Reviewerly/exHarmony huggingface dataset:
FedCNNAvg: Federated Learning for Preserving-Privacy of Multi-clients Decentralized Medical Image Classification

Federated Learning (FL) permits the cooperative training of a joint model for several medical facilities while maintaining the decentralization of the data owing to privacy considerations. However, Federated optimizations often struggle with the heterogeneity of data dissemination among medical facilities. Nowadays, the domains of medical image classification, compression, and privacy are particularly difficult for diagnosing disease. The transmission of these medical images through the internet for diagnostic reasons must be protected against cyberattacks. In this proposed method, a Federated Learning approach with a Convolutional Neural Network (FedCNN) and Federated Averaging (FedAVG) is employed for classification problems. This technique adjusts the contribution of each data sample to the local goal during optimization based on knowledge of the client’s label distribution, thereby minimizing the instability caused by data heterogeneity. The model utilizes a hybrid approach to ensure consistency in time-series data. The datasets, namely, COVIDx-19 X-ray and malaria that are freely accessible are the subject of our in-depth investigations. The experimental results have been analyzed by evaluation metrics, namely, accuracy (78.79 and 98.92), precision (73.72 and 95.73), and recall (71.91 and 93.91) for proper validation. The findings demonstrate that FedCNN achieves better convergence performance than the main FL optimization methods under comparison.
Scene Matching Techniques Using Satellite Imagery Data

The problem of scene matching is a challenging problem in the field of image processing and pattern recognition. Therefore, it is modeled and its influencing factors are analyzed. According to sources, influence factors can be catalogued into three types: 1- changes of scenes 2- changes of image conditions 3- changes of sensors. For each factor, its mechanism is discussed. Given a pictorial description of a region of a scene, it is desired to determine which region in another scene is similar. The most efficient algorithms for scene matching are discussed. Those are the sequential hierarchical scenes matching algorithms for grey-scale and binary images and the two-stage template-matching algorithm. Experimental results are presented for matching satellite images of AI-Minea (EGYPT) and Montana (USA) using those approaches. The results prove efficiency and success in reaching the best match location with minimum required computations. A comment on the results is presented as well as a comparison between the applied methods.
Audit of A Web-Based Electronic Documents and Record Management System (WEDRMS): Oversight Efforts to Improve Administration in Higher Educational Institutions

An electronic records had important role in organizations including a university or higher educational institutions. It was important to stay abreast of best practices and new technologies to manage these records effectively. A Web-Based Electronic Documents and Records Management System (WEDRMS) was a web-based system that was expected to reach a wide range of system users. This system had functions to store, archive and retrieve electronic documents. The purpose of this research was to explain further an audit implication for Web-Based Electronic Documents and Record Management System (WEDRMS) as an effort to supervise administration of higher educational Institutions. This research used a descriptive research approach with observation and document analysis for the data collection process. With the existence of an audit, it can become an alternative to managing electronic documents in the higher education environment. This research provides perspective and urgency regarding the audit implication of web-based electronic documentation dan recording management.
Knowledge regarding of Iron Deficiency Anemia among Adolescent Girls of Chitwan

Growth spurts and menarche in adolescent Girls increase iron requirements. Iron deficiency anemia, one of the common nutritional deficiency condition of adolescent girls, is related to poor dietary intake and lack of iron supplementation puts them into the high risk group. The objective of this study is to find out the “Knowledge regarding Iron Deficiency Anemia among Adolescent girls. Descriptive cross-sectional study design was used for the study. Among the 5 government school of this Municipality, three were selected by simple random method and among those schools probability proportional sampling was used for the selection of the adolescent girls of the schools. Data were collected from 156 adolescent school girls through self-administered structured questionnaire developed by researcher and analysis was done using SPSS version 20. Descriptive and inferential statistics was used for the analysis. This study revealed that more than half (53.2%) of the adolescents girls had low knowledge regarding iron deficiency anemia and remaining had just moderate knowledge. There was significant association between age (p=0.03), education (p= 0.001) and father’s occupation (p=0.004) with level of knowledge on iron deficiency anemia. The study concludes that adolescent girls have low knowledge regarding iron deficiency anemia, so school administration need to plan and organize anemia preventive campaign and provide information, education and communication (IEC) materials and screening services related to anemia.
Digital Forensic Research for Analyzing Drone Pilot: Focusing on DJI Remote Controller

Drones, also known as unmanned aerial vehicles (UAVs) and sometimes referred to as ‘Mobile IoT’ or ‘Flying IoT’, are widely adopted worldwide, with their market share continuously increasing. While drones are generally harnessed for a wide range of positive applications, recent instances of drones being employed as lethal weapons in conflicts between countries like Russia, Ukraine, Israel, Palestine, and Hamas have demonstrated the potential consequences of their misuse. Such misuse poses a significant threat to cybersecurity and human lives, thereby highlighting the need for research to swiftly and accurately analyze drone-related crimes, identify the responsible pilot, and establish when and what illegal actions were carried out. In contrast to existing research, involving limited data collection and analysis of the drone, our study focused on collecting and rigorously analyzing data without restrictions from the remote controller used to operate the drone. This comprehensive approach allowed us to unveil essential details, including the pilot’s account information, the specific drone used, pairing timestamps, the pilot’s operational location, the drone’s flight path, and the content captured during flights. We developed methodologies and proposed artifacts to reveal these specifics, which were supported by real-world data. Significantly, this study is the pioneering digital forensic investigation of remote controller devices. We meticulously collected and analyzed all internal data, and we even employed reverse engineering to decrypt critical information files. These achievements hold substantial significance. The outcomes of this research are expected to serve as a digital forensic methodology for drone systems, thereby making valuable contributions to numerous investigations.
Modulation-agnostic single-shot estimation of quantum measurement confidence

We experimentally explore single-shot state identification using long alphabets of states and employing different modulation schemes. We use time-resolved quantum measurement and Bayesian inference to identify the input state and demonstrate the advantage of this single-shot measurement over classical state identification. For each single-shot measurement, we estimate the confidence of state identification based on the quantum measurement and demonstrate the physical significance of confidence estimates. Particularly, we show that a set of confidence values correctly represents the probabilities of successful state identification for a given experimental outcome. We investigate the alphabets of coherent states with different modulations and show that confidence estimates yield the reliability of each act of measurement independently of the modulation used.
Message from the Editor-in-Chief and from the Associate Editor-in-Chief

Welcome to the November 2023 issue of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Transactions on Visualization and Computer Graphics (TVCG)</i> . This issue contains selected papers accepted at the IEEE International Symposium on Mixed and Augmented Reality (ISMAR). The conference took place from October 16 to 20, 2023 in Sydney, Australia, in a hybrid mode.
The Application of Cooperative Learning (Talking Stick) with Multimedia to Improve Javanese Reading Skills to Fifth Grade Students of SD Negeri 1 Panjer in Academic Year of 2022/2023

&lt;p&gt;&lt;em&gt;The study aimed to improve Javanese script reading skills to fifth grade of SD Negeri 1 Panjer in academic year of 2022/2023 through the application of Talking Stick with multimedia. It was a collaborative Classroom Action Research conducted in three cycles. The subjects were teachers and students of fifth grade at SD Negeri 1 Panjer in academic year of 2022/2023. The data were qualitative and quantitative. Data collection techniques were observation, interviews, and tests. Data validity used triangulation of sources and triangulation of sources techniques. Data analysis included data reduction, data presentation, and drawing conclusions. The percentages of students’ passing grades in Javanese script reading skills were 72% in the first cycle, 80% in the second cycle, 92% in the third cycle. It concludes that the application of Talking Stick with multimedia improves Javanese script reading skills to fifth grade students of SD Negeri 1 Panjer in academic year of 2022/2023.&lt;/em&gt;&lt;/p&gt;
A Comparative Study of Deterministic and Stochastic Policies for Q-learning

Q-learning is a form of reinforcement learning that employs agents to perform actions in an environment under a policy to reach ultimate goals. Q-learning is also thought as a goal-directed learning to maximize the expected value of the cumulative rewards via optimizing policies. Deterministic and scholastic policies are commonly used in reinforcement learning. However, they perform quite different in Markov decision processes. In this study, we conduct a comparative study on these two policies in the context of a grid world problem with Q-learning and provide an insight into the superiority of the deterministic policy over the scholastic one.
Publishing under suspicion: Harmful hurdles in scholarly publishing

Academic Library Workers in Conversation is a bimonthly C&amp;RL News series focused on elevating the everyday conversations of library professionals. The wisdom of the watercooler has long been heralded, but this series hopes to go further by minimizing barriers to traditional publishing with an accessible format. Each of the topics in the series were proposed by the authors and they were given space to explore. We encourage you to follow and share these conversations about transforming libraries with ideas from the frontlines. This issue’s conversation addresses discrimination in scholarly publishing and makes it clear that we have not arrived, wherever we are. —Dustin Fife, series editor
Analysis of the degree of the establishment of a learning organization in innovative middle schools' professional learning community

This study aims to examine the disparity in the degree of establishment of a professional learning community learning organization between innovative schools and general schools in Chungnam. To achieve this research objectives, we examined the characteristics of innovative schools based on prior research and summarized the relationship between the professional learning community and Senge's learning organization theory. We also developed a questionnaire asking the degree of establishment of a learning organization in a professional learning community drawing from prior research and expert advice. The survey was conducted among middle school teachers in Chungnam and analyzed using basic statistical analysis and independent sample t-test. The main findings indicated a significant difference in the degree of establishment of a professional learning community learning organization between innovative schools and general schools. Innovative schools scored an average of 4.31 points out of 5, while general schools scored an average of 3.84 points. The difference between innovative schools and general schools was statistically significant at the significance level of 0.001. The analysis revealed that innovative schools have relatively more professional learning communities than general schools, especially in terms of team learning and system thinking. Based on the results of this study, it is recommended to actively disseminate the various activities of professional learning communities centered on innovative schools to other general schools. While existing studies have discussed the effectiveness of professional learning communities, this study adds value by analyzing professional learning communities in detail within the framework of learning organizational theory. Further research are needed on the degree of establishment of a learning organization in a professional learning community for the 1st to 8th graders of innovative schools.
Classification Of Maternal Health Risk Using Three Models Naive Bayes Method

Lack of information related to maternal health care during pregnancy and post-pregnancy, especially in rural areas, results in many cases of pregnancy complications. Risk analysis for pregnant women is really needed as a reference in handling pregnant women so that the risk to pregnant women can be minimized. To analyze the risk of pregnant women can use data mining techniques by classifying the risk of pregnant women. This study proposes to classify Maternal Health Risk using the Naive Bayes method with three models, namely Gaussian, Multinomial, and Bournolli. The data used is the health data of pregnant women based on risk intensity which is grouped into three classes, namely low, mid and high risk. while the attributes are Age, Systolic Blood Pressure as SystolicBP, Diastolic BP as DiastolicBP, Blood Sugar as BS, Body Temperature as BodyTemp, and HeartRate. The results show that among the three Naïve Bayes models that have the best performance are the Multinomial and Bournolli with an accuracy of 84.8% while the Gaussian produces an accuracy of 82.6%.
BoCB: Performance Benchmarking by Analysing Impacts of Cloud Platforms on Consortium Blockchain

Consortium blockchain has recently witnessed unprecedented popularity due to its implicit features and potential capabilities. On the other hand, cloud computing has become a mature technology and has reshaped numerous other innovative technologies through its flexible and efficient on-demand computing services. Consortium blockchain's performance issues undermine its wider acceptance but unleashing the cloud’s computing capabilities can help to develop the full potential of blockchain. In other words, cloud technology and blockchain's potential integration can be envisaged as a next-generation information technology, highly characterised by scalable and secure solutions, respectively. In this context, it is important to understand what benefits blockchain gains from cloud integration in terms of performance. This article presents a comprehensive, empirical analysis for an in-depth study of the performance of the blockchain, specifically consortium (but not limited to) implemented on the cloud, ranging from identifying potential performance bottlenecks, to configuring system parameters. Furthermore, this article presents a novel framework for blockchain on cloud benchmarking (BoCB) and implement it by a Hyperledger Fabric application on four different commercial Cloud platforms. The evaluation results of the blockchain performance on heterogeneous cloud platforms can help developers select the best possible configuration and resources to optimise their applications accordingly.
Exploring the Potential of Image Overlay in Self-supervised Learning: A Study on SimSiam Networks and Strategies for Preventing Model Collapse

Self-supervised representation learning (SSL) has rapidly become a compelling avenue of research in visual tasks. Its performance, comparable to that of supervised learning, combined with its ability to mimic human perception, makes it an attractive area of study. The inherent simplicity of SSL also makes it easily accessible. However, data augmentation and collapse avoidance remain significant challenges. To address these issues, we have explored the use of image overlay in combination with principal Convolutional Neural Network models and an efficient SimSiam network. Our investigation led to three main findings. Firstly, while image overlay did not perform as efficiently as existing optimized augmentation methods, it showed potential to enhance the effectiveness of SSL tasks. Secondly, our research underscored the critical roles of data volume and augmentation in preventing a model collapse in SSL. This stands in contrast to pre-trained supervised learning, which places more emphasis on input image resolution and model size. Finally, our results indicated stability in the loss function within SSL, hinting at its potential to refine the model training process and encouraging the exploration of innovative augmentation methods. These empirical insights could be instrumental in simplifying and democratising deep learning techniques, making them more accessible and appealing to a broader audience. These findings will stimulate further research in this domain and contribute to the ongoing evolution of deep learning.
Sentiment Analysis for Organizational Research

Sentiment analysis is a text analysis method that is developed for systematically detecting, identifying, or extracting the emotional intent of words to infer if the text expresses a positive or negative tone. Although this novel method has opened an exciting new avenue for organizational research – mainly due to the abundantly available text data in organizations and the well-developed sentiment analysis techniques, it has also posed a serious challenge to many organizational researchers. This chapter aims to introduce the sentiment analysis method in the text mining area to the organizational research community. In this chapter, the authors first briefly discuss the central role of sentiment in organizational research and then introduce the traditional and modern approaches to sentiment analysis. The authors further delineate research paradigms for text analysis research, advocating the iterative research paradigm (cf., inductive and deductive research paradigms) that is more suitable for text mining research, and also introduce the analytical procedures for sentiment analysis with three stages – discovery, measurement, and inference. More importantly, the authors highlight both the dictionary-based and machine learning (ML) approaches in the measurement stage, with special coverage on deep learning and word embedding techniques as the latest breakthroughs in sentiment and text analyses. Lastly, the authors provide two illustrative examples to demonstrate the applications of sentiment analysis in organizational research. It is the authors’ hope that this chapter – by providing these practical guidelines – will help facilitate more applications of this novel method in organizational research in the future.
An open source blockchain as a service solution for health science applications

The use of big data in the field of omics and biomedical studies is the enabling factor for finding new insights with sufficient statistical confidence. When dealing with such data, several issues have to be addressed, related to the personal identifiable information (PII) often included in datasets and subject to the European General Data Protection Regulation (GDPR), which imposes particular organizational and technical measures, aimed to protect patients’ privacy. In this contribution we describe an open-source Blockchain-as-a-service solution, developed and deployed on the INFN Cloud infrastructure, and a health-related decentralized application (DApp) using this service. The main target of this DApp is to manage personal information while preserving patients’ rights, thanks to the trusted, tamper-proof, traceable and accountable distributed digital ledger provided by the blockchain.
MODELLING ROAD ACCIDENTS IN SELANGOR: A COMPARATIVE ANALYSIS BY USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (SARIMA) AND ARTIFICIAL NEURAL NETWORK (ANN)

Road accidents have become one of the major problems globally. It was reported that the total number of road accidents in Malaysia has increased by 0.92% within the last ten years. Modelling road accident occurrences is critical for policymakers to understand the trend and pattern of road accidents to provide an appropriate countermeasure. This study attempts to forecast the road accident occurrences on federal and state roads in Selangor, Malaysia. This study utilised monthly road accident data from January 2011 to December 2021. The traditional univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Network (ANN) models were employed and the performance of each model was assessed. The findings found that the ANN model outperformed the SARIMA model in training and validation. Furthermore, the ANN model has the lowest RMSE, MAE, MAPE and MASE values for both sets. This study demonstrates the potential of machine learning in forecasting and predicting road accident occurrences, giving more flexibility and assumption-free methodology.
Definition of etc. Behavior in the Crime of Assisting Information Network Criminal Activities

Various kinds of cybercrime have emerged after entering the Internet era. In the Criminal Law Amendment (IX), the crime of assisting information network criminal activities has been added. However, there is an inconsistent understanding of the crime of assisting information network criminal activities among all parties in the practical circles. It is found that the judicial application of the crime of assisting information network criminal activities is more confusing by combing the relevant adjudication documents. For the purpose of eliminating this phenomenon, the etc. assistance in the crime of assisting information network criminal activities can be defined through legal norms analysis or case studies. This paper clarifies the boundary between crime and non-crime and between this crime and the other crime based on the following criteria. The objective assisting behavior needs to have the legal benefit infringement. The degree of scienter is relatively specific and determined. Meanwhile, the circumstances are severe. Besides, in order to clarify the scope of etc., this paper analyzes the legal norms of the act of using the account number in online games to transfer money, which provides a more significant reference value for judicial application.
Exploring the Impact of Online Language Teaching on the Development of Communicative Competence in Second Language Learners

The advent of online language teaching has revolutionized the way second language learners acquire communicative competence. This qualitative study aims to explore the impact of online language teaching on the development of communicative competence in second language learners. By employing a qualitative research design, including interviews and observations, this study examines the experiences and perceptions of learners engaged in online language learning programs. The findings shed light on the advantages, challenges, and strategies associated with online language teaching, offering insights into how it influences the development of communicative competence in second language learners.