Search Results for author:
Found 15 papers, 5 papers with code
FedFormer: Contextual Federation with Attention in Reinforcement Learning
We evaluate our methods on the Meta-World environment and find that our approach yields significant improvements over FedAvg and non-federated Soft Actor-Critic single-agent methods.
Online abuse detection: the value of preprocessing and neural attention models
We propose an attention-based neural network approach to detect abusive speech in online social networks.
TruPercept: Trust Modelling for Autonomous Vehicle Cooperative Perception from Synthetic Data
Inter-vehicle communication for autonomous vehicles (AVs) stands to provide significant benefits in terms of perception robustness.
Multi-Modal Discussion Transformer: Integrating Text, Images and Graph Transformers to Detect Hate Speech on Social Media
We present the Multi-Modal Discussion Transformer (mDT), a novel methodfor detecting hate speech in online social networks such as Reddit discussions.
Investigating the Characteristics of One-Sided Matching Mechanisms Under Various Preferences and Risk Attitudes
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.
Random Serial Dictatorship versus Probabilistic Serial Rule: A Tale of Two Random Mechanisms
For assignment problems where agents, specifying ordinal preferences, are allocated indivisible objects, two widely studied randomized mechanisms are the Random Serial Dictatorship (RSD) and Probabilistic Serial Rule (PS).
A Coordinated MDP Approach to Multi-Agent Planning for Resource Allocation, with Applications to Healthcare
This paper considers a novel approach to scalable multiagent resource allocation in dynamic settings.
Annotation of Rhetorical Moves in Biochemistry Articles
In particular, we conduct a detailed study with human annotators to confirm that our selection of semantic roles is effective in determining the underlying rhetorical structure of existing biomedical articles in an extensive dataset.
A two-level solution to fight against dishonest opinions in recommendation-based trust systems
In this paper, we propose a mechanism to deal with dishonest opinions in recommendation-based trust models, at both the collection and processing levels.
Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations
Based on the results, we distill twelve practical design recommendations for AV visual signals, with focus on signal pattern design and placement.
Towards A Multi-agent System for Online Hate Speech Detection
This paper envisions a multi-agent system for detecting the presence of hate speech in online social media platforms such as Twitter and Facebook.
Personalized multi-faceted trust modeling to determine trust links in social media and its potential for misinformation management
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 modeling.
Predicting Hateful Discussions on Reddit using Graph Transformer Networks and Communal Context
We propose a system to predict harmful discussions on social media platforms.
Qualitative Analysis of a Graph Transformer Approach to Addressing Hate Speech: Adapting to Dynamically Changing Content
Included is an exploration of the kinds of posts that permeate social media today, including the use of hateful images.
A Survey on Explainable Artificial Intelligence for Cybersecurity
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
Explainable Artificial Intelligence (XAI)
