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1,772 papers
João Carlos Amaro Ferreira (ISCTE-IUL, 16/07/2021), showcasing the research group's work in IoT, edge/fog computing, and data science. The group has expertise in edge/fog architectures (open-source and commercial platforms), IoT communications (short and long range, lightweight protocols such as MQTT/CoAP, V2X), and di...
The Wisdom Continuum: A Unified Theory of Human Development, Learning, Intelligence, Artificial Intelligence, and Civilizational Evolution Description / Abstract The Wisdom Continuum presents a civilizational-scale framework that unifies human development, moral philosophy, educational reform, and artificial intelligen...
Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data, preserving data privacy. However, this approach is increasingly recognized to be vulnerable to algorithmic bias. The inherent diversity and potential imbalances within the data contributed by numerous clien...
This survey critically examines significant research contributions highlighting AI applications across intrusion detection, malware analysis, phishing detection, ransomware mitigation, distributed denial-of-service prevention, and network security and outlines prospective directions to advance the development of intell...
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Edge translation systems must balance limited computation, data locality, and weak performance on low-resource language pairs. This study presents Multitask Bayesian Federated Learning (MT-BayesFL), a multilingual translation framework that combines a lightweight shared encoder, task-specific decoders, matrix-normal po...
Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data, preserving data privacy. However, this approach is increasingly recognized to be vulnerable to algorithmic bias. The inherent diversity and potential imbalances within the data contributed by numerous clien...
A novel federated transfer learning framework for accurate classification of renal abnormalities using 12,446 kidney CT scan images and simultaneously preserves data privacy is proposed, highlighting its potential for deployment in distributed clinical environments for kidney disease diagnosis.
João Carlos Amaro Ferreira (ISCTE-IUL, 16/07/2021), showcasing the research group's work in IoT, edge/fog computing, and data science. The group has expertise in edge/fog architectures (open-source and commercial platforms), IoT communications (short and long range, lightweight protocols such as MQTT/CoAP, V2X), and di...
Federated learning (FL) lets distributed intrusion-detection systems (IDS) train on private traffic, but exposes the global model to poisoning and backdoor attacks under non-IID client data. A popular defense family scores or filters clients using a small trusted "root" dataset and the similarity of client behaviour to...
Blockchain-Anchored Provenance for Federated Learning under Variable IoT Device Availability
Modern computing environments integrate Linux servers, Windows workstations, and Android devices, creating fragmented security telemetry that limits the detection of coordinated cross-platform attacks. This paper proposes DSSN-V4, a unified distributed framework for cross-platform threat monitoring and global risk asse...
Modern computing environments integrate Linux servers, Windows workstations, and Android devices, creating fragmented security telemetry that limits the detection of coordinated cross-platform attacks. This paper proposes DSSN-V4, a unified distributed framework for cross-platform threat monitoring and global risk asse...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
StudentSim is a research system for evaluating and improving AI tutoring tools before they are tested with learners. It models how individuals may engage with tutors, giving feedback to improve tutor performance across subjects like chess, writing, and mathematics. The post Simulating student learning for better AI tutoring appeared first on Microsoft Research.
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.
The effort aims to help U.S. learners from kindergarten to community college, with an emphasis on math, making, and the constructive use of AI.
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