For universities, HR is the first element and the first resource to build university resources. Systematic construction and management of HR and effective development and utilization are the key factors to promote the...
详细信息
For universities, HR is the first element and the first resource to build university resources. Systematic construction and management of HR and effective development and utilization are the key factors to promote the development of universities. In this paper, the Apriori correlation rule mining algorithm is studied, the optimal allocation model of university HR based on the Apriori algorithm is studied, and the strategy of optimal allocation of university HR is discussed. DM can discover the talent patterns and laws within the organization, so as to find the talent model within the organization. The results show that the method proposed in this paper can be used to mine the correlation rules of the university HR data set, and good results can be achieved. The system can realize the standardization, safety, and paperless work of university personnel, which has changed the original manual work mode and greatly improved the work efficiency. The method in this paper can be used to determine a reasonable HR structure according to the development goals and plans of our school, establish a scientific talent evaluation mechanism, clarify the age structure of teachers, provide an important scientific method, and provide objective decision support for formulating talent demand planning, talent recruitment, and training.
Carbon functional materials (CFMs) such as biochar and hydrochar can be obtained from hundreds of biomass precursors varying from urban sludge to agriculture wastes. They can be produced through tens of synthesis meth...
详细信息
Carbon functional materials (CFMs) such as biochar and hydrochar can be obtained from hundreds of biomass precursors varying from urban sludge to agriculture wastes. They can be produced through tens of synthesis methods and postsynthesis processing steps tuned at specific conditions (e.g., temperature, time, and chemical concentrations). To achieve a "rational design" platform for a system with a high dimensional parameter space such as CFMs, we processed 10,975 scientific articles (from years 2000 to 2020) related to the subject with automatic reading-interpreting-extracting computational routines (namely, the *** engine). The *** engine automatically recognized more than a hundred precursors, among which wheat straw, rice husk, and rice straw were the most studied for CFM synthesis and application in agriculture (e.g., as an amendment), as fuel (energy generation), and as an adsorbent. Parameters related to the CFMs' synthesis conditions, such as carbonization temperature and time, and parameters related to CFMs' properties, such as surface area and heavy metals adsorption capacity, can also be extracted from the articles. Correlations between the CFM precursors and synthesis conditions indicated very little statistical difference between the carbonization temperature and time used for the CFMs' synthesis from different precursors. Essentially, precursors are carbonized at temperatures varying from 100 to 900 degrees C for 30 min to 6 h using pyrolysis, hydrothermal carbonization, and gasification. When focusing the analysis on just CFMs produced by pyrolysis (biochar), we observed that peanut shells can produce materials with higher surface areas than other precursors (P < 0.05). When performing correlations between biochar synthesis conditions and their properties, general trends can be confirmed: (i) the higher the carbonization temperature, the lower the H/C and O/C ratios, and (ii) the increase in the surface area can be achieved by preserving a high aro
In order to improve the effect of piano information teaching, a piano information teaching mode based on deep learning algorithm is proposed. The teaching objectives are divided into three levels: classroom teaching o...
详细信息
In order to improve the effect of piano information teaching, a piano information teaching mode based on deep learning algorithm is proposed. The teaching objectives are divided into three levels: classroom teaching objectives, curriculum objectives, and education and training objectives. A piano information classroom integrating cloud application platform, teaching platform, resource platform, learning space, and interactive classroom is built. The previous teaching mode is optimized to build an innovative teaching mode of piano information classroom. The evaluation index system of piano informatization classroom teaching quality is constructed, and the hierarchical structure model of each evaluation index is established by using the analytic hierarchy process. The hierarchical analysis method is used to establish a hierarchical structure model of each evaluation index. The judgment matrix is determined by the nine-digit scale method. After the consistency verification of the judgment matrix, the weight of the quality evaluation of piano information classroom teaching is calculated. The new mode optimizes the weight and threshold of BP neural network in deep learning algorithm by genetic algorithm (GA). The weight of each classroom teaching quality evaluation index is input into the GA-BP neural network, and the network output result is the piano information classroom teaching quality evaluation score. The test results show that the optimal number of hidden layer nodes for the BP neural network is 7, when the GA-BP neural network iterations are 95. This method can evaluate the quality of piano information classroom teaching, with high evaluation accuracy and strong practical application.
In general, grammar polishing systems recombine words and their order of combination based on the comparison results of word combinations and large vocabulary grammar data to achieve error polishing. However, this pro...
详细信息
Aging is the trend of the global population in the 21st century. Physical degradation of the elderly and related care is a major challenge in the face of an aging society. Exercise can delay physiological aging and pr...
详细信息
Aging is the trend of the global population in the 21st century. Physical degradation of the elderly and related care is a major challenge in the face of an aging society. Exercise can delay physiological aging and promote the metabolism of body functions. Although aging is an irreversible natural law, proper physical training can help prevent aging. Therefore, relevant personnel attach great importance to the training of physical fitness. To this end, a 12-week elderly functional fitness training experiment was conducted with elderly residents in a village in Nanjing. In the detection process, the gait analysis system is mainly used for the subject's motion detection and recording and records the data into the gait analysis software system based on the improved deep learning algorithm for sports training simulation analysis. After completing the physical training simulation experiment, the RTM model is used for simulation analysis. The results were evaluated. The evaluation data show that the homogeneity test results of the designed physical training simulation experiment are very reasonable. Since the result is much larger than 0.10, it can be inferred that the results of the physical training simulation analysis have been expected and also meet the national GB/T 31054-2014 standard requirements.
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that...
详细信息
We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (Phys Rev D 99(1):015014, 2019, arXiv:1806.02350 [hep-ph]), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.
Autism spectrum disorder is an inherited long-living and neurological disorder that starts in the early age of childhood with complicated causes. Autism spectrum disorder can lead to mental disorders such as anxiety, ...
详细信息
Autism spectrum disorder is an inherited long-living and neurological disorder that starts in the early age of childhood with complicated causes. Autism spectrum disorder can lead to mental disorders such as anxiety, miscommunication, and limited repetitive interest. If the autism spectrum disorder is detected in the early childhood, it will be very beneficial for children to enhance their mental health level. In this study, different machine and deep learning algorithms were applied to classify the severity of autism spectrum disorder. Moreover, different optimization techniques were employed to enhance the performance. The deep neural network performed better when compared with other approaches.
We explored the role of ML (ML) to improve English proficiency. With more people interested in English skills, the importance of learners' diversified approaches has been emphasized. ML algorithms are used to auto...
详细信息
Auction games have been widely used in plenty of trading environments such as online advertising and real estate. The complexity of real-world scenarios, characterized by diverse auction mechanisms and bidder asymmetr...
详细信息
Auction games have been widely used in plenty of trading environments such as online advertising and real estate. The complexity of real-world scenarios, characterized by diverse auction mechanisms and bidder asymmetries, poses significant challenges in efficiently solving for equilibria. Traditional learning approaches often face limitations due to their specificity to certain settings and high resource demands. Addressing this, we introduce Auctionformer, an efficient transformer-based method to solve equilibria of diverse auctions in a unified framework. Leveraging the flexible tokenization schemes, Auctionformer translates varying auction games into a standard token series, making use of renowned Transformer architectures. Moreover, we employ Nash error as the loss term, sidestepping the need for underlying equilibrium solutions and enabling efficient training and inference. Furthermore, a few-shot framework supports adaptability to new mechanisms, reinforced by a self-supervised fine-tuning approach. Extensive experimental results affirm the superior performance of Auctionformer over contemporary methods, heralding its potential for broad real-world applications. Copyright 2024 by the author(s)
The full-span log-linear (FSLL) model introduced in this letter is considered an nth order Boltzmann machine, where n is the number of all variables in the target system. Let X = (X-0, ..., Xn-1) be finite discrete ra...
详细信息
The full-span log-linear (FSLL) model introduced in this letter is considered an nth order Boltzmann machine, where n is the number of all variables in the target system. Let X = (X-0, ..., Xn-1) be finite discrete random variables that can take vertical bar X vertical bar = vertical bar X-0 vertical bar...vertical bar Xn-1 vertical bar different values. The FSLL model has vertical bar X vertical bar - 1 parameters and can represent arbitrary positive distributions of X. The FSLL model is a highest-order Boltzmann machine;nevertheless, we can compute the dual parameter of the model distribution, which plays important roles in exponential families in O(vertical bar X vertical bar log vertical bar X vertical bar) time. Furthermore, using properties of the dual parameters of the FSLL model, we can construct an efficient learning algorithm. The FSLL model is limited to small probabilistic models up to vertical bar X vertical bar approximate to 2(25);however, in this problem domain, the FSLL model flexibly fits various true distributions underlying the training data without any hyperparameter tuning. The experiments showed that the FSLL successfully learned six training data sets such that vertical bar X vertical bar = 2(20) within 1 minute with a laptop PC.
暂无评论