64 research outputs found

    A selective dynamic compiler for embedded Java virtual machine targeting ARM processors

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    Tableau d’honneur de la Faculté des études supérieures et postdoctorales, 2004-2005Ce travail présente une nouvelle technique de compilation dynamique sélective pour les systèmes embarqués avec processeurs ARM. Ce compilateur a été intégré dans la plateforme J2ME/CLDC (Java 2 Micro Edition for Connected Limited Device Con- figuration). L’objectif principal de notre travail est d’obtenir une machine virtuelle accélérée, légère et compacte prête pour l’exécution sur les systèmes embarqués. Cela est atteint par l’implémentation d’un compilateur dynamique sélectif pour l’architecture ARM dans la Kilo machine virtuelle de Sun (KVM). Ce compilateur est appelé Armed E-Bunny. Premièrement, on présente la plateforme Java, le Java 2 Micro Edition(J2ME) pour les systèmes embarqués et les composants de la machine virtuelle Java. Ensuite, on discute les différentes techniques d’accélération pour la machine virtuelle Java et on détaille le principe de la compilation dynamique. Enfin, on illustre l’architecture, le design (la conception), l’implémentation et les résultats expérimentaux de notre compilateur dynamique sélective Armed E-Bunny. La version modifiée de KVM a été portée sur un ordinateur de poche (PDA) et a été testée en utilisant un benchmark standard de J2ME. Les résultats expérimentaux de la performance montrent une accélération de 360 % par rapport à la dernière version de la KVM de Sun avec un espace mémoire additionnel qui n’excède pas 119 kilobytes.This work presents a new selective dynamic compilation technique targeting ARM 16/32-bit embedded system processors. This compiler is built inside the J2ME/CLDC (Java 2 Micro Edition for Connected Limited Device Configuration) platform. The primary objective of our work is to come up with an efficient, lightweight and low-footprint accelerated Java virtual machine ready to be executed on embedded machines. This is achieved by implementing a selective ARM dynamic compiler called Armed E-Bunny into Sun’s Kilobyte Virtual Machine (KVM). We first present the Java platform, Java 2 Micro Edition (J2ME) for embedded systems and Java virtual machine components. Then, we discuss the different acceleration techniques for Java virtual machine and we detail the principle of dynamic compilation. After that we illustrate the architecture, design, implementation and experimental results of our selective dynamic compiler Armed E-Bunny. The modified KVM is ported on a handheld PDA and is tested using standard J2ME benchmarks. The experimental results on its performance demonstrate that a speedup of 360% over the last version of Sun’s KVM is accomplished with a footprint overhead that does not exceed 119 kilobytes

    An aspect-oriented framework for systematic security hardening of software

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    In this thesis, we address the problems related to the security hardening of open source software. Accordingly, we first propose an aspect-oriented and pattern-based approach for systematic security hardening. It is based on the full separation between the roles and duties of the security experts and the developers performing the hardening. Such proposition constitutes a bridge that allows the security experts to provide the best solutions to particular security problems with the details on why, how and where to apply them. Moreover, it allows the developers to use these solutions to harden open source software without the need to have high security expertise. We realize the proposed approach by elaborating a programming independent and aspect-oriented based language for security hardening called SHL, developing its corresponding parser, compiler and facilities and integrating all of them into a framework for software security hardening. We also illustrate the feasibility of the elaborated framework by developing several security hardening case studies that deal with known security requirements and vulnerabilities and applying them on large scale software. Second, we enrich SHL and the aspect-oriented languages with new pointcut and primitive constructs ( GAFlow, GDFlow, ExportParameter and ImportParameter ) that provide features missing in the current AOP proposals and needed for systematic security hardening concerns. We also explore the viability of the proposed pointcuts and primitives by elaborating and implementing their algorithms and presenting the result of explanatory case studies. Finally, we improve the proposed framework by proposing a new approach for applying security hardening on the Gimple representation of software and elaborating formal syntax for SHL and Gimple together with an operational semantics for SHL weaving based on Gimple. We realize our proposition by integrating into the GCC compiler few features described in the SHL weaving semantics and developing a demonstrative case stud

    A Federated Learning Scheme for Neuro-developmental Disorders: Multi-Aspect ASD Detection

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    Autism Spectrum Disorder (ASD) is a neuro-developmental syndrome resulting from alterations in the embryological brain before birth. This disorder distinguishes its patients by special socially restricted and repetitive behavior in addition to specific behavioral traits. Hence, this would possibly deteriorate their social behavior among other individuals, as well as their overall interaction within their community. Moreover, medical research has proved that ASD also affects the facial characteristics of its patients, making the syndrome recognizable from distinctive signs within an individual's face. Given that as a motivation behind our work, we propose a novel privacy-preserving federated learning scheme to predict ASD in a certain individual based on their behavioral and facial features, embedding a merging process of both data features through facial feature extraction while respecting patient data privacy. After training behavioral and facial image data on federated machine learning models, promising results are achieved, with 70\% accuracy for the prediction of ASD according to behavioral traits in a federated learning environment, and a 62\% accuracy is reached for the prediction of ASD given an image of the patient's face. Then, we test the behavior of regular as well as federated ML on our merged data, behavioral and facial, where a 65\% accuracy is achieved with the regular logistic regression model and 63\% accuracy with the federated learning model

    On the feasibility of Federated Learning towards on-demand client deployment at the edge

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    Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed

    On the Feasibility of Federated Learning for Neurodevelopmental Disorders: ASD Detection Use-Case

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    Autism Spectrum Disorder (ASD) is a neurodevelopmental syndrome resulting from alterations in the embryological brain pre-birth. This disorder distinguishes its patients by special socially restricted and repetitive behavior, in addition to specific behavioral traits, deteriorating their social behavior and interaction within their community. Moreover, medical research has proved that ASD affects the facial features of its patients, making the syndrome recognizable from distinctive signs within an individual\u27s face. Given that as a motivation behind our work, we propose a novel privacy-preserving FL model, in order to predict ASD in a certain individual based on their behavioral traits or facial features, while respecting patient data privacy, as ASD data is medical and hence sensitive to leakage. After training behavioral and facial image data on Federated Machine Learning (FL) models, promising results are achieved, with 70% accuracy for prediction of ASD according to behavioral traits in a federated learning private environment, and a 62% accuracy is reached for prediction of ASD given an image of the patient\u27s face

    Analyzing social web services\u27 capabilities

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    © 2015 IEEE. This paper looks into ways of supporting social Web services react to the behaviors that their peers expose at run time. Examples of behaviors include selfishness and unfairness. These reactions are associated with actions packaged into capabilities. A capability allows a social Web service to stop exchanging private details with a peer and/or to suspend collaborating with another peer, for example. The analysis of capability results into three types referred to as functional (what a social Web service does), non-functional (how a social Web service runs), and social (how a social Web service reacts to peers). To avoid cross-cutting concerns among these capabilities aspect-oriented programming is used for implementing a system

    A Blockchain based Architecture for the Detection of Fake Sensing in Mobile Crowdsensing

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    © 2019 University of Split, FESB. With the emergence of mobile crowdsensing (MCS), we now have the possibility of leveraging the sensing capabilities of mobile devices to collect information and intelligence about cities and events. Despite the promise that MCS brings, this new concept opens the door to a multitude of security and privacy threats and attacks. Indeed, the human involvement in the crowdsensing process and the openness of this process to any participant, render the task of securing MCS environments very challenging. In this work, we propose a Blockchain-based hybrid architecture for the detection and prevention of fake sensing activities in MCS. Our architecture leverages the capabilities of the Blockchain network and introduces a new role to the MCS architecture to ensure the validation of the collected information. Combining both data quality metrics along with behavioral analysis based participants\u27 reliability scoring, our solution is able to detect variations in behavior and quality of contributions. The proposed solution was tested with real life data collected from 200 mobile users, over the span of 2 years, and the results obtained are very promising

    Ontology based recommender system using social network data

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    Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Network (OSN). Domain knowledge is used to define tailored strategies that can decrease the budget and time required for mining while increasing the recall. An ontology supports our filtering layer in evaluating the relatedness of nodes. Our approach demonstrates that the same mechanism can be advanced to prompt recommendations to users. Our test cases and experimental results emphasize the importance of the strategy definition step in our social miner and the application of ontologies on the knowledge graph in the domain of recommendation analysis

    Reinforcement Learning Framework for Server Placement and Workload Allocation in Multi-Access Edge Computing

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    Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low latency besides the higher processing power, is increasingly becoming a vital factor for the success of modern applications. This paper addresses the problem of minimizing both, the network delay, which is the main objective of MEC, and the number of edge servers to provide a MEC design with minimum cost. This MEC design consists of edge servers placement and base stations allocation, which makes it a joint combinatorial optimization problem (COP). Recently, reinforcement learning (RL) has shown promising results for COPs. However, modeling real-world problems using RL when the state and action spaces are large still needs investigation. We propose a novel RL framework with an efficient representation and modeling of the state space, action space and the penalty function in the design of the underlying Markov Decision Process (MDP) for solving our problem

    Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

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    News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19 communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This paper presents a large-scale study based on data mined from Twitter. Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users profiles, meta-data and tweets context. The study noted various interesting conclusions including the critical impact of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.Comment: 11 pages, 10 figures, Journal Articl
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