OpenRaiser/Intern-Atlas huggingface dataset:
Video Compressive Sensing for Spatial Multiplexing Cameras using Motion-Flow Models
Venue: arXiv cs.CV 2015
Year: 2015

Spatial multiplexing cameras (SMCs) acquire a (typically static) scene through a series of coded projections using a spatial light modulator (e.g., a digital micro-mirror device) and a few optical sensors. This approach finds use in imaging applications where full-frame sensors are either too expensive (e.g., for short-wave infrared wavelengths) or unavailable. Existing SMC systems reconstruct static scenes using techniques from compressive sensing (CS). For videos, however, existing acquisition and recovery methods deliver poor quality. In this paper, we propose the CS multi-scale video (CS-MUVI) sensing and recovery framework for high-quality video acquisition and recovery using SMCs. Our framework features novel sensing matrices that enable the efficient computation of a low-resolution video preview, while enabling high-resolution video recovery using convex optimization. To further improve the quality of the reconstructed videos, we extract optical-flow estimates from the low-resolution previews and impose them as constraints in the recovery procedure. We demonstrate the efficacy of our CS-MUVI framework for a host of synthetic and real measured SMC video data, and we show that high-quality videos can be recovered at roughly $60\times$ compression.
Unravelling Graph-Exchange File Formats
Venue: arXiv cs.DB 2015
Year: 2015

A graph is used to represent data in which the relationships between the objects in the data are at least as important as the objects themselves. Over the last two decades nearly a hundred file formats have been proposed or used to provide portable access to such data. This paper seeks to review these formats, and provide some insight to both reduce the ongoing creation of unnecessary formats, and guide the development of new formats where needed.
A Universal Channel Model for Molecular Communication Systems with Metal-Oxide Detectors
Venue: arXiv cs.ET 2015
Year: 2015

In this paper, we propose an end-to-end channel model for molecular communication systems with metal-oxide sensors. In particular, we focus on the recently developed table top molecular communication platform. The system is separated into two parts: the propagation and the sensor detection. There is derived, based on this, a more realistic end-to-end channel model. However, since some of the coefficients in the derived models are unknown, we collect a great deal of experimental data to estimate these coefficients and evaluate how they change with respect to the different system parameters. Finally, a noise model is derived for the system to complete an end-to-end system model for the tabletop platform.
Ham-Sandwich Cuts for Abstract Order Types
Venue: arXiv cs.CG 2015
Year: 2015

The linear-time ham-sandwich cut algorithm of Lo, Matoušek, and Steiger for bi-chromatic finite point sets in the plane works by appropriately selecting crossings of the lines in the dual line arrangement with a set of well-chosen vertical lines. We consider the setting where we are not given the coordinates of the point set, but only the orientation of each point triple (the order type) and give a deterministic linear-time algorithm for the mentioned sub-algorithm. This yields a linear-time ham-sandwich cut algorithm even in our restricted setting. We also show that our methods are applicable to abstract order types.
Stability Analysis of Large-Scale Distributed Networked Control Systems with Random Communication Delays: A Switched System Approach
Venue: arXiv cs.SY 2015
Year: 2015

In this paper, we consider the stability analysis of large-scale distributed networked control systems with random communication delays between linearly interconnected subsystems. The stability analysis is performed in the Markov jump linear system framework. There have been considerable researches on stability analysis of Markov jump systems, however, these methods are not applicable to large-scale systems because large numbers of subsystems result in an extremely large number of the switching modes. To avoid this scalability issue, we propose a new reduced mode model for stability analysis, which is computationally efficient. We also consider the case in which the transition probabilities for the Markov jump process contain uncertainties. We provide a new method that estimates bounds for uncertain Markov transition probability matrix to guarantee the system stability. The efficiency and the usefulness of the proposed methods are verified through examples.
Towards a Science of Trust
Venue: arXiv cs.CR 2015
Year: 2015

The diverse views of science of security have opened up several alleys towards applying the methods of science to security. We pursue a different kind of connection between science and security. This paper explores the idea that security is not just a suitable subject for science, but that the process of security is also similar to the process of science. This similarity arises from the fact that both science and security depend on the methods of inductive inference. Because of this dependency, a scientific theory can never be definitely proved, but can only be disproved by new evidence, and improved into a better theory. Because of the same dependency, every security claim and method has a lifetime, and always eventually needs to be improved. In this general framework of security-as-science, we explore the ways to apply the methods of scientific induction in the process of trust. The process of trust building and updating is viewed as hypothesis testing. We propose to formulate the trust hypotheses by the methods of algorithmic learning, and to build more robust trust testing and vetting methodologies on the solid foundations of statistical inference.
Reconciling a component and process view
Venue: arXiv cs.SE 2015
Year: 2015

In many cases we need to represent on the same abstraction level not only system components but also processes within the system, and if for both representation different frameworks are used, the system model becomes hard to read and to understand. We suggest a solution how to cover this gap and to reconcile component and process views on system representation: a formal framework that gives the advantage of solving design problems for large-scale component systems.
A Multi-Gene Genetic Programming Application for Predicting Students Failure at School
Venue: arXiv cs.CY 2015
Year: 2015

Several efforts to predict student failure rate (SFR) at school accurately still remains a core problem area faced by many in the educational sector. The procedure for forecasting SFR are rigid and most often times require data scaling or conversion into binary form such as is the case of the logistic model which may lead to lose of information and effect size attenuation. Also, the high number of factors, incomplete and unbalanced dataset, and black boxing issues as in Artificial Neural Networks and Fuzzy logic systems exposes the need for more efficient tools. Currently the application of Genetic Programming (GP) holds great promises and has produced tremendous positive results in different sectors. In this regard, this study developed GPSFARPS, a software application to provide a robust solution to the prediction of SFR using an evolutionary algorithm known as multi-gene genetic programming. The approach is validated by feeding a testing data set to the evolved GP models. Result obtained from GPSFARPS simulations show its unique ability to evolve a suitable failure rate expression with a fast convergence at 30 generations from a maximum specified generation of 500. The multi-gene system was also able to minimize the evolved model expression and accurately predict student failure rate using a subset of the original expression
Convolutional Neural Network Architectures for Matching Natural Language Sentences
Venue: arXiv cs.CL 2015
Year: 2015

Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.
Epidemic Information Diffusion: A Simple Solution to Support Community-based Recommendations in P2P Overlays
Venue: arXiv cs.SI 2015
Year: 2015

Epidemic protocols proved to be very efficient solutions for supporting dynamic and complex information diffusion in highly dis- tributed computing infrastructures, like P2P environments. They are useful bricks for building and maintaining virtual network topologies, in the form of overlay networks as well as to support pervasive diffusion of information when it is injected into the network. This paper proposes a simple architecture exploiting the features of epidemic approaches to foster a collaborative percolation of information between computing nodes belonging to the network aimed at building a system that groups similar users and spread useful information among them.
Generalized Spatial Modulation in Indoor Wireless Visible Light Communication
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we investigate the performance of generalized spatial modulation (GSM) in indoor wireless visible light communication (VLC) systems. GSM uses $N_t$ light emitting diodes (LED), but activates only $N_a$ of them at a given time. Spatial modulation and spatial multiplexing are special cases of GSM with $N_{a}=1$ and $N_{a}=N_t$, respectively. We first derive an analytical upper bound on the bit error rate (BER) for maximum likelihood (ML) detection of GSM in VLC systems. Analysis and simulation results show that the derived upper bound is very tight at medium to high signal-to-noise ratios (SNR). The channel gains and channel correlations influence the GSM performance such that the best BER is achieved at an optimum LED spacing. Also, for a fixed transmission efficiency, the performance of GSM in VLC improves as the half-power semi-angle of the LEDs is decreased. We then compare the performance of GSM in VLC systems with those of other MIMO schemes such as spatial multiplexing (SMP), space shift keying (SSK), generalized space shift keying (GSSK), and spatial modulation (SM). Analysis and simulation results show that GSM in VLC outperforms the other considered MIMO schemes at moderate to high SNRs; for example, for 8 bits per channel use, GSM outperforms SMP and GSSK by about 21 dB, and SM by about 10 dB at $10^{-4}$ BER.
The Feedback Capacity of the $(1,\infty)$-RLL Input-Constrained Erasure Channel
Venue: arXiv cs.IT 2015
Year: 2015

The input-constrained erasure channel with feedback is considered, where the binary input sequence contains no consecutive ones, i.e., it satisfies the $(1,\infty)$-RLL constraint. We derive the capacity for this setting, which can be expressed as $C_ε=\max_{0 \leq p \leq \frac{1}{2}}\frac{H_{b}(p)}{p+\frac{1}{1-ε}}$, where $ε$ is the erasure probability and $ H_{b}(\cdot)$ is the binary entropy function. Moreover, we prove that a-priori knowledge of the erasure at the encoder does not increase the feedback capacity. The feedback capacity was calculated using an equivalent dynamic programming (DP) formulation with an optimal average-reward that is equal to the capacity. Furthermore, we obtained an optimal encoding procedure from the solution of the DP, leading to a capacity-achieving, zero-error coding scheme for our setting. DP is thus shown to be a tool not only for solving optimization problems such as capacity calculation, but also for constructing optimal coding schemes. The derived capacity expression also serves as the only non-trivial upper bound known on the capacity of the input-constrained erasure channel without feedback, a problem that is still open.
Bublz! : Playing with Bubbles to Develop Mathematical Thinking
Venue: arXiv cs.HC 2015
Year: 2015

We encounter mathematical problems in various forms in our lives, thus making mathematical thinking an important human ability. In this paper, we present Bublz!, a simple, click-driven game for children to engage in and develop mathematical thinking in an enjoyable manner.
Upper Bounds on the Relative Entropy and Rényi Divergence as a Function of Total Variation Distance for Finite Alphabets
Venue: arXiv cs.IT 2015
Year: 2015

A new upper bound on the relative entropy is derived as a function of the total variation distance for probability measures defined on a common finite alphabet. The bound improves a previously reported bound by Csiszár and Talata. It is further extended to an upper bound on the Rényi divergence of an arbitrary non-negative order (including $\infty$) as a function of the total variation distance.
A mathematical motivation for complex-valued convolutional networks
Venue: arXiv cs.LG 2015
Year: 2015

A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an input vector of nonnegative real numbers: (1) convolution with complex-valued vectors followed by (2) taking the absolute value of every entry of the resulting vectors followed by (3) local averaging. For processing real-valued random vectors, complex-valued convnets can be viewed as "data-driven multiscale windowed power spectra," "data-driven multiscale windowed absolute spectra," "data-driven multiwavelet absolute values," or (in their most general configuration) "data-driven nonlinear multiwavelet packets." Indeed, complex-valued convnets can calculate multiscale windowed spectra when the convnet filters are windowed complex-valued exponentials. Standard real-valued convnets, using rectified linear units (ReLUs), sigmoidal (for example, logistic or tanh) nonlinearities, max. pooling, etc., do not obviously exhibit the same exact correspondence with data-driven wavelets (whereas for complex-valued convnets, the correspondence is much more than just a vague analogy). Courtesy of the exact correspondence, the remarkably rich and rigorous body of mathematical analysis for wavelets applies directly to (complex-valued) convnets.
State of the art parallel approaches for RSA public key based cryptosystem
Venue: arXiv cs.CR 2015
Year: 2015

RSA is one of the most popular Public Key Cryptography based algorithm mainly used for digital signatures, encryption/decryption etc. It is based on the mathematical scheme of factorization of very large integers which is a compute-intensive process and takes very long time as well as power to perform. Several scientists are working throughout the world to increase the speedup and to decrease the power consumption of RSA algorithm while keeping the security of the algorithm intact. One popular technique which can be used to enhance the performance of RSA is parallel programming. In this paper we are presenting the survey of various parallel implementations of RSA algorithm involving variety of hardware and software implementations.
A divisive hierarchical clustering-based method for indexing image information
Venue: arXiv cs.IR 2015
Year: 2015

In most practical applications of image retrieval, high-dimensional feature vectors are required, but current multi-dimensional indexing structures lose their efficiency with growth of dimensions. Our goal is to propose a divisive hierarchical clustering-based multi-dimensional indexing structure which is efficient in high-dimensional feature spaces. A projection pursuit method has been used for finding a component of the data, which data's projections onto it maximizes the approximation of negentropy for preparing essential information in order to partitioning of the data space. Various tests and experimental results on high-dimensional datasets indicate the performance of proposed method in comparison with others.
Single image super-resolution by approximated Heaviside functions
Venue: arXiv cs.CV 2015
Year: 2015

Image super-resolution is a process to enhance image resolution. It is widely used in medical imaging, satellite imaging, target recognition, etc. In this paper, we conduct continuous modeling and assume that the unknown image intensity function is defined on a continuous domain and belongs to a space with a redundant basis. We propose a new iterative model for single image super-resolution based on an observation: an image is consisted of smooth components and non-smooth components, and we use two classes of approximated Heaviside functions (AHFs) to represent them respectively. Due to sparsity of the non-smooth components, a $L_{1}$ model is employed. In addition, we apply the proposed iterative model to image patches to reduce computation and storage. Comparisons with some existing competitive methods show the effectiveness of the proposed method.
On Computing the Translations Norm in the Epipolar Graph
Venue: arXiv cs.CV 2015
Year: 2015

This paper deals with the problem of recovering the unknown norm of relative translations between cameras based on the knowledge of relative rotations and translation directions. We provide theoretical conditions for the solvability of such a problem, and we propose a two-stage method to solve it. First, a cycle basis for the epipolar graph is computed, then all the scaling factors are recovered simultaneously by solving a homogeneous linear system. We demonstrate the accuracy of our solution by means of synthetic and real experiments.
QoS Guaranteed Intelligent Routing Using Hybrid PSO-GA in Wireless Mesh Networks
Venue: arXiv cs.NI 2015
Year: 2015

In Multi-Channel Multi-Radio Wireless Mesh Networks (MCMR-WMN), finding the optimal routing by satisfying the Quality of Service (QoS) constraints is an ambitious task. Multiple paths are available from the source node to the gateway for reliability, and sometimes it is necessary to deal with failures of the link in WMN. A major challenge in a MCMR-WMN is finding the routing with QoS satisfied and an interference free path from the redundant paths, in order to transmit the packets through this path. The Particle Swarm Optimization (PSO) is an optimization technique to find the candidate solution in the search space optimally, and it applies artificial intelligence to solve the routing problem. On the other hand, the Genetic Algorithm (GA) is a population based meta-heuristic optimization algorithm inspired by the natural evolution, such as selection,mutation and crossover. PSO can easily fall into a local optimal solution, at the same time GA is not suitable for dynamic data due to the underlying dynamic network. In this paper we propose an optimal intelligent routing, using a Hybrid PSO-GA, which also meets the QoS constraints. Moreover, it integrates the strength of PSO and GA. The QoS constraints, such as bandwidth, delay, jitter and interference are transformed into penalty functions. The simulation results show that the hybrid approach outperforms PSO and GA individually, and it takes less convergence time comparatively, keeping away from converging prematurely. Keywords: Wireless mesh networks, Multi-radio, Multi-channel, Particle swarm optimization, Genetic algorithm, Quality of service.
Capturing, Documenting and Visualizing Search Contexts for building Multimedia Corpora
Venue: arXiv cs.IR 2015
Year: 2015

In Social Science research, multimedia documents are often collected to answer particular research questions like: "Which of the aesthetic properties of a photo are considered important on the web" or "How has Street Art developed over the past 50 years". Therefore, a researcher generally issues multiple queries to a number of search engines. This activity may span over long time intervals and results in a collection which can be further analyzed. Documenting the collection building process which includes the context of the carried out searches is imperative for social scientists to reproduce their research. Such context documentation consists of several user actions and search attributes like: the issued queries; the results clicked and saved; duration a particular result was viewed for; the set of results that was displayed but neither clicked, nor saved; as well as user annotations like comments or tags. In this work we will describe a search process tracking module and a search history visualization module. These modules can be integrated into keyword based search systems through a REST API which was developed to help capture, document and revisit past search contexts while building a web corpora. Finally, we detail the implementation of how the module was integrated into the LearnWeb2.0 platform - a multimedia web2.0 search and sharing application which can obtain resources from various web2.0 tools such as Youtube, Bing, Flickr, etc using keyword search.
On Graduated Optimization for Stochastic Non-Convex Problems
Venue: arXiv cs.LG 2015
Year: 2015

The graduated optimization approach, also known as the continuation method, is a popular heuristic to solving non-convex problems that has received renewed interest over the last decade. Despite its popularity, very little is known in terms of theoretical convergence analysis. In this paper we describe a new first-order algorithm based on graduated optimiza- tion and analyze its performance. We characterize a parameterized family of non- convex functions for which this algorithm provably converges to a global optimum. In particular, we prove that the algorithm converges to an ε-approximate solution within O(1/ε^2) gradient-based steps. We extend our algorithm and analysis to the setting of stochastic non-convex optimization with noisy gradient feedback, attaining the same convergence rate. Additionally, we discuss the setting of zero-order optimization, and devise a a variant of our algorithm which converges at rate of O(d^2/ε^4).
Sound-Proof: Usable Two-Factor Authentication Based on Ambient Sound
Venue: arXiv cs.CR 2015
Year: 2015

Two-factor authentication protects online accounts even if passwords are leaked. Most users, however, prefer password-only authentication. One reason why two-factor authentication is so unpopular is the extra steps that the user must complete in order to log in. Currently deployed two-factor authentication mechanisms require the user to interact with his phone to, for example, copy a verification code to the browser. Two-factor authentication schemes that eliminate user-phone interaction exist, but require additional software to be deployed. In this paper we propose Sound-Proof, a usable and deployable two-factor authentication mechanism. Sound-Proof does not require interaction between the user and his phone. In Sound-Proof the second authentication factor is the proximity of the user's phone to the device being used to log in. The proximity of the two devices is verified by comparing the ambient noise recorded by their microphones. Audio recording and comparison are transparent to the user, so that the user experience is similar to the one of password-only authentication. Sound-Proof can be easily deployed as it works with current phones and major browsers without plugins. We build a prototype for both Android and iOS. We provide empirical evidence that ambient noise is a robust discriminant to determine the proximity of two devices both indoors and outdoors, and even if the phone is in a pocket or purse. We conduct a user study designed to compare the perceived usability of Sound-Proof with Google 2-Step Verification. Participants ranked Sound-Proof as more usable and the majority would be willing to use Sound-Proof even for scenarios in which two-factor authentication is optional.