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With the gradual implementation of the strategic goal of strengthening the country in education, people's attention to students' learning is also increasing day by day. At the same time, the teaching effect of individual teachers depends to a large extent on the identity of teachers themselves, so it is necessary to improve the construction of teachers' identity. Most of the research on teachers' identity construction starts from the theory of identity, which does not play a significant role in the critical application in practical teaching activities. In response to these problems, this paper will use the ReliefF algorithm and the Spark algorithm in the feature selection algorithm to scientifically process the identity construction, and implement the application of the improved ReliefF feature selection algorithm and the feature selection application steps based on the Spark algorithm respectively. The experimental results show that the improved ReliefF algorithm has better feature selection accuracy when the feature range is 30% to 40%. It shows that the construction of teacher identity based on feature selection algorithm can provide an objective basis for the realization of teacher identity. [ABSTRACT FROM AUTHOR]

Great deluge (GD) algorithm same as other metaheuristics can solve feature selection problem. The GD imitates that in a great deluge someone climbing a hill and attempt to progress in any direction that does not get his/her feet wet in the expectation of discovering a way up when the water Level rises. The drawbacks of GD are: 1) a local search, which may lead the algorithm toward a local optima and 2) a challenging estimation of quality of the final solution in solving most of the problems. In this paper, for the first issue, a population-based great deluge (popGD) algorithm with additional recurrence operation is proposed. This operation is an imitation of no progress of hill climber after a long time; the climber tries to move small steps even downward in hope of finding better way to climb. For the second problem, a technique with an automate alteration of the Level is proposed. The statistical analysis of the results from 25 test functions and 18 benchmark feature selection problems supports the ability of the method. Finally a real-world academician data are employed to perform feature selection and execute classification result with selected features. [ABSTRACT FROM AUTHOR]

In recent years, with the development and extensive application of Internet technology and computer technology, and under the role of the continuous expansion and enrollment expansion of colleges and universities, how to effectively solve the Internet problems faced by teachers and students in colleges and universities has gradually become an important problem that the university information management department should solve in time. In order to continuously improve the efficiency of Internet access in universities, the Internet authentication and billing system for universities proposed in this paper take J2EE as the development platform and combines RADIUS protocol and PPPoE protocol to provide users with diversified Internet billing methods and strategies. At the same time, based on the Fourier fast transform algorithm, the development environment for the Internet authentication and billing system for colleges and universities is deployed, and after the Internet authentication and billing system is implemented, from the functional point of view of the system, the main functional modules are designed and implemented, including the authentication management function. module, billing management function module. The experimental results have shown that the throughput of PPPoE access server in the campus network using the Fourier rapid transform algorithm for traffic load balancing delay is small and stable between 200 ms and 300 ms after the optimization, which provides a feasible solution to promote the healthy and harmonious development of the university campus network. [ABSTRACT FROM AUTHOR]

The paper is devoted to the Evidence Algorithm, initiated by Academician Glushkov in 1970 and implemented as Russian-language and English-language SAD systems intended for automated deduction. Some of their characteristics and features are described. Examples of their application to solving mathematical and common problems that require deductive constructions are provided. Possible ways of the further development of the English-language SAD system are given. [ABSTRACT FROM AUTHOR]

Because of the increasing adoption and use of technology in primary health care (PHC), public health informatics competencies (PHIC) are becoming essential for public health workers. Unfortunately, no studies have measured PHIC in resource-limited setting. This paper describes the process of developing and validating Public Health Informatics Competencies for Primary Health Care (PHIC4PHC), an instrument for measuring PHC workers' competencies in public health informatics. Method: This study developed a questionnaire that had three stages: the Delphi technique, a pretest, and field test. Eleven academicians from a university and 13 PHC workers joined 2 rounds of group discussion in the first stage. The second stage comprised two pilot studies with 75 PHC workers in Semarang Municipality. The third stage involved validating the questionnaire with 462 PHC workers in Kendal District. This study used Pearson's product-moment correlation for the validity check and Cronbach's alpha coefficient for determining the internal consistency. This study used the K-means algorithm for clustering the results of the PHIC4PHC questionnaire. Results and Conclusion: PHIC4PHC is the first comprehensive PHIC questionnaire administered in a resource-limited setting, consisting of 11 indicators and 42 measurement items concerning knowledge of health information systems, skills required for health data management, ethical aspects of data sharing and health information literacy. The final results of PHIC4PHC were clustered into three classes based on the K-means algorithm. Overall, 45.7% PHC workers achieved medium competency, whereas 25.6% and 27.7% achieved low and high competency, respectively. Men had higher competency than women. The higher the worker's level of education, the higher the PHIC level; the longer the worker's work experience, the lower the PHIC score; and the greater the worker's age, the lower the PHIC score. Measuring and monitoring PHIC is vital to support successful health IT adoption in PHC. [ABSTRACT FROM AUTHOR]

The article profiles Herbert Eli "Herb" Scarf, an economics professor at Yale University and a member of Cowles Foundation for Research in Economics. It states that the use of numerical algorithms to compute equilibrium prices in a general equilibrium model of the economy was pioneered by Scarf. It also mentions that he won the 1983 John von Neumann Theory Prize and is a fellow of Institute for Operations Research and the Management Sciences (INFORMS).

In this paper, the problem of secure transmission of sensitive contents over the public network Internet is addressed by proposing a novel data hiding method in encrypted images with dual-level security. The secret information is divided into three blocks using a specific pattern, followed by an encryption mechanism based on the three-level encryption algorithm (TLEA). The input image is scrambled using a secret key, and the encrypted sub-message blocks are then embedded in the scrambled image by cyclic18 least significant bit (LSB) substitution method, utilizing LSBs and intermediate LSB planes. Furthermore, the cover image and its planes are rotated at different angles using a secret key prior to embedding, deceiving the attacker during data extraction. The usage of message blocks division, TLEA, image scrambling, and the cyclic18 LSB method results in an advanced security system, maintaining the visual transparency of resultant images and increasing the security of embedded data. In addition, employing various secret keys for image scrambling, data encryption, and data hiding using the cyclic18 LSB method makes the data recovery comparatively more challenging for attackers. Experimental results not only validate the effectiveness of the proposed framework in terms of visual quality and security compared to other state-of-the-art methods, but also suggest its feasibility for secure transmission of diagnostically important keyframes to healthcare centers and gastroenterologists during wireless capsule endoscopy. [ABSTRACT FROM AUTHOR]

The article presents the authors' views on the responses of several scholars to their article "Birnbaum-Saunders Distribution (BSD): A review of models, analysis and applications." These scholars include college professors Augustine Wong, Artur Lemonte and Gauss Cordeiro, and Biswabrata Pradhan. Topics include inferential methods for classical BSD parameters, expectation-maximization algorithms for determining maximum-likelihood estimate, and unified skew-ellipitical BS distribution.

With the popularization of standardized classrooms in colleges and universities, it is possible to collect video data of students' class status through the camera device in the classroom. With abundant video data sources, it is easy to obtain big data of students' class status images. Unstructured video big data is a topic worthy of research in improving teaching quality. First, the current teaching ability of teachers in colleges and universities is investigated, and its problems are found. Then, the You Only Look Once (YOLO) network in the object detection network is mainly studied. The deficiencies in the network structure are further explored and optimized. It is used in real classroom scenarios as well as on student expression detection problems. Finally, the proposed scheme is tested. The test results show that at present, 20% and 38% of teachers in higher vocational colleges think that they are "dissatisfied" with their classroom teaching and practical guidance ability. And 38% of teachers wanted to improve the bad situation. The accuracy of the proposed model for student expression detection is higher than that of faster-region convolutional neural network and mask-region convolutional neural network by more than 8%, higher than the YOLO v3 model by more than 4%, and higher than YOLO v3 Tiny model above 6%. The proposed model provides some ideas for the application of deep learning technology in the improvement of teachers' teaching ability. [ABSTRACT FROM AUTHOR]

This Article focuses on what it calls the "algorithm method," a common method used to teach tax classes that presents students with unambiguous problems that guide students through complex statutes and regulations. The Article describes a novel teaching tool created by the author: a website that randomly generates tax problems with objectively correct answers; multiple choice answers that reflect common errors that students make; and explanations for each answer that either respond to the underlying error or give a full explanation of the correct answer. The Article explains the purpose and use of the website for professors and students, respectively, and proposes approaches to make using the website, and indeed the algorithm method, more effective. [ABSTRACT FROM AUTHOR]

Shuffled frog leaping algorithm (SFLA) is a recent addition to the family of memetic algorithms that takes its inspiration from the natural foraging behavior of frogs. In SFLA the colony of frogs is divided into memeplexes of equal size. SFLA gathered the interest of research fraternity to solve many real world complex optimization problems. The basic structure of SFLA posses some inherent limitations. In order to overcome the limitation, in this study an enhanced variant of SFLA is proposed and named as NL-SFLA. Generally the initial population is generated using a traditional pseudo-random numbers which may not be much efficient. In NL-SFLA an attempt has been made to initialize the population of frog by integrating the concept of nonlinear simplex method of Nelder and Mead. Later modification is done in the frog distribution scheme in memeplexes to handle continuous optimization problems. Numerical results of NL-SFLA are compared with the state-of-art algorithms over a set of benchmark problems. Also the efficiency of the proposal is investigated on four real world problems. Simulated results signify the efficacy of the proposal. [ABSTRACT FROM AUTHOR]

The article focuses on professor Stephen C. Graves, who holds joint professorships in the Mechanical Engineering Department and the Engineering Systems Division at Massachusetts Institute of Technology (MIT). It says that Graves has been an influential scholar and his research had an impact on practice and theory. It mentions that Graves' fields of interest are operations management and applied operations research. Moreover, his development of a modeling convention and algorithm is discussed.

The diversity of bibliometric indices today poses the challenge of exploiting the relationships among them. Our research uncovers the best core set of relevant indices for predicting other bibliometric indices. An added difficulty is to select the role of each variable, that is, which bibliometric indices are predictive variables and which are response variables. This results in a novel multioutput regression problem where the role of each variable (predictor or response) is unknown beforehand. We use Gaussian Bayesian networks to solve the this problem and discover multivariate relationships among bibliometric indices. These networks are learnt by a genetic algorithm that looks for the optimal models that best predict bibliometric data. Results show that the optimal induced Gaussian Bayesian networks corroborate previous relationships between several indices, but also suggest new, previously unreported interactions. An extended analysis of the best model illustrates that a set of 12 bibliometric indices can be accurately predicted using only a smaller predictive core subset composed of citations, g-index, q2-index, and hr-index. This research is performed using bibliometric data on Spanish full professors associated with the computer science area. [ABSTRACT FROM AUTHOR]

An innovative algorithm is developed for obtaining spreadsheet regression measures used in computing out-of-sample statistics. This algorithm alleviates the leave-one-out computational simulation complexity and memory size problems perceived in computing these statistics. Hence, the purpose of this article is to describe a computationally enhanced algorithm that gives spreadsheet users advanced regression capabilities thereby adding a new dimension to spreadsheet regression operations. These statistics include diagonals of the hat matrix, legitimate forecasting intervals, and PRESS residuals. These computational innovations promote learning while eliminating spreadsheet inadequacies thereby making spreadsheet regression attractive to academicians in teaching and practitioners in acquiring further application competence. [ABSTRACT FROM AUTHOR]

An interview with Mario A. de Oliveira, associate professor at Mato Grosso Federal Institute of Technology in Brazil, on his research Embedded Application of Convolutional Neural Networks on Raspberry Pi for Structural Health Monitoring, is presented. Topics of the interview include structural health monitoring (SHM), the significance of using convolutional neural networks algorithm in SHM, and his plan for the development of his work.