214,123 research outputs found

    Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

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    In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and KK-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.Comment: Accepted for the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC, June 14-18, 2015, Zakopane, Polan

    Development of mobile indoor positioning system application using android and bluetooth low energy with trilateration method

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    This proceedings volume contains papers presented at the fifth International Conference on Soft Computing, Intelligent System and Information Technology (the 5th ICSIIT) held in Bali, Indonesia, 26-29 September 2017. Main theme of this international conference is �Building Intelligence through IoT and Big Data�, and it was organized and hosted by Informatics Engineering Department, Petra Christian University, Surabaya, Indonesia. The Program Committee received 106 submissions for the conference from across Indonesia and around the world. After peer-review process by at least two reviewers per paper, 64 papers were accepted and included in the proceedings. The papers were divided into ten groups: Classification and Correlation Techniques, Feature Extraction and Image Recognition Methods, Algorithms for Intelligent Computation, Distributed Systems and Computer Networks, Mobile and Pervasive IoT Applications, Assessments of Integrated IS/IT, Simulation and Virtual Reality Applications, Smart Assistive Technologies, Smart Mobile Applications, Case Studies of Knowledge Discovery and Management

    Development of Interactive Learning Media for Simulating Human Blood Circulatory System

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    This proceedings volume contains papers presented at the fifth International Conference on Soft Computing, Intelligent System and Information Technology (the 5th ICSIIT) held in Bali, Indonesia, 26-29 September 2017. Main theme of this international conference is “Building Intelligence through IoT and Big Data”, and it was organized and hosted by Informatics Engineering Department, Petra Christian University, Surabaya, Indonesia. The Program Committee received 106 submissions for the conference from across Indonesia and around the world. After peer-review process by at least two reviewers per paper, 64 papers were accepted and included in the proceedings. The papers were divided into ten groups: Classification and Correlation Techniques, Feature Extraction and Image Recognition Methods, Algorithms for Intelligent Computation, Distributed Systems and Computer Networks, Mobile and Pervasive IoT Applications, Assessments of Integrated IS/IT, Simulation and Virtual Reality Applications, Smart Assistive Technologies, Smart Mobile Applications, Case Studies of Knowledge Discovery and Management

    Development of Interactive Learning Media for Simulating Human Digestive System

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    This proceedings volume contains papers presented at the fifth International Conference on Soft Computing, Intelligent System and Information Technology (the 5th ICSIIT) held in Bali, Indonesia, 26-29 September 2017. Main theme of this international conference is “Building Intelligence through IoT and Big Data”, and it was organized and hosted by Informatics Engineering Department, Petra Christian University, Surabaya, Indonesia. The Program Committee received 106 submissions for the conference from across Indonesia and around the world. After peer-review process by at least two reviewers per paper, 64 papers were accepted and included in the proceedings. The papers were divided into ten groups: Classification and Correlation Techniques, Feature Extraction and Image Recognition Methods, Algorithms for Intelligent Computation, Distributed Systems and Computer Networks, Mobile and Pervasive IoT Applications, Assessments of Integrated IS/IT, Simulation and Virtual Reality Applications, Smart Assistive Technologies, Smart Mobile Applications, Case Studies of Knowledge Discovery and Management

    Agreement technologies and their use in cloud computing environments

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s13748-012-0031-9[EN] Nowadays, cloud computing is revolutionizing the services provided through the Internet to adapt itself in order to keep the quality of its services. Recent research foresees the advent of a new discipline of agent-based cloud computing systems that can make decisions about adaption in an uncertain environment. This paper discusses the role of argumentation in the next generation of agreement technologies and its use in cloud computing environments.This work is supported by the Spanish government (MICINN), project reference: TIN2012-36586-C03-01.Heras Barberá, SM.; De La Piedra, F.; Julian Inglada, VJ.; Rodríguez, S.; Botti Navarro, VJ.; Bajo, J.; Corchado, JM. (2012). Agreement technologies and their use in cloud computing environments. Progress in Artificial Intelligence. 1(4):277-290. https://doi.org/10.1007/s13748-012-0031-9S27729014European Comission: The Future of Cloud Computing. Technical report (2010)Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: SOSP03 Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pp. 164–177. ACM, New York (2003)Wang, L., et. al.: Scientific cloud computing: early definition and experience. In: 10th IEEE International Conference on High Performance Computing and Communications (HPCC-08), pp. 825–830. IEEE Press (2008)Talia, D.: Clouds meet agents: toward intelligent cloud services. Internet Comput. IEEE 16(2), 78–81 (2012). doi: 10.1109/MIC.2012.28Heras, S.: Case-Based Argumentation Framework for Agent Societies. PhD thesis, Universitat Politècnica de València. http://hdl.handle.net/10251/12497 (2011)Ashton, K.: That ‘internet of things’ thing. RFID J. (2009). http://www.rfidjournal.com/article/view/4986Klusch, M.: Information agent technology for the Internet: a Survey. Data Knowl. Eng. 36, 337–372 (2001)Schaffer, H.E.: X as a Service. Cloud Computing, and the Need for Good Judgment IT Professional 11(5), 4–5 (2009). doi: 10.1109/MITP.2009.112Richardson, L., Ruby, S.: RESTful Web Services, Web services for the real world O’Reilly, Media, May, p. 454 (2007)GlusterFS Developers. The Gluster web site. http://www.gluster.org (2012)Chodorow, K., Dirolf, M.: The Definitive Guide. O’Reilly Media, MongoDB (2010)Fuentes-Fernandez, R., Hassan, S., Pavon, J., Galan, J.M., Lopez-Paredes, A.: Metamodels for role-driven agent-based modelling. Comput. Math. Organ. Theory 18(1), 91–112 (2012)Jordán, J., et al.: A customer support application using argumentation in multi-agent systems. In: 14th International Conference on, Information Fusion, pp. 772–778 (2011)Heras, S., Jordán, J., Botti, V., Julián, V.: Argue to agree: a case-based argumentation approach. Int. J. Approx. Reasoning (2012, in press)Walton, D., Reed, C., Macagno, F.: Argumentation Schemes. Cambridge University Press, Cambridge (2008)Bench-Capon, T., Sartor, G.: A model of legal reasoning with cases incorporating theories and values. Artif. Intell. 150(1–2), 97–143 (2003)Dignum, F., Weigand, H.: Communication and deontic logic. In: Information Systems Correctness and Reusability, pp. 242–260. World Scientific, Singapore (1995)Wooldridge, M., Jennings, N.R.: Intelligent agents: theory and practice. Knowl. Eng. Rev. 10(2), 115–152 (1995)Lopez-Rodriguez, I., Hernandez-Tejera, M.: Software agents as cloud computing services. In: 9th International Conference on Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol. 88, pp. 271–276. Springer, Berlin (2011)Sim, K.M.: Towards complex negotiation for cloud economy. In: 5th International Conference on Advances in Grid and Pervasive Computing. LNCS, vol. 6104, pp. 395–406. Springer, Berlin (2010)Aversa, R., et al.: Cloud agency: a mobile agent based cloud system. In: International Conference on Complex, Intelligent and Software Intensive Systems, pp. 132–137. IEEE Computer Society Press, Washington, DC (2010)Cao, B., et al.: A service-oriented qos-assured and multi-agent cloud computing architecture. In: 1st International Conference on Cloud Computing. LNCS, vol. 5931, pp. 644–649. Springer, Berlin (2009)Rahwan, I., Simari, G. (eds.): Argumentation in Artificial Intelligence. Springer, Berlin (2009

    A self-growing Bayesian network classifier for online learning of human motion patterns

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    This paper proposes a new self-growing Bayesian network classifier for online learning of human motion patterns (HMPs) in dynamically changing environments. The proposed classifier is designed to represent HMP classes based on a set of historical trajectories labeled by unsupervised clustering. It then assigns HMP class labels to current trajectories. Parameters of the proposed classifier are recalculated based on the augmented dataset of labeled trajectories and all HMP classes are accordingly updated. As such, the proposed classifier allows current trajectories to form new HMP classes when they are sufficiently different from existing HMP classes. The performance of the proposed classifier is evaluated by a set of real-world data. The results show that the proposed classifier effectively learns new HMP classes from current trajectories in an online manner. © 2010 IEEE.published_or_final_versionThe 2010 International Conference of Soft Computing and Pattern Recognition (SoCPaR 2010), Paris, France, 7-10 December 2010. In Proceedings of SoCPaR2010, 2010, p. 182-18
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