Network Community Detection on Metric Space

Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings

Learning through playing for or against each other? Promoting collaborative learning in digital game based learning

The process of learning through Game Based Learning (GBL) presents both positive aspects and challenges to be faced in order to support the achievement of learning goals and knowledge creation. This study aims to characterise game dynamics in the adoption of multi-player GBL. In particular, we examine the multi-player GBL dynamics may enhance collaborative learning through a relation of positive interdependence while at the same time maintaining a certain level of competition for ensuring multi-player GBL gameplay. The first section of the paper introduces collaborative GBL and describes the combination of intragroup dynamics of cooperation and positive interdependence and an intergroup dynamic of competition to maintain gameplay. The second part of the paper describes two multi-player GBL scenarios: the multi-player game with interpersonal competition and the multiplayer game with intergroup competition. For each scenario a case analysis of existing collaborative games is provided, which may help instructional and game designers when defining the collaborative GBL dynamics. Technological requirements and best practices in the use of collaborative GBL are described in the last sections

PENGEMBANGAN PEMBELAJARAN BERBASIS SOCIAL LEARNING NETWORKS (SLN) DENGAN MICROSOFT TEAMS PADA MATA KULIAH ANIMASI

Penelitian ini bertujuan untuk menghasilkan produk berupa rancangan pembelajaran berbasis Social Learning Networks (SLN) dengan menggunakan platform Microsoft Teams yang dapat digunakan oleh mahasiswa yang sedang mengikuti mata kuliah Animasi di Program Studi Teknologi Universitas Negeri Jakarta. Penelitian ini dilakukan dengan mengikuti prosedur model pengembangan ADDIE, yang terdiri dari 5 tahap yaitu (1) Analysis: melakukan analisis kebutuhan, peserta didik dan materi, (2) Design: merancang desain pembelajaran, (3) Development: mengembangkan rancangan pembelajaran, (4) Implementation: melakukan uji coba kepada para ahli dan pengguna, (5) Evaluation: mengevaluasi hasil pengembangan produk berdasarkan uji coba yang telah dilakukan. Berdasarkan hasil uji coba para ahli diperoleh nilai rata-rata keseluruhan 3,36 yang berarti sangat baik. Sedangkan hasil uji coba one to one dan small group didapatkan hasil rata-rata keseluruhan 3,55 yang berarti sangat baik. Hasil penelitian pengembangan ini menunjukkan rancangan pembelajaran yang dikembangkan dapat digunakan untuk memfasilitasi mahasiswa dalam proses pembelajaran khususnya pembelajaran online. This study aims to produce a product in the form of a Social Learning Networks (SLN) based learning design using the Microsoft Teams platform that can be used by students who are taking Animation courses at the Jakarta State University Technology Study Program. This research was conducted by following the ADDIE development model procedure, which consisted of 5 stages, namely (1) Analysis: conducting a needs analysis, students and materials, (2) Design: designing a learning design, (3) Development: developing a learning design, (4) ) Implementation: conducting trials on experts and users, (5) Evaluation: evaluating the results of product development based on the trials that have been done. Based on the trial results of the experts, the overall average value was 3.36 which means very good. While the results of the one to one and small group trials obtained an overall average of 3.55 which means very good. The results of this development research indicate that the learning design developed can be used to facilitate students in the learning process, especially online learning

Identifying the Leverage Points in the Household Solid Waste Management System for Harare, Zimbabwe, Using Network Analysis Techniques

Managing household solid waste (HSW) has gone beyond what the Harare local government can handle. Inadequate knowledge of the interactions existing between issues that affect the efficient running of waste management systems is one of the major hindrances in waste management planning in developing countries like Zimbabwe. The complexity of the waste management system for a given municipal area needs to be identified and understood to generate appropriate and efficient waste management strategies. Network analysis (NA) is a methodology extensively used in research to help reveal a comprehensive picture of the relationships and factors related to a particular phenomenon. The methodology reduces the intricacy of large systems such as waste management to smaller and more understandable structures. In this study, NA, which was done mainly using the R software environment, showed a result of 1.5% for network density, thus signifying that for Harare, waste management strategies need to be ‘seeded’ in various parts of the system. The Pareto principle and the 3Rs (Reduce, Reuse, Recycle) concept were applied to suggest the issues to prioritize and generate strategies that could potentially affect significant change to the city’s waste management system. The key issues identified, in their order of importance, are an increase in uncollected waste, low waste collection efficiency, increase in illegal waste dumping, the deteriorating country’s economy, reduced municipal financial capacity, reduced municipal workforce capacity, inadequate or unreliable waste data, increase in waste volume, increase in the number of street vendors, no waste planning and monitoring unit, no engineered landfills in the city, increase in waste collection pressure, low waste collection frequency, increase in the unemployment rate, reduced municipal technical capacity, few waste collection vehicles, limited vehicles maintenance, distinct socio-economic classes, high vehicles breakdown, and increase in population

Evolving MCDM Applications Using Hybrid Expert-Based ISM and DEMATEL Models: An Example of Sustainable Ecotourism

Ecological degradation is an escalating global threat. Increasingly, people are expressing awareness and priority for concerns about environmental problems surrounding them. Environmental protection issues are highlighted. An appropriate information technology tool, the growing popular social network system (virtual community, VC), facilitates public education and engagement with applications for existent problems effectively. Particularly, the exploration of related involvement behavior of VC member engagement is an interesting topic. Nevertheless, member engagement processes comprise interrelated sub-processes that reflect an interactive experience within VCs as well as the value co-creation model. To address the top-focused ecotourism VCs, this study presents an application of a hybrid expert-based ISM model and DEMATEL model based on multi-criteria decision making tools to investigate the complex multidimensional and dynamic nature of member engagement. Our research findings provide insightful managerial implications and suggest that the viral marketing of ecotourism protection is concerned with practitioners and academicians alike

SCIENTIFIC CONTRIBUTION TO TECHNOLOGICAL INNOVATION: ANALYSIS OF RESEARCH STRATEGIES IN REFERENCE DATABASES.

Tesis doctoral en período de exposición públicaDoctorado en Tecnología de Invernaderos e Ingeniería Industrial (RD99/11) (8909

Strategies Global Virtual Team Leaders Use to Improve Trust and Communication

Global virtual team (GVT) members’ inability to effectively build trust and communication has the potential to negatively impact organizational outcomes. Organizational leaders are concerned with team members’ inability to build trust and communication, as it is the leading cause of reduced productivity and efficiency levels within GVTs. Grounded in the social exchange theory, the purpose of this qualitative multiple case study was to explore strategies GVT leaders use to improve trust and communication among GVT members. The participants were 18 GVT business leaders from six organizations located in the Pacific Northwest of the United States. Data were collected using semistructured interviews and a review of organizational documentation. Through thematic analysis, four themes were identified: (a) information sharing through transparency, (b) the creation and iteration of best practices/strategies, (c) localization development, and (d) the development of cross-functional work tools. A key recommendation is for GVT leaders to define team meeting styles/frequency, which leads to trust development, improved communication, productivity, and team efficiency. The implications for positive social change include the potential for organizations to increase human resources in other regions of the globe and support the local communities and economies of their workforce

Social Media Analytics of Smoking Cessation Intervention: User Behavior Analysis, Classification, and Prediction

Tobacco use causes a large number of diseases and deaths in the United States. Traditional intervention programs are based on face-to-face consulting, and social support is offered to help smoking quitters control stress and achieve better intervention outcomes. However, the scalability of these traditional intervention programs is limited by time and location. With the development of Web 2.0, many intervention programs of smoking cessation are developed online to reach a wider population. QuitNet is a popular website for smoking cessation that provides different services to help users quit smoking. It builds communities on different social media for people to discuss issues of smoking cessation and provide social support for each other. In this dissertation, we develop a comprehensive study to understand user behavior and their discussion interactions in online communities of smoking cessation. We compare user features and behaviors on different social media channels, analyze user interactions from the perspective of social support exchange, and apply data mining techniques to analyze discussion content and recommend threads for users. Health communities are developed on different types of social media. For example, QuitNet has Web forums on its own Web site while it also has its appearance on Facebook. The user participation may vary on different social media platforms. Users may also behave differently depending on the functions and design of the social media platforms. So, as the first step in this dissertation, we carry out a preliminary study to compare smoking cessation communities on different social media channels. We analyze user characteristics and behaviors in QuitNet Forum and QuitNet Facebook with statistical analysis and social network analysis. It is found that most users of QuitNet Forum are early smoking quitters, and they participate in discussions more actively than users of QuitNet Facebook. However, users of QuitNet Facebook have a wider spectrum of quitting statuses and interaction behaviors. Second, we are interested in user behaviors and how they exchange social support in online communities. Social support is "an exchange of resources between two individuals perceived by the provider or the recipient to be intended to enhance the well-being of the recipient". As QuitNet Forum attracts much more active users than QuitNet Facebook, it provides a better platform for our research purpose. So, we focus on QuitNet Forum, developing a classification scheme through qualitative analysis to categorize discussion topics and types of social support on the forum. Patterns of user behaviors are defined and identified. Social networks are built to analyze user interactions of social support exchange. It is found that users at different quit stages have different behaviors to exchange social support, and different types of social support flow between users at different quit stages. Discussion topics, user behaviors and patterns of social support exchanges are thoroughly analyzed. However, due to a huge amount of information on QuitNet Forum, it is difficult for users to find proper topics or peers to discuss or interact with. It would be helpful if we could apply machine learning techniques to understand user generated information in online health communities, and recommend discussion topics to users to participate in. We develop classifiers to categorize posts and comments on QuitNet Forum in terms of user intentions and social support types. User behaviors and patterns are used to help developing various feature sets. Then, we develop recommendation techniques to recommend threads for users to participate in. Based on traditional Collaborative Filtering and content-based approaches, we integrate classification results and user quit stages to develop recommendation systems. The experiments show that integrating classification results or user health statuses can achieve the best recommendation results with different percentages of unknown data. In this dissertation, we implement all-sided studies for online smoking cessation communities, including comprehensive analytics and applications. The proposed frameworks and approaches could be applied to other health communities. In the future, we will apply more analytics and techniques to a larger data set, and develop user-end applications to serve and improve online health intervention programs and communities.Ph.D., Computer Science -- Drexel University, 201

Advances and Applications of Dezert-Smarandache Theory (DSmT) for Information Fusion (Collected Works), Vol. 4

The fourth volume on Advances and Applications of Dezert-Smarandache Theory (DSmT) for information fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics. The contributions (see List of Articles published in this book, at the end of the volume) have been published or presented after disseminating the third volume (2009, http://fs.unm.edu/DSmT-book3.pdf) in international conferences, seminars, workshops and journals. First Part of this book presents the theoretical advancement of DSmT, dealing with Belief functions, conditioning and deconditioning, Analytic Hierarchy Process, Decision Making, Multi-Criteria, evidence theory, combination rule, evidence distance, conflicting belief, sources of evidences with different importance and reliabilities, importance of sources, pignistic probability transformation, Qualitative reasoning under uncertainty, Imprecise belief structures, 2-Tuple linguistic label, Electre Tri Method, hierarchical proportional redistribution, basic belief assignment, subjective probability measure, Smarandache codification, neutrosophic logic, Evidence theory, outranking methods, Dempster-Shafer Theory, Bayes fusion rule, frequentist probability, mean square error, controlling factor, optimal assignment solution, data association, Transferable Belief Model, and others. More applications of DSmT have emerged in the past years since the apparition of the third book of DSmT 2009. Subsequently, the second part of this volume is about applications of DSmT in correlation with Electronic Support Measures, belief function, sensor networks, Ground Moving Target and Multiple target tracking, Vehicle-Born Improvised Explosive Device, Belief Interacting Multiple Model filter, seismic and acoustic sensor, Support Vector Machines, Alarm classification, ability of human visual system, Uncertainty Representation and Reasoning Evaluation Framework, Threat Assessment, Handwritten Signature Verification, Automatic Aircraft Recognition, Dynamic Data-Driven Application System, adjustment of secure communication trust analysis, and so on. Finally, the third part presents a List of References related with DSmT published or presented along the years since its inception in 2004, chronologically ordered

El Libro de los libros: 1160 Libros profesionales de descarga gratuita y legal para Bibliotecarios y Documentalistas

[ES]Compilación de 1060 libros de Información y Documentación con enlace al texto completo de la obra, resumen y referencia bibliográfica, de interés para profesionales de las bibliotecas y servicios de información[EN] Compilation books 1060 on Information and Documentation with link to the full text of the work, abstract and bibliographic references of interest to library professionals and information service