OpenRaiser/Intern-Atlas huggingface dataset:
Priority Based Energy-Efficient Data Forwarding Algorithm in Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

A Wireless Sensor Network(WSN) can be described as a collection of untethered sensor nodes. An important application of WSNs is in the field of real-time communication. Real-time communication is a critical service which requires a qualitative routing protocol for energy-efficient network communication. The judicious use of energy of the network nodes is essential and important for sustainability and longevity of a WSN. This paper proposes an algorithm namely Priority-Energy Based Data Forwarding Algorithm(PEDF) which empowers the node to choose the most suitable packet forwarding path, based on the priority of the packet and the current energy status of the forwarding node. The algorithm hence dynamically adapts to the prevailing energy-scenario of the network and takes routing decisions accordingly, based on packet priority. Minimizing delay, minimizing energy utilization, maximizing throughput and maximizing network lifetime are the key elements of the proposed algorithm.
Asymptotic metrics on the space of matrices under the commutation relation
Venue: arXiv cs.NA 2013
Year: 2013

We show that the norm of the commutator defines "almost a metric" on the quotient space of commuting matrices, in the sense that it is a semi-metric satisfying the triangle inequality asymptotically for large matrices drawn from a "good" distribution.
Interference Alignment in Distributed Antenna Systems
Venue: arXiv cs.IT 2013
Year: 2013

Interference alignment (IA) is a cooperative transmission strategy that improves spectral efficiency in high signal-to-noise ratio (SNR) environments, yet performs poorly in low-SNR scenarios. This limits IA's utility in cellular systems as it is ineffective in improving cell-edge data rates. Modern cellular architectures such as distributed antenna systems (DAS), however, promise to boost cell-edge SNR, creating the environment needed to realize practical IA gains. Existing IA solutions cannot be applied to DAS as they neglect the per-remote-radio power constraints imposed on distributed precoders. This paper considers two types of distributed antenna IA systems: ones with a limit on maximum per-radio power, and ones with a strict equality constraint on per-radio power. The rate-loss incurred by a simple power back-off strategy, used in systems with maximum power constraints, is characterized analytically. It is also shown that enforcing strict power constraints avoids such a rate-loss but negatively affects IA feasibility. For such systems, an IA algorithm is proposed and feasibility conditions are derived based on the concept of system properness. Finally, numerical results validate the analysis and demonstrate that IA and DAS can be successfully combined to mitigate inter-cell interference and improve performance for most mobile users, especially those at the cell-edge.
On Min-Cut Algorithms for Half-Duplex Relay Networks
Venue: arXiv cs.IT 2013
Year: 2013

Computing the cut-set bound in half-duplex relay networks is a challenging optimization problem, since it requires finding the cut-set optimal half-duplex schedule. This subproblem in general involves an exponential number of variables, since the number of ways to assign each node to either transmitter or receiver mode is exponential in the number of nodes. We present a general technique that takes advantage of specific structures in the topology of a given network and allows us to reduce the complexity of computing the half-duplex schedule that maximizes the cut-set bound (with i.i.d. input distribution). In certain classes of network topologies, our approach yields polynomial time algorithms. We use simulations to show running time improvements over alternative methods and compare the performance of various half-duplex scheduling approaches in different SNR regimes.
Full Square Rhomboids and Their Algebraic Expressions
Venue: arXiv cs.DS 2013
Year: 2013

The paper investigates relationship between algebraic expressions and graphs. We consider a digraph called a full square rhomboid that is an example of non-series-parallel graphs. Our intention is to simplify the expressions of full square rhomboids and eventually find their shortest representations. With that end in view, we describe two decomposition methods for generating expressions of full square rhomboids and carry out their comparative analysis.
The Multi-Sender Multicast Index Coding
Venue: arXiv cs.IT 2013
Year: 2013

We focus on the following instance of an index coding problem, where a set of receivers are required to decode multiple messages, whilst each knows one of the messages a priori. In particular, here we consider a generalized setting where they are multiple senders, each sender only knows a subset of messages, and all senders are required to collectively transmit the index code. For a single sender, Ong and Ho (ICC, 2012) have established the optimal index codelength, where the lower bound was obtained using a pruning algorithm. In this paper, the pruning algorithm is simplified, and used in conjunction with an appending technique to give a lower bound to the multi-sender case. An upper bound is derived based on network coding. While the two bounds do not match in general, for the special case where no two senders know any message bit in common, the bounds match, giving the optimal index codelength. The results are derived based on graph theory, and are expressed in terms of strongly connected components.
Conceptual Framework of Redundant Link Aggregation
Venue: arXiv cs.NI 2013
Year: 2013

This is era of information blast. A huge quantity of information is pouring in from various sources. The revolutionary advancement of Information and Communication technologies bring the world close together. A pile of information in different formats is just a click away. Which motivate the organizations to get more internet bandwidth to consume and publish the information over exploding cloud of Internet. The standard router redundancy protocol is used to handle backup links however it cannot aggregate them. Whereas the link standard aggregation protocol can aggregate the link but it support only Ethernet technology. In this research paper a concept of Redundant Link Aggregation (RLA) is proposed. RLA can aggregate and handle backup links with main links regardless of carrier technology. Furthermore a data forwarding mechanism Odd Load Balancing (OLB) is also proposed for RLA scheme. For the sake of performance evaluation, Redundant Link Aggregation (RLA) is compared with Virtual Router Redundancy Protocol (VRRP). The simulation result reveals that Redundant Link Aggregation (RLA) can cover the bandwidth demand of the network in peak hours by consuming backup links as well which with Virtual Router Redundancy Protocol (VRRP) cannot. It is further noted that Odd Load Balancing (OLB) feature can be used to save the cost in terms of money per annum.
Time Estimation Model of Concurrent Computing Systems
Venue: arXiv cs.LO 2013
Year: 2013

We consider an asynchronous system with transitions corresponding to the instructions of a computer system. For each instruction, a runtime is given. We propose a mathematical model, allowing us to construct an algorithm for finding the minimum time of the parallel process with a given trace. We consider a problem of constructing a parallel process which transforms the initial state to given and has the minimum execution time. We show that it is reduced to the problem of finding the shortest path in a directed graph with edge lengths equal to 1.
Personal Information Privacy Settings of Online Social Networks and their Suitability for Mobile Internet Devices
Venue: arXiv cs.SI 2013
Year: 2013

Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users privacy. These risks include the misuse of personal information which may lead to illegal acts such as identity theft. This study aims to measure the awareness of users on protecting their personal information privacy, as well as the suitability of the privacy systems which they use to modify privacy settings. Survey results show high percentage of the use of smart phones for web services but the current privacy settings for online social networks need to be improved to support different type of mobile phones screens. Because most users use their mobile phones for Internet services, privacy settings that are compatible with mobile phones need to be developed. The method of selecting privacy settings should also be simplified to provide users with a clear picture of the data that will be shared with others. Results of this study can be used to develop a new privacy system which will help users control their personal information easily from different devices, including mobile Internet devices and computers.
OBDD-Based Representation of Interval Graphs
Venue: arXiv cs.DS 2013
Year: 2013

A graph $G = (V,E)$ can be described by the characteristic function of the edge set $χ_E$ which maps a pair of binary encoded nodes to 1 iff the nodes are adjacent. Using \emph{Ordered Binary Decision Diagrams} (OBDDs) to store $χ_E$ can lead to a (hopefully) compact representation. Given the OBDD as an input, symbolic/implicit OBDD-based graph algorithms can solve optimization problems by mainly using functional operations, e.g. quantification or binary synthesis. While the OBDD representation size can not be small in general, it can be provable small for special graph classes and then also lead to fast algorithms. In this paper, we show that the OBDD size of unit interval graphs is $O(\ | V \ | /\log \ | V \ |)$ and the OBDD size of interval graphs is $O(\ | V \ | \log \ | V \ |)$ which both improve a known result from Nunkesser and Woelfel (2009). Furthermore, we can show that using our variable order and node labeling for interval graphs the worst-case OBDD size is $Ω(\ | V \ | \log \ | V \ |)$. We use the structure of the adjacency matrices to prove these bounds. This method may be of independent interest and can be applied to other graph classes. We also develop a maximum matching algorithm on unit interval graphs using $O(\log \ | V \ |)$ operations and a coloring algorithm for unit and general intervals graphs using $O(\log^2 \ | V \ |)$ operations and evaluate the algorithms empirically.
Infrastructure to Vehicle Real Time Secured Communication
Venue: arXiv cs.NI 2013
Year: 2013

Among civilian communication systems, vehicular networks emerge as one of the most is convincing and yet most challenging instantiations of the mobile ad hoc networking technology. Towards the deployment of vehicular communication systems, security and privacy are critical factors and significant challenges to be met. This Vehicular communication (VC) system has the potential to improve road safety and driving comfort. Nevertheless, securing the operation is a prerequisite for deployment so in this paper we are focusing on real time experimental design of infrastructure to vehicle communication. We outline how VANET will be a better option than GPS technology. We also try to discuss IP address passing using DHCP in the network and the security issues.
Metrics for Computing Trust in a Multi-Agent Environment
Venue: arXiv cs.CY 2013
Year: 2013

One of the risks involved in multi agent community is in the identification of trustworthy agent partners for transaction. In this paper we aim to describe a trust model for measuring trust in the interacting agents. The trust metric model works on the basis of the parameters that we have identified. The model primarily analyses trust value on the basis of the agents reputation, as provided by the agent itself, and the agents aggregate rating as provided by the witness agents. The final computation of the trust value is given by a weighted average of these two components. While computing the aggregate rating, a weight based method has been adopted to discount the contribution of possibly unfair ratings by the witness agents.
Dynamic Spectrum Refarming of GSM Spectrum for LTE Small Cells
Venue: arXiv cs.IT 2013
Year: 2013

In this paper we propose a novel solution called dynamic spectrum refarming (DSR) for deploying LTE small cells using the same spectrum as existing GSM networks. The basic idea of DSR is that LTE small cells are deployed in the GSM spectrum but suppress transmission of all signals including the reference signals in some specific physical resource blocks corresponding to a portion of the GSM carriers to ensure full GSM coverage. Our study shows that the proposed solution can provide LTE mobile terminals with high speed data services when they are in the coverage of the LTE small cells while minimally affecting the service provided to GSM terminals located within the LTE small cell coverage area. Thus, the proposal allows the normal operation of the existing GSM networks even with LTE small cells deployed in that spectrum. Though the focus of this paper is about GSM spectrum refarming, an analogous approach can be applied to reuse code division multiple access (CDMA) spectrum for LTE small cells.
Novel variational model for inpainting in the wavelet domain
Venue: arXiv cs.CV 2013
Year: 2013

Wavelet domain inpainting refers to the process of recovering the missing coefficients during the image compression or transmission stage. Recently, an efficient algorithm framework which is called Bregmanized operator splitting (BOS) was proposed for solving the classical variational model of wavelet inpainting. However, it is still time-consuming to some extent due to the inner iteration. In this paper, a novel variational model is established to formulate this reconstruction problem from the view of image decomposition. Then an efficient iterative algorithm based on the split-Bregman method is adopted to calculate an optimal solution, and it is also proved to be convergent. Compared with the BOS algorithm the proposed algorithm avoids the inner iteration and hence is more simple. Numerical experiments demonstrate that the proposed method is very efficient and outperforms the current state-of-the-art methods, especially in the computational time.
Running Consensus for Decentralized Detection
Venue: arXiv cs.SY 2013
Year: 2013

This thesis represents a culmination of work and learning that has taken place over a period of almost three years (2007 - 2010) at the University of Salerno, and at the University of Connecticut. It is mostly an unified mathematical dissertation of the running consensus procedures. In the recent years, the detection using the paradigm of the running consensus has been recognized as one of the three possible classes of distributed detection in which the phases of sensing and communication need not be mutually exclusive, i.e., sensing and communication occur simultaneously. Considering that the running consensus paradigm is just an intuitive inference procedure, i.e. sub-optimal w.r.t. an ideal centralized system scheme which is optimal, the most important result is that it asymptotically reaches the performance of this ideal scheme.
FPT is Characterized by Useful Obstruction Sets
Venue: arXiv cs.CC 2013
Year: 2013

Many graph problems were first shown to be fixed-parameter tractable using the results of Robertson and Seymour on graph minors. We show that the combination of finite, computable, obstruction sets and efficient order tests is not just one way of obtaining strongly uniform FPT algorithms, but that all of FPT may be captured in this way. Our new characterization of FPT has a strong connection to the theory of kernelization, as we prove that problems with polynomial kernels can be characterized by obstruction sets whose elements have polynomial size. Consequently we investigate the interplay between the sizes of problem kernels and the sizes of the elements of such obstruction sets, obtaining several examples of how results in one area yield new insights in the other. We show how exponential-size minor-minimal obstructions for pathwidth k form the crucial ingredient in a novel OR-cross-composition for k-Pathwidth, complementing the trivial AND-composition that is known for this problem. In the other direction, we show that OR-cross-compositions into a parameterized problem can be used to rule out the existence of efficiently generated quasi-orders on its instances that characterize the NO-instances by polynomial-size obstructions.
A Mining-Based Compression Approach for Constraint Satisfaction Problems
Venue: arXiv cs.AI 2013
Year: 2013

In this paper, we propose an extension of our Mining for SAT framework to Constraint satisfaction Problem (CSP). We consider n-ary extensional constraints (table constraints). Our approach aims to reduce the size of the CSP by exploiting the structure of the constraints graph and of its associated microstructure. More precisely, we apply itemset mining techniques to search for closed frequent itemsets on these two representation. Using Tseitin extension, we rewrite the whole CSP to another compressed CSP equivalent with respect to satisfiability. Our approach contrast with previous proposed approach by Katsirelos and Walsh, as we do not change the structure of the constraints.
Online Learning in a Contract Selection Problem
Venue: arXiv cs.LG 2013
Year: 2013

In an online contract selection problem there is a seller which offers a set of contracts to sequentially arriving buyers whose types are drawn from an unknown distribution. If there exists a profitable contract for the buyer in the offered set, i.e., a contract with payoff higher than the payoff of not accepting any contracts, the buyer chooses the contract that maximizes its payoff. In this paper we consider the online contract selection problem to maximize the sellers profit. Assuming that a structural property called ordered preferences holds for the buyer's payoff function, we propose online learning algorithms that have sub-linear regret with respect to the best set of contracts given the distribution over the buyer's type. This problem has many applications including spectrum contracts, wireless service provider data plans and recommendation systems.
Approximate Congestion Games for Load Balancing in Distributed Environment
Venue: arXiv cs.NI 2013
Year: 2013

The use of game theoretic models has been quite successful in describing various cooperative and non-cooperative optimization problems in networks and other domains of computer systems. In this paper, we study an application of game theoretic models in the domain of distributed system, where nodes play a game to balance the total processing loads among themselves. We have used congestion gaming model, a model of game theory where many agents compete for allocating resources, and studied the existence of Nash Equilibrium for such types of games. As the classical congestion game is known to be PLS-Complete, we use an approximation, called the ε-Congestion game, which converges to ε-Nash equilibrium within finite number of steps under selected conditions. Our focus is to define the load balancing problem using the model of ε-congestion games, and finally provide a greedy algorithm for load balancing in distributed systems. We have simulated our proposed system to show the effect of ε-congestion game, and the distribution of load at equilibrium state.
Transfer Learning for Content-Based Recommender Systems using Tree Matching
Venue: arXiv cs.LG 2013
Year: 2013

In this paper we present a new approach to content-based transfer learning for solving the data sparsity problem in cases when the users' preferences in the target domain are either scarce or unavailable, but the necessary information on the preferences exists in another domain. We show that training a system to use such information across domains can produce better performance. Specifically, we represent users' behavior patterns based on topological graph structures. Each behavior pattern represents the behavior of a set of users, when the users' behavior is defined as the items they rated and the items' rating values. In the next step we find a correlation between behavior patterns in the source domain and behavior patterns in the target domain. This mapping is considered a bridge between the two domains. Based on the correlation and content-attributes of the items, we train a machine learning model to predict users' ratings in the target domain. When we compare our approach to the popularity approach and KNN-cross-domain on a real world dataset, the results show that on an average of 83$%$ of the cases our approach outperforms both methods.
Almost Lossless Analog Signal Separation
Venue: arXiv cs.IT 2013
Year: 2013

We propose an information-theoretic framework for analog signal separation. Specifically, we consider the problem of recovering two analog signals from a noiseless sum of linear measurements of the signals. Our framework is inspired by the groundbreaking work of Wu and Verdú (2010) on almost lossless analog compression. The main results of the present paper are a general achievability bound for the compression rate in the analog signal separation problem, an exact expression for the optimal compression rate in the case of signals that have mixed discrete-continuous distributions, and a new technique for showing that the intersection of generic subspaces with subsets of sufficiently small Minkowski dimension is empty. This technique can also be applied to obtain a simplified proof of a key result in Wu and Verdú (2010).
Nyquist Filter Design using POCS Methods: Including Constraints in Design
Venue: arXiv cs.IT 2013
Year: 2013

The problem of constrained finite impulse response (FIR) filter design is central to signal processing and arises in a variety of disciplines. This paper surveys the design of such filters using Projection onto convex sets (POCS) and discusses certain commonly encountered time and frequency domain constraints. We study in particular the design of Nyquist filters and propose a simple extension to the work carried out by Haddad, Stark, and Galatsanos in [1]. The flexibility and the ease that this design method provides in terms of accommodating constraints is one of its outstanding features.
Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network
Venue: arXiv cs.CV 2013
Year: 2013

In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a large acoustic dataset containing 24 months of nearly continuous recordings. The results show a significant improvement in performance of the detection-classification system; yielding as much as 20% improvement in true positive rate for a given false positive rate.