Reviewerly/exHarmony huggingface dataset:
Maintaining Image Size by Developing the Cropping Rectangle Method on CT-Scan Images

CT-Scan is a computer-based imaging technology, but it presents certain weaknesses, including interpretation challenges, lengthy analysis times, and visualization limitations. The progress in digital image processing science empowers computers to identify objects through recognizing distinctive features. To facilitate further data processing, an image cropping method is essential to narrow down the area of interest, as not all parts of an object require processing. However, using a rectangular cropping method can lead to a reduction in image size, potentially compromising image quality and causing blurring when displayed on the same axes. To uphold image quality and avert blurring, maintaining the cropped image’s size becomes crucial. The developed rectangular cropping method successfully preserves the cropped image’s size by adjusting the intensity values of pixels surrounding the cutting point. Coordinate points within the cropping rectangle delineate the region where pixel intensity values are modified. In this study, input images were sourced from CT-scan images of M. Djamil Hospital, Padang, West Sumatra, in jpg format. The developed algorithm underwent testing using the Matlab program, employing the rectangle method for image cropping to ensure the image size remained constant. The study’s outcomes reveal no disparity in the number of pixels obtained when employing the developed rectangular cropping method. Based on the experiments carried out in this study using 8 input images that were successfully maintained, the size of the image from the cropped image was 8 images and 0 images were unsuccessful, so the accuracy obtained in this study was 100%.
The study of current functional responsibilities of pharmaceutical workers and their acquisition of soft and hard skills adapted to modern conditions

Aim. To study trends and changes in the list of functional responsibilities of pharmaceutical workers (PW) and their acquisition of soft and hard skills adapted to modern conditions taking into account the professional opinion of PW.&#x0D; Materials and methods. For our study, information on the state regulation of healthcare institutions (HCIs) in the conditions of military aggression and the current state of crisis, a review of scientific literature, and the results of personal research were used. Such scientific methods as analysis, comparison, comparison, questionnaires and direct observations were applied. The results were processed and verified using licensed Microsoft Office Excel software products.&#x0D; Results. The study involved 212 respondents in such positions as the head of a pharmacy, pharmacist, assistant pharmacist, clinical pharmacist (CP). The research has shown that employers need to update job descriptions and balance them with job cards to better understand the employees’ responsibilities and achievements and analyze the employer’s key performance indicators (KPIs) that show how a particular employee or an entire department is moving toward the achievement of the goal set during the daily work.&#x0D; Conclusions. A sociological study has been conducted among the employees of HCIs, such as pharmacies, pharmacy branches, hospitals, and treatment and prevention institutions. For each position, the most significant functional responsibilities and desired KRI have been determined. It has been found that for the further effective development of pharmaceutical support for the population it is necessary to update the job descriptions and balance them with job cards to better understand employees’ responsibilities and achievements and analyze the employer’s KPIs.
CIPP Evaluation on Japanese Language Training Apprenticeship Program at Job Training Institute (LPK) Akeno Gakuen Bawen

This study aims to describe the evaluation of Japanese language training using the CIPP Model developed by Stufflebeam, including the evaluation of context, input, process, and product at LPK Akeno Gakuen. This research uses a qualitative approach. The study participants consisted of LPK managers, 2 tutors, and 5 study residents. Data collection techniques are carried out through observation, interview, and documentation methods. Data analysis techniques include data collection, data reduction, data presentation, and conclusions. The results showed that: (1) context evaluation: the training program has been achieved in accordance with the needs of learning residents and in accordance with the objectives of the institution or learning residents. (2) input evaluation: all aspects can support the implementation of Japanese language learning. However, the need for additional Japanese language tutors and infrastructure facilities requires additional or updated classrooms that are lacking. (3) process evaluation: the components of the process in LPK run well, it's just that the aspects related to tutor activities have not been maximized. It is necessary to provide appropriate training and professional development for tutors in order to maximize learning activities in class. In addition to learning obstacles, there is a need for tutors to develop effective classroom management strategies so that the specified learning goals can be achieved. (4) product evaluation: Japanese language training at LPK Akeno Gakuen has reached all components, LPK has succeeded in equipping citizens to learn by improving their knowledge and skills in Japanese.
Health Information Media Literacy on Instagram Sehat Surabayaku

This study aims to examine the level of media literacy and public response to health information provided by the Instagram account @sehatsurabayaku during the period May to June 2023. Media literacy is a crucial aspect in understanding, assessing and interpreting health information circulating on social media. This study uses the content analysis method to identify the type, source, and accuracy of health information published by the Instagram account @sehatsurabayaku. In addition, a survey was conducted to collect data on people's understanding and perception of health information received from these accounts. Survey respondents were randomly selected from followers of the Instagram account @sehatsurabayaku who were active during the study period. The results of this study are expected to provide an understanding of the extent to which people's media literacy processes health information from certain social media, especially the Instagram account @sehatsurabayaku. The findings of this study can provide insight for stakeholders in the health and social media fields in developing a more effective and responsible communication strategy
PENGGUNAAN MEDIA SPINNER UNTUK MENINGKATKAN HASIL BELAJAR BAHASA INDONESIA MATERI KALIMAT AKTIF DAN PASIF PADA PESERTA DIDIK KELAS II SDI WAHID HASYIM SIDOARJO

The aim of this research is to determine the improvement in learning outcomes in Indonesian, the difference between active and passive sentences using spinner media. This research uses classroom action research (PTK) carried out in several cycles. Each cycle consists of 4 stages, namely planning, implementation, observation and reflection.. The results of the research show that Indonesian language learning activities using Spinner media have increased. This is shown by an increase in the percentage of teacher activity in cycle I reaching 63.15% and cycle II reaching 89.50%. Student activity during Indonesian language learning activities using Spinner learning media has increased. This is shown by an increase in the percentage of student activity in cycle I reaching 67.4% and cycle II reaching 88.22%. Increased student learning outcomes after participating in Indonesian language learning activities using Spinner learning media. This can be shown by an increase in the percentage of learning outcomes for students in cycle I reaching 72.6% and cycle II reaching 90.13%.
Pemanfaat Aplikasi Mendeley dalam Penulisan Tugas Akhir oleh Mahasiswa Program Studi Ilmu Perpustakaan Universitas Islam Negeri Sumatera Utara

Lots of library processing applications can be used to support students in completing their final assignments, one of which is through Mendeley. Mendeley is an application that has the ability to process scientific databases in the form of e-books, e-journals, and other libraries. Mendeley is claimed to be important in writing the final project because reference management can make it easier for the civitas academica to cite citations as well as good reference management and can minimize plagiarism. This study discusses the Utilization of the Mendeley Application in Writing Final Projects by Library Science Study Program Students at the State Islamic University of North Sumatra in writing scientific articles for Library Science study program students. This study aims to find out how to use Mendeley in writing their final assignment by Library Science students at the State Islamic University of North Sumatra. This research method uses qualitative methods with observation and interview approaches. The research subjects were students of the 2019 Library Science study program who were writing their final assignment.
5G Mobile Technology in Education

5G mobile networks are developing rapidly and are increasingly being deployed in more and more places in our country not to mention around the world. These services are available not only to the general public but also to several industries. Industrial applications are often implemented using 5G SA (Stand Alone). For this reason, I created two separate measurement exercises to familiarize students with 5G technology on an all-encompassing systematic level. This will allow students studying at the university to keep up with today's modern mobile network systems. From this up-to-date knowledge they can benefit as the engineers of the future.
Application and Certification of Software Models Used in Development

As the complexity of tasks increases, developers have to use different tools. We talked about this in details in some previous publications. In this paper, we will touch on a special area that is increasingly coming to the fore these days, which is the question of the application of models. However, the application of models in safety-critical systems raises the question of the reliability of the models and the questions of the certification of these software tools.
Side Benefits of Cybersecurity Measures an Empirical Study

Cybersecurity is mostly seen as a necessary evil to protect us from the increasing threat of hacker attacks. While this is definitely justified in terms of the damage caused, there are also unexpected benefits to this growing concern that are rarely highlighted. Cyber attacks have the potential to result in financial loss and reputation damage for an organization, so it is a given that enhancing cybersecurity will lead to protecting against these attacks. However, the other advantages are rarely highlighted. While many companies believe that cybersecurity is a burden and obstruct their processes, in this empirical study we examine the positive effects of security measures on organizations. Our primary hypothesis is that increased focus on cybersecurity improves organizational resilience, process maturity and product quality. We show how technological controls and security processes motivate the organization to keep its processes current, well structured, and well thought out. We also find that the concern for cybersecurity opens the doors for innovation, which drives growth. By examining the main areas covered by security measures: people, processes, and technology, we conclude that the investment made in these areas for cybersecurity risk mitigation, or as a response to regulations, also have significant benefits outside the security domain.
Digital device use as a function of digital socialisation among secondary school students

Digital socialisation is a new dimension of socialisation arenas. The issue of socialisation has long been the focus of research and articles, as its success determines the attitudes and attitudes of children and later adults in many areas of life. Prior to the 21st century, the study of socialisation processes focused primarily on the family and the wider and narrower social context (village, region, country) from which young people drew their basic patterns. However, the fourth industrial revolution (now the fifth industrial revolution) has also brought about a fundamental change in this area. The unprecedented advance of digitalisation, the IT and robotics explosion has changed the socialisation landscape and its direction. From the family and the community to the unknown (celebrities and influencers) and unknown content (social media platforms), huge - and in many cases negative - changes have taken place in the socialisation of young people. New areas of socialisation have also come to the fore, such as financial socialisation or even digital socialisation. In our study, we want to examine the issue of digital socialization based on the results of a questionnaire survey conducted in Hungary, based on the opinions of Generation Z, secondary school students. Our aim is to assess the trends and influencing factors that determine the digital awareness of the young generation, the patterns to follow and the factors of digital device use along primary data.
Enhancing Sentiment Analysis Accuracy on IMDB Reviews Through Ensemble Machine Learning Techniques

In the rapidly evolving field of sentiment analysis, the IMDB movie review dataset has become one of the key benchmarks for evaluating the performance of machine learning models. This paper presents a comprehensive study of various machine learning models applied to the dataset, focusing on positive and negative reviews. We delve into the intricacies of advanced models such as BERT, LSTM, and GRU. The novelty of this work lies in the application of ensemble methods, specifically voting functions and stacking, to improve the accuracy of sentiment classification. We propose original techniques that leverage the strengths of individual models, mitigating their weaknesses through a collaborative approach. The ensemble methods used outperform single-model approaches, demonstrating improved accuracies in the classification of both positive and negative reviews. This research contributes to the ongoing discourse in sentiment analysis, offering fresh perspectives and techniques that enhance sentiment classification accuracy. The findings underscore the potential of ensemble methods in machine learning.
A Hands-on Tutorial for Implementing Eye-in-Hand Visual Servoing in Robot Applications

In this tutorial paper, we aim to provide a comprehensive and replicable example of implementing image-based visual servoing within the realm of robotic manipulation. Our primary objective is to furnish concise yet robust learning resources, including practical ROS-based source code, to assist those pursuing robotic manipulation projects that leverage the advantages of motion coordination using geometric information derived from image processing. Image-based visual servoing, a technique recognized and employed in numerous applications, often lacks in-depth, instructive tutorials elucidating both its theoretical underpinnings and practical applications, complete with step-by-step instructions and source code. Consequently, we tailor our paper towards students, engineers, researchers, and educators seeking to utilize holistic learning materials in this field.
Exploring the Identification with Agile Values in Different Generations<sup>1</sup>

In today's rapidly and frequently changing world, the agile approach is becoming more and more widespread, and the need to be aware of it is slowly becoming almost inescapable. One of the main strengths of this approach, which has become a familiar one in the software development field, is its ability to adapt to change, which promises to deliver real value and thus higher levels of customer satisfaction. The authors of the agile approach have built agility on four main pillars (values) and twelve additional points (principles). The aim of our research is to investigate attitudes towards these four agile values and the extent to which people identify with them in their work, generation by generation.
A Comprehensive Review and Open Challenges on Visual Question Answering Models

Users are now able to actively interact with images and pose different questions based on images, thanks to recent developments in artificial intelligence. In turn, a response in a natural language answer is expected. The study discusses a variety of datasets that can be used to examine applications for visual question-answering (VQA), as well as their advantages and disadvantages. Four different forms of VQA models—simple joint embedding-based models, attention-based models, knowledge-incorporated models, and domain-specific VQA models—are in-depth examined in this article. We also critically assess the drawbacks and future possibilities of all current state-of-the-art (SoTa), end-to-end VQA models. Finally, we present the directions and guidelines for further development of the VQA models.
Pi-Sniffer: Privacy Sniffing through the Wall with WiFi CSI

The popularity and development of smart homes and the Internet of Things also bring the risk of privacy leaks. Illegal sniffing devices outside the target building may receive wireless signals from legitimate signal-sending devices (such as routers, mobile phones, laptops, cameras, etc.), so that the attacker (privacy thief) can steal the privacy information of the target person (victim) without the victim’s awareness. We have designed and implemented Pi-Sniffer, a system that uses off-the-shelf WIFI devices to conduct privacy theft attacks based on wireless sensing, and evaluate the privacy theft ability (accuracy) of attackers under different scenarios. First, we use the RSS signal attenuation model and signal source positioning algorithm to find one or more signal sources located in the target building. And we collect information about the victim by monitoring the signal source or the combination of multiple signal sources in the target building. We then carefully craft features that are independent of the environment. Lastly, we apply LSTM to classify the signals through the wall. Our experiments show that the accuracy of privacy theft attack is affected by the number of active WiFi devices in the victim’s room, and the accuracy of classifying the four types of activity directions of the victim can reach up to 66.7% under different scenarios of monitoring combinations of various active WiFi devices.
D2V-DDQN: Influence Maximization of Positive Opinions Based on Deep Reinforcement Learning

The problem of maximizing influence aims to select a portion of seed users in social networks, making the diffusion wider of users’ opinions. For a topic, the diffusion width is the number of activated users, and the diffusion depth is the average opinion value of all activated users after a certain period of information diffusion on the social network. This paper proposes an opinion-based independent cascade (OPIC) model to quantify the diffusion width and depth of the topic. Then, we propose a deep reinforcement learning algorithm named D2V-DDQN. This algorithm aims to find a seed node set to maximize the positive influence of the target topic so that there are more positive opinions with a large number of activated nodes under the OPIC diffusion model. The experimental results verify the superiority of the method relative to existing methods.
Personalized Multi-task Federated Learning on Non-IID Medical Health Care Data

Medical institutions’ internal data is closely related to individual privacy information, and has a high degree of confidentiality and sensitivity in data storage and data use. Federated Learning (FL) is a distributed computing paradigm proposed to improve data privacy and quality issues in edge computing. One challenge in FL is data heterogeneity, which reduces training quality because the training data on local devices usually does not have the ideal Independent-and-Identically-Distributed (IID) property. The normalization layers in neural network structures play a key role in improving model accuracy in FL. In this work, the authors investigate the efficiency of applying group normalization (GN) instead of the commonly used batch normalization, aiming to improve the performance of the global model. The authors further identify a shortcoming of using GN, which is its degraded performance given the large batch size in training. Towards this end, they propose a federated learning system that features a normalization layer symGN, which introduces stochasticity into the GN model and improves its performance in the large-batch training scenario. A thorough evaluation of different non-IID datasets has demonstrated the efficiency of their proposed method. They use this personalized FL framework to protect privacy of medical data and share safe global models in different medical organizations.
LMIE-BERT: A Learnable Method for Inter-Layer Ensembles to Accelerate Inference of BERT-Style Pre-trained Models

Pre-trained models have brought tremendous accuracy improvements to Natural Language Processing(NLP) and Computer Vision tasks, but they suffer from slow inference speed due to the heavy model, which hinders their deployment in production. The early exit methods have been proposed to accelerate the inference speed of large pre-trained models. However, these methods will lose control of accuracy at higher speed ratios. In order to balance the trade-off between model speed and accuracy better, we propose a novel early-exit mechanism called LMIE-BERT. To achieve this, we introduce a learnable method for inter-layer ensemble strategy in the internal classifier, it trains the model to fit the information from both the previous and current layers, which enables the early exit method to get more robust results. The experimental results demonstrate that LMIE-BERT can maintain over 90% of the accuracy of the original model while achieving a 4× inference speed up in multiple tasks. Our method is ahead of other early exit methods in terms of model accuracy for the same speed ratio.
Sequence to Sequence Load Recognition Model Based On Sparse Self-attention Transformer

Load recognition is a technique for monitoring the status of different appliances from the aggregate measurement signal. Traditional load recognition methods either rely on high frequency sampled data or have significantly degraded performance as the amount of devices increases. Recently, research has shown that deep learning-based load recognition solutions offer better performance. However, the recognition accuracy still needs to be improved. In this paper, we propose a load recognition model based on tranformer, and we introduce a sparse self-attention mechanism to reduce the computational complexity. By conducting experiments on the low-frequency sampled UKDALE dataset, the results show that our proposed method outperforms previous methods, demonstrating the effectiveness of the proposed method.