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

With the advancements in 5G technology, Multi-access Edge Computing (MEC) has always been at the forefront due to its ability to provide additional computational power with reduced network latency compared to cloud computing. Most applications from the Vehicle to Everything (V2X) and Intelligent Transport Systems (ITS) domains are stringent in latency requirements, for which MEC is an excellent enabler. As per MEC architecture, MEC applications can leverage the benefits of storing essential information as a part of MEC services to further reduce the latency. In this work, we enhance an in-house 5G emulation framework by integrating it with ETSI MEC Location Service API for supporting various ITS use cases. We demonstrate the use case of providing proximity alerts to the drivers using SUMO platform and a mobile application. © 2023 IEEE.

World University root node

2023 15th International Conference on COMmunication Systems and NETworkS, COMSNETS 2023

Demonstration of a V2X Use Case Using MEC-assisted 5G Emulation Framework

The increasing popularity of virtual reality and 360-degree video streaming has created a demand for streaming high-quality video content over cellular networks. However, streaming 360-degree videos over cellular networks poses a challenge due to the need for ultra-low latency and high bandwidth. Although viewport adaptive streaming presents a promising solution to reduce the bandwidth requirement of <inline-formula><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video, it remains challenging to predict the Field of View (FoV) due to the adverse impact of prediction errors on the user&#x0027;s Quality of Experience (QoE). Moreover, bitrate adaptation gets complicated due to <inline-formula><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> videos and the dynamic nature of the cellular network. Therefore, in this paper, we propose an intelligent framework, termed Seer, to fully realize <inline-formula><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> video streaming over cellular networks by leveraging Multi-access Edge Computing (MEC). It incorporates an FoV prediction scheme and a bitrate adaptation policy into the MEC platform. In particular, the FoV prediction scheme uses perceived video features coupled with time-series based viewport prediction to achieve higher prediction accuracy. For an effective bitrate selection in cellular network conditions, a Reinforcement Learning (RL) based bitrate adaptation policy is further employed. The results demonstrate that Seer&#x0027;s FoV prediction scheme delivers higher prediction accuracy, while the streaming framework significantly reduces the bandwidth requirements and improves the user&#x0027;s QoE. IEEE

IEEE Transactions on Network Science and Engineering

Seer: Learning-Based <inline-formula><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> Video Streaming for MEC-Equipped Cellular Networks

In this paper, we present ComplAI, a unique framework to enable, observe, analyze and quantify explainability, robustness, performance, fairness, and model's behavior in drift scenarios, and to provide a single Trust Factor that evaluates different supervised ML models from an overall responsibility perspective. It helps users to (a) connect their models and enable explanations, (b) assess and visualize different aspects of the model and (c) compare different models from an overall perspective thereby facilitating actionable recourse for improvement of the models. ComplAI is model agnostic and works with different supervised machine learning scenarios and frameworks, seamlessly integrable with any ML life-cycle framework. Thus, this already deployed framework aims to unify critical aspects of Responsible AI systems for regulating the development process of such real systems. The theory version of the paper and a demo version is available in this link (use the Zip Password: Welcome2022!). An detailed version of the paper is available in Arxiv. © 2023 Owner/Author(s).

Proceedings of the ACM Symposium on Applied Computing

ComplAI: Framework for Multi-factor Assessment of Black-Box Supervised Machine Learning Models

Network Functions Virtualization (NFV) enables carriers to replace dedicated middleboxes with Virtual Network Functions (VNFs) consolidated on a few shared servers. However, the question of how (and even whether) one can achieve performance related Service Level Objectives (SLOs) with software packet processing in NFV remains open. VNF consolidation causes high variability and unpredictability in throughput and latency of VNFs deployed together. It was shown in our prior work that isolating the processor's Last Level Cache (LLC) and limiting Memory Bandwidth (MB) directly helps in achieving performance isolation among the co-located VNFs. So, in this work, we formulate VNF placement problem with exclusive allocation of LLC and MB resources as a Mixed Integer Linear Program (MILP). Due to its hardness to solve, we also present a heuristic solution named RAVIN that enforces performance SLOs for multi-tenant NFV servers while being as much resource-efficient as possible. We demonstrate RAVIN's effectiveness in improving resource utilization and in reducing the total number of required servers to deploy VNFs compared to state-of-the-art and baseline approaches. © 2023 IEEE.

Proceedings of IEEE/IFIP Network Operations and Management Symposium 2023, NOMS 2023

RAVIN: A Resource-aware VNF Placement Scheme with Performance Guarantees

The analysis of traffic policies, for instance, the duration of green and red phases at intersections, can be quite challenging. While the introduction of communication systems can potentially lead to better solutions, it is important to analyse and formulate policies in the presence of potential communication failures and delays. Given the stochastic nature of traffic, posing the problem as a model checking problem in probabilistic epistemic temporal logic seems promising.In this work, we propose an approach that uses epistemic modalities to model the effect of communication between multiple intersections and temporal modalities to model the progression of traffic volumes over time. We validate our approach in a non-stochastic setting, using the tool Model Checker for Multi-Agent Systems (MCMAS). We develop a Statistical Model Checking module and use it in conjunction with a tool chain that integrates a traffic simulator (SUMO) and a network simulator (OMNeT++/Veins) to study the impact of communications on traffic policies. © 2023 ACM.

Traffic Intersections as Agents: A model checking approach for analysing communicating agents

In PATH SET PACKING, the input is an undirected graph G, a collection of simple paths in G, and a positive integer k. The problem is to decide whether there exist k edge-disjoint paths in. We study the parameterized complexity of PATH SET PACKING with respect to both natural and structural parameters. We show that the problem is W[1]-hard with respect to vertex cover plus the maximum length of a path in, and W[1]-hard with respect to pathwidth plus maximum degree plus solution size. These results answer an open question raised in [17]. On the positive side, we present an FPT algorithm parameterized by feedback vertex set plus maximum degree, and also provide an FPT algorithm parameterized by treewidth plus maximum degree plus maximum length of a path in. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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

Parameterized Complexity of Path Set Packing

Hand gesture recognition has a wide range of applications, including in the automotive and industrial sectors, health assistive systems, authentication, and so on. Thermal images are more resistant to environmental changes than red-green-blue (RGB) images for hand gesture recognition. However, one disadvantage of using thermal images for the aforementioned task is the scarcity of labeled thermal datasets. To tackle this problem, we propose a method that combines unsupervised domain adaptation (UDA) techniques with deep-learning (DL) technology to remove the need for labeled data in the learning process. There are several types and methods for implementing UDA, with adversarial UDA being one of the most common. In this article, the first time in this field, we propose a novel adversarial UDA model that uses channel attention and bottleneck layers to learn domain-invariant features across RGB and thermal domains. Thus, the proposed model leverages the information from the labeled RGB data to solve the hand gesture recognition task using thermal images. We evaluate the proposed model on two hand gesture datasets, namely, Sign Digit Classification and Alphabet Gesture Classification, and compare it to other benchmark models in terms of accuracy, model size, and model parameters. Our model outperforms the other state-of-the-art methods on the Sign Digit Classification and Alphabet Gesture Classification datasets and achieves 91.32% and 80.91% target test accuracy, respectively. © 2001-2012 IEEE.

IEEE Sensors Journal

Adversarial Unsupervised Domain Adaptation for Hand Gesture Recognition Using Thermal Images

In this demo, we present FENCE, a privacy-preserving, scalable solution for enterprise networks that continuously collects Internet access artifacts in real-time to help post-incident Internet forensics. We have built a data lakehouse to store these artifacts in a secure and compact query-friendly format. We implemented FENCE using open-source frameworks, deployed in a large enterprise with thousands of bring-your-own-devices (BYOD), and demonstrated that it works effectively in production environments. © 2023 IEEE.

FENCE: A Real-Time Privacy-Preserving Solution for Enterprise Internet Forensics at Scale

Various real world applications in science and industry are often recorded over time as asynchronous event sequences. These event sequences comprise of the time of occurrence of events. Different applications including such event sequences are crime analysis, earthquake prediction, neural spiking train study, infectious disease prediction etc. A principled framework for modeling asynchronous event sequences is temporal point process. Recent works on neural temporal point process have combined the theoretical foundation of point process with universal approximation ability of neural networks. However, the predictions made by these models are uncertain due to incorrect model inference. Therefore, it is highly desirable to associate uncertainty with the predictions as well. In this paper, we propose a novel model, Ensemble Neural Hawkes Process, which is capable of predicting event occurrence time along with uncertainty, hence improving the generalization capability. We also propose evaluation metric which captures the uncertainty modelling capability for event prediction. The efficacy of proposed model is demonstrated using various simulated and real world datasets. © 2023 ACM.

ACM International Conference Proceeding Series

Event Uncertainty using Ensemble Neural Hawkes Process

Autonomous navigation, while still facing a lot of challenges, has become a reality in the last few years. It has been successfully deployed in limited environments, showing the potential such technologies offer. The availability of increased computational power, coupled with advances in machine learning techniques, including deep learning, have enabled this success. However, there are many challenges that need to be overcome to enable massive adoption of autonomous navigation in all environments. One such challenge is to provide reliable, low-latency, and cost-effective data processing solutions for compute-heavy applications. To address this challenge, data processing near the data source, that is, at an edge cloud has been proposed. In this paper, we share our experience in implementing one such edge cloud, designed to bring compute power close to resource-constrained end devices in an autonomous navigation testbed, named as Technology Innovation Hub on Autonomous Navigation and Data Acquisition Systems (TiHAN). By considering some use cases, we show how deploying this edge cloud at the testbed has greatly benefited the performance of autonomous navigation applications. © 2023 IEEE.