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Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

Computers, Materials & Continua 2024年第5期79卷 2063-2083页

作者： Xiaoqin Ma Jun Wang Wenchang Yu Qinli Zhang School of big data and Artificial Intelligence Chizhou UniversityChizhou247000China Anhui Education big data Intelligent Perception and application engineering research center Chizhou247000China

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable *** available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal *** order to address the aforementioned issues,this paper delves into the study of attribute reduction *** of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute *** newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute ***,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute ***,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy *** effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information ***,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is *** experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction *** comprehensive analysis o

关键词： Hybrid decision information systems fuzzy conditional information entropy attribute reduction fuzzy relationship rough set theory(RST)

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Investigation into the Methodology and Implementation of Life Cycle engineering under China’s Carbon Reduction Target in the Process Industry

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engineering 2024年第9期40卷 87-99页

作者： Mingyang Li Feng Gao Zuoren Nie Boxue Sun Yu Liu Xianzheng Gong national engineering Laboratory for Industrial big-data application Technology College of Material Science and EngineeringBeijing University of TechnologyBeijing 100124China

The industrial sector is the primary source of carbon emissions in *** pursuit of meeting its carbon reduction targets,China aims to promote resource consumption sustainability,reduce energy consumption,and achieve carbon neutrality within its processing *** effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering *** strategy aims to attain an optimal solution for material performance,resource consumption,and environmental *** this study,five types of technologies were considered:raw material replacement,process reengineering,fuel replacement,energy recycling and reutilization,and material recycling and *** meaning,methodology,and development status of life cycle engineering technology abroad and domestically are discussed in detail.A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption,carbon emissions,product performance,and recycling of secondary resources in a manufacturing *** coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry,as an *** results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

关键词： Carbon neutrality Life cycle engineering Process Industry Carbon reduction technology Eco-design

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IoTDQ: An Industrial IoT data analysis Library for Apache IoTDB

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big data Mining and Analytics 2024年第1期7卷 29-41页

作者： Pengyu Chen Wendi He Wenxuan Ma Xiangdong Huang Chen Wang School of Software Tsinghua UniversityBeijing 100084China national engineering research center for big data Software(NERCBDS) Tsinghua UniversityBeijing 100084China

There is a growing demand for time series data analysis in industry *** loTDB is a time series database designed for the Internet of Things(loT)with enhanced storage and I/O *** User-Defined Functions(UDF)provided,computation for time series can be executed on Apache loTDB *** satisfy most of the common requirements in industrial time series analysis,we create a UDF library,loTDQ,on Apache *** library integrates stream computation functions on data quality analysis,data profiling,anomaly detection,data repairing,*** enables users to conduct a wide range of analyses,such as monitoring,error diagnosis,equipment reliability *** provides a framework for users to examine loT time series with data quality *** show that loTDQ keeps the same level of performance compared to mainstream alternatives,and shortens I/O consumption for Apache loTDB users.

关键词： industrial big data data quality data mining and analytics

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Understanding and improving fairness in cognitive diagnosis

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Science China(Information Sciences) 2024年第5期67卷 99-114页

作者： Zheng ZHANG Le WU Qi LIU Jiayu LIU Zhenya HUANG Yu YIN Yan ZHUANG Weibo GAO Enhong CHEN Anhui Province Key Laboratory of big data analysis and application School of Computer Science and Technology& School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence School of Computer Science and Information engineering Hefei University of Technology

Intelligent education is a significant application of artificial intelligence. One of the key research topics in intelligence education is cognitive diagnosis, which aims to gauge the level of proficiency among students on specific knowledge concepts(e.g., Geometry). To the best of our knowledge, most of the existing cognitive models primarily focus on improving diagnostic accuracy while rarely considering fairness issues; for instance, the diagnosis of students may be affected by various sensitive attributes(e.g., region). In this paper,we aim to explore fairness in cognitive diagnosis and answer two questions:(1) Are the results of existing cognitive diagnosis models affected by sensitive attributes?(2) If yes, how can we mitigate the impact of sensitive attributes to ensure fair diagnosis results? To this end, we first empirically reveal that several wellknown cognitive diagnosis methods usually lead to unfair performances, and the trend of unfairness varies among different cognitive diagnosis models. Then, we make a theoretical analysis to explain the reasons behind this phenomenon. To resolve the unfairness problem in existing cognitive diagnosis models, we propose a general fairness-aware cognitive diagnosis framework, FairCD. Our fundamental principle involves eliminating the effect of sensitive attributes on student proficiency. To achieve this, we divide student proficiency in existing cognitive diagnosis models into two components: bias proficiency and fair *** design two orthogonal tasks for each of them to ensure that fairness in proficiency remains independent of sensitive attributes and take it as the final diagnosed result. Extensive experiments on the Program for International Student Assessment(PISA) dataset clearly show the effectiveness of our framework.

关键词： fairness intelligent education cognitive diagnosis psychometrics adversarial learning

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Answering reachability queries with ordered label constraints over labeled graphs

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Frontiers of Computer Science 2024年第1期18卷 105-117页

作者： Daoliang HE Pingpeng YUAN Hai JIN national engineering research center for big data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science&TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Reachability query plays a vital role in many graph analysis *** researches proposed many methods to efficiently answer reachability queries between vertex *** many real graphs are labeled graph,it highly demands Label-Constrained Reachability(LCR)query inwhich constraint includes a set of labels besides vertex *** researches proposed several methods for answering some LCR queries which require appearance of some labels specified in constraints in the *** that constraint may be a label set,query constraint may be ordered labels,namely OLCR(Ordered-Label-Constrained Reachability)queries which retrieve paths matching a sequence of ***,no solutions are available for ***,we propose DHL,a novel bloom filter based indexing technique for answering OLCR *** can be used to check reachability between vertex *** the answers are not no,then constrained DFS is ***,we employ DHL followed by performing constrained DFS to answer OLCR *** show that DHL has a bounded false positive rate,and it's powerful in saving indexing time and *** experiments on 10 real-life graphs and 12 synthetic graphs demonstrate that DHL achieves about 4.8-22.5 times smaller index space and 4.6-114 times less index construction time than two state-of-art techniques for LCR queries,while achieving comparable query response *** results also show that our algorithm can answer OLCR queries effectively.

关键词： graph ordered-label-constrained reachability partial index bloom filter query processing

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Ghost Module Based Residual Mixture of Self-Attention and Convolution for Online Signature Verification

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Computers, Materials & Continua 2024年第4期79卷 695-712页

作者： Fangjun Luan Xuewen Mu Shuai Yuan Department of Computer Technology School of Computer Science and EngineeringShenyang Jianzhu UniversityShenyang110168China Liaoning Province big data Management and analysis Laboratory of Urban Construction Shenyang Jianzhu UniversityShenyang110168China Shenyang Branch of national Special Computer engineering Technology research center Shenyang Jianzhu UniversityShenyang110168China

Online Signature Verification (OSV), as a personal identification technology, is widely used in various ***, it faces challenges, such as incomplete feature extraction, low accuracy, and computational heaviness. Toaddress these issues, we propose a novel approach for online signature verification, using a one-dimensionalGhost-ACmix Residual Network (1D-ACGRNet), which is a Ghost-ACmix Residual Network that combines convolutionwith a self-attention mechanism and performs improvement by using Ghost method. The Ghost-ACmix Residualstructure is introduced to leverage both self-attention and convolution mechanisms for capturing global featureinformation and extracting local information, effectively complementing whole and local signature features andmitigating the problem of insufficient feature extraction. Then, the Ghost-based Convolution and Self-Attention(ACG) block is proposed to simplify the common parts between convolution and self-attention using the Ghostmodule and employ feature transformation to obtain intermediate features, thus reducing computational ***, feature selection is performed using the random forestmethod, and the data is dimensionally reducedusing Principal Component analysis (PCA). Finally, tests are implemented on the MCYT-100 datasets and theSVC-2004 Task2 datasets, and the equal error rates (EERs) for small-sample training using five genuine andforged signatures are 3.07% and 4.17%, respectively. The EERs for training with ten genuine and forged signaturesare 0.91% and 2.12% on the respective datasets. The experimental results illustrate that the proposed approacheffectively enhances the accuracy of online signature verification.

关键词： Online signature verification feature selection ACG block ghost-ACmix residual structure

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analysis on the causes of massive stranding of Yellow Sea green tide on Lianyungang and Rizhao coasts in 2022

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Journal of Oceanology and Limnology 2024年第3期42卷 816-830页

作者： Lingjuan WU Juan HUANG Yi DING Guiyan LIU Shuai HUANG Song GAO Chao YUAN Jiangling XU Ping WU Rui HUANG Ruobing WEN Jie MEI North China Sea Marine Forecasting and Hazard Mitigation center Ministry of Natural ResourcesQingdao 266061China Shandong Key Laboratory of Marine ecological Environment and Disaster Prevention and Mitigation Qingdao 266061China national engineering Laboratory for Integrated Aero-Space-Ground-Ocean big data application Technology Xi’an 710129China East China Sea Marine Forecasting and Hazard Mitigation center Ministry of Natural ResourcesShanghai 200136China North China Sea ecological center Ministry of Natural ResourcesQingdao 266033China

In 2022,Yellow Sea green tide caused by Ulva prolifera reached a historic minimum and the coastal areas of Shandong Peninsula were less ***,the largest amount of seaweed biomass has been washed ashore on Lianyungang and Rizhao coasts since *** studied the development pattern of Yellow Sea green tide in 2022,and analyzed the key environmental factors on the growth and drifting,then discussed the possible reasons that resulted in the massive stranding of green tide biomass in Lianyungang and *** show under the combined influence of the east to southeast winds and currents with shoreward anomalies,green tide drifted to the coastal waters between Shandong and Jiangsu provinces and the distribution areas located westward compared with previous years(2008–2021).Floating *** rafts from the coastal waters of Binhai and Sheyang drifted continuously into the coastal waters of Lianyungang and Rizhao,providing important supplements for Yellow Sea green *** green tide in 2022 distributed close to the coastal waters,the abundant nutrients might support their continuous high growth *** addition,the amount of rainfall around Shandong Peninsula from late June to early July were significantly higher than in previous years,which might promote the development of green tide to some extent.

关键词： Yellow Sea green tide stranding biomass development pattern environmental factor

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Visible-frequency nonvolatile reconfigurable Janus metasurfaces for dual-wavelength-switched and spin-asymmetric holograms

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Photonics research 2024年第2期12卷 356-368页

作者： HUAN YUAN ZHEQIANG ZHONG BIN ZHANG College of Electronics and Information engineering Sichuan UniversityChengdu 610064China big data analysis and Fusion application Technology engineering Laboratory of Sichuan Province Chengdu 610065China

Janus metasurface holography with asymmetric transmission characteristics provides new degrees of freedom for multiplexing ***,earlier metasurfaces with asymmetrical transmission faced limitations in terms of tunability and *** this study,we propose a metasurface color holographic encryption scheme with dynamic switching and asymmetric transmission at visible frequencies using a low-loss nonvolatile optical phase-change material,Sb_(2)S_(3).Using a modified holographic optimization strategy,we achieved highfidelity asymmetric holographic imaging of a nanostructured *** controlling the incident direction and wavelength of visible light,as well as the level of crystallization of Sb_(2)S_(3),this reconfigurable metasurface enables the precise manipulation of tunable color holographic image *** particular,in the semi-crystalline state of Sb_(2)S_(3),the encoded information can be securely encrypted using a two-channel color-holographic image,whereas only a preset camouflaged image is displayed in the crystalline or amorphous state of Sb_(2)S_(3).The proposed multiencrypted Janus metasurface provides a potential approach for dynamic holographic displays with ultrahigh capacity,holographic encryption,and information storage.

关键词： holographic asymmetric visible

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A survey on dynamic graph processing on GPUs: concepts, terminologies and systems

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Frontiers of Computer Science 2024年第4期18卷 1-23页

作者： Hongru GAO Xiaofei LIAO Zhiyuan SHAO Kexin LI Jiajie CHEN Hai JIN national engineering research center for big data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graphs that are used to model real-world entities with vertices and relationships among entities with edges,have proven to be a powerful tool for describing real-world problems in *** most real-world scenarios,entities and their relationships are subject to constant *** that record such changes are called dynamic *** recent years,the widespread application scenarios of dynamic graphs have stimulated extensive research on dynamic graph processing systems that continuously ingest graph updates and produce up-to-date graph analytics *** the scale of dynamic graphs becomes larger,higher performance requirements are demanded to dynamic graph processing *** the massive parallel processing power and high memory bandwidth,GPUs become mainstream vehicles to accelerate dynamic graph processing ***-based dynamic graph processing systems mainly address two challenges:maintaining the graph data when updates occur(i.e.,graph updating)and producing analytics results in time(i.e.,graph computing).In this paper,we survey GPU-based dynamic graph processing systems and review their methods on addressing both graph updating and graph *** comprehensively discuss existing dynamic graph processing systems on GPUs,we first introduce the terminologies of dynamic graph processing and then develop a taxonomy to describe the methods employed for graph updating and graph *** addition,we discuss the challenges and future research directions of dynamic graph processing on GPUs.

关键词： dynamic graphs graph processing graph algorithms GPUs

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Enhancing Tea Leaf Disease Identification with Lightweight MobileNetV2

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Computers, Materials & Continua 2024年第7期80卷 679-694页

作者： Zhilin Li Yuxin Li Chunyu Yan Peng Yan Xiutong Li Mei Yu Tingchi Wen Benliang Xie College of big data and Information engineering Guizhou UniversityGuiyang550025China Power Semiconductor Device Reliability engineering center of the Ministry of Education Guiyang550025China State Key Laboratory of Public big data Guizhou UniversityGuiyang550025China The Mushroom research Centre Guizhou UniversityGuiyang550025China The engineering research center of Southwest Bio-Pharmaceutical Resources Ministry of EducationGuizhou UniversityGuiyang550025China

Diseases in tea trees can result in significant losses in both the quality and quantity of tea *** monitoring can help to prevent the occurrence of large-scale diseases in tea ***,existingmethods face challenges such as a high number of parameters and low recognition accuracy,which hinders their application in tea plantation monitoring *** paper presents a lightweight I-MobileNetV2 model for identifying diseases in tea leaves,to address these *** proposed method first embeds a Coordinate Attention(CA)module into the originalMobileNetV2 network,enabling the model to locate disease regions ***,a Multi-branch Parallel Convolution(MPC)module is employed to extract disease features across multiple scales,improving themodel’s adaptability to different disease ***,the AutoML for Model Compression(AMC)is used to compress themodel and reduce computational *** results indicate that our proposed algorithm attains an average accuracy of 96.12%on our self-built tea leaf disease dataset,surpassing the original MobileNetV2 by 1.91%.Furthermore,the number of model parameters have been reduced by 40%,making itmore suitable for practical application in tea plantation environments.

关键词： Disease identification coordinate attention mechanism multi-scale feature extraction model pruning

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