CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

We describe the state-of-the-art in performance modeling and prediction for Information Retrieval (IR), Natural Language Processing (NLP) and Recommender Systems (RecSys) along with its shortcomings and strengths. We present a framework for further research, identifying five major problem areas: understanding measures, performance analysis, making underlying assumptions explicit, identifying application features determining performance, and the development of prediction models describing the relationship between assumptions, features and resulting performanc

From Evaluating to Forecasting Performance: How to Turn Information Retrieval, Natural Language Processing and Recommender Systems into Predictive Sciences

Non peer reviewe

Modelling Emotional Effects of Music: Key Areas of Improvement

Modelling emotions perceived in music and induced by music has garnered increased attention during the last five years. The present paper attempts to put together observations of the areas that need attention in order to make progress in the modelling emotional effects of music. These broad areas are divided into theory, data and context, which are reviewed separately. Each area is given an overview in terms of the present state of the art and promising further avenues, and the main limitations are presented. In theory, there are discrepancies in the terminology and justifications for particular emotion models and focus. In data, reliable estimation of high-level musical concepts and data collection and evaluation routines require systematic attention. In context, which is the least developed area of modelling, the primary area of improvement is incorporating musical context (music genres) into the modelling emotions. In a broad sense, better acknowledgement of music consumption and everyday life context, such as the data provided by social media, may offer novel insights into the modelling emotional effects of music

Classification design : understanding the decisions between theory and consequence

Classification systems are systems of terms and term relationships intended to sort and gather like concepts and documents. These systems are ubiquitous as the substrate of our interactions with library collections, retail websites, and bureaucracies. Through their design and impact, classification systems share with other technologies an unavoidable though often ignored relationship to politics, power, and authority (Fleischmann & Wallace, 2007). Despite concern among scholars that classification systems embody values and bias, there is little work examining how these qualities are built into a classification system. Specifically, we do not adequately understand classification construction, in which classification designers make decisions by applying classification theory to the specific context of a project (Park, 2008). If systems embody values— particularly values that might either cause harm (Berman, 1971) or provide an additional means of communicating the creator’s position (Feinberg, 2007)— we must understand how and when the system takes on these qualities. This dissertation bridges critical classification theory with design-oriented classification theory. Where critical classification theory is concerned with the outcomes of classification system design, design-oriented classification theory is concerned with the correct processes by which to build a classification system. To connect the consequences of classification system design to designers’ methods and intentions, I use the research lens of infrastructure studies, particularly infrastructural inversion (Star & Ruhleder, 1996) or making visible the work behind infrastructures such as classification systems. Accordingly, my research focuses on designers’ decisions and rethinks our assumptions regarding the factors that classification designers consider in making their design decisions. I adopted an ethnographic approach to the study of classification design that would make visible design decisions and designers’ consideration of factors. Using this approach, I studied the daily design work of volunteer classification designers who maintain a curated folksonomy. Using the grounded theory method (Strauss & Corbin, 1998), I analyzed the designers’ decisions. My analysis identified the implications of the designers’ convergences and divergences from established classification methods for the character of the system and for the connection between classification theory and classification methods. I show how the factors—and the prioritization of factors—that these designers considered in making their decisions were consistent with the values and needs of the community. Therefore, I argue that classification designers have an important role in creating the values or bias of a classification system. In particular, designers’ divergence from universal guidelines and designers’ choices among sources of evidence represent opportunities to align a classification system to its community. I recommend that classification research focus on such instances of divergence and choice to understand the connection between classification design and the values of classification systems. The Introduction motivates the problem space around values in classification systems and outlines my approach in focusing on classification design. The Literature Review outlines the dominant theories in classification scholarship according to three elements of classification design: what decisions designers make, what information designers use in their decisions, and what skills designers apply to their decisions. In the Methods chapter, I introduce the site of my ethnographic research (The Fanwork Repository), detail my ethnographic methods, summarize the types of data I collected, and describe my grounded analysis. Three findings chapters examine one type of complex decision each: Names, Works, and Guidelines, respectively. In the fourth findings chapter, Synthesis, I define 10 factors designers considered across these complex design decisions. I then discuss how the factors figured into complex design decisions, how the factors overlapped and conflicted in design decisions, and how designers understood their role in making complex design decisions. In the Discussion chapter I connect the findings from the site of my ethnography to classification scholarship. In the Conclusion, I consider the contribution of examining classification systems as infrastructure, highlight the differences in accounts of classification design decisions made visible through classification theory and infrastructure studies approaches, and present suggestions for future research in classification design and the study of classification systems as infrastructure.Informatio

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

Linking Research and Policy: Assessing a Framework for Organic Agricultural Support in Ireland

This paper links social science research and agricultural policy through an analysis of support for organic agriculture and food. Globally, sales of organic food have experienced 20% annual increases for the past two decades, and represent the fastest growing segment of the grocery market. Although consumer interest has increased, farmers are not keeping up with demand. This is partly due to a lack of political support provided to farmers in their transition from conventional to organic production. Support policies vary by country and in some nations, such as the US, vary by state/province. There have been few attempts to document the types of support currently in place. This research draws on an existing Framework tool to investigate regionally specific and relevant policy support available to organic farmers in Ireland. This exploratory study develops a case study of Ireland within the framework of ten key categories of organic agricultural support: leadership, policy, research, technical support, financial support, marketing and promotion, education and information, consumer issues, inter-agency activities, and future developments. Data from the Irish Department of Agriculture, Fisheries and Food, the Irish Agriculture and Food Development Authority (Teagasc), and other governmental and semi-governmental agencies provide the basis for an assessment of support in each category. Assessments are based on the number of activities, availability of information to farmers, and attention from governmental personnel for each of the ten categories. This policy framework is a valuable tool for farmers, researchers, state agencies, and citizen groups seeking to document existing types of organic agricultural support and discover policy areas which deserve more attention