Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence o...Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.展开更多
Scientific knowledge on the chemical compositions of fine particulate matter(PM_(2.5)) is essential for properly assessing its health and climate effects,and for decisionmakers to develop efficient mitigation strategi...Scientific knowledge on the chemical compositions of fine particulate matter(PM_(2.5)) is essential for properly assessing its health and climate effects,and for decisionmakers to develop efficient mitigation strategies.A high-resolution PM_(2.5) chemical composition dataset(CAQRA-aerosol)is developed in this study,which provides hourly maps of organic carbon,black carbon,ammonium,nitrate,and sulfate in China from 2013 to 2020 with a horizontal resolution of 15 km.This paper describes the method,access,and validation results of this dataset.It shows that CAQRA-aerosol has good consistency with observations and achieves higher or comparable accuracy with previous PM_(2.5) composition datasets.Based on CAQRA-aerosol,spatiotemporal changes of different PM_(2.5) compositions were investigated from a national viewpoint,which emphasizes different changes of nitrate from other compositions.The estimated annual rate of population-weighted concentrations of nitrate is 0.23μg m^(−3)yr^(−1) from 2015 to 2020,compared with−0.19 to−1.1μg m^(−3)yr^(−1) for other compositions.The whole dataset is freely available from the China Air Pollution Data Center(https://doi.org/10.12423/capdb_PKU.2023.DA).展开更多
This data set collects,compares and contrasts the capacities and structures of a series of hard carbon materials,and then searches for correlations between structure and electrochemical performance.The capacity data o...This data set collects,compares and contrasts the capacities and structures of a series of hard carbon materials,and then searches for correlations between structure and electrochemical performance.The capacity data of the hard carbons were obtained by charge/discharge tests and the materials were characterized by XRD,gas adsorption,true density tests and SAXS.In particular,the fitting of SAXS gave a series of structural parameters which showed good characterization.The related test details are given with the structural data of the hard carbons and the electrochemical performance of the sodium-ion batteries.展开更多
The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.How...The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.However,existing datasets for IDS training often lack relevance to modern IIoT environments,limiting their applicability for research and development.To address the latter gap,this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems.As a consequence,that can be used by an IDS to detect imminent threats.Likewise,HiTar-2024 was generated using the AREZZO simulator,which replicates realistic smart manufacturing scenarios.The generated dataset includes five distinct classes:Normal,Probing,Remote to Local(R2L),User to Root(U2R),and Denial of Service(DoS).Furthermore,comprehensive experiments with popular Machine Learning(ML)models using various classifiers,including BayesNet,Logistic,IBK,Multiclass,PART,and J48 demonstrate high accuracy,precision,recall,and F1-scores,exceeding 0.99 across all ML metrics.The latter result is reached thanks to the rigorous applied process to achieve this quite good result,including data pre-processing,features extraction,fixing the class imbalance problem,and using a test option for model robustness.This comprehensive approach emphasizes meticulous dataset construction through a complete dataset generation process,a careful labelling algorithm,and a sophisticated evaluation method,providing valuable insights to reinforce IIoT system security.Finally,the HiTar-2024 dataset is compared with other similar datasets in the literature,considering several factors such as data format,feature extraction tools,number of features,attack categories,number of instances,and ML metrics.展开更多
When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes...When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets.展开更多
Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveill...Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.展开更多
Climate change significantly affects environment,ecosystems,communities,and economies.These impacts often result in quick and gradual changes in water resources,environmental conditions,and weather patterns.A geograph...Climate change significantly affects environment,ecosystems,communities,and economies.These impacts often result in quick and gradual changes in water resources,environmental conditions,and weather patterns.A geographical study was conducted in Arizona State,USA,to examine monthly precipi-tation concentration rates over time.This analysis used a high-resolution 0.50×0.50 grid for monthly precip-itation data from 1961 to 2022,Provided by the Climatic Research Unit.The study aimed to analyze climatic changes affected the first and last five years of each decade,as well as the entire decade,during the specified period.GIS was used to meet the objectives of this study.Arizona experienced 51–568 mm,67–560 mm,63–622 mm,and 52–590 mm of rainfall in the sixth,seventh,eighth,and ninth decades of the second millennium,respectively.Both the first and second five year periods of each decade showed accept-able rainfall amounts despite fluctuations.However,rainfall decreased in the first and second decades of the third millennium.and in the first two years of the third decade.Rainfall amounts dropped to 42–472 mm,55–469 mm,and 74–498 mm,respectively,indicating a downward trend in precipitation.The central part of the state received the highest rainfall,while the eastern and western regions(spanning north to south)had significantly less.Over the decades of the third millennium,the average annual rainfall every five years was relatively low,showing a declining trend due to severe climate changes,generally ranging between 35 mm and 498 mm.The central regions consistently received more rainfall than the eastern and western outskirts.Arizona is currently experiencing a decrease in rainfall due to climate change,a situation that could deterio-rate further.This highlights the need to optimize the use of existing rainfall and explore alternative water sources.展开更多
Lithology identification is a critical aspect of geoenergy exploration,including geothermal energy development,gas hydrate extraction,and gas storage.In recent years,artificial intelligence techniques based on drill c...Lithology identification is a critical aspect of geoenergy exploration,including geothermal energy development,gas hydrate extraction,and gas storage.In recent years,artificial intelligence techniques based on drill core images have made significant strides in lithology identification,achieving high accuracy.However,the current demand for advanced lithology identification models remains unmet due to the lack of high-quality drill core image datasets.This study successfully constructs and publicly releases the first open-source Drill Core Image Dataset(DCID),addressing the need for large-scale,high-quality datasets in lithology characterization tasks within geological engineering and establishing a standard dataset for model evaluation.DCID consists of 35 lithology categories and a total of 98,000 high-resolution images(512×512 pixels),making it the most comprehensive drill core image dataset in terms of lithology categories,image quantity,and resolution.This study also provides lithology identification accuracy benchmarks for popular convolutional neural networks(CNNs)such as VGG,ResNet,DenseNet,MobileNet,as well as for the Vision Transformer(ViT)and MLP-Mixer,based on DCID.Additionally,the sensitivity of model performance to various parameters and image resolution is evaluated.In response to real-world challenges,we propose a real-world data augmentation(RWDA)method,leveraging slightly defective images from DCID to enhance model robustness.The study also explores the impact of real-world lighting conditions on the performance of lithology identification models.Finally,we demonstrate how to rapidly evaluate model performance across multiple dimensions using low-resolution datasets,advancing the application and development of new lithology identification models for geoenergy exploration.展开更多
Standardized datasets are foundational to healthcare informatization by enhancing data quality and unleashing the value of data elements.Using bibliometrics and content analysis,this study examines China's healthc...Standardized datasets are foundational to healthcare informatization by enhancing data quality and unleashing the value of data elements.Using bibliometrics and content analysis,this study examines China's healthcare dataset standards from 2011 to 2025.It analyzes their evolution across types,applications,institutions,and themes,highlighting key achievements including substantial growth in quantity,optimized typology,expansion into innovative application scenarios such as health decision support,and broadened institutional involvement.The study also identifies critical challenges,including imbalanced development,insufficient quality control,and a lack of essential metadata—such as authoritative data element mappings and privacy annotations—which hampers the delivery of intelligent services.To address these challenges,the study proposes a multi-faceted strategy focused on optimizing the standard system's architecture,enhancing quality and implementation,and advancing both data governance—through authoritative tracing and privacy protection—and intelligent service provision.These strategies aim to promote the application of dataset standards,thereby fostering and securing the development of new productive forces in healthcare.展开更多
The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficu...The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.展开更多
The unprecedented scale of large models,such as large language models(LLMs)and text-to-image diffusion models,has raised critical concerns about the unauthorized use of copyrighted data during model training.These con...The unprecedented scale of large models,such as large language models(LLMs)and text-to-image diffusion models,has raised critical concerns about the unauthorized use of copyrighted data during model training.These concerns have spurred a growing demand for dataset copyright auditing techniques,which aim to detect and verify potential infringements in the training data of commercial AI systems.This paper presents a survey of existing auditing solutions,categorizing them across key dimensions:data modality,model training stage,data overlap scenarios,and model access levels.We highlight major trends,including the prevalence of black-box auditing methods and the emphasis on fine-tuning rather than pre-training.Through an in-depth analysis of 12 representative works,we extract four key observations that reveal the limitations of current methods.Furthermore,we identify three open challenges and propose future directions for robust,multimodal,and scalable auditing solutions.Our findings underscore the urgent need to establish standardized benchmarks and develop auditing frameworks that are resilient to low watermark densities and applicable in diverse deployment settings.展开更多
The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area,primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational sit...The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area,primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational site was established to monitor climate, land surface dynamics, and hydrological variability in this region. Over a 10-year period(2010–19), an extensive observational dataset was compiled, now available to the scientific community. This dataset includes comprehensive details on site characteristics, instrumentation, and data processing methods, covering meteorological and radiative fluxes, energy exchanges, soil moisture dynamics, and heat transfer properties. The dataset is particularly valuable for researchers studying land surface processes, land–atmosphere interactions, and climate modeling, and may also benefit ecological, hydrological, and water resource studies. The report ends with a discussion on perspectives and challenges of continued observational monitoring in this region, focusing on issues such as cryosphere influences, complex topography,and ecological changes like the encroachment of weeds and scrubland.展开更多
Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers t...Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers to techniques that increase the amount of image data.Common operations for image augmentation include changes in illumination,rotation,contrast,size,viewing angle,and others.Recently,Generative Adversarial Networks(GANs)have been employed for image generation.However,like image augmentation methods,GAN approaches can only generate images that are similar to the original images.Therefore,they also cannot generate new classes of data.Texture images presentmore challenges than general images,and generating textures is more complex than creating other types of images.This study proposes a gradient-based deep neural network method that generates a new class of texture.It is possible to rapidly generate new classes of textures using different kernels from pre-trained deep networks.After generating new textures for each class,the number of textures increases through image augmentation.During this process,several techniques are proposed to automatically remove incomplete and similar textures that are created.The proposed method is faster than some well-known generative networks by around 4 to 10 times.In addition,the quality of the generated textures surpasses that of these networks.The proposed method can generate textures that surpass those of someGANs and parametric models in certain image qualitymetrics.It can provide a big texture dataset to train deep networks.A new big texture dataset is created artificially using the proposed method.This dataset is approximately 2 GB in size and comprises 30,000 textures,each 150×150 pixels in size,organized into 600 classes.It is uploaded to the Kaggle site and Google Drive.This dataset is called BigTex.Compared to other texture datasets,the proposed dataset is the largest and can serve as a comprehensive texture dataset for training more powerful deep neural networks and mitigating overfitting.展开更多
Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variabil...Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.展开更多
Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus o...Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively.展开更多
Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,...This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures.展开更多
In industry,it is becoming common to detect and recognize industrial workpieces using deep learning methods.In this field,the lack of datasets is a big problem,and collecting and annotating datasets in this field is v...In industry,it is becoming common to detect and recognize industrial workpieces using deep learning methods.In this field,the lack of datasets is a big problem,and collecting and annotating datasets in this field is very labor intensive.The researchers need to perform dataset annotation if a dataset is generated by themselves.It is also one of the restrictive factors that the current method based on deep learning cannot expand well.At present,there are very few workpiece datasets for industrial fields,and the existing datasets are generated from ideal workpiece computer aided design(CAD)models,for which few actual workpiece images were collected and utilized.We propose an automatic industrial workpiece dataset generation method and an automatic ground truth annotation method.Included in our methods are three algorithms that we proposed:a point cloud based spatial plane segmentation algorithm to segment the workpieces in the real scene and to obtain the annotation information of the workpieces in the images captured in the real scene;a random multiple workpiece generation algorithm to generate abundant composition datasets with random rotation workpiece angles and positions;and a tangent vector based contour tracking and completion algorithm to get improved contour images.With our procedures,annotation information can be obtained using the algorithms proposed in this paper.Upon completion of the annotation process,a json format file is generated.Faster R-CNN(Faster R-convolutional neural network),SSD(single shot multibox detector)and YOLO(you only look once:unified,real-time object detection)are trained using the datasets proposed in this paper.The experimental results show the effectiveness and integrity of this dataset generation and annotation method.展开更多
文摘Knowing the influence of the size of datasets for regression models can help in improving the accuracy of a solar power forecast and make the most out of renewable energy systems.This research explores the influence of dataset size on the accuracy and reliability of regression models for solar power prediction,contributing to better forecasting methods.The study analyzes data from two solar panels,aSiMicro03036 and aSiTandem72-46,over 7,14,17,21,28,and 38 days,with each dataset comprising five independent and one dependent parameter,and split 80–20 for training and testing.Results indicate that Random Forest consistently outperforms other models,achieving the highest correlation coefficient of 0.9822 and the lowest Mean Absolute Error(MAE)of 2.0544 on the aSiTandem72-46 panel with 21 days of data.For the aSiMicro03036 panel,the best MAE of 4.2978 was reached using the k-Nearest Neighbor(k-NN)algorithm,which was set up as instance-based k-Nearest neighbors(IBk)in Weka after being trained on 17 days of data.Regression performance for most models(excluding IBk)stabilizes at 14 days or more.Compared to the 7-day dataset,increasing to 21 days reduced the MAE by around 20%and improved correlation coefficients by around 2.1%,highlighting the value of moderate dataset expansion.These findings suggest that datasets spanning 17 to 21 days,with 80%used for training,can significantly enhance the predictive accuracy of solar power generation models.
基金support from the National Key Scientific and Technological Infrastructure project “Earth System Science Numerical Simulator Facility” (Earth Lab)sponsored by the National Natural Science Foundation of China (Grant Nos. 42175132, 92044303, and 42205119)+2 种基金the National Key R&D Program (Grant Nos. 2020YFA0607802 and 2022YFC3703003)the CAS Information Technology Program (Grant No. CAS-WX2021SF-0107-02)the fellowship of China Postdoctoral Science Foundation (Grant No. 2022M723093)
文摘Scientific knowledge on the chemical compositions of fine particulate matter(PM_(2.5)) is essential for properly assessing its health and climate effects,and for decisionmakers to develop efficient mitigation strategies.A high-resolution PM_(2.5) chemical composition dataset(CAQRA-aerosol)is developed in this study,which provides hourly maps of organic carbon,black carbon,ammonium,nitrate,and sulfate in China from 2013 to 2020 with a horizontal resolution of 15 km.This paper describes the method,access,and validation results of this dataset.It shows that CAQRA-aerosol has good consistency with observations and achieves higher or comparable accuracy with previous PM_(2.5) composition datasets.Based on CAQRA-aerosol,spatiotemporal changes of different PM_(2.5) compositions were investigated from a national viewpoint,which emphasizes different changes of nitrate from other compositions.The estimated annual rate of population-weighted concentrations of nitrate is 0.23μg m^(−3)yr^(−1) from 2015 to 2020,compared with−0.19 to−1.1μg m^(−3)yr^(−1) for other compositions.The whole dataset is freely available from the China Air Pollution Data Center(https://doi.org/10.12423/capdb_PKU.2023.DA).
基金supported by the National Natural Science Foundation of China(22379157)CAS Project for Young Scientists in Basic Research(YSBR-102)+2 种基金Institute of Coal Chemistry,Chinese Academy of Sciences(SCJC-XCL-2023-13,SCJCXCL-2023-10)Talent Projects for Outstanding Doctoral Students to Work in Shanxi Province(E3SWR4791Z)Fundamental Research Program of Shanxi Province(202403021222485).
文摘This data set collects,compares and contrasts the capacities and structures of a series of hard carbon materials,and then searches for correlations between structure and electrochemical performance.The capacity data of the hard carbons were obtained by charge/discharge tests and the materials were characterized by XRD,gas adsorption,true density tests and SAXS.In particular,the fitting of SAXS gave a series of structural parameters which showed good characterization.The related test details are given with the structural data of the hard carbons and the electrochemical performance of the sodium-ion batteries.
文摘The increasing adoption of Industrial Internet of Things(IIoT)systems in smart manufacturing is leading to raise cyberattack numbers and pressing the requirement for intrusion detection systems(IDS)to be effective.However,existing datasets for IDS training often lack relevance to modern IIoT environments,limiting their applicability for research and development.To address the latter gap,this paper introduces the HiTar-2024 dataset specifically designed for IIoT systems.As a consequence,that can be used by an IDS to detect imminent threats.Likewise,HiTar-2024 was generated using the AREZZO simulator,which replicates realistic smart manufacturing scenarios.The generated dataset includes five distinct classes:Normal,Probing,Remote to Local(R2L),User to Root(U2R),and Denial of Service(DoS).Furthermore,comprehensive experiments with popular Machine Learning(ML)models using various classifiers,including BayesNet,Logistic,IBK,Multiclass,PART,and J48 demonstrate high accuracy,precision,recall,and F1-scores,exceeding 0.99 across all ML metrics.The latter result is reached thanks to the rigorous applied process to achieve this quite good result,including data pre-processing,features extraction,fixing the class imbalance problem,and using a test option for model robustness.This comprehensive approach emphasizes meticulous dataset construction through a complete dataset generation process,a careful labelling algorithm,and a sophisticated evaluation method,providing valuable insights to reinforce IIoT system security.Finally,the HiTar-2024 dataset is compared with other similar datasets in the literature,considering several factors such as data format,feature extraction tools,number of features,attack categories,number of instances,and ML metrics.
基金supported by the Natural Science Basic Research Program of Shaanxi(Program No.2024JC-YBMS-026).
文摘When dealing with imbalanced datasets,the traditional support vectormachine(SVM)tends to produce a classification hyperplane that is biased towards the majority class,which exhibits poor robustness.This paper proposes a high-performance classification algorithm specifically designed for imbalanced datasets.The proposed method first uses a biased second-order cone programming support vectormachine(B-SOCP-SVM)to identify the support vectors(SVs)and non-support vectors(NSVs)in the imbalanced data.Then,it applies the synthetic minority over-sampling technique(SV-SMOTE)to oversample the support vectors of the minority class and uses the random under-sampling technique(NSV-RUS)multiple times to undersample the non-support vectors of the majority class.Combining the above-obtained minority class data set withmultiple majority class datasets can obtainmultiple new balanced data sets.Finally,SOCP-SVM is used to classify each data set,and the final result is obtained through the integrated algorithm.Experimental results demonstrate that the proposed method performs excellently on imbalanced datasets.
基金funded by A’Sharqiyah University,Sultanate of Oman,under Research Project grant number(BFP/RGP/ICT/22/490).
文摘Detecting faces under occlusion remains a significant challenge in computer vision due to variations caused by masks,sunglasses,and other obstructions.Addressing this issue is crucial for applications such as surveillance,biometric authentication,and human-computer interaction.This paper provides a comprehensive review of face detection techniques developed to handle occluded faces.Studies are categorized into four main approaches:feature-based,machine learning-based,deep learning-based,and hybrid methods.We analyzed state-of-the-art studies within each category,examining their methodologies,strengths,and limitations based on widely used benchmark datasets,highlighting their adaptability to partial and severe occlusions.The review also identifies key challenges,including dataset diversity,model generalization,and computational efficiency.Our findings reveal that deep learning methods dominate recent studies,benefiting from their ability to extract hierarchical features and handle complex occlusion patterns.More recently,researchers have increasingly explored Transformer-based architectures,such as Vision Transformer(ViT)and Swin Transformer,to further improve detection robustness under challenging occlusion scenarios.In addition,hybrid approaches,which aim to combine traditional andmodern techniques,are emerging as a promising direction for improving robustness.This review provides valuable insights for researchers aiming to develop more robust face detection systems and for practitioners seeking to deploy reliable solutions in real-world,occlusionprone environments.Further improvements and the proposal of broader datasets are required to developmore scalable,robust,and efficient models that can handle complex occlusions in real-world scenarios.
文摘Climate change significantly affects environment,ecosystems,communities,and economies.These impacts often result in quick and gradual changes in water resources,environmental conditions,and weather patterns.A geographical study was conducted in Arizona State,USA,to examine monthly precipi-tation concentration rates over time.This analysis used a high-resolution 0.50×0.50 grid for monthly precip-itation data from 1961 to 2022,Provided by the Climatic Research Unit.The study aimed to analyze climatic changes affected the first and last five years of each decade,as well as the entire decade,during the specified period.GIS was used to meet the objectives of this study.Arizona experienced 51–568 mm,67–560 mm,63–622 mm,and 52–590 mm of rainfall in the sixth,seventh,eighth,and ninth decades of the second millennium,respectively.Both the first and second five year periods of each decade showed accept-able rainfall amounts despite fluctuations.However,rainfall decreased in the first and second decades of the third millennium.and in the first two years of the third decade.Rainfall amounts dropped to 42–472 mm,55–469 mm,and 74–498 mm,respectively,indicating a downward trend in precipitation.The central part of the state received the highest rainfall,while the eastern and western regions(spanning north to south)had significantly less.Over the decades of the third millennium,the average annual rainfall every five years was relatively low,showing a declining trend due to severe climate changes,generally ranging between 35 mm and 498 mm.The central regions consistently received more rainfall than the eastern and western outskirts.Arizona is currently experiencing a decrease in rainfall due to climate change,a situation that could deterio-rate further.This highlights the need to optimize the use of existing rainfall and explore alternative water sources.
基金support from the National Natural Science Foundation of China(Nos.U24B2034,U2139204)the China Petroleum Science and Technology Innovation Fund(2021DQ02-0501)the Science and Technology Support Project of Langfang(2024011073).
文摘Lithology identification is a critical aspect of geoenergy exploration,including geothermal energy development,gas hydrate extraction,and gas storage.In recent years,artificial intelligence techniques based on drill core images have made significant strides in lithology identification,achieving high accuracy.However,the current demand for advanced lithology identification models remains unmet due to the lack of high-quality drill core image datasets.This study successfully constructs and publicly releases the first open-source Drill Core Image Dataset(DCID),addressing the need for large-scale,high-quality datasets in lithology characterization tasks within geological engineering and establishing a standard dataset for model evaluation.DCID consists of 35 lithology categories and a total of 98,000 high-resolution images(512×512 pixels),making it the most comprehensive drill core image dataset in terms of lithology categories,image quantity,and resolution.This study also provides lithology identification accuracy benchmarks for popular convolutional neural networks(CNNs)such as VGG,ResNet,DenseNet,MobileNet,as well as for the Vision Transformer(ViT)and MLP-Mixer,based on DCID.Additionally,the sensitivity of model performance to various parameters and image resolution is evaluated.In response to real-world challenges,we propose a real-world data augmentation(RWDA)method,leveraging slightly defective images from DCID to enhance model robustness.The study also explores the impact of real-world lighting conditions on the performance of lithology identification models.Finally,we demonstrate how to rapidly evaluate model performance across multiple dimensions using low-resolution datasets,advancing the application and development of new lithology identification models for geoenergy exploration.
文摘Standardized datasets are foundational to healthcare informatization by enhancing data quality and unleashing the value of data elements.Using bibliometrics and content analysis,this study examines China's healthcare dataset standards from 2011 to 2025.It analyzes their evolution across types,applications,institutions,and themes,highlighting key achievements including substantial growth in quantity,optimized typology,expansion into innovative application scenarios such as health decision support,and broadened institutional involvement.The study also identifies critical challenges,including imbalanced development,insufficient quality control,and a lack of essential metadata—such as authoritative data element mappings and privacy annotations—which hampers the delivery of intelligent services.To address these challenges,the study proposes a multi-faceted strategy focused on optimizing the standard system's architecture,enhancing quality and implementation,and advancing both data governance—through authoritative tracing and privacy protection—and intelligent service provision.These strategies aim to promote the application of dataset standards,thereby fostering and securing the development of new productive forces in healthcare.
文摘The aim of this article is to explore potential directions for the development of artificial intelligence(AI).It points out that,while current AI can handle the statistical properties of complex systems,it has difficulty effectively processing and fully representing their spatiotemporal complexity patterns.The article also discusses a potential path of AI development in the engineering domain.Based on the existing understanding of the principles of multilevel com-plexity,this article suggests that consistency among the logical structures of datasets,AI models,model-building software,and hardware will be an important AI development direction and is worthy of careful consideration.
基金supported in part by NSFC under Grant Nos.62402379,U22A2029 and U24A20237.
文摘The unprecedented scale of large models,such as large language models(LLMs)and text-to-image diffusion models,has raised critical concerns about the unauthorized use of copyrighted data during model training.These concerns have spurred a growing demand for dataset copyright auditing techniques,which aim to detect and verify potential infringements in the training data of commercial AI systems.This paper presents a survey of existing auditing solutions,categorizing them across key dimensions:data modality,model training stage,data overlap scenarios,and model access levels.We highlight major trends,including the prevalence of black-box auditing methods and the emphasis on fine-tuning rather than pre-training.Through an in-depth analysis of 12 representative works,we extract four key observations that reveal the limitations of current methods.Furthermore,we identify three open challenges and propose future directions for robust,multimodal,and scalable auditing solutions.Our findings underscore the urgent need to establish standardized benchmarks and develop auditing frameworks that are resilient to low watermark densities and applicable in diverse deployment settings.
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars (Grant No.42325502)the 2nd Scientific Expedition to the Qinghai–Tibet Plateau (Grant No.2019QZKK0102)+3 种基金the West Light Foundation of the Chinese Academy of Sciences (Grant No.xbzg-zdsys-202215)the Science and Technology Research Plan of Gansu Province (Grant Nos.23JRRA654 and 20JR10RA070)the Youth Innovation Promotion Association of the Chinese Academy of Sciences (Grant No.QCH2019004)iLEAPS (integrated Land Ecosystem–Atmosphere Processes Study)。
文摘The source region of the Yellow River, accounting for over 38% of its total runoff, is a critical catchment area,primarily characterized by alpine grasslands. In 2005, the Maqu land surface processes observational site was established to monitor climate, land surface dynamics, and hydrological variability in this region. Over a 10-year period(2010–19), an extensive observational dataset was compiled, now available to the scientific community. This dataset includes comprehensive details on site characteristics, instrumentation, and data processing methods, covering meteorological and radiative fluxes, energy exchanges, soil moisture dynamics, and heat transfer properties. The dataset is particularly valuable for researchers studying land surface processes, land–atmosphere interactions, and climate modeling, and may also benefit ecological, hydrological, and water resource studies. The report ends with a discussion on perspectives and challenges of continued observational monitoring in this region, focusing on issues such as cryosphere influences, complex topography,and ecological changes like the encroachment of weeds and scrubland.
基金supported via funding from Prince Sattam bin Abdulaziz University(PSAU/2025/R/1446)Princess Nourah bint Abdulrahman University(PNURSP2025R300)Prince Sultan University.
文摘Deep neural networks provide accurate results for most applications.However,they need a big dataset to train properly.Providing a big dataset is a significant challenge in most applications.Image augmentation refers to techniques that increase the amount of image data.Common operations for image augmentation include changes in illumination,rotation,contrast,size,viewing angle,and others.Recently,Generative Adversarial Networks(GANs)have been employed for image generation.However,like image augmentation methods,GAN approaches can only generate images that are similar to the original images.Therefore,they also cannot generate new classes of data.Texture images presentmore challenges than general images,and generating textures is more complex than creating other types of images.This study proposes a gradient-based deep neural network method that generates a new class of texture.It is possible to rapidly generate new classes of textures using different kernels from pre-trained deep networks.After generating new textures for each class,the number of textures increases through image augmentation.During this process,several techniques are proposed to automatically remove incomplete and similar textures that are created.The proposed method is faster than some well-known generative networks by around 4 to 10 times.In addition,the quality of the generated textures surpasses that of these networks.The proposed method can generate textures that surpass those of someGANs and parametric models in certain image qualitymetrics.It can provide a big texture dataset to train deep networks.A new big texture dataset is created artificially using the proposed method.This dataset is approximately 2 GB in size and comprises 30,000 textures,each 150×150 pixels in size,organized into 600 classes.It is uploaded to the Kaggle site and Google Drive.This dataset is called BigTex.Compared to other texture datasets,the proposed dataset is the largest and can serve as a comprehensive texture dataset for training more powerful deep neural networks and mitigating overfitting.
文摘Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.
基金supported by the National Key R&D Program of China(2022YFD1401600)the National Science Foundation for Distinguished Young Scholars of Zhejang Province,China(LR23C140001)supported by the Key Area Research and Development Program of Guangdong Province,China(2018B020205003 and 2020B0202090001).
文摘Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively.
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
基金supported by a project entitled Loess Plateau Region-Watershed-Slope Geological Hazard Multi-Scale Collaborative Intelligent Early Warning System of the National Key R&D Program of China(2022YFC3003404)a project of the Shaanxi Youth Science and Technology Star(2021KJXX-87)public welfare geological survey projects of Shaanxi Institute of Geologic Survey(20180301,201918,202103,and 202413).
文摘This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures.
文摘In industry,it is becoming common to detect and recognize industrial workpieces using deep learning methods.In this field,the lack of datasets is a big problem,and collecting and annotating datasets in this field is very labor intensive.The researchers need to perform dataset annotation if a dataset is generated by themselves.It is also one of the restrictive factors that the current method based on deep learning cannot expand well.At present,there are very few workpiece datasets for industrial fields,and the existing datasets are generated from ideal workpiece computer aided design(CAD)models,for which few actual workpiece images were collected and utilized.We propose an automatic industrial workpiece dataset generation method and an automatic ground truth annotation method.Included in our methods are three algorithms that we proposed:a point cloud based spatial plane segmentation algorithm to segment the workpieces in the real scene and to obtain the annotation information of the workpieces in the images captured in the real scene;a random multiple workpiece generation algorithm to generate abundant composition datasets with random rotation workpiece angles and positions;and a tangent vector based contour tracking and completion algorithm to get improved contour images.With our procedures,annotation information can be obtained using the algorithms proposed in this paper.Upon completion of the annotation process,a json format file is generated.Faster R-CNN(Faster R-convolutional neural network),SSD(single shot multibox detector)and YOLO(you only look once:unified,real-time object detection)are trained using the datasets proposed in this paper.The experimental results show the effectiveness and integrity of this dataset generation and annotation method.
