Objective: This study aims to inherit the national sports culture of throwing embroidered balls, combine technological innovation with the application of technological information, improve the competitive level and competition management level of the backbasket throwing embroidered ball. Method: Utilizing methods such as literature review and mathematical statistics to elucidate the evolutionary history of embroidered ball culture, analyze the development process of the backbasket throwing embroidery ball sport and throwing technology using biomechanics; Research innovative throwing techniques and tactical applications through comparative analysis of competition results. Result: There are problems with the lack of theoretical throwing techniques, imperfect competition rules, and inconsistent embroidery balls in competition. The training results of the short fast low (α ≤ 30°) throwing method are outstanding, but insufficient stability leads to easy mistakes in competition, it requires athletes with good psychological qualities; The training performance of the long slow high (α > 30°) throwing method is poor, but it has good stability and is easy to exert competitive ability in competitions. Conclusion: Emphasize the inheritance of ethnic sports culture and enhance social participation interest. The mixed throwing method, with short fast low throwing as the main method and long slow high throwing as the auxiliary method, can effectively improve the level of competition. Utilizing modern elements such as unmanned aerial vehicles and information technology management to improve the level of referees and promote the sustainable development of ethnic sports.
With the rapid growth of Internet of Things (IoT) applications, fog computing enables the IoT to provide efficient services by extending the cloud computing paradigm to the edge of the network. However, the existing attribute-based encryption schemes rarely focus on user and attribute revocation in fog computing and most of them still impose high computational and storage overhead on resource-limited IoT devices. In this article, we propose an efficient attribute-based encryption outsourcing scheme with user and attribute revocation for fog-enabled IoT. The proposed scheme improves the existing encryption scheme that uses the concept of attribute groups to achieve attribute revocation, making it suitable for fog computing and improving the efficiency of ciphertext update. In addition, it implements a novel method of user revocation in fog computing based on the characteristics of fog computing. In order to reduce the computing and storage burden of IoT devices, the heavy computation operations of encryption and decryption are outsourced to fog nodes and part of secret keys are stored in fog nodes. The security analysis shows that the proposed scheme is secure under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. The performance analysis shows that the proposed scheme has high revocation efficiency and is efficient enough to be deployed in practical fog computing.
We present a reduction framework from ordinal ranking to binary classification. The framework consists of three steps: extracting extended examples from the original examples, learning a binary classifier on the extended examples with any binary classification algorithm, and constructing a ranker from the binary classifier. Based on the framework, we show that a weighted 0/1 loss of the binary classifier upper-bounds the mislabeling cost of the ranker, both error-wise and regret-wise. Our framework allows not only the design of good ordinal ranking algorithms based on well-tuned binary classification approaches, but also the derivation of new generalization bounds for ordinal ranking from known bounds for binary classification. In addition, our framework unifies many existing ordinal ranking algorithms, such as perceptron ranking and support vector ordinal regression. When compared empirically on benchmark data sets, some of our newly designed algorithms enjoy advantages in terms of both training speed and generalization performance over existing algorithms. In addition, the newly designed algorithms lead to better cost-sensitive ordinal ranking performance, as well as improved listwise ranking performance.
Cloud computing is an evolutionary technology that offers on-demand resources and elastic services through the Internet. Most providers adopt fixed-price mechanisms (e.g. pay-as-you-go). However, a few providers have recently employed auction-like approaches to price cloud services. Meanwhile, cloud consumers pay more attention to Quality of Service (QoS) such as availability, which measures how well a service is performed. This paper proposes a novel auction approach that can efficiently allocate resources according to customers' QoS preferences. The QoS-based pricing can generate more revenue than a fixed-price strategy. This research lies at the intersection of cloud computing, economics, and information systems.
A blockchain consists of an ordered list with nodes and links where the nodes store information and are connected through links called chains. This technology supports the availability of a publicly maintained ledger of transactions, first gaining mainstream attraction with cryptocurrencies. A myriad of other applications have emerged ever since. There has been a steady growth in the number of research studies conducted in this field; as such, there is a need to review the research in this field. This paper conducts an extensive review on 76 journal publications in the field of blockchain from 2016 to 2018 available in Science Citation Index (SCI) and Social Science Citation Index (SSCI) database. The aim of this paper is to present scholars and practitioners with a detailed overview of the available research in the field of blockchain. The selected papers have been grouped into 14 categories. The contents of papers in each category are summarized and future research direction for each category is outlined. This overview indicates that the research in blockchain is becoming more prominent and requires more effort in developing new methodologies and framework to integrate blockchain. It is the need of today's growing business that ventures into new technologies like cloud computing and Internet of Things (IoT).
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Bacterial Foraging Optimization(BFO) is a comparatively new optimization algorithm which learn from the foraging behavior of bacteria. The elimination and dispersal step is the major step of BFO to get the global search ability. An Enhanced Bacterial Foraging Algorithm (EBFO) is presented in this paper, which is a variation of the original BFO algorithm. This new algorithm uses an adaptive elimination-dispersal probability according to bacterial fitness, meanwhile it carry out an Particle Swarm Optimization (PSO) operator just after the swim step. From the result of experiment on 6 benchmark functions, we can draw the following conclusions that the new algorithm has the advantages of better global searching ability, speeder convergence and more precise convergence.
A Maturity Model (MM) is essential for laboratories aiming to improve and compete globally. Despite being accredited for over 20 years, some laboratories lack evidence of system maturity necessary for international competitiveness. A higher maturity level indicates a robust quality management system, leading to improved efficiency, accuracy, and customer satisfaction, while also assuring stakeholders, such as customers and regulatory bodies, of the laboratory's commitment to continuous improvement. The MM is designed to assess the success of laboratory processes, management styles, and the development of quality management practices, based on the 4M model (Manpower, Method, Machine, Material). The MM was developed using Analytes Accurate Certain Score (AACS) for the ten most common tests and System Maturity Scores (SMS), which incorporate proficiency testing and audit scores over two accreditation cycles (six years). While the model identifies inefficiencies, it helps organizations pinpoint areas of improvement and devise strategies to enhance their operations. This study applied the MM to a commercial laboratory, ABC (anonymized), accredited since the early 1990s and having undergone eight assessment cycles. The laboratory was found to be at a “leading” maturity level with a score above 80%, although improvements are still needed. Key areas for improvement include: 1) **Manpower**: maintaining competent staff by adjusting management strategies, 2) **Method**: validating all in-house methods according to Analytical Laboratory Accreditation Criteria Committee (ALACC) guidelines, 3) **Machine**: applying good laboratory practices (GLP) for equipment sharing, especially for specific analytes, and 4) **Material**: ensuring metrologically traceable reference materials and proficiency testing for all analytes. These improvements will help the laboratory further enhance its global competitiveness.
In this paper,we analyzed some key problems that must be solved in classification.Then,the idea and characteristic of main kinds of classification algorithms are reviewed.Decision tree algorithm can handle noise data well but is only effective to small datasets.Bayesian has the merits of high accuracy,fast speed,low mistake rate and demerits of low accuracy.Classification based on association rule has advantages of high accuracy but is limited to random access memory.Support vector machine has the merits of high accuracy,low complexity but shows bad time complexity.According to the advantages and disadvantages of the well-known algorithms,some recent proposed classification algorithms which achieve better performance are addressed,such as multi-decision fusion technology,the hybrid classification algorithm based on Bayesian and information gain,and neural network classification algorithm based on rough set and genetic algorithm etc.Finally,research emphasis in the future is discussed.
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