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Federated learning (FL), a privacy-preserving distributed machine learning, has been rapidly applied in wireless communication networks. FL enables Internet of Things (IoT) clients to obtain well-trained models while preventing privacy leakage. Person detection can be deployed on edge devices with limited computing power if combined with FL to process the video data directly at the edge. However, due to the different hardware and deployment scenarios of different cameras, the data collected by the camera present non-independent and identically distributed (non-IID), and the global model derived from FL aggregation is less effective. Meanwhile, existing research lacks public data set for real-world FL object detection, which is not conducive to studying the non-IID problem on IoT cameras. Therefore, we open source a non-IID IoT person detection (NIPD) data set, which is collected from five different cameras. To our knowledge, this is the first true device-based non-IID person detection data set. Based on this data set, we explain how to establish a FL experimental platform and provide a benchmark for non-IID person detection. NIPD is expected to promote the application of FL and the security of smart city., Comment: 8 pages, 5 figures, 3 tables, FL-IJCAI 23 conference

Covalent organic frameworks (COFs) are emerging porous polymers that have 2D or 3D long-range ordering. Currently available COFs are typically insoluble or decompose upon dissolution, which remarkably restricts their practical implementations. For 3D COFs, the achievement of noninterpenetration, which maximizes their porosity-derived applications, also remains a challenge synthetically. Here, we report the synthesis of the first highly water-soluble 3D COF (sCOF-101) from irreversible polymerization of a preorganized supramolecular organic framework through cucurbit[8]uril (CB[8])-controlled [2 + 2] photodimerization. Synchrotron X-ray scattering and diffraction analyses confirm that sCOF-101 exhibits porosity periodicity, with a channel diameter of 2.3 nm, in both water and the solid state and retains the periodicity under both strongly acidic and basic conditions. As an ordered 3D polymer, sCOF-101 can enrich [Ru(bpy)3]2+ photosensitizers and redox-active polyoxometalates in water, which leads to remarkable increase of their photocatalytic activity for proton reduction to produce H2.

Covalent organic frameworks (COFs) are emerging porous polymers that have 2D or 3D long-range ordering. Currently available COFs are typically insoluble or decompose upon dissolution, which remarkably restricts their practical implementations. For 3D COFs, the achievement of noninterpenetration, which maximizes their porosity-derived applications, also remains a challenge synthetically. Here, we report the synthesis of the first highly water-soluble 3D COF (sCOF-101) from irreversible polymerization of a preorganized supramolecular organic framework through cucurbit[8]uril (CB[8])-controlled [2 + 2] photodimerization. Synchrotron X-ray scattering and diffraction analyses confirm that sCOF-101 exhibits porosity periodicity, with a channel diameter of 2.3 nm, in both water and the solid state and retains the periodicity under both strongly acidic and basic conditions. As an ordered 3D polymer, sCOF-101 can enrich [Ru(bpy)3]2+ photosensitizers and redox-active polyoxometalates in water, which leads to remarkable increase of their photocatalytic activity for proton reduction to produce H2.

Mining the shared features of same identity in different scene, and the unique features of different identity in same scene, are most significant challenges in the field of person re-identification (ReID). Online Instance Matching (OIM) loss function and Triplet loss function are main methods for person ReID. Unfortunately, both of them have drawbacks. OIM loss treats all samples equally and puts no emphasis on hard samples. Triplet loss processes batch construction in a complicated and fussy way and converges slowly. For these problems, we propose a Triplet Online Instance Matching (TOIM) loss function, which lays emphasis on the hard samples and improves the accuracy of person ReID effectively. It combines the advantages of OIM loss and Triplet loss and simplifies the process of batch construction, which leads to a more rapid convergence. It can be trained on-line when handle the joint detection and identification task. To validate our loss function, we collect and annotate a large-scale benchmark dataset (UESTC-PR) based on images taken from surveillance cameras, which contains 499 identities and 60,437 images. We evaluated our proposed loss function on Duke, Marker-1501 and UESTC-PR using ResNet-50, and the result shows that our proposed loss function outperforms the baseline methods by a maximum of 21.7%, including Softmax loss, OIM loss and Triplet loss.

Person search generally involves three important parts: person detection, feature extraction and identity comparison. However, person search integrating detection, extraction and comparison has the following drawbacks. Firstly, the accuracy of detection will affect the accuracy of comparison. Secondly, it is difficult to achieve real-time in real-world applications. To solve these problems, we propose a Multi-task Joint Framework for real-time person search (MJF), which optimizes the person detection, feature extraction and identity comparison respectively. For the person detection module, we proposed the YOLOv5-GS model, which is trained with person dataset. It combines the advantages of the Ghostnet and the Squeeze-and-Excitation (SE) block, and improves the speed and accuracy. For the feature extraction module, we design the Model Adaptation Architecture (MAA), which could select different network according to the number of people. It could balance the relationship between accuracy and speed. For identity comparison, we propose a Three Dimension (3D) Pooled Table and a matching strategy to improve identification accuracy. On the condition of 1920*1080 resolution video and 500 IDs table, the identification rate (IR) and frames per second (FPS) achieved by our method could reach 93.6% and 25.7

Body Sensor Network (BSN) is a typical Internet-of-Things (IoT) application for personalized health care. It consists of economically powered, wireless and implanted medical monitoring sensor nodes, which are designed to continually collect the medical information of the target patients. Multichannel is often used in BSNs to reduce the spectrum competition of the tremendous sensor nodes and the problem of channel assignment has attracted much research attention. The health sensing data in BSNs is often required to be delivered to a sink node (or server) before a certain deadline for real time monitoring or health emergency alarm. Therefore, deadline is of significant importance for multichannel allocation and scheduling. The existing works, though designed to meet the deadline, often overlook the impact of the unreliable wireless links. As a result, the health sensing data can still be overdue because of the scheduled lossy links. Besides, potential collisions in the schedules also incur considerable delay in delivering the sensing data. In this paper, we propose a novel deadline- driven Link quality Aware Channel Assignment scheme (LACA), where link quality, deadlines and collisions are jointly considered. LACA prioritizes links with urgent deadlines and heavy collisions. Besides, LACA allows the exploition of the spare slots for retransmissions on lossy links, which can further reduce the retransmission delay. Extensive simulation experiments show that compared to the existing approaches, LACA can better utilize the wireless spectrum and achieve higher packet delivery ratio before the deadline., Comment: PMC