Noncoercive human intelligence gathering

Despite widespread recognition that coercive methods for intelligence gathering are unethical and counterproductive, there is an absence of empirical evidence for effective alternatives. We compared two non-coercive methods - the Modified Cognitive Interview (MCI) and Controlled Cognitive Engagement (CCE), adapted for intelligence gathering by adding a moral frame to encourage interviewees to consciously consider sharing intelligence. Participants from the general population experienced an unexpected live event where equipment was damaged, and an argument ensued. Prior to interview, participants were incentivised to withhold information about a target individual implicated in the event. CCE yielded more target information, more frequently than MCI (67% vs. 36%). Similarly, framing yielded target information more often (65% vs. 39%). The effects of interview and framing appear to be additive rather than interactive. Our results indicate combining non-coercive interview methods with moral framing can enhance intelligence gain

Mean field variational Bayesian inference for support vector machine classification

A mean field variational Bayes approach to support vector machines (SVMs) using the latent variable representation on Polson & Scott (2012) is presented. This representation allows circumvention of many of the shortcomings associated with classical SVMs including automatic penalty parameter selection, the ability to handle dependent samples, missing data and variable selection. We demonstrate on simulated and real datasets that our approach is easily extendable to non-standard situations and outperforms the classical SVM approach whilst remaining computationally efficient.Comment: 18 pages, 4 figure

Why bad ideas are a good idea

What would happen if we wrote an Abstract that was the exact opposite of what the paper described? This is a bad idea, but it makes us think more carefully than usual about properties of Abstracts. This paper describes BadIdeas, a collection of techniques that uses ???bad??? or ???silly??? ideas to inspire creativity, explore design domains and teach critical thinking in interaction design. We describe the approach, some evidence, how it is performed in practice and experience in its use.published or submitted for publicationis peer reviewe

Improving professional observers’ veracity judgments by tactical interviewing

Understanding whether a person of interest is being truthful during an investigative interview is a constant challenge and is of concern to numerous criminal justice professionals, most of whom are not involved in conducting the interview itself. Here, we investigated police observers’ veracity detection performance having viewed interviews with truthtellers and deceivers using either the tactical use of evidence (TUE), strategic use of evidence (SUE) or a control technique. Thirty serving police officers participated as post-interview observers and each viewed 12 interviews in a counterbalanced order. After each interview, the officer made a veracity judgement. Overall, untrained police observers were significantly more accurate (68%) when making veracity judgements post-TUE interviews, whereas for both SUE and control performance was around chance (51% and 48%, respectively). Veracity performance for liars and truthtellers revealed a similar pattern of results (67% liars; 70% truthtellers) in the TUE condition. These results lend further support to the psychological literature highlighting the importance of how and when to reveal evidence or any other relevant event information during an investigative interview for ‘outing’ deceivers as well as allowing truthtellers early opportunities to demonstrate their innocence

Understanding participation and non-participation in sport amongst Black and minority ethnic groups in Wales

Like the other home nation sports councils, Sport Wales has a responsibility to increase participation, improve sporting performance and raise standards in sport and physical recreation. For some time the sports councils in the UK have been concerned that people from Black and minority ethnic (BME) groups may not be getting as much from sport as they might (e.g. the Sports Equity Index (Sport England, 2001)). The concern with increasing participation is multidimensional: it might serve to recruit new talent; allows sectors of society to enjoy what are thought to be the benefits of sport; and in so doing help to unite the nation. However, as recognised by the Equality Impact Assessment that was conducted by Sport Wales, there has been a shortage of research around sport participation by Black and minority ethnic communities. Thus, the current research is closely aligned with the aim of increasing participation and understanding non-participation. The research also addresses the identified need for further investigation into identified differences in participation between different equalities groups. This project, commissioned by Sport Wales, has been undertaken by the Institute for Sport, Physical Activity and Leisure (ISPAL) in conjunction with Ecorys. It is a response to the strategy, action plans and operational plans of Sport Wales that embrace diversity and inclusiveness. These policy and operational documents consider ethnicity alongside other protected characteristics and a concern to address poverty and deprivation by operating in conjunction with other agencies. The research reported here will help to establish what resonance people in Black and minority ethnic groups have with the goal of ensuring ‘a thriving sporting community, where all individuals feel safe, welcome and free from discrimination’ (Sport Wales, Equality And Diversity Operational Action Plan)

Theory of Gaussian Variational Approximation for a Poisson Mixed Model

Likelihood-based inference for the parameters of generalized linear mixed models is hindered by the presence of intractable integrals. Gaussian variational approximation provides a fast and effective means of approximate inference. We provide some theory for this type of approximation for a simple Poisson mixed model. In particular, we establish consistency at rate m−1/2 + n−1, where m is the number of groups and n is the number of repeated measurements

On semiparametric regression and data mining

Semiparametric regression is playing an increasingly large role in the analysis of datasets exhibiting various complications (Ruppert, Wand & Carroll, 2003). In particular semiparametric regression a plays prominent role in the area of data mining where such complications are numerous (Hastie, Tibshirani & Friedman, 2001). In this thesis we develop fast, interpretable methods addressing many of the difficulties associated with data mining applications including: model selection, missing value analysis, outliers and heteroscedastic noise. We focus on function estimation using penalised splines via mixed model methodology (Wahba 1990; Speed 1991; Ruppert et al. 2003). In dealing with the difficulties associated with data mining applications many of the models we consider deviate from typical normality assumptions. These models lead to likelihoods involving analytically intractable integrals. Thus, in keeping with the aim of speed, we seek analytic approximations to such integrals which are typically faster than numeric alternatives. These analytic approximations not only include popular penalised quasi-likelihood (PQL) approximations (Breslow & Clayton, 1993) but variational approximations. Originating in physics, variational approximations are a relatively new class of approximations (to statistics) which are simple, fast, flexible and effective. They have recently been applied to statistical problems in machine learning where they are rapidly gaining popularity (Jordan, Ghahramani, Jaakkola & Sau11999; Corduneanu & Bishop, 2001; Ueda & Ghahramani, 2002; Bishop & Winn, 2003; Winn & Bishop 2005). We develop variational approximations to: generalized linear mixed models (GLMMs); Bayesian GLMMs; simple missing values models; and for outlier and heteroscedastic noise models, which are, to the best of our knowledge, new. These methods are quite effective and extremely fast, with fitting taking minutes if not seconds on a typical 2008 computer. We also make a contribution to variational methods themselves. Variational approximations often underestimate the variance of posterior densities in Bayesian models (Humphreys & Titterington, 2000; Consonni & Marin, 2004; Wang & Titterington, 2005). We develop grid-based variational posterior approximations. These approximations combine a sequence of variational posterior approximations, can be extremely accurate and are reasonably fast