Reviewerly/exHarmony huggingface dataset:
EVALUATION OF APPLICABILITY OF THE BDS SYSTEM IN ESTABLISHING A SURVEYING CONTROL NETWORK

The paper introduces some experimental results of absolute positioning and relative positioning of BDS system, use US-made Trimble R8, and Chinese-made S82C, Galaxy G6 GPS devices to conduct experimental measurements. Then based on the indicators SV, HDOP, VDOP, GDOP, Ratio, RMS and relative errors measuring of the Baslines make some evaluation comments on the positioning accuracy of BDS system. Research results show that absolute positioning has an accuracy of less than 5 m; Relative positioning accuracy is about 5-9 mm; the BDS system can be used in measuring a surveying control networks and deformation monitoring.
Performance Evaluation and Analysis of Federated Learning in Edge Computing Environment

The recent development of the Internet of Things (IoT) in various fields has dramatically increased the size of the IoT market. This development has increased the amount of local data and opened up new opportunities for artificial intelligence technologies. Federated learning has emerged to protect personal information during data processing and mitigate communication costs during data transmission. In this article, we construct a federated learning environment using Django, a python web framework, and analyze the optimal conditions for edge computing through performance evaluations in various scenarios. The performance evaluations show that the accuracy and training time can be improved by increasing the size of training data, reducing the number of clients and by taking higher data ratio of a particular client when the total training dataset is given. These findings can help in significantly improving the performance of federated learning in an edge computing environment.
Analisa kelayakan kredit usaha mikro berjalan pada perbankan dengan metode naive bayes

The development of information systems currently has a very influential on human life, especially in the world of work and in the education of information and communication technology lessions. Almost all business activities in banking now require an information system. Information syatem can help companies in the banking world to determine their performane in predicting the credit worth hiness of micro-enterprises to run optimally and efficiently. To connect to the internet, a company must integrated with one of the information system services by running on system sofware, which is provided according to the needs of the banking world. Data mining classification can be done using the naive bayes algorithm for knowledge result that are easy to understand and easy to understand. How ever, if the use of information systems in micro business credit performance is running more and more, there will be inaccuracy in a business credit, so customers will not believe in the performance of running micro business loans.
Validating Switch Variable of Flower Pollination Algorithm in Regression Test Case Prioritization

This study aims at analysing and validating the impact of switch variable of Flower Pollination Algorithm (FPA) in regression test case prioritization. The value of switch variable decides whether local or global optimization will take place. The present study makes an attempt to analyse the impact of switch variable with values ranging from 0.8 to 0.1 on test case prioritization in regression testing. The present study is an extension of the prior work done to implement FPA for test case prioritization in regression testing. To validate the study two case studies written in Java programming were used. The results were validated using the APFD metrics. From the empirical results it was deduced that values ranging between 0.8 and 0.5 of switch variable gave promising results for test case prioritization.
Web-Based E-commerce Application at Narmada Furniture Store

Narmada Furniture Store is a store engaged in the sale of household furniture items such as tables, chairs, mattresses, cabinets, and others. At the Narmada Furniture Store, the sales system used today is a conventional system, namely the buyer must come directly to the store to see the products offered. At this time the Narmada Furniture Store does not yet have an e-commerce application, so it is difficult to offer products to consumers who are outside the city. In addition, it also results in consumer ignorance of the existence of Narmada Furniture Stores. The selling process of the Narmada Furniture Store is only carried out if there are consumers who come to the store, while many consumers both in Cirebon and outside the city of Cirebon cannot come or do not know the existence of the Narmada Furniture Store.&#x0D; In analyzing the system, a description of the current system procedures is carried out, so that the system's strengths and weaknesses can be evaluated. In the process of making the system begins with the system analysis stage. Then the process modeling is described using flowmaps, context diagrams, data flow diagrams (Data Flow Diagrams) and Entity Relationship Diagrams (ERD). After the analysis stage is complete, it is continued with the design stage, by making table designs, table relations, program structures, program input and output designs. With this e-commerce application, it is expected to make it easier for consumers to obtain information about product specifications offered by Narmada Furniture Stores. Make it easy for Narmada Furniture Stores in selling their products to consumers who are inside and outside the city of Cirebon. Consumers can find out where the Narmada Furniture store is by using this e-commerce application service and can buy products without having to come to the store. And make it easier for leaders to make decisions based on the output of sales transaction reports and sales charts per month.
Research on Teaching Reform of Track and Field Course for Physical Education Majors Based on OBE mode

Under the background of increasing attention to physical education, track and field teaching has become an important part of physical education curriculum in primary and secondary schools, and the requirements for physical education teachers' track and field teaching ability are getting higher and higher.Applying the concept of OBE mode to the undergraduate teaching reform of physical education can not only make the teaching more suitable for the development needs of students, but also help to cultivate physical education teachers with all-round development of knowledge, skills, innovative ability and other comprehensive qualities.
Heterogeneous Attention Based Transformer for Sign Language Translation

Sign language translation (SLT) has attracted significant interest both from research and industry, enabling convenient communications with the deaf-mute community. While recent transformer-based models have shown improved sign translation performance, it is still under-explored how to design an efficient transformer-based deep network architecture that effectively extracts joint visual-text features by exploiting multi-level spatial and temporal contextual information. In this paper, we propose heterogeneous attention based transformer(HAT), a novel SLT model to generate attentions from diverse spatial and temporal contextual levels. These are further used to enhance a light dual-stream sparse attention-based transformer module to yield more effective visual-text representations compared to conventional transformers. Extensive experiments demonstrate that our HAT achieves state-of-the-art performance on the challenging PHOENIX2014T benchmark dataset with a BLEU-4 score of 25.33 on the test set.
AN EFFECTIVE ACTION RECOGNITION APPROACH USED IN PHYSICAL EXERCISE FOR COPD PATIENTS

This study was conducted to evaluate the effect of computer vision-based respiratory rehabilitation. Chronic obstructive pulmonary disease (COPD) is one of the primary respiratory diseases worldwide. Recently, image-capturing devices are increasingly used for physical therapy during rehabilitation treatment. Among these technologies, Action recognition plays a critical role in physical exercise and rehabilitation evaluation. This study aimed to propose an action series of a respiratory training program consisting of six actions. A video camera was placed in front of the participants to record their movements. Then, a hybrid algorithm combined with a convolution neural network and long short-term memory models was employed for action recognition from a video recording. The results indicated that the model achieved a reliable classification level of 82.35% on six actions. This demonstrated the validity of the proposed approach for multi-category action recognition. It was effective for action evaluation without medical guidance under home-based rehabilitation. Furthermore, the model for weight estimation was light-weight, with no need to consider the processing time.
OPTIMASI IMAGE CLASSIFICATION PADA JENIS SAMPAH DENGAN DATA AUGMENTATION DAN CONVOLUTIONAL NEURAL NETWORK

Garbage is useless goods/materials used normally or specifically in production, goods damaged during production or useless materials which mainly come from households. Moreover, inorganic waste is very difficult and takes a longer time to be decomposed by the soil. The lack of public knowledge about the classification of types of waste and how to process it causes a very serious problem in Indonesia. Therefore, this research creates a waste type recognition program using the Convolutional Neural Network (CNN) algorithm, which can be used to detect and recognize objects in an image. CNN is a technique inspired by the way mammals, humans, produce visual perception. CNN is included in the type of deep neural network because of its high network depth and widely applied to imagery. 2 Types of waste classification, namely inorganic waste and organic waste. The implementation of garbage image recognition uses 2 test models, Sequential and on top VGG16 which runs on the Google Collaboratory application, and Keras. After carrying out the Augmentation process, the number of test data in this study was 1489 images on the training data and 182 on the testing data resulting in an evaluation value with an accuracy of 90.97% and a loss value of 0.307 on the Sequential model, and an accuracy value of 97.99% with a loss value of 0.069 on the on top model. VGG16.
Augmented reality in sport and healthcare sciences

The term "augmented reality," refers to the adding of new data to an existing environment. When contrasted to other innovations, augmented reality may provide benefits not previously available. This research aimed to describe and get a better understanding of the advantages that augmented reality may bring to the training of athletes. In this review we propose the findings were evaluated in terms of their significance in athletics in sports and healthcare sectors. According to the findings of our study, a variety of augmented reality methods could be employed for teaching as well as delivering feedback. It may be possible to change some of the rules in order to level the playing field amongst players with varying levels of expertise. The perception of the audience might be enhanced further by the addition of more information. We also investigated the limits of existing augmented reality systems, as well as their usefulness in training, and offered some pointers for the creation of various types of training situations. This paper describes augmented reality techniques and reviews augmented reality tools for sport and health sciences. Current study describes augmented reality tools. Firstly, we explain what the augmented reality stands for.
Personalized Energy Expenditure Estimation: Visual Sensing Approach With Deep Learning

Background Calorimetry is both expensive and obtrusive but provides the only way to accurately measure energy expenditure in daily living activities of any specific person, as different people can use different amounts of energy despite performing the same actions in the same manner. Deep learning video analysis techniques have traditionally required a lot of data to train; however, recent advances in few-shot learning, where only a few training examples are necessary, have made developing personalized models without a calorimeter a possibility. Objective The primary aim of this study is to determine which activities are most well suited to calibrate a vision-based personalized deep learning calorie estimation system for daily living activities. Methods The SPHERE (Sensor Platform for Healthcare in a Residential Environment) Calorie data set is used, which features 10 participants performing 11 daily living activities totaling 4.5 hours of footage. Calorimeter and video data are available for all recordings. A deep learning method is used to regress calorie predictions from video. Results Models are personalized with 32 seconds from all 11 actions in the data set, and mean square error (MSE) is taken against a calorimeter ground truth. The best single action for calibration is wipe (1.40 MSE). The best pair of actions are sweep and sit (1.09 MSE). This compares favorably to using a whole 30-minute sequence containing 11 actions to calibrate (1.06 MSE). Conclusions A vision-based deep learning energy expenditure estimation system for a wide range of daily living activities can be calibrated to a specific person with footage and calorimeter data from 32 seconds of sweeping and 32 seconds of sitting.
Development of artificial intelligence indicator for elementary students

This research aimed to develop the Artificial Intelligence (AI) indicator for elementary students by synthesizing the components of artificial intelligence learning for elementary students which is designed to be aware of the issue of mental health of children as well. The results were assessed for appropriateness by five experts. Findings demonstrated that the indicator consisted of five aspects ordering by the complex from Grade 1 to 5. Firstly, it was Perception which the student could explain the origin of interaction between the AI and human by designing a reception system to conduct perception and response,and draw the response diagram. Secondly, it was Representation &amp; Reasoning which the student could explain the reasoning algorithm of the AI. Thirdly, it was Machine Learning which the student could explain the machine learning, criticize the input data defect. Fourthly, the Natural interaction, which the student could justify the purpose of data trending and improvement of AI to be more natural by creating a realistic response system. Lastly, it was the Societal Impact which the student could suggest the solution to AI system problems to be more accurate and appropriate with the fewest impact on humans and facilitate human living efficiently.
The Enactment of Online Learning for Special Need Students during COVID 19 Pandemic: A Case Study

The present study focuses on the implement of online learning in special education and how Information and Telecommunications Technology being used to strengthen and support the achievement of learning and the learning goals at special needs junior high school students (SMPLB) Jambi. The research method used in this study is a case study where the researchers observe how the patterns and processes of online education in the learning of SMPLB students, distributing questionnaires on the use of ICT to the teachers, interviews with parents/guardians of the students, and interviews with special junior high school teacher Sri Soedewi Maschun Sofyan, S.H Jambi. The results of the research indicate that the students in special needs schools have difficulty in obtaining technology that suits with their needs in learning. The learning process cannot be carried out as expected because the teachers have difficulty in making technology-based learning materials for the students, especially for students with the deaf and the blind. Reading and writing skills are also very limited, and the student learning is very low because the online method greatly limits opportunities for direct interaction between the students and the teachers. The involvement of parental assistance is also very low because the parents do not understand how to aid with children in online learning.
An efficient anonymous revocable ring signature scheme

Compared with other schemes, the ring signature scheme proposed in this paper has the characteristics of revocable anonymity.When malicious users publish false messages, we can find an effective algorithm to revoke anonymity, calculate the real signer identity in the ring signature members, and ensure the reliability and robustness of the ring signature system.Finally, the scheme is analyzed from the aspects of security and performance, The results show that the ring signature scheme with revocable anonymity in this paper is more secure, more efficient and lower operation cost.
The Effect of Youth Activities on Cooperative Competence: Focusing on the Mediating Effect of Creativity and Self-Esteem

본 연구는 청소년활동과 창의성, 자아존중감, 협동역량의 구조적 관계를 파악하고, 청소년활동과 협동역량의 관계에서 창의성과 자아존중감의 이중매개효과를 검증하는 데에 목적이 있다. 이를 위해 한국청소년정책연구원에서 수행한 한국아동·청소년패널조사 2018(Korean Children & Youth Panel Survey 2018; KCYPS 2018)의 3차년도 중1 코호트(중학교 3학년) 패널 자료를 활용하였다. 구조방정식 모형을 통해 변인 간 구조적 관계와 매개효과를 분석하였으며, 주요 결과는 다음과 같다. 우선, 청소년활동이 협동역량에 정적인 영향을 미치는 것으로 나타났다. 이뿐만 아니라 청소년활동이 내적 인지·정서 요인인 창의성과 자아존중감에도 정적인 영향을 미치는 것으로 나타났다. 창의성은 협동역량에 직접적으로 유의한 영향을 미치진 않지만, 자아존중감을 매개하여 협동역량에 간접적으로 정적인 영향을 미치는 것으로 나타났다. 반면 자아존중감은 협동역량에 직접적으로 정적 영향을 미치는 것으로 나타났다. 또한, 청소년활동이 협동역량에 미치는 영향에 있어 창의성의 매개효과는 유의하지 않았으나, 자아존중감의 부분매개효과가 유의하게 나타났다. 더하여 청소년활동이 협동역량에 미치는 영향이 창의성과 자아존중감의 이중매개효과로 더욱 강화되는 것을 확인하였다. 본 연구는 청소년활동과 창의성, 자아존중감, 협동역량의 구조적 관계를 규명하고, 이러한 연구 결과를 바탕으로 협동역량 함양을 위한 시사점과 지원방안을 제시하였다. The purpose of this study is to test the structural relationship between youth activities, creativity, self-esteem, and cooperative competence, and to verify the dual mediating effect of creativity and self-esteem. For this purpose, a structural equation modeling was applied to the Korean Children and Youth Panel Survey 2018 (KCYPS 2018) data set (9th grade). The results are as follows. Youth activities had a positive effect on cooperative competence. In addition, youth activities had a positive effect on creativity and self-esteem. Creativity did not directly have a significant effect on cooperative competency, but it indirectly had a positive effect on cooperative competence by mediating self-esteem. On the other hand, self-esteem had a direct effect on cooperative competence. In terms of the effect of youth activities on cooperative competence, the mediating effect of creativity was not significant, but the partial mediating effect of self-esteem was. Besides, the effect of youth activities on cooperative competence was further strengthened by the dual mediating effect of creativity and self-esteem. Based on these results, implications and suggestions for developing cooperative competence are presented.
INFORMATION SYSTEM FOR MANAGEMENT PROSPERITY COUNCIL OF MOSQUE

This study discusses the proposed information system design for the Mosque Prosperity Council. This SI is expected to facilitate every management, such as recording asset information, cash management information, and religious activities. The purpose of this research is in addition to reducing the control of the mosque organization as well as to facilitate the delivery of information to the local community because this system is designed how the data can be accessed anywhere and anytime without time limits and to support this, one of them is by integrating it on a website. The method used in this research is to use the System Development Life Cycle (SDLC) to develop a software system, which consists of several stages, including planning, analysis, design, implementation, testing, and management, that form a workflow for planning and controlling. Information Systems. The result of this research is the application of the SI website, which helps facilitate the management of the Mosque Prosperity Council and increasing the ease with which the public can receive all this information.
Quantification of Market Potential for Blockchain Technology Service Providers

Blockchain is an emerging technology perceived as ground-breaking by many domain experts. Prior studies and market reports keep signaling the overall potential of blockchain technology globally in the last decade. However, the predictions from these studies may not be adequate for technology service providers to realize the true economic prospective of the technology and jump into the bandwagon and start delivering blockchain-based solutions. It is often difficult for both internal and external technology service providers to convert existing qualitative and quantitative reports into meaningful numbers they can act upon. Yet, the understanding of market potential is critical for drawing interests and adoptions. As technology service providers recognize the opportunities and challenges of blockchain, they could customize their existing solution to minimize development costs and associated risks. This study uses the total addressable market (TAM) method to quantify the opportunity of blockchain-based solution. It further proposes a three-step approach for execution, through exploring a case of quantifying the market potential of blockchain in the primary industries of New Zealand.
APPLICATION DEVELOPMENT OF EARTHQUAKES DISASTER PREPAREDNESS FOR CHILDREN

&lt;div&gt;&lt;p class="Els-history-head"&gt;Indonesia is a country in the ring of fire and one of the most disaster-prone countries on the planet. As a result of these characteristics, Indonesia is vulnerable to earthquakes at any time. For the children, the earthquake was a traumatic experience. They were overcome with panic and dread. The victims of earthquakes and tsunamis are also susceptible to nightmares related to the natural disasters they have witnessed. As a result, there is a need for early childhood learning development media to deal with earthquakes, one of which is an android application. This program is designed for early childhood earthquakes and children’s attempts to comprehend earthquakes. For media development, this study uses the System Development Life Cycle method with Waterfall methodology and BlackBox testing. The results of the application test using BlackBox when generating all scenarios were successfully executed according to the expected results using three different device brands, including Xiaomi, Asus, and Samsung. &lt;/p&gt;&lt;/div&gt;
Massive Machine Type Communication using Non-Orthogonal Multiple Access with Convolutional Neural Network Approach

The 5G cellular network supports massive Machine Type Communication (mMTC) for Wireless Sensor Network (WSN) application.In this paper, High Altitude Platforms (HAPs) is used as a replacement for Base Station (BS).So that the cluster head (CH) from every cluster will send information owned to the HAPs by using the Power Domain Non-Orthogonal Multiple Access (PD NOMA) as a multiple access technique.PD NOMA uses the Successive Interference Cancellation (SIC) technique on the receiver side.SIC process is proven effective for detecting PD NOMA signal by sorting the received signal strength and then decoding it.However, error from the prioritized signal that has high decoding has a tremendous impact on the prioritized signal that has a way lower decoding, and this error can then further spread with the SIC process.In this paper, we propose a Convolutional Neural Network (CNN) approach to decode information from multiple CH without performing traditional communication signal processing.The simulation is already done by the Rician channel with 11 CH that is connected to the HAP.From the series of simulations that have been done, we can see that the CNN used to replace the conventional SIC on the uplink PD NOMA can detect NOMA signals without the use of conventional signal processing.The CH node nearest to the HAP requires a lower SNR than the CH node farthest from the HAP to achieve BER = 10-4 in both conventional uplink PD NOMA and uplink PD NOMA with CNN.Uplink PD NOMA with CNN has a lower complexity than conventional uplink PD NOMA.