Co-located with the ACM India Annual Event, ARCS (formerly IRISS) is a premier ACM India conference spanning two days. The program features:
The audience comprises students, faculty and industry leaders from across India coming together in an interactive environment, such as panel discussions, informal "Ask me Anything" sessions with the ARCS speakers as well as Annual Event invitees like Turing Award laureates.
|2021:||PSG College of Technology|
|2020:||IIT Gandhi Nagar|
|2019:||Rajagiri School of Engineering and Technology - Kochi|
|2017:||Calcutta University/Amity University - Kolkata|
|2016:||Techno Park - Trivandrum|
|2015:||BITS Pilani - Goa|
Prof. Amit Kumar
Department of Computer Science and Engineering
Indian Institute of Technology
New Delhi - 110016
ACM India announces the 16th Academic Research and Careers for Students
(ARCS) Symposium, Coimbatore, India. Earlier, the event was known as
IRISS. ARCS invites research scholars of computer science and allied areas
in India to showcase their recent work (either published in 2021 or accepted
in 2021 for publication) to a conclave of researchers and potential employers.
Apart from the contributed talks and poster presentations, ARCS 2022 will comprise talks by the ACM-India Doctoral Dissertation Award recipient (DDA), Early Career Research (ECR) Awardee and an invited Keynote Speaker. Further to this there will be four talks by early career researchers discussing the speaker’s transition from PhD to their career and job, their expectations, disappointments, why they chose what they did etc. Finally, we will also have several panel discussions.
Submissions will be in electronic form via EasyChair. Submissions must not exceed 2 pages (including the title page, but excluding bibliography). Submissions should begin with a title followed by the names and affiliations of all co-authors. This should be followed by a detailed abstract and the details of the venue where the work has either appeared or been accepted for publication. Submissions should be no more than two pages long. The usage of pdflatex and the ARCS style file are mandatory; no changes to font size, page geometry, etc. are permitted. Because of ongoing pandemic we will accommodate virtual presentations, if needed. Click << here >> to download the ARCS style file.
Abstract Submission deadline extended: 22nd October, 2021 30th October, 2021
Notification to Authors: 1st December, 2021 11th December, 2021
Paper submission and notification process is complete for ARCS 2022.
Download Call for Papers
Fairness, Accountability and Transparency
Relevance of Computer Science Theory in Industry
|Sr. No.||Authors||Paper title||Publication Venue|
|1||Debanjan Konar, Siddhartha Bhattacharyya, Bijaya K. Panigrahi and Elizabeth C Behrman||Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Image Segmentation||IEEE Transaction on Neural Networks and Learning Systems|
|2||Siddharth Barman, K Ramakrishnan and Saladi Rahul||Optimal Algorithms for Range Searching over Multi-Armed Bandits||IJCAI 2021|
|3||Nishat Koti, Mahak Pancholi, Arpita Patra and Ajith Suresh||SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning||Usenix Security Symposium|
|4||Jayant Jain, Vrittika Bagadia, Sahil Manchanda and Sayan Ranu||NeuroMLR: Robust & Reliable Route Recommendation on Road Networks||NeurIPS 21|
|5||Vinayak Gupta, Srikanta Bedathur, Sourangshu Bhattacharya and Abir De||A Neural Approach for Modeling Continuous Time Sequences with Intermittent Observations||AISTATS 21|
|6||Sruthi Gorantla, Amit Deshpande and Anand Louis||On the Problem of Underranking in Group-Fair Ranking||ICML 21|
|7||Yash Khanna, Anand Louis and Rameesh Paul||Independent Sets in Semi-random Hypergraphs||17th Algorithms and Data Structures Symposium|
|8||Arindam Bhattacharya, Sumanth Varambally, Amitabha Bagchi and Srikanta Bedathur||Fast One-class Classification using Class Boundary-preserving Random Projections||KDD 21|
|9||Nilesh Gupta, Sakina Bohra, Yashoteja Prabhu, Saurabh Purohit and Manik Varma||Generalized Zero-Shot Extreme Classification||KDD 21|
|10||Yatin Nandwani, Deepanshu Jindal, Mausam Mausam and Parag Singla||Neural Learning of One-of-Many Solutions for Combinatorial Problems in Structured Output Spaces||ICLR 21|
|11||Soumi Das, Harikrishna Patibandla, Suparna Bhattacharya, Kshounis Bera, Niloy Ganguly and Sourangshu Bhattacharya||TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving||ICCV 21|
|12||Punyajoy Saha, Binny Mathew, Kiran Garimella and Animesh Mukherjee||"Short is the Road that Leads from Fear to Hate": Fear Speech in Indian WhatsApp Groups||WWW 21|
|13||Sunil Nishad, Shubhangi Agarwal, Arnab Bhattacharya and Sayan Ranu||GraphReach: Position-Aware Graph Neural Network using Reachability Estimations||IJCAI 21|
|14||Anshul Mittal, Noveen Sachdeva, Sheshansh Agrawal, Sumeet Agarwal, Purushottam Kar and Manik Varma||ECLARE: Extreme Classification with Label Graph Correlations||WWW 21|
|15||Ismi Abidi, Ishan Nangia, Paarijaat Aditya and Rijurekha Sen||Privacy in Urban Sensing with Instrumented Fleets, Using Air Pollution Monitoring As A Usecase||NDSS 22|
|16||Kunal Dahiya, Ananye Agarwal, Deepak Saini, Gururaj K, Jian Jiao, Amit Singh, Sumeet Agarwal, Purushottam Kar and Manik Varma||SiameseXML: Siamese Networks meet Extreme Classifiers with 100M Labels||ICML 21|
|17||Bhaskar Mukhoty, Subhajit Dutta and Purushottam Kar||Robust non-Parametric Regression via Incoherent Subspace Projections||ECML 2021|
|18||Rajdeep Mukherjee, Uppada Vishnu, Chandana Peruri, Sourangshu Bhattacharya, Niloy Ganguly, Pawan Goyal and Koustav Rudra||MTLVS: A Multi-Task Framework to Verify and Summarize Crisis-Related Microblogs||WSDM 2022|
|19||Rajdeep Mukherjee, Tapas Nayak, Yash Butala, Sourangshu Bhattacharya and Pawan Goyal||PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction||EMNLP 2021|
|Sr. No.||Authors||Paper title|
|1||Pranjal Dutta||tau-Conjecture for sum-of-squares: A unified approach to lower bound and derandomization|
|2||Deepankar Nankani||Cardiac Abnormality Detection from Multichannel ECG using Feature Fused Parallel CNN-GAP|
|3||Prachi Kashikar and Sharad Sinha||Model Compression for Edge AI|
|4||Jayant Vyas, Debasis Das and Santanu Chaudhury||SAFER: Safe and Fuel Efficient Driving Recommendation|
|5||Pavitra Prakash Bhade and Dr. Sharad Sinha||Detection of Microarchitectural Side-Channel Attacks using Thread Level Performance Counters|
|6||Meghana Nasre, Prajakta Nimbhorkar, Keshav Ranjan and Ankita Sarkar||Popular Matchings in the Hospital-Residents Problem with Two-sided Lower Quotas|
|7||Debajyoti Bera and Tharrmashastha Sapv||Quantum and Randomized Algorithms for Non-linearity Estimation|
|8||Ashwin Jacob, Diptapriyo Majumdar and Venkatesh Raman||Faster FPT Algorithms for Deletion to Pairs of Graph Classes|
|9||Mohit Chandra, Manvith Reddy, Shradha Sehgal, Saurabh Gupta, Arun Balaji Buduru and Ponnurangam Kumaraguru||“A Virus Has No Religion”: Analyzing Islamophobia on Twitter During the COVID-19 Outbreak|
|10||Asmit Kumar Singh, Chirag Jain, Jivitesh Jain, Rishi Raj Jain, Shradha Sehgal, Tanisha Pandey and Ponnurangam Kumaraguru||What's Kooking? Characterizing India's Emerging Social Network, Koo|
|11||Raghavendra Sridharamurthy||Comparative Analysis of Merge Trees using Local Tree Edit Distance|
|12||Sriram Bhyravarapu, Tim A. Hartmann, Subrahmanyam Kalyanasundaram and Vinod Reddy||Conflict-Free Coloring: Graphs of Bounded Clique Width and Intersection Graphs|
|13||Shivangi Singhal, Mudit Dhawan, Rajiv Ratn Shah and Ponnurangam Kumaraguru||Inter-modality Discordance for Multimodal Fake News Detection|
|14||Indu Joshi, Ayush Utkarsh, Riya Kothari, Vinod Kurmi, Antitza Dantcheva, Sumantra Dutta Roy and Prem Kalra||Data Uncertainty Guided Noise-Aware Fingerprint Preprocessing|
|15||Debojyoti Dey, Bhaskar Mukhoty and Purushottam Kar||AGGLIO: Global Optimization for Locally Convex Functions|
The Symposium is open to all research scholars, researchers and academicians pursuing research in Computer Science and related areas. Registration is free and the page will open in the month of January 2022. Registration is mandatory to attend the event.
Sayan Ranu (IIT Delhi), Chair
Ankit Anand (DeepMind)
Amit Awekar (IIT Guwahati)
Nipun Batra (IIT Gandhinagar)
Abhijnan Chakraborty (IIT Delhi)
Ayon Chakraborty (IIT Madras)
Syamantak Das (IIIT Delhi)
Manoj Gupta (IIT Gandhinagar)
Neelima Gupta (University of Delhi)
Aritra Hazra (IIT Kharagpur)
Neeldhara Misra (IIT Gandhinagar)
Adway Mitra (IIT Kharagpur)
Meghna Nasre (IIT Madras)
Swaprava Nath (IIT Kanpur)
Biswabandan Panda (IIT Bombay)
Rohan Paul (IIT Delhi)
Krithika Ramaswamy (IIT Palakkad)
Aishwarya Thiruvengadam (IIT Madras)
Rohit Vaish (TIFR)
Hamim Zafar (IIT Kanpur)
(Chair, IIIT, Bangalore)
Jayant R. Haritsa
(Executive Director, ACM India)
(COO, ACM India)
(PSG TECH, CBE)
(Director & CEO, ACENET Technologies, CBE)
(PSG TECH, CBE)
Dean (Research & Academics), Chitkara University
A Kaja Mohideen
PSG College of Technology
The Inter-Research-Institute Student Seminar in Computer Science (IRISS) is an annual student seminar conducted by ACM India, and is also co-located with the ACM India Annual Event. The IRISS 2020, organized at IIT Gandhinagar, attracted many eminent researchers from both academia and industry along with many fellow research students. What sets apart IRISS for me from most of the other conferences is the diversity in the backgrounds of the attendees, ranging from core CS theory to various disciplines of computer systems and artificial intelligence, and also the variety of carefully designed sessions that I will allude to next.
IRISS 2020 had several sessions covering the various areas of computer science. The two keynotes included one in the area of Privacy Amplification and the other in the area of Testing SQL queries. A special attraction was the talks given by the recipients of the ACM India Doctoral Dissertation Award and the Honorable Mention. These talks were exceptionally well structured to cover the overview of the PhD thesis. Another particularly refreshing session included the talks by Early Career Researchers. These included young leaders in diverse fields of industry, academics and start-up. They discussed their experiences as a graduate student, what drove their career choices and their life after graduation. For the crowd that included many graduate students like me, getting to understand the flavour and challenges of research that usually come on our way as we graduate and join the research community was very enlightening. The panel discussions were also on some extremely relevant topics including pedagogy in computer science, and industry awareness in terms of the opportunities and challenges. What appealed a lot to me was an “ask me anything” session where the prominent researchers who were part of the IRISS event and the speakers from the ACM India Annual day that followed the next day were present. This informal session was extremely inspiring and informative.
The various contributed talks, lightning talks and poster presentations provide students with a platform for showcasing recent research work. These include many presentations on papers accepted in top-tier international conferences. I also got an opportunity to present my work on DBMS testing as part of the contributed talks at IRISS 2019, which was held in Kochi. The feedback from the peers and experienced researchers was extremely useful.
The event included lunches, dinner and cultural show. These also provided a great networking opportunity with researchers from various universities in the country. Being co-located with ACM Annual Event, I also got a chance to listen to some eminent researchers, including Yann Lecun (2018 ACM Turing Award Winner), Shwetak Patel (Winner of 2018 ACM Prize in Computing), Susanne Albers (Gottfried Wilhelm Leibniz Prize) and M. Balakrishnan (ACM Eugene L. Lawler Award) deliver really illuminating talks. I found the talk by M. Balakrishnan on "Assistive Technology Solutions for Mobility & Education of Visually Impaired" particularly extremely motivating and informative.
Lastly, the event venue being IIT Gandhinagar, situated on the banks of Sabarmati River, provided aesthetic surroundings. The facilities and hospitality at IIT Gandhinagar were amazing and I had a wonderful time during my stay there.
IRISS 2020 indeed was a good experience for me. Various informative panel discussions like Pedagogy in Computer Science are useful for graduating students. It also provides a platform to interact with students of other premier institutes as well. It gave us opportunity to get to know about various career options.
Last but not the least the venue IIT Gandinagar was great. The accommodation was awesome. The organisation by Dr.Neeldhara Mishra was great.
Peelamedu, Avinashi Road
Coimbatore, Tamilnadu, India
Name: Program Committee
Name: Prof. R. Nadarajan
Abstract: In this talk we will give a brief introduction to the area of Multivariate algorithms. The talk will consist of challenges, excitement, thrills and road ahead of the area, from the perspective of speakers' work.
Abstract: Modern machine learning is characterized by two trends: First, the training data is huge, and often a mixture of several distributions, and model training incurs tremendous energy costs. Second, a model once trained is required to serve diverse real-world settings where the training distribution may not match the test distribution. In this talk, I will discuss current research on handling this distribution mismatch. The talk will span over topics like domain adaptation, domain generalization, out of distribution detection, and robustness.
Abstract: Graphs are all around us, ranging from citation and social networks to Knowledge Graphs (KGs). They are one of the most expressive data structures which have been used to model a variety of problems. Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them. Recent research has resulted in the development of several large KGs; examples include DBpedia, YAGO, NELL, and Freebase. However, all of them tend to be sparse with very few facts per entity. For instance, NELL KG consists of only 1.34 facts per entity. In the first part of the thesis, we propose three solutions to alleviate this problem: (1) KG Canonicalization, i.e., identifying and merging duplicate entities in a KG, (2) Relation Extraction which involves automating the process of extracting semantic relationships between entities from unstructured text, and (3) Link prediction which includes inferring missing facts based on the known facts in a KG. For KG Canonicalization, we propose CESI (Canonicalization using Embeddings and Side Information), a novel approach that performs canonicalization over learned embeddings of Open KGs. The method extends recent advances in KG embedding by incorporating relevant NP and relation phrase side information in a principled manner. For relation extraction, we propose RESIDE, a distantly-supervised neural relation extraction method which utilizes additional side information from KGs for improved relation extraction. Finally, for link prediction, we propose InteractE which extends ConvE, a convolutional neural network-based link prediction method, by increasing the number of feature interactions through three key ideas -- feature permutation, a novel feature reshaping, and circular convolution. Through extensive experiments on multiple datasets, we demonstrate the effectiveness of our proposed methods.
Abstract: In this thesis we consider several (di)graph cut problems and study them from the perspective of parameterized complexity and kernelization. The goal of the study is three-fold: first to extend the otherwise limited understanding of parameterized cut problems on directed graphs; second to extend, and present novel applications of, the existing rich toolkit for undirected cut problems and; third to develop tools that allow the reuse of algorithms to solve the respective problems in the presence of an additional constraint. The concrete questions addressed in the thesis are inspired from some major open problems and concerns in the area. Some of these being the famously active open problem of the existence of a polynomial kernel for Directed Feedback Vertex/Arc Set, sub-exponentiality in FPT beyond tournaments, parameterized algorithms for partitioning problems beyond the classical partitioning problems, the existence of single exponential FPT algorithms for stable versions of classical cut problems and the parameterized complexity of Stable Multicut. We address the above questions either in full, or extend (possibly all) the results known in literature that take steps towards resolving the respective question.
Abstract: This talk is on building compiler infrastructure for emerging programming models for the multicore and accelerator era. The advent of high-productivity programming models and high-performance accelerator chips (especially for machine learning and AI) brings in new challenges on how the next generation of compilers should be built for modularity and reusability. This talk will cover the role of the polyhedral compiler abstraction and techniques towards achieving these goals.