text: 38 P P Based on the above discussions, we arrive at the conclusion that Problem and Problem 1 7 can achieve the same optimal set of selected UEs. REFERENCES [1] J. Andrews, S. Buzzi, W. Choi, S. Hanly, A. Lozano, A. Soong, and J. Zhang, “What will 5G be?” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1065–1082, Jun. 2014. [2] M. Peng, Y. Sun, X. Li, Z. Mao, and C. Wang, “Recent advances in cloud radio access networks: System architectures, key techniques, and open issues,” IEEE Commun. Surveys Tut., vol. 18, no. 3, pp. 2282–2308, thirdquarter 2016. [3] Y. Shi, J. Zhang, K. B. Letaief, B. Bai, and W. Chen, “Large-scale convex optimization for ultra-dense cloud-RAN,” IEEE Wireless Commun. Mag., vol. 22, no. 3, pp. 84–91, Jun. 2015. [4] R. G. Stephen and R. Zhang, “Joint millimeter-wave fronthaul and OFDMA resource allocation in ultra-dense CRAN,” IEEE Trans. Commun., vol. 65, no. 3, pp. 1411–1423, Mar. 2017. [5] Y. Shi, J. Zhang, and K. Letaief, “Group sparse beamforming for green Cloud-RAN,” IEEE Trans. Wireless Commun., vol. 13, no. 5, pp. 2809–2823, May 2014. [6] B. Dai and W. Yu, “Energy efﬁciency of downlink transmission strategies for cloud radio access networks,” IEEE J. Sel. Areas Commun., vol. 34, no. 4, pp. 1037–1050, Apr. 2016. [7] ——, “Sparse beamforming and user-centric clustering for downlink cloud radio access network,” IEEE Access,, vol. 2, pp. 1326–1339, Oct. 2014. [8] V. N. Ha, L. B. Le, and N. D. Dao, “Coordinated multipoint transmission design for Cloud-RANs with limited fronthaul capacity constraints,” IEEE Trans. Veh. Technol., vol. 65, no. 9, pp. 7432–7447, Sep. 2016. [9] A. Abdelnasser and E. Hossain, “Resource allocation for an OFDMA Cloud-RAN of small cells underlaying a macrocell,” IEEE Trans. Mobile Comput., vol. 15, no. 11, pp. 2837–2850, Nov. 2016. [10] C. Pan, H. Zhu, N. J. Gomes, and J. Wang, “Joint precoding and RRH selection for user-centric green MIMO C-RAN,” IEEE Trans. Wireless Commun., vol. 16, no. 5, pp. 2891–2906, May 2017. [11] P. Luong, L. N. Tran, C. Despins, and F. Gagnon, “Joint beamforming and remote radio head selection in limited fronthaul C-RAN,” in 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Sept 2016, pp. 1–6. [12] P. Luong, F. Gagnon, C. Despins, and L. N. Tran, “Optimal joint remote radio head selection and beamforming design for limited fronthaul C-RAN,” IEEE Trans. Signal Process., vol. 65, no. 21, pp. 5605–5620, Nov. 2017. [13] Y. Shi, J. Zhang, and K. B. Letaief, “CSI overhead reduction with stochastic beamforming for cloud radio access networks,” in 2014 IEEE International Conference on Communications (ICC), 2014, pp. 5154–5159. [14] T. R. Lakshmana, A. Tolli, R. Devassy, and T. Svensson, “Precoder design with incomplete feedback for joint transmission,” IEEE Trans. Wireless Commun., vol. 15, no. 3, pp. 1923–1936, Mar. 2016. [15] C. Fan, Y. J. Zhang, and X. Yuan, “Dynamic nested clustering for parallel PHY-layer processing in Cloud-RANs,” IEEE Trans. Wireless Commun., vol. 15, no. 3, pp. 1881–1894, Mar. 2016. [16] C. Pan, H. Zhu, N. J. Gomes, and J. Wang, “Joint user selection and energy minimization for ultra-dense multi-channel C-RAN with incomplete CSI,” IEEE J. Sel. Areas Commun., vol. 35, no. 8, pp. 1809–1824, Aug. 2017. [17] Z. Bai, “Evolved universal terrestrial radio access (E-UTRA); physical layer procedures,” 3GPP, Sophia Antipolis, Technical Speciﬁcation 36.213 v. 11.4. 0, 2013.

text: P

text: P

text: Based

text: on

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text: above

text: discussions,

text: we

text: arrive

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text: conclusion

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text: Problem

text: and

text: Problem

text: can

text: achieve

text: the

text: same

text: optimal

text: set

text: of

text: selected

text: UEs.

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text: A Generation Method of Filtering Rules of Twitter Via Smartphone Based Participatory Sensing System for Tourist by Interactive GHSOM and C4.5 Takumi Ichimura Shin Kamada Faculty of Management and Information Systems, Graduate School of Comprehensive Scientiﬁc Research, Prefectural University of Hiroshima Prefectural University of Hiroshima 1-1-71, Ujina-Higashi, Minami-ku, 1-1-71, Ujina-Higashi, Minami-ku, Hiroshima, 734-8559, Japan Hiroshima, 734-8559, Japan Email: ichimura@pu-hiroshima.ac.jp Email: q222003qr@pu-hiroshima.ac.jp Abstract— Mobile Phone based Participatory Sensing (MPPS) For example, the technology for determining the geographic systems involve a community of users sending personal infor- location of cell phones and other hand-held devices is becom- mation and participating in autonomous sensing through their ing increasingly available. The tourist subjective data collec- mobile phones. Sensed data can also be obtained from external tion system with Android smartphone has been developed [2]. sensing devices that can communicate wirelessly to the phone. We have developed the tourist subjective data collection system The application can collect subjective data such as pictures with Android smartphone. The tourist can tweet the information with GPS, geographic location name, the evaluation, and com- of sightseeing spots by using the application. The application can ments in real sightseeing spots where a tourist visits and more determine the ﬁltering rules to provide the important information than 500 subjective data are stored in the database. Attractive of sightseeing spot. The rules are automatically generated by knowledge discovery for sight seeing spots is required to Interactive Growing Hierarchical SOM and C4.5. Index Terms—SOM, Growing Hierarchical SOM, Interactive promote the sightseeing industries. GHSOM, Smartphone based Participatory Sensing System, Clus- Data mining is the process of applying these methods to tering, Tourist Informatics, Knowledge Discovery, C4.5 data with the intention of uncovering hidden patterns. An unavoidable fact of data mining is that the subsets of data I. INTRODUCTION being analyzed may not be representative of the whole domain, and therefore may not contain examples of certain critical The current information technology can collect various relationships and behaviors that exist across other parts of the data sets because the recent tremendous technical advances domain. Moreover, the probability of inclusion of missing data in processing power, storage capacity and network connected and/or contradictory data becomes high because the means or cloud computing. The sample record in such data set includes instrumentality for storing information is to store the raw data not only numerical values but also language, evaluation, and in the storage by the automated collecting process. Data mining binary data such as pictures. The technical method to discover is seen as an increasingly important tool by modern business to knowledge in such databases is known to be a ﬁeld of data transform unprecedented quantities into business intelligence mining and developed in various research ﬁelds. giving an informational advantage. Some data mining tools Mobile Phone based Participatory Sensing (MPPS) systems are currently used in a wide range of proﬁling practices involve a community of users sending personal information such as marketing, fraud detection and medical information. and participating in autonomous sensing through their mobile As data sets have growth in size and complexity, automatic phones [1]. Sensed data can be obtained from sensing devices data processing technique in the ﬁeld of computer sciences is present on mobiles such as audio, video, and motion sensors, required. the latter available in high-end mobile phones. Sensed data can also be obtained from external sensing devices that can In this paper, we examined to classify them to investigate communicate wirelessly to the phone. Participation of mobile the effectiveness of the interactive GHSOM and the knowledge phone users in sensorial data collection both from the indi- is extracted by using the classiﬁcation results of the interactive vidual and from the surrounding environment presents a wide GHSOM [3] and C4.5 [4]. Rauber et al. proposed the tradi- range of opportunities for truly pervasive applications. tional algorithm of the growing hierarchical self organizing map (GHSOM) [6]. The algorithm has been chosen for its (cid:13)c 2012 IEEE. Personal use of this material is permitted. Permission from capability to develop a hierarchical structure of clustering and IEEE must be obtained for all other uses, in any current or future media, for the intuitive outputs which help the interpretation of the including reprinting/republishing this material for advertising or promotional clusters. However, GHSOM divides a data set into sub clusters purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. immoderately if the distribution of samples is complex. There

text: A

label: title

text: Generation

text: Method

text: of

text: Filtering

text: Rules

text: of

text: Twitter

text: Via

text: Smartphone

text: Based

text: Participatory

text: Sensing

text: System

text: for

text: Tourist

text: by

text: Interactive

text: GHSOM

text: and

text: Takumi

label: author

text: Ichimura

text: Shin

label: paragraph

text: Kamada

text: Faculty

text: of

text: Management

text: and

text: Information

text: Systems,

text: Graduate

text: School

text: of

text: Comprehensive

text: Scientiﬁc

text: Research,

text: Prefectural

text: University

text: of

text: Hiroshima

text: Prefectural

text: University

text: of

text: Hiroshima

text: Ujina-Higashi,

text: Minami-ku,

text: Ujina-Higashi,

text: Minami-ku,

text: Hiroshima,

text: Japan

text: Hiroshima,

text: Japan

text: Email:

text: ichimura@pu-hiroshima.ac.jp

text: Email:

text: Abstract—

text: Mobile

label: abstract

text: Phone

text: based

text: Participatory

text: Sensing

text: (MPPS)

text: For

text: example,

text: the

text: technology

text: for

text: determining

text: the

text: geographic

text: systems

text: involve

text: a

text: community

text: of

text: users

text: sending

text: personal

text: infor-

text: location

text: of

text: cell

text: phones

text: and

text: other

text: hand-held

text: devices

text: is

text: becom-

text: mation

text: and

text: participating

text: in

text: autonomous

text: sensing

text: through

text: their

text: ing

text: increasingly

text: available.

text: The

text: tourist

text: subjective

text: data

text: collec-

text: mobile

text: phones.

text: Sensed

text: data

text: can

text: also

text: be

text: obtained

text: from

text: external

text: tion

text: system

text: with

text: Android

text: smartphone

text: has

text: been

text: developed

text: sensing

text: devices

text: that

text: can

text: communicate

text: wirelessly

text: to

text: the

text: phone.

text: We

text: have

text: developed

text: the

text: tourist

text: subjective

text: data

text: collection

text: system

text: The

text: application

text: can

text: collect

text: subjective

text: data

text: such

text: as

text: pictures

text: with

text: Android

text: smartphone.

text: The

text: tourist

text: can

text: tweet

text: the

text: information

text: with

text: GPS,

text: geographic

text: location

text: name,

text: the

text: evaluation,

text: and

text: com-

text: of

text: sightseeing

text: spots

text: by

text: using

text: the

text: application.

text: The

text: application

text: can

text: ments

text: in

text: real

text: sightseeing

text: spots

text: where

text: a

text: tourist

text: visits

text: and

text: more

text: determine

text: the

text: ﬁltering

text: rules

text: to

text: provide

text: the

text: important

text: information

text: than

text: subjective

text: data

text: are

text: stored

text: in

text: the

text: database.

text: Attractive

text: of

text: sightseeing

text: spot.

text: The

text: rules

text: are

text: automatically

text: generated

text: by

text: knowledge

text: discovery

text: for

text: sight

text: seeing

text: spots

text: is

text: required

text: to

text: Interactive

text: Growing

text: Hierarchical

text: SOM

text: and

text: Index

text: Terms—SOM,

text: Growing

text: Hierarchical

text: SOM,

text: Interactive

text: promote

text: the

text: sightseeing

text: industries.

text: GHSOM,

text: Smartphone

text: based

text: Participatory

text: Sensing

text: System,

text: Clus-

text: Data

text: mining

text: is

text: the

text: process

text: of

text: applying

text: these

text: methods

text: to

text: tering,

text: Tourist

text: Informatics,

text: Knowledge

text: Discovery,

text: data

text: with

text: the

text: intention

text: of

text: uncovering

text: hidden

text: patterns.

text: An

text: unavoidable

text: fact

text: of

text: data

text: mining

text: is

text: that

text: the

text: subsets

text: of

text: data

text: I.

text: INTRODUCTION

text: being

text: analyzed

text: may

text: not

text: be

text: representative

text: of

text: the

text: whole

text: domain,

text: and

text: therefore

text: may

text: not

text: contain

text: examples

text: of

text: certain

text: critical

text: The

text: current

text: information

text: technology

text: can

text: collect

text: various

text: relationships

text: and

text: behaviors

text: that

text: exist

text: across

text: other

text: parts

text: of

text: the

text: data

text: sets

text: because

text: the

text: recent

text: tremendous

text: technical

text: advances

text: domain.

text: Moreover,

text: the

text: probability

text: of

text: inclusion

text: of

text: missing

text: data

text: in

text: processing

text: power,

text: storage

text: capacity

text: and

text: network

text: connected

text: and/or

text: contradictory

text: data

text: becomes

text: high

text: because

text: the

text: means

text: or

text: cloud

text: computing.

text: The

text: sample

text: record

text: in

text: such

text: data

text: set

text: includes

text: instrumentality

text: for

text: storing

text: information

text: is

text: to

text: store

text: the

text: raw

text: data

text: not

text: only

text: numerical

text: values

text: but

text: also

text: language,

text: evaluation,

text: and

text: in

text: the

text: storage

text: by

text: the

text: automated

text: collecting

text: process.

text: Data

text: mining

text: binary

text: data

text: such

text: as

text: pictures.

text: The

text: technical

text: method

text: to

text: discover

text: is

text: seen

text: as

text: an

text: increasingly

text: important

text: tool

text: by

text: modern

text: business

text: to

text: knowledge

text: in

text: such

text: databases

text: is

text: known

text: to

text: be

text: a

text: ﬁeld

text: of

text: data

text: transform

text: unprecedented

text: quantities

text: into

text: business

text: intelligence

text: mining

text: and

text: developed

text: in

text: various

text: research

text: ﬁelds.

text: giving

text: an

text: informational

text: advantage.

text: Some

text: data

text: mining

text: tools

text: Mobile

text: Phone

text: based

text: Participatory

text: Sensing

text: (MPPS)

text: systems

text: are

text: currently

text: used

text: in

text: a

text: wide

text: range

text: of

text: proﬁling

text: practices

text: involve

text: a

text: community

text: of

text: users

text: sending

text: personal

text: information

text: such

text: as

text: marketing,

text: fraud

text: detection

text: and

text: medical

text: information.

text: and

text: participating

text: in

text: autonomous

text: sensing

text: through

text: their

text: mobile

text: As

text: data

text: sets

text: have

text: growth

text: in

text: size

text: and

text: complexity,

text: automatic

text: phones

text: Sensed

text: data

text: can

text: be

text: obtained

text: from

text: sensing

text: devices

text: data

text: processing

text: technique

text: in

text: the

text: ﬁeld

text: of

text: computer

text: sciences

text: is

text: present

text: on

text: mobiles

text: such

text: as

text: audio,

text: video,

text: and

text: motion

text: sensors,

text: required.

text: the

text: latter

text: available

text: in

text: high-end

text: mobile

text: phones.

text: Sensed

text: data

text: can

text: also

text: be

text: obtained

text: from

text: external

text: sensing

text: devices

text: that

text: can

text: In

text: this

text: paper,

text: we

text: examined

text: to

text: classify

text: them

text: to

text: investigate

text: communicate

text: wirelessly

text: to

text: the

text: phone.

text: Participation

text: of

text: mobile

text: the

text: effectiveness

text: of

text: the

text: interactive

text: GHSOM

text: and

text: the

text: knowledge

text: phone

text: users

text: in

text: sensorial

text: data

text: collection

text: both

text: from

text: the

text: indi-

text: is

text: extracted

text: by

text: using

text: the

text: classiﬁcation

text: results

text: of

text: the

text: interactive

text: vidual

text: and

text: from

text: the

text: surrounding

text: environment

text: presents

text: a

text: wide

text: GHSOM

text: and

text: Rauber

text: et

text: al.

text: proposed

text: the

text: tradi-

text: range

text: of

text: opportunities

text: for

text: truly

text: pervasive

text: applications.

text: tional

text: algorithm

text: of

text: the

text: growing

text: hierarchical

text: self

text: organizing

text: map

text: (GHSOM)

text: The

text: algorithm

text: has

text: been

text: chosen

text: for

text: its

text: IEEE.

text: Personal

text: use

text: of

text: this

text: material

text: is

text: permitted.

text: Permission

text: from

text: capability

text: to

text: develop

text: a

text: hierarchical

text: structure

text: of

text: clustering

text: and

text: IEEE

text: must

text: be

text: obtained

text: for

text: all

text: other

text: uses,

text: in

text: any

text: current

text: or

text: future

text: media,

text: for

text: the

text: intuitive

text: outputs

text: which

text: help

text: the

text: interpretation

text: of

text: the

text: including

text: reprinting/republishing

text: this

text: material

text: for

text: advertising

text: or

text: promotional

text: clusters.

text: However,

text: GHSOM

text: divides

text: a

text: data

text: set

text: into

text: sub

text: clusters

text: purposes,

text: creating

text: new

text: collective

text: works,

text: for

text: resale

text: or

text: redistribution

text: to

text: servers

text: or

text: lists,

text: or

text: reuse

text: of

text: any

text: copyrighted

text: component

text: of

text: this

text: work

text: in

text: other

text: works.

text: immoderately

text: if

text: the

text: distribution

text: of

text: samples

text: is

text: complex.

text: There


text: IV. CONCLUSIVE DISCUSSION The interactive GHSOM can restrain the growing of hi- erarchy in GHSOM by reforming the map in each layer interactively. The interactive GHSOM and its graphical tool can give intuitive comprehension of two or more dimensional data. In order to verify the effectiveness of our proposed method, we developed Android smartphone based Participatory Sensing system for Hiroshima tourism information which can collect the tourist subjective data in sightseeing spots. As computational results, the tree structure by GHSOM almost equals the knowledge structure extracted by C4.5. The dif- ferences between classiﬁcation results as shown in Fig.6 are represented knowledge in If-Then rule form. The knowledge Fig. 7. The overview of Filtering rules of Twitter obtained from the calculation result by GHSOM is complex structure because the tree is nesting under the same condition without the numerical value of criteria. After the interactive (cid:13) (cid:13) denoted by D1 and D2 as shown in Fig. 6(b) GHSOM implementation, the obtained knowledge represents in the clear structure intuitively. The GHSOM analysis tool on These 136 samples divided into the 4 maps in the lower the Android smartphone is expected to be solved the problems layer were allocated in the unit [R][11] before the interactive in real world and within real time, because Android is equipped GHSOM implementation as shown in Fig. 6(a), because the with the easy human computer interaction. method classiﬁed not only the corresponding node but also Located in Hiroshima, which is remote from big cities, it the samples included in the above map connected to the node is well-known widely as a sightseeing spot of World Heritage simultaneously. As a result, one condition of classiﬁcation Site and a lot of guests seem to come all the way from Kyoto, knowledge arranged some granular parts of knowledge and Osaka, and Tokyo. Certainly, Hiroshima has two spots of the overall structure of knowledge can be a lucid expression. UNESCO World Heritage Site. However, the recent utilization Note that the simulation result described here depends on the of local resources for tourism makes no difference to our desire trials. to travel, because the means of transport has been developed The complex condition such as a result is often seen in from downtown to peripheral suburbs. Not only tourism asso- the classiﬁcation by using softcomputing methodologies. Our ciation but the local citizens should give the innovative and proposed interactive GHSOM can sometimes classify the par- attractive information in sightseeing to visitors. Our developed tially knowledge into the distinct subcategories and sometimes clustering system and MPPS for tourism information can arrange the other way around, because the interactive GHSOM discover valuable information in the unknown user subjective can classify the samples allocated into the speciﬁed node. data and spread the information on social networking service. Then, the complex structure of knowledge becomes a clear. However, the analysis of the main subjective data such as Such results appeared in other empirical studies show that the pictures does not implemented. We will develop the image GHSOM tool can give the intuitive graphical comprehension analysis method in tourism information in future. for real world data such as tourist subjective data in MPPS. REFERENCES C. Filtering Rules of Twitter [1] Lane, N.D., Miluzzo, E., Hong L., Peebles, D., Choudhury, T., Campbell, This subsection describes the ﬁltering rules of tourist sub- A.T. (2010) ‘A survey of mobile phone sensing’, IEEE Communications Magazine, Vol.48, No.9, pp.140-150. jective data to be tweeted. Fig. 7 shows the overview of the [2] ITProducts (2011) ‘Hiroshima Tourist Map’, clustering results by the interactive GHSOM and the decision https://market.android.com/details? id=jp.itproducts.KankouMap, tree by C4.5 via our developed Android Application. The Retrieved 2011-11-15. [3] Ichimura, T., Yamaguchi, T. (2011) ‘A Proposal of Interactive Growing following ﬁltering rule is generated according to the acquired Hierarchical SOM’, Proc. of 2011 IEEE SMC2011, pp.3149-3154. knowledge. The idea of rule is drawn in the left side of Fig.7. [4] Quinlan, J.R. (1996) ‘Improved use of continuous attributes in c4.5’, Journal of Artiﬁcial Intelligence Research, No.4, pp.77-90. Rule: [5] Kohonen, T. (1995) ‘Self-Organizing Maps’, Springer Series in Informa- IF ‘evaluation’ > 2 & ‘lon’ < 132.386 tion Sciences, Vol. 30, Springer. & ‘lat’ < 34.4323 & ‘tfidf’ > 0.4022 [6] Rauber, A., Merkl, D., Dittenbach, M., ‘The growing hierarchical self- organizing map: exploratory analysis of high-dimensional data’, IEEE THEN Area is ‘Miyajima’. Transactions on Neural Networks, vol.13, pp.1331-1341. [7] Fisher (1936) ‘Iris Dataset, UCI Machine Learning Repository’, If the message to be tweeted matches the rule, the corre- http://archive.ics.uci.edu/ml/datasets/Iris. Retrieved 2011-11-15. sponding tourist information is important and the message [8] Kuehni, R.G. (2002) ‘The early development of the Munsell system’, is outgoing on social networking service and microblogging Color Research and Application, Vol.27, No.1, pp.2027. [9] Wu, H.C., Luk, R.W.P., Wong, K.F., Kwok, K.L. (2008) ‘Interpreting TF- service. The output messages with the hush tag (#KankouMap) IDF term weights as making relevance decisions’, ACM Transactions on are appeared in the Twietter as the speciﬁed user. Information Systems, Vol.26, No.3, pp.137, 2008. ##LTFigure## ##LTFigure##

text: IV.

label: section

text: CONCLUSIVE

text: DISCUSSION

text: The

text: interactive

text: GHSOM

text: can

text: restrain

text: the

text: growing

text: of

text: hi-

text: erarchy

text: in

text: GHSOM

text: by

text: reforming

text: the

text: map

text: in

text: each

text: layer

text: interactively.

text: The

text: interactive

text: GHSOM

text: and

text: its

text: graphical

text: tool

text: can

text: give

text: intuitive

text: comprehension

text: of

text: two

text: or

text: more

text: dimensional

text: data.

text: In

text: order

text: to

text: verify

text: the

text: effectiveness

text: of

text: our

text: proposed

text: method,

text: we

text: developed

text: Android

text: smartphone

text: based

text: Participatory

text: Sensing

text: system

text: for

text: Hiroshima

text: tourism

text: information

text: which

text: can

text: collect

text: the

text: tourist

text: subjective

text: data

text: in

text: sightseeing

text: spots.

text: As

text: computational

text: results,

text: the

text: tree

text: structure

text: by

text: GHSOM

text: almost

text: equals

text: the

text: knowledge

text: structure

text: extracted

text: by

text: The

text: dif-

text: ferences

text: between

text: classiﬁcation

text: results

text: as

text: shown

text: in

text: are

text: represented

text: knowledge

text: in

text: If-Then

text: rule

text: form.

text: The

text: knowledge

text: Fig.

text: The

text: overview

text: of

text: Filtering

text: rules

text: of

text: Twitter

text: obtained

text: from

text: the

text: calculation

text: result

text: by

text: GHSOM

text: is

text: complex

text: structure

text: because

text: the

text: tree

text: is

text: nesting

text: under

text: the

text: same

text: condition

text: without

text: the

text: numerical

text: value

text: of

text: criteria.

text: After

text: the

text: interactive

text: denoted

text: by

text: and

text: as

text: shown

text: in

text: Fig.

text: GHSOM

text: implementation,

text: the

text: obtained

text: knowledge

text: represents

text: in

text: the

text: clear

text: structure

text: intuitively.

text: The

text: GHSOM

text: analysis

text: tool

text: on

text: These

text: samples

text: divided

text: into

text: the

text: maps

text: in

text: the

text: lower

text: the

text: Android

text: smartphone

text: is

text: expected

text: to

text: be

text: solved

text: the

text: problems

text: layer

text: were

text: allocated

text: in

text: the

text: unit

text: before

text: the

text: interactive

text: in

text: real

text: world

text: and

text: within

text: real

text: time,

text: because

text: Android

text: is

text: equipped

text: GHSOM

text: implementation

text: as

text: shown

text: in

text: Fig.

text: because

text: the

text: with

text: the

text: easy

text: human

text: computer

text: interaction.

text: method

text: classiﬁed

text: not

text: only

text: the

text: corresponding

text: node

text: but

text: also

text: Located

text: in

text: Hiroshima,

text: which

text: is

text: remote

text: from

text: big

text: cities,

text: it

text: the

text: samples

text: included

text: in

text: the

text: above

text: map

text: connected

text: to

text: the

text: node

text: is

text: well-known

text: widely

text: as

text: a

text: sightseeing

text: spot

text: of

text: World

text: Heritage

text: simultaneously.

text: As

text: a

text: result,

text: one

text: condition

text: of

text: classiﬁcation

text: Site

text: and

text: a

text: lot

text: of

text: guests

text: seem

text: to

text: come

text: all

text: the

text: way

text: from

text: Kyoto,

text: knowledge

text: arranged

text: some

text: granular

text: parts

text: of

text: knowledge

text: and

text: Osaka,

text: and

text: Tokyo.

text: Certainly,

text: Hiroshima

text: has

text: two

text: spots

text: of

text: the

text: overall

text: structure

text: of

text: knowledge

text: can

text: be

text: a

text: lucid

text: expression.

text: UNESCO

text: World

text: Heritage

text: Site.

text: However,

text: the

text: recent

text: utilization

text: Note

text: that

text: the

text: simulation

text: result

text: described

text: here

text: depends

text: on

text: the

text: of

text: local

text: resources

text: for

text: tourism

text: makes

text: no

text: difference

text: to

text: our

text: desire

text: trials.

text: to

text: travel,

text: because

text: the

text: means

text: of

text: transport

text: has

text: been

text: developed

text: The

text: complex

text: condition

text: such

text: as

text: a

text: result

text: is

text: often

text: seen

text: in

text: from

text: downtown

text: to

text: peripheral

text: suburbs.

text: Not

text: only

text: tourism

text: asso-

text: the

text: classiﬁcation

text: by

text: using

text: softcomputing

text: methodologies.

text: Our

text: ciation

text: but

text: the

text: local

text: citizens

text: should

text: give

text: the

text: innovative

text: and

text: proposed

text: interactive

text: GHSOM

text: can

text: sometimes

text: classify

text: the

text: par-

text: attractive

text: information

text: in

text: sightseeing

text: to

text: visitors.

text: Our

text: developed

text: tially

text: knowledge

text: into

text: the

text: distinct

text: subcategories

text: and

text: sometimes

text: clustering

text: system

text: and

text: MPPS

text: for

text: tourism

text: information

text: can

text: arrange

text: the

text: other

text: way

text: around,

text: because

text: the

text: interactive

text: GHSOM

text: discover

text: valuable

text: information

text: in

text: the

text: unknown

text: user

text: subjective

text: can

text: classify

text: the

text: samples

text: allocated

text: into

text: the

text: speciﬁed

text: node.

text: data

text: and

text: spread

text: the

text: information

text: on

text: social

text: networking

text: service.

text: Then,

text: the

text: complex

text: structure

text: of

text: knowledge

text: becomes

text: a

text: clear.

text: However,

text: the

text: analysis

text: of

text: the

text: main

text: subjective

text: data

text: such

text: as

text: Such

text: results

text: appeared

text: in

text: other

text: empirical

text: studies

text: show

text: that

text: the

text: pictures

text: does

text: not

text: implemented.

text: We

text: will

text: develop

text: the

text: image

text: GHSOM

text: tool

text: can

text: give

text: the

text: intuitive

text: graphical

text: comprehension

text: analysis

text: method

text: in

text: tourism

text: information

text: in

text: future.

text: for

text: real

text: world

text: data

text: such

text: as

text: tourist

text: subjective

text: data

text: in

text: MPPS.

text: REFERENCES

text: C.

text: Filtering

text: Rules

text: of

text: Twitter

text: Lane,

text: N.D.,

text: Miluzzo,

text: E.,

text: Hong

text: L.,

text: Peebles,

text: D.,

text: Choudhury,

text: T.,

text: Campbell,

text: This

text: subsection

text: describes

text: the

text: ﬁltering

text: rules

text: of

text: tourist

text: sub-

text: A.T.

text: ‘A

text: survey

text: of

text: mobile

text: phone

text: sensing’,

text: IEEE

text: Communications

text: Magazine,

text: jective

text: data

text: to

text: be

text: tweeted.

text: Fig.

text: shows

text: the

text: overview

text: of

text: the

text: ITProducts

text: ‘Hiroshima

text: Tourist

text: Map’,

text: clustering

text: results

text: by

text: the

text: interactive

text: GHSOM

text: and

text: the

text: decision

text: id=jp.itproducts.KankouMap,

text: tree

text: by

text: via

text: our

text: developed

text: Android

text: Application.

text: The

text: Retrieved

text: Ichimura,

text: T.,

text: Yamaguchi,

text: T.

text: ‘A

text: Proposal

text: of

text: Interactive

text: Growing

text: following

text: ﬁltering

text: rule

text: is

text: generated

text: according

text: to

text: the

text: acquired

text: Hierarchical

text: SOM’,

text: Proc.

text: of

text: IEEE

text: knowledge.

text: The

text: idea

text: of

text: rule

text: is

text: drawn

text: in

text: the

text: left

text: side

text: of

text: Quinlan,

text: J.R.

text: ‘Improved

text: use

text: of

text: continuous

text: attributes

text: in

text: Journal

text: of

text: Artiﬁcial

text: Intelligence

text: Research,

text: Rule:

text: Kohonen,

text: T.

text: ‘Self-Organizing

text: Maps’,

text: Springer

text: Series

text: in

text: Informa-

text: IF

text: ‘evaluation’

text: ‘lon’

text: tion

text: Sciences,

text: Vol.

text: Springer.

text: ‘lat’

text: ‘tfidf’

text: Rauber,

text: A.,

text: Merkl,

text: D.,

text: Dittenbach,

text: M.,

text: ‘The

text: growing

text: hierarchical

text: self-

text: organizing

text: map:

text: exploratory

text: analysis

text: of

text: high-dimensional

text: data’,

text: IEEE

text: THEN

text: Area

text: is

text: ‘Miyajima’.

text: Transactions

text: on

text: Neural

text: Networks,

text: Fisher

text: ‘Iris

text: Dataset,

text: UCI

text: Machine

text: Learning

text: Repository’,

text: If

text: the

text: message

text: to

text: be

text: tweeted

text: matches

text: the

text: rule,

text: the

text: corre-

text: Retrieved

text: sponding

text: tourist

text: information

text: is

text: important

text: and

text: the

text: message

text: Kuehni,

text: R.G.

text: ‘The

text: early

text: development

text: of

text: the

text: Munsell

text: system’,

text: is

text: outgoing

text: on

text: social

text: networking

text: service

text: and

text: microblogging

text: Color

text: Research

text: and

text: Application,

text: Wu,

text: H.C.,

text: Luk,

text: R.W.P.,

text: Wong,

text: K.F.,

text: Kwok,

text: K.L.

text: ‘Interpreting

text: TF-

text: service.

text: The

text: output

text: messages

text: with

text: the

text: hush

text: tag

text: (#KankouMap)

text: IDF

text: term

text: weights

text: as

text: making

text: relevance

text: decisions’,

text: ACM

text: Transactions

text: on

text: are

text: appeared

text: in

text: the

text: Twietter

text: as

text: the

text: speciﬁed

text: user.

text: Information

text: Systems,

text: ##LTFigure##

text: ##LTFigure##


text: [10] Ichimura, T., Kamada, S., Kato, K. (2012) ‘Analysis of Tourist Sub- jective Text Data in Smartphone based Participatory Sensing System by Interactive Growing Hierarchical SOM’ Proc. of KES-IDT2012, Vol. 2, SIST 16, pp. 225–235, 2012.

text: Ichimura,

text: T.,

text: Kamada,

text: S.,

text: Kato,

text: K.

text: ‘Analysis

text: of

text: Tourist

text: Sub-

text: jective

text: Text

text: Data

text: in

text: Smartphone

text: based

text: Participatory

text: Sensing

text: System

text: by

text: Interactive

text: Growing

text: Hierarchical

text: SOM’

text: Proc.

text: of

text: Vol.

text: SIST

text: pp.


text: An Estimation of Favorite Value in Emotion Generating Calculation by Fuzzy Petri Net Takumi Ichimura Kousuke Tanabe Faculty of Management and Information Systems, Graduate School of Comprehensive Scientiﬁc Research, Prefectural University of Hiroshima Prefectural University of Hiroshima 1-1-71, Ujina-Higashi, Minami-ku, 1-1-71, Ujina-Higashi, Minami-ku, Hiroshima, 734-8559, Japan Hiroshima, 734-8559, Japan Email: ichimura@pu-hiroshima.ac.jp Email: bakabonn009@gmail.com Abstract— Emotion Generating Calculations (EGC) method However, the weak point in EGC is Favorite Value (FV ) based on the Emotion Eliciting Condition Theory can decide as the personal taste information. In order to improve the whether an event arouses pleasure or not and quantify the problem, Mera et al. challenged to establish a method to degree under the event. An event in the form of Case Frame learn speaker’s taste information from dialog [8]. The learning representation is classiﬁed into 12 types of calculations. However, the weak point in EGC is Favorite Value (FV ) as the personal method consists of four following parts:(1)Directly expression taste information. In order to improve the problem, this paper according to the degree of like/dislike, (2)Decision Making challenges to establish a learning method to learn speaker’s taste based on the speaker’s environment, (3)Relevance to the degree information from dialog. Especially, the learning method employs of displeasure to the object, and (4)Backward calculation from Fuzzy Petri Net to ﬁnd an appropriate FV to a word which has the emotional expression. The method seems to be effective in the unknown FV . This paper discusses the effective learning method to improve a weak point of EGC when a missing value theory, but the procedure (2) is still an theoretical query into of FV exists. emotions, because it does not include the reasoning model. Index Terms—Emotion Generating Calculations, Mental State In this paper, the learning method employs Fuzzy Petri Net Transition Network, Fuzzy Petri Net, Favorite Value, Learning (FPN) to ﬁnd an appropriate FV for a word which has the personal taste information unknown FV . The FV given by the method can change the value itself according to the meaning or intent of discourse I. INTRODUCTION in a dialog. Moreover, the reasoning method can control the degree of changing FV , because the threshold value in FPN Our research group proposed an estimation method to varies as the mood in MSTN changes. This paper discusses calculate the agent’s emotion from the contents of utterances the effective learning method to improve a weak point of EGC and to express emotions which are aroused in computer agent when a missing value of FV exists. by using synthesized facial expression [1], [2], [3]. Emotion The remainder of this paper is organized as follows. In Generating Calculations (EGC) method [3] based on the the section , the brief explanation to understand the EGC is Emotion Eliciting Condition Theory [4] can decide whether described. Section explains to MSTN to measure the current an event arouses pleasure or not and quantify the degree of mental state by the stimulus of the EGC. Section describes pleasure under the event. the Fuzzy Petri Net model and Section proposes the reasoning Calculated emotions by EGC will change the mood of method and learning method of FV modiﬁcation. In Section the agent. Ren [5] describes Mental State Transition Network , we give some discussions to conclude this paper. (MSTN) which is the basic concept of approximating to II. EMOTION GENERATING CALCULATIONS human psychological and mental responses. The assumption A. An Overview of Emotion Generating Process of discrete emotion state is that human emotion is classiﬁed into some kinds of stable discrete states, called “mental state,” Fig.1 shows the emotion generating process where the user’s and the variance of emotions occurs in the transition from utterance is transcribed into a case frame representation based a state to other state with an arbitrary probability. Mera and on the results of morphological analysis and parsing. The agent Ichimura [6], [7] developed a computer agent that can transit works to determine the degree of pleasure/displeasure from a mental state in MSTN based on analysis of emotion by EGC the event in case frame representation by using EGC. EGC method. EGC calculates emotion and the type of the aroused consists of 2 or 3 terms such as subject, object and predicate, emotion is used to transit mental state [6]. which have Favorite Value (FV ), the strength of the feelings described in section . (cid:13)c 2013 IEEE. Personal use of this material is permitted. Permission from Then, the agent divides this simple emotion (plea- IEEE must be obtained for all other uses, in any current or future media, sure/displeasure) into 20 various emotions based on the El- including reprinting/republishing this material for advertising or promotional liott’s “Emotion Eliciting Condition Theory[4].” Elliott’s the- purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. ory requires judging conditions such as “feeling for another,”

text: An

text: Estimation

text: of

text: Favorite

text: Value

text: in

text: Emotion

text: Generating

text: Calculation

text: by

text: Fuzzy

text: Petri

text: Net

text: Takumi

text: Ichimura

text: Kousuke

text: Tanabe

text: Faculty

text: of

text: Management

text: and

text: Information

text: Systems,

text: Graduate

text: School

text: of

text: Comprehensive

text: Scientiﬁc

text: Research,

text: Prefectural

text: University

text: of

text: Hiroshima

text: Prefectural

text: University

text: of

text: Hiroshima

text: Ujina-Higashi,

text: Minami-ku,

text: Ujina-Higashi,

text: Minami-ku,

text: Hiroshima,

text: Japan

text: Hiroshima,

text: Japan

text: Email:

text: ichimura@pu-hiroshima.ac.jp

text: Email:

text: Abstract—

text: Emotion

text: Generating

text: Calculations

text: (EGC)

text: method

text: However,

text: the

text: weak

text: point

text: in

text: EGC

text: is

text: Favorite

text: Value

text: (FV

text: based

text: on

text: the

text: Emotion

text: Eliciting

text: Condition

text: Theory

text: can

text: decide

text: as

text: the

text: personal

text: taste

text: information.

text: In

text: order

text: to

text: improve

text: the

text: whether

text: an

text: event

text: arouses

text: pleasure

text: or

text: not

text: and

text: quantify

text: the

text: problem,

text: Mera

text: et

text: al.

text: challenged

text: to

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text: a

text: method

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text: degree

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text: event.

text: An

text: event

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text: form

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text: Case

text: Frame

text: learn

text: speaker’s

text: taste

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text: dialog

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text: learning

text: representation

text: is

text: classiﬁed

text: into

text: types

text: of

text: calculations.

text: However,

text: the

text: weak

text: point

text: in

text: EGC

text: is

text: Favorite

text: Value

text: (FV

text: as

text: the

text: personal

text: method

text: consists

text: of

text: four

text: following

text: expression

text: taste

text: information.

text: In

text: order

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text: the

text: problem,

text: this

text: paper

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text: degree

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text: Making

text: challenges

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text: establish

text: a

text: learning

text: method

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text: learn

text: speaker’s

text: taste

text: based

text: on

text: the

text: speaker’s

text: environment,

text: to

text: the

text: degree

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text: from

text: dialog.

text: Especially,

text: the

text: learning

text: method

text: employs

text: of

text: displeasure

text: to

text: the

text: object,

text: and

text: calculation

text: from

text: Fuzzy

text: Petri

text: Net

text: to

text: ﬁnd

text: an

text: appropriate

text: FV

text: to

text: a

text: word

text: which

text: has

text: the

text: emotional

text: expression.

text: The

text: method

text: seems

text: to

text: be

text: effective

text: in

text: the

text: unknown

text: FV

text: This

text: paper

text: discusses

text: the

text: effective

text: learning

text: method

text: to

text: improve

text: a

text: weak

text: point

text: of

text: EGC

text: when

text: a

text: missing

text: value

text: theory,

text: but

text: the

text: procedure

text: is

text: still

text: an

text: theoretical

text: query

text: into

text: of

text: FV

text: exists.

text: emotions,

text: because

text: it

text: does

text: not

text: include

text: the

text: reasoning

text: model.

text: Index

text: Terms—Emotion

text: Generating

text: Calculations,

text: Mental

text: State

text: In

text: this

text: paper,

text: the

text: learning

text: method

text: employs

text: Fuzzy

text: Petri

text: Net

text: Transition

text: Network,

text: Fuzzy

text: Petri

text: Net,

text: Favorite

text: Value,

text: Learning

text: (FPN)

text: to

text: ﬁnd

text: an

text: appropriate

text: FV

text: for

text: a

text: word

text: which

text: has

text: the

text: personal

text: taste

text: information

text: unknown

text: FV

text: The

text: FV

text: given

text: by

text: the

text: method

text: can

text: change

text: the

text: value

text: itself

text: according

text: to

text: the

text: meaning

text: or

text: intent

text: of

text: discourse

text: I.

text: INTRODUCTION

text: in

text: a

text: dialog.

text: Moreover,

text: the

text: reasoning

text: method

text: can

text: control

text: the

text: degree

text: of

text: changing

text: FV

text: because

text: the

text: threshold

text: value

text: in

text: FPN

text: Our

text: research

text: group

text: proposed

text: an

text: estimation

text: method

text: to

text: varies

text: as

text: the

text: mood

text: in

text: MSTN

text: changes.

text: This

text: paper

text: discusses

text: calculate

text: the

text: agent’s

text: emotion

text: from

text: the

text: contents

text: of

text: utterances

text: the

text: effective

text: learning

text: method

text: to

text: improve

text: a

text: weak

text: point

text: of

text: EGC

text: and

text: to

text: express

text: emotions

text: which

text: are

text: aroused

text: in

text: computer

text: agent

text: when

text: a

text: missing

text: value

text: of

text: FV

text: exists.

text: by

text: using

text: synthesized

text: facial

text: expression

text: Emotion

text: The

text: remainder

text: of

text: this

text: paper

text: is

text: organized

text: as

text: follows.

text: In

text: Generating

text: Calculations

text: (EGC)

text: method

text: based

text: on

text: the

text: the

text: section

text: the

text: brief

text: explanation

text: to

text: understand

text: the

text: EGC

text: is

text: Emotion

text: Eliciting

text: Condition

text: Theory

text: can

text: decide

text: whether

text: described.

text: Section

text: explains

text: to

text: MSTN

text: to

text: measure

text: the

text: current

text: an

text: event

text: arouses

text: pleasure

text: or

text: not

text: and

text: quantify

text: the

text: degree

text: of

text: mental

text: state

text: by

text: the

text: stimulus

text: of

text: the

text: EGC.

text: Section

text: describes

text: pleasure

text: under

text: the

text: event.

text: the

text: Fuzzy

text: Petri

text: Net

text: model

text: and

text: Section

text: proposes

text: the

text: reasoning

text: Calculated

text: emotions

text: by

text: EGC

text: will

text: change

text: the

text: mood

text: of

text: method

text: and

text: learning

text: method

text: of

text: FV

text: modiﬁcation.

text: In

text: Section

text: the

text: agent.

text: Ren

text: describes

text: Mental

text: State

text: Transition

text: Network

text: we

text: give

text: some

text: discussions

text: to

text: conclude

text: this

text: paper.

text: (MSTN)

text: which

text: is

text: the

text: basic

text: concept

text: of

text: approximating

text: to

text: II.

text: EMOTION

text: GENERATING

text: CALCULATIONS

text: human

text: psychological

text: and

text: mental

text: responses.

text: The

text: assumption

text: A.

text: An

text: Overview

text: of

text: Emotion

text: Generating

text: Process

text: of

text: discrete

text: emotion

text: state

text: is

text: that

text: human

text: emotion

text: is

text: classiﬁed

text: into

text: some

text: kinds

text: of

text: stable

text: discrete

text: states,

text: called

text: “mental

text: state,”

text: shows

text: the

text: emotion

text: generating

text: process

text: where

text: the

text: user’s

text: and

text: the

text: variance

text: of

text: emotions

text: occurs

text: in

text: the

text: transition

text: from

text: utterance

text: is

text: transcribed

text: into

text: a

text: case

text: frame

text: representation

text: based

text: a

text: state

text: to

text: other

text: state

text: with

text: an

text: arbitrary

text: probability.

text: Mera

text: and

text: on

text: the

text: results

text: of

text: morphological

text: analysis

text: and

text: parsing.

text: The

text: agent

text: Ichimura

text: developed

text: a

text: computer

text: agent

text: that

text: can

text: transit

text: works

text: to

text: determine

text: the

text: degree

text: of

text: pleasure/displeasure

text: from

text: a

text: mental

text: state

text: in

text: MSTN

text: based

text: on

text: analysis

text: of

text: emotion

text: by

text: EGC

text: the

text: event

text: in

text: case

text: frame

text: representation

text: by

text: using

text: EGC.

text: EGC

text: method.

text: EGC

text: calculates

text: emotion

text: and

text: the

text: type

text: of

text: the

text: aroused

text: consists

text: of

text: or

text: terms

text: such

text: as

text: subject,

text: object

text: and

text: predicate,

text: emotion

text: is

text: used

text: to

text: transit

text: mental

text: state

text: which

text: have

text: Favorite

text: Value

text: (FV

text: the

text: strength

text: of

text: the

text: feelings

text: described

text: in

text: section

text: IEEE.

text: Personal

text: use

text: of

text: this

text: material

text: is

text: permitted.

text: Permission

text: from

text: Then,

text: the

text: agent

text: divides

text: this

text: simple

text: emotion

text: (plea-

text: IEEE

text: must

text: be

text: obtained

text: for

text: all

text: other

text: uses,

text: in

text: any

text: current

text: or

text: future

text: media,

text: sure/displeasure)

text: into

text: various

text: emotions

text: based

text: on

text: the

text: El-

text: including

text: reprinting/republishing

text: this

text: material

text: for

text: advertising

text: or

text: promotional

text: liott’s

text: “Emotion

text: Eliciting

text: Condition

text: Elliott’s

text: the-

text: purposes,

text: creating

text: new

text: collective

text: works,

text: for

text: resale

text: or

text: redistribution

text: to

text: servers

text: or

text: lists,

text: or

text: reuse

text: of

text: any

text: copyrighted

text: component

text: of

text: this

text: work

text: in

text: other

text: works.

text: ory

text: requires

text: judging

text: conditions

text: such

text: as

text: “feeling

text: for

text: another,”


text: TABLE IV CLASSIFICATION OF GENERATED EMOTION dj µi dk No. Emotion 1 admirgaltoioatni,ngli,khinogp,eg, rsaattiitsufdaec,tigorna,tirﬁecliaetfi,onp,rliodvee,, shy Pj ti Pk 2 joy, happy for Fig. 6. Fuzzy Petri Net of If-Then rule 3 sorry-for, shame, remorse 4 fear-conﬁrmed, disappointment, sadness 5 distress, perplexity 6 disliking, hate the system is to pick the choice from many enumerated 7 resentment, reproach, anger possibilities. 8 fear The goal driven reasoning can deduce the user’s require- 9 surprise ment from the conversation in the speciﬁed goal driven rea- soning such as tourist information system. Then, IF-THEN rules for the interactive system as knowledge representations Happy Sad are prepared according to the sightseeing spots and goods. A fuzzy production rule is a rule which describes the fuzzy relation between 2 proposition. Let R b a set of fuzzy { · · · } production rule R = R , R , , R . The general form of 1 2 n the ith fuzzy production rule R is as follows: i Surprise Quiet Anger R : IF d Then d , CF = µ (4) i j k j A Fuzzy Petri Net (FPN)[11] is an effective model to implement goal driven reasoning. A FPN structure is deﬁned Fear Disgust as 8-tuple[11]; { } Fig. 5. MSTN with EGC FPN = P, T, D, I, O, f, α, β , (5) The emotion vector consists of 9 kinds of emotion groups which are classiﬁed 28 kinds of emotions as shown in Table IV. where P = {p , p , · · · , p } is a ﬁnite set of places, Fig.5 shows the MSTN by using EGC. The circled numbers T = {t , t , · · · , t1 }2is a ﬁnnite set of transitions, D = i(n1 ≤Figk.5≤ar9e) tshheownusmthbeerstrinentghteh loefftemsidoetioonf gTraobulpe kIVa.nTdhteakeeks {andd1, d|P2,|·1·=· ,2d|Dn}|.isIamﬁ: nTite s→et oPf ∞propisostihtieonisn,pPut∩fTun∩ctDion=, φa the maximum value of elements belonged in each set ek as mapping from transition to bags of places. O : T → P∞ follows. is the output function, a mapping from transition to bags of · · · → e1 = max(egloating, ehope, , eshy) places. f : T [0, 1] is an association function, a mapping → e2 = max(ejoy, ehappy for) from transitions to real values in [0, 1]. α : P [0, 1] is . . an association function, a mapping from places to real values . → in [0,1]. β : P D is an association function, a bijective e = max(e ) 9 surprise mapping from laces to propositions. The emo in Eq.(3) calculates the maximum emotion group ∈ Let A be a set of directed arcs. If p I(t ), then there according to the transition cost between current state and next ∈ j i exists a directed arc a ( A) from the place p to the state. ∈ ji j transition t . if p O(t ), then there exists a directed arc ∈ j k i a ( A) from the transition t to place p . If f(t ) = µ , ik ∈ i k i i emok = arg mkax cost(Scur, neekxt(Scur, k)), 1 ≤ k ≤ 9,(3) (wµiith a [r0e,a1l ]v)a, lutheenµit.hIef tβra(npsi)iti=ondtii, (idsi s∈aidD)to, thbeenatshseocpialatecde p is said to be associated with the proposition d . i ∈ j S The token value in a place p ( P) is denoted by α(p ), where next( cur, k) is next mental state from the current where α(p ) ∈ [0.1]. If α(p ) =i y (y ∈ [0, 1]) and β(p ) i= state by selecting emotion group k. d( ), then iit indicates that thi e degrieeiof truth of proposiition i d is y . IV. FUZZY PETRI NET i i By using a FPN, the fuzzy production rule such as Eq.(4) The interactive system for tourist concierge is implemented can be modeled as shown in Fig.6. The FPN model has places, by the goal driven reasoning. The inference technique which transitions, and tokens. A transition may be enabled to ﬁre. A ∈ ≥ uses IF-Then rules to repetitively breaks a goal into smaller transition t is enabled if for all p ( I(t )), α(p ) λ, where i j i j sub-goals. It is an efﬁcient way to solve problems that can λ is a threshold value in [0, 1]. A transition t ﬁres by removing i be modeled as “structured selection problems.” The aim of the tokens from its input places and then depositing one token ##LTFigure## ##LTFigure## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine## ##LTLine##

text: TABLE

text: IV

text: CLASSIFICATION

label: caption

text: OF

text: GENERATED

text: EMOTION

text: dj

font: default

text: µi

text: dk

text: No.

label: table

text: Emotion

text: admirgaltoioatni,ngli,khinogp,eg,

text: shy

text: Pj

text: ti

text: Pk

text: joy,

text: happy

text: for

text: Fig.

text: Fuzzy

text: Petri

text: Net

text: of

text: If-Then

text: rule

text: sorry-for,

text: shame,

text: remorse

text: fear-conﬁrmed,

text: disappointment,

text: sadness

text: distress,

text: perplexity

text: disliking,

text: hate

text: the

text: system

text: is

text: to

text: pick

text: the

text: choice

text: from

text: many

text: enumerated

text: resentment,

text: reproach,

text: anger

text: possibilities.

text: fear

text: The

text: goal

text: driven

text: reasoning

text: can

text: deduce

text: the

text: user’s

text: require-

text: surprise

text: ment

text: from

text: the

text: conversation

text: in

text: the

text: speciﬁed

text: goal

text: driven

text: rea-

text: soning

text: such

text: as

text: tourist

text: information

text: system.

text: Then,

text: IF-THEN

text: rules

text: for

text: the

text: interactive

text: system

text: as

text: knowledge

text: representations

text: Happy

text: Sad

text: are

text: prepared

text: according

text: to

text: the

text: sightseeing

text: spots

text: and

text: goods.

text: A

text: fuzzy

text: production

text: rule

text: is

text: a

text: rule

text: which

text: describes

text: the

text: fuzzy

text: relation

text: between

text: proposition.

text: Let

text: R

text: b

text: a

text: set

text: of

text: fuzzy

text: production

text: rule

text: R

text: R

text: R

text: R

text: The

text: general

text: form

text: of

text: n

text: the

text: ith

text: fuzzy

text: production

text: rule

text: R

text: is

text: as

text: follows:

text: i

text: Surprise

text: Quiet

text: Anger

text: R

text: IF

text: d

text: Then

text: d

text: CF

text: µ

text: i

text: j

text: k

text: j

text: A

text: Fuzzy

text: Petri

text: Net

text: is

text: an

text: effective

text: model

text: to

text: implement

text: goal

text: driven

text: reasoning.

text: A

text: FPN

text: structure

text: is

text: deﬁned

text: Fear

text: Disgust

text: as

text: Fig.

text: MSTN

text: with

text: EGC

text: FPN

text: P,

text: T,

text: D,

text: I,

text: O,

text: f,

text: α,

text: β

text: The

text: emotion

text: vector

text: consists

text: of

text: kinds

text: of

text: emotion

text: groups

text: which

text: are

text: classiﬁed

text: kinds

text: of

text: emotions

text: as

text: shown

text: in

text: Table

text: IV.

text: where

text: P

text: {p

text: p

text: p

text: is

text: a

text: ﬁnite

text: set

text: of

text: places,

text: shows

text: the

text: MSTN

text: by

text: using

text: EGC.

text: The

text: circled

text: numbers

text: T

text: {t

text: t

text: a

text: ﬁnnite

text: set

text: of

text: transitions,

text: D

text: tshheownusmthbeerstrinentghteh

text: loefftemsidoetioonf

text: gTraobulpe

text: kIVa.nTdhteakeeks

text: nTite

text: s→et

text: oPf

text: ∞propisostihtieonisn,pPut∩fTun∩ctDion=,

text: φa

text: the

text: maximum

text: value

text: of

text: elements

text: belonged

text: in

text: each

text: set

text: ek

text: as

text: mapping

text: from

text: transition

text: to

text: bags

text: of

text: places.

text: O

text: T

text: P∞

text: follows.

text: is

text: the

text: output

text: function,

text: a

text: mapping

text: from

text: transition

text: to

text: bags

text: of

text: max(egloating,

text: ehope,

text: eshy)

text: places.

text: f

text: T

text: is

text: an

text: association

text: function,

text: a

text: mapping

text: max(ejoy,

text: ehappy

text: for)

text: from

text: transitions

text: to

text: real

text: values

text: in

text: α

text: P

text: is

text: an

text: association

text: function,

text: a

text: mapping

text: from

text: places

text: to

text: real

text: values

text: in

text: β

text: P

text: D

text: is

text: an

text: association

text: function,

text: a

text: bijective

text: e

text: max(e

text: surprise

text: mapping

text: from

text: laces

text: to

text: propositions.

text: The

text: emo

text: in

text: calculates

text: the

text: maximum

text: emotion

text: group

text: Let

text: A

text: be

text: a

text: set

text: of

text: directed

text: arcs.

text: If

text: p

text: I(t

text: then

text: there

text: according

text: to

text: the

text: transition

text: cost

text: between

text: current

text: state

text: and

text: next

text: j

text: i

text: exists

text: a

text: directed

text: arc

text: a

text: A)

text: from

text: the

text: place

text: p

text: to

text: the

text: state.

text: ji

text: j

text: transition

text: t

text: if

text: p

text: O(t

text: then

text: there

text: exists

text: a

text: directed

text: arc

text: j

text: k

text: i

text: a

text: A)

text: from

text: the

text: transition

text: t

text: to

text: place

text: p

text: If

text: f(t

text: µ

text: ik

text: i

text: k

text: i

text: i

text: emok

text: arg

text: mkax

text: cost(Scur,

text: neekxt(Scur,

text: k)),

text: k

text: (wµiith

text: a

text: ]v)a,

text: lutheenµit.hIef

text: tβra(npsi)iti=ondtii,

text: (idsi

text: s∈aidD)to,

text: thbeenatshseocpialatecde

text: p

text: is

text: said

text: to

text: be

text: associated

text: with

text: the

text: proposition

text: d

text: i

text: j

text: S

text: The

text: token

text: value

text: in

text: a

text: place

text: p

text: P)

text: is

text: denoted

text: by

text: α(p

text: where

text: next(

text: cur,

text: k)

text: is

text: next

text: mental

text: state

text: from

text: the

text: current

text: where

text: α(p

text: If

text: α(p

text: =i

text: y

text: (y

text: and

text: β(p

text: i=

text: state

text: by

text: selecting

text: emotion

text: group

text: k.

text: d(

text: then

text: iit

text: indicates

text: that

text: thi

text: e

text: degrieeiof

text: truth

text: of

text: proposiition

text: i

text: d

text: is

text: y

text: IV.

text: FUZZY

text: PETRI

text: NET

text: i

text: i

text: By

text: using

text: a

text: FPN,

text: the

text: fuzzy

text: production

text: rule

text: such

text: as

text: The

text: interactive

text: system

text: for

text: tourist

text: concierge

text: is

text: implemented

text: can

text: be

text: modeled

text: as

text: shown

text: in

text: The

text: FPN

text: model

text: has

text: places,

text: by

text: the

text: goal

text: driven

text: reasoning.

text: The

text: inference

text: technique

text: which

text: transitions,

text: and

text: tokens.

text: A

text: transition

text: may

text: be

text: enabled

text: to

text: ﬁre.

text: A

text: uses

text: IF-Then

text: rules

text: to

text: repetitively

text: breaks

text: a

text: goal

text: into

text: smaller

text: transition

text: t

text: is

text: enabled

text: if

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text: PREPRINT SUBMITTED TO IEEE TRANSACTIONS ON MOBILE COMPUTING, 2018 2 zation schemes for gait-based pairing, an improved shapes as collection without speciﬁc order [22]. For all above quantization approach for one of the investigated methods, gait recognition can be improved by combining approaches to mitigate the identiﬁed security weak- statistical gait features from real and synthetic templates [21] ness, as well as the ﬁrst ever empirical demonstration Due to the increasing availability of wearable sensors that video is capable to estimate gait sufﬁciently such as gyroscopes (rotation), accelerometers (acceleration) accurate to break gait-based security schemes. or force sensors (force during walking), gait recognition via such wearable sensors is increasingly investigated [11], [30], sectioncolor [31], [32], [33], [34], [35]. In these approaches, acceleration 2 RELATED WORK sequences are recorded from devices located at various body We discuss recent related studies on gait-based security locations, most prominently at the waist. The acceleration mechanisms. In particular, we ﬁrst discuss gait recognition signal is then denoised e.g. by applying wavelet transfor- approaches, before we summarize recent progress in gait- mation [34] and changes in walking speed are mitigated based authentication and gait-based pairing. In the remain- utilizing dynamic time warping [36] or similar approaches. der of the discussion, we are then focusing on using ac- Individual steps are identiﬁed from the resulting signal by celeration sequences from natural gait for device pairing. searching for minima and by applying pattern or template In particular, we do not consider the use of gait for user matching [34]. Similarity can be estimated by the computa- authentication. As discussed in Section , gait as a biometric tion of cross-correlation [33]. Alternatively, machine learn- measure has several limitations and its strength as the seed ing classiﬁers are trained and applied [32]. for the authentication is, even after many years of research, Finally, a recent technique employed to acquire human questioned by some. In contrast, we consider attempts to gait is to monitor phase changes of an electromagnetic signal exploit gait as a stimuli acceleration-based ad-hoc pairing, reﬂected from a subject walking towards a transceiver [37], which utilizes the correlation in sensor readings for devices [38]. The authors exploit changes in channel state informa- in the same context. In contrast to authentication, ad-hoc tion (CSI) from WiFi devices for the detection of gait. After pairing generates instantaneous one-time keys. It is there- generating spectograms from CSI measurements, similar to fore not affected by stolen historical gait sequences but Doppler radars, and applying autocorrelation on the torso relies on the entropy in short-time ﬂuctuation of simultane- reﬂection to remove imperfection in these spectrograms, ous readings from correlated sources. Speciﬁcally, machine ﬁne-grained gait patterns are extracted. learning is not feasible for ad-hoc pairing as the challenges Note that frequently, sensors installed in the ﬂoor such as created are always fresh and shall not follow common pressure sensing mats are also mentioned as modalities for predictable or learnable patterns. The features exploited for gait recognition [39], [40]. However, in these cases, not gait key generation can further not have their origin in activity itself is extracted but other features such as footprints [40], recognition, as such coarse classes would result in small, ground reaction force [41] or heel-to-toe ratio [42]. and therefore weak key spaces. sectioncolor sectioncolor 2.2 Gait as a Biometric Pattern for Authentication 2.1 Gait Recognition Authentication systems comprise sensors converting Traditionally, Gait recognition has been applied exploit- analog stimuli to digital input that can then be quantized ing machine vision [20], [21], [22], [23]. Systems then com- and compared to a database of previously stored features. prise one or multiple cameras to capture natural gait and Thus, these systems allow authenticaton based on biometric contain image recognition steps including background sub- features. Gait as a discriminating feature was ﬁrst studied traction, feature extraction and recognition [24]. First work in [10], [43]. It has been realized that characteristic features goes back to perception experiments on light point dis- in gait enable identiﬁcation of subjects also in larger gait plays conducted in [10]. This work was further developed databases [44], [45], [46], [47]. In addition, multiple studies in [25] with computer vision approaches to recognize people have demonstrated that the success probability of an im- from gait. In preceding years drastic improvements have poster trying to mimic a subjects gait are low [14] even when been made in gait recognition algorithms [26], [27]. Gait trained professionals with similar physical characteristics recognition approaches can be grouped into (1) temporal are employed [8]. For instance, Hoang et al. [48] generated a alignment-based, (2) static parameter-based and (3) silhou- key ﬁngerprint from the difference of a mean gait spanning ette shape-based approaches [22]. From these, [28] found the complete population to the individual’s mean gait. In that shape is more signiﬁcant for person identiﬁcation than this way, the authors assured that the resulting sequence is kinematics. well balanced and uniformly distributed. A good overview Temporal alignment-based approaches emphasize both on gait-based user authentication is provided in [49], [50]. shape and dynamics and ﬁrst extract silhouette features However, despite studies asserting that gait can be used before aligning sequences of these e.g. with temporal cor- as biometric feature [11], [12], [13], we remark that there relation, dynamic time warping or hidden Markov models. is a lack of studies investigating the security features and Static parameter-based approaches exploit gait dynamics entropy of gait as an authentication mechanism. such as stride length, cadence and stride speed [29]. How- Several attacks though, most signiﬁcantly impersonation ever, they are least successful for gait-based identiﬁcation attacks, have been considered (cf. Table 1). For instance, due to their need for 3D calibration information. Mjaaland et al [53] trained seven individuals to imitate one Finally, silhouette shape-based approaches use silhou- speciﬁc victim. Even after intensive training over two weeks ette shape similarity and disregard temporal information, (5 hours every day), and for one subject even for six weeks, often considering averaged silhouettes or treating silhouette it was not possible for the subjects to accurately imitate the

text: PREPRINT

font: TQNYLE+NimbusSanL-Regu

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text: COMPUTING,

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text: schemes

text: for

text: gait-based

text: pairing,

text: an

text: improved

text: shapes

text: as

text: collection

text: without

text: speciﬁc

text: order

text: For

text: all

text: above

text: quantization

text: approach

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text: one

text: of

text: the

text: investigated

text: methods,

text: gait

text: recognition

text: can

text: be

text: improved

text: by

text: combining

text: approaches

text: to

text: mitigate

text: the

text: identiﬁed

text: security

text: weak-

text: statistical

text: gait

text: features

text: from

text: real

text: and

text: synthetic

text: templates

text: ness,

text: as

text: well

text: as

text: the

text: ﬁrst

text: ever

text: empirical

text: demonstration

text: Due

text: to

text: the

text: increasing

text: availability

text: of

text: wearable

text: sensors

text: that

text: video

text: is

text: capable

text: to

text: estimate

text: gait

text: sufﬁciently

text: such

text: as

text: gyroscopes

text: (rotation),

text: accelerometers

text: (acceleration)

text: accurate

text: to

text: break

text: gait-based

text: security

text: schemes.

text: or

text: force

text: sensors

text: (force

text: during

text: walking),

text: gait

text: recognition

text: via

text: such

text: wearable

text: sensors

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text: increasingly

text: investigated

text: sectioncolor

text: In

text: these

text: approaches,

text: acceleration

text: RELATED

text: WORK

text: sequences

text: are

text: recorded

text: from

text: devices

text: located

text: at

text: various

text: body

text: We

text: discuss

text: recent

text: related

text: studies

text: on

text: gait-based

text: security

text: locations,

text: most

text: prominently

text: at

text: the

text: waist.

text: The

text: acceleration

text: mechanisms.

text: In

text: particular,

text: we

text: ﬁrst

text: discuss

text: gait

text: recognition

text: signal

text: is

text: then

text: denoised

text: e.g.

text: by

text: applying

text: wavelet

text: transfor-

text: approaches,

text: before

text: we

text: summarize

text: recent

text: progress

text: in

text: gait-

text: mation

text: and

text: changes

text: in

text: walking

text: speed

text: are

text: mitigated

text: based

text: authentication

text: and

text: gait-based

text: pairing.

text: In

text: the

text: remain-

text: utilizing

text: dynamic

text: time

text: warping

text: or

text: similar

text: approaches.

text: der

text: of

text: the

text: discussion,

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text: the

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text: celeration

text: sequences

text: from

text: natural

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text: device

text: pairing.

text: searching

text: for

text: minima

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text: by

text: applying

text: pattern

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text: template

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text: particular,

text: we

text: do

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text: consider

text: the

text: use

text: of

text: gait

text: for

text: user

text: matching

text: Similarity

text: can

text: be

text: estimated

text: by

text: the

text: computa-

text: authentication.

text: As

text: discussed

text: in

text: Section

text: gait

text: as

text: a

text: biometric

text: tion

text: of

text: cross-correlation

text: Alternatively,

text: machine

text: learn-

text: measure

text: has

text: several

text: limitations

text: and

text: its

text: strength

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text: the

text: seed

text: ing

text: classiﬁers

text: are

text: trained

text: and

text: applied

text: for

text: the

text: authentication

text: is,

text: even

text: after

text: many

text: years

text: of

text: research,

text: Finally,

text: a

text: recent

text: technique

text: employed

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text: acquire

text: human

text: questioned

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text: we

text: consider

text: attempts

text: to

text: gait

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text: to

text: monitor

text: phase

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text: of

text: an

text: electromagnetic

text: signal

text: exploit

text: gait

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text: a

text: stimuli

text: acceleration-based

text: ad-hoc

text: pairing,

text: reﬂected

text: from

text: a

text: subject

text: walking

text: towards

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text: changes

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text: the

text: same

text: context.

text: In

text: contrast

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text: (CSI)

text: from

text: WiFi

text: devices

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text: detection

text: of

text: gait.

text: After

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text: generates

text: instantaneous

text: one-time

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text: It

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text: gait

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text: Doppler

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text: the

text: torso

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text: entropy

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text: short-time

text: ﬂuctuation

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text: reﬂection

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text: gait

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text: the

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text: modalities

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text: footprints

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text: classes

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text: ground

text: reaction

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text: or

text: heel-to-toe

text: ratio

text: and

text: therefore

text: weak

text: key

text: spaces.

text: sectioncolor

text: sectioncolor

text: Gait

text: as

text: a

text: Biometric

text: Pattern

text: for

text: Authentication

text: Gait

text: Recognition

text: Authentication

text: systems

text: comprise

text: sensors

text: converting

text: Traditionally,

text: Gait

text: recognition

text: has

text: been

text: applied

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text: analog

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text: digital

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text: authenticaton

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text: people

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text: success

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text: is

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text: Static

text: parameter-based

text: approaches

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text: gait

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text: entropy

text: of

text: gait

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text: an

text: authentication

text: mechanism.

text: such

text: as

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text: cadence

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text: et

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text: trained

text: seven

text: individuals

text: to

text: imitate

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text: silhouette

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text: approaches

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text: speciﬁc

text: victim.

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text: after

text: intensive

text: training

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text: weeks

text: ette

text: shape

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text: disregard

text: temporal

text: information,

text: hours

text: every

text: day),

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text: for

text: one

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text: weeks,

text: often

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text: treating

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