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Performance issue monitoring, identification and diagnosis of SaaS software:a survey

Frontiers of computer science 2025年第1期19卷 71-95页

作者： Rui WANG Xiangbo TIAN Shi YING College of Mining and Safety Engineering Shandong University of Science and TechnologyQingdao 266590China School of Computer Science Wuhan UniversityWuhan 430072China

SaaS (Software-as-a-Service) is a service model provided by cloud computing. It has a high requirement for QoS (Quality of Software) due to its method of providing software service. However, manual identification and diagnosis for performance issues is typically expensive and laborious because of the complexity of the application software and the dynamic nature of the deployment environment. Recently, substantial research efforts have been devoted to automatically identifying and diagnosing performance issues of SaaS software. In this survey, we comprehensively review the different methods about automatically identifying and diagnosing performance issues of SaaS software. We divide them into three steps according to their function: performance log generation, performance issue identification and performance issue diagnosis. We then comprehensively review these methods by their development history. Meanwhile, we give our proposed solution for each step. Finally, the effectiveness of our proposed methods is shown by experiments.

关键词： SaaS software performance log generation performance issue identification performance issue diagnosis

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Towards Privacy Protection: A Non-Invasive Human Detection Method via Edge-Cloud Collaboration 10

Towards Privacy Protection: A Non-Invasive Human Detection M...

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10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Yin, Luxiu Huang, Qihui Wang, Xuanya Liang, Wei Wang, Chun Hunan University of Science and Technology College of Computer Science and Engineering China Hunan Normal University College of Information Science and Engineering China

ISBN: (纸本)9798350389524

Traditional human detection technologies, such as those based on computer vision or wearable devices, often require the storage and analysis of vast amounts of personal data in the cloud, which increases the risk of data breaches. Therefore, the rapid processing of large-scale detection data while protecting user privacy has become a critical issue that needs urgent resolution. The approaches based on wireless sensing will achieve privacy protection, non-contact perception, massively scalable, and long-range detection. In this paper, we propose an innovative solution that combines data collection from terminal devices, real-time processing by edge computing, and deep analysis capabilities of cloud computing, to realize efficient and privacy-friendly human detection. First, we propose a non-invasive human detection method based on edge-cloud collaboration, integrating wireless sensing, data transmission, and processing within edge nodes, and utilizing drones equipped with edge nodes to process a large amount of wireless sensing data in real-time. Secondly, we designed a human presence prediction module based on a lightweight convolutional neural network to analyze signal data and achieve non-invasive human detection. Finally, with commodity software defined radio devices, we implement a non-invasive human detection prototype that can accurately differentiate multiple states of targets while protecting their privacy. Experimental results indicate that the proposed solution effectively reduces the risk of privacy leakage while maintaining a high detection accuracy. This provides strong support for the application of non-invasive personnel detection technology in the aspect of privacy protection. © 2024 IEEE.

关键词： Digital storage

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A bio-inspired optical imaging method for robotic perception in turbid water

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science China(Technological sciences) 2025年第1期68卷 75-77页

作者： Xudong SUN Yafeng LI Peng LIN Xiaodi SHANG Xianping FU School of Information Science and Technology Dalian Maritime UniversityDalian 116026China College of Computer Science and Technology Qingdao UniversityQingdao 266071China

In the realm of underwater robotics,optical imaging plays a pivotal role in many scientific *** to the effects of absorption and scattering,images captured in turbid water are severely ***,enhancing the quality of underwater optical images stands paramount in ensuring the continued advancement and efficacy of underwater robots across its multifarious applications.

关键词： underwater optical continued

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A Dual-Layer Attention Based CAPTCHA Recognition Approach with Guided Visual Attention

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computer Modeling in Engineering & sciences 2025年第3期142卷 2841-2867页

作者： Zaid Derea Beiji Zou Xiaoyan Kui Alaa Thobhani Amr Abdussalam School of Computer Science and Engineering Central South UniversityChangsha410083China College of Computer Science and Information Technology Wasit UniversityWasit52001Iraq Electronic Engineering and Information Science Department University of Science and Technology of ChinaHefei230026China

Enhancing website security is crucial to combat malicious activities,and CAPTCHA(Completely Automated Public Turing tests to tell computers and Humans Apart)has become a key method to distinguish humans from *** text-based CAPTCHAs are designed to challenge machines while remaining human-readable,recent advances in deep learning have enabled models to recognize them with remarkable *** this regard,we propose a novel two-layer visual attention framework for CAPTCHA recognition that builds on traditional attention mechanisms by incorporating Guided Visual Attention(GVA),which sharpens focus on relevant visual *** have specifically adapted the well-established image captioning task to address this *** approach utilizes the first-level attention module as guidance to the second-level attention component,incorporating two LSTM(Long Short-Term Memory)layers to enhance CAPTCHA *** extensive evaluation across four diverse datasets—Weibo,BoC(Bank of China),Gregwar,and Captcha 0.3—shows the adaptability and efficacy of our *** approach demonstrated impressive performance,achieving an accuracy of 96.70%for BoC and 95.92%for *** results underscore the effectiveness of our method in accurately recognizing and processing CAPTCHA datasets,showcasing its robustness,reliability,and ability to handle varied challenges in CAPTCHA recognition.

关键词： Text-based CAPTCHA image recognition guided visual attention web security computer vision

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Predicting Age and Gender in Author Profiling: A Multi-Feature Exploration

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computers, Materials & Continua 2024年第5期79卷 3333-3353页

作者： Aiman Muhammad Arshad Bilal Khan Sadique Ahmad Muhammad Asim Department of Computer Science City University of Science and Information TechnologyPeshawar25000Pakistan EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia School of Computer Science and Technology Guangdong University of TechnologyGuangzhou510006China

Author Profiling (AP) is a subsection of digital forensics that focuses on the detection of the author’s personalinformation, such as age, gender, occupation, and education, based on various linguistic features, e.g., stylistic,semantic, and syntactic. The importance of AP lies in various fields, including forensics, security, medicine, andmarketing. In previous studies, many works have been done using different languages, e.g., English, Arabic, French,***, the research on RomanUrdu is not up to the ***, this study focuses on detecting the author’sage and gender based on Roman Urdu text messages. The dataset used in this study is Fire’18-MaponSMS. Thisstudy proposed an ensemble model based on AdaBoostM1 and Random Forest (AMBRF) for AP using multiplelinguistic features that are stylistic, character-based, word-based, and sentence-based. The proposed model iscontrasted with several of the well-known models fromthe literature, including J48-Decision Tree (J48),Na飗e Bays(NB), K Nearest Neighbor (KNN), and Composite Hypercube on Random Projection (CHIRP), NB-Updatable,RF, and AdaboostM1. The overall outcome shows the better performance of the proposed AdaboostM1 withRandom Forest (ABMRF) with an accuracy of 54.2857% for age prediction and 71.1429% for gender predictioncalculated on stylistic features. Regarding word-based features, age and gender were considered in 50.5714% and60%, respectively. On the other hand, KNN and CHIRP show the weakest performance using all the linguisticfeatures for age and gender prediction.

关键词： Digital forensics author profiling for security AdaBoostM1 random forest ensemble learning

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Underwater Biological Target Detection Algorithm and Research Based on YOLOv7 Algorithm

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第6期51卷 594-601页

作者： Zhuang, Hongwei Liu, Weisheng School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan China College of Computer Science and Software Engineering University of Science and Technology Liaoning CO Anshan114051 China

Underwater target detection is an important method for detecting marine organisms. However, due to the image occlusion of underwater targets, blurred water quality, poor lighting conditions, small targets, and complex backgrounds, the detection of underwater biological targets has posed significant challenges. In the intricate underwater environment, the conventional feature extraction method has a few drawbacks, including imprecise feature extraction, sluggish detection speed, and inadequate robustness. Consequently, an underwater target detection method based on the enhanced You Only Look Once 7 (YOLOv7) is proposed in this study. The network architecture is reconstructed, and the Deformable Convolutional Network (DCN) modules replace some 3×3 convolutional blocks in the ELAN structure to offset sampling points and reduce background interference. Skip connections and 1× 1 convolutional architecture are added to the DCN module to improve the model’s perception of image details. In addition, Contextual Transformer 3 (COT3) is also incorporated to improve visual performance. Finally, to improve the detection efficiency of small objects, the CIoU loss function is finally replaced by the Normalized Wasserstein Distance (NWD) algorithm. The mAP of DCCN-YOLOv7 on the URPC dataset is 80.4%, according to the experimental results, 2.8% higher than the YOLOv7 network model that is used as a baseline. Furthermore, in contrast to the original YOLOv7 algorithm, the detection speed and accuracy are higher, making it more appropriate for target recognition underwater. © (2024), (International Association of Engineers). All rights reserved.

关键词： Feature extraction

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Micro-Expression Recognition Based on Spatio-Temporal Feature Extraction of Key Regions

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computers, Materials & Continua 2023年第10期77卷 1373-1392页

作者： Wenqiu Zhu Yongsheng Li Qiang Liu Zhigao Zeng College of Computer Science Hunan University of TechnologyZhuzhou412007China Intelligent Information Perception and Processing Technology Hunan Province Key LaboratoryZhuzhou412007China

Aiming at the problems of short duration,low intensity,and difficult detection of micro-expressions(MEs),the global and local features of ME video frames are extracted by combining spatial feature extraction and temporal feature *** on traditional convolution neural network(CNN)and long short-term memory(LSTM),a recognition method combining global identification attention network(GIA),block identification attention network(BIA)and bi-directional long short-term memory(Bi-LSTM)is *** the BIA,the ME video frame will be cropped,and the training will be carried out by cropping into 24 identification blocks(IBs),10 IBs and uncropped *** alleviate the overfitting problem in training,we first extract the basic features of the preprocessed sequence through the transfer learning layer,and then extract the global and local spatial features of the output data through the GIA layer and the BIA layer,*** the BIA layer,the input data will be cropped into local feature vectors with attention weights to extract the local features of the ME frames;in the GIA layer,the global features of the ME frames will be ***,after fusing the global and local feature vectors,the ME time-series information is extracted by *** experimental results show that using IBs can significantly improve the model’s ability to extract subtle facial features,and the model works best when 10 IBs are used.

关键词： Micro-expression recognition attention mechanism long and short-term memory network transfer learning identification block

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Single Image Desnow Based on Vision Transformer and Conditional Generative Adversarial Network for Internet of Vehicles

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computer Modeling in Engineering & sciences 2023年第11期137卷 1975-1988页

作者： Bingcai Wei Di Wang Zhuang Wang Liye Zhang College of Computer Science and Technology Shandong University of TechnologyZibo255049China

With the increasing popularity of artificial intelligence applications,machine learning is also playing an increasingly important role in the Internet of Things(IoT)and the Internet of Vehicles(IoV).As an essential part of the IoV,smart transportation relies heavily on information obtained from ***,inclement weather,such as snowy weather,negatively impacts the process and can hinder the regular operation of imaging equipment and the acquisition of conventional image *** only that,but the snow also makes intelligent transportation systems make the wrong judgment of road conditions and the entire system of the Internet of Vehicles *** paper describes the single image snowremoval task and the use of a vision transformer to generate adversarial *** residual structure is used in the algorithm,and the Transformer structure is used in the network structure of the generator in the generative adversarial networks,which improves the accuracy of the snow removal ***,the vision transformer has good scalability and versatility for larger models and has a more vital fitting ability than the previously popular convolutional neural *** Snow100K dataset is used for training,testing and comparison,and the peak signal-to-noise ratio and structural similarity are used as evaluation *** experimental results show that the improved snow removal algorithm performs well and can obtain high-quality snow removal images.

关键词： Artificial intelligence Internet of Things vision transformer deep learning image desnow

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Lactobacillus johnsonii-derived leucic acid promotes fatty acid absorption and deposition by targeting CD36

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science China Life sciences 2025年第6期68卷 1727-1739页

作者： Yunxia Li Zhiming Zhang Qi Han Gang Liu Yulong Yin Jie Yin College of Animal Science and Technology Hunan Agriculture University Yuelushan Laboratory College of Bioscience and Biotechnology Hunan Agriculture University Institute of Subtropical Agriculture Chinese Academy of Sciences

Lactobacillus johnsonii is a microbial biomarker associated with lipid deposition, but the mechanism by which it accelerates fatty acid absorption and deposition remains unclear. In this study, we isolated a strain of L. johnsonii MS0621 from the feces of Ningxiang pigs, an obese animal model, and evaluated its probiotic properties with high resistance to temperature and intestinal fluids. Colonization by L. johnsonii MS0621 increased the abundance of gut Lactobacillus in lean DLY pigs, concomitant with increases in fatty acid absorption in the intestine and lipid depositions in the fat and muscle tissues. The lipid absorption-promoting effect was further detected in IPEC-J2 cells treated with live L. johnsonii MS0621 and the bacteria-free supernatants, as evidenced by high triglyceride synthesis and the expression of CD36, a key lipid transporter. Metabolomics analysis showed that(R)-leucic acid is a potential metabolite targeting CD36 expression to guarantee lipid absorption and deposition. The mechanism might involve direct interaction with CD36, as molecular docking and inhibition of CD36 blocked L. johnsonii MS0621 or derived metabolite-mediated lipid absorption. In conclusion, we uncovered an important role of L. johnsonii MS0621 derived(R)-leucic acid in regulating the absorption and deposition of intestinal fatty acids via regulation of CD36 expression.

关键词： L. johnsonii MS0621 (R)-leucic acid fatty acid absorption

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AsyCo: an asymmetric dual-task co-training model for partial-label learning

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science China(Information sciences) 2025年第5期68卷 332-347页

作者： Beibei LI Yiyuan ZHENG Beihong JIN Tao XIANG Haobo WANG Lei FENG College of Computer Science Chongqing University State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences School of Software Technology Zhejiang University School of Computer Science and Engineering Nanyang Technological University

Partial-label learning(PLL) is a typical problem of weakly supervised learning, where each training instance is annotated with a set of candidate labels. Self-training PLL models achieve state-of-the-art performance but suffer from error accumulation problems caused by mistakenly disambiguated instances. Although co-training can alleviate this issue by training two networks simultaneously and allowing them to interact with each other, most existing co-training methods train two structurally identical networks with the same task, i.e., are symmetric, rendering it insufficient for them to correct each other due to their similar limitations. Therefore, in this paper, we propose an asymmetric dual-task co-training PLL model called AsyCo,which forces its two networks, i.e., a disambiguation network and an auxiliary network, to learn from different views explicitly by optimizing distinct tasks. Specifically, the disambiguation network is trained with a self-training PLL task to learn label confidence, while the auxiliary network is trained in a supervised learning paradigm to learn from the noisy pairwise similarity labels that are constructed according to the learned label confidence. Finally, the error accumulation problem is mitigated via information distillation and confidence refinement. Extensive experiments on both uniform and instance-dependent partially labeled datasets demonstrate the effectiveness of AsyCo.

关键词： machine learning weakly supervised learning partial-label learning co-training models candidate label sets

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