Reviewerly/exHarmony huggingface dataset:
Vicinal Feature Statistics Augmentation for Federated 3D Medical Volume Segmentation

Federated learning (FL) enables multiple client medical institutes collaboratively train a deep learning (DL) model with privacy protection. However, the performance of FL can be constrained by the limited availability of labeled data in small institutes and the heterogeneous (i.e., non-i.i.d.) data distribution across institutes. Though data augmentation has been a proven technique to boost the generalization capabilities of conventional centralized DL as a “free lunch”, its application in FL is largely underexplored. Notably, constrained by costly labeling, 3D medical segmentation generally relies on data augmentation. In this work, we aim to develop a vicinal feature-level data augmentation (VFDA) scheme to efficiently alleviate the local feature shift and facilitate collaborative training for privacy-aware FL segmentation. We take both the inner- and inter-institute divergence into consideration, without the need for cross-institute transfer of raw data or their mixup. Specifically, we exploit the batch-wise feature statistics (e.g., mean and standard deviation) in each institute to abstractly represent the discrepancy of data, and model each feature statistic probabilistically via a Gaussian prototype, with the mean corresponding to the original statistic and the variance quantifying the augmentation scope. From the vicinal risk minimization perspective, novel feature statistics can be drawn from the Gaussian distribution to fulfill augmentation. The variance is explicitly derived by the data bias in each individual institute and the underlying feature statistics characterized by all participating institutes. The added-on VFDA consistently yielded marked improvements over six advanced FL methods on both 3D brain tumor and cardiac segmentation.
Meningkatkan Minat dan Hasil Belajar Bahasa Indonesia melaui Penerapan Belajar Kelompok Siswa Kelas IX.A SMP Negeri 17 Mataram

The goal to be achieved from this research is to find out Increasing Interest and Learning Outcomes of Indonesian Language through the Application of Study Groups for Class IX.A Students of SMP Negeri 17 Mataram in the Academic Year 2022/2023. This research is a classroom action research. The subjects in this study were 32 students of class IX.A SMP Negeri 17 Mataram with 20 male students and 14 female students. Data collection techniques used are observation, documentation, and tests. Observations are used to collect data on learning interest. The test is used to measure learning achievement in Indonesian subjects. Data analysis used is descriptive qualitative and descriptive quantitative. The increase in students' cognitive learning outcomes before and after the implementation of group learning at SMP Negeri 17 Mataram can be seen from the percentage gain of each indicator, namely: The average value of the initial data students obtained 74.10% increased to 77.69 in cycle I and obtained 82.84 in cycle II. While students who completed the data were 17 people then obtained 25 in cycle I and experienced an increase to 30 people in cycle II with each classical completeness obtained initial data from 64.10 to 78.13 in cycle I and increased to 193.75 in cycle II, this shows that all the indicators expected by the researcher on the reading ability of grade I students were successfully achieved. Lesson planning carried out by the teacher increased from 75.00% in cycle I and 85.00% in cycle II. So the lesson planning carried out by the teacher is very good. While the implementation of learning carried out by the teacher also experienced an increase from 79.17% in cycle I to 87.50% in cycle II. So the implementation of learning carried out by the teacher has also increased. Student learning activities that the average percentage in the first cycle is 80.00%. But in cycle II, student learning activities increased to 90.00%. So student learning activities in learning Indonesian are very good.
The Effectiveness of Teaching English Vocabulary Using Dictionary Online Media in STTR Cepu in Academic Year of 2020-2021

The Covid-19 pandemic, apart from providing a bad experience, can also make someone challenged and offer to develop learning but in Engineering English lectures. At first the learning was carried out face-to-face, this time it was carried out boldly. Vocabulary is a basic and important knowledge in Mastering English. One of the vocabulary learning strategies during the pandemic is through online media. This research was conducted in order to determine the effectiveness of vocabulary learning by using an online dictionary for Mechanical Engineering Students STTR Cepu. This study used a descriptive approach. There are 30 students as respondents. Based on the questionnaire and strongly agree by 43.3%, agree by 46.7% say disagree and strongly agree. So that it can be said that the effectiveness of learning vocabulary using online dictionaries is weak in mechanical engineering students at STTR Cepu.
THE EFFECTIVENESS OF USING RANDOM TEXT STRATEGY IN TEACHING READING

This pre-experimental research aimed at finding out whether or not the use of Random Text Strategy is effective to improve the students’ literal and interpretative reading comprehension at the 7th grade Students of SMPS Ahlu Shuffah Muhammadiyah, Bantaeng Regency, Province of South Sulawesi. The sample of this research was the 7th grade Students of SMPS Ahlu Shuffah Muhammadiyah.The researcher used purposive sampling method because that class was able to fulfill the rules and they had the characteristics that the researcher wants. Hence, the researcher want to overcome the students’ problem in reading and improve their reading skill through the use of random text strategy then the researcher chose the class with the low English learning achievement compared with the others classes especially for their reading skill and find out the effectiveness of this strategy. The findings of this research shown that there is a significant improvement toward the students’ reading comprehension in the post-test compared with the students score in pre-test. The use of random text strategy is effective to improve the students' literal reading comprehension. It based on the result of the students’ reading test in pre-test compared with the students’ reading test result in post-test.
INCREASING STUDENTS’ WIRITING ABILITY THROUGH INTERACTIVE WRITING MODEL AT SMAN 1 ENREKANG

This research aimed to know the increasing students’ writing ability in descripitive and recount text through Interactive Writing Model at the first grade SMAN 1 ENREKANG. The method of this research was pre-experimental research. The sample of this research. The sample of this research consisted of experiment class. Namely class X IPA 5 was 30 students. In the class used Interactive Writing Model. The sample was taken by Random Sampling Technique. Based on the findings, it was found that the value of t-test in writing is descriptive text is greater than the ratio on t-table (3.770) &gt; (2.756) and in recount text t-test is greater than ratio on t-table also (5.420) &gt; (2.756). Based on the table it can be conclude that the alternative hypothesis is accepted. It means that there was increased in the students’ writing descriptive and recount text before and after gave treatment used interactive writing model. Therefore, that result of the pre-test and post-test was significance different. Based on the descriptive, the pre-test and post-test prove the post-test is higher. Based on the explanation, Interactive Writing Model can increase the students’ writing ability in descriptive and recount text.
Face Mask Detection System

Abstract: Since the infectious coronavirus disease (COVID-19) was first reported, it has become a public health problem in and even around the world. This pandemic is having devastating effects on societies and economies around the world. Healthcare officials from the World Health Organization and local authorities are propelling people to wear face masks as it is one of the comprehensive strategies to overcome the transmission. However, wearing a face mask that prevents the transmission of droplets in the air and maintaining an appropriate physical distance between people, and reducing close contact with each other can still be beneficial in combating this pandemic. Therefore, we focus on implementing a face mask detection model as a vision system. People violating social distancing or not wearing masks were detected using this model. It has been trained with the dataset that contains around 4000 images and compiled for differentiating accuracy to choose the best for this type of model. This solution tracks the people with or without masks in a scenario and this can be used with the existing embedded infrastructure to enable these analytics which can be applied to various verticals, as well as in an office building or at airport terminals/gates Mask has been a comprehensive strategy to overcome the transmission. So, the face mask detection model detects the face of individuals and concludes whether they are wearing masks or not at that particular moment when they are captured in the image. Existing system uses manpower. It indicates whether a person is wearing a face mask or not manually by means of manpower. Due to COVID pandemic masks are essential in public places such as airports, railways, schools, offices, shopping malls etc... So, manual checking is a hectic process. Face Mask Detection model is an application to detect people if they are wearing a mask or not. This system is capable of training the dataset of both persons wearing masks and without wearing masks. It uses python and machine learning. After training the model the system can predict whether the person is wearing the mask or not.
IOT Based Heart Attack Detection and Heart Rate Monitor

Abstract: In today's world, many people are losing their lives due to heart attacks and the shortage of specialist doctors available to take immediate action. Hence this system provides the implementation of heart rate monitoring and controlling of a patient. For this, we have used the technology called the "GSM" to detect and monitor the heart rate of a patient. In this system, the patient will be equipped with the hardware consisting of sensors and other devices for measuring the heartbeat along with the notification unit to notify and provide data in real-time. The heartbeat sensor with advance measuring technique will calculate the heartbeat of the patient, and transmit it over the internet that can be easily accessed by the patient itself and the doctors through different electronic devices such as tablets, phones, and computers. The heartbeat limits are set on a system that informs about the high and low rate of heartbeat. It also provides continuous data for analyzing the chance of an attack on a patient
IOT Based Remote Health Monitoring System for Discharged Patients

Abstract: In this automated world, biomedical field is not superior.But the rapid advancements in engineering have proven their importance in the biomedical field.Rapid development of technologies such as the Internet and Internet of Things has brought improvements in healthcare. IoT-based health monitoring system has been widely studied and developed in recent years, as they can improve the quality and accessibility of health care services, especially for patients with chronic diseases or in rural areas.The existing IoT-based health monitoring systems have some similarities and differences in terms of their hardware and software components,features, performance, etc. All the systems use sensors to measure the vital signs of patients but they use different types and models of sensors with different specifications and accuracy. This paper presents multiple improvements to the current approach by introducing a system that provides remote and continuous monitoring of patients' health status and enables early detection and intervention of potential health problems. This system utilizes Arduino nano and Wemos D1 Mini, two popular microcontrollers, collect data from various sensors attached to the patient's body and transmit it wirelessly to a central monitoring station. Moreover, the cost of this system is substantially reduced as compared to previous systems.
Accessibility barriers of wheelchair users in public spaces

Background: Wheelchair users encounter many barriers when traveling in public spaces. This research aims to explore the experiences wheelchair users have when facing physical and non-physical barriers in public areas. Methods: This study used qualitative research methods. Data were collected through observation and semi-structured in-depth interviews with six wheelchair users using purposive sampling. First, the data were analyzed using thematic analysis, and then the trustworthiness of the data was checked using triangulation of data sources and member checking. Results: Three themes were found. The first theme: accessibility barriers, includes two subthemes: Physical barriers; Non-physical barriers. The second theme: accessibility barrier strategies, consists of two subthemes: Physical barrier strategies; Non-physical barrier strategies. The third theme: the ability level of wheelchair users, consists of two subthemes: Level of independence; Level of incapacity. In this study, wheelchair users face accessibility when accessing public spaces. The emergence of barriers makes wheelchair users create strategies to deal with them to make public spaces more accessible for wheelchair users.
Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space

We consider the reinforcement learning (RL) problem with general utilities which consists in maximizing a function of the state-action occupancy measure. Beyond the standard cumulative reward RL setting, this problem includes as particular cases constrained RL, pure exploration and learning from demonstrations among others. For this problem, we propose a simpler single-loop parameter-free normalized policy gradient algorithm. Implementing a recursive momentum variance reduction mechanism, our algorithm achieves $\tilde{\mathcal{O}}(\epsilon^{-3})$ and $\tilde{\mathcal{O}}(\epsilon^{-2})$ sample complexities for $\epsilon$-first-order stationarity and $\epsilon$-global optimality respectively, under adequate assumptions. We further address the setting of large finite state action spaces via linear function approximation of the occupancy measure and show a $\tilde{\mathcal{O}}(\epsilon^{-4})$ sample complexity for a simple policy gradient method with a linear regression subroutine.
Discussion Paper: The Threat of Real Time Deepfakes

Generative deep learning models are able to create realistic audio and video. This technology has been used to impersonate the faces and voices of individuals. These ``deepfakes'' are being used to spread misinformation, enable scams, perform fraud, and blackmail the innocent. The technology continues to advance and today attackers have the ability to generate deepfakes in real-time. This new capability poses a significant threat to society as attackers begin to exploit the technology in advances social engineering attacks. In this paper, we discuss the implications of this emerging threat, identify the challenges with preventing these attacks and suggest a better direction for researching stronger defences.
Book review of Mills, K. A., Unsworth, L., &amp; Scholes, L. 2022. Literacy for Digital Futures: Mind, Body, Text

Their volume is presented in three parts: Mind and Materiality; Body and Senses; and Texts and Digital Semiotics (as stated in the title), and is relevant for educational sectors, especially literacy educators and students of digital media.This volume "assists new and seasoned researchers in rethinking dynamic changes in the materiality of texts and their implications for the mind and body.
Post Shannon Information Theory

&lt;p&gt;We all know and often use compression software, why the compression software cannot reduce the file size after compressing the compressed file, and even multiple compressions will make the file size larger. Through research on compression software and compression algorithms, we found that based on previous theories, data compression has a limit. This article provides a new theory, expounds a new mathematical method, and implements a new compression algorithm, which can compress files multiple times and accumulate the compression rate, for example, compressing a 1Gb file into 1Kb. &lt;/p&gt;
Embedding Digital Fluency in Our Courses: Moving from Theory to Practice

An increasing amount of literature from academic, governmental and non-governmental organizations (Kluzer, Reyna, Hanham, &amp; Meier, 2018; Kluzer &amp; Pujol Priego, 2018, etc.) points to the need for post-secondary institutions to do more to prepare students to be fluent in the consumption, understanding and use of digital media and tools. Digital fluency is increasingly seen as an essential skillset for graduates’ employability and for their citizenship, but while there is strong consensus about the need for students to be more digitally fluent, there is less consensus about what digitally fluency is, or about how to teach it.&#x0D; This paper will offer a working definition of digital fluency and describe an approach to fostering student digital fluency development that can be applied to a wide range of courses. Drawing on frameworks by various governmental and non-governmental organizations, as well as current scholarship in the field of teaching digital literacy (Ng, 2012.; Caufiled, 2017, Ungerer, 2016, Hinrichsen &amp; Coombs, 2013, etc.), the paper will provide an explanation of the rationale behind the framework tool that the author has developed and the paper will conclude with an explanation of how to use the tool to embed specific and authentic digital fluency skill development at the course level.
Continuing education in times of Covid-19

The chances of contamination of health professionals who work in symptomatic patients are high. The nurse sees in his role as caregiver, leader and educator, a reference in the team, in the prevention of COVID-19. The study is a bibliographical, systematic, and reflective review, using the descriptors Nursing, Continuing Education and COVID-19, accessing and selecting data in the Virtual Health Library. The need to update and modify the practices of health professionals made Permanent Education (PE) a health policy, turning the theme into a challenge to be faced by all health categories. The arrival of the COVID-19 pandemic required a rapid response, especially in diagnosis and preparation to receive the demand. The need to work with the techniques of contagion prevention, especially with the paramentation and undressing, demanded from the Nursing revision of techniques, in the use of the main individual protective equipment. Finally, the review and correction of contagion prevention techniques should be a daily practice, using PE as a resource to promote educational processes.
APLIKASI MODEL ARIMA GARCH DALAM PERAMALAN DATA NILAI TUKAR RUPIAH TERHADAP DOLAR TAHUN 2017-2022

The Indonesian rupiah (IDR) exchange rate is used to gauge Indonesia's economic stability. Maintaining the IDR exchange rate's stability is critical since it has a direct impact on Indonesia's national monetary situation, particularly during the Covid-19 pandemic. Forecasting the rupiah exchange rate is important to do and is one way to assess government policy. The data series to be used here are IDR exchange rate from the Yahoo Finance. It consists of 271 data taken from August 2017 to October 2022. This study aims to use the Autoregressive Integrated Moving Average (ARIMA) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) modeling method using the R-studio software and predict the IDR exchange rate. The ARIMA method describes the data based on a certain time series. ARCH-Lagrange Multiplier (ARCH-LM) was applied on the residuals of the best ARIMA model to test whetoer the data is heteroscedasticity. The testing result shows that the residual of the IDR exchange rate is heteroscedasticity. Therefore, the GARCH model can be used to handle it. The results of this study are obtained for the ARIMA(2,1,3) GARCH(3,6) model as the best and describe the actual data pattern with a mean absolute percentage error (MAPE) forecasting value is 1,99%.
The Application of the Least Squares Method to Multicollinear Data

Regression analysis is an analysis that aims to determine whether there is a statistically dependent relationship between two variables, namely the predictor variable and the response variable. One of the methods for estimating multiple linear regression parameters is the Least Squares Method. Therefore, careful and meticulous analysis and selection of appropriate techniques are required to overcome the multicollinearity problem and ensure accurate and meaningful regression analysis results. Descriptive statistical table of response variables and predictor variables, where the average results are rounded. The regression equation using the OLS method is as follows: Y ̂=2,037+0.302X_1+0.206X_2+0.172X_3+0.342X_4. Therefore, it is important to use special techniques such as regularization or PCA to overcome the multicollinearity problem in the data before applying the least squares method. Thus, we can obtain more stable and accurate regression coefficient estimates and a more reliable linear regression model.
MARKET BASKET ANALYSIS METHOD ON SALES DATA USING FP-GROWTH ALGORITHM

Product promotion is a way for business owners to increase sales of existing goods. Business owners could use association rules as a consideration of product promotion policies. Determination of association rules can be determined using the market basket analysis method with the fp-growth algorithm. From the research, there are 248 association rules for goods that are often purchased simultaneously with minimum support of 0.01 and minimum confidence of 0.1. Of the 248 rules, there are seven rules that have a confidence value of more than 0.5. Of the seven rules, flour appeared five times, and the cake mat and cake box had the highest confidence value of 0.62 and a lift of 5.54. Therefore, we recommend shop owners to place wheat flour on the main display close to items often purchased together, such as sweetened condensed milk, sugar, powdered margarine, and margarine. In addition, shop owners can also promote bundling cake boxes and cake mats.
eLearning Gamification Prototype Development using User Centered Design Approach

Abstract eLearning is one of the tools used by the educational institution. Unfortunately, many eLearning systems were not fully utilized by the user. These problems occur because of a lack of interaction in the eLearning system, dulling the learning process. Another problem is the lack of preparation from the user. This research tried to tackle these problems using gamification. The gamification adopted in this prototype is Medals, Ratings, and Discussion. The development prototype development using User Centred Design to gather the requirements and system flows. The use of these elements is hoped to be able to elevate the eLearning adoptions. The results of this research are the prototype of the gamification features and the business process of discussion in the eLearning system&#x0D; &#x0D;