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).

Automated Detection of Glaucoma From Topographic Features of the Optic Nerve Head in Color Fundus Photographs

Abstract: Objective:To describe and evaluate the performance of an automated CAD system for detection of glaucoma from color fundus photographs.Design and Setting:Color fundus photographs of 2252 eyes from 1126 subjects were collected from 2 centers: Aravind Eye Hospital, Madurai and Coimbatore, India. The im

Using prefix-trees for efficiently computing set joins

Abstract: Joins on set-valued attributes (set joins) have numerous database applications. In this paper we propose PRETTI (PREfix Tree based seT joIn) – a suite of set join algorithms for containment, overlap and equality join predicates. Our algorithms use prefix trees and inverted indices. These structures are constructed on-the-fly if they are not already precomputed. This feature makes our algorithms usable for relations without indices and when joining intermediate results during join queries with more than two relations. Another feature of our algorithms is that results are output continuously during their execution and not just at the end. Experiments on real life datasets show that the total execution time of our algorithms is significantly less than that of previous approaches, even when the indices required by our algorithms are not precomputed.

WYSWYE: shoulder surfing defense for recognition based graphical passwords

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

Automatic Question Generation using Relative Pronouns and Adverbs

Abstract: This paper presents a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. Our system is syntax-based, runs on dependency parse information of a single-sentence input, and achieves high accuracy in terms of syntactic correctness, semantic adequacy, fluency and uniqueness. One of the key advantages of our system, in comparison with other rule-based approaches, is that we nearly eliminate the chances of getting a wrong wh-word in the generated question, by fetching the requisite wh-word from the input sentence itself. Depending upon the input, we generate both factoid and descriptive type questions. To the best of our information, the exploitation of wh-pronouns and wh-adverbs to generate questions is novel in the Automatic Question Generation task.

Universal Semi-Supervised Semantic Segmentation

Abstract: In recent years, the need for semantic segmentation has arisen across several different applications and environments. However, the expense and redundancy of annotation often limits the quantity of labels available for training in any domain, while deployment is easier if a single model works well across domains. In this paper, we pose the novel problem of universal semi-supervised semantic segmentation and propose a solution framework, to meet the dual needs of lower annotation and deployment costs. In contrast to counterpoints such as fine tuning, joint training or unsupervised domain adaptation, universal semi-supervised segmentation ensures that across all domains: (i) a single model is deployed, (ii) unlabeled data is used, (iii) performance is improved, (iv) only a few labels are needed and (v) label spaces may differ. To address this, we minimize supervised as well as within and cross-domain unsupervised losses, introducing a novel feature alignment objective based on pixel-aware entropy regularization for the latter. We demonstrate quantitative advantages over other approaches on several combinations of segmentation datasets across different geographies (Germany, England, India) and environments (outdoors, indoors), as well as qualitative insights on the aligned representations.