AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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2,057 papers
Federated learning (FL) trains a shared network-intrusion detector across organisations without pooling raw traffic, and such deployments increasingly demand explainability. Yet FL traffic is deeply **non-IID**, and heterogeneity's effect on a model's *explanations* is uncharacterised. We audit it. Sweeping the Dirichl...
Autonomous aerial and vehicular systems leverage edge computing and AI for real-time intelligent transportation. However, they face critical security challenges due to distributed architectures and emerging quantum threats. This chapter proposes a quantum-resilient framework integrating post-quantum cryptography with e...
The rapid iteration of generative Artificial Intelligence (AI) is reshaping the cybersecurity offense-defense landscape. This paper systematically reviews research progress in this field from 2021 to 2026, focusing on the dual use of generative AI in cybersecurity. On the one hand, malicious actors exploit generative A...
The growing elderly population in Indonesia is directly associated with an increasing burden of non- communicable diseases such as hypertension, heart disease, diabetes mellitus, and chronic obstructive pulmonary disease (COPD), creating a need for continuous rather than periodic health-monitoring mechanisms. This stud...
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To address fluctuations in multimodal data quality, client heterogeneity, and insufficient feedback timeliness in university classrooms, this paper proposes a cloud-edge-device collaborative method, HE-EdgePF. The method integrates visual, audio, textual, and platform-behavior features through quality-aware dynamic gat...
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Obstacle detection is the perception task where delay is measured against the laws of motion, and cloud centred inference cannot meet that budget without also spending bandwidth and exposing sensor data. This chapter develops Federated Edge Intelligence, a three tier design uniting Federated Learning with Multi-Access...
The advancement of AI, IoT, 6G, and UAV technologies is enabling Intelligent Transportation Systems (ITS) to evolve into connected and autonomous mobility ecosystems. This chapter proposes an integrated framework for developing ITS and discusses how EdgeAI enables low-latency and privacy-aware decision-making by integr...
Precision weed management is critical to sustainable cotton production because overuse of herbicides can have detrimental environmental and health effects and increase production costs. The problem of weed recognition from field images is still difficult even with complex background, intra-class variation, class imbala...
The rapid proliferation of Internet of Things devices has intensified demands on distributed computing infrastructures, making fog computing — a paradigm that positions computational resources at the network edge — a critical enabler of low-latency real-time applications. Optimally placing services across fog nodes is...
Spatial Crowdsourcing (SC) coordinates workers and requesters through personal mobile devices to complete location‐dependent tasks in real time. The combination of continuous worker mobility, self‐interested participants, and real‐time service requirements creates a learning environment that is more adversarial, more...
Cloud-centric Air Traffic Management architectures are poorly suited to Urban Air Mobility, where dense, three-dimensional flight paths require conflict-resolution decisions within tens of milliseconds. This chapter proposes a four-layer, edge-native reference architecture combining Deep Reinforcement Learning for adap...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
A Blog post by IBM Research on Hugging Face
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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