Least squares twin support vector machines for pattern classification

In this paper we formulate a least squares version of the recently proposed twin support vector machine (TSVM) for binary classification. This formulation leads to extremely simple and fast algorithm for generating binary classifiers based on two non-parallel hyperplanes. Here we attempt to solve two modified primal problems of TSVM, instead of two dual problems usually solved. We show that the solution of the two modified primal problems reduces to solving just two systems of linear equations as opposed to solving two quadratic programming problems along with two systems of linear equations in TSVM. Classification using nonlinear kernel also leads to systems of linear equations. Our experiments on publicly available datasets indicate that the proposed least squares TSVM has comparable classification accuracy to that of TSVM but with considerably lesser computational time. Since linear least squares TSVM can easily handle large datasets, we further went on to investigate its efficiency for text categorization applications. Computational results demonstrate the effectiveness of the proposed method over linear proximal SVM on all the text corpuses considered.     

Experimental realization of quantum cheque using a five-qubit quantum computer

Quantum cheques could be a forgery-free way to make transaction in a quantum networked banking system with perfect security against any no-signalling adversary. Here, we demonstrate the implementation of quantum cheque, proposed by Moulick and Panigrahi (Quantum Inf Process 15:2475–2486, 2016), using the five-qubit IBM quantum computer. Appropriate single qubit, CNOT and Fredkin gates are used in an optimized configuration. The accuracy of implementation is checked and verified through quantum state tomography by comparing results from the theoretical and experimental density matrices.     

Recommendation engine based on derived wisdom for more similar item neighbors

Information Systems and e-Business Management - Collaborative filtering (CF) is a popular and widely accepted recommendation technique. CF is an automated form of word-of-mouth communication...     

A synopsis on node compromise detection in wireless sensor networks using sequential analysis (Invited Review Article)

By exploiting the unattended nature of the wireless sensor networks, an attacker can physically capture and compromise sensor nodes and then launch a variety of attacks. He can additionally create many replicas of a few compromised nodes and spread these replicas over the network, thus launching further attacks with their help. In order to minimize the damage incurred by compromised and replicated nodes, it is very important to detect such malicious nodes as quickly as possible. In this review article, we synthesize our previous works on node compromise detection in sensor networks while providing the extended analysis in terms of performance comparison to the related work. More specifically, we use the methodology of the sequential analysis to detect static and mobile compromised nodes, as well as mobile replicated nodes in sensor networks. With the help of analytical and simulation results, we also demonstrate that our schemes provide robust and efficient node compromise detection capability.     

Wavefront diffusion and LMSR: algorithms for dynamic repartitioningof adaptive meshes

Current multilevel repartitioning schemes tend to perform well on certain types of problems while obtaining worse results for other types of problems. We present two new multilevel algorithms for repartitioning adaptive meshes that improve the performance of multilevel schemes for the types of problems that current schemes perform poorly while maintaining similar or better results for those problems that current schemes perform well. Specifically, we present a new scratch-remap scheme called Locally-matched Multilevel Scratch-remap (or simply LMSR) for repartitioning of adaptive meshes. LMSR tries to compute a high-quality partitioning that has a large amount of overlap with the original partitioning. We show that LMSR generally decreases the data redistribution costs required to balance the load compared to current scratch-remap schemes. We present a new diffusion-based scheme that we refer to as Wavefront Diffusion. In Wavefront Diffusion, the flow of vertices moves in a wavefront from overweight to underweight subdomains. We show that Wavefront Diffusion obtains significantly lower data redistribution costs while maintaining similar or better edge-cut results compared to existing diffusion algorithms. We also compare Wavefront Diffusion with LMSR and show that these provide a trade-off between edge-cut and data redistribution costs for a wide range of problems. Our experimental results on a Gray T3E, an IBM SP2, and a cluster of Pentium Pro workstations show that both schemes are fast and scalable. For example, both are capable of repartitioning a seven million vertex graph in under three seconds on 128 processors of a Gray T3E. Our schemes obtained relative speedups of between nine and 12 when the number of processors was increased by a factor of 16 on a Gray T3E     

Modeling Instrumental Rationality, Land Tenure and Conflict Resolution

Full instrumental rationality and perfect institutions are two cornerstoneassumptions underlying neoclassical models. However, in the real world, thesetwo assumptions never hold, especially not in developing countries. In thispaper, we develop a game theoretical model to investigate if relaxations inthe full instrumental rationality and perfect institutions premise can explainthe conflicts that have been occurring between the various principals in theNarok district in Kenya with regard to land tenure and use.     

Scalable parallel data mining for association rules

The authors propose two new parallel formulations of the Apriori algorithm (R. Agrawal and R. Srikant, 1994) that is used for computing association rules. These new formulations, IDD and HD, address the shortcomings of two previously proposed parallel formulations CD and DD. Unlike the CD algorithm, the IDD algorithm partitions the candidate set intelligently among processors to efficiently parallelize the step of building the hash tree. The IDD algorithm also eliminates the redundant work inherent in DD, and requires substantially smaller communication overhead than DD. But IDD suffers from the added cost due to communication of transactions among processors. HD is a hybrid algorithm that combines the advantages of CD and DD. Experimental results on a 128-processor Cray T3E show that HD scales just as well as the CD algorithm with respect to the number of transactions, and scales as well as IDD with respect to increasing candidate set size