探讨局域网网络维护及其管理措施

现在局域网在日常办公中起到越来越重要的作用,随之而来的就是其安全和管理问题。局域网络的有效维护和妥善管理对于其正常运行显得尤为重要。笔者将以局域网中的各类不安全因素为切入点,深入分析威胁网络安全的因素,并提出切实可行的局域网安全管理措施,为提高局域网管理水平提供借鉴意义。     

基于局域网网络维护和管理措施探讨

改革开放以来,我国经济迅猛发展,信息水平也不断提高。工作对计算机的需求量也慢慢变大,计算机已经成为工作中不可缺少的一部分。加强对局域网管理的相关工作就显得尤为重要,本文由局域网中各类不安全因素切入,对网络安全威胁因素深入分析,并提出切实可行的管理措施。     

Selecting among five common modelling approaches for integrated environmental assessment and management

The design and implementation of effective environmental policies need to be informed by a holistic understanding of the system processes (biophysical, social and economic), their complex interactions, and how they respond to various changes. Models, integrating different system processes into a unified framework, are seen as useful tools to help analyse alternatives with stakeholders, assess their outcomes, and communicate results in a transparent way. This paper reviews five common approaches or model types that have the capacity to integrate knowledge by developing models that can accommodate multiple issues, values, scales and uncertainty considerations, as well as facilitate stakeholder engagement. The approaches considered are: systems dynamics, Bayesian networks, coupled component models, agent-based models and knowledge-based models (also referred to as expert systems). We start by discussing several considerations in model development, such as the purpose of model building, the availability of qualitative versus quantitative data for model specification, the level of spatio-temporal detail required, and treatment of uncertainty. These considerations and a review of applications are then used to develop a framework that aims to assist modellers and model users in the choice of an appropriate modelling approach for their integrated assessment applications and that enables more effective learning in interdisciplinary settings.     

一种面向大规模系统域网络性能管理系统

当前系统域网络规模日益庞大,如何监控系统域网络复杂的流量行为、发现性能瓶颈以及可能的网络故障点,为系统域网络性能优化提供有效支持的需求已经日益迫切。首先提出了一种系统域网络的性能管理体系结构SNPMA,SNPMA采用了松耦合的分层结构,通过各层之间的协同实现性能管理的自动化和可操作性。在此基础上提出了一种网络性能评估模型NPEM,解决大规模网络中对现有网络设备性能状况无法正确评估、对网络运行状态无法进行有效预测的问题,进而提出了自适应并发策略性能监控方法STM,能动态调整采集数据的策略,较好地提高了采集数据的效率。在"天河二号"真实的网络环境下,对网络设备的性能进行分级评估和分析,验证了网络性能评估和分析模型。     

基于代价敏感间隔分布优化的软件缺陷定位

在大型软件项目的开发与维护中,从大量的代码文件中定位软件缺陷费时、费力,有效地进行软件缺陷自动定位,将能极大地降低开发成本.软件缺陷报告通常包含了大量未发觉的软件缺陷的信息,精确地寻找与缺陷报告相关联的代码文件,对于降低维护成本具有重要意义.目前,已有一些基于深度神经网络的缺陷定位技术相对于传统方法,其效果有所提升,但相关工作大多关注网络结构的设计,缺乏对训练过程中损失函数的研究,而损失函数对于预测任务的性能会有极大的影响.在此背景下,提出了代价敏感的间隔分布优化（cost-sensitive margin distribution optimization,简称CSMDO）损失函数,并将代价敏感的间隔分布优化层应用到深度卷积神经网络中,能够良好地处理软件缺陷数据的不平衡性,进一步提高缺陷定位的准确度.     

独立于网格计算经济模型的记账和支付

针对当前网格计算经济中记账和支付的不足,提出了一种能够独立于计算经济模型的记账和支付体系结构.以独立的第3方完成记账数据采集;依据GSP和GSC的支付策略实现资源选择和支付;完成了一个支付算法,实现了和现实商业银行的仿真连接.     

计算机网络安全的主要隐患及管理措施分析

随着我国科技的不断发展,我国已经步入信息化时代的潮流中,全国各地对计算机网络的应用越来越广泛,计算机网络应用极大的为人们生活带来方便和快捷、同时不断促使着我国市场经济的发展、带领我国的经济和文化走向国际化发展前沿,但是在计算机网络的使用中,出现着种种弊端,计算机网络其实就是一把双刃剑,给人们带来优越生活的同时,也出现着各种危害,本文针对计算机网络安全现状进行分析,主要研究计算机网络安全的主要隐患,并提出合理化管理措施。     

A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability

The experimental analysis on the performance of a proposed method is a crucial and necessary task to carry out in a research. This paper is focused on the statistical analysis of the results in the field of genetics-based machine Learning. It presents a study involving a set of techniques which can be used for doing a rigorous comparison among algorithms, in terms of obtaining successful classification models. Two accuracy measures for multi-class problems have been employed: classification rate and Cohen’s kappa. Furthermore, two interpretability measures have been employed: size of the rule set and number of antecedents. We have studied whether the samples of results obtained by genetics-based classifiers, using the performance measures cited above, check the necessary conditions for being analysed by means of parametrical tests. The results obtained state that the fulfillment of these conditions are problem-dependent and indefinite, which supports the use of non-parametric statistics in the experimental analysis. In addition, non-parametric tests can be satisfactorily employed for comparing generic classifiers over various data-sets considering any performance measure. According to these facts, we propose the use of the most powerful non-parametric statistical tests to carry out multiple comparisons. However, the statistical analysis conducted on interpretability must be carefully considered.     

计算机网络安全的主要隐患及管理措施分析

随着现代科学技术以及网络信息技术的不断更新换代,电子计算机网络已经被十分广泛的应用于各个领域,并且在各个领域中发挥越来越重要的作用。然而,网络信息技术的发展也为网络用户带来了很多的安全隐患。从微观角度,网络安全隐患可能会导致个人隐私与重要信息泄露。从宏观角度,网络安全隐患可能会导致整个国家的国防以及其他核心机密受到威胁。本文主要研究和分析了当前计算机网络安全的主要隐患,并且提出了几点管理措施。     

Location does not have to be destiny: student evaluation and integrity controls in a management accounting class

As accounting education transitions to more distance-learning formats, the integrity of student evaluation continues to serve as an obstacle to adoption. Greater technological possibilities will be opposed if faculty members believe that testing is compromised. This article investigates whether students taking exams remotely (and under no surveillance) performed better than students taking exams under conventional exam security. The results suggest an equivalent degree of achievement. The lack of human-based integrity controls over students taking exams did not produce a situation where grades were no longer accurate manifestations of student abilities. Although the results may be the results of the specific testing environment that was in use, they are encouraging for the proliferation of distance education in the accounting discipline. Integrity controls can be designed into the programs that administer the assessment.     

Comparison of Algorithmic and Machine Learning Approaches for the Automatic Fitting of Gaussian Peaks

Fitting Gaussian peaks to experimental data is important in many disciplines, including nuclear spectroscopy. Nonlinear least squares fitting methods have been in use for a long time, but these are iterative, computationally intensive, and require user intervention. Machine learning approaches automate and speed up the fitting procedure. However, for a single pure Gaussian, there exists a simple and automatic analytical approach based on linearisation followed by a weighted linear Least Squares (LS) fit. This paper compares this algorithmic method with an abductive machine learning approach based on AIM ¹ (Abductory Induction Mechanism). Both techniques are briefly described and their performance compared for analysing simulated and actual spectral peaks. Evaluated on 500 peaks with statistical uncertainties corresponding to a peak count of 100, average absolute errors for the peak height, position and width are 4.9%, 2.9% and 4.2% for AIM, versus 3.3%, 0.5% and 7.7% for the LS. AIM is better for the width, while LS is more accurate for the position. LS errors are more biased, under-estimating the peak position and over-estimating the peak width. Tentative CPU time comparison indicates a five-fold speed advantage for AIM, which also has a constant execution time, while LS time depends upon the peak width.     

基于奇异值分解的雷达性能组合预测方法

对雷达实施健康管理过程中,预测是重要的功能环节。雷达的性能参数监测序列反映其健康状态,在对其进行建模预测过程中,单一模型难以满足预测准确度要求。为了提高预测准确度,需选用与雷达失效机理相适应的模型。在自回归模型、径向基函数神经网络和奇异值滤波算法的基础上,提出了一种联合两类模型的最优化组合预测方法,将奇异值分解滤波恰当地应用于辨识雷达性能的非同源影响因素并对雷达性能监测序列进行最优拆分。仿真结果表明,该方法相较于单一模型预测和传统的组合预测算法,预测准确度指标提升至少一个数量级。     

High performance accelerators for deep neural networks: A review

The availability of huge structured and unstructured data, advanced highly dense memory and high performance computing machines have provided a strong push for the development in artificial intelligence (AI) and machine learning (ML) domains. AI and machine learning has rekindled the hope of efficiently solving complex problems which was not possible in the recent past. The generation and availability of big-data is a strong driving force for the development of AI/ML applications, however, several challenges need to be addressed, like processing speed, memory requirement, high bandwidth, low latency memory access, and highly conductive and flexible connections between processing units and memory blocks. The conventional computing platforms are unable to address these issues with machine learning and AI. Deep neural networks (DNNs) are widely employed for machine learning and AI applications, like speech recognition, computer vison, robotics, and so forth, efficiently and accurately. However, accuracy is achieved at the cost of high computational complexity, sacrificing energy efficiency and throughput like performance measuring parameters along with high latency. To address the problems of latency, energy efficiency, complexity, power consumption, and so forth, a lot of state of the art DNN accelerators have been designed and implemented in the form of application specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs). This work provides the state of the art of all these DNN accelerators which have been developed recently. Various DNN architectures, their computing units, emerging technologies used in improving the performance of DNN accelerators will be discussed. Finally, we will try to explore the scope for further improvement in these accelerator designs, various opportunities and challenges for the future research.     

Context-aware temporal network representation of event logs: Model and methods for process performance analysis

Analysing performance of business processes is an important vehicle to improve their operation. Specifically, an accurate assessment of sojourn times and remaining times enables bottleneck analysis and resource planning. Recently, methods to create respective performance models from event logs have been proposed. These works have several limitations, though: They either consider control-flow and performance information separately, or rely on an ad-hoc selection of temporal relations between events. In this paper, we introduce the Temporal Network Representation (TNR) of a log. It is based on Allen’s interval algebra, comprises the pairwise temporal relations for activity executions, and potentially incorporates the context in which these relations have been observed. We demonstrate the usefulness of the TNR for detecting (unrecorded) delays and for probabilistic mining of variants when modelling the performance of a process. In order to compare different models from the performance perspective, we further develop a framework for measuring performance fitness. Under this framework, TNR-based process discovery is guaranteed to dominate existing techniques in measuring performance characteristics of a process. In addition, we show how contextual information in terms of the congestion levels of the process can be mined in order to further improve capabilities for performance analysis. To illustrate the practical value of the proposed models, we evaluate our approaches with three real-life datasets. Our experiments show that the TNR yields an improvement in performance fitness over state-of-the-art algorithms, while congestion learning is able to accurately reconstruct congestion levels from event data.     

Federated learning for green shipping optimization and management

Many shipping companies are unwilling to share their raw data because of data privacy concerns. However, certain problems in the maritime industry become much more solvable or manageable if data are shared—for instance, the problem of reducing ship fuel consumption and thus emissions. In this study, we develop a two-stage method based on federated learning (FL) and optimization techniques to predict ship fuel consumption and optimize ship sailing speed. Because FL only requires parameters rather than raw data to be shared during model training, it can achieve both information sharing and data privacy protection. Our experiments show that FL develops a more accurate ship fuel consumption prediction model in the first stage and thus helps obtain the optimal ship sailing speed setting in the second stage. The proposed two-stage method can reduce ship fuel consumption by 2.5%–7.5% compared to models using the initial individual data. Moreover, our proposed FL framework protects the data privacy of shipping companies while facilitating the sharing of information among shipping companies.     

ANN based self tuned PID like adaptive controller design for high performance PMSM position control

Proportional-integral-derivative (PID) being the most simple and the widely deployed controller in the industrial drives is not quite amenable to the solution for high performance drives as these drives are subjected to the parametric uncertainty, unmodeled dynamics and variable load conditions during operation. In order to expand the robustness and adaptive capabilities of conventional PID controller, a neural network based PID (NNPID) like controller which is tuned when the controller is operating in an on line mode for high performance permanent magnet synchronous motor (PMSM) position control is proposed in this paper. The NN based PID like controller is composed of a mixed locally recurrent neural network and contains at most three hidden nodes which form a PID like structure. A novel training algorithm for the PID controller gain initialization based upon the minimum norm least square solution is proposed. An on line sequential training algorithm based on recursive least square is then derived to update controller gains in an on line manner. The proposed controller is not only easy to implement but also requires least number of parameters to be tuned prior to the implementation. The performance of the proposed controller is evaluated in the presence of parametric uncertainties and load disturbances also the result outcomes are compared with the conventional PID controller, optimized using Cuckoo search based optimization method.     

Social networks,communication styles,and learning performance in a CSCL community

The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning social networks, when they had work together on the design of aerospace systems using online collaboration tools. The results showed that both individual and structural factors (i.e., communication styles and a pre-existing friendship network) significantly affected the way the learners developed collaborative learning social networks. More specifically, learners who possessed high willingness to communicate (WTC) or occupied initially peripheral network positions were more likely to explore new network linkages. We also found that the resultant social network properties significantly influenced learners’ performance to the extent that central actors in the emergent collaborative social network tended to get higher final grades. The study suggests that communication and social networks should be central elements in a distributed learning environment. We also propose that the addition of personality theory (operationalized here as communication styles) to structural analysis (SNA) contributes to an enhanced picture of how distributed learners build their social and intellectual capital in the context of CSCL.     

基于局部权值阈值调整的BP算法的研究

文章针对BP算法收敛速度慢的问题,提出一种基于局部权值阈值调整的BP算法。该算法结合生物神经元学习与记忆形成的特点,针对特定的训练样本,只激发网络中的部分神经元以产生相应的输出,而未被激发的神经元产生的输出则与目标输出相差较大,那么我们就需要对未被激发的神经元权值阈值进行调整。所以该论文提出的算法是对局部神经元权值阈值的调整,而不是传统的BP算法需要对所有神经元权值阈值进行调整,这样有助于加快网络的学习速度。     

Mixing logics and rewards for the component-oriented specification of performance measures

Formal notations for system performance modeling need to be equipped with suitable notations for specifying performance measures. These companion notations have been traditionally based on reward structures and, more recently, on temporal logics. In this paper we propose an approach that combines logics and rewards, together with a definition mechanism that allows performance measures to be specified in a component-oriented way, thus facilitating the task for non-experts. The resulting Measure Specification Language (MSL) is interpreted both on action-labeled continuous-time Markov chains and on stochastic process algebras. The latter interpretation provides a compositional framework for performance-sensitive model manipulations and emphasizes the increased expressiveness with respect to traditional reward structures for implicit-state modeling notations.     

Finite-time optimal tracking control for a class of nonlinear systems with prescribed performance

In this paper, a finite-time optimal tracking control scheme based on integral reinforcement learning is developed for partially unknown nonlinear systems. In order to realize the prescribed performance, the original system is transformed into an equivalent unconstrained system so as to a composite system is constructed. Subsequently, a modified nonlinear quadratic performance function containing the auxiliary tracking error is designed. Furthermore, the technique of experience replay is used to update the critic neural network, which eliminates the persistent of excitation condition in traditional optimal methods. By combining the prescribed performance control with the finite-time optimization control technique, the tracking error is driven to a desired performance in finite time. Consequently, it has been shown that all signals in the partially unknown nonlinear system are semiglobally practical finite-time stable by stability analysis. Finally, the provided comparative simulation results verify the effectiveness of the developed control scheme.