Department of Computer Science & Engineering

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IIT Jodhpur

With advancement in the field of vehicle automation and wireless communication, Vehicular Ad Hoc network (VANET) based solutions are emerging as the answer to the dire problem of road traffic management. As dedicated short-range communications (DSRC) devices, vehicles can be organized in a peer- To-peer network to manage their movement for a smoother traffic flow. Our paper targets traffic management at road intersections. Our approach calls for the selection of a leader node in every lane so that they may mutually decide a safe and efficient order of crossing the intersection. Generally leader node selection algorithms have a computational complexity of O(nlog(n)). Our proposed algorithm uses geographic routing coupled with sequential flow of communication to select the leader node with a computational complexity of O(n) where n is the number of vehicles present in a lane at Road Intersection.

World University root node

2016 6th International Workshop on Computer Science and Engineering, WCSE 2016

Algorithm for leader node selection in vehicular adhoc networks at road intersections

Cloud computing is a rising technology that is still unclear to many security issues. The most challenging issue today in cloud servers is to ensure data security and privacy of the users. The proposed schema presents HE-RSA or hybrid encryption RSA along with Advanced Encryption Standard or AES to ensure efficiency, consistency and trustworthiness in cloud servers. The goal of this paper is to use various cryptography concepts during communication along with its application in cloud computing and to enhance the security of ciphertext or encrypted data in cloud servers along with minimizing the consumption of time, cost and memory size during encryption and decryption. © 2016 IEEE.

Proceedings of the 2016 6th International Conference - Cloud System and Big Data Engineering, Confluence 2016

Secure algorithm for cloud computing and its applications

Vehicular Ad hoc Networks (VANETs) allow vehicles to form a self-organized network without the need for a permanent infrastructure and routing in VANETs is a challenging task due to the high mobility and high density of mobile nodes. Position-based routing protocols, which are mostly based on greedy routing, are more suited to highly dynamic and mobile network. A focus of the scientific community is to design network oriented position-based routing protocols, and this has resulted in a very high number of algorithms, different in approach and performance and each suited only to particular applications. However, though numerous, very few positions based algorithms have actually been adopted for commercial purposes. In this paper, we survey state of art position based routing protocols previously used in VANETs, present open research challenges and possible future direction. We also outlined the main problems which must be solved for this class of routing protocols and even presented the solution for the particular problem. We will also propose an algorithm for position based routing at Road Intersection in VANETs. The discussed protocols are also compared with respect to the other proposed routing protocols, and thus aims at implementing the following characteristics that are obstacle awareness, street awareness, no bottleneck and no full path selection. © 2015 IEEE.

6th International Conference on Computing, Communications and Networking Technologies, ICCCNT 2015

Specific opportunistic routing at road intersection in VANETs

The Kalyna block cipher has recently been established as the Ukranian encryption standard in June, 2015. It was selected in a Ukrainian National Public Cryptographic Competition running from 2007 to 2010. Kalyna supports block sizes and key lengths of 128, 256 and 512 bits. Denoting variants of Kalyna as Kalyna-b / k, where b denotes the block size and k denotes the keylength, the design specifies k∈{b,2b}k∈{b,2b}. In this work, we re-evaluate the security bound of some reduced round Kalyna variants, specifically Kalyna-128 / 256 and Kalyna-256 / 512 against key recovery attacks in the single key model. We first construct new 6-round distinguishers and then use these distinguishers to demonstrate 9-round attacks on these Kalyna variants. These attacks improve the previous best 7-round attacks on the same. Our 9-round attack on Kalyna-128/256 has data, time and memory complexity of 2105, 245.83 and 2226.86 respectively. For our 9-round attack on Kalyna-256/512, the data/time/memory complexities are 2217, 2477.83 and 2451.45 respectively. The attacks presented in this work are the current best on Kalyna. We apply multiset attack - a variant of meet-in-the-middle attack to achieve these results. © Springer International Publishing Switzerland 2016.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Single key recovery attacks on 9-Round Kalyna-128/256 and Kalyna-256/512

The Simultaneous Localization and Mapping problem (SLAM) in robotics is typically modeled as a dyadic graph of relative pose measurements taken by the robot. The graph nodes store the values representing the absolute pose of the robot at a given point of time. An edge connecting two nodes represents robot movement and it stores the measurements taken by the robot sensor while moving between two nodes. The objective of the SLAM problem is to find the optimal global measurements best satisfying the noisy relative measurements [12]. This problem of optimal estimation on a graph given relative measurements is a well-studied problem within the control community, for which several results and algorithms are known [3, 4]. SLAM is generally solved as a least squares problem. Robust kernels which are less sensitive to outliers are used to deal with noise and outlier measurements. However, robust kernels tend to be dependent on initialization and can fail as the number of outliers increase. Therefore, it's important to identify and prune the outlier (noisy) measurements represented by incorrect loop closure edges for an accurate pose estimate. In this paper we propose a multi-scale Heat-Kernel analysis based loop closure edge pruning algorithm for the SLAM graph. We show that compared to other pruning algorithms, our algorithm has a substantially higher precision and recall when compared and is able to handle a large amount of outlier measurements.We have corroborated results on several publicly available datasets and several types of noise. Our algorithm is not restricted to SLAM graphs only, but has a much wider applicability to other types of geometric graphs. © 2016 IEEE.

2016 IEEE 55th Conference on Decision and Control, CDC 2016

SLAM pose-graph robustification via multi-scale Heat-Kernel analysis

Vertex Bisection Minimization problem (VBMP) consists of partitioning a vertex set into two sets B and B′ where |B|=|V|/2 such that vertex width, VW, is minimized which is defined as the number of vertices in B which are adjacent to at least one vertex in B′. It is an NP-complete problem in general. VBMP has applications in fault tolerance and is related to the complexity of sending messages to processors in interconnection networks via vertex disjoint paths. In this paper, a memetic algorithm has been designed for this problem (MAVBMP) in which four construction heuristics have been proposed to generate the initial population. These heuristics are analyzed statistically and accordingly used in proportion to generate the initial population for MAVBMP. A new crossover type search operator has been proposed for recombination and a local improvement operator has also been developed. Extensive experiments have been conducted on several classes of graphs such as complete bipartite graphs, 2-dimensional ordinary meshes, 2-dimensional toroidal meshes, 3-dimensional toroidal meshes, complete split graph, join of hypercubes and Harwell-boeing graphs which are a subset of public domain Matrix Market library. Trends observed in the experimental results for some of the classes of graphs have led to conjectures for vertex width. © 2016 Elsevier Inc.

Information Sciences

On minimizing vertex bisection using a memetic algorithm

Accurately annotated large video data is critical for the development of reliable surveillance and automotive related vision solutions. In this work, we propose an efficient and yet accurate annotation scheme for objects in videos (pedestrians in this case) with minimal supervision. We annotate objects with tight bounding boxes. We propagate the annotations across the frames with a self training based approach. An energy minimization scheme for the segmentation is the central component of our method. Unlike the popular grab cut like segmentation schemes, we demand minimal user intervention. Since our annotation is built on an accurate segmentation, our bounding boxes are tight. We validate the performance of our approach on multiple publicly available datasets.

2016 IEEE Winter Applications of Computer Vision Workshops, WACVW 2016

Efficient object annotation for surveillance and automotive applications

In this paper, we introduce the cartoon faces in the wild (IIIT-CFW) database and associated problems. This database contains 8,928 annotated images of cartoon faces of 100 public figures. It will be useful in conducting research on spectrum of problems associated with cartoon understanding. Note that to our knowledge, such realistic and large databases of cartoon faces are not available in the literature. © Springer International Publishing Switzerland 2016.

IIIT-CFW: A benchmark database of cartoon faces in the wild

Recognizing scene text is a challenging problem, even more so than the recognition of scanned documents. This problem has gained significant attention from the computer vision community in recent years, and several methods based on energy minimization frameworks and deep learning approaches have been proposed. In this work, we focus on the energy minimization framework and propose a model that exploits both bottom-up and top-down cues for recognizing cropped words extracted from street images. The bottom-up cues are derived from individual character detections from an image. We build a conditional random field model on these detections to jointly model the strength of the detections and the interactions between them. These interactions are top-down cues obtained from a lexicon-based prior, i.e., language statistics. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We evaluate our proposed algorithm extensively on a number of cropped scene text benchmark datasets, namely Street View Text, ICDAR 2003, 2011 and 2013 datasets, and IIIT 5K-word, and show better performance than comparable methods. We perform a rigorous analysis of all the steps in our approach and analyze the results. We also show that state-of-the-art convolutional neural network features can be integrated in our framework to further improve the recognition performance. © 2016 Elsevier Inc. All rights reserved.

Fulltext

Computer Vision and Image Understanding

Enhancing energy minimization framework for scene text recognition with top-down cues

A heterogeneous social network is characterized by multiple link types which makes the task of link prediction in such networks more involved. In the last few years collective link prediction methods have been proposed for the problem of link prediction in heterogeneous networks. These methods capture the correlation between different types of links and utilize this information in the link prediction task. In this paper we pose the problem of link prediction in heterogeneous networks as a multi-task, metric learning (MTML) problem. For each link-type (relation) we learn a corresponding distance measure, which utilizes both network and node features. These link-type specific distance measures are learnt in a coupled fashion by employing the Multi-Task Structure Preserving Metric Learning (MT-SPML) setup. We further extend the MT-SPML method to account for task correlations, robustness to noninformative features and non-stationary degree distribution across networks. Experiments on the Flickr and DBLP network demonstrates the effectiveness of our proposed approach vis-à-vis competitive baselines. © 2016 ACM.