检索结果-内蒙古大学图书馆

9th IEEE International Conference on data science in Cyberspace, DSC 2024

作者： Li, Min Han, Delong Shao, Rui He, Jinghui City University of Macau Faculty of Data Science China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350391367

In the process of large-scale industrial production, complex defects of different shapes and sizes can occur in industrial products and their components, and these defects seriously affect the performance of industrial products and their components, and being able to quickly and accurately identify defective industrial products and their components is an important task. Memory-based methods are usually used for anomaly detection, and these methods often rely on pre-trained models for feature extraction, but have high requirements on feature quality. In addition, patch-based methods mainly focus on local differences in feature embedding when detecting anomalies without explicitly exploiting broader contextual information, thus greatly affecting the robustness and accuracy of anomaly detection. Aiming at the above problems and requirements, this paper proposes a Feature Aggregation Network for Memory-Based Industrial Anomaly Detection (FANet), which is based on the pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-level and multi-scale features, and the FANet provides advanced performance in industrial image anomaly detection and localisation by pre-extraction of anomalous features, attentional attention to key features, information aggregation of multi-layered and multi-scale features, and introduction of environmental information in the anomaly score computation to perform industrial anomaly detection tasks, and using core set subsampling to reduce storage requirements. On the MVTec AD, FANet has an image-level AUROC score of 99.1% and a pixel-level AUROC score of 98.2%. Similarly high detection results were achieved on the ViSA dataset, further demonstrating the robustness of FANet. © 2024 IEEE.

关键词： Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Developing a Visual Dashboard to Improve Research Activity Assessment

Developing a Visual Dashboard to Improve Research Activity A...

引用

International Conference on Computer, Control, Informatics and Its Applications (IC3INA)

作者： Muhammad Zaki Nur Rahman Esa Prakasa Moch Arif Bijaksana Bachelor of Data Science Faculty of Informatics Telkom University Bandung Indonesia National Research and Innovation Agency (BRIN) Research Center for Data and Information Sciences Bandung Indonesia School of Computing Telkom University Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

In research institutions, the ability to monitor and analyze publication output is crucial for assessing productivity and achieving strategic goals. This paper presents a dashboard designed to visualize and analyze publication data from a research center of the Indonesian public institution. The dashboard addresses several key questions: (1) whether the current number of publications is on track to meet the institution’s targets, (2) the comparison between journal and proceeding publications, (3) individual researcher productivity, (4) research group productivity, and (5) the collaborative efforts among researchers and groups. By leveraging this tool, stakeholders can gain insights into publication trends and identify areas for improvement. The findings reveal significant trends in publication types and collaboration patterns, offering a comprehensive overview of the institution’s research output. This study contributes to the understanding of research productivity metrics and their implications for strategic planning in academic research.

关键词： Productivity Measurement Visualization Target tracking Collaboration data visualization Strategic planning Market research Stakeholders Monitoring

来源：评论

学校读者我要写书评

暂无评论

Analytical Study for Cyber Threat Intelligence (CTI) 7

Analytical Study for Cyber Threat Intelligence (CTI)

引用

7th IET Smart Cities Symposium, SCS 2023

作者： Alkhateeb, Ibrahim R. Al-Haija, Qasem Abu Abu-Soud, Saleh Dept. of Cybersecurity/ Data Science King Hussein School of Computing Sciences Prince Sumaya University for Technology P.O. Box 1438 Amman11941 Jordan Dept. of Cybersecurity Faculty of Computer & Information Technology Jordan University of Science and Technology P.O. Box 3030 Irbid22110 Jordan

ISBN: (纸本)9781839539831

Cyber Threat Intelligence (CTI) involves collecting and analyzing cyber security data using advanced algorithms and rigorous techniques to identify and address potential threats, harmful incidents, and existing vulnerabilities that cybercriminals exploit. Threat intelligence facilitates faster and well-informed security decisions while understanding threat actors' motives, targets, and attack patterns. Defenders employ diverse strategies to prevent, detect, and disrupt incoming attacks. However, traditional security solutions are inadequate for today's complex security landscape, often proving ineffective against the rapid proliferation of cyber threats. Automating information sharing related to cyber threats presents new challenges for researchers and analysts, requiring them to detect deceptive practices and produce accurate, timely, and pertinent intelligence. This research explores the profound impact of Artificial Intelligence (AI) and Machine Learning (ML) in cyber security, including their various types, applications, challenges, and promising future. Leveraging AI knowledge, particularly ML technology, organizations can strengthen their defenses against malicious threats, reduce cyber risks, and establish a dynamic, automated, and up-to-date security system by analyzing security data. © The Institution of Engineering & Technology 2023.

关键词： Cybersecurity

来源：评论

学校读者我要写书评

暂无评论

Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

引用

Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

来源：评论

学校读者我要写书评

暂无评论

MI-MCF: A Mutual Information-Based Multilabel Causal Feature Selection

引用

IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 9864-9878页

作者： Lin Ma Liang Hu Yonghao Li Weiping Ding Wanfu Gao Department of Computer Science and Technology Jilin University Changchun Jilin China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu Sichuan China School of Artificial Intelligence and Computer Science Nantong University Nantong China Faculty of Data Science City University of Macau Macau China

Multilabel causal feature selection has attracted extensive attention in recent years. Current multilabel causal feature selection algorithms typically employ existing Markov Blanket (MB) search methods for the initial construction of the MB, followed by further optimization. These methods generally treat labels and features as equally weighted nodes during the MB construction process. However, the search for spouse sets often involves extensive conditional independence (CI) tests, which are time-consuming. Furthermore, they fail to consider the distinct contributions of labels and features to the target nodes. Information theory is often used to evaluate the contributions of nodes. Inspired by this, we carry out a theoretical investigation into the causal relationships within multilabel datasets and propose the mutual information-based multilabel causal feature selection (MI-MCF) method. First, MI-MCF employs MI and conditional MI (CMI) instead of CI test when constructing the MB of labels without incurring significant time overhead. Then, MI-MCF uses MI to compare the contributions of features and labels to the target nodes. This helps identify which nodes should be retained when recovering features hindered by strong label correlation. Finally, MI-MCF eliminates spurious nodes through a symmetry check. Experiments on real-world datasets demonstrate that MI-MCF can autonomously determine the optimal number of selected features and consistently outperform compared methods. The code is available at https://***/malinjlu/MI-MCF .

关键词： Feature extraction Correlation Learning systems Redundancy Optimization Mutual information Manifolds Electronic mail Computational complexity Accuracy

来源：评论

学校读者我要写书评

暂无评论

A Review on data Quality Dimensions for Big data

引用

Procedia Computer science 2024年 234卷 341-348页

作者： Fakhitah Ridzuan Wan Mohd Nazmee Wan Zainon Faculty of Data Science and Computing Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kota Bharu Kelantan Institute for Artificial Intelligence & Big Data Universiti Malaysia Kelantan Pengkalan Chepa 16100 Kota Bharu Kelantan School of Computer Sciences Universiti Sains Malaysia 11800 Penang

Big data wave has led to a rapid increase in the amount of data being collected by organizations. While the accuracy and reliability of prediction models are often prioritized, the quality of the collected data is frequently overlooked. Poor data quality can result in the common problem of ‘garbage in, garbage out’. Traditional measures of data quality, such as accuracy, consistency, completeness, and timeliness, are no longer adequate in the era of Big data. Therefore, this paper proposes a taxonomy of data quality dimensions specifically for Big data, addressing emerging challenges by formulating 20 dimensions and categorizing them into four distinct categories.

关键词： big data data quality data quality dimension data management

来源：评论

学校读者我要写书评

暂无评论

Privacy-aware data Fusion and Prediction for Smart City Services in Edge computing Environment 15

Privacy-aware Data Fusion and Prediction for Smart City Serv...

引用

2022 IEEE Congress on Cybermatics: 15th IEEE International Conferences on Internet of Things, iThings 2022, 18th IEEE International Conferences on Green computing and Communications, GreenCom 2022, 2022 IEEE International Conference on Cyber, Physical and Social computing, CPSCom 2022 and 8th IEEE International Conference on Smart data, Smartdata 2022

作者： Qi, Lianyong Chi, Xiaoxiao Zhou, Xiaokang Liu, Qi Dai, Fei Xu, Xiaolong Zhang, Xuyun School of Computer Science Qufu Normal University China Faculty of Data Science Shiga University Japan Riken Center for Advanced Intelligence Project Japan School of Computer and Software Nanjing University of Information Science and Technology China School of Big Data and Intelligence Engineering Southwest Forestry University China Department of Computing Macquarie University Australia

ISBN: (数字)9781665454179

ISBN: (纸本)9781665454179

With fast development of Cyber Physical System, the variety and volume of data generated from different edge servers are fairly considerable. Mining and exploiting the data would definitely bring huge advantages. However, there are two major issues exposed in the process. First, utilizing the data with high efficiency is challenging in edge computing environment. Second, data from multiple edge servers often contain sensitive information such as user preferences, which will give rise to privacy breach risks when using the data. Locality-Sensitive Hashing and amplified technology are utilized for dealing with two problems. In this paper, we propose a novel privacy-protection approach named SRchain-LSH in edge computing environment which is based on amplified Locality-Sensitive Hashing technique. In order to verify high accuracy and efficiency of our proposal, large sets of experiments are conducted. According to experimental results, our proposal shows better performance than those of comparative methods in terms of MAE , RMSE and time costs. For our method, the best value of MAE is about 0.41 and time cost is about 0.1 seconds, which are more accurate and efficient than comparative methods. © 2022 IEEE.

关键词： Smart city

来源：评论

学校读者我要写书评

暂无评论

LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition

arXiv

引用

arXiv 2024年

作者： Hu, Youbing Cheng, Yun Lu, Anqi Cao, Zhiqiang Wei, Dawei Liu, Jie Li, Zhijun AIoT Lab. Faculty of Computing Harbin Institute of Technology China Swiss Data Science Center Zurich Switzerland School of Cyber Engineering Xidian University China

The Vision Transformer (ViT) excels in accuracy when handling high-resolution images, yet it confronts the challenge of significant spatial redundancy, leading to increased computational and memory requirements. To address this, we present the Localization and Focus Vision Transformer (LF-ViT). This model operates by strategically curtailing computational demands without impinging on performance. In the Localization phase, a reduced-resolution image is processed;if a definitive prediction remains elusive, our pioneering Neighborhood Global Class Attention (NGCA) mechanism is triggered, effectively identifying and spotlighting class-discriminative regions based on initial findings. Subsequently, in the Focus phase, this designated region is used from the original image to enhance recognition. Uniquely, LF-ViT employs consistent parameters across both phases, ensuring seamless end-to-end optimization. Our empirical tests affirm LF-ViT’s prowess: it remarkably decreases Deit-S’s FLOPs by 63% and concurrently amplifies throughput twofold. Code of this project is at https://***/edgeai1/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Image recognition

来源：评论

学校读者我要写书评

暂无评论

Explaining Decision-Tree Predictions by Addressing Potential Conflicts between Predictions and Plausible Expectations 14

Explaining Decision-Tree Predictions by Addressing Potential...

引用

14th International Conference on Natural Language Generation, INLG 2021

作者： Maruf, Sameen Zukerman, Ingrid Reiter, Ehud Haffari, Gholamreza Dept. of Data Science and AI Faculty of IT Monash University VIC Australia Dept. of Computing Science University of Aberdeen United Kingdom

ISBN: (纸本)9781954085510

We offer an approach to explain Decision Tree (DT) predictions by addressing potential conflicts between aspects of these predictions and plausible expectations licensed by background information. We define four types of conflicts, operationalize their identification, and specify explanatory schemas that address them. Our human evaluation focused on the effect of explanations on users' understanding of a DT's reasoning and their willingness to act on its predictions. The results show that (1) explanations that address potential conflicts are considered at least as good as baseline explanations that just follow a DT path;and (2) the conflict-based explanations are deemed especially valuable when users' expectations disagree with the DT's predictions. © 2021 Association for Computational Linguistics

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Machine Unlearning Through Fine-Grained Model Parameters Perturbation

arXiv

引用

arXiv 2024年

作者： Zuo, Zhiwei Tang, Zhuo Li, Kenli Datta, Anwitaman College of Computer Science and Electronic Engineering Hunan University China College of Computing and Data Science Nanyang Technological University Singapore Shenzhen Research Institute Hunan University China Faculty of Computing Engineering & Media De Montfort University Leicester United Kingdom

Machine unlearning involves retracting data records and reducing their influence on trained models, aiding user privacy protection, at a significant computational cost potentially. Weight perturbation-based unlearning is common but typically modifies parameters globally. We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning that address the privacy needs while keeping the computational costs tractable. However, commonly used training data are independent and identically distributed, for inexact machine unlearning, current metrics are inadequate in quantifying unlearning degree that occurs after unlearning. To address this quantification issue, we introduce SPD-GAN, which subtly perturbs data distribution targeted for unlearning. Then, we evaluate unlearning degree by measuring the performance difference of the models on the perturbed unlearning data before and after unlearning. Furthermore, to demonstrate efficacy, we tackle the challenge of evaluating machine unlearning by assessing model generalization across unlearning and remaining data. To better assess the unlearning effect and model generalization, we propose novel metrics, namely, the forgetting rate and memory retention rate. By implementing these innovative techniques and metrics, we achieve computationally efficacious privacy protection in machine learning applications without significant sacrifice of model performance. A by-product of our work is a novel method for evaluating and quantifying unlearning degree. © 2024, CC BY.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：