Quantum circuit-based proxy blind signatures:A novel approach and experimental evaluation on the IBM quantum cloud platform

This paper presents a novel approach to proxy blind signatures in the realm of quantum circuits,aiming to enhance security while safeguarding sensitive information.The main objective of this research is to introduce a quantum proxy blind signature(QPBS)protocol that utilizes quantum logical gates and quantum measurement techniques.The QPBS protocol is constructed by the initial phase,proximal blinding message phase,remote authorization and signature phase,remote validation,and de-blinding phase.This innovative design ensures a secure mechanism for signing documents without revealing the content to the proxy signer,providing practical security authentication in a quantum environment under the assumption that the CNOT gates are securely implemented.Unlike existing approaches,our proposed QPBS protocol eliminates the need for quantum entanglement preparation,thus simplifying the implementation process.To assess the effectiveness and robustness of the QPBS protocol,we conduct comprehensive simulation studies in both ideal and noisy quantum environments on the IBM quantum cloud platform.The results demonstrate the superior performance of the QPBS algorithm,highlighting its resilience against repudiation and forgeability,which are key security concerns in the realm of proxy blind signatures.Furthermore,we have established authentic security thresholds(82.102%)in the presence of real noise,thereby emphasizing the practicality of our proposed solution.

Research on Network Security Level Protection Measurement Strategy in the Context of Cloud Platforms

Platforms facilitate information exchange,streamline resources,and reduce production and management costs for companies.However,some viral information may invade and steal company resources,or lead to information leakage.For this reason,this paper discusses the standards for cybersecurity protection,examines the current state of cybersecurity management and the risks faced by cloud platforms,expands the time and space for training on cloud platforms,and provides recommendations for measuring the level of cybersecurity protection within cloud platforms in order to build a solid foundation for them.

Quafu-Qcover:Explore combinatorial optimization problems on cloud-based quantum computers

We introduce Quafu-Qcover,an open-source cloud-based software package developed for solving combinatorial optimization problems using quantum simulators and hardware backends.Quafu-Qcover provides a standardized and comprehensive workflow that utilizes the quantum approximate optimization algorithm(QAOA).It facilitates the automatic conversion of the original problem into a quadratic unconstrained binary optimization(QUBO)model and its corresponding Ising model,which can be subsequently transformed into a weight graph.The core of Qcover relies on a graph decomposition-based classical algorithm,which efficiently derives the optimal parameters for the shallow QAOA circuit.Quafu-Qcover incorporates a dedicated compiler capable of translating QAOA circuits into physical quantum circuits that can be executed on Quafu cloud quantum computers.Compared to a general-purpose compiler,our compiler demonstrates the ability to generate shorter circuit depths,while also exhibiting superior speed performance.Additionally,the Qcover compiler has the capability to dynamically create a library of qubits coupling substructures in real-time,utilizing the most recent calibration data from the superconducting quantum devices.This ensures that computational tasks can be assigned to connected physical qubits with the highest fidelity.The Quafu-Qcover allows us to retrieve quantum computing sampling results using a task ID at any time,enabling asynchronous processing.Moreover,it incorporates modules for results preprocessing and visualization,facilitating an intuitive display of solutions for combinatorial optimization problems.We hope that Quafu-Qcover can serve as an instructive illustration for how to explore application problems on the Quafu cloud quantum computers.

Quafu-RL:The cloud quantum computers based quantum reinforcement learning

With the rapid advancement of quantum computing,hybrid quantum–classical machine learning has shown numerous potential applications at the current stage,with expectations of being achievable in the noisy intermediate-scale quantum(NISQ)era.Quantum reinforcement learning,as an indispensable study,has recently demonstrated its ability to solve standard benchmark environments with formally provable theoretical advantages over classical counterparts.However,despite the progress of quantum processors and the emergence of quantum computing clouds,implementing quantum reinforcement learning algorithms utilizing parameterized quantum circuits(PQCs)on NISQ devices remains infrequent.In this work,we take the first step towards executing benchmark quantum reinforcement problems on real devices equipped with at most 136 qubits on the BAQIS Quafu quantum computing cloud.The experimental results demonstrate that the policy agents can successfully accomplish objectives under modified conditions in both the training and inference phases.Moreover,we design hardware-efficient PQC architectures in the quantum model using a multi-objective evolutionary algorithm and develop a learning algorithm that is adaptable to quantum devices.We hope that the Quafu-RL can be a guiding example to show how to realize machine learning tasks by taking advantage of quantum computers on the quantum cloud platform.

Research on Construction Scheme of Cloud Platform for Core Network Equipment of Railway 5G Private Network

This paper studies and analyzes the rigorous requirements of railway 5G private network core network(5GC)equipment based on network function virtualization(NFV)technology in terms of reliability,security,latency and other aspects of communication cloud,compares cloud platform schemes with different decoupling modes,and proposes that railway 5GC should be implemented by software and hardware integration scheme or software and hardware two-layer decoupling scheme.At the same time,the redundancy and disaster recovery schemes and measures that can be taken by 5GC based on cloud platform are proposed.Finally,taking the products of ZTE Corporation as an example,the implementation architecture of railway 5GC cloud platform in 1+1 redundancy mode is given.It serves as a reference for the engineering construction of 5G-R core network.

Saviynt Meets GCP: A Deep Dive into Integrated IAM for Modern Cloud Security

The Google Cloud Platform (GCP) is a popular choice for companies seeking a comprehensive cloud computing solution because it provides everything from essential computing resources to powerful data analytics and machine learning capabilities. Saviynt is a cloud-based Identity and Access Management (IAM) system that integrates with Google Cloud Platform (GCP) and other services for additional functionality. However, other problems are associated with the transition, such as the requirement to correctly integrate IAM Saviynt into current IT infrastructures and provide comprehensive training to users on the new system. The paper will give a detailed review of the advantages, disadvantages, and best practices related to this transition.

基于SpringCloud框架微服务架构的融合媒体云平台设计

设计基于SpringCloud框架微服务架构的融合媒体云平台,提高媒体业务流程效率。对SpringCloud的基本情况与相关组件进行概述,提出融合媒体云平台设计思路与原则,以此为基础构建有三层服务层的融合媒体云平台,设计融合媒体云平台的安全,基于“一次采集、多种生成、多元传播”的融合媒体云平台设计思路,最终构成融合媒体云平台的业务流程。

Design and Implementation of Cloud Platform for Intelligent Logistics in the Trend of Intellectualization

Intellectualization has become a new trend for telecom industry, driven by intelligent technology including cloud computing, big data, and Internet of things. In order to satisfy the service demand of intelligent logistics, this paper designed an intelligent logistics platform containing the main applications such as e-commerce, self-service transceiver, big data analysis, path location and distribution optimization. The intelligent logistics service platform has been built based on cloud computing to collect, store and handling multi-source heterogeneous mass data from sensors, RFID electronic tag, vehicle terminals and APP, so that the open-access cloud services including distribution, positioning, navigation, scheduling and other data services can be provided for the logistics distribution applications. And then the architecture of intelligent logistics cloud platform containing software layer(SaaS), platform layer(PaaS) and infrastructure(IaaS) has been constructed accordance with the core technology relative high concurrent processing technique, heterogeneous terminal data access, encapsulation and data mining. Therefore, intelligent logistics cloud platform can be carried out by the service mode for implementation to accelerate the construction of the symbiotic win-winlogistics ecological system and the benign development of the ICT industry in the trend of intellectualization in China.

Distributed energy storage node controller and control strategy based on energy storage cloud platform architecture

Based on the energy storage cloud platform architecture,this study considers the extensive configuration of energy storage devices and the future large-scale application of electric vehicles at the customer side to build a new mode of smart power consumption with a flexible interaction,smooth the peak/valley difference of the load side power,and improve energy efficiency.A plug and play device for customer-side energy storage and an internet-based energy storage cloud platform are developed herein to build a new intelligent power consumption mode with a flexible interaction suitable for ordinary customers.Based on the load perception of the power grid,this study aims to investigate the operating state and service life of distributed energy storage devices.By selecting an integrated optimal control scheme,this study designs a kind of energy optimization and deployment strategy for stratified partition to reduce the operating cost of the energy storage device on the client side.The effectiveness of the system and the control strategy is verified through the Suzhou client-side distributed energy storage demonstration project.

The Construction of Human Resource Management Cloud Service Platform

Human resource management service faced the challenges of promoting efficiency, costs saving, quick responding and so on. In order to face these challenges, this paper puts forward a “6 + 1” structure of human resource management service combined with some characteristics of cloud computing, elaborates the service mode of the cloud service platform based on this structure, the characteristics and challenges of the platform, and hopes to provide a new service perspective to human resource management.

An Efficient Adaptive Failure Detection Mechanism for Cloud Platform Based on Volterra Series

Failure detection module is one of important components in fault-tolerant distributed systems,especially cloud platform.However,to achieve fast and accurate detection of failure becomes more and more difficult especially when network and other resources' status keep changing.This study presented an efficient adaptive failure detection mechanism based on volterra series,which can use a small amount of data for predicting.The mechanism uses a volterra filter for time series prediction and a decision tree for decision making.Major contributions are applying volterra filter in cloud failure prediction,and introducing a user factor for different QoS requirements in different modules and levels of IaaS.Detailed implementation is proposed,and an evaluation is performed in Beijing and Guangzhou experiment environment.

Exploring the Innovative Teaching Mode of Geomatics Based on the Teaching Cloud Platform

Geomatics is an interdisciplinary subject.Many disciplines have teaching demands in this field.A new course on“Geomatics Technology”has been suggested by the Weiyang College of Tsinghua University of China for the major of“Mathematical and Scientific Basic Science+Civil,Hydraulic and Marine Engineering”.This paper offers a data-led geomatics teaching mode,developing a customized teaching cloud platform,to explore the cross-integrated innovative teaching methods.Teachers and students can assign and submit assignments on this platform.The platform constitutes a data flow with the data download,data processing and result sharing.It encourages communication among students in various majors,grades and units using data as the medium,from data processing to application upstream and downstream.In the“Geomatics Technology”course,geospatial data has emerged as a vital element of the multidisciplinary approach.This kind of teaching mode has been used in the postgraduate remote sensing course offered by Tsinghua University’s Department of Civil Engineering and Construction Management.Furthermore,the mode will be used for the first time in the autumn semester of 2022 in the undergraduate teaching of Weiyang College and civil engineering,to offer a novel idea for the reform of courses linked to geospatial informatics.

Education Technology Cloud Platform Framework Establishment and Security

With more educational business absorbed into information management system at universities, traditional information management platform seems unable to provide efficient service for teaching and research. Some universities then resort to cloud computing platform. In view of the problems existing in the traditional information platform, this study presented an information management framework designed with cloud technology, and introduced the security techniques for its protection.

Fine-Grained Binary Analysis Method for Privacy Leakage Detection on the Cloud Platform

Nowadays cloud architecture is widely applied on the internet.New malware aiming at the privacy data stealing or crypto currency mining is threatening the security of cloud platforms.In view of the problems with existing application behavior monitoring methods such as coarse-grained analysis,high performance overhead and lack of applicability,this paper proposes a new fine-grained binary program monitoring and analysis method based on multiple system level components,which is used to detect the possible privacy leakage of applications installed on cloud platforms.It can be used online in cloud platform environments for fine-grained automated analysis of target programs,ensuring the stability and continuity of program execution.We combine the external interception and internal instrumentation and design a variety of optimization schemes to further reduce the impact of fine-grained analysis on the performance of target programs,enabling it to be employed in actual environments.The experimental results show that the proposed method is feasible and can achieve the acceptable analysis performance while consuming a small amount of system resources.The optimization schemes can go beyond traditional dynamic instrumentation methods with better analytical performance and can be more applicable to online analysis on cloud platforms.

Comprehensive Online Management Based on Cloud Platform for Breast Cancer Patients Using Dual-Targeted Therapy with Macromolecular Monoclonal Antibodies

Objective: The purpose of the study was to explore the application effects of the cloud platform-based comprehensive online management for breast cancer patients using dual-targeted therapy with macromolecular monoclonal antibodies. Methods: 120 breast cancer patients treated by dual-targeted therapy with macromolecular monoclonal antibodies were managed by a cloud platform from March to November 2019. Comprehensive online management included consultation about drugs and side effects and frequently asked questions in the dual-targeted therapy with macromolecular monoclonal antibodies. Results: In the consultation about drugs and side effects, there were five patients with fever, neutrophil, cough, and fatigue;24 with diarrhea;25 with nausea;11 with oral mucosal inflammation;10 with rashes and dry skin;8 with insomnia;and 1 with palpitation. Moreover, 110 patients with anxiety about the missed or delayed treatment were properly handled. Conclusion: The comprehensive online management of dual-targeted therapy with macromolecular monoclonal antibodies based on the cloud platform is helpful to satisfy the at-home breast cancer patients’ needs, ensure the continuity of dual-targeted therapy with macromolecular monoclonal antibodies for breast cancer patients, prevent misinformation, alleviate patients’ negative psychological emotions, and reduce patients’ economic losses. The online cloud platform integrated management model is crucial for managing patients with breast cancer treated by dual-targeted therapy.

Medication Regularity of Ye Tianshi's Prescription in Treatment of Tinnitus Based on Ancient and Modern Consilia Cloud Platform

[Objectives]Through data mining and analysis,the medication characters and ideas of ye Tianshi,a famous physician in Qing Dynasty,in the treatment of tinnitus were explored,so as to provide reference for clinical tinnitus treatment.[Methods]The medical records of Ye Tianshi's treatment of tinnitus in the Ancient and Modern Consilia Cloud Platform were retrieved and sorted,and the use frequency,properties,metabolic organ and compatibility of related Chinese herbs in the cases were analyzed through system-integrated statistical analysis,cluster analysis,association rules analysis and complex network analysis.[Results]A total of 46 medical cases were finally included,with 15 frequently used Chinese herbs,including poria with hostwood,Radix Rehmanniae Preparata,magnetitum,etc.Most of the drugs are cold and neutral in nature,sweet and bitter in taste,and metabolized in liver and heart.The results of cluster analysis show that the therapeutic drugs for tinnitus are mainly divided into four categories:strengthening spleen,nourishing yin,suppressing symptoms,and clearing heat.The results of association rules analysis show that the commonly used traditional Chinese medicine combination is composed of three herbs,poria with hostwood,Radix Ophiopogonis,and Radix et Rhizoma Glycyrrhizae,which have spleen-strengthening and stomach-tonifying effects.The results of complex network analysis show that the core prescription is based on Siwu Decoction.[Conclusions]In the treatment of tinnitus,Ye Tianshi paid attention to the three internal organs of heart,kidney and gallbladder,and made good use of lung qi-dispersing drugs to clear heat,sweet and cold drugs to tonify stomach and nourish yin,and tonic drugs to suppress symptoms.The core prescription is mainly composed of Radix Rehmanniae,Radix Rehmanniae Preparata,oyster,poria with hostwood,Fructus Forsythiae,Radix Paeoniae Alba,antelope horn,magnetitum and Radix Asparagi.The commonly used single herbs are poria with hostwood,Radix Rehmanniae Preparata and magnetitum.The combination of poria with hostwood,Radix Ophiopogonis and Radix et Rhizoma Glycyrrhizae is more common.

ERP Virtual Simulation Experiment Teaching Platform based on UFIDA Cloud Computing

Economic Management Professional Academic Education are increasingly becoming personalization,intelligence and application.Colleges and universities should actively use cloud computing and big data.Also Internet of Things and other advanced information technologies to build an economics and management ERP virtual simulation experiment teaching platform.Cloud computing and big data,virtual simulation experiment teaching resources with"resource library+project library+enterprise management simulation sandbox training"as the core can build an online and offline collaborative and practical experiment teaching platform.It is expected to achieve the ideal effect of integration of three spaces.Such as physics and resources and social digital teaching.Moreover,it can also benefit human-computer collaboration and interactive teaching and inquiry learning.

Research on Cloud Platform Teaching Mode--Take Literary Psychology as an example

With the rapid development of information technology,the impact of the internet on education reformation is growing.Various courses in colleges and universities have gradually applied the combination of internet platform and course teaching to explain knowledge for students.In this context,literary psychology should also introduce information technology,and keep pace with the times,actively carry out practical teaching.

IoT data analytic algorithms on edge-cloud infrastructure:A review

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime services.However,it is constrained by limited data storage and processing power.It offloads its massive data stream to edge devices and the cloud for adequate storage and processing.This further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data streams.This paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load balancing.The review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load balancing.In addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been reviewed.The classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.