Max-Margin Early Event Detectors

Size: px
Start display at page:

Download "Max-Margin Early Event Detectors"

Transcription

1 Max-Margn 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 human-robot nteracton to vdeo securty. Whle temporal event detecton has been extensvely studed, early detecton s a relatvely unexplored problem. Ths paper proposes a maxmum-margn 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., rsk-of-fallng 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 Max-Margn 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 frame-by-frame 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 bag-of-words 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 change-pont 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 n-gram 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 well-defned 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 two-stage 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 log-lkelhood 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. Max-Margn 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 non-negatve functon, and n general, t should be a non-decreasng functon n (, ]. In our experments, we found the followng pece-wse 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 non-empty 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 sequence-event 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/cplex-optmzer/

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) []. F-score 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 frame-based 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 F-score 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 F-score 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 I-love-you sentence whch was preceded and succeeded by 5 random sgns. The I-love-you 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 I-love-you 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- love-you 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 I-love-you sentences. Inspred by the hgh recognton rate of HMM, we constructed the feature representaton for SVM-based detectors (SOSVM and MMED) as follows. We frst traned a Gaussan Mxture Model of Gaussans for the frames extracted from the I-love-you sentences. Each frame was then assocated wth a log-lkelhood vector. We retaned the top three values of ths vector, zerong out the other values, to create a frame-level 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 I-love-you sentences n addton to the full sentences as postve tranng examples. Negatve tranng examples were random segments that had no overlappng wth the I- love-you 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 I-love-you 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 Cohn-Kanade 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 frame-based SVMs: Frmpeak was traned on peak frames of the tranng sequences whle Frm-all was traned usng all frames between the onset and offset of the facal acton. Frame-based 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): F-score 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 segment-based. 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, segment-based SVMs outperformed framebased SVMs. The ROC areas (mean and standard devaton) for Frm-peak, Frm-all, 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, Frm-all, 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 sngle-acton sequences. Followng [5], we extracted bnary masks and computed Eucldean dstance transform for frame-level features. Frame-level feature vectors were clustered usng k-means 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 leave-one-out cross valdaton; each cross valdaton fold traned the event detector on 8 sequences and tested t on the leave-out 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 Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna wangtngzhong2@sna.cn Abstract.

More information

What is Candidate Sampling

What 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 information

Vision Mouse. Saurabh Sarkar a* University of Cincinnati, Cincinnati, USA ABSTRACT 1. INTRODUCTION

Vision 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 information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit 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 information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting 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 information

An Alternative Way to Measure Private Equity Performance

An 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 information

Project Networks With Mixed-Time Constraints

Project Networks With Mixed-Time Constraints Project Networs Wth Mxed-Tme 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 information

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification

Logistic 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 information

Feature selection for intrusion detection. Slobodan Petrović NISlab, Gjøvik University College

Feature 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 information

Gender Classification for Real-Time Audience Analysis System

Gender Classification for Real-Time Audience Analysis System Gender Classfcaton for Real-Tme 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 information

An Interest-Oriented Network Evolution Mechanism for Online Communities

An Interest-Oriented Network Evolution Mechanism for Online Communities An Interest-Orented 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 information

Support Vector Machines

Support 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 information

Single and multiple stage classifiers implementing logistic discrimination

Single 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 information

The OC Curve of Attribute Acceptance Plans

The 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 information

Can Auto Liability Insurance Purchases Signal Risk Attitude?

Can Auto Liability Insurance Purchases Signal Risk Attitude? Internatonal Journal of Busness and Economcs, 2011, Vol. 10, No. 2, 159-164 Can Auto Lablty Insurance Purchases Sgnal Rsk Atttude? Chu-Shu L Department of Internatonal Busness, Asa Unversty, Tawan Sheng-Chang

More information

A Secure Password-Authenticated Key Agreement Using Smart Cards

A Secure Password-Authenticated Key Agreement Using Smart Cards A Secure Password-Authentcated Key Agreement Usng Smart Cards Ka Chan 1, Wen-Chung Kuo 2 and Jn-Chou Cheng 3 1 Department of Computer and Informaton Scence, R.O.C. Mltary Academy, Kaohsung 83059, Tawan,

More information

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network

Forecasting 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 information

A Hierarchical Anomaly Network Intrusion Detection System using Neural Network Classification

A 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 information

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS

INVESTIGATION OF VEHICULAR USERS FAIRNESS IN CDMA-HDR NETWORKS 21 22 September 2007, BULGARIA 119 Proceedngs of the Internatonal Conference on Informaton Technologes (InfoTech-2007) 21 st 22 nd September 2007, Bulgara vol. 2 INVESTIGATION OF VEHICULAR USERS FAIRNESS

More information

IMPACT ANALYSIS OF A CELLULAR PHONE

IMPACT 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 information

L10: Linear discriminants analysis

L10: 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 information

Detecting Credit Card Fraud using Periodic Features

Detecting 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 information

J. Parallel Distrib. Comput.

J. 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 information

ANALYZING 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 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, 610-519-4390,

More information

How To Understand The Results Of The German Meris Cloud And Water Vapour Product

How To Understand The Results Of The German Meris Cloud And Water Vapour Product Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller

More information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 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 information

Recurrence. 1 Definitions and main statements

Recurrence. 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 information

CS 2750 Machine Learning. Lecture 3. Density estimation. CS 2750 Machine Learning. Announcements

CS 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 information

Face Verification Problem. Face Recognition Problem. Application: Access Control. Biometric Authentication. Face Verification (1:1 matching)

Face 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 information

Fault tolerance in cloud technologies presented as a service

Fault 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 information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

Eye Center Localization on a Facial Image Based on Multi-Block Local Binary Patterns

Eye Center Localization on a Facial Image Based on Multi-Block Local Binary Patterns Eye Center Localzaton on a Facal Image Based on Mult-Bloc Local Bnary Patterns Anatoly tn, Vladmr Khryashchev, Olga Stepanova Yaroslavl State Unversty Yaroslavl, Russa anatolyntnyar@gmal.com, vhr@yandex.ru,

More information

Learning from Multiple Outlooks

Learning 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 information

AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE

AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE AN APPOINTMENT ORDER OUTPATIENT SCHEDULING SYSTEM THAT IMPROVES OUTPATIENT EXPERIENCE Yu-L Huang Industral Engneerng Department New Mexco State Unversty Las Cruces, New Mexco 88003, U.S.A. Abstract Patent

More information

Course outline. Financial Time Series Analysis. Overview. Data analysis. Predictive signal. Trading strategy

Course 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 information

How To Solve An Onlne Control Polcy On A Vrtualzed Data Center

How To Solve An Onlne Control Polcy On A Vrtualzed Data Center 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}@docomolabs-usa.com, mjneely@usc.edu

More information

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services

An 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, Hsn-yng Wu b a Professor (Management Scence), Natonal Chao

More information

Descriptive Models. Cluster Analysis. Example. General Applications of Clustering. Examples of Clustering Applications

Descriptive 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 information

Activity Scheduling for Cost-Time Investment Optimization in Project Management

Activity Scheduling for Cost-Time 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 information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

Mining Feature Importance: Applying Evolutionary Algorithms within a Web-based Educational System

Mining Feature Importance: Applying Evolutionary Algorithms within a Web-based Educational System Mnng Feature Importance: Applyng Evolutonary Algorthms wthn a Web-based Educatonal System Behrouz MINAEI-BIDGOLI 1, and Gerd KORTEMEYER 2, and Wllam F. PUNCH 1 1 Genetc Algorthms Research and Applcatons

More information

Statistical Methods to Develop Rating Models

Statistical 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 information

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

How 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 information

Statistical Approach for Offline Handwritten Signature Verification

Statistical Approach for Offline Handwritten Signature Verification Journal of Computer Scence 4 (3): 181-185, 2008 ISSN 1549-3636 2008 Scence Publcatons Statstcal Approach for Offlne Handwrtten Sgnature Verfcaton 2 Debnath Bhattacharyya, 1 Samr Kumar Bandyopadhyay, 2

More information

Detecting Global Motion Patterns in Complex Videos

Detecting 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 information

Stochastic Protocol Modeling for Anomaly Based Network Intrusion Detection

Stochastic Protocol Modeling for Anomaly Based Network Intrusion Detection Stochastc Protocol Modelng for Anomaly Based Network Intruson Detecton Juan M. Estevez-Tapador, Pedro Garca-Teodoro, and Jesus E. Daz-Verdejo Department of Electroncs and Computer Technology Unversty of

More information

Bayesian Network Based Causal Relationship Identification and Funding Success Prediction in P2P Lending

Bayesian 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 information

Performance Analysis and Coding Strategy of ECOC SVMs

Performance Analysis and Coding Strategy of ECOC SVMs Internatonal Journal of Grd and Dstrbuted Computng Vol.7, No. (04), pp.67-76 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 information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Luby 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 information

Latent Class Regression. Statistics for Psychosocial Research II: Structural Models December 4 and 6, 2006

Latent 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 information

On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features

On-Line Fault Detection in Wind Turbine Transmission System using Adaptive Filter and Robust Statistical Features On-Lne 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 information

An interactive system for structure-based ASCII art creation

An interactive system for structure-based ASCII art creation An nteractve system for structure-based ASCII art creaton Katsunor Myake Henry Johan Tomoyuk Nshta The Unversty of Tokyo Nanyang Technologcal Unversty Abstract Non-Photorealstc Renderng (NPR), whose am

More information

Properties of Indoor Received Signal Strength for WLAN Location Fingerprinting

Properties 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 E-mal: kakst2,prashk@ptt.edu

More information

An Enhanced Super-Resolution System with Improved Image Registration, Automatic Image Selection, and Image Enhancement

An Enhanced Super-Resolution System with Improved Image Registration, Automatic Image Selection, and Image Enhancement An Enhanced Super-Resoluton System wth Improved Image Regstraton, Automatc Image Selecton, and Image Enhancement Yu-Chuan Kuo ( ), Chen-Yu Chen ( ), and Chou-Shann Fuh ( ) Department of Computer Scence

More information

A Genetic Programming Based Stock Price Predictor together with Mean-Variance Based Sell/Buy Actions

A Genetic Programming Based Stock Price Predictor together with Mean-Variance Based Sell/Buy Actions Proceedngs of the World Congress on Engneerng 28 Vol II WCE 28, July 2-4, 28, London, U.K. A Genetc Programmng Based Stock Prce Predctor together wth Mean-Varance Based Sell/Buy Actons Ramn Rajaboun and

More information

CONSISTENT VEHICLES TRACKING BY USING A COOPERATIVE DISTRIBUTED VIDEO SURVEILLANCESYSTEM

CONSISTENT 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 2251-838X / Vol, 4 (12):3658-3663 Scence Explorer Publcatons CONSISTENT VEHICLES TRACKING BY USING A COOPERATIVE

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

Extending Probabilistic Dynamic Epistemic Logic

Extending 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 σ-algebra: a set

More information

BUSINESS PROCESS PERFORMANCE MANAGEMENT USING BAYESIAN BELIEF NETWORK. 0688, dskim@ssu.ac.kr

BUSINESS 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 Yeong-bn Mn 1, Yongwoo Shn 2, Km Jeehong 1, Dongsoo

More information

The Application of Fractional Brownian Motion in Option Pricing

The Application of Fractional Brownian Motion in Option Pricing Vol. 0, No. (05), pp. 73-8 http://dx.do.org/0.457/jmue.05.0..6 The Applcaton of Fractonal Brownan Moton n Opton Prcng Qng-xn Zhou School of Basc Scence,arbn Unversty of Commerce,arbn zhouqngxn98@6.com

More information

Data Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network *

Data Broadcast on a Multi-System Heterogeneous Overlayed Wireless Network * JOURNAL OF INFORMATION SCIENCE AND ENGINEERING 24, 819-840 (2008) Data Broadcast on a Mult-System Heterogeneous Overlayed Wreless Network * Department of Computer Scence Natonal Chao Tung Unversty Hsnchu,

More information

Efficient Striping Techniques for Variable Bit Rate Continuous Media File Servers æ

Efficient 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 information

Analysis of Premium Liabilities for Australian Lines of Business

Analysis 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 information

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña

A DYNAMIC CRASHING METHOD FOR PROJECT MANAGEMENT USING SIMULATION-BASED OPTIMIZATION. Michael E. Kuhl Radhamés A. Tolentino-Peña 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 SIMULATION-BASED OPTIMIZATION

More information

Disagreement-Based Multi-System Tracking

Disagreement-Based Multi-System Tracking Dsagreement-Based Mult-System Trackng Quannan L 1, Xnggang Wang 2, We Wang 3, Yuan Jang 3, Zh-Hua Zhou 3, Zhuowen Tu 1 1 Lab of Neuro Imagng, Unversty of Calforna, Los Angeles 2 Huazhong Unversty of Scence

More information

Calculation of Sampling Weights

Calculation 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 two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) 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 information

THE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION

THE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION Internatonal Journal of Electronc Busness Management, Vol. 3, No. 4, pp. 30-30 (2005) 30 THE APPLICATION OF DATA MINING TECHNIQUES AND MULTIPLE CLASSIFIERS TO MARKETING DECISION Yu-Mn Chang *, Yu-Cheh

More information

New Approaches to Support Vector Ordinal Regression

New 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@yahoo-nc.com

More information

A Replication-Based and Fault Tolerant Allocation Algorithm for Cloud Computing

A Replication-Based and Fault Tolerant Allocation Algorithm for Cloud Computing A Replcaton-Based and Fault Tolerant Allocaton Algorthm for Cloud Computng Tork Altameem Dept of Computer Scence, RCC, Kng Saud Unversty, PO Box: 28095 11437 Ryadh-Saud Araba Abstract The very large nfrastructure

More information

Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm

Document Clustering Analysis Based on Hybrid PSO+K-means Algorithm Document Clusterng Analyss Based on Hybrd PSO+K-means Algorthm Xaohu Cu, Thomas E. Potok Appled Software Engneerng Research Group, Computatonal Scences and Engneerng Dvson, Oak Rdge Natonal Laboratory,

More information

Distributed Multi-Target Tracking In A Self-Configuring Camera Network

Distributed Multi-Target Tracking In A Self-Configuring Camera Network Dstrbuted Mult-Target Trackng In A Self-Confgurng Camera Network Crstan Soto, B Song, Amt K. Roy-Chowdhury Department of Electrcal Engneerng Unversty of Calforna, Rversde {cwlder,bsong,amtrc}@ee.ucr.edu

More information

Software project management with GAs

Software 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 information

A New Quality of Service Metric for Hard/Soft Real-Time Applications

A New Quality of Service Metric for Hard/Soft Real-Time Applications A New Qualty of Servce Metrc for Hard/Soft Real-Tme Applcatons Shaoxong Hua and Gang Qu Electrcal and Computer Engneerng Department and Insttute of Advanced Computer Study Unversty of Maryland, College

More information

Searching for Interacting Features for Spam Filtering

Searching for Interacting Features for Spam Filtering Searchng for Interactng Features for Spam Flterng Chuanlang Chen 1, Yun-Chao Gong 2, Rongfang Be 1,, and X. Z. Gao 3 1 Department of Computer Scence, Bejng Normal Unversty, Bejng 100875, Chna 2 Software

More information

Abstract. 1. Introduction

Abstract. 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 information

1 Example 1: Axis-aligned rectangles

1 Example 1: Axis-aligned 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 information

RESEARCH ON DUAL-SHAKER SINE VIBRATION CONTROL. Yaoqi FENG 1, Hanping QIU 1. China Academy of Space Technology (CAST) yaoqi.feng@yahoo.

RESEARCH ON DUAL-SHAKER 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 DUAL-SHAKER SINE VIBRATION CONTROL Yaoq FENG, Hanpng QIU Dynamc Test Laboratory, BISEE Chna Academy of Space Technology (CAST) yaoq.feng@yahoo.com Abstract

More information

Damage detection in composite laminates using coin-tap method

Damage detection in composite laminates using coin-tap method Damage detecton n composte lamnates usng con-tap method S.J. Km Korea Aerospace Research Insttute, 45 Eoeun-Dong, Youseong-Gu, 35-333 Daejeon, Republc of Korea yaeln@kar.re.kr 45 The con-tap test has the

More information

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign

PAS: A Packet Accounting System to Limit the Effects of DoS & DDoS. Debish Fesehaye & Klara Naherstedt University of Illinois-Urbana Champaign PAS: A Packet Accountng System to Lmt the Effects of DoS & DDoS Debsh Fesehaye & Klara Naherstedt Unversty of Illnos-Urbana Champagn DoS and DDoS DDoS attacks are ncreasng threats to our dgtal world. Exstng

More information

VoIP Playout Buffer Adjustment using Adaptive Estimation of Network Delays

VoIP 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 information

Lecture 2: Single Layer Perceptrons Kevin Swingler

Lecture 2: Single Layer Perceptrons Kevin Swingler Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCulloch-Ptts 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 information

The Greedy Method. Introduction. 0/1 Knapsack Problem

The 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 information

320 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 information

Data Visualization by Pairwise Distortion Minimization

Data 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 information

A DATA MINING APPLICATION IN A STUDENT DATABASE

A DATA MINING APPLICATION IN A STUDENT DATABASE JOURNAL OF AERONAUTICS AND SPACE TECHNOLOGIES JULY 005 VOLUME NUMBER (53-57) A DATA MINING APPLICATION IN A STUDENT DATABASE Şenol Zafer ERDOĞAN Maltepe Ünversty Faculty of Engneerng Büyükbakkalköy-Istanbul

More information

Conferencing protocols and Petri net analysis

Conferencing 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 information

Automated Network Performance Management and Monitoring via One-class Support Vector Machine

Automated Network Performance Management and Monitoring via One-class Support Vector Machine Automated Network Performance Management and Montorng va One-class Support Vector Machne R. Zhang, J. Jang, and S. Zhang Dgtal Meda & Systems Research Insttute, Unversty of Bradford, UK Abstract: In ths

More information

How To Create An Emoton Recognzer

How To Create An Emoton Recognzer Recognzng Low/Hgh Anger n Speech for Call Centers FU-MING LEE*, LI-HUA LI, RU-YI HUANG Department of Informaton Management Chaoyang Unversty of Technology 168 Jfong E. Rd., Wufong Townshp Tachung County,

More information

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc 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 information

Improved Mining of Software Complexity Data on Evolutionary Filtered Training Sets

Improved 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, SI-2000 Marbor SLOVENIA vl.podgorelec@un-mb.s

More information

"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *

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, 789-794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC

More information

How To Detect An 802.11 Traffc From A Network With A Network Onlne Onlnet

How To Detect An 802.11 Traffc From A Network With A Network Onlne Onlnet 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 ACK-Pars We We, Member, IEEE, Kyoungwon Suh, Member, IEEE,

More information

Traffic State Estimation in the Traffic Management Center of Berlin

Traffic State Estimation in the Traffic Management Center of Berlin Traffc State Estmaton n the Traffc Management Center of Berln Authors: Peter Vortsch, PTV AG, Stumpfstrasse, D-763 Karlsruhe, Germany phone ++49/72/965/35, emal peter.vortsch@ptv.de Peter Möhl, PTV AG,

More information

Evaluating credit risk models: A critique and a new proposal

Evaluating 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 information

A Suspect Vehicle Tracking System Based on Video

A Suspect Vehicle Tracking System Based on Video 3rd Internatonal Conference on Multmeda Technology ICMT 2013) A Suspect Vehcle Trackng System Based on Vdeo Yad Chen 1, Tuo Wang Abstract. Vdeo survellance systems are wdely used n securty feld. The large

More information

Support Vector Machine Model for Currency Crisis Discrimination. Arindam Chaudhuri 1. Abstract

Support 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 information

Who are you with and Where are you going?

Who 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

1. Measuring association using correlation and regression

1. 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 information

A study on the ability of Support Vector Regression and Neural Networks to Forecast Basic Time Series Patterns

A study on the ability of Support Vector Regression and Neural Networks to Forecast Basic Time Series Patterns A study on the ablty of Support Vector Regresson and Neural Networks to Forecast Basc Tme Seres Patterns Sven F. Crone, Jose Guajardo 2, and Rchard Weber 2 Lancaster Unversty, Department of Management

More information