9th International Conference on Computer Science, Engineering and Applications (CCSEA 2019)

July 13~14, 2019, Toronto, Canada

Accepted Papers


    Context-Aware Trust-Based Access Control For Ubiquitous Systems
    Malika Yaici, Faiza Ainennas and Nassima Zidi, Computer Department, University of Bejaia, Bejaia, Algeria
    ABSTRACT
    The ubiquitous computing and context-aware applications experience at the present time a very important development. This has led organizations to open more of their information systems, making them available anywhere, at any time and integrating the dimension of mobile users. This cannot be done without taking into account thoughtfully the access security: a pervasive information system must henceforth be able to take into account the contextual features to ensure a robust access control. In this paper, access control and a few existing mechanisms have been exposed. It is intended to show the importance of taking into account context during a request for access. In this regard, our proposal incorporates the concept of trust to establish a trust relationship according to three contextual constraints (location, social situation and time) in order to decide to grant or deny the access request of a user to a service
    KEYWORDS

    Pervasive systems, Access Control, RBAC, Context-awareness, Trust management


    An Ontology Based Approach to Improve Process Mining Result In Univer sity Information system
    Maryem Dellai and Yemna Sayeb, Research Laboratory, ISAMM, Manouba, Tunisia
    ABSTRACT
    Process mining algorithms use event logs to extract process-related information, to discover, analyze conformance, or to enhance processes. Event logs can be used to analyze and visualize the processes with better insight and improved formal access to the data. Most process mining (PM) applications are based on event logs with keyword-based activity and resource descriptions. In recent years, lots of efforts are dedicated to explore logic-based ontology formalisms.In this research work, we use ontologies that are intended to define the semantics of recorded events. The highest quality of event logs requires the existence of ontologies to which events and attributes point. Many human-designed processes are based on explicit workflow or lifecycle models which can be described using taxonomies or more complicated ontologies. Ontologies have been successfully applied to represent the knowledge in many domains. In this paper, we introduce an approach for enriching event logs using Process mining with associated ontology structures. Our proposal is to provide features that help integrating event logs from event sources in order to extract data and put it into a suitable format semantically enriched so that the data can be exploited with process mining tools (ProM).
    KEYWORDS

    process mining, event logs, ontologies, process model, Petri net.


    Construction Of an Oral Cancer Auto-Classify system Based On Machine- Learning for Artificial Intelligence
    Meng-Jia Lian1, Chih-Ling Huang2, Tzer-Min Lee1,3
    1 School of Dentistry, Kaohsiung Medical University, Kaohsiung, Taiwan
    2 Center for Fundamental Science, Kaohsiung Medical University, Kaohsiung, Taiwan
    3Institute of Oral Medicine, National Cheng Kung University Medical College, Tainan
    ABSTRACT
    Oral cancer is one of the most prevalent tumors of the head and neck region. An earlier diagnosis can help dentist getting a better therapy plan, giving patients a better treatment and the reliable techniques for detecting oral cancer cells are urgently required. This study proposes an optic and automation method using reflection images obtained with scanned laser pico-projection system, and Gray-Level Cooccurrence Matrix for sampling. Moreover, the artificial intelligence technology, Support Vector Machine, was used to classify samples. Normal Oral Keratinocyte and dysplastic oral keratinocyte were simulating the evolvement of cancer to be classified. The accuracy in distinguishing two cells has reached 85.22%. Compared to existing diagnosis methods, the proposed method possesses many advantages, including a lower cost, a larger sample size, an instant, a non-invasive, and a more reliable diagnostic performance. As a result, it provides a highly promising solution for the early diagnosis of oral squamous carcinoma.
    KEYWORDS

    Oral Cancer Cell, Normal Oral Keratinocyte (NOK), Dysplastic oral keratinocyte (DOK), Gray-Level Cooccurrence Matrix (GLCM), Scanned Laser Pico-Projection (SLPP), Support Vector Machine (SVM), Machine-Learning


    Automatic Extraction of Feature Lines on 3D Surface
    Zhihong Mao,Division of Intelligent Manufacturing, Wuyi University, Jiangmen, China
    ABSTRACT
    Many applications in mesh processing require the detection of feature lines. Feature lines convey the inherent features of the shape. Existing techniques to find feature lines in discrete surfaces are relied on user-specified thresholds and are inaccurate and time-consuming. We use an automatic approximation technique to estimate the optimal threshold for detecting feature lines. Some examples are presented to show our method is effective, which leads to improve the feature lines visualization.
    KEYWORDS

    Feature Lines; Extraction; Meshes .


    HMM-Based Dari Named Entity Recognition for Information Extraction
    Ghezal Ahmad Jan, Zia
    Department of Models and Theory of Distributed Systems, TU Berlin Straße des 17. Juni 135, 10623 Berlin, Germany
    ABSTRACT
    Named Entity Recognition (NER) is the fundamental subtask of information extraction systems that labels elements into categories such as persons, organizations or locations. The task of NER is to detect and classify words that are parts of sentences. This paper describes a statistical approach to modeling NER on the Dari language.Dari and Pashto are low resources languages, spoken as official languages in Afghanistan. Unlike other languages, named entity detection approaches differ in Dari. Since in Dari language there is no capitalization for identifying named entities. We seek to bridge the gap between Dari linguistic structure and supervised learning model that predict the sequences of words paired with a sequence of tags as outputs. Dari corpus was developed from the collection of news, reports and articles based on the original orthographic structure of the Dari language. The experimental result presents the named entity recognition performance 95% accuracy.
    KEYWORDS

    Natural Language Processing (NLP), Hidden Markov Model (HMM), Named Entity Recognition (NER), Part-of-Speech (POS) Tagging


    Designing Dynamic Protocol for Real-Time IIoT-based Applications by Efficient Management of System Resources
    Farzad Kiani, Sajjad Nematzadehmiandoab, Amir Seyyedabbasi
    Computer Engineering Dept., Engineering and Natural Sciences Faculty at Istanbul Sabahattin Zaim University, Kucukcekmece, 34303, Istanbul, Turkey
    ABSTRACT
    Due to increased applicability, wireless sensor networks have captured the attention of researchers from various fields. These networks still suffer from various challenges and limitations regardless. These problems are even much more pronounced in some areas of the field such as real time IoT based applications. Here, a dynamic protocol that efficiently utilizes the available resources is proposed. The protocol employs five developed algorithms that aid the data transmission, neighbor and optimal path finding processes. The protocol can be utilized in, but not limited to, real-time large data streaming applications.. In this paper is defined a structure that enables the sensor devices to communicate with each other over their local network or internet as required in order to preserve the available resources. Both theoretical and experimental result analysis of the entire protocol in general and individual algorithms is also performed.
    KEYWORDS

    Big data wireless sensor networks, real-time systems, energy efficiency, routing protocol, IoT.

      Interactive Mesh Cutout Using Graph Cuts
      Zhihong Mao,Division of Intelligent Manufacturing, Wuyi University, Jiangmen529020, China
      ABSTRACT
      Mesh segmentation is a foundational operation for many computer graphics applications. Although various automatic segmentation schemes have been proposed, to precisely obtain the meaningful part of a mesh is a challenging issue. In this paper, we introduce an Interactive system to efficiently extract meaningful objects from a triangular mesh. The algorithm proposed in this paper extends min-cut based on 2D-image segmentation techniques to the domain of 3D mesh. We also provide a screen-space user interface that allows the user to indicate the meaningful object easily. In our system, quadric-based surface simplification is adopted for a large mesh, we use min-cut in the simplified mesh, then graph cuts are used to refine the previous cuts in the original mesh. The results show that our proposed method is relatively simple and effective as a powerful tool for mesh cutout.
      KEYWORDS

      Mesh Segmentation, Mesh Cutout, Graph cuts.

      Tough Random Symmetric 3-SAT Generator
      Robert Amador1, Chang-Yu Hsieh2 , Chen-Fu Chiang3
      1,3Department of Computer Science, State University of New York Polytechnic Institute, Utica, NY 13502, USA,2Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
      ABSTRACT
      We designed and implemented an efficient tough random symmetric 3-SAT generator. We quantify the hardness in terms of CPU time, numbers of restarts, decisions, propagations, conflicts and conflicted literals that occur when a solver tries to solve 3-SAT instances. In this experiment, the clause variable ratio was chosen to be around the conventional critical phase transition number 4.24. The experiment shows that instances generated by our generator are significantly harder than instances generated by the Tough K-SAT generator. The difference in hardness between two SAT instance generators exponentiates as the number of boolean variables used increases.

      Data Analysis of Wireless Networks Using Classification Techniques
      Daniel Rosa Canêdo1,2 and Alexandre Ricardo Soares Romariz1
      1Department of Electrical Engineering, University of Brasília, Brasília, Brazil ,2Federal Institute of Goiás, Luziânia, Brazil
      ABSTRACT
      In the last decade, there has been a great technological advance in the infrastructure of mobile technologies. The increase in the use of wireless local area networks and the use of satellite services are also noticed. The high utilization rate of mobile devices for various purposes makes clear the need to monitor wireless networks to ensure the integrity and confidentiality of the information transmitted. Therefore, it is necessary to quickly and efficiently identify the normal and abnormal traffic of such networks, so that administrators can take action. This work aims to analyze classification techniques in relation to data from Wireless Networks, using some classes of anomalies pre-established according to some defined criteria of the MAC layer. For data analysis, WEKA Data Mining software (Waikato Environment for Knowledge Analysis) is used. The classification algorithms present a success rate in the classification of viable data, being indicated in the use of intrusion detection systems for wireless networks.
      KEYWORDS

      Wireless Networks, Classification Thecniques, Weka.