In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mo...In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.展开更多
A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems(ITSs).Although the existing approaches have shown promising performance,the different design choices,incl...A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems(ITSs).Although the existing approaches have shown promising performance,the different design choices,including the different textual fields,feature representation methods and machine learning algorithms adopted by existing approaches,have not been comprehensively compared and analyzed.To fill this gap,we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches.Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification,including:(1)Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification;(2)Word embedding with Long Short-Term Memory(LSTM)can better extract features from the textual fields in the issues,and hence,lead to better issue classification models;(3)There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues;(4)The performance of the issue classification model is not sensitive to the choices of ML algorithms.Based on our study outcomes,we further propose an advanced issue classification approach,DEEPLABEL,which can achieve better performance compared with the existing issue classification approaches.展开更多
A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer prefe...A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.展开更多
The behavior issues of preschoolers are closely related to their parents'parenting styles.This editorial discusses the value and strategies for solving behavior issues in preschoolers from the perspectives of mind...The behavior issues of preschoolers are closely related to their parents'parenting styles.This editorial discusses the value and strategies for solving behavior issues in preschoolers from the perspectives of mindfulness and mindful parenting.We expect that upcoming studies will place greater emphasis on the behavioral concerns of preschoolers and the parenting practices that shape them,particularly focusing on proactive interventions for preschoolers'behavioral issues.展开更多
In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of Ch...In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.展开更多
The development of this technology has favored the advances noted in recent years in the field of precise positioning. It has also paved the way for a wide range of research into the evaluation of their performance an...The development of this technology has favored the advances noted in recent years in the field of precise positioning. It has also paved the way for a wide range of research into the evaluation of their performance and reliability, their potential use in different fields, the improvement of performance and combined systems, etc. Single-frequency GNSS receivers, which for a long time remained the only category of low-cost GNSS receivers, often limited by their level of accuracy (metric) mainly due to their single-frequency nature, have been joined in the last decade by dual-frequency GNSS receivers developed by certain manufacturers of positioning equipment. These receivers now offer possible alternatives to the relatively expensive conventional (topographic quality) or geodetic receivers and. In this study, the performance of these low-cost dual-frequency receivers was evaluated in static and real-time kinematic GNSS positioning modes. Static positioning was carried out on three points with sessions of 2 h and 4 h over three days with antenna swapping (CHC i50, Leica GS14 and Emlid Reach RS2+). Real-time observations were carried out on eleven (11) points in open, poorly open and not at all open environments, in order to assess not only performance but also receiver sensitivity in environments with a high risk of multipath. The results obtained showed an average agreement of 2 cm in planimetry between the low-cost Emlid RS2+ receiver and the Leica GS14 and CHC i50 receivers. The differences in altimetry are nevertheless greater (sometimes up to decimetres for certain points). Real-time positioning results provided an average convergence of around 1 cm on the E, N and H components with the results from the low-cost Emlid Reach RS2+ and Ublox ZED-F9P receivers and the CHC i50 receiver. Analysis of the results obtained has enabled us to highlight the various issues and challenges associated with this new generation of GNSS receivers, with a view to enhancing their appropriation and optimal integration in the professional and research worlds.展开更多
Objective To study the content of China’s guiding principles on multiplicity issues in clinical trials,and to provide reference for the revision of China’s relevant guiding principles.Methods Based on ICH E9,the sim...Objective To study the content of China’s guiding principles on multiplicity issues in clinical trials,and to provide reference for the revision of China’s relevant guiding principles.Methods Based on ICH E9,the similarities and differences of the guiding principles of US Food and Drug Administration(FDA),European Medicines Agency(EMA),and National Medical Products Administration(NMPA)on the multiplicity issues in clinical trials were compared one by one.Results and Conclusion In general,NMPA guidelines are based on ICH E9,but in detail,the guidelines of FDA and EMA focus differently on the multiplicity issues.Therefore,NMPA guidelines need to be detailed and comprehensive.NMPA guidelines can be refined by referring to foreign guidelines to improve the practical guiding significance for clinical research and promote the level of domestic clinical trials in line with international standards.展开更多
提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型...提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。展开更多
文摘In the last decade, technical advancements and faster Internet speeds have also led to an increasing number ofmobile devices and users. Thus, all contributors to society, whether young or old members, can use these mobileapps. The use of these apps eases our daily lives, and all customers who need any type of service can accessit easily, comfortably, and efficiently through mobile apps. Particularly, Saudi Arabia greatly depends on digitalservices to assist people and visitors. Such mobile devices are used in organizing daily work schedules and services,particularly during two large occasions, Umrah and Hajj. However, pilgrims encounter mobile app issues such asslowness, conflict, unreliability, or user-unfriendliness. Pilgrims comment on these issues on mobile app platformsthrough reviews of their experiences with these digital services. Scholars have made several attempts to solve suchmobile issues by reporting bugs or non-functional requirements by utilizing user comments.However, solving suchissues is a great challenge, and the issues still exist. Therefore, this study aims to propose a hybrid deep learningmodel to classify and predict mobile app software issues encountered by millions of pilgrims during the Hajj andUmrah periods from the user perspective. Firstly, a dataset was constructed using user-generated comments fromrelevant mobile apps using natural language processing methods, including information extraction, the annotationprocess, and pre-processing steps, considering a multi-class classification problem. Then, several experimentswere conducted using common machine learning classifiers, Artificial Neural Networks (ANN), Long Short-TermMemory (LSTM), and Convolutional Neural Network Long Short-Term Memory (CNN-LSTM) architectures, toexamine the performance of the proposed model. Results show 96% in F1-score and accuracy, and the proposedmodel outperformed the mentioned models.
基金This research was supported by the National Natural Science Foundation of China(Grant No.61972193)the Program B for Outstanding PhD Candidate of Nanjing University。
文摘A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems(ITSs).Although the existing approaches have shown promising performance,the different design choices,including the different textual fields,feature representation methods and machine learning algorithms adopted by existing approaches,have not been comprehensively compared and analyzed.To fill this gap,we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches.Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification,including:(1)Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification;(2)Word embedding with Long Short-Term Memory(LSTM)can better extract features from the textual fields in the issues,and hence,lead to better issue classification models;(3)There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues;(4)The performance of the issue classification model is not sensitive to the choices of ML algorithms.Based on our study outcomes,we further propose an advanced issue classification approach,DEEPLABEL,which can achieve better performance compared with the existing issue classification approaches.
文摘A Recommender System(RS)is a crucial part of several firms,particularly those involved in e-commerce.In conventional RS,a user may only offer a single rating for an item-that is insufficient to perceive consumer preferences.Nowadays,businesses in industries like e-learning and tourism enable customers to rate a product using a variety of factors to comprehend customers’preferences.On the other hand,the collaborative filtering(CF)algorithm utilizing AutoEncoder(AE)is seen to be effective in identifying user-interested items.However,the cost of these computations increases nonlinearly as the number of items and users increases.To triumph over the issues,a novel expanded stacked autoencoder(ESAE)with Kernel Fuzzy C-Means Clustering(KFCM)technique is proposed with two phases.In the first phase of offline,the sparse multicriteria rating matrix is smoothened to a complete matrix by predicting the users’intact rating by the ESAE approach and users are clustered using the KFCM approach.In the next phase of online,the top-N recommendation prediction is made by the ESAE approach involving only the most similar user from multiple clusters.Hence the ESAE_KFCM model upgrades the prediction accuracy of 98.2%in Top-N recommendation with a minimized recommendation generation time.An experimental check on the Yahoo!Movies(YM)movie dataset and TripAdvisor(TA)travel dataset confirmed that the ESAE_KFCM model constantly outperforms conventional RS algorithms on a variety of assessment measures.
基金Supported by The Education and Teaching Reform Project of the First Clinical College of Chongqing Medical University,No.CMER202305The Natural Science Foundation of Tibet Autonomous Region,No.XZ2024ZR-ZY100(Z).
文摘The behavior issues of preschoolers are closely related to their parents'parenting styles.This editorial discusses the value and strategies for solving behavior issues in preschoolers from the perspectives of mindfulness and mindful parenting.We expect that upcoming studies will place greater emphasis on the behavioral concerns of preschoolers and the parenting practices that shape them,particularly focusing on proactive interventions for preschoolers'behavioral issues.
基金supported by the National Key Research and Development Program of China under the sub-theme“Research on the Path of Enhancing the Sustainable Development Capacity of Cities and Towns under the Carbon Neutral Goal”[Grant No.2022YFC3802902-04].
文摘In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.
文摘The development of this technology has favored the advances noted in recent years in the field of precise positioning. It has also paved the way for a wide range of research into the evaluation of their performance and reliability, their potential use in different fields, the improvement of performance and combined systems, etc. Single-frequency GNSS receivers, which for a long time remained the only category of low-cost GNSS receivers, often limited by their level of accuracy (metric) mainly due to their single-frequency nature, have been joined in the last decade by dual-frequency GNSS receivers developed by certain manufacturers of positioning equipment. These receivers now offer possible alternatives to the relatively expensive conventional (topographic quality) or geodetic receivers and. In this study, the performance of these low-cost dual-frequency receivers was evaluated in static and real-time kinematic GNSS positioning modes. Static positioning was carried out on three points with sessions of 2 h and 4 h over three days with antenna swapping (CHC i50, Leica GS14 and Emlid Reach RS2+). Real-time observations were carried out on eleven (11) points in open, poorly open and not at all open environments, in order to assess not only performance but also receiver sensitivity in environments with a high risk of multipath. The results obtained showed an average agreement of 2 cm in planimetry between the low-cost Emlid RS2+ receiver and the Leica GS14 and CHC i50 receivers. The differences in altimetry are nevertheless greater (sometimes up to decimetres for certain points). Real-time positioning results provided an average convergence of around 1 cm on the E, N and H components with the results from the low-cost Emlid Reach RS2+ and Ublox ZED-F9P receivers and the CHC i50 receiver. Analysis of the results obtained has enabled us to highlight the various issues and challenges associated with this new generation of GNSS receivers, with a view to enhancing their appropriation and optimal integration in the professional and research worlds.
基金supported by the Special Foundation of Research Institute of Drug Regulatory Science,Shenyang Pharmaceutical University(2021jgkx004).
文摘Objective To study the content of China’s guiding principles on multiplicity issues in clinical trials,and to provide reference for the revision of China’s relevant guiding principles.Methods Based on ICH E9,the similarities and differences of the guiding principles of US Food and Drug Administration(FDA),European Medicines Agency(EMA),and National Medical Products Administration(NMPA)on the multiplicity issues in clinical trials were compared one by one.Results and Conclusion In general,NMPA guidelines are based on ICH E9,but in detail,the guidelines of FDA and EMA focus differently on the multiplicity issues.Therefore,NMPA guidelines need to be detailed and comprehensive.NMPA guidelines can be refined by referring to foreign guidelines to improve the practical guiding significance for clinical research and promote the level of domestic clinical trials in line with international standards.
文摘提出一种高精度的ZWD模型(tianjin_zwd,TZ)。TZ基于2016-2018年逐小时气压分层的ERA5,欧洲中尺度气象预报中心第五代再分析产品数据,采用BP神经网络建立。然后,根据2019年的ERA5产品导出的ZWD对TZ模型进行了验证。结果表明:相比GPT3模型,TZ模型可提供更贴近真值的ZWD估值;并且,其RMSE由5.0 cm (GPT3)降至4.5 cm,表明10%的精度提升。上述结果表明TZ模型实现了更优的预测性能,该模型的构建策略可为全国其他地区的ZWD建模提供借鉴。
