This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works ...This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works in synchronous model. Compared with the method of using secret share protocol m times to share m secrets, our protocol is quite efficient. The protocol can be used to improve the efficiency of secure multi-party computation （MPC） greatly in asynchronous network.展开更多
Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrain...Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.展开更多
In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network e...In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.展开更多
Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based met...Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based methods,tracking on a single CPU core,or parallelizing the computation across multiple cores via the message passing interface(MPI).Although these approaches work well for single-bunch tracking,scaling them to multiple bunches significantly increases the computational load,which often necessitates the use of a dedicated multi-CPU cluster.To address this challenge,alternative methods leveraging General-Purpose computing on Graphics Processing Units(GPGPU)have been proposed,enabling tracking studies on a standalone desktop personal computer(PC).However,frequent CPU-GPU interactions,including data transfers and synchronization operations during tracking,can introduce communication overheads,potentially reducing the overall effectiveness of GPU-based computations.In this study,we propose a novel approach that eliminates this overhead by performing the entire tracking simulation process exclusively on the GPU,thereby enabling the simultaneous processing of all bunches and their macro-particles.Specifically,we introduce MBTRACK2-CUDA,a Compute Unified Device Architecture(CUDA)ported version of MBTRACK2,which facilitates efficient tracking of single-and multi-bunch collective effects by leveraging the full GPU-resident computation.展开更多
As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays...As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.展开更多
Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and v...Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.展开更多
Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purel...Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purely theoretical study to an object of practical interest,with a growing interest in practical applications such as privacy-preserving machine learning(PPML).In this paper,we comprehensively survey existing work on concretely ecient MPC protocols with both semi-honest and malicious security,in both dishonest-majority and honest-majority settings.We focus on considering the notion of security with abort,meaning that corrupted parties could prevent honest parties from receiving output after they receive output.We present high-level ideas of the basic and key approaches for designing di erent styles of MPC protocols and the crucial building blocks of MPC.For MPC applications,we compare the known PPML protocols built on MPC,and describe the eciency of private inference and training for the state-of-the-art PPML protocols.Further-more,we summarize several challenges and open problems to break though the eciency of MPC protocols as well as some interesting future work that is worth being addressed.This survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing,improving,and applying concretely ecient MPC protocols.展开更多
To solve the data island problem,federated learning(FL)provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation.Peer-to-peer(P2P)federated learning f...To solve the data island problem,federated learning(FL)provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation.Peer-to-peer(P2P)federated learning further improves the robustness of the system,in which there is no server and each client communicates directly with the other.For secure aggregation,secure multi-party computing(SMPC)protocols have been utilized in peer-to-peer manner.However,the ideal SMPC protocols could fail when some clients drop out.In this paper,we propose a robust peer-to-peer learning(RP2PL)algorithm via SMPC to resist clients dropping out.We improve the segmentbased SMPC protocol by adding a check and designing the generation method of random segments.In RP2PL,each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training.Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance.展开更多
The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches l...The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches lack built-in privacy mechanisms,exposing sensitive data to risks,which motivates the development of Privacy-Preserving Machine Learning(PPML)methods.Despite significant advances in PPML,a comprehensive and focused exploration of Secure Multi-Party Computing(SMPC)within this context remains underdeveloped.This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML,offering a structured overviewof current techniques,challenges,and future directions.Using a semi-systematicmapping studymethodology,this paper surveys recent literature spanning SMPC protocols,PPML frameworks,implementation approaches,threat models,and performance metrics.Emphasis is placed on identifying trends,technical limitations,and comparative strengths of leading SMPC-based methods.Our findings reveal thatwhile SMPCoffers strong cryptographic guarantees for privacy,challenges such as computational overhead,communication costs,and scalability persist.The paper also discusses critical vulnerabilities,practical deployment issues,and variations in protocol efficiency across use cases.展开更多
Privacy-Preserving Computation(PPC)comprises the techniques,schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis.Most of the current PPC techniques rely...Privacy-Preserving Computation(PPC)comprises the techniques,schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis.Most of the current PPC techniques rely on the complexity of cryptographic operations,which are expected to be efficiently solved by quantum computers soon.This review explores how PPC can be built on top of quantum computing itself to alleviate these future threats.We analyze quantum proposals for Secure Multi-party Computation,Oblivious Transfer and Homomorphic Encryption from the last decade focusing on their maturity and the challenges they currently face.Our findings show a strong focus on purely theoretical works,but a rise on the experimental consideration of these techniques in the last 5 years.The applicability of these techniques to actual use cases is an underexplored aspect which could lead to the practical assessment of these techniques.展开更多
Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability an...Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the grid.Recently,much attention has been paid to the research on smart grid,especially in protecting privacy and data aggregation.However,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog computation.In this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog computing.VPDC-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing scheme.Besides,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’report.Furthermore,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart grid.Besides,VPDC-MPC tolerates fault of cloud servers and resists collusion.We also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.展开更多
Universality is an important property in software and hardware design.This paper concentrates on the universality of quantum secure multi-party computation(SMC)protocol.First of all,an in-depth study of universality h...Universality is an important property in software and hardware design.This paper concentrates on the universality of quantum secure multi-party computation(SMC)protocol.First of all,an in-depth study of universality has been conducted,and then a nearly universal protocol is proposed by using the Greenberger-Horne-Zeilinger(GHZ)-like state and stabilizer formalism.The protocol can resolve the quantum SMC problem which can be deduced as modulo subtraction,and the steps are simple and effective.Secondly,three quantum SMC protocols based on the proposed universal protocol:Quantum private comparison(QPC)protocol,quantum millionaire(QM)protocol,and quantum multi-party summation(QMS)protocol are presented.These protocols are given as examples to explain universality.Thirdly,analyses of the example protocols are shown.Concretely,the correctness,fairness,and efficiency are confirmed.And the proposed universal protocol meets security from the perspective of preventing inside attacks and outside attacks.Finally,the experimental results of the example protocols on the International Business Machines(IBM)quantum platform are consistent with the theoretical results.Our research indicates that our protocol is universal to a certain degree and easy to perform.展开更多
The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical r...The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.展开更多
Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets.To enhance privacy in federated learning,multi-party comp...Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets.To enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model training.This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training efficiency.We also pinpoint future directions to deploy federated learning to a wider range of applications.展开更多
In software-defined networking(SDN),controllers are sinks of information such as network topology collected from switches.Organizations often like to protect their internal network topology and keep their network poli...In software-defined networking(SDN),controllers are sinks of information such as network topology collected from switches.Organizations often like to protect their internal network topology and keep their network policies private.We borrow techniques from secure multi-party computation(SMC)to preserve the privacy of policies of SDN controllers about status of routers.On the other hand,the number of controllers is one of the most important concerns in scalability of SMC application in SDNs.To address this issue,we formulate an optimization problem to minimize the number of SDN controllers while considering their reliability in SMC operations.We use Non-Dominated Sorting Genetic Algorithm II(NSGA-II)to determine the optimal number of controllers,and simulate SMC for typical SDNs with this number of controllers.Simulation results show that applying the SMC technique to preserve the privacy of organization policies causes only a little delay in SDNs,which is completely justifiable by the privacy obtained.展开更多
In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this neces...In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.展开更多
The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digit...The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.展开更多
As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational ...As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.展开更多
To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Un...To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.展开更多
Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for t...Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.展开更多
基金the National Natural Science Foundation of China(No.60803146)
文摘This paper proposes an efficient batch secret sharing protocol among n players resilient to t 〈 n/4 players in asynchronous network. The construction of our protocol is along the line of Hirt＇s protocol which works in synchronous model. Compared with the method of using secret share protocol m times to share m secrets, our protocol is quite efficient. The protocol can be used to improve the efficiency of secure multi-party computation （MPC） greatly in asynchronous network.
基金supported by Key Science and Technology Program of Henan Province,China(Grant Nos.242102210147,242102210027)Fujian Province Young and Middle aged Teacher Education Research Project(Science and Technology Category)(No.JZ240101)(Corresponding author:Dong Yuan).
文摘Vehicle Edge Computing(VEC)and Cloud Computing(CC)significantly enhance the processing efficiency of delay-sensitive and computation-intensive applications by offloading compute-intensive tasks from resource-constrained onboard devices to nearby Roadside Unit(RSU),thereby achieving lower delay and energy consumption.However,due to the limited storage capacity and energy budget of RSUs,it is challenging to meet the demands of the highly dynamic Internet of Vehicles(IoV)environment.Therefore,determining reasonable service caching and computation offloading strategies is crucial.To address this,this paper proposes a joint service caching scheme for cloud-edge collaborative IoV computation offloading.By modeling the dynamic optimization problem using Markov Decision Processes(MDP),the scheme jointly optimizes task delay,energy consumption,load balancing,and privacy entropy to achieve better quality of service.Additionally,a dynamic adaptive multi-objective deep reinforcement learning algorithm is proposed.Each Double Deep Q-Network(DDQN)agent obtains rewards for different objectives based on distinct reward functions and dynamically updates the objective weights by learning the value changes between objectives using Radial Basis Function Networks(RBFN),thereby efficiently approximating the Pareto-optimal decisions for multiple objectives.Extensive experiments demonstrate that the proposed algorithm can better coordinate the three-tier computing resources of cloud,edge,and vehicles.Compared to existing algorithms,the proposed method reduces task delay and energy consumption by 10.64%and 5.1%,respectively.
基金supported by the National Natural Science Foundation of China(62202215)Liaoning Province Applied Basic Research Program(Youth Special Project,2023JH2/101600038)+4 种基金Shenyang Youth Science and Technology Innovation Talent Support Program(RC220458)Guangxuan Program of Shenyang Ligong University(SYLUGXRC202216)the Basic Research Special Funds for Undergraduate Universities in Liaoning Province(LJ212410144067)the Natural Science Foundation of Liaoning Province(2024-MS-113)the science and technology funds from Liaoning Education Department(LJKZ0242).
文摘In the field of edge computing,achieving low-latency computational task offloading with limited resources is a critical research challenge,particularly in resource-constrained and latency-sensitive vehicular network environments where rapid response is mandatory for safety-critical applications.In scenarios where edge servers are sparsely deployed,the lack of coordination and information sharing often leads to load imbalance,thereby increasing system latency.Furthermore,in regions without edge server coverage,tasks must be processed locally,which further exacerbates latency issues.To address these challenges,we propose a novel and efficient Deep Reinforcement Learning(DRL)-based approach aimed at minimizing average task latency.The proposed method incorporates three offloading strategies:local computation,direct offloading to the edge server in local region,and device-to-device(D2D)-assisted offloading to edge servers in other regions.We formulate the task offloading process as a complex latency minimization optimization problem.To solve it,we propose an advanced algorithm based on the Dueling Double Deep Q-Network(D3QN)architecture and incorporating the Prioritized Experience Replay(PER)mechanism.Experimental results demonstrate that,compared with existing offloading algorithms,the proposed method significantly reduces average task latency,enhances user experience,and offers an effective strategy for latency optimization in future edge computing systems under dynamic workloads.
基金supported by the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(MSIT)(No.RS-2022-00143178)the Ministry of Education(MOE)(Nos.2022R1A6A3A13053896 and 2022R1F1A1074616),Republic of Korea.
文摘Beam-tracking simulations have been extensively utilized in the study of collective beam instabilities in circular accelerators.Traditionally,many simulation codes have relied on central processing unit(CPU)-based methods,tracking on a single CPU core,or parallelizing the computation across multiple cores via the message passing interface(MPI).Although these approaches work well for single-bunch tracking,scaling them to multiple bunches significantly increases the computational load,which often necessitates the use of a dedicated multi-CPU cluster.To address this challenge,alternative methods leveraging General-Purpose computing on Graphics Processing Units(GPGPU)have been proposed,enabling tracking studies on a standalone desktop personal computer(PC).However,frequent CPU-GPU interactions,including data transfers and synchronization operations during tracking,can introduce communication overheads,potentially reducing the overall effectiveness of GPU-based computations.In this study,we propose a novel approach that eliminates this overhead by performing the entire tracking simulation process exclusively on the GPU,thereby enabling the simultaneous processing of all bunches and their macro-particles.Specifically,we introduce MBTRACK2-CUDA,a Compute Unified Device Architecture(CUDA)ported version of MBTRACK2,which facilitates efficient tracking of single-and multi-bunch collective effects by leveraging the full GPU-resident computation.
基金supported by Youth Talent Project of Scientific Research Program of Hubei Provincial Department of Education under Grant Q20241809Doctoral Scientific Research Foundation of Hubei University of Automotive Technology under Grant 202404.
文摘As Internet of Things(IoT)applications expand,Mobile Edge Computing(MEC)has emerged as a promising architecture to overcome the real-time processing limitations of mobile devices.Edge-side computation offloading plays a pivotal role in MEC performance but remains challenging due to complex task topologies,conflicting objectives,and limited resources.This paper addresses high-dimensional multi-objective offloading for serial heterogeneous tasks in MEC.We jointly consider task heterogeneity,high-dimensional objectives,and flexible resource scheduling,modeling the problem as a Many-objective optimization.To solve it,we propose a flexible framework integrating an improved cooperative co-evolutionary algorithm based on decomposition(MOCC/D)and a flexible scheduling strategy.Experimental results on benchmark functions and simulation scenarios show that the proposed method outperforms existing approaches in both convergence and solution quality.
基金supported by the Major Project for the Integration of ScienceEducation and Industry (Grant No.2025ZDZX02)。
文摘Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials.
基金the National Key Research and Development Program of China(Grant No.2018YFB0804105)in part by the National Natural Science Foundation of China(Grant Nos.62102037,61932019).
文摘Secure multi-party computation(MPC)allows a set of parties to jointly compute a function on their private inputs,and reveals nothing but the output of the function.In the last decade,MPC has rapidly moved from a purely theoretical study to an object of practical interest,with a growing interest in practical applications such as privacy-preserving machine learning(PPML).In this paper,we comprehensively survey existing work on concretely ecient MPC protocols with both semi-honest and malicious security,in both dishonest-majority and honest-majority settings.We focus on considering the notion of security with abort,meaning that corrupted parties could prevent honest parties from receiving output after they receive output.We present high-level ideas of the basic and key approaches for designing di erent styles of MPC protocols and the crucial building blocks of MPC.For MPC applications,we compare the known PPML protocols built on MPC,and describe the eciency of private inference and training for the state-of-the-art PPML protocols.Further-more,we summarize several challenges and open problems to break though the eciency of MPC protocols as well as some interesting future work that is worth being addressed.This survey aims to provide the recent development and key approaches of MPC to researchers,who are interested in knowing,improving,and applying concretely ecient MPC protocols.
基金supported by the National Key R&D Program of China(2022YFB3102100)Shenzhen Fundamental Research Program(JCYJ20220818102414030)+2 种基金the Major Key Project of PCL(PCL2022A03)Shenzhen Science and Technology Program(ZDSYS20210623091809029)Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies(2022B1212010005).
文摘To solve the data island problem,federated learning(FL)provides a solution paradigm where each client sends the model parameters but not the data to a server for model aggregation.Peer-to-peer(P2P)federated learning further improves the robustness of the system,in which there is no server and each client communicates directly with the other.For secure aggregation,secure multi-party computing(SMPC)protocols have been utilized in peer-to-peer manner.However,the ideal SMPC protocols could fail when some clients drop out.In this paper,we propose a robust peer-to-peer learning(RP2PL)algorithm via SMPC to resist clients dropping out.We improve the segmentbased SMPC protocol by adding a check and designing the generation method of random segments.In RP2PL,each client aggregates their models by the improved robust secure multi-part computation protocol when finishes the local training.Experimental results demonstrate that the RP2PL paradigm can mitigate clients dropping out with no significant degradation in performance.
文摘The rapid adoption of machine learning in sensitive domains,such as healthcare,finance,and government services,has heightened the need for robust,privacy-preserving techniques.Traditional machine learning approaches lack built-in privacy mechanisms,exposing sensitive data to risks,which motivates the development of Privacy-Preserving Machine Learning(PPML)methods.Despite significant advances in PPML,a comprehensive and focused exploration of Secure Multi-Party Computing(SMPC)within this context remains underdeveloped.This review aims to bridge this knowledge gap by systematically analyzing the role of SMPC in PPML,offering a structured overviewof current techniques,challenges,and future directions.Using a semi-systematicmapping studymethodology,this paper surveys recent literature spanning SMPC protocols,PPML frameworks,implementation approaches,threat models,and performance metrics.Emphasis is placed on identifying trends,technical limitations,and comparative strengths of leading SMPC-based methods.Our findings reveal thatwhile SMPCoffers strong cryptographic guarantees for privacy,challenges such as computational overhead,communication costs,and scalability persist.The paper also discusses critical vulnerabilities,practical deployment issues,and variations in protocol efficiency across use cases.
基金supported by the Basque Government through the ELKARTEK program for Research and Innovation,under the BRTAQUANTUM project(Grant Agreement No.KK-2022/00041)。
文摘Privacy-Preserving Computation(PPC)comprises the techniques,schemes and protocols which ensure privacy and confidentiality in the context of secure computation and data analysis.Most of the current PPC techniques rely on the complexity of cryptographic operations,which are expected to be efficiently solved by quantum computers soon.This review explores how PPC can be built on top of quantum computing itself to alleviate these future threats.We analyze quantum proposals for Secure Multi-party Computation,Oblivious Transfer and Homomorphic Encryption from the last decade focusing on their maturity and the challenges they currently face.Our findings show a strong focus on purely theoretical works,but a rise on the experimental consideration of these techniques in the last 5 years.The applicability of these techniques to actual use cases is an underexplored aspect which could lead to the practical assessment of these techniques.
基金This work was supported in part by the National Key Research and Development Project of China(Grant No.2020YFA0712300)in part by the National Natural Science Foundation of China(Grant Nos.62132005,61632012,62172162 and 62072404).
文摘Incorporation of fog computing with low latency,preprocession(e.g.,data aggregation)and location awareness,can facilitate fine-grained collection of smart metering data in smart grid and promotes the sustainability and efficiency of the grid.Recently,much attention has been paid to the research on smart grid,especially in protecting privacy and data aggregation.However,most previous works do not focus on privacy-preserving data aggregation and function computation query on enormous data simultaneously in smart grid based on fog computation.In this paper,we construct a novel verifiable privacy-preserving data collection scheme supporting multi-party computation(MPC),named VPDC-MPC,to achieve both functions simultaneously in smart grid based on fog computing.VPDC-MPC realizes verifiable secret sharing of users’data and data aggregation without revealing individual reports via practical cryptosystem and verifiable secret sharing scheme.Besides,we propose an efficient algorithm for batch verification of share consistency and detection of error reports if the external adversaries modify the SMs’report.Furthermore,VPDC-MPC allows both the control center and users with limited resources to obtain arbitrary arithmetic analysis(not only data aggregation)via secure multi-party computation between cloud servers in smart grid.Besides,VPDC-MPC tolerates fault of cloud servers and resists collusion.We also present security analysis and performance evaluation of our scheme,which indicates that even with tradeoff on computation and communication overhead,VPDC-MPC is practical with above features.
基金supported by the National Key Research and Development Program of China(2020YFB1805405)the 111 Project(B21049)+1 种基金the Foundation of Guizhou Provincial Key Laboratory of Public Big Data(2019BDKFJJ014)the Fundamental Research Funds for the Central Universities(2020RC38)
文摘Universality is an important property in software and hardware design.This paper concentrates on the universality of quantum secure multi-party computation(SMC)protocol.First of all,an in-depth study of universality has been conducted,and then a nearly universal protocol is proposed by using the Greenberger-Horne-Zeilinger(GHZ)-like state and stabilizer formalism.The protocol can resolve the quantum SMC problem which can be deduced as modulo subtraction,and the steps are simple and effective.Secondly,three quantum SMC protocols based on the proposed universal protocol:Quantum private comparison(QPC)protocol,quantum millionaire(QM)protocol,and quantum multi-party summation(QMS)protocol are presented.These protocols are given as examples to explain universality.Thirdly,analyses of the example protocols are shown.Concretely,the correctness,fairness,and efficiency are confirmed.And the proposed universal protocol meets security from the perspective of preventing inside attacks and outside attacks.Finally,the experimental results of the example protocols on the International Business Machines(IBM)quantum platform are consistent with the theoretical results.Our research indicates that our protocol is universal to a certain degree and easy to perform.
文摘The purpose of this review is to explore the intersection of computational engineering and biomedical science,highlighting the transformative potential this convergence holds for innovation in healthcare and medical research.The review covers key topics such as computational modelling,bioinformatics,machine learning in medical diagnostics,and the integration of wearable technology for real-time health monitoring.Major findings indicate that computational models have significantly enhanced the understanding of complex biological systems,while machine learning algorithms have improved the accuracy of disease prediction and diagnosis.The synergy between bioinformatics and computational techniques has led to breakthroughs in personalized medicine,enabling more precise treatment strategies.Additionally,the integration of wearable devices with advanced computational methods has opened new avenues for continuous health monitoring and early disease detection.The review emphasizes the need for interdisciplinary collaboration to further advance this field.Future research should focus on developing more robust and scalable computational models,enhancing data integration techniques,and addressing ethical considerations related to data privacy and security.By fostering innovation at the intersection of these disciplines,the potential to revolutionize healthcare delivery and outcomes becomes increasingly attainable.
基金partially supported by the National Natural Science Foundation of China(NSFC)(Grant Nos.U21A20516,62076017,and 62141605)the Funding of Advanced Innovation Center for Future Blockchain and Privacy Computing(No.ZF226G2201)+1 种基金the Beihang University Basic Research Funding(No.YWF-22-L-531)the Funding(No.22-TQ23-14-ZD-01-001)and WeBank Scholars Program.
文摘Federated learning is a promising learning paradigm that allows collaborative training of models across multiple data owners without sharing their raw datasets.To enhance privacy in federated learning,multi-party computation can be leveraged for secure communication and computation during model training.This survey provides a comprehensive review on how to integrate mainstream multi-party computation techniques into diverse federated learning setups for guaranteed privacy,as well as the corresponding optimization techniques to improve model accuracy and training efficiency.We also pinpoint future directions to deploy federated learning to a wider range of applications.
文摘In software-defined networking(SDN),controllers are sinks of information such as network topology collected from switches.Organizations often like to protect their internal network topology and keep their network policies private.We borrow techniques from secure multi-party computation(SMC)to preserve the privacy of policies of SDN controllers about status of routers.On the other hand,the number of controllers is one of the most important concerns in scalability of SMC application in SDNs.To address this issue,we formulate an optimization problem to minimize the number of SDN controllers while considering their reliability in SMC operations.We use Non-Dominated Sorting Genetic Algorithm II(NSGA-II)to determine the optimal number of controllers,and simulate SMC for typical SDNs with this number of controllers.Simulation results show that applying the SMC technique to preserve the privacy of organization policies causes only a little delay in SDNs,which is completely justifiable by the privacy obtained.
基金supported by the National Key Research and Development Program of China(No.2021YFB2900504).
文摘In 6th Generation Mobile Networks(6G),the Space-Integrated-Ground(SIG)Radio Access Network(RAN)promises seamless coverage and exceptionally high Quality of Service(QoS)for diverse services.However,achieving this necessitates effective management of computation and wireless resources tailored to the requirements of various services.The heterogeneity of computation resources and interference among shared wireless resources pose significant coordination and management challenges.To solve these problems,this work provides an overview of multi-dimensional resource management in 6G SIG RAN,including computation and wireless resource.Firstly it provides with a review of current investigations on computation and wireless resource management and an analysis of existing deficiencies and challenges.Then focusing on the provided challenges,the work proposes an MEC-based computation resource management scheme and a mixed numerology-based wireless resource management scheme.Furthermore,it outlines promising future technologies,including joint model-driven and data-driven resource management technology,and blockchain-based resource management technology within the 6G SIG network.The work also highlights remaining challenges,such as reducing communication costs associated with unstable ground-to-satellite links and overcoming barriers posed by spectrum isolation.Overall,this comprehensive approach aims to pave the way for efficient and effective resource management in future 6G networks.
文摘The Literary Lab at Stanford University is one of the birthplaces of digital humanities and has maintained significant influence in this field over the years.Professor Hui Haifeng has been engaged in research on digital humanities and computational criticism in recent years.During his visiting scholarship at Stanford University,he participated in the activities of the Literary Lab.Taking this opportunity,he interviewed Professor Mark Algee-Hewitt,the director of the Literary Lab,discussing important topics such as the current state and reception of DH(digital humanities)in the English Department,the operations of the Literary Lab,and the landscape of computational criticism.Mark Algee-Hewitt's research focuses on the eighteenth and early nineteenth centuries in England and Germany and seeks to combine literary criticism with digital and quantitative analyses of literary texts.In particular,he is interested in the history of aesthetic theory and the development and transmission of aesthetic and philosophical concepts during the Enlightenment and Romantic periods.He is also interested in the relationship between aesthetic theory and the poetry of the long eighteenth century.Although his primary background is English literature,he also has a degree in computer science.He believes that the influence of digital humanities within the humanities disciplines is growing increasingly significant.This impact is evident in both the attraction and assistance it offers to students,as well as in the new interpretations it brings to traditional literary studies.He argues that the key to effectively integrating digital humanities into the English Department is to focus on literary research questions,exploring how digital tools can raise new questions or provide new insights into traditional research.
基金in part by the National Natural Science Foundation of China(NSFC)under Grant 62371012in part by the Beijing Natural Science Foundation under Grant 4252001.
文摘As an essential element of intelligent trans-port systems,Internet of vehicles(IoV)has brought an immersive user experience recently.Meanwhile,the emergence of mobile edge computing(MEC)has enhanced the computational capability of the vehicle which reduces task processing latency and power con-sumption effectively and meets the quality of service requirements of vehicle users.However,there are still some problems in the MEC-assisted IoV system such as poor connectivity and high cost.Unmanned aerial vehicles(UAVs)equipped with MEC servers have become a promising approach for providing com-munication and computing services to mobile vehi-cles.Hence,in this article,an optimal framework for the UAV-assisted MEC system for IoV to minimize the average system cost is presented.Through joint consideration of computational offloading decisions and computational resource allocation,the optimiza-tion problem of our proposed architecture is presented to reduce system energy consumption and delay.For purpose of tackling this issue,the original non-convex issue is converted into a convex issue and the alternat-ing direction method of multipliers-based distributed optimal scheme is developed.The simulation results illustrate that the presented scheme can enhance the system performance dramatically with regard to other schemes,and the convergence of the proposed scheme is also significant.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2023YJS053)the National Natural Science Foundation of China(Grant No.52278386).
文摘To fundamentally alleviate the excavation chamber clogging during slurry tunnel boring machine(TBM)advancing in hard rock,large-diameter short screw conveyor was adopted to slurry TBM of Qingdao Jiaozhou Bay Second Undersea Tunnel.To evaluate the discharging performance of short screw conveyor in different cases,the full-scale transient slurry-rock two-phase model for a short screw conveyor actively discharging rocks was established using computational fluid dynamics-discrete element method(CFD-DEM)coupling approach.In the fluid domain of coupling model,the sliding mesh technology was utilized to describe the rotations of the atmospheric composite cutterhead and the short screw conveyor.In the particle domain of coupling model,the dynamic particle factories were established to produce rock particles with the rotation of the cutterhead.And the accuracy and reliability of the CFD-DEM simulation results were validated via the field test and model test.Furthermore,a comprehensive parameter analysis was conducted to examine the effects of TBM operating parameters,the geometric design of screw conveyor and the size of rocks on the discharging performance of short screw conveyor.Accordingly,a reasonable rotational speed of screw conveyor was suggested and applied to Jiaozhou Bay Second Undersea Tunnel project.The findings in this paper could provide valuable references for addressing the excavation chamber clogging during ultra-large-diameter slurry TBM tunneling in hard rock for similar future.
基金supported by National Natural Science Foundation of China No.62231012Natural Science Foundation for Outstanding Young Scholars of Heilongjiang Province under Grant YQ2020F001Heilongjiang Province Postdoctoral General Foundation under Grant AUGA4110004923.
文摘Low earth orbit(LEO)satellites with wide coverage can carry the mobile edge computing(MEC)servers with powerful computing capabilities to form the LEO satellite edge computing system,providing computing services for the global ground users.In this paper,the computation offloading problem and resource allocation problem are formulated as a mixed integer nonlinear program(MINLP)problem.This paper proposes a computation offloading algorithm based on deep deterministic policy gradient(DDPG)to obtain the user offloading decisions and user uplink transmission power.This paper uses the convex optimization algorithm based on Lagrange multiplier method to obtain the optimal MEC server resource allocation scheme.In addition,the expression of suboptimal user local CPU cycles is derived by relaxation method.Simulation results show that the proposed algorithm can achieve excellent convergence effect,and the proposed algorithm significantly reduces the system utility values at considerable time cost compared with other algorithms.
