NIPD: A Federated Learning Person Detection Benchmark Based on Real-World Non-IID Data

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 conferenc

Water-Soluble 3D Covalent Organic Framework that Displays an Enhanced Enrichment Effect of Photosensitizers and Catalysts for the Reduction of Protons to 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

A panther chameleon skin-inspired core@shell supramolecular hydrogel with spatially organized multi-luminogens enables programmable color change

Organization of different iridophores into a core@shell structure constitutes an evolutionary novelty for panther chameleons that allows their skins to display diverse color change. Inspired by this natural color-changing design, we present a responsive core@shell-structured multi-luminogen supramolecular hydrogel system that generates a programmable multi-color fluorescent change. Specifically, red Eu$^{3+}$-amidopicolinate (R) luminogen is incorporated into the core hydrogel, while blue naphthalimide (B) and green perylene-tetracarboxylic acid (G) luminogens are grown into two supramolecular shell hydrogels. The intensities of G/B luminogens could then be controlled independently, which enables its emission color to be programmed easily from red to blue or green, nearly covering the full visible spectrum. Because of the differential excitation energies between these luminogens, a desirable excitation wavelength-dependent fluorescence is also achieved. Colorful materials with a patterned core@shell structure are also demonstrated for anti-counterfeiting, opening up the possibility of utilizing a bioinspired core@shell structure to develop an efficient multi-color fluorescent system with versatile uses

Link quality aware channel allocation for multichannel body sensor networks.

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.N/

Surface Stability of Spinel MgNi0.5Mn1.5O4 and MgMn2O4 as Cathode Materials for Magnesium Ion Batteries

Rechargeable ion batteries based on the intercalation of multivalent ions are attractive due to their high energy density and structural stability. Surface of cathode materials plays an important role for the electrochemical performance of the rechargeable ion batteries. In this work we calculated surface energies of (001), (110) and (111) facets with different terminations in spinel MgMn2O4 and MgNi0.5Mn1.5O4 cathodes. Results showed clearly that atomic reconstruction occurred due to surface relaxation. The surface energies for the (001), (110) and (111) surfaces of the MgNi0.5Mn1.5O4 were 0.08, 0.13 and 0.11 J/m2, respectively, whereas those of the Ni-doped MgMn2O4 showed less dependence on the surface structures

In-situ formation and densification of MgAl2O4-SmAlO3 ceramics by a single-stage reaction sintering process

Stoichiometric magnesium aluminate spinel (MgAl2O4, MA)-samarium aluminate (SmAlO3, SA) ceramics have been prepared at 1580°C for 4h from calcined magnesia (MgO), commercial alumina (Al2O3) and samarium oxide (Sm2O3) by a single-stage in-situ reaction sintering (SIRS) method. The phase compositions, microstructures, shrinkage ratio, bulk density and cold compressive strength of the MA-SA ceramics have been investigated. The ceramics with 2.5 - 7.5 wt. % Sm2O3 are composed of MA and SA phases. The microstructures of the ceramics are dense. MA particles exist as angular shape, and their grain size varies between 2 and 10μm but the average grain size is about 5 μm. SmAlO3 particles form due to the reaction of Sm2O3 and Al2O3, and they distribute in the intergranular space of MA grains. The diameter shrinkage ratio, volume shrinkage ratio, bulk density and cold compressive strength of MA-SA ceramics are greatly improved due to the addition of Sm2O3

Effect of nitrogen doping and external electric field on the adsorption of hydrogen on graphene

Effect of doping and external electric field on the adsorption of hydrogen on graphene was studied by using density functional theory. Substitutional nitrogen, pyridinic and pyrrolic nitrogen doping has been considered. It was found that hydrogen prefers to be physically adsorbed on the pristine and substitutional nitrogen doped graphene, whereas hydrogen prefers to be chemically adsorbed on the pyridinic and pyrrolic nitrogen doped graphene. An external electric field can enhance the chemical adsorption of hydrogen on the pyridinic and pyrrolic N-doped graphene. These demonstrate nitrogen doping combined with external electric field can increase the capacity of hydrogen storage in graphene

Hybrid Pyramid Convolutional Network for Multiscale Face Detection

Face detection remains a challenging problem due to the high variability of scale and occlusion despite the strong representational power of deep convolutional neural networks and their implicit robustness. To handle hard face detection under extreme circumstances especially tiny faces detection, in this paper, we proposed a multiscale Hybrid Pyramid Convolutional Network (HPCNet), which is a one-stage fully convolutional network. Our HPCNet consists of three newly presented modules: firstly, we designed the Hybrid Dilated Convolution (HDC) module to replace the fully connected layers in VGG16, which enlarges receptive field and reduces its loss of local information; secondly, we constructed the Hybrid Feature Pyramid (HFP) to combine semantic information from higher layers together with details from lower layers; and thirdly, to deal with the problem of occlusion and blurring effectively, we introduced Context Information Extractor (CIE) in HPCNet. In addition, we presented an improved Online Hard Example Mining (OHEM) strategy, which can enhance the average precision of face detection by balancing the number of positive and negative samples. Our method has achieved an accuracy of 0.933, 0.924, and 0.848 on the Easy, Medium, and Hard subset of WIDER FACE, respectively, which surpasses most of the advanced algorithms