Search

Abstract In the era of cloud service popularization, the trustworthiness of service is particularly important. If users cannot prevent the potential trustworthiness problem of the service during long-term use, once the trustworthiness problem occurs, it will cause significant losses. In order to objectively assess the cloud service trustworthiness, and predict its change, this paper establishes a special hierarchical model of cloud service trustworthiness attributes. This paper proposes corresponding management countermeasures around the model, defines the cloud service trustworthiness level, defines the cloud service trusted state based on fuzzy entropy and Markov chain, constructs the membership function of the cloud service trusted state, and realizes the assessment of cloud service trustworthiness and its changes according to the prediction method of Markov chain. Through case analysis and method comparison, it shows that the method proposed in this paper is effective and feasible. This method can provide objective and comprehensive assessment data for the cloud service trustworthiness and its change, makes up the deficiency of fuzzy entropy assessment method. This research has important reference value and significance for the research of cloud service trustworthiness assessment.

To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than 50 % OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement. [ABSTRACT FROM AUTHOR]

The immense volume of online content linked to digital libraries has given emergence to the advancement of screening and recommendation systems. A recommendation system is vitally important in both academic institutions and elibraries to assist professors, instructors, students, and researchers in finding appropriate sources of information. Distributed or collaborative screening is the most common method used in current recommendation systems. However, collaborative approaches cannot promote library repositories, including unrated or unpurchased electronic information. Thus, this paper deals with the automated classification and recommendation of a multiclass corpus found in virtual repositories (cloud databases). In various stages, Neuro-Fuzzy (NF) and Support Vector Machine (SVM) techniques are used as the base classifiers for the categorization of the essential subjects (contents). Later, a high-level ensemble learning strategy is utilized to recommend appropriate subjects from the available multiclass corpus. The methods use a CoC (Coherence of Content)-based inference mechanism to extract and filter the critical components before beginning the recommendation process. Experiments demonstrated that a recommended approach based on detailed conceptual descriptions instead of a handful of phrases/words might help academic and research communities to find relevant sources. Observing the results over a period of months shows that the suggested method increases user comfort, proving the system's acceptability to users in this way. In addition, compared to previous models, the accuracy in categorizing the requisite subjects is more than 97.16 per cent. [ABSTRACT FROM AUTHOR]

Abstract Water is the most basic need for humans and a source of livelihood for humans. Lack of human awareness to maintain water quality, causing water to become polluted, by both industrial and household waste, impacts on human health and material loss. Thus, it is important to create technology that can monitor water pollution automatically and quickly. This research aims to create a system which utilizes the Internet of Things (IoT) technology that can facilitate quality of water by measuring parameters such as pH and turbidity. The methodology of the system progresses by the usage of a controller which is ATmega328P-AU, pH sensor to measure acidity, turbidity sensor to measure turbidity level, LPWAN LoRa works like a communication of data transmission as well as cloud service, namely Antares, to store data that are sent via Android. Based on the outcomes, the proposed system has achieved a reliable accuracy with percentage error of 99.73% in pH sensor and 99.41% in the turbidity sensor. Also, 2.6 s is the average required time to deliver the results to the cloud service.

To solve the privacy-preserving problem of both the user and the server in indoor positioning, outsourcing part of the calculation to cloud server in the process of using Paillier encryption was considered.The scheme not only protected the privacy of the user and the positioning server, but also avoided excessive computing and communication overhead.The main idea of the scheme was that the fingerprint database in the offline stage was established by the server firstly.The k-anonymity algorithm was combined with Paillier encryption in the online stage by the user, and the encrypted Wi-Fi fingerprints were sent to the positioning server.An aggregation of the received Wi-Fi fingerprints and database fingerprints were performed by the server.Then they were outsourced to the cloud server for decryption and distance calculation by the positioning server.Finally, the positioning result was obtained.Theoretical analysis and experimental results show that the proposed scheme is safe, effective and practical.

Water is the most basic need for humans and a source of livelihood for humans. Lack of human awareness to maintain water quality, causing water to become polluted, by both industrial and household waste, impacts on human health and material loss. Thus, it is important to create technology that can monitor water pollution automatically and quickly. This research aims to create a system which utilizes the Internet of Things (IoT) technology that can facilitate quality of water by measuring parameters such as pH and turbidity. The methodology of the system progresses by the usage of a controller which is ATmega328P-AU, pH sensor to measure acidity, turbidity sensor to measure turbidity level, LPWAN LoRa works like a communication of data transmission as well as cloud service, namely Antares, to store data that are sent via Android. Based on the outcomes, the proposed system has achieved a reliable accuracy with percentage error of 99.73% in pH sensor and 99.41% in the turbidity sensor. Also, 2.6 s is the average required time to deliver the results to the cloud service. [ABSTRACT FROM AUTHOR]

Many cloud services are relying on distributed key-value stores such as ZooKeeper, Cassandra, HBase, etc. However, distributed key-value stores are notoriously difficult to design and implement without any mistakes. Because data consistency is the contract for clients that defines what the correct values to read are for a given history of operations under a specific consistency model, consistency violations can confuse client applications by showing invalid values. As a result, serious consequences such as data loss, data corruption, and unexpected behavior of client applications can occur. Software bugs are one of main reasons why consistency violations may occur. Formal verification techniques may be used to make designs correct and minimize the risks of having bugs in the implementation. However, formal verification is not a panacea due to limitations such as the cost of verification, inability to verify existing implementations, and human errors involved. Implementation-level model checking has been heavily explored by researchers for the past decades to formally verify whether the underlying implementation of distributed systems have bugs or not. Nevertheless, previous proposals are limited because their invariant checking is not versatile enough to check for the wide spectrum of consistency models, from eventual consistency to strong consistency. In this work, consistency oracles are employed for consistency invariant checking that can be used by implementation-level model checkers to formally verify data consistency model implementations of distributed key-value stores. To integrate consistency oracles with implementation-level distributed system model checkers, the partial-order information obtained via API is leveraged to avoid the exhaustive search during consistency invariant checking. Our evaluation results show that, by using the proposed method for consistency invariant checking, our prototype model checker, VConMC, can detect consistency violations caused by several real-world software bugs in a well-known distributed key-value store, ZooKeeper. [ABSTRACT FROM AUTHOR]

To solve the privacy-preserving problem of both the user and the server in indoor positioning, outsourcing part of the calculation to cloud server in the process of using Paillier encryption was considered. The scheme not only protected the privacy of the user and the positioning server, but also avoided excessive computing and communication overhead. The main idea of the scheme was that the fingerprint database in the offline stage was established by the server firstly. The k-anonymity algorithm was combined with Paillier encryption in the online stage by the user, and the encrypted Wi-Fi fingerprints were sent to the positioning server. An aggregation of the received Wi-Fi fingerprints and database fingerprints were performed by the server. Then they were outsourced to the cloud server for decryption and distance calculation by the positioning server. Finally, the positioning result was obtained. Theoretical analysis and experimental results show that the proposed scheme is safe, effective and practical. [ABSTRACT FROM AUTHOR]

This study addresses significant challenges within the field of remote sensing monitoring, including operational inefficiency, data confidentiality concerns, high hardware costs, and issues with data management and distribution. To tackle these problems, we introduce a synergistic remote sensing monitoring framework that leverages a bimodal cloud infrastructure, facilitated by cloud services to provide on-demand resource allocation and efficient management. Our research focuses on designing and developing an integrated operating system that optimizes remote sensing monitoring processes and enhances operational efficiency through the use of a mutual scheduling mechanism and rapid data indexing capabilities. The system is underpinned by a dual-state cloud service mechanism, combining the Memory Cloud (Flash Cloud) known for its high-speed data processing and the Storage Cloud (Persistent Cloud) for long-term data retention. This dual-state approach establishes a multi-level caching system to ensure quick access to frequently requested spatial data. Additionally, a two-tier security system is implemented to safeguard data integrity and confidentiality. Our “YunYao” geographic information service rendering engine, operating on this dual-state cloud platform, demonstrated remarkable performance advantages over mainstream platforms in identical testing environments. Specifically, it outperformed ArcGIS Desktop by over two times, exceeded GeoServer by more than four times, and was over seven times faster than ArcGIS Server in rendering speeds. Experimental and practical applications have shown that our system streamlines routine workflows and enhances work efficiency, making it a critical reference for remote sensing monitoring. Furthermore, a comparative analysis was conducted to quantitatively demonstrate the superior performance of our method in handling large volumes of remote sensing data(Including satellite imagery and UAV imagery). Despite these advancements, the integration of cloud service technology in the field of satellite remote sensing requires further development, particularly regarding the establishment of private clouds and the internal collaborative computing mechanisms within the remote sensing domain. Our research paves the way for future advancements and the eventual full integration of cloud service models into remote sensing monitoring.

To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than 50% OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement.

Personalized quality of service （QoS） prediction is crucial for developing high-quality cloud service system.However, the traditional collaborative filtering method, based on centralized training, presents challenges in protecting user privacy.In order to effectively protect user privacy while obtaining highly accurate prediction effect, a distributed user privacy adjustable personalized QoS prediction model for cloud services （DUPPA） was proposed.The model adopted a “server-multi-user” architecture, in which the server coordinated multiple users, handled multiple users’ requests for uploading model gradients and downloading global model, and maintained global model parameters.To further protect user privacy, a user privacy adjustment strategy was proposed to balance privacy and prediction accuracy by adjusting the initialization proportion of local model parameters and gradient upload proportion.In the local model initialization stage, the user calculated the difference matrix between the local model and the global model, and selected the global model parameters corresponding to the larger elements in the difference matrix to initialize the local model parameters.In the gradient upload stage, the user can select some important gradients to upload to the server to meet the privacy protection requirements of different application scenarios.To evaluate the privacy degree of DUPPA, a data reconstruction attack method was proposed for the distributed matrix factorization model gradient sharing scheme.The experimental results show that when DUPPA sets the gradient upload proportion to 0.1 and the local model parameter initialization proportion to 0.5, the predicted MAE and RMSE are reduced by 1.27% and 0.91%, respectively, compared with the traditional centralized matrix factorization model.Besides, when DUPPA sets the gradient upload proportion to 0.1, the privacy degree is 5 times higher than when the gradient upload proportion is 1.And when DUPPA sets the local model parameter initialization proportion to 0.5, the privacy degree is 3.44 times higher than when the local model parameter initialization proportion is 1.

Abstract Many currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis.

It is difficult for users to understand the complex cloud product information for product selection. Using this information to recommend satisfactory cloud products is a challenge. Previous studies focused on similar information of users and products while neglecting relevance; therefore, they could not create recommendation approaches that account for functional dependencies among cloud products. To overcome this challenge, this study proposes a cloud product set recommendation model based on a hierarchical knowledge graph (KG) with a pre-post correlation of product functionality. There are two main contributions: First, we constructed a cloud product functionality and performance KG using the dependency information of layers and entities to represent complicated pre-post logical connections. The KG was designed according to the cloud service model. Second, we designed an improved PageRank algorithm to obtain the importance weight for each functionality and performance, which replaces the original average method with the proportion of connection weight. We considered the release time of the functionality, launch time of the product, and last update time of the product as crucial factors in the recommendation score to reflect the importance of the functionality and current development stage of the product. Finally, our method recommended a product set based on the weighted scores from the above results. In addition, we constructed a cloud product functionality dataset containing 339 functionalities. The experimental results show that the proposed method can generate a closely related set of products, leading to improved accuracy and higher satisfaction compared to mainstream methods.

In a smart parking system, the license plate recognition service controls the car's entry and exit and plays the core role in the parking lot system. When the Internet is interrupted, the parking lot's business will also be interrupted. Hence, we proposed an Edge-Cloud-Dew architecture for the mobile industry in order to tackle this critical problem. The architecture has an innovative design, including LAN-level deployment, Platform-as-a-Dew Service (PaaDS), the dew version of license plate recognition, and the dew type of machine learning model training. Based on these designs, the architecture presents many benefits, such as: (1) reduced maintenance and deployment issues and increased dew service reliability and sustainability; (2) effective release of the network constraint on cloud computing and increase in the horizontal and vertical scalability of the system; (3) enhancement of dew computing to resolve the heavy computing process problem; and (4) proposal of a dew type of machine learning training mechanism without requiring periodic retraining, but with acceptable accuracy. Finally, business owners can reduce their burdens when introducing machine learning technology. Our research goal is to make parking systems smarter in edge computing through the integration of cloud and dew architecture technology. [ABSTRACT FROM AUTHOR]

Cloud services are distributed from manifold sources with autonomous security procedures. The conventional authentication methods result in asynchronous security towards multi‐resource access and sharing. This provides volatile authentication for sequential service sessions. For alleviating this issue, a Rendezvous Block‐based Authentication Framework (RBAF) is proposed. This framework is backboned with a blockchain paradigm that differentiates authentication based on rendezvous and asynchronous attributes. The modifications in initial and final attributes are observed, intended for training, and supplanted with service‐dependent authentications. In the different sessions, attribute‐based authentication and agreed end‐to‐end security are administered using agreed keys that are valid within the sessions. This key generation is modified using the new session and user attributes based on learning recommendations. The ledger paradigm records the session and its associated attributes for different training instances, based on which service flexibility is ensured. The proposed framework's performance is verified using the metrics service distribution ratio, false rate, session uptime, and service delay. [ABSTRACT FROM AUTHOR]

Cloud Computing is no longer just a fad and announcement. Since the beginning of this decade and up to the present, cloud computing has changed and evolved. Workflows are employed in many different scientific disciplines to coordinate data demanding applications, but their virtualized apps, platforms, compute, and storage must be promptly provisioned, scaled, and released instantly. The vast majority of researchers agree that using scientific workflows as a paradigm to define, arrange, and communicate complex scientific analysis is helpful. Workflow scheduling aims to optimally assign and execute sequence of tasks on various virtual machine instances shared and controlled by the workflow scheduler. Numerous scheduling algorithms have been proposed to tackle difficult problems more quickly than metaheuristic ones due to the NP-complete nature of this problem and its dependence on theproblem size. Energy consumption has emerged as a major issue in the cloud computing environment, in addition to the standard quality of service(QoS) restrictions like time and cost to handle this issue. The goal of this paper is to develop the Dynamic Provisioning Based on Demand (DPBD) algorithm for workflow scheduling. While sequential task completion and task priority are design restrictions, the dual objectives of the cloud service are to minimize makespan and energy usage. Using the non-dominated sorting genetic algorithm (NSGA-II), the multi-objective optimization problem's Pareto optimal solutions are found. Our approach is validated by simulating a complex workflow application. [ABSTRACT FROM AUTHOR]

The use of unified communication; video conferencing, audio conferencing, and instant messaging has skyrocketed during the COVID-19 pandemic. However, security and privacy considerations have often been neglected. This article provides a comprehensive survey of security and privacy in Unified Communication (UC). We systematically analyze security and privacy threats and mitigations in a generic UC scenario. Based on this, we analyze security and privacy features of the major UC market leaders, and we draw conclusions on the overall UC landscape. While confidentiality in communication channels is generally well protected through encryption, other privacy properties are mostly lacking on UC platforms. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Network & Information Security is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A variety of data-based services such as cloud services and big data-based services have emerged in recent times. These services store data and derive the value of the data. The reliability and integrity of the data must be ensured. Unfortunately, attackers have taken valuable data as hostage for money in attacks called ransomware. It is difficult to recover original data from files in systems infected by ransomware because they are encrypted and cannot be accessed without keys. There are cloud services to backup data; however, encrypted files are synchronized with the cloud service. Therefore, the original file cannot be restored even from the cloud when the victim systems are infected. Therefore, in this paper, we propose a method to effectively detect ransomware for cloud services. The proposed method detects infected files by estimating the entropy to synchronize files based on uniformity, one of the characteristics of encrypted files. For the experiment, files containing sensitive user information and system files for system operation were selected. In this study, we detected 100% of the infected files in all file formats, with no false positives or false negatives. We demonstrate that our proposed ransomware detection method was very effective compared to other existing methods. Based on the results of this paper, we expect that this detection method will not synchronize with a cloud server by detecting infected files even if the victim systems are infected with ransomware. In addition, we expect to restore the original files by backing up the files stored on the cloud server. [ABSTRACT FROM AUTHOR]

Many currently existing cloud cost optimisation solutions are aimed at cloud infrastructure providers, and they often deal only with specific types of application services. Unlike infrastructure providers, the providers of cloud applications are often left without a suitable cost optimisation solution, especially concerning the wide range of different application types. This paper presents an approach that aims to provide an optimisation solution for the providers of applications hosted in the cloud environments, applicable at the early phase of a cloud application lifecycle and for a wide range of application services. The focus of this research is the development of the method for identifying optimised service deployment option in available cloud environments based on the model of the service and its context, intending to minimise the operational cost of the cloud service while fulfilling the requirements defined by the service level agreement. A cloud application context metamodel is proposed that includes parameters related to both the application service and the cloud infrastructure relevant for the cost and quality of service. By using the proposed optimisation method, knowledge is gained about the effects of the cloud application context parameters on the service cost and quality of service, which is then used to determine the optimal service deployment option. The service models are validated using cloud applications deployed in laboratory conditions, and the optimisation method is validated using the simulations based on the proposed cloud application context metamodel. The experimental results based on two cloud application services demonstrate the ability of the proposed approach to provide relevant information about the impact of cloud application context parameters on service cost and quality of service and use this information for reducing service operational cost while preserving the acceptable service quality level. The results indicate the applicability and relevance of the proposed approach for cloud applications in the early service lifecycle phase since application providers can gain valuable insights regarding service deployment decision without acquiring extensive datasets for the analysis. [ABSTRACT FROM AUTHOR]

As the traditional power grid has evolved into smart grid， testing of relay protection devices has shifted from manual mode to automatic model. By analyzing the shortcomings of the existing automatic testing systems， the paper proposes a new relay protection testing system based on 5G and image recognition technology to reduce labor cost and improve the testing efficiency of relay protection. The new system adopts a modular design idea and uses 5G network to connect the cloud server， automatic testing platform and mobile phone terminal so that testing solutions can be downloaded on the client， remote and real-time testing monitoring can be achieved， and testing report can be generated fast. In order to quickly obtain the pressure plate status of the protection device， image recognition technology is introduced， and corresponding solutions are proposed for the intelligent generation of test solutions， automatic configuration of virtual terminals and other key technologies. The system has been successfully applied to the acceptance of 500 kV Xiaowei substation and 220 kV Lyuting substation with satisfactory test results.

• SIMANFOR is a Decision Support System that simulates forest management scenarios. • Growth and yield models for different levels are implemented in SIMANFOR. • The SIMANFOR website provides cloud-based service with a clean interface and no installation. • Tree and stand variables for ecosystem services support silviculture selection. Technological progress in the last decades has driven great advances in many fields of knowledge. A wide range of tools and services are now available and constantly evolving to handle vast amounts of available data as well as the increased complexity of real-world case studies and analytical alternatives. Most sectors have embraced new methodologies to provide solutions to their problems, and the forestry sector is no exception. Important steps have been taken to update the forestry sector and introduce new large-scale experimental designs, digital tools and more extensive forestry databases. However, assimilation of this progress by forest managers remains largely pending. The more specialized technical knowledge and computing skills required to use this new generation of tools constitutes a known barrier to uptake. In this work, we present the SIMANFOR cloud-based Decision Support System service for simulating forest management alternatives. Its evolution, internal structure and potential applications are described. A case study was developed to demonstrate simulator performance under diverse management scenarios and highlight the benefits of this tool for forest managers. SIMANFOR cloud services are free and can be accessed at www.simanfor.es. [Display omitted] [ABSTRACT FROM AUTHOR]

Accurate and timely classification of diseases during strawberry planting can help growers deal with them in timely manner, thereby reducing losses. However, the classification of strawberry diseases in real planting environments is facing severe challenges, including complex planting environments, multiple disease categories with small differences, and so on. Although recent mobile vision technology based deep learning has achieved some success in overcoming the above problems, a key problem is how to construct a non-destructive, fast and convenient method to improve the efficiency of strawberry disease identification for the multi-region, multi-space and multi-time classification requirements. We develop and evaluate a rapid, low-cost system for classifying diseases in strawberry cultivation. This involves designing an easy-to-use cloud-based strawberry disease identification system, combined with our novel self-supervised multi-network fusion classification model, which consists of a Location network, a Feedback network and a Classification network to identify the categories of common strawberry diseases. With the help of a novel self-supervision mechanism, the model can effectively identify diseased regions of strawberry disease images without the need for annotations such as bounding boxes. Using accuracy, precision, recall and F1 to evaluate the classification effect, the results of the test set are 92.48, 90.68, 86.32 and 88.45%, respectively. Compared with popular Convolutional Neural Networks (CNN) and five other methods, our network achieves better disease classification effect. Currently, the client (mini program) has been released on the WeChat platform. The mini program has perfect classification effect in the actual test, which verifies the feasibility and effectiveness of the system, and can provide a reference for the intelligent research and application of strawberry disease identification.

A number of targets aimed at promoting digital transformation have been set up within the framework of the 2020-2030 Digital Decade. An abundance of scientific research has shown the importance of digital technologies and the impact they have on the competitiveness of businesses. The objective of this study is to conduct a comparative analysis of the digital technology applications used by European Union enterprises in an effort to identify any potential linkages. This study primarily analyses the use of artificial intelligence (AI), cloud services, and Big Data, as well as the current level of digital intensity and skills. In addition, the research evaluates the extent to which the strategy document 2030 Digital Compass of the European Union, which delineated the goals pertaining to technology applications, has been achieved so far. The purpose of this assessment is to provide guidance for the subsequent seven years of the development stage. The evaluation is conducted with data acquired from the Network Readiness Index (NRI) and the Digital Economy and Society Index (DESI) for the year 2022. [ABSTRACT FROM AUTHOR]

The development of cloud storage makes people willing to upload personal video to the cloud,but the data security problems brought by it have become increasingly prominent,selective encryption is one of the effective ways to protect video privacy.Aiming at the problem of insufficient security in the current H.264/AVC video selective encryption method,a novel H.264/AVC video selective encryption method based on adaptive permutation of macroblock coding information is proposed.The method adaptively generates pseudo-random sequence frame by frame according to the macroblock types,uses the pseudo-random sequence to randomly permute the coded block pattern (CBP) and the residual data in the coding information of a macroblock between macroblocks,changes the intra prediction modes of I macroblocks,and flips the signs of motion vector differences of P macroblocks and B macroblocks.Experimental results show that the proposed method can preserve format compatibility with H.264/AVC coding standard,and has characteristics of large encryption space,good key sensitivity,and small video bitrate variation.Compared with the existing encryption schemes,the proposed method performs better in terms of visual security and resistance to state of the art of sketch attack.

As a pioneer part of the major project of space-integrated-ground information network (SIGN), the typical application services provided by the ground information port (GIP) are the basic starting point and footing point for the implementation of major project to serve our national strategy and economic construction.Based on the basic framework of the ground information port and the idea of cloud service construction, the typical case of the ground information port was explored, providing reference for the development of the related technical services in the ground information port.

Introduction. This publication summarizes the experience of the use of applied containerized software tools in cloud environment, which the authors gained during the project “Development of methods, algorithms and intellectual analytical system for processing and analysis of heterogeneous clinical and biomedical data in order to improve the diagnosis of complex diseases”, accomplished by the team from the United Institute of Informatics Problems of the NAS of Belarus and V.M. Glushkov Institute of Cybernetics of the NAS of Ukraine. In parallel, the features of biomedical data and the main approaches to their processing and classification, implemented within the framework of an intelligent analytical system, and the possibility of their implementation as part of a container application are described. The purpose of the paper is to describe modern technologies that ensure the reproducibility of numerical experiments in this field and the tools aimed to integrate several sources of biomedical information in order to improve the diagnostics and prognosis of complex diseases. Special attention is also paid to the methods of handling data received from different sources of biomedical information. Particular attention is paid to methods of processing data obtained from various sources of biomedical information and included to the intelligent analytical system. Results. The experience of the use of applied containerized biomedical software tools in cloud environment is summarized. The reproducibility of scientific computing in relation with modern technologies of scientific calculations is discussed. The main approaches to biomedical data preprocessing and integration in the framework of the intelligent analytical system are described. The developed hybrid classification model presents the basis of the intelligent analytical system and aims to integrate several sources of biomedical information. Conclusions. The experience of using the developed classification module NonSmoothSVC, which is part of the developed intelligent analytical system, gained during its testing on artificial and real data, allows us to conclude about several advantages provided by the containerized form of the created application. Namely: • It permits to provide access to real data located in cloud environment, • It is possible to perform calculations to solve research problems on cloud resources both with the help of developed tools and with the help of cloud services, • Such a form of research organization makes numerical experiments reproducible, i.e. any other researcher can compare the results of their developments on specific data that have already been studied by others, in order to verify the conclusions and technical feasibility of new results, • There exists a universal opportunity to use the developed tools on technical devices of various classes from a personal computer to powerful cluster. The hybrid classification model as a core of the intelligent system will make it possible to integrate multidimensional, heterogeneous biomedical data with the aim to better understand the molecular courses of disease origin and development, to improve the identification of disease subtypes and disease prognosis.

Several Cloud services may be composed in order to respond quickly to the users’ needs. Unfortunately, when running such a service some faults may occur. The outcome of fault control is a big challenge. In this paper, the authors propose a new approach based back recovery and multi-agent planning methods. The proposed architecture based MAS (Multi-Agent System) is composed of two main types of Agents: a Composition Manager Agent (CMA) and a Supervisor Agent (SA). The role of the CMA is to create a set of plans as an oriented graph where the nodes are the Cloud services and the valued arcs represent the composition order of these services. This agent saves checkpoints (nodes) in a stable memory so that there are at least one possible path. However, the SA ensures that the running plan is working properly; otherwise, it informs the CMA to select another sub-plan. Experimental results show the performance of the proposed approach in term of different metrics such as response time, cost and total cost of different alternatives of cloud services. [ABSTRACT FROM AUTHOR]

To prevent privacy leakage, cloud services need to take corresponding methods, so participants often face the dilemma of service utility and privacy protection. In this paper, we propose a dynamic adaptive access control model based on trust permission and privacy protection to solve the problem of privacy disclosure and utility in the cloud service. Firstly, we add the concept of obligation and purpose into access control and establish the privacy information tree and privacy policy tree. Secondly, we establish a new trust evaluation and give the corresponding weight algorithm. Thirdly, we quantify the privacy information with the normal space and correlation coefficient method. Further, we propose a tradeoff relationship model between trust permission and privacy protection, each participant can select the corresponding parameters according to the actual requirement and personal preference. Experimental analysis and comparison results verify the feasibility, effectiveness, and superiority of our method. Finally, we summarize the work of this paper and point out the future development direction.

In view of the problems existing in the safety monitoring system of China’s mining enterprises,such as poor reliability,small coverage,high cost,weak maintenance and inaccurate early-warning,etc.,a new safety monitoring IoT system with micro power consumption was proposed for Chinese mines to provide effective technical support for the safety of mine production and operation.The key technologies such as micro-power consumption information collection,fault diagnosis and auto-recovery,energy-saving wireless communication,online intelligent data analysis and early warning had been broken through by the proposed system,which had been successfully applied in typical cases such as online monitoring of tailings ponds,open-pit slop and ground pressure of domestic mining enterprises.Field practice shows that the proposed system is more suitable for mine operation conditions than the existing conventional safety monitoring system,the SNR of new sensor is increased by 1.4 times,the comprehensive energy consumption is reduced by 10%,which is suitable for online intelligent analysis and rapid warning.

The article reveals and interprets the key features of project-based learning based on cloud-based services: social activity; convenient communication in a team during the project implementation and at the resulting stage; open educational space; self-learning and self-improvement; use of interdisciplinary links to combine students of different years of study (1--4 degrees of the first (bachelor's) level and students of the second master's level) to joint research teams to study through research; purposeful motivation of cognitive and research activity of students within the discipline with the use of interdisciplinary connections; formation of digital literacy of students. The advantages and disadvantages of the Yammer cloud service are presented and a comparative analysis of this service with similar cloud services is performed. Examples of using Yammer in professional project activity are given. The stages of using project methods using the small group method are analyzed and detailed: initiation; planning; conducting/implementation; presentation; assessment/defense.

Copyright of Journal of Technology & Information Education is the property of Palacky University in Olomouc and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As cloud services become so ubiquitous that they can be used anytime, anywhere, there is an increasing interest in IoT privacy protection. Manufacturers of small and medium-sized businesses that produce IT devices are increasingly using a variety of technologies to easily connect to other devices, with ease and affordability. However, the importance of protection against IoT privacy is growing as devices connected to the Internet do not know what information is transmitted by third parties. In this paper, we propose an IoT privacy protection technique in which users' privacy-related elements are classified into block chains and non-block chains so that the third parties do not use the information used in the cloud environment maliciously, so that users' privacy can be handled probabilistically by grouping them into service chains. The proposed model uses user privacy information as a block chain to handle identity attributes and access control policies so that IoT privacy information is not exposed to third parties. This process can be used to protect users' privacy, regardless of the size or purpose of the cloud environment. In the proposed technique, IoT privacy was grouped into hierarchical multilevel forms, while efficiency was improved by an average of 11.1% using computational-intensive cryptographic policies. In addition, because the proposed technique has improved the accessibility of IoT privacy information over the existing one, it has reduced the overhead of cloud servers by more than 17.2%. [ABSTRACT FROM AUTHOR]

Copyright of Space-Integrated-Ground Information Networks is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Featured Application: This article proposes a semantic web framework and application able to perform semantic analysis to create a common understanding about objects across varied robots, enabling robots to learn from each other's experience. Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots' object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots. [ABSTRACT FROM AUTHOR]

Objective: The emergence of the Quid 19 global epidemic crisis has increasingly increased the users of e-learning systems. One of the important challenges of these systems is to provide the appropriate hardware and software infrastructure to respond to its growing users and maintain efficiency. The advent of cloud computing has revolutionized the way data storage and access resources are accessed. Easy access through standard network communication mechanisms and automatic resource allocation has made cloud computing an ideal infrastructure for e-learning systems. However, due to the multiplicity of cloud suppliers and the diversity of their services, as well as the various quality of these services components, the issue of choosing infrastructure as a service is a serious challenge for educational institutions. The purpose of this study is to design a conceptual framework for selecting the appropriate cloud service for e-learning systems. Materials and Methods: The present study has tried to follow a mixed approach and use the methods of systematic literature review, fuzzy Delphi and prioritization of the fuzzy best worst in the form of an exploratory plan, a coherent and comprehensive view of "factors influencing cloud service selection for e-learning" Provide. In the first step, using the method of SLR and meta-combination of findings of 105 scientific sources in the field of cloud service selection, the concepts and categories of the initial framework were extracted. The initial research framework has been used as the input of the fuzzy Delphi study and based on the opinions of experts, the initial framework has been developed, refined and approved. In the final step, with the help of the fuzzy best worst method, the weight of each of the specific dimensions and elements and the conceptual framework of the research have been compiled and explained. Result and Discussion: The framework for selecting the appropriate cloud service for e-learning systems consists of 259 conceptual elements organized in 5 dimensions and 27 components. Functional dimensions (7 components) 34.8%, security (6 components) 22.1%, organizational and environmental (7 components) 20.3%, personal data protection (5 components) 16.1% and data management (3 components) 6.7%, respectively Cloud services are effective. The results show that in choosing a cloud service, respectively, the components of availability, reliability, governance, virtual machine hardware features (such as throughput, memory, etc.), total cost (sum of rent cost, ISP cost and data transfer cost) and the geographical location of the data storage are most important. [ABSTRACT FROM AUTHOR]

Analytics firm Cyence estimated Amazon's four-hour cloud computing outage in 2017 “cost S&P 500 companies at least $ \$ $150 million” and traffic monitoring firm Apica claimed “54 of the top 100 online retailers saw site performance slump by at least 20 percent”. According to Ponemon, 2015 data center outages cost Fortune 1000 companies between $ \$ $1.25 and $ \$ $2.5 billion. Despite potential risks, the cloud computing industry continues to grow. For example, Internet of Things, which is projected to grow 266% between 2013 and 2020, will drive increased demand on cloud computing as data across multiple industries is collected and sent back to cloud data centers for processing. RightScale estimates enterprises will continue to increase cloud demand with 85% having multi-cloud strategies. This growth and dependency will influence risk exposure and potential for impact (e.g. availability, performance, security, financial). The research in this paper and proposed solution calculates cloud service provider (CSP) trustworthiness levels and predicts cloud service and cloud service level agreement (SLA) availability performance. Evolving industry standards (e.g. NIST, ISO/IEC) for cloud SLAs and existing work regarding CSP trustworthiness will be leveraged as regression-based predictive models are constructed to analyze CSP cloud computing services, SLA performance and CSP trustworthiness.

More engaging and accessible solutions that offer outstanding user experience are needed. The virtual desktop infrastructure (VDI) is the first user access part of the cloud. Therefore, the performance of the VDI has a significant influence on the network. The graphics processing unit (GPU) can accelerate performance by assigning each virtual machine a dedicated GPU for performance-boosting to improve the user experience. In this paper, we propose a high-performance VDI using an integration of a GPU on OpenStack with a PCI pass-through. It might have certain benefits over using virtualized hardware, such as lower latency, better performance, and more functionality. The evaluation and comparison of the computer processing unit (CPU) and GPU are presented among the different benchmark tools.

The greenhouse vegetable industry play an important strategic role in the adjustment of agricultural transformation mode and the reform of supply side in Heilongjiang Province. Facility horticulture in Heilongjiang Province develops rapidly in recent years, technical support is in great demand, but the experts' technology support for facility horticulture is far from enough. Experts' on-site guidance costs much time and money in the countryside, while the service efficiency is very low. To solve this urgent problem, the architecture of "greenhouse vegetable intelligent terminal system based on cloud service" and the key technologies of implementation (low-cost IoT, distributed real-time operating architecture, virtual expert service, neural network image recognition and mobile terminal service) were put forward. Based on expert services, supplemented by data mining technology, IoT devices were used as expert's remote perception means, smart phones as user terminals, cloud service for integrating knowledge, resources and Internet of Things data to provide vegetable experts and greenhouse vegetable users with high information acquisition, storage, analysis,decision-making capabilities and effective solutions. Experts could view vegetable production status in greenhouses remotely through the Internet, get image and growth environment data, then provide remote guidance to vegetable farmers through the system, expert knowledge would be stored, mined and reused by the system. The Internet of Things system could automatically send out early warning information by judging the air temperature, humidity, illumination intensity and soil moisture in greenhouse. The application of knowledge map and neural network technology would reduce the workload of experts and increase concurrent processing capability of services at the same time. At present, part of this research has been applied in different user groups such as agricultural research departments, enterprises, vegetable cooperatives and farmers in Heilongjiang Province. The system can provide experts with remote inquiry means of greenhouse vegetable production environment, and has the characteristics of simple deployment and low cost. It is suitable for various greenhouse vegetable scenarios, including fruit and edible fungi. In order to popularize this technology in greenhouse vegetable production in China, and achieve an efficient experts' technical support, this research also proposed technical solutions of a large-scale application scenario through cloud computing in future.

Author’s methodology of forming modeling skills involves 4 steps: Step 1 – the teacher step by step constructs the curve by means of cloud based service GeoGebra; Step 2 – the teacher offers a description-definition of the curve and provides a ready-made algorithm by which students model the curve independently in GeoGebra; Step 3 – the teacher offers an algorithm for constructing a curve model, and students need to characterize the properties of the curve or give its definition based on the results, Step 4 – students are offered definitions of curves that they have to model in GeoGebra). An example of realization of the author’s methodology is given, the pedagogical experiment on proof of its effectiveness is described.

Electronic scales and cash registers play a universal role in transactions at the checkout systems of businesses. To reduce the number of errors occurring at the checkouts of buffet restaurants using electronic scales, a novel electronic scale measuring system is designed and investigated in this study. This designed checkout system was constructed using a Tibbo embedded chip in the cash register management system with a cloud connection. This proposed system is different from the point of sale (POS) system: it can obtain all the information of sale items at the same time, and it has the advantages of a low cost and simple operation. Furthermore, the system can transfer each trading record to be saved in a remote server through a cloud transfer service, and restaurants using this system can construct an effective accounting management mechanism. A weighed quantity is transformed into a digital quantity and sent to the cloud system by the embedded system. As compared with traditional, expensive, and large POS systems, the proposed system has higher measuring accuracy and is less affected by staff errors. The low-cost cash register management system with a cloud connection will be a competitive digital product for future commercial business activities. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has dominated the digital world by enabling thousands of physical devices to communicate with each other and share useful data over the Internet. With use cases spanning every sphere of modern life, the IoT is all set to revolutionize the traveling sector by extending services associated with safety, security, surveillance, tracking, comfort, and a lot more. This paper presents the design and implementation of a prototype Easy-Weigh-Out, based on the principles of the IoT which enables a user to run Android-based mobile application and view the weight of luggage and track its location without any contact with it. The hardware of the system is built around Arduino UNO microcontroller which interfaces with a load cell (20 kg) via A/D converter and a NEO-6M Global Positioning System (GPS) module for tracking of the bag. The system uploads data to a Cloud server using ESP8266 Wi-Fi module. Our results show the real-time weight and tracking of the bag at a click of a button as well as long-term data logging. [ABSTRACT FROM AUTHOR]

In order to achieve universal and personalized cloud service choices in a social network environment, we propose a cloud service selection method based on trust and user preference clustering. The method performs a comprehensive trust evaluation, which is used to evaluate and select cloud services. Meanwhile, we propose an improved condensed hierarchical clustering method based on user preference similarity to further improve the accuracy of recommendation trust. A cloud model-based approach is used to measure similarities between users, and then a hierarchical clustering method is used to divide users into different domains according to user similarity. The final recommendation trust will be obtained, which includes the intra-domain recommendation trust and the extra-domain recommendation trust. The comprehensive trust of cloud services, which consists of direct trust and recommended trust. Simulation experiments verify the accuracy and superiority of the clustering algorithm. Experimental results show that the cloud service selection method improves the transaction success rate and enables users to select more satisfactory cloud services.

Nowadays, mobile applications (Apps) have become a main form of mobile Internet services, and related applications in the geological disaster monitoring domain must follow this development trend. In this study, an innovative remote and intelligent landslide monitoring system was designed and developed, which can capture the in-depth sliding force state of the slope in real time. When it reaches the early warning threshold, the system immediately transmits the warning information to user terminals and warns users to initiate corresponding risk-avoidance plans. Next, using the developed system, an App of early warning information publishing program was developed to transmit the acquired sliding-force data by field monitoring devices to servers via Beidou Satellite or GPRS base station. The App can inquire background servers via WiFi or 4G for acquiring the monitoring data and curves of the side slope. Finally, the developed system was applied for the monitoring of the sliding mass in Zhoujiawan, Badong County, the Three Gorges Reservoir Region. The monitoring personnel could locate and inspect the failure characteristics of the deformation region in a timely manner using the developed App. The App data exhibited significant correlation and consistency with the monitored results, thus enhancing the inspection efficiency and allowing an effective emergency response.

Agricultural machinery rental is a new service form that uses big data in agriculture to improve the utilization rate of agricultural machinery and promote the development of the agricultural economy. To realize agricultural machinery scheduling optimization in cloud services, a dynamic artificial bee-ant colony algorithm (DABAA) is proposed to solve the above problem. First, to improve the practicability of the mathematical model in agricultural production, a dynamic coefficient is proposed. Then the mutation operation is combined with the artificial bee colony (ABC) algorithm to improve the algorithm. Then, iterative threshold adjustment and optimal fusion point evaluation are used to combine the ABC algorithm with the ant colony optimization (ACO) algorithm, which not only improves the search precision but also improves the running speed. Finally, two groups of comparison experiments are carried out, and the results show that the DABAA can obviously improve the running speed and accuracy of cloud services in agricultural machinery rental.

The principal aim of research is to develop a mechanism for the organization of research work of students toward mastery of ICT literacy. In the article it has been formed the model of students’ research work organization in the learning process of informatics disciplines; produced the algorithm of cloud-based learning technologies application in the organization of history students’ research work; described recommendations for the structure and the content of e-learning materials in the study of cloud-based learning technologies (as a part of the training course in LMS Moodle). The several recommendations can be made on the basis of performed experience analysis of implementing a cloud oriented technologies of Informatics disciplines among the different specialties. It is advisable to use the model-table "Using a cloud oriented technologies of teaching", with appropriate adaptation to scientific-pedagogical and logistical capacity of a particular educational institution (specific structural unit or Department). The introduction of the designated model promotes: productive activity of students; autonomy of learning and the maximum approximation of the accumulated experience to the requirements of the labour market; the formation of an active research position of the project participants; the formation of students ‘ critical thinking by phased activation of the operations of thinking. Using the model need to be creative in overcoming some negative aspects: to develop the motivational aspect of attracting students to research activities; to form the definition of future student’s research activities; to maximize the interaction of science with the educational process and with the requirements of the labour market; to overcome the stereotype that not all students should be involved in scientific work.