Reviewerly/exHarmony huggingface dataset:
Perancangan Aplikasi Lajena (Pelaporan Angka Bebas Jentik Nyamuk) Secara Online Mendukung Gerakan Satu Rumah Satu Jumantik Menggunakan Metode Waterfall Di Puskesmas Kedungmundu Kota Semarang

The Covid-19 pandemic has made it impossible to carry out the activities of examining mosquito larvae directly in people's homes. So that the reporting of mosquito larvae is done through the WhatsApp group. Each home must report the status of the larvae in their respective homes. This is considered less objective, because there is no cross- examination. During the Covid-19 pandemic, we learned that there are conditions under which people have to limit their activities and mobility. The purpose of designing this app is that larvae tracking should be more accurate and precise and can help solve the larva-free numbers problem in the Kedungmundu Health Center area. This type of research is qualitative research with descriptive analysis using the Waterfall method. Subject was imaged using intentional sampling technique at 5 Jumantic Cadres, 1 P2DBD program leader at Kedungmundu Health Center, and 5 One House One Jumantic Cadres. The subject of this study is the examination of mosquito larvae. The results is the design of the LAJENA (Mosquito Larva-Free Numbers Report) application in the form of design analysis in form of a flowchart, decomposition diagram, data and database flowchart and user interface in the form of Appearance of the application in the form of login and registration pages, dashboard pages, reporting pages, and report history pages that conform to user needs for user needs to independently report maggot examination.
An Effective Runge-Kutta Optimizer Based on Adaptive Population Size and Search Step Size

A newly proposed competent population-based optimization algorithm called RUN, which uses the principle of slope variations calculated by applying the Runge Kutta method as the key search mechanism, has gained wider interest in solving optimization problems. However, in high-dimensional problems, the search capabilities, convergence speed, and runtime of RUN deteriorate. This work aims at filling this gap by proposing an improved variant of the RUN algorithm called the Adaptive-RUN. Population size plays a vital role in both runtime efficiency and optimization effectiveness of metaheuristic algorithms. Unlike the original RUN where population size is fixed throughout the search process, Adaptive-RUN automatically adjusts population size according to two population size adaptation techniques, which are linear staircase reduction and iterative halving, during the search process to achieve a good balance between exploration and exploitation characteristics. In addition, the proposed methodology employs an adaptive search step size technique to determine a better solution in the early stages of evolution to improve the solution quality, fitness, and convergence speed of the original RUN. Adaptive-RUN performance is analyzed over 23 IEEE CEC-2017 benchmark functions for two cases, where the first one applies linear staircase reduction with adaptive search step size (LSRUN), and the second one applies iterative halving with adaptive search step size (HRUN), with the original RUN. To promote green computing, the carbon footprint metric is included in the performance evaluation in addition to runtime and fitness. Simulation results based on the Friedman and Wilcoxon tests revealed that Adaptive-RUN can produce high-quality solutions with lower runtime and carbon footprint values as compared to the original RUN and three recent metaheuristics. Therefore, with its higher computation efficiency, Adaptive-RUN is a much more favorable choice as compared to RUN in time stringent applications.
Research on Manufacturing Matrix Data Complementation Method Based on Generative Adversarial Network

In this paper, we propose a missing data reconstruction method based on the improved CGAN model for the problem of missing measurement data in the inventory system of manufacturing enterprises and design a deep neural network structure inside the CGAN. The developed model can automatically learn the complex spatiotemporal relationships between measurement variables, such as correlations and fluctuation patterns, which are challenging to model in the form of unsupervised training explicitly. Since the method proposed in this paper reconstructs the data through the learned correlation patterns, it can maintain stable and accurate reconstruction results even when missing measurements. In addition, the reconstructed data in this paper have similar spatiotemporal sequence properties to the actual data, which ensures the feasibility of using the reconstructed data as pseudo-measurements.
Design conversacional de chatbots: cultura, linguagem e participação

The challenges in conversational design of chatbots that consider domain and/or user cultural issues are diverse. Among them, the lack of conceptual and/or theoretical approaches to guide the designer during the conversational design process. As a result, designers often rely on their own assumptions and linguistic preferences in conversation design. In this work, we discuss how the areas of HCI and CSCW can contribute to the inclusion of cultural issues in the participatory design process of conversational agents.
Research on autonomous charging of robot based on fuzzy improved DWA algorithm

The fuzzy improved DWA autonomous charging algorithm is proposed for the problems of high hardware cost, complex docking process and low docking efficiency of robot autonomous charging technology. A horizontal attitude adjustment function and a vertical attitude adjustment function have been added to the azimuth evaluation function of the traditional DWA algorithm. During the robot electrode docking process, the robot uses a fuzzy control algorithm to adjust the weight coefficients of the horizontal posture adjustment function and the weight coefficients of the vertical posture adjustment function according to the horizontal distance between itself and the charging pile, in order to control the robot's direct navigation to the charging pile.
Multi-UAV cooperative task planning based on an improved adaptive simulated annealing and genetic algorithm

Aiming at the shortcomings of traditional optimization algorithms in solving speed and flexibility of multi-UAV cooperative task planning, a multi-UAV cooperative task planning method based on improved adaptive simulated annealing and genetic algorithm is proposed. Starting from the problem of motion encounter and the starting state of UAV and target, the multi-UAV cooperative task planning model based on action sequence is constructed, and the mathematical analysis method based on task time is given. By introducing the idea of edge filling, the algorithm structure, coding, and population update strategy are improved, and an adaptive optimization algorithm is designed. The simulation results show that the above algorithm can improve the model optimization speed and task adaptability, which is suitable for the optimization of multi-UAV cooperative task planning problem.
Graph convolutional network with target-attention for aspect-based sentiment analysis

Aspect Sentiment Classification (ASC) is aimed at determining the sentiment disposition towards a specific target. Most previous approaches use RNN and attention mechanisms to model context and target words. However, these approaches ignore the syntactic relationship between the aspect terms and the corresponding context words, resulting in the models wrongly focusing on syntax-independent words. To solve this problem, we propose an attention fusion model based on a Graph Convolutional Network (GCN) that considers syntactic and semantic information of sentences as well as captures more comprehensive information using a Feature Interactive Learning mechanism. We also apply the Pre-training model Bert to this task and obtain new state-of-the-art results. Experiments and analysis demonstrate the efficacy of the model.
Designing and Validation of e-module on Agriculture for Nutrition and Health (A4NH)

There is less awareness about health and nutrition problems and at the same time lack of availability of relevant and credible modules for ready reference for the needy people in India. This has aggravated the health and nutrition problems among rural women. The very low ratio of extension workers to farmers is aggravating the problem of knowledge transfer through the traditional system, so e-learning which is supported by growing connectivity and increased penetration of electronics in rural India can be used as an alternative solution to give faster and easily accessible information to the rural women, hence e module was developed using Microsoft Office PowerPoint 2013, e-learning module was created. The module was published as a CD-ROM. The module was validated through the sample group of researchers using the validation tool conceived by Vijayragavan and Singh [1] adapted for research purposes. Validation is done using four parameters, out of 4 parameters content and design were ranked first with a mean score of 3.88, followed by the satisfaction of felt information need with a mean score of 3.85. It was found that the utility of content and ease of understanding were scored low by the experts when compared to the content and satisfaction of the need for felt information. However, in absolute terms, all the parameters had a mean score of more than 3.5 indicating high acceptability of the complete module.
THE IMPACT OF THE CORONAVIRUS PANDEMIC ON STUDENTS' PROGRESS IN THE DISCIPLINE «MATHEMATICS»

The coronavirus pandemic has changed all areas of life, including education. Universities urgently began to introduce distance-learning technologies in an attempt to prevent the risk of the spread of infection. Their implementation has not gone unnoticed. The article analyzes the performance of students of the Omsk State Technical University in the discipline «Mathematics» during the period of online learning, compared with previous and subsequent years, in order to determine the degree of influence of new learning technologies on student performance and the quality of their knowledge. It is shown, that the use of distance technologies in the educational process has reduced the effectiveness of learning. The main reasons influencing the change in the indicators of students' progress are listed.
ANALYSIS OF PRE-UNIVERSITY MATHEMATICAL TRAINING OMSTU FIRST-YEAR STUDENTS

The adaptation of first-year students to the learning process at a technical university depends, in particular, on residual knowledge in the disciplines previously studied in the school curriculum, including mathematics. The article presents the results of written control («entrance control») in elementary mathematics, conducted annually in September to test the residual knowledge of first-year students of the Omsk State Technical University (OmSTU). Analysis of the data obtained showed that during the observation period (2017-2022) there were no significant changes in the level of mathematical training
Faster R-CNN-based pedestrian detection and tracking

Pedestrian detection and tracking is an important research area in the field of computer vision. It has extensive application in many areas, such as intelligent transportation,intelligent video surveillance. This paper uses Fast region-based convolutional neural network( Fast RCNN) for reference，propose pedestrian detection method based on Faster R-CNN in target recognition field. Image features are extracted by CNN. A region proposal network (RPN) is built up to extract regions that might contain pedestrians. And the region is identified and classified by detection network.According to the Kalman filter and Camshift tracking algorithm, this paper proposes the Camshift algorithm based on Kalman filter to track the detected pedestrians. Experimental results show that Faster R-CNN model and Camshift algorithm based on Kalman filter can be used to detect and track pedestrians in surveillance video.
The Effect of Social Skills and Emotional Intelligence on Learning Outcomes of Elementary School Students

The purpose of this research is to determine the influence of social skills and emotional intelligence on student learning outcomes. The research design uses a quantitative approach with a descriptive analysis design. The research population included students in grades IV, V, and VI (AB) at Muhamadiyah 4 Kandang Sapi Elementary School, Jebres, Surakarta, while the sample consisted of 111 students from these classes. Data was collected through observation, questionnaires regarding social skills and emotional intelligence, and documentation of learning outcomes from odd semester report cards. Data analysis used the multiple linear regression method with the help of the IBM SPSS Statistical 26 application. The research results show a positive and significant relationship between social skills and emotional intelligence and the learning achievement of students in grades IV, V, and VI (AB). Social skills have a t-value of 2.972 with a significance level of 0.004, while emotional intelligence has a t-value of 2.469 with a significance level of 0.015. Thus, it can be concluded that social skills and emotional intelligence positively influence student learning outcomes. This study uses a quantitative method with a descriptive analysis approach.
4.62 “Ask” vs “Inquire”: Quantifying Lexical Complexity With Etymology

Individuals with schizophrenia (SCZ) have less complex language. This deficit may be related to greater use of short, common words of Proto-Germanic origin (eg, “ask,” “old”) vs more formal Old French/Latinate words (“inquire,” “ancient”). We hypothesized that individuals with SCZ have less lexical richness and use relatively more words with Proto-Germanic etymology and fewer with Old French/Latinate, and that these features are related.
Effects of outdoor volleyball exercise on cardiorespiratory function under a heavy haze environment

Haze has been one of the originators of the impact on human health. It is noteworthy that all functions of the human body can decline or even fail in a heavy haze environment. In this paper, the environmental monitoring website was used to count the data of meteorological and other natural factors as well as socio-economic influencing factors in the target cities over the past year. On this basis, the spatial and temporal distribution patterns of heavy haze weather were analyzed and studied. For the 100 volunteers who had been exposed to heavy haze pollution for a long time, the physical activity time of volleyball was classified into four levels using the quadratic method. Apart from that, a mixed linear model with fixed and random effects was constructed to explore the effects on cardiorespiratory fitness after outdoor volleyball exercise under a heavy haze environment. According to the model analysis results, the outdoor volleyball exercise had a significant interaction effect on pulmonary ventilation function in men only. Moreover, volleyball could be beneficial to the target group when the physical activity had not yet reached the level of high-level volleyball physical activity. This resulted in an improvement of 0.2L, 0.04L, 1.19%, and 0.03L in their pulmonary ventilation function indexes, respectively. However, the negative effects of a heavy haze environment were heavier after reaching a high-level degree. In addition, the indicator kept decreasing, from 2.04L, 1.13L, 63.63%, 1.99L to 1.98L, 1.04L, 60.78%, and 1.83L, respectively.
Automatic Weight of Color, Texture, and Shape Features in Content-Based Image Retrieval Using Artificial Neural Network

Image retrieval is the process of finding images in the database that are similar to the query image by measuring how close the feature values of the query image are to other images. Image retrieval is currently dominated by approaches that combine several different representations or features. The optimal weight of each feature is needed in combining the image features such as color features, texture features, and shape features. In this study, we use a multi-layer perceptron artificial neural network (MLP) method to obtain feature weights automatically and simultaneously look for optimal weights. The color moment is used to find nine color features, Gray Level Co-occurrence Matrix (GLCM) to find four texture features, and Hu Moment to find seven shape features totaling 20 neurons and all of these features become the input layer in our MLP network. Three neurons in output layers become the automatic weight of each feature. These weights are used to combine each feature's role in obtaining the relevant image. Euclidean Distance is used to measure similarity. The average precision values obtained using automatic feature weights are 93.94% for the synthetic dataset, 91.19% for the Coil-100 dataset, and 54.31% for the Wang dataset. These results have an average difference of 5.06% with the target so automatic feature weighting works well. This value is obtained at a hidden layer size of 11 and a learning rate of 0.1. In addition, the use of automatic feature weighting gives more accurate results compared to manual feature weighting.
A Study on Behavior Intention of Vacation-based Volunteer Tourists Applying the Theory of Planned Behavior

The purpose of this study was to predict the behavioral intentions by expanding the planned behavior theory to analyze the role of psychological benefits in the decision-making process of vacation-based volunteer tourists. In order to achieve the purpose of the study, reliability and validity were tested for 301 tourists who had experience of vacation-based volunteer work within the past 5 years, and the effect relationship was performed through regression analysis. As a result of the analysis, the psychological benefits were confirmed in three sub-dimensions, such as warm feelings, self-expression benefits, and natural experiences. And, all sub-factors of psychological benefits had a significant effect on the attitude of the planned action theory, and attitude, subjective norms, and perceived behavioral control had a significant effect on the behavioral intention. In addition, the psychological benefits had an indirect effect on the behavioral intention through the three structures of the planned action theory. Therefore, volunteer tourism suggests that it can become a sustainable tourism paradigm that can incorporate a reciprocal spirit of sharing social responsibilities into tourism activities, away from the perspective of mass tourism that simply visits and enjoys local areas. Also, In addition, it can be a good way to develop the environment and community for local governments or related organizations to select volunteer activities suitable for the characteristics of the region or space and establish a systematical support plan.
Security Check Points and Police Extortion: A Study of Road Transport Corridors in Imo State, Nigeria

Police checkpoints have come to be associated with extortion in Imo State, and motorists frequently become victims of a well-established pattern of unethical behavior. This work, ‘Security Check Points and Police Extortion: A Study of Transport Corridors in Imo State, Nigeria’, sought to investigate and understand the nature and pattern of such extortion, its significant effects on drivers, and society at large. The objective of the study is to comprehend the dynamics of this widespread problem and shed light on its deep-seated roots and wide-ranging effects. Data was primarily gathered, methodically coded, and analysed using an interpretivist philosophy and qualitative methods to help the study delve deeply into the various tactics used by the police to extort money from motorists, such as arbitrary searches, illegal fines, and intimidation tactics. Adopting Strain Anomie Theory, the study analysed the methods used at these checkpoints. The effects of this extortion scheme go beyond just the financial losses for drivers. But there is a deterioration of public confidence among the police organisation and personnel. Considering a multifaceted approach to dealing with this issue The study recommends that the police force urgently needs fundamental reforms, covering greater training, stricter supervision procedures, and increased accountability standards. Imo State can aim to eliminate this harmful behaviour and restore faith in the police institution through a concerted effort involving other law enforcement agencies, civic society, and the general public, ultimately building a safer and more just society for all.
An Analysis of Current Advancements: Elderly Fall Detection Systems Using Machine Learning Techniques

According to the NPHCE (National Program for Health Care of the Elderly), by 2025, 12% of India’s population will be elderly, and the majority of them will favor independent living. Among them 8–10% needs the highest level of care. Therefore, the use of machine learning in AALS (Ambient Assisted Living Systems) areas like fall detection has the potential to significantly affect society. Falling is one of the most frequent accidents that can cause elderly individuals to sustain severe injuries or even die. Therefore, early fall detection is essential to minimizing the negative consequences of falls. The early detection and reporting of human falls may result in lifesaving. There have been numerous suggested fall monitoring systems. However, a large number of them identify a daily life activity as a fall. This paper describes the most current advances in machine learning (ML)-based fall detection and prevention systems. Recent works, ML algorithm, datasets, and age groups are thoroughly studied. Additionally, it offers a detailed analysis of current fall detection system trends as well as potential future directions by addressing the existing issues. This summary can aid in understanding existing systems and suggest new methodologies.
Explaining deep residual networks predictions with symplectic adjoint method

Understanding deep residual networks (ResNets) decisions are receiving much attention as a way to ensure their security and reliability. Recent research, however, lacks theoretical analysis to guarantee the faithfulness of explanations and could produce an unreliable explanation. In order to explain ResNets predictions, we suggest a provably faithful explanation for ResNet using a surrogate explainable model, a neural ordinary differential equation network (Neural ODE). First, ResNets are proved to converge to a Neural ODE and the Neural ODE is regarded as a surrogate model to explain the decision-making attribution of the ResNets. And then the decision feature and the explanation map of inputs belonging to the target class for Neural ODE are generated via the symplectic adjoint method. Finally, we prove that the explanations of Neural ODE can be sufficiently approximate to ResNet. Experiments show that the proposed explanation method has higher faithfulness with lower computational cost than other explanation approaches and it is effective for troubleshooting and optimizing a model by the explanation.
Development of the Use of Prezi Media on Short Story Writing Skills for Class Xi Indonesian Language Learning

: Learning media is a paraga tool that is useful as an intermediary in the process of conveying learning information so that it will have a big influence on learning success. One of the varied learning media is prezi, where the lesson material will be displayed in the form of online presentation media and includes illustrations and explanations of the material. The aim of this research is to determine the feasibility of Prezi-based learning media in short story writing skills in Indonesian language learning and determine students' responses to Prezi-based learning media in short story writing skills in Indonesian language learning. Keywords: prezi learning media, short stories, Indonesian language. DOI: http://dx.doi.org/10.61436/rietm/v1i2.pp42-44