Kamel Barkaoui

Rateb Jabbar, Mohammed Shinoy, Mohamed Kharbeche et al.

A sleepy driver is arguably much more dangerous on the road than the one who is speeding as he is a victim of microsleeps. Automotive researchers and manufacturers are trying to curb this problem with several technological solutions that will avert such a crisis. This article focuses on the detection of such micro sleep and drowsiness using neural network based methodologies. Our previous work in this field involved using machine learning with multi-layer perceptron to detect the same. In this paper, accuracy was increased by utilizing facial landmarks which are detected by the camera and that is passed to a Convolutional Neural Network (CNN) to classify drowsiness. The achievement with this work is the capability to provide a lightweight alternative to heavier classification models with more than 88% for the category without glasses, more than 85% for the category night without glasses. On average, more than 83% of accuracy was achieved in all categories. Moreover, as for model size, complexity and storage, there is a marked reduction in the new proposed model in comparison to the benchmark model where the maximum size is 75 KB. The proposed CNN based model can be used to build a real-time driver drowsiness detection system for embedded systems and Android devices with high accuracy and ease of use.

Aymen Louati, Chadlia Jerad, Kamel Barkaoui

Thanks to its graphical notation and simplicity, Unified Modeling Language (UML) is a de facto standard and a widespread language used in both industry and academia, despite the fact that its semantics is still informal. The Interaction Overview Diagram (IOD) is introduced in UML2; it allows the specification of the behavior in the hierarchical way. This paper is a contribution towards a formal dynamic semantics of UML2. We start by formalizing the Hierarchical use of IOD. Afterward, we complete the mapping of IOD, Sequence Diagrams and Timing Diagrams into Hierarchical Colored Petri Nets (HCPNs) using the Timed colored Petri Nets (timed CP-net). Our approach helps designers to get benefits from abstraction as well as refinement at more than two levels of hierarchy which reduces verification complexity.

Sonia Alouane-Ksouri, Minyar Sassi-Hidri, Kamel Barkaoui

In a context of document co-clustering, we define a new similarity measure which iteratively computes similarity while combining fuzzy sets in a three-partite graph. The fuzzy triadic similarity (FT-Sim) model can deal with uncertainty offers by the fuzzy sets. Moreover, with the development of the Web and the high availability of storage spaces, more and more documents become accessible. Documents can be provided from multiple sites and make similarity computation an expensive processing. This problem motivated us to use parallel computing. In this paper, we introduce parallel architectures which are able to treat large and multi-source data sets by a sequential, a merging or a splitting-based process. Then, we proceed to a local and a central (or global) computing using the basic FT-Sim measure. The idea behind these architectures is to reduce both time and space complexities thanks to parallel computation.

Zohra Sbaï, Kamel Barkaoui

In this paper, we present a method of verification of collaborative workflow processes based on model checking techniques. In particular, we propose to verify soundness properties of these processes using SPIN model checker. First we translate the adopted specification of workflows (i.e. the WF-net) to Promela which is the description language of models to be verified by SPIN. Then we express the soundness properties in Linear Temporal Logic (LTL) and use SPIN to test whether each property is satisfied by the Promela model of the WF-net in question. Finally, we express the properties of k-soundness for WF-nets modeling multiple instances and (k,R)-soundness for workflow processes with multiple instances and sharing resources.