bracis: A Pathfinder

This is a computer-generated pathfinder created against the Distant Reader study called bracis.

Each Distant Reader study carrel is composed of many individual items. Each item is bibliographically described with author, title, date, summary, and keyword values. Below is a list of the items' most signficant keywords as well as lists of the items themselves. Purpusing the content of this pathfinder provides the student, researcher, or scholar with one way to get their heads around the scope of the carrel. The keywords include:

Scholar; Model; Learning; Data; Results; Models; Table; Dataset; Training; Performance; Time; Algorithm; Approach; Set; Search; \mathcal; State; \in; Analysis; System; Policy; Documents; Intelligence; Computer; Problems

Depending on how this pathfinder was created, many of the bibliographic sections will include elaborations on the meaning(s) of the given keywords. These elaborations were generated by feeding the items' summaries to a large langauge model and asking the model to address the question, "What is X?", where "X" is the keyword. The result will be a few sentences of elaboration. Be forewarned. The elaborations are often plausible, but they should not be take as truth. Instead, they should be taken as points for consideration.

• A Conversational Agent to Support Hospital Bed Allocation by Engelmann, Débora C.; Cezar, Lucca Dornelles; Panisson, Alison R.; Bordini, Rafael H. (2021) - 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
• A Protocol for Argumentation-Based Persuasive Negotiation Dialogues by Morveli-Espinoza, Mariela; Possebom, Ayslan; Tacla, Cesar Augusto (2021) - 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
• MAS4GC: Multi-agent System for Glycemic Control of Intensive Care Unit Patients by Segato, Tiago Henrique Faccio; Serafim, Rafael Moura da Silva; Fernandes, Sérgio Eduardo Soares; Ralha, Célia Ghedini (2021) - 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
• Slot Sharing Mechanism in Multi-domain Dialogue Systems by Nishimoto, Bruno Eidi; Costa, Anna Helena Reali (2021) - 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
• Dynamic Learning in Hyper-Heuristics to Solve Flowshop Problems by Pavelski, Lucas Marcondes; Kessaci, Marie-Éléonore; Delgado, Myriam (2021) - 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
• Hyper-Heuristic Based NSGA-III for the Many-Objective Quadratic Assignment Problem by Senzaki, Bianca N. K.; Venske, Sandra M.; Almeida, Carolina P. (2021) - 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
• I2DE: Improved Interval Differential Evolution for Numerical Constrained Global Optimization by Cassenote, Mariane R. S.; Derenievicz, Guilherme A.; Silva, Fabiano (2021) - 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
• Improving a Genetic Clustering Approach with a CVI-Based Objective Function by Flexa, Caio; Gomes, Walisson; Moreira, Igor; Santos, Reginaldo; Sales, Claudomiro; Silva, Moisés (2021) - 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
• Improving Particle Swarm Optimization with Self-adaptive Parameters, Rotational Invariance, and Diversity Control by Vasconcelos, Matheus; Flexa, Caio; Moreira, Igor; Santos, Reginaldo; Sales, Claudomiro (2021) - 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
• On the Analysis of CGP Mutation Operators When Inferring Gene Regulatory Networks Using ScRNA-Seq Time Series Data by Silva, José Eduardo H. da; Bernardino, Heder S.; Oliveira, Itamar L. de; Vieira, Alex B.; Barbosa, Helio J. C. (2021) - 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
• Active Learning and Case-Based Reasoning for the Deceptive Play in the Card Game of Truco by Vargas, Daniel P.; Paulus, Gustavo B.; Silva, Luis A. L. (2021) - 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
• A Co-occurrence Based Approach for Mining Overlapped Co-clusters in Binary Data by Santos, Yuri Santa Rosa Nassar dos; Santiago, Rafael; Perego, Raffaele; Schaly, Matheus Henrique; Alvares, Luis Otávio; Renso, Chiara; Bogorny, Vania (2021) - 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
• Classifying Potentially Unbounded Hierarchical Data Streams with Incremental Gaussian Naive Bayes by Tieppo, Eduardo; Barddal, Jean Paul; Nievola, Júlio Cesar (2021) - 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
• Coarsening Algorithm via Semi-synchronous Label Propagation for Bipartite Networks by Valejo, Alan Demétrius Baria; Althoff, Paulo Eduardo; Faleiros, Thiago de Paulo; Chuerubim, Maria Lígia; Yan, Jianglong; Liu, Weiguang; Zhao, Liang (2021) - 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
• Evaluating Clustering Meta-features for Classifier Recommendation by Garcia, Luís P. F.; Campelo, Felipe; Ramos, Guilherme N.; Rivolli, Adriano; Carvalho, André C. P. de L. F. de (2021) - 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
• Interpreting Classification Models Using Feature Importance Based on Marginal Local Effects by Silva Filho, Rogério Luiz Cardoso; Adeodato, Paulo Jorge Leitão; Brito, Kellyton dos Santos (2021) - 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
• On the Generalizations of the Choquet Integral for Application in FRBCs by Lucca, Giancarlo; Borges, Eduardo N.; Berri, Rafael A.; Emmendorfer, Leonardo; Dimuro, Graçaliz P.; Asmus, Tiago C. (2021) - 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
• Optimizing Diffusion Rate and Label Reliability in a Graph-Based Semi-supervised Classifier by Afonso, Bruno Klaus de Aquino; Berton, Lilian (2021) - 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}$$; \mathbf; accuracy; data; diffusion; google; graph; label; learning; lgc}\_\texttt; matrix; scholar
• Tactical Asset Allocation Through Random Walk on Stock Network by Freitas, Washington Burkart; Bertini Junior, João Roberto (2021) - 5.2 Portfolio Performance Metrics To measure the performance of stock portfolios, some methods can be used, such as: the Sharpe ratio, the maximum drawdown (difference between the highest and lowest value in a given period) and the cumulative wealth. The lower the MDD index the better. $$\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
• A Heterogeneous Network-Based Positive and Unlabeled Learning Approach to Detect Fake News by Souza, Mariana C. de; Nogueira, Bruno M.; Rossi, Rafael G.; Marcacini, Ricardo M.; Rezende, Solange O. (2021) - 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
• Anomaly Detection in Brazilian Federal Government Purchase Cards Through Unsupervised Learning Techniques by Nunes, Breno; Colliri, Tiago; Lauretto, Marcelo; Liu, Weiguang; Zhao, Liang (2021) - 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
• De-Identification of Clinical Notes Using Contextualized Language Models and a Token Classifier by Santos, Joaquim; Santos, Henrique D. P. dos; Tabalipa, Fábio; Vieira, Renata (2021) - 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
• Detecting Early Signs of Insufficiency in COVID-19 Patients from CBC Tests Through a Supervised Learning Approach by Colliri, Tiago; Minakawa, Marcia; Zhao, Liang (2021) - 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
• Ensemble of Protein Stability upon Point Mutation Predictors by Freitas, Eduardo Kenji Hasegawa de; Camargo, Alex Dias; Balboni, Maurício; Werhli, Adriano V.; Machado, Karina dos Santos (2021) - 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
• Ethics of AI: Do the Face Detection Models Act with Prejudice? by Ferreira, Marcos Vinícius; Almeida, Ariel; Canario, João Paulo; Souza, Matheus; Nogueira, Tatiane; Rios, Ricardo (2021) - 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
• Evaluating Topic Models in Portuguese Political Comments About Bills from Brazil’s Chamber of Deputies by 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 (2021) - 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
• Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rays by Zeiser, Felipe André; Costa, Cristiano André da; Ramos, Gabriel de Oliveira; Bohn, Henrique; Santos, Ismael; Righi, Rodrigo da Rosa (2021) - 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
• Long-Term Map Maintenance in Complex Environments by Oliveira, Josias; Mutz, Filipe; Forechi, Avelino; Azevedo, Pedro; Oliveira-Santos, Thiago; Souza, Alberto F. De; Badue, Claudine (2021) - 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
• The Future of AI: Neat or Scruffy? by Gonçalves, Bernardo; Cozman, Fabio Gagliardi (2021) - 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
• Weapon Engagement Zone Maximum Launch Range Estimation Using a Deep Neural Network by Dantas, Joao P. A.; Costa, Andre N.; Geraldo, Diego; Maximo, Marcos R. O. A.; Yoneyama, Takashi (2021) - 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
• Deep Convolutional Features for Fingerprint Indexing by Costa, Leonardo F. da; Fernandes, Lucas S.; Andrade, João P. B.; Rego, Paulo A. L.; Maia, José G. R. (2021) - 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
• How to Generate Synthetic Paintings to Improve Art Style Classification by Pérez, Sarah Pires; Cozman, Fabio Gagliardi (2021) - 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
• Iris-CV: Classifying Iris Flowers Is Not as Easy as You Thought by 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 (2021) - 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
• Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis by Granero, Marco Aurélio; Hernández, Cristhian Xavier; Valle, Marcos Eduardo (2021) - 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
• Sea State Estimation with Neural Networks Based on the Motion of a Moored FPSO Subjected to Campos Basin Metocean Conditions by Bisinotto, Gustavo A.; Cotrim, Lucas P.; Cozman, Fabio Gagliardi; Tannuri, Eduardo A. (2021) - [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
• Aggressive Language Detection Using VGCN-BERT for Spanish Texts by Mamani-Condori, Errol; Ochoa-Luna, José (2021) - 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
• An Empirical Study of Text Features for Identifying Subjective Sentences in Portuguese by Oliveira, Miguel de; Melo, Tiago de (2021) - 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
• Entity Relation Extraction from News Articles in Portuguese for Competitive Intelligence Based on BERT by Reyes, Daniel De Los; Trajano, Douglas; Manssour, Isabel Harb; Vieira, Renata; Bordini, Rafael H. (2021) - 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
• Experiments on Kaldi-Based Forced Phonetic Alignment for Brazilian Portuguese by Batista, Cassio; Neto, Nelson (2021) - 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
• Named Entity Recognition for Brazilian Portuguese Product Titles by 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 (2021) - 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
• Portuguese Neural Text Simplification Using Machine Translation by Lima, Tiago B. de; Nascimento, André C. A.; Valença, George; Miranda, Pericles; Mello, Rafael Ferreira; Si, Tapas (2021) - 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
• Speech2Phone: A Novel and Efficient Method for Training Speaker Recognition Models by 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 (2021) - 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
• Text Classification in Legal Documents Extracted from Lawsuits in Brazilian Courts by Aguiar, André; Silveira, Raquel; Pinheiro, Vládia; Furtado, Vasco; Araújo Neto, João (2021) - 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
• Regulation and Ethics of Facial Recognition Systems: An Analysis of Cases in the Court of Appeal in the State of São Paulo by Oliveira, Cristina Godoy B. de; Albuquerque, Otávio de Paula; Belotti, Emily Liene; Lopes, Isabella Ferreira; Silva, Rodrigo Brandão de A.; Arbix, Glauco (2023) - 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
• Efficient Density-Based Models for Multiple Machine Learning Solutions over Large Datasets by Batista, Natanael F. Dacioli; Nunes, Bruno Leonel; Naldi, Murilo Coelho (2023) - 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
• Exploring Text Decoding Methods for Portuguese Legal Text Generation by Sakiyama, Kenzo; Montanari, Raphael; Junior, Roseval Malaquias; Nogueira, Rodrigo; Romero, Roseli A. F. (2023) - 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
• Community Detection for Multi-label Classification by Gatto, Elaine Cecília; Valejo, Alan Demétrius Baria; Ferrandin, Mauri; Cerri, Ricardo (2023) - 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
• Specifying Preferences over Policies Using Branching Time Temporal Logic by Machado, Warlles Carlos Costa; Santos, Viviane Bonadia dos; Barros, Leliane Nunes de; Menezes, Maria Viviane de (2023) - 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
• Logic-Based Explanations for Linear Support Vector Classifiers with Reject Option by Rocha, Francisco Mateus; Rocha, Thiago Alves; Ribeiro, Reginaldo Pereira Fernandes; Rocha, Ajalmar Rêgo (2023) - 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
• A Custom Bio-Inspired Algorithm for the Molecular Distance Geometry Problem by Carneiro, Sarah Ribeiro Lisboa; Souza, Michael Ferreira de; Cardoso, Douglas O.; Tarrataca, Luís; Assis, Laura S. (2023) - 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
• Allocating Dynamic and Finite Resources to a Set of Known Tasks by Silva, João da; Peres, Sarajane; Cordeiro, Daniel; Freire, Valdinei (2023) - 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
• d-CC Integrals: Generalizing CC-Integrals by Restricted Dissimilarity Functions with Applications to Fuzzy-Rule Based Systems by Sartori, Joelson; Lucca, Giancarlo; Asmus, Tiago; Santos, Helida; Borges, Eduardo; Bedregal, Benjamin; Bustince, Humberto; Dimuro, Graçaliz Pereira (2023) - 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
• Hierarchical Time-Aware Approach for Video Summarization by Cardoso, Leonardo Vilela; Gomes, Gustavo Oliveira Rocha; Guimarães, Silvio Jamil Ferzoli; Patrocínio Júnior, Zenilton Kleber Gonçalves do (2023) - 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
• Analyzing College Student Dropout Risk Prediction in Real Data Using Walk-Forward Validation by Santos, Rodolfo Sanches; Ponti, Moacir Antonelli; Rodrigues, Kamila Rios (2023) - 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
• The Artificial Intelligence as a Technological Resource in the Application of Tasks for the Development of Joint Attention in Children with Autism by Valentim, Nathália Assis; Dorça, Fabiano Azevedo; Asnis, Valéria Peres; Elias, Nassim Chamel (2023) - 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
• Machine Teaching: An Explainable Machine Learning Model for Individualized Education by Ferreira, Eduardo Vargas; Lorena, Ana Carolina (2023) - 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
• BLUEX: A Benchmark Based on Brazilian Leading Universities Entrance eXams by Almeida, Thales Sales; Laitz, Thiago; Bonás, Giovana K.; Nogueira, Rodrigo (2023) - 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
• Towards Generating P-Contrastive Explanations for Goal Selection in Extended-BDI Agents by Jasinski, Henrique; Morveli-Espinoza, Mariela; Tacla, Cesar Augusto (2023) - 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
• Applying Theory of Mind to Multi-agent Systems: A Systematic Review by Rocha, Michele; Silva, Heitor Henrique da; Morales, Analúcia Schiaffino; Sarkadi, Stefan; Panisson, Alison R. (2023) - 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
• A Spin-off Version of Jason for IoT and Embedded Multi-Agent Systems by Pantoja, Carlos Eduardo; Jesus, Vinicius Souza de; Lazarin, Nilson Mori; Viterbo, José (2023) - 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
• Hybrid Multilevel Explanation: A New Approach for Explaining Regression Models by Miranda Filho, Renato; Pappa, Gisele L. (2023) - 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
• Explainability of COVID-19 Classification Models Using Dimensionality Reduction of SHAP Values by Kuhn, Daniel Matheus; Loreto, Melina Silva de; Recamonde-Mendoza, Mariana; Comba, João Luiz Dihl; Moreira, Viviane Pereira (2023) - 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
• Bayes and Laplace Versus the World: A New Label Attack Approach in Federated Environments Based on Bayesian Neural Networks by Barros, Pedro H.; Murai, Fabricio; Ramos, Heitor S. (2023) - ) 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
• MAT-Tree: A Tree-Based Method for Multiple Aspect Trajectory Clustering by Santos, Yuri; Giuliani, Ricardo; Bogorny, Vania; Grellert, Mateus; Carvalho, Jônata Tyska (2023) - 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
• bracis-28375 by None (None) - 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
• Dog Face Recognition Using Vision Transformer by Canto, Victor Hugo Braguim; Manesco, João Renato Ribeiro; Souza, Gustavo Botelho de; Marana, Aparecido Nilceu (2023) - 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
• Convolutional Neural Networks for the Molecular Detection of COVID-19 by Jr., Anisio P. Santos; Filho, Anage C. Mundim; Sabino-Silva, Robinson; Carneiro, Murillo G. (2023) - 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
• Hierarchical Graph Convolutional Networks for Image Classification by Batisteli, João Pedro Oliveira; Guimarães, Silvio Jamil Ferzoli; Patrocínio Júnior, Zenilton Kleber Gonçalves do (2023) - 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
• Enhancing Stock Market Predictions Through the Integration of Convolutional and Recursive LSTM Blocks: A Cross-market Analysis by Ramos, Filipe; Silva, Guilherme; Luz, Eduardo; Silva, Pedro (2023) - 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
• Ensemble Architectures and Efficient Fusion Techniques for Convolutional Neural Networks: An Analysis on Resource Optimization Strategies by Costa, Cícero L.; Lima, Danielli A.; Barcelos, Celia A. Zorzo; Travençolo, Bruno A. N. (2023) - 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
• Dog Face Recognition Using Deep Features Embeddings by Andrade, João P. B.; Costa, Leonardo F.; Fernandes, Lucas S.; Rego, Paulo A. L.; Maia, José G. R. (2023) - 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
• EfficientDeepLab for Automated Trachea Segmentation on Medical Images by Fernandes, Arthur Guilherme Santos; Junior, Geraldo Braz; Diniz, João Otávio Bandeira; Silva, Aristófanes Correa; Matos, Caio Eduardo Falcõ (2023) - 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
• Multi-label Classification of Pathologies in Chest Radiograph Images Using DenseNet by Mendes, Alison Corrêa; Pessoa, Alexandre César Pinto; Paiva, Anselmo Cardoso de (2023) - 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
• Does Pre-training on Brain-Related Tasks Results in Better Deep-Learning-Based Brain Age Biomarkers? by Pacheco, Bruno M.; Oliveira, Victor H. R. de; Antunes, Augusto B. F.; Pedro, Saulo D. S.; Silva, Danilo (2023) - 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
• Performance Analysis of Generative Adversarial Networks and Diffusion Models for Face Aging by Kemmer, Bruno; Simões, Rodolfo; Ivamoto, Victor; Lima, Clodoaldo (2023) - 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
• Occluded Face In-painting Using Generative Adversarial Networks—A Review by Ivamoto, Victor; Simões, Rodolfo; Kemmer, Bruno; Lima, Clodoaldo (2023) - 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
• 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 by 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 (2023) - 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
• Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design by Silva, Rodney Renato de Souza; Cerri, Ricardo (2023) - 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
• Merging Traditional Feature Extraction and Deep Learning for Enhanced Hop Variety Classification: A Comparative Study Using the UFOP-HVD Dataset by Castro, Pedro; Fortuna, Gabriel; Silva, Pedro; Bianchi, Andrea G. C.; Moreira, Gladston; Luz, Eduardo (2023) - 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
• Feature Selection and Hyperparameter Fine-Tuning in Artificial Neural Networks for Wood Quality Classification by Roder, Mateus; Passos, Leandro Aparecido; Papa, João Paulo; Rossi, André Luis Debiaso (2023) - 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
• A Feature-Based Out-of-Distribution Detection Approach in Skin Lesion Classification by Carvalho, Thiago; Vellasco, Marley; Amaral, José Franco; Figueiredo, Karla (2023) - 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
• A Framework for Characterizing What Makes an Instance Hard to Classify by Valeriano, Maria Gabriela; Paiva, Pedro Yuri Arbs; Kiffer, Carlos Roberto Veiga; Lorena, Ana Carolina (2023) - 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
• Physicochemical Properties for Promoter Classification by Moraes, Lauro; Luz, Eduardo; Moreira, Gladston (2023) - 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
• The Effect of Statistical Hypothesis Testing on Machine Learning Model Selection by Gonçalves, Marcel Chacon; Silva, Rodrigo (2023) - 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
• Genetic Algorithms with Optimality Cuts to the Max-Cut Problem by Soares, Pablo Luiz Braga; Araújo, Carlos Victor Dantas (2023) - 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
• Assessment of Robust Multi-objective Evolutionary Algorithms on Robust and Noisy Environments by Sousa, Mateus Clemente de; Meneghini, Ivan Reinaldo; Guimarães, Frederico Gadelha (2023) - 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
• Binary Flying Squirrel Optimizer for Feature Selection by Sementille, Luiz Fernando Merli de Oliveira; Rodrigues, Douglas; Souuza, André Nunes de; Papa, João Paulo (2023) - 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
• FlexCon-CE: A Semi-supervised Method with an Ensemble-Based Adaptive Confidence by Medeiros, Arthur; Gorgônio, Arthur C.; Vale, Karliane Medeiros Ovidio; Gorgônio, Flavius L.; Canuto, Anne Magály de Paula (2023) - (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
• Single Image Super-Resolution Based on Capsule Neural Networks by Araújo, George Corrêa de; Jordão, Artur; Pedrini, Helio (2023) - 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
• Development of a Deep Learning Model for the Classification of Mosquito Larvae Images by Martins, Ramon Mayor; Espíndola, Bruno Manarin; Araujo, Pedro Philippi; Wangenheim, Christiane Gresse von; Pinto, Carlos José de Carvalho; Caminha, Gisele (2023) - 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
• A Simple and Low-Cost Method for Leaf Surface Dimension Estimation Based on Digital Images by Silva, Karla Gabriele Florentino da; Moreira, Jonas Magalhães; Calixto, Gabriel Barreto; Maciel, Luiz Maurílio da Silva; Miranda, Márcio Assis; Morais, Leandro Elias (2023) - 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
• Crop Row Line Detection with Auxiliary Segmentation Task by Costa, Igor Ferreira da; Caarls, Wouter (2023) - 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
• Multiple Object Tracking in Native Bee Hives: A Case Study with Jataí in the Field by Leocádio, Rodolfo R. V.; Segundo, Alan Kardek Rêgo; Pessin, Gustavo (2023) - 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
• An Open Source Eye Gaze Tracker System to Perform Remote User Testing Evaluations by Capdevila, Marc G.; Rodrigues, Karine Aparecida P.; Jardim, Camila F.; Silva, Renato M. (2023) - 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
• Who Killed the Winograd Schema Challenge? by Neri, Hugo; Cozman, Fabio G. (2023) - 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
• Sabiá: Portuguese Large Language Models by Pires, Ramon; Abonizio, Hugo; Almeida, Thales Sales; Nogueira, Rodrigo (2023) - 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
• LegalBert-pt: A Pretrained Language Model for the Brazilian Portuguese Legal Domain by Silveira, Raquel; Ponte, Caio; Almeida, Vitor; Pinheiro, Vládia; Furtado, Vasco (2023) - 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
• A Framework for Controversial Political Topics Identification Using Twitter Data by Sakiyama, Kenzo; Rodrigues, Lucas de Souza; Nogueira, Bruno Magalhães; Matsubara, Edson Takashi; Romero, Roseli A. F. (2023) - [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
• Leveraging Sign Language Processing with Formal SignWriting and Deep Learning Architectures by Freitas, Fernando de Almeida; Peres, Sarajane Marques; Albuquerque, Otávio de Paula; Fantinato, Marcelo (2023) - 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
• A Clustering Validation Index Based on Semantic Description by Aquino, Roberto Douglas Guimarães de; Curtis, Vitor Venceslau; Verri, Filipe Alves Neto (2023) - 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
• Detecting Multiple Epidemic Sources in Network Epidemics Using Graph Neural Networks by Haddad, Rodrigo Gonçalves; Figueiredo, Daniel Ratton (2023) - 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
• Time Series Forecasting of COVID-19 Cases in Brazil with GNN and Mobility Networks by Duarte, Fernando Henrique Oliveira; Moreira, Gladston J. P.; Luz, Eduardo J. S.; Santos, Leonardo B. L.; Freitas, Vander L. S. (2023) - 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
• Federated Learning and Mel-Spectrograms for Physical Violence Detection in Audio by Silva, Victor E. de S.; Lacerda, Tiago B.; Miranda, Péricles; Câmara, André; Chagas, Amerson Riley Cabral; Furtado, Ana Paula C. (2023) - 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
• Police Report Similarity Search: A Case Study by Araújo, José Alan Firmiano; Silva, Ticiana L. Coelho da; Rocha, Atslands Rego da; Lira, Vinicius Cezar Monteiro de (2023) - 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
• A Tool for Measuring Energy Consumption in Data Stream Mining by Onuki, Eric Kenzo Taniguchi; Malucelli, Andreia; Barddal, Jean Paul (2023) - 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
• Exploring Artificial Intelligence Methods for the Automatic Measurement of a New Biomarker Aiming at Glaucoma Diagnosis by Fernandes, Gabriel C.; Lavinsky, Fabio; Rigo, Sandro José; Bohn, Henrique C. (2023) - 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
• A Contrastive Objective for Training Continuous Generative Flow Networks by Silva, Tiago da; Mesquita, Diego (2024) - 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
• A Data Distribution-Based Ensemble Generation Applied to Wind Speed Forecasting by Almeida, Diogo M.; Mattos Neto, Paulo S. G. de; Cunha, Daniel C. (2024) - 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
• A Multi-level Semantics Formalism for Multi-Agent Microservices by Panisson, Alison R.; Farias, Giovani P. (2024) - 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
• A Novel Genetic Algorithm Approach for Discriminative Subspace Optimization by Gatto, Bernardo B.; Mollinetti, Marco A. F.; Santos, Eulanda M. dos; Koerich, Alessandro L.; Silva Junior, Waldir S. da (2024) - 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
• A Performance Increment Strategy for Semantic Segmentation of Low-Resolution Images from Damaged Roads by Toledo, Rafael S.; Oliveira, Cristiano S.; Oliveira, Vitor H. T.; Antonelo, Eric A.; Wangenheim, Aldo von (2024) - 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
• An Ensemble of LLMs Finetuned with LoRA for NER in Portuguese Legal Documents by Nunes, Rafael Oleques; Puttlitz, Letícia Maria; Boll, Antonio Oss; Spritzer, Andre; Freitas, Carla Maria Dal Sasso; Balreira, Dennis Giovani; Tavares, Anderson Rocha (2024) - 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
• Analyzing the Impact of Coarsening on k-Partite Network Classification by Faleiros, Thiago de Paulo; Althoff, Paulo Eduardo; Valejo, Alan Demétrius Baria (2024) - 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
• Applying Transformers for Anomaly Detection in Bus Trajectories by Cruz, Michael; Barbosa, Luciano (2024) - 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
• Aroeira: A Curated Corpus for the Portuguese Language with a Large Number of Tokens by Lira, Thiago; Cação, Flávio; Souza, Cinthia; Valentini, João; Bollis, Edson; Oliveira, Otavio; Almeida, Renato; Magalhães, Marcio; Poloni, Katia; Oliveira, Andre; Pellicer, Lucas (2024) - 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
• Assessing European and Brazilian Portuguese LLMs for NER in Specialised Domains by Nunes, Rafael Oleques; Santos, Joaquim; Spritzer, Andre; Balreira, Dennis Giovani; Freitas, Carla Maria Dal Sasso; Olival, Fernanda; Cameron, Helena Freire; Vieira, Renata (2024) - 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
• Beyond Audio Signals: Generative Model-Based Speaker Diarization in Portuguese by Boll, Antônio Oss; Puttlitz, Letícia Maria; Boll, Heloísa Oss; Malossi, Rodrigo Mor (2024) - 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
• Classification of Non-alcoholic Fatty Liver Disease in Thermal Images of the Liver Using a Siamese Neural Network by Silva, Maxwell Pires; Silva, Aristófanes Corrêa; Paiva, Anselmo Cardoso de (2024) - 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
• Deep Learning Approach to Temporal Dimensionality Reduction of Volumetric Computed Tomography by Silva, Lucas Almeida da; Santos, Eulanda Miranda dos; Giusti, Rafael (2024) - 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
• Detection of Pathological Regions of the Gastrointestinal Tract in Capsule Images Using EfficientNetV2 and YOLOv8 by 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 (2024) - 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
• Dual-Bandwidth Spectrogram Analysis for Speaker Verification by Virgilli, Rafaello; Candido Junior, Arnaldo; Rosa, Augusto Seben da; Oliveira, Frederico S.; Soares, Anderson da Silva (2024) - 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
• Enhancing Graph Data Quality by Leveraging Heterogeneous Node Features and Embeddings by Angonese, Silvio Fernando; Galante, Renata (2024) - 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
• Ensemble of CNNs for Enhanced Leukocyte Classification in Acute Myeloid Leukemia Diagnosis by Sousa, Leonardo P.; Silva, Romuere R. V.; Claro, Maíla L.; Araújo, Flávio H. D.; Borges, Rodrigo N.; Machado, Vinicius P.; Veras, Rodrigo M. S. (2024) - 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
• Explaining Biomarker Response to Anticoagulant Therapy in Atrial Fibrillation: A Study of Warfarin and Rivaroxaban with Machine Learning Models by Veloso, Adriano; Zuin, Gianlucca; Sena, Luan (2024) - 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
• Growing Self-Organizing Maps for Multi-label Classification by Casarotto, Pedro Henrique; Cerri, Ricardo (2024) - 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
• HEACT: Hybrid Evolutionary Algorithm for the Multi-region Multi-objective Cloud Task Scheduling Problem. A Study of Workflow Scheduling in AWS EC2 by Carvalho, Vinicius Renan de; Sichman, Jaime Simão (2024) - 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
• Improving Short-Content Misinformation Detection Using Multiple Aspect Trajectories Classification Techniques by Sanchez, Juan Pablo Chavarro; Portela, Tarlis Tortelli; Carvalho, Jônata Tyska (2024) - 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
• Investigating Universal Adversarial Attacks Against Transformers-Based Automatic Essay Scoring Systems by Silveira, Igor Cataneo; Barbosa, André; Costa, Daniel Silva Lopes da; Mauá, Denis Deratani (2024) - 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
• LLM-Driven Chest X-Ray Report Generation With a Modular, Reduced-Size Architecture by Vargas, Talles Viana; Pedrini, Helio; Santanchè, André (2024) - [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
• Multimodal and Hybrid Models for Predicting SCD Risk in Chagas Cardiomyopathy by Costa, Isabelly P. da; Takazono, Bruno M. P.; Cavalcante, Carlos H. L.; Madeiro, João P. V.; Pedrosa, Roberto C. (2024) - 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
• Optimizing CleanUNet Architecture Parameters for Enhancing Speech Denoising by Silva, Matheus Vieira da; Mari, João Fernando; Backes, André Ricardo (2024) - 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
• Portuguese Emotion Detection Model Using BERTimbau Applied to COVID-19 News and Replies by Oliveira, Francisco Bráulio; Sichman, Jaime Simão (2024) - 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
• Predicting Bull and Bear Markets: A Deep Learning and Linear Regression Study in Cryptocurrencies by Costa e Souza, João Paulo; Meneguette, Rodolfo I.; Gonçalves, Vinícius P.; Mendonça, Fábio L. L. de; Silva, Francisco Airton; Rocha Filho, Geraldo P. (2024) - [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
• Predicting Energy Consumption Data Using Deep Learning: An LSTM Approach by 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 (2024) - 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
• ptt5-v2: A Closer Look at Continued Pretraining of T5 Models for the Portuguese Language by Piau, Marcos; Lotufo, Roberto; Nogueira, Rodrigo (2024) - 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
• Semi-supervised Predictive Clustering Trees for Multi-label Protein Subcellular Localization by Alcantara, Leonardo U.; Triguero, Isaac; Cerri, Ricardo (2024) - 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
• Siamese Network-Based Prioritization for Enhanced Multi-document Summarization by Garcia, Klaifer; Berton, Lilian (2024) - 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
• Standing on the Shoulders of Giants by Cardoso, Lucas F. F.; Ribeiro Filho, José de Sousa; Santos, Vitor C. A.; Francês, Regiane S. Kawasaki; Alves, Ronnie C. O. (2024) - 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
• SurveySum: A Dataset for Summarizing Multiple Scientific Articles into a Survey Section by Fernandes, Leandro Carísio; Guedes, Gustavo Bartz; Laitz, Thiago Soares; Almeida, Thales Sales; Nogueira, Rodrigo; Lotufo, Roberto; Pereira, Jayr (2024) - 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
• Traffic Forecasting Using Federated Randomized High-Order Fuzzy Cognitive Maps by Orang, Omid; Silva, Felipe A. R. da; Silva, Petrônio C. L.; Barros, Pedro H. S. S.; Ramos, Heitor S.; Guimarães, Frederico G. (2024) - 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
• Using Complex Networks to Improve Legal Text Hierarchical Classification by Pires, Rilder S.; Silveira, Raquel; Fernandes, Carlos G. O.; Monteiro Neto, João A.; Furtado, Vasco (2024) - 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
• bracis-33615 by None (None) - 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
• Affective States in Novice Programmers: Automatically Detecting and Analyzing the Impact on Learning by Rocha, Hemilis Joyse Barbosa; Pimentel, Bruno Almeida; Costa, Evandro de Barros; Tedesco, Patricia Cabral de Azevedo Restelli (2024) - 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
• AnisotropicBreast-ViT: Breast Cancer Classification in Ultrasound Images Using Anisotropic Filtering and Vision Transformer by 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. (2024) - 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
• Anomalies Diagnostic in Endoscopic Images Using Deep Learning Ensemble Models by Viana, Pedro da S.; Cruz, Luana B. da; Dias Jr., Domingos A.; Diniz, João Otávio Beira (2024) - 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
• Automated Segmentation of Computed Tomography Images for COVID-19 Patient Evaluation by Marques, Júlio Vitor Monteiro; Gonçalves, Clésio de Araújo; Carvalho Filho, Antonio Oseas de; Veras, Rodrigo de Melo Souza; Veloso e Silva, Romuere Rodrigues (2024) - 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
• Comparative Analysis of Machine Learning Algorithms for Identifying Genetic Markers Linked to Alzheimer’s Disease by Alves, Juliana; Costa, Eduardo; Xavier, Alencar; Brito, Luiz; Cerri, Ricardo; Neuroimaging Initiative, Alzheimer’s Disease (2024) - 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
• Alzheimer’s Disease Neuroimaging Initiative Comparing LIME and SHAP Global Explanations for Human Activity Recognition by Alves, Patrick; Delgado, Jaime; Gonzalez, Luis; Rocha, Anderson R.; Boccato, Levy; Borin, Edson (2024) - 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
• Damage Identification of Wind Turbine Blades by Oliveira, Alberto Régio Alves de; Medeiros, Cláudio Marques de Sá; Ramalho, Geraldo Luis Bezerra (2024) - [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
• Enhancing Multiobjective Genetic Algorithms for Pharmaceutical Batch Scheduling: A Study on Partitioned Selection with Constraints and Mutation with Greedy Local Search Strategy by Kohara, Debora T.; Oliveira, Gina M. B. de; Martins, Luiz G. A. (2024) - 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
• Evaluating Sentiment Quantification Methods in Brazilian Portuguese Corpora by Silva, Lucas Nildaimon dos Santos; Silva, Diego Furtado; Caseli, Helena de Medeiros (2024) - 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
• Evaluating Short Text Stream Clustering on Large E-commerce Datasets by Andrade, Cesar; Ribeiro, Rita P.; Gama, João (2024) - 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
• Evolutionary Adjustment of a Cellular Automata-BasedModel for Wildfire Spreading by Murilo, Lucas V.; Oliveira, Gina M. B.; Martins, Luiz G. A. (2024) - (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
• Exploring Score-Based Ranking Fairness in Marketplace Environments Through Simulation by Silva, Lucas C.; Lucrédio, Daniel (2024) - 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
• HAVANA: Hybrid Attentional Graph Convolutional Network Semantic Venue Annotation Model by Santos, Germano B. dos; Silva, Paulo H. C.; Silva, Fabrício A.; Silva, Thais R. M. Braga; Aylon, Linnyer B. R. (2024) - 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
• Improving Colorectal Cancer Diagnosis Using MIRNet and InceptionV3 on Histopathological Images by 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 (2024) - 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
• Integrating Tensor-Based Data Analytics and Adaptive Prediction for Informed Decision-Making Support by Campello, Betania; Duarte, Leonardo Tomazeli (2024) - 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
• Investigating Methods to Detect Off-Topic Essays by Silva, Joyce M.; Anchiêta, Rafael T.; Sousa, Rogério F. de; Moura, Raimundo S. (2024) - 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
• Machine Learning and Time Series Analysis to Forecast Hotel Room Prices by Oliveira, Francisco B.; Silva-Filho, Moesio W.; Barbosa, Gabriel A.; Freitas, João Paulo; Penna, Chris; Miranda, Péricles B. C. (2024) - 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
• Modeling and Predicting Crimes in the City of São Paulo Using Graph Neural Networks by Hassan, Waqar; Cabral, Marvin Mendes; Ramos, Thiago Rodrigo; Castelo Filho, Antonio; Nonato, Luis Gustavo (2024) - 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
• RLPortfolio: Reinforcement Learning for Financial Portfolio Optimization by Costa, Caio de Souza Barbosa; Costa, Anna Helena Reali (2024) - 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
• Special-Crowd-Distance Boosted MESH Applied to the Operation of Cascade Hydro-Power Plants by Araújo Júnior, Ronald Albert de; Leite, Gabriel Matos Cardoso; Jiménez-Fernández, Silvia; Salcedo-Sanz, Sancho; Delgado, Carla Amor Divino Moreira; Marcelino, Carolina Gil (2024) - 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
• The Impact of Double Transfer Learning in VGG Architectures for Metastasis Breast Cancer Detection by Silva e Silva, Danyllo Carlos; Cortes, Omar Andres Carmona; Diniz, João Otávio Beira (2024) - 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
• A Transformer-Based Tabular Approach to Detect Toxic Comments by Damas, Ghivvago; Anchiêta, Rafael Torres; Moura, Raimundo Santos; Machado, Vinicius Ponte (2024) - 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
• Automatic Text Simplification for the Legal Domain in Brazilian Portuguese by Pereira, Francielle Vasconcellos; Frazão, Ana; Moreira, Viviane P. (2024) - 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
• Developing Resource-Efficient Clinical LLMs for Brazilian Portuguese by Pinto, João Gabriel de Souza; Freitas, Andrey Rodrigues de; Martins, Anderson Carlos Gomes; Sawazaki, Caroline Midori Rozza; Vidal, Caroline; Silva e Oliveira, Lucas Emanuel (2024) - 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
• One-Class Learning for Data Stream Through Graph Neural Networks by Gôlo, Marcos Paulo Silva; Gama, João; Marcacini, Ricardo Marcondes (2024) - 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
• Semi-periodic Activation for Time Series Classification by Medeiros Júnior, José Gilberto Barbosa de; Mitri, André Guarnier de; Silva, Diego Furtado (2024) - (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
• Classifying Potentially Non-compliant Portuguese Language Sentences Concerning Privacy Policies by Tocchini, Matheus; Rocha, Igor M.; Barros, Raphael M. de; O. e Silva, Jéssica; Garcia, Ananda F.; Zular, Felipe; Maranhão, Juliano; Sichman, Jaime Simão (2024) - 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
• Diversity in Data for Speech Processing in Brazilian Portuguese by Craveiro, Giovana Meloni; Galdino, Julio Cesar (2024) - 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
• Elevating Healthcare AI: Achieving Efficiency and Accuracy in Medical Applications with Surrogate-Based Multiobjective Compression of ResNet50 CNNs by Ferreira, Gabriel Bicalho; Silva, Pedro; Silva, Rodrigo (2024) - 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
• Explainability of Machine Learning Models with XGBoost and SHAP Values in the Context of Coping with Disasters by Teixeira, Lucas; Matos, Augusto; Carvalho, Gabriel; Valencio, Norma; Camargo, Heloisa (2024) - 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
• Gender-Neutral English to Portuguese Machine Translator: Promoting Inclusive Language by Rabonato, Ricardo Trainotti; Milios, Evangelos; Berton, Lilian (2024) - 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
• Hybrid Artificial Intelligence Model for Detecting Signs of Delayed Child Development by 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 (2024) - 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
• Low Birth Weight in Brazil Vulnerable Groups: An Analysis Based on Data Mining and Big Data by Cunha, José Gustavo; Lucas, Tarcísio Daniel Pontes; Lucas, Andreza Daniela Pontes; Ferreira, Monaliza de Oliveira (2024) - 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
• Modeling EEG Data into Graphs for the Prognostic of Patients in Coma Using Graph Neural Networks by Nascimento Junior, Odelmo O.; Assis, Dhara L. C.; Destro Filho, João B.; Zhao, Liang; Carneiro, Murillo G. (2024) - 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

• Gradient Estimation in Model-Based Reinforcement Learning: A Study on Linear Quadratic Environments by Lovatto, Ângelo Gregório; Bueno, Thiago Pereira; Barros, Leliane Nunes de (2021) - 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
• A Systematic Approach to Define Semantics for Prioritised Logic Programs by Cordeiro, Renan; Fernandes, Guilherme; Alcântara, João; Viana, Henrique (2021) - 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
• Ontology Based Classification of Electronic Health Records to Support Value-Based Health Care by Dal Bosco, Avner; Vieira, Renata; Zanotto, Bruna; Etges, Ana Paula Beck da Silva (2021) - 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
• Encoding Physical Conditioning from Inertial Sensors for Multi-step Heart Rate Estimation by Aguiar, Davi Pedrosa de; Murai, Fabricio (2021) - [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
• Experiments on Portuguese Clinical Question Answering by Silva e Oliveira, Lucas Emanuel; Schneider, Elisa Terumi Rubel; Gumiel, Yohan Bonescki; Luz, Mayara Aparecida Passaura da; Paraiso, Emerson Cabrera; Moro, Claudia (2021) - 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
• Supervised Training of a Simple Digital Assistant for a Free Crop Clinic by 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 (2021) - 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
• Code Autocomplete Using Transformers by Meyrer, Gabriel T.; Araújo, Denis A.; Rigo, Sandro J. (2021) - 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
• Performance Analysis of YOLOv3 for Real-Time Detection of Pests in Soybeans by Silveira, Fábio Amaral Godoy da; Tetila, Everton Castelão; Astolfi, Gilberto; Costa, Anderson Bessa da; Amorim, Willian Paraguassu (2021) - 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
• Transfer Learning of Shapelets for Time Series Classification Using Convolutional Neural Network by Souza, Alexandre Felipe Muller de; Cassenote, Mariane R. S.; Silva, Fabiano (2021) - 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
• A Deep Learning Approach for Aspect Sentiment Triplet Extraction in Portuguese by Barros, José Meléndez; De Bona, Glauber (2021) - 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
• Deep Active-Self Learning Applied to Named Entity Recognition by S. Neto, José Reinaldo C. S. A. V.; Faleiros, Thiago de Paulo (2021) - 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
• DEEPAGÉ: Answering Questions in Portuguese About the Brazilian Environment by Cação, Flávio Nakasato; José, Marcos Menon; Oliveira, André Seidel; Spindola, Stefano; Costa, Anna Helena Reali; Cozman, Fabio Gagliardi (2021) - 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
• Enriching Portuguese Word Embeddings with Visual Information by Consoli, Bernardo Scapini; Vieira, Renata (2021) - 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
• Incorporating Text Specificity into a Convolutional Neural Network for the Classification of Review Perceived Helpfulness by Lima, Beatriz; Nogueira, Tatiane (2021) - 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
• Joint Event Extraction with Contextualized Word Embeddings for the Portuguese Language by Sacramento, Anderson da Silva Brito; Souza, Marlo (2021) - 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
• mRAT-SQL+GAP: A Portuguese Text-to-SQL Transformer by José, Marcelo Archanjo; Cozman, Fabio Gagliardi (2021) - 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
• Rhetorical Role Identification for Portuguese Legal Documents by Aragy, Roberto; Fernandes, Eraldo Rezende; Caceres, Edson Norberto (2021) - 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
• Embracing Data Irregularities in Multivariate Time Series with Recurrent and Graph Neural Networks by Barros, Marcel Rodrigues de; Rissi, Thiago Lizier; Cabrera, Eduardo Faria; Tannuri, Eduardo Aoun; Gomi, Edson Satoshi; Barreira, Rodrigo Augusto; Costa, Anna Helena Reali (2023) - 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
• A Combinatorial Optimization Model and Polynomial Time Heuristic for a Problem of Finding Specific Structural Patterns in Networks by Sampaio, Igor M.; Lima, Karla Roberta P. S. (2023) - 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
• The Multi-attribute Fairer Cover Problem by Dantas, Ana Paula S.; Oliveira, Gabriel Bianchin de; Pedrini, Helio; Souza, Cid C. de; Dias, Zanoni (2023) - 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
• FeatGeNN: Improving Model Performance for Tabular Data with Correlation-Based Feature Extraction by Silva, Sammuel Ramos; Silva, Rodrigo (2023) - 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
• Interpreting Convolutional Neural Networks for Brain Tumor Classification: An Explainable Artificial Intelligence Approach by Schiavon, Dieine Estela Bernieri; Becker, Carla Diniz Lopes; Botelho, Viviane Rodrigues; Pianoski, Thatiane Alves (2023) - 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
• Optimization Strategies for BERT-Based Named Entity Recognition by Monteiro, Monique; Zanchettin, Cleber (2023) - 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
• Disambiguation of Universal Dependencies Part-of-Speech Tags of Closed Class Words in Portuguese by Lopes, Lucelene; Fernandes, Paulo; Inacio, Marcio L.; Duran, Magali S.; Pardo, Thiago A. S. (2023) - 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
• Prediction of Cancer-Related miRNA Targets Using an Integrative Heterogeneous Graph Neural Network-Based Method by Fabiano, Emanoel Aurelio Vianna; Recamonde-Mendoza, Mariana (2023) - 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
• Investigation of Deep Active Self-learning Algorithms Applied to Named Entity Recognition by Silva Neto, José Reinaldo Cunha Santos A. V.; Faleiros, Thiago de Paulo (2023) - 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
• A Large Dataset of Spontaneous Speech with the Accent Spoken in São Paulo for Automatic Speech Recognition Evaluation by Lima, Rodrigo; Leal, Sidney E.; Candido Junior, Arnaldo; Aluísio, Sandra M. (2024) - 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
• Adaptive Client-Dropping in Federated Learning: Preserving Data Integrity in Medical Domains by Negrão, Arthur; Silva, Guilherme; Pedrosa, Rodrigo; Luz, Eduardo; Silva, Pedro (2024) - 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
• Classifying Graphs of Elementary Mathematical Functions Using Convolutional Neural Networks by Viana, Joaquim; Matos, Helder; Mota, Marcelle; Santos, Reginaldo (2024) - 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
• Deployment of IBM Federated Learning Platform and Aggregation Algorithm Comparison: A Case Study Using the MNIST Dataset by Schulz, Hans Herbert; Moreira, Benjamin Grando (2024) - 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
• Embedding Representations for AutoML Pipelines by Braz, Camila Santana; Teixeira, Matheus Cândido; Pappa, Gisele Lobo (2024) - 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
• GovBERT-BR: A BERT-Based Language Model for Brazilian Portuguese Governmental Data by Silva, Mariana O.; Oliveira, Gabriel P.; Costa, Lucas G. L.; Pappa, Gisele L. (2024) - 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
• InRanker: Distilled Rankers for Zero-Shot Information Retrieval by Laitz, Thiago Soares; Papakostas, Konstantinos; Lotufo, Roberto; Nogueira, Rodrigo (2024) - 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
• Likelihood Estimator for Multi Model-Based Reinforcement Learning by Albarrans, Guilherme; Freire, Valdinei (2024) - 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
• Pseudonymization in Legal Texts According to the LGPD: A Named Entity Recognition Approach by Anselmo, Marcelo; Ribas, Bruno César (2024) - 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
• Acoustic Features and Autoencoders for Fault Detection in Rotating Machines: A Case Study by Bortoni, Leonardo Afonso Ferreira; Jaskowiak, Pablo Andretta (2024) - 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
• bracis-33618 by None (None) - 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
• Impact of Pre-training Datasets on Human Activity Recognition with Contrastive Predictive Coding by Silva, Betania E. R. da; Napoli, Otávio O.; Delgado, J. V.; Rocha, Anderson R.; Boccato, Levy; Borin, Edson (2024) - 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
• Predicting Engagement of Brazilian Politicians on TikTok: A Machine Learning Approach by Santana, Maria; Santana, José; Sampaio, Pablo; Brito, Kellyton (2024) - 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
• Preserving Privacy, Enhancing Robustness: Federated Learning for Lung Disease Identification in Chest X-Ray Images by Cunha, Weld Lucas; Castelo-Fernez, Cesar; Simionato, Rafael; Lacerda, Matheus Soares de; Martins, Samuel Botter (2024) - 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
• A Topology-Inspired Approach to AGM Belief Change by Souza, Marlo (2024) - 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
• Automated and Intelligent Vocational Guidance System for Classifying Specialties Based on POSCOMP Microdata by Costa, Jean Carlos; Santos, Reginaldo (2024) - 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
• Exploring Climatic Shifts in Brazilian Climates: Insights from ARMAX, Decision Trees, and Artificial Neural Networks by 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. (2024) - 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
• Tackling Low-Resource ECG Classification with Self-supervised Learning by Silva, Rafael da Costa; Silva, Diego Furtado (2024) - 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
• Tuning Hypothesis Creation: Combining Discrete and Continuous Spaces for Zero-Shot Hate Speech Detection by Vecchi, Lorenzo Puppi; Barbon Junior, Sylvio; Paraiso, Emerson Cabrera (2024) - 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

• On the Impact of MDP Design for Reinforcement Learning Agents in Resource Management by Cunha, Renato Luiz de Freitas; Chaimowicz, Luiz (2021) - 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
• Online Selection of Heuristic Operators with Deep Q-Network: A Study on the HyFlex Framework by Dantas, Augusto; Pozo, Aurora (2021) - 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
• A Kullback-Leibler Divergence-Based Locally Linear Embedding Method: A Novel Parametric Approach for Cluster Analysis by Levada, Alexandre L. M.; Haddad, Michel F. C. (2021) - 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
• Time-Dependent Item Embeddings for Collaborative Filtering by Pires, Pedro R.; Pascon, Amanda C.; Almeida, Tiago A. (2021) - 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
• An Incremental MaxSAT-Based Model to Learn Interpretable and Balanced Classification Rules by Ferreira Júnior, Antônio Carlos Souza; Rocha, Thiago Alves (2023) - 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
• Clinical Oncology Textual Notes Analysis Using Machine Learning and Deep Learning by Silva, Diego Pinheiro da; Fröhlich, William da Rosa; Schwertner, Marco Antonio; Rigo, Sandro José (2023) - 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
• Applying Reinforcement Learning for Multiple Functions in Swarm Intelligence by Ribeiro, André A. V. Escorel; Lira, Rodrigo Cesar; Macedo, Mariana; Siqueira, Hugo Valadares; Bastos-Filho, Carmelo (2023) - 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
• Deep Reinforcement Learning for Voltage Control in Power Systems by 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 (2023) - 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
• Estimating Code Running Time Complexity with Machine Learning by Pfitscher, Ricardo J.; Rodenbusch, Gabriel B.; Dias, Anderson; Vieira, Paulo; Fouto, Nuno M. M. D. (2023) - 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
• Fitness Landscape Analysis of TPOT Using Local Optima Network by Teixeira, Matheus Cândido; Pappa, Gisele Lobo (2023) - 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
• An Instance Level Analysis of Classification Difficulty for Unlabeled Data by Ueda, Patricia S. M.; Rivolli, Adriano; Lorena, Ana Carolina (2024) - 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
• Investigating Behavior Cloning from Few Demonstrations for Autonomous Driving Based on Bird’s-Eye View in Simulated Cities by Antonelo, Eric Aislan; Couto, Gustavo Claudio Karl; Möller, Christian; Fernandes, Pedro Henrique (2024) - 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
• Question Answering with Texts and Tables Through Deep Reinforcement Learning by José, Marcos M.; Cação, Flávio N.; Ribeiro, Maria F.; Cheang, Rafael M.; Pirozelli, Paulo; Cozman, Fabio G. (2024) - 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
• Reinforcement Learning with Utility-Based Semantic for Goals by Polar, Christian Delgado; Delgado, Karina Valdivia; Freire, Valdinei (2024) - 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
• An Evaluation of Temporal Neighborhood Coding Variants in Smartphone-Based Human Activity Recognition by Luz, Gustavo P. C. P. da; Napoli, Otávio O.; Delgado, J. V.; Rocha, Anderson R.; Boccato, Levy; Borin, Edson (2024) - 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
• Emotion Recognition in Instrumental Music Using AI by Alves, Camila Ferreira; Mozart, Thiago Garcia; Kowada, Luis Antônio Brasil (2024) - 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
• Scaling and Adapting Large Language Models for Portuguese Open Information Extraction: A Comparative Study of Fine-Tuning and LoRA by Melo, Alan; Cabral, Bruno; Claro, Daniela Barreiro (2024) - 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

• A Comparative Study on Concept Drift Detectors for Regression by Lima, Marília; Silva Filho, Telmo; Fagundes, Roberta Andrade de A. (2021) - 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
• Evaluating Contextualized Embeddings for Topic Modeling in Public Bidding Domain by Hott, Henrique R.; Silva, Mariana O.; Oliveira, Gabriel P.; Brandão, Michele A.; Lacerda, Anisio; Pappa, Gisele (2023) - [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
• Assessing Adversarial Effects of Noise in Missing Data Imputation by Mangussi, Arthur Dantas; Pereira, Ricardo Cardoso; Abreu, Pedro Henriques; Lorena, Ana Carolina (2024) - 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
• BASWE: Balanced Accuracy-Based Sliding Window Ensemble for Classification in Imbalanced Data Streams with Concept Drift by Oliveira, Douglas Amorim de; Delgado, Karina Valdivia; Lauretto, Marcelo de Souza (2024) - 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
• ERASMO: Leveraging Large Language Models for Enhanced Clustering Segmentation by Silva, Fillipe dos Santos; Kakimoto, Gabriel Kenzo; Reis, Julio Cesar dos; Reis, Marcelo S. (2024) - 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
• Euclidean Alignment for Transfer Learning in Multi-band Common Spatial Pattern by Amorim, Marcelo M.; Prata, Leonardo; Maurício, João Stephan; Borges, Alex; Bernardino, Heder; Souza, Gabriel de (2024) - 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
• A Case Study on Water Demand Forecasting in a Coastal Tourist City by Stefaniak, Antoniel Kleber; Jaskowiak, Pablo Andretta; Weihmann, Lucas (2024) - 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

• Comparing Contextual Embeddings for Semantic Textual Similarity in Portuguese by Andrade Junior, José E.; Cardoso-Silva, Jonathan; Bezerra, Leonardo C. T. (2021) - 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
• When External Knowledge Does Not Aggregate in Named Entity Recognition by Privatto, Pedro Ivo Monteiro; Guilherme, Ivan Rizzo (2021) - 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
• Dynamicity Analysis in the Selection of Classifier Ensembles Parameters by Silva, Jesaías Carvalho Pereira; Canuto, Anne Magaly de Paula; Santos, Araken de Medeiros (2024) - 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
• Evaluating CNN-Based Classification Models Combined with the Smoothed Pseudo Wigner-Ville Distribution to Identify Low Probability of Interception Radar Signals by Alves, Edgard B.; Alves, Jorge A.; Goldschmidt, Ronaldo R. (2024) - 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
• Heuristic Solutions for the 2D Bin-Packing Problem with Varied Size by Fernandes, Eduardo Augusto Militão; Noronha, Thiago Ferreira de; Coco, Amadeu Almeida (2024) - 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
• Unsupervised Statistical Keyword Extraction Pipeline: Is LLM All You Need? by Zagatti, Fernando Rezende; Lucrédio, Daniel; Caseli, Helena de Medeiros (2024) - 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

• Evaluating Recent Legal Rhetorical Role Labeling Approaches Supported by Transformer Encoders by Lima, Alexandre Gomes de; Moreno, José G.; Dkaki, Taoufiq; Aranha, Eduardo Henrique da S.; Boughanem, Mohand (2023) - 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
• Bete: A Brazilian Portuguese Dataset for Named Entity Recognition and Relation Extraction in the Diabetes Healthcare Domain by Pavanelli, Lucas; Gumiel, Yohan Bonescki; Ferreira, Thiago; Pagano, Adriana; Laber, Eduardo (2023) - Experiments for entity recognition models. Experiments for relation extraction models. Keywords: annotation; bert; dataset; diabetes; entities; entity; extraction; language; models; portuguese; recognition; relation; table
• Evaluating Large Language Models for Tax Law Reasoning by Presa, João Paulo Cavalcante; Camilo Junior, Celso Gonçalves; Oliveira, Sávio Salvarino Teles de (2024) - 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
• Improving LLMs’ Reasoning and Planning with Finite-State Machines by Cabrera, Eduardo Faria; Barros, Marcel Rodrigues de; Costa, Anna Helena Reali (2024) - 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
• Multilingual Extractive Summarization: Investigating State-of-the-Art Methods for English and Brazilian Portuguese by Jorge, Germano Antonio Zani; Bezerra, Davi Alves; Xavier, Clarissa Castellã; Pardo, Thiago Alexre Salgueiro (2024) - 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

• Improving Rule Based and Equivalent Decision Simplifications for Bloat Control in Genetic Programming Using a Dynamic Operator by Oliveira, Gustavo F. V. de; Mendes, Marcus H. S. (2021) - 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
• Universal Dependencies-Based PoS Tagging Refinement Through Linguistic Resources by Lopes, Lucelene; Duran, Magali S.; Pardo, Thiago A. S. (2021) - 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
• A Multi-algorithm Approach to the Optimization of Thermal Power Plants Operation by 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 (2023) - 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

• Fast Movelet Extraction and Dimensionality Reduction for Robust Multiple Aspect Trajectory Classification by Portela, Tarlis Tortelli; Silva, Camila Leite da; Carvalho, Jonata Tyska; Bogorny, Vania (2021) - 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

• AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification by Del Valle, Aline Marques; Mantovani, Rafael Gomes; Cerri, Ricardo (2023) - 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

• Impact of Parent Selection Operator on the FDEA Algorithm by Oliveira, João Pedro A. de; Castro Jr., Olacir R. (2024) - 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

• An Enhanced TSP-Based Approach for Active Debris Removal Mission Planning by Rodrigues Neto, João Batista; Ramos, Gabriel de Oliveira (2021) - 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
• A Monte Carlo Algorithm for Time-Constrained General Game Playing by Putrich, Victor Scherer; Tavares, Anderson Rocha; Meneguzzi, Felipe (2023) - \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
• Comparing Neural Network Encodings for Logic-Based Explainability by Carvalho, Levi Cordeiro; Oliveira, Saulo A. F.; Rocha, Thiago Alves (2024) - 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

• A Modified NSGA-DO for Solving Multiobjective Optimization Problems by Machado, Jussara Gomes; Pires, Matheus Giovanni; Bertoni, Fabiana Cristina; Pimenta, Adinovam Henriques de Macedo; Camargo, Heloisa de Arruda (2021) - 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
• Combining Clustering and Genetic Algorithms for Portfolio Optimization: A Case Study with B3 Companies by Alonso, Edsson Israel Andonaegui; Delgado, Karina Valdivia; Santos, Francisco Carlos B. dos (2024) - 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

• Multiobjective Evolutionary Algorithms Applied to the Optimization of Expanded Genetic Codes by Silva, Maísa de Carvalho; Pereira, Paulo Guilherme Pinheiro; Oliveira, Lariza Laura de; Tinós, Renato (2023) - 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

• A Graph-Based Crossover and Soft-Repair Operators for the Steiner Tree Problem by Godoi, Giliard Almeida de; Tinós, Renato; Sanches, Danilo Sipoli (2021) - 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

• Lackadaisical Quantum Walk in the Hypercube to Search for Multiple Marked Vertices by Souza, Luciano S. de; Carvalho, Jonathan H. A. de; Ferreira, Tiago A. E. (2021) - 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

• ASPIC? and the Postulates of Non-interference and Crash-Resistance by Silva, Rafael; Alcântara, João (2021) - $$ 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
• On the Refinement of Compensation-Based Semantics for Weighted Argumentation Frameworks by Viana, Henrique; Alcântara, João (2021) - 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
• α-MCMP: Trade-Offs Between Probability and Cost in SSPs with the MCMP Criterion by Crispino, Gabriel Nunes; Freire, Valdinei; Delgado, Karina Valdivia (2023) - 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

• Intelligent Agents for Observation and Containment of Malicious Targets Organizations by 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 (2021) - 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

• On the Equivalence Between Logic Programs and Bipolar Argumentation Frameworks by Cordeiro, Renan; Alcântara, João (2024) - 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

• Critical Analysis of AI Indicators in Terms of Weighting and Aggregation Approaches by Pelissari, Renata; Campello, Betania; Pelegrina, Guilherme Dean; Suyama, Ricardo; Duarte, Leonardo Tomazeli (2023) - 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

• Improved Fuzzy Decision System for Energy Bill Reduction in the Context of the Brazilian White Tariff Scenario by Branco, Raimunda; Saraiva, Filipe (2023) - 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

• A Unified Framework for Average Reward Criterion and Risk by Reis, Willy Arthur Silva; Delgado, Karina Valdivia; Freire, Valdinei (2024) - 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

• SARA - A Generative AI for Legal Process Summarization Based on Chain of Density Prompt Engineering by Bomfim, Francisco das Chagas Jucá; Monteiro Neto, Joao Araujo; Bezerra Filho, Gilson; Furtado, Vasco; Pinheiro, Vládia (2024) - 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

• Front-Matter by none (2023) - 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

• Humanities and AI: Ethical Education in Technology Careers by Camargo, Laura Simões; Blay, Enio Alterman; Schmidt, Gabriela Soares (2024) - 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

• Going Bananas! - Unfolding Program Synthesis with Origami by Fernandes, Matheus Campos; França, Fabrício Olivetti de; Francesquini, Emilio (2024) - 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

Epilogue

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Created: 2025-12-24