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We present a randomized distributed maximal independent set (MIS) algorithm for arbitrary graphs of size n that halts in time O(log n) with probability 1 − o(n −1 ), and only needs messages containing 1 bit. Thus, its bit complexity par channel is O(log n). We assume that the graph is anonymous: unique identities are not available to distinguish between the processes; we only assume that each vertex distinguishes between its neighbours by locally known channel names. Furthermore we do not assume that the size (or an upper bound on the size) of the graph is known. This algorithm is optimal (modulo a multiplicative constant) for the bit complexity and improves the best previous randomized distributed MIS algorithms (deduced from the randomized PRAM algorithm due to Luby (SIAM J. Comput. 15:1036-1053, 1986)) for general graphs which is O(log 2 n) per channel (it halts in time O(log n) and the size of each message is log n). This result is based on a powerful and general technique for converting unrealistic exchanges of messages containing real numbers drawn at

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Human Action Recognition is one of the key tasks in video understanding. Deep Convolutional Neural Networks (CNN) are often used for this purpose. Although they usually perform impressively, their decision interpretation remains challenging. We propose a novel visual CNN features understanding technique. Its objective is to find salient features that played a key role in decision making of the network. The technique only uses the features from the last convolutional layer before the fully connected layers of a trained model and builds an importance map of features. The map is propagated to the original frame thus highlighting the regions in them that contribute to the final decision. The method is fast as it does not require gradient computation as many state-of-the-art methods do. Proposed technique is applied to the Twin Spatio-Temporal 3D Convolutional Neural Network (TSTCNN), designed for Table Tennis Actions recognition. Features visualization is performed at the RGB and Optical flow branches of the network. Obtained results are compared to other visualization techniques both in terms of human understanding and similarity metrics. The metrics show that generated maps are similar to those obtained with known Grad-CAM method, e.g. Pearson Correlation Coefficient between the maps generated of RGB data for Grad-CAM and our method is 0.7 ± 0.05 and 0.72 ± 0.06 on Optical Flow data.

International audienceWe present new efficient deterministic and randomized distributed algorithms for decomposing a graph with $n$ nodes into a disjoint set of connected clusters with radius at most $k-1$ and having $O(n^{1+1/k})$ intercluster edges. We show how to implement our algorithms in the distributed $\mathcal{CONGEST}$ model of computation, i.e., limited message size, which improves the time complexity of previous algorithms~\cite{MS00,Awe85,Peleg00b} from $O(n)$ to $O(n^{1-1/k})$. We apply our algorithms for constructing low stretch graph spanners and network synchronizers in sublinear deterministic time in the $\mathcal{CONGEST}$ model

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With the digital breakthrough, smart phones have become very essential component for many routine tasks like shopping, paying bills, transferring money, instant messaging, emails etc. Mobile devices are very attractive attack surface for cyber thieves as they hold personal details (accounts, locations, contacts, photos) and have potential capabilities for eavesdropping (with cameras/microphone, wireless connections). Android, being the most popular, is the target of malicious hackers who are trying to use Android app as a tool to break into and control device. Android malware authors use many anti-analysis techniques to hide from analysis tools. Academic researchers and commercial anti-malware companies are putting great effort to detect such malicious apps. They are making use of the combinations of static, dynamic and behavior based analysis techniques.Despite of all the security mechanisms provided by Android, apps can carry out malicious actions through inter-app communication. One such inter-app communication threats is collusion. In collusion malicious functionality is divided across multiple apps. Each participating app accomplish its part and communicate information to another app through Inter Component Communication (ICC). ICC does not require any special permissions. Also there is no compulsion to inform user about the communication. Each participating app needs to request a minimal set of privileges, which may make it appear benign to current state-of-the-art techniques that analyze one app at a time.There are many surveys on app analysis techniques in Android; however they focus on single-app analysis. This survey highlights several inter-app communication threats, in particular collusion among multiple-apps. In this paper, we present Android vulnerabilities that may be exploited for carrying privilege escalation attacks, privacy leakage and collusion attacks. We cover the existing threat analysis, scenarios, and a detailed comparison of tools for intra and inter-app analysis. To the best of our knowledge this is the first survey on interapp communication threats, app collusion and state-of-the-art detection tools in Android.

Landmarks are one of the important concepts in morphometry analysis. They are anatomical points that can be located consistently (e.g., corner of the eyes) and used to establish correspondence or divergence among morphologies of biological or non-biological specimens. Currently, the landmarks are mostly positioned manually by entomologists on numerical images. In this work, we propose a method to automatically predict the landmarks on entomological images based on Deep Learning methods, more specifically by using Convolutional Neural Network (CNN). We propose a CNN architecture, EB-Net, which is built in a modular way the concept of "Elementary Blocks", each made up of usual layer types of CNN. After using a custom data augmentation procedure, the network has been trained and tested on a data set of different anatomical part of carabids (pronotum, head and elytra). In this numerical experiment, we have generated two strategies to evaluate the network and to improve the obtained results: training from scratch or applying a fine-tuning step. The predicted landmark coordinates have been compared to the coordinates of the manual landmarks provided by the biologists. The statistical analysis of the distances between predicted and manual coordinates has shown that our predictions can replace efficiently manual landmarking and allows to propose automatization of such operation.