Department of Computer Science & Engineering

&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

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

World University root node

Face and gender classification in crowd video

In this paper we present the first differential fault analysis (DFA) of SHA-3. This attack can recover the internal state of two versions of SHA-3 (namely, SHA3-512 and SHA3-384) and can be used to forge MAC’s which are using these versions of SHA-3. Assuming that the attacker can inject a random single bit fault on the intermediate state of the hash computation, and given the output of the SHA-3 version for a correct message and 80 faulty messages, we can extract 1592 out of the 1600 bits of the compression function’s internal state. To the best of our knowledge, this is the first public analysis of SHA-3 against DFA. Although our results do not compromise any security claim of SHA-3, it shows the feasibility of DFA on this scheme and possibly other Sponge based MACs and increases our understanding of SHA-3. © Springer International Publishing Switzerland 2015.

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

Differential fault analysis of SHA-3

In this paper, we describe a classifier based retrieval scheme for efficiently and accurately retrieving relevant documents. We use SVM classifiers for word retrieval, and argue that the classifier based solutions can be superior to the OCR based solutions in many practical situations. We overcome the practical limitations of the classifier based solution in terms of limited vocabulary support, and availability of training data. In order to overcome these limitations, we design a one-shot learning scheme for dynamically synthesizing classifiers. Given a set of SVM classifiers, we appropriately join them to create novel classifiers. This extends the classifier based retrieval paradigm to an unlimited number of classes (words) present in a language. We validate our method on multiple datasets, and compare it with popular alternatives like OCR and word spotting. Even on a language like English, where OCRs have been fairly advanced, our method yields comparable or even superior results. Our results are significant since we do not use any language specific post-processing for obtaining this performance. For better accuracy of the retrieved list, we use query expansion. This also allows us to seamlessly adapt our solution to new fonts, styles and collections. © 2015 IEEE.

Proceedings - 2015 IEEE Winter Conference on Applications of Computer Vision, WACV 2015

Document retrieval with unlimited vocabulary

Geographic routing scheme (such as Greedy perimeter stateless routing) makes use of location information to forward packets greedily. Nodes need to keep only this location information in stateless routing. When the greedy forwarding fails, routing switches to perimeter forwarding based on either planar graph (GG and RNG) or cross link detection protocol approaches. However, it has drawback in terms of cost and computational overheads. We propose a coverage-tree heuristic based routing instead of face routing in geographic routing schemes when greedy forwarding fails. We prove that the coverage tree based routing problem is NP hard by reduction using minimum rooted spanning tree of depth 2 (RST 2). We also show that coverage-tree based geographic routing problem is APX hard and not approximable with a factor of (1-∈)lnn for any fixed (Formula Presented.). Our proposed scheme of coverage-tree heuristics based geographic routing is (Formula Presented.)-approximation algorithm, a polynomial time algorithm using minimum distance topology knowledge. On performance comparison using simulation, our proposed scheme outperforms with the competitive schemes in term of success rate, network overhead and success rate against varying node density. © 2014, Springer Science+Business Media New York.

Wireless Networks

Approximating geographic routing using coverage tree heuristics for wireless network

The vehicular ad-hoc networks (VANETs) encountered frequent handoff due to high mobility of vehicles. The existing schemes are based on the context transfer for fast handoff, but needs to reduce the latency of handoff for VANETs. Fast handoffs in vehicle scans at any instance of time depends upon the association pattern of the AP. In this paper, we have proposed proactive approach for fast handoff using access point (AP) in VANETs. The fast handoff algorithm using AP graph with multiple vehicles for VANETs improves the re-association latency and context transfer procedure must be singled out from re-association process. From the simulation studies, we find that the use of AP graphs reduces the handoff latency due to re-association process and that the effectiveness of the approach improves dramatically as user mobility increases. The protocol for context transfer is defined in inter-access point protocol (IAPP), which infuses proactive transfer technique for frequent handoffs. Copyright © 2015 Inderscience Enterprises Ltd.

International Journal of Communication Networks and Distributed Systems

Caching algorithm for fast handoff using AP graph with multiple vehicles for VANETs

Apriori Algorithm is one of the most important algorithm which is used to extract frequent itemsets from large database and get the association rule for discovering the knowledge. It basically requires two important things: minimum support and minimum confidence. First, we check whether the items are greater than or equal to the minimum support and we find the frequent itemsets respectively. Secondly, the minimum confidence constraint is used to form association rules. Based on this algorithm, this paper indicates the limitation of the original Apriori algorithm of wasting time and space for scanning the whole database searching on the frequent itemsets, and present an improvement on Apriori. © 2015 The Authors.

Fulltext

Procedia Computer Science

Improvised apriori algorithm using frequent pattern tree for real time applications in data mining

Multiplying two sparse matrices, denoted spmm, is a fundamental operation in linear algebra with several applications. Hence, efficient and scalable implementation of spmm has been a topic of immense research. Recent efforts are aimed at implementations on GPUs, multicore architectures, FPGAs, and such emerging computational platforms. Owing to the highly irregular nature of spmm, it is observed that GPUs and CPUs can offer comparable performance. In this paper, we study CPU+GPU heterogeneous algorithms for spmm where the matrices exhibit a scale-free nature. Focusing on such matrices, we propose an algorithm that multiplies two sparse matrices exhibiting scale-free nature on a CPU+GPU heterogeneous platform. Our experiments on a wide variety of real-world matrices from standard datasets show an average of 25% improvement over the best possible algorithm on a CPU+GPU heterogeneous platform. We show that our approach is both architecture-aware, and workload-aware. © 2015 IEEE.

Proceedings - 2015 IEEE 29th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015

A Novel Heterogeneous Algorithm for Multiplying Scale-Free Sparse Matrices

Histopathological grading of cancer not only offers an insight to the patients' prognosis but also helps in making individual treatment plans. Mitosis counts in histopathological slides play a crucial role for invasive breast cancer grading using the Nottingham grading system. Pathologists perform this grading by manual examinations of a few thousand images for each patient. Hence, finding the mitotic figures from these images is a tedious job and also prone to observer variability due to variations in the appearances of the mitotic cells. We propose a fast and accurate approach for automatic mitosis detection from histopathological images. We employ area morphological scale space for cell segmentation. The scale space is constructed in a novel manner by restricting the scales with the maximization of relative-entropy between the cells and the background. This results in precise cell segmentation. The segmented cells are classified in mitotic and non-mitotic category using the random forest classifier. Experiments show at least 12% improvement in F1 score on more than 450 histopathological images at 40× magnification. © 2014 IEEE.

IEEE Transactions on Image Processing

Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images

We propose a fast and accurate method for counting the mitotic figures from histopathological slides using regenerative random forest. Our method performs automatic feature selection in an integrated manner with classification. The proposed random forest assigns a weight to each feature (dimension) of the feature vector in a novel manner based on the importance of the feature (dimension). The forest also assigns a misclassification-based penalty term to each tree in the forest. The trees are then regenerated to make a new population of trees (new forest) and only the more important features survive in the new forest. The feature vector is constructed from domain knowledge using the intensity features of nucleus, features of nuclear membrane and features of the possible stroma region surrounding the cell. The use of domain knowledge improves the classification performance. Experiments show at least 4% improvement in F-measure with an improvement in time complexity on the MITOS dataset from ICPR 2012 grand challenge. © Springer International Publishing Switzerland 2015.

Regenerative random forest with automatic feature selection to detect mitosis in histopathological breast cancer images

We present a learning based method for image super resolution problem. Our approach uses kernel methods to build an efficient representation and also to learn the regression model. For constructing an efficient set of features, we apply Kernel Principal Component Analysis (Kernel-PCA) with a Gaussian kernel on a patch based data-base constructed from 69 training images up-scaled using bi-cubic interpolation. These features were given as input to a non-linear Support Vector Regression (SVR) model, with Gaussian kernel, to predict the pixels of the high resolution image. The model selection for SVR was performed using grid search. We tested our algorithm on an unseen data-set of 13 images. Our method out-performed a state-of the-art method and achieved an average of 0.92 dB higher Peak signal-to-noise ratio (PSNR). The average improvement in PSNR over bi-cubic interpolation was found to be 3.38 dB. © Springer International Publishing Switzerland 2015.