id	sid	tid	token	lemma	pos
ajst-27521	1	1	academic	academic	ADJ
ajst-27521	1	2	journal	journal	NOUN
ajst-27521	1	3	of	of	ADP
ajst-27521	1	4	science	science	NOUN
ajst-27521	1	5	and	and	CCONJ
ajst-27521	1	6	technology	technology	NOUN
ajst-27521	1	7	issn	issn	NOUN
ajst-27521	1	8	:	:	PUNCT
ajst-27521	1	9	2771	2771	NUM
ajst-27521	1	10	-	-	SYM
ajst-27521	1	11	3032	3032	NUM
ajst-27521	1	12	|	|	NOUN
ajst-27521	1	13	vol	vol	NOUN
ajst-27521	1	14	.	.	PROPN
ajst-27521	1	15	13	13	NUM
ajst-27521	1	16	,	,	PUNCT
ajst-27521	1	17	no	no	INTJ
ajst-27521	1	18	.	.	NOUN
ajst-27521	1	19	2	2	NUM
ajst-27521	1	20	,	,	PUNCT
ajst-27521	1	21	2024	2024	NUM
ajst-27521	1	22	165	165	NUM
ajst-27521	1	23	time‐series	time‐serie	NOUN
ajst-27521	1	24	anomaly	anomaly	NOUN
ajst-27521	1	25	detection	detection	NOUN
ajst-27521	1	26	study	study	NOUN
ajst-27521	1	27	based	base	VERB
ajst-27521	1	28	on	on	ADP
ajst-27521	1	29	deep	deep	ADJ
ajst-27521	1	30	learning	learn	VERB
ajst-27521	1	31	wenjuan	wenjuan	PROPN
ajst-27521	1	32	wu	wu	PROPN
ajst-27521	1	33	school	school	PROPN
ajst-27521	1	34	of	of	ADP
ajst-27521	1	35	henan	henan	PROPN
ajst-27521	1	36	polytechnic	polytechnic	PROPN
ajst-27521	1	37	university	university	PROPN
ajst-27521	1	38	,	,	PUNCT
ajst-27521	1	39	jiaozuo	jiaozuo	PROPN
ajst-27521	1	40	454000	454000	NUM
ajst-27521	1	41	,	,	PUNCT
ajst-27521	1	42	china	china	PROPN
ajst-27521	1	43	abstract	abstract	PROPN
ajst-27521	1	44	:	:	PUNCT
ajst-27521	1	45	with	with	ADP
ajst-27521	1	46	the	the	DET
ajst-27521	1	47	rapid	rapid	ADJ
ajst-27521	1	48	development	development	NOUN
ajst-27521	1	49	of	of	ADP
ajst-27521	1	50	deep	deep	ADJ
ajst-27521	1	51	learning	learning	NOUN
ajst-27521	1	52	technology	technology	NOUN
ajst-27521	1	53	,	,	PUNCT
ajst-27521	1	54	anomaly	anomaly	NOUN
ajst-27521	1	55	detection	detection	NOUN
ajst-27521	1	56	based	base	VERB
ajst-27521	1	57	on	on	ADP
ajst-27521	1	58	deep	deep	ADJ
ajst-27521	1	59	learning	learning	NOUN
ajst-27521	1	60	has	have	AUX
ajst-27521	1	61	become	become	VERB
ajst-27521	1	62	an	an	DET
ajst-27521	1	63	important	important	ADJ
ajst-27521	1	64	research	research	NOUN
ajst-27521	1	65	direction	direction	NOUN
ajst-27521	1	66	.	.	PUNCT
ajst-27521	2	1	this	this	DET
ajst-27521	2	2	paper	paper	NOUN
ajst-27521	2	3	classifies	classify	VERB
ajst-27521	2	4	and	and	CCONJ
ajst-27521	2	5	summarizes	summarize	NOUN
ajst-27521	2	6	anomaly	anomaly	NOUN
ajst-27521	2	7	detection	detection	NOUN
ajst-27521	2	8	methods	method	NOUN
ajst-27521	2	9	,	,	PUNCT
ajst-27521	2	10	covering	cover	VERB
ajst-27521	2	11	traditional	traditional	ADJ
ajst-27521	2	12	methods	method	NOUN
ajst-27521	2	13	,	,	PUNCT
ajst-27521	2	14	machine	machine	NOUN
ajst-27521	2	15	learning	learning	NOUN
ajst-27521	2	16	-	-	PUNCT
ajst-27521	2	17	based	base	VERB
ajst-27521	2	18	methods	method	NOUN
ajst-27521	2	19	,	,	PUNCT
ajst-27521	2	20	and	and	CCONJ
ajst-27521	2	21	deep	deep	ADJ
ajst-27521	2	22	learning	learning	NOUN
ajst-27521	2	23	-	-	PUNCT
ajst-27521	2	24	based	base	VERB
ajst-27521	2	25	methods	method	NOUN
ajst-27521	2	26	.	.	PUNCT
ajst-27521	3	1	it	it	PRON
ajst-27521	3	2	particularly	particularly	ADV
ajst-27521	3	3	introduces	introduce	VERB
ajst-27521	3	4	typical	typical	ADJ
ajst-27521	3	5	deep	deep	ADJ
ajst-27521	3	6	learning	learning	NOUN
ajst-27521	3	7	models	model	NOUN
ajst-27521	3	8	,	,	PUNCT
ajst-27521	3	9	analyzing	analyze	VERB
ajst-27521	3	10	their	their	PRON
ajst-27521	3	11	respective	respective	ADJ
ajst-27521	3	12	advantages	advantage	NOUN
ajst-27521	3	13	and	and	CCONJ
ajst-27521	3	14	disadvantages	disadvantage	NOUN
ajst-27521	3	15	.	.	PUNCT
ajst-27521	4	1	experiments	experiment	NOUN
ajst-27521	4	2	were	be	AUX
ajst-27521	4	3	conducted	conduct	VERB
ajst-27521	4	4	on	on	ADP
ajst-27521	4	5	two	two	NUM
ajst-27521	4	6	public	public	ADJ
ajst-27521	4	7	datasets	dataset	NOUN
ajst-27521	4	8	,	,	PUNCT
ajst-27521	4	9	and	and	CCONJ
ajst-27521	4	10	the	the	DET
ajst-27521	4	11	performance	performance	NOUN
ajst-27521	4	12	of	of	ADP
ajst-27521	4	13	each	each	DET
ajst-27521	4	14	model	model	NOUN
ajst-27521	4	15	in	in	ADP
ajst-27521	4	16	anomaly	anomaly	NOUN
ajst-27521	4	17	detection	detection	NOUN
ajst-27521	4	18	was	be	AUX
ajst-27521	4	19	compared	compare	VERB
ajst-27521	4	20	in	in	ADP
ajst-27521	4	21	detail	detail	NOUN
ajst-27521	4	22	.	.	PUNCT
ajst-27521	5	1	keywords	keyword	NOUN
ajst-27521	5	2	:	:	PUNCT
ajst-27521	5	3	anomaly	anomaly	NOUN
ajst-27521	5	4	detection	detection	NOUN
ajst-27521	5	5	;	;	PUNCT
ajst-27521	5	6	deep	deep	ADJ
ajst-27521	5	7	learning	learning	NOUN
ajst-27521	5	8	;	;	PUNCT
ajst-27521	5	9	time	time	NOUN
ajst-27521	5	10	series	series	NOUN
ajst-27521	5	11	.	.	PUNCT
ajst-27521	6	1	1	1	X
ajst-27521	6	2	.	.	X
ajst-27521	6	3	introduction	introduction	NOUN
ajst-27521	6	4	time	time	PROPN
ajst-27521	6	5	series	series	PROPN
ajst-27521	6	6	data	data	PROPN
ajst-27521	6	7	is	be	AUX
ajst-27521	6	8	an	an	DET
ajst-27521	6	9	ordered	order	VERB
ajst-27521	6	10	collection	collection	NOUN
ajst-27521	6	11	of	of	ADP
ajst-27521	6	12	data	datum	NOUN
ajst-27521	6	13	that	that	PRON
ajst-27521	6	14	changes	change	VERB
ajst-27521	6	15	over	over	ADP
ajst-27521	6	16	time	time	NOUN
ajst-27521	6	17	,	,	PUNCT
ajst-27521	6	18	widely	widely	ADV
ajst-27521	6	19	existing	exist	VERB
ajst-27521	6	20	in	in	ADP
ajst-27521	6	21	various	various	ADJ
ajst-27521	6	22	fields	field	NOUN
ajst-27521	6	23	such	such	ADJ
ajst-27521	6	24	as	as	ADP
ajst-27521	6	25	financial	financial	ADJ
ajst-27521	6	26	trading	trading	NOUN
ajst-27521	6	27	,	,	PUNCT
ajst-27521	6	28	industrial	industrial	ADJ
ajst-27521	6	29	operations	operation	NOUN
ajst-27521	6	30	,	,	PUNCT
ajst-27521	6	31	environmental	environmental	ADJ
ajst-27521	6	32	monitoring	monitoring	NOUN
ajst-27521	6	33	,	,	PUNCT
ajst-27521	6	34	and	and	CCONJ
ajst-27521	6	35	network	network	NOUN
ajst-27521	6	36	traffic	traffic	NOUN
ajst-27521	6	37	monitoring	monitoring	NOUN
ajst-27521	6	38	.	.	PUNCT
ajst-27521	7	1	in	in	ADP
ajst-27521	7	2	time	time	NOUN
ajst-27521	7	3	series	series	PROPN
ajst-27521	7	4	data	data	PROPN
ajst-27521	7	5	,	,	PUNCT
ajst-27521	7	6	there	there	PRON
ajst-27521	7	7	are	be	VERB
ajst-27521	7	8	always	always	ADV
ajst-27521	7	9	some	some	DET
ajst-27521	7	10	data	datum	NOUN
ajst-27521	7	11	that	that	PRON
ajst-27521	7	12	differ	differ	VERB
ajst-27521	7	13	significantly	significantly	ADV
ajst-27521	7	14	from	from	ADP
ajst-27521	7	15	the	the	DET
ajst-27521	7	16	overall	overall	ADJ
ajst-27521	7	17	characteristics	characteristic	NOUN
ajst-27521	7	18	of	of	ADP
ajst-27521	7	19	the	the	DET
ajst-27521	7	20	dataset	dataset	NOUN
ajst-27521	7	21	,	,	PUNCT
ajst-27521	7	22	which	which	PRON
ajst-27521	7	23	are	be	AUX
ajst-27521	7	24	known	know	VERB
ajst-27521	7	25	as	as	ADP
ajst-27521	7	26	anomalies	anomaly	NOUN
ajst-27521	7	27	.	.	PUNCT
ajst-27521	8	1	timely	timely	ADJ
ajst-27521	8	2	detection	detection	NOUN
ajst-27521	8	3	of	of	ADP
ajst-27521	8	4	these	these	DET
ajst-27521	8	5	anomalies	anomaly	NOUN
ajst-27521	8	6	can	can	AUX
ajst-27521	8	7	effectively	effectively	ADV
ajst-27521	8	8	reduce	reduce	VERB
ajst-27521	8	9	losses	loss	NOUN
ajst-27521	8	10	in	in	ADP
ajst-27521	8	11	industrial	industrial	ADJ
ajst-27521	8	12	production	production	NOUN
ajst-27521	8	13	and	and	CCONJ
ajst-27521	8	14	can	can	AUX
ajst-27521	8	15	even	even	ADV
ajst-27521	8	16	prevent	prevent	VERB
ajst-27521	8	17	the	the	DET
ajst-27521	8	18	occurrence	occurrence	NOUN
ajst-27521	8	19	of	of	ADP
ajst-27521	8	20	some	some	DET
ajst-27521	8	21	disasters	disaster	NOUN
ajst-27521	8	22	.	.	PUNCT
ajst-27521	9	1	therefore	therefore	ADV
ajst-27521	9	2	,	,	PUNCT
ajst-27521	9	3	research	research	NOUN
ajst-27521	9	4	into	into	ADP
ajst-27521	9	5	anomaly	anomaly	NOUN
ajst-27521	9	6	detection	detection	NOUN
ajst-27521	9	7	has	have	VERB
ajst-27521	9	8	very	very	ADV
ajst-27521	9	9	important	important	ADJ
ajst-27521	9	10	practical	practical	ADJ
ajst-27521	9	11	significance	significance	NOUN
ajst-27521	9	12	.	.	PUNCT
ajst-27521	10	1	currently	currently	ADV
ajst-27521	10	2	,	,	PUNCT
ajst-27521	10	3	research	research	NOUN
ajst-27521	10	4	on	on	ADP
ajst-27521	10	5	time	time	NOUN
ajst-27521	10	6	series	series	PROPN
ajst-27521	10	7	anomaly	anomaly	PROPN
ajst-27521	10	8	detection	detection	NOUN
ajst-27521	10	9	mainly	mainly	ADV
ajst-27521	10	10	focuses	focus	VERB
ajst-27521	10	11	on	on	ADP
ajst-27521	10	12	traditional	traditional	ADJ
ajst-27521	10	13	statistical	statistical	ADJ
ajst-27521	10	14	models	model	NOUN
ajst-27521	10	15	and	and	CCONJ
ajst-27521	10	16	machine	machine	NOUN
ajst-27521	10	17	learning	learning	NOUN
ajst-27521	10	18	models	model	NOUN
ajst-27521	10	19	,	,	PUNCT
ajst-27521	10	20	but	but	CCONJ
ajst-27521	10	21	there	there	PRON
ajst-27521	10	22	are	be	VERB
ajst-27521	10	23	issues	issue	NOUN
ajst-27521	10	24	such	such	ADJ
ajst-27521	10	25	as	as	ADP
ajst-27521	10	26	low	low	ADJ
ajst-27521	10	27	detection	detection	NOUN
ajst-27521	10	28	accuracy	accuracy	NOUN
ajst-27521	10	29	and	and	CCONJ
ajst-27521	10	30	weak	weak	ADJ
ajst-27521	10	31	generalization	generalization	NOUN
ajst-27521	10	32	ability	ability	NOUN
ajst-27521	10	33	.	.	PUNCT
ajst-27521	11	1	with	with	ADP
ajst-27521	11	2	the	the	DET
ajst-27521	11	3	rapid	rapid	ADJ
ajst-27521	11	4	development	development	NOUN
ajst-27521	11	5	of	of	ADP
ajst-27521	11	6	deep	deep	ADJ
ajst-27521	11	7	learning	learning	NOUN
ajst-27521	11	8	technology	technology	NOUN
ajst-27521	11	9	,	,	PUNCT
ajst-27521	11	10	researchers	researcher	NOUN
ajst-27521	11	11	are	be	AUX
ajst-27521	11	12	gradually	gradually	ADV
ajst-27521	11	13	applying	apply	VERB
ajst-27521	11	14	some	some	DET
ajst-27521	11	15	deep	deep	ADJ
ajst-27521	11	16	learning	learning	NOUN
ajst-27521	11	17	algorithms	algorithm	NOUN
ajst-27521	11	18	,	,	PUNCT
ajst-27521	11	19	including	include	VERB
ajst-27521	11	20	convolutional	convolutional	ADJ
ajst-27521	11	21	neural	neural	ADJ
ajst-27521	11	22	networks	network	NOUN
ajst-27521	11	23	(	(	PUNCT
ajst-27521	11	24	cnn	cnn	PROPN
ajst-27521	11	25	)	)	PUNCT
ajst-27521	12	1	[	[	X
ajst-27521	12	2	1	1	X
ajst-27521	12	3	]	]	PUNCT
ajst-27521	12	4	and	and	CCONJ
ajst-27521	12	5	long	long	ADJ
ajst-27521	12	6	short	short	ADJ
ajst-27521	12	7	-	-	PUNCT
ajst-27521	12	8	term	term	NOUN
ajst-27521	12	9	memory	memory	NOUN
ajst-27521	12	10	networks	network	NOUN
ajst-27521	12	11	(	(	PUNCT
ajst-27521	12	12	lstm	lstm	NOUN
ajst-27521	12	13	)	)	PUNCT
ajst-27521	13	1	[	[	X
ajst-27521	13	2	2	2	NUM
ajst-27521	13	3	]	]	PUNCT
ajst-27521	13	4	,	,	PUNCT
ajst-27521	13	5	to	to	PART
ajst-27521	13	6	anomaly	anomaly	VERB
ajst-27521	13	7	detection	detection	NOUN
ajst-27521	13	8	in	in	ADP
ajst-27521	13	9	fields	field	NOUN
ajst-27521	13	10	such	such	ADJ
ajst-27521	13	11	as	as	ADP
ajst-27521	13	12	medical	medical	ADJ
ajst-27521	13	13	image	image	NOUN
ajst-27521	13	14	processing	processing	NOUN
ajst-27521	13	15	[	[	X
ajst-27521	13	16	3	3	NUM
ajst-27521	13	17	]	]	PUNCT
ajst-27521	13	18	,	,	PUNCT
ajst-27521	13	19	pest	pest	VERB
ajst-27521	13	20	detection	detection	NOUN
ajst-27521	13	21	[	[	X
ajst-27521	13	22	4	4	NUM
ajst-27521	13	23	]	]	PUNCT
ajst-27521	13	24	,	,	PUNCT
ajst-27521	13	25	and	and	CCONJ
ajst-27521	13	26	natural	natural	ADJ
ajst-27521	13	27	language	language	NOUN
ajst-27521	13	28	processing	processing	NOUN
ajst-27521	13	29	[	[	X
ajst-27521	13	30	5	5	NUM
ajst-27521	13	31	]	]	PUNCT
ajst-27521	13	32	.	.	PUNCT
ajst-27521	14	1	2	2	X
ajst-27521	14	2	.	.	X
ajst-27521	14	3	introduction	introduction	NOUN
ajst-27521	14	4	to	to	PART
ajst-27521	14	5	anomaly	anomaly	NOUN
ajst-27521	14	6	detection	detection	NOUN
ajst-27521	14	7	2.1	2.1	NUM
ajst-27521	14	8	classification	classification	NOUN
ajst-27521	14	9	of	of	ADP
ajst-27521	14	10	anomalies	anomaly	NOUN
ajst-27521	14	11	time	time	PROPN
ajst-27521	14	12	series	series	PROPN
ajst-27521	14	13	data	data	PROPN
ajst-27521	14	14	is	be	AUX
ajst-27521	14	15	a	a	DET
ajst-27521	14	16	collection	collection	NOUN
ajst-27521	14	17	of	of	ADP
ajst-27521	14	18	data	datum	NOUN
ajst-27521	14	19	points	point	NOUN
ajst-27521	14	20	arranged	arrange	VERB
ajst-27521	14	21	in	in	ADP
ajst-27521	14	22	chronological	chronological	ADJ
ajst-27521	14	23	order	order	NOUN
ajst-27521	14	24	,	,	PUNCT
ajst-27521	14	25	with	with	ADP
ajst-27521	14	26	each	each	DET
ajst-27521	14	27	time	time	NOUN
ajst-27521	14	28	point	point	NOUN
ajst-27521	14	29	corresponding	correspond	VERB
ajst-27521	14	30	to	to	ADP
ajst-27521	14	31	an	an	DET
ajst-27521	14	32	observation	observation	NOUN
ajst-27521	14	33	.	.	PUNCT
ajst-27521	15	1	it	it	PRON
ajst-27521	15	2	is	be	AUX
ajst-27521	15	3	commonly	commonly	ADV
ajst-27521	15	4	used	use	VERB
ajst-27521	15	5	to	to	PART
ajst-27521	15	6	describe	describe	VERB
ajst-27521	15	7	events	event	NOUN
ajst-27521	15	8	that	that	PRON
ajst-27521	15	9	change	change	VERB
ajst-27521	15	10	over	over	ADP
ajst-27521	15	11	time	time	NOUN
ajst-27521	15	12	.	.	PUNCT
ajst-27521	16	1	in	in	ADP
ajst-27521	16	2	time	time	NOUN
ajst-27521	16	3	series	series	PROPN
ajst-27521	16	4	anomaly	anomaly	PROPN
ajst-27521	16	5	detection	detection	NOUN
ajst-27521	16	6	,	,	PUNCT
ajst-27521	16	7	an	an	DET
ajst-27521	16	8	anomaly	anomaly	NOUN
ajst-27521	16	9	refers	refer	VERB
ajst-27521	16	10	to	to	ADP
ajst-27521	16	11	individual	individual	ADJ
ajst-27521	16	12	data	datum	NOUN
ajst-27521	16	13	points	point	NOUN
ajst-27521	16	14	that	that	PRON
ajst-27521	16	15	are	be	AUX
ajst-27521	16	16	significantly	significantly	ADV
ajst-27521	16	17	deviant	deviant	ADJ
ajst-27521	16	18	from	from	ADP
ajst-27521	16	19	the	the	DET
ajst-27521	16	20	sample	sample	NOUN
ajst-27521	16	21	dataset	dataset	NOUN
ajst-27521	16	22	and	and	CCONJ
ajst-27521	16	23	lie	lie	VERB
ajst-27521	16	24	outside	outside	ADP
ajst-27521	16	25	a	a	DET
ajst-27521	16	26	specific	specific	ADJ
ajst-27521	16	27	range	range	NOUN
ajst-27521	16	28	.	.	PUNCT
ajst-27521	17	1	anomalies	anomaly	NOUN
ajst-27521	17	2	are	be	AUX
ajst-27521	17	3	generally	generally	ADV
ajst-27521	17	4	classified	classify	VERB
ajst-27521	17	5	into	into	ADP
ajst-27521	17	6	three	three	NUM
ajst-27521	17	7	types	type	NOUN
ajst-27521	17	8	:	:	PUNCT
ajst-27521	17	9	point	point	NOUN
ajst-27521	17	10	anomalies	anomaly	NOUN
ajst-27521	17	11	,	,	PUNCT
ajst-27521	17	12	contextual	contextual	ADJ
ajst-27521	17	13	anomalies	anomaly	NOUN
ajst-27521	17	14	,	,	PUNCT
ajst-27521	17	15	and	and	CCONJ
ajst-27521	17	16	collective	collective	ADJ
ajst-27521	17	17	anomalies	anomaly	NOUN
ajst-27521	17	18	:	:	PUNCT
ajst-27521	17	19	(	(	PUNCT
ajst-27521	17	20	1	1	X
ajst-27521	17	21	)	)	PUNCT
ajst-27521	17	22	point	point	NOUN
ajst-27521	17	23	anomalies	anomaly	NOUN
ajst-27521	17	24	:	:	PUNCT
ajst-27521	17	25	these	these	PRON
ajst-27521	17	26	are	be	AUX
ajst-27521	17	27	values	value	NOUN
ajst-27521	17	28	that	that	PRON
ajst-27521	17	29	are	be	AUX
ajst-27521	17	30	different	different	ADJ
ajst-27521	17	31	from	from	ADP
ajst-27521	17	32	most	most	ADJ
ajst-27521	17	33	of	of	ADP
ajst-27521	17	34	the	the	DET
ajst-27521	17	35	data	data	NOUN
ajst-27521	17	36	points	point	NOUN
ajst-27521	17	37	in	in	ADP
ajst-27521	17	38	the	the	DET
ajst-27521	17	39	dataset	dataset	NOUN
ajst-27521	17	40	.	.	PUNCT
ajst-27521	18	1	point	point	NOUN
ajst-27521	18	2	anomalies	anomaly	NOUN
ajst-27521	18	3	can	can	AUX
ajst-27521	18	4	cause	cause	VERB
ajst-27521	18	5	biases	bias	NOUN
ajst-27521	18	6	in	in	ADP
ajst-27521	18	7	data	datum	NOUN
ajst-27521	18	8	models	model	NOUN
ajst-27521	18	9	,	,	PUNCT
ajst-27521	18	10	as	as	SCONJ
ajst-27521	18	11	these	these	DET
ajst-27521	18	12	anomalies	anomaly	NOUN
ajst-27521	18	13	represent	represent	VERB
ajst-27521	18	14	errors	error	NOUN
ajst-27521	18	15	in	in	ADP
ajst-27521	18	16	data	datum	NOUN
ajst-27521	18	17	collection	collection	NOUN
ajst-27521	18	18	or	or	CCONJ
ajst-27521	18	19	recording	recording	NOUN
ajst-27521	18	20	,	,	PUNCT
ajst-27521	18	21	or	or	CCONJ
ajst-27521	18	22	they	they	PRON
ajst-27521	18	23	may	may	AUX
ajst-27521	18	24	indicate	indicate	VERB
ajst-27521	18	25	true	true	ADJ
ajst-27521	18	26	rare	rare	ADJ
ajst-27521	18	27	events	event	NOUN
ajst-27521	18	28	within	within	ADP
ajst-27521	18	29	the	the	DET
ajst-27521	18	30	dataset	dataset	NOUN
ajst-27521	18	31	.	.	PUNCT
ajst-27521	19	1	(	(	PUNCT
ajst-27521	19	2	2	2	X
ajst-27521	19	3	)	)	PUNCT
ajst-27521	19	4	contextual	contextual	ADJ
ajst-27521	19	5	anomalies	anomaly	NOUN
ajst-27521	19	6	:	:	PUNCT
ajst-27521	19	7	these	these	PRON
ajst-27521	19	8	have	have	VERB
ajst-27521	19	9	relatively	relatively	ADV
ajst-27521	19	10	larger	large	ADJ
ajst-27521	19	11	or	or	CCONJ
ajst-27521	19	12	smaller	small	ADJ
ajst-27521	19	13	values	value	NOUN
ajst-27521	19	14	within	within	ADP
ajst-27521	19	15	a	a	DET
ajst-27521	19	16	context	context	NOUN
ajst-27521	19	17	or	or	CCONJ
ajst-27521	19	18	subset	subset	NOUN
ajst-27521	19	19	,	,	PUNCT
ajst-27521	19	20	but	but	CCONJ
ajst-27521	19	21	not	not	PART
ajst-27521	19	22	globally	globally	ADV
ajst-27521	19	23	.	.	PUNCT
ajst-27521	20	1	contextual	contextual	ADJ
ajst-27521	20	2	anomalies	anomaly	NOUN
ajst-27521	20	3	are	be	AUX
ajst-27521	20	4	useful	useful	ADJ
ajst-27521	20	5	for	for	ADP
ajst-27521	20	6	understanding	understand	VERB
ajst-27521	20	7	how	how	SCONJ
ajst-27521	20	8	data	datum	NOUN
ajst-27521	20	9	behaves	behave	VERB
ajst-27521	20	10	under	under	ADP
ajst-27521	20	11	different	different	ADJ
ajst-27521	20	12	conditions	condition	NOUN
ajst-27521	20	13	,	,	PUNCT
ajst-27521	20	14	but	but	CCONJ
ajst-27521	20	15	they	they	PRON
ajst-27521	20	16	can	can	AUX
ajst-27521	20	17	also	also	ADV
ajst-27521	20	18	lead	lead	VERB
ajst-27521	20	19	to	to	ADP
ajst-27521	20	20	the	the	DET
ajst-27521	20	21	omission	omission	NOUN
ajst-27521	20	22	of	of	ADP
ajst-27521	20	23	important	important	ADJ
ajst-27521	20	24	local	local	ADJ
ajst-27521	20	25	information	information	NOUN
ajst-27521	20	26	in	in	ADP
ajst-27521	20	27	global	global	ADJ
ajst-27521	20	28	analysis	analysis	NOUN
ajst-27521	20	29	.	.	PUNCT
ajst-27521	21	1	(	(	PUNCT
ajst-27521	21	2	3	3	X
ajst-27521	21	3	)	)	PUNCT
ajst-27521	21	4	collective	collective	ADJ
ajst-27521	21	5	anomalies	anomaly	NOUN
ajst-27521	21	6	:	:	PUNCT
ajst-27521	21	7	these	these	PRON
ajst-27521	21	8	are	be	AUX
ajst-27521	21	9	composed	compose	VERB
ajst-27521	21	10	of	of	ADP
ajst-27521	21	11	multiple	multiple	ADJ
ajst-27521	21	12	related	relate	VERB
ajst-27521	21	13	data	datum	NOUN
ajst-27521	21	14	points	point	NOUN
ajst-27521	21	15	that	that	SCONJ
ajst-27521	21	16	,	,	PUNCT
ajst-27521	21	17	as	as	ADP
ajst-27521	21	18	a	a	DET
ajst-27521	21	19	whole	whole	NOUN
ajst-27521	21	20	,	,	PUNCT
ajst-27521	21	21	are	be	AUX
ajst-27521	21	22	anomalous	anomalous	ADJ
ajst-27521	21	23	with	with	ADP
ajst-27521	21	24	respect	respect	NOUN
ajst-27521	21	25	to	to	ADP
ajst-27521	21	26	the	the	DET
ajst-27521	21	27	entire	entire	ADJ
ajst-27521	21	28	dataset	dataset	NOUN
ajst-27521	21	29	.	.	PUNCT
ajst-27521	22	1	collective	collective	ADJ
ajst-27521	22	2	anomalies	anomaly	NOUN
ajst-27521	22	3	represent	represent	VERB
ajst-27521	22	4	new	new	ADJ
ajst-27521	22	5	patterns	pattern	NOUN
ajst-27521	22	6	or	or	CCONJ
ajst-27521	22	7	trends	trend	NOUN
ajst-27521	22	8	in	in	ADP
ajst-27521	22	9	the	the	DET
ajst-27521	22	10	data	datum	NOUN
ajst-27521	22	11	,	,	PUNCT
ajst-27521	22	12	or	or	CCONJ
ajst-27521	22	13	they	they	PRON
ajst-27521	22	14	may	may	AUX
ajst-27521	22	15	indicate	indicate	VERB
ajst-27521	22	16	some	some	DET
ajst-27521	22	17	kind	kind	NOUN
ajst-27521	22	18	of	of	ADP
ajst-27521	22	19	systemic	systemic	ADJ
ajst-27521	22	20	problem	problem	NOUN
ajst-27521	22	21	.	.	PUNCT
ajst-27521	23	1	2.2	2.2	NUM
ajst-27521	23	2	challenges	challenge	NOUN
ajst-27521	23	3	faced	face	VERB
ajst-27521	23	4	in	in	ADP
ajst-27521	23	5	anomaly	anomaly	NOUN
ajst-27521	23	6	detection	detection	NOUN
ajst-27521	23	7	time	time	NOUN
ajst-27521	23	8	series	series	PROPN
ajst-27521	23	9	data	data	PROPN
ajst-27521	23	10	is	be	AUX
ajst-27521	23	11	characterized	characterize	VERB
ajst-27521	23	12	by	by	ADP
ajst-27521	23	13	its	its	PRON
ajst-27521	23	14	continuity	continuity	NOUN
ajst-27521	23	15	and	and	CCONJ
ajst-27521	23	16	complexity	complexity	NOUN
ajst-27521	23	17	.	.	PUNCT
ajst-27521	24	1	in	in	ADP
ajst-27521	24	2	the	the	DET
ajst-27521	24	3	vast	vast	ADJ
ajst-27521	24	4	amount	amount	NOUN
ajst-27521	24	5	of	of	ADP
ajst-27521	24	6	data	datum	NOUN
ajst-27521	24	7	,	,	PUNCT
ajst-27521	24	8	accurately	accurately	ADV
ajst-27521	24	9	and	and	CCONJ
ajst-27521	24	10	efficiently	efficiently	ADV
ajst-27521	24	11	detecting	detect	VERB
ajst-27521	24	12	anomalies	anomaly	NOUN
ajst-27521	24	13	is	be	AUX
ajst-27521	24	14	crucial	crucial	ADJ
ajst-27521	24	15	for	for	ADP
ajst-27521	24	16	system	system	NOUN
ajst-27521	24	17	safety	safety	NOUN
ajst-27521	24	18	.	.	PUNCT
ajst-27521	25	1	currently	currently	ADV
ajst-27521	25	2	,	,	PUNCT
ajst-27521	25	3	time	time	NOUN
ajst-27521	25	4	series	series	PROPN
ajst-27521	25	5	modeling	modeling	NOUN
ajst-27521	25	6	methods	method	NOUN
ajst-27521	25	7	based	base	VERB
ajst-27521	25	8	on	on	ADP
ajst-27521	25	9	deep	deep	ADJ
ajst-27521	25	10	learning	learning	NOUN
ajst-27521	25	11	have	have	AUX
ajst-27521	25	12	shown	show	VERB
ajst-27521	25	13	good	good	ADJ
ajst-27521	25	14	results	result	NOUN
ajst-27521	25	15	in	in	ADP
ajst-27521	25	16	real	real	ADJ
ajst-27521	25	17	-	-	PUNCT
ajst-27521	25	18	time	time	NOUN
ajst-27521	25	19	status	status	NOUN
ajst-27521	25	20	detection	detection	NOUN
ajst-27521	25	21	,	,	PUNCT
ajst-27521	25	22	fault	fault	NOUN
ajst-27521	25	23	detection	detection	NOUN
ajst-27521	25	24	,	,	PUNCT
ajst-27521	25	25	and	and	CCONJ
ajst-27521	25	26	have	have	AUX
ajst-27521	25	27	effectively	effectively	ADV
ajst-27521	25	28	improved	improve	VERB
ajst-27521	25	29	operational	operational	ADJ
ajst-27521	25	30	efficiency	efficiency	NOUN
ajst-27521	25	31	.	.	PUNCT
ajst-27521	26	1	against	against	ADP
ajst-27521	26	2	this	this	DET
ajst-27521	26	3	backdrop	backdrop	NOUN
ajst-27521	26	4	,	,	PUNCT
ajst-27521	26	5	a	a	DET
ajst-27521	26	6	large	large	ADJ
ajst-27521	26	7	number	number	NOUN
ajst-27521	26	8	of	of	ADP
ajst-27521	26	9	deep	deep	ADJ
ajst-27521	26	10	learning	learning	NOUN
ajst-27521	26	11	research	research	NOUN
ajst-27521	26	12	results	result	NOUN
ajst-27521	26	13	have	have	AUX
ajst-27521	26	14	emerged	emerge	VERB
ajst-27521	26	15	in	in	ADP
ajst-27521	26	16	the	the	DET
ajst-27521	26	17	field	field	NOUN
ajst-27521	26	18	of	of	ADP
ajst-27521	26	19	time	time	NOUN
ajst-27521	26	20	series	series	PROPN
ajst-27521	26	21	anomaly	anomaly	PROPN
ajst-27521	26	22	detection	detection	NOUN
ajst-27521	26	23	,	,	PUNCT
ajst-27521	26	24	while	while	SCONJ
ajst-27521	26	25	also	also	ADV
ajst-27521	26	26	facing	face	VERB
ajst-27521	26	27	many	many	ADJ
ajst-27521	26	28	challenges	challenge	NOUN
ajst-27521	26	29	:	:	PUNCT
ajst-27521	26	30	(	(	PUNCT
ajst-27521	26	31	1	1	X
ajst-27521	26	32	)	)	PUNCT
ajst-27521	26	33	the	the	DET
ajst-27521	26	34	recall	recall	NOUN
ajst-27521	26	35	rate	rate	NOUN
ajst-27521	26	36	of	of	ADP
ajst-27521	26	37	anomaly	anomaly	NOUN
ajst-27521	26	38	detection	detection	NOUN
ajst-27521	26	39	is	be	AUX
ajst-27521	26	40	low	low	ADJ
ajst-27521	26	41	.	.	PUNCT
ajst-27521	27	1	due	due	ADP
ajst-27521	27	2	to	to	ADP
ajst-27521	27	3	the	the	DET
ajst-27521	27	4	rarity	rarity	NOUN
ajst-27521	27	5	and	and	CCONJ
ajst-27521	27	6	diversity	diversity	NOUN
ajst-27521	27	7	of	of	ADP
ajst-27521	27	8	anomalous	anomalous	ADJ
ajst-27521	27	9	events	event	NOUN
ajst-27521	27	10	,	,	PUNCT
ajst-27521	27	11	it	it	PRON
ajst-27521	27	12	is	be	AUX
ajst-27521	27	13	difficult	difficult	ADJ
ajst-27521	27	14	to	to	PART
ajst-27521	27	15	identify	identify	VERB
ajst-27521	27	16	all	all	DET
ajst-27521	27	17	anomalies	anomaly	NOUN
ajst-27521	27	18	.	.	PUNCT
ajst-27521	28	1	even	even	ADV
ajst-27521	28	2	many	many	ADJ
ajst-27521	28	3	normal	normal	ADJ
ajst-27521	28	4	instances	instance	NOUN
ajst-27521	28	5	are	be	AUX
ajst-27521	28	6	misclassified	misclassifie	VERB
ajst-27521	28	7	as	as	ADP
ajst-27521	28	8	anomalies	anomaly	NOUN
ajst-27521	28	9	,	,	PUNCT
ajst-27521	28	10	while	while	SCONJ
ajst-27521	28	11	true	true	ADJ
ajst-27521	28	12	anomalies	anomaly	NOUN
ajst-27521	28	13	are	be	AUX
ajst-27521	28	14	overlooked	overlook	VERB
ajst-27521	28	15	.	.	PUNCT
ajst-27521	29	1	existing	exist	VERB
ajst-27521	29	2	methods	method	NOUN
ajst-27521	29	3	have	have	VERB
ajst-27521	29	4	a	a	DET
ajst-27521	29	5	high	high	ADJ
ajst-27521	29	6	false	false	ADJ
ajst-27521	29	7	positive	positive	ADJ
ajst-27521	29	8	rate	rate	NOUN
ajst-27521	29	9	,	,	PUNCT
ajst-27521	29	10	and	and	CCONJ
ajst-27521	29	11	reducing	reduce	VERB
ajst-27521	29	12	this	this	DET
ajst-27521	29	13	rate	rate	NOUN
ajst-27521	29	14	and	and	CCONJ
ajst-27521	29	15	improving	improve	VERB
ajst-27521	29	16	the	the	DET
ajst-27521	29	17	recall	recall	NOUN
ajst-27521	29	18	rate	rate	NOUN
ajst-27521	29	19	is	be	AUX
ajst-27521	29	20	an	an	DET
ajst-27521	29	21	important	important	ADJ
ajst-27521	29	22	challenge	challenge	NOUN
ajst-27521	29	23	.	.	PUNCT
ajst-27521	30	1	(	(	PUNCT
ajst-27521	30	2	2	2	X
ajst-27521	30	3	)	)	PUNCT
ajst-27521	30	4	insufficient	insufficient	ADJ
ajst-27521	30	5	high	high	ADV
ajst-27521	30	6	-	-	PUNCT
ajst-27521	30	7	dimensional	dimensional	ADJ
ajst-27521	30	8	data	datum	NOUN
ajst-27521	30	9	processing	processing	NOUN
ajst-27521	30	10	capability	capability	NOUN
ajst-27521	30	11	.	.	PUNCT
ajst-27521	31	1	in	in	ADP
ajst-27521	31	2	low	low	ADJ
ajst-27521	31	3	-	-	PUNCT
ajst-27521	31	4	dimensional	dimensional	ADJ
ajst-27521	31	5	space	space	NOUN
ajst-27521	31	6	,	,	PUNCT
ajst-27521	31	7	anomalous	anomalous	ADJ
ajst-27521	31	8	features	feature	NOUN
ajst-27521	31	9	are	be	AUX
ajst-27521	31	10	more	more	ADV
ajst-27521	31	11	apparent	apparent	ADJ
ajst-27521	31	12	,	,	PUNCT
ajst-27521	31	13	but	but	CCONJ
ajst-27521	31	14	in	in	ADP
ajst-27521	31	15	high	high	ADJ
ajst-27521	31	16	-	-	PUNCT
ajst-27521	31	17	dimensional	dimensional	ADJ
ajst-27521	31	18	space	space	NOUN
ajst-27521	31	19	,	,	PUNCT
ajst-27521	31	20	they	they	PRON
ajst-27521	31	21	become	become	VERB
ajst-27521	31	22	hidden	hidden	ADJ
ajst-27521	31	23	and	and	CCONJ
ajst-27521	31	24	less	less	ADV
ajst-27521	31	25	obvious	obvious	ADJ
ajst-27521	31	26	.	.	PUNCT
ajst-27521	32	1	this	this	PRON
ajst-27521	32	2	increases	increase	VERB
ajst-27521	32	3	the	the	DET
ajst-27521	32	4	difficulty	difficulty	NOUN
ajst-27521	32	5	of	of	ADP
ajst-27521	32	6	anomaly	anomaly	NOUN
ajst-27521	32	7	detection	detection	NOUN
ajst-27521	32	8	.	.	PUNCT
ajst-27521	33	1	(	(	PUNCT
ajst-27521	33	2	3	3	X
ajst-27521	33	3	)	)	PUNCT
ajst-27521	33	4	susceptibility	susceptibility	NOUN
ajst-27521	33	5	to	to	PART
ajst-27521	33	6	noise	noise	VERB
ajst-27521	33	7	.	.	PUNCT
ajst-27521	34	1	many	many	ADJ
ajst-27521	34	2	weakly	weakly	ADV
ajst-27521	34	3	supervised	supervised	ADJ
ajst-27521	34	4	or	or	CCONJ
ajst-27521	34	5	semi	semi	ADJ
ajst-27521	34	6	-	-	ADJ
ajst-27521	34	7	supervised	supervised	ADJ
ajst-27521	34	8	anomaly	anomaly	NOUN
ajst-27521	34	9	detection	detection	NOUN
ajst-27521	34	10	methods	method	NOUN
ajst-27521	34	11	assume	assume	VERB
ajst-27521	34	12	that	that	SCONJ
ajst-27521	34	13	the	the	DET
ajst-27521	34	14	labels	label	NOUN
ajst-27521	34	15	in	in	ADP
ajst-27521	34	16	the	the	DET
ajst-27521	34	17	training	training	NOUN
ajst-27521	34	18	data	datum	NOUN
ajst-27521	34	19	are	be	AUX
ajst-27521	34	20	correct	correct	ADJ
ajst-27521	34	21	,	,	PUNCT
ajst-27521	34	22	but	but	CCONJ
ajst-27521	34	23	such	such	ADJ
ajst-27521	34	24	data	datum	NOUN
ajst-27521	34	25	may	may	AUX
ajst-27521	34	26	contain	contain	VERB
ajst-27521	34	27	noise	noise	NOUN
ajst-27521	34	28	samples	sample	NOUN
ajst-27521	34	29	that	that	PRON
ajst-27521	34	30	are	be	AUX
ajst-27521	34	31	incorrectly	incorrectly	ADV
ajst-27521	34	32	marked	mark	VERB
ajst-27521	34	33	as	as	ADP
ajst-27521	34	34	another	another	DET
ajst-27521	34	35	category	category	NOUN
ajst-27521	34	36	,	,	PUNCT
ajst-27521	34	37	thereby	thereby	ADV
ajst-27521	34	38	affecting	affect	VERB
ajst-27521	34	39	the	the	DET
ajst-27521	34	40	detection	detection	NOUN
ajst-27521	34	41	results	result	NOUN
ajst-27521	34	42	.	.	PUNCT
ajst-27521	35	1	(	(	PUNCT
ajst-27521	35	2	4	4	X
ajst-27521	35	3	)	)	PUNCT
ajst-27521	35	4	limitations	limitation	NOUN
ajst-27521	35	5	in	in	ADP
ajst-27521	35	6	model	model	NOUN
ajst-27521	35	7	training	training	NOUN
ajst-27521	35	8	time	time	NOUN
ajst-27521	35	9	and	and	CCONJ
ajst-27521	35	10	computational	computational	ADJ
ajst-27521	35	11	resources	resource	NOUN
ajst-27521	35	12	.	.	PUNCT
ajst-27521	36	1	anomaly	anomaly	NOUN
ajst-27521	36	2	detection	detection	NOUN
ajst-27521	36	3	methods	method	NOUN
ajst-27521	36	4	based	base	VERB
ajst-27521	36	5	on	on	ADP
ajst-27521	36	6	deep	deep	ADJ
ajst-27521	36	7	learning	learning	NOUN
ajst-27521	36	8	typically	typically	ADV
ajst-27521	36	9	require	require	VERB
ajst-27521	36	10	longer	long	ADJ
ajst-27521	36	11	training	training	NOUN
ajst-27521	36	12	times	time	NOUN
ajst-27521	36	13	and	and	CCONJ
ajst-27521	36	14	a	a	DET
ajst-27521	36	15	large	large	ADJ
ajst-27521	36	16	amount	amount	NOUN
ajst-27521	36	17	of	of	ADP
ajst-27521	36	18	computational	computational	ADJ
ajst-27521	36	19	resources	resource	NOUN
ajst-27521	36	20	,	,	PUNCT
ajst-27521	36	21	which	which	PRON
ajst-27521	36	22	can	can	AUX
ajst-27521	36	23	be	be	AUX
ajst-27521	36	24	a	a	DET
ajst-27521	36	25	limiting	limit	VERB
ajst-27521	36	26	factor	factor	NOUN
ajst-27521	36	27	in	in	ADP
ajst-27521	36	28	situations	situation	NOUN
ajst-27521	36	29	with	with	ADP
ajst-27521	36	30	limited	limited	ADJ
ajst-27521	36	31	resources	resource	NOUN
ajst-27521	36	32	.	.	PUNCT
ajst-27521	37	1	166	166	NUM
ajst-27521	37	2	3	3	NUM
ajst-27521	37	3	.	.	PUNCT
ajst-27521	37	4	classification	classification	NOUN
ajst-27521	37	5	of	of	ADP
ajst-27521	37	6	anomaly	anomaly	NOUN
ajst-27521	37	7	detection	detection	NOUN
ajst-27521	37	8	methods	method	NOUN
ajst-27521	37	9	the	the	DET
ajst-27521	37	10	anomaly	anomaly	NOUN
ajst-27521	37	11	detection	detection	NOUN
ajst-27521	37	12	method	method	NOUN
ajst-27521	37	13	can	can	AUX
ajst-27521	37	14	be	be	AUX
ajst-27521	37	15	divided	divide	VERB
ajst-27521	37	16	into	into	ADP
ajst-27521	37	17	three	three	NUM
ajst-27521	37	18	categories	category	NOUN
ajst-27521	37	19	,	,	PUNCT
ajst-27521	37	20	supervised	supervised	ADJ
ajst-27521	37	21	,	,	PUNCT
ajst-27521	37	22	unsupervised	unsupervised	ADJ
ajst-27521	37	23	and	and	CCONJ
ajst-27521	37	24	semi	semi	ADJ
ajst-27521	37	25	-	-	ADJ
ajst-27521	37	26	supervised	supervised	ADJ
ajst-27521	37	27	according	accord	VERB
ajst-27521	37	28	to	to	ADP
ajst-27521	37	29	whether	whether	SCONJ
ajst-27521	37	30	the	the	DET
ajst-27521	37	31	data	datum	NOUN
ajst-27521	37	32	set	set	NOUN
ajst-27521	37	33	has	have	AUX
ajst-27521	37	34	labels	label	NOUN
ajst-27521	37	35	:	:	PUNCT
ajst-27521	37	36	(	(	PUNCT
ajst-27521	37	37	1	1	X
ajst-27521	37	38	)	)	PUNCT
ajst-27521	37	39	supervised	supervise	VERB
ajst-27521	37	40	anomaly	anomaly	NOUN
ajst-27521	37	41	detection	detection	NOUN
ajst-27521	37	42	means	mean	VERB
ajst-27521	37	43	that	that	SCONJ
ajst-27521	37	44	all	all	DET
ajst-27521	37	45	the	the	DET
ajst-27521	37	46	data	datum	NOUN
ajst-27521	37	47	in	in	ADP
ajst-27521	37	48	the	the	DET
ajst-27521	37	49	training	training	NOUN
ajst-27521	37	50	set	set	NOUN
ajst-27521	37	51	have	have	VERB
ajst-27521	37	52	labels	label	NOUN
ajst-27521	37	53	,	,	PUNCT
ajst-27521	37	54	but	but	CCONJ
ajst-27521	37	55	it	it	PRON
ajst-27521	37	56	is	be	AUX
ajst-27521	37	57	very	very	ADV
ajst-27521	37	58	difficult	difficult	ADJ
ajst-27521	37	59	to	to	PART
ajst-27521	37	60	obtain	obtain	VERB
ajst-27521	37	61	the	the	DET
ajst-27521	37	62	accurate	accurate	ADJ
ajst-27521	37	63	labeled	label	VERB
ajst-27521	37	64	training	training	NOUN
ajst-27521	37	65	data	datum	NOUN
ajst-27521	37	66	set	set	VERB
ajst-27521	37	67	,	,	PUNCT
ajst-27521	37	68	which	which	PRON
ajst-27521	37	69	usually	usually	ADV
ajst-27521	37	70	requires	require	VERB
ajst-27521	37	71	manual	manual	ADJ
ajst-27521	37	72	annotation	annotation	NOUN
ajst-27521	37	73	,	,	PUNCT
ajst-27521	37	74	so	so	SCONJ
ajst-27521	37	75	the	the	DET
ajst-27521	37	76	application	application	NOUN
ajst-27521	37	77	scope	scope	NOUN
ajst-27521	37	78	of	of	ADP
ajst-27521	37	79	supervised	supervised	ADJ
ajst-27521	37	80	anomaly	anomaly	NOUN
ajst-27521	37	81	detection	detection	NOUN
ajst-27521	37	82	is	be	AUX
ajst-27521	37	83	small	small	ADJ
ajst-27521	37	84	;	;	PUNCT
ajst-27521	37	85	(	(	PUNCT
ajst-27521	37	86	2	2	X
ajst-27521	37	87	)	)	PUNCT
ajst-27521	37	88	unsupervised	unsupervised	ADJ
ajst-27521	37	89	anomaly	anomaly	NOUN
ajst-27521	37	90	detection	detection	NOUN
ajst-27521	37	91	means	mean	VERB
ajst-27521	37	92	that	that	SCONJ
ajst-27521	37	93	the	the	DET
ajst-27521	37	94	data	datum	NOUN
ajst-27521	37	95	in	in	ADP
ajst-27521	37	96	the	the	DET
ajst-27521	37	97	training	training	NOUN
ajst-27521	37	98	set	set	NOUN
ajst-27521	37	99	are	be	AUX
ajst-27521	37	100	unmarked	unmarked	ADJ
ajst-27521	37	101	,	,	PUNCT
ajst-27521	37	102	and	and	CCONJ
ajst-27521	37	103	unsupervised	unsupervised	ADJ
ajst-27521	37	104	anomaly	anomaly	NOUN
ajst-27521	37	105	detection	detection	NOUN
ajst-27521	37	106	is	be	AUX
ajst-27521	37	107	widespread	widespread	ADJ
ajst-27521	37	108	;	;	PUNCT
ajst-27521	37	109	semi	semi	ADJ
ajst-27521	37	110	-	-	ADJ
ajst-27521	37	111	supervised	supervised	ADJ
ajst-27521	37	112	abnormality	abnormality	NOUN
ajst-27521	37	113	detection	detection	NOUN
ajst-27521	37	114	means	mean	VERB
ajst-27521	37	115	that	that	SCONJ
ajst-27521	37	116	some	some	DET
ajst-27521	37	117	samples	sample	NOUN
ajst-27521	37	118	in	in	ADP
ajst-27521	37	119	the	the	DET
ajst-27521	37	120	training	training	NOUN
ajst-27521	37	121	set	set	NOUN
ajst-27521	37	122	have	have	VERB
ajst-27521	37	123	labels	label	NOUN
ajst-27521	37	124	,	,	PUNCT
ajst-27521	37	125	and	and	CCONJ
ajst-27521	37	126	the	the	DET
ajst-27521	37	127	use	use	NOUN
ajst-27521	37	128	of	of	ADP
ajst-27521	37	129	labeled	label	VERB
ajst-27521	37	130	data	datum	NOUN
ajst-27521	37	131	can	can	AUX
ajst-27521	37	132	improve	improve	VERB
ajst-27521	37	133	the	the	DET
ajst-27521	37	134	detection	detection	NOUN
ajst-27521	37	135	performance	performance	NOUN
ajst-27521	37	136	of	of	ADP
ajst-27521	37	137	the	the	DET
ajst-27521	37	138	model	model	NOUN
ajst-27521	37	139	.	.	PUNCT
ajst-27521	38	1	therefore	therefore	ADV
ajst-27521	38	2	,	,	PUNCT
ajst-27521	38	3	the	the	DET
ajst-27521	38	4	semi	semi	ADJ
ajst-27521	38	5	-	-	ADJ
ajst-27521	38	6	supervised	supervised	ADJ
ajst-27521	38	7	anomaly	anomaly	NOUN
ajst-27521	38	8	detection	detection	NOUN
ajst-27521	38	9	is	be	AUX
ajst-27521	38	10	also	also	ADV
ajst-27521	38	11	widely	widely	ADV
ajst-27521	38	12	used	use	VERB
ajst-27521	38	13	.	.	PUNCT
ajst-27521	39	1	anomaly	anomaly	PROPN
ajst-27521	39	2	detection	detection	NOUN
ajst-27521	39	3	can	can	AUX
ajst-27521	39	4	be	be	AUX
ajst-27521	39	5	roughly	roughly	ADV
ajst-27521	39	6	divided	divide	VERB
ajst-27521	39	7	into	into	ADP
ajst-27521	39	8	three	three	NUM
ajst-27521	39	9	categories	category	NOUN
ajst-27521	39	10	according	accord	VERB
ajst-27521	39	11	to	to	ADP
ajst-27521	39	12	the	the	DET
ajst-27521	39	13	way	way	NOUN
ajst-27521	39	14	of	of	ADP
ajst-27521	39	15	processing	processing	NOUN
ajst-27521	39	16	data	datum	NOUN
ajst-27521	39	17	:	:	PUNCT
ajst-27521	39	18	statistical	statistical	ADJ
ajst-27521	39	19	based	base	VERB
ajst-27521	39	20	methods	method	NOUN
ajst-27521	39	21	,	,	PUNCT
ajst-27521	39	22	machine	machine	NOUN
ajst-27521	39	23	learning	learning	NOUN
ajst-27521	39	24	based	base	VERB
ajst-27521	39	25	methods	method	NOUN
ajst-27521	39	26	and	and	CCONJ
ajst-27521	39	27	deep	deep	ADJ
ajst-27521	39	28	learning	learning	NOUN
ajst-27521	39	29	based	base	VERB
ajst-27521	39	30	methods	method	NOUN
ajst-27521	39	31	.	.	PUNCT
ajst-27521	40	1	3.1	3.1	NUM
ajst-27521	40	2	statistics	statistic	NOUN
ajst-27521	40	3	-	-	PUNCT
ajst-27521	40	4	based	base	VERB
ajst-27521	40	5	methods	method	NOUN
ajst-27521	40	6	the	the	DET
ajst-27521	40	7	advantages	advantage	NOUN
ajst-27521	40	8	of	of	ADP
ajst-27521	40	9	statistics	statistic	NOUN
ajst-27521	40	10	-	-	PUNCT
ajst-27521	40	11	based	base	VERB
ajst-27521	40	12	anomaly	anomaly	NOUN
ajst-27521	40	13	detection	detection	NOUN
ajst-27521	40	14	methods	method	NOUN
ajst-27521	40	15	are	be	AUX
ajst-27521	40	16	low	low	ADJ
ajst-27521	40	17	complexity	complexity	NOUN
ajst-27521	40	18	and	and	CCONJ
ajst-27521	40	19	fast	fast	ADJ
ajst-27521	40	20	calculation	calculation	NOUN
ajst-27521	40	21	speed	speed	NOUN
ajst-27521	40	22	,	,	PUNCT
ajst-27521	40	23	which	which	PRON
ajst-27521	40	24	are	be	AUX
ajst-27521	40	25	suitable	suitable	ADJ
ajst-27521	40	26	for	for	ADP
ajst-27521	40	27	scenarios	scenario	NOUN
ajst-27521	40	28	without	without	ADP
ajst-27521	40	29	historical	historical	ADJ
ajst-27521	40	30	data	datum	NOUN
ajst-27521	40	31	.	.	PUNCT
ajst-27521	41	1	this	this	DET
ajst-27521	41	2	method	method	NOUN
ajst-27521	41	3	ignores	ignore	VERB
ajst-27521	41	4	the	the	DET
ajst-27521	41	5	temporal	temporal	ADJ
ajst-27521	41	6	indexing	indexing	NOUN
ajst-27521	41	7	in	in	ADP
ajst-27521	41	8	the	the	DET
ajst-27521	41	9	time	time	NOUN
ajst-27521	41	10	series	series	NOUN
ajst-27521	41	11	and	and	CCONJ
ajst-27521	41	12	takes	take	VERB
ajst-27521	41	13	the	the	DET
ajst-27521	41	14	points	point	NOUN
ajst-27521	41	15	in	in	ADP
ajst-27521	41	16	the	the	DET
ajst-27521	41	17	time	time	NOUN
ajst-27521	41	18	series	series	NOUN
ajst-27521	41	19	as	as	ADP
ajst-27521	41	20	statistical	statistical	ADJ
ajst-27521	41	21	sample	sample	NOUN
ajst-27521	41	22	points	point	NOUN
ajst-27521	41	23	.	.	PUNCT
ajst-27521	42	1	assuming	assume	VERB
ajst-27521	42	2	that	that	SCONJ
ajst-27521	42	3	the	the	DET
ajst-27521	42	4	data	data	NOUN
ajst-27521	42	5	follows	follow	VERB
ajst-27521	42	6	some	some	DET
ajst-27521	42	7	statistical	statistical	ADJ
ajst-27521	42	8	model	model	NOUN
ajst-27521	42	9	,	,	PUNCT
ajst-27521	42	10	the	the	DET
ajst-27521	42	11	data	data	NOUN
ajst-27521	42	12	points	point	VERB
ajst-27521	42	13	with	with	ADP
ajst-27521	42	14	a	a	DET
ajst-27521	42	15	small	small	ADJ
ajst-27521	42	16	probability	probability	NOUN
ajst-27521	42	17	of	of	ADP
ajst-27521	42	18	occurrence	occurrence	NOUN
ajst-27521	42	19	are	be	AUX
ajst-27521	42	20	anomalies	anomaly	NOUN
ajst-27521	42	21	.	.	PUNCT
ajst-27521	43	1	the	the	DET
ajst-27521	43	2	statistics	statistic	NOUN
ajst-27521	43	3	-	-	PUNCT
ajst-27521	43	4	based	base	VERB
ajst-27521	43	5	abnormality	abnormality	NOUN
ajst-27521	43	6	detection	detection	NOUN
ajst-27521	43	7	algorithm	algorithm	NOUN
ajst-27521	43	8	[	[	X
ajst-27521	43	9	5	5	NUM
ajst-27521	43	10	]	]	PUNCT
ajst-27521	43	11	is	be	AUX
ajst-27521	43	12	an	an	DET
ajst-27521	43	13	early	early	ADJ
ajst-27521	43	14	and	and	CCONJ
ajst-27521	43	15	mature	mature	ADJ
ajst-27521	43	16	technology	technology	NOUN
ajst-27521	43	17	,	,	PUNCT
ajst-27521	43	18	and	and	CCONJ
ajst-27521	43	19	n	n	CCONJ
ajst-27521	43	20	-	-	PUNCT
ajst-27521	43	21	sigma	sigma	NOUN
ajst-27521	43	22	and	and	CCONJ
ajst-27521	43	23	boxplot	boxplot	NOUN
ajst-27521	43	24	are	be	AUX
ajst-27521	43	25	the	the	DET
ajst-27521	43	26	most	most	ADV
ajst-27521	43	27	common	common	ADJ
ajst-27521	43	28	statistical	statistical	ADJ
ajst-27521	43	29	methods	method	NOUN
ajst-27521	43	30	.	.	PUNCT
ajst-27521	44	1	the	the	DET
ajst-27521	44	2	n	n	NOUN
ajst-27521	44	3	-	-	PUNCT
ajst-27521	44	4	sigma	sigma	PROPN
ajst-27521	44	5	assumes	assume	VERB
ajst-27521	44	6	that	that	SCONJ
ajst-27521	44	7	the	the	DET
ajst-27521	44	8	data	datum	NOUN
ajst-27521	44	9	follow	follow	VERB
ajst-27521	44	10	a	a	DET
ajst-27521	44	11	normal	normal	ADJ
ajst-27521	44	12	distribution	distribution	NOUN
ajst-27521	44	13	,	,	PUNCT
ajst-27521	44	14	and	and	CCONJ
ajst-27521	44	15	the	the	DET
ajst-27521	44	16	data	datum	NOUN
ajst-27521	44	17	whose	whose	DET
ajst-27521	44	18	distance	distance	NOUN
ajst-27521	44	19	from	from	ADP
ajst-27521	44	20	the	the	DET
ajst-27521	44	21	mean	mean	NOUN
ajst-27521	44	22	exceeds	exceed	NOUN
ajst-27521	44	23	n	n	PRON
ajst-27521	44	24	times	time	NOUN
ajst-27521	44	25	the	the	DET
ajst-27521	44	26	standard	standard	ADJ
ajst-27521	44	27	deviation	deviation	NOUN
ajst-27521	44	28	are	be	AUX
ajst-27521	44	29	labeled	label	VERB
ajst-27521	44	30	as	as	ADV
ajst-27521	44	31	abnormal	abnormal	ADJ
ajst-27521	44	32	.	.	PUNCT
ajst-27521	45	1	classical	classical	ADJ
ajst-27521	45	2	parametric	parametric	ADJ
ajst-27521	45	3	statistical	statistical	ADJ
ajst-27521	45	4	models	model	NOUN
ajst-27521	45	5	such	such	ADJ
ajst-27521	45	6	as	as	ADP
ajst-27521	45	7	mobile	mobile	ADJ
ajst-27521	45	8	autoregressive	autoregressive	ADJ
ajst-27521	45	9	models	model	NOUN
ajst-27521	45	10	also	also	ADV
ajst-27521	45	11	perform	perform	VERB
ajst-27521	45	12	anomaly	anomaly	NOUN
ajst-27521	45	13	detection	detection	NOUN
ajst-27521	45	14	by	by	ADP
ajst-27521	45	15	assuming	assume	VERB
ajst-27521	45	16	that	that	SCONJ
ajst-27521	45	17	the	the	DET
ajst-27521	45	18	basic	basic	ADJ
ajst-27521	45	19	distribution	distribution	NOUN
ajst-27521	45	20	of	of	ADP
ajst-27521	45	21	normal	normal	ADJ
ajst-27521	45	22	data	datum	NOUN
ajst-27521	45	23	fits	fit	VERB
ajst-27521	45	24	the	the	DET
ajst-27521	45	25	preset	preset	ADJ
ajst-27521	45	26	distribution	distribution	NOUN
ajst-27521	45	27	.	.	PUNCT
ajst-27521	46	1	the	the	DET
ajst-27521	46	2	gaussian	gaussian	ADJ
ajst-27521	46	3	mixture	mixture	NOUN
ajst-27521	46	4	model	model	NOUN
ajst-27521	46	5	estimates	estimate	VERB
ajst-27521	46	6	the	the	DET
ajst-27521	46	7	probability	probability	NOUN
ajst-27521	46	8	density	density	NOUN
ajst-27521	46	9	of	of	ADP
ajst-27521	46	10	normal	normal	ADJ
ajst-27521	46	11	class	class	NOUN
ajst-27521	46	12	data	datum	NOUN
ajst-27521	46	13	,	,	PUNCT
ajst-27521	46	14	and	and	CCONJ
ajst-27521	46	15	introduces	introduce	VERB
ajst-27521	46	16	a	a	DET
ajst-27521	46	17	confidence	confidence	NOUN
ajst-27521	46	18	measure	measure	NOUN
ajst-27521	46	19	to	to	PART
ajst-27521	46	20	estimate	estimate	VERB
ajst-27521	46	21	the	the	DET
ajst-27521	46	22	reliability	reliability	NOUN
ajst-27521	46	23	of	of	ADP
ajst-27521	46	24	normal	normal	ADJ
ajst-27521	46	25	data	datum	NOUN
ajst-27521	46	26	.	.	PUNCT
ajst-27521	47	1	when	when	SCONJ
ajst-27521	47	2	the	the	DET
ajst-27521	47	3	confidence	confidence	NOUN
ajst-27521	47	4	takes	take	VERB
ajst-27521	47	5	a	a	DET
ajst-27521	47	6	large	large	ADJ
ajst-27521	47	7	value	value	NOUN
ajst-27521	47	8	,	,	PUNCT
ajst-27521	47	9	the	the	DET
ajst-27521	47	10	algorithm	algorithm	NOUN
ajst-27521	47	11	updates	update	VERB
ajst-27521	47	12	the	the	DET
ajst-27521	47	13	parameter	parameter	NOUN
ajst-27521	47	14	once	once	ADV
ajst-27521	47	15	.	.	PUNCT
ajst-27521	48	1	non	non	ADJ
ajst-27521	48	2	-	-	ADJ
ajst-27521	48	3	parametric	parametric	ADJ
ajst-27521	48	4	statistical	statistical	ADJ
ajst-27521	48	5	models	model	NOUN
ajst-27521	48	6	are	be	AUX
ajst-27521	48	7	based	base	VERB
ajst-27521	48	8	on	on	ADP
ajst-27521	48	9	kernel	kernel	PROPN
ajst-27521	48	10	functions	function	NOUN
ajst-27521	48	11	,	,	PUNCT
ajst-27521	48	12	and	and	CCONJ
ajst-27521	48	13	they	they	PRON
ajst-27521	48	14	learn	learn	VERB
ajst-27521	48	15	the	the	DET
ajst-27521	48	16	underlying	underlie	VERB
ajst-27521	48	17	distribution	distribution	NOUN
ajst-27521	48	18	of	of	ADP
ajst-27521	48	19	normal	normal	ADJ
ajst-27521	48	20	behavior	behavior	NOUN
ajst-27521	48	21	directly	directly	ADV
ajst-27521	48	22	from	from	ADP
ajst-27521	48	23	a	a	DET
ajst-27521	48	24	given	give	VERB
ajst-27521	48	25	data	datum	NOUN
ajst-27521	48	26	,	,	PUNCT
ajst-27521	48	27	but	but	CCONJ
ajst-27521	48	28	are	be	AUX
ajst-27521	48	29	difficult	difficult	ADJ
ajst-27521	48	30	for	for	ADP
ajst-27521	48	31	the	the	DET
ajst-27521	48	32	processing	processing	NOUN
ajst-27521	48	33	of	of	ADP
ajst-27521	48	34	high	high	ADJ
ajst-27521	48	35	-	-	PUNCT
ajst-27521	48	36	dimensional	dimensional	ADJ
ajst-27521	48	37	data	datum	NOUN
ajst-27521	48	38	.	.	PUNCT
ajst-27521	49	1	statistics	statistic	NOUN
ajst-27521	49	2	-	-	PUNCT
ajst-27521	49	3	based	base	VERB
ajst-27521	49	4	methods	method	NOUN
ajst-27521	49	5	are	be	AUX
ajst-27521	49	6	highly	highly	ADV
ajst-27521	49	7	hypothetical	hypothetical	ADJ
ajst-27521	49	8	,	,	PUNCT
ajst-27521	49	9	and	and	CCONJ
ajst-27521	49	10	the	the	DET
ajst-27521	49	11	efficiency	efficiency	NOUN
ajst-27521	49	12	of	of	ADP
ajst-27521	49	13	abnormality	abnormality	NOUN
ajst-27521	49	14	detection	detection	NOUN
ajst-27521	49	15	will	will	AUX
ajst-27521	49	16	decrease	decrease	VERB
ajst-27521	49	17	when	when	SCONJ
ajst-27521	49	18	the	the	DET
ajst-27521	49	19	amount	amount	NOUN
ajst-27521	49	20	of	of	ADP
ajst-27521	49	21	data	datum	NOUN
ajst-27521	49	22	and	and	CCONJ
ajst-27521	49	23	dimensionality	dimensionality	NOUN
ajst-27521	49	24	increase	increase	NOUN
ajst-27521	49	25	.	.	PUNCT
ajst-27521	50	1	3.2	3.2	NUM
ajst-27521	50	2	machine	machine	NOUN
ajst-27521	50	3	learning	learning	NOUN
ajst-27521	50	4	-	-	PUNCT
ajst-27521	50	5	based	base	VERB
ajst-27521	50	6	methods	method	NOUN
ajst-27521	50	7	machine	machine	NOUN
ajst-27521	50	8	learning	learning	NOUN
ajst-27521	50	9	-	-	PUNCT
ajst-27521	50	10	based	base	VERB
ajst-27521	50	11	anomaly	anomaly	NOUN
ajst-27521	50	12	detection	detection	NOUN
ajst-27521	50	13	algorithms	algorithm	NOUN
ajst-27521	50	14	can	can	AUX
ajst-27521	50	15	be	be	AUX
ajst-27521	50	16	classified	classify	VERB
ajst-27521	50	17	into	into	ADP
ajst-27521	50	18	clustering	clustering	NOUN
ajst-27521	50	19	-	-	PUNCT
ajst-27521	50	20	based	base	VERB
ajst-27521	50	21	,	,	PUNCT
ajst-27521	50	22	classification	classification	NOUN
ajst-27521	50	23	-	-	PUNCT
ajst-27521	50	24	based	base	VERB
ajst-27521	50	25	,	,	PUNCT
ajst-27521	50	26	and	and	CCONJ
ajst-27521	50	27	density	density	NOUN
ajst-27521	50	28	-	-	PUNCT
ajst-27521	50	29	based	base	VERB
ajst-27521	50	30	anomaly	anomaly	NOUN
ajst-27521	50	31	detection	detection	NOUN
ajst-27521	50	32	algorithms	algorithm	NOUN
ajst-27521	50	33	.	.	PUNCT
ajst-27521	51	1	clusteringbased	clusteringbase	VERB
ajst-27521	51	2	anomaly	anomaly	NOUN
ajst-27521	51	3	detection	detection	NOUN
ajst-27521	51	4	combines	combine	VERB
ajst-27521	51	5	clustering	cluster	VERB
ajst-27521	51	6	with	with	ADP
ajst-27521	51	7	anomaly	anomaly	NOUN
ajst-27521	51	8	detection	detection	NOUN
ajst-27521	51	9	algorithms	algorithm	NOUN
ajst-27521	51	10	,	,	PUNCT
ajst-27521	51	11	using	use	VERB
ajst-27521	51	12	existing	exist	VERB
ajst-27521	51	13	clustering	clustering	ADJ
ajst-27521	51	14	algorithms	algorithm	NOUN
ajst-27521	51	15	directly	directly	ADV
ajst-27521	51	16	or	or	CCONJ
ajst-27521	51	17	indirectly	indirectly	ADV
ajst-27521	51	18	to	to	PART
ajst-27521	51	19	alleviate	alleviate	VERB
ajst-27521	51	20	the	the	DET
ajst-27521	51	21	problem	problem	NOUN
ajst-27521	51	22	of	of	ADP
ajst-27521	51	23	low	low	ADJ
ajst-27521	51	24	detection	detection	NOUN
ajst-27521	51	25	efficiency	efficiency	NOUN
ajst-27521	51	26	in	in	ADP
ajst-27521	51	27	high	high	ADV
ajst-27521	51	28	-	-	PUNCT
ajst-27521	51	29	dimensional	dimensional	ADJ
ajst-27521	51	30	sparse	sparse	ADJ
ajst-27521	51	31	data	datum	NOUN
ajst-27521	51	32	.	.	PUNCT
ajst-27521	52	1	this	this	DET
ajst-27521	52	2	algorithm	algorithm	NOUN
ajst-27521	52	3	considers	consider	VERB
ajst-27521	52	4	data	datum	NOUN
ajst-27521	52	5	that	that	PRON
ajst-27521	52	6	is	be	AUX
ajst-27521	52	7	isolated	isolate	VERB
ajst-27521	52	8	or	or	CCONJ
ajst-27521	52	9	far	far	ADV
ajst-27521	52	10	from	from	ADP
ajst-27521	52	11	the	the	DET
ajst-27521	52	12	cluster	cluster	NOUN
ajst-27521	52	13	center	center	NOUN
ajst-27521	52	14	as	as	ADP
ajst-27521	52	15	anomalies	anomaly	NOUN
ajst-27521	52	16	[	[	X
ajst-27521	52	17	6	6	NUM
ajst-27521	52	18	-	-	SYM
ajst-27521	52	19	8	8	NUM
ajst-27521	52	20	]	]	PUNCT
ajst-27521	52	21	.	.	PUNCT
ajst-27521	53	1	for	for	ADP
ajst-27521	53	2	example	example	NOUN
ajst-27521	53	3	,	,	PUNCT
ajst-27521	53	4	the	the	DET
ajst-27521	53	5	dbscan	dbscan	ADJ
ajst-27521	53	6	algorithm	algorithm	NOUN
ajst-27521	53	7	clusters	cluster	NOUN
ajst-27521	53	8	data	datum	NOUN
ajst-27521	53	9	in	in	ADP
ajst-27521	53	10	wireless	wireless	ADJ
ajst-27521	53	11	sensor	sensor	NOUN
ajst-27521	53	12	networks	network	NOUN
ajst-27521	53	13	and	and	CCONJ
ajst-27521	53	14	considers	consider	VERB
ajst-27521	53	15	lowdensity	lowdensity	NOUN
ajst-27521	53	16	areas	area	NOUN
ajst-27521	53	17	as	as	ADP
ajst-27521	53	18	anomalies	anomaly	NOUN
ajst-27521	53	19	.	.	PUNCT
ajst-27521	54	1	the	the	DET
ajst-27521	54	2	k	k	NOUN
ajst-27521	54	3	-	-	PUNCT
ajst-27521	54	4	means	means	NOUN
ajst-27521	54	5	algorithm	algorithm	NOUN
ajst-27521	54	6	clusters	cluster	VERB
ajst-27521	54	7	traffic	traffic	NOUN
ajst-27521	54	8	data	datum	NOUN
ajst-27521	54	9	[	[	X
ajst-27521	54	10	9	9	NUM
ajst-27521	54	11	]	]	PUNCT
ajst-27521	54	12	,	,	PUNCT
ajst-27521	54	13	and	and	CCONJ
ajst-27521	54	14	makes	make	VERB
ajst-27521	54	15	judgments	judgment	NOUN
ajst-27521	54	16	on	on	ADP
ajst-27521	54	17	normal	normal	ADJ
ajst-27521	54	18	and	and	CCONJ
ajst-27521	54	19	abnormal	abnormal	ADJ
ajst-27521	54	20	results	result	NOUN
ajst-27521	54	21	based	base	VERB
ajst-27521	54	22	on	on	ADP
ajst-27521	54	23	the	the	DET
ajst-27521	54	24	assumption	assumption	NOUN
ajst-27521	54	25	principle	principle	NOUN
ajst-27521	54	26	.	.	PUNCT
ajst-27521	55	1	anomaly	anomaly	PROPN
ajst-27521	55	2	detection	detection	NOUN
ajst-27521	55	3	based	base	VERB
ajst-27521	55	4	on	on	ADP
ajst-27521	55	5	bayesian	bayesian	NOUN
ajst-27521	55	6	networks	network	NOUN
ajst-27521	55	7	[	[	X
ajst-27521	55	8	10	10	NUM
ajst-27521	55	9	]	]	PUNCT
ajst-27521	55	10	estimates	estimate	VERB
ajst-27521	55	11	the	the	DET
ajst-27521	55	12	posterior	posterior	ADJ
ajst-27521	55	13	probability	probability	NOUN
ajst-27521	55	14	distribution	distribution	NOUN
ajst-27521	55	15	of	of	ADP
ajst-27521	55	16	normal	normal	ADJ
ajst-27521	55	17	and	and	CCONJ
ajst-27521	55	18	abnormal	abnormal	ADJ
ajst-27521	55	19	data	datum	NOUN
ajst-27521	55	20	by	by	ADP
ajst-27521	55	21	constructing	construct	VERB
ajst-27521	55	22	a	a	DET
ajst-27521	55	23	naive	naive	ADJ
ajst-27521	55	24	bayesian	bayesian	NOUN
ajst-27521	55	25	network	network	NOUN
ajst-27521	55	26	,	,	PUNCT
ajst-27521	55	27	aggregates	aggregate	VERB
ajst-27521	55	28	the	the	DET
ajst-27521	55	29	posterior	posterior	ADJ
ajst-27521	55	30	probability	probability	NOUN
ajst-27521	55	31	distribution	distribution	NOUN
ajst-27521	55	32	of	of	ADP
ajst-27521	55	33	data	datum	NOUN
ajst-27521	55	34	attributes	attribute	NOUN
ajst-27521	55	35	,	,	PUNCT
ajst-27521	55	36	and	and	CCONJ
ajst-27521	55	37	extends	extend	VERB
ajst-27521	55	38	the	the	DET
ajst-27521	55	39	univariate	univariate	ADJ
ajst-27521	55	40	classification	classification	NOUN
ajst-27521	55	41	algorithm	algorithm	NOUN
ajst-27521	55	42	to	to	PART
ajst-27521	55	43	multivariate	multivariate	VERB
ajst-27521	55	44	anomaly	anomaly	NOUN
ajst-27521	55	45	detection	detection	NOUN
ajst-27521	55	46	tasks	task	NOUN
ajst-27521	55	47	.	.	PUNCT
ajst-27521	56	1	it	it	PRON
ajst-27521	56	2	has	have	AUX
ajst-27521	56	3	already	already	ADV
ajst-27521	56	4	been	be	AUX
ajst-27521	56	5	applied	apply	VERB
ajst-27521	56	6	in	in	ADP
ajst-27521	56	7	areas	area	NOUN
ajst-27521	56	8	such	such	ADJ
ajst-27521	56	9	as	as	ADP
ajst-27521	56	10	network	network	NOUN
ajst-27521	56	11	intrusion	intrusion	NOUN
ajst-27521	56	12	and	and	CCONJ
ajst-27521	56	13	image	image	NOUN
ajst-27521	56	14	anomaly	anomaly	NOUN
ajst-27521	56	15	detection	detection	NOUN
ajst-27521	56	16	,	,	PUNCT
ajst-27521	56	17	but	but	CCONJ
ajst-27521	56	18	due	due	ADP
ajst-27521	56	19	to	to	ADP
ajst-27521	56	20	its	its	PRON
ajst-27521	56	21	conditional	conditional	ADJ
ajst-27521	56	22	independence	independence	NOUN
ajst-27521	56	23	assumption	assumption	NOUN
ajst-27521	56	24	,	,	PUNCT
ajst-27521	56	25	it	it	PRON
ajst-27521	56	26	often	often	ADV
ajst-27521	56	27	performs	perform	VERB
ajst-27521	56	28	poorly	poorly	ADV
ajst-27521	56	29	in	in	ADP
ajst-27521	56	30	practical	practical	ADJ
ajst-27521	56	31	applications	application	NOUN
ajst-27521	56	32	.	.	PUNCT
ajst-27521	57	1	clustering	clustering	NOUN
ajst-27521	57	2	-	-	PUNCT
ajst-27521	57	3	based	base	VERB
ajst-27521	57	4	anomaly	anomaly	NOUN
ajst-27521	57	5	detection	detection	NOUN
ajst-27521	57	6	algorithms	algorithm	NOUN
ajst-27521	57	7	are	be	AUX
ajst-27521	57	8	an	an	DET
ajst-27521	57	9	unsupervised	unsupervised	ADJ
ajst-27521	57	10	detection	detection	NOUN
ajst-27521	57	11	method	method	NOUN
ajst-27521	57	12	,	,	PUNCT
ajst-27521	57	13	with	with	ADP
ajst-27521	57	14	the	the	DET
ajst-27521	57	15	greatest	great	ADJ
ajst-27521	57	16	advantage	advantage	NOUN
ajst-27521	57	17	being	be	AUX
ajst-27521	57	18	the	the	DET
ajst-27521	57	19	lack	lack	NOUN
ajst-27521	57	20	of	of	ADP
ajst-27521	57	21	labeled	label	VERB
ajst-27521	57	22	data	datum	NOUN
ajst-27521	57	23	and	and	CCONJ
ajst-27521	57	24	ease	ease	NOUN
ajst-27521	57	25	of	of	ADP
ajst-27521	57	26	understanding	understanding	NOUN
ajst-27521	57	27	,	,	PUNCT
ajst-27521	57	28	but	but	CCONJ
ajst-27521	57	29	the	the	DET
ajst-27521	57	30	detection	detection	NOUN
ajst-27521	57	31	effect	effect	NOUN
ajst-27521	57	32	and	and	CCONJ
ajst-27521	57	33	computational	computational	ADJ
ajst-27521	57	34	complexity	complexity	NOUN
ajst-27521	57	35	decrease	decrease	NOUN
ajst-27521	57	36	with	with	ADP
ajst-27521	57	37	increasing	increase	VERB
ajst-27521	57	38	dimensions	dimension	NOUN
ajst-27521	57	39	.	.	PUNCT
ajst-27521	58	1	classification	classification	NOUN
ajst-27521	58	2	-	-	PUNCT
ajst-27521	58	3	based	base	VERB
ajst-27521	58	4	anomaly	anomaly	NOUN
ajst-27521	58	5	detection	detection	NOUN
ajst-27521	58	6	algorithms	algorithm	NOUN
ajst-27521	58	7	assume	assume	VERB
ajst-27521	58	8	that	that	SCONJ
ajst-27521	58	9	normal	normal	ADJ
ajst-27521	58	10	data	datum	NOUN
ajst-27521	58	11	can	can	AUX
ajst-27521	58	12	be	be	AUX
ajst-27521	58	13	smoothly	smoothly	ADV
ajst-27521	58	14	mapped	map	VERB
ajst-27521	58	15	to	to	ADP
ajst-27521	58	16	a	a	DET
ajst-27521	58	17	feature	feature	NOUN
ajst-27521	58	18	subspace	subspace	NOUN
ajst-27521	58	19	,	,	PUNCT
ajst-27521	58	20	and	and	CCONJ
ajst-27521	58	21	data	datum	NOUN
ajst-27521	58	22	points	point	NOUN
ajst-27521	58	23	that	that	PRON
ajst-27521	58	24	can	can	AUX
ajst-27521	58	25	not	not	PART
ajst-27521	58	26	be	be	AUX
ajst-27521	58	27	mapped	map	VERB
ajst-27521	58	28	to	to	ADP
ajst-27521	58	29	the	the	DET
ajst-27521	58	30	subspace	subspace	NOUN
ajst-27521	58	31	are	be	AUX
ajst-27521	58	32	considered	consider	VERB
ajst-27521	58	33	as	as	ADP
ajst-27521	58	34	anomalies	anomaly	NOUN
ajst-27521	58	35	.	.	PUNCT
ajst-27521	59	1	the	the	DET
ajst-27521	59	2	isolation	isolation	NOUN
ajst-27521	59	3	forest	forest	NOUN
ajst-27521	59	4	anomaly	anomaly	NOUN
ajst-27521	59	5	detection	detection	NOUN
ajst-27521	59	6	algorithm	algorithm	NOUN
ajst-27521	59	7	uses	use	VERB
ajst-27521	59	8	a	a	DET
ajst-27521	59	9	binary	binary	ADJ
ajst-27521	59	10	tree	tree	NOUN
ajst-27521	59	11	data	datum	NOUN
ajst-27521	59	12	structure	structure	NOUN
ajst-27521	59	13	to	to	PART
ajst-27521	59	14	classify	classify	VERB
ajst-27521	59	15	anomalies	anomaly	NOUN
ajst-27521	59	16	at	at	ADP
ajst-27521	59	17	the	the	DET
ajst-27521	59	18	root	root	NOUN
ajst-27521	59	19	of	of	ADP
ajst-27521	59	20	the	the	DET
ajst-27521	59	21	tree	tree	NOUN
ajst-27521	59	22	and	and	CCONJ
ajst-27521	59	23	normal	normal	ADJ
ajst-27521	59	24	data	datum	NOUN
ajst-27521	59	25	into	into	ADP
ajst-27521	59	26	deeper	deep	ADJ
ajst-27521	59	27	nodes	node	NOUN
ajst-27521	59	28	.	.	PUNCT
ajst-27521	60	1	the	the	DET
ajst-27521	60	2	support	support	NOUN
ajst-27521	60	3	vector	vector	NOUN
ajst-27521	60	4	machine	machine	NOUN
ajst-27521	60	5	(	(	PUNCT
ajst-27521	60	6	svm	svm	PROPN
ajst-27521	60	7	)	)	PUNCT
ajst-27521	60	8	anomaly	anomaly	NOUN
ajst-27521	60	9	detection	detection	NOUN
ajst-27521	60	10	algorithm	algorithm	NOUN
ajst-27521	60	11	[	[	X
ajst-27521	60	12	11	11	NUM
ajst-27521	60	13	-	-	SYM
ajst-27521	60	14	12	12	NUM
ajst-27521	60	15	]	]	PUNCT
ajst-27521	60	16	maps	map	VERB
ajst-27521	60	17	normal	normal	ADJ
ajst-27521	60	18	and	and	CCONJ
ajst-27521	60	19	anomalous	anomalous	ADJ
ajst-27521	60	20	data	datum	NOUN
ajst-27521	60	21	to	to	ADP
ajst-27521	60	22	a	a	DET
ajst-27521	60	23	low	low	ADJ
ajst-27521	60	24	-	-	PUNCT
ajst-27521	60	25	dimensional	dimensional	ADJ
ajst-27521	60	26	space	space	NOUN
ajst-27521	60	27	through	through	ADP
ajst-27521	60	28	svm	svm	NOUN
ajst-27521	60	29	,	,	PUNCT
ajst-27521	60	30	and	and	CCONJ
ajst-27521	60	31	finds	find	VERB
ajst-27521	60	32	a	a	DET
ajst-27521	60	33	decision	decision	NOUN
ajst-27521	60	34	boundary	boundary	ADJ
ajst-27521	60	35	in	in	ADP
ajst-27521	60	36	the	the	DET
ajst-27521	60	37	feature	feature	NOUN
ajst-27521	60	38	space	space	NOUN
ajst-27521	60	39	to	to	PART
ajst-27521	60	40	separate	separate	VERB
ajst-27521	60	41	normal	normal	ADJ
ajst-27521	60	42	and	and	CCONJ
ajst-27521	60	43	anomalous	anomalous	ADJ
ajst-27521	60	44	data	datum	NOUN
ajst-27521	60	45	.	.	PUNCT
ajst-27521	61	1	due	due	ADP
ajst-27521	61	2	to	to	ADP
ajst-27521	61	3	the	the	DET
ajst-27521	61	4	difficulty	difficulty	NOUN
ajst-27521	61	5	in	in	ADP
ajst-27521	61	6	obtaining	obtain	VERB
ajst-27521	61	7	negative	negative	ADJ
ajst-27521	61	8	class	class	NOUN
ajst-27521	61	9	samples	sample	NOUN
ajst-27521	61	10	in	in	ADP
ajst-27521	61	11	anomaly	anomaly	NOUN
ajst-27521	61	12	detection	detection	NOUN
ajst-27521	61	13	,	,	PUNCT
ajst-27521	61	14	a	a	DET
ajst-27521	61	15	variant	variant	NOUN
ajst-27521	61	16	of	of	ADP
ajst-27521	61	17	this	this	DET
ajst-27521	61	18	algorithm	algorithm	NOUN
ajst-27521	61	19	,	,	PUNCT
ajst-27521	61	20	one	one	NUM
ajst-27521	61	21	-	-	PUNCT
ajst-27521	61	22	class	class	NOUN
ajst-27521	61	23	svm	svm	NOUN
ajst-27521	61	24	,	,	PUNCT
ajst-27521	61	25	has	have	AUX
ajst-27521	61	26	been	be	AUX
ajst-27521	61	27	developed	develop	VERB
ajst-27521	61	28	that	that	PRON
ajst-27521	61	29	can	can	AUX
ajst-27521	61	30	make	make	VERB
ajst-27521	61	31	use	use	NOUN
ajst-27521	61	32	of	of	ADP
ajst-27521	61	33	normal	normal	ADJ
ajst-27521	61	34	data	datum	NOUN
ajst-27521	61	35	for	for	ADP
ajst-27521	61	36	anomaly	anomaly	NOUN
ajst-27521	61	37	detection	detection	NOUN
ajst-27521	61	38	.	.	PUNCT
ajst-27521	62	1	however	however	ADV
ajst-27521	62	2	,	,	PUNCT
ajst-27521	62	3	this	this	DET
ajst-27521	62	4	algorithm	algorithm	NOUN
ajst-27521	62	5	also	also	ADV
ajst-27521	62	6	has	have	VERB
ajst-27521	62	7	some	some	DET
ajst-27521	62	8	drawbacks	drawback	NOUN
ajst-27521	62	9	,	,	PUNCT
ajst-27521	62	10	such	such	ADJ
ajst-27521	62	11	as	as	ADP
ajst-27521	62	12	the	the	DET
ajst-27521	62	13	computation	computation	NOUN
ajst-27521	62	14	of	of	ADP
ajst-27521	62	15	its	its	PRON
ajst-27521	62	16	kernel	kernel	NOUN
ajst-27521	62	17	function	function	NOUN
ajst-27521	62	18	requiring	require	VERB
ajst-27521	62	19	a	a	DET
ajst-27521	62	20	significant	significant	ADJ
ajst-27521	62	21	amount	amount	NOUN
ajst-27521	62	22	of	of	ADP
ajst-27521	62	23	computational	computational	ADJ
ajst-27521	62	24	resources	resource	NOUN
ajst-27521	62	25	.	.	PUNCT
ajst-27521	63	1	density	density	NOUN
ajst-27521	63	2	-	-	PUNCT
ajst-27521	63	3	based	base	VERB
ajst-27521	63	4	anomaly	anomaly	NOUN
ajst-27521	63	5	detection	detection	NOUN
ajst-27521	63	6	considers	consider	VERB
ajst-27521	63	7	high	high	ADJ
ajst-27521	63	8	-	-	PUNCT
ajst-27521	63	9	density	density	NOUN
ajst-27521	63	10	areas	area	NOUN
ajst-27521	63	11	as	as	ADP
ajst-27521	63	12	normal	normal	ADJ
ajst-27521	63	13	and	and	CCONJ
ajst-27521	63	14	low	low	ADJ
ajst-27521	63	15	-	-	PUNCT
ajst-27521	63	16	density	density	NOUN
ajst-27521	63	17	areas	area	NOUN
ajst-27521	63	18	as	as	ADP
ajst-27521	63	19	abnormal	abnormal	ADJ
ajst-27521	63	20	.	.	PUNCT
ajst-27521	64	1	the	the	DET
ajst-27521	64	2	fundamental	fundamental	ADJ
ajst-27521	64	3	idea	idea	NOUN
ajst-27521	64	4	is	be	AUX
ajst-27521	64	5	to	to	PART
ajst-27521	64	6	detect	detect	VERB
ajst-27521	64	7	anomalies	anomaly	NOUN
ajst-27521	64	8	by	by	ADP
ajst-27521	64	9	comparing	compare	VERB
ajst-27521	64	10	the	the	DET
ajst-27521	64	11	density	density	NOUN
ajst-27521	64	12	of	of	ADP
ajst-27521	64	13	data	datum	NOUN
ajst-27521	64	14	with	with	ADP
ajst-27521	64	15	its	its	PRON
ajst-27521	64	16	neighboring	neighboring	NOUN
ajst-27521	64	17	points	point	NOUN
ajst-27521	64	18	.	.	PUNCT
ajst-27521	65	1	in	in	ADP
ajst-27521	65	2	this	this	DET
ajst-27521	65	3	category	category	NOUN
ajst-27521	65	4	of	of	ADP
ajst-27521	65	5	algorithms	algorithm	NOUN
ajst-27521	65	6	,	,	PUNCT
ajst-27521	65	7	the	the	DET
ajst-27521	65	8	local	local	ADJ
ajst-27521	65	9	outlier	outlier	NOUN
ajst-27521	65	10	factor	factor	NOUN
ajst-27521	65	11	(	(	PUNCT
ajst-27521	65	12	lof	lof	NOUN
ajst-27521	65	13	)	)	PUNCT
ajst-27521	65	14	algorithm	algorithm	NOUN
ajst-27521	65	15	views	view	NOUN
ajst-27521	65	16	anomalies	anomaly	NOUN
ajst-27521	65	17	not	not	PART
ajst-27521	65	18	as	as	ADP
ajst-27521	65	19	a	a	DET
ajst-27521	65	20	binary	binary	ADJ
ajst-27521	65	21	attribute	attribute	NOUN
ajst-27521	65	22	but	but	CCONJ
ajst-27521	65	23	as	as	ADP
ajst-27521	65	24	a	a	DET
ajst-27521	65	25	measure	measure	NOUN
ajst-27521	65	26	,	,	PUNCT
ajst-27521	65	27	with	with	ADP
ajst-27521	65	28	a	a	DET
ajst-27521	65	29	higher	high	ADJ
ajst-27521	65	30	lof	lof	NOUN
ajst-27521	65	31	value	value	NOUN
ajst-27521	65	32	indicating	indicate	VERB
ajst-27521	65	33	a	a	DET
ajst-27521	65	34	greater	great	ADJ
ajst-27521	65	35	likelihood	likelihood	NOUN
ajst-27521	65	36	of	of	ADP
ajst-27521	65	37	the	the	DET
ajst-27521	65	38	data	datum	NOUN
ajst-27521	65	39	being	be	AUX
ajst-27521	65	40	anomalous	anomalous	ADJ
ajst-27521	65	41	.	.	PUNCT
ajst-27521	66	1	its	its	PRON
ajst-27521	66	2	variant	variant	NOUN
ajst-27521	66	3	,	,	PUNCT
ajst-27521	66	4	the	the	DET
ajst-27521	66	5	connectivity	connectivity	NOUN
ajst-27521	66	6	-	-	PUNCT
ajst-27521	66	7	based	base	VERB
ajst-27521	66	8	outlier	outlier	NOUN
ajst-27521	66	9	factor	factor	NOUN
ajst-27521	66	10	(	(	PUNCT
ajst-27521	66	11	cof	cof	PROPN
ajst-27521	66	12	)	)	PUNCT
ajst-27521	66	13	,	,	PUNCT
ajst-27521	66	14	calculates	calculate	VERB
ajst-27521	66	15	the	the	DET
ajst-27521	66	16	density	density	NOUN
ajst-27521	66	17	of	of	ADP
ajst-27521	66	18	different	different	ADJ
ajst-27521	66	19	data	datum	NOUN
ajst-27521	66	20	regions	region	NOUN
ajst-27521	66	21	within	within	ADP
ajst-27521	66	22	the	the	DET
ajst-27521	66	23	dataset	dataset	NOUN
ajst-27521	66	24	and	and	CCONJ
ajst-27521	66	25	considers	consider	VERB
ajst-27521	66	26	regions	region	NOUN
ajst-27521	66	27	with	with	ADP
ajst-27521	66	28	lower	low	ADJ
ajst-27521	66	29	density	density	NOUN
ajst-27521	66	30	as	as	ADP
ajst-27521	66	31	outliers	outlier	NOUN
ajst-27521	66	32	.	.	PUNCT
ajst-27521	67	1	normal	normal	ADJ
ajst-27521	67	2	data	datum	NOUN
ajst-27521	67	3	points	point	NOUN
ajst-27521	67	4	have	have	VERB
ajst-27521	67	5	strong	strong	ADJ
ajst-27521	67	6	connectivity	connectivity	NOUN
ajst-27521	67	7	with	with	ADP
ajst-27521	67	8	other	other	ADJ
ajst-27521	67	9	data	data	NOUN
ajst-27521	67	10	points	point	NOUN
ajst-27521	67	11	within	within	ADP
ajst-27521	67	12	their	their	PRON
ajst-27521	67	13	neighborhood	neighborhood	NOUN
ajst-27521	67	14	,	,	PUNCT
ajst-27521	67	15	whereas	whereas	SCONJ
ajst-27521	67	16	anomalous	anomalous	ADJ
ajst-27521	67	17	points	point	NOUN
ajst-27521	67	18	have	have	VERB
ajst-27521	67	19	weaker	weak	ADJ
ajst-27521	67	20	connectivity	connectivity	NOUN
ajst-27521	67	21	.	.	PUNCT
ajst-27521	68	1	the	the	DET
ajst-27521	68	2	classic	classic	ADJ
ajst-27521	68	3	knearest	knearest	NOUN
ajst-27521	68	4	neighbor	neighbor	NOUN
ajst-27521	68	5	(	(	PUNCT
ajst-27521	68	6	knn	knn	PROPN
ajst-27521	68	7	)	)	PUNCT
ajst-27521	68	8	anomaly	anomaly	NOUN
ajst-27521	68	9	detection	detection	NOUN
ajst-27521	68	10	[	[	X
ajst-27521	68	11	13	13	NUM
ajst-27521	68	12	]	]	PUNCT
ajst-27521	68	13	employs	employ	VERB
ajst-27521	68	14	pruning	prune	VERB
ajst-27521	68	15	techniques	technique	NOUN
ajst-27521	68	16	to	to	PART
ajst-27521	68	17	improve	improve	VERB
ajst-27521	68	18	running	run	VERB
ajst-27521	68	19	speed	speed	NOUN
ajst-27521	68	20	when	when	SCONJ
ajst-27521	68	21	dealing	deal	VERB
ajst-27521	68	22	with	with	ADP
ajst-27521	68	23	high	high	ADJ
ajst-27521	68	24	-	-	PUNCT
ajst-27521	68	25	dimensional	dimensional	ADJ
ajst-27521	68	26	data	datum	NOUN
ajst-27521	68	27	.	.	PUNCT
ajst-27521	69	1	density	density	NOUN
ajst-27521	69	2	-	-	PUNCT
ajst-27521	69	3	based	base	VERB
ajst-27521	69	4	bias	bias	NOUN
ajst-27521	69	5	sampling	sample	VERB
ajst-27521	69	6	algorithms	algorithm	NOUN
ajst-27521	69	7	combine	combine	VERB
ajst-27521	69	8	probabilistic	probabilistic	ADJ
ajst-27521	69	9	analysis	analysis	NOUN
ajst-27521	69	10	with	with	ADP
ajst-27521	69	11	density	density	NOUN
ajst-27521	69	12	calculations	calculation	NOUN
ajst-27521	69	13	to	to	PART
ajst-27521	69	14	reduce	reduce	VERB
ajst-27521	69	15	the	the	DET
ajst-27521	69	16	time	time	NOUN
ajst-27521	69	17	and	and	CCONJ
ajst-27521	69	18	space	space	NOUN
ajst-27521	69	19	complexity	complexity	NOUN
ajst-27521	69	20	of	of	ADP
ajst-27521	69	21	the	the	DET
ajst-27521	69	22	algorithm	algorithm	NOUN
ajst-27521	69	23	,	,	PUNCT
ajst-27521	69	24	enhancing	enhance	VERB
ajst-27521	69	25	the	the	DET
ajst-27521	69	26	efficiency	efficiency	NOUN
ajst-27521	69	27	of	of	ADP
ajst-27521	69	28	anomaly	anomaly	NOUN
ajst-27521	69	29	detection	detection	NOUN
ajst-27521	69	30	.	.	PUNCT
ajst-27521	70	1	although	although	SCONJ
ajst-27521	70	2	density	density	NOUN
ajst-27521	70	3	-	-	PUNCT
ajst-27521	70	4	based	base	VERB
ajst-27521	70	5	anomaly	anomaly	NOUN
ajst-27521	70	6	detection	detection	NOUN
ajst-27521	70	7	algorithms	algorithm	NOUN
ajst-27521	70	8	do	do	AUX
ajst-27521	70	9	not	not	PART
ajst-27521	70	10	require	require	VERB
ajst-27521	70	11	labels	label	NOUN
ajst-27521	70	12	,	,	PUNCT
ajst-27521	70	13	they	they	PRON
ajst-27521	70	14	have	have	VERB
ajst-27521	70	15	high	high	ADJ
ajst-27521	70	16	complexity	complexity	NOUN
ajst-27521	70	17	and	and	CCONJ
ajst-27521	70	18	are	be	AUX
ajst-27521	70	19	sensitive	sensitive	ADJ
ajst-27521	70	20	to	to	ADP
ajst-27521	70	21	the	the	DET
ajst-27521	70	22	choice	choice	NOUN
ajst-27521	70	23	of	of	ADP
ajst-27521	70	24	parameters	parameter	NOUN
ajst-27521	70	25	such	such	ADJ
ajst-27521	70	26	as	as	ADP
ajst-27521	70	27	the	the	DET
ajst-27521	70	28	anomaly	anomaly	NOUN
ajst-27521	70	29	factor	factor	NOUN
ajst-27521	70	30	threshold	threshold	NOUN
ajst-27521	70	31	.	.	PUNCT
ajst-27521	71	1	in	in	ADP
ajst-27521	71	2	addition	addition	NOUN
ajst-27521	71	3	to	to	ADP
ajst-27521	71	4	the	the	DET
ajst-27521	71	5	more	more	ADV
ajst-27521	71	6	common	common	ADJ
ajst-27521	71	7	machine	machine	NOUN
ajst-27521	71	8	learning	learning	NOUN
ajst-27521	71	9	-	-	PUNCT
ajst-27521	71	10	based	base	VERB
ajst-27521	71	11	anomaly	anomaly	NOUN
ajst-27521	71	12	detection	detection	NOUN
ajst-27521	71	13	methods	method	NOUN
ajst-27521	71	14	,	,	PUNCT
ajst-27521	71	15	there	there	PRON
ajst-27521	71	16	are	be	VERB
ajst-27521	71	17	also	also	ADV
ajst-27521	71	18	anomaly	anomaly	NOUN
ajst-27521	71	19	detection	detection	NOUN
ajst-27521	71	20	methods	method	NOUN
ajst-27521	71	21	based	base	VERB
ajst-27521	71	22	on	on	ADP
ajst-27521	71	23	information	information	NOUN
ajst-27521	71	24	theory	theory	NOUN
ajst-27521	71	25	.	.	PUNCT
ajst-27521	72	1	assuming	assume	VERB
ajst-27521	72	2	that	that	SCONJ
ajst-27521	72	3	anomalies	anomaly	NOUN
ajst-27521	72	4	have	have	VERB
ajst-27521	72	5	a	a	DET
ajst-27521	72	6	greater	great	ADJ
ajst-27521	72	7	impact	impact	NOUN
ajst-27521	72	8	on	on	ADP
ajst-27521	72	9	content	content	NOUN
ajst-27521	72	10	information	information	NOUN
ajst-27521	72	11	compared	compare	VERB
ajst-27521	72	12	to	to	ADP
ajst-27521	72	13	normal	normal	ADJ
ajst-27521	72	14	points	point	NOUN
ajst-27521	72	15	,	,	PUNCT
ajst-27521	72	16	algorithms	algorithm	NOUN
ajst-27521	72	17	determine	determine	VERB
ajst-27521	72	18	whether	whether	SCONJ
ajst-27521	72	19	the	the	DET
ajst-27521	72	20	removed	removed	ADJ
ajst-27521	72	21	data	data	NOUN
ajst-27521	72	22	is	be	AUX
ajst-27521	72	23	anomalous	anomalous	ADJ
ajst-27521	72	24	based	base	VERB
ajst-27521	72	25	on	on	ADP
ajst-27521	72	26	the	the	DET
ajst-27521	72	27	extent	extent	NOUN
ajst-27521	72	28	of	of	ADP
ajst-27521	72	29	change	change	NOUN
ajst-27521	72	30	in	in	ADP
ajst-27521	72	31	content	content	NOUN
ajst-27521	72	32	information	information	NOUN
ajst-27521	72	33	after	after	ADP
ajst-27521	72	34	data	data	NOUN
ajst-27521	72	35	removal	removal	NOUN
ajst-27521	72	36	.	.	PUNCT
ajst-27521	73	1	information	information	NOUN
ajst-27521	73	2	entropy	entropy	PROPN
ajst-27521	73	3	167	167	NUM
ajst-27521	73	4	anomaly	anomaly	NOUN
ajst-27521	73	5	detection	detection	NOUN
ajst-27521	73	6	uses	use	VERB
ajst-27521	73	7	information	information	NOUN
ajst-27521	73	8	entropy	entropy	NOUN
ajst-27521	73	9	to	to	PART
ajst-27521	73	10	identify	identify	VERB
ajst-27521	73	11	anomalies	anomaly	NOUN
ajst-27521	73	12	.	.	PUNCT
ajst-27521	74	1	if	if	SCONJ
ajst-27521	74	2	the	the	DET
ajst-27521	74	3	entropy	entropy	NOUN
ajst-27521	74	4	of	of	ADP
ajst-27521	74	5	the	the	DET
ajst-27521	74	6	dataset	dataset	NOUN
ajst-27521	74	7	decreases	decrease	NOUN
ajst-27521	74	8	after	after	ADP
ajst-27521	74	9	removing	remove	VERB
ajst-27521	74	10	a	a	DET
ajst-27521	74	11	data	data	NOUN
ajst-27521	74	12	point	point	NOUN
ajst-27521	74	13	,	,	PUNCT
ajst-27521	74	14	then	then	ADV
ajst-27521	74	15	the	the	DET
ajst-27521	74	16	removed	removed	ADJ
ajst-27521	74	17	point	point	NOUN
ajst-27521	74	18	is	be	AUX
ajst-27521	74	19	considered	consider	VERB
ajst-27521	74	20	an	an	DET
ajst-27521	74	21	anomaly	anomaly	NOUN
ajst-27521	74	22	.	.	PUNCT
ajst-27521	75	1	the	the	DET
ajst-27521	75	2	information	information	NOUN
ajst-27521	75	3	bottleneck	bottleneck	NOUN
ajst-27521	75	4	method	method	NOUN
ajst-27521	75	5	defines	define	VERB
ajst-27521	75	6	anomalous	anomalous	ADJ
ajst-27521	75	7	data	data	NOUN
ajst-27521	75	8	clusters	cluster	NOUN
ajst-27521	75	9	using	use	VERB
ajst-27521	75	10	the	the	DET
ajst-27521	75	11	information	information	NOUN
ajst-27521	75	12	bottleneck	bottleneck	NOUN
ajst-27521	75	13	and	and	CCONJ
ajst-27521	75	14	detects	detect	NOUN
ajst-27521	75	15	anomalies	anomaly	NOUN
ajst-27521	75	16	based	base	VERB
ajst-27521	75	17	on	on	ADP
ajst-27521	75	18	the	the	DET
ajst-27521	75	19	difference	difference	NOUN
ajst-27521	75	20	between	between	ADP
ajst-27521	75	21	the	the	DET
ajst-27521	75	22	distribution	distribution	NOUN
ajst-27521	75	23	of	of	ADP
ajst-27521	75	24	anomalous	anomalous	ADJ
ajst-27521	75	25	data	datum	NOUN
ajst-27521	75	26	clusters	cluster	NOUN
ajst-27521	75	27	and	and	CCONJ
ajst-27521	75	28	normal	normal	ADJ
ajst-27521	75	29	data	datum	NOUN
ajst-27521	75	30	clusters	cluster	NOUN
ajst-27521	75	31	.	.	PUNCT
ajst-27521	76	1	anomaly	anomaly	PROPN
ajst-27521	76	2	detection	detection	NOUN
ajst-27521	76	3	algorithms	algorithm	NOUN
ajst-27521	76	4	based	base	VERB
ajst-27521	76	5	on	on	ADP
ajst-27521	76	6	principal	principal	ADJ
ajst-27521	76	7	component	component	NOUN
ajst-27521	76	8	analysis	analysis	NOUN
ajst-27521	76	9	use	use	VERB
ajst-27521	76	10	orthogonal	orthogonal	ADJ
ajst-27521	76	11	transformations	transformation	NOUN
ajst-27521	76	12	to	to	PART
ajst-27521	76	13	map	map	VERB
ajst-27521	76	14	data	datum	NOUN
ajst-27521	76	15	into	into	ADP
ajst-27521	76	16	a	a	DET
ajst-27521	76	17	low	low	ADJ
ajst-27521	76	18	-	-	PUNCT
ajst-27521	76	19	dimensional	dimensional	ADJ
ajst-27521	76	20	subspace	subspace	NOUN
ajst-27521	76	21	,	,	PUNCT
ajst-27521	76	22	where	where	SCONJ
ajst-27521	76	23	the	the	DET
ajst-27521	76	24	features	feature	NOUN
ajst-27521	76	25	in	in	ADP
ajst-27521	76	26	the	the	DET
ajst-27521	76	27	low	low	ADJ
ajst-27521	76	28	-	-	PUNCT
ajst-27521	76	29	dimensional	dimensional	ADJ
ajst-27521	76	30	space	space	NOUN
ajst-27521	76	31	retain	retain	VERB
ajst-27521	76	32	the	the	DET
ajst-27521	76	33	key	key	ADJ
ajst-27521	76	34	components	component	NOUN
ajst-27521	76	35	of	of	ADP
ajst-27521	76	36	the	the	DET
ajst-27521	76	37	original	original	ADJ
ajst-27521	76	38	data	datum	NOUN
ajst-27521	76	39	to	to	ADP
ajst-27521	76	40	the	the	DET
ajst-27521	76	41	greatest	great	ADJ
ajst-27521	76	42	extent	extent	NOUN
ajst-27521	76	43	.	.	PUNCT
ajst-27521	77	1	vector	vector	NOUN
ajst-27521	77	2	-	-	PUNCT
ajst-27521	77	3	based	base	VERB
ajst-27521	77	4	anomaly	anomaly	NOUN
ajst-27521	77	5	detection	detection	NOUN
ajst-27521	77	6	uses	use	VERB
ajst-27521	77	7	the	the	DET
ajst-27521	77	8	variance	variance	NOUN
ajst-27521	77	9	of	of	ADP
ajst-27521	77	10	vector	vector	NOUN
ajst-27521	77	11	angles	angle	NOUN
ajst-27521	77	12	as	as	ADP
ajst-27521	77	13	a	a	DET
ajst-27521	77	14	criterion	criterion	NOUN
ajst-27521	77	15	for	for	ADP
ajst-27521	77	16	identifying	identify	VERB
ajst-27521	77	17	anomalies	anomaly	NOUN
ajst-27521	77	18	,	,	PUNCT
ajst-27521	77	19	which	which	PRON
ajst-27521	77	20	to	to	ADP
ajst-27521	77	21	some	some	DET
ajst-27521	77	22	extent	extent	NOUN
ajst-27521	77	23	alleviates	alleviate	VERB
ajst-27521	77	24	the	the	DET
ajst-27521	77	25	difficulties	difficulty	NOUN
ajst-27521	77	26	of	of	ADP
ajst-27521	77	27	dealing	deal	VERB
ajst-27521	77	28	with	with	ADP
ajst-27521	77	29	high	high	ADJ
ajst-27521	77	30	-	-	PUNCT
ajst-27521	77	31	dimensional	dimensional	ADJ
ajst-27521	77	32	data	datum	NOUN
ajst-27521	77	33	.	.	PUNCT
ajst-27521	78	1	3.3	3.3	NUM
ajst-27521	78	2	deep	deep	ADJ
ajst-27521	78	3	learning	learning	NOUN
ajst-27521	78	4	-	-	PUNCT
ajst-27521	78	5	based	base	VERB
ajst-27521	78	6	methods	method	NOUN
ajst-27521	78	7	deep	deep	ADJ
ajst-27521	78	8	learning	learning	NOUN
ajst-27521	78	9	-	-	PUNCT
ajst-27521	78	10	based	base	VERB
ajst-27521	78	11	time	time	NOUN
ajst-27521	78	12	series	series	PROPN
ajst-27521	78	13	anomaly	anomaly	PROPN
ajst-27521	78	14	detection	detection	NOUN
ajst-27521	78	15	has	have	AUX
ajst-27521	78	16	become	become	VERB
ajst-27521	78	17	a	a	DET
ajst-27521	78	18	popular	popular	ADJ
ajst-27521	78	19	research	research	NOUN
ajst-27521	78	20	direction	direction	NOUN
ajst-27521	78	21	in	in	ADP
ajst-27521	78	22	recent	recent	ADJ
ajst-27521	78	23	years	year	NOUN
ajst-27521	78	24	,	,	PUNCT
ajst-27521	78	25	using	use	VERB
ajst-27521	78	26	various	various	ADJ
ajst-27521	78	27	neural	neural	ADJ
ajst-27521	78	28	networks	network	NOUN
ajst-27521	78	29	to	to	PART
ajst-27521	78	30	extract	extract	VERB
ajst-27521	78	31	data	data	NOUN
ajst-27521	78	32	features	feature	NOUN
ajst-27521	78	33	and	and	CCONJ
ajst-27521	78	34	identify	identify	VERB
ajst-27521	78	35	anomalies	anomaly	NOUN
ajst-27521	78	36	that	that	PRON
ajst-27521	78	37	do	do	AUX
ajst-27521	78	38	not	not	PART
ajst-27521	78	39	conform	conform	VERB
ajst-27521	78	40	to	to	ADP
ajst-27521	78	41	patterns	pattern	NOUN
ajst-27521	78	42	from	from	ADP
ajst-27521	78	43	these	these	DET
ajst-27521	78	44	features	feature	NOUN
ajst-27521	78	45	.	.	PUNCT
ajst-27521	79	1	it	it	PRON
ajst-27521	79	2	can	can	AUX
ajst-27521	79	3	be	be	AUX
ajst-27521	79	4	divided	divide	VERB
ajst-27521	79	5	into	into	ADP
ajst-27521	79	6	three	three	NUM
ajst-27521	79	7	categories	category	NOUN
ajst-27521	79	8	:	:	PUNCT
ajst-27521	79	9	prediction	prediction	NOUN
ajst-27521	79	10	-	-	PUNCT
ajst-27521	79	11	based	base	VERB
ajst-27521	79	12	time	time	NOUN
ajst-27521	79	13	series	series	PROPN
ajst-27521	79	14	anomaly	anomaly	PROPN
ajst-27521	79	15	detection	detection	NOUN
ajst-27521	79	16	algorithms	algorithm	NOUN
ajst-27521	79	17	,	,	PUNCT
ajst-27521	79	18	reconstruction	reconstruction	NOUN
ajst-27521	79	19	-	-	PUNCT
ajst-27521	79	20	based	base	VERB
ajst-27521	79	21	anomaly	anomaly	NOUN
ajst-27521	79	22	detection	detection	NOUN
ajst-27521	79	23	algorithms	algorithm	NOUN
ajst-27521	79	24	,	,	PUNCT
ajst-27521	79	25	and	and	CCONJ
ajst-27521	79	26	generation	generation	NOUN
ajst-27521	79	27	-	-	PUNCT
ajst-27521	79	28	based	base	VERB
ajst-27521	79	29	anomaly	anomaly	NOUN
ajst-27521	79	30	detection	detection	NOUN
ajst-27521	79	31	algorithms	algorithm	NOUN
ajst-27521	79	32	.	.	PUNCT
ajst-27521	80	1	prediction	prediction	NOUN
ajst-27521	80	2	-	-	PUNCT
ajst-27521	80	3	based	base	VERB
ajst-27521	80	4	time	time	NOUN
ajst-27521	80	5	series	series	PROPN
ajst-27521	80	6	anomaly	anomaly	PROPN
ajst-27521	80	7	detection	detection	NOUN
ajst-27521	80	8	uses	use	VERB
ajst-27521	80	9	neural	neural	ADJ
ajst-27521	80	10	networks	network	NOUN
ajst-27521	80	11	to	to	PART
ajst-27521	80	12	predict	predict	VERB
ajst-27521	80	13	time	time	NOUN
ajst-27521	80	14	series	series	NOUN
ajst-27521	80	15	and	and	CCONJ
ajst-27521	80	16	determines	determine	VERB
ajst-27521	80	17	anomalies	anomaly	NOUN
ajst-27521	80	18	by	by	ADP
ajst-27521	80	19	comparing	compare	VERB
ajst-27521	80	20	the	the	DET
ajst-27521	80	21	distance	distance	NOUN
ajst-27521	80	22	between	between	ADP
ajst-27521	80	23	predicted	predict	VERB
ajst-27521	80	24	values	value	NOUN
ajst-27521	80	25	and	and	CCONJ
ajst-27521	80	26	original	original	ADJ
ajst-27521	80	27	data	datum	NOUN
ajst-27521	80	28	.	.	PUNCT
ajst-27521	81	1	convolutional	convolutional	ADJ
ajst-27521	81	2	anomaly	anomaly	NOUN
ajst-27521	81	3	detection	detection	NOUN
ajst-27521	81	4	[	[	X
ajst-27521	81	5	15	15	NUM
ajst-27521	81	6	]	]	PUNCT
ajst-27521	81	7	learns	learn	VERB
ajst-27521	81	8	the	the	DET
ajst-27521	81	9	geometric	geometric	ADJ
ajst-27521	81	10	shape	shape	NOUN
ajst-27521	81	11	of	of	ADP
ajst-27521	81	12	data	datum	NOUN
ajst-27521	81	13	features	feature	NOUN
ajst-27521	81	14	by	by	ADP
ajst-27521	81	15	using	use	VERB
ajst-27521	81	16	the	the	DET
ajst-27521	81	17	jacobian	jacobian	ADJ
ajst-27521	81	18	matrix	matrix	NOUN
ajst-27521	81	19	output	output	NOUN
ajst-27521	81	20	from	from	ADP
ajst-27521	81	21	a	a	DET
ajst-27521	81	22	regularized	regularized	ADJ
ajst-27521	81	23	encoder	encoder	NOUN
ajst-27521	81	24	,	,	PUNCT
ajst-27521	81	25	achieving	achieve	VERB
ajst-27521	81	26	a	a	DET
ajst-27521	81	27	smoother	smooth	ADJ
ajst-27521	81	28	data	datum	NOUN
ajst-27521	81	29	representation	representation	NOUN
ajst-27521	81	30	space	space	NOUN
ajst-27521	81	31	,	,	PUNCT
ajst-27521	81	32	and	and	CCONJ
ajst-27521	81	33	the	the	DET
ajst-27521	81	34	algorithm	algorithm	NOUN
ajst-27521	81	35	can	can	AUX
ajst-27521	81	36	more	more	ADV
ajst-27521	81	37	effectively	effectively	ADV
ajst-27521	81	38	detect	detect	VERB
ajst-27521	81	39	anomalies	anomaly	NOUN
ajst-27521	81	40	from	from	ADP
ajst-27521	81	41	normal	normal	ADJ
ajst-27521	81	42	data	datum	NOUN
ajst-27521	81	43	.	.	PUNCT
ajst-27521	82	1	lstm	lstm	NOUN
ajst-27521	82	2	anomaly	anomaly	NOUN
ajst-27521	82	3	detection	detection	NOUN
ajst-27521	82	4	[	[	X
ajst-27521	82	5	16	16	NUM
ajst-27521	82	6	-	-	SYM
ajst-27521	82	7	17	17	NUM
ajst-27521	82	8	]	]	PUNCT
ajst-27521	82	9	predicts	predict	VERB
ajst-27521	82	10	time	time	NOUN
ajst-27521	82	11	series	series	PROPN
ajst-27521	82	12	,	,	PUNCT
ajst-27521	82	13	taking	take	VERB
ajst-27521	82	14	adjacent	adjacent	ADJ
ajst-27521	82	15	data	datum	NOUN
ajst-27521	82	16	segments	segment	NOUN
ajst-27521	82	17	as	as	ADP
ajst-27521	82	18	true	true	ADJ
ajst-27521	82	19	values	value	NOUN
ajst-27521	82	20	,	,	PUNCT
ajst-27521	82	21	and	and	CCONJ
ajst-27521	82	22	identifies	identify	VERB
ajst-27521	82	23	anomalies	anomaly	NOUN
ajst-27521	82	24	by	by	ADP
ajst-27521	82	25	comparing	compare	VERB
ajst-27521	82	26	predicted	predict	VERB
ajst-27521	82	27	values	value	NOUN
ajst-27521	82	28	with	with	ADP
ajst-27521	82	29	true	true	ADJ
ajst-27521	82	30	values	value	NOUN
ajst-27521	82	31	.	.	PUNCT
ajst-27521	83	1	multilayer	multilayer	ADJ
ajst-27521	83	2	convolutional	convolutional	ADJ
ajst-27521	83	3	neural	neural	ADJ
ajst-27521	83	4	networks	network	NOUN
ajst-27521	83	5	[	[	X
ajst-27521	83	6	18	18	NUM
ajst-27521	83	7	]	]	PUNCT
ajst-27521	83	8	reduce	reduce	VERB
ajst-27521	83	9	the	the	DET
ajst-27521	83	10	dimensionality	dimensionality	NOUN
ajst-27521	83	11	of	of	ADP
ajst-27521	83	12	the	the	DET
ajst-27521	83	13	original	original	ADJ
ajst-27521	83	14	data	datum	NOUN
ajst-27521	83	15	with	with	ADP
ajst-27521	83	16	multiple	multiple	ADJ
ajst-27521	83	17	layers	layer	NOUN
ajst-27521	83	18	of	of	ADP
ajst-27521	83	19	cnns	cnn	NOUN
ajst-27521	83	20	and	and	CCONJ
ajst-27521	83	21	then	then	ADV
ajst-27521	83	22	increase	increase	VERB
ajst-27521	83	23	it	it	PRON
ajst-27521	83	24	again	again	ADV
ajst-27521	83	25	,	,	PUNCT
ajst-27521	83	26	training	train	VERB
ajst-27521	83	27	the	the	DET
ajst-27521	83	28	network	network	NOUN
ajst-27521	83	29	so	so	SCONJ
ajst-27521	83	30	that	that	SCONJ
ajst-27521	83	31	the	the	DET
ajst-27521	83	32	result	result	NOUN
ajst-27521	83	33	after	after	SCONJ
ajst-27521	83	34	dimensionality	dimensionality	NOUN
ajst-27521	83	35	increase	increase	NOUN
ajst-27521	83	36	approximates	approximate	VERB
ajst-27521	83	37	the	the	DET
ajst-27521	83	38	original	original	ADJ
ajst-27521	83	39	data	datum	NOUN
ajst-27521	83	40	segments	segment	NOUN
ajst-27521	83	41	,	,	PUNCT
ajst-27521	83	42	and	and	CCONJ
ajst-27521	83	43	anomaly	anomaly	NOUN
ajst-27521	83	44	detection	detection	NOUN
ajst-27521	83	45	is	be	AUX
ajst-27521	83	46	performed	perform	VERB
ajst-27521	83	47	by	by	ADP
ajst-27521	83	48	comparing	compare	VERB
ajst-27521	83	49	the	the	DET
ajst-27521	83	50	differences	difference	NOUN
ajst-27521	83	51	before	before	ADP
ajst-27521	83	52	and	and	CCONJ
ajst-27521	83	53	after	after	ADP
ajst-27521	83	54	training	training	NOUN
ajst-27521	83	55	.	.	PUNCT
ajst-27521	84	1	reconstruction	reconstruction	NOUN
ajst-27521	84	2	-	-	PUNCT
ajst-27521	84	3	based	base	VERB
ajst-27521	84	4	time	time	NOUN
ajst-27521	84	5	series	series	PROPN
ajst-27521	84	6	anomaly	anomaly	PROPN
ajst-27521	84	7	detection	detection	NOUN
ajst-27521	84	8	is	be	AUX
ajst-27521	84	9	achieved	achieve	VERB
ajst-27521	84	10	by	by	ADP
ajst-27521	84	11	minimizing	minimize	VERB
ajst-27521	84	12	reconstruction	reconstruction	NOUN
ajst-27521	84	13	errors	error	NOUN
ajst-27521	84	14	,	,	PUNCT
ajst-27521	84	15	with	with	ADP
ajst-27521	84	16	the	the	DET
ajst-27521	84	17	most	most	ADV
ajst-27521	84	18	classic	classic	ADJ
ajst-27521	84	19	reconstruction	reconstruction	NOUN
ajst-27521	84	20	algorithm	algorithm	NOUN
ajst-27521	84	21	being	be	AUX
ajst-27521	84	22	the	the	DET
ajst-27521	84	23	autoencoder	autoencoder	NOUN
ajst-27521	84	24	and	and	CCONJ
ajst-27521	84	25	its	its	PRON
ajst-27521	84	26	variants	variant	NOUN
ajst-27521	84	27	.	.	PUNCT
ajst-27521	85	1	spectral	spectral	ADJ
ajst-27521	85	2	encoding	encoding	PROPN
ajst-27521	85	3	anomaly	anomaly	NOUN
ajst-27521	85	4	detection	detection	NOUN
ajst-27521	85	5	[	[	X
ajst-27521	85	6	19	19	NUM
ajst-27521	85	7	]	]	PUNCT
ajst-27521	85	8	constructs	construct	VERB
ajst-27521	85	9	a	a	DET
ajst-27521	85	10	denoising	denoise	VERB
ajst-27521	85	11	autoencoder	autoencoder	NOUN
ajst-27521	85	12	based	base	VERB
ajst-27521	85	13	on	on	ADP
ajst-27521	85	14	bidirectional	bidirectional	ADJ
ajst-27521	85	15	lstm	lstm	PROPN
ajst-27521	85	16	,	,	PUNCT
ajst-27521	85	17	mapping	map	VERB
ajst-27521	85	18	data	datum	NOUN
ajst-27521	85	19	to	to	ADP
ajst-27521	85	20	spectral	spectral	ADJ
ajst-27521	85	21	features	feature	NOUN
ajst-27521	85	22	,	,	PUNCT
ajst-27521	85	23	with	with	ADP
ajst-27521	85	24	the	the	DET
ajst-27521	85	25	decoder	decoder	NOUN
ajst-27521	85	26	reconstructing	reconstruct	VERB
ajst-27521	85	27	the	the	DET
ajst-27521	85	28	original	original	ADJ
ajst-27521	85	29	data	datum	NOUN
ajst-27521	85	30	and	and	CCONJ
ajst-27521	85	31	determining	determine	VERB
ajst-27521	85	32	whether	whether	SCONJ
ajst-27521	85	33	the	the	DET
ajst-27521	85	34	input	input	NOUN
ajst-27521	85	35	data	datum	NOUN
ajst-27521	85	36	is	be	AUX
ajst-27521	85	37	anomalous	anomalous	ADJ
ajst-27521	85	38	based	base	VERB
ajst-27521	85	39	on	on	ADP
ajst-27521	85	40	the	the	DET
ajst-27521	85	41	difference	difference	NOUN
ajst-27521	85	42	between	between	ADP
ajst-27521	85	43	the	the	DET
ajst-27521	85	44	reconstructed	reconstructed	ADJ
ajst-27521	85	45	data	datum	NOUN
ajst-27521	85	46	and	and	CCONJ
ajst-27521	85	47	the	the	DET
ajst-27521	85	48	original	original	ADJ
ajst-27521	85	49	data	datum	NOUN
ajst-27521	85	50	.	.	PUNCT
ajst-27521	86	1	group	group	NOUN
ajst-27521	86	2	-	-	PUNCT
ajst-27521	86	3	robust	robust	ADJ
ajst-27521	86	4	deep	deep	ADJ
ajst-27521	86	5	autoencoders	autoencoder	NOUN
ajst-27521	86	6	decompose	decompose	VERB
ajst-27521	86	7	data	datum	NOUN
ajst-27521	86	8	into	into	ADP
ajst-27521	86	9	reconstructed	reconstructed	ADJ
ajst-27521	86	10	data	datum	NOUN
ajst-27521	86	11	and	and	CCONJ
ajst-27521	86	12	anomalous	anomalous	ADJ
ajst-27521	86	13	noise	noise	NOUN
ajst-27521	86	14	data	datum	NOUN
ajst-27521	86	15	,	,	PUNCT
ajst-27521	86	16	using	use	VERB
ajst-27521	86	17	sparse	sparse	ADJ
ajst-27521	86	18	regularization	regularization	NOUN
ajst-27521	86	19	terms	term	NOUN
ajst-27521	86	20	to	to	PART
ajst-27521	86	21	constrain	constrain	VERB
ajst-27521	86	22	the	the	DET
ajst-27521	86	23	anomalous	anomalous	ADJ
ajst-27521	86	24	noise	noise	NOUN
ajst-27521	86	25	data	datum	NOUN
ajst-27521	86	26	,	,	PUNCT
ajst-27521	86	27	thereby	thereby	ADV
ajst-27521	86	28	achieving	achieve	VERB
ajst-27521	86	29	robust	robust	ADJ
ajst-27521	86	30	anomaly	anomaly	NOUN
ajst-27521	86	31	detection	detection	NOUN
ajst-27521	86	32	results	result	NOUN
ajst-27521	86	33	.	.	PUNCT
ajst-27521	87	1	bayesian	bayesian	NOUN
ajst-27521	87	2	convolutional	convolutional	ADJ
ajst-27521	87	3	autoencoders	autoencoder	NOUN
ajst-27521	87	4	[	[	X
ajst-27521	87	5	20	20	NUM
ajst-27521	87	6	]	]	PUNCT
ajst-27521	87	7	show	show	VERB
ajst-27521	87	8	a	a	DET
ajst-27521	87	9	significant	significant	ADJ
ajst-27521	87	10	difference	difference	NOUN
ajst-27521	87	11	between	between	ADP
ajst-27521	87	12	the	the	DET
ajst-27521	87	13	reconstructed	reconstructed	ADJ
ajst-27521	87	14	data	datum	NOUN
ajst-27521	87	15	and	and	CCONJ
ajst-27521	87	16	the	the	DET
ajst-27521	87	17	original	original	ADJ
ajst-27521	87	18	data	datum	NOUN
ajst-27521	87	19	when	when	SCONJ
ajst-27521	87	20	anomalous	anomalous	ADJ
ajst-27521	87	21	data	datum	NOUN
ajst-27521	87	22	is	be	AUX
ajst-27521	87	23	input	input	VERB
ajst-27521	87	24	into	into	ADP
ajst-27521	87	25	the	the	DET
ajst-27521	87	26	autoencoder	autoencoder	NOUN
ajst-27521	87	27	.	.	PUNCT
ajst-27521	88	1	ensemble	ensemble	ADJ
ajst-27521	88	2	methods	method	NOUN
ajst-27521	88	3	integrate	integrate	VERB
ajst-27521	88	4	different	different	ADJ
ajst-27521	88	5	types	type	NOUN
ajst-27521	88	6	of	of	ADP
ajst-27521	88	7	autoencoders	autoencoder	NOUN
ajst-27521	88	8	to	to	PART
ajst-27521	88	9	detect	detect	VERB
ajst-27521	88	10	anomalous	anomalous	ADJ
ajst-27521	88	11	data	datum	NOUN
ajst-27521	88	12	,	,	PUNCT
ajst-27521	88	13	replacing	replace	VERB
ajst-27521	88	14	fully	fully	ADV
ajst-27521	88	15	connected	connected	ADJ
ajst-27521	88	16	autoencoders	autoencoder	NOUN
ajst-27521	88	17	with	with	ADP
ajst-27521	88	18	autoencoders	autoencoder	NOUN
ajst-27521	88	19	of	of	ADP
ajst-27521	88	20	different	different	ADJ
ajst-27521	88	21	structures	structure	NOUN
ajst-27521	88	22	and	and	CCONJ
ajst-27521	88	23	connection	connection	NOUN
ajst-27521	88	24	methods	method	NOUN
ajst-27521	88	25	,	,	PUNCT
ajst-27521	88	26	reducing	reduce	VERB
ajst-27521	88	27	the	the	DET
ajst-27521	88	28	computational	computational	ADJ
ajst-27521	88	29	complexity	complexity	NOUN
ajst-27521	88	30	of	of	ADP
ajst-27521	88	31	the	the	DET
ajst-27521	88	32	algorithm	algorithm	NOUN
ajst-27521	88	33	.	.	PUNCT
ajst-27521	89	1	deep	deep	ADJ
ajst-27521	89	2	anomaly	anomaly	NOUN
ajst-27521	89	3	detection	detection	NOUN
ajst-27521	89	4	based	base	VERB
ajst-27521	89	5	on	on	ADP
ajst-27521	89	6	variational	variational	ADJ
ajst-27521	89	7	autoencoders	autoencoder	NOUN
ajst-27521	89	8	[	[	X
ajst-27521	89	9	21	21	NUM
ajst-27521	89	10	]	]	X
ajst-27521	89	11	uses	use	VERB
ajst-27521	89	12	variational	variational	ADJ
ajst-27521	89	13	inference	inference	NOUN
ajst-27521	89	14	mechanisms	mechanism	NOUN
ajst-27521	89	15	to	to	PART
ajst-27521	89	16	learn	learn	VERB
ajst-27521	89	17	the	the	DET
ajst-27521	89	18	generative	generative	ADJ
ajst-27521	89	19	distribution	distribution	NOUN
ajst-27521	89	20	information	information	NOUN
ajst-27521	89	21	of	of	ADP
ajst-27521	89	22	normal	normal	ADJ
ajst-27521	89	23	data	datum	NOUN
ajst-27521	89	24	.	.	PUNCT
ajst-27521	90	1	improved	improve	VERB
ajst-27521	90	2	variational	variational	ADV
ajst-27521	90	3	lower	lower	ADV
ajst-27521	90	4	bound	bind	VERB
ajst-27521	90	5	anomaly	anomaly	NOUN
ajst-27521	90	6	detection	detection	NOUN
ajst-27521	90	7	trains	train	VERB
ajst-27521	90	8	the	the	DET
ajst-27521	90	9	model	model	NOUN
ajst-27521	90	10	with	with	ADP
ajst-27521	90	11	an	an	DET
ajst-27521	90	12	improved	improved	ADJ
ajst-27521	90	13	version	version	NOUN
ajst-27521	90	14	of	of	ADP
ajst-27521	90	15	the	the	DET
ajst-27521	90	16	variational	variational	ADV
ajst-27521	90	17	lower	lower	ADV
ajst-27521	90	18	bound	bind	VERB
ajst-27521	90	19	and	and	CCONJ
ajst-27521	90	20	uses	use	VERB
ajst-27521	90	21	markov	markov	NOUN
ajst-27521	90	22	chain	chain	NOUN
ajst-27521	90	23	monte	monte	PROPN
ajst-27521	90	24	carlo	carlo	PROPN
ajst-27521	90	25	strategies	strategy	NOUN
ajst-27521	90	26	to	to	PART
ajst-27521	90	27	detect	detect	VERB
ajst-27521	90	28	anomalies	anomaly	NOUN
ajst-27521	90	29	in	in	ADP
ajst-27521	90	30	the	the	DET
ajst-27521	90	31	data	datum	NOUN
ajst-27521	90	32	.	.	PUNCT
ajst-27521	91	1	the	the	DET
ajst-27521	91	2	variational	variational	ADJ
ajst-27521	91	3	deviation	deviation	NOUN
ajst-27521	91	4	network	network	NOUN
ajst-27521	91	5	model	model	NOUN
ajst-27521	91	6	uses	use	VERB
ajst-27521	91	7	variational	variational	ADJ
ajst-27521	91	8	autoencoders	autoencoder	NOUN
ajst-27521	91	9	to	to	PART
ajst-27521	91	10	generate	generate	VERB
ajst-27521	91	11	reference	reference	NOUN
ajst-27521	91	12	scores	score	NOUN
ajst-27521	91	13	,	,	PUNCT
ajst-27521	91	14	offering	offer	VERB
ajst-27521	91	15	better	well	ADJ
ajst-27521	91	16	scalability	scalability	NOUN
ajst-27521	91	17	and	and	CCONJ
ajst-27521	91	18	stronger	strong	ADJ
ajst-27521	91	19	interpretability	interpretability	NOUN
ajst-27521	91	20	for	for	ADP
ajst-27521	91	21	anomalous	anomalous	ADJ
ajst-27521	91	22	data	datum	NOUN
ajst-27521	91	23	.	.	PUNCT
ajst-27521	92	1	however	however	ADV
ajst-27521	92	2	,	,	PUNCT
ajst-27521	92	3	this	this	DET
ajst-27521	92	4	model	model	NOUN
ajst-27521	92	5	also	also	ADV
ajst-27521	92	6	has	have	VERB
ajst-27521	92	7	certain	certain	ADJ
ajst-27521	92	8	limitations	limitation	NOUN
ajst-27521	92	9	,	,	PUNCT
ajst-27521	92	10	as	as	SCONJ
ajst-27521	92	11	it	it	PRON
ajst-27521	92	12	uses	use	VERB
ajst-27521	92	13	a	a	DET
ajst-27521	92	14	normal	normal	ADJ
ajst-27521	92	15	distribution	distribution	NOUN
ajst-27521	92	16	to	to	PART
ajst-27521	92	17	fit	fit	VERB
ajst-27521	92	18	the	the	DET
ajst-27521	92	19	probability	probability	NOUN
ajst-27521	92	20	distribution	distribution	NOUN
ajst-27521	92	21	of	of	ADP
ajst-27521	92	22	normal	normal	ADJ
ajst-27521	92	23	samples	sample	NOUN
ajst-27521	92	24	.	.	PUNCT
ajst-27521	93	1	when	when	SCONJ
ajst-27521	93	2	training	training	NOUN
ajst-27521	93	3	data	datum	NOUN
ajst-27521	93	4	and	and	CCONJ
ajst-27521	93	5	test	test	NOUN
ajst-27521	93	6	data	datum	NOUN
ajst-27521	93	7	come	come	VERB
ajst-27521	93	8	from	from	ADP
ajst-27521	93	9	different	different	ADJ
ajst-27521	93	10	data	datum	NOUN
ajst-27521	93	11	distributions	distribution	NOUN
ajst-27521	93	12	,	,	PUNCT
ajst-27521	93	13	variational	variational	ADJ
ajst-27521	93	14	autoencoders	autoencoder	NOUN
ajst-27521	93	15	can	can	AUX
ajst-27521	93	16	not	not	PART
ajst-27521	93	17	be	be	AUX
ajst-27521	93	18	used	use	VERB
ajst-27521	93	19	for	for	ADP
ajst-27521	93	20	anomaly	anomaly	NOUN
ajst-27521	93	21	detection	detection	NOUN
ajst-27521	93	22	on	on	ADP
ajst-27521	93	23	the	the	DET
ajst-27521	93	24	data	datum	NOUN
ajst-27521	93	25	.	.	PUNCT
ajst-27521	94	1	generation	generation	NOUN
ajst-27521	94	2	-	-	PUNCT
ajst-27521	94	3	based	base	VERB
ajst-27521	94	4	time	time	NOUN
ajst-27521	94	5	series	series	PROPN
ajst-27521	94	6	anomaly	anomaly	NOUN
ajst-27521	94	7	detection	detection	NOUN
ajst-27521	94	8	[	[	X
ajst-27521	94	9	22	22	NUM
ajst-27521	94	10	]	]	PUNCT
ajst-27521	94	11	adopts	adopt	VERB
ajst-27521	94	12	a	a	DET
ajst-27521	94	13	game	game	NOUN
ajst-27521	94	14	-	-	PUNCT
ajst-27521	94	15	theoretic	theoretic	NOUN
ajst-27521	94	16	approach	approach	NOUN
ajst-27521	94	17	to	to	PART
ajst-27521	94	18	learn	learn	VERB
ajst-27521	94	19	the	the	DET
ajst-27521	94	20	marginal	marginal	ADJ
ajst-27521	94	21	distribution	distribution	NOUN
ajst-27521	94	22	information	information	NOUN
ajst-27521	94	23	of	of	ADP
ajst-27521	94	24	normal	normal	ADJ
ajst-27521	94	25	data	datum	NOUN
ajst-27521	94	26	.	.	PUNCT
ajst-27521	95	1	detection	detection	NOUN
ajst-27521	95	2	generative	generative	VERB
ajst-27521	95	3	adversarial	adversarial	ADJ
ajst-27521	95	4	networks	network	NOUN
ajst-27521	95	5	[	[	X
ajst-27521	95	6	23	23	NUM
ajst-27521	95	7	]	]	PUNCT
ajst-27521	95	8	use	use	VERB
ajst-27521	95	9	convolutional	convolutional	ADJ
ajst-27521	95	10	generative	generative	ADJ
ajst-27521	95	11	adversarial	adversarial	ADJ
ajst-27521	95	12	networks	network	NOUN
ajst-27521	95	13	to	to	PART
ajst-27521	95	14	learn	learn	VERB
ajst-27521	95	15	the	the	DET
ajst-27521	95	16	population	population	NOUN
ajst-27521	95	17	distribution	distribution	NOUN
ajst-27521	95	18	of	of	ADP
ajst-27521	95	19	normal	normal	ADJ
ajst-27521	95	20	data	datum	NOUN
ajst-27521	95	21	,	,	PUNCT
ajst-27521	95	22	and	and	CCONJ
ajst-27521	95	23	when	when	SCONJ
ajst-27521	95	24	the	the	DET
ajst-27521	95	25	model	model	NOUN
ajst-27521	95	26	is	be	AUX
ajst-27521	95	27	input	input	NOUN
ajst-27521	95	28	with	with	ADP
ajst-27521	95	29	anomalous	anomalous	ADJ
ajst-27521	95	30	data	datum	NOUN
ajst-27521	95	31	,	,	PUNCT
ajst-27521	95	32	the	the	DET
ajst-27521	95	33	generated	generate	VERB
ajst-27521	95	34	anomalous	anomalous	ADJ
ajst-27521	95	35	data	datum	NOUN
ajst-27521	95	36	differs	differ	VERB
ajst-27521	95	37	significantly	significantly	ADV
ajst-27521	95	38	from	from	ADP
ajst-27521	95	39	the	the	DET
ajst-27521	95	40	original	original	ADJ
ajst-27521	95	41	data	datum	NOUN
ajst-27521	95	42	,	,	PUNCT
ajst-27521	95	43	with	with	ADP
ajst-27521	95	44	the	the	DET
ajst-27521	95	45	difference	difference	NOUN
ajst-27521	95	46	serving	serve	VERB
ajst-27521	95	47	as	as	ADP
ajst-27521	95	48	an	an	DET
ajst-27521	95	49	anomaly	anomaly	NOUN
ajst-27521	95	50	score	score	NOUN
ajst-27521	95	51	.	.	PUNCT
ajst-27521	96	1	conditional	conditional	ADJ
ajst-27521	96	2	generative	generative	ADJ
ajst-27521	96	3	anomaly	anomaly	NOUN
ajst-27521	96	4	detection	detection	NOUN
ajst-27521	97	1	[	[	X
ajst-27521	97	2	24	24	NUM
ajst-27521	97	3	]	]	X
ajst-27521	97	4	uses	use	VERB
ajst-27521	97	5	conditional	conditional	ADJ
ajst-27521	97	6	generative	generative	ADJ
ajst-27521	97	7	adversarial	adversarial	ADJ
ajst-27521	97	8	networks	network	NOUN
ajst-27521	97	9	to	to	PART
ajst-27521	97	10	learn	learn	VERB
ajst-27521	97	11	the	the	DET
ajst-27521	97	12	distribution	distribution	NOUN
ajst-27521	97	13	of	of	ADP
ajst-27521	97	14	the	the	DET
ajst-27521	97	15	data	data	NOUN
ajst-27521	97	16	generation	generation	NOUN
ajst-27521	97	17	space	space	NOUN
ajst-27521	97	18	and	and	CCONJ
ajst-27521	97	19	the	the	DET
ajst-27521	97	20	data	data	NOUN
ajst-27521	97	21	inference	inference	NOUN
ajst-27521	97	22	space	space	NOUN
ajst-27521	97	23	,	,	PUNCT
ajst-27521	97	24	constructing	construct	VERB
ajst-27521	97	25	data	data	NOUN
ajst-27521	97	26	encoders	encoder	NOUN
ajst-27521	97	27	and	and	CCONJ
ajst-27521	97	28	data	datum	NOUN
ajst-27521	97	29	decoders	decoder	NOUN
ajst-27521	97	30	to	to	PART
ajst-27521	97	31	extract	extract	VERB
ajst-27521	97	32	features	feature	NOUN
ajst-27521	97	33	and	and	CCONJ
ajst-27521	97	34	reconstruct	reconstruct	VERB
ajst-27521	97	35	data	datum	NOUN
ajst-27521	97	36	,	,	PUNCT
ajst-27521	97	37	respectively	respectively	ADV
ajst-27521	97	38	,	,	PUNCT
ajst-27521	97	39	while	while	SCONJ
ajst-27521	97	40	other	other	ADJ
ajst-27521	97	41	encoders	encoder	NOUN
ajst-27521	97	42	learn	learn	VERB
ajst-27521	97	43	the	the	DET
ajst-27521	97	44	latent	latent	NOUN
ajst-27521	97	45	space	space	NOUN
ajst-27521	97	46	representation	representation	NOUN
ajst-27521	97	47	of	of	ADP
ajst-27521	97	48	the	the	DET
ajst-27521	97	49	reconstructed	reconstructed	ADJ
ajst-27521	97	50	data	datum	NOUN
ajst-27521	97	51	.	.	PUNCT
ajst-27521	98	1	active	active	ADJ
ajst-27521	98	2	learning	learning	PROPN
ajst-27521	98	3	anomaly	anomaly	PROPN
ajst-27521	98	4	detection	detection	NOUN
ajst-27521	98	5	constructs	construct	VERB
ajst-27521	98	6	multiple	multiple	ADJ
ajst-27521	98	7	generators	generator	NOUN
ajst-27521	98	8	to	to	PART
ajst-27521	98	9	generate	generate	VERB
ajst-27521	98	10	different	different	ADJ
ajst-27521	98	11	potential	potential	ADJ
ajst-27521	98	12	outlier	outlier	NOUN
ajst-27521	98	13	data	datum	NOUN
ajst-27521	98	14	to	to	PART
ajst-27521	98	15	avoid	avoid	VERB
ajst-27521	98	16	the	the	DET
ajst-27521	98	17	problem	problem	NOUN
ajst-27521	98	18	of	of	ADP
ajst-27521	98	19	mode	mode	NOUN
ajst-27521	98	20	collapse	collapse	NOUN
ajst-27521	98	21	.	.	PUNCT
ajst-27521	99	1	adversarial	adversarial	ADJ
ajst-27521	99	2	inference	inference	NOUN
ajst-27521	99	3	anomaly	anomaly	NOUN
ajst-27521	99	4	detection	detection	NOUN
ajst-27521	100	1	[	[	X
ajst-27521	100	2	25	25	NUM
ajst-27521	100	3	]	]	PUNCT
ajst-27521	100	4	is	be	AUX
ajst-27521	100	5	an	an	DET
ajst-27521	100	6	inferencebased	inferencebased	ADJ
ajst-27521	100	7	anomaly	anomaly	NOUN
ajst-27521	100	8	detection	detection	NOUN
ajst-27521	100	9	algorithm	algorithm	NOUN
ajst-27521	100	10	that	that	PRON
ajst-27521	100	11	uses	use	VERB
ajst-27521	100	12	bidirectional	bidirectional	ADJ
ajst-27521	100	13	generative	generative	ADJ
ajst-27521	100	14	adversarial	adversarial	ADJ
ajst-27521	100	15	networks	network	NOUN
ajst-27521	100	16	to	to	PART
ajst-27521	100	17	infer	infer	VERB
ajst-27521	100	18	generated	generate	VERB
ajst-27521	100	19	data	datum	NOUN
ajst-27521	100	20	and	and	CCONJ
ajst-27521	100	21	learn	learn	VERB
ajst-27521	100	22	the	the	DET
ajst-27521	100	23	joint	joint	ADJ
ajst-27521	100	24	distribution	distribution	NOUN
ajst-27521	100	25	of	of	ADP
ajst-27521	100	26	normal	normal	ADJ
ajst-27521	100	27	and	and	CCONJ
ajst-27521	100	28	anomalous	anomalous	ADJ
ajst-27521	100	29	data	datum	NOUN
ajst-27521	100	30	.	.	PUNCT
ajst-27521	101	1	anomalies	anomaly	NOUN
ajst-27521	101	2	are	be	AUX
ajst-27521	101	3	determined	determine	VERB
ajst-27521	101	4	by	by	ADP
ajst-27521	101	5	calculating	calculate	VERB
ajst-27521	101	6	the	the	DET
ajst-27521	101	7	reconstruction	reconstruction	NOUN
ajst-27521	101	8	error	error	NOUN
ajst-27521	101	9	of	of	ADP
ajst-27521	101	10	data	data	NOUN
ajst-27521	101	11	features	feature	NOUN
ajst-27521	101	12	.	.	PUNCT
ajst-27521	102	1	4	4	X
ajst-27521	102	2	.	.	X
ajst-27521	102	3	deep	deep	ADJ
ajst-27521	102	4	anomaly	anomaly	NOUN
ajst-27521	102	5	detection	detection	NOUN
ajst-27521	102	6	model	model	NOUN
ajst-27521	102	7	4.1	4.1	NUM
ajst-27521	102	8	deep	deep	ADJ
ajst-27521	102	9	anomaly	anomaly	NOUN
ajst-27521	102	10	detection	detection	NOUN
ajst-27521	102	11	model	model	NOUN
ajst-27521	102	12	based	base	VERB
ajst-27521	102	13	on	on	ADP
ajst-27521	102	14	transformer	transformer	NOUN
ajst-27521	102	15	the	the	DET
ajst-27521	102	16	transformer	transformer	NOUN
ajst-27521	102	17	model	model	NOUN
ajst-27521	102	18	is	be	AUX
ajst-27521	102	19	a	a	DET
ajst-27521	102	20	deep	deep	ADJ
ajst-27521	102	21	learning	learning	NOUN
ajst-27521	102	22	model	model	NOUN
ajst-27521	102	23	architecture	architecture	NOUN
ajst-27521	102	24	for	for	ADP
ajst-27521	102	25	sequence	sequence	NOUN
ajst-27521	102	26	-	-	PUNCT
ajst-27521	102	27	to	to	ADP
ajst-27521	102	28	-	-	PUNCT
ajst-27521	102	29	sequence	sequence	NOUN
ajst-27521	102	30	tasks	task	NOUN
ajst-27521	102	31	in	in	ADP
ajst-27521	102	32	natural	natural	ADJ
ajst-27521	102	33	language	language	NOUN
ajst-27521	102	34	processing	processing	NOUN
ajst-27521	102	35	,	,	PUNCT
ajst-27521	102	36	introducing	introduce	VERB
ajst-27521	102	37	self	self	NOUN
ajst-27521	102	38	-	-	PUNCT
ajst-27521	102	39	attention	attention	NOUN
ajst-27521	102	40	mechanism	mechanism	NOUN
ajst-27521	102	41	and	and	CCONJ
ajst-27521	102	42	multi	multi	ADJ
ajst-27521	102	43	-	-	ADJ
ajst-27521	102	44	head	head	ADJ
ajst-27521	102	45	attention	attention	NOUN
ajst-27521	102	46	mechanism	mechanism	NOUN
ajst-27521	102	47	to	to	PART
ajst-27521	102	48	escape	escape	VERB
ajst-27521	102	49	the	the	DET
ajst-27521	102	50	sequence	sequence	NOUN
ajst-27521	102	51	dependence	dependence	NOUN
ajst-27521	102	52	problem	problem	NOUN
ajst-27521	102	53	inherent	inherent	ADJ
ajst-27521	102	54	in	in	ADP
ajst-27521	102	55	traditional	traditional	ADJ
ajst-27521	102	56	recurrent	recurrent	ADJ
ajst-27521	102	57	neural	neural	ADJ
ajst-27521	102	58	networks	network	NOUN
ajst-27521	102	59	.	.	PUNCT
ajst-27521	103	1	it	it	PRON
ajst-27521	103	2	is	be	AUX
ajst-27521	103	3	mainly	mainly	ADV
ajst-27521	103	4	composed	compose	VERB
ajst-27521	103	5	of	of	ADP
ajst-27521	103	6	four	four	NUM
ajst-27521	103	7	parts	part	NOUN
ajst-27521	103	8	:	:	PUNCT
ajst-27521	103	9	input	input	NOUN
ajst-27521	103	10	part	part	NOUN
ajst-27521	103	11	,	,	PUNCT
ajst-27521	103	12	multi	multi	ADJ
ajst-27521	103	13	-	-	ADJ
ajst-27521	103	14	layer	layer	ADJ
ajst-27521	103	15	encoder	encoder	NOUN
ajst-27521	103	16	,	,	PUNCT
ajst-27521	103	17	multi	multi	ADJ
ajst-27521	103	18	-	-	ADJ
ajst-27521	103	19	layer	layer	ADJ
ajst-27521	103	20	decoder	decoder	NOUN
ajst-27521	103	21	and	and	CCONJ
ajst-27521	103	22	output	output	NOUN
ajst-27521	103	23	part	part	NOUN
ajst-27521	103	24	.	.	PUNCT
ajst-27521	104	1	(	(	PUNCT
ajst-27521	104	2	1	1	X
ajst-27521	104	3	)	)	PUNCT
ajst-27521	104	4	input	input	NOUN
ajst-27521	104	5	part	part	NOUN
ajst-27521	104	6	:	:	PUNCT
ajst-27521	104	7	generate	generate	VERB
ajst-27521	104	8	a	a	DET
ajst-27521	104	9	position	position	NOUN
ajst-27521	104	10	vector	vector	NOUN
ajst-27521	104	11	for	for	ADP
ajst-27521	104	12	each	each	DET
ajst-27521	104	13	position	position	NOUN
ajst-27521	104	14	of	of	ADP
ajst-27521	104	15	the	the	DET
ajst-27521	104	16	input	input	NOUN
ajst-27521	104	17	sequence	sequence	NOUN
ajst-27521	104	18	,	,	PUNCT
ajst-27521	104	19	so	so	SCONJ
ajst-27521	104	20	that	that	SCONJ
ajst-27521	104	21	the	the	DET
ajst-27521	104	22	model	model	NOUN
ajst-27521	104	23	can	can	AUX
ajst-27521	104	24	understand	understand	VERB
ajst-27521	104	25	the	the	DET
ajst-27521	104	26	position	position	NOUN
ajst-27521	104	27	information	information	NOUN
ajst-27521	104	28	in	in	ADP
ajst-27521	104	29	the	the	DET
ajst-27521	104	30	sequence.(2	sequence.(2	NOUN
ajst-27521	104	31	)	)	PUNCT
ajst-27521	104	32	encoder	encoder	NOUN
ajst-27521	104	33	part	part	NOUN
ajst-27521	104	34	:	:	PUNCT
ajst-27521	104	35	composed	compose	VERB
ajst-27521	104	36	of	of	ADP
ajst-27521	104	37	n	n	PRON
ajst-27521	104	38	encoder	encoder	NOUN
ajst-27521	104	39	layers	layer	NOUN
ajst-27521	104	40	.	.	PUNCT
ajst-27521	105	1	each	each	DET
ajst-27521	105	2	encoder	encoder	NOUN
ajst-27521	105	3	layer	layer	NOUN
ajst-27521	105	4	consists	consist	VERB
ajst-27521	105	5	of	of	ADP
ajst-27521	105	6	two	two	NUM
ajst-27521	105	7	sub	sub	ADJ
ajst-27521	105	8	-	-	ADJ
ajst-27521	105	9	layer	layer	ADJ
ajst-27521	105	10	connected	connect	VERB
ajst-27521	105	11	structures	structure	NOUN
ajst-27521	105	12	:	:	PUNCT
ajst-27521	105	13	multi	multi	ADJ
ajst-27521	105	14	-	-	ADJ
ajst-27521	105	15	head	head	ADJ
ajst-27521	105	16	self	self	NOUN
ajst-27521	105	17	-	-	PUNCT
ajst-27521	105	18	attention	attention	NOUN
ajst-27521	105	19	sublayer	sublayer	NOUN
ajst-27521	105	20	and	and	CCONJ
ajst-27521	105	21	feedforward	feedforward	NOUN
ajst-27521	105	22	fully	fully	ADV
ajst-27521	105	23	connected	connect	VERB
ajst-27521	105	24	sublayer.(3	sublayer.(3	NOUN
ajst-27521	105	25	)	)	PUNCT
ajst-27521	105	26	decoder	decoder	NOUN
ajst-27521	105	27	part	part	NOUN
ajst-27521	105	28	:	:	PUNCT
ajst-27521	105	29	stacked	stack	VERB
ajst-27521	105	30	of	of	ADP
ajst-27521	105	31	n	n	PRON
ajst-27521	105	32	decoder	decoder	NOUN
ajst-27521	105	33	layers	layer	NOUN
ajst-27521	105	34	.	.	PUNCT
ajst-27521	106	1	each	each	DET
ajst-27521	106	2	decoder	decoder	NOUN
ajst-27521	106	3	layer	layer	NOUN
ajst-27521	106	4	consists	consist	VERB
ajst-27521	106	5	of	of	ADP
ajst-27521	106	6	three	three	NUM
ajst-27521	106	7	sub	sub	ADJ
ajst-27521	106	8	-	-	ADJ
ajst-27521	106	9	layer	layer	ADJ
ajst-27521	106	10	connected	connect	VERB
ajst-27521	106	11	structures	structure	NOUN
ajst-27521	106	12	:	:	PUNCT
ajst-27521	106	13	a	a	DET
ajst-27521	106	14	masked	masked	ADJ
ajst-27521	106	15	multi	multi	ADJ
ajst-27521	106	16	-	-	ADJ
ajst-27521	106	17	head	head	ADJ
ajst-27521	106	18	self	self	NOUN
ajst-27521	106	19	-	-	PUNCT
ajst-27521	106	20	attention	attention	NOUN
ajst-27521	106	21	sub	sub	NOUN
ajst-27521	106	22	-	-	NOUN
ajst-27521	106	23	layer	layer	NOUN
ajst-27521	106	24	,	,	PUNCT
ajst-27521	106	25	a	a	DET
ajst-27521	106	26	multi	multi	ADJ
ajst-27521	106	27	-	-	ADJ
ajst-27521	106	28	head	head	ADJ
ajst-27521	106	29	attention	attention	NOUN
ajst-27521	106	30	sub	sub	NOUN
ajst-27521	106	31	-	-	NOUN
ajst-27521	106	32	layer	layer	NOUN
ajst-27521	106	33	,	,	PUNCT
ajst-27521	106	34	and	and	CCONJ
ajst-27521	106	35	a	a	DET
ajst-27521	106	36	feedforward	feedforward	NOUN
ajst-27521	106	37	fully	fully	ADV
ajst-27521	106	38	connected	connect	VERB
ajst-27521	106	39	sub	sub	NOUN
ajst-27521	106	40	-	-	NOUN
ajst-27521	106	41	layer	layer	NOUN
ajst-27521	106	42	.	.	PUNCT
ajst-27521	107	1	each	each	DET
ajst-27521	107	2	sublayer	sublayer	NOUN
ajst-27521	107	3	is	be	AUX
ajst-27521	107	4	connected	connect	VERB
ajst-27521	107	5	by	by	ADP
ajst-27521	107	6	a	a	DET
ajst-27521	107	7	normalized	normalize	VERB
ajst-27521	107	8	layer	layer	NOUN
ajst-27521	107	9	and	and	CCONJ
ajst-27521	107	10	a	a	DET
ajst-27521	107	11	residual	residual	ADJ
ajst-27521	107	12	connection.(4	connection.(4	NOUN
ajst-27521	107	13	)	)	PUNCT
ajst-27521	107	14	output	output	NOUN
ajst-27521	107	15	part	part	NOUN
ajst-27521	107	16	:	:	PUNCT
ajst-27521	107	17	convert	convert	VERB
ajst-27521	107	18	the	the	DET
ajst-27521	107	19	vector	vector	NOUN
ajst-27521	107	20	of	of	ADP
ajst-27521	107	21	the	the	DET
ajst-27521	107	22	output	output	NOUN
ajst-27521	107	23	of	of	ADP
ajst-27521	107	24	the	the	DET
ajst-27521	107	25	decoder	decoder	NOUN
ajst-27521	107	26	into	into	ADP
ajst-27521	107	27	the	the	DET
ajst-27521	107	28	linear	linear	ADJ
ajst-27521	107	29	layer	layer	NOUN
ajst-27521	107	30	of	of	ADP
ajst-27521	107	31	the	the	DET
ajst-27521	107	32	final	final	ADJ
ajst-27521	107	33	output	output	NOUN
ajst-27521	107	34	dimension	dimension	NOUN
ajst-27521	107	35	and	and	CCONJ
ajst-27521	107	36	the	the	DET
ajst-27521	107	37	softmax	softmax	NOUN
ajst-27521	107	38	layer	layer	NOUN
ajst-27521	107	39	of	of	ADP
ajst-27521	107	40	converting	convert	VERB
ajst-27521	107	41	the	the	DET
ajst-27521	107	42	output	output	NOUN
ajst-27521	107	43	of	of	ADP
ajst-27521	107	44	the	the	DET
ajst-27521	107	45	linear	linear	ADJ
ajst-27521	107	46	layer	layer	NOUN
ajst-27521	107	47	into	into	ADP
ajst-27521	107	48	the	the	DET
ajst-27521	107	49	probability	probability	NOUN
ajst-27521	107	50	distribution	distribution	NOUN
ajst-27521	107	51	.	.	PUNCT
ajst-27521	108	1	168	168	NUM
ajst-27521	108	2	encoder	encoder	NOUN
ajst-27521	108	3	decoder	decoder	NOUN
ajst-27521	108	4	encoder	encoder	NOUN
ajst-27521	108	5	encoder	encoder	NOUN
ajst-27521	108	6	encoder	encoder	NOUN
ajst-27521	108	7	encoder	encoder	NOUN
ajst-27521	108	8	decoder	decoder	NOUN
ajst-27521	108	9	decoder	decoder	NOUN
ajst-27521	108	10	decoder	decoder	NOUN
ajst-27521	108	11	decoder	decoder	NOUN
ajst-27521	108	12	coding	code	VERB
ajst-27521	108	13	information	information	NOUN
ajst-27521	108	14	input	input	NOUN
ajst-27521	108	15	output	output	NOUN
ajst-27521	108	16	figure	figure	NOUN
ajst-27521	108	17	1	1	NUM
ajst-27521	108	18	.	.	PUNCT
ajst-27521	108	19	transformer	transformer	NOUN
ajst-27521	108	20	brief	brief	ADJ
ajst-27521	108	21	diagram	diagram	NOUN
ajst-27521	108	22	of	of	ADP
ajst-27521	108	23	the	the	DET
ajst-27521	108	24	structure	structure	NOUN
ajst-27521	108	25	4.2	4.2	NUM
ajst-27521	108	26	deep	deep	ADJ
ajst-27521	108	27	anomaly	anomaly	NOUN
ajst-27521	108	28	detection	detection	NOUN
ajst-27521	108	29	model	model	NOUN
ajst-27521	108	30	based	base	VERB
ajst-27521	108	31	on	on	ADP
ajst-27521	108	32	autoencoder	autoencoder	NOUN
ajst-27521	108	33	the	the	DET
ajst-27521	108	34	autoencoder	autoencoder	NOUN
ajst-27521	108	35	consists	consist	VERB
ajst-27521	108	36	of	of	ADP
ajst-27521	108	37	two	two	NUM
ajst-27521	108	38	components	component	NOUN
ajst-27521	108	39	,	,	PUNCT
ajst-27521	108	40	the	the	DET
ajst-27521	108	41	encoder	encoder	NOUN
ajst-27521	108	42	and	and	CCONJ
ajst-27521	108	43	the	the	DET
ajst-27521	108	44	decoder	decoder	NOUN
ajst-27521	108	45	,	,	PUNCT
ajst-27521	108	46	where	where	SCONJ
ajst-27521	108	47	the	the	DET
ajst-27521	108	48	input	input	NOUN
ajst-27521	108	49	is	be	AUX
ajst-27521	108	50	compressed	compress	VERB
ajst-27521	108	51	into	into	ADP
ajst-27521	108	52	the	the	DET
ajst-27521	108	53	latent	latent	NOUN
ajst-27521	108	54	spatial	spatial	ADJ
ajst-27521	108	55	representation	representation	NOUN
ajst-27521	108	56	through	through	ADP
ajst-27521	108	57	the	the	DET
ajst-27521	108	58	encoder	encoder	NOUN
ajst-27521	108	59	,	,	PUNCT
ajst-27521	108	60	and	and	CCONJ
ajst-27521	108	61	the	the	DET
ajst-27521	108	62	decoder	decoder	NOUN
ajst-27521	108	63	uses	use	VERB
ajst-27521	108	64	these	these	DET
ajst-27521	108	65	latent	latent	NOUN
ajst-27521	108	66	spatial	spatial	ADJ
ajst-27521	108	67	representations	representation	NOUN
ajst-27521	108	68	as	as	ADP
ajst-27521	108	69	inputs	input	NOUN
ajst-27521	108	70	for	for	ADP
ajst-27521	108	71	the	the	DET
ajst-27521	108	72	reconstruction	reconstruction	NOUN
ajst-27521	108	73	output	output	NOUN
ajst-27521	108	74	.	.	PUNCT
ajst-27521	109	1	by	by	ADP
ajst-27521	109	2	adding	add	VERB
ajst-27521	109	3	constraints	constraint	NOUN
ajst-27521	109	4	to	to	ADP
ajst-27521	109	5	the	the	DET
ajst-27521	109	6	autoencoder	autoencoder	NOUN
ajst-27521	109	7	,	,	PUNCT
ajst-27521	109	8	the	the	DET
ajst-27521	109	9	input	input	NOUN
ajst-27521	109	10	and	and	CCONJ
ajst-27521	109	11	output	output	NOUN
ajst-27521	109	12	are	be	AUX
ajst-27521	109	13	approximately	approximately	ADV
ajst-27521	109	14	identical	identical	ADJ
ajst-27521	109	15	to	to	PART
ajst-27521	109	16	effectively	effectively	ADV
ajst-27521	109	17	learn	learn	VERB
ajst-27521	109	18	the	the	DET
ajst-27521	109	19	data	data	NOUN
ajst-27521	109	20	features	feature	NOUN
ajst-27521	109	21	.	.	PUNCT
ajst-27521	110	1	the	the	DET
ajst-27521	110	2	autoencoder	autoencoder	NOUN
ajst-27521	110	3	is	be	AUX
ajst-27521	110	4	a	a	DET
ajst-27521	110	5	widely	widely	ADV
ajst-27521	110	6	used	use	VERB
ajst-27521	110	7	data	data	NOUN
ajst-27521	110	8	compression	compression	NOUN
ajst-27521	110	9	technology	technology	NOUN
ajst-27521	110	10	that	that	PRON
ajst-27521	110	11	can	can	AUX
ajst-27521	110	12	not	not	PART
ajst-27521	110	13	only	only	ADV
ajst-27521	110	14	be	be	AUX
ajst-27521	110	15	used	use	VERB
ajst-27521	110	16	in	in	ADP
ajst-27521	110	17	both	both	PRON
ajst-27521	110	18	for	for	ADP
ajst-27521	110	19	dimension	dimension	NOUN
ajst-27521	110	20	reduction	reduction	NOUN
ajst-27521	110	21	and	and	CCONJ
ajst-27521	110	22	feature	feature	NOUN
ajst-27521	110	23	learning	learning	NOUN
ajst-27521	110	24	,	,	PUNCT
ajst-27521	110	25	but	but	CCONJ
ajst-27521	110	26	also	also	ADV
ajst-27521	110	27	as	as	ADP
ajst-27521	110	28	a	a	DET
ajst-27521	110	29	good	good	ADJ
ajst-27521	110	30	data	data	NOUN
ajst-27521	110	31	noise	noise	NOUN
ajst-27521	110	32	reduction	reduction	NOUN
ajst-27521	110	33	algorithm	algorithm	NOUN
ajst-27521	110	34	.	.	PUNCT
ajst-27521	111	1	autoencoder	autoencoder	NOUN
ajst-27521	111	2	and	and	CCONJ
ajst-27521	111	3	its	its	PRON
ajst-27521	111	4	variants	variant	NOUN
ajst-27521	111	5	have	have	AUX
ajst-27521	111	6	been	be	AUX
ajst-27521	111	7	widely	widely	ADV
ajst-27521	111	8	used	use	VERB
ajst-27521	111	9	in	in	ADP
ajst-27521	111	10	anomaly	anomaly	NOUN
ajst-27521	111	11	detection	detection	NOUN
ajst-27521	111	12	and	and	CCONJ
ajst-27521	111	13	data	datum	NOUN
ajst-27521	111	14	generation	generation	NOUN
ajst-27521	111	15	,	,	PUNCT
ajst-27521	111	16	and	and	CCONJ
ajst-27521	111	17	it	it	PRON
ajst-27521	111	18	is	be	AUX
ajst-27521	111	19	an	an	DET
ajst-27521	111	20	unsupervised	unsupervised	ADJ
ajst-27521	111	21	deep	deep	ADJ
ajst-27521	111	22	learning	learning	NOUN
ajst-27521	111	23	method	method	NOUN
ajst-27521	111	24	.	.	PUNCT
ajst-27521	112	1	encoder	encoder	NOUN
ajst-27521	112	2	hidden	hide	VERB
ajst-27521	112	3	layer	layer	NOUN
ajst-27521	112	4	output	output	NOUN
ajst-27521	112	5	layer	layer	NOUN
ajst-27521	112	6	decoder	decoder	NOUN
ajst-27521	112	7	input	input	NOUN
ajst-27521	112	8	layer	layer	NOUN
ajst-27521	112	9	figure	figure	NOUN
ajst-27521	112	10	2	2	NUM
ajst-27521	112	11	.	.	PUNCT
ajst-27521	112	12	structure	structure	NOUN
ajst-27521	112	13	diagram	diagram	NOUN
ajst-27521	112	14	of	of	ADP
ajst-27521	112	15	the	the	DET
ajst-27521	112	16	autoencoder	autoencoder	NOUN
ajst-27521	112	17	in	in	ADP
ajst-27521	112	18	unsupervised	unsupervised	ADJ
ajst-27521	112	19	training	training	NOUN
ajst-27521	112	20	,	,	PUNCT
ajst-27521	112	21	the	the	DET
ajst-27521	112	22	loss	loss	NOUN
ajst-27521	112	23	function	function	NOUN
ajst-27521	112	24	helps	help	VERB
ajst-27521	112	25	to	to	PART
ajst-27521	112	26	correct	correct	VERB
ajst-27521	112	27	errors	error	NOUN
ajst-27521	112	28	generated	generate	VERB
ajst-27521	112	29	by	by	ADP
ajst-27521	112	30	the	the	DET
ajst-27521	112	31	model	model	NOUN
ajst-27521	112	32	,	,	PUNCT
ajst-27521	112	33	with	with	ADP
ajst-27521	112	34	the	the	DET
ajst-27521	112	35	goal	goal	NOUN
ajst-27521	112	36	to	to	PART
ajst-27521	112	37	make	make	VERB
ajst-27521	112	38	the	the	DET
ajst-27521	112	39	inputs	input	NOUN
ajst-27521	112	40	and	and	CCONJ
ajst-27521	112	41	outputs	output	NOUN
ajst-27521	112	42	of	of	ADP
ajst-27521	112	43	the	the	DET
ajst-27521	112	44	model	model	NOUN
ajst-27521	112	45	as	as	ADV
ajst-27521	112	46	close	close	ADJ
ajst-27521	112	47	as	as	ADP
ajst-27521	112	48	possible	possible	ADJ
ajst-27521	112	49	.	.	PUNCT
ajst-27521	113	1	as	as	SCONJ
ajst-27521	113	2	shown	show	VERB
ajst-27521	113	3	in	in	ADP
ajst-27521	113	4	the	the	DET
ajst-27521	113	5	following	follow	VERB
ajst-27521	113	6	formula	formula	NOUN
ajst-27521	113	7	,	,	PUNCT
ajst-27521	113	8	the	the	DET
ajst-27521	113	9	input	input	NOUN
ajst-27521	113	10	is	be	AUX
ajst-27521	113	11	sent	send	VERB
ajst-27521	113	12	to	to	ADP
ajst-27521	113	13	the	the	DET
ajst-27521	113	14	autoencoder	autoencoder	NOUN
ajst-27521	113	15	and	and	CCONJ
ajst-27521	113	16	reconstructed	reconstruct	VERB
ajst-27521	113	17	,	,	PUNCT
ajst-27521	113	18	the	the	DET
ajst-27521	113	19	encoder	encoder	NOUN
ajst-27521	113	20	by	by	ADP
ajst-27521	113	21	the	the	DET
ajst-27521	113	22	function	function	NOUN
ajst-27521	113	23	f	f	PROPN
ajst-27521	113	24	and	and	CCONJ
ajst-27521	113	25	the	the	DET
ajst-27521	113	26	decoder	decoder	NOUN
ajst-27521	113	27	by	by	ADP
ajst-27521	113	28	the	the	DET
ajst-27521	113	29	function	function	NOUN
ajst-27521	113	30	g	g	NOUN
ajst-27521	113	31	:	:	PUNCT
ajst-27521	113	32	ℎ	ℎ	PART
ajst-27521	114	1	𝑥	𝑥	PROPN
ajst-27521	114	2	𝑓	𝑓	ADP
ajst-27521	114	3	𝑤	𝑤	PART
ajst-27521	114	4	𝑥	𝑥	X
ajst-27521	114	5	𝑏	𝑏	NOUN
ajst-27521	114	6	𝑜	𝑜	NOUN
ajst-27521	114	7	𝑥	𝑥	PRON
ajst-27521	114	8	𝑔	𝑔	NOUN
ajst-27521	114	9	𝑤	𝑤	ADP
ajst-27521	114	10	ℎ	ℎ	PART
ajst-27521	114	11	𝑥	𝑥	NOUN
ajst-27521	114	12	𝑏	𝑏	NOUN
ajst-27521	114	13	where	where	SCONJ
ajst-27521	114	14	x	x	PRON
ajst-27521	114	15	is	be	AUX
ajst-27521	114	16	the	the	DET
ajst-27521	114	17	input	input	NOUN
ajst-27521	114	18	;	;	PUNCT
ajst-27521	114	19	w1	w1	NOUN
ajst-27521	114	20	and	and	CCONJ
ajst-27521	114	21	w2	w2	NOUN
ajst-27521	114	22	are	be	AUX
ajst-27521	114	23	the	the	DET
ajst-27521	114	24	weight	weight	NOUN
ajst-27521	114	25	matrices	matrix	NOUN
ajst-27521	114	26	of	of	ADP
ajst-27521	114	27	the	the	DET
ajst-27521	114	28	input	input	NOUN
ajst-27521	114	29	-	-	PUNCT
ajst-27521	114	30	output	output	NOUN
ajst-27521	114	31	layers	layer	NOUN
ajst-27521	114	32	and	and	CCONJ
ajst-27521	114	33	the	the	DET
ajst-27521	114	34	hidden	hidden	ADJ
ajst-27521	114	35	layers	layer	NOUN
ajst-27521	114	36	,	,	PUNCT
ajst-27521	114	37	respectively	respectively	ADV
ajst-27521	114	38	;	;	PUNCT
ajst-27521	114	39	and	and	CCONJ
ajst-27521	114	40	b1	b1	NOUN
ajst-27521	114	41	and	and	CCONJ
ajst-27521	114	42	b2	b2	NOUN
ajst-27521	114	43	are	be	AUX
ajst-27521	114	44	the	the	DET
ajst-27521	114	45	bias	bias	NOUN
ajst-27521	114	46	corresponding	correspond	VERB
ajst-27521	114	47	to	to	ADP
ajst-27521	114	48	each	each	DET
ajst-27521	114	49	stage	stage	NOUN
ajst-27521	114	50	.	.	PUNCT
ajst-27521	115	1	the	the	DET
ajst-27521	115	2	autoencoder	autoencoder	NOUN
ajst-27521	115	3	structure	structure	NOUN
ajst-27521	115	4	is	be	AUX
ajst-27521	115	5	clear	clear	ADJ
ajst-27521	115	6	and	and	CCONJ
ajst-27521	115	7	easy	easy	ADJ
ajst-27521	115	8	to	to	PART
ajst-27521	115	9	understand	understand	VERB
ajst-27521	115	10	,	,	PUNCT
ajst-27521	115	11	suitable	suitable	ADJ
ajst-27521	115	12	for	for	ADP
ajst-27521	115	13	different	different	ADJ
ajst-27521	115	14	types	type	NOUN
ajst-27521	115	15	of	of	ADP
ajst-27521	115	16	data	datum	NOUN
ajst-27521	115	17	,	,	PUNCT
ajst-27521	115	18	and	and	CCONJ
ajst-27521	115	19	many	many	ADJ
ajst-27521	115	20	powerful	powerful	ADJ
ajst-27521	115	21	variants	variant	NOUN
ajst-27521	115	22	are	be	AUX
ajst-27521	115	23	derived	derive	VERB
ajst-27521	115	24	.	.	PUNCT
ajst-27521	116	1	however	however	ADV
ajst-27521	116	2	,	,	PUNCT
ajst-27521	116	3	abnormalities	abnormality	NOUN
ajst-27521	116	4	in	in	ADP
ajst-27521	116	5	the	the	DET
ajst-27521	116	6	training	training	NOUN
ajst-27521	116	7	data	datum	NOUN
ajst-27521	116	8	may	may	AUX
ajst-27521	116	9	bias	bias	VERB
ajst-27521	116	10	the	the	DET
ajst-27521	116	11	learned	learn	VERB
ajst-27521	116	12	feature	feature	NOUN
ajst-27521	116	13	representation	representation	NOUN
ajst-27521	116	14	.	.	PUNCT
ajst-27521	117	1	moreover	moreover	ADV
ajst-27521	117	2	,	,	PUNCT
ajst-27521	117	3	the	the	DET
ajst-27521	117	4	objective	objective	ADJ
ajst-27521	117	5	function	function	NOUN
ajst-27521	117	6	of	of	ADP
ajst-27521	117	7	data	datum	NOUN
ajst-27521	117	8	reconstruction	reconstruction	NOUN
ajst-27521	117	9	is	be	AUX
ajst-27521	117	10	mainly	mainly	ADV
ajst-27521	117	11	aimed	aim	VERB
ajst-27521	117	12	at	at	ADP
ajst-27521	117	13	dimension	dimension	NOUN
ajst-27521	117	14	reduction	reduction	NOUN
ajst-27521	117	15	and	and	CCONJ
ajst-27521	117	16	data	datum	NOUN
ajst-27521	117	17	compression	compression	NOUN
ajst-27521	117	18	,	,	PUNCT
ajst-27521	117	19	not	not	PART
ajst-27521	117	20	specifically	specifically	ADV
ajst-27521	117	21	for	for	ADP
ajst-27521	117	22	anomaly	anomaly	NOUN
ajst-27521	117	23	detection	detection	NOUN
ajst-27521	117	24	.	.	PUNCT
ajst-27521	118	1	this	this	PRON
ajst-27521	118	2	causes	cause	VERB
ajst-27521	118	3	the	the	DET
ajst-27521	118	4	learned	learn	VERB
ajst-27521	118	5	feature	feature	NOUN
ajst-27521	118	6	representation	representation	NOUN
ajst-27521	118	7	tends	tend	VERB
ajst-27521	118	8	to	to	PART
ajst-27521	118	9	generalize	generalize	VERB
ajst-27521	118	10	latent	latent	NOUN
ajst-27521	118	11	patterns	pattern	NOUN
ajst-27521	118	12	and	and	CCONJ
ajst-27521	118	13	is	be	AUX
ajst-27521	118	14	not	not	PART
ajst-27521	118	15	optimized	optimize	VERB
ajst-27521	118	16	for	for	ADP
ajst-27521	118	17	anomaly	anomaly	NOUN
ajst-27521	118	18	detection	detection	NOUN
ajst-27521	118	19	.	.	PUNCT
ajst-27521	119	1	4.3	4.3	NUM
ajst-27521	119	2	deep	deep	ADJ
ajst-27521	119	3	anomaly	anomaly	NOUN
ajst-27521	119	4	detection	detection	NOUN
ajst-27521	119	5	model	model	NOUN
ajst-27521	119	6	based	base	VERB
ajst-27521	119	7	on	on	ADP
ajst-27521	119	8	lstm	lstm	NOUN
ajst-27521	119	9	as	as	ADP
ajst-27521	119	10	a	a	DET
ajst-27521	119	11	variant	variant	NOUN
ajst-27521	119	12	of	of	ADP
ajst-27521	119	13	recurrent	recurrent	ADJ
ajst-27521	119	14	neural	neural	ADJ
ajst-27521	119	15	network	network	NOUN
ajst-27521	119	16	(	(	PUNCT
ajst-27521	119	17	rnn	rnn	PROPN
ajst-27521	119	18	)	)	PUNCT
ajst-27521	119	19	,	,	PUNCT
ajst-27521	119	20	lstm	lstm	NOUN
ajst-27521	119	21	inherits	inherit	VERB
ajst-27521	119	22	the	the	DET
ajst-27521	119	23	ability	ability	NOUN
ajst-27521	119	24	of	of	ADP
ajst-27521	119	25	rnn	rnn	NOUN
ajst-27521	119	26	to	to	PART
ajst-27521	119	27	process	process	NOUN
ajst-27521	119	28	time	time	NOUN
ajst-27521	119	29	series	series	PROPN
ajst-27521	119	30	data	data	PROPN
ajst-27521	119	31	,	,	PUNCT
ajst-27521	119	32	which	which	PRON
ajst-27521	119	33	can	can	AUX
ajst-27521	119	34	make	make	VERB
ajst-27521	119	35	use	use	NOUN
ajst-27521	119	36	of	of	ADP
ajst-27521	119	37	historical	historical	ADJ
ajst-27521	119	38	data	datum	NOUN
ajst-27521	119	39	for	for	ADP
ajst-27521	119	40	predict	predict	NOUN
ajst-27521	119	41	,	,	PUNCT
ajst-27521	119	42	while	while	SCONJ
ajst-27521	119	43	overcoming	overcome	VERB
ajst-27521	119	44	the	the	DET
ajst-27521	119	45	problems	problem	NOUN
ajst-27521	119	46	of	of	ADP
ajst-27521	119	47	short	short	ADJ
ajst-27521	119	48	-	-	PUNCT
ajst-27521	119	49	term	term	NOUN
ajst-27521	119	50	memory	memory	NOUN
ajst-27521	119	51	and	and	CCONJ
ajst-27521	119	52	gradient	gradient	ADJ
ajst-27521	119	53	disappearance	disappearance	NOUN
ajst-27521	119	54	of	of	ADP
ajst-27521	119	55	rnn	rnn	PROPN
ajst-27521	119	56	.	.	PUNCT
ajst-27521	120	1	through	through	ADP
ajst-27521	120	2	its	its	PRON
ajst-27521	120	3	unique	unique	ADJ
ajst-27521	120	4	memory	memory	NOUN
ajst-27521	120	5	module	module	NOUN
ajst-27521	120	6	,	,	PUNCT
ajst-27521	120	7	lstm	lstm	NOUN
ajst-27521	120	8	is	be	AUX
ajst-27521	120	9	able	able	ADJ
ajst-27521	120	10	to	to	PART
ajst-27521	120	11	learn	learn	VERB
ajst-27521	120	12	and	and	CCONJ
ajst-27521	120	13	retain	retain	VERB
ajst-27521	120	14	long	long	ADJ
ajst-27521	120	15	-	-	PUNCT
ajst-27521	120	16	term	term	NOUN
ajst-27521	120	17	dependent	dependent	ADJ
ajst-27521	120	18	169	169	NUM
ajst-27521	120	19	information	information	NOUN
ajst-27521	120	20	,	,	PUNCT
ajst-27521	120	21	thus	thus	ADV
ajst-27521	120	22	enabling	enable	VERB
ajst-27521	120	23	the	the	DET
ajst-27521	120	24	model	model	NOUN
ajst-27521	120	25	to	to	PART
ajst-27521	120	26	perform	perform	VERB
ajst-27521	120	27	well	well	ADV
ajst-27521	120	28	when	when	SCONJ
ajst-27521	120	29	processing	processing	NOUN
ajst-27521	120	30	time	time	NOUN
ajst-27521	120	31	-	-	PUNCT
ajst-27521	120	32	series	series	NOUN
ajst-27521	120	33	data	datum	NOUN
ajst-27521	120	34	.	.	PUNCT
ajst-27521	121	1	recently	recently	ADV
ajst-27521	121	2	,	,	PUNCT
ajst-27521	121	3	models	model	NOUN
ajst-27521	121	4	based	base	VERB
ajst-27521	121	5	on	on	ADP
ajst-27521	121	6	lstm	lstm	NOUN
ajst-27521	121	7	models	model	NOUN
ajst-27521	121	8	and	and	CCONJ
ajst-27521	121	9	their	their	PRON
ajst-27521	121	10	variants	variant	NOUN
ajst-27521	121	11	have	have	AUX
ajst-27521	121	12	been	be	AUX
ajst-27521	121	13	widely	widely	ADV
ajst-27521	121	14	used	use	VERB
ajst-27521	121	15	in	in	ADP
ajst-27521	121	16	various	various	ADJ
ajst-27521	121	17	research	research	NOUN
ajst-27521	121	18	fields	field	NOUN
ajst-27521	121	19	,	,	PUNCT
ajst-27521	121	20	solving	solve	VERB
ajst-27521	121	21	many	many	ADJ
ajst-27521	121	22	problems	problem	NOUN
ajst-27521	121	23	that	that	PRON
ajst-27521	121	24	are	be	AUX
ajst-27521	121	25	difficult	difficult	ADJ
ajst-27521	121	26	to	to	PART
ajst-27521	121	27	overcome	overcome	VERB
ajst-27521	121	28	by	by	ADP
ajst-27521	121	29	traditional	traditional	ADJ
ajst-27521	121	30	ai	ai	ADJ
ajst-27521	121	31	algorithms	algorithm	NOUN
ajst-27521	121	32	.	.	PUNCT
ajst-27521	122	1	for	for	ADP
ajst-27521	122	2	a	a	DET
ajst-27521	122	3	given	give	VERB
ajst-27521	122	4	time	time	NOUN
ajst-27521	122	5	series	series	PROPN
ajst-27521	122	6	input	input	PROPN
ajst-27521	122	7	xt	xt	PROPN
ajst-27521	122	8	,	,	PUNCT
ajst-27521	122	9	lstm	lstm	NOUN
ajst-27521	122	10	jointly	jointly	ADV
ajst-27521	122	11	controls	control	VERB
ajst-27521	122	12	its	its	PRON
ajst-27521	122	13	unit	unit	NOUN
ajst-27521	122	14	state	state	NOUN
ajst-27521	122	15	ct	ct	PROPN
ajst-27521	122	16	and	and	CCONJ
ajst-27521	122	17	output	output	NOUN
ajst-27521	122	18	ht	ht	INTJ
ajst-27521	122	19	by	by	ADP
ajst-27521	122	20	forgetting	forget	VERB
ajst-27521	122	21	the	the	DET
ajst-27521	122	22	gate	gate	NOUN
ajst-27521	122	23	,	,	PUNCT
ajst-27521	122	24	input	input	NOUN
ajst-27521	122	25	gate	gate	NOUN
ajst-27521	122	26	and	and	CCONJ
ajst-27521	122	27	output	output	NOUN
ajst-27521	122	28	gate	gate	NOUN
ajst-27521	122	29	:	:	PUNCT
ajst-27521	122	30	𝑓	𝑓	PROPN
ajst-27521	122	31	𝜎	𝜎	X
ajst-27521	122	32	𝑊	𝑊	NOUN
ajst-27521	122	33	∙	∙	NOUN
ajst-27521	122	34	𝑥	𝑥	X
ajst-27521	122	35	𝑊	𝑊	NOUN
ajst-27521	122	36	∙	∙	NOUN
ajst-27521	122	37	ℎ	ℎ	ADP
ajst-27521	122	38	𝑏	𝑏	NOUN
ajst-27521	122	39	𝑖	𝑖	SYM
ajst-27521	122	40	𝜎	𝜎	NOUN
ajst-27521	122	41	𝑊	𝑊	NOUN
ajst-27521	122	42	∙	∙	NOUN
ajst-27521	122	43	𝑥	𝑥	X
ajst-27521	122	44	𝑊	𝑊	NOUN
ajst-27521	122	45	∙	∙	NOUN
ajst-27521	122	46	ℎ	ℎ	PART
ajst-27521	122	47	𝑏	𝑏	NOUN
ajst-27521	122	48	𝑜	𝑜	NOUN
ajst-27521	122	49	𝜎	𝜎	NOUN
ajst-27521	122	50	𝑊	𝑊	NOUN
ajst-27521	122	51	∙	∙	NOUN
ajst-27521	122	52	𝑥	𝑥	X
ajst-27521	122	53	𝑊	𝑊	NOUN
ajst-27521	122	54	∙	∙	NOUN
ajst-27521	122	55	ℎ	ℎ	PART
ajst-27521	122	56	𝑏	𝑏	PROPN
ajst-27521	122	57	𝑐	𝑐	PROPN
ajst-27521	122	58	𝑓	𝑓	DET
ajst-27521	122	59	⨂𝑐	⨂𝑐	PROPN
ajst-27521	122	60	𝑖	𝑖	SYM
ajst-27521	122	61	⨂𝑡𝑎𝑛ℎ	⨂𝑡𝑎𝑛ℎ	NOUN
ajst-27521	123	1	𝑊	𝑊	VERB
ajst-27521	123	2	∙	∙	NOUN
ajst-27521	123	3	𝑥	𝑥	X
ajst-27521	123	4	𝑊	𝑊	NOUN
ajst-27521	123	5	∙	∙	NOUN
ajst-27521	123	6	ℎ	ℎ	ADP
ajst-27521	123	7	𝑏	𝑏	NOUN
ajst-27521	123	8	ℎ	ℎ	PROPN
ajst-27521	123	9	𝑜	𝑜	NOUN
ajst-27521	123	10	⨂𝑡𝑎𝑛ℎ	⨂𝑡𝑎𝑛ℎ	VERB
ajst-27521	123	11	𝑐	𝑐	PROPN
ajst-27521	123	12	where	where	SCONJ
ajst-27521	123	13	w	w	NOUN
ajst-27521	123	14	and	and	CCONJ
ajst-27521	123	15	b	b	PROPN
ajst-27521	123	16	represent	represent	VERB
ajst-27521	123	17	the	the	DET
ajst-27521	123	18	weight	weight	NOUN
ajst-27521	123	19	matrix	matrix	NOUN
ajst-27521	123	20	and	and	CCONJ
ajst-27521	123	21	the	the	DET
ajst-27521	123	22	bias	bias	NOUN
ajst-27521	123	23	vector	vector	NOUN
ajst-27521	123	24	,	,	PUNCT
ajst-27521	123	25	respectively	respectively	ADV
ajst-27521	123	26	.	.	PUNCT
ajst-27521	124	1	the	the	DET
ajst-27521	124	2	forgetting	forget	VERB
ajst-27521	124	3	gate	gate	NOUN
ajst-27521	124	4	is	be	AUX
ajst-27521	124	5	responsible	responsible	ADJ
ajst-27521	124	6	for	for	ADP
ajst-27521	124	7	discarding	discard	VERB
ajst-27521	124	8	the	the	DET
ajst-27521	124	9	useless	useless	ADJ
ajst-27521	124	10	information	information	NOUN
ajst-27521	124	11	existing	exist	VERB
ajst-27521	124	12	in	in	ADP
ajst-27521	124	13	the	the	DET
ajst-27521	124	14	past	past	ADJ
ajst-27521	124	15	cell	cell	NOUN
ajst-27521	124	16	state	state	NOUN
ajst-27521	124	17	of	of	ADP
ajst-27521	124	18	the	the	DET
ajst-27521	124	19	lstm	lstm	NOUN
ajst-27521	124	20	,	,	PUNCT
ajst-27521	124	21	retaining	retain	VERB
ajst-27521	124	22	the	the	DET
ajst-27521	124	23	information	information	NOUN
ajst-27521	124	24	added	add	VERB
ajst-27521	124	25	to	to	ADP
ajst-27521	124	26	the	the	DET
ajst-27521	124	27	current	current	ADJ
ajst-27521	124	28	cell	cell	NOUN
ajst-27521	124	29	state	state	NOUN
ajst-27521	124	30	of	of	ADP
ajst-27521	124	31	the	the	DET
ajst-27521	124	32	lstm	lstm	NOUN
ajst-27521	124	33	,	,	PUNCT
ajst-27521	124	34	and	and	CCONJ
ajst-27521	124	35	determining	determine	VERB
ajst-27521	124	36	the	the	DET
ajst-27521	124	37	information	information	NOUN
ajst-27521	124	38	output	output	NOUN
ajst-27521	124	39	.	.	PUNCT
ajst-27521	125	1	due	due	ADP
ajst-27521	125	2	to	to	ADP
ajst-27521	125	3	the	the	DET
ajst-27521	125	4	presence	presence	NOUN
ajst-27521	125	5	of	of	ADP
ajst-27521	125	6	these	these	DET
ajst-27521	125	7	gates	gate	NOUN
ajst-27521	125	8	,	,	PUNCT
ajst-27521	125	9	lstm	lstm	NOUN
ajst-27521	125	10	can	can	AUX
ajst-27521	125	11	more	more	ADV
ajst-27521	125	12	easily	easily	ADV
ajst-27521	125	13	simulate	simulate	VERB
ajst-27521	125	14	long	long	ADJ
ajst-27521	125	15	-	-	PUNCT
ajst-27521	125	16	term	term	NOUN
ajst-27521	125	17	change	change	NOUN
ajst-27521	125	18	patterns	pattern	NOUN
ajst-27521	125	19	in	in	ADP
ajst-27521	125	20	time	time	NOUN
ajst-27521	125	21	series	series	NOUN
ajst-27521	125	22	.	.	PUNCT
ajst-27521	126	1	∂∂	∂∂	SYM
ajst-27521	126	2	  	  	SPACE
ajst-27521	126	3	∂	∂	NUM
ajst-27521	126	4	tanh	tanh	PROPN
ajst-27521	126	5	tanh	tanh	PROPN
ajst-27521	126	6	ct‐1	ct‐1	PROPN
ajst-27521	127	1	ct	ct	PROPN
ajst-27521	127	2	  	  	SPACE
ajst-27521	127	3	ht‐1	ht‐1	PROPN
ajst-27521	127	4	  	  	SPACE
ajst-27521	127	5	f	f	PROPN
ajst-27521	127	6	 	 	SPACE
ajst-27521	128	1	i	i	PRON
ajst-27521	128	2	  	  	SPACE
ajst-27521	128	3	g	g	PROPN
ajst-27521	128	4	  	  	SPACE
ajst-27521	128	5	o	o	NOUN
ajst-27521	128	6	  	  	SPACE
ajst-27521	128	7	xt‐1	xt‐1	PROPN
ajst-27521	128	8	 	 	SPACE
ajst-27521	129	1	ht	ht	PROPN
ajst-27521	129	2	figure	figure	NOUN
ajst-27521	129	3	3	3	NUM
ajst-27521	129	4	.	.	PUNCT
ajst-27521	129	5	structural	structural	ADJ
ajst-27521	129	6	diagram	diagram	NOUN
ajst-27521	129	7	of	of	ADP
ajst-27521	129	8	the	the	DET
ajst-27521	129	9	lstm	lstm	PROPN
ajst-27521	129	10	cells	cell	NOUN
ajst-27521	129	11	lstm	lstm	ADJ
ajst-27521	129	12	network	network	NOUN
ajst-27521	129	13	model	model	NOUN
ajst-27521	129	14	of	of	ADP
ajst-27521	129	15	time	time	NOUN
ajst-27521	129	16	series	series	NOUN
ajst-27521	129	17	in	in	ADP
ajst-27521	129	18	the	the	DET
ajst-27521	129	19	study	study	NOUN
ajst-27521	129	20	of	of	ADP
ajst-27521	129	21	various	various	ADJ
ajst-27521	129	22	time	time	NOUN
ajst-27521	129	23	series	series	NOUN
ajst-27521	129	24	,	,	PUNCT
ajst-27521	129	25	which	which	PRON
ajst-27521	129	26	can	can	AUX
ajst-27521	129	27	learn	learn	VERB
ajst-27521	129	28	adaptively	adaptively	ADV
ajst-27521	129	29	from	from	ADP
ajst-27521	129	30	a	a	DET
ajst-27521	129	31	large	large	ADJ
ajst-27521	129	32	number	number	NOUN
ajst-27521	129	33	of	of	ADP
ajst-27521	129	34	normal	normal	ADJ
ajst-27521	129	35	samples	sample	NOUN
ajst-27521	129	36	and	and	CCONJ
ajst-27521	129	37	realize	realize	VERB
ajst-27521	129	38	real	real	ADJ
ajst-27521	129	39	-	-	PUNCT
ajst-27521	129	40	time	time	NOUN
ajst-27521	129	41	anomaly	anomaly	NOUN
ajst-27521	129	42	detection	detection	NOUN
ajst-27521	129	43	.	.	PUNCT
ajst-27521	130	1	there	there	PRON
ajst-27521	130	2	are	be	VERB
ajst-27521	130	3	also	also	ADV
ajst-27521	130	4	some	some	DET
ajst-27521	130	5	deficiencies	deficiency	NOUN
ajst-27521	130	6	,	,	PUNCT
ajst-27521	130	7	including	include	VERB
ajst-27521	130	8	high	high	ADJ
ajst-27521	130	9	computational	computational	ADJ
ajst-27521	130	10	costs	cost	NOUN
ajst-27521	130	11	,	,	PUNCT
ajst-27521	130	12	overfitting	overfitte	VERB
ajst-27521	130	13	risks	risk	NOUN
ajst-27521	130	14	,	,	PUNCT
ajst-27521	130	15	complex	complex	ADJ
ajst-27521	130	16	parameter	parameter	NOUN
ajst-27521	130	17	adjustments	adjustment	NOUN
ajst-27521	130	18	,	,	PUNCT
ajst-27521	130	19	and	and	CCONJ
ajst-27521	130	20	insufficient	insufficient	ADJ
ajst-27521	130	21	sensitivity	sensitivity	NOUN
ajst-27521	130	22	to	to	ADP
ajst-27521	130	23	some	some	DET
ajst-27521	130	24	abnormal	abnormal	ADJ
ajst-27521	130	25	types	type	NOUN
ajst-27521	130	26	,	,	PUNCT
ajst-27521	130	27	which	which	PRON
ajst-27521	130	28	may	may	AUX
ajst-27521	130	29	affect	affect	VERB
ajst-27521	130	30	their	their	PRON
ajst-27521	130	31	performance	performance	NOUN
ajst-27521	130	32	and	and	CCONJ
ajst-27521	130	33	efficiency	efficiency	NOUN
ajst-27521	130	34	in	in	ADP
ajst-27521	130	35	practical	practical	ADJ
ajst-27521	130	36	applications	application	NOUN
ajst-27521	130	37	.	.	PUNCT
ajst-27521	131	1	4.4	4.4	NUM
ajst-27521	131	2	deep	deep	ADJ
ajst-27521	131	3	anomaly	anomaly	NOUN
ajst-27521	131	4	detection	detection	NOUN
ajst-27521	131	5	model	model	NOUN
ajst-27521	131	6	based	base	VERB
ajst-27521	131	7	on	on	ADP
ajst-27521	131	8	gan	gan	PROPN
ajst-27521	131	9	generating	generating	PROPN
ajst-27521	131	10	generative	generative	ADJ
ajst-27521	131	11	adversarial	adversarial	ADJ
ajst-27521	131	12	network	network	NOUN
ajst-27521	131	13	(	(	PUNCT
ajst-27521	131	14	gan	gan	PROPN
ajst-27521	131	15	)	)	PUNCT
ajst-27521	131	16	is	be	AUX
ajst-27521	131	17	mainly	mainly	ADV
ajst-27521	131	18	composed	compose	VERB
ajst-27521	131	19	of	of	ADP
ajst-27521	131	20	the	the	DET
ajst-27521	131	21	generator	generator	NOUN
ajst-27521	131	22	and	and	CCONJ
ajst-27521	131	23	the	the	DET
ajst-27521	131	24	discriminator	discriminator	NOUN
ajst-27521	131	25	,	,	PUNCT
ajst-27521	131	26	in	in	ADP
ajst-27521	131	27	which	which	PRON
ajst-27521	131	28	the	the	DET
ajst-27521	131	29	discriminator	discriminator	NOUN
ajst-27521	131	30	is	be	AUX
ajst-27521	131	31	responsible	responsible	ADJ
ajst-27521	131	32	for	for	ADP
ajst-27521	131	33	the	the	DET
ajst-27521	131	34	global	global	ADJ
ajst-27521	131	35	judgment	judgment	NOUN
ajst-27521	131	36	,	,	PUNCT
ajst-27521	131	37	and	and	CCONJ
ajst-27521	131	38	the	the	DET
ajst-27521	131	39	generator	generator	NOUN
ajst-27521	131	40	focuses	focus	VERB
ajst-27521	131	41	on	on	ADP
ajst-27521	131	42	the	the	DET
ajst-27521	131	43	generation	generation	NOUN
ajst-27521	131	44	of	of	ADP
ajst-27521	131	45	local	local	ADJ
ajst-27521	131	46	details	detail	NOUN
ajst-27521	131	47	.	.	PUNCT
ajst-27521	132	1	the	the	DET
ajst-27521	132	2	goal	goal	NOUN
ajst-27521	132	3	of	of	ADP
ajst-27521	132	4	the	the	DET
ajst-27521	132	5	generator	generator	NOUN
ajst-27521	132	6	is	be	AUX
ajst-27521	132	7	to	to	PART
ajst-27521	132	8	maximize	maximize	VERB
ajst-27521	132	9	capturing	capture	VERB
ajst-27521	132	10	the	the	DET
ajst-27521	132	11	characteristics	characteristic	NOUN
ajst-27521	132	12	of	of	ADP
ajst-27521	132	13	the	the	DET
ajst-27521	132	14	training	training	NOUN
ajst-27521	132	15	sample	sample	NOUN
ajst-27521	132	16	to	to	PART
ajst-27521	132	17	generate	generate	VERB
ajst-27521	132	18	realistic	realistic	ADJ
ajst-27521	132	19	samples	sample	NOUN
ajst-27521	132	20	sufficient	sufficient	ADJ
ajst-27521	132	21	to	to	PART
ajst-27521	132	22	fool	fool	VERB
ajst-27521	132	23	the	the	DET
ajst-27521	132	24	discriminator	discriminator	NOUN
ajst-27521	132	25	.	.	PUNCT
ajst-27521	133	1	the	the	DET
ajst-27521	133	2	purpose	purpose	NOUN
ajst-27521	133	3	of	of	ADP
ajst-27521	133	4	the	the	DET
ajst-27521	133	5	discriminator	discriminator	NOUN
ajst-27521	133	6	is	be	AUX
ajst-27521	133	7	to	to	PART
ajst-27521	133	8	compare	compare	VERB
ajst-27521	133	9	the	the	DET
ajst-27521	133	10	two	two	NUM
ajst-27521	133	11	,	,	PUNCT
ajst-27521	133	12	to	to	PART
ajst-27521	133	13	distinguish	distinguish	VERB
ajst-27521	133	14	the	the	DET
ajst-27521	133	15	authenticity	authenticity	NOUN
ajst-27521	133	16	of	of	ADP
ajst-27521	133	17	the	the	DET
ajst-27521	133	18	input	input	NOUN
ajst-27521	133	19	data	datum	NOUN
ajst-27521	133	20	as	as	ADV
ajst-27521	133	21	much	much	ADV
ajst-27521	133	22	as	as	ADP
ajst-27521	133	23	possible	possible	ADJ
ajst-27521	133	24	,	,	PUNCT
ajst-27521	133	25	and	and	CCONJ
ajst-27521	133	26	to	to	PART
ajst-27521	133	27	narrow	narrow	VERB
ajst-27521	133	28	the	the	DET
ajst-27521	133	29	deviation	deviation	NOUN
ajst-27521	133	30	between	between	ADP
ajst-27521	133	31	the	the	DET
ajst-27521	133	32	generated	generate	VERB
ajst-27521	133	33	sample	sample	NOUN
ajst-27521	133	34	and	and	CCONJ
ajst-27521	133	35	the	the	DET
ajst-27521	133	36	real	real	ADJ
ajst-27521	133	37	sample	sample	NOUN
ajst-27521	133	38	.	.	PUNCT
ajst-27521	134	1	真实数据x	真实数据x	PROPN
ajst-27521	134	2	随机噪声z	随机噪声z	PROPN
ajst-27521	134	3	生成器	生成器	PROPN
ajst-27521	134	4	判别器	判别器	VERB
ajst-27521	134	5	g（z	g（z	PROPN
ajst-27521	134	6	）	）	NOUN
ajst-27521	134	7	判别真伪	判别真伪	ADJ
ajst-27521	134	8	figure	figure	NOUN
ajst-27521	134	9	3	3	X
ajst-27521	134	10	.	.	PUNCT
ajst-27521	134	11	gan	gan	PROPN
ajst-27521	134	12	structure	structure	NOUN
ajst-27521	134	13	diagram	diagram	NOUN
ajst-27521	134	14	in	in	ADP
ajst-27521	134	15	the	the	DET
ajst-27521	134	16	training	training	NOUN
ajst-27521	134	17	process	process	NOUN
ajst-27521	134	18	,	,	PUNCT
ajst-27521	134	19	while	while	SCONJ
ajst-27521	134	20	the	the	DET
ajst-27521	134	21	generator	generator	NOUN
ajst-27521	134	22	improves	improve	VERB
ajst-27521	134	23	the	the	DET
ajst-27521	134	24	falsification	falsification	NOUN
ajst-27521	134	25	ability	ability	NOUN
ajst-27521	134	26	,	,	PUNCT
ajst-27521	134	27	the	the	DET
ajst-27521	134	28	discriminator	discriminator	NOUN
ajst-27521	134	29	also	also	ADV
ajst-27521	134	30	improves	improve	VERB
ajst-27521	134	31	the	the	DET
ajst-27521	134	32	discrimination	discrimination	NOUN
ajst-27521	134	33	ability	ability	NOUN
ajst-27521	134	34	.	.	PUNCT
ajst-27521	135	1	the	the	DET
ajst-27521	135	2	confrontation	confrontation	NOUN
ajst-27521	135	3	between	between	ADP
ajst-27521	135	4	the	the	DET
ajst-27521	135	5	generator	generator	NOUN
ajst-27521	135	6	and	and	CCONJ
ajst-27521	135	7	the	the	DET
ajst-27521	135	8	discriminator	discriminator	NOUN
ajst-27521	135	9	forms	form	VERB
ajst-27521	135	10	a	a	DET
ajst-27521	135	11	dynamic	dynamic	ADJ
ajst-27521	135	12	game	game	NOUN
ajst-27521	135	13	process	process	NOUN
ajst-27521	135	14	.	.	PUNCT
ajst-27521	136	1	nash	nash	PROPN
ajst-27521	136	2	equilibrium	equilibrium	NOUN
ajst-27521	136	3	is	be	AUX
ajst-27521	136	4	reached	reach	VERB
ajst-27521	136	5	when	when	SCONJ
ajst-27521	136	6	the	the	DET
ajst-27521	136	7	generator	generator	NOUN
ajst-27521	136	8	can	can	AUX
ajst-27521	136	9	generate	generate	VERB
ajst-27521	136	10	data	datum	NOUN
ajst-27521	136	11	that	that	PRON
ajst-27521	136	12	the	the	DET
ajst-27521	136	13	discriminant	discriminant	NOUN
ajst-27521	136	14	has	have	VERB
ajst-27521	136	15	difficulty	difficulty	NOUN
ajst-27521	136	16	distinguishing	distinguish	VERB
ajst-27521	136	17	between	between	ADP
ajst-27521	136	18	true	true	ADJ
ajst-27521	136	19	and	and	CCONJ
ajst-27521	136	20	false	false	ADJ
ajst-27521	136	21	.	.	PUNCT
ajst-27521	137	1	the	the	DET
ajst-27521	137	2	gan	gan	PROPN
ajst-27521	137	3	optimization	optimization	NOUN
ajst-27521	137	4	objective	objective	NOUN
ajst-27521	137	5	is	be	AUX
ajst-27521	137	6	to	to	PART
ajst-27521	137	7	maximize	maximize	VERB
ajst-27521	137	8	discriminant	discriminant	ADJ
ajst-27521	137	9	parameters	parameter	NOUN
ajst-27521	137	10	,	,	PUNCT
ajst-27521	137	11	while	while	SCONJ
ajst-27521	137	12	minimizing	minimize	VERB
ajst-27521	137	13	generator	generator	NOUN
ajst-27521	137	14	parameters	parameter	NOUN
ajst-27521	137	15	.	.	PUNCT
ajst-27521	138	1	the	the	DET
ajst-27521	138	2	objective	objective	ADJ
ajst-27521	138	3	function	function	NOUN
ajst-27521	138	4	formula	formula	NOUN
ajst-27521	138	5	is	be	AUX
ajst-27521	138	6	as	as	SCONJ
ajst-27521	138	7	follows	follow	VERB
ajst-27521	138	8	:	:	PUNCT
ajst-27521	138	9	𝑀𝑖𝑛	𝑀𝑖𝑛	PROPN
ajst-27521	138	10	𝑀𝑎𝑥	𝑀𝑎𝑥	PROPN
ajst-27521	138	11	𝑉	𝑉	PROPN
ajst-27521	138	12	𝐷	𝐷	PROPN
ajst-27521	138	13	,	,	PUNCT
ajst-27521	138	14	𝐺	𝐺	PROPN
ajst-27521	138	15	𝐸	𝐸	PROPN
ajst-27521	138	16	~	~	PUNCT
ajst-27521	138	17	𝑙𝑜𝑔𝐷	𝑙𝑜𝑔𝐷	ADJ
ajst-27521	139	1	𝑥	𝑥	X
ajst-27521	139	2	𝐸	𝐸	PROPN
ajst-27521	139	3	~	~	PUNCT
ajst-27521	139	4	𝑙𝑜𝑔	𝑙𝑜𝑔	NOUN
ajst-27521	139	5	1	1	NUM
ajst-27521	139	6	𝐷	𝐷	NOUN
ajst-27521	139	7	𝐺	𝐺	NOUN
ajst-27521	139	8	𝑧	𝑧	PROPN
ajst-27521	139	9	where	where	SCONJ
ajst-27521	139	10	z	z	NOUN
ajst-27521	139	11	represents	represent	VERB
ajst-27521	139	12	the	the	DET
ajst-27521	139	13	random	random	ADJ
ajst-27521	139	14	noise	noise	NOUN
ajst-27521	139	15	of	of	ADP
ajst-27521	139	16	the	the	DET
ajst-27521	139	17	input	input	NOUN
ajst-27521	139	18	,	,	PUNCT
ajst-27521	139	19	pz	pz	NOUN
ajst-27521	139	20	(	(	PUNCT
ajst-27521	139	21	z	z	NOUN
ajst-27521	139	22	)	)	PUNCT
ajst-27521	139	23	represents	represent	VERB
ajst-27521	139	24	the	the	DET
ajst-27521	139	25	distribution	distribution	NOUN
ajst-27521	139	26	of	of	ADP
ajst-27521	139	27	the	the	DET
ajst-27521	139	28	generated	generate	VERB
ajst-27521	139	29	network	network	NOUN
ajst-27521	139	30	,	,	PUNCT
ajst-27521	139	31	x	x	PRON
ajst-27521	139	32	represents	represent	VERB
ajst-27521	139	33	the	the	DET
ajst-27521	139	34	real	real	ADJ
ajst-27521	139	35	data	datum	NOUN
ajst-27521	139	36	,	,	PUNCT
ajst-27521	139	37	and	and	CCONJ
ajst-27521	139	38	pdata	pdata	NOUN
ajst-27521	139	39	(	(	PUNCT
ajst-27521	139	40	x	x	X
ajst-27521	139	41	)	)	PUNCT
ajst-27521	139	42	represents	represent	VERB
ajst-27521	139	43	the	the	DET
ajst-27521	139	44	distribution	distribution	NOUN
ajst-27521	139	45	of	of	ADP
ajst-27521	139	46	the	the	DET
ajst-27521	139	47	real	real	ADJ
ajst-27521	139	48	data	datum	NOUN
ajst-27521	139	49	.	.	PUNCT
ajst-27521	140	1	the	the	DET
ajst-27521	140	2	discriminant	discriminant	NOUN
ajst-27521	140	3	will	will	AUX
ajst-27521	140	4	bring	bring	VERB
ajst-27521	140	5	d(g(z	d(g(z	NOUN
ajst-27521	140	6	)	)	PUNCT
ajst-27521	140	7	)	)	PUNCT
ajst-27521	140	8	to	to	ADP
ajst-27521	140	9	0	0	NUM
ajst-27521	140	10	,	,	PUNCT
ajst-27521	140	11	the	the	DET
ajst-27521	140	12	generator	generator	NOUN
ajst-27521	140	13	will	will	AUX
ajst-27521	140	14	bring	bring	VERB
ajst-27521	140	15	d(g(z	d(g(z	NOUN
ajst-27521	140	16	)	)	PUNCT
ajst-27521	140	17	)	)	PUNCT
ajst-27521	140	18	to	to	ADP
ajst-27521	140	19	1	1	NUM
ajst-27521	140	20	,	,	PUNCT
ajst-27521	140	21	and	and	CCONJ
ajst-27521	140	22	when	when	SCONJ
ajst-27521	140	23	d(g(z	d(g(z	NOUN
ajst-27521	140	24	)	)	PUNCT
ajst-27521	140	25	)	)	PUNCT
ajst-27521	141	1	=	=	SYM
ajst-27521	141	2	0.5	0.5	NUM
ajst-27521	141	3	,	,	PUNCT
ajst-27521	141	4	the	the	DET
ajst-27521	141	5	generated	generate	VERB
ajst-27521	141	6	sample	sample	NOUN
ajst-27521	141	7	can	can	AUX
ajst-27521	141	8	be	be	AUX
ajst-27521	141	9	false	false	ADJ
ajst-27521	141	10	,	,	PUNCT
ajst-27521	141	11	theoretically	theoretically	ADV
ajst-27521	141	12	reaching	reach	VERB
ajst-27521	141	13	nash	nash	ADJ
ajst-27521	141	14	equilibrium	equilibrium	NOUN
ajst-27521	141	15	.	.	PUNCT
ajst-27521	142	1	gan	gan	PROPN
ajst-27521	142	2	has	have	AUX
ajst-27521	142	3	been	be	AUX
ajst-27521	142	4	very	very	ADV
ajst-27521	142	5	mature	mature	ADJ
ajst-27521	142	6	in	in	ADP
ajst-27521	142	7	image	image	NOUN
ajst-27521	142	8	data	datum	NOUN
ajst-27521	142	9	processing	processing	NOUN
ajst-27521	142	10	,	,	PUNCT
ajst-27521	142	11	and	and	CCONJ
ajst-27521	142	12	a	a	DET
ajst-27521	142	13	large	large	ADJ
ajst-27521	142	14	number	number	NOUN
ajst-27521	142	15	of	of	ADP
ajst-27521	142	16	existing	exist	VERB
ajst-27521	142	17	gan	gan	NOUN
ajst-27521	142	18	correlation	correlation	NOUN
ajst-27521	142	19	models	model	NOUN
ajst-27521	142	20	and	and	CCONJ
ajst-27521	142	21	theories	theory	NOUN
ajst-27521	142	22	provide	provide	VERB
ajst-27521	142	23	a	a	DET
ajst-27521	142	24	theoretical	theoretical	ADJ
ajst-27521	142	25	basis	basis	NOUN
ajst-27521	142	26	for	for	ADP
ajst-27521	142	27	anomaly	anomaly	NOUN
ajst-27521	142	28	detection	detection	NOUN
ajst-27521	142	29	.	.	PUNCT
ajst-27521	143	1	however	however	ADV
ajst-27521	143	2	,	,	PUNCT
ajst-27521	143	3	there	there	PRON
ajst-27521	143	4	are	be	VERB
ajst-27521	143	5	still	still	ADV
ajst-27521	143	6	some	some	DET
ajst-27521	143	7	defects	defect	NOUN
ajst-27521	143	8	in	in	ADP
ajst-27521	143	9	gan	gan	NOUN
ajst-27521	143	10	-	-	PUNCT
ajst-27521	143	11	based	base	VERB
ajst-27521	143	12	abnormality	abnormality	NOUN
ajst-27521	143	13	detection	detection	NOUN
ajst-27521	143	14	:	:	PUNCT
ajst-27521	143	15	the	the	DET
ajst-27521	143	16	gan	gan	PROPN
ajst-27521	143	17	training	training	NOUN
ajst-27521	143	18	process	process	NOUN
ajst-27521	143	19	may	may	AUX
ajst-27521	143	20	face	face	VERB
ajst-27521	143	21	the	the	DET
ajst-27521	143	22	non	non	ADJ
ajst-27521	143	23	-	-	ADJ
ajst-27521	143	24	convergence	convergence	ADJ
ajst-27521	143	25	problem	problem	NOUN
ajst-27521	143	26	,	,	PUNCT
ajst-27521	143	27	which	which	PRON
ajst-27521	143	28	increases	increase	VERB
ajst-27521	143	29	the	the	DET
ajst-27521	143	30	training	training	NOUN
ajst-27521	143	31	difficulty	difficulty	NOUN
ajst-27521	143	32	;	;	PUNCT
ajst-27521	143	33	the	the	DET
ajst-27521	143	34	generator	generator	NOUN
ajst-27521	143	35	network	network	NOUN
ajst-27521	143	36	may	may	AUX
ajst-27521	143	37	be	be	AUX
ajst-27521	143	38	misled	mislead	VERB
ajst-27521	143	39	when	when	SCONJ
ajst-27521	143	40	the	the	DET
ajst-27521	143	41	true	true	ADJ
ajst-27521	143	42	distribution	distribution	NOUN
ajst-27521	143	43	of	of	ADP
ajst-27521	143	44	the	the	DET
ajst-27521	143	45	data	datum	NOUN
ajst-27521	143	46	set	set	VERB
ajst-27521	143	47	is	be	AUX
ajst-27521	143	48	complex	complex	ADJ
ajst-27521	143	49	or	or	CCONJ
ajst-27521	143	50	the	the	DET
ajst-27521	143	51	training	training	NOUN
ajst-27521	143	52	data	data	NOUN
ajst-27521	143	53	contains	contain	VERB
ajst-27521	143	54	inaccurate	inaccurate	ADJ
ajst-27521	143	55	outliers	outlier	NOUN
ajst-27521	143	56	.	.	PUNCT
ajst-27521	144	1	170	170	NUM
ajst-27521	144	2	4.5	4.5	NUM
ajst-27521	144	3	deep	deep	ADJ
ajst-27521	144	4	anomaly	anomaly	NOUN
ajst-27521	144	5	detection	detection	NOUN
ajst-27521	144	6	model	model	NOUN
ajst-27521	144	7	based	base	VERB
ajst-27521	144	8	on	on	ADP
ajst-27521	144	9	cnn	cnn	PROPN
ajst-27521	144	10	when	when	SCONJ
ajst-27521	144	11	processing	processing	NOUN
ajst-27521	144	12	images	image	NOUN
ajst-27521	144	13	,	,	PUNCT
ajst-27521	144	14	cnn	cnn	PROPN
ajst-27521	144	15	treats	treat	VERB
ajst-27521	144	16	the	the	DET
ajst-27521	144	17	images	image	NOUN
ajst-27521	144	18	as	as	ADP
ajst-27521	144	19	twodimensional	twodimensional	ADJ
ajst-27521	144	20	matrices	matrix	NOUN
ajst-27521	144	21	,	,	PUNCT
ajst-27521	144	22	and	and	CCONJ
ajst-27521	144	23	gradually	gradually	ADV
ajst-27521	144	24	extracts	extract	NOUN
ajst-27521	144	25	and	and	CCONJ
ajst-27521	144	26	combines	combine	VERB
ajst-27521	144	27	features	feature	NOUN
ajst-27521	144	28	through	through	ADP
ajst-27521	144	29	multi	multi	ADJ
ajst-27521	144	30	-	-	ADJ
ajst-27521	144	31	layer	layer	ADJ
ajst-27521	144	32	convolution	convolution	NOUN
ajst-27521	144	33	operation	operation	NOUN
ajst-27521	144	34	and	and	CCONJ
ajst-27521	144	35	pooling	pool	VERB
ajst-27521	144	36	operation	operation	NOUN
ajst-27521	144	37	to	to	PART
ajst-27521	144	38	realize	realize	VERB
ajst-27521	144	39	the	the	DET
ajst-27521	144	40	efficient	efficient	ADJ
ajst-27521	144	41	processing	processing	NOUN
ajst-27521	144	42	of	of	ADP
ajst-27521	144	43	images	image	NOUN
ajst-27521	144	44	.	.	PUNCT
ajst-27521	145	1	in	in	ADP
ajst-27521	145	2	the	the	DET
ajst-27521	145	3	time	time	NOUN
ajst-27521	145	4	series	series	PROPN
ajst-27521	145	5	analysis	analysis	NOUN
ajst-27521	145	6	,	,	PUNCT
ajst-27521	145	7	the	the	DET
ajst-27521	145	8	time	time	NOUN
ajst-27521	145	9	series	series	PROPN
ajst-27521	145	10	can	can	AUX
ajst-27521	145	11	be	be	AUX
ajst-27521	145	12	regarded	regard	VERB
ajst-27521	145	13	as	as	ADP
ajst-27521	145	14	a	a	DET
ajst-27521	145	15	one	one	NUM
ajst-27521	145	16	-	-	PUNCT
ajst-27521	145	17	dimensional	dimensional	ADJ
ajst-27521	145	18	vector	vector	NOUN
ajst-27521	145	19	of	of	ADP
ajst-27521	145	20	1*n	1*n	NUM
ajst-27521	145	21	.	.	PUNCT
ajst-27521	146	1	the	the	DET
ajst-27521	146	2	advantage	advantage	NOUN
ajst-27521	146	3	of	of	ADP
ajst-27521	146	4	applying	apply	VERB
ajst-27521	146	5	convolutional	convolutional	ADJ
ajst-27521	146	6	neural	neural	ADJ
ajst-27521	146	7	networks	network	NOUN
ajst-27521	146	8	in	in	ADP
ajst-27521	146	9	time	time	NOUN
ajst-27521	146	10	series	series	NOUN
ajst-27521	146	11	prediction	prediction	NOUN
ajst-27521	146	12	is	be	AUX
ajst-27521	146	13	that	that	SCONJ
ajst-27521	146	14	their	their	PRON
ajst-27521	146	15	multi	multi	ADJ
ajst-27521	146	16	-	-	ADJ
ajst-27521	146	17	layer	layer	ADJ
ajst-27521	146	18	network	network	NOUN
ajst-27521	146	19	structure	structure	NOUN
ajst-27521	146	20	enables	enable	VERB
ajst-27521	146	21	massively	massively	ADV
ajst-27521	146	22	parallel	parallel	ADJ
ajst-27521	146	23	processing	processing	NOUN
ajst-27521	146	24	.	.	PUNCT
ajst-27521	147	1	this	this	DET
ajst-27521	147	2	structure	structure	NOUN
ajst-27521	147	3	can	can	AUX
ajst-27521	147	4	build	build	VERB
ajst-27521	147	5	a	a	DET
ajst-27521	147	6	deep	deep	ADJ
ajst-27521	147	7	learning	learning	NOUN
ajst-27521	147	8	network	network	NOUN
ajst-27521	147	9	,	,	PUNCT
ajst-27521	147	10	improving	improve	VERB
ajst-27521	147	11	performance	performance	NOUN
ajst-27521	147	12	while	while	SCONJ
ajst-27521	147	13	also	also	ADV
ajst-27521	147	14	saving	save	VERB
ajst-27521	147	15	time	time	NOUN
ajst-27521	147	16	.	.	PUNCT
ajst-27521	148	1	5	5	X
ajst-27521	148	2	.	.	X
ajst-27521	148	3	experiment	experiment	NOUN
ajst-27521	148	4	5.1	5.1	NUM
ajst-27521	148	5	dataset	dataset	NOUN
ajst-27521	148	6	to	to	PART
ajst-27521	148	7	compare	compare	VERB
ajst-27521	148	8	the	the	DET
ajst-27521	148	9	effects	effect	NOUN
ajst-27521	148	10	of	of	ADP
ajst-27521	148	11	different	different	ADJ
ajst-27521	148	12	models	model	NOUN
ajst-27521	148	13	in	in	ADP
ajst-27521	148	14	anomaly	anomaly	NOUN
ajst-27521	148	15	detection	detection	NOUN
ajst-27521	148	16	applications	application	NOUN
ajst-27521	148	17	,	,	PUNCT
ajst-27521	148	18	this	this	DET
ajst-27521	148	19	paper	paper	NOUN
ajst-27521	148	20	selected	select	VERB
ajst-27521	148	21	two	two	NUM
ajst-27521	148	22	public	public	ADJ
ajst-27521	148	23	datasets	dataset	NOUN
ajst-27521	148	24	to	to	PART
ajst-27521	148	25	conduct	conduct	VERB
ajst-27521	148	26	experiments	experiment	NOUN
ajst-27521	148	27	on	on	ADP
ajst-27521	148	28	various	various	ADJ
ajst-27521	148	29	models	model	NOUN
ajst-27521	148	30	.	.	PUNCT
ajst-27521	149	1	one	one	NUM
ajst-27521	149	2	of	of	ADP
ajst-27521	149	3	the	the	DET
ajst-27521	149	4	datasets	dataset	NOUN
ajst-27521	149	5	is	be	AUX
ajst-27521	149	6	the	the	DET
ajst-27521	149	7	kpi	kpi	ADJ
ajst-27521	149	8	dataset	dataset	NOUN
ajst-27521	149	9	released	release	VERB
ajst-27521	149	10	by	by	ADP
ajst-27521	149	11	the	the	DET
ajst-27521	149	12	aiops	aiop	NOUN
ajst-27521	149	13	data	datum	NOUN
ajst-27521	149	14	competition	competition	NOUN
ajst-27521	149	15	[	[	X
ajst-27521	149	16	26	26	NUM
ajst-27521	149	17	]	]	PUNCT
ajst-27521	149	18	,	,	PUNCT
ajst-27521	149	19	which	which	PRON
ajst-27521	149	20	consists	consist	VERB
ajst-27521	149	21	of	of	ADP
ajst-27521	149	22	multiple	multiple	ADJ
ajst-27521	149	23	kpi	kpi	ADJ
ajst-27521	149	24	curves	curve	NOUN
ajst-27521	149	25	,	,	PUNCT
ajst-27521	149	26	and	and	CCONJ
ajst-27521	149	27	the	the	DET
ajst-27521	149	28	anomaly	anomaly	NOUN
ajst-27521	149	29	labels	label	NOUN
ajst-27521	149	30	come	come	VERB
ajst-27521	149	31	from	from	ADP
ajst-27521	149	32	several	several	ADJ
ajst-27521	149	33	internet	internet	NOUN
ajst-27521	149	34	companies	company	NOUN
ajst-27521	149	35	including	include	VERB
ajst-27521	149	36	sogou	sogou	NOUN
ajst-27521	149	37	,	,	PUNCT
ajst-27521	149	38	tencent	tencent	NOUN
ajst-27521	149	39	,	,	PUNCT
ajst-27521	149	40	and	and	CCONJ
ajst-27521	149	41	ebay	ebay	NOUN
ajst-27521	149	42	.	.	PUNCT
ajst-27521	150	1	most	most	ADJ
ajst-27521	150	2	of	of	ADP
ajst-27521	150	3	the	the	DET
ajst-27521	150	4	kpi	kpi	PROPN
ajst-27521	150	5	curves	curve	NOUN
ajst-27521	150	6	have	have	VERB
ajst-27521	150	7	data	datum	NOUN
ajst-27521	150	8	points	point	NOUN
ajst-27521	150	9	with	with	ADP
ajst-27521	150	10	a	a	DET
ajst-27521	150	11	1	1	NUM
ajst-27521	150	12	-	-	PUNCT
ajst-27521	150	13	minute	minute	NOUN
ajst-27521	150	14	interval	interval	NOUN
ajst-27521	150	15	,	,	PUNCT
ajst-27521	150	16	while	while	SCONJ
ajst-27521	150	17	some	some	PRON
ajst-27521	150	18	have	have	VERB
ajst-27521	150	19	a	a	DET
ajst-27521	150	20	5	5	NUM
ajst-27521	150	21	-	-	PUNCT
ajst-27521	150	22	minute	minute	NOUN
ajst-27521	150	23	interval	interval	NOUN
ajst-27521	150	24	.	.	PUNCT
ajst-27521	151	1	these	these	DET
ajst-27521	151	2	datasets	dataset	NOUN
ajst-27521	151	3	cover	cover	VERB
ajst-27521	151	4	time	time	NOUN
ajst-27521	151	5	series	series	NOUN
ajst-27521	151	6	with	with	ADP
ajst-27521	151	7	different	different	ADJ
ajst-27521	151	8	time	time	NOUN
ajst-27521	151	9	intervals	interval	NOUN
ajst-27521	151	10	and	and	CCONJ
ajst-27521	151	11	a	a	DET
ajst-27521	151	12	wide	wide	ADJ
ajst-27521	151	13	range	range	NOUN
ajst-27521	151	14	of	of	ADP
ajst-27521	151	15	patterns	pattern	NOUN
ajst-27521	151	16	,	,	PUNCT
ajst-27521	151	17	and	and	CCONJ
ajst-27521	151	18	are	be	AUX
ajst-27521	151	19	commonly	commonly	ADV
ajst-27521	151	20	used	use	VERB
ajst-27521	151	21	to	to	PART
ajst-27521	151	22	evaluate	evaluate	VERB
ajst-27521	151	23	the	the	DET
ajst-27521	151	24	performance	performance	NOUN
ajst-27521	151	25	of	of	ADP
ajst-27521	151	26	time	time	NOUN
ajst-27521	151	27	series	series	PROPN
ajst-27521	151	28	anomaly	anomaly	PROPN
ajst-27521	151	29	detection	detection	NOUN
ajst-27521	151	30	.	.	PUNCT
ajst-27521	152	1	the	the	DET
ajst-27521	152	2	other	other	ADJ
ajst-27521	152	3	dataset	dataset	NOUN
ajst-27521	152	4	comes	come	VERB
ajst-27521	152	5	from	from	ADP
ajst-27521	152	6	huawei	huawei	PROPN
ajst-27521	152	7	's	's	PART
ajst-27521	152	8	naie	naie	PROPN
ajst-27521	152	9	platform	platform	NOUN
ajst-27521	152	10	,	,	PUNCT
ajst-27521	152	11	which	which	PRON
ajst-27521	152	12	is	be	AUX
ajst-27521	152	13	a	a	DET
ajst-27521	152	14	kpi	kpi	PROPN
ajst-27521	152	15	anomaly	anomaly	NOUN
ajst-27521	152	16	detection	detection	NOUN
ajst-27521	152	17	dataset	dataset	VERB
ajst-27521	152	18	based	base	VERB
ajst-27521	152	19	on	on	ADP
ajst-27521	152	20	huawei	huawei	PROPN
ajst-27521	152	21	's	's	PART
ajst-27521	152	22	real	real	ADJ
ajst-27521	152	23	business	business	NOUN
ajst-27521	152	24	.	.	PUNCT
ajst-27521	153	1	it	it	PRON
ajst-27521	153	2	includes	include	VERB
ajst-27521	153	3	a	a	DET
ajst-27521	153	4	labeled	label	VERB
ajst-27521	153	5	training	training	NOUN
ajst-27521	153	6	set	set	NOUN
ajst-27521	153	7	and	and	CCONJ
ajst-27521	153	8	an	an	DET
ajst-27521	153	9	unlabeled	unlabele	VERB
ajst-27521	153	10	testing	testing	NOUN
ajst-27521	153	11	set	set	NOUN
ajst-27521	153	12	,	,	PUNCT
ajst-27521	153	13	and	and	CCONJ
ajst-27521	153	14	its	its	PRON
ajst-27521	153	15	data	data	NOUN
ajst-27521	153	16	format	format	NOUN
ajst-27521	153	17	is	be	AUX
ajst-27521	153	18	basically	basically	ADV
ajst-27521	153	19	consistent	consistent	ADJ
ajst-27521	153	20	with	with	ADP
ajst-27521	153	21	the	the	DET
ajst-27521	153	22	aiops	aiop	NOUN
ajst-27521	153	23	dataset	dataset	VERB
ajst-27521	153	24	.	.	PUNCT
ajst-27521	154	1	5.2	5.2	NUM
ajst-27521	154	2	evaluation	evaluation	NOUN
ajst-27521	154	3	metrics	metric	NOUN
ajst-27521	154	4	in	in	ADP
ajst-27521	154	5	evaluating	evaluate	VERB
ajst-27521	154	6	model	model	NOUN
ajst-27521	154	7	performance	performance	NOUN
ajst-27521	154	8	,	,	PUNCT
ajst-27521	154	9	precision	precision	NOUN
ajst-27521	154	10	,	,	PUNCT
ajst-27521	154	11	recall	recall	NOUN
ajst-27521	154	12	,	,	PUNCT
ajst-27521	154	13	f1score	f1score	NOUN
ajst-27521	154	14	,	,	PUNCT
ajst-27521	154	15	and	and	CCONJ
ajst-27521	154	16	accuracy	accuracy	NOUN
ajst-27521	154	17	(	(	PUNCT
ajst-27521	154	18	acc	acc	PROPN
ajst-27521	154	19	)	)	PUNCT
ajst-27521	154	20	are	be	AUX
ajst-27521	154	21	commonly	commonly	ADV
ajst-27521	154	22	used	use	VERB
ajst-27521	154	23	metrics	metric	NOUN
ajst-27521	154	24	,	,	PUNCT
ajst-27521	154	25	which	which	PRON
ajst-27521	154	26	are	be	AUX
ajst-27521	154	27	defined	define	VERB
ajst-27521	154	28	as	as	SCONJ
ajst-27521	154	29	follows	follow	VERB
ajst-27521	154	30	:	:	PUNCT
ajst-27521	154	31	𝐴𝑐𝑐	𝐴𝑐𝑐	PROPN
ajst-27521	154	32	𝑇	𝑇	PROPN
ajst-27521	154	33	𝑇	𝑇	PROPN
ajst-27521	154	34	𝑇	𝑇	PROPN
ajst-27521	154	35	𝐹	𝐹	PROPN
ajst-27521	154	36	𝑇	𝑇	PROPN
ajst-27521	154	37	𝐹	𝐹	PRON
ajst-27521	154	38	𝑃	𝑃	VERB
ajst-27521	154	39	𝑇	𝑇	PROPN
ajst-27521	154	40	𝑇	𝑇	PROPN
ajst-27521	155	1	𝐹	𝐹	PROPN
ajst-27521	155	2	𝑅	𝑅	PROPN
ajst-27521	155	3	𝑇	𝑇	PROPN
ajst-27521	155	4	𝑇	𝑇	PROPN
ajst-27521	155	5	𝐹	𝐹	PROPN
ajst-27521	155	6	𝐹	𝐹	PROPN
ajst-27521	155	7	2	2	NUM
ajst-27521	155	8	𝑃𝑅	𝑃𝑅	PROPN
ajst-27521	155	9	𝑃	𝑃	NOUN
ajst-27521	155	10	𝑅	𝑅	PROPN
ajst-27521	155	11	among	among	ADP
ajst-27521	155	12	them	they	PRON
ajst-27521	155	13	,	,	PUNCT
ajst-27521	155	14	tp	tp	NOUN
ajst-27521	155	15	represents	represent	VERB
ajst-27521	155	16	the	the	DET
ajst-27521	155	17	number	number	NOUN
ajst-27521	155	18	of	of	ADP
ajst-27521	155	19	instances	instance	NOUN
ajst-27521	155	20	correctly	correctly	ADV
ajst-27521	155	21	detected	detect	VERB
ajst-27521	155	22	as	as	ADP
ajst-27521	155	23	normal	normal	ADJ
ajst-27521	155	24	and	and	CCONJ
ajst-27521	155	25	are	be	AUX
ajst-27521	155	26	actually	actually	ADV
ajst-27521	155	27	normal	normal	ADJ
ajst-27521	155	28	,	,	PUNCT
ajst-27521	155	29	fp	fp	X
ajst-27521	155	30	represents	represent	VERB
ajst-27521	155	31	the	the	DET
ajst-27521	155	32	number	number	NOUN
ajst-27521	155	33	of	of	ADP
ajst-27521	155	34	instances	instance	NOUN
ajst-27521	155	35	detected	detect	VERB
ajst-27521	155	36	as	as	ADP
ajst-27521	155	37	normal	normal	ADJ
ajst-27521	155	38	but	but	CCONJ
ajst-27521	155	39	are	be	AUX
ajst-27521	155	40	actually	actually	ADV
ajst-27521	155	41	abnormal	abnormal	ADJ
ajst-27521	155	42	,	,	PUNCT
ajst-27521	155	43	tn	tn	PROPN
ajst-27521	155	44	represents	represent	VERB
ajst-27521	155	45	the	the	DET
ajst-27521	155	46	number	number	NOUN
ajst-27521	155	47	of	of	ADP
ajst-27521	155	48	instances	instance	NOUN
ajst-27521	155	49	correctly	correctly	ADV
ajst-27521	155	50	detected	detect	VERB
ajst-27521	155	51	as	as	ADV
ajst-27521	155	52	abnormal	abnormal	ADJ
ajst-27521	155	53	and	and	CCONJ
ajst-27521	155	54	are	be	AUX
ajst-27521	155	55	actually	actually	ADV
ajst-27521	155	56	abnormal	abnormal	ADJ
ajst-27521	155	57	,	,	PUNCT
ajst-27521	155	58	and	and	CCONJ
ajst-27521	155	59	fn	fn	NOUN
ajst-27521	155	60	represents	represent	VERB
ajst-27521	155	61	the	the	DET
ajst-27521	155	62	number	number	NOUN
ajst-27521	155	63	of	of	ADP
ajst-27521	155	64	instances	instance	NOUN
ajst-27521	155	65	detected	detect	VERB
ajst-27521	155	66	as	as	ADV
ajst-27521	155	67	abnormal	abnormal	ADJ
ajst-27521	155	68	but	but	CCONJ
ajst-27521	155	69	are	be	AUX
ajst-27521	155	70	actually	actually	ADV
ajst-27521	155	71	normal	normal	ADJ
ajst-27521	155	72	.	.	PUNCT
ajst-27521	156	1	5.3	5.3	NUM
ajst-27521	156	2	experimental	experimental	ADJ
ajst-27521	156	3	analysis	analysis	NOUN
ajst-27521	156	4	the	the	DET
ajst-27521	156	5	experiment	experiment	NOUN
ajst-27521	156	6	compares	compare	VERB
ajst-27521	156	7	the	the	DET
ajst-27521	156	8	effectiveness	effectiveness	NOUN
ajst-27521	156	9	of	of	ADP
ajst-27521	156	10	five	five	NUM
ajst-27521	156	11	representative	representative	ADJ
ajst-27521	156	12	deep	deep	ADJ
ajst-27521	156	13	learning	learning	NOUN
ajst-27521	156	14	anomaly	anomaly	NOUN
ajst-27521	156	15	detection	detection	NOUN
ajst-27521	156	16	algorithms	algorithm	NOUN
ajst-27521	156	17	,	,	PUNCT
ajst-27521	156	18	which	which	PRON
ajst-27521	156	19	are	be	AUX
ajst-27521	156	20	introduced	introduce	VERB
ajst-27521	156	21	in	in	ADP
ajst-27521	156	22	detail	detail	NOUN
ajst-27521	156	23	in	in	ADP
ajst-27521	156	24	the	the	DET
ajst-27521	156	25	text	text	NOUN
ajst-27521	156	26	,	,	PUNCT
ajst-27521	156	27	on	on	ADP
ajst-27521	156	28	time	time	NOUN
ajst-27521	156	29	series	series	PROPN
ajst-27521	156	30	data	data	PROPN
ajst-27521	156	31	.	.	PUNCT
ajst-27521	157	1	to	to	PART
ajst-27521	157	2	ensure	ensure	VERB
ajst-27521	157	3	the	the	DET
ajst-27521	157	4	consistency	consistency	NOUN
ajst-27521	157	5	and	and	CCONJ
ajst-27521	157	6	comparability	comparability	NOUN
ajst-27521	157	7	of	of	ADP
ajst-27521	157	8	the	the	DET
ajst-27521	157	9	experiment	experiment	NOUN
ajst-27521	157	10	,	,	PUNCT
ajst-27521	157	11	the	the	DET
ajst-27521	157	12	evaluation	evaluation	NOUN
ajst-27521	157	13	was	be	AUX
ajst-27521	157	14	conducted	conduct	VERB
ajst-27521	157	15	on	on	ADP
ajst-27521	157	16	two	two	NUM
ajst-27521	157	17	public	public	ADJ
ajst-27521	157	18	kpi	kpi	PROPN
ajst-27521	157	19	anomaly	anomaly	NOUN
ajst-27521	157	20	detection	detection	NOUN
ajst-27521	157	21	datasets	dataset	NOUN
ajst-27521	157	22	.	.	PUNCT
ajst-27521	158	1	this	this	PRON
ajst-27521	158	2	ensures	ensure	VERB
ajst-27521	158	3	the	the	DET
ajst-27521	158	4	applicability	applicability	NOUN
ajst-27521	158	5	and	and	CCONJ
ajst-27521	158	6	representativeness	representativeness	NOUN
ajst-27521	158	7	of	of	ADP
ajst-27521	158	8	the	the	DET
ajst-27521	158	9	research	research	NOUN
ajst-27521	158	10	results	result	NOUN
ajst-27521	158	11	,	,	PUNCT
ajst-27521	158	12	providing	provide	VERB
ajst-27521	158	13	a	a	DET
ajst-27521	158	14	reliable	reliable	ADJ
ajst-27521	158	15	basis	basis	NOUN
ajst-27521	158	16	for	for	ADP
ajst-27521	158	17	technology	technology	NOUN
ajst-27521	158	18	selection	selection	NOUN
ajst-27521	158	19	in	in	ADP
ajst-27521	158	20	different	different	ADJ
ajst-27521	158	21	application	application	NOUN
ajst-27521	158	22	scenarios	scenario	NOUN
ajst-27521	158	23	.	.	PUNCT
ajst-27521	159	1	experiments	experiment	NOUN
ajst-27521	159	2	conducted	conduct	VERB
ajst-27521	159	3	on	on	ADP
ajst-27521	159	4	the	the	DET
ajst-27521	159	5	kpi	kpi	ADJ
ajst-27521	159	6	dataset	dataset	NOUN
ajst-27521	159	7	released	release	VERB
ajst-27521	159	8	by	by	ADP
ajst-27521	159	9	the	the	DET
ajst-27521	159	10	aiops	aiop	NOUN
ajst-27521	159	11	data	data	NOUN
ajst-27521	159	12	competition	competition	NOUN
ajst-27521	159	13	yielded	yield	VERB
ajst-27521	159	14	a	a	DET
ajst-27521	159	15	comparison	comparison	NOUN
ajst-27521	159	16	of	of	ADP
ajst-27521	159	17	performance	performance	NOUN
ajst-27521	159	18	metrics	metric	NOUN
ajst-27521	159	19	including	include	VERB
ajst-27521	159	20	precision	precision	NOUN
ajst-27521	159	21	,	,	PUNCT
ajst-27521	159	22	recall	recall	NOUN
ajst-27521	159	23	,	,	PUNCT
ajst-27521	159	24	f1	f1	NOUN
ajst-27521	159	25	score	score	NOUN
ajst-27521	159	26	,	,	PUNCT
ajst-27521	159	27	and	and	CCONJ
ajst-27521	159	28	accuracy	accuracy	NOUN
ajst-27521	159	29	,	,	PUNCT
ajst-27521	159	30	as	as	SCONJ
ajst-27521	159	31	shown	show	VERB
ajst-27521	159	32	in	in	ADP
ajst-27521	159	33	the	the	DET
ajst-27521	159	34	following	follow	VERB
ajst-27521	159	35	table	table	NOUN
ajst-27521	159	36	:	:	PUNCT
ajst-27521	159	37	table	table	NOUN
ajst-27521	159	38	1	1	NUM
ajst-27521	159	39	.	.	PUNCT
ajst-27521	159	40	results	result	NOUN
ajst-27521	159	41	of	of	ADP
ajst-27521	159	42	models	model	NOUN
ajst-27521	159	43	’	'	PUNCT
ajst-27521	159	44	anomaly	anomaly	NOUN
ajst-27521	159	45	detection	detection	NOUN
ajst-27521	159	46	metrics	metric	NOUN
ajst-27521	159	47	detection	detection	NOUN
ajst-27521	159	48	methods	method	NOUN
ajst-27521	159	49	precision	precision	NOUN
ajst-27521	159	50	recall	recall	VERB
ajst-27521	159	51	f1	f1	NOUN
ajst-27521	159	52	-	-	PUNCT
ajst-27521	159	53	score	score	NOUN
ajst-27521	159	54	accuracy	accuracy	NOUN
ajst-27521	159	55	ae	ae	PROPN
ajst-27521	159	56	0.80	0.80	NUM
ajst-27521	159	57	0.78	0.78	NUM
ajst-27521	159	58	0.85	0.85	NUM
ajst-27521	159	59	0.77	0.77	NUM
ajst-27521	159	60	transformer	transformer	NOUN
ajst-27521	159	61	0.73	0.73	NUM
ajst-27521	159	62	0.80	0.80	NUM
ajst-27521	159	63	0.83	0.83	NUM
ajst-27521	159	64	0.75	0.75	NUM
ajst-27521	159	65	lstm	lstm	NOUN
ajst-27521	159	66	0.87	0.87	NUM
ajst-27521	159	67	0.90	0.90	NUM
ajst-27521	159	68	0.87	0.87	NUM
ajst-27521	159	69	0.86	0.86	NUM
ajst-27521	159	70	gan	gan	NOUN
ajst-27521	159	71	0.85	0.85	NUM
ajst-27521	159	72	0.88	0.88	NUM
ajst-27521	159	73	0.86	0.86	NUM
ajst-27521	159	74	0.83	0.83	NUM
ajst-27521	159	75	cnn	cnn	NOUN
ajst-27521	159	76	0.82	0.82	NUM
ajst-27521	159	77	0.87	0.87	NUM
ajst-27521	159	78	0.86	0.86	NUM
ajst-27521	159	79	0.85	0.85	NUM
ajst-27521	159	80	figure	figure	NOUN
ajst-27521	159	81	5	5	NUM
ajst-27521	159	82	.	.	PUNCT
ajst-27521	159	83	detection	detection	NOUN
ajst-27521	159	84	results	result	NOUN
ajst-27521	159	85	of	of	ADP
ajst-27521	159	86	models	model	NOUN
ajst-27521	159	87	on	on	ADP
ajst-27521	159	88	kpi	kpi	PROPN
ajst-27521	159	89	data	datum	NOUN
ajst-27521	159	90	171	171	NUM
ajst-27521	159	91	on	on	ADP
ajst-27521	159	92	the	the	DET
ajst-27521	159	93	kpi	kpi	PROPN
ajst-27521	159	94	anomaly	anomaly	PROPN
ajst-27521	159	95	detection	detection	NOUN
ajst-27521	159	96	dataset	dataset	NOUN
ajst-27521	159	97	released	release	VERB
ajst-27521	159	98	by	by	ADP
ajst-27521	159	99	huawei	huawei	PROPN
ajst-27521	159	100	's	's	PART
ajst-27521	159	101	naie	naie	PROPN
ajst-27521	159	102	platform	platform	NOUN
ajst-27521	159	103	,	,	PUNCT
ajst-27521	159	104	only	only	ADV
ajst-27521	159	105	the	the	DET
ajst-27521	159	106	f1	f1	ADJ
ajst-27521	159	107	score	score	NOUN
ajst-27521	159	108	performance	performance	NOUN
ajst-27521	159	109	metric	metric	NOUN
ajst-27521	159	110	was	be	AUX
ajst-27521	159	111	evaluated	evaluate	VERB
ajst-27521	159	112	.	.	PUNCT
ajst-27521	160	1	it	it	PRON
ajst-27521	160	2	can	can	AUX
ajst-27521	160	3	be	be	AUX
ajst-27521	160	4	observed	observe	VERB
ajst-27521	160	5	that	that	SCONJ
ajst-27521	160	6	different	different	ADJ
ajst-27521	160	7	models	model	NOUN
ajst-27521	160	8	perform	perform	VERB
ajst-27521	160	9	relatively	relatively	ADV
ajst-27521	160	10	well	well	ADV
ajst-27521	160	11	on	on	ADP
ajst-27521	160	12	the	the	DET
ajst-27521	160	13	aiops	aiop	NOUN
ajst-27521	160	14	dataset	dataset	VERB
ajst-27521	160	15	.	.	PUNCT
ajst-27521	161	1	table	table	NOUN
ajst-27521	161	2	2	2	NUM
ajst-27521	161	3	.	.	PUNCT
ajst-27521	162	1	the	the	DET
ajst-27521	162	2	f1	f1	NOUN
ajst-27521	162	3	-	-	PUNCT
ajst-27521	162	4	score	score	NOUN
ajst-27521	162	5	of	of	ADP
ajst-27521	162	6	models	model	NOUN
ajst-27521	162	7	on	on	ADP
ajst-27521	162	8	the	the	DET
ajst-27521	162	9	huawei	huawei	PROPN
ajst-27521	162	10	dataset	dataset	NOUN
ajst-27521	162	11	detection	detection	NOUN
ajst-27521	162	12	methods	method	NOUN
ajst-27521	162	13	huawei	huawei	PROPN
ajst-27521	162	14	ae	ae	PROPN
ajst-27521	162	15	0.73	0.73	NUM
ajst-27521	162	16	transformer	transformer	NOUN
ajst-27521	162	17	0.72	0.72	NUM
ajst-27521	162	18	lstm	lstm	NOUN
ajst-27521	162	19	0.74	0.74	NUM
ajst-27521	162	20	gan	gan	NOUN
ajst-27521	162	21	0.73	0.73	NUM
ajst-27521	162	22	cnn	cnn	PROPN
ajst-27521	162	23	0.69	0.69	NUM
ajst-27521	162	24	the	the	DET
ajst-27521	162	25	experimental	experimental	ADJ
ajst-27521	162	26	results	result	NOUN
ajst-27521	162	27	from	from	ADP
ajst-27521	162	28	two	two	NUM
ajst-27521	162	29	datasets	dataset	NOUN
ajst-27521	162	30	indicate	indicate	VERB
ajst-27521	162	31	that	that	SCONJ
ajst-27521	162	32	lstm	lstm	NOUN
ajst-27521	162	33	,	,	PUNCT
ajst-27521	162	34	gan	gan	PROPN
ajst-27521	162	35	,	,	PUNCT
ajst-27521	162	36	and	and	CCONJ
ajst-27521	162	37	cnn	cnn	PROPN
ajst-27521	162	38	perform	perform	VERB
ajst-27521	162	39	well	well	ADV
ajst-27521	162	40	in	in	ADP
ajst-27521	162	41	anomaly	anomaly	NOUN
ajst-27521	162	42	detection	detection	NOUN
ajst-27521	162	43	tasks	task	NOUN
ajst-27521	162	44	,	,	PUNCT
ajst-27521	162	45	achieving	achieve	VERB
ajst-27521	162	46	high	high	ADJ
ajst-27521	162	47	scores	score	NOUN
ajst-27521	162	48	in	in	ADP
ajst-27521	162	49	evaluation	evaluation	NOUN
ajst-27521	162	50	metrics	metric	NOUN
ajst-27521	162	51	such	such	ADJ
ajst-27521	162	52	as	as	ADP
ajst-27521	162	53	precision	precision	NOUN
ajst-27521	162	54	,	,	PUNCT
ajst-27521	162	55	recall	recall	NOUN
ajst-27521	162	56	,	,	PUNCT
ajst-27521	162	57	f1	f1	NOUN
ajst-27521	162	58	score	score	NOUN
ajst-27521	162	59	,	,	PUNCT
ajst-27521	162	60	and	and	CCONJ
ajst-27521	162	61	accuracy	accuracy	NOUN
ajst-27521	162	62	.	.	PUNCT
ajst-27521	163	1	different	different	ADJ
ajst-27521	163	2	types	type	NOUN
ajst-27521	163	3	of	of	ADP
ajst-27521	163	4	deep	deep	ADJ
ajst-27521	163	5	learning	learning	NOUN
ajst-27521	163	6	models	model	NOUN
ajst-27521	163	7	have	have	VERB
ajst-27521	163	8	their	their	PRON
ajst-27521	163	9	own	own	ADJ
ajst-27521	163	10	focuses	focus	NOUN
ajst-27521	163	11	in	in	ADP
ajst-27521	163	12	anomaly	anomaly	NOUN
ajst-27521	163	13	detection	detection	NOUN
ajst-27521	163	14	tasks	task	NOUN
ajst-27521	163	15	.	.	PUNCT
ajst-27521	164	1	lstm	lstm	NOUN
ajst-27521	164	2	is	be	AUX
ajst-27521	164	3	effective	effective	ADJ
ajst-27521	164	4	in	in	ADP
ajst-27521	164	5	capturing	capture	VERB
ajst-27521	164	6	complex	complex	ADJ
ajst-27521	164	7	features	feature	NOUN
ajst-27521	164	8	and	and	CCONJ
ajst-27521	164	9	long	long	ADJ
ajst-27521	164	10	-	-	PUNCT
ajst-27521	164	11	term	term	NOUN
ajst-27521	164	12	dependencies	dependency	NOUN
ajst-27521	164	13	in	in	ADP
ajst-27521	164	14	the	the	DET
ajst-27521	164	15	data	datum	NOUN
ajst-27521	164	16	;	;	PUNCT
ajst-27521	164	17	gan	gan	PROPN
ajst-27521	164	18	has	have	VERB
ajst-27521	164	19	an	an	DET
ajst-27521	164	20	advantage	advantage	NOUN
ajst-27521	164	21	in	in	ADP
ajst-27521	164	22	generating	generate	VERB
ajst-27521	164	23	deceptive	deceptive	ADJ
ajst-27521	164	24	anomaly	anomaly	NOUN
ajst-27521	164	25	samples	sample	NOUN
ajst-27521	164	26	;	;	PUNCT
ajst-27521	164	27	while	while	SCONJ
ajst-27521	164	28	lstm	lstm	NOUN
ajst-27521	164	29	and	and	CCONJ
ajst-27521	164	30	cnn	cnn	PROPN
ajst-27521	164	31	excel	excel	VERB
ajst-27521	164	32	in	in	ADP
ajst-27521	164	33	handling	handle	VERB
ajst-27521	164	34	the	the	DET
ajst-27521	164	35	sequential	sequential	ADJ
ajst-27521	164	36	and	and	CCONJ
ajst-27521	164	37	local	local	ADJ
ajst-27521	164	38	features	feature	NOUN
ajst-27521	164	39	of	of	ADP
ajst-27521	164	40	time	time	NOUN
ajst-27521	164	41	series	series	PROPN
ajst-27521	164	42	data	data	PROPN
ajst-27521	164	43	.	.	PUNCT
ajst-27521	165	1	additionally	additionally	ADV
ajst-27521	165	2	,	,	PUNCT
ajst-27521	165	3	although	although	SCONJ
ajst-27521	165	4	the	the	DET
ajst-27521	165	5	performance	performance	NOUN
ajst-27521	165	6	of	of	ADP
ajst-27521	165	7	the	the	DET
ajst-27521	165	8	five	five	NUM
ajst-27521	165	9	algorithms	algorithm	NOUN
ajst-27521	165	10	on	on	ADP
ajst-27521	165	11	huawei	huawei	PROPN
ajst-27521	165	12	’s	’s	PART
ajst-27521	165	13	kpi	kpi	PROPN
ajst-27521	165	14	dataset	dataset	NOUN
ajst-27521	165	15	is	be	AUX
ajst-27521	165	16	slightly	slightly	ADV
ajst-27521	165	17	inferior	inferior	ADJ
ajst-27521	165	18	,	,	PUNCT
ajst-27521	165	19	the	the	DET
ajst-27521	165	20	average	average	ADJ
ajst-27521	165	21	f1	f1	NOUN
ajst-27521	165	22	score	score	NOUN
ajst-27521	165	23	exceeds	exceed	VERB
ajst-27521	165	24	0.7	0.7	NUM
ajst-27521	165	25	.	.	PUNCT
ajst-27521	166	1	this	this	PRON
ajst-27521	166	2	suggests	suggest	VERB
ajst-27521	166	3	that	that	SCONJ
ajst-27521	166	4	deep	deep	ADJ
ajst-27521	166	5	learning	learning	NOUN
ajst-27521	166	6	-	-	PUNCT
ajst-27521	166	7	based	base	VERB
ajst-27521	166	8	anomaly	anomaly	NOUN
ajst-27521	166	9	detection	detection	NOUN
ajst-27521	166	10	algorithms	algorithm	NOUN
ajst-27521	166	11	have	have	VERB
ajst-27521	166	12	good	good	ADJ
ajst-27521	166	13	generalization	generalization	NOUN
ajst-27521	166	14	capabilities	capability	NOUN
ajst-27521	166	15	and	and	CCONJ
ajst-27521	166	16	can	can	AUX
ajst-27521	166	17	be	be	AUX
ajst-27521	166	18	somewhat	somewhat	ADV
ajst-27521	166	19	applied	apply	VERB
ajst-27521	166	20	to	to	ADP
ajst-27521	166	21	real	real	ADJ
ajst-27521	166	22	-	-	PUNCT
ajst-27521	166	23	world	world	NOUN
ajst-27521	166	24	production	production	NOUN
ajst-27521	166	25	environments	environment	NOUN
ajst-27521	166	26	.	.	PUNCT
ajst-27521	167	1	6	6	X
ajst-27521	167	2	.	.	X
ajst-27521	167	3	summary	summary	NOUN
ajst-27521	167	4	time	time	NOUN
ajst-27521	167	5	series	series	PROPN
ajst-27521	167	6	anomaly	anomaly	PROPN
ajst-27521	167	7	detection	detection	NOUN
ajst-27521	167	8	has	have	VERB
ajst-27521	167	9	important	important	ADJ
ajst-27521	167	10	practical	practical	ADJ
ajst-27521	167	11	value	value	NOUN
ajst-27521	167	12	and	and	CCONJ
ajst-27521	167	13	is	be	AUX
ajst-27521	167	14	a	a	DET
ajst-27521	167	15	subject	subject	NOUN
ajst-27521	167	16	that	that	PRON
ajst-27521	167	17	has	have	AUX
ajst-27521	167	18	been	be	AUX
ajst-27521	167	19	studied	study	VERB
ajst-27521	167	20	extensively	extensively	ADV
ajst-27521	167	21	.	.	PUNCT
ajst-27521	168	1	in	in	ADP
ajst-27521	168	2	actual	actual	ADJ
ajst-27521	168	3	work	work	NOUN
ajst-27521	168	4	,	,	PUNCT
ajst-27521	168	5	it	it	PRON
ajst-27521	168	6	is	be	AUX
ajst-27521	168	7	often	often	ADV
ajst-27521	168	8	difficult	difficult	ADJ
ajst-27521	168	9	to	to	PART
ajst-27521	168	10	obtain	obtain	VERB
ajst-27521	168	11	high	high	ADJ
ajst-27521	168	12	-	-	PUNCT
ajst-27521	168	13	quality	quality	NOUN
ajst-27521	168	14	labels	label	NOUN
ajst-27521	168	15	for	for	ADP
ajst-27521	168	16	supervised	supervised	ADJ
ajst-27521	168	17	learning	learn	VERB
ajst-27521	168	18	anomaly	anomaly	NOUN
ajst-27521	168	19	detection	detection	NOUN
ajst-27521	168	20	algorithms	algorithm	NOUN
ajst-27521	168	21	,	,	PUNCT
ajst-27521	168	22	so	so	ADV
ajst-27521	168	23	unsupervised	unsupervised	ADJ
ajst-27521	168	24	or	or	CCONJ
ajst-27521	168	25	semi	semi	ADJ
ajst-27521	168	26	-	-	ADJ
ajst-27521	168	27	supervised	supervised	ADJ
ajst-27521	168	28	deep	deep	ADJ
ajst-27521	168	29	learning	learning	NOUN
ajst-27521	168	30	anomaly	anomaly	NOUN
ajst-27521	168	31	detection	detection	NOUN
ajst-27521	168	32	algorithms	algorithm	NOUN
ajst-27521	168	33	have	have	VERB
ajst-27521	168	34	higher	high	ADJ
ajst-27521	168	35	universality	universality	NOUN
ajst-27521	168	36	and	and	CCONJ
ajst-27521	168	37	a	a	DET
ajst-27521	168	38	wider	wide	ADJ
ajst-27521	168	39	range	range	NOUN
ajst-27521	168	40	of	of	ADP
ajst-27521	168	41	application	application	NOUN
ajst-27521	168	42	scenarios	scenario	NOUN
ajst-27521	168	43	.	.	PUNCT
ajst-27521	169	1	this	this	DET
ajst-27521	169	2	paper	paper	NOUN
ajst-27521	169	3	provides	provide	VERB
ajst-27521	169	4	a	a	DET
ajst-27521	169	5	classification	classification	NOUN
ajst-27521	169	6	overview	overview	NOUN
ajst-27521	169	7	of	of	ADP
ajst-27521	169	8	existing	exist	VERB
ajst-27521	169	9	anomaly	anomaly	NOUN
ajst-27521	169	10	detection	detection	NOUN
ajst-27521	169	11	methods	method	NOUN
ajst-27521	169	12	and	and	CCONJ
ajst-27521	169	13	focuses	focus	VERB
ajst-27521	169	14	on	on	ADP
ajst-27521	169	15	introducing	introduce	VERB
ajst-27521	169	16	five	five	NUM
ajst-27521	169	17	classic	classic	ADJ
ajst-27521	169	18	deep	deep	ADJ
ajst-27521	169	19	learning	learning	NOUN
ajst-27521	169	20	models	model	NOUN
ajst-27521	169	21	.	.	PUNCT
ajst-27521	170	1	the	the	DET
ajst-27521	170	2	performance	performance	NOUN
ajst-27521	170	3	of	of	ADP
ajst-27521	170	4	different	different	ADJ
ajst-27521	170	5	deep	deep	ADJ
ajst-27521	170	6	learning	learning	NOUN
ajst-27521	170	7	time	time	NOUN
ajst-27521	170	8	series	series	PROPN
ajst-27521	170	9	anomaly	anomaly	PROPN
ajst-27521	170	10	detection	detection	NOUN
ajst-27521	170	11	algorithms	algorithm	NOUN
ajst-27521	170	12	was	be	AUX
ajst-27521	170	13	evaluated	evaluate	VERB
ajst-27521	170	14	on	on	ADP
ajst-27521	170	15	two	two	NUM
ajst-27521	170	16	public	public	ADJ
ajst-27521	170	17	kpi	kpi	PROPN
ajst-27521	170	18	anomaly	anomaly	NOUN
ajst-27521	170	19	detection	detection	NOUN
ajst-27521	170	20	datasets	dataset	NOUN
ajst-27521	170	21	.	.	PUNCT
ajst-27521	171	1	the	the	DET
ajst-27521	171	2	experimental	experimental	ADJ
ajst-27521	171	3	results	result	NOUN
ajst-27521	171	4	show	show	VERB
ajst-27521	171	5	that	that	SCONJ
ajst-27521	171	6	lstm	lstm	NOUN
ajst-27521	171	7	,	,	PUNCT
ajst-27521	171	8	gan	gan	PROPN
ajst-27521	171	9	,	,	PUNCT
ajst-27521	171	10	and	and	CCONJ
ajst-27521	171	11	cnn	cnn	PROPN
ajst-27521	171	12	perform	perform	VERB
ajst-27521	171	13	well	well	ADV
ajst-27521	171	14	in	in	ADP
ajst-27521	171	15	the	the	DET
ajst-27521	171	16	anomaly	anomaly	NOUN
ajst-27521	171	17	detection	detection	NOUN
ajst-27521	171	18	task	task	NOUN
ajst-27521	171	19	.	.	PUNCT
ajst-27521	172	1	meanwhile	meanwhile	ADV
ajst-27521	172	2	,	,	PUNCT
ajst-27521	172	3	the	the	DET
ajst-27521	172	4	average	average	ADJ
ajst-27521	172	5	f1	f1	NOUN
ajst-27521	172	6	score	score	NOUN
ajst-27521	172	7	of	of	ADP
ajst-27521	172	8	the	the	DET
ajst-27521	172	9	five	five	NUM
ajst-27521	172	10	deep	deep	ADJ
ajst-27521	172	11	learning	learning	NOUN
ajst-27521	172	12	algorithms	algorithm	NOUN
ajst-27521	172	13	exceeded	exceed	VERB
ajst-27521	172	14	0.7	0.7	NUM
ajst-27521	172	15	,	,	PUNCT
ajst-27521	172	16	proving	prove	VERB
ajst-27521	172	17	that	that	SCONJ
ajst-27521	172	18	the	the	DET
ajst-27521	172	19	deep	deep	ADJ
ajst-27521	172	20	learning	learning	NOUN
ajst-27521	172	21	-	-	PUNCT
ajst-27521	172	22	based	base	VERB
ajst-27521	172	23	anomaly	anomaly	NOUN
ajst-27521	172	24	detection	detection	NOUN
ajst-27521	172	25	model	model	NOUN
ajst-27521	172	26	has	have	VERB
ajst-27521	172	27	good	good	ADJ
ajst-27521	172	28	generalization	generalization	NOUN
ajst-27521	172	29	ability	ability	NOUN
ajst-27521	172	30	.	.	PUNCT
ajst-27521	173	1	deep	deep	ADJ
ajst-27521	173	2	learning	learning	NOUN
ajst-27521	173	3	technology	technology	NOUN
ajst-27521	173	4	has	have	VERB
ajst-27521	173	5	certain	certain	ADJ
ajst-27521	173	6	application	application	NOUN
ajst-27521	173	7	value	value	NOUN
ajst-27521	173	8	in	in	ADP
ajst-27521	173	9	time	time	NOUN
ajst-27521	173	10	series	series	PROPN
ajst-27521	173	11	anomaly	anomaly	PROPN
ajst-27521	173	12	detection	detection	NOUN
ajst-27521	173	13	,	,	PUNCT
ajst-27521	173	14	but	but	CCONJ
ajst-27521	173	15	there	there	PRON
ajst-27521	173	16	are	be	VERB
ajst-27521	173	17	also	also	ADV
ajst-27521	173	18	certain	certain	ADJ
ajst-27521	173	19	limitations	limitation	NOUN
ajst-27521	173	20	,	,	PUNCT
ajst-27521	173	21	such	such	ADJ
ajst-27521	173	22	as	as	ADP
ajst-27521	173	23	high	high	ADJ
ajst-27521	173	24	training	training	NOUN
ajst-27521	173	25	cost	cost	NOUN
ajst-27521	173	26	,	,	PUNCT
ajst-27521	173	27	complex	complex	ADJ
ajst-27521	173	28	model	model	NOUN
ajst-27521	173	29	,	,	PUNCT
ajst-27521	173	30	and	and	CCONJ
ajst-27521	173	31	insufficient	insufficient	ADJ
ajst-27521	173	32	sensitivity	sensitivity	NOUN
ajst-27521	173	33	to	to	ADP
ajst-27521	173	34	certain	certain	ADJ
ajst-27521	173	35	anomaly	anomaly	NOUN
ajst-27521	173	36	types	type	NOUN
ajst-27521	173	37	.	.	PUNCT
ajst-27521	174	1	references	reference	NOUN
ajst-27521	174	2	[	[	X
ajst-27521	174	3	1	1	NUM
ajst-27521	174	4	]	]	X
ajst-27521	174	5	imani	imani	PROPN
ajst-27521	174	6	m.	m.	PROPN
ajst-27521	174	7	collaborative	collaborative	PROPN
ajst-27521	174	8	representation	representation	PROPN
ajst-27521	174	9	based	base	VERB
ajst-27521	174	10	unsupervised	unsupervised	ADJ
ajst-27521	174	11	cnn	cnn	PROPN
ajst-27521	174	12	for	for	ADP
ajst-27521	174	13	hyperspectral	hyperspectral	ADJ
ajst-27521	174	14	anomaly	anomaly	NOUN
ajst-27521	174	15	detection[j	detection[j	PROPN
ajst-27521	174	16	]	]	PUNCT
ajst-27521	174	17	.	.	PUNCT
ajst-27521	175	1	infrared	infrared	PROPN
ajst-27521	175	2	physics	physics	PROPN
ajst-27521	175	3	&	&	CCONJ
ajst-27521	175	4	technology	technology	PROPN
ajst-27521	175	5	,	,	PUNCT
ajst-27521	175	6	2024	2024	NUM
ajst-27521	175	7	,	,	PUNCT
ajst-27521	175	8	141	141	NUM
ajst-27521	175	9	:	:	SYM
ajst-27521	175	10	105498	105498	NUM
ajst-27521	175	11	.	.	PUNCT
ajst-27521	176	1	[	[	X
ajst-27521	176	2	2	2	NUM
ajst-27521	176	3	]	]	X
ajst-27521	176	4	cai	cai	X
ajst-27521	176	5	y	y	PROPN
ajst-27521	176	6	,	,	PUNCT
ajst-27521	176	7	tu	tu	PROPN
ajst-27521	176	8	y	y	PROPN
ajst-27521	176	9	x	x	PROPN
ajst-27521	176	10	,	,	PUNCT
ajst-27521	176	11	teng	teng	PROPN
ajst-27521	176	12	y	y	PROPN
ajst-27521	176	13	t	t	PROPN
ajst-27521	176	14	,	,	PUNCT
ajst-27521	176	15	et	et	PROPN
ajst-27521	176	16	al	al	PROPN
ajst-27521	176	17	.	.	PROPN
ajst-27521	176	18	anomaly	anomaly	PROPN
ajst-27521	176	19	detection	detection	NOUN
ajst-27521	176	20	of	of	ADP
ajst-27521	176	21	earthquake	earthquake	NOUN
ajst-27521	176	22	precursor	precursor	NOUN
ajst-27521	176	23	data	datum	NOUN
ajst-27521	176	24	using	use	VERB
ajst-27521	176	25	long	long	ADJ
ajst-27521	176	26	short	short	ADJ
ajst-27521	176	27	-	-	PUNCT
ajst-27521	176	28	term	term	NOUN
ajst-27521	176	29	memory	memory	NOUN
ajst-27521	176	30	networks	network	NOUN
ajst-27521	176	31	[	[	X
ajst-27521	176	32	j	j	X
ajst-27521	176	33	]	]	X
ajst-27521	176	34	.	.	PUNCT
ajst-27521	177	1	applied	apply	VERB
ajst-27521	177	2	geophysics	geophysic	NOUN
ajst-27521	177	3	,	,	PUNCT
ajst-27521	177	4	2019	2019	NUM
ajst-27521	177	5	,	,	PUNCT
ajst-27521	177	6	16(03	16(03	NUM
ajst-27521	177	7	):	):	PUNCT
ajst-27521	177	8	257	257	NUM
ajst-27521	177	9	-	-	SYM
ajst-27521	177	10	266	266	NUM
ajst-27521	177	11	.	.	PUNCT
ajst-27521	178	1	[	[	X
ajst-27521	178	2	3	3	X
ajst-27521	178	3	]	]	X
ajst-27521	178	4	xue	xue	PROPN
ajst-27521	178	5	j	j	PROPN
ajst-27521	178	6	,	,	PUNCT
ajst-27521	178	7	yan	yan	PROPN
ajst-27521	178	8	s	s	PART
ajst-27521	178	9	,	,	PUNCT
ajst-27521	178	10	qu	qu	PROPN
ajst-27521	178	11	jh	jh	PROPN
ajst-27521	178	12	,	,	PUNCT
ajst-27521	178	13	et	et	PROPN
ajst-27521	178	14	al	al	PROPN
ajst-27521	178	15	.	.	PUNCT
ajst-27521	179	1	deep	deep	ADJ
ajst-27521	179	2	membrane	membrane	NOUN
ajst-27521	179	3	systems	system	NOUN
ajst-27521	179	4	for	for	ADP
ajst-27521	179	5	multitask	multitask	ADJ
ajst-27521	179	6	segmentation	segmentation	NOUN
ajst-27521	179	7	in	in	ADP
ajst-27521	179	8	diabetic	diabetic	ADJ
ajst-27521	179	9	retinopathy[j	retinopathy[j	NOUN
ajst-27521	179	10	]	]	PUNCT
ajst-27521	179	11	.	.	PUNCT
ajst-27521	180	1	knowledge	knowledge	NOUN
ajst-27521	180	2	-	-	PUNCT
ajst-27521	180	3	based	base	VERB
ajst-27521	180	4	system	system	NOUN
ajst-27521	180	5	,	,	PUNCT
ajst-27521	180	6	2019	2019	NUM
ajst-27521	180	7	,	,	PUNCT
ajst-27521	180	8	183(1	183(1	NUM
ajst-27521	180	9	):	):	PUNCT
ajst-27521	180	10	1	1	NUM
ajst-27521	180	11	-	-	SYM
ajst-27521	180	12	10	10	NUM
ajst-27521	180	13	.	.	PUNCT
ajst-27521	181	1	[	[	X
ajst-27521	181	2	4	4	X
ajst-27521	181	3	]	]	X
ajst-27521	181	4	wang	wang	PROPN
ajst-27521	181	5	q	q	PROPN
ajst-27521	181	6	,	,	PUNCT
ajst-27521	181	7	qi	qi	PROPN
ajst-27521	181	8	f	f	PROPN
ajst-27521	181	9	,	,	PUNCT
ajst-27521	181	10	sun	sun	PROPN
ajst-27521	181	11	m	m	PROPN
ajst-27521	181	12	,	,	PUNCT
ajst-27521	181	13	et	et	PROPN
ajst-27521	181	14	al	al	PROPN
ajst-27521	181	15	.	.	PUNCT
ajst-27521	181	16	identification	identification	NOUN
ajst-27521	181	17	of	of	ADP
ajst-27521	181	18	tomato	tomato	NOUN
ajst-27521	181	19	disease	disease	NOUN
ajst-27521	181	20	types	type	NOUN
ajst-27521	181	21	and	and	CCONJ
ajst-27521	181	22	detection	detection	NOUN
ajst-27521	181	23	of	of	ADP
ajst-27521	181	24	infected	infected	ADJ
ajst-27521	181	25	areas	area	NOUN
ajst-27521	181	26	based	base	VERB
ajst-27521	181	27	on	on	ADP
ajst-27521	181	28	deep	deep	ADJ
ajst-27521	181	29	convolutional	convolutional	ADJ
ajst-27521	181	30	neural	neural	ADJ
ajst-27521	181	31	networks	network	NOUN
ajst-27521	181	32	and	and	CCONJ
ajst-27521	181	33	object	object	VERB
ajst-27521	181	34	detection	detection	NOUN
ajst-27521	181	35	techniques[j	techniques[j	NOUN
ajst-27521	181	36	]	]	PUNCT
ajst-27521	181	37	.	.	PUNCT
ajst-27521	182	1	computational	computational	ADJ
ajst-27521	182	2	intelligence	intelligence	NOUN
ajst-27521	182	3	and	and	CCONJ
ajst-27521	182	4	neuroscience	neuroscience	NOUN
ajst-27521	182	5	,	,	PUNCT
ajst-27521	182	6	2019	2019	NUM
ajst-27521	182	7	,	,	PUNCT
ajst-27521	182	8	2019(2	2019(2	NUM
ajst-27521	182	9	):	):	PUNCT
ajst-27521	182	10	1	1	NUM
ajst-27521	182	11	-	-	SYM
ajst-27521	182	12	15	15	NUM
ajst-27521	182	13	.	.	PUNCT
ajst-27521	183	1	[	[	X
ajst-27521	183	2	5	5	X
ajst-27521	183	3	]	]	PUNCT
ajst-27521	183	4	pittino	pittino	NOUN
ajst-27521	183	5	f	f	PROPN
ajst-27521	183	6	,	,	PUNCT
ajst-27521	183	7	puggl	puggl	PROPN
ajst-27521	183	8	m	m	NOUN
ajst-27521	183	9	,	,	PUNCT
ajst-27521	183	10	moldaschl	moldaschl	PROPN
ajst-27521	183	11	t	t	PROPN
ajst-27521	183	12	,	,	PUNCT
ajst-27521	183	13	et	et	PROPN
ajst-27521	183	14	al	al	PROPN
ajst-27521	183	15	.	.	PROPN
ajst-27521	184	1	automatic	automatic	ADJ
ajst-27521	184	2	anomaly	anomaly	NOUN
ajst-27521	184	3	detection	detection	NOUN
ajst-27521	184	4	on	on	ADP
ajst-27521	184	5	in	in	ADP
ajst-27521	184	6	-	-	PUNCT
ajst-27521	184	7	production	production	NOUN
ajst-27521	184	8	manufacturing	manufacturing	NOUN
ajst-27521	184	9	machines	machine	NOUN
ajst-27521	184	10	using	use	VERB
ajst-27521	184	11	statistical	statistical	ADJ
ajst-27521	184	12	learning	learn	VERB
ajst-27521	184	13	methods[j	methods[j	PROPN
ajst-27521	184	14	]	]	PUNCT
ajst-27521	184	15	.	.	PUNCT
ajst-27521	185	1	sensors	sensor	NOUN
ajst-27521	185	2	,	,	PUNCT
ajst-27521	185	3	2020	2020	NUM
ajst-27521	185	4	,	,	PUNCT
ajst-27521	185	5	20(8	20(8	NUM
ajst-27521	185	6	):	):	PUNCT
ajst-27521	185	7	2344	2344	NUM
ajst-27521	185	8	.	.	PUNCT
ajst-27521	186	1	[	[	X
ajst-27521	186	2	6	6	NUM
ajst-27521	186	3	]	]	X
ajst-27521	186	4	wu	wu	PROPN
ajst-27521	186	5	s	s	PROPN
ajst-27521	186	6	,	,	PUNCT
ajst-27521	186	7	fang	fang	PROPN
ajst-27521	186	8	l	l	PROPN
ajst-27521	186	9	,	,	PUNCT
ajst-27521	186	10	zhang	zhang	PROPN
ajst-27521	186	11	j	j	PROPN
ajst-27521	186	12	,	,	PUNCT
ajst-27521	186	13	et	et	PROPN
ajst-27521	186	14	al	al	PROPN
ajst-27521	186	15	.	.	PROPN
ajst-27521	186	16	unsupervised	unsupervised	ADJ
ajst-27521	186	17	anomaly	anomaly	NOUN
ajst-27521	186	18	detection	detection	NOUN
ajst-27521	186	19	and	and	CCONJ
ajst-27521	186	20	diagnosis	diagnosis	NOUN
ajst-27521	186	21	in	in	ADP
ajst-27521	186	22	power	power	NOUN
ajst-27521	186	23	electronic	electronic	ADJ
ajst-27521	186	24	networks	network	NOUN
ajst-27521	186	25	:	:	PUNCT
ajst-27521	186	26	informative	informative	ADJ
ajst-27521	186	27	leverage	leverage	NOUN
ajst-27521	186	28	and	and	CCONJ
ajst-27521	186	29	multivariate	multivariate	NOUN
ajst-27521	186	30	functional	functional	ADJ
ajst-27521	186	31	clustering	clustering	ADJ
ajst-27521	186	32	approaches[j	approaches[j	PROPN
ajst-27521	186	33	]	]	PUNCT
ajst-27521	186	34	.	.	PUNCT
ajst-27521	187	1	ieee	ieee	NOUN
ajst-27521	187	2	transactions	transaction	NOUN
ajst-27521	187	3	on	on	ADP
ajst-27521	187	4	smart	smart	ADJ
ajst-27521	187	5	grid	grid	NOUN
ajst-27521	187	6	,	,	PUNCT
ajst-27521	187	7	2023	2023	NUM
ajst-27521	187	8	.	.	PUNCT
ajst-27521	188	1	[	[	X
ajst-27521	188	2	7	7	X
ajst-27521	188	3	]	]	X
ajst-27521	188	4	wang	wang	PROPN
ajst-27521	188	5	c	c	PROPN
ajst-27521	188	6	,	,	PUNCT
ajst-27521	188	7	zhou	zhou	PROPN
ajst-27521	188	8	h	h	PROPN
ajst-27521	188	9	,	,	PUNCT
ajst-27521	188	10	hao	hao	PROPN
ajst-27521	188	11	z	z	PROPN
ajst-27521	188	12	,	,	PUNCT
ajst-27521	188	13	et	et	PROPN
ajst-27521	188	14	al	al	PROPN
ajst-27521	188	15	.	.	PUNCT
ajst-27521	188	16	network	network	NOUN
ajst-27521	188	17	traffic	traffic	NOUN
ajst-27521	188	18	analysis	analysis	NOUN
ajst-27521	188	19	over	over	ADP
ajst-27521	188	20	clustering	cluster	VERB
ajst-27521	188	21	-	-	PUNCT
ajst-27521	188	22	based	base	VERB
ajst-27521	188	23	collective	collective	ADJ
ajst-27521	188	24	anomaly	anomaly	NOUN
ajst-27521	188	25	detection[j	detection[j	PROPN
ajst-27521	188	26	]	]	PUNCT
ajst-27521	188	27	.	.	PUNCT
ajst-27521	189	1	computer	computer	NOUN
ajst-27521	189	2	networks	network	NOUN
ajst-27521	189	3	,	,	PUNCT
ajst-27521	189	4	2022	2022	NUM
ajst-27521	189	5	,	,	PUNCT
ajst-27521	189	6	205	205	NUM
ajst-27521	189	7	:	:	SYM
ajst-27521	189	8	108760	108760	NUM
ajst-27521	189	9	.	.	PUNCT
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ajst-27521	190	2	8	8	NUM
ajst-27521	190	3	]	]	SYM
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ajst-27521	190	5	e	e	NOUN
ajst-27521	190	6	,	,	PUNCT
ajst-27521	190	7	mortazavi	mortazavi	ADJ
ajst-27521	190	8	r	r	NOUN
ajst-27521	190	9	,	,	PUNCT
ajst-27521	190	10	basiri	basiri	NOUN
ajst-27521	190	11	a	a	NOUN
ajst-27521	190	12	,	,	PUNCT
ajst-27521	190	13	et	et	PROPN
ajst-27521	190	14	al	al	PROPN
ajst-27521	190	15	.	.	PROPN
ajst-27521	190	16	time	time	PROPN
ajst-27521	190	17	series	series	PROPN
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ajst-27521	190	20	via	via	ADP
ajst-27521	190	21	clustering	cluster	VERB
ajst-27521	190	22	-	-	PUNCT
ajst-27521	190	23	based	base	VERB
ajst-27521	190	24	representation[j	representation[j	PROPN
ajst-27521	190	25	]	]	PUNCT
ajst-27521	190	26	.	.	PUNCT
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ajst-27521	191	2	systems	system	NOUN
ajst-27521	191	3	,	,	PUNCT
ajst-27521	191	4	2024	2024	NUM
ajst-27521	191	5	,	,	PUNCT
ajst-27521	191	6	15(4	15(4	NUM
ajst-27521	191	7	):	):	PUNCT
ajst-27521	191	8	1115	1115	NUM
ajst-27521	191	9	-	-	SYM
ajst-27521	191	10	1136	1136	NUM
ajst-27521	191	11	.	.	PUNCT
ajst-27521	192	1	[	[	X
ajst-27521	192	2	9	9	NUM
ajst-27521	192	3	]	]	SYM
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ajst-27521	192	5	,	,	PUNCT
ajst-27521	192	6	m.	m.	NOUN
ajst-27521	192	7	,	,	PUNCT
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ajst-27521	192	9	,	,	PUNCT
ajst-27521	192	10	d.	d.	PROPN
ajst-27521	192	11	,	,	PUNCT
ajst-27521	192	12	zhang	zhang	PROPN
ajst-27521	192	13	,	,	PUNCT
ajst-27521	192	14	d.	d.	PROPN
ajst-27521	192	15	et	et	PROPN
ajst-27521	192	16	al	al	PROPN
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ajst-27521	193	1	the	the	DET
ajst-27521	193	2	seeding	seed	VERB
ajst-27521	193	3	algorithms	algorithm	NOUN
ajst-27521	193	4	for	for	ADP
ajst-27521	193	5	spherical	spherical	ADJ
ajst-27521	193	6	k	k	ADJ
ajst-27521	193	7	-	-	PUNCT
ajst-27521	193	8	means	means	NOUN
ajst-27521	193	9	clustering[j	clustering[j	NOUN
ajst-27521	193	10	]	]	PUNCT
ajst-27521	193	11	.	.	PUNCT
ajst-27521	194	1	glob	glob	PROPN
ajst-27521	194	2	optim,2020	optim,2020	NOUN
ajst-27521	194	3	:	:	PUNCT
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ajst-27521	194	5	.	.	PUNCT
ajst-27521	195	1	[	[	X
ajst-27521	195	2	10	10	NUM
ajst-27521	195	3	]	]	X
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ajst-27521	195	8	d	d	PROPN
ajst-27521	195	9	,	,	PUNCT
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ajst-27521	195	11	z	z	PROPN
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ajst-27521	195	13	et	et	PROPN
ajst-27521	195	14	al	al	PROPN
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ajst-27521	195	18	telemetry	telemetry	NOUN
ajst-27521	195	19	data	datum	NOUN
ajst-27521	195	20	anomaly	anomaly	NOUN
ajst-27521	195	21	detection	detection	NOUN
ajst-27521	195	22	model	model	NOUN
ajst-27521	195	23	based	base	VERB
ajst-27521	195	24	on	on	ADP
ajst-27521	195	25	bayesian	bayesian	NOUN
ajst-27521	195	26	lstm[j	lstm[j	PROPN
ajst-27521	195	27	]	]	PUNCT
ajst-27521	195	28	.	.	PUNCT
ajst-27521	196	1	acta	acta	PROPN
ajst-27521	196	2	astronautica	astronautica	PROPN
ajst-27521	196	3	,	,	PUNCT
ajst-27521	196	4	2021	2021	NUM
ajst-27521	196	5	,	,	PUNCT
ajst-27521	196	6	180	180	NUM
ajst-27521	196	7	:	:	SYM
ajst-27521	196	8	232	232	NUM
ajst-27521	196	9	-	-	SYM
ajst-27521	196	10	242	242	NUM
ajst-27521	196	11	.	.	PUNCT
ajst-27521	197	1	[	[	X
ajst-27521	197	2	11	11	NUM
ajst-27521	197	3	]	]	X
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ajst-27521	197	6	,	,	PUNCT
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ajst-27521	197	8	a	a	DET
ajst-27521	197	9	m	m	NOUN
ajst-27521	197	10	,	,	PUNCT
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ajst-27521	197	13	,	,	PUNCT
ajst-27521	197	14	et	et	PROPN
ajst-27521	197	15	al	al	PROPN
ajst-27521	197	16	.	.	PUNCT
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ajst-27521	198	2	security	security	NOUN
ajst-27521	198	3	using	use	VERB
ajst-27521	198	4	svm	svm	PROPN
ajst-27521	198	5	-	-	PUNCT
ajst-27521	198	6	based	base	VERB
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ajst-27521	198	8	detection	detection	NOUN
ajst-27521	198	9	:	:	PUNCT
ajst-27521	198	10	issues	issue	NOUN
ajst-27521	198	11	and	and	CCONJ
ajst-27521	198	12	challenges[j	challenges[j	NOUN
ajst-27521	198	13	]	]	PUNCT
ajst-27521	198	14	.	.	PUNCT
ajst-27521	199	1	soft	soft	ADJ
ajst-27521	199	2	computing	computing	NOUN
ajst-27521	199	3	,	,	PUNCT
ajst-27521	199	4	2021	2021	NUM
ajst-27521	199	5	,	,	PUNCT
ajst-27521	199	6	25(4	25(4	NOUN
ajst-27521	199	7	):	):	PUNCT
ajst-27521	199	8	3195	3195	NUM
ajst-27521	199	9	-	-	SYM
ajst-27521	199	10	3223	3223	NUM
ajst-27521	199	11	.	.	PUNCT
ajst-27521	200	1	[	[	X
ajst-27521	200	2	12	12	NUM
ajst-27521	200	3	]	]	X
ajst-27521	200	4	li	li	PROPN
ajst-27521	200	5	y	y	PROPN
ajst-27521	200	6	,	,	PUNCT
ajst-27521	200	7	lei	lei	PROPN
ajst-27521	200	8	m	m	PROPN
ajst-27521	200	9	,	,	PUNCT
ajst-27521	200	10	liu	liu	PROPN
ajst-27521	200	11	p	p	PROPN
ajst-27521	200	12	,	,	PUNCT
ajst-27521	200	13	et	et	PROPN
ajst-27521	200	14	al	al	PROPN
ajst-27521	200	15	.	.	PUNCT
ajst-27521	201	1	a	a	DET
ajst-27521	201	2	novel	novel	ADJ
ajst-27521	201	3	framework	framework	NOUN
ajst-27521	201	4	for	for	ADP
ajst-27521	201	5	anomaly	anomaly	NOUN
ajst-27521	201	6	detection	detection	NOUN
ajst-27521	201	7	for	for	ADP
ajst-27521	201	8	satellite	satellite	NOUN
ajst-27521	201	9	momentum	momentum	NOUN
ajst-27521	201	10	wheel	wheel	NOUN
ajst-27521	201	11	based	base	VERB
ajst-27521	201	12	on	on	ADP
ajst-27521	201	13	optimized	optimize	VERB
ajst-27521	201	14	svm	svm	PROPN
ajst-27521	201	15	and	and	CCONJ
ajst-27521	201	16	huffman	huffman	PROPN
ajst-27521	201	17	-	-	PUNCT
ajst-27521	201	18	multi	multi	ADJ
ajst-27521	201	19	-	-	ADJ
ajst-27521	201	20	scale	scale	ADJ
ajst-27521	201	21	entropy[j	entropy[j	PROPN
ajst-27521	201	22	]	]	PUNCT
ajst-27521	201	23	.	.	PUNCT
ajst-27521	202	1	entropy	entropy	PROPN
ajst-27521	202	2	,	,	PUNCT
ajst-27521	202	3	2021	2021	NUM
ajst-27521	202	4	,	,	PUNCT
ajst-27521	202	5	23(8	23(8	NUM
ajst-27521	202	6	):	):	PUNCT
ajst-27521	202	7	1062	1062	NUM
ajst-27521	202	8	.	.	PUNCT
ajst-27521	203	1	[	[	X
ajst-27521	203	2	13	13	NUM
ajst-27521	203	3	]	]	PUNCT
ajst-27521	203	4	djenouri	djenouri	PROPN
ajst-27521	203	5	y	y	PROPN
ajst-27521	203	6	,	,	PUNCT
ajst-27521	203	7	belhadi	belhadi	VERB
ajst-27521	203	8	a	a	PRON
ajst-27521	203	9	,	,	PUNCT
ajst-27521	203	10	lin	lin	PROPN
ajst-27521	203	11	j	j	PROPN
ajst-27521	203	12	c	c	PROPN
ajst-27521	203	13	w	w	PROPN
ajst-27521	203	14	,	,	PUNCT
ajst-27521	203	15	et	et	PROPN
ajst-27521	203	16	al	al	PROPN
ajst-27521	203	17	.	.	PROPN
ajst-27521	203	18	adapted	adapt	VERB
ajst-27521	203	19	k	k	ADV
ajst-27521	203	20	-	-	PUNCT
ajst-27521	203	21	nearest	near	ADJ
ajst-27521	203	22	neighbors	neighbor	NOUN
ajst-27521	203	23	for	for	ADP
ajst-27521	203	24	detecting	detect	VERB
ajst-27521	203	25	anomalies	anomaly	NOUN
ajst-27521	203	26	on	on	ADP
ajst-27521	203	27	spatio	spatio	PROPN
ajst-27521	203	28	–	–	PUNCT
ajst-27521	203	29	temporal	temporal	ADJ
ajst-27521	203	30	traffic	traffic	NOUN
ajst-27521	203	31	flow[j	flow[j	NOUN
ajst-27521	203	32	]	]	PUNCT
ajst-27521	203	33	.	.	PUNCT
ajst-27521	204	1	ieee	ieee	NOUN
ajst-27521	204	2	access	access	NOUN
ajst-27521	204	3	,	,	PUNCT
ajst-27521	204	4	2019	2019	NUM
ajst-27521	204	5	,	,	PUNCT
ajst-27521	204	6	7	7	NUM
ajst-27521	204	7	:	:	SYM
ajst-27521	204	8	10015	10015	NUM
ajst-27521	204	9	-	-	SYM
ajst-27521	204	10	10027	10027	NUM
ajst-27521	204	11	.	.	PUNCT
ajst-27521	205	1	[	[	X
ajst-27521	205	2	14	14	NUM
ajst-27521	205	3	]	]	X
ajst-27521	205	4	ruff	ruff	PROPN
ajst-27521	205	5	l	l	PROPN
ajst-27521	205	6	,	,	PUNCT
ajst-27521	205	7	kauffmann	kauffmann	PROPN
ajst-27521	205	8	j	j	PROPN
ajst-27521	205	9	r	r	PROPN
ajst-27521	205	10	,	,	PUNCT
ajst-27521	205	11	vandermeulen	vandermeulen	ADJ
ajst-27521	205	12	r	r	NOUN
ajst-27521	205	13	a	a	X
ajst-27521	205	14	,	,	PUNCT
ajst-27521	205	15	et	et	PROPN
ajst-27521	205	16	al	al	PROPN
ajst-27521	205	17	.	.	PUNCT
ajst-27521	206	1	a	a	DET
ajst-27521	206	2	unifying	unifying	ADJ
ajst-27521	206	3	review	review	NOUN
ajst-27521	206	4	of	of	ADP
ajst-27521	206	5	deep	deep	ADJ
ajst-27521	206	6	and	and	CCONJ
ajst-27521	206	7	shallow	shallow	ADJ
ajst-27521	206	8	anomaly	anomaly	NOUN
ajst-27521	206	9	detection[j	detection[j	PROPN
ajst-27521	206	10	]	]	PUNCT
ajst-27521	206	11	.	.	PUNCT
ajst-27521	207	1	the	the	DET
ajst-27521	207	2	ieee	ieee	NOUN
ajst-27521	207	3	,	,	PUNCT
ajst-27521	207	4	2021	2021	NUM
ajst-27521	207	5	,	,	PUNCT
ajst-27521	207	6	109(5	109(5	NUM
ajst-27521	207	7	):	):	PUNCT
ajst-27521	207	8	756	756	NUM
ajst-27521	207	9	-	-	SYM
ajst-27521	207	10	795	795	NUM
ajst-27521	207	11	.	.	PUNCT
ajst-27521	208	1	[	[	X
ajst-27521	208	2	15	15	NUM
ajst-27521	208	3	]	]	X
ajst-27521	208	4	zhao	zhao	PROPN
ajst-27521	208	5	h	h	PROPN
ajst-27521	208	6	,	,	PUNCT
ajst-27521	208	7	liu	liu	PROPN
ajst-27521	208	8	m	m	PROPN
ajst-27521	208	9	,	,	PUNCT
ajst-27521	208	10	qiu	qiu	PROPN
ajst-27521	208	11	s	s	PROPN
ajst-27521	208	12	,	,	PUNCT
ajst-27521	208	13	et	et	PROPN
ajst-27521	208	14	al	al	PROPN
ajst-27521	208	15	.	.	PROPN
ajst-27521	208	16	satellite	satellite	PROPN
ajst-27521	208	17	unsupervised	unsupervised	ADJ
ajst-27521	208	18	anomaly	anomaly	NOUN
ajst-27521	208	19	detection	detection	NOUN
ajst-27521	208	20	based	base	VERB
ajst-27521	208	21	on	on	ADP
ajst-27521	208	22	deconvolution	deconvolution	NOUN
ajst-27521	208	23	-	-	PUNCT
ajst-27521	208	24	reconstructed	reconstruct	VERB
ajst-27521	208	25	temporal	temporal	ADJ
ajst-27521	208	26	convolutional	convolutional	ADJ
ajst-27521	208	27	autoencoder[j	autoencoder[j	NOUN
ajst-27521	208	28	]	]	PUNCT
ajst-27521	208	29	.	.	PUNCT
ajst-27521	209	1	ieee	ieee	NOUN
ajst-27521	209	2	transactions	transaction	NOUN
ajst-27521	209	3	on	on	ADP
ajst-27521	209	4	consumer	consumer	NOUN
ajst-27521	209	5	electronics	electronic	NOUN
ajst-27521	209	6	,	,	PUNCT
ajst-27521	209	7	2023	2023	NUM
ajst-27521	209	8	.	.	PUNCT
ajst-27521	210	1	[	[	X
ajst-27521	210	2	16	16	NUM
ajst-27521	210	3	]	]	X
ajst-27521	210	4	kong	kong	PROPN
ajst-27521	210	5	f	f	PROPN
ajst-27521	210	6	,	,	PUNCT
ajst-27521	210	7	li	li	PROPN
ajst-27521	210	8	j	j	PROPN
ajst-27521	210	9	,	,	PUNCT
ajst-27521	210	10	jiang	jiang	PROPN
ajst-27521	210	11	b	b	PROPN
ajst-27521	210	12	,	,	PUNCT
ajst-27521	210	13	et	et	PROPN
ajst-27521	210	14	al	al	PROPN
ajst-27521	210	15	.	.	PROPN
ajst-27521	210	16	integrated	integrate	VERB
ajst-27521	210	17	generative	generative	ADJ
ajst-27521	210	18	model	model	NOUN
ajst-27521	210	19	for	for	ADP
ajst-27521	210	20	industrial	industrial	ADJ
ajst-27521	210	21	anomaly	anomaly	NOUN
ajst-27521	210	22	detection	detection	NOUN
ajst-27521	210	23	via	via	ADP
ajst-27521	210	24	bidirectional	bidirectional	ADJ
ajst-27521	210	25	lstm	lstm	NOUN
ajst-27521	210	26	and	and	CCONJ
ajst-27521	210	27	attention	attention	NOUN
ajst-27521	210	28	mechanism[j	mechanism[j	PROPN
ajst-27521	210	29	]	]	PUNCT
ajst-27521	210	30	.	.	PUNCT
ajst-27521	211	1	ieee	ieee	NOUN
ajst-27521	211	2	transactions	transaction	NOUN
ajst-27521	211	3	on	on	ADP
ajst-27521	211	4	industrial	industrial	ADJ
ajst-27521	211	5	informatics	informatic	NOUN
ajst-27521	211	6	,	,	PUNCT
ajst-27521	211	7	2021	2021	NUM
ajst-27521	211	8	,	,	PUNCT
ajst-27521	211	9	19(1	19(1	NUM
ajst-27521	211	10	):	):	PUNCT
ajst-27521	211	11	541	541	NUM
ajst-27521	211	12	-	-	SYM
ajst-27521	211	13	550	550	NUM
ajst-27521	211	14	.	.	PUNCT
ajst-27521	212	1	[	[	X
ajst-27521	212	2	17	17	NUM
ajst-27521	212	3	]	]	SYM
ajst-27521	212	4	su	su	PROPN
ajst-27521	212	5	y	y	PROPN
ajst-27521	212	6	,	,	PUNCT
ajst-27521	212	7	zhao	zhao	PROPN
ajst-27521	212	8	y	y	PROPN
ajst-27521	212	9	,	,	PUNCT
ajst-27521	212	10	niu	niu	PROPN
ajst-27521	212	11	c	c	PROPN
ajst-27521	212	12	,	,	PUNCT
ajst-27521	212	13	et	et	PROPN
ajst-27521	212	14	al	al	PROPN
ajst-27521	212	15	.	.	PROPN
ajst-27521	212	16	robust	robust	ADJ
ajst-27521	212	17	anomaly	anomaly	NOUN
ajst-27521	212	18	detection	detection	NOUN
ajst-27521	212	19	for	for	ADP
ajst-27521	212	20	multivariatetime	multivariatetime	PROPN
ajst-27521	212	21	series	series	NOUN
ajst-27521	212	22	through	through	ADP
ajst-27521	212	23	stochastic	stochastic	ADJ
ajst-27521	212	24	recurrent	recurrent	ADJ
ajst-27521	212	25	neural	neural	ADJ
ajst-27521	212	26	network[c]//proceedings	network[c]//proceeding	NOUN
ajst-27521	212	27	of	of	ADP
ajst-27521	212	28	the	the	DET
ajst-27521	212	29	25th	25th	ADJ
ajst-27521	212	30	acm	acm	PROPN
ajst-27521	212	31	sigkdd	sigkdd	NOUN
ajst-27521	212	32	international	international	ADJ
ajst-27521	212	33	conference	conference	NOUN
ajst-27521	212	34	on	on	ADP
ajst-27521	212	35	knowledge	knowledge	PROPN
ajst-27521	212	36	discovery	discovery	PROPN
ajst-27521	212	37	&	&	CCONJ
ajst-27521	212	38	data	data	PROPN
ajst-27521	212	39	mining.2019:2828	mining.2019:2828	NOUN
ajst-27521	212	40	-	-	PUNCT
ajst-27521	212	41	2837	2837	NUM
ajst-27521	212	42	.	.	PUNCT
ajst-27521	213	1	[	[	X
ajst-27521	213	2	18	18	NUM
ajst-27521	213	3	]	]	X
ajst-27521	213	4	zhao	zhao	PROPN
ajst-27521	213	5	p	p	PROPN
ajst-27521	213	6	,	,	PUNCT
ajst-27521	213	7	chang	chang	PROPN
ajst-27521	213	8	x	x	PROPN
ajst-27521	213	9	,	,	PUNCT
ajst-27521	213	10	wang	wang	PROPN
ajst-27521	213	11	m.	m.	VERB
ajst-27521	213	12	a	a	DET
ajst-27521	213	13	novel	novel	ADJ
ajst-27521	213	14	multivariate	multivariate	NOUN
ajst-27521	213	15	time	time	NOUN
ajst-27521	213	16	-	-	PUNCT
ajst-27521	213	17	series	series	NOUN
ajst-27521	213	18	anomaly	anomaly	NOUN
ajst-27521	213	19	detection	detection	NOUN
ajst-27521	213	20	approach	approach	NOUN
ajst-27521	213	21	using	use	VERB
ajst-27521	213	22	an	an	DET
ajst-27521	213	23	unsupervised	unsupervised	ADJ
ajst-27521	213	24	deep	deep	ADJ
ajst-27521	213	25	neural	neural	ADJ
ajst-27521	213	26	network[j	network[j	NOUN
ajst-27521	213	27	]	]	PUNCT
ajst-27521	213	28	.	.	PUNCT
ajst-27521	214	1	ieee	ieee	NOUN
ajst-27521	214	2	access	access	NOUN
ajst-27521	214	3	,	,	PUNCT
ajst-27521	214	4	2021	2021	NUM
ajst-27521	214	5	,	,	PUNCT
ajst-27521	214	6	9	9	NUM
ajst-27521	214	7	:	:	SYM
ajst-27521	214	8	109025	109025	NUM
ajst-27521	214	9	-	-	SYM
ajst-27521	214	10	109041	109041	NUM
ajst-27521	214	11	.	.	PUNCT
ajst-27521	215	1	[	[	X
ajst-27521	215	2	19	19	NUM
ajst-27521	215	3	]	]	X
ajst-27521	215	4	yao	yao	NOUN
ajst-27521	215	5	x	x	X
ajst-27521	215	6	,	,	PUNCT
ajst-27521	215	7	zhu	zhu	PROPN
ajst-27521	215	8	j	j	PROPN
ajst-27521	215	9	,	,	PUNCT
ajst-27521	215	10	jiang	jiang	PROPN
ajst-27521	215	11	q	q	PROPN
ajst-27521	215	12	,	,	PUNCT
ajst-27521	215	13	et	et	PROPN
ajst-27521	215	14	al	al	PROPN
ajst-27521	215	15	.	.	PUNCT
ajst-27521	215	16	rul	rul	PROPN
ajst-27521	215	17	prediction	prediction	NOUN
ajst-27521	215	18	method	method	NOUN
ajst-27521	215	19	for	for	ADP
ajst-27521	215	20	rolling	rolling	ADJ
ajst-27521	215	21	bearing	bearing	NOUN
ajst-27521	215	22	using	use	VERB
ajst-27521	215	23	convolutional	convolutional	ADJ
ajst-27521	215	24	denoising	denoising	NOUN
ajst-27521	215	25	autoencoder	autoencoder	NOUN
ajst-27521	215	26	and	and	CCONJ
ajst-27521	215	27	bidirectional	bidirectional	NOUN
ajst-27521	215	28	lstm[j	lstm[j	PROPN
ajst-27521	215	29	]	]	PUNCT
ajst-27521	215	30	.	.	PUNCT
ajst-27521	216	1	measurement	measurement	NOUN
ajst-27521	216	2	science	science	NOUN
ajst-27521	216	3	and	and	CCONJ
ajst-27521	216	4	technology	technology	NOUN
ajst-27521	216	5	,	,	PUNCT
ajst-27521	216	6	2023	2023	NUM
ajst-27521	216	7	,	,	PUNCT
ajst-27521	216	8	35(3	35(3	NUM
ajst-27521	216	9	):	):	PUNCT
ajst-27521	216	10	035111	035111	NUM
ajst-27521	216	11	.	.	PUNCT
ajst-27521	217	1	[	[	X
ajst-27521	217	2	20	20	NUM
ajst-27521	217	3	]	]	PUNCT
ajst-27521	217	4	zou	zou	PROPN
ajst-27521	217	5	l	l	PROPN
ajst-27521	217	6	,	,	PUNCT
ajst-27521	217	7	zhuang	zhuang	PROPN
ajst-27521	217	8	k	k	PROPN
ajst-27521	217	9	j	j	PROPN
ajst-27521	217	10	,	,	PUNCT
ajst-27521	217	11	zhou	zhou	PROPN
ajst-27521	217	12	a	a	X
ajst-27521	217	13	,	,	PUNCT
ajst-27521	217	14	et	et	PROPN
ajst-27521	217	15	al	al	PROPN
ajst-27521	217	16	.	.	PUNCT
ajst-27521	217	17	bayesian	bayesian	NOUN
ajst-27521	217	18	optimization	optimization	NOUN
ajst-27521	217	19	and	and	CCONJ
ajst-27521	217	20	channel	channel	NOUN
ajst-27521	217	21	-	-	PUNCT
ajst-27521	217	22	fusion	fusion	NOUN
ajst-27521	217	23	-	-	PUNCT
ajst-27521	217	24	based	base	VERB
ajst-27521	217	25	convolutional	convolutional	ADJ
ajst-27521	217	26	autoencoder	autoencoder	NOUN
ajst-27521	217	27	network	network	NOUN
ajst-27521	217	28	for	for	ADP
ajst-27521	217	29	172	172	NUM
ajst-27521	217	30	fault	fault	NOUN
ajst-27521	217	31	diagnosis	diagnosis	NOUN
ajst-27521	217	32	of	of	ADP
ajst-27521	217	33	rotating	rotate	VERB
ajst-27521	217	34	machinery[j	machinery[j	NOUN
ajst-27521	217	35	]	]	PUNCT
ajst-27521	217	36	.	.	PUNCT
ajst-27521	218	1	engineering	engineering	NOUN
ajst-27521	218	2	structures	structure	NOUN
ajst-27521	218	3	,	,	PUNCT
ajst-27521	218	4	2023	2023	NUM
ajst-27521	218	5	,	,	PUNCT
ajst-27521	218	6	280	280	NUM
ajst-27521	218	7	:	:	SYM
ajst-27521	218	8	115708	115708	NUM
ajst-27521	218	9	.	.	PUNCT
ajst-27521	219	1	[	[	X
ajst-27521	219	2	21	21	NUM
ajst-27521	219	3	]	]	X
ajst-27521	219	4	moon	moon	PROPN
ajst-27521	219	5	j	j	PROPN
ajst-27521	219	6	,	,	PUNCT
ajst-27521	219	7	noh	noh	PROPN
ajst-27521	219	8	y	y	PROPN
ajst-27521	219	9	,	,	PUNCT
ajst-27521	219	10	jung	jung	PROPN
ajst-27521	219	11	s	s	PROPN
ajst-27521	219	12	,	,	PUNCT
ajst-27521	219	13	et	et	PROPN
ajst-27521	219	14	al	al	PROPN
ajst-27521	219	15	.	.	PROPN
ajst-27521	219	16	anomaly	anomaly	PROPN
ajst-27521	219	17	detection	detection	NOUN
ajst-27521	219	18	using	use	VERB
ajst-27521	219	19	a	a	DET
ajst-27521	219	20	model	model	ADJ
ajst-27521	219	21	-	-	ADJ
ajst-27521	219	22	agnostic	agnostic	ADJ
ajst-27521	219	23	meta	meta	ADJ
ajst-27521	219	24	-	-	PUNCT
ajst-27521	219	25	learning	learning	NOUN
ajst-27521	219	26	-	-	PUNCT
ajst-27521	219	27	based	base	VERB
ajst-27521	219	28	variational	variational	ADJ
ajst-27521	219	29	auto	auto	NOUN
ajst-27521	219	30	-	-	PUNCT
ajst-27521	219	31	encoder	encoder	NOUN
ajst-27521	219	32	for	for	ADP
ajst-27521	219	33	facility	facility	NOUN
ajst-27521	219	34	management[j	management[j	NOUN
ajst-27521	219	35	]	]	PUNCT
ajst-27521	219	36	.	.	PUNCT
ajst-27521	220	1	journal	journal	PROPN
ajst-27521	220	2	of	of	ADP
ajst-27521	220	3	building	build	VERB
ajst-27521	220	4	engineering	engineering	NOUN
ajst-27521	220	5	,	,	PUNCT
ajst-27521	220	6	2023	2023	NUM
ajst-27521	220	7	,	,	PUNCT
ajst-27521	220	8	68	68	NUM
ajst-27521	220	9	:	:	SYM
ajst-27521	220	10	106099	106099	NUM
ajst-27521	220	11	.	.	PUNCT
ajst-27521	221	1	[	[	X
ajst-27521	221	2	22	22	NUM
ajst-27521	221	3	]	]	X
ajst-27521	221	4	de	de	X
ajst-27521	221	5	albuquerque	albuquerque	PROPN
ajst-27521	221	6	filho	filho	PROPN
ajst-27521	221	7	j	j	PROPN
ajst-27521	221	8	e	e	PROPN
ajst-27521	221	9	,	,	PUNCT
ajst-27521	221	10	brandão	brandão	PROPN
ajst-27521	221	11	l	l	PROPN
ajst-27521	222	1	c	c	PROPN
ajst-27521	222	2	p	p	X
ajst-27521	222	3	,	,	PUNCT
ajst-27521	222	4	fernandes	fernandes	PROPN
ajst-27521	222	5	b	b	PROPN
ajst-27521	222	6	j	j	PROPN
ajst-27521	222	7	t	t	PROPN
ajst-27521	222	8	,	,	PUNCT
ajst-27521	222	9	et	et	PROPN
ajst-27521	222	10	al	al	PROPN
ajst-27521	222	11	.	.	PUNCT
ajst-27521	223	1	a	a	DET
ajst-27521	223	2	review	review	NOUN
ajst-27521	223	3	of	of	ADP
ajst-27521	223	4	neural	neural	ADJ
ajst-27521	223	5	networks	network	NOUN
ajst-27521	223	6	for	for	ADP
ajst-27521	223	7	anomaly	anomaly	PROPN
ajst-27521	223	8	detection[j	detection[j	PROPN
ajst-27521	223	9	]	]	PUNCT
ajst-27521	223	10	.	.	PUNCT
ajst-27521	224	1	ieee	ieee	NOUN
ajst-27521	224	2	access	access	NOUN
ajst-27521	224	3	,	,	PUNCT
ajst-27521	224	4	2022	2022	NUM
ajst-27521	224	5	,	,	PUNCT
ajst-27521	224	6	10	10	NUM
ajst-27521	224	7	:	:	SYM
ajst-27521	224	8	112342	112342	NUM
ajst-27521	224	9	-	-	SYM
ajst-27521	224	10	112367	112367	NUM
ajst-27521	224	11	.	.	PUNCT
ajst-27521	225	1	[	[	X
ajst-27521	225	2	23	23	NUM
ajst-27521	225	3	]	]	X
ajst-27521	225	4	zhang	zhang	PROPN
ajst-27521	225	5	x	x	PROPN
ajst-27521	225	6	,	,	PUNCT
ajst-27521	225	7	mu	mu	PROPN
ajst-27521	225	8	j	j	PROPN
ajst-27521	225	9	,	,	PUNCT
ajst-27521	225	10	zhang	zhang	PROPN
ajst-27521	225	11	x	x	PROPN
ajst-27521	225	12	,	,	PUNCT
ajst-27521	225	13	et	et	PROPN
ajst-27521	225	14	al	al	PROPN
ajst-27521	225	15	.	.	PUNCT
ajst-27521	226	1	deep	deep	ADJ
ajst-27521	226	2	anomaly	anomaly	NOUN
ajst-27521	226	3	detection	detection	NOUN
ajst-27521	226	4	with	with	ADP
ajst-27521	226	5	self	self	NOUN
ajst-27521	226	6	-	-	PUNCT
ajst-27521	226	7	supervised	supervise	VERB
ajst-27521	226	8	learning	learning	NOUN
ajst-27521	226	9	and	and	CCONJ
ajst-27521	226	10	adversarial	adversarial	ADJ
ajst-27521	226	11	training[j	training[j	NOUN
ajst-27521	226	12	]	]	PUNCT
ajst-27521	226	13	.	.	PUNCT
ajst-27521	227	1	pattern	pattern	NOUN
ajst-27521	227	2	recognition	recognition	NOUN
ajst-27521	227	3	,	,	PUNCT
ajst-27521	227	4	2022	2022	NUM
ajst-27521	227	5	,	,	PUNCT
ajst-27521	227	6	121	121	NUM
ajst-27521	227	7	:	:	SYM
ajst-27521	227	8	108234	108234	NUM
ajst-27521	227	9	.	.	PUNCT
ajst-27521	228	1	[	[	X
ajst-27521	228	2	24	24	NUM
ajst-27521	228	3	]	]	X
ajst-27521	228	4	chen	chen	PROPN
ajst-27521	228	5	z	z	PROPN
ajst-27521	228	6	,	,	PUNCT
ajst-27521	228	7	duan	duan	PROPN
ajst-27521	228	8	j	j	PROPN
ajst-27521	228	9	,	,	PUNCT
ajst-27521	228	10	kang	kang	PROPN
ajst-27521	228	11	l	l	PROPN
ajst-27521	228	12	,	,	PUNCT
ajst-27521	228	13	et	et	PROPN
ajst-27521	228	14	al	al	PROPN
ajst-27521	228	15	.	.	PROPN
ajst-27521	228	16	supervised	supervise	VERB
ajst-27521	228	17	anomaly	anomaly	NOUN
ajst-27521	228	18	detection	detection	NOUN
ajst-27521	228	19	via	via	ADP
ajst-27521	228	20	conditional	conditional	ADJ
ajst-27521	228	21	generative	generative	ADJ
ajst-27521	228	22	adversarial	adversarial	ADJ
ajst-27521	228	23	network	network	NOUN
ajst-27521	228	24	and	and	CCONJ
ajst-27521	228	25	ensemble	ensemble	VERB
ajst-27521	228	26	active	active	ADJ
ajst-27521	228	27	learning[j	learning[j	NOUN
ajst-27521	228	28	]	]	PUNCT
ajst-27521	228	29	.	.	PUNCT
ajst-27521	229	1	ieee	ieee	NOUN
ajst-27521	229	2	transactions	transaction	NOUN
ajst-27521	229	3	on	on	ADP
ajst-27521	229	4	pattern	pattern	NOUN
ajst-27521	229	5	analysis	analysis	NOUN
ajst-27521	229	6	and	and	CCONJ
ajst-27521	229	7	machine	machine	NOUN
ajst-27521	229	8	intelligence	intelligence	NOUN
ajst-27521	229	9	,	,	PUNCT
ajst-27521	229	10	2022	2022	NUM
ajst-27521	229	11	,	,	PUNCT
ajst-27521	229	12	45(6	45(6	NUM
ajst-27521	229	13	):	):	PUNCT
ajst-27521	229	14	7781	7781	NUM
ajst-27521	229	15	-	-	SYM
ajst-27521	229	16	7798	7798	NUM
ajst-27521	229	17	.	.	PUNCT
ajst-27521	230	1	[	[	X
ajst-27521	230	2	25	25	NUM
ajst-27521	230	3	]	]	PUNCT
ajst-27521	230	4	schlegl	schlegl	PROPN
ajst-27521	230	5	t	t	PROPN
ajst-27521	230	6	,	,	PUNCT
ajst-27521	230	7	seeböck	seeböck	NOUN
ajst-27521	230	8	p	p	NOUN
ajst-27521	230	9	,	,	PUNCT
ajst-27521	230	10	waldstein	waldstein	NOUN
ajst-27521	230	11	s	s	PART
ajst-27521	230	12	m	m	PROPN
ajst-27521	230	13	,	,	PUNCT
ajst-27521	230	14	et	et	PROPN
ajst-27521	230	15	al	al	PROPN
ajst-27521	230	16	.	.	PUNCT
ajst-27521	231	1	f	f	X
ajst-27521	231	2	-	-	PUNCT
ajst-27521	231	3	anogan	anogan	ADJ
ajst-27521	231	4	:	:	PUNCT
ajst-27521	231	5	fast	fast	ADJ
ajst-27521	231	6	unsupervised	unsupervised	ADJ
ajst-27521	231	7	anomaly	anomaly	NOUN
ajst-27521	231	8	detection	detection	NOUN
ajst-27521	231	9	with	with	ADP
ajst-27521	231	10	generative	generative	ADJ
ajst-27521	231	11	adversarial	adversarial	ADJ
ajst-27521	231	12	networks[j	networks[j	PROPN
ajst-27521	231	13	]	]	PUNCT
ajst-27521	231	14	.	.	PUNCT
ajst-27521	232	1	medical	medical	ADJ
ajst-27521	232	2	image	image	NOUN
ajst-27521	232	3	analysis	analysis	NOUN
ajst-27521	232	4	,	,	PUNCT
ajst-27521	232	5	2019	2019	NUM
ajst-27521	232	6	,	,	PUNCT
ajst-27521	232	7	54	54	NUM
ajst-27521	232	8	:	:	SYM
ajst-27521	232	9	30	30	NUM
ajst-27521	232	10	-	-	SYM
ajst-27521	232	11	44	44	NUM
ajst-27521	232	12	.	.	PUNCT
ajst-27521	233	1	[	[	X
ajst-27521	233	2	26	26	NUM
ajst-27521	233	3	]	]	X
ajst-27521	233	4	li	li	PROPN
ajst-27521	233	5	z	z	PROPN
ajst-27521	233	6	,	,	PUNCT
ajst-27521	233	7	zhao	zhao	PROPN
ajst-27521	233	8	n	n	CCONJ
ajst-27521	233	9	,	,	PUNCT
ajst-27521	233	10	zhang	zhang	PROPN
ajst-27521	233	11	s	s	PROPN
ajst-27521	233	12	,	,	PUNCT
ajst-27521	233	13	et	et	PROPN
ajst-27521	233	14	al	al	PROPN
ajst-27521	233	15	.	.	PUNCT
ajst-27521	234	1	constructing	construct	VERB
ajst-27521	234	2	large	large	ADJ
ajst-27521	234	3	-	-	PUNCT
ajst-27521	234	4	scale	scale	NOUN
ajst-27521	234	5	realworld	realworld	NOUN
ajst-27521	234	6	benchmark	benchmark	NOUN
ajst-27521	234	7	datasets	dataset	NOUN
ajst-27521	234	8	for	for	ADP
ajst-27521	234	9	aiops[j	aiops[j	NOUN
ajst-27521	234	10	]	]	PUNCT
ajst-27521	234	11	.	.	PUNCT
ajst-27521	235	1	arxiv	arxiv	PROPN
ajst-27521	235	2	preprint	preprint	NOUN
ajst-27521	235	3	arxiv:2208.03938	arxiv:2208.03938	NOUN
ajst-27521	235	4	,	,	PUNCT
ajst-27521	235	5	2022	2022	NUM
ajst-27521	235	6	.	.	PUNCT