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RETRACTED: Contourlet-based non-local mean via Retinex theory for robot infrared image enhancement [EAI Endorsed Scal Inf Syst (2022), Online First] 撤下:基于contourlet的基于Retinex理论的非局部均值机器人红外图像增强[EAI背书尺度信息系统(2022),在线第一]
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-04-08 DOI: 10.4108/eai.8-4-2022.173789
Xi Zhang, Jiyue Wang
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引用次数: 0
RETRACTED: An automatic scoring method for Chinese-English spoken translation based on attention LSTM [EAI Endorsed Scal Inf Syst (2022), Online First] 一种基于注意力LSTM的汉英口语翻译自动评分方法[EAI背书尺度信息系统(2022),Online First]
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-04-08 DOI: 10.4108/eai.8-4-2022.173786
Xiaobin Guo
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引用次数: 0
RETRACTED: A novel Gauss-Laplace operator based on multi-scale convolution for dance motion image enhancement [EAI Endorsed Scal Inf Syst (2022), Online First] 撤下:一种基于多尺度卷积的新型高斯拉普拉斯算子用于舞蹈运动图像增强[EAI背书尺度信息系统(2022),Online First]
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-04-08 DOI: 10.4108/eai.8-4-2022.173797
Dianhuai Shen, X. Jiang, Lin Teng
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引用次数: 0
A Fuzzy TOPSIS Based Analysis to Prioritize Enabling Factors for Strategic Information Technology Management 基于模糊TOPSIS的战略信息技术管理使能因素优先排序分析
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-04-04 DOI: 10.4108/eai.4-4-2022.173782
Raziya Siddiqui, N. Khan, S. Ahmad
Strategic management of information technology (IT) requires the attention provided to internal and external organizational factors. This paper discusses different enabling factors that allow strategic management of IT, making advances not only for using the approaches independently as well as in using them in a corresponding and adaptive way. A questionnaire-based survey and in-depth discussions were performed with 40 primary stakeholders to assess the relevance of enabling factors. Using available resources-based analysis, enabling factors were defined in four different categories: organizational, business, technological, and operational assessment. Subsequently, these four enabling factors were prioritized using the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) multi-criteria decision analysis method. Finally, technological assessments were given high priority on the basis of the findings to allow more successful strategic IT management.
信息技术(IT)的战略管理需要关注内部和外部组织因素。本文讨论了允许IT战略管理的不同启用因素,不仅在独立使用这些方法以及以相应和自适应的方式使用它们方面取得了进展。对40个主要利益攸关方进行了基于问卷的调查和深入讨论,以评估促成因素的相关性。使用可用的基于资源的分析,将启用因素定义为四个不同的类别:组织、业务、技术和操作评估。随后,采用模糊TOPSIS (Similarity to Ideal Solution)多准则决策分析法对这4个使能因素进行排序。最后,根据调查结果给予技术评估以高度优先级,以允许更成功的战略IT管理。
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引用次数: 0
Majority Voting and Feature Selection Based Network Intrusion Detection System 基于多数投票和特征选择的网络入侵检测系统
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-04-04 DOI: 10.4108/eai.4-4-2022.173780
D. Patil, T. Pattewar
Attackers continually foster new endeavours and attack strategies meant to keep away from safeguards. Many attacks have an effect on other malware or social engineering to collect consumer credentials that grant them get access to network and data. A network intrusion detection system (NIDS) is essential for network safety because it empowers to understand and react to malicious traffic. In this paper, we propose a feature selection and majority voting based solutions for detecting intrusions. A multi-model intrusion detection system is designed using Majority Voting approach. Our proposed approach was tested on a NSL-KDD benchmark dataset. The experimental results show that models based on Majority Voting and Chi-square features selection method achieved the best accuracy of 99.50% with error-rate of 0.501%, FPR of 0.005 and FNR of 0.005 using only 14 features.
攻击者不断培育新的努力和攻击策略,以避开安全措施。许多攻击对其他恶意软件或社会工程产生影响,以收集授予他们访问网络和数据的消费者凭证。网络入侵检测系统(NIDS)对网络安全至关重要,因为它能够理解恶意流量并对其做出反应。本文提出了一种基于特征选择和多数投票的入侵检测方案。采用多数投票法设计了一个多模型入侵检测系统。我们提出的方法在NSL-KDD基准数据集上进行了测试。实验结果表明,基于多数票和卡方特征选择方法的模型仅使用14个特征,准确率达到99.50%,错误率为0.501%,FPR为0.005,FNR为0.005。
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引用次数: 1
An Efficient Neuro Deep Learning Intrusion Detection System for Mobile Adhoc Networks 一种高效的移动自组网神经深度学习入侵检测系统
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-04-04 DOI: 10.4108/eai.4-4-2022.173781
N. Venkateswaran, S. Prabaharan
As of late mobile ad hoc networks (MANETs) have turned into a very popular explore the theme. By giving interchanges without a fixed infrastructure MANETs are an appealing innovation for some applications, for ex, reassigning tasks, strategic activities, nature observing, meetings, & so forth. This paper proposes the use of a neuro Deep learning wireless intrusion detection system that distinguishes the attacks in MANETs. Executing security is a hard task in MANET due to its immutable vulnerabilities. Deep learning gives extra security to such systems and the proposed framework comprises a hybrid conspiracy that joins the determination and abnormality-based methodologies. Executing the partial IDS utilizing neuro Deep learning improves the identification rate in MANETs. The proposed plan utilizes deep neural networks and a cross breed neural system. It demonstrates that Recurrent neural networks can successfully improve the identification and diminish the rate of false caution and failure.
最近,移动自组织网络(manet)已经变成了一个非常流行的探索主题。通过在没有固定基础设施的情况下提供交换,manet对于一些应用来说是一个有吸引力的创新,例如,重新分配任务,战略活动,自然观察,会议等等。本文提出了一种基于神经深度学习的无线入侵检测系统,用于识别无线网络中的攻击。由于其不可变的漏洞,在MANET中执行安全是一项艰巨的任务。深度学习为这样的系统提供了额外的安全性,所提出的框架包括一个混合阴谋,它结合了确定和基于异常的方法。利用神经深度学习来执行部分IDS,可以提高自适应神经网络的识别率。该方案利用深度神经网络和杂交神经系统。结果表明,递归神经网络可以有效地提高识别效率,降低误报率和失败率。
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引用次数: 0
A Taxonomy for Large-Scale Cyber Security Attacks 大规模网络安全攻击的分类
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-03-02 DOI: 10.4108/eai.2-3-2022.173548
Fadi Mohsen, C. Zwart, D. Karastoyanova, G. Gaydadjiev
In an e ff ort to examine the spread of large-scale cyber attacks, researchers have created various taxonomies. These taxonomies are purposefully built to facilitate the understanding and the comparison of these attacks , and hence counter their spread. Yet, existing taxonomies focus mainly on the technical aspects of the attacks, with little or no information about how to defend against them. As such, the aim of this work is to extend existing taxonomies by incorporating new features pertaining the defense strategy, scale, and others. We will compare the proposed taxonomy with existing state of the art taxonomies. We also present the analysis of 174 large cyber security attacks based on our taxonomy. Finally, we present a web tool that we developed to allow researchers to explore exiting data sets of attacks and contribute new ones. We are convinced that our work will allow researchers gain deeper insights into emerging attacks by facilitating their categorization, sharing and analysis, which results in boosting the defense e ff orts against cyber attack.
为了研究大规模网络攻击的传播,研究人员创建了各种分类。这些分类法的建立是为了便于理解和比较这些攻击,从而阻止它们的传播。然而,现有的分类法主要关注攻击的技术方面,很少或根本没有关于如何防御它们的信息。因此,这项工作的目的是通过合并与防御策略、规模和其他相关的新特征来扩展现有的分类法。我们将比较拟议的分类法与现有的最先进的分类法。我们还根据我们的分类法对174次大型网络安全攻击进行了分析。最后,我们展示了一个我们开发的网络工具,允许研究人员探索现有的攻击数据集并贡献新的攻击数据集。我们相信,我们的工作将使研究人员通过促进对新兴攻击的分类、共享和分析,从而提高对网络攻击的防御能力,从而更深入地了解新兴攻击。
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引用次数: 0
A credible predictive model for employment of college graduates based on LightGBM 基于LightGBM的可信大学毕业生就业预测模型
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-02-17 DOI: 10.4108/eai.17-2-2022.173456
Yangzi He, Jiawen Zhu, Weina Fu
INTRODUCTION: "Improving the employment rate of college students" directly affects the stability of the country and society and the healthy development of the industry market. The traditional graduate employment rate model only predicts the future employment rate based on changes in historical employment data in previous years. OBJECTIVES: Quantify the employment factors and solve the employment problems in colleges and universities in a targeted manner. METHODS: We construct a credible employment prediction model for college graduates based on LightGBM. RESULTS: We use the model to predict the employment status of students and obtain the special importance which is important to employment of college students . CONCLUSION: The final result shows that our Model performs well in the two indicators of accuracy and model quality.
引言:“提高大学生就业率”直接关系到国家和社会的稳定以及行业市场的健康发展。传统的毕业生就业率模型只是根据往年历史就业数据的变化来预测未来的就业率。目的:量化就业因素,有针对性地解决高校就业问题。方法:基于LightGBM构建可信的大学毕业生就业预测模型。结果:运用该模型对大学生就业状况进行了预测,得到了对大学生就业具有特殊重要性的信息。结论:最终结果表明,我们的模型在准确率和模型质量两个指标上表现良好。
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引用次数: 0
Quality evaluation system of engineering cost education curriculum based on data clustering 基于数据聚类的工程造价教育课程质量评价体系
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-02-11 DOI: 10.4108/eai.11-2-2022.173451
K. Liang, Xiao-qing Cai, Lu Hui
Aiming at the problems of poor evaluation effect and long system response time in the existing project cost course quality evaluation system, a project cost education course quality evaluation system based on data clustering is designed. The data acquisition module of infrastructure layer is used to collect the quality evaluation data of engineering cost education course, and the collected data is transmitted to the upper computer by can communication module. The processor control module in the upper computer transmits the data to the course quality evaluation module, and the processor control module selects 32-bit fixed-point chip TMS320F2812; After receiving the data, the course quality evaluation module uses the fuzzy matter-element proximity clustering evaluation method in data mining to evaluate the quality of engineering cost education courses. The evaluation results are transmitted to the application layer for users to use, and the evaluation results are displayed to users through the display interface of the display layer to complete the system design. The experimental results show that the proposed system can complete the quality evaluation of engineering cost education course, the response time of system evaluation is controlled within 400ms, and the response efficiency of the system is improved.
针对现有工程造价课程质量评价体系存在的评价效果差、系统响应时间长等问题,设计了一种基于数据聚类的工程造价教育课程质量评价体系。基础层数据采集模块用于采集工程造价教育课程的质量评价数据,并通过can通信模块将采集到的数据传输到上位机。上位机的处理器控制模块将数据传输给课程质量评估模块,处理器控制模块选择32位定点芯片TMS320F2812;课程质量评价模块接收到数据后,采用数据挖掘中的模糊物元接近聚类评价方法对工程造价教育课程质量进行评价。将评估结果传输到应用层供用户使用,并通过显示层的显示界面将评估结果显示给用户,完成系统设计。实验结果表明,所提出的系统能够完成工程造价教育课程的质量评价,系统评价的响应时间控制在400ms以内,提高了系统的响应效率。
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引用次数: 0
Design of music training assistant system based on artificial intelligence 基于人工智能的音乐训练辅助系统设计
IF 1.3 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Pub Date : 2022-02-11 DOI: 10.4108/eai.11-2-2022.173450
Hua Zhihan, Liang Yuan, Tao Jin
In order to improve the input accuracy and response speed of music training, this paper designs an intelligent assistant system. The architecture is divided into infrastructure layer, data layer, application layer and presentation layer. In the hardware design, the combination of ARM and digital signal processor (DSP) is used to realize the interaction between data analysis and human and interface. In the software design, cepstrum algorithm is used to extract cepstrum features of music signals, linear smoothing algorithm is used to filter, dynamic time warping method is used to match patterns, and radial basis function algorithm is used to output the results. Thus, the overall design of the music-assisted training system is completed. Experimental results show that the signal-to-noise ratio of music signal transmission is more than 14dB, the accuracy is higher than 99.5%, and the response speed of serving 240 users is only 1s. The system has strong operability and good performance of music assistant training.
为了提高音乐训练的输入精度和响应速度,本文设计了一个智能辅助系统。该体系结构分为基础设施层、数据层、应用层和表示层。在硬件设计上,采用ARM与数字信号处理器(DSP)相结合的方式,实现数据分析与人机交互。在软件设计中,采用倒谱算法提取音乐信号的倒谱特征,采用线性平滑算法进行滤波,采用动态时间规整法进行模式匹配,采用径向基函数算法输出结果。至此,完成了音乐辅助训练系统的总体设计。实验结果表明,该方法传输音乐信号的信噪比大于14dB,准确率高于99.5%,服务240个用户的响应速度仅为15秒。该系统具有较强的可操作性和较好的音乐助理培训效果。
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引用次数: 2
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EAI Endorsed Transactions on Scalable Information Systems
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