Application-based authentication on an inter-VM traffic in a Cloud environment

Cloud Computing (CC) is an innovative computing model in which resources are provided as a service over the Internet, on an as-needed basis. It is a large-scale distributed computing paradigm that is driven by economies of scale, in which a pool of abstracted, virtualized, dynamically-scalable, managed computing power, storage, platforms, and services are delivered on demand to external customers over the Internet. Since cloud is often enabled by virtualization and share a common attribute, that is, the allocation of resources, applications, and even OSs, adequate safeguards and security measures are essential. In fact, Virtualization creates new targets for intrusion due to the complexity of access and difficulty in monitoring all interconnection points between systems, applications, and data sets. This raises many questions about the appropriate infrastructure, processes, and strategy for enacting detection and response to intrusion in a Cloud environment. Hence, without strict controls put in place within the Cloud, guests could violate and bypass security policies, intercept unauthorized client data, and initiate or become the target of security attacks. This article shines the light on the issues of security within Cloud Computing, especially inter-VM traffic visibility. In addition, the paper lays the proposition of an Application Based Security (ABS) approach in order to enforce an application-based authentication between VMs, through various security mechanisms, filtering, structures, and policies

Content based Medical Image Retrieval: use of Generalized Gaussian Density to model BEMD's IMF.

In this paper, we address the problem of medical ddiagnosis aid through content based image retrieval methods. We propose to characterize images without extracting local features, by using global information extracted from the image Bidimensional Empirical Mode Decomposition (BEMD). This method decompose image into a set of functions named Intrinsic Mode Functions (IMF) and a residu. The generalized Gaussian density function (GGD) is used to represent the coefficients derived from each IMF, and the Kullback–Leibler Distance (KLD) compute the similarity between GGDs. Retrieval efficiency is given for different databases including a diabetic retinopathy, and a face database. Results are promising: the retrieval efficiency is higher than 85% for some cases

Improving Content Based Video Retrieval Performance by Using Hadoop-MapReduce Model

In this paper, we present a distributed Content- Based Video Retrieval (CBVR) system based on MapReduce pro- gramming model. A CBVR system called Bounded Coordinate of Motion Histogram (BCMH) has been implemented as case study by using Hadoop framework. Our work consists of proposing a distributed model to extract videos signatures and compute similarity with the BCMH system based on a set of Mapreduce jobs assigned to multiple nodes of the Hadoop cluster in order to reduce computation time of training process. The proposed approach is tested on HOLLYWOOD2 dataset and the obtained results demonstrate efficiency of the proposed approach

Indexation de l'information médicale. Application à la recherche d'images et de vidéos par le contenu

Dans ce travail de thèse, nous nous intéressons à l'utilisation des bases de données médicales multimédia pour l'aide à la décision diagnostique et le suivi thérapeutique. Notre objectif est de définir des méthodes, et un système, pour sélectionner dans les bases de documents multimédia des documents similaires à un document proposé en requête. Ces documents contiennent des informations sous forme texte, numérique, des images et parfois des séquences vidéos. Pour l'aide au diagnostic, l'interrogation du système s'effectue en lui présentant en requête le dossier patient, ou une partie de ce dossier. Notre travail va donc mettre en oeuvre des méthodes relatives au raisonnement à base de cas (CBR : Case Based Reasoning), à la fouille de données, à la recherche d images par le contenu (CBIR : Content Based Image Retrieval) et à la rechercher de vidéo par le contenu (CBVR : Content Based Video Retrieval). Les méthodes sont évaluées sur trois bases de données médicales multimodales. La première base de données étudiée est une base d images rétiniennes, constituée au LaTIM pour l aide au suivi de la rétinopathie diabétique. La seconde base est une base publique de mammographies (Digital Database for Screening Mammography, DDSM University of South Florida). La troisième base de données est une base de video gastro-entérologie constituée aussi au LaTIM. Nous utilisons cette base pour étudier les possibilités d'utilisation des méthodes développées dans le cadre de la recherche d images fixes, pour la recherche de séquences vidéos couleurs. Dans la première partie de notre travail, nous cherchons à caractériser individuellement chaque image du dossier patient. Nous avons poursuivi les travaux effectués dans le laboratoire sur l utilisation des méthodes globales de caractérisation des images dans le domaine compressé (quantification vectorielle, DCT, JPEG-ondelettes, ondelettes adaptées) pour la recherche d images. Les résultats obtenus avec les ondelettes, comparés aux autres méthodes de compression ont montré une grande amélioration en terme de retrouvaille. Cependant, les ondelettes nécessitent la spécification d'un noyau ou d'une fonction de base pour effectuer la décomposition. Pour pallier ce problème, nous avons proposé une méthode originale de caractérisation à partir de la décomposition BEMD des images (Bidimensionnal Empirical Mode Decomposition) : elle permet de décomposer une image en plusieurs modes BIMFs (Bidimensionnel Intrinsic Mode Functions), qui permettent d'accéder à des informations sur le contenu fréquentiel des images. Une des originalités de la méthode provient de l auto-adaptativite de la BEMD, qui ne nécessite pas une fonction de base pour effectuer la décomposition. Une fois les images caractérisées, la recherche s'effectue en calculant, au sens d'une métrique donnée, la distance entre la signature de l image requête et les signatures des images de la base. Ce calcul permet de sélectionner des images en réponse à la requête en dehors de toute signification sémantique. Pour améliorer le résultat de retrouvaille, nous introduisons une technique d optimisation pour le calcul de la distance entre signature, en utilisant les algorithmes génétiques. Nous abordons ensuite le problème de la recherche de vidéos par le contenu. Pour cela, nous introduisons une méthode pour le calcul des signatures vidéo à partir des images clefs extraites par l analyse du mouvement. La distance entre signatures video est calculée en utilisant une technique basée sur l analyse en composantes principales. Enfin, nous intégrons les travaux précédents dans la requète par dossiers patients, qui contiennent plusieurs images ainsi que des informations textuelles, sémantiques et numériques. Pour cela nous utilisons trois méthodes développées dans le cadre d une these récemment soutenue dans notre laboratoire : la première est basée sur les arbres de décision, la deuxième sur les réseaux bayésiens et la troisième sur la théorie de Dezert-Smarandache (DSmT).This PhD thesis addresses the use of multimedia medical databases for diagnostic decision and therapeutic follow-up. Our goal is to develop methods and a system to select in multimedia databases documents similar to a query document. These documents consist of text information, numeric images and sometimes videos. In the proposed diagnosis aid system, the database is queried with the patient file, or a part of it, as input. Our work therefore involves implementing methods related to Case-Based Reasoning (CBR), datamining, Content Based Image Retrieval (CBIR) and Content Based Video Retrieval (CBVR). These methods are evaluated on three multimodal medical databases. The first database consists of retinal images collected by the LaTIM laboratory for aided diabetic retinopathy follow-up. The second database is a public mammography database (Digital Database for Screening Mammography DDSM ) collected by the University of South Florida. The third database consists of gastroenterology videos also collected by the LaTIM laboratory. This database is used to discover whether methods developed for fixed image retrieval can also be used for color video retrieval. The first part of this work focuses on the characterization of each image in the patient file. We continued the work started in our laboratory to characterize images globally in the compressed domain (vector quantization, DCT-JPEG, wavelets, adapted wavelets) for image retrieval. Compared to other compression methods, the wavelet decomposition led to a great improvement in terms of retrieval performance. However, the wavelet decomposition requires the specification of a kernel or basis function. To overcome this problem, we proposed an original image characterization method based on the BEMD (Bidimensionnal Empirical Mode Decomposition). It allows decomposing an image into several BIMFs (Bidimensionnal Intrinsic Mode Functions) that provide access to frequency information of the image content. An originality of the method comes from the self-adaptivity of BEMD: it does not require the specification of a basic function. Once images are characterized, a similarity search is performed by computing the distance between the signature of the query image and the signature of each image in the database, given a metric. This process leads to the selection of similar images, without semantic meaning. An optimization process, based on genetic algorithms, is used to adapt the distance metric and thus improve retrieval performance. Then, the problem of content based video retrieval is addressed. A method to generate video signatures is presented. This method relies on key video frames extracted by movement analysis. The distance between video signatures is computed using a Principal Component Analysis (PCA) based technique. Finally, the proposed methods are integrated into the framework of patient file retrieval (each patient file consisting of several images and textual information). Three methods developed during a PhD thesis recently defended in our laboratory are used for patient file retrieval: the first approach is based on decision trees and their extensions, the second on Bayesian networks and the third on the Dezert-Smarandache theory (DSmT)..RENNES1-BU Sciences Philo (352382102) / SudocCESSON SEVIGNE-Télécom Breta (350512301) / SudocBREST-Télécom Bretagne (290192306) / SudocSudocFranceF

A distributed Content-Based Video Retrieval system for large datasets

Abstract With the rapid growth in the amount of video data, efficient video indexing and retrieval methods have become one of the most critical challenges in multimedia management. For this purpose, Content-Based Video Retrieval (CBVR) is nowadays an active area of research. In this article, a CBVR system providing similar videos from a large multimedia dataset based on query video has been proposed. This approach uses vector motion-based signatures to describe the visual content and uses machine learning techniques to extract key frames for rapid browsing and efficient video indexing. The proposed method has been implemented on both single machine and real-time distributed cluster to evaluate the real-time performance aspect, especially when the number and size of videos are large. Experiments were performed using various benchmark action and activity recognition datasets and the results reveal the effectiveness of the proposed method in both accuracy and processing time compared to previous studies

Advancing human action recognition: A hybrid approach using attention-based LSTM and 3D CNN

In this paper, we propose a novel approach to video action recognition that integrates a modified and optimized 3D Convolutional Neural Network, a Long Short-Term Memory network, and attention mechanisms. This synergy enhances the overall performance, offering an advantage over existing methods in managing the intricacies of real-world scenarios. The uniqueness of our approach lies in its capacity to capture both spatial and temporal information from video sequences and the incorporation of an attention mechanism that selectively emphasizes key areas within the sequences, thereby enhancing recognition accuracy. The model is particularly tailored to handle complex scenarios, such as those with multiple actors or objects, or instances of occlusion. It effectively addresses the subjectivity and variability inherent in action annotations within datasets. We also apply an array of preprocessing techniques to further optimize model performance. Through rigorous experimental evaluations on benchmark datasets, namely UCF101 and HMDB51, we demonstrate that our proposed approach significantly outperforms existing state-of-the-art methods in action recognition. These results underscore the potential of our approach for further advancements in video action recognition research

Identification of Online Learning Challenges During the COVID-19 Pandemic in Developing Countries: A Case Study of a Metropolis Faculty of Sciences

The unexpected outbreak of the Corona virus (COVID-19) disrupted schools and universities around the world. Traditional classes were canceled, forcing schools and universities to switch to online learning. While developed countries have already adopted e-learning and online learning into their teaching practices, which made the transition relatively easy during the COVID-19 crisis, other developing countries continue to struggle with problems of electricity and information technology infrastructure. The purpose of this paper is to investigate the challenges of online learning faced by students and teachers at the Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Morocco. Thus, two anonymous structured Google Forms questionnaires were sent to participants via email. 498 responses were returned from students and 105 from teachers. We use descriptive statistics to better understand the distribution of study participants. The study reveals that the faculty during the lockdown ensured educational continuity and offered a reliable online learning in terms of digital and educational materials. However, technical problems such as the slow speed of the Internet connection, lack of knowledge about the use of information and communication technologies to teach and learn, low motivation of students, were significant challenges to students’ and teachers’ use of the digital tools

Recherche d'images médicales par leur contenu numérique : utilisation de signatures construites à partir de la BEMD

Nous nous intéressons à la recherche d'images médicales par leur contenu. Pour construire un vecteur caractéristique d'une image, nous effectuons une analyse fréquentielle de l'image basée sur la méthode BEMD (Bidimensionnel Empirical Mode Decomposition). La BEMD permet de décomposer une image en plusieurs modes BIMFs (Bidimensionnel Intrinsic Mode Functions). Le vecteur caractéristique ou signature d'une image est construit en utilisant les sorties de bancs de filtres de Gabor, appliqués à chaque BIMF. La recherche d'images s'effectuer en calculant, au sens d'une métrique donnée, la distance entre les signatures dans la base et la signature de l'image requête

Identification of Online Learning Challenges During the COVID-19 Pandemic in Developing Countries

The unexpected outbreak of the Corona virus (COVID-19) disrupted schools and universities around the world. Traditional classes were canceled, forcing schools and universities to switch to online learning. While developed countries have already adopted e-learning and online learning into their teaching practices, which made the transition relatively easy during the COVID-19 crisis, other developing countries continue to struggle with problems of electricity and information technology infrastructure. The purpose of this paper is to investigate the challenges of online learning faced by students and teachers at the Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Morocco. Thus, two anonymous structured Google Forms questionnaires were sent to participants via email. 498 responses were returned from students and 105 from teachers. We use descriptive statistics to better understand the distribution of study participants. The study reveals that the faculty during the lockdown ensured educational continuity and offered a reliable online learning in terms of digital and educational materials. However, technical problems such as the slow speed of the Internet connection, lack of knowledge about the use of information and communication technologies to teach and learn, low motivation of students, were significant challenges to students’ and teachers’ use of the digital tools

Content based Medical Image Retrieval: use of Generalized Gaussian Density to model BEMD's IMF.

In this paper, we address the problem of medical ddiagnosis aid through content based image retrieval methods. We propose to characterize images without extracting local features, by using global information extracted from the image Bidimensional Empirical Mode Decomposition (BEMD). This method decompose image into a set of functions named Intrinsic Mode Functions (IMF) and a residu. The generalized Gaussian density function (GGD) is used to represent the coefficients derived from each IMF, and the Kullback–Leibler Distance (KLD) compute the similarity between GGDs. Retrieval efficiency is given for different databases including a diabetic retinopathy, and a face database. Results are promising: the retrieval efficiency is higher than 85% for some cases