Abstract:
Popular programming languages offer thousands of add-on libraries or modules. Popular applications offer thousands of add-on plug-in modules. We consider the problem of recommending a small set of related modules when someone visits a page describing a module. There are three promising sources of information for these recommendations. The first is textual similarity among the descriptions of modules. The second is co-mention of the modules in forum conversations. The third is co-installation of modules in public sites that report on which modules are used. I will report on a deployment on drupal.org, comparing the effectiveness (in terms of user click-throughs) of recommenders that use the three sources. I will also describe a further experiment, about to be released on drupal.org, that dynamically updates the recommendations based on users‘ click behaviors, using a Multi-Armed Bandit learning algorithm. I will describe how we are using the three sources of information to set „priors“ for the bandit algorithm, and how we will assess the amount of improvement in recommendations from using the

priors as compared to a prior-free version of the bandit algorithm.
Background paper: http://portal.acm.org/citation.cfm?id=1639714.1639737

Paul Resnick is a Professor at the University of Michigan School of Information. He previously worked as a researcher at AT&T Labs and AT&T Bell Labs, and as an Assistant Professor at the MIT Sloan School of Management. He received the master’s and Ph.D. degrees in Electrical Engineering and Computer Science from MIT, and a bachelor’s degree in mathematics from the University of Michigan.
Professor Resnick’s research focuses on SocioTechnical Capital, productive social relations that are enabled by the ongoing use of information and communication technology. His current projects include analyzing and designing reputation systems, ride share coordination services, and applying principles from economics and social psychology to the design of on-line communities.
Resnick was a pioneer in the field of recommender systems (sometimes called collaborative filtering or social filtering). Recommender systems guide people to interesting materials based on recommendations from other people. His articles have appeared in Scientific American, Wired, Communications of the ACM, The American Economic Review, Management Science, and many other publications.

Mit der Entwicklung der Informationstheorie gelang C. E. Shannon bereits im Jahre 1948 der Beweis, das mittels geeigneter Codierverfahren auch über gestörte Übertragungskanäle prinzipiell eine fehlerfreie digitale Übertragung möglich ist, solange nicht versucht wird, mehr Daten zu übertragen als die Kapazität des Kanals zulässt. Dieses Kanalcodierungstheorem leitete eine breite Forschungstätigkeit auf dem Gebiet der Kanalcodierung ein. Dennoch wurden über viele Jahre trotz des Einsatzes anspruchsvollster mathematischer Methoden nur eher bescheidene Fortschritte erreicht und das Ziel, die informationstheoretische Kapazität von Übertragungskanälen in der Praxis nutzbar zu machen, erschien grundsätzlich  unerreichbar. Erst durch die Zufallserfindung der sog. „Turbo-Codes

Abstract: Visual information retrieval (VIR) is an active and vibrant research area which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) form large, unstructured repositories.  In its early years (1995-2000) the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community.  Later, it was widely recognized that the challenges imposed by the semantic gap (the lack of coincidence between an image’s visual contents and its semantic interpretation) required a clever use of textual metadata (in addition to information extracted from the image’s pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on.

This talk revisits the field of content-based image retrieval (CBIR) 10 years after „the end of the early years“ (as announced in a seminal paper in the field) and highlights the most relevant advances, pending challenges, and promising opportunities in CBIR and related areas. Particularly, it includes an overview of  the important field of medical image retrieval, its main challenges and opportunities.

Dr. Oge Marques is an Assoc

iate Professor and Associate Chairman in the Department of Computer Science and Engineering at Florida Atlantic University in Boca Raton, Florida. He received his Ph.D. in Computer Engineering from Florida Atlantic University in 2001, his Masters in Electronics Engineering from Philips International Institute (Eindhoven, NL) in 1989 and his Bachelor’s Degree in Electrical Engineering from UTFPR (Curitiba, Brazil), where he also taught for more than 10 years before moving to the USA.

His research interests include: image processing, analysis, annotation, search, and retrieval; human and computer vision; and video processing and analysis. He has more than 20 years of teaching and research experience in the fields of image processing and computer vision, in different countries (USA, Austria, Brazil, Netherlands, Spain, France, and India) and capacities. He is the (co-) author of 4 (four) books in these topics, including the forthcoming textbook “Practical Image and Video Processing Using MATLAB” (Wiley, 2011). He has also published several book chapters and more than 50 refereed journal and conference papers in these fields. He serves as a reviewer and Editorial Board member for several leading journals in computer science and engineering. He is a senior member of the ACM, senior member of the IEEE, and a member of the IEEE Computer Society, IEEE Education Society, and the honor societies of Tau Beta Pi, Sigma Xi, Phi Kappa Phi and Upsilon Pi Epsilon. 

Abstract: The energy sector will undergo fundamental changes over the next ten years. Prices for fossil energy resources are continuously increasing, there is an urgent need to reduce CO2 emissions, and the United States and European Union are strongly motivated to become more independent from foreign energy imports. These factors will lead to installation of large numbers of distributed renewable energy generators, which are often intermittent in nature.

This trend conflicts with the current power grid control infrastructure and strategies, where a few centralized control centers manage a limited number of large power plants such that their output meets the energy demands in real time. As the proportion of distributed and intermittent power production capacity increases, this task becomes much harder, especially as the local and regional distribution grids where renewable energy producers are usually installed are currently virtually unmanaged, lack real time metering and in many cases are not built to cope with power flow inversions.

While the hierarchical command-and-control approach served well in a world with a few large scale generation facilities and many small consumers, a more flexible, decentralized, and self-organizing control infrastructure will have to be developed that can be actively managed to balance both the large grid as a whole, as well as the many lower voltage sub-grids. One strong candidate for this control infrastructure is to create energy markets at the retail level. To help mitigate the risk of instituting such markets in the real world, we are deve

loping a competitive market simulation testbed. We expect that this testbed will stimulate research and development of market structures along with automated software agents that can support decision making in these markets. Participants in the competition will design intelligent agents that will act as brokers, building portfolios of energy producers and consumers, and matching energy supply from producers with energy demand from consumers. The competition will closely model reality by bootstrapping the simulation environment with real historic load, generation, and weather data.

Wolfgang Ketter is Assistant Professor at the Department of Decision and Information Sciences at the Rotterdam School of Management of the Erasmus University. He received his Ph.D. in Computer Science from the University of Minnesota in 2007. He founded and runs the Learning Agents Research Group at Erasmus (LARGE). The primary objective of LARGE is to research, develop, and apply autonomous and mixed-initiative intelligent agent systems to support human decision making in the area of business networks, electronic markets, information systems and supply-chain management. He was co-chairing the TADA workshop at AAAI 2008, the general chair of TAC 2009, and is member of the board of directors of the association for trading agent research since 2009. He is the program co-chair of the International Conference of Electronic Commerce 2011. His research has been published in various information systems, and computer science journals such as AI Magazine, Decision Support Systems, Electronic Commerce Research and Applications, European Journal of Information Systems, INFORMS OR/MS Today, and International Journal of Electronic Commerce. He serves on the editorial board of Electronic Commerce Research and Applications.