Using Big Five Personality Model to Detect Cultural Aspects in Crowds

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1 Usng Bg Fve Personalty Model to Detect Cultural Aspects n Crowds Rodolfo Favaretto, Leandro Dhl, Soraa Raupp Musse Pontfcal Catholc Unversty of Ro Grande do Sul Graduate Studes on Computer Scence Porto Alegre, RS, Brazl Emal: soraa.musse@pucrs.br Felpe Vlanova and Angelo Brandell Costa Pontfcal Catholc Unversty of Ro Grande do Sul Graduate Studes on Psychology Porto Alegre, RS, Brazl Emal: angelo.costa@pucrs.br Abstract The use of nformaton technology n the study of human behavor s a subject of great scentfc nterest. Cultural and personalty aspects are factors that nfluence how people nteract wth one another n a crowd. Ths paper presents a methodology to detect cultural characterstcs of crowds n vdeo sequences. Based on flmed sequences, pedestrans are detected, tracked and characterzed. Such nformaton s then used to fnd out cultural dfferences n those vdeos, based on the Bg-fve personalty model. Regardng cultural dfferences of each country, results ndcate that ths model generates coherent nformaton when compared to data provded n lterature. I. INTRODUCTION Crowd analyss s a phenomenon of great nterest n a large number of applcatons. Survellance, entertanment and socal scences are examples of felds that can beneft from the development of ths area of study. Lterature dealt wth dfferent applcatons of crowd analyss, for example countng people n crowds [1], [2], group and crowd movement and formaton [3] [6] and detecton of socal groups n crowds [7] [10]. Normally, these approaches are based on personal trackng or optcal flow algorthms, and handle as features: speed, drectons and dstances over tme. Recently, some studes nvestgated cultural dfference n vdeos from dfferent countres. Chattaraj et al. [11] suggested that cultural and populaton dfferences could produce devatons n speed, densty and flow of the crowd. Favaretto et al. [12] dscussed cultural dmensons accordng to Hofstede analyss [13] and presented a methodology to map data from vdeo sequences to the dmensons of Hofstede cultural dmensons theory. In ths paper, we propose to detect crowd-cultural aspects based on the Bg-fve personalty model (or OCEAN) [14] (Brazlan verson) from NEO PI-R [15] usng ndvduals behavors automatcally detected n vdeo sequences. For ths, we used the NEO PI-R [14] that s the standard questonnare measure of the Bg-Fve Factor Model. The questonnare provdes a detaled personalty descrpton that can be a valuable resource for a varety of professonals. We frstly selected NEO PI-R tems related to ndvdual-level crowd characterstcs and the correspondng factor, as descrbed later n ths paper. For example: Lke beng part of crowd at sportng events correspondng to the factor Extroverson. More detals about personalty models are dscussed n Secton II. After the NEO PI-R tems selecton (related to crowds characterstcs), we propose a way to map data extracted from vdeo sequences to Bg-Fve parameters, as descrbed n Secton III. Snce there are dfferent dstrbutons of each of the Bg- Fve factors n dfferent countres [14], we hypothesze that t would be possble to detect cultural dfferences from vdeos processng crowd behavor from dfferent countres. Ths dscusson s addressed n Secton IV. Conclusons and future work are presented n Secton V. II. RELATED WORK Ths secton dscusses some topcs concerned wth personalty and also assocated wth crowd smulaton. Personalty may be labeled as deep psychologcal ndvdual level trat [16]. Trat s an nference made after observed behavors that seeks to explan ts regularty [17]. In general, researchers agree that there are fve robust orthogonal trats whch effectvely matched personalty attrbutes [18], known as the Bg Fve: Openness to experence (the actve seekng and apprecaton of new experences); Conscentousness (degree of organzaton, persstence, control and motvaton n goal drected behavor); Extraverson (quantty and ntensty of energy drected outwards n the socal world); Agreeableness (the knds of nteracton an ndvdual prefers from compasson to tough mndedness); Neurotcsm (how much prone to psychologcal dstress the ndvdual s) [19]. The development of the Bg Fve personalty model has ts roots n the work by Allport and Odbert (1936) who tred to dentfy ndvdual dfferences extractng relevant words n the Websters Unabrdged Dctonary. They worked wth the hypothess that the most mportant ndvdual dfferences would be coded n language, snce as they are the most mportant, there would be an evolutonary necessty to communcate t. Although Allport and Odbert (1936) found words whch referred to generalzed and stable personalty trats, ther technque couldnt orgnate few personalty trats whch explaned most part of behavors varance. Raymond Cattel s commonly referred as the one who developed the methodology whch permtted the objectve groupng of hundreds of trat descrptors n a set of hgher level factors [18]. Cattell [20] developed a taxonomy of ndvdual

2 dfferences that conssted of 16 prmary factors and 8 secondorder factors. Nevertheless, attempts to replcate hs work were unsuccessful [21] and researchers agreed that only the 5-factor model matched hs data, orgnatng the Bg Fve personalty model. The NEO PI-R [15] s one of the most used nstrument based on the Bg Fve personalty theory. It assesses the normal adult personalty and s nternatonally recognzed as a gold standard for personalty assessment. One of ts advantages s that t further specfes sx facets wthn each personalty trat and have data from several countres whch easly allows crosscultural comparsons [22], [23]. Although the current emprcal evdence matchng ndvdual level trats, such personalty and crowd behavor s not strong (one of the few examples s [24]), the Bg-Fve personalty model s wdely used to model computatonal crowd smulaton [25] [27]. The model allows to smulate a crowd wth ndvdual level parameters based on the expected behavors of the agents. Recently, research has shown that dgtal records can be an effectve tool n predctng personalty trats. Facebook lkes, for example, can predct the actual score of the Bg- Fve personalty model, especally the Openness trat [28], provdng roughly as much nformaton as the self-reported personalty test score tself. Ths makes room for the use of computatonal methods n predctng an ndvduals personalty as effectvely as through the analyss of self-reported scores. A computatonal method to assess personalty score can be also useful snce there are ssues concernng tradtonal self-report technques: 1) ndvduals may deceve themselves and unntentonally dstort ther ratngs of socally desrable trats n a postve drecton [29]; 2) ndvduals can fake ther responses to personalty measures, especally n contexts whch the test s used as a selecton crteron, such as n job ntervews; 3) ndvduals can dstort ther answers n dfferent levels and ways, makng t harder to apply a general statstcal correcton whch serves equally to everyone [30]. One effectve alternatve to the self-report method s the observer ratngs of personalty (.e., acquantances, frends, colleagues). A meta-analyss has shown that observatonal ratng provdes substantal ncremental valdty over self-reports of personalty [30]. One of the possble reasons for t s that self-reports assess the nternal dynamcs of an ndvdual, whereas observer ratngs analyze the behavoral performance. As the behavor s a better predctor of the future performance than the nner dynamcs of an ndvdual [31] t mght be the reason of the better predctve valdty. Therefore, we propose that t s possble to predct facets of personalty trats of ndvduals through computer vson of crowd behavor as effectvely as through the self-report method and observer ratngs such as a collegue or a frend. The ratonale behnd ths proposal s that snce observer ratngs mght be as vald as the self-report, computer vson mght be effectve as well - snce the behavoral component s beng analyzed and not solely the nner dynamcs of an ndvdual. One example s the way we can successfully predct players personalty scores through behavoural cue of ther avatars n vrtual worlds and games [32], [33]. Concernng cultural smulaton, Lala et al. [34] ntroduced a vrtual envronment that allows the creaton of dfferent types of cultural crowds. The crowd parameterzaton s based on the cultural dmensons presented by Hofstede [35]. The work proposed by Kamnka [36] presents data that am to dfferentate populatons wth regard to ther behavor of movement n crowds. Cultural parameters are proposed and analyzed n vdeos from dfferent countres, for later comparson. Some of the analyzed parameters are: speed, personal space, collson quanttes and populaton flow. In ths paper, the dea s to map parameters from ndvdual behavors (automatcally detected from vdeo sequences of dfferent countres) to generate a Bg-Fve personalty model score (OCEAN) [14] for each of them. In ths sense, our contrbuton s a model based on a set of equatons that handle the ndvdual parameters related to crowd behavors obtaned from vdeos and mapped to crowd-related Bg-Fve personalty trats, generatng profles of each ndvdual/analyzed vdeo. Snce personalty dfferences n the Bg-fve model between countres are establshed n the lterature [22], [23], one can compare each specfc result extracted from the vdeo wth the related country/cultural score. III. THE PROPOSED APPROACH Our model presents two man steps: vdeo data extracton and cultural analyss. The frst step ams to obtan the ndvdual trajectores from observed pedestrans n real vdeos. Usng these trajectores, we extracted data that are useful for the second step, that s responsble for the personalty and cultural analyss. A. Indvduals Data Extracton Intally, the nformaton about people from real vdeos s obtaned usng a tracker [37] to recover people trajectores. The features are followng descrbed. We compute frstly the geometrc nformaton for each person at each tmestep: ) 2D poston x (meters); ) speed s (meters/frame); ) angular varaton α (degrees) w.r.t. a reference vector r = (1, 0). In addton, three other features are also computed: v) collectvty φ, v) socalzaton ϑ and v) solaton levels ϕ. These features were chosen because two reasons: Frstly, they are strongly related wth the questons concerned wth groups actvtes n Neo-P survey [14]. The second reason s the theory behnd socalzaton/solaton that easly can be represented through geometrc data (postons and dstances), and collectvty that has been already explored n the context of crowd behavors detecton [4]. To compute the collectvty affectng one ndvdual from all n ndvduals n hs/her socal space (as presented n [38]), we used Equaton 1: n 1 φ = γe ( βϖ(,j)2), (1) j=0 where the collectvty between two ndvduals and j s calculated as a decay functon of ϖ(, j) = s(s, s j ).w 1 +

3 o(α, α j ).w 2, consderng s and o respectvely the speed and orentaton dfferences between the two people and w 1 and w 2 are constants that should regulate the offset n meters and radans. We have used w 1 = 1 and w 2 = 1. So, values for ϖ(, j) are ncluded n nterval 0 ϖ(, j) γ = 1 s the maxmum collectvty value when ϖ(, j) = 0, and β = 0.3 s emprcally defned as decay constant. Hence, φ s a value n the nterval [0; 1]. To compute the socalzaton level ϑ we use a classcal supervsed learnng algorthm proposed by Moller [39]. The artfcal neural network (ANN) (llustrated n Fgure 1) uses a Scaled Conjugate Gradent (SCG) algorthm n the tranng process to calculate the socalzaton ϑ level for each ndvdual. Fg. 1. Neural network used to learn the socalzaton level. As descrbed n Fgure 1, the ANN has 3 nputs (collectvty φ of person, mean Eucldean dstance from a person to others d,j and the number of people n the Socal Space 1 accordng to Hall s proxemcs [17] around the person n ). In addton, the network has 10 hdden layers and 2 outputs (the probablty of socalzaton and the probablty of non socalzaton). The fnal accuracy from the tranng processes was 96%. We used samples (70% of tranng and 30% of valdatng). These samples were obtaned from the 25 ntal frames from each of the vdeos from our dataset. The remanng frames were used to test the ANN as descrbed n Secton IV. The ground truth (GT) was generated as follows: Frstly, we defne f a person has a hgh socalzaton level GT ϑ based on Hall s proxemcs, calculated accordng to the Equaton 2: { 0, f n = 0 GT ϑ = n, (2) ρ, otherwse where n s the number of ndvduals n the socal space around the person and ρ s the number of ndvduals n the analyzed frame. If GT ϑ >= 0.5, we consdered ths person as a socal person, otherwse the person s consdered not socal n the tranng processes. Secondly, we proceed a vsual nspecton manually correctng false postves or false negatves n comparson to our personal opnon. Usng ths GT and the neural network we evaluate ϑ for each ndvdual at each frame, for each vdeo n the test group. Once we get the socalzaton level ϑ, we compute the solaton level ϕ = 1 ϑ, that corresponds to ts nverse. Fnally, for each ndvdual n a frame f of a certan vdeo v, we wll have a features vector V f,v = 1 Socal space s related to 3.6 meters [17]. [, α f,v, φ f,v, ϑ f,v ], ϕ f,v. Then, computng the aver- x f,v, s f,v age for ndvdual, for all frames of a vdeo v, we wll have vector V v for each person. In ths paper, we are nterested about mappng the features vector from each ndvdual n a specfc vdeo V v to OCEAN dmensons, detaled n next secton. B. Mappng crowd features n Cultural Dmensons Our goal s to map data from V to BF, where the last one s related to the Bg-Fve dmensons (or OCEAN) for each ndvdual for a certan vdeo and descrbed as a features vector: BF = [O, C, E, A, N ]. Therefore, n our method BF s computed based on NEO PI-R. Wth human bengs, OCEAN s calculated based on ther answers to the full verson of NEO PI-R, wth 240 tems. Our goal s to fnd out NEO PI-R answers for each ndvdual n the vdeo sequence, based on ther features ( V ). So, we have proposed a seres of emprcally defned equatons to map ndvdual and crowd characterstcs (n vdeo sequences) to OCEAN cultural dmensons. As stated before, the complete verson of NEO PI-R has 240 tems. Frstly, we selected 25 tems from NEO PI-R nventory that had a drect relatonshp wth crowd behavor. From the 25 tems selected, 18 (72%) are from Extroverson, 3 (12%) are from Neurotcsm, 2 (8%) are from Agreeableness, 1 (4%) s from Openness and 1 (4%) s from Conscentousness. One example of tems presented n NEO PI-R s 1 - Have clear goals, work to them n orderly way and possble answers are n the nterval [0;4] whch respectvely represent: Strongly Dsagree, Dsagree, Neutral, Agree and Strongly Agree. Our proposal s to answer these 25 tems (see Table I) for each ndvdual at each frame n the vdeo through the Equatons on the rght n Table I. For example, n order to represent the tem 1 - Have clear goals, work to them n orderly way, we consder that the ndvdual should have a hgh velocty s and low angular varaton α to have answer compatble wth 4. So the equaton for ths tem s Q 1 = s + 1 α. In ths way, we emprcally defned equatons for all 25 tems, as presented n Table I. Once all questons k (n the nterval [1; 25]) have been answered for all ndvduals, we wll have Q f,k for each frame f. We computed the average values to have one vector Q,k per vdeo. As already mentoned, NEO PI-R tems answers vary from 0 to 4. We converted the values obtaned n Q,k n one of the 5 score possble optons (0, 1, 2, 3 and 4) by smply normalzng the answers n 5 unformly dstrbuted levels, snce we know the maxmum level for each tem at each vdeo. We called ths normalzed vector as Q,k. In NEO PI-R defntons, some questons should nvert the values, because an tem score 4 (Strongly Agree) can represent a hgh value of Extroverson or low, dependng on the queston. For example, let us analyze questons 4 and 16. A score=4 to both of them represents completely opposte answers n terms of socablty. So, to get the correct values, we appled a factor to the questons whch score should be nverted: Q,k = 4 Q,k.

4 NEO PI-R Item TABLE I EQUATIONS FROM EACH NEO PI-R ITEM SELECTED. Equaton 1 - Have clear goals, work to them n orderly way Q 1 = s + 1 α 2. Follow same route when go somewhere Q 2 = α 3. Shy away from crowds 4. Dont get much pleasure chattng wth people 5. Usually prefer to do thngs alone 6. Prefer jobs that let me work alone, unbothered 7. Wouldnt enjoy holday n Las Vegas 8. Many thnk of me as somewhat cold, dstant 9. Rather cooperate wth others than compete 10. Try to be courteous to everyone I meet Q 3 8 = ϕ Q 9 10 = φ 11. Socal gatherngs usually bore me Q 11 = ϕ + std(α ) 12. Usually seem n hurry Q 12 = s + α 13. Often dsgusted wth people I have to deal wth Q 13 = ϕ + 1 φ 14. Have often been leader of groups belonged to Q 14 = φ + ϑ + 1 α 15. Would rather go my own way than be a leader Q 15 = 1 Q Lke to have lots of people around me 17. Enjoy partes wth lots of people 18. Lke beng part of crowd at sportng events 19. Would rather a popular beach than solated cabn 20. Really enjoy talkng to people 21. Lke to be where acton s Q = ϑ N = Q, ϱ q=24 Q,q, (9) where ϱ represents the percentage of questons from the total, n each dmenson (O, C, E, A and N), respectvely 4%, 4%, 72%, 8% and 12%, as explaned prevously. Once we get the OCEAN values of each person, we calculate the OCEAN of the vdeo by the mean of people s OCEAN. In a smlar way, the OCEAN of a country s the mean of vdeos from that country. In the next secton we present some obtaned results of our method. IV. EXPERIMENTAL RESULTS In ths secton we dscuss some results obtaned wth our approach. We evaluated our method n a set of 20 vdeos from 4 countres (9 from Brazl, 5 from Chna, 3 from Austra and 3 from Japan). These vdeos, wth a duraton varyng between 100 and 900 frames, were collected from dfferent publc databases avalable on the Internet, such as [4], [40], [41]. Frstly, we get the OCEAN of each ndvdual n the scene (Fgure 2 shows some examples). In Fgure 2 (a) we can observe the hgher E that was found n an ndvdual, part of a group of people, whle the opposte happens n (b) when lower E was computed for ndvdual alone and far from the others. 22. Feel need for other people f by myself for long 23. Fnd t easy to smle, be outgong wth strangers 24. Rarely feel lonely or blue 25. Seldom feel self-conscous around people Q = ϑ + φ (a) Hgher E (Brazl) (b) Lower E - Hgher N (Chna) In addton, n NEO PI-R defnton, each of the questons Q k are assocated to one of the Bg Fve dmensons, as shown n next equatons: O = Q,2 ϱ, (3) (c) Hgher A (Brazl) (d) Lower A (Chna) C = Q,1 ϱ, (4) E = Q,3 + Q,12 + Q,14 + E = 23 q=16 Q,q, (5) 8 Q,q + Q,11 + Q,15, (6) q=4 E = (E + E ), (7) ϱ A = 10 q=9 Q,q, (8) ϱ (e) Hgher O (Japan) (f) Lower O (Brazl) Fg. 2. Examples of some ndvduals OCEAN levels: a) the hghlght person has the hghest Extraverson, b) shows the person wth the lowest Extraverson (and hghest Neurotcsm), c) shows the person wth the hghest Agreeableness and the person hghlghted n d) has the lowest Agreeableness. The hghlght person n e) has the hghest Openness and the person hghlghted n d) has the lowest Openness. Same knd of analyss can be done for mages (c) and (d) relatng to ther collectvty (hgher and lower respectvely) as descrbed n Equaton 1. Although t s more dffcult to vsual

5 Fg. 3. OCEAN comparson between our approach and lterature values. nspect the dmensons O, C and N we present the qualtatve results. For example n Fgure 2 (e) the hghlghted ndvdual has lower angular varaton n comparson to all others (hgher O value), whle n (f) ths s the ndvdual wth hgher angular varaton, consequently havng lower value of O. In addton, n Fgure 2 (b) we obtaned the hgher value of N, snce t s dependent of the nverse of collectvty and socalzaton. Once the ndvdual OCEAN values are computed, we get the mean OCEAN value for each vdeo. The country s OCEAN, n turn, s calculated by the average OCEANs of that country s vdeos. Fgure 4 shows the results obtaned by the country Brazl n all OCEAN dmensons, n comparson wth the lterature [14], consdered as ground-truth n our approach. It s nterestng to hghlght that results acheved for ths country showed the hgher accuracy, when compared to the other countres (see the Fgure 3). Ths was the country wth more avalable vdeos to be processed n our method (9 vdeos), n comparson wth other countres. In addton, we computed the perceptual error when accumulatng each dmenson from all vdeos and compared wth lterature for those Countres. Fgure 6 shows such errors and also ndcates that the presented error of dmenson E has lower value; that s an nterestng observaton snce ths was the dmenson that had more questons to be analyzed, as shown n Equatons 5, 6 and 7. In terms of cultural aspects of ndvduals n the vdeos, Table II shows the countres that get the hgher and lower values n each dmenson, accordng to our approach. For example, Brazl s the most extrovert country, whle the less neurotc s Japan. TABLE II COUNTRIES WITH HIGHER AND LOWER VALUES IN EACH BIG FIVE DIMENSION. O C E A N Hgher Lower Chna Austra Brazl Japan Austra (0.89) (0.53) (0.50) (0.60) (0.52) Brazl Japan Chna Chna Japan (0.53) (0.30) (0.33) (0.51) (0.42) Fg. 4. OCEAN results from Brazl. Accordng to prevous work, another classcal cultural dmenson s proposed by Hofstede [13]. In a recent paper,

6 Favaretto et al. [12] presented the cultural aspects of people n vdeo usng Hofstede s cultural dmensons theory (Fgure 5). We compared our error usng Bg-Fve (Fgure 6) wth ths method, when usng Hofstede s. Fg. 5. Hofstede percentual of dfferences. Fg. 6. Bg-Fve percentual of dfferences. The accuracy of each approach (OCEAN and Hofstede) can be found n terms of the mean dfference percentual when compared wth the lterature results, consderng all dmensons among all vdeos. Wth an average error of 30% from the results presented n lterature, the OCEAN method proved to be more promsng than Hofstede (wth an average error of 53%) for culturalty mappng. It s mportant to note that the mappng to OCEAN dmensons was emprcally defned through equatons usng data extracted from computer vson. NEO PI-R measured these dmensons by consderng a dfferent type of nformaton (subjectve responses of ndvduals collected through questonnares). In ths sense, t s possble to affrm that, even wth few vdeos used, the results obtaned wth the proposed approach are coherent wth NEO PI-R results and more effectve f compared wth Hofstede dmensons. The factor Extroverson (E) s the one that seems to be more predctable wth our model. Probably because ths factor comprehends the majorty of tems related to crowd behavors. V. CONCLUSIONS In ths paper we descrbed a way to map equatons to compute ndvdual-level trats from vdeo sequences, based on ndvduals and groups features. Our model computed, from vdeo sequences, OCEAN personalty trats and compared wth data from dfferent countres exstent n the lterature. In addton, we compared wth some prevous work that computed Hofstede dmensons usng a smlar approach. We beleve the results are promsng and vdeo sequences can be used to detect crowd cultural aspects. For our future work we ntend to valdate our model askng partcpants how much they agree wth the assgned score of each tem n the Bg-Fve questonnare that resulted n our model for ndvduals wth hgh scores n selected vdeos. By dong ths we can compare human score wth computer generated score of the same vdeos. We also ntend to ncrease our set of vdeo data. Both aspects, number of countres and the among of vdeos from each of them, should be consdered. One of the major dffcultes of ths work was to fnd a sutable set of vdeos to perform the experments. In addton, we ntend to make vdeo-recordngs of group stuatons where each ndvdual presented n the vdeo has prevously evaluated OCEAN scores. For ths, one plausble opton s evaluate our method wth the SALSA dataset [42], whch provdes Bg-fve personalty trats for a group of people n vdeo sequences. We may thus have another evdence of the valdty of the presented model. In addton, we plan to create a new model comprehendng dfferent psychologcal domans related to crowd characterstcs that have documented cultural dfferences: extraverson from the bg-fve model [14], Hofstedes collectvsm [13], Halls personal space [43], fundamental dagram [11] and the subjectve pace of tme [44]. REFERENCES [1] A. B. Chan and N. Vasconcelos, Bayesan posson regresson for crowd countng, n 12th IEEE ICCV, Sept 2009, pp [2] Z. Ca, Z. L. Yu, H. Lu, and K. Zhang, Countng people n crowded scenes by vdeo analyzng, n 9th IEEE ICIEA, June 2014, pp [3] B. Solmaz, B. E. Moore, and M. Shah, Identfyng behavors n crowd scenes usng stablty analyss for dynamcal systems, IEEE PAMI, vol. 34, no. 10, pp , Oct [Onlne]. Avalable: [4] B. Zhou, X. Tang, H. Zhang, and X. Wang, Measurng crowd collectveness, IEEE PAMI, vol. 36, no. 8, pp , Aug [5] R. J. Seth, Towards defnng groups and crowds n vdeo usng the atomc group actons dataset, n 2015 IEEE Internatonal Conference on Image Processng, ICIP 2015, Quebec Cty, QC, Canada, September 27-30, 2015, 2015, pp [Onlne]. Avalable: [6] H. Jo, K. Chug, and R. J. Seth, A revew of physcs-based methods for group and crowd analyss n computer vson, Journal of Postdoctoral Research, vol. 1, no. 1, pp. 4 7, [7] F. Solera, S. Calderara, and R. Cucchara, Structured learnng for detecton of socal groups n crowd, n 10th IEEE AVSS, Aug [8] J. Shao, C. C. Loy, and X. Wang, Scene-ndependent group proflng n crowd, n IEEE CVPR, June 2014, pp [9] L. Feng and B. Bhanu, Understandng dynamc socal groupng behavors of pedestrans, IEEE STSP, vol. 9, no. 2, pp , March 2015.

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