Reviewerly/exHarmony huggingface dataset:
Efektivitas Keterampilan Vokasional Membuat Minuman Bandrek melalui Teknik Modeling bagi Anak Tunarungu Kelas X di SLB Negeri 1 Linggo Sari Baganti

The purpose of this study was to prove that modeling techniques were effective in improving vocational skills in making bandrek drinks in class X deaf children at SLB Negeri 1 Lingo Sari Baganti. This type of research used is an experimental method in the form of pre-experimental with the type of one-group pretest-posttest design. then given treatment using modeling techniques and continued by giving a posttest processed and compared using the Wilcoxon Rank Test test. From the results of statistical test studies that have been analyzed using the SPSS 23 research program, the data obtained on average during the pretest was 54.40 while for the posttest there was an increase to 83.80. The data were processed more scientifically using the Wilcoxon test and obtained a rank test score between the pretest and posttest of 2.023 with Asymp. Sig (2-tailed) 0.043. Predetermined probability α = 0.05. The alternative hypothesis is accepted because the probability &lt;of the probability that has been determined is 0.039 &lt;0.05.
Dampak Kecanduan Game Online Terhadap Siswa SMK PGRI Kabupaten Kepulauan Aru

This research is directed to find the factors that cause the rise of online games among students, especially at the high school level, and its impact on their attitudes and behavior both at school and at home.The data collection process used a descriptive qualitative method with a purposive sampling technique to determine informants.Through observation, interviews, and documentation techniques, data and information in the field were processed, analyzed, interpreted, and then concluded for 30 students, involving guidance, counseling, homeroom teachers, and school principals at SMK PGRI Dobo, Aru Islands Regency.Based on the results of the study, it was found that online games had a psychological impact on students so that it influenced their attitudes and behavior in communicating, acting, socializing, and in the learning process in class.There are efforts made by the school in overcoming the impact of online games on student behavior through providing guidance, advice, sanctions, and establishing good coordination with parents.Collaboration and coaching collaboration between schools and parents is a model that is considered capable of solving problems faced by students both now and in the future.
Analisis Kesulitan Belajar Siswa di SMA Negeri 2 Bilah Hulu

This study aims to determine students' learning difficulties in the matter of the digestive system in humans from the aspect of learning indicators and the factors that cause students' learning difficulties in studying the material of the Digestive System in Humans in class XI IPA SMA Negeri 2 Bilah Huluh. In this study the approach and type of research used is to use a qualitative descriptive approach. The population and sample of this study were taken by means of a total sampling of 89 students. Data collection techniques used were interviews, documentation and questionnaires. Presentation of students' learning difficulties in the aspects of learning indicators, namely (1) structure and function of 61.89% in the medium difficulty category; (2) The digestive process is 80.80% with a high difficulty category; (3) The gear section is 41.29% in the low difficulty category; (4) Enzyme function is 72.00% with high difficulty category; (5) substance in food is 48.50% with medium difficulty category; (6) The function of food is 61.61% in the medium difficulty category; (7) Variation and composition of 61.70% in the medium difficulty category; (8) Abnormalities of the digestive structure of 60.11% with moderate difficulty category. Analysis of students' learning difficulties showed 6 factors causing learning difficulties which consisted of (1) too much Latin as much as 88.01%; (2) Difficulty understanding the material as much as 81.09%; (3) The teacher's explanation is not understood by 67.60%; Lack of books from other sources as much as 61.42%; (5) Lack of learning media as much as 60.11%; and (6) The material seems too abstract as much as 53.75%.
MEDIA MIND MAPPING UNTUK MENINGKATKAN PEMAHAMAN MATERI KALIMAT BERPOLA

This study aims to determine the effect of using mind mapping media on patterned sentence material on the understanding of SMA/SMK/MA equivalent students. This research is a quantitative research. The research method used is One Group Pretest-Posttest Design. The population in this study were SMA/SMK/MA equivalent students. The sampling technique used was the snowball sampling technique. Samples were obtained from disseminating from small groups to large groups. The data collection instrument used was multiple choice test sheets which were given through pre-test and post-test via google form and WhatsApp to provide mind mapping media. The data from this study were analyzed using descriptive statistics by analyzing the mean, median and mode obtained from the pre-test and post-test. From the research results, it is known that the average, median and post-test mode results after being given mind mapping media are more improved than before being given mind mapping media by doing a pre-test. So it can be concluded that there is a significant effect after the use of mind mapping media with patterned sentence material on the understanding of SMA/SMK/MA students on an equal footing.
Facilitating writing performance of EFL learners via virtual reality: Immersion, presence, embodiment

Immersive virtual reality (IVR) technology is being used to help EFL learners overcome their difficulties with their language skills, especially writing skills. Past studies showed that the instrument that provides immersion will positively affect learners’ written performance. In line with that, this study aims to investigate the vocabulary usage and writing performance of learners who learn vocabulary via IVR versus those who learn from conventional classroom-based instruction. A total of 144 Chinese-speaking English learners, who were divided into experimental group ( N = 69) and control group ( N = 75), experienced the treatments related to the writing tasks. The results show that the learners in the experimental group wrote more informatively and presented more details. Comparative analysis revealed that learners using IVR performed significantly better on target word usage, lexical density, distribution richness, and completion of task than those in the conventional classroom. Based on the results, it would seem that the positive transfer of learning may be related to the experience of exploring in a virtual environment. The immersion of IVR and the sense of presence and embodiment enable learners to benefit from their immersive experience which aids the use of vocabulary in their writing. The implication of the study demonstrated the impact of the technological factors, whereby what causes the improvement in writing performance is due to the learners’ virtual experience and their sense of embodiment.
Efektivitas Penerapan Video YouTube dan Google Form dalam Pembelajaran PAI di SMAN 2 Kota Sukabumi

Referring to preventive measures to overcome the spread of Covid-19 in schools, the application of distance learning (Online) must be implemented so that all educators are required to use technology-based learning. The use of Information and Communication Technology (ICT) in learning Islamic Religious Education (PAI) in accordance with the 2013 curriculum can be a variety of media and media that can be used as a reference are YouTube and Google Form. This research is a descriptive qualitative research-based research conducted at SMAN 2 Sukabumi City. The results of this study found that YouTube-based learning media is more effective than Google Form in Islamic Religious Education Learning (PAI). From the results of the questionnaire, it can be seen that the Google Form media is more suitable as a medium for evaluating students than as the main medium for learning.&#x0D;
Application of Internet of Things in smart environmental protection in the context of big data

With the gradual formation of a national digital sharing economy and the growing international concern about environmental issues in developing countries, the emergence of a new "smart environmental protection" ecosystem is imperative. In this paper, the future development needs, architecture, and applications of IoT in environmental protection are presented to integrate IoT into environmental protection with the help of computers and cloud computing to achieve the integration of human society and environmental business systems for environmental management and decision-making in a more granular and dynamic manner. The application of IoT + Big Data in smart environmental protection will greatly accelerate the construction of industrial ecology.
EXTENDED COMPOSITION

In the sphere of contemporary composition and performance, the material for composers and musicians is not only sound, but extends to different forms of visuality, objects, movements and language. The Extended Composition project poses some fundamental questions: what new strategies for composition and performance will have to be developed to master the multitude of sign systems emergent from music’s expanded material array? What new significance is emerging from the layers in an extended composition of sound, language and movements, and how do we evaluate it? This presentation contains reflections and presentations of three artistic works done in collaboration between composers, musicians, dancers and other artists. The project group has been Henrik Hellstenius, Tanja Orning, Christian Blom, Ellen Ugelvik and Camilla Eeg-Tverbakk.
A Study on Key Issues by Major Industries through Criticality Assessment: From the GRI Perspective

In the past, companies focused on growth-oriented, or "shareholder capitalism" management, with the aim of maximizing profits, but through the 4th Industrial Revolution and the COVID-19 era, they are shifting to "stakeholder capitalism" that recognizes the protection and safety of human resources as a top priority. Companies select key issues through the interest and business impact of various stakeholders, which is called criticality evaluation. The importance evaluation in the annual sustainability report can be seen as a management strategy to increase corporate competitiveness by prioritizing key issues. This study selected the construction industry, transportation and warehouse industry, and manufacturing industry as representative industries, and extracted key issues in the criticality evaluation of 134 sustainability reports from five companies. Key keywords were selected from the GRI perspective through the extracted issues, and frequency analysis and weight analysis by industry were conducted. In the construction industry, "customers" of product responsibility and "eco-friendly" of the environment were high in frequency analysis. In the transportation and warehouse industries, the frequency of "Employment" of labor and "social" of society was high. In the manufacturing industry, "products" of product responsibility and "partner company" of society showed high frequency. As a result of the weight analysis, the environment was the highest in all three industries, with the construction industry having the highest "economy," the transportation warehouse industry having the highest "society," and the manufacturing industry having the highest "product." Through this, it was confirmed that the characteristics of each industry and the analysis results were consistent. Through this study, it will be basic academic and practical data by recognizing the importance of criticality evaluation in terms of corporate management strategies, identifying key issues of various stakeholders and business impact, and comparing them by industry.
Atmospheric Air Temperature Observation from the Landmark University Weather Station, Omu-Aran, Nigeria for Long Term Prediction

Air temperatures on the Earth are largely dependent upon how much energy is received from the Sun, and varies both with latitude on the Earth and the time of year. A convenient way to measure climate quantitatively, is by considering the average meteorological parameters. In this work, primary daily hourly data of the air temperature obtained from the Atmospheric Weather Station of Landmark University, Omu-Aran, Nigeria (Geographic Lat. 8.14°N, long. 5.09°E) was employed to study its morphological pattern both quantitatively and qualitatively. The data spans July 2014 to December 2017. The temperature pattern was further compared annually with those of other parameters including solar radiation, relative humidity, soil temperature etc., measured from the same station. Results obtained revealed consistency in the air temperature pattern across months of study period revealing the stability of the parameter over Omu-Aran atmosphere. Across the months, peak values were obtained around 16 local time (L T) with the highest (33.3°C) in March, and the least (25.5°C) in August. The air temperature increases with increasing solar radiation leading to increased soil temperature and evapotranspiration; but reduction in relative humidity. Additionally, the soil water level is not completely dependent on the air temperature. The observations from this work will assist Meteorologists, Agricultural experts and local farmers, especially around the North Central geo-political zone of Nigeria, in long- term prediction of weather activities and forecast.
Small and medium-sized enterprises’ time to default: an analysis using an improved mixture cure model with time-varying covariates

The analysis of the survival time of small and medium-sized enterprises (SMEs) in credit default is as important as that of their credit defaults. This analysis is complicated by the fact that not all SMEs ultimately default on their loans, making a standard time-to-event analysis unsuitable. Based on our recently proposed mixture cure model with time-varying covariates, we identify issues in earlier studies and, to update the incidence component of the model, we propose a more flexible method that employs a support vector machine to enhance the exploration of nonlinear covariate effects if SMEs never default. In addition, we consider the time-varying and fixed covariates for the incidence and latency of an event. Our estimation method is tested and verified via a simulation study, which shows that our proposed model outperforms existing models. In our empirical study, an association network was established using fuzzy cognitive maps for feature selection. The results of our analysis reveal that several factors have significant impacts on the survival time to default. The proposed model also gives a favorable performance in the prediction of probability of default.
Perception-preserving Color Ocular-image Enhancement Through Multi-scale and Multiresolution Features

This paper presents a color ocular image enhancement method based on multi-scale and multiresolution features obtained using Mathematical Morphology (MM) and Wavelet Transform (WT), respectively. In addition to enhancing the image quality and highlighting the minute features, the proposed algorithm also preserves the perceptual appearance of the image. The algorithm first decomposes the luminance component of the input image into different sub-bands. All the coefficients (excluding approximation) of these sub-bands are remapped using local–global image statistics followed by performing morphological filtering on only coarsest-level coefficients. These modified coefficients are used to reconstruct the luminance component of the image using WT sub-band coding. Enhanced images are generated post reconstructing the luminance component. Experimental results on the UBIRIS and Berkeley datasets demonstrate that the proposed method outperforms various state-of-the-art techniques. Also, results of mobile-iris liveness detection demonstrate the potential and generalization ability of our method.
Improving Drone Efficiency for Emergency Package Delivery through Payload Optimization and Traffic Avoidance

The purpose of this study is to improve drone efficiency for emergency package delivery through payload optimization and traffic avoidance. By modifying the drone design to carry more payload, the drone can be used over shorter distances to deliver small packages such as food, letters, medicine, etc. The use of drones for emergency purposes can greatly reduce road traffic and increase speed of delivery. To optimize payload, the weight of the drone and its components must be carefully considered and reduced where possible. This can be achieved through the use of lighter materials and streamlining the design. In addition, the route the drone takes must be carefully planned to avoid traffic and obstacles. This can be done through the use of advanced navigation systems and real-time traffic monitoring. By implementing these changes, the efficiency and speed of emergency package delivery can be significantly improved, leading to better outcomes for those in need.
Case Study on Youth Enlightenment Through the Library Movement

This case study is on an organization which is engaged in spreading libraries. The reading habits have fallen down substantially due to rise in e-content. The libraries which were the source of transforming ideas earlier, are all abandoned. Can an organization survive in such a difficult conditions. Should the organization change its strategy? How should an organization try to achieve the objective, which is very difficult to achieve in the present day context. This case study describes the overall working of this social development organization and presents options for its revival. The original case study is written in 2006. The present day situation has been added in this case study and the questions remain the same.
On Min–Max Storage for Resource-Restricted Clients in Coded Blockchain Systems

Blockchain is the foundation of emerging applications, such as smart contracts, nonfungible token (NFT), and metaverse. A key issue is that blockchain requires massive storage space, which limits its deployment in resource-limited end devices, e.g., Internet of Things. Recently, coded blockchain is proposed to reduce the storage requirement of blockchain while guaranteeing its security and data integrity. Coded blockchain encodes blocks into coded symbols, which are then distributively stored by clients. A key challenge when applying coded blockchain in resource-restricted networks is to ensure all clients store the same, and also the minimum, number of coded blocks. To this end, this article addresses a novel problem that minimizes the maximum (min–max) storage requirement of clients. It formulates the said problem as an integer linear program (ILP). It then proposes centralized algorithms to improve the computational efficiency of storage assignments. Moreover, it presents distributed algorithms that satisfy the distributive property of blockchain. Numerical results show that the proposed distributed algorithm with a short length code reduces the min–max storage of clients by 80% compared with traditional blockchain. In addition, the computational complexity of distributed algorithms is significantly lower than centralized algorithms.
High accuracy inference by an optical neural network implementation

In recent years, sensing and imaging have significantly progressed due to AI algorithms such as Neural Network (NN). The main issues of applying NNs to information processing are the limited processing speed and high energy consumption of electronic processors. Optical Neural Network (ONN), which utilizes diffraction and propagation of light for processing, is an intriguing implementation of an ultra-fast and low-energy-consuming NN. However, previous studies of ONN are mainly on simulations due to the experimental difficulty of processing more than hundreds of input data. In hardware implementations, the performance or the classification accuracy of ONNs can be reduced by the noise and the displacements. Therefore, not only must the ONN achieve high theoretical accuracy, but it must also be robust to these experimental errors. In this study, the classification of 1,000 MNIST input data (100 data for each of 10 classes) was realized experimentally as well as theoretically, taking advantage of our novel setup with a variable spatial light modulator (SLM). With our experimental configuration, we investigated the classification accuracy with several loss functions for the ONN training. The inference accuracy of the MNIST classification task was up to 97% in the simulation and ~95% in the experiment by softmax-cross-entropy (SCE) loss function. Also, the classification accuracy of 98% for a Surface crack classification and 93% for a Pollen classification was achieved experimentally. These results show that SCE can realize high-accuracy classification in the ONN implementation. Our results revealed the high application capability of the optical neural network for practical sensing tasks.
Research and implementation of Android fraud software detection technology based on machine learning

People's daily lives are getting more and more entangled with the Internet as technology advances and the level of living rises. The diversity of mobile Internet applications has also attracted some unscrupulous elements to use counterfeit APPs, fraud-related APPs, gambling-related APPs, and other forms to obtain ill-gotten gains, and users have suffered huge economic losses as a result. To solve the current problems, it is especially important to extract APKs quickly for such APPs and the subsequent related analysis of APKs through relevant tools and determine whether they are involved in fraud. In this paper, we propose a dynamic and static method combining unknown APK download links, running screenshots, and text data to detect whether unknown APKs are involved in fraud and to classify fraudulent APKs in detail.
A prototype for crowd location and counting via deep learning over 5G SA network using 24/7 panoramic imaging for crowd handling during earthquakes

This research shows a prototype for crowd location and counting for earthquakes based on deep learning and the infrastructure of a state-of-the-art 5G standalone network deployed at the Universidad de Concepcion, Chile. The system uses an 8 MP panoramic network camera to capture real-time crowd images, which are sent to a Deep Learning Server (DLS) over the 5G network. The camera provides visible color images, and its sensor technology can provide color images even at night. The DLS uses frames from the video feed and generates Focal Inverse Distance Transform (FIDT) maps, in which the counting and location of people are carried out. In particular, the FIDT maps are generated from the crowd images using a deep-learning model composed of two cascaded autoencoders. The 5G technology allows the system to transfer data from the camera to DLS at high speed, an essential feature for a system that will help authorities make critical decisions during natural disasters. Under this scenario, and considering that the number of rescuers is usually limited, our system enables a better distribution of them among several crowded places by instantly knowing the number of people at any time of the day or night.