Sukumar Nandi

Bio: Sukumar Nandi is an academic researcher from Indian Institute of Technology Guwahati. The author has contributed to research in topics: Network packet & Wireless sensor network. The author has an hindex of 27, co-authored 390 publications receiving 4202 citations. Previous affiliations of Sukumar Nandi include Indian Institute of Technology Patna & University of Messina.

Theory and applications of cellular automata in cryptography

Abstract: This paper deals with the theory and application of Cellular Automata (CA) for a class of block ciphers and stream ciphers. Based on CA state transitions certain fundamental transformations are defined which are block ciphering functions of the proposed enciphering scheme, These fundamental transformations are found to generate the simple (alternating) group of even permutations which in turn is a subgroup of the permutation group, These functions are implemented with a class of programmable cellular automata (PCA) built around rules 51, 153, and 195. Further, high quality pseudorandom pattern generators built around rule 90 and 150 programmable cellular automata with a rule selector (i.e., combining function) has been proposed as running key generators in stream ciphers, Both the schemes provide better security against different types of attacks. With a simple, regular, modular and cascadable structure of CA, hardware implementation of such schemes idealy suit VLSI implementation. >

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Abstract: Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users Therefore, these measures are not suitable in a sparse data In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics

QoS support in mobile ad hoc networks

Abstract: Due to resource constraints and dynamic topology of mobile ad hoc networks (MANETs), supporting quality of service (QoS) in MANETs is a challenging task. Although lots of research have been done on supporting QoS in the Internet and other networks, they are not suitable for the MANET environment. While traditional protocol layering is an important abstraction to reduce complexity of network design in wired networks, it is not well suited to wireless networks to provide complex functionalities like QoS due to interdependencies of different layers. This paper provides a brief review of the current researches on QoS support in MANETs specially the different existing QoS models and proposes a general framework which is based on a cross layer approach. A thorough investigation of the different techniques at different layers and their adaptation has lead to this simple framework which gives better performance under different traffic loads and mobility scenarios.

[서평]「Applied Cryptography」

Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

IEEE transactions on pattern analysis and machine intelligence

Abstract: In the real world, a realistic setting for computer vision or multimedia recognition problems is that we have some classes containing lots of training data and many classes contain a small amount of training data. Therefore, how to use frequent classes to help learning rare classes for which it is harder to collect the training data is an open question. Learning with Shared Information is an emerging topic in machine learning, computer vision and multimedia analysis. There are different level of components that can be shared during concept modeling and machine learning stages, such as sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, etc. Regarding the specific methods, multi-task learning, transfer learning and deep learning can be seen as using different strategies to share information. These learning with shared information methods are very effective in solving real-world large-scale problems. This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis. Both state-of-the-art works, as well as literature reviews, are welcome for submission. Papers addressing interesting real-world computer vision and multimedia applications are especially encouraged. Topics of interest include, but are not limited to: • Multi-task learning or transfer learning for large-scale computer vision and multimedia analysis • Deep learning for large-scale computer vision and multimedia analysis • Multi-modal approach for large-scale computer vision and multimedia analysis • Different sharing strategies, e.g., sharing generic object parts, sharing attributes, sharing transformations, sharing regularization parameters and sharing training examples, • Real-world computer vision and multimedia applications based on learning with shared information, e.g., event detection, object recognition, object detection, action recognition, human head pose estimation, object tracking, location-based services, semantic indexing. • New datasets and metrics to evaluate the benefit of the proposed sharing ability for the specific computer vision or multimedia problem. • Survey papers regarding the topic of learning with shared information. Authors who are unsure whether their planned submission is in scope may contact the guest editors prior to the submission deadline with an abstract, in order to receive feedback.