Privacy-Preserving Map-Free Exploration for Confirming the Absence of a Radioactive Source

Performing an inspection task while maintaining the privacy of the inspected site is a challenging balancing act. In this work, we are motivated by the future of nuclear arms control verification, which requires both a high level of privacy and guaranteed correctness. For scenarios with limitations on sensors and stored information due to the potentially secret nature of observable features, we propose a robotic verification procedure that provides map-free exploration to perform a source verification task without requiring, nor revealing, any task-irrelevant, site-specific information. We provide theoretical guarantees on the privacy and correctness of our approach, validated by extensive simulated and hardware experiments.Comment: 10 pages, 6 figures, in submissio

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Big data for monitoring educational systems

This report considers “how advances in big data are likely to transform the context and methodology of monitoring educational systems within a long-term perspective (10-30 years) and impact the evidence based policy development in the sector”, big data are “large amounts of different types of data produced with high velocity from a high number of various types of sources.” Five independent experts were commissioned by Ecorys, responding to themes of: students' privacy, educational equity and efficiency, student tracking, assessment and skills. The experts were asked to consider the “macro perspective on governance on educational systems at all levels from primary, secondary education and tertiary – the latter covering all aspects of tertiary from further, to higher, and to VET”, prioritising primary and secondary levels of education

Many Episode Learning in a Modular Embodied Agent via End-to-End Interaction

In this work we give a case study of an embodied machine-learning (ML) powered agent that improves itself via interactions with crowd-workers. The agent consists of a set of modules, some of which are learned, and others heuristic. While the agent is not "end-to-end" in the ML sense, end-to-end interaction is a vital part of the agent's learning mechanism. We describe how the design of the agent works together with the design of multiple annotation interfaces to allow crowd-workers to assign credit to module errors from end-to-end interactions, and to label data for individual modules. Over multiple automated human-agent interaction, credit assignment, data annotation, and model re-training and re-deployment, rounds we demonstrate agent improvement

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 199

This bibliography lists 82 reports, articles, and other documents introduced into the NASA scientific and technical information system in October 1979

Reinforcement Learning

Brains rule the world, and brain-like computation is increasingly used in computers and electronic devices. Brain-like computation is about processing and interpreting data or directly putting forward and performing actions. Learning is a very important aspect. This book is on reinforcement learning which involves performing actions to achieve a goal. The first 11 chapters of this book describe and extend the scope of reinforcement learning. The remaining 11 chapters show that there is already wide usage in numerous fields. Reinforcement learning can tackle control tasks that are too complex for traditional, hand-designed, non-learning controllers. As learning computers can deal with technical complexities, the tasks of human operators remain to specify goals on increasingly higher levels. This book shows that reinforcement learning is a very dynamic area in terms of theory and applications and it shall stimulate and encourage new research in this field

Impact assessment of AI-enabled automation on the workplace and employment. The case of Portugal

Artificial intelligence (AI) has the potential to lead to a wave of innovation in organiza-tional design, changes in the workplace and create disruptive effects in the employment sys-tems across the world. Moreover, the future deployment of broad-spectrum algorithms capa-ble of being used in wide areas of application (e.g., industrial robotics, software and data anal-ysis, decision-making) can lead to considerable changes in current work patterns, swiftly render many unemployed across the globe and profoundly destabilize labour relations. The impacts of AI are estimated to lead to a reduction of millions of workplaces. But qualitative research about AI and its governance is scarce. An emergent technology requires a technology assess-ment (TA) approach to understand the implications of AI in firms. Mechanisms of industrial democracy can help to adopt AI by ensuring adequate arrangements for employees and avoid-ing conflicts (mitigating negative effects, promoting reskilling, etc.). In this research work, the probable penetration of AI in the manufacturing sector is identified to study its effects in work organization and employment in Portugal. Is the employ-ment changing alongside recent AI trends in Portugal? What are the expectable changes in work organisation due to AI-enabled automation? Are there signs of work qualification to go with AI systems implementation? Are there visions on the role of humans on the interaction with the features of industry 4.0? Does that imply new forms of human interaction with AI? These are the questions this research work will try to answer. A TA approach using mixed methods was applied to conduct statistical analyses of relevant databases, interviews with ac-ademic, industrial and social actors and exploratory scenarios of AI-based automation systems, on work organization and employment. The manufacturing industry was the chosen sector since it is the sector where most cases of AI-based automation systems are in place. Findings suggest that, until now, it seems AI is still not able to replace most of the human skills and cognitive capacities but can replace humans on simple tasks. In the future, four different possible states may occur, according to the various initial conditions, the com-pany's motivation, their business strategy, the public policies in place and main social actors involved: Re-organisation of work; Substitution of the workforce; People at the centre and Fo-cus on Efficiency. These were the basis for our scenario outcomes.A inteligência artificial (IA) tem o potencial de levar a uma onda de inovação no desenho das organizações, nas mudanças no local de trabalho e em criar efeitos disruptivos nos sistemas de emprego em todo o mundo. Além disso, a futura implementação de algoritmos de amplo espectro, capazes de serem usados em muitas áreas de aplicação (por exemplo, robótica industrial, software e análise de dados, tomada de decisão), pode levar a mudanças consideráveis nos padrões de trabalho atuais, e rapidamente, levar ao desemprego em todo o mundo e à desestabilização profunda das relações laborais. Estima-se que os impactos da IA levem a uma redução de milhões de locais de trabalho. Mas a investigação qualitativa sobre IA é escassa. Uma tecnologia emergente requer uma abordagem de avaliação de tecnologia (AT) para entender as suas implicações. Mecanismos de democracia industrial podem ajudar a adotar a IA, garantindo condições adequadas para os trabalhadores e evitando conflitos (mitigando efeitos negativos, promovendo requalificação, etc.). Neste trabalho de investigação identifica-se a provável penetração da IA no setor da indústria transformadora para estudar os seus efeitos na organização do trabalho e emprego em Portugal. O emprego está a mudar a par das tendências recentes da IA em Portugal? Quais são as mudanças na organização do trabalho devido à automação baseada em IA? Há indícios de qualificação do trabalho para acompanhar a implementação dos sistemas de IA? Existem visões sobre o papel do ser humano na interação com os recursos da indústria 4.0? Isso implica novas formas de interação humana com a IA? Estas são as perguntas que este trabalho de investigação tentará responder. Na abordagem de AT, foram usados métodos mistos para realizar análises estatísticas de bases de dados, entrevistas com atores do ecossistema académico, industrial e social e cenários exploratórios sobre os efeitos da adoção de sistemas de automação baseados em IA, na organização do trabalho e emprego. A indústria transformadora foi escolhida por ser onde existem a maioria de casos de aplicação de sistemas de auto-mação baseados em IA. Os resultados sugerem que, até ao momento, que a IA não tem a capacidade de subs-tituir a maioria das competências e raciocínio humanos, mas apenas tarefas simples. No futuro, poderão ocorrer quatro situações, dependendo das condições iniciais, motivação e estratégia da empresa, das políticas e incentivos públicos existentes e do envolvimento de atores sociais: Reorganização do trabalho; Substituição da mão-de-obra; Pessoas no centro da transformação e foco na Eficiência. Estas foram a base para os nossos cenários de referência