OpenRaiser/Intern-Atlas huggingface dataset:
On the hardness of the Learning with Errors problem with a discrete reproducible error distribution
Venue: arXiv cs.CR 2016
Year: 2016

In this work we show that the hardness of the Learning with Errors problem with errors taken from the discrete Gaussian distribution implies the hardness of the Learning with Errors problem with errors taken from the symmetric Skellam distribution. Due to the sample preserving search-to-decision reduction by Micciancio and Mol the same result applies to the decisional version of the problem. Thus, we provide a variant of the Learning with Errors problem that is hard based on conjecturally hard lattice problems and uses a discrete error distribution that is similar to the continuous Gaussian distribution in that it is closed under convolution. As an application of this result we construct a post-quantum cryptographic protocol for differentially private data anlysis in the distributed model. The security of this protocol is based on the hardness of the new variant of the Decisional Learning with Errors problem. A feature of this protocol is the use of the same noise for security and for differential privacy resulting in an efficiency boost.
Secrecy Rate Maximization for MISO Multicasting SWIPT System with Power Splitting Scheme
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers transmit covariance matrix design for secrecy rate maximization problem in a multiple-input single-output (MISO) multicasting simultaneous wireless information and power transfer (SWIPT) system. In order to enhance the performance of the system, artificial noise (AN) is added to the transmit signal in the design for the following purposes: to reduce the received signal-to-noise ratio (SNR) at the eavesdroppers and increase the harvested energy. We assume that all the channel-state-information (CSI) is perfectly known at the transmitter and all legitimate users are capable of simultaneously receiving information and harvesting energy. In addition, all the eavesdroppers are passive and they can harvest energy only when they are not intercepting or eavesdropping the messages intended for the legitimate users. The original secrecy rate maximization problem is not convex in terms of transmit and artificial covariance matrices as well as the power splitting (PS) ratio. In order to circumvent this non-convexity issue, we exploit the \emph{Charnes-Cooper} Transformation and semidefinite relaxation (SDR) to convert this original problem into a convex one. However, this convex problem does not always yield the rank-one transmit and AN covariance matrices to obtain the solution of the original problem. Therefore, we analyze the optimal conditions and utilize a Gaussian randomization (GR) method to construct the rank-one solutions from the non-rank one results. Simulation results have been provided to demonstrate the performance of the proposed transmit covariance matrices design for MISO multicasting SWIPT system.
Robust Bayesian Method for Simultaneous Block Sparse Signal Recovery with Applications to Face Recognition
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we present a novel Bayesian approach to recover simultaneously block sparse signals in the presence of outliers. The key advantage of our proposed method is the ability to handle non-stationary outliers, i.e. outliers which have time varying support. We validate our approach with empirical results showing the superiority of the proposed method over competing approaches in synthetic data experiments as well as the multiple measurement face recognition problem.
Optimal Reliability in Energy Harvesting Industrial Wireless Sensor Networks
Venue: arXiv cs.NI 2016
Year: 2016

For Industrial Wireless Sensor Networks, it is essential to reliably sense and deliver the environmental data on time to avoid system malfunction. While energy harvesting is a promising technique to extend the lifetime of sensor nodes, it also brings new challenges for system reliability due to the stochastic nature of the harvested energy. In this paper, we investigate the optimal energy management policy to minimize the weighted packet loss rate under delay constraint, where the packet loss rate considers the lost packets both during the sensing and delivering processes. We show that the above energy management problem can be modeled as an infinite horizon average reward constraint Markov decision problem. In order to address the well-known curse of dimensionality problem and facilitate distributed implementation, we utilize the linear value approximation technique. Moreover, we apply stochastic online learning with post-decision state to deal with the lack of knowledge of the underlying stochastic processes. A distributed energy allocation algorithm with water-filling structure and a scheduling algorithm by auction mechanism are obtained. Experimental results show that the proposed algorithm achieves nearly the same performance as the optimal offline value iteration algorithm while requiring much less computation complexity and signaling overhead, and outperforms various existing baseline algorithms.
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
Venue: arXiv cs.LG 2016
Year: 2016

The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real-world tasks since they involve non-realistic 2D environments and the third-person perspective. Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world. The software, called ViZDoom, is based on the classical first-person shooter video game, Doom. It allows developing bots that play the game using the screen buffer. ViZDoom is lightweight, fast, and highly customizable via a convenient mechanism of user scenarios. In the experimental part, we test the environment by trying to learn bots for two scenarios: a basic move-and-shoot task and a more complex maze-navigation problem. Using convolutional deep neural networks with Q-learning and experience replay, for both scenarios, we were able to train competent bots, which exhibit human-like behaviors. The results confirm the utility of ViZDoom as an AI research platform and imply that visual reinforcement learning in 3D realistic first-person perspective environments is feasible.
End-to-end evaluation of research organizations
Venue: arXiv cs.DL 2016
Year: 2016

End-to-end research evaluation needs to separate out the bibliometric part of the chain from the econometric part. We first introduce the role of size-dependent and size-independent indicators in the bibliometric part of the evaluation chain. We show that performance can then be evaluated at various levels, namely a zeroth-order, a first-order or even a second-order. To complete the evaluation chain, we take up the econometric part where efficiency of the research production process is represented in terms of output and outcome productivities. Both size-dependent and size-independent terms play a crucial role to combine quantity and quality (impact) in a meaningful way. Output or outcome at the bibliometric level can be measured using zeroth, first or second-order composite indicators, and the productivity terms follow accordingly using the input to output or outcome factors.
Fast and High-Quality Bilateral Filtering Using Gauss-Chebyshev Approximation
Venue: arXiv cs.CV 2016
Year: 2016

The bilateral filter is an edge-preserving smoother that has diverse applications in image processing, computer vision, computer graphics, and computational photography. The filter uses a spatial kernel along with a range kernel to perform edge-preserving smoothing. In this paper, we consider the Gaussian bilateral filter where both the kernels are Gaussian. A direct implementation of the Gaussian bilateral filter requires $O(σ_s^2)$ operations per pixel, where $σ_s$ is the standard deviation of the spatial Gaussian. In fact, it is well-known that the direct implementation is slow in practice. We present an approximation of the Gaussian bilateral filter, whereby we can cut down the number of operations to $O(1)$ per pixel for any arbitrary $σ_s$, and yet achieve very high-quality filtering that is almost indistinguishable from the output of the original filter. We demonstrate that the proposed approximation is few orders faster in practice compared to the direct implementation. We also demonstrate that the approximation is competitive with existing fast algorithms in terms of speed and accuracy.
Effect of group organization on the performance of cooperative processes
Venue: arXiv cs.SI 2016
Year: 2016

Problem-solving competence at group level is influenced by the structure of the social networks and so it may shed light on the organization patterns of gregarious animals. Here we use an agent-based model to investigate whether the ubiquity of hierarchical networks in nature could be explained as the result of a selection pressure favoring problem-solving efficiency. The task of the agents is to find the global maxima of NK fitness landscapes and the agents cooperate by broadcasting messages informing on their fitness to the group. This information is then used to imitate, with a certain probability, the fittest agent in their influence networks. For rugged landscapes, we find that the modular organization of the hierarchical network with its high degree of clustering eases the escape from the local maxima, resulting in a superior performance as compared with the scale-free and the random networks. The optimal performance in a rugged landscape is achieved by letting the main hub to be only slightly more propense to imitate the other agents than vice versa. The performance is greatly harmed when the main hub carries out the search independently of the rest of the group as well as when it compulsively imitates the other agents.
The complexity of cylindrical algebraic decomposition with respect to polynomial degree
Venue: arXiv cs.SC 2016
Year: 2016

Cylindrical algebraic decomposition (CAD) is an important tool for working with polynomial systems, particularly quantifier elimination. However, it has complexity doubly exponential in the number of variables. The base algorithm can be improved by adapting to take advantage of any equational constraints (ECs): equations logically implied by the input. Intuitively, we expect the double exponent in the complexity to decrease by one for each EC. In ISSAC 2015 the present authors proved this for the factor in the complexity bound dependent on the number of polynomials in the input. However, the other term, that dependent on the degree of the input polynomials, remained unchanged. In the present paper the authors investigate how CAD in the presence of ECs could be further refined using the technology of Groebner Bases to move towards the intuitive bound for polynomial degree.
Critical Point Computations on Smooth Varieties: Degree and Complexity bounds
Venue: arXiv cs.SC 2016
Year: 2016

Let V $\subset$ C n be an equidimensional algebraic set and g be an n-variate polynomial with rational coefficients. Computing the critical points of the map that evaluates g at the points of V is a cornerstone of several algorithms in real algebraic geometry and optimization. Under the assumption that the critical locus is finite and that the projective closure of V is smooth, we provide sharp upper bounds on the degree of the critical locus which depend only on deg(g) and the degrees of the generic polar varieties associated to V. Hence, in some special cases where the degrees of the generic polar varieties do not reach the worst-case bounds, this implies that the number of critical points of the evaluation map of g is less than the currently known degree bounds. We show that, given a lifting fiber of V , a slight variant of an algorithm due to Bank, Giusti, Heintz, Lecerf, Matera and Solern{ó} computes these critical points in time which is quadratic in this bound up to logarithmic factors, linear in the complexity of evaluating the input system and polynomial in the number of variables and the maximum degree of the input polynomials.
On the 5G Wireless Communications at the Low Terahertz Band
Venue: arXiv cs.NI 2016
Year: 2016

Initiation of fourth generation (4G) mobile telecommunication system rollouts fires the starting pistol for beyond 4G research activities. Whereas technologies enhancing spectral efficiency have traditionally been the solution to data rate and network capacity increase demands, due to the already advanced techniques, returns of the even more complicated algorithms hardly worth the complexity increase any longer. In addition, surging number of connected devices now enables operative use of short-range communication methods. Also considering the recently approved standards for the 60 gigahertz (GHz) industrial, scientific and medical radio band, in this paper the transmission windows around 300 GHz is proposed to be utilized for the fifth generation wireless communication systems. Motivations for the low end of the terahertz (THz) band are provided in accordance with market trends, and standardization activities in higher frequencies are listed. Unified mathematical expressions of the electromagnetic wave propagation mechanisms in the low-THz band are presented. THz band device technologies are outlined and a complete survey of the state-of-the-art low-THz band circuit blocks which are suitable for mass market production is given. Future research directions are specified before the conclusion of the paper.
Detecting Code Clones: A review
Venue: arXiv cs.SE 2016
Year: 2016

Code clone detection is involved with detecting duplicated fragments of code within a code base. Detecting these clones is useful for maintenance operations which require editing the clones. The tools developed are expected to be robust enough to identify clones even when they have been modified, whilst preserving reasonable recall and precision rates. It is also expected that these tools be easily adaptable to different programming languages. The major approaches to this problem has involve the use of direct string matching, token comparison or comparison using abstract syntax trees. It is difficult to compare detection tools due to the absence of a standardized framework for measurement. More work should be done to make the existing tools useful for other practical/industrial purposes.
Theano: A Python framework for fast computation of mathematical expressions
Venue: arXiv cs.SC 2016
Year: 2016

Theano is a Python library that allows to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements. Theano is being actively and continuously developed since 2008, multiple frameworks have been built on top of it and it has been used to produce many state-of-the-art machine learning models. The present article is structured as follows. Section I provides an overview of the Theano software and its community. Section II presents the principal features of Theano and how to use them, and compares them with other similar projects. Section III focuses on recently-introduced functionalities and improvements. Section IV compares the performance of Theano against Torch7 and TensorFlow on several machine learning models. Section V discusses current limitations of Theano and potential ways of improving it.
A Theoretical Analysis of Deep Neural Networks for Texture Classification
Venue: arXiv cs.CV 2016
Year: 2016

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity.
Anytime Bi-Objective Optimization with a Hybrid Multi-Objective CMA-ES (HMO-CMA-ES)
Venue: arXiv cs.NE 2016
Year: 2016

We propose a multi-objective optimization algorithm aimed at achieving good anytime performance over a wide range of problems. Performance is assessed in terms of the hypervolume metric. The algorithm called HMO-CMA-ES represents a hybrid of several old and new variants of CMA-ES, complemented by BOBYQA as a warm start. We benchmark HMO-CMA-ES on the recently introduced bi-objective problem suite of the COCO framework (COmparing Continuous Optimizers), consisting of 55 scalable continuous optimization problems, which is used by the Black-Box Optimization Benchmarking (BBOB) Workshop 2016.
Synthesizing Probabilistic Invariants via Doob's Decomposition
Venue: arXiv cs.PL 2016
Year: 2016

When analyzing probabilistic computations, a powerful approach is to first find a martingale---an expression on the program variables whose expectation remains invariant---and then apply the optional stopping theorem in order to infer properties at termination time. One of the main challenges, then, is to systematically find martingales. We propose a novel procedure to synthesize martingale expressions from an arbitrary initial expression. Contrary to state-of-the-art approaches, we do not rely on constraint solving. Instead, we use a symbolic construction based on Doob's decomposition. This procedure can produce very complex martingales, expressed in terms of conditional expectations. We show how to automatically generate and simplify these martingales, as well as how to apply the optional stopping theorem to infer properties at termination time. This last step typically involves some simplification steps, and is usually done manually in current approaches. We implement our techniques in a prototype tool and demonstrate our process on several classical examples. Some of them go beyond the capability of current semi-automatic approaches.
Estimacion de carga muscular mediante imagenes
Venue: arXiv cs.CV 2016
Year: 2016

Un problema de gran interes en disciplinas como la ocupacional, ergonomica y deportiva, es la medicion de variables biomecanicas involucradas en el movimiento humano (como las fuerzas musculares internas y torque de articulaciones). Actualmente este problema se resuelve en un proceso de dos pasos. Primero capturando datos con dispositivos poco prácticos, intrusivos y costosos. Luego estos datos son usados como entrada en modelos complejos para obtener las variables biomecanicas como salida. El presente trabajo representa una alternativa automatizada, no intrusiva y economica al primer paso, proponiendo la captura de estos datos a traves de imagenes. En trabajos futuros la idea es automatizar todo el proceso de calculo de esas variables. En este trabajo elegimos un caso particular de medicion de variables biomecanicas: el problema de estimar el nivel discreto de carga muscular que estan ejerciendo los musculos de un brazo. Para estimar a partir de imagenes estaticas del brazo ejerciendo la fuerza de sostener la carga, el nivel de la misma, realizamos un proceso de clasificacion. Nuestro enfoque utiliza Support Vector Machines para clasificacion, combinada con una etapa de pre-procesamiento que extrae caracterısticas visuales utilizando variadas tecnicas (Bag of Keypoints, Local Binary Patterns, Histogramas de Color, Momentos de Contornos) En los mejores casos (Local Binary Patterns y Momentos de Contornos) obtenemos medidas de performance en la clasificacion (Precision, Recall, F-Measure y Accuracy) superiores al 90 %.
Analysis of SQL Injection Detection Techniques
Venue: arXiv cs.CR 2016
Year: 2016

SQL Injection is one of the vulnerabilities in OWASPs Top Ten List for Web Based Application Exploitation.These types of attacks takes place on Dynamic Web applications as they interact with the databases for the various operations.Current Content Management System like Drupal, Joomla or Wordpress have all the information stored in their databases. A single intrusion into these types of websites can lead to overall control of websites by the attacker. Researchers are aware of the basic SQL Injection attacks but there are numerous SQL Injection attacks which are yet to be Prevented and Detected. Over here, we present the extensive review for the Advanced SQL Injection attack such as Fast Flux Sql Injection, Compounded SQL Injection and Deep Blind SQL Injection. We also analyze the detection and prevention using the classical methods as well as modern approaches. We will be discussing the Comparative Evaluation for prevention of SQL Injection.
Structured Compute-and-Forward with phase precoding scheme: Beyond integers
Venue: arXiv cs.IT 2016
Year: 2016

-In this work, we focus on the K--user Gaussian Symmetric Complex-valued Interference Channels (GS-CIC). The Compute-and-Forward (CoF) protocol in wireless networks have been employed for Gaussian Symmetric Real-valued Interference Channels (GS-RIC) to achieve approximately the capacity of such channels and making Interference Alignment (IA) by solving a system of linear equations at destinations. We aim to adapt this protocol to GS-CIC. At high Signal-to-Noise Ratio (SNR), this protocol shows a fading behavior of the achievable sum-rate for strong and very strong interference regimes. In complex field this random-like behavior is related to the phase of complex cross channel coefficients. To avoid this fading-like behavior, we consider Z[i]--lattice codes and we propose a phase precoding scheme for CoF protocol with partial feedback. In this method the phase of channel coefficients will then be fed back to the transmitters in order to choose the best precoder factors to control this random behavior. We further simulate the achievable sum-rate given by this scheme and showing significant gain improvement can be obtained in terms of limiting the random behavior of achievable sum-rate.
Proposing A Ciphering Protocol
Venue: arXiv cs.CR 2016
Year: 2016

This paper describes a novel bit level stream cipher based symmetric key cryptographic technique. At first, sender and receiver agree upon a symmetric key. Then the symmetric key is formed using Greatest Common Divisor (G.C.D) of sum of even or odd bit positional weightth even or odd number and a symmetric key value is derived from the symmetric by the sender. The symmetric key value is XORed with the ASCII value of the plain text character to get the cipher text character. This is repeated for the entire plain text file. The symmetric key and the entire cipher text file is sent by the sender to the receiver. From the symmetric key, the receiver calculates the symmetric key value. The symmetric key value is XORed with the cipher text character to get back the plain text character. When this process is repeated for every cipher text character, then entire plain text file is formed by the decryption process. This technique has several advantages like formation of symmetric key dynamically, more emphasis on symmetric key value rather than the symmetric key, security and equal applicability for encryption and decryption of a large number of files of almost any type or extension.