OpenRaiser/Intern-Atlas huggingface dataset:
Simple Regenerating Codes: Network Coding for Cloud Storage
Venue: arXiv cs.IT 2011
Year: 2011

Network codes designed specifically for distributed storage systems have the potential to provide dramatically higher storage efficiency for the same availability. One main challenge in the design of such codes is the exact repair problem: if a node storing encoded information fails, in order to maintain the same level of reliability we need to create encoded information at a new node. One of the main open problems in this emerging area has been the design of simple coding schemes that allow exact and low cost repair of failed nodes and have high data rates. In particular, all prior known explicit constructions have data rates bounded by 1/2. In this paper we introduce the first family of distributed storage codes that have simple look-up repair and can achieve arbitrarily high rates. Our constructions are very simple to implement and perform exact repair by simple XORing of packets. We experimentally evaluate the proposed codes in a realistic cloud storage simulator and show significant benefits in both performance and reliability compared to replication and standard Reed-Solomon codes.
Compressive Matched-Field Processing
Venue: arXiv cs.IT 2011
Year: 2011

Source localization by matched-field processing (MFP) generally involves solving a number of computationally intensive partial differential equations. This paper introduces a technique that mitigates this computational workload by "compressing" these computations. Drawing on key concepts from the recently developed field of compressed sensing, it shows how a low-dimensional proxy for the Green's function can be constructed by backpropagating a small set of random receiver vectors. Then, the source can be located by performing a number of "short" correlations between this proxy and the projection of the recorded acoustic data in the compressed space. Numerical experiments in a Pekeris ocean waveguide are presented which demonstrate that this compressed version of MFP is as effective as traditional MFP even when the compression is significant. The results are particularly promising in the broadband regime where using as few as two random backpropagations per frequency performs almost as well as the traditional broadband MFP, but with the added benefit of generic applicability. That is, the computationally intensive backpropagations may be computed offline independently from the received signals, and may be reused to locate any source within the search grid area.
A KIF Formalization for the IFF Category Theory Ontology
Venue: arXiv cs.LO 2011
Year: 2011

This paper begins the discussion of how the Information Flow Framework can be used to provide a principled foundation for the metalevel (or structural level) of the Standard Upper Ontology (SUO). This SUO structural level can be used as a logical framework for manipulating collections of ontologies in the object level of the SUO or other middle level or domain ontologies. From the Information Flow perspective, the SUO structural level resolves into several metalevel ontologies. This paper discusses a KIF formalization for one of those metalevel categories, the Category Theory Ontology. In particular, it discusses its category and colimit sub-namespaces.
On discrete cosine transform
Venue: arXiv cs.IT 2011
Year: 2011

The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases.
How Open Should Open Source Be?
Venue: arXiv cs.CR 2011
Year: 2011

Many open-source projects land security fixes in public repositories before shipping these patches to users. This paper presents attacks on such projects - taking Firefox as a case-study - that exploit patch metadata to efficiently search for security patches prior to shipping. Using access-restricted bug reports linked from patch descriptions, security patches can be immediately identified for 260 out of 300 days of Firefox 3 development. In response to Mozilla obfuscating descriptions, we show that machine learning can exploit metadata such as patch author to search for security patches, extending the total window of vulnerability by 5 months in an 8 month period when examining up to two patches daily. Finally we present strong evidence that further metadata obfuscation is unlikely to prevent information leaks, and we argue that open-source projects instead ought to keep security patches secret until they are ready to be released.
Approximate Counting via Correlation Decay in Spin Systems
Venue: arXiv cs.DS 2011
Year: 2011

We give the first deterministic fully polynomial-time approximation scheme (FPTAS) for computing the partition function of a two-state spin system on an arbitrary graph, when the parameters of the system satisfy the uniqueness condition on infinite regular trees. This condition is of physical significance and is believed to be the right boundary between approximable and inapproximable. The FPTAS is based on the correlation decay technique introduced by Bandyopadhyay and Gamarnik [SODA 06] and Weitz [STOC 06]. The classic correlation decay is defined with respect to graph distance. Although this definition has natural physical meanings, it does not directly support an FPTAS for systems on arbitrary graphs, because for graphs with unbounded degrees, the local computation that provides a desirable precision by correlation decay may take super-polynomial time. We introduce a notion of computationally efficient correlation decay, in which the correlation decay is measured in a refined metric instead of graph distance. We use a potential method to analyze the amortized behavior of this correlation decay and establish a correlation decay that guarantees an inverse-polynomial precision by polynomial-time local computation. This gives us an FPTAS for spin systems on arbitrary graphs. This new notion of correlation decay properly reflects the algorithmic aspect of the spin systems, and may be used for designing FPTAS for other counting problems.
Beyond the Boundaries of Open, Closed and Pirate Archives: Lessons from a Hybrid Approach
Venue: arXiv cs.DL 2011
Year: 2011

The creation of open archives i.e. archives where access is regulated by open licensing models (content, source, data), should be seen as part of a broader socio-economic phenomenon that finds legal expression in specific organizational and technical formats.This paper examines the origins and main characteristics of the open archives phenomenon. We investigate the extent to which different models of production of economic or social value can be expressed in different forms of licensing in the context of open archives. Through this process, we assess the extent to which the digital archive is moving towards providing access that is deeper (meaning, that offers more access rights) and wider (in the sense that most of the information given is in open content licensing) or face a gradual stratification and polarization of the content. Such stratification entails the emergence of two types of content: content to which access is extremely limited and content to which access remains completely open. This differentiation between classes of content is the result of multiple factors: from purely legislative, administrative and contractual restrictions (e.g. data protection and confidentiality restrictions) to information economics (e.g. peer production) or social (minimum universal access). We claim that with respect to the access management model, most of the current archiving processes include elements of openness. Usually, this is the result of economic necessity expressed in licensing instruments or organisational arrangements. The viability and the socio-economic importance of the digital archives also contributes to the use of open archiving practices. In such a context, although pure forms of open digital archives may remain an ideal, the reality of hybrid open digital archives is a necessity.
An improved distributed routing algorithm for Benes based optical NoC
Venue: arXiv cs.AR 2011
Year: 2011

Integrated optical interconnect is believed to be one of the main technologies to replace electrical wires. Optical Network-on-Chip (ONoC) has attracted more attentions nowadays. Benes topology is a good choice for ONoC for its rearrangeable non-blocking character, multistage feature and easy scalability. Routing algorithm plays an important role in determining the performance of ONoC. But traditional routing algorithms for Benes network are not suitable for ONoC communication, we developed a new distributed routing algorithm for Benes ONoC in this paper. Our algorithm selected the routing path dynamically according to network condition and enables more path choices for the message traveling in the network. We used OPNET to evaluate the performance of our routing algorithm and also compared it with a well-known bit-controlled routing algorithm. ETE delay and throughput were showed under different packet length and network sizes. Simulation results show that our routing algorithm can provide better performance for ONoC.
Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach
Venue: arXiv cs.SI 2011
Year: 2011

In this paper, we propose a probabilistic generative model, called unified model, which naturally unifies the ideas of social influence, collaborative filtering and content-based methods for item recommendation. To address the issue of hidden social influence, we devise new algorithms to learn the model parameters of our proposal based on expectation maximization (EM). In addition to a single-machine version of our EM algorithm, we further devise a parallelized implementation on the Map-Reduce framework to process two large-scale datasets we collect. Moreover, we show that the social influence obtained from our generative models can be used for group recommendation. Finally, we conduct comprehensive experiments using the datasets crawled from last.fm and whrrl.com to validate our ideas. Experimental results show that the generative models with social influence significantly outperform those without incorporating social influence. The unified generative model proposed in this paper obtains the best performance. Moreover, our study on social influence finds that users in whrrl.com are more likely to get influenced by friends than those in last.fm. The experimental results also confirm that our social influence based group recommendation algorithm outperforms the state-of-the-art algorithms for group recommendation.
Efficient and Correct Stencil Computation via Pattern Matching and Static Typing
Venue: arXiv cs.PL 2011
Year: 2011

Stencil computations, involving operations over the elements of an array, are a common programming pattern in scientific computing, games, and image processing. As a programming pattern, stencil computations are highly regular and amenable to optimisation and parallelisation. However, general-purpose languages obscure this regular pattern from the compiler, and even the programmer, preventing optimisation and obfuscating (in)correctness. This paper furthers our work on the Ypnos domain-specific language for stencil computations embedded in Haskell. Ypnos allows declarative, abstract specification of stencil computations, exposing the structure of a problem to the compiler and to the programmer via specialised syntax. In this paper we show the decidable safety guarantee that well-formed, well-typed Ypnos programs cannot index outside of array boundaries. Thus indexing in Ypnos is safe and run-time bounds checking can be eliminated. Program information is encoded as types, using the advanced type-system features of the Glasgow Haskell Compiler, with the safe-indexing invariant enforced at compile time via type checking.
Tutorial on Online Partial Evaluation
Venue: arXiv cs.PL 2011
Year: 2011

This paper is a short tutorial introduction to online partial evaluation. We show how to write a simple online partial evaluator for a simple, pure, first-order, functional programming language. In particular, we show that the partial evaluator can be derived as a variation on a compositionally defined interpreter. We demonstrate the use of the resulting partial evaluator for program optimization in the context of model-driven development.
Trimming the Multipath for Efficient Dynamic Routing
Venue: arXiv cs.NI 2011
Year: 2011

Multipath routing is a trivial way to exploit the path diversity to leverage the network throughput. Technologies such as OSPF ECMP use all the available paths in the network to forward traffic, however, we argue that is not necessary to do so to load balance the network. In this paper, we consider multipath routing with only a limited number of end-to-end paths for each source and destination, and found that this can still load balance the traffic. We devised an algorithm to select a few paths for each source-destination pair so that when all traffic are forwarded over these paths, we can achieve a balanced load in the sense that the maximum link utilization is comparable to that of ECMP forwarding. When the constraint of only shortest paths (i.e. equal paths) are relaxed, we can even outperform ECMP in certain cases. As a result, we can use a few end-to-end tunnels between each source and destination nodes to achieve the load balancing of traffic.
A Trellis Coded Modulation Scheme for the Fading Relay Channel
Venue: arXiv cs.IT 2011
Year: 2011

A decode and forward protocol based Trellis Coded Modulation (TCM) scheme for the half-duplex relay channel, in a Rayleigh fading environment, is presented. The proposed scheme can achieve any spectral efficiency greater than or equal to one bit per channel use (bpcu). A near-ML decoder for the suggested TCM scheme is proposed. It is shown that the high SNR performance of this near-ML decoder approaches the performance of the optimal ML decoder. The high SNR performance of this near-ML decoder is independent of the strength of the Source-Relay link and approaches the performance of the optimal ML decoder with an ideal Source-Relay link. Based on the derived Pair-wise Error Probability (PEP) bounds, design criteria to maximize the diversity and coding gains are obtained. Simulation results show a large gain in SNR for the proposed TCM scheme over uncoded communication as well as the direct transmission without the relay. Also, it is shown that even for the uncoded transmission scheme, the choice of the labelling scheme (mapping from bits to complex symbols) used at the source and the relay significantly impacts the BER vs SNR performance. We provide a good labelling scheme for $2^l$-PSK signal set, where $l\geq 2$ is an integer.
Boolean logic gate design principles in unconventional computers: an NMR case study
Venue: arXiv cs.ET 2011
Year: 2011

We present a general method for analysing novel computational substrates to determine which of their parameters can be manipulated to exhibit the complete set of 2-input boolean logical operations. We demonstrate this approach with an NMR-based case study, showing which NMR parameters can be used to perform boolean logic.
Reliability in Source Coding with Side Information
Venue: arXiv cs.IT 2011
Year: 2011

We study error exponents for source coding with side information. Both achievable exponents and converse bounds are obtained for the following two cases: lossless source coding with coded information (SCCSI) and lossy source coding with full side information (Wyner-Ziv). These results recover and extend several existing results on source-coding error exponents and are tight in some circumstances. Our bounds have a natural interpretation as a two-player game between nature and the code designer, with nature seeking to minimize the exponent and the code designer seeking to maximize it. In the Wyner-Ziv problem our analysis exposes a tension in the choice of test channel with the optimal test channel balancing two competing error events. The Gaussian and binary-erasure cases are examined in detail.
Attack Prevention for Collaborative Spectrum Sensing in Cognitive Radio Networks
Venue: arXiv cs.CR 2011
Year: 2011

Collaborative spectrum sensing can significantly improve the detection performance of secondary unlicensed users (SUs). However, the performance of collaborative sensing is vulnerable to sensing data falsification attacks, where malicious SUs (attackers) submit manipulated sensing reports to mislead the fusion center's decision on spectrum occupancy. Moreover, attackers may not follow the fusion center's decision regarding their spectrum access. This paper considers a challenging attack scenario where multiple rational attackers overhear all honest SUs' sensing reports and cooperatively maximize attackers' aggregate spectrum utilization. We show that, without attack-prevention mechanisms, honest SUs are unable to transmit over the licensed spectrum, and they may further be penalized by the primary user for collisions due to attackers' aggressive transmissions. To prevent such attacks, we propose two novel attack-prevention mechanisms with direct and indirect punishments. The key idea is to identify collisions to the primary user that should not happen if all SUs follow the fusion center's decision. Unlike prior work, the proposed simple mechanisms do not require the fusion center to identify and exclude attackers. The direct punishment can effectively prevent all attackers from behaving maliciously. The indirect punishment is easier to implement and can prevent attacks when the attackers care enough about their long-term reward.
Automatic Diagnosis of Abnormal Tumor Region from Brain Computed Tomography Images Using Wavelet Based Statistical Texture Features
Venue: arXiv cs.CV 2011
Year: 2011

The research work presented in this paper is to achieve the tissue classification and automatically diagnosis the abnormal tumor region present in Computed Tomography (CT) images using the wavelet based statistical texture analysis method. Comparative studies of texture analysis method are performed for the proposed wavelet based texture analysis method and Spatial Gray Level Dependence Method (SGLDM). Our proposed system consists of four phases i) Discrete Wavelet Decomposition (ii) Feature extraction (iii) Feature selection (iv) Analysis of extracted texture features by classifier. A wavelet based statistical texture feature set is derived from normal and tumor regions. Genetic Algorithm (GA) is used to select the optimal texture features from the set of extracted texture features. We construct the Support Vector Machine (SVM) based classifier and evaluate the performance of classifier by comparing the classification results of the SVM based classifier with the Back Propagation Neural network classifier(BPN). The results of Support Vector Machine (SVM), BPN classifiers for the texture analysis methods are evaluated using Receiver Operating Characteristic (ROC) analysis. Experimental results show that the classification accuracy of SVM is 96% for 10 fold cross validation method. The system has been tested with a number of real Computed Tomography brain images and has achieved satisfactory results.
A Framework for Predicting Phishing Websites using Neural Networks
Venue: arXiv cs.NE 2011
Year: 2011

In India many people are now dependent on online banking. This raises security concerns as the banking websites are forged and fraud can be committed by identity theft. These forged websites are called as Phishing websites and created by malicious people to mimic web pages of real websites and it attempts to defraud people of their personal information. Detecting and identifying phishing websites is a really complex and dynamic problem involving many factors and criteria. This paper discusses about the prediction of phishing websites using neural networks. A neural network is a multilayer system which reduces the error and increases the performance. This paper describes a framework to better classify and predict the phishing sites using neural networks.
Multi Agent Communication System for Online Auction with Decision Support System by JADE and TRACE
Venue: arXiv cs.MA 2011
Year: 2011

The success of online auctions has given buyers access to greater product diversity with potentially lower prices. It has provided sellers with access to large numbers of potential buyers and reduced transaction costs by enabling auctions to take place without regard to time or place. However it is difficult to spend more time period with system and closely monitor the auction until auction participant wins the bid or closing of the auction. Determining which items to bid on or what may be the recommended bid and when to bid it are difficult questions to answer for online auction participants. The multi agent auction advisor system JADE and TRACE, which is connected with decision support system, gives the recommended bid to buyers for online auctions. The auction advisor system relies on intelligent agents both for the retrieval of relevant auction data and for the processing of that data to enable meaningful recommendations, statistical reports and market prediction report to be made to auction participants.
P2P Simulator for Queries Routing using Data Mining
Venue: arXiv cs.NI 2011
Year: 2011

Data mining is used to extract hidden information from large databases. In Peer-to-Peer context, a challenging problem is how to find the appropriate Peer to deal with a given query without overly consuming bandwidth. Different methods proposed routing strategies of queries taking into account the P2P network at hand. An unstructured P2P system based on an organization of Peers around Super-Peers that are connected to Super-Super-Peer according to their semantic domains is considered. This paper integrates Decision Trees in P2P architectures for predicting Query-Suitable Super-Peers representing a community of Peers, where one among them is able to answer the given query. In fact, by analyzing the queries' log file, a predictive model that avoids flooding queries in the P2P networks constructed by predicting the appropriate Super-Peer, and hence the Peer to answer the query. The proposed architecture is based on a Decision Tree (Base-Knowledge - BK). The efficiency of these architectures is discussed considering architecture without knowledge (Baseline) using only the flooding queries method to answer queries. The advantage of this knowledge based model is the robustness in Queries routing mechanism and scalability in P2P Network.