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Edge computing has enabled users to experience ubiquitous artificial intelligence (AI) through distributed learning and inference. Continuous efforts to reduce computing burden from the edge devices and increased preservation of privacy have popularized remote inference and federated learning (FL). However, network-level side-channel information can still expose sensitive operational states. In this paper, we demonstrate that network-level telemetry data can be used to fingerprint edge-assisted learning and inference workflows and reveal their operational phases. We propose a hierarchical classification framework, where the first stage separates learning from inference, and the second distinguishes learning phases. In addition, we develop a testbed with convolutional and recurrent neural network-based FL and remote inference systems, alongside an attacker device collecting network sniffing data. Using features derived from flow volumes, transfer speeds, ratios, and latency, the system achieves fingerprinting accuracy of 100% between learning and inference tasks, and 95.9% across different learning phases. These results highlight the vulnerability of edge-assisted distributed AI systems to network-based side-channel fingerprinting. 

We introduce a data capture system and a new dataset, HO-Cap, for 3D reconstruction and pose tracking of hands and objects in videos. The system leverages multiple RGB-D cameras and a HoloLens headset for data collection, avoiding the use of expensive 3D scanners or motion capture systems. We propose a semi-automatic method for annotating the shape and pose of hands and objects in the collected videos, significantly reducing the annotation time and cost compared to manual labeling. With this system, we captured a video dataset of humans performing various single- and dual-hand manipulation tasks, including simple pick-and-place actions, handovers between hands, and using objects according to their affordance. This dataset can serve as human demonstrations for research in embodied AI and robot manipulation. Our capture setup and annotation framework will be made available to the community for reconstructing 3D shapes of objects and human hands, as well as tracking their poses in videos. 

Smart cities seek to improve urban living by embedding advanced technologies into infrastructures, services, and governance. Edge Artificial Intelligence (Edge AI) has emerged as a critical enabler by moving computation and learning closer to data sources, enabling real-time decision-making, improving privacy, and reducing reliance on centralized cloud infrastructure. This survey provides a comprehensive review of the foundations, challenges, and opportunities of edge AI in smart cities. In particular, we begin with an overview of layer-wise designs for edge AI-enabled smart cities, followed by an introduction to the core components of edge AI systems, including applications, sensing data, models, and infrastructure. Then, we summarize domain-specific applications spanning manufacturing, healthcare, transportation, buildings, and environments, highlighting both the softcore (e.g., AI algorithm design) and the hardcore (e.g., edge device selection) in heterogeneous applications. Next, we analyze the sources of sensing data generation, model design strategies, and hardware infrastructure that underpin edge AI deployment. Building on these, we finally identify several open challenges and provide future research directions in this domain. Our survey outlines a future research roadmap to advance edge AI technologies, thereby supporting the development of adaptive, harmonic, and sustainable smart cities. 

Metal additive manufacturing has significantly evolved since the 1990s, achieving a market valuation of USD 6.36 billion in 2022, with an anticipated compound annual growth rate of 24.2% from 2023 to 2030. While powder-bed-based methods like powder bed fusion and binder jetting dominate the market due to their high accuracy and resolution, they face challenges such as lengthy build times, excessive costs, and safety concerns. Non-powder-bed-based techniques, including direct energy deposition, material extrusion, and sheet lamination, offer advantages such as larger build sizes and lower energy consumption but also encounter issues like residual stress and poor surface finish. The existing reviews of non-powder-bed-based metal additive manufacturing are restricted to one technical branch or one specific material. This survey investigates and analyzes each non-powder-bed-based technique in terms of its manufacturing method, materials, product quality, and summary for easy understanding and comparison. Innovative designs and research status are included.