Reviewerly/exHarmony huggingface dataset:
PSIKIS PROBLEMS OF THE CHARACTER OF BARRA SADEWA IN NOVEL HABIBIE YA NOUR EL AIN KARYA MAYA LESTARI GF

This research is motivated by the existence of psychological problems described by the author in the novel Habibie Ya Nour El Ain by Maya Lestari GF. The formulation of the problem in this study is, how are the psychological problems of the character Barra Sadewad in the novel Habibie Ya Nour El Ain by Maya Lestari GF. In accordance with the formulation of the problem, the purpose of this study is to describe the psychological problems of the character Barra Sadewa in the novel Habibie Ya Nour El Ain by Maya Lestari GF. The type and method of this research are qualitative research and descriptive methods of analysis. The data in this study are words and sentences related to psychological problems in the novel Habibie Ya Nour El Ain by Maya Lestari GF. The results of this study indicate that in the novel Habibie Ya Nour El Ain by Maya Lestari GF there are psychological problems experienced by the character Barra Sadewa. The psychological problems in the novel Habibie Ya Nour El Ain by Maya Lestari GF are depression, obsession, anxiety, guilt, inadequacy, anger, hurt, dissatisfaction, and concern. Some of the psychological problems that occur in the novel Habibie Ya Nour El Ain by Maya Lestari GF are depression which dominates the occurrence of psychological problems in characters. The cause of depression in the character in the novel Habibie Ya Nour El Ain by Maya Lestari GF is feeling disappointed with her life and presence which her parents did not want.
Finding Ways to Improve Korean Standard Response Procedures by Analyzing Disaster Management Systems in Foreign Countries

The aim of this research is to establish a comparative analysis framework of domestic and foreign disaster response procedures and to suggest ways to improve Korean standard response procedures. Major variables in this study are as follows: ① command and support system of Countermeasures Headquarters, ② cooperative relationships among related organizations, ③ The response organizations’ activity characteristics in operation stage. The authors schematized each countries’ characteristics which are defined as vertical(command and support) and horizontal(cooperative) relationships to identify the connections among factors in disaster response procedure. The findings suggest that it is necessary to strengthen the coordination role of Ministry of the Interior and Safety(MOIS), to build the integrated command system, to write a customized disaster response plan, to strengthen the regional governments’ coordination functions, and to develop specific details on inter-organization collaboration.
Prediction of the Spread of Covid-19 in Indonesia Using the SEIRD Model and Hybrid Model with ARIMA Correction

Building an SEIRD segmentation model with life dynamics, estimating its parameters, and utilizing the ARIMA model to determine and predict the difference between the SEIRD model solution and the observed and fitted data constitute the machine learning approach used in this study. We use machine learning techniques to accomplish this. A hybrid method is used to process the newly collected data, using the model predictions and the residuals from the data. The historical values of the infected, recovering, and dying populations divided by the confidence intervals serve as the parameters for the SEIRD model, which in turn serve as parameters for the models' applicability. confidence level range. Long-term and short-term estimates with confidence intervals can be provided by the model, which can assess incoming data in real-time. We tested the model's predictions using actual data on COVID-19 cases in Indonesia. All current models are tested with the maximum allowed deviation during validation using MAE, MSE, MLSE, normalized MAE, and normalized MSE. The findings shown that the features of the current model are highly accurate for infected patients, patients who are recuperating, and patients who have passed away. Governments, business, and policy makers can use the findings of our suggested model to forecast controllable health hazards. This methodology can also be applied to additional research to progress science.
Materials of the control work with digital competencies for the areas of training in the IT sphere of agricultural universities

The teaching manual sets out the basic laws and formulas for all sections of the discipline "Physics", provides examples of problem solving and variants of control works containing, among other things, tasks on digital competencies. This educational and methodological manual is intended for use in the educational process in the preparation of bachelors in the areas of 09.03.02 "Information Systems and Technologies" and 09.03.03 "Applied Informatics" in full-time and part-time education at the K.A. Timiryazev Moscow State Agricultural Academy.
Personalised recommendation model for senior learning: a case study of Thai seniors to enhance social network skills

To promote senior learning, this study proposes a personalised recommendation model using the combination of performance, preference, and physical condition factors of the learners, namely the 3P personalised recommendation model. Presented in the set of pre-defined rules, it consists of learning object selection and content presentation. A total of 68 Thai seniors aged between 60 and 83 years old from senior school participated in the case study for enhancing social media skills, how to use LINE application, on electronic learning platform. The learners were required to complete all learning objects with flexible learning paces. The recommendation was given to everyone individually after having each test in a learning object. The proposed model was evaluated in learning enhancement ability and learner satisfaction. The result revealed that the recommendation provided by the proposed model could effectively enhance learning efficiency of seniors. Also, the proposed model obtained satisfaction at the 'very satisfied' level.
Low-Latency LiDAR Semantic Segmentation

Several methods of semantic segmentation using light detection and ranging (LiDAR) sensors have been proposed for the recognition of surrounding objects by autonomous driving cars. LiDAR is a sensor that compensates for the weaknesses of other sensors, such as cameras or radar systems, and semantic segmentation assigns a class label to each point in the LiDAR point cloud. Recently, real-time semantic segmentation methods that are capable of processing LiDAR point clouds at frame rates have been proposed. Real-time semantic segmentation is essential for the autonomous driving system because it can output class labels for LiDAR point clouds at high speeds. However, this segmentation method suffers from a delay equal to processing time. To address this challenge, we propose a novel method that combines SalsaNext [1], a method of real-time LiDAR semantic segmentation, and semantic forecasting, which predicts the results of future semantic segmentation. We quantitatively evaluate our method using the Semantic-KITTI dataset, which comprises point cloud data acquired from the LiDAR sensor in the real world, and compare the latency and accuracy of our method with other semantic segmentation methods. Consequently, our method is found to be capable of operating in real-time and with low-latency, and it can achieve a performance similar to that of previously reported real-time semantic segmentation methods.
Survey of Deep Learning Approaches for Twitter Text Classification

Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine. With the rise of deep language models, such as RoBERTa, also more difficult data domains can be analyzed, e.g., news texts where authors typically express their opinion/sentiment less explicitly. Sentiment analysis aims to extract opinion automatically from data and classify them as positive and negative. Twitter widely used social media tools, been seen as an important source of information for acquiring people’s attitudes, emotions, views, and feedbacks. Within this context, Twitter sentiment analysis techniques were developed to decide whether textual tweets express a positive or negative opinion. In contrast to lower classification performance of traditional algorithms, deep learning models, including Convolution Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM), have achieved a significant result in sentiment analysis. Keras is a Deep Learning (DL) framework that provides an embedding layer to produce the vector representation of words present in the document. The objective of this work is to analyze the performance of deep learning models namely Convolutional Neural Network (CNN), Simple Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), bidirectional Long Short-Term Memory (Bi-LSTM), BERT and RoBERTa for classifying the twitter reviews. From the experiments conducted, it is found that RoBERTa model performs better than CNN and simple RNN for sentiment classification.
Research on Intelligent Acknowledgement Algorithm of Host Lecture Mood Based on Deep Knowledge

With the continuous improvement of artificial intelligence based on deep knowledge, more and more jobs in specific brain-assisted fields will be replaced in the future. The field of artificial intelligence has been widely used, and lecture acknowledgement is one of the very meaningful researches. Speech is one of the most natural and ideal ways of human-computer interaction, which carries the rich moodal content of the speaker. The ultimate goal of lecture mood acknowledgement is to enable machines to recognize human moods through lecture like humans, and achieve better human-machine communication, which has a very broad future in technology application scenarios. The artificial intelligence recognizes the moods expressed by the host in real time through lecture acknowledgement, and then uses the "mood tag" to call the corresponding background music and images to achieve automatic soundtrack and background by calling the large database prepared in advance. This paper is based on deep knowledge. Higher-order thinking is the core trait of deep knowledge. Developing higher-order thinking capacity helps to realize and promote deep knowledge. At the same time, deep knowledge helps to improve learners' thinking quality and knowledge efficiency.
Status istraživanja komunikacione kompetentnosti nastavnika u proteklih deset godina

The complexity of new teachers’ roles during their continuous professional development requires equally developed communication skills. The paper presents a short theoretical framework of the development of teachersʼ communication competences, and its primary aim was the selection and analysis of papers in the field of the development of teachersʼ communication competence within the past decade, usingprimarily samples in higher education. To this end, content analysis was carried out. The results show great heterogeneity and diversity in terms of approaches to studying and improving the communication competence of teachers and future teachers. There is a need for a more precise definition of these competences in order to emphasize the importance of communication in higher education teaching.
The Contemporary Philosophy of Science: An Eternal Return

The article explores the contemporary philosophy of science in the context of the idea of eternal return. The problematization of the intellectual field “after postpositivism” runs through the renewed questions “what?”, “how?”, “who?” and “for what?” of scientific research. This questioning is a search for bearings in the historical space and time that determines “after what?” or “back to whom?” the thinking about science unfolds. Such a reflexive appeal to the origins leads to the ideas of the philosophy of science of the first half of the twentieth century. It is then that the main differences within the research of science were formed: between sociological and methodological approaches, between philosophy and disciplines that study science, between the goals of forming a worldview and managing science. The philosophy of science “after postpositivism” expresses itself in the controversial interpretation of the subject matter and method of the study of science, in the division of labor between disciplines and approaches that lose the possibility of constructive interaction and reach the point of “science wars”. In conclusion, it is argued that philosophy as the "hard core” of scientific research, a historical appeal to the origins of scientific activity and the interpretation of the scientific revolution as a renewal of tradition can make such modernity valuable for return.
Virtual Reality for Delivering Swimming Practice Through Water-Free Immersive Training System

Abstract Learning to swim is essential at any age as a great way to exercise and part of safety measures. Not everyone feels comfortable to start practicing from being in the water due to specific psychological or physical difficulties. Similarly, the in-water practice lacks real-time feedback for postures or stroke corrections. However, Virtual Reality (VR) technology presents a great potential to enable a water-free approach as preparation for, or supplementary to, in-water practice. Such technology is still under-explored. This paper proposes a Water-Free Immersive Training System (WITS) which verifies the feasibility of a Water-free Immersive Training System using system construction and experience tests. WITS features whole-body physical feedback (e.g.through distributed resistance materials or controllable air cushions) and body movement tracking to provide visual and embodied immersion and real-time training feedback. A Presence Questionnaire (PQ) is adopted to analyze users immersive experience. This paper discusses the user experience of WITS and the broader directions and implications for future development. This paper is intended to propose a novel design application and expand the possibility of other innovative forms of immersive sports. At the same time, it provides a set of design-oriented insights for creating tangible immersive experiences in VR systems.
Keywords extraction method based on two-way feature fusion

In order to improve the accuracy of keyword extraction, an improved method was proposed to solve the problem of missing keywords in traditional TF-IDF keyword extraction algorithm. In this method, two routes are used to extract the features of the text, one is responsible for storing the dependency syntactic relations in the preprocessing of the text, and the other is responsible for calculating the tF-IDF value, part of speech and position of the words, and constructing the comprehensive scoring weighting formula of the candidate keywords. Finally, the feature information of these two paths is fused in the stage of combinatorial optimization and sorted to form formal extracted keywords. Experimental results show that compared with the traditional TF-IDF algorithm and Textrank algorithm, the F value of the proposed method in Sina news data set is improved by 14.4% and 16.3% respectively, which effectively verifies that the proposed method can significantly improve the keyword extraction performance.
A Smart Wireless IoT Ring for Real-Time Keystroke Recognition Using Edge Computing

Enabling a single ring to recognize 3D keystrokes is a daunting challenge, especially for real-time keystrokes recognition. In this work, we integrated edge computing and machine learning algorithms into a microcontroller unit (MCU) of a smart ring so that keystrokes motion can be accurately recognized in real-time. We developed a multi-level decision (MLD) algorithm and embed a lightweight support vector machine (SVM) algorithm to execute computation for keystroke recognition on the edge of the smart ring. With this method, we can reduce the time for data transmission and avoid the data redundancy problem with the huge dispersion calculation workload; thus, improving the real-time performance of the smart ring device. Consequently, the application of this smart ring-based virtual keyboard system has minimum requirements for hardware, such as memory space and computing capacity. This study demonstrates that the use of low-performance chips in future virtual keyboard systems is possible in order to achieve lower development cost, reduced device size, and improved ease of use. The proposed smart ring is expected to provide a novel and convenient technology for real-world human-computer-interface applications in the future.
Symmetric Key Based Scheme for Verification Token Generation in Internet of Things Communication Environment

The traditional power grid systems are being replaced with smart grids so as to offer the required levels of flexibility, reliability, efficiency and dynamic power adjustments. However, security and privacy challenges are serious issues in smart grid environment due to a myriad of heterogeneous appliances that interconnect with the grid system. Consequently, many security and privacy preservation schemes have been developed based on techniques such as elliptic curve cryptography, blockchains, homomorphic encryption, public key certificates and bilinear pairing operations among others. However, these schemes have numerous performance and security constraints. In addition, some of these protocols require tamper proof devices which ride on unrealistic security assumptions. In this paper, a symmetric key based scheme for verification token generation is developed for internet of things communication environment. Extensive performance evaluation is executed in terms of computation, communication and space complexities. The results show that the proposed scheme has relatively low storage complexity, and the least computation and communication complexities. This renders it applicable in smart grid communication environment. In terms of security and privacy, a number of hypotheses are formulated and proofed to show that this scheme is secure under both the Dolev-Yao (DY) and the Canetti-Krawczyk threat models.
A Novel Specific Emitter Identification Algorithm Based on Amplitude Probability Distribution

We propose a novel specific emitter identification (SEI) algorithm based on amplitude probability distribution (APD). By studying the subtle differences in nonlinear distortion of power amplifiers, the proposed algorithm leverages the real signal amplitude interval probability distribution (AIPD) and analytical signal’s constellation circle interval probability distribution (CIPD) to extract the local distortion features. The simulation results on both the simulated signal and actual signal show that compared to the benchmark methods, the proposed algorithm can achieve significant performance gains with relatively low complexity and has high robustness to training set size and prior information of signal to noise ratio (SNR).
SISTEM REPOSITORI DAN PENGARSIPAN UNTUK MANAJEMEN PENYIMPANAN DATA SURAT RESMI PADA DESA KARANGDUREN KABUPATEN MALANG

At the village level, there are many obstacles faced by village government officials in their efforts to provide services to the community. One of which is storing official letters that have been issued by the Village Office. So far, official letter storage at the village office was still in the form of hard files (paper) and stacked on storage shelves, without any soft files and filing numbers. With such a letter storage model, as a result, the village office correspondents had difficulty especially in finding the letter if needed. Therefore, a Repository and Archiving System for Data Storage Management of Official Letters was created so as to improve the service and efficiency of the Karangduren Village Office's performance in serving the community. In other words, this PPM (Community Service) activity produced a prototype repository and archiving system application for data storage management for official letters at the Karangduren Village Office, Kec. Pakisaji, Kab. Malang, by involving all members of the PPM team for its implementation. Students were involved in survey activities to analyze system requirements in Karangduren Village, Pakisaji Sub-district, Malang Regency and making application usage modules.
Forecasting Model for Hanwoo Considering the Change in the Net Number of Breeding Cattle

Decision-making on the number of slaughtered heads considering the change in the number of breeding heads is an important issue for Hanwoo-breeding farmers. While previous studies treat breeding heads of Hanwoo in terms of supply function, this study differs in the way that it develops a model for breeding heads that incorporate both supply and demand sides. The number of cattle raised is forecasted using the estimates of slaughtered heads, the estimates of breeding cattle under one year of age, and the actual number of breeding cattle in the previous period. The result of the precision of the model for estimating the number of cattle raised indicates that the accuracy of the model is very high.
Automatic Sales Order Collection from A Top Indonesian Retailer's E-Procurement Website Using Selenium

The utilization of E-Procurement services is increasing rapidly over the years. This can be seen in several cases of mainstream supermarket retailers in Indonesia such as Carrefour, Hero, and Indogrosir. They will have a centralized order placing system which has been proven to provide numerous benefits. However, from the suppliers' point of view, it might be perceived as a difficulty since they are obligated to check multiple websites throughout the day. Hence, this solution is proposed to solve the suppliers' problem of being required to continuously monitor the e-procurement websites. Selenium will be used to automatically harvest sales orders data from the e-procurement website with the appropriate credentials. The data will be inserted into a database after undergoing data cleansing which will then be displayed via a User Interface. The system have been built and the testing shown 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> success of data collection.
Semantic-Augmented Local Decision Aggregation Network for Action Recognition

It is challenging for an intelligent system to recognize the actions recorded in an RGB video due to the large amount of information and wide variations in the RGB video. On the other side, skeleton data focuses on the region of human body but lacks the interaction information with the background, which is complementary to the RGB data. Recently, some works focus on combining the RGB and skeleton data together to boost the performance of action recognition. However, the semantic information between joints is missing in existing works, which is important for action recognition. In this paper, we propose a novel semantic-augmented local decision aggregation network for action recognition. Specifically, we regard the area of body joints as the attention region to extract a local spatio-temporal feature for each body joint. In order to take advantage of the semantic information between joints, we propose a semantic information module, which jointly encodes the spatial and temporal index of body joints to enhance the representation ability of the local features. For better learning ability, instead of aggregating the local features, we first make decisions based on each individual local feature and then aggregate the local decisions for final recognition, which reflects the idea of resemble learning. Extensive experiments demonstrate the effectiveness of our proposed module which improves the performance of action recognition on three commonly used datasets.