Department of Computer Science and Engineering

Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

Online social networks have created a platform for the human to share information at an unprecedented scale. However, misinformation on various topics also gets spread across the social media. Misinformation on vaccination especially anti-vaccination campaigns could adversely affect public health by preventing many people from taking the vaccination, resulting in poor health conditions and deaths. It is crucial to detect misinformation related to vaccination in social media. Here, we study anti-vaccination campaigns in Twitter and develop predictive models which could detect anti-vaccination tweets with high accuracy. This is challenging since the number of the anti-vaccination tweets are typically very less and results in highly imbalanced data. We develop a predictive model to take care of this data imbalance based on synthetic minority undersampling techniques which could overcome these limitations and detect anti-vaccination tweets with high accuracy and a very low False Positive rate. © 2019 IEEE.

World University root node

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

Leveraging Social Media Towards Understanding Anti-Vaccination Campaigns

In this work we prove the first Fixed-depth Size-Hierarchy Theorem for uniform AC0[]. In particular, we show that for any fixed d, the class Cd,k of functions that have uniform AC0[] formulas of depth d and size nk form an infinite hierarchy. We show this by exhibiting the first class of explicit functions where we have nearly (up to a polynomial factor) matching upper and lower bounds for the class of AC0[] formulas. The explicit functions are derived from the δ-Coin Problem, which is the computational problem of distinguishing between coins that are heads with probability (1 + δ)/2 or (1 − δ)/2, where δ is a parameter that is going to 0. We study the complexity of this problem and make progress on both upper bound and lower bound fronts. Upper bounds. For any constant d ≥ 2, we show that there are explicit monotone AC0 formulas (i.e. made up of AND and OR gates only) solving the δ-coin problem that have depth d, size exp(O(d(1/δ)1/(d−1))), and sample complexity (i.e. number of inputs) poly(1/δ). This matches previous upper bounds of O’Donnell and Wimmer (ICALP 2007) and Amano (ICALP 2009) in terms of size (which is optimal) and improves the sample complexity from exp(O(d(1/δ)1/(d−1))) to poly(1/δ). Lower bounds. We show that the above upper bounds are nearly tight (in terms of size) even for the significantly stronger model of AC0[] formulas (which are also allowed NOT and Parity gates): formally, we show that any AC0[] formula solving the δ-coin problem must have size exp(Ω(d(1/δ)1/(d−1))). This strengthens a result of Shaltiel and Viola (SICOMP 2010), who prove a exp(Ω((1/δ)1/(d+2))) lower bound for AC0[], and a lower bound of exp(Ω((1/δ)1/(d−1))) shown by Cohen, Ganor and Raz (APPROX-RANDOM 2014) for the class AC0. The upper bound is a derandomization involving a use of Janson’s inequality and classical combinatorial designs. The lower bound involves proving an optimal degree lower bound for polynomials over F2 solving the δ-coin problem. © 2019 Association for Computing Machinery.

Proceedings of the Annual ACM Symposium on Theory of Computing

A fixed-depth size-hierarchy theorem for AC0[] via the coin problem

Tax evasion is an illegal practice where a person or a business entity intentionally avoids paying his/her true tax liability. Any business entity is required by the law to file their tax return statements following a periodical schedule. Avoiding to file the tax return statement is one among the most rudimentary forms of tax evasion. The dealers committing tax evasion in such a way are called return defaulters. In this paper, we construct a logistic regression model that predicts with high accuracy whether a business entity is a potential return defaulter for the upcoming tax-filing period. For the same, we analyzed the effect of the amount of sales/purchases transactions among the business entities (dealers) and the mean absolute deviation (MAD) value of the first digit Benford's law on sales transactions by a business entity. We developed this model for the commercial taxes department, government of Telangana, India. © 2018 IEEE.

Proceedings - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics, DSAA 2018

Predictive modeling for identifying return defaulters in goods and services tax

This paper explains an application of big data analytics to detect illegitimate transactions performed by fraudulent communities of people who are engaged in a notorious tax evasion practice called circular trading. We designed and implemented this technique for the commercial taxes department, government of Telangana, India. This problem is solved in two steps. In step one, the problem is formulated as detecting fraudulent communities in a social network, where the vertices correspond to dealers and edges correspond to sales transactions. In step two, specific type of cycles are removed from each fraudulent community, which were identified in step one, to detect the illegitimate transactions. We used RHadoop framework for implementing this technique. © Springer Nature Switzerland AG 2019.

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

Big data analytics for nabbing fraudulent transactions in taxation system

Circular trading is a fraudulent trading scheme used by notorious tax evaders with the motivation to trick the tax enforcement authorities from identifying their suspicious transactions. Dealers make use of this technique to collude with each other and hence do heavy illegitimate trade among themselves to hide suspicious sales transactions. In this paper, we develop an algorithm to detect the group of colluding dealers who do heavy illegitimate trading among themselves. We formulate the problem as finding clusters in a weighted directed graph. Novelty of our approach is that we used Benford’s analysis to define weights and defined a measure similar to F1 score to find similarity between two clusters. The proposed algorithm is run on the commercial tax data set given by the government of Telangana, India, and the results obtained contains a group of several colluding dealers. © Springer Nature Switzerland AG 2019.

Advances in Intelligent Systems and Computing

A collusion set detection in value added tax using benford’s analysis

Vision-based detection of road accidents using traffic surveillance video is a highly desirable but challenging task. In this paper, we propose a novel framework for automatic detection of road accidents in surveillance videos. The proposed framework automatically learns feature representation from the spatiotemporal volumes of raw pixel intensity instead of traditional hand-crafted features. We consider the accident of the vehicles as an unusual incident. The proposed framework extracts deep representation using denoising autoencoders trained over the normal traffic videos. The possibility of an accident is determined based on the reconstruction error and the likelihood of the deep representation. For the likelihood of the deep representation, an unsupervised model is trained using one class support vector machine. Also, the intersection points of the vehicle's trajectories are used to reduce the false alarm rate and increase the reliability of the overall system. We evaluated out proposed approach on real accident videos collected from the CCTV surveillance network of Hyderabad City in India. The experiments on these real accident videos demonstrate the efficacy of the proposed approach. © 2000-2011 IEEE.

IEEE Transactions on Intelligent Transportation Systems

Deep Spatio-Temporal Representation for Detection of Road Accidents Using Stacked Autoencoder

Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today. We introduce a mini-batch selection strategy based on submodular function maximization. Our novel submodular formulation captures the informativeness of each sample and diversity of the whole subset. We design an efficient, greedy algorithm which can give high-quality solutions to this NP-hard combinatorial optimization problem. Our extensive experiments on standard datasets show that the deep models trained using the proposed batch selection strategy provide better generalization than Stochastic Gradient Descent as well as a popular baseline sampling strategy across different learning rates, batch sizes, and distance metrics. © 2019 International Joint Conferences on Artificial Intelligence. All rights reserved.

IJCAI International Joint Conference on Artificial Intelligence

Submodular batch selection for training deep neural networks

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a pseudo-multimodal object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approach. © 2019 IEEE.

IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

Borrow from anywhere: Pseudo multi-modal object detection in thermal imagery

This paper presents the privacy preserving End-To-End (E2E) Quality of Service (QoS) mechanism in multi-domain Software Defined Network (SDN). Guaranteed E2E QoS needs complete information about the network. But, in multi-domain SDN, information sharing among different operators mainly have privacy and security issues; thus, operators may not share such network information. Moreover, such information collection is not only computationally expensive but also it requires a lot of message exchange between the domain controllers. Hence, we propose PRIME-Q, a PRIvacy aware E2E QoS framework in Multi-domain SDN. In PRIME-Q, instead of collecting the required information about the network at a single location, the data is distributively processed, and only the binary decision is reported at a single point to take the final decision about E2E QoS. To quantify the gain of PRIME-Q, we define Privacy Index to measure the privacy of E2E QoS coordination in multidomain SDN. The results show that the learning approach of PRIME-Q can achieve the required E2E QoS with maximum privacy. Further, the numerical results for PRIME-Q show a significant reduction in the communication and computation overhead in the network. © 2019 IEEE.

Proceedings of the 2019 IEEE Conference on Network Softwarization: Unleashing the Power of Network Softwarization, NetSoft 2019

PRIME-Q: Privacy Aware End-To-End QoS Framework in Multi-Domain SDN

Background: Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming. Therefore, quick and precise estimation of heading date of paddy rice is highly essential. Results: In this work, we propose a simple pipeline to detect regions containing flowering panicles from ground level RGB images of paddy rice. Given a fixed region size for an image, the number of regions containing flowering panicles is directly proportional to the number of flowering panicles present. Consequently, we use the flowering panicle region counts to estimate the heading date of the crop. The method is based on image classification using Convolutional Neural Networks. We evaluated the performance of our algorithm on five time series image sequences of three different varieties of rice crops. When compared to the previous work on this dataset, the accuracy and general versatility of the method has been improved and heading date has been estimated with a mean absolute error of less than 1 day. Conclusion: An efficient heading date estimation method has been described for rice crops using time series RGB images of crop under natural field conditions. This study demonstrated that our method can reliably be used as a replacement of manual observation to detect the heading date of rice crops. © 2019 The Author(s).