Raw results and figures for the paper Schema-Augmented LLM Prompting for Converting ML Training Scripts to Federated Learning Clients (ACM TOSEM, resubmission 2026). v2.0 adds the corrected reruns (validator stage classification, sample-weighted FedAvg simulations, overall and macro accuracy) and the resubmission exper...
Yen‐Jung Chiu, Chao-Chun Chuang· Zenodo (CERN European Organi...· 0 citations
In Internet of Things(IoT) environments, most intrusion detection systems are trained under a static closed-set assumption, where attack categories are predefined, and the model is deployed without mechanisms for continuous adaptation. However, severe device heterogeneity and rapidly evolving attacks undermine this ass...
Zhendong Wang, Ribao Wang, Shuxin Yang et al.· Engineering Applications of...· 0 citations
Raw results and figures for the paper Schema-Augmented LLM Prompting for Converting ML Training Scripts to Federated Learning Clients (ACM TOSEM, resubmission 2026). v2.0 adds the corrected reruns (validator stage classification, sample-weighted FedAvg simulations, overall and macro accuracy) and the resubmission exper...
holiday· Zenodo (CERN European Organi...· 0 citations
This package contains the results of the experiments conducted for the paper "Dynamic Runtime Adaptation of Multiple Architectural Patterns in Federated Learning" which is currently under revision. To use the tool and replicate the experiments, please refer to FLiP Github Repository
Luciano Baresi, Ivan Compagnucci, Livia Lestingi et al.· Zenodo (CERN European Organi...· 0 citations
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Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables : (EPC Art. 54(2); French IPC Art. L 611-11; cf....
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking + Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables : art. 54(2) CBE (Convention sur le brevet europé...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables : (EPC Art. 54(2); French IPC Art. L 611-11; cf....
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking et Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre dans l’état de la technique au sens des législations applicables (EPC Art. 54(2); French IPC Art. L 611-11; cf. 35 U.S.C. §1...
Xavier Pillet· Zenodo (CERN European Organi...· 0 citations
Résumé FRCe document, produit avec l’assistance de ChatGPT 5.2 Thinking + Gemini 3 Raisonnement, est publié sous licence Apache 2.0. Il constitue une publication défensive (antériorité) et entre de ce fait dans l’état de la technique au sens des législations applicables : art. 54(2) CBE (Convention sur le brevet europé...
Pillet, Xavier· Zenodo (CERN European Organi...· 0 citations
The research provides a new security situation awareness solution with real-time, privacy and scalability for the Industrial Internet of Things, which has practical application value for collaborative security protection in complex industrial environments.
Hui-Nian He· Discover Internet of Things· 0 citations
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
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