Reviewerly/exHarmony huggingface dataset:
Schinzophrenia Detection using Machine Learning

Schizophrenia is a complex mental disorder characterized by disruptions in thinking, perception, and emotional regulation. Early diagnosis is crucial for effective treatment and improved patient outcomes. This abstract explores the application of machine learning in schizophrenia detection. By analyzing diverse data sources, including neuroimaging, genetic, and clinical data, machine learning models can aid in identifying patterns and biomarkers associated with schizophrenia. This approach offers the potential for early and accurate diagnosis, enabling timely interventions and personalized treatment plans. The integration of machine learning into the diagnostic process holds promise for enhancing the understanding and management of schizophrenia, ultimately improving the quality of life for affected individuals.
Enhancing Data Backup and Recovery in Cloud Computing with Secure Database Monitoring

The company uses unregistered data to improve performance, but this comes at the cost of limited backup options, data loss, and a voluntarily damaging week-long downtime. Even if they have backup systems, they cannot meet the SLA recovery time. To solve these problems, this work presents a secure database monitoring method for cloud computing. This approach adjusts the backup speed according to the data volume; This is important when data grows at least 30% per year. For the some companies, the data doubles every 3-4 years or more and SLAs and recovery targets need to be updated. At the same time, business needs for data recovery continue to grow, highlighting the need for more robust and responsive data management strategies.
Implementasi Fungsi Polinomial pada Algoritma Gradient Boosting Regressor: Studi Regresi pada Dataset Obat-Obatan Kadaluarsa Sebagai Material Antikorosi

Corrosion is an electrochemical process between the metal surface and a corrosive environment that can lead to significant losses in various industries, especially in the oil and gas sector. Experimental studies are conducted to evaluate the performance of corrosion inhibitors and available resources. In this research, a machine learning (ML) approach is employed to assess the effectiveness of expired drug compounds as corrosion inhibitors. The primary challenge in machine learning is obtaining a highly accurate model to ensure that predictions are relevant to the properties of the tested materials. Therefore, the polynomial function is tested in the gradient-boosting regressor (GBR) algorithm to enhance the accuracy of the developed ML model. The results indicate that the implementation of the polynomial function in the GBR algorithm can improve the accuracy of the prediction model based on R2 and RMSE metrics.
English Language Minority in Indonesian EFL (English Foreign Language) Learners Rural Area

English as a communication tool has been vital aspect into worldwide society. The element of English has a more profound influence through the implementation of life and daily human habit. Nowadays, English has been normalized as a language necessity for academics, basic internet norms, social status, and business. Hence, it is standard for English as a requirement for many fields. Although the fact that society uses English as language instruction, there is a majority of Indonesian districts that could not be able to apply the language vice versa. It is due to internal and external factors such as geographic area, difficulty, and national curriculum design. Consequently, it created imbalance of education distribution aspect and rights. Therefore, this research elaborates and finds out the minority elements of English language involved in EFL learners in rural area. The primary method of inquiry in this research is a literature review which means the technique intended to gain a collection of previous research from the journal, internet, book, and the latest research. The data were analyzed, synthesized into a findings unit, and elaborated on how to broaden the result to be discursive.
Analysis of Language Strategies in the Digital Promotion of the Mulyosari Group Through Instagram Media

The research data consists of 10 Instagram content from several brands under Mulyosari Group. The analysis was conducted by paying attention to the use of language, communication style, and promotional strategies applied in Instagram content. The results of this study show that the language strategy used in digital promotion at the Mulyosari Group company through Instagram media involves the use of foreign languages, creative, simple, and interesting language. The language used tends to be informal and adopts a more relaxed communication style to attract the attention of Instagram users, the majority of whom are the younger generation
Social Media a Concern for Foreign Relationships with Neighbor Countries

Indian Sub-continent, especially Bangladesh, India, and Pakistan were divided by Islamic ideology. Bangladesh gained independence from West Pakistan in 1971 with the unbelievable support of the neighboring country India. The present article attempts to find out about the recent time controversy through social media between these countries. In the 2023 Men’s World Cup Cricket Final match India vs Australia held on November 19, 2023, Australia won by 6 Wickets. Here Bangladeshi young cricket lovers enjoyed supporting Team Australia but Indians could not take it positively. They think Bangladesh as a Muslim country avoids Hindus and does not respect and love them. However, the study reveals that 83% disagree that Islam is related to support. 59.50% voted that a player’s behavior is responsible for the support of India and only 3% think that religion may affect supporting the cricket matches. Finally, an effective recommendation has been structured for resolving the crisis.
Evaluation of the Performance of Perumda Air Minum Tirta Khayangan Sungai Penuh City Using the Balanced Scorecard Method for 2019-2022

The purpose of this study is to examine and describe how the Regional Public Company (Perumda) Drinking water, Tirta Khayangan, Sungai Penuh City uses the Balanced Scorecard with the perspectives of finance, customers, internal business processes, growth and learning. Customers and employees of Perumda Air Minum Tirta Khayangan and the company's financial statements for 2019, 2020, 2021 and 2022 are used as the population of this study. The simple random sampling technique with the slovin formula in this study obtained a total sample of 100 customer respondents and 101 respondents from the Perumda Air Minum Tirta Khayangan City of Sungai Penuh. Descriptive research with a quantitative focus was used for this study.
Improving Syntax Learning Achievement using Lesson Study Based Learning Management: A Classroom Action Research

Classroom Action research is motivated by the low value of English, especially when it comes to syntax learning, and students are not brave, fearful or confident in syntax English. The aim of this research was to learn about the process of lesson study based learning management and to increase syntax learning achievement using lesson study based learning management on four semesters at STKIP PGRI Sidoarjo. The descriptive qualitative and classroom action research methods were used in this research. This research was conducted from April to June 2022, located in STKIP PGRI Sidoarjo on four semester of English Education. This research was designed as a classroom action research project with lesson study-based learning management. Two cycles were used to implement the improvements. The lesson study observation sheet and the students' activity observation sheet were used as research instruments during the learning process. The results of the process of lesson study based learning management revealed that it was a cyclical process involving the development of lesson plans, the implementation of learning (do, open class), and the evaluation of learning (see), and that there was an improvement in terms of syntax learning achievement through lesson study based learning management on four semesters at STKIP PGRI Sidoarjo when lesson study based learning management was implemented
Research on the Material and Human Resource Allocation in Rural Schools Based on Social Capital Theory

The allocation of resources in rural schools directly affects the quality of rural education development. However, rural schools have long faced inadequacies in both material and human resource allocation. Social capital, as an intangible resource, can play a significant role. This article first analyzes the current problems in the allocation of material and human resources in rural schools, highlighting issues of inadequacy and imbalance. Then, from the perspective of social capital, it analyzes its impact on resource allocation, which can not only expand resource supply channels but also enhance allocation efficiency and promote sharing. In addition, the article proposes strategies and recommendations for leveraging social capital to optimize resource allocation in rural schools. Finally, empirical research using typical cases and questionnaire surveys verifies the effectiveness of social capital in resource allocation. The study demonstrates that social capital can effectively improve resource allocation efficiency and play a crucial role in enhancing rural education development.
The Function of Humanities Education in Russian Language at University

Humanities education emphasizes humanistic education, aims at perfecting personality, and focuses on achieving and promoting the harmonious development of individual body and mind. Humanities education in universities is mainly embodied in general education courses, but specialized courses education also plays a non-negligible and irreplaceable role in cultivating humanities education for college students. In this paper, we explore the functions of specialized Russian courses in disseminating humanistic knowledge, enlightening humanistic thoughts, cultivating humanistic spirit and shaping a sound personality from the perspective of university Russian education, and discuss the qualities that teachers should possess and the role they should play in it.
Students' Viewpoints Toward Part of Speech as the Fulcrum of Grammar

This study concerned with students’ viewpoints toward part of speech which attempted to figure out the students’ viewpoints about parts of speech, to explore students’ problems in learning parts of speech, and to reveal the students’ strategies to be acquainted with part of speech. This research was a qualitative study where the data were collected by interview. The subjects of this research were 28 students (15 females and 13 males) in the third semester, English Education Department at Faculty of Training and Education of a State University in West Sulawesi who were chosen randomly. All of the students were interviewed about their viewpoints about parts of speech. Based on the result of the research, the researcher found that some students misunderstood the definition of part of speech, and some of them understand it. The students were found to have problems in learning parts of speech such as being less practice, not mastering the parts of speech well, lack of vocabulary, and inability to differentiate parts of speech, as well as lack of understanding. In the mean time, the students’ way to become familiar with parts of speech was by memorizing the word classes, knowing the characteristics of the word, paying attention to the position of the word in a sentence, knowing the meaning of the word, guessing the word, and looking at the word before and after in a sentence or text. The result of the study could be useful for future lectures about parts of speech.
On the Training of Yangqin's Wheel Tone Technique

The dulcimer is one of China's national musical instruments, and with the development of the times, it is loved by many people. As a representative playing technique, dulcimer wheel sound has its own artistic characteristics and three particularities: linear beauty, tension and other characteristics. representative playing technique, dulcimer wheel sound has its own artistic characteristics and three particularities: linear beauty, tension beauty and emotional beauty. This paper aims to analyze the use of dulcimer wheel tone in music, the problems existing in practice and the corresponding solutions, hoping to provide useful help for the development of dulcimer wheel sound. This paper aims to analyze the use of dulcimer wheel tone in music, the problems existing in practice and the corresponding solutions, hoping to provide useful help for dulcimer beginners to master wheel tone skills.
Students’ Anxiety in Learning English Writing Skills in Senior High School Level

The purpose of this study was to investigate the factors that contribute to students' English writing anxiety in the Science 11 class at UPT SMAN 11 Luwu. Students' levels and causes of writing anxiety in English are described (cognitive anxiety, somatic anxiety, and avoidance behavior). Descriptive statistics were used for this investigation. Simple random sampling was used to select a sample of 68 students from the population of eleventh-grade students in a science class. The Causes of Second Language Writing Anxiety Inventory (CSLWAI) by Rezaei and Jafari was used to describe the main factors that contribute to students' writing anxiety, and the Second Language Writing Anxiety Inventory (SLWAI) by Cheng was used to analyze students' anxiety levels. SPSS version 22 and Microsoft Excel 2019 were used to find the mean and percentage of the data, respectively. Students who reported high levels of writing anxiety were statistically typical (M=73.06&gt;65). Inadequate writing technique, linguistic difficulties, a lack of practice, and time constraints are also major contributors to students' writing anxieties.
Impacto del confinamiento por COVID-19 en el estado emocional y conductual de niños con discapacidad

The measures implemented due to COVID-19 significantly affected the life of society in general, however, diverse and differentiated effects are recognized in the population of children with disabilities, both in their emotional and behavioralwell-being. Therefore, the objective of this study was to determine the effects of home lockdown, because of COVID-19 in the year 2020, on the emotional and behavioral state of children with disabilities. For this purpose, a descriptive, nonexperimental research was implemented, with a cross-sectional non-probabilistic selective design, being the study variables, home lockdown, and the emotional and behavioral state of children with disabilities. The participants were 68 parents of children with disabilities in Special Basic Education (SBE) and Regular Basic Education (RBE) in Metropolitan Lima, selected by non-probabilistic convenience sampling. The instruments used were a sociodemographic form and an online questionnaire based on the COVID-19 and ASD Questionnaire. The results found an increase of children who decreased their speech skills, conversation initiation and voice volume; eye contact, following instructions and rules; facial expression, recognizing others’ feelings and communicating emotional states; listening to stories, sharing toys and belongings.
Prediction of Transfusion among In-patient Population using Temporal Pattern based Clinical Similarity Graphs.

Intelligent prediction of risk of blood transfusion among hospitalized patients can identify at-risk patients and provide timely information to the hospital to plan and reserve resources to meet the demand of blood transfusion. While previously proposed solutions focus on sub-populations such as patients admitted to ICU after gastrointestinal bleeding or postpartum patients with hemorrhage, we design a predictive model applicable to complete in-patient population. Our model relies on patients' similarity graph based on temporal patterns among clinical history of the patients. These graphs are processed through graph convolutional neural network (GCNN) to estimate node or patient level risk of blood transfusion. Thus, our model not only learns from the patient's own clinical history but also from other patients with similar clinical history. The model is also capable of fusing diverse data elements from electronic health records (EHR) such as demographic information, billing codes, and recorded vital signs. Our model was validated on both internal and external sets and outperformed all comparative baseline models.
Web-based Prototype for Graphical Exploration of FHIR® Questionnaire Responses.

The evaluation of clinical questionnaires is an important part of gaining knowledge in empirical research. The electronically captured responses are encoded in a standard format such as HL7 FHIR® that facilitates data exchange and systems interoperability. However, this also complicates access of the information to explore and interpret the results without appropriate tools. In this work, we present the design of a web-based graphical exploration tool for categorical questionnaire response data that can interact with FHIR-conformant HTTP endpoints. The web app enables non-technical users with simplified, direct visual access to highly structured FHIR questionnaire response data and preserves the applicability in arbitrary data exploration tasks. We describe the abstract feature design with the derived technical implementation to allow a universal, user-configurable data subselection mechanism to generate conditional one- and two-data-dimensional charts. The applicability of our developed prototype is demonstrated on synthetic FHIR data with the source code available at https://github.com/frankkramer-lab/FHIR-QR-Explorer.
Clinical Feature Vector Generation using Unsupervised Graph Representation Learning from Heterogeneous Medical Records.

The diversity of patient information recorded on electronic medical records generally, presents a challenge for converting it into fixed-length vectors that align with clinical characteristics. To address this issue, this study aimed to utilize an unsupervised graph representation learning method to transform the unstructured inpatient information from electronic medical records into a fixed-length vector. Infograph, one of the unsupervised graph representation learning algorithms was applied to the graphed inpatient information, resulting in embedded vectors of fixed length. The embedded vectors were then evaluated for whether the clinical information was preserved in it. The results indicated that the embedded representation contained information that could predict readmission within 30 days, demonstrating the feasibility of using unsupervised graph representation learning to transform patient information into fixed-length vectors that retain clinical characteristics.
Caregivers Attitude Detection From Clinical Notes.

Caregivers' attitudes impact healthcare quality and disparities. Clinical notes contain highly specialized and ambiguous language that requires extensive domain knowledge to understand, and using negative language does not necessarily imply a negative attitude. This study discusses the challenge of detecting caregivers' attitudes from their clinical notes. To address these challenges, we annotate MIMIC clinical notes and train state-of-the-art language models from the Hugging Face platform. The study focuses on the Neonatal Intensive Care Unit and evaluates models in zero-shot, few-shot, and fully-trained scenarios. Among the chosen models, RoBERTa identifies caregivers' attitudes from clinical notes with an F1-score of 0.75. This approach not only enhances patient satisfaction, but opens up exciting possibilities for detecting and preventing care provider syndromes, such as fatigue, stress, and burnout. The paper concludes by discussing limitations and potential future work.
Promoting TEFCA with Blockchain Technology: A Decentralized Approach to Patient-centered Healthcare Data Management.

The Trusted Exchange Framework and Common Agreement (TEFCA) is a framework consisting of seven principles designed to create a secure and seamless health information exchange system across various healthcare settings. The ultimate goal of TEFCA is to facilitate public health surveillance, increase interoperability, promote data sharing, and ensure patient-centered healthcare data management. While the implementation of these principles is challenging, blockchain technology, with its unique features such as transparency, auditability, immutability, and anonymity, can provide a promising solution to the development of TEFCA. This article delves into the potential of blockchain technology to promote TEFCA design. By providing an immutable and transparent ledger, blockchain ensures data integrity, openness, and patient privacy. Overall, the use of blockchain technology can help address the challenges of implementing TEFCA principles and promote patient empowerment and control over their health data, improve data interoperability, and enhance healthcare quality.
Strip-MLP: Efficient Token Interaction for Vision MLP

Token interaction operation is one of the core modules in MLP-based models to exchange and aggregate information between different spatial locations. However, the power of token interaction on the spatial dimension is highly dependent on the spatial resolution of the feature maps, which limits the model’s expressive ability, especially in deep layers where the feature are down-sampled to a small spatial size. To address this issue, we present a novel method called Strip-MLP to enrich the token interaction power in three ways. Firstly, we introduce a new MLP paradigm called Strip MLP layer that allows the token to interact with other tokens in a cross-strip manner, enabling the tokens in a row (or column) to contribute to the information aggregations in adjacent but different strips of rows (or columns). Secondly, a Cascade Group Strip Mixing Module (CGSMM) is proposed to overcome the performance degradation caused by small spatial feature size. The module allows tokens to interact more effectively in the manners of within-patch and cross-patch, which is independent to the feature spatial size. Finally, based on the Strip MLP layer, we propose a novel Local Strip Mixing Module (LSMM) to boost the token interaction power in the local region. Extensive experiments demonstrate that Strip-MLP significantly improves the performance of MLP-based models on small datasets and obtains comparable or even better results on ImageNet. In particular, Strip-MLP models achieve higher average Top-1 accuracy than existing MLP-based models by +2.44% on Caltech-101 and +2.16% on CIFAR-100. The source codes will be available at https://github.com/Med-Process/Strip_MLP.
Diffusion-Guided Reconstruction of Everyday Hand-Object Interaction Clips

We tackle the task of reconstructing hand-object interactions from short video clips. Given an input video, our approach casts 3D inference as a per-video optimization and recovers a neural 3D representation of the object shape, as well as the time-varying motion and hand articulation. While the input video naturally provides some multi-view cues to guide 3D inference, these are insufficient on their own due to occlusions and limited viewpoint variations. To obtain accurate 3D, we augment the multi-view signals with generic data-driven priors to guide reconstruction. Specifically, we learn a diffusion network to model the conditional distribution of (geometric) renderings of objects conditioned on hand configuration and category label, and leverage it as a prior to guide the novel-view renderings of the reconstructed scene. We empirically evaluate our approach on egocentric videos across 6 object categories, and observe significant improvements over prior single-view and multi-view methods. Finally, we demonstrate our system’s ability to reconstruct arbitrary clips from YouTube, showing both 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> and 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> person interactions.