N. V. Narendra Kumar
Institute for Development and Research in Banking Technology
9 Papers
6 Citations
N. V. Narendra Kumar is an academic researcher from Institute for Development and Research in Banking Technology. The author has contributed to research in topics: Computer science & Skew. The author has an hindex of 2, co-authored 7 publications.
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Papers
GssMILP for anomaly classification in surveillance videos
TL;DR: Wang et al. as discussed by the authors proposed a novel anomaly classifier with a new objective function (GssMILP) by leveraging the benefits from graph-based semi-supervised and multiple-instance learning approaches.
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Secure Synthesis of IoT via Readers-Writers Flow Model
Shashank Khobragade,N. V. Narendra Kumar,R. K. Shyamasundar +2 more
- 11 Jan 2018
TL;DR: This paper arrives at a synthesis methodology for the IoT and demonstrates how information flow among the connected devices using a three tier architecture enables us to assess the required security and privacy of the IoT based on the given security andprivacy capabilities of the components.
3
Security Analysis of EMV Protocol and Approaches for Strengthening It
Khedkar Shrikrishna,N. V. Narendra Kumar,R. K. Shyamasundar +2 more
- 11 Jan 2018
TL;DR: Although EMV cards are widely adopted around the world, it is still amenable to attacks as the analysis reveals.
3
Design Strategies for Handling Data Skew in MapReduce Framework
Avinash Potluri,S. Nagesh Bhattu,N. V. Narendra Kumar,R. B. V. Subramanyam +3 more
- 29 Aug 2019
TL;DR: This study gives a solution to address the issue of skew and to minimize the cost for communication in a network using two strategies for addressing skew and proposes two algorithms, which study the trade-offs between the two strategies.
2
Structure-sensitive graph-based multiple-instance semi-supervised learning
Satya Krishna Nunna,S. Nagesh Bhattu,D V L N Somayajulu,D V L N Somayajulu,N. V. Narendra Kumar +4 more
TL;DR: A non-convex formulation for instance-level MIL to find the instance- level labels by combining the benefits of both MIL and graph-based label propagation methods is proposed and the comparison of the performance of the proposed method to those of several state-of-the-art base-lines in MIL is presented.
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