,.,,., [1], [3], [4], [5] [6]. [2]. ,,, Tree-to-String. 1,f) input 1 (Python) : if x % 5 == 0: output 2 (Comment): # y x 5
|
|
|
- Ανάργυρος Λόντος
- 5 χρόνια πριν
- Προβολές:
Transcript
1 1,a) 2,b) 1,c) 1,d) 1,) 1,f) Tr-to-String 1.,.,,., [1], [2].,,,, 1.,,,. 1, 1, 1 Nara Institut of Scinc and Tchnology 2 Shinshu Univrsity a) oda.yusuk.on9@is.naist.jp b) dlihiros@gmail.com c) nubig@is.naist.jp d) ssakti@is.naist.jp ) tomoki@is.naist.jp f) s-nakamura@is.naist.jp input 1 (Python) : if x % 5 == 0: output 1 (Commnt): # x 5 input 2 (Python) : y = x / 5 output 2 (Commnt): # y x 5 1 Python., [3], [4], [5] [6].,., (, ),,.,,.,,,., c 2014 Information Procssing Socity of Japan 1
2 , Tr-to-String,.,, Tr-to-String.,. Python vr.3., [7], f ê (1). ê := arg max Pr( f) (1) f = f f 1 = [f 1, f 2,, f f ], = 1 = [ 1, 2,, ]. f., f ( ) [8], [9], Tr-to-String [10]. Tr-to-String,. 2.2 Tr-to-String f,. T f.,., [11]. Tr-to-String., (1), Tr-to-String. ê := arg max Pr( f) 2 = arg max arg max Tr-to-String Pr( f, T f )Pr(T f f) T f T f Pr( T f )Pr(T f f) (2) arg max Pr( Tf ) (3) Tr-to-String, 2,,. 2,., d. d, Tr-to-String. Pr( T f ), (4) d. d D Pr( T f ) := xp (w ϕ(t f,, d)) d D xp (w ϕ(t f,, d)) (4) D, ϕ d, w. ϕ,. Pr(T f f) f T f.,,. (3), Trto-String Tf = arg max Pr(T f f), T f., c 2014 Information Procssing Socity of Japan 2
3 ., (2) [12]. 2.3 Tr-to-String Tr-to-String (1), (2), (3) ,.., 2., [13], [14], [15] ,. GHKM [16]., ϕ, , w [17]. 3., Tr-to-String.,.,.,,., Tr-to-String. Tr-to-String. 3.1,.,. Python,., if,.,.,, 1, , Tr-to-String.,,. 3, if x is divisibl by 5 (a), Python if x % 5 == 0: (b). Ckylark *1, Python ast. x 5., Python. 3(b) Load,,. if,. Tr-to-String 2.3,,. 3,, 3(b)., Load,. *1 ckylark c 2014 Information Procssing Socity of Japan 3
4 1 Python : pass if a == b: if a == b:pass df foo(a, b, c): df foo(a, b, c):pass lif if Tru:pass\n lif foo(): if Tru:pass\nlif foo():pass ls ls: if Tru:pass\nls:pass xcpt try:pass\n xcpt: try:pass\nxcpt:pass 3,,.,.,, [18], [19], [20]., Tr-to-String,,.. 3.3, Python. (1), (2) 2, (3) (b) if,,., [21].,.,,. [21].,.,. 3(b) ,. 4,., c 2014 Information Procssing Socity of Japan 4
5 2 (Nam... (id (str x))...) (Nam x) (Num... (n (int x))...) (int x) (BinOp... (lft x) (op y) (right z)...) (BinOp (lft x) (right z) (op y)) (Compar... (lft x) (ops y) (comparators z)...) (Compar (lft x) (comparators z) (ops y)) 4 5,,.,., , (b) 5,, ,.,,. if x % 5 == 0: x 5, x 5,. 0,. 3,,.,. 6.,,. 6 x 5, 0, NAME1, INT1, INT2.,,.,,.,.,, Tr-to-String.. 3.4, 2.3 Tr-to-String c 2014 Information Procssing Socity of Japan 5
6 6,.. 4. : Python 4.1 Tr-to-String,. Python [22]. *2 Python 3., 722, Python, Tr-to-String 1,., ,., Tr-to-String. (1). (2) ( ),. *2 MCab[23]. MCab, ASCII,. foo _ bar 1-3. (3) Python 3.1,. Python ast. (4) (3), 3.3. (5) (2) (4),. (6) (5),. (invrsion transduction grammar: ITG) pialign[15]. ( ) 10.,, 80. (7) (5).,. n-gram KnLM[24], 5-gram. (8)Tr-to-String c 2014 Information Procssing Socity of Japan 6
7 7 Tr-to-String,,,, Tr-to-String. GHKM [16] Travatar[25].. 3 Accptability 5 (AA),. 4 (A),. 3 (B),. 2 (C),. 1 (F),. 4.3,.. raw,, Python ast. had,. rducd,,. constraind,.,,. 4.4,.,. BLEU[26] RIBES[27]. n-gram, BLEU, RIBES., 0, 1. Accptability[28]. 3 5, BLEU % RIBES % raw had rducd constraind Accptability Accptability raw had rducd constraind ,., 9, 1 10, 10. BLEU, RIBES rducd ( +), had ( ), raw ( ).,., constraind( ++).,. c 2014 Information Procssing Socity of Japan 7
8 5.2 8 Accptability 5 Accptability. 8, Accptability. Accptability,, rducd constraind 70%,. raw, Tr-to-String,., rducd, had, raw,., constraind rducd. constraind,,. 8, Accptability 5( ) 1().,, , 2 if x % 5 == 0: if x % 5 == 1:., 1 1, 0. 0.,,, Tr-to-String,.. constraind,.,.,,. 4, 5. constraind,,. Tr-to-String, Tr-to-String [12]. 7. Tr-to-String,.,., 1,.,.,,.,, Python.,., c 2014 Information Procssing Socity of Japan 8
9 Python if x % 5 == 0: if x % 5 == 1: raw x 5 x 5 1 had x 5 x 5 1 rducd x 5 x 5 1 constraind x 5 0 x [20],.,.,,.,,,.,. [1] DLin, R., Vnolia, G. and Rowan, K.: Softwar Dvlopmnt with Cod Maps, Commun. ACM, Vol. 53, No. 8, pp (2010). [2] Rahman, M. M. and Roy, C. K.: SurfClips: Contxt- Awar Mta Sarch in th IDE, Proc. ICSME, pp (2014). [3] Sridhara, G., Hill, E., Muppanni, D., Pollock, L. and Vijay-Shankr, K.: Towards automatically gnrating summary commnts for Java mthods, Proc. ASE, pp (2010). [4] Bus, R. P. and Wimr, W. R.: Automatic Documntation Infrnc for Excptions, Proc. ISSTA, pp (2008). [5] Sridhara, G., Pollock, L. and Vijay-Shankr, K.: Automatically Dtcting and Dscribing High Lvl Actions Within Mthods, Proc. ICSE, pp (2011). [6] Wong, E., Yang, J. and Tan, L.: AutoCommnt: Mining qustion and answr sits for automatic commnt gnration, Proc. ASE, pp (2013). [7] Brown, P. F., Pitra, V. J. D., Pitra, S. A. D. and Mrcr, R. L.: Th mathmatics of statistical machin translation: Paramtr stimation, Computational Linguistics, Vol. 19, No. 2, pp (1993). [8] Kohn, P., Och, F. J. and Marcu, D.: Statistical phras-basd translation, Proc. NAACL-HLT, pp (2003). [9] Chiang, D.: Hirarchical phras-basd translation, Computational Linguistics, Vol. 33, No. 2, pp (2007). [10] Huang, L., Knight, K. and Joshi, A.: Statistical syntaxdirctd translation with xtndd domain of locality, Proc. AMTA, Vol. 2006, pp (2006). [11] Nubig, G. and Duh, K.: On th Elmnts of an Accurat Tr-to-String Machin Translation Systm, Proc. ACL, Baltimor, USA (2014). [12] Mi, H., Huang, L. and Liu, Q.: Forst-Basd Translation, Proc. ACL-HLT, Columbus, Ohio, pp (2008). [13] Brown, P. F., Pitra, V. J. D., Pitra, S. A. D. and Mrcr, R. L.: Th Mathmatics of Statistical Machin Translation: Paramtr Estimation, Computational Linguistics, Vol. 19, No. 2, pp (1993). [14] Och, F. J. and Ny, H.: A Systmatic Comparison of Various Statistical Alignmnt Modls, Computational Linguistics, Vol. 29, No. 1, pp (2003). [15] Nubig, G., Watanab, T., Sumita, E., Mori, S. and Kawahara, T.: An Unsuprvisd Modl for Joint Phras Alignmnt and Extraction, Proc. ACL-HLT, Portland, Orgon, USA, pp (2011). [16] Gally, M., Hopkins, M., Knight, K. and Marcu, D.: What s in a Translation Rul?, Proc. NAACL-HLT, pp (2004). [17] Och, F. J.: Minimum Error Rat Training in Statistical Machin Translation, Proc. ACL, Sapporo, Japan, pp (2003). [18] Isozaki, H., Sudoh, K., Tsukada, H. and Duh, K.: Had Finalization: A Simpl Rordring Rul for SOV Languags, Proc. WMT, Uppsala, Swdn, pp (2010). [19] Hatakoshi, Y., Nubig, G., Sakti, S., Toda, T. and Nakamura, S.: Rul-basd Syntactic Prprocssing for Syntax-basd Machin Translation, Proc. SSST, Doha, Qatar, pp (2014). [20] Burktt, D. and Klin, D.: Transforming Trs to Improv Syntactic Convrgnc, Proc. EMNLP, Jju Island, South Kora (2012). [21] Li, P., Liu, Y. and Sun, M.: An Extndd GHKM Algorithm for Inducing λ-scfg, Proc. AAAI (2013). [22] Vol. 2014, No. 22, pp. 1 8 (2014). [23] Kudo, T., Yamamoto, K. and Matsumoto, Y.: Applying Conditional Random Filds to Japans Morphological Analysis., Proc. EMNLP, Vol. 4, pp (2004). [24] Hafild, K., Pouzyrvsky, I., Clark, J. H. and Kohn, P.: Scalabl Modifid Knsr-Ny Languag Modl Estimation, Proc. ACL, Sofia, Bulgaria, pp (2013). [25] Nubig, G.: Travatar: A Forst-to-String Machin Translation Engin basd on Tr Transducrs, Proc. ACL, Sofia, Bulgaria, pp (2013). [26] Papinni, K., Roukos, S., Ward, T. and Zhu, W.-J.: BLEU: A Mthod for Automatic Evaluation of Machin Translation, Proc. ACL, pp (2002). [27] Isozaki, H., Hirao, T., Duh, K., Sudoh, K. and Tsukada, H.: Automatic Evaluation of Translation Quality for Distant Languag Pairs, Proc. EMNLP, pp (2010). [28] Goto, I., Chow, K. P., Lu, B., Sumita, E. and Tsou, B. K.: Ovrviw of th patnt machin translation task at th NTCIR-10 workshop, NTCIR-10 (2013). c 2014 Information Procssing Socity of Japan 9
1 n-gram n-gram n-gram [11], [15] n-best [16] n-gram. n-gram. 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e)
![1 n-gram n-gram n-gram [11], [15] n-best [16] n-gram. n-gram. 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e)](/thumbs/39/20145714.jpg "1 n-gram n-gram n-gram [11], [15] n-best [16] n-gram. n-gram. 1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e)")
1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 1. [11], [15] 1 Nara Institute of Science and Technology a) akabe.koichi.zx8@is.naist.jp b) neubig@is.naist.jp c) ssakti@is.naist.jp d) tomoki@is.naist.jp
(Statistical Machine Translation: SMT[1]) [2]
![(Statistical Machine Translation: SMT[1]) [2]](/thumbs/71/65378195.jpg "(Statistical Machine Translation: SMT[1]) [2]")
1,a) Graham Neubig 1,b) Sakriani Sakti 1,c) 1,d) 1,e) 2 1. (Statistical Machine Translation: SMT[1]) [2] [3] [4][5][6] 2 1 (a) 3 approach 1 Nara Institute of Science and Technology a) miura.akiba.lr9@is.naist.jp
(Statistical Machine Translation: SMT [1])
![(Statistical Machine Translation: SMT [1])](/thumbs/71/65378250.jpg "(Statistical Machine Translation: SMT [1])")
1,a) Graham Neubig 1,b) Michael Paul 2,c) 1,d) n-gram 1. (Statistical Machine Translation: SMT [1]) (Active Learning) [2][3][4][5][6][7] [2] 1 Nara Institute of Science and Technology 2 ATR-Trek a) miura.akiba.lr9@is.naist.jp
[15], [16], [17] [6] [2] [5] Jiang [6] 2.1 [6], [10] Score(x, y) y ( 1) ( 1 ) b e ( 1 ) b e. O(n 2 ) 2.3. 2.2 Jiang [6] (word lattice reranking)
![[15], [16], [17] [6] [2] [5] Jiang [6] 2.1 [6], [10] Score(x, y) y ( 1) ( 1 ) b e ( 1 ) b e. O(n 2 ) 2.3. 2.2 Jiang [6] (word lattice reranking)](/thumbs/39/20145561.jpg "[15], [16], [17] [6] [2] [5] Jiang [6] 2.1 [6], [10] Score(x, y) y ( 1) ( 1 ) b e ( 1 ) b e. O(n 2 ) 2.3. 2.2 Jiang [6] (word lattice reranking)")
1,a) 1 2 10 1. [6] [1], [6], [8], [10], [11] 2 n n+1 C 2 O(n 2 ) 1 153-8505 4-6-1 a) kaji@tkl.iis.u-tokyo.ac.jp [10] [19], [23] [6] [6] (3 ) 10 (1) (2) 3 c 2012 Information Processing Society of Japan
(String-to-Tree ) KJ [11] best 1-best 2. SMT 2. [9] Brockett [2] Mizumoto [10] Brockett [2] [10] [15] ê = argmax e P(e f ) = argmax e M m=1 λ
![(String-to-Tree ) KJ [11] best 1-best 2. SMT 2. [9] Brockett [2] Mizumoto [10] Brockett [2] [10] [15] ê = argmax e P(e f ) = argmax e M m=1 λ](/thumbs/89/97909699.jpg "(String-to-Tree ) KJ [11] best 1-best 2. SMT 2. [9] Brockett [2] Mizumoto [10] Brockett [2] [10] [15] ê = argmax e P(e f ) = argmax e M m=1 λ")
1,a) 1,b) 2 2 Shared Task 4 n-best 1-best 1. Web SNS Lang-8 *1 GIN- GER *2 2 HOO 2011 2012 [7], [8] CoNLL Shared Task 2013 [14] 2014 CoNLL Shared Task 1 Rozovskaya and Roth [16] Tajiri [19] Rozovskaya
MIDI [8] MIDI. [9] Hsu [1], [2] [10] Salamon [11] [5] Song [6] Sony, Minato, Tokyo , Japan a) b)
![MIDI [8] MIDI. [9] Hsu [1], [2] [10] Salamon [11] [5] Song [6] Sony, Minato, Tokyo , Japan a) b)](/thumbs/93/113459486.jpg "MIDI [8] MIDI. [9] Hsu [1], [2] [10] Salamon [11] [5] Song [6] Sony, Minato, Tokyo , Japan a) b)")
1,a) 1,b) 1,c) 1. MIDI [1], [2] U/D/S 3 [3], [4] 1 [5] Song [6] 1 Sony, Minato, Tokyo 108 0075, Japan a) Emiru.Tsunoo@jp.sony.com b) AkiraB.Inoue@jp.sony.com c) Masayuki.Nishiguchi@jp.sony.com MIDI [7]
{takasu, Conditional Random Field
DEIM Forum 2016 C8-6 CRF 700 8530 3 1 1 700 8530 3 1 1 101 8430 2-1-2 E-mail: pobp52cw@s.okayama-u.ac.jp, ohta@de.cs.okayama-u.ac.jp, {takasu, adachi}@nii.ac.jp Conditional Random Field 1. Conditional
An Analysis of Problems in Grammatical Error Correction of ESL Writings Using a Large Learner Corpus of English
1,a) 1,b) 1,c) 2,d) 1,e) An Analysis of Problems in Grammatical Error Correction of ESL Writings Using a Large Learner Corpus of English Abstract: English as a Second Language (ESL) learners writings contain
Text Mining using Linguistic Information
630-0101 8916-5 {taku-kukaoru-yayuuta-tmatsu}@isaist-naraacjp PrefixSpan : PrefixSpan Text Mining using Linguistic Information Taku Kudo Kaoru Yamamoto Yuta Tsuboi Yuji Matsumoto Graduate School of Information
[1, 2, 3, 4, 5, 6, 7, 8]
![[1, 2, 3, 4, 5, 6, 7, 8]](/thumbs/103/158256424.jpg "[1, 2, 3, 4, 5, 6, 7, 8]")
208 5 [, 2, 3, 4, 5, 6, 7, 8] 2 () ϕ = λ θ () ϕ [W/m 2 ] θ [K] λ [W/(m K)] Schmatic rprsantation of Fourir s law (2) ρc θ = ϕ + f (2) ρ [kg/m 3 ] c [J/(kg K)] θ = t f [W/m3 ] () (2) (3) ρc θ = (λ θ) +
DEIM Forum 2016 G7-5 152-8565 2-12-1 152-8565 2-12-1 889-1601 5200 E-mail: uragaki.k.aa@m.titech.ac.jp,,,.,,,,,,, 1. 1. 1,,,,,,.,,,,, 1. 2 [1],,,,, [2] (, SPM),,,,,,,. [3],, [4]. 2 A,B, A B, B A, B, 2,,,
Shortness Ambiguity TEAM Ungrammaticality
DEIM Forum 2017 D8-4 Twitter 305 8573 1 1 1 305 8573 1 1 1 E-mail: s.nagaki@kde.cs.tsukuba.ac.jp, kitagawa@cs.tsukuba.ac.jp Twitter Twitter 1 Twitter 1. Twitter Wikipedia 2 Twitter 1 1 Twitter [1] Twitter
Novel rotor position detection method of line back EMF for BLDCM
14 12 2010 12 ELECTRI C MACHINES AND CONTROL Vol. 14 No. 12 Dc. 2010 1 2 2 1. 430073 2. 430074 TM 351 A 1007-449X 2010 12-0096- 05 Novl rotor position dtction mthod of lin back EMF for BLDCM LI Zi-chng
Fourier transform, STFT 5. Continuous wavelet transform, CWT STFT STFT STFT STFT [1] CWT CWT CWT STFT [2 5] CWT STFT STFT CWT CWT. Griffin [8] CWT CWT
![Fourier transform, STFT 5. Continuous wavelet transform, CWT STFT STFT STFT STFT [1] CWT CWT CWT STFT [2 5] CWT STFT STFT CWT CWT. Griffin [8] CWT CWT](/thumbs/91/105754605.jpg "Fourier transform, STFT 5. Continuous wavelet transform, CWT STFT STFT STFT STFT [1] CWT CWT CWT STFT [2 5] CWT STFT STFT CWT CWT. Griffin [8] CWT CWT")
1,a) 1,2,b) Continuous wavelet transform, CWT CWT CWT CWT CWT 100 1. Continuous wavelet transform, CWT [1] CWT CWT CWT [2 5] CWT CWT CWT CWT CWT Irino [6] CWT CWT CWT CWT CWT 1, 7-3-1, 113-0033 2 NTT,
2. N-gram IDF. DEIM Forum 2016 A1-1. N-gram IDF IDF. 5 N-gram. N-gram. N-gram. N-gram IDF.
DEIM Forum 216 A1-1 N-gram IDF 565 871 1-5 E-mail: {hirakawa.maumi,hara}@it.oaka-u.ac.jp N-gram IDF IDF N-gram N-gram N-gram N-gram IDF N-gram N-gram IDF N-gram N-gram IDF Web Wikipedia 1 N-gram IDF [3]
Faruqui [7] WordNet [15] FrameNet [2] PPDB [8]
![Faruqui [7] WordNet [15] FrameNet [2] PPDB [8]](/thumbs/81/84029477.jpg "Faruqui [7] WordNet [15] FrameNet [2] PPDB [8]")
1,a) 1,b) 1,c) 2,d) 2,e) word2vec WordNet fine-tuning fine-tuning 1. [10] Faruqui [7] WordNet[15] fine-tuning Retrofitting Retrofitting WordNet[11] 1 2 a) taguchi-y2@asahi.com b) tamori-h@asahi.com c)
1530 ( ) 2014,54(12),, E (, 1, X ) [4],,, α, T α, β,, T β, c, P(T β 1 T α,α, β,c) 1 1,,X X F, X E F X E X F X F E X E 1 [1-2] , 2 : X X 1 X 2 ;
![1530 ( ) 2014,54(12),, E (, 1, X ) [4],,, α, T α, β,, T β, c, P(T β 1 T α,α, β,c) 1 1,,X X F, X E F X E X F X F E X E 1 [1-2] , 2 : X X 1 X 2 ;](/thumbs/91/107463875.jpg "1530 ( ) 2014,54(12),, E (, 1, X ) [4],,, α, T α, β,, T β, c, P(T β 1 T α,α, β,c) 1 1,,X X F, X E F X E X F X F E X E 1 [1-2] , 2 : X X 1 X 2 ;")
ISSN1000-0054 CN11-2223/N ( ) 2014 54 12 JTsinghuaUniv(Sci& Technol), 2014,Vol.54, No.12 4/20 1529-1533,, (,, (), 100084) [1-2] :,,,,,,,, :, 0.3~ [3] 0.8BLEU,, : ; ; [4], ; :TP391.2 :A, :1000-0054(2014)12-1529-05,
DEIM Forum 2014 A8-1, 606 8501 E-mail: {tsukuda,ohshima,kato,tanaka}@dl.kuis.kyoto-u.ac.jp 1 2,, 1. Google 1 Yahoo 2 Bing 3 Web Web BM25 [1] HITS [2] PageRank [3] Web 1 [4] 1http://www.google.com 2http://www.yahoo.com
ΓΡΑΜΜΙΚΟΣ & ΔΙΚΤΥΑΚΟΣ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΣ
ΓΡΑΜΜΙΚΟΣ & ΔΙΚΤΥΑΚΟΣ ΠΡΟΓΡΑΜΜΑΤΙΣΜΟΣ Ενότητα 12: Συνοπτική Παρουσίαση Ανάπτυξης Κώδικα με το Matlab Σαμαράς Νικόλαος Άδειες Χρήσης Το παρόν εκπαιδευτικό υλικό υπόκειται σε άδειες χρήσης Creative Commons.
An Automatic Modulation Classifier using a Frequency Discriminator for Intelligent Software Defined Radio
C IEEJ Transactions on Electronics, Information and Systems Vol.133 No.5 pp.910 915 DOI: 10.1541/ieejeiss.133.910 a) An Automatic Modulation Classifier using a Frequency Discriminator for Intelligent Software
α A G C T 國立交通大學生物資訊及系統生物研究所林勇欣老師
A G C T Juks and Cantor s (969) on-aramtr modl A T C G A G 0 0 0-3 C T A() A( t ) ( 3 ) ( ) A() A() ( 3 ) ( ) A( A( A( A( t ) A( 3 A( t ) ( ) A( A( Juks and Cantor s (969) on-aramtr modl A( A( t ) A( d
DEIM Forum 2013 A2-2 606 8501 E-mail: kato@dl.kuis.kyoto-u.ac.jp 1. 2. 1 4 A B C D A B C D A : B :: C : D : :: : : :: : A B C D A= B= C= D= D 3 Turney [20] A B C D A B C D Bollegala [5] Web SVM A B C D
Maude 6. Maude [1] UIUC J. Meseguer. Maude. Maude SRI SRI. Maude. AC (Associative-Commutative) Maude. Maude Meseguer OBJ LTL SPIN
![Maude 6. Maude [1] UIUC J. Meseguer. Maude. Maude SRI SRI. Maude. AC (Associative-Commutative) Maude. Maude Meseguer OBJ LTL SPIN](/thumbs/84/90701332.jpg "Maude 6. Maude [1] UIUC J. Meseguer. Maude. Maude SRI SRI. Maude. AC (Associative-Commutative) Maude. Maude Meseguer OBJ LTL SPIN")
78 Maude 1 Maude [1] UIUC J. Meseguer ( 1 ) ( ) Maude Maude SRI 90 UIUC SRI Maude SRI S. Eker C++ Maude 2 Maude Meseguer OBJ 1983-84 OBJ2[3] OBJ Maude OBJ 1 CafeOBJ 3 Maude 4 Maude CafeOBJ Maude: A Computer
LS series ALUMINUM ELECTROLYTIC CAPACITORS CAT.8100D. Specifications. Drawing. Type numbering system ( Example : 200V 390µF)
Snap-in Terminal Type, 85 C Standard Withstanding 3000 hours application of rated ripple current at 85 C. Compliant to the RoHS directive (2011/65/EU). LS Smaller LG Specifications Item Category Temperature
Applying Markov Decision Processes to Role-playing Game
1,a) 1 1 1 1 2011 8 25, 2012 3 2 MDPRPG RPG MDP RPG MDP RPG MDP RPG MDP RPG Applying Markov Decision Processes to Role-playing Game Yasunari Maeda 1,a) Fumitaro Goto 1 Hiroshi Masui 1 Fumito Masui 1 Masakiyo
Razor. [1], [2] (typical) LSI V/F. Razor. (Timing Fault: TF) [7] Razor [3], [4], [5] DVFS - Dynamic Voltage and Frequency Scaling [6]
![Razor. [1], [2] (typical) LSI V/F. Razor. (Timing Fault: TF) [7] Razor [3], [4], [5] DVFS - Dynamic Voltage and Frequency Scaling [6]](/thumbs/103/161217973.jpg "Razor. [1], [2] (typical) LSI V/F. Razor. (Timing Fault: TF) [7] Razor [3], [4], [5] DVFS - Dynamic Voltage and Frequency Scaling [6]")
,a),.,.,.,,,,.,. [], [] (typial) LSI (Timing Fault: TF) TF Razor [], [], [] DVFS - Dynami Voltage an Frequeny Saling [] TF (V) (F) TF TF Grauate Shool of Information Siene an Tehnology, The University
Web DEIM Forum 2009 A7-1. Web. Web. Web. Web. 4 Wikipedia. Wikipedia. Web.
DEIM Forum 2009 A7-1 Web 606-8501 E-mail: {nakatani,adam,ohshima,tanaka}@dl.kuis.kyoto-u.ac.jp Web Web Web Web Wikipedia Web Wikipedia 1. Web Nakamura 2007 1000 [1] (46%) (ii) 40 (36.8%) 2 Web Web (i)
DEIM Forum 2 D3-6 819 39 744 66 8 E-mail: kawamoto@inf.kyushu-u.ac.jp, tawara@db.soc.i.kyoto-u.ac.jp, {asano,yoshikawa}@i.kyoto-u.ac.jp 1.,, Amazon.com The Internet Movie Database (IMDb) 1 Social spammers
ACTA MATHEMATICAE APPLICATAE SINICA Nov., ( µ ) ( (
35 Þ 6 Ð Å Vol. 35 No. 6 2012 11 ACTA MATHEMATICAE APPLICATAE SINICA Nov., 2012 È ÄÎ Ç ÓÑ ( µ 266590) (E-mail: jgzhu980@yahoo.com.cn) Ð ( Æ (Í ), µ 266555) (E-mail: bbhao981@yahoo.com.cn) Þ» ½ α- Ð Æ Ä
ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΣΤΗΝ ΕΠΙΣΤΗΜΗ ΤΩΝ ΥΠΟΛΟΓΙΣΤΩΝ. Διπλωματική Εργασία Μεταπτυχιακού Διπλώματος Ειδίκευσης
ΟΙΚΟΝΟΜΙΚΟ ΠΑΝΕΠΙΣΤΗΜΙΟ ΑΘΗΝΩΝ ΠΡΟΓΡΑΜΜΑ ΜΕΤΑΠΤΥΧΙΑΚΩΝ ΣΠΟΥΔΩΝ ΣΤΗΝ ΕΠΙΣΤΗΜΗ ΤΩΝ ΥΠΟΛΟΓΙΣΤΩΝ Διπλωματική Εργασία Μεταπτυχιακού Διπλώματος Ειδίκευσης «Γλωσσικά μοντέλα μορφολογικά περίπλοκων γλωσσών» Δημήτρης
Buried Markov Model Pairwise
Buried Markov Model 1 2 2 HMM Buried Markov Model J. Bilmes Buried Markov Model Pairwise 0.6 0.6 1.3 Structuring Model for Speech Recognition using Buried Markov Model Takayuki Yamamoto, 1 Tetsuya Takiguchi
i M-1 1. ij f(i, j) N-1. (pixel) 2. M N (x, y) (x, y ) = 256. R(x, y), G(x, y), B(x, y)
D4 2 2. (pixel) 2 ( ) M N (x, y) (x, y ) ( )f(x, y) j N- i j i f(i, j) M-. ij f(i, j) 8 2 8 = 256, 2 2 f(x, y) 3,, R(x, y), G(x, y), B(x, y) 256 2 2.2 ( ) JPEG (Joint Photographic Experts Group), GIF (Graphics
ITU-R P (2012/02) &' (
ITU-R P.530-4 (0/0) $ % " "#! &' ( P ITU-R P. 530-4 ii.. (IPR) (ITU-T/ITU-R/ISO/IEC).ITU-R http://www.itu.int/itu-r/go/patents/en. ITU-T/ITU-R/ISO/IEC (http://www.itu.int/publ/r-rec/en ) () ( ) BO BR BS
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr (T t N n) Pr (max (X 1,..., X N ) t N n) Pr (max
Automatic Domain2Specific Term Extraction and Its Application in Text Cla ssification
2 2007 2 ACTA ELECTRONICA SINICA Vol. 35 No. 2 Feb. 2007,,, (, 150001) :,,,,..,,,. : ; ; ; ; : TP39112 : A : 037222112 (2007) 0220328205 Automatic Domain2Specific Term Extraction and Its Application in
02 Αντικειμενοστρεφής Προγραμματισμός
02 Αντικειμενοστρεφής Προγραμματισμός Τεχνολογία Λογισμικού Τμήμα Πληροφορικής & Τηλεπικοινωνιών, ΕΚΠΑ Εαρινό εξάμηνο 2016 17 Δρ. Κώστας Σαΐδης saiko@di.uoa.gr Αντικειμενοστρέφεια Στον προγραμματισμό object
ES440/ES911: CFD. Chapter 5. Solution of Linear Equation Systems
ES440/ES911: CFD Chapter 5. Solution of Linear Equation Systems Dr Yongmann M. Chung http://www.eng.warwick.ac.uk/staff/ymc/es440.html Y.M.Chung@warwick.ac.uk School of Engineering & Centre for Scientific
NoC. SoC (Systems-on-Chip) NoC (Network-on-Chip) (2) (3) 45% Flattened Butterfly [7] SoC. (QAP, quadratic assignment 2.1. (NoC) NoC.
![NoC. SoC (Systems-on-Chip) NoC (Network-on-Chip) (2) (3) 45% Flattened Butterfly [7] SoC. (QAP, quadratic assignment 2.1. (NoC) NoC.](/thumbs/84/91102379.jpg "NoC. SoC (Systems-on-Chip) NoC (Network-on-Chip) (2) (3) 45% Flattened Butterfly [7] SoC. (QAP, quadratic assignment 2.1. (NoC) NoC.")
IPSJ SIG Tchnical Rport NoC 1,a) 1,2,b) (NoC) NoC 512 NoC 45% 1. SoC (Systms-on-Chip) [1 4] 1,000 [5, 6] NoC (Ntwork-on-Chip) NoC NoC 2 Flattn Buttrfly [7] 1 32256 SoC 1 National Institut of Informatics,
Bundle Adjustment for 3-D Reconstruction: Implementation and Evaluation
3 2 3 2 3 undle Adjustment or 3-D Reconstruction: Implementation and Evaluation Yuuki Iwamoto, Yasuyuki Sugaya 2 and Kenichi Kanatani We describe in detail the algorithm o bundle adjustment or 3-D reconstruction
Vol.4-DCC-8 No.8 Vol.4-MUS-5 No.8 4// 3 3 Hanning (T ) 3 Hanning 3T (y(t)w(t)) dt =.5 T y (t)dt. () STRAIGHT F 3 TANDEM-STRAIGHT[] 3 F F 3 [] F []. :
![Vol.4-DCC-8 No.8 Vol.4-MUS-5 No.8 4// 3 3 Hanning (T ) 3 Hanning 3T (y(t)w(t)) dt =.5 T y (t)dt. () STRAIGHT F 3 TANDEM-STRAIGHT[] 3 F F 3 [] F []. :](/thumbs/85/92640788.jpg "Vol.4-DCC-8 No.8 Vol.4-MUS-5 No.8 4// 3 3 Hanning (T ) 3 Hanning 3T (y(t)w(t)) dt =.5 T y (t)dt. () STRAIGHT F 3 TANDEM-STRAIGHT[] 3 F F 3 [] F []. :")
Vol.4-DCC-8 No.8 Vol.4-MUS-5 No.8 4//,a) Vocoder (F) F F. PSOLA [] sinusoidal model [] phase vocoder Vocoder [3] (F) F 3 [4], [5], [6], [7], [8], [9] [], [], [], [3], [4] [5], [6] [7], [8], University
DEIM Forum 2018 F3-5 657 8501 1-1 657 8501 1-1 E-mail: yuta@cs25.scitec.kobe-u.ac.jp, eguchi@port.kobe-u.ac.jp, ( ) ( )..,,,.,.,.,,..,.,,, 2..., 1.,., (Autoencoder: AE) [1] (Generative Stochastic Networks:
Smaller. 6.3 to 100 After 1 minute's application of rated voltage at 20 C, leakage current is. not more than 0.03CV or 4 (µa), whichever is greater.
Low Impedance, For Switching Power Supplies Low impedance and high reliability withstanding 5000 hours load life at +05 C (3000 / 2000 hours for smaller case sizes as specified below). Capacitance ranges
RMT Tick RMT. Application of the RMT-test on Real Data: Hash Function and Tick Data of Stock Prices
RMT Tick 1,a) 1 1,b) RMT 2 SHA-1 MD5 RMT SHA-1 2007 2009 TOPIX500 2010 2011 RMT Application of the RMT-test on Real Data: Hash Function and Tick Data of Stock Prices Xin Yang 1,a) Yuta Mikamori 1 Mieko
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM
SOLUTIONS TO MATH38181 EXTREME VALUES AND FINANCIAL RISK EXAM Solutions to Question 1 a) The cumulative distribution function of T conditional on N n is Pr T t N n) Pr max X 1,..., X N ) t N n) Pr max
Bayesian statistics. DS GA 1002 Probability and Statistics for Data Science.
Bayesian statistics DS GA 1002 Probability and Statistics for Data Science http://www.cims.nyu.edu/~cfgranda/pages/dsga1002_fall17 Carlos Fernandez-Granda Frequentist vs Bayesian statistics In frequentist
VBA Microsoft Excel. J. Comput. Chem. Jpn., Vol. 5, No. 1, pp (2006)
J. Comput. Chem. Jpn., Vol. 5, No. 1, pp. 29 38 (2006) Microsoft Excel, 184-8588 2-24-16 e-mail: yosimura@cc.tuat.ac.jp (Received: July 28, 2005; Accepted for publication: October 24, 2005; Published on
Quick algorithm f or computing core attribute
24 5 Vol. 24 No. 5 Cont rol an d Decision 2009 5 May 2009 : 100120920 (2009) 0520738205 1a, 2, 1b (1. a., b., 239012 ; 2., 230039) :,,.,.,. : ; ; ; : TP181 : A Quick algorithm f or computing core attribute
ΔΙΠΛΩΜΑΤΙΚΕΣ ΕΡΓΑΣΙΕΣ
ΔΙΠΛΩΜΑΤΙΚΕΣ ΕΡΓΑΣΙΕΣ ΤΜ. ΜΗΧΑΝΙΚΩΝ ΠΛΗΡΟΦΟΡΙΚΗΣ & ΤΗΛΕΠΙΚΟΙΝΩΝΙΩΝ 2018-2019 Επιβλέπουσα: Μπίμπη Ματίνα Ανάλυση της πλατφόρμας ανοιχτού κώδικα Home Assistant Το Home Assistant είναι μία πλατφόρμα ανοιχτού
The Finite Element Method
Th Finit Elmnt Mthod Plan (D) Truss and Fram Elmnts Rad: Sctions 4.6 and 5.4 CONTENTS Rviw of bar finit lmnt in th local coordinats Plan truss lmnt Rviw of bam finit lmnt in th local coordinats Plan fram
ss rt çã r s t Pr r Pós r çã ê t çã st t t ê s 1 t s r s r s r s r q s t r r t çã r str ê t çã r t r r r t r s
P P P P ss rt çã r s t Pr r Pós r çã ê t çã st t t ê s 1 t s r s r s r s r q s t r r t çã r str ê t çã r t r r r t r s r t r 3 2 r r r 3 t r ér t r s s r t s r s r s ér t r r t t q s t s sã s s s ér t
n 1 n 3 choice node (shelf) choice node (rough group) choice node (representative candidate)
THE INSTITUTE OF ELECTRONICS, INFORMATION AND COMMUNICATION ENGINEERS TECHNICAL REPORT OF IEICE. y y yy y 1565 0871 2 1 yy 525 8577 1 1 1 E-mail: yfmakihara,shiraig@cv.mech.eng.osaka-u.ac.jp, yyshimada@ci.ritsumei.ac.jp
Monolithic Crystal Filters (M.C.F.)
Monolithic Crystal Filters (M.C.F.) MCF (MONOLITHIC CRYSTAL FILTER) features high quality quartz resonators such as sharp cutoff characteristics, low loss, good inter-modulation and high stability over
Applying Conditional Random Fields to Japanese Morphologiaical Analysis
Conditional Random Fields 1 CREST JST {taku-kumatsu}@isnaistp kaoru@lrpititechacp Conditonal Random Fields (CRF) CRF CRF CRF CRF (HMM MEMM) CRF label bias length bias HMM MEMM 2 (L1-CRF/L2-CRF) Applying
P P Ó P. r r t r r r s 1. r r ó t t ó rr r rr r rí st s t s. Pr s t P r s rr. r t r s s s é 3 ñ
P P Ó P r r t r r r s 1 r r ó t t ó rr r rr r rí st s t s Pr s t P r s rr r t r s s s é 3 ñ í sé 3 ñ 3 é1 r P P Ó P str r r r t é t r r r s 1 t r P r s rr 1 1 s t r r ó s r s st rr t s r t s rr s r q s
1,a) 1,b) 2 3 Sakriani Sakti 1 Graham Neubig 1 1. A Study on HMM-Based Speech Synthesis Using Rich Context Models
HMM 1,a 1,b 3 Sakriani Sakti 1 Graham Neubig 1 1 Hidden Markov Model HMM HMM HMM HMM HMM A Study on HMM-Based Speech Synthesis Using Rich Context Models Shinnosuke Takamichi 1,a Toda Tomoki 1,b Shiga Yoshinori
ibemo Kazakhstan Republic of Kazakhstan, West Kazakhstan Oblast, Aksai, Pramzone, BKKS office complex Phone: ; Fax:
Assessment of otoacoustic emission probe fit at the workfloor
Assessment of otoacoustic emission probe fit at the workfloor t s st tt r st s s r r t rs t2 t P t rs str t t r 1 t s ér r tr st tr r2 t r r t s t t t r t s r ss r rr t 2 s r r 1 s r r t s s s r t s t
Dynamic types, Lambda calculus machines Section and Practice Problems Apr 21 22, 2016
Harvard School of Engineering and Applied Sciences CS 152: Programming Languages Dynamic types, Lambda calculus machines Apr 21 22, 2016 1 Dynamic types and contracts (a) To make sure you understand the
Supplementary Materials for Evolutionary Multiobjective Optimization Based Multimodal Optimization: Fitness Landscape Approximation and Peak Detection
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. XX, NO. X, XXXX XXXX Supplementary Materials for Evolutionary Multiobjective Optimization Based Multimodal Optimization: Fitness Landscape Approximation
MnZn. MnZn Ferrites with Low Loss and High Flux Density for Power Supply Transformer. Abstract:
MnZn JFE No. 8 5 6 p. 32 37 MnZn Ferrites with Low Loss and High Flux Density for Power Supply Transformer FUJITA Akira JFE Ph. D. FUKUDA Yutaka JFE NISHIZAWA Keitarou JFE TOGAWA Jirou MnZn Fe2O3 1 C NiO
Japanese Fuzzy String Matching in Cooking Recipes
1 Japanese Fuzzy String Matching in Cooking Recipes Michiko Yasukawa 1 In this paper, we propose Japanese fuzzy string matching in cooking recipes. Cooking recipes contain spelling variants for recipe
Estimation for ARMA Processes with Stable Noise. Matt Calder & Richard A. Davis Colorado State University
Estimation for ARMA Processes with Stable Noise Matt Calder & Richard A. Davis Colorado State University rdavis@stat.colostate.edu 1 ARMA processes with stable noise Review of M-estimation Examples of
Newman Modularity Newman [4], [5] Newman Q Q Q greedy algorithm[6] Newman Newman Q 1 Tabu Search[7] Newman Newman Newman Q Newman 1 2 Newman 3
![Newman Modularity Newman [4], [5] Newman Q Q Q greedy algorithm[6] Newman Newman Q 1 Tabu Search[7] Newman Newman Newman Q Newman 1 2 Newman 3](/thumbs/50/25933291.jpg "Newman Modularity Newman [4], [5] Newman Q Q Q greedy algorithm[6] Newman Newman Q 1 Tabu Search[7] Newman Newman Newman Q Newman 1 2 Newman 3")
DEWS2007 D3-6 y yy y y y y yy / DC 7313194 341 E-mail: yfktamura,mori,kuroki,kitakamig@its.hiroshima-cu.ac.jp, yymakoto@db.its.hiroshima-cu.ac.jp Newman Newman Newman Newman Newman A Clustering Algorithm
A Method for Creating Shortcut Links by Considering Popularity of Contents in Structured P2P Networks
P2P 1,a) 1 1 1 P2P P2P P2P P2P A Method for Creating Shortcut Links by Considering Popularity of Contents in Structured P2P Networks NARISHIGE Yuki 1,a) ABE Kota 1 ISHIBASHI Hayato 1 MATSUURA Toshio 1
Design and Fabrication of Water Heater with Electromagnetic Induction Heating
U Kamphaengsean Acad. J. Vol. 7, No. 2, 2009, Pages 48-60 ก 7 2 2552 ก ก กก ก Design and Fabrication of Water Heater with Electromagnetic Induction Heating 1* Geerapong Srivichai 1* ABSTRACT The purpose
RDN-250i Application / Benefits Model No. Servo Motor Maker SPECIFICATIONS Table Dia. Horizontal [kg] Center Height Resister Dia.
![RDN-250i Application / Benefits Model No. Servo Motor Maker SPECIFICATIONS Table Dia. Horizontal [kg] Center Height Resister Dia.](/thumbs/90/101861915.jpg "RDN-250i Application / Benefits Model No. Servo Motor Maker SPECIFICATIONS Table Dia. Horizontal [kg] Center Height Resister Dia.")
RDN-250i Zero Backlash High-Definition Split System with ROLLER CAM DRIVE Zero Backlash with ROLLER CAM DRIVE Table Dia. 232 Center Height 210 105 Spindle Through Hole Dia. 70 Pneumatic Allowable Work
Εισαγωγή στον Προγ/μό Υπολογιστών
Εισαγωγή στον Προγ/μό Υπολογιστών Διάλεξη 1 Εκφράσεις, κλητικές εκφράσεις, ονόματα Περιεχόμενα Εκφράσεις (expressions) Τελεστές (operators) της Python Κλητικές εκφράσεις (call expressions) Ονόματα (names)
[1] DNA ATM [2] c 2013 Information Processing Society of Japan. Gait motion descriptors. Osaka University 2. Drexel University a)
![[1] DNA ATM [2] c 2013 Information Processing Society of Japan. Gait motion descriptors. Osaka University 2. Drexel University a)](/thumbs/99/141682136.jpg "[1] DNA ATM [2] c 2013 Information Processing Society of Japan. Gait motion descriptors. Osaka University 2. Drexel University a)")
1,a) 1,b) 2,c) 1,d) Gait motion descriptors 1. 12 1 Osaka University 2 Drexel University a) higashiyama@am.sanken.osaka-u.ac.jp b) makihara@am.sanken.osaka-u.ac.jp c) kon@drexel.edu d) yagi@am.sanken.osaka-u.ac.jp
Vol. 38 No Journal of Jiangxi Normal University Natural Science Nov. 2014
38 6 Vol 38 No 6 204 Journal o Jiangxi Normal UniversityNatural Science Nov 204 000-586220406-055-06 2 * 330022 Nevanlinna 2 2 2 O 74 52 0 B j z 0j = 0 φz 0 0 λ - φ= C j z 0j = 0 ab 0 arg a arg b a = cb0
HIV HIV HIV HIV AIDS 3 :.1 /-,**1 +332
,**1 The Japanese Society for AIDS Research The Journal of AIDS Research +,, +,, +,, + -. / 0 1 +, -. / 0 1 : :,**- +,**. 1..+ - : +** 22 HIV AIDS HIV HIV AIDS : HIV AIDS HIV :HIV AIDS 3 :.1 /-,**1 HIV
Consolidated Drained
Consolidated Drained q, 8 6 Max. Shear c' =.185 φ' =.8 tan φ' =.69 Deviator, 8 6 6 8 1 1 p', 5 1 15 5 Axial, Symbol Sample ID Depth Test Number Height, in Diameter, in Moisture Content (from Cuttings),
Η Αυτοματοποιημένη και μη-αυτοματοποιημένη αξιολόγηση συστήματος Στατιστικής Μηχανικής Μετάφρασης για το γλωσσικό ζεύγος Ελληνικά - Ιταλικά
111 Η Αυτοματοποιημένη και μη-αυτοματοποιημένη αξιολόγηση συστήματος Στατιστικής Μηχανικής Μετάφρασης για το γλωσσικό ζεύγος Ελληνικά - Ιταλικά Πολυξένη Κανελλιάδου* Κωνσταντίνος Χατζηθεοδώρου** Machine
Automatic extraction of bibliography with machine learning
Automatic extraction of bibliography with machine learning Takeshi ABEKAWA Hidetsugu NANBA Hiroya TAKAMURA Manabu OKUMURA Abstract In this paper, we propose an extraction method of bibliography using support
Κβαντική Επεξεργασία Πληροφορίας
Κβαντική Επεξεργασία Πληροφορίας Ενότητα 36: Προοπτικές και Εφαρμογές Κβαντικών Αλγορίθμων Σγάρμπας Κυριάκος Πολυτεχνική Σχολή Τμήμα Ηλεκτρολόγων Μηχανικών και Τεχνολογίας Υπολογιστών Σκοποί ενότητας Προοπτικές
Συµβολοσειρές - Strings
Συµβολοσειρές - Strings 1 Συµβολοσειρέςστην C/C++ 2 Χαρακτήρες 'a', 'z', '0', Χαρακτήρες σαν int 'z' επίσης αναπαριστά την ακεραία τιµή του χαρακτήρα z Strings-Συµβολοσειρές Σειρές από χαρακτήρες σαν µια
Exhaustive Topic Detection and Query Expansion Support Based on Substance-Oriented Term Clustering
DEWS2006 2C-i4 101 8430 2 1 2 101 8430 2 1 2 E-mail: {hiromi,masada,takasu,adachi}@nii.ac.jp Tangibility Tangibility Tangibility WWW Exhaustive Topic Detection and Query Expansion Support Based on Substance-Oriented
Anomaly Detection with Neighborhood Preservation Principle
27 27 Workshop on Information-Based Induction Sciences (IBIS27) Tokyo, Japan, November 5-7, 27. Anomaly Detection with Neighborhood Preservation Principle Tsuyoshi Idé Abstract: We consider a task of anomaly
A Method of Trajectory Tracking Control for Nonminimum Phase Continuous Time Systems
IIC-11-8 A Method of Trajectory Tracking Control for Nonminimum Phase Continuous Time Systems Takayuki Shiraishi, iroshi Fujimoto (The University of Tokyo) Abstract The purpose of this paper is achievement
❷ s é 2s é í t é Pr 3
❷ s é 2s é í t é Pr 3 t tr t á t r í í t 2 ➄ P á r í3 í str t s tr t r t r s 3 í rá P r t P P á í 2 rá í s é rá P r t P 3 é r 2 í r 3 t é str á 2 rá rt 3 3 t str 3 str ýr t ý í r t t2 str s í P á í t
070-A
764 070-A543-50 www.tektronix.com Copyright Tektronix Japan, Ltd. All rights reserved. 141 0001 5 9 31 TektronixTek Tektronix, Inc. i v ix xi 1 11 12 12 12 13 19 110 110 2 21 21 22 23 24 24 26 211
Re-Pair n. Re-Pair. Re-Pair. Re-Pair. Re-Pair. (Re-Merge) Re-Merge. Sekine [4, 5, 8] (highly repetitive text) [2] Re-Pair. Blocked-Repair-VF [7]
![Re-Pair n. Re-Pair. Re-Pair. Re-Pair. Re-Pair. (Re-Merge) Re-Merge. Sekine [4, 5, 8] (highly repetitive text) [2] Re-Pair. Blocked-Repair-VF [7]](/thumbs/78/77313882.jpg "Re-Pair n. Re-Pair. Re-Pair. Re-Pair. Re-Pair. (Re-Merge) Re-Merge. Sekine [4, 5, 8] (highly repetitive text) [2] Re-Pair. Blocked-Repair-VF [7]")
Re-Pair 1 1 Re-Pair Re-Pair Re-Pair Re-Pair 1. Larsson Moffat [1] Re-Pair Re-Pair (Re-Pair) ( ) (highly repetitive text) [2] Re-Pair [7] Re-Pair Re-Pair n O(n) O(n) 1 Hokkaido University, Graduate School
P Ë ³μ,.. μ μ³μ²μ,.. ŠμÎ μ,.. μ μ,.. Š μ. ˆ œ ˆ Š Œˆ ŠˆŒ ƒ Œ Ÿ ˆŸ Š ˆ ˆ -ˆ ˆŠ
P9-2008-102.. Ë ³μ,.. μ μ³μ²μ,.. ŠμÎ μ,.. μ μ,.. Š μ ˆ œ ˆ Š Œˆ ŠˆŒ ƒ Œ Ÿ ˆŸ Š ˆ ˆ -ˆ ˆŠ Ë ³μ... P9-2008-102 ˆ μ²ó μ Ô± μ³ Î ± ³ μ³ ²Ö μ²êî Ö Êα μ μ - ÉμÎ ± μ²êî É ÒÌ Ê ±μ ÒÌ Êαμ 48 Ö ²Ö É Ö μ μ ±²ÕÎ
Optimization, PSO) DE [1, 2, 3, 4] PSO [5, 6, 7, 8, 9, 10, 11] (P)
![Optimization, PSO) DE [1, 2, 3, 4] PSO [5, 6, 7, 8, 9, 10, 11] (P)](/thumbs/85/92153760.jpg "Optimization, PSO) DE [1, 2, 3, 4] PSO [5, 6, 7, 8, 9, 10, 11] (P)")
( ) 1 ( ) : : (Differential Evolution, DE) (Particle Swarm Optimization, PSO) DE [1, 2, 3, 4] PSO [5, 6, 7, 8, 9, 10, 11] 2 2.1 (P) (P ) minimize f(x) subject to g j (x) 0, j = 1,..., q h j (x) = 0, j
Ανάλυση σχημάτων βασισμένη σε μεθόδους αναζήτησης ομοιότητας υποακολουθιών (C589)
Ανάλυση σχημάτων βασισμένη σε μεθόδους αναζήτησης ομοιότητας υποακολουθιών (C589) Μεγαλοοικονόμου Βασίλειος Τμήμα Μηχ. Η/ΥκαιΠληροφορικής Επιστημονικός Υπεύθυνος Στόχος Προτεινόμενου Έργου Ανάπτυξη μεθόδων
DEIM Forum 2012 D2-1 606 8501 150 0002 2-15-1 28F E-mail: {tsukuda,ohshima,tanaka}@dl.kuis.kyoto-u.ac.jp, {miyamamoto,hiwasaki}@d-itlab.co.jp 1 Wikipedia Wikipedia HITS 1. Web Web Web 1 3 Wikipedia 2 Web
- (VAR) : :.. (VAR)... e-mail: nasr_reza@hotmail.com e-mail: Nemata@yahoo.com e-mail: Bidram@hotmail.com ..... :. VAR ........ (VAR)..... ( ) ().-. Output Gap. ... ( )...... ) - ) ( (. ). ( -. ( ).. *
NATIONAL HERALD VOL. 96 No.31747 GREEK-AMERICAN DAILY NY, NJ, PA, MA, CT $1.50
Γράφει την ιστορία του Ελληνισμού των ΗΠΑ από το 1915 a b Σάββατο 4 - Κυριακή 5 Δεκεμβρίου 2010 / Saturday - Sunday, December 4-5, 2010 www.ekirikas.com Προτάσεις κινητοποίησης της Νεολαίας για την Παρέλαση
Twitter 6. DEIM Forum 2014 A Twitter,,, Wikipedia, Explicit Semantic Analysis,
DEIM Forum 2014 A5-2 Twitter 565 0871 1 5 E-mail: {shirakawa.masumi,hara,nishio}@ist.osaka-u.ac.p 9 24 Twitter,,, Wikipedia, Explicit Semantic Analysis, 1. political leaning Twitter Cision 2013 1 90% 9
Πέμπτη, 8 Αυγούστου 2013 / Τhursday, Αugust 8, 2013. NATIONAL HERALD VOL. 98 No.32566 GREEK-AMERICAN DAILY NY, NJ, PA, MA $1.25 - CT $1.
News Γράφει την ιστορία του Ελληνισμού των ΗΠΑ από το 1915 a b Πέμπτη, 8 Αυγούστου 2013 / Τhursday, Αugust 8, 2013 www.ekirikas.com Οι Κυκλαδίτες της Αμερικής και οι Κυκλάδες Αυτό το Σαββατοκύριακο στο
l 0 l 2 l 1 l 1 l 1 l 2 l 2 l 1 l p λ λ µ R N l 2 R N l 2 2 = N x i l p p R N l p N p = ( x i p ) 1 p i=1 l 2 l p p = 2 l p l 1 R N l 1 i=1 x 2 i 1 = N x i i=1 l p p p R N l 0 0 = {i x i 0} R
A multipath QoS routing algorithm based on Ant Net
3 4 Vol. 3. 4 2008 8 CAA I Transactions on Intelligent System s Aug. 2008 AntNet QoS, (, 200237) : AntNet,, QoS, AntNet QoS. QoS,, AntNet QoS,, QoS : ; ; QoS; : TP393 : A : 67324785 (2008) 0420349206 A
Γ7.2 Συμβολοσειρές (Strings) Γ Λυκείου Κατεύθυνσης
Γ7.2 Συμβολοσειρές (Strings) Γ Λυκείου Κατεύθυνσης Εισαγωγή Στη C++ υπάρχει η δυνατότητα να δηλώσουμε μία συμβολοσειρά ως αντικείμενο, χρησιμοποιώντας τη βιβλιοθήκη . Επειδή οι συμβολοσειρές είναι
Ερευνητική+Ομάδα+Τεχνολογιών+ Διαδικτύου+
Ερευνητική+Ομάδα+Τεχνολογιών+ Διαδικτύου+ Ερευνητικές,Δραστηριότητες,και, Ενδιαφέροντα,, Τμήμα,Μηχανικών,Η/Υ,&,Πληροφορικής, Τομέας,Λογικού,των,Υπολογιστών, Εργαστήριο,Γραφικών,,Πολυμέσων,και,Γεωγραφικών,
Wiki. Wiki. Analysis of user activity of closed Wiki used by small groups
Wiki Wiki Wiki Wiki qwikweb Wiki Wiki Wiki Analysis of user activity of closed Wiki used by small groups Satoshi V. Suzuki, Koichiro Eto, Keiki Shimada, Shinobu Shibamura and Takuichi Nishimura Wikis are
GF GF 3 1,2) KP PP KP Photo 1 GF PP GF PP 3) KP ULultra-light 2.KP 2.1KP KP Fig. 1 PET GF PP 4) 2.2KP KP GF 2 3 KP Olefin film Stampable sheet
JFE No. 4 20045 p 82 Composite Material for Automotive Headliners Expandable Stampable Sheet with Light Weight and High Stiffness A JFE SUZU JFE HA KP 50 mass 30 UL 800 g/m 2 7.2 N/mm Abstract: KP-Sheet
Psychological Evaluation on Interactions between People and Robot
362 Vol. 19 No. 3, pp.362 371, 2001 Psychological Evaluation on Interactions between People and Robot Takayuki Kanda, Hiroshi Ishiguro and Toru Ishida For realizing robots working in human society, interaction
Kenta OKU and Fumio HATTORI
DEIM Forum 2012 A1-3 525 8577 1 1 1 E-mail: oku@fc.ritsumei.ac.jp, fhattori@is.ritsumei.ac.jp Kenta OKU and Fumio HATTORI College of Information Science and Engineering, 1 1 1 Nojihigashi, Kusatsu-city,
2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems
2016 IEEE/ACM International Conference on Mobile Software Engineering and Systems Multiple User Interfaces MobileSoft'16, Multi-User Experience (MUX) S1: Insourcing S2: Outsourcing S3: Responsive design
ΠΛΗΡΟΦΟΡΙΚΗ Ι JAVA Τμήμα θεωρίας με Α.Μ. σε 3, 7, 8 & 9 22/11/07
Ακαδ έτος 2007-2008 ΠΛΗΡΟΦΟΡΙΚΗ Ι Φερεντίνος 22/11/07 ΠΛΗΡΟΦΟΡΙΚΗ Ι JAVA Τμήμα θεωρίας με ΑΜ σε 3, 7, 8 & 9 22/11/07 Παράδειγμα με if/else if και user input: import javautil*; public class Grades public
EM Baum-Welch. Step by Step the Baum-Welch Algorithm and its Application 2. HMM Baum-Welch. Baum-Welch. Baum-Welch Baum-Welch.
Baum-Welch Step by Step the Baum-Welch Algorithm and its Application Jin ichi MURAKAMI EM EM EM Baum-Welch Baum-Welch Baum-Welch Baum-Welch, EM 1. EM 2. HMM EM (Expectationmaximization algorithm) 1 3.