296 citations · 777 across the 23 of their papers we have counts for
27 papers
Evaluating Privacy Leakage in Split Learning
Xinchi Qiu, Ilias Leontiadis, Luca Melis +2
Privacy-Preserving machine learning (PPML) can help us train and deploy models that utilize private information. In particular, on-device machine learning allows us to avoid sharin…
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Meisam Hejazinia, Dzmitry Huba, Ilias Leontiadis +7
Federated learning (FL) has emerged as an effective approach to address consumer privacy needs. FL has been successfully applied to certain machine learning tasks, such as training…
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…
How to Reach Real-Time AI on Consumer Devices? Solutions for Programmable and Custom Architectures
Stylianos I. Venieris, Ioannis Panopoulos, Ilias Leontiadis +1
The unprecedented performance of deep neural networks (DNNs) has led to large strides in various Artificial Intelligence (AI) inference tasks, such as object and speech recognition…
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device
Mario Almeida, Stefanos Laskaridis, Stylianos I. Venieris +2
Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs). To alleviate their excessive computational demands, deve…
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation
Ilias Leontiadis, Stefanos Laskaridis, Stylianos I. Venieris +1
On-device machine learning is becoming a reality thanks to the availability of powerful hardware and model compression techniques. Typically, these models are pretrained on large G…
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud
Stefanos Laskaridis, Stylianos I. Venieris, Mario Almeida +2
Despite the soaring use of convolutional neural networks (CNNs) in mobile applications, uniformly sustaining high-performance inference on mobile has been elusive due to the excess…
DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments
Fan Mo, Ali Shahin Shamsabadi, Kleomenis Katevas +4
We present DarkneTZ, a framework that uses an edge device's Trusted Execution Environment (TEE) in conjunction with model partitioning to limit the attack surface against Deep Neur…
A Retrospective Analysis of User Exposure to (Illicit) Cryptocurrency Mining on the Web
Ralph Holz, Diego Perino, Matteo Varvello +6
In late 2017, a sudden proliferation of malicious JavaScript was reported on the Web: browser-based mining exploited the CPU time of website visitors to mine the cryptocurrency Mon…
Detecting Cyberbullying and Cyberaggression in Social Media
Despoina Chatzakou, Ilias Leontiadis, Jeremy Blackburn +4
Cyberbullying and cyberaggression are increasingly worrisome phenomena affecting people across all demographics. More than half of young social media users worldwide have been expo…
EmBench: Quantifying Performance Variations of Deep Neural Networks across Modern Commodity Devices
Mario Almeida, Stefanos Laskaridis, Ilias Leontiadis +2
In recent years, advances in deep learning have resulted in unprecedented leaps in diverse tasks spanning from speech and object recognition to context awareness and health monitor…
A Self-Attentive Emotion Recognition Network
Harris Partaourides, Kostantinos Papadamou, Nicolas Kourtellis +2
Modern deep learning approaches have achieved groundbreaking performance in modeling and classifying sequential data. Specifically, attention networks constitute the state-of-the-a…
Disturbed YouTube for Kids: Characterizing and Detecting Inappropriate Videos Targeting Young Children
Kostantinos Papadamou, Antonis Papasavva, Savvas Zannettou +5
A large number of the most-subscribed YouTube channels target children of a very young age. Hundreds of toddler-oriented channels on YouTube feature inoffensive, well-produced, and…
LOBO -- Evaluation of Generalization Deficiencies in Twitter Bot Classifiers
Juan Echeverría, Emiliano De Cristofaro, Nicolas Kourtellis +3
Botnets in online social networks are increasingly often affecting the regular flow of discussion, attacking regular users and their posts, spamming them with irrelevant or offensi…
Finding Dory in the Crowd: Detecting Social Interactions using Multi-Modal Mobile Sensing
Kleomenis Katevas, Katrin Hänsel, Richard Clegg +3
Remembering our day-to-day social interactions is challenging even if you aren't a blue memory challenged fish. The ability to automatically detect and remember these types of inte…
There goes Wally: Anonymously sharing your location gives you away
Apostolos Pyrgelis, Nicolas Kourtellis, Ilias Leontiadis +2
With current technology, a number of entities have access to user mobility traces at different levels of spatio-temporal granularity. At the same time, users frequently reveal thei…
"You Know What to Do": Proactive Detection of YouTube Videos Targeted by Coordinated Hate Attacks
Enrico Mariconti, Guillermo Suarez-Tangil, Jeremy Blackburn +5
Video sharing platforms like YouTube are increasingly targeted by aggression and hate attacks. Prior work has shown how these attacks often take place as a result of "raids," i.e.,…
A Unified Deep Learning Architecture for Abuse Detection
Antigoni-Maria Founta, Despoina Chatzakou, Nicolas Kourtellis +3
Hate speech, offensive language, sexism, racism and other types of abusive behavior have become a common phenomenon in many online social media platforms. In recent years, such div…
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior
Antigoni-Maria Founta, Constantinos Djouvas, Despoina Chatzakou +6
In recent years, offensive, abusive and hateful language, sexism, racism and other types of aggressive and cyberbullying behavior have been manifesting with increased frequency, an…
Continual Prediction of Notification Attendance with Classical and Deep Network Approaches
Kleomenis Katevas, Ilias Leontiadis, Martin Pielot +1
We investigate to what extent mobile use patterns can predict -- at the moment it is posted -- whether a notification will be clicked within the next 10 minutes. We use a data set…
Practical Processing of Mobile Sensor Data for Continual Deep Learning Predictions
Kleomenis Katevas, Ilias Leontiadis, Martin Pielot +1
We present a practical approach for processing mobile sensor time series data for continual deep learning predictions. The approach comprises data cleaning, normalization, capping,…
The Web Centipede: Understanding How Web Communities Influence Each Other Through the Lens of Mainstream and Alternative News Sources
Savvas Zannettou, Tristan Caulfield, Emiliano De Cristofaro +5
As the number and the diversity of news outlets on the Web grow, so does the opportunity for "alternative" sources of information to emerge. Using large social networks like Twitte…
Hot or not? Forecasting cellular network hot spots using sector performance indicators
Joan Serrà, Ilias Leontiadis, Alexandros Karatzoglou +1
To manage and maintain large-scale cellular networks, operators need to know which sectors underperform at any given time. For this purpose, they use the so-called hot spot score,…
Kek, Cucks, and God Emperor Trump: A Measurement Study of 4chan's Politically Incorrect Forum and Its Effects on the Web
Gabriel Emile Hine, Jeremiah Onaolapo, Emiliano De Cristofaro +5
The discussion-board site 4chan has been part of the Internet's dark underbelly since its inception, and recent political events have put it increasingly in the spotlight. In parti…