Ashoka University is a private university located in Sonipat, Haryana, India, focusing on liberal arts and sciences. It was established in 2014.&nbsp;It is guided by an academic council of academics and scholars from around the world. The council sets the university&#39;s academic standard, advising on matters of curriculum, faculty hiring and research.&nbsp;

Ashoka University offers a full-time 3 (+ 1) year undergraduate programme. The University comprises of&nbsp;2500 plus students, with a mix of both national and international students.&nbsp; &nbsp;

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Ashoka University

CLIPStyler demonstrated image style transfer with realistic textures using only the style text description (instead of requiring a reference style image). However, the ground semantics of objects in style transfer output is lost due to style spillover on salient and background objects (content mismatch) or over-stylization. To solve this, we propose Semantic CLIPStyler (Sem-CS) that performs semantic style transfer. Sem-CS first segments the content image into salient and non-salient objects and then transfers artistic style based on a given style text description. The semantic style transfer is achieved using global foreground loss (for salient objects) and global background loss (for non-salient objects). Our empirical results, including DISTS, NIMA and user study scores, show that our proposed framework yields superior qualitative and quantitative performance.

Department of Computer Science

<a href="https://arxiv.org/abs/2303.06334">SEM-CS: Semantic CLIPStyler for Text-Based Image Style Transfer</a>

World University root node

Proceedings of the 36th AAAI Conference on Artificial Intelligence, AAAI 2022

NEUROCRYPT: Coercion-Resistant Implicit Memory Authentication (Student Abstract)

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

India’s “Aadhaar” Biometric ID: Structure, Security, and Vulnerabilities

Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter exchange may still leak information. Several approaches have been proposed to overcome this, based on multi-party computation, fully homomorphic encryption, etc.; many of these protocols are slow and impractical for real-world use as they involve a large number of cryptographic operations. In this paper, we propose the use of Trusted Execution Environments (TEE), which provide a platform for isolated execution of code and handling of data, for this purpose. We describe Flatee, an efficient privacy-preserving federated learning framework across TEEs, which considerably reduces training and communication time. Our framework can handle malicious parties (we do not natively solve adversarial data poisoning, though we describe a preliminary approach to handle this). © 2021 IEEE.

Proceedings - 2021 IEEE European Symposium on Security and Privacy, Euro S and P 2021

Poster: FLATEE: Federated learning across trusted execution environments

We present a novel radar-based system for real-time indoor positioning and detection of objects and human-bodies with low-quality, inexpensive sensors. Using modern deep learning methods, we avoid the use of expensive hardware and computationally-expensive signal processing methods for object detection.We train our model on mini-Doppler maps, collected via software defined radios. Crucially, our system is different from existing RF-based detection systems as it operates in a less crowded frequency range of 433 MHz, allowing us to use inexpensive off-the shelf hardware. Our system, based on the VGG-16 model, reports high-accuracy results on: (1) classification of different objects/materials (plastic, glass, metal); (2) detection and classification of multiple visually and materially similar objects and the human-body; and (3) Simple object detection at different distances between the transmitter Tx and the receiver Rx.WALDO, using low frequency radio waves, is able to handle occlusions and bad lighting environments. Our results demonstrate that Deep Learning methods can be combined with inexpensive, low-frequency radars to achieve high accuracy in real-time on various useful tasks. © 2021 IEEE.

IEEE National Radar Conference - Proceedings

WALDO Finds You using Machine Learning: Wireless Adaptive Location and Detection of Objects

Affirmative action in the form of reservations is a divisive and contentious topic of policy in India. In this paper, we aim to create a principled and data-driven model to design the reservations policy in India. We look at some arguments against current policy and try to resolve them. We use statistical modeling to create our new framework, RAMSES (Rigorous and Adaptive Measurement of Socio-Economic Status). RAMSES measures the multidimensional disadvantage faced by an individual as an "adjusted income", which attempts to calibrate the quantum of compensatory aid in the form of reservations for that individual to have a level playing field. We illustrate our model using a case study. © 2020 Owner/Author.

COMPASS 2020 - Proceedings of the 2020 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

An Alternative to India's Reservation Policy: A Unified Framework for Rigorous and Adaptive Measurement of Socio-Economic Status

The average user has between 90–130 online accounts [17], and around 3 × 1011 passwords are in use this year [10]. Most people are terrible at remembering “random” passwords, so they reuse or create similar passwords using a combination of predictable words, numbers, and symbols [16]. Previous password-generation or management protocols have imposed so large a cognitive load that users have abandoned them in favor of insecure yet simpler methods (e.g., writing them down or reusing minor variants). We describe a range of candidate human-computable “hash” functions suitable for use as password generators - as long as the human (with minimal education assumptions) keeps a single, easily-memorizable ‘master’ secret - and rate them by various metrics, including effective security. These functions hash master-secrets with user accounts to produce sub-secrets that can be used as passwords; FR(s, w) ⟶ y, which takes a website w and produces a password y, parameterized by the master secret s, which may or may not be a string. We exploit the unique configuration R of each user’s associative and implicit memory (detailed in Sect. 2) to ensure that sources of randomness unique to each user are present in each F. An adversary cannot compute or verify FR efficiently since R is unique to each individual; in that sense, our hash function is similar to a physically unclonable function [37]. For the algorithms we propose, the user need only complete primitive operations such as addition, spatial navigation or searching. Critically, most of our methods are also accessible to neurodiverse, or cognitively or physically differently-abled persons. Given the nature of these functions, it is not possible to directly use traditional cryptographic methods for analysis; so, we use an array of approaches, mainly related to entropy, to illustrate and analyze the same. We draw on cognitive, neuroscientific, and cryptographic research to use these functions as improved password management and creation systems, and present results from a survey (n = 134 individuals, with each candidate performing 2 schemes) investigating real-world usage of these methods and how people currently come up with their passwords. We also survey 400 websites to collate current password advice. © Springer Nature Switzerland AG 2020.