Reviewerly/exHarmony huggingface dataset:
Teaching of English Reading Based on Large Unit Teaching Mode in High School

As a popular teaching concept in recent years, Large Unit Teaching (LUT) has its own advantages in solving the problems existing in the teaching of English reading compared with other methods. The purpose of this study is to address the aforementioned issues with the use of LUT. The study suggested that vertical and horizontal integration of teaching content in LUT satisfies the needs of different levels of students who are restricted by the one-fits-all teaching materials. The processing of reading materials in LUT is guided by a core at the macro level, which avoids the scattered distribution of knowledge points and reduces the cognitive burden of students. In addition, with students as the subject, LUT pays attention to the connection between teaching content and students' life, arousing students' interest in learning as well as encouraging students to apply what they have learned. The corresponding suggestion are offered to better implement LUT in classroom practice.
Encryption method of annual bad debt accumulation information and reimbursement based on ant colony algorithm

In the enterprise financial information management, the information security of reimbursement and annual bad debt accumulation cannot be ignored. In order to solve the problem that the plaintext exceeds the byte limit in information encryption and affects the encryption calculation time, an annual bad debt accumulation information and reimbursement encryption method is proposed based on ant colony algorithm. We first define all the variables including encryption method, organizations’ information and reimbursement encryption, Ant colony algorithm and then we utilized elliptic curve digital signature to process the data and to prevent the annual bad debt accumulation information data from being tampered with or replaced in the transmission process. Furthermore, one dimensional Logistic chaotic map and Tent chaotic map with good randomness are selected to generate chaotic sequences as keys, and they are grouped based on ant colony algorithm. Each group is XOR with the previous group of ciphertext, which shortens the plaintext bytes. The design of the encryption method for the accumulated information of annual bad debt accrual is completed through the operation and the second round of encryption. The plaintext data files with file sizes of 10MB, 50MB, 100MB and 200MB are selected for serial and parallel encryption calculation. It shows that the average speedup ratio of this method is 3.90, which is 0.44 and 0.46 higher than the encryption methods based on genetic algorithm and particle swarm optimization algorithm, so it has better encryption performance.
FORMATION OF INFORMATION AND DIGITAL COMPETENCE OF PRIMARY SCHOOL PUPILS IN THE CONTEXT OF THEIR CYBER SOCIALIZATION

The paper substantiates and meaningfully develops information and digital competence of primary school pupils in the context of their cyber socialization. The most significant features of cyber socialization are highlighted: opportunities to communicate in the Internet environment in real time; restructuring of the leisure and information sphere, changes in characteristics of the educational process in the process of cyber socialization. Human activity in the virtual socializing environment of cyberspace is not limited to the position of a viewer, reader or listener, the pupils can organize and be engaged in action, they can influence what is happening; using modern information and communication technologies which leads to the development and transformation of human activities at almost any age due to the emergence of new skills, operations, procedures and methods of performing actions, new target and motivational-semantic structures in a virtual socializing environment. It is determined that social pedagogy, the subject of which is socialization and social education, fits into the concept of cyberpedagogics, digital pedagogy, since it is also aimed at developing the skills of interaction and communication, education and training in cyberspace, the development of socio-pedagogical technologies of cyber socialization, the implementation of optimal, depending on the age of the child, socio-pedagogical support and socio-pedagogical support for this process, timely adequate assistance to the child in the process of their cyber socialization. It is shown that the success of the process of a child’s cyber socialization is largely determined by the necessary level of information and digital competence formation, adequate for the age, on the one hand, and the digital information technologies, on the other. Based on the analysis, it is established that the younger pupils manifest themselves differently in each of these spheres of life. At the same time, the content of their digital competence is influenced by their age, social environment, educational environment, family, material and technical capabilities, and conditions. It should be especially taken into account that the process of active introduction of information and computer technologies in all spheres of life is accompanied by uneven access to digital technologies due to the differentiation of territories and uneven development in different social and age groups. The theoretical analysis of scientific concepts that are caused by the process of pupils cyber socialization, allowed to formulate the initial concept of research, where the information and digital competence of a primary school pupil is understood as the ability acquired by the student at the basic level to constructive and responsible social interaction and communication with adults and peers, to personal development based on the acquisition and updating of knowledge, skills and abilities of information and digital technologies, to high behavioural motivation and network ethics in the school digital environment.
Cranio-maxillofacial post-operative face prediction by deep spatial multiband VGG-NET CNN.

Current plastic and reconstructive surgery computational techniques are not precise and take a long time to perform. Therefore, these limitations reduced the adoption of computational techniques. Although computer-aided surgical preparation systems may help to enhance clinical results, minimize operating time and costs, they are too complicated and require detailed manual information, which restricts their usage in doctor-patient communication and clinical decision-making. In order to obtain the optimal aesthetic and reconstruction treatment results, these techniques must be designed and implemented carefully. Computer-aided modeling, planning, and simulation techniques enable the preoperational evaluation of various therapeutic strategies based on the 3D patient models. We offer the new deep-learning architecture for diagnostics, risk stratification, and post-operative simulation for face prediction. Initially, preprocessing was done by using the weighted adaptive median filter and Laplacian partial differential equation-based histogram equalization. Then the target area was converted to 3D for clear visualization by using the Smart restorative frustum model. Finally, the post-operative face prediction was constructed by using the deep spatial Multiband VGG NET CNN. We obtained a face dataset of 313,318 CT and their clinical records from different centers. The algorithms were developed by 21,095 scans (Qure25k data set). In addition, CQ500 datasets from various centers were compiled in two batches, B1 and B2, to validate the algorithms clinically. Four hundred ninety-one scans used the CQ500 dataset. Initially, we reconstructed the input image and then devised the post-operative face computationally. The suggested deep spatial Multiband VGG NET CNN showed the high range of post-operative face prediction accuracy. Therefore, successful metrics such as the Jaccard and dice scores have shown accurate outcomes compared to other traditional methods. MATLAB was used to obtain the output of proposed work. With the help of the suggested classifier, the prediction accuracy was 93.7%, sensitivity was 99.9%, and specificity was 99.8%, all of which were higher than traditional approaches. Here, the suggested method provides better results for post-operative face prediction to the applied dataset than any other existing mechanisms. It is a generalized attempt that can apply to other similar datasets as well.
Practice-oriented Technologies in the Training of Competitive Specialists in the Information and Documentation Industry

In the article examines the problem of training competitive specialists in information, library and archival affairs in higher education institutions. Substantiation of the concepts «technology», «practice-oriented technologies», «professional culture of a specialist in the information and documentation industry» are presented. Through clarifying the essence of the concept of «learning technology», defining its structure, the expediency of using practice-oriented technologies as the most appropriate tool of the educational process, which results in the skilful application of acquired knowledge and skills in practice. It is noted that the use of practice-oriented technologies in the training of competitive specialists in the information and documentation industry contributes to a clearer and more regulated influence (both on the part of the teacher and the student) on all stages of the learning process: from providing students with knowledge, skills and abilities – to control of their absorption. In this case, an important condition is to provide feedback (quality, selection of material, consideration of the situation, control, self-control, etc.).
Discovering Boundary Values of Feature-based Machine Learning Classifiers through Exploratory Datamorphic Testing

Testing has been widely recognised as difficult for AI applications. This paper proposes a set of testing strategies for testing machine learning applications in the framework of the datamorphism testing methodology. In these strategies, testing aims at exploring the data space of a classification or clustering application to discover the boundaries between classes that the machine learning application defines. This enables the tester to understand precisely the behaviour and function of the software under test. In the paper, three variants of exploratory strategies are presented with the algorithms implemented in the automated datamorphic testing tool Morphy. The correctness of these algorithms are formally proved. Their capability and cost of discovering borders between classes are evaluated via a set of controlled experiments with manually designed subjects and a set of case studies with real machine learning models.
Electric Democracy: Proof of Work to secure Elections

Electronic voting consistently fails to supplant conventional paper ballot due to a plethora of security shortcomings. Not only are traditional voting methods mediocre in terms of convenience and interface, they also encompass principal-agent problem, where the state may have vested interest in the outcome of the ballot. Electronic voting protocol using cryptography to deliver in zero trust environments is long overdue. Here I propose using Proof of Work algorithm at user devices in combination with other well-known security primitives to build a zero-trust voting system. The state would only issue single-use voting authorizations to its citizens, while zero trust design would allow conducting elections by an open-source platform with enhanced observability. It is also hypothesized that the heighten availability of plebiscite by means of the proposed design may ultimately change the way our society participates in the policy making.
Does Code Quality Affect Pull Request Acceptance? An empirical study

Background. Pull requests are a common practice for contributing and reviewing contributions, and are employed both in open-source and industrial contexts. One of the main goals of code reviews is to find defects in the code, allowing project maintainers to easily integrate external contributions into a project and discuss the code contributions. Objective. The goal of this paper is to understand whether code quality is actually considered when pull requests are accepted. Specifically, we aim at understanding whether code quality issues such as code smells, antipatterns, and coding style violations in the pull request code affect the chance of its acceptance when reviewed by a maintainer of the project. Method. We conducted a case study among 28 Java open-source projects, analyzing the presence of 4.7 M code quality issues in 36 K pull requests. We analyzed further correlations by applying Logistic Regression and seven machine learning techniques (Decision Tree, Random Forest, Extremely Randomized Trees, AdaBoost, Gradient Boosting, XGBoost). Results. Unexpectedly, code quality turned out not to affect the acceptance of a pull request at all. As suggested by other works, other factors such as the reputation of the maintainer and the importance of the feature delivered might be more important than code quality in terms of pull request acceptance. Conclusions. Researchers already investigated the influence of the developers' reputation and the pull request acceptance. This is the first work investigating if quality of the code in pull requests affects the acceptance of the pull request or not. We recommend that researchers further investigate this topic to understand if different measures or different tools could provide some useful measures.
Reconnaissance and Confirmation Task Planning of Multiple Fixed-Wing UAVs with Specific Payloads: A Comparison Study

In this study, the reconnaissance and confirmation task planning of multiple fixed-wing unmanned aerial vehicles (UAV) with specific payloads, which is an NP-hard problem with strong constraints and mixed variables, is decomposed into two subproblems, task allocation with “payload-target” matching constraints, and fast path planning of the UAV group, for which two mathematical models are respectively established. A bi-layer collaborative solution framework is also proposed. The outer layer optimizes the allocation scheme between the UAVs and targets, whereas the inner layer generates the UAV path and evaluates the outer scheme. In the outer layer, a unified encoding based on the grouping and pairing relationship between UAVs and targets is proposed. The corresponding combinatorial mutation operators are then designed for the representative NSGA-II, MOEA/D-AWA, and DMOEA-ϵ C algorithms. In the inner layer, an efficient heuristic algorithm is used to solve the path planning of each UAV group. The simulation results verify the effectiveness of the cooperative bi-layer solution scheme and the combined mutation operators. At the same time, compared with the NSGA-II and MOEA/D-AWA, DMOEA-ϵ C can obtain a significantly better Pareto front and can weigh the assigned number of UAVs and the total task completion time to generate more diversified reconnaissance confirmation execution schemes.
Text Reconstruction Method of College English Textbooks from the Perspective of Language Images

With the continuous development of social economy, English learning plays an increasingly important role in daily communication. However, the update speed of English textbooks is far lower than the development speed of English. How to reconstruct the text of English textbooks in colleges and universities to improve the learning effect of college English has become an urgent problem to be solved. In order to solve this problem more effectively, this paper proposes a text reconstruction method for college English textbooks from the perspective of language images. First, this paper proposes a text reconstruction network model for college English textbooks, which includes two self-network models, namely, a text feature extraction network and an image feature extraction network. Second, text designs an English text feature reconstruction network to fuse image features and text features to guide the generation of new English texts according to the generated emotions. Finally, through a large number of experiments, it is proved that the text reconstruction method of college English textbooks from the perspective of language images can effectively generate new texts of college English textbooks, enhance the emotional color of college textbooks, and improve the effect of English learning.
Navigator of block programming environments

The theme "Algorithmization and programming" is one of the key themes in the school informatics course. The material of this theme is present in the tasks of the Main State Examination and the Unifed State Examination. Most often, its study takes place in grades 8–9 in the programming languages Python, Pascal, C++. However, for successful study, it is very important to lay the foundations and basic concepts of the theme in the lower grades. To do this, you can use block programming, in which the program is not typed manually by the student, but is assembled from pre-prepared blocks, like a puzzle. There are a fairly large number of such block programming environments, and the authors of this article decided to create the so-called Navigator of Block Programming Environments, in which to visually structure the available information. For each environment represented, the Navigator contains brief information about the environment, links to the ofcial website and a number of useful materials for acquaintance and study of this environment. It was decided to include environments in the Navigator that are free and cross-platform. It can be useful for informatics teachers to get acquainted with new block programming environments that can be used as а part of regular and extracurricular activities.
Experiences of English as Foreign Language student using ICT in their learning process

This article presents the results of a narrative analysis that looked into some EFL students’ experiences related to their English learning processes and the use of ICT (Information and Communication Technologies). The study was carried out at a university in southern Colombia. The main objective was to understand the participants’ experiences in the institutional English courses at a university while using ICT during their language learning process. The data was collected through in-depth interviews and life stories. The English students’ experiences are relevant to validating a new teaching tool or strategy in a specific context. The main conclusion indicates that ICT in language teaching needs to be articulated with other appropriate tools, practices, methodologies, and approaches to achieve meaningful learning.
Investigating and Auditing Through Facial Recognition Using LBPH Algorithm

Technology is constantly advancing, and many individuals submit various video material to social networking websites such as YouTube or Facebook. Since it has become a source of income for many individuals across the world, it is becoming increasingly vital to utilize it in situations when you need to discover a specific person in several video recordings. Another option is to manually go through each video and try to discover the individual segment in order to extract it. Manual searching can take a long time, and it's practically difficult to find a specific video in which a person appears. A person can only focus for 20 to 30 minutes on average to recognize or identify the person in the video, and a video stream may take much longer. Due to the huge quantity of data gathered in the multimedia application, such as videos, a human conducting a video search manually may be difficult to do so properly in such cases. It is critical to automate the procedure in order to eliminate human error and the time it takes to identify the individual in the video footage. Given its popularity and use in applications ranging from our mobile phone games to high-end computers for future forecasting, artificial intelligence may be used to address many difficulties, including this one. In this work, a pre-trained facial recognition classifier known as the Linear Binary Pattern Histogram (LBPH) is utilized to recognize a person in video clips and provide recorded proof of each video in which a person appears, along with the time stamp of his or her visibility. Here, a method is proposed for identifying and tracking down a missing individual utilizing massive video data and Artificial Intelligence without the need for human participation.
A Drug Safety Traceability Model Based on Big Data

In order to solve the storage and management problems of heterogeneous drug data, this paper uses big data technology to complete the cleaning and distributed storage of drug data, and improve the function of data sharing and traceability. At the same time, in order to improve the drug traceability function, ensure the reliable storage of traceability information, and make the traceability process more reliable. This paper will put forward a drug traceability system model based on big data on the basis of existing research. Secondly, an evidence chain framework is proposed to verify evidence files. At last, the simulation experiment is carried out to test and illustrate the credibility of the traceability verification model.
Research on Automatic Tracking of Marking Points in Sports Image Sequences Based on Fuzzy Control Algorithm

research-article Research on Automatic Tracking of Marking Points in Sports Image Sequences Based on Fuzzy Control Algorithm Author: Jun Zhao Wuhan Railway Vocational College of Technology, China Wuhan Railway Vocational College of Technology, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 817–821https://doi.org/10.1145/3544109.3544360Published:18 July 2022Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Computer Aided Analysis of Sports Sequence Images Based on SIFT Algorithm

research-article Computer Aided Analysis of Sports Sequence Images Based on SIFT Algorithm Authors: Lishui Wang Hefei Technology College, China Hefei Technology College, ChinaView Profile , Jie Zhang Hefei Technology College, China Hefei Technology College, ChinaView Profile , Hao Zhu Hefei Technology College, China Hefei Technology College, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 863–867https://doi.org/10.1145/3544109.3544369Published:18 July 2022Publication History 0citation7DownloadsMetricsTotal Citations0Total Downloads7Last 12 Months7Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access