移动办公平台架构及关键技术

本文针对目前移动办公终端类型多样,移动办公系统对视频业务支持力度不够和不能有效利用Internet资源的问题。本文通过对3G IMS、J2ME和J2EE的研究分析,提出了适应3G发展的移动办公平台架构。该移动办公平台架构下实现的移动办公业务可以运行于不同类型的移动终端并且能够与Internet中的办公终端协同工作,同时支持包括视频在内的各种多媒体业务。     

Ultra-Wideband Electrostrictive Mechanical Antenna

Mechanical antennas based on piezoelectric materials can effectively reduce the size of long wave antennas down to 1/1000 of the wavelength (from km scale to mm level). However, the narrow bandwidth and weak field intensity seriously restrict its practical applications in transmission distance and channel capacity. Here, a mechanical antenna-based electrostrictive effect of relaxor ferroelectric ceramic (PMN-PT) is proposed to improve radiation capacity and achieve ultra-wideband characteristics (10 kHz–1 MHz). Due to the ultra-high dielectric constant at working temperature and the relationship between the strain and applied field intensity, the proposed antenna gets rid of the dependence on the poled materials and exhibits excellent communication properties beyond traditional mechanical antennas, which are experimentally demonstrated by a practical wireless communication system. Only using a single proposed mechanical antenna with 8 mm diameter and 3 mm thickness, the effective communication with a transmission distance of 200 m can be realized. This design offers a promising way of constructing mechanical antennas for long-wave communication.     

基于动态门控特征融合的轻量深度补全算法

稠密深度图在自动驾驶和机器人等领域至关重要,但是现今的深度传感器只能产生稀疏的深度测量,所以有必要对其进行补全.在所有辅助模态中, RGB图像是常用且易得的信息.现今的许多方法都采用RGB和稀疏深度信息结合进行补全.然而它们绝大部分都是利用通道拼接或逐元素求和简单的对两种模态的信息进行融合,没有考虑到不用场景下不同模态特征的置信度.提出一种以输入深度稀疏分布为指导,结合双模态信息量的动态门控融合模块,通过动态产生融合权重的方式对两个模态特征进行更高效的结合.并且根据不同模态的数据特征设计了精简的网络结构.实验结果表明所提出模块和改进的有效性,提出的网络在两个有挑战性的公开数据集KITTI depth completion和NYU depth v2上,使用了很少的参数量达到了先进的结果,取得了性能和速度的优秀平衡.     

Few-shot node classification via local adaptive discriminant structure learning

Node classification has a wide range of application scenarios such as citation analysis and social network analysis. In many real-world attributed networks, a large portion of classes only contain limited labeled nodes. Most of the existing node classification methods cannot be used for few-shot node classification. To train the model effectively and improve the robustness and reliability of the model with scarce labeled samples, in this paper, we propose a local adaptive discriminant structure learning (LADSL) method for few-shot node classification. LADSL aims to properly represent the nodes in the attributed graphs and learn a metric space with a strong discriminating power by reducing the intra-class variations and enlarging inter-class differences. Extensive experiments conducted on various attributed networks datasets demonstrate that LADSL is superior to the other methods on few-shot node classification task.     

数据要素市场化的理论内涵、现实挑战和实践路径

数据要素是数字经济时代的全新生产要素,数据要素市场化在数字经济推进经济高质量发展中具有显著的战略意义.目前,全球对于数据要素的认识仍然处在积极探索中,正确把握数据要素市场化的新内涵、新特征、新要求、新挑战至关重要.在对数据要素市场化的理论内涵和重大意义进行系统阐述的基础上,通过数据要素市场化指数测度发现我国当前数据要素市场化起步晚、起步缓,面临数据产权确权难、数据价值评估难、数据交易流通难、数据保护监管难等挑战,从而从加快数据汇聚、推动数据流通、深化数据应用、强化数据安全等方面提出具有实践价值的解决路径.     

Long-term ultra-precision phase synchronization technique for locking the repetition rate of OFCs based on FLOM-PD

Long-term ultra-precision synchronization between optical frequency combs (OFCs) and microwave oscillators is important for various fields, including scientific observation, smart grid, positioning and navigation, etc. Here, a phase-locked loop system based on fiber loop optical-microwave phase detector (FLOM-PD) is proposed to realize the synchronization of the repetition rate of OFCs and rubidium atomic clocks. Firstly, the scheme and locking process of the system are elaborated, then the mathematical model of the system is established, and the feasibility of the scheme is proved by theoretical analysis and experimental verification. After synchronization, the instability of the system reaches 8.69×10-12 at 1 s and 2.94×10-13 at 1 000 s, indicating that the phase synchronization system can achieve ultra-precision and stability of OFCs repetition rate.     

基于拓扑链路识别的光网络流量数据合成算法

基于深度学习的光网络流量诊断与预测等场景中,由于保密等原因,光链路的流量数据采集和存储工作受限。针对数据量少而无法支撑深度学习的问题,文章提出了一种基于拓扑链路识别的光网络流量数据合成算法,其核心思想是在生成对抗网络框架下,联合基于光网络拓扑的条件生成模型和基于光网络流量的数据合成模型,以自监督的方式合成指定光链路的流量数据。仿真结果表明,所提算法合成的光网络流量数据在自相关系数指标上与真实数据接近且使得基于全连接神经网络的流量预测模型准确率达到95%以上。     

Theories of Social Media: Philosophical Foundations

Although many different views of social media coexist in the field of information systems (IS), such theories are usually not introduced in a consistent framework based on philosophical foundations. This paper introduces the dimensions of lifeworld and consideration of others. The concept of lifeworld includes Descartes’ rationality and Heidegger’s historicity, and consideration of others is based on instrumentalism and Heidegger’s “being-with.” These philosophical foundations elaborate a framework where different archetypal theories applied to social media may be compared: Goffman’s presentation of self, Bourdieu’s social capital, Sartre’s existential project, and Heidegger’s “shared-world.” While Goffman has become a frequent reference in social media, the three other references are innovative in IS research. The concepts of these four theories of social media are compared with empirical findings in IS literature. While some of these concepts match the empirical findings, some other concepts have not yet been investigated in the use of social media, suggesting future research directions.     

A Secure and Effective Energy-Aware Fixed-Point Quantization Scheme for Asynchronous Federated Learning

Asynchronous federated learning (AsynFL) can effectively mitigate the impact of heterogeneity of edge nodes on joint training while satisfying participant user privacy protection and data security. However, the frequent exchange of massive data can lead to excess communication overhead between edge and central nodes regardless of whether the federated learning (FL) algorithm uses synchronous or asynchronous aggregation. Therefore, there is an urgent need for a method that can simultaneously take into account device heterogeneity and edge node energy consumption reduction. This paper proposes a novel Fixed-point Asynchronous Federated Learning (FixedAsynFL) algorithm, which could mitigate the resource consumption caused by frequent data communication while alleviating the effect of device heterogeneity. FixedAsynFL uses fixed-point quantization to compress the local and global models in AsynFL. In order to balance energy consumption and learning accuracy, this paper proposed a quantization scale selection mechanism. This paper examines the mathematical relationship between the quantization scale and energy consumption of the computation/communication process in the FixedAsynFL. Based on considering the upper bound of quantization noise, this paper optimizes the quantization scale by minimizing communication and computation consumption. This paper performs pertinent experiments on the MNIST dataset with several edge nodes of different computing efficiency. The results show that the FixedAsynFL algorithm with an 8-bit quantization can significantly reduce the communication data size by 81.3% and save the computation energy in the training phase by 74.9% without significant loss of accuracy. According to the experimental results, we can see that the proposed AsynFixedFL algorithm can effectively solve the problem of device heterogeneity and energy consumption limitation of edge nodes.     

Container-Based Internet of Vehicles Edge Application Migration Mechanism

Internet of Vehicles (IoV) applications integrating with edge computing will significantly drive the growth of IoV. However, the contradiction between the high-speed mobility of vehicles, the delay sensitivity of corresponding IoV applications and the limited coverage and resource capacity of distributed edge servers will pose challenges to the service continuity and stability of IoV applications. IoV application migration is a promising solution that can be supported by application containerization, a technology forseamless cross-edge-server application migration without user perception. Therefore, this paper proposes the container-based IoV edge application migration mechanism, consisting of three parts. The first is the migration trigger determination algorithm for cross-border migration and service degradation migration, respectively, based on trajectory prediction and traffic awareness to improve the determination accuracy. The second is the migration target decision calculation model for minimizing the average migration time and maximizing the average service time to reduce migration times and improve the stability and adaptability of migration decisions. The third is the migration decision algorithm based on the improved artificial bee colony algorithm to avoid local optimal migration decisions. Simulation results show that the proposed migration mechanism can reduce migration times, reduce average migration time, improve average service time and enhance the stability and adaptability of IoV application services.