The pervasiveness of online web tracking poses a constant threat to the privacy of Internet users. Millions of users currently employ content-blockers in their web browsers to block tracking resources in real time. Although content-blockers are based on blacklists, which are known to be difficult to maintain and easy to evade, the research community has not succeeded in replacing them with better alternatives yet. Most of the methods recently proposed in the literature obtain good detection accuracy, but at the expense of increasing their complexity and making them more difficult to maintain and configure by the end user. In this paper, we present a new web tracking detection method, called Deep Tracking Detector (DTD), that analyzes the properties of URL strings to detect tracking resources, without using any other external features. Consequently, DTD can easily be implemented in a browser plugin and operate in real time. Our experimental results, with more than 5M HTTP requests from 100K websites, show that DTD achieves a detection accuracy higher than 97% by looking only at the URL of the resources.

This special issue presents recent contributions and results in the fields of cloud computing, IoT, and big data technologies.It includes a set of selected articles that have been presented at the 2017 International Conference on Cloud Computing and Applications (CloudTech'17). 1 CloudTech'17 was held in Rabat on October 24-26, 2017, and it is one of the most successful events in the fields of cloud computing and big data technologies.CloudTech addresses topics related to cloud computing and big data technologies, architectures, and applications, including data privacy and security.The set of papers accepted for publication in this special issue are briefly described in the rest of this document and are structured in four main research areas: (i) platforms and architectures, (ii) cloud computing and big data analysis, (iii) virtualization techniques, and (iv) data security and privacy.

Summary Digital phenotyping is an emergent science mainly based on imagery techniques. The tremendous amount of data generated needs important cloud computing for their processing. The coupling of recent advance of distributed databases and cloud computing offers new possibilities of big data management and data sharing for the scientific research. In this paper, we present a solution combining a lambda architecture built around Apache Druid and a hosting platform leaning on Apache Mesos. Lambda architecture has already proved its performance and robustness. However, the capacity of ingesting and requesting of the database is essential and can constitute a bottleneck for the architecture, in particular, for in terms of availability and response time of data. We focused our experimentation on the response time of different databases to choose the most adapted for our phenotyping architecture. Apache Druid has shown its ability to respond to typical queries of phenotyping applications in times generally inferior to the second.

Summary The last few years have been marked by the presence of very large sets of images and videos in our everyday lives. These multimedia objects have a very fast frequency of creation and sharing since images and videos can come from different devices such as smartphones, satellites, cameras, or drones. They are generally used to illustrate objects in different situations (public areas, train stations, hospitals, political and sport events and competitions, etc). As consequence, image and video processing algorithms have got increasing importance for several computer vision applications that should be adapted for managing large‐scale volumes and exploiting high performance computing resources (local or cloud). In this work, we propose a cloud‐based toolbox (platform) for computer vision applications. This platform integrates a toolbox of image and video processing algorithms that can (i) exploit high performance computing cloud resources, (ii) execute applications in real time, and (iii) manage large‐scale database using Big Data technologies. The related libraries and hardware drivers are automatically integrated and configured in order to offer to users an access to the different applications without the need to download, install, and configure software or hardware. Experiments were conducted using three kinds of applications: (i) image and video processing applications, (ii) deep learning techniques for images classification and multiobject localization, and (iii) images indexation and retrieval. These experiments demonstrated the interest of our platform for sharing, in an efficient way, our scientific contributions and annotated databases in order to improve the quality and performance of computer vision applications.

Cloud computing

peer reviewed

This work presents a new flexible parameterizable architecture for image and video processing with reduced latency and memory requirements, supporting a variable input resolution. The proposed architecture is optimized for feature detection, more specifically, the Canny edge detector and the Harris corner detector. The architecture contains neighborhood extractors and threshold operators that can be parameterized at runtime. Also, algorithm simplifications are employed to reduce mathematical complexity, memory requirements, and latency without losing reliability. Furthermore, we present the proposed architecture implementation on an FPGA-based platform and its analogous optimized implementation on a GPU-based architecture for comparison. A performance analysis of the FPGA and the GPU implementations, and an extra CPU reference implementation, shows the competitive throughput of the proposed architecture even at a much lower clock frequency than those of the GPU and the CPU. Also, the results show a clear advantage of the proposed architecture in terms of power consumption and maintain a reliable performance with noisy images, low latency and memory requirements.

The purpose of this paper is to solve the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) on Grid'5000 using the ParadisEO framework. In this respect, four packages developed in ParadisEO are exploited. First, EO package (Evolving Objects) is used to create an evolutionary algorithm to solve the mono-objective CVRPTW. Then, a related multi-objective problem is solved with MOEO package (Multi-Objective Evolving Objects). The package PEO (Parallel Evolving Objects) permitted us to use a particular hybridization scheme: the cooperative island model. Exchanges were performed between three evolutionary algorithms. With this strategy, an improvement in the solutions has been noticed. The final part of the work is concerned with developing hybridization between an evolutionary algorithm and a simulated annealing (created with the Moving Objects Package MO). The goal is to take advantage of intensification by simulated annealing and diversification by evolutionary algorithm. One important feature is the exploitation of the Grid'5000 infrastructure which permits us to reduce the execution time by 50 compared to a sequential execution.

This paper discusses well balanced implementations of sparse matrix-vector multiplication on heterogeneous environments. A new heuristic is proposed for balancing the computing load over the processors proportionally to their power. This is done by defining a distribution model which splits the sparse matrix in k-way partitions, in order to minimize the total execution time. An implementation of the sparse matrix vector multiplication in heterogeneous environment using parallel object-oriented programming model POP-C++ shows that this ID-partitioning heuristic improve greatly the performance of the product, in comparison with block row decomposition