Reviewerly/exHarmony huggingface dataset:
Teaching English for Intensive Program: A Need Analysis

In UIN Prof. K.H. Saifuddin Zuhri, most students, who had taken an English Proficiency Test at the beginning of the academic year to recognize the students' English proficiency, did not achieve a passing grade (450 points) of the test. This condition drives the institution to improve the student's English proficiency through the intensive language program that the Language Development Unit organizes. The research uses a descriptive study to analyze the data from the questionnaire, in which each item is analyzed individually. Fifty students' questionnaire answers and the interview of ten lecturers are grouped and analyzed to identify their real needs. The data is evaluated descriptively based on the percentage of student responses. The study results show that the language skills needed in intensive English courses are listening, structure and written expression, and reading. Most of the lecturers said that the students lacked grammar. Therefore, students need listening, structure and written expression materials, and reading skills—the essential skills to improve our structure and writing. Also, vocabulary and grammar are the aspects of the language that should be improved in the intensive English class. When developing the material, the target needs analysis should be considered, including language skills, language aspects, English learning objectives, and the time commitment of the Intensive English language program.
Tax Data Processing System

The expense information handling framework at the Shibgah Islam Nusantara Amanah Foundation is as yet done physically, according to the ongoing framework, information filing and duty information recovery have not been completed as expected and on time, where away it is constantly put away haphazardly, causing harm or harm. lost information, so looking for information for data requirements of benefactors consumes most of the day since they need to every year find and match documents. This framework is broke down and planned utilizing spellbinding investigation research strategies and OOAD (Object Oriented Analysis Design) framework improvement methods. The programming language utilized by this framework is PHP with XAMPP web server and PHPMyAdmin information base. This expense information handling data framework that has been made can assist establishment with staffing to find, and recap charge information that is given consequently and can decrease the degree of document harm and blunders in recording charge reports.
Role Based Secure Data Access Control for Cost Optimized Cloud Storage Using Data Fragmentation While Maintaining Data Confidentiality

The paper proposes a role-based secure data access control framework for cost-optimized cloud storage, addressing the challenge of maintaining data security, privacy, integrity, and availability at lower cost. The proposed framework incorporates a secure authenticity scheme to protect data during storage or transfer over the cloud. The framework leverages storage cost optimization by compressing high-resolution images and fragmenting them into multiple encrypted chunks using the owner's private key. The proposed approach offers two layers of security, ensuring that only authorized users can decrypt and reconstruct data into its original format. The implementation results depicts that the proposed scheme outperforms existing systems in various aspects, making it a reliable solution for cloud service providers to enhance data security while reducing storage costs.
Supervised Learning of Physical Activity Features From Functional Accelerometer Data

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> We propose a new health informatics framework to analyze physical activity (PA) from accelerometer devices. Accelerometry data enables scientists to extract personal digital features useful for precision health decision making. Existing methods in accelerometry data analysis typically begin with discretizing summary counts by certain fixed cutoffs into activity categories. One well-known limitation is that the chosen cutoffs are often validated under restricted settings, and cannot be generalizable across populations, devices, or studies. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> We develop a data-driven approach to overcome this bottleneck in PA data analysis, in which we holistically summarize a subject's activity profile using Occupation-Time curves (OTCs), which describe the percentage of time spent at or above a continuum of activity count levels. We develop multi-step adaptive learning algorithms to perform supervised learning via a scalar-on-function model that involves OTC as the functional predictor of interest as well as other scalar covariates. Our learning analytic first incorporates a hybrid approach of fused lasso for clustering and Hidden Markov Model for changepoint detection, then executes refinement procedures to determine activity windows of interest. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> We evaluate and illustrate the performance of the proposed learning analytic through simulation experiments and real-world data analyses to assess the influence of PA on biological aging. Our findings indicate a different directional relationship between biological age and PA depending on the specific outcome of interest. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> Our bioinformatics methodology involves the biomedical outcome of interest to detect different critical points, and is thus adaptive to the specific data, study population, and health outcome under investigation.
Multimodal Body Sensor for Recognizing the Human Activity Using DMOA Based FS with DL

The relevance of automated recognition of human behaviors or actions stems from the breadth of its potential uses, which includes, but is not limited to, surveillance, robots, and personal health monitoring. Several computer vision-based approaches for identifying human activity in RGB and depth camera footage have emerged in recent years. Techniques including space-time trajectories, motion indoctrination, key pose extraction, tenancy patterns in 3D space, motion maps in depth, and skeleton joints are all part of the mix. These camera-based methods can only be used inside a constrained area and are vulnerable to changes in lighting and clutter in the backdrop. Although wearable inertial sensors offer a potential answer to these issues, they are not without drawbacks, including a reliance on the user’s knowledge of their precise location and orientation. Several sensing modalities are being used for reliable human action detection due to the complimentary nature of the data acquired from the sensors. This research therefore introduces a two-tiered hierarchical approach to activity recognition by employing a variety of wearable sensors. Dwarf mongoose optimization process is used to extract the handmade features and pick the best features (DMOA). It predicts the composite’s behavior by emulating how DMO searches for food. The DMO hive is divided into an alpha group, scouts, and babysitters. Every community has a different strategy to corner the food supply. In this study, we tested out a number of different methods for video categorization and action identification, including ConvLSTM, LRCN and C3D. The projected human action recognition (HAR) framework is evaluated using the UTD-MHAD dataset, which is a multimodal collection of 27 different human activities that is available to the public. The suggested feature selection model for HAR is trained and tested using a variety of classifiers. It has been shown experimentally that the suggested technique outperforms in terms of recognition accuracy.
Tracking the Effects of Groundswell Communication on the Attitudinal and Purchasing Behaviour of Consumers of Eco-Friendly Products

The expansion of the dialogue and individual customer values due to active development of digital communication, which is related to innovation of digital communication and its integration into all areas of marketing communication, requires examination of emotions and emotional responses in the "business - online community - customer" communication process. Globalization but also the development of digital technologies have led to the integration of traditional and digital presentation of business entities into a unified whole, contributing to the emergence of the currently popular phenomenon of the groundswell. The presence of new forms of social interaction, particularly in the realm of social media, supports the creation and dissemination of so-called community communication about businesses and their products, often without their knowledge. As a result of this phenomenon and the possibility of quickly obtaining information and comparing companies across various online channels, there is a change in consumer behaviour. The groundswell defines a new trend in communication between organizations and unofficial institutions (fans, customers, consumers), which has a significant impact on business in any industry. However, the spread of hoaxes and false information about eco-products can be a problem. Social media users become victims of community opinion, and their belief in the need for eco-friendly behaviour in the purchasing process often has a decreasing tendency. Uncoordinated management of the groundswell indicates a societal issue, which is why the authors of the research article aimed to investigate and systematically describe the impact of groundswell activities on previously environmentally conscious customers, as well as on customers who were unaware of eco-products. In their scientific study, the authors present the findings of theoretical research aimed at ecological brands, products and users who have been exposed to the effect of groundswell as well as the potential of neuromarketing usage in the given field. &#x0D;
Fast Diff Algorithms

In this chapter, we will discuss a third way to compare the old and new sets of sub-nodes: the fast Diff algorithm. As the name suggests, this algorithm is very fast. This algorithm was first applied to the ivi and inferno frameworks, and Vue.js 3 draws on and extends it. Figure 11.1 compares the performance of ivi, inferno, and Vue.js 2.
First Steps Towards Taming Description Logics with Strings

We consider the description logic $$\mathcal {A}\mathcal {L}\mathcal {C}\mathcal {F}^{\mathcal {P}}(\mathcal {D}_{\varSigma })$$ over the concrete domain $$\mathcal {D}_{\varSigma } = (\varSigma ^*,\prec ,=,(=_{\mathfrak {w}})_{\mathfrak {w}\in \varSigma ^*})$$ , where $$\prec $$ is the strict prefix order over finite strings in $$\varSigma ^*$$ . Using an automata-based approach, we show that the concept satisfiability problem w.r.t. general TBoxes for $$\mathcal {A}\mathcal {L}\mathcal {C}\mathcal {F}^{\mathcal {P}}(\mathcal {D}_{\varSigma })$$ is ExpTime-complete for all finite alphabets $$\varSigma $$ . As far as we know, this is the first complexity result for an expressive description logic with a nontrivial concrete domain on strings.
Intelligent Educational Recommendation Platform with AI Chatbots

The objectives of this research were as follows. 1) Analyze the intelligent educational recommendation platform with AI Chatbots. 2) Design the architecture of the intelligent educational recommendation platform with AI Chatbots. 3) Develop the architecture of the intelligent educational recommendation platform with AI Chatbots. 4) Study the appropriateness of developing the intelligent educational recommendation platform with AI Chatbots. The sample used in the research was seven experts in information system development from various institutions in higher education. The architecture of the intelligent educational recommendation platform with AI Chatbots there is two main components: 1) Stakeholders consisting of system administrators and external users, and 2) The working process of the intelligent educational recommendation platform with AI Chatbots consists of four parts including natural language processing, dialog management, database and application programming interface (API), and response generation. Assessment of the appropriateness of the architecture of the intelligent educational recommendation platform with AI Chatbots found that 1) the architecture of the intelligent educational recommendation platform with AI Chatbots, overall at a high appropriated, 2) the architecture of the intelligent educational recommendation platform with AI Chatbots, an individual element at a high appropriated, and 3) the architecture of the intelligent educational recommendation platform with AI Chatbots, Integrated elements at a high appropriated. As described earlier, the architecture of the intelligent educational recommendation platform with AI Chatbots can be a guideline for developing with AI Chatbots in the future.
Voices of kindergarten and daycare center teachers on the integration of early childhood education and child care

Objectives The purpose of this study were to provide basic data for integration of early childhood education and&#x0D; child care that can be stably settled in the field by listening to what kind of thoughts kindergarten and daycare center&#x0D; teachers have on the integration of early childhood education and child care that is currently being promoted.&#x0D; Methods For this study, 5 kindergarten teachers and 5 daycare center teachers were selected as research participants,&#x0D; individual interviews were conducted to analyze the data through the process of ‘coding - categorization&#x0D; - topic derivation’ based on the transcribed data and researcher's diary.&#x0D; Results First, teachers pointed out the ‘speed of integration of ealry childhood education and child care’ and ‘voices&#x0D; from the excluded educational site’ as problems in the promotion process of integration of early childhood education&#x0D; and child care. Second, opinions on the integration of early childhood education and child care ways to&#x0D; were analyzed as “point of view on the gap between kindergartens and daycare centers” “Proposal of a new integrated&#x0D; institution model” and “opinion on teacher qualification”.&#x0D; Conclusions Therefore, this study listened to the voices of teachers at kindergartens and daycare centers at the&#x0D; present time, when the first stage of the integration of early childhood education and child care was in progress.&#x0D; Through this, it is meaningful in that it confirmed the voices of various teachers' opinions for stable integration of&#x0D; early childhood education and child care in the future.
The Problems an Improvement Direction of High School Music Appreciation and Criticism Textbook

Objectives This study examines the problems in the contents of high school textbook Music Appreciation and&#x0D; Criticism, and proposes improvement directions accordingly.&#x0D; Methods For this purpose, the 2015 revised textbook Music Appreciation and Criticism's unit composition, organization,&#x0D; and Gugak contents were analyzed.&#x0D; Results The problems of high school music appreciation and criticism education are, first, that Gugak is not universally&#x0D; covered in music. Second, the majority of music pieces overlapped with general Music textbooks, and&#x0D; third, the fact that the description of Gugak was remarkably lacking compared to Western music history, and&#x0D; fourth, there was no concept and critical awareness of Gugak criticism. Since music appreciation and criticism education&#x0D; is a special subject for students majored in music, it should have more advanced content than general&#x0D; Music textbooks, but there was room to instill musical prejudice.&#x0D; Conclusions The improvement direction for these problems is, first, to understand Korean traditional music&#x0D; universally. Second, a critical mind about the criticism of Gugak will be preceded. Third, future-oriented education&#x0D; with the context of the times was to be pursued. Fourth, appreciation music was to be presented in a more diverse&#x0D; way and learning contents were to be converged. When these suggestions for improvement are fully considered&#x0D; and improved, the appreciation and criticism of Gugak can also develop in a creative direction.
Mediating Effect of Self-Efficacy on Effect of Learning Participation Motivation of Preliminary Social Workers as Mature-are Students on Participation Continuance Intention: Focusing on students majoring in social welfare

Objectives The purpose of this study was to examine whether the self-efficacy has a mediating effect on the effect of the learning participation motivation of preliminary social workers as mature-age students on their participation continuance intention.&#x0D; Methods To this end, it administered questionnaires to 259 preliminary social workers as mature-age students enrolled in collages located at D- and J-si, K-do, and it tested the relation among variables, the mediating effect and the significance level from the data collected by using SPSS/PC(21.0) and Process macro.&#x0D; Results First, it was fond that the learning participation motivation of preliminary social workers as mature-age students has an effect on the self-efficacy and the participation continuance intention, and the self-efficacy also has a significant effect on the participation continuance intention. Second, it was found that the self-efficacy mediates the relationship between the learning participation motivation of preliminary social workers as mature-age students and their participation continuance intention. The findings suggest that the higher the learning participation motivation and the self-efficacy of preliminary social workers as mature-age students, the higher the participation continuance intention.&#x0D; Conclusions On the basis of the findings, this study suggests that academic advisers’ interest and communication are required to enhance the learning participation motivation and the self-efficacy of preliminary social workers as mature-age students, and the participation continuance intention for learning can be also improved based on the effort to develop education programs for mature-age students.
Machine Learning Approaches for MRI Image Analysis-Based Prostate Cancer Detection

The sooner the patient receives a diagnosis for their condition, the higher their chances will be of surviving it. As is the case with conventional diagnosis, medical imaging is analyzed by trained professionals who look for any signs that the body may be displaying cancerous tendencies. The great quality and multidimensionality of MRI images need the use of an appropriate diagnostic system in addition to CAD tools. Because it is useful, researchers are now concentrating their efforts on developing methods to improve the accuracy, specificity, and speed of these systems. A model that is efficient in terms of image processing, feature extraction, and machine learning is presented in this study. This chapter presents machine learning techniques for prostate cancer detection by analyzing MRI images. Image preprocessing is done using histogram equalization. It improves image quality. Image segmentation is performed using the fuzzy C means algorithm. Features are extracted using the gray level co-occurrence matrix algorithm. Classification is performed using the KNN.
Multi-Task Learning-Based CSI Feedback Design in Multiple Scenarios

For frequency division duplex (FDD) systems, downlink channel state information (CSI) feedback is essential. Deep learning-based auto-encoder (AE) structures have shown promise in reducing feedback overhead. However, designing a super-large AE network to handle the CSI of all scenarios is not practical. A more practical approach is to divide the CSI dataset by region/scenario and use multiple simple AE networks. However, this method requires high memory capacity, making it unsuitable for low-end user equipment (UE). In this paper, we propose a new UE-friendly framework based on multi-tasking mode. Our framework, called single-encoder-to-multiple-decoders (S-to-M), uses multi-task-learning to design multiple independent AEs into a joint architecture with a shared encoder that corresponds to multiple task-specific decoders. We also integrate GateNet as a classifier to enable the base station to autonomously select the right task-specific decoder for the subregion. Our experiments on a simulated multi-scenario CSI dataset show that our proposed S-to-M framework outperforms other benchmark modes by significantly reducing model complexity and UE memory consumption.
Negativity and Positivity in the ICU: Exploratory Development of Automated Sentiment Capture in the Electronic Health Record

To develop proof-of-concept algorithms using alternative approaches to capture provider sentiment in ICU notes.Retrospective observational cohort study.The Multiparameter Intelligent Monitoring of Intensive Care III (MIMIC-III) and the University of California, San Francisco (UCSF) deidentified notes databases.Adult (≥18 yr old) patients admitted to the ICU.We developed two sentiment models: 1) a keywords-based approach using a consensus-based clinical sentiment lexicon comprised of 72 positive and 103 negative phrases, including negations and 2) a Decoding-enhanced Bidirectional Encoder Representations from Transformers with disentangled attention-v3-based deep learning model (keywords-independent) trained on clinical sentiment labels. We applied the models to 198,944 notes across 52,997 ICU admissions in the MIMIC-III database. Analyses were replicated on an external sample of patients admitted to a UCSF ICU from 2018 to 2019. We also labeled sentiment in 1,493 note fragments and compared the predictive accuracy of our tools to three popular sentiment classifiers. Clinical sentiment terms were found in 99% of patient visits across 88% of notes. Our two sentiment tools were substantially more predictive (Spearman correlations of 0.62-0.84, p values < 0.00001) of labeled sentiment compared with general language algorithms (0.28-0.46).Our exploratory healthcare-specific sentiment models can more accurately detect positivity and negativity in clinical notes compared with general sentiment tools not designed for clinical usage.
Indonesia Student Can’t Read the Qur’an? The Bilhikmah Method Solution

Although Islam emphasizes that its followers should be able to read the Qur`ān, most students, unfortunately, cannot. Results showed that only 15% of elementary school students, 25% junior high school, 35% of high school, and 40% of university students could read the Qur`ān. Also, the 2021 pre-test results from the UPI showed that only 34% of students could read the Qur`ān. The findings from the UNISBA were better, as the 2021 pre-test results revealed that up to 60% of students were able to read it and only 40% could not. Therefore, the problem of this study is regarding how Al-Qur`ān illiteracy among students can be eradicated. Several innovative reading methods are employed in Indonesia, including the bilhikmah technique. This research aims to test the effectiveness of this method in eradicating Al-Qur`ān illiteracy from UNISBA students. It used a quasi-experimental research method, and the sample comprised 998 students of the 2021-2022 class that were unable to read the Al-Qur`ān. A total of 499 students from 14 classes attended the offline learning program, while the other half from 20 classes participated online. Before and after learning, pre-tests and post-tests were provided. Consequently, the study discovered that 84% and 74% of students from the offline and online classes, respectively, managed to improve their ability to read the Qur`ān. Based on the statistical test results, the offline program was much more effective than the online equivalent. Hence, the study recommends that the eradication of Al-Qur`ān illiteracy for students should use the bilhikmah method for both offline and online classes