Anticipative Generation and In-Situ Adaptation of Maneuvering Affordance in a Naturally Complex Scene

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Advancing Economic Research on the Free and Open Source Software Mode of Production

Early contributions to the academic literature on free/libre and open source software (F/LOSS) movements have been directed primarily at identifying the motivations that account for the sustained and often intensive involvement of many people in this non-contractual and unremunerated productive activity. This issue has been particularly prominent in economists’ contributions to the literature, and it reflects a view that widespread voluntary participation in the creation of economically valuable goods that is to be distributed without charge constitutes a significant behavioral anomaly. Undoubtedly, the motivations of F/LOSS developers deserve to be studied more intensively, but not because their behaviors are unique, or historically unprecedented. In this essay we argue that other aspects of the “open source” phenomenon are just as intriguing, if not more so, and possibly are also more consequential topics for economic analysis. We describe the re-focusing and re-direction of empirical and theoretical research in an integrated international project (based at Stanford University/SIEPR) that aims at better understanding a set of less widely discussed topics: the modes of organization, governance and performance of F/LOSS development -- viewed as a collective distributed mode of production.. We discuss of the significance of tackling those questions in order to assess the potentialities of the “open source way of working” as a paradigm for a broader class of knowledge and information- goods production, and conclude with proposals for the trajectory of future research along that line.

Transforming Energy Networks via Peer to Peer Energy Trading: Potential of Game Theoretic Approaches

Peer-to-peer (P2P) energy trading has emerged as a next-generation energy management mechanism for the smart grid that enables each prosumer of the network to participate in energy trading with one another and the grid. This poses a significant challenge in terms of modeling the decision-making process of each participant with conflicting interest and motivating prosumers to participate in energy trading and to cooperate, if necessary, for achieving different energy management goals. Therefore, such decision-making process needs to be built on solid mathematical and signal processing tools that can ensure an efficient operation of the smart grid. This paper provides an overview of the use of game theoretic approaches for P2P energy trading as a feasible and effective means of energy management. As such, we discuss various games and auction theoretic approaches by following a systematic classification to provide information on the importance of game theory for smart energy research. Then, the paper focuses on the P2P energy trading describing its key features and giving an introduction to an existing P2P testbed. Further, the paper zooms into the detail of some specific game and auction theoretic models that have recently been used in P2P energy trading and discusses some important finding of these schemes.Comment: 38 pages, single column, double spac

Trust Model for Protection of Personal Health Data in a Global Environment

Successful health care, eHealth, digital health, and personal health systems increasingly take place in cross-jurisdictional, dynamic and risk-encumbered information space. They require rich amount of personal health information (PHI). Trust is and will be the cornerstone and prerequisite for successful health services. In global environments, trust cannot be expected as granted. In this paper, health service in the global environment is perceived as a meta-system, and a trust management model is developed to support it. The predefined trusting belief currently used in health care is not transferable to global environments. In the authors' model, the level of trust is dynamically calculated from measurable attributes. These attributes describe trust features of the service provider and its environment. The calculated trust value or profile can be used in defining the risk service user has to accept when disclosing PHI, and in definition of additional privacy and security safeguards before disclosing PHI and/or using services

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

Towards Analytics for Wholistic School Improvement: Hierarchical Process Modelling and Evidence Visualization

Central to the mission of most educational institutions is the task of preparing the next generation of citizens to contribute to society. Schools, colleges, and universities value a range of outcomes — e.g., problem solving, creativity, collaboration, citizenship, service to community — as well as academic outcomes in traditional subjects. Often referred to as “wider outcomes,” these are hard to quantify. While new kinds of monitoring technologies and public datasets expand the possibilities for quantifying these indices, we need ways to bring that data together to support sense-making and decision-making. Taking a systems perspective, the hierarchical process modelling (HPM) approach and the “Perimeta” visual analytic provides a dashboard that informs leadership decision-making with heterogeneous, often incomplete evidence. We report a prototype of Perimeta modelling from education, aggregating wider outcomes data across a network of schools, and calculating their cumulative contribution to key performance indicators, using the visual analytic of the Italian flag to make explicit not only the supporting evidence, but also the challenging evidence, as well as areas of uncertainty. We discuss the nature of the modelling decisions and implicit values involved in quantifying these kinds of educational outcomes

A Gang of Adversarial Bandits

We consider running multiple instances of multi-armed bandit (MAB) problems in parallel. A main motivation for this study are online recommendation systems, in which each of N users is associated with a MAB problem and the goal is to exploit users' similarity in order to learn users' preferences to K items more efficiently. We consider the adversarial MAB setting, whereby an adversary is free to choose which user and which loss to present to the learner during the learning process. Users are in a social network and the learner is aided by a-priori knowledge of the strengths of the social links between all pairs of users. It is assumed that if the social link between two users is strong then they tend to share the same action. The regret is measured relative to an arbitrary function which maps users to actions. The smoothness of the function is captured by a resistance-based dispersion measure Ψ. We present two learning algorithms, GABA-I and GABA-II which exploit the network structure to bias towards functions of low Ψ values. We show that GABA-I has an expected regret bound of O(pln(N K/Ψ)ΨKT) and per-trial time complexity of O(K ln(N)), whilst GABA-II has a weaker O(pln(N/Ψ) ln(N K/Ψ)ΨKT) regret, but a better O(ln(K) ln(N)) per-trial time complexity. We highlight improvements of both algorithms over running independent standard MABs across users

Deep Learning-Based Machinery Fault Diagnostics

This book offers a compilation for experts, scholars, and researchers to present the most recent advancements, from theoretical methods to the applications of sophisticated fault diagnosis techniques. The deep learning methods for analyzing and testing complex mechanical systems are of particular interest. Special attention is given to the representation and analysis of system information, operating condition monitoring, the establishment of technical standards, and scientific support of machinery fault diagnosis

Value Creation through Co-Opetition in Service Networks

Well-defined interfaces and standardization allow for the composition of single Web services into value-added complex services. Such complex Web Services are increasingly traded via agile marketplaces, facilitating flexible recombination of service modules to meet heterogeneous customer demands. In order to coordinate participants, this work introduces a mechanism design approach - the co-opetition mechanism - that is tailored to requirements imposed by a networked and co-opetitive environment