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.

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IIT Jodhpur

The Worldwide road traffic problems, accident rates, transportation issues are increasing despite the improvements in Intelligent Transportation Systems (ITS). Internet of Vehicles (IoV) is a promising paradigm to the better and safe future of the automotive field. This complex, dynamic mobile network system allows data sharing between vehicles, its surrounding portable devices, road-side units, infrastructures, sensors, cloud, etc. However, the IoV provides several benefits and opportunities in different applications, it introduces challenges in security. In this paper, we propose an efficient authentication system for secure Vehicle-to- Vehicle (V2V) communications in an IoV scenario. The security analysis reveals that our scheme can defend against strong attacks. Our experimental outcomes on a simulated environment (on a desktop computer and Raspberry Pi) reveal that our proposed scheme can work with less computation time while ensuring integrity of the message in a strong adversarial scenario. © 2019 IEEE.

World University root node

IEEE Vehicular Technology Conference

An efficient authentication and secure vehicle-to-vehicle communications in an IoV

As the vehicles become a formidable sensor platform, the concept of VANETs is being transformed into a pervasive and ubiquitous concept of IoV (Internet of Vehicles). We discuss the scope of IoV over the limitations of VANET and further, we see the standards specified for automated vehicles and the aspects related to their deployment. We also discuss the various techniques under development that facilitate the transfer of anonymous user data upload in automated vehicle environment to the open public data repositories for the research community. We give the abstract of a model proposed to answer the privacy issue and see its controlled deployment. We finally propose the newly modelled approach of Google and suggest some modifications to the approach to answer the privacy issue. © 2019 IEEE.

International Conference on Information Networking

Privacy in the Internet of Vehicles: Models, Algorithms, and Applications

The evolution of Internet of Things (IoT) has given a new direction to our day to day activities. As IoT strongly relies on sensors of several kinds, a large amount of sensory data is generated daily. The vast heterogeneity among the generated data items induces new challenges. Moreover, Quality of Service (\mathrm {Q}\mathrm {o}\mathrm {S}) requirements vary widely based on the application versatility. Traditionally, the processing and analysis of these data are performed at a distant server. However, off-site data processing tends to waste a lot of system resources and incurs high overhead with low QoS for the running applications. This brings in the need of low-cost in-network data management techniques that also suits the resource constrained nature of the sensor networks. This work aims to study the need of context adaptive sensing coupled with invocation of in-network data fusion strategy in a smart building environment. Based on the planned testbed we also justify the effectiveness of the proposed system by considering contextual parameters and in-network data fusion using Kalman Filter. © 2019 IEEE.

2019 11th International Conference on Communication Systems and Networks, COMSNETS 2019

Motivating In-network Fusion for Smart Infrastructure Monitoring

The tremendous growth in ubiquitous sensor networks and IoT has led to Human Activity Recognition (HAR) emerging to be an exciting challenge. Machine learning approaches proposed in this direction have proved to be competent solutions. HAR data treatment in a recurrent form, and further analysis using deep networks such as RNNs or LSTM is largely unexplored. In this work, we treat data generated from a smart home setup towards HRA, as a series of events and perform feature extraction using a the popular LSTM variant of LSTMs. We use the Aruba dataset from the CASAS project [2]. We apply LSTM to extract features and perform annotations. Additionally we apply standard classification techniques to recognize and evaluate the different activities in the newly annotated data. From experimentations using our method we can achieve annotation accuracies of up to 79.5% which is 13.6% better than other state-of-the-art solutions. © 2019 IEEE.

Activity Recognition through Feature Learning and Annotations using LSTM

A document is rich in its layout. The entities of interest can be scattered over the document page. Traditional layout matching has involved modeling layout structure as grids, graphs, and spatial histograms of patches. In this paper we propose a new way of representing layout, which we call attributed paths. This representation admits a string edit distance based match measure. Our experiments show that layout based retrieval using attributed paths is computationally efficient and more effective. It also offers flexibility in tuning the match criterion. We have demonstrated effectiveness of attributed paths in performing layout based retrieval tasks on datasets of floor plan images [14] and journal pages [1]. © Springer Nature Singapore Pte Ltd. 2019.

Communications in Computer and Information Science

Attributed Paths for Layout-Based Document Retrieval

3D Human Body Reconstruction from a monocular image is an important problem in computer vision with applications in virtual and augmented reality platforms, animation industry, en-commerce domain, etc. While several of the existing works formulate it as a volumetric or parametric learning with complex and indirect reliance on re-projections of the mesh, we would like to focus on implicitly learning the mesh representation. To that end, we propose a novel model, HumanMeshNet, that regresses a template mesh's vertices, as well as receives a regularization by the 3D skeletal locations in a multi-branch, multi-task setup. The image to mesh vertex regression is further regularized by the neighborhood constraint imposed by mesh topology ensuring smooth surface reconstruction. The proposed paradigm can theoretically learn local surface deformations induced by body shape variations and can therefore learn high-resolution meshes going ahead. We show comparable performance with SoA (in terms of surface and joint error) with far lesser computational complexity, modeling cost and therefore real-time reconstructions on three publicly available datasets. We also show the generalizability of the proposed paradigm for a similar task of predicting hand mesh models. Given these initial results, we would like to exploit the mesh topology in an explicit manner going ahead. © 2019 IEEE.

Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

HumanMeshNet: Polygonal mesh recovery of humans

In vertex deletion problems on graphs, the task is to find a set of minimum number of vertices whose deletion results in a graph with some specific property. The class of vertex deletion problems contains several classical optimization problems, and has been studied extensively in algorithm design. Recently, there was a study on vertex deletion problems on split graphs. One of the results shown was that transforming a split graph into a block graph and a threshold graph using minimum number of vertex deletions is NP-hard. We call the decision version of these problems as Split to Block Vertex Deletion (SBVD) and Split to Threshold Vertex Deletion (STVD), respectively. In this paper, we study these problems in the realm of parameterized complexity with respect to the number of vertex deletions k as parameter. These problems are “implicit” 4-Hitting Set, and thus admit an algorithm with running time, a kernel with vertices, and a 4-approximation algorithm. In this paper, we exploit the structure of the input graph to obtain a kernel for SBVD with vertices and FPT algorithms for SBVD and STVD with running times and. © 2019, Springer Nature Switzerland AG.

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

Vertex Deletion on Split Graphs: Beyond 4-Hitting Set

In a multiwinner election based on the Condorcet criterion, we are given a set of candidates, and a set of voters with strict preference ranking over the candidates. A committee is weakly Gehrlein stable (WGS) if each committee member is preferred to each non-member by at least half of the voters. Recently, Aziz et al. [IJCAI 2017] studied the computational complexity of finding a WGS committee of size k. They show that this problem is NP-hard in general and and polynomial time solvable when the number of voters is odd. In this article, we initiate a systematic study of the problem in the realm of parameterized complexity. We first show that the problem is W[1]-hard when parameterized by the size of the committee. To overcome this intractability result, we use a known reformulation of WGS as a problem on directed graphs and then use parameters that measure the "structure" of these directed graphs. In particular, we consider the majority graph, defined as follows: there is a vertex corresponding to each candidate, and there is a directed arc from a candidate c to c' if the number of voters that prefer c over c' is more than those that prefer c' over c. The problem of finding WGS committee of size k corresponds to finding a vertex subset X of size k in the majority graph with the following property: the set X contains no vertex outside the committee that has an in-neighbor in X. Observe that the polynomial time algorithm of Aziz et al. [IJCAI 2017] corresponds to solving the problem on a tournament (a complete graph with orientation on edges). Thus, natural parameters to study our problem are "closeness" to being a tournament. We define closeness as the number of missing arcs in the given directed graph and the number of vertices we need to delete from the given directed graph such that the resulting graph is a tournament. We show that the problem is fixed parameter tractable (FPT) and admits linear kernels with respect to closeness parameters. Finally, we also design an exact exponential time algorithm running in time 0(1.2207nn0(1) Here, n denotes the number of candidates. © 2019 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS

Gehrlein stability in committee selection: Parameterized hardness and algorithms

In this paper, we address the problem of hand-drawn sketch recognition. Inspired by the Bayesian decision theory, we present a deep metric learning loss with the objective to minimize the Bayesian risk of misclassification. We estimate this risk for every mini-batch during training, and learn robust deep embeddings by backpropagating it to a deep neural network in an end-to-end trainable paradigm. Our learnt embeddings are discriminative and robust despite of intra-class variations and inter-class similarities naturally present in hand-drawn sketch images. Outperforming the state of the art on sketch recognition, our method achieves 82.2% and 88.7% on TU-Berlin-250 and TU-Berlin-160 benchmarks respectively. © Springer Nature Switzerland AG 2019.

Deep Embedding Using Bayesian Risk Minimization with Application to Sketch Recognition

The problem of answering questions about an image is popularly known as visual question answering (or VQA in short). It is a well-established problem in computer vision. However, none of the VQA methods currently utilize the text often present in the image. These 'texts in images' provide additional useful cues and facilitate better understanding of the visual content. In this paper, we introduce a novel task of visual question answering by reading text in images, i.e., by optical character recognition or OCR. We refer to this problem as OCR-VQA. To facilitate a systematic way of studying this new problem, we introduce a large-scale dataset, namely OCRVQA-200K. This dataset comprises of 207,572 images of book covers and contains more than 1 million question-answer pairs about these images. We judiciously combine well-established techniques from OCR and VQA domains to present a novel baseline for OCR-VQA-200K. The experimental results and rigorous analysis demonstrate various challenges present in this dataset leaving ample scope for the future research. We are optimistic that this new task along with compiled dataset will open-up many exciting research avenues both for the document image analysis and the VQA communities. © 2019 IEEE.