9 research outputs found

    A Dynamic Application Partitioning and Offloading Framework to Enhance the Capabilities of Transient Clouds Using Mobile Agents

    Get PDF
    Mobile cloud computing has emerged as a prominent area of research, a natural extension of cloud computing that proposes to offer solutions for enhancing the capabilities of smart mobile devices commonly plagued by resource constraints. As one of its promising models, transient clouds aim to address the internet connectivity shortfall inherent in most solutions through the formation of ad hoc networks by devices in close proximity, then the offloading some computations (Cyber Foraging) to the created cloud. However, transient clouds, at their current state, have several limitations, concerning their expansion on a local network having a large number of devices and the management of the instability of the network due to the constant mobility of the devices. Another issue is the fact code partitioning and offloading are not addressed to fit the need of such networks, thereby rendering the distributed computing mechanism barely efficient for the Transient Cloud. In this study, we propose a transient cloud-based framework that exploits the use of multi-agent systems, enabling a dynamic partitioning and offloading of code, and facilitating the movement and the execution of code partition packets in a multi-hop ad-hoc mesh network. When created and deployed, these intelligent mobile agents operate independently or collaboratively and adapt to the continual entry and exit of devices in the neighbourhood. The integration of these trending concepts in distributed computing within a framework offers a new architecture for resource-sharing among cooperating devices that addresses the varied issues that arise in dynamic environments

    Detection of breast pathologies in digital mammography images by thresholding and mathematical morphology

    Get PDF
    This paper proposes an algorithm for mass and micro-calcification detection by manual thresholding and prewitt detector. This algorithm has been tested using mammography images of different densities from multiple databases of a health clinic and images taken from the internet (40 images in total). The results are very accurate, allowing better detection of breast pathologies (mass and micro-calcification). Finally, the detection of breast pathologies was performed using as input a detection algorithm specially designed for this purpose. After segmentation by manual thresholding, morphological opening, morphological dilatation and Prewitt contour detection we have a demarcation of the masses and breast micro-calcification. The results obtained show the robustness of the proposed manual thresholding method. In order to evaluate the efficiency of our pathology detector, we compared our results with those in the literature and performed a qualitative evaluation with a rate of 98.04% for the detection of breast pathologies.  A radiologist from the health clinic evaluated the results and considers them acceptable to the CAD

    Identifying the exact fixing actions of static rule violation

    Get PDF
    International audience—We study good programming practices expressed in rules and detected by static analysis checkers such as PMD or FindBugs. To understand how violations to these rules are corrected and whether this can be automated, we need to identify in the source code where they appear and how they were fixed. This presents some similarities with research on understanding software bugs, their causes, their fixes, and how they could be avoided. The traditional method to identify how a bug or a rule violation were fixed consists in finding the commit that contains this fix and identifying what was changed in this commit. If the commit is small, all the lines changed are ascribed to the fixing of the rule violation or the bug. However, commits are not always atomic, and several fixes and even enhancements can be mixed in a single one (a large commit). In this case, it is impossible to detect which modifications contribute to which fix. In this paper, we are proposing a method that identifies precisely the modifications that are related to the correction of a rule violation. The same method could be applied to bug fixes, providing there is a test illustrating this bug. We validate our solution on a real world system and actual rules

    Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series

    No full text
    CARI, Ecole Polytechnique de Thiès, SénégalInternational audienceL'analyse prédictive permet d'estimer les tendances des évènements futurs. De nos jours, les algorithmes Deep Learning permettent de faire de bonnes prédictions. Cependant, pour chaque type de problème donné, il est nécessaire de choisir l'architecture optimale. Dans cet article, les modèles Stack-LSTM, CNN-LSTM et ConvLSTM sont appliqués à une série temporelle d'images radar sentinel-1, le but étant de prédire la prochaine occurrence dans une séquence. Les résultats expérimentaux évalués à l'aide des indicateurs de performance tels que le RMSE et le MAE, le temps de traitement et l'index de similarité SSIM, montrent que chacune des trois architectures peut produire de bons résultats en fonction des paramètres utilisés.Predictive analytics allow to estimate future trends of events. Nowadays, Deep Learning algorithms allow making good predictions. However, it is necessary to choose the architecture that produces the most efficient results for each kind of problem. In this paper, the Stack-LSTM, the CNN-LSTM and the ConvLSTM models are applied to a time series of sentinel-1 radar images. The goal is to predict the next occurrence in a sequence of images. Experimental results are evaluated with performance metrics such as the RMSE and MAE loss, the processing time and the SSIM index. The values show that each of the three architectures can produce good results depending on used parameters

    Ontologies-based Architecture for Sociocultural Knowledge Co-Construction Systems

    No full text
    International audienceConsidering the evolution of the semantic wiki engine based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires existing ontologies to be imported. Most of them use the RDF-based (Resource Description Framework) systems in conjunction with the standard SQL (Structured Query Language) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for SMW (Semantic MediaWiki) is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology based architecture, which aims to implement this idea. The architecture mainly includes three layered functional architectures: Web User Interface Layer, Semantic Layer and Persistence Layer

    Ontologies-based Architecture for Sociocultural Knowledge Co-Construction Systems

    No full text
    International audienceConsidering the evolution of the semantic wiki engine based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires existing ontologies to be imported. Most of them use the RDF-based (Resource Description Framework) systems in conjunction with the standard SQL (Structured Query Language) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for SMW (Semantic MediaWiki) is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology based architecture, which aims to implement this idea. The architecture mainly includes three layered functional architectures: Web User Interface Layer, Semantic Layer and Persistence Layer

    Ontologies-based Architecture for Sociocultural Knowledge Co-Construction Systems

    No full text
    International audienceConsidering the evolution of the semantic wiki engine based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires existing ontologies to be imported. Most of them use the RDF-based (Resource Description Framework) systems in conjunction with the standard SQL (Structured Query Language) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for SMW (Semantic MediaWiki) is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology based architecture, which aims to implement this idea. The architecture mainly includes three layered functional architectures: Web User Interface Layer, Semantic Layer and Persistence Layer
    corecore