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IEEE Trans. Robot. Autom. 16, 382–399 (2000)\nBalduzzi, F., Giua, A., Seatzu, C.: Modelling and simulation of manufacturing systems using first-order hybrid Petri nets. Int. J. Prod. Res. 39, 255–282 (2001)\nBalduzzi, F., Di Febbraro, A., Giua, A., Seatzu, C.: Decidability results in first-order hybrid Petri nets. Discret. Event Dyn. Syst. 11, 41–58 (2001)\nČapkovič, F.: Supervision of agents modelling evacuation at crisis situations. In: Jezic, G., Kusek, M., Nguyen, N.T., Howlett, R.J., Lakhmi, C.J. (eds.) Agent and Multi-Agent Systems: Technologies and Applications. LNAI, vol. 7327, pp. 24–33. Springer, Heidelberg (2012)\nČapkovič, F.: Modelling evacuation at crisis situations by Petri net-based supervision. In: Nguyen, N.T. (ed.) Transactions on CCI XII. LNCS, vol. 8240, pp. 143–170. Springer, Heidelberg (2013)\nČapkovič, F.: Agent-based modelling the evacuation of endangered areas. In: Nguyen, N.T., Attachoo, B., Trawiňski, B., Somboonviwat, K. (eds.) Intelligent Information and Database Systems. LNAI, vol. 8397, pp. 281–290, Springer, Cham Heidelberg, New York, Dordrecht, London (2014)\nDotoli, M., Fanti, M., Giua, A., Seatzu, C.: First-order hybrid Petri nets. An application to distributed manufacturing systems. Nonlinear Anal. Hybrid Syst. 2, 408–430 (2008)\nDavid, R., Alla, H.: On hybrid Petri nets. Discret. Event Dyn. Syst.: Theory Appl. 11, 9–40 (2001)\nDotoli, M., Fanti, M., Giua, A., Seatzu, C.: Modeling systems by hybrid Petri nets: an application to supply chains. In: Kordic, V. (ed.) Petri Net Theory and Applications. Chap. 5, pp. 91–109. I-Tech Education and Publishing, Vienna, Austria (2008)\nDotoli, M., Fanti, M., Iacobellis, G., Mangini, A.M.: A first-order hybrid Petri net model for supply chain management. IEEE Trans. Autom. Sci. Eng. 6, 744–758 (2009)\nHofman, U., Veichtlbauer, A., Miloucheva, T.: Dynamic evacuation architecture using context-aware policy management. Int. J. Comput. Sci. Appl. 6, 38–49 (2009)\nIordache, M.V., Antsaklis, P.J.: Supervision based on place invariants: a survey. Discret. Event Dyn. Syst. 16, 451–492 (2006)\nIordache, M.V., Antsaklis, P.J.: Supervisory Control of Concurrent Systems: A Petri Net Structural Approach. Birkhäuser, Boston (2006)\nIordache, M.V.: Methods for the Supervisory Control of Concurrent Systems Based on Petri Nets Abstraction. Ph.D. Dissertation, University of Notre Dame, Notre Dame, Indiana, USA (2003)\nLino, P., Maione, G.: Applying a discrete event system approach to problems of collective motion in emergency situations. In: Klingsch, W.W.F., Rogsch, Ch., Schadschneider, A., Schreckenberg, M. (eds.) Pedestrian and Evacuation Dynamics 2008, pp. 465–477. Springer, Heidelberg (2010)\nMurata, T.: Petri nets: properties, analysis and applications. Proc. IEEE 77, 541–580 (1989)\nPeterson, J.L.: Petri nets theory and the modelling of systems. Prentice-Hall Inc., Englewood Cliffs, New York (1981)\nPopova-Zeugmann, L.: Time Petri Nets: Theory, Tools and Applications, Part 1, Part 2. [Online]. http:\u002F\u002Fwww2.informatik.hu-berlin.de\u002F~popova\u002F1-part-short, http:\u002F\u002Fwww2.informatik.hu-berlin.de\u002F~popova\u002F2-part-short (2008)\nSessego, F., Giua, A., Seatzu, C.: HYPENS: a matlab tool for timed discrete, continuous and hybrid Petri nets. In: van Hee, K.M., Valk, R. (eds.) Applications and Theory of Petri Nets. LNCS, vol. 5062, pp. 419–428. Springer, Heidelberg (2008)\nSessego, F., Giua, A., Seatzu, C.: HYPENS manual. [Online]. http:\u002F\u002Fwww.diee.unica.it\u002Fautomatica\u002Fhypens\u002FManual_HYPENS (2008)",{"EN":197},"The evacuation process from endangered areas (EA) into safe spaces in crisis situations is modelled by means of simple agents (gate-ways equipped by sensors). Petri nets (PN) are utilized here to model the EA structure as well as the agents and their cooperation. More precisely, timed PN (TPN) and first-order hybrid PN (FOHPN) are used to do this. Rooms, other spaces to be evacuated (corridors) and the safe spaces out of EA (where people are evacuated) are modelled by TPN places and FOHPN continuous places. Gate-ways are modelled by TPN subnets and by FOHPN continuous transitions. While the supervisor for the TPN gate-ways can be synthesized by means of place\u002Ftransition PN (P\u002FT PN), the blocks of FOHPN discrete places and transitions are used to affect the gate-ways. Depending on the immediate throughput of the gate-ways the escape time behaviour is found in the process of simulation. This paper is the extended version of the paper (Čapkovič Intelligent Information and Database Systems. LNAI. Springer, Cham Heidelberg 2014) presented in the ACIIDS 2014 conference.",{"EN":199},"Agent-based modelling of the evacuation of endangered areas in crisis situations",{"VOID":201},"10.1007\u002Fs40595-014-0029-2","PUBLICATION","VERIFIED","Auto Verify","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40595-014-0029-2",[207],{"id":208,"sortIndex":21,"researcher":20,"roles":209,"affiliations":211,"properties":221},"a4fd55a4-f122-4e47-aa62-57eccf174bb0",[210],"AUTHOR",[212],{"id":20,"sortIndex":21,"affiliation":213,"properties":20},{"id":214,"createTime":215,"updateTime":215,"relativeEntities":216,"slug":217,"properties":218,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e940b33f-1ffd-43b7-804f-84f56e254cb4","2024-04-06T17:53:08.101+00:00",[],"Institute-of-Informatics-Slovak-Academy-of-Sciences-Bratislava-Slovakia",{"title":219},{"VI":220},"Institute of Informatics, Slovak Academy of Sciences, Bratislava, Slovakia",{"title":222},{"VI":223},"František Čapkovič","ARTICLE",{"url":205,"publisher":226,"properties":261},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":227,"slug":10,"properties":228,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":232,"manageAffiliations":233,"indexDatabases":234,"url":20,"thumbnailPath":20,"statistic":256,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":229,"eissn":230,"title":231},{"VOID":13},{"VOID":15},{"EN":17},[],[],[235,242,249],{"id":97,"indexDatabase":236,"url":112,"indexYears":20,"academicFieldIds":241,"indexDatabaseRanking":20},{"id":99,"createTime":100,"updateTime":101,"relativeEntities":237,"label":238,"description":239,"key":108,"publicationTags":240,"standard":20},[],{"EN":104,"VI":104},{"VI":106,"EN":107},[110,111],[114],{"id":140,"indexDatabase":243,"url":153,"indexYears":154,"academicFieldIds":248,"indexDatabaseRanking":20},{"id":142,"createTime":143,"updateTime":144,"relativeEntities":244,"label":245,"description":246,"key":150,"publicationTags":247,"standard":20},[],{"EN":147,"VI":147},{"VI":149,"EN":149},[152],[156],{"id":116,"indexDatabase":250,"url":129,"indexYears":130,"academicFieldIds":255,"indexDatabaseRanking":138},{"id":118,"createTime":119,"updateTime":120,"relativeEntities":251,"label":252,"description":253,"key":126,"publicationTags":254,"standard":20},[],{"EN":123,"VI":123},{"EN":123,"VI":125},[128],[132,133,134,135,136,137],{"impactFactor":21,"impactFactorByYear":257,"i10Index":164,"i10IndexLast5Year":21,"totalPublication":165,"totalPublicationByYear":258,"totalCitation":172,"totalCitationByYear":259,"totalCitationPerPublication":177,"totalCitationPerPublicationByYear":260,"hindexLast5Year":176,"hindex":176},{"2015":159,"2016":160,"2017":161,"2018":159,"2019":162,"2020":163},{"2013":82,"2014":167,"2015":168,"2016":169,"2017":170,"2018":171},{"2014":174,"2015":175,"2016":164,"2017":170,"2018":176},{"2014":179,"2015":180,"2016":181,"2017":164,"2018":182},{"volume":262,"pages":264},{"VOID":263},"2",{"VOID":265},"35-45","2014-09-04",2014,false,{"id":270,"createTime":271,"updateTime":272,"relativeEntities":273,"slug":274,"properties":275,"entityType":202,"verifyStatus":203,"verifyTime":272,"verifyNote":204,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":284,"fullTextUrl":20,"authors":285,"publicationType":224,"publisherRelationship":325,"citationCount":20,"citationInfo":20,"publishDate":366,"publishYear":367,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":268},"36bee01f-4a28-4cd9-9c2d-8879fbc68577","2024-01-11T10:41:55.565+00:00","2025-01-15T23:35:05.711+00:00",[],"Towards-an-enhanced-user-s-preferences-integration-into-ranking-process-using-dominance-approach",{"references":276,"abstract":278,"title":280,"doi":282},{"VOID":277},"Ait-Mlouk, A., Gharnati, F., Agouti, T.: Multi-agent-based modeling for extracting relevant association rules using a multi-criteria analysis approach. Vietnam J. Comput. Sci. 3(4), 235–245 (2016). doi:10.1007\u002Fs40595-016-0070-4\nArvanitis, A., Koutrika, G.: PrefDB: supporting preferences as first-class citizens in relational databases. IEEE Trans. Knowl. Data Eng. 26(6), 1430–1446 (2014). doi:10.1109\u002FTKDE.2013.28\nAsha, P., Srinivasan, S.: Analysing the associations between infected genes using data mining techniques. Int. J. Data Mining Bioinf. 15(3), 250–271 (2016). doi:10.1504\u002FIJDMB.2016.0770\nBouker, S., Saidi, R., Ben Yahia, S., Mephu Nguifo, E.: Mining undominated association rules through interestingness measures. Int J Artif. Intell. Tools. 23(4), 1460011 (2014). doi:10.1142\u002FS0218213014600112\nBranke, J., Corrente, S., Greco, S., Słowiński, R., Zielniewicz, P.: Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. Eur. J. Oper. Res. 250(3), 884–901 (2016). doi:10.1016\u002Fj.ejor.2015.10.027\nBranke, J.: MCDA and multiobjective evolutionary algorithms. Multiple Criteria Decision Analysis, pp. 977–1008 (2016). doi:10.1007\u002F978-1-4939-3094-4_23\nDe Amo, S., Saliou Diallo, M., Talibouya Diop, C., Giacometti, A., Li, D., Soulet, A.: Contextual preference mining for user profile construction. Inf. Syst. 49, 182–199 (2015). doi:10.1016\u002Fj.is.2014.11.009\nGheorghiu, R., Labrinidis, A., Chrysanthis, P.: Unifying Qualitative and Quantitative Database Preferences to Enhance Query Personalization. Proceedings of the Second International Workshop on Databases and the Web - ExploreDB’15, pp. 6–8 (2015). doi:10.1145\u002F2795218.2795223\nGupta, G.: Introduction to data mining with case studies. PHI Learning Pvt, Ltd (2014)\nJiang, B., Pei, J., L, X., Cheung, D., Han, J.: Mining preferences from superior and inferior examples. Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp. 390–398 (2008)\nKongchai, P., Kerdprasop, N., Kerdprasop, K.: Dissimilar Rule Mining and Ranking Technique for Associative Classification. Proceedings of the International MultiConference of Engineers and Computer Scientists 2013, IMECS 2013. 1 (2013)\nMallik, S., Mukhopadhyay, A., Maulik, U.: RANWAR: Rank-based weighted association rule mining from gene expression and methylation data. IEEE Trans. NanoBiosci. 14(1), 59–66 (2015)\nMehrotra, A., Hendley, R., Musolesi, M.: PrefMiner. Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing-UbiComp ’16, pp. 1223–1234 (2016). doi:10.1145\u002F2971648.2971747\nMiao, X., Gao, Y., Chen, G., Cui, H., Guo, C., Pan, W.: Si2p: a restaurant recommendation system using preference queries over incomplete information. Proc. VLDB Endow. 9(13), 1509–1512 (2016). doi:10.14778\u002F3007263.3007296\nMouhir, M., Gadi, T., Balouki, Y., El Far, M.: A new way to select the valuable association rules. 2015 7th International Conference on Knowledge and Smart Technology (KST), pp. 81–86 (2015). doi:10.1109\u002FKST.2015.7051464\nNajeeb, M. M., El Sheikh, A., Nababteh, M.: A new rule ranking model for associative classification using a hybrid artificial intelligence technique. In: Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on IEEE, pp. 231–235 (2011)\nRolfsnes, T., Moonen, L., Di Alesio, S., Behjati, R., Binkley, D.: Improving change recommendation using aggregated association rules. Proceedings of the 13th International Workshop on Mining Software Repositories—MSR ’16, pp. 73–84 (2016). doi:10.1145\u002F2901739.2901756\nShmueli, G., Peter Bruce, C., Nitin, Patel R.: Data mining for business analytics: concepts, techniques, and applications with XLMiner. Wiley, Hoboken (2016)\nSoulet, A., Raïssi, C., Plantevit, M., Cremilleux, B.: Mining Dominant Patterns in the Sky. 2011 IEEE 11th International Conference on Data Mining, pp. 655–664 (2011). doi:10.1109\u002FICDM.2011.100\nUgarte, W., Boizumault, P., Loudni, S., Crémilleux, B., Lepailleur, A.: Mining (Soft-) skypatterns using constraint programming. Advances in Knowledge Discovery and Management, pp. 105–136 (2015). doi:10.1007\u002F978-3-319-23751-0_6\nYang, G., Mabu, S. M., Shimada, K., Gong, Y., Hirasawa, K.: Ranking association rules for classification based on genetic network programming. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation ACM, pp. 1917–1918 (2009)\nZhang, J., Lin, Y., Lin, M., Liu, J.: An effective collaborative filtering algorithm based on user preference clustering. Appl. Intell. 45(2), 230–240 (2016). doi:10.1007\u002Fs10489-015-0756-9\nZhang, J., Jiang, X., Ku, W.S., Qin, X.: Efficient parallel skyline evaluation using mapreduce. IEEE Trans. Parallel Distrib. Syst. 27(7), 1996–2009 (2016)\nZhu, H., Chen, E., Xiong, H., Yu, K., Cao, H., Tian, J.: Mining mobile user preferences for personalized context-aware recommendation. ACM Trans. Intell. Syst. Technol. 5(4), 1–27 (2014). doi:10.1145\u002F253251",{"EN":279},"User preference is very important in orienting data miner, and this is the reason why these user preferences are integrated in the mining process, where they are coupled with Association Rules Mining “ARM” Algorithms to select only Association Rules “ARs” that satisfy the user’s wishes and expectations. Within this framework, several approaches were proposed to overcome some problems which persist with the traditional ARM algorithms mainly dimensionality phenomenon engendered by thresholding and the subjective choice of measures. “MDP\n                  \n                    \n                  \n                  $$_{\\mathrm {REF}}$$\n                  \n                    \n                  \n                 Algorithm” is one of these approaches; it prunes, filters to select the relevant ARs, while ”Rank-Sort-MDP\n                  \n                    \n                  \n                  $$_{\\mathrm {REF}}$$\n                  \n                    \n                  \n                ” sorts, ranks, and stores ARs to complete the MDP\n                  \n                    \n                  \n                  $$_{\\mathrm {REF}}$$\n                  \n                    \n                  \n                 algorithm mining operation. Experiment result on real database showed the advantages of MDP\n                  \n                    \n                  \n                  $$_{\\mathrm {REF}}$$\n                  \n                    \n                  \n                 algorithm and Rank-Sort-MDP\n                  \n                    \n                  \n                  $$_{\\mathrm {REF}}$$\n                  \n                    \n                  \n                 algorithm over the other algorithms.",{"EN":281},"Towards an enhanced user’s preferences integration into ranking process using dominance approach",{"VOID":283},"10.1007\u002Fs40595-017-0098-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40595-017-0098-0",[286,301,313],{"id":287,"sortIndex":82,"researcher":20,"roles":288,"affiliations":289,"properties":298},"ef1cb50f-b73c-457a-9cef-8a576392116a",[210],[290],{"id":20,"sortIndex":21,"affiliation":291,"properties":20},{"id":292,"createTime":293,"updateTime":293,"relativeEntities":294,"slug":20,"properties":295,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"f9654722-eb98-4165-bc45-d6363f7c93b5","2024-01-11T10:41:55.575+00:00",[],{"title":296},{"VI":297},"Laboratory of Informatics, Imaging and Modeling of Complex Systems in University of Hassan, Settat, Morocco",{"title":299},{"VI":300},"Taoufiq Gadi",{"id":302,"sortIndex":21,"researcher":20,"roles":303,"affiliations":304,"properties":310},"2b67a6e3-b7eb-4a73-ac08-0c3fefb6895c",[210],[305],{"id":20,"sortIndex":21,"affiliation":306,"properties":20},{"id":292,"createTime":293,"updateTime":293,"relativeEntities":307,"slug":20,"properties":308,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":309},{"VI":297},{"title":311},{"VI":312},"Mohammed Mouhir",{"id":314,"sortIndex":164,"researcher":20,"roles":315,"affiliations":316,"properties":322},"004b09f5-f41d-4529-9be9-49bfbf434c7e",[210],[317],{"id":20,"sortIndex":21,"affiliation":318,"properties":20},{"id":292,"createTime":293,"updateTime":293,"relativeEntities":319,"slug":20,"properties":320,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":321},{"VI":297},{"title":323},{"VI":324},"Youssef Balouki",{"url":284,"publisher":326,"properties":361},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":327,"slug":10,"properties":328,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":332,"manageAffiliations":333,"indexDatabases":334,"url":20,"thumbnailPath":20,"statistic":356,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":329,"eissn":330,"title":331},{"VOID":13},{"VOID":15},{"EN":17},[],[],[335,342,349],{"id":97,"indexDatabase":336,"url":112,"indexYears":20,"academicFieldIds":341,"indexDatabaseRanking":20},{"id":99,"createTime":100,"updateTime":101,"relativeEntities":337,"label":338,"description":339,"key":108,"publicationTags":340,"standard":20},[],{"EN":104,"VI":104},{"VI":106,"EN":107},[110,111],[114],{"id":140,"indexDatabase":343,"url":153,"indexYears":154,"academicFieldIds":348,"indexDatabaseRanking":20},{"id":142,"createTime":143,"updateTime":144,"relativeEntities":344,"label":345,"description":346,"key":150,"publicationTags":347,"standard":20},[],{"EN":147,"VI":147},{"VI":149,"EN":149},[152],[156],{"id":116,"indexDatabase":350,"url":129,"indexYears":130,"academicFieldIds":355,"indexDatabaseRanking":138},{"id":118,"createTime":119,"updateTime":120,"relativeEntities":351,"label":352,"description":353,"key":126,"publicationTags":354,"standard":20},[],{"EN":123,"VI":123},{"EN":123,"VI":125},[128],[132,133,134,135,136,137],{"impactFactor":21,"impactFactorByYear":357,"i10Index":164,"i10IndexLast5Year":21,"totalPublication":165,"totalPublicationByYear":358,"totalCitation":172,"totalCitationByYear":359,"totalCitationPerPublication":177,"totalCitationPerPublicationByYear":360,"hindexLast5Year":176,"hindex":176},{"2015":159,"2016":160,"2017":161,"2018":159,"2019":162,"2020":163},{"2013":82,"2014":167,"2015":168,"2016":169,"2017":170,"2018":171},{"2014":174,"2015":175,"2016":164,"2017":170,"2018":176},{"2014":179,"2015":180,"2016":181,"2017":164,"2018":182},{"volume":362,"pages":364},{"VOID":363},"5",{"VOID":365},"15-25","2017-07-15",2017,{"id":369,"createTime":370,"updateTime":371,"relativeEntities":372,"slug":373,"properties":374,"entityType":202,"verifyStatus":203,"verifyTime":371,"verifyNote":204,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":383,"fullTextUrl":20,"authors":384,"publicationType":224,"publisherRelationship":431,"citationCount":20,"citationInfo":20,"publishDate":472,"publishYear":267,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":268},"1ab621c7-f93f-44d2-a6d8-a02e567ae9b0","2024-01-15T05:35:52.146+00:00","2025-02-02T23:34:15.600+00:00",[],"IMSR-PreTree-an-improved-algorithm-for-mining-sequential-rules-based-on-the-prefix-tree",{"references":375,"abstract":377,"title":379,"doi":381},{"VOID":376},"Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th international conference very large data, bases, pp. 487–499 (1994)\nAgrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of the 11th international conference on data engineering, pp. 3–14. IEEE (1995)\nAyres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 429–435. ACM (2002)\nBaralis, E., Chiusano, S., Dutto, R.: Applying sequential rules to protein localization prediction. Comput. Math. Appl. 55(5), 867–878 (2008)\nVo, B., Hong, T.P., Le, B.: A lattice-based approach for mining most generalization association rules. Knowl. Based Syst. 45, 20–30 (2013)\nEl-Sayed, M., Ruiz, C., Rundensteiner, E.A.: FS-Miner: efficient and incremental mining of frequent sequence patterns in web logs. In: Proceedings of the 6th annual ACM international workshop on web information and data management, pp. 128–135 (2004)\nEzeife, C.I., Lu, Y., Liu, Y.: PLWAP sequential mining: open source code. In: Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations, pp. 26–35 (2005)\nFournier-Viger, P., Faghihi, U., Nkambou, R., Nguifo, E.M.: CMRules: mining sequential rules common to several sequences. Knowl. Based Syst. 25(1), 63–76 (2012)\nFournier-Viger, P., Nkambou, R., Tseng, V.S.M.: RuleGrowth: mining sequential rules common to several sequences by pattern-growth. In: Proceedings of the 2011 ACM symposium on applied computing, pp. 956–961 (2011)\nFournier-Viger, P., Wu, C.W., Tseng, V.S., Nkambou, R.: Mining sequential rules common to several sequences with the window size constraint. Adv. Artif. Intell. 299–304 (2012)\nGouda, K., Hassaan, M., Zaki, M.J.: Prism: an effective approach for frequent sequence mining via prime-block encoding. J. Comput. Syst. Sci. 76(1), 88–102 (2010)\nHan, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.C. FreeSpan: frequent pattern-projected sequential pattern mining. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 355–359 (2000)\nLo, D., Khoo, S.-C., Liu, C.: Efficient mining of recurrent rules from a sequence database. In: DASFAA 2008, LNCS vol. 4947, pp. 67–83 (2008)\nLo, D., Khoo, S.C., Wong, L.: Non-redundant sequential rules—theory and algorithm. Inf. Syst. 34(4), 438–453 (2009)\nMasseglia, F., Cathala, F., Poncelet, P.: The PSP approach for mining sequential patterns. In: PKDD’98, Nantes, France, LNCS vol. 1510, pp. 176–184 (1998)\nPei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Hsu, M.C.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)\nPei, J., Han, J., Mortazavi-Asl, B., Zhu, H.: Mining access patterns efficiently from web logs. In: Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’00), Kyoto, Japan, pp. 396–407 (2000)\nSpiliopoulou, M.: Managing interesting rules in sequence mining. In: Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery, Prague, Czech Republic, pp. 554–560 (1999)\nSrikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, Avignon, France, LNCS, pp. 3–17 (1996)\nUCI Machine Learning Repository. http:\u002F\u002Fwww.ics.uci.edu\u002Fmlearn\u002FMLRepository.html\nVan, T.T., Vo, B., Le, B.: Mining sequential rules based on prefix-tree. In: Proceedings of the 3rd Asian Conference on Intelligent Information and Database Systems, Daegu, Korea, pp. 147–156 (2011)\nZaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1–2), 31–60 (2001)",{"EN":378},"Sequential rules generated from sequential patterns express temporal relationships among patterns. Sequential rule mining is an important research problem because it has broad application such as the analyses of customer purchases, web log, DNA sequences, and so on. However, developing an efficient algorithm for mining sequential rules is a difficult problem due to the large size of the sequential pattern set. The larger the sequential pattern set, the longer the mining time. In this paper, we propose a new algorithm called IMSR_PreTree which is an improved algorithm of MSR_PreTree that mines sequential rules based on prefix-tree. IMSR_PreTree also generates rules from frequent sequences stored in a prefix-tree but it prunes the sub trees which give non-significant rules very early in the process of rule generation and avoids tree scanning as much as possible. Thus, IMSR_PreTree can significantly reduce the search space during the mining process. Our performance study shows that IMSR_PreTree outperforms MSR_PreTree, especially on large sequence databases.",{"EN":380},"IMSR_PreTree: an improved algorithm for mining sequential rules based on the prefix-tree",{"VOID":382},"10.1007\u002Fs40595-013-0012-3","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40595-013-0012-3",[385,401,416],{"id":386,"sortIndex":164,"researcher":20,"roles":387,"affiliations":388,"properties":398},"445bd0c0-d38a-4160-a850-f9056e8e58b9",[210],[389],{"id":20,"sortIndex":21,"affiliation":390,"properties":20},{"id":391,"createTime":392,"updateTime":392,"relativeEntities":393,"slug":394,"properties":395,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"9f00d37c-5d86-4aab-a7d1-9b67fea542b1","2025-12-12T09:20:27.512+00:00",[],"Faculty-of-Information-Technology-Ton-Duc-Thang-University-Ho-Chi-Minh-Vietnam",{"title":396},{"EN":397},"Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh, Vietnam",{"title":399},{"VI":400},"Bay Vo",{"id":402,"sortIndex":82,"researcher":20,"roles":403,"affiliations":404,"properties":413},"cc612170-52cb-422c-bbbd-abadff69c508",[210],[405],{"id":20,"sortIndex":21,"affiliation":406,"properties":20},{"id":407,"createTime":408,"updateTime":408,"relativeEntities":409,"slug":20,"properties":410,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"16dcec72-fd90-4d13-9a67-48f647c1a9af","2024-01-15T05:35:52.217+00:00",[],{"title":411},{"VI":412},"Faculty of Information Technology, University of Science, VNU, Ho Chi Minh, Vietnam",{"title":414},{"VI":415},"Bac Le",{"id":417,"sortIndex":21,"researcher":20,"roles":418,"affiliations":419,"properties":428},"57b198dc-d659-4986-930f-fc8b15a4c463",[210],[420],{"id":20,"sortIndex":21,"affiliation":421,"properties":20},{"id":422,"createTime":423,"updateTime":423,"relativeEntities":424,"slug":20,"properties":425,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"a3dc7693-8fbc-48f4-abf0-2cffc1528e30","2024-01-15T05:35:52.173+00:00",[],{"title":426},{"VI":427},"Faculty of Information Technology, Ho Chi Minh City University of Technology, Ho Chi Minh, Vietnam",{"title":429},{"VI":430},"Thien-Trang Van",{"url":383,"publisher":432,"properties":467},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":433,"slug":10,"properties":434,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":438,"manageAffiliations":439,"indexDatabases":440,"url":20,"thumbnailPath":20,"statistic":462,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":435,"eissn":436,"title":437},{"VOID":13},{"VOID":15},{"EN":17},[],[],[441,448,455],{"id":97,"indexDatabase":442,"url":112,"indexYears":20,"academicFieldIds":447,"indexDatabaseRanking":20},{"id":99,"createTime":100,"updateTime":101,"relativeEntities":443,"label":444,"description":445,"key":108,"publicationTags":446,"standard":20},[],{"EN":104,"VI":104},{"VI":106,"EN":107},[110,111],[114],{"id":140,"indexDatabase":449,"url":153,"indexYears":154,"academicFieldIds":454,"indexDatabaseRanking":20},{"id":142,"createTime":143,"updateTime":144,"relativeEntities":450,"label":451,"description":452,"key":150,"publicationTags":453,"standard":20},[],{"EN":147,"VI":147},{"VI":149,"EN":149},[152],[156],{"id":116,"indexDatabase":456,"url":129,"indexYears":130,"academicFieldIds":461,"indexDatabaseRanking":138},{"id":118,"createTime":119,"updateTime":120,"relativeEntities":457,"label":458,"description":459,"key":126,"publicationTags":460,"standard":20},[],{"EN":123,"VI":123},{"EN":123,"VI":125},[128],[132,133,134,135,136,137],{"impactFactor":21,"impactFactorByYear":463,"i10Index":164,"i10IndexLast5Year":21,"totalPublication":165,"totalPublicationByYear":464,"totalCitation":172,"totalCitationByYear":465,"totalCitationPerPublication":177,"totalCitationPerPublicationByYear":466,"hindexLast5Year":176,"hindex":176},{"2015":159,"2016":160,"2017":161,"2018":159,"2019":162,"2020":163},{"2013":82,"2014":167,"2015":168,"2016":169,"2017":170,"2018":171},{"2014":174,"2015":175,"2016":164,"2017":170,"2018":176},{"2014":179,"2015":180,"2016":181,"2017":164,"2018":182},{"volume":468,"pages":470},{"VOID":469},"1",{"VOID":471},"97-105","2014-01-30",{"id":474,"createTime":475,"updateTime":476,"relativeEntities":477,"slug":478,"properties":479,"entityType":202,"verifyStatus":203,"verifyTime":476,"verifyNote":204,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":488,"fullTextUrl":20,"authors":489,"publicationType":224,"publisherRelationship":544,"citationCount":20,"citationInfo":20,"publishDate":584,"publishYear":585,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":268},"20d4109a-517c-43c9-a9c0-80813b0d5d56","2024-01-09T01:51:05.847+00:00","2025-02-07T23:16:27.196+00:00",[],"Evaluation-of-SPARQL-compliant-semantic-search-user-interfaces",{"references":480,"abstract":482,"title":484,"doi":486},{"VOID":481},"Berners-Lee, T., Hendler, J., Lassila, O.: The semantic web. Sci. Am. 284(5), 34–43 (2001)\nBizer, C., Auer, S., Kobilarov, G., Lehmann, J., Becker, C., Hellmann, S.: DBpedia—querying wikipedia like a database and an interlinking-hub in the web of data. In: Querying Wikipedia Like a Database (4\u002F4\u002F2009) FU Berlin, Universitt Leipzig (2009)\nCarroll, J., Dickinson, I., Dollin, C., Reynolds, D., Seaborne, A., Wilkinson, K.: Jena: implementing the semantic web recommendations. Tech. rep., Hewlett Packard (2003)\nDamljanovic, D., Agatonovic, M., Cunningham, H.: Natural language interfaces to ontologies: Combining syntactic analysis and ontology-based lookup through the user interaction. In: Aroyo, L., Antoniou, G., Hyvnen, E., ten Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) ESWC (1). Lecture Notes in Computer Science, vol. 6088, pp. 106–120. Springer, Berlin (2010)\nElbedweihy, K., Wrigley, S.N., Ciravegna, F.: Evaluating semantic search query approaches with expert and casual users. In: Proceedings of the 11th International Conference on The Semantic Web (ISWC’12), vol. Part II, pp. 274–286. Springer, Berlin (2012)\nGoutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In: Losada, D.E., Fernndez-Luna, J.M. (eds.) ECIR. Lecture Notes in Computer Science, vol. 3408, pp. 345–359. Springer, Berlin (2005)\nHan, L., Finin, T., Joshi, A.: GoRelations: an intuitive query system for DBpedia. In: Pan, J.Z., Chen, H., Kim, H.G., Li, J., Wu, Z., Horrocks, I., Mizoguchi, R., Wu, Z. (eds.) JIST. Lecture Notes in Computer Science, vol. 7185, pp. 334–341. Springer, Berlin (2011)\nKasneci, G., Suchanek, F.M., Ifrim, G., Ramanath, M., Weikum, G.: NAGA: searching and ranking knowledge. In: 24th International Conference on Data Engineering (ICDE’08). IEEE, IEEE Press, Cancun (2008)\nLopez, V., Fernndez, M., Motta, E., Stieler, N.: Poweraqua: supporting users in querying and exploring the semantic web. Semantic Web 3(3), 249–265 (2012)\nRussell, A., Smart, P.R., Braines, D., Shadbolt, N.R.: Nitelight: A graphical tool for semantic query construction. In: Semantic Web User Interaction Workshop (SWUI’08) (2008), event Dates: 5th April 2008\nStyperek, A., Ciesielczyk, M., Szwabe, A.: Sparql compliant semantic search engine with an intuitive user interface. In: Nguyen, N., Attachoo, B., Trawiski, B., Somboonviwat, K. (eds.) Intelligent Information and Database Systems. Lecture Notes in Computer Science, vol. 8397, pp. 201–210. Springer International Publishing, NY (2014)\nSuchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge unifying WordNet and Wikipedia. In: Proceedings of the 16th International World Wide Web Conference (WWW’07), pp. 697–706. Banff (2007)\nThe Apache Software Foundation: Apache Lucene Core. http:\u002F\u002Flucene.apache.org\u002Fcore\u002F (2014). Accessed 5 Apr 2015\nTran, T., Math, T., Haase, P.: Usability of keyword-driven schema-agnostic search. In: Aroyo, L., Antoniou, G., Hyvnen, E., Teije, A., Stuckenschmidt, H., Cabral, L., Tudorache, T. (eds.) The Semantic Web: Research and Applications. Lecture Notes in Computer Science, vol. 6089, pp. 349–364. Springer, Berlin (2010)\nWang, C., Xiong, M., Zhou, Q., Yu, Y.: Panto: a portable natural language interface to ontologies. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC. Lecture Notes in Computer Science, vol. 4519, pp. 473–487. Springer, Berlin (2007)\nWrigley, S.N., Reinhard, D., Elbedweihy, K., Bernstein, A., Ciravegna, F.: Methodology and campaign design for the evaluation of semantic search tools. In: Proceedings of the 3rd International Semantic Search Workshop (SEMSEARCH’10), pp. 10:1–10:10. ACM, New York (2010)\nZhou, Q., Wang, C., Xiong, M., Wang, H., Yu, Y.: Spark: adapting keyword query to semantic search. In: Aberer, K., Choi, K.S., Noy, N., Allemang, D., Lee, K.I., Nixon, L., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudr-Mauroux, P. (eds.) The Semantic Web. Lecture Notes in Computer Science, vol. 4825, pp. 694–707. Springer, Berlin (2007)",{"EN":483},"A regular user of a semantic search system frequently posses no knowledge about the SPARQL language nor about the ontology of a given knowledge base, especially when it provides domain-unspecific data obtained from heterogeneous sources. Nevertheless, he\u002Fshe should be provided with tools enabling both intuitive and effective exploration of RDF-compliant knowledge bases. Natural language querying is one of the solutions that have been proposed so far as means for making knowledge bases more user-friendly. However, the results of natural language querying usually have lower precision and recall than analogical results of graph-based querying. In the paper, we introduce an evaluation methodology based on the 2011 QALD workshop queries that allows to measure the accuracy of a semantic search system as well as the complexity of the query formulation process. The obtained results confirm the intuition that graph-based querying, although assuring comparatively high accuracy of the results, is usually still too difficult for regular users. On the other hand, on the basis of results obtained for an experimental search system referred to as Semantic Focused Crawler, we claim that enhancing a SPARQL-compliant graph-based system by an entity-type recommendation feature may reduce the number of query elements necessary to formulate a query without compromising the quality of the results.",{"EN":485},"Evaluation of SPARQL-compliant semantic search user interfaces",{"VOID":487},"10.1007\u002Fs40595-015-0044-y","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40595-015-0044-y",[490,508,520,532],{"id":491,"sortIndex":492,"researcher":20,"roles":493,"affiliations":494,"properties":505},"40bd860d-a69b-4a6a-b230-2dd1768367e6",3,[210],[495],{"id":20,"sortIndex":21,"affiliation":496,"properties":20},{"id":497,"createTime":498,"updateTime":499,"relativeEntities":500,"slug":501,"properties":502,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"8ecd5e7b-2483-42f5-9767-4cb6535c0201","2024-01-20T11:22:01.715+00:00","2024-09-01T20:20:59.730+00:00",[],"Poznan-University-of-Technology-Poznan-Poland",{"title":503},{"VI":504},"Poznan University of Technology, Poznan, Poland",{"title":506},{"VI":507},"Pawel Misiorek",{"id":509,"sortIndex":164,"researcher":20,"roles":510,"affiliations":511,"properties":517},"d5bea03f-5536-43d7-b86a-ccdd997411da",[210],[512],{"id":20,"sortIndex":21,"affiliation":513,"properties":20},{"id":497,"createTime":498,"updateTime":499,"relativeEntities":514,"slug":501,"properties":515,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":516},{"VI":504},{"title":518},{"VI":519},"Michal Ciesielczyk",{"id":521,"sortIndex":21,"researcher":20,"roles":522,"affiliations":523,"properties":529},"83c3910a-6401-42d6-b933-c586d01d0099",[210],[524],{"id":20,"sortIndex":21,"affiliation":525,"properties":20},{"id":497,"createTime":498,"updateTime":499,"relativeEntities":526,"slug":501,"properties":527,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":528},{"VI":504},{"title":530},{"VI":531},"Adam 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H., Wang, Z., Nie, F.: Orthogonal least squares regression for feature extraction. Neurocomputing 216, 200–207 (2016)\nZhao, M., Jiang, B., Luo, B., et al.: Common visual patterns discovery with an elastic matching model. Cogn. Comput 8(5), 839–846 (2016)\nLi, J., Li, X., Yang, B., et al.: Segmentation-based image copy-move forgery detection scheme. IEEE Trans. Inf. Forensics Secur. 10(3), 507–518 (2015)\nCox, I.J., Kilian, J., Leighton, F.T., et al.: Secure spread spectrum watermarking for multimedia. Secure spread spectrum watermarking for images, audio and video. 3, 243-246 (1996)\nCoatrieux, G., Guillou, C.L., Cauvin, J.M., et al.: Reversible watermarking for knowledge digest embedding and reliability control in medical images. IEEE Trans. Inf. Technol. Biomed. Publ. 13(2), 158–165 (2009)\nBarton, J.M.: Method and apparatus for embedding authentication information within digital data: US, US 6047374 A[P] (2000)\nYin, Z., Niu, X., Zhou, Z, et al.: Improved reversible image authentication scheme. Cogn. Comput. 1–10 (2016)\nLee, S., Yoo, C.D., Kalker, T.: Reversible image watermarking based on integer-to-integer wavelet transform. IEEE Trans. Inf. Forensics Secur. 2(3), 321–330 (2007)\nHwang, K., Li, D.: Trusted cloud computing with secure resources and data coloring. IEEE Internet Comput. 14(5), 14–22 (2010)\nTian, J.: Reversible data embedding using a difference expansion. IEEE Trans. Circuits Syst. Video Technol. 13(8), 890–896 (2003)\nKim, K.S., Lee, M.J., Lee, H.Y., et al.: Reversible data hiding exploiting spatial correlation between sub-sampled images. Pattern Recognit. 42(11), 3083–3096 (2009)\nLi, X., Zhang, W., Gui, X., et al.: Efficient reversible data hiding based on multiple histograms modification. IEEE Trans. Inf. Forensics Secur. 10(9), 2016–2027 (2015)\nLuo, H., Yu, F.X., Chen, H., et al.: Reversible data hiding based on block median preservation. Inf. Sci. 181(2), 308–328 (2011)\nLou, D.C., Hu, M.C., Liu, J.L.: Multiple layer data hiding scheme for medical images. Comput. Stand. Interfaces 31(2), 329–335 (2009)\nChang, C.C., Nguyen, T.S., Lin, C.C.: A reversible data hiding scheme for VQ indices using locally adaptive coding. J. Vis. Commun. Image Represent. 22(7), 664–672 (2011)\nThodi, D.M., Rodrguez, J.J.: Expansion embedding techniques for reversible watermarking. IEEE Trans. Image Process. 16(3), 721–730 (2007)\nNi, Z., Shi, Y.Q., Ansari, N., et al.: Reversible data hiding. IEEE Trans. Circuits Syst. Video Technol. 16(3), 354–362 (2006)\nLi, Y.C., Yeh, C.M., Chang, C.C.: Data hiding based on the similarity between neighboring pixels with reversibility. Digit. Signal Process. 20(4), 1116–1128 (2010)\nTai, W.L., Yeh, C.M., Chang, C.C.: Reversible data hiding based on histogram modification of pixel differences. IEEE Trans. Circuits Syst. Video Technol. 19(6), 906–910 (2009)\nLi, X., Li, B., Yang, B., et al.: General framework to histogram-shifting-based reversible data hiding. IEEE Trans. Image Process. 22(6), 2181–2191 (2013)\nQin, C., Chang, C.C., Liao, L.T.: An adaptive prediction-error expansion oriented reversible information hiding scheme. Pattern Recognit. Lett. 33(16), 2166–2172 (2012)\nGui, X., Li, X., Yang, B.: A high capacity reversible data hiding scheme based on generalized prediction-error expansion and adaptive embedding. Signal Process. 98, 370–380 (2014)\nHong, W., Chen, T.S.: Reversible data embedding for high quality images using interpolation and reference pixel distribution mechanism. J. Vis. Commun. Image Represent. 22(2), 131–140 (2011)\nLu, T.C., Chang, C.C., Huang, Y.H.: High capacity reversible hiding scheme based on interpolation, difference expansion, and histogram shifting. Multimed. Tools Appl. 72(1), 417–435 (2014)\nLin, C.C., Tai, W.L., Chang, C.C.: Multilevel reversible data hiding based on histogram modification of difference images. Pattern Recognit. 41(12), 3582–3591 (2008)\nTsai, P., Hu, Y.C., Yeh, H.L.: Reversible image hiding scheme using predictive coding and histogram shifting. Signal Process. 89(6), 1129–1143 (2009)\nLiu, L., Chang, C.C., Wang, A.: Reversible data hiding scheme based on histogram shifting of n-bit planes. Multimed. Tools Appl. 1–16 (2015)\nFallahpour, M., Sedaaghi, M.H.: High capacity lossless data hiding based on histogram modification. IEICE Electron Express 4(7), 205–210 (2007)\nKuo, W.C., Jiang, D.J., Huang, Y.C.A.: Reversible data hiding scheme based on block division. In: IEEE Congress on Image and Signal Processing, CISP’08, vol. 2008, no. (1), pp. 365–369 (2008)\nLee, C.W., Tsai, W.H.: A Lossless Data Hiding Method by Histogram Shifting Based on an Adaptive Block Division Scheme. Pattern Recognition and Machine Vision, pp. 1–14. River Publishers, Aalborg (2010)\nHe, W.: Improved block redundancy mining based reversible data hiding using multi-sub-blocking. Signal Process. Image Commun. 60, 199–210 (2018)\nHe, W., Xiong, G., Zhou, K., et al.: Reversible data hiding based on multilevel histogram modification and pixel value grouping. J. Vis. Commun. Image Represent. 40, 459–469 (2016)\nChang, J.C., Lu, Y.Z., Wu, H.L.: A separable reversible data hiding scheme for encrypted JPEG bitstreams. Signal Process. 133, 135–143 (2017)",{"EN":596},"In this paper, we proposed a new technique for reversible data hiding based on efficient compressed domain with multiple bit planes. We conducted a sequence of experiments to use block division scheme to appraise the result with different parameters and amended the probability of zero point in every block of histogram. This scheme attained more embedding capacity and high-quality of stego-image. Experimental consequences prove that the proposed method effectively achieved the objective of high embedding capacity and sustaining the quality of image.",{"EN":598},"A new multilevel reversible bit-planes data hiding technique based on histogram shifting of efficient compressed domain",{"VOID":600},"10.1007\u002Fs40595-018-0114-z","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40595-018-0114-z",[603,619,634,649,661,673],{"id":604,"sortIndex":605,"researcher":20,"roles":606,"affiliations":607,"properties":616},"abd29c33-de1a-4c4f-9811-8abd7568ef9a",5,[210],[608],{"id":20,"sortIndex":21,"affiliation":609,"properties":20},{"id":610,"createTime":611,"updateTime":611,"relativeEntities":612,"slug":20,"properties":613,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"51a951fb-996f-4e52-a767-1fad7280f30a","2024-02-18T04:20:48.696+00:00",[],{"title":614},{"VI":615},"School of Computer and Technology, Anhui University, Hefei, People’s Republic of China",{"title":617},{"VI":618},"Lixiang Xu",{"id":620,"sortIndex":164,"researcher":20,"roles":621,"affiliations":622,"properties":631},"d29d527a-b9ca-48f2-af4a-c1375a6ebdef",[210],[623],{"id":20,"sortIndex":21,"affiliation":624,"properties":20},{"id":625,"createTime":626,"updateTime":626,"relativeEntities":627,"slug":20,"properties":628,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"50b4c985-4222-4d57-b546-6d4b21cc3d24","2024-02-18T04:20:48.704+00:00",[],{"title":629},{"VI":630},"Key Lab of Intelligent Computing and Signal Processing of MOE and School of Computer and Technology, Anhui University, Hefei, People’s Republic of China",{"title":632},{"VI":633},"Bin Luo",{"id":635,"sortIndex":82,"researcher":20,"roles":636,"affiliations":637,"properties":646},"d75c03b4-50db-4c04-81c8-ec1c1bc722b1",[210],[638],{"id":20,"sortIndex":21,"affiliation":639,"properties":20},{"id":640,"createTime":641,"updateTime":641,"relativeEntities":642,"slug":20,"properties":643,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"4b0d4739-ce09-4232-8787-353233262c60","2024-02-18T04:20:48.714+00:00",[],{"title":644},{"VI":645},"College of Computer Science Technology, Chongqing University, Chongqing, People’s Republic of China",{"title":647},{"VI":648},"Gohar Rehman",{"id":650,"sortIndex":176,"researcher":20,"roles":651,"affiliations":652,"properties":658},"1dd27e0a-90ad-487d-b09c-7d7a28f4396c",[210],[653],{"id":20,"sortIndex":21,"affiliation":654,"properties":20},{"id":610,"createTime":611,"updateTime":611,"relativeEntities":655,"slug":20,"properties":656,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":657},{"VI":615},{"title":659},{"VI":660},"Muhammad Shahid Iqbal",{"id":662,"sortIndex":492,"researcher":20,"roles":663,"affiliations":664,"properties":670},"7ec49685-c22b-4284-ac00-33376abb2bf8",[210],[665],{"id":20,"sortIndex":21,"affiliation":666,"properties":20},{"id":610,"createTime":611,"updateTime":611,"relativeEntities":667,"slug":20,"properties":668,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":669},{"VI":615},{"title":671},{"VI":672},"Haseeb Hassan",{"id":674,"sortIndex":21,"researcher":20,"roles":675,"affiliations":676,"properties":682},"1919101f-2f25-45c0-8f5f-3bd1111dec16",[210],[677],{"id":20,"sortIndex":21,"affiliation":678,"properties":20},{"id":610,"createTime":611,"updateTime":611,"relativeEntities":679,"slug":20,"properties":680,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":681},{"VI":615},{"title":683},{"VI":684},"Rashid 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K., Katsifarakis, N., Orlowski, C. Sarzyński A.: Urban air quality forecasting: a regression and a classification approach. In: In Nguyen N.T. et al. (eds.): Intelligent information and database systems, \\(9^{\\text{th}}\\) Asian Conference on Intelligent Information and Database Systems, Part II, Lecture Notes in Artificial Intelligence vol. 10192, pp. 1–10 (2017). https:\u002F\u002Fdoi.org\u002F10.1007\u002F978-3-319-54430-4_52\nRiffat, S., Powell, R., Aydin, D.: Future cities and environmental sustainability. Future Cities Environ. 2, 1 (2016). https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs40984-016-0014-2\nWebel, S.: Forecasting Software that’s a Breath of Fresh Air. Pictures of the Future Siemens Magazine, (2016) http:\u002F\u002Fwww.siemens.com\u002Finnovation\u002Fen\u002Fhome\u002Fpictures-of-the-future\u002Finfrastructure-and-finance\u002Fsmart-cities-air-pollution-forecasting-models.html. Accessed 18 Aug 2017\nDawe, S. Paradice, D.: A systems approach to smart city infrastructure: a small city perspective. In: Proceedings of the Thirty Seventh International Conference on Information Systems, Dublin, http:\u002F\u002Fiot-smartcities.lero.ie\u002Fwp-content\u002Fuploads\u002F2016\u002F12\u002FA-Systems-Approach-to-Smart-City-Infrastructure-A-Small-City-Perspective.pdf. Accessed 18 Aug 2017\nMarinov, M.B., Topalov, I., Gieva, E., Nikolov, G.: Air quality monitoring in urban environments. In: 39th International Spring Seminar on Electronics Technology (ISSE), Pilsen, pp. 443–448. (2016). https:\u002F\u002Fdoi.org\u002F10.1109\u002FISSE.2016.7563237\nBukoski, B., Taylor, E.M.: Air quality forecasting. Air quality management 129–138 (2014)\nKukkonen, J., Olsson, T., Schultz, D.M., Baklanov, A., Klein, T., Miranda, A.I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lehtinen, K.E.J., Karatzas, K., San José, R., Astitha, M., Kallos, G., Schaap, M., Reimer, E., Jakobs, H., Eben, K.: A review of operational, regional-scale, chemical weather forecasting models in Europe. Atmos. Chem. Phys. 12, 1–87 (2012)\nKaratzas, K., Kaltsatos, S.: Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simul. Modelling Pract. Theory 15(10), 1310–1319 (2007)\nEEA, 2016: Air quality in Europe—2016 report, European Environment Agency, https:\u002F\u002Fdoi.org\u002F10.2800\u002F80982. https:\u002F\u002Fwww.eea.europa.eu\u002F\u002Fpublications\u002Fair-quality-in-europe-2016. Accessed 18 Aug 2017\nJuda-Rezler, K., Trapp, W., Reizer, M.: Modelling the impact of climate changes on particulate matter levels over Poland. In: Steyn, D.G., Rao, S.T. (eds.) Air pollution modeling and its application XX, pp. 499–450 (2010)\nMoussiopoulos, N., Vlachokostas, C., Tsilingiridis, G., Douros, I., Hourdakis, E., Naneris, C., Sidiropoulos, C.: Air quality status in Greater Thessaloniki Area and the emission reductions needed for attaining the EU air quality legislation. Sci. Total Environ. 407(4), 1268–1285 (2009)\nAndrews, A.: The clean air handbook, a practical guideline to EU air quality law, https:\u002F\u002Fwww.clientearth.org\u002Freports\u002F20140515-clientearth-air-pollution-clean-air-handbook.pdf. Accessed 18 Aug 2017\nWHO: Air Quality Guidelines, global update 2005, ISBN 92 890 2192 6 via http:\u002F\u002Fwww.euro.who.int. Accessed 18 Aug. 2017\nSiwek, K., Osowski, S.: Improving the accuracy of prediction of PM10 pollution by the wavelet transformation and an ensemble of neural predictors. Eng. Appl. Artif. Intel. 25(6), 1246–1258 (2012)\nZhou, Q., Jiang, H., Wang, J., Zhou, J.: A hybrid model for PM2.5 forecasting based on ensemble empirical mode decomposition and a general regression neural network. Sci. Total Environ. 496, 264–274 (2014)\nBiancofiore, F., Busilacchio, M., Verdecchia, M., Tomassetti, B., Aruffo, E., Bianco, S., Di Tommaso, S., Colangeli, C., Rosatelli, G., Carlo, P.: Recursive neural network model for analysis and forecast of PM10 and PM2.5. atmospheric. Pollut. Res. 8(4), 652–659 (2017)\nKhokhlov, V.N., Glushkov, A.V., Loboda, N.S., Bunyakova, Y.Y.: Short-range forecast of atmospheric pollutants using non-linear prediction method. Atmos. Environ. 42(31), 7284–7292 (2008)\nOrłowski, C., Sarzyński, A.: A model for forecasting pm10 levels with the use of artificial neural networks. In: Information Systems Architecture and Technology—the use of IT Technologies to Support Organizational Management in Risky Environment, Wrocław (2014)\nOrłowski, C., Sarzyński, A., Karatzas, K., Katsifarakis, N., Nazarko J.: Adaptation of an ANN-based air quality forecasting model to a new application area. In: Król D., Nguyen N., Shirai K. (eds) Advanced Topics in Intelligent Information and Database Systems 479-488 (2017)\nKaratzas, K., Kaltsatos, S.: Air pollution modelling with the aid of computational intelligence methods in Thessaloniki, Greece. Simul. Model. Pract. Theory 15(10), 1310–1319 (2007)\nVoukantsis, D., Karatzas, K., Kukkonen, J., Räsänen, T., Karppinen, A., Kolehmainen, M.: Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci. Total Environ. 409, 1266–1276 (2011)\nSzczepaniak, K., Astel, A., Bode, P., Sârbu, C., Biziuk, M., Raińska, E., Gos, K.: Assessment of atmospheric inorganic pollution in the urban region of Gdańsk. J. Radioanal. Nuclear Chem. 270(1), 35–42 (2006)\nHall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. SIGKDD Explorations 11(1), 10–18 (2009)\nBreiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)\nKohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. Proc. Fourteenth Int. Joint Conf. Artif. Intel. 2(12), 1137–1143 (1995)\nEPA: Guidelines for developing an air quality (ozone and PM2.5) forecasting program, U.S. Environmental Protection Agency report EPA-456\u002FR-03-002, https:\u002F\u002Fwww3.epa.gov\u002Fairnow\u002Faq_forecasting_guidance-1016.pdf. Accessed 18 Aug 2017\nVoukantsis, D., Niska, H., Karatzas, K., Riga, M., Damialis, A., Vokou, D.: Forecasting daily pollen concentrations using data-driven modeling methods in Thessaloniki, Greece. Atmos. Environ. 44(39), 5101–5111 (2010)\nTzima, F., Mitkas, P., Voukantsis, D., Karatzas, K.: Sparse episode identification in environmental datasets: the case of air quality assessment. Expert Syst. with Appl. 38(5), 5019–5027 (2011)",{"EN":737},"We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, accompanied by Random Forest models, for five locations per study area and for a dataset of limited feature dimensionality. An ensemble approach is also used for generating and testing AQ forecasting models. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the daily mean \n                  \n                    \n                  \n                  $$\\hbox {PM}_{10}$$\n                  \n                    \n                  \n                 concentration one day in advance reaching 0.765 for one of the TMA locations and 0.64 for one of the GMA locations. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts on the basis of limited feature space dimensionality and by employing simple linear regression models.",{"EN":739},"Revisiting urban air quality forecasting: a regression approach",{"VOID":741},"10.1007\u002Fs40595-018-0113-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs40595-018-0113-0",[744,759,774,786],{"id":745,"sortIndex":492,"researcher":20,"roles":746,"affiliations":747,"properties":756},"4f176d66-62aa-42f7-becb-72de9ce536d8",[210],[748],{"id":20,"sortIndex":21,"affiliation":749,"properties":20},{"id":750,"createTime":751,"updateTime":751,"relativeEntities":752,"slug":20,"properties":753,"entityType":81,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"90fa13fa-a5f1-4381-a3a2-b071c788fe5d","2024-02-11T13:18:23.390+00:00",[],{"title":754},{"VI":755},"Department of Applied 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Sci. 1(3), 277–293 (1990)",{"doi":984},"10.1142\u002FS0129054190000205",{"id":20,"text":986,"url":20,"identifiers":987},"Gorrieri, R., Marchetti, S., Montanari, U.: A $$^2$$ 2 CCS: atomic actions for CCS. Theor. Comput. Sci. 72(2–3), 203–223 (1990)",{"doi":988},"10.1016\u002F0304-3975(90)90035-G",{"id":20,"text":990,"url":20,"identifiers":991},"Gorrieri, R., Versari, C.: A process calculus for expressing finite place\u002Ftransition Petri nets. In: Proceedings of the EXPRESS’10, EPTCS, 2010. doi: 10.4204\u002FEPTCS.41.6 . arXiv:1011.6433v1",{"doi":992},"10.4204\u002FEPTCS.41.6",{"id":20,"text":994,"url":20,"identifiers":995},"Gorrieri, R., Versari, C.: EATCS Text in Computer Science. Introduction to concurrency theory: transition systems and CCS. Springer, New York (2015)",{},{"id":20,"text":997,"url":20,"identifiers":998},"Hoare, C.A.R.: Communicating Sequential Processes. 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Springer, New York (2009)",{"doi":1014},"10.1007\u002F978-3-642-04081-8_31",{"id":20,"text":1016,"url":20,"identifiers":1017},"Milner, R.: Communication and Concurrency. Prentice-Hall, New York (1989)",{},{"id":20,"text":1019,"url":20,"identifiers":1020},"Milner, R.: Communicating and Mobile Systems: The $$\\pi $$ π -Calculus. Cambridge University Press, Cambridge (1999)",{},{"id":20,"text":1022,"url":20,"identifiers":1023},"Nielsen, M., Thiagarajan, P.S.: Degrees of non-determinism and concurrency: a Petri net view. In: Proceedings of the Fourth Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS’84). LNCS, vol. 181, pp. 89–117. Springer, New York (1984)",{"doi":1024},"10.1007\u002F3-540-13883-8_66",{"id":20,"text":1026,"url":20,"identifiers":1027},"Olderog, E.R.: Nets, terms and formulas. In: Cambridge Tracts in Theoretical Computer Science, vol. 23. 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Res. 17(4), 315–337 (1998)\nBartoň, A.: Control of Autonomous Robot Using Neural Networks (in Czech), Master thesis, University of Ostrava, Czech Republic (2015)\nBarton, A., Volna, E., Kotyrba, M.: Big data filtering through adaptive resonance theory. In: Asian Conference on Intelligent Information and Database Systems ACIIDS 2017, pp. 382–391. Springer, Cham (2017)\nDudek, G., Jenkin, M.: Computational Principles of Mobile Robotics. Cambridge University Press, Cambridge (2010)\nMatarić, M.J.: The Robotics Primer. Mit Press, London (2007)\nMironovova, M., Bíla, J.: Fast fourier transform for feature extraction and neural network for classification of electrocardiogram signals. In: 2015 Fourth International Conference on Future Generation Communication Technology (FGCT), pp. 1–6, IEEE (2015)\nRajaraman, A., Ullman, J.: Mining of Massive Datasets. Cambridge University Press, Cambridge (2011)\nRojas, R.: Neutral Networks: A Systematic Introduction. Springer, Berlin (1996)\nSingh, Y., Chauhan, A.S.: Neural networks in data mining. J. Theor. Appl. Inf. Technol. 5(6), 36–42 (2009)\nVolná, E., Kotyrba, M., Žáček, M., Bartoň, A.: Emergence of an autonomous robot‘s behavior. In: Proc. 29th European Conference on Modellingand Simulation, ECMS 2015, Albena, Bulgaria, pp. 462–468 (2015)\nTripathi, G.N., Rihani, V.: Motion planning of an autonomous mobile robot using artificial neural network. arXiv:1207.4931 (2012) (preprint)\nKim, P.K., Jung, S.: Experimental studies of neural network control for one-wheel mobile robot. J. Control Sci. Eng. 2012, 12 (2012) (Article ID 194397)\nMarkoski, B., Vukosavljev, S., Kukolj, D., Pletl, S.: Mobile robot control using self-learning neural network. In: 7th International Symposium on Intelligent Systems and Informatics, 2009. SISY’09, IEEE, pp. 45–48 (2009)\nFarooq, U., Amar, M., Asad, M.U., Hanif, A., Saleh, S.O.: Design and implementation of neural network based controller for mobile robot navigation in unknown environments. Int. J. Comput. Electr. Eng. 6(2), 83–89 (2014)\nReynoso, J.S.C.: A neural network for Java Lego robots. Learn to program intelligent Lego Mindstorms robots with Java. Javaworld. [Online] 16 April 2005. https:\u002F\u002Fwww.javaworld.com\u002Farticle\u002F2071879\u002Fenterprise-java\u002Fa-neural-network-for-java-lego-robots.html (2005)\nBlack, L.: A worm’s mind. In a lego body. I Programmer. [Online] 16 Nov 2014. http:\u002F\u002Fwww.i-programmer.info\u002Fnews\u002F105-artificial-intelligence\u002F7985-a-worms-mind-in-a-lego-body.html",{"EN":1057},"The aim of the article is to use neural networks to control autonomous robot behavior. The type of the controlling neural network was chosen a backpropagation neural network with a sigmoidal transfer function. 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In: ISCRAM Vietnam (Information Systems for Crisis Response and Management) (2013)\nLe, N.N.T., Hanachi, C., Stinckwich, S., Vinh, H.T.: Combining process simulation and agent organizational structure evaluation in order to analyze disaster response plans. In: 9th International KES Conference on Agents and Multi-Agent Systems—Technologies and Applications (2015)\nBénaben, F., Hanachi, C., Lauras, M., Couget, P., Chapurlat, V.: A metamodel and its ontology to guide crisis characterization and its collaborative management. In: Proceedings of the 5th International Conference ISCRAM (2008)\nViorica Epure, E., Martin-Rodilla, P., Hug, C., Deneckere, R., Salinesi, C.: Automatic process model discovery from textual methodologies. In: Research Challenges in Information Science (RCIS), IEEE 9th International Conference (2015)\nVan der Aalst, W.M.P.: Process mining: discovery, conformance and enhancement of business processes. In: Springer Publishing Company Incorporated, ISBN 978-3-642-19345-3 (2011)\nKüster, T., Lützenberger, M., Heßler, A., Hirsch, B.: Integrating process modelling into multi-agent system engineering. In: Multiagent and Grid Systems, vol. 8, no. 1, pp. 105–124. IOS Press, Amsterdam (2012)\nKüster, T., Heßler, A., Albayrak, S.: Towards process-oriented modelling and creation of multi-agent systems. In: Engineering Multi-Agent Systems, pp. 163–180. Springer, New York (2014)\nOnggo, B.S.S.: BPMN pattern for agent-based simulation model representation. In: Winter Simulation Conference (WSC), pp. 1–10. IEEE (2012)\nOnggo, B.S.S.: Agent-based simulation model representation using BPMN. In: Formal Languages for Computer Simulation: Transdisciplinary Models and Applications, pp. 378–399 (2013)\nEndert, H., Küster, T., Hirsch, B., Albayrak, S.: Mapping BPMN to agents: an analysis. In: Agents, Web-Services, and Ontologies Integrated Methodologies, pp. 43–58 (2007)\nFerber, J., Gutknecht, O., Michel, F.: From agents to organizations: an organizational view of multi-agent systems. In: Agent-Oriented Software Engineering IV: 4th International Workshop, AOSE 2003, Melbourne, Australia, pp. 214–230. Springer, Berlin, Heidelberg (2004)\nSzpyrka, M., Nalepa, G. J., Ligȩza, A., Kluza, K.: Proposal of formal verification of selected BPMN models with Alvis modeling language. In: Intelligent Distributed Computing V: Proceedings of the 5th International Symposium on Intelligent Distributed Computing - IDC 2011, Delft, the Netherlands, pp. 249–255. Springer, Berlin, Heidelberg (2012)\nGrossi, D., Dignum, F., Dignum, V., Dastani, M., Royakkers, L.: Structural aspects of the evaluation of agent organizations. In: Coordination, Organizations, Institutions, and Norms in Agent Systems II, pp. 3–18. Springer-Verlag, New york (2007)\nCardoso, J.: Business process control-flow complexity: metric, evaluation, and validation. In: International Journal of Web Services Research (IJWSR), vol. 5, no. 2, pp. 49–76. IGI Global, USA (2008)",{"EN":1233},"Recently, we have witnessed an increasing number of crises, not only natural disasters but also man-made ones. Coordination among several stakeholders is the key factor to reduce the damage caused by a crisis. However, the plan for coordination can be expressed under various representations, including textual format—the most used one in reality but hard to analyze its efficiency. We consider in this paper a combination of process and organization aspects of a coordination plan. Process models (e.g Petri Net, Business Process Model and Notation) could be used to capture the processes of activities and messages exchanged between the actors involved in a crisis, while organization models (e.g. Role graph, agent-centred multi-agent system, organization centred multi-agent system) are used to highlight the roles, their interactions and the organizational structures. We then describe a proposal that allows performing an automatic transformation from process models to organization models. 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