Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new user-post pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).Comment: accepted in IJCAI-1

09101 Abstracts Collection -- Interactive Information Retrieval

From 01.03. to 06.03.2009, the Dagstuhl Seminar 09101 ``Interactive Information Retrieval \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Personalization in cultural heritage: the road travelled and the one ahead

Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

Multivariate Fairness for Paper Selection

Peer review is the process by which publishers select the best publications for inclusion in a journal or a conference. Bias in the peer review process can impact which papers are selected for inclusion in conferences and journals. Although often implicit, race, gender and other demographics can prevent members of underrepresented groups from presenting at major conferences. To try to avoid bias, many conferences use a double-blind review process to increase fairness during reviewing. However, recent studies argue that the bias has not been removed completely. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To address this, we present fair algorithms that explicitly incorporate author diversity in paper recommendation using multidimensional author profiles that include five demographic features, i.e., gender, ethnicity, career stage, university rank, and geolocation. The Overall Diversity method ranks papers based on an overall diversity score whereas the Multifaceted Diversity method selects papers that fill the highest-priority demographic feature first. We evaluate these algorithms with Boolean and continuous-valued features by recommending papers for SIGCHI 2017 from a pool of SIGCHI 2017, DIS 2017 and IUI 2017 papers and compare the resulting set of papers with the papers accepted by the conference. Both methods increase diversity with small decreases in utility using profiles with either Boolean or continuous feature values. Our best method, Multifaceted Diversity, recommends a set of papers that match demographic parity, selecting authors who are 42.50% more diverse with a 2.45% gain in utility. This approach could be applied when selecting conference papers, journal papers, grant proposals, or other tasks within academia

Multivariate Fairness for Paper Selection

Peer review is the process by which publishers select the best publications for inclusion in a journal or a conference. Bias in the peer review process can impact which papers are selected for inclusion in conferences and journals. Although often implicit, race, gender and other demographics can prevent members of underrepresented groups from presenting at major conferences. To try to avoid bias, many conferences use a double-blind review process to increase fairness during reviewing. However, recent studies argue that the bias has not been removed completely. Our research focuses on developing fair algorithms that correct for these biases and select papers from a more demographically diverse group of authors. To address this, we present fair algorithms that explicitly incorporate author diversity in paper recommendation using multidimensional author profiles that include five demographic features, i.e., gender, ethnicity, career stage, university rank, and geolocation. The Overall Diversity method ranks papers based on an overall diversity score whereas the Multifaceted Diversity method selects papers that fill the highest-priority demographic feature first. We evaluate these algorithms with Boolean and continuous-valued features by recommending papers for SIGCHI 2017 from a pool of SIGCHI 2017, DIS 2017 and IUI 2017 papers and compare the resulting set of papers with the papers accepted by the conference. Both methods increase diversity with small decreases in utility using profiles with either Boolean or continuous feature values. Our best method, Multifaceted Diversity, recommends a set of papers that match demographic parity, selecting authors who are 42.50% more diverse with a 2.45% gain in utility. This approach could be applied when selecting conference papers, journal papers, grant proposals, or other tasks within academia

Sea level rise adaptation: emerging lessons for local policy development

Many coastal communities across the United States are beginning to plan for climate-related sea level rise. While impacts and solutions will vary with local conditions, jurisdictions which have begun this process seem to pass through three common stages when developing policy for local sea level rise adaptation: l) building awareness about local sea level rise threats, 2) undertaking analyses of local vulnerabilities, and 3) developing plans and policies to deal with these vulnerabilities. The purpose of this paper is to help advance community dialogue and further inform local decision-makers about key elements and steps for addressing climate-related sea level rise. It summarizes the results of a project the Marine Policy Institute (MPI) undertook during 2011-12 to review experiences from fourteen U.S. coastal jurisdictions representing a variety of city, county, and state efforts with sea level adaptation. There are many more initiatives underway than those reflected in this sample, but the “focus jurisdictions” were selected because of the extensive information publically available on their experiences and lessons being learned that could provide insights for coastal communities, especially in Southwest Florida