Reviewerly/exHarmony huggingface dataset:
Introduction to the special section “pattern recognition for Recent and Future Advances On intelligent systems” (SS:ISPR22)

Stereo image super-resolution (StereoISR) aims at recovering high resolution (HR) details and realistic textures from low-resolution (LR) left-right pairs with stereo correspondence. Recently, some advanced 2D methods have achieved good performance in realistic texture reconstruction via texture attention mechanism in a reference-based training way. However, the existing 3D super-resolution (SR) datasets lack the HR reference to obtain the LR-to-HR reference pair for the reference-based training. To overcome this problem, we propose a novel StereoISR network with a joint texture and parallax attention module (JTPAM), where the down-sampled LR (named super-low-resolution (SLR)) image and the LR image are taken as the SLR-to-LR reference pair, to simulate the texture conversion relationship between the LR and HR images. Additionally, the parallax attention mechanism is utilized to maintain the stereo correspondence by calculating the feature similarity along the Epipolar line. Experiment results on the Flickr1024, Middlebury, KITTI 2012 and KITTI 2015 datasets show that our network achieves the state-of-the-art (SOTA) performance over the compared methods, and the stereo SR images generated by our network have abundant binocular information and clear texture details.
Curriculum Design of Chemical Knowledge in Courses of Environmental Geotechnical and Ecological Protection for New Agricultural Sciences

The startup of new agricultural science projects in China provides the new development opportunities for the universities and their specialty as well as course reforms.Because of the serious shortage of chemical knowledge in the courses of environmental, geotechnical and ecological protection related to agricultural and rural infrastructure of civil engineering in new agricultural science, this paper systematically analyzes and optimizes the course contents, frame and structures of basic chemical knowledge in related courses of civil engineering.After years of teaching practice, the curriculum reform has greatly improved the chemical knowledge application in engineering design and practical ability of civil engineering students, and is proved to provide a good theoretical and methodological guidance for engineering practice.
Teaching Hanzi Using Correct Stroke Order and Bujian: An Analysis of CEGEP Students' Learning Experiences

Learning hanzi (Chinese characters) has been regarded as a challenging task due to the complex strokes, the rupture between shape and sound, and the memorization required.Targeting a Chinese as a Foreign Language (CFL) student audience, this paper demonstrates the pedagogical benefits of learning the correct Chinese order of strokes (COS) and bujian (component) for hanzi acquisition.This research was conducted at a CEGEP (Collège d'enseignement général et professionnel in French; General and Vocational College in Quebec in English) located in metropolitan Montreal.Results showed that students' knowledge of COS and bujian improves the outcome of their handwriting.When writing hanzi without first being demonstrated COS, students tended to make mistakes in strokes, shapes or structure, such as an extra hook or an asymmetrical appearance.However, after being instructed the correct COS, the mistakes decreased.Moreover, it is noticeable that the effects of COS interweaved with students' previous knowledge of bujian.When students wrote new hanzi that were comprised of bujian that they had been previously exposed to, they often wrote correctly, with appropriate shapes and space arrangements.Students' surveys further affirmed their appreciation of COS and their preference of an instructor's in-person guidance while taking advantage of multimedia teaching tools for assistance.Following these findings, this paper analyzes several useful pedagogical approaches, including the phenomenographic teaching approach, that allow instructors to prioritize learners' perceptual experiences through engaging and proactive learning processes.
Controllable Wireless Spoofing Attack Based on Conditional BEGAN and Auxiliary Channel Sensing

This paper investigates how to build a controllable wireless spoofing attack launch framework that is driven by fundamental channel modeling and practical wireless datasets. First, we propose a wireless spoofing attack scheme against the defense mechanism with adversarial deep learning. To obtain channel characteristics and facilitate offline training of the attack model, auxiliary channel sensing is proposed with fundamental channel modeling. Based on these, a conditional boundary equilibrium generative adversarial network (CBEGAN) is designed with adversarial autoencoder (AAE), which takes true labels of signals and channel characteristics as conditions and enables the generation of controllable spoofing signals to fool the protected legitimate classifier. We verify the performance of the proposed spoofing attack scheme with CBEGAN and channel sensing by using wireless datasets, which contain signal data of multiple emitters and modulation types. Results show that the proposed scheme outperforms random attack, replay attack, and the recent attack scheme based on generative adversarial network (GAN) when a single legitimate emitter sends a fixed modulation type. It is also shown that the average attack success probability of the proposed CBEGAN attack model can reach more than 80% while mimicking multiple emitters and modulation types. The performance of the proposed scheme on different channel conditions including signal-to-noise ratio (SNR) and K-factor of the Rician fading channel is evaluated.
Speech-to-Gender Recognition Based on Machine Learning Algorithms

Speech recognition has several application areas such as human machine interaction, classification of phone calls by gender, voice tagging, STT, etc. Predicting gender from audio signals is a problem that is easy for humans to solve, difficult to solve by a computer. In this study, a model based on MFCC and classification with machine learning is proposed for gender estimation from Turkish voice signals. Within the scope of the study, 58 different series and films were examined and a new original dataset was created with 894 audio recordings consisting of 5 sec sections taken from them. Mel-frequency cepstral coefficients (MFCC) and spectrogram, which are frequently used in the literature, were used for feature extraction from audio data. The results were first evaluated separately using two features in one way. A hybrid feature vector was then created using two feature vectors. Different machine learning algorithms (LR, DT, RF, XGB etc.) were tested in the classification process and it was seen that the best accuracy was achieved in the hybrid model and logistic regression with 89%. Recall, precision and f-score values were obtained as 86.8%, 92% and 89.3%, respectively. The obtained test results revealed that the proposed model, together with the hybrid feature vector used, the original dataset and the classifier based on machine learning, showed classification success in terms of accuracy and was a stable and robust model.
Development Of Economic Mathematics Learning Media Based on Android

The design thinking development model, which covers the processes of empathizing, defining challenges, ideation, prototyping, and testing, is the subject of this research, which is a sort of research and development.Students and lecturers are the research's subjects.An android-based application for economic mathematics classes is the focus of this study.The goal of this work is to create a learning tool for economic mathematics based on an Android application.An android-based application product for economic mathematics content is the study's output, and it is available for download from the Google Play store.Users claim that the validation test findings are practical for the application's presentation and menus and helpful for autonomous as well as online and offline learning.This application is used to connect with students and professors and to support learning.
A Covert Communication Method Combining Secret Sharing and Steganography

In the distributed environment, when all parties carry out the cooperative computing, there are problems of low scalability and privacy leakage. To solve the above problems, this paper proposes a scheme that integrates the secret sharing and distributed steganography. Arnold transform is performed to encrypt the secret before the communication, and this transform can effectively improve the robustness of secret transmission. After a secret is embedded in a cover, the scheme uses random numbers to generate multiple sub-stego-objects, then carries out the distributed storage and communication for these sub-stego-objects. The identity of the sender is verified by the receiver using Schnorr zero-knowledge proof at the local, which reduced interaction between the sender and the receiver, and no more information is not obtained by the receiver except the stego-object. As a result, the receiver does not communicate directly with the sender, which, to some extent, can avoid the attention of the steganography from external attackers. Additionally, this scheme can still work even in the case that some users were disconnected from the system after a stego-object is transmitted. After analyzing how the scheme supports more senders, the correctness of the scheme is also proved in detail.
Anomaly Detection of Spatiotemporal Sub-trajectories Based on Local Outlier Factor

In the field of early warning and surveillance, it is of great significance to detect abnormal behaviors of targets from massive spatiotemporal trajectories for situation cognition. At present, most trajectory anomaly detection methods are based on the whole trajectory and are insensitive to local anomaly sub- trajectories. In this paper, Spatiotemporal Sub-trajectories Anomaly Detector (SSAD) is proposed. Firstly, Hausdorff distance measurement is applied to sub-trajectory in spatiotemporal dimension to solve the problem of insensitivity to course and velocity anomalies. Then, Spatiotemporal Sub-trajectories Hausdorff Local Outlier Factor (SSHLOF) is constructed based on Local outlier factor (LOF), which can effectively identify local abnormal behaviors in the whole trajectory. Finally, SSAD is implemented in simulation and measured trajectory data respectively. Experiment results show that our method can effectively detect local abnormal behavior of sub-trajectory, which has a good application prospect in intelligent surveillance tasks.
Optimization and Comparative Modeling of Technology English Automatic Translation Algorithms for Mobile Platforms

Educator Bruner said, “The best stimulus to learning is interest in the material.” According to research: in teaching activities, images are more likely to attract people's attention than words. And it is very convenient to disseminate images in smart mobile devices. The design of mobile learning platform is mainly to facilitate learners to use mobile devices to access the resources on the server through wireless network at any time. The system mainly includes three parts of the learning resource server, wireless network, mobile terminal. In data search, different modes of complete matching and fuzzy matching can be selected, and the fuzzy matching mode is mainly used to improve translation memory. Translation memory is the key feature and core part of automatic translation system. At present, the evaluation of all translation products revolves around a certain fixed standard to evaluate a certain attribute of the product. There is no fixed evaluation standard to evaluate the quality of machine automatic translation. Therefore, it is difficult to evaluate the quality of machine automatic translation with high accuracy. Therefore, an automatic translation machine speech signal recognition method based on spectrum features is proposed. The frequency spectrum energy function is used to separate the noise characteristic coefficients and adjust the speech signal filter. According to the frequency threshold of core region, the filter parameter correction is determined, and the attenuation coefficient is introduced. Related experiments and simulations show that this newly designed Chinese-English phrase translation combined machine automatic translation system has a higher translation accuracy and recall rate of 11.2%, an error rate of 1.3%, and a strong intelligence improvement of 6.8%.
Computer-Aided Diagnosis of White Blood Cell Leukemia using VGG16 Convolution Neural Network

Leukemia is a type of blood cancer caused by the uncontrolled multiplication of white blood cells (WBCs) within the bone marrow. This kind of white blood cell (WBC). Current Convolution Neural Networks are the best solution for medical image analysis, such as detection and classification (CNN). The structure AlexNet, VGG16, GoogLeNet, and ResNet50 was evaluated with complete learning and transfer learning when training CNN, which is the cornerstone of the Convolution layer. The technique successfully determined WBC cells 100 percent of the time at the end of the research. With transfer learning, VGG16, one of the CNN designs, has shown the greatest results. Lymphocyte cell types were determined with a 98.53 percent accuracy rate, Monocyte cell types were determined with a 97.41 percent accuracy rate, Basophil cell types were determined with a 98.48 percent accuracy rate, Eosinophil cell types were determined with a 96.16 percent accuracy rate, and Neutrophil cell types were determined with a 94.05 percent accuracy rate. On each of these VGG16 datasets, their results were some of the best, outperforming some convolutional network models. The average accuracy of the methods applied in image analysis to detect leukemia was 97.16%.
The Evaluation Data Mining of the Comprehensive Practice Platform of Single-Chip Microcomputer in Vocational Colleges based on the Intelligence of Skill Competition Assessment

Based on summarizing the basics of my own training attempts in the single-chip skill competition, this paper thinks about the teaching reform of secondary vocational single-chip microcomputer courses from the perspectives of teaching content, teaching methods, and assessment methods. The current situation and development trend of the experimental teaching platform of single-chip microcomputer are investigated and studied, and its success and shortcomings are analyzed. Introduced, and other related knowledge. And use data warehouse and online analysis and processing technology to build a comprehensive evaluation system of teaching quality combining “plane” and “three-dimensional”, and establish a multi-dimensional comprehensive evaluation online analysis mining model.
Rich Media and Virtual Cloud Service Platform Guide the Design of the Ideological Integration Guidance Platform for College Students

The article made a brief analysis of the current status of rich media and the development planning and design of virtual cloud platforms in the future, and clarified that the establishment of rich media must be based on comprehensive cloud computing services. Therefore, in order to ensure the quality of the ideological integration guidance platform for college students, a corresponding cloud platform must be established. The system architecture of the public Baidu cloud and mobile Internet integration and the overall software framework of the CQCSMIS system are given, and Android, J2EE and MySQL cloud databases are applied. Related technologies have completed the development, deployment and operation of the system.
Observer Design for GNSS-aided Position Estimation of Autonomous Surface Vessels

State estimation is one of the crucial technologies in the navigation system of unmanned surface vehicles (USVs). However, many previous works on Kalman Filter design ignores the accuracy loss due to the velocity change. In this paper, a Time-varying Extended Kalman Filter (TEFK) is designed for ASV north-east position estimation in the presence of time-variant noise and unknown external disturbances to address the challenge. The observer is verified through the simulation with Cybership II. The simulation results have shown its robustness and effectiveness in estimating the states compared to the nonlinear observer. The simulation results have provided strong evidence that the proposed method can be considered as a practical approach for state estimation.
Ideological Format of the Children's First Reader/Alphabet Book

This article about ideological format of the children's first reader/alphabet book (on the material of the Russian-language textbooks).The article elaborates upon the role of ideologemes in the first textbooks that teach literacy to children.The stable verbal compounds as well as visual means in educational texts can act as representatives of ideological attitudes.The empirical research embraces the selection of ABC books (primers, alphabet books) and first readers published in Russian in pre-revolutionary, Soviet and post-Soviet Russia in the period from 1903 to the present.The authors conclude that the basic universal values actualized by ideologemes do not undergo significant changes over time, while the socio-political artificially formed (phantom) ones transform or disappear.
Balanced Allocation: Patience Is Not a Virtue

Load balancing is a well-studied problem, with balls-in-bins being the primary framework. The greedy algorithm of Azar et al. [SIAM J. Comput., 29 (1999), pp. 180–200] places each ball by probing random bins and placing the ball in the least loaded of them. With high probability, the maximum load under is exponentially lower than the result when balls are placed uniformly randomly. Vöcking [J. ACM, 50 (2003), pp. 568–589] showed that a slightly asymmetric variant, , provides a further significant improvement. However, this improvement comes at the additional computational cost of imposing structure on the bins. Here, we present a fully decentralized and easy-to-implement algorithm called that combines the simplicity of and the improved balance of . The key idea in is to probe until a different bin size from the first observation is located and then place the ball. Although the number of probes could be quite large for some of the balls, we show that requires only at most probes on average per ball (in both the standard and the heavily loaded settings). Thus the number of probes is no greater than that of either or . More importantly, we show that closely matches the improved maximum load ensured by in both the standard and heavily loaded settings. We further provide a tight lower bound on the maximum load up to terms. We additionally give experimental data that is indeed as good as , if not better, in practice.
Models: An Assembly of Schematics

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Criticism of Policy Changes in Korean Higher Education

This study looked at the higher education law from the perspective of university education as well as school education to explore authenticity. Starting with the Higher Education Act, which was first enforced in 1998, among the Higher Education Acts revised and enacted until 2022, we will focus on systems that have become a policy change point or have had a significant impact on higher education. Through this, the problem was overviewed and a solution was sought. Korea has achieved policy and institutional improvements through various institutional reforms, and has demonstrated the power of Korean education that could not be found anywhere else in the international community. However, at this point, it is necessary to review the flow of higher education in the past and discuss the policy direction to find consistency and originality in the future. It is also pointed out as a problem that the majority of studies take a bottom-up approach to designing simple policies or systems or implementing them efficiently. In order to find the original meaning of university, major courses of various characteristics must be developed and operated, and learner-centered self-directed learning must be realized. A cquisition o f a c reative p roblem-solving p rocess a s w ell a s t he a bility t o u tilize i t a nd t he exploration of self are privileges of university education, so future university policies should be promoted in this direction. Lastly, active support for the formation of a network of policy researchers is needed at the government level so that higher education researchers can maximize their expertise and expand the scope of higher education research. Finally, in order to solve the problems of university education, the process of change was reviewed centering on the higher education law, which is most direct to the field of university education, policy changes in future university education were examined, and alternatives to university education in the future were presented. In this respect, I would like to give meaning to this study.
Design and implementation of dance teaching live broadcast system of computer network platform

In artistic aesthetic, dance art is the most beautiful form of expression with special beauty. It can not only convey emotion, but also present beauty. It integrates time and space to express harmonious rhythm. It is not only an art to beautify human action, but also a wonderful appreciation beyond the soul. Dance education presents artistic image based on aesthetic way, which causes influence and inspiration to people under imperceptible influence. In order to improve people's dance appreciation and appreciation level, it is necessary to vigorously popularize dance education for amateurs and non-dance professional students, so as to cultivate their dance aesthetic appreciation, training, performance and other abilities. Dance teaching needs to combine body movements with music, clothing, lighting and other artistic elements, which is limited by time and space, and cannot be carried out on a large scale and on a large scale. The mobile terminal learning mode, based on the video live broadcast and communication mode, directly provides a new development path for dance popularization and education. Therefore, this paper designs the dance teaching live broadcast system with the computer network platform as the carrier.
Smart Portfolio Trading Strategy Based on Random Forest Model with Limited Information

Quantitative trading refers to using advanced mathematical models instead of people's subjective judgment and the use of computer technology and big data to formulate trading strategies. Quantitative trading dramatically reduces people's irrational investment strategies due to emotional fluctuations. With the continuous development of global financial markets, quantitative trading has been welcomed by more and more investors. This paper takes gold and bitcoin as the research object, takes machine learning as the method, and aims to maximize the return of their portfolio. Before December 26, 2016, the daily stock price changes are selected as the training set, and the daily stock price changes from December 26, 2016, to September 10, 2021, are selected as the test set. Establish the rate of the return prediction model and backtest system, select the trading decision through a reasonable investment portfolio and buying and selling time points, and conduct sensitivity analysis. This paper provides an investment strategy on bitcoin and gold, which has the advantages of high yield, strong stability, and high application value.