SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

The Internet of Things(IoT)has characteristics such as node mobility,node heterogeneity,link heterogeneity,and topology heterogeneity.In the face of the IoT characteristics and the explosive growth of IoT nodes,which brings about large-scale data processing requirements,edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions.However,the defense mechanism of Edge Computing-enabled IoT Nodes(ECIoTNs)is still weak due to their limited resources,so that they are susceptible to malicious software spread,which can compromise data confidentiality and network service availability.Facing this situation,we put forward an epidemiology-based susceptible-curb-infectious-removed-dead(SCIRD)model.Then,we analyze the dynamics of ECIoTNs with different infection levels under different initial conditions to obtain the dynamic differential equations.Additionally,we establish the presence of equilibrium states in the SCIRD model.Furthermore,we conduct an analysis of the model’s stability and examine the conditions under which malicious software will either spread or disappear within Edge Computing-enabled IoT(ECIoT)networks.Lastly,we validate the efficacy and superiority of the SCIRD model through MATLAB simulations.These research findings offer a theoretical foundation for suppressing the propagation of malicious software in ECIoT networks.The experimental results indicate that the theoretical SCIRD model has instructive significance,deeply revealing the principles of malicious software propagation in ECIoT networks.This study solves a challenging security problem of ECIoT networks by determining the malicious software propagation threshold,which lays the foundation for buildingmore secure and reliable ECIoT networks.

Static Analysis Techniques for Fixing Software Defects in MPI-Based Parallel Programs

The Message Passing Interface (MPI) is a widely accepted standard for parallel computing on distributed memorysystems.However, MPI implementations can contain defects that impact the reliability and performance of parallelapplications. Detecting and correcting these defects is crucial, yet there is a lack of published models specificallydesigned for correctingMPI defects. To address this, we propose a model for detecting and correcting MPI defects(DC_MPI), which aims to detect and correct defects in various types of MPI communication, including blockingpoint-to-point (BPTP), nonblocking point-to-point (NBPTP), and collective communication (CC). The defectsaddressed by the DC_MPI model include illegal MPI calls, deadlocks (DL), race conditions (RC), and messagemismatches (MM). To assess the effectiveness of the DC_MPI model, we performed experiments on a datasetconsisting of 40 MPI codes. The results indicate that the model achieved a detection rate of 37 out of 40 codes,resulting in an overall detection accuracy of 92.5%. Additionally, the execution duration of the DC_MPI modelranged from 0.81 to 1.36 s. These findings show that the DC_MPI model is useful in detecting and correctingdefects in MPI implementations, thereby enhancing the reliability and performance of parallel applications. TheDC_MPImodel fills an important research gap and provides a valuable tool for improving the quality ofMPI-basedparallel computing systems.

Dynamic software allocation algorithm for saving power in pervasive computing

A novel dynamic software allocation algorithm suitable for pervasive computing environments is proposed to minimize power consumption of mobile devices. Considering the power cost incurred by the computation, communication and migration of software components, a power consumption model of component assignments between a mobile device and a server is set up. Also, the mobility of components and the mobility relationships between components are taken into account in software allocation. By using network flow theory, the optimization problem of power conservation is transformed into the optimal bipartition problem of a flow network which can be partitioned by the max-flow rain-cut algorithm. Simulation results show that the proposed algorithm can save si^nificantlv more energy than existing algorithms.

Domain-Oriented Software Defined Computing Architecture

With the introduction of software defined hardware by DARPA Electronics Resurgence Initiative,software definition will be the basic attribute of information system.Benefiting from boundary certainty and algorithm aggregation of domain applications,domain-oriented computing architecture has become the technical direction that considers the high flexibility and efficiency of information system.Aiming at the characteristics of data-intensive computing in different scenarios such as Internet of Things(IoT),big data,artificial intelligence(AI),this paper presents a domain-oriented software defined computing architecture,discusses the hierarchical interconnection structure,hybrid granularity computing element and its computational kernel extraction method,finally proves the flexibility and high efficiency of this architecture by experimental comparison.

Autonomic Software Component QoS Matchmaking Algorithm Based on Fidelity Factor in Agent-Based Autonomic Computing System

Autonomic software component (ASC) QoS matchmaking problem for autonomic element has been taken as one of the most important issue in field of autonomic computing based on agent. Aimed at overcoming drawbacks such as subjec-tiveness and unfairness, and improving the self-configuration capability for autonomic element, we introduce evalua-tion mechanism of confidence of individual QoS attributes during ASC QoS matchmaking, i.e., fidelity factor for each attribute, and propose an ASC QoS matchmaking algorithm based on fidelity factor. Simulation experiments demon-strate that our proposed algorithm performs best performance in terms of QoS than other existing algorithms, and has better compromise between attribute quality and users’ evaluation when selecting ASC.

Social Computing in Open Source Community:A Study of Software Reuse

Software projects are not developed in isolation but often build upon other open source resources. These projects form a kind of reference ecosystem regarded as a software world. Most of social computing works focus on social networks such as Facebook and weibo to mine information. However, few previous works analyze Open Source Community which could help developers conduct collaborative development. In this paper, we model the Java reference ecosystem as a network based on the reuse relationships of GitHub-hosted Java projects and analyze the characteristics and the patterns of this reference ecosystem by using community detection and pattern discovery algorithms. Our study indicates that (1) Developers prefer to reuse software limited in only a small part of projects with cross cutting functionality or advanced applications. (2) Developers usually select software reused with similar function widely depending on different requirements, resulting to different patterns. Based on these collective intelligence, our study opens up several possible future directions of reuse recommendation,which are considered as guidance of collaborative development.

Artificial Intelligence Model for Software Reusability Prediction System

The most significant invention made in recent years to serve various applications is software.Developing a faultless software system requires the soft-ware system design to be resilient.To make the software design more efficient,it is essential to assess the reusability of the components used.This paper proposes a software reusability prediction model named Flexible Random Fit(FRF)based on aging resilience for a Service Net(SN)software system.The reusability predic-tion model is developed based on a multilevel optimization technique based on software characteristics such as cohesion,coupling,and complexity.Metrics are obtained from the SN software system,which is then subjected to min-max nor-malization to avoid any saturation during the learning process.The feature extrac-tion process is made more feasible by enriching the data quality via outlier detection.The reusability of the classes is estimated based on a tool called Soft Audit.Software reusability can be predicted more effectively based on the pro-posed FRF-ANN(Flexible Random Fit-Artificial Neural Network)algorithm.Performance evaluation shows that the proposed algorithm outperforms all the other techniques,thus ensuring the optimization of software reusability based on aging resilient.The model is then tested using constraint-based testing techni-ques to make sure that it is perfect at optimizing and making predictions.

A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple levels.Combining different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve performance.During the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these errors.Numerous studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming models.Despite the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been developed.Therefore,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot detect.For the first time,this paper presents a classification of operational errors that can result from the integration of the three models.This paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and OpenACC.This hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic technology.The hybrid technique can detect more errors because it combines two distinct technologies.The proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environment are left to the dynamic technology,which completes the validation.

Teaching Reform Based on the Competency Model for Software Engineering-related Courses

This paper explores the reform and practice of software engineering-related courses based on the competency model of the Computing Curricula,and proposes some measures of teaching reform and talent cultivation in software engineering.The teaching reform emphasizes student-centered education,and focuses on the cultivation and enhancement of students’knowledge,skills,and dispositions.Based on the three elements of the competency model,specific measures of teaching reform are proposed for some professional courses in software engineering,to strengthen course relevance,improve knowledge systems,reform practical modes with a focus on skill development,and cultivate good dispositions through student-centered education.The teaching reform’s attempts and practice are conducted in some courses such as Advanced Web Technologies,Software Engineering,and Intelligent Terminal Systems and Application Development.Through the analysis and comparison of the implementation effects,significant improvements are observed in teaching effectiveness,students’mastery of knowledge and skills are noticeably improved,and the expected goals of the teaching reform are achieved.

大跨度公铁双层斜拉桥主梁涡激共振机理与控制

以拟建的某主跨808 m公铁双层斜拉桥为工程依托,采用节段模型风洞试验研究不同攻角下双层桁架梁断面的涡振性能及5种气动控制措施的抑振效果,结合计算流体动力学(CFD)静态绕流模拟,对比分析双层桁架梁断面的涡振机理及控制方法.研究表明:主梁断面原设计方案在+3°和0°风攻角下存在明显的竖向和扭转涡振现象,且振幅超过规范允许值;间隔封闭上层桥面栏杆或增设抑流板可有效抑制主梁扭转涡振,但竖向涡振振幅仍不满足规范要求;上弦杆外侧增设风嘴可有效抑制主梁竖向和扭转涡振,而下弦杆外侧增设风嘴对主梁涡振抑振效果有限.气流经主梁原设计断面上层桥面分离后,在其上下表面形成周期性脱落的大尺度旋涡,并在上层桥面后部再附,这是主梁发生竖向涡振的主要诱因;上弦杆外侧增设风嘴可引导气流平稳通过上层桥面,消除了周期性的旋涡脱落,并在其上表面形成一段狭长“回流区”,从而有效抑制了涡振的发生.

基于CT扫描数据的黄鳍金枪鱼鱼体三维重构

为研究鱼类分类与其内部结构,实验利用X射线计算机断层(CT)扫描技术对黄鳍金枪鱼鱼体进行扫描,通过医学影像软件Mimics (MIS_Research_21.0)对CT扫描数据进行处理,对黄鳍金枪鱼的骨骼、鳔和肌肉进行三维重构并测定了相应部分的体积和表面积。结果显示:(1) CT扫描可获得清晰的黄鳍金枪鱼骨骼、肌肉和鳔的影像;(2)医学影像软件Mimics可计算出黄鳍金枪鱼的骨骼、鳔和肌肉的体积和表面积,其体积分别占鱼体体积的7.76%、0.88%和91.36%,表面积分别占鱼体表面积的29.52%、0.74%和69.74%。研究表明:(1)使用X射线CT扫描技术是目前对鱼类损伤较小、鱼体骨骼、鳔和肌肉成像最便捷、最直观的方法;(2)医学影像软件Mimics可用于鱼体三维重构、计算鱼类骨骼、鳔和肌肉的体积和表面积;(3) CT扫描可用于鱼类学、鱼病学和鱼类声学回波强度的深入研究。实验可为鱼类学、鱼病学和鱼类声学回波强度等的深入研究提供参考。

蒽与马来酸酐Diels-Alder反应的量化计算研究——介绍一个计算化学实验

蒽与马来酸酐的Diels-Alder反应是基础有机化学实验课程中普遍开展的一个经典实验。本文推荐蒽与马来酸酐Diels-Alder反应的计算化学实验,该实验采用Gaussian 16程序中的密度泛函方法对蒽和马来酸酐Diels-Alder反应的反应机理进行了研究。旨在通过本实验激发学生对有机化学和计算化学的兴趣,加深学生对化学学科的理解,提升学生在科研工作中学以致用的能力。

面向ET-DQN的卫星网络任务部署算法研究

卫星边缘计算服务已经成为网络的重要组成部分,特别在地面网络稀疏环境下,可显著的提高用户的服务质量.但卫星网络的资源有限性为边缘计算任务的部署带来了巨大困难.因此,本文针对部署位置和计算资源分配不合理而导致任务处理时延过长、卫星能耗过大的问题,在SDN(Software Defined Network)的卫星-地面联合部署网络架构(SDN-Space-Ground Integrated Network,SSGIN)的系统模型下.提出了ET-DQN(Experience Tournament-DQN)的任务部署算法,该算法通过引入经验竞选机制提高经验池利用率和网络训练的效率,解决了经验池利用率低,而造成的网络过估计、难收敛的问题.仿真结果表明,该方法能够有效降低任务的响应时延以及能耗,很好地改善了传统DQN算法收敛速度慢和样本的利用率低下的问题.

高性能CAE软件性能评测平台的设计与应用

高性能计算机辅助工程(CAE)软件在复杂多物理过程仿真、大规模高逼真度数值模拟中发挥重要作用。高性能CAE软件的性能评测是衡量它性能指标的重要手段。传统手工基于算例评测的方式难以适应批量软件大量算例的并发评测,数据采集、性能分析等各阶段的数据管理、资源调度、性能指标计算以及评测报告撰写等活动脱节,制约高性能CAE软件的研发效率和评估效率。从高性能CAE软件研发模式和性能评测的需求出发,提出性能评测的数据模型和执行模型,基于科学工作流技术实现多软件、多算例、多规模的三层嵌套并发评测,设计并实现了高性能CAE软件性能评测平台,平台采用松耦合的可执行组件封装技术,提供软件和算例等信息管理、超算资源无缝对接、评测流程自动化执行和评测报告自动生成功能,在实际项目中采用该平台完成了数十个软件上百算例的并发评测。

智能算力核心基础系统软件的现状与展望

智能算力对中国人工智能技术的进步具有重要意义。发展智能算力平台,做好核心基础系统软件尤其重要。梳理了智能算力平台中的10个核心基础系统软件,对这些软件的全球现状进行了详细介绍,并探讨了当前中国算力平台上系统软件栈建设的机遇和挑战。

基于边缘计算的可重构波束控制技术研究

随着社会和技术的发展,相控阵软件无线电技术得到了快速推广和普及,其应用领域从传统的军事预警探测逐步拓展到气象探测、航空安全、无人驾驶、空间探测、天地通信中继等等。同时,新方法新体制应用对天线波束形成算法复杂度、数据传输量和波束切换的低延时等提出了更高的要求,商业应用要求高效低成本部署,并能够基于软件定义具备良好的可扩展性,传统集中式波束控制方法已无法有效应对需求。波束控制节点部署利用边缘计算理念可实现相控阵天线射频前端的就近波束形成运算与数据处理,有效减小数据传输带宽和降低延时,波束控制平台采用软件可重构体制可适应新兴领域和用户需求的不断发展变化。介绍了相控阵射频前端波束控制原理,提出了一种基于边缘计算的可重构波束控制技术,采用嵌入式分布化运算处理技术,通过建模仿真与计算,证明该技术可有效改善相控阵天线多通道数据传输压力,提升波束敏捷性,实现功能、孔径可重构,具有广泛的工程应用前景。

基于改进LSD的斜拉桥索力非接触鲁棒识别

拉索是斜拉桥的关键承载构件,其受力状态是评估桥梁安全状态的重要指标,准确测量拉索索力是保障桥梁结构安全的重要环节.基于此,建立一套可实现远距离、无靶标、高精度的斜拉索索力识别系统.该系统利用改进的直线检测(Line Segment Detector,LSD)算法对拉索进行追踪,从而识别拉索的振动信息;再通过模态分解(VMD)离散出斜拉索的有效振动信号,得到斜拉索的频率信息;最后利用振动频率法,从斜拉索的频率信息来估算出斜拉索的索力.在实验室条件下对长细比较大、像素占比较少的钢尺模型以及在大跨度斜拉桥上对斜拉索索力进行识别对比,确定了改进的LSD算法以及该系统对索力识别的精确性及鲁棒性.在钢尺模型试验中,改进的LSD算法识别到的钢尺振动信息与激光位移计对比误差仅为0.63%;在大跨度斜拉桥上的试验中,该系统识别到斜拉索的索力与接触式传感器对比误差小于3.0%.以上两个试验结果表明,本文提出的系统能够在复杂的实际工程现场环境中对斜拉索索力进行高精度识别.

模拟计算与课程思政在教学中深度融合的策略——以材料科学与工程基础课程教学为例

为提高学生的材料设计与性能优化能力,改善职业道德素养,我们在材料科学与工程基础课程教学中进行了“模拟计算+课程思政”改革与设计。以纯钨和钨铁合金的中子辐照分子动力学模拟实验教学为例,详细介绍了EAM势函数构建、中子辐照模拟及模拟结果分析三部分教学过程。同时,我们在教学中融合思想政治元素,进行了模拟结果分析与人生观教育。实践表明,“模拟计算+课程思政”不仅强化了学生的科研动手能力,而且激发了学生的学习兴趣,提升了其学习效率及专业认同感。

电磁计算与电磁工业软件

概要介绍了电磁计算与电磁工业软件的研究进展。1)介绍了电磁计算方法的研究进展,对解析方法、高频近似方法、全波计算方法、快速计算方法,以及近年发展起来的混合方法进行了阐述;2)概述了基于时域有限差分方法(FDTD)、有限元法(FEM)、矩量法(MoM)等全波计算方法的3款国际主流商业电磁计算软件的特点和发展历程;3)介绍了我国具有自主知识产权的“摇光”高频电磁工业软件的算法体系、软件功能以及实际应用情况;4)对电磁计算与电磁工业软件的未来发展进行了总结和展望。

基于云计算的软件工程方法在通信网络中的应用与效益

文章探讨了云计算与软件工程方法的结合,以及这种结合在通信网络中的应用和效益。首先,概述了云计算的基本概念和软件工程方法,分析了两者的结合点。其次,深入探讨了基于云计算的软件工程方法如何满足通信网络的软件需求并解决存在的问题,并评估了这种方法带来的效益,包括提高工作效率、降低运营成本,以及提高网络的可靠性和安全性等。最后,总结了软件工程方法的未来发展趋势,并对未来通信网络的发展趋势和预期效益进行了预测。