Temporal Expertise Profiling

Abstract. We introduce the temporal expertise profiling task: identifying the skills and knowledge of an individual and tracking how they change over time. To be able to capture and distinguish meaningful changes, we propose the concept of a hierarchical expertise profile, where topical areas are organized in a taxonomy. Snapshots of hierarchical profiles are then taken at regular time intervals. Further, we develop methods for detecting and characterizing changes in a person’s profile, such as, switching the main field of research or narrowing/broadening the topics of research. Initial results demonstrate the potential of our approach.

Temporal word embeddings for dynamic user profiling in Twitter

The research described in this paper focused on exploring the domain of user profiling, a nascent and contentious technology which has been steadily attracting increased interest from the research community as its potential for providing personalised digital services is realised. An extensive review of related literature revealed that limited research has been conducted into how temporal aspects of users can be captured using user profiling techniques. This, coupled with the notable lack of research into the use of word embedding techniques to capture temporal variances in language, revealed an opportunity to extend the Random Indexing word embedding technique such that the interests of users could be modelled based on their use of language. To achieve this, this work concerned itself with extending an existing implementation of Temporal Random Indexing to model Twitter users across multiple granularities of time based on their use of language. The product of this is a novel technique for temporal user profiling, where a set of vectors is used to describe the evolution of a Twitter user’s interests over time through their use of language. The vectors produced were evaluated against a temporal implementation of another state-of-the-art word embedding technique, the Word2Vec Dynamic Independent Skip-gram model, where it was found that Temporal Random Indexing outperformed Word2Vec in the generation of temporal user profiles

BCAS: A Web-enabled and GIS-based Decision Support System for the Diagnosis and Treatment of Breast Cancer

For decades, geographical variations in cancer rates have been observed but the precise determinants of such geographic differences in breast cancer development are unclear. Various statistical models have been proposed. Applications of these models, however, require that the data be assembled from a variety of sources, converted into the statistical models’ parameters and delivered effectively to researchers and policy makers. A web-enabled and GIS-based system can be developed to provide the needed functionality. This article overviews the conceptual web-enabled and GIS-based system (BCAS), illustrates the system’s use in diagnosing and treating breast cancer and examines the potential benefits and implications for breast cancer research and practice

Finding a needle in a haystack: toward a psychologically informed method for aviation security screening

Current aviation security systems identify behavioral indicators of deception to assess risks to flights, but they lack a strong psychological basis or empirical validation. We present a new method that tests the veracity of passenger accounts. In an in vivo double-blind randomized-control trial conducted in international airports, security agents detected 66% of deceptive passengers using the veracity test method compared with less than 5% using behavioral indicator recognition. As well as revealing advantages of veracity testing over behavioral indicator identification, the study provides the highest levels to date of deception detection in a realistic setting where the known base rate of deceptive individuals is low

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

Computerized crime linkage systems: A critical review and research agenda

Computerized crime linkage systems are meant to assist the police in determining whether crimes have been committed by the same offender. In this article, the authors assess these systems critically and identify four assumptions that affect the effectiveness of these systems. These assumptions are that (a) data in the systems can be coded reliably, (b) data in the systems are accurate, (c) violent serial offenders exhibit consistent but distinctive patterns of behavior, and (d) analysts have the ability to use the data in the systems to link crimes accurately. The authors argue that there is no compelling empirical support for any of the four assumptions, and they outline a research agenda for testing each assumption. Until evidence supporting these assumptions becomes available, the value of linkage systems will remain open to debate

A Decision Technology System To Advance the Diagnosis and Treatment of Breast Cancer

Geographical variations in cancer rates have been observed for decades. Described spatial patterns and trends have provided clues for generating hypotheses about the etiology of cancer. For breast cancer, investigators have demonstrated that some variation can be explained by differences in the population distribution of known breast cancer risk factors such as menstrual and reproductive variables (Laden, Spiegelman, and Neas, 1997; Robbins, Bescianini, and Kelsey, 1997; Sturgeon, Schairer, and Gail, 1995). However, regional patterns also may reflect the effects of Workshop on Hormones, Hormone Metabolism, Environment, and Breast Cancer (1995): (a) environmental hazards (such as air and water pollution), (b) demographics and the lifestyle of a mobile population, (c) subgroup susceptibility, (d) changes and advances in medical practice and healthcare management, and (e) other factors. To accurately measure breast cancer risk in individuals and population groups, it is necessary to singly and jointly assess the association between such risk and the hypothesized factors. Various statistical models will be needed to determine the potential relationships between breast cancer development and estimated exposures to environmental contamination. To apply the models, data must be assembled from a variety of sources, converted into the statistical models’ parameters, and delivered effectively to researchers and policy makers. A Web-enabled decision technology system can be developed to provide the needed functionality. This chapter will present a conceptual architecture for such a decision technology system. First, there will be a brief overview of a typical geographical analysis. Next, the chapter will present the conceptual Web-based decision technology system and illustrate how the system can assist users in diagnosing and treating breast cancer. The chapter will conclude with an examination of the potential benefits from system use and the implications for breast cancer research and practice