A Critical Look at Decentralized Personal Data Architectures

While the Internet was conceived as a decentralized network, the most widely used web applications today tend toward centralization. Control increasingly rests with centralized service providers who, as a consequence, have also amassed unprecedented amounts of data about the behaviors and personalities of individuals. Developers, regulators, and consumer advocates have looked to alternative decentralized architectures as the natural response to threats posed by these centralized services. The result has been a great variety of solutions that include personal data stores (PDS), infomediaries, Vendor Relationship Management (VRM) systems, and federated and distributed social networks. And yet, for all these efforts, decentralized personal data architectures have seen little adoption. This position paper attempts to account for these failures, challenging the accepted wisdom in the web community on the feasibility and desirability of these approaches. We start with a historical discussion of the development of various categories of decentralized personal data architectures. Then we survey the main ideas to illustrate the common themes among these efforts. We tease apart the design characteristics of these systems from the social values that they (are intended to) promote. We use this understanding to point out numerous drawbacks of the decentralization paradigm, some inherent and others incidental. We end with recommendations for designers of these systems for working towards goals that are achievable, but perhaps more limited in scope and ambition

Artificial Intelligence for the Financial Services Industry: What Challenges Organizations to Succeed?

As a research field, artificial intelligence (AI) exists for several years. More recently, technological breakthroughs, coupled with the fast availability of data, have brought AI closer to commercial use. Internet giants such as Google, Amazon, Apple or Facebook invest significantly into AI, thereby underlining its relevance for business models worldwide. For the highly data driven finance industry, AI is of intensive interest within pilot projects, still, few AI applications have been implemented so far. This study analyzes drivers and inhibitors of a successful AI application in the finance industry based on panel data comprising 22 semi-structured interviews with experts in AI in finance. As theoretical lens, we structured our results using the TOE framework. Guidelines for applying AI successfully reveal AI-specific role models and process competencies as crucial, before trained algorithms will have reached a quality level on which AI applications will operate without human intervention and moral concerns

Social architecture and the emergence of power laws in online social games

This paper explores the concept of the “social architecture” of games, and tests the theory that it is possible to analyse game mechanics based on the effect they have on the social behaviour of the players. Using tools from Social Network Analysis, these studies confirm that social activity in games reliably follows a power distribution: a few players are responsible for a disproportionate amount of social interactions. Based on this, the scaling exponent is highlighted as a simple measure of sociability that is constant for a game design. This allows for the direct comparison of social activity in very different games. In addition, it can act as a powerful analytical tool for highlighting anomalies in game designs that detrimentally affect players’ ability to interact socially. Although the social architectures of games are complicated systems, SNA allows for quantitative analysis of social behaviours of players in meaningful ways, which are to the benefit of game designers

The Platformisation of the European Mobile Industry

This paper argues that the structure of the mobile communications industry is being decisively affected by 'platformisation', yet in a present context of strong 'platform ambiguity'. It introduces the concept of gatekeeper roles to compare current mobile platform initiatives, and proposes a typology of platforms to characterise the various models encountered.Mobile Platforms, Business Models, Gatekeeping, Platform Typology

WARP: A ICN architecture for social data

Social network companies maintain complete visibility and ownership of the data they store. However users should be able to maintain full control over their content. For this purpose, we propose WARP, an architecture based upon Information-Centric Networking (ICN) designs, which expands the scope of the ICN architecture beyond media distribution, to provide data control in social networks. The benefit of our solution lies in the lightweight nature of the protocol and in its layered design. With WARP, data distribution and access policies are enforced on the user side. Data can still be replicated in an ICN fashion but we introduce control channels, named \textit{thread updates}, which ensures that the access to the data is always updated to the latest control policy. WARP decentralizes the social network but still offers APIs so that social network providers can build products and business models on top of WARP. Social applications run directly on the user's device and store their data on the user's \textit{butler} that takes care of encryption and distribution. Moreover, users can still rely on third parties to have high-availability without renouncing their privacy

MLPerf Inference Benchmark

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and five orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. In this paper, we present our benchmarking method for evaluating ML inference systems. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf prescribes a set of rules and best practices to ensure comparability across systems with wildly differing architectures. The first call for submissions garnered more than 600 reproducible inference-performance measurements from 14 organizations, representing over 30 systems that showcase a wide range of capabilities. The submissions attest to the benchmark's flexibility and adaptability.Comment: ISCA 202

Higher education and the promises and perils of social network

The last decade has produced tremendous innovation in how people connect with one another online. Social networks have experienced a rapid increase in popularity, producing both concerns (privacy, content ownership) and opportunities. The articles in this journal can be viewed as attempts to answer the question: What should educators do about social networks

FABRIC: A National-Scale Programmable Experimental Network Infrastructure

FABRIC is a unique national research infrastructure to enable cutting-edge and exploratory research at-scale in networking, cybersecurity, distributed computing and storage systems, machine learning, and science applications. It is an everywhere-programmable nationwide instrument comprised of novel extensible network elements equipped with large amounts of compute and storage, interconnected by high speed, dedicated optical links. It will connect a number of specialized testbeds for cloud research (NSF Cloud testbeds CloudLab and Chameleon), for research beyond 5G technologies (Platforms for Advanced Wireless Research or PAWR), as well as production high-performance computing facilities and science instruments to create a rich fabric for a wide variety of experimental activities

Platforms, Markets and Innovation: An Introduction

The emergence of platforms, whether used inside firms, across supply chains, or as building blocks that act as engines of innovation and redefine industrial architectures, is a novel phenomenon affecting most industries today, from products to services. This book, the first of its kind dedicated to the emerging field of platform research, presents leading-edge contributions from top international scholars from strategy, economics, innovation, organizations and knowledge management. This book represents a milestone for the vibrant field of platform research. It is the outcome of an ambitious international collaboration, regrouping and making connections between the research work of 24 scholars, affiliated with 19 universities, in seven countries over four continents. The novel insights assembled in the 14 chapters of this volume constitute a fundamental step towards an empirically based, nuanced understanding of the nature of platforms and the implications they hold for the evolution of industrial innovation. But what exactly are platforms? Why should we care about them? And, why do we need a book about them