Publications

Social Media as a Vector for Escort Ads:A Study on OnlyFans advertisements on Twitter
Maricarmen Arenas
Pratheeksha Nair
Reihaneh Rabbany
Golnoosh Farnadi
Online sex trafficking is on the rise and a majority of trafficking victims report being advertised online. The use of OnlyFans as a platfor… (voir plus)m for adult content is also increasing, with Twitter as its main advertising tool. Furthermore, we know that traffickers usually work within a network and control multiple victims. Consequently, we suspect that there may be networks of traffickers promoting multiple OnlyFans accounts belonging to their victims. To this end, we present the first study of OnlyFans advertisements on Twitter in the context of finding organized activities. Preliminary analysis of this space shows that most tweets related to OnlyFans contain generic text, making text-based methods less reliable. Instead, focusing on what ties the authors of these tweets together, we propose a novel method for uncovering coordinated networks of users based on their behaviour. Our method, called Multi-Level Clustering (MLC), combines two levels of clustering that considers both the network structure as well as embedded node attribute information. It focuses jointly on user connections (through mentions) and content (through shared URLs). We apply MLC to real-world data of 2 million tweets pertaining to OnlyFans and analyse the detected groups. We also evaluate our method on synthetically generated data (with injected ground truth) and show its superior performance compared to competitive baselines. Finally, we discuss examples of organized clusters as case studies and provide interesting conclusions to our study.
2023-04-30
Proceedings of the 15th ACM Web Science Conference 2023 (publié)
doi.org
Can ChatGPT Pass An Introductory Level Functional Language Programming Course?
Chuqin Geng
Yihan Zhang
Brigitte Pientka
Xujie Si
The recent introduction of ChatGPT has drawn significant attention from both industry and academia due to its impressive capabilities in sol… (voir plus)ving a diverse range of tasks, including language translation, text summarization, and computer programming. Its capability for writing, modifying, and even correcting code together with its ease of use and access is already dramatically impacting computer science education. This paper aims to explore how well ChatGPT can perform in an introductory-level functional language programming course. In our systematic evaluation, we treated ChatGPT as one of our students and demonstrated that it can achieve a grade B- and its rank in the class is 155 out of 314 students overall. Our comprehensive evaluation provides valuable insights into ChatGPT's impact from both student and instructor perspectives. Additionally, we identify several potential benefits that ChatGPT can offer to both groups. Overall, we believe that this study significantly clarifies and advances our understanding of ChatGPT's capabilities and potential impact on computer science education.
2023-04-29
ArXiv (prépublication)
doi.org
arxiv.org
Leveraging Data Mining Algorithms to Recommend Source Code Changes
AmirHossein Naghshzan
Saeed Khalilazar
Pierre Poilane
Olga Baysal
Latifa Guerrouj
Foutse Khomh
2023-04-29
ArXiv (prépublication)
doi.org
arxiv.org
Group Fairness in Reinforcement Learning
Harsh Satija
Alessandro Lazaric
Matteo Pirotta
Joelle Pineau
We pose and study the problem of satisfying fairness in the online Reinforcement Learning (RL) setting. We focus on the group notions of fai… (voir plus)rness, according to which agents belonging to different groups should have similar performance based on some given measure. We consider the setting of maximizing return in an unknown environment (unknown transition and reward function) and show that it is possible to have RL algorithms that learn the best fair policies without violating the fairness requirements at any point in time during the learning process. In the tabular finite-horizon episodic setting, we provide an algorithm that combines the principle of optimism and pessimism under uncertainty to achieve zero fairness violation with arbitrarily high probability while also maintaining sub-linear regret guarantees. For the high-dimensional Deep-RL setting, we present algorithms based on the performance-difference style approximate policy improvement update step and we report encouraging empirical results on various traditional RL-inspired benchmarks showing that our algorithms display the desired behavior of learning the optimal policy while performing a fair learning process.
2023-04-28
TMLR (accepté)
openreview.net
openreview.net
Overcoming Interpretability and Accuracy Trade-off in Medical Imaging
Ivaxi Sheth
Samira Ebrahimi Kahou
2023-04-28
MIDL.io/2023/Short_Paper_Track (poster)
openreview.net
openreview.net
The Influence of Age, Sex, and Socioeconomic Status on Glycemic Control Among People With Type 1 and Type 2 Diabetes in Canada: Patient-Led Longitudinal Retrospective Cross-sectional Study With Multiple Time Points of Measurement
Seyedmostafa Mousavi
Dana Tannenbaum Greenberg
Ruth Ndjaboué
Michelle Greiver
Olivia Drescher
Selma Chipenda Dansokho
Denis Boutin
Jean-Marc Chouinard
Sylvie Dostie
Robert Fenton
Marley Greenberg
Jonathan McGavock
Adhiyat Najam
Monia Rekik
Tom Weisz
Donald J Willison
Audrey Durand
Holly O Witteman
2023-04-27
JMIR Diabetes (publié)
doi.org
Controllable Image Generation via Collage Representations
Arantxa Casanova
Marlene Careil
Adriana Romero Soriano
Chris Pal
Jakob Verbeek
Michal Drozdzal
2023-04-26
ArXiv (prépublication)
doi.org
openreview.net
Towards ethical multimodal systems
Alexis Roger
Esma Aimeur
Irina Rish
2023-04-26
ArXiv (prépublication)
doi.org
arxiv.org
Tri-process model of interpersonal mindfulness: theoretical framework and study protocol
Bassam Khoury
Viktoriya Manova
Lena Adel
Guillaume Dumas
Michael Lifshitz
Rodrigo C. Vergara
Harmehr Sekhon
Soham Rej
According to the Center for Disease Control and Prevention, over 14% of the US population practice mindfulness meditation. The effects of mi… (voir plus)ndfulness training on physical and mental health have been consistently documented, but its effects on interpersonal relationships are not yet fully understood or investigated. Interpersonal relationships play a crucial role in the wellbeing of individuals and society, and therefore, warrants further study. The aim of this paper is to present a tri-process theoretical model of interpersonal mindfulness and a study protocol to validate the proposed model. Specifically, according to the proposed model, mindfulness meditation training increases the self-awareness, self-regulation, and prosociality of those receiving the training, which ameliorates the quality of interpersonal interactions and the socioemotional support provided to other individuals. Finally, better socioemotional support increases the support receiver’s ability to regulate their emotions. Using a multiphasic longitudinal design involving 640 participants randomized into 480 dyads, the proposed protocol aims to validate the tri-process model and to investigate its mechanisms of actions. The proposed study has important theoretical and social implications and will allow devising new and more effective interpersonal mindfulness programs with applications in multiple fields.
2023-04-26
Frontiers in Psychology (publié)
doi.org
Understanding the normative leadership of the world health organization (who): a mixed-method approach
Catherine Régis
Miriam Cohen
Jean-Louis Denis
Pierre Larouche
Gaelle Foucault
Marie-Andree Girard
2023-04-26
Population Medicine (publié)
doi.org
Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation
Junde Wu
Rao Fu
Huihui Fang
Yuanpei Liu
Zhao-Yang Wang
Yanwu Xu
Yueming Jin
Tal Arbel
The Segment Anything Model (SAM) has recently gained popularity in the field of image segmentation due to its impressive capabilities in var… (voir plus)ious segmentation tasks and its prompt-based interface. However, recent studies and individual experiments have shown that SAM underperforms in medical image segmentation, since the lack of the medical specific knowledge. This raises the question of how to enhance SAM's segmentation capability for medical images. In this paper, instead of fine-tuning the SAM model, we propose the Medical SAM Adapter (Med-SA), which incorporates domain-specific medical knowledge into the segmentation model using a light yet effective adaptation technique. In Med-SA, we propose Space-Depth Transpose (SD-Trans) to adapt 2D SAM to 3D medical images and Hyper-Prompting Adapter (HyP-Adpt) to achieve prompt-conditioned adaptation. We conduct comprehensive evaluation experiments on 17 medical image segmentation tasks across various image modalities. Med-SA outperforms several state-of-the-art (SOTA) medical image segmentation methods, while updating only 2\% of the parameters. Our code is released at https://github.com/KidsWithTokens/Medical-SAM-Adapter.
2023-04-25
ArXiv (prépublication)
doi.org
arxiv.org
Ranking code clones to support maintenance activities
Osama Ehsan
Foutse Khomh
Ying Zou
Dong Qiu
2023-04-25
Empirical Software Engineering (publié)
doi.org