OpenRaiser/Intern-Atlas huggingface dataset:
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
Venue: arXiv cs.CV 2016
Year: 2016

In the scope of gestural action recognition, the size of the feature vector representing movements is in general quite large especially when full body movements are considered. Furthermore, this feature vector evolves during the movement performance so that a complete movement is fully represented by a matrix M of size DxT , whose element M i, j represents the value of feature i at timestamps j. Many studies have addressed dimensionality reduction considering only the size of the feature vector lying in R D to reduce both the variability of gestural sequences expressed in the reduced space, and the computational complexity of their processing. In return, very few of these methods have explicitly addressed the dimensionality reduction along the time axis. Yet this is a major issue when considering the use of elastic distances which are characterized by a quadratic complexity along the time axis. We present in this paper an evaluation of straightforward approaches aiming at reducing the dimensionality of the matrix M for each movement, leading to consider both the dimensionality reduction of the feature vector as well as its reduction along the time axis. The dimensionality reduction of the feature vector is achieved by selecting remarkable joints in the skeleton performing the movement, basically the extremities of the articulatory chains composing the skeleton. The temporal dimen-sionality reduction is achieved using either a regular or adaptive down-sampling that seeks to minimize the reconstruction error of the movements. Elastic and Euclidean kernels are then compared through support vector machine learning. Two data sets 1 that are widely referenced in the domain of human gesture recognition, and quite distinctive in terms of quality of motion capture, are used for the experimental assessment of the proposed approaches. On these data sets we experimentally show that it is feasible, and possibly desirable, to significantly reduce simultaneously the size of the feature vector and the number of skeleton frames to represent body movements while maintaining a very good recognition rate. The method proves to give satisfactory results at a level currently reached by state-of-the-art methods on these data sets. We experimentally show that the computational complexity reduction that is obtained makes this approach eligible for real-time applications.
What is LTE actually used for? An answer through multi-operator, crowd-sourced measurement
Venue: arXiv cs.NI 2016
Year: 2016

LTE networks are commonplace nowadays; however, comparatively little is known about where (and why) they are deployed, and the demand they serve. We shed some light on these issues through large-scale, crowd-sourced measurement. Our data, collected by users of the WeFi app, spans multiple operators and multiple cities, allowing us to observe a wide variety of deployment patterns. Surprisingly, we find that LTE is frequently used to improve the {\em coverage} of network rather than the capacity thereof, and that no evidence shows that video traffic be a primary driver for its deployment. Our insights suggest that such factors as pre-existing networks and commercial policies have a deeper impact on deployment decisions than purely technical considerations.
Static Analysis of Communicating Processes using Symbolic Transducers
Venue: arXiv cs.SE 2016
Year: 2016

We present a general model allowing static analysis based on abstract interpretation for systems of communicating processes. Our technique, inspired by Regular Model Checking, represents set of program states as lattice automata and programs semantics as symbolic transducers. This model can express dynamic creation/destruction of processes and communications. Using the abstract interpretation framework, we are able to provide a sound over-approximation of the reachability set of the system thus allowing us to prove safety properties. We implemented this method in a prototype that targets the MPI library for C programs.
dMath: Distributed Linear Algebra for DL
Venue: arXiv cs.DC 2016
Year: 2016

The paper presents a parallel math library, dMath, that demonstrates leading scaling when using intranode, internode, and hybrid-parallelism for deep learning (DL). dMath provides easy-to-use distributed primitives and a variety of domain-specific algorithms including matrix multiplication, convolutions, and others allowing for rapid development of scalable applications like deep neural networks (DNNs). Persistent data stored in GPU memory and advanced memory management techniques avoid costly transfers between host and device. dMath delivers performance, portability, and productivity to its specific domain of support.
Routing Number Of A Pyramid
Venue: arXiv cs.DM 2016
Year: 2016

In this short note we give the routing number of pyramid graph under the \textit{routing via matching} model introduced by Alon et al\cite{5}. This model can be viewed as a communication scheme on a distributed network. The nodes in the network can communicate via matchings (a step), where a node exchanges data with its partner. Formally, given a connected graph $G$ with vertices labeled from $[1,...,n]$ and a permutation $π$ giving the destination of pebbles on the vertices the problem is to find a minimum step routing scheme. This is denoted as the routing time $rt(G,π)$ of $G$ given $π$. We show that a $d$-dimensional pyramid with $m$ levels has a routing number of $O(dN^{1/d})$.
At Ease with Your Warnings: The Principles of the Salutogenesis Model Applied to Automatic Static Analysis
Venue: arXiv cs.SE 2016
Year: 2016

The results of an automatic static analysis run can be overwhelming, especially for beginners. The overflow of information and the resulting need for many decisions is mentally tiring and can cause stress symptoms. There are several models in health care which are designed to fight stress. One of these is the salutogenesis model created by Aaron Antonovsky. In this paper, we will present an idea on how to transfer this model into a triage and recommendation model for static analysis tools and give an example of how this can be implemented in FindBugs, a static analysis tool for Java.
On ($1$, $ε$)-Restricted Max-Min Fair Allocation Problem
Venue: arXiv cs.DM 2016
Year: 2016

We study the max-min fair allocation problem in which a set of $m$ indivisible items are to be distributed among $n$ agents such that the minimum utility among all agents is maximized. In the restricted setting, the utility of each item $j$ on agent $i$ is either $0$ or some non-negative weight $w_j$. For this setting, Asadpour et al. showed that a certain configuration-LP can be used to estimate the optimal value within a factor of $4+δ$, for any $δ>0$, which was recently extended by Annamalai et al. to give a polynomial-time $13$-approximation algorithm for the problem. For hardness results, Bezakova and Dani showed that it is \NP-hard to approximate the problem within any ratio smaller than $2$. In this paper we consider the $(1,ε)$-restricted max-min fair allocation problem in which each item $j$ is either heavy $(w_j = 1)$ or light $(w_j = ε)$, for some parameter $ε\in (0,1)$. We show that the $(1,ε)$-restricted case is also \NP-hard to approximate within any ratio smaller than $2$. Hence, this simple special case is still algorithmically interesting. Using the configuration-LP, we are able to estimate the optimal value of the problem within a factor of $3+δ$, for any $δ>0$. Extending this idea, we also obtain a quasi-polynomial time $(3+4ε)$-approximation algorithm and a polynomial time $9$-approximation algorithm. Moreover, we show that as $ε$ tends to $0$, the approximation ratio of our polynomial-time algorithm approaches $3+2\sqrt{2}\approx 5.83$.
Recalling Holistic Information for Semantic Segmentation
Venue: arXiv cs.CV 2016
Year: 2016

Semantic segmentation requires a detailed labeling of image pixels by object category. Information derived from local image patches is necessary to describe the detailed shape of individual objects. However, this information is ambiguous and can result in noisy labels. Global inference of image content can instead capture the general semantic concepts present. We advocate that high-recall holistic inference of image concepts provides valuable information for detailed pixel labeling. We build a two-stream neural network architecture that facilitates information flow from holistic information to local pixels, while keeping common image features shared among the low-level layers of both the holistic analysis and segmentation branches. We empirically evaluate our network on four standard semantic segmentation datasets. Our network obtains state-of-the-art performance on PASCAL-Context and NYUDv2, and ablation studies verify its effectiveness on ADE20K and SIFT-Flow.
Online Scaling of NFV Service Chains across Geo-distributed Datacenters
Venue: arXiv cs.NI 2016
Year: 2016

Network Function Virtualization (NFV) is an emerging paradigm that turns hardware-dependent implementation of network functions (i.e., middleboxes) into software modules running on virtualized platforms, for significant cost reduction and ease of management. Such virtual network functions (VNFs) commonly constitute service chains, to provide network services that traffic flows need to go through. Efficient deployment of VNFs for network service provisioning is key to realize the NFV goals. Existing efforts on VNF placement mostly deal with offline or one-time placement, ignoring the fundamental, dynamic deployment and scaling need of VNFs to handle practical time-varying traffic volumes. This work investigates dynamic placement of VNF service chains across geo-distributed datacenters to serve flows between dispersed source and destination pairs, for operational cost minimization of the service chain provider over the entire system span. An efficient online algorithm is proposed, which consists of two main components: (1) A regularization-based approach from online learning literature to convert the offline optimal deployment problem into a sequence of one-shot regularized problems, each to be efficiently solved in one time slot; (2) An online dependent rounding scheme to derive feasible integer solutions from the optimal fractional solutions of the one-shot problems, and to guarantee a good competitive ratio of the online algorithm over the entire time span. We verify our online algorithm with solid theoretical analysis and trace-driven simulations under realistic settings.
On the Feasibility of Attribute-Based Encryption on Internet of Things Devices
Venue: arXiv cs.CR 2016
Year: 2016

Attribute-Based Encryption (ABE) could be an effective cryptographic tool for the secure management of Internet-of-Things (IoT) devices, but its feasibility in the IoT has been under-investigated thus far. This article explores such feasibility for well-known IoT platforms, namely, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero, and concludes that adopting ABE in the IoT is indeed feasible.
Interference alignment for downlink cellular networks: Joint scheduling and precoding
Venue: arXiv cs.NI 2016
Year: 2016

Interference Alignment (IA) is technique that, in a large sense, makes use of the increasing signal dimensions available in the system through MIMO and OFDM technologies in order to globally reduce the interference suffered by users in a network. In this paper, we address the problem of downlink cellular networks, the so-called interfering broadcast channels, where mobile users at cell edges may suffer from high interference and thus, poor performance. Starting from the downlink IA scheme proposed by Suh et al., a new approach is proposed where each user feeds back multiple selected received signal directions with high signal-to-interference gain. A exhaustive search based scheduler selects a subset of users to be served simultaneously, balancing between sum-rate performance and fairness, but becomes untractable in dense network scenarios where many users send simultaneous requests. Therefore, we develop a sub-optimal scheduler that greatly decreases the complexity while preserving a near-optimal data rate gain. More interestingly, our simulations show that the IA scheme becomes valuable only in correlated channels, whereas the matched filtering based scheme performs the best in the uncorrelated scenarios.
An Integrated approach for the Secure Transmission of Images based on DNA Sequences
Venue: arXiv cs.CR 2016
Year: 2016

As long as human beings exist on this earth, there will be confidential images intended for limited audience. These images have to be transmitted in such a way that no unauthorized person gets knowledge of them. DNA sequences play a vital role in modern cryptography and DNA sequence based cryptography renders a helping hand for transmission of such confidential images over a public insecure channel as the intended recipient alone can decipher them. This paper outlines an integrated encryption scheme based on DNA sequences and scrambling according to magic square of doubly even order pattern. Since there is negligible correlation between the original and encrypted image this method is robust against any type of crypt attack.
Software-Defined Networking-based Crypto Ransomware Detection Using HTTP Traffic Characteristics
Venue: arXiv cs.CR 2016
Year: 2016

Ransomware is currently the key threat for individual as well as corporate Internet users. Especially dangerous is crypto ransomware that encrypts important user data and it is only possible to recover it once a ransom has been paid. Therefore devising efficient and effective countermeasures is a rising necessity. In this paper we present a novel Software-Defined Networking (SDN) based detection approach that utilizes characteristics of ransomware communication. Based on the observation of network communication of two crypto ransomware families, namely CryptoWall and Locky we conclude that analysis of the HTTP messages' sequences and their respective content sizes is enough to detect such threats. We show feasibility of our approach by designing and evaluating the proof-of-concept SDN-based detection system. Experimental results confirm that the proposed approach is feasible and efficient.
Robust Sequential Path Planning Under Disturbances and Adversarial Intruder
Venue: arXiv cs.MA 2016
Year: 2016

Provably safe and scalable multi-vehicle path planning is an important and urgent problem due to the expected increase of automation in civilian airspace in the near future. Although this problem has been studied in the past, there has not been a method that guarantees both goal satisfaction and safety for vehicles with general nonlinear dynamics while taking into account disturbances and potential adversarial agents, to the best of our knowledge. Hamilton-Jacobi (HJ) reachability is the ideal tool for guaranteeing goal satisfaction and safety under such scenarios, and has been successfully applied to many small-scale problems. However, a direct application of HJ reachability in most cases becomes intractable when there are more than two vehicles due to the exponentially scaling computational complexity with respect to system dimension. In this paper, we take advantage of the guarantees HJ reachability provides, and eliminate the computation burden by assigning a strict priority ordering to the vehicles under consideration. Under this sequential path planning (SPP) scheme, vehicles reserve "space-time" portions in the airspace, and the space-time portions guarantee dynamic feasibility, collision avoidance, and optimality of the paths given the priority ordering. With a computation complexity that scales quadratically when accounting for both disturbances and an intruder, and linearly when accounting for only disturbances, SPP can tractably solve the multi-vehicle path planning problem for vehicles with general nonlinear dynamics in a practical setting. We demonstrate our theory in representative simulations.
Nondeterministic Communication Complexity of Random Boolean Functions
Venue: arXiv cs.DM 2016
Year: 2016

We study nondeterministic communication complexity and related concepts (fooling sets, fractional covering number) of random functions $f\colon X\times Y \to \{0,1\}$ where each value is chosen to be 1 independently with probability $p=p(n)$, $n := |X|=|Y|$.
Investigating Low Level Protocols for Wireless Body Sensor Networks
Venue: arXiv cs.DC 2016
Year: 2016

The rapid development of medical sensors has increased the interest in Wireless Body Area Network (WBAN) applications where physiological data from the human body and its environment is gathered, monitored, and analyzed to take the proper measures. In WBANs, it is essential to design MAC protocols that ensure adequate Quality of Service (QoS) such as low delay and high scalability. This paper investigates Medium Access Control (MAC) protocols used in WBAN, and compares their performance in a high traffic environment. Such scenario can be induced in case of emergency for example, where physiological data collected from all sensors on human body should be sent simultaneously to take appropriate action. This study can also be extended to cover collaborative WBAN systems where information from different bodies is sent simultaneously leading to high traffic. OPNET simulations are performed to compare the delay and scalability performance of the different MAC protocols under the same experimental conditions and to draw conclusions about the best protocol to be used in a high traffic environment.
Geometric deep learning on graphs and manifolds using mixture model CNNs
Venue: arXiv cs.CV 2016
Year: 2016

Deep learning has achieved a remarkable performance breakthrough in several fields, most notably in speech recognition, natural language processing, and computer vision. In particular, convolutional neural network (CNN) architectures currently produce state-of-the-art performance on a variety of image analysis tasks such as object detection and recognition. Most of deep learning research has so far focused on dealing with 1D, 2D, or 3D Euclidean-structured data such as acoustic signals, images, or videos. Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics. In this paper, we propose a unified framework allowing to generalize CNN architectures to non-Euclidean domains (graphs and manifolds) and learn local, stationary, and compositional task-specific features. We show that various non-Euclidean CNN methods previously proposed in the literature can be considered as particular instances of our framework. We test the proposed method on standard tasks from the realms of image-, graph- and 3D shape analysis and show that it consistently outperforms previous approaches.
Semantic Segmentation using Adversarial Networks
Venue: arXiv cs.CV 2016
Year: 2016

Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets.
Card games as pointer structures: case studies in mobile CSP modelling
Venue: arXiv cs.SE 2016
Year: 2016

The author has long enjoyed using the CSP refinement checker FDR to solve puzzles, as witnessed by examples in \cite{tpc,ucs}. Recent experiments have shown that a number of games of patience (card games for one) are now well within bounds. We discuss the modelling approaches used to tackle these and avoid symmetric states. For two such games we reveal much higher percentages of winnable games than was previously believed. The techniques developed for some of these card games -which employ various dynamic patterns of cards - suggest techniques for modelling pointer structures in CSP and FDR analogous to those used with the pi-calculus. Most of these use CSP's ability to express mobile systems.
Vertex-centred Method to Detect Communities in Evolving Networks
Venue: arXiv cs.SI 2016
Year: 2016

Finding communities in evolving networks is a difficult task and raises issues different from the classic static detection case. We introduce an approach based on the recent vertex-centred paradigm. The proposed algorithm, named DynLOCNeSs, detects communities by scanning and evaluating each vertex neighbourhood, which can be done independently in a parallel way. It is done by means of a preference measure, using these preferences to handle community changes. We also introduce a new vertex neighbourhood preference measure, CWCN, more efficient than current existing ones in the considered context. Experimental results show the relevance of this measure and the ability of the proposed approach to detect classical community evolution patterns such as grow-shrink and merge-split.
Person Re-Identification by Unsupervised Video Matching
Venue: arXiv cs.CV 2016
Year: 2016

Most existing person re-identification (ReID) methods rely only on the spatial appearance information from either one or multiple person images, whilst ignore the space-time cues readily available in video or image-sequence data. Moreover, they often assume the availability of exhaustively labelled cross-view pairwise data for every camera pair, making them non-scalable to ReID applications in real-world large scale camera networks. In this work, we introduce a novel video based person ReID method capable of accurately matching people across views from arbitrary unaligned image-sequences without any labelled pairwise data. Specifically, we introduce a new space-time person representation by encoding multiple granularities of spatio-temporal dynamics in form of time series. Moreover, a Time Shift Dynamic Time Warping (TS-DTW) model is derived for performing automatically alignment whilst achieving data selection and matching between inherently inaccurate and incomplete sequences in a unified way. We further extend the TS-DTW model for accommodating multiple feature-sequences of an image-sequence in order to fuse information from different descriptions. Crucially, this model does not require pairwise labelled training data (i.e. unsupervised) therefore readily scalable to large scale camera networks of arbitrary camera pairs without the need for exhaustive data annotation for every camera pair. We show the effectiveness and advantages of the proposed method by extensive comparisons with related state-of-the-art approaches using two benchmarking ReID datasets, PRID2011 and iLIDS-VID.
Reliability of k-out-of-n Data Storage System with Deterministic Parallel and Serial Repair
Venue: arXiv cs.IT 2016
Year: 2016

In this paper, we find the Laplace Stieltjes transform of the probability of data loss for the k-out-of-n distributed storage system with deterministic repair times. We consider two repair models, namely the serial and parallel repair. We show that for failure rate much lower than the repair rate, mean time of data loss for the two models is the same unlike the case for exponential repair models.