Cell death is a crucial process required for development,tissue homeostasis,and pathological cell loss in multicellular organisms.Cell death mainly occurs in two alternative modes:apoptosis or necrosis.Apoptosis is a ...Cell death is a crucial process required for development,tissue homeostasis,and pathological cell loss in multicellular organisms.Cell death mainly occurs in two alternative modes:apoptosis or necrosis.Apoptosis is a form of programmed cell death with typical morphological features,including cell shrinkage,chromatin condensation,and DNA fragmentation(Degterev and Yuan,2008).The dying cell is eventually fragmented into membrane-bound展开更多
Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to i...Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.展开更多
Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardwar...Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.展开更多
Emerging Internet of Things(IoT)applications require faster execution time and response time to achieve optimal performance.However,most IoT devices have limited or no computing capability to achieve such stringent ap...Emerging Internet of Things(IoT)applications require faster execution time and response time to achieve optimal performance.However,most IoT devices have limited or no computing capability to achieve such stringent application requirements.To this end,computation offloading in edge computing has been used for IoT systems to achieve the desired performance.Nevertheless,randomly offloading applications to any available edge without considering their resource demands,inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation.We introduce Edge-IoT,a machine learning-enabled orchestration framework in this paper,which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency.We further propose a variant bin-packing optimization model that co-locates applications firmly on edge resources to fully utilize available resources.Extensive experiments show the effectiveness and resource efficiency of the proposed approach.展开更多
Internet of things(IoT)devices are being increasingly used in numerous areas.However,the low priority on security and various IoT types have made these devices vulnerable to attacks.To prevent this,recent studies have...Internet of things(IoT)devices are being increasingly used in numerous areas.However,the low priority on security and various IoT types have made these devices vulnerable to attacks.To prevent this,recent studies have analyzed firmware in an emulation environment that does not require actual devices and is efficient for repeated experiments.However,these studies focused only on major firmware architectures and rarely considered exotic firmware.In addition,because of the diversity of firmware,the emulation success rate is not high in terms of large-scale analyses.In this study,we propose the adaptive emulation framework for multi-architecture(AEMA).In the field of automated emulation frameworks for IoT firmware testing,AEMA considers the following issues:(1)limited compatibility for exotic firmware architectures,(2)emulation instability when configuring an automated environment,and(3)shallow testing range resulting from structured inputs.To tackle these problems,AEMAcan emulate not onlymajor firmware architectures but also exotic firmware architectures not previously considered,such as Xtensa,ColdFire,and reduced instruction set computer(RISC)version five,by implementing a minority emulator.Moreover,we applied the emulation arbitration technique and input keyword extraction technique for emulation stability and efficient test case generation.We compared AEMA with other existing frameworks in terms of emulation success rates and fuzz testing.As a result,AEMA succeeded in emulating 864 out of 1,083 overall experimental firmware and detected vulnerabilities at least twice as fast as the experimental group.Furthermore,AEMAfound a 0-day vulnerability in realworld IoT devices within 24 h.展开更多
Nowadays,with the significant growth of the mobile market,security issues on the Android Operation System have also become an urgent matter.Trusted execution environment(TEE)technologies are considered an option for s...Nowadays,with the significant growth of the mobile market,security issues on the Android Operation System have also become an urgent matter.Trusted execution environment(TEE)technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security.However,for Android,TEE technologies still contain restrictions and limitations.The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE.Another issue of hardware-based TEE is the cross-platform problem.Since every mobile device supports different TEE vendors,it becomes an obstacle for developers to migrate their trusted applications to other Android devices.A software-based TEE solution is a potential approach that allows developers to customize,package and deliver the product efficiently.Motivated by that idea,this paper introduces a VTEE model,a software-based TEE solution,on Android devices.This research contributes to the analysis of the feasibility of using a virtualized TEE on Android devices by considering two metrics:computing performance and security.The experiment shows that the VTEE model can host other software-based TEE services and deliver various cryptography TEE functions on theAndroid environment.The security evaluation shows that adding the VTEE model to the existing Android does not addmore security issues to the traditional design.Overall,this paper shows applicable solutions to adjust the balance between computing performance and security.展开更多
The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. H...The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.展开更多
Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open por...Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.展开更多
Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems.In the meanwhile,the overhead cost of channel state information and beam training is considerable,especially in dynami...Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems.In the meanwhile,the overhead cost of channel state information and beam training is considerable,especially in dynamic environments.To reduce the overhead cost,we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method.With online learning of users’moving trajectories,the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate.Considering practical implementation,we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity.Furthermore,we propose a scalable state-action-reward design for scenarios with different users and antenna numbers.Simulation results verify the effectiveness of the designed method.展开更多
The widespread usage of clean and sustainable energy sources is leading to a significant transformation of the world’s energy systems. Over-reliance on only the national grid energy system has made institutions fail ...The widespread usage of clean and sustainable energy sources is leading to a significant transformation of the world’s energy systems. Over-reliance on only the national grid energy system has made institutions fail to sustain energy systems. The council is only connected to the national grid electricity supply system, with diesel generators as the only alternative, which is unhealthy and unsafe. Surprisingly, even with such alternatives, power shortages have persisted despite government efforts to provide a solution to the shortages by installing numerous off-grid systems. Due to such a situation, the council would construct a sustainable energy system as a remedy. Thus, the purpose of this study was to establish critical success factors influencing the implementation of a sustainable energy system at the Inter-University Council of East Africa (IUCEA) Head Quarters, Kampala-Uganda. A cross-sectional survey design was used;a sample size of 84 participants was selected. Questionnaire survey and interview methods were utilized. The study found that the most significant (p < 0.05) critical factors in the implementation of sustainable energy in institutions are;the use of innovative technologies and infrastructure, the use of efficient zero emissions for heating and cooling, integration of renewable energy use in the existing buildings, building and renovating in an energy-efficient way, integrating regional energy systems, improving energy efficiency in the buildings, enhanced zero emission power technologies, energy efficient equipment in place and stakeholder empowerment in energy management. This study concludes that institutions like;the Inter-University Council of East Africa (IUCEA) need to clearly state policies and actions of energy management. The roles and responsibilities of each member have to be clearly stated while capturing the activities involved in energy conservation, energy security and energy efficiency.展开更多
Debugging software code has been a challenge for software developers since the early days of computer programming. A simple need, because the world is run by software. So perhaps the biggest engineering challenge is f...Debugging software code has been a challenge for software developers since the early days of computer programming. A simple need, because the world is run by software. So perhaps the biggest engineering challenge is finding ways to make software more reliable. This review provides an overview of techniques developed over time in the field of software model checking to solve the problem of detecting errors in program code. In addition, the challenges posed by this technology are discussed and ways to mitigate them in future research and applications are proposed. A comprehensive examination of the various model verification methods used to detect program code errors is intended to lay the foundation for future research in this area.展开更多
The extended enterprise is formed according to the philosophy of dispersednetworked manufacturing. Manufacturing execution system (MES) can close the information gap whichexists between device control system and produ...The extended enterprise is formed according to the philosophy of dispersednetworked manufacturing. Manufacturing execution system (MES) can close the information gap whichexists between device control system and production information management system. The functions andthe web-based architecture of the MES in the extended enterprise are introduced. Using thecooperating system models of object-oriented and distributed agents and CORBA, all objects keep touniform interface standards and are easily inserted to object request broker. The utilization ofdistributed MES in extended enterprise can adapt fast change of manufacturing environment andresource. It also can improve the independent management capability of manufacturing cell and theenterprise response capability to global economic competition.展开更多
Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationall...Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.展开更多
To solve the problem of distributed tasks-platforms scheduling in holonic command and control(C2) organization,the basic elements of the organization are analyzed firstly and the formal description of organizational e...To solve the problem of distributed tasks-platforms scheduling in holonic command and control(C2) organization,the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality,a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally,a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.展开更多
Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principle...Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.展开更多
In this paper, an event-triggered sliding mode control approach for trajectory tracking problem of nonlinear input affine system with disturbance has been proposed. A second order robotic manipulator system has been m...In this paper, an event-triggered sliding mode control approach for trajectory tracking problem of nonlinear input affine system with disturbance has been proposed. A second order robotic manipulator system has been modeled into a general nonlinear input affine system. Initially, the global asymptotic stability is ensured with conventional periodic sampling approach for reference trajectory tracking. Then the proposed approach of event-triggered sliding mode control is discussed which guarantees semi-global uniform ultimate boundedness. The proposed control approach guarantees non-accumulation of control updates ensuring lower bounds on inter-event triggering instants avoiding Zeno behavior in presence of the disturbance. The system shows better performance in terms of reduced control updates, ensures system stability which further guarantees optimization of resource usage and cost. The simulation results are provided for validation of proposed methodology for tracking problem by a robotic manipulator. The number of aperiodic control updates is found to be approximately 44% and 61% in the presence of constant and time-varying disturbances respectively.展开更多
Objective:To investigate the effects of mirror neuron theory-based visual feedback therapy(VFT)on restoration of upper limb function of stroke patients and motor-related cortical function using functional magnetic res...Objective:To investigate the effects of mirror neuron theory-based visual feedback therapy(VFT)on restoration of upper limb function of stroke patients and motor-related cortical function using functional magnetic resonance imaging(fMRI).Methods:Hemiplegic stroke patients were randomly divided into two groups:a VFT group and a control(CTL)group.Sixteen patients in the VFT group received conventional rehabilitation(CR)and VFT for 8 weeks,while 15 patients in the CTL group received only CR.The Barthel Index(BI)was used to assess the activities of daily living at baseline and the 8th week of the recovery training period.The Fugl-Meyer assessment(FMA)scale,somatosensory evoked potential(SEP),and fMRI were used to evaluate the recovery effect of the training therapies.The latencies and amplitudes of N9 and N20 were measured.Before recovery training,fMRI was performed for all patients in the VFT and CTL groups.In addition,17 patients(9 in the VFT group and 8 in the CTL group)underwent fMRI for follow-up 2 months after treatment.Qualitative data were analyzed using the x2 test.The independent sample t-test was used to compare normally distributed data among different groups,the paired sample t-test was used to compare data between groups,and the non-parametric test was used to comparing data without normal distribution among groups.Results:There were no significant differences between the VFT and CTL group in all indexes.However,after 8 weeks of recovery training,these indexes were all significantly improved(P<0.05).As compared with the CTL group,the FMA scores,BI,and N9/N20 latencies and amplitudes of SEP in the VFT group were significantly improved(P<0.05).Two months after recovery training,fMRI showed that the degree of activation of the bilateral central anterior gyrus.parietal lobe,and auxiliary motor areas was significantly higher in the VFT group than the CTL group(P<0.05).Conclusions:VFT based on mirror neuron theory is an effective approach to improve upper extremity motor function and daily activity performance of stroke patients.The therapeutic mechanism promotes motor relearning by activating the mirror neuron system and motor cortex.SEP amplitudes increased only for patients who participated in visual feedback.VFT promotes sensory-motor plasticity and behavioral changes in both the motor and sensory domains.展开更多
基金supported by the National Basic Research Program of China (2013CB910102)the National Natural Science Foundation of China (31471303)a project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions
文摘Cell death is a crucial process required for development,tissue homeostasis,and pathological cell loss in multicellular organisms.Cell death mainly occurs in two alternative modes:apoptosis or necrosis.Apoptosis is a form of programmed cell death with typical morphological features,including cell shrinkage,chromatin condensation,and DNA fragmentation(Degterev and Yuan,2008).The dying cell is eventually fragmented into membrane-bound
文摘Software security analysts typically only have access to the executable program and cannot directly access the source code of the program.This poses significant challenges to security analysis.While it is crucial to identify vulnerabilities in such non-source code programs,there exists a limited set of generalized tools due to the low versatility of current vulnerability mining methods.However,these tools suffer from some shortcomings.In terms of targeted fuzzing,the path searching for target points is not streamlined enough,and the completely random testing leads to an excessively large search space.Additionally,when it comes to code similarity analysis,there are issues with incomplete code feature extraction,which may result in information loss.In this paper,we propose a cross-platform and cross-architecture approach to exploit vulnerabilities using neural network obfuscation techniques.By leveraging the Angr framework,a deobfuscation technique is introduced,along with the adoption of a VEX-IR-based intermediate language conversion method.This combination allows for the unified handling of binary programs across various architectures,compilers,and compilation options.Subsequently,binary programs are processed to extract multi-level spatial features using a combination of a skip-gram model with self-attention mechanism and a bidirectional Long Short-Term Memory(LSTM)network.Finally,the graph embedding network is utilized to evaluate the similarity of program functionalities.Based on these similarity scores,a target function is determined,and symbolic execution is applied to solve the target function.The solved content serves as the initial seed for targeted fuzzing.The binary program is processed by using the de-obfuscation technique and intermediate language transformation method,and then the similarity of program functions is evaluated by using a graph embedding network,and symbolic execution is performed based on these similarity scores.This approach facilitates cross-architecture analysis of executable programs without their source codes and concurrently reduces the risk of symbolic execution path explosion.
基金supported by ZTE Industry-University-Institute Cooperation Funds under Grant No.2022ZTE09.
文摘Real-time system timing analysis is crucial for estimating the worst-case execution time(WCET)of a program.To achieve this,static or dynamic analysis methods are used,along with targeted modeling of the actual hardware system.This literature review focuses on calculating WCET for multi-core processors,providing a survey of traditional methods used for static and dynamic analysis and highlighting the major challenges that arise from different program execution scenarios on multi-core platforms.This paper outlines the strengths and weaknesses of current methodologies and offers insights into prospective areas of research on multi-core analysis.By presenting a comprehensive analysis of the current state of research on multi-core processor analysis for WCET estimation,this review aims to serve as a valuable resource for researchers and practitioners in the field.
基金supported by the National Natural Science Foundation of China under Grant Nos.61571401 and 61901416(part of the China Postdoctoral Science Foundation under Grant No.2021TQ0304)the Innovative Talent Colleges and the University of Henan Province under Grant No.18HASTIT021.
文摘Emerging Internet of Things(IoT)applications require faster execution time and response time to achieve optimal performance.However,most IoT devices have limited or no computing capability to achieve such stringent application requirements.To this end,computation offloading in edge computing has been used for IoT systems to achieve the desired performance.Nevertheless,randomly offloading applications to any available edge without considering their resource demands,inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation.We introduce Edge-IoT,a machine learning-enabled orchestration framework in this paper,which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency.We further propose a variant bin-packing optimization model that co-locates applications firmly on edge resources to fully utilize available resources.Extensive experiments show the effectiveness and resource efficiency of the proposed approach.
基金This work was supported by the Ministry of Science and ICT(MSIT)Korea,under the Information Technology Research Center(ITRC)support program(IITP-2022-2018-0-01423)+2 种基金supervised by the Institute for Information&Communications Technology Planning&Evaluation(IITP)by MSIT,Korea under the ITRC support program(IITP-2021-2020-0-01602)supervised by the IITP.
文摘Internet of things(IoT)devices are being increasingly used in numerous areas.However,the low priority on security and various IoT types have made these devices vulnerable to attacks.To prevent this,recent studies have analyzed firmware in an emulation environment that does not require actual devices and is efficient for repeated experiments.However,these studies focused only on major firmware architectures and rarely considered exotic firmware.In addition,because of the diversity of firmware,the emulation success rate is not high in terms of large-scale analyses.In this study,we propose the adaptive emulation framework for multi-architecture(AEMA).In the field of automated emulation frameworks for IoT firmware testing,AEMA considers the following issues:(1)limited compatibility for exotic firmware architectures,(2)emulation instability when configuring an automated environment,and(3)shallow testing range resulting from structured inputs.To tackle these problems,AEMAcan emulate not onlymajor firmware architectures but also exotic firmware architectures not previously considered,such as Xtensa,ColdFire,and reduced instruction set computer(RISC)version five,by implementing a minority emulator.Moreover,we applied the emulation arbitration technique and input keyword extraction technique for emulation stability and efficient test case generation.We compared AEMA with other existing frameworks in terms of emulation success rates and fuzz testing.As a result,AEMA succeeded in emulating 864 out of 1,083 overall experimental firmware and detected vulnerabilities at least twice as fast as the experimental group.Furthermore,AEMAfound a 0-day vulnerability in realworld IoT devices within 24 h.
基金This work was partly supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea Government(MSIT),(No.2020-0-00952,Development of 5G edge security technology for ensuring 5G+service stability and availability,50%)the Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the MSIT(Ministry of Science and ICT),Korea(No.IITP-2022-2020-0-01602,ITRC(Information Technology Research Center)support program,50%).
文摘Nowadays,with the significant growth of the mobile market,security issues on the Android Operation System have also become an urgent matter.Trusted execution environment(TEE)technologies are considered an option for satisfying the inviolable property by taking advantage of hardware security.However,for Android,TEE technologies still contain restrictions and limitations.The first issue is that non-original equipment manufacturer developers have limited access to the functionality of hardware-based TEE.Another issue of hardware-based TEE is the cross-platform problem.Since every mobile device supports different TEE vendors,it becomes an obstacle for developers to migrate their trusted applications to other Android devices.A software-based TEE solution is a potential approach that allows developers to customize,package and deliver the product efficiently.Motivated by that idea,this paper introduces a VTEE model,a software-based TEE solution,on Android devices.This research contributes to the analysis of the feasibility of using a virtualized TEE on Android devices by considering two metrics:computing performance and security.The experiment shows that the VTEE model can host other software-based TEE services and deliver various cryptography TEE functions on theAndroid environment.The security evaluation shows that adding the VTEE model to the existing Android does not addmore security issues to the traditional design.Overall,this paper shows applicable solutions to adjust the balance between computing performance and security.
基金supported in part by the National Science Foundation of China (61973247, 61673315, 62173268)the Key Research and Development Program of Shaanxi (2022GY-033)+2 种基金the Nationa Postdoctoral Innovative Talents Support Program of China (BX20200272)the Key Program of the National Natural Science Foundation of China (61833015)the Fundamental Research Funds for the Central Universities (xzy022021050)。
文摘The smart grid utilizes the demand side management technology to motivate energy users towards cutting demand during peak power consumption periods, which greatly improves the operation efficiency of the power grid. However, as the number of energy users participating in the smart grid continues to increase, the demand side management strategy of individual agent is greatly affected by the dynamic strategies of other agents. In addition, the existing demand side management methods, which need to obtain users’ power consumption information,seriously threaten the users’ privacy. To address the dynamic issue in the multi-microgrid demand side management model, a novel multi-agent reinforcement learning method based on centralized training and decentralized execution paradigm is presented to mitigate the damage of training performance caused by the instability of training experience. In order to protect users’ privacy, we design a neural network with fixed parameters as the encryptor to transform the users’ energy consumption information from low-dimensional to high-dimensional and theoretically prove that the proposed encryptor-based privacy preserving method will not affect the convergence property of the reinforcement learning algorithm. We verify the effectiveness of the proposed demand side management scheme with the real-world energy consumption data of Xi’an, Shaanxi, China. Simulation results show that the proposed method can effectively improve users’ satisfaction while reducing the bill payment compared with traditional reinforcement learning(RL) methods(i.e., deep Q learning(DQN), deep deterministic policy gradient(DDPG),QMIX and multi-agent deep deterministic policy gradient(MADDPG)). The results also demonstrate that the proposed privacy protection scheme can effectively protect users’ privacy while ensuring the performance of the algorithm.
基金funded by National Key Research and Development Program of China under Grant No.2019YFC1520904 from January 2020 to April 2023funded by Shaanxi Innovation Program under Grant 2023-CX-TD-04 January 2023 to December 2025.
文摘Trusted Execution Environment(TEE)is an important part of the security architecture of modern mobile devices,but its secure interaction process brings extra computing burden to mobile devices.This paper takes open portable trusted execution environment(OP-TEE)as the research object and deploys it to Raspberry Pi 3B,designs and implements a benchmark for OP-TEE,and analyzes its program characteristics.Furthermore,the application execution time,energy consumption and energy-delay product(EDP)are taken as the optimization objectives,and the central processing unit(CPU)frequency scheduling strategy of mobile devices is dynamically adjusted according to the characteristics of different applications through the combined model.The experimental result shows that compared with the default strategy,the scheduling method proposed in this paper saves 21.18%on average with the Line Regression-Decision Tree scheduling model with the shortest delay as the optimization objective.The Decision Tree-Support Vector Regression(SVR)scheduling model,which takes the lowest energy consumption as the optimization goal,saves 22%energy on average.The Decision Tree-K-Nearest Neighbor(KNN)scheduling model with the lowest EDP as the optimization objective optimizes about 33.9%on average.
文摘Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems.In the meanwhile,the overhead cost of channel state information and beam training is considerable,especially in dynamic environments.To reduce the overhead cost,we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method.With online learning of users’moving trajectories,the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate.Considering practical implementation,we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity.Furthermore,we propose a scalable state-action-reward design for scenarios with different users and antenna numbers.Simulation results verify the effectiveness of the designed method.
文摘The widespread usage of clean and sustainable energy sources is leading to a significant transformation of the world’s energy systems. Over-reliance on only the national grid energy system has made institutions fail to sustain energy systems. The council is only connected to the national grid electricity supply system, with diesel generators as the only alternative, which is unhealthy and unsafe. Surprisingly, even with such alternatives, power shortages have persisted despite government efforts to provide a solution to the shortages by installing numerous off-grid systems. Due to such a situation, the council would construct a sustainable energy system as a remedy. Thus, the purpose of this study was to establish critical success factors influencing the implementation of a sustainable energy system at the Inter-University Council of East Africa (IUCEA) Head Quarters, Kampala-Uganda. A cross-sectional survey design was used;a sample size of 84 participants was selected. Questionnaire survey and interview methods were utilized. The study found that the most significant (p < 0.05) critical factors in the implementation of sustainable energy in institutions are;the use of innovative technologies and infrastructure, the use of efficient zero emissions for heating and cooling, integration of renewable energy use in the existing buildings, building and renovating in an energy-efficient way, integrating regional energy systems, improving energy efficiency in the buildings, enhanced zero emission power technologies, energy efficient equipment in place and stakeholder empowerment in energy management. This study concludes that institutions like;the Inter-University Council of East Africa (IUCEA) need to clearly state policies and actions of energy management. The roles and responsibilities of each member have to be clearly stated while capturing the activities involved in energy conservation, energy security and energy efficiency.
文摘Debugging software code has been a challenge for software developers since the early days of computer programming. A simple need, because the world is run by software. So perhaps the biggest engineering challenge is finding ways to make software more reliable. This review provides an overview of techniques developed over time in the field of software model checking to solve the problem of detecting errors in program code. In addition, the challenges posed by this technology are discussed and ways to mitigate them in future research and applications are proposed. A comprehensive examination of the various model verification methods used to detect program code errors is intended to lay the foundation for future research in this area.
文摘The extended enterprise is formed according to the philosophy of dispersednetworked manufacturing. Manufacturing execution system (MES) can close the information gap whichexists between device control system and production information management system. The functions andthe web-based architecture of the MES in the extended enterprise are introduced. Using thecooperating system models of object-oriented and distributed agents and CORBA, all objects keep touniform interface standards and are easily inserted to object request broker. The utilization ofdistributed MES in extended enterprise can adapt fast change of manufacturing environment andresource. It also can improve the independent management capability of manufacturing cell and theenterprise response capability to global economic competition.
基金supported by the NSFC (61602126)the scientific and technological project of Henan province (162102210214)
文摘Fog computing is an emerging paradigm of cloud computing which to meet the growing computation demand of mobile application. It can help mobile devices to overcome resource constraints by offloading the computationally intensive tasks to cloud servers. The challenge of the cloud is to minimize the time of data transfer and task execution to the user, whose location changes owing to mobility, and the energy consumption for the mobile device. To provide satisfactory computation performance is particularly challenging in the fog computing environment. In this paper, we propose a novel fog computing model and offloading policy which can effectively bring the fog computing power closer to the mobile user. The fog computing model consist of remote cloud nodes and local cloud nodes, which is attached to wireless access infrastructure. And we give task offloading policy taking into account executi+on, energy consumption and other expenses. We finally evaluate the performance of our method through experimental simulations. The experimental results show that this method has a significant effect on reducing the execution time of tasks and energy consumption of mobile devices.
基金supported by the National Natural Science Foundation of China(6157301761703425)+2 种基金the Aeronautical Science Fund(20175796014)Shaanxi Province Natural Science Foundation(2016JQ60622017JM6062)
文摘To solve the problem of distributed tasks-platforms scheduling in holonic command and control(C2) organization,the basic elements of the organization are analyzed firstly and the formal description of organizational elements and structure is provided. Based on the improvement of task execution quality,a single task resource scheduling model is established and the solving method based on the m-best algorithm is proposed. For the problem of tactical decision-holon cannot handle tasks with low priority effectively, a distributed resource scheduling collaboration mechanism based on platform pricing and a platform exchange mechanism based on resource capacities are designed. Finally,a series of experiments are designed to prove the effectiveness of these methods. The results show that the proposed distributed scheduling methods can realize the effective balance of platform resources.
文摘Current orchestration and choreography process engines only serve with dedicate process languages.To solve these problems,an Event-driven Process Execution Model(EPEM) was developed.Formalization and mapping principles of the model were presented to guarantee the correctness and efficiency for process transformation.As a case study,the EPEM descriptions of Web Services Business Process Execution Language(WS-BPEL) were represented and a Process Virtual Machine(PVM)-OncePVM was implemented in compliance with the EPEM.
文摘In this paper, an event-triggered sliding mode control approach for trajectory tracking problem of nonlinear input affine system with disturbance has been proposed. A second order robotic manipulator system has been modeled into a general nonlinear input affine system. Initially, the global asymptotic stability is ensured with conventional periodic sampling approach for reference trajectory tracking. Then the proposed approach of event-triggered sliding mode control is discussed which guarantees semi-global uniform ultimate boundedness. The proposed control approach guarantees non-accumulation of control updates ensuring lower bounds on inter-event triggering instants avoiding Zeno behavior in presence of the disturbance. The system shows better performance in terms of reduced control updates, ensures system stability which further guarantees optimization of resource usage and cost. The simulation results are provided for validation of proposed methodology for tracking problem by a robotic manipulator. The number of aperiodic control updates is found to be approximately 44% and 61% in the presence of constant and time-varying disturbances respectively.
基金This study was supported in part by grants from Zhejiang province medical and health technology achievement Funding project(2018ZH044)Zhejiang province medical and health science and technology project.(2020KY317)+1 种基金Zhejiang province natural science foundation(LQ19H170001)2019-2021 period key discipline construction plan funded project of traditional Chinese medicine in Jiaxing city(2019 XK-A07).
文摘Objective:To investigate the effects of mirror neuron theory-based visual feedback therapy(VFT)on restoration of upper limb function of stroke patients and motor-related cortical function using functional magnetic resonance imaging(fMRI).Methods:Hemiplegic stroke patients were randomly divided into two groups:a VFT group and a control(CTL)group.Sixteen patients in the VFT group received conventional rehabilitation(CR)and VFT for 8 weeks,while 15 patients in the CTL group received only CR.The Barthel Index(BI)was used to assess the activities of daily living at baseline and the 8th week of the recovery training period.The Fugl-Meyer assessment(FMA)scale,somatosensory evoked potential(SEP),and fMRI were used to evaluate the recovery effect of the training therapies.The latencies and amplitudes of N9 and N20 were measured.Before recovery training,fMRI was performed for all patients in the VFT and CTL groups.In addition,17 patients(9 in the VFT group and 8 in the CTL group)underwent fMRI for follow-up 2 months after treatment.Qualitative data were analyzed using the x2 test.The independent sample t-test was used to compare normally distributed data among different groups,the paired sample t-test was used to compare data between groups,and the non-parametric test was used to comparing data without normal distribution among groups.Results:There were no significant differences between the VFT and CTL group in all indexes.However,after 8 weeks of recovery training,these indexes were all significantly improved(P<0.05).As compared with the CTL group,the FMA scores,BI,and N9/N20 latencies and amplitudes of SEP in the VFT group were significantly improved(P<0.05).Two months after recovery training,fMRI showed that the degree of activation of the bilateral central anterior gyrus.parietal lobe,and auxiliary motor areas was significantly higher in the VFT group than the CTL group(P<0.05).Conclusions:VFT based on mirror neuron theory is an effective approach to improve upper extremity motor function and daily activity performance of stroke patients.The therapeutic mechanism promotes motor relearning by activating the mirror neuron system and motor cortex.SEP amplitudes increased only for patients who participated in visual feedback.VFT promotes sensory-motor plasticity and behavioral changes in both the motor and sensory domains.