International Institute of Information Technology, Hyderabad

About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).

Multi modal semantic indexing for image retrieval

Abstract: Popular image retrieval schemes generally rely only on a single mode, (either low level visual features or embedded text) for searching in multimedia databases. Many popular image collections (eg. those emerging over Internet) have associated tags, often for human consumption. A natural extension is to combine information from multiple modes for enhancing effectiveness in retrieval. In this paper, we propose two techniques: Multi-modal Latent Semantic Indexing (MMLSI) and Multi-Modal Probabilistic Latent Semantic Analysis (MMpLSA). These methods are obtained by directly extending their traditional single mode counter parts. Both these methods incorporate visual features and tags by generating simultaneous semantic contexts. The experimental results demonstrate an improved accuracy over other single and multi-modal methods.

Role of Urea-Aromatic Stacking Interactions in Stabilizing the Aromatic Residues of the Protein in Urea-Induced Denatured State.

Abstract: A delicate balance of different types of intramolecular interactions makes the folded states of proteins marginally more stable than the unfolded states. Experiments use thermal, chemical or mechanical stress to perturb the folding equilibrium for examining protein stability and protein folding process. Elucidation of the mechanism by which chemical denaturants unfold proteins is crucial; this study explores the nature of urea-aromatic interactions relevant in urea assisted protein denaturation. Free energy profiles corresponding to the unfolding of Trp-cage mini-protein in the presence and absence of urea at three different temperatures demonstrate the distortion of the hydrophobic core to be a crucial step. Exposure of the Trp6 residue to the solvent is found to be favored in the presence of urea. Previous experiments showed that urea has high affinity for aromatic groups of proteins. We show here that this is due to the remarkable ability of urea to form stacking and NH-π interactions with aromatic gro...

Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis

Abstract: Humans involuntarily tend to infer parts of the conversation from lip movements when the speech is absent or corrupted by external noise. In this work, we explore the task of lip to speech synthesis, i.e., learning to generate natural speech given only the lip movements of a speaker. Acknowledging the importance of contextual and speaker-specific cues for accurate lip-reading, we take a different path from existing works. We focus on learning accurate lip sequences to speech mappings for individual speakers in unconstrained, large vocabulary settings. To this end, we collect and release a large-scale benchmark dataset, the first of its kind, specifically to train and evaluate the single-speaker lip to speech task in natural settings. We propose a novel approach with key design choices to achieve accurate, natural lip to speech synthesis in such unconstrained scenarios for the first time. Extensive evaluation using quantitative, qualitative metrics and human evaluation shows that our method is four times more intelligible than previous works in this space. Please check out our demo video for a quick overview of the paper, method, and qualitative results. this https URL

A provably secure biometrics-based authenticated key agreement scheme for multi-server environments

Abstract: An authentication scheme handling multiple servers offers a feasible environment to users to conveniently access the rightful services from various servers using one-time registration. The practical realization of distribution of online services efficiently and transparently in multiple-server systems has come true by virtue of multi-server user authentication schemes. Due to distinguished properties like, difficulty to forge or copy, in-feasibility to lose or guess or forget, etc., biometrics have been widely preferred as a third authenticating factor in password and smart card based user authentication protocols. In this paper, we design a new biometrics-based multi-server authentication scheme based on trusted multiple-servers. We harness the concept of fuzzy extractor to provide the proper matching of biometric patterns. We evaluate our scheme through informal discussions on performance and also using Burrows-Abadi-Needham logic (BAN-logic) & random oracle model for formal security analysis. We also compose a comparative assessment of our scheme and the related ones. Outcome of the analysis and assessment shows our scheme an edge above many related and contemporary schemes.

Universal Dependency Parsing for Hindi-English Code-switching

Abstract: Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike standard corpora, these data often need to go through additional processes such as language identification, normalization and/or back-transliteration for their efficient processing. In this paper, we investigate these indispensable processes and other problems associated with syntactic parsing of code-switching data and propose methods to mitigate their effects. In particular, we study dependency parsing of code-switching data of Hindi and English multilingual speakers from Twitter. We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose a neural stacking model for parsing that efficiently leverages the part-of-speech tag and syntactic tree annotations in the code-switching treebank and the preexisting Hindi and English treebanks. We also present normalization and back-transliteration models with a decoding process tailored for code-switching data. Results show that our neural stacking parser is 1.5% LAS points better than the augmented parsing model and 3.8% LAS points better than the one which uses first-best normalization and/or back-transliteration.