Artificial intelligence (AI) is increasingly integrated into digital commerce through intelligent services that support consumer purchasing. However, limited empirical evidence explains how technology acceptance translates into AI adoption and subsequent online purchase behavior, particularly in digital MSMEs. This stu...
Dewi Novianti, Susanta Susanta, Didik Indarwanta· RSF Conference Series Busine...· 0 citations
Maritime systems operate in highly dynamic environments where unexpected equipment failures can compromise safety, reliability, and operational efficiency. Recent advances in artificial intelligence (AI), machine learning, digital twins, and predictive maintenance enable proactive failure prediction and prevention. How...
Dionisis Kalogeropoulos, Georgia Sovatzidi, P. Kalozoumis et al.· 0 citations
VeriRAN is a lightweight runtime-verified AI-RAN control architecture that separates intelligence from authorization, and uses AI agents as intelligent action proposers that generate radio-aware and service-level-agreement (SLA)-aware decisions.
Osman Tugay Başaran, Falko Dressler· Proceedings of the 3rd ACM W...· 0 citations
A fully local, autonomous incident-response framework for edge gateways that combines a compact classifier that routes traffic by confidence, a tool-using language-model agent restricted to vetted mitigation actions, and an Explanation Engine grounded in the agent's recorded observations is presented.
Shaghayegh Shajarian, Sajad Khorsandroo, Mahmoud Abdelsalam· Proceedings of the 17th ACM...· 0 citations
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This work recovers DU-side scheduling behavior and radio-side execution, including the per-beam and per-layer beamforming weights carried on the C-plane, separating measured quantities from those conditioned on an array hypothesis, and ground an eleven-agent platform in which a language model only interprets measuremen...
Sridhar Rajagopal, Eran Pisek, Gabriele Gemmi et al.· Proceedings of the 3rd ACM W...· 0 citations
This work introduces EIO-Agents, an open specification for interoperable AI agent evaluation built on two layers: EIO provides that missing semantic contract, while PER preserves the resulting evaluation as a portable and verifiable system of record.
It is claimed that future TTRSs, in addition to offering personalized information filtering, should become more flexible advisors that support decision making, integrating multiple data types and AI techniques, from data mining to natural language processing.
Alejandro Bellogín, Linus W. Dietz, Francesco Ricci et al.· 0 citations
BARE-AI, a runtime framework that detects, localizes, and mitigates BFAs during inference, and introduces AI Performance Counters, lightweight hardware monitors in the accelerator datapath that capture per-layer activation statistics such as sparsity, entropy, kurtosis, and spectral shift.
Habibur Rahaman, Swastik Bhattacharya, Sanjay Das et al.· 0 citations
The results show that the GNN model can accurately predict the optimal values of the manipulated variables, and application of explainable AI algorithms reveals equality and inequality constraints that are the most important for predicting the optimal solution.
This study maps how AI ethics is taught within postgraduate/CPD HPE, drawing on principlism, an ethical framework espousing the principles of autonomy, beneficence, non-maleficence and justice and transformative learning theory (TLT), which explains how critical reflection transforms professional assumptions, perspecti...
T. Wong, Ang Yu Chien Constance, M. S. Hussein et al.· The Clinical Teacher· 0 citations
In the field of Explainable AI (XAI), counterfactual (CF) explanations interpret a model's decision by suggesting the changes to the input that would lead to a more favourable outcome. To be useful in practice, such an explanation should change few features and change them as little as possible, properties known as spa...
A theory-oriented agentic AI system that helps researchers identify potential theorizing opportunities by incorporating established theorizing approaches into literature exploration and evaluation and demonstrates the system through an end-to-end analysis of human oversight of agentic AI systems in organizations.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 6, 2026
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
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