| Vol-3649 urn:nbn:de:0074-3649-5 Copyright © 2024 for the individual papers by the papers' authors. Copyright © 2024 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# ML4CMH 2024
Machine Learning for Cognitive and Mental Health Workshop 2024

### Proceedings of Machine Learning for Cognitive and Mental Health Workshop (ML4CMH 2024)
Co-located with the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI 2024)

### Vancouver, BC, Canada, February 26th, 2024.

Edited by

### Marija Stanojevic *

* Cambridge Cognition, Toronto, ON, Canada

## Table of Contents

- Preface
  Summary: There were 20 papers submitted for peer-review to this workshop. Out of these, 15 papers were accepted for this volume, 9 as regular papers and 6 as short papers.

### Oral Presentations

- Knowledge-enhanced Memory Model for Emotional Support Conversation 1-11
  Mengzhao Jia, Qianglong Chen, Liqiang Jing, Dawei Fu, Renyu Li
- Learning to Generate Context-Sensitive Backchannel Smiles for Embodied AI Agents with Applications in Mental Health Dialogues 12-22
  Maneesh Bilalpur, Mert Inan, Dorsa Zeinali, Jeffrey F. Cohn, Malihe Alikhani
- A Pretrained Language Model for Mental Health Risk Detection (short paper) 23-28
  Diego Maupomé, Fanny Rancourt, Raouf Belbahar, Marie-Jean Meurs
- PMC: Paired Multi-Contrast MRI Dataset at 1.5T and 3T for Supervised Image2Image Translation (short paper) 29-34
  Fatemeh Bagheri, Kamil Uludag
- Dance of the Neurons: Unraveling Sex from Brain Signals (short paper) 35-40
  Mohammad-Javad Darvishi-Bayazi, Mohammad Sajjad Ghaemi, Jocelyn Faubert, Irina Rish
- Mental Health Stigma across Diverse Genders in Generative Large Language Models - Abstract (abstract) 41-41
  Lucille Njoo, Lee Janzen-Morel, Inna Wanyin Lin, Yulia Tsvetkov

### Poster Presentations

- ConversationMoC: Encoding Conversational Dynamics using Multiplex Network for Identifying Moment of Change in Mood and Mental Health Classification 42-56
  Loitongbam Gyanendro Singh, Stuart E. Middleton, Tayyaba Azim, Elena Nichele, Pinyi Lyu, Santiago De Ossorno Garcia
- A Privacy-preserving Unsupervised Speaker Disentanglement Method for Depression Detection from Speech (short paper) 57-63
  Vijay Ravi, Jinhan Wang, Jonathan Flint, Abeer Alwan
- Ordinal Scale Evaluation of Smiling Intensity using Comparison-Based Network 64-73
  Kei Shimonishi, Kazuaki Kondo, Hirotada Ueda, Yuichi Nakamura
- Natural Language Explanations for Suicide Risk Classification Using Large Language Models 74-83
  William Stern, Seng Jhing Goh, Nasheen Nur, Patrick J Aragon, Thomas Mercer, Siddhartha Bhattacharyya, Chiradeep Sen, Van Minh Nguyen
- Deploying AI Methods for Mental Health in Singapore: From Mental Wellness to Serious Mental Health Conditions 84-93
  Creighton Heaukulani, Ye Sheng Phang, Janice Huiqin Weng, Jimmy Lee, Robert J.T. Morris
- Investigating Bias in Affective State Detection Using Eye Biometrics (short paper) 94-100
  Yuxin Zhi, Bilal Taha, Dimitrios Hatzinakos
- Towards remote differential diagnosis of mental and neurological disorders using automatically extracted speech and facial features 101-111
  Vanessa Richter, Michael Neumann, Vikram Ramanarayanan
- Prediction of Relapse in Adolescent Depression using Fusion of Video and Speech Data (short paper) 112-117
  Christopher Lucasius, Mai Ali, Deepa Kundur, Marco Battaglia, Peter Szatmari, John Strauss
- Toward A Reinforcement-Learning-Based System for Adjusting Medication to Minimize Speech Disfluency 118-127
  Pavlos Constas, Vikram Rawal, Matthew Honorio Oliveira, Andreas Constas, Aditya Khan, Kaison Cheung, Najma Sultani, Carrie Chen, Micol Altomare, Michael Akzam, Jiacheng Chen, Vhea He, Lauren Altomare, Heraa Murqi, Asad Khan, Nimit Amikumar Bhanshali, Youssef Rachad, Michael Guerzhoy

2024-02-16: submitted by Marija Stanojevic, metadata incl. bibliographic data published under Creative Commons CC0
2024-03-14: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|