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2024 Advanced Fiber Laser conference, AFL 2024

作者： Duan, Yunfei Song, Yi Lin, Xiang Huang, Huannan Li, Yueyang Sun, Wangning Li, Zhili Deng, Chunjian Yang, Jianjun Gao, Qingguo Zhang, Zhi Cai, Yi Pan, Xinjian Zhongshan Branch of State Key Laboratory of Electronic Thin Films and Integrated Devices College of Electronic and Information University of Electronic Science and Technology of China Zhongshan Institute Zhongshan528402 China Shenzhen Univ Shenzhen Key Lab Micronano Photon Informat Technol Optoelect Devices & Syst Coll Phys & Optoelect Eng Minist Educ & Guangdong Prov State Key Lab Radio F Shenzhen518060 China

ISBN: (数字)9781510688889

ISBN: (纸本)9781510688872

In recent years, the field of glass ceramics has witnessed rapid advancements, leading to a substantial expansion in their varieties, structures, properties, and application domains. Glass ceramics have found applications in cutting-edge sectors such as astronomy, laser technology, and optoelectronics, solidifying their status as advanced materials. This study aims to investigate the impact of processing parameters on the picosecond laser cutting of glass ceramics to achieve high-quality cuts. A series of cutting experiments were conducted, revealing that processing point spacing, incident laser energy, and the number of pulse trains significantly influence sample processing. Atomic Force Microscopy (AFM) analysis indicates that a processing point spacing of 1 micron facilitates the connection of microholes into an ablative groove, resulting in favorable surface roughness. An optimal number of two pulses yields the most consistent and effective improvement, achieving a local optimum. At an incident laser power of 12.5 W, the lowest average roughness and most uniform ablation are observed, also attaining a local optimum. In this study, glass ceramics with a thickness of 600 nm exhibited superior processing quality, characterized by an average roughness of 219 nm, a local optimal roughness of 208 nm. © 2025 SPIE.

关键词： Glass ceramics

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Distributed Denial of Service Attack Detection by Machine Learning Techniques 22

Distributed Denial of Service Attack Detection by Machine Le...

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22nd international Learning and Technology conference, L and T 2025

作者： Jewani, Varkha K. P.E., Ajmire M., Atique B., Sharma I.B., Dhaou S., Awadhesh Chaurasia Sant Gadge Baba Amravati University Amravati India Chitkara University Centre of Research Impact and Outcome Punjab Rajpura140401 India Dar Al-Hekma University Hekma School of Engineering Computing and Design Department of Computer Science Jeddah Saudi Arabia RTM Nagpur University Department of Computer Science Nagpur India

ISBN: (纸本)9798331532215

The exponential expansion of computer networks and the internet makes it clear that there is a chance of being attacked and harmed. In the meantime, one of the most crucial defensive measures against the more complex and frequent network attacks is the intrusion detection and prevention systems (IDSs and IPSs). Due to insufficient datasets, anomaly-based research in intrusion detection systems is plagued by imprecise deployment, analysis, and evaluation. The researchers analyzed a variety of datasets, including DARPA98, KDD99, ISC2012, and ADFA13, to assess how well their suggested intrusion detection and intrusion prevention techniques performed. There are several issues: lack of adequate attack coverage, unrepresentative payloads, and anonymised packet information, insufficient traffic volume and diversity, or a deficiency in feature set and metadata. This study is focused on CICIDS-2017 which is the recently updated IDS dataset that meets real-world requirements and is accessible publicly. In addition to benign network flows and distributed denial of service (DDoS) attacks, it contains seven common attack network flows as well. To provide the optimal combination of features for the identification of the attack category, it also assesses the efficacy of several machine learning (ML) algorithms and network traffic feature sets. Even though various ML techniques like Linear Regression, Random Forests, and Decision Tree can be used, and performed very well, Hybrid Algorithm is designed to get better analysis on dataset. © 2025 IEEE.

关键词： Network intrusion

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Insights into the High-Quality Development of Agriculture under the Wave of Digital Economy 20th

Insights into the High-Quality Development of Agriculture un...

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20th international conference on Metaverse, METAVERSE 2024, Held as Part of the Services conference Federation, SCF 2024

作者： ChaoYu, Liang XiangQiong, Wu Hui, Ou Shengping, Liang College of Information Engineering Capital Normal University Beijing100048 China College of Mathematics and Statistics Hunan Normal University Changsha410081 China China Forestry Group Corporation Beijing100026 China

ISBN: (纸本)9783031769764

Based on panel data from 30 provinces, autonomous regions, and municipalities in China from 2012 to 2021, this study conducted a quantitative analysis of the high-quality development of agriculture amidst the wave of the digital economy. The research revealed that the digital economy plays a significant role in promoting the high-quality development of agriculture. It not only directly enhances the high-quality development of agriculture in various provinces, autonomous regions, and municipalities but also indirectly provides a material foundation for such development by elevating the level of agricultural talent cultivation. However, the impact of the digital economy on the high-quality development of agriculture exhibits regional heterogeneity, with notable effects in the northern region and less significant impacts in the southern region. Consequently, this study’s analysis points out that strengthening strategies to empower agriculture with the digital economy, optimizing talent attraction and cultivation mechanisms, and enhancing differentiated policy support are crucial strategies for promoting the high-quality development of agriculture. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Agricultural economics

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Photoacoustic tomography based on interference stimulated narrow-band ultrasound 7

Photoacoustic tomography based on interference stimulated na...

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7th international Symposium on Laser Ultrasonics and Advanced Sensing, LU 2024

作者： Ye, Jun Zhang, Yang Yan, Peng Tang, Zhongduo Ni, Bin Xu, Bin Liu, Xuefeng Mehmood, Muhammad Qasim Xiong, Jichuan School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Electrical Engineering and Automation Luoyang Institute of Science and Technology Luoyang471023 China Electrical Engineering Department Information Technology University of the Punjab Lahore54000 Pakistan

Photoacoustic tomography (PAT) has emerged as a promising noninvasive imaging technology with significant potential for various clinical applications in medical settings. Traditionally, PAT utilizes a single laser beam to generate the excitation light (Wang & Hu, 2012). Nonetheless, this method often suffers from low signal-to-noise ratios (SNR) and poor image contrast due to environmental noise and other interfering factors. This paper introduces an interference laser excitation technique designed to focus the distribution of excitation light more effectively. By processing the signals generated by the interference, environmental noise can be effectively distinguished from the desired signal, thereby reducing its impact on image quality. The results demonstrate that this proposed method yields a more concentrated photoacoustic signal with high SNR after filtering and produces highly contrasted reconstructed images. This approach holds practical value and provides a novel excitation scheme for clinical photoacoustic tomography. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Laser excitation

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Cross-Domain Calibration and Boundary Denoising Network for Weakly Supervised Semantic Segmentation 27th

Cross-Domain Calibration and Boundary Denoising Network for...

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27th international conference on Pattern Recognition, ICPR 2024

作者： Liu, Zhoufeng Li, Bingrui Ding, Shumin Xi, Jiangtao Li, Chunlei School of Information and Communication Engineering Zhongyuan University of Technology Henan450007 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW Australia

ISBN: (纸本)9783031781216

Weakly Supervised Semantic Segmentation (WSSS) methods based on image-level labeling alleviate the burden of annotation due to image-level labels only providing object categories. However, the generated Class Activation Maps (CAM) can only localize the most discriminative region rather than the entire object region. To remedy this issue, a framework of WSSS algorithms for Cross-Domain Calibration and Boundary Denoising Network (CD-CBN) is presented. Specifically, a Spatial Feature Calibration Network (SFCN) is proposed to align cross-dimensional features with class prototypes, focusing on intra-class feature consistency. Then, a Class-Specific Distance Model (CSDM) is adopted to separate features from different classes, and feature activation in the object region surpasses the background area. Finally, a Full-domain-aware Noise Reduction Model (FNRM) is designed to refine the object boundary pixels by capturing global contextual features and further filtering out pixel-level noise. A comprehensive experimental evaluation of the highly challenging Pascal VOC 2012 dataset and MS COCO 2014 is presented in this study, illustrating the effectiveness of our suggested approach. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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TBA-GNN: A Traffic Behavior Analysis Model with Graph Neural Networks for Malicious Traffic Detection 18th

TBA-GNN: A Traffic Behavior Analysis Model with Graph Neura...

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18th international conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Han, Xinbo Zhang, Meng Yang, Zheng Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783031714634

Given the surge in network attack behavior, detecting malicious traffic has become a pivotal cybersecurity task. Many existing methods for malicious traffic detection rely on machine learning and deep learning, but they exhibit certain shortcomings: A considerable number of these methods rely on statistical features, which may lose their relevance as networks evolve and lead to the loss of important information. Additionally, Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) face limitations in extracting features from network traffic, specifically, their inability to capture traffic interaction behavior information within a network flow. In this paper, we propose a Traffic Behavior Analysis model with Graph Neural Networks (TBA-GNN), which works directly with raw bytes and leverages the hierarchy structure of traffic (byte-packet-flow) to delve into valuable information. Firstly, we devise the PacketCNN to extract packet-level features from raw bytes. Subsequently, we construct a network flow as a Traffic Interaction Graph, containing both the traffic interaction behavior information and packet-level traffic information, and utilize the GNN to extract flow-level features. Finally, we perform a classification task to detect malicious traffic. We conduct extensive experiments on the ISCXIDS2012 and CICIDS2017 datasets, and the experimental results demonstrate that our model effectively identifies malicious traffic, outperforming baselines significantly. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graph neural networks

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Spatial Frequency-Aware Self-Distillation for Weakly-Supervised Semantic Segmentation

Spatial Frequency-Aware Self-Distillation for Weakly-Supervi...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jingyuan Fang Yang Ning Xiushan Nie School of Computer Science and Technology Shandong Jianzhu University Jinan P.R. China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Weakly-supervised semantic segmentation (WSSS) aims to achieve pixel-level classification under image-level supervision. Recent class activation map (CAM)-based methods seek to expand foreground activation while suppressing background. However, they often overlook the uncertainty of CAM, where non-salient activation in some regions complicates semantic classification. These regions are typically dismissed as noise, resulting in inappropriate activations due to inadequate regularization. To resolve this, we introduce a Spatial Frequency-Aware Self-Distillation strategy (SFS). Firstly, to enhance the perception of high-frequency spatial information in uncertain regions, we propose a boundary self-distillation and uncertain region reconstruction strategy, which captures high-frequency boundary information and fine-grained spatial context in these regions. Secondly, to enhance the discrimination of low-frequency semantic features, we propose a contrastive attention mechanism that guides the Vision Transformer (ViT) to focus more on the foreground, thereby improving the distinction between foreground and background. Finally, our SFS demonstrates outstanding performance on both the VOC 2012 and COCO 2014 datasets, attributed to its superior spatial frequency perception capabilities. The code is available at https://***/fjoybest/SFS.

关键词： Uncertainty Semantic segmentation Semantics Noise Frequency conversion Transformers Data mining Speech processing Spatial resolution Image reconstruction

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CA-UAP: Content-Agnostic Universal Adversarial Perturbation for Enhanced Generalization

CA-UAP: Content-Agnostic Universal Adversarial Perturbation ...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Rui Lu Ziqiang He Jingyang Wen Xiangui Kang Z. Jane Wang Guangdong Key Lab of Information Security School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China College of Electrical and Information Engineering North Minzu University Yinchuan China Department of Electrical and Computer Engineering University of British Columbia Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep Neural Networks (DNNs) have been shown vulnerable to universal adversarial perturbation (UAP), which are imperceptible and capable of fooling the target model for most samples. Existing universal attack methods mainly focus on aggregating the gradient obtained from global image features to directly optimize (noise-based) or indirectly generate (generator-based) UAP. However, such methods do not yet consider improving the generalization of UAP from the perspective of making the perturbation irrelevant to image content. We note that minimizing self-similarity is helpful to make the UAP irrelevant to the image content. Therefore, we propose a novel Content-Agnostic UAP (CA-UAP), which combines global image features and local patch features to optimize UAP. Specifically, we introduce a self-similarity loss that encourages minimizing the similarity between adversarial perturbed global images and their randomly cropped local regions, making the UAP agnostic to image content and consequently enhancing UAP generalization. Extensive experiments on the ILSVRC 2012 dataset demonstrate that our proposed method outperforms existing methods in both untargeted and targeted attacks, e.g., improving the average fooling rate from 79.12% (achieved by the state-of-the-art method) to 82.25% in targeted attacks.

关键词： Correlation Perturbation methods Buildings Artificial neural networks Signal processing Transformers Filling Acoustics Speech processing

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DisenSemi: Semi-Supervised Graph Classification via Disentangled Representation Learning

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IEEE Transactions on Neural Networks and Learning Systems 2025年第5期36卷 8192-8204页

作者： Wang, Yifan Luo, Xiao Chen, Chong Hua, Xian-Sheng Zhang, Ming Ju, Wei University of International Business and Economics School of Information Technology and Management Beijing100029 China University of California at Los Angeles Department of Computer Science Los AngelesCA90095 United States Terminus Group Beijing100027 China Peking University-Anker Embodied AI Laboratory Peking University School of Computer Science National Key Laboratory for Multimedia Information Processing Beijing100871 China Sichuan University College of Computer Science Chengdu610064 China

Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and social networks. Nevertheless, in the real world, labeled graph data are always limited or scarce. To address this issue, we focus on the semi-supervised graph classification task, which involves both supervised and unsupervised models learning from labeled and unlabeled data. In contrast to recent approaches that transfer the entire knowledge from the unsupervised model to the supervised one, we argue that an effective transfer should only retain the relevant semantics that align well with the supervised task. We introduce a novel framework termed DisenSemi in this article, which learns disentangled representation for semi-supervised graph classification. Specifically, a disentangled graph encoder is proposed to generate factorwise graph representations for both supervised and unsupervised models. Then, we train two models via supervised objective and mutual information (MI)-based constraints, respectively. To ensure the meaningful transfer of knowledge from the unsupervised encoder to the supervised one, we further define an MI-based disentangled consistency regularization between two models and identify the corresponding rationale that aligns well with the current graph classification task. Experiments conducted on various publicly available datasets demonstrate the effectiveness of our DisenSemi. © 2012 IEEE.

关键词： Semi-supervised learning

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Review of Emerging Threats in Cyberspace: A Replicated Study Based on a Year 2022 Research

Review of Emerging Threats in Cyberspace: A Replicated Study...

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28th international conference on Image processing, computer Vision, and Pattern Recognition, IPCV 2024, and 23rd international conference on information and Knowledge Engineering, IKE 2024, held as part of the World Congress in computer science, computer Engineering and Applied Computing, CSCE 2024

作者： Bethea, Devonte W. Olatunbosun, Samuel B. O. Department of Computer Science Norfolk State University Norfolk NorfolkVA United States

ISBN: (纸本)9783031859328

The rapid explosion of the Internet and computer interconnections has led to a progressive incident of cybersecurity attacks. The news media have reported several attacks that have resulted in devastating and grievous consequences for the victims. Malware has been the primary weapon of choice that perpetrators have used repeatedly to carry out many of these malicious cyber-attacks. Perpetrators either have exploited loopholes in existing network systems to attack or have used the weaknesses discovered in several emerging technologies to facilitate these malware related crimes. There is therefore the necessity to keep developing more effective and innovative malware defense mechanisms to meet head-on, the existing cybersecurity challenges. A study and an article published in 2022 titled "A Survey of Emerging Threats in Cybersecurity" provided updated information that include statistical data, figures, and tables to report incidents of malware attack cases. In this replicated study, we investigated and provided further information on the latest trends in the Cyber Malware war. An overview of the most exploited vulnerabilities in existing hardware, software, and network layers are discussed, followed by critiques of the existing mitigation techniques, in terms of why some have proven effective and why some have not. New attack patterns evolving in the use of technologies such as social media, cloud computing, smartphones, and other critical infrastructure are also discussed. Finally, this report compares today’s current situation with the earlier year 2012 study to make recommendations for future research on the subject. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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