item: #1 of 282
          id: bracis-19019
      author: Engelmann, Débora C.; Cezar, Lucca Dornelles; Panisson, Alison R.; Bordini, Rafael H.
       title: A Conversational Agent to Support Hospital Bed Allocation
        date: 2021
       words: 6269
      flesch: 48
     summary: As part of this work, we developed a web-based simulation of hospital bed allocation system integrated with a chatbot for interaction with the user. Our objective was to understand the real scenario of hospital bed allocation.
    keywords: agent; allocation; approach; bed; bed allocation; chatbot; google; hospital; patient; plan; professionals; rules; scholar; system
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        item: #2 of 282
          id: bracis-19020
      author: Morveli-Espinoza, Mariela; Possebom, Ayslan; Tacla, Cesar Augusto
       title: A Protocol for Argumentation-Based Persuasive Negotiation Dialogues
        date: 2021
       words: 6443
      flesch: 59
     summary: orcid.org/0000-0002-1347-585211 & Cesar Augusto Tacla  ORCID: orcid.org/0000-0002-8244-897010  Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13073)) Included in the following conference series: Brazilian Conference on Intelligent Systems 802 Accesses 2 Citations Abstract Argumentation-based persuasive negotiation is a form of negotiation dialogue in which agents, with different interests and goals, exchange proposals that are supported by rhetorical arguments such as threats, rewards, or appeals. Besides rhetorical arguments, additional kinds of illocutions may also be exchanged during the dialogue, for instance, agents may ask for explanations, give explanations, or attack (or contradict) previous arguments.
    keywords: \(\mathtt; \rangle; agent; argumentation; arguments; attacks; definition; dialogue; google; negotiation; opponent; protocol; scholar
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        item: #3 of 282
          id: bracis-19021
      author: Lovatto, Ângelo Gregório; Bueno, Thiago Pereira; Barros, Leliane Nunes de
       title: Gradient Estimation in Model-Based Reinforcement Learning: A Study on Linear Quadratic Environments
        date: 2021
       words: 5654
      flesch: 52
     summary: It is not clear, however, if better policy gradient estimation translates to more stability or faster convergence in SVG algorithms. 3.3 Stochastic Value Gradient Methods In the broader RL context, methods that learn parameterized policies, often called policy optimization methods, have gained traction in the recent decade.
    keywords: control; estimation; function; gradient; learning; lqg; methods; model; optimization; policy; reinforcement; state; svg; value; work
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        item: #4 of 282
          id: bracis-19022
      author: Silva, Thayanne França da; Araújo, Matheus Santos; Ferro Junior, Raimundo Juracy Campos; Costa, Leonardo Ferreira da; Andrade, João Pedro Bernardino; Campos, Gustavo Augusto Lima de
       title: Intelligent Agents for Observation and Containment of Malicious Targets Organizations
        date: 2021
       words: 6282
      flesch: 51
     summary: Based on these concepts of an image capture agent, the use of the computer vision technique, and the limitation of target communication to maintain the organization in CMOMMT, we can assist robots in classifying target structures through the application of computer vision in the simulation scenario, such as [5, 15]. The state machine that demonstrates target team communication is shown in Fig.
    keywords: agents; coalition; communication; coordinator; environment; google; robots; state; strategy; sub; targets; team
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        item: #5 of 282
          id: bracis-19023
      author: Segato, Tiago Henrique Faccio; Serafim, Rafael Moura da Silva; Fernandes, Sérgio Eduardo Soares; Ralha, Célia Ghedini
       title: MAS4GC: Multi-agent System for Glycemic Control of Intensive Care Unit Patients
        date: 2021
       words: 5987
      flesch: 51
     summary: In the literature review, AI-based works for glycemic control of ICU patients are presented [5,6,7], as well as the application of Multi-Agent System (MAS) for patients glycemic control [8], and MAS in the ICU context [9, 10]. Table 1 summarizes the qualitative aspects of the related work, limited to the application context (glycemic control, ICU patients) and technologies used (agent-based, prediction model).
    keywords: agent; blood; care; control; glucose; google; health; icu; mas; monitoring; patients; professionals; recommendations; scholar; system; treatment
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        item: #6 of 282
          id: bracis-19024
      author: Cunha, Renato Luiz de Freitas; Chaimowicz, Luiz
       title: On the Impact of MDP Design for Reinforcement Learning Agents in Resource Management
        date: 2021
       words: 6973
      flesch: 56
     summary: Resource management, the process by which we map computational resources to the tasks and jobs (programs) that require them, in particular, is an area in which recent learning approaches have demonstrated superior performance over classical algorithms and optimization techniques. DeepRM presented an approach of using Policy Gradients to schedule jobs based on CPU and memory requirements.
    keywords: \theta; agent; fig; image; jobs; learning; mdp; performance; policy; processors; reinforcement; representation; scheduling; state; time
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        item: #7 of 282
          id: bracis-19025
      author: Nishimoto, Bruno Eidi; Costa, Anna Helena Reali
       title: Slot Sharing Mechanism in Multi-domain Dialogue Systems
        date: 2021
       words: 6560
      flesch: 58
     summary: Dealing with multi-domain dialogue systems is a problem much harder since the complexity of user goals and conversations increases a lot. Cuayáhuitl, H., Yu, S., Williamson, A., Carse, J.: Scaling up deep reinforcement learning for multi-domain dialogue systems.
    keywords: agent; dialogue; domain; google; hotel; information; learning; mechanism; policy; restaurant; scholar; sharing; slot; systems; user
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        item: #8 of 282
          id: bracis-19026
      author: Godoi, Giliard Almeida de; Tinós, Renato; Sanches, Danilo Sipoli
       title: A Graph-Based Crossover and Soft-Repair Operators for the Steiner Tree Problem
        date: 2021
       words: 6900
      flesch: 60
     summary: A portal vertex connects a partition to another through common edges. Moreover, there are two sets of common edges: one formed by the edge \(\{(5, 9)\}\) and the other formed by the edges \(\{(55, 12), (12, 11), (23, 11), (17, 11)\}\).
    keywords: cost; crossover; edges; graph; offspring; operator; partitions; pxst; set; solution; tree; vertices
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        item: #9 of 282
          id: bracis-19027
      author: Machado, Jussara Gomes; Pires, Matheus Giovanni; Bertoni, Fabiana Cristina; Pimenta, Adinovam Henriques de Macedo; Camargo, Heloisa de Arruda
       title: A Modified NSGA-DO for Solving Multiobjective Optimization Problems
        date: 2021
       words: 5176
      flesch: 53
     summary: For each solution i, contained in the population of solutions, two values are calculated: \(nd_i\), the number of solutions that dominate solution i; and \(U_i\), the set of solutions that are dominated by solution i. Solutions with \(nd_i = 0\) are contained in the \(F_1\) front (Pareto front). Recently, a modification on the NSGA-II was proposed by [15], seeking to improve the diversity of the set of non-dominated solutions.
    keywords: algorithm; distance; google; nsga; objective; optimization; pareto; points; problems; set; solutions
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        item: #10 of 282
          id: bracis-19028
      author: Rodrigues Neto, João Batista; Ramos, Gabriel de Oliveira
       title: An Enhanced TSP-Based Approach for Active Debris Removal Mission Planning
        date: 2021
       words: 5667
      flesch: 56
     summary: https://doi.org/10.1029/JA083iA06p02637 Article  Google Scholar  Li, H., Baoyin, H.: Optimization of multiple debris removal missions using an evolving elitist club algorithm. Evolving solutions to tsp variants for active space debris removal.
    keywords: \sum; algorithm; approach; cost; debris; iterations; mission; problem; removal; run; s2opt; space; time; work
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        item: #11 of 282
          id: bracis-19029
      author: Pavelski, Lucas Marcondes; Kessaci, Marie-Éléonore; Delgado, Myriam
       title: Dynamic Learning in Hyper-Heuristics to Solve Flowshop Problems
        date: 2021
       words: 6250
      flesch: 49
     summary: The shaking is adapted by maintaining a tabu-list of non-improving heuristics, while different local searches are chosen greedily on a rank metric based on improving moves. Res. 3(9), 397–422 (2002) Google Scholar  Baker, K.R., Trietsch, D.: Principles of Sequencing and Scheduling.
    keywords: adaptation; algorithm; components; destruction; dynamic; google; heuristics; hyper; learning; reward; scholar; search; size; strategies; table
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        item: #12 of 282
          id: bracis-19030
      author: Senzaki, Bianca N. K.; Venske, Sandra M.; Almeida, Carolina P.
       title: Hyper-Heuristic Based NSGA-III for the Many-Objective Quadratic Assignment Problem
        date: 2021
       words: 6687
      flesch: 56
     summary: 52, 10–25 (2016) Google Scholar  Senzaki, B.N.K., Venske, S.M., Almeida, C.P.: Multi-objective quadratic assignment problem: an approach using a hyper-heuristic based on the choice function. IEEE Press (2009) Google Scholar  Çela, E.: The Quadratic Assignment Problem: Theory and Algorithms, ser.
    keywords: algorithm; failure; google; heuristic; hyper; nsga; objective; operators; problem; scholar; success; table; work
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        item: #13 of 282
          id: bracis-19031
      author: Cassenote, Mariane R. S.; Derenievicz, Guilherme A.; Silva, Fabiano
       title: I2DE: Improved Interval Differential Evolution for Numerical Constrained Global Optimization
        date: 2021
       words: 8019
      flesch: 50
     summary: Optim. 65(4), 837–866 (2016) Article  MathSciNet  Google Scholar  Berge, C.: Graphs and Hypergraphs. Elsevier Science Ltd., Oxford (1985) Google Scholar  Brest, J., Maučec, M.S., Bošković, B.: iL-SHADE: Improved L-SHADE algorithm for single objective real-parameter optimization.
    keywords: \texttt; box; constraint; google; i2de; ieee; instance; interval; multi; ogre; optimization; population; scholar; search; solvers
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        item: #14 of 282
          id: bracis-19032
      author: Flexa, Caio; Gomes, Walisson; Moreira, Igor; Santos, Reginaldo; Sales, Claudomiro; Silva, Moisés
       title: Improving a Genetic Clustering Approach with a CVI-Based Objective Function
        date: 2021
       words: 5940
      flesch: 55
     summary: 1793–1801, September 2016 Google Scholar  Daniel, W.W.: Applied nonparametric statistics. 3–9 (2016) Google Scholar  Esfandian, N., Razzazi, F., Behrad, A.: A clustering based feature selection method in spectro-temporal domain for speech recognition.
    keywords: \kappa; algorithm; analysis; article; clustering; clusters; data; gadba; genetic; google; google scholar; mec; number; results; scholar
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        item: #15 of 282
          id: bracis-19033
      author: Vasconcelos, Matheus; Flexa, Caio; Moreira, Igor; Santos, Reginaldo; Sales, Claudomiro
       title: Improving Particle Swarm Optimization with Self-adaptive Parameters, Rotational Invariance, and Diversity Control
        date: 2021
       words: 5624
      flesch: 46
     summary: Empirical study on rotation and information exchange in particle swarm optimization. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings (1998) Google Scholar  Spears, W., Green, D., Spears, D.: Biases in particle swarm optimization.
    keywords: \vec; diversity; google; gradient; optimization; particle; pso; results; scholar; search; swarm; xpso
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        item: #16 of 282
          id: bracis-19034
      author: Oliveira, Gustavo F. V. de; Mendes, Marcus H. S.
       title: Improving Rule Based and Equivalent Decision Simplifications for Bloat Control in Genetic Programming Using a Dynamic Operator
        date: 2021
       words: 5539
      flesch: 57
     summary: 3 Proposed Improvements The main idea of the improvements to the simplification with RBS and EDS flow is to increase RBS rules table R dynamically as soon as new rules, or more efficient ones, are discovered. It also optimizes the access of RBS rules using the any keyword in a hash-table implementation, as well as introduces a warm-up stage to grow RBS rules before the evolutionary process begins.
    keywords: benchmark; dore; eds; individuals; operator; problems; rbs; rules; simplification; size; table
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        item: #17 of 282
          id: bracis-19035
      author: Souza, Luciano S. de; Carvalho, Jonathan H. A. de; Ferreira, Tiago A. E.
       title: Lackadaisical Quantum Walk in the Hypercube to Search for Multiple Marked Vertices
        date: 2021
       words: 5769
      flesch: 66
     summary: In Sect. 3, we characterize the probability distribution along with the space, adjust the self-loop weight for multiple marked vertices, and search for adjacent marked vertices. Carvalho [6] shows that the optimal value of the self-loop for quantum walks in D-dimensional grids with multiple marked vertices is $$\begin{aligned} l = \frac{2Dm}{N}, \end{aligned}$$ where 2D is the number of movements the walker can do, not counting the self-loop, m the number of marked vertices, and N the number of vertices of the grid.
    keywords: hypercube; probability; quantum; search; shows; success; value; vertex; vertices; walk
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        item: #18 of 282
          id: bracis-19036
      author: Silva, José Eduardo H. da; Bernardino, Heder S.; Oliveira, Itamar L. de; Vieira, Alex B.; Barbosa, Helio J. C.
       title: On the Analysis of CGP Mutation Operators When Inferring Gene Regulatory Networks Using ScRNA-Seq Time Series Data
        date: 2021
       words: 5826
      flesch: 57
     summary: Mech. 1860(1), 41–52 (2017) Google Scholar  Chan, T.E., Stumpf, M.P., Babtie, A.C.: Cell Syst. 5(3), 251–267 (2017) Google Scholar  Chen, S., Mar, J.C.:
    keywords: cgp; data; gene; genie3; google; mutation; networks; number; performance; results; sam; scholar; somo
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        item: #19 of 282
          id: bracis-19037
      author: Dantas, Augusto; Pozo, Aurora
       title: Online Selection of Heuristic Operators with Deep Q-Network: A Study on the HyFlex Framework
        date: 2021
       words: 5046
      flesch: 54
     summary: Average selection of operators on VRP instance 5 Full size image Figure 13 shows the frequencies of operator selection on one Homberger instance. 1033–1036 (2014) Google Scholar  DaCosta, L., Fialho, A., Schoenauer, M., Sebag, M.: Adaptive operator selection with dynamic multi-armed bandits.
    keywords: dqn; fig; heuristic; instances; learning; operators; performance; search; selection; size; state; table
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        item: #20 of 282
          id: bracis-19038
      author: Cordeiro, Renan; Fernandes, Guilherme; Alcântara, João; Viana, Henrique
       title: A Systematic Approach to Define Semantics for Prioritised Logic Programs
        date: 2021
       words: 9715
      flesch: 60
     summary: For each \(o_1, o_2\) in \(\mathcal {O}_P\), the closure \(\varPhi ^*\) of \(\varPhi \) is defined as follows: \(o_1 \preceq o_1 \in \varPhi ^*\); if \(o_1 \preceq o_2 \in \varPhi \), then \(o_1 \preceq o_2 \in \varPhi ^*\); if \(o_1 \preceq o_2 \in \varPhi ^*\) and \(o_2 \preceq o_3 \in \varPhi ^*\), then \(o_1 \preceq o_3 \in \varPhi ^*\). Then for any \(x \in \{ st , opt ,\) \( pes , opp \}\), \(\forall o \in \mathcal X\) and \(\forall o' \in \mathcal Y\), it holds \(o' \not \prec o \in \varPhi \) iff \(\forall o \in \mathcal X\), it holds \(\exists o' \in \mathcal Y\) such that \(o' \not \prec o \in \varPhi \) iff \(o_2 \not \prec o_1 \in \varPhi \) iff \(\exists o \in \mathcal X\) such that \(\forall o' \in \mathcal Y\), it holds \(o' \not \prec o \in \varPhi \) iff \(o_2 \not \prec o_1 \in \varPhi \) iff \(\exists o \in \mathcal X\) and \(\exists o' \in \mathcal Y\) such that \(o' \not \prec o \in \varPhi \).
    keywords: \((p; \exists; \in; \in \left\; \in \mathcal; \in \varphi; \left\; \mathcal; \subseteq; \varphi; iff; model; w.r.t
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        item: #21 of 282
          id: bracis-19039
      author: Vargas, Daniel P.; Paulus, Gustavo B.; Silva, Luis A. L.
       title: Active Learning and Case-Based Reasoning for the Deceptive Play in the Card Game of Truco
        date: 2021
       words: 7062
      flesch: 58
     summary: 3.1 The Case Base Formation A web-based system was developed to permit the collection of Truco cases, where these cases were the result of Truco matches played between two human opponents who had various levels of Truco experience. Instead of using active learning to collect any kind of expert experience of game playing, this work direct such learning to the improvement of the deceptive capabilities of card playing agents.
    keywords: agents; base; card; case; case base; game; google; hand; learning; problem; reuse; scholar; strength; truco
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        item: #22 of 282
          id: bracis-19040
      author: Silva, Rafael; Alcântara, João
       title: ASPIC? and the Postulates of Non-interference and Crash-Resistance
        date: 2021
       words: 9791
      flesch: 61
     summary: $$ Given a \( SAF SA \) defined by an argumentation theory \( AT \) and an \( AF _2\, AF \) corresponding to \( SA \), we will refer to \( AF \) as the resulting \( AF _2\) from \( AT \). For a compatible set S in \( AF \), we say 1) S is an admissible set of \( AF \) iff \(S \subseteq F_ AF (S)\); 2) S is a complete extension of \( AF \) iff \(f_ AF (S) = S\); 3) S is a preferred extension of \( AF \) iff it is a set inclusion maximal complete extension of \( AF \); 4) S is the grounded extension iff it is the set inclusion minimal complete extension of \( AF \); 5) S is a stable extension iff S is complete extension of \( AF \) and \(\forall Y \not \in S\), \(\exists X \in S\) s.t. \((X, Y )
    keywords: \cup; \in \mathcal; \ldots; \left\; \mathcal; \mathtt; \phi; \rightarrow; \subseteq; \texttt; argumentation; arguments; aspic
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        item: #23 of 282
          id: bracis-19041
      author: Viana, Henrique; Alcântara, João
       title: On the Refinement of Compensation-Based Semantics for Weighted Argumentation Frameworks
        date: 2021
       words: 7765
      flesch: 58
     summary: Although they are t-conorms, weighted Łukasiewicz and weighted probabilistic sum semantics go in a direction different from weighted max-based Semantics and satisfy (Compensation), along with all the 1–12 principles. As it happened with the maximum t-conorm, which has a higher acceptability degree when compared with the other t-conorms, the acceptability degree of an argument is higher for the h-categorizer when compared to cumulative sum semantics.
    keywords: \(\mathbf; \in; \mathcal; \text; argument; att}_\mathbf; deg}^{\mathbf; g}}(a; precedence; quality; semantics; sum; s}}_{\mathbf
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        item: #24 of 282
          id: bracis-19042
      author: Dal Bosco, Avner; Vieira, Renata; Zanotto, Bruna; Etges, Ana Paula Beck da Silva
       title: Ontology Based Classification of Electronic Health Records to Support Value-Based Health Care
        date: 2021
       words: 4393
      flesch: 57
     summary: Ontology based model resultsFull size table [13] domain ontologies are used to classify sentences using rules, based on the relations between concepts.
    keywords: classification; health; indexes; model; ontology; records; results; score; sentences; table; terms; words
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        item: #25 of 282
          id: bracis-19043
      author: Santos, Yuri Santa Rosa Nassar dos; Santiago, Rafael; Perego, Raffaele; Schaly, Matheus Henrique; Alvares, Luis Otávio; Renso, Chiara; Bogorny, Vania
       title: A Co-occurrence Based Approach for Mining Overlapped Co-clusters in Binary Data
        date: 2021
       words: 7489
      flesch: 61
     summary: Proposition 1 Let K be the maximum number of non-overlapped co-clusters, N the total number of objects, M the total number of attributes, and P the number of overlapped co-clusters. Regarding the overall complexity of our algorithm, OCoClus calls findPureCocluster and expandPureCocluster methods, then builds D\(_{r}\) for each of the K (or less) non-overlapped co-clusters and finalizes with the findOverlap method.
    keywords: \(\epsilon; attributes; clustering; clusters; cost; data; function; google; matrix; method; noise; number; objects; ococlus; scholar
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        item: #26 of 282
          id: bracis-19044
      author: Lima, Marília; Silva Filho, Telmo; Fagundes, Roberta Andrade de A.
       title: A Comparative Study on Concept Drift Detectors for Regression
        date: 2021
       words: 4473
      flesch: 62
     summary: A comparative study on concept drift detectors. Conclusion: our experiments were executed in a framework that can easily be extended to include new CD detectors and base learners, allowing future studies to use it.
    keywords: average; base; concept; data; datasets; detection; detectors; drift; google; learner
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        item: #27 of 282
          id: bracis-19045
      author: Levada, Alexandre L. M.; Haddad, Michel F. C.
       title: A Kullback-Leibler Divergence-Based Locally Linear Embedding Method: A Novel Parametric Approach for Cluster Analysis
        date: 2021
       words: 5731
      flesch: 52
     summary: Although more efficient than linear methods, the LLE still has some important limitations. Regarding the means and medians, one may realize that the proposed method performs better in comparison with the established LLE as well as two of its variations (i.e., Hessian LLE and LTSA).
    keywords: \end{aligned}$$; \sum; \vec; data; learning; linear; lle; matrix; method; w}_i
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        item: #28 of 282
          id: bracis-19046
      author: Tieppo, Eduardo; Barddal, Jean Paul; Nievola, Júlio Cesar
       title: Classifying Potentially Unbounded Hierarchical Data Streams with Incremental Gaussian Naive Bayes
        date: 2021
       words: 6230
      flesch: 52
     summary: 139–148 (2009) Google Scholar  Bifet, A., Kirkby, R.: Data stream mining a practical approach (2009) Google Scholar  Bishop, C.M.: Citeseer (2003) Google Scholar  de Campos Merschmann, L.H., Freitas, A.A.:
    keywords: bayes; classification; data; data streams; gnb; google; hds; instances; knn; learning; method; model; node; scholar; streams; time
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        item: #29 of 282
          id: bracis-19047
      author: Valejo, Alan Demétrius Baria; Althoff, Paulo Eduardo; Faleiros, Thiago de Paulo; Chuerubim, Maria Lígia; Yan, Jianglong; Liu, Weiguang; Zhao, Liang
       title: Coarsening Algorithm via Semi-synchronous Label Propagation for Bipartite Networks
        date: 2021
       words: 6174
      flesch: 56
     summary: However, few of these algorithms have been specifically designed to deal with bipartite networks and they still face theoretical limitations that need to be explored. Over the last years, there has been a growing scientific interest in bipartite networks given their occurrence in many data analytic problems, such as community detection and text classification.
    keywords: \mathcal; algorithm; bipartite; bipartite networks; clpb; coarsening; google; label; networks; nodes; number; propagation; scholar; size; strategy
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        item: #30 of 282
          id: bracis-19048
      author: Garcia, Luís P. F.; Campelo, Felipe; Ramos, Guilherme N.; Rivolli, Adriano; Carvalho, André C. P. de L. F. de
       title: Evaluating Clustering Meta-features for Classifier Recommendation
        date: 2021
       words: 6269
      flesch: 52
     summary: J. Cybern. 4(1), 95–104 (1974) Article  MathSciNet  Google Scholar  Filchenkov, A., Pendryak, A.: Datasets meta-feature description for recommending feature selection algorithm. https://doi.org/10.1007/978-3-540-73263-1 Book  MATH  Google Scholar  Breiman, L.: Random forests.
    keywords: \end{aligned}$$; \mathbf; algorithms; classification; classifier; clustering; dataset; features; google; learning; measures; meta; mtl; performance; problem; scholar
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        item: #31 of 282
          id: bracis-19049
      author: Portela, Tarlis Tortelli; Silva, Camila Leite da; Carvalho, Jonata Tyska; Bogorny, Vania
       title: Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification
        date: 2021
       words: 6686
      flesch: 47
     summary: SUPERMovelets also automatically finds a threshold \(\lambda \) for limiting the maximum number of trajectory dimensions required in each movelet candidate, as an alternative for not exploring all dimension combinations. Equation (1) describes this quality function for a pivot candidate (\(\mathcal {P}\)) of a class: $$\begin{aligned} quality_{piv} = \frac{\sum ^{d=|C|}_{d=1} freq_{piv}(\mathcal {P},d,\mathbf{T}' )}{|C|} \end{aligned}$$ (1) Where C is the set of trajectory dimensions of \(\mathcal {P}\), and the quality is the average proportion that \(\mathcal {P}\) occurred in trajectories of the class in each dimension d of C. As there are different combinations of dimensions in each \(\mathcal {P}\), we measure the relative frequency as the average count that a \(\mathcal {P}\) occurred in each dimension, as described in (2). $$\begin{aligned} freq_{piv}(\mathcal {P}, d,\mathbf{T}' )
    keywords: aspect; candidate; classification; dataset; dimensions; mastermovelets; method; movelet; number; pivot; subtrajectory; supermovelets; time; trajectories; trajectory
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        item: #32 of 282
          id: bracis-19050
      author: Silva Filho, Rogério Luiz Cardoso; Adeodato, Paulo Jorge Leitão; Brito, Kellyton dos Santos
       title: Interpreting Classification Models Using Feature Importance Based on Marginal Local Effects
        date: 2021
       words: 5831
      flesch: 49
     summary: Feature importance (MUA) by groups during the period (RF and AB means) Full size image 5 Discussion and Conclusion This paper has proposed new model-agnostic metrics of feature importance in an attempt to circumvent the drawbacks and constraints of the existing methods, such as the β- coefficients of additive models and feature importance from tree-based algorithms, widely used for this purpose. In this paper, the main goal of this paper is to support applied research providing single metrics that, in a more realistic scenario, are able to report the overall contribution of model features.
    keywords: \({x}_{1}\; ale; data; effects; feature; feature importance; google; importance; learning; machine; metrics; models; paper; scholar
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        item: #33 of 282
          id: bracis-19051
      author: Lucca, Giancarlo; Borges, Eduardo N.; Berri, Rafael A.; Emmendorfer, Leonardo; Dimuro, Graçaliz P.; Asmus, Tiago C.
       title: On the Generalizations of the Choquet Integral for Application in FRBCs
        date: 2021
       words: 6779
      flesch: 55
     summary: Res. 8, 1–33 (2007) MathSciNet  MATH  Google Scholar  Ishibuchi, H., Nakashima, T., Nii, M.: Classification and Modeling with Linguistic Information Granules, Advanced Approaches to Linguistic Data Mining. In: Proceedings of the Institution of Electrical Engineers, vol. 121, issue number 12, pp. 1585–1588 (1974) Google Scholar  Ishibuchi, H., Nakashima, T.: Effect of rule weights in fuzzy rule-based classification systems.
    keywords: aggregation; averaging; choquet; classification; frm; functions; generalizations; google; information; integral; rule; scholar; systems
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        item: #34 of 282
          id: bracis-19052
      author: Afonso, Bruno Klaus de Aquino; Berton, Lilian
       title: Optimizing Diffusion Rate and Label Reliability in a Graph-Based Semi-supervised Classifier
        date: 2021
       words: 6406
      flesch: 56
     summary: In order to separate the labeled data \(\mathcal {L}\) from the unlabeled data \(\mathcal {U}\), we divide our matrices as following: $$\begin{aligned} \mathbf {X}&= \left[ \mathbf {X_\mathcal {L}}^\top , \mathbf {X_\mathcal {U}}^\top \right] ^\top \end{aligned}$$ (4) $$\begin{aligned} \mathbf {Y}&= \left[ \mathbf {Y_\mathcal {L}}^\top , \mathbf {Y_\mathcal {U}}^\top \right] ^\top \end{aligned}$$ (5) $$\begin{aligned} \mathbf {F}&= \left[ \mathbf {F_\mathcal {L}}^\top , \mathbf {F_\mathcal {U}}^\top \right] ^\top \end{aligned}$$ (6) The idea of SSL is appealing for many reasons. \mathbf {x})\).
    keywords: \(\alpha; \(\texttt; \end{aligned}$$; \mathbf; accuracy; data; diffusion; google; graph; label; learning; lgc}\_\texttt; matrix; scholar
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        item: #35 of 282
          id: bracis-19053
      author: Freitas, Washington Burkart; Bertini Junior, João Roberto
       title: Tactical Asset Allocation Through Random Walk on Stock Network
        date: 2021
       words: 5585
      flesch: 55
     summary: 5.2 Portfolio Performance Metrics To measure the performance of stock portfolios, some methods can be used, such as: the Sharpe ratio, the maximum drawdown (difference between the highest and lowest value in a given period) and the cumulative wealth. The lower the MDD index the better. $$\begin{aligned} MDD = \frac{{ max(r_{t}) - min(r_{t}) }}{ max(r_{t}) } \end{aligned}$$ (11) Cumulative Wealth (CW): is the cumulative wealth of stock portfolio over the period \( \tau \).
    keywords: \(\lambda; algorithm; asset; google; network; performance; portfolio; results; return; risk; scholar; stock; value
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        item: #36 of 282
          id: bracis-19054
      author: Souza, Mariana C. de; Nogueira, Bruno M.; Rossi, Rafael G.; Marcacini, Ricardo M.; Rezende, Solange O.
       title: A Heterogeneous Network-Based Positive and Unlabeled Learning Approach to Detect Fake News
        date: 2021
       words: 7135
      flesch: 53
     summary: Our network incorporates different linguistic features to characterize fake news, such as representative terms, emotiveness, pausality, and average sentence size. Categorization Heterodox Economics Learning algorithms Machine Learning Probabilistic data networks Self-serving bias 1 Introduction Detecting fake news is a challenging task since fake news constantly evolves, influencing the formation of the opinion of social groups as accepted [6].
    keywords: \mathcal; algorithms; approach; classification; data; features; google; information; interest; learning; network; news; scholar; set
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        item: #37 of 282
          id: bracis-19055
      author: Nunes, Breno; Colliri, Tiago; Lauretto, Marcelo; Liu, Weiguang; Zhao, Liang
       title: Anomaly Detection in Brazilian Federal Government Purchase Cards Through Unsupervised Learning Techniques
        date: 2021
       words: 5537
      flesch: 51
     summary: J. Classif. 1(1), 7–24 (1984) Article  Google Scholar  Ferreira, L.N., Zhao, L.: Detecting time series periodicity using complex networks. Disc. 29(3), 626–688 (2015) Article  MathSciNet  Google Scholar  de Andrade, P.H.M.A., Meira, W., Cerqueira, B., Cruz, G.:
    keywords: anomalies; approach; clustering; cpgf; data; detection; google; means; network; results; scholar; series; values
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        item: #38 of 282
          id: bracis-19056
      author: Santos, Joaquim; Santos, Henrique D. P. dos; Tabalipa, Fábio; Vieira, Renata
       title: De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier
        date: 2021
       words: 3808
      flesch: 50
     summary: In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 1638–1649 (2018) Google Scholar  Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. US Department of Commerce, Technology Administration, National Institute of \(\ldots \) (2005) Google Scholar  Hochreiter, S., Schmidhuber, J.: Long short-term memory.
    keywords: corpus; embeddings; experiments; google; identification; language; models; notes; santos; scholar
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        item: #39 of 282
          id: bracis-19057
      author: Colliri, Tiago; Minakawa, Marcia; Zhao, Liang
       title: Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning Approach
        date: 2021
       words: 6584
      flesch: 53
     summary: MATH  Google Scholar  Breiman, L.: Random forests. Nature 410(6825), 268–276 (2001) Article  Google Scholar  Valejo, A., Ferreira, V., Fabbri, R., Oliveira, M.C.F.d., Lopes, A.D.A.: A critical survey of the multilevel method in complex networks.
    keywords: classification; covid-19; data; dataset; google; insufficiency; model; network; patients; scholar; signs; technique; testing; tests; training
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        item: #40 of 282
          id: bracis-19058
      author: Aguiar, Davi Pedrosa de; Murai, Fabricio
       title: Encoding Physical Conditioning from Inertial Sensors for Multi-step Heart Rate Estimation
        date: 2021
       words: 7075
      flesch: 56
     summary: [13] consists of data from 40 sensors (accelerometers, gyroscopes, magnetometers, thermometers and HR sensor) sampled 100 Hz of 9 subjects performing 18 different activities (e.g., rope jumping, running, sitting). The Physical Conditional Embedding LSTM Model Here we describe PCE-LSTM, our proposed neural network architecture for HR prediction.
    keywords: data; estimation; imu; lstm; model; network; pce; ppg; rate; signals; state; task; time; vectors
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        item: #41 of 282
          id: bracis-19059
      author: Freitas, Eduardo Kenji Hasegawa de; Camargo, Alex Dias; Balboni, Maurício; Werhli, Adriano V.; Machado, Karina dos Santos
       title: Ensemble of Protein Stability upon Point Mutation Predictors
        date: 2021
       words: 6036
      flesch: 53
     summary: Anal. 9(2), 340–361 (2016) MathSciNet  Google Scholar  Parthiban, V., Gromiha, M.M., Schomburg, D.: CUPSAT: prediction of protein stability upon point mutations. FEBS Lett. 325(1–2), 5–16 (1993) Google Scholar  Freund, Y., Schapire, R.E., et al.: Experiments with a new boosting algorithm.
    keywords: \vardelta; bagging; data; ensemble; google; learning; models; mutations; point; protein; results; scholar; stability; table; tools
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        item: #42 of 282
          id: bracis-19060
      author: Ferreira, Marcos Vinícius; Almeida, Ariel; Canario, João Paulo; Souza, Matheus; Nogueira, Tatiane; Rios, Ricardo
       title: Ethics of AI: Do the Face Detection Models Act with Prejudice?
        date: 2021
       words: 5967
      flesch: 52
     summary: In summary, face detection is a subarea of object detection, devoted to finding regions in images that contain faces [22, 25]. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016) Google Scholar  Jain, V., Learned-Miller, E.: FDDB: a benchmark for face detection in unconstrained settings.
    keywords: bounding; dataset; detection; detectors; error; face; gender; google; images; models; race; recognition; results; scholar; users
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        item: #43 of 282
          id: bracis-19061
      author: Silva, Nádia F. F. da; Silva, Marília Costa R.; Pereira, Fabíola S. F.; Tarrega, João Pedro M.; Beinotti, João Vitor P.; Fonseca, Márcio; Andrade, Francisco Edmundo de; Carvalho, André C. P. de L. F. de
       title: Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies
        date: 2021
       words: 6154
      flesch: 49
     summary: A brief survey of text mining: classification, clustering and extraction techniques (2017) Google Scholar  Andrade, M.D.D., Rosa, B.D.C., Pinto, E.R.G.D.C.: Legal tech: analytics, inteligência artificial e as novas perspectivas para a prática da advocacia privada. (2020) Google Scholar  Angelov, D.: Top2Vec: distributed representations of topics (2020).
    keywords: author; bills; clustering; comments; conference; data; embeddings; google; google scholar; learning; modeling; models; portuguese; scholar; sentence; topic; word
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        item: #44 of 282
          id: bracis-19062
      author: Zeiser, Felipe André; Costa, Cristiano André da; Ramos, Gabriel de Oliveira; Bohn, Henrique; Santos, Ismael; Righi, Rodrigo da Rosa
       title: Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays
        date: 2021
       words: 4889
      flesch: 55
     summary: Science 369(6508), 1255–1260 (2020) Google Scholar  CDC COVID-19 Response Team: Article  Google Scholar  EPICOVID: Covid-19 no brasil: várias epidemias num só país (2020).
    keywords: article; author; cases; chest; classification; covid-19; google; images; learning; models; pneumonia; scholar; set; use; work
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        item: #45 of 282
          id: bracis-19063
      author: Silva e Oliveira, Lucas Emanuel; Schneider, Elisa Terumi Rubel; Gumiel, Yohan Bonescki; Luz, Mayara Aparecida Passaura da; Paraiso, Emerson Cabrera; Moro, Claudia
       title: Experiments on Portuguese Clinical Question Answering
        date: 2021
       words: 4892
      flesch: 49
     summary: Another aspect that hinders the research of clinical QA is the complexity of the data that are stored in the patient’s Electronic Health Records (EHR). The aim of this article is to carry out preliminary experiments to help define the next steps towards a robust model of clinical QA.
    keywords: answering; answers; biomedical; dataset; fine; language; learning; model; notes; portuguese; question; squad
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        item: #46 of 282
          id: bracis-19064
      author: Oliveira, Josias; Mutz, Filipe; Forechi, Avelino; Azevedo, Pedro; Oliveira-Santos, Thiago; Souza, Alberto F. De; Badue, Claudine
       title: Long-Term Map Maintenance in Complex Environments
        date: 2021
       words: 5712
      flesch: 54
     summary: The system receives as input one or more log files containing the surveying missions (or sessions) from a single or multiple vehicles, and it outputs the AVs’ poses in a global coordinate frame, the parameters of the odometry bias calibration for each vehicle and different types of grid maps. Despite being able to build many types of grid maps, the next sections will focus only on OGMs.
    keywords: \left; bias; calibration; google; gps; hypergraph; loop; map; mapping; maps; merging; new; odometry; scholar; system
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        item: #47 of 282
          id: bracis-19065
      author: Barros, Mariana da Silva; Philippini, Igor de Moura; Silva, Ladson Gomes; Netto, Antonio Barros da Silva; Blawid, Rosana; Barros, Edna Natividade da Silva; Blawid, Stefan
       title: Supervised Training of a Simple Digital Assistant for a Free Crop Clinic
        date: 2021
       words: 5686
      flesch: 48
     summary: In this case, the digital assistant shall forward to experts image segments that possibly show symptoms for further inspection. In a pioneering work, Mohanty et al. [3] suggested using a deep learning approach based on image classification to identify selected plant diseases through leaf images.
    keywords: classification; dataset; disease; images; learning; leaves; model; plant; recall; symptoms; system; table; training
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        item: #48 of 282
          id: bracis-19066
      author: Gonçalves, Bernardo; Cozman, Fabio Gagliardi
       title: The Future of AI: Neat or Scruffy?
        date: 2021
       words: 7548
      flesch: 59
     summary: So according to Minsky, one is led to think, AI systems shall be untidy like the brain. They complained that AI systems back then (e.g., expert systems) were sufficiently successful as task-specific cognitive artifacts and yet were seen as a failure because of “Turing’s ghost” (p. 976).
    keywords: artificial; brain; google; human; intelligence; knowledge; minsky; neats; research; scholar; science; scruffy; simon; systems
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        item: #49 of 282
          id: bracis-19067
      author: Dantas, Joao P. A.; Costa, Andre N.; Geraldo, Diego; Maximo, Marcos R. O. A.; Yoneyama, Takashi
       title: Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network
        date: 2021
       words: 5384
      flesch: 54
     summary: Master’s Thesis, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, Brazil (2019) Google Scholar  Dantas, J.P.A., Costa, A.N., Geraldo, D., Maximo, M.R.A.O., Yoneyama, T.: Engagement decision support for beyond visual range air combat. 1–6 (2021), Accepted for publication Google Scholar  Dantas, J.P.A.:
    keywords: air; author; fig; google; launch; maximum; missile; model; range; scholar; simulation; target; training; wez; zone
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        item: #50 of 282
          id: bracis-19068
      author: Meyrer, Gabriel T.; Araújo, Denis A.; Rigo, Sandro J.
       title: Code Autocomplete Using Transformers
        date: 2021
       words: 4273
      flesch: 52
     summary: Throughout this article, we’ll refer to our model as Java8G. 4 Evaluation In this section, we define our evaluation methodology, where we initially propose the creation of a new metric to measure the applicability of the model’s suggestions in code completion, and then we detail how we proceed with the experiments. 4.1 DG Evaluation Metric This far we already know that language models can also be applied to problems involving code intelligence. Download conference paper PDF Similar content being viewed by others Statistical Approach to Increase Source Code Completion Accuracy Chapter © 2018 Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction Article 03 June 2024 On Source Code Completion Assistants and the Need of a Context-Aware Approach Chapter © 2017 Explore related subjects Discover the latest articles, books and news in related subjects, suggested using machine learning.
    keywords: code; completion; edit; evaluation; language; metric; model; similarity; software; suggestions; tasks
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        item: #51 of 282
          id: bracis-19069
      author: Costa, Leonardo F. da; Fernandes, Lucas S.; Andrade, João P. B.; Rego, Paulo A. L.; Maia, José G. R.
       title: Deep Convolutional Features for Fingerprint Indexing
        date: 2021
       words: 5345
      flesch: 57
     summary: Fingerprint indexing can be done using different fingerprint features, such as singular points [25], minutiae [7], and texture [12]. This work presents a method for fingerprint indexing, which uses both exact and approximation methods of nearest neighbors (ANNs), which are very efficient in terms of runtime, even if they sacrifice a little accuracy by presenting approximate solutions.
    keywords: ann; cnn; features; fingerprint; fvc; google; image; indexing; method; rate; results; scholar; search; size
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        item: #52 of 282
          id: bracis-19070
      author: Pérez, Sarah Pires; Cozman, Fabio Gagliardi
       title: How to Generate Synthetic Paintings to Improve Art Style Classification
        date: 2021
       words: 5803
      flesch: 52
     summary: In: Neural Information Processing Systems, pp. 2180–2188 (2016) Google Scholar  Chu, W.T., Wu, Y.L.: Image style classification based on learnt deep correlation features. Recognizing image style.
    keywords: art; artwork; augmentation; classification; conference; data; gan; google; image; learning; model; networks; performance; scholar; style
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        item: #53 of 282
          id: bracis-19071
      author: Rocha Filho, Itamar de Paiva; Teixeira, João Pedro Vasconcelos; Lins, João Wallace Lucena; Sousa, Felipe Honorato de; Sousa, Ana Clara Chaves; Ferreira Junior, Manuel; Ramos, Thaís; Silva, Cecília; Rêgo, Thaís Gaudencio do; Malheiros, Yuri de Almeida; Silva Filho, Telmo
       title: Iris-CV: Classifying Iris Flowers Is Not as Easy as You Thought
        date: 2021
       words: 4066
      flesch: 51
     summary: This work uses the iNaturalist dataset as a single source, but some other researchers used it combined with different datasets. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017) Google Scholar  Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database.
    keywords: author; computer; dataset; google; images; iris; learning; machine; scholar; search; table
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        item: #54 of 282
          id: bracis-19072
      author: Silveira, Fábio Amaral Godoy da; Tetila, Everton Castelão; Astolfi, Gilberto; Costa, Anderson Bessa da; Amorim, Willian Paraguassu
       title: Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans
        date: 2021
       words: 5956
      flesch: 51
     summary: abs/1903.10827, http://arxiv.org/abs/1903.10827 Deng, L., Wang, Y., Han, Z., Yu, R.: Research on insect pest image detection and recognition based on bio-inspired methods. ISBN 978-85-7035-139-5 Tetila, E.C., Machado, B.B., Menezes, G.V., de Souza Belete, N.A., Astolfi, G., Pistori, H.: A deep-learning approach for automatic counting of soybean insect pests.
    keywords: accuracy; batch; classification; cnn; dataset; detection; images; insect; learning; methods; model; object; pest; recognition; results; size; soybean; species; yolov3
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        item: #55 of 282
          id: bracis-19073
      author: Granero, Marco Aurélio; Hernández, Cristhian Xavier; Valle, Marcos Eduardo
       title: Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis
        date: 2021
       words: 5483
      flesch: 48
     summary: Quaternion convolutional neural networks for end-to-end automatic speech recognition. https://doi.org/10.1016/j.engappai.2018.04.024 Zhu, X., Xu, Y., Xu, H., Chen, C.: Quaternion convolutional neural networks.
    keywords: accuracy; article; blood; classification; color; conference; diagnosis; google; image; information; leukemia; lymphoblast; networks; quaternion; scholar
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        item: #56 of 282
          id: bracis-19074
      author: Bisinotto, Gustavo A.; Cotrim, Lucas P.; Cozman, Fabio Gagliardi; Tannuri, Eduardo A.
       title: Sea State Estimation with Neural Networks Based on the Motion of a Moored FPSO Subjected to Campos Basin Metocean Conditions
        date: 2021
       words: 6137
      flesch: 50
     summary: [6], time series of movements from an in-service frigate type vessel and data from numerical simulation were considered to output wave height, period and direction. 3. Power and directional wave spectra Full size image Due to the irregular nature of waves, it is not reasonable to assume a single wave height or wave period.
    keywords: data; direction; estimation; fig; google; height; motion; networks; parameters; scholar; sea; size; state; time; wave
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        item: #57 of 282
          id: bracis-19075
      author: Pires, Pedro R.; Pascon, Amanda C.; Almeida, Tiago A.
       title: Time-Dependent Item Embeddings for Collaborative Filtering
        date: 2021
       words: 5798
      flesch: 49
     summary: This study shows how to adapt a pioneering method of item embeddings by adding a sliding window over time, in conjunction with a split in the user’s interaction history. The Item2Vec was a pioneering technique in introducing neural embedding techniques from Natural Language Processing to the recommendation scenario, adapting the skip-gram network to generate item embeddings.
    keywords: conference; data; embeddings; information; item; item2vec; learning; neural; proceedings; recommendation; recommender; results; systems; time; user
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        item: #58 of 282
          id: bracis-19076
      author: Souza, Alexandre Felipe Muller de; Cassenote, Mariane R. S.; Silva, Fabiano
       title: Transfer Learning of Shapelets for Time Series Classification Using Convolutional Neural Network
        date: 2021
       words: 5833
      flesch: 53
     summary: Cross validation, shapelet series training, importing model from 4; 7. One of the works that supports several others in the field of time series classification is the use of a collection of classifiers called COTE
    keywords: classification; data; experiments; learning; model; network; results; series; shapelets; time; time series; training
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        item: #59 of 282
          id: bracis-19077
      author: Barros, José Meléndez; De Bona, Glauber
       title: A Deep Learning Approach for Aspect Sentiment Triplet Extraction in Portuguese
        date: 2021
       words: 5887
      flesch: 55
     summary: To the best of our knowledge, we have developed the most fine-grained model to deal with aspect sentiment tasks in Portuguese. http://arxiv.org/abs/1901.05287 Han, H., Li, X., Zhi, S., Wang, H.: Multi-attention network for aspect sentiment analysis.
    keywords: \mathbf; aspect; attention; bert; dependency; extraction; information; layer; model; opinion; portuguese; sentiment; triplet; vectors; word
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        item: #60 of 282
          id: bracis-19078
      author: Mamani-Condori, Errol; Ochoa-Luna, José
       title: Aggressive Language Detection Using VGCN-BERT for Spanish Texts
        date: 2021
       words: 6480
      flesch: 55
     summary: To do so, we use the combination of BERT model and Vocabulary Graph Convolutional Network which improves local information encoded in BERT embeddings by adding global information between words and concepts (Vocabulary GCN). TecNM at MEX-A3T 2020: Fake news and aggressiveness analysis in Mexican Spanish (2020) Google Scholar  Plaza-del Arco, F.M., Molina-González, M.D., Ureña-López, L.A., Martín-Valdivia, M.T.: Comparing pre-trained language models for Spanish hate speech detection.
    keywords: aggressiveness; bert; content; detection; google; graph; information; language; model; results; scholar; spanish; vgcn; vocabulary; words
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        item: #61 of 282
          id: bracis-19079
      author: Oliveira, Miguel de; Melo, Tiago de
       title: An Empirical Study of Text Features for Identifying Subjective Sentences in Portuguese
        date: 2021
       words: 5135
      flesch: 52
     summary: However, the vast majority of them did not handle texts in the Brazilian Portuguese language, and there is no one to consider the combination of sets of text features of NLP tasks with classifiers. Many types of text features have been proposed and evaluated in the literature, such as syntactic features and part-of-speech features.
    keywords: classification; features; google; language; learning; portuguese; scholar; sentences; sentiment; set; subjectivity; table; text
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        item: #62 of 282
          id: bracis-19080
      author: Andrade Junior, José E.; Cardoso-Silva, Jonathan; Bezerra, Leonardo C. T.
       title: Comparing Contextual Embeddings for Semantic Textual Similarity in Portuguese
        date: 2021
       words: 6889
      flesch: 56
     summary: First, we compare the performance of pre-trained SBERT models with the state-of-the-art BERT models for the ASSIN datasets [8]. Furthermore, multilingual pre-trained SBERT models have been made available in an open source SBERT repository (https://www.sbert.net).
    keywords: art; assin; embeddings; fine; language; models; portuguese; results; sbert; sentence; sts; training; tuning
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        item: #63 of 282
          id: bracis-19081
      author: S. Neto, José Reinaldo C. S. A. V.; Faleiros, Thiago de Paulo
       title: Deep Active-Self Learning Applied to Named Entity Recognition
        date: 2021
       words: 5516
      flesch: 54
     summary: For once, deep learning models are slow to be retrained from scratch for each active learning iteration when compared to shallow models. [6, 17] with the use of deep learning models and key changes to alleviate the sensitivity of the self learning process to the initial labeled set.
    keywords: algorithm; asl; cnn; datasets; experiments; learning; model; performance; samples; self; set; training
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        item: #64 of 282
          id: bracis-19082
      author: Cação, Flávio Nakasato; José, Marcos Menon; Oliveira, André Seidel; Spindola, Stefano; Costa, Anna Helena Reali; Cozman, Fabio Gagliardi
       title: DEEPAGÉ: Answering Questions in Portuguese About the Brazilian Environment
        date: 2021
       words: 5945
      flesch: 54
     summary: In this work, we start to fill this gap by putting together QA systems that enhance existing architectures and that are built from a knowledge base (KB) consisting of 17K Wikipedia articles in PortugueseFootnote 1 and 29K news. On the dataset availability side, [1] recently released Pirá, the first Bilingual Portuguese-English crowdsourced QA dataset about the ocean and, in particular, the Brazilian coast, based on UN reports and abstracts from scientific papers.
    keywords: brazil; dataset; domain; environment; google; language; model; news; pairs; portuguese; question; reader; retriever; system
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        item: #65 of 282
          id: bracis-19083
      author: Consoli, Bernardo Scapini; Vieira, Renata
       title: Enriching Portuguese Word Embeddings with Visual Information
        date: 2021
       words: 5804
      flesch: 49
     summary: It involved the development of word embedding models which were then put through a test battery for multimodal Word Embedding models which included the following tasks: Word Relatedness, Sentence Similarity, Analogy Prediction and Named Entity Recognition. Beyond these efforts to further enhance the usage of text in the training of word embedding models, be it Portuguese language text or otherwise, an effort to enrich these embeddings with other modes of information also arose.
    keywords: architecture; embeddings; fusion; language; model; multimodal; portuguese; results; test; text; textual; word
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        item: #66 of 282
          id: bracis-19084
      author: Reyes, Daniel De Los; Trajano, Douglas; Manssour, Isabel Harb; Vieira, Renata; Bordini, Rafael H.
       title: Entity Relation Extraction from News Articles in Portuguese for Competitive Intelligence Based on BERT
        date: 2021
       words: 6585
      flesch: 51
     summary: Cristina Mota; Diana Santos (ed) Desafios na avaliação conjunta do reconhecimento de entidades mencionadas: O Segundo HAREM Linguateca 2008 (2008) Google Scholar  Cheng, W., Xiong, J.: Entity relationship extraction based on bi-channel neural network. IEEE (2020) Google Scholar  Zhou, Z., Zhang, H.: Research on entity relationship extraction in financial and economic field based on deep learning.
    keywords: bert; conference; data; entities; entity; extraction; google; information; language; market; model; portuguese; relationships; scholar; sentence; work
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        item: #67 of 282
          id: bracis-19085
      author: Batista, Cassio; Neto, Nelson
       title: Experiments on Kaldi-Based Forced Phonetic Alignment for Brazilian Portuguese
        date: 2021
       words: 6546
      flesch: 57
     summary: On the other hand, among Kaldi models, tri-\(\Updelta \) stands out as being virtually the best one. With respect to ASR-based frameworks, we found only three forced aligners that provide pre-trained models for Brazilian Portuguese (BP): EasyAlign [9], Montreal Forced Aligner (MFA)
    keywords: acoustic; alignment; audio; dataset; evaluation; google; kaldi; mfa; models; phonemes; portuguese; scholar; speech; table; training; ufpalign
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        item: #68 of 282
          id: bracis-19086
      author: Lima, Beatriz; Nogueira, Tatiane
       title: Incorporating Text Specificity into a Convolutional Neural Network for the Classification of Review Perceived Helpfulness
        date: 2021
       words: 6991
      flesch: 49
     summary: Because of the good performance of this technique on review domains (restaurant and movie reviews), it was employed by the present work to infer the specificity degree of the unlabeled sentences from our product reviews. a case study of product review helpfulness prediction.
    keywords: classification; cnn; experiments; features; helpfulness; length; model; prediction; results; reviews; sentences; specificity; task; text
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        item: #69 of 282
          id: bracis-19087
      author: Sacramento, Anderson da Silva Brito; Souza, Marlo
       title: Joint Event Extraction with Contextualized Word Embeddings for the Portuguese Language
        date: 2021
       words: 6745
      flesch: 52
     summary: Our experimental results show that our method is able to predict event types and arguments automatically, and the proposed method of data augmentation, in one of the two evaluated samples, contributes to the performance of the tested models in the subtask of argument role prediction. We developed a method to identify and classify a closed set of event types whose arguments and their roles were previously specified based on machine learning.
    keywords: annotation; argument; corpus; data; event; extraction; language; model; portuguese; representation; role; sentence; task; trigger; types; word
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        item: #70 of 282
          id: bracis-19088
      author: José, Marcelo Archanjo; Cozman, Fabio Gagliardi
       title: mRAT-SQL+GAP: A Portuguese Text-to-SQL Transformer
        date: 2021
       words: 5576
      flesch: 60
     summary: orcid.org/0000-0001-7153-040210 & Fabio Gagliardi Cozman  ORCID: orcid.org/0000-0003-4077-493511  Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13074)) Included in the following conference series: Brazilian Conference on Intelligent Systems 1360 Accesses 16 Citations 5 Altmetric Abstract The translation of natural language questions to SQL queries has attracted growing attention, in particular in connection with transformers and similar language models. Machine learning approaches are based on supervised learning, in which training data contains natural language questions and paired SQL queries [3].
    keywords: dataset; english; language; line; model; portuguese; query; questions; rat; sql; sql+gap; table; training
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        item: #71 of 282
          id: bracis-19089
      author: Silva, Diego F.; M. e Silva, Alcides; Lopes, Bianca M.; Johansson, Karina M.; Assi, Fernanda M.; Jesus, Júlia T. C. de; Mazo, Reynold N.; Lucrédio, Daniel; Caseli, Helena M.; Real, Livy
       title: Named Entity Recognition for Brazilian Portuguese Product Titles
        date: 2021
       words: 6236
      flesch: 58
     summary: The same seeds (seed1 and seed2) used for training BERT NER models were also used for training MITIE NER models with one difference: MITIE does not explicitly use a validation set. Table 2. F1 scores for BERT models in test setFull size table It is interesting to notice that, although by a little difference, the model fine-tuned from the BERTimbau-Base performed better than the one fine-tuned from a version trained for NER in HAREM dataset.
    keywords: attribute; author; bert; bertimbau; celular; google; mitie; model; ner; product; results; scholar; seed2; smartphone; titles; training
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        item: #72 of 282
          id: bracis-19090
      author: Lima, Tiago B. de; Nascimento, André C. A.; Valença, George; Miranda, Pericles; Mello, Rafael Ferreira; Si, Tapas
       title: Portuguese Neural Text Simplification Using Machine Translation
        date: 2021
       words: 5487
      flesch: 52
     summary: 26–32 (2015) Google Scholar  Cooper, M., Shardlow, M.: CombiNMT: an exploration into neural text simplification models. Exploring neural text simplification models.
    keywords: ats; corpus; google; language; machine; methods; model; nmt; portuguese; scholar; score; sentence; simplification; table; text; translation
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        item: #73 of 282
          id: bracis-19091
      author: Aragy, Roberto; Fernandes, Eraldo Rezende; Caceres, Edson Norberto
       title: Rhetorical Role Identification for Portuguese Legal Documents
        date: 2021
       words: 6236
      flesch: 52
     summary: Rhetorical role identification (RRI) is an NLP task that consists of labeling the sentences of a document according to a given set of semantic functions (rhetorical roles). At the best of our knowledge, this is the first work to deal with rhetorical role identification for petitions, given that previous works focused only on judicial decisions.
    keywords: bert; civil; classification; corpus; court; decisions; documents; identification; language; learning; model; petitions; portuguese; representation; roles; sentences; text; work
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        item: #74 of 282
          id: bracis-19092
      author: Casanova, Edresson; Candido Junior, Arnaldo; Shulby, Christopher; Oliveira, Frederico Santos de; Gris, Lucas Rafael Stefanel; Silva, Hamilton Pereira da; Aluísio, Sandra Maria; Ponti, Moacir Antonelli
       title: Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models
        date: 2021
       words: 6650
      flesch: 55
     summary: In this work, we propose a new method for training speaker recognition models, called Speech2Phone. 5 Conclusions and Future Work In this article, we proposed a novel training method for speaker recognition models, called Speech2Phone.
    keywords: dataset; experiment; google; loss; method; model; recognition; scenario; scholar; speaker; speech; speech2phone; training; voice
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        item: #75 of 282
          id: bracis-19093
      author: Aguiar, André; Silveira, Raquel; Pinheiro, Vládia; Furtado, Vasco; Araújo Neto, João
       title: Text Classification in Legal Documents Extracted from Lawsuits in Brazilian Courts
        date: 2021
       words: 6366
      flesch: 48
     summary: We assume that the relevance of cited legislations in lawsuit texts is an important information for the learning model: legislation cited only in lawsuits of the same class should be considered more important than legislation cited in lawsuits of several classes. Results in terms of F1 score macro for lawsuit classification in different scenarios and models.
    keywords: brazilian; classes; classification; court; documents; embeddings; google; lawsuit; legislation; model; petitions; scenario; scholar; text
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        item: #76 of 282
          id: bracis-19094
      author: Lopes, Lucelene; Duran, Magali S.; Pardo, Thiago A. S.
       title: Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources
        date: 2021
       words: 5263
      flesch: 51
     summary: Table 8 presents the obtained accuracy starting from the original system annotation, followed by the application of our automatic correction of non-ambiguous single tokens (ACS) and automatic correction of non-ambiguous co-occurring tokens (ACC). The technique is based on the development and use of lists of non-ambiguous single tokens and non-ambiguous co-occuring tokens in Portuguese (regardless of whether they constitute multiword expressions or not).
    keywords: annotation; classes; pos; sentences; set; table; tagging; tokens
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        item: #77 of 282
          id: bracis-19095
      author: Privatto, Pedro Ivo Monteiro; Guilherme, Ivan Rizzo
       title: When External Knowledge Does Not Aggregate in Named Entity Recognition
        date: 2021
       words: 5025
      flesch: 51
     summary: Further, we also aggregate the aim of validation: External Knowledge embeddings. An approach for named entity recognition in poorly structured data.
    keywords: association; conference; embeddings; entity; information; knowledge; linguistics; methods; models; recognition; results; use; word
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        item: #78 of 282
          id: bracis-28345
      author: Barros, Marcel Rodrigues de; Rissi, Thiago Lizier; Cabrera, Eduardo Faria; Tannuri, Eduardo Aoun; Gomi, Edson Satoshi; Barreira, Rodrigo Augusto; Costa, Anna Helena Reali
       title: Embracing Data Irregularities in Multivariate Time Series with Recurrent and Graph Neural Networks
        date: 2023
       words: 5528
      flesch: 51
     summary: Being invariant to this symmetry means that gated RNNs can associate patterns in time series data even if they are distorted in time as long as order is preserved. Models like the Transformer [17] have generated groundbreaking results in tasks such as question answering and text classification, thereby prompting the question: Can such successes be replicated in the context of time series?
    keywords: \mathcal; architecture; author; data; encoding; fig; function; graph; information; model; mts; networks; representation; series; size; time; time series
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        item: #79 of 282
          id: bracis-28346
      author: Oliveira, Cristina Godoy B. de; Albuquerque, Otávio de Paula; Belotti, Emily Liene; Lopes, Isabella Ferreira; Silva, Rodrigo Brandão de A.; Arbix, Glauco
       title: Regulation and Ethics of Facial Recognition Systems: An Analysis of Cases in the Court of Appeal in the State of São Paulo
        date: 2023
       words: 7520
      flesch: 44
     summary: Section 3 presents and discusses the obtained results, highlighting the dependence of facial recognition systems on sensitive personal data and noting that the Judiciary of the State of São Paulo has given little importance to the requirement of free and informed consent for the treatment of such data. Such data can be explained by a conjuncture of phenomena, among them, the “esteira invertida”, in which fraudsters—usually bank correspondentsFootnote 3 or financial market operators—deposit an uncontracted loan in the account of the retiree or pensioner, without their authorization, to receive up to 6% of the transaction amount as commission.
    keywords: appeal; article; artificial; cases; court; data; decisions; google; intelligence; paulo; recognition; research; scholar; state; systems; são; technology; use
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        item: #80 of 282
          id: bracis-28347
      author: Sampaio, Igor M.; Lima, Karla Roberta P. S.
       title: A Combinatorial Optimization Model and Polynomial Time Heuristic for a Problem of Finding Specific Structural Patterns in Networks
        date: 2023
       words: 5601
      flesch: 56
     summary: In general the problem of searching for tropical subgraphs in vertex colored graphs has been explored extensively for some classes of graphs; for more details see [1, 3, 6]. Results of the computational experiments on real-world instances for graphs in generalFull size table From the perspective of random graph instances in general, the heuristic algorithm found the integer optimal solution for \(28.26\%\) of the instances; \(86.96\%\) of the instances presented a gap, that is, a difference, in percent, between the integer optimal solution presented by the ILP model and the solution of the heuristic algorithm, of less than \(30\%\); and \(60.87\%\) of the instances presented a gap of less than \(10\%\).
    keywords: \in; algorithm; cactus; colors; graph; heuristic; instances; model; number; problem; solution; vertex; vertices
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        item: #81 of 282
          id: bracis-28348
      author: Batista, Natanael F. Dacioli; Nunes, Bruno Leonel; Naldi, Murilo Coelho
       title: Efficient Density-Based Models for Multiple Machine Learning Solutions over Large Datasets
        date: 2023
       words: 6888
      flesch: 55
     summary: J. 3(4), 209–235 (2010) Article  MathSciNet  MATH  Google Scholar  Zerhari, B., Lahcen, A.A., Mouline, S.: Big data clustering: Algorithms and challenges. In: Proceedings of International Conference on Big Data, Cloud and Applications (BDCA-5) (2015) Google Scholar  Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases.
    keywords: article; clustering; clusters; core; data; dbs; density; distance; google; graph; minpts; objects; scholar; ssg; summarization
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        item: #82 of 282
          id: bracis-28349
      author: Sakiyama, Kenzo; Montanari, Raphael; Junior, Roseval Malaquias; Nogueira, Rodrigo; Romero, Roseli A. F.
       title: Exploring Text Decoding Methods for Portuguese Legal Text Generation
        date: 2023
       words: 6190
      flesch: 55
     summary: In this article, we investigate text decoding methods for automating the writing of keyphrases, a sequence of key terms present in documents used in courts throughout Brazil. Specifically, we seek to investigate text decoding methods in order to generate keyphrases that aid retrieval in the legal domain.
    keywords: decoding; dockets; documents; generation; google; keyphrases; language; methods; metrics; retrieval; sampling; scholar; text; tokens
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        item: #83 of 282
          id: bracis-28350
      author: Gatto, Elaine Cecília; Valejo, Alan Demétrius Baria; Ferrandin, Mauri; Cerri, Ricardo
       title: Community Detection for Multi-label Classification
        date: 2023
       words: 6406
      flesch: 49
     summary: Figure 1 presents our idea of multi-label partitions, where squares are the partitions, circles are label clusters, and diamonds are labels. Appl. 203, 117215 (2022) Google Scholar  Chang, W., Yu, H., Zhong, K., Yang, Y., Dhillon, I.S.: A modular deep learning approach for extreme multi-label text classification.
    keywords: approach; classification; classifier; community; correlations; global; google; hybrid; label; local; methods; partitions; results; scholar
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        item: #84 of 282
          id: bracis-28351
      author: Putrich, Victor Scherer; Tavares, Anderson Rocha; Meneguzzi, Felipe
       title: A Monte Carlo Algorithm for Time-Constrained General Game Playing
        date: 2023
       words: 5960
      flesch: 56
     summary: \end{aligned}$$ (3) Here, \(Q_{s,a}\) is the mean reward from action a, when selected from state s. \(N_s\) is the number of visits on state s, while \(n_{s,a}\) is the number of times action a has been selected in state s. Ludii’s GDL is robust and straightforward, it allows researchers and game designers to create new games and even reproduce historical ones
    keywords: \(\text; algorithm; carlo; game; ggp; halving; node; regret; search; sh}}}}\; time; tree; uct; uct\(_{{\sqrt{\text
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        item: #85 of 282
          id: bracis-28352
      author: Crispino, Gabriel Nunes; Freire, Valdinei; Delgado, Karina Valdivia
       title: α-MCMP: Trade-Offs Between Probability and Cost in SSPs with the MCMP Criterion
        date: 2023
       words: 6649
      flesch: 61
     summary: in(s) represents the expected flow entering s, while out(s) represents the expected flow leaving s. Constraint (C4) restricts that except for \(s_0\) and goal states, the expected flow entering a state must be equal to the expected flow leaving it. In this LP, the objective function maximizes the total expected flow of reaching goal states, which is equivalent to the probability-to-goal from the initial state \(s_0\).
    keywords: \(\alpha; \le; \mathcal; cost; criterion; goal; gubs; policy; priority; probability; value
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        item: #86 of 282
          id: bracis-28353
      author: Machado, Warlles Carlos Costa; Santos, Viviane Bonadia dos; Barros, Leliane Nunes de; Menezes, Maria Viviane de
       title: Specifying Preferences over Policies Using Branching Time Temporal Logic
        date: 2023
       words: 7411
      flesch: 56
     summary: Up to our knowledge, this is the first work to solve non-deterministic planning problems with preferences using a CTL temporal logic. The work in [27] presents a planning algorithm that aims to solve non-deterministic planning problems with temporally extended goals (complex goals), while also considering the quality of the policy (weak, strong, or strong-cyclic).
    keywords: \(\alpha; \in; \mathcal; actions; goal; non; path; planning; policy; preferences; problem; scholar; set; state
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        item: #87 of 282
          id: bracis-28354
      author: Rocha, Francisco Mateus; Rocha, Thiago Alves; Ribeiro, Reginaldo Pereira Fernandes; Rocha, Ajalmar Rêgo
       title: Logic-Based Explanations for Linear Support Vector Classifiers with Reject Option
        date: 2023
       words: 7603
      flesch: 55
     summary: While most of the related work has developed means to give such explanations for machine learning models, to the best of our knowledge none have done so for when reject option is present. Due to this, such explanations are provably correct and hold for any point in the space, which therefore makes them trustworthy [7].
    keywords: \ge; \le; anchors; approach; classification; explanations; feature; google; instance; linear; models; option; reject; scholar
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        item: #88 of 282
          id: bracis-28355
      author: Dantas, Ana Paula S.; Oliveira, Gabriel Bianchin de; Pedrini, Helio; Souza, Cid C. de; Dias, Zanoni
       title: The Multi-attribute Fairer Cover Problem
        date: 2023
       words: 6891
      flesch: 60
     summary: We followed this configuration for both fair and random selection of images for age regression models. 2.2 Proposed Method In this subsection, we describe the proposed method for fair age regression, divided into ilp and age regression models.
    keywords: age; color; cover; dataset; elements; model; number; problem; regression; size; training; value
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        item: #89 of 282
          id: bracis-28356
      author: Carneiro, Sarah Ribeiro Lisboa; Souza, Michael Ferreira de; Cardoso, Douglas O.; Tarrataca, Luís; Assis, Laura S.
       title: A Custom Bio-Inspired Algorithm for the Molecular Distance Geometry Problem
        date: 2023
       words: 5787
      flesch: 57
     summary: Optimization, and Machine Learning (1989) Google Scholar  Goncalves, D.S., Lavor, C., Liberti, L., Souza, M.: A new algorithm for the \(^k\)dmdgp subclass of distance geometry problems (2020) Google Scholar  Gong, Y.J., et al.: 10(03), 1242009 (2012) Article  MATH  Google Scholar  Mucherino, A., Liberti, L., Lavor, C.: MD-jeep: an implementation of a branch and prune algorithm for distance geometry problems.
    keywords: algorithm; atoms; distance; figa; geometry; google; instances; molecular; problem; scholar; search; set; size; solution
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        item: #90 of 282
          id: bracis-28357
      author: Silva, João da; Peres, Sarajane; Cordeiro, Daniel; Freire, Valdinei
       title: Allocating Dynamic and Finite Resources to a Set of Known Tasks
        date: 2023
       words: 6921
      flesch: 59
     summary: Section 4 gives an overview of related works on the state of the art of task allocation. In recent works, social search engines have become very useful as it uses information about the resources so as to improve task allocation
    keywords: \end{aligned}$$; \in; \in \mathcal; \mathcal; allocation; google; number; problem; r \in; resources; scholar; solutions; t \in; tasks
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        item: #91 of 282
          id: bracis-28358
      author: Justino, Gabriela T.; Freitas, Gabriela C.; Batista, Camilla B.; Cotta, Kleyton P.; Deon, Bruno; Loução Jr., Flávio L.; Almeida, Rodrigo J. S. de; Araújo Jr., Carlos A. A. de
       title: A Multi-algorithm Approach to the Optimization of Thermal Power Plants Operation
        date: 2023
       words: 5598
      flesch: 46
     summary: https://doi.org/10.1016/j.asoc.2022.109021 Article  Google Scholar  Tian, J., Wei, H., Tan, J.: Global optimization for power dispatch problems based on theory of moments. References Al-Amyal, F., Al-attabi, K.J., Al-khayyat, A.: Multistage ant colony algorithm for economic emission dispatch problem.
    keywords: algorithm; author; dispatch; energy; fuel; function; generating; objective; operation; optimization; plants; power; problem; system; table; time
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        item: #92 of 282
          id: bracis-28359
      author: Ferreira Júnior, Antônio Carlos Souza; Rocha, Thiago Alves
       title: An Incremental MaxSAT-Based Model to Learn Interpretable and Balanced Classification Rules
        date: 2023
       words: 7035
      flesch: 62
     summary: Efficient learning of interpretable classification rules. Res. 74, 1823–1863 (2022) Article  MathSciNet  MATH  Google Scholar  Ghosh, B., Meel, K.S.: IMLI: an incremental framework for MaxSAT-based learning of interpretable classification rules.
    keywords: \({\textbf; \bigwedge; \in; \lnot; \wedge; imli; learning; rules; samples; set; size
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        item: #93 of 282
          id: bracis-28360
      author: Sartori, Joelson; Lucca, Giancarlo; Asmus, Tiago; Santos, Helida; Borges, Eduardo; Bedregal, Benjamin; Bustince, Humberto; Dimuro, Graçaliz Pereira
       title: d-CC Integrals: Generalizing CC-Integrals by Restricted Dissimilarity Functions with Applications to Fuzzy-Rule Based Systems
        date: 2023
       words: 6886
      flesch: 60
     summary: Article  MATH  Google Scholar  Marco-Detchart, C., Lucca, G., Lopez-Molina, C., De Miguel, L., Pereira Dimuro, G., Bustince, H.: Neuro-inspired edge feature fusion using Choquet integrals. Article  Google Scholar  Lucca, G., Dimuro, G.P., Fernandez, J., Bustince, H., Bedregal, B., Sanz, J.A.: Improving the performance of fuzzy rule-based classification systems based on a nonaveraging generalization of CC-integrals named \(C_{F_1F_2}\)-integrals.
    keywords: 0,1]\; \(\delta; \in; \ldots; \le; cc}_{\delta; classification; functions; google; integrals; scholar; table
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        item: #94 of 282
          id: bracis-28361
      author: Silva, Sammuel Ramos; Silva, Rodrigo
       title: FeatGeNN: Improving Model Performance for Tabular Data with Correlation-Based Feature Extraction
        date: 2023
       words: 5551
      flesch: 47
     summary: The proposed method consists of four phases: Pre-processing to reduce dimensionality, where the authors perform feature selection based on information gain (IG); Mining of correlated features to define and search for pairwise correlated features, where the distance correlation [8] is calculated to determine if there is an interesting predictive relationship between a pair of features; Feature generation, where regularized regression algorithms are used to search for associations between features and generate new features; and Feature selection, where features that do not add new information to the dataset are discarded. To address these challenges, we propose a novel convolutional method called FeatGeNN that extracts and creates new features using correlation as a pooling function.
    keywords: correlation; data; dataset; featgenn; features; learning; machine; max; model; number; performance; pooling; selection
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        item: #95 of 282
          id: bracis-28362
      author: Cardoso, Leonardo Vilela; Gomes, Gustavo Oliveira Rocha; Guimarães, Silvio Jamil Ferzoli; Patrocínio Júnior, Zenilton Kleber Gonçalves do
       title: Hierarchical Time-Aware Approach for Video Summarization
        date: 2023
       words: 5910
      flesch: 51
     summary: Similarly to recent deep-learning-based approaches, the proposed method uses pre-trained neural networks to generate video frame descriptions. It uses pre-trained neural networks to generate video frame descriptions with a hierarchical graph-based clustering strategy.
    keywords: approach; frames; google; graph; hietasumm; keyframes; method; results; scholar; similarity; summarization; summary; time; user; video
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        item: #96 of 282
          id: bracis-28363
      author: Santos, Rodolfo Sanches; Ponti, Moacir Antonelli; Rodrigues, Kamila Rios
       title: Analyzing College Student Dropout Risk Prediction in Real Data Using Walk-Forward Validation
        date: 2023
       words: 6423
      flesch: 51
     summary: Master’s thesis, Universidade de Brasília (2011) Google Scholar  Meedech, P., Iam-On, N., Boongoen, T.: Prediction of student dropout using personal profile and data mining approach. Psicothema 30 (2018) Google Scholar  Eisenberg, D., Gollust, S., Golberstein, E., Hefner, J.: Prevalence and correlates of depression, anxiety, and suicidality among university students.
    keywords: college; course; data; dropout; education; features; google; google scholar; information; learning; results; risk; scholar; students; table; training; university; years
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        item: #97 of 282
          id: bracis-28364
      author: Valentim, Nathália Assis; Dorça, Fabiano Azevedo; Asnis, Valéria Peres; Elias, Nassim Chamel
       title: The Artificial Intelligence as a Technological Resource in the Application of Tasks for the Development of Joint Attention in Children with Autism
        date: 2023
       words: 1684
      flesch: 39
     summary: Master’s thesis, Universidade Federal Fluminense (2021) Google Scholar  American Psychiatric Association: DSM-5: manual diagnóstico e estatístico de transtornos mentais. UEL (2019) Google Scholar  Gera, D., Balasubramanian, S.: Landmark guidance independent spatio-channel attention and complementary context information based facial expression recognition.
    keywords: attention; author; autism; children; google; intelligence; scholar; spectrum
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        item: #98 of 282
          id: bracis-28365
      author: Ferreira, Eduardo Vargas; Lorena, Ana Carolina
       title: Machine Teaching: An Explainable Machine Learning Model for Individualized Education
        date: 2023
       words: 6403
      flesch: 50
     summary: In: Contemporary Psychometrics: A Festschrift for Roderick P. McDonald (2013) Google Scholar  Ansari, A., Essegaier, S., Kohli, R.: Internet recommendation systems. Res. 11, 1803–1831 (2010) MathSciNet  MATH  Google Scholar  Béguin, A.A., Glas, C.A.: MCMC estimation and some model-fit analysis of multidimensional IRT models.
    keywords: \displaystyle; \left; \sigma; \textbf{y; \theta; data; education; google; items; latent; learning; machine; model; parameters; responses; scholar; traits
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        item: #99 of 282
          id: bracis-28366
      author: Almeida, Thales Sales; Laitz, Thiago; Bonás, Giovana K.; Nogueira, Rodrigo
       title: BLUEX: A Benchmark Based on Brazilian Leading Universities Entrance eXams
        date: 2023
       words: 3994
      flesch: 48
     summary: Citations Abstract One common trend in recent studies of language models (LMs) is the use of standardized tests for evaluation. Palm: scaling language modeling with pathways (2022) Google Scholar  FitzGerald, J., et al.:
    keywords: bluex; brazilian; dataset; exams; google; language; models; performance; portuguese; questions; scholar
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        item: #100 of 282
          id: bracis-28367
      author: Jasinski, Henrique; Morveli-Espinoza, Mariela; Tacla, Cesar Augusto
       title: Towards Generating P-Contrastive Explanations for Goal Selection in Extended-BDI Agents
        date: 2023
       words: 6751
      flesch: 57
     summary: This is important when the high priority of such goal can explain its selection over any other goals. This process is known as goal selection.
    keywords: \in; \mathcal; agent; beliefs; causes; conditions; explanations; generating; goal; preference; scholar; selection; set; status
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        item: #101 of 282
          id: bracis-28368
      author: Rocha, Michele; Silva, Heitor Henrique da; Morales, Analúcia Schiaffino; Sarkadi, Stefan; Panisson, Alison R.
       title: Applying Theory of Mind to Multi-agent Systems: A Systematic Review
        date: 2023
       words: 6496
      flesch: 54
     summary: 280, 103216 (2020) Article  MathSciNet  MATH  Google Scholar  Baron-Cohen, S.: Mindblindness: An Essay on Autism and Theory of Mind. MIT Press (1997) Google Scholar  Baron-Cohen, S., Leslie, A.M., Frith, U.: Does the autistic child have a “theory of mind?’’.
    keywords: agents; article; authors; conference; google; google scholar; mas; mind; research; scholar; systems; table; theory; tom; works
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        item: #102 of 282
          id: bracis-28369
      author: Pantoja, Carlos Eduardo; Jesus, Vinicius Souza de; Lazarin, Nilson Mori; Viterbo, José
       title: A Spin-off Version of Jason for IoT and Embedded Multi-Agent Systems
        date: 2023
       words: 6396
      flesch: 51
     summary: Thus, only the below four works [8, 12, 15, 25] present a framework for programming IoT agents. https://doi.org/10.1109/ACCESS.2020.3027357 Article  Google Scholar  Pantoja, C., Soares, H.D., Viterbo, J., Seghrouchni, A.E.F.: An architecture for the development of ambient intelligence systems managed by embedded agents.
    keywords: agents; argo; communicator; control; devices; embedded; framework; hardware; iot; jason; mas; mobility; scholar; systems
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        item: #103 of 282
          id: bracis-28370
      author: Miranda Filho, Renato; Pappa, Gisele L.
       title: Hybrid Multilevel Explanation: A New Approach for Explaining Regression Models
        date: 2023
       words: 5992
      flesch: 48
     summary: In Sect. 2, we review the existing literature on explanation methods for regression models. As per MAME, little attention has been given to obtaining insights at an intermediate level of model explanations.
    keywords: explanation; features; google; humie; instances; level; model; output; prototypes; regression; scholar; tree; values
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        item: #104 of 282
          id: bracis-28371
      author: Kuhn, Daniel Matheus; Loreto, Melina Silva de; Recamonde-Mendoza, Mariana; Comba, João Luiz Dihl; Moreira, Viviane Pereira
       title: Explainability of COVID-19 Classification Models Using Dimensionality Reduction of SHAP Values
        date: 2023
       words: 6268
      flesch: 47
     summary: 2 Related Work This section discusses existing works that addressed mortality prediction for COVID-19 patients based on the information available at hospital admission. We considered works evaluating classical risk scores (including those designed especially for COVID-19 patients and those that precede the pandemic) and ML classifiers.
    keywords: \(\text; classifiers; covid-19; data; dataset; features; google; models; mortality; patients; prediction; scholar; sensitivity; shap; values
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        item: #105 of 282
          id: bracis-28373
      author: Barros, Pedro H.; Murai, Fabricio; Ramos, Heitor S.
       title: Bayes and Laplace Versus the World: A New Label Attack Approach in Federated Environments Based on Bayesian Neural Networks
        date: 2023
       words: 6630
      flesch: 43
     summary: ) Article  Google Scholar  Fang, M., Cao, X., Jia, J., Gong, N.Z.: Local model poisoning attacks to byzantine-robust federated learning. Fedequal: defending model poisoning attacks in heterogeneous federated learning.
    keywords: \textbf; approach; attack; data; defense; distribution; google; label; laplace; learning; model; poisoning; scholar; training
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        item: #106 of 282
          id: bracis-28374
      author: Santos, Yuri; Giuliani, Ricardo; Bogorny, Vania; Grellert, Mateus; Carvalho, Jônata Tyska
       title: MAT-Tree: A Tree-Based Method for Multiple Aspect Trajectory Clustering
        date: 2023
       words: 6232
      flesch: 50
     summary: 2. Example of multiple aspect trajectory clustering tree. In this paper, we propose a novel hierarchical clustering algorithm for multiple aspect trajectories using a decision tree structure that chooses the best aspect to branch and group the most similar trajectories according to different criteria.
    keywords: article; aspect; clustering; clusters; data; frequency; google; mat; method; scholar; semantic; trajectories; trajectory; tree
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        item: #107 of 282
          id: bracis-28375
      author: None
       title: bracis-28375
        date: None
       words: 5981
      flesch: 47
     summary: Springer (2018) Google Scholar  Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: U-Net++: redesigning skip connections to exploit multiscale features in image segmentation. The results demonstrate the effectiveness of this approach in segmenting seismic images from a heterogeneous environment, such as the pre-salt layer, indicating potential applications of this architecture in various configurations for identifying and extracting geological structures in the field of seismic imaging.
    keywords: architecture; data; fault; fig; google; image; models; network; scholar; segmentation; seismic; transformer; transunet
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        item: #108 of 282
          id: bracis-28376
      author: Lima, Alexandre Gomes de; Moreno, José G.; Dkaki, Taoufiq; Aranha, Eduardo Henrique da S.; Boughanem, Mohand
       title: Evaluating Recent Legal Rhetorical Role Labeling Approaches Supported by Transformer Encoders
        date: 2023
       words: 6574
      flesch: 52
     summary: We also implement the following baselines: SingleSC: These are single sentence classification models, i.e. models that do not rely on chunks. Fine-tuning is a common training procedure for one working with pre-trained models, whose goal is the adjustment of the model weights to the task at hand.
    keywords: approaches; cohan; conference; dataset; dfcsc; embeddings; incaselaw; longformer; mixup; models; pre; roberta; roles; sentence; training; transformer
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        item: #109 of 282
          id: bracis-28377
      author: Canto, Victor Hugo Braguim; Manesco, João Renato Ribeiro; Souza, Gustavo Botelho de; Marana, Aparecido Nilceu
       title: Dog Face Recognition Using Vision Transformer
        date: 2023
       words: 4909
      flesch: 51
     summary: Results obtained on DogFaceNet, a public database of dog face images, show that the proposed method, which uses the EfficientFormer-L1 architecture, outperforms the state-of-the-art method proposed previously in literature based on ResNet, a deep convolutional neural network. Figure 2 shows examples of dog muzzle images that were discarded in the work by Jang et al.
    keywords: architecture; dog; efficientformer; face; features; fig; google; identification; image; recognition; scholar; vision
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        item: #110 of 282
          id: bracis-28378
      author: Jr., Anisio P. Santos; Filho, Anage C. Mundim; Sabino-Silva, Robinson; Carneiro, Murillo G.
       title: Convolutional Neural Networks for the Molecular Detection of COVID-19
        date: 2023
       words: 4645
      flesch: 47
     summary: This paper aims to contribute to the development and evaluation of CNNs for the detection of COVID-19 using Raman spectra of serum samples, in order to obtain more accurate models. 3 Model Description This section describes the one-dimensional CNN proposed in this study for the detection of COVID-19 using Raman spectra of serum samples.
    keywords: analysis; article; cnn; covid-19; detection; google; layer; learning; raman; samples; scholar; spectra; spectroscopy; techniques
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        item: #111 of 282
          id: bracis-28379
      author: Batisteli, João Pedro Oliveira; Guimarães, Silvio Jamil Ferzoli; Patrocínio Júnior, Zenilton Kleber Gonçalves do
       title: Hierarchical Graph Convolutional Networks for Image Classification
        date: 2023
       words: 5798
      flesch: 48
     summary: Full size image To overcome the limitations of existing methods, we propose a novel approach that leverages hierarchical segmentation techniques to generate graph vertices for image classification from graph representation. We can briefly describe the two major contributions of this work to graph-based image analysis: (i) the proposition of a novel graph representation method that leverages hierarchical image segmentation to capture hierarchical representations of the underlying image structure; and (ii) the introduction of a novel graph convolutional network (GCN) architecture that can extract and use the essential information from our new graph representation.
    keywords: architecture; classification; edges; features; google; graph; image; information; methods; model; networks; representation; scholar; segmentation; set; vertices
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        item: #112 of 282
          id: bracis-28380
      author: Schiavon, Dieine Estela Bernieri; Becker, Carla Diniz Lopes; Botelho, Viviane Rodrigues; Pianoski, Thatiane Alves
       title: Interpreting Convolutional Neural Networks for Brain Tumor Classification: An Explainable Artificial Intelligence Approach
        date: 2023
       words: 5900
      flesch: 43
     summary: In addition, we use Explainable Artificial Intelligence (XAI) techniques to visualize and interpret the behavior of CNN models. Our results show that CNN models accurately classified MRI images with brain tumors.
    keywords: accuracy; brain; cam; classification; cnn; fig; image; layers; learning; model; techniques; tumor; xai; xception
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        item: #113 of 282
          id: bracis-28381
      author: Ramos, Filipe; Silva, Guilherme; Luz, Eduardo; Silva, Pedro
       title: Enhancing Stock Market Predictions Through the Integration of Convolutional and Recursive LSTM Blocks: A Cross-market Analysis
        date: 2023
       words: 5031
      flesch: 64
     summary: Despite the works presented in this section using different datasets, the objective is stock price prediction. Article  Google Scholar  Lu, W., Li, J., Wang, J., Qin, L.: A CNN-BiLSTM-am method for stock price prediction.
    keywords: cnn; data; google; learning; lstm; market; model; prediction; price; scholar; size; stock; table
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        item: #114 of 282
          id: bracis-28382
      author: Costa, Cícero L.; Lima, Danielli A.; Barcelos, Celia A. Zorzo; Travençolo, Bruno A. N.
       title: Ensemble Architectures and Efficient Fusion Techniques for Convolutional Neural Networks: An Analysis on Resource Optimization Strategies
        date: 2023
       words: 5195
      flesch: 48
     summary: By examining these four aspects, we gain a comprehensive understanding of the individual and fused CNN models, their performance metrics, optimal training configurations, and resource requirements. Full size table Upon analyzing the results, it is evident that the performance of the fusion models varies depending on the specific combination of CNN models used.
    keywords: article; classification; cnn; fusion; gastrointestinal; google; gpu; models; performance; results; scholar; score; table
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        item: #115 of 282
          id: bracis-28383
      author: Andrade, João P. B.; Costa, Leonardo F.; Fernandes, Lucas S.; Rego, Paulo A. L.; Maia, José G. R.
       title: Dog Face Recognition Using Deep Features Embeddings
        date: 2023
       words: 5680
      flesch: 55
     summary: 76(14), 15325–15340 (2017) Google Scholar  Chaturvedi, K.: Wolf and dog breed image classification using deep learning techniques. Thus, the present work seeks to consolidate results in this area by investigating the use of deep feature embedding vectors for dog face recognition, focusing on the facial identification task.
    keywords: accuracy; dataset; detection; dog; dogfacenet; dogs; face; flickr; google; identification; images; learning; recognition; scholar; training
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        item: #116 of 282
          id: bracis-28384
      author: Silva, Diego Pinheiro da; Fröhlich, William da Rosa; Schwertner, Marco Antonio; Rigo, Sandro José
       title: Clinical Oncology Textual Notes Analysis Using Machine Learning and Deep Learning
        date: 2023
       words: 5400
      flesch: 47
     summary: Machine learning classifiers experiments results. Therefore, two main experiments were performed: a) Machine learning - several machine learning classifiers have been experimented with and their performance compared (described in Sect. 3.1); b) Deep learning - an experiment with a deep learning recurrent neural network was performed (described in Sect. 3.2).
    keywords: classification; classifier; clinical; corpus; data; experiments; information; learning; machine; machine learning; notes; oncology; patient; text
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        item: #117 of 282
          id: bracis-28385
      author: Fernandes, Arthur Guilherme Santos; Junior, Geraldo Braz; Diniz, João Otávio Bandeira; Silva, Aristófanes Correa; Matos, Caio Eduardo Falcõ
       title: EfficientDeepLab for Automated Trachea Segmentation on Medical Images
        date: 2023
       words: 4291
      flesch: 51
     summary: Sci. 9(3), 193 (2012) Article  Google Scholar  Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFs (2016) Google Scholar  Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. Driven by recent advances in transformer architecture for image segmentation, [24] used it alongside U-Net to perform segmentation on various OaRs, including the trachea.
    keywords: architecture; author; efficientnet; fig; google; image; method; model; network; organs; scholar; segmentation; size; trachea
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        item: #118 of 282
          id: bracis-28386
      author: Mendes, Alison Corrêa; Pessoa, Alexandre César Pinto; Paiva, Anselmo Cardoso de
       title: Multi-label Classification of Pathologies in Chest Radiograph Images Using DenseNet
        date: 2023
       words: 4890
      flesch: 54
     summary: In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623–2631 (2019) Google Scholar  Bhusal, D., Panday, D., Prasad, S.: Multi-label classification of thoracic diseases using dense convolutional network on chest radiographs. arXiv https://doi.org/10.1007/978-1-4612-0919-5_24 Chapter  Google Scholar  He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition.
    keywords: auc; chestx; class; dataset; images; labels; layers; loss; model; pathologies; performance; ray14; scholar
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        item: #119 of 282
          id: bracis-28387
      author: Pacheco, Bruno M.; Oliveira, Victor H. R. de; Antunes, Augusto B. F.; Pedro, Saulo D. S.; Silva, Danilo
       title: Does Pre-training on Brain-Related Tasks Results in Better Deep-Learning-Based Brain Age Biomarkers?
        date: 2023
       words: 6424
      flesch: 58
     summary: This is particularly true for the use of the validation set for training brain age models, which significantly reduced the reliability in all scenarios. Therefore, our paper stands out by comparing brain age models pre-trained on brain tumor segmentation to models without pre-training or pre-trained on natural image classification.
    keywords: age; article; backbone; brain; brain age; data; google; learning; models; pre; prediction; scholar; set; training; validation
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        item: #120 of 282
          id: bracis-28388
      author: Ribeiro, André A. V. Escorel; Lira, Rodrigo Cesar; Macedo, Mariana; Siqueira, Hugo Valadares; Bastos-Filho, Carmelo
       title: Applying Reinforcement Learning for Multiple Functions in Swarm Intelligence
        date: 2023
       words: 4978
      flesch: 48
     summary: https://doi.org/10.1080/0305215X.2020.1867120 Article  MathSciNet  MATH  Google Scholar  Xu, Y., Pi, D.: A reinforcement learning-based communication topology in particle swarm optimization. Springer, US, Boston, MA (2006) Google Scholar  Lira, R.C., Macedo, M., Siqueira, H.V., Bastos-Filho, C.: Integrating reinforcement learning and optimization task: Evaluating an agent to dynamically select PSO communication topology.
    keywords: agent; functions; intelligence; learning; optimization; particle; pso; reinforcement; scenario; swarm; topology
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        item: #121 of 282
          id: bracis-28389
      author: Barg, Mauricio W.; Rodrigues, Barbara S.; Justino, Gabriela T.; Cotta, Kleyton Pontes; Portuita, Hugo R. V.; Loução Jr., Flávio L.; Abreu, Iran Pereira; Pigossi Jr., Antônio Carlos
       title: Deep Reinforcement Learning for Voltage Control in Power Systems
        date: 2023
       words: 5596
      flesch: 55
     summary: Besides the traditional deep reinforcement learning approach, three novel reinforcement learning variations named windowed, ensemble and windowed ensemble Q-Learning, which alter the agent’s learning process for voltage control, are presented and tested on IEEE 13, 37 and 123 bus systems, simulated on OpenDSS. One of these tasks known as voltage control regards keeping voltage on all system buses between certain limits which are usually defined by regulatory associations and take into account the systems’ correct functioning.
    keywords: actions; agent; author; control; distribution; equipment; fig; ieee; learning; power; reinforcement; reinforcement learning; system; time; voltage
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        item: #122 of 282
          id: bracis-28390
      author: Kemmer, Bruno; Simões, Rodolfo; Ivamoto, Victor; Lima, Clodoaldo
       title: Performance Analysis of Generative Adversarial Networks and Diffusion Models for Face Aging
        date: 2023
       words: 6559
      flesch: 54
     summary: There are a few ways to perform image editing with diffusion models. Train a diffusion model to do image editing, as in Production-Ready Face Re-Aging for Visual Effects [40] and in Instruct-pix2pix [2]. DDIM Inversion.
    keywords: aging; authors; diffusion; editing; face; google; image; input; models; networks; original; pre; results; scholar; text
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        item: #123 of 282
          id: bracis-28391
      author: Ivamoto, Victor; Simões, Rodolfo; Kemmer, Bruno; Lima, Clodoaldo
       title: Occluded Face In-painting Using Generative Adversarial Networks—A Review
        date: 2023
       words: 6144
      flesch: 50
     summary: ACM Press/Addison-Wesley Publishing Co., USA (2000) Google Scholar  Burgos-Artizzu, X.P., Perona, P., Dollár, P.: Robust face landmark estimation under occlusion. Institute of Electrical and Electronics Engineers Inc. (2020) Google Scholar  Chen, M., Liu, Z., Ye, L., Wang, Y.: Attentional coarse-and-fine generative adversarial networks for image inpainting.
    keywords: computer; conference; discriminator; face; generator; google; google scholar; ieee; image; inpainting; network; occlusion; scholar
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        item: #124 of 282
          id: bracis-28392
      author: Michelassi, Gabriel C.; Bortoletti, Henrique S.; Pinheiro, Tuany D.; Nobayashi, Thiago; Barros, Fabio R. D. de; Testa, Rafael L.; Silva, Andréia F.; Revers, Mirian C.; Portolese, Joana; Pedrini, Helio; Brentani, Helena; Nunes, Fatima L. S.; Machado-Lima, Ariane
       title: Classification of Facial Images to Assist in the Diagnosis of Autism Spectrum Disorder: A Study on the Effect of Face Detection and Landmark Identification Algorithms
        date: 2023
       words: 5894
      flesch: 46
     summary: American Psychiatric Pub (2013) Google Scholar  Baltrusaitis, T., Robinson, P., Morency, L.P.: Constrained local neural fields for robust facial landmark detection in the wild. CoRR abs/1907.05047 (2019) Google Scholar  Boehringer, S., et al.:
    keywords: algorithms; article; author; autism; classifier; detection; experiment; face; google; identification; images; landmark; mediapipe; scholar; table
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        item: #125 of 282
          id: bracis-28393
      author: Silva, Rodney Renato de Souza; Cerri, Ricardo
       title: Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design
        date: 2023
       words: 6015
      flesch: 54
     summary: Thus, we propose to evaluate our molecules by classifying them into a taxonomy, using a hierarchical multi-label classifier previously trained using molecules with known taxonomy information. Considering different possibilities to evaluate the methods and the generated molecules, we propose classifying generated molecules in a taxonomy, using a hierarchical multi-label classifier previously trained in a dataset of molecules with known taxonomy information.
    keywords: article; chebi; distance; diversity; drug; google; graph; groups; information; learning; molecules; properties; scholar; table; taxonomy
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        item: #126 of 282
          id: bracis-28394
      author: Del Valle, Aline Marques; Mantovani, Rafael Gomes; Cerri, Ricardo
       title: AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification
        date: 2023
       words: 6275
      flesch: 49
     summary: MathSciNet  MATH  Google Scholar  de Sá, A.G.C., Freitas, A.A., Pappa, G.L.: Automated selection and configuration of multi-label classification algorithms with grammar-based genetic programming. Most multi-objective algorithms use the concept of dominance to find Pareto optimal solutions.
    keywords: \({\textrm{autommlc}_\textrm{mors}}\; algorithms; autommlc; label; mlc; objective; optimization; score; search; solutions; space; time; training
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        item: #127 of 282
          id: bracis-28395
      author: Castro, Pedro; Fortuna, Gabriel; Silva, Pedro; Bianchi, Andrea G. C.; Moreira, Gladston; Luz, Eduardo
       title: Merging Traditional Feature Extraction and Deep Learning for Enhanced Hop Variety Classification: A Comparative Study Using the UFOP-HVD Dataset
        date: 2023
       words: 5405
      flesch: 51
     summary: Sci. 10(15), 5074 (2020) Google Scholar  Azlah, M.A.F., Chua, L.S., Rahmad, F.R., Abdullah, F.I., Wan Alwi, S.R.: Computers 8(4), 77 (2019) Google Scholar  Bhattarai, U., Karkee, M.: A weakly-supervised approach for flower/fruit counting in apple orchards.
    keywords: accuracy; author; classification; dataset; feature; google; hop; image; learning; models; plant; scholar; size; table; techniques; varieties
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        item: #128 of 282
          id: bracis-28396
      author: Roder, Mateus; Passos, Leandro Aparecido; Papa, João Paulo; Rossi, André Luis Debiaso
       title: Feature Selection and Hyperparameter Fine-Tuning in Artificial Neural Networks for Wood Quality Classification
        date: 2023
       words: 6617
      flesch: 47
     summary: Default MLP hyperparameter values defined by Weka and dimensionality reduction performed by Principal Components Analysis (PCA); Method 5 (M5) : MLP hyperparameter values tuned by PSO and dimensionality reduction performed by PCA; Random Search (RS) : Random selection of MLP hyperparameter values and feature subset. Finally, RS represents the analysis of random combinations of MLP hyperparameter values and selected features.
    keywords: classification; feature; google; hyperparameter; mlp; optimization; performance; problem; pso; quality; scholar; selection; set; tuning; values; wood
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        item: #129 of 282
          id: bracis-28397
      author: Carvalho, Thiago; Vellasco, Marley; Amaral, José Franco; Figueiredo, Karla
       title: A Feature-Based Out-of-Distribution Detection Approach in Skin Lesion Classification
        date: 2023
       words: 6177
      flesch: 49
     summary: In such a context, detecting Out-of-Distribution (OOD) samples plays an important role as an auxiliary task, generally solved by OOD detection methods. In such cases, OOD detection methods can play an essential role in identifying whether a new sample belongs to any of the known classes of the problem, thus providing an auxiliary task for DL-based approaches.
    keywords: class; classes; classification; detection; distribution; feature; google; method; ood; ood detection; openpcs; samples; scholar; skin; space
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        item: #130 of 282
          id: bracis-28398
      author: Valeriano, Maria Gabriela; Paiva, Pedro Yuri Arbs; Kiffer, Carlos Roberto Veiga; Lorena, Ana Carolina
       title: A Framework for Characterizing What Makes an Instance Hard to Classify
        date: 2023
       words: 6925
      flesch: 51
     summary: The purity of the footprint, which corresponds to the percentage of instances enclosed by the footprint from a given category, that is, the percentage of hard instances in the case of the hardness footprint and of easy instances in the easiness footprint. Easy instances have a low kDN value (are surrounded by elements sharing their class label), while hard instances have a high kDN value (are close to elements from the opposite class), despite the class they belong to.
    keywords: \(\mathcal; analysis; class; data; dataset; features; footprints; google; hardness; instances; learning; meta; models; patients; performance; scholar; values
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        item: #131 of 282
          id: bracis-28399
      author: Moraes, Lauro; Luz, Eduardo; Moreira, Gladston
       title: Physicochemical Properties for Promoter Classification
        date: 2023
       words: 5255
      flesch: 47
     summary: IEEE (2019) Google Scholar  Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: 785–794 (2016) Google Scholar  Chen, W., Lei, T.Y., Jin, D.C., Lin, H., Chou, K.C.:
    keywords: article; dataset; dna; fold; google; learning; machine; models; performance; prediction; promoter; properties; property; scholar; sequence; validation
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        item: #132 of 282
          id: bracis-28400
      author: Pelissari, Renata; Campello, Betania; Pelegrina, Guilherme Dean; Suyama, Ricardo; Duarte, Leonardo Tomazeli
       title: Critical Analysis of AI Indicators in Terms of Weighting and Aggregation Approaches
        date: 2023
       words: 6404
      flesch: 47
     summary: For each criterion \(g_j\) is given a relative importance \(w_j\) called criterion weight, \(j=1, \ldots , n\). 4.1 SMAA SMAA (Stochastic Multicriteria Acceptability Analysis) is a simulation-based method for discrete multicriteria decision makings problems where model parameters are uncertain, imprecise, or, specifically in the case of criteria weights, partially or totally missing [7, 11].
    keywords: acceptability; analysis; choquet; countries; criteria; decision; index; integral; interaction; ranking; smaa; tortoise; weights
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        item: #133 of 282
          id: bracis-28401
      author: Pfitscher, Ricardo J.; Rodenbusch, Gabriel B.; Dias, Anderson; Vieira, Paulo; Fouto, Nuno M. M. D.
       title: Estimating Code Running Time Complexity with Machine Learning
        date: 2023
       words: 6295
      flesch: 49
     summary: However, the recent advances in artificial intelligence propelled the development of models that estimate code complexity. Second, it shows that the Random Forest model achieved the best results for predicting code complexity, with an accuracy of 71.84% using code features as attributes and 83.57% when Abstract Syntax Tree (AST) applies to generate code embedding used in training.
    keywords: accuracy; classes; code; complexity; dataset; efficiency; forest; google; learning; machine; models; results; table; time
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        item: #134 of 282
          id: bracis-28402
      author: Gonçalves, Marcel Chacon; Silva, Rodrigo
       title: The Effect of Statistical Hypothesis Testing on Machine Learning Model Selection
        date: 2023
       words: 4334
      flesch: 49
     summary: The selection of machine learning models based on statistical tests of hypothesis lead to higher quality models after a large number of iterations? 2. The use of statistical tests of hypothesis is fundamental in many scientific fields, and it is crucial to better understand their impact on the selection of machine learning models.
    keywords: difference; google; hypothesis; learning; machine; model; number; performance; samples; scholar; selection; test
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        item: #135 of 282
          id: bracis-28403
      author: Silva, Maísa de Carvalho; Pereira, Paulo Guilherme Pinheiro; Oliveira, Lariza Laura de; Tinós, Renato
       title: Multiobjective Evolutionary Algorithms Applied to the Optimization of Expanded Genetic Codes
        date: 2023
       words: 4695
      flesch: 54
     summary: Expanded genetic codes have been created without considering the robustness of the code. In this work, multi-objective genetic algorithms are proposed for the optimization of expanded genetic codes.
    keywords: acids; amino; approach; codes; codons; genetic; new; objective; pareto; solutions
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        item: #136 of 282
          id: bracis-28404
      author: Soares, Pablo Luiz Braga; Araújo, Carlos Victor Dantas
       title: Genetic Algorithms with Optimality Cuts to the Max-Cut Problem
        date: 2023
       words: 5797
      flesch: 58
     summary: 529–535 (2003) Google Scholar  Alidaee, B., Sloan, H., Wang, H.: Lett. 2(3), 107–111 (1983) Article  MathSciNet  MATH  Google Scholar  Barahona, F., Grötschel, M., Jünger, M., Reinelt, G.: An application of combinatorial optimization to statistical physics and circuit layout design.
    keywords: \sum; algorithm; cut; cuts; google; instances; max; optimality; problem; scholar; search
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        item: #137 of 282
          id: bracis-28405
      author: Sousa, Mateus Clemente de; Meneghini, Ivan Reinaldo; Guimarães, Frederico Gadelha
       title: Assessment of Robust Multi-objective Evolutionary Algorithms on Robust and Noisy Environments
        date: 2023
       words: 5836
      flesch: 53
     summary: 7 shows these results for tests of the function generator extension with intermediate noise intensity and \(\delta _x=0.1\). Results of the IGD metric for intermediate intensity noise and \(\delta _x = 0.1\).Full size table Table 3 displays the variability of the IGD metric across 30 simulations, using the Robust Front as the reference, as indicated by the standard deviation.
    keywords: algorithms; function; google; intensity; noise; objective; optimization; rmoea; scholar; uncertainties
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        item: #138 of 282
          id: bracis-28406
      author: Sementille, Luiz Fernando Merli de Oliveira; Rodrigues, Douglas; Souuza, André Nunes de; Papa, João Paulo
       title: Binary Flying Squirrel Optimizer for Feature Selection
        date: 2023
       words: 4024
      flesch: 52
     summary: Over the last few years, bio-inspired algorithms have successfully addressed feature selection problems, for they can obtain good solutions in a reasonable time, even if the problem is complex. The Flying Squirrel Optimizer belongs to the family of bio-inspired algorithms and simulates the movement of flying squirrels from tree to tree in search of food.
    keywords: algorithm; bfso; binary; datasets; feature; flying; google; optimization; optimizer; scholar; selection; squirrel
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        item: #139 of 282
          id: bracis-28407
      author: Teixeira, Matheus Cândido; Pappa, Gisele Lobo
       title: Fitness Landscape Analysis of TPOT Using Local Optima Network
        date: 2023
       words: 6178
      flesch: 52
     summary: To shed light on this matter, the present study conducts an examination of AutoML search spaces generated by the Tree-based Pipeline Optimization Tool (TPOT) Essentially, our grammar and search space represent 69,960 valid machine learning pipelines that comprise a classification algorithm and potentially a preprocessing algorithmFootnote 1.
    keywords: algorithm; automl; edges; fitness; landscape; learning; lon; machine; number; optima; pipelines; search; solutions; space; tpot
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        item: #140 of 282
          id: bracis-28408
      author: Monteiro, Monique; Zanchettin, Cleber
       title: Optimization Strategies for BERT-Based Named Entity Recognition
        date: 2023
       words: 4979
      flesch: 48
     summary: Language models are the most classic examples among these pre-trained models. Furthermore, qualitative analysis for the intermediary language model shows example outputs for predicting masked term tasks, i.e., the public accounts auditing language model can generate texts related to themes such as contracts and biddings, as seen in Table 6. 4.3 Causal Language Modeling - Few/zero-Shot Learning This subsection summarizes experiments conducted on GPT 3.5, a large language model pre-trained with a causal language modeling objective.
    keywords: adaptation; bertimbau; domain; entity; experiments; language; learning; model; ner; recognition; results; task; training
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        item: #141 of 282
          id: bracis-28409
      author: Medeiros, Arthur; Gorgônio, Arthur C.; Vale, Karliane Medeiros Ovidio; Gorgônio, Flavius L.; Canuto, Anne Magály de Paula
       title: FlexCon-CE: A Semi-supervised Method with an Ensemble-Based Adaptive Confidence
        date: 2023
       words: 5928
      flesch: 47
     summary: (2011) Google Scholar  Chapelle, O., Scholkopf, B., Zien, A.: Semi-supervised Learning, vol. IEEE (2014) Google Scholar  Gorgônio, A.C., et al.:
    keywords: algorithm; analysis; classifier; confidence; data; ensemble; flexcon; google; instances; learning; method; results; scholar
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        item: #142 of 282
          id: bracis-28410
      author: Araújo, George Corrêa de; Jordão, Artur; Pedrini, Helio
       title: Single Image Super-Resolution Based on Capsule Neural Networks
        date: 2023
       words: 7498
      flesch: 55
     summary: In: ICPR, pp. 2366–2369 (2010) Google Scholar  Hsu, J., Kuo, C., Chen, D.: Image super-resolution using capsule neural networks. [18] developed two frameworks to incorporate capsules into image SR convolutional networks: Capsule Image Restoration Neural Network (CIRNN) and Capsule Attention and Reconstruction Neural Network (CARNN).
    keywords: capsule; cvpr; function; google; google scholar; image; image super; layer; loss; model; network; resolution; results; scholar; super
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        item: #143 of 282
          id: bracis-28411
      author: Martins, Ramon Mayor; Espíndola, Bruno Manarin; Araujo, Pedro Philippi; Wangenheim, Christiane Gresse von; Pinto, Carlos José de Carvalho; Caminha, Gisele
       title: Development of a Deep Learning Model for the Classification of Mosquito Larvae Images
        date: 2023
       words: 5382
      flesch: 46
     summary: With regard to evaluation metric bias, we used multiple standard evaluation metrics that cover different aspects of model performance and to report the results of all metrics used. 31–36 (2020) Google Scholar  Fast.ai (2023).
    keywords: aedes; aegypti; classification; culex; google; images; larvae; learning; models; mosquito; performance; research; scholar; species; table
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        item: #144 of 282
          id: bracis-28412
      author: Silva, Karla Gabriele Florentino da; Moreira, Jonas Magalhães; Calixto, Gabriel Barreto; Maciel, Luiz Maurílio da Silva; Miranda, Márcio Assis; Morais, Leandro Elias
       title: A Simple and Low-Cost Method for Leaf Surface Dimension Estimation Based on Digital Images
        date: 2023
       words: 6647
      flesch: 59
     summary: Res. 100(1), 117–124 (2007) Google Scholar  Siswantoro, J., Artadana, I.B.M.: Image based leaf area measurement method using artificial neural network. Download conference paper PDF Similar content being viewed by others Lower-dimensional intrinsic structural representation of leaf images and plant recognition Article 13 July 2021 A New Approach for Measuring Leaf Projected Area for Potted Plant Based on Computer Vision Chapter © 2016 Leaf Image-Based Plant Identification Using Morphological Feature Extraction Chapter © 2024 Explore related subjects Discover the latest articles, books and news in related subjects, suggested using machine learning.
    keywords: \textbf; area; contour; dimensions; google; image; leaf; leaf area; leaves; length; method; pattern; perimeter; plant; results; scale; scholar; width
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        item: #145 of 282
          id: bracis-28413
      author: Costa, Igor Ferreira da; Caarls, Wouter
       title: Crop Row Line Detection with Auxiliary Segmentation Task
        date: 2023
       words: 4424
      flesch: 56
     summary: 7. Field modelled for testing with five growth stages Full size image Each model was tested by crossing the field in both directions across all growth stages and the error between the theoretical perfect line and also the mean absolute error of the robot position were both recorded. Bottom camera line loss - dotted line mark the minimum value obtained Full size image Meanwhile, the top camera, in Fig. 9, has better training results than the bottom one.
    keywords: camera; crop; field; fig; google; growth; image; line; model; robot; row; scholar; task
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        item: #146 of 282
          id: bracis-28414
      author: Leocádio, Rodolfo R. V.; Segundo, Alan Kardek Rêgo; Pessin, Gustavo
       title: Multiple Object Tracking in Native Bee Hives: A Case Study with Jataí in the Field
        date: 2023
       words: 6059
      flesch: 57
     summary: https://doi.org/10.5897/AJAR2020.15203 Article  Google Scholar  Sánchez-Bayo, F., Wyckhuys, K.A.G.: Worldwide decline of the entomofauna: a review of its drivers. https://doi.org/10.1371/journal.pone.0251572 Article  Google Scholar  Perez-Cham, O.E., et al.:
    keywords: article; bees; computer; data; detection; error; fig; google; hive; images; insects; jataí; monitoring; object; scholar; tracking; vision
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        item: #147 of 282
          id: bracis-28415
      author: Capdevila, Marc G.; Rodrigues, Karine Aparecida P.; Jardim, Camila F.; Silva, Renato M.
       title: An Open Source Eye Gaze Tracker System to Perform Remote User Testing Evaluations
        date: 2023
       words: 6860
      flesch: 55
     summary: Citeseer (2009) Google Scholar  Biedert, R., Buscher, G., Dengel, A.: The eye book. V Congreso Internacional de Ciencias de la Computación y Sistemas de Información 2021 (2022) Google Scholar  Carter, B.T., Luke, S.G.:
    keywords: calibration; date; eye; gaze; google; google scholar; remote; research; scholar; september; source; system; testing; tracker; tracking; usability; user
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        item: #148 of 282
          id: bracis-28416
      author: Neri, Hugo; Cozman, Fabio G.
       title: Who Killed the Winograd Schema Challenge?
        date: 2023
       words: 7221
      flesch: 59
     summary: 39–45 (2015) Google Scholar  Bobrow, D.: Precision-focussed textual inference. Eng. 15(4), i-xvii (2009) Google Scholar  Dagan, I., Glickman, O., Magnini, B.: The PASCAL Recognising Textual Entailment Challenge.
    keywords: challenge; cloze; commonsense; dataset; google; google scholar; language; models; paper; performance; roberta; schema; scholar; test; winograd; wsc
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        item: #149 of 282
          id: bracis-28417
      author: Pires, Ramon; Abonizio, Hugo; Almeida, Thales Sales; Nogueira, Rodrigo
       title: Sabiá: Portuguese Large Language Models
        date: 2023
       words: 6434
      flesch: 52
     summary: As the capabilities of language models continue to advance, it is conceivable that “one-size-fits-all” model will remain as the main paradigm. Scaling expert language models with unsupervised domain discovery. arXiv preprint arXiv:2303.14177 (2023)
    keywords: arxiv; association; conference; datasets; english; google; language; language models; learning; models; portuguese; preprint; pretraining; scholar
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        item: #150 of 282
          id: bracis-28418
      author: Lopes, Lucelene; Fernandes, Paulo; Inacio, Marcio L.; Duran, Magali S.; Pardo, Thiago A. S.
       title: Disambiguation of Universal Dependencies Part-of-Speech Tags of Closed Class Words in Portuguese
        date: 2023
       words: 5822
      flesch: 53
     summary: In such work the authors point out the processing burden associated using CRF to perform the task, but they deliver PoS tag accuracies around 97%. The set of UD PoS tags is formed by: ADJ - adjectives, as “bonito” (“beautiful” in English); ADP - adpositions, as “de” (“of” in English); ADV - adverbs, as “não” (“no” in English); AUX - auxiliary verbs, as “foi” (“was” in English); CCONJ - coordinating conjunctions, as “e” (“and” in English); DET - determiners, as “cujo” (“whose” in English); INTJ - interjections, as “tchau” (“goodbye” in English); NOUN - nouns, as “vida” (“life” in English); NUM - numerals, as “cinco” (“five” in English); PART - particles, which is not employed in Portuguese; PRON - pronouns, as “ele” (“he” in English); PROPN - proper nouns, as “Brasil” (“Brazil” in English); PUNCT - punctuations, as “?”; SCONJ - subordinating conjunctions, as “porque” (“because” in English); SYM - symbols, as “$”; VERB - verbs, as “jogamos” (“(we) play” in English); X - others, as foreign words.
    keywords: accuracy; bertimbau; conference; dependencies; english; language; methods; model; portuguese; pos; proceedings; set; tags; words
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        item: #151 of 282
          id: bracis-28419
      author: Pavanelli, Lucas; Gumiel, Yohan Bonescki; Ferreira, Thiago; Pagano, Adriana; Laber, Eduardo
       title: Bete: A Brazilian Portuguese Dataset for Named Entity Recognition and Relation Extraction in the Diabetes Healthcare Domain
        date: 2023
       words: 4260
      flesch: 50
     summary: Experiments for entity recognition models. Experiments for relation extraction models.
    keywords: annotation; bert; dataset; diabetes; entities; entity; extraction; language; models; portuguese; recognition; relation; table
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        item: #152 of 282
          id: bracis-28420
      author: Silveira, Raquel; Ponte, Caio; Almeida, Vitor; Pinheiro, Vládia; Furtado, Vasco
       title: LegalBert-pt: A Pretrained Language Model for the Brazilian Portuguese Legal Domain
        date: 2023
       words: 6475
      flesch: 50
     summary: For our study, we developed two variations of the pretraining of legal domain language models in Brazilian Portuguese: (i) pretraining from scratch using a specific domain corpus (LegalBert-pt SC) and (ii) an adaptation of BERTimbau with pretraining using a specific domain corpus (LegalBert-pt FP). LEGAL-BERT [7] was among the pioneers in developing legal language models, utilizing a corpus of approximately 12 GB with texts from European and North American legislation and cases.
    keywords: classification; documents; domain; language; language models; legalbert; model; results; scholar; score; tasks; text
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        item: #153 of 282
          id: bracis-28421
      author: Sakiyama, Kenzo; Rodrigues, Lucas de Souza; Nogueira, Bruno Magalhães; Matsubara, Edson Takashi; Romero, Roseli A. F.
       title: A Framework for Controversial Political Topics Identification Using Twitter Data
        date: 2023
       words: 6251
      flesch: 52
     summary: [19] algorithm to cluster tweets and extract common topics, inspired by the success of previous works [4, 14]. We propose a framework that enriches text representations, combining state-of-the-art unsupervised (HDBSCAN) and supervised (BERTimbau) techniques to identify controversial political topics in social media publications in Brazilian Portuguese.
    keywords: analysis; clustering; clusters; data; examples; google; hdbscan; number; scholar; sentiment; table; text; topics; tweets; twitter
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        item: #154 of 282
          id: bracis-28422
      author: Freitas, Fernando de Almeida; Peres, Sarajane Marques; Albuquerque, Otávio de Paula; Fantinato, Marcelo
       title: Leveraging Sign Language Processing with Formal SignWriting and Deep Learning Architectures
        date: 2023
       words: 6355
      flesch: 47
     summary: The challenge is even more difficult for deaf babies born into families that are not familiar with sign language, as it is essential for them to gain literacy in sign language to facilitate appropriate cognitive and socioemotional development, as well as equitable and effective communication [38]. Besides acquiring sign language, access to diverse forms of knowledge throughout their lives is essential for their intellectual and civic development.
    keywords: conference; descriptions; google; hand; information; language; language processing; learning; models; processing; recognition; representation; scholar; sign; sign language; signwriting; symbols
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        item: #155 of 282
          id: bracis-28423
      author: Aquino, Roberto Douglas Guimarães de; Curtis, Vitor Venceslau; Verri, Filipe Alves Neto
       title: A Clustering Validation Index Based on Semantic Description
        date: 2023
       words: 5945
      flesch: 56
     summary: 487–499 (1994) Google Scholar  Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Pérez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Math. 20, 53–65 (1987) Article  MATH  Google Scholar  Saha, J., Mukherjee, J.: Cnak: cluster number assisted k-means.
    keywords: \end{aligned}$$; \in; \text; clustering; clusters; data; google; index; indices; number; points; scholar; set; sets
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        item: #156 of 282
          id: bracis-28424
      author: Haddad, Rodrigo Gonçalves; Figueiredo, Daniel Ratton
       title: Detecting Multiple Epidemic Sources in Network Epidemics Using Graph Neural Networks
        date: 2023
       words: 6406
      flesch: 54
     summary: It is not surprising that, over the past decades, many works have focused on developing models to predict real network epidemics sources for various kinds of phenomena. The performance of the proposed approach will be characterized using two different criteria: identifying epidemic source nodes and identifying neighbors of source nodes.
    keywords: epidemic; epidemic sources; google; graph; information; model; neighbors; network; nodes; number; performance; scenarios; scholar; sources
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        item: #157 of 282
          id: bracis-28425
      author: Fabiano, Emanoel Aurelio Vianna; Recamonde-Mendoza, Mariana
       title: Prediction of Cancer-Related miRNA Targets Using an Integrative Heterogeneous Graph Neural Network-Based Method
        date: 2023
       words: 5740
      flesch: 46
     summary: However, miRNA target prediction is still considered an open problem due to several challenges. 2 Related Works Several tools have been developed for miRNA target prediction, with ML and DL being recurrent among solutions.
    keywords: data; experiment; expression; graph; interactions; mirna; model; number; prediction; results; target; test; training
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        item: #158 of 282
          id: bracis-28426
      author: Duarte, Fernando Henrique Oliveira; Moreira, Gladston J. P.; Luz, Eduardo J. S.; Santos, Leonardo B. L.; Freitas, Vander L. S.
       title: Time Series Forecasting of COVID-19 Cases in Brazil with GNN and Mobility Networks
        date: 2023
       words: 5901
      flesch: 56
     summary: This work presents time series forecasting models to predict the number of COVID-19 cases in Brazilian cities [12] and Prophet [24] are time series forecasting models, and each one has a different approach.
    keywords: cases; covid-19; data; forecasting; gclstm; google; graph; mobility; models; networks; results; rmse; scholar; series; time; values
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        item: #159 of 282
          id: bracis-28427
      author: Silva, Victor E. de S.; Lacerda, Tiago B.; Miranda, Péricles; Câmara, André; Chagas, Amerson Riley Cabral; Furtado, Ana Paula C.
       title: Federated Learning and Mel-Spectrograms for Physical Violence Detection in Audio
        date: 2023
       words: 5823
      flesch: 48
     summary: Deep learning and mel-spectrograms for physical violence detection in audio. arXiv preprint arXiv:2007.14390 (2020) Choi, K., Fazekas, G., Sandler, M.: Automatic tagging using deep convolutional neural networks (2016) Google Scholar  Durães, D., Marcondes, F.S., Gonçalves, F., Fonseca, J., Machado, J., Novais, P.: Detection violent behaviors: a survey.
    keywords: architectures; audio; author; dataset; detection; experiment; google; learning; mel; model; network; results; scholar; table; violence
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        item: #160 of 282
          id: bracis-28428
      author: Araújo, José Alan Firmiano; Silva, Ticiana L. Coelho da; Rocha, Atslands Rego da; Lira, Vinicius Cezar Monteiro de
       title: Police Report Similarity Search: A Case Study
        date: 2023
       words: 7001
      flesch: 47
     summary: Another contribution of this work is the development of trained embedding models specifically tailored for the domain of police reports. We employ a two-step approach: generating similarity matrices for different sentence representations and validating the accuracy and Mean Reciprocal Rank (MRR) of police report representations.
    keywords: data; embedding; google; models; police; police reports; reports; representation; scholar; sentence; similarity; text; use; vectors; word
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        item: #161 of 282
          id: bracis-28429
      author: Hott, Henrique R.; Silva, Mariana O.; Oliveira, Gabriel P.; Brandão, Michele A.; Lacerda, Anisio; Pappa, Gisele
       title: Evaluating Contextualized Embeddings for Topic Modeling in Public Bidding Domain
        date: 2023
       words: 6105
      flesch: 43
     summary: [16] use topic models for analyzing and visualizing Brazilian comments about legislation. Evaluating topic models in Portuguese political comments about bills from Brazil’s chamber of deputies.
    keywords: bertopic; clustering; data; diversity; documents; evaluation; language; modeling; models; performance; procurement; sentence; text; topic
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        item: #162 of 282
          id: bracis-28430
      author: Onuki, Eric Kenzo Taniguchi; Malucelli, Andreia; Barddal, Jean Paul
       title: A Tool for Measuring Energy Consumption in Data Stream Mining
        date: 2023
       words: 4842
      flesch: 55
     summary: Consequently, it is important to quantify how real-time learning algorithms tailored for data streams and edge computing behave in terms of accuracy, processing time, memory usage, and energy consumption. In this work, we bring forward a tool for measuring energy consumption in the Massive Online Analysis (MOA).
    keywords: classifiers; consumption; data; energy; energy consumption; google; hoeffding; learning; mining; results; scholar; stream; time; tool
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        item: #163 of 282
          id: bracis-28431
      author: Branco, Raimunda; Saraiva, Filipe
       title: Improved Fuzzy Decision System for Energy Bill Reduction in the Context of the Brazilian White Tariff Scenario
        date: 2023
       words: 5219
      flesch: 49
     summary: Grid power flow on simple (a) and fuzzy (b) systems over one year Full size image 7 Conclusion The main purpose of this article was to improve the use of photovoltaic energy in the context of white tariff in Brazil by using fuzzy systems. Fuzzy logic based coordinated control of battery energy storage system and dispatchable distributed generation for microgrid.
    keywords: battery; consumption; distribution; electricity; energy; grid; logic; management; photovoltaic; power; system; tariff; use
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        item: #164 of 282
          id: bracis-28432
      author: Fernandes, Gabriel C.; Lavinsky, Fabio; Rigo, Sandro José; Bohn, Henrique C.
       title: Exploring Artificial Intelligence Methods for the Automatic Measurement of a New Biomarker Aiming at Glaucoma Diagnosis
        date: 2023
       words: 6090
      flesch: 51
     summary: 3.2 CNN Architecture The present work used a neural network to perform tasks related to image segmentation. The U-Net [14] and FCN [19] inspire most architectures presented for clinical image segmentation.
    keywords: data; disc; fig; glaucoma; google; images; layer; measurement; network; oct; optic; region; results; retina; scholar; segmentation
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        item: #165 of 282
          id: bracis-28433
      author: Silva Neto, José Reinaldo Cunha Santos A. V.; Faleiros, Thiago de Paulo
       title: Investigation of Deep Active Self-learning Algorithms Applied to Named Entity Recognition
        date: 2023
       words: 6389
      flesch: 54
     summary: In Sect. 5, we propose a novel Active Self-learning algorithm based on token-level querying, where both the human annotator and machine learning model cooperatively annotate tokens from the same sentence. An illustration of the collaborative configuration where an oracle and machine learning model annotate the same sentence jointly.
    keywords: algorithm; confidence; data; entity; learning; level; model; oracle; samples; self; sentence; tokens; training
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        item: #166 of 282
          id: bracis-28434
      author: none
       title: Front-Matter
        date: 2023
       words: 3703
      flesch: 8
     summary: André Rossi Universidade Estadual Paulista, Brazil André Ruela Marinha do Brasil, Brazil André Takahata Universidade Federal do ABC, Brazil Andrés E. C. Salazar Universidade Tecnológica Federal do Paraná, Brazil Anna H. R. Costa Universidade de São Paulo, Brazil Anne Canuto Universidade Federal do Rio Grande do Norte, Brazil Araken Santos Universidade Federal Rural do Semi-árido, Brazil Artur Jordão Universidade de São Paulo, Brazil Aurora Pozo Universidade Federal do Paraná, Brazil Bernardo Gonçalves Universidade de São Paulo, Brazil Bruno Masiero Universidade Estadual de Campinas, Brazil Bruno Nogueira Universidade Federal de Mato Grosso Brazil Lucelene Lopes Universidade de São Paulo - São Carlos, Brazil Luciano Digiampietri Universidade de São Paulo, Brazil Luis Garcia Universidade de Brasília, Brazil Luiz H. Merschmann Universidade Federal de Lavras, Brazil Marcela Ribeiro Universidade Federal de São Carlos, Brazil Marcelo Finger Universidade de São Paulo, Brazil Marcilio de Souto Université d’Orléans, France Marcos Domingues Universidade Estadual de Maringá, Brazil Marcos Quiles Universidade Federal de São Paulo, Brazil Maria Claudia Castro Centro Universitario FEI, Brazil Maria do C. Nicoletti Universidade Federal de São Carlos, Brazil Marilton Aguiar Universidade Federal de Pelotas, Brazil Marley M. B. R. Vellasco Pontifícia Universidade Católica do R. de J., Brazil Marlo Souza Universidade Federal da Bahia, Brazil Marlon Mathias Universidade de São Paulo, Brazil Mauri Ferrandin Universidade Federal de Santa Catarina, Brazil Márcio Basgalupp Universidade Federal de São Paulo, Brazil Mário Benevides Universidade Federal Fluminense, Brazil Moacir Ponti Universidade de São Paulo, Brazil Murillo Carneiro Universidade Federal de Uberlândia, Brazil Murilo Loiola Universidade Federal
    keywords: abc; brazil; brazil paulo; brazilian; campinas; carlos; centro; de são; estadual de; federal de; fei; grande; intelligence; josé; lucas; organization; paraná; paulo; rio; santos; silva; são carlos; são paulo; thiago; universidade de; universidade estadual; universidade federal; university
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        item: #167 of 282
          id: bracis-33548
      author: Silva, Tiago da; Mesquita, Diego
       title: A Contrastive Objective for Training Continuous Generative Flow Networks
        date: 2024
       words: 6445
      flesch: 51
     summary: In this context, inspired by the success of contrastive learning for variational inference, we propose the continuous contrastive loss (CCL) as the first objective function natively enabling off-policy training of continuous GFlowNets without reliance on the approximation of high-dimensional integrals via SGD, extending previous work based on discrete distributions. We derive a contrastive balance condition for continuous GFlowNets and rigorously show that it is a sufficient for ensuring sampling correctness; 2.
    keywords: \(\kappa; \in; \mathbb; \mathcal; \tau; gflownets; google; learning; loss; objective; policy; scholar; training
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        item: #168 of 282
          id: bracis-33549
      author: Almeida, Diogo M.; Mattos Neto, Paulo S. G. de; Cunha, Daniel C.
       title: A Data Distribution-Based Ensemble Generation Applied to Wind Speed Forecasting
        date: 2024
       words: 6820
      flesch: 53
     summary: Ensembles can be used as an alternative to address the complex patterns over time in wind speed time series. However, wind speed time series display complex patterns [6].
    keywords: data; ensemble; forecasting; google; individual; locdist; method; models; partitions; scholar; series; set; speed; time; training; wind
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        item: #169 of 282
          id: bracis-33550
      author: Lima, Rodrigo; Leal, Sidney E.; Candido Junior, Arnaldo; Aluísio, Sandra M.
       title: A Large Dataset of Spontaneous Speech with the Accent Spoken in São Paulo for Automatic Speech Recognition Evaluation
        date: 2024
       words: 6322
      flesch: 55
     summary: To the best of our knowledge, this is the first large Paulistano accented spontaneous speech corpus dedicated to the ASR task in Portuguese. Spontaneous speech has phenomena that make its recognition more complex than that of read or prepared speech.
    keywords: asr; audio; corpus; dataset; distil; fine; language; model; nurc; paulo; portuguese; recognition; speech; são; training; whisper
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        item: #170 of 282
          id: bracis-33551
      author: Panisson, Alison R.; Farias, Giovani P.
       title: A Multi-level Semantics Formalism for Multi-Agent Microservices
        date: 2024
       words: 6443
      flesch: 48
     summary: In this section, we discuss some of such approaches in order to clarify the existence of such multiple levels in MAS, which supports the manner we are going to define the operational semantics for multi-agents microservices. However, in the literature, there are few attempts of formalising multi-level operational semantics for multi-agent systems which allows the formalisation of all levels of abstraction in those systems.
    keywords: \mathcal; agent; google; levels; message; microservices; rule; scholar; semantics; set; systems
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        item: #171 of 282
          id: bracis-33552
      author: Gatto, Bernardo B.; Mollinetti, Marco A. F.; Santos, Eulanda M. dos; Koerich, Alessandro L.; Silva Junior, Waldir S. da
       title: A Novel Genetic Algorithm Approach for Discriminative Subspace Optimization
        date: 2024
       words: 6232
      flesch: 42
     summary: We develop a Genetic Algorithm (GA) for integrating OMSM and GDS discriminative subspaces. We can describe two main findings from the obtained results: 1) the employed initialization strategy frequently shows advantages over conventional methods, suggesting that the proper use of OMSM and GDS discriminative subspaces improves the classification accuracy; 2) the GA presents discriminative subspaces capable of achieving even higher classification results by iteratively selecting merged subspaces of random dimensions.
    keywords: algorithm; discriminative; eigenvectors; gds; google; image; methods; omsm; optimization; pattern; results; scholar; set; solutions; subspace
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        item: #172 of 282
          id: bracis-33553
      author: Toledo, Rafael S.; Oliveira, Cristiano S.; Oliveira, Vitor H. T.; Antonelo, Eric A.; Wangenheim, Aldo von
       title: A Performance Increment Strategy for Semantic Segmentation of Low-Resolution Images from Damaged Roads
        date: 2024
       words: 4844
      flesch: 56
     summary: In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3640–3649 (2016) Google Scholar  Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. 801–818 (2018) Google Scholar  CNT: Pesquisa CNT de rodovias 2021.
    keywords: conference; google; ieee; images; miou; objects; performance; pisss; road; rtk; scholar; segmentation; size; table; training
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        item: #173 of 282
          id: bracis-33554
      author: Reis, Willy Arthur Silva; Delgado, Karina Valdivia; Freire, Valdinei
       title: A Unified Framework for Average Reward Criterion and Risk
        date: 2024
       words: 6092
      flesch: 61
     summary: Section 2 reviews the definitions of MDP, average reward criterion, and risk-sensitive average reward literature. 2.1 Risk-Neutral Criterion The expected total reward of a policy \(\pi \) from the initial state s up to the decision epoch \(N+1\) is a function \(v^\pi _{N+1}\) defined by $$\begin{aligned} v^\pi _{N+1}(s) = E \Bigg \{ \sum _{n=1}^{N} r(S_n,A_n) \Big | S_1=s,\pi \Bigg \} = E \Bigg \{ \sum _{n=1}^{N} r_n \Bigg \}, \end{aligned}$$ (1) where \(S_n\) and \(A_n\) refer to the random variables of the state and action in the time step n.
    keywords: \(\pi; \end{aligned}$$; average; criterion; gain; policies; policy; reward; risk; utility
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        item: #174 of 282
          id: bracis-33555
      author: Negrão, Arthur; Silva, Guilherme; Pedrosa, Rodrigo; Luz, Eduardo; Silva, Pedro
       title: Adaptive Client-Dropping in Federated Learning: Preserving Data Integrity in Medical Domains
        date: 2024
       words: 6098
      flesch: 48
     summary: These results demonstrate that the proposed strategy is resilient against corrupted data and does not negatively impact scenarios without corrupted clients. Specially on cases where accuracy drops are mild, where the high uncertainty on corrupted client predictions would probably be masked, those metrics can provide good intel whether a client is corrupted or not.
    keywords: accuracy; approach; client; conformal; data; dataset; dropping; experiments; google; learning; model; prediction; results; strategy; training
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        item: #175 of 282
          id: bracis-33556
      author: Nunes, Rafael Oleques; Puttlitz, Letícia Maria; Boll, Antonio Oss; Spritzer, Andre; Freitas, Carla Maria Dal Sasso; Balreira, Dennis Giovani; Tavares, Anderson Rocha
       title: An Ensemble of LLMs Finetuned with LoRA for NER in Portuguese Legal Documents
        date: 2024
       words: 5609
      flesch: 53
     summary: To the best of our knowledge, our work is the first to analyze the efficacy of using LoRA to fine-tune BERT models specifically for legal NER tasks and to explore the design and implementation of prompt engineering techniques for using LLM to ensemble legal NER models. [19] investigated how a semi-supervised technique can improve BERTimbau’s performance in the legislative domain, demonstrating that such techniques can enhance model results.
    keywords: bert; brazilian; corpus; ensemble; entity; google; language; lora; models; portuguese; results; scholar
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        item: #176 of 282
          id: bracis-33557
      author: Ueda, Patricia S. M.; Rivolli, Adriano; Lorena, Ana Carolina
       title: An Instance Level Analysis of Classification Difficulty for Unlabeled Data
        date: 2024
       words: 6050
      flesch: 53
     summary: This paper proposes alternative instance hardness measures when the instances do not have a label. The adapted measures show an increased correlation to the original values of the instance hardness measures and prove to be an adequate alternative to estimate instance hardness in the deployment stage, driving the solutions to a more refined level and contributing toward a more trustful use of ML models.
    keywords: class; classes; dataset; hardness; ihm; instance; label; learning; measures; meta; values
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        item: #177 of 282
          id: bracis-33558
      author: Faleiros, Thiago de Paulo; Althoff, Paulo Eduardo; Valejo, Alan Demétrius Baria
       title: Analyzing the Impact of Coarsening on k-Partite Network Classification
        date: 2024
       words: 5074
      flesch: 47
     summary: This study introduces a novel coarsening method designed explicitly for k-partite networks, aiming to preserve classification performance while addressing storage and processing issues. In this context, this study introduces the development of a novel coarsening method designed explicitly for k-partite networks.
    keywords: algorithm; bipartite; classification; coarsening; google; information; network; partition; scholar; storage; target; vertex; vertices
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        item: #178 of 282
          id: bracis-33559
      author: Cruz, Michael; Barbosa, Luciano
       title: Applying Transformers for Anomaly Detection in Bus Trajectories
        date: 2024
       words: 6382
      flesch: 51
     summary: Particularly in the traffic context, which is easily influenced by external factors (e.g., accidents, detours, events, and weather conditions), trajectory anomaly detection is crucial to understand traffic behavior and to support better decision-making by transit authorities. Although trajectory anomaly detection has been a research hotspot [1], some challenges remain.
    keywords: anomaly; approach; bus; decoder; detection; encoder; google; model; points; scholar; sequence; table; trajectories; trajectory; transformer
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        item: #179 of 282
          id: bracis-33560
      author: Lira, Thiago; Cação, Flávio; Souza, Cinthia; Valentini, João; Bollis, Edson; Oliveira, Otavio; Almeida, Renato; Magalhães, Marcio; Poloni, Katia; Oliveira, Andre; Pellicer, Lucas
       title: Aroeira: A Curated Corpus for the Portuguese Language with a Large Number of Tokens
        date: 2024
       words: 6003
      flesch: 52
     summary: https://doi.org/10.18653/v1/2023.sustainlp-1.20 Almeida, T.S., Abonizio, H., Nogueira, R., Pires, R.: Sabi\(\backslash \)’a-2: a new generation of Portuguese large language models. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3360–3362 (2022) Google Scholar  Pires, R., Abonizio, H., Almeida, T.S., Nogueira, R.: Sabiá: Portuguese large language models.
    keywords: author; bias; content; corpora; corpus; data; documents; google; language; models; portuguese; quality; scholar; text; training; words
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        item: #180 of 282
          id: bracis-33561
      author: Mangussi, Arthur Dantas; Pereira, Ricardo Cardoso; Abreu, Pedro Henriques; Lorena, Ana Carolina
       title: Assessing Adversarial Effects of Noise in Missing Data Imputation
        date: 2024
       words: 6119
      flesch: 52
     summary: Regarding the interplay of MD and noise inconsistencies, when initial noisy data is used to extract patterns for missing data imputation, whether through simple statistics or more sophisticated strategies, the harmful effects of noise can propagate to other instances. https://ijhr.iums.ac.ir/article_171438.html Pereira, R.C., Abreu, P.H., Rodrigues, P.P.: Siamese autoencoder-based approach for missing data imputation.
    keywords: data; datasets; imputation; instances; methods; mvi; noise; quality; results; synthetic; table; values; work; world
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        item: #181 of 282
          id: bracis-33562
      author: Nunes, Rafael Oleques; Santos, Joaquim; Spritzer, Andre; Balreira, Dennis Giovani; Freitas, Carla Maria Dal Sasso; Olival, Fernanda; Cameron, Helena Freire; Vieira, Renata
       title: Assessing European and Brazilian Portuguese LLMs for NER in Specialised Domains
        date: 2024
       words: 6232
      flesch: 46
     summary: This framework provides pre-trained language models, named entity recognition models, and neural networks for language model training and sequence tagging. With Flair, we can construct pipelines for training token classifiers and feed them with various types of language models, such as Word Embeddings, Transformer-based models, and Flair Embeddings itself.
    keywords: albertina; corpus; entities; entity; european; google; language; models; ner; performance; portuguese; results; scholar; table; texts
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        item: #182 of 282
          id: bracis-33563
      author: Oliveira, Douglas Amorim de; Delgado, Karina Valdivia; Lauretto, Marcelo de Souza
       title: BASWE: Balanced Accuracy-Based Sliding Window Ensemble for Classification in Imbalanced Data Streams with Concept Drift
        date: 2024
       words: 6159
      flesch: 57
     summary: This strategy is tailored to address imbalanced data streams, effectively reducing the imbalance ratio during the model training phase with new data chunks. CALMID, CSARF, ROSE, UOB, and SMOTE-OB were selected as the top 5 algorithms by a recent survey on imbalanced data streams [1].
    keywords: baswe; concept; concept drift; data; data streams; drift; ensemble; experiments; kappa; score; streams
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        item: #183 of 282
          id: bracis-33564
      author: Boll, Antônio Oss; Puttlitz, Letícia Maria; Boll, Heloísa Oss; Malossi, Rodrigo Mor
       title: Beyond Audio Signals: Generative Model-Based Speaker Diarization in Portuguese
        date: 2024
       words: 5305
      flesch: 47
     summary: 3 Related Work There are several diarization models that support the English language, including Pyannote [8]. PMLR (2023) Google Scholar  Reynolds, D.A.: Speaker identification and verification using Gaussian mixture speaker models.
    keywords: approach; audio; diarization; generative; google; language; method; model; recognition; scholar; speaker; speech; task; text
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        item: #184 of 282
          id: bracis-33565
      author: Silva, Maxwell Pires; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de
       title: Classification of Non-alcoholic Fatty Liver Disease in Thermal Images of the Liver Using a Siamese Neural Network
        date: 2024
       words: 4267
      flesch: 49
     summary: This work presents an innovative approach using Siamese neural networks to classify thermal images of the liver in order to identify the presence of NAFLD. The research is motivated by the need to use artificial intelligence to analyze thermal images, especially when the number of images is limited, making it difficult for ordinary neural networks to learn, a difficulty that the Siamese network already faces with ease.
    keywords: classification; disease; fatty; google; images; liver; method; nafld; network; patients; scholar
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        item: #185 of 282
          id: bracis-33566
      author: Viana, Joaquim; Matos, Helder; Mota, Marcelle; Santos, Reginaldo
       title: Classifying Graphs of Elementary Mathematical Functions Using Convolutional Neural Networks
        date: 2024
       words: 3740
      flesch: 42
     summary: The CNN architecture proposed for classifying elementary function graph images was named F-Graphs. To improve the performance of ResNet-50, MobileNet-V3, and EfficientNet-B0 on the dataset of elementary function images, parameter tuning would be necessary.
    keywords: accuracy; architecture; author; dataset; function; graphs; images; layers; learning; model; networks
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        item: #186 of 282
          id: bracis-33567
      author: Carvalho, Levi Cordeiro; Oliveira, Saulo A. F.; Rocha, Thiago Alves
       title: Comparing Neural Network Encodings for Logic-Based Explainability
        date: 2024
       words: 7007
      flesch: 55
     summary: Experiments showed similar running times for computing explanations, but the adapted encoding performed up to 18% better in building logical constraints and up to 16% better in overall time. Moreover, these bounds can aid the solver in computing explanations more rapidly.
    keywords: \le; \wedge; anns; computing; constraints; encoding; explanations; formula; time; variables
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        item: #187 of 282
          id: bracis-33568
      author: Silva, Lucas Almeida da; Santos, Eulanda Miranda dos; Giusti, Rafael
       title: Deep Learning Approach to Temporal Dimensionality Reduction of Volumetric Computed Tomography
        date: 2024
       words: 5683
      flesch: 51
     summary: Article  Google Scholar  Becker, A.S., et al.: In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017) Google Scholar  da Cruz, L.B., et al.:
    keywords: approach; article; data; google; images; information; learning; method; model; number; scholar; selection; slices; volumetric
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        item: #188 of 282
          id: bracis-33569
      author: Schulz, Hans Herbert; Moreira, Benjamin Grando
       title: Deployment of IBM Federated Learning Platform and Aggregation Algorithm Comparison: A Case Study Using the MNIST Dataset
        date: 2024
       words: 4495
      flesch: 53
     summary: The collected data will be used to train machine learning models to improve predictive maintenance routines. The primary goal of this study is to deploy an FL platform using Docker container technology to train models and check its functionality by comparing the performance of machine learning models using different FL strategies.
    keywords: data; device; fedavg; fedsdg; fig; ipt; learning; model; network; parties; platform; training; usage
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        item: #189 of 282
          id: bracis-33570
      author: Silva, Anderson Lopes; França, Hellen Guterres; Santos Neto, Carlos Mendes dos; Pessoa, Alexandre César Pinto; Quintanilha, Darlan Bruno Pontes; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de
       title: Detection of Pathological Regions of the Gastrointestinal Tract in Capsule Images Using EfficientNetV2 and YOLOv8
        date: 2024
       words: 6187
      flesch: 49
     summary: The experiment carried out, and the impact of the results on the advancement of detection models in GI Tract images are also described. For this task, CNNs widely used in the literature in the field of image classification were chosen.
    keywords: capsule; classification; dataset; detection; google; images; method; model; pathologies; results; scholar; table; tract; wce; yolov8
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        item: #190 of 282
          id: bracis-33571
      author: Virgilli, Rafaello; Candido Junior, Arnaldo; Rosa, Augusto Seben da; Oliveira, Frederico S.; Soares, Anderson da Silva
       title: Dual-Bandwidth Spectrogram Analysis for Speaker Verification
        date: 2024
       words: 4922
      flesch: 42
     summary: The average training duration was 1.8 h per epoch for single spectrogram models and 2.21 h for dual-bandwidth spectrogram models. This sums to 360 h for single spectrogram models and 442 h for dual-bandwidth models.
    keywords: approach; audio; bandwidth; broadband; eer; google; model; narrowband; performance; scholar; speaker; spectrograms; verification
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        item: #191 of 282
          id: bracis-33572
      author: Silva, Jesaías Carvalho Pereira; Canuto, Anne Magaly de Paula; Santos, Araken de Medeiros
       title: Dynamicity Analysis in the Selection of Classifier Ensembles Parameters
        date: 2024
       words: 5880
      flesch: 52
     summary: Different dynamic selection methods have been proposed in the literature, mainly for ensemble members and features, but very little effort has been done to propose dynamic selection methods for combination methods. 107(1), 177–207 (2018) Article  MathSciNet  Google Scholar  Ko, A.H.R., Sabourin, R., Britto, A.S., Jr.: From dynamic classifier selection to dynamic ensemble selection.
    keywords: classifier; combination; des; ensemble; knora; meta; methods; results; selection; test
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        item: #192 of 282
          id: bracis-33573
      author: Braz, Camila Santana; Teixeira, Matheus Cândido; Pappa, Gisele Lobo
       title: Embedding Representations for AutoML Pipelines
        date: 2024
       words: 5151
      flesch: 47
     summary: The main contributions of this study are: Comparison of two models to represent AutoML pipelines representations: tree and embeddings; Analysis and evaluation of the use of this linear representation in the context of AutoML; Investigation of semantic preservation in the representation through embeddings; Development of a method for visualizing the search spaces. Moreover, visual and qualitative analysis of the search space and pipelines distance are performed to better asses the proposed representation.
    keywords: algorithm; automl; distance; embeddings; google; learning; machine; model; pipelines; representations; search; space; tree
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        item: #193 of 282
          id: bracis-33574
      author: Angonese, Silvio Fernando; Galante, Renata
       title: Enhancing Graph Data Quality by Leveraging Heterogeneous Node Features and Embeddings
        date: 2024
       words: 6067
      flesch: 43
     summary: Node embedding is a technique that maps graph nodes to low-dimensional vectors, preserving the graph structure and node features, representing the nodes [16]. Enhancing Graph Data Quality by Leveraging Heterogeneous Node Features and Embeddings Download book PDF Download book EPUB Silvio Fernando Angonese  ORCID: orcid.org/0009-0001-6441-43209 & Renata Galante  ORCID: orcid.org/0000-0003-3589-16199  Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15412)) Included in the following conference series: Brazilian Conference on Intelligent Systems 478 Accesses Abstract Heterogeneous Graphs are important data sources due to their rich representation of knowledge, primarily based on node features and relationships.
    keywords: algorithm; data; embeddings; experiments; features; google; graph; image; information; node; paper; publisher; scholar; types
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        item: #194 of 282
          id: bracis-33575
      author: Sousa, Leonardo P.; Silva, Romuere R. V.; Claro, Maíla L.; Araújo, Flávio H. D.; Borges, Rodrigo N.; Machado, Vinicius P.; Veras, Rodrigo M. S.
       title: Ensemble of CNNs for Enhanced Leukocyte Classification in Acute Myeloid Leukemia Diagnosis
        date: 2024
       words: 5828
      flesch: 42
     summary: Article  MATH  Google Scholar  Dasariraju, S., Huo, M., McCalla, S.: Detection and classification of immature leukocytes for diagnosis of acute myeloid leukemia using random forest algorithm. Springer (2000) Google Scholar  Khan, F.H., Saadeh, W.: An eeg-based hypnotic state monitor for patients during general anesthesia.
    keywords: accuracy; acute; bagging; blood; classification; ensemble; google; images; learning; leukemia; leukocytes; network; results; scholar; table
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        item: #195 of 282
          id: bracis-33576
      author: Silva, Fillipe dos Santos; Kakimoto, Gabriel Kenzo; Reis, Julio Cesar dos; Reis, Marcelo S.
       title: ERASMO: Leveraging Large Language Models for Enhanced Clustering Segmentation
        date: 2024
       words: 6131
      flesch: 46
     summary: ERASMO’s tailored embeddings for tabular datasets and integration of feature order permutation provide more precise and contextually relevant clusters, offering superior versatility and robustness in various clustering applications. However, there is no spatial ordering relationship between features in tabular datasets.
    keywords: chi; clustering; clusters; data; dataset; dbi; embeddings; erasmo; erasmobase; erasmonv; feature; language; models; tabular; tuning
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        item: #196 of 282
          id: bracis-33577
      author: Amorim, Marcelo M.; Prata, Leonardo; Maurício, João Stephan; Borges, Alex; Bernardino, Heder; Souza, Gabriel de
       title: Euclidean Alignment for Transfer Learning in Multi-band Common Spatial Pattern
        date: 2024
       words: 5724
      flesch: 56
     summary: This study introduces new BCI architecture with multi-band temporal filters and EA. We proposed Euclidean Alignment (EA) with multi-band temporal filters to reduce the impact of these two conditions.
    keywords: alignment; band; bci; brain; csp; data; electrodes; filter; models; motor; multi; results; signal; stroke
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        item: #197 of 282
          id: bracis-33578
      author: Alves, Edgard B.; Alves, Jorge A.; Goldschmidt, Ronaldo R.
       title: Evaluating CNN-Based Classification Models Combined with the Smoothed Pseudo Wigner-Ville Distribution to Identify Low Probability of Interception Radar Signals
        date: 2024
       words: 5311
      flesch: 45
     summary: Due to the use of robust automatic recognition algorithms of intrapulse modulations (ATR - Automatic Target Recognition) of LPI radar signals, ELINT systems have good performance, even in environments with low signal-to-noise ratio (SNR) The SPWVD is one of the most effective TFA techniques for estimating various temporal and spectral parameters of LPI radar signals, especially in noisy environments [7].
    keywords: accuracy; atr; cnn; combinations; cwd; fig; lpi; radar; results; signal; snr; spwvd; tfa; tfi
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        item: #198 of 282
          id: bracis-33579
      author: Presa, João Paulo Cavalcante; Camilo Junior, Celso Gonçalves; Oliveira, Sávio Salvarino Teles de
       title: Evaluating Large Language Models for Tax Law Reasoning
        date: 2024
       words: 5411
      flesch: 42
     summary: Fedjudge: federated legal large language model. arXiv preprint arXiv:2309.08173 (2023) DISC-LawLLM [38] employs large language models trained on supervised datasets in the legal domain and incorporates a retrieval module to access and utilize external legal knowledge.
    keywords: answers; dataset; evaluation; language; law; llms; metrics; models; questions; reasoning; responses; tasks; tax
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        item: #199 of 282
          id: bracis-33580
      author: Veloso, Adriano; Zuin, Gianlucca; Sena, Luan
       title: Explaining Biomarker Response to Anticoagulant Therapy in Atrial Fibrillation: A Study of Warfarin and Rivaroxaban with Machine Learning Models
        date: 2024
       words: 4775
      flesch: 45
     summary: 120, 102161 (2021) Google Scholar  Bayer, S., Gimpel, H., Markgraf, M.: The role of domain expertise in trusting and following explainable ai decision support systems. Article  MATH  Google Scholar  Costa, A.B.D., Moreira, L., Andrade, D.C.D., Veloso, A., Ziviani, N.: Predicting the evolution of pain relief: Ensemble learning by diversifying model explanations.
    keywords: article; biomarkers; data; features; fibrillation; google; learning; machine; model; patients; rivaroxaban; scholar; warfarin
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        item: #200 of 282
          id: bracis-33581
      author: Fernandes, Matheus Campos; França, Fabrício Olivetti de; Francesquini, Emilio
       title: Going Bananas! - Unfolding Program Synthesis with Origami
        date: 2024
       words: 6235
      flesch: 56
     summary: Springer Nature Singapore, Singapore (2024).https://doi.org/10.1007/978-981-99-8413-8_14 Forstenlechner, S., Fagan, D., Nicolau, M., O’Neill, M.: A grammar design pattern for arbitrary program synthesis problems in genetic programming. For each dataset, we executed 30 seeds of each pattern starting from the simplest and testing other patterns if none of the seeds succeeded in finding a solution (i.e., the success rate was \(0\%\)).
    keywords: 0}\; arg\(_\texttt; i\(_\texttt; n}\; origami; pattern; problems; program; programming; recursion; synthesis; type
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        item: #201 of 282
          id: bracis-33582
      author: Silva, Mariana O.; Oliveira, Gabriel P.; Costa, Lucas G. L.; Pappa, Gisele L.
       title: GovBERT-BR: A BERT-Based Language Model for Brazilian Portuguese Governmental Data
        date: 2024
       words: 5623
      flesch: 41
     summary: Such government-related models can be divided into two distinct categories according to their purpose: legal domain and administrative domain models. On the other hand, administrative domain models are designed to support various governmental functions outside the legal sphere, such as managing bidding processes and processing information from the official gazettes.
    keywords: classification; data; documents; domain; govbert; governmental; language; model; performance; pre; tasks; text; training
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        item: #202 of 282
          id: bracis-33583
      author: Casarotto, Pedro Henrique; Cerri, Ricardo
       title: Growing Self-Organizing Maps for Multi-label Classification
        date: 2024
       words: 6051
      flesch: 58
     summary: Neural Netw. 11(3), 601–614 (2000) Article  MATH  Google Scholar  Alshanqiti, A., Namoun, A.: Predicting student performance and its influential factors using hybrid regression and multi-label classification. IEEE (2008) Google Scholar  Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification.
    keywords: classification; data; google; grid; gsom; instance; label; learning; maps; mll; neuron; number; organizing; scholar; self
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        item: #203 of 282
          id: bracis-33584
      author: Carvalho, Vinicius Renan de; Sichman, Jaime Simão
       title: HEACT: Hybrid Evolutionary Algorithm for the Multi-region Multi-objective Cloud Task Scheduling Problem. A Study of Workflow Scheduling in AWS EC2
        date: 2024
       words: 6334
      flesch: 56
     summary: Over the years, genetic algorithms (GA) were also considered for scheduling tasks in this domain. In [24], the authors introduced two hybrid heuristics based on genetic algorithms for task scheduling.
    keywords: algorithm; aws; cloud; cost; google; heuristic; machine; objective; problem; scheduling; scholar; solutions; task; time; workflow
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        item: #204 of 282
          id: bracis-33585
      author: Fernandes, Eduardo Augusto Militão; Noronha, Thiago Ferreira de; Coco, Amadeu Almeida
       title: Heuristic Solutions for the 2D Bin-Packing Problem with Varied Size
        date: 2024
       words: 5512
      flesch: 55
     summary: However, when observing the boxplot with the distribution of solution depths, it is evident that, especially in the most challenging sets of instances in Ortmann’s dataset - Nice300i, Nice400i, Nice500i, Path300i, Path400i and Path500i -, the solutions found by the proposed algorithm have depths smaller, less varied and without extreme values than those displayed by the original algorithm. Heuristic Solutions for the 2D Bin-Packing Problem with Varied Size Download book PDF Download book EPUB Eduardo Augusto Militão Fernandes9, Thiago Ferreira de Noronha9 & Amadeu Almeida Coco10  Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15413)) Included in the following conference series: Brazilian Conference on Intelligent Systems 400 Accesses Abstract This work addresses the 2D Bin-Packing Problem with Varied Size and proposes heuristic solutions for it.
    keywords: algorithm; bin; constraint; depth; instances; items; packing; problem; results; size; solutions
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        item: #205 of 282
          id: bracis-33586
      author: Camargo, Laura Simões; Blay, Enio Alterman; Schmidt, Gabriela Soares
       title: Humanities and AI: Ethical Education in Technology Careers
        date: 2024
       words: 5072
      flesch: 45
     summary: Download conference paper PDF Similar content being viewed by others AI Ethics in Higher Education: A Review of Ethical Challenges Chapter © 2026 Ethics and AI in Higher Education: A Study on Students’ Perceptions Chapter © 2024 Source: Author’s elaboration, 2024.Full size table Based on a survey of ethics subjects and related topics (such as social sciences and human rights) in curricular matrices and Political Pedagogical Plans, a total of 204 subjects were found.
    keywords: authors; computer; computing; courses; disciplines; education; ethics; science; subjects; universities; university
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        item: #206 of 282
          id: bracis-33587
      author: Oliveira, João Pedro A. de; Castro Jr., Olacir R.
       title: Impact of Parent Selection Operator on the FDEA Algorithm
        date: 2024
       words: 5839
      flesch: 41
     summary: These characteristics are desired to ensure the optimizer is efficient and provides meaningful options to the decision-maker in multi-objective problems [4]. This algorithm employs a fuzzy decomposition approach to divide a Multi-Objective Optimization Problem (MOP) into a set of subproblems, each solved individually to enhance the adaptability and precision of solutions in multi-objective problems [11].
    keywords: algorithm; fdea; multi; objective; optimization; pareto; performance; problem; selection; solutions; variants
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        item: #207 of 282
          id: bracis-33588
      author: Cabrera, Eduardo Faria; Barros, Marcel Rodrigues de; Costa, Anna Helena Reali
       title: Improving LLMs’ Reasoning and Planning with Finite-State Machines
        date: 2024
       words: 5726
      flesch: 51
     summary: We suspect this issue is exacerbated by the requirement to return action numbers instead of names in the restricted setting. \(\mathcal {G} \subset \mathcal {S}\) is the set of goal states where the goal condition is satisfied.
    keywords: \mathcal; actions; goal; language; llms; methods; models; number; planning; plans; prompt; reasoning; state
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        item: #208 of 282
          id: bracis-33589
      author: Sanchez, Juan Pablo Chavarro; Portela, Tarlis Tortelli; Carvalho, Jônata Tyska
       title: Improving Short-Content Misinformation Detection Using Multiple Aspect Trajectories Classification Techniques
        date: 2024
       words: 6555
      flesch: 42
     summary: The main contributions of this work are: a new dataset of information trajectories, a new application domain for trajectory-based classification methods, and a new classification method with great potential for detecting misinformation in social networks. 312–320 (2019) Google Scholar  da Silva, C.L., Petry, L.M., Bogorny, V.: A survey and comparison of trajectory classification methods.
    keywords: approach; aspect; classification; content; data; detection; google; messages; misinformation; models; networks; news; propagation; scholar; trajectories; trajectory
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        item: #209 of 282
          id: bracis-33590
      author: Laitz, Thiago Soares; Papakostas, Konstantinos; Lotufo, Roberto; Nogueira, Rodrigo
       title: InRanker: Distilled Rankers for Zero-Shot Information Retrieval
        date: 2024
       words: 5670
      flesch: 50
     summary: One such approach is model distillation [17]. This has shown that knowledge transfer via model distillation is not only feasible but also effective.
    keywords: beir; datasets; distillation; domain; effectiveness; information; labels; model; queries; retrieval; synthetic; table; teacher; training
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        item: #210 of 282
          id: bracis-33591
      author: Antonelo, Eric Aislan; Couto, Gustavo Claudio Karl; Möller, Christian; Fernandes, Pedro Henrique
       title: Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving Based on Bird’s-Eye View in Simulated Cities
        date: 2024
       words: 4955
      flesch: 53
     summary: Although training is offline in town01 environment, we still evaluate both BC and weighted BC agents as their model’s parameters are updated. Our approach enhances BC by integrating a kernel density estimator to adjust training sample weights based on action density, thereby improving the learning of rare but critical actions such as stopping at red lights and accelerating at green lights, specially in scenarios of scarce number of expert demonstrations.
    keywords: actions; agent; bev; density; driving; expert; function; learning; loss; policy; training; vehicle
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        item: #211 of 282
          id: bracis-33592
      author: Silveira, Igor Cataneo; Barbosa, André; Costa, Daniel Silva Lopes da; Mauá, Denis Deratani
       title: Investigating Universal Adversarial Attacks Against Transformers-Based Automatic Essay Scoring Systems
        date: 2024
       words: 7226
      flesch: 57
     summary: In: Proceedings of the 16th International Conference on Computational Processing of Portuguese, vol. 1. pp. 228–237 (2024) Google Scholar  Singh, A., Pandey, N., Shirgaonkar, A., Manoj, P., Aski, V.: A study of optimizations for fine-tuning large language models (2024) Google Scholar  Souza, F., Nogueira, R., Lotufo, R.: BERTimbau: pretrained BERT models for Brazilian Portuguese. Curran Associates, Inc. (2020) Google Scholar  Chang, L.H., Ginter, F.: Automatic short answer grading for finnish with chatgpt.
    keywords: adjectives; adverbs; attacks; bert; competence; essay; features; gemini; google; google scholar; model; phi-3; scholar; scoring; systems
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        item: #212 of 282
          id: bracis-33593
      author: Albarrans, Guilherme; Freire, Valdinei
       title: Likelihood Estimator for Multi Model-Based Reinforcement Learning
        date: 2024
       words: 6162
      flesch: 40
     summary: By incorporating learned models into the decision-making process, these methods can adapt more readily to new situations and exploit previously acquired knowledge to achieve better performance. Through empirical evaluation and analysis, we aim to provide insights into the comparative advantages and limitations of both traditional single-model and proposed segmented model approaches in addressing challenges posed by environments with heterogeneous dynamics.
    keywords: \mathcal; approach; dynamics; environment; episode; function; learning; likelihood; model; parameters; reinforcement; reinforcement learning; state
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        item: #213 of 282
          id: bracis-33594
      author: Vargas, Talles Viana; Pedrini, Helio; Santanchè, André
       title: LLM-Driven Chest X-Ray Report Generation With a Modular, Reduced-Size Architecture
        date: 2024
       words: 5484
      flesch: 50
     summary: [6] took a different approach by first multi-classifying diseases on chest X-ray images with bounding boxes, and then using these abnormality features as input for the LLM. Med-PaLM [26, 27] is a large-scale generalist biomedical AI system, capable of interpreting various biomedical data modalities, including tasks like chest X-ray report generation and medical visual question answering. The architecture, as illustrated in Fig. 1, starts with the image encoder, responsible for extracting pertinent features from chest X-ray images.
    keywords: chest; generation; image; language; metrics; models; ray; report; scholar; text; training; vision
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        item: #214 of 282
          id: bracis-33595
      author: Jorge, Germano Antonio Zani; Bezerra, Davi Alves; Xavier, Clarissa Castellã; Pardo, Thiago Alexre Salgueiro
       title: Multilingual Extractive Summarization: Investigating State-of-the-Art Methods for English and Brazilian Portuguese
        date: 2024
       words: 4577
      flesch: 45
     summary: https://aclanthology.org/W17-1003 Ruan, Q., Ostendorff, M., Rehm, G.: HiStruct+: improving extractive text summarization with hierarchical structure information. On the other hand, extractive summarization methods generally do not suffer from these disadvantages.
    keywords: blanc; cstnews; document; language; methods; models; portuguese; presumm; rouge; summaries; summarization; summary; text
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        item: #215 of 282
          id: bracis-33596
      author: Costa, Isabelly P. da; Takazono, Bruno M. P.; Cavalcante, Carlos H. L.; Madeiro, João P. V.; Pedrosa, Roberto C.
       title: Multimodal and Hybrid Models for Predicting SCD Risk in Chagas Cardiomyopathy
        date: 2024
       words: 5720
      flesch: 49
     summary: Eng. 20(5), 9159–9178 (2023) Article  MATH  Google Scholar  Elman, J.L.: Article  MATH  Google Scholar  Johnston, L.: Student’s t-test.
    keywords: chagas; data; ecg; features; google; mlp; model; patients; risk; rnn; scd; scenario; scholar; series; tabular; time
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        item: #216 of 282
          id: bracis-33597
      author: Cordeiro, Renan; Alcântara, João
       title: On the Equivalence Between Logic Programs and Bipolar Argumentation Frameworks
        date: 2024
       words: 8084
      flesch: 48
     summary: Let \(\mathcal {L}\) be a labelling of \(\mathcal B\) respecting \(\mathfrak {Sup}\) and \(\mathcal M\) be an interpretation of \(P_\mathcal {B}\). From Lemma 1, we obtain a similar result to Theorem 4: Theorem 7 Let \(\mathcal B = (\mathcal {A}, Att , Sup )\) be a \(\textit{BAF}\) with corresponding \(\textit{NLP}\) \(P_\mathcal {B}\), \(\mathcal {L}\) be a labelling of \(\mathcal B\) respecting \(\mathfrak {Sup}\) and \(\mathcal M\) be an interpretation of \(P_\mathcal {B}\).
    keywords: \(\beta; \(\mathcal; \(\textit{baf}\; \in; argumentation; arguments; att; b}\; labelling; logic; l}2\mathcal; semantics
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        item: #217 of 282
          id: bracis-33598
      author: Silva, Matheus Vieira da; Mari, João Fernando; Backes, André Ricardo
       title: Optimizing CleanUNet Architecture Parameters for Enhancing Speech Denoising
        date: 2024
       words: 4888
      flesch: 48
     summary: Article  MATH  Google Scholar  Defossez, A., Synnaeve, G., Adi, Y.: Real time speech enhancement in the waveform domain (2020) Google Scholar  Ding, J., et al.: Syst. 27 (2014) Google Scholar  Gu, A., Dao, T.: Mamba: Linear-time sequence modeling with selective state spaces.
    keywords: architecture; attention; bottleneck; cleanunet; google; mamba; model; noise; number; parameters; scholar; self; speech; time; training
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        item: #218 of 282
          id: bracis-33599
      author: Oliveira, Francisco Bráulio; Sichman, Jaime Simão
       title: Portuguese Emotion Detection Model Using BERTimbau Applied to COVID-19 News and Replies
        date: 2024
       words: 5748
      flesch: 52
     summary: The main kinds of emotion models include discrete and dimensional models A systematic review on affective computing: emotion models, databases, and recent advances.
    keywords: covid-19; detection; distribution; emotions; google; language; media; model; news; portuguese; prevalence; replies; scholar; table; topic; tweets
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        item: #219 of 282
          id: bracis-33600
      author: Costa e Souza, João Paulo; Meneguette, Rodolfo I.; Gonçalves, Vinícius P.; Mendonça, Fábio L. L. de; Silva, Francisco Airton; Rocha Filho, Geraldo P.
       title: Predicting Bull and Bear Markets: A Deep Learning and Linear Regression Study in Cryptocurrencies
        date: 2024
       words: 5733
      flesch: 44
     summary: [15] conducted a comparative analysis of three machine learning approaches: Recurrent Neural Network (RNN), Long Short-Term Memory, and Convolutional Neural Network, focusing on their performance in predicting financial market price. Predicting Bull and Bear Markets: A Deep Learning and Linear Regression Study in Cryptocurrencies Download book PDF Download book EPUB João Paulo Costa e Souza9, Rodolfo I. Meneguette10, Vinícius P. Gonçalves9, Fábio L. L. de Mendonça9, Francisco Airton Silva11 & … Geraldo P. Rocha Filho9,12  Show authors Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15413)) Included in the following conference series: Brazilian Conference on Intelligent Systems 408 Accesses Abstract Despite the growing popularity and increasingly widespread use of cryptocurrencies in contemporary financial markets, understanding market trends and predicting their future movements is a formidable challenge in financial analysis.
    keywords: bilstm; candles; cnn; data; forecasting; google; linear; lstm; market; model; prediction; price; regression; scholar; slope; trend
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        item: #220 of 282
          id: bracis-33601
      author: Chaves, Julio Macedo; Ohata, Elene Firmeza; Santos, Matheus Araujo dos; Santos, José Daniel de Alencar; Bernardes, Matheus Jardim; Dora, Daniel Seleme; Rebouças Filho, Pedro Pedrosa
       title: Predicting Energy Consumption Data Using Deep Learning: An LSTM Approach
        date: 2024
       words: 4684
      flesch: 53
     summary: Article  Google Scholar  Xiao, Z.: Impacts of data preprocessing and selection on energy consumption prediction model of HVAC systems based on deep learning. 4.1 Modeling of the Regression Method In Table 2, we present the results of energy consumption prediction using different models; we used the default settings for all models.
    keywords: article; author; consumption; data; energy; google; google scholar; horizon; learning; math; model; prediction; results; scholar; search
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        item: #221 of 282
          id: bracis-33602
      author: Anselmo, Marcelo; Ribas, Bruno César
       title: Pseudonymization in Legal Texts According to the LGPD: A Named Entity Recognition Approach
        date: 2024
       words: 4778
      flesch: 47
     summary: Their results provide an essential foundation for validating NER models in the Brazilian legal context. This practice not only allows for the personalization of online experiences, but it also exposes users to privacy risks, including the illegal sale of such data in dark markets, potentially for use in criminal activities
    keywords: data; entities; entity; fig; information; lgpd; model; ner; pseudonymization; recognition; study; terms; text
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        item: #222 of 282
          id: bracis-33603
      author: Piau, Marcos; Lotufo, Roberto; Nogueira, Rodrigo
       title: ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language
        date: 2024
       words: 5867
      flesch: 51
     summary: Language models scale reliably with over-training and on downstream tasks (2024) Google Scholar  Garcia, G.L., et al.: MS MARCO: a human generated machine reading comprehension dataset (2018) Google Scholar  de Barros, T.M., Pedrini, H., Dias, Z.: Leveraging emoji to improve sentiment classification of tweets.
    keywords: dataset; google; language; models; performance; portuguese; pretraining; ptt5; scholar; size; tasks; text
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        item: #223 of 282
          id: bracis-33604
      author: José, Marcos M.; Cação, Flávio N.; Ribeiro, Maria F.; Cheang, Rafael M.; Pirozelli, Paulo; Cozman, Fabio G.
       title: Question Answering with Texts and Tables Through Deep Reinforcement Learning
        date: 2024
       words: 6359
      flesch: 52
     summary: As the linker may provide an excessive amount of information for the reader to process, the Chainer’s role is to select the top 50 chains (table row and text passage), which consist of a table row and a corresponding text passage. It starts with 8 text or table passages and iteratively expands the search.
    keywords: agent; answer; answering; encoder; information; learning; passages; question; reader; retrieval; retriever; tables; texts; training
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        item: #224 of 282
          id: bracis-33605
      author: Polar, Christian Delgado; Delgado, Karina Valdivia; Freire, Valdinei
       title: Reinforcement Learning with Utility-Based Semantic for Goals
        date: 2024
       words: 7257
      flesch: 64
     summary: orcid.org/0000-0001-6123-64969,10, Karina Valdivia Delgado  ORCID: orcid.org/0000-0002-9120-89879 & Valdinei Freire  ORCID: orcid.org/0000-0003-0330-39319  Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15413)) Included in the following conference series: Brazilian Conference on Intelligent Systems 378 Accesses Abstract Stochastic shortest path problems (SSPs) are Markov decision processes with goal states and the problem is to find policies to achieve the goal with the lowest possible expected cost. Download conference paper PDF Similar content being viewed by others $$\alpha $$ -MCMP: Trade-Offs Between Probability and Cost in SSPs with the MCMP Criterion Chapter © 2023 Trading Utility and Uncertainty: Applying the Value of Information to Resolve the Exploration–Exploitation Dilemma in Reinforcement Learning Chapter © 2021 A Linear Online Guided Policy Search Algorithm Chapter © 2017 1 Introduction Stochastic shortest path problems (SSPs) are Markov decision processes (MDPs) with a set of goal states.
    keywords: \in; algorithm; cost; criterion; goal; gubs; learning; policy; probability; state; value
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        item: #225 of 282
          id: bracis-33606
      author: Bomfim, Francisco das Chagas Jucá; Monteiro Neto, Joao Araujo; Bezerra Filho, Gilson; Furtado, Vasco; Pinheiro, Vládia
       title: SARA - A Generative AI for Legal Process Summarization Based on Chain of Density Prompt Engineering
        date: 2024
       words: 5189
      flesch: 39
     summary: Although we focused on a specific type of summary, the generic nature of the process report, which involves various elements, suggests that our approach is broadly applicable to other types of legal document summaries. Quantitative metrics for evaluating legal summaries remain limited.
    keywords: abstractive; cod; density; documents; evaluation; judicial; process; prompt; report; sara; summaries; summarization; summary; version
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        item: #226 of 282
          id: bracis-33607
      author: Alcantara, Leonardo U.; Triguero, Isaac; Cerri, Ricardo
       title: Semi-supervised Predictive Clustering Trees for Multi-label Protein Subcellular Localization
        date: 2024
       words: 6191
      flesch: 53
     summary: Thus, in this paper, we propose a new semi-supervised algorithm for multi-label protein subcellular localization. 6 Conclusions and Future Work In this paper, we proposed a Semi-Supervised Predictive Clustering Tree (SSL-PCT) algorithm capable of classifying multi-label instances exploiting labeled and unlabeled data.
    keywords: classification; clustering; data; datasets; google; instances; label; learning; localization; math; protein; scholar; set; trees
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        item: #227 of 282
          id: bracis-33608
      author: Garcia, Klaifer; Berton, Lilian
       title: Siamese Network-Based Prioritization for Enhanced Multi-document Summarization
        date: 2024
       words: 6739
      flesch: 46
     summary: Siamese Network-Based Prioritization for Enhanced Multi-document Summarization Download book PDF Download book EPUB Klaifer Garcia  ORCID: orcid.org/0000-0002-9983-67349 & Lilian Berton  ORCID: orcid.org/0000-0003-1397-60059  Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15413)) Included in the following conference series: Brazilian Conference on Intelligent Systems 369 Accesses Abstract Methods for document summarization hold significance in numerous applications, particularly in scenarios involving extensive content, such as news and social media monitoring. We believe the combination of input documents can help provide a more accurate description of the content, and our goal is to improve summarization by leveraging this variety of input documents.
    keywords: association; documents; google; input; linguistics; methods; multi; network; news; results; scholar; sentences; summaries; summarization; training
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        item: #228 of 282
          id: bracis-33609
      author: Cardoso, Lucas F. F.; Ribeiro Filho, José de Sousa; Santos, Vitor C. A.; Francês, Regiane S. Kawasaki; Alves, Ronnie C. O.
       title: Standing on the Shoulders of Giants
        date: 2024
       words: 5720
      flesch: 56
     summary: 1, this work aims to explore how IRT can be useful for evaluating ML models considering the confusion matrix obtained in a classical classification problemFootnote 1. Full size table 5 Final Considerations The empirical evaluation of ML models remains the most common way to analyze the performance of a classifier on a dataset.
    keywords: dataset; google; instances; irt; item; matrix; metrics; models; performance; scholar; score; test
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        item: #229 of 282
          id: bracis-33610
      author: Fernandes, Leandro Carísio; Guedes, Gustavo Bartz; Laitz, Thiago Soares; Almeida, Thales Sales; Nogueira, Rodrigo; Lotufo, Roberto; Pereira, Jayr
       title: SurveySum: A Dataset for Summarizing Multiple Scientific Articles into a Survey Section
        date: 2024
       words: 4722
      flesch: 50
     summary: In: ACL (2020) Google Scholar  Fabbri, A.R., Li, I., She, T., Li, S., Radevm, D.R.: Multi-news: a large-scale multi-document summarization dataset and abstractive hierarchical model (2019) Google Scholar  Ghalandari, D.G., Hokamp, C., Pham, N.T., Glover, J., Ifrim, G.: A large-scale multi-document summarization dataset from the wikipedia current events portal (2020) Google Scholar  Gupta, V., Bharti, P., Nokhiz, P., Karnick, H.: SumPubMed: summarization dataset of PubMed scientific articles. ScisummNet: a large annotated corpus and content-impact models for scientific paper summarization with citation networks.
    keywords: articles; chunks; dataset; document; google; model; scholar; section; summarization; survey; text
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        item: #230 of 282
          id: bracis-33611
      author: Orang, Omid; Silva, Felipe A. R. da; Silva, Petrônio C. L.; Barros, Pedro H. S. S.; Ramos, Heitor S.; Guimarães, Frederico G.
       title: Traffic Forecasting Using Federated Randomized High-Order Fuzzy Cognitive Maps
        date: 2024
       words: 6127
      flesch: 57
     summary: For instance, in [33], DBN, k-means clustering, and Dempster-Shafer theory are utilized for traffic flow prediction, while in [34], CNN with Pearson correlation-based theory is employed to predict traffic speed. https://doi.org/10.1007/978-3-319-26404-2_16 Chapter  MATH  Google Scholar  Lv, Y., Duan, Y., Kang, W., Li, Z., Wang, F.-Y.: Traffic flow prediction with big data: a deep learning approach.
    keywords: data; flow; forecasting; google; google scholar; learning; methods; model; prediction; rhfcm; scholar; series; time; traffic
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        item: #231 of 282
          id: bracis-33612
      author: Zagatti, Fernando Rezende; Lucrédio, Daniel; Caseli, Helena de Medeiros
       title: Unsupervised Statistical Keyword Extraction Pipeline: Is LLM All You Need?
        date: 2024
       words: 5919
      flesch: 49
     summary: Our work seeks to offer a direct comparison between classical keyword extraction methods and LLM-based techniques, focusing on their respective strengths and weaknesses in a more generalizable context. In this way, keyword extraction plays a crucial role in various applications for Natural Language Processing (NLP), such as information retrieval
    keywords: extraction; google; keyword; language; list; llms; methods; models; prompt; results; similarity; table; text; words
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        item: #232 of 282
          id: bracis-33613
      author: Pires, Rilder S.; Silveira, Raquel; Fernandes, Carlos G. O.; Monteiro Neto, João A.; Furtado, Vasco
       title: Using Complex Networks to Improve Legal Text Hierarchical Classification
        date: 2024
       words: 6277
      flesch: 49
     summary: Legal topics are typically organized into hierarchical trees, where each branch, from the root (e.g., consumer law) to the leaf (e.g., moral/material damage), categorizes the vocabulary that describes lawsuits. Additional background knowledge about the hierarchical structure of legal topics and provisions is used.
    keywords: approach; brazil; citations; classification; google; graph; hierarchy; label; model; petition; provisions; scholar; set; text; topics; vertices
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        item: #233 of 282
          id: bracis-33614
      author: Stefaniak, Antoniel Kleber; Jaskowiak, Pablo Andretta; Weihmann, Lucas
       title: A Case Study on Water Demand Forecasting in a Coastal Tourist City
        date: 2024
       words: 5711
      flesch: 49
     summary: Relevant months in terms of water demand forecasting are zoomed in to highlight the performance of both models in Fig.  Machine learning for water demand forecasting: case study in a Brazilian coastal city.
    keywords: arima; data; demand; forecasting; methods; models; results; series; study; term; time; water; window
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        item: #234 of 282
          id: bracis-33615
      author: None
       title: bracis-33615
        date: None
       words: 6583
      flesch: 44
     summary: To develop the model, the authors initially created a list of coders’ interpretations of student behavior, whether related to gaming behavior or not, and these interpretations were used to create the patterns (Fig. 1). In Table 2, we offer a list and description of the elements of student behaviors identified during the knowledge elicitation process with the expert, in line with the findings of previous work [21].
    keywords: approach; attitudes; behavior; data; engineering; gaming; google; help; knowledge; learning; model; scholar; students; system; table
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        item: #235 of 282
          id: bracis-33616
      author: Bortoni, Leonardo Afonso Ferreira; Jaskowiak, Pablo Andretta
       title: Acoustic Features and Autoencoders for Fault Detection in Rotating Machines: A Case Study
        date: 2024
       words: 6628
      flesch: 54
     summary: Acoustic signals, in particular, are quite appealing in the context of MFD, as they are often among the first manifestations of machine failure. Given these compelling characteristics, MFD based exclusively on acoustic signals can be highly beneficial.
    keywords: approach; autoencoders; baselines; data; detection; fault; features; frame; input; machine; mafaulda; mfd; model; results; signals
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        item: #236 of 282
          id: bracis-33617
      author: Rocha, Hemilis Joyse Barbosa; Pimentel, Bruno Almeida; Costa, Evandro de Barros; Tedesco, Patricia Cabral de Azevedo Restelli
       title: Affective States in Novice Programmers: Automatically Detecting and Analyzing the Impact on Learning
        date: 2024
       words: 6103
      flesch: 46
     summary: Springer, Cham (2018) Google Scholar  Medeiros, M.G., Nunes, R., Prates, R.O.: In: Proceedings of the International Conference on Software Engineering and Knowledge Engineering, pp. 463–468 (2018) Google Scholar  Liu, Y., Liu, Y., Yao, Y., Li, H.: Predicting students’ emotions in programming based on multimodal data.
    keywords: affective; average; concepts; google; learning; novice; performance; problem; programmers; programming; scholar; solving; states; students; table
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        item: #237 of 282
          id: bracis-33618
      author: None
       title: bracis-33618
        date: None
       words: 6378
      flesch: 54
     summary: This highlights the effectiveness of classifying human activity recognition data using TF-C, surpassing the performance of individual supervised CNNs with same architecture. This work demonstrates the feasibility of utilizing TF-C to perform HAR as downstream task, achieving an accuracy of 96% utilizing all data of the training dataset in fine-tuning.
    keywords: activity; data; dataset; frequency; google; har; learning; model; performance; recognition; task; time; training
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        item: #238 of 282
          id: bracis-33619
      author: Luz, Gustavo P. C. P. da; Napoli, Otávio O.; Delgado, J. V.; Rocha, Anderson R.; Boccato, Levy; Borin, Edson
       title: An Evaluation of Temporal Neighborhood Coding Variants in Smartphone-Based Human Activity Recognition
        date: 2024
       words: 5432
      flesch: 53
     summary: In this work, we evaluate different TNC variants, i.e., using the RNN and the TS2Vec encoders and using the ADF and the Cosine Similarity test functions at the window selector. Our evaluation of different TNC variants using the UCI-Raw dataset demonstrated that the TS2Vec encoder significantly outperforms the RNN encoder for this task, achieving accuracies that are 15 to 17% points higher.
    keywords: adf; dataset; encoder; features; learning; raw; rnn; time; tnc; ts2vec; uci
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        item: #239 of 282
          id: bracis-33620
      author: Diniz, João Otávio Bandeira; Ribeiro, Neilson P.; Dias Jr., Domingos A.; Cruz, Luana B. da; Silva, Giovanni L. F. da; Gomes Jr, Daniel L.; Paiva, Anselmo C. de; Silva, Aristófanes C.
       title: AnisotropicBreast-ViT: Breast Cancer Classification in Ultrasound Images Using Anisotropic Filtering and Vision Transformer
        date: 2024
       words: 5610
      flesch: 50
     summary: This study introduces AnisotropicBreast-ViT, a method that integrates anisotropic filtering, balanced data augmentation, and Vision Transformer to aid in the classification of breast ultrasound images. References Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images.
    keywords: breast; cancer; classification; data; google; images; method; model; results; roi; scholar; techniques; training; ultrasound; vit
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        item: #240 of 282
          id: bracis-33621
      author: Viana, Pedro da S.; Cruz, Luana B. da; Dias Jr., Domingos A.; Diniz, João Otávio Beira
       title: Anomalies Diagnostic in Endoscopic Images Using Deep Learning Ensemble Models
        date: 2024
       words: 5054
      flesch: 44
     summary: Jones & Bartlett Publishers (2013) Google Scholar  da Cruz, L.B., et al.: ) Google Scholar  da Cruz, L.B., et al.:
    keywords: accuracy; augmentation; classes; classification; data; google; images; learning; method; model; results; roi; scholar; voting
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        item: #241 of 282
          id: bracis-33622
      author: Marques, Júlio Vitor Monteiro; Gonçalves, Clésio de Araújo; Carvalho Filho, Antonio Oseas de; Veras, Rodrigo de Melo Souza; Veloso e Silva, Romuere Rodrigues
       title: Automated Segmentation of Computed Tomography Images for COVID-19 Patient Evaluation
        date: 2024
       words: 6105
      flesch: 48
     summary: 3.5 Segmentation U-Net [18] is a Convolutional Neural Network (CNN) architecture for image segmentation. However, analyzing CT images is labor-intensive and demands significant manual effort, making the process exhaustive for specialists.
    keywords: covid-19; google; images; lesions; method; model; net; preprocessing; results; scholar; segmentation
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        item: #242 of 282
          id: bracis-33623
      author: Alonso, Edsson Israel Andonaegui; Delgado, Karina Valdivia; Santos, Francisco Carlos B. dos
       title: Combining Clustering and Genetic Algorithms for Portfolio Optimization: A Case Study with B3 Companies
        date: 2024
       words: 6068
      flesch: 52
     summary: Then the algorithm selects one asset per cluster of companies with the aim of diversifying the portfolio and reducing the correlation between assets, thus reducing the risk. This optimization problem can be modeled as a multi-objective problem (maximizing return and minimizing risk) with constraints to make it more realistic, such as cardinality constraints (limiting the maximum amount of assets that can make up the portfolio), penalty systems and transaction costs [3, 7].
    keywords: algorithm; assets; companies; dtw; means; portfolio; ratio; return; risk; sharpe; weights
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        item: #243 of 282
          id: bracis-33624
      author: Alves, Juliana; Costa, Eduardo; Xavier, Alencar; Brito, Luiz; Cerri, Ricardo; Neuroimaging Initiative, Alzheimer’s Disease
       title: Comparative Analysis of Machine Learning Algorithms for Identifying Genetic Markers Linked to Alzheimer’s Disease
        date: 2024
       words: 5597
      flesch: 43
     summary: Classification of Alzheimer’s Disease using robust tabnet neural networks on genetic data. Role of genes and environments for explaining Alzheimer disease.
    keywords: algorithms; alzheimer; analysis; data; disease; forest; gene; learning; machine; markers; model; regression; scholar; snps; study
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        item: #244 of 282
          id: bracis-33625
      author: Alves, Patrick; Delgado, Jaime; Gonzalez, Luis; Rocha, Anderson R.; Boccato, Levy; Borin, Edson
       title: Alzheimer’s Disease Neuroimaging Initiative Comparing LIME and SHAP Global Explanations for Human Activity Recognition
        date: 2024
       words: 5763
      flesch: 45
     summary: To measure the agreement among global explanations, it is necessary to calculate feature importance globally for LIME to compare the XAI techniques globally. Interpretation of correlation coefficientsFull size table 5 Experimental Results This section presents the results of our experiments, the analysis, and the answers to the research questions proposed to evaluate the disagreement between LIME and SHAP when explaining feature importance and if the divergence in the explanations is a problem for the interpretability of the models.
    keywords: activity; correlation; data; datasets; explanations; features; google; importance; lime; model; scholar; shap; techniques; xai
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        item: #245 of 282
          id: bracis-33626
      author: Oliveira, Alberto Régio Alves de; Medeiros, Cláudio Marques de Sá; Ramalho, Geraldo Luis Bezerra
       title: Damage Identification of Wind Turbine Blades
        date: 2024
       words: 5379
      flesch: 56
     summary: [15] with Inception-ResNet-V2 backbone to detect wind blades damages. The objective of this work was also to detect wind blades damage using bounding boxes.
    keywords: blades; classification; crops; damage; google; images; learning; lightning; model; scholar; set; size; training; turbine; wind
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        item: #246 of 282
          id: bracis-33627
      author: Alves, Camila Ferreira; Mozart, Thiago Garcia; Kowada, Luis Antônio Brasil
       title: Emotion Recognition in Instrumental Music Using AI
        date: 2024
       words: 3364
      flesch: 41
     summary: 3. Mel Spectrograms Full size image 2.6 Model Training The Scikit-Learn libraryFootnote 2 was used for model training. However, when attempting to generalize the models using different datasets, a considerable reduction in generalization capability was observed.
    keywords: audio; dataset; emotions; learning; mel; models; music; table; training; validation
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        item: #247 of 282
          id: bracis-33628
      author: Kohara, Debora T.; Oliveira, Gina M. B. de; Martins, Luiz G. A.
       title: Enhancing Multiobjective Genetic Algorithms for Pharmaceutical Batch Scheduling: A Study on Partitioned Selection with Constraints and Mutation with Greedy Local Search Strategy
        date: 2024
       words: 6234
      flesch: 47
     summary: A set of non-dominated solutions, known as Pareto optimal \(P^*\), exists if no solution within the search space dominates any in \(P^*\)  It is defined as the percentage of elements of the Pareto set \(P*\) that are not contained in P. Given two sets of non-dominated solutions A and B, the set coverage metric, or Coverage of two Sets (CS), represents the percentage of elements in B that are dominated by A. Therefore, CS(A,B)=1 indicates that all solutions in B are dominated by A, while CS(A,B)=0 indicates that none of the elements in B is dominated by A [20].
    keywords: bat; batch; google; heu; ini; model; mutation; number; scheduling; scholar; search; solutions
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        item: #248 of 282
          id: bracis-33629
      author: Silva, Lucas Nildaimon dos Santos; Silva, Diego Furtado; Caseli, Helena de Medeiros
       title: Evaluating Sentiment Quantification Methods in Brazilian Portuguese Corpora
        date: 2024
       words: 6203
      flesch: 40
     summary: In contrast to the existing body of work, our study focuses specifically on the application of sentiment quantification methods to Brazilian Portuguese texts, a research problem that has received less attention. Accesses Abstract This paper evaluates sentiment quantification methods applied to Brazilian Portuguese corpora.
    keywords: class; data; dataset; distribution; google; methods; performance; prevalence; quantification; quantification methods; scholar; sentiment; shifts; test
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        item: #249 of 282
          id: bracis-33630
      author: Andrade, Cesar; Ribeiro, Rita P.; Gama, João
       title: Evaluating Short Text Stream Clustering on Large E-commerce Datasets
        date: 2024
       words: 5931
      flesch: 47
     summary: This study aims to fill this gap by evaluating the effectiveness of short text clustering methods in a large and diverse e-commerce dataset. A significant challenge in evaluating the performance of clustering methods is the reliance on metrics like Normalized Mutual Information (NMI).
    keywords: aic; clustering; clusters; datasets; google; gtin; information; methods; model; nmi; performance; scholar; semantic; size; text
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        item: #250 of 282
          id: bracis-33631
      author: Murilo, Lucas V.; Oliveira, Gina M. B.; Martins, Luiz G. A.
       title: Evolutionary Adjustment of a Cellular Automata-BasedModel for Wildfire Spreading
        date: 2024
       words: 7076
      flesch: 50
     summary: (Color figure online) Full size image Experiments have shown that the proposed approach can replicate the behavior of other fire propagation models with different parameters, as well as adapt satisfactorily to the data dynamics generated from different sampling rates. The approach proposed in [7] is a reference for fire spread models using cellular automata.
    keywords: approach; automata; dataset; evolution; fig; fire; google; model; parameters; propagation; reference; scholar; time
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        item: #251 of 282
          id: bracis-33632
      author: Silva, Lucas C.; Lucrédio, Daniel
       title: Exploring Score-Based Ranking Fairness in Marketplace Environments Through Simulation
        date: 2024
       words: 6328
      flesch: 44
     summary: Findings reveal how utility in ranking fairness algorithms can be affected by the application of fairness techniques and how data drift impacts regular and fair ranking algorithms in a long-term scenario. The fairness-utility trade-off, coupled with the challenge posed by constantly changing group size distributions, has underscored limitations in state-of-the-art methods and metrics for optimizing ranking fairness in the proposed simulated environment.
    keywords: agent; algorithms; bias; conference; data; distribution; fairness; function; google; marketplace; premium; ranking; scholar; sellers; simulation
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        item: #252 of 282
          id: bracis-33633
      author: Santos, Germano B. dos; Silva, Paulo H. C.; Silva, Fabrício A.; Silva, Thais R. M. Braga; Aylon, Linnyer B. R.
       title: HAVANA: Hybrid Attentional Graph Convolutional Network Semantic Venue Annotation Model
        date: 2024
       words: 6171
      flesch: 50
     summary: MATH  Google Scholar  Bianchi, F.M., Grattarola, D., Livi, L., Alippi, C.: Graph neural networks with convolutional ARMA filters. Ad Hoc Netw. 138103016 (2023) Google Scholar  Chen, D., Lin, Y., Li, W., Li, P., Zhou, J., Sun, X.: Measuring and relieving the over-smoothing problem for graph neural networks from the topological view.
    keywords: \mathbf; aggregation; annotation; convolution; data; features; google; graph; havana; hybrid; learning; matrix; model; networks; neural; scholar; venue
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        item: #253 of 282
          id: bracis-33634
      author: Silva, Betania E. R. da; Napoli, Otávio O.; Delgado, J. V.; Rocha, Anderson R.; Boccato, Levy; Borin, Edson
       title: Impact of Pre-training Datasets on Human Activity Recognition with Contrastive Predictive Coding
        date: 2024
       words: 5647
      flesch: 54
     summary: Impact of Pre-training Datasets on Human Activity Recognition with Contrastive Predictive Coding | Springer Nature Link (formerly SpringerLink) Skip to main content Advertisement Log in Menu Find a journal Publish with us Track your research Search Cart Home Intelligent Systems Conference paper Impact of Pre-training Datasets on Human Activity Recognition with Contrastive Predictive Coding Conference paper First Online: 30 January 2025 pp 306–320 Cite this conference paper Access provided by University of Notre Dame Hesburgh Library Download book PDF Download book EPUB Intelligent Systems (BRACIS 2024)
    keywords: backbone; cpc; datasets; downstream; har; model; performance; pre; table; target; training
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        item: #254 of 282
          id: bracis-33635
      author: Ribeiro, Neilson P.; Teles, Felipe R. S.; Diniz, João Otávio Beira; Cruz, Luana B. da; Dias Jr., Domingos A.; Braz Junior, Geraldo; Almeida, João D. S. de; Paiva, Anselmo C. de
       title: Improving Colorectal Cancer Diagnosis Using MIRNet and InceptionV3 on Histopathological Images
        date: 2024
       words: 4952
      flesch: 47
     summary: Therefore, this study presents a method for diagnosing CRC histopathological images using convolutional neural networks (CNNs) for image enhancement and classification between benign and malignant. Addressing this gap, the proposed method introduces the use of a CNN called MIRNet for the automatic enhancement of CRC histopathological images.
    keywords: author; benign; cancer; classification; colon; crc; features; google; images; inceptionv3; method; metrics; mirnet; scholar
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        item: #255 of 282
          id: bracis-33636
      author: Campello, Betania; Duarte, Leonardo Tomazeli
       title: Integrating Tensor-Based Data Analytics and Adaptive Prediction for Informed Decision-Making Support
        date: 2024
       words: 4885
      flesch: 53
     summary: Prod. 413, 137445 (2023) Article  MATH  Google Scholar  Watrobski, J., Salabun, W., Ladorucki, G.: The temporal supplier evaluation model based on multicriteria decision analysis methods. orcid.org/0000-0001-9609-87249 & Leonardo Tomazeli Duarte  ORCID: orcid.org/0000-0003-0290-00809  Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15414)) Included in the following conference series: Brazilian Conference on Intelligent Systems 374 Accesses Abstract This work proposes a novel approach to support multi-criteria decision analysis (MCDA) using tensor-based data structures and an adaptive prediction method.
    keywords: \({\textbf; \times; analysis; approach; criteria; data; decision; google; mcda; method; prediction; ranking; scholar; tensor
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        item: #256 of 282
          id: bracis-33637
      author: Silva, Joyce M.; Anchiêta, Rafael T.; Sousa, Rogério F. de; Moura, Raimundo S.
       title: Investigating Methods to Detect Off-Topic Essays
        date: 2024
       words: 4695
      flesch: 58
     summary: Association for Computational Linguistics, Valencia, Spain (2017) Google Scholar  Beigman Klebanov, B., Flor, M., Gyawali, B.: Topicality-based indices for essay scoring. Res. 3(Jan), 993–1022 (2003) Google Scholar  Caseli, H.M., Nunes, M.G.V. (eds.):
    keywords: conference; corpus; essays; feature; google; language; model; prompt; scholar; table; topic; topic essays
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        item: #257 of 282
          id: bracis-33638
      author: Oliveira, Francisco B.; Silva-Filho, Moesio W.; Barbosa, Gabriel A.; Freitas, João Paulo; Penna, Chris; Miranda, Péricles B. C.
       title: Machine Learning and Time Series Analysis to Forecast Hotel Room Prices
        date: 2024
       words: 5378
      flesch: 49
     summary: We employ time series models, including AutoRegressors and Prophet, to capture underlying trends and seasonal variations. Even after normalizing values to account for the number of nights and adults in the reservation, time series prediction models did not perform comparably to tree-based models.
    keywords: analysis; average; data; forecasting; google; hotel; learning; machine; models; prices; room; scholar; series; time; values
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        item: #258 of 282
          id: bracis-33639
      author: Hassan, Waqar; Cabral, Marvin Mendes; Ramos, Thiago Rodrigo; Castelo Filho, Antonio; Nonato, Luis Gustavo
       title: Modeling and Predicting Crimes in the City of São Paulo Using Graph Neural Networks
        date: 2024
       words: 5944
      flesch: 50
     summary: Projecting crime data: Crime data is extracted from São Paulo’s Department of Public Safety [27]. [22] both adapted to operate in crime data.
    keywords: crime; data; dysat; evolvegcn; gnns; google; graph; learning; models; networks; prediction; scholar; street; são; time
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        item: #259 of 282
          id: bracis-33640
      author: Santana, Maria; Santana, José; Sampaio, Pablo; Brito, Kellyton
       title: Predicting Engagement of Brazilian Politicians on TikTok: A Machine Learning Approach
        date: 2024
       words: 6703
      flesch: 46
     summary: This resulted in two categories: high engagement posts and low engagement posts. It is clear, therefore, that the platform most used by researchers for political data analysis is Twitter, with most applied methods focusing on volume and sentiment analysis of mentions.
    keywords: analysis; bolsonaro; data; dataset; engagement; features; lula; machine; media; model; posts; results; tiktok; transformations
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        item: #260 of 282
          id: bracis-33641
      author: Cunha, Weld Lucas; Castelo-Fernez, Cesar; Simionato, Rafael; Lacerda, Matheus Soares de; Martins, Samuel Botter
       title: Preserving Privacy, Enhancing Robustness: Federated Learning for Lung Disease Identification in Chest X-Ray Images
        date: 2024
       words: 3983
      flesch: 46
     summary: This work introduces a federated-learning-based approach for automatically detecting lung diseases in chest X-ray images, focusing on preserving data privacy and enhancing robustness. [7, 11] is a machine learning methodology tailored to situations where there are decentralized clients and/or when data privacy is a major concern, as exemplified by sensitive medical examination data.
    keywords: chest; data; dataset; diseases; images; learning; lung; model; privacy; training
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        item: #261 of 282
          id: bracis-33642
      author: Costa, Caio de Souza Barbosa; Costa, Anna Helena Reali
       title: RLPortfolio: Reinforcement Learning for Financial Portfolio Optimization
        date: 2024
       words: 6207
      flesch: 51
     summary: Despite its suitability, there are few libraries that developers and researchers can use to design, implement, train and test the performance of portfolio optimization agents with reinforcement learning considering the state-of-the-art formulation of the problem and using novel deep learning and mathematical frameworks. [3] introduced a simulation that applies Jiang’s formulation and that can be easily used to train reinforcement learning agents, but a modern open-source implementation of the training algorithm for portfolio optimization agents remains, to the best of our knowledge, nonexistent.
    keywords: agent; article; environment; google; learning; optimization; policy; portfolio; portfolio optimization; reinforcement; scholar; state; time; training; value
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        item: #262 of 282
          id: bracis-33643
      author: Melo, Alan; Cabral, Bruno; Claro, Daniela Barreiro
       title: Scaling and Adapting Large Language Models for Portuguese Open Information Extraction: A Comparative Study of Fine-Tuning and LoRA
        date: 2024
       words: 6071
      flesch: 43
     summary: As language models have seen significant usage employing neural networks, OpenIE evolved in describing the task and generating triples as prompting, particularly with English languages. At the core of LoRA is the idea that transformation matrices in language models, such as those found in the attention and feed-forward layers of transformers, can be approximated by products of lower-dimensional matrices.
    keywords: adaptation; data; extraction; fine; information; information extraction; language; language models; learning; lora; models; openie; portuguese; training; tuning
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        item: #263 of 282
          id: bracis-33644
      author: Araújo Júnior, Ronald Albert de; Leite, Gabriel Matos Cardoso; Jiménez-Fernández, Silvia; Salcedo-Sanz, Sancho; Delgado, Carla Amor Divino Moreira; Marcelino, Carolina Gil
       title: Special-Crowd-Distance Boosted MESH Applied to the Operation of Cascade Hydro-Power Plants
        date: 2024
       words: 6231
      flesch: 52
     summary: Article  MATH  Google Scholar  Wang, H., et al.: Multi-reservoir system operation theory and practice. Res. 47(8) (2011) Google Scholar  Sharifi, M.R., Akbarifard, S., Madadi, M.R., Qaderi, K., Akbarifard, H.: Optimization of hydropower energy generation by 14 robust evolutionary algorithms.
    keywords: algorithm; article; crowd; distance; google; math; mesh; multi; objective; optimization; plants; power; scholar; water
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        item: #264 of 282
          id: bracis-33645
      author: Silva e Silva, Danyllo Carlos; Cortes, Omar Andres Carmona; Diniz, João Otávio Beira
       title: The Impact of Double Transfer Learning in VGG Architectures for Metastasis Breast Cancer Detection
        date: 2024
       words: 4339
      flesch: 51
     summary: In: International Conference on Learning Representations (2014) Google Scholar  Spanhol, F., Oliveira, L.S., Petitjean, C., Heutte, L.: A dataset for breast cancer histopathological image classification. https://doi.org/10.1016/j.jrras.2024.100885 Article  MATH  Google Scholar  Matos, J.D., Britto, A.D.S., Oliveira, L.E.S., Koerich, A.L.: Double transfer learning for breast cancer histopathologic image classification.
    keywords: breakhis; breast; camelyon; cancer; dataset; detection; dtl; google; images; learning; patch; scholar; transfer; vgg16
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        item: #265 of 282
          id: bracis-33646
      author: Souza, Marlo
       title: A Topology-Inspired Approach to AGM Belief Change
        date: 2024
       words: 7599
      flesch: 50
     summary: Exploring well-known connections between Logic, Algebra and Topology, our work explores a general notion of belief change contraction, which can be connected to well-studied operations in the literature, such as AGM rational contractions, partial meet contractions, and multiple contractions, employing the framework of Abstract Model Theory and Topological Semantics. In this work, we investigate a general notion of belief change contraction and the definability of AGM belief contraction operators based on results from abstract model theory and its connections to topology (Theorems 1 and 2, and Lemma 5), generalising the results of Ribeiro et al.
    keywords: \(\mathcal; \in; \langle; \subseteq; abstract; agm; belief; change; contraction; logic; l}\; model; theory
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        item: #266 of 282
          id: bracis-33647
      author: Damas, Ghivvago; Anchiêta, Rafael Torres; Moura, Raimundo Santos; Machado, Vinicius Ponte
       title: A Transformer-Based Tabular Approach to Detect Toxic Comments
        date: 2024
       words: 5540
      flesch: 48
     summary: In: Proceedings of the 16th International Conference on Computational Processing of Portuguese, ACL (2024) Google Scholar  Bertaglia, T.F.C., Nunes, M.d. EPJ Data Sci. 5 (2016) Google Scholar  Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., Liu, Z.:
    keywords: approach; comments; detection; embedding; google; hate; language; learning; media; models; portuguese; scholar; speech; text
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        item: #267 of 282
          id: bracis-33648
      author: Pereira, Francielle Vasconcellos; Frazão, Ana; Moreira, Viviane P.
       title: Automatic Text Simplification for the Legal Domain in Brazilian Portuguese
        date: 2024
       words: 6276
      flesch: 55
     summary: This is a lengthy process that may take years to materialize, still, it can benefit from advancements resulting from the use of automatic tools for text simplification. Text simplification (TS) is a subfield of Natural Language Processing (NLP).
    keywords: domain; evaluation; google; language; metrics; models; portuguese; ptt5; results; scholar; sentences; simplification; table; text
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        item: #268 of 282
          id: bracis-33649
      author: Pinto, João Gabriel de Souza; Freitas, Andrey Rodrigues de; Martins, Anderson Carlos Gomes; Sawazaki, Caroline Midori Rozza; Vidal, Caroline; Silva e Oliveira, Lucas Emanuel
       title: Developing Resource-Efficient Clinical LLMs for Brazilian Portuguese
        date: 2024
       words: 5657
      flesch: 43
     summary: Language models are few-shot learners (2020) Google Scholar  Chen, Z., Cano, A.H., et al.: Download conference paper PDF Similar content being viewed by others Human level information extraction from clinical reports with finetuned language models Article Open access 24 November 2025 Evaluation and mitigation of the limitations of large language models in clinical decision-making Article Open access
    keywords: clinical; data; google; language; llama-2; llms; mistral-7b; models; portuguese; scholar; text; training; v0.2
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        item: #269 of 282
          id: bracis-33650
      author: Gôlo, Marcos Paulo Silva; Gama, João; Marcacini, Ricardo Marcondes
       title: One-Class Learning for Data Stream Through Graph Neural Networks
        date: 2024
       words: 6268
      flesch: 59
     summary: Rev. Methods Primers 4(1), 17 (2024) Article  MATH  Google Scholar  de Faria, E.R., Ponce de Leon Ferreira Carvalho, A.C., Gama, J.: Minas: multiclass learning algorithm for novelty detection in data streams. Article  MATH  Google Scholar  Bifet, A., et al.:
    keywords: class; data; google; graph; interest; learning; loss; methods; networks; neural; ocl; opencast; representations; scholar; stream
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        item: #270 of 282
          id: bracis-33651
      author: Medeiros Júnior, José Gilberto Barbosa de; Mitri, André Guarnier de; Silva, Diego Furtado
       title: Semi-periodic Activation for Time Series Classification
        date: 2024
       words: 5817
      flesch: 50
     summary: (Color figure online) Full size image To analyze the boundness of LeakySineLU activation function we rely on the limits of \(f(x) \rightarrow +\infty \) and \(f(x) \rightarrow -\infty \). Semi-periodic Activation for Time Series Classification Download book PDF Download book EPUB José Gilberto Barbosa de Medeiros Júnior9, André Guarnier de Mitri9 & Diego Furtado Silva9  Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 15415)) Included in the following conference series: Brazilian Conference on Intelligent Systems 242 Accesses 1 Citation Abstract This paper investigates the lack of research on activation functions for neural network models in time series tasks.
    keywords: \end{aligned}$$; \mathbb; activation; classification; function; google; leakysinelu; network; relu; scholar; series; time
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        item: #271 of 282
          id: bracis-33652
      author: Costa, Jean Carlos; Santos, Reginaldo
       title: Automated and Intelligent Vocational Guidance System for Classifying Specialties Based on POSCOMP Microdata
        date: 2024
       words: 5019
      flesch: 43
     summary: They employed methods to extract useful information from student performance data and predict future outcomes using machine learning techniques, specifically decision tree. Additionally, several studies have been conducted on exam data analysis using data mining and machine learning techniques [5, 9, 22].
    keywords: algorithm; analysis; computer; computing; data; exam; model; participants; performance; poscomp; research; science; specialties; system; techniques
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        item: #272 of 282
          id: bracis-33653
      author: Tocchini, Matheus; Rocha, Igor M.; Barros, Raphael M. de; O. e Silva, Jéssica; Garcia, Ananda F.; Zular, Felipe; Maranhão, Juliano; Sichman, Jaime Simão
       title: Classifying Potentially Non-compliant Portuguese Language Sentences Concerning Privacy Policies
        date: 2024
       words: 6595
      flesch: 56
     summary: References Al-Khalifa, H., Mashaabi, M., Al-Yahya, G., Alnashwan, R.: The Saudi privacy policy dataset (2023) Google Scholar  Alshamsan, A.R., Chaudhry, S.A.: A GDPR compliant approach to assign risk levels to privacy policies. Surv. 34(1), 1–47 (2002) Google Scholar  Shankar, A., Waldis, A., Bless, C., Andueza Rodriguez, M., Mazzola, L.: Privacyglue: a benchmark dataset for general language understanding in privacy policies.
    keywords: categories; compliance; corpus; data; google; guideline; information; language; non; policies; ppol; privacy; scholar; sentences; task; work
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        item: #273 of 282
          id: bracis-33654
      author: Craveiro, Giovana Meloni; Galdino, Julio Cesar
       title: Diversity in Data for Speech Processing in Brazilian Portuguese
        date: 2024
       words: 6185
      flesch: 50
     summary: Revista da Sociedade Brasileira de Fonoaudiologia 14, 293–299 (2009) Google Scholar  Biron, T., et al.: https://doi.org/10.1371/journal.pone.0250969 Article  MATH  Google Scholar  Bisol, L., Brescancini, C.: Fonologia e variação: recortes do português brasileiro.
    keywords: age; brazil; corpora; corpus; diversity; education; google; nurc; portuguese; scholar; segmentation; speakers; speech; state; vowels
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        item: #274 of 282
          id: bracis-33655
      author: Ferreira, Gabriel Bicalho; Silva, Pedro; Silva, Rodrigo
       title: Elevating Healthcare AI: Achieving Efficiency and Accuracy in Medical Applications with Surrogate-Based Multiobjective Compression of ResNet50 CNNs
        date: 2024
       words: 5986
      flesch: 45
     summary: It is evident that most of the non-dominated solutions involve the joint application of pruning and quantization techniques for model compression. The variability in these outcomes underscores the need for broader experimentation to fully understand the typical effects of model compression across various datasets.
    keywords: accuracy; applications; compression; dataset; google; healthcare; models; network; objective; pruning; quantization; results; scholar; surrogate; time
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        item: #275 of 282
          id: bracis-33656
      author: Teixeira, Lucas; Matos, Augusto; Carvalho, Gabriel; Valencio, Norma; Camargo, Heloisa
       title: Explainability of Machine Learning Models with XGBoost and SHAP Values in the Context of Coping with Disasters
        date: 2024
       words: 5324
      flesch: 43
     summary: 25–56 (2015) Google Scholar  Valencio, N.: Para além do ‘dia do desastre’: o caso brasileiro. LUMINA 12, 19–39 (2018) Article  Google Scholar  United Nations: The Sustainable Development Goals Report 2023, Special United Nations Publications, New York (2023) Google Scholar  Arrieta, A., et al.:
    keywords: article; author; class; data; disasters; fig; google; learning; model; period; scholar; shap; values; variables; xgboost
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        item: #276 of 282
          id: bracis-33657
      author: Andrade, João V. R. de; Silva, Igor L. B. da; Souza Junior, Teobaldo G. de; Silva, Leandro H. de S.; Freire, Agostinho; Lucena, Daisy; Fernandes, Bruno J. T.
       title: Exploring Climatic Shifts in Brazilian Climates: Insights from ARMAX, Decision Trees, and Artificial Neural Networks
        date: 2024
       words: 4388
      flesch: 45
     summary: This methodology enhances our ability to predict temperature changes and deepens our understanding of the interplay between human activity and environmental conditions, paving the way for more informed urban and environmental planning. Consequently, the output yields two series of delta values relative to 1990, facilitating a more precise analysis of temperature changes over time while mitigating the effects of seasonal variability.
    keywords: armax; cajazeiras; change; data; forest; félix; land; learning; machine; model; series; são; table; temperature
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        item: #277 of 282
          id: bracis-33658
      author: Rabonato, Ricardo Trainotti; Milios, Evangelos; Berton, Lilian
       title: Gender-Neutral English to Portuguese Machine Translator: Promoting Inclusive Language
        date: 2024
       words: 6715
      flesch: 46
     summary: Fleisig, E., Fellbaum, C.: Mitigating gender bias in machine translation through adversarial learning (2022) Google Scholar  Font, J.E., Costa-Jussa, M.R.: Equalizing gender biases in neural machine translation with word embeddings techniques. The experiments conducted in this study demonstrated the effectiveness of fine-tuning as a technique for reducing gender bias in machine translation models.
    keywords: bias; english; fairness; gender; gender bias; google; language; learning; machine; machine translation; model; portuguese; processing; research; scholar; sentences; translation; tuning
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        item: #278 of 282
          id: bracis-33659
      author: Souza, Daniel Leal; Santos, Isadora Mendes dos; Soares, Caio Johnston; Oliveira Neto, José Pires de; Cassiano, Lucas; Proença Neto, Marco Aurélio; Ramos, Aline Maria Pereira Cruz; Oliveira, Liliane Afonso de; Araújo, Flávia Luciana Guimaraes Marçal Pantoja de; Araújo, Fabrício Almeida; Souza Junior, Gilberto Nerino de; Braga, Marcus de Barros
       title: Hybrid Artificial Intelligence Model for Detecting Signs of Delayed Child Development
        date: 2024
       words: 4438
      flesch: 44
     summary: Periodic assessments of child development indicators are important since birth, with most of them having preventive purposes or for the early diagnosis of disorders that may affect child development. The present study proposes a hybrid artificial intelligence model, combining first-order logic and fuzzy logic to identify delays in child development.
    keywords: age; author; child; development; google; intelligence; milestones; model; orcid; rules; scholar; search; systems; table
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        item: #279 of 282
          id: bracis-33660
      author: Cunha, José Gustavo; Lucas, Tarcísio Daniel Pontes; Lucas, Andreza Daniela Pontes; Ferreira, Monaliza de Oliveira
       title: Low Birth Weight in Brazil Vulnerable Groups: An Analysis Based on Data Mining and Big Data
        date: 2024
       words: 5347
      flesch: 60
     summary: Render: About us (2024) Google Scholar  Romero, C., González, P., Ventura, S., Del Jesús, M.J., Herrera, F.: Evolutionary algorithms for subgroup discovery in e-learning: a practical application using Moodle data. Article  MATH  Google Scholar  Carmona, C.J., Ramírez-Gallego, S., Torres, F., Bernal, E., del Jesús, M.J., García, S.: Web usage mining to improve the design of an e-commerce website: OrOliveSur.com.
    keywords: birth; brazil; data; dataset; discovery; features; google; groups; lbw; mothers; rate; scholar; subgroup; table; weight
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        item: #280 of 282
          id: bracis-33661
      author: Nascimento Junior, Odelmo O.; Assis, Dhara L. C.; Destro Filho, João B.; Zhao, Liang; Carneiro, Murillo G.
       title: Modeling EEG Data into Graphs for the Prognostic of Patients in Coma Using Graph Neural Networks
        date: 2024
       words: 6520
      flesch: 48
     summary: IEEE (2023) Google Scholar  Chen, M., Wei, Z., Huang, Z., Ding, B., Li, Y.: IEEE (2021) Google Scholar  Di Perri, C., Thibaut, A., Heine, L., Soddu, A., Demertzi, A., Laureys, S.: Measuring consciousness in coma and related states.
    keywords: analysis; architectures; article; data; eeg; eegraph; gnns; google; graph; lstm; modeling; networks; patients; results; scholar; table
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        item: #281 of 282
          id: bracis-33662
      author: Silva, Rafael da Costa; Silva, Diego Furtado
       title: Tackling Low-Resource ECG Classification with Self-supervised Learning
        date: 2024
       words: 5894
      flesch: 51
     summary: This observation inspired us to investigate the question: Can a high-resource dataset be used to tackle a low-resource ECG classification task with pre-trained SSL models with good performance? Conversely, ESPCN configured most of the best workflow scenarios for the ECG fragment classification task using TS2Vec as SSL model.
    keywords: dataset; ecg; espcn; learning; model; performance; pre; resource; results; scenarios; series; ssl; task; time
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        item: #282 of 282
          id: bracis-33663
      author: Vecchi, Lorenzo Puppi; Barbon Junior, Sylvio; Paraiso, Emerson Cabrera
       title: Tuning Hypothesis Creation: Combining Discrete and Continuous Spaces for Zero-Shot Hate Speech Detection
        date: 2024
       words: 5851
      flesch: 42
     summary: 2 Background The evaluation of hate speech detection models often suffers from overestimation issues, impacting both state-of-the-art (SOTA) and baseline models. [16] is a suite of tests for evaluating hate speech detection models.
    keywords: detection; hate; hate speech; hypothesis; language; model; nli; offensive; performance; speech; table; tokens; tuning; zshsd
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