Reviewerly/exHarmony huggingface dataset:
Sistem Informasi Akuntansi Penerimaan Dan Pengeluaran Kas Terhadap Perencanaan Dan Pengendalian Keuangan

An accounting information system is a system that is connected to each other as a whole, from procedures, forms, records, until finally financial reports can be created. There are two accounting information systems used by researchers, namely the cash receipt system and the cash disbursement system. The aim of this research is to find out how the accounting information system for cash receipts and disbursements is and the role of internal control at UD Dadhen Kurnia Abadi. UD Dadhen Kurnia Abadi is an MSME that operates in the catfish fillet industry so the organizational structure is more concise and the functions involved in a transaction are also quite small. Data was obtained by conducting interviews, observation and documentation. The research results show that the cash receipts and disbursements accounting information system running at UD. Dadhen Kurnia Abadi cannot be said to be good because it still uses manual recording which results in weaknesses, namely transactions recorded are not up to date so there are often discrepancies in recording so that the information produced is not accurate and precise and does not rule out the possibility of abuse of authority or embezzlement of funds. Apart from that, internal control is still less effective, there should be a strict separation of duties and responsibilities for each function, and the purchasing, receiving and cash functions should be separated.
Analysis of Informatization Teaching Pathway of English in Colleges and Universities under Constructivism

Constructivism is a guiding ideology that has been used for a long time in English teaching in colleges and universities, and it can meet the requirements of China's new curriculum standard for English education in colleges and universities. With the continuous development of China's information technology and high technology, the informatization of college English teaching based on the constructivism framework needs to expand the teaching methods in combination with the development of information technology and high technology, to improve the quality and level of English learning of college students in China.
THE USAGE OF EDUCATIONAL WEBSITES AS A DIDACTIC TOOL IN THE EDUCATIONAL PROCESS

In this article, an educational website is defined as a didactic tool that can be used by a teacher during the educational process. An attempt has also been made to present the most popular and sought-after educational websites. An analysis of the three most popular websites was conducted: Bilimland, TopIQ and Daryn.online, whose positive aspects and disadvantages were identified. Virtual education platforms, websites, and digital games provide an unlimited number of additional tools that can be used anywhere, anytime, where there is an electronic device and an Internet connection. This allows for both synchronous and asynchronous interactions and constant communication between student-teacher and student-student. The result is effective classroom collaboration. Therefore, we define educational websites as an indispensable didactic tool for teaching.
Are Local Government Information Systems in Indonesia Successfully Implemented?

The increasing development of information and communication technology has an impact on changes in all fields including the government sector which is marked by the enactment of a new policy, namely the Minister of Home Affairs Regulation Number 70 of 2019 concerning Regional Government Information Systems (SIPD). This study evaluates the success of implementing the Local Government Information System (SIPD) application in the Metro City Local Government through the IS Success Model. The Grand Theory used in this research is the Information Success Model. The variables in this study use constructs from the IS Success Model, including information quality, system quality, service quality, user satisfaction, and usage benefits. The sampling technique in this study uses non-probability sampling which uses Purposive Sampling with several criteria for respondents so that a sample of 93 respondents is obtained. Data analysis in this study used SPSS version 26 with path analysis testing. The results showed that information quality, service quality, and user satisfaction had a positive effect on user benefits but not system quality. In this study, user satisfaction mediates between the independent and dependent variables.
Deep learning in computed tomography super resolution using multi‐modality data training

Abstract Background One of the limitations in leveraging the potential of artificial intelligence in X‐ray imaging is the limited availability of annotated training data. As X‐ray and CT shares similar imaging physics, one could achieve cross‐domain data sharing, so to generate labeled synthetic X‐ray images from annotated CT volumes as digitally reconstructed radiographs (DRRs). To account for the lower resolution of CT and the CT‐generated DRRs as compared to the real X‐ray images, we propose the use of super‐resolution (SR) techniques to enhance the CT resolution before DRR generation. Purpose As spatial resolution can be defined by the modulation transfer function kernel in CT physics, we propose to train a SR network using paired low‐resolution (LR) and high‐resolution (HR) images by varying the kernel's shape and cutoff frequency. This is different to previous deep learning‐based SR techniques on RGB and medical images which focused on refining the sampling grid. Instead of generating LR images by bicubic interpolation, we aim to create realistic multi‐detector CT (MDCT) like LR images from HR cone‐beam CT (CBCT) scans. Methods We propose and evaluate the use of a SR U‐Net for the mapping between LR and HR CBCT image slices. We reconstructed paired LR and HR training volumes from the same CT scans with small in‐plane sampling grid size of . We used the residual U‐Net architecture to train two models. SRUN: trained with kernel‐based LR images, and SRUN: trained with bicubic downsampled data as baseline. Both models are trained on one CBCT dataset ( n = 13 391). The performance of both models was then evaluated on unseen kernel‐based and interpolation‐based LR CBCT images ( n = 10 950), and also on MDCT images ( n = 1392). Results Five‐fold cross validation and ablation study were performed to find the optimal hyperparameters. Both SRUN and SRUN models show significant improvements ( p ‐value 0.05) in mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR) and structural similarity index measures (SSIMs) on unseen CBCT images. Also, the improvement percentages in MAE, PSNR, and SSIM by SRUN is larger than SRUN. For SRUN, MAE is reduced by 14%, and PSNR and SSIMs increased by 6 and 8%, respectively. To conclude, SRUN outperforms SRUN, which the former generates sharper images when tested with kernel‐based LR CBCT images as well as cross‐modality LR MDCT data. Conclusions Our proposed method showed better performance than the baseline interpolation approach on unseen LR CBCT. We showed that the frequency behavior of the used data is important for learning the SR features. Additionally, we showed cross‐modality resolution improvements to LR MDCT images. Our approach is, therefore, a first and essential step in enabling realistic high spatial resolution CT‐generated DRRs for deep learning training.
A Study of the Spread of Virtual YouTubers according to the Popularization of Virtual Production Technology

Virtual production technology has emerged as a significant alternative in film production, overcoming the limitations of linear pipelines and enabling real-time adjustments and collaborations on the set while reducing uncertainty. The non-linear workflow of virtual production is made possible by the advantages inherent in game engines. These engines provide real-time feedback, facilitate rapid iteration and experimentation, and transform the production process into an agile methodology. This technology extends its influence beyond the realm of filmmaking, and interestingly there are technological similarities found in the field of virtual YouTubers (VTubers). Virtual production technology can be classified into three main types: performance capture, real-time hybrid, and the live LED wall types. Among these, VTubers predominantly leverage performance capture virtual production, capturing character movements and utilizing game engines to construct virtual environments. To achieve mainstream adoption of virtual YouTuber activities, it is crucial to bridge the technological gap and actively embrace virtual production techniques. Popularization of technology typically involves overcoming what is known as the "chasm," a transitional phase. To vault over this chasm, improving technological accessibility has become paramount. With increasing competition in the 3D software industry, software companies are quietly engaged in a battle to enhance user accessibility. Looking ahead, advancements in virtual production technology and increased accessibility are expected to create an environment where both individuals and businesses can readily participate in virtual YouTuber activities. This development is poised to have a positive impact on the entire YouTuber ecosystem.
Interference of Indonesian Language on Japanese Language in the Use of Aizuchi (相槌) by Japanese Language Students in Medan

Language interference or disorder often occurs in individuals who have acquired proficiency in multiple languages, including Japanese language learners at the college level. This research aims to analyze the interference of the Indonesian language in the use of aizuchi (backchanneling) in Japanese by Japanese language students in Medan, Indonesia. The study employs a qualitative approach with a descriptive method. The data consists of transcriptions of conversations between students and lecturers that contain instances of aizuchi interference. Data collection is conducted through observation using techniques such as eavesdropping and note-taking. The collected data is analyzed through data condensation, data presentation, and conclusion drawing. The findings reveal that the interference of the Indonesian language in aizuchi in Japanese primarily occurs at the lexical level, specifically in the utilization of Indonesian words as aizuchi in Japanese conversations. An example of this interference is replacing the Japanese word "hai" with the Indonesian word "iya." The students' experiences influence the identified interference in learning foreign languages and cultural factors. This research contributes to a better understanding of the interference of the Indonesian language in the use of aizuchi in Japanese and provides insights for teaching the Japanese language at the tertiary level.
Intelligent Surveillance Drone System for Health Care Enhancement in a Smart City

Accelerating medical services to provide appropriate health care within the available time is essential, especially in critical cases. Achieving this goal depends on the response speed of the health care teams in reaching the accident scene and the people who are seeking or needing health care services. Response speed depends entirely on the data provided to these teams by the requesters of these services. This work utilizes drone technologies through a set of developed algorithms to ensure the provision of the required data in the shortest possible time. Drones fly for a specific time to carry out the required tasks over a specified area. The area of interest is divided into a group of zones, and a set of drones is allocated to each zone. The provided algorithms will minimize the maximum completion time required to perform all assigned tasks for all regions. Practical experiments were performed using two classes of 280 different instances to assess the performance of the developed algorithms; different criteria were used to measure the performance of these algorithms. The obtained results showed the ability of the presented algorithms to achieve the assumed goal by reducing the maximum completion time. Results showed that the best algorithm is MCZ with a percentage of 100% and a 0.00 average gap.
An Effective Reversible Data Hiding Scheme Used in Compressible Encryption Which Can Prevent Loss

This paper introduces a novel approach that combines reversible data hiding (RDH) and encryption-then-compression (ETC) for images. The proposed method involves encrypting the image first, followed by hiding secret information within it. The encrypted image can then be compressed, and upon decompression, the recipient can retrieve the complete hidden information and decrypt the image. This method effectively safeguards against potential damage to the embedded information caused by lossy compression. To validate the integrity and reversibility of the embedded information, we conducted simulation tests, which also demonstrated the method’s favorable compression performance.
REVERSIBLE DATA HIDING TECHNIQUES AND ITS APPLICATION IN INFORMATION SECURITY

Reversible data hiding is widely recognized as one of the most employed methods for copyright protection and data security. This technique involves embedding a unique piece of information, referred to as a watermark or secret data, without being detected by human or machine visual systems. Moreover, the original image is completely restored after embedded information is extracted by the recipient. Reversible data hiding technique has many vital applications, such as camouflage in communication to protect confidential information or embedding information into photos to protect digital image content. However, the problem with this technique is to maintain a good trade-off between imperceptibility and a higher bit embedding rate. This research provides a thorough review of existing methods of image steganography. Moreover, the paper also discusses the strategy to select different cover media for numerous applications in modern life.
AdaptPSOFL: Adaptive Particle Swarm Optimization-Based Layer Offloading Framework for Federated Learning

Following the rising privacy requirement enforced by regulations such as the European General Data Protection Regulation and the HIPAA, the centralized training of machine learning models has become difficult. Federated learning has come under extensive adoption because it allows clients to train a local model on a local dataset and then send the model to a server for aggregation. This aspect allows clients to preserve the privacy of their local data, while also allowing the training of a global model. A new area of study that is emerging is the possibility of offloading the training of the local model between a client and a server to influence the training time of the model. Studies such as FedAdapt use a reinforcement learning agent to decide the optimal split between a client and a server. In our study, we determine the optimal split between a client and server by formulating an optimization problem that simultaneously minimizes the CPU utilization and the round execution time while considering the number of layers placed on the client as decision variables. We apply particle swarm optimization to approach the optimization problem. Based on our experiments, we observed that our scheme outperforms FedAdapt and classic federated learning on the combined objectives by 36.60% and 36.73% on average respectively.
Using Unmanned Aerial Vehicles for Traffic Flow Analysis at Large Multi-Lane Roundabout

Traditional traffic turning movement survey of large multi-lane roundabout is a daunting task as the traffic surveyors are unable to track the vehicles’ movement. The direct method to obtain the turning movement requires the survey equipment to be placed at a higher level, usually at masts or high-rise buildings adjacent to the intersection. Indirect methods such as license plate matching may yield inaccurate results. With the advancement of technology, the use of unmanned aerial vehicles (UAV) in various fields has seen an increase, including traffic and transportation, due to its versatility. This paper compares the method of collecting traffic turning movement data using (i) video camera recording; and (ii) UAV to analyse the traffic flow at a 200 m inscribed diameter roundabout in Kuching, Malaysia. The results give an insight into the capability of a single unit UAV in determining the turning movement. Description of the challenges and limitations of UAV application are documented. The differences in the data obtained from methods were considered small and negligible. Due to the battery limitation, the UAV has to be flown down for battery change, which cause approximately 10 min of data loss for an hour’s survey. Continuous data captured with a video camera shows that data loss is within the range of 20% of the total volume. With the expected uptrend of UAV applications in the near future, this kind of research serves as a useful reference for industries and researchers.
CHASING THE RETENTION: EXAMINING THE DIVERSE EFFECTS OF JOB SATISFACTION AND WORK ENVIRONMENT ON EMPLOYEE RETENTION IN THE INFORMATION TECHNOLOGY SECTOR

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; &nbsp;In this study, we investigate employee retention in the dynamic IT sector in India, employing an exploratory research approach. Our research included 110 participants from various organizational levels, encompassing both established and prominent organizations such as Infosys, TCS, Ernst &amp; Young, HCL, and Wipro, situated in key Indian cities. Data was collected through a structured questionnaire comprising 24 questions, utilizing a five-point rating scale, and meticulously recorded using Google Forms. To analyze the data, we utilized the robust Statistical Package for Social Sciences (SPSS) and descriptive statistics. Our findings reveal crucial insights into the determinants of employee satisfaction and retention within the IT industry. Firstly, personal, and individual context factors, such as competitive compensation and robust career development initiatives, were identified as powerful predictors of employee satisfaction and retention. Factors like recognition systems, training programs, and promotional opportunities significantly motivated employees to remain loyal to their organizations. Secondly, our research underscores the significance of work context factors in predicting employee satisfaction. Favourable working environment like flexible schedules, collaborative team dynamics, supportive leadership, and alignment with organizational values all significantly contributed to retaining employees. Technical support, particularly for high-performing individuals, played a pivotal role in offering developmental opportunities and on-site learning experiences. The performance appraisal process emerged as a critical factor, providing employees with valuable feedback, enhancing their understanding of their role within the organization, and boosting motivation and commitment. Demographic data revealed that most respondents (83.64%) were aged between 20-40 years, providing important insights into the age composition of our sample. This research offers valuable guidance to organizations in the Indian IT sector, allowing them to refine their strategies and practices to foster higher levels of satisfaction and loyalty among their workforce effectively.
Practical Example: Face Recognition

Face recognition is a computer vision problem to detect and identify human faces in an image or video. The first step of facial recognition is to detect and locate the position of the face in the input image. This is a typical object detection task that we explored in the previous chapters. After the face is detected, a feature set, also called a facial footprint or face embedding, is created from various key points on the face. A human face has 80 nodal points or distinguishing landmarks that are used to create the feature set (USPTO Patent Number US7634662B2, ). The face embedding is then compared against a database to establish the identity of the face.
Practical Example: Object Tracking in Videos

The focus of this chapter is on two critical capabilities of computer vision: object detection and object tracking. In general, and in the context of a set of images, object detection provides the ability to identify one or more objects in an image, and object tracking provides the ability to track a detected object across a set of images. In previous chapters, we explored the technical aspects of training deep learning models to detect objects. In this chapter, we will delve into the practical application of our object detection knowledge within the realm of video.
Prerequisites and Software Installation

This book is a practical guide that explores the process of building computer vision applications using the Python programming language. Throughout this book, you will gain a comprehensive understanding of leveraging OpenCV for image manipulation and harnessing the power of TensorFlow to build machine learning models.
Performance Analysis of ExpressJS and Fastify in NestJS

A lot of research and resources are available on the back-end development using NodeJS because of its flexibility and ease among develop ers, but in the recent years, there has been a rise in migration of back-end framework to NestJSNestJS by developers due to the various shortcomings of NodeJS. In this paper, we have given an insight on the shortcomings of NodeJS compared to NestJSNestJS. Fundamental concepts of NestJSNestJS are discussed along with the construction of the structure of the application. We have also highlighted features of NestJSNestJS including unit testing, validation, and database connection with PostgreSQL. A comparative analysis is drawn between these ExpressJSExpressJS and FastifyFastify based on latency and throughput varying with respect to the number of concurrent HTTP requests sent to both Express.js and FastifyFastify using Apache Jmeter. Results are drawn in graphical form and inference is discussed. FastifyFastify proved to be 5% faster in performance than Express.
Deep Learning in Object Detection

Chapter 5 covered how to classify images using a standard multilayer perceptron (MLP) and a convolutional neural network (CNN). In classification tasks, our focus is predicting the class of the entire image, without considering the specific objects present within it. In this chapter, we will explore how to detect objects and determine their locations within the image.
Techniques of Image Processing

In a computer vision application, images are normally ingested from their source, such as cameras, files stored on a computer disk, or streams from another application. In most cases, these input images are converted from one form into another. For instance, they may need to be resized or rotated or their colors may need to be altered. In some cases, background pixels may need to be removed or two images may need to be merged. Additionally, finding boundaries around specific objects within an image may be a necessary task.