Reviewerly/exHarmony huggingface dataset:
Deep Learning Based Automatic Detection of Audiological Symbols in Audiogram Images

Hard copy documents in clinical archives need to be digitized and registered to the digital health systems in order to be assessed and tracked through these systems. As it is known, thanks to the machine learning and image processing-based approaches, basic tasks such as automatic detection, segmentation and classification can be performed on a large number of handwritten documents very quickly and these documents can be digitized. In this paper, a research study on the automatic detection and digitization of various audiological symbols in audiogram images is presented. In the proposed study, it is aimed to detect automatically 19 different audiological symbols that are likely to appear on audiogram images by using deep learning-based object detection methods. By using the YOLO (You Only Look Once) object detection algorithm, quite successful results were observed in the experimental studies performed on 1000 audiogram images, and a value of 0.9600 precision, 0.9700 recall, 0.9650 F1-score, 0.9580 mAP 50 and 0.6730 mAP50-95 were measured for all 19 different audiological symbols. The proposed study is one of the limited studies in the current literature on the automatic detection of audiological symbols from the audiogram images, and it is also the first national study, to the best of our knowledge, on this topic.
Optimizing Software Development: A Comparative Study of Python and R for Enhanced Efficiency and Quality

Abstract: The constantly changing and evolving realm of software development requires incessant improvement to meet the growing demand for effective top-quality solutions. The main objective of this paper is to present an encompassing comparative analysis of Python and R programming languages which will be focusing on the software efficiency and features through the use of several algorithms. This paper attempts to delve into these languages and understand their weaknesses and strengths in order to provide professionals to make abreast decisions. The results of this comparative analysis are intended to give decisionmakers, data scientists and computer scientists a useful understanding of the pros and cons of R and Python in different scenarios related to software efficiency and development.
Analisis dan Perancangan Interoperabilitas Data Pemonitoran SPM (Standar Pelayanan Minimal) Bidang Kesehatan dengan Web Services

System interoperability is a key factor in the transformation of the minimum service standards (SPM) reporting system in the health sector. This article presents an in-depth analysis to design an Application Programming Interface (API) model that aims to increase the effectiveness of sending and reporting SPM data from primary health facilities under the district health office. Through analytical studies, we identified system interoperability needs at the district health department level, including aspects such as electronic medical record (EMR) data formats, minimum service standards, and patient identification systems. One of the main challenges is the diversity of data formats and sources that must be integrated. Based on this analysis, we designed an API specifically designed to facilitate the exchange of important data related to reporting minimum service standards in the health sector. The resulting API follows the principles of RESTful architecture, prioritizing scalability, flexibility, and security. API specifications include nationally recognized data standards for health reporting as well as stringent authentication and authorization systems to protect sensitive data. Initial implementation and testing results show that the proposed API successfully connects diverse health reporting systems with high effectiveness. Evaluation of API performance through measuring response time and resource usage indicates adequate performance for use in a production environment. Through the design of this API, it is hoped that it can increase interoperability between minimum service standard reporting systems in the health sector, reduce data duplication, and speed up the reporting process. The conclusions of this study underscore the important role of APIs in supporting healthcare quality, data-driven decision-making, and more efficient integration of medical systems.
SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing

In the modern era, mobile phones have become ubiquitous, and Short Message Service (SMS) has grown to become a multi-million-dollar service due to the widespread adoption of mobile devices and the millions of people who use SMS daily. However, SMS spam has also become a pervasive problem that endangers users' privacy and security through phishing and fraud. Despite numerous spam filtering techniques, there is still a need for a more effective solution to address this problem [1]. This research addresses the pervasive issue of SMS spam, which poses threats to users' privacy and security. Despite existing spam filtering techniques, the high false-positive rate persists as a challenge. The study introduces a novel approach utilizing Natural Language Processing (NLP) and machine learning models, particularly BERT (Bidirectional Encoder Representations from Transformers), for SMS spam detection and classification. Data preprocessing techniques, such as stop word removal and tokenization, are applied, along with feature extraction using BERT.&#x0D; Machine learning models, including SVM, Logistic Regression, Naive Bayes, Gradient Boosting, and Random Forest, are integrated with BERT for differentiating spam from ham messages. Evaluation results revealed that the Naïve Bayes classifier + BERT model achieves the highest accuracy at 97.31% with the fastest execution time of 0.3 seconds on the test dataset. This approach demonstrates a notable enhancement in spam detection efficiency and a low false-positive rate.&#x0D; The developed model presents a valuable solution to combat SMS spam, ensuring faster and more accurate detection. This model not only safeguards users' privacy but also assists network providers in effectively identifying and blocking SMS spam messages.
Analisis Risiko Bencana pada kasus Curah Hujan Ekstrem Provinsi Sulawesi Barat dengan Metode Moran's I &amp; Local Indicator Of Spatial Association

High rainfall in an area makes the area vulnerable to various disasters. To reduce the risk of damage caused by various disasters, it is important to look at the pattern of spatial distribution of areas that have a high risk of being affected by extreme rains. Information about distribution patterns of rainfall can assist policy makers in making decisions regarding extreme rain events in West Sulawesi which have the potential to cause various losses. Therefore, spatial analysis is carried out to detect connections between regions so that rainfall distribution patterns can be known. Based on this, this study aims to analyze the characteristics of the spatial distribution of rainfall in each sub-district of extreme rainfall on an annual scale in West Sulawesi. The results showed that there was a significant spatial autocorrelation. This showed that there was a correlation between the rainfall values between districts in West Sulawesi based on the spatial autocorrelation analysis with the Moran index test. Furthermore, the results of the Local Indicator Spatial Autocorrelation (LISA) analysis of rainfall data in each sub-district show that there are 10 sub-districts in 2019, 7 sub-districts in 2020, 13 sub-districts in 2021, and 7 sub-districts in 2022 which are identified as having spatial autocorrelation with a level significance 0.05. From all the results of the analysis carried out, namely the analysis of the moran index and LISA, it was obtained that the areas that have the greatest risk of high rainfall are the Kalukku, Tabang and Pana sub-districts. The research results obtained can be useful as information and evaluation material that is useful for preparing natural hazard mitigation programs and assisting the government in establishing policies based on information on areas that have a high risk of being affected by extreme rainfall.
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification

Bayesian neural networks (BNNs) have been long considered an ideal, yet unscalable solution for improving the robustness and the predictive uncertainty of deep neural networks. While they could capture more accurately the posterior distribution of the network parameters, most BNN approaches are either limited to small networks or rely on constraining assumptions such as parameter independence. These drawbacks have enabled prominence of simple, but computationally heavy approaches such as Deep Ensembles, whose training and testing costs increase linearly with the number of networks. In this work we aim for efficient deep BNNs amenable to complex computer vision architectures, e.g. ResNet50 DeepLabV3+, and tasks, e.g. semantic segmentation, with fewer assumptions on the parameters. We achieve this by leveraging variational autoencoders (VAEs) to learn the interaction and the latent distribution of the parameters at each network layer. Our approach, Latent-Posterior BNN (LP-BNN), is compatible with the recent BatchEnsemble method, leading to highly efficient ({in terms of computation and} memory during both training and testing) ensembles. LP-BNN s attain competitive results across multiple metrics in several challenging benchmarks for image classification, semantic segmentation and out-of-distribution detection.
Foreign Aid, Decentralization, Local Governance and Service Delivery in Local Governments: A Case Study of Rumbek Central County South Sudan

The study focused on foreign aid, decentralization, and local administration to examine the gap in service delivery in Rumbek County, South Sudan. The study's goals were to investigate the connections between foreign assistance and service provision, the connections between foreign assistance, local governance, and service provision, and the study factor structure between foreign assistance, decentralization, local governance, and service provision. The study population consisted of 75,000 respondents, and the research style was a case study and descriptive research design. It used both qualitative and quantitative methodologies to gather data from this population. The sample of 382 respondents was chosen using simple random sampling. Only 352 were considered for analysis. The descriptive analysis option of SPSS version 20.0 was used to analyze the data before it was presented using Spearman's correlation, multiple regression, and factor structure loadings to show the relationship and prediction for international aid, decentralization, local governance, and service delivery, respectively. The major findings of the study were that there is a significant positive correlation between; foreign aid and service delivery (r = 0.728, P-value &lt; 0.01), decentralization and service delivery (r = 0.544, P-value &lt; 0.01), local governance and service delivery (r = 0.776, P-value &lt; 0.01) and foreign aid, decentralization and local governance influenced 62.9% the level of service delivery. The study suggests that the county implements control measures to guarantee that funds are used for the proper purposes in the counties because research revealed that services were not worth the district budget. Adequate research should be conducted at the community level to identify services needed by the people to draw sufficient budgets for the implementation of foreign aid, decentralization, and local government.
SOCIO-LINGUISTIC FEATURES OF THE STATE LANGUAGE IN THE DIGITIZATION OF KAZAKHSTAN

The article is intended to determine the features of the state language during the process of digitization in the public social life of the citizens of Kazakhstan. Digitization of Kazakhstan is going on intensively. The rapid development of IT technology has made changes to the structure of the language. In this regard, the article considers the social, political, and economic reasons for determining the peculiarities of the Kazakh language in the field of digital technology. Language units collected from texts on social networks, state portals, media channels, business platforms were taken as materials of the article. Socio-linguistic methods were used in the article: the method of external observation on field research, the method of synchronous analysis in modeling the features of language in society, general linguistic methods (description, analysis, systematization). The current state of the Kazakh language was described using the method of external observation, the sociolinguistic phenomena that occurred in the linguistic situation of the Kazakh language in a heterogeneous linguistic environment (linguistic situation, language conflict, mixing of languages, bilingualism, multilingualism, language choice, recognition of language status, etc. ) was determined. It has been proven that the use of IT technology and digital system products contributes to the expansion of the functional scope of the Kazakh language. Although the "Digital Kazakhstan" program is not directly related to the implementation of the language policy of the state, it was determined that it had a positive effect on the development of the state language.
Defect Management and Root Cause Analysis: Pillars of Excellence in Software Quality Engineering

Defect Management and Root Cause Analysis: Pillars of Excellence in Software Quality Engineering
PENERAPAN METODE RANDOM FOREST DALAM MENDETEKSI BERITA HOAX

Hoax is information that is not true. The Ministry of Communication and Informatics (Kominfo) found that there was 2,099 hoax news that was spread thousands of times via social media. This generally impacts the community so it can lead to a crisis of confidence in the government. This arises because many message recipients have different literacy levels, which will affect how people analyze the information conveyed. This research uses the Random Forest method, which is used to classify large amounts of data to detect hoax news. The research results show that the Random Forest method is proven to be able to classify hoax news based on data that has been weighted and entered into the system. From the results of the study using 200 data sets, which were divided by 80% in the form of training data and 20% of testing data, the classification results obtained from the testing data were in the form of 28 positive sentiments and 23 negative sentiments with an accuracy rate of 98%.
Monitoring and Automation System for Bird Feeding and Drinking Based on Internet of Things Using ESP32

Birds are vertebrate animals characterized by feathers and wings. Their melodious sounds and colorful plumage make them a popular choice for bird breeders, which presents a promising business opportunity. Manual feeding and drinking requires farmers to feed and drink every day, which often results in delayed feeding and drinking or inconsistent dosing, causing birds to become susceptible to disease and death. Therefore, this study is intended to create an automated system for monitoring feed and drink at predetermined times utilizing the Internet of Things (IoT) concept. The system's schedule can be remotely accessed and monitored through a website, and it operates in real-time without requiring manual intervention. This allows farmers to monitor and provide birds with food and water even from a distance. The Internet of Things (IoT) system utilized in this research employs an ESP32 microcontroller as the primary controller, which connects to a Wi-Fi network. It features a servo motor designed to refill bird feeders and drinkers. Further, a mini pump is used to replenish water in the drinker based on the owner's predetermined schedule set via the application. The research concludes that the tool functions based on the input time on the website. The system sends notifications to the breeder's smartphone regarding successful feeding and drinking, with an error rate of less than 20% in system testing and over 80% accuracy in system functioning. The outcome is in the form of a tool dispensing feed and drink daily, in accordance with the breeder's specified hours
FPGA-Based Image Denoising Using Adaptive Vector Directional Distance Filter: Performance Analysis and Comparison

Image processing is essential in a wide range of applications, like medical imaging and remote sensing, where precise visual scene interpretation is critical. However, noise is typically introduced during the process of creating, editing, transmitting, and storing color images, diminishing their visual quality and the efficacy of later image processing operations. Digital image processing relies heavily on noise reduction, also known as denoising, which seeks to get rid of unwanted noise without altering the quality of the image’s essential details. In this research, we show how to use a hardware/software codesign strategy to efficiently construct the Adaptive Vector Directional Distance Filter (AVDDF) on a Field-Programmable Gate Array (FPGA). To streamline the development process, we use a high-level synthesis (HLS) pipeline with the Xilinx Vivado HLS tool. The software module is written in C/C++ and runs on a high-performance Reduced Instruction Set Computer (RISC). Comparisons are done with well-established denoising techniques like the Vector Directional Distance Filter (VDDF) and the Vector Directional Filter (VDF) to evaluate the effectiveness of the AVDDF technique. For performance comparisons, evaluation criteria such as the Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE) are used. In addition, the performance of the FPGA is measured in terms of its Look-Up Tables (LUTs), Flip-Flops (FFs), BRAM_18K (Block RAMs), and DSP48E (Digital Signal Processing Blocks) using Verilog. The area, power, and delay performance of the suggested ASIC implementation is evaluated as well. The findings of this study prove that the AVDDF denoising method is effective in enhancing image quality while keeping crucial image properties unchanged.
Chat GPT and its Capabilities

Abstract: ChatGPT is a large language model that uses deep learning techniques to generate human-like text.It is based on the GPT (Generative Pre-Trained Transformer)architecture, which uses a transformer neural network to process and generate text. The model is pre trained on a massive dataset of text such as books, articles and websites, so it can understand the pattern and structure of natural language when given a prompt or a starting point, the model uses this pre-trained knowledge to generate text that continues the given input in a coherent and natural way
Crisis state of sciences and methods to resolve it.

The article is devoted to the crisis state of modern science. This crisis has two aspects. Firstly, scientific cognition is separated from philosophy, metaphysics, religion, and theology. Secondly, there many philosophical ways of thinking and sciences which are in opposition to each other. This crisis can be resolved by turning to scientific knowledge, as Plato and Aristotle represented it. The actualization of the philosophers of ancient Greece will make scientific knowledge not an independent and separate unit, but part of a wider whole — metaphysics, theology and religion. The second aspect of the crisis can be resolved by turning to the philosophical concepts of the neo-Kantian and the philosopher of culture E. Cassirer and the author of phenomenology E. Husserl. Turning to their projects will return philosophy to its dominant position over science as a whole, and will remove the contradiction between the humanities and natural sciences. Such contradiction can be removed by considering each science as a closed symbolic form, having its own and different semantic relationship and truth criterion (E. Cassirer). The integration of sciences is possible by making the centre not the object of research, but the eidetic structures of consciousness — acts of making sense of the studied objects (E. Husserl).
Art Students’ Error Analysis in Using Past Tense in Narrative Essay

As EFL learners, writing a narrative essay with proper grammatical rules and good chronological order is not easy. Although simple past tense has been introduced to students, previous studies found that the misuse of past tense remains a problem. A similar case also happened to art students at Sekolah Tinggi Kesenian Wilwatikta Surabaya. Thus, this study aimed to find out the students’ errors in using past tense in their narrative essays. It is seen as an urgent point since the literature about how art students deal with English subjects is very limited. Help is still required, and it begins by identifying their errors in writing. By employing qualitative content analysis of 30 students’ narrative essays, this study found a total of 197 errors. These errors were grouped into three categories: omission (98 errors), addition (67 errors), and misinformation (32 errors). A conclusion was drawn that the students committed grammatical errors in their narrative essays due to several factors such as L1 interference, different grammatical rules between Indonesia and English, insufficient grammatical knowledge, overgeneralization, rule restriction ignorance, and false hypothesis.
Examination of Cybersickness in Virtual Reality: The Role of Individual Differences, Effects on Cognitive Functions &amp; Motor Skills, and Intensity Differences During and After Immersion.pdf

Background: Given that VR is applied in multiple domains, understanding the effects of cybersickness on human cognition and motor skills and the factors contributing to cybersickness gains urgency.This study aimed to explore the predictors of cybersickness and its interplay with cognitive and motor skills.Methods: 30 participants, 20-45 years old, completed the MSSQ and the CSQ-VR, and were immersed in VR.During immersion, they were exposed to a roller coaster ride.Before and after the ride, participants responded to CSQ-VR and performed VR-based cognitive and psychomotor tasks.Post VR session, participants completed the CSQ-VR again.Results: Motion sickness susceptibility, during adulthood, was the most prominent predictor of cybersickness.Pupil dilation emerged as a significant predictor of cybersickness.Experience in videogaming was a significant predictor of both cybersickness and cognitive/motor functions.Cybersickness negatively affected visuospatial working memory and psychomotor skills.Overall cybersickness', nausea and vestibular symptoms' intensities significantly decreased after removing the VR headset.Conclusions: In order of importance, motion sickness susceptibility and gaming experience are significant predictors of cybersickness.Pupil dilation appears as a cybersickness' biomarker.Cybersickness negatively affects visuospatial working memory and psychomotor skills.Cybersickness and its effects on performance should be examined during and not after immersion.