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This paper describes and evaluates CONSPECT, a service that analyses states in a learner΄s conceptual development. CONSPECT combines two technologies – Latent Semantic Analysis (LSA) and Network Analysis (NA) into a technique called Meaningful Interaction Analysis (MIA). It uses LSA for the language analysis and NA to provide visualisations of the semantic relatedness information calculated by LSA. CONSPECT was designed to help both learners and tutors monitor conceptual development. This paper reports on the verification activities undertaken to show how well LSA matches humans in clustering similar concepts. The verification used the edit distance between card sorts to quantitatively evaluate the service. [ABSTRACT FROM AUTHOR]

Finding suitable analysis techniques for networks generated from social processes is a difficult task when the population changes over time. Traditional social network analysis measures may not work in such circumstances. It is argued that agent-based social networks should not be constrained by apriori assumptions about the evolved network and/or the analysis techniques. In most agent-based social simulation models, the number of agents remains fixed throughout the simulation; this paper considers the case when this does not hold. Thus the aim of this paper is to demonstrate how the network signatures change when the agents΄ population depends upon endogenous social processes. We argue for a much wider attention from the social simulation community in addressing this open research problem. [ABSTRACT FROM AUTHOR]

This paper presents two new methods for network analysis. Ant colony optimization is a nature inspired algorithm succesfull in graph traversal and network path finding whereas network reduction based on stability introduces two new properties of network vertices based on their long-term behavior, their role in the network and the understanding of how memory works. We illustrate the algorithms on applications in social network analysis and information retrieval using the DBLP dataset and a small network of hyperlinked documents. [ABSTRACT FROM AUTHOR]

This research-in-progress paper presents a new approach called Link Proximity Analysis (LPA) for identifying related web pages based on link analysis. In contrast to current techniques, which ignore intra-page link analysis, the one put forth here examines the relative positioning of links to each other within websites. The approach uses the fact that a clear correlation between the proximity of links to each other and the subject-relatedness of the linked websites can be observed on nearly every web page. By statistically analyzing this relationship and measuring the amount of sentences, paragraphs, etc. between two links, related websites can be automatically, identified as a first study has proven. [ABSTRACT FROM AUTHOR]

In this paper, we aim to investigate strong herb-herb interactions in TCM for effective treatment of insomnia. Given that extraction of herb interactions is quite similar to gene epistasis study due to non-linear interactions among their study factors, we propose to apply Multifactor Dimensionality Reduction (MDR) that has shown useful in discovering hidden interaction patterns in biomedical domains. However, MDR suffers from high computational overhead incurred in its exhaustive enumeration of factors combinations in its processing. To address this drawback, we introduce a two-stage analytical approach which first uses hierarchical core sub-network analysis to pre-select the subset of herbs that have high probability in participating in herb-herb interactions, which is followed by applying MDR to detect strong attribute interactions in the pre-selected subset. Experimental evaluation confirms that this approach is able to detect effective high order herb-herb interaction models in high dimensional TCM insomnia dataset that also has high predictive accuracies. [ABSTRACT FROM AUTHOR]

Developing a whole brain simulator, a computer simulation in modeling brain structure and functionality of human, is the ultimate goal of Brain Informatics. Brain simulator helps researchers cross the bridge between the cognitive behavior/decease, and the neurophysiology. Brain simulators development is still in infant stage. Current simulators mostly consider the neuron as the basic functional component. This paper starts with introducing the background and current status of brain simulator. Then, an extensible brain simulator development framework is proposed. From information technology perspective, we adopt overlay and peer-to-peer network to deal with the complexity of brain network. Moreover, layered design with object-oriented brain class hierarchy forms the flexible development framework of the proposed simulator. The proposed brain simulator is evolved in case-based incremental delivery style. The power of the simulator will grow along with more research cases from cognitive and clinical neuroscience. [ABSTRACT FROM AUTHOR]

New business models and theories are developing nowadays towards collaborative environments direction, and many new tools in sustaining companies involved in these organizations are emerging. Among them, a plethora of methodologies to analyze their needs are already developed for single companies. Few academic works are available about Enterprise Networks (ENs) need analysis. This paper presents the learning from an action research (AR) in the mechatronics sector: AR has been used in order to experience the issue of evaluating network needs and therefore define, develop, and test a complete framework for network evaluation. Reflection on the story in the light of the experience and the theory is presented, as well as extrapolation to a broader context and articulation of usable knowledge. [ABSTRACT FROM AUTHOR]

A digital humanities technique for the network analysis of words with a text is applied to capture the subtle and sensitive contents of essays written by a contemporary composer of classical music. Based on analysis findings, the possible contributions of digital humanities to affective technology are discussed. This paper also provides a systematic view of digital humanities and affective technology. [ABSTRACT FROM AUTHOR]

The eighth in a series of publications dealing with aspects of intercultural communication, this volume focuses on approaches to intercultural communication research. The 16 chapters of the volume are arranged into three sections: diverse approaches to research, quantitative approaches, and qualitative approaches. Specific chapters discuss the following topics: (1) general issues in intercultural research, (2) the implications of the sociology of knowledge for conducting research, (3) methodological problems in cross-cultural studies, (4) choices researchers must make in conducting a study, (5) the nature of quantitative research, (6) network analysis, (7) an international standard for the measurement of human emotion, (8) a technique to test whether equivalence has been achieved in translations of research instruments from one language to another, (9) quantitative content analysis, (10) measuring responses to filmed interpersonal conflict, (11) qualitative research methods, (12) qualitative content analysis from a Burkean perspective, (13) naturalistic field research, (14) observational techniques, (15) metaphor analysis, and (16) intercultural rhetoric. (FL)

The PKK/KONGRA-GEL terrorist group makes extensive use of the internet, notably for propaganda. The prominent PKK websites are listed in a dataset which shows the way these sites relate to each other with links. An overview of their content is given, then various software and analysis tools, notably Unicet, are used to reveal different aspects of this network of Websites; Centrality Analyses to show prominence and hierarchical structure, Density and Geodesic Distances Analyses, and Connectivity Analyses. [ABSTRACT FROM AUTHOR]

The standard, transactional setting of pattern mining assumes that data is subdivided in transactions; the aim is to find patterns that can be mapped onto at least a minimum number of transactions. However, this setting can be hard to apply when the aim is to find graph patterns in databases consisting of large graphs. For instance, the web, or any social network, is a single large graph that one may not wish to split into small parts. The focus in network analysis is on finding structural regularities or anomalies in one network, rather than finding structural regularities common to a set of them. This requires us to revise the definition of key concepts in pattern mining, such as support, in the single-graph setting. Our contribution is a support measure that we prove to be computationally less expensive and often closer to intuition than other measures proposed. Further we prove several properties between these measures and experimentally validate the efficiency of our measure. [ABSTRACT FROM AUTHOR]