International audience

We propose a new approach for Arabic Word Sense Disambiguation (AWSD) by hybridization of single-layer Convolutional Neural Network (CNN) with contextual representation (BERT). WSD is the task of automatically detecting the correct meaning of a word used in a given context. WSD can be performed as a classification task, and the context is generally a short sentence. Kim [ 26 ] proved that combining a CNN with an RNN (recurrent neural network) provides a good result for text classification. Here, we use a concatenation of BERT models as a word embedding to get simultaneously the target and context representation. Our approach improves the performance of WSD in Arabic languages. The experimental results show that our model outperforms the state-of-the-art approaches and improves the accuracy of 96.42% on the Arabic WordNet dataset.

The paper presents a middleware implemented in Java, RT-MED, which corresponds to a software layer to be placed above the operating system. This software component is designed to execute and evaluate the performance, reliability and correctness of some real-time scheduling approaches which are theoretically validated. It describes a transition from the theory to the actual implementation of the proposed solutions. These solutions are based on a combinatorial optimization approach to solve the problem of feasibility in a system which is dynamically reconfigurable. RT-MED also presents a patch between the system and its environment under different constraints such as time and energy. It offers a set of adjustable parameters to control the flow of the execution. The middleware can be integrated into many operating systems and provides good quality both in terms of execution time and energy consumption. The implementation of this tool is based on java technology with embedded and real-time systems supported by the Real-Time Specification for Java. We have used a UML profile to describe various states and run-time reconfiguration of the embedded system. Results show that the middleware can effectively maintain the control and the stability of the system.

The paper deals with low-power adaptive scheduling of synchronous and flexible real-time OS tasks. A software reconfiguration scenario is assumed to be any run-time operation allowing the addition-removal-update of OS tasks to adapt the system to its environment under well-defined conditions. The problem is that any reconfiguration can push the system to an unfeasible behavior where temporal properties are violated or the energy consumption is possibly high and unacceptable. A task in the system can change its characteristics at any time when a reconfiguration scenario is applied, it can also be stopped or replaced by another one. The difficulty is how to find the new temporal parameters of the systems tasks after any reconfiguration. We use a DVS processor which is with a variable speed to support run-time solutions that re-obtain the system's feasibility. The challenge is how to compute the best combinations between available processor speeds for a good compromise between execution time and energy consumption. We propose a combinatorial optimization method based on integer programming and heuristics. We propose also a solution when the available speeds do not allow the feasibility of the system. Both approaches include a mechanism to adjust the deadlines of tasks to satisfy the feasibility conditions and overcome the problem of rejected tasks. This mechanism makes the scheduling more flexible and able to react in accordance with its environment.