The influence of national culture on the attitude towards mobile recommender systems

This is the post-print version of the final paper published in Technological Forecasting and Social Change. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.This study aimed to identify factors that influence user attitudes towards mobile recommender systems and to examine how these factors interact with cultural values to affect attitudes towards this technology. Based on the theory of reasoned action, belief factors for mobile recommender systems are identified in three dimensions: functional, contextual, and social. Hypotheses explaining different impacts of cultural values on the factors affecting attitudes were also proposed. The research model was tested based on data collected in China, South Korea, and the United Kingdom. Findings indicate that functional and social factors have significant impacts on user attitudes towards mobile recommender systems. The relationships between belief factors and attitudes are moderated by two cultural values: collectivism and uncertainty avoidance. The theoretical and practical implications of applying theory of reasoned action and innovation diffusion theory to explain the adoption of new technologies in societies with different cultures are also discussed.National Research Foundation of Korea Grant funded by the Korean governmen

Towards an Intellectual Property Rights Strategy for Innovation in Europe

On October 13, 2009 the Science and Technology Options Assessment Panel (STOA) together with Knowledge4Innovation/The Lisbon Forum, supported by Technopolis Consulting Group and TNO, organised a half-day workshop entitled ‘Towards an Intellectual Property Rights Strategy for Innovation in Europe’. This workshop was part of the 1st European Innovation Summit at the European Parliament which took place on 13 October and 14 October 2009. It addressed the topics of the evolution and current issues concerning the European Patent System as well as International Protection and Enforcement of IPR (with special consideration of issues pertaining to IP enforcement in the Digital Environment). Conclusions drawn point to the benefits of a comprehensive European IPR strategy, covering a broad range of IP instruments and topics

Recommendations for a core outcome set for measuring standing balance in adult populations: a consensus-based approach

Standing balance is imperative for mobility and avoiding falls. Use of an excessive number of standing balance measures has limited the synthesis of balance intervention data and hampered consistent clinical practice.To develop recommendations for a core outcome set (COS) of standing balance measures for research and practice among adults.A combination of scoping reviews, literature appraisal, anonymous voting and face-to-face meetings with fourteen invited experts from a range of disciplines with international recognition in balance measurement and falls prevention. Consensus was sought over three rounds using pre-established criteria.The scoping review identified 56 existing standing balance measures validated in adult populations with evidence of use in the past five years, and these were considered for inclusion in the COS.Fifteen measures were excluded after the first round of scoring and a further 36 after round two. Five measures were considered in round three. Two measures reached consensus for recommendation, and the expert panel recommended that at a minimum, either the Berg Balance Scale or Mini Balance Evaluation Systems Test be used when measuring standing balance in adult populations.Inclusion of two measures in the COS may increase the feasibility of potential uptake, but poses challenges for data synthesis. Adoption of the standing balance COS does not constitute a comprehensive balance assessment for any population, and users should include additional validated measures as appropriate.The absence of a gold standard for measuring standing balance has contributed to the proliferation of outcome measures. These recommendations represent an important first step towards greater standardization in the assessment and measurement of this critical skill and will inform clinical research and practice internationally

Reflections on the EU objectives in addressing aggressive tax planning and harmful tax practices Final Report. CEPS Report November 2019

This Report analyses the EU’s instruments to tackle aggressive tax planning and harmful tax practices. Based on desk research, interviews with stakeholders and expert assessments, it considers the coherence, relevance, and added value of the EU’s approach. The instruments under analysis are found to be internally coherent and consistent with other EU policies and with the international tax agenda, in particular with the OECD/G20 BEPS framework. The Report also confirms the continued relevance of most of the original needs and problems addressed by the EU’s initiatives in the field of tax avoidance. There is also EU added value in having common EU instruments in the field to bolster coordination and harmonise the implementation of tax measures. One cross-cutting issue identified is the impact of digitalisation on corporate taxation. Against this background, the Report outlines potential improvements to the EU tax strategy such as: making EU tax systems fit for the digital era; leading the international debate on tax avoidance; enabling capacity building in Member States and developing countries; strengthening tax good governance in third countries; ensuring a consistent approach at home and abroad; achieving a level playing field for all companies; and increasing tax certainty and legal certainty

Pervasive Gaming: Testing Future Context Aware Applications

Over the last few years, many discussions have centred around the issue of interconnection rates and their economic impact on the market. Interconnection charging in Europe is still based mainly on the calling party pays (CPP) principle combined with element based charging (EBC). Due to the convergence of the classical PSTN/ISDN and the IP world to next generation networks (NGN), the different charging principles and systems are being reviewed to determine the optimal solution for the future. In its working program for the year 2008, the Austrian Regulatory Authority (RTR) launched an industry working group on charging principles and systems for wholesale services. This paper highlights some of the central issues of the discussions that have taken place and contains the authors’ views and conclusions .1 Further, the paper identifies possible charging systems, as well as economic assessment criteria for these systems and how the different charging systems may be evaluated with respect to those criteria. Regarding the usefulness of industry working groups, the work has shown that these lead to a higher degree of transparency between regulator and market players as well as a better understanding between the market players themselves. The main drawback is that working groups are time consuming and that it is almost impossible to agree on meaningful outcomes. Regarding the assessment of the charging models it was possible to derive a set of 10 criteria according to which charging systems can be evaluated. There was a rather broad consensus on the delineation of charging models as well as the economic criteria. When it comes to the results of the evaluation, the discussions brought forward very controversial views amongst the participants. No common views could be achieved on which the charging model fulfills the defined criteria in the best manner.Interconnection, NGN, charging principles, CPP, Bill&Keep.

Equality of Voice: Towards Fair Representation in Crowdsourced Top-K Recommendations

To help their users to discover important items at a particular time, major websites like Twitter, Yelp, TripAdvisor or NYTimes provide Top-K recommendations (e.g., 10 Trending Topics, Top 5 Hotels in Paris or 10 Most Viewed News Stories), which rely on crowdsourced popularity signals to select the items. However, different sections of a crowd may have different preferences, and there is a large silent majority who do not explicitly express their opinion. Also, the crowd often consists of actors like bots, spammers, or people running orchestrated campaigns. Recommendation algorithms today largely do not consider such nuances, hence are vulnerable to strategic manipulation by small but hyper-active user groups. To fairly aggregate the preferences of all users while recommending top-K items, we borrow ideas from prior research on social choice theory, and identify a voting mechanism called Single Transferable Vote (STV) as having many of the fairness properties we desire in top-K item (s)elections. We develop an innovative mechanism to attribute preferences of silent majority which also make STV completely operational. We show the generalizability of our approach by implementing it on two different real-world datasets. Through extensive experimentation and comparison with state-of-the-art techniques, we show that our proposed approach provides maximum user satisfaction, and cuts down drastically on items disliked by most but hyper-actively promoted by a few users.Comment: In the proceedings of the Conference on Fairness, Accountability, and Transparency (FAT* '19). Please cite the conference versio

Leveraging Epidemiology to Improve Risk Assessment.

The field of environmental public health is at an important crossroad. Our current biomonitoring efforts document widespread exposure to a host of chemicals for which toxicity information is lacking. At the same time, advances in the fields of genomics, proteomics, metabolomics, genetics and epigenetics are yielding volumes of data at a rapid pace. Our ability to detect chemicals in biological and environmental media has far outpaced our ability to interpret their health relevance, and as a result, the environmental risk paradigm, in its current state, is antiquated and ill-equipped to make the best use of these new data. In light of new scientific developments and the pressing need to characterize the public health burdens of chemicals, it is imperative to reinvigorate the use of environmental epidemiology in chemical risk assessment. Two case studies of chemical assessments from the Environmental Protection Agency Integrated Risk Information System database are presented to illustrate opportunities where epidemiologic data could have been used in place of experimental animal data in dose-response assessment, or where different approaches, techniques, or studies could have been employed to better utilize existing epidemiologic evidence. Based on the case studies and what can be learned from recent scientific advances and improved approaches to utilizing human data for dose-response estimation, recommendations are provided for the disciplines of epidemiology and risk assessment for enhancing the role of epidemiologic data in hazard identification and dose-response assessment

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