When large numbers of alerts are reported by intrusion detection (ID) systems in very fine granularity, it prevents system administrators from handling the alerts effectively. This in turn degrades the usability of an intrusion detection system. Aside from detection, timely responses of...

Machine learning has become one of the most active and exciting areas of computer science research, in large part because of its wide-spread applicability to problems as diverse as natural language processing, speech recognition, spam detection, search, computer vision, gene discovery, medical...

Intelligent Product Recommendation Agents have been used for some time now by large, well known Internet businesses such as Amazon and Netflix. Unfortunately there is little research assessing the effectiveness of these systems in influencing online consumer behavior. Businesses that sell...

Enterprise systems interoperability (ESI) is an important topic for business currently. This situation is evidenced, at least in part, by the number and extent of potential candidate protocols for such process interoperation, viz., ebXML, BPML, BPEL, and WSCI. Wide-ranging support for each of...

Much research has been devoted over the years to investigating and advancing the techniques and tools used by analysts when they model. As opposed to what academics, software providers and their resellers promote as should be happening, the aim of this research was to determine whether...

We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance...