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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Ding, Xiaoou Li, Genglong Wang, Hongzhi Wang, Chen Song, Yichen School of Computer Science and Technology Harbin Institute of Technology China Tsinghua University National Engineering Laboratory for Big Data Software EIRI China

ISBN: (纸本)9798350317152

Time series data generated by thousands of sensors are suffering data quality problems. Traditional constraint-based techniques have greatly contributed to data cleaning applications. However, cleaning methods that support expressive constraints on time series data remain insufficient. Given the notable characteristics of time series data, existing cleaning approaches are challenged to provide good repair solutions. To address the challenges, we propose a novel data cleaning method for time series which incorporates expressive constraints that support arithmetic operations between attributes and time context. In the violation detection phase, we introduce specialized violation degree quantification functions and design a violation cell discovery algorithm to identify errors hidden in time series data. In the data repairing phase, we formalize the cleaning task as a constrained optimization problem and develop a novel repair objective function that considers both modification costs and conformance degrees of constraints. We effectively reduce the repair search space through the evaluation of time-context constraints and propose a bidirectional repairing algorithm. We also provide theoretical analysis of the proposed repairing method. Experimental results on three real-world IoT datasets across five metrics demonstrate that our proposed method outperforms seven state-of-the-art cleaning techniques specialized for time series data. Specifically, we achieve a 60% improvement in repairing effectiveness and a 70% reduction in time costs with our designed cleaning strategy. © 2024 IEEE.

关键词： Time series

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Pre-trained SAM as data augmentation for image segmentation

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CAAI Transactions on Intelligence Technology 2025年第1期10卷 268-282页

作者： Junjun Wu Yunbo Rao Shaoning Zeng Bob Zhang Yangtze Delta Region Institute(Huzhou) University of Electronic Science and Technology of ChinaHuzhouChina School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengduChina Pattern Analysis and Machine Intelligence Research Group Department of Computer and Information ScienceUniversity of MacaoMacaoChina

Data augmentation plays an important role in training deep neural model by expanding the size and diversity of the ***,data augmentation mainly involved some simple transformations of ***,in order to increase the diversity and complexity of data,more advanced methods appeared and evolved to sophisticated generative ***,these methods required a mass of computation of training or *** this paper,a novel training-free method that utilises the Pre-Trained Segment Anything Model(SAM)model as a data augmentation tool(PTSAM-DA)is proposed to generate the augmented annotations for *** the need for training,it obtains prompt boxes from the original annotations and then feeds the boxes to the pre-trained SAM to generate diverse and improved *** this way,annotations are augmented more ingenious than simple manipulations without incurring huge computation for training a data augmentation *** comparative experiments on three datasets are conducted,including an in-house dataset,ADE20K and *** this in-house dataset,namely Agricultural Plot Segmentation Dataset,maximum improvements of 3.77%and 8.92%are gained in two mainstream metrics,mIoU and mAcc,***,large vision models like SAM are proven to be promising not only in image segmentation but also in data augmentation.

关键词： data augmentation image segmentation large model segment anything model

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Common Problems in software Requirement engineering Process: An Overview of Pakistani software Industry 4

Common Problems in Software Requirement Engineering Process:...

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4th International Conference on Advancements in Computational sciences, ICACS 2023

作者： Khalid, Sadia Rasheed, Uzair Muneer, Mishal Butt, Wasi Haider Mehmood, Rabia Qamar, Usman College of Electrical and Mechanical Engineering NUST Department of Computer and Software Engineering Islamabad Pakistan University of Lahore Department of Computer Science and IT Lahore Pakistan Lahore College for Women University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781665463775

Requirement engineering is a major phase of software development process. A project's success mainly depends on an efficient and effective requirement engineering process. Practices have been defined to ensure successful requirement engineering of software projects. Yet the professionals face numerous issues during this phase. This paper explores the software requirement engineering practices from in the software industry of Pakistan. It highlights the common problems faced by the software professionals, as well as commonly deployed solutions and practices. © 2023 IEEE.

关键词： Requirements engineering

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Mask Wearing Detection System for Epidemic Control Based on STM32 6th

Mask Wearing Detection System for Epidemic Control Based on ...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Luoli Yadav, Amit Khan, Asif Varish, Naushad Singh, Priyanka Thakkar, Hiren Kumar Department of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University DarwinNT Australia Department of Computer Application Integral University Lucknow India Department of Computer Science and Engineering GITAM University Hyderabad502329 India Department of Computer Science and Engineering SRM University Andhra Pradesh Amravati522502 India Department of Computer Science and Engineering Pandit Deendayal Energy Univrsity Gujarat Gandhinagar382007 India

ISBN: (纸本)9789819940707

This paper designs an epidemic prevention and control mask wearing detection system based on STM32, which is used to monitor the situation of people wearing masks. Tiny-YOLO detection algorithm is adopted in the system, combined with image recognition technology, and two kinds of image data with and without masks are used for network training. Then, the trained model can be used to carry out real-time automatic supervision on the wearing of masks in the surveillance video. When the wrong wearing or not wearing masks are detected, the buzzer will send an alarm, so as to effectively monitor the wearing of masks and remind relevant personnel to wear masks correctly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image recognition

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Source identification and prediction of nitrogen and phosphorus pollution of Lake Taihu by an ensemble machine learning technique

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Frontiers of Environmental science & engineering 2023年第5期17卷 27-40页

作者： Yirong Hu Wenjie Du Cheng Yang Yang Wang Tianyin Huang Xiaoyi Xu Wenwei Li CAS Key Laboratory of Urban Pollutant Conversion Department of Environmental Science and EngineeringUniversity of Science and Technology of ChinaHefei 230026China School of Software Engineering University of Science and Technology of ChinaHefei 230026China USTC-CityU Joint Advanced Research Center Suzhou Institute for Advanced Research of USTCSuzhou 215123China National and Local Joint Engineering Laboratory for Municipal Sewage Resource Utilization Technology Suzhou University of Science and TechnologySuzhou 215009China

Effective control of lake eutrophication necessitates a full understanding of the complicated nitrogen and phosphorus pollution sources,for which mathematical modeling is commonly *** contrast to the conventional knowledge-based models that usually perform poorly due to insufficient knowledge of pollutant geochemical cycling,we employed an ensemble machine learning(ML)model to identify the key nitrogen and phosphorus sources of *** ML models were developed based on 13 years of historical data of Lake Taihu’s water quality,environmental input,and meteorological conditions,among which the XGBoost model stood out as the best model for total nitrogen(TN)and total phosphorus(TP)*** results suggest that the lake TN is mainly affected by the endogenous load and inflow river water quality,while the lake TP is predominantly from endogenous *** prediction of the lake TN and TP concentration changes in response to these key feature variations suggests that endogenous source control is a highly desirable option for lake eutrophication ***,one-month-ahead prediction of lake TN and TP concentrations(R2 of 0.85 and 0.95,respectively)was achieved based on this model with sliding time window lengths of 9 and 6 months,*** work demonstrates the great potential of using ensemble ML models for lake pollution source tracking and prediction,which may provide valuable references for early warning and rational control of lake eutrophication.

关键词： Eutrophication Machine learning Water quality Nutrients Prediction

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Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 695-706页

作者： Peiyuan Sun Long Huo Xin Chen Siyuan Liang the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China the School of Electrical Engineering and the Center of Nanomaterials for Renewable EnergyState Key Laboratory of Electrical Insulation and Power EquipmentXi’an Jiaotong UniversityXi’an 710049China the Department of Computer Science and Engineering The Chinese University of Hong KongShatin999077Hong KongChina

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS *** paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU *** grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power *** the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are *** studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN *** show that the TTEDNN model performs best among existing deep learning ***,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关键词： Rotor angle stability prediction topological embedding deep learning graph convolution network

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A New Cybersecurity Approach Enhanced by xAI-Derived Rules to Improve Network Intrusion Detection and SIEM

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computers, Materials & Continua 2025年第5期83卷 1607-1621页

作者： Federica Uccello Marek Pawlicki Salvatore D'Antonio RafałKozik MichałChoras Centro Direzionale Department of EngineeringUniversity of Naples‘Parthenope’Isola C4Napoli80133Italy Department of Computer and Information Science Software and SystemsLinköping UniversityLinköping58183Sweden ITTI Sp.z o.o. Pozna´n61-612Poland Faculty of Telecommunications Computer Science and Electrical EngineeringBydgoszcz University of Science and TechnologyBydgoszcz85-796Poland

The growing sophistication of cyberthreats,among others the Distributed Denial of Service attacks,has exposed limitations in traditional rule-based Security Information and Event Management *** machine learning–based intrusion detection systems can capture complex network behaviours,their“black-box”nature often limits trust and actionable insight for security *** study introduces a novel approach that integrates Explainable Artificial Intelligence—xAI—with the Random Forest classifier to derive human-interpretable rules,thereby enhancing the detection of Distributed Denial of Service(DDoS)*** proposed framework combines traditional static rule formulation with advanced xAI techniques—SHapley Additive exPlanations and Scoped Rules-to extract decision criteria from a fully trained *** methodology was validated on two benchmark datasets,CICIDS2017 and *** rules were evaluated against conventional Security Information and Event Management Systems rules with metrics such as precision,recall,accuracy,balanced accuracy,and Matthews Correlation *** results demonstrate that xAI-derived rules consistently outperform traditional static ***,the most refined xAI-generated rule achieved near-perfect performance with significantly improved detection of DDoS traffic while maintaining high accuracy in classifying benign traffic across both datasets.

关键词： Cybersecurity explainable artificial intelligence intrusion detection system rule-based SIEM distributed denial of service

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Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation 24

Devil is in Details: Locality-Aware 3D Abdominal CT Volume G...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yuran Wan, Zhijing Qiu, Yansheng Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource requirements and privacy constraints. Numerous efforts employ generative models to generate high-fidelity, unlabeled 3D volumes across diverse modalities and anatomical regions. However, the intricate and indistinguishable anatomical structures within the abdomen pose a unique challenge to abdominal CT volume generation compared to other anatomical regions. To address the overlooked challenge, we introduce the Locality-Aware Diffusion (Lad), a novel method tailored for exquisite 3D abdominal CT volume generation. We design a locality loss to refine crucial anatomical regions and devise a condition extractor to integrate abdominal priori into generation, thereby enabling the generation of large quantities of high-quality abdominal CT volumes essential for SSL tasks without the need for additional data such as labels or radiology reports. Volumes generated through our method demonstrate remarkable fidelity in reproducing abdominal structures, achieving a decrease in FID score from 0.0034 to 0.0002 on AbdomenCT-1K dataset, closely mirroring authentic data and surpassing current methods. Extensive experiments demonstrate the effectiveness of our method in self-supervised organ segmentation tasks, resulting in an improvement in mean Dice scores on two abdominal datasets effectively. These results underscore the potential of synthetic data to advance self-supervised learning in medical image analysis. © 2024 ACM.

关键词： Self-supervised learning

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Sequential Kernel Goodness-of-fit Testing 41

Sequential Kernel Goodness-of-fit Testing

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Zhengyu Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Goodness-of-fit testing, a classical statistical tool, has been extensively explored in the batch setting, where the sample size is ***, practitioners often prefer methods that adapt to the complexity of a problem rather than fixing the sample size *** batch tests are generally unsuitable for streaming data, as valid inference after data peeking requires multiple testing corrections, resulting in reduced statistical *** address this issue, we delve into the design of consistent sequential goodness-of-fit *** the principle of testing by betting, we reframe this task as selecting a sequence of payoff functions that maximize the wealth of a fictitious bettor, betting against the null in a repeated *** conduct experiments to demonstrate the adaptability of our sequential test across varying difficulty levels of problems while maintaining control over type-I errors. Copyright 2024 by the author(s)

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PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research Center Wuxi 214082China Taihu Laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

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