Reviewerly/exHarmony huggingface dataset:
SISTEM PENGENDALIAN INTERNAL BERDASARKAN COSO FRAMEWORK PADA PERUSAHAAN KONSTRUKSI

Companies that are growing rapidly must be able to increase their competitiveness by continuously improving their operational activities in order to survive. One of the improvements that must be made is the company's internal control system. The COSO framework can serve as a basis for internal control systems. The research method used in this research is descriptive qualitative. Data collection using a questionnaire. The sample in this study were 83 employees of PT Brantas Abipraya Persero East Jakarta Head Office. Based on the research results, it is known that the implementation of the internal control system at PT Brantas Abipraya Persero, East Jakarta Head Office using the 5-dimensional COSO framework has been going well, but some improvements still need to be made so that the control system can be implemented according to what the company expects. The implications of this research can be input for improving the performance of the internal control system, so that operations can run smoothly without errors and omissions that are detrimental to PT Brantas Abipraya Persero East Jakarta Head Office.
Application of Branch Current Method in Power Flow Reverse Analysis of Single-phase Intelligent Meter

In the low-voltage three-phase four-wire power supply line, the phase line behind the single-phase smart meter is wrongly connected, resulting in the negative current of the single-phase smart meter, the inconsistency between the phase line and the neutral current, and the reverse flow of the power flow, resulting in the risk of inaccurate measurement and high-quality service, causing complaints, and affecting the quality of power supply service. According to the stagger connection of the rear phase lines of two single-phase smart meters, collect the curve data of the system current and other electrical parameters, and analyze in detail the relationship between electrical parameters, the cause of reverse flow, the source of measurement error, and the correct method of electricity settlement. This paper puts forward detailed preventive measures from management and technology, evaluates and manages the whole process of the electric power metering device, and effectively guards against various risks.
Research on Talent Cultivation Index Model of High-Tech Enterprises Based on Improved AHP

Talents are the core competitiveness of enterprise development, while talent cultivation plays an important role in the stable development of enterprises. As a result, building a scientific evaluation model of talent cultivation is an effective way to establish talent development path and optimize human resource structure. Based on three first-level indexes, four second-level indexes and three third-level indexes, this paper establishes an evaluation model for talent cultivation program, which uses AHP method to calculate index weights. However, in AHP, there are cases where the judgment matrixes fail the consistency tests. To solve this problem, an improved AHP is proposed to amend flawed matrix which can’t pass consistency test for successful operation. After calculation, all matrixes finally pass consistency test and the weights of each level are obtained. Through ranking weights and analyzing results, we can get some guidances and suggestions to assist company in cultivating talent and help talents improve themselves better.
E-Learning Platform for Digital Customization of Garments

Virtual prototyping and digital customization of garments via specialized 3D software is a current trend in clothing production. This approach leads to faster garment design cycles, reduction of physical samples, faster time-to-market and efficient interactions between various stakeholders of the fashion supply chain. E-learning instruments provide more attractive and rapid access to educational materials and the trend of learning via Open Educational Resources and Open Educational Practices was recently fostered by various factors, including the restrictions of COVID-19 pandemics. A consortium of six research and educational providers in Europe identified the need of training of Higher Education students and young professionals in textiles in the field of virtual prototyping via e-learning. An e-learning platform with educational materials of virtual prototyping of garments was conceived and designed. The platform is under development and will include a database of physical and digital fabrics, digitalized patterns and styles, a knowledge base as well as an adapted user interface for e-learning of an online software application for virtual prototyping. This ambitious work is ongoing within the Erasmus+ project DigitalFashion – “Collaborative Online International Learning in Digital Fashion”, funded by the European Commission during 2022-2025 (www.digitalfashionproject.eu).
Design of College English Guiding Resources Information Management Platform Based on MVC Architecture

The English guiding resource management system is the container of resource construction and the basis for the realization of education informatization.Aiming at the limitations of traditional educational resource platforms, an English teaching resource management system based on the MVC model was designed.The system is based on J2EE platform and MVC framework application development, using Struts framework design, and using MySQL as the back-end database.The article first introduces the main technology of the system design, then carries on the in-depth function module design to the system, and gives a detailed description of the realization of the main function modules of the system.On the basis of the system design, using the object-oriented software engineering design model, the English teaching resource management system was realized, and the management efficiency increased by 7.2%.
Cipher Block Chaining SVM for Secured Decentralized Cloud using Cryptographic Algorithms

Cloud computing is used in multiple companies and institutes and is growing rapidly.It provides services via the internet to access shared resources like network storage servers.Cryptography is one of the strong methods we use in cloud computing Cloud provides data storage we assume that whatever we are storing data or sending data to a server we think that our data is secured but we can't give a guarantee about whether our data where is safe or not.Hence there are many methods and technologies which provide security but still, there is the problem of leakage of data due to collusion attacks or any physical attacks.To reduce these problems, we are applying some algorithms to secure data.To entrust user security, we proposed this cryptography process.We are providing security using different techniques such as the key generation for the SVM-based encryption service model is done with various encryption mode operations with several enhancements.And A block cipher's method we are used by applying a cypher key to the entire block, cypher block chaining (CBC) encrypts a set of bits as a single unit or block.In cypher block chaining, an initialization vector (IV) of a particular length is required.With this and a single encryption key, companies and individuals may safely encrypt and decrypt large volumes of plaintext.Compared to an existing system The SVM algorithm uses less storage and even has a reduced execution time.This result helps us to use a smaller number of keys and provides strong data secrecy.
IMAGE TO PERTURBATION: A GRAPHICALLY SECURED DIGITAL FILTERING SYSTEM FOR PRIVATE INFORMATION USING DEEP NEURAL NETWORKS

For the purpose of creating aesthetically protected images, we tend to suggest a distinctive image transformation network.Regarding deep neural networks that protect privacy (DNNs).Plain image datasets are used to train the projected transformation network, resulting in the regeneration of plain image area units into optically protected ones.Standard sensory activity coding techniques degrade the accuracy of image classification and are insufficiently resistant to persistent attacks.While deep learning is highly successful in algorithms based, it has also been explained to be subject to even handed modifications of the input.These perturbations are signals implemented to the input which was created specifically to manipulate the system while maintaining nearly normal for humans.Additionally, there are objective techniques commonly that are the source of this network on the number of inputs.The planned network, but at the other hand, not only allows the cutting-edge to keep up However, the image classification accuracy obtained by using plain photos is highly resistive to DNN-based attacks.
Solar irradiance nowcasting based on a network of all-sky imagers: the value of high-resolution data on variability information

The transition to a fossil-free energy system requires the rapid installation of photovoltaic (PV) systems. For Germany, the government is targeting an installed PV capacity of 215 Gigawatts (GW) by 2030 (70 GW today). This target is associated with an increased installation rate of 22 gigawatts PV per year (around three times compared to the 2021 rate). In urban areas, the majority of systems will be installed on rooftops connected to the low-voltage grid. It is therefore likely, that the majority of suitable rooftops will be equipped with PV systems in the next 10 years. In parallel, significant changes in load patterns (e.g. e-mobility, heat pumps) and the integration of battery storages can be expected.&amp;#160; The efficient integration of the additional PV systems into the electrical grid also requires a detailed understanding of the generation profiles at different levels from the household to the transformer. Therefore, the impact of very short-term solar irradiance variability on ramp rates and balancing effects should be investigated for scenarios with decentralized, but much denser PV generation than today. Since this variability is mainly caused by small scale cloud dynamics, high resolution information on temporal and spatial cloud cover and irradiance distribution is needed. In northwestern Germany, DLR has installed and is operating Eye2Sky, a dense network of allsky imagers (ASI). At 30 different locations, high-resolution fisheye images of the sky are taken every 30 seconds. At 10 locations, the images are complemented by radiation and meteorological measurements. The Eye2Sky network covers about 100 km x 100 km centered at the city of Oldenburg. It has a low ASI density in rural areas and a high density in city of Oldenburg, thus providing an almost complete coverage of the city.&amp;#160; Eye2Sky is used to study solar irradiance variability in the city of Oldenburg at a high spatial (50 meters) and temporal (30 seconds) resolution. This enables simulations of single rooftop PV systems. Compared to state-of-the art radiation data sources like satellite images or numerical weather models (NWP), the camera information in Eye2Sky resolves cloud details that cause solar irradiance fluctuations on small scales down to household level. In this work, we would like to present a solar irradiance nowcasting validation from the ASI network in Oldenburg and its comparison with methods based on satellite (MSG) as well as NWP (ICON-D2) data. Emphasis will be made on the ability of the different methods to reproduce the spatio-temporal variability under different cloud conditions.
Gradient domain weighted guided image filtering

Guided image filter is a well-known local filter in image processing. However, the presence of halo artifacts is a common issue associated with this type of filter. This paper proposes an algorithm that utilizes gradient information to accurately identify the edges of an image. Furthermore, the algorithm uses weighted information to distinguish flat areas from edge areas, resulting in sharper edges and reduced blur in flat areas. This approach mitigates the excessive blurring near edges that often leads to halo artifacts. Experimental results demonstrate that the proposed algorithm significantly suppresses halo artifacts at the edges, making it highly effective for both image denoising and detail enhancement.
Sistem Informasi Pemesanan dan Laporan Keuangan Jasa Sablon Pakaian di Yuan Shop Menggunakan Metode Agile Development

Yuan Shop is one of the business places to order screen printing services in Kebonagung Village, Kraksaan District, which provides screen printing services according to customer requests. Orders at Yuan Shop are made offline and via the WhatsApp application. There are various problems with this system, namely that it still requires customers to come to the place to order screen printing services and make payments, so that they are constrained by distance and time to go to where to order screen printing. Orders made through the WhatsApp application also have drawbacks, namely when too many chat orders come in so it will be difficult to record the number of orders. The owner records orders and recapitulates financial reports manually using a notebook, so that it can have an impact on the performance of business owners in recording orders and recapitulating financial statements every month. Therefore we need an ordering information system and financial reports that can overcome existing problems. The development method used in this study is the Agile Development Method, for system design using Flowcharts, DFD, and ERD. The results of the research conducted are to produce an ordering information system and financial reports for clothing screen printing services at the Yuan Shop which can be used to simplify the process of ordering clothing screen printing services and to make it easier for screen printing business owners to find out the results of financial reports every month.
Indonesian Language Diplomacy in the United States of America Through the Bahasa Indonesia Bagi Penutur Asing (BIPA) Program

Indonesian language (bahasa Indonesia) can be a soft power in promoting Indonesia in the international arena. The government through the Language Development and Development Agency took the initiative to facilitate foreign speakers to learn Indonesian through the BIPA program. The purpose of this study is to find out how Indonesian language diplomacy is in the United States through the Indonesian Language for Foreign Speakers or Bahasa Indonesia bagi Penutur Asing (BIPA) program. This study uses the concept of Cultural Diplomacy. The method used in this research is descriptive qualitative research method. Data collection techniques were carried out by means of literature studies and interviews with BIPA Coordinators, BIPA teachers and students in the United States. The results of this study showed that cultural and Indonesian language diplomacy carried out through the BIPA program in the United States has been running since 2017 until today, where since the Covid-19 pandemic until now learning has been carried out online. The process of delivering materials is carried out by BIPA teaching staff who have been selected through various stages of selection. To find out how the results of BIPA learning in the United States, students must display their Indonesian language skills through a virtual Indonesian language showcase.
MapReduce Solutions Classification by Their Implementation

Distributed Systems are widely used in industrial projects and scientific research. The Apache Hadoop environment, which works on the MapReduce paradigm, lost popularity because new, modern tools were developed. For example, Apache Spark is preferred in some cases since it uses RAM resources to hold intermediate calculations; therefore, it works faster and is easier to use. In order to take full advantage of it, users must think about the MapReduce concept. In this paper, a usual solution and MapReduce solution of ten problems were compared by their pseudocodes and categorized into five groups. According to these groups’ descriptions and pseudocodes, readers can get a concept of MapReduce without taking specific courses. This paper proposes a five-category classification methodology to help distributed-system users learn the MapReduce paradigm fast. The proposed methodology is illustrated with ten tasks. Furthermore, statistical analysis is carried out to test if the proposed classification methodology affects learner performance. The results of this study indicate that the proposed model outperforms the traditional approach with statistical significance, as evidenced by a p-value of less than 0.05. The policy implication is that educational institutions and organizations could adopt the proposed classification methodology to help learners and employees acquire the necessary knowledge and skills to use distributed systems effectively.
Innovative Application and Research of Flipped Classroom in Vocal Music Teaching Under the Background of Internet +

At present, flipped classroom is a new teaching model in the Internet age.It makes education freer, breaks through the limitations of classroom knowledge teaching, realizes teaching tasks outside the classroom, and strengthens the communication between teachers and students to a certain extent.Taking vocal music courses in higher vocational colleges as an example, vocal music teaching has always adopted the traditional "collective singing" teaching mode, and the efficiency of classroom teaching has not been significantly improved.It provides a new development direction for vocal music teaching in colleges and universities, which not only meets the actual requirements of the new curriculum standard teaching, but also fully improves the effect of vocal music teaching in colleges and universities.Therefore, this paper mainly studies and analyzes the optimization methods and strategies of "flipped classroom" in vocal music teaching in colleges and universities.
WVC: Towards Secure Device Paring for Mobile Augmented Reality

In mobile augmented reality applications, how to build a secure device connection between two previously unassociated devices without prior set-up is challenging, which also refers to the problem of device pairing. Most existing approaches to device pairing either have certain limits to be utilized in the environment of augmented reality or lack considerations of security issues. In this article, we design WVC, an intuitive, user-friendly, and secure device paring system for mobile augmented reality. It enables users to connect to a neighboring device by waving a finger to click towards it in the air. The system uses critical features from finger tracking trajectories to understand which device a user wants to interact with. Then, it designs key generation and correction algorithms to enhance security in device communication. In addition, we present solutions to defend against malicious attacks. We implement and test the system with 10 volunteers. The experimental results demonstrate the feasibility and effectiveness of the system under varied scenarios in which devices are close to each other at a small angle or located at different heights within 2 m .
ON-SITE FAULT DIAGNOSIS FOR MECHANICAL EQUIPMENT BASED ON COMPRESSED SENSING OF MULTISOURCE DATA

With the development of the Internet of Things (IoT), on-site fault diagnosis of multisource sensor data is becoming more and more important.Thus, for on-site fault diagnosis implemented on edge computing platform, efficient multisource data fusion and low-cost computation is essential for fault diagnosis.In this study, a fault diagnosis scheme based on multisource sensor data com-pression is proposed, and its advantages include high data compression & fusion efficiency, low computational cost, and fast online training.The method includes reference matrix construction, compression and fusion, sparse vectors calculation, testing sample reconstruction & quality evaluation.First, a reference matrix is constructed with labelled multisource sensor data.Then, the reference matrix is compressed using a measurement matrix, meanwhile, the multisource data samples are fused.Later, the testing sample is sparsely represented based on batch matching pursuit algorithm, and outputs a sparse vector.Finally, based on reconstruction quality evaluation, the pattern of the testing sample is determined.Two cases are employed to validate the effective-ness of the proposed approach, including landfill gas power generator maintenance pattern recognition and multiple redundancy aileron actuator fault diagnosis, and the accuracy is 96.13% and 97.50%, respectively.
Role of Artificial Intelligence and Machine Learning for Enabling IoT-Enabled Healthcare Systems

The Internet of Things (IoT) and artificial intelligence (AI) technologies are a natural match for future expansion. IoT uses the internet to link everything in the globe. IoT devices have limitless information in chips and sensors since there are many linked devices, which means there is a lot of data that can be used to empower individuals in all facets of their life. The significant amount of data created by IoT devices is too much for even humans and computer algorithms to analyse and analyse. Algorithms for machine learning and artificial intelligence so aid in controlling them. The functional answer to controlling the numerous linked IoT components is provided by AI. Learning capabilities and limitless data processing that are produced by IoT devices are the crucial concern. The businesses are employing machine learning (ML), a potent branch of artificial intelligence (AI), to solve this problem. The smart systems give an accurate forecast for applying ML to IoT data. IoT AI applications help businesses reduce unscheduled downtime, create new services and products, run more efficiently, and manage risk better. In healthcare, smart homes, autonomous cars, agriculture, and marketing, this combination is mostly employed. This survey study addresses healthcare applications based on artificial intelligence (AI) and the internet of things (IoT) in these numerous applications. Additionally, it presents a review of several IoT AI algorithms for early illness prediction in the medical field.
Performance Analysis of Different Classifiers for the Application of Human Activity Identification

Recognizing human movements through computer vision is an important field of research, which can be used in various applications such as patient monitoring, observation, and human-machine interface. The ability to perceive these movements requires extremely complex judgments. Generally, the above-mentioned applications need to automatically recognize advanced operations, such as: a pair of easy movements of a man and a woman. If the action is well classified, then the proper information can be provided to the system. This paper addresses various machine learning algorithms such as logistic regression, RBF SVM, decision tree, random forest, linear SVM, gradient boosting DT by grouping different activities. This article classifies complex human behaviors by observing, comparing and evaluating the performance of algorithms by using large set of information.