Reviewerly/exHarmony huggingface dataset:
HEALTH MONITORING TO FALL DETECTION USING DEEP LEARNING TRANSFER

Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This article provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.
VALUES IN THE PROCESS OF CHANGE IN THE FIELD OF EDUCATION IN SLOVENIA

Values are final lines that separate a person from society, whereas a person may be forced to prioritize his or her own values to social-based values. Society influences values and children are educated in those underlying values, which exist in certain society. Therefore, even transmitted knowledge cannot be seen as neutral. We argue that values in education must represent an important topic to practice intellect by establishing dialogues between students; implanting cultural values and contributing to the person’s spiritual and ethical development. In the presented study we focus on the review of the bibliographic sources in Slovenia to analyse which values Slovenia prioritizes, because those values may be reflected in the Slovenian education. The key findings show that values in education are changing, their priority is changing, and some are disappearing altogether. Resource reflection gives us important information on how to introduce learning and socialization of values into the educational processes.
MENINGKATKAN MOTIVASI DAN AKTIVITAS BELAJAR SISWA DI KELAS VII.C MTsN PANINJAUAN DENGAN MENGGUNAKAN MODEL PEMBELAJARAN KOOPERATIF TIPE JIGSAW

AbstractGood motivation is a key for success. Equipped with good classroom activities, it will be easy for students to increase their result. In grade VII MTsN Paninjauan, Some student have got good result in study, except for students in VII.c. To increase their result, we have to increase their motivation and classroom activities. All of this effort, we do in a series of activities in classroom action research that we do in two cycle activities. Base on first observation, the model that suitable for student is cooperative learning type Jigsaw. As the result, we find that student motivation and classroom are increase. It is also followed by increasing of their study result
PENGARUH KECERDASAN EMOSIONAL TERHADAP KEMAMPUAN MENULIS ARGUMENTASI SISWA DI SMP NEGERI 1 TANJUNGBALAI

Writing arguments as one type of writing that aims to express ideas or opinions based on perceived experiences and observations. In addition, lifting things closer to the students makes students easy in expressing their ideas because the emotional role is very tight there. The purpose of this study is to prove the Influence of Emotional Intelligence Against the ability of writing arguments for students of SMP 1 Tanjungbalai. The population of this study were students of SMP 1 Tanjungbalai with a sample of 64 grade VII students from the school. The data analysis used linear regression and correlation test to find out the functional relationship and the closeness between the two variables. The results of this study showed that emotional intelligence significantly influence the ability of writing arguments. Meanwhile, the relationship between emotional intelligence and the ability of writing arguments is very high with correlation coefficient 0.945
PENGARUH PENERAPAN STRATEGI PEMBELAJARAN PQ4R TERHADAP HASIL BELAJAR SISWA KELAS IX MTsN 1 PESISIR SELATAN

This Research was based on student’s mathematics learning outcome was still under Minimum Achievement Criteria (KKM). The research was aimed at finding out whether student’s mathematical learning outcome by implementing PQ4R strategy was better than student’s mathematics learning outcome by implementing conventional learning at class IX MTsN 1 Pesisir Selatan. This was an experimental research with research result is random to subject. The population was all students at IXMTsN 1 Pesisir Selatan. Sampling classes were class IX.6 as the experimental class and IX.5 as the control class. The instrument was final test with its reliability 0,98. Technique of data analysis used one-tailed t-test. Based on the result of data analysis, it is concluded that student’s mathematical learning outcome by implementing PQ4R strategy was better than student’s mathematics learning outcome by implementing convensional learning.
Several Appropriate Statistical Techniques in Research

This paper has the main purpose to assist researchers and students in choosing the appropriate statistical test for studies that examine one variable or more variables. The aim of this article is to consider the role which statistical methods can sensibly take in some relevant fields. Other objective of writing this paper is to provide the algorithm of choosing the statistical techniques in the data analysis. This article is concentrated to define data analysis and the concept of data preparation. Then, the data analysis will be discussed. The article covers a brief outline of the variables, an understanding of quantitative and qualitative variables and measure of central tendency. This article will also try to apprise the reader with the basic research techniques that are utilized while conducting various studies. Finally, there is a focus on parametric and nonparametric tests for data analysis and various strategies in this concept.
Teaching students in high school using multimedia technologies

The peculiarities of the training of specialists in higher education using multimedia technologies are clarified and their importance and necessity for modern education are shown. Based on the recorded didactic and technical pedagogical capabilities of multimedia, we will identify potential functions that can be differentially implemented in the educational space. Manipulation, deformation, contamination, toning of images, discrete presentation of audiovisual information is considered; fixation of a selected part of visual information for movement, its further examination "under a magnifying glass", demonstration of processes and events in the real-time, multi-window presentation of audiovisual information, which intensify the educational process and strengthen the motivational effect in the educational field. Research and experimental work were carried out, which made it possible to find out the peculiarities of the training of specialists in higher education using multimedia technologies and to prove their importance and necessity for modern education. We have reasonably identified the stages that are important for the educational space for the qualitative application of knowledge and skills in the training of specialists; advantages of the introduction of multimedia technologies; various aspects of the use of computer software multimedia tools in the process of professional training of a specialist are named.
The Importance of Employee Trust as a Mediating Job Satisfaction, Employee Relations and Employee Performance

Purpose – The aim of the study sought to ascertain the impact of employee relations and job satisfaction. Relations with mediation of Employee Trust on employee performance at the delivery service provider company PT TAB in Indonesia. Methodology/approach – These studies employ quantitative descriptive methods. Using a quota sampling type with a non-probability sampling technique, there are 203 couriers at DKI Jakarta who have been employed for over a year. SEM-PLS data analysis with the SmartPLS 3.2.9 program. Findings – According to research, job happiness and employee relations have a favorable and strong direct relationship to employee performance, whereas employee trust has no bearing. Another conclusion was that there was no evidence for an indirect association between work satisfaction and employee relations on employee performance. Novelty/value – It is recommended that the company maintain and maintain the current svstem and promotions and consider reviewing matters related to the distribution of information and policies within the company and continue to consider the recruitment and training system to improve the capabilities of couriers at PT TAB.
Generative Adversarial Network and ResNet Comparison for Video Super Resolution in Smartphones

With the latest evolutions of smartphone hardware regarding neural network processing and advances in Image Super Resolution, one question remains: What is the applicability of SR techniques already consolidated in smartphones? Several works focused on different characteristics of images have been developed and seek to understand how such networks can meet different segments. This work focuses on applying two already consolidated networks in the processing of photo-realistic images, SRResNet and SRGAN, in mobile devices to identify how such networks behave in smartphones of different generations, either in the image quality or in the response time of these networks. After evaluation, the SRResNet network had a better performance both in inferred image quality and lower latency, with a PSNR of 27.7075 versus 21.3843 and 0.19 milliseconds latency, compared to 0.20 milliseconds for SRGAN, thus showing that it is feasible to apply SR techniques already consolidated in smartphones.
Treatment of mentally disordered offenders in Italy: prisons and REMS

In Italy, the treatment of dangerous patients found not guilty by reason of insanity (NGRI) underwent a radical reform in 2014 with the Law 81, that promulgated the closure of secure hospitals, Ospedali Psichiatrici Giudiziari (OPG), that provided psychiatric care within a secure setting. OPG were replaced by new psychiatric facilities called Residence for the Execution of a Security Measure (REMS) which currently host NGRI patients who cannot be sent to prison. Eight years after the creation of the REMS, many questions still need to be addressed in relation to their functions, limitations, and cooperation with the other services. This article describes the process of deinstitutionalisation of psychiatric care in Italy over the past forty years, which culminated in the creation of the REMS. The functioning of the REMS and some of the issues that have emerged since their creation are explored, and possible solutions are proposed. Finally, the impact of the 2014 reform on psychiatric care in Italian prisons is highlighted.
Posture Pattern Recognition Analysis in Lectures

The Study of Posture Analysis and Non-Verbal Communication plays a pivotal role in enhancing communication among individuals in various contexts. The ability to decode and comprehend messages conveyed through gestures, facial expressions, and body movements is crucial for fostering more effective and meaningful interactions. Accordingly, this present work aims to conduct an exploratory analysis of posture patterns among speakers worldwide. To achieve this, the Openpifpaf algorithm was employed in videos of lectures for pose extraction, and the K-means clustering algorithm was utilized to distinguish commonly adopted postures during this lectures. The evaluation regarding the representativeness of keyposes involved an online questionnaire in which participants were asked to classify certain speaker poses into one of the clusters. The results revealed that the K-means algorithm achieved an accuracy rate of 85.71%.
Cross-Database in Deepfake Detection Based on a Convolutional Neural Network and Vision Transformer

The proliferation of Deepfake techniques has raised concerns due to their potential to generate misleading multimedia content, leading to ethical, social, and political implications. In response to this emerging issue, collaborative efforts between academia and leading technological entities have committed on developing robust detection methods. Initially, Convolutional Neural Networks (CNNs) were prominent, recently proposed methods, which combine features of CNNs with Vision Transformers (ViT) have shown improved performance. This research centers on evaluating the generalization capacity of these advanced models by subjecting them to cross-database tests with different datasets than those used in their training phases. Our analysis reveals that while both models perform well on known datasets, they face challenges related to overfitting when transitioning to new datasets. Consequently, this study underscores the need for further research in Deepfake detection, ensuring its adaptability and effectiveness in diverse scenarios.
Comparing U-Net based architectures in monocular depth estimation

Monocular depth estimation is a computer vision problem which has diverse applications ranging from augmented reality to surgical procedures. Given the similarity between the segmentation and monocular depth estimation tasks, in addition to the good performance of the U-net network and its variations in the segmentation task, this study aims to compare the performance of variations of U-Net and UNet++ architectures, each one adopting a different network as encoder, and the TransUnet architecture in monocular depth estimation. The results achieved on the NYU Depth V2 dataset shows that U-Net using Mix Transformer (MiT-B2) as encoder outperforms all other evaluated approaches.
Detection of Breast Cancer through Histopathological Images using Deep Learning

One of the most prevalent occurrences of cancer amongst women is that of the breast cancer. The occurrence of this cancer in the female body leads to the impairment of various mental and physical related health issues. A large amount of complexities are however related to the presence of this disease which majorly includes the processing of images and its detection through image processing techniques. In addition to image processing techniques; various deep learning and transfer learning based techniques and algorithms can also be used in order to determine the same. For the purpose of implementation of the proposed research study; the author of the paper tends to utilize the Kaggle dataset which comprises of detailed information of breast cancer images which are obtained using the histopathological process. The dataset comprises of 800 cancer images and 250 healthy images which are undetermined with tumor. The entire process of defining the image takes place through the normalization technique using the concepts of histogram. Further, the author proposes to implement the working theory of CNN as a deep learning algorithm and Dense-Net-121 as transfer learning based algorithm. The transfer learning based algorithm however uses a pre-trained model to implement the same and further hyper-parameter tuning is also performed so that higher values of accuracy and precision can be obtained.
Prediction Analysis of Pancreatic Tumour using Transfer Learning

Pancreatic cancer is one of the most common types of cancer that affect men. The development of this cancer in the male body impairs a number of health difficulties, both physical and mental. The existence of this illness is associated with a great deal of complexity, mostly linked to image processing and image detecting methods. Different deep learning and transfer learning based approaches and algorithms can be employed to ascertain the same, in addition to image processing techniques. The author of the work typically uses the Kaggle dataset, which has precise information on images of pancreatic cancer that were collected through the data collection process, in order to carry out the proposed research study. 750 photos of cancer and 150 healthy, tumor-undetermined images make up the dataset. Using the notions of a histogram, the normalization technique defines the image in its entirety. In addition, the author suggests using YOLO as a transfer learning-based method and CNN's working theory as a deep learning algorithm. However, the transfer learning-based technique implements the same using a pre-trained model, and in order to get higher accuracy and precision values, further hyper-parameter tuning is also carried out.
Edge-Based Real-Time Sensor Data Processing for Anomaly Detection in Industrial IoT Applications

The Industrial Internet of Things (IIoT), which uses devices with sensors to provide real-time insights into crucial processes, has completely changed how industries function. However, there are many problems associated with the sheer volume and speed of data created in industrial environments, particularly when it comes to anomaly detection. The development of edge-based real-time sensor data processing techniques was required because traditional cloud-based solutions frequently experience latency problems and privacy issues. This study suggests a novel method for IIoT applications that focuses on processing sensor data at the edge, close to the data source, for anomaly identification. We offer real-time analysis of sensor data without the need for continuous data transfer to the cloud by utilising the processing capabilities of edge devices, such as industrial gateways and embedded systems. To find anomalies in streams of real-time sensor data, our methodology integrates data pre-processing, feature engineering, and machine learning algorithms. This strategy not only lessens the strain on the network's bandwidth but also ensures quick reaction to urgent situations, cutting downtime and boosting operational effectiveness. Proposed system has adaptive learning features that enable it to continuously adjust to altering ambient factors and sensor properties, enhancing the precision of anomaly detection over time. We provide experimental findings that show how our edge-based anomaly detection system performs well in diverse industrial situations. The results show that, while protecting data privacy and minimising latency, our methodology outperforms conventional cloud-based methods in terms of anomaly detection performance.
Sosialisasi Pengenalan dan Cara Mengembangkan Knowledge Management System (KMS) pada Institusi Pendidikan

A knowledge base is one component that is needed in any field and institution. As is known, especially in the field of education, several educational institutions, both private and state, have different rules and academic systems. One of them is an academic information system that can be applied to elementary schools. Basically, every educational institution must have an academic information system, but sometimes what is the manufacturing process or system flow like? Many educational institutions still use Ms.Office, especially Ms.Excel, in processing the results of academic activities that have been carried out and are still limited to grade processing. This socialization is more about introducing how an educational institution can develop knowledge for an academic information system by utilizing the Knowledge Management System (KMS). From the results of socialization, especially in Tambaksari Village, Pedes Karawang, there are higher education institutions and other similar institutions such as schools and so on, they can develop an academic information system that has appropriate values to be more efficient.Â Keywords: Knowledge base, knowledge management, knowledge management system, education
Android-Based Cryptography Applications Using The Rail Fence Cipher Algorithm

Android-based cryptographic applications using the rail fence cipher algorithm are made using the Java programming language (AndroidStudio), the PHP programming language (JSON File Web Service) and the MySQL database. This application has a url link for storing PHP files and message data in the database, while the url link is http://railfencecipher.my.id/. An Android-based cryptographic application using the rail fence cipher algorithm has been uploaded to Google Playstore by entering the rail keyword fence cipher. An android-based cryptographic application using the rail fence cipher algorithm can be used to assist communication between the two parties, both the sender and the recipient of the message. In addition, this application has implemented a rail fence cipher algorithm that is used to maintain the confidentiality of the message stored in the application's database. The records in the message body column in the database are in random form, therefore they cannot be read. This of course makes the application maintain the confidentiality of the messages sent, because there is a process of scrambling (encryption) of the message. Based on the results of the tests that have been carried out, the functionality of the android-based cryptographic application using the rail fence cipher algorithm runs well as expected and the message sent between the recipient and the sender is kept confidential because the message is encrypted.
A Modular Encryption Framework in Cloud and Mobile Environments for Cybersecurity Solutions in Health Information

The rapidly evolving landscape of healthcare information systems, ensuring the security and privacy of sensitive health data is paramount. This abstract introduces an encryption framework designed for the unique challenges posed by cloud and mobile environments, offering robust cybersecurity solutions for health information. The framework integrates cutting-edge encryption technologies tailored to the specific requirements of both cloud-based storage and mobile devices, addressing the dynamic nature of healthcare data access. In cloud environments, the framework employs advanced encryption algorithms to protect data at rest, in transit, and during processing. Additionally, it leverages key management strategies to ensure secure access control and mitigate unauthorized breaches. For mobile platforms, the framework focuses on end-to-end encryption, safeguarding health information across diverse devices while considering the inherent vulnerabilities of mobile networks. Furthermore, it incorporates secure authentication mechanisms to fortify access points and prevent unauthorized entry. This comprehensive Modular encryption framework represents a proactive approach to healthcare cybersecurity, acknowledging the critical importance of safeguarding patient data. By seamlessly integrating encryption measures into both cloud and mobile environments, it provides a robust solution to the multifaceted challenges in health information security, fostering a resilient and protected digital ecosystem for healthcare providers and patients alike.