Department of Computer Science & Engineering

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Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

Cloud Computing is a rising field in the history of computing. It is a way to maximise the capacity and capabilities without spending a lot to buy a new infrastructure and software. When users are online, they can get faster access to their data due to the massive storage. Although Cloud computing has many advantages due to large number of organizations moving towards it, it comes up with lots of security issues and breaches faced by both cloud service providers and users which are addressed in this paper. An efficient framework is devised for dealing with such issues. Proposed framework can protect data while transferring, sharing and storing in data centers using classification of data, Hashed Message Authentication codes and Index Building. The data is divided into three sections and accordingly the user is asked for authentication. User is provided the digital signature which can be verified with cloud directory. Using indexing, search can be made on the encrypted data. © 2016 IEEE.

World University root node

2016 International Conference on Computational Techniques in Information and Communication Technologies, ICCTICT 2016 - Proceedings

A framework for data security and storage in Cloud Computing

This paper proposes an efficient sensor deployment model, called 'MOdel Driven GRAdient Deployment for Irregular Terrain' (MoDGraDIT), for increasing network lifetime and data gathering accuracy. MoDGraDIT uses two steps for computing the sensor deployment density. First, the number of children for any intermediate node has been estimated based on the connected-coverage criteria. Second, sensors are deployed in high redundancy to sustain node failures. The density of deployment has been estimated that depends on the computed 'Energy Dissipation Factor' (EDF), considering the gradient effect of energy depletion. The performance of MoDGraDIT has been analyzed using theoretical and simulation results. © 2016 Elsevier Ltd. All rights reserved.

Journal of Network and Computer Applications

Impact of redundant sensor deployment over data gathering performance: A model based approach

There are many exercises which are repetitive in nature and are required to be done with perfection to derive maximum benefits. Sun Salutation or Surya Namaskar is one of the oldest yoga practice known. It is a sequence of ten actions or 'asanas' where the actions are synchronized with breathing and each action and its transition should be performed with minimal jerks. Essentially, it is important that this yoga practice be performed with Grace and Consistency. In this context, Grace is the ability of a person to perform an exercise with smoothness i.e. without sudden movements or jerks during the posture transition and Consistency measures the repeatability of an exercise in every cycle. We propose an algorithm that assesses how well a person practices Sun Salutation in terms of grace and consistency. Our approach works by training individual HMMs for each asana using STIP features[11] followed by automatic segmentation and labeling of the entire Sun Salutation sequence using a concatenated-HMM. The metric of grace and consistency are then laid down in terms of posture transition times. The assessments made by our system are compared with the assessments of the yoga trainer to derive the accuracy of the system. We introduce a dataset for Sun Salutation videos comprising 30 sequences of perfect Sun Salutation performed by seven experts and used this dataset to train our system. While Sun Salutation can be judged on multiple parameters, we focus mainly on judging Grace and Consistency. © 2016 ACM.

ACM International Conference Proceeding Series

A framework to assess sun salutation videos

We target the problem of Image Denoising using Gaussian Processes Regression (GPR). Being a non-parametric regression technique, GPR has received much attention in the recent past and here we further explore its versatility by applying it to a denoising problem. The focus is primarily on the design of a local gradient sensitive kernel that captures pixel similarity in the context of image denoising. This novel kernel formulation is used to shape the smoothness of the joint GP prior. We apply the GPR denoising technique to small patches and then stitch back these patches, this allows the priors to be local and relevant, also this helps us in dealing with GPR complexity. We demonstrate that our GPR based technique gives better PSNR values in comparison to existing popular denoising techniques. © 2015 IEEE.

2015 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics, NCVPRIPG 2015

Gradient sensitive kernel for image denoising, using Gaussian Process Regression

We propose a framework for synthesis of natural semi cursive handwritten Latin script that can find application in text personalization, or in generation of synthetic data for recognition systems. Our method is based on the generation of synthetic n-gram letter glyphs and their subsequent concatenation. We propose a non-parametric data driven generation scheme that is able to mimic the variation observed in handwritten glyph samples to synthesize natural looking synthetic glyphs. These synthetic glyphs are then stitched together to form complete words, using a spline based concatenation scheme. Further, as a refinement, our method is able to generate pen-lifts, giving our results a natural semicursive look. Through subjective experiments and detailed analysis of the results, we demonstrate the effectiveness of our formulation in being able to generate natural looking synthetic script. © 2016 ACM.

Generating synthetic handwriting using n-gram letter glyphs

This paper addresses the problem of segmenting handwritten annotations on scientific research papers. The motivation of this work is to geometrically segment the complex cases of handwritten annotations, including marks, cuts and special symbols along, with the regular text. Our work particularly focuses on documents that have multi-oriented handwritten [1] annotations rather than annotations in controlled scenario [2]. Spectral Partitioning is adopted as the segmentation scheme to separate the printed text and annotations. A new feature Envelope Straightness is developed and included in our feature set. This leads to an improvement of accuracy over the state-of-the-art features. The experiments are performed on two datasets: 40 documents authored by two writers from IAM dataset, comprising only printed and handwritten text and a self created dataset of 40 scientific papers from various proceedings annotated by a reader, comprising varied types of annotations. In the framework of spectral partitioning, our feature set has achieved a recall of 98.39% for printed text and precision of 85.40% for handwritten annotations on our dataset. For IAM dataset our feature set has achieved a recall of 81.89% for printed text and a precision of 69.67% for handwritten annotations. The results achieved on both dataset are better compared with results obtained using [3] [1]. © 2015 IEEE.

Segmenting printed text and handwritten annotation by Spectral Partitioning

The ongoing CAESAR competition, aimed at finding robust and secure authenticated encryption schemes provides many new submissions for analysis. We analyzed many schemes and came across a plenitude of techniques, design ideals and security notions. In view of the above, we present key recovery attacks using DPA for Deoxys, Joltik and ELmD, and a forgery attack on AEGIS. In our analysis of the various schemes, we found out that, schemes using Sponge constructions with pre-initialized keys such as Ascon, ICEPOLE, Keyak, NORX, PRIMATEs, etc. were significantly harder to attack than contemporary designs using standard building blocks from a side channel perspective. We also implement and demonstrate an attack on Joltik-BC, to recover the key in roughly 50–60 traces. © Springer International Publishing AG 2016.

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

Exploiting the leakage: Analysis of some authenticated encryption schemes

Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier. We propose a reinforced random forest (RRF) classifier that exploits reinforcement learning to improve classification accuracy. Our algorithm is initialized with a forest. Then the entire training data is tested using the initial forest. In order to reinforce learning, we use mis-classified data points to grow certain number of new trees. A subset of the new trees is added to the existing forest using a novel graph-based approach. We show that addition of these trees ensures improvement in classification accuracy. This process is continued iteratively until classification accuracy saturates. The proposed RRF has low computational burden. We achieve at least 3% improvement in F-measure compared to random forest in three breast cancer datasets. Results on benchmark datasets show significant reduction in average classification error. © 2016 ACM.

Reinforced random forest

We present an approach for automatically identifying the script of the text localized in the scene images. Our approach is inspired by the advancements in mid-level features. We represent the text images using mid-level features which are pooled from densely computed local features. Once text images are represented using the proposed mid-level feature representation, we use an off-the-shelf classifier to identify the script of the text image. Our approach is efficient and requires very less labeled data. We evaluate the performance of our method on a recently introduced CVSI dataset, demonstrating that the proposed approach can correctly identify script of 96.70% of the text images. In addition, we also introduce and benchmark a more challenging Indian Language Scene Text (ILST) dataset for evaluating the performance of our method. © 2016 IEEE.

Proceedings - 12th IAPR International Workshop on Document Analysis Systems, DAS 2016

A Simple and Effective Solution for Script Identification in the Wild

Promoting visual diversity in image search results has been of immense interest in the MIR (multimedia information retrieval) community. However, most of the work done in this space and the test collections used therein have ignored the use of social tags (user generated metadata) for image search result diversification on social multimedia platforms such as Flickr, Picasa etc. Unlike traditional multimedia content, content generated on such social media platforms are usually annotated with a rich set of explicit and implicit human generated metadata (referred here as social tags) such as keywords, textual description, category information, author's profile, user-To-user and user-To-content interaction etc which can be useful for the image search-result diversity task. In this paper we demonstrate how existing image search result diversification method can be extended to incorporate social tag information. Experiments on a real-world dataset shows that incorporating social tag features in some of the popular diversification algorithms results in improvement over baseline numbers. © 2016 IEEE.