66 citations · 116 across the 10 of their papers we have counts for
12 papers
MarcoPolo: A Zero-Permission Attack for Location Type Inference from the Magnetic Field using Mobile Devices
Beatrice Perez, Abhinav Mehrotra, Mirco Musolesi
Location information extracted from mobile devices has been largely exploited to reveal our routines, significant places, and interests just to name a few. Given the sensitivity of…
Federated Self-supervised Speech Representations: Are We There Yet?
Yan Gao, Javier Fernandez-Marques, Titouan Parcollet +2
The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio data being produced at the edge. The integration of self-supervised learning (SSL) and fede…
Inferring Facing Direction from Voice Signals
Yu-Lin Wei, Rui Li, Abhinav Mehrotra +2
Consider a home or office where multiple devices are running voice assistants (e.g., TVs, lights, ovens, refrigerators, etc.). A human user turns to a particular device and gives a…
Smart at what cost? Characterising Mobile Deep Neural Networks in the wild
Mario Almeida, Stefanos Laskaridis, Abhinav Mehrotra +3
With smartphones' omnipresence in people's pockets, Machine Learning (ML) on mobile is gaining traction as devices become more powerful. With applications ranging from visual filte…
Zero-Cost Proxies for Lightweight NAS
Mohamed S. Abdelfattah, Abhinav Mehrotra, Łukasz Dudziak +1
Neural Architecture Search (NAS) is quickly becoming the standard methodology to design neural network models. However, NAS is typically compute-intensive because multiple models n…
Bunched LPCNet : Vocoder for Low-cost Neural Text-To-Speech Systems
Ravichander Vipperla, Sangjun Park, Kihyun Choo +6
LPCNet is an efficient vocoder that combines linear prediction and deep neural network modules to keep the computational complexity low. In this work, we present two techniques to…
Iterative Compression of End-to-End ASR Model using AutoML
Abhinav Mehrotra, Łukasz Dudziak, Jinsu Yeo +9
Increasing demand for on-device Automatic Speech Recognition (ASR) systems has resulted in renewed interests in developing automatic model compression techniques. Past research hav…
Graph Input Representations for Machine Learning Applications in Urban Network Analysis
Alessio Pagani, Abhinav Mehrotra, Mirco Musolesi
Understanding and learning the characteristics of network paths has been of particular interest for decades and has led to several successful applications. Such analysis becomes ch…
Intelligent Notification Systems: A Survey of the State of the Art and Research Challenges
Abhinav Mehrotra, Mirco Musolesi
Notifications provide a unique mechanism for increasing the effectiveness of real-time information delivery systems. However, notifications that demand users' attention at inopport…
Towards Deep Learning Models for Psychological State Prediction using Smartphone Data: Challenges and Opportunities
Gatis Mikelsons, Matthew Smith, Abhinav Mehrotra +1
There is an increasing interest in exploiting mobile sensing technologies and machine learning techniques for mental health monitoring and intervention. Researchers have effectivel…
Sensing and Modeling Human Behavior Using Social Media and Mobile Data
Abhinav Mehrotra, Mirco Musolesi
In the past years we have witnessed the emergence of the new discipline of computational social science, which promotes a new data-driven and computation-based approach to social s…
Anticipatory Mobile Digital Health: Towards Personalised Proactive Therapies and Prevention Strategies
Veljko Pejovic, Abhinav Mehrotra, Mirco Musolesi
The last two centuries saw groundbreaking advances in the field of healthcare: from the invention of the vaccine to organ transplant, and eradication of numerous deadly diseases. Y…