Charalampos Bratsas

On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications

Recent neuroscience findings demonstrate the fundamental role of emotion in the maintenance of physical and mental health. In the present study, a novel architecture is proposed for the robust discrimination of emotional physiological signals evoked upon viewing pictures selected from the International Affective Picture System (IAPS). Biosignals are multichannel recordings from both the central and the autonomic nervous systems. Following the bidirectional emotion theory model, IAPS pictures are rated along two dimensions, namely, their valence and arousal. Following this model, biosignals in this paper are initially differentiated according to their valence dimension by means of a data mining approach, which is the C4.5 decision tree algorithm. Then, the valence and the gender information serve as an input to a Mahalanobis distance classifier, which dissects the data into high and low arousing. Results are described in Extensible Markup Language (XML) format, thereby accounting for platform independency, easy interconnectivity, and information exchange. The average recognition (success) rate was 77.68% for the discrimination of four emotional states, differing both in their arousal and valence dimension. It is, therefore, envisaged that the proposed approach holds promise for the efficient discrimination of negative and positive emotions, and it is hereby discussed how future developments may be steered to serve for affective healthcare applications, such as the monitoring of the elderly or chronically ill people.

Internationalization of Linked Data: The case of the Greek DBpedia edition

This paper describes the deployment of the Greek DBpedia and the contribution to the DBpedia information extraction framework with regard to internationalization (I18n) and multilingual support. I18n filters are proposed as pluggable components in order to address issues when extracting knowledge from non-English Wikipedia editions. We report on our strategy for supporting the International Resource Identifier (IRI) and introduce two new extractors to complement the I18n filters. Additionally, the paper discusses the definition of Transparent Content Negotiation (TCN) rules for IRIs to address de-referencing and IRI serialization problems. The aim of this research is to establish best practices (complemented by software) to allow the DBpedia community to easily generate, maintain and properly interlink language-specific DBpedia editions. Furthermore, these best practices can be applied for the publication of Linked Data in non-Latin languages in general.

KnowBaSICS-M: An ontology-based system for semantic management of medical problems and computerised algorithmic solutions

In this paper, an ontology-based system (KnowBaSICS-M) is presented for the semantic management of Medical Computational Problems (MCPs), i.e., medical problems and computerised algorithmic solutions. The system provides an open environment, which: (1) allows clinicians and researchers to retrieve potential algorithmic solutions pertinent to a medical problem and (2) enables incorporation of new MCPs into its underlying Knowledge Base (KB). KnowBaSICS-M is a modular system for MCP acquisition and discovery that relies on an innovative ontology-based model incorporating concepts from the Unified Medical Language System (UMLS). Information retrieval (IR) is based on an ontology-based Vector Space Model (VSM) that estimates the similarity among user-defined MCP search criteria and registered MCP solutions in the KB. The results of a preliminary evaluation and specific examples of use are presented to illustrate the benefits of the system. KnowBaSICS-M constitutes an approach towards the construction of an integrated and manageable MCP repository for the biomedical research community.

A semantic wiki within moodle for Greek medical education

Medical education requires a learning environment that enables medical students to acquire knowledge in a “hands on” and organized way. This, in turn, requires that content can be accessed, evaluated, organized and reused with ease by the students. Social Software (i.e. Weblogs, Wikis, ePortfolios, Instant Messaging) and Semantic Web technology could play an important role in such learning environments. Where Social Software gives users freedom to choose their own processes and supports the collaboration of people anytime, anywhere, Semantic Web technology gives the possibility to structure information for easy retrieval, reuse, and exchange between different systems and tools. In this article a very specific technology that combines Social Software and the Semantic Web, that is Semantic Wikis are presented, together with their possible role in medical education Moreover the first Medical Semantic Wiki in Greek Language and its use in medical education are illustrated.

Smart City Ontologies: Improving the effectiveness of smart city applications

This paper addresses the problem of low impact of smart city applications observed in the fields of energy and transport, which constitute high-priority domains for the development of smart cities. However, these are not the only fields where the impact of smart cities has been limited. The paper provides an explanation for the low impact of various individual applications of smart cities and discusses ways of improving their effectiveness. We argue that the impact of applications depends primarily on their ontology, and secondarily on smart technology and programming features. Consequently, we start by creating an overall ontology for the smart city, defining the building blocks of this ontology with respect to the most cited definitions of smart cities, and structuring this ontology with the Protege 5.0 editor, defining entities, class hierarchy, object properties, and data type properties. We then analyze how the ontologies of a sample of smart city applications fit into the overall Smart City Ontology, the consistency between digital spaces, knowledge processes, city domains targeted by the applications, and the types of innovation that determine their impact. In conclusion, we underline the relationships between innovation and ontology, and discuss how we can improve the effectiveness of smart city applications, combining expert and user-driven ontology design with the integration and or-chestration of applications over platforms and larger city entities such as neighborhoods, districts, clusters, and sectors of city activities.

An Integrated Approach to Emotion Recognition for Advanced Emotional Intelligence

Emotion identification is beginning to be considered as an essential feature in human-computer interaction. However, most of the studies are mainly focused on facial expression classifications and speech recognition and not much attention has been paid until recently to physiological pattern recognition. In this paper, an integrative approach is proposed to emotional interaction by fusing multi-modal signals. Subjects are exposed to pictures selected from the International Affective Picture System (IAPS). A feature extraction procedure is used to discriminate between four affective states by means of a Mahalanobis distance classifier. The average classifications rate (74.11%) was encouraging. Thus, the induced affective state is mirrored through an avatar by changing its facial characteristics and generating a voice message sympathising with the users mood. It is argued that multi-physiological patterning in combination with anthropomorphic avatars may contribute to the enhancement of affective multi-modal interfaces and the advancement of machine emotional intelligence.

Ontology-based Vector Space Model and Fuzzy Query Expansion to Retrieve Knowledge on Medical Computational Problem Solutions

Medical Computational Problem (MCP) solving is related to medical problems and their computerized algorithmic solutions. In this paper, an extension of an ontology-based model to fuzzy logic is presented, as a means to enhance the information retrieval (IR) procedure in semantic management of MCPs. We present herein the methodology followed for the fuzzy expansion of the ontology model, the fuzzy query expansion procedure, as well as an appropriate ontology-based Vector Space Model (VSM) that was constructed for efficient mapping of user-defined MCP search criteria and MCP acquired knowledge. The relevant fuzzy thesaurus is constructed by calculating the simultaneous occurrences of terms and the term-to-term similarities derived from the ontology that utilizes UMLS (Unified Medical Language System) concepts by using Concept Unique Identifiers (CUI), synonyms, semantic types, and broader- narrower relationships for fuzzy query expansion. The current approach constitutes a sophisticated advance for effective, semantics-based MCP-related IR.

Towards exergaming commons: composing the exergame ontology for publishing open game data

Background It has been shown that exergames have multiple benefits for physical, mental and cognitive health. Only recently, however, researchers have started considering them as health monitoring tools, through collection and analysis of game metrics data. In light of this and initiatives like the Quantified Self, there is an emerging need to open the data produced by health games and their associated metrics in order for them to be evaluated by the research community in an attempt to quantify their potential health, cognitive and physiological benefits. Methods We have developed an ontology that describes exergames using the Web Ontology Language (OWL); it is available at http://purl.org/net/exergame/ns#. After an investigation of key components of exergames, relevant ontologies were incorporated, while necessary classes and properties were defined to model these components. A JavaScript framework was also developed in order to apply the ontology to online exergames. Finally, a SPARQL Endpoint is provided to enable open data access to potential clients through the web. Results Exergame components include details for players, game sessions, as well as, data produced during these game-playing sessions. The description of the game includes elements such as goals, game controllers and presentation hardware used; what is more, concepts from already existing ontologies are reused/repurposed. Game sessions include information related to the player, the date and venue where the game was played, as well as, the results/scores that were produced/achieved. These games are subsequently played by 14 users in multiple game sessions and the results derived from these sessions are published in a triplestore as open data. Conclusions We model concepts related to exergames by providing a standardized structure for reference and comparison. This is the first work that publishes data from actual exergame sessions on the web, facilitating the integration and analysis of the data, while allowing open data access through the web in an effort to enable the concept of Open Trials for Active and Healthy Ageing.

Federating learning management systems for medical education: A persuasive technologies perspective

In the current explosion of internet technologies and exponentially increasing usage of the web there exists a need for effective searching and retrieval of information and resources. This is particularly true in medical and health care education. In the mEducator EUC funded project (www.mEducator.net), the collbaorators are attempting to develop technologies that will enable the above notion by means of multiple technological approaches, including one based on Web 2.0 principles and mashup technologies, and another based on web 3.0 and semantic (or linked) services. In this paper we view this problem through the recent notions of persuasive technologies and we explain how these are taken into account in the problem of metadata completion, a necessary action for linking resources in open educational repositories or learning management systems. The basic principles of persuasive technologies taken into account together with their application in the above field are explained and demonstrated with examples

Bringing mathematics to the web of data: the case of the mathematics subject classification

The Mathematics Subject Classification (MSC), maintained by the American Mathematical Societys Mathematical Reviews (MR) and FIZ Karlsruhes Zentralblatt fur Mathematik (Zbl), is a scheme for classifying publications in mathematics. While it is widely used, its traditional, idiosyncratic conceptualization and representation did not encourage wide reuse on the Web, and it made the scheme hard to maintain. We have reimplemented its current version MSC2010 as a Linked Open Dataset using SKOS, and our focus is concentrated on turning it into the new MSC authority. This paper explains the motivation and details of our design considerations and how we realized them in the implementation, presents use cases, and future applications.