OpenRaiser/Intern-Atlas huggingface dataset:
On the conjecture about the nonexistence of rotation symmetric bent functions
Venue: arXiv cs.CR 2013
Year: 2013

In this paper, we describe a different approach to the proof of the nonexistence of homogeneous rotation symmetric bent functions. As a result, we obtain some new results which support the conjecture made in this journal, i.e., there are no homogeneous rotation symmetric bent functions of degree >2. Also we characterize homogeneous degree 2 rotation symmetric bent functions by using GCD of polynomials.
Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks
Venue: arXiv cs.SI 2013
Year: 2013

Viral campaigns are crucial methods for word-of-mouth marketing in social communities. The goal of these campaigns is to encourage people for activity. The problem of incentivised and non-incentivised campaigns is studied in the paper. Based on the data collected within the real social networking site both approaches were compared. The experimental results revealed that a highly motivated campaign not necessarily provides better results due to overlapping effect. Additional studies have shown that the behaviour of individual community members in the campaign based on their service profile can be predicted but the classification accuracy may be limited.
Linear NDCG and Pair-wise Loss
Venue: arXiv cs.LG 2013
Year: 2013

Linear NDCG is used for measuring the performance of the Web content quality assessment in ECML/PKDD Discovery Challenge 2010. In this paper, we will prove that the DCG error equals a new pair-wise loss.
Broadening the Scope of Nanopublications
Venue: arXiv cs.DL 2013
Year: 2013

In this paper, we present an approach for extending the existing concept of nanopublications --- tiny entities of scientific results in RDF representation --- to broaden their application range. The proposed extension uses English sentences to represent informal and underspecified scientific claims. These sentences follow a syntactic and semantic scheme that we call AIDA (Atomic, Independent, Declarative, Absolute), which provides a uniform and succinct representation of scientific assertions. Such AIDA nanopublications are compatible with the existing nanopublication concept and enjoy most of its advantages such as information sharing, interlinking of scientific findings, and detailed attribution, while being more flexible and applicable to a much wider range of scientific results. We show that users are able to create AIDA sentences for given scientific results quickly and at high quality, and that it is feasible to automatically extract and interlink AIDA nanopublications from existing unstructured data sources. To demonstrate our approach, a web-based interface is introduced, which also exemplifies the use of nanopublications for non-scientific content, including meta-nanopublications that describe other nanopublications.
Context-aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware
Venue: arXiv cs.NI 2013
Year: 2013

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a substantial acceleration of the growth rate in the future. It is also evident that the increasing number of IoT middleware solutions are developed in both research and commercial environments. However, sensor search and selection remain a critical requirement and a challenge. In this paper, we present CASSARAM, a context-aware sensor search, selection, and ranking model for Internet of Things to address the research challenges of selecting sensors when large numbers of sensors with overlapping and sometimes redundant functionality are available. CASSARAM proposes the search and selection of sensors based on user priorities. CASSARAM considers a broad range of characteristics of sensors for search such as reliability, accuracy, battery life just to name a few. Our approach utilises both semantic querying and quantitative reasoning techniques. User priority based weighted Euclidean distance comparison in multidimensional space technique is used to index and rank sensors. Our objectives are to highlight the importance of sensor search in IoT paradigm, identify important characteristics of both sensors and data acquisition processes which help to select sensors, understand how semantic and statistical reasoning can be combined together to address this problem in an efficient manner. We developed a tool called CASSARA to evaluate the proposed model in terms of resource consumption and response time.
Distributed Intrusion Detection of Byzantine Attacks in Wireless Networks with Random Linear Network Coding
Venue: arXiv cs.NI 2013
Year: 2013

Network coding is an elegant technique where, instead of simply relaying the packets of information they receive, the nodes of a network are allowed to combine \emph{several} packets together for transmission and this technique can be used to achieve the maximum possible information flow in a network and save the needed number of packet transmissions. Moreover, in an energy-constraint wireless network such as Wireless Sensor Network (a typical type of wireless ad hoc network), applying network coding to reduce the number of wireless transmissions can also prolong the life time of sensor nodes. Although applying network coding in a wireless sensor network is obviously beneficial, due to the operation that one transmitting information is actually combination of multiple other information, it is possible that an error propagation may occur in the network. This special characteristic also exposes network coding system to a wide range of error attacks, especially Byzantine attacks. When some adversary nodes generate error data in the network with network coding, those erroneous information will be mixed at intermeidate nodes and thus corrupt all the information reaching a destination. Recent research efforts have shown that network coding can be combined with classical error control codes and cryptography for secure communication or misbehavior detection. Nevertheless, when it comes to Byzantine attacks, these results have limited effect. In fact, unless we find out those adversary nodes and isolate them, network coding may perform much worse than pure routing in the presence of malicious nodes. In this paper, a distributed hierarchical algorithm based on random linear network coding is developed to detect, locate and isolate malicious nodes.
On f-Symmetries of the Independence Polynomial
Venue: arXiv cs.DM 2013
Year: 2013

An independent set in a graph is a set of pairwise non-adjacent vertices, and a(G) is the size of a maximum independent set in the graph G. If s_{k} is the number of independent sets of cardinality k in G, then I(G;x)=s_0+s_1*x+s_2*x^2+...+s_a*x^a,a=a(G), is called the independence polynomial of G (I. Gutman and F. Harary, 1983). If s_{a-i}=f(i)*s_{i} holds for every i, then I(G;x) is called f-symmetric (f-palindromic). If f(i)=1, then I(G;x) is symmetric (palindromic). The corona of the graphs G and H is the graph G*H obtained by joining each vertex of G to all the vertices of a copy of H. In this paper we show that if H is a graph with p vertices, q edges, and alpha(H)=2, then I(G*H;x) is f-symmetric for some elegant function f. In particular, if H = K_{r}-e, we show that I(G*H;x) is symmetric and unimodal, with a unique mode. This finding generalizes results due to (Stevanovic, 1998) and (Mandrescu, 2012) claiming that I(G*(K_2-e);x)=I(G*2K_1;x) is symmetric and unimodal for every graph G.
One-shot source coding with coded side information available at the decoder
Venue: arXiv cs.IT 2013
Year: 2013

One-shot achievable rate region for source coding when coded side information is available at the decoder (source coding with a helper) is proposed. The achievable region proposed is in terms of conditional smooth max Renyi entropy and smooth max Renyi divergence. Asymptotically (in the limit of large block lengths) this region is quantified in terms of spectral-sup conditional entropy rate and spectral- sup mutual information rate. In particular, it coincides with the rate region derived in the limit of unlimited independent and identically distributed copies of the sources.
Proof of the hypothesis Edmonds's, not polynomial of NPC-problems and classification of the problems with polynomial certificates
Venue: arXiv cs.CC 2013
Year: 2013

We show that the affirmation $P\subseteq NP$ (in computer science) erroneously and we prove the justice of the hypotesis J.Edmonds's $P\neq NP$. We show further that all the $NP$-complete problems is not polynomial and we give the classification of the problems with the polynomial certificates.
Bilateral Filter: Graph Spectral Interpretation and Extensions
Venue: arXiv cs.CV 2013
Year: 2013

In this paper we study the bilateral filter proposed by Tomasi and Manduchi, as a spectral domain transform defined on a weighted graph. The nodes of this graph represent the pixels in the image and a graph signal defined on the nodes represents the intensity values. Edge weights in the graph correspond to the bilateral filter coefficients and hence are data adaptive. Spectrum of a graph is defined in terms of the eigenvalues and eigenvectors of the graph Laplacian matrix. We use this spectral interpretation to generalize the bilateral filter and propose more flexible and application specific spectral designs of bilateral-like filters. We show that these spectral filters can be implemented with k-iterative bilateral filtering operations and do not require expensive diagonalization of the Laplacian matrix.
Resilient Continuous-Time Consensus in Fractional Robust Networks
Venue: arXiv cs.SY 2013
Year: 2013

In this paper, we study the continuous-time consensus problem in the presence of adversaries. The networked multi-agent system is modeled as a switched system, where the normal agents have integrator dynamics and the switching signal determines the topology of the network. We consider several models of omniscient adversaries under the assumption that at most a fraction of any normal agent's neighbors may be adversaries. Under this fractional assumption on the interaction between normal and adversary agents, we show that a novel graph theoretic metric, called fractional robustness, is useful for analyzing the network topologies under which the normal agents achieve consensus.
Evolutionary Approaches to Expensive Optimisation
Venue: arXiv cs.NE 2013
Year: 2013

Surrogate assisted evolutionary algorithms (EA) are rapidly gaining popularity where applications of EA in complex real world problem domains are concerned. Although EAs are powerful global optimizers, finding optimal solution to complex high dimensional, multimodal problems often require very expensive fitness function evaluations. Needless to say, this could brand any population-based iterative optimization technique to be the most crippling choice to handle such problems. Use of approximate model or surrogates provides a much cheaper option. However, naturally this cheaper option comes with its own price. This paper discusses some of the key issues involved with use of approximation in evolutionary algorithm, possible best practices and solutions. Answers to the following questions have been sought: what type of fitness approximation to be used; which approximation model to use; how to integrate the approximation model in EA; how much approximation to use; and how to ensure reliable approximation.
A Cooperative Q-learning Approach for Real-time Power Allocation in Femtocell Networks
Venue: arXiv cs.MA 2013
Year: 2013

In this paper, we address the problem of distributed interference management of cognitive femtocells that share the same frequency range with macrocells (primary user) using distributed multi-agent Q-learning. We formulate and solve three problems representing three different Q-learning algorithms: namely, centralized, distributed and partially distributed power control using Q-learning (CPC-Q, DPC-Q and PDPC-Q). CPCQ, although not of practical interest, characterizes the global optimum. Each of DPC-Q and PDPC-Q works in two different learning paradigms: Independent (IL) and Cooperative (CL). The former is considered the simplest form for applying Qlearning in multi-agent scenarios, where all the femtocells learn independently. The latter is the proposed scheme in which femtocells share partial information during the learning process in order to strike a balance between practical relevance and performance. In terms of performance, the simulation results showed that the CL paradigm outperforms the IL paradigm and achieves an aggregate femtocells capacity that is very close to the optimal one. For the practical relevance issue, we evaluate the robustness and scalability of DPC-Q, in real time, by deploying new femtocells in the system during the learning process, where we showed that DPC-Q in the CL paradigm is scalable to large number of femtocells and more robust to the network dynamics compared to the IL paradigm
Almost sure convergence of a randomized algorithm for relative localization in sensor networks
Venue: arXiv cs.SY 2013
Year: 2013

This paper regards the relative localization problem in sensor networks. We study a randomized algorithm, which is based on input-driven consensus dynamics and involves pairwise "gossip" communications and updates. Due to the randomness of the updates, the state of this algorithm ergodically oscillates around a limit value. Exploiting the ergodicity of the dynamics, we show that the time-average of the state almost surely converges to the least-squares solution of the localization problem. Remarkably, the computation of the time-average does not require the sensors to share any common clock. Hence, the proposed algorithm is fully distributed and asynchronous.
A Competitive Ratio Approximation Scheme for the k-Server Problem in Fixed Finite Metrics
Venue: arXiv cs.DS 2013
Year: 2013

We show how to restrict the analysis of a class of online problems that includes the $k$-server problem in finite metrics such that we only have to consider finite sequences of request. When applying the restrictions, both the optimal offline solutions and the best possible deterministic or randomized online solutions only differ by at most an arbitrarily small constant factor from the corresponding solutions without restrictions. Furthermore, we show how to obtain an algorithm with best possible deterministic or randomized competitive ratio for the restricted setup. Thus, for each fixed finite metrics our result qualifies as a competitive ratio approximation scheme as defined by Günther et al.
Nepenthes Honeypots based Botnet Detection
Venue: arXiv cs.CR 2013
Year: 2013

The numbers of the botnet attacks are increasing day by day and the detection of botnet spreading in the network has become very challenging. Bots are having specific characteristics in comparison of normal malware as they are controlled by the remote master server and usually dont show their behavior like normal malware until they dont receive any command from their master server. Most of time bot malware are inactive, hence it is very difficult to detect. Further the detection or tracking of the network of theses bots requires an infrastructure that should be able to collect the data from a diverse range of data sources and correlate the data to bring the bigger picture in view. In this paper, we are sharing our experience of botnet detection in the private network as well as in public zone by deploying the nepenthes honeypots. The automated framework for malware collection using nepenthes and analysis using anti-virus scan are discussed. The experimental results of botnet detection by enabling nepenthes honeypots in network are shown. Also we saw that existing known bots in our network can be detected.
Convex Hull-Based Multi-objective Genetic Programming for Maximizing ROC Performance
Venue: arXiv cs.NE 2013
Year: 2013

ROC is usually used to analyze the performance of classifiers in data mining. ROC convex hull (ROCCH) is the least convex major-ant (LCM) of the empirical ROC curve, and covers potential optima for the given set of classifiers. Generally, ROC performance maximization could be considered to maximize the ROCCH, which also means to maximize the true positive rate (tpr) and minimize the false positive rate (fpr) for each classifier in the ROC space. However, tpr and fpr are conflicting with each other in the ROCCH optimization process. Though ROCCH maximization problem seems like a multi-objective optimization problem (MOP), the special characters make it different from traditional MOP. In this work, we will discuss the difference between them and propose convex hull-based multi-objective genetic programming (CH-MOGP) to solve ROCCH maximization problems. Convex hull-based sort is an indicator based selection scheme that aims to maximize the area under convex hull, which serves as a unary indicator for the performance of a set of points. A selection procedure is described that can be efficiently implemented and follows similar design principles than classical hyper-volume based optimization algorithms. It is hypothesized that by using a tailored indicator-based selection scheme CH-MOGP gets more efficient for ROC convex hull approximation than algorithms which compute all Pareto optimal points. To test our hypothesis we compare the new CH-MOGP to MOGP with classical selection schemes, including NSGA-II, MOEA/D) and SMS-EMOA. Meanwhile, CH-MOGP is also compared with traditional machine learning algorithms such as C4.5, Naive Bayes and Prie. Experimental results based on 22 well-known UCI data sets show that CH-MOGP outperforms significantly traditional EMOAs.
Design and Analysis of a Multi-Carrier Differential Chaos Shift Keying Communication System
Venue: arXiv cs.OH 2013
Year: 2013

A new Multi-Carrier Differential Chaos Shift Keying (MC-DCSK) modulation is presented in this paper. The system endeavors to provide a good trade-off between robustness, energy efficiency and high data rate, while still being simple compared to conventional multi-carrier spread spectrum systems. This system can be seen as a parallel extension of the DCSK modulation where one chaotic reference sequence is transmitted over a predefined subcarrier frequency. Multiple modulated data streams are transmitted over the remaining subcarriers. This transmitter structure increases the spectral efficiency of the conventional DCSK system and uses less energy. The receiver design makes this system easy to implement where no radio frequency (RF) delay circuit is needed to demodulate received data. Various system design parameters are discussed throughout the paper, including the number of subcarriers, the spreading factor, and the transmitted energy. Once the design is explained, the bit error rate performance of the MC-DCSK system is computed and compared to the conventional DCSK system under an additive white Gaussian noise (AWGN) and Rayleigh channels. Simulation results confirm the advantages of this new hybrid design.
Tiled Algorithms for Matrix Computations on Multicore Architectures
Venue: arXiv cs.NA 2013
Year: 2013

The current computer architecture has moved towards the multi/many-core structure. However, the algorithms in the current sequential dense numerical linear algebra libraries (e.g. LAPACK) do not parallelize well on multi/many-core architectures. A new family of algorithms, the tile algorithms, has recently been introduced to circumvent this problem. Previous research has shown that it is possible to write efficient and scalable tile algorithms for performing a Cholesky factorization, a (pseudo) LU factorization, and a QR factorization. The goal of this thesis is to study tiled algorithms in a multi/many-core setting and to provide new algorithms which exploit the current architecture to improve performance relative to current state-of-the-art libraries while maintaining the stability and robustness of these libraries.
Performance of a random-access wireless network with a mix of full- and half-duplex stations
Venue: arXiv cs.IT 2013
Year: 2013

In this paper, we consider the performance of a random-access time-slotted wireless network with a single access point and a mix of half- and full- duplex stations. Full-duplex transmissions involve data transmitted simultaneously in both directions, and this influences the dynamics of the queue at the access point. Given the probabilities of channel access by the nodes, this paper provides generalized analytical formulations for the throughputs for each station. Special cases related to a 802.11 DCA based system as well as a full-fairness system are discussed, which provide insights into the changes introduced by the new technology of full-duplex wireless.
Multi-Stage Robust Chinese Remainder Theorem
Venue: arXiv cs.IT 2013
Year: 2013

It is well-known that the traditional Chinese remainder theorem (CRT) is not robust in the sense that a small error in a remainder may cause a large error in the reconstruction solution. A robust CRT was recently proposed for a special case when the greatest common divisor (gcd) of all the moduli is more than 1 and the remaining integers factorized by the gcd of all the moduli are co-prime. In this special case, a closed-form reconstruction from erroneous remainders was proposed and a necessary and sufficient condition on the remainder errors was obtained. It basically says that the reconstruction error is upper bounded by the remainder error level $τ$ if $τ$ is smaller than a quarter of the gcd of all the moduli. In this paper, we consider the robust reconstruction problem for a general set of moduli. We first present a necessary and sufficient condition for the remainder errors for a robust reconstruction from erroneous remainders with a general set of muduli and also a corresponding robust reconstruction method. This can be thought of as a single stage robust CRT. We then propose a two-stage robust CRT by grouping the moduli into several groups as follows. First, the single stage robust CRT is applied to each group. Then, with these robust reconstructions from all the groups, the single stage robust CRT is applied again across the groups. This is then easily generalized to multi-stage robust CRT. Interestingly, with this two-stage robust CRT, the robust reconstruction holds even when the remainder error level $τ$ is above the quarter of the gcd of all the moduli. In this paper, we also propose an algorithm on how to group a set of moduli for a better reconstruction robustness of the two-stage robust CRT in some special cases.
Relay Selection and Resource Allocation for Two Way DF-AF Cognitive Radio Networks
Venue: arXiv cs.IT 2013
Year: 2013

In this letter, the problem of optimal resource power allocation and relay selection for two way relaying cognitive radio networks using half duplex Decode and Forward (DF) and Amplify and Forward (AF) systems are investigated. The primary and secondary networks are assumed to access the spectrum at the same time, so that the interference introduced to the primary network caused by the secondary network should be below a certain interference threshold. In addition, a selection strategy between the AF and DF schemes is applied depending on the achieved secondary sum rate without affecting the quality of service of the primary network. A suboptimal approach based on a genetic algorithm is also presented to solve our problem. Selected simulation results show that the proposed suboptimal algorithm offers a performance close to the performance of the optimal solution with a considerable complexity saving.