The new ear of AI is brought about by three converging forces:the advance of AI algorithms,the availability of big data,and the increasing popularity of high performance computing platforms.Data-driven intelligence,or...The new ear of AI is brought about by three converging forces:the advance of AI algorithms,the availability of big data,and the increasing popularity of high performance computing platforms.Data-driven intelligence,or data intelligence,is a new form of AI technologies that leverages the展开更多
The new ear of AI is brought about by three eonverging forees: the advanee of AI algorithms, the availability of big data, and the inereasing popularity of high performanee computing platforms. Data-driven intelligen...The new ear of AI is brought about by three eonverging forees: the advanee of AI algorithms, the availability of big data, and the inereasing popularity of high performanee computing platforms. Data-driven intelligenee, or data intelligenee, is a new fore1 of AI teehnologies that leverages the power of big data.展开更多
This paper describes the function,structure and working status of the data buffer unitDBU,one of the most important functional units on ITM-1.It also discusses DBU’s supportto the multiprocessor system and Prolog lan...This paper describes the function,structure and working status of the data buffer unitDBU,one of the most important functional units on ITM-1.It also discusses DBU’s supportto the multiprocessor system and Prolog language.展开更多
Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain ...Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.展开更多
This paper explores the development logic,trends,and challenges of digital finance in the era of the digital economy.As a crucial component of the digital economy,digital finance has completely transformed the traditi...This paper explores the development logic,trends,and challenges of digital finance in the era of the digital economy.As a crucial component of the digital economy,digital finance has completely transformed the traditional financial services model through factors such as technological innovation,data intelligence,and personalized user experiences,paving the way for new business models and market opportunities.However,the rapid development of digital finance also faces challenges such as competition,security,and regulation.This paper emphasizes the importance of finding a balance between innovation and security in the development of digital finance and discusses the potential of digital finance in promoting financial inclusion and sustainable development.Through comprehensive analysis,this paper aims to provide valuable insights for academic researchers and industry practitioners,promoting the healthy development of digital finance.展开更多
The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information.However,it...The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information.However,it is difficult to predict the congestion state of the link-end accurately at the source.In this paper,we presented an improved NUMFabric algorithm for calculating the overall congestion price.In the proposed scheme,the whole network structure had been obtained by the central control server in the Software Defined Network,and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated.In this scheme,the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information.Based on the historical data,the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm.The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm.We evaluate this evolved NUMFabric algorithm in NS3,which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.展开更多
There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities be...There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.展开更多
The regularities of the solid solutions between the scheelite-type compounds and rare earth molybdates or tungstates were investigated by the atomic parameter-support vector machine method and the intelligent database...The regularities of the solid solutions between the scheelite-type compounds and rare earth molybdates or tungstates were investigated by the atomic parameter-support vector machine method and the intelligent database of phase diagrams of molten salt systems. The crystal structure of scheelite-type compounds having M^1M^′Ⅲ (XO4)2(X = Mo, W) as common formula and the formability of the continuous solid solution between these compounds and rare earth molybdates or tungstates were also investigated. Besides, the cell constants of these compounds can be calculated by some semi-empirical equations. Based on the obtained relationships, the results of computerized prediction of the solid solubility of T1Pr (MoO4)2-Pr2 (MoO4)3 system have good agreement with experimental results.展开更多
There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities be...There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.展开更多
ESCAP/WMO Typhoon Committee Members are directly or indirectly affected by typhoons every year.Members have accumulated rich experiences dealing with typhoons'negative impact and developed the technologies and mea...ESCAP/WMO Typhoon Committee Members are directly or indirectly affected by typhoons every year.Members have accumulated rich experiences dealing with typhoons'negative impact and developed the technologies and measures on typhoon-related disaster risk forecasting and early warning in various ways to reduce the damage caused by typhoon.However,it is still facing many difficulties and challenges to accurately forecast the occurrence of typhoons and warning the potential impacts in an early stage due to the continuously changing weather conditions.With the development of information technology(IT)and computing science,and increasing accumulated hydro-meteorological data in recent decades,scientists,researchers and operationers keep trying to improve forecasting models based on the application of big data and artificial intelligent(AI)technology to promote the capacity of typhoon-related disaster risk forecasting and early warning.This paper reviewed the current status of application of big data and AI technology in the aspect of typhoon-related disaster risk forecasting and early warning,and discussed the challenges and limitations that must be addressed to effectively harness the power of big data and AI technology application in typhoon-related disaster risk reduction in the future.展开更多
The fast-developing intelligent infrastructure landscape catalyzes transformative new relationships of human,technology,and environment and requires new socio-technical configurations of information practice and knowl...The fast-developing intelligent infrastructure landscape catalyzes transformative new relationships of human,technology,and environment and requires new socio-technical configurations of information practice and knowledge work.With a focus on data as the source of intelligence,this paper aims to explore the shifting scenarios and indicative features of data science solutions for intelligent system applications and identify the evolving knowledge spaces and integrative learning practices in the“smart”landscape.It projects and discusses the democratization of data science platforms,the distribution of data intelligence on the edge,and the transition from vertical to horizontal data solutions in solving intelligent system problems.Through mapping the changing data research landscape,this work further reveals essential new roles of knowledge architects and social engineers in enabling dynamic data linking,interaction,and exploration for transdisciplinary data convergence.展开更多
文摘The new ear of AI is brought about by three converging forces:the advance of AI algorithms,the availability of big data,and the increasing popularity of high performance computing platforms.Data-driven intelligence,or data intelligence,is a new form of AI technologies that leverages the
文摘The new ear of AI is brought about by three eonverging forees: the advanee of AI algorithms, the availability of big data, and the inereasing popularity of high performanee computing platforms. Data-driven intelligenee, or data intelligenee, is a new fore1 of AI teehnologies that leverages the power of big data.
基金the High Technology Research and Development Programme of china.
文摘This paper describes the function,structure and working status of the data buffer unitDBU,one of the most important functional units on ITM-1.It also discusses DBU’s supportto the multiprocessor system and Prolog language.
文摘Artificial intelligence is a new technological science that researches and develops theories,methods,technologies and application systems for simulating,extending and expanding human intelligence.It simulates certain human thought processes and intelligent behaviors(such as learning,reasoning,thinking,planning,etc.),and produces a new type of intelligent machine that can respond in a similar way to human intelligence.In the past 30 years,it has achieved rapid development in various industries and related disciplines such as manufacturing,medical care,finance,and transportation.
文摘This paper explores the development logic,trends,and challenges of digital finance in the era of the digital economy.As a crucial component of the digital economy,digital finance has completely transformed the traditional financial services model through factors such as technological innovation,data intelligence,and personalized user experiences,paving the way for new business models and market opportunities.However,the rapid development of digital finance also faces challenges such as competition,security,and regulation.This paper emphasizes the importance of finding a balance between innovation and security in the development of digital finance and discusses the potential of digital finance in promoting financial inclusion and sustainable development.Through comprehensive analysis,this paper aims to provide valuable insights for academic researchers and industry practitioners,promoting the healthy development of digital finance.
基金supported by National Key R&D Program of China—Industrial Internet Application Demonstration-Sub-topic Intelligent Network Operation and Security Protection(2018YFB1802400).
文摘The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information.However,it is difficult to predict the congestion state of the link-end accurately at the source.In this paper,we presented an improved NUMFabric algorithm for calculating the overall congestion price.In the proposed scheme,the whole network structure had been obtained by the central control server in the Software Defined Network,and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated.In this scheme,the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information.Based on the historical data,the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm.The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm.We evaluate this evolved NUMFabric algorithm in NS3,which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.
文摘There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.
文摘The regularities of the solid solutions between the scheelite-type compounds and rare earth molybdates or tungstates were investigated by the atomic parameter-support vector machine method and the intelligent database of phase diagrams of molten salt systems. The crystal structure of scheelite-type compounds having M^1M^′Ⅲ (XO4)2(X = Mo, W) as common formula and the formability of the continuous solid solution between these compounds and rare earth molybdates or tungstates were also investigated. Besides, the cell constants of these compounds can be calculated by some semi-empirical equations. Based on the obtained relationships, the results of computerized prediction of the solid solubility of T1Pr (MoO4)2-Pr2 (MoO4)3 system have good agreement with experimental results.
基金This work is supported by the Information Technology Department,College of Computer,Qassim University,6633,Buraidah 51452,Saudi Arabia.
文摘There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.
文摘ESCAP/WMO Typhoon Committee Members are directly or indirectly affected by typhoons every year.Members have accumulated rich experiences dealing with typhoons'negative impact and developed the technologies and measures on typhoon-related disaster risk forecasting and early warning in various ways to reduce the damage caused by typhoon.However,it is still facing many difficulties and challenges to accurately forecast the occurrence of typhoons and warning the potential impacts in an early stage due to the continuously changing weather conditions.With the development of information technology(IT)and computing science,and increasing accumulated hydro-meteorological data in recent decades,scientists,researchers and operationers keep trying to improve forecasting models based on the application of big data and artificial intelligent(AI)technology to promote the capacity of typhoon-related disaster risk forecasting and early warning.This paper reviewed the current status of application of big data and AI technology in the aspect of typhoon-related disaster risk forecasting and early warning,and discussed the challenges and limitations that must be addressed to effectively harness the power of big data and AI technology application in typhoon-related disaster risk reduction in the future.
文摘The fast-developing intelligent infrastructure landscape catalyzes transformative new relationships of human,technology,and environment and requires new socio-technical configurations of information practice and knowledge work.With a focus on data as the source of intelligence,this paper aims to explore the shifting scenarios and indicative features of data science solutions for intelligent system applications and identify the evolving knowledge spaces and integrative learning practices in the“smart”landscape.It projects and discusses the democratization of data science platforms,the distribution of data intelligence on the edge,and the transition from vertical to horizontal data solutions in solving intelligent system problems.Through mapping the changing data research landscape,this work further reveals essential new roles of knowledge architects and social engineers in enabling dynamic data linking,interaction,and exploration for transdisciplinary data convergence.