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

The deployment of LTE in unlicensed spectrum is a plausible solution to meet explosive traffic demand from mobile users. However, fair coexistence with the existing unlicensed technologies, mainly Wi-Fi, needs to be ensured before any such deployment. Duty cycled LTE (LTE-U) is a simple and an easily adaptable scheme which helps in fair coexistence with the Wi-Fi. Nonetheless, the immense deployment of Wi-Fi necessitates a user-oriented study to find the effects of LTE-U operation, primarily in scenarios where the LTE-U eNB remains hidden from Wi-Fi Access Point. To comprehend these effects, we perform a user-level throughput study of Wi-Fi in the presence of LTE-U using a testbed and observe a clear unfairness in throughput distribution among Wi-Fi users. Furthermore, we also notice inability among the disadvantaged users to receive the periodic Wi-Fi beacon frames successfully. The reasons and the subsequent consequences, of throughput unfairness and beacon losses, are carefully elaborated. Also, to validate the beacon loss results, we present a beacon loss analysis which provides a mathematical expression to find the beacon loss percentage. Finally, we examine the results and highlight a need for incorporating additional functionalities in either LTE-U or Wi-Fi to overcome the present challenges. © 2018 IEEE.

World University root node

2018 10th International Conference on Communication Systems and Networks, COMSNETS 2018

LTE-U and Wi-Fi hidden terminal problem: How serious is it for deployment consideration?

In 3GPP the architecture of a New Radio Access Network (New RAN) has been defined where the evolved NodeB (eNB) functions can be split between a Distributed Unit (DU) and Central Unit (CU). Furthermore, in the virtual RAN (VRAN) approach, such functions can be virtualized (e.g., in simple terms, deployed in virtual machines). Based on the split type, different performance in terms of capacity and latency are requested to the network (i.e., fronthaul) connecting DU and CU. This study experimentally evaluates, in the 5G segment of the Advanced Research on NetwOrking (ARNO) testbed (ARNO-5G), the fronthaul latency requirements specified by Standard Developing Organizations (SDO) (3GPP in this specific case). Moreover it evaluates how much virtualization impacts the fronthaul latency budget for the the Option 7-1 functional split. The obtained results show that, in the considered Option 7-1 functional split, the fronthaul latency requirements are about 250 μs but they depend on the radio channel bandwidth and the number of the connected UEs. Finally virtualization further decreases the latency budget. © 2017 IEEE.

2017 IEEE Globecom Workshops, GC Wkshps 2017 - Proceedings

Impact of RAN virtualization on fronthaul latency budget: An experimental evaluation

In the classic Integer Programming (IP) problem, the objective is to decide whether, for a given m × n matrix A and an m-vector b = (b1,⋯, bm), there is a non-negative integer n-vector x such that Ax = b. Solving (IP) is an important step in numerous algorithms and it is important to obtain an understanding of the precise complexity of this problem as a function of natural parameters of the input. The classic pseudo-polynomial time algorithm of Papadimitriou [J. ACM 1981] for instances of (IP) with a constant number of constraints was only recently improved upon by Eisenbrand and Weismantel [SODA 2018] and Jansen and Rohwedder [ArXiv 2018]. We continue this line of work and show that under the Exponential Time Hypothesis (ETH), the algorithm of Jansen and Rohwedder is nearly optimal. We also show that when the matrix A is assumed to be non-negative, a component of Papadimitriou's original algorithm is already nearly optimal under ETH. This motivates us to pick up the line of research initiated by Cunningham and Geelen [IPCO 2007] who studied the complexity of solving (IP) with non-negative matrices in which the number of constraints may be unbounded, but the branch-width of the column-matroid corresponding to the constraint matrix is a constant. We prove a lower bound on the complexity of solving (IP) for such instances and obtain optimal results with respect to a closely related parameter, path-width. Specifically, we prove matching upper and lower bounds for (IP) when the path-width of the corresponding column-matroid is a constant. © Fedor V. Fomin, Fahad Panolan, M.S. Ramanujan, and Saket Saurabh.

Leibniz International Proceedings in Informatics, LIPIcs

On the optimality of pseudo-polynomial algorithms for integer programming

We present two new combinatorial tools for the design of parameterized algorithms. The first is a simple linear time randomized algorithm that given as input a d-degenerate graph G and an integer k, outputs an independent set Y, such that for every independent set X in G of size at-most k, the probability that X is a subset of Y is at least (d+1)k kí k(d + 1)-1. The second is a new (deterministic) polynomial time graph sparsification procedure that given a graph G, a set T = ffs1; t1g; fs2; t2g; : : : ; fs'; t'gg of terminal pairs and an integer k, returns an induced subgraph G? of G that maintains all the inclusion minimal multicuts of G of size at most k, and does not contain any (k +2)-vertex connected set of size 2O(k). In particular, G? excludes a clique of size 2O(k) as a topological minor. Put together, our new tools yield new randomized fixed parameter tractable (FPT) algorithms for Stable s-t Separator, Stable Odd Cycle Transversal and Stable Multicut on general graphs, and for Stable Directed Feedback Vertex Set on d-degenerate graphs, resolving two problems left open by Marx et al. [ACM Transactions on Algorithms, 2013]. All of our algorithms can be derandomized at the cost of a small overhead in the running time. © Copyright 2018 by SIAM.

Proceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms

Covering small independent sets and separators with applications to parameterized algorithms

There has been a massive rise in the use of Community Question and Answering (CQA) forums to get solutions to various technical and non-technical queries. One common problem faced in CQA is the small number of experts, which leaves many questions unanswered. This paper addresses the challenging problem of predicting the best answerer for a new question and thereby recommending the best expert for the same. Although there are work in the literature that aim to find possible answerers for questions posted in CQA, very few algorithms exist for finding the best answerer whose answer will satisfy the information need of the original Poster. For finding answerers, existing approaches mostly use features based on content and tags associated with the questions. There are few approaches that additionally consider the users' history. In this paper, we propose an approach that considers a comprehensive set of features including but not limited to text representation, tag based similarity as well as multiple user-based features that target users' availability, agility as well as expertise for predicting the best answerer for a given question. We also include features that give incentives to users who answer less but more important questions over those who answer a lot of questions of less importance. A learning to rank algorithm is used to find the weight of each feature. Experiments conducted on a real dataset from Stack Exchange show the efficacy of the proposed method in terms of multiple evaluation metrics for accuracy, robustness and real time performance. © 2018 Association for Computing Machinery.

RecSys 2018 - 12th ACM Conference on Recommender Systems

Get me the best: Predicting best answerers in community question answering sites

As the social networking sites get more popular, spammers target these sites to spread spam posts. Twitter is one of the most popular online social networking sites where users communicate and interact on various topics. Most of the current spam filtering methods in Twitter focus on detecting the spammers and blocking them. However, spammers can create a new account and start posting new spam tweets again. So there is a need for robust spam detection techniques to detect the spam at tweet level. These types of techniques can prevent the spam in real time. To detect the spam at tweet level, often features are defined, and appropriate machine learning algorithms are applied in the literature. Recently, deep learning methods are showing fruitful results on several natural language processing tasks. We want to use the potential benefits of these two types of methods for our problem. Toward this, we propose an ensemble approach for spam detection at tweet level. We develop various deep learning models based on convolutional neural networks (CNNs). Five CNNs and one feature-based model are used in the ensemble. Each CNN uses different word embeddings (Glove, Word2vec) to train the model. The feature-based model uses content-based, user-based, and n-gram features. Our approach combines both deep learning and traditional feature-based models using a multilayer neural network which acts as a meta-classifier. We evaluate our method on two data sets, one data set is balanced, and another one is imbalanced. The experimental results show that our proposed method outperforms the existing methods. © 2014 IEEE.

IEEE Transactions on Computational Social Systems

A Neural Network-Based Ensemble Approach for Spam Detection in Twitter

Recommendation systems create personalized list of items that might interest the user by analyzing the user’s history of past purchases and/or consumption. For rating based systems, most of the traditional methods for recommendation focus on the absolute ratings provided by the users to the items. In this paper, we extend the traditional Matrix Factorization approach for recommendation and propose pairwise relation based factor modeling. While modeling the items in the system, the use of pairwise preferences allow information flow between the items through the preference relations as an additional information. Item feedbacks are available in the form of reviews apart from the rating information. The reviews have textual information that can be really helpful to represent the item’s latent feature vector appropriately. We perform topic modeling of the item reviews and use the topic vectors to guide the joint factor modeling of the users and items and learn their final representations. The proposed method shows promising results in comparison to the state-of-the-art methods in our experiments. Copyright © 2018 by the paper’s authors.

CEUR Workshop Proceedings

PReFacTO: Preference relations based factor model with topic awareness and offset

Proper formulation of features plays an important role in short-text classification tasks as the amount of text available is very little. In literature, Term Frequency - Inverse Document Frequency (TF-IDF) is commonly used to create feature vectors for such tasks. However, TF-IDF formulation does not utilize the class information available in supervised learning. For classification problems, if it is possible to identify terms that can strongly distinguish among classes, then more weight can be given to those terms during feature construction phase. This may result in improved classifier performance with the incorporation of extra class label related information. We propose a supervised feature construction method to classify tweets, based on the actionable information that might be present, posted during different disaster scenarios. Improved classifier performance for such classification tasks can be helpful in the rescue and relief operations. We used three benchmark datasets containing tweets posted during Nepal and Italy earthquakes in 2015 and 2016 respectively. Experimental results show that the proposed method obtains better classification performance on these benchmark datasets. © 2018 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC BY 4.0 License.

The Web Conference 2018 - Companion of the World Wide Web Conference, WWW 2018

Class Specific TF-IDF Boosting for Short-text Classification: Application to Short-texts Generated during Disasters

Social media is currently one of the most important means of news communication. Since people are consuming a large fraction of their daily news through social media, most of the traditional news channels are using social media to catch the attention of users. Each news channel has its own strategies to attract more users. In this paper, we analyze how the news channels use sentiment to garner users’ attention in social media. We compare the sentiment of social media news posts of television, radio and print media, to show the differences in the ways these channels cover the news. We also analyze users’ reactions and opinion sentiment on news posts with different sentiments. We perform our experiments on a dataset extracted from Facebook Pages of five popular news channels. Our dataset contains 0.15 million news posts and 1.13 billion users reactions. The results of our experiments show that the sentiment of user opinion has a strong correlation with the sentiment of the news post and the type of information source. Our study also illustrates the differences among the social media news channels of different types of news sources. © 2018 Elsevier B.V.

Fulltext

Online Social Networks and Media

Sentiment dynamics in social media news channels

Actions can be recognized effectively when the various atomic attributes forming the action are identified and combined in the form of a representation. In this paper, a low-dimensional representation is extracted from a pool of attributes learned in a universal Gaussian mixture model using factor analysis. However, such a representation cannot adequately discriminate between actions with similar attributes. Hence, we propose to classify such actions by leveraging the corresponding class labels. We train a Siamese deep neural network with a contrastive loss on the low-dimensional representation. We show that Siamese networks allow effective discrimination even between similar actions. The efficacy of the proposed approach is demonstrated on two benchmark action datasets, HMDB51 and MPII Cooking Activities. On both the datasets, the proposed method improves the state-of-the-art performance considerably. © 2018 IEEE.