Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Adaptive Stochastic Optimisation of Nonconvex Composite Objectives

In this paper, we propose and analyse a family of generalised stochastic composite mirror descent algorithms. With adaptive step sizes, the proposed algorithms converge without requiring prior knowledge of the problem. Combined with an entropy-like update-generating function, these algorithms perform gradient descent in the space equipped with the maximum norm, which allows us to exploit the low-dimensional structure of the decision sets for high-dimensional problems. Together with a sampling method based on the Rademacher distribution and variance reduction techniques, the proposed algorithms guarantee a logarithmic complexity dependence on dimensionality for zeroth-order optimisation problems.Comment: arXiv admin note: substantial text overlap with arXiv:2208.0457

Transmission rate sampling and selection for reliable wireless multicast

The multicast communication concept offers a scalable and efficient method for many classes of applications; however, its potential remains largely unexploited when it comes to link-layer multicasting in wireless local area networks. The fundamental lacking feature for this is a transmission rate control mechanism that offers higher transmission performance and lower channel utilization, while ensuring the reliability of wireless multicast transmissions. This is much harder to achieve in a scalable manner for multicast when compared with unicast transmissions, which employs explicit acknowledgment mechanisms for rate control. This article introduces EWRiM, a reliable multicast transmission rate control protocol for IEEE 802.11 networks. It adapts the transmission rate sampling concept to multicast through an aggregated receiver feedback scheme and combines it with a sliding window forward error correction (FEC) mechanism for ensuring reliability at the link layer. An inherent novelty of EWRiM is the close interaction of its FEC and transmission rate selection components to address the performance-reliability tradeoff in multicast communications. The performance of EWRiM was tested in three scenarios with intrinsically different traffic patterns; namely, music streaming scenario, large data frame delivery scenario, and an IoT scenario with frequent distribution of small data packets. Evaluation results demonstrate that the proposed approach adapts well to all of these realistic multicast traffic scenarios and provides significant improvements over the legacy multicast- and unicast-based transmissions.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Augmenting the Task of Exercise Gamification: An Expert View on the Adoption of a New Technology for Deploying Existing Virtual Environments in Virtual Urban Exergames

Exergames commonly denote serious games and gamified systems that were developed for the sake of improving health and exercise adherence. One of the recent trends in exergaming are urban games. They are defined as “highly interdisciplinary digital games which root in such diverse fields as architecture and urban planning, healthcare sciences, and serious games research” (Knoell et al., 2014). Besides having various ‘physical benefits’, such as promoting movement patterns, urban exergames have the core task of psychologically motivating players to exercise more and inspire them to be physically active. While offering an innovative and an immersive way to exercise, urban games come also with the typical drawbacks which outdoor exercising generally has (e.g. being dependent on good weather and intimidation problems for obese people). A possible solution would be simulating urban games for indoor exercise. On top of augmenting the sedentary game input to a motion-based one, designing and developing 3D environments for virtual urban games is not an obvious task and it takes a vast amount of knowledge, time and budget to create a realistic world with a “tremendous appeal and a powerful attraction”. To bypass this challenge, we introduce in this work a new technology for accessing and gamifying existing game environments. Furthermore, we validate our approach by presenting the results of a qualitative research that we have conducted with the help of gamification experts and exergame designers

FABRIC: A Framework for the Design and Evaluation of Collaborative Robots with Extended Human Adaptation

A limitation for collaborative robots (cobots) is their lack of ability to adapt to human partners, who typically exhibit an immense diversity of behaviors. We present an autonomous framework as a cobot's real-time decision-making mechanism to anticipate a variety of human characteristics and behaviors, including human errors, toward a personalized collaboration. Our framework handles such behaviors in two levels: 1) short-term human behaviors are adapted through our novel Anticipatory Partially Observable Markov Decision Process (A-POMDP) models, covering a human's changing intent (motivation), availability, and capability; 2) long-term changing human characteristics are adapted by our novel Adaptive Bayesian Policy Selection (ABPS) mechanism that selects a short-term decision model, e.g., an A-POMDP, according to an estimate of a human's workplace characteristics, such as her expertise and collaboration preferences. To design and evaluate our framework over a diversity of human behaviors, we propose a pipeline where we first train and rigorously test the framework in simulation over novel human models. Then, we deploy and evaluate it on our novel physical experiment setup that induces cognitive load on humans to observe their dynamic behaviors, including their mistakes, and their changing characteristics such as their expertise. We conduct user studies and show that our framework effectively collaborates non-stop for hours and adapts to various changing human behaviors and characteristics in real-time. That increases the efficiency and naturalness of the collaboration with a higher perceived collaboration, positive teammate traits, and human trust. We believe that such an extended human adaptation is key to the long-term use of cobots.Comment: The article is in review for publication in International Journal of Robotics Researc

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Very Short-Term Power System Frequency Forecasting

Power system frequency plays a pivotal role in ensuring the security, adequacy, and integrity of a power system. While some frequency response services are automatically delivered to maintain the frequency within the stipulated limits, certain cases may require that system operators (SOs) manually intervene-against the clock-to take the necessary preventive or corrective actions. As such, SOs can be greatly aided by practical tools that afford them greater temporal leeway. To this end, we propose a methodology to forecast the power system frequency in the subsequent minute. We perform an extensive analysis so as to identify the factors that influence power system frequency. By effectively exploiting the identified factors, we develop a forecasting methodology that harnesses the long short-term memory model. We demonstrate the effectiveness of the proposed methodology on Great Britain transmission system frequency data using comparative assessments with selected benchmarks based on various evaluation metrics.Publisher's Versio

Agile Principles Applied to a Complex Long Term Research Activity - The PERIMETER Approach

Agile software development is a group of software development methodologies that are based on similar principles, as defined in the Agile Manifesto. Agile software projects are characterized by iterative and incremental development, accommodation of changes and active customer participation. The popularity of agile principles is steadily increasing. Their adopters report that this development process leads to higher software quality and customer satisfaction ratings when compared to using traditional methods, with more productive and motivated developers. Whilst smaller developer teams have cited higher success rates than larger teams, agile principles can and have been applied successfully to large scale projects and distributed teams. Despite these advantages, there are very few research activities that apply agile principles in their development. Perhaps this is due to the nature of research projects, which usually span years rather than months, frequently involve experimental work, and consist of team members with varying levels of experience, often coming from different organizations, research groups and countries. This paper examines how agile principles can be adapted to suit one such long term research activity; PERIMETER