Reviewerly/exHarmony huggingface dataset:
Functional transposition of<i>TILL</i>and<i>UNTIL</i>from a diachronic perspective

Abstract The research traces the development of functional transposition of TILL and UNTIL in English. The study covers 16 historical periods and is based on 1,243 examples extracted from the writings from 850 to 1710, which are analyzed without resorting to any corpus software; 2,225 examples from 1710 to 1920, which are automatically tagged utilizing the corpus toolbox Lancsbox; and the statistical data on 127,544 examples over the period of 1920–2020. It is hypothesized that despite being synonyms and representatives of the same grammatical categories of prepositions and conjunctions in modern English, the lexical units TILL and UNTIL were initially formed as prepositions and later, following their own transpositional patterns, they were transposed into conjunctions.
Investigating the Impact of Workload Characteristics on the Scalability and Performance of Permissioned Blockchain

Abstract Blockchain Technology has grown exponentially in recent years due to its decentralized, immutable, transparent data storage, transaction sharing, and processing. With the emergence of different blockchain platforms, it is substantial to analyze and evaluate the performance of these platforms in various scenarios. The popularity of the public blockchain, i.e., Bitcoin and Ethereum, has increased manifold. But in distinction to a public blockchain, there is a permissioned and private blockchain that allows restricted involvement of users in the network. To make a well-informed decision regarding the selection of an appropriate platform for utilization, it is crucial to evaluate diverse performance metrics among the numerous available blockchain platforms. In this study, we conducted an assessment of the performance of the Hyperledger Fabric blockchain (HLF), taking into account various metrics such as resource consumption, throughput, success rate, and latency. We have incorporated parameters such as the ordering service, programming language to write chaincode/smart contracts, number of transactions, transactions per second, and organizations to evaluate the system’s performance. Along with our analysis, we also suggested potential areas of research for the future development of blockchain technology.
Recognizing Indian Classical Dance Forms Using Transfer Learning

The rise of cultural tourism in India and massive digitization over the last decade has necessitated preserving Indian art forms. Recent advances in artificial intelligence (AI) have provided the tools and techniques to make these efforts more efficient. The potential research areas involved are digital image processing, computer vision, and machine intelligence. Since researchers have not much explored the scope of the digital preservation of Indian classical dance, this paper contributes a method to classify different Indian classical dance forms. Computerized dance modeling offers a wide range of uses, including cultural conservation, online learning, entertainment, and even remedial treatment. Because the dataset is small, the paper uses transfer learning models for feature extraction and deep neural networks for classification. The proposed method showed a remarkable accuracy of 97.05 percent.
Steganalysis of Neural Networks Based on Symmetric Histogram Distribution

Deep neural networks have achieved remarkable success in various fields of artificial intelligence. However, these models, which contain a large number of parameters, are widely distributed and disseminated by researchers, engineers, and even unauthorized users. Except for intelligent tasks, typically overparameterized deep neural networks have become new digital covers for data hiding, which may pose significant security challenges to AI systems. To address this issue, this paper proposes a symmetric steganalysis scheme specifically designed for neural networks trained for image classification tasks. The proposed method focuses on detecting the presence of additional data without access to the internal structure or parameters of the host network. It employs a well-designed method based on histogram distribution to find the optimal decision threshold, with a symmetric determining rule where the original networks and stego networks undergo two highly symmetrical flows to generate the classification labels; the method has been shown to be practical and effective. SVM and ensemble classifiers were chosen as the binary classifier for their applicability to feature vectors output from neural networks based on different datasets and network structures. This scheme is the first of its kind, focusing on steganalysis for neural networks based on the distribution of network output, compared to conventional digital media such as images, audio, and video. Overall, the proposed scheme offers a promising approach to enhancing the security of deep neural networks against data hiding attacks.
Development of E-Leaflet Based Learning Media to Improve Student Learning Outcomes in Excretion System Materials

The excretory system is material that is considered relatively difficult by students, because teachers experience limitations in using learning media. Therefore, learning media are needed to attract students' attention such as E-Leaflets. This study aims to develop media-based learning E-Leaflet on excretory system material in improving student learning outcomes. This study uses research methods Research and Development using the ADDIE model. The subject of this study were 34 students. Limited trials in this study using the design one group pretest-postest. The instruments used in this study consisted of expert validation sheets, multiple choice instruments and student response questionnaires E-Leaflet. The multiple choice instrument is used to measure all levels of learning objectives. While the student questionnaire sheet is used to determine student responses to learning media. The expert validation instrument is used to find out. The results of the study show that media validationE-Leaflet expert included in the valid category without revision. While the validation of material content experts gets valid and appropriate category for use in the learning process. The results of the student response questionnaire received a positive response . The results of the N-Gain analysis show that the criteria are moderate. This shows that learning media E-Leaflet can improve students' biology learning outcomes.
E-SOLUTION FOR MINDANAO RESOURCES: AN ONLINE ANNOTATED BIBLIOGRAPHY

Mindanao is rich in culture and its natural resources, but little is known about it. Providing available resources online is one way of learning about this beautiful island. This study was undertaken to identify the available Mindanao resources and to make an annotation of the collected local resources through the use of Librarika. Furthermore, the bibliographic information of the Mindanao resources becomes accessible and retrievable, thus, making the list of an online annotated bibliography available in the different libraries in Mindanao. This project study aimed to design an E-Solution for Mindanao resources online annotated bibliography. Using quantitative and qualitative descriptive design, test and evaluation of the functionality and aesthetic design of the “E-Solution for Mindanao Resources: An Online Annotated Bibliography” was developed. E-Solution for Mindanao resources project helps the users to easily access the availability of online annotated bibliography of Mindanao resources because of its eight functional tabs namely; home, catalog, search catalog, database A-Z, top collections, new collections, my account and ask a librarian. Results showed that E-Solution for Mindanao Resources: An Online Annotated Bibliography is functional and competitive in its aesthetic design. It is recommended that this E-Solution for Mindanao resources project be visible and accessible online and be continually updated by the researcher regarding developments in Mindanao resources and annotated bibliography. KEYWORDS: Mindanao Resources, Online Annotated Bibliography, Librarika, E-Solution for Mindanao resources
Classifying High School Scholarship Recipients Using the K-Nearest Neighbor Algorithm

YBM PLN UIWS2JB provides scholarships for high school students who cannot afford school tuition due to poverty or other economic conditions. Since the target is specific, the foundation must carefully select the recipients to ensure the scholarship is granted to those who deserve it. The predetermined criteria combined with the limited quota available become a difficulty in itself as a large number of applications are coming in. Data mining with a classification method using the K-Nearest Neighbor algorithm is believed to be one of the alternative solutions to solve this problem. This study aims to examine how this method could help in the selection process to determine who is eligible to receive the scholarship, and it also aims to evaluate the algorithm's performance with the optimal K value. The findings of this research showed that the classification method using K-Nearest Neighbor is the potential to be applied in a case such as this. The results found an accuracy of 91% in the selection process and are included in the Excellent Classification category. The optimal K value obtained is K = 5.
Perception of self-esteem in elderly boarding schools in the Institution

Introduction: Old age is characterized by a series of changes at a physical, cognitive, emotional and social level, so it is necessary for the elderly to have an adjustment (balance) to the demands that derive from this stage, since this way they diagnoses better development.However, self-esteem, being a central axis in the way experiences are interpreted and assumed, must be considered as a priority in the health sector, since emotional strategies can be generated from it that can facilitate their adaptation.and therefore their quality of life.Aim.To evaluate the perception of self-esteem in older adults hospitalized in a Public Institution.Method.This research is based on a quantitative methodology, descriptive-correlational, with a non-experimental and cross-sectional design: the Rosenberg scale was used.In addition, a non-probabilistic sampling was applied, selected for convenience, where 48 older adults participated, whose age ranges from 69 to 93 years and who were in a situation of institutionalization at the ISSSTE in Morelia, Michoacán.Results.The main findings report that two out of three older adults have a regular to high self-esteem, likewise, a relationship was found with schooling, the decision to institutionalize and with the fact that the couple is in a situation of institutionalization. conclusions.Through the results, the need to provide attention to that percentage that is affected by their low self-esteem is shown.
Morphological Analysis of "The Flag of Albania" in the Context of Linguistics Development of Nineteenth-Century. The Case of Pronouns

The newspapers of late nineteenth-century are considered an important vector in Italian linguistic history due to their undoubted impact on the common language. What characterizes the journalistic prose of the time, valid to a certain extent even today, are the great heterogeneity of the expressive registers on the one hand and the linguistic homogeneity on the other. In this context, the journalistic language must undoubtedly be placed on the tracks of the literary language of tradition, that’s why our morphological analysis in this article is in full harmony with the developments of the contemporary literary language. The linguistic analysis of the examined journal, «The flag of Albania», will prove to be true. Our analysis will not be broad-based grammar, but will only touch the grammatical category of pronouns. We will examine the most salient forms of personal, indefinite, demonstrative, relative and interrogative pronouns accepted by the author in various contexts of use, explaining the reason for this choice and why one form is more frequent than the other.
A Cybersecurity and Digital Risk Assessment: A Family Case Study

Digitalization is not limited merely to business companies and high-tech industries; it has increasingly changed families' behaviors and attitudes as they are exposed to the digital world using different technological aspects. Therefore, numerous risks can be raised between all members of the family. For example, if IoT devices in a smart home are not embedded with high-security standards, they would be vulnerable to being attacked by hackers. Cyberattacks will not be limited to attacking virtually, but also they could unlock the home's door from the phone, and accordingly, the criminal will enter the home, and they can lose much more than credit cards. In this paper we identified various types of risks, with providing an analysis about the vulnerabilities and protecting families from digital attackers.
Open your black box classifier

The transparency of machine learning models is central to good practice when they are applied in high stakes applications. Recent developments make this feasible for tabular data, which is prevalent in risk modelling and computer-based decision support across multiple domains including healthcare. Important motivating factors for interpretability are outlined and practical approaches are summarised, signposting the main methods available, with pointers to the supporting literature. A key finding is that any black box classifier making probabilistic predictions of class membership from data in tabular form can be represented with a globally interpretable model without loss of performance.
Implementation of Monte Carlo Simulation in Evaluation of The Uncertainty of Rainfall Measurement

Many factors trigger the uncertainty of rainfall measurement. Several factors can be related to the instruments, weather conditions, and acquisition methods. The degree of uncertainty could be obtained through the calibration process. In principle, rain gauges are calibrated based on the standard process ruled by ISO/IEC 17025 using the law of propagation of uncertainty (LPU). However, LPU requires complex and complicated mathematical calculations. An alternative approach is needed to evaluate measurement uncertainty besides the LPU method. This research used the Monte Carlo method to determine the uncertainty during the rainfall measurement. This method involves repeated random simulations by providing probability distribution on the input and output of rainfall measurement. The results showed that the Monte Carlo method can accurately determine the uncertainty of rainfall measurement. In addition, the uncertainty analysis also showed that instrument inaccuracy is the most significant factor that causes the uncertainty of rainfall measurement.
60P Deep learning-based digital pathology for risk stratification of atypical ductal hyperplasia/ductal carcinoma in situ

Clinical trials are exploring the possibility of active surveillance for low-risk ductal carcinoma in situ (DCIS) and atypical ductal hyperplasia (ADH). However, interobserver variability in grading DCIS exists. We aim to train and evaluate a deep learning model (Deep DCIS) on breast biopsy with ADH/DCIS to improve the risk stratification of these precursor lesions. Patients diagnosed with ADH or DCIS by breast biopsy and who received wide excision within six months were included (n=592 and 112 for primary and independent validation cohorts). Two pathologists independently evaluated nuclear grade, comedonecrosis, and focus suspicious for microinvasion and annotated patch-level labels by consensus. Estrogen receptor immunostain and apocrine features were also assessed. Clinical features (e.g., biopsy method, lesion size, BIRADS classification of ultrasound and mammogram) were collected. The model adopted Inception-V3 with a train-valid-test split and Adam optimization. The outputs of Deep DCIS comprised five parameters: total patches, lesion extent, Deep Grade, Deep Necrosis, and Deep Stroma. These parameters were combined with clinical information and hormone status to predict upstaging to invasive breast carcinoma. Deep Grade strongly correlated with the pathologists' grading on both slide- and patient-level labels. All five parameters from Deep DCIS were associated with upstaging to invasive carcinoma. Deep DCIS successfully predicted upstaging with an area under the curve (AUC) of 0.81 and an accuracy (acc) of 0.75, outperforming pathologists' evaluation (AUC 0.71 and 0.69; acc 0.67 and 0.65 from two pathologists). The prediction improved after combining Deep DCIS with clinical factors and hormone status (AUC 0.87; acc 0.79). This combined model retained its predictive power in the subgroup of ADH and low-/intermediate-grade DCIS (AUC 0.81; acc 0.76). The independent validation cohort confirmed the model's performance (AUC 0.82; acc 0.72). The deep learning model refines histological evaluation of ADH and DCIS on breast biopsy and improves prediction of upstaging to invasive carcinoma on wide excision, which may help guide treatment planning in future clinical studies.
Penerapan Metode Bayes Pada Aplikasi Deteksi Ketergantungan Terhadap Pengguna Game Online

In this era, online gamems are ectends all over the world. Almost all circles of people have a very high attachment to online games. Online games are a kind of fun game for players, because the can be played not only alone (Singleplayer), but cab also be played by two more players (multiplayer) from different places and different countries. One of the problems in society is the magnitude of the influence of online games, because online games themselves have an attraction that makes people more fun playing than learning. Based on the results of observations, it is proven by the large number of teenagers spending their daily time playing online games instead of reading books and can have other negative impacts such as lack of socializing, having a negative impact on health. To solve this problem, an expert system is needed by applying the Bayes method algorithm, based on the results of interviews with a counseling teacher, the results are in the form of data based on name, 3 types of dependency levels and 18 symptom data from online games. Therefore, the Bayesian method can predict future opportunities based on previous experience. From the results of this study, students were tested by answering 18 questions for each online game dependency symptom data. From the results of this study it can be concluded that there was a student who was diagnosed with heavy dependence on the use of online games with a bayes score of 0.84% or 84%. In addition, this research produced an application for detecting dependence on online game users by applying the Bayes method and has been successfully created and running well to help reduce online game users from being dependent anymore.
Pengelolaan Dokumen Arsip Laboratorium Menggunakan OwnCloud Sebagai Media Cloud Storage Berbasis Infrastructure as a Service (IaaS)

Archive document management of three laboratories namely Database Laboratory, Network Laboratory and Programming Laboratory under the Department of Informatics, Faculty of Information Technology and Business, Institute of Science &amp; Technology Akprind Yogyakarta in managing laboratory archive documents still uses public applications, so they are vulnerable to exposure hack. In addition, the management of archival documents in each laboratory has not been managed centrally by the Informatics Department. Based on the problem of archival document management in each laboratory that has not been managed centrally by the Informatics Department and document storage that still uses public applications so it is vulnerable to being hacked, this research will develop an Infrastructure as a Service (IaaS) private cloud storage application. ) by using the OwnCloud application. The PPDIOO method developed by CISCO is used as a research method. The steps of the PPDIOO method are Prepare, which is preparing a work plan both from a technical and strategic point of view which is used to build a Cloud Server, Plan, which is a needs analysis used as a parameter before designing a private cloud network system, Design, which is making a design for monitoring virtual servers that will implemented on a cloud computing network, Implement is the process of building a Cloud Server using OwnCloud, Operate is the process of operating OwnCloud such as user management, uploading and downloading documents, and sharing documents with other users, and Optimize, namely the process of increasing capacity (storage, resources) according to needs users so that OwnCloud's performance is optimal. After testing using the blackbox testing method, the result is that the Cloud Computing application based on Infrastructure as a Service (IaaS) using OwnCloud can work properly starting from the login process to the process of storing data, uploading and downloading files and sharing files with other users. All forms and features work according to their function.
Study of the Utilization of WRF Model Output Data to Produce Daily Flood Forecast Maps in the North Sumatra Region

Flood is a natural disaster that often occurs in Indonesia. Currently, flood events are relatively difficult to predict because floods generally occur suddenly in uncertain periods. Extreme rainfall is a major factor for the occurrence of floods. Considering that floods can be caused by heavy rainfall events within a few hours, it is necessary to produce daily flood forecasts for flood disaster mitigation. This study aims to test the accuracy of utilizing rainfall forecast data from the Weather Research and Forecasting (WRF) model to create daily flood forecast maps. The data used in this study include Global Forecast System (GFS) data, BMKG rainfall measurement data which spread across several points in North Sumatra Province, and flood incident reports from BNPB. Data processing is carried out by Geospatial Information System (GIS) using Quantum-GIS, which includes weighting and scoring the parameters of soil type, slope, land elevation, river density, and land cover to produce Flood Prone Maps, then integrated with rainfall data to produce Daily Flood Forecast Maps. The case studies of flood events in this study are August 28 and November 28, 2022. The results showed that the spatial forecast of flood potential (WRF) has a pattern in accordance with the flood event area. Therefore, the WRF model output rainfall prediction data can be used to create a daily flood forecast map in the North Sumatra region.