AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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1,703 papers
This chapter discusses mobile health (mHealth), teledermatology, and AI-based remote monitoring in the management of vitiligo. It investigates mHealth platforms based on CNNs and Vision Transformers to detect lesions and automatically score the severity of VASI/VETF with standardised photography protocols. The models o...
This software and reproducibility package supports the study “Label-Induced Bias and Verification-Cost Trade-offs in Federated Learning: A Budget-Constrained Crowd-Intelligence Approach.” It contains source code, configuration information, analysis scripts, and final five-seed experimental outputs for federated learnin...
Federated learning (FL) enables collaborative maritime perception without transferring raw observations, but solar-powered clients must preserve energy for platform-specific duties. Existing energy-aware FL generally applies a common battery constraint and cannot jointly protect persistent buoy service and the safe ret...
Information and communication technologies (ICT) have become essential for delivering healthcare services in a faster, more accessible, and data-driven manner. At the same time, the growing scale of digital health data has raised concerns about privacy, security, and the effective use of distributed data sources. In th...
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Artificial intelligence in medical imaging is becoming more common to improve clinical decision-making and accuracy of the diagnostic findings. Nevertheless, the high-level centralization of medical image storage and processing brings up serious challenges related to the privacy, security of data, regulatory advocacy,...
Decentralized learning ecosystems powered by blockchain, federated machine learning, and immersive metaverse environments are fundamentally reshaping educational data architecture and student engagement paradigms. This chapter presents a comprehensive investigation into AI-driven student performance prediction within t...
Objective. The objective of the research is to develop an architecture for a self-regulating Security Operations Center (SOC) that ensures autonomous adaptation of agents to new threats by combining federated learning with post-quantum secure model aggregation mechanisms based on lattice cryptography, while simultaneou...
Federated learning across device tiers that differ in sensing capability, using echo state networks with validation-gated aggregation and per-tier personalised readouts, evaluated on the PhysioNet/CinC Challenge 2021 ECG data.
Federated learning across device tiers that differ in sensing capability, using echo state networks with validation-gated aggregation and per-tier personalised readouts, evaluated on the PhysioNet/CinC Challenge 2021 ECG data.
Artificial Intelligence of Things (AIoT) refers to the deliberate pairing of artificial intelligence with the sensing and connectivity that the Internet of Things (IoT) already provides, so that the data streaming in from distributed devices turns into insight and, ultimately, action [1]. A properly designed AIoT syste...
Artificial Intelligence of Things (AIoT) refers to the deliberate pairing of artificial intelligence with the sensing and connectivity that the Internet of Things (IoT) already provides, so that the data streaming in from distributed devices turns into insight and, ultimately, action [1]. A properly designed AIoT syste...
The fraud of money laundering costs the global financial system USD 800 billion to USD 2 trillion annually, while digital banking contributes to the increasing number and complexity of money laundering transactions. If there are adversarial forces that are constantly adapting their approach to avoid complying with a co...
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.
Associate Professor Cathy Wu uses reinforcement learning to help map out improvements to transportation and other multifaceted systems.
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