检索结果-内蒙古大学图书馆

1st Asian Conference on Communication and networks, ASIANComNet 2024

作者： Jawad, Samer Kadhim Alnajjar, Satea Hikmat Al-Iraqia University Department of Computer Engineering Baghdad Iraq Al-Iraqia University Department of Network Engineering Baghdad Iraq

ISBN: (纸本)9798350367003

Phishing attacks are the most common cyberattacks nowadays. Phishing attacks rely on social engineering concepts to trick victims into reaching the goals of malicious attackers. In addition, phishing attacks are the largest vector for various cyberattacks. However, URLs are a fulcrum for phishing attacks. The difficulty distinguishing between legitimate and phishing URLs is the reason for the increased success rates of these attacks. An integrated framework is proposed in this study to detect phishing attacks based on classifying URLs into phishing or legitimate URLs through machine learning models such as decision tree (DT) and random forest (RF), which have high power and prediction accuracy in binary classification tasks. The RF model, using the cross validation (CV) technique, achieved an accuracy score of 9 8.2. This methodology is embedded in a web application with a graphical user interface to provide ease of handling and show alerts in real time and visually. This contributes to providing the field of cybersecurity with a highly accurate verification system to reduce users falling victim to these dangerous attacks. © 2024 IEEE.

关键词： Phishing

来源：评论

学校读者我要写书评

暂无评论

Research on intelligent perception of computer vision environment for autonomous driving in complex environment based on cloud computing and improved Bayesian algorithm 3

Research on intelligent perception of computer vision enviro...

引用

3rd International Conference on Electronic Information engineering and Data Processing, EIEDP 2024

作者： Xu, Wang Department of Network Engineering School of Computer Science Guangdong Neusoft Institute Guangdong Foshan528225 China

ISBN: (纸本)9781510680531

In the field of autonomous driving, vision-based environmental perception methods usually use monocular or multi-ocular cameras to capture images, and use computer vision technology to analyze images to complete perceptual tasks such as object recognition, target detection and tracking, semantic segmentation, three-dimensional reconstruction, and distance measurement. Where there is a lack of prior knowledge or relational structure, the application of existing methods is limited. A method for calculating similarity matching between azimuth and polarity eigenvectors of components is proposed, and 5 defects of common components are accurately detected. The experimental results show that the proposed algorithm can quickly and accurately locate and identify PCB components, and effectively detect the defects of different components of the circuit board, because information is only passed between tightly connected nodes, this approach fails to capture novel interactions between distant nodes. The way to solve this limitation is to introduce an attention layer with induced sparsity to find. Therefore, based on specific projects in the laboratory, this paper conducts in-depth research on the visual environment perception for automatic driving in complex environments, aiming at the actual needs of intelligent unmanned units for automatic driving on complex off-road road conditions, and the problems such as low accuracy and insufficient robustness of existing algorithms. © 2024 SPIE.

关键词： Autonomous vehicles

来源：评论

学校读者我要写书评

暂无评论

Enhancing Video Quality Assessment Through Brightness Information 4

Enhancing Video Quality Assessment Through Brightness Inform...

引用

4th International Conference on computer Vision and Data Mining, ICCVDM 2023

作者： Yuan, Jinchun Peng, Yuxin Zhang, Aoxiang Guangzhou Broadcasting Network Guangzhou China Computer Science and Cyber Engineering Guangzhou University Guangzhou China

ISBN: (纸本)9781510674448

Video quality assessment (VQA) plays a crucial role in video transmission and storage to ensure optimal video compression effect. In existing research, deep neural networks are commonly employed to align with the complexities of the Human Visual System (HVS) and achieve superior performance. However, due to the complexity of the HVS, many characteristics of HVS have not been considered, resulting in a suboptimal fit to the HVS. To address this issue, this paper proposes a novel VQA method grounded in the HVS. Notably, our method introduces brightness information from video frames as embeddings, thereby enhancing the model's alignment with the HVS. Additionally, we identify that feeding every single frame into the network for feature extraction leads to a lot of redundant information. To improve efficiency while maintaining comparable performance, our method samples the video frames at an interval of eight, resulting in an eightfold improvement in efficiency. The effectiveness of our method is validated through extensive experiments conducted on four mainstream VQA datasets. Ablation studies further corroborate the effectiveness and efficiency of our method by demonstrating the positive impact of incorporating brightness information into VQA. © 2024 SPIE.

关键词： Luminance

来源：评论

学校读者我要写书评

暂无评论

Inverse Scattering of Two-dimensional Circular Cylindrical Conductors by Convolutional Neural network 4

Inverse Scattering of Two-dimensional Circular Cylindrical C...

引用

4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chen, Po-Hsiang Hsu, Hsiu-Hui Chiu, Chien-Ching Chien, Wei Yu, Kai-Xu Tamkang University Department of Electrical and Computer Engineering New Taipei City Taiwan Lunghwa University of Science and Technology Department of Computer Information and Network Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350360721

We used a Convolutional Neural network (CNN) to reconstruct the electromagnetic imaging of a perfect conductor in half-space through the traverse magnetic wave. Since the conventional iterative algorithm is time-consuming, we developed a real-time electromagnetic imaging technique to apply to the CNN to reconstruct the half-space conducting cylinders. The sizes and positions of the cylinders were calculated through the Direct Sampling Method (DSM) in the scattered field. The original ground truth was fed to CNN for training to predict accurate sizes and positions of the shape. The accurate size and position of the shape for the perfect conductor were reconstructed. © 2024 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning 30

Few-Shot Semantic Dependency Parsing via Graph Contrastive L...

引用

Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Li, Bin Fan, Yunlong Sataer, Yikemaiti Shi, Chuanqi Gao, Miao Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education China

ISBN: (纸本)9782493814104

Graph neural networks (GNNs) have achieved promising performance on semantic dependency parsing (SDP), owing to their powerful graph representation learning ability. However, training a high-performing GNN-based model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labeled data. To address this drawback, we propose a syntax-guided graph contrastive learning framework to pre-train GNNs with plenty of unlabeled data and fine-tune pre-trained GNNs with few-shot labeled SDP data. Through extensive experiments conducted on the SemEval-2015 Task 18 English dataset in three formalisms (DM, PAS, and PSD), we demonstrate that our framework achieves promising results when few-shot training samples are available. Furthermore, benefiting from the pre-training process, our framework exhibits notable advantages in the out-of-domain test sets. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Developing an Esports Assistant System for First Person Shooting Game Based on Multimodal Learning 14th

Developing an Esports Assistant System for First Person Shoo...

引用

14th International Conference on Frontier Computing, FC 2024

作者： Chiu, Wei-Hsiang Hung, Jason C. Ma, Hao-Shang Huang, Chun-Hong Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan City Taiwan

ISBN: (纸本)9789819623945

We plan to develop a specialized training system to enhance the competitive skills of players in the first-person shooter game "Valorant", aiming to improve their abilities and tactical understanding within the game. We will adopt a multimodal image-text fusion technique to enhance the model’s learning of sample similarity. Specifically, we will use the cross-entropy function to maximize the diagonal values of the similarity matrix, thereby capturing the features of similar samples and reducing the similarity between dissimilar samples. Through this process, we ensure that the model can map the same entity across different modalities into similar embedding representations, while distinguishing unrelated samples. Our goal is to find a way of embedding representation during the learning process that enables the model to better understand and represent multimodal data. We will utilize the YOLOv7 model for object detection to identify destructible skills. Based on the results of object detection, we will generate relevant textual descriptions for each object. This will enable novice players to quickly understand the function of diverse and complex skills and take appropriate actions when encountering them. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

New Systematic MDS Array Codes with Two Parities

IEEE Open Journal of the Communications Society

引用

IEEE Open Journal of the Communications Society 2024年 5卷 6329-6342页

作者： Ma, Lan Zhou, Liyang Liu, Shaoteng Chen, Xiangyu Sun, Qifu University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Huawei Technologies Company Ltd. Network Technology Lab Shenzhen518000 China

Row-diagonal parity (RDP) code is a classical (k + 2, k) systematic maximum distance separable (MDS) array code with k ≤ L - 1 under sub-packetization level l = L - 1, where L is a prime integer. When k = L-1, its encoding requires 2 - 2/k XORs per original data bit, which exactly achieves theoretical optimal lower bound. In this paper, we present three new constructions of (k+2, k) systematic MDS array codes. First, under sub-packetization level l = 4, we novelly design a (17, 15) array code C1, where k can reach the largest possible value to satisfy the MDS property. Moreover, when k ≤ 7, the encoding complexity of its subcodes can exactly achieve the theoretical optimal 2 - 2/k XORs per original data bit, and likewise, the decoding complexity of the subcodes with k ≤ 4 is also exactly optimal. Under sub-packetization level l = L - 1 with certain primes L, the second construction yields an MDS array code C2 with k ≤ L(L-1)/2, and the encoding complexity of C2 is also exactly optimal for k = L-1, 2L-3. Furthermore, based on bit permutation, the third MDS array code C3 is obtained with k ≤ L(L-1) under sub-packetization level l = 2(L-1) with certain primes L. In particular, as an extension of C2, C3 exactly achieves the optimal encoding complexity for k = 2(2L - 3), which does not hold for other array codes in the literature. © 2020 IEEE.

关键词： Encoding (symbols)

来源：评论

学校读者我要写书评

暂无评论

Enhancing Parameter-Efficient Transformers with Contrastive Syntax and Regularized Dropout for Neural Machine Translation 21st

Enhancing Parameter-Efficient Transformers with Contrastive...

引用

21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Fan, Yunlong Yang, Zhiheng Li, Baixuan Gao, Zhiqiang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

ISBN: (纸本)9789819601219

Transformers have significantly improved Neural Machine Translation (NMT) models, accompanied by the inherent space complexity of O(n2). While recent approaches aim to be parameter-efficient, they often exhibit limited generalization capabilities with fewer parameters and decreased performances in longer sentences. To this end, we propose two methods, Syntax-enhanced Contrastive Learning (Syn-CL) and JS divergence-based Regularized Dropout (JSR-Drop) for the cross-layer parameter-sharing Transformer (baseline) in NMT. In Syn-CL, we add the corresponding target-to-source instances for the same batch in an extra bidirectional training phase, and minimize the distances of syntax-enhanced representation between the bilingual sentence-pair while maximizing the ones among others. In JSR-Drop, we refine the regularized dropout strategy with JS divergence, and improve generalization capabilities by reducing Transformers’ inconsistencies between training and inference introduced by dropout. Extensive experiments on six NMT tasks, IWSLT2014 (German↔English) and IWSLT2017 (English↔French and English↔Romanian), show an average BLEU score of 37.55, surpassing the vanilla Transformer and the baseline up to 1.75 BLEU, and outperforming models with 1.5×, 2.0× parameters in translating long sentences. Our code is available at https://***/Anon1214/EnPeNMT. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection System with Customized Machine Learning Techniques for NSL-KDD Dataset

引用

computers, Materials & Continua 2023年第12期77卷 4025-4054页

作者： Mohammed Zakariah Salman A.AlQahtani Abdulaziz M.Alawwad Abdullilah A.Alotaibi Department of Computer Science College of Computer and Information ScienceKing Saud UniversityRiyadh11495Saudi Arabia New Emerging Technologies and 5G Network and Beyond Research Chair Department of Computer EngineeringCollege of Computer and Information SciencesKing Saud UniversityRiyadh11495Saudi Arabia Department of Computer Engineering College of Computer and Information ScienceKing Saud UniversityRiyadh11495Saudi Arabia

Modern networks are at risk from a variety of threats as a result of the enormous growth in internet-based *** consuming time and resources,intrusive traffic hampers the efficient operation of network *** effective strategy for preventing,detecting,and mitigating intrusion incidents will increase productivity.A crucial element of secure network traffic is Intrusion Detection System(IDS).An IDS system may be host-based or network-based to monitor intrusive network *** unusual internet traffic has become a severe security risk for intelligent *** systems are negatively impacted by several attacks,which are slowing *** addition,networked communication anomalies and breaches must be detected using Machine Learning(ML).This paper uses the NSL-KDD data set to propose a novel IDS based on Artificial Neural networks(ANNs).As a result,the ML model generalizes sufficiently to perform well on untried *** NSL-KDD dataset shall be utilized for both training and *** this paper,we present a custom ANN model architecture using the Keras open-source software *** specific arrangement of nodes and layers,along with the activation functions,enhances the model’s ability to capture intricate patterns in network *** performance of the ANN is carefully tested and evaluated,resulting in the identification of a maximum detection accuracy of 97.5%.We thoroughly compared our suggested model to industry-recognized benchmark methods,such as decision classifier combinations and ML classifiers like k-Nearest Neighbors(KNN),Deep Learning(DL),Support Vector Machine(SVM),Long Short-Term Memory(LSTM),Deep Neural network(DNN),and *** is encouraging to see that our model consistently outperformed each of these tried-and-true techniques in all *** result underlines the effectiveness of the suggested methodology by demonstrating the ANN’s capacity to accurately assess the effectiveness of the developed strategy

关键词： Artificial neural networks intrusion detection system classification NSL-KDD dataset machine and deep-learning neural network

来源：评论

学校读者我要写书评

暂无评论

DRSC: Dual-Reweighted Siamese Contrastive Learning network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

引用

IEEE Internet of Things Journal 2025年第13期12卷 23678-23693页

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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：