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Winston, Artificial Intelligence, 2nd Edition (Addison-Wesley, Reading, MA, 1984).",{},{"id":796,"createTime":797,"updateTime":798,"relativeEntities":799,"slug":800,"properties":801,"entityType":199,"verifyStatus":200,"verifyTime":812,"verifyNote":202,"languages":813,"translateLanguages":18,"viewCount":19,"primaryUrl":814,"fullTextUrl":18,"authors":815,"publicationType":272,"publisherRelationship":835,"citationCount":19,"citationInfo":888,"publishDate":891,"publishYear":889,"citationAnalyzeStatus":329,"lastCitationAnalyze":892,"indexDatabases":893,"openAccess":18,"references":894,"isForceReanalyzing":432},"7732ea4d-73a5-4e1b-813a-9c6ffb4c3513","2024-04-22T01:26:24.287+00:00","2026-07-28T09:19:57.030+00:00",[],"Reasoning-on-temporal-class-diagrams-Undecidability-results",{"openalex":802,"mag":804,"title":806,"gsPaper":808,"doi":810},{"VOID":803},"W2042470127",{"VOID":805},"2042470127",{"EN":807},"Reasoning on temporal class diagrams: Undecidability 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Artale and E. Franconi, Temporal ER modeling with description logics, in: Proc. of the International Conference on Conceptual Modeling (ER'99), Lecture Notes in Computer Science (Springer, 1999).",{"doi":898},"10.1007\u002F3-540-47866-3_6",{"id":18,"text":900,"url":18,"identifiers":901},"A. Artale and E. Franconi, A survey of temporal extensions of description logics, Annals of Mathematics and Artificial Intelligence 30(1–4) (2001).",{"doi":902},"10.1023\u002FA:1016636131405",{"id":18,"text":904,"url":18,"identifiers":905},"A. Artale, Reasoning on temporal conceptual schemas with dynamic constraints, in: 11th International Symposium on Temporal Representation and Reasoning (TIME04) (IEEE Computer Society, 2004). Also in Proc. of the 2004 International Workshop on Description Logics (DL'04).",{"doi":906},"10.1109\u002FTIME.2004.1314423",{"id":18,"text":908,"url":18,"identifiers":909},"A. Artale, E. Franconi and F. Mandreoli, Description logics for modelling dynamic information, in: Logics for Emerging Applications of Databases, eds. J. Chomicki, R. van der Meyden and G. Saake, Lecture Notes in Computer Science (Springer, 2003).",{"doi":910},"10.1007\u002F978-3-642-18690-5_7",{"id":18,"text":912,"url":18,"identifiers":913},"A. Artale, E. Franconi, F. Wolter and M. Zakharyaschev, A temporal description logic for reasoning about conceptual schemas and queries, in: Proceedings of the 8th Joint European Conference on Logics in Artificial Intelligence (JELIA-02), volume 2424 of LNAI, eds. S. Flesca, S. Greco, N. Leone and G. Ianni (Springer, 2002) pp. 98–110.",{"doi":914},"10.1007\u002F3-540-45757-7_9",{"id":18,"text":916,"url":18,"identifiers":917},"F. Baader, D. Calvanese, D. McGuinness, D. Nardi and P.F. Patel-Schneider, eds., in: Description Logic Handbook: Theory, Implementation and Applications (Cambridge University Press, 2002).",{},{"id":18,"text":919,"url":18,"identifiers":920},"D. Berardi, D. Calvanese and G. De Giacomo, Reasoning on UML class diagrams, Artificial Intelligence 168(1–2) (2005) 70–118.",{"doi":921},"10.1016\u002Fj.artint.2005.05.003",{"id":18,"text":923,"url":18,"identifiers":924},"D. Calvanese, G. De Giacomo and M. Lenzerini, On the decidability of query containment under constraints, in: Proc. of the 17th ACM SIGACT SIGMOD SIGART Sym. on Principles of Database Systems (PODS'98) (1998) pp. 149–158.",{"doi":925},"10.1145\u002F275487.275504",{"id":18,"text":927,"url":18,"identifiers":928},"D. Calvanese, M. Lenzerini and D. Nardi, Unifying class-based representation formalisms, Journal of Artificial Intelligence Research 11 (1999) 199–240.",{"doi":929},"10.1613\u002Fjair.548",{"id":18,"text":931,"url":18,"identifiers":932},"J. Chomicki and D. Toman, Temporal logic in information systems, in: Logics for Databases and Information Systems, chapter 1, eds. J. Chomicki and G. Saake (Kluwer, 1998).",{"doi":933},"10.1007\u002F978-1-4615-5643-5_3",{"id":18,"text":935,"url":18,"identifiers":936},"R. Elmasri and S.B. Navathe, Fundamentals of Database Systems, 2nd edition (Benjamin\u002FCummings, 1994).",{},{"id":18,"text":938,"url":18,"identifiers":939},"D. Gabbay, A. Kurucz, F. Wolter and M. Zakharyaschev, Many-Dimensional Modal Logics: Theory and Applications, Studies in Logic (Elsevier, 2003).",{},{"id":18,"text":941,"url":18,"identifiers":942},"H. Gregersen and J.S. Jensen, Conceptual modeling of time-varying information, Technical Report TimeCenter TR-35 (Aalborg University, Denmark, 1998).",{},{"id":18,"text":944,"url":18,"identifiers":945},"H. Gregersen and J.S. Jensen, Temporal entity-relationship models – A survey, IEEE Transactions on Knowledge and Data Engineering 11(3) (1999) 464–497.",{"doi":946},"10.1109\u002F69.774104",{"id":18,"text":948,"url":18,"identifiers":949},"R. Gupta and G. Hall, Modeling transition, in: Proc. of ICDE'91 (1991) pp. 540–549.",{},{"id":18,"text":951,"url":18,"identifiers":952},"C.S. Jensen, J. Clifford, S.K. Gadia, P. Hayes and S. Jajodia et al., The consensus glossary of temporal database concepts, Temporal Databases – Research and Practice, eds. O. Etzion, S. Jajodia and S. Sripada (Springer, 1998) pp. 367–405.",{"doi":953},"10.1007\u002FBFb0053710",{"id":18,"text":955,"url":18,"identifiers":956},"C.S. Jensen and R.T. Snodgrass, Temporal data management, IEEE Transactions on Knowledge and Data Engineering 111(1) (1999) 36–44.",{"doi":957},"10.1109\u002F69.755613",{"id":18,"text":959,"url":18,"identifiers":960},"C.S. Jensen, M. Soo and R.T. Snodgrass, Unifying temporal data models via a conceptual model, Information Systems 9(7) (1994) 513–547.",{"doi":961},"10.1016\u002F0306-4379(94)90013-2",{"id":18,"text":963,"url":18,"identifiers":964},"S. Spaccapietra, C. Parent and E. Zimanyi, Modeling time from a conceptual perspective, in: Int. Conf. on Information and Knowledge Management (CIKM98) (1998).",{"doi":965},"10.1145\u002F288627.288693",{"id":18,"text":967,"url":18,"identifiers":968},"C. Theodoulidis, P. Loucopoulos and B. Wangler, A conceptual modelling formalism for temporal database applications, Information Systems 16(3) (1991) 401–416.",{"doi":969},"10.1016\u002F0306-4379(91)90031-4",{"id":18,"text":971,"url":18,"identifiers":972},"F. Wolter and M. Zakharyaschev, Satisfiability problem in description logics with modal operators, in: Proc. of the 6 $$^{th}$$ International Conference on Principles of Knowledge Representation and Reasoning (KR'98) (Trento, Italy, June 1998) pp. 512–523.",{},{"id":974,"createTime":975,"updateTime":976,"relativeEntities":977,"slug":978,"properties":979,"entityType":199,"verifyStatus":200,"verifyTime":990,"verifyNote":202,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":991,"fullTextUrl":18,"authors":992,"publicationType":272,"publisherRelationship":1023,"citationCount":107,"citationInfo":1074,"publishDate":1077,"publishYear":1075,"citationAnalyzeStatus":566,"lastCitationAnalyze":1078,"indexDatabases":1079,"openAccess":18,"references":18,"isForceReanalyzing":432},"498c5d91-3974-4457-8107-714eae497914","2024-01-10T10:31:21.922+00:00","2026-07-26T23:05:01.712+00:00",[],"Schema-versioning-and-database-conversion-techniques-for-bi-temporal-databases",{"abstract":980,"title":982,"gsPaper":984,"references":986,"doi":988},{"EN":981},"Schema evolution and schema versioning are two techniques used for managing database evolution. Schema evolution keeps only the current version of a schema and database after applying schema changes. Schema versioning creates new schema versions and converts the corresponding data while preserving the old schema versions and data. To provide the most generality, bi-temporal databases can be used to realize schema versioning, since they allow both retroactive and proactive updates to the schema and database. In this paper we first study two proposed database conversion approaches for supporting schema evolution and schema versioning: single table version approach and multiple table version approach. We then propose the partial table version approach to solve the problems encountered in these approaches when applied to bi-temporal databases.",{"EN":983},"Schema versioning and database conversion techniques for bi-temporal databases",{"VOID":985},"[\"7351343613754084505\"]",{"VOID":987},"J. Banerjee, H.-T. Chou, H.J. Kim and H.F. Korth, Semantics and implementation of schema evolution in object-oriented databases, SIGMOD RECORD 16(3) (1987) 311-322.\nN. Beckmann, H.P. Kriegel, R. Schneider and B. Seeger, The R*-tree: an efficient and robust access method for points and rectangles, in: Proceedings of ACM SIGMOD (1990) pp. 322-331.\nJ. Clifford, C. Dyreson, T. Isakowitz, C.S. Jensen and R.T. Snodgrass, On the semantics of \"now\" in databases, ACM Transactions on Database Systems 22(2) (1997) 171-214.\nJ. Clifford and D.S. Warren, Formal semantics for time in databases, ACM Transactions on Database Systems (1983) 214-254.\nC. DeCastro, F. Grandi and M.R. Scalas, Schema versioning for multitemporal relational databases, Information Systems (1997) 249-290.\nR. Elmasri, G. Wuu and Y. Kim, The time index: An access structure for temporal data, in: Proceedings of the 16th VLDB Conference (1990).\nO. Etzion, A. Gal and A. Segev, Retroactive and proactive database processing, in: Fourth International Workshop on Research Issues in Data Engineering: Active Database Systems, Houston, TX (February 14-15, 1994).\nH. Gunadhi and A. Segev, Query processing algorithms for temporal intersection joins, in: Proceedings of 7th International Conference on Data Engineering (IEEE, 1991).\nA. Guttman, R-trees: A dynamic index structure for spatial searching, in: Proceedings of ACM SIGMOD (1984) pp. 47-57.\nC. Jensen et al., A consensus glossary of temporal database concepts, SIGMOD RECORD 23(1) (1994) 52-64.\nW. Kim and H.-T. Chou, Versions of schema for object-oriented databases, in: Proceedings of the 14th International Conference on Very Large Databases (1988) pp. 148-159.\nC. Kolovson and M. Stonebraker, Segment indexes: Dynamic indexing techniques for multidimensional interval data, in: Proceedings ACM SIGMOD (1991) pp. 138-147.\nA. Kumar, V.J. Tsotras and C. Faloutsos, Access methods for bi-temporal databases, in: Recent Advances in Temporal Databases, eds. J. Clifford and A. Tuzhilin (1995) pp. 235-254.\nB.S. Lerner and A.N. Habermann, Beyond schema evolution to database reorganization, SIGPLAN Notices 25(10) (1990) 67-76.\nN.G. Martin, S.B. Navathe and R. Ahmed, Dealing with schema anomalies in history databases, in: Proceedings of the 13th International Conference on VLDB (1987).\nP. Mishra and M.H. Eich, Join processing in relational databases, ACM Computing Surveys 24(1) (1992).\nM.A. Nascimento, M.H. Dunham and R. Elmasri, M-IVTT: A practical index for bi-temporal database, in: DEXA'96 Proceedings, Zurich, Switzerland (September 1996) pp. 779-790.\nS.B. Navathe and R. Ahmed, A temporal relation model and a query langue, Information Sciences (1989) 147-175.\nJ.F. Roddick, Dynamically changing schemas within database models, Australian Computer Journal (1991) 105-109.\nJ.F. Roddick, Schema evolution in database systems-an annotated bibliography, Technical Report No. CIS-92-004, School of Computer and Information Science, University of South Australia (1992).\nJ.F. Roddick, SQL\u002FSE-A query language extension for databases supporting schema evolution, SIGMOD RECORD (1992) 10-16.\nJ.F. Roddick, A survey of schema versioning issues for database systems, Information and Software Technology 37(7) (1995).\nB. Salzberg and V.J. Tsotras, A comparison of access methods for time-evolving data, Computing Surveys 31(2) (1999) 158-121.\nM.R. Scalas, A. Cappelli and C. De Castro, A model for schema evolution in temporal relational databases, in: Proceedings of 1993 CompEuro, Computers in Design, Manufacturing, and Production (May 1993) pp. 223-231.\nA. Segev, Join processing and optimization in temporal relational databases, in: Temporal Databases: Theory, Design, and Implementation (Benjamin\u002FCummings, 1993) chapter 15.\nA. Segev and H. Gunadhi, Event-join optimization in temporal relational databases, in: Proceedings of the Conference on Very Large Data Base (August, 1989) pp. 205-215.\nA. Shrufi and T. Topaloglou, Query processing for knowledge bases using join indices, in: Proceedings of the 4th International Conference on Information and Knowledge Management (CIKM), 1995.\nD. Sjøberg, Quantifying schema evolution, Information and Software Technology 35(1) (1993) 35-44.\nR.T. Snodgrass, The temporal query language TQuel, ACMTransactions on Database Systems (1987) 247-298.\nR.T. Snodgrass (ed.), The TSQL2 Temporal Query Language (Kluwer Academic, 1995) chapter 10.\nM.D. Soo, R.T. Snodgrass and C.S. Jensen, Efficient evaluation of the valid-time natural join, in: Proceedings of the 10th International Conference on Data Engineering (IEEE, 1994).\nD. Son and R. Elamsri, Efficient temporal join processing using time index, in: Proceedings of the 8th International Conference on Scientific and Statistical Database Management (June 18-20, 1996) pp. 252-261.\nT. Topaloglou, Storage management for knowledge bases, in: Proceedings of the 2nd International Conference on Information and Knowledge Management (CIKM'93), 1993.\nM. Tresch and M.H. Scholl, Schema transformation without database reorganization, SIGMOD RECORD 22(1) (1993) 21-27.\nV.J. Tsotras, C.S. Jensen and R.T. Snodgrass, A notation for spatiotemporal queries, TimeCenter Technical Report TR-10 (April 1997).\nP. Valduriez, Join indices, ACM Transactions on Database Systems 12(2) (1987) 218-246.\nH.C. Wei and R. Elmasri, Study and comparison of schema versioning and database conversion techniques for bi-temporal databases, in: Proceedings of the 6th International Workshop on Temporal Representation and Reasoning (TIME-99) (May 1-2, 1999) pp. 88-98.\nR. Zicari, A framework for schema updates in an object-oriented database system, in: Proceedings of the 7th International Conference on Data Engineering (April 1991) pp. 2-13.",{"VOID":989},"10.1023\u002FA:1016622202755","2024-05-16T14:05:41.822+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1016622202755",[993,1008],{"id":994,"sortIndex":19,"researcher":18,"roles":995,"affiliations":996,"properties":1005,"displayName":1007,"givenName":18,"familyName":18},"2161dca7-5043-4e15-acfe-5d8e54a6a1b3",[208],[997],{"id":998,"sortIndex":19,"affiliation":999,"properties":18},"0cdc162c-f2ab-4985-8994-446daffbe16d",{"id":998,"createTime":18,"updateTime":18,"relativeEntities":1000,"slug":18,"properties":1001,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1004,"statistic":18},[],{"title":1002},{"VI":1003},"Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, USA E-mail",[],{"title":1006},{"VI":1007},"Han-Chieh Wei",{"id":1009,"sortIndex":225,"researcher":18,"roles":1010,"affiliations":1011,"properties":1018,"displayName":1020,"givenName":18,"familyName":18},"7080a5c3-600f-4c1f-a46c-838cdb82b020",[208],[1012],{"id":998,"sortIndex":19,"affiliation":1013,"properties":18},{"id":998,"createTime":18,"updateTime":18,"relativeEntities":1014,"slug":18,"properties":1015,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1017,"statistic":18},[],{"title":1016},{"VI":1003},[],{"title":1019,"gsAuthor":1021},{"VI":1020},"Ramez 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paper develops and examines an innovative methodology for training an artificial neural network to identify and tag target visual objects in a given database. While the field of Artificial Intelligence in general, and computer vision in particular, has greatly advanced in recent years, fast and efficient methods for tagging (i.e., labeling) visual targets are still lacking. Tagging data is important to train, as it allow to train supervised learning models. However, this is a tiresome task that often creates bottlenecks in academic and industrial research projects. In order to develop an algorithm that improves data tagging processes, this study utilizes the advantages of human cognition and machine learning by combining Brain Computer Interface, Human-In-The-Loop, and Deep Learning. Combining these three fields into one algorithm could enable the rapid annotation of large visual databases that have no prior references and cannot be described as a mathematical optimization function. Human-In-The-Loop is an increasingly researched area that refers to the integration of human feedback in computation processes. At present, computer-based deep learning can only be incorporated in the process of identifying and tagging target objects of interest if a predefined database exists – one that has already been defined by a human user. To reduce the scope of this timely and costly process, our algorithm uses machine learning techniques (i.e., active learning) to minimize the number of target objects a human user needs to identify before the computer can successfully carry out the task independently. In our method, users are connected to electroencephalograms electrodes and shown images using rapid serial visual presentation – a fast method for presenting users with images. Some images are target objects, while others are not. Based on users’ brainwave activity when target objects are shown, the computer learns to identify and tag target objects – already in the learning stage (unlike naïve uniform sampling methods that first require human input, and only then begin the learning stage). As such, our work is proof of concept for the effectiveness of involving humans in the computer’s learning stage, i.e., human-in-the-loop as opposed to the traditional method of humans first tagging the data and the machines then learning and creating a model.",{"EN":1090},"Human-in-the-loop active learning via brain computer interface",{"VOID":1092},"[\"1943779793067325552\"]",{"VOID":1094},"10.1007\u002Fs10472-020-09689-0","2024-05-03T18:04:06.910+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs10472-020-09689-0",[1098,1113],{"id":1099,"sortIndex":19,"researcher":18,"roles":1100,"affiliations":1101,"properties":1110,"displayName":1112,"givenName":18,"familyName":18},"05881f68-6fde-4838-9e88-71478e43e6de",[208],[1102],{"id":1103,"sortIndex":19,"affiliation":1104,"properties":18},"0670b39d-8bb6-4c8f-8a76-01f14b91bf12",{"id":1103,"createTime":18,"updateTime":18,"relativeEntities":1105,"slug":18,"properties":1106,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1109,"statistic":18},[],{"title":1107},{"VI":1108},"InnerEye Ltd and Department of Electrical and Computer Engineering, Ben-Gurion University, Beer Sheva, Israel",[],{"title":1111},{"VI":1112},"Eitan Netzer",{"id":1114,"sortIndex":225,"researcher":18,"roles":1115,"affiliations":1116,"properties":1123,"displayName":1125,"givenName":18,"familyName":18},"00b47717-5f1a-4043-b558-4ae920769deb",[208],[1117],{"id":1103,"sortIndex":19,"affiliation":1118,"properties":18},{"id":1103,"createTime":18,"updateTime":18,"relativeEntities":1119,"slug":18,"properties":1120,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1122,"statistic":18},[],{"title":1121},{"VI":1108},[],{"title":1124,"gsAuthor":1126},{"VI":1125},"Amir B. 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In Advances in neural information processing systems, pages 1097–1105, 2012","https:\u002F\u002Fdl.acm.org\u002Fdoi\u002F10.1145\u002F3065386",{"doi":1191},"10.1145\u002F3065386",{"id":334,"text":1193,"url":336,"identifiers":1194},"Schirner, G., Erdogmus, D., Chowdhury, K., Padir, T.: The future of human-in-the-loop cyber-physical systems. Computer. 46(1), 36–45 (2013)",{"doi":338},{"id":334,"text":1196,"url":336,"identifiers":1197},"Amazon Mechanical Turk. Amazon mechanical turk. Retrieved August, 17:2012, 2012",{"doi":338},{"id":18,"text":1199,"url":18,"identifiers":1200},"Zhu, X.,: Semi-Supervised Learning Literature Survey. 2005",{},{"id":18,"text":1202,"url":18,"identifiers":1203},"Gal, Y., Islam, R., Ghahramani, Z.: Deep bayesian active learning with image data. arXiv preprint arXiv:1703.02910, 2017",{"arxiv":1204},"arXiv:1703.02910",{"id":334,"text":1206,"url":336,"identifiers":1207},"Alpert, G.F., Manor, R., Spanier, A.B., Deouell, L.Y., Geva, A.B.: Spatiotemporal representations of rapid visual target detection: A single-trial EEG classification algorithm. IEEE Transactions on Biomedical Engineering. 61(8), 2290–2303 (2014)",{"doi":338},{"id":334,"text":1209,"url":336,"identifiers":1210},"Manor, R., Geva, A.B.: Convolutional neural network for multi-category rapid serial visual presentation bci. Frontiers in computational neuroscience. 9, 146 (2015)",{"doi":338},{"id":18,"text":1212,"url":18,"identifiers":1213},"Sajda, P., Pohlmeyer, E., Wang, J., Parra, L.C., Christoforou, C., Dmochowski, J., Hanna, B., Bahlmann, C., Singh, M.K., Chang, S.-F.: In a blink of an eye and a switch of a transistor: cortically coupled computer vision. Proceedings of the IEEE. 98(3), 462–478 (2010)",{},{"id":18,"text":1215,"url":18,"identifiers":1216},"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. nature. 521(7553), 436 (2015)",{},{"id":18,"text":1218,"url":18,"identifiers":1219},"Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013",{"arxiv":1220},"arXiv:1312.5602",{"id":334,"text":1222,"url":336,"identifiers":1223},"Taigman, Y., Yang, M., Ranzato, M’A., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701–1708, 2014",{"doi":338},{"id":1225,"text":1226,"url":1227,"identifiers":1228},"e8b4842c-7df7-4c08-b6d6-17187c644a67","Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representations by back-propagating errors. nature. 323(6088), 533 (1986)","https:\u002F\u002Fwww.nature.com\u002Farticles\u002F323533a0",{"doi":1229},"10.1038\u002F323533a0",{"id":334,"text":1231,"url":336,"identifiers":1232},"Nguyen, A., Yosinski, J., Clune, J.: Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 427–436, (2015)",{"doi":338},{"id":18,"text":1234,"url":18,"identifiers":1235},"Sharif, M., Bhagavatula, S., Bauer, L., Reiter, M.K.: Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pages 1528–1540. ACM, 2016",{},{"id":334,"text":1237,"url":336,"identifiers":1238},"Pohlmeyer, E.A., Jangraw, D.C., Wang, J., Chang, S.-F., Sajda, P.: Combining computer and human vision into a bci: Can the whole be greater than the sum of its parts? In Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE, pages 138–141. IEEE, (2010)",{"doi":338},{"id":334,"text":1240,"url":336,"identifiers":1241},"Gerson, A.D., Parra, L.C., Sajda, P.: Cortically coupled computer vision for rapid image search. IEEE Transactions on neural systems and rehabilitation engineering. 14(2), 174–179 (2006)",{"doi":338},{"id":334,"text":1243,"url":336,"identifiers":1244},"Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pages 17–36, (2012)",{"doi":338},{"id":18,"text":1246,"url":18,"identifiers":1247},"Fei-Fei, L., Knowledge transfer in learning to recognize visual objects classes. In Proceedings of the International Conference on Development and Learning (ICDL), page 11, 2006",{},{"id":18,"text":1249,"url":18,"identifiers":1250},"Mesnil, G., Dauphin, Y., Glorot, X., Rifai, S, Bengio, Y., Goodfellow, I., Lavoie, E., Muller, X., Desjardins, G., Warde-Farley, D.: et al. Unsupervised and transfer learning challenge: a deep learning approach. In Proceedings of the 2011 International Conference on Unsupervised and Transfer Learning workshop –Volume 27, pages 97–111. JMLR. org, (2011)",{},{"id":18,"text":1252,"url":18,"identifiers":1253},"Simonyan, K., Zisserman, A.,: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014",{"arxiv":1254},"arXiv:1409.1556",{"id":334,"text":1256,"url":336,"identifiers":1257},"Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision. 115(3), 211–252 (2015)",{"doi":338},{"id":334,"text":1259,"url":336,"identifiers":1260},"Daniel, C., Mahajan, M., Dale, J., Pepper, S., Lin, Y., Yoo, S., A transfer learning approach to parking lot classification in aerial imagery. In Scientific Data Summit (NYSDS), 2017 New York, pages 1–5. IEEE, (2017)",{"doi":338},{"id":334,"text":1262,"url":336,"identifiers":1263},"Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In Advances in neural information processing systems, pages 3320–3328, (2014)",{"doi":338},{"id":1265,"text":1266,"url":1267,"identifiers":1268},"185e4b78-6f93-43fb-9065-647d907422db","Bengio, Y., et al.: Learning deep architectures for ai. Foundations and trends® in Machine Learning. 2(1), 1–127 (2009)","https:\u002F\u002Fwww.goodreads.com\u002Fbook\u002Fshow\u002F11342787-learning-deep-architectures-for-ai",{"isbn":1269,"isbn13":1270},"1601982941","9781601982940",{"id":334,"text":1272,"url":336,"identifiers":1273},"Salvador, S., Chan, P.: Determining the number of clusters\u002Fsegments in hierarchical clustering\u002Fsegmentation algorithms. 16th IEEE International Conference on Tools with Artificial Intelligence. IEEE, 2004",{"doi":338},{"id":18,"text":1275,"url":18,"identifiers":1276},"Donchin, E., Ritter, W., McCallum, W.C., et al. Cognitive psychophysiology: The endogenous components of the erp. Event-related brain potentials in man, pages 349–411, (1978)",{},{"id":334,"text":1278,"url":336,"identifiers":1279},"Kutas, M., McCarthy, G., Donchin, E.: Augmenting mental chronometry: the p300 as a measure of stimulus evaluation time. Science. 197(4305), 792–795 (1977)",{"doi":338},{"id":334,"text":1281,"url":336,"identifiers":1282},"Donchin, E., Coles, M.G.H.: Is the p300 component a manifestation of context updating? Behavioral and brain sciences. 11(3), 357–374 (1988)",{"doi":338},{"id":1284,"text":1285,"url":1286,"identifiers":1287},"dd898d31-913c-484f-a7e2-c744e1227157","Polich, J.: Updating p300: an integrative theory of p3a and p3b. Clin. Neurophysiol. 118(10), 2128–2148 (2007)","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS1388245707001897",{"doi":1288},"10.1016\u002Fj.clinph.2007.04.019",{"id":334,"text":1290,"url":336,"identifiers":1291},"Li, K., Sankar, R., Arbel, Y., Donchin, E.: Single trial independent component analysis for p300 bci system. In Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE, Pages 4035–4038. IEEE, (2009)",{"doi":338},{"id":18,"text":1293,"url":18,"identifiers":1294},"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.,: Striving for simplicity: The all convolutional net. arXiv preprint arXiv:1412.6806, 2014",{"arxiv":1295},"arXiv:1412.6806",{"id":1297,"createTime":1298,"updateTime":1299,"relativeEntities":1300,"slug":1301,"properties":1302,"entityType":199,"verifyStatus":200,"verifyTime":1311,"verifyNote":202,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1312,"fullTextUrl":18,"authors":1313,"publicationType":272,"publisherRelationship":1346,"citationCount":19,"citationInfo":1397,"publishDate":1400,"publishYear":1398,"citationAnalyzeStatus":17,"lastCitationAnalyze":1401,"indexDatabases":1402,"openAccess":18,"references":1403,"isForceReanalyzing":432},"3e94066b-d70e-496b-8040-2ed36ec3e88c","2024-01-28T18:05:23.129+00:00","2026-07-22T05:43:47.065+00:00",[],"Introducing-the-mathematical-category-of-artificial-perceptions",{"abstract":1303,"title":1305,"gsPaper":1307,"doi":1309},{"EN":1304},"Perception is the recognition of elements and events in the environment, usually through integration of sensory impressions. It is considered here as a broad, high-level, concept (different from the sense in which computer vision\u002Faudio research takes the concept of perception). We propose and develop premises for a formal approach to a fundamental phenomenon in AI: the diversity of artificial perceptions. A mathematical substratum is proposed as a basis for a rigorous theory of artificial perceptions. A basic mathematical category is defined. Its objects are perceptions, consisting of world elements, connotations, and a three-valued (true, false, undefined) predicative correspondence between them. Morphisms describe paths between perceptions. This structure serves as a basis for a mathematical theory. This theory provides a way of extending and systematizing certain intuitive pre-theoretical conceptions about perception, about improving and\u002For completing an agent's perceptual grasp, about transition between various perceptions, etc. Some example applications of the theory are analyzed.",{"EN":1306},"Introducing the mathematical category of artificial perceptions",{"VOID":1308},"[\"322327180330278602\"]",{"VOID":1310},"10.1023\u002FA:1018928627501","2024-05-01T09:51:08.820+00:00","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1023\u002FA:1018928627501",[1314,1329],{"id":1315,"sortIndex":19,"researcher":18,"roles":1316,"affiliations":1317,"properties":1326,"displayName":1328,"givenName":18,"familyName":18},"f108bd53-7e28-4988-b613-19f022af713a",[208],[1318],{"id":1319,"sortIndex":19,"affiliation":1320,"properties":18},"ad6a9e2c-aa8e-4c34-9c41-db118ff2c659",{"id":1319,"createTime":18,"updateTime":18,"relativeEntities":1321,"slug":18,"properties":1322,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1325,"statistic":18},[],{"title":1323},{"VI":1324},"Typographics Ltd, Jerusalem, Israel",[],{"title":1327},{"VI":1328},"Z. 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Manes, Arrows, Structures and Functors–The Categorical Imperative(Academic Press, New York, 1975).",{},{"id":334,"text":1408,"url":336,"identifiers":1409},"Z. Arzi-Gonczarowski and D. Lehmann, Categorical tools for artificial perception, in: Proceedings of the 11th European Conference on Artificial Intelligence ECAI ’94, ed. A. Cohn (Wiley, Amsterdam, 1994) pp. 757–761.",{"doi":338},{"id":334,"text":1411,"url":336,"identifiers":1412},"Z. Arzi-Gonczarowski and D. Lehmann, From environments to representations–A mathematical theory of artificial perceptions, Artificial Intelligence, forthcoming.",{"doi":338},{"id":18,"text":1414,"url":18,"identifiers":1415},"A. Asperti and G. Longo, Categories, Types, and Structures(MIT Press, 1991).",{},{"id":334,"text":1417,"url":336,"identifiers":1418},"R.B. Banerji, Similarities in problem solving strategies, in: Change of Representation and Inductive Bias, ed. D.P. Benjamin (Kluwer Academic, 1990) pp. 183–191.",{"doi":338},{"id":18,"text":1420,"url":18,"identifiers":1421},"M. Barr and C. Wells, Category Theory for Computing Science(Prentice-Hall, Englewood Cliffs, NJ, 2nd ed., 1995).",{},{"id":334,"text":1423,"url":336,"identifiers":1424},"E.A. Bender, Mathematical Methods in Artificial Intelligence(IEEE, Los Alamitos, CA, 1995).",{"doi":338},{"id":18,"text":1426,"url":18,"identifiers":1427},"D.P. Benjamin, A review of [31], SIGART Bulletin 3(4) (October 1992).",{},{"id":334,"text":1429,"url":336,"identifiers":1430},"F. Borceux, Handbook of Categorical Algebra(Cambridge University Press, Cambridge, 1994).",{"doi":338},{"id":334,"text":1432,"url":336,"identifiers":1433},"R. Casati and A.C. Varzi, Basic issues in spatial representation, in: Proceedings of WOCFAI ’95, Second World Conference on the Fundamentals of AI, eds. M. DeGlas and Z. Pawlak (Angkor, Paris, 1995) pp. 63–72.",{"doi":338},{"id":18,"text":1435,"url":18,"identifiers":1436},"M.A. Croon and F.J.R. Van de Vijver, eds., Viability of Mathematical Models in the Social and Behavioral Sciences(Swets and Zeitlinger B.V., Lisse, 1994).",{},{"id":18,"text":1438,"url":18,"identifiers":1439},"E.R. Doughherty and C.R. Giardina, Mathematical Methods for Artificial Intelligence and Autonomous Systems(Prentice-Hall, Englewood Cliffs, NJ, 1988).",{},{"id":18,"text":1441,"url":18,"identifiers":1442},"W.D. Ellis, ed., A Source Book of Gestalt Psychology(Routledge and Kegan Paul, London, 1938).",{},{"id":18,"text":1444,"url":18,"identifiers":1445},"N. Fridman and C.D. Hafner, The state of the art in ontology design, AI Magazine 18(3) (1997).",{},{"id":334,"text":1447,"url":336,"identifiers":1448},"P. Gärdenfors, Induction, conceptual spaces and AI, Philosophy of Science 57 (1990) 78–95.",{"doi":338},{"id":18,"text":1450,"url":18,"identifiers":1451},"H. Herrlich and G.E. Strecker, Category Theory(Allyn and Bacon, 1973).",{},{"id":18,"text":1453,"url":18,"identifiers":1454},"J.P.E. Hodgson, Pushouts and problem solving, in: Working Proceedings of the First International Workshop on Category Theory in AI and Robotics, eds. I. Mandhyan, D.P. Benjamin and E.G. Manes (Philips Laboratories, 1989) pp. 89–112.",{},{"id":1456,"text":1457,"url":1458,"identifiers":1459},"d609b4e1-5d72-49c1-8a54-ca15593bed97","B. Indurkhya, Approximate semantic transference: A computational theory of metaphors and analogies, Cognitive Science 11 (1987) 445–480.","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0364021387800162",{"doi":1460},"10.1016\u002Fs0364-0213(87)80016-2",{"id":18,"text":1462,"url":18,"identifiers":1463},"S. Kraus and D. Lehmann, Designing and building a negotiating automated agent, Computational Intelligence 11(1) (1995) 132–171.",{},{"id":334,"text":1465,"url":336,"identifiers":1466},"S. Kripke, Semantical considerations on modal logic, Acta Philosophica Fennica 16 (1963) 83–94.",{"doi":338},{"id":18,"text":1468,"url":18,"identifiers":1469},"G. Lakoff, Women, Fire, and Dangerous Things–What Categories Reveal about the Mind(The University of Chicago Press, Chicago, 1987).",{},{"id":18,"text":1471,"url":18,"identifiers":1472},"F.W. Lawvere, Tools for the advancement of objective logic: Closed categories and toposes, in: The Logical Foundations of Cognition, eds. J. Macnamara and G.E. Reyes (Oxford University Press, Oxford, 1994) pp. 43–55.",{},{"id":334,"text":1474,"url":336,"identifiers":1475},"M.R. Lowry, Algorithm synthesis through problem reformulation, Ph.D. thesis, Stanford University (June 1989).",{"doi":338},{"id":334,"text":1477,"url":336,"identifiers":1478},"M.R. Lowry, Strata: Problem reformulation and abstract data types, in: Change of Representation and Inductive Bias, ed. D.P. Benjamin (Kluwer Academic Publishers, 1990) pp. 41–66.",{"doi":338},{"id":18,"text":1480,"url":18,"identifiers":1481},"S. MacLane, Categories for the Working Mathematician(Springer, Berlin, 1972).",{},{"id":18,"text":1483,"url":18,"identifiers":1484},"F. Magnan and G.E. Reyes, Category theory as a conceptual tool in the study of cognition, in: The Logical Foundations of Cognition, eds. J. Macnamara and G.E. Reyes (Oxford University Press, Oxford, 1994) pp. 57–90.",{},{"id":334,"text":1486,"url":336,"identifiers":1487},"J.A. Makowsky, Mental images and the architecture of concepts, in: The Universal Turing Machine–A Half Century Survey, ed. R. Herken (Oxford University Press, Oxford, 1988) pp. 453–465.",{"doi":338},{"id":18,"text":1489,"url":18,"identifiers":1490},"A. Newell, Unified Theories of Cognition(Harvard University Press, Cambridge, MA, 1990).",{},{"id":334,"text":1492,"url":336,"identifiers":1493},"N.J. Nilsson, Artificial intelligence prepares for 2001, AI Magazine 4(4) (1983).",{"doi":338},{"id":18,"text":1495,"url":18,"identifiers":1496},"N.J. Nilsson, Eye on the prize, AI Magazine 16(2) (1995) 9–17.",{},{"id":1498,"text":1499,"url":1500,"identifiers":1501},"9113c6d0-4559-4159-aa76-aa96f8dac9ec","B.C. Pierce, Basic Category Theory for Computer Scientists(MIT Press, 1991).","https:\u002F\u002Fwww.goodreads.com\u002Fbook\u002Fshow\u002F1810837.Basic_Category_Theory_for_Computer_Scientists",{"isbn":1502,"isbn13":1503},"0262660717","9780262660716",{"id":18,"text":1505,"url":18,"identifiers":1506},"R.F.C. Walters, Categories and Computer Science(Cambridge University Press, Cambridge, 1991).",{},{"id":18,"text":1508,"url":18,"identifiers":1509},"R.M. Zimmer, Representation engineering and category theory, in: Change of Representation and Inductive Bias, ed. D.P. Benjamin (Kluwer Academic Publishers, 1990) pp. 169–182.",{},{"id":1511,"createTime":1512,"updateTime":1513,"relativeEntities":1514,"slug":1515,"properties":1516,"entityType":199,"verifyStatus":200,"verifyTime":1525,"verifyNote":202,"languages":18,"translateLanguages":18,"viewCount":19,"primaryUrl":1526,"fullTextUrl":18,"authors":1527,"publicationType":272,"publisherRelationship":1558,"citationCount":19,"citationInfo":1609,"publishDate":1612,"publishYear":1610,"citationAnalyzeStatus":17,"lastCitationAnalyze":1513,"indexDatabases":1613,"openAccess":18,"references":1614,"isForceReanalyzing":432},"b841da7a-2c11-40fe-8009-d8a6a5461f91","2023-12-08T05:02:18.455+00:00","2026-07-20T12:42:18.623+00:00",[],"Challenges-to-complexity-shields-that-are-supposed-to-protect-elections-against-manipulation-and-control-a-survey",{"abstract":1517,"title":1519,"gsPaper":1521,"doi":1523},{"EN":1518},"In the context of voting, manipulation and control refer to attempts to influence the outcome of elections by either setting some of the votes strategically (i.e., by reporting untruthful preferences) or by altering the structure of elections via adding, deleting, or partitioning either candidates or voters. Since by the celebrated Gibbard–Satterthwaite theorem (and other results expanding its scope) all reasonable voting systems are manipulable in principle and since many voting systems are in principle susceptible to many control types modeling natural control scenarios, much work has been done to use computational complexity as a shield to protect elections against manipulation and control. However, most of this work has merely yielded NP-hardness results, showing that certain voting systems resist certain types of manipulation or control only in the worst case. Various approaches, including studies of the typical case (where votes are given according to some natural distribution), pose serious challenges to such worst-case complexity results and might allow successful manipulation or control attempts, despite the NP-hardness of the corresponding problems. We survey and discuss some recent results on these challenges to complexity results for manipulation and control, including typical-case analyses and experiments, fixed-parameter tractability, domain restrictions (single-peakedness), and approximability.",{"EN":1520},"Challenges to complexity shields that are supposed to protect elections against manipulation and control: a survey",{"VOID":1522},"[\"12550968250801878677\"]",{"VOID":1524},"10.1007\u002Fs10472-013-9359-5","2024-04-29T14:37:48.778+00:00","http:\u002F\u002Flink.springer.com\u002F10.1007\u002Fs10472-013-9359-5",[1528,1545],{"id":1529,"sortIndex":19,"researcher":18,"roles":1530,"affiliations":1531,"properties":1540,"displayName":1542,"givenName":18,"familyName":18},"4b8c1344-b2cb-4780-990b-e70b128f9fff",[208],[1532],{"id":1533,"sortIndex":19,"affiliation":1534,"properties":18},"0ab276ff-8ce4-4d64-b2f6-45f487e36ce1",{"id":1533,"createTime":18,"updateTime":18,"relativeEntities":1535,"slug":18,"properties":1536,"entityType":18,"verifyStatus":18,"verifyTime":18,"verifyNote":18,"languages":18,"translateLanguages":18,"viewCount":18,"url":18,"parentIds":1539,"statistic":18},[],{"title":1537},{"VI":1538},"Institut für Informatik, Heinrich-Heine-Univ. 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J.: The technique of the Nanson preferential majority system of election. 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The resulting complication makes it difficult to implement a Prolog-type query answering procedure for disjunctive logic programs. However, SLO-resolution provides a mechanism which is similar to SLD-resolution and hence offers a solution to this problem. The Warren Abstract Machine has been a very effective model for implementing Prolog. In this paper, we extend the WAM model of Prolog and adapt it for DISLOG — a language for disjunctive logic programming. We describe the extensions and additional instructions needed to make WAM viable for modeling and executing DISLOG. The extension is made in such a way that the original architecture is not disturbed and a Prolog program will execute as efficiently as it does in the original WAM.",{"EN":2040},"DWAM — A WAM model extension for disjunctive logic programming",{"VOID":2042},"[\"5478951549811906085\"]",{"VOID":2044},"H. Ait-Kaci,Warren's Abstract Machine: A Tutorial Reconstruction (MIT Press, Cambridge, MA, 1991). 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