Publications

Can ChatGPT Pass An Introductory Level Functional Language Programming Course?

Chuqin Geng

Yihan Zhang

Brigitte Pientka

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

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

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