On potential cognitive abilities in the machine kingdom

The final publication is available at Springer via http://dx.doi.org/10.1007/s11023-012-9299-6Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different for the ‘machine kingdom’. While machines can be characterised by a set of cognitive abilities, and measuring them is already a big challenge, known as ‘universal psychometrics’, a more informative, and yet more challenging, goal would be to also determine the potential cognitive abilities of a machine. In this paper we investigate the notion of potential cognitive ability for machines, focussing especially on universality and intelligence. We consider several machine characterisations (non-interactive and interactive) and give definitions for each case, considering permanent and temporal potentials. From these definitions, we analyse the relation between some potential abilities, we bring out the dependency on the environment distribution and we suggest some ideas about how potential abilities can be measured. Finally, we also analyse the potential of environments at different levels and briefly discuss whether machines should be designed to be intelligent or potentially intelligent.We thank the anonymous reviewers for their comments, which have helped to significantly improve this paper. This work was supported by the MEC-MINECO projects CONSOLIDER-INGENIO CSD2007-00022 and TIN 2010-21062-C02-02, GVA project PROMETEO/2008/051, the COST - European Cooperation in the field of Scientific and Technical Research IC0801 AT. Finally, we thank three pioneers ahead of their time(s). We thank Ray Solomonoff (1926-2009) and Chris Wallace (1933-2004) for all that they taught us, directly and indirectly. And, in his centenary year, we thank Alan Turing (1912-1954), with whom it perhaps all began.Hernández-Orallo, J.; Dowe, DL. (2013). On potential cognitive abilities in the machine kingdom. Minds and Machines. 23(2):179-210. https://doi.org/10.1007/s11023-012-9299-6S179210232Amari, S., Fujita, N., Shinomoto, S. (1992). Four types of learning curves. Neural Computation 4(4), 605–618.Aristotle (Translation, Introduction, and Commentary by Ross, W.D.) (1924). Aristotle’s Metaphysics. Oxford: Clarendon Press.Barmpalias, G. & Dowe, D. L. (2012). 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Formal Introduction to Fuzzy Implications

SummaryIn the article we present in the Mizar system the catalogue of nine basic fuzzy implications, used especially in the theory of fuzzy sets. This work is a continuation of the development of fuzzy sets in Mizar; it could be used to give a variety of more general operations, and also it could be a good starting point towards the formalization of fuzzy logic (together with t-norms and t-conorms, formalized previously).Institute of Informatics, University of Białystok, PolandMichał Baczyński and Balasubramaniam Jayaram. Fuzzy Implications. Springer Publishing Company, Incorporated, 2008. doi:10.1007/978-3-540-69082-5.Adam Grabowski. Basic formal properties of triangular norms and conorms. Formalized Mathematics, 25(2):93–100, 2017. doi:10.1515/forma-2017-0009.Adam Grabowski. The formal construction of fuzzy numbers. Formalized Mathematics, 22(4):321–327, 2014. doi:10.2478/forma-2014-0032.Adam Grabowski. On the computer certification of fuzzy numbers. In M. Ganzha, L. Maciaszek, and M. Paprzycki, editors, 2013 Federated Conference on Computer Science and Information Systems (FedCSIS), Federated Conference on Computer Science and Information Systems, pages 51–54, 2013.Adam Grabowski. Lattice theory for rough sets – a case study with Mizar. Fundamenta Informaticae, 147(2–3):223–240, 2016. doi:10.3233/FI-2016-1406.Adam Grabowski and Magdalena Jastrzębska. Rough set theory from a math-assistant perspective. In Rough Sets and Intelligent Systems Paradigms, International Conference, RSEISP 2007, Warsaw, Poland, June 28–30, 2007, Proceedings, pages 152–161, 2007. doi:10.1007/978-3-540-73451-2_17.Adam Grabowski and Takashi Mitsuishi. Extending Formal Fuzzy Sets with Triangular Norms and Conorms, volume 642: Advances in Intelligent Systems and Computing, pages 176–187. Springer International Publishing, Cham, 2018. doi:10.1007/978-3-319-66824-6_16.Adam Grabowski and Takashi Mitsuishi. Initial comparison of formal approaches to fuzzy and rough sets. In Leszek Rutkowski, Marcin Korytkowski, Rafal Scherer, Ryszard Tadeusiewicz, Lotfi A. Zadeh, and Jacek M. Zurada, editors, Artificial Intelligence and Soft Computing - 14th International Conference, ICAISC 2015, Zakopane, Poland, June 14-18, 2015, Proceedings, Part I, volume 9119 of Lecture Notes in Computer Science, pages 160–171. Springer, 2015. doi:10.1007/978-3-319-19324-3_15.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi:10.1007/s10817-015-9345-1.Takashi Mitsuishi, Noboru Endou, and Yasunari Shidama. The concept of fuzzy set and membership function and basic properties of fuzzy set operation. Formalized Mathematics, 9(2):351–356, 2001.Zdzisław Pawlak. Rough sets. International Journal of Parallel Programming, 11:341–356, 1982. doi:10.1007/BF01001956.Lotfi Zadeh. Fuzzy sets. Information and Control, 8(3):338–353, 1965.25324124

Automatic classification of human facial features based on their appearance

[EN] Classification or typology systems used to categorize different human body parts have existed for many years. Nevertheless, there are very few taxonomies of facial features. Ergonomics, forensic anthropology, crime prevention or new human-machine interaction systems and online activities, like e-commerce, e-learning, games, dating or social networks, are fields in which classifications of facial features are useful, for example, to create digital interlocutors that optimize the interactions between human and machines. However, classifying isolated facial features is difficult for human observers. Previous works reported low inter-observer and intra-observer agreement in the evaluation of facial features. This work presents a computer-based procedure to automatically classify facial features based on their global appearance. 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Cross-Language Plagiarism Detection

Cross-language plagiarism detection deals with the automatic identification and extraction of plagiarism in a multilingual setting. In this setting, a suspicious document is given, and the task is to retrieve all sections from the document that originate from a large, multilingual document collection. Our contributions in this field are as follows: (1) a comprehensive retrieval process for cross-language plagiarism detection is introduced, highlighting the differences to monolingual plagiarism detection, (2) state-of-the-art solutions for two important subtasks are reviewed, (3) retrieval models for the assessment of cross-language similarity are surveyed, and, (4) the three models CL-CNG, CL-ESA and CL-ASA are compared. Our evaluation is of realistic scale: it relies on 120,000 test documents which are selected from the corpora JRC-Acquis and Wikipedia, so that for each test document highly similar documents are available in all of the six languages English, German, Spanish, French, Dutch, and Polish. The models are employed in a series of ranking tasks, and more than 100 million similarities are computed with each model. The results of our evaluation indicate that CL-CNG, despite its simple approach, is the best choice to rank and compare texts across languages if they are syntactically related. CL-ESA almost matches the performance of CL-CNG, but on arbitrary pairs of languages. CL-ASA works best on "exact" translations but does not generalize well.This work was partially supported by the TEXT-ENTERPRISE 2.0 TIN2009-13391-C04-03 project and the CONACyT-Mexico 192021 grant.Potthast, M.; Barrón Cedeño, LA.; Stein, B.; Rosso, P. (2011). Cross-Language Plagiarism Detection. Language Resources and Evaluation. 45(1):45-62. https://doi.org/10.1007/s10579-009-9114-zS4562451Ballesteros, L. A. (2001). Resolving ambiguity for cross-language information retrieval: A dictionary approach. PhD thesis, University of Massachusetts Amherst, USA, Bruce Croft.Barrón-Cedeño, A., Rosso, P., Pinto, D., & Juan A. (2008). On cross-lingual plagiarism analysis using a statistical model. In S. Benno, S. Efstathios, & K. Moshe (Eds.), ECAI 2008 workshop on uncovering plagiarism, authorship, and social software misuse (PAN 08) (pp. 9–13). Patras, Greece.Baum, L. E. (1972). An inequality and associated maximization technique in statistical estimation of probabilistic functions of a Markov process. Inequalities, 3, 1–8.Berger, A., & Lafferty, J. (1999). Information retrieval as statistical translation. In SIGIR’99: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval (vol. 4629, pp. 222–229). Berkeley, California, United States: ACM.Brin, S., Davis, J., & Garcia-Molina, H. (1995). Copy detection mechanisms for digital documents. In SIGMOD ’95 (pp. 398–409). New York, NY, USA: ACM Press.Brown, P. F., Della Pietra, S. A., Della Pietra, V. J., & Mercer R. L. (1993). The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2), 263–311.Ceska, Z., Toman, M., & Jezek, K. (2008). Multilingual plagiarism detection. In AIMSA’08: Proceedings of the 13th international conference on artificial intelligence (pp. 83–92). Berlin, Heidelberg: Springer.Clough, P. (2003). Old and new challenges in automatic plagiarism detection. National UK Plagiarism Advisory Service, http://www.ir.shef.ac.uk/cloughie/papers/pas_plagiarism.pdf .Dempster A. P., Laird N. M., Rubin D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 39(1), 1–38.Dumais, S. T., Letsche, T. A., Littman, M. L., & Landauer, T. K. (1997). Automatic cross-language retrieval using latent semantic indexing. In D. Hull & D. Oard (Eds.), AAAI-97 spring symposium series: Cross-language text and speech retrieval (pp. 18–24). Stanford University, American Association for Artificial Intelligence.Gabrilovich, E., & Markovitch, S. (2007). Computing semantic relatedness using Wikipedia-based explicit semantic analysis. In Proceedings of the 20th international joint conference for artificial intelligence, Hyderabad, India.Hoad T. C., & Zobel, J. (2003). Methods for identifying versioned and plagiarised documents. American Society for Information Science and Technology, 54(3), 203–215.Levow, G.-A., Oard, D. W., & Resnik, P. (2005). Dictionary-based techniques for cross-language information retrieval. Information Processing & Management, 41(3), 523–547.Littman, M., Dumais, S. T., & Landauer, T. K. (1998). Automatic cross-language information retrieval using latent semantic indexing. In Cross-language information retrieval, chap. 5 (pp. 51–62). Kluwer.Maurer, H., Kappe, F., & Zaka, B. (2006). Plagiarism—a survey. Journal of Universal Computer Science, 12(8), 1050–1084.McCabe, D. (2005). Research report of the Center for Academic Integrity. http://www.academicintegrity.org .Mcnamee, P., & Mayfield, J. (2004). Character N-gram tokenization for European language text retrieval. Information Retrieval, 7(1–2), 73–97.Meyer zu Eissen, S., & Stein, B. (2006). Intrinsic plagiarism detection. In M. Lalmas, A. MacFarlane, S. M. Rüger, A. Tombros, T. Tsikrika, & A. Yavlinsky (Eds.), Proceedings of the European conference on information retrieval (ECIR 2006), volume 3936 of Lecture Notes in Computer Science (pp. 565–569). Springer.Meyer zu Eissen, S., Stein, B., & Kulig, M. (2007). Plagiarism detection without reference collections. In R. Decker & H. J. Lenz (Eds.), Advances in data analysis (pp. 359–366), Springer.Och, F. J., & Ney, H. (2003). A systematic comparison of various statistical alignment models. Computational Linguistics, 29(1), 19–51.Pinto, D., Juan, A., & Rosso, P. (2007). Using query-relevant documents pairs for cross-lingual information retrieval. In V. Matousek & P. Mautner (Eds.), Lecture Notes in Artificial Intelligence (pp. 630–637). Pilsen, Czech Republic.Pinto, D., Civera, J., Barrón-Cedeño, A., Juan, A., & Rosso, P. (2009). A statistical approach to cross-lingual natural language tasks. Journal of Algorithms, 64(1), 51–60.Potthast, M. (2007). Wikipedia in the pocket-indexing technology for near-duplicate detection and high similarity search. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 909–909). ACM.Potthast, M., Stein, B., & Anderka, M. (2008). A Wikipedia-based multilingual retrieval model. In C. Macdonald, I. Ounis, V. Plachouras, I. Ruthven, & R. W. White (Eds.), 30th European conference on IR research, ECIR 2008, Glasgow , volume 4956 LNCS of Lecture Notes in Computer Science (pp. 522–530). Berlin: Springer.Pouliquen, B., Steinberger, R., & Ignat, C. (2003a). Automatic annotation of multilingual text collections with a conceptual thesaurus. In Proceedings of the workshop ’ontologies and information extraction’ at the Summer School ’The Semantic Web and Language Technology—its potential and practicalities’ (EUROLAN’2003) (pp. 9–28), Bucharest, Romania.Pouliquen, B., Steinberger, R., & Ignat, C. (2003b). Automatic identification of document translations in large multilingual document collections. In Proceedings of the international conference recent advances in natural language processing (RANLP’2003) (pp. 401–408). Borovets, Bulgaria.Stein, B. (2007). Principles of hash-based text retrieval. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 527–534). ACM.Stein, B. (2005). Fuzzy-fingerprints for text-based information retrieval. In K. Tochtermann & H. Maurer (Eds.), Proceedings of the 5th international conference on knowledge management (I-KNOW 05), Graz, Journal of Universal Computer Science. (pp. 572–579). Know-Center.Stein, B., & Anderka, M. (2009). Collection-relative representations: A unifying view to retrieval models. In A. M. Tjoa & R. R. Wagner (Eds.), 20th International conference on database and expert systems applications (DEXA 09) (pp. 383–387). IEEE.Stein, B., & Meyer zu Eissen, S. (2007). Intrinsic plagiarism analysis with meta learning. In B. Stein, M. Koppel, & E. Stamatatos (Eds.), SIGIR workshop on plagiarism analysis, authorship identification, and near-duplicate detection (PAN 07) (pp. 45–50). CEUR-WS.org.Stein, B., & Potthast, M. (2007). Construction of compact retrieval models. In S. Dominich & F. Kiss (Eds.), Studies in theory of information retrieval (pp. 85–93). Foundation for Information Society.Stein, B., Meyer zu Eissen, S., & Potthast, M. (2007). Strategies for retrieving plagiarized documents. In C. Clarke, N. Fuhr, N. Kando, W. Kraaij, & A. de Vries (Eds.), 30th Annual international ACM SIGIR conference (pp. 825–826). ACM.Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufis, D., & Varga, D. (2006). The JRC-Acquis: A multilingual aligned parallel corpus with 20+ languages. In Proceedings of the 5th international conference on language resources and evaluation (LREC’2006).Steinberger, R., Pouliquen, B., & Ignat, C. (2004). Exploiting multilingual nomenclatures and language-independent text features as an interlingua for cross-lingual text analysis applications. In Proceedings of the 4th Slovenian language technology conference. Information Society 2004 (IS’2004).Vinokourov, A., Shawe-Taylor, J., & Cristianini, N. (2003). Inferring a semantic representation of text via cross-language correlation analysis. In S. Becker, S. Thrun, & K. Obermayer (Eds.), NIPS-02: Advances in neural information processing systems (pp. 1473–1480). MIT Press.Yang, Y., Carbonell, J. G., Brown, R. D., & Frederking, R. E. (1998). Translingual information retrieval: Learning from bilingual corpora. Artificial Intelligence, 103(1–2), 323–345

Formal Development of Rough Inclusion Functions

Rough sets, developed by Pawlak [15], are important tool to describe situation of incomplete or partially unknown information. In this article, continuing the formalization of rough sets [12], we give the formal characterization of three rough inclusion functions (RIFs). We start with the standard one, κ£, connected with Łukasiewicz [14], and extend this research for two additional RIFs: κ 1, and κ 2, following a paper by Gomolińska [4], [3]. We also define q-RIFs and weak q-RIFs [2]. The paper establishes a formal counterpart of [7] and makes a preliminary step towards rough mereology [16], [17] in Mizar [13].Institute of Informatics, University of Białystok, PolandAnna Gomolinska. A comparative study of some generalized rough approximations. Fundamenta Informaticae, 51:103–119, 2002.Anna Gomolinska. Rough approximation based on weak q-RIFs. In James F. Peters, Andrzej Skowron, Marcin Wolski, Mihir K. Chakraborty, and Wei-Zhi Wu, editors, Transactions on Rough Sets X, volume 5656 of Lecture Notes in Computer Science, pages 117–135, Berlin, Heidelberg, 2009. Springer. ISBN 978-3-642-03281-3. doi:10.1007/978-3-642-03281-3_4.Anna Gomolinska. On three closely related rough inclusion functions. In Marzena Kryszkiewicz, James F. Peters, Henryk Rybinski, and Andrzej Skowron, editors, Rough Sets and Intelligent Systems Paradigms, volume 4585 of Lecture Notes in Computer Science, pages 142–151, Berlin, Heidelberg, 2007. Springer. doi:10.1007/978-3-540-73451-2_16.Anna Gomolinska. On certain rough inclusion functions. In James F. Peters, Andrzej Skowron, and Henryk Rybinski, editors, Transactions on Rough Sets IX, volume 5390 of Lecture Notes in Computer Science, pages 35–55. Springer Berlin Heidelberg, 2008. doi:10.1007/978-3-540-89876-4_3.Adam Grabowski. On the computer-assisted reasoning about rough sets. In B. Dunin-Kęplicz, A. Jankowski, A. Skowron, and M. Szczuka, editors, International Workshop on Monitoring, Security, and Rescue Techniques in Multiagent Systems Location, volume 28 of Advances in Soft Computing, pages 215–226, Berlin, Heidelberg, 2005. Springer-Verlag. doi:10.1007/3-540-32370-8_15.Adam Grabowski. Efficient rough set theory merging. Fundamenta Informaticae, 135(4): 371–385, 2014. doi:10.3233/FI-2014-1129.Adam Grabowski. Building a framework of rough inclusion functions by means of computerized proof assistant. In Tamás Mihálydeák, Fan Min, Guoyin Wang, Mohua Banerjee, Ivo Düntsch, Zbigniew Suraj, and Davide Ciucci, editors, Rough Sets, volume 11499 of Lecture Notes in Computer Science, pages 225–238, Cham, 2019. Springer International Publishing. ISBN 978-3-030-22815-6. doi:10.1007/978-3-030-22815-6_18.Adam Grabowski. Lattice theory for rough sets – a case study with Mizar. Fundamenta Informaticae, 147(2–3):223–240, 2016. doi:10.3233/FI-2016-1406.Adam Grabowski. Relational formal characterization of rough sets. Formalized Mathematics, 21(1):55–64, 2013. doi:10.2478/forma-2013-0006.Adam Grabowski. Binary relations-based rough sets – an automated approach. Formalized Mathematics, 24(2):143–155, 2016. doi:10.1515/forma-2016-0011.Adam Grabowski and Christoph Schwarzweller. On duplication in mathematical repositories. In Serge Autexier, Jacques Calmet, David Delahaye, Patrick D. F. Ion, Laurence Rideau, Renaud Rioboo, and Alan P. Sexton, editors, Intelligent Computer Mathematics, 10th International Conference, AISC 2010, 17th Symposium, Calculemus 2010, and 9th International Conference, MKM 2010, Paris, France, July 5–10, 2010. Proceedings, volume 6167 of Lecture Notes in Computer Science, pages 300–314. Springer, 2010. doi:10.1007/978-3-642-14128-7_26.Adam Grabowski and Michał Sielwiesiuk. Formalizing two generalized approximation operators. Formalized Mathematics, 26(2):183–191, 2018. doi:10.2478/forma-2018-0016.Adam Grabowski, Artur Korniłowicz, and Adam Naumowicz. Four decades of Mizar. Journal of Automated Reasoning, 55(3):191–198, 2015. doi:10.1007/s10817-015-9345-1.Jan Łukasiewicz. Die logischen Grundlagen der Wahrscheinlichkeitsrechnung. In L. Borkowski, editor, Jan Łukasiewicz – Selected Works, pages 16–63. North Holland, Polish Scientific Publ., Amsterdam London Warsaw, 1970. First published in Kraków, 1913.Zdzisław Pawlak. Rough sets. International Journal of Parallel Programming, 11:341–356, 1982. doi:10.1007/BF01001956.Lech Polkowski. Rough mereology. In Approximate Reasoning by Parts, volume 20 of Intelligent Systems Reference Library, pages 229–257, Berlin, Heidelberg, 2011. Springer. ISBN 978-3-642-22279-5. doi:10.1007/978-3-642-22279-5_6.Lech Polkowski and Andrzej Skowron. Rough mereology: A new paradigm for approximate reasoning. International Journal of Approximate Reasoning, 15(4):333–365, 1996. doi:10.1016/S0888-613X(96)00072-2.Andrzej Skowron and Jarosław Stepaniuk. Tolerance approximation spaces. Fundamenta Informaticae, 27(2/3):245–253, 1996. doi:10.3233/FI-1996-272311.William Zhu. Generalized rough sets based on relations. Information Sciences, 177: 4997–5011, 2007.27433734

A B2B Architecture and Protocol for Researchers Cooperation

Acknowledgement: Electronic version of an article published as International Journal of Cooperative Information Systems, Volume 22, Issue 02, 2013, DOI: 10.1142/S021884301350010X © World Scientific Publishing Company http://www.worldscientific.com/Some works on the researchers cooperation's literature provide the key lines for building research networks and propose new protocols and standards for business to business (B2B) data exchange, but none of them explains how researchers should contact and the procedure to select the most appropriate partner of a research enterprise, institute or university. In this paper, we propose a B2B architecture and protocol between research entities, that uses ebXML protocol. The contacts for cooperation are established based on some defined parameters and an information retrieval system. We explain the information retrieval system, the researcher selection procedure, the XML-based protocol and the workflow of our proposal. We also show the information that has to be exchanged to contact other researchers. Several simulations demonstrate that our proposal is a feasible architecture and may be used to promote the research cooperation. The main purpose of this paper is to propose an efficient procedure for searching project partners.Lloret, J.; Tomás Gironés, J.; García Pineda, M.; Lacuesta Contreras, R. (2013). A B2B Architecture and Protocol for Researchers Cooperation. International Journal of Cooperative Information Systems. 22(2):1-27. doi:10.1142/S021884301350010XS127222B. Wellman and S. D. Berkowitz, Social Structures: A Network Approach (Cambridge University Press, Cambridge, 1988) pp. 19–61.Wasserman, S., & Faust, K. (1994). Social Network Analysis. doi:10.1017/cbo9780511815478Wellman, B., Salaff, J., Dimitrova, D., Garton, L., Gulia, M., & Haythornthwaite, C. (1996). Computer Networks as Social Networks: Collaborative Work, Telework, and Virtual Community. Annual Review of Sociology, 22(1), 213-238. doi:10.1146/annurev.soc.22.1.213Fulk, J., & Steinfield, C. (1990). Organizations and Communication Technology. doi:10.4135/9781483325385B. Wellman and M. Gulia, Networks in the Global Village (Westview Press, Boulder, CO, 1997) pp. 331–367.Marsden, P. V., & Campbell, K. E. (1984). Measuring Tie Strength. Social Forces, 63(2), 482-501. doi:10.1093/sf/63.2.482Wellman, B., & Wortley, S. (1990). Different Strokes from Different Folks: Community Ties and Social Support. American Journal of Sociology, 96(3), 558-588. doi:10.1086/229572Adamic, L., & Adar, E. (2005). How to search a social network. Social Networks, 27(3), 187-203. doi:10.1016/j.socnet.2005.01.007Ebel, H., Mielsch, L.-I., & Bornholdt, S. (2002). Scale-free topology of e-mail networks. Physical Review E, 66(3). doi:10.1103/physreve.66.035103Jung, J.-Y., Kim, H., & Kang, S.-H. (2006). Standards-based approaches to B2B workflow integration. Computers & Industrial Engineering, 51(2), 321-334. doi:10.1016/j.cie.2006.02.011Lloret, J., Palau, C., Boronat, F., & Tomas, J. (2008). Improving networks using group-based topologies. Computer Communications, 31(14), 3438-3450. doi:10.1016/j.comcom.2008.05.030Lloret, J., Garcia, M., Tomás, J., & Boronat, F. (2008). GBP-WAHSN: A Group-Based Protocol for Large Wireless Ad Hoc and Sensor Networks. Journal of Computer Science and Technology, 23(3), 461-480. doi:10.1007/s11390-008-9147-6Lloret, J., Garcia, M., Bri, D., & Diaz, J. R. (2009). Study and performance of a group-based Content Delivery Network. Journal of Network and Computer Applications, 32(5), 991-999. doi:10.1016/j.jnca.2009.03.008Lloret, J., Garcia, M., Tomas, J., & Sendra, S. (2010). A group-based architecture for grids. Telecommunication Systems, 46(2), 117-133. doi:10.1007/s11235-010-9279-1Lin, T.-C., & Huang, C.-C. (2010). Withholding effort in knowledge contribution: The role of social exchange and social cognitive on project teams. Information & Management, 47(3), 188-196. doi:10.1016/j.im.2010.02.001Maron, M. E., & Kuhns, J. L. (1960). On Relevance, Probabilistic Indexing and Information Retrieval. Journal of the ACM, 7(3), 216-244. doi:10.1145/321033.321035Tomás, J., Lloret, J., & Casacuberta, F. (2005). Phrase-Based Alignment Models for Statistical Machine Translation. Lecture Notes in Computer Science, 605-613. doi:10.1007/11492542_74Turel, O., & Zhang, Y. (Jenny). (2011). Should I e-collaborate with this group? A multilevel model of usage intentions. Information & Management, 48(1), 62-68. doi:10.1016/j.im.2010.12.004Okuda, T., Tanaka, E., & Kasai, T. (1976). A Method for the Correction of Garbled Words Based on the Levenshtein Metric. IEEE Transactions on Computers, C-25(2), 172-178. doi:10.1109/tc.1976.500923

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future