检索结果-内蒙古大学图书馆

IEEE International Conference on Signal and Image Processing (ICSIP)

作者： Naixin Kang Pengfei Yi Qingshu Geng Jing Dong Rui Liu Ling Wang Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China

ISBN: (数字)9798350350920

ISBN: (纸本)9798350350937

Human Action Recognition is one of the most applied research directions in the field of Computer Vision, which is widely used in human-computer interaction, Augmented Reality (AR) technology, security monitoring, and other scenarios. However, due to the complexity of human action gestures, existing Human Action Recognition methods have certain deficiencies in dealing with variable human gestures and action information, and the accuracy needs to be improved. To improve the accuracy, We propose a multi-dimensional network model based on SC-LSTM(Skip-Connection + LSTM). First, a Temporal Feature Extraction Module is designed based on SC-LSTM, and a Spatial Feature Extraction Module is designed based on CNN and Multi-Attention Mechanism to extract potential human action features from both temporal and spatial dimensions, respectively. Then, a separate SC-LSTM classification network is utilized to process these spatio-temporal features to obtain the final HAR results. The experimental results show that compared to other algorithms, the present model can more fully utilize the information in the temporal dimension, and thus performs better in terms of HAR accuracy.

关键词： Human computer interaction Computer vision Accuracy Image recognition Feature extraction Complexity theory Human activity recognition

来源：评论

学校读者我要写书评

暂无评论

IP SafeGuard-An AI-Driven Malicious IP Detection Framework

引用

IEEE Access 2025年 13卷 90249-90261页

作者： Siam, Abdullah Al Alazab, Moutaz Awajan, Albara Hasan, Md Rakibul Obeidat, Areej Faruqui, Nuruzzaman Daffodil International University Department of Software Engineering Dhaka Daffodil Smart City1216 Bangladesh Liverpool John Moores University School of Computing and Data Sciences Oryx Universal College Cybersecurity Department Doha Qatar Al-Balqa Applied University Faculty of Artificial Intelligence Department of Intelligent Systems As-Salt19385 Jordan Department of Computer Science and Engineering Dhaka1216 Bangladesh Independent Researcher As-Salt19385 Jordan

The rising frequency and sophistication of cyberattacks have made real-time malicious IP detection a critical challenge for modern Security Operations Center (SOC). Traditional solutions, such as static blacklists and manual IP reputation checks, are no longer sufficient in today's dynamic threat scenario. To overcome these constraints, we present IP SafeGuard, an AI-driven platform that incorporates multi-source threat intelligence, sophisticated feature engineering, and machine learning (ML)for real-time IP categorization. The framework collects data from AbuseIPDB, VirusTotal, and other sources to compute a Dynamic Threat Score (DTS) for each IP address. It leverages an XGBoost-based classification model to achieve high accuracy and low false-positive rates, even in skewed datasets. Experimental findings indicate the improved performance of IP SafeGuard, with an accuracy of 98.2%, a precision of 97.8%, and a recall of 98.5%. The average detection duration of 45 milliseconds makes it appropriate for real-time SOC integration, enabling automated incident response through Security Information and Event Management (SIEM) alerting and firewall blocking. The framework's modular design assures scalability and adaptability, making it a vital tool for high-volume situations. By overcoming the limits of old approaches and using the power of ML, IP SafeGuard considerably boosts the efficiency and efficacy of current cybersecurity systems. Future work involves expanding the system to enable new threat intelligence sources and studying federated learning for secure and privacy-preserving threat information exchange. © 2013 IEEE.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

HOTFUZ: Cost-effective higher-order mutation-based fault localization

HOTFUZ: Cost-effective higher-order mutation-based fault loc...

引用

作者： Jang, Jong-In Ryu, Duksan Baik, Jongmoon School of Computing Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Software Engineering Jeonbuk National University Jeonju Korea Republic of

Fault localization techniques are used to deduce the exact source of a failure from a set of failure indications while debugging software and play a crucial role in improving software quality. Mutation-based fault localization (MBFL) techniques are proposed to localize faults at a finer granularity and with higher accuracy than traditional fault localization techniques. Despite the technique's effectiveness, the immense cost of mutation analysis hinders MBFL's practical application in the industry. Various mutation alternative strategies are utilized to lower the cost of MBFL, but they sacrifice the accuracy of localization results. Higher-order mutation testing was proposed to search for valuable mutants that drive testing harder and reduce the overall test effort. However, higher-order mutants (HOMs) never have been used to address the cost problem of MBFL to the extent of our knowledge. This paper proposes a novel, cost-effective MBFL technique called HOTFUZ, Higher-Order muTation-based FaUlt localiZation, that employs HOMs to reduce the cost while minimizing the accuracy degradation. HOTFUZ combines mutants of a program under test into HOMs to decrease the number of mutants by more than half, depending on the order of HOMs. An experimental study is conducted using 65 real-world faults of CoREBench to assess the proposed approach's cost-effectiveness. The experimental results show that HOTFUZ outperforms the extant mutation alternative strategies by localizing faults more accurately using the same number of mutants executed. HOTFUZ has three main benefits over existing mutant reduction techniques for MBFL: (a) It keeps the advantage of using the whole set of mutation operators;(b) it does not discard generated mutants randomly for the sake of efficiency;and, finally, (c) it significantly decreases the proportion of equivalent mutants. © 2021 John Wiley & Sons, Ltd.

关键词： Cost effectiveness

来源：评论

学校读者我要写书评

暂无评论

Robust Psoriasis Severity Classification by using Data Augmentation

引用

Transactions of the Korean Institute of Electrical Engineers 2022年第12期71卷 1841-1847页

作者： Moon, Cho-I Baek, Yoo Sang Choi, Min Hyung Lee, Onseok Dept. of Software Convergence Graduate School Soonchunhyang University Korea Republic of Dept. of Dermatology Guro Hospital Korea University College of Medicine Korea Republic of Dept. of Computer Science and Engineering College of Engineering Design and Computing University of Colorado Denver United States

Psoriasis is a chronic recurrent disease formed by lesions such as erythema and scale. To evaluate the severity of psoriasis, the psoriasis area and severity index (PASI) score have been used in clinical trials and studies. This clinical indicator is subjective, so to overcome these shortcoming, various automatic psoriasis analysis methods based on deep learning have been studied. However, the limited number of data and psoriasis characteristic such as ambiguity of severity deteriorate model performance. One of the simple and powerful methods to overcome these problem is data augmentation. Data augmentation should be used according to data characteristics. Therefore, we analyzed and compared the classification results applied with five data augmentation methods, Geometric transformation, CutMix, Visual Corruptions, AutoAugment, RandAugment, and explored data augmentation method suitable for psoriasis severity classification. We used the EfficientNet B2 for psoriasis severity classification. As a result, when RandAugment or the combination of Geometric transform and Visual Corruptions were used, it showed the best classification performance with an accuracy of 87.5%. In addition, we confirmed the effect of data augmentation for improving model performance and the difference in performance according to single or multiple applications of the data augmentation methods. Through these results, our study can be applied to various studies as a data augmentation method suitable for psoriasis disease image. Copyright © The Korean Institute of Electrical Engineers.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Toward Intelligent Callout and Semantic Interpretation of ISO Geometrical Production Specification 17

Toward Intelligent Callout and Semantic Interpretation of IS...

引用

17th CIRP Conference on Computer Aided Tolerancing, CAT 2022

作者： Dai, Xiangcheng Guo, Benjun Xu, Yuanping Li, Tukun Jiang, Xiangqian Gai, Qiuyan He, Jia Zhou, Jiliu Huang, Jian School of Software Engineering Chengdu University of Information Technology Chengdu China School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom

ISO Geometrical Product Specification and Verification standards (GPS) is a worldwide used technique language to control the geometrical variation of a workpiece and its digital twins. More than 150 standards documents have been developed. Normally, the call out of a symbol needs to check many standards documents. It is difficult to check the complete callouts manually and lead to an increase of the specification uncertainty. To this end, this paper presents an ontology-based semantic model to graphically and integrally represent the multi-dimensional knowledge in ISO GPS. In the knowledge modelling stage, this study focuses on integrating tolerance specification and verification knowledge seamlessly. To support automatic advanced rule extractions and reasonings, this study applies Semantic Web Rule Language (SWRL) to extract and represent sufficient multi-dimensional relationships inheriting from GPS documents. Then the constraint relations and reasoners are used to support the intelligent creations and semantic interpretations of the complete tolerance specification callouts. A case study on a typical workpiece-RV reducer is undertaken to test and evaluate the validity, usability and universality of the devised knowledge model. © 2022 The Author(s).

关键词： Ontology

来源：评论

学校读者我要写书评

暂无评论

Ppt4j: Patch Presence Test for Java Binaries 24

Ppt4j: Patch Presence Test for Java Binaries

引用

44th ACM/IEEE International Conference on software engineering, ICSE 2024

作者： Pan, Zhiyuan Hu, Xing Xia, Xin Zhan, Xian Lo, David Yang, Xiaohu Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Huawei Software Engineering Application Technology Lab Hangzhou China The Hong Kong Polytechnic University Hong Kong School of Computing and Information Systems Singapore Management University Singapore

ISBN: (纸本)9798400702174

The number of vulnerabilities reported in open source software has increased substantially in recent years. Security patches provide the necessary measures to protect software from attacks and vulnerabilities. In practice, it is difficult to identify whether patches have been integrated into software, especially if we only have binary files. Therefore, the ability to test whether a patch is applied to the target binary, a.k.a. patch presence test, is crucial for practitioners. However, it is challenging to obtain accurate semantic information from patches, which could lead to incorrect results. In this paper, we propose a new patch presence test framework named Ppt4j (Patch Presence Test forJava Binaries). Ppt4j is designed for open-source Java libraries. It takes Java binaries (i.e. bytecode files) as input, extracts semantic information from patches, and uses feature-based techniques to identify patch lines in the binaries. To evaluate the effectiveness of our proposed approach Ppt4j, we construct a dataset with binaries that include 110 vulnerabilities. The results show that Ppt4j achieves an F1 score of 98.5% with reasonable efficiency, improving the baseline by 14.2%. Furthermore, we conduct an in-the-wild evaluation of Ppt4j on JetBrains IntelliJ IDEA. The results suggest that a third-party library included in the software is not patched for two CVEs, and we have reported this potential security problem to the vendor. © 2024 ACM.

关键词： Open source software

来源：评论

学校读者我要写书评

暂无评论

Wind Energy Forecasting: A Comparative Analysis of Machine Learning Approaches 8

Wind Energy Forecasting: A Comparative Analysis of Machine L...

引用

8th International Scientific Conference on Applying New Technology in Green Buildings, ATiGB 2023

作者： Pham, Thanh Son Nguyen, Duc Huy Phan, Nguyen Duy Minh FPT University Faculty of Computing FPT Greenwich Centre Danang Viet Nam FPT Software Quy Nhon Viet Nam The University of Danang University of Technology and Education Faculty of Mechanical Engineering Danang Viet Nam

ISBN: (纸本)9798350343977

This paper conducts a comprehensive comparative analysis of machine learning and deep learning techniques for wind energy forecasting. Wind energy prediction is a crucial aspect of renewable energy management. We study and assess the application of tree-based algorithms (LightGBM, CatBoost, and Random Forest) and deep learning approaches (feedforward networks with skip connection and FT-Transformers). In the numerical study, we evaluate forecasting accuracy, robustness, computational efficiency, and analyze the reasons why some methods are better than others. This research contributes to advancing wind energy forecasting methodologies in the renewable energy sector. Accurate predictive models play the most crucial role in optimizing wind energy integration into the grid, supporting a sustainable energy future. © 2023 IEEE.

关键词： Wind power

来源：评论

学校读者我要写书评

暂无评论

A Heuristic-based Dynamic Scheduling and Routing Method for Industrial TSN Networks 10

A Heuristic-based Dynamic Scheduling and Routing Method for ...

引用

10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Chen, Honglong Liu, Mindong Huang, Jing Zheng, Zhiling Huang, Weihong Xiao, Yufeng Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Hunan Industry Polytechnic Information Center Changsha410125 China

ISBN: (纸本)9798350312461

In the industrial environment, machines often need to reflect the anomaly detection results to the total control center in time, and the general industrial network can not achieve high real-time. In order to solve such challenges, a set of protocol standards developed by IEEE802.1 working group, namely Time-sensitive Networking (TSN), has been introduced into industrial networks. TSN can provide high real-time and reliability for data transmission, where the reliability is achieved by Frame duplication and Frame Elimination (FRER). In the realization process of FRER, it is necessary to determine the source node, destination node, and multiple disjoint paths to transmit redundant data. However, the transmission of these redundant traffic may result in the delay of other flows, and then affects the user experience. Therefore, it is very important to choose excellent redundant traffic paths to ensure reliability and reduce the impact on other flows. In the existing research, there are many dynamic scheduling and routing heuristics to determine the path, but they do not consider the influence of the location of the source node on the whole route scheduling. This paper proposes an improved dynamic scheduling and routing heuristic method, which takes the source node into account in the routing selection. In the flow test experiments of different magnitudes, it is found that the total delay of all flows is reduced by 1.4%-4.5% under the same magnitude of schedulability compared with Ant Colony Optimization. © 2023 IEEE.

关键词： Heuristic algorithms

来源：评论

学校读者我要写书评

暂无评论

HyPRE: Hybrid Proxy Re-Encryption for Secure Multimedia Data Sharing on Mobile Devices

HyPRE: Hybrid Proxy Re-Encryption for Secure Multimedia Data...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Xinyu Feng Cong Li Qingni Shen Jisheng Dong Wenjun Qian Yuejian Fang Zhonghai Wu School of Software and Microelectronics Peking University National Engineering Research Center for Software Engineering Peking University PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University School of Computer Science Peking University

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Due to the rapid growth of mobile internet, massive multimedia data (e.g., movies, photos, notes, etc.) on mobile devices is synchronized and shared through the cloud. During this process, public key encryption plays an important role in ensuring the confidentiality of data. However, due to the bottleneck of computing and storage resources in mobile devices, it is difficult to execute complex cryptographic algorithms on them. In this paper, we present a novel Hybrid Proxy Reencryption (HyPRE) scheme for the sharing of multimedia data on mobile devices, which empowers a semi-trusted proxy to convert a ciphertext under an identity to a new one under an expressive policy without revealing the underlying plaintext. Our scheme allows mobile devices with limited resources to encrypt data efficiently, and then to share the encrypted data to multiple entities securely. We define the HRA security for our HyPRE scheme to improve the incompleteness of the security under chosen plaintext attacks (CPA) in traditional proxy re-encryption schemes and prove it selectively secure under HRA. Experimental analysis indicates that HyPRE achieves 2× to 3× improvement in terms of re-encryption performance compared with the state-of-the-art ones.

关键词： Performance evaluation Cloud computing Public key Data collection Motion pictures Mobile handsets Encryption

来源：评论

学校读者我要写书评

暂无评论

Client Segmentation and Customization in E-Commerce: Applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

引用

2024 International Conference on Communication, Computer Sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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：