^February March – 2011 ^April

The Internet has enabled the creation of virtual worlds and communities, where user interactions imitate and, to some extent, even replace the more traditional "real-life" equivalents on a larger scale. The existence of easily accessible virtual communities makes it both possible and legitimate to communicate with total strangers. We can now anonymously interact with other virtual community members whom we do not really know in ways that break the boundaries and limitations of the real world. Trust and reputation systems are considered key enablers of virtual communities, especially communities of strangers, where users are not required to reveal their real identities and use nicknames or pseudonyms instead. These systems support the accumulation of member reputation information and leverage this information to increase the likelihood of successful member interactions and to better protect the community from fraudulent members. Reputation information is a valuable resource both for the users and for the communities. We developed the Cross-Community Reputation (CCR) model for the sharing of reputation knowledge across virtual communities. The CCR model defines the major stages required to compute the cross-community reputation of a community member based on the reputation of that member in other communities. We addressed major privacy concerns that are not present or that are less significant in single community domains. In this talk we provide a brief introduction to trust and reputation systems. We outline our major research directions, and focus on the CCR model and its related privacy concerns.

In the square jigsaw puzzle problem one is required to reconstruct the complete image from a set of non-overlapping, unordered, square puzzle parts. Here we propose a fully automatic solver for this problem, where unlike some previous work, it assumes no clues regarding parts location and requires no prior knowledge about the original image or its simplified (e.g., lower resolution) versions. To do so, we introduce a greedy solver which combines both informed piece placement and rearrangement of puzzle segments to find the final solution. Among our other contributions are new compatibility metrics which better predict the chances of two given parts to be neighbors, and a novel estimation measure which evaluates the quality of puzzle solutions without the need for ground-truth information. Incorporating these contributions, our approach facilitates solutions that surpass state-of-the-art solvers on puzzles of size larger than ever attempted before.

RNA molecules are fundamental participants in many biological processes, where recent discoveries revile that the role of RNA is much more important than was previously known. A key property for analyzing the functionality of RNA is its structure. Due to their size, it is difficult to determine structures of RNA molecules physically, and thus computational RNA structure prediction (a.k.a "RNA folding") is an important bioinformatic application.

In this talk, we present a new approach which significantly improves the accuracy of RNA folding. The method combines rich feature representations of RNA molecules with a light-weight machine learning algorithm (the "online passive aggressive" algorithm), and obtains about 50% reduction of error rate with respect to the best previous result.