A Dynamic Embedding Model of the Media Landscape

Information about world events is disseminated through a wide variety of news channels, each with specific considerations in the choice of their reporting. Although the multiplicity of these outlets should ensure a variety of viewpoints, recent reports suggest that the rising concentration of media ownership may void this assumption. This observation motivates the study of the impact of ownership on the global media landscape and its influence on the coverage the actual viewer receives. To this end, the selection of reported events has been shown to be informative about the high-level structure of the news ecosystem. However, existing methods only provide a static view into an inherently dynamic system, providing underperforming statistical models and hindering our understanding of the media landscape as a whole. In this work, we present a dynamic embedding method that learns to capture the decision process of individual news sources in their selection of reported events while also enabling the systematic detection of large-scale transformations in the media landscape over prolonged periods of time. In an experiment covering over 580M real-world event mentions, we show our approach to outperform static embedding methods in predictive terms. We demonstrate the potential of the method for news monitoring applications and investigative journalism by shedding light on important changes in programming induced by mergers and acquisitions, policy changes, or network-wide content diffusion. These findings offer evidence of strong content convergence trends inside large broadcasting groups, influencing the news ecosystem in a time of increasing media ownership concentration

The Global People toolbook: managing the life cycle of intercultural partnerships

This Toolbook has been designed for those who are planning and running international projects and who feel a need for guidance. It has its origins in a major educational project, the eChina-UK Programme, that created new collaborations between UK and Chinese Higher Education Institutions around the development of e-learning materials. The rich intercultural learning that emerged from that programme prompted the development of a new and evidence-based set of resources for other individuals and institutions undertaking international collaborative projects. Although the main focus of the work is on intercultural effectiveness in international contexts, we believe that many of the resources have a more general value and are useful for those planning collaboration in any situation of diversity – national, regional, sectoral or institutional

Micro protocol engineering for unstructured carriers: On the embedding of steganographic control protocols into audio transmissions

Network steganography conceals the transfer of sensitive information within unobtrusive data in computer networks. So-called micro protocols are communication protocols placed within the payload of a network steganographic transfer. They enrich this transfer with features such as reliability, dynamic overlay routing, or performance optimization --- just to mention a few. We present different design approaches for the embedding of hidden channels with micro protocols in digitized audio signals under consideration of different requirements. On the basis of experimental results, our design approaches are compared, and introduced into a protocol engineering approach for micro protocols.Comment: 20 pages, 7 figures, 4 table

Avatar: A Time- and Space-Efficient Self-Stabilizing Overlay Network

Overlay networks present an interesting challenge for fault-tolerant computing. Many overlay networks operate in dynamic environments (e.g. the Internet), where faults are frequent and widespread, and the number of processes in a system may be quite large. Recently, self-stabilizing overlay networks have been presented as a method for managing this complexity. \emph{Self-stabilizing overlay networks} promise that, starting from any weakly-connected configuration, a correct overlay network will eventually be built. To date, this guarantee has come at a cost: nodes may either have high degree during the algorithm's execution, or the algorithm may take a long time to reach a legal configuration. In this paper, we present the first self-stabilizing overlay network algorithm that does not incur this penalty. Specifically, we (i) present a new locally-checkable overlay network based upon a binary search tree, and (ii) provide a randomized algorithm for self-stabilization that terminates in an expected polylogarithmic number of rounds \emph{and} increases a node's degree by only a polylogarithmic factor in expectation

The mechanics of trust: a framework for research and design

With an increasing number of technologies supporting transactions over distance and replacing traditional forms of interaction, designing for trust in mediated interactions has become a key concern for researchers in human computer interaction (HCI). While much of this research focuses on increasing users’ trust, we present a framework that shifts the perspective towards factors that support trustworthy behavior. In a second step, we analyze how the presence of these factors can be signalled. We argue that it is essential to take a systemic perspective for enabling well-placed trust and trustworthy behavior in the long term. For our analysis we draw on relevant research from sociology, economics, and psychology, as well as HCI. We identify contextual properties (motivation based on temporal, social, and institutional embeddedness) and the actor's intrinsic properties (ability, and motivation based on internalized norms and benevolence) that form the basis of trustworthy behavior. Our analysis provides a frame of reference for the design of studies on trust in technology-mediated interactions, as well as a guide for identifying trust requirements in design processes. We demonstrate the application of the framework in three scenarios: call centre interactions, B2C e-commerce, and voice-enabled on-line gaming

Learning to Communicate with Deep Multi-Agent Reinforcement Learning

We consider the problem of multiple agents sensing and acting in environments with the goal of maximising their shared utility. In these environments, agents must learn communication protocols in order to share information that is needed to solve the tasks. By embracing deep neural networks, we are able to demonstrate end-to-end learning of protocols in complex environments inspired by communication riddles and multi-agent computer vision problems with partial observability. We propose two approaches for learning in these domains: Reinforced Inter-Agent Learning (RIAL) and Differentiable Inter-Agent Learning (DIAL). The former uses deep Q-learning, while the latter exploits the fact that, during learning, agents can backpropagate error derivatives through (noisy) communication channels. Hence, this approach uses centralised learning but decentralised execution. Our experiments introduce new environments for studying the learning of communication protocols and present a set of engineering innovations that are essential for success in these domains

Value Propagation Networks

We present Value Propagation (VProp), a set of parameter-efficient differentiable planning modules built on Value Iteration which can successfully be trained using reinforcement learning to solve unseen tasks, has the capability to generalize to larger map sizes, and can learn to navigate in dynamic environments. We show that the modules enable learning to plan when the environment also includes stochastic elements, providing a cost-efficient learning system to build low-level size-invariant planners for a variety of interactive navigation problems. We evaluate on static and dynamic configurations of MazeBase grid-worlds, with randomly generated environments of several different sizes, and on a StarCraft navigation scenario, with more complex dynamics, and pixels as input.Comment: Updated to match ICLR 2019 OpenReview's versio