Building a Mobile Advertising System for Target Marketing

Mobile advertising has become one of the most exciting new technological frontiers in advertising area in recent years. The ubiquitous nature of mobile phones makes it possible for advertisers to target users effectively. This paper proposes a targeted mobile advertising system (TMAS) that works as a platform to provide consumers personalized ads based on the consumers’ contextual and preference. The platform allows shops to provide contextual and time-sensitive ads and consumers to locate ads and promotion information using their smart phone. A demonstration is conducted to show the validity of the key process in the TMAS

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning

Web usage mining for click fraud detection

Estágio realizado na AuditMark e orientado pelo Eng.º Pedro FortunaTese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Second CLIPS Conference Proceedings, volume 2

Papers presented at the 2nd C Language Integrated Production System (CLIPS) Conference held at the Lyndon B. Johnson Space Center (JSC) on 23-25 September 1991 are documented in these proceedings. CLIPS is an expert system tool developed by the Software Technology Branch at NASA JSC and is used at over 4000 sites by government, industry, and business. During the three days of the conference, over 40 papers were presented by experts from NASA, Department of Defense, other government agencies, universities, and industry

Self-adaptive unobtrusive interactions of mobile computing systems

[EN] In Pervasive Computing environments, people are surrounded by a lot of embedded services. Since pervasive devices, such as mobile devices, have become a key part of our everyday life, they enable users to always be connected to the environment, making demands on one of the most valuable resources of users: human attention. A challenge of the mobile computing systems is regulating the request for users¿ attention. In other words, service interactions should behave in a considerate manner by taking into account the degree to which each service intrudes on the user¿s mind (i.e., the degree of obtrusiveness). The main goal of this paper is to introduce self-adaptive capabilities in mobile computing systems in order to provide non-disturbing interactions. We achieve this by means of an software infrastructure that automatically adapts the service interaction obtrusiveness according to the user¿s context. This infrastructure works from a set of high-level models that define the unobtrusive adaptation behavior and its implication with the interaction resources in a technology-independent way. Our infrastructure has been validated through several experiments to assess its correctness, performance, and the achieved user experience through a user study.This work has been developed with the support of MINECO under the project SMART-ADAPT TIN2013-42981-P, and co-financed by the Generalitat Valenciana under the postdoctoral fellowship APOSTD/2016/042.Gil Pascual, M.; Pelechano Ferragud, V. (2017). Self-adaptive unobtrusive interactions of mobile computing systems. Journal of Ambient Intelligence and Smart Environments. 9(6):659-688. https://doi.org/10.3233/AIS-170463S65968896Aleksy, M., Butter, T., & Schader, M. (2008). Context-Aware Loading for Mobile Applications. 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Proceedings of the 2nd ACM SIGCHI symposium on Engineering interactive computing systems - EICS ’10. doi:10.1145/1822018.1822022D.M. Brown, Communicating Design: Developing Web Site Documentation for Design and Planning, 2nd edn, New Riders Press, 2010.J. Bruin, Statistical Analyses Using SPSS, 2011, http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm#1sampt.J. Cámara, G. Moreno and D. Garlan, Reasoning about human participation in self-adaptive systems, in: SEAMS 2015, 2015, pp. 146–156.Campbell, A., & Choudhury, T. (2012). From Smart to Cognitive Phones. IEEE Pervasive Computing, 11(3), 7-11. doi:10.1109/mprv.2012.41Y. Cao, M. Theune and A. Nijholt, Modality effects on cognitive load and performance in high-load information presentation, in: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI’09, ACM, New York, 2009, pp. 335–344.Chang, F., & Ren, J. (2007). Validating system properties exhibited in execution traces. 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Future Generation Computer Systems, 34, 110-123. doi:10.1016/j.future.2013.12.010Fagin, R., Halpern, J. Y., & Megiddo, N. (1990). A logic for reasoning about probabilities. Information and Computation, 87(1-2), 78-128. doi:10.1016/0890-5401(90)90060-uFerscha, A. (2012). 20 Years Past Weiser: What’s Next? IEEE Pervasive Computing, 11(1), 52-61. doi:10.1109/mprv.2011.78Floch, J., Frà, C., Fricke, R., Geihs, K., Wagner, M., Lorenzo, J., … Scholz, U. (2012). Playing MUSIC - building context-aware and self-adaptive mobile applications. Software: Practice and Experience, 43(3), 359-388. doi:10.1002/spe.2116Gibbs, W. W. (2005). Considerate Computing. Scientific American, 292(1), 54-61. doi:10.1038/scientificamerican0105-54Gil, M., Giner, P., & Pelechano, V. (2011). Personalization for unobtrusive service interaction. Personal and Ubiquitous Computing, 16(5), 543-561. doi:10.1007/s00779-011-0414-0Gil Pascual, M. (s. f.). Adapting Interaction Obtrusiveness: Making Ubiquitous Interactions Less Obnoxious. A Model Driven Engineering approach. doi:10.4995/thesis/10251/31660Haapalainen, E., Kim, S., Forlizzi, J. F., & Dey, A. K. (2010). Psycho-physiological measures for assessing cognitive load. Proceedings of the 12th ACM international conference on Ubiquitous computing - Ubicomp ’10. doi:10.1145/1864349.1864395Hallsteinsen, S., Geihs, K., Paspallis, N., Eliassen, F., Horn, G., Lorenzo, J., … Papadopoulos, G. A. (2012). A development framework and methodology for self-adapting applications in ubiquitous computing environments. Journal of Systems and Software, 85(12), 2840-2859. doi:10.1016/j.jss.2012.07.052Hassenzahl, M. (2004). The Interplay of Beauty, Goodness, and Usability in Interactive Products. Human-Computer Interaction, 19(4), 319-349. doi:10.1207/s15327051hci1904_2Hassenzahl, M., & Tractinsky, N. (2006). User experience - a research agenda. Behaviour & Information Technology, 25(2), 91-97. doi:10.1080/01449290500330331Ho, J., & Intille, S. S. (2005). Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. Proceedings of the SIGCHI conference on Human factors in computing systems - CHI ’05. doi:10.1145/1054972.1055100Horvitz, E., Kadie, C., Paek, T., & Hovel, D. (2003). Models of attention in computing and communication. Communications of the ACM, 46(3), 52. doi:10.1145/636772.636798Horvitz, E., Koch, P., Sarin, R., Apacible, J., & Subramani, M. (2005). Bayesphone: Precomputation of Context-Sensitive Policies for Inquiry and Action in Mobile Devices. Lecture Notes in Computer Science, 251-260. doi:10.1007/11527886_33Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41-50. doi:10.1109/mc.2003.1160055Korpipaa, P., Malm, E.-J., Rantakokko, T., Kyllonen, V., Kela, J., Mantyjarvi, J., … Kansala, I. (2006). Customizing User Interaction in Smart Phones. IEEE Pervasive Computing, 5(3), 82-90. doi:10.1109/mprv.2006.49S. Lemmelä, A. Vetek, K. Mäkelä and D. Trendafilov, Designing and evaluating multimodal interaction for mobile contexts, in: Proceedings of the 10th International Conference on Multimodal Interfaces, ICMI’08, ACM, New York, 2008, pp. 265–272.Lim, B. Y. (2010). Improving trust in context-aware applications with intelligibility. Proceedings of the 12th ACM international conference adjunct papers on Ubiquitous computing - Ubicomp ’10. doi:10.1145/1864431.1864491J.-Y. Mao, K. Vredenburg, P.W. Smith and T. Carey, User-centered design methods in practice: A survey of the state of the art, in: Proceedings of the 2001 Conference of the Centre for Advanced Studies on Collaborative Research, CASCON’01, IBM Press, 2001, p. 12.Maoz, S. (2009). Using Model-Based Traces as Runtime Models. Computer, 42(10), 28-36. doi:10.1109/mc.2009.336Mayer, R. E., & Moreno, R. (2003). Nine Ways to Reduce Cognitive Load in Multimedia Learning. Educational Psychologist, 38(1), 43-52. doi:10.1207/s15326985ep3801_6Motti, V. G., & Vanderdonckt, J. (2013). A computational framework for context-aware adaptation of user interfaces. IEEE 7th International Conference on Research Challenges in Information Science (RCIS). doi:10.1109/rcis.2013.6577709R. Murch, Autonomic Computing, IBM Press, 2004.Obrenovic, Z., Abascal, J., & Starcevic, D. (2007). Universal accessibility as a multimodal design issue. Communications of the ACM, 50(5), 83-88. doi:10.1145/1230819.1241668Patterson, D. J., Baker, C., Ding, X., Kaufman, S. J., Liu, K., & Zaldivar, A. (2008). Online everywhere. Proceedings of the 10th international conference on Ubiquitous computing - UbiComp ’08. doi:10.1145/1409635.1409645Pielot, M., de Oliveira, R., Kwak, H., & Oliver, N. (2014). Didn’t you see my message? Proceedings of the 32nd annual ACM conference on Human factors in computing systems - CHI ’14. doi:10.1145/2556288.2556973Poppinga, B., Heuten, W., & Boll, S. (2014). Sensor-Based Identification of Opportune Moments for Triggering Notifications. IEEE Pervasive Computing, 13(1), 22-29. doi:10.1109/mprv.2014.15S. Ramchurn, B. Deitch, M. Thompson, D. De Roure, N. Jennings and M. Luck, Minimising intrusiveness in pervasive computing environments using multi-agent negotiation, in: Mobile and Ubiquitous Systems: Networking and Services, MOBIQUITOUS 2004. The First Annual International Conference on, 2004, pp. 364–371.C. Roda, Human Attention and Its Implications for Human-Computer Interaction, Cambridge University Press, 2011.S. Rosenthal, A.K. Dey and M. Veloso, Using decision-theoretic experience sampling to build personalized mobile phone interruption models, in: Proceedings of the 9th International Conference on Pervasive Computing, Pervasive 2011, Springer-Verlag, Berlin, 2011, pp. 170–187.E. Rukzio, K. Leichtenstern and V. Callaghan, An experimental comparison of physical mobile interaction techniques: Touching, pointing and scanning, in: 8th International Conference on Ubiquitous Computing, UbiComp 2006, Orange County, California, 2006.Serral, E., Valderas, P., & Pelechano, V. (2010). Towards the Model Driven Development of context-aware pervasive systems. 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High Performance Data Mining Techniques For Intrusion Detection

The rapid growth of computers transformed the way in which information and data was stored. With this new paradigm of data access, comes the threat of this information being exposed to unauthorized and unintended users. Many systems have been developed which scrutinize the data for a deviation from the normal behavior of a user or system, or search for a known signature within the data. These systems are termed as Intrusion Detection Systems (IDS). These systems employ different techniques varying from statistical methods to machine learning algorithms. Intrusion detection systems use audit data generated by operating systems, application softwares or network devices. These sources produce huge amount of datasets with tens of millions of records in them. To analyze this data, data mining is used which is a process to dig useful patterns from a large bulk of information. A major obstacle in the process is that the traditional data mining and learning algorithms are overwhelmed by the bulk volume and complexity of available data. This makes these algorithms impractical for time critical tasks like intrusion detection because of the large execution time. Our approach towards this issue makes use of high performance data mining techniques to expedite the process by exploiting the parallelism in the existing data mining algorithms and the underlying hardware. We will show that how high performance and parallel computing can be used to scale the data mining algorithms to handle large datasets, allowing the data mining component to search a much larger set of patterns and models than traditional computational platforms and algorithms would allow. We develop parallel data mining algorithms by parallelizing existing machine learning techniques using cluster computing. These algorithms include parallel backpropagation and parallel fuzzy ARTMAP neural networks. We evaluate the performances of the developed models in terms of speedup over traditional algorithms, prediction rate and false alarm rate. Our results showed that the traditional backpropagation and fuzzy ARTMAP algorithms can benefit from high performance computing techniques which make them well suited for time critical tasks like intrusion detection

When Does Retargeting Work? Information Specificity in Online Advertising

Firms can now offer personalized recommendations to consumers who return to their website, using consumers' previous browsing history on that website. In addition, online advertising has greatly improved in its use of external browsing data to target Internet ads. Dynamic retargeting integrates these two advances by using information from the browsing history on the firm's website to improve advertising content on external websites. When surfing the Internet, consumers who previously viewed products on the firm's website are shown ads with images of those same products. To examine whether this is more effective than simply showing generic brand ads, the authors use data from a field experiment conducted by an online travel firm. Surprisingly, the data suggest that dynamic retargeted ads are, on average, less effective than their generic equivalents. However, when consumers exhibit browsing behavior that suggests their product preferences have evolved (e.g., visiting review websites), dynamic retargeted ads no longer underperform. One explanation for this finding is that when consumers begin a product search, their preferences are initially construed at a high level. As a result, they respond best to higher-level product information. Only when they have narrowly construed preferences do they respond positively to ads that display detailed product information. This finding suggests that in evaluating how best to reach consumers through ads, managers should be aware of the multistage nature of consumers' decision processes and vary advertising content along these stages.London Business School. Centre for MarketingNational Science Foundation (U.S.) (CAREER Award 1053398

Second CLIPS Conference Proceedings, volume 1

Topics covered at the 2nd CLIPS Conference held at the Johnson Space Center, September 23-25, 1991 are given. Topics include rule groupings, fault detection using expert systems, decision making using expert systems, knowledge representation, computer aided design and debugging expert systems