Reviewerly/exHarmony huggingface dataset:
Design of Computerized Patient Registration Application System at Tanjung Pati Health Center

Authors:
raw_author_name: Oktamianiza Oktamianiza
raw_affiliation_string: D3 Medical Records and Health Information Study Program, STIKES Dharma Landbouw Padang, 
Jl. S. Parman No. 120 Lolong Belanti 25136, Indonesia
raw_author_name: Vitratul Ilahi
raw_affiliation_string: D3 Medical Records and Health Information Study Program, STIKES Dharma Landbouw Padang, 
Jl. S. Parman No. 120 Lolong Belanti 25136, Indonesia
raw_author_name: Rahmadhani Rahmadhani
raw_affiliation_string: D3 Medical Records and Health Information Study Program, STIKES Dharma Landbouw Padang, 
Jl. S. Parman No. 120 Lolong Belanti 25136, Indonesia

Abstract: One of the shortcomings of manual patient registration systems is the procedures that affect the efficiency of service care to patients. Therefore, researchers are interested in designing a computerized patient registration application using Microsoft Access software. The type of research used is Research and Development (R&amp;D). This research was conducted at Tanjung Pati Community Health Center, with in-depth interviews with two registration officers. The research results show that the data elements needed to design the patient registration application system, sourced from the Patient Registration Book, will be developed by creating an application system using UML modeling. The application system undergoes Black Box Testing with the result that all components run well. The application system is efficient, with an average comparison of manual patient registration time to system patient registration of 420:180 seconds for new patients and 360:33 seconds for existing patients. The application system is efficient according to the results of interviews with informants. Therefore, the design of the computerized patient registration application system using Microsoft Access meets the needs of the community health center and is efficient in terms of patient registration time. It is recommended that the patient registration application be used continuously in the medical records unit at Tanjung Pati Community Health Center.
Semantic Relatedness and the Efficacy of Retrieval Practice

Authors:
raw_author_name: Steven C. Pan
raw_author_name: Timothy Charles Rickard

Abstract: There is growing interest in the role that semantic memory plays in the retrieval practice (or testing) effect. We investigated how semantic relatedness impacts the efficacy of testing, relative to a restudy control, using a cued-recall paradigm with immediate correct answer feedback. High and low relatedness word pairs were created by selecting two different response words for each cue word based on associative norms. The parameter-free predictions of the dual-memory model of test-enhanced learning for low relatedness pairs were used as an empirically well-supported scaling benchmark for evaluating the effectiveness of testing for high relatedness pairs, allowing us to overcome the issue of removable interactions. Results showed a robust testing effect at both relatedness levels. For the high relatedness group, however, the testing effect was about 26% smaller than expected by the model. Nevertheless, the predicted quadratic relation between test and restudy proportion correct in the cumulative distribution held for both groups. One candidate account of those results within the dual-memory framework is that, for the high relatedness case, study and test memory strengths for tested items are positively correlated rather than independent. A simulation implementing that version of the model provided a good distribution fit for the high relatedness group. Other theoretical accounts of the semantic relatedness effect are considered.
NeVer2: Learning and Verification of Neural Networks

Authors:
raw_author_name: Stefano Demarchi
raw_affiliation_string: Universita degli Studi di Genova
raw_author_name: Dario Guidotti
raw_affiliation_string: Universita degli Studi di Sassari
raw_author_name: Luca Pulina
raw_affiliation_string: Universita degli Studi di Sassari
raw_author_name: Armando Tacchella
raw_affiliation_string: Universita degli Studi di Genova

Abstract: Abstract NeVer2 is a cross-platform open-source tool to design, train and verify neural networks. NeVer2 integrates off-the-shelf learning libraries with our verification backend and makes their functionalities available through a graphical interface. A user can work out the structure of a neural network by dragging and dropping blocks on a canvas and then train the network by supplying a dataset source and the hyper-parameters through dialog boxes. Once the network is trained, verification amounts to (i) adding input pre-conditions and output post-conditions to the network through special blocks and (ii) starting verification in a "push-button" fashion. To the best of our knowledge, there is no other publicly available tool that integrates all such features at present. Here we provide a detailed description of NeVer2 including examples to demonstrate the seamless integration between design, training and verification. We further provide an experimental analysis on several verification benchmarks in order to show the trade-off between completeness and computability using different algorithms, including a comparison with the state-of-the-art tools α,β-CROWN and NNV for reference.
Pengaruh Content Creation, Content Sharing, Connecting dan Community Building terhadap Keputusan Pembelian Konsumen pada 3D Stable Equestrian

Authors:
raw_author_name: Dessy Yunita
raw_affiliation_string: Program Studi Manajemen, Fakultas Ekonomi, Universitas Sriwijaya

Abstract: This research aims to determine the influence of content creation, content sharing, connecting and community building on consumer purchasing decisions on 3D Stable Equestrian. The research method used is a quantitative method. The data used in this research is primary data. The sampling technique uses purposive sampling technique. The sample studied in this research was 100 respondents who were 3D Stable Equestrian consumers. Based on the t test carried out, it can be concluded that partially content creation, content sharing, connecting and community building have a significant effect on consumer purchasing decisions at 3D Stable Equestrian. Content creation, content sharing, connecting and community building simultaneously have a significant influence on consumer purchasing decisions at 3D Stable Equestrian. The coefficient of determination of the content creation, content sharing, connecting and communitu building variables on the purchasing decision variable is 46,3%
Analysis and Development of a Football Scouting App based on Flutter: A Case Study of A3N

Authors:
raw_author_name: Nungki Selviandro
raw_author_name: Gia Septiana Wulandari

Abstract: Scouting is a crucial aspect of football, where the team's success depends heavily on the ability to select the right player. To minimize risk and optimize resources, teams can utilize the football scouting application. Unfortunately, existing applications still have limitations, especially in support of multiple platforms, and are open source. Therefore, this research is focused on developing a football scouting application that supports multiple platforms and is open source, using a single codebase-based Flutter Framework. Using Flutter, the app can be created based on just one code base, which can be compiled directly on Android and iOS platforms. The developed application has several superior features. First, the application allows the admin to insert player data into the database. Another feature is the ability to search for players based on names that users include. In addition, the application is also able to display a list of players based on the position chosen by the user. Finally, the application can display the complete data and statistics of the currently active player. Through the implementation of the selected development method, the implementation of this system can contribute significantly to supporting the scouting process in football teams. Thus, this application can increase the efficiency and effectiveness of decision-making within the scope of football scouting activities.
Dynamic Knowledge Expansion: Real-time Text Classification with Deep Convolutional Neural Networks

Authors:
raw_author_name: Imen Ferjani
raw_affiliation_string: Imam Abdulrahman Bin Faisal University
raw_author_name: Minyar Sassi Hidri
raw_affiliation_string: Imam Abdulrahman Bin Faisal University
raw_author_name: Ali Frihida
raw_affiliation_string: École Nationale d'Ingénieurs de Tunis: Ecole Nationale d'Ingenieurs de Tunis

Abstract: Abstract Convolutional Neural Networks (CNNs) have shown exceptional prowess in text classification in the dynamic landscape of Natural Language Processing (NLP). It is difficult, however, to adapt to evolving concepts when overwhelmed with streaming data and retraining is cumbersome. We present an incremental learning solution tailored for real-world, computationally intensive applications in this paper. Using dynamic updates, we introduce a new taxonomy for text data stream classification that addresses the formidable challenge of concept drift. Using a modest dataset, we train a lean CNN. Using transfer learning, this model intelligently gleans insights from the data stream, leveraging knowledge gained from previous datasets. In order to classify data streams in real time, an online learning mechanism is deployed. A pretrained CNN is updated periodically to capture emerging features, accelerating learning even further. Our experimental results demonstrate the superiority of our approach, setting new benchmarks in performance. Our method allows NLP systems to remain agile, efficient, and state-ofthe- art in an era of relentless data flow.
Development of a mobile learning network for science with augmented reality and its impact on students’ literacy and numeracy

Authors:
raw_author_name: Ucu Cahyana
raw_affiliation_string: Department of Chemistry Education, Universitas Negeri Jakarta, Jakarta 13220, Indonesia.
raw_author_name: Ika Lestari
raw_affiliation_string: Universitas Negeri Jakarta, Jakarta 13220, Indonesia.
raw_author_name: Irwanto Irwanto
raw_affiliation_string: Department of Chemistry Education, Universitas Negeri Jakarta, Jakarta 13220, Indonesia.

Abstract: The aim of the current research is to develop the Mobile Learning Network for Science application with Augmented Reality (MLNFS-AR) application on the topic of atomic structure and chemical equilibrium. The study was conducted in four public schools in Jakarta, Indonesia. The Borg and Gall development cycle was adopted in this study, including preliminary research, planning and developing learning media, and validation, evaluation, and revision. Regarding the assessment results from media experts, the mobile learning application was declared very feasible with a percentage of 91.7% for the topic of atomic structure and 87.2% for the topic of chemical equilibrium. The results of the material and language feasibility assessments were 81.6% for the topic of atomic structure and 82.3% for the topic of chemical equilibrium. The results of the media assessments for students were 92.9% for the topic of atomic structure and 89.9% for the topic of chemical equilibrium. The results of the media assessment by chemistry teachers were 97.1% for the topic of atomic structure and 94.7% for the topic of chemical equilibrium. Overall, this mobile learning medium is categorized as very feasible to use as a learning support medium. In addition, the MLNFS-AR app is effective in improving student literacy and numeracy. In sum, the mobile learning app, MLNFS-AR, can be used as a flexible learning medium, can be used anytime and anywhere, and makes it easier for students to learn chemistry.
Enhancing Marketing Transparency and Trust through Blockchain Technology

Authors:
raw_author_name: Bernardus Franco Maseke
raw_affiliation_string: University of Namibia, Namibia.

Abstract: This research delved into the intersection of blockchain technology and marketing, aiming to investigate its transformative potential in enhancing transparency, trust, and reducing ad fraud. Employing a mixed-methods research design, both quantitative and qualitative approaches were employed to comprehensively address the research objectives. In the quantitative phase, a stratified random sampling technique was utilized to select participants from diverse demographic segments, including both consumers and marketing professionals. Surveys and digital analytics tools were employed to collect data on trust levels, perceptions, and customer engagement in traditional and blockchain-based marketing initiatives. Statistical analyses, including chi-square tests and ANOVA, were conducted to determine the significance of blockchain's impact. The qualitative phase involved purposive sampling of key informants for in-depth interviews, providing nuanced insights into experiences and perceptions regarding blockchain technology in marketing. Content analysis and thematic analysis were applied to marketing materials and qualitative data, respectively. The integration of findings from both phases allowed for a comprehensive understanding of blockchain's impact on marketing, emphasizing its transformative potential. Limitations, such as the challenge of capturing the rapidly evolving nature of blockchain technology, were acknowledged.
The comparison of students' academic stress in biology class at public and private high schools

Authors:
raw_author_name: Adi Rahmat
raw_affiliation_string: Universitas Pendidikan Indonesia

Abstract: &lt;p&gt;Students are demanded by school assignments, and many of them feel under pressure when learning biology. This situation is a trigger for academic stress which has an impact on student achievement. This study aims to identify and compare the factors that influence academic stress on students studying biology at the senior high school level. The research method used was an ex post facto survey using a sample of 54 students with an age range between 16-18 years. Data were obtained through a questionnaire with 9 scales of answer choices and interviews. The results showed that the level of preference for biology subjects for both public and private high school students was quite good, but most students experienced academic stress with a fairly high category in terms of motivation, self-confidence, the education system, teachers, and school conditions. This research implies that teachers are expected to create learning strategies by paying attention to the psychological conditions of students so that student learning outcomes can be maximized.&lt;/p&gt;
Clustering Fake News with K-Means and Agglomerative Clustering Based on Word2Vec

Authors:
raw_author_name: M.  Fariz Fadillah  Mardianto
raw_affiliation_string: Statistics Program, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia
raw_author_name: Dita Amelia
raw_affiliation_string: Statistics Program, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia
raw_author_name: Elly Ana
raw_affiliation_string: Statistics Program, Faculty of Science and Technology, Universitas Airlangga, Surabaya, Indonesia

Abstract: Fake News on digital platforms is a major problem in this digital age. Many people want to find methods to detect Fake News. This research looks at a way to group Fake News articles using K-Means and Agglomerative Clustering techniques, using the semantic representations from Word2Vec embeddings. The researchers use natural language translation methods and advanced machine learning to improve the accuracy and efficiency of Fake News detection. The study involves getting meaningful features from textual data, turning them into vector representations using Word2Vec, and then applying clustering algorithms to sort similar articles. The methodology aims to improve the most recent state of the art in Fake News detection, helping to create more reliable and robust tools to fight misinformation in the digital age, In the comparative analysis of clustering metrics, K-Means clustering exhibits a Purity Score of 88.09% and an Adjusted Rand Score of 58.03%. Conversely, Agglomerative Clustering with the Ward method yields a Purity Score of 85.13% and an Adjusted Rand Score of 49.36%.The Purity Score of 88.09% for K-Means suggests a strong ability to form clusters where the majority of data points share the same true class. Agglomerative Clustering with Ward, though slightly lower at 85.13%, also demonstrates effective class separation within clusters. When considering the Adjusted Rand Score, which accounts for chance and measures the agreement between true and predicted labels, K-Means significantly outperforms Agglomerative Clustering with Ward. The scores are 58.03% and 49.36%, respectively
Using mHealth for Primary Prevention of Dementia: A Proof-of-Concept Study on Usage Patterns, Appreciation, and Beliefs and Attitudes Regarding Prevention

Authors:
raw_author_name: Frans Verhey
raw_affiliation_string: Alzheimer Centrum Limburg, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
raw_author_name: Anke Oenema
raw_affiliation_string: Department of Health Promotion, CAPHRI Care and Public Health Research Institute Maastricht, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands

Abstract: Background: Health- and lifestyle factors account for a substantial part of all dementia cases, which opens the opportunity for primary prevention. However, the required behavioral change is complex and involves targeting multiple risk factors. mHealth interventions can potentially contribute to improving motivation in a low-cost and scalable way. Objective: To explore usage patterns, appreciation, and beliefs and attitudes regarding dementia risk reduction during the use of the MyBraincoach mobile app. Methods: Participants were community-dwelling middle-aged adults from the Netherlands and used either the standard (education) or extended (education+motivational triggers) app version for three months. Two panel studies were combined in this paper. Chi-square tests, t-tests and linear mixed models were used, adjusted for age, sex, and education. Results: Of all participants (n = 299, 50.2% male), 167 (55.9%) had installed the app. The most reported reason for non-use was technical problems (47%). Those who used the app were at baseline already more positive about dementia risk reduction than those who did not use the app. Of all users who completed the evaluation (n = 102), 78.4% (n = 80) stated that the app provided a positive approach towards brain health and 80.4% (n = 82) felt better informed. Younger (&lt;60y) and lower educated participants evaluated the app most positively. Conclusion: Usage of the app was low, but users showed more positive beliefs and attitudes regarding dementia risk reduction. Most users evaluated the app positively and stated to have gained knowledge on the topic. Improving the use of the app must keep high priority in future studies.
Pengembangan Media Pembelajaran Berbasis Augmented Reality (AR) pada Mata Pelajaran IPAS Kelas VIII SMP

Authors:
raw_author_name: Nofri Hendri
raw_affiliation_string: Universitas Negeri Padang

Abstract: Augmented Reality (AR) learning media is a new learning innovation that displays interesting 3D animation visuals and makes complex and abstract concepts more real so that it triggers students' interest and curiosity in learning, so it can be used as an alternative in learning problems. This study aims to develop learning media for IPAS Class VIII Junior High School in order to produce valid media that is practical and effective for use by teachers and students This type of research is a development research (Reseacrh an Development) with the 4D (Four-D) development model. The average score for media validity assessment using Likert scale percentage presentation with media assessment by validator I is 4.93 and the percentage of 98% is categorized as "very valid", validator II is 4.73 with a percentage of 94% categorized as "very valid". And the average score of the material validator is 4.79 with a percentage of 95% categorized as "very valid". The research was conducted in 4 stages, the first stage by conducting curriculum analysis, learner analysis, and concept analysis. Then the initial product development was carried out by making flowcharts and storyboards and then making learning media with the Aseemblr edu application. Furthermore, the validity test stage is carried out by media and material validators, the last one is conducting a practicality trial and effectiveness trial for VIII grade junior high school students.
Research on a Feature Enhancement Extraction Method for Underwater Targets Based on Deep Autoencoder Networks

Authors:
raw_author_name: Fang Ji
raw_affiliation_string: China Ship Research and Development Academy, Beijing 100192, China
raw_author_name: Shaoqing Lu
raw_affiliation_string: China Ship Research and Development Academy, Beijing 100192, China

Abstract: The low-frequency line spectrum of the radiated noise signals of hydroacoustic targets contains features describing the intrinsic properties of the target that make the target susceptible to exposure. In order to extract the line spectral features of underwater acoustic targets, a method combining image processing and a deep autoencoder network (DAE) is proposed in this paper to enhance the low-frequency weak line spectrum of underwater targets in an extremely low signal-to-noise ratio environment based on the measured data of large underwater vehicles. A Gauss–Bernoulli restricted Boltzmann machine (G–BRBM) for real-value signal processing was designed and programmed by introducing a greedy algorithm. On this basis, the encoding and decoding mechanism of the DAE network was used to eliminate interference from environmental noise. The weak line spectrum features were effectively enhanced and extracted under an extremely low signal-to-noise ratio of 10–300 Hz, after which the reconstruction results of the line spectrum features were obtained. Data from large underwater vehicles detected by far-field sonar arrays were processed and the results show that the method proposed in this paper was able to adaptively enhance the line spectrum in a data-driven manner. The DAE method was able to achieve more than double the extractable line spectral density in the frequency band of 10–300 Hz. Compared with the traditional feature enhancement extraction method, the DAE method has certain advantages for the extraction of weak line spectra.