Sotiris Diplaris

Protein classification with multiple algorithms

Nowadays, the number of protein sequences being stored in central protein databases from labs all over the world is constantly increasing. From these proteins only a fraction has been experimentally analyzed in order to detect their structure and hence their function in the corresponding organism. The reason is that experimental determination of structure is labor-intensive and quite time-consuming. Therefore there is the need for automated tools that can classify new proteins to structural families. This paper presents a comparative evaluation of several algorithms that learn such classification models from data concerning patterns of proteins with known structure. In addition, several approaches that combine multiple learning algorithms to increase the accuracy of predictions are evaluated. The results of the experiments provide insights that can help biologists and computer scientists design high-performance protein classification systems of high quality.

Emerging, Collective Intelligence for Personal, Organisational and Social Use

The main objective of this chapter is to present novel technologies for exploiting multiple layers of intelligence from user-contributed content, which together constitute Collective Intelligence, a form of intelligence that emerges from the collaboration and competition among many individuals, and that seemingly has a mind of its own. User contributed content is analysed by integrating research and development in media analysis, mass content processing, user feedback, social analysis and knowledge management to automatically extract the hidden intelligence and make it accessible to end users and organisations. The exploitation of the emerging Collective Intelligence results is showcased in two distinct case studies: an Emergency Response and a Consumers Social Group case study.

SocialSensor: sensing user generated input for improved media discovery and experience

SocialSensor will develop a new framework for enabling real-time multimedia indexing and search in the Social Web. The project moves beyond conventional text-based indexing and retrieval models by mining and aggregating user inputs and content over multiple social networking sites. Social Indexing will incorporate information about the structure and activity of the users social network directly into the multimedia analysis and search process. Furthermore, it will enhance the multimedia consumption experience by developing novel user-centric media visualization and browsing paradigms. For example, SocialSensor will analyse the dynamic and massive user contributions in order to extract unbiased trending topics and events and will use social connections for improved recommendations. To achieve its objectives, SocialSensor introduces the concept of Dynamic Social COntainers (DySCOs), a new layer of online multimedia content organisation with particular emphasis on the real-time, social and contextual nature of content and information consumption. Through the proposed DySCOs-centered media search, SocialSensor will integrate social content mining, search and intelligent presentation in a personalized, context and network-aware way, based on aggregation and indexing of both UGC and multimedia Web content.

WikiRate.org – Leveraging Collective Awareness to Understand Companies’ Environmental, Social and Governance Performance

WikiRate is a Collective Awareness Platform for Sustainability and Social Innovation (CAPS) project with the aim of “crowdsourcing better companies” through analysis of their Environmental Social and Governance (ESG) performance. Research to inform the design of the platform involved surveying the current corporate ESG information landscape, and identifying ways in which an open approach and peer production ethos could be effectively mobilised to improve this landscape’s fertility. The key requirement identified is for an open public repository of data tracking companies’ ESG performance. Corporate Social Responsibility reporting is conducted in public, but there are barriers to accessing the information in a standardised analysable format. Analyses of and ratings built upon this data can exert power over companies’ behaviour in certain circumstances, but the public at large have no access to the data or the most influential ratings that utilise it. WikiRate aims to build an open repository for this data along with tools for analysis, to increase public demand for the data, allow a broader range of stakeholders to participate in its interpretation, and in turn drive companies to behave in a more ethical manner. This paper describes the quantitative Metrics system that has been designed to meet those objectives and some early examples of its use.

Making sense of it all: an attempt to aid journalists in analysing and filtering user generated content

This position paper explores how journalists can embrace new ways of content provision and authoring, by aggregating and analyzing content gathered from Social Media. Current challenges in the news media industry are reviewed and a new system for capturing emerging knowledge from Social Media is described. Novel features that assist professional journalists in processing sheer amounts of Social Media information are presented with a reference to the technical requirements of the system. First implementation steps are also discussed, particularly focusing in event detection and user influence identification.

Collective Intelligence in Mobile Consumer Social Applications

This paper presents a mobile software application for the provision of mobile guidance, supporting functionalities, which are based on automatically extracted Collective Intelligence. Collective Intelligence is the intelligence which emerges from the collaboration, competition and coordination among individuals and can be extracted by the analysis of mass amount of user-contributed data currently available in Web 2.0 applications. More specifically, services including automatic Point of Interest (POI) detection, raking, search and aggregation with semi-structured sources (e.g. Wikipedia) are developed, which are based on lexical and statistical analysis of mass data coming from Wikipedia, Yahoo! Geoplanet, query logs and flickr tags. These services together with personalization functionalities are integrated in a travel mobile application, enabling their efficient usage exploiting on the same time user location information. Evaluation with real users depicts the application’s potential for providing a higher degree of satisfaction compared to existing travel information management solutions and also directions for future enhancements.

easIE: Easy-to-Use Information Extraction for Constructing CSR Databases From the Web

Public awareness of and concerns about companies’ social and environmental impacts have seen a marked increase over recent decades. In parallel, the quantity of relevant information has increased, as states pass laws requiring certain forms of reporting, researchers investigate companies’ performance, and companies themselves seek to gain a competitive advantage by being seen to operate fairly and transparently. However, this information is typically dispersed and non-standardized, making it complicated to collect and analyze. To address this challenge, the WikiRate platform aims to collect this information and store it in a standardized format within a centralized public repository, making it much more amenable to analysis. In the context of WikiRate, this article introduces easIE, an easy-to-use information extraction (IE) framework that leverages general Web IE principles for building datasets with environmental, social, and governance information from the Web. To demonstrate the flexibility and value of easIE, we built a large-scale corporate social responsibility database comprising 654,491 metrics related to 49,009 companies spending less than 16 hours for data engineering, collection, and indexing. Finally, a data collection exercise involving 12 subjects was performed to showcase the ease of use of the developed framework.

Large-Scale Open Corporate Data Collection and Analysis as an Enabler of Corporate Social Responsibility Research

During the last years, citizens and transparency initiatives put increasing pressure on governments, organizations, and companies to be more transparent and to publicize information pertaining to their operations. Although several organizations have started engaging in open data practices, data quality, structure and availability is still highly inconsistent across organizations, which makes it challenging and effort-intensive to obtain and analyze large-scale high-quality datasets. To this end, this paper examines how publicly available financial and corporate data can be leveraged to extract useful inferences regarding the financial and social performance of companies. Numerous reports have been collected from the Securities Exchange Commission (SEC) and analyzed to study hypotheses regarding the corporate practices and social responsibility of companies.

Extracting Emergent Semantics from Large-Scale User-Generated Content

This paper presents a survey of novel technologies for uncovering implicit knowledge through the analysis of user-contributed content in Web2.0 applications. The special features of emergent semantics are herein described, along with the various dimensions that the techniques should be able to handle. Consequently a series of application domains is given where the extracted information can be consumed. The relevant techniques are reviewed and categorised according to their capability for scaling, multi-modal analysis, social networks analysis, semantic representation, real-time and spatio-temporal processing. A showcase of such an emergent semantics extraction application, namely ClustTour, is also presented, and open issues and future challenges in this new field are discussed.

The STEP Project: Societal and Political Engagement of Young People in Environmental Issues

Decisions on environmental topics taken today are going to have long-term consequences that will affect future generations. Young people will have to live with the consequences of these decisions and undertake special responsibilities. Moreover, as tomorrow’s decision makers, they themselves should learn how to negotiate and debate issues before final decisions are made. Therefore, any participation they can have in environmental decision making processes will prove essential in developing a sustainable future for the community.