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...
Jing-Xuan Fang, Hong Chen· International journal of pat...· 0 citations
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...
Meghna B. Patel, Patel Devarshi, Neha Mewani et al.· Advances in computational in...· 0 citations
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...
Ankur Kulhari, Sanjeev Patwa· International Journal of Ele...· 0 citations
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...
Oleksandr Sbitnev, Lyudmila Voloshchuk· Computer Systems and Informa...· 0 citations
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...
Md. Mujibur Rahman, Q. Mamun, Michael Bewong et al.· WIREs Data Mining and Knowle...· 0 citations
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...
Nitesh Kumar, Vikash Kumar· Advances in computational in...· 0 citations
This report argues that the challenges facing the protection of personal data in the digital age are complex and interwoven, arising from the interaction between rapid technical development, notably artificial intelligence and big data, and legislative deficiency. It adopts a descriptive-analytical and comparative meth...
Abderrahmane Belouafi Ben Haiba· Social Reports· 0 citations
The rapid deployment of artificial intelligence (AI) in higher education raises questions of equity, transparency, accountability, privacy, and governance in adaptive personalized learning. While AI-enabled personalization can increase educational responsiveness, top-down personalized learning architectures may reinfor...
Kamal Singh Kunwar· Journal of Innovative Techno...· 0 citations
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...
Anonymous· Zenodo (CERN European Organi...· 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
