Malika Yaici, Faiza Ainennas and Nassima Zidi, Computer Department, University of Bejaia, Bejaia, Algeria
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
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
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).
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
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.
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
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.
Feature Lines; Extraction; Meshes .