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It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
On-device machine learning is becoming a reality thanks to the availabil...
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SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Despite the soaring use of convolutional neural networks (CNNs) in mobil...
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A Retrospective Analysis of User Exposure to (Illicit) Cryptocurrency Mining on the Web
In late 2017, a sudden proliferation of malicious JavaScript was reporte...
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DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
We present DarkneTZ, a framework that uses an edge device's Trusted Exec...
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Detecting Cyberbullying and Cyberaggression in Social Media
Cyberbullying and cyberaggression are increasingly worrisome phenomena a...
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EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
In recent years, advances in deep learning have resulted in unprecedente...
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A Self-Attentive Emotion Recognition Network
Modern deep learning approaches have achieved groundbreaking performance...
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Disturbed YouTube for Kids: Characterizing and Detecting Disturbing Content on YouTube
A considerable number of the most-subscribed YouTube channels feature co...
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LOBO -- Evaluation of Generalization Deficiencies in Twitter Bot Classifiers
Botnets in online social networks are increasingly often affecting the r...
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Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing
Remembering our day-to-day social interactions is challenging even if yo...
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There goes Wally: Anonymously sharing your location gives you away
With current technology, a number of entities have access to user mobili...
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"You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks
Over the years, the Web has shrunk the world, allowing individuals to sh...
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A Unified Deep Learning Architecture for Abuse Detection
Hate speech, offensive language, sexism, racism and other types of abusi...
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Continual Prediction of Notification Attendance with Classical and Deep Network Approaches
We investigate to what extent mobile use patterns can predict -- at the ...
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