International Conference Papers

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    Better Space-Time-Robustness Trade-Offs for Set Reconciliation
    (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2024-07) Belazzougui , Djamal; Kucherov, Gregory; Walzer, Stefan
    We consider the problem of reconstructing the symmetric difference between similar sets from their representations (sketches) of size linear in the number of differences. Exact solutions to this problem are based on error-correcting coding techniques and suffer from a large decoding time. Existing probabilistic solutions based on Invertible Bloom Lookup Tables (IBLTs) are time-efficient but offer insufficient success guarantees for many applications. Here we propose a tunable trade-off between the two approaches combining the efficiency of IBLTs with exponentially decreasing failure probability. The proof relies on a refined analysis of IBLTs proposed in (Bæk Tejs Houen et al. SOSA 2023) which has an independent interest. We also propose a modification of our algorithm that enables telling apart the elements of each set in the symmetric difference.
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    Towards Big Data Analytics over Mobile User Data using Machine Learning
    (IEEE, 2023-01) Ichou, Sabrina; Hammoudi, Slimane; Cuzzocrea, Alfredo; Meziane, Abdelkrim; Benna, Amel
    Machine Learning (ML) is a science that forces computers to learn and behave like humans. As these systems interact with data, networks, and people, they automatically become smarter so that they can eventually solve or predict a practical issue in the world for us. The use of ML can be a giant leap for cannot simply be integrated as the top layer. This requires redefining workflow, architecture, data collection and storage, analytics, and other modules. This paper aims to discuss the issue of machine learning technique for analysis data of mobile user. First, we identified the machine learning benefits and drawbacks, challenges, advantages of using Machine Learning. Then, we propose a generic model of analytic mobile user data using ML, the model is centered on the machine learning component, which interacts with two other components, including mobile user data, and system. The interactions go in both directions. For instance, mobile user data serves as inputs to the learning component and the latter generates outputs; system architecture has impact on how learning algorithms should run and how efficient it is to run them, and simultaneously meeting. Mobile user data goes through several stages: prepossessing which includes the steps we need to follow to transform or encode the data so that it can be easily analyzed by the machine. Then, modelling in this step we will be clustering and classification the data obtained. Finally, evaluation, various measures of performance, accuracy, recall, precision, and F-measure were used to analyze the results of the naive Bayes, SVM, and K-nearest neighbor classification algorithms.
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    Low-cost haptic glove for grasp precision improvement in Virtual Reality-Based Post-Stroke Hand Rehabilitation
    (IEEE) Masmoudi, Mostefa; Zenati, Nadia; Benbelkacem , Samir; Hadjadj, Zineb
    Stroke in Algeria is one of the most important causes of severe physical disability. Upper limb paralysis is also most common in stroke patients, which severely affecting their daily life. Therefore, it is important to help stroke patients to improve the quality of their life. In this article, we have proposed a novel system based on virtual reality for fine motor rehabilitation. Because the sense of touch is essential to the patient's daily activities, we have integrated haptic feedback into our system (vibrating glove), this is to help the patient to perform rehabilitation exercises. The proposed vibrating glove is equipped with five small and flat vibrating motor discs (one on each finger); these motors are controlled by ESP8266 board. This system has been tested on two patients with stroke. The preliminary results show that the system can help patients recover fine motor skills.
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    Optimizing Cloud Energy Consumption Using Static Task Scheduling Algorithms: A Comparative Study
    (IEEE, 2023-12) Khiat, Abdelhamid
    Cloud data centers, comprising a diverse set of heterogeneous resources working collaboratively to achieve high-performance computing, face the challenge of resource dynamism, where performance fluctuates over time. This dynamism poses complexities in task scheduling, warranting further research on the resilience of existing static task scheduling algorithms when deployed in dynamic cloud environments. This study adapts three well-known task scheduling algorithms to the cloud computing context and conducts a comprehensive comparison to assess their resilience to dynamic conditions. The evaluation, employing simulation techniques, analyzes total energy consumption and total response time as key metrics. The results offer detailed insights into the effectiveness of the adapted algorithms, providing valuable guidance for optimizing task scheduling in dynamic cloud data centers.
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    Intrusion Detection Systems using Data Mining Techniques: A comparative study
    (IEEE, 2022-01-20) Haddadi, Mohamed; Khiat, Abdelhamid; Bahnes, Nacera
    Data mining tools are widely used in computer networks. The well-known and mostly used tools to secure computers and network systems are WEKA and TANAGRA. The purpose of this study is to compare these two tools in terms of detection accuracy and computation time. This comparison was conducted using a well-known NSL-KDD dataset. Experiments show that TANAGRA achieves better results than WEKA in detection accuracy. But, TANAGRA is competitive with WEKA in terms of computation time.
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    Combining Tags and Reviews to Improve Social Book Search Performance
    (Springer, 2018-08-15) Chaa, Messaoud; Nouali, Omar; Bellot, Patrice
    The emergence of Web 2.0 and social networks have provided important amounts of information that led researchers from different fields to exploit it. Social information retrieval is one of the areas that aim to use this social information to improve the information retrieval performance. This information can be textual, like tags or reviews, or non textual like ratings, number of likes, number of shares, etc. In this paper, we focus on the integration of social textual information in the research model. As it seems logical that integrating tags in the retrieval model should not be in the same way taken to integrate reviews, we will analyze the different influences of using tags and reviews on both the settings of retrieval parameters and the retrieval effectiveness. After several experiments, on the CLEF social book search collection, we concluded that combining the results obtained from two separate indexes and two models with specific parameters for tags and reviews gives good results compared to when using a single index and a single model.
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    DPFTT: Distributed Particle Filter for Target Tracking in the Internet of Things
    (IEEE, 2023-11-07) Boulkaboul, Sahar; Djenouri, Djamel; Bagaa, Miloud
    A novel distributed particle filter algorithm for target tracking is proposed in this paper. It uses new metrics and addresses the measurement uncertainty problem by adapting the particle filter to environmental changes and estimating the kinematic (motion-related) parameters of the target. The aim is to calculate the distance between the Gaussian-distributed probability densities of kinematic data and to generate the optimal distribution that maximizes the precision. The proposed data fusion method can be used in several smart environments and Internet of Things (IoT) applications that call for target tracking, such as smart building applications, security surveillance, smart healthcare, and intelligent transportation, to mention a few. The diverse estimation techniques were compared with the state-of-the-art solutions by measuring the estimation root mean square error in different settings under different conditions, including high-noise environments. The simulation results show that the proposed algorithm is scalable and outperforms the standard particle filter, the improved particle filter based on KLD, and the consensus-based particle filter algorithm.
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    On the challenges of mobility prediction in smart cities
    (Copernicus Publications, 2020) Boukhedouma, H.; Meziane, Abdelkrim; Hammoudi, S.; Benna, Amel
    The mass of data generated from people’s mobility in smart cities is constantly increasing, thus making a new business for large companies. These data are often used for mobility prediction in order to improve services or even systems such as the development of location-based services, personalized recommendation systems, and mobile communication systems. In this paper, we identify the mobility prediction issues and challenges serving as guideline for researchers and developers in mobility prediction. To this end, we first identify the key concepts and classifications related to mobility prediction. We then, focus on challenges in mobility prediction from a deep literature study. These classifications and challenges are for serving further understanding, development and enhancement of the mobility prediction vision.
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    Leap motion controller for upper limbs physical rehabilitation in post-stroke patients: a usability evaluation
    (2022-05) Hadjadj, Zineb; Masmoudi, Mostefa; Meziane, Abdelkrim; Zenati, Nadia
    Stroke in Algeria is one of the most important causes of severe physical disability. Since the disease strongly influences the quality of life of patients, optimal solutions for the treatment of post-stroke patients are needed. The use of new technologies in the field of rehabilitation aims to reduce the impact of functional problems. Recent studies have shown that technologies such as virtual reality and video games can provide a way that can motivate and help patients recover their motor skills. In this paper, our objective is to evaluate the usability of the Leap Motion Controller virtual reality system (LMC), which is a sensor that captures the movement of the patient's hands and fingers without the need to place sensors or devices on the body, with serious games specifically designed for upper limbs rehabilitation in post-stroke patients. We measured the usability of the LMC system used with serious games as well as the level of satisfaction among healthy participants and post-stroke patients from Bounaama Djilali Hospital (CHU Douera) in Algeria. The results show favorable data, for the first time, the LMC is a usable tool, measured by the System Usability Scale (SUS). In addition, participants demonstrated good motivation, enjoyment and the majority of them said that they would like to use the proposed system in future treatment. Nevertheless, further studies are needed to confirm these preliminary findings.
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    PReDIHERO – Privacy-Preserving Remote Deep Learning Inference based on Homomorphic Encryption and Reversible Obfuscation for Enhanced Client-side Overhead in Pervasive Health Monitoring
    (IEEE, 2021) Boulemtafes, Amine; Derhab, Abdelouahid; Ait Ali Braham, Nassim; Challal, Yacine
    Homomorphic Encryption is one of the most promising techniques to deal with privacy concerns, which is raised by remote deep learning paradigm, and maintain high classification accuracy. However, homomorphic encryption-based solutions are characterized by high overhead in terms of both computation and communication, which limits their adoption in pervasive health monitoring applications with constrained client-side devices. In this paper, we propose PReDIHERO, an improved privacy-preserving solution for remote deep learning inferences based on homomorphic encryption. The proposed solution applies a reversible obfuscation technique that successfully protects sensitive information, and enhances the client-side overhead compared to the conventional homomorphic encryption approach. The solution tackles three main heavyweight client-side tasks, namely, encryption and transmission of private data, refreshing encrypted data, and outsourcing computation of activation functions. The efficiency of the client-side is evaluated on a healthcare dataset and compared to a conventional homomorphic encryption approach. The evaluation results show that PReDIHERO requires increasingly less time and storage in comparison to conventional solutions when inferences are requested. At two hundreds inferences, the improvement ratio could reach more than 30 times in terms of computation overhead, and more than 8 times in terms of communication overhead. The same behavior is observed in sequential data and batch inferences, as we record an improvement ratio of more than 100 times in terms of computation overhead, and more than 20 times in terms of communication overhead.