OpenRaiser/Intern-Atlas huggingface dataset:
Expansion coding: Achieving the capacity of an AEN channel
Venue: arXiv cs.IT 2012
Year: 2012

A general method of coding over expansions is proposed, which allows one to reduce the highly non-trivial problem of coding over continuous channels to a much simpler discrete ones. More specifically, the focus is on the additive exponential noise (AEN) channel, for which the (binary) expansion of the (exponential) noise random variable is considered. It is shown that each of the random variables in the expansion corresponds to independent Bernoulli random variables. Thus, each of the expansion levels (of the underlying channel) corresponds to a binary symmetric channel (BSC), and the coding problem is reduced to coding over these parallel channels while satisfying the channel input constraint. This optimization formulation is stated as the achievable rate result, for which a specific choice of input distribution is shown to achieve a rate which is arbitrarily close to the channel capacity in the high SNR regime. Remarkably, the scheme allows for low-complexity capacity-achieving codes for AEN channels, using the codes that are originally designed for BSCs. Extensions to different channel models and applications to other coding problems are discussed.
Robust seed selection algorithm for k-means type algorithms
Venue: arXiv cs.CV 2012
Year: 2012

Selection of initial seeds greatly affects the quality of the clusters and in k-means type algorithms. Most of the seed selection methods result different results in different independent runs. We propose a single, optimal, outlier insensitive seed selection algorithm for k-means type algorithms as extension to k-means++. The experimental results on synthetic, real and on microarray data sets demonstrated that effectiveness of the new algorithm in producing the clustering results
Allocations for Heterogenous Distributed Storage
Venue: arXiv cs.IT 2012
Year: 2012

We study the problem of storing a data object in a set of data nodes that fail independently with given probabilities. Our problem is a natural generalization of a homogenous storage allocation problem where all the nodes had the same reliability and is naturally motivated for peer-to-peer and cloud storage systems with different types of nodes. Assuming optimal erasure coding (MDS), the goal is to find a storage allocation (i.e, how much to store in each node) to maximize the probability of successful recovery. This problem turns out to be a challenging combinatorial optimization problem. In this work we introduce an approximation framework based on large deviation inequalities and convex optimization. We propose two approximation algorithms and study the asymptotic performance of the resulting allocations.
FastSIR Algorithm: A Fast Algorithm for simulation of epidemic spread in large networks by using SIR compartment model
Venue: arXiv cs.DS 2012
Year: 2012

The epidemic spreading on arbitrary complex networks is studied in SIR (Susceptible Infected Recovered) compartment model. We propose our implementation of a Naive SIR algorithm for epidemic simulation spreading on networks that uses data structures efficiently to reduce running time. The Naive SIR algorithm models full epidemic dynamics and can be easily upgraded to parallel version. We also propose novel algorithm for epidemic simulation spreading on networks called the FastSIR algorithm that has better average case running time than the Naive SIR algorithm. The FastSIR algorithm uses novel approach to reduce average case running time by constant factor by using probability distributions of the number of infected nodes. Moreover, the FastSIR algorithm does not follow epidemic dynamics in time, but still captures all infection transfers. Furthermore, we also propose an efficient recursive method for calculating probability distributions of the number of infected nodes. Average case running time of both algorithms has also been derived and experimental analysis was made on five different empirical complex networks.
Trust Based Scheme for QoS Assurance in Mobile Ad-Hoc Networks
Venue: arXiv cs.NI 2012
Year: 2012

A mobile ad-hoc network (MANET) is a peer-to-peer wireless network where nodes can communicate with each other without the use of infrastructure such as access points or base stations. These networks are self-configuring, capable of self-directed operation and hastily deployable. Nodes cooperate to provide connectivity, operates without centralized administration. Nodes are itinerant, topology can be very dynamic and nodes must be able to relay traffic since communicating nodes might be out of range. The dynamic nature of MANET makes network open to attacks and unreliability. Routing is always the most significant part for any networks. Each node should not only work for itself, but should be cooperative with other nodes. Node misbehaviour due to selfish or malicious intention could significantly degrade the performance of MANET. The Qos parameters like PDR, throughput and delay are affected directly due to such misbehaving nodes. We focus on trust management framework, which is intended to cope with misbehaviour problem of node and increase the performance of MANETs. A trust-based system can be used to track this misbehaving of nodes, spot them and isolate them from routing and provide reliability. In this paper a Trust Based Reliable AODV [TBRAODV] protocol is presented which implements a trust value for each node. For every node trust value is calculated and based trust value nodes are allowed to participate in routing or else identified to become a misbehaving node. This enhances reliability in AODV routing and results in increase of PDR, decrease in delay and throughput is maintained. This work is implemented and simulated on NS-2. Based on simulation results, the proposed protocol provides more consistent and reliable data transfer compared with general AODV, if there are misbehaving nodes in the MANET
Design of wireless electronic stethoscope based on zigbee
Venue: arXiv cs.OH 2012
Year: 2012

Heart sound stethoscope is primary stage to access diseases. In this paper design of an electronic stethoscope with the functions of wireless transmission is discussed. This electronic stethoscope based on embedded processor. The data can be transmitted through wireless transmission using Zigbee module. A microphone is used to pick up the sound of the heart beat. Acoustic stethoscope can be changed into a digital stethoscope by inserting an electric capacity microphone into its head. The signal is processed and amplified to play with or without earphone. Heart sounds are processed, sampled and sent wirelessly using Zigbee module so that multiple doctors can do auscultation. PC connectivity is provided through serial port where from audio and video can be made available through LAN and internet for telemedicine consultation. Heart beat signals are sensed, sent, displayed, monitored, stored, reviewed, and analysed with ease.
Learning to Place New Objects in a Scene
Venue: arXiv cs.RO 2012
Year: 2012

Placing is a necessary skill for a personal robot to have in order to perform tasks such as arranging objects in a disorganized room. The object placements should not only be stable but also be in their semantically preferred placing areas and orientations. This is challenging because an environment can have a large variety of objects and placing areas that may not have been seen by the robot before. In this paper, we propose a learning approach for placing multiple objects in different placing areas in a scene. Given point-clouds of the objects and the scene, we design appropriate features and use a graphical model to encode various properties, such as the stacking of objects, stability, object-area relationship and common placing constraints. The inference in our model is an integer linear program, which we solve efficiently via an LP relaxation. We extensively evaluate our approach on 98 objects from 16 categories being placed into 40 areas. Our robotic experiments show a success rate of 98% in placing known objects and 82% in placing new objects stably. We use our method on our robots for performing tasks such as loading several dish-racks, a bookshelf and a fridge with multiple items.
Review of syn-flooding attack detection mechanism
Venue: arXiv cs.CR 2012
Year: 2012

Denial of Service (DoS) is a security threat which compromises the confidentiality of information stored in Local Area Networks (LANs) due to unauthorized access by spoofed IP addresses. SYN Flooding is a type of DoS which is harmful to network as the flooding of packets may delay other users from accessing the server and in severe cases, the server may need to be shut down, wasting valuable resources, especially in critical real-time services such as in e-commerce and the medical field. The objective of this paper is to review the state-of-the art of detection mechanisms for SYN flooding. The detection schemes for SYN Flooding attacks have been classified broadly into three categories - detection schemes based on the router data structure, detection schemes based on statistical analysis of the packet flow and detection schemes based on artificial intelligence. The advantages and disadvantages for various detection schemes under each category have been critically examined. The performance measures of the categories have also been compared.
Network Coded Gossip with Correlated Data
Venue: arXiv cs.IT 2012
Year: 2012

We design and analyze gossip algorithms for networks with correlated data. In these networks, either the data to be distributed, the data already available at the nodes, or both, are correlated. This model is applicable for a variety of modern networks, such as sensor, peer-to-peer and content distribution networks. Although coding schemes for correlated data have been studied extensively, the focus has been on characterizing the rate region in static memory-free networks. In a gossip-based scheme, however, nodes communicate among each other by continuously exchanging packets according to some underlying communication model. The main figure of merit in this setting is the stopping time -- the time required until nodes can successfully decode. While Gossip schemes are practical, distributed and scalable, they have only been studied for uncorrelated data. We wish to close this gap by providing techniques to analyze network coded gossip in (dynamic) networks with correlated data. We give a clean framework for oblivious network models that applies to a multitude of network and communication scenarios, specify a general setting for distributed correlated data, and give tight bounds on the stopping times of network coded protocols in this wide range of scenarios.
Principle of Virtual Use Method in Common Gateway Interface Program on the DACS Scheme
Venue: arXiv cs.NI 2012
Year: 2012

In the world of the Internet, Web Servers such as Apache and Internet Information Server (IIS) were developed to exchange information among client computers having different Operation System. They have only the function of displaying static information such as HTML files and image files into the Web Browser. However, when the information is updated, the administrator updates it by manual operation. In some cases, because it is necessary to update several places about the same information, the work load becomes high than it is assume and update error and update omission may occur. These problems were solved by use of a Common Gateway Interface (CGI) program such as a bulletin board system and a Blog system. However, these programs opened to Internet have often no user authentication mechanism and no access control mechanism. That is, they have the problem that user can access it freely only by getting the URL and inputting it to a Web Browser. Therefore, in this paper, we show a method to add the user authentication and access control mechanism for them. It is called virtual use method of CGI and is realized in the case of introducing the Destination Addressing Control System (DACS) Scheme, which is a kind of Policy Based Network Management Scheme (PBNM). As the result, this kind of the CGI program can be used in the organization with the above two functions.
A personalized web page content filtering model based on segmentation
Venue: arXiv cs.IR 2012
Year: 2012

In the view of massive content explosion in World Wide Web through diverse sources, it has become mandatory to have content filtering tools. The filtering of contents of the web pages holds greater significance in cases of access by minor-age people. The traditional web page blocking systems goes by the Boolean methodology of either displaying the full page or blocking it completely. With the increased dynamism in the web pages, it has become a common phenomenon that different portions of the web page holds different types of content at different time instances. This paper proposes a model to block the contents at a fine-grained level i.e. instead of completely blocking the page it would be efficient to block only those segments which holds the contents to be blocked. The advantages of this method over the traditional methods are fine-graining level of blocking and automatic identification of portions of the page to be blocked. The experiments conducted on the proposed model indicate 88% of accuracy in filtering out the segments.
A Reliable Semi-Distributed Load Balancing Architecture of Heterogeneous Wireless Networks
Venue: arXiv cs.NI 2012
Year: 2012

Now a day's Heterogeneous wireless network is a promising field of research interest. Various challenges exist in this hybrid combination like load balancing, resource management and so on. In this paper we introduce a reliable load balancing architecture for heterogeneous wireless communications to ensure certain level of quality of service. To conquer the problem of centralized and distributed design, a semi distributed load balancing architecture for multiple access networks is introduced. In this grid based design multiple Load and Mobile Agent Management Units is incorporated. To prove the compactness of the design, integrated reliability, signalling overhead and total processing time is calculated. And finally simulation result shows that overall system performance is improved by enhancing reliability, reducing signalling overhead and processing time.
First-Order Logic on Higher-Order Nested Pushdown Trees
Venue: arXiv cs.LO 2012
Year: 2012

We introduce a new hierarchy of higher-order nested pushdown trees generalising Alur et al.'s concept of nested pushdown trees. Nested pushdown trees are useful representations of control flows in the verification of programs with recursive calls of first-order functions. Higher-order nested pushdown trees are expansions of unfoldings of graphs generated by higher-order pushdown systems. Moreover, the class of nested pushdown trees of level n is uniformly first-order interpretable in the class of collapsible pushdown graphs of level n+1. The relationship between the class of higher-order pushdown graphs and the class of collapsible higher-order pushdown graphs is not very well understood. We hope that the further study of the nested pushdown tree hierarchy leads to a better understanding of these two hierarchies. In this paper, we are concerned with the first-order model checking problem on higher-order nested pushdown trees. We show that the first-order model checking on the first two levels of this hierarchy is decidable. Moreover, we obtain an 2-EXPSPACE algorithm for the class of nested pushdown trees of level 1. The proof technique involves a pseudo-local analysis of strategies in the Ehrenfeucht-Fraisse games on two identical copies of a nested pushdown tree. Ordinary locality arguments in the spirit of Gaifman's lemma do not apply here because nested pushdown trees tend to have small diameters. We introduce the notion of relevant ancestors which provide a sufficient description of the FO_k -type of each element in a higher-order nested pushdown tree. The local analysis of these ancestors allows us to prove the existence of restricted winning strategies in the Ehrenfeucht-Fraisse game. These strategies are then used to create a first-order model checking algorithm.
Non-parametric convolution based image-segmentation of ill-posed objects applying context window approach
Venue: arXiv cs.CV 2012
Year: 2012

Context-dependence in human cognition process is a well-established fact. Following this, we introduced the image segmentation method that can use context to classify a pixel on the basis of its membership to a particular object-class of the concerned image. In the broad methodological steps, each pixel was defined by its context window (CW) surrounding it the size of which was fixed heuristically. CW texture defined by the intensities of its pixels was convoluted with weights optimized through a non-parametric function supported by a backpropagation network. Result of convolution was used to classify them. The training data points (i.e., pixels) were carefully chosen to include all variety of contexts of types, i) points within the object, ii) points near the edge but inside the objects, iii) points at the border of the objects, iv) points near the edge but outside the objects, v) points near or at the edge of the image frame. Moreover the training data points were selected from all the images within image-dataset. CW texture information for 1000 pixels from face area and background area of images were captured, out of which 700 CWs were used as training input data, and remaining 300 for testing. Our work gives the first time foundation of quantitative enumeration of efficiency of image-segmentation which is extendable to segment out more than 2 objects within an image.
A Comparison of Soft and Hard Coded Relaying
Venue: arXiv cs.IT 2012
Year: 2012

"Amplify and Forward" and "Decode and Forward" are the two main relaying functions that have been proposed since the advent of cooperative communication. "\textit{Soft} Decode and Forward" is a recently introduced relaying principle that is to combine the benefits of the classical two relaying algorithms. In this work, we thoroughly investigate \textit{soft} relaying algorithms when convolutional or turbo codes are applied. We study the error performance of two cooperative scenarios employing soft-relaying. A novel approach, the mutual information loss due to data processing, is proposed to analyze the relay-based soft encoder. We also introduce a novel approach to derive the estimated bit error rate and the equivalent channel SNR for the relaying techniques considered in the paper.
Multiuser Detection and Channel Estimation for Multibeam Satellite Communications
Venue: arXiv cs.NI 2012
Year: 2012

In this paper, iterative multi-user detection techniques for multi-beam communications are presented. The solutions are based on a successive interference cancellation architecture and a channel decoding to treat the co-channel interference. Beams forming and channels coefficients are estimated and updated iteratively. A developed technique of signals combining allows power improvement of the useful received signal; and then reduction of the bit error rates with low signal to noise ratios. The approach is applied to a synchronous multi-beam satellite link under an additive white Gaussian channel. Evaluation of the techniques is done with computer simulations, where a noised and multi-access environment is considered. The simulations results show the good performance of the proposed solutions.
Predicting Contextual Sequences via Submodular Function Maximization
Venue: arXiv cs.AI 2012
Year: 2012

Sequence optimization, where the items in a list are ordered to maximize some reward has many applications such as web advertisement placement, search, and control libraries in robotics. Previous work in sequence optimization produces a static ordering that does not take any features of the item or context of the problem into account. In this work, we propose a general approach to order the items within the sequence based on the context (e.g., perceptual information, environment description, and goals). We take a simple, efficient, reduction-based approach where the choice and order of the items is established by repeatedly learning simple classifiers or regressors for each "slot" in the sequence. Our approach leverages recent work on submodular function maximization to provide a formal regret reduction from submodular sequence optimization to simple cost-sensitive prediction. We apply our contextual sequence prediction algorithm to optimize control libraries and demonstrate results on two robotics problems: manipulator trajectory prediction and mobile robot path planning.
Abstract Representations and Frequent Pattern Discovery
Venue: arXiv cs.AI 2012
Year: 2012

We discuss the frequent pattern mining problem in a general setting. From an analysis of abstract representations, summarization and frequent pattern mining, we arrive at a generalization of the problem. Then, we show how the problem can be cast into the powerful language of algorithmic information theory. This allows us to formulate a simple algorithm to mine for all frequent patterns.
QRB-Domains and the Probabilistic Powerdomain
Venue: arXiv cs.PL 2012
Year: 2012

Is there any Cartesian-closed category of continuous domains that would be closed under Jones and Plotkin's probabilistic powerdomain construction? This is a major open problem in the area of denotational semantics of probabilistic higher-order languages. We relax the question, and look for quasi-continuous dcpos instead. We introduce a natural class of such quasi-continuous dcpos, the omega-QRB-domains. We show that they form a category omega-QRB with pleasing properties: omega-QRB is closed under the probabilistic powerdomain functor, under finite products, under taking bilimits of expanding sequences, under retracts, and even under so-called quasi-retracts. But... omega-QRB is not Cartesian closed. We conclude by showing that the QRB domains are just one half of an FS-domain, merely lacking control.
An evaluation of local shape descriptors for 3D shape retrieval
Venue: arXiv cs.CV 2012
Year: 2012

As the usage of 3D models increases, so does the importance of developing accurate 3D shape retrieval algorithms. A common approach is to calculate a shape descriptor for each object, which can then be compared to determine two objects' similarity. However, these descriptors are often evaluated independently and on different datasets, making them difficult to compare. Using the SHREC 2011 Shape Retrieval Contest of Non-rigid 3D Watertight Meshes dataset, we systematically evaluate a collection of local shape descriptors. We apply each descriptor to the bag-of-words paradigm and assess the effects of varying the dictionary's size and the number of sample points. In addition, several salient point detection methods are used to choose sample points; these methods are compared to each other and to random selection. Finally, information from two local descriptors is combined in two ways and changes in performance are investigated. This paper presents results of these experiment
Cyber threats in social networking websites
Venue: arXiv cs.CR 2012
Year: 2012

A social network is a social structure made up of individuals or organizations called nodes, which are connected by one or more specific types of interdependency, such as friendship, common interest, and exchange of finance, relationships of beliefs, knowledge or prestige. A cyber threat can be both unintentional and intentional, targeted or non targeted, and it can come from a variety of sources, including foreign nations engaged in espionage and information warfare, criminals, hackers, virus writers, disgruntled employees and contractors working within an organization. Social networking sites are not only to communicate or interact with other people globally, but also one effective way for business promotion. In this paper, we investigate and study the cyber threats in social networking websites. We go through the amassing history of online social websites, classify their types and also discuss the cyber threats, suggest the anti-threats strategies and visualize the future trends of such hoppy popular websites.