Tootfinder

Trust@Health: A Trust-Based Multilayered Network for Scalable Healthcare Service Management
Avijit Gayen, Somyajit Chakraborty, Joydeep Chakraborty, Angshuman Jana
https://arxiv.org/abs/2508.11942

Trust@Health: A Trust-Based Multilayered Network for Scalable Healthcare Service Management
We study the intricate relationships within healthcare systems, focusing on interactions among doctors, departments, and hospitals. Leveraging an evolutionary graph framework, the proposed model emphasizes both intra-layer and inter-layer trust relationships to better understand and optimize healthcare services. The trust-based network facilitates the identification of key healthcare entities by integrating their social and professional interactions, culminating in a trust-based algorithm that …

FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
Prajit Sengupta, Islem Rekik
https://arxiv.org/abs/2509.10510

FireGNN: Neuro-Symbolic Graph Neural Networks with Trainable Fuzzy Rules for Interpretable Medical Image Classification
Medical image classification requires not only high predictive performance but also interpretability to ensure clinical trust and adoption. Graph Neural Networks (GNNs) offer a powerful framework for modeling relational structures within datasets; however, standard GNNs often operate as black boxes, limiting transparency and usability, particularly in clinical settings. In this work, we present an interpretable graph-based learning framework named FireGNN that integrates trainable fuzzy rules i…

A Curriculum Learning Approach to Reinforcement Learning: Leveraging RAG for Multimodal Question Answering
Chenliang Zhang, Lin Wang, Yuanyuan Lu, Yusheng Qi, Kexin Wang, Peixu Hou, Wenshi Chen
https://arxiv.org/abs/2508.10337

A Curriculum Learning Approach to Reinforcement Learning: Leveraging RAG for Multimodal Question Answering
This paper describes the solutions of the Dianping-Trust-Safety team for the META CRAG-MM challenge. The challenge requires building a comprehensive retrieval-augmented generation system capable for multi-modal multi-turn question answering. The competition consists of three tasks: (1) answering questions using structured data retrieved from an image-based mock knowledge graph, (2) synthesizing information from both knowledge graphs and web search results, and (3) handling multi-turn conversati…

Fraud detection and risk assessment of online payment transactions on e-commerce platforms based on LLM and GCN frameworks
RuiHan Luo, Nanxi Wang, Xiaotong Zhu
https://arxiv.org/abs/2509.09928

Fraud detection and risk assessment of online payment transactions on e-commerce platforms based on LLM and GCN frameworks
With the rapid growth of e-commerce, online payment fraud has become increasingly complex, posing serious threats to financial security and consumer trust. Traditional detection methods often struggle to capture the intricate relational structures inherent in transactional data. This study presents a novel fraud detection framework that combines Large Language Models (LLM) with Graph Convolutional Networks (GCN) to effectively identify fraudulent activities in e-commerce online payment transact…

Climate Policy Radar is developing an open-source knowledge graph for climate policy. At Berlin Buzzwords, Harrison Pim and Fred O'Loughlin discussed how they combine in-house expertise with a scalable data infrastructure in order to identify key concepts within thousands of global climate policy documents.
Watch the full session: https://youtu.be/H6BhF6zSvp4?si=Jzioutp8n__2XY2c
Berlin Buzzwords returns on 7-9 June 2026! Get 36% off with our Trust Us Ticket: https://tickets.plainschwarz.com/bbuzz26/c/8Hvk0ZvJA/

Context-Aware Risk Estimation in Home Environments: A Probabilistic Framework for Service Robots
Sena Ishii, Akash Chikhalikar, Ankit A. Ravankar, Jose Victorio Salazar Luces, Yasuhisa Hirata
https://arxiv.org/abs/2508.19788

Context-Aware Risk Estimation in Home Environments: A Probabilistic Framework for Service Robots
We present a novel framework for estimating accident-prone regions in everyday indoor scenes, aimed at improving real-time risk awareness in service robots operating in human-centric environments. As robots become integrated into daily life, particularly in homes, the ability to anticipate and respond to environmental hazards is crucial for ensuring user safety, trust, and effective human-robot interaction. Our approach models object-level risk and context through a semantic graph-based propaga…

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation
David Egea, Barproda Halder, Sanghamitra Dutta
https://arxiv.org/abs/2508.18933

VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation
Automated detection of vulnerabilities in source code is an essential cybersecurity challenge, underpinning trust in digital systems and services. Graph Neural Networks (GNNs) have emerged as a promising approach as they can learn structural and logical code relationships in a data-driven manner. However, their performance is severely constrained by training data imbalances and label noise. GNNs often learn 'spurious' correlations from superficial code similarities, producing detectors that fai…

Crosslisted article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Beyond Fixed Morphologies: Learning Graph Policies with Trust Region Compensation in Variable Act...
Thomas Gallien

Crosslisted article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Beyond Fixed Morphologies: Learning Graph Policies with Trust Region Compensation in Variable Act...
Thomas Gallien