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: Computer science & Authentication. 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).

Analysis of emotional speech at subsegmental level.

Abstract: Emotional speech is produced when a speaker is in a state different from normal state. The objective of this study is to explore the deviations in the excitation source features of an emotional speech compared to normal speech. The features used for analysis are extracted at subsegmental level (1-3 ms) of speech. A comparative study of these features across different emotions indicates that there are significant deviations in the subsegmental level features of speech in emotional state when compared to normal state.

A Dynamic Privacy-Preserving Key Management Protocol for V2G in Social Internet of Things

Abstract: With the smart grid (SG) and the social Internet of Things (SIoT), an electric vehicle operator can use reliable, flexible, and efficient charging service with vehicle-to-grid (V2G). However, open channels can be vulnerable to various attacks by a malicious adversary. Therefore, secure mutual authentication for V2G has become essential, and numerous related protocols have been proposed. In 2018, Shen et al. proposed a privacy-preserving and lightweight key agreement protocol for V2G in SIoT to ensure security. However, we demonstrate that their protocol does not withstand impersonation, privileged-insider, and offline password guessing attacks, and it does not also guarantee secure mutual authentication, session key security, and perfect forward secrecy. Therefore, this paper proposes a dynamic privacy-preserving and lightweight key agreement protocol for V2G in SIoT to resolve the security weaknesses of Shen et al.'s protocol. The proposed protocol resists several attacks including impersonation, offline password guessing, man-in-the-middle, replay, and trace attacks, ensures anonymity, perfect forward secrecy, session key security, and secure mutual authentication. We evaluate the security of the proposed protocol using formal security analysis under the broadly-accepted real-or-random (ROR) model, secure mutual authentication proof using the widely-accepted Burrows-Abadi-Needham (BAN) logic, informal (non-mathematical) security analysis, and also the formal security verification using the broadly-accepted automated validation of Internet security protocols and applications (AVISPA) tool. We then compare computation costs, and security and functionality features of the proposed protocol with related protocols. Overall, the proposed protocol provides superior security, and it can be efficiently deployed to practical SIoT-based V2G environment.

Measurement-device-independent randomness from local entangled states

Abstract: Nonlocal correlations are useful for device-independent (DI) randomness certification (Pironio S. et al. , Nature (London) , 464 (2010) 1021). The advantage of this DI protocol over the conventional quantum protocol is that randomness can be certified even when experimental apparatuses are not trusted. Quantum entanglement is the necessary physical source for the nonlocal correlation required for such DI task. However, nonlocality and entanglement are distinct concepts. There exist entangled states which produce no nonlocal correlation and hence are not useful for the DI randomness certification task. Here we introduce the measurement-device–independent randomness certification task where one has trusted quantum state preparation devices but the mesurement devices are completely unspecified. Interestingly we show that there exist entangled states, with local description, that are a useful resource in such task but are useless in the corresponding DI scenario.

FNNC: Achieving Fairness through Neural Networks

Abstract: In classification models fairness can be ensured by solving a constrained optimization problem. We focus on fairness constraints like Disparate Impact, Demographic Parity, and Equalized Odds, which are non-decomposable and non-convex. Researchers define convex surrogates of the constraints and then apply convex optimization frameworks to obtain fair classifiers. Surrogates serve only as an upper bound to the actual constraints, and convexifying fairness constraints might be challenging. We propose a neural network-based framework, \emph{FNNC}, to achieve fairness while maintaining high accuracy in classification. The above fairness constraints are included in the loss using Lagrangian multipliers. We prove bounds on generalization errors for the constrained losses which asymptotically go to zero. The network is optimized using two-step mini-batch stochastic gradient descent. Our experiments show that FNNC performs as good as the state of the art, if not better. The experimental evidence supplements our theoretical guarantees. In summary, we have an automated solution to achieve fairness in classification, which is easily extendable to many fairness constraints.

Offline Handwriting Recognition on Devanagari Using a New Benchmark Dataset

Abstract: Handwriting recognition (HWR) in Indic scripts, like Devanagari is very challenging due to the subtleties in the scripts, variations in rendering and the cursive nature of the handwriting. Lack of public handwriting datasets in Indic scripts has long stymied the development of offline handwritten word recognizers and made comparison across different methods a tedious task in the field. In this paper, we release a new handwritten word dataset for Devanagari, IIIT-HW-Dev to alleviate some of these issues. We benchmark the IIIT-HW-Dev dataset using a CNN-RNN hybrid architecture. Furthermore, using this architecture, we empirically show that usage of synthetic data and cross lingual transfer learning helps alleviate the issue of lack of training data. We use this proposed pipeline on a public dataset, RoyDB and achieve state of the art results.