OpenRaiser/Intern-Atlas huggingface dataset:
A Comprehensive Survey of Data Mining-based Fraud Detection Research
Venue: arXiv cs.AI 2010
Year: 2010

This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.
Multi-standard programmable baseband modulator for next generation wireless communication
Venue: arXiv cs.AR 2010
Year: 2010

Considerable research has taken place in recent times in the area of parameterization of software defined radio (SDR) architecture. Parameterization decreases the size of the software to be downloaded and also limits the hardware reconfiguration time. The present paper is based on the design and development of a programmable baseband modulator that perform the QPSK modulation schemes and as well as its other three commonly used variants to satisfy the requirement of several established 2G and 3G wireless communication standards. The proposed design has been shown to be capable of operating at a maximum data rate of 77 Mbps on Xilinx Virtex 2-Pro University field programmable gate array (FPGA) board. The pulse shaping root raised cosine (RRC) filter has been implemented using distributed arithmetic (DA) technique in the present work in order to reduce the computational complexity, and to achieve appropriate power reduction and enhanced throughput. The designed multiplier-less programmable 32-tap FIR-based RRC filter has been found to withstand a peak inter-symbol interference (ISI) distortion of -41 dBs
Heuristic approach to optimize the number of test cases for simple circuits
Venue: arXiv cs.OH 2010
Year: 2010

In this paper a new solution is proposed for testing simple stwo stage electronic circuits. It minimizes the number of tests to be performed to determine the genuinity of the circuit. The main idea behind the present research work is to identify the maximum number of indistinguishable faults present in the given circuit and minimize the number of test cases based on the number of faults that has been detected. Heuristic approach is used for test minimization part, which identifies the essential tests from overall test cases. From the results it is observed that, test minimization varies from 50% to 99% with the lowest one corresponding to a circuit with four gates .Test minimization is low in case of circuits with lesser input leads in gates compared to greater input leads in gates for the boolean expression with same number of symbols. Achievement of 99% reduction is due to the fact that the large number of tests find the same faults. The new approach is implemented for simple circuits. The results show potential for both smaller test sets and lower cpu times.
A Novel and Highly Efficient AES Implementation Robust against Differential Power Analysis
Venue: arXiv cs.CR 2010
Year: 2010

Developed by Paul Kocher, Joshua Jaffe, and Benjamin Jun in 1999, Differential Power Analysis (DPA) represents a unique and powerful cryptanalysis technique. Insight into the encryption and decryption behavior of a cryptographic device can be determined by examining its electrical power signature. This paper describes a novel approach for implementation of the AES algorithm which provides a significantly improved strength against differential power analysis with a minimal additional hardware overhead. Our method is based on randomization in composite field arithmetic which entails an area penalty of only 7% while does not decrease the working frequency, does not alter the algorithm and keeps perfect compatibility with the published standard. The efficiency of the proposed technique was verified by practical results obtained from real implementation on a Xilinx Spartan-II FPGA.
The complexity of global cardinality constraints
Venue: arXiv cs.LO 2010
Year: 2010

In a constraint satisfaction problem (CSP) the goal is to find an assignment of a given set of variables subject to specified constraints. A global cardinality constraint is an additional requirement that prescribes how many variables must be assigned a certain value. We study the complexity of the problem CCSP(G), the constraint satisfaction problem with global cardinality constraints that allows only relations from the set G. The main result of this paper characterizes sets G that give rise to problems solvable in polynomial time, and states that the remaining such problems are NP-complete.
Polynomial-time sortable stacks of burnt pancakes
Venue: arXiv cs.DS 2010
Year: 2010

Pancake flipping, a famous open problem in computer science, can be formalised as the problem of sorting a permutation of positive integers using as few prefix reversals as possible. In that context, a prefix reversal of length k reverses the order of the first k elements of the permutation. The burnt variant of pancake flipping involves permutations of signed integers, and reversals in that case not only reverse the order of elements but also invert their signs. Although three decades have now passed since the first works on these problems, neither their computational complexity nor the maximal number of prefix reversals needed to sort a permutation is yet known. In this work, we prove a new lower bound for sorting burnt pancakes, and show that an important class of permutations, known as "simple permutations", can be optimally sorted in polynomial time.
Using Stochastic Models to Describe and Predict Social Dynamics of Web Users
Venue: arXiv cs.CY 2010
Year: 2010

Popularity of content in social media is unequally distributed, with some items receiving a disproportionate share of attention from users. Predicting which newly-submitted items will become popular is critically important for both hosts of social media content and its consumers. Accurate and timely prediction would enable hosts to maximize revenue through differential pricing for access to content or ad placement. Prediction would also give consumers an important tool for filtering the ever-growing amount of content. Predicting popularity of content in social media, however, is challenging due to the complex interactions between content quality and how the social media site chooses to highlight content. Moreover, most social media sites also selectively present content that has been highly rated by similar users, whose similarity is indicated implicitly by their behavior or explicitly by links in a social network. While these factors make it difficult to predict popularity \emph{a priori}, we show that stochastic models of user behavior on these sites allows predicting popularity based on early user reactions to new content. By incorporating the various mechanisms through which web sites display content, such models improve on predictions based on simply extrapolating from the early votes. Using data from one such site, the news aggregator Digg, we show how a stochastic model of user behavior distinguishes the effect of the increased visibility due to the network from how interested users are in the content. We find a wide range of interest, identifying stories primarily of interest to users in the network (``niche interests'') from those of more general interest to the user community. This distinction is useful for predicting a story's eventual popularity from users' early reactions to the story.
Steepest Ascent Hill Climbing For A Mathematical Problem
Venue: arXiv cs.AI 2010
Year: 2010

The paper proposes artificial intelligence technique called hill climbing to find numerical solutions of Diophantine Equations. Such equations are important as they have many applications in fields like public key cryptography, integer factorization, algebraic curves, projective curves and data dependency in super computers. Importantly, it has been proved that there is no general method to find solutions of such equations. This paper is an attempt to find numerical solutions of Diophantine equations using steepest ascent version of Hill Climbing. The method, which uses tree representation to depict possible solutions of Diophantine equations, adopts a novel methodology to generate successors. The heuristic function used help to make the process of finding solution as a minimization process. The work illustrates the effectiveness of the proposed methodology using a class of Diophantine equations given by a1. x1 p1 + a2. x2 p2 + ...... + an . xn pn = N where ai and N are integers. The experimental results validate that the procedure proposed is successful in finding solutions of Diophantine Equations with sufficiently large powers and large number of variables.
Reflection-based language support for the heterogeneous capture and restoration of running computations
Venue: arXiv cs.PL 2010
Year: 2010

This work is devoted to the study of the problem of user-level capture and restoration of running computations in heterogeneous environments. Support for those operations has traditionally been offered through ready-made solutions for specific applications, which are difficult to tailor or adapt to different needs. We believe that a more promising approach would be to build specific solutions as needed, over a more general framework for capture and restoration. In this work, in order to explore the basic mechanisms a language should provide to support the implementation of different policies, we extend the Lua programming language with an API that allows the programmer to reify the internal structures of execution into fine-grained language values.
Convolutional Matching Pursuit and Dictionary Training
Venue: arXiv cs.CV 2010
Year: 2010

Matching pursuit and K-SVD is demonstrated in the translation invariant setting
The Impact of Data Replicatino on Job Scheduling Performance in Hierarchical data Grid
Venue: arXiv cs.DC 2010
Year: 2010

In data-intensive applications data transfer is a primary cause of job execution delay. Data access time depends on bandwidth. The major bottleneck to supporting fast data access in Grids is the high latencies of Wide Area Networks and Internet. Effective scheduling can reduce the amount of data transferred across the internet by dispatching a job to where the needed data are present. Another solution is to use a data replication mechanism. Objective of dynamic replica strategies is reducing file access time which leads to reducing job runtime. In this paper we develop a job scheduling policy and a dynamic data replication strategy, called HRS (Hierarchical Replication Strategy), to improve the data access efficiencies. We study our approach and evaluate it through simulation. The results show that our algorithm has improved 12% over the current strategies.
Selfish Response to Epidemic Propagation
Venue: arXiv cs.SY 2010
Year: 2010

An epidemic spreading in a network calls for a decision on the part of the network members: They should decide whether to protect themselves or not. Their decision depends on the trade-off between their perceived risk of being infected and the cost of being protected. The network members can make decisions repeatedly, based on information that they receive about the changing infection level in the network. We study the equilibrium states reached by a network whose members increase (resp. decrease) their security deployment when learning that the network infection is widespread (resp. limited). Our main finding is that the equilibrium level of infection increases as the learning rate of the members increases. We confirm this result in three scenarios for the behavior of the members: strictly rational cost minimizers, not strictly rational, and strictly rational but split into two response classes. In the first two cases, we completely characterize the stability and the domains of attraction of the equilibrium points, even though the first case leads to a differential inclusion. We validate our conclusions with simulations on human mobility traces.
A Fault Analytic Method against HB+
Venue: arXiv cs.CR 2010
Year: 2010

The search for lightweight authentication protocols suitable for low-cost RFID tags constitutes an active and challenging research area. In this context, a family of protocols based on the LPN problem has been proposed: the so-called HB-family. Despite the rich literature regarding the cryptanalysis of these protocols, there are no published results about the impact of fault analysis over them. The purpose of this paper is to fill this gap by presenting a fault analytic method against a prominent member of the HB-family: HB+ protocol. We demonstrate that the fault analysis model can lead to a flexible and effective attack against HB-like protocols, posing a serious threat over them.
Astronomy in the Cloud: Using MapReduce for Image Coaddition
Venue: arXiv cs.DC 2010
Year: 2010

In the coming decade, astronomical surveys of the sky will generate tens of terabytes of images and detect hundreds of millions of sources every night. The study of these sources will involve computation challenges such as anomaly detection and classification, and moving object tracking. Since such studies benefit from the highest quality data, methods such as image coaddition (stacking) will be a critical preprocessing step prior to scientific investigation. With a requirement that these images be analyzed on a nightly basis to identify moving sources or transient objects, these data streams present many computational challenges. Given the quantity of data involved, the computational load of these problems can only be addressed by distributing the workload over a large number of nodes. However, the high data throughput demanded by these applications may present scalability challenges for certain storage architectures. One scalable data-processing method that has emerged in recent years is MapReduce, and in this paper we focus on its popular open-source implementation called Hadoop. In the Hadoop framework, the data is partitioned among storage attached directly to worker nodes, and the processing workload is scheduled in parallel on the nodes that contain the required input data. A further motivation for using Hadoop is that it allows us to exploit cloud computing resources, e.g., Amazon's EC2. We report on our experience implementing a scalable image-processing pipeline for the SDSS imaging database using Hadoop. This multi-terabyte imaging dataset provides a good testbed for algorithm development since its scope and structure approximate future surveys. First, we describe MapReduce and how we adapted image coaddition to the MapReduce framework. Then we describe a number of optimizations to our basic approach and report experimental results comparing their performance.
Superselectors: Efficient Constructions and Applications
Venue: arXiv cs.DS 2010
Year: 2010

We introduce a new combinatorial structure: the superselector. We show that superselectors subsume several important combinatorial structures used in the past few years to solve problems in group testing, compressed sensing, multi-channel conflict resolution and data security. We prove close upper and lower bounds on the size of superselectors and we provide efficient algorithms for their constructions. Albeit our bounds are very general, when they are instantiated on the combinatorial structures that are particular cases of superselectors (e.g., (p,k,n)-selectors, (d,\ell)-list-disjunct matrices, MUT_k(r)-families, FUT(k, a)-families, etc.) they match the best known bounds in terms of size of the structures (the relevant parameter in the applications). For appropriate values of parameters, our results also provide the first efficient deterministic algorithms for the construction of such structures.
On the Capacity of Multi-Hop Wireless Networks with Partial Network Knowledge
Venue: arXiv cs.IT 2010
Year: 2010

In large wireless networks, acquiring full network state information is typically infeasible. Hence, nodes need to flow the information and manage the interference based on partial information about the network. In this paper, we consider multi-hop wireless networks and assume that each source only knows the channel gains that are on the routes from itself to other destinations in the network. We develop several distributed strategies to manage the interference among the users and prove their optimality in maximizing the achievable normalized sum-rate for some classes of networks.
Secrecy Capacity of the Gaussian Wire-Tap Channel with Finite Complex Constellation Input
Venue: arXiv cs.IT 2010
Year: 2010

The secrecy capacity of a discrete memoryless Gaussian Wire-Tap Channel when the input is from a finite complex constellation is studied. It is shown that the secrecy capacity curves of a finite constellation plotted against the SNR, for a fixed noise variance of the eavesdropper's channel has a global maximum at an internal point. This is in contrast to what is known in the case of Gaussian codebook input where the secrecy capacity curve is a bounded, monotonically increasing function of SNR. Secrecy capacity curves for some well known constellations like BPSK, 4-QAM, 16-QAM and 8-PSK are plotted and the SNR at which the maximum occurs is found through simulation. It is conjectured that the secrecy capacity curves for finite constellations have a single maximum.
Spherical harmonic transform with GPUs
Venue: arXiv cs.DC 2010
Year: 2010

We describe an algorithm for computing an inverse spherical harmonic transform suitable for graphic processing units (GPU). We use CUDA and base our implementation on a Fortran90 routine included in a publicly available parallel package, S2HAT. We focus our attention on the two major sequential steps involved in the transforms computation, retaining the efficient parallel framework of the original code. We detail optimization techniques used to enhance the performance of the CUDA-based code and contrast them with those implemented in the Fortran90 version. We also present performance comparisons of a single CPU plus GPU unit with the S2HAT code running on either a single or 4 processors. In particular we find that use of the latest generation of GPUs, such as NVIDIA GF100 (Fermi), can accelerate the spherical harmonic transforms by as much as 18 times with respect to S2HAT executed on one core, and by as much as 5.5 with respect to S2HAT on 4 cores, with the overall performance being limited by the Fast Fourier transforms. The work presented here has been performed in the context of the Cosmic Microwave Background simulations and analysis. However, we expect that the developed software will be of more general interest and applicability.
Probing Capacity
Venue: arXiv cs.IT 2010
Year: 2010

We consider the problem of optimal probing of states of a channel by transmitter and receiver for maximizing rate of reliable communication. The channel is discrete memoryless (DMC) with i.i.d. states. The encoder takes probing actions dependent on the message. It then uses the state information obtained from probing causally or non-causally to generate channel input symbols. The decoder may also take channel probing actions as a function of the observed channel output and use the channel state information thus acquired, along with the channel output, to estimate the message. We refer to the maximum achievable rate for reliable communication for such systems as the 'Probing Capacity'. We characterize this capacity when the encoder and decoder actions are cost constrained. To motivate the problem, we begin by characterizing the trade-off between the capacity and fraction of channel states the encoder is allowed to observe, while the decoder is aware of channel states. In this setting of 'to observe or not to observe' state at the encoder, we compute certain numerical examples and note a pleasing phenomenon, where encoder can observe a relatively small fraction of states and yet communicate at maximum rate, i.e. rate when observing states at encoder is not cost constrained.
Multi Layer Approach to Defend DDoS Attacks Caused by Spam
Venue: arXiv cs.CR 2010
Year: 2010

Corporate mail services are designed to perform better than public mail services. Fast mail delivery, large size file transfer as an attachments, high level spam and virus protection, commercial advertisement free environment are some of the advantages worth to mention. But these mail services are frequent target of hackers and spammers. Distributed Denial of service attacks are becoming more common and sophisticated. The researchers have proposed various solutions to the DDOS attacks. Can we stop these kinds of attacks with available technology? These days the DDoS attack through spam has increased and disturbed the mail services of various organizations. Spam penetrates through all the filters to establish DDoS attacks, which causes serious problems to users and the data. In this paper we propose a multilayer approach to defend DDoS attack caused by spam mails. This approach is a combination of fine tuning of source filters, content filters, strictly implementing mail policies, educating user, network monitoring and logical solutions to the ongoing attack. We have conducted several experiments in corporate mail services; the results show that this approach is highly effective to prevent DDoS attack caused by spam. The defense mechanism reduced 60% of the incoming spam traffic and repelled many DDoS attacks caused by spam
High-SIR Transmission Capacity of Wireless Networks with General Fading and Node Distribution
Venue: arXiv cs.IT 2010
Year: 2010

In many wireless systems, interference is the main performance-limiting factor, and is primarily dictated by the locations of concurrent transmitters. In many earlier works, the locations of the transmitters is often modeled as a Poisson point process for analytical tractability. While analytically convenient, the PPP only accurately models networks whose nodes are placed independently and use ALOHA as the channel access protocol, which preserves the independence. Correlations between transmitter locations in non-Poisson networks, which model intelligent access protocols, makes the outage analysis extremely difficult. In this paper, we take an alternative approach and focus on an asymptotic regime where the density of interferers $η$ goes to 0. We prove for general node distributions and fading statistics that the success probability $\p \sim 1-γη^κ$ for $η\rightarrow 0$, and provide values of $γ$ and $κ$ for a number of important special cases. We show that $κ$ is lower bounded by 1 and upper bounded by a value that depends on the path loss exponent and the fading. This new analytical framework is then used to characterize the transmission capacity of a very general class of networks, defined as the maximum spatial density of active links given an outage constraint.
Algorithmic and Statistical Perspectives on Large-Scale Data Analysis
Venue: arXiv cs.DS 2010
Year: 2010

In recent years, ideas from statistics and scientific computing have begun to interact in increasingly sophisticated and fruitful ways with ideas from computer science and the theory of algorithms to aid in the development of improved worst-case algorithms that are useful for large-scale scientific and Internet data analysis problems. In this chapter, I will describe two recent examples---one having to do with selecting good columns or features from a (DNA Single Nucleotide Polymorphism) data matrix, and the other having to do with selecting good clusters or communities from a data graph (representing a social or information network)---that drew on ideas from both areas and that may serve as a model for exploiting complementary algorithmic and statistical perspectives in order to solve applied large-scale data analysis problems.