MaxMargin Early Event Detectors


 Colleen Bates
 1 years ago
 Views:
Transcription
1 MaxMargn Early Event Detectors Mnh Hoa Fernando De la Torre Robotcs Insttute, Carnege Mellon Unversty Abstract The need for early detecton of temporal events from sequental data arses n a wde spectrum of applcatons rangng from humanrobot nteracton to vdeo securty. Whle temporal event detecton has been extensvely studed, early detecton s a relatvely unexplored problem. Ths paper proposes a maxmummargn framework for tranng temporal event detectors to recognze partal events, enablng early detecton. Our method s based on Structured Output SVM, but extends t to accommodate sequental data. Experments on datasets of varyng complexty, for detectng facal expressons, hand gestures, and human actvtes, demonstrate the benefts of our approach. To the best of our knowledge, ths s the frst paper n the lterature of computer vson that proposes a learnng formulaton for early event detecton.. Introducton The ablty to make relable early detecton of temporal events has many potental applcatons n a wde range of felds, rangng from securty (e.g., pandemc attack detecton), envronmental scence (e.g., tsunam warnng) to healthcare (e.g., rskoffallng detecton) and robotcs (e.g., affectve computng). A temporal event has a duraton, and by early detecton, we mean to detect the event as soon as possble, after t starts but before t ends, as llustrated n Fg.. To see why t s mportant to detect events before they fnsh, consder a concrete example of buldng a robot that can affectvely nteract wth humans. Arguably, a key requrement for such a robot s ts ablty to accurately and rapdly detect the human emotonal states from facal expresson so that approprate responses can be made n a tmely manner. More often than not, a socally acceptable response s to mtate the current human behavor. Ths requres facal events such as smlng or frownng to be detected even before they are complete; otherwse, the mtaton response would be out of synchronzaton. Despte the mportance of early detecton, few machne learnng formulatons have been explctly developed for early detecton. Most exstng methods (e.g., [5, 3, 6,,, 9]) for event detecton are desgned for offlne process #"$%&'(" #/"&/.%)'(*("$%&'(".,(/*" )#$*" +,*,(" Fgure. How many frames do we need to detect a smle relably? Can we even detect a smle before t fnshes? Exstng event detectors are traned to recognze complete events only; they requre seeng the entre event for a relable decson, preventng early detecton. We propose a learnng formulaton to recognze partal events, enablng early detecton. ng. They have a lmtaton for processng sequental data as they are only traned to detect complete events. But for early detecton, t s necessary to recognze partal events, whch are gnored n the tranng process of exstng event detectors. Ths paper proposes MaxMargn Early Event Detectors (MMED), a novel formulaton for tranng event detectors that recognze partal events, enablng early detecton. MMED s based on Structured Output SVM (SOSVM) [7], but extends t to accommodate the nature of sequental data. In partcular, we smulate the sequental framebyframe data arrval for tranng tme seres and learn an event detector that correctly classfes partally observed sequences. Fg. llustrates the key dea behnd MMED: partal events are smulated and used as postve tranng examples. It s mportant to emphasze that we tran a sngle event detector to recognze all partal events. But MMED does more than augmentng the set of tranng examples; t trans a detector to localze the temporal extent of a target event, even when the target event has yet fnshed. Ths requres monotoncty of the detecton functon wth respect to the ncluson relatonshp between partal events the detecton score (confdence) of a partal event cannot exceed the score of an encompassng partal event. MMED provdes a prncpled mechansm to acheve ths monotoncty, whch cannot be assured by a nave soluton that smply augments the set of tranng examples. The learnng formulaton of MMED s a constraned quadratc optmzaton problem. Ths formulaton s the
2 &%,'#*(" )#3#'"$%&'($" #".%)'(*("$%&'(" Fgure. Gven a tranng tme seres that contans a complete event, we smulate the sequental arrval of tranng data and use partal events as postve tranng examples. The red segments ndcate the temporal extents of the partal events. We tran a sngle event detector to recognze all partal events, but our method does more than augmentng the set of tranng examples. oretcally justfed. In Sec. 3., we dscuss two ways for quantfyng the loss for contnuous detecton on sequental data. We prove that, n both cases, the objectve of the learnng formulaton s to mnmze an upper bound of the true loss on the tranng data. MMED has numerous benefts. Frst, MMED nherts the advantages of SOSVM, ncludng ts convex learnng formulaton and ts ablty for accurate localzaton of event boundares. Second, MMED, specfcally desgned for early detecton, s superor to SOSVM and other competng methods regardng the tmelness of the detecton. Experments on datasets of varyng complexty, rangng from sgn language to facal expresson and human actons, showed that our method often made faster detectons whle mantanng comparable or even better accuracy.. Prevous work Ths secton dscusses prevous work on early detecton and event detecton... Early detecton Whle event detecton has been studed extensvely n the lterature of computer vson, lttle attenton has been pad to early detecton. Davs and Tyag [] addressed rapd recognton of human actons usng the probablty rato test. Ths s a passve method for early detecton; t assumes that a generatve HMM for an event class, traned n a standard way, can also generate partal events. Smlarly, Ryoo [5] took a passve approach for early recognton of human actvtes; he developed two varants of the bagofwords representaton to manly address the computatonal ssues, not tmelness or accuracy, of the detecton process. Prevous work on early detecton exsts n other felds, but ts applcablty n computer vson s unclear. Nell et al. [] studed dsease outbreak detecton. Ther approach, lke onlne changepont detecton [3], s based on detectng the locatons where abrupt statstcal changes occur. Ths technque, however, cannot be appled to detect temporal events such as smlng and frownng, whch must and can be detected and recognzed ndependently of the background. Brown et al. [] used the ngram model for predctve typng,.e., predctng the next word from prevous words. However, t s hard to apply ther method to computer vson, whch does not have a welldefned language model yet. Early detecton has also been studed n the context of spam flterng, where mmedate and rreversble decsons must be made whenever an emal arrves. Assumng spam messages were smlar to one another, Hader et al. [6] developed a method for detectng batches of spam messages based on clusterng. But vsual events such as smlng or frownng cannot be detected and recognzed just by observng the smlarty between consttuent frames, because ths characterstc s nether requste nor exclusve to these events. It s mportant to dstngush between forecastng and detecton. Forecastng predcts the future whle detecton nterprets the present. For example, fnancal forecastng (e.g., [8]) predcts the next day s stock ndex based on the current and past observatons. Ths technque cannot be drectly used for early event detecton because t predcts the raw value of the next observaton nstead of recognzng the event class of the current and past observatons. Perhaps, forecastng the future s a good frst step for recognzng the present, but ths twostage approach has a dsadvantage because the former may be harder than the latter. For example, t s probably easer to recognze a partal smle than to predct when t wll end or how t wll progress... Event detecton Ths secton revews SVM, HMM, and SOSVM, whch are among the most popular algorthms for tranng event detectors. None of them are specfcally desgned for early detecton. Let (X,y ),, (X n,y n ) be the set of tranng tme seres and ther assocated ground truth annotatons for the events of nterest. Here we assume each tranng sequence contans at most one event of nterest, as a tranng sequence contanng several events can always be dvded nto smaller subsequences of sngle events. Thus y = [s, e ] conssts of two numbers ndcatng the start and the end of the event n tme seres X. Suppose the length of an event s bounded by l mn and l max and we denote Y(t) be the set of lengthbounded tme ntervals from the st to the t th frame: Y(t) = {y N y [, t], l mn y l max } { }. Here s the length functon. For a tme seres X of length l, Y(l) s the set of all possble locatons of an event; the empty segment, y =, ndcates no event occurrence. For an nterval y = [s, e] Y(l), let X y denote the subsegment of X from frame s to e nclusve. Let g(x) denote the output of the detector, whch s the segment that maxmzes
3 the detecton score: g(x) = argmaxf(x y ; θ). () y Y(l) The output of the detector may be the empty segment, and f t s, we report no detecton. f(x y ; θ) s the detecton score of segment X y, and θ s the parameter of the score functon. Note that the detector searches over temporal scales from l mn to l max. In testng, ths process can be repeated to detect multple target events, f more than one event occur. How s θ learned? Bnary SVM methods learn θ by requrng the score of postve tranng examples to be greater than or equal to,.e., f(x y ; θ), whle constranng the score of negatve tranng examples to be smaller than or equal to. Negatve examples can be selected n many ways; a smple approach s to choose random segments of tranng tme seres that do not overlap wth postve examples. HMM methods defne f(, θ) as the loglkelhood and learn θ that maxmzes the total loglkelhood of postve tranng examples,.e., maxmzng f(x y ; θ). HMM methods gnore negatve tranng examples. SOSVM methods learn θ by requrng the score of a postve tranng example X y to be greater than the score of any other segment from the same tme seres,.e., f(x y ; θ) > f(x y ; θ) y y. SOSVM further requres ths constrant to be well satsfed by a margn: f(x y ; θ) f(x y ; θ) + (y,y) y y, where (y,y) s the loss of the detector for outputtng y when the desred output s y []. Though optmzng dfferent learnng objectves and constrants, all of these aforementoned methods use the same set of postve examples. They are traned to recognze complete events only, nadequately prepared for the task of early detecton. 3. MaxMargn Early Event Detectors As explaned above, exstng methods do not tran detectors to recognze partal events. Consequently, usng these methods for onlne predcton would lead to unrelable decsons as we wll llustrate n the expermental secton. Ths secton derves a learnng formulaton to address ths problem. We use the same notatons as descrbed n Sec Learnng wth smulated sequental data Let ϕ(x y ) be the feature vector for segment X y. We consder a lnear detecton score functon: { w f(x y ; θ) = T ϕ(x y ) + b f y, () otherwse. Here θ = (w, b), w s the weght vector and b s the bas term. From now on, for brevty, we use f(x y ) nstead of f(x y ; θ) to denote the score of segment X y. To support early detecton of events n tme seres data, we propose to use partal events as postve tranng examples (Fg. ). In partcular, we smulate the sequental arrval of tranng data as follows. Suppose the length of X s l. For each tme t =,, l, let y t be the part of event y that has already happened,.e., y t = y [, t], whch s possbly empty. Ideally, we want the output of the detector on tme seres X at tme t to be the partal event,.e., g(x [,t] ) = y t. (3) Note that g(x [,t]) s not the output of the detector runnng on the entre tme seres X. It s the output of the detector on the subsequence of tme seres X from the frst frame to the t th frame only,.e., g(x [,t] ) = argmax f(x y ). () y Y(t) From (3)(), the desred property of the score functon s: f(x y t) f(x y) y Y(t). (5) Ths constrant requres the score of the partal event yt to be hgher than the score of any other tme seres segment y whch has been seen n the past, y [, t]. Ths s llustrated n Fg. 3. Note that the score of the partal event s not requred to be hgher than the score of a future segment. As n the case of SOSVM, the prevous constrant can be requred to be well satsfed by an adaptve margn. Ths margn s (yt,y), the loss of the detector for outputtng y when the desred output s yt (n our case (yt,y) = y t y yt + y ). The desred constrant s: f(x y t) f(x y) + (y t,y) y Y(t). (6) Ths constrant should be enforced for all t =,, l. As n the formulatons of SVM and SOSVM, constrants are allowed to be volated by ntroducng slack varables, and we obtan the followng learnng formulaton: mnmze w,b,ξ w + C n n ξ, (7) = s.t. f(x y f(x t) y) + (yt,y) µ ξ ( y y, t =,, l, y Y(t). (8) ( ) y t Here denotes the length functon, and µ y s a functon of( the proporton ) of the event that has occurred y at tme t. µ s a slack varable rescalng factor and y should correlate wth the mportance of correctly detectng at tme t whether the eventy has happened. µ( ) can be any )
4 X s t e t t t t t #$%&"" %'()'*&" y t #$$3"" ''*&" )#3'&'"" ''*&" +,&,'"" %'()'*&" "*%&$/*&5" > f(x y t) f(x y past ).'%/'."%'"+,**"f( ) Fgure 3. The desred score functon for early event detecton: the complete event must have the hghest detecton score, and the detecton score of a partal event must be hgher than that of any segment that ends before the partal event. To learn ths functon, we explctly consder partal events durng tranng. At tme t, the score of the truncated event (red segment) s requred to be hgher than the score of any segment n the past (e.g., blue segment); however, t s not requred to be hgher than the score of any future segment (e.g., green segment). Ths fgure s best seen n color. arbtrary nonnegatve functon, and n general, t should be a nondecreasng functon n (, ]. In our experments, we found the followng pecewse lnear functon a reasonable choce: µ(x) = for < x α; µ(x) = (x α)/(β α) for α < x β; and µ(x) = for β < x or x =. Here, α and β are tunable parameters. µ() = µ() emphaszes that true rejecton s as mportant as true detecton of the complete event. Ths learnng formulaton s an extenson of SOSVM. From ths formulaton, we obtan SOSVM by not smulatng the sequental arrval of tranng data,.e., to set t = l nstead of t =,, l n Constrant (8). Notably, our method does more than augmentng the set of tranng examples; t enforces the monotoncty of the detector functon, as shown n Fg.. For a better understandng of Constrant (8), let us analyze the constrant wthout the slack varable term and break t nto three cases: ) t < s (event has not started); ) t s, y = (event has started; compare the partal event aganst the detecton threshold); ) t s, y (event has started; compare the partal event aganst any nonempty segment). Recall f(x ) = and y t = for t < s, cases (), (), () lead to Constrants (9), (), (), respectvely: f(x y) y Y(s ) \ { }, (9) f(x y t ) t s, () f(x y t ) f(x y) + (y t,y) t s,y Y(t) \ { }. () Constrant (9) prevents false detecton when the event has $%&'(%$#&)*(%#+,).*#f( ) Fgure. Monotoncty requrement the detecton score of a partal event cannot exceed the score of an encompassng partal event. MMED provdes a prncpled mechansm to acheve ths monotoncty, whch cannot be assured by a nave soluton that smply augments the set of tranng examples. not started. Constrant () requres successful recognton of partal events. Constrant () trans the detector to accurately localze the temporal extent of the partal events. The proposed learnng formulaton Eq. (7) s convex, but t contans a large number of constrants. Followng [7], we propose to use constrant generaton n optmzaton,.e., we mantan a smaller subset of constrants and teratvely update t by addng the most volated ones. Constrant generaton s guaranteed to converge to the global mnmum. In our experments descrbed n Sec., ths usually converges wthn teratons. Each teraton requres mnmzng a convex quadratc objectve. Ths objectve s optmzed usng Cplex n our mplementaton. 3.. Loss functon and emprcal rsk mnmzaton In Sec. 3., we have proposed a formulaton for tranng early event detectors. Ths secton provdes further dscusson on what exactly s beng optmzed. Frst, we brefly revew the loss of SOSVM and ts surrogate emprcal rsk. We then descrbe two general approaches for quantfyng the loss of a detector on sequental data. In both cases, what Eq. (7) mnmzes s an upper bound on the loss. As prevously explaned, (y, ŷ) s the functon that quantfes the loss assocated wth a predcton ŷ, f the true output value s y. Thus, n the settng of offlne detecton, the loss of a detector g( ) on a sequenceevent par (X, y) s quantfed as (y, g(x)). Suppose the sequenceevent pars (X, y) are generated accordng to some dstr P(X,y), the loss of the detector g s R true (g) = buton X Y (y, g(x))dp(x,y). However, P s unknown so the performance of g(.) s descrbed by the emprcal rsk www.bm.com/software/ntegraton/optmzaton/cplexoptmzer/
5 on the tranng data {(X,y )}, assumng they are generated..d accordng to P. The emprcal rsk s R emp(g) = n n = (y, g(x )). It has been shown that SOSVM mnmzes an upper bound on the emprcal rsk R emp [7]. Due to the nature of contnual evaluaton, quantfyng the loss of an onlne detector on streamng data requres aggregatng the losses evaluated throughout the course of the data sequence. Let us consder the loss assocated wth a predcton y = g(x [,t] ) for tme seres X at tme t as (y t,y)µ ( y y ). Here (y t,y) accounts for the dfference ( ) between the output y and true truncated event yt. y µ s the scalng factor; t depends on how much the y temporal event y has happened. Two possble ways for aggregatng these loss quanttes s to use ther maxmum or average. They lead to two dfferent emprcal rsks for a set of tranng tme seres: R,µ max(g) = n R,µ mean (g) = n n = n = { ( y max (yt, g(x t [,t] ))µ y mean t { (y t, g(x [,t] ))µ ( y y )}, )}. In the followng, we state and prove a proposton that establshes that the learnng formulaton gven n Eq. 7 mnmzes an upper bound of the above two emprcal rsks. Proposton: Denote by ξ (g) the optmal soluton of the slack varables n Eq. (7) for a gven detector g, then n n = ξ s an upper bound on the emprcal rsks (g) and R,µ R,µ max mean (g). Proof: Consder Constrant (8) wth y = g(x [,t] ) and together wth the fact that f(x g(x )) f(x we y [,t] ( ) t), have ξ (yt, g(x [,t] ))µ y y t. Thus ξ ( ) max t { (yt, g(x [,t] ))µ y }. Hence n n = ξ y R,µ max(g) R,µ mean(g). Ths completes the proof of the proposton. Ths proposton justfes the objectve of the learnng formulaton.. Experments Ths secton descrbes our experments on several publcly avalable datasets of varyng complexty... Evaluaton crtera Ths secton descrbes several crtera for evaluatng the accuracy and tmelness of detectors. We used the area under the ROC curve for accuracy comparson, Normalzed Tme to Detecton (NTtoD) for benchmarkng the tmelness of detecton, and F score for evaluatng localzaton qualty. Area under the ROC curve: Consder testng a detector on a set of tme seres. The False Postve Rate (FPR) of the detector s defned as the fracton of tme seres that the detector fres before the event of nterest starts. The True Postve Rate (TPR) s defned as the fracton of tme seres that the detector fres durng the event of nterest. A detector typcally has a detecton threshold that can be adjusted to trade off hgh TPR for low FPR and vse versa. By varyng ths detecton threshold, we can generate the ROC curve whch s the functon of TPR aganst FPR. We use the area under the ROC for evaluatng the detector accuracy. AMOC curve: To evaluate the tmelness of detecton we used Normalzed Tme to Detecton (NTtoD) whch s defned as follows. Gven a testng tme seres wth the event of nterest occurs from s to e. Suppose the detector starts to fre at tme t. For a successful detecton, s t e, we defne the NTtoD as the fracton of event that has occurred, t s+.e., e s+. NTtoD s defned as for a false detecton (t < s) and for a false rejecton (t > e). By adjustng the detecton threshold, one can acheve lower NTtoD at the cost of hgher FPR and vce versa. For a complete characterstc pcture, we vared the detecton threshold and plotted the curve of NToD versus FPR. Ths s referred as the Actvty Montorng Operatng Curve (AMOC) []. Fscore curve: The ROC and AMOC curves, however, do not provde a measure for how well the detector can localze the event of nterest. For ths purpose, we propose to use the framebased F scores. Consder runnng a detector on a tmes seres. At tme t the detector output the segment y whle the ground truth (possbly) truncated event s y. The Fscore s defned as the harmonc mean of precson and recall values: F := Precson Recall Precson+Recall, wth Precson := y y y and Recall := y y y. For a new test tme seres, we can smulate the sequental arrval of data and record the F scores as the event of nterest unroll from % to %. We refer to ths as the Fscore curve... Synthetc data We frst valdated the performance of MMED on a synthetcally generated dataset of tme seres. Each tme seres contaned one nstance of the event of nterest, sgnal 5(a)., and several nstances of other events, sgnals 5(a). v. Some examples of these tme seres are shown n Fg. 5(b). We randomly splt the data nto tranng and testng subsets of equal szes. Durng testng we smulated the sequental arrval of data and recorded the moment that MMED started to detect the start of the event of nterest. Wth % precson, MMED detected the event when t had completed 7.5% of the event. For comparson, SOSVM requred observng 77.5% of the event for a postve detecton. Examples of testng tme seres and results are depcted n Fg. 5(b). The events of nterest are drawn n
6 5 5 v 5 (a) Fgure 5. Synthetc data experment. (a): tme seres were created by concatenatng the event of nterest () and several nstances of other events () (v). (b): examples of testng tme seres; the sold vertcal red lnes mark the moments that our method starts to detect the event of nterest whle the dash blue lnes are the results of SOSVM. green and the sold vertcal red lnes mark the moments that our method started to detect these events. The dash vertcal blue lnes are the results of SOSVM. Notably, ths result reveals an nterestng capablty of MMED. For the tme seres n ths experment, the change n sgnal values from 3 to s exclusve to the target events. MMED was traned to recognze partal events, t mplctly dscovered ths unque behavor, and t detected the target events as soon as ths behavor occurred. In ths experment, we represented each tme seres segment by the L normalzed hstogram of sgnal values n the segment (normalzed to have unt norm). We used lnear SVM wth C =, α =, β =..3. Auslan dataset Australan sgn language (b) Ths secton descrbes our experments on a publcly avalable dataset [7] that contans 95 Auslan sgns, each wth 7 examples. The sgns were captured from a natve sgner usng poston trackers and nstrumented gloves; the locaton of two hands, the orentaton of the palms, and the bendng of the fngers were recorded. We consdered detectng the sentence I love you n monologues obtaned by concatenatng multple sgns. In partcular, each monologue contaned an Iloveyou sentence whch was preceded and succeeded by 5 random sgns. The Iloveyou sentence was ordered concatenaton of random samples of three sgns: I, love, and you. We created tranng and testng monologues from dsjont sets of sgn samples; the frst 5 examples of each sgn were used to create tranng monologues whle the last examples were used for testng monologues. The average lengths and standard devatons of the monologues and the Iloveyou sentences were 836 ± 38 and 58 ± 6 respectvely. Prevous work [7] reported hgh recognton performance on ths dataset usng HMMs. Followng ther success, we mplemented a contnuous densty HMM for I loveyou sentences. Our HMM mplementaton conssted of states, each was a mxture of Gaussans. To use the HMM for detecton, we adopted a sldng wndow approach; the wndow sze was fxed to the average length of the Iloveyou sentences. Inspred by the hgh recognton rate of HMM, we constructed the feature representaton for SVMbased detectors (SOSVM and MMED) as follows. We frst traned a Gaussan Mxture Model of Gaussans for the frames extracted from the Iloveyou sentences. Each frame was then assocated wth a loglkelhood vector. We retaned the top three values of ths vector, zerong out the other values, to create a framelevel feature representaton. Ths s often referred to as a soft quantzaton approach. To compute the feature vector for a gven wndow, we dvded the wndow nto two roughly equal halves, the mean feature vector of each half was calculated, and the concatenaton of these mean vectors was used as the feature representaton of the wndow. A nave strategy for early detecton s to use truncated events as postve examples. For comparson, we mplemented Seg[.5,], a bnary SVM that used the frst halves of the Iloveyou sentences n addton to the full sentences as postve tranng examples. Negatve tranng examples were random segments that had no overlappng wth the I loveyou sentences. We repeated our experment tmes and recorded the average performance. Regardng the detecton accuracy, all methods except SVM[.5,] performed smlarly well. The ROC areas for HMM, SVM[.5,], SOSVM, and MMED were.97,.9,.99, and.99, respectvely. However, when comparng the tmelness of detecton, MMED outperformed the others by a large margn. For example, at % false postve rate, our method detected the Iloveyou sentence when t observed the frst 37% of the sentence. At the same false postve rate, the best alternatve method requred seeng 6% of the sentence. The full AMOC curves are depcted n Fg. 6(a). In ths experment, we used lnear SVM wth C =, α =.5, β =... Extended Cohn Kanade dataset expresson The Extended CohnKanade dataset (CK+) [] contans 37 facal mage sequences from 3 subjects performng one of seven dscrete emotons: anger, contempt, dsgust, fear, happness, sadness, and surprse. Each of the sequences contans mages from onset (neutral frame) to peak expresson (last frame). We consdered the task of detectng negatve emotons: anger, dsgust, fear, and sadness. We used the same representaton as [], where each frame s represented by the canoncal normalzed appearance feature, referred as CAPP n []. For comparson purposes, we mplemented two framebased SVMs: Frmpeak was traned on peak frames of the tranng sequences whle Frmall was traned usng all frames between the onset and offset of the facal acton. Framebased SVMs can be used for detecton by classfyng ndvdual frames. In
7 Normalzed Tme to Detect HMM Seg [.5,] SOSVM MMED False Postve Rate (a) Auslan, AMOC Normalzed Tme to Detect Frm peak Frm all SOSVM MMED False Postve Rate (b) CK+, AMOC F score Seg [] Seg [.5,]. SOSVM MMED Fracton of the event seen (c) Wezmann, F curve Fgure 6. Performance curves. (a, b): AMOC curves on Auslan and CK+ datasets; at the same false postve rate, MMED detects the event of nterest sooner than the others. (c): Fscore curves on Wezmann dataset; MMED provdes better localzaton for the event of nterest, especally when the fracton of the event observed s small. Ths fgure s best seen n color. contrast, SOSVM and MMED are segmentbased. Snce a facal expresson s a devaton of the neutral expresson, we represented each segment of an emoton sequence by the dfference between the end frame and the start frame. Even though the start frame was not necessary a neutral face, ths representaton led to good recognton results. We randomly dvded the data nto dsjont tranng and testng subsets. The tranng set contaned sequences wth equal numbers of postve and negatve examples. For relable results, we repeated our experment tmes and recorded the average performance. Regardng the detecton accuracy, segmentbased SVMs outperformed framebased SVMs. The ROC areas (mean and standard devaton) for Frmpeak, Frmall, SOSVM, MMED are.8 ±.,.8 ±.3,.96 ±., and.97 ±., respectvely. Comparng the tmelness of detecton, our method was sgnfcantly better than the others, especally at low false postve rate. For example, at % false postve rate, Frmpeak, Frmall, SOSVM, and MMED can detect the expresson when t completes 7%, 6%, 55%, and 7% respectvely. Fg. 6(b) plots the AMOC curves, and Fg. 7 dsplays some qualtatve results. In ths experment, we used a lnear SVM wth C =, α =, β = Wezmann dataset human acton The Wezmann dataset contans 9 vdeo sequences of 9 people, each performng actons. Each vdeo sequence n ths dataset only conssts of a sngle acton. To measure the accuracy and tmelness of detecton, we performed experments on longer vdeo sequences whch were created by concatenatng exstng sngleacton sequences. Followng [5], we extracted bnary masks and computed Eucldean dstance transform for framelevel features. Framelevel feature vectors were clustered usng kmeans to create a codebook of temporal words. Subsequently, each frame (a) (b) dsgust fear...6. Fgure 7. Dsgust (a) and fear (b) detecton on CK+ dataset. From left to rght: the onset frame, the frame at whch MMED fres, the frame at whch SOSVM fres, and the peak frame. The number n each mage s the correspondng NTtoD. was represented by the ID of the correspondng codebook entry and each segment of a tme seres was represented by the hstogram of temporal words assocated wth frames nsde the segment. We traned a detector for each acton class, but consdered them one by one. We created 9 long vdeo sequences, each composed of vdeos of the same person and had the event of nterest at the end of the sequence. We performed leaveoneout cross valdaton; each cross valdaton fold traned the event detector on 8 sequences and tested t on the leaveout sequence. For the testng sequence, we computed the normalzed tme to detecton at % false postve rate. Ths false postve rate was acheved by rasng the threshold for detecton so that the detector would not fre before the event started. We calculated the medan normalzed tme to detecton across 9 cross valdaton folds and averaged these medan values across acton classes; the resultng values for Seg[], Seg[.5,], SOSVM, MMED are.6,.3,.6, and. respectvely. Here Seg[] was
The Development of Web Log Mining Based on ImproveKMeans Clustering Analysis
The Development of Web Log Mnng Based on ImproveKMeans Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationVision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION
Vson Mouse Saurabh Sarkar a* a Unversty of Cncnnat, Cncnnat, USA ABSTRACT The report dscusses a vson based approach towards trackng of eyes and fngers. The report descrbes the process of locatng the possble
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationForecasting the Direction and Strength of Stock Market Movement
Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye cjngwe@stanford.edu mchen5@stanford.edu nanye@stanford.edu Abstract  Stock market s one of the most complcated systems
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationProject Networks With MixedTime Constraints
Project Networs Wth MxedTme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationLogistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification
Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson
More informationAn InterestOriented Network Evolution Mechanism for Online Communities
An InterestOrented Network Evoluton Mechansm for Onlne Communtes Cahong Sun and Xaopng Yang School of Informaton, Renmn Unversty of Chna, Bejng 100872, P.R. Chna {chsun,yang}@ruc.edu.cn Abstract. Onlne
More informationGender Classification for RealTime Audience Analysis System
Gender Classfcaton for RealTme Audence Analyss System Vladmr Khryashchev, Lev Shmaglt, Andrey Shemyakov, Anton Lebedev Yaroslavl State Unversty Yaroslavl, Russa vhr@yandex.ru, shmaglt_lev@yahoo.com, andrey.shemakov@gmal.com,
More informationFeature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College
Feature selecton for ntruson detecton Slobodan Petrovć NISlab, Gjøvk Unversty College Contents The feature selecton problem Intruson detecton Traffc features relevant for IDS The CFS measure The mrmr measure
More informationSupport Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
More informationSingle and multiple stage classifiers implementing logistic discrimination
Sngle and multple stage classfers mplementng logstc dscrmnaton Hélo Radke Bttencourt 1 Dens Alter de Olvera Moraes 2 Vctor Haertel 2 1 Pontfíca Unversdade Católca do Ro Grande do Sul  PUCRS Av. Ipranga,
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationCan Auto Liability Insurance Purchases Signal Risk Attitude?
Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? ChuShu L Department of Internatonal Busness, Asa Unversty, Tawan ShengChang
More informationA Secure PasswordAuthenticated Key Agreement Using Smart Cards
A Secure PasswordAuthentcated Key Agreement Usng Smart Cards Ka Chan 1, WenChung Kuo 2 and JnChou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,
More informationForecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network
700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School
More informationA Hierarchical Anomaly Network Intrusion Detection System using Neural Network Classification
IDC IDC A Herarchcal Anomaly Network Intruson Detecton System usng Neural Network Classfcaton ZHENG ZHANG, JUN LI, C. N. MANIKOPOULOS, JAY JORGENSON and JOSE UCLES ECE Department, New Jersey Inst. of Tech.,
More informationINVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMAHDR NETWORKS
21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS
More informationIMPACT ANALYSIS OF A CELLULAR PHONE
4 th ASA & μeta Internatonal Conference IMPACT AALYSIS OF A CELLULAR PHOE We Lu, 2 Hongy L Bejng FEAonlne Engneerng Co.,Ltd. Bejng, Chna ABSTRACT Drop test smulaton plays an mportant role n nvestgatng
More informationL10: Linear discriminants analysis
L0: Lnear dscrmnants analyss Lnear dscrmnant analyss, two classes Lnear dscrmnant analyss, C classes LDA vs. PCA Lmtatons of LDA Varants of LDA Other dmensonalty reducton methods CSCE 666 Pattern Analyss
More informationDetecting Credit Card Fraud using Periodic Features
Detectng Credt Card Fraud usng Perodc Features Alejandro Correa Bahnsen, Djamla Aouada, Aleksandar Stojanovc and Björn Ottersten Interdscplnary Centre for Securty, Relablty and Trust Unversty of Luxembourg,
More informationJ. Parallel Distrib. Comput.
J. Parallel Dstrb. Comput. 71 (2011) 62 76 Contents lsts avalable at ScenceDrect J. Parallel Dstrb. Comput. journal homepage: www.elsever.com/locate/jpdc Optmzng server placement n dstrbuted systems n
More informationANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING
ANALYZING THE RELATIONSHIPS BETWEEN QUALITY, TIME, AND COST IN PROJECT MANAGEMENT DECISION MAKING Matthew J. Lberatore, Department of Management and Operatons, Vllanova Unversty, Vllanova, PA 19085, 6105194390,
More informationMAPP. MERIS level 3 cloud and water vapour products. Issue: 1. Revision: 0. Date: 9.12.1998. Function Name Organisation Signature Date
Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPPATBDClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More informationCS 2750 Machine Learning. Lecture 3. Density estimation. CS 2750 Machine Learning. Announcements
Lecture 3 Densty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 5329 Sennott Square Next lecture: Matlab tutoral Announcements Rules for attendng the class: Regstered for credt Regstered for audt (only f there
More informationFace Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching)
Face Recognton Problem Face Verfcaton Problem Face Verfcaton (1:1 matchng) Querymage face query Face Recognton (1:N matchng) database Applcaton: Access Control www.vsage.com www.vsoncs.com Bometrc Authentcaton
More informationFault tolerance in cloud technologies presented as a service
Internatonal Scentfc Conference Computer Scence 2015 Pavel Dzhunev, PhD student Fault tolerance n cloud technologes presented as a servce INTRODUCTION Improvements n technques for vrtualzaton and performance
More informationCausal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting
Causal, Explanatory Forecastng Assumes causeandeffect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of
More informationLearning from Multiple Outlooks
Learnng from Multple Outlooks Maayan Harel Department of Electrcal Engneerng, Technon, Hafa, Israel She Mannor Department of Electrcal Engneerng, Technon, Hafa, Israel maayanga@tx.technon.ac.l she@ee.technon.ac.l
More informationAN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE
AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE YuL Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent
More informationEye Center Localization on a Facial Image Based on MultiBlock Local Binary Patterns
Eye Center Localzaton on a Facal Image Based on MultBloc Local Bnary Patterns Anatoly tn, Vladmr Khryashchev, Olga Stepanova Yaroslavl State Unversty Yaroslavl, Russa anatolyntnyar@gmal.com, vhr@yandex.ru,
More informationUsing Mixture Covariance Matrices to Improve Face and Facial Expression Recognitions
Usng Mxture Covarance Matrces to Improve Face and Facal Expresson Recogntons Carlos E. homaz, Duncan F. Glles and Raul Q. Fetosa 2 Imperal College of Scence echnology and Medcne, Department of Computng,
More informationDescriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications
CMSC828G Prncples of Data Mnng Lecture #9 Today s Readng: HMS, chapter 9 Today s Lecture: Descrptve Modelng Clusterng Algorthms Descrptve Models model presents the man features of the data, a global summary
More informationDynamic Resource Allocation and Power Management in Virtualized Data Centers
Dynamc Resource Allocaton and Power Management n Vrtualzed Data Centers Rahul Urgaonkar, Ulas C. Kozat, Ken Igarash, Mchael J. Neely urgaonka@usc.edu, {kozat, garash}@docomolabsusa.com, mjneely@usc.edu
More informationCourse outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy
Fnancal Tme Seres Analyss Patrck McSharry patrck@mcsharry.net www.mcsharry.net Trnty Term 2014 Mathematcal Insttute Unversty of Oxford Course outlne 1. Data analyss, probablty, correlatons, vsualsaton
More informationActivity Scheduling for CostTime Investment Optimization in Project Management
PROJECT MANAGEMENT 4 th Internatonal Conference on Industral Engneerng and Industral Management XIV Congreso de Ingenería de Organzacón Donosta San Sebastán, September 8 th 10 th 010 Actvty Schedulng
More informationDEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMISP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
More informationStatistical Methods to Develop Rating Models
Statstcal Methods to Develop Ratng Models [Evelyn Hayden and Danel Porath, Österrechsche Natonalbank and Unversty of Appled Scences at Manz] Source: The Basel II Rsk Parameters Estmaton, Valdaton, and
More informationAn Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services
An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsnyng Wu b a Professor (Management Scence), Natonal Chao
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationCS 2750 Machine Learning. Lecture 17a. Clustering. CS 2750 Machine Learning. Clustering
Lecture 7a Clusterng Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square Clusterng Groups together smlar nstances n the data sample Basc clusterng problem: dstrbute data nto k dfferent groups such that
More informationStatistical Approach for Offline Handwritten Signature Verification
Journal of Computer Scence 4 (3): 181185, 2008 ISSN 15493636 2008 Scence Publcatons Statstcal Approach for Offlne Handwrtten Sgnature Verfcaton 2 Debnath Bhattacharyya, 1 Samr Kumar Bandyopadhyay, 2
More informationMining Feature Importance: Applying Evolutionary Algorithms within a Webbased Educational System
Mnng Feature Importance: Applyng Evolutonary Algorthms wthn a Webbased Educatonal System Behrouz MINAEIBIDGOLI 1, and Gerd KORTEMEYER 2, and Wllam F. PUNCH 1 1 Genetc Algorthms Research and Applcatons
More informationDetecting Global Motion Patterns in Complex Videos
Detectng Global Moton Patterns n Complex Vdeos Mn Hu, Saad Al, Mubarak Shah Computer Vson Lab, Unversty of Central Florda {mhu,sal,shah}@eecs.ucf.edu Abstract Learnng domnant moton patterns or actvtes
More informationBayesian Network Based Causal Relationship Identification and Funding Success Prediction in P2P Lending
Proceedngs of 2012 4th Internatonal Conference on Machne Learnng and Computng IPCSIT vol. 25 (2012) (2012) IACSIT Press, Sngapore Bayesan Network Based Causal Relatonshp Identfcaton and Fundng Success
More informationStochastic Protocol Modeling for Anomaly Based Network Intrusion Detection
Stochastc Protocol Modelng for Anomaly Based Network Intruson Detecton Juan M. EstevezTapador, Pedro GarcaTeodoro, and Jesus E. DazVerdejo Department of Electroncs and Computer Technology Unversty of
More informationLatent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006
Latent Class Regresson Statstcs for Psychosocal Research II: Structural Models December 4 and 6, 2006 Latent Class Regresson (LCR) What s t and when do we use t? Recall the standard latent class model
More informationPerformance Analysis and Coding Strategy of ECOC SVMs
Internatonal Journal of Grd and Dstrbuted Computng Vol.7, No. (04), pp.6776 http://dx.do.org/0.457/jgdc.04.7..07 Performance Analyss and Codng Strategy of ECOC SVMs Zhgang Yan, and Yuanxuan Yang, School
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More informationAn interactive system for structurebased ASCII art creation
An nteractve system for structurebased ASCII art creaton Katsunor Myake Henry Johan Tomoyuk Nshta The Unversty of Tokyo Nanyang Technologcal Unversty Abstract NonPhotorealstc Renderng (NPR), whose am
More informationProperties of Indoor Received Signal Strength for WLAN Location Fingerprinting
Propertes of Indoor Receved Sgnal Strength for WLAN Locaton Fngerprntng Kamol Kaemarungs and Prashant Krshnamurthy Telecommuncatons Program, School of Informaton Scences, Unversty of Pttsburgh Emal: kakst2,prashk@ptt.edu
More informationAn Enhanced SuperResolution System with Improved Image Registration, Automatic Image Selection, and Image Enhancement
An Enhanced SuperResoluton System wth Improved Image Regstraton, Automatc Image Selecton, and Image Enhancement YuChuan Kuo ( ), ChenYu Chen ( ), and ChouShann Fuh ( ) Department of Computer Scence
More informationOnLine Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features
OnLne Fault Detecton n Wnd Turbne Transmsson System usng Adaptve Flter and Robust Statstcal Features Ruoyu L Remote Dagnostcs Center SKF USA Inc. 3443 N. Sam Houston Pkwy., Houston TX 77086 Emal: ruoyu.l@skf.com
More informationA Genetic Programming Based Stock Price Predictor together with MeanVariance Based Sell/Buy Actions
Proceedngs of the World Congress on Engneerng 28 Vol II WCE 28, July 24, 28, London, U.K. A Genetc Programmng Based Stock Prce Predctor together wth MeanVarance Based Sell/Buy Actons Ramn Rajaboun and
More informationData Broadcast on a MultiSystem Heterogeneous Overlayed Wireless Network *
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 819840 (2008) Data Broadcast on a MultSystem Heterogeneous Overlayed Wreless Network * Department of Computer Scence Natonal Chao Tung Unversty Hsnchu,
More informationCONSISTENT VEHICLES TRACKING BY USING A COOPERATIVE DISTRIBUTED VIDEO SURVEILLANCESYSTEM
Internatonal Research Journal of Appled and Basc Scences 2013 Avalable onlne at www.rjabs.com ISSN 2251838X / Vol, 4 (12):36583663 Scence Explorer Publcatons CONSISTENT VEHICLES TRACKING BY USING A COOPERATIVE
More informationExtending Probabilistic Dynamic Epistemic Logic
Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σalgebra: a set
More informationA Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy Scurve Regression
Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy Scurve Regresson ChengWu Chen, Morrs H. L. Wang and TngYa Hseh Department of Cvl Engneerng, Natonal Central Unversty,
More informationBUSINESS PROCESS PERFORMANCE MANAGEMENT USING BAYESIAN BELIEF NETWORK. 0688, dskim@ssu.ac.kr
Proceedngs of the 41st Internatonal Conference on Computers & Industral Engneerng BUSINESS PROCESS PERFORMANCE MANAGEMENT USING BAYESIAN BELIEF NETWORK Yeongbn Mn 1, Yongwoo Shn 2, Km Jeehong 1, Dongsoo
More informationAnalysis of Premium Liabilities for Australian Lines of Business
Summary of Analyss of Premum Labltes for Australan Lnes of Busness Emly Tao Honours Research Paper, The Unversty of Melbourne Emly Tao Acknowledgements I am grateful to the Australan Prudental Regulaton
More informationEfficient Striping Techniques for Variable Bit Rate Continuous Media File Servers æ
Effcent Strpng Technques for Varable Bt Rate Contnuous Meda Fle Servers æ Prashant J. Shenoy Harrck M. Vn Department of Computer Scence, Department of Computer Scences, Unversty of Massachusetts at Amherst
More informationA DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATIONBASED OPTIMIZATION. Michael E. Kuhl Radhamés A. TolentinoPeña
Proceedngs of the 2008 Wnter Smulaton Conference S. J. Mason, R. R. Hll, L. Mönch, O. Rose, T. Jefferson, J. W. Fowler eds. A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATIONBASED OPTIMIZATION
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a twostage stratfed cluster desgn. 1 The frst stage conssted of a sample
More informationDisagreementBased MultiSystem Tracking
DsagreementBased MultSystem Trackng Quannan L 1, Xnggang Wang 2, We Wang 3, Yuan Jang 3, ZhHua Zhou 3, Zhuowen Tu 1 1 Lab of Neuro Imagng, Unversty of Calforna, Los Angeles 2 Huazhong Unversty of Scence
More informationPSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 12
14 The Chsquared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationThe Application of Fractional Brownian Motion in Option Pricing
Vol. 0, No. (05), pp. 738 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qngxn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com
More informationNew Approaches to Support Vector Ordinal Regression
New Approaches to Support Vector Ordnal Regresson We Chu chuwe@gatsby.ucl.ac.uk Gatsby Computatonal Neuroscence Unt, Unversty College London, London, WCN 3AR, UK S. Sathya Keerth selvarak@yahoonc.com
More informationTHE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION
Internatonal Journal of Electronc Busness Management, Vol. 3, No. 4, pp. 3030 (2005) 30 THE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION YuMn Chang *, YuCheh
More informationA ReplicationBased and Fault Tolerant Allocation Algorithm for Cloud Computing
A ReplcatonBased and Fault Tolerant Allocaton Algorthm for Cloud Computng Tork Altameem Dept of Computer Scence, RCC, Kng Saud Unversty, PO Box: 28095 11437 RyadhSaud Araba Abstract The very large nfrastructure
More informationDocument Clustering Analysis Based on Hybrid PSO+Kmeans Algorithm
Document Clusterng Analyss Based on Hybrd PSO+Kmeans Algorthm Xaohu Cu, Thomas E. Potok Appled Software Engneerng Research Group, Computatonal Scences and Engneerng Dvson, Oak Rdge Natonal Laboratory,
More informationDistributed MultiTarget Tracking In A SelfConfiguring Camera Network
Dstrbuted MultTarget Trackng In A SelfConfgurng Camera Network Crstan Soto, B Song, Amt K. RoyChowdhury Department of Electrcal Engneerng Unversty of Calforna, Rversde {cwlder,bsong,amtrc}@ee.ucr.edu
More informationSoftware project management with GAs
Informaton Scences 177 (27) 238 241 www.elsever.com/locate/ns Software project management wth GAs Enrque Alba *, J. Francsco Chcano Unversty of Málaga, Grupo GISUM, Departamento de Lenguajes y Cencas de
More informationRecognizing Low/High Anger in Speech for Call Centers
Recognzng Low/Hgh Anger n Speech for Call Centers FUMING LEE*, LIHUA LI, RUYI HUANG Department of Informaton Management Chaoyang Unversty of Technology 168 Jfong E. Rd., Wufong Townshp Tachung County,
More informationA New Quality of Service Metric for Hard/Soft RealTime Applications
A New Qualty of Servce Metrc for Hard/Soft RealTme Applcatons Shaoxong Hua and Gang Qu Electrcal and Computer Engneerng Department and Insttute of Advanced Computer Study Unversty of Maryland, College
More informationAbstract. 1. Introduction
System and Methodology for Usng Moble Phones n Lve Remote Montorng of Physcal Actvtes Hamed Ketabdar and Matt Lyra Qualty and Usablty Lab, Deutsche Telekom Laboratores, TU Berln hamed.ketabdar@telekom.de,
More informationSearching for Interacting Features for Spam Filtering
Searchng for Interactng Features for Spam Flterng Chuanlang Chen 1, YunChao Gong 2, Rongfang Be 1,, and X. Z. Gao 3 1 Department of Computer Scence, Bejng Normal Unversty, Bejng 100875, Chna 2 Software
More informationThe Analysis of Outliers in Statistical Data
THALES Project No. xxxx The Analyss of Outlers n Statstcal Data Research Team Chrysses Caron, Assocate Professor (P.I.) Vaslk Karot, Doctoral canddate Polychrons Economou, Chrstna Perrakou, Postgraduate
More information1 Example 1: Axisaligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
More informationRESEARCH ON DUALSHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo.
ICSV4 Carns Australa 9 July, 007 RESEARCH ON DUALSHAKER SINE VIBRATION CONTROL Yaoq FENG, Hanpng QIU Dynamc Test Laboratory, BISEE Chna Academy of Space Technology (CAST) yaoq.feng@yahoo.com Abstract
More informationDamage detection in composite laminates using cointap method
Damage detecton n composte lamnates usng contap method S.J. Km Korea Aerospace Research Insttute, 45 EoeunDong, YouseongGu, 35333 Daejeon, Republc of Korea yaeln@kar.re.kr 45 The contap test has the
More informationThe Greedy Method. Introduction. 0/1 Knapsack Problem
The Greedy Method Introducton We have completed data structures. We now are gong to look at algorthm desgn methods. Often we are lookng at optmzaton problems whose performance s exponental. For an optmzaton
More informationPAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of IllinoisUrbana Champaign
PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of IllnosUrbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng
More informationLecture 2: Single Layer Perceptrons Kevin Swingler
Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCullochPtts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses
More informationVoIP Playout Buffer Adjustment using Adaptive Estimation of Network Delays
VoIP Playout Buffer Adjustment usng Adaptve Estmaton of Network Delays Mroslaw Narbutt and Lam Murphy* Department of Computer Scence Unversty College Dubln, Belfeld, Dubln, IRELAND Abstract The poor qualty
More informationData Visualization by Pairwise Distortion Minimization
Communcatons n Statstcs, Theory and Methods 34 (6), 005 Data Vsualzaton by Parwse Dstorton Mnmzaton By Marc Sobel, and Longn Jan Lateck* Department of Statstcs and Department of Computer and Informaton
More information320 The Internatonal Arab Journal of Informaton Technology, Vol. 5, No. 3, July 2008 Comparsons Between Data Clusterng Algorthms Osama Abu Abbas Computer Scence Department, Yarmouk Unversty, Jordan Abstract:
More informationA DATA MINING APPLICATION IN A STUDENT DATABASE
JOURNAL OF AERONAUTICS AND SPACE TECHNOLOGIES JULY 005 VOLUME NUMBER (5357) A DATA MINING APPLICATION IN A STUDENT DATABASE Şenol Zafer ERDOĞAN Maltepe Ünversty Faculty of Engneerng BüyükbakkalköyIstanbul
More informationConferencing protocols and Petri net analysis
Conferencng protocols and Petr net analyss E. ANTONIDAKIS Department of Electroncs, Technologcal Educatonal Insttute of Crete, GREECE ena@chana.tecrete.gr Abstract: Durng a computer conference, users desre
More informationOn the Optimal Control of a Cascade of HydroElectric Power Stations
On the Optmal Control of a Cascade of HydroElectrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;
More information1 Approximation Algorithms
CME 305: Dscrete Mathematcs and Algorthms 1 Approxmaton Algorthms In lght of the apparent ntractablty of the problems we beleve not to le n P, t makes sense to pursue deas other than complete solutons
More informationImproved Mining of Software Complexity Data on Evolutionary Filtered Training Sets
Improved Mnng of Software Complexty Data on Evolutonary Fltered Tranng Sets VILI PODGORELEC Insttute of Informatcs, FERI Unversty of Marbor Smetanova ulca 17, SI2000 Marbor SLOVENIA vl.podgorelec@unmb.s
More information"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *
Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC
More information1. Measuring association using correlation and regression
How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a
More informationEvaluating credit risk models: A critique and a new proposal
Evaluatng credt rsk models: A crtque and a new proposal Hergen Frerchs* Gunter Löffler Unversty of Frankfurt (Man) February 14, 2001 Abstract Evaluatng the qualty of credt portfolo rsk models s an mportant
More informationTHE deployment of IEEE 802.11 wireless networks
IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. X, NO. X, XXX 2008 1 Passve Onlne Detecton of 802.11 Traffc Usng Sequental Hypothess Testng wth TCP ACKPars We We, Member, IEEE, Kyoungwon Suh, Member, IEEE,
More informationSupport Vector Machine Model for Currency Crisis Discrimination. Arindam Chaudhuri 1. Abstract
Support Vector Machne Model for Currency Crss Dscrmnaton Arndam Chaudhur Abstract Support Vector Machne (SVM) s powerful classfcaton technque based on the dea of structural rsk mnmzaton. Use of kernel
More informationWho are you with and Where are you going?
Who are you wth and Where are you gong? Kota Yamaguch Alexander C. Berg Lus E. Ortz Tamara L. Berg Stony Brook Unversty Stony Brook Unversty, NY 11794, USA {kyamagu, aberg, leortz, tlberg}@cs.stonybrook.edu
More information