diffusion models

Research on AIGC-driven Footwear Intelligent Design Methods and Applications

ObjectiveThis research is aimed at solving the problems exposed by AIGC technology in the field of footwear design, such as generation homogeneity, low process feasibility, and market demand disconnection. A dedicated AIGC generation model and an intelligent design methodology for footwear design are built, which reali...

Analysis of the Knowledge Structure, Thematic Evolution, and Emerging and Future Research Directions in User Interface Design for Explainable Artificial Intelligence (XAI) in Decision-Making Dashboards: A Scientometric Study

Purpose: The rapid diffusion of artificial intelligence (AI) in organizational decision-making environments has intensified concerns about transparency, interpretability, and user trust. As complex machine learning models increasingly support managerial and policy decisions, the need for Explainable Artificial Intellig...

Copyright-aware adversarial perturbation for preventing unauthorized image generation in diffusion models

An Analysis of Object Detection in Bad Weather Conditions using Deep Learning Models

Object detection, a task, in the field of computer vision faces obstacles when dealing with weather conditions such as fog, rain, snow, and low light situations. This paper provides an overview of advancements in the realm of object detection under challenging weather conditions. It delves into groundbreaking research...

Exploring the Landscape of Cloud Robotics: A Comprehensive Review

Cloud robotics is an innovative field that leverages cloud technologies-including cloud computing (CC), cloud storage, deep learning, big data, and the Internet of Things to augment the capabilities of robotics. This integration facilitates the execution of robotic functions through a converged infrastructure and share...

AI Agentic Architectures for Autonomous Data Engineering Pipelines

This study delves into the notion of AI agentic architectures for autonomous data engineering pipelines and investigates the potential benefits of intelligent agents in enhancing automation, resilience, and decision-making processes in contemporary data ecosystems.

Economic impact and productivity modeling of AI agents

The paper provides a comprehensive analytical tool to make sense of micro and macro evidence, unpacks scenarios when AI agents will drive inclusive productivity growth, and outlines a policy roadmap focused on complementary investments, incentives for task-redesign, and workforce transition support measures.

Ethical challenges and bias mitigation in Artificial Intelligence systems

It is concluded that future research should prioritize interdisciplinary collaboration, robust regulatory frameworks, and continuous monitoring to promote the ethical use of AI.

Policy Drift in Learning AI Agents: A Dynamical Systems Perspective on Security Degradation

Policy drift takes shape through a nonlinear differential equation - framed within the policy state space - with support from Lyapunov stability concepts alongside bifurcation methods alongside bifurcation methods, and the Intent Drift Rate appears: a concrete number per dialogue turn built as the time-based change in...

Agentic workflows for end-to-end software engineering automation

This conceptual paper theorizes agentic workflows systems, where an AI agent or agents proactively perceive, plan, act and reflect throughout the entire software development lifecycle (SDLC); its implications for end to end software engineering automation are discussed.

AI-driven cybersecurity in software engineering

AI-driven cybersecurity in the software engineering field is discussed, where machine learning, deep learning, natural language processing, and reinforcement learning can be applied throughout the software development lifecycle to provide increased security.

Intent-Based Security: Replacing Identity-Centric Trust in Autonomous AI Agentic Systems

Intent-Based Security (IBS), a structured approach built on foundational ideas from access control, zero-trust models, and principal-agent dynamics, shows why trusting identities fails against invisible threats.