Towards a new ITU-T recommendation for subjective methods evaluating gaming QoE

This paper reports on activities in Study Group 12 of the International Telecommunication Union (ITU-T SG12) to define a new Recommendation on subjective evaluation methods for gaming Quality of Experience (QoE). It first resumes the structure and content of the current draft which has been proposed to ITU-T SG12 in September 2014 and then critically discusses potential gaming content and evaluation methods for inclusion into the upcoming Recommendation. The aim is to start a discussion amongst experts on potential evaluation methods and their limitations, before finalizing a Recommendation. Such a recommendation might in the end be applied by non -expert users, hence wrong decisions in the evaluation design could negatively affect gaming QoE throughout the evaluation

Representation Learning for Attributed Multiplex Heterogeneous Network

Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.Comment: Accepted to KDD 2019. Website: https://sites.google.com/view/gatn

The role of economics in ecosystem based management:The case of the EU Marine Strategy Framework Directive; first lessons learnt and way forward

The EU Marine Strategy Framework Directive (MSFD) sets out a plan of action relating to marine environmental policy and in particular to achieving ‘good environmental status’ (GES) in European marine waters by 2020. Article 8.1 (c) of the Directive calls for ‘an economic and social analysis of the use of those waters and of the cost of degradation of the marine environment’. The MSFD is ‘informed’ by the Ecosystem Approach to management, with GES interpreted in terms of ecosystem functioning and services provision. Implementation of the Ecosystem Approach is expected to be by adaptive management policy and practice. The initial socio-economic assessment was made by maritime EU Member States between 2011 and 2012, with future updates to be made on a regular basis. For the majority of Member States, this assessment has led to an exercise combining an analysis of maritime activities both at national and coastal zone scales, and an analysis of the non-market value of marine waters. In this paper we examine the approaches taken in more detail, outline the main challenges facing the Member States in assessing the economic value of achieving GES as outlined in the Directive and make recommendations for the theoretically sound and practically useful completion of the required follow-up economic assessments specified in the MSFD