Reviewerly/exHarmony huggingface dataset:
A visual analysis of domestic county medical community research based on clustering algorithms

To systematically explore the research hotspots and frontier trends in the field of data mining then domestic county medical community, and to provide data reference for future in-depth research in this field. Co-occurrence analysis, mutual information algorithms, LLR algorithms and LSI algorithms, etc. were used to retrieve and summarize the literature collected from 2016 to 2023 using the CNKI database as the data source, to draw the knowledge map and to perform visualization and analysis. The amount of literature on county medical community has increased rapidly from 2018 onwards, and county medical community have become a widely followed field. The collaborative network among authors is relatively loose, and no core group of authors has been formed yet. The research hotspots are mainly divided into three categories, which are the basic theoretical research related to medical communities, the optimal allocation of resources within medical communities, and the analysis of medical community operation models. In terms of cutting-edge trends, information construction, financial management, talent training, medical insurance payment, and medical prevention integration have become new themes and directions for research on county-level medical communities. The field of county-level medical community should further explore cross-sectoral and cross-regional collaborative research mechanisms and strengthen cross-collaboration in different fields. In the future, we should build a big data information system to support existing research, as well as deepen the theory of county-level medical communities by conducting diverse collaborative research models and practical theories combined with research methods.
The effect of SMEs' total quality management activities (TQM) on corporate competency and business performance

Regardless of the size of scale, the top quality management of a corporation is a measure enabled to strengthen the corporate capabilities and maximize management results. The present study was conducted to demonstrate the influence of top quality management(TQM) for small and medium businesses on the corporate capabilities and management results. Based on the research model, the study investigated precedent researches and established hypothesis to prove based on logicality. Afterwards, the statistical analysis program was used to conduct the empirical analysis. As a result of analysis, partial relationships that the TQM of small and medium businesses influenced corporate capabilities. In addition, as a result of analyzing the relationship between corporate capabilities and management, this part was also selected. Based on the results of the present study, the summary and implications can be explained as follows. First, the source of competition for corporations begin with the product quality that they manufacture. It could be favorable for a corporation devote their work on product sales but they can only devote on product sales if the product quality can support that. If the product quality is not good, corporations cannot only receive various claims from their customers and it also is bad in the aspect of evaluation and therefore, it is the case where the corporation deteriorates its own competitive power. When combining the contents of description, it is not important to establish external-internal strategies for a corporation to develop with competitive powers but is desirable for corporations to first remove the root cause that hinder corporate growth and then establish directions for growth.
ARENA FUTSAL UPAUJAYA DI KECAMATAN UPAU KABUPATEN TABALONG

Sport is one of the routine activities that a person does to maintain health, Futsal becomes a trend in the pleasure of young people and also in all circles, futsal is also no different from the game of football because both are played by kicking, the difference is that futsal is very easy not to require a large field and a lot of players. Based on the background of the problems raised from this report, namely how to realize the design of a Futsal Arena (Indoor) Type C equivalent that is creative and has a profit in the district of Upau. The method used to solve this problem is the design method is a method of solving problems with programmatic concepts. The design method is the answer to a problem that will encompass the needs in a building. By focusing on space, shape, function, construction.
Value determination of personal self-fulfillment of gifted youth students at a teaching university

The study of the problem of personal self-realization of gifted youth is recognized as one of the priority directions in the conditions of European integration of higher education, as is proven in the article. Supporting gifted students becomes an important task for society. The value determination of the process of personal self-realization of gifted students is recognized as a priority way of solving this problem in the article. It is noted that this problem acquires special significance when it comes to the future teacher, who must acquire the appropriate skills for raising gifted children during the period of study in institutions of higher education. The concept of «giftedness» will be interpreted in the article as favorable prerequisites for the development, personal attitude, and orientation of a developing young person. At the same time, it is also an individual peculiarity of a person's abilities, thanks to which he achieves success in the chosen field of activity. The article also presents other interpretations of giftedness: as general abilities that determine a person's capabilities for certain activities; as a system of endowments, which are natural prerequisites for abilities; as individual characteristics of cognitive abilities; as the appropriate level of development of abilities that allow the individual to achieve success in various fields of activity; as a peculiar system of abilities in a person, the unity that they form and, in sum, contribute to high achievements. The authors single out the following types of giftedness: artistic giftedness in the fields of art, authorship, music, literature, and sculpture; leadership talent (high intelligence; ability to make decisions and plan for the future; flexibility; sense of responsibility; persistence and enthusiasm; self-confidence; ability to express oneself clearly); creative talent; and academic and intellectual giftedness (observation; sharp thinking; special memory; problem-solving abilities).
A survey of gradient normalization adversarial attack methods

Recent research has found that deep neural networks are vulnerable and easily been attacked by adversarial samples. Improving the success rate of attacks against adversarial samples is a key issue in testing and improving the security and robustness of models, making adversarial samples a hot topic in current neural network research. The researchers found that the general adversarial attack problem can be transformed into an optimization problem to maximize the objective function. Currently, a important algorithms for solving such optimization problems are based on gradient methods, such as FGSM, I-FGSM, and other gradient symbol normalization methods. This article summarizes the research status of several typical adversarial attack algorithms and gradient-based normalization methods. The application of several gradient normalization methods in adversarial attacks and their algorithm convergence are prospected.
Intelligent wardrobe based on machine vision technology and new rotary guide rail

According to statistics, the scale of China's smart home market is increasing year by year, and the market has great prospects and development potential. According to the analysis of the research level of smart homes at home and abroad, the research level of smart homes in China is still in its infancy, and the development of smart wardrobes is relatively slow. At present, the smart wardrobes on the market can only achieve basic functions such as sterilization and dehumidification, and there are generally fewer smart wardrobes with multi-function. But with the advancement of technology, many user groups are no longer satisfied with single-function smart homes. In addition, the development of the times has also led human beings to a fast-paced life, and many young people are reluctant to spend time sorting and sorting their clothes, resulting in a lot of time when looking for clothes in the changing seasons and going out to change outfits. In the face of the current social situation, the project has analyzed and designed according to user needs, and used the Internet of Things, artificial intelligence and other technologies to create a smart wardrobe that integrates intelligent matching, intelligent finding of clothes, intelligent cooling and dehumidification to meet market demand and solve the problems of the times.
The Nexus Between Youth and Mobile Phones in Botswana

Almost two decades since the advent of mobile telephony countrywide, Botswana has recorded a significant growth in mobile phone subscription and use. In all the villages, towns, and cities across Botswana, many adolescents, adults, and elderly people own and use a mobile phone. In its 2021 report, the Botswana Communications and Regulatory Authority (BOCRA) documented that mobile phone subscription across Botswana's three network operators, BTC beMobile, Mascom, and Orange, had increased dramatically from 2,953,116 in 2012 to 3,876,773 subscribers among a population of around 2.3 million (Botswana Communications Regulatory Authority Telecommunications Statistics. Gaborone; 2021). High mobile phone adoption in Botswana is a result of both the local networks' and the Botswana government's commitment to diffuse network signals to many places countrywide (Lesitaokana, Journal of New Media and Society 16:840-855, 2014). For instance, the local networks have continued to extend mobile phone signals and mobile-enabled services countrywide, while the Botswana government has committed itself to networking the villages that have been underserved by the local networks. Subsequently, young people in Botswana continued to adopt mobile phones, as the devices promise them extensive connectedness with their family, relatives, and friends, and also use them to access public and social services, including mobile broadband Internet. Nonetheless, as part of young people's interactive relationship with mobile phones, the devices also influence cultural practices and inappropriate behaviors among youth. Given this situation, we are presented with an ideal opportunity to study both the socioeconomic importance and the cultural implications of the mobile phone in the Botswana context, as this will add to the sociological literature of the nexus between youth and mobile communication technology.
Research and Application of an Improved Apriori Algorithm in Market Basket Data

In data mining, association rule mining has been proven to be a valuable strategy. The Apriori algorithm, proposed in 1993, is one of the most classic association rule mining algorithms and has been widely used in various domains to date. However, the algorithm itself requires multiple scans of the dataset, making the efficiency improvement of the algorithm an important topic. In this study, focusing on the association rule mining problem in shopping basket datasets, we address the efficiency and accuracy issues of the traditional Apriori algorithm in this domain and propose a novel improved Apriori algorithm. This algorithm enhances the accuracy and time efficiency of rule mining by introducing deletion and identification modules that exploit the characteristics of shopping basket datasets. Through mathematical reasoning and experimental validation, it is demonstrated that the improved Apriori algorithm achieves higher accuracy and time efficiency, thus proving its superior
Research on evaluation methods of news hotspots based on media attention and user attention

Analyzing the distribution and evolution characteristics of hot topics of online public opinion can effectively reveal the trend of hot topics of public opinion, and has certain significance for strengthening supervision of online public opinion, guiding online public opinion and providing opinions on public opinion governance. According to the Single-Pass algorithm, daily topics were discovered and extracted. The heat evaluation formula was designed from the two aspects of media attention and user attention, and the heat analysis was carried out on the discovered topics. The hot topics were extracted and displayed in a visual display way, and the evolution process of the topics was analyzed in combination with current events. The evolution analysis method of network public opinion topics proposed in this paper can effectively mine the space-time law of the evolution of network public opinion topics, and provide more scientific decision support for public opinion monitoring and early warning.
Leveraging Transformer Neural Networks for Enhanced Sentiment Analysis on Online Platform Comments

The relentless surge in the volume of data generated across online platforms necessitates sophisticated approaches to glean actionable insights, particularly in discerning the sentiment encased within user-generated comments. This paper presents an intricate exploration of applying deep learning technologies, specifically the Transformer neural network model, in classifying sentiments expressed in a copious corpus of 1.6 million tweets from the Sentiment140 dataset. Endeavoring beyond conventional models such as Support Vector Machines (SVM) and Long Short-Term Memory networks (LSTM), this research meticulously evaluates the performance enhancements offered by the Transformer architecture in the realm of sentiment analysis. Our methodology entails the comprehensive utilization of the Transformer model, renowned for its prowess in handling sequential data, to unearth the underlying sentiments in the tweets, categorizing them aptly as positive, neutral, or negative. The empirical results emanating from this study delineate a noteworthy improvement in classification accuracy and model robustness, solidifying the Transformer's position as a formidable architecture for sentiment analysis tasks. The enlightening findings of this research offer a pivotal contribution to the continuous efforts in augmenting sentiment analysis methodologies, emphasizing the substantial potentials of integrating advanced deep learning models in processing and analyzing online platform comments. Through this work, we envisage fostering innovative advancements in the field, cultivating enhanced techniques for accurate, efficient, and impactful sentiment analysis in the burgeoning digital landscapes.
Investigation the Impact of Features on Malicious Traffic Identification Based on Different Machine Learning Algorithms Combined with Dimensionality Reduction

With the Internet's rapid evolution, the incidence of cyberattacks has surged significantly. Employing machine learning to precisely detect and thwart malicious network traffic has emerged as a novel and effective solution for safeguarding computer networks. This research program centers on the identification of suitable machine learning models and the meticulous curation of data features. Within this study, a total of 13 features, encompassing conventional timestamps, the volume of traffic packets in data streams, and their associated sizes, are extracted as key features following the dataset's traffic packet consolidation process. Three algorithms such as Random Forest, Decision Tree and Support Vector Machine were chosen for training and testing the dataset. In addition, Principle Component Analysis dimensionality reduction is performed for these 13 features to determine the effect on the accuracy of the results before and after the dimensionality reduction process. The final result is that the Random Forest algorithm achieves best processing power, but produces large fluctuations in the accuracy in one dimension. In the face of large-scale network traffic analysis, the random forest model should be preferred as the machine learning model, while ensuring that the dimension is greater than one dimension after dimensionality reduction.
Research on Personalized Recommendation Algorithm for Housing Rental Based on Data Mining

As China's economy develops and people's living standards improve, the demand for housing is increasing day by day. Many people choose to rent a house as a temporary solution to their housing needs. In view of the large amount of data in the housing rental industry and the difficulty for users to obtain information, this paper proposes a personalized recommendation algorithm for house rental based on data mining. A user interest model is extracted based on the user's historical behaviour, and a grey correlation analysis is used to calculate the correlation between all housing data and the user interest model, and the housing information with a high correlation is recommended to the user. In this paper, the personalized recommendation algorithm will be studied to provide a method for personalized recommendation service for users' rental properties.
A Survey of Behavioral Biometric Authentication on Smartphones

User authentication is an important technology to ensure the legal use of smartphones. With the development of smartphones, More and more authentication methods are appearing. Compared with traditional password authentication and face authentication, behavioral biometric authentication can keep authenticating the user's identity during use and does not require the user's collaboration in the authentication process. This paper summarizes the structure of smartphone authentication systems and the processing of behavioral biometric authentication and investigates user authentication approaches based on behavioral biometrics, including touchscreen-based authentication, motion-based authentication, and voice-based authentication. The research work of user authentication based on different behavioral biometrics is described and compared in detail. Finally, the paper discusses the advantages, challenges, and future trends of behavioral biometrics authentication.
Fine Grained Access Control Algorithm for Sensitive Data Based on Deep Learning and Security Domain

Traditional fine-grained access control methods for sensitive data are vulnerable to the impact of access filtering assumptions, and the algorithm has a high Time complexity. Therefore, design a fine-grained sensitive data access control algorithm based on deep learning and security domain. Design a fine-grained access control mechanism for sensitive data based on deep learning, construct a fine-grained access control model for sensitive data combined with security domains, and generate an optimization process for fine-grained access control for sensitive data. The experimental results show that the designed fine-grained access control algorithm for sensitive data has low Time complexity, which proves that the designed fine-grained access control algorithm for sensitive data has good performance, high efficiency and certain application value.
Transformer-based a Automatic Scoring Model for Translation Jobs

In the field of natural language processing, evaluating translation quality has been a long-standing challenge. Traditionally, the assessment of translation quality has relied heavily on manual assessment, which can be time-consuming, subjective and resource-intensive. These limitations spur the need for automated methods to streamline processes and increase objectivity and efficiency. To solve this problem, this paper introduces an automatic scoring system based on the Transformer model for evaluating the quality of translation tasks. The Transformer is a powerful sequence-to-sequence model that has achieved significant success in the field of natural language processing. By using the Transformer model, we can transform the translation task into a machine learning problem and automatically analyze the differences between the target language and the reference language. The training process of this automatic scoring system involves using a large amount of parallel corpora for data training, enabling the model to learn effective translation rules and semantic representations. In the testing phase, the system can accept the translation text to be evaluated and assign it an automatic score, representing the quality of its translation. This automatic scoring system can improve the efficiency and accuracy of translation tasks while reducing reliance on manual evaluation.
A Secure Storage System for High-Speed Railway Monitoring Data Based on Blockchain Technology

To address the issues of malicious data tampering and sharing difficulties in the distributed storage of high-speed railway monitoring data, this study proposes a decentralized dual chain storage model based on alliance chain. Using wireless sensor networks and blockchain technology to construct a high-speed railway alliance chain perception node distribution framework and a data storage alliance chain model, a data blockchain is formed through data collection base station consensus. Based on the attributes, hash values, and other information of the data, a specific type of hash chain is formed to facilitate the analysis and summary of historical data of the same type of monitoring project. Security performance analysis shows that the data alliance storage chain can achieve safe and reliable storage of monitoring data along the railway.
Research on Blockchain-based Direct Transaction Model of Distributed Power Supply

Blockchain technology can provide distributed power generation projects and power users with direct trading opportunities, thus promoting distributed power consumption and reducing transaction costs and transaction risks. This study first carries out the design of a blockchain-based distributed power direct transaction process, proposing six steps: transaction application, information sharing, transaction matching, transaction audit, transaction settlement, and transaction completion. Secondly, the smart contract design of the relevant process is carried out, combining the DE-GWO-SVR prediction model, CP-ABE attribute encryption method, k-prototype clustering algorithm, blockchain storage based on location partition, unconsumed output model, consensus group and entropy weight-TOPSIS method for the design of key smart contracts. Finally, the architecture of blockchain-based distributed power direct trading is designed, including the base layer, platform layer, service layer, user layer, and support model.
Client Selection for Mobile Edge Computing-Assisted Hierarchical Federated Learning with Non-Independent and Identically Distributed Data

This paper focuses on the Mobile Edge Computing-assisted Hierarchical Federated Learning (MEC-HFL) and the Non-Independent and Identically Distributed (Non-IID) data among edges, especially. To improve the learning performance of MEC-HFL systems with fixedly associated edge servers and clients, this paper analyzes the learning process of MEC-HFL and proposes a multi-objective optimization problem which simultaneously minimizes the data distribution distance and maximizes the amount of data through client selection. A Nondominated Sorting Genetic Algorithm-based Client Selection Algorithm (NSGA-CSA) is proposed to solve the above problem. Simulation results show that our proposed NSGA-CSA converges quickly. Under different datasets and models, while improving the learning performance by 2.16%, the NSGA-CSA reduces the learning cost by 21.87%.