Linux环境下PC机CLUSTER实验平台的设计与实现

介绍了有关机群(CLUSTER)计算机的基本概念、并提出了如何利用低档微机建构一个PC机CLUSTER实验平台的方法.介绍了实验平台硬件环境,阐述了CLUSTER环境的设计、网络监控和调试的过程,并试图通过开发相应的实验程序,使用户得到与实用PC机群系统相仿的仿真环境.

基于Linux Cluster的并行CMAQ研究

CMAQ（Community Multiscale Air Quality）涉及海量空间数据、复杂的处理模型和苛刻的时间需求，但高密集的计算操作使得串行CMaQ面临计算瓶颈问题，昂贵的巨型高性能专用机对于普通研究者望尘莫及，因此基于Linux Cluster的并行CMAQ研究是解决该问题的重要途径。本文以开源CMAQ为研究对象，探讨基于Linux Cluster的并行CMAQ的计算模式、体系结构、并行模式、软件框架等，并构建了相应的原型系统。实验表明相对于传统的串行架构，所提出的并行架构在计算效率上有了显著提高。

Turbo Linux Cluster集群技术研究及在ISP/ICP上的应用

在分析NLB的基础上,探讨了一些Turbo Linux Cluster集群关键技术和配置方案,提出了一个MC系统的解决方案.

A novel method for clustering cellular data to improve classification

Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.

Multi-Step Clustering of Smart Meters Time Series:Application to Demand Flexibility Characterization of SME Customers

Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’participation in the energy transition.This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons.Smart meter data is split between daily and hourly normalized time series to assess monthly,weekly,daily,and hourly seasonality patterns separately.The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series data.The intraday clustering procedure sequentially identifies representative hourly day-unit profiles for each customer and the entire population.For the first time,a step function approach is applied to reduce time series dimensionality.Customer attributes embedded in surveys are employed to build external clustering validation metrics using Cramer’s V correlation factors and to identify statistically significant determinants of load-shape in energy usage.In addition,a time series features engineering approach is used to extract 16 relevant demand flexibility indicators that characterize customers and corresponding clusters along four different axes:available Energy(E),Temporal patterns(T),Consistency(C),and Variability(V).The methodology is implemented on a real-world electricity consumption dataset of 325 Small and Medium-sized Enterprise(SME)customers,identifying 4 daily and 6 hourly easy-to-interpret,well-defined clusters.The application of the methodology includes selecting key parameters via grid search and a thorough comparison of clustering distances and methods to ensure the robustness of the results.Further research can test the scalability of the methodology to larger datasets from various customer segments(households and large commercial)and locations with different weather and socioeconomic conditions.

A porous⁃layered aluminoborate built by mixed oxoboron clusters and AlO_(4)tetrahedra

An aluminoborate,Na_(2.5)Rb[Al{B_(5)O_(10)}{B_(3)O_(5)}]·0.5NO_(3)·H_(2)O(1),was synthesized under hydrothermal condition,which was built by mixed oxoboron clusters and AlO_(4)tetrahedra.In the structure,the[B_(5)O_(10)]^(5-)and[B_(3)O_(7)]^(5-)clusters are alternately connected to form 1D[B_(8)O_(15)]_(n)^(6n-)chains,which are further linked by AlO_(4)units to form a 2D monolayer with 7‑membered ring and 10‑membered ring windows.Two adjacent monolayers with opposite orientations further form a porous‑layered structure with six channels through B—O—Al bonds.Compound 1 was characterized by single crystal X‑ray diffraction,powder X‑ray diffraction(PXRD),IR spectroscopy,UV‑Vis diffuse reflection spectroscopy,and thermogravimetric analysis(TGA),respectively.UV‑Vis diffuse reflectance analysis indicates that compound 1 shows a wide transparency range with a short cutoff edge of 201 nm,suggesting it may have potential application in UV regions.CCDC:2383923.

基于项目驱动法的“Linux操作系统管理”课程教学改革探索

“Linux操作系统管理”是计算机科学和软件工程领域的核心课程,能够帮助学生掌握使用各种工具和功能来解决多用户管理、网络通信等任务。文章分析了国内外关于Linux课程驱动教学的研究及课程教学现状,重点从教学内容、教学方式和评价体系3个层面梳理了存在的问题。基于此,提出了一种基于项目驱动法的“Linux操作系统管理”课程教学模式,旨在坚持学生主体地位,强化项目驱动式教学,建立多维度的考核体系。通过实施项目驱动法,深入探讨了该方法在“Linux操作系统管理”课程中的应用,力求通过课程优化提升学生解决实际项目问题的能力,为培养高素质的技术技能人才提供参考。

Weak Merging Scenario of CLASH Cluster A209

We study the structural and dynamical properties of A209 based on Chandra and XMM-Newton observations.We obtain detailed temperature,pressure,and entropy maps with the contour binning method,and find a hot region in the NW direction.The X-ray brightness residual map and corresponding temperature profiles reveal a possible shock front in the NW direction and a cold front feature in the SE direction.Combined with the galaxy luminosity density map we propose a weak merger scenario.A young sub-cluster passing from the SE to NW direction could explain the optical subpeak,the intracluster medium temperature map,the X-ray surface brightness excess,and the X-ray peak offset together.

A Study of Triangle Inequality Violations in Social Network Clustering

Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hierarchical, use the distance function to measure the dissimilarities among actors. These distance functions need to fulfill various properties, including the triangle inequality (TI). However, in some cases, the triangle inequality might be violated, impacting the quality of the resulting clusters. With experiments, this paper explains how TI violates while performing traditional clustering techniques: k-medoids, hierarchical, DENGRAPH, and spectral clustering on social networks and how the violation of TI affects the quality of the resulting clusters.

Linux实时化研究

本文根据Linux和实时操作系统的特点,分析了国外几种典型的实时化Linux的实现思想,讨论了其实时特性和调度特点,并展望了Linux实时化的研究工作。

基于Linux系统多课程融合的教学方法研究

Linux系统作为各种系统、应用软件的开发和支撑平台,在计算机学科体系中发挥了非常基础的作用,是后续多门专业课程的先修课程。为了培养计算机专业学生的实践和创新能力,提升学生整体素质,将“Linux系统管理与编程环境”与多门专业课程进行融合,并通过设计具有生产性、实践性的教学案例来提升学生学习积极性,使课程更具“高阶性、创新性、挑战度”,体现了突出以学生为中心,注重能力培养的教学模式。

基于大语言模型的Linux课程问答系统

基于国产主流大语言模型,设计一个Linux课程知识问答系统.该系统结合检索增强技术,能根据人类反馈持续学习,有助于解决Linux课程教学中如何更有效辅助学生学习的问题.实验结果表明,该系统提高了大语言模型回答的事实性,能有效回答学生提问.此外,该系统以较低成本积累了以自然语言形式呈现的专业领域知识库,降低了教师教学资料搜集整理的工作量.

Unknown Application Layer Protocol Recognition Method Based on Deep Clustering

In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extraction ability,and they cannot mine the discriminating features of the protocol data thoroughly.To address the issue,we propose an unknown application layer protocol recognition method based on deep clustering.Deep clustering which consists of the deep neural network and the clustering algorithm can automatically extract the features of the input and cluster the data based on the extracted features.Compared with the traditional clustering methods,deep clustering boasts of higher clustering accuracy.The proposed method utilizes network-in-network(NIN),channel attention,spatial attention and Bidirectional Long Short-term memory(BLSTM)to construct an autoencoder to extract the spatial-temporal features of the protocol data,and utilizes the unsupervised clustering algorithm to recognize the unknown protocols based on the features.The method firstly extracts the application layer protocol data from the network traffic and transforms the data into one-dimensional matrix.Secondly,the autoencoder is pretrained,and the protocol data is compressed into low dimensional latent space by the autoencoder and the initial clustering is performed with K-Means.Finally,the clustering loss is calculated and the classification model is optimized according to the clustering loss.The classification results can be obtained when the classification model is optimal.Compared with the existing unknown protocol recognition methods,the proposed method utilizes deep clustering to cluster the unknown protocols,and it can mine the key features of the protocol data and recognize the unknown protocols accurately.Experimental results show that the proposed method can effectively recognize the unknown protocols,and its performance is better than other methods.

Linux终端检测响应系统的文件防护绕过技术研究

目前,国内外很多厂商推出了Linux系统中的终端检测响应(Endpoint Detection and Response,EDR)系统,为云平台、物联网、大数据计算等基础设施提供全面的安全检测和防护服务。但是,针对EDR文件防护功能的绕过攻击能够帮助恶意行为规避监控,造成严重的系统和数据安全风险。针对开源和商业闭源的Linux EDR系统,首先,阐述了文件防护功能的底层实现机制,对其核心技术原理进行了分析;其次,重点梳理了4种现有公开的文件防护绕过技术,提出了3种尚未公开的绕过技术,并且总结提炼为3种攻击类型;再次,基于上述绕过技术编写了验证工具,通过测试证明了这些技术方法对于部分Linux EDR系统的文件防护绕过能力;最后,给出了相应的安全防护建议。

基于线上线下混合式教育的LINUX操作系统课程思政探索与实践

“课程思政”是实现“立德树人”根本任务的重要举措。近年来,课程团队基于线上线下混合式教学模式开展了LINUX操作系统课程思政的探索和实践。从课程目标、教师教学能力、线上线下教学各个环节等方面全程全方位将课程思政有机融入,旨在培养“德才兼备”LINUX操作系统人才。

高职“Linux操作系统”课程建设与实践

以高职“Linux操作系统”课程为例,以专业教师、思政教师与企业导师相互配合,组建高素质结构化教学团队、建设“双师型”教师队伍为基础,以开发校企合编新型活页式教材、在线课程和数字化实训室等数字资源为载体,依托校企共建的“华为信息与网络技术学院”“北斗导航协同创新中心”,融入“云、大、物、智”等新一代信息技术,采用任务驱动、理实一体、行动导向教学理论开展教学。应用研究表明,课程实施教师、教法、教材的“三教”改革对培养学生的知识、技能、素质等方面有明显的促进作用,有效达成了人才培养既定教学目标。

基于虚拟机自省的Linux恶意软件检测方案

随着物联网和云计算技术的快速发展,Linux恶意软件的数量和种类急剧增加,因此如何有效检测Linux恶意软件成为安全领域的重要研究方向之一。为了解决这一问题,文章提出一种基于虚拟机自省的Linux恶意软件检测方案。该方案利用虚拟机自省技术在沙箱外部安全获取内部运行状态,在实现全方位监控的同时,规避了恶意软件的反动态分析问题。与其他沙箱监控方案相比,文章所提方案增加了恶意软件在沙箱中的恶意行为表现的数量。针对特征之间的时序性,采用时序处理模型对沙箱获取的特征信息进行建模和训练,旨在判断Linux应用是否属于恶意软件。文章使用了3种神经网络,包括循环神经网络、长短期记忆网络和门控循环单元网络。实验结果表明,长短期记忆网络在该应用场景下检测效果更好,准确率达98.02%,同时具有较高的召回率,将虚拟机自省技术与神经网络模型结合应用于恶意软件检测,既能在虚拟机外部监控虚拟机内部,又考虑特征之间的时序性。

高职专业群共享课程的改革与实践——以“Linux操作系统”课程为例

本文以健康信息服务专业群“Linux操作系统”课程为例,分析专业群共享课程针对群内各专业的培养目标以及该课程作为专业群共享课程的课程特点,从课程教学的改革思路、课程内容的提取与分配、特色教材的开发、课程资源的建设、教学方法的配套等方面阐述专业群共享课程的建设实践,以期为高职院校信息类专业群共享课程建设提供参考。

高职《Linux操作系统应用》课程思政的设计与实践——以工匠精神为例

在高职院校专业课程教学中要重视课程思政,贯彻“各门课程都具有育人功能”的精神。本文通过在高职《Linux操作系统应用》课程教学中融入工匠精神,给出了高职专业课程课程思政的设计全过程,包括课前自我思政、课中沉浸思政、课后思政再强化三个环节,使学生逐步感受、认识,从而达到课程思政育人目标。