Publications (20)
A Coordinated MDP Approach to Multi-Agent Planning for Resource Allocation, with Applications to Healthcare
Hadi Hosseini, Jesse Hoey, Robin Cohen
This paper considers a novel approach to scalable multiagent resource allocation in dynamic settings. We propose an approximate solution in which each resource consumer is represen…
Investigating the Characteristics of One-Sided Matching Mechanisms Under Various Preferences and Risk Attitudes
Hadi Hosseini, Kate Larson, Robin Cohen
One-sided matching mechanisms are fundamental for assigning a set of indivisible objects to a set of self-interested agents when monetary transfers are not allowed. Two widely-stud…
Machine Learning Innovations in CPR: A Comprehensive Survey on Enhanced Resuscitation Techniques
Saidul Islam, Gaith Rjoub, Hanae Elmekki +3
This survey paper explores the transformative role of Machine Learning (ML) and Artificial Intelligence (AI) in Cardiopulmonary Resuscitation (CPR). It examines the evolution from…
Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content
Liam Hebert, Hong Yi Chen, Robin Cohen +1
Our work advances an approach for predicting hate speech in social media, drawing out the critical need to consider the discussions that follow a post to successfully detect when h…
Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations
Henry Chen, Robin Cohen, Kerstin Dautenhahn +2
Autonomous Vehicles (AV) will transform transportation, but also the interaction between vehicles and pedestrians. In the absence of a driver, it is not clear how an AV can communi…
Random Serial Dictatorship versus Probabilistic Serial Rule: A Tale of Two Random Mechanisms
Hadi Hosseini, Kate Larson, Robin Cohen
For assignment problems where agents, specifying ordinal preferences, are allocated indivisible objects, two widely studied randomized mechanisms are the Random Serial Dictatorship…
Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
Liam Hebert, Lukasz Golab, Robin Cohen
We propose a system to predict harmful discussions on social media platforms. Our solution uses contextual deep language models and proposes the novel idea of integrating state-of-…
FedFormer: Contextual Federation with Attention in Reinforcement Learning
Liam Hebert, Lukasz Golab, Pascal Poupart +1
A core issue in multi-agent federated reinforcement learning is defining how to aggregate insights from multiple agents. This is commonly done by taking the average of each partici…
GASTON: Graph-Aware Social Transformer for Online Networks
Olha Wloch, Liam Hebert, Robin Cohen +1
Online communities have become essential places for socialization and support, yet they also possess toxicity, echo chambers, and misinformation. Detecting this harmful content is…
Towards A Multi-agent System for Online Hate Speech Detection
Gaurav Sahu, Robin Cohen, Olga Vechtomova
This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook. We introduce a novel framework em…
Community Norms in the Spotlight: Enabling Task-Agnostic Unsupervised Pre-Training to Benefit Online Social Media
Liam Hebert, Lucas Kopp, Robin Cohen
Modelling the complex dynamics of online social platforms is critical for addressing challenges such as hate speech and misinformation. While Discussion Transformers, which model c…
A Multi-Modal Unsupervised Machine Learning Approach for Biomedical Signal Processing in CPR
Saidul Islam, Jamal Bentahar, Robin Cohen +1
Cardiopulmonary resuscitation (CPR) is a critical, life-saving intervention aimed at restoring blood circulation and breathing in individuals experiencing cardiac arrest or respira…
Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management
Alexandre Parmentier, Robin Cohen, Xueguang Ma +2
In this paper, we present an approach for predicting trust links between peers in social media, one that is grounded in the artificial intelligence area of multiagent trust modelin…
Improving LLM Performance Through Black-Box Online Tuning: A Case for Adding System Specs to Factsheets for Trusted AI
Yonas Atinafu, Henry Lin, Robin Cohen
In this paper, we present a novel black-box online controller that uses only end-to-end measurements over short segments, without internal instrumentation, and hill climbing to max…
A Survey on Explainable Artificial Intelligence for Cybersecurity
Gaith Rjoub, Jamal Bentahar, Omar Abdel Wahab +5
The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) i…
RewardHackingAgents: Benchmarking Evaluation Integrity for LLM ML-Engineering Agents
Yonas Atinafu, Robin Cohen
LLM agents increasingly perform end-to-end ML engineering tasks where success is judged by a single scalar test metric. This creates a structural vulnerability: an agent can increa…
Rumour Evaluation with Very Large Language Models
Dahlia Shehata, Robin Cohen, Charles Clarke
Conversational prompt-engineering-based large language models (LLMs) have enabled targeted control over the output creation, enhancing versatility, adaptability and adhoc retrieval…
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
Braden Hurl, Robin Cohen, Krzysztof Czarnecki +1
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness. We propose a novel approach for AVs to communica…
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
Omar Abdel Wahab, Jamal Bentahar, Robin Cohen +2
In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels. We consider a scenario in…
Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media
Liam Hebert, Gaurav Sahu, Yuxuan Guo +3
We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions. In contrast to traditional co…