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School enterprise cooperation is of great strategic significance to China's manufacturing industry. The Party Central Committee has repeatedly proposed to strengthen the integration of industry and education and deepen the cooperation between schools and enterprises. In recent years, experts and scholars at home and abroad have been from different perspectives, although they have achieved fruitful results, there are also problems such as insufficient attention to theoretical research, relatively backward research, narrow research scope, and so on. In view of this phenomenon, this paper will study a new school-enterprise cooperation mechanism based on the improved decision tree algorithm. The research of this paper is divided into three parts. First, after analyzing the advantages and disadvantages of the algorithm, the algorithm of the decision tree is improved, which makes the improved algorithm more suitable for the field of school-enterprise cooperation. Then, based on cloud computing and intelligence, this paper establishes a new model of school-enterprise cooperation platform, which solves some problems of data management and information exchange in school-enterprise cooperation. Finally, in order to make the cooperation mechanism of this paper better used in practice, this paper builds an online and offline hybrid training base, hoping to make the cooperation between schools and enterprises closer through the training base. In order to test the effect of the cooperation model, this paper takes school as the experimental model. After investigation and research, it is believed that thanks to the school-enterprise cooperation mechanism in this paper, the cooperative enterprise of school has been greatly improved in the past three years, and the willingness of enterprises to cooperate has become more and more strong. No matter students, teachers, or enterprises are reaping huge benefits under this cooperation mechanism, it is a suit for extensive promotion The school-enterprise cooperation mechanism of Guangyun. [ABSTRACT FROM AUTHOR]

As there is a continuous delivery of big data, the researchers are showing interest in the applications of cloud computing concerning privacy, and security. On the other hand, many researchers and experts of cybersecurity have commenced on a quest for improving the data encryption to the models of big data and applications of cloud computing. Since many users of the cloud become public cloud services, confidentiality turns out to be a more compound problem. To solve the confidentiality problem, cloud clients maintain the data on the public cloud. Under this circumstance, Homomorphic Encryption (HE) appears as a probable solution, in which the information of the client is encrypted on the cloud in such a process that it permits few manipulation operations without decryption. The main intent of this paper is to present the systematic review of research papers published in the field of Fully Homomorphic Encryption (FHE) over the past 10 years. The encryption scheme is considered full when it consists of plaintext, a ciphertext, a keyspace, an encryption algorithm, and a decryption algorithm. Hence, the review mostly concentrates on reviewing more powerful and recent FHE. The contributions using different algorithms in FHE like Lattice-based, integer-based, Learning With Errors (LWE), Ring Learning With Errors (RLWE), and Nth degree Truncated polynomial Ring Units (NTRU) are also discussed. Finally, it highlights the challenges and gaps to be addressed in modeling and learning about competent, effectual, and vigorous FHE for the cloud sector and pays attention to directions for better future research. [ABSTRACT FROM AUTHOR]

To solve the problem of choosing the appropriate cloud computing vendors in small and medium-sized enterprises (SMEs), this paper boils it down to a group decision making (GDM) problem. To facilitate the judgment, this paper uses preference relation as the decision making technology. Considering the situation where uncertain positive and negative judgments exist simultaneously, interval-valued intuitionistic fuzzy preference relations (IVIFPRs) are employed to express the decision makers' judgments. In view of the multiplicative consistency and consensus analysis, a new GDM algorithm with IVIFPRs is offered. To accomplish this goal, a new multiplicative consistency is first defined, which can avoid the limitations of the previous ones. Then, a programming model is built to check the consistency of IVIFPRs. To deal with incomplete IVIFPRs, two programming models are constructed to determine the missing values with the goal of maximizing the level of multiplicative consistency and minimizing the total uncertainty. To achieve the minimum adjustment of original preference information, a programming model is established to repair inconsistent IVIFPRs. In addition, programming models for getting the decision makers (DMs)' weights and improving the consensus degree are offered. Finally, a practical decision making example is given to illustrate the effectiveness of the proposed method and to compare it with previous methods. [ABSTRACT FROM AUTHOR]

At present, resource configuration of mobile cloud computing has received extensive attention from the outside world. Most of the similar resource scheduling configuration fails to comprehensively consider the dynamics of mobile terminals and the difference in user requested resources. Therefore, considering uncertainty in paging scheduling under mobile cloud resource environment from the perspective of consumers has become the key to solving the problem of resource allocation in the mobile cloud computing environment. This paper proposes an adaptive matching resource allocation algorithm based on uncertain factors under mobile cloud computing environment. Uncertain factors of the mobile terminal are derived via QoS attribute, and then user information and load characteristics of the user requested resources are analyzed through CLIQUE similarity matching. Afterwards, based on the mapping between similarity and resources, resource paging allocation can be carried out based on adaptive matching resource allocation algorithm. From the perspective of consumers, dynamics of mobile terminals and uncertainty of paging scheduling in the mobile cloud resource environment under different user requested resources can be considered to allow minimized delay and optimized paging strategies. [ABSTRACT FROM AUTHOR]

At present, resource configuration of mobile cloud computing has received extensive attention from the outside world. Most of the similar resource scheduling configuration fails to comprehensively consider the dynamics of mobile terminals and the difference in user requested resources. Therefore, considering uncertainty in paging scheduling under mobile cloud resource environment from the perspective of consumers has become the key to solving the problem of resource allocation in the mobile cloud computing environment. This paper proposes an adaptive matching resource allocation algorithm based on uncertain factors under mobile cloud computing environment. Uncertain factors of the mobile terminal are derived via QoS attribute, and then user information and load characteristics of the user requested resources are analyzed through CLIQUE similarity matching. Afterwards, based on the mapping between similarity and resources, resource paging allocation can be carried out based on adaptive matching resource allocation algorithm. From the perspective of consumers, dynamics of mobile terminals and uncertainty of paging scheduling in the mobile cloud resource environment under different user requested resources can be considered to allow minimized delay and optimized paging strategies. [ABSTRACT FROM AUTHOR]

Fuzzy logic has wide adoption in every field of study due to its immense advantages and techniques. In cloud computing, there are many challenges that can be resolved with the help of fuzzy logic. The core objective of this paper is to analyze the application of fuzzy logic in most demanding research areas of cloud computing. We also analyzed the fuzzy methods that were used in the solving of problems relates to cloud computing. A systematic literature review was conducted to enlist the all the challenging areas of research relates to cloud computing, categorized the most critical and challenging areas of cloud research, studied existing problem-solving techniques of each challenging cloud area, and finally studied the application of fuzzy logic in each aforementioned areas to redress different problems. The authors concluded that fuzzy logic can be used in every area of research including cloud computing to solve the problems and optimized the performance, as well as fuzzy logic techniques, were opted by many cloud computing researchers to conduct their study to optimize the performance of the system. [ABSTRACT FROM AUTHOR]

Dynamic Replica Technology can solve the problem of load balancing in cloud computing. Multiple replicas are distributed in different nodes and the load of a node can be dispersed to other nodes that have the replicas. This paper presents a replica manage strategy based on file access frequency to solve the problem of load balancing, including the time for creating the replicas, the number of the replicas, the location of the replicas, the time for deleting the replicas and deleting the replicas. The strategy determines the number and the location of replicas according to the file access frequency, node's storage space, network bandwidth, replica consistency maintenance cost and other factors. The replica will be deleted when the file access frequency is low or it has not been accessed for a long time. Experimental results reveal that the algorithm can not only achieve load balancing but also improve the performance of the system. [ABSTRACT FROM AUTHOR]

This paper suggests a new stochastic framework based on cloud theory to model the uncertainty effects in the optimal energy management in renewable micro-grids (MGs). Cloud theory is constructed based on the fuzzy theory and probability concept to provide an intelligent model called cloud model (CM). In comparison with the popular Monte Carlo simulation (MCS) method, CM can capture more uncertainty of the problem through the cloud drops. The basic idea is to incorporate the fuzziness and randomness of qualitative concepts and afterward transform them to the quantitative model. According to the high complexity and nonlinearity of the problem, a novel optimization algorithm called modified krill herd (MKH) algorithm is suggested to explore the problem search space globally. The proposed modification method will increase both the search ability and convergence of the algorithm effectively. The feasibility and satisfying performance of the proposed intelligent stochastic framework is demonstrated though a typical MG including different types of renewable energy sources and storage device. Comparative studies reveal the reliable superiority of the proposed stochastic framework to solve the problem. [ABSTRACT FROM AUTHOR]

Granular computing is one of the important methods for extracting knowledge from data and has got great achievements. However, it is still a puzzle for granular computing researchers to imitate the human cognition process of choosing reasonable granularities automatically for dealing with difficult problems. In this paper, a Gaussian cloud transformation method is proposed to solve this problem, which is based on Gaussian Mixture Model and Gaussian Cloud Model. Gaussian Mixture Model (GMM) is used to transfer an original data set to a sum of Gaussian distributions, and Gaussian Cloud Model (GCM) is used to represent the extension of a concept and measure its confusion degree. Extensive experiments on data clustering and image segmentation have been done to evaluate this method and the results show its performance and validity. [ABSTRACT FROM AUTHOR]

Resource allocation in cloud computing is an optimization problem that determines the allocation of computer and network resources of service providers to requested services of users for meeting user service requirements. A distributed cloud computing environment for IT services requires resource allocation in a decentralized manner through the coordination of service providers and service users. Achieving the optimal solution of service planning through the decentralized service provider-user coordination remains as a challenge. This paper looks into elements of service provider-user coordination first in the formulation of the resource allocation problem in a centralized manner and then in the formulation of the problem in a decentralized manner for various problem cases. By examining differences between the centralized, optimal solutions and the decentralized solutions for those problem cases, the analysis of how the decentralized service provider-user coordination breaks down the optimal solutions is performed. Based on this analysis, strategies of decentralized service provider-user coordination are developed. [ABSTRACT FROM AUTHOR]