Abstract

Internal repetition within proteins is a commonly observed phenomenon and presents multiple binding and structural roles to proteins. Ankyrin repeat, one of the most widely existing protein motifs in nature, forms helix-turn-helix motif, and exclusively functions to mediate proteinprotein interactions, some of which are directly involved in the development of human cancer and other diseases. It has been observed that the ankyrin repeat motif is defined by its fold rather than by its function, and as there is no specific sequence underlying the fold, its identification at the structural level is desirable. We propose graph based approach in the identification and analysis of these repeats. The topological details of protein structures has been shown to be effectively captured by protein graphs. In this study we analyzed about twelve graph measures for the identification of repeated structural motifs. Of these, degree, Katz status, Page rank, closeness vitality and eigenvectors corresponding to the principal eigenvalue of the adjacency matrix are found to be promising. We observe that on considering the secondary structure information along with the analysis of various graph measures, it is possible to accurately identify the boundaries of individual repeats. This approach is computationally very efficient compared to the structure-structure self-comparison. Design and engineering of repeat proteins may help to elucidate their structural and biophysical properties, such as the dependence of stability and folding on the number of repeats, the importance of key intra- and inter-repeat interactions and identifying novel binding molecules suitable for biotechnological or medical applications.

Abstract

Recognition based graphical passwords are inherently vulnerable to shoulder surfing attacks because of their visual mode of interaction. In this paper, we propose and evaluate two novel shoulder-surfing defense techniques for recognition based graphical passwords. These techniques are based on WYSWYE (Where You See is What You Enter) strategy, where the user identifies a pattern of password images within a presented grid of images and replicates it onto another grid. We conducted controlled laboratory experiments to evaluate the usability and security of the proposed techniques. Both the schemes had high login success rates with no failures in authentication. More than seventy percent of participants successfully logged on to the system in their first attempt in both the schemes. The participants were satisfied with the schemes and were willing to use it in public places. In addition, both the schemes were significantly secure against shoulder surfing than normal unprotected recognition based graphical passwords. The login efficiency improved with practice in one of the proposed scheme. We believe, WYSWYE strategy has considerable potential and can easily be extended to other types of authentication systems such as text passwords and PINS.

Abstract

Similar to the traditional approach, we consider the task of summarization as selection of top ranked sentences from ranked sentence-clusters. To achieve this goal, we rank the sentence clusters by using the importance of words calculated by using page rank algorithm on reverse directed word graph of sentences. Next, to rank the sentences in every cluster we introduce the use of weighted clustering coefficient. We use page rank score of words for calculation of weighted clustering coefficient. Finally the most important issue is the presence of a lot of noisy entries in the text, which downgrades the performance of most of the text mining algorithms. To solve this problem, we introduce the use of Wikipedia anchor text based phrase mapping scheme. Our experimental results on DUC-2002 and DUC-2004 dataset show that our system performs better than unsupervised systems and better than/comparable with novel supervised systems of this area.

Abstract

The use of predefined phrase patterns like: N-grams (N>=2), longest common sub sequences or pre defined linguistic patterns etc do not give any credit to non-matching/smaller-size useful patterns and thus, may result in loss of information. Next, the use of 1-gram based model results in several noisy matches. Additionally, due to presence of more than one topic with different levels of importance in summary, we consider summarization evaluation task as topic based evaluation of information content. Means at first stage, we identify the topics covered in given model/reference summary and calculate their importance. At the next stage, we calculate the information coverage in test / machine generated summary, w.r.t. every identified topic. We introduce a graph based mapping scheme and the concept of closeness centrality measure to calculate the information depth and sense of the co-occurring words in every identified topic. Our experimental results show that devised system is better than/comparable with best results of TAC 2011 AESOP dataset.

Abstract

The problem of Authenticated Byzantine Generals (ABG) aims to simulate a virtual reliable broadcast channel from the General to all the players via a protocol over a real (point-to-point) network in the presence of faults. We propose a new model to study the self-composition of ABG protocols. The central dogma of our approach can be phrased as follows: Consider a player who diligently executes (only) the delegated protocol but the adversary steals some private information from him. Should such a player be considered faulty? With respect to ABG protocols, we argue that the answer has to be no. In the new model we show that in spite of using unique session identifiers, if , there cannot exist any ABG protocol that composes in parallel even twice. Further, for , we design ABG protocols that compose for any number of parallel executions. Besides investigating the composition of ABG under a new light, our work also brings out several new insights into Canetti's Universal Composability framework. Specifically, we show that there are several undesirable effects if one deviates from our dogma. This provides further evidence as to why our dogma is the right framework to study the composition of ABG protocols.

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