Reviewerly/exHarmony huggingface dataset:
The Effects of Textual Enhancement Technique on the Acquisition of Comparison Degrees of Indonesian Secondary School Students

Textual enhancement technique is a technique that refers to the specific linguistics target through the text. The main purpose of this study was to determine whether the technique of using textual enhancement affects the students' grammar mastery, especially in the comparison degree material. Comparison degree is the process of comparing things, persons, or places through the level of quality, or relation and is formed by adjectives and adverbs. This research method was quantitative in the form of a pre-experimental design. There were 40 Indonesian Secondary School students participating in the study. They were randomly assigned to one control and one experimental group by 20 participants in each group. During the four-week period of the current study, the participants received the instructions and treatment. Then the experimental group was exposed to comparison degrees presented with four different textual enhancement techniques. The control group was exposed to comparison degrees used with a different technique (traditional technique) in teaching activities. The data were collected after instructional sessions during the four weeks through paragraphs focusing on comparison degrees production written by the participants. After the treatment, a comparison degree was also administered to the control group, as a posttest. The results showed that the textual enhancement technique can be an alternative strategy in learning English, significantly affecting Indonesian secondary school students’ comparison degrees.
A Novel Dynamic Pricing Approach for Preemptible Cloud Services

Dynamic pricing for preemptible cloud services (DPPCS) is highly demanded to effectively utilize the excess capacity in cloud computing. However, the dynamic nature of excess capacity exhibits high non-stationarity, which is characterized by multi-temporal stochastic patterns with time-varying statistical properties. The non-stationarity results in the DPPCS problem being a Non-Stationary Markov Decision Process (NSMDP) with unknown transition probabilities. Moreover, DPPCS is constrained by a certain maximum preemption rate, further complicating the DPPCS problem as a Constrained NSMDP (CNSMDP). We transform the CNSMDP into a piecewise Lagrangian dual model, which converts the CNSMDP into an unconstrained optimization problem. To solve the above problem, we propose a novel Q-Learning approach for DPPCS. We first present estimation methods for the unknown environment parameters, including a detection method for identifying temporal pattern changes, and a diffusion approximation method for estimating the actual preemption rate. Then, we introduce a Lagrange multiplier updating method, which can strike a balance between revenue and the preemption rate in the reward function. Building upon the above methods, we develop a Constrained Non-Stationary Q-Learning (CNSQL) algorithm for DPPCS, which dynamically adjusts its learning process to adapt to the multi-temporal patterns. Through simulated experiments, we demonstrate the effectiveness of our proposed approach compared to state-of-the-art algorithms. It performs well in improving revenue generated from excess capacity while maintaining the actual preemption rate within the specified constraint.
Micro-Level Language Planning of Torwali Language in KPK: A Roadmap for Indigenous Languages and Digital Census-2023

This study analyzes the case of an indigenous language in Khyber Pakhtunkhwa province within the paradigm of language policy and planning (LPP) where the local linguists along with NGOs and community members are promoting Torwali Language. Language Planning and Policy is a state’s doctrine about the languages to be used within the state and for international communication. Therefore, this study utilizes the three categories status, corpus, and acquisition of LPP as a theoretical model to examine Torwali’s role in society and its impact on the digital census 2023 and indigenous languages. Hence, revitalization and promotion of Torwali language at micro-level put forwards a roadmap for language policy makers, local linguist, and state to follow these steps in promoting the indigenous languages in other provinces. This study presents the case of Torwali language with respect to the three categories, the same steps must be taken by the other indigenous speakers and state to promote their languages.
Quality Assessment for Research Data Management in Research Projects

In the context of research data management (RDM), researchers are confronted with a multitude of new tasks and responsibilities. The totality of all tasks to ensure the re-use of data, long-term archiving, and access to data through data management planning, further data documentation, and provinces of data collection and analysis are described as research data management [1]. Often, the process of RDM is represented with data life cycle models, which include the basic phases of planning, data collection, analysis, archiving, access, and reuse [2].
Pengaruh Media Dadu Kata Bergambar terhadap Kemampuan Membaca Permulaan Peserta Didik

The purpose of writing this research to determine the effect of pictorial word dice learning media to improve students' initial reading skills at SD IT. This type of research is a quasi-experimental. The population in this study were students of class III A and III B as many 46 students. Sampling was carried out using the Saturated Sampling technique, the class selected as the experimental class was III A and the control class was III B. Data collection techniques were carried out by observation, tests, questionnaires, and documentation. The data analysis technique was carried out by normality test, homogeneity test, and hypothesis testing using SPSS.The results showed that the average post-test score for the experimental class was 88.38%. Based on the hypothesis test with SPSS obtained a significant in two sides (2-tailed) of = 0.000, it can be concluded that (0.000 &lt;0.05). Therefore, it can be said that H0 which reads that there is no effect of using pictorial word dice learning media to improve students' initial reading ability is rejected and Ha is accepted, which means that there is an effect of using pictorial word dice learning media to improve students' initial reading ability at SD IT.
The Federal State Initiatives for RDM as Intermediaries in a Dynamic Landscape of RDM Infrastructures and Services

A number of German federal states have established initiatives to support the institutionalization of RDM infrastructures and services. They can serve as intermediaries between disciplinary approaches to RDM like NFDI and RDM services in individual research institutions. This presentation gives an overview of the current state of these initiatives and provides an outlook on their role in creating synergies in RDM with a focus on their integrating potential for NFDI.
PC Automation using Hand Gestures using Machine Learning

Pattern recognition and Gesture recognition are the developing fields of research.Hand gestures can add another dimension to contactless computer operating.Hence in this project, we develop a software which presents a system prototype that is able to automatically recognize gestures and runs pc commands for particular gestures.We are using python to develop our solution by using the integrated modules like cv2(OpenCV(cv2)-Open source computer vision is a library of programming functions mainly aimed at real-time computer vision.) and Mediapipe(detects hand and fingers data points.)
The Effect of Youth Sports Club Participants Coach’s Support on Problem Solving and Self-Control Ability

The purpose of this study is to empirically investigate "the effect of coach’s support of Youth Sports Club Participants on the Problem Solving Ability and Self-Regulation. For this purpose, data from 213 valid samples were used for analysis using convenience sampling among Youth Sport player training at a Youth Sport center in Dong-du-cheon & Yeoncheon city. As for the data analysis method for hypothesis testing, exploratory factor analysis, correlation analysis, and multiple regression analysis are performed using SPSS/PC version 21.0. The conclusions of this study were as follows; First, the coach’s support of Youth Sport player was found to have a positive effect on Problem Solving Ability Second, experience of coach’s support was found to have a positive effect on Self-Regulation.
Open Science Best Practices in Data Science and Artificial Intelligence

In the past years, scientific research in Data Science and Artificial Intelligence has witnessed vast progress. The number of published papers and digital objects (data, code, models) is growing exponentially. However, not all of these research artifacts are findable, accessible, interoperable and reusable (FAIR), contributing to a rather low level of reproducibility of the experimental findings reported in scholarly publications (reproducibility crisis). In this paper, we focus on Data Science and Artificial Intelligence Open Science best practices, i.e., a set of recommendations that eventually contribute to the management and development of digital artefacts that are as FAIR as possible. While several guidelines exist, we add best practices for the FAIR collection, processing, storing and sharing of scholarly findings via Research Knowledge Graphs. The final list of recommendations will be openly available on the NFDI4DS webpage as an interactive web application.
WissKI

WissKI is a free and open source virtual research environment based on the free and open source content management system Drupal. It features everything that the content management system provides while using a triplestore for authorative data storage. Thus changes can be made in the triplestore and they are directly reflected by the system. Furthermore WissKI provides all features that are necessary for a full Linked Open Data Semantic Web platform.
FAIR Data for Energy System Research

The NFDI4Energy consortium aims to establish new services filling a variety of needs for the energy system research community, from making FAIR research data easily accessible to promoting collaboration among community entities. Seven Task Areas (TAs) have been defined to achieve the consortium’s objectives, each with a specific focus. Task Area 4 (TA4): FAIR Data for Energy System Research shall develop ontologies, metadata standards, and services to promote semantic consistency and improve interoperability of energy research projects, thereby supporting the harmonization of data management among various institutions and research fields.
NFDI4Health Local Data Hubs for Finding and Accessing Health Data

To support federated data structuring and sharing for sensitive health data from clinical trial, epidemiological and public health studies in the context of the German National Research Data Infrastructure for Personal Health Data (NFDI4Health), we have developed Local Data Hubs (LDHs) based on the FAIRDOM-SEEK platform. Those LDHs connect to the German Central Health Study Hub (CSH) to make the health data searchable and findable. This decentralised approach supports researchers to make health studies with their data FAIR (Findable, Accessible, Interoperable and Reusable), and at the same time fully preserves data protection for sensitive data.
PERANCANGAN METODE MACHINE LEARNING BERBASIS WEB UNTUK PREDIKSI SIFAT MEKANIK ALUMINIUM

The main objective of this research is to design a web-based machine learning model that can predict the mechanical properties of aluminum based on its chemical composition. By inputting nine variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, and Si, the model is able to provide predictions for two output data, Yield Strength (YS) and Tensile Strength (TS). The research aims to understand the relationship between chemical composition and mechanical properties of aluminum, and to develop a tool that can be used to predict these properties with a high level of accuracy. Overall, the goal of this study is to enhance the understanding of the properties of aluminum and how it can be utilized in various applications. This study designs a web-based machine learning modeling to predict the mechanical properties of aluminum in the percentage of chemical composition, where the input data in the modeling consists of 9 variables of chemical elements such as Al, Mg, Zn, Ti, Cu, Mn, Cr, Fe, Si, and has 2 output data consisting of Yield Strength (YS) and Tensile Strength (TS). The modeling machine learning is designed using the Python programming language and additional libraries such as Pandas, Numpy, Scikit-learn, and Streamlit. The modeling in this study uses three algorithms consisting of Decision Trees (DT), Random Forest (RF), and Artificial Neural Network (ANN). Each algorithm is optimized with the best search parameters, and where the RF algorithm has better performance than DT and JST. The best modeling uses the RF algorithm with optimal parameters of number of trees at 20 and maximum depth of 10, with MAE values of 11.44, RMSE of 14.282, and R of 0.93 for Yield Strength (YS) predictions, and for Tensile Strength (TS) predictions, MAE values are obtained. 21,669, RMSE 27,301, and R 0.871.
Because Data Shall Grow (and we With it)

Research data are a valuable asset of their own and individual researchers as well as the research community as a whole can benefit through data sharing practices and open science. These benefits include but are not limited to higher data quality or the more efficient use of time and financial resources. Despite these potential gains, data sharing is not widespread yet and processes of cultural change are needed to reap the benefits of data sharing. Against this background, the NFDI can function as a platform for discussion and provides a network that extends beyond individual research bubbles in the name of common interests and facilitate cultural change processes towards data sharing. A first collaborative workshop in April 2023 identified four central clusters of interest including 1. policies, strategies and funding; 2. communities, workshops and multipliers; 3. publications and 4. collaboration, communication and error cultures. Departing from this, we contemplate developing a vision on how to further encourage cultural change in the NFDI through a series of workshops. As a result, a vision of cultural change within and with the NFDI will be published by the end of 2023 to further guide the processes to facilitate cultural change. A first step could be to change the guiding metaphor for data: Not “Gold” or “Oil”, but “Humus” for healthy growth of a collaborative data ecosystem.
Automatically Assembling a Custom-Built Training Corpus for Improving the Learning of In-Domain Word/Document Embeddings

Embedding models turn words/documents into real-number vectors via co-occurrence data from unrelated texts. Crafting domain-specific embeddings from general corpora with limited domain vocabulary is challenging. Existing solutions retrain models on small domain datasets, overlooking potential of gathering rich in-domain texts. We exploit Named Entity Recognition and Doc2Vec for autonomous in-domain corpus creation. Our experiments compare models from general and in-domain corpora, highlighting that domain-specific training attains the best outcome.
Sentimen Analisis Inisiatif Vaksin Nasional Menggunakan Naïve Bayes dan Laplacian Smoothing Pada Komentar Video Youtube

COVID-19 pandemic that has been declared by who in march 2020 Has been Indonesia biggest health crisis end in the decade. WHO said one of the quickest way to end the pandemic is through immunity through vaccine thu's theory is a national vaccine program initiated by the government in the middle of 2021. YouTube is of de facto public space in Indonesia cyberspace for its netizen for various conversation. from gossiping to discuss in public policy YouTube has been a gold mine for social media data mining enthusiast since 2010. but has been not utilized much by Indonesia Academic. do lack of popularity compared to Twitter which has been a media darling what Indonesian Acdemic ever since This research is focused on sentiment analysis pantydeal YouTube about the national vaccine initiation on a news channel in YouTube. This research is primarily consist of naive bayes classifier a a popular algorithm Indonesian data mining enthusiast which has some limitation such as the problem known as zero probability problem and also the use of non-public data which can be fixed by the use of Laplacian smoothing algorithm which when tested Using 100 of random comments as a data testing has resulted in 71% percent of succes rate and when we do a statistical analysis the precision , recall rate and the F-meassurement score of the classifier all resulted in above 75% score which is satisfactory.
An exploration of search-as-learning in digital archives of an online museum

Purpose Recent studies on search-as-learning (SAL) have recognized the significance of identifying users' learning needs as they evolve for acquiring knowledge during the search process. In this study, the authors clarify the extent to which search behaviors reflect the learning outcome and foster the users' knowledge of Chinese art. Design/methodology/approach The authors conducted an exploratory-sequential mixed-methods approach using simulated work task situations to collect empirical data. The authors used two types of simulated learning tasks for topics related to painting and antique knowledge. A lot of 25 users participated in this evaluation of digital archives (DAs) at the National Palace Museum (NPM) in Taiwan. For each set of topics, a close-ended task related to lower-level learning goals and an open-ended task related to higher-level learning goals. Findings The learning criteria reflect changes in the users' knowledge structure, revealing the SAL process. Furthermore, users achieved better task performance on the higher-level creative-learning task, which suggests that they met more learning criteria, exhibited a greater variety of search patterns when exploring the topics via interaction with various sources. Finally, there is a close relationship between creative-learning tasks, prior knowledge, keyword search actions and learning outcomes. Originality/value The authors discuss implications with respect to the design of DAs in practice and contributions to the body of SAL knowledge in DAs of online museums. For future reference, the authors provide implications for the development of learning measures from the perspective of user search behavior with associated learning outcomes in the context of DAs.
Diagnosis of Cardiovascular Disease Using Machine Learning Algorithms and Feature Selection Method for Class Imbalance Problem

In the worldwide, cardiovascular disease is the leading cause of death. Risk factors like high blood pressure, an irregular heartbeat, etc. make the disease harder to detect. Therefore, preventing heart failure at an early stage with an accurate and timely diagnosis may increase patient's survival. With this regard, the machine learning algorithms (MLAs) are used by many researchers for the detection of diseases as they are reliable and efficient. In this paper, we use four MLAs namely Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF) and Voting Classifier (VC) with Statlog (Heart) dataset. As the dataset suffers from Class Imbalance Problem (CIP), we apply data preprocessing technique i.e., SVM_balance, a variant of Synthetic Minority Over-Sampling Technique (SMOTE), Standardization and Feature Selection (FS) technique i.e., Correlation based FS (CFS). Furthermore, we evaluate the performance of these algorithms with ALL features and with Selected features in terms of various performance metrics such as Balanced Accuracy, F1 score and Matthews Correlation Coefficient (MCC). Finally, the experimental results show that SVM and VC achieved the admirably better values and may be used in various domains for healthcare industry.
Integrating big data and survey data for efficient estimation of the median

An ever-increasing deluge of big data is becoming available to national statistical offices globally, but it is well documented that statistics produced by big data alone often suffer from selection bias and are not usually representative of the population at large. In this paper, we construct a new design-based estimator of the median by integrating big data and survey data. Our estimator is asymptotically unbiased and has a smaller variance than a median estimator produced using survey data alone.
A Lightweight Real-Time System for Object Detection in Enterprise Information Systems for Frequency-Based Feature Separation

In the domain of target detection in mobile and embedded devices, neural network model inference speed is a crucial metric. This paper introduces YOLO-FLNet, a lightweight algorithm for detecting people in open scenes. The model utilizes the DFEM structure to capture and process high-frequency and low-frequency information in the feature map. Additionally, the VoV-DFEM structure, based on the concept of one-shot aggregation, enhances feature aggregation from different scales and frequencies in the backbone network. To validate its performance, experiments were conducted using publicly available datasets on a computer with dedicated GPUs. As a result, compared to YOLOv7-tiny, YOLO-FLNet achieved a 0.3% mAP@0.5 improvement, reduced parameter size by 52.9%, and increased inference speed by 30.2%. These characteristics make it valuable for person detection in engineering domains, providing theoretical guidance for lightweight models in edge computing.
The regulatory ecosystem of data driven campaigning in the UK

Political campaigns are increasingly described as data-driven, as parties collect and analyse large quantities of voter data to target their campaign messages in ever more granular ways, particularly online. These practices have increasingly been facing calls for greater regulation due to the range of harms they are seen to pose for citizens and democracy more generally. Such harms include the intrusions on voter privacy, reduced transparency in how messages are constructed and targeted at voters and exposure to increasingly divisive and polarizing political content. Given that data-driven campaigning (DDC) encompasses a range of different practices that are likely to fall under the remit of multiple agencies, it is not evident how suitable current regulatory frameworks are for addressing the harms associated with the growth of DDC. This paper takes a first step toward addressing that question by mapping an emergent regulatory “ecosystem” for DDC in the particular case of the UK. Specifically, we collect and analyse interview data from a range of regulators working directly or indirectly in the election campaigns and communication arena. Our analysis shows that while privacy violations associated with DDC are seen by regulators to be largely well covered by current legislation, other potential harms are given lesser to no priority. These gaps appear to be due to regulators lacking either the powers or the incentives to intervene.
Open Science and Language Data: Expectations vs. Reality

Language data are essential for any scientific endeavor. However, unlike numerical data, language data are often protected by copyright, as they easily meet the threshold of originality. The role of research infrastructures (such CLARIN, DARIAH, and Text+) is to bridge the gap between uses allowed by statutory exceptions and the requirements of Open Science. This is achieved on the one hand by sharing language data produced by research organisations with the widest possible circle of persons, and on the other by mutualizing efforts towards copyright clearance and appropriate licensing of datasets.