Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

(1998). Human cognitive abilities: A survey of factor analytic studies. Gifted and Talented International: Vol. 13, No. 2, pp. 97-98.

Human cognitive abilities: A survey of factor analytic studies

Home PURPOSE OF THE CENTER: To develop the center to address state-of-the-art research, create innovating educational programs, and support technology transfers using commercially viable results to assist the Army Research Laboratory to develop the next generation Future Combat System in the telecommunications sector that assures prevention of perceived threats, and Non Line of Sight/Beyond Line of Sight lethal support.

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We address the problems of 1/ assessing the confidence of the standard point estimates, precision, recall and F-score, and 2/ comparing the results, in terms of precision, recall and F-score, obtained using two different methods. To do so, we use a probabilistic setting which allows us to obtain posterior distributions on these performance indicators, rather than point estimates. This framework is applied to the case where different methods are run on different datasets from the same source, as well as the standard situation where competing results are obtained on the same data.

A probabilistic interpretation of precision, recall and F -score, with implication for evaluation

Within the emerging context of the digitization of health care, electronic health records (EHRs) constitute a significant technological advance in the way medical information is stored, communicated, and processed by the multiple parties involved in health care delivery. However, in spite of the anticipated value potential of this technology, there is widespread concern that consumer privacy issues may impede its diffusion. In this study, we pose the question: Can individuals be persuaded to change their attitudes and opt-in behavioral intentions toward EHRs, and allow their medical information to be digitized even in the presence of significant privacy concerns? To investigate this question, we integrate an individual's concern for information privacy (CFIP) with the elaboration likelihood model (ELM) to examine attitude change and likelihood of opting-in to an EHR system. We theorize that issue involvement and argument framing interact to influence attitude change, and that concern for information privacy further moderates the effects of these variables. We also propose that likelihood of adoption is driven by concern for information privacy and attitude. We test our predictions using an experiment with 366 subjects where we manipulate the framing of the arguments supporting EHRs. We find that an individual's CFIP interacts with argument framing and issue involvement to affect attitudes toward the use of EHRs. In addition, results suggest that attitude toward EHR use and CFIP directly influence opt-in behavioral intentions. An important finding for both theory and practice is that even when people have high concerns for privacy, their attitudes can be positively altered with appropriate message framing. These results as well as other theoretical and practical implications are discussed.

Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion

A statistical analysis of the TREC-3 data shows that performance differences across queries is greater than performance differences across participants runs. Generally, groups of runs which do not differ significantly at lerge, sometimes accounting for over half the runs. Correlation among the various performance measures is high.

A Statistical Analysis of the TREC-3 Data

Applying a multi-dimensional hedonic concept of intrinsic motivation on social tagging tools: A theoretical model and empirical validation

Traditional document clustering techniques group similar documents without any user interaction. Although such methods minimize user effort, the clusters they generate are often not in accord with their users' conception of the document collection. In this paper we describe a new framework and experiments with it exploring how clustering might be improved by including user supervision at the level of selecting features that are used to distinguish between documents. Our features are based on the words that appear in documents (see §4.1 for details.) We conjecture that clusters better matching user expectations can be generated with user input at the feature level. In order to verify our conjecture, we propose a novel iterative framework which involves users interactively selecting the features used to cluster documents. Unlike existing semi-supervised clustering, which asks users to label constraints between documents, this framework interactively asks users to label features. The proposed method ranks all features based on the recent clusters using cluster-based feature selection and presents a list of highly ranked features to users for labeling. The feature set for next clustering iteration includes both features accepted by users and other highly ranked features. The experimental results on several real datasets demonstrate that the feature set obtained using the new interactive framework can produce clusters that better match the user's expectations. Moreover, we quantify and evaluate the effect of reweighting previously accepted features and of user effort.

https://web.cs.dal.ca/~eem/cvWeb/pubs/2011-Yeming-SAC.pdf

Interactive feature selection for document clustering

This work is a report on efforts to improve the usability of intrusion detection systems. Specifically, we first conducted a worldwide survey of system administrators from different countries and economic sectors to understand the state of practice in security management with a particular focus on intrusion detection systems (IDS). Then, based on these survey results and in depth interviews, we developed new heuristics to measure the effectiveness and efficiency of IDS. The comparison of our refined heuristics and Nielsen's general heuristics on Snort, Snortsnarf and our proposed interface show that evaluators using our heuristics find significantly (p<0.0002) more of the problems. Also, evaluations with both sets find fewer problems in our interface than in Snort or Snortsnarf.

http://web.cs.dal.ca/~jamie/pubs/PDF/Zhou%2bCCECE04.pdf

Improving intrusion detection systems through heuristic evaluation

While recent progress has been made in understanding the structure and dynamics of social tagging systems, we know little about the users' underlying motivations for tagging, and how these motivations influence the resulting use of tagging systems. In this article, we propose and empirically validate a conceptual model of key factors that affect users' attitude and intention to use social tagging systems. Our findings highlight three new factors and confirm two previous factors. In addition to Perceived Enjoyment and Perceived Ease-of-use, we introduce Content Generation, Information Retrievability, and Information Re-find ability as new dimensions affecting the use of social tagging systems. Our goal is to help researchers, designers, and managers of tagging systems and other social systems on the Web understand how to motivate users to increase their use and hence harvest the collaborative and sharing benefits associated with these tools.

James Blustein

Papers

A Statistical Analysis of the TREC-3 Data

Applying a multi-dimensional hedonic concept of intrinsic motivation on social tagging tools: A theoretical model and empirical validation

Interactive feature selection for document clustering

Improving intrusion detection systems through heuristic evaluation

Exploring Factors Impacting Users' Attitude and Intention towards Social Tagging Systems