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Devising a complete and correct set of roles has been recognized as one of the most important and challenging tasks in implementing role based access control. A key problem related to this is the notion of goodness/interestingness -- when is a role good/interesting? In this paper, we define the <i>role mining problem</i> (RMP) as the problem of discovering(More)
An improved understanding of the relationship between search intent, result quality, and searcher behavior is crucial for improving the effectiveness of web search. While recent progress in user behavior mining has been largely focused on aggregate server-side click logs, we present a new class of search behavior models that also exploit fine-grained user(More)
A better understanding of strategies and behavior of successful searchers is crucial for improving the experience of all searchers. However, research of search behavior has been struggling with the tension between the relatively small-scale, but controlled lab studies, and the large-scale log-based studies where the searcher intent and many other important(More)
A key problem in information retrieval is inferring the searcher's interest in the results, which can be used for implicit feedback, query suggestion, and result ranking and summarization. One important indicator of searcher interest is gaze position - that is, the results or the terms in a result listing where a searcher concentrates her attention.(More)
Devising a complete and correct set of roles has been recognized as one of the most important and challenging tasks in implementing role based access control. A key problem related to this is the notion of goodness - when is a set of roles good? Recently, the <i>role mining problem</i> (RMP) has been defined as the problem of discovering an optimal set of(More)
As a fundamental task in computer architecture research, performance comparison has been continuously hampered by the variability of computer performance. In traditional performance comparisons, the impact of performance variability is usually ignored (i.e., the means of performance observations are compared regardless of the variability), or in the few(More)
Detecting and predicting searcher success is essential for automatically evaluating and improving Web search engine performance. In the past, Web searcher behavior data, such as result clickthrough, dwell time, and query reformulation sequences, have been successfully used for a variety of tasks, including prediction of success in a search session. However,(More)
Type 2 diabetes is characterized by impaired glucose homeostasis due to defects in insulin secretion, insulin resistance and the incretin response. GPR40 (FFAR1 or FFA1) is a G-protein-coupled receptor (GPCR), primarily expressed in insulin-producing pancreatic β-cells and incretin-producing enteroendocrine cells of the small intestine. Several GPR40(More)
FFA1 (GPR40) and GPR120 are G-protein-coupled receptors activated by long-chain fatty acids. FFA1 is expressed in pancreatic β-cells, where it regulates glucose-dependent insulin secretion, and GPR120 has been implicated in mediating GLP-1 secretion. We show here that FFA1 co-localizes with GLP-1 in enteroendocrine cells and plays a critical role in glucose(More)
Clickthrough on search results have been successfully used to infer user interest and preferences, but are often noisy and potentially ambiguous. We explore the potential of a complementary, more sensitive signal -mouse movements- in providing insights into the intent behind a web search query. We report preliminary results of studying user mouse movements(More)