Building Social Decision Support Mechanisms with Friend Networks

Size: px
Start display at page:

Download "Building Social Decision Support Mechanisms with Friend Networks"

Transcription

1 h Hawaii Inernaional Conference on Sysem Sciences Building Social Decision Suppor Mechanisms wih Friend Neworks Yung-Ming Li Insiue of Informaion Managemen, Naional Chiao Tung Universiy, Hsinchu, Taiwan 300, ROC Yi-Lin Lee Insiue of Informaion Managemen, Naional Chiao Tung Universiy, Hsinchu, Taiwan 300, ROC Absrac Many Inerne users are geing used o make decisions based on opinions colleced from heir own social neworks. While convenional decision suppor sysem has been exensively invesigaed, lile specific mechanism, however, on how social neworks can help users wih decision-making is developed. Using social nework analysis along wih regression model, fuzzy Delphi and fuzzy AHP mehods as ools, his paper designs a social nework based decision suppor sysem wih beer effeciveness. 1. Inroducion Social Neworking Sies (SNS) are increasingly popular nowadays. Sies like Facebook and Twier are compeing o give he bes feaures in erms of socializaion, ineracion and even enerainmen. However, he main purpose of using social neworks should be socializing wih people who you like and are ineresed in. I is no surprising ha many people are hooked on wih hese sies, bu predicions abou he fuure of social neworks are various Curren applicaions of social nework As social neworking echnology evolves and increasing online aciviies make more daa ses available, social neworks are beginning o be uilized in many ways. Mos social neworking services are used for sharing wha you ve done or wha you re doing, bu he applicaions should no be focused on personal aciviies only. Curren developmen of social nework analysis (SNA) is limied o discovering friend connecions, leaders, influenial people and friends. For example, markeing applicaions have been widely discussed o discover poenial opporuniies for aracing cusomers, bu he facors considered in hese applicaions are limied o personal connecions. Human decision making is ofen based on heurisics han on pure logic or weighing advanages and disadvanages. When people make decisions hey are ofen influenced by wha oher people like hemselves are saying and doing. Missing from social nework sudies, however, has been discussions of decision mechanisms. The social nework approach focuses on relaions beween people, raher han on aribues of people. For example, i measures he exisence of friendship beween individuals, raher han he friendliness of each individual [16]. In moving from he analysis of aribues oward relaion-cenered analysis, nework researchers appeared o have los sigh of ime and is poenial effecs on he srengh of relaions. Wha new insigh could we discover if social nework analysis akes ime ino accoun, in addiion o only connecions beween people? If new facors could be aken ino consideraion, specifically changes over ime, hen social nework analysis could discover hings like emergence of leadership and changes in rus over ime. And ha will be very valuable informaion ha he social neworks have barely begun o capure Saemen of he research problem Wih respec o decision making in groups, wo aspecs are imporan [7]: normaive and informaional influence. The former one is based on he desire o conform o he expecaions of ohers, and he laer one is based on he accepance of informaion from ohers [14]. Recenly, some simple decision suppor funcions like Quesions on Facebook have been developed (Figure 1). By using Quesions you can ge recommendaions or conduc polls. However, i is no a real decision suppor ool because only voing funcion is provided. Besides, he crieria have o be seup before he quesion is presened o friends. Tha is, he /12 $ IEEE DOI /HICSS

2 decision maker should have clear undersanding abou quesions, and have he abiliy o lis decision crieria. Typically, we may ask friends quesions like: Wha o buy? You may ge a lis of recommended producs from recommendaion mechanism like conen-based or collaboraive filering, bu lile informaion abou how he producs are seleced is known. If users ry o make decision based on funcions like Quesion, hey should have clear undersanding abou decision problems and have o be capable of defining decision crieria. However, if his is no he case, exising funcions won work well. This aricle aemps wo asks. Firs, he main obecive is o design a social decision suppor sysem running on online social nework. By disclosing he power of online social nework, we inroduce ime facor o expand radiional social nework analysis, in which he inerconnecions are core. Second, an improved similariy aggregaion mehod was proposed o beer fi he characerisics of social nework. We inend o answer quesions like: How o buy? Tha is, we ake a sep forward by supporing decision makers wih crieria discovering, decision group selecing and decision crieria synhesizing. A decision maker does no o be familiar wih decision quesions, nor does he ake care of decision processes. When a problem is issued, our sysem help users wih selecing decision group members from users social nework, collecing opinions and aggregaing hem ino alernaives. Users can undersand he whole picure of wha he alernaives are based on. on Facebook. Third, an adapive fuzzy similariy aggregaion mehod was also presened o beer fi he characerisics of informaional influence wihin social neworks. The remaining par of his paper is organized as follows. In secion 2, we discuss exising lieraure relaed o our research opics. Secion 3 demonsraes our research model and in secion 4 we describe he experimenal daa source, seings, and procedures. The experimenal resuls and evaluaions are discussed in secion 5. Finally, secion 6 concludes our research conribuions and presens fuure research direcions. 2. Lieraure review 2.1. Social decision suppor Social decision making is he process ha akes every individual s local decisions and generaes a collecive response [25]. Social decision suppor (SDS) heory is composed of individual preferences, group composiion, group influence processes, and collecive responses [26]. I has also been applied o problem-solving asks [19] and collecive recall [10]. A social decision suppor sysem (SDSS) allows users in a nework-based environmen o form a decision group and paricipae in a collaboraive decision making process. The social decision suppor sysem (SDSS) idea was firs inroduced in [29] as a compuer suppored group decision making sysem. A social decision suppor sysem draws is foundaion from a number of relaed areas such as Delphi mehod [20], compuer mediaed communicaion [9], decision heory [2, 8] and online communiies [24]. However, we hink ha he design of sysems o suppor online socieal collaboraive processes for formulaion of decision crieria and alernaives requires some new and challenging developmen for successful operaion Social nework analysis Figure 1. Quesions funcion on Facebook 1.3. Significance of he sudy In our sudy, we seleced famous social nework sie Facebook o be our experimen plaform. Our sudy differeniaes from exising works in hree ways. Firs, we measured he friendship by combining ime facor wih cenraliy from social nework analysis. Second, a regression model was used o build up decision group selecing mechanism based on he friend relaionships Social nework analysis is becoming increasingly popular as a general mehodology for undersanding complex paerns of ineracion. I provides a visual represenaion of users and inerconnecions. The mos popular merics used are degree, beweenness and closeness cenraliy [5]. A user wih higher beweenness cenraliy is ofen considered as an opinion leader [5], and a higher closeness cenraliy indicaes ha a user is highly relaed o all ohers [23]. The sudies of social neworks have examined a diverse se of properies, and hese properies are classified as relaional properies and srucural properies [27]. Relaional properies focus on he 1778

3 conen of he relaionship beween nework members and on he form of hese relaionships, while srucural properies describe he way members fi ogeher o form social neworks. Human relaionships are mainained, are renewed, or deeriorae over ime [28], bu ime facor is missing from he above properies Fuzzy Delphi and Fuzzy AHP Delphi mehod is a ype of collecive decision-making mehod [20, 33], and i requires several rounds of anonymous wrien quesionnaire surveys for he purpose of collecing expers opinion. This mus coninue asking expers for heir opinion unil he expers arrive a a consensus. As a resul, i generally has weaknesses such as ime and cos consuming. Fuzzy Delphi Mehod (FDM) is an analyical mehod based on he Delphi Mehod ha draws on he ideas of he Fuzzy Theory. To solve he problem of fuzziness in exper consensus in group decision making, researchers from around he world came up wih new mehods [15, 22]. Combining wih cumulaive frequency and fuzzy scoring, Ishikawa e al. [13] proposed he Maximum-Minimum Mehod o compile he expers opinions ino fuzzy numbers. Hsu and Chen [11] proposed a fuzzy similariy aggregaion mehod, in which similariies beween expers were collaed and fuzzy numbers assigned direcly o each exper o deermine he agreemen degree beween hem. The consensus coefficien was hen used o aggregae all expers fuzzy evaluaion values. Analyic Hierarchy Process (AHP) is a srucured, muli-crieria decision-making approach for dealing wih complex, ill-srucured problems by organizing he decision facors in a hierarchical srucure. I is one of he mos popular and powerful mehods for group decision making. Besides, AHP is widely used for dealing wih quanifiable and inangible crieria ha can be applied o numerous areas such as decision heory [31]. While AHP can be used o capure he prioriies of individual decision paricipans, i is necessary o combine hese individual assessmens ino a consensus. Though he purpose of convenional AHP is o capure he expers knowledge, i sill canno reflec he human hinking syle. Therefore, Laarhoven and Pedrycz [18] proposed a fuzzy exension of AHP o solve his issue. The linguisic scale of radiional AHP mehod could express he fuzzy uncerainy when a decision maker is making a decision. Therefore, FAHP ranslaes he opinions of expers from definie values ino fuzzy numbers and membership funcions, presens riangular fuzzy numbers in paired comparison of marices o develop FAHP. Consequenly, he opinions of expers approach human hinking model, so as o achieve more reasonable evaluaion crieria. 3. Sysem framework Our requiremens for his sysem are governed by he obecive of designing a sysem o suppor decision processes on social nework. Typically, a group decision process includes choosing he expers, deermining he evaluaion crieria, aggregaing expers crieria and suggesing alernaives. For more vivid picure of he sudy, Figure 2 served as he research paradigm. In he following, we describe our imporan sysem modules in deail. Figure 2. Sysem flow 3.1. Expers discovering People's brains are more responsive o friends han o srangers, even if he sranger has more in common [17]. There are psychological and evoluionary argumens for he idea ha he social facors of similariy and closeness could ge privileged reamen in he brain. However, a sudy suggess ha social closeness is he primary facor, raher han social similariy, as previously assumed [17]. As SNA is inroduced o analyze complex neworks [3], in our model we choose closeness from hree commonly used cenraliy merics o be one of our firs exper selecion facors. Closeness cenraliy is defined as he oal disance of a user from all oher users, and can be formulaed as [3]: n C ( p ) = 1/ d p, p (1) ( ) c i i = 1 where n is he number of users and d( pi, p) is he disance beween decision maker i and his friend. Beweenness cenraliy racks he number of geodesic pahs hrough he enire social nework, which pass hrough he concep whose influence is measured. I is an approximaion of is influence on he 1779

4 discussion in general [3]. Besides, beweenness cenraliy bes measures which members, in a se of members, are viewed mos frequenly as a leader, han oher social nework analysis measures [5]. The beweenness cenraliy evaluaes he capabiliy of ineracions beween wo friends and is defined as [6]: C () i = ( g () i )/ G (2) B l l i l where G l is he number of he shores pahs linking wo friends (, i ) and g l () i is he number of shores pahs linking he wo nodes ( l, ) conaining node i. However, even wo people are close friends; friendship may evaporae as ime goes by if hey do no inerac frequenly. To measure how he friendship changes wihin a ime period, we define an evaporaion funcion o be a new facor of evaluaing friendship among friends. The evaporaion funcion is formulaed as: 1 τ + i = (1 ρi ) τi + τi (3) where: τ : friendship deposied for friend i ρ i : friendship evaporaion coefficien, L i L ρi = (4) S N τ i : amoun of friendship changed in ime, / if ineracion exiss beween (, ) Q Li i τ i = (5) 0 oherwise where: L : average number of ineracion over ime periods L : coun of muual ineracion of (, i ) in ime i S : sandard deviaion of ineracion beween (, i ) N : number of ime periods used o calculae Q : difference beween L and 1 L, i.e., L L 1 Regression analysis is a ool for he invesigaion of relaionships beween variables, and is maor use is predicion or forecasing [4]. Usually, he invesigaor seeks o ascerain he causal effec of one variable upon anoher. To explore he friendship beween friends, we assemble daa on he underlying variables of ineres (in our work, closeness, beweenness and evaporaion) and employ regression o esimae he quaniaive effec of hese hree variables upon friendship. In our work, we use he following regression model o esimae he friendship beween decision maker i and friend in ime : F = β + β C ( p ) + β C ( p ) + βτ + ε (6) i 0 1 c i 2 b i 3 i i L where β, β, β and β are parameers, ε i is error erm, and E( ε i ) = 0, Cov( ε, ε ) = 0. i ik Var( εi ) 2 = σ, Afer he regression model is build, we can use his o measure decision maker s friend and selec required decision group. In our sysem, we selec op-n friends by ranking heir friendship and form he decision group Decision crieria discovering Our sudy used FDM for he screening of alernae facors [12]. The fuzziness of common undersanding of expers could be solved by using he fuzzy heory, and he efficiency and qualiy of quesionnaires could be improved. In our work, we followed ypical FDM process o implemen our sysem, bu made furher improvemen. To implemen FDM, we have o collec opinions of decision group firs. However, radiional quesionnaire survey for crieria collecing is ime consuming, so we design an online crieria collecing module o do he ob. To mainain basic requiremen of Delphi mehod, during he process, individual opinions are unknown o ohers. Afer collecing all he opinions, we calculaed he value of riangular fuzzy number of all facors and discovered he signi cance riangular fuzzy number of facors. By using simple cener of graviy mehod o defuzzify, he fuzzy weigh of each opinion can be convered o de nie value. Finally proper opinions can be screened ou as decision crieria from numerous facors by predefined hreshold value Decision crieria synhesizing In deermining evaluaion crieria phase, our sysem has screened he imporan facors conforming o a decision problem hrough FDM invesigaing expers crieria o se up he hierarchy archiecure. Here we modify ypical FAHP o calculae he weighs of individual crieria of a decision problem. Hsu and Chen [11] proposed a fuzzy similariy aggregaion mehod (SAM), in which similariies beween expers were collaed and fuzzy numbers assigned direcly o each exper o deermine he agreemen degree beween hem. Taking he degree of imporance of each exper ino consideraion, we modified he original weighing mehod as below. In [11], he average agreemen degree of exper E is given by n 1 A( E ) = Sk n (7) 1 k = 1 k 1780

5 where S k is he agreemen degree, and n is he number of expers. Besides, RAD is he relaive agreemen degree of exper E, which is formulaed as: AE ( ) RAD = (8) n A( E ) = 1 The relaive imporance of expers is formulaed as: r w =, = 1,2,..., n (9) n r = 1 Meanwhile, he consensus degree coefficien of exper E, = 1,2,... n is defined as: CDC = β w + (1 β) RAD (10) where 0 β 1. In our work, we improve he calculaion of relaive imporance of expers ( w ) and consensus degree coefficien of exper ( CDC ) o capure he spiri of social nework. For w, in he original definiion he weigh of he mos imporan exper is 1, ha is, r = 1. Then he kh exper is compared wih he mos imporan exper, and a relaive weigh r k is assigned. Since he decision group was seleced based on friendship F, in our i design he exper wih highes friendship index is considered o be he mos imporan exper wih r = 1, for all oher expers, rk = F F. Therefore, we can ik i reformulae he relaive imporance of expers as Fi w =, = 1,2,..., n (11) n F = 1 i 4. Experimen To furher prove he feasibiliy of our design, an empirical sudy alone wih sysem developmen was conduced. The procedures of our experimen are described as follows: 1. Build up regression model o compue friendship index beween decision maker and friends. 2. Issue decision problem by a user, hereinafer referred as decision maker, wihin a social nework. 3. Choose expers o form he decision group based on friendship index. 4. Build and exrac decision crieria online. 5. Synhesize decision crieria and form he alernaives. In he following secions, he imporan procedures are described in deail o clearly explain our sudy. To implemen our sysem, one of he mos popular social nework sies Facebook was seleced o be our experimen plaform. Firs we need o build up linear regression model o measure friendship index beween friends. For he purpose of collecing basic daa required by building regression model, a group of social nework users were invied o be our paricipans. Snowball sampling is a feasible way when sudying social nework issues [1], so i was used o consruc our experimen. A snowball sampling procedure wih S sages K names is defined as follows. A random sample of individuals is drawn from a given populaion. Each one in he sample is asked o name K differen persons, where K is a predefined number. For example, each person is asked o name K bes friends. The persons who were no in he random sample bu were named by individuals in i form he firs sage. Each of he individuals in he firs sage is hen asked o name K differen persons. This procedure repeas S imes o complee he sampling process. In he iniial sage, we drew 18 Facebook users randomly and divided hem ino hree groups by heir lifesyle. Afer filering ou hose users who are no willing o oin our experimen, we have suden group, office worker and random member groups. By using 3 (S) sages 3 (K) names snowball sampling we have 120 paricipans for each social nework (group). Tha is, a specific nework is formed by a coninuous node expending process unil a predefined maximum disance of connecions (i.e. 3 hops in our experimen) is reached. Afer filering he people no ineresed in our experimen, finally we have oal 181 unique paricipans, who form our experimenal social neworks. Of all he 181 paricipans, we randomly seleced 51 users o collec daa needed for consrucing our friendship regression model. The average year of usage is 1.6 years and he average number of friends is 155. An online survey package, including a cover leer explaining he research obecives and he quesionnaire was disribued o all he paricipans o survey heir online friendship. Since he closeness and beweenness cenraliy can be calculaed wihou paricipans help, our online quesionnaire lised all he friends of each paricipan, and asked hem o rae heir friendship. A 5-poin scale of nodding acquainance, ordinary friend, good friend, grea friend, bes friend is used o measure heir relaionship. Friends who are no raed by our paricipans are no included in our daase. Afer collecing he friendship daase, we used SPSS o build our regression model by enering friendship, closeness and beweenness cenraliy as base daa. This 1781

6 model was hen used o predic he friendship index a he ime when a decision problem is issued. Afer he regression model was buil, 8 random seleced users were invied o be our decision makers. In our experimen, hey can issue a decision problem and evaluae he effeciveness of decision crieria and alernaives. 73 problems abou consumer producs purchasing were issued during our wo monhs experimen, and hey were delivered o he decision group seleced by our sysem. Users wih op-5 friendship index were seleced as our decision group, and he processes repeaed when a problem was issued. Tha is, here are differen decision groups for differen problems. When a decision problem was presened o decision group, members can express heir opinions online. They were asked o choose some keywords from a predefined daabase WordNe [30], and describe why hese keywords relaed o decision problem. Follow he mehod proposed in our research, decision crieria and alernaives are generaed and presened o decision makers. Noe ha o precisely evaluae he effeciveness of our sysem, conen-based and collaboraive filering are used as benchmark mehods. The alernaives generaed from our work and benchmark mehods are presened o decision makers in he same page, and he links are lised randomly o minimize inerfere of presenaion order. To avoid informaion overloading, he firs wo alernaives of each mehod were seleced, and oal six alernaives were presened o decision makers for each problem. Finally, he click coun and say ime of each page linked o alernaives are recorded. If here exiss any overlap alernaives beween hree mehods, all he clicksream daa are duplicaed. 5. Discussion 5.1. Resuls and performance evaluaion The resul of social nework analysis is shown in Table 1, and he average friendship index in our experimen is In our experimen we colleced clicksream daa of every alernaive presened, as shown in Table 2. As we can see, here are no significan differences beween number of clicks, bu he average say ime varies. To furher examine if here are significan differences beween say ime, a saisical mehod is required. Thus we used Tukey es for simulaneous muliple comparisons of our approach and all oher wo approaches, he es resul is presened in Table 3. As we can see, here is no significan difference beween he average say ime of collaboraive filering and conen based mehod, bu our approach has higher say ime. This means he decision makers spen more ime on our proposed alernaives han ohers. Since here is a srong endency for users o spend a greaer lengh of ime reading aricles of ineres o ha user [21, 32], we may conclude ha our approach is more effecive when compared wih oher mehods. Table 1. Saisics of beweenness closeness Beweenness Closeness Beweenness nbeweenness incloseness oucloseness Mean Sd Dev Sum Variance Minimum Maximu Table 2. Saisics of clicksream daa Number of alernaives Collaboraive Filering Conen Based Our Approach Toal Table 3. Muliple comparisons of approaches 5.2. Decision group Number of click Mean say ime Sd. Deviaion Sd. Error Minimum Maximum 95% (I) Mehod (J) Mehod Mean Difference (I-J) Sig. Lower Bound Upper Bound Conen Based Collaboraive Filering Our Approach * Conen Based Our Approach Collaboraive Filering Collaboraive Filering * Our Approach Conen Based * * *. The mean difference is significan a he 0.05 level. To es he influence of friendship index in he selecion of decision group, all he 73 decision problems were issued a differen ime period. Since he selecion rule of decision group was based on friendship index which is influenced parially by ime, we waned o know if he members change across differen ime. For example, when decision maker A issued his firs problem, a decision group consising of 5 members was buil, say ( M1, M2, M3, M4, M 5). A he ime when second problem was issued, anoher decision group, say ( M1, M2, M3, M7, M 8), was buil. In hese wo decision process, here were 10 users seleced as decision group, bu only 7 unique users since ( M1, M2, M3) was overlapped. The deail numbers are lised in Table 4. Furher analysis found ha decision makers wih a large number of unique users are more acive han hose who wih small number. However, no evidence showed ha here was significan difference of ime spen on he alernaives suggesed by differen decision groups even if here 1782

7 exiss differen number of unique users. This observaion showed ha our mechanism was sable enough and won be influenced by he uniqueness of decision group. Table 4. Users included in decision group Decision Maker Toal number of users seleced as decision group 6. Conclusion and fuure works In our paper, we inroduced ime facor ino social nework analysis. By using regression a friendship index calculaion model is proposed and served as our ool o predic friendship beween wo users in specific ime period. By equipping FDM wih online decision crieria mechanism, ime consuming problem of References Number of unique users A B 45* 33 C D E F G H *In our experimen, B issued exra one quesion and we decided o include i in our experimen [1]. Y.Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong, Analysis of Topological Characerisics of Huge Online Social Neworking Services, In Proceedings of he 16h Inernaional Conference on World Wide Web, 2007, Albera, Canada, ACM, USA. [2]. B. Brehmer, Social Judgmen Theory and he Analysis of Inerpersonal Conflic, Psychological Bullein, 1976, 83(6), pp [3]. L. Chi, C.W. Holsapple and C. Srinivasan, Compeiive Dynamics in Elecronic Neworks: A Model and he Case of Inerorganizaional Sysems, Inernaional Journal of Elecronic Commerce, 2007, 11(3), pp [4]. J. B. Copas, "Regression, predicion and shrinkage", Journal of he Royal Saisical Sociey, 1983, 45(3), pp [5]. L.C. Freeman, A Se of Measures of Cenraliy Based on Beweenness, Sociomery, 1977, 40(1), pp [6]. L.C. Freeman, Cenraliy in Social Neworks Concepual Clarificaion, Social neworks, 1979, 1(3), pp [7]. G. Groh and C. Ehmig, Recommendaions in Tase Relaed Domains: Collaboraive Filering Vs. Social Filering, In Proceedings of he 2007 inernaional ACM Conference on Supporing Group Work, 2007, Florida, USA, ACM, USA. convenional Delphi mehod was solved. Furhermore, an adopive SAM was also suggesed o furher improve he applicaion of FAHP on social nework relaed research. An empirical sudy furher proved he feasibiliy and effeciveness of our work. Being one of he pilo sudies in he developmen of social nework based decision suppor mechanism, alhough he research has reached is aims, here were some unavoidable limiaions. Firs, because of he ime limi, his research was conduced only on a small size of populaion who were he users of Facebook. Therefore, o generalize he resuls over differen social nework plaforms, he sudy should have involved more paricipans from oher social neworks. Second, o build regression model we needed basic daa. Alhough beweenness and closeness can be acquired direcly by analyzing exising connecions beween users, he iniial friendship daa requires manual collecion. To make our sysem more feasible, an auomaic friendship daa collecing mechanism would be necessary. Third, here are various measuremen used in he research of social nework analysis. In our work, only beweenness and closeness were considered. I would make our proposed friendship index more comprehensive by inroducing facors such as ie srengh and rus. [8]. J. Harmon and J. Rohrbaugh, Social Judgmen Analysis and Small Group Decision Making: Cogniive Feedback Effecs on Individual and Collecive Performance, Organizaional Behavior and Human Decision Processes, 1990, 46(1), pp [9]. S.R. Hilz and M. Turoff, The Nework Naion: Human Communicaion Via Compuer, The MIT Press, Cambridge, Massachuses, USA, [10]. V.B. Hinsz, Cogniive and Consensus Processes in Group Recogniion Memory Performance, Journal of Personaliy and Social Psychology, 1990, 59(4), pp [11]. H.M. Hsu and C.T. Chen, Aggregaion of Fuzzy Opinions under Group Decision Making, Fuzzy Ses and Sysems, 1996, 79(3), pp [12]. Y.L. Hsu, C.H. Lee and V.B. Kreng, The Applicaion of Fuzzy Delphi Mehod and Fuzzy Ahp in Lubrican Regeneraive Technology Selecion, Exper Sysems wih Applicaions, 2010, 37(1), pp [13]. A. Ishikawa, M. Amagasa, T. Shiga, G. Tomizawa, R. Tasua, and H. Mieno, The Max-Min Delphi Mehod and Fuzzy Delphi Mehod Via Fuzzy Inegraion, Fuzzy Ses and Sysems, 1993, 55(3), pp [14]. M.F. Kaplan and C.E. Miller, Group Decision Making and Normaive Versus Informaional Influence: Effecs of Type of Issue and Assigned Decision Rule, Journal of Personaliy and Social Psychology, 1987, 53(2), pp

8 [15]. G.L. Klir and T.A. Folger, Fuzzy Ses, Uncerainy, and Informaion, Prenice Hall, Upper Saddle River, New Jersey, USA, [16]. D. Krackhard and L.W. Porer, When Friends Leave: A Srucural Analysis of he Relaionship beween Turnover and Sayers' Aiudes, Adminisraive Science Quarerly, 1985, 30(2), pp [17]. F.M. Krienen, P.C. Tu and R.L. Buckner, Clan Menaliy: Evidence Tha he Medial Prefronal Corex Responds o Close Ohers, The Journal of Neuroscience, 2010, 30(41), pp [18]. P.J.M. van Laarhoven and W. Pedrycz, A Fuzzy Exension of Saay's Prioriy Theory, Fuzzy Ses and Sysems, 1983, 11(3), pp [19]. P.R. Laughlin and A.L. Ellis, Demonsrabiliy and Social Combinaion Processes on Mahemaical Inellecive Tasks, Journal of Experimenal Social Psychology, 1986, 22(3), pp [20]. H.A. Linsone and M. Turoff, The Delphi Mehod: Techniques and Applicaions, Addison-Wesley, Boson, Massachuses, USA,2002. [21]. M. Moria and Y. Shinoda. Informaion Filering Based on User Behavior Analysis and Bes Mach Tex Rerieval, In Proceedings of he 17h Annual Inernaional ACM SIGIR Conference on Research and Developmen in Informaion Rerieval, 1994, Dublin, Ireland, Springer, USA. [22]. T.J. Murray, L.L. Pipino and J.P.V. Gigch, A Pilo Sudy of Fuzzy Se Modificaion of Delphi, Human Sysems Managemen, 1985, 5(1), pp [23]. E. Oe and R. Rousseau, Social Nework Analysis: A Powerful Sraegy, Also for he Informaion Sciences, Journal of Informaion Science, 2002, 28(6), pp [24]. J. Preece, Sociabiliy and Usabiliy in Online Communiies: Deermining and Measuring Success, Behaviour & Informaion Technology, 2001, 20(5), pp [25]. M.A. Rodriguez, Social Decision Making wih Muli-Relaional Neworks and Grammar-Based Paricle Swarms, In Proceedings of he 40h Annual Hawaii Inernaional Conference on Sysem Sciences, 2007, Hawaii, USA, IEEE, USA. [26]. G. Sasser, A Primer of Social Decision Scheme Theory: Models of Group Influence, Compeiive Model-Tesing, and Prospecive Modeling, Organizaional Behavior and Human Decision Processes, 1999, 80(1), pp [27]. C.L. Sreeer and D.F. Gillespie, Social Nework Analysis, Journal of Social Service Research, 1993, 16(1), pp [28]. H.T. Reis, W.A. Collins and E. Berscheid, The Relaionship Conex of Human Behavior and Developmen, Psychological Bullein, 2000, 126(6), pp [29]. M. Turoff, S.R. Hilz, H.K. Cho, Z. Li, and Y. Wang, Social Decision Suppor Sysems (SDSS), In Proceedings of he 35h Annual Hawaii Inernaional Conference on Sysem Sciences, 2002, Hawaii, USA, IEEE, USA. [30]. Princeon Uinversiy, WordNe: A Large Lexical Daabase of English, Available from: hp://wordne.princeon.edu/. [31]. O.S. Vaidya and S. Kumar, Analyic Hierarchy Process: An Overview of Applicaions, European Journal of operaional research, 2006, 169(1), pp [32]. G. Velayahan and S. Yamada, Behavior-Based Web Page Evaluaion, In Proceedings of he Inernaional Conference on Web Inelligence and Inelligen Agen Technology, 2006, Hong Kong, China, IEEE, USA. [33]. M.I. Yousuf, Using Expers' Opinions hrough Delphi Technique, Pracical Assessmen, Research & Evaluaion, 2007, 12(4), pp

Neural Network Model of the Backpropagation Algorithm

Neural Network Model of the Backpropagation Algorithm Neural Nework Model of he Backpropagaion Algorihm Rudolf Jakša Deparmen of Cyberneics and Arificial Inelligence Technical Universiy of Košice Lená 9, 4 Košice Slovakia jaksa@neuron.uke.sk Miroslav Karák

More information

MyLab & Mastering Business

MyLab & Mastering Business MyLab & Masering Business Efficacy Repor 2013 MyLab & Masering: Business Efficacy Repor 2013 Edied by Michelle D. Speckler 2013 Pearson MyAccouningLab, MyEconLab, MyFinanceLab, MyMarkeingLab, and MyOMLab

More information

Fast Multi-task Learning for Query Spelling Correction

Fast Multi-task Learning for Query Spelling Correction Fas Muli-ask Learning for Query Spelling Correcion Xu Sun Dep. of Saisical Science Cornell Universiy Ihaca, NY 14853 xusun@cornell.edu Anshumali Shrivasava Dep. of Compuer Science Cornell Universiy Ihaca,

More information

An Effiecient Approach for Resource Auto-Scaling in Cloud Environments

An Effiecient Approach for Resource Auto-Scaling in Cloud Environments Inernaional Journal of Elecrical and Compuer Engineering (IJECE) Vol. 6, No. 5, Ocober 2016, pp. 2415~2424 ISSN: 2088-8708, DOI: 10.11591/ijece.v6i5.10639 2415 An Effiecien Approach for Resource Auo-Scaling

More information

Information Propagation for informing Special Population Subgroups about New Ground Transportation Services at Airports

Information Propagation for informing Special Population Subgroups about New Ground Transportation Services at Airports Downloaded from ascelibrary.org by Basil Sephanis on 07/13/16. Copyrigh ASCE. For personal use only; all righs reserved. Informaion Propagaion for informing Special Populaion Subgroups abou New Ground

More information

1 Language universals

1 Language universals AS LX 500 Topics: Language Uniersals Fall 2010, Sepember 21 4a. Anisymmery 1 Language uniersals Subjec-erb agreemen and order Bach (1971) discusses wh-quesions across SO and SO languages, hypohesizing:...

More information

More Accurate Question Answering on Freebase

More Accurate Question Answering on Freebase More Accurae Quesion Answering on Freebase Hannah Bas, Elmar Haussmann Deparmen of Compuer Science Universiy of Freiburg 79110 Freiburg, Germany {bas, haussmann}@informaik.uni-freiburg.de ABSTRACT Real-world

More information

Channel Mapping using Bidirectional Long Short-Term Memory for Dereverberation in Hands-Free Voice Controlled Devices

Channel Mapping using Bidirectional Long Short-Term Memory for Dereverberation in Hands-Free Voice Controlled Devices Z. Zhang e al.: Channel Mapping using Bidirecional Long Shor-Term Memory for Dereverberaion in Hands-Free Voice Conrolled Devices 525 Channel Mapping using Bidirecional Long Shor-Term Memory for Dereverberaion

More information

The Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma

The Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma International Journal of Computer Applications (975 8887) The Use of Statistical, Computational and Modelling Tools in Higher Learning Institutions: A Case Study of the University of Dodoma Gilbert M.

More information

A Coding System for Dynamic Topic Analysis: A Computer-Mediated Discourse Analysis Technique

A Coding System for Dynamic Topic Analysis: A Computer-Mediated Discourse Analysis Technique A Coding System for Dynamic Topic Analysis: A Computer-Mediated Discourse Analysis Technique Hiromi Ishizaki 1, Susan C. Herring 2, Yasuhiro Takishima 1 1 KDDI R&D Laboratories, Inc. 2 Indiana University

More information

Reduce the Failure Rate of the Screwing Process with Six Sigma Approach

Reduce the Failure Rate of the Screwing Process with Six Sigma Approach Proceedings of the 2014 International Conference on Industrial Engineering and Operations Management Bali, Indonesia, January 7 9, 2014 Reduce the Failure Rate of the Screwing Process with Six Sigma Approach

More information

In Workflow. Viewing: Last edit: 10/27/15 1:51 pm. Approval Path. Date Submi ed: 10/09/15 2:47 pm. 6. Coordinator Curriculum Management

In Workflow. Viewing: Last edit: 10/27/15 1:51 pm. Approval Path. Date Submi ed: 10/09/15 2:47 pm. 6. Coordinator Curriculum Management 1 of 5 11/19/2015 8:10 AM Date Submi ed: 10/09/15 2:47 pm Viewing: Last edit: 10/27/15 1:51 pm Changes proposed by: GODWINH In Workflow 1. BUSI Editor 2. BUSI Chair 3. BU Associate Dean 4. Biggio Center

More information

ACTIVITY: Comparing Combination Locks

ACTIVITY: Comparing Combination Locks 5.4 Compound Events outcomes of one or more events? ow can you find the number of possible ACIVIY: Comparing Combination Locks Work with a partner. You are buying a combination lock. You have three choices.

More information

Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design

Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design Session 2B From understanding perspectives to informing public policy the potential and challenges for Q findings to inform survey design Paper #3 Five Q-to-survey approaches: did they work? Job van Exel

More information

Applying Fuzzy Rule-Based System on FMEA to Assess the Risks on Project-Based Software Engineering Education

Applying Fuzzy Rule-Based System on FMEA to Assess the Risks on Project-Based Software Engineering Education Journal of Software Engineering and Applications, 2017, 10, 591-604 http://www.scirp.org/journal/jsea ISSN Online: 1945-3124 ISSN Print: 1945-3116 Applying Fuzzy Rule-Based System on FMEA to Assess the

More information

WHY SOLVE PROBLEMS? INTERVIEWING COLLEGE FACULTY ABOUT THE LEARNING AND TEACHING OF PROBLEM SOLVING

WHY SOLVE PROBLEMS? INTERVIEWING COLLEGE FACULTY ABOUT THE LEARNING AND TEACHING OF PROBLEM SOLVING From Proceedings of Physics Teacher Education Beyond 2000 International Conference, Barcelona, Spain, August 27 to September 1, 2000 WHY SOLVE PROBLEMS? INTERVIEWING COLLEGE FACULTY ABOUT THE LEARNING

More information

Level 1 Mathematics and Statistics, 2015

Level 1 Mathematics and Statistics, 2015 91037 910370 1SUPERVISOR S Level 1 Mathematics and Statistics, 2015 91037 Demonstrate understanding of chance and data 9.30 a.m. Monday 9 November 2015 Credits: Four Achievement Achievement with Merit

More information

Identification of Opinion Leaders Using Text Mining Technique in Virtual Community

Identification of Opinion Leaders Using Text Mining Technique in Virtual Community Identification of Opinion Leaders Using Text Mining Technique in Virtual Community Chihli Hung Department of Information Management Chung Yuan Christian University Taiwan 32023, R.O.C. chihli@cycu.edu.tw

More information

Analyzing the Usage of IT in SMEs

Analyzing the Usage of IT in SMEs IBIMA Publishing Communications of the IBIMA http://www.ibimapublishing.com/journals/cibima/cibima.html Vol. 2010 (2010), Article ID 208609, 10 pages DOI: 10.5171/2010.208609 Analyzing the Usage of IT

More information

On-the-Fly Customization of Automated Essay Scoring

On-the-Fly Customization of Automated Essay Scoring Research Report On-the-Fly Customization of Automated Essay Scoring Yigal Attali Research & Development December 2007 RR-07-42 On-the-Fly Customization of Automated Essay Scoring Yigal Attali ETS, Princeton,

More information

School Size and the Quality of Teaching and Learning

School Size and the Quality of Teaching and Learning School Size and the Quality of Teaching and Learning An Analysis of Relationships between School Size and Assessments of Factors Related to the Quality of Teaching and Learning in Primary Schools Undertaken

More information

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method

Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method Malicious User Suppression for Cooperative Spectrum Sensing in Cognitive Radio Networks using Dixon s Outlier Detection Method Sanket S. Kalamkar and Adrish Banerjee Department of Electrical Engineering

More information

On the Design of Group Decision Processes for Electronic Meeting Rooms

On the Design of Group Decision Processes for Electronic Meeting Rooms On the Design of Group Decision Processes for Electronic Meeting Rooms Abstract Pedro Antunes Department of Informatics, Faculty of Sciences of the University of Lisboa, Campo Grande, Lisboa, Portugal

More information

Improving software testing course experience with pair testing pattern. Iyad Alazzam* and Mohammed Akour

Improving software testing course experience with pair testing pattern. Iyad Alazzam* and Mohammed Akour 244 Int. J. Teaching and Case Studies, Vol. 6, No. 3, 2015 Improving software testing course experience with pair testing pattern Iyad lazzam* and Mohammed kour Department of Computer Information Systems,

More information

Capturing and Organizing Prior Student Learning with the OCW Backpack

Capturing and Organizing Prior Student Learning with the OCW Backpack Capturing and Organizing Prior Student Learning with the OCW Backpack Brian Ouellette,* Elena Gitin,** Justin Prost,*** Peter Smith**** * Vice President, KNEXT, Kaplan University Group ** Senior Research

More information

Implementing a tool to Support KAOS-Beta Process Model Using EPF

Implementing a tool to Support KAOS-Beta Process Model Using EPF Implementing a tool to Support KAOS-Beta Process Model Using EPF Malihe Tabatabaie Malihe.Tabatabaie@cs.york.ac.uk Department of Computer Science The University of York United Kingdom Eclipse Process Framework

More information

International Series in Operations Research & Management Science

International Series in Operations Research & Management Science International Series in Operations Research & Management Science Volume 240 Series Editor Camille C. Price Stephen F. Austin State University, TX, USA Associate Series Editor Joe Zhu Worcester Polytechnic

More information

Evolutive Neural Net Fuzzy Filtering: Basic Description

Evolutive Neural Net Fuzzy Filtering: Basic Description Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:

More information

2017? Are you skilled for. Market Leader. Prize Winner. Pass Insurance. Online Learning F7, F8 & F9. Classroom Learning P1-P7

2017? Are you skilled for. Market Leader. Prize Winner. Pass Insurance. Online Learning F7, F8 & F9. Classroom Learning P1-P7 Are you skilled for 2017? ACCA June 2017 Association of Chartered Certified Accountants Market Leader More than 50 years of professional accounting experience worldwide with the biggest professional accounting

More information

Developing True/False Test Sheet Generating System with Diagnosing Basic Cognitive Ability

Developing True/False Test Sheet Generating System with Diagnosing Basic Cognitive Ability Developing True/False Test Sheet Generating System with Diagnosing Basic Cognitive Ability Shih-Bin Chen Dept. of Information and Computer Engineering, Chung-Yuan Christian University Chung-Li, Taiwan

More information

The Enterprise Knowledge Portal: The Concept

The Enterprise Knowledge Portal: The Concept The Enterprise Knowledge Portal: The Concept Executive Information Systems, Inc. www.dkms.com eisai@home.com (703) 461-8823 (o) 1 A Beginning Where is the life we have lost in living! Where is the wisdom

More information

Linking the Ohio State Assessments to NWEA MAP Growth Tests *

Linking the Ohio State Assessments to NWEA MAP Growth Tests * Linking the Ohio State Assessments to NWEA MAP Growth Tests * *As of June 2017 Measures of Academic Progress (MAP ) is known as MAP Growth. August 2016 Introduction Northwest Evaluation Association (NWEA

More information

A Note on Structuring Employability Skills for Accounting Students

A Note on Structuring Employability Skills for Accounting Students A Note on Structuring Employability Skills for Accounting Students Jon Warwick and Anna Howard School of Business, London South Bank University Correspondence Address Jon Warwick, School of Business, London

More information

ECON 365 fall papers GEOS 330Z fall papers HUMN 300Z fall papers PHIL 370 fall papers

ECON 365 fall papers GEOS 330Z fall papers HUMN 300Z fall papers PHIL 370 fall papers Assessing Critical Thinking in GE In Spring 2016 semester, the GE Curriculum Advisory Board (CAB) engaged in assessment of Critical Thinking (CT) across the General Education program. The assessment was

More information

Generic Skills and the Employability of Electrical Installation Students in Technical Colleges of Akwa Ibom State, Nigeria.

Generic Skills and the Employability of Electrical Installation Students in Technical Colleges of Akwa Ibom State, Nigeria. IOSR Journal of Research & Method in Education (IOSR-JRME) e-issn: 2320 7388,p-ISSN: 2320 737X Volume 1, Issue 2 (Mar. Apr. 2013), PP 59-67 Generic Skills the Employability of Electrical Installation Students

More information

Conceptual and Procedural Knowledge of a Mathematics Problem: Their Measurement and Their Causal Interrelations

Conceptual and Procedural Knowledge of a Mathematics Problem: Their Measurement and Their Causal Interrelations Conceptual and Procedural Knowledge of a Mathematics Problem: Their Measurement and Their Causal Interrelations Michael Schneider (mschneider@mpib-berlin.mpg.de) Elsbeth Stern (stern@mpib-berlin.mpg.de)

More information

Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees

Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Impact of Cluster Validity Measures on Performance of Hybrid Models Based on K-means and Decision Trees Mariusz Łapczy ski 1 and Bartłomiej Jefma ski 2 1 The Chair of Market Analysis and Marketing Research,

More information

A Case Study: News Classification Based on Term Frequency

A Case Study: News Classification Based on Term Frequency A Case Study: News Classification Based on Term Frequency Petr Kroha Faculty of Computer Science University of Technology 09107 Chemnitz Germany kroha@informatik.tu-chemnitz.de Ricardo Baeza-Yates Center

More information

Data Fusion Models in WSNs: Comparison and Analysis

Data Fusion Models in WSNs: Comparison and Analysis Proceedings of 2014 Zone 1 Conference of the American Society for Engineering Education (ASEE Zone 1) Data Fusion s in WSNs: Comparison and Analysis Marwah M Almasri, and Khaled M Elleithy, Senior Member,

More information

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur)

Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) Quantitative analysis with statistics (and ponies) (Some slides, pony-based examples from Blase Ur) 1 Interviews, diary studies Start stats Thursday: Ethics/IRB Tuesday: More stats New homework is available

More information

Australian Journal of Basic and Applied Sciences

Australian Journal of Basic and Applied Sciences AENSI Journals Australian Journal of Basic and Applied Sciences ISSN:1991-8178 Journal home page: www.ajbasweb.com Feature Selection Technique Using Principal Component Analysis For Improving Fuzzy C-Mean

More information

Seminar - Organic Computing

Seminar - Organic Computing Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts

More information

Team Formation for Generalized Tasks in Expertise Social Networks

Team Formation for Generalized Tasks in Expertise Social Networks IEEE International Conference on Social Computing / IEEE International Conference on Privacy, Security, Risk and Trust Team Formation for Generalized Tasks in Expertise Social Networks Cheng-Te Li Graduate

More information

Instructor: Mario D. Garrett, Ph.D. Phone: Office: Hepner Hall (HH) 100

Instructor: Mario D. Garrett, Ph.D.   Phone: Office: Hepner Hall (HH) 100 San Diego State University School of Social Work 610 COMPUTER APPLICATIONS FOR SOCIAL WORK PRACTICE Statistical Package for the Social Sciences Office: Hepner Hall (HH) 100 Instructor: Mario D. Garrett,

More information

Different Requirements Gathering Techniques and Issues. Javaria Mushtaq

Different Requirements Gathering Techniques and Issues. Javaria Mushtaq 835 Different Requirements Gathering Techniques and Issues Javaria Mushtaq Abstract- Project management is now becoming a very important part of our software industries. To handle projects with success

More information

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X

The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, / X The 9 th International Scientific Conference elearning and software for Education Bucharest, April 25-26, 2013 10.12753/2066-026X-13-154 DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE Adela BÂRA,

More information

Discovering Statistics

Discovering Statistics School of Psychology Module Handbook 2015/2016 Discovering Statistics Module Convenor: Professor Andy Field NOTE: Most of the questions you need answers to about this module are in this document. Please

More information

Logical Soft Systems Methodology for Education Programme Development

Logical Soft Systems Methodology for Education Programme Development Issues in Informing Science and Information Technology Logical Soft Systems Methodology for Education Programme Development Ho-Leung Tsoi Caritas Francis Hsu College, Hong Kong hltsoi@yahoo.com Abstract

More information

Probabilistic Latent Semantic Analysis

Probabilistic Latent Semantic Analysis Probabilistic Latent Semantic Analysis Thomas Hofmann Presentation by Ioannis Pavlopoulos & Andreas Damianou for the course of Data Mining & Exploration 1 Outline Latent Semantic Analysis o Need o Overview

More information

When!Identifying!Contributors!is!Costly:!An! Experiment!on!Public!Goods!

When!Identifying!Contributors!is!Costly:!An! Experiment!on!Public!Goods! !! EVIDENCE-BASED RESEARCH ON CHARITABLE GIVING SPI$FUNDED$ When!Identifying!Contributors!is!Costly:!An! Experiment!on!Public!Goods! Anya!Samek,!Roman!M.!Sheremeta!! University!of!WisconsinFMadison! Case!Western!Reserve!University!&!Chapman!University!!

More information

Travis Park, Assoc Prof, Cornell University Donna Pearson, Assoc Prof, University of Louisville. NACTEI National Conference Portland, OR May 16, 2012

Travis Park, Assoc Prof, Cornell University Donna Pearson, Assoc Prof, University of Louisville. NACTEI National Conference Portland, OR May 16, 2012 Travis Park, Assoc Prof, Cornell University Donna Pearson, Assoc Prof, University of Louisville NACTEI National Conference Portland, OR May 16, 2012 NRCCTE Partners Four Main Ac5vi5es Research (Scientifically-based)!!

More information

COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR

COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR COMPUTATIONAL COMPLEXITY OF LEFT-ASSOCIATIVE GRAMMAR ROLAND HAUSSER Institut für Deutsche Philologie Ludwig-Maximilians Universität München München, West Germany 1. CHOICE OF A PRIMITIVE OPERATION The

More information

Abstractions and the Brain

Abstractions and the Brain Abstractions and the Brain Brian D. Josephson Department of Physics, University of Cambridge Cavendish Lab. Madingley Road Cambridge, UK. CB3 OHE bdj10@cam.ac.uk http://www.tcm.phy.cam.ac.uk/~bdj10 ABSTRACT

More information

ATW 202. Business Research Methods

ATW 202. Business Research Methods ATW 202 Business Research Methods Course Outline SYNOPSIS This course is designed to introduce students to the research methods that can be used in most business research and other research related to

More information

From Empire to Twenty-First Century Britain: Economic and Political Development of Great Britain in the 19th and 20th Centuries 5HD391

From Empire to Twenty-First Century Britain: Economic and Political Development of Great Britain in the 19th and 20th Centuries 5HD391 Provisional list of courses for Exchange students Fall semester 2017: University of Economics, Prague Courses stated below are offered by particular departments and faculties at the University of Economics,

More information

Improving Fairness in Memory Scheduling

Improving Fairness in Memory Scheduling Improving Fairness in Memory Scheduling Using a Team of Learning Automata Aditya Kajwe and Madhu Mutyam Department of Computer Science & Engineering, Indian Institute of Tehcnology - Madras June 14, 2014

More information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina

More information

Development of Multistage Tests based on Teacher Ratings

Development of Multistage Tests based on Teacher Ratings Development of Multistage Tests based on Teacher Ratings Stéphanie Berger 12, Jeannette Oostlander 1, Angela Verschoor 3, Theo Eggen 23 & Urs Moser 1 1 Institute for Educational Evaluation, 2 Research

More information

A Case-Based Approach To Imitation Learning in Robotic Agents

A Case-Based Approach To Imitation Learning in Robotic Agents A Case-Based Approach To Imitation Learning in Robotic Agents Tesca Fitzgerald, Ashok Goel School of Interactive Computing Georgia Institute of Technology, Atlanta, GA 30332, USA {tesca.fitzgerald,goel}@cc.gatech.edu

More information

EDCI 699 Statistics: Content, Process, Application COURSE SYLLABUS: SPRING 2016

EDCI 699 Statistics: Content, Process, Application COURSE SYLLABUS: SPRING 2016 EDCI 699 Statistics: Content, Process, Application COURSE SYLLABUS: SPRING 2016 Instructor: Dr. Katy Denson, Ph.D. Office Hours: Because I live in Albuquerque, New Mexico, I won t have office hours. But

More information

Measures of the Location of the Data

Measures of the Location of the Data OpenStax-CNX module m46930 1 Measures of the Location of the Data OpenStax College This work is produced by OpenStax-CNX and licensed under the Creative Commons Attribution License 3.0 The common measures

More information

BLACKBOARD TRAINING PHASE 2 CREATE ASSESSMENT. Essential Tool Part 1 Rubrics, page 3-4. Assignment Tool Part 2 Assignments, page 5-10

BLACKBOARD TRAINING PHASE 2 CREATE ASSESSMENT. Essential Tool Part 1 Rubrics, page 3-4. Assignment Tool Part 2 Assignments, page 5-10 BLACKBOARD TRAINING PHASE 2 CREATE ASSESSMENT Essential Tool Part 1 Rubrics, page 3-4 Assignment Tool Part 2 Assignments, page 5-10 Review Tool Part 3 SafeAssign, page 11-13 Assessment Tool Part 4 Test,

More information

Axiom 2013 Team Description Paper

Axiom 2013 Team Description Paper Axiom 2013 Team Description Paper Mohammad Ghazanfari, S Omid Shirkhorshidi, Farbod Samsamipour, Hossein Rahmatizadeh Zagheli, Mohammad Mahdavi, Payam Mohajeri, S Abbas Alamolhoda Robotics Scientific Association

More information

MAINTAINING CURRICULUM CONSISTENCY OF TECHNICAL AND VOCATIONAL EDUCATIONAL PROGRAMS THROUGH TEACHER DESIGN TEAMS

MAINTAINING CURRICULUM CONSISTENCY OF TECHNICAL AND VOCATIONAL EDUCATIONAL PROGRAMS THROUGH TEACHER DESIGN TEAMS Man In India, 95(2015) (Special Issue: Researches in Education and Social Sciences) Serials Publications MAINTAINING CURRICULUM CONSISTENCY OF TECHNICAL AND VOCATIONAL EDUCATIONAL PROGRAMS THROUGH TEACHER

More information

Evaluating Collaboration and Core Competence in a Virtual Enterprise

Evaluating Collaboration and Core Competence in a Virtual Enterprise PsychNology Journal, 2003 Volume 1, Number 4, 391-399 Evaluating Collaboration and Core Competence in a Virtual Enterprise Rainer Breite and Hannu Vanharanta Tampere University of Technology, Pori, Finland

More information

College Pricing. Ben Johnson. April 30, Abstract. Colleges in the United States price discriminate based on student characteristics

College Pricing. Ben Johnson. April 30, Abstract. Colleges in the United States price discriminate based on student characteristics College Pricing Ben Johnson April 30, 2012 Abstract Colleges in the United States price discriminate based on student characteristics such as ability and income. This paper develops a model of college

More information

Developing Autonomy in an East Asian Classroom: from Policy to Practice

Developing Autonomy in an East Asian Classroom: from Policy to Practice DOI: 10.7763/IPEDR. 2013. V68. 2 Developing Autonomy in an East Asian Classroom: from Policy to Practice Thao Thi Thanh PHAN Thanhdo University Hanoi Vietnam Queensland University of Technology Brisbane

More information

DESIGN, DEVELOPMENT, AND VALIDATION OF LEARNING OBJECTS

DESIGN, DEVELOPMENT, AND VALIDATION OF LEARNING OBJECTS J. EDUCATIONAL TECHNOLOGY SYSTEMS, Vol. 34(3) 271-281, 2005-2006 DESIGN, DEVELOPMENT, AND VALIDATION OF LEARNING OBJECTS GWEN NUGENT LEEN-KIAT SOH ASHOK SAMAL University of Nebraska-Lincoln ABSTRACT A

More information

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,

More information

DIANA: A computer-supported heterogeneous grouping system for teachers to conduct successful small learning groups

DIANA: A computer-supported heterogeneous grouping system for teachers to conduct successful small learning groups Computers in Human Behavior Computers in Human Behavior 23 (2007) 1997 2010 www.elsevier.com/locate/comphumbeh DIANA: A computer-supported heterogeneous grouping system for teachers to conduct successful

More information

University of Groningen. Systemen, planning, netwerken Bosman, Aart

University of Groningen. Systemen, planning, netwerken Bosman, Aart University of Groningen Systemen, planning, netwerken Bosman, Aart IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document

More information

OCR LEVEL 3 CAMBRIDGE TECHNICAL

OCR LEVEL 3 CAMBRIDGE TECHNICAL Cambridge TECHNICALS OCR LEVEL 3 CAMBRIDGE TECHNICAL CERTIFICATE/DIPLOMA IN IT SYSTEMS ANALYSIS K/505/5481 LEVEL 3 UNIT 34 GUIDED LEARNING HOURS: 60 UNIT CREDIT VALUE: 10 SYSTEMS ANALYSIS K/505/5481 LEVEL

More information

National Survey of Student Engagement at UND Highlights for Students. Sue Erickson Carmen Williams Office of Institutional Research April 19, 2012

National Survey of Student Engagement at UND Highlights for Students. Sue Erickson Carmen Williams Office of Institutional Research April 19, 2012 National Survey of Student Engagement at Highlights for Students Sue Erickson Carmen Williams Office of Institutional Research April 19, 2012 April 19, 2012 Table of Contents NSSE At... 1 NSSE Benchmarks...

More information

CS Machine Learning

CS Machine Learning CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing

More information

Critical Issues and Problems in Technology Education

Critical Issues and Problems in Technology Education Utah State University DigitalCommons@USU Publications Research 00 Critical Issues and Problems in echnology Education Robert C. Wicklein University of Georgia Follow this and additional works at: http://digitalcommons.usu.edu/ncete_publications

More information

Linking the Common European Framework of Reference and the Michigan English Language Assessment Battery Technical Report

Linking the Common European Framework of Reference and the Michigan English Language Assessment Battery Technical Report Linking the Common European Framework of Reference and the Michigan English Language Assessment Battery Technical Report Contact Information All correspondence and mailings should be addressed to: CaMLA

More information

Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining

Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining Dave Donnellan, School of Computer Applications Dublin City University Dublin 9 Ireland daviddonnellan@eircom.net Claus Pahl

More information

Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining

Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining Evaluation of Usage Patterns for Web-based Educational Systems using Web Mining Dave Donnellan, School of Computer Applications Dublin City University Dublin 9 Ireland daviddonnellan@eircom.net Claus Pahl

More information

Software Maintenance

Software Maintenance 1 What is Software Maintenance? Software Maintenance is a very broad activity that includes error corrections, enhancements of capabilities, deletion of obsolete capabilities, and optimization. 2 Categories

More information

Effects of Anonymity and Accountability During Online Peer Assessment

Effects of Anonymity and Accountability During Online Peer Assessment INFORMATION SCIENCE PUBLISHING 302 Wadhwa, Schulz & Mann 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.idea-group.com ITB11759 This chapter

More information

National Taiwan Normal University - List of Presidents

National Taiwan Normal University - List of Presidents National Taiwan Normal University - List of Presidents 1st Chancellor Li Ji-gu (Term of Office: 1946.5 ~1948.6) Chancellor Li Ji-gu (1895-1968), former name Zong Wu, from Zhejiang, Shaoxing. Graduated

More information

PRODUCT COMPLEXITY: A NEW MODELLING COURSE IN THE INDUSTRIAL DESIGN PROGRAM AT THE UNIVERSITY OF TWENTE

PRODUCT COMPLEXITY: A NEW MODELLING COURSE IN THE INDUSTRIAL DESIGN PROGRAM AT THE UNIVERSITY OF TWENTE INTERNATIONAL CONFERENCE ON ENGINEERING AND PRODUCT DESIGN EDUCATION 6 & 7 SEPTEMBER 2012, ARTESIS UNIVERSITY COLLEGE, ANTWERP, BELGIUM PRODUCT COMPLEXITY: A NEW MODELLING COURSE IN THE INDUSTRIAL DESIGN

More information

The Effect of Discourse Markers on the Speaking Production of EFL Students. Iman Moradimanesh

The Effect of Discourse Markers on the Speaking Production of EFL Students. Iman Moradimanesh The Effect of Discourse Markers on the Speaking Production of EFL Students Iman Moradimanesh Abstract The research aimed at investigating the relationship between discourse markers (DMs) and a special

More information

PSIWORLD Keywords: self-directed learning; personality traits; academic achievement; learning strategies; learning activties.

PSIWORLD Keywords: self-directed learning; personality traits; academic achievement; learning strategies; learning activties. Available online at www.sciencedirect.com ScienceDirect Procedia - Social and Behavioral Scien ce s 127 ( 2014 ) 640 644 PSIWORLD 2013 Self-directed learning, personality traits and academic achievement

More information

DESIGN-BASED LEARNING IN INFORMATION SYSTEMS: THE ROLE OF KNOWLEDGE AND MOTIVATION ON LEARNING AND DESIGN OUTCOMES

DESIGN-BASED LEARNING IN INFORMATION SYSTEMS: THE ROLE OF KNOWLEDGE AND MOTIVATION ON LEARNING AND DESIGN OUTCOMES DESIGN-BASED LEARNING IN INFORMATION SYSTEMS: THE ROLE OF KNOWLEDGE AND MOTIVATION ON LEARNING AND DESIGN OUTCOMES Joycelyn Streator Georgia Gwinnett College j.streator@ggc.edu Sunyoung Cho Georgia Gwinnett

More information

Active Learning. Yingyu Liang Computer Sciences 760 Fall

Active Learning. Yingyu Liang Computer Sciences 760 Fall Active Learning Yingyu Liang Computer Sciences 760 Fall 2017 http://pages.cs.wisc.edu/~yliang/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed by Mark Craven,

More information

TU-E2090 Research Assignment in Operations Management and Services

TU-E2090 Research Assignment in Operations Management and Services Aalto University School of Science Operations and Service Management TU-E2090 Research Assignment in Operations Management and Services Version 2016-08-29 COURSE INSTRUCTOR: OFFICE HOURS: CONTACT: Saara

More information

Teachers Attitudes Toward Mobile Learning in Korea

Teachers Attitudes Toward Mobile Learning in Korea Boise State University ScholarWorks Educational Technology Faculty Publications and Presentations Department of Educational Technology 1-1-2017 Teachers Attitudes Toward Mobile Learning in Korea Youngkyun

More information

Matching Similarity for Keyword-Based Clustering

Matching Similarity for Keyword-Based Clustering Matching Similarity for Keyword-Based Clustering Mohammad Rezaei and Pasi Fränti University of Eastern Finland {rezaei,franti}@cs.uef.fi Abstract. Semantic clustering of objects such as documents, web

More information

Chamilo 2.0: A Second Generation Open Source E-learning and Collaboration Platform

Chamilo 2.0: A Second Generation Open Source E-learning and Collaboration Platform Chamilo 2.0: A Second Generation Open Source E-learning and Collaboration Platform doi:10.3991/ijac.v3i3.1364 Jean-Marie Maes University College Ghent, Ghent, Belgium Abstract Dokeos used to be one of

More information

SOME MINIMAL NOTES ON MINIMALISM *

SOME MINIMAL NOTES ON MINIMALISM * In Linguistic Society of Hong Kong Newsletter 36, 7-10. (2000) SOME MINIMAL NOTES ON MINIMALISM * Sze-Wing Tang The Hong Kong Polytechnic University 1 Introduction Based on the framework outlined in chapter

More information

An Estimating Method for IT Project Expected Duration Oriented to GERT

An Estimating Method for IT Project Expected Duration Oriented to GERT An Estimating Method for IT Project Expected Duration Oriented to GERT Li Yu and Meiyun Zuo School of Information, Renmin University of China, Beijing 100872, P.R. China buaayuli@mc.e(iuxn zuomeiyun@263.nct

More information

Curriculum Vitae of Chiang-Ju Chien

Curriculum Vitae of Chiang-Ju Chien Contact Information Curriculum Vitae of Chiang-Ju Chien Affiliation : Department of Electronic Engineering, Huafan University, Taiwan Address : Department of Electronic Engineering, Huafan University,

More information

Concept mapping instrumental support for problem solving

Concept mapping instrumental support for problem solving 40 Int. J. Cont. Engineering Education and Lifelong Learning, Vol. 18, No. 1, 2008 Concept mapping instrumental support for problem solving Slavi Stoyanov* Open University of the Netherlands, OTEC, P.O.

More information

Problems of the Arabic OCR: New Attitudes

Problems of the Arabic OCR: New Attitudes Problems of the Arabic OCR: New Attitudes Prof. O.Redkin, Dr. O.Bernikova Department of Asian and African Studies, St. Petersburg State University, St Petersburg, Russia Abstract - This paper reviews existing

More information

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF)

SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) SINGLE DOCUMENT AUTOMATIC TEXT SUMMARIZATION USING TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Hans Christian 1 ; Mikhael Pramodana Agus 2 ; Derwin Suhartono 3 1,2,3 Computer Science Department,

More information

12- A whirlwind tour of statistics

12- A whirlwind tour of statistics CyLab HT 05-436 / 05-836 / 08-534 / 08-734 / 19-534 / 19-734 Usable Privacy and Security TP :// C DU February 22, 2016 y & Secu rivac rity P le ratory bo La Lujo Bauer, Nicolas Christin, and Abby Marsh

More information

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation

A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation A New Perspective on Combining GMM and DNN Frameworks for Speaker Adaptation SLSP-2016 October 11-12 Natalia Tomashenko 1,2,3 natalia.tomashenko@univ-lemans.fr Yuri Khokhlov 3 khokhlov@speechpro.com Yannick

More information

What is a Mental Model?

What is a Mental Model? Mental Models for Program Understanding Dr. Jonathan I. Maletic Computer Science Department Kent State University What is a Mental Model? Internal (mental) representation of a real system s behavior,

More information