Reviewerly/exHarmony huggingface dataset:
A Systematic Temporal Extraction Pipeline for Medical Concepts in Clinical Notes.

With increased application of natural language processing (NLP) in medicine, many NLP models are being developed for uncovering relevant clinical features from electronic health records. Temporal information plays a key role in understanding the context, significance, and interpretation of medical concepts extracted from clinical notes. This is particularly true in situations where the behavior, value, or status of a medical concept changes over time. In this paper, we introduce a systematic framework, NLP annotation-Relaxation-Generation (NRG). NRG compiles incidents of medical concept changes from status annotations and timestamps of multiple clinical notes. We demonstrate the effectiveness of the NRG pipeline by applying it to two medical concepts related to patients with inflammatory bowel disease: extra-intestinal manifestations and medications. We show that the NRG pipeline offers not only insights into medical concept changes over time, but can help convey longitudinal changes in clinical features at both individual and population level.
Hybrid Learning-Based Educational Game Frog to Improve Speaking Skills

The educational game Frog is implemented with a hybrid learning model to improve the speech skills of pre-schoolers. This type of research uses Research and Development (R&amp;D) with a Four-D (4D) development model. The research development adopts the 4D model of the figures of Thiagarajan, et al, namely: define, design, develop, and disseminate. The research subject focuses on preschool children group B (aged 5-6 years) as the object of research. The data collection technique used expert validation questionnaires, observations, and tests to measure learning outcomes against the media. The usefulness of research answers learning to improve children's speech so they grow into active and confident children. This hybrid learning-based frog educational game is designed to simulate existing problems so that essence or knowledge can be obtained that can be used to solve these problems. The results from media experts get a percentage of 78%, including the valid category and material experts get 89% in the beneficial type. Students' and teachers' responses to the frog educational media based on hybrid learning on speaking skills are fascinating. This can be seen from the small group trial, with a percentage of 84% and the field group, with a rate of 82%. The effectiveness of the hybrid learning-based frog educational game learning media on speaking skills in speaking skills with IK (an indicator of success) is 87.5%.
Pseudo pairs based unsupervised deep hashing for image retrieval

The research presents a new deep learning framework, the pseudo-pair-based unsupervised deep hashing (PPUDH), designed to enhance image retrieval systems. PPUDH employs a soft clustering approach that iteratively trains clusters with strong discriminative capabilities and creates binary codes (BCs) with heightened correlation sensitivity. These clusters are then amalgamated to form an additional distribution for deriving hash codes (HCs). The model undergoes optimization via standard stochastic gradient descent (SGD). This optimization process marries the reconstruction loss from the encoder tasked with auto-reconstruction with the loss incurred from meeting binary code requirements. The efficacy of PPUDH has been validated through comprehensive evaluations of three renowned datasets. The outcomes of these tests demonstrate that PPUDH offers a considerable advancement over existing top-tier methods in the field.
Object detection algorithm based on channel-spatial-skip connection attention mechanisms

A channel-spatial-skip connection attention mechanism(CSSCAM) is proposed to address the problems of low detection accuracy and high miss rate for small objects in one-stage object detection models. The CSSCAM technique combines spatial attention and channel attention mechanisms, and can be easily integrated into any object detection model with flexibility, such as YoloV4, to improve its performance. The attention mechanism is improved in two aspects: firstly, Separate fusion of channel attention and spatial attention is performed to extract more complementary features. and secondly, To address the issue of information loss resulting from multiple convolutions in the model, a skip connection mechanism is incorporated to link shallow detail information with deep semantic information. We selected four typical object detection algorithms and conducted comparative experiments on public datasets such as PASCAL VOC2007 and PASCAL VOC2012. Our approach yields significant performance improvements for object detection models, particularly on the PASCAL VOC2007 datasets, as evidenced by the experimental results. After embedding this method into YoloV4, the mAP index of the original model increased by 4.87%.
A Study on Image Production in Mixed Reality Spaces Using 3D Anamorphic Illusion

This study aims to gain insights into effective methods for image production based on the "Presence Theory" at a time when the use of mixed-reality outdoor advertising with 3D anamorphic technology is progressively on the rise. 3D anamorphic outdoor advertising possesses non-material characteristics as it coexists in both physical and virtual environments. In this way, dematerialized spaces induce a sense of presence in people, and the determinants that trigger this presence were categorized into "content realism" and "expressive realism". The analysis of the correlation with presence revealed that both content realism and expressive realism have a significant impact on the sense of presence. When measuring the presence effect, it was found that with higher levels of expressive realism, even if it contains unrealistic content, greater levels of presence effects from enjoyment, memory and arousal are observed. Based on these research findings, methods for effective visual production in mixed reality using 3D anamorphic technology have been proposed. The significance of this study lies in examining 3D anamorphic mixed reality spaces from the perspective of the Presence Theory, and in offering presence-based visual production methods.
Concept of Workplace Ostrscism under the Labor Standards Act and Effective Meaning of Preventive Education

The revision of the Labor Standards Act in 2019 legalized behavioral requirements and measures to ban workplace bullying, however in particular, the law has not effectively alleviated the phenomenon of workplace ostracism. Furthermore, academia and the media tend not to observe disharmony among members separately instead of focusing on abuse or unfair instructions by managers, called Gapjil. This paper reviews the concept of ostracism included in workplace bullying under the revised Labor Standards Act in 2019 and contributes to the preparation of measures applicable to actual workplaces by analyzing characteristics distinct from general abuse. To this end, I first consider domestic and international studies on the causes and attributes of workplace ostracism, introduce the research content discussed before the revised Labor Standards Act in 2019, and explain the nature of bullying in the workplace in light of the relevant regulations. As an alternative, this research considers the physical and mental integrity of workers as protective legal interests and emphasizes preventive education that alleviates the discriminatory factors prevalent in the workplace rather than post-processing that incompletely restores the damaged state.
Extraction of Key Frames from Dance Videos and Movement Recognition by Multi-feature Fusion

Dance videos contain numerous complex movements that make movement recognition difficult. The multi-feature fusion method proposed in this paper first takes color and texture features of video frames for clustering, and then extracts key frames. Based on the key frames, spatial and temporal features, respectively, are extracted by using long short-term memory and a three-dimensional convolutional neural network. A multi-feature fusion method designed for movement recognition is used in experiments conducted with both the DanceDB dataset and a selfbuilt dataset. The results show that the proposed multi-feature fusion method has high recall and precision ratios for key frame extraction, and achieved recognition accuracy of 42.67% and 50.64% with DanceDB and the self-built dataset, respectively. This paper validates the effectiveness of the proposed method for key frame extraction and movement recognition in dance videos, and suggests potential practical applications. The approach improves the reliability of video processing and provides theoretical support for further research on deep learning methods in the field of video processing.
The Utilization of Blockchain Technology for Mitigating The Prevalence of Counterfeit Electronic Drug Prescriptions

The use of electronic or digital prescription has been widely implemented in several countries. However, even though electronic or digital prescriptions are used until now, some research reported there are still irregularities in the use of electronic prescriptions. Some of the previous studies mentioned the utilization of blockchain technology for issuing electronic drug prescriptions to improve the quality and reliability of electronic drug prescriptions, and to minimize the falsify the electronic drug prescription. Previous research reported there has been conducted directly practicing on blockchain technology still infancy. Therefore, this study tries to conduct a simulation trial using an existing prototype of blockchain technology. The sample data used uses several real digital prescriptions, but some important information is masked to maintain the privacy of the patient. The research began with interviews with several active doctors to be able to validate the problems of electronic prescriptions, and the resulting validated business process for issuing electronic drug prescriptions. After that, the prototype was tested, and it was found that blockchain technology (Multichain) can be used to record issuing electronic drug prescriptions. The positive things found from the results of this trial are electronic drug prescription tracking, and open electronic drug prescription. During the trial, there were also obstacles, namely that it was not possible to correct the data that had been inputted because of the nature of blockchain technology, incorrect data could not be deleted. In general, the prototype test results show that blockchain technology can be used for issuing electronic drug prescriptions.
Evaluation of Large Scale Language Models on Solving Math Word Problems with Difficulty Grading

With the advancement of artificial intelligence, large language models have achieved remarkable success, particularly in natural language processing. However, their performance in solving Math Word Problems (MWPs) has been challenged by the difficulty of these problems, especially in high-difficulty scenarios. This research aims to evaluate the answer accuracy of large language models, taking GPT-3.5 as an example, in MWP tasks, focusing on graded difficulty levels of the problems. We transform the existing math23K dataset into a math application problem dataset with multiple difficulty levels, aiming to reflect the diverse complexities of real-world application scenarios. Through systematic experimental analysis, we delve into the performance of these models on problems of varying difficulty and reveal the relationship between model performance and problem difficulty. Experimental results indicate that these large language models excel in solving simple and moderately low-difficulty problems but exhibit a general decline in performance when faced with moderately high and complicated problems. Furthermore, our study unveils that models are more susceptible to the complexity and ambiguity of problem descriptions, particularly in high-difficulty scenarios.
Interdependent Mission Impact Assessment of an IoT System with Hypergame- heoretic Attack-Defense Behavior Modeling

Mission Impact Assessment (MIA) is a critical endeavor for evaluating the performance of mission systems, encompassing intricate elements such as assets, services, tasks, vulnerability, attacks, and defenses. This study introduces an innovative MIA framework that transcends existing methodologies by intricately modeling the interdependencies among these components. Additionally, we integrate hypergame theory to address the strategic dynamics of attack-defense interactions. To illustrate its practicality, we apply the framework to an Internet-of-Things (IoT)-based mission system tailored for accurate, time-sensitive object detection. Rigorous simulation experiments affirm the framework's robustness across a spectrum of scenarios. Our results prove that the developed MIA framework shows a sufficiently high inference accuracy (e.g., 80 %) even with a small portion of the training dataset (e.g., 20–50 %).
PENERAPAN MEDIA KARTU KATA BERGAMBAR UNTUK MENINGKATKAN SKILL MEMBACA ANAK USIA DINI DI RA ALWASLIYAH BANGUN 17 KECAMATAN GUNUNG MALELA KABUPATEN SIMALUNGUN

The purpose of this study was to analyze the application of picture word card media to improve early childhood reading skills in Ra Alwasliyah Bangun 17, Gunung Malela District, Simalungun Regency. Learning media is an important aspect that supports the learning process for early childhood in order to develop all aspects of development for educational preparation at the next level. Very many learning media have been applied and developed in early childhood education, therefore this study aims to describe early childhood learning media. This type of research is qualitative research. The process of data collection in this type of research is: observation and interview. The results of this study explain that so many learning media can be applied by educators according to certain aspects such as aspects of vocabulary development, cognitive, speaking, creativity, numeracy, language, social emotional, physical motor and reading. One of the learning media that plays an active role in developing early childhood reading skills is picture word card media. Thus, the application of picture word card media in Ra Alwasliyah Bangun 17 can be done well.
COMPARISON OF LOGISTIC REGRESSION, MULTINOMIALNB, SVM, AND K-NN METHODS ON SENTIMENT ANALYSIS OF GOJEK APP REVIEWS ON THE GOOGLE PLAY STORE

Today's community activities cannot be separated from the name of transportation because it makes it very easy for people's daily activities. With the existence of transportation, people can more quickly reach their destination. With the Gojek application on the Google Play Store, it will help people travel or deliver goods. To determine service quality, sentiment analysis can be used to classify reviews. The purpose of this study is to compare which method has the best accuracy, so that it can classify reviews into positive or negative sentiments. The methods tested in this study are Logistic Regression, MultinomialNB, SVM, and K-NN. Performance assessment methods include score accuracy, recall, and precision, classification reports, and confusion matrix to determine the appropriate method for classifying reviews into positive or negative categories. Of the four methods tested, the one with the highest performance is the Logistic Regression method. Accuracy, recall and precision scores of the Logistic Regression method were 82.45%, 82.49%, 82.45% and 82.43%, respectively. Classification report also shows good results. In the confusion matrix, there are 111 and 124 True positives and True negatives. There are only 22 and 28 False positive and False negative results respectively. The method that has the lowest score is K-NN, with score accuracy, recall, and precision respectively were 52.28%, 59.43%, 93, 52%, and 65.65%. Classification report shows quite bad results. In the connection matrix, it produces True positives and True negatives 130 and 19. There are only 127 and 9 False positive and False negative results respectively. The results of this study state that using the Logistic Regression method is suitable for use in classifying positive and negative reviews in the review dataset on the Gojek application on the Google Play Store.
Prototype and Application Implementation of Domestic and Foreign Labor Distribution

In today's globalized world, many people are looking for domestic and international work for various reasons, such as higher salaries and better work experience. This application will assist job seekers in finding suitable job vacancies. With their skills and interests, as well as helping companies find candidates that suit their needs, information on job vacancies in the country and abroad. In the initial phase of developing this application, an application prototype will be created that includes the main features of this application. These features include information on job vacancies in the country and abroad, a job search system based on skills and interests, and a system design method using UML (Unified Modeling Language) design. This modeling is a system implementation of how to put a strategy into an image (visual) in the form of a diagram. This model facilitates the making of an application program or implementation and can be used in the long term, not only at this time but continuously and continuously. Because application programs are used for a long time, it is necessary to have a good and precise analysis of planning, design, and modeling, such as flow for application programs. In this study, using the UML method, submitting online job applications, as well as a notification.
Enhancing Speaking Proficiency through Self-Discovery: Utilizing Johari Window Techniques in Student Learning

Effective speaking hinges on confidence and comfort, yet many students grapple with these aspects, often feeling lost and unsure about their abilities. This study investigates the application of Johari Windows to aid first-year English Program students at IAIN Parepare, who commonly encounter challenges in speaking. Our research focuses on two primary questions: How does the Johari Window facilitate students' self-discovery, and how does it contribute to the development of their speaking skills? Adopting a mixed-method approach, this study blends quantitative and qualitative data. We engaged 48 first-year English Program students at IAIN Parepare, utilizing observations, questionnaires, and focus group discussions as our primary data collection tools. Additionally, the data were analyzed using SPSS. Our findings reveal that many students exhibit uncertainty or neutrality in identifying with the characteristics and traits of the 'open self,' highlighting the complexities of self-awareness and the fluid nature of self-perceptions, which vary among individuals. The study also notes differing self-views across various characteristics. Crucially, implementing the Johari Window in speaking skill development, particularly in forum discussion groups, has significantly bolstered students' awareness of their public speaking strengths and weaknesses. This enhanced awareness has been instrumental in their journey toward becoming more confident and effective speakers.
A Multi-Scale Feature Attention Image Recognition Algorithm

The accuracy of small-sample image classification depends on the ability of neural network models to extract image representations from sample data. A small-sample image classification system based on attention mechanisms and meta-learning is suggested in order to extract more comprehensive information from pictures. The approach may extract richer multiscale features from pictures and enhance classification outcomes through meta-learning because the attention mechanism of multiscale features can concentrate on the data in the sample feature space. In order to demonstrate the efficacy of the suggested strategy, tests are conducted on the two industry-standard datasets miniImageNet and tieredImageNet for both 5-way 1-shot and 5-way 5-shot tasks. The results are compared with the best existing methods.
Live Demonstration: Speech reconstruction for speech impaired individuals using Assistive Magnetic Skin System

People diagnosed with a speech-sound disorder (SSD) may have any combination of problems creating or forming speech sounds needed to communicate with others, resulting in frustration, depression and social isolation. Enhanced by advances in artificial wearable skins and machine learning, natural verbal communication for SSD afflicted individuals is more feasible than ever before. An Assistive Magnetic Skin System (AM2S) is designed to assist speech impaired individuals to communicate with others by reading the mouth movement. The system measures the changes in the magnetic skin patches attached next to the lower lip through magnetic field sensors embedded in Magnetphones. The measured magnetic field signal data is processed using a Fine k-Nearest Neighbor (KNN) classifier model. Ultimately, the classified data can be deployed visually on a display or verbally on speakers. AM2S successfully identifies full English alphabets with an average accuracy rate of 94.96%.
Advanced Recursive Spherical Quantization for Image Retrieval

In recent years, quantization technology has proven to be very effective in the field of supervised image retrieval, owing to its capacity to provide both high accuracy and swift retrieval speeds. However, the challenge of balancing retrieval accuracy and speeds, as denoted by variable code lengths, persists as a fundamental consideration across different applications. Recently, there are a few methods that employ recursive strategies for quantization encoding, and there remains substantial room for improvement in their effectiveness. In this paper, we introduce the concept of a Recursive Spherical Quantization (RSQ) architecture, capable of generating sequential binary codes. It can reuse unit vector codebook to recursively quantize real vectors. Empirical findings from benchmark datasets demonstrate that our model is competitive or even superior performance in image retrieval, while simultaneously demanding a significantly reduced number of parameters.
Effectiveness of English Debating In Teaching Speaking For Senior High School Students

The aim of this study is to find out the effect of using debating toward students’ motivation to speak English. The populations of this study were the second year students of MAN Dompu consists of 7 classes so the total number of them is 193. To facilitate it, the researchers used random sampling consisting of 32 students. They were divided in two groups namely experimental and control group, to collect the data the researcher used two tests they were pre-test and post-test, the researcher gave different treatment for experimental and control group. From the result of the test, it can be seen that the mean score of experimental was (24.36) is higher than the control group (15.96). Then it was continued to analyze both mean score by using t-test. The result shows that t-test is (1.63), the computation of correlation between the mean scores of the groups has a result of (8.40). Based on the result of the data analysis the alternative hypothesis which said that “The English debating has positive effect in teaching speaking of the second year students of MAN Dompu” is accepted, and the null hypothesis which is said, “The debating has not positive effect in teaching speaking of the second year students MAN Dompu” is rejected. This study quantitatively investigated the effectiveness of English debating program in teaching English. Similar studies which qualitatively explore English debating in teaching English are highly recommended