Reviewerly/exHarmony huggingface dataset:
DESIGNING A TECHNOLOGY FOR THE FORMATION OF PROFESSIONAL RESPONSIBILITY AMONG CADETS OF A DEPARTMENTAL UNIVERSITY

The problem of forming the professional responsibility of cadets of departmental universities, which is relevant for the pedagogy of vocational education, is considered in a technological context - through the stages of design. The stages of process design are singled out: goal-setting, determination of the level characteristics of the formation of the required professional responsibility, design of the Program and the criteria-evaluation apparatus with the determination of its component formation on the basis of specially selected and developed by the author of control, measuring and diagnostic tools. When designing a program for the formation ofprofessional responsibility, cadets identified different types of risk-preventive situations as the main content unit of design.
Scope and Closures

A scope or environment is something functions and expressions are associated with that tells them what value each variable refers to. It is used to figure out which environment expressions are evaluated in. The same variable name can be used in many places in a program, referring to different things in different locations, but the scope of an expression tells R exactly how to map names to values. In R, you have both implicit environments—that I will tend to call scopes—and explicit environments. The former is what you will use in most everyday programming, while the latter is used in more advanced techniques, which mostly add flexibility in how you can provide interfaces to users. The way that R maps names to values is the same in both settings, however, and understanding how this works can make it easier to write functions and understand advanced packages.
Improvement of CenterNet Based on Feature Pyramid Networks

To solve the problem of lacking multi-scale feature fusion in anchor-free object detection algorithm CenterNet, an improved CenterNet-F algorithm combined with feature pyramid is proposed. Based on the network structure of CenterNet, the data processing method batch normalization is replaced by group normalization with better performance. The multi-scale fusion of features is carried out in the network output head combined with feature pyramid structure, which effectively improves the accuracy of small-scale object detection. The experimental results show that the detection accuracy of the improved algorithm is improved from 30.1 AP to 30.6 AP for all scale objects on coco dataset, and 3.5 AP for small scale objects.
Supplemental Material: Big data to support geoscience recruitment: Novel adoption of topological data analysis in geoscience education

&lt;p&gt;Supplemental Text S1: An illustrative example of topological data analysis: Determining organ function through cellular data. Figure S1: Point cloud data of cellular data in multidimensional space. Figure S2: Visualization of a series of data frames through the lens of the number of mitochondria in the cell with a frame length of 100 mitochondria and 50% overlap. Figure S3: Algorithm clustering. Table S1: Career motivation items. Table S2: Scales incorporated into survey.&lt;/p&gt;
Robust Image Hashing in Encrypted Domain

In recent decades, a large number of robust image hashing methods have been designed for image authentication. However, existing hashing methods generate hash sequences from the plaintext images. To protect the privacy of image content, we propose a robust image hashing method for encrypted images in this article. In our method, the hash sequence of an image can be extracted from its encrypted version without decryption, and the obtained hash sequence is consistent with that extracted from the corresponding plaintext image. To achieve this, the plaintext image is encrypted using Paillier cryptosystem. Then, according to the homomorphism, hash sequence can be extracted using Walsh-Hadamard transform (WHT) and histogram statistic. For application, a general framework for image authentication in encrypted domain is also presented, including three main entities, i.e., image owner, cloud server and authentication user. An image can be efficiently authenticated by the cloud server without disclosing the image content of the image owner. Compared with typical hashing methods that designed for plaintext domain, our method achieves satisfactory privacy-preserving performance, and also has better performances of image authentication, i.e., robustness and discrimination.
COMPARATIVE STUDY OF CENTROID DEFUZZIFICATION AND MAXIMUM DEFUZZIFIER MODEL MEAN OF MAXIMA ALGORITHM FOR CORONARY ARTERY DISEASE DIAGNOSIS

Objective: Cardiovascular, especially coronary artery disease (CAD), is the number one cause of death globally. Every year, more people die from cardiovascular disease than any other disease. There are several factors and symptoms of coronary artery disease that are easily recognized. To find out the severity of CAD, a diagnosis of CAD is needed to facilitate medical treatment. This study aims to explain the diagnostic process for CAD using a fuzzy system and to determine the accuracy of the fuzzy system. Design and method: This study uses a fuzzy system to diagnose the severity of coronary artery disease. Input variables used in the study were gender, age, pulse, systolic blood pressure, cholesterol, blood sugar, triglycerides, electrocardiogram (ECG), chest pain, shortness of breath, and cough. In making the system, 90 data were used which were then divided into two types of data, namely 70 training data and 20 testing data. The fuzzy inference system used in this study is the Mamdani inference system which uses centroid defuzzification and maximum defuzzifier mean of maxima (MOM). This defuzzification process is used to diagnose types of coronary artery disease, namely: CAD level 1 (Asymptomatic), CAD level 2 (Angina Pectoris), and CAD level 3 (Acute Myocardial Infarction). Results: The results of the research on the application of the fuzzy system for diagnosing coronary artery disease were the accuracy of the defuzzification centroid method of 98.5% for training data and 95% for testing data, while for the MOM defuzzification method, the accuracy level of training data was 98.5% and 90% for testing data. Conclusions: Based on the research results, it can be concluded that the centroid defuzzification method is better than the MOM defuzzification system for the coronary artery disease diagnosis system, so, it can be said that by using a fuzzy system with centroid defuzzification, the probability of correctly diagnosing one patient is 95%.
Design and implementation of a face recognition classroom attendance system based on the Django framework

Compare the current situation of classroom management at home and abroad, take certain feasibility analyses of classroom attendance management systems at home and abroad, and investigate and explore the problems encountered in classroom management in universities nowadays. We design a platform for classroom attendance management of college students and build a modular classroom attendance system, which is mainly divided into a face recognition module, student management module, classroom management module, and database interface module.
Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes.

Humans and animals have a rich and flexible understanding of the physical world, which enables them to infer the underlying dynamical trajectories of objects and events, plausible future states, and use that to plan and anticipate the consequences of actions. However, the neural mechanisms underlying these computations are unclear. We combine a goal-driven modeling approach with dense neurophysiological data and high-throughput human behavioral readouts that contain thousands of comparisons to directly impinge on this question. Specifically, we construct and evaluate several classes of sensory-cognitive networks to predict the future state of rich, ethologically-relevant environments, ranging from self-supervised end-to-end models with pixel-wise or object-slot objectives, to models that future predict in the latent space of purely static image-pretrained or dynamic video-pretrained foundation models. We find that "scale is not all you need", and that many state-of-the-art machine learning models fail to perform well on our neural and behavioral benchmarks for future prediction. In fact, only one class of models matches these data well overall. We find that neural responses are currently best predicted by models trained to predict the future state of their environment in the latent space of pretrained foundation models optimized for dynamic scenes in a self-supervised manner. These models also approach the neurons' ability to predict the environmental state variables that are visually hidden from view, despite not being explicitly trained to do so. Finally, we find that not all foundation model latents are equal. Notably, models that future predict in the latent space of video foundation models that are optimized to support a diverse range of egocentric sensorimotor tasks, reasonably match both human behavioral error patterns and neural dynamics across all environmental scenarios that we were able to test. Overall, these findings suggest that the neural mechanisms and behaviors of primate mental simulation have strong inductive biases associated with them, and are thus far most consistent with being optimized to future predict on reusable visual representations that are useful for Embodied AI more generally.
FunLexia: An Intelligent Game for Children with Dyslexia to Learn Arabic

Learning games have been widely used in international educational programs, however, there is a scarcity of educational games that improve the learning abilities of children with disabilities especially in the Moroccan regions. This paper presents the design of an educational game called ‘FunLexia’ for children with dyslexia. The game has two main characteristics, its design is based on the basis of the linguistic analysis of spelling errors in Arabic made by children with dyslexia and an intelligent character named ‘Lexia’ that accompanies the child in their learning journey and provides real-time feedback to guide the child toward the correct behavior without diminishing the challenge of the game.
GWNN-HF: beyond assortativity in graph wavelet neural network

Graph wavelet neural network exerts a powerful learning ability in assortative networks where most of the adjacent nodes have the same label as the target node. However, it does not perform well in disassortative networks where most of the adjacent nodes have different label than the target node. So graph wavelet neural network cannot extract the most useful information based on different types of networks. On the one hand, graph wavelet neural network is not able to extract the similarity information of the same labeled neighbor nodes and the difference information of different labeled neighbor nodes in a flexible way. On the other hand, graph wavelet neural network only aggregates neighbor nodes so that it cannot obtain information of nodes which have similar feature with the target node and are far from the target node. To solve the above problems, we propose the GWNN-HF model, which can effectively adapt to different types of networks and get a better node representation. Specifically speaking, firstly, we design low-pass filter and high-pass filter convolution kernels to get low-pass and high-pass signals and then use the adaptive fusion method to fuse them, which effectively get commonality of same label nodes and difference of different label nodes. Secondly, we use the Relaxed Minimum Spanning Tree algorithm to construct a feature correlation graph and use an attention mechanism to fuse the original graph and feature correlation graph representation. Extensive experiments on benchmark datasets clearly indicate that GWNN-HF has a good performance in different types of network structures.
Expressibility Of Constraint Satisfaction Problems In Extensions Of First-Order Logic

&lt;p&gt;A fundamental open problem in finite model theory and, specifically, descriptive complexity is the question whether there exists a logic which characterizes solvability of algorithmic decision polynomial time on the class of finite relational structures. A prominent candidate is the logic of Choiceless Polynomial Time (CPT) (optionally counting), a strict extension of the first-order logic. The expressibility problem for properties of finite relational structures in CPT (or CPT+C) is intrinsically hard, but CPT can be replaced by a more standard model-theoretic construction.&lt;/p&gt; &lt;p&gt;We show that constraint satisfaction problems over Maltsev templates can be expressed in the logic PIL+H. Furthermore, we show that the algorithm for Maltsev templates also solves said instances of constraint satisfaction problems over finite, idempotent Taylor algebras. This proves that for every finite, idempotent algebra A, the problem CSP(A) is either NP-complete or its solvability can be defined in the logic PIL+H.&lt;/p&gt;
A Real-Time Semantic Segmentation Method Guided by Boundary Information for Substation Scene

Automatic inspection robots are widely used in the management of unattended substations. For higher-level tasks or understanding its road scenes, the robot needs to extract semantic information from environment, such as roads, buildings, and equipment. However, small devices in substations and real-time requirements are quite a challenging task in semantic segmentation. To meet these demands, we propose a new semantic segmentation network with high real-time. The proposed method contains a contextual branch and a boundary-guided branch, which continuously interacts with another branch through an attention mechanism to improve the accuracy of boundary segmentation between neighbouring objects. Our approach is an excellent balance of accuracy and speed on a new dataset from a transformer substation. It can overcome the problems of missing segmentation of small targets and blurry object boundaries in semantic segmentation, and has good real-time performance. Additionally, compared with the state-of-the-art methods, the proposed method gets advanced accuracy and speed on a publicly available road scene dataset—Cityscapes.
An Application Of Machine Learning Methods On Complicated Large Scale Dataset

&lt;p&gt; Machine learning research has been an upcoming trend over the last few years. With more computational power and increasing volume of data available thanks to the development of the Internet, machine learning methods could be applied to real life problems and produce fascinating outcomes. Furthermore, with the rise of deep learning methodologies, machine learning practitioners can work on unstructured datasets and achieve human level accuracy. The present thesis focuses on a structured dataset with unstructured fields and information, aiming to apply multiple machine learning methods from a supervised learning perspective. Firstly, linear regression models and extreme gradient boosting machine, as conventional machine learning methods, are applied on certain selected features of the dataset, achieving remarkable results. They serve as base models. Next, an ensembled neural network model with four modules, i.e., fully connected module, embedding module, convolutional network module, and recurrent network module, is created and implemented with the transfer learning technique, which yields better outcomes. This work leads to a typical supervised learning structure and can be considered as a stepping stone for similar practical implementations. &lt;/p&gt;
Accessing, Extracting, and Analyzing the Textual Content within Reg A Form 1-A Part II Filings Using Python

This article explores the methodology of accessing, extracting, and analyzing the textual content within Regulation A Form 1-A Part II filings using Python. Reg A offerings provide companies with a flexible framework to raise capital through public offerings while adhering to relaxed filing requirements set by the Securities and Exchange Commission (SEC). Part II of the Form 1-A contains crucial textual content such as business descriptions, risk factors, and financial information. By leveraging Python libraries like BeautifulSoup and TextBlob, users can preprocess the data, extract desired textual content, and perform textual analysis. This paper serves as a guide for researchers, analysts, and investors looking to gain valuable insights from Reg A filings using Python, enabling them to make more informed decisions regarding these unique securities offerings. The article also discusses the challenges, limitations, and gaps in the existing methods, as well as displaying the Python code and libraries utilized for text extraction and analysis. The results and analysis section demonstrates sentiment analysis on different sections of a Reg A filing, providing insights into the polarity and subjectivity of the regulatory text.
Interpersonal Relations and Task Performance

Purpose: The study draws on the social network theory to examine the influence of interpersonal relations on the task performance at the individual employee, team and divisional levels in the Nigerian Police Force.&#x0D; Design/methodology/approach: The study adopted a cross sectional survey design and a sample of 556 police men was selected through the application of multi-stage sampling procedures. The data collection instrument was designed in line with the 5-point Likert scale while the instrument was validated through content validity approach and the reliability of the instrument is determined using the test-retest method.&#x0D; Findings: Data were collected through the self-report of the policemen while analysis was carried out using descriptive statistics, run mean test and Chi-square test and the results indicated that interpersonal relations influence employee, team and divisional task performance in the Nigerian Police Force.&#x0D; Practical implications: We recommended that instrumental ties and social communities in the fabric of the Nigerian Police Force should be promoted to further advance the critical roles of crime fighting and prevention with a view to reducing criminality in Nigeria. &#x0D; Originality/value: To the best of the authors' knowledge, this research is original and adds new value to the field by being the first of its kind.&#x0D; Paper type: Research paper
THE USE OF VIRTUAL REALITY TECHNOLOGIES IN EDUCATION

Background. This article presents the results of a study of the impact of virtual reality on modern education. The comparison of traditional education and education using virtual reality is carried out.&#x0D; Materials and methods. Methods of system analysis, data analysis, comparison, systems theory were used.&#x0D; Results. The importance of virtual reality in education is revealed, which allows expanding the possibilities of remote interaction between people. The possibility of using virtual reality in an educational environment in order to improve the quality of assimilation of educational material, visualization of educational material is shown.&#x0D; Conclusion. The article examines the role of virtual reality in modern education. It has been established that the material learned through the VR simulator is much more effective in contrast to classical teaching methods.
Non-EFL Students’ Spontaneous Technique to Reduce Anxiety in ELT Speaking Practice Class

This research investigated non-EFL students’ spontaneous technique to reduce their English-speaking anxiety. This research proposes an instrument in three areas and the findings are parallel with them. The first scope is the use of various instruments which could give both strengths and lacks to students. The proper instruments help students to maintain the time necessary. The inappropriate use of the instrument lengthens students’ time to perform their speaking. The second finding scope is students' method of reducing anxiety through managing language use which covers diction, synonym, intonation management, length of speech, punctuation use, and repetition. The verbal language used represents students' previous experience and their readiness. The third finding scope is students’ method of reducing anxiety through managing non-linguistic aspects that cover facial expression, eye contact, body language, and changing of standing up position. The main conclusion is that reducing anxiety needs a well-prepared method or program.
Motion Detection and Multiple Faces Identification using Webcam

As technology is marking the highest peak, all things are being automated using Machine Learning and Deep Learning. By using Machine Learning models, we can detect the movements, actions, multiple faces, their recognition and many more in snap of time. Just like that, this project is implemented using Machine Learning models, like OpenCV for detecting motions of an object and Face Recognition for multiple faces recognition to provide the advanced security. In this paper, we are going to see the project named as “Motion Detection and Multiple Face Identification using Webcam” were detection of motion and multiple faces will be done with the help of a system Webcam or Security Cameras. This is implemented using Python’s OpenCV a Computer Vision module and Cascade Classifier. This research paper will guide, how this machine learning project helps to detect and verify the motion and faces of people more efficiently and provide advanced security to an organization or to a home. Key Words- Machine Learning (ML), Artificial Intelligence (AI), MYSQL, Haar-Cascade Classifier, Gaussian-Blur, Face Recognition, MySQL Database.
The Principles of Teaching Reading Skill for Educational Section

Abstract&#x0D; Language is a means of communication. Human beings use a language to communicate with each other in order to express their minds and accomplish their needs. World wide communication, technology, politics, literature, agriculture and science mostly used English as the language. Reading is an important activity in every language. Reading enables people to find out information from a variety of texts, written or printed information from newspapers, magazines, advertisements, brochures and soon. Good English reading comprehension is really needed to be mastered especially by students to face this global era in which the English is the language that is used. Reading comprehension is taught since the first year of junior high school till the university. It indicates that reading comprehension is important.By this skill students can get more information not only from the school, they also can get information from the English magazine, book, journal or everything written in English&#x0D; Keywords:Principles, Teaching Reading