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It is our great pleasure to welcome you to the 2019 ACM Workshop on Hot Topics in Video Analytics and Intelligent Edges - HotEdgeVideo'19. The basis for this workshop is the pervasive deployment of cameras everywhere! Analyzing live videos from these cameras has great potential to impact science and society. Analyzing live video streams is arguably the most challenging of domains for "systems-for-AI". Unlike text or numeric processing, video analytics require higher bandwidth, consume considerable compute cycles for processing, necessitate richer query semantics, and demand tighter security&privacy guarantees. This workshop will provide the forum for presentation and discussion of research results and experience reports on upcoming issues of video analytics systems, edge computing, storage of videos, security &privacy implications, as well as novel applications. The mission of the workshop is to enable debate on the challenges and implications of video analytics systems, beneficial application scenarios, and identify new directions for future research and development. HotEdgeVideo gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of video analytics. The call for papers attracted submissions from Europe, China, India, and the United States. The accepted papers cover topics ranging from deep neural networks for video analytics, network considerations in streaming the videos, tamper-proof video processing, and evolution of smart cameras with compute onboard. Please check out the program and we hope you can attend the sessions.

Data compression (Computer science)

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The self-driving car, one of the most disruptive innovations of this era, has recently received great attention. This transportation mode could benefit all groups of society. However, the great public is not ready to hand over full control (i.e. driving style, steering wheel, avoiding obstacles, etc.) to that self-driving car. This paper is aimed at studying people’s willingness to give full control to the self-driving car. We address this issue through linking this willingness of people to hand over control to the vehicle to certain personality traits (i.e. conscientiousness and openness) they have. Drawing on quantitative evidence, the results of the study show that conscientiousness and openness do not directly influence people’s willingness to give control to a self-driving car. Nevertheless, these personality traits do have an influence when moderated by age or gender. Following a discussion on the results, the paper provides implications for car developers on the group of people who should be targeted first, and what control features remain important to them.

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This master’s thesis digs into the research field of technology-related trust, focusing on trust in AI-equipped devices in a medical context. It is obvious that AI in the medical sector offers a myriad of new possibilities, but their effective implementation is seriously tempered by the lack of trust shown by potential users, and this limits the evolution of this promising technology. Those stakes underscore the relevance of the present work, not in the least because the existing literature on the subject seems to focus mainly on trust in automation. In the context of this study, a survey was used to measure trust in AI. This survey featured on the one hand a fictive scenario involving either AI or a regular doctor, and on the other the measurement of a number of trust antecedents. Results show that trust and intention to use is significantly higher when granted to a human doctor than when granted to AI. Second, demographics, propensity to trust and locus of control have no effect on trust or intention to use. Third, medical health literacy enhances trust in a doctor, but not the intention to reuse the process involving a doctor. Fourth, medical literacy does not shift either trust in a diagnosis expressed by an AI-equipped machine, or the intention to reuse such a system. Fifth, there is no difference in attitude whether AI is explicitly mentioned to the respondent or only described.