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    NSSSD: A new semantic hierarchical storage for sensor data

    , Article 20th IEEE International Conference on Computer Supported Cooperative Work in Design, 4 May 2016 through 6 May 2016 ; 2016 , Pages 174-179 ; 9781509019151 (ISBN) Gheisari, M ; Movassagh, A. A ; Qin, Y ; Yong, J ; Tao, X ; Zhang, J ; Shen, H ; Liu, X. P ; Yong, J ; Barthes, J. P ; Shen, W ; Yang, C ; Luo, J ; Chen, L ; IEEE Systems, Man, and Cybernetics Society; International Working Group on Computer Supported Cooperative Work in Design (CSCWD) ; Sharif University of Technology
    Institute of Electrical and Electronics Engineers Inc 
    Sensor networks usually generate mass of data, which if not structured for future applications, will require much effort on analytical processing and interpretations. Thus, storing sensor data in an effective and structured format is a key issue in the area of sensor networks. In the meantime, even a little improvement on data storing structure may lead to a significant effect on the lifetime and performance of the sensor network. This paper describes a new method for sensor storage that combines semantic web concepts, a data aggregation method along with aligning sensors in hierarchical form. This solution is able to reduce the amount of data stored at the sink nodes significantly. At the... 

    Advantages of dependency parsing for free word order natural languages

    , Article 41st International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2015, 24 January 2015 through 29 January 2015 ; Volume 8939 , 2015 , Pages 511-518 ; 03029743 (ISSN) ; 9783662460771 (ISBN) Mirlohi Falavarjani, S. A ; Ghassem Sani, G ; Sharif University of Technology
    Springer Verlag  2015
    An important reason to prefer dependency parsing over classical phrased based methods, especially for languages such as Persian, with the property of being “free word order”, is that this particular property has a negative impact on the accuracy of conventional parsing methods. In Persian, some words such as adverbs can freely be moved within a sentence without affecting its correctness or meaning. In this paper, we illustrate the robustness of dependency parsing against this particular problem by training two well-known dependency parsers, namely MST Parser and Malt Parser, using a Persian dependency corpus called Dadegan. We divided the corpus into two separate parts including only...