DOI: 10.3897/jucs.143553

Authors: Aluizio Haendchen Filho, Jonathan Nau, Hércules Antonio do Prado, Edilson Ferneda

Abstract: This paper explores Natural Language Processing (NLP) in automatic comprehension and discourse analysis, focusing on argument mining. While previous works have focused on English, this study addresses the lack of adequate corpora and methodologies for Brazilian Portuguese. The researchers employed a corpus of essays from Brazil's National High School Exam (ENEM) to investigate the impact of discourse markers on identifying argumentative structures using feature engineering with machine learning. The proposed methodology offers key advantages over transformer-based approaches: enhanced interpretability of feature selection, computational efficiency, and improved adaptability across different linguistic domains. By systematically 'opening the black box' of machine learning models, this approach provides insights into the discourse marker identification decision-making process, in contrast to the opaque neural network models. Unlike the transformers-based solutions, this approach offers a transparent solution based on feature engineering allowing insights into the linguistic patterns underlying argumentative structures in Portuguese. While acknowledging the relatively small corpus size as a limitation, the researchers suggest that future work should focus on expanding the dataset for further evaluation. This work lays the groundwork for advancing NLP in Portuguese by providing valuable features and methodologies for feature engineering in automated linguistic analysis tasks such as essay scoring, opinion mining, and text summarization. The findings demonstrated a significant breakthrough, revealing that a concise set of only five argument mining-derived features dramatically improved the model accuracy, surpassing the performance of an initial, extensive set of over 600 features. These features specifically enhanced the evaluation of Competence 5, which assesses students' ability to develop intervention proposals grounded in scientific concepts.

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DOI: 10.3897/jucs.68852

Authors: Agnes Kukulska-Hulme, Jorge Arus-Hota, Jesus Garcia Laborda

Abstract: The emerging paradigm of mobile open social learning for languages (MOSL4L) integrates the three elements of mobile, open and social, and in so doing it creates the idea of a conceptually different language learning space. It is a space full of opportunity and challenge, relevant to a post-pandemic world in which we are looking for ways to build back better. The paper discusses tensions between formal and informal language learning and the nature of learning outcomes in MOSL4L. It focuses on the needs of individuals while also considering the characteristics of the virtual spaces in which they participate. It highlights the potential of micro experiences and learning moments as structures that are well aligned with MOSL4L. It suggests developments in language curricula to take account of communication challenges being identified in the contemporary world. Many more new learning designs and software architectures will have to be developed to match the possibilities generated by the MOSL4L space.

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DOI: 10.3897/jucs.2020.062

Authors: Angela Fessl, Viktoria Pammer-Schindler, Kai Pata, Sandra Feyertag, Mati Mõttus, Jörgen Janus, Tobias Ley

Abstract: This paper presents cooperative design as method to address the needs of SMEs to gain sufficient knowledge about new technologies in order for them to decide about adoption for knowledge management. We developed and refined a cooperative design method iteratively over nine use cases. In each use case, the goal was to match the SME's knowledge management needs with offerings of new (to the SMEs) technologies. Where traditionally, innovation adoption and diffusion literature assume new knowledge to be transferred from knowledgeable stakeholders to less knowledgeable stakeholders, our method is built on cooperative design. In this, the relevant knowledge is constructed by the SMEs who wish to decide upon the adoption of novel technologies through the cooperative design process. The presented method is constituted of an analysis stage based on activity theory and a design stage based on paper prototyping and design workshops. In all nine cases, our method led to a good understanding a) of the domain by researchers - validated by the creation of meaningful first-version paper prototypes and b) of new technologies - validated by meaningful input to design and plausible assessment of technologies' benefit for the respective SME. Practitioners and researchers alike are invited to use the here documented tools to cooperatively match the domain needs of practitioners with the offerings of new technologies. The value of our work lies in providing a concrete implementation of the cooperative design paradigm that is based on an established theory (activity theory) for work analysis and established tools of cooperative design (paper prototypes and design workshops as media of communication); and a discussion based on nine heterogeneous use cases.

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DOI: 10.3897/jucs.2020.032

Authors: João Gabriel Lopes De Oliveira, Pedro Moreira Menezes Da Costa, Flavio De Mello

Abstract: Artificial Intelligence (AI) pervades industry, entertainment, transportation, finance, and health. It seems to be in a kind of golden age, but today AI is based on the strength of techniques that bear little relation to the thought mechanism. Contemporary techniques of machine learning, deep learning and case-based reasoning seem to be occupied with delivering functional and optimized solutions, leaving aside the core reasons of why such solutions work. This paper, in turn, proposes a theoretical study of perception, a key issue for knowledge acquisition and intelligence construction. Its main concern is the formal representation of a perceived phenomenon by a casual observer and its relationship with machine intelligence. This work is based on recently proposed geometric theory, and represents an approach that is able to describe the inuence of scope, development paradigms, matching process and ground truth on phenomenon perception. As a result, it enumerates the perception variables and describes the implications for AI.

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DOI: 10.3897/jucs.2020.030

Authors: Sana Debbech, Simon Collart-Dutilleul, Philippe Bon

Abstract: Dysfunctional analysis is an essential and demanding task in the early development stages of safety-critical systems (SCSs). Nevertheless, current practices present several drawbacks. Generally, a common dysfunctional analysis conceptualization is missing and it is dependent on safety analysis techniques. Moreover, some safety analysis methods require well-known system behaviors expressed by dynamic models such as sequence diagrams and finite automata. However, the dynamic character of these models increases their susceptibility to changes and then they are not obtainable in the early design stages. Since dysfunctional analysis highly relies on the experience of safety analysts and the feedback (REX) obtained from previous systems development, there is a need to formalize this knowledge domain in a structured way to ensure its future reuse. Furthermore, safety measures derived from this dysfunctional analysis approach must be strongly linked to a goal-oriented perspective and adapted to a specific context. For this purpose, this paper presents a real-world semantics interpretation and conceptualization of dysfunctional analysis related concepts based on the Unified Foundational Ontology (UFO) and well-known standards to avoid ambiguities. The proposed Dysfunctional Analysis Ontology (DAO) aims to provide a systematization of the goal-oriented dysfunctional analysis through a terminological clarification in order to prevent hazards in the first design phases. Then, a DAO formalization is proposed using the Web Ontology Language (OWL). Finally, the DAO pattern is applied to two different real critical scenarios from the railway domain in order to illustrate and evaluate this ontological approach.

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DOI: 10.3217/jucs-025-10-1373

Authors: Khaled Halimi, Hassina Seridi-Bouchelaghem

Abstract: The aim of the paper is to describe the design of a Web 3.0-based Intelligent Learning System (ILS) that addressing the students' needs in the 21st century. The design is based theoretically, on the principles of the connectivism theory and technically, it implements the semantic web representations combining with the use of learning analytics techniques. The work emphasises that implementing a learning analytics approach that uses: text classification, sentiment analysis, topics extraction, and text clustering on the basis of a semantic web and ontologies can support the connectivist learning. The semantic learning analytics process, represents the key element of the proposed intelligent learning analytics system to infer and deduce hidden data in the massive learning data thanks to semantic models of i-SoLearn. The aim is to guide students to understand through recommendations, charts and visualisations their learning behaviour and to give teachers feedbacks, enabling them to examine both students' learning and activities. An experimental study using i-SoLearn (an intelligent social learning environment), indicates that designing an ILS based on Web 3.0 techniques is effective and expected to show a great advantage in enhancing the connectivist learning of students in the digital age.

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DOI: 10.3217/jucs-025-08-1018

Authors: Rodrigo Barbosa Sounited States Of America Orrego, Jorge Luis Victória Barbosa

Abstract: Resources of a city are urban assets such as hospitals and pharmacies (health facilities) or accessible ramps and adapted toilets (accessibility resources). This paper addresses the problem of resource management for smart cities combining crowdsourcing with gamification, and proposes a model called CORE-MM. This model allows the use of crowdsourcing techniques so that the management of cities resources is done by the citizens, without having to rely on an organization or public administration. To encourage participation in this resource management, this model also uses techniques of gamification. CORE-MM proposes the use of crowdsourcing integrated with gamification to manage the resources of a smart city, with two interdependent objectives: to motivate the use of the system by the users, and to encourage their participation in the sharing and management of information. The scientific contribution of this work is that CORE-MM treats the resource management considering a generic resources approach for smart cities. A prototype of CORE-MM was offered to volunteers and a questionnaire was developed to collect data and to evaluate the model, its performance and relevance. Results with volunteers indicated good perceived ease of use and good perceived utility. From the affirmations of the questionnaire that the 10 volunteers that tested the CORE-MM prototype had to answer, 91.67% agreed on the ease of use of the system and 8.33% manifested indifference in their responses. Regarding the utility of the system, 99.17% agreed and only 0.83% were indifferent. These results point to positive perspectives regarding the use of the application in possible situations and real locations.

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DOI: 10.3217/jucs-024-11-1651

Authors: Yoan Gutiérrez, Elena Lloret, José Gómez

Abstract: Ontologies are appropriate structures for capturing and representing the knowledge about a domain or task. However, the design and further population of them are both di_cult tasks, normally addressed in a manual or in a semi-automatic manner. The goal of this article is to de_ne and extend a task-oriented ontology schema that semantically represents the information contained in texts. This information can be extracted using Human Language Technologies, and throughout this work, the whole process to design such ontology schema is described. Then, we also describe an algorithm to automatically populate ontologies based our Human Language Technology oriented schema, avoiding the unnecessary duplication of instances, and having as a result the required information in a more compact and useful format ready to exploit. Tangible results are provided, such as permanent online access points to the ontology schema, an example bucket (i.e. ontology instance repository) based on a real scenario, and a documentation Web page.

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DOI: 10.3217/jucs-024-07-0935

Authors: Alaa Qaffas, Alexandra Cristea, Mohamed Mead

Abstract: Adaptive online advertising is a rapidly expanding marketing tool that delivers personalised messages and adverts to Internet users. At a time when the Internet is burgeoning, many websites use an adaptation process to tailor their advertisements, however, often in an ad-hoc manner. Thus, a new model that guarantees a systematic integration of adaptive features on existing business websites has become an urgent requirement to satisfy customers. This paper aims to solve this issue, by presenting an innovative model for e-advertising adaptation: the Layered Adaptive Advertising Integration (LAAI). LAAI is building upon previous models and frameworks from different domains, by selecting and adding novel features appropriate for e-advertising. Based on this model, a new adaptation system -AEADS - is developed, to test and evaluate the LAAI model. This research also reports on the perception on the methods towards obtaining generalisation, portability and efficiency, as proposed by the LAAI model, by evaluating how a range of businesses are enabled to adapt their advertisements based on user profiles and behaviours.

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DOI: 10.3217/jucs-024-01-0043

Authors: Erick Nilsen Pereira De Souza, Daniela Claro, Rafael Glauber

Abstract: Open information extraction (Open IE) discovers facts as triples of relationships in texts. A major challenge to Open IE task is to reduce the proportion of invalid extractions. Current methods based on a set of specific features eliminate many inconsistent and incomplete facts. However, these solutions have the disadvantage of being highly language-dependent. This dependence arises from the difficulty in finding the most representative set of features, considering the peculiarities of each language. These solutions require extensive training sets, usually produced with the aid of a specialized linguistic knowledge. Furthermore, although linguistic knowledge resources are common in English, they are scarce in most other languages. Therefore, we propose a method for classifying extracted facts based on the similarity of grammatical structures, which builds models from morphological structures contained in the extraction through the application of algorithms for the detection of isomorphism in sub-graphs. In particular, Portuguese was chosen for the implementation and validation of the proposed approach as it is one of the languages that lack this type of resource.

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DOI: 10.3217/jucs-023-08-0755

Authors: Akrivi Krouska, Christos Troussas, Maria Virvou

Abstract: Twitter is a highly popular social networking service and a web-based communication platform with million users exchanging daily public messages, namely tweets, expressing their opinion and feelings towards various issues. Twitter represents one of the largest and most dynamic datasets for data mining and sentiment analysis. Therefore, Twitter Sentiment Analysis constitutes a prominent and an active research area with significant applications in industry and academia. The purpose of this paper is to provide a guideline for the decision of optimal algorithms for sentiment analysis services. In this context, five well-known learning-based classifiers (Naive Bayes, Support Vector Machine, k-Nearest Neighbor, Logistic Regression and C4.5) and a lexicon-based approach (SentiStrength) have been evaluated based on confusion matrices, using three different datasets (OMD, HCR and STS-Gold) and two test models (percentage split and cross validation). The results demonstrate the superiority of Naive Bayes and Support Vector Machine regardless of datasets and test methods.

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DOI: 10.3217/jucs-022-05-0709

Authors: Evangelos Kalampokis, Areti Karamanou, Efthimios Tambouris, Konstantinos Tarabanis

Abstract: Billions of people everyday use Social Media (SM), such as Facebook and Twitter, to express their opinions and experiences with brands. Companies are highly interested in understanding such SM brand-related content. Consequently, many studies have been conducted and many applications have been developed to analyse this content. For analysis purposes, the main SM metrics used include volume and sentiment. Interestingly, however, brand equity theory proposes different metrics for assessing brand reputation. These include brand image, brand satisfaction and purchase intention (henceforth referred to as marketing metrics). The objective of this paper is to explore the feasibility of applying marketing metrics in Twitter brand-related content. For this purpose, we collect, study and analyse tweets that mention two brands, namely IKEA and Gatorade. The manual analysis suggests that a significant amount of brand tweets is related to brand image, brand satisfaction and purchase intention. We thereafter design an algorithm that classifies tweets into relevant categories to enable automatic marketing metrics computation. We implement the algorithm using statistical learning approaches and prove that its classification accuracy is good. We anticipate that this article will motivate other studies as well as applications' designers in adopting marketing theories when evaluating brand reputation through SM content.

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DOI: 10.3217/jucs-022-06-0742

Authors: Kyeong-Jin Oh, Myung-Duk Hong, Ui-Nyoung Yoon, Geun-Sik Jo

Abstract: Videos are one of the most frequently used forms of multimedia resources. People want to interact with videos to find a specific part or to obtain relevant information. To support user interactions, current videos should be transformed to interactive videos. This paper proposes an interactive cooking video system to generate automatically interactive cooking videos. To do this, the proposed system performs semantic video annotation on cooking videos. Semantic video annotation process includes three parts: synchronization between recipes and corresponding cooking videos based on a caption-recipe alignment algorithm, information extraction on food recipes using lexico-syntactic patterns, and semantic entity interconnection between recognized entities and semantic web entities. Cooking video annotation ontology is modeled to handle annotation data. To evaluate the proposed system, comparative experiments are performed on the caption-recipe alignment algorithm. The accuracy of information extraction and semantic entity interconnection is also measured. Experimental results show that the proposed system is superior to compared algorithms in alignment perspectives. Information extraction and semantic interconnection method also achieve high accuracy over 95%, respectively. Consequently, the proposed system generates interactive cooking videos in high accuracy and support user interactions by providing a user interface which allows users to easily find specific scenes and obtain detailed information on objects users have interested in.

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DOI: 10.3217/jucs-022-02-0247

Authors: Dragos Datcu, Stephan Lukosch, Heide Lukosch

Abstract: While augmented reality research has grown into a mature field over the last years, the aspects of situational awareness and presence of augmented reality (AR) are still quite open research topics. This paper introduces a collaborative game to explore the different perception of situational awareness and presence in a physical and an AR environment. The game is employed as an approximation of collaboratively solving complex problems. The goal of the game is to jointly build a tower with either physical blocks in a physical environment or virtual blocks in an augmented reality environment. A first study with 18 users shows the feasibility of the game and questionnaire design for studying the different perception of situational awareness and presence in a physical and an AR environment. The study further identifies necessary future research with regard to the perception of presence and awareness in AR.

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DOI: 10.3217/jucs-021-01-0093

Authors: Benham Taraghi, Anna Saranti, Martin Ebner, Vinzent Müller, Arndt Großmann

Abstract: Learner profiling is a methodology that draws a parallel from user profiling. Implicit feedback is often used in recommender systems to create and adapt user profiles. In this work the implicit feedback is based on the learner's answering behaviour in the Android application UnlockYourBrain, which poses different basic mathematical questions to the learners. We introduce an analytical approach to model the learners' profile according to the learner's answering behaviour. Furthermore, similar learner's profiles are grouped together to construct a learning behaviour cluster. The choice of hierarchical clustering as a means of classification of learners' profiles derives from the observations of learners behaviour. This in turn reflects the similarities and subtle differences of learner behaviour, which are further analysed in more detail. Building awareness about the learner's behaviour is the first and necessary step for future learning-aware applications.

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DOI: 10.3217/jucs-020-15-2080

Authors: Yen-Chun Wu, Chan-Lan Chang, Ying-Jiun Hsieh

Abstract: The purpose of the study is to propose a completely barrier-less, or "accessible" tourism platform and makes suggestions to facilitate current travel information for disabled persons. Drawing on in-depth interviews with tourism industry representatives and academics, and applying Long Tail theory's three "forces" and nine "rules", the study makes assessments as to the viability of the disabled tourism niche, creating an accessible tourism communication network to connect upstream and downstream travel agencies in a Web 2.0 environment. Web 2.0 is an appropriate platform that can be seen as a launching pad or accelerator to share knowledge between business and customer, as well as customer and customer. This platform can provide adoptive learning experiences and behavioural enhancements. This study sets up an accessible tourism communications network based on Web 2.0 concepts, contributing a real platform that travel agents can refer to as they take their first steps to provide travel packages that accommodate the needs of the disabled, a previously marginally represented group in the sustainable tourism literature.

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DOI: 10.3217/jucs-020-10-1443

Authors: María García-Álvarez, Laura Varela-Candamio, Isabel Novo-Corti

Abstract: The current higher education programs use information and communication technologies to conduct interactive teaching and learning activities. This paper creates an educational method based on an Interaction Analysis Model through the use of chats in higher education. Compared to the traditional functions of the chats in education, our proposal introduces discussions of current economic events and real cases. This contributes to develop the problem-based learning and leads to students not only to improve their knowledge but to develop skills such as teamwork or leadership, which should be important characteristics of a graduate in Business Degree. As a result, students transfer their knowledge to solve current case studies improving their interest in the subject greatly and, therefore, their motivation and the social knowledge construction of the whole group.

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DOI: 10.3217/jucs-019-13-1914

Authors: Carlos García-Moreno, Yolanda Hernández-González, Miguel Rodríguez-García, José Miñarro-Giménez, Rafael Valencia-García, Angela Almela

Abstract: Innovation is one of the keys to success in business and industry world, especially within the current economic context. R&D projects are a building-block in the innovation process, hence the importance of managing them efficiently. Ontologies and semantic technologies have proven highly effective at this task. Within this context, the present study explores the use of ontologies to model R&D related data and the application of semantic technologies to the building of an enhanced management system. Findings confirm the success of the system proposed, and reveal that it may bring considerable benefits to project management, such as the definition of a completely explicit information model and improved management capabilities.

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DOI: 10.3217/jucs-019-12-1808

Authors: Josiane Michalak Hauagge Dall Agnol, Cesar Tacla

Abstract: This paper proposes a framework of the negotiation process for solving divergences in the collaborative ontology development. Such framework is obtained through the use of philosophical principles deriving from the theories of essence, identity, unity and dependence (preconized by the OntoClean methodology) as to justify part of the argumentation used in the negotiation process among the participants, besides helping reach a consensus and reduce the conceptual gap among models. The evaluation of the experiments conducted with the use of the proposed method suggests the feasibility and implementability of our approach in practice.

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DOI: 10.3217/jucs-019-11-1661

Authors: Francisco García-Peñalvo, Miguel Conde, Valentina Zangrando, Alicia García-Holgado, Anton Seoane, Marc Forment, Nikolas Galanis, Francis Brouns, Hubert Vogten, David Griffiths, Aleksandra Mykowska, Gustavo Ribeiro-Alves, Miroslav Minović

Abstract: The application of information technologies (specially the Internet, Web 2.0 and social tools) make informal learning more visible. This kind of learning is not linked to an institution or a period of time, but it is important enough to be taken into account. On the one hand, learners should be able to communicate to the institutions they are related to, what skills they possess, whether they were achieved in a formal or informal way. On the other hand the companies and educational institutions need to have a deeper knowledge about the competencies of their staff. The TRAILER project provides a methodology supported by a technological framework to facilitate communication about informal learning between businesses, employees and learners. The paper presents the project and some of the work carried out, an exploratory analysis about how informal learning is considered and the technological framework proposed. Whilst challenges remain in terms of establishing the meaningfulness of technological engagement for employees and businesses, the continuing transformation of the social, technological and educational environment is likely to lead to greater emphasis for the effective exploitation of informal learning.

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DOI: 10.3217/jucs-019-09-1257

Authors: Pilar Garrido, Irene Ruiz, Miguel Gómez-Nieto

Abstract: This paper describes an alert system for people monitoring based on multi-agent using maps. This system monitors the users' physical context using their mobile phone. The data acquisition is made using the available sensors on mobile phone. A set of agents on mobile phones are responsible for collecting, processing and sending data to the server. Another set of agents on server stores the data and checks the preconditions of the restrictions associated with the user, in order to trigger the appropriate alarms. These alarms are sent not only to the user that violates a restriction, but also to the one responsible for supervising the person monitored. The supervisor can control all the supervised people through a map interface with functionality such as sending a SMS or making a call directly from the map. The applicability of the system will be illustrated with an example for Alzheimer patient monitoring. These patients will carry on normal activity in the home environment or home for the elderly, monitored by their family or by nurses.

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DOI: 10.3217/jucs-018-16-2301

Authors: Atif Latif, Muhammad Afzal, Hermann Maurer

Abstract: The Linked Open Data project provides a new publishing paradigm for creating machine readable and structured data on the Web. Currently, the significant presence of data sets describing scholarly publications in the Linked Data cloud underpins the importance of Linked Data for the scientific community and for the open access movement. However, these semantically rich datasets need to be exploited and linked with real time applications. In the project we report on this. We have exploited numerous scholarly datasets and have created semantic links to papers in an online journal, particularly Journal of Universal Computer Science (J.UCS). The J. UCS plays an important part in the computer science publishing community and provides a number of innovative features and datasets to its web users. However, the legacy HTML format in which these features are made available makes it difficult for machines to understand and query. Keeping in mind the impressive benefits of the Linked Open Data project, this paper presents an approach to convert J.UCS legacy HTML data from its current form to machine understandable format (RDF). It also interlinks this data with other important Linked Data resources. The approach developed has successfully disambiguated and interlinked J.UCS authors and publications datasets with DBpedia, DBLP, CiteULike and faceted DBLP. Additionally, triplified and interlinked datasets are made available to the scientific and semantic web community for downloading and posing SPARQL queries. This semantically linked dataset can further be used by researchers and semantic agents to identify semantic associations, to build inferencing systems, and to extract useful knowledge.

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DOI: 10.3217/jucs-018-08-0973

Authors: Wilas Chamlertwat, Pattarasinee Bhattarakosol, Tippakorn Rungkasiri, Choochart Haruechaiyasak

Abstract: Traditional approaches for studying consumer behavior, such as marketing survey and focus group, require a large amount of time and resources. Moreover, some products, such as smartphones, have a short product life cycle. As an alternative solution, we propose a system, the Micro-blog Sentiment Analysis System (MSAS), based on sentiment analysis to automatically analyze customer opinions from the Twitter micro-blog service. The MSAS consists of five main functions to (1) collect Twitter posts, (2) filter for opinionated posts, (3) detect polarity in each post, (4) categorize product features and (5) summarize and visualize the overall results. We used the product domain of smartphone as our case study. The experiments on 100,000 collected posts related to smartphones showed that the system could help indicating the customers' sentiments towards the product features, such as Application, Screen, and Camera. Further evaluation by experts in smartphone industry confirmed that the system yielded some valid results.

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DOI: 10.3217/jucs-018-04-0577

Authors: Anna Zygmunt, Piotr Bródka, Przemysław Kazienko, Jarosław Koźlak

Abstract: Identifying key persons active in social groups in the blogosphere is performed by means of social network analysis. Two main independent approaches are considered in the paper: (i) discovery of the most important individuals in persistent social communities and (ii) regular centrality measures applied either to social groups or the entire network. A new method for separating of groups stable over time, fulfilling given conditions of activity level of their members is proposed. Furthermore, a new concept for extracting user roles and key persons in such groups is also presented. This new approach was compared to the typical clustering method and the structural node position measure applied to rank users. The experimental studies have been carried out on real two-year blogosphere data.

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DOI: 10.3217/jucs-017-14-1931

Authors: Ana Cristina B. Garcia, Adriana Vivacqua, Thiago Tavares

Abstract: Democratic governments constantly need to make sense of their citizens' needs to make appropriate decisions that reflect the overall wishes and needs of the population. However, except for mandatory voting scenarios, a low rate of citizen participation in government decisions through democratic processes is an aspect that defies democracy itself. Brazil's participatory budget policy emphasizes people's direct guidance regarding certain budget allocations though group meetings. This paper presents mParticipation, an agent-based model for eliciting and answering citizen demands in a participatory government structure using mobile technology. A prototype system applied to the domain of public budget allocation domain demonstrates that it is feasible to provide effective computational support to participatory collective decision-making.

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DOI: 10.3217/jucs-017-10-1430

Authors: Alice Comi, Martin Eppler

Abstract: As suggested by several scholars, inter-organizational collaboration is an important vehicle for innovation, but working across organizational boundaries entails great complexity. In this paper, we argue that visual facilitation may act as a catalyst of inter-organizational teamwork, leading to increased knowledge sharing quality (H1), team performance (H2) and satisfaction (H3). On the other hand, we suggest that the aesthetic beauty of visual representations may exert a manipulatory effect, inducing inter-organizational actors to overestimate the collaboration value potential (H4). We adopt an experimental design (N=145 participants) in order to assess the advantages and disadvantages of visual facilitation in inter-organizational teamwork. In particular, we compare inter-organizational teams working with i) software-based visualization, ii) poster-based visualization, and iii) text-based facilitation (control condition). By comparing results across the two treatment conditions (software and poster), we disentangle the effects of visual facilitation and computer interactivity, therefore making a unique contribution to research on information visualization. The experiment findings show that software-supported teams outperform the control groups in terms of performance (H2), and exhibit greater satisfaction with the inter-organizational meetings (H3). We extend our experimental study by conducting focus groups with 17 experiment participants to gain an in-depth understanding of the users' experience with the different support systems. After discussing relevant implications for both researchers and practitioners, we point out the limitations of our study and suggest directions for future research.

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DOI: 10.3217/jucs-017-10-1412

Authors: Rui Ferreira, Jorge Silva, Faimara do Rocio Strauhs, Antonio Soares

Abstract: Performance management in collaborative networks of organisations is a complex process due to the multiplicity of competing perspectives upon it. One of the more sensitive phases of this process is the agreement of the actors in the network regarding the performance evaluation model whose design is considered of great importance in the research literature. This paper proposes a method for the design of performance evaluation systems in collaborative networks through an innovative combination of performance information classification and multi-criteria decision model. The method is implemented in a web-based collaborative platform that enables the members of a collaborative network to efficiently achieve specific performance models that result from a collective and negotiated construction.

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DOI: 10.3217/jucs-017-07-1089

Authors: Valentina Janev, Sanja Vraneš

Abstract: Semantic-based technologies have been steadily increasing their relevance in recent years in both the research world and business world. Considering this, the present article discusses the process of design and implementation of a competency management system in information and communication technologies domain utilizing the latest Semantic Web tools and technologies including D2RQ server, TopBraid Composer, OWL 2, SPARQL, SPARQL Rules and common human resources related public vocabularies. In particular, the paper discusses the process of building individual and enterprise competence models in a form of ontology database, as well as different ways of meaningful search and retrieval of expertise data on the Semantic Web. The ontological knowledge base aims at storing the extracted and integrated competences from structured, as well as unstructured sources. By using the illustrative case study of deployment of such a system in the Human Resources sector at the Mihajlo Pupin Institute, this paper shows an example of new approaches to data integration and information management. The proposed approach extends the functionalities of existing enterprise information systems and offers possibilities for development of future Internet services. This allows organizations to express their core competences and talents in a standardized, machine processable and understandable format, and hence, facilitates their integration in the European Research Area and beyond.

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DOI: 10.3217/jucs-017-04-0548

Authors: Marco Bertoni, Koteshwar Chirumalla

Abstract: The paper explores the application of Web 2.0 technologies to support product development efforts in a global, virtual and cross-functional setting. It analyses the dichotomy between the prevailing hierarchical structure of CAD/PLM/PDM systems and the principles of the Social Web under the light of the emerging product development trends. Further it introduces the concept of Engineering 2.0, intended as a more bottom up and lightweight knowledge sharing approach to support early stage design decisions within virtual and cross-functional product development teams. The lessons learned collected from a cross-company study highlight how to further developblogs, wikis, forums and tags for the benefit of new product development teams, highlighting opportunities, challenges and no-go areas.

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DOI: 10.3217/jucs-017-04-0532

Authors: Johannes Müller, Alexander Stocker

Abstract: Siemens is well known for ambitious efforts in knowledge management, providing a series of innovative tools and applications within the intranet. References@BT is such a web-based application with currently more than 7,300 registered users from more than 70 countries. Its goal is to support the sharing of knowledge, experiences and best-practices globally within the Building Technologies division. Launched in 2005, References@BT features structured knowledge references, discussion forums, and a basic social networking service. In response to use demand, a new microblogging service, tightly integrated into References@BT, was implemented in March 2009. More than 500 authors have created around 2,600 microblog postings since then. Following a brief introduction into the community platform References@BT, we comprehensively describe the motivation, experiences and advantages for an organization in providing internal microblogging services. We provide detailed microblog usage statistics, analyzing the top ten users regarding postings and followers as well as the top ten topics. In doing so, we aim to shed light on microblogging usage and adoption within a globally distributed organization.

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DOI: 10.3217/jucs-017-02-0261

Authors: Gustavo Carvalho, Adriana Vivacqua, Jano Souza, Sérgio Palma J. Medeiros

Abstract: Decision-making involves choosing between one ore more alternatives, to achieve one or more goals. To support this process, there are decision support systems that employ different approaches, supporting groups or not. Generally, however, these systems do not have great flexibility; their users have to follow preestablished decision methods. This paper, after exposing some decision-making processes, describes a system, LaSca (from Large Scale), to support decisions in large-scale groups. This system, besides allowing effective achievement of the benefits of deciding in large groups through the proper structuring of the group, also allows its users to define themselves how this structuring will happen, based or not in the existing theories on the subject. So, in addition to facilitate the decision-making process, LaSca also allows its users to decide how to decide.

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DOI: 10.3217/jucs-016-08-1089

Authors: Katri-Liis Lepik, Merle Krigul, Erik Terk

Abstract: The present article aims to describe the Living Lab’s method as a method innovation in institutional activities and the problems of taking this innovation into use. Possibilities to transfer the Living Lab's method from one country, Finland, to other, Estonia, potential implementation fields and obstacles are studied. Considerations on the process of utilising the Living Lab's method in Tallinn are given. Living Lab's is a human-centric research and development approach in which new technologies are co-created, tested, and evaluated in the users’ own private context. This method is coming into use in several countries among which Finland is in the forefront but is not yet in use in Tallinn, Estonia. The empirical part of the research is based on the analyses of fourteen interviews conducted among Tallinn and Helsinki city officials, representatives of technology enterprises, experts of the fields that are internationally most wide-spread Living Labs' testing grounds, using structured interviews and discussions. The article concludes by discussing possibilities to use the Living Lab's method in enhancing Helsinki-Tallinn cross-border co-operation and thus metropolitan regional integration.

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DOI: 10.3217/jucs-015-13-2701

Authors: Thanh-Quang Chu, Alexis Drogoul, Alain Boucher, Jean-Daniel Zucker

Abstract: Efficient response to natural disasters has an increasingly important role in limiting the toll on human life and property. The work we have undertaken seeks to improve existing models by building a Decision Support System (DSS) of resource allocation and planning for natural disaster emergencies in urban areas. A multi-agent environment is used to simulate disaster response activities, taking into account geospatial, temporal and rescue organizational information. The problem we address is the acquisition of situated expert knowledge that is used to organize rescue missions. We propose an approach based on participatory design and interactive learning which incrementally elicits experts’ preferences by online analysis of their interventions with rescue simulations. An additive utility functions are used, assuming mutual preferential independence between decision criteria, as a preference for the elicitation process. The learning algorithm proposed refines the coefficients of the utility function by resolving incremental linear programming. For testing our algorithm, we run rescue scenarios of ambulances saving victims. This experiment makes use of geographical data for the Ba-Dinh district of Hanoi and damage parameters from well-regarded local statistical and geographical resources. The preliminary results show that our approach is initially confident in solving this problem.

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DOI: 10.3217/jucs-015-09-1812

Authors: Sergio Ochoa, José Pino, Fabián Poblete

Abstract: The Software Engineering community has been trying to get fast and accurate software estimations for many years. Most of the proposed methods require historical information and/or experts’ judgment. Because of that, the current methods are not suitable for novice developers or persons who do not know the company development capability. In order to help overcome such need, this paper proposes a software estimation method named CEBON (Collaborative Estimation Based On Negotiation). The method is applicable to small/medium-size projects (1-6 months). It focuses on supporting estimation of Web information systems in scenarios where historical data is not available. The CEBON method has been used to estimate eight real projects. The obtained results were compared with the real projects execution, which were carried out by novice developers in Chile. The comparison indicates the method is able to deliver quite accurate results. In addition, a survey applied to the involved developers shows they feel comfortable using the estimation method. The article also describes a collaborative software application supporting the CEBON process and a preliminary evaluation of both the estimation method and the supporting tool.

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DOI: 10.3217/jucs-015-08-1722

Authors: Steffen Lohmann, Jörg Niesenhaus, Philipp Heim, Jürgen Ziegler

Abstract: Due to an increasing professionalization, specialization, and globalization in the development of interactive entertainment new demands for comprehensive knowledge management support emerge. This article aims at sensitizing and systematizing the needs and potentials for continuous knowledge flow and community engagement in this application area. It starts with an analysis of typical development activities and involved parties that could benefit from a continuous knowledge management support. Then, a general framework architecture and implementation examples are presented that provide different levels of knowledge management support for interactive entertainment development.

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DOI: 10.3217/jucs-015-07-1526

Authors: Diego Alonso Gómez Aguilar, Roberto Therón, Francisco García-Peñalvo

Abstract: This article presents Semantic Spiral Timelines (SST) as an interactive visual tool aimed at the exploration and analysis of additional academic information stored in current e-learning platforms. Despite the development of contents specifically for these platforms, and in spite of the various features they provide, knowledge of the actual use made by individual participants is emerging as an unavoidable necessity, so as to ensure proper operation and effective use of e-learning platforms. SST supports the discovery of temporal patterns by incorporating an innovative highly interactive visual representation, which can be explored at various levels. This tool makes it possible to assess, at first glance, the use of the e-learning platform during the development of courses; one can also perceive how it is used by class participants. Then, through different interaction mechanisms, it is possible for students and professors to uncover specific details about courses, which would otherwise remain hidden.

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DOI: 10.3217/jucs-015-03-0665

Authors: Andreas Schmeil, Martin Eppler

Abstract: In this paper we propose a classification and systematic description structure based on the pattern paradigm for interaction scripts in Second Life that aim at facilitating on the one side knowledge sharing and knowledge integration in groups, and on the other side knowledge creation in formal and informal ways. We present 13 examples of interaction patterns, a description structure to formalize them, and classify them into four classes according to their design effort and added value. Based on this classification we distinguish among sophisticated 3D collaboration patterns, seamless patterns, decorative patterns, and pseudo patterns.

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DOI: 10.3217/jucs-014-18-2912

Authors: Josep Lladós, Dimosthenis Karatzas, Joan Mas, Gemma Sánchez

Abstract: Mass digitization of document collections with further processing and semantic annotation is an increasing activity among libraries and archives at large for preservation, browsing and navigation, and search purposes. In this paper we propose a software architecture for the process of converting high volumes of document collections to semantically annotated digital libraries. The proposed architecture recognizes two sources of knowledge in the conversion pipeline, namely document images and humans. The Image Analysis module and the Correction and Validation module cover the initial conversion stages. In the former information is automatically extracted from document images. The latter involves human intervention at a technical level to define workflows and to validate the image processing results. The second stage, represented by the Knowledge Capture modules requires information specific to the particular knowledge domain and generally calls for expert practitioners. These two principal conversion stages are coupled with a Knowledge Management module which provides the means to organise the extracted and acquired knowledge. In terms of data propagation, the architecture follows a bottom-up process, starting with document image units, called terms, and progressively building meaningful concepts and their relationships. In the second part of the paper we describe a real scenario with historical document archives implemented according to the proposed architecture.

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DOI: 10.3217/jucs-014-14-2288

Authors: Longbing Cao, Yuming Ou

Abstract: Market Surveillance plays important mechanism roles in constructing market models. From data analysis perspective, we view it valuable for smart trading in designing legal and profitable trading strategies and smart regulation in maintaining market integrity, transparency and fairness. The existing trading pattern analysis only focuses on interday data which discloses explicit and high-level market dynamics. In the mean time, the existing market surveillance systems available from large exchanges are facing crucial challenges of diversified, dynamic, distributed and cyber-based misuse, mis-disclosure and misdealing of information, announcement and orders in one market or crossing multiple markets. Therefore, there is a crucial need to develop innovative and workable methods for smart trading and surveillance. To deal with such issues, we propose the innovative concept microstructure pattern analysis and corresponding approaches in this paper. Microstructure pattern analysis studies trading behaviour patterns of traders in market microstructure data by utilizing market microstructure knowledge. The identified market microstructure patterns are then used for powering market trading and surveillance agents for automatically detecting/designing profitable and legal trading strategies or monitoring abnormal market dynamics and trader’s behaviour. Such trading/surveillance agent-driven market trading/surveillance systems can greatly enhance the analytical, discovery and decision-support capability of market trading/surveillance than the current predefined rule/alert-based systems.

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DOI: 10.3217/jucs-014-14-2263

Authors: Damian Dudek

Abstract: Adaptive behavior and learning are required of software agents in many application domains. At the same time agents are often supposed to be resource-bounded systems, which do not consume much CPU time, memory or disk space. In attempt to satisfy both requirements, we propose a novel framework, called APS (standing for Analysis of Past States), which provides agent with learning capabilities with respect to saving system resources. The new solution is based on incremental association rule mining and maintenance. The APS process runs periodically in a cycle, in which phases of agent's normal performance intertwine with learning phases. During the former ones an agent stores observations in a history. After a learning phase has been triggered, the history facts are analyzed to yield new association rules, which are added to the knowledge base by the maintenance algorithm. Then the old observations are removed from the history, so that in the next learning runs only recent facts are processed in search of new association rules. Keeping the history small can save both processing time and disk space as compared to batch learning approaches.

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DOI: 10.3217/jucs-013-12-1936

Authors: Domenico Beneventano, Sonia Bergamaschi, Francesco Guerra, Maurizio Vincini

Abstract: Integration of heterogeneous information in the context of Internet becomes a key activity to enable a more organized and semantically meaningful access to data sources. As Internet can be viewed as a data-sharing network where sites are data sources, the challenge is twofold. Firstly, sources present information according to their particular view of the matter, i.e. each of them assumes a specific ontology. Then, data sources are usually isolated, i.e. they do not share any topological information concerning the content or the structure of other sources. The classical approach to solve these issues is provided by mediator systems which aim at creating a unified virtual view of the underlying data sources in order to hide the heterogeneity of data and give users a transparent access to the integrated information. In this paper we propose to use a multi-agent architecture to build and manage a mediators network. While a single peer (i.e. a mediator agent) independently carries out data integration activities, it exchanges knowledge with other peers by means of specialized agents (i.e. brokers) which provide a coherent access plan to access information in the peer network. This defines two layers in the system: at local level, peers maintain an integrated view of local sources, while at network level agents maintain mappings among the different peers. The result is the definition of a new networked mediator system intended to operate in web economies, which we realized in the SEWASIE (SEmantic Webs and AgentS in Integrated Economies) project. SEWASIE is a RDT project supported by the 5th Framework IST program of the European Community successfully ended on September 2005.

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DOI: 10.3217/jucs-013-12-1881

Authors: Khaled Khelif, Rose Dieng-Kuntz, Pascal Barbry

Abstract: This paper describes an ontology-based approach aiming at helping biologists to annotate their documents and at facilitating their information retrieval task. Our approach, based on semantic web technologies, relies on formalised ontologies, semantic annotations of scientific articles and knowledge extraction from texts. We propose a method/system for the generation of ontology-based semantic annotations (MeatAnnot) and a system allowing biologists to draw advanced inferences on these annotations (MeatSearch). This approach was proposed to support biologists working on DNA microarray experiments in the validation and the interpretation of their results, but it can probably be extended to other massive analyses of biological events (as provided by proteomics, metabolomics...).

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DOI: 10.3217/jucs-013-10-1471

Authors: Chunguo Wu, Maurizio Marchese, Jingqing Jiang, Alexander Ivanyukovich, Yanchun Liang

Abstract: With the currently growing interest in the Semantic Web, keywords/metadata extraction is coming to play an increasingly important role. Keywords extraction from documents is a complex task in natural languages processing. Ideally this task concerns sophisticated semantic analysis. However, the complexity of the problem makes current semantic analysis techniques insufficient. Machine learning methods can support the initial phases of keywords extraction and can thus improve the input to further semantic analysis phases. In this paper we propose a machine learning-based keywords extraction for given documents domain, namely scientific literature. More specifically, the least square support vector machine is used as a machine learning method. The proposed method takes the advantages of machine learning techniques and moves the complexity of the task to the process of learning from appropriate samples obtained within a domain. Preliminary experiments show that the proposed method is capable to extract keywords from the domain of scientific literature with promising results.

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DOI: 10.3217/jucs-013-10-1434

Authors: Gustavo Zurita, Pedro Antunes, Nelson Baloian, Felipe Baytelman

Abstract: We propose mobile sensemaking as a collaborative mechanism to explore and understand information in highly mobile and fluid situations, where people engage in multiple parallel, rapid and ad-hoc interactions, rather than participating in large highly-structured decision processes. Mobile sensemaking is explored in the classroom context, where it has been recognized that the traditional lectures should be reconstructed as active processes centered on collaborative activities. Mobile sensemaking relies on mobile computing devices and a proximity model, both organizing collaborative activities according to the domain context and physical proximity. The paper describes in detail the proposed proximity model and the developed mobile application.

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DOI: 10.3217/jucs-013-09-1270

Authors: Amber Settle, Chad Settle

Abstract: Student satisfaction with distance learning is impacted by a variety of factors, including interaction with the instructor and the structure of the course. Satisfaction with distance-learning courses also has a strong impact on retention. In an earlier article, we determined that student satisfaction as measured by course evaluation scores in an online discrete mathematics course taught by the first author was not statistically significantly different from that of students in traditional versions of the same course, supporting some previous studies on distance-learning student satisfaction. However, the model of distance-learning studied in our initial work is not the dominant model used for distance learning at the institution in question. In this article we obtain statistically significant results different from the earlier article when a distance-learning course that uses the dominant model is considered. In particular, the course evaluations for online and traditional sections of introductory Java programming courses varied in some notable ways.

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DOI: 10.3217/jucs-013-09-1246

Authors: Lihong Li, Vadim Bulitko, Russell Greiner

Abstract: Classification-based reinforcement learning (RL) methods have recently been pro-posed as an alternative to the traditional value-function based methods. These methods use a classifier to represent a policy, where the input (features) to the classifier is the state and theoutput (class label) for that state is the desired action. The reinforcement-learning community knows that focusing on more important states can lead to improved performance. In this paper,we investigate the idea of focused learning in the context of classification-based RL. Specifically, we define a useful notation of state importance, which we use to prove rigorous bounds on policyloss. Furthermore, we show that a classification-based RL agent may behave arbitrarily poorly if it treats all states as equally important.

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DOI: 10.3217/jucs-013-09-1234

Authors: Muhammad Afzal, Narayanan Kulathuramaiyer, Hermann Maurer

Abstract: We are approaching an era where research materials will be stored more and more as digital resources on the World Wide Web. This of course will enable easier access to online publications. As the number of electronic publications expands, it will, however, become a challenge for individuals to find related or relevant papers. Related papers could be papers written by the same team of authors or by one of the authors, or even papers that deal with the same topic but were written by other authors. This, of course, raises the issue of linking to papers forward in time, or as we call it "links into the future". To be concrete, while reading a paper written in the year 1980, it would be nice to know if the same author has written another related paper in 1990’s or if the same author has written a paper earlier, all this without making an explicit search. Based on the ascertained interest of a person reading a particular paper from a digital repository, an auto-suggestion facility could be useful to indicate papers in the same area, category and subject that might potentially be of interest to the reader. One is typically interested in finding related papers by the same author or by one of the authors of a paper. This feature can be implemented in two ways. The first is by creating links from this paper to all the relevant papers and updating it periodically for new papers appearing on the World Wide Web. Another way is by going through the references of all papers appearing on the WWW. Based on the references, one can create mutual links to the papers that are referred to.

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DOI: 10.3217/jucs-013-09-1213

Authors: Juan Dodero, Salvador Sánchez-Alonso, Dirk Frosch-Wilke

Abstract: Competence development programmes are collections of units of learning and learning activities used to increase the overall effective performance of a learner within a certain task. The definition of a competence development programme is fairly complex and subject to variability, depending on the available learning units and components. Some instructional engineering approaches have been successfully used to create courseware by the combination of existing learning resources within a systematic and iterative method. In this work, a generative, model-driven engineering approach is used to create and adapt competence development programmes from families of available learning components, such as units of learning, learning designs, and learning services. The process begins from the statement of the learning goals as feature models, and carries out a number of transformations from the analysis model down to learning designs and implementation components. However, shared definitions for competence-related terms and computational semantics are essential in this effort. In this paper, ontologies are proposed as a means to that end. In particular, the transformations between models are defined with the help of a general competence ontology.

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DOI: 10.3217/jucs-013-07-0959

Authors: José Chaves-González, Miguel A. Vega-Rodríguez, Juan Gómez-Pulido, Juan Sánchez-Pérez

Abstract: This paper presents a project that provides both, to professors and to students, a tool that is useful for studying, teaching and learning how pipelines work and how they can be scheduled in an easy and widespread way. The project is called PipeSim, and features static and dynamic pipelines with a very attractive, dynamic and intuitive interface. It is well known that pipeline and pipeline-scheduling are very relevant concepts in computer science studies and it is very important that students can learn these in an easy and reliable way. The simulator makes easy both working in depth about pipeline scheduling and working slowly paying attention in the different stages of the scheduling. However, we designed the simulator knowing that principal users would be students with no experience, so both the execution and the presentation of the results have been carefully developed. In addition to this, to check the success of PipeSim, a survey has been made among some students that used the simulator. Results reveal that this kind of applications has a great acceptance among students, thought they consider that simulators are complements to the lessons given by the professor and never a substitute for them.

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DOI: 10.3217/jucs-013-07-0948

Authors: Maximiliano Paredes, Pedro Sánchez-Villalón, Manuel Ortega Cantero, J. Velázquez-Iturbide

Abstract: Writing applications are currently designed for desktop personal computers. Mobile devices like PDAs or smart phones are increasingly being used for mobile applications such as access to information sources or local work on the device, but they are seldom used for collaborative tasks. Here we present AULA and AWLA, two applications that put mobile devices and collaborative educational environments together inside and outside the classroom. They are designed under the paradigm of collaborative composition writing in language learning courses, in particular English as a Foreign Language (EFL).

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DOI: 10.3217/jucs-013-07-0924

Authors: Daniel Burgos, Colin Tattersall, Martin Dougiamas, Hubert Vogten, Rob Koper

Abstract: Mapping the specification IMS Learning Design and the Course Management System Moodle is a logical step forward on interoperability between eLearning systems and specifications in order to increase the best acceptance of the specifications into the widespread world of the eLearning systems and to ensure the standardization of the outputs from the systems to be used in others. IMS Learning Design and Moodle look for a common understanding focused on the integration of information packages modelled by each part in the other. The final goal aims at Moodle playing an IMS LD package. A second step will map a Moodle course to be used in any IMS LD complaint tool. The Unit of Learning in IMS LD and the course in Moodle become the perfect couple where to find several elements that should match each other. This paper shows how to make this understanding, mapping related elements in both to get a list of pairs easy to translate from one to another, and to define also a list of requirements for this protocol.

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DOI: 10.3217/jucs-013-04-0504

Authors: Denis Helic

Abstract: Nowadays, advanced E-Learning systems are generally pedagogy-aware. Commonly, these systems include facilities for defining so-called learning scenarios that reflect sophisticated pedagogical approaches such as collaborative writing or project-oriented learning. To support different learning activities from such scenarios the technological infrastructure of these systems must be appropriately adjusted and configured. Usually, this configuration process is laced with a number of difficulties. Most of these difficulties are caused by the fact that scenario capturing is achieved through informal user-developer dialogues. Typically, the result of such informal dialogues contains inconsistent and incomplete information because of misunderstandings and the complexity of the interactions within a scenario. Consequently, the configuration of the system is suboptimal and a number of iterations are required in order to achieve better results. In this paper an approach to improve this situation is presented. This approach is based on a general formal representation model for describing learning scenarios. A particular formal description of a concrete learning scenario is obtained through a user dialogue with a wizard tool. At the next step, this formal description might be automatically processed to facilitate configuration process. The paper is concluded with some experiences gained by applying this approach in two E-Learning projects.

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DOI: 10.3217/jucs-013-02-0287

Authors: Leszek Borzemski

Abstract: In this paper we propose an application of data mining methods in the prediction of the availability and performance of Internet paths. We deploy a general decision-making method for advising the users in further usage of Internet path at particular time and date. The method is based on the clustering and tree classification data mining techniques. The usefulness of our method for prediction the Internet path behavior has been confirmed in real-life experiment. The active Internet measurements were performed to gather the end-to-end latency and packet routing information. The knowledge gathered has been analyzed using a professional data mining package via neural clustering and decision tree algorithms. The results show that the data mining can be efficiently used for the purpose of the forecasting the network behavior. We propose to build a network performance monitoring and prediction service based on proposed data mining procedure. We address our approach especially to the non-networkers of such networking frameworks as Grid and overlay networks who want to schedule their network activity but who want to be left free from networking issues to concentrate on their work.

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DOI: 10.3217/jucs-013-02-0209

Authors: Cesar Sanín, Edward Szczerbicki, Carlos Toro

Abstract: Collecting, distributing and sharing knowledge in a knowledge-explicit way is a significant task for any company. However, collecting decisional knowledge in the form of formal decision events as the fingerprints of a company is an utmost advance. Such decisional fingerprint is called decisional DNA. Set of experience knowledge structure can assist on accomplishing this purpose. In addition, Ontology-based technology applied to set of experience knowledge structure would facilitate distributing and sharing companies' decisional DNA. Such possibility would assist in the development of an e-decisional community, which will support decision-makers on their overwhelming job. The purpose of this paper is to explain the development of .an OWL decisional Ontology built upon set of experience, which would make decisional DNA, that is, explicit knowledge of formal decision events, a useful element in multiple systems and technologies, as well as in the construction of the e-decisional community.

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DOI: 10.3217/jucs-013-02-0177

Authors: Ken Saito, Hidekazu Kubota, Yasuyuki Sumi, Toyoaki Nishida

Abstract: In this paper, we present a computational approach to understanding and augmenting the conversational knowledge process. We introduce the concept of the conversation quantization, a technique of approximating a continuous flow of conversation by a series of conversation quanta that represent points of the discourse. To investigate what the nature of conversation quanta is, we attempt to extract conversation quanta from two types of the meeting videos by hand. As a result, we have obtained some profitable suggestions about conversation quanta.

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DOI: 10.3217/jucs-012-01-0080

Authors: Adel Taweel, Alan Rector, Jeremy Rogers

Abstract: The convergence of need between improved clinical care and post genomics research presents a unique challenge to restructuring information flow so that it benefits both without compromising patient safety or confidentiality. The CLEF project aims to link-up heath care with bioinformatics to build a collaborative research platform that enables a more effective biomedical research. In that, it addresses various barriers and issues, including privacy both by policy and by technical means, towards establishing its eventual system. It makes extensive use of language technology for information extraction and presentation, and its shared repository is based around coherent "chronicles" of patients' histories that go beyond traditional health record structure. It makes use of a collaborative research workbench that encompasses several technologies and uses many tools providing a rich platform for clinical researcher.

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DOI: 10.3217/jucs-012-01-0059

Authors: Anthony Glascock, David Kutzik

Abstract: The lack of appropriate and accurate information on the ability of a frail individual to accomplish specific task oriented activities can place the individual at risk or result in the allocation of costly and unnecessary care. Although there have been previous attempts to use computer technology to obtain this information, they have proved to be costly and complex and therefore not widely used. However, a behavioral monitoring technology, based on smart-home and telemedicine applications, has been developed that obtains more accurate and timely information on the ability of frail individuals to accomplish specific tasks in their own residences than any other existing method. During a twelve month pilot study, this system has been used by care providers to assess the status of their clients, respond to immediate needs and alter overall care plans, thus resulting in better care and greater peace of mind for the individual.

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