Abstract

This study evaluates improvement in energy efficiency of an air conditioned building block employing energy conservation measures (ECMs) recommended by Indian Energy Conservation Building Code 2007 (ECBC) through a prescriptive route. First part evaluates energy savings by implementing five ECMs of envelope independently and two combinations of ECMs keeping constant thermostat setting throughout the year. In the second part of the study same ECMs are considered to the subject building model allowing thermostat settings as per thermal adaptation resulting from change in outdoor temperature. Actual measurements were taken and simulation model was fine-tuned. Annual energy consumption of building is used to evaluate the effect of individual ECMs and their combinations on both part of the study, i.e. fixed thermostat and adaptive thermostat settings. The simulation result shows that together …

Abstract

Regression is the study of functional dependency of one variable with respect to other variables. In this paperwe propose a novel regression algorithm, BINGR, for predicting dependent variable, having the advantage oflow computational complexity. The algorithm is interesting because instead of directly predicting the valueof the response variable, it recursively narrows down the range in which response variable lies. BINGRreduces the computation order to logarithmic which is much better than that of existing standard algorithms.As BINGR is parameterless, it can be employed by any naive user. Our experimental study shows that ourtechnique is as accurate as the state of the art, and faster by an order of magnitude

Abstract

In this paper we are proposing neural network based feature transformation framework for developing emotion independent speaker identification system. Most of the present speaker recognition systems may not perform well during emotional environments. In real life, humans extensively express emotions during conversations for effectively conveying the messages. Therefore, in this work we propose the speaker recognition system, robust to variations in emotional moods of speakers. Neural network models are explored to transform the speaker specific spectral features from any specific emotion to neutral. In this work, we have considered eight emotions namely, Anger, Sad, Disgust, Fear, Happy, Neutral, Sarcastic and Surprise. The emotional databases developed in Hindi, Telugu and German are used in this work for analyzing the effect of proposed feature transformation on the performance of speaker identification system. In this work, spectral features are represented by mel-frequency cepstral coefficients, and speaker models are developed using Gaussian mixture models. Performance of the speaker identification system is analyzed with various feature mapping techniques. Results have demonstrated that the proposed neural network based feature transformation has improved the speaker identification performance by 20 %. Feature transformation at the syllable level has shown the better performance, compared to sentence level.

Abstract

Pattern discovery is at the heart of bioinformatics, and algorithms from computer science have been widely used for identifying biological patterns. The assumption behind pattern discovery approaches is that a pattern that occurs often enough in biological sequences/structures or is conserved across organisms is expected to play a role in defining the respective sequence’s or structure’s functional behavior and/or evolutionary relationships. The pattern recognition problem addressed here is at the genomic level and involves identifying horizontally transferred regions, called genomic islands. A horizontally transferred event is defined as the movement of genetic material between phylogenetically unrelated organisms by mechanisms other than parent to progeny inheritance. Increasing evidence suggests the importance of horizontal transfer events in the evolution of bacteria, influencing traits such as antibiotic …

Abstract

Internal repetition within proteins is a commonly observed phenomenon as these provide multiple binding sites for protein-protein interactions by forming integrated multi-repeat assemblies in three-dimension. However, at the sequence level the similarity between internal copies of the repeats is generally low, and identification at the structural level is desired. In recent studies it has been shown that graph representation of protein structures is capable of capturing the 3-dimensional fold of the protein very well, thus providing a computationally efficient approach for protein structure analysis. Here we exploit this feature of protein structure graphs and carry out a comparative analysis of eight graph centrality measures for the identification of tandem structural repeats. We observe that principal eigen spectra of the adjacency matrix and Katz status index are able to capture the repetitive pattern of the structural units. The spectral analysis being computationally efficient, in this paper we present the analysis of the principal eigen spectra of the adjacency matrix of proteins containing Ankyrin (ANK) repeats, the most commonly occurring structural motifs in proteins.

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.