OpenRaiser/Intern-Atlas huggingface dataset:
Privacy Protection for Mobile Cloud Data: A Network Coding Approach
Venue: arXiv cs.CR 2017
Year: 2017

Taking into account of both the huge computing power of intruders and untrusted cloud servers, we develop an enhanced secure pseudonym scheme to protect the privacy of mobile cloud data. To face the huge computing power challenge, we develop an unconditionally secure lightweight network coding pseudonym scheme. For the privacy issue of untrusted cloud server, we further design a two tier network coding to decouple the stored mobile cloud data from the owner pseudonyms. Therefore, our proposed network coding based pseudonym scheme can simultaneously defend against attackers from both outside and inside. We implement our proposed two-tier light-weight network coding mechanism in a group location based service (LBS) using untrusted cloud database. Compared to computationally secure Hash-based pseudonym, our proposed scheme is not only unconditionally secure, but also can reduce more than 90 percent of processing time as well as 10 percent of energy consumption.
On Hardening Problems in Critical Infrastructure Systems
Venue: arXiv cs.NI 2017
Year: 2017

The power and communication networks are highly interdependent and form a part of the critical infrastructure of a country. Similarly, dependencies exist within the networks itself. It is essential to have a model which captures these dependencies precisely. Previous research has proposed certain models but these models have certain limitations. The limitations of the aforementioned models have been overcome by the Implicative Interdependency Model, which uses Boolean Logic to denote the dependencies. This paper formulates the Entity Hardening problem and the Targeted Entity Hardening problem using the Implicative Interdependency Model. The Entity Hardening problem describes a situation where an operator, with a limited budget, must decide which entities to harden, which in turn would minimize the damage, provided a set of entities fail initially. The Targeted Entity Hardening problem is a restricted version of the Entity Hardening problem. This problem presents a scenario where, the protection of certain entities is of higher priority. If these entities were to be nonfunctional, the economic and societal damage would be higher when compared to other entities being nonfunctional. It has been shown that both problems are NP-Complete. An Integer Linear Program (ILP) has been devised to find the optimal solution. A heuristic has been described whose accuracy is found by comparing its performance with the optimal solution using real-world and simulated data.
By chance is not enough: Preserving relative density through non uniform sampling
Venue: arXiv cs.GR 2017
Year: 2017

Dealing with visualizations containing large data set is a challenging issue and, in the field of Information Visualization, almost every visual technique reveals its drawback when visualizing large number of items. To deal with this problem we introduce a formal environment, modeling in a virtual space the image features we are interested in (e.g, absolute and relative density, clusters, etc.) and we define some metrics able to characterize the image decay. Such metrics drive our automatic techniques (i.e., not uniform sampling) rescuing the image features and making them visible to the user. In this paper we focus on 2D scatter-plots, devising a novel non uniform data sampling strategy able to preserve in an effective way relative densities.
Performance of Dynamic and Static TDD in Self-backhauled mmWave Cellular Networks
Venue: arXiv cs.IT 2017
Year: 2017

Initial deployments of millimeter wave (mmWave) cellular networks are likely to be enabled with self-backhauling. In this work, we propose a random spatial model to analyze uplink (UL) and downlink (DL) SINR distribution and mean rates corresponding to different access-backhaul and UL-DL resource allocation schemes in a self-backhauled mmWave cellular network with Poisson point process (PPP) deployment of users and base stations. In particular, we focus on heuristic implementations of static and dynamic time division duplexing (TDD) for access links with synchronized or unsynchronized access-backhaul (SAB or UAB) time splits. We propose PPP approximations to characterize the distribution of the new types of interference encountered with dynamic TDD and UAB. These schemes offer better resource utilization than static TDD and SAB, however potentially higher interference makes their choice non-trivial and the offered gains sensitive to different network parameters, including UL/DL traffic asymmetry, user load per BS or number of slave BSs per master BS. One can harness notable gains from UAB and/or dynamic TDD only if backhaul links are designed to have much larger throughput than the access links.
Coded Caching with Linear Subpacketization is Possible using Ruzsa-Szeméredi Graphs
Venue: arXiv cs.IT 2017
Year: 2017

Coded caching is a problem where encoded broadcasts are used to satisfy users requesting popular files and having caching capabilities. Recent work by Maddah-Ali and Niesen showed that it is possible to satisfy a scaling number of users with only a constant number of broadcast transmissions by exploiting coding and caching. Unfortunately, all previous schemes required the splitting of files into an exponential number of packets before the significant coding gains of caching appeared. The question of what can be achieved with polynomial subpacketization (in the number of users) has been a central open problem in this area. We resolve this problem and present the first coded caching scheme with polynomial (in fact, linear) subpacketization. We obtain a number of transmissions that is not constant, but can be any polynomial in the number of users with an exponent arbitrarily close to zero. Our central technical tool is a novel connection between Ruzsa-Szeméredi graphs and coded caching.
Tactical Diagrammatic Reasoning
Venue: arXiv cs.LO 2017
Year: 2017

Although automated reasoning with diagrams has been possible for some years, tools for diagrammatic reasoning are generally much less sophisticated than their sentential cousins. The tasks of exploring levels of automation and abstraction in the construction of proofs and of providing explanations of solutions expressed in the proofs remain to be addressed. In this paper we take an interactive proof assistant for Euler diagrams, Speedith, and add tactics to its reasoning engine, providing a level of automation in the construction of proofs. By adding tactics to Speedith's repertoire of inferences, we ease the interaction between the user and the system and capture a higher level explanation of the essence of the proof. We analysed the design options for tactics by using metrics which relate to human readability, such as the number of inferences and the amount of clutter present in diagrams. Thus, in contrast to the normal case with sentential tactics, our tactics are designed to not only prove the theorem, but also to support explanation.
Fog-Assisted Operational Cost Reduction for Cloud Data Centers
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we intend to reduce the operational cost of cloud data centers with the help of fog devices, which can avoid the revenue loss due to wide-area network propagation delay and save network bandwidth cost by serving nearby cloud users. Since fog devices may not be owned by a cloud service provider, they should be compensated for serving the requests of cloud users. When taking economical compensation into consideration, the optimal number of requests processed locally by each fog device should be decided. As a result, existing load balancing schemes developed for cloud data centers can not be applied directly and it is very necessary to redesign a cost-ware load balancing algorithm for the fog-cloud system. To achieve the above aim, we first formulate a fog-assisted operational cost minimization problem for the cloud service provider. Then, we design a parallel and distributed load balancing algorithm with low computational complexity based on Proximal Jacobian Alternating Direction Method of Multipliers (PJ-ADMM). Finally, extensive simulation results show the effectiveness of the proposed algorithm.
Exploring Students Blended Learning Through Social Media
Venue: arXiv cs.CY 2017
Year: 2017

Information technology (IT) has been used widely in many aspects of our daily life. After discuss politics related aspects for some articles. In this article author would like to discuss social media for students learning environment. Social media as a leading application on the internet has changed many aspects of life become more globalized. This article discusses the use of social media to support learning activities for students in the faculty of computer science. The author uses Facebook and WordPress as an alternative to electronic learning: 1) online attendance tool, 2) media storage and dissemination of course materials, 3) event scheduling for the lectures. Social media succeed to change the way of modern learning styles and environment. The results of this study are some learning activities such as : 1) Preparation, 2) Weekly meeting activities, 3) Course Page, 4) Social Media as Online Attendance Tool, 5) Social Media as Learning Repository and Dissemination, and 6) Social Media as Online Event Scheduling.
Community-aware network sparsification
Venue: arXiv cs.SI 2017
Year: 2017

Network sparsification aims to reduce the number of edges of a network while maintaining its structural properties; such properties include shortest paths, cuts, spectral measures, or network modularity. Sparsification has multiple applications, such as, speeding up graph-mining algorithms, graph visualization, as well as identifying the important network edges. In this paper we consider a novel formulation of the network-sparsification problem. In addition to the network, we also consider as input a set of communities. The goal is to sparsify the network so as to preserve the network structure with respect to the given communities. We introduce two variants of the community-aware sparsification problem, leading to sparsifiers that satisfy different connectedness community properties. From the technical point of view, we prove hardness results and devise effective approximation algorithms. Our experimental results on a large collection of datasets demonstrate the effectiveness of our algorithms.
Identifying Key Cyber-Physical Terrain (Extended Version)
Venue: arXiv cs.CR 2017
Year: 2017

The high mobility of Army tactical networks, combined with their close proximity to hostile actors, elevates the risks associated with short-range network attacks. The connectivity model for such short range connections under active operations is extremely fluid, and highly dependent upon the physical space within which the element is operating, as well as the patterns of movement within that space. To handle these dependencies, we introduce the notion of "key cyber-physical terrain": locations within an area of operations that allow for effective control over the spread of proximity-dependent malware in a mobile tactical network, even as the elements of that network are in constant motion with an unpredictable pattern of node-to-node connectivity. We provide an analysis of movement models and approximation strategies for finding such critical nodes, and demonstrate via simulation that we can identify such key cyber-physical terrain quickly and effectively.
Statistical Decoding
Venue: arXiv cs.CR 2017
Year: 2017

The security of code-based cryptography relies primarily on the hardness of generic decoding with linear codes. The best generic decoding algorithms are all improvements of an old algorithm due to Prange: they are known under the name of information set decoding techniques (ISD). A while ago a generic decoding algorithm which does not belong to this family was proposed: statistical decoding. It is a randomized algorithm that requires the computation of a large set of parity-check equations of moderate weight. We solve here several open problems related to this decoding algorithm. We give in particular the asymptotic complexity of this algorithm, give a rather efficient way of computing the parity-check equations needed for it inspired by ISD techniques and give a lower bound on its complexity showing that when it comes to decoding on the Gilbert-Varshamov bound it can never be better than Prange's algorithm.
A Convex Similarity Index for Sparse Recovery of Missing Image Samples
Venue: arXiv cs.LG 2017
Year: 2017

This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal. We derive mathematical properties for the proposed index and show how to optimally choose the parameters of the proposed criterion, investigating the Restricted Isometry (RIP) and error-sensitivity properties. We also propose an iterative sparse recovery method based on a constrained $l_1$-norm minimization problem, incorporating CSIM as the fidelity criterion. The resulting convex optimization problem is solved via an algorithm based on Alternating Direction Method of Multipliers (ADMM). Taking advantage of the convexity of the CSIM index, we also prove the convergence of the algorithm to the globally optimal solution of the proposed optimization problem, starting from any arbitrary point. Simulation results confirm the performance of the new similarity index as well as the proposed algorithm for missing sample recovery of image patch signals.
Asynchronous iterations in ultrametric spaces
Venue: arXiv cs.NI 2017
Year: 2017

Some iterative calculations can be carried out by parallel communicating processors, and yield the same results whether or not the processors are synchronized. We show that this is the case if and only if the iteration is a contraction that is strict on orbits, with respect to an ultrametric defined on the state space. The maximum number of independent processors is given by the dimension of the space. We apply this theorem to interdomain routing, and are able to provide two advances over the previous state of the art. Firstly, multipath routing problems have unique solutions, if certain conditions are satisfied that are analogous to known correctness conditions for the single-path case. Secondly, these solutions can be computed asynchronously in a variety of ways, which go beyond methods that are currently used.
Sorting by Reversals and the Theory of 4-Regular Graphs
Venue: arXiv cs.DM 2017
Year: 2017

We show that the theory of sorting by reversals fits into the well-established theory of circuit partitions of 4-regular multigraphs (which also involves the combinatorial structures of circle graphs and delta-matroids). In this way, we expose strong connections between the two theories that have not been fully appreciated before. We also discuss a generalization of sorting by reversals involving the double-cut-and-join (DCJ) operation. Finally, we also show that the theory of sorting by reversals is closely related to that of gene assembly in ciliates.
Implementation of Tetris as a Model Counter
Venue: arXiv cs.DS 2017
Year: 2017

Solving SharpSAT problems is an important area of work. In this paper, we discuss implementing Tetris, an algorithm originally designed for handling natural joins, as an exact model counter for the SharpSAT problem. Tetris uses a simple geometric framework, yet manages to achieve the fractional hypertree-width bound. Its design allows it to handle complex problems involving extremely large numbers of clauses on which other state-of-the-art model counters do not perform well, yet still performs strongly on standard SAT benchmarks. We have achieved the following objectives. First, we have found a natural set of model counting benchmarks on which Tetris outperforms other model counters. Second, we have constructed a data structure capable of efficiently handling and caching all of the data Tetris needs to work on over the course of the algorithm. Third, we have modified Tetris in order to move from a theoretical, asymptotic-time-focused environment to one that performs well in practice. In particular, we have managed to produce results keeping us within a single order of magnitude as compared to other solvers on most benchmarks, and outperform those solvers by multiple orders of magnitude on others.
Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature Embedding
Venue: arXiv cs.LG 2017
Year: 2017

The widespread availability of electronic health records (EHRs) promises to usher in the era of personalized medicine. However, the problem of extracting useful clinical representations from longitudinal EHR data remains challenging. In this paper, we explore deep neural network models with learned medical feature embedding to deal with the problems of high dimensionality and temporality. Specifically, we use a multi-layer convolutional neural network (CNN) to parameterize the model and is thus able to capture complex non-linear longitudinal evolution of EHRs. Our model can effectively capture local/short temporal dependency in EHRs, which is beneficial for risk prediction. To account for high dimensionality, we use the embedding medical features in the CNN model which hold the natural medical concepts. Our initial experiments produce promising results and demonstrate the effectiveness of both the medical feature embedding and the proposed convolutional neural network in risk prediction on cohorts of congestive heart failure and diabetes patients compared with several strong baselines.
Group Testing using left-and-right-regular sparse-graph codes
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of non-adaptive group testing of $N$ items out of which $K$ or less items are known to be defective. We propose a testing scheme based on left-and-right-regular sparse-graph codes and a simple iterative decoder. We show that for any arbitrarily small $ε>0$ our scheme requires only $m=c_εK\log \frac{c_1N}{K}$ tests to recover $(1-ε)$ fraction of the defective items with high probability (w.h.p) i.e., with probability approaching $1$ asymptotically in $N$ and $K$, where the value of constants $c_ε$ and $\ell$ are a function of the desired error floor $ε$ and constant $c_1=\frac{\ell}{c_ε}$ (observed to be approximately equal to 1 for various values of $ε$). More importantly the iterative decoding algorithm has a sub-linear computational complexity of $\mathcal{O}(K\log \frac{N}{K})$ which is known to be optimal. Also for $m=c_2 K\log K\log \frac{N}{K}$ tests our scheme recovers the \textit{whole} set of defective items w.h.p. These results are valid for both noiseless and noisy versions of the problem as long as the number of defective items scale sub-linearly with the total number of items, i.e., $K=o(N)$. The simulation results validate the theoretical results by showing a substantial improvement in the number of tests required when compared to the testing scheme based on left-regular sparse-graphs.
Switched control for quantized feedback systems: invariance and limit cycle analysis
Venue: arXiv cs.SY 2017
Year: 2017

We study feedback control for discrete-time linear time-invariant systems in the presence of quantization both in the control action and in the measurement of the controlled variable. While in some application the quantization effects can be neglected, when high-precision control is needed, they have to be explicitly accounted for in control design. In this paper we propose a switched control solution for minimizing the effect of quantization of both the control and controlled variables in the case of a simple integrator with unitary delay, a model that is quite common in the computing systems domain, for example in thread scheduling, clock synchronization, and resource allocation. We show that the switched solution outperforms the one without switching, designed by neglecting quantization, and analyze necessary and sufficient conditions for the controlled system to exhibit periodic solutions in the presence of an additive constant disturbance affecting the control input. Simulation results provide evidence of the effectiveness of the approach.
Monitoring and Intervention: Concepts and Formal Models
Venue: arXiv cs.CY 2017
Year: 2017

Our machines, products, utilities, and environments have long been monitored by embedded software systems. Our professional, commercial, social and personal lives are also subject to monitoring as they are mediated by software systems. Data on nearly everything now exists, waiting to be collected and analysed for all sorts of reasons. Given the rising tide of data we pose the questions: What is monitoring? Do diverse and disparate monitoring systems have anything in common? We attempt answer these questions by proposing an abstract conceptual framework for studying monitoring. We argue that it captures a structure common to many different monitoring practices, and that from it detailed formal models can be derived, customised to applications. The framework formalises the idea that monitoring is a process that observes the behaviour of people and objects in a context. The entities and their behaviours are represented by abstract data types and the observable attributes by logics. Since monitoring usually has a specific purpose, we extend the framework with protocols for detecting attributes or events that require interventions and, possibly, a change in behaviour. Our theory is illustrated by a case study from criminal justice, that of electronic tagging.
Floor Scale Modulo Lifting for QC-LDPC codes
Venue: arXiv cs.IT 2017
Year: 2017

In the given paper we present a novel approach for constructing a QC-LDPC code of smaller length by lifting a given QC-LDPC code. The proposed method can be considered as a generalization of floor lifting. Also we prove several probabilistic statements concerning a theoretical improvement of the method with respect to the number of small cycles. Making some offline calculation of scale parameter it is possible to construct a sequence of QC-LDPC codes with different circulant sizes generated from a single exponent matrix using only floor and scale operations. The only parameter we store in memory is a constant needed for scaling.