Abstract

Perfectly reliable message transmission (PRMT) is one of the fundamental problems in distributed computing. It allows a sender to reliably transmit a message to a receiver in an unreliable network, even in the presence of a computationally unbounded adversary. In this article, we study the inherent trade-off between the three important parameters of the PRMT protocols, namely, the network connectivity (n), the round complexity (r), and the communication complexity by considering the following generic question (which can be considered as the holy grail problem) in the context of the PRMT protocols. Given an n-connected network, a message of size ℓ (to be reliably communicated) and a limit c for the total communication allowed between the sender and the receiver, what is the minimum number of communication rounds required by a PRMT protocol to send the message, such that the communication complexity of the protocol is O(c)? We answer this interesting question by deriving a nontrivial lower bound on the round complexity. Moreover, we show that the lower bound is tight in the amortized sense, by designing a PRMT protocol whose round complexity matches the lower bound. The lower bound is the first of its kind, that simultaneously captures the inherent tradeoff between the three important parameters of a PRMT protocol.

Abstract

Regression is the study of functional dependency of one nu-meric variable with respect to another. In this paper, we present a novel,efficient, binary search based regression algorithm having the advantageof low computational complexity. These desirable features make BINERa very attractive alternative to existing approaches. The algorithm isinteresting because instead of directly predicting the value of responsevariable, it recursively narrows down the range in which the responsevariable lies. Our empirical experiments with several real world datasetsshow that our algorithm, outperforms current state of art approachesand is faster by an order of magnitude.

Abstract

Measuring inter-document similarity is one of the most essential steps in text document clustering. Traditional methods rely on representing text documents using the simple Bag-of-Words (BOW) model. A document is an organized structure consisting of various text segments or passages. Such single term analysis of the text treats whole document as a single semantic unit and thus, ignores other semantic units like sentences, passages etc. In this paper, we attempt to take advantage of underlying subtopic structure of text documents and investigate whether clustering of text documents can be improved if text segments of two documents are utilized, while calculating similarity between them. We concentrate on examining effects of combining suggested inter-document similarities (based on inter-passage similarities) with traditional inter-document similarities following a simple approach for the same. Experimental results on standard data sets suggest improvement in clustering of text documents

Abstract

User authentication is necessary to secure the data and process on Internet and in digital devices. Static text based authentication are most widely employed authentication systems for being inexpensive and highly scalable. But they are prone to various types of active and passive attacks. The constant need of extending them to increase security is making them less usable. One promising alternative is Graphical authentication systems, which if implemented properly are more secure but have their own drawbacks. In this paper, we discuss in detail the extension of our previous work Passblot [18], a unique graphical authentication system. It generates pseudo random one time passwords using a set of inkblots, unique to each user. Properties of one time passwords ensure the resistance towards various common attacks and the uniqueness of human perception makes it usable. We demonstrate how our system effectively mitigates various attacks and analyse the results from various experiments conducted.

Abstract

In this paper, we explore the use of Bessel features derived from speech utterances, to develop Gaussian mixture speaker models for text independent Speaker Identification. The proposed approach to speaker identification is based on existing methods that employ Gaussian mixtures for the modeling of speakers. However, we have developed the speaker models from the Bessel features derived from the speech utterances, as an alternative to Mel-frequency cepstral coefficients for developing the speaker models. The proposed approach is tested on two databases of ten and twenty speakers respectively and their performance is evaluated. Finally, we have made some suggestions for future work involving the use of Bessel features for text independent speaker identification

Abstract

Edge detection is an important image processing technique used in face recognition. In this paper we propose an edge detection algorithm suitable for extracting edge maps from facial images under noisy conditions. We employ several improvised techniques like composite gradient variation measures, weighted wide convolution kernels and dual maxima detection. This helps in removing spurious edge points along stronger edges, extracting fine edge lines along facial boundaries and curvatures and suppressing noise. This is demonstrated by extracting edges from facial images degraded by Gaussian, speckle and salt and pepper noise. For performance measurement and evaluation Pratt's Figure of Merit and Signal to Noise statistics is used

Abstract

The current research focused on the issues related to human-bird conflicts and subsequent alternatives adopted by the migratory birds at Uppalapadu Lake (India). The study collates the information from field observations and literature surveys and draws conclusions based on the observation data. The study highlights the ways the local community, Forest Department, activist groups and other organizations participate in protecting the birds from harm. It also focuses on some of the management initiatives taken by the Forest Department that are helping the birds during their breeding periods. Overall, the research identifies the ecological importance of the lake with respect to birds and stresses the need for its protection and further expansion.

Abstract

In the present study, three large ecological plots of 3 ha each were surveyed in 3 different forest types of North Andaman Islands. Each 3-ha plot (30 sub-plots of 0.1 ha each) was classified into different classes based on site quality as either excellent, good, moderate or poor using an index developed by utilizing vegetation parameters such as species richness, diversity, density, among others. Analysis revealed most of the area to be under the good category in three forest communities, indicating that forests of North Andaman are potential sites of species richness and diversity. The “general limit of species assemblage” with respect to higher angiosperm taxa in North Andaman was observed based on two independent approaches of sampling: stratified random plots and the large area ecological plots. The general limit of species assemblage was in the range of 14-33 species. The present analysis provides a base …

Abstract

In this work, we study the problem of reporting and counting maximal points in a query rectangle for a set of n integer points that lie on an n×n grid. A point is said to be maximal inside a query rectangle if it is not dominated by any other point inside the query rectangle. Our model of computation is unit-cost RAM model with word size of O(logn) bits. For the reporting version of the problem, we present a data structure of size words and support querying in time where k is the size of the output. For the counting version, we present a data structure of size words which supports querying in . Both the data structures are static in nature. The reporting version of the problem has been studied in [1] and [5]. To the best of our knowledge, this is the first sub-logarithmic result for the reporting version and the first work for the counting version of the problem.

Abstract

The use of manycore architectures and accelerators, such as GPUs, with good programmability has allowed them to be deployed for vital computational work. The ability to use randomness in computation is known to help in several situations. For such computations to be made possible on a general purpose computer, a source of randomness, or in general a pseudo random generator (PRNG), is essential. However, most of the PRNGs currently available on GPUs suffer from some basic drawbacks that we highlight in this paper. It is of high interest therefore to develop a parallel, quality PRNG that also works in an on demand model. In this paper we investigate a CPU+GPU hybrid technique to create an efficient PRNG. The basic technique we apply is that of random walks on expander graphs. Unlike existing generators available in the GPU programming environment, our generator can produce random numbers on demand as opposed to a onetime generation. Our approach produces 0.07 GNumbers per second. The quality of our generator is tested with industry standard tests. We also demonstrate two applications of our PRNG. We apply our PRNG to design a list ranking algorithm which demonstrates the on-demand nature of the algorithm and a Monte Carlo simulation which shows the high quality of our generator.