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Institution

International Institute of Information Technology, Hyderabad

EducationHyderabad, India
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).


Papers
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Journal ArticleDOI
TL;DR: This paper proposes a new efficient three-factor user authentication scheme for a renewable energy-based smart grid environment (TUAS-RESG), which uses the lightweight cryptographic computations such as one-way hash functions, bitwise XOR operations, and elliptic curve cryptography.
Abstract: Smart grid (SG) technology has recently received significant attention due to its usage in maintaining demand response management in power transmission systems. In SG, charging of electric vehicles becomes one of the emerging applications. However, authentication between a vehicle user and a smart meter is required so that both of them can securely communicate for managing demand response during peak hours. To address the above mentioned issues, in this paper, we propose a new efficient three-factor user authentication scheme for a renewable energy-based smart grid environment (TUAS-RESG), which uses the lightweight cryptographic computations such as one-way hash functions, bitwise XOR operations, and elliptic curve cryptography. The detailed security analysis shows the robustness of TUAS-RESG against various well-known attacks. Moreover, TUAS-RESG provides superior security with additional features, such as dynamic smart meter addition, flexibility for password and biometric update, user and smart meter anonymity, and untraceability as compared to other related existing schemes. The practical demonstration of TUAS-RESG is also proved using the widely accepted NS2 simulation.

110 citations

Proceedings ArticleDOI
13 May 2013
TL;DR: An efficient, scalable system to detect events from tweets (ET), which automatically detects events from a set of tweets using an extraction scheme for event representative keywords, and a hierarchical clustering technique based on the common co-occurring features of keywords.
Abstract: Social media sites such as Twitter and Facebook have emerged as popular tools for people to express their opinions on various topics The large amount of data provided by these media is extremely valuable for mining trending topics and events In this paper, we build an efficient, scalable system to detect events from tweets (ET) Our approach detects events by exploring their textual and temporal components ET does not require any target entity or domain knowledge to be specified; it automatically detects events from a set of tweets The key components of ET are (1) an extraction scheme for event representative keywords, (2) an efficient storage mechanism to store their appearance patterns, and (3) a hierarchical clustering technique based on the common co-occurring features of keywords The events are determined through the hierarchical clustering process We evaluate our system on two data-sets; one is provided by VAST challenge 2011, and the other published by US based users in January 2013 Our results show that we are able to detect events of relevance efficiently

109 citations

Journal ArticleDOI
TL;DR: This work proposes a new secure protocol to realize anonymous mutual authentication and confidential transmission for star two-tier WBAN topology using the widely-accepted Burrows-Abadi-Needham (BAN) logic and informal security analysis to prove that the protocol achieves the necessary security requirements and goals of an authentication service.

106 citations

Proceedings ArticleDOI
25 Aug 2013
TL;DR: This work presents a holistic word recognition framework that represents the scene text image and synthetic images generated from lexicon words using gradient-based features, and recognizes the text in the image by matching the scene and synthetic image features with the novel weighted Dynamic Time Warping (wDTW) approach.
Abstract: Recognizing text in images taken in the wild is a challenging problem that has received great attention in recent years. Previous methods addressed this problem by first detecting individual characters, and then forming them into words. Such approaches often suffer from weak character detections, due to large intra-class variations, even more so than characters from scanned documents. We take a different view of the problem and present a holistic word recognition framework. In this, we first represent the scene text image and synthetic images generated from lexicon words using gradient-based features. We then recognize the text in the image by matching the scene and synthetic image features with our novel weighted Dynamic Time Warping (wDTW) approach. We perform experimental analysis on challenging public datasets, such as Street View Text and ICDAR 2003. Our proposed method significantly outperforms our earlier work in Mishra et al. (CVPR 2012), as well as many other recent works, such as Novikova et al. (ECCV 2012), Wang et al. al.(ICPR 2012), Wang et al.(ICCV 2011).

106 citations

Proceedings ArticleDOI
18 Dec 2006
TL;DR: MARGIN is a maximal subgraph mining algorithm that moves among promising nodes of the search space along the "border" of the infrequent and frequent subgraphs, which drastically reduces the number of candidate patterns considered in thesearch space.
Abstract: The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. The set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs, thus providing ample scope for pruning. MARGIN is a maximal subgraph mining algorithm that moves among promising nodes of the search space along the "border" of the infrequent and frequent subgraphs. This drastically reduces the number of candidate patterns considered in the search space. Experimental results validate the efficiency and utility of the technique proposed.

105 citations


Authors

Showing all 2066 results

NameH-indexPapersCitations
Ravi Shankar6667219326
Joakim Nivre6129517203
Aravind K. Joshi5924916417
Ashok Kumar Das562789166
Malcolm F. White5517210762
B. Yegnanarayana5434012861
Ram Bilas Pachori481828140
C. V. Jawahar454799582
Saurabh Garg402066738
Himanshu Thapliyal362013992
Monika Sharma362384412
Ponnurangam Kumaraguru332696849
Abhijit Mitra332407795
Ramanathan Sowdhamini332564458
Helmut Schiessel321173527
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202310
202229
2021373
2020440
2019367
2018364