The Edge Effects Boosting Hydrogen Evolution Performance of Platinum/Transition Bimetallic Phosphide Hybrid Electrocatalysts

Platinum (Pt) is regarded as a promising electrocatalyst for hydrogen evolution reaction (HER). However, its application in an alkaline medium is limited by the activation energy of water dissociation, diffusion of H⁺, and desorption of H*. Moreover, the formation of effective structures with a low Pt usage amount is still a challenge. Herein, guided by the simulation discovery that the edge effect can boost local electric field (LEF) of the electrocatalysts for faster proton diffusion, platinum nanocrystals on the edge of transition metal phosphide nanosheets are fabricated. The unique heterostructure with ultralow Pt amount delivered an outstanding HER performance in an alkaline medium with a small overpotential of 44.5 mV and excellent stability for 80 h at the current density of −10 mA cm⁻². The mass activity of as-prepared electrocatalyst is 2.77 A mg⁻¹_Pt, which is 15 times higher than that of commercial Pt/C electrocatalysts (0.18 A mg⁻¹_Pt). The density function theory calculation revealed the efficient water dissociation, fast adsorption, and desorption of protons with hybrid structure. The study provides an innovative strategy to design unique nanostructures for boosting HER performances via achieving both synergistic effects from hybrid components and enhanced LEF from the structural edge effect.     

Portable and Efficient Implementation of CRYSTALS-Kyber Based on WebAssembly

With the rapid development of quantum computers capable of realizing Shor’s algorithm, existing public key-based algorithms face a significant security risk. Crystals-Kyber has been selected as the only key encapsulation mechanism (KEM) algorithm in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography (PQC) competition. In this study, we present a portable and efficient implementation of a Crystals-Kyber post-quantum KEM based on WebAssembly (Wasm), a recently released portable execution framework for high-performance web applications. Until now, most Kyber implementations have been developed with native programming languages such as C and Assembly. Although there are a few previous Kyber implementations based on JavaScript for portability, their performance is significantly lower than that of implementations based on native programming languages. Therefore, it is necessary to develop a portable and efficient Kyber implementation to secure web applications in the quantum computing era. Our Kyber software is based on JavaScript and Wasm to provide portability and efficiency while ensuring quantum security. Namely, the overall software is written in JavaScript, and the performance core parts (secure hash algorithm-3-based operations and polynomial multiplication) are written in Wasm. Furthermore, we parallelize the number theoretic transform (NTT)-based polynomial multiplication using single instruction multiple data (SIMD) functionality, which is available in Wasm. The three steps in the NTT-based polynomial multiplication have been parallelized with Wasm SIMD intrinsic functions. Our software outperforms the latest reference implementation of Kyber developed in JavaScript by ×4.02 (resp. ×4.32 and ×4.1), ×3.42 (resp. ×3.52 and ×3.44), and ×3.41 (resp. ×3.44 and ×3.38) in terms of key generation, encapsulation, and decapsulation on Google Chrome (resp. Firefox, and Microsoft Edge). As far as we know, this is the first software implementation of Kyber with Wasm technology in the web environment.     

Optimal and Effective Resource Management in Edge Computing

Edge computing is a cloud computing extension where physical computers are installed closer to the device to minimize latency. The task of edge data centers is to include a growing abundance of applications with a small capability in comparison to conventional data centers. Under this framework, Federated Learning was suggested to offer distributed data training strategies by the coordination of many mobile devices for the training of a popular Artificial Intelligence (AI) model without actually revealing the underlying data, which is significantly enhanced in terms of privacy. Federated learning (FL) is a recently developed decentralized profound learning methodology, where customers train their localized neural network models independently using private data, and then combine a global model on the core server together. The models on the edge server use very little time since the edge server is highly calculated. But the amount of time it takes to download data from smartphone users on the edge server has a significant impact on the time it takes to complete a single cycle of FL operations. A machine learning strategic planning system that uses FL in conjunction to minimise model training time and total time utilisation, while recognising mobile appliance energy restrictions, is the focus of this study. To further speed up integration and reduce the amount of data, it implements an optimization agent for the establishment of optimal aggregation policy and asylum architecture with several employees’ shared learners. The main solutions and lessons learnt along with the prospects are discussed. Experiments show that our method is superior in terms of the effective and elastic use of resources.     

Simulation and analysis of edge ghosting for microcapsule e-paper based on particle dynamics and light scattering model

Electronic paper (e-paper) is a reflective display technology with unique advantages, such as bistability, low-power consumption, and high ambient contrast ratio. These features make e-paper a promising candidate for future Internet of Things applications. Among different technologies of e-paper, electrophoretic display (EPD) is the most successful one for commercialization. However, the edge ghosting (also known as the fringing effect) still limits the performance of EPD. Herein, we established a model of particle dynamics of electrophoresis, simulated the edge ghosting of microcapsule EPD, analyzed the edge ghosting effect, and revealed the relationship between thicknesses, dielectric constants of the back binder layer, and the edge ghosting. Two EPD panels with different thicknesses of back binder layer were demonstrated, which verifies the accuracy of this simulation model. With the proposed model, many device mechanisms and product issues can be analyzed and illustrated, which is supposed to guide the researchers in optimizing the device structure design of EPD.     

身份保持约束下的面部图像超分辨率重建方法

低分辨率是影响人脸识别精度的重要因素。一种有效方法是使用图像超分辨率技术对低分辨率图像重建,生成超分辨率图像后再对其作人脸识别,从而克服低分辨率面部图像对人脸识别的限制。但是,现有超分辨率方法在重建过程中往往忽略了保持其原始身份信息,这直接影响生成图像的人脸识别结果。针对上述问题,提出了一种身份保持约束下的面部超分辨率重建方法IPNet,在提高低分辨率面部图像质量的同时,能保持重建后的面部图像身份。IPNet方法将语义分割网络和面部生成器相结合,通过语义分割网络提取低维隐码和多分辨率空间特征,进而指导面部生成器输出接近于原图的真实面部图像。在此基础上引入人脸识别网络,将身份信息整合到超分辨率方法中,从而约束重建前后的面部图像身份保持一致。实验结果表明,IPNet方法在超分辨率图像质量和身份保持上均优于其他对比方法。     

基于实时视频感知的虚拟体育交互系统

针对疫情常态化背景下,传统体育项目受场地、器材等限制,市场上相关产品价格昂贵、可扩展性不足等问题,提出了一种基于实时视频感知的虚拟体育交互系统.该系统设计视频数据采集模块和人体关节点提取模块,结合OpenPose获取人体的关节点坐标,实时捕捉人体手势以及肢体动作.动作语义理解模块包括运动动作理解和绘图动作理解.前者根据运动中肢体关节点的相对位置关系,识别运动动作语义.后者将手腕部关节点绘图动作轨迹生成为草图图像,使用AlexNet进行识别分类,解析为对应的绘制动作语义.该模型在边缘端设备的分类准确率为98.83%.采用基于Unity设计的草图游戏应用作为可视化交互界面,实现在虚拟场景中的运动交互.该系统使用实时视频感知交互方式实现居家运动健身,无需其他的外部设备,具有更强的参与度和趣味性.     

一种结合机器学习的移动边缘计算的切换预测方法

随着移动通信的发展,减少通信延时成为关键性问题,因此,提出了一种使用机器学习方法的移动边缘计算(MEC)移动性管理。移动性决策基于参考信号接收功率(RSRP)值和不确定性预测器。使用神经网络建立预测器,输出不同相邻单元的RSRP平均值和标准偏差,推导了切换概率的封闭表达式。基于这些可能性,MEC服务器能够提前缓存用户服务,以便将切换期间的中断降至最低。实验结果表明,提出的方法能够满足实际需求。     

基于博弈论的多服务器多用户视频分析任务卸载算法

在过去的近10年中,人工智能相关的服务和应用大规模出现,它们要求高算力、高带宽和低时延.边缘计算目前被认为是这些应用最适合的计算模式,尤其是视频分析相关应用.研究多服务器多用户异构视频分析任务卸载问题,其中用户选择合适的边缘服务器,并将他们的原始视频数据上传至服务器进行视频分析.为了有效处理众多用户对有限网络资源的竞争和共享,并且能够获得稳定的网络资源分配局面,即每个用户不会单方面地改变自己的任务卸载决策,该多服务器多用户异构视频分析任务卸载问题被建模为一个多玩家的博弈问题.基于最小化整体时延的优化目标,先后研究非分布式视频分析场景和分布式视频分析场景两种情形,分别提出基于博弈论的潜在最优服务器选择算法和视频单元分配算法.通过严格的数学证明,两种情形下提出的算法均可以达到纳什均衡,同时保证较低的整体时延.最后,基于真实数据集的大量实验表明,所提方法比其他现有算法降低了平均26.3%的整体时延.     

A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

With the rapid development of mobile technology and smart devices, crowdsensing has shown its large potential to collect massive data. Considering the limitation of calculation power, edge computing is introduced to release unnecessary data transmission. In edge-computing-enabled crowdsensing, massive data is required to be preliminary processed by edge computing devices (ECDs). Compared with the traditional central platform, these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data synthetically. ECDs involved in one task are required to cooperate to process the task data. The privacy of participants is important in crowdsensing, so blockchain is used due to its decentralization and tamper-resistance. In crowdsensing tasks, it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is introduced. As mentioned before, ECDs can’t process task data comprehensively and they are required to cooperate quality assessment. Therefore, a blockchain-based framework for data quality in edge-computing-enabled crowdsensing (BFEC) is proposed in this paper. DPoR (Delegated Proof of Reputation), which is proposed in our previous work, is improved to be suitable in BFEC. Iteratively, the final result is calculated without revealing the privacy of participants. Experiments on the open datasets Adult, Blog, and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.     

Bragg Edge Neutron Transmission Strain Tomography in Granular Systems

The advent of pixelated detectors for time‐of‐flight neutron transmission experiments has raised significant interest in terms of the potential for tomographic reconstructions of triaxial strain distributions. A recent publication by Lionheart and Withers [WRB Lionheart and PJ Withers, “Diffraction tomography of strain”, Inverse Problems, v31:045005, 2015] has demonstrated that reconstruction is not possible in the general sense; however, various special cases may exist. In this paper, we outline a process by which it is possible to tomographically reconstruct average triaxial elastic strains within individual particles in a granular assembly from a series of Bragg edge strain measurements. This algorithm is tested on simulated data in two and three dimensions and is shown to be capable of rejecting Gaussian measurement noise. Sources of systematic error that may present problems in an experimental implementation are briefly discussed.