Author: Jack Smith

Authors: Beatrice Balbierer、Lukas Heinlein、Domenique Zipperling、Niklas Kühl Paper: https://arxiv.org/abs/2408.08666 Introduction In the digital age, the proliferation of data has opened up new avenues for innovation, particularly in the realm of machine learning (ML). However, the decentralized nature of data storage across various clients, such as edge devices or organizations, poses significant challenges. Federated Learning (FL) emerges as a promising solution by enabling decentralized training of ML models, thus mitigating privacy risks associated with centralized data storage. Despite its advantages, FL is not immune to privacy concerns, as model updates can still leak sensitive information. Additionally, ensuring fairness in FL, particularly in…

Authors: Daniel Omeiza、Pratik Somaiya、Jo-Ann Pattinson、Carolyn Ten-Holter、Jack Stilgoe、Marina Jirotka、Lars Kunze Paper: https://arxiv.org/abs/2408.08584 Introduction As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among AI developers create misaligned evaluation metrics, complicating the assessment and certification of safety-critical and complex AI systems such as autonomous driving (AD) agents. To address this challenge, the Simulation-Based Robustness Assessment Framework (S-RAF) for autonomous driving has been introduced. S-RAF leverages the CARLA Driving simulator to rigorously assess AD agents across diverse conditions, including faulty sensors, environmental changes, and complex traffic situations. By quantifying robustness…

Authors: Lukas Kirchdorfer、Robert Blümel、Timotheus Kampik、Han van der Aa、Heiner Stuckenschmidt Paper: https://arxiv.org/abs/2408.08571 Introduction Business Process Simulation (BPS) is a technique used to estimate the impact of changes in business processes. Traditional BPS approaches often focus on control-flow-first perspectives, which may not accurately capture the dynamics of real-world processes involving distinct resource behaviors and decentralized decision-making. This paper introduces AgentSimulator, a resource-first BPS approach that models distinct resource behaviors and interaction patterns through a multi-agent system (MAS) discovered from event logs. AgentSimulator achieves state-of-the-art simulation accuracy with significantly lower computation times and high interpretability. Motivation The motivation for shifting from control-flow-first to…

Authors: Kshitij Bhardwaj Paper: https://arxiv.org/abs/2408.08437 Introduction Vision Transformers (ViTs) have emerged as a powerful alternative to Convolutional Neural Networks (CNNs) for various image recognition tasks. They offer significant improvements in computational efficiency and accuracy. However, ViTs are complex and memory-intensive, making them unsuitable for resource-constrained mobile and edge systems. To address this challenge, the paper introduces PQV-Mobile, a combined pruning and quantization toolkit designed to optimize ViTs for mobile applications. PQV-Mobile Tool The PQV-Mobile tool supports various structured pruning strategies and quantization methods to optimize ViTs for mobile deployment. The tool’s workflow is illustrated in Figure 1. Pruning Method PQV-Mobile…

Authors: Alejandro Carrasco、Victor Rodriguez-Fernandez、Richard Linares Paper: https://arxiv.org/abs/2408.08676 Introduction Large Language Models (LLMs) have revolutionized artificial intelligence, expanding their applications beyond text generation to becoming autonomous agents capable of making decisions based on contextual information. This study explores the use of fine-tuned LLMs for autonomous spacecraft control within the Kerbal Space Program Differential Games suite (KSPDG). Traditional Reinforcement Learning (RL) approaches face limitations in this domain due to insufficient simulation capabilities and data. By leveraging LLMs, specifically fine-tuning models like GPT-3.5 and LLaMA, this research demonstrates how these models can effectively control spacecraft using language-based inputs and outputs. Background Limitations of…

Authors: Harry Cheng、Yangyang Guo、Qingpei Guo、Ming Yang、Tian Gan、Liqiang Nie Paper: https://arxiv.org/abs/2408.06569 Social Debiasing for Fair Multi-modal LLMs: A Comprehensive Overview Introduction Multi-modal Large Language Models (MLLMs) have significantly advanced the field of vision-language understanding, enabling powerful zero-shot performance and fine-tuning capabilities for various applications. However, these models often inherit severe social biases from their training datasets, leading to unfair predictions based on attributes like race and gender. This paper addresses these biases by introducing a comprehensive Counterfactual dataset with Multiple Social Concepts (CMSC) and proposing an Anti-Stereotype Debiasing strategy (ASD). Abstract The paper focuses on mitigating social biases in MLLMs by:…