With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and r...With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and rapidly becomes a main research focus.In recent years,deep learning has achieved tremendous success in image processing,natural language processing,language analysis and other research fields.Despite the task performance has been greatly improved,the resources required to run these models have increased significantly.This poses a major challenge for deploying such applications on resource-restricted mobile devices.Mobile intelligence needs faster mobile processors,more storage space,smaller but more accurate models,and even the assistance of other network nodes.To help the readers establish a global concept of the entire research direction concisely,we classify the latest works in this field into two categories,which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks.We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications.Finally,we conjecture what the future may hold for deploying deep learning applications on mobile devices research,which may help to stimulate new ideas.展开更多
1.Background and motivation Cyber-Physical System(CPS)refers to the seamless integration of the physical processes,computational components,and Internet-of-Things(IoT)devices such as sensors,actuators,and so on.Exampl...1.Background and motivation Cyber-Physical System(CPS)refers to the seamless integration of the physical processes,computational components,and Internet-of-Things(IoT)devices such as sensors,actuators,and so on.Examples of CPS include smart grids,autonomous transportation systems,medical monitoring,process control systems,robotic systems,and automatic pilot avionics.展开更多
Aim: To investigate the expression levels of receptor activator of NF-κB ligand (RANKL) and oste-oprotegerin (OPG) in human periodontal ligament fibroblasts when stimulated with heat in infective conditions. Methods:...Aim: To investigate the expression levels of receptor activator of NF-κB ligand (RANKL) and oste-oprotegerin (OPG) in human periodontal ligament fibroblasts when stimulated with heat in infective conditions. Methods: Periodontal ligament fibroblasts were subjected to various temperature increases for 5 min with or without 10 ng/mL lipopolysaccharide (LPS) and then maintained at 37℃ for 6 h. After that, the expression levels of RANKL and OPG were investigated using real-time RT-PCR and ELISA. As a positive or negative control, the cells were cultured at 37℃ with or without 10 ng/mL LPS. Data were analyzed using one-way ANOVA at a significant level of p = 0.05. Results: The mRNA expression levels of RANKL and OPG were both down-regulated when the cells were heated in infective conditions. The release of sRANKL was increased at low temperatures in such infection;while at high temperatures heat treatment down-regulated the release of sRANKL induced by LPS. The relative RANKL/OPG expression ratios were increased at low temperatures in infective conditions. Conclusions: The interactions between heat and LPS would affect the balance between RANKL and OPG in periodontal ligament fibroblasts when they were heated in infective conditions. Such infection may be the reason why bone resorption occurs in the local area after warm vertical compaction.展开更多
High-speed Maglev is a cutting-edge technology brought back into the focus of research by plans of the Chinese government for the development of a new 600 km/h Maglev train.A Chinese‐German cooperation with industria...High-speed Maglev is a cutting-edge technology brought back into the focus of research by plans of the Chinese government for the development of a new 600 km/h Maglev train.A Chinese‐German cooperation with industrial and academic partners has been established to pursue this ambitious goal and bring together experts from multiple disciplines.This contribution presents the joint work and achievements of CRRC Qingdao Sifang,thyssenkrupp Transrapid,CDFEB,and the ITM of the University of Stuttgart,regarding research and development in the field of high‐speed Maglev systems.Furthermore,an overview is given of the historical development of the Transrapid in Germany,the associated development of dynamical simulation models,and recent developments regarding high-speed Maglev trains in China.展开更多
Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information ...Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive attention.To promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide.More specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk detection.It then focuses on data-mining-based tax risk detection methods utilized around the world.Based on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and analyzed.Finally,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled information.After investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.展开更多
SQL injection poses a major threat to the application level security of the database and there is no systematic solution to these attacks.Different from traditional run time security strategies such as IDS and fire-wa...SQL injection poses a major threat to the application level security of the database and there is no systematic solution to these attacks.Different from traditional run time security strategies such as IDS and fire-wall,this paper focuses on the solution at the outset;it presents a method to find vulnerabilities by analyzing the source codes.The concept of validated tree is developed to track variables referenced by database operations in scripts.By checking whether these variables are influenced by outside inputs,the database operations are proved to be secure or not.This method has advantages of high accuracy and efficiency as well as low costs,and it is universal to any type of web application platforms.It is implemented by the software code vulnerabilities of SQL injection detector(CVSID).The validity and efficiency are demonstrated with an example.展开更多
基金supported by the National Key Research and Development Program of China with grant number 2020AAA0108800the National Science Foundation of China under Grant Nos.61772414,61532015,61532004,61721002,61472317,and 61502379+1 种基金the MOE Innovation Research Team No.IRT 17R86the Project of China Knowledge Centre for Engineering Science and Technology.
文摘With the rapid development of mobile devices and deep learning,mobile smart applications using deep learning technology have sprung up.It satisfies multiple needs of users,network operators and service providers,and rapidly becomes a main research focus.In recent years,deep learning has achieved tremendous success in image processing,natural language processing,language analysis and other research fields.Despite the task performance has been greatly improved,the resources required to run these models have increased significantly.This poses a major challenge for deploying such applications on resource-restricted mobile devices.Mobile intelligence needs faster mobile processors,more storage space,smaller but more accurate models,and even the assistance of other network nodes.To help the readers establish a global concept of the entire research direction concisely,we classify the latest works in this field into two categories,which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks.We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications.Finally,we conjecture what the future may hold for deploying deep learning applications on mobile devices research,which may help to stimulate new ideas.
基金This work is supported in part by the National Natural Science Foundation of China under Grant 62072351in part by the open research project of ZheJiang Lab under Grant 2021PD0AB01in part by the 111 Project under Grant B16037.
文摘1.Background and motivation Cyber-Physical System(CPS)refers to the seamless integration of the physical processes,computational components,and Internet-of-Things(IoT)devices such as sensors,actuators,and so on.Examples of CPS include smart grids,autonomous transportation systems,medical monitoring,process control systems,robotic systems,and automatic pilot avionics.
文摘Aim: To investigate the expression levels of receptor activator of NF-κB ligand (RANKL) and oste-oprotegerin (OPG) in human periodontal ligament fibroblasts when stimulated with heat in infective conditions. Methods: Periodontal ligament fibroblasts were subjected to various temperature increases for 5 min with or without 10 ng/mL lipopolysaccharide (LPS) and then maintained at 37℃ for 6 h. After that, the expression levels of RANKL and OPG were investigated using real-time RT-PCR and ELISA. As a positive or negative control, the cells were cultured at 37℃ with or without 10 ng/mL LPS. Data were analyzed using one-way ANOVA at a significant level of p = 0.05. Results: The mRNA expression levels of RANKL and OPG were both down-regulated when the cells were heated in infective conditions. The release of sRANKL was increased at low temperatures in such infection;while at high temperatures heat treatment down-regulated the release of sRANKL induced by LPS. The relative RANKL/OPG expression ratios were increased at low temperatures in infective conditions. Conclusions: The interactions between heat and LPS would affect the balance between RANKL and OPG in periodontal ligament fibroblasts when they were heated in infective conditions. Such infection may be the reason why bone resorption occurs in the local area after warm vertical compaction.
基金CRRC Sifang received partial funding for this project from the National Natural Science Foundation of China under Grant Number 52232013.This support is highly appreciated.
文摘High-speed Maglev is a cutting-edge technology brought back into the focus of research by plans of the Chinese government for the development of a new 600 km/h Maglev train.A Chinese‐German cooperation with industrial and academic partners has been established to pursue this ambitious goal and bring together experts from multiple disciplines.This contribution presents the joint work and achievements of CRRC Qingdao Sifang,thyssenkrupp Transrapid,CDFEB,and the ITM of the University of Stuttgart,regarding research and development in the field of high‐speed Maglev systems.Furthermore,an overview is given of the historical development of the Transrapid in Germany,the associated development of dynamical simulation models,and recent developments regarding high-speed Maglev trains in China.
基金supported by the Key Research and Development Project in Shaanxi Province (2023GXLH-024)the National Natural Science Foundation of China (62250009,62002282,62037001,and 62192781).
文摘Tax risk behavior causes serious loss of fiscal revenue,damages the country’s public infrastructure,and disturbs the market economic order of fair competition.In recent years,tax risk detection,driven by information technology such as data mining and artificial intelligence,has received extensive attention.To promote the high-quality development of tax risk detection methods,this paper provides the first comprehensive overview and summary of existing tax risk detection methods worldwide.More specifi-cally,it first discusses the causes and negative impacts of tax risk behaviors,along with the development of tax risk detection.It then focuses on data-mining-based tax risk detection methods utilized around the world.Based on the different principles employed by the algorithms,existing risk detection methods can be divided into two categories:relationship-based and non-relationship-based.A total of 14 risk detection methods are identified,and each method is thoroughly explored and analyzed.Finally,four major technical bottlenecks of current data-driven tax risk detection methods are analyzed and discussed,including the difficulty of integrating and using fiscal and tax fragmented knowledge,unexplainable risk detection results,the high cost of risk detection algorithms,and the reliance of existing algorithms on labeled information.After investigating these issues,it is concluded that knowledge-guided and datadriven big data knowledge engineering will be the development trend in the field of tax risk in the future;that is,the gradual transition of tax risk detection from informatization to intelligence is the future development direction.
基金supported by the National Natural Science Foundation of China (Grant No.60574087)the Hi-Tech Research and Development Program of China (Nos.2007AA01Z475,2007AA01Z480,2007AA01Z464)the 111 International Collaboration Program of China.
文摘SQL injection poses a major threat to the application level security of the database and there is no systematic solution to these attacks.Different from traditional run time security strategies such as IDS and fire-wall,this paper focuses on the solution at the outset;it presents a method to find vulnerabilities by analyzing the source codes.The concept of validated tree is developed to track variables referenced by database operations in scripts.By checking whether these variables are influenced by outside inputs,the database operations are proved to be secure or not.This method has advantages of high accuracy and efficiency as well as low costs,and it is universal to any type of web application platforms.It is implemented by the software code vulnerabilities of SQL injection detector(CVSID).The validity and efficiency are demonstrated with an example.
