Existing work in trust management does not satisfactorily provide a combination of functionality and generality. A model that sufficiently addresses the aspects of trust management and is general enough to be applicable in multiple problem scenarios is needed. This paper presents a base trust model that can be used in multiple problem scenarios (content management, service provision, and routing). In addition, smart spaces introduce new issues that are not addressed sufficiently by the available trust management models. The base trust model is designed to be simple to facilitate its enrichment to accommodate the additional requirements of Smart Spaces in the future.

Using comment information available from Digg we define a co-participation network between users. We focus on the analysis of this implicit network, and study the behavioral characteristics of users. Using an entropy measure, we infer that users at Digg are not highly focused and participate across a wide range of topics. We also use the comment data and social network derived features to predict the popularity of online content linked at Digg using a classification and regression framework. We show promising results for predicting the popularity scores even after limiting our feature extraction to the first few hours of comment activity that follows a Digg submission.

Document classification is a key task for many text mining applications. However, traditional text classification requires labeled data to construct reliable and accurate classifiers. Unfortunately, labeled data are seldom available, and often too expensive to obtain. In this work, we propose a universal text classifier, which does not require any labeled training document. Our approach simulates the capability of people to classify documents based on background knowledge. As such, we build a classifier that can effectively group documents based on their content, under the guidance of few words, which we call discriminant words, describing the classes of interest. Background knowledge is modeled using encyclopedic knowledge, namely Wikipedia. Wikipedia's articles related to the specific problem domain at hand are selected, and used during the learning process for predicting labels of test documents. The universal text classifier can also be used to perform document retrieval, in which the pool of test documents may or may not be relevant to the topics of interest for the user. In our experiments with real data we test the feasibility of our approach for both the classification and retrieval tasks. The results demonstrate the advantage of incorporating background knowledge through Wikipedia, and the effectiveness of modeling such knowledge via probabilistic topic modeling. The accuracy achieved by the universal text classifier is comparable to that of a supervised learning technique for transfer learning.

Despite the large body of work in both motion planning and multi-agent simulation, little work has focused on the problem of planning motion for groups of robots using external "controller" agents. We call this problem the group control problem. This problem is complex because it is highly underactuated, dynamic, and requires multi-agent cooperation. In this paper, we present a variety of new motion planning algorithms based on ESt, RRT, and PRM methods for shepherds to guide flocks of robots through obstacle-filled environments. We show using simulation on several environments that under certain circumstances, motion planning can find paths that are too complicated for naive "simulation only" approaches. However, inconsistent results indicate that this problem is still in need of additional study.

The field of mobile robotics is on the forefront of robotics research around the world. Control architectures for complex autonomous mobile robots have largely settled on hybrid architectures for their suitability at dealing with the opposing forces of planning and reactivity. We present a general, heterogeneous 3-Tier hybrid architecture for control of an autonomous mobile robot and discuss an implementation in the domain of campus navigation. The architecture features a useful organization structure for high-level skills and offers flexible construction options for low-level behavior hierarchies.

An administrative role-based access control (ARBAC) model specifies administrative policies over a role-based access control (RBAC) system, where an administrative permission may change an RBAC policy by updating permissions assigned to roles, or assigning/revoking users to/from roles. Consequently, enforcing ARBAC policies over an active access controller while some users are using protected resources would result in conflicts: a policy may be in effect in the RBAC system while being updated by an ARBAC operation. Towards solving this concurrency problem, we propose a session-aware administrative model for RBAC. We show how the concurrency problem can be resolved by enhancing the eXtensible Access Control Markup Language (XACML) reference implementation. In order to do so, we develop an XACML-ARBAC profile to specify ARBAC policies, and enforce these polices by building an ARBAC enforcement module and a session administrative module. The former synchronizes with the evaluation of access control requests. The latter revokes conflicting ongoing user sessions immediately prior to enforcing administrative operations. Experimental shows reasonable performance characteristics of our initial enhancement to Sun's reference implementation

The need to engineer novel therapeutics and functional materials is driving the in-silico design of molecular complexes. This paper proposes a method to compute symmetric homo-oligomeric protein complexes when the structure of the replicated protein monomer is known and rigid. The relationship between the structure of a protein and its biological function brings the in-silico determination of protein structures associated with functional states to the forefront of computational biology. While protein complexes, arising from associations of protein monomers, are pervasive in every genome, determination of their structures remains challenging. Given the difficulty in computing structures of a protein monomer, computing arrangements of monomers in a complex is mainly limited to dimers. A growth in the number of protein complexes studied in wet labs is allowing classification of their structures. A recent database shows that most naturally-occurring protein complexes are symmetric homo-oligomers. The method presented here exploits this database to propose structures of symmetric homo-oligomers that can accommodate spatial replications of a given protein monomer. The method searches the database for documented structures of symmetric homo-oligomers where the replicated monomer has a geometrically-similar structure to that of the input protein monomer. The proposed method is a first step towards the in-silico design of novel protein complexes that upon further refinement and characterization can serve as molecular machines or fundamental units in therapeutics or functional materials.

We consider scheduling weighted packets with time constraints over a fading channel. Packets arrive at the transmitter in an online manner. Each packet has a value and a hard deadline by which it should be sent. The fade state of the channel determines the throughput obtained per unit of time and the channel's quality may change over time. In this paper, we design both offline and online algorithms to maximize weighted throughput, which is defined as the total value of the packets sent by their respective deadlines. We first present polynomial-time exact offline algorithms for this problem. Then, we present online algorithms and their competitive analysis as well as the lower bounds of competitive ratios. Our work is the first one addressing weighted throughput for this important problem in the areas of information theory and communications.