Reviewerly/exHarmony huggingface dataset:
Awareness and Image-Telling of Gaya History in Middle and High School History Textbooks - Focusing on Text Network Analysis -

This article examines the correlation between key keywords and keywords through text network analysis of Gaya history in middle and high school history textbooks, and studies the causality of Gaya"s perception and image-telling process. In history textbooks, first, Gaya tends to be dealt with in relations with the Three Kingdoms. This was confirmed through the frequency of simultaneous appearance of ‘Three Kingdoms’ and ‘Gaya’. Second, the frequent appearance of ‘Baekje’ and ‘Gaya’ was mobilized to explain the destruction of Gaya, and the appearance with other keywords was poor. Third, the key keyword with the highest frequency in the narrative related to Gayasa was ‘Federation’. The ‘union’ correlates with the ‘centralized state’ and has a network structure that leads to ‘growth’ and ‘development’. It was also confirmed that “Federation” was a keyword mobilized to describe Gaya as the same “Federal Kingdom” as Buyeo or to describe a country that failed to grow into a centralized state from the “Federal Kingdom.” It describes the process of the fall of Gaya as not being able to achieve centralization by maintaining its own power or maintaining its own political base. Compared to Goguryeo, Baekje, and Silla, which coexisted at the same time, it is seen as lacking and weak. Through history textbooks, Gaya"s national image is image-telling in a way that falls short of ‘growth and development’.
The Interaction of Contextual Performance Dimensions of the Working Woman in Governmental Institutions: A Case of Ministry of Health in Gadarif State/ Sudan

This study aims to investigatethe contextual performance and the interaction of its dimensions represented by the organizational commitment and organizational citizenship behavior of the working woman. The study followed the descriptive analytical approach and a questionnaire was designed and distributed to a sample of 92 clerks in the headquarters of the Ministry of Health in Gadarif State /Sudan. The results of the study found that the contextual performance is moderate (medium). Also, there is a statistically significant partial impact of organizational commitment on organizational citizenship behavior through emotional and normative commitments only. Interestingly, there is no significant impact of the continual commitment on organizational citizenship behavior at the individual and organizational levels. Finally, the study offers several recommendations that would help decision-makers in building and strengthening contextual performance to positively drive the overall performance.
The influence of motivation and independence of learning on the mathematics student learning outcomes of class VIII SMPN 12 Padang

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Ninik Puspita Rahayu, Ahmad Fauzan; The influence of motivation and independence of learning on the mathematics student learning outcomes of class VIII SMPN 12 Padang. AIP Conference Proceedings 7 February 2023; 2698 (1): 060045. https://doi.org/10.1063/5.0122435 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
The improved discretization of peak ground acceleration variable using K-medoids

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Devni Prima Sari, Media Rosha; The improved discretization of peak ground acceleration variable using K-medoids. AIP Conference Proceedings 7 February 2023; 2698 (1): 020003. https://doi.org/10.1063/5.0122442 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Differential calculus mind map as output concept analysis in preliminary research phase

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Saddam Al Aziz, Rara Sandhy Winanda; Differential calculus mind map as output concept analysis in preliminary research phase. AIP Conference Proceedings 7 February 2023; 2698 (1): 060004. https://doi.org/10.1063/5.0122341 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Multiobject Tracking via Discriminative Embeddings for the Internet of Things

Multiobject tracking (MOT) technology can be deployed to the Internet of Things (IoT) devices to enhance the security and reliability of some video analysis applications, such as video surveillance and intelligent security system. However, since the IoT devices with limited computing capacity and storage, most existing MOT methods are difficult to deploy to IoT devices and exhibit poor tracking robustness in scenes with frequent occlusions, severe crowded, and scale variations. To alleviate the aforementioned issues, we propose a regression-based online MOT method. First, an object-aware embedding extraction module (OAEM) is designed to extract preliminary discriminative embedding of the object in the current frame. Then, an embedding aggregation module (EAM) is proposed to obtain high-quality aggregated embedding in temporal. Finally, the temporal embedding combined with the preliminary embedding extracted from the current frame to obtain a refined embedding feature for subsequent position prediction and association. Importantly, we achieve a beneficial interaction between embedding extraction, position prediction and association task. The proposed method does not suffer from significant memory consumption. Therefore, our method is a potential solution for intelligent video analysis on IoT devices. To evaluate the proposed method, we have conducted numerous experiments on the MOT16, MOT17, and MOT20 benchmark data sets. Results demonstrate that the proposed method can provide a more robust tracking performance compared to other optimal methods.
A Study on the Anxiety of Chinese Korean Learners in Online Korean Classes

This study is intended to analyze the types and extent of anxiety felt by Chinese students in online Korean classes. In this study, a survey was conducted on 48 Chinese students who was currently taking online courses at Busan University of Foreign Studies. According to the results, Chinese students have a relatively high level of anxiety about the COVID-19 situation, and most of them have a low level of anxiety about their ability, learning efficiency, concentration, learning methods, questions, interaction with teachers, and missing class information in online Korean classes. On the other hand, most students showed low levels of anxiety about online teaching methods and online tests. Most of the students prefer the method of taking face-to-face and non-face-to-face classes together. Therefore, not only should we consider improving effective teaching methods for online Korean language classes, but also how students and teachers adapt when returning from online classes to face-to-face classes.
Analysis of Short Answer in Question Answering System for Online Descriptive Examination

Globally due to the COVID-19 situation, the examination was taken online. The majority of online exams are objective exams, however, it is challenging to auto-correct a subjective exam. As checking the answer found in the subjective type-examination has many things to be considered like spelling mistakes, grammatical errors, words missing or extra words added, and the meaning of the answer. The system that is present today does not handle this type of answer and generates the result as wrong. The Microsoft team template was used for conducting the test. In this paper, we analyze the performance of student answers with model answers. The responses were checked manually and analyze the performance. The analysis was done in two ways: Part of speech Tagger and Levenshtein distance. The first stage is to use the Part of speech Tagger for tagging the student’s answer and then check the model answer type with the student’s answer type, and calculate the percentage of nouns and verbs and secondly calculate the distance using Levenshtein amongst the answers. Observation during the experiment is that 85% of answers are noun oriented which helps in matching between the answers. The results of the Levenshtein distance are used to calculate the performance of the system for that accuracy and precision measures were considered.
Conclusion

This book presents the achievements in the research of a special underactuated system called mobile wheeled inverted pendulum (MWIP) over recent years. Taking the dynamic modeling, control, and simulation as the mainline, this book first introduces the particularity, control challenges, and applications of the MWIP system. The Lagrange function is adopted to model the dynamics of two-dimensional and three-dimensional MWIP systems. Based on the special dynamical characteristics, a new design of sliding mode controller incorporated with a novel high-order disturbance observer is introduced. Aiming at overcoming the chattering problem appeared in the traditional sliding mode control, several chattering avoidance methods are presented in detail. Besides, some intelligent algorithms related to the interval type-2 fuzzy logic control are applied to the MWIP system. All the proposed methods mentioned above are verified on a physical MWIP system. The future development of underactuated robots has been prospected. This book is intended for researchers and engineers in the field of robotics and control. It can also be used as supplementary reading for nonlinear systems at the graduate level. The in-depth theory and detailed platform construction provide a great convenience for readers to build their own platforms and learn the knowledge they need.
gTop: An Efficient SPARQL Query Engine

In this demonstration, we present gTop, a top-k query engine based on gStore which supports SPARQL queries over RDF databases. gTop can answer top-k queries with high efficiency and scalability. We use the DP-B algorithm for top-k queries and the DP-any algorithm for any-k queries. We break cyclic queries into pieces of tree queries and use DP-any to solve queries generated before assembling the results to retrieve the origin answers. Experiments show the efficiency of gTop. We provide a demonstration website to show the usage of gTop, where users can query YAGO2 with top-k SPARQL queries.
Performance of the nanopost single-photon source: beyond the single-mode model

We present a detailed analysis of the physics governing the collection efficiency and the Purcell enhancement of the nanopost single-photon source. We show that a standard single-mode Fabry-P\'erot model is insufficient to describe the device performance, which benefits significantly from scattering from the fundamental mode to radiation modes. We show how the scattering mechanism decouples the collection efficiency from the Purcell enhancement, such that maximum collection efficiency is obtained off-resonance. Finally, we discuss how this scattering mechanism can be beneficial for future single-photon source designs.
Coordinated Multi-robot Exploration Method Based on GBPlanner in Subterranean Environments

Focusing on some subterranean environments which have similar vertical structure, multiple branches and large space, such as tunnels, urban buildings and caves, this paper proposes a coordinated exploration method for multi-robot, Multi-robot Graph-Based exploration path Planner (M-GBPlanner). In order to tackle the problem of repeated exploration paths and low efficiency in coordinated multi-UAV(Unmanned Aerial Vehicle) exploration, the coordinated exploration team employing M-GBPlanner method performs exploration based on shared perception information and information of exploration paths to be executed which effectively reduces duplication of paths, thus improving exploration efficiency and coverage. The coordinated exploration method proposed in this paper has been validated in the simulation environment built in Gazebo. Compared to Graph-Based exploration path Planner (GBPlanner) and traditional RRT(Rapidly-exploring Random Tree) exploration method which do not share perception and path information, exploration team adopting M-GBPlanner produces 39% and 62% more efficiency and coverage respectively.
A Kind of Syntax Parsing Algorithm Based on The Predictive Parsing Table

Syntax parsing algorithms are important algorithms in natural language processing area. If the Context free grammar is belong to LL(1) grammars, the predictive parsing table can be constructed for it according to the grammar. The syntax parsing algorithm can be constructed based on the predictive parsing table. The syntax parsing can be completed by using the predictive parsing table and the syntax parsing algorithm based on the predictive parsing table.
Analysis of Content Benefits of Audiovisual Learning Models For Nonformal Early Children and Basic Education

This research is to describe the duties and responsibilities of the Bengkulu Province PAUD and Dikmas Development Center (BP PAUD) which must be carried out by tutors and teachers, namely, the development of learning models. The Bengkulu Province PAUD and Dikmas Development Center (BP PAUD) has created a development product entitled social-financial learning through Audio Visual for Non-Formal PAUD Children and Basic Education to support children's learning from home. This research method uses descriptive qualitative research methods, data collection techniques use interview techniques, observation and documentation, data validation techniques use time triangulation techniques, subject triangulation and technical triangulation, the subjects in this study are Non-Formal PAUD managers and Basic Education managers who implement development the Daily Learning Implementation Plan (RPPH) model in Non-Formal PAUD and Basic Education. To ensure the benefits of the model development product by the manager, this research was carried out. The purpose of this study was to find out the benefits of teaching materials about financial social learning strategies in the Daily Learning Implementation Plan (RPPH), namely to help educators and students focus on learning in accordance with the teaching materials that have been provided, and the benefits of financial learning activities through Audio Visual for PAUD Children Non-formal and Basic Education in the Daily Learning Implementation Plan (RPPH), namely to support learning from and make the implementation of learning more focused, directed because it takes advantage of children's hearing and sight so that children become interested and active in learning while the benefits of RPPH content are about Audio Visual learning for identity because in the RPPH there are already themes and sub-themes, another benefit of the Daily Learning Implementation Plan (RPPH) is to make the implementation of learning more focused and directed, especially for Non-formal PAUD Children and Basic Education
Applying Project-Based-Learning to improve English speaking skills of remote learners at tertiary education

English speaking skills are important in global communication and interaction. To speak English effectively at an acceptable level for a non-native speaker, various sub-skills are considered such as lexical resources, grammatical structures, pronunciation, rhythm, intonation, and the like. Nevertheless, a vast number of remote learners at Ho Chi Minh City Open University (HCMCOU) seemed to struggle in representing their thoughts and speaking sub-skills. Thus, this study aims to explore the challenges encountered by remote learners, the effectiveness of Project-Based Learning (PBL) applied in teaching speaking skills online, and learners’ attitudes towards the applied project. To do this, a mixed research method was employed. Twenty-two remote learners participated in responding to the survey questionnaire, and five of them were randomly selected for interview in depth. The finding shows that the most of remote learners had problems of speaking sub-skills, inadequate environments for speaking practice, communication, and interaction. Also, PBL is found as a unique method that enables learners to solve their existing problems, and improve their speaking skills, and all remote learners expressed their positive attitudes of towards PBL applied in their learning process.
MOVING OBJECT DETECTION USING SEMANTIC CONVOLUTIONAL FEATURES

Moving object detection from aerial images remains an unsolved problem in computer vision research domain. Detection results are not precise due to blurry aerial images, thin edges and noise. Various methods were previously proposed for moving object detection which could not provide robust results due many challenges, i.e., noise, motion detection, lack of appropriate features, lack of effective classification approach, complex background and variations in illumination. This research proposes an efficient method for moving object detection using convolutional semantic features from VGG-16 to use motion patterns to facilitate detection in each frame and provides smaller area as region of interest. Proposed method reduces probability motion intensity information getting lost in case of same coloured object in the background and thus minimizes background complexity. After that, proposed method performs semantic features distance measurement to calculate linear distances in each frame. In this context, if there is any frame loss due to noise or illumination variation, proposed method uses Kalman filter to process that frame by illuminating noise. Finally, decision for final moving detection is determined using random forest classifier from semantic convolutional feature vector by generating a set of probabilities for each class. Experimental results show that the proposed method can detect moving objects efficiently, which in turn will decrease the operating time and increase the detection rate compared to previous research methods.
INNOVATION AND ACTIVITY ENVIRONMENT OF HIGHER EDUCATION INSTITUTIONS AS A LEADING FACTOR IN THE CREATION OF INNOVATIVE STUDENT CULTURE

The article analyzes the theoretical and scientific-methodical aspects of the issue of creating the student’s innovative culture in the innovative activity environment of a higher education institution. Attention is drawn to the main differences in innovative education, its leading functions, and key concepts. The justified necessity and the main requirements for the optimization of the innovative activity environment of a higher education institution, which objectively or subjectively acts on the student with the help of pedagogically controlled action and includes a set of interacting objects united by functional connections. The main principles of the system approach to innovative activity have been highlighted.
Pathway For a More Discrete Fintech Application

The addition of the Secure Hash Algorithm SHA-256, the Advanced Encryption Standard AES-128, and dynamic polynomialswitchingintheLinearFeedbackShiftRegistersenhancesthesecurityof stream cipher. Numerous studies concluded that this introduction strengthens the stream cipher's resistance to cryptanalysis. In order to provide a distinct understanding of these designs, this study intends to present a thorough assessment that highlights current attempts to put this concept into practice. The process of creating new designs will be aided by this vision.
Assistant Zone – Homeschooling Assistance System based on Natural Language Processing

As a developing country, most people give their highest priority to education. When focusing on building an e-learning platform to improve the knowledge of students and teacher-student interactivity, the pandemic season can be mentioned as the main blocker which highly impacted the education field. Not only by considering the pandemic situation but also by addressing the concerns when it comes to teacher and student evaluation and psychological levels of students who are undergoing different difficulties, the “Home Schooling Assistance System” (Assistant Zone) has been introduced as a solution. The Assistant Zone has been initiated with three unique features which are valuable for both students and teachers. This system analyzes the strengths, weaknesses and evaluates the student performance, suggests study materials to improve themselves, provides solutions to the problems faced by the students, teachers, and parents and measures the performance of teachers based on their students, and recommends learning materials for the low-performing teachers. The Assistant Zone fulfills the targeted problems and introduces the above-mentioned three unique features with the use of Natural Language Processing (NLP) such as the BERT algorithm and Machine Learning models such as the Recurrent Neural Network, Forward Neural Network, and Gaussian Model.
Network Anomaly Detection Using Federated Learning

Due to the veracity and heterogeneity in network traffic, detecting anomalous events is challenging. The computational load on global servers is a significant challenge in terms of efficiency, accuracy, and scalability. Our primary motivation is to introduce a robust and scalable framework that enables efficient network anomaly detection. We address the issue of scalability and efficiency for network anomaly detection by leveraging federated learning, in which multiple participants train a global model jointly. Unlike centralized training architectures, federated learning does not require participants to upload their training data to the server, preventing attackers from exploiting the training data. Moreover, most prior works have focused on traditional centralized machine learning, making federated machine learning under-explored in network anomaly detection. Therefore, we propose a deep neural network framework that could work on low to mid-end devices detecting network anomalies while checking if a request from a specific IP address is malicious or not. Compared to multiple traditional centralized machine learning models, the deep neural federated model reduces training time overhead. The proposed method performs better than baseline machine learning techniques on the UNSW-NB15 data set as measured by experiments conducted with an accuracy of 97.21% and a faster computation time.