Reviewerly/exHarmony huggingface dataset:
Developing and Applying a Chinese Character Learning Game App to Enhance Primary School Students’ Abilities in Identifying and Using Characters

The Chinese language is the mother tongue that most students in Singapore need to master. However, for many local students, due to the use of English as the main language in Singapore’s families and the living environment, the time and frequency of using Chinese and the exposure to Chinese characters are relatively insufficient, which leads to a high forgetting rate, confusion of the characters and the improper use of Chinese characters. This study attempts to develop an app of a Chinese character learning game for Singapore primary school students, aiming to stimulate students’ interest in learning Chinese, increase their frequency of contact and use of Chinese characters, and ultimately strengthen their ability to remember, understand and use Chinese characters. By collecting the data from students’ questionnaire surveys, teachers’ questionnaire surveys, students’ literacy tests, and classroom observations, the research team found that the designed app can enhance the interest of lower grade primary school students in learning Chinese and strengthen their ability to memorize and use Chinese characters.
Multi Strategy Search with Crow Search Algorithm

Crow Search Algorithm (CSA) is one of the recently proposed swarm intelligence algorithms developed inspiring of the social behaviour of crow flocks. One of the drawbacks of the original CSA is that it tends to randomly select a neighbour on search strategy due to its low convergence rate, which pushes the search to stick in local optima due to the same search strategy applied across iterations. The multi-strategy search for CSA (CSA-MSS) has been proposed to enrich the search facilities and provide diversity to overcome these drawbacks. The multi-strategy search implies utilising a pool of strategies consists of six different types of search operators. The multi-strategy approach with a selection mechanism has not been proposed for CSA before and implemented first time. The comparative performance analysis for the proposed algorithm has been conducted over solving 24 benchmark problems. The results demonstrate that the proposed approach is outperforming well-known state-of-the-art methods.
Bearings-only Tracking Based on Distributed Multisensor Pseudolinear Kalman Filter

For bearings-only tracking (BOT), there are mainly two problems of nonlinear filtering and poor range observability. In the paper, a new distributed multisensor pseudolinear Kalman filter (PLKF) algorithm is proposed. The sensors use an instrumental vector PLKF (IV-PLKF) to process the measurements of the target independently, which can tackle the bias arising from the correlation between the measurement vector and pseudolinear noise by the bias compensation PLKF (BC-PLKF). The IV-PLKF embeds the recursive instrumental vector estimation method into the BC-PLKF, uses it to construct the instrumental vector, and applies the method of selective angle measurement to modify the local target state estimation and covariance. In the fusion center, the target state can be estimated by using the multisensor optimal information fusion criterion. Then the Cramer-Rao lower bound (CRLB) of multisensor BOT is derived. Simulation results show the effectiveness of the algorithm.
Detection of Moving Objects and Enhancement Using Motion Features in Various Video Sequences

Due to its lack of performance and flexibility, the traditional frame difference method does not handle precise moving object detection by motion of the object region in each frame of the various video sequences. So, the object cannot be seen in the foreground accurately. It remains a serious concern. The time computation for object detection using the three frame difference and five frame difference approaches is longer, and frame information is lost. To address these flaws, a new method known as the inter frame difference method is suggested (MIFD). It detects moving objects in video under various environmental conditions with little data loss in a short period of time. MIFD involves constructing a reference frame, computing inter frame difference, a motion frame and detecting moving object(s) in a frame by drawing a rectangle blobs using connected components in the video sequence. The performance of the proposed algorithm is compared with the previous results of the code book model (CB), self-organizing background subtraction method (SOBS), local binary pattern histogram (LBPH), robust background subtraction for network surveillance in H.264, GMM, VIBE, frame difference, three frame difference, improved three frame difference, and combined three frame difference & background subtraction model. The experimental results demonstrate that the proposed methodology performance is better than the other methods in accurately detecting moving object(s) in video under challenging environmental conditions.
Peningkatan Kapasitas Dosen dan Mahasiswa dalam Pemanfaatan Software Lisrel di STT Renatus Pematang Siantar

The purpose of implementing this community service program was to improve the ability of lecturers and students in processing research data using Lisrel software. This activity was carried out through three stages, namely the planning stage, the implementation stage and the evaluation stage. The method used in the implementation of the event is the lecture method and practice. The resource person who became the instructor in this training was a lecturer at the 17 Augustus 1945 Jakarta University as a form of community service. This training was very much needed at STT Renatus Pematang Siantar because there were no lecturers and students who have mastered the use of this software properly. The results of the evaluation were carried out by comparing the pretest and posttest scores. There was an increase in the mastery of software in the majority of the trainees. The majority of participants already understand well how to input data, how to process data and how to interpret the results of data processing. It is hoped that the participants will continue to learn and practice their skills in processing research data with Lisrel software, so that these skills can support the careers of the participants and support the performance of their institutions.
INDEPENDENSI AUDITOR DI MASA PANDEMI COVID-19 (STUDI PADA KAP "X" MALANG)

The Covid-19 pandemic has implications for business activities in general and has implications for the Public Accounting Firm (KAP) business, especially in terms of an auditor's independence. Meanwhile, independence is generally related to the ability of an auditor to be impartial and not easily influenced by certain parties to support audit quality during the Covid-19 Pandemic. This study aims to determine the auditor’s perception of independence during the Covid-19 Pandemic at KAP “X” Office. The paradigm in this study is the Interpretive Paradigm. This type of research is a qualitative approach. Data collection techniques using the method of observation, interviews and documentation. Test the validity of the data using the source triangulation method. The stages in this research are using data reduction, data presentation and concluding (verification). The results showed a statement from one of the informants who stated that there was a request from the client who asked the KAP auditor “X” to give a fair opinion so that there was a continuity of cooperation with the client. Based on this statement, it can be interpreted that during the Covid-19 Pandemic, there was pressure from the client on the auditor to give a fair opinion on the company’s financial performance. Therefore, the auditor needs to consistently adhere to the principle of independence to maintain the audit quality of the financial statements.&#x0D; &#x0D;
Institutions of Higher Education in the Russian Federation: Budgetary and Private Impact of Education at the University Level

This article considers at the university level the graduates’ income information, as well as budgetary and private cost estimates to determine budgetary and private impact of education using the calculation of internal rate of return. Since the graduates’ income information is available for only three-year period after the graduation, the synthetic profile of the graduates’ income during their lives is constructed according to the regional microdata. Results of the calculation show that the average impact of higher education is about 9 %, which is consistent with other estimates. In addition to list of universities indicating their impact, the article presents conclusions and recommendations, based on the analysis of the impact’s types and the correlation with other parameters, particularly with Unified State Examination scores for entry into higher education. The article’s value consists in demonstration of the possible approach for estimates, considering impact of investment in higher education at the university, faculty or department level. Researchers of the future can build on methodology and estimates, presented in this article, to obtain more accurate and reliable data on private impact of investment in higher education.
Anomaly Detection for Solar Panel Joint Clamps Using Deep Learning Based Detection and Recognition Techniques

Expectations for solar power generation, one of the renewable energy sources, are increasing in order to reduce greenhouse gas emissions. However, solar panels are industrial products, and there is always a risk of local failure of the products. Several automatic anomaly detection systems have been developed for solar panels and their roofs. However, there is no precedent for automatic anomaly detection of joint clamps supporting roofs and panels, and visual inspection by technicians is still performed as a regular inspection. Thus, accurate and quick evaluation of the joint clamps of solar panels will contribute to reducing the labor and time required for inspections. In this study, we attempted to develop an automatic anomaly detection system for joint clamps, using object detection and recognition technologies based on deep learning. In the first stage, the joint clamp is detected by object detection technology. In the second stage, the system judges the abnormality from the detected the joint clamp by recognition technology. The effective of the proposed system was evaluated through the field experiments.
Statistical Analysis and Comparison of the Performance of Meta-Heuristic Methods Based on their Powerfulness and Effectiveness

In this paper, the performance of meta-heuristic algorithms is compared using statistical analysis based on new criteria (powerfulness and effectiveness). Due to the large number of meta-heuristic methods reported so far, choosing one of them by researchers has always been challenging. In fact, the user does not know which of these methods are able to solve his complex problem. In this paper, in order to compare the performance of several methods from different categories of meta-heuristic methods new criteria are proposed. In fact, by using these criteria, the user is able to choose an effective method for his problem. For this reason, statistical analysis is conducted on each of these methods to clarify the application of each of these methods for the users. Also, powerfulness and effectiveness criteria are defined to compare the performance of the meta-heuristic methods to introduce suitable substrate and suitable quantitative parameters for this purpose. The results of these criteria clearly show the ability of each method for different applications and problems.
An Overview of Obstacle Avoidance Methods for Unmanned Vehicles

Autonomous obstacle avoidance is one of the popular research elements in the field of intelligent unmanned vehicles, and it is a key technology to realize the automatic travel of intelligent unmanned vehicles.This paper introduces the traditional algorithms and intelligent algorithms related to autonomous obstacle avoidance, and analyze the advantages and drawbacks of the corresponding algorithms.The development of the corresponding control strategies is also summarized and summarized in order to provide some reference for the research on obstacle avoidance of intelligent unmanned vehicles.
Experience in Organizin IPMITE events in the Conditions of Distance Learning

The article discusses the features of the organization of the educational process of students of a pedagogical university during the period of distance learning. The difficulties and limitations faced by the teachers are considered. The tasks of the implementation of the educational process in a pedagogical university in this period are presented. The purpose of the article is to present the experience of teaching and educating students, taking into account the possibilities and limitations of distance technologies at the Novosibirsk State Pedagogical University, from the position of a systematic approach. Methodology. The research was carried out within the framework of the implementation of pedagogical years of practice and the organization of educational online events for graduates of IPMITE. We have identified the problems of students: educational level related to the level of mastering the disciplines of the psychological and pedagogical block and general cultural disciplines; insufficient integration of interdisciplinary connections among students; problems of a personal nature: students’ lack of confidence in their own pedagogical powers and creativity, a tendency to algorithmization; problems of self-discipline and activity. In conclusion, it is concluded that the current stage of training future teachers requires the creation of integrated courses for students, for testing and updating digital technologies in the educational process of the university; to reveal the problems of using psychological and pedagogical tools in a virtual environment, problems of psychological and pedagogical safety support.
Preliminary Evaluation of Convolutional Neural Network Acoustic Model for Iban Language Using NVIDIA NeMo

For the past few years, artificial neural networks (ANNs) have been one of the most common solutions relied upon while developing automated speech recognition (ASR) acoustic models.There are several variants of ANNs, such as deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs).A CNN model is widely used as a method for improving image processing performance.In recent years, CNNs have also been utilized in ASR techniques, and this paper investigates the preliminary result of an end-to-end CNN-based ASR using NVIDIA NeMo on the Iban corpus, an under-resourced language.Studies have shown that CNNs have also managed to produce excellent word error (WER) rates for the acoustic model on ASR for speech data.Conversely, results and studies concerned with under-resourced languages remain unsatisfactory.Hence, by using NVIDIA NeMo, a new ASR engine developed by NVIDIA, the viability and the potential of this alternative approach are evaluated in this paper.Two experiments were conducted: the number of resources used in the works of our ASR's training was manipulated, as was the internal parameter of the engine used, namely the epochs.The results of those experiments are then analyzed and compared with the results shown in existing papers.
Person detection and identification surrounding gas facilities based on Improved-Yolov4-tiny

Abstract The safety of the gas facilities is an important guarantee for the production and life of the society, and the monitoring of people entering the area is an important means to guarantee the safety of the facility. In order to solve the problem of non-contact monitoring of people entering the gas facilities, an Improved-Yolov4-tiny method is proposed in this paper. This method can be used to monitor people entering the area in real time and determine whether the type of person is a worker or non-worker based on characteristics such as clothing. Among them, the model increases a large-scale feature layer to improve the recognition and detection of small targets. The introduction of attention mechanism for multi-scale feature fusion increases the effectiveness of fine-grained detection of person. The Improved-Yolov4-tiny detection algorithm not only meets the requirements of real-time monitoring, but also achieves better results at different resolutions. Compared with the traditional Yolov4-tiny detection algorithm, the Improved-Yolov4-tiny detection algorithm improved the mAP metric by 3.1%.
Path discovering in maze area using mobile robot

Robotic maze pathfinding problems deal with detecting the correct route from the start point to the end-point in a virtual maze environment consisting of walls. Automated robot mobility is a significant feature, which enables a mobile robot to traverse a maze independently, from one position to another, without human intervention. There is a myriad of autonomous industrial mobile robot applications, including the transportation of goods and parts, domestic cleaning, indoor security surveillance, airport baggage couriering, and a plethora of other applications to traverse dangerous locations. This paper proposes a pathfinding mobile robot in a virtual maze based on a combination of a simplified left-hand algorithm and a line-following control algorithm. The mobile robot works in any maze to determine a route from the initial starting point to the end-point. The approach outlined in this paper uses a left-hand algorithm to solve the maze problem and a line-follower control algorithm to enable the robot to move in a straight line through the virtual maze. The algorithm used is less complicated and prevents the robot from falling into infinity loops compared to the traditional wall-follower algorithm.
Differentiated effects of COVID-19 on university students

The confinement due to the coronavirus had effects after implementing virtual education at all educational levels. To identify the students' perception about the teaching-learning conditions that prevailed during COVID-19, distinguishing those that allowed them to continue learning from those that did not work in the academic areas of higher education in health sciences. And economic-administrative, exploratory, descriptive and correlational research was carried out with students from the Universidad Veracruzana de México. The sample consisted of 665 non-probabilistic and non-random with a general population. It was inquired about the teacher's teaching activities, learning, academic interaction, digital knowledge, new understanding acquired, socio-emotional difficulties experienced and management strategies, and the technological resources and connectivity used. Among other results, it was found that students from the first semesters and women were the populations most likely to put metacognitive skills into practice.
Some classes of complete permutation polynomials in the form of (<i>x<sup>p<sup>m</sup> </sup> </i> − <i>x</i> + <i>δ</i> )<i> <sup>s</sup> </i> + <i>ax<sup>p<sup>m</sup> </sup> </i> + <i>bx</i> over <i>F<sub> <sub>p</sub> </sub> </i> <sub>2<i>m</i> </sub>

Abstract Several classes of complete permutation polynomials (CPPs) in the form of ( x p m − x + δ ) s + ax p m + bx over F p 2 m are described in this paper. For fractional exponent s , the permutation properties of the given polynomials are proved by the AGW criterion and for determining the bijectivity of the corresponding piecewise functions. For exponent s satisfying s = i ( p m − 1) + p j , the main proofs here depend on the AGW criterion and the quantity of solutions for certain linearised equations. Several results generalise some earlier results on permutation polynomials (PPs).
The Use of Statistic Complexity for Security and Performance Analysis in Autonomic Component Ensembles

The paper proposes a new technique for detecting malware threats in autonomic component ensembles. The technique is based on the statistic complexity metrics, which relate objects to random variables and (unlike other complexity measures considering objects as individual symbol strings) are ensemble based. This transforms the classic problem of assessing the complexity of an object into the realm of statistics. The proposed technique requires implementation of the process X (which generates ‘healthy’ flows containing no malware threats) and objects generated by the actual (possible infected) process Y. The component flows files are used as objects of the processes X and Y. The result of the proposed procedure gives us the distribution of probabilities of malware infection among autonomic components. The possibility to use the results obtained to perform quantitative probabilistic verification and analysis of ASEs using the probabilistic model checking tool PRISM is demonstrated.
Interactive Teaching Model - An Effective Way to Cultivate Students’ Communicative Competence

The subject matter of communicative competencies of teachers is an issue, which is extremely important in the process of educational change that takes place in contemporary school. However, even defining the concept of communicative competences itself is not an easy task, due to the fact that literature of the subject gives many ambiguous interpretations. Frequently, it is identified with the level of knowledge and skills indispensable for effective communication with the interlocutor, with ability to adjust both verbal and non-verbal language to the recipient and encouraging him to cooperation. In this article, author gives the characteristic of the process of communication in the teacher-student relation, with special focus on types and styles of communication in education. He also indicates some of the competence features, improvement of which is expected to upgrade the quality of functioning of the school class.
Python as an automation tool in IS. Monitoring the relevance of protection against malicious code

Abstract One of the necessary, periodically performed by information security (IS) department procedures is to assess the level of compliance of the actual state of any IS measure with the established requirements. These procedures involve a significant amount of manual work, which leads to a waste of time, human, technological resources. In this case it is not always possible to provide a high level of reliability of the results. To reduce costs, increase the degree of reliability and accuracy of the results of assessment of the IS state, and, accordingly, increase the level of confidence in the IS state assessment, it is possible by the use of “small automation” means when performing routine operations of collection, correlation, normalization, analysis of data sets. The most acceptable means of automation is the programming language python, which, in addition to convenience of use, powerful functionality, has an extensive analytical apparatus. This article discusses the use of python as a “small automation” tool for monitoring the relevance of antivirus protection system.
On the finite convergence of the NN classification learning on mistakes

The paper establishes an analog of well-known Novikoff’s theorem on the perceptron learning algorithm’s finite convergence in linearly separated classes. We obtain a similar result concerning the nearest neighbor classification algorithm in the case of compact classes in a general metric space for the case of non-intersecting classes. The learning process consists of gradual modification of the algorithm in misclassification cases. The process is studied in the deterministic setting. Classes are understood as compacts in complete metric space, and class separation is defined as the non-intersection of compacts. The number of learning steps is bounded by the number of elements in some ε-net for the considered classes.