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

This paper presents a context-aware ontology driven approach to water resource management in smart cities for providing adequate water supply to the citizens. The appropriate management of water requires exploitation of efficient action plan to review the prevailing causes of water shortage in a geospatial environment. This involves analysis of historical and real-time water specific information captured through heterogeneous sensors. Since the gathered contextual data is available in different formats so interoperability across diverse data requires converting it into a common perceivable RDF format. As the perceptual model of the Smart Water domain comprises of observable media properties of the concepts so to achieve context-aware data fusion we have employed multimedia ontology based semantic mapping. The multimedia ontology encoded in Multimedia Web Ontology Language (MOWL) forms the core of our IoT based smart water application. It supports Dynamic Bayesian Network based probabilistic reasoning to predict the changing situations in a real-time irregular environment patterns. Ultimately, the paper presents a context-aware approach to deal with uncertainties in water resource in the face of environment variability and offer timely conveyance to water authorities by circulating warnings via text-messages or emails. To illustrate the usability of the presented approach we have utilized the online available sample water data-sets. © 2017, Springer International Publishing AG.

World University root node

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

Smart Water Management: An Ontology-Driven Context-Aware IoT Application

This paper presents a new hybrid Kinect-variety-based synthesis scheme that renders artifact-free multiple views for autostereoscopic/automultiscopic displays. The proposed approach does not explicitly require dense scene depth information for synthesizing novel views from arbitrary viewpoints. Instead, the integrated framework first constructs a consistent minimal image-space parameterization of the underlying 3D scene. The compact representation of scene structure is formed using only implicit sparse depth information of a few reference scene points extracted from raw RGB depth data. The views from arbitrary positions can be inferred by moving the novel camera in parameterized space by enforcing Euclidean constraints on reference scene images under a full-perspective projection model. Unlike the state-of-the-art depth image-based rendering (DIBR) methods, in which input depth map accuracy is crucial for high-quality output, our proposed algorithm does not depend on precise per-pixel geometry information. Therefore, it simply sidesteps to recover and refine the incomplete or noisy depth estimates with advanced filling or upscaling techniques. Our approach performs fairly well in unconstrained indoor/outdoor environments, where the performance of range sensors or dense depth-based algorithms could be seriously affected due to scene complex geometric conditions. We demonstrate that the proposed hybrid scheme provides guarantees on the completeness, optimality with respect to the inter-view consistency of the algorithm. In the experimental validation, we performed a quantitative evaluation as well as subjective assessment of the scene with complex geometric or surface properties. A comparison with the latest representative DIBR methods is additionally performed to demonstrate the superior performance of the proposed scheme. © 2017 IEEE.

IEEE Transactions on Circuits and Systems for Video Technology

A Novel Hybrid Kinect-Variety-Based High-Quality Multiview Rendering Scheme for Glass-Free 3D Displays

Unsupervised feature extraction is gaining a lot of research attention following its success to represent any kind of noisy data. Owing to the presence of a lot of training parameters, these feature learning models are prone to overfitting. Different regularization methods have been explored in the literature to avoid overfitting in deep learning models. In this research, we consider autoencoder as the feature learning architecture and propose ℓ2,1-norm based regularization to improve its learning capacity, called as Group Sparse AutoEncoder (GSAE). ℓ2,1-norm is based on the postulate that the features from the same class will have a common sparsity pattern in the feature space. We present the learning algorithm for group sparse encoding using majorization–minimization approach. The performance of the proposed algorithm is also studied on three baseline image datasets: MNIST, CIFAR-10, and SVHN. Further, using GSAE, we propose a novel deep learning based image representation for minutia detection from latent fingerprints. Latent fingerprints contain only a partial finger region, very noisy ridge patterns, and depending on the surface it is deposited, contain significant background noise. We formulate the problem of minutia extraction as a two-class classification problem and learn the descriptor using the novel formulation of GSAE. Experimental results on two publicly available latent fingerprint datasets show that the proposed algorithm yields state-of-the-art results for automated minutia extraction. © 2017 Elsevier B.V.

Image and Vision Computing

Group sparse autoencoder

Image databases are getting larger and diverse with the coming up of new imaging devices and advancements in technology. Content-based image classification (CBIC) is a method to classify images from large databases into different categories, on the basis of image content. An efficient image representation is an important component of a CBIC system. In this paper, we demonstrate that Self-Organizing Maps (SOM)-based clustering can be used to form an efficient representation of an image for a CBIC system. The proposed method first extracts Scale-Invariant Feature Transform (SIFT) features from images. Then it uses SOM for clustering of descriptors and forming a Bag of Features (BOF) or Vector of Locally Aggregated Descriptors (VLAD) representation of image. The performance of proposed method has been compared with systems using k-means clustering for forming VLAD or BOF representations of an image. The classification performance of proposed method is found to be better in terms of F-measure (FM) value and execution time. © Springer Science+Business Media Singapore 2017.

Advances in Intelligent Systems and Computing

Applicability of self-organizing maps in content-based image classification

Face recognition for forensics

The social network being one of the most disruptive innovations of the last decade has gathered a huge amount of attention of the people. The posts of the users of the social media are used by many companies in the world to find the mentality of the users, the current trend of the market and many more things. But still, there is a latent potential in the social network. One of the aspect that we were able to discover was about finding the relationship between the users (i.e., especially, the negative link) on the social network using the posts that the users make and the reaction of the other users towards it. The prediction of the negative link can be applied in the cyber security field, to observe the aberrations in the network and further find the malicious nodes in the social network; say, if two nodes are doing things together even though there is no relation between them. It can also be used in improving the recommendation system in social media as if there is some probability between the two nodes of being the enemy or disliking each other then we can remove them from each other's recommendation list or could assign a lower weight to them in our recommendation algorithm. To achieve all this relationship between the nodes we first need to find whether the user is posting posts with positive emotion (like happy, excited, etc.) or negative emotion (like angry, sad, etc.) so that we can further analyze the mentality of the user and use it to recommend the people who we have previously classified with the similar personality. For that, we have used the sentiment analysis, which divides the users into five simple categories: Extremely +ve(i.e.,positive), +ve, Neutral, -ve (i.e.,negative) and Extremely -ve. This research paper explains the methodologies that we have used to achieve the prediction of negative links between the nodes in the social network. © 2017 IEEE.

2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017

Algorithm for prediction of negative links using sentiment analysis in social networks

In authenticated encryption schemes, there are two techniques for handling long ciphertexts while working within the constraints of a low buffer size: releasing unverified plaintext (RUP) or producing intermediate tags (PIT). In this paper, in addition to the two techniques, we propose another way to handle a long ciphertext with a low buffer size by storing and releasing only one (generally, or only few) intermediate state without releasing or storing any part of an unverified plaintext and without need of generating any intermediate tag. In this paper we explain this generalised technique using our new construction sp-AELM. sp-AELM is a sponge-based authenticated encryption scheme that provides support for limited memory devices. We also provide its security proof for privacy and authenticity in an ideal permutation model, using a code-based game playing framework. Furthermore, we also present two more variants of sp-AELM that serve the same purpose and are more efficient than sp-AELM. The ongoing CAESAR competition has nine submissions which are based on the sponge construction. We apply our generalised technique of storing single intermediate state to all these submissions, to determine their suitability with a crypto module having limited memory. Our findings show that only ASCON and one of the PRIMATE's modes (namely GIBBON) satisfy the limited memory constraint using this technique, while the remaining schemes (namely, Artemia, ICEPOLE, Ketje, Keyak, NORX, π-cipher, STRIBOB and two of the PRIMATEs modes: APE and HANUMAN) are not suitable for this scenario directly. © 2017 Inderscience Enterprises Ltd.

Fulltext

International Journal of Applied Cryptography

A new authenticated encryption technique for handling long ciphertexts in memory constrained devices

This paper proposes a novel recommendation engine to suggest coordinated outfits to the users that complements each other. The proposed recommendation model encodes subjective knowledge of clothing experts in Multimedia Web Ontology Language (MOWL) and makes use of evidential and causal reasoning scheme to deal with the media properties of concepts. Our approach automatically identifies the user visual personality and interprets the contextual meaning of media features of the garments in the context of input query image. As a result, personalized complementary garments based on occasion of wear are recommended to the user. We have validated our approach with garment preferences of various models with a large collection of shirts and trousers, collected from various websites. © 2015 IEEE.

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

Recommendation of complementary garments using ontology

Video, rich in visual real-time content, is however, difficult to interpret and analyse. Video collections necessarily have large data volume. Video analytics strives to automatically discover patterns and correlations present in the large volume of video data, which can help the end-user to take informed and intelligent decisions as well as predict the future based on the patterns discovered across space and time. In this study, the authors discuss various issues and problems in video analytics, proposed solutions and present some of the important current applications of video analytics. © The Institution of Engineering and Technology 2016.

IET Computer Vision

Video analytics revisited

We propose a novel approach of utilizing phenomic traits to automatically quantify stress in plants using machine learning techniques. Moisture deficit conditions cause change in leaf color due to decrease in chlorophyll content as chloroplast is damaged by active oxygen species. Therefore, the proposed technique uses leaf color as the phenomic trait to assess stress levels using Relative water content (RWC) as a quantitative proxy. We extracted the change in leaf color in response to drought stress using the color features obtained using Random forest. A regressor has been modeled to predict the stress level of rice genotypes via RWC by employing colour histogram as a feature vector. The experiment was performed with pot images of different rice genotypes under normal and drought stressed conditions. We report a correlation coefficient of 0.89 obtained using this model demonstrating the capability of the presented technique for stress level predictions. © 2015 IEEE.