Authenticating computer access based on keystroke dynamics using a probabilistic neural network

Comunicação apresentada na 2nd Annual International Conference on Global e-Security, Docklands, UK, 20 - 22 April 2006.Most computer systems are secured using a login id and password. When computers are connected to the internet, they become more vulnerable as more machines are available to attack them. In this paper, we present a novel method for protecting/enhancing login protection that can reduce the potential threat of internet connected computers. Our method is based on and enhancement to login id/password based on keystroke dynamics. We employ a novel authentication algorithm based on a probabilistic neural network. Our results indicate that we can achieve an equal error rate of less than 5%, comparable to what is achieved with hardware based solutions such as fingerprint scanners and facial recognition systems

Personalisation of eSearch Services – Concepts, Techniques, and Market Overview

The importance of information in today’s society is still growing and information search has become an essential task in both the workplace and in private life. eSearch services provide access to the abundance of information available on the Internet by means of search engine technology. However, conventional search engines have certain limitations in dealing with the typical information overload problems. With the application of personalisation techniques search engine providers aim at moderating some of the problems by providing users with information access individualised to their needs. The aim of this paper is twofold. Firstly, techniques for personalisation of eSearch services are introduced. Secondly, the results of an empirical study of the market for eSearch services are presented. Typical examples illustrate eSearch personalisation in practice, and the diffusion of techniques and implications for further research in the domain are discussed

XML Matchers: approaches and challenges

Schema Matching, i.e. the process of discovering semantic correspondences between concepts adopted in different data source schemas, has been a key topic in Database and Artificial Intelligence research areas for many years. In the past, it was largely investigated especially for classical database models (e.g., E/R schemas, relational databases, etc.). However, in the latest years, the widespread adoption of XML in the most disparate application fields pushed a growing number of researchers to design XML-specific Schema Matching approaches, called XML Matchers, aiming at finding semantic matchings between concepts defined in DTDs and XSDs. XML Matchers do not just take well-known techniques originally designed for other data models and apply them on DTDs/XSDs, but they exploit specific XML features (e.g., the hierarchical structure of a DTD/XSD) to improve the performance of the Schema Matching process. The design of XML Matchers is currently a well-established research area. The main goal of this paper is to provide a detailed description and classification of XML Matchers. We first describe to what extent the specificities of DTDs/XSDs impact on the Schema Matching task. Then we introduce a template, called XML Matcher Template, that describes the main components of an XML Matcher, their role and behavior. We illustrate how each of these components has been implemented in some popular XML Matchers. We consider our XML Matcher Template as the baseline for objectively comparing approaches that, at first glance, might appear as unrelated. The introduction of this template can be useful in the design of future XML Matchers. Finally, we analyze commercial tools implementing XML Matchers and introduce two challenging issues strictly related to this topic, namely XML source clustering and uncertainty management in XML Matchers.Comment: 34 pages, 8 tables, 7 figure

An Abstract Framework for Non-Cooperative Multi-Agent Planning

[EN] In non-cooperative multi-agent planning environments, it is essential to have a system that enables the agents¿ strategic behavior. It is also important to consider all planning phases, i.e., goal allocation, strategic planning, and plan execution, in order to solve a complete problem. Currently, we have no evidence of the existence of any framework that brings together all these phases for non-cooperative multi-agent planning environments. In this work, an exhaustive study is made to identify existing approaches for the different phases as well as frameworks and different applicable techniques in each phase. Thus, an abstract framework that covers all the necessary phases to solve these types of problems is proposed. In addition, we provide a concrete instantiation of the abstract framework using different techniques to promote all the advantages that the framework can offer. A case study is also carried out to show an illustrative example of how to solve a non-cooperative multi-agent planning problem with the presented framework. This work aims to establish a base on which to implement all the necessary phases using the appropriate technologies in each of them and to solve complex problems in different domains of application for non-cooperative multi-agent planning settings.This work was partially funded by MINECO/FEDER RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by Universitat Politecnica de Valencia (UPV) PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana Fondo Social Europeo.Jordán, J.; Bajo, J.; Botti, V.; Julian Inglada, VJ. (2019). An Abstract Framework for Non-Cooperative Multi-Agent Planning. Applied Sciences. 9(23):1-18. https://doi.org/10.3390/app9235180S118923De Weerdt, M., & Clement, B. (2009). Introduction to planning in multiagent systems. Multiagent and Grid Systems, 5(4), 345-355. doi:10.3233/mgs-2009-0133Dunne, P. E., Kraus, S., Manisterski, E., & Wooldridge, M. (2010). Solving coalitional resource games. Artificial Intelligence, 174(1), 20-50. doi:10.1016/j.artint.2009.09.005Torreño, A., Onaindia, E., Komenda, A., & Štolba, M. (2018). Cooperative Multi-Agent Planning. ACM Computing Surveys, 50(6), 1-32. doi:10.1145/3128584Fikes, R. E., & Nilsson, N. J. (1971). Strips: A new approach to the application of theorem proving to problem solving. Artificial Intelligence, 2(3-4), 189-208. doi:10.1016/0004-3702(71)90010-5Hoffmann, J., & Nebel, B. (2001). The FF Planning System: Fast Plan Generation Through Heuristic Search. Journal of Artificial Intelligence Research, 14, 253-302. doi:10.1613/jair.855Dukeman, A., & Adams, J. A. (2017). Hybrid mission planning with coalition formation. Autonomous Agents and Multi-Agent Systems, 31(6), 1424-1466. doi:10.1007/s10458-017-9367-7Hadad, M., Kraus, S., Ben-Arroyo Hartman, I., & Rosenfeld, A. (2013). Group planning with time constraints. Annals of Mathematics and Artificial Intelligence, 69(3), 243-291. doi:10.1007/s10472-013-9363-9Guo, Y., Pan, Q., Sun, Q., Zhao, C., Wang, D., & Feng, M. (2019). Cooperative Game-based Multi-Agent Path Planning with Obstacle Avoidance*. 2019 IEEE 28th International Symposium on Industrial Electronics (ISIE). doi:10.1109/isie.2019.8781205v. Neumann, J. (1928). Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, 100(1), 295-320. doi:10.1007/bf01448847Mookherjee, D., & Sopher, B. (1994). Learning Behavior in an Experimental Matching Pennies Game. Games and Economic Behavior, 7(1), 62-91. doi:10.1006/game.1994.1037Ochs, J. (1995). Games with Unique, Mixed Strategy Equilibria: An Experimental Study. Games and Economic Behavior, 10(1), 202-217. doi:10.1006/game.1995.1030Applegate, C., Elsaesser, C., & Sanborn, J. (1990). An architecture for adversarial planning. IEEE Transactions on Systems, Man, and Cybernetics, 20(1), 186-194. doi:10.1109/21.47820Sailer, F., Buro, M., & Lanctot, M. (2007). Adversarial Planning Through Strategy Simulation. 2007 IEEE Symposium on Computational Intelligence and Games. doi:10.1109/cig.2007.368082Willmott, S., Richardson, J., Bundy, A., & Levine, J. (2001). Applying adversarial planning techniques to Go. Theoretical Computer Science, 252(1-2), 45-82. doi:10.1016/s0304-3975(00)00076-1Nau, D. S., Au, T. C., Ilghami, O., Kuter, U., Murdock, J. W., Wu, D., & Yaman, F. (2003). SHOP2: An HTN Planning System. Journal of Artificial Intelligence Research, 20, 379-404. doi:10.1613/jair.1141Knuth, D. E., & Moore, R. W. (1975). An analysis of alpha-beta pruning. Artificial Intelligence, 6(4), 293-326. doi:10.1016/0004-3702(75)90019-3Vickrey, W. (1961). COUNTERSPECULATION, AUCTIONS, AND COMPETITIVE SEALED TENDERS. The Journal of Finance, 16(1), 8-37. doi:10.1111/j.1540-6261.1961.tb02789.xClarke, E. H. (1971). Multipart pricing of public goods. Public Choice, 11(1), 17-33. doi:10.1007/bf01726210Groves, T. (1973). Incentives in Teams. Econometrica, 41(4), 617. doi:10.2307/1914085Savaux, J., Vion, J., Piechowiak, S., Mandiau, R., Matsui, T., Hirayama, K., … Silaghi, M. (2016). DisCSPs with Privacy Recast as Planning Problems for Self-Interested Agents. 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI). doi:10.1109/wi.2016.0057Buzing, P., Mors, A. ter, Valk, J., & Witteveen, C. (2006). Coordinating Self-interested Planning Agents. Autonomous Agents and Multi-Agent Systems, 12(2), 199-218. doi:10.1007/s10458-005-6104-4Ter Mors, A., & Witteveen, C. (s. f.). Coordinating Non Cooperative Planning Agents: Complexity Results. IEEE/WIC/ACM International Conference on Intelligent Agent Technology. doi:10.1109/iat.2005.60Hrnčíř, J., Rovatsos, M., & Jakob, M. (2015). Ridesharing on Timetabled Transport Services: A Multiagent Planning Approach. Journal of Intelligent Transportation Systems, 19(1), 89-105. doi:10.1080/15472450.2014.941759Galuszka, A., & Swierniak, A. (2009). Planning in Multi-agent Environment Using Strips Representation and Non-cooperative Equilibrium Strategy. Journal of Intelligent and Robotic Systems, 58(3-4), 239-251. doi:10.1007/s10846-009-9364-4Rosenthal, R. W. (1973). A class of games possessing pure-strategy Nash equilibria. International Journal of Game Theory, 2(1), 65-67. doi:10.1007/bf01737559Jordán, J., Torreño, A., de Weerdt, M., & Onaindia, E. (2017). A better-response strategy for self-interested planning agents. Applied Intelligence, 48(4), 1020-1040. doi:10.1007/s10489-017-1046-5Veloso, M., Muñoz-Avila, H., & Bergmann, R. (1996). Case-based planning: selected methods and systems. AI Communications, 9(3), 128-137. doi:10.3233/aic-1996-9305VOORNEVELD, M., BORM, P., VAN MEGEN, F., TIJS, S., & FACCHINI, G. (1999). CONGESTION GAMES AND POTENTIALS RECONSIDERED. International Game Theory Review, 01(03n04), 283-299. doi:10.1142/s0219198999000219Han-Lim Choi, Brunet, L., & How, J. P. (2009). Consensus-Based Decentralized Auctions for Robust Task Allocation. IEEE Transactions on Robotics, 25(4), 912-926. doi:10.1109/tro.2009.2022423Monderer, D., & Shapley, L. S. (1996). Potential Games. Games and Economic Behavior, 14(1), 124-143. doi:10.1006/game.1996.0044Friedman, J. W., & Mezzetti, C. (2001). Learning in Games by Random Sampling. Journal of Economic Theory, 98(1), 55-84. doi:10.1006/jeth.2000.2694Aamodt, A., & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications, 7(1), 39-59. doi:10.3233/aic-1994-7104Bertsekas, D. P. (1988). The auction algorithm: A distributed relaxation method for the assignment problem. Annals of Operations Research, 14(1), 105-123. doi:10.1007/bf02186476Bertsekas, D. P., & Castanon, D. A. (1989). The auction algorithm for the transportation problem. Annals of Operations Research, 20(1), 67-96. doi:10.1007/bf0221692

Interactive Agent-Based Simulation for Experimentation: A Case Study with Cooperatve Game Theory

Incorporating human behavior is a current challenge for agent-based modeling and simulation (ABMS). Human behavior includes many different aspects depending on the scenario considered. The scenario context of this paper is strategic coalition formation, which is traditionally modeled using cooperative game theory, but we use ABMS instead; as such, it needs to be validated. One approach to validation is to compare the recorded behavior of humans to what was observed in our simulation. We suggest that using an interactive simulation is a good approach to collecting the necessary human behavior data because the humans would be playing in precisely the same context as the computerized agents. However, such a validation approach may be suspectable to extraneous effects. In this paper, we conducted a correlation research experiment that included an investigation into whether game theory experience, an extraneous variable, affects human behavior in our interactive simulation; our results indicate that it did not make a significant difference. However, in only 42 percent of the trials did the human participants’ behavior result in an outcome predicted by the underlying theory used in our model, i.e., cooperative game theory. This paper also provides a detailed case study for creating an interactive simulation for experimentation

Classifying Attitude by Topic Aspect for English and Chinese Document Collections

The goal of this dissertation is to explore the design of tools to help users make sense of subjective information in English and Chinese by comparing attitudes on aspects of a topic in English and Chinese document collections. This involves two coupled challenges: topic aspect focus and attitude characterization. The topic aspect focus is specified by using information retrieval techniques to obtain documents on a topic that are of interest to a user and then allowing the user to designate a few segments of those documents to serve as examples for aspects that she wishes to see characterized. A novel feature of this work is that the examples can be drawn from documents in two languages (English and Chinese). A bilingual aspect classifier which applies monolingual and cross-language classification techniques is used to assemble automatically a large set of document segments on those same aspects. A test collection was designed for aspect classification by annotating consecutive sentences in documents from the Topic Detection and Tracking collections as aspect instances. Experiments show that classification effectiveness can often be increased by using training examples from both languages. Attitude characterization is achieved by classifiers which determine the subjectivity and polarity of document segments. Sentence attitude classification is the focus of the experiments in the dissertation because the best presently available test collection for Chinese attitude classification (the NTCIR-6 Chinese Opinion Analysis Pilot Task) is focused on sentence-level classification. A large Chinese sentiment lexicon was constructed by leveraging existing Chinese and English lexical resources, and an existing character-based approach for estimating the semantic orientation of other Chinese words was extended. A shallow linguistic analysis approach was adopted to classify the subjectivity and polarity of a sentence. Using the large sentiment lexicon with appropriate handling of negation, and leveraging sentence subjectivity density, sentence positivity and negativity, the resulting sentence attitude classifier was more effective than the best previously reported systems