Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation

Recent success stories in automated object or face recognition, partly fuelled by deep learning artificial neural network (ANN) architectures, has led to the advancement of biometric research platforms and, to some extent, the resurrection of Artificial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have taken place to automate the recognition of emotions in adults or children for the benefit of various applications such as identification of children emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straight forward with several challenges arising for both science(e.g., methodology underpinned by psychology) and technology (e.g., iMotions biometric research platform). In this paper, we present a methodology, experiment and interesting findings, which raise the following research questions for the recognition of emotions and attention in humans: a) adequacy of well-established techniques such as the International Affective Picture System (IAPS), b) adequacy of state-of-the-art biometric research platforms, c) the extent to which emotional responses may be different among children or adults. Our findings and first attempts to answer some of these research questions, are all based on a mixed sample of adults and children, who took part in the experiment resulting into a statistical analysis of numerous variables. These are related with, both automatically and interactively, captured responses of participants to a sample of IAPS pictures

Neighborhood Crime and Travel Behavior: An Investigation of the Influence of Neighborhood Crime Rates on Mode Choice – Phase II, MTI Report 11-04

There are considerable environmental and public health benefits if people choose to walk, bicycle, or ride transit, instead of drive. However, little work has been done on the effects of neighborhood crimes on mode choice. Instinctively, we understand that the threats posed by possible criminal activity in one’s neighborhood can play a major role in the decision to drive, take transit, walk or ride a bicycle, but so far little empirical evidence supports this notion, let alone guides public infrastructure investments, land use planning, or the allocation of police services. This report--describing Phase 2 of a research study conducted for the Mineta Transportation Institute on crime and travel behavior – finds that high crime neighborhoods tend to discourage residents from walking or riding a bicycle. When comparing a high crime to a lower crime neighborhood the odds of walking over choosing auto decrease by 17.25 percent for work trips and 61 percent for non-work trips. For transit access to work trips, the odds of choosing walk/bike to a transit station over auto decrease by 48.1 percent. Transit trips, on the other hand, are affected by neighborhood crime levels in a similar way to auto trips, wherein high crime neighborhoods appear to encourage transit mode choice. The odds of taking transit over choosing auto increase by 17.25 percent for work trips and 164 percent for non-work trips. Surprised by this last finding, the research team tested two possible explanations for why high levels of neighborhood crime would increase transit use: 1) the mode choice models do not adequately account for the effects and interplay between urban form and crime levels and mode choice; and 2) people who ride in cars or take transit may feel more protected when riding in a vehicle (termed here, the “neighborhood exposure hypothesis”). To investigate the first explanation, the researchers tested a number of alternative urban form and crime interaction variables to no effect. Digging deeper into the second hypothesis, the researchers tested whether the access portion of transit trips (walking, bicycling, or driving to a transit stop) is sensitive to neighborhood crimes as well, wherein high crime neighborhoods discourage walking and bicycling and encourage driving to transit stations. The report provides evidence that high crime neighborhoods encourage driving to transit stops and discourage walking or bicycling, lending support to the neighborhood exposure hypothesis

Biometrics and the United Kingdom National Identity Register: Exploring the privacy dilemmas of proportionality and secondary use of biometric information

Despite the obvious importance of privacy concerns in the information age, “privacy” remains a messy concept in the academic literature. Scholars are thus attempting to clarify and systematize the privacy concept. They have proposed two important dimensions of privacy concerns: 1) proportionality, or the adequate, relevant and non-excessive collection of personal data, and 2) secondary usage, or the prohibition of subsequent, unspecified uses of personal information. This paper takes measure of the proportionality and potential secondary uses of biometric data in the proposed United Kingdom (UK) National Identity Register (NIR). It argues that the UK Identity Cards Act 2006 fails to guard against violations of the principles of proportionality and secondary usage of biometric data. After reviewing the modern literature on informational privacy protection, I analyze biometrics and their privacy implications. I then discuss these implications in the context of the UK government’s NIR plans. The analysis yields insights into how biometrics on the proposed NIR interplay with purpose specifications, architectural concerns, knowledge asymmetries and public anxieties. I also explore potential secondary uses of the types of biometric data that could be stored in the NIR. Last, a brief note is offered about the possible means of regulating against privacy infringements

Designing and Operating Safe and Secure Transit Systems: Assessing Current Practices in the United States and Abroad, MTI Report 04-05

Public transit systems around the world have for decades served as a principal venue for terrorist acts. Today, transit security is widely viewed as an important public policy issue and is a high priority at most large transit systems and at smaller systems operating in large metropolitan areas. Research on transit security in the United States has mushroomed since 9/11; this study is part of that new wave of research. This study contributes to our understanding of transit security by (1) reviewing and synthesizing nearly all previously published research on transit terrorism; (2) conducting detailed case studies of transit systems in London, Madrid, New York, Paris, Tokyo, and Washington, D.C.; (3) interviewing federal officials here in the United States responsible for overseeing transit security and transit industry representatives both here and abroad to learn about efforts to coordinate and finance transit security planning; and (4) surveying 113 of the largest transit operators in the United States. Our major findings include: (1) the threat of transit terrorism is probably not universal—most major attacks in the developed world have been on the largest systems in the largest cities; (2) this asymmetry of risk does not square with fiscal politics that seek to spread security funding among many jurisdictions; (3) transit managers are struggling to balance the costs and (uncertain) benefits of increased security against the costs and (certain) benefits of attracting passengers; (4) coordination and cooperation between security and transit agencies is improving, but far from complete; (5) enlisting passengers in surveillance has benefits, but fearful passengers may stop using public transit; (6) the role of crime prevention through environmental design in security planning is waxing; and (7) given the uncertain effectiveness of antitransit terrorism efforts, the most tangible benefits of increased attention to and spending on transit security may be a reduction in transit-related person and property crimes

ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning

We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a diagnostic dataset of event causality inference grounded in visio-linguistic social scenarios. ECHo employs real-world human-centric deductive information building on a television crime drama. ECHo requires the Theory-of-Mind (ToM) ability to understand and reason about social interactions based on multimodal information. Using ECHo, we propose a unified Chain-of-Thought (CoT) framework to assess the reasoning capability of current AI systems. Our ToM-enhanced CoT pipeline accommodates various large foundation models in both zero-shot and few-shot visio-linguistic reasoning. We use this framework to scrutinize recent large foundation models such as InstructGPT and MiniGPT-4 on three diagnostic human-centric tasks. Further analysis demonstrates ECHo as a challenging dataset to expose imperfections and inconsistencies in reasoning. Our data and code are publicly available at https://github.com/YuxiXie/ECHo.Comment: Findings of EMNLP 2023. 10 pages, 6 figures, 5 tables (22 pages, 8 figures, 15 tables including references and appendices

Perceived liveability, transport, and mental health: A story of overlying inequalities

Introduction: This paper examines the links between perceived liveability and mental health, questioning the role transport-related variables and features of the built environment play in the relationship between the two concepts. By exploring a topic not often tackled from the perspective of transport and health studies, the paper positions the concept of perceived liveability as a mechanism to capture the subjective interpretations of the built environment by residents of different socioeconomic backgrounds and mobility behaviours. Methods: The paper uses Cali, Colombia as an example of a rapidly growing city in the global South. We analyse data collected from an online participatory planning instrument where over 300 participants responded to questions on their mental health and their perceptions of the built environment, urban design, access to leisure facilities, and so forth. We use a Structural Equations Model that incorporates mental health and perceived liveability as latent variables. The paper also draws from secondary data to map both the spatial distribution of the various determinants of perceived liveability as well as the scores of the two latent constructs analysed. Results: We demonstrate that perceived liveability can be expressed as a latent variable, causing scores and correlations in measured variables associated with the urban form, the environment, access to transport, and fear of crime. On the whole, higher liveability scores are linked with higher mental health scores, and car users tend to score higher in both perceived liveability and mental health scores. Conclusions: There are meaningful links between perceived liveability and mental health influenced by transport-related drivers such as mode choice. Findings concerning car users suggest that transport investments in cities like Cali tend to accommodate already socio-economically advantaged residents. When testing our hypothesis that proximity to mass transit infrastructure could increase liveability, the results were inconclusive, which suggests a limited “liveability footprint” of public transport infrastructure