Reviewerly/exHarmony huggingface dataset:
Application of Compressed Sensing Method With <i>ℓ</i>1/2-Norm in Fan/Compressor Mode Detection

Abstract In this paper, ℓ1/2-norm method is used to replace the traditional ℓ1-norm to improve the compressed sensing technique. Numerical experiments and fan/compressor tests were performed to validate the applicability and robustness of the CS method based on ℓ1/2-norm. Numerical experiments show that ℓ1/2-norm method can effectually eliminate the spurious mode peaks generated by failed recovery of ℓ1-norm method. Further study with random sensor failure shows the stronger robustness and applicability of ℓ1/2-norm method. In the low-order acoustic mode detection experiment on cooling fan, 24 sensors were used to conduct mode decomposition at blade passing frequency (BPF). The recovered signal using ℓ1/2-norm method was sparser than that using ℓ1-norm method. In the case two sensors were randomly removed as failed sensors, the deviation between results of ℓ1/2-norm method and original signal was also found smaller. In the condition that more sensors failed and ℓ1-norm method did not work properly, ℓ1/2-norm method could still detect the main mode. For further validation, the data from a high-order aerodynamic pressure mode detection experiment on axial compressor with two sensors damaged was processed through ℓ1/2-norm method. The mode decomposition results at both BPF and the rotating instability frequency (RIF) agreed with the conclusion of the first experiment. Numerical experiments and fan/compressor tests show that using ℓ1/2-norm can significantly improve the application and robustness of compressed sensing in mode detection.
Dragonfly: Higher Perceptual Quality For Continuous 360° Video Playback

When streaming 360° video, it is possible to reduce bandwidth by 5× with approaches that spatially segment video into tiles and only stream the user's viewport. Unfortunately, it is difficult to accurately predict a user's viewport even 2--3 seconds before playback. This results in rebuffering events owing to misprediction of a user's viewport or network bandwidth dips, which hurts interactive experience. However, avoiding rebuffering by naively skipping tiles that do not arrive by the playback deadline may lead to incomplete viewports and degraded experience.
Causal Inference Applied to Explaining the Appearance of Shadow Phenomena in an Image

Due to the complexity and lack of transparency of recent advances in artificial intelligence, Explainable AI (XAI) emerged as a solution to enable the development of causal image-based models. This study examines shadow detection across several fields, including computer vision and visual effects. Three-fold approaches were used to construct a diverse dataset, integrate structural causal models with shadow detection, and apply interventions simultaneously for detection and inferences. While confounding factors have only a minimal impact on cause identification, this study illustrates how shadow detection enhances understanding of both causal inference and confounding variables.
PENGARUH METODE PEMBELAJARAN TOTAL PHYSICAL RESPONSE TERHADAP KEMAMPUAN KOSAKATA BARU BAHASA INDONESIA YANG BAKU OLEH SISWA KELAS II SEKOLAH DASAR NEGERI NAIKOTEN 2 KOTA KUPANG

This study aims to determine the effect of the method total physical response on the new standard vocabulary of Indonesian in use by students in class II in Naikoten 2 Public Elementary School, Kupang City. This research uses a randomized pretest-posttest control group in a true experiment design. All students at SDN Naikoten 2 made up the population of this study, and all students in classes II-A (the control class) and II-B made up the sample (experimental class). For the purpose of this study, documentation, tests, and observations are used as data gathering methods. Using the Wilcoxon formula in the SPSS version 21 application, the hypothesis was tested, and if Asymp.Sig.(2-tailed) 0.05, H0 is rejected and H1 is accepted based on the analysis results.
Sewing Service Order Information Web-Based (a Case Study at Iendandy Modiste)

Ordering sewing services is a crucial fashion industry process requiring efficiency and easy customer access. It aims to make customer easier to place orders and improve operational efficiency for sewing service providers. Through the implementation of this system, customers can easily choose and order sewing services according to their needs through a responsive and easy-to-use web platform. Sewing service providers will also benefit from this system by improving the order process, managing orders more organized, and increasing customer satisfaction. The design of this system is built using PHP and MySQL programming languages as databases. Workflow design on this system uses DFD (Data Flow Diagram) and ERD (Entity Relationship Diagram). With this system, it can help Iendandy Modiste in increasing bookings.
Development of CORE-based Electronic-modules to Improve Students' Problem-Solving Skills

Students must be trained and develop problem-solving skills as part of 21st-century skills. The study aims to determine the feasibility and implementation of learning CORE-based Electronic-modules and the improvement of students' problem-solving skills on impulse-momentum material. The research method used is Research and Development with the ADDIE model (Analysis, Design, Development, Implementation, and Evaluation). The instrument used is a validation sheet (expert of material and media and teachers), student worksheets, and tests. The results show CORE-based Electronic-modules after validation an average feasibility of 80% and the implementation of learning activities using CORE-based Electronic-modules an average of 84% including in the very good category. Student worksheets with an implementation percentage of 81% with effective criteria Enhancing students' problem-solving skills of 0.64 with moderate interpretation. The results of hypothesis testing using a paired obtained a tcount of 31.00 &gt; ttable of 2.04. The results show there are differences in problem-solving skills after the implementation of CORE-based electronic modules. The CORE-based electronic modules are feasible to use in the classroom learning process and can improve students' problem-solving skills on impulse-momentum material
APLIKASI KONSULTASI DOKTER-PASIEN SECARA ONLINE BERBASIS ANDROID MENGGUNAKAN REACT NATIVE

The use of communication technology has penetrated into every field, including those related to medical consulting services. Smartphone devices not only function as a verbal communication tool, but can be used for online communication. With the development of computer programming languages, smartphone devices can be used more widely to serve consultations about health. Javascript is a programming language that is widely used in developing mobile applications. The React Native framework is used to design mobile applications so that they are easy to use via smartphone devices without going through a web application. In this study UML is used as an application design method and firebase as a data storage medium. The trials were carried out using BlackBox Testing on 4 smartphone devices based on the Android operating system with different specifications. In addition, trials were also carried out on every function in the Doctor-Patient Consultation application involving 25 respondents by providing an assessment. The test results show that the use of the React Native framework maximizes the design of Android-based mobile applications. Of the 4 smartphone devices, all functions in the application went well, apart from that, 25 respondents who gave an assessment of more than 65% stated that they agreed and strongly agreed with each statement regarding the application.
ANALISIS PENGARUH DIMENSI GAMBAR PADA KLASIFIKASI MOTIF BATIK DENGAN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

This study focuses on the classification of batik patterns using image data, specifically with three classes: kawung, parang, and pekalongan batik patterns. A total of 180 images were used for the research, divided equally among the three motifs. The dataset was collected through observations from Google. Data preprocessing involved two stages. In the first stage, the data was split into training and validation sets, with a 70% - 30% ratio, respectively. This separation allowed for evaluating the model's performance and generalization ability on unseen data. In the second stage, the Image Data Generator library was utilized to enhance data diversity and improve the model's ability to generalize patterns. Data augmentation techniques, such as rotation, shear, zoom, horizontal flip, and shift, were applied to the training data. The augmented data was then fed into a pre-defined Convolutional Neural Network (CNN) architecture. The CNN model processed the input data through convolutional layers with max pooling and ReLU activation functions. The outputs from the first convolutional layer were used as inputs for subsequent convolutional layers. The resulting feature maps were flattened and passed through fully connected layers for classification. The model's performance was assessed by evaluating accuracy measures. The CNN model achieved the highest accuracy of 98% on the training data and 100% on the validation data after 7 epochs.. The implementation was done using Python programming language and the Google Colab platform, along with required libraries. Model evaluation involved assessing the trained model's performance by inputting test data and computing evaluation metrics, including accuracy, precision, recall, and F1-score. Confusion matrix analysis provided insights into true positive, true negative, false positive, and false negative predictions. The classification report summarized the performance metrics of the model. In conclusion, the CNN method proved effective in classifying batik patterns. The size of the input images significantly influenced the accuracy of the model. A 148x148 pixel image size yielded 100% accuracy. As a suggestion, future research should consider using a larger dataset to maximize the accuracy of the classification model.
HRM AND PUBLIC SERVANT’S HEALTH: EPISTEMOLOGICAL AND METHODOLOGICAL POSSIBILITIES

The objective of this theoretical essay is to reflect and present epistemological and methodological possibilities about the processes of health, illness and normality in the human resource management practices adopted by the Public Administration in Brazil. The Circle of Epistemic Matrices was used, with its dimensions of technical, practical and emancipatory interest. The analytical approach adopted allowed the identification of alternatives for each of these dimensions, besides discussing the new arrangements of service delivery due to the Covid-19 pandemic, in face of the implementation of telework and the use of new technologies in people management.
PERSON FIT ANALYSIS FOR ASSESSING ACADEMIC WRITING PERFORMANCE USING RASCH MODEL

Assessing academic writing performance is a critical aspect of educational practices. The thesis is derived from writing is an essential skill that students need to develop to succeed academically and professionally. To fulfill this, the Rasch model offers important test statistics that play a crucial role in constructing tests related to evaluation and item selection problems, as well as in decision-making regarding the generated test scores. Building upon this narrative, this research is conducted with the aim of identifying person fit in assessing academic writing performance using the Rasch model. The sample in the form of essays was obtained from a group of 40 students who had previously undergone a six-month academic writing program. The data was then analyzed using Ministep 4.8.2.0 built for Rasch Model analysis. The analysis results indicated that there were 31 students in the person misfit category, suggesting a need to reconsider the appropriateness of the conducted treatment and whether there were other factors contributing to it. Additionally, a discrepancy was found in 5 students with persons misfit or overfit, and their better performance was observed on more difficult items, contrary to the Rasch model's profile
Time Series Analysis for Customs Revenue Prediction using Arima Model in Python

The Directorate General of Customs and Excise (DJBC) serves as a revenue collector in the field of customs and excise. This revenue plays an essential role in supporting infrastructure development. Predictions are needed to plan a good State Revenue and Expenditure Budget (APBN). Predictions serve as a tool for revenue optimization and control. However, forecasting is problematic because unpredictable external factors also influence these receipts. A logical and accountable approach is needed to predict acceptance to overcome this problem. The prediction method used is Autoregressive Integrated Moving Average (ARIMA). According to the computations, the Root Mean Square Percentage Error (RMSPE) value is less than 10%, indicating that the ARIMA model estimation is excellent
Performance Evaluation Between HarperDB, Mongo DB and PostgreSQL

Several modern-day problems, like information overload and big data, need to deal with large amounts of data. As such, to meet the application requirements, for instance, performance and consistency, more and more systems are adapting to the specificities. The existing relational database management system (RDBMS)’s processing of massive data has become an issue because these databases do not deal with a massive amount of data. NoSQL is a database management system that makes processing massive and/or unstructured data easier because it uses key-value to store the data, collections or document stores instead of tables. Many companies today tend to start a project using NoSQL. However, HarperDB aims to produce a relational and non-relational DBMS, allowing developers to choose between different solutions. This paper aims to show the most relevant differences between HarperDB, MongoDB and PostgreSQL and compare their performances. Preliminary results show that PostgreSQL performs better with structured data, but HarperDB can integrate NoSQL and SQL, which can be a significant advantage to HarperDB compared with the other solutions.
University Education Standards in the Postmodern Context of Sustainable Growth

In the period of postmodern changes, trends for global cooperation and the development of the contemporary standards of university education are focused on the convergence of state higher education layouts in Europe to design an Area for Higher Education in Europe (EHEA), which will mainly strengthen worldwide competitiveness. The principles of university standards system modernization in the postmodern era are not the same but similar in both European and global educational environments. In general, they may be described as the triple-phased process of standardized assessment, combining a triangle of education, research, and innovation. Global cooperation trends and the adoption of common standards in European and world education enable university students and staff at different levels to be mobile without legal or other restrictions within the international community. The university has always been and remains a powerful structure that has survived many historical epochs. Thus, postmodern transformation processes in the university education sector have led to the need to review and reorganize the whole system of standardized assessment of a threefold set of knowledge, based on exploration and alteration, correlated with the conventional and contemporary university function, connected with producing new knowledge, that is not restricted to the national frameworks, but is worldwide, directed on the global cooperation, and development.
Shared Leadership, Competencies, and Relationship to Performance: Project Management Perspective

Systematic accumulation and sharing of experience knowledge is so important that the construction industry is said to be an experience industry. Due to the lack of information sharing on prior and subsequent projects by concealing information by the project execution organization, sufficiently preventable mistakes often recur, resulting in losses due to overlapping trial and error. Due to the nature of the construction industry, knowledge sharing within the organization can be greatly affected by the leadership of the person in charge. For example, in the case of construction projects, similar projects are often carried out at a certain time, and if information on previous projects or successful projects can be shared in a timely manner, subsequent projects will not fail, which will have a significant impact on project results.&#x0D; Shared leadership has been found to increase organizational performance by affecting organizational innovation and job satisfaction (Hwang Mi-jung et al., 2020). In addition, previous studies have also confirmed the positive effects of shared leadership on individuals and organizations, such as innovative behavior (Song Jung-soo, 2019). In order to increase the efficiency of the low-productivity construction industry and to have continuous competitiveness, this paper suggests the need to increase the level of shared leadership of organizational members to enhance project management personal competencies. By verifying how organizational members' capabilities affect corporate performance through shared leadership, and proving that shared leadership within the organization is an important factor in controlling project performance relationships, we aim to derive meaningful implications for improving construction companies' productivity. The results of this empirical study suggest that in order for construction companies to be continuously competitive in the changing industrial ecosystem and corporate paradigm of the Fourth Industrial Revolution, it is necessary to enhance project management capabilities by increasing the level of shared leadership of organizational members. In addition, we would like to derive meaningful strategic implications for the project management of Korean construction companies.
Scalable Delay Line-Free All-Optical Reservoir

All-optical neural networks (NN) are an exciting opportunity to increase computing speed and efficiency [1]. For this purpose Reservoir Computing (RC) [2] (see Fig. 1a) has attracted a lot of interest as it provides performance competitive with conventional NNs, but with a simpler implementation. Particular interest is on RC based on integrated photonics, as it is compact, scalable and provides intrinsic nonlinear effects [3] necessary for a NN.
Research on the Artificial Intelligence in the Field of Multisource Information Processing

Artificial Intelligence is one of the most popular research focus in the world. Facing more and more urgent needs in multisource information processing, such as data analysis, image identification and object positioning, multisource information processing are trying to apply AI technologies in more activities. Based on reports and thesis published in recent years, this paper intends to study why taking AI into multisource information processing, and try to figure out how does it be applied. Artificial Intelligence technologies is playing an important role in realizing auto information processing, and the efficiency and accuracy could be both improved.
Optical Injection-Induced Timing Jitter Reduction in Gain-Switched 1550-nm Discrete Mode Semiconductor Lasers

Advanced laser modulation techniques have enabled the development of versatile and efficient light sources for quantum communications [1]. In fact, semiconductor lasers are normally used as single-photon sources in most commercial and research quantum key distribution (QKD) systems [2]. In particular, modulated single-mode semiconductor lasers, utilizing gain-switching, optical injection locking and direct phase modulations have been used to implement a great variety of QKD protocols [1]. In recently introduced protocols, like the measurement-device-independent one (MDI) based on the interference of pulses generated by two remote semiconductor laser sources, timing-jitter in gain-switched lasers affects the temporal overlap of the pulses producing a dramatic reduction in the interference visibility [1]. Very recently MDI-QKD systems using optical injection from a semiconductor laser into a gain-switched semiconductor laser have been demonstrated [1]. A reduction of timing-jitter induced by optical injection is then desirable to improve the interference visibility in this type of MDI-QKD systems.
THE CORRELATION BETWEEN STUDENTS' VOCABULARY MASTERY AND THEIR ENGLISH LEARNING ACHIEVEMENT AT SMP NEGERI 2 KOTA TERNATE

The objective of this research to know the correlation between students’ vocabulary mastery and their English learning achievement at SMP Negeri 2 Kota Ternate. Data ware collected by through tests. A sample of 30 students of class VIII6 were selected purposively. The results of this research indicate that the average score of the students' vocabulary mastery test was moderate (56.86) and the score of the students' English achievement was also moderate (57). The test value of the correlation between the variables X and Y (correlated) was found a very high and r count result (0.9233) exceeded the t-table value (0.361) at a significant level of 5%. Thus the alternative hypothesis (Hi) is accepted and the hypothesis (Ho) is rejected. It can be concluded that the students’ vocabulary mastery was highly and significant correlated with the students’ English learning achievement.
Multilayer Semantic Features Adaptive Distillation for Object Detectors

Knowledge distillation (KD) is a well-established technique for compressing neural networks and has gained increasing attention in object detection tasks. However, typical object detection distillation methods use fixed-level semantic features for distillation, which might not be best for all training stages and samples. In this paper, a multilayer semantic feature adaptive distillation (MSFAD) method is proposed that uses a routing network composed of a teacher and a student detector, along with an agent network for decision making. Specifically, the inputs to the proxy network consist of the features output by the neck structures of the teacher and student detectors, and the output is a decision on which features to choose for distillation. The MSFAD method improves the distillation training process by enabling the student detector to automatically select valuable semantic-level features from the teacher detector. Experimental results demonstrated that the proposed method increased the mAP50 of YOLOv5s by 3.4% and the mAP50–90 by 3.3%. Additionally, YOLOv5n with only 1.9 M parameters achieved detection performance comparable to that of YOLOv5s.