Reviewerly/exHarmony huggingface dataset:
Integrated approach of learning content using digital technology in primary education

Integrated teaching currently intervenes as a regulatory organizing principle through an effort to systematize active concepts, strategies, methods and principles more or less brought together by paradigms and educational orientations recognized worldwide. Integrated teaching currently intervenes as a regulatory organizing principle through an effort to systematize active concepts, strategies, methods and principles more or less brought together by paradigms and educational orientations recognized worldwide. The integrated teaching of knowledge in relation to the new perspective dictated by a reference idea or integrative principle that reconstructs in ideational plan the unity and complexity of the real world. The different paradigms for integrating the curriculum with wide openings in the plan of integrated teaching bring transformations to the approaches of elaboration of the didactic strategies. The design of a strategic path takes into account primarily the type of learning promoted, the specifics of organizing learning experiences in a broad, global context promoted by the new perspectives of curricular integration.
ANALYSIS OF RIVER WATER POLLUTION DUE TO DISPOSAL OF NATURAL STONE INDUSTRIAL WASTE IN CIREBON REGENCY

The development of the industrial sector has caused many changes in environmental quality, including the natural stone industry in Cirebon Regency. Water pollution that occurs is mostly caused by the disposal of waste to the river. Wastewater containing sludge causes changes in water quality including turbidity, high total and suspended solids, and sedimentation. The purpose of this study was to examine the condition of water quality and the status of river pollution due to the disposal of natural stone industrial waste using the STORET method and the pollution index. As a result, the concentration of TSS reached 1,042.33 mg/L and has exceeded the class III quality standard based on Government Regulation Number 22 of 2021. Pollution status based on the STORET method shows stations 1 and 2 are in the moderately polluted category, while station 3 is included in the category of lightly polluted. Meanwhile, based on the pollution index method, each station is included in the lightly polluted category. The results of both the STORET and the pollution index showed that station 2 had the highest score. Thus, efforts to treat waste from natural stone industrial activities are needed to reduce the rate of increase in river pollution.
Risk Stratification of Breast Cancer Patients: Integrating Epidemiology, Risk Factors, and Prognostic Markers for Sustainable Development

The classification of breast cancer risk into high and low categories is essential for individualized treatment planning and enhanced patient outcomes. This research paper provides a comprehensive analysis of the classification process utilizing machine learning algorithms, with a particular emphasis on cancerous growth rate, hormone receptor status, and lymph node involvement as key factors. Classification models were created using a variety of machine learning procedures, including logistic regression, support vector machines, random forest, and k-nearest neighbours. The evaluation of performance was based on precision, recall, Accuracy and F1-score. The random forest model outperformed all other algorithms with an accuracy of 95.9%. Analysing the significance of characteristics revealed important factors that influence the classification process. The top ten characteristics, including hormone receptor status, lymph node involvement, and tumour size, exhibited strong predictive power. This study demonstrates the ability of machine learning algorithms, specifically the random forest model, to accurately classify breast cancer patients into risk categories based on cell nuclei images. The implications of these findings for personalized treatment planning and improved patient outcomes are discussed. Accurate risk classification enables healthcare professionals to tailor interventions, ensuring that high-risk patients receive the appropriate treatment and averting superfluous interventions for low-risk patients.
JUDGMENT VALUE ON CORRUPTION IN ONLINE NEWS: SYSTEMIC FUNCTIONAL LINGUISTICS

AbstractExploring the judgment evaluation is conducted to see the pattern of the social sanction and its impact in giving self-reflection to readers as public figures in Indonesia through systemic functional linguistics theory: appraisal. The news text about corruption that are chosen to be analyzed are namely: Lukas Enembe case as a governor, Sudrajat Dimyati case as a judge, and Ferdy Sambo as policeman that involve in criminal case. The data are analyzed by using appraisal theory in judgment process. The data source is the three online news texts that have mentioned some criticisms to the Indonesian officials, namely: National Tempo News, National Okezone News, and Kompas News. The findings show that each news text has their own way to express the social sanction which uses high negative propriety and negative veracity. The finding is benefit to see how the news text show critical expression that can influence the readers’ emotion. The novelty is finding the pattern of judgment expressions used in three online news texts through appraisal theory but it is limited on the evaluation of judgment.Keywords: judgment, value, corruption cases, appraisal. AbstrakEksplorasi nilai judgment dilaksanakan untuk mengetahui bentuk sanksi sosial dan dampaknya dalam memberikan refleksi diri pada pembaca sebagai tokoh masyarakat di Indonesia melalui teori linguistic sistemik fungsional: appraisal. Teks berita tentang korupsi yang dipilih untuk dianalisa terdiri dari: kasus Lukas Enenbe sebagai seorang gubernur, kasus Sudrajat Dimyati sebagai seorang hakim, dan kasus Ferdy Sambo sebagai seorang polisi yang terlibat kasus kriminal. Data dianalisa dengan menggunakan teori appraisal dalam bentuk proses judgment. Sumber data merupakan tiga teks berita online, yaitu Berita Nasional Tempo, Berita Nasional Okezone, dan Berita Kompas. Temuan menunjukkan bahwa masing-masing teks berita memiliki cara sendiri untuk mengekspresikan sanksi social yang menggunakan negative propriety dan negative veracity yang tinggi. Temuan ini penting untuk melihat bagaimana teks berita menunjukkan ekspresi kritis yang dapat mempengaruhi emosi pembaca. Kebaruan penelitian dalam bentuk pola ekspesi judgment yang digunakan dalam tiga teks berita online melalui teori appraisal namun penelitian ini masih terbatas pada penilaian judgment. Kata Kunci judgment, nilai, kasus korupsi, appraisal.
Pola Peresepan Obat Glaukoma di Poli Spesialis Mata Rumah Sakit X di Kabupaten Sidoarjo Periode Desember 2022 – Februari 2023

Glaucoma is a chronic optic neuropathy disease characterized by occlusion of the optic disc, narrowing of the field of view, and increased intraocular pressure. This study aimed to determine the pattern of glaucoma medication adherence in the ophthalmology polyclinic of Hospital X in Sidoarjo Regency for the period December 2022 - February 2023. This study was descriptive, non-experimental, and retrospective data collection. The results of this study obtained 127 prescription sheets. Based on the gender, 94 women (74.04%) and 33 men (25.98%). The most common age group suffered by patients aged over 65 years was 44 people (34.65%). 3 groups are most widely prescribed, namely the non-selective Beta blocker group (Timolol 0.5% eye drops) obtained 63 prescriptions (49.61%) with the rule of daily use 2 x 1 drop, the Systemic Carbonic Anhydrase Inhibitor group (Acetazolamide 500 mg tablets ) obtained 9 prescriptions (7.09%) with the rule of daily use 1 to 2 times 1 tablet, the Prostaglandin Analog group (Latanoprost 0.005% eye drops) obtained 4 prescriptions (3.15%) with the rule of using 1 drop at night. The prescribing pattern for the glaucoma drug that was most often prescribed was Timolol 0.5% eye drops, which obtained 63 prescriptions (49.61%).
KWT Pelangi Communication Strategy in Using Human Society 5.0 Promotional Media in Wonogiri Organic Village

The development of tourism objects in Beji cannot be separated from the role of the local KWT. KWT assisted by PPL acts as a benchmark for the success of developing tourism objects in the area. The main obstacle KWT in promoting organic village tourism is the lack of knowledge about the use of social media. This social media issue makes organic village tourism not so familiar to the wider community, with the aim of knowing the KWT communication strategy in utilizing the Human Society 5.0 Promotional Media to increase tourist visits in organic villages. Society 5.0 is a concept which states that technology will coexist with humans to improve the quality of life in a sustainable manner. The method used in this study is a qualitative research method. This type of research uses a qualitative descriptive type, which aims to explain the phenomenon in depth through data collection. The informants interviewed were KWT, PPL and Tourists. The result of this research is the existence of a KWT Communication Strategy in Utilizing the Promotional Media of Human Society 5.0 in Wonogiri Organic Village. In recognizing this target through meetings of socialization activities with tourists. The choice of communication media used was Facebook and PPT presentations which contained organic villages. The study of the purpose of KWT communication messages uses persuasive techniques with the role of a communicator (KWT) as an interesting caption maker for uploads on Facebook social media.
Aplicação de um Modelo Neural para Reconhecimento de Fala em Áudios com Características de Comunicação via Rádio

Automatic speech recognition is essential for machines to understand the content of words and sentences in a spoken language. Machine learning models known as deep neural networks are the focus of actual research in the artificial intelligence area, obtaining superior results compared with classical models and enabling the learning of features through unlabeled data. Despite the significant advance in applying these models to languages with a low volume of labeled data, there is still a barrier to the practical use of speech recognition models caused by the domain mismatch between training and inference data. This article proposes a methodology for simulating radio communication characteristics, enabling the development of datasets oriented to the robust training of neural models. The simulation was carried out through the implementation via software of a narrowband FM transmitter and receiver, together with the noisy communication channel. A state-of-the-art speech recognition architecture is also implemented and trained using advanced regularization techniques. When performing the training with the simulated data, it is was observed a relative reduction of 51.7% in the character error rate considering the most challenging noise level (SNR of 0 dB), with a similar decrease at all noise levels. We expected that the methodology developed in this work would open space to develop more robust speech recognition models with future applications in radio communication.
Previsão de Irradiância Solar de Curto Prazo Utilizando Modelo de Envelopes para os Preditores

In this work, we employ the recent envelope estimation method to fit a multiple linear regression model in order to make predictions of solar irradiance at horizons of 10, 20 and 30 min ahead. The advantage of using this method lies in the fact that there is a reduction in the variance of the estimated coefficients. Such a reduction is obtained by enveloping the material part, while excluding the variation of the immaterial part, which also contributes to a reduction in dimensionality, since there is a decrease in the estimation variance without an increase in the number of observations. The model was used in a dataset formed by LDR luminosity signals and statistical attributes extracted from images of the sky. The integration of the LDRs and the camera was done using a Raspberry Pi 3. Its performance was compared to that of linear (LASSO and Ridge) and non-linear (Multi-Layer Perceptron) models, being evaluated by the metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Root Mean Square Error Relative (rRMSE).
Interação com o Ambiente Mediante Classificação por Distância Euclidiana

Applications based on gestures or actions are increasingly common in everyday life. This is because it allows the user to activate the equipment without the need to touch it, besides being possible to activate it remotely. Thus, this work proposes a gesture recognition method using Principal Component Analysis (PCA) as a dimensionality reducer of variables, and the Euclidean distance as a classifier. The objective here is o interact with an environment through the actions of a user. To do this, a own database with 14 classes of actions were created. The classifier is validated through an online operation and confusion matrix are used to analize the results. The classifier achieved a hit rate higher than 80% for 6 of the 14 classes. This shows that the present work can be improved. However, it is worth noting that the main contribution was the formalization of a simplistic classifier, which makes use of a small database.
Protótipo de Rastreador Solar de Eixo Duplo

This article presents the design and practical implementation of a dual-axis solar tracker using low-cost components and recyclable materials. The prototype has an open and circular structure, offering a natural cooling. The tilting of the photovoltaic panel is performed using two servomotors to obtain highest intensity of sunlight captured by 4 LDR sensors, placed to the left of the panel and separated by two opaque barriers, generating the shadow caused by the sun movement. For the system supervision and control, a Scada was developed using the ScadaBR. The prototype was tested and results compared, considering a system with fixed panel, one and dual-axis tracker. Results showed that the dual-axis tracker system proved to be more efficient, considering a generated voltage, around a 12, 45% compared to the single-axis tracker.
Effectiveness of Machine Translation in Rendering Yemeni Culture-Specific Items into English: Sana'ani Dialect as a Case-in-Point

Undoubtedly, in any given society cultural elements are manifested in all aspects of life. In this globalized age, efforts have been exerted in translating cultural elements. However, since each culture has its own linguistic peculiarities and social aspects, it has become a complicated issue in the field of MT. Despite the recent technological developments, MT still lacks the ability to produce adequate translation in terms of author-based accuracy and reader-oriented cultural acceptability. This study aims to assess the effectiveness of MT in rendering Yemeni culture-specific items into English by comparing the translation outcomes of three MT programs (Google Translate, Yandex Translate and Bing Translator) to find out which program can produce the closest translation to the source text. Moreover, the study aims to identify translation problems of Yemeni culture-specific items resulting from MT. To achieve these aims, the researchers followed a descriptive qualitative method within a comparative framework. The study sample includes 14 Yemeni culture-specific items in their contexts taken from the book Social Issues in Popular Yemeni Culture by Abdu Al Rahman Mutahar and inserted into the three MT systems. The study concluded that MT lacks the ability to translate Yemeni culture-specific items adequately. The three programs failed to render most of the selected sample adequately. Google and Yandex failed to translate 13 cultural items whereas Bing made 12 errors in translating the same cultural items. The study results also showed that MT faced challenges in translating Yemeni culture-specific items, resulting in lexical, semantic and pragmatic problems.
Identificação de Relevância em Textos de Sistemas de Help Desk usando Técnicas Clássicas de Aprendizado de Máquina

Service Desk systems have a rich information base made up of the history of calls made, which can and should be used as a reference base for subsequent calls. Standard search tools, such as keyword searches, prove to be unfeasible for searching large databases, due to the long query time and the return of results unrelated to the problem. This work aims to investigate the ability of some classical classification algorithms to find the characteristic defined here as “relevance”: the characteristic of texts with some knowledge that can be reused. The motivation is that non-relevant texts can be removed early from the dataset, allowing complex algorithms to be employed on a smaller amount of information. In the tests performed, the Naive-Bayes, Adaptive Boosting, Random Forest, Stochastic Gradient Descent, Logistic Regression, Support Vector Machine, and Light Gradient Boosting Machine classifiers were used. The classifiers showed accuracy below 0.8, indicating that, in this scenario, other more efficient approaches should be used.
Segmentation and Visualization of Water Pollution Based on the K-means Method

The paper presents a study of the current state of water pollution detection systems. A formalization of the centroid map for a three-channel aerial photograph is proposed. An example of using the Kmeans algorithm for clustering terrain and water areas on test aerial photographs is considered. The visualization of the results of clustering of aerial photographs for a different number of centroids is given as well as the results of pollution segmentation. A block diagram of the clustering algorithm is presented. Its advantages and disadvantages are identified. The structure of the developed software using Python and cross-platform computer graphics libraries is described. An assessment of the accuracy of using the clustering algorithm using the F1-measure is performed. Preliminary experimental studies showed that the inclusion of an expert in the contour of decision-making allows increasing the flexibility of the program, due to the possibility of selecting a target area, choosing the number of clusters and segmentation accuracy.
Optimized DBSCAN Parameter Selection: Stratified Sampling for Epsilon and Gridsearch for Minimum Samples

This research presents an advanced methodology for estimating the epsilon and minimum samples parameters in the DBSCAN clustering algorithm using a Stratified Sampling and Grid-Search approach. Our method showcased notable improvement in eps estimation precision across nine diverse datasets compared to conventional techniques. By accounting for dataset variations in structure and density, stratified sampling leads to superior cluster formations. The k-nearest distance graph further refines these relationships, ensuring a comprehensive understanding of data densities. Additionally, our method underscores the importance of each dataset’s unique stratum, providing holistic insights. We also introduced a Grid-Search technique for MinPts estimation with the help of silhouette score, challenging traditional rule-of-thumb settings. Our approach suggests setting MinPts flexibly, considering the dataset’s specific attributes and has proven its efficacy by enhancing clustering results, with implications for both SS-DBSCAN and traditional DBSCAN frameworks. This study highlights the potential of parameter estimation in optimizing clustering outcomes and computational efficiency.
Bandoneon Explorations

This master's thesis explores the augmentation of the bandoneon, an iconic Argentine instrument traditionally associated with tango music, through the integration of live electronics and extended performance techniques. The research delves into the development of a unique system that enables real-time interaction between the performer and the electronics, transforming the bandoneon into a dynamic and expressive instrument. The study investigates the implications of this augmented approach on musical expression, embodiment, and the relationship between the performer and the instrument. Through a series of performances, including collaborations with other musicians and participation in festivals, the thesis examines the practical application and reception of the augmented bandoneon in diverse contexts. The findings contribute to the discourse on the intersection of traditional instruments, technology, and contemporary musical practices, offering insights into the possibilities and challenges of augmenting a culturally significant instrument like the bandoneon.
Big Data: Storage, Analysis and Implementation

We are in an age where data is not only readily available, but abundant. However, it is what we do with this data, through analyzing it, that is most important. While traditional means of data analytics works well for smaller amounts of information, it may not be able to handle larger amounts, called big data. In this paper, we will discuss the different ways in which big data can best be stored, what methods work best in analyzing it, and how the results of this analysis can be implemented in real-world scenarios.
A Survey Paper on Tiffindia - An Android Application for On-Demand Food

Abstract: Online meal ordering and delivery services are expanding quickly thanks to developing technology because of changes in consumer behavior and technological advancements. The way that Indian consumers have responded to the Internet has created opportunities for a wide range of enterprises, from startups to major multinationals. Businesses now have the chance to grow their online services, such as online meal ordering and delivery services, thanks to online banking. The demands of those who lead hectic lives are satisfied by online meal ordering, which allows customers to place orders using laptops, tablets, or smartphones and have their food delivered to their homes in a matter of minutes. But there aren't many platforms that provide nutritious meals, particularly for people on particular diets. Due to their hectic schedules, the majority of students end up ordering junk food online or struggle to find the right meals for their particular diet online. The "tiffin-India-app" is an online ordering and delivery system for healthy food that is suggested for Higher Education Institutions to offer staff and students a variety of healthy food alternatives that can be tailored to their individual diets. The online ordering and delivery system will be developed using an extreme programming technique. The results showed that the suggested method, which makes use of the improved web service, is a successful effort to encourage students and employees of higher education institutions to lead healthy lifestyles and adopt appropriate eating habits. I
Software Development Life Cycle (SDLC)

Abstract: The software development life cycle (SDLC) process plays an important role in the development and delivery of a software product. This research article provides an in-depth analysis of various SDLC approaches and their unique capabilities, benefits, and challenges. By reviewing traditional and modern SDLC models,this article aims to provide insight to software developers, project managers, and project partners to help them make decisions. Information and development into the entire software development process.