As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large lang...As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large language models(LLMs)for Salmonella resistance prediction,data presentation,and data sharing.To overcome this issue,we firstly propose a two-step feature-selection process based on the chi-square test and conditional mutual information maximization to find the key Salmonella resistance genes in a pan-genomics analysis and develop an LLM-based Salmonella antimicrobial-resistance predictive(SARPLLM)algorithm to achieve accurate antimicrobial-resistance prediction,based on Qwen2 LLM and low-rank adaptation.Secondly,we optimize the time complexity to compute the sample distance from the linear to logarithmic level by constructing a quantum data augmentation algorithm denoted as QSMOTEN.Thirdly,we build up a user-friendly Salmonella antimicrobial-resistance predictive online platform based on knowledge graphs,which not only facilitates online resistance prediction for users but also visualizes the pan-genomics analysis results of the Salmonella datasets.展开更多
The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language mod...The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.展开更多
As a new computing mode,cloud computing can provide users with virtualized and scalable web services,which faced with serious security challenges,however.Access control is one of the most important measures to ensure ...As a new computing mode,cloud computing can provide users with virtualized and scalable web services,which faced with serious security challenges,however.Access control is one of the most important measures to ensure the security of cloud computing.But applying traditional access control model into the Cloud directly could not solve the uncertainty and vulnerability caused by the open conditions of cloud computing.In cloud computing environment,only when the security and reliability of both interaction parties are ensured,data security can be effectively guaranteed during interactions between users and the Cloud.Therefore,building a mutual trust relationship between users and cloud platform is the key to implement new kinds of access control method in cloud computing environment.Combining with Trust Management(TM),a mutual trust based access control(MTBAC) model is proposed in this paper.MTBAC model take both user's behavior trust and cloud services node's credibility into consideration.Trust relationships between users and cloud service nodes are established by mutual trust mechanism.Security problems of access control are solved by implementing MTBAC model into cloud computing environment.Simulation experiments show that MTBAC model can guarantee the interaction between users and cloud service nodes.展开更多
The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not o...The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.展开更多
After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtuali...After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.展开更多
The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achievi...The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.展开更多
In cloud computing, the risk of data leakage exists between users and virtual machines. Whether it is direct or indirect data leakage, it can be regarded as illegal information flow. Methods such as access control mod...In cloud computing, the risk of data leakage exists between users and virtual machines. Whether it is direct or indirect data leakage, it can be regarded as illegal information flow. Methods such as access control models can control the information flow rather than the covert information flow. Therefore, it needs to use the noninterference models to detect the existence of illegal information flow in cloud computing. Typical noninterference models are not suitable to verificate information flow in cloud computing. When concurrent access actions execute in the cloud architecture, security domains do not affect each other, because there is no information flow between security domains. Based on this, we propose noninterference for cloud architecture in which concurrent access and sequential access coexist. When the sequential actions execute, the information flow between security domains can flow in accordance with established rules. When concurrent access actions execute, there should not be the information flow between security domains.展开更多
Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different ...Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different models exhibit distinct strengths and preferences,resulting in varying levels of performance.In this paper,we compare the capabilities of the most advanced LLMs—DeepSeek,ChatGPT,and Claude—along with their reasoning-optimized versions in addressing computational challenges.Specifically,we evaluate their proficiency in solving traditional numerical problems in scientific computing as well as leveraging scientific machine learning techniques for PDE-based problems.We designed all our experiments so that a nontrivial decision is required,e.g,defining the proper space of input functions for neural operator learning.Our findings show that reasoning and hybrid-reasoning models consistently and significantly outperform non-reasoning ones in solving challenging problems,with ChatGPT o3-mini-high generally offering the fastest reasoning speed.展开更多
Cloud computing provides services to users through Internet.This open mode not only facilitates the access by users,but also brings potential security risks.In cloud computing,the risk of data leakage exists between u...Cloud computing provides services to users through Internet.This open mode not only facilitates the access by users,but also brings potential security risks.In cloud computing,the risk of data leakage exists between users and virtual machines.Whether direct or indirect data leakage,it can be regarded as illegal information flow.Methods,such as access control models can control the information flow,but not the covert information flow.Therefore,it needs to use the noninterference models to detect the existence of illegal information flow in cloud computing architecture.Typical noninterference models are not suitable to certificate information flow in cloud computing architecture.In this paper,we propose several information flow models for cloud architecture.One model is for transitive cloud computing architecture.The others are for intransitive cloud computing architecture.When concurrent access actions execute in the cloud architecture,we want that security domain and security domain do not affect each other,that there is no information flow between security domains.But in fact,there will be more or less indirect information flow between security domains.Our models are concerned with how much information is allowed to flow.For example,in the CIP model,the other domain can learn the sequence of actions.But in the CTA model,the other domain can’t learn the information.Which security model will be used in an architecture depends on the security requirements for that architecture.展开更多
In this letter,we propose a novel three-dimensional conceptual model for an emerging service-oriented simulation paradigm. The model can be used as a guideline or an analytic means to find the potential and possible f...In this letter,we propose a novel three-dimensional conceptual model for an emerging service-oriented simulation paradigm. The model can be used as a guideline or an analytic means to find the potential and possible future directions of the current simulation frameworks. In particular, the model inspects the crossover between the disciplines of modeling and simulation, service-orientation,and software/systems engineering. Finally, two specific simulation frameworks are studied as examples.展开更多
According to the requirement of computer forensic and network forensic, a novel forensic computing model is presented, which exploits XML/OEM/RM data model, Data fusion technology, forensic knowledgebase, inference me...According to the requirement of computer forensic and network forensic, a novel forensic computing model is presented, which exploits XML/OEM/RM data model, Data fusion technology, forensic knowledgebase, inference mechanism of expert system and evidence mining engine. This model takes advantage of flexility and openness, so it can be widely used in mining evidence.展开更多
Pneumonia is a highly transmissible disease in children.According to the World Health Organization(WHO),the most affected regions include south Asia and sub-Saharan Africa.Worldwide,15%of pediatric deaths can be attri...Pneumonia is a highly transmissible disease in children.According to the World Health Organization(WHO),the most affected regions include south Asia and sub-Saharan Africa.Worldwide,15%of pediatric deaths can be attributed to pneumonia.Computing techniques have a significant role in science,engineering,and many other fields.In this study,we focused on the efficiency of numerical techniques via computer programs.We studied the dynamics of the pneumonia-like infections of epidemic models using numerical techniques.We discuss two types of analysis:dynamical and numerical.The dynamical analysis included positivity,boundedness,local stability,reproduction number,and equilibria of the model.We also discusswell-known computing techniques including Euler,Runge Kutta,and non-standard finite difference(NSFD)for the model.The non-standard finite difference(NSFD)technique shows convergence to the true equilibrium points of the model for any time step size.However,Euler and Runge Kutta do not work well over large time intervals.Computing techniques are the suitable tool for crosschecking the theoretical analysis of the model.展开更多
IaaS (Infrastructure as a Platform) public cloud is one mainstream service mode for public cloud computing. The design aim of one IaaS public cloud is to enlarge the hardware-usage of whole platform, optimize the virt...IaaS (Infrastructure as a Platform) public cloud is one mainstream service mode for public cloud computing. The design aim of one IaaS public cloud is to enlarge the hardware-usage of whole platform, optimize the virtual machine deployment and enhance the accept rate of service demand. In this paper we create one service model for IaaS public cloud, and based on the waiting-line theory to optimize the service model, the queue length and the configuration of scheduling server. And create one demand-vector based scheduling model, to filter the available host machine according to the match of demand and metadata of available resource. The scheduling model can be bonded with the virtual machine motion to reallocate the resources to guarantee the available rate of the whole platform. The feasibility of the algorithm is verified on our own IaaS public cloud computing platform.展开更多
Trust is one of the most important security requirements in the design and implementation of peer-to-peer （P2P） systems. In an environment where peers＇ identity privacy is important, it may conflict with trustworth...Trust is one of the most important security requirements in the design and implementation of peer-to-peer （P2P） systems. In an environment where peers＇ identity privacy is important, it may conflict with trustworthiness that is based on the knowledge related to the peer＇s identity, while identity privacy is usually achieved by hiding such knowledge. A trust model based on trusted computing （TC） technology was proposed to enhance the identity privacy of peers during the trustworthiness evaluation process between peers from different groups. The simulation results show that, the model can be implemented in an efficient way, and when the degree of anonymity within group （DAWG） is up to 0.6 and the percentage of malicious peers is up to 70%7 the service selection failure rate is less than 0.15.展开更多
Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures.It offers tremendous advantages for enterprises and service providers.It is anticipated tha...Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures.It offers tremendous advantages for enterprises and service providers.It is anticipated that this situation will evolve to foster cross-tenant collaboration supported by Authorization as a service.To realize access control in a multi-tenant cloud computing environment,this study proposes a multi-tenant cloud computing access control model based on the traditional usage access control model by building trust relations among tenants.The model consists of three sub-models,which achieve trust relationships between tenants with different granularities and satisfy the requirements of different application scenarios.With an established trust relation in MT-UCON(Multi-tenant Usage Access Control),the trustee can precisely authorize cross-tenant accesses to the trustor’s resources consistent with constraints over the trust relation and other components designated by the trustor.In addition,the security of the model is analyzed by an information flow method.The model adapts to the characteristics of a dynamic and open multi-tenant cloud computing environment and achieves fine-grained access control within and between tenants.展开更多
Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its ...Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its development.The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks.In this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment.Specifically,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the timeliness.In the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks.Finally,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary changes.Simulation experiments are introduced to verify the performance of the model.Compared with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.展开更多
Based on FEM (finite element method) program ANSYS and the OpenGL graphics, this paper develops the parametric modeling module and the computing module of the multi-tower suspension bridge, the modules being embedded ...Based on FEM (finite element method) program ANSYS and the OpenGL graphics, this paper develops the parametric modeling module and the computing module of the multi-tower suspension bridge, the modules being embedded into the ANSYS system, and the parametric modeling module parameters can be entered by way of interface, which can fast establish a multi-tower suspension bridge model. Calculation module can establish load conditions for the features of road bridge and specifications, in which multiple conditions can be defined and solved automatically. Post-processing part of the solution also serves the results of the subtotals and selects the output, so that the results of the output and finishing work have become more convenient and easier, and also the results can be saved in word, excel and other different file types.展开更多
Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into accou...Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.展开更多
This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular compu...This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular computing which are induced by coverings of the given universe.展开更多
With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,prov...With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.展开更多
基金supported by the National Science and Technology Major Project(2021YFF1201200)the National Natural Science Foundation of China(62372316)the Sichuan Science and Technology Program key project(2024YFHZ0091).
文摘As a common foodborne pathogen,Salmonella poses risks to public health safety,common given the emergence of antimicrobial-resistant strains.However,there is currently a lack of systematic platforms based on large language models(LLMs)for Salmonella resistance prediction,data presentation,and data sharing.To overcome this issue,we firstly propose a two-step feature-selection process based on the chi-square test and conditional mutual information maximization to find the key Salmonella resistance genes in a pan-genomics analysis and develop an LLM-based Salmonella antimicrobial-resistance predictive(SARPLLM)algorithm to achieve accurate antimicrobial-resistance prediction,based on Qwen2 LLM and low-rank adaptation.Secondly,we optimize the time complexity to compute the sample distance from the linear to logarithmic level by constructing a quantum data augmentation algorithm denoted as QSMOTEN.Thirdly,we build up a user-friendly Salmonella antimicrobial-resistance predictive online platform based on knowledge graphs,which not only facilitates online resistance prediction for users but also visualizes the pan-genomics analysis results of the Salmonella datasets.
文摘The rapid advancement of deep learning and the emergence of largescale neural models,such as bidirectional encoder representations from transformers(BERT),generative pre-trained transformer(GPT),and large language model Meta AI(LLaMa),have brought significant computational and energy challenges.Neuromorphic computing presents a biologically inspired approach to addressing these issues,leveraging event-driven processing and in-memory computation for enhanced energy efficiency.This survey explores the intersection of neuromorphic computing and large-scale deep learning models,focusing on neuromorphic models,learning methods,and hardware.We highlight transferable techniques from deep learning to neuromorphic computing and examine the memoryrelated scalability limitations of current neuromorphic systems.Furthermore,we identify potential directions to enable neuromorphic systems to meet the growing demands of modern AI workloads.
基金ACKNOWLEDGEMENT This paper is supported by the Opening Project of State Key Laboratory for Novel Software Technology of Nanjing University, China （Grant No.KFKT2012B25） and National Science Foundation of China （Grant No.61303263）.
文摘As a new computing mode,cloud computing can provide users with virtualized and scalable web services,which faced with serious security challenges,however.Access control is one of the most important measures to ensure the security of cloud computing.But applying traditional access control model into the Cloud directly could not solve the uncertainty and vulnerability caused by the open conditions of cloud computing.In cloud computing environment,only when the security and reliability of both interaction parties are ensured,data security can be effectively guaranteed during interactions between users and the Cloud.Therefore,building a mutual trust relationship between users and cloud platform is the key to implement new kinds of access control method in cloud computing environment.Combining with Trust Management(TM),a mutual trust based access control(MTBAC) model is proposed in this paper.MTBAC model take both user's behavior trust and cloud services node's credibility into consideration.Trust relationships between users and cloud service nodes are established by mutual trust mechanism.Security problems of access control are solved by implementing MTBAC model into cloud computing environment.Simulation experiments show that MTBAC model can guarantee the interaction between users and cloud service nodes.
文摘The distribution of the various organic and inorganic constituents and their influences on the combustion of coal has been comprehensively studied.However,the combustion characteristics of pulverized coal depend not only on rank but also on the composition,distribution,and combination of the macerals.Unlike the proximate and ultimate analyses,determining the macerals in coal involves the use of sophisticated microscopic instrumentation and expertise.In this study,an attempt was made to predict the amount of macerals(vitrinite,inertinite,and liptinite)and total mineral matter from the Witbank Coalfields samples using the multiple input single output white-box artificial neural network(MISOWB-ANN),gene expression programming(GEP),multiple linear regression(MLR),and multiple nonlinear regression(MNLR).The predictive models obtained from the multiple soft computing models adopted are contrasted with one another using difference,efficiency,and composite statistical indicators to examine the appropriateness of the models.The MISOWB-ANN provides a more reliable predictive model than the other three models with the lowest difference and highest efficiency and composite statistical indicators.
文摘After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.
文摘The rapid advent in artificial intelligence and big data has revolutionized the dynamic requirement in the demands of the computing resource for executing specific tasks in the cloud environment.The process of achieving autonomic resource management is identified to be a herculean task due to its huge distributed and heterogeneous environment.Moreover,the cloud network needs to provide autonomic resource management and deliver potential services to the clients by complying with the requirements of Quality-of-Service(QoS)without impacting the Service Level Agreements(SLAs).However,the existing autonomic cloud resource managing frameworks are not capable in handling the resources of the cloud with its dynamic requirements.In this paper,Coot Bird Behavior Model-based Workload Aware Autonomic Resource Management Scheme(CBBM-WARMS)is proposed for handling the dynamic requirements of cloud resources through the estimation of workload that need to be policed by the cloud environment.This CBBM-WARMS initially adopted the algorithm of adaptive density peak clustering for workloads clustering of the cloud.Then,it utilized the fuzzy logic during the process of workload scheduling for achieving the determining the availability of cloud resources.It further used CBBM for potential Virtual Machine(VM)deployment that attributes towards the provision of optimal resources.It is proposed with the capability of achieving optimal QoS with minimized time,energy consumption,SLA cost and SLA violation.The experimental validation of the proposed CBBMWARMS confirms minimized SLA cost of 19.21%and reduced SLA violation rate of 18.74%,better than the compared autonomic cloud resource managing frameworks.
基金Supported by the National Natural Science Foundation of China(61640220)the Natural Science Research Project of Jiangsu Province Universities and Colleges(17KJD520005)the Government Audit Research Foundation of Nanjing Audit University
文摘In cloud computing, the risk of data leakage exists between users and virtual machines. Whether it is direct or indirect data leakage, it can be regarded as illegal information flow. Methods such as access control models can control the information flow rather than the covert information flow. Therefore, it needs to use the noninterference models to detect the existence of illegal information flow in cloud computing. Typical noninterference models are not suitable to verificate information flow in cloud computing. When concurrent access actions execute in the cloud architecture, security domains do not affect each other, because there is no information flow between security domains. Based on this, we propose noninterference for cloud architecture in which concurrent access and sequential access coexist. When the sequential actions execute, the information flow between security domains can flow in accordance with established rules. When concurrent access actions execute, there should not be the information flow between security domains.
基金supported by the ONR Vannevar Bush Faculty Fellowship(Grant No.N00014-22-1-2795).
文摘Large language models(LLMs)have emerged as powerful tools for addressing a wide range of problems,including those in scientific computing,particularly in solving partial differential equations(PDEs).However,different models exhibit distinct strengths and preferences,resulting in varying levels of performance.In this paper,we compare the capabilities of the most advanced LLMs—DeepSeek,ChatGPT,and Claude—along with their reasoning-optimized versions in addressing computational challenges.Specifically,we evaluate their proficiency in solving traditional numerical problems in scientific computing as well as leveraging scientific machine learning techniques for PDE-based problems.We designed all our experiments so that a nontrivial decision is required,e.g,defining the proper space of input functions for neural operator learning.Our findings show that reasoning and hybrid-reasoning models consistently and significantly outperform non-reasoning ones in solving challenging problems,with ChatGPT o3-mini-high generally offering the fastest reasoning speed.
基金Natural Science Research Project of Jiangsu Province Universities and Colleges(No.17KJD520005,Congdong Lv).
文摘Cloud computing provides services to users through Internet.This open mode not only facilitates the access by users,but also brings potential security risks.In cloud computing,the risk of data leakage exists between users and virtual machines.Whether direct or indirect data leakage,it can be regarded as illegal information flow.Methods,such as access control models can control the information flow,but not the covert information flow.Therefore,it needs to use the noninterference models to detect the existence of illegal information flow in cloud computing architecture.Typical noninterference models are not suitable to certificate information flow in cloud computing architecture.In this paper,we propose several information flow models for cloud architecture.One model is for transitive cloud computing architecture.The others are for intransitive cloud computing architecture.When concurrent access actions execute in the cloud architecture,we want that security domain and security domain do not affect each other,that there is no information flow between security domains.But in fact,there will be more or less indirect information flow between security domains.Our models are concerned with how much information is allowed to flow.For example,in the CIP model,the other domain can learn the sequence of actions.But in the CTA model,the other domain can’t learn the information.Which security model will be used in an architecture depends on the security requirements for that architecture.
基金Project (Nos.60574056 and 60674069) supported by the National Natural Science Foundation of China
文摘In this letter,we propose a novel three-dimensional conceptual model for an emerging service-oriented simulation paradigm. The model can be used as a guideline or an analytic means to find the potential and possible future directions of the current simulation frameworks. In particular, the model inspects the crossover between the disciplines of modeling and simulation, service-orientation,and software/systems engineering. Finally, two specific simulation frameworks are studied as examples.
基金Supported by the Scientific and TechnologicalBureau of the Ministry of Public Security of P.R.China ,the Projectof the Network Supervising Bureau(2005yycxhbst117) the Project ofthe 15th Overall Plan of Education Department of Hubei Province(2004d349) the Project of the 15th Overall Plan of Social ScienceFund of Hubei Province([2005]073)
文摘According to the requirement of computer forensic and network forensic, a novel forensic computing model is presented, which exploits XML/OEM/RM data model, Data fusion technology, forensic knowledgebase, inference mechanism of expert system and evidence mining engine. This model takes advantage of flexility and openness, so it can be widely used in mining evidence.
文摘Pneumonia is a highly transmissible disease in children.According to the World Health Organization(WHO),the most affected regions include south Asia and sub-Saharan Africa.Worldwide,15%of pediatric deaths can be attributed to pneumonia.Computing techniques have a significant role in science,engineering,and many other fields.In this study,we focused on the efficiency of numerical techniques via computer programs.We studied the dynamics of the pneumonia-like infections of epidemic models using numerical techniques.We discuss two types of analysis:dynamical and numerical.The dynamical analysis included positivity,boundedness,local stability,reproduction number,and equilibria of the model.We also discusswell-known computing techniques including Euler,Runge Kutta,and non-standard finite difference(NSFD)for the model.The non-standard finite difference(NSFD)technique shows convergence to the true equilibrium points of the model for any time step size.However,Euler and Runge Kutta do not work well over large time intervals.Computing techniques are the suitable tool for crosschecking the theoretical analysis of the model.
文摘IaaS (Infrastructure as a Platform) public cloud is one mainstream service mode for public cloud computing. The design aim of one IaaS public cloud is to enlarge the hardware-usage of whole platform, optimize the virtual machine deployment and enhance the accept rate of service demand. In this paper we create one service model for IaaS public cloud, and based on the waiting-line theory to optimize the service model, the queue length and the configuration of scheduling server. And create one demand-vector based scheduling model, to filter the available host machine according to the match of demand and metadata of available resource. The scheduling model can be bonded with the virtual machine motion to reallocate the resources to guarantee the available rate of the whole platform. The feasibility of the algorithm is verified on our own IaaS public cloud computing platform.
基金The National High-Tech Research and Development (863) Program of China (No. 2005AA145110, No. 2006AA01Z436) The Natural Science Foundation of Shanghai (No. 05ZR14083) The Pudong New Area Technology Innovation Public Service Platform of China (No. PDPT2005-04)
文摘Trust is one of the most important security requirements in the design and implementation of peer-to-peer （P2P） systems. In an environment where peers＇ identity privacy is important, it may conflict with trustworthiness that is based on the knowledge related to the peer＇s identity, while identity privacy is usually achieved by hiding such knowledge. A trust model based on trusted computing （TC） technology was proposed to enhance the identity privacy of peers during the trustworthiness evaluation process between peers from different groups. The simulation results show that, the model can be implemented in an efficient way, and when the degree of anonymity within group （DAWG） is up to 0.6 and the percentage of malicious peers is up to 70%7 the service selection failure rate is less than 0.15.
文摘Most cloud services are built with multi-tenancy which enables data and configuration segregation upon shared infrastructures.It offers tremendous advantages for enterprises and service providers.It is anticipated that this situation will evolve to foster cross-tenant collaboration supported by Authorization as a service.To realize access control in a multi-tenant cloud computing environment,this study proposes a multi-tenant cloud computing access control model based on the traditional usage access control model by building trust relations among tenants.The model consists of three sub-models,which achieve trust relationships between tenants with different granularities and satisfy the requirements of different application scenarios.With an established trust relation in MT-UCON(Multi-tenant Usage Access Control),the trustee can precisely authorize cross-tenant accesses to the trustor’s resources consistent with constraints over the trust relation and other components designated by the trustor.In addition,the security of the model is analyzed by an information flow method.The model adapts to the characteristics of a dynamic and open multi-tenant cloud computing environment and achieves fine-grained access control within and between tenants.
基金supported in part by the National Science Foundation Project of P.R.China (No.61931001)the Fundamental Research Funds for the Central Universities under Grant (No.FRFAT-19-010)the Scientific and Technological Innovation Foundation of Foshan,USTB (No.BK20AF003)。
文摘Dispersed computing is a new resourcecentric computing paradigm.Due to its high degree of openness and decentralization,it is vulnerable to attacks,and security issues have become an important challenge hindering its development.The trust evaluation technology is of great significance to the reliable operation and security assurance of dispersed computing networks.In this paper,a dynamic Bayesian-based comprehensive trust evaluation model is proposed for dispersed computing environment.Specifically,in the calculation of direct trust,a logarithmic decay function and a sliding window are introduced to improve the timeliness.In the calculation of indirect trust,a random screening method based on sine function is designed,which excludes malicious nodes providing false reports and multiple malicious nodes colluding attacks.Finally,the comprehensive trust value is dynamically updated based on historical interactions,current interactions and momentary changes.Simulation experiments are introduced to verify the performance of the model.Compared with existing model,the proposed trust evaluation model performs better in terms of the detection rate of malicious nodes,the interaction success rate,and the computational cost.
基金National Science and Technology Support Program of China(No.2009BAG15B01)Key Programs for Science and Technology Development of Chinese Transportation Industry(No.2008-353-332-190)"333 High-level Personnel Training Project"Special Funded Projects in Jiangsu Province
文摘Based on FEM (finite element method) program ANSYS and the OpenGL graphics, this paper develops the parametric modeling module and the computing module of the multi-tower suspension bridge, the modules being embedded into the ANSYS system, and the parametric modeling module parameters can be entered by way of interface, which can fast establish a multi-tower suspension bridge model. Calculation module can establish load conditions for the features of road bridge and specifications, in which multiple conditions can be defined and solved automatically. Post-processing part of the solution also serves the results of the subtotals and selects the output, so that the results of the output and finishing work have become more convenient and easier, and also the results can be saved in word, excel and other different file types.
基金Project supported by the National Natural Science Foundation of China(Grant No.61332003)High Performance Computing Laboratory,China(Grant No.201501-02)
文摘Memristors, as memristive devices, have received a great deal of interest since being fabricated by HP labs. The forgetting effect that has significant influences on memristors＇ performance has to be taken into account when they are employed. It is significant to build a good model that can express the forgetting effect well for application researches due to its promising prospects in brain-inspired computing. Some models are proposed to represent the forgetting effect but do not work well. In this paper, we present a novel window function, which has good performance in a drift model. We analyze the deficiencies of the previous drift diffusion models for the forgetting effect and propose an improved model. Moreover,the improved model is exploited as a synapse model in spiking neural networks to recognize digit images. Simulation results show that the improved model overcomes the defects of the previous models and can be used as a synapse model in brain-inspired computing due to its synaptic characteristics. The results also indicate that the improved model can express the forgetting effect better when it is employed in spiking neural networks, which means that more appropriate evaluations can be obtained in applications.
文摘This paper reviews a class of important models of granular computing which are induced by equivalence relations,or by general binary relations,or by neighborhood systems,and propose a class of models of granular computing which are induced by coverings of the given universe.
文摘With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.
