Reviewerly/exHarmony huggingface dataset:
Analisis dan perancangan aplikasi Augmented Reality anatomi tubuh manusia berbasis Android

This study focuses on designing and analyzing an Android-based Augmented Reality (AR) device for practical learning of human anatomy. Its objective is to analyze the effectiveness of AR technology in enhancing practical learning of human anatomy and identifying possible issues in implementing such technology. The study utilizes the software development method based on SDLC with a waterfall model. The results demonstrate that the use of AR technology in practical learning of human anatomy can improve the effectiveness of student learning. Students who use AR devices can better understand and recall the material faster than those who do not. Additionally, the AR device facilitates students in visualizing the human body structure interactively and in 3D. However, the study also identifies some issues that may arise during the implementation of AR technology in practical learning of human anatomy. Educators and software developers can take note of these issues when developing AR applications for practical learning of human anatomy. In conclusion, this study proves that the use of AR technology in practical learning of human anatomy can enhance the effectiveness of student learning and provide useful information for educators and software developers in developing effective AR applications for practical learning of human anatomy.
EVALUATION OF IMPLEMENTATION OF ACCOUNTING INFORMATION SYSTEMS AND INTERNAL CONTROL SYSTEMS IN FINISHED GOODS INVENTORY AT PT ISKANDAR INDAH PRINTING TEXTILE SURAKARTA

This research was conducted at companies engaged in the textile sector. The purpose of this research is to find out how the accounting information system and internal control system are applied to the finished goods inventory at PT Iskandar Indah Printing Textile and to find out how the accounting information system and internal control system for finished goods inventory at PT Iskandar Indah Printing Textile are by the theory stated. There is. The method used in this research is qualitative. Data collection techniques used are interviews, observation and documentation. The data analysis technique used in this study is by (1) Collecting data to get the desired information. (2) Reducing data or summarizing the main things. (3) Presenting data to conclude. (4) Summarize the research results.The results showed that the finished goods inventory accounting information system implemented at PT Iskandar Indah Printing Textile indicated that the tasks or functions carried out and the recording and reporting system regarding dead goods inventory management activities had been practical. Meanwhile, the internal control system for finished goods inventory still has a dual function/task of the warehouse department. Based on the practice that occurred at PT Iskandar Indah Printing Textile, by looking at the existing theory, the situation is still not by the idea. Still, the company can adequately run the accounting information and internal control systems on finished goods inventory.
Efektivitas Penggunaan Media Instagram Komik dalam Pembelajaran Menulis Sinopsis Teks Cerita Bima Bungkus pada Siswa Kelas X Di SMA Negeri 9 semarang Tahun ajaran 2022-2023

This study aims to describe in detail the effectiveness of using Instagram Comic media in learning to write a synopsis of the Bima Bungkus story text for class X students at SMA Negeri 9 Semarang in the 2022-2023 Academic Year. This is motivated by students' lack of interest in reading and students' lack of interest in wayang stories. Inadequate use will affect student learning outcomes. So we need media that is adequate and can attract students' learning interest in the process of understanding the story of wayang Bima Bungkus. Thus, the formulation of the problem obtained is how effective is the use of Instagram Comic media in learning to write a synopsis of the story text Bima Bungkus in class X students.This research is included in the experimental research, with the type of research One-Group Pretests-Posttest Design. The data obtained will be used to determine the effect of treatment on controlled conditions. The approach used is descriptive qualitative. The data obtained from this study were in the form of test results, questionnaires and interviews.After analyzing the data, the following research results were obtained: From the results of the learning competency test scores using Instagram Comic media, the final average score of the competency test was 93.58. While learning that only uses Power Point media, gets an average final competency test score of 34.70. From the acquisition of the final average score of the competency test, it can be said that the final average score of the first meeting competency test using only Power Point media is lower than the predetermined KKM score of 75 and the final average score of the second meeting competency test those using Instagram Comic media are higher than the specified KKM score. Thus, the use of Instagram Comic media in learning to write a synopsis of the Bima Bungkus story text for class X students can have an influence on the achievement of the specified learning objectives.
An Assessment of the Fundamental Concepts of Blockchain Technology and its Application in Cryptocurrency using Bibliometric Methods

In past few years, blockchain has attracted attention as a potential area of study on a worldwide scale. It is a decentralized database system that is both autonomous and unreliable. Since its first implementation, blockchain technology has been used by a growing number of nations and academic institutions. In order to get a broad understanding of blockchain technology and the present research focus, this article makes use of the quantitative analysis tools CiteSpace. For this study, we relied heavily on data from the Web of Sciences core collection dataset. The studies included 1000 publications published between 2015 and 2022. To be more precise, the patterns and fields of publishing may provide information on the directions and foci of recent blockchain studies. This bibliometric study included the most prominent and prolific writers, universities, nations, and publications. Smart contracts, Bitcoin, Ethereum, Dogecoin and cryptography were the most popular theoretical and research issues related to blockchain technology. To identify developing tendencies and new frontiers in blockchain, a keyword-based cluster analysis was conducted. The findings of this study demonstrate the growing interest in blockchain technology and its potential applications in various domains, including cryptocurrency. The analysis also identified key research areas and gaps in the literature that can guide future research in this field. Overall, this paper provides a comprehensive overview of the current state of research on blockchain technology and cryptocurrency and contributes to the growing body of knowledge on this topic.
The Design and implementation of a double layered Security Network with an Authentication System through Block chain Technology

With more and more connected devices joining IoT networks, protecting their communications is becoming more difficult. The distributed ledger technology known as blockchain has the potential to solve the security issues plaguing 5G-enabled IoT networks. This article provides a layered blockchain security strategy to protect Internet of Things networks and make their deployment simpler. Using clustering, the complex multi-layer structure is simplified. The K-unknown clusters in the IoT network are generated by use of a hybrid Evolutionary Computation Methodology involving Simulated Annealing and Genetic Algorithms. The chosen cluster chiefs have authority over local authentication and permissions. By creating a private blockchain network on a local level, cluster administrators and critical nodes can streamline their interactions. This type of blockchain also facilitates authentication on a network, but it also adds an extra layer of security and integrity. The proposed framework makes use of Hyperledger Fabric, an open-source implementation of Distributed ledger technology. The encryption of data transmitted between nodes is ensured by the use of a global distributed ledger technology, which is instated in the base stations. As opposed to previously disclosed methods, simulation results show that the proposed clustering algorithm is effective. Finally, it is shown that the envisaged portable cryptocurrency architecture achieves a better latency/capacity tradeoff than a conventional global blockchain.
Implementation and Execution of Blockchain Technology in the Field of Education for the Special Education

A blockchain is a data structure made up of data blocks that are organised chronologically. Decentralization, reliability, data exchange, security, etc. are some of its features. It is frequently employed in a variety of sectors, including credit encryption, smart contracts, and digital currency. Online education, a revolutionary educational method, has become extremely well-liked with the advancement of Internet technology. However, there are still several issues with this style of education, including issues with course authenticity, verification of credits and certificates, student privacy, and course sharing. This paper explores the fundamental technological concepts and application characteristics of blockchain technology through a review of the literature and a case study. It then suggests a blockchainbased solution to the issues with online education. The blockchain technology enables the trusted, distributed storage of learning records, the provision of reliable digital certificates, the sharing of learning resources via smart contracts, and the protection of intellectual property via data encryption. According to the study, incorporating blockchain technology into online education is a trend that has a lot of potential.
Blockchain Technology for Protecting Ingenuity of Image

Hash link is exploited for digital images to obtain imagecord concepts derived from cryptographic formation. The primary difference from the Blockchain imagecord concept is that the data block will not be kept as a collection of individual blocks but rather will be joined by an embedding method. Accordingly, standard image files are used in the same way as it was created before. An extra data block is also cultivated in it to preserve the imagecord from its origin point. The proposed method requires no supplementary data apart from the original image. This solution is also suitable for multiple graphics file formats, making it a portable version with platform independence. Simultaneously, this method also gives higher security against the forgery of the original file without losing its properties. This was attained by embedding the hashing data with the image’s data block without losing its integrity by preventing alternation. This paper deals with the concept of Blockchain distributed ledger to apply in imagecord by embedding algorithms and data blocks in the image.
Human- Centred Artificial Intelligence In Education, Present And Future Opportunities

High achievers of advantages, including accuracy, effectiveness, as well as minimization as well as the execution of repetitive or time-consuming activities, have been raised as a result of artificial intelligence (AI) technologies' growing importance in a variety of fields. Artificial intelligence applications have tremendous promises for creativity, especially in the area of education. In order to generate training assessments and design personalised learning experiences, this subject place a lot of emphasis on studying and assessing user features. These AI-driven systems include various issues in terms of privacy, morals, and dignity because they include the knowledge of trainees. In just this essay, we define the term "human-centred AI" and discuss how an AI system might be created with row with human principles despite endangering people. Therefore, think this is an appropriate moment to increase consciousness more about application of concepts that nurture human-centred values and assist in the creation of accountable, ethically, & real worth AI since this education industry is just beginning to incorporate AI into learning resources.
PELATIHAN KETERAMPILAN MENGETIK SEPULUH JARI BAGI SISWA SMK LINK &amp; MATCH KOTA TANGERANG SELATAN

Speed in typing is needed to make it easier when creating documents. This community service activity aims to share the knowledge by the lecturer team regarding fast typing techniques using the ten-finger approach with the help of the typing master application. With the Typing Master application, it will help training participants to be able to carry out all their daily administrative activities in processing documents quickly and precisely, besides that the ability or skill to process words can also be an added value or provision for training participants as a competency value (soft-skill). in the world of work that needs HR who are proficient at typing with ten-fingers quickly and precisely in preparing documents. The Training attended by 76 SMK Link &amp; Match students for two days on November 3-4 2020. The results showed that the knowledge and skills of SMK Link &amp; Match students in typing with the ten-finger technique were increasing, especially since students could continue to deepen the material obtained by trying to keep typing fast using the typing master application.
A Privacy-preserving Proof-of-Reputation

The sharing of high-quality information is essential for improving the user experience in distributed systems such as vehicular networks or IoT-based monitoring systems. Crowd-sourced data collection systems rely heavily on a secure and efficient consensus algorithm. However, consensus algorithms are often considered a bottleneck for scalability. With the emergence of blockchains, proof-of-work (PoW) has become the most popular and secure consensus algorithm for open and asynchronous distributed networks. However, PoW has been criticized for its energy consumption and waste of resources. To address this issue, alternative consensus protocols such as proof-of-stake (PoS) and proof-of-authority (PoA) have been proposed. Reputation-based consensus algorithms have recently gained attention but suffer from limitations in terms of privacy and predictability. In this paper, we propose a privacy-preserving proof-of-reputation (PoR) mechanism that combines ring signatures and dlog-based zero-knowledge proof systems. The proposed PoR algorithm aims to build unpredictable blockchain consensus algorithms that are privacy-friendly for permissioned blockchains. We describe the main algorithms used in PoR, demonstrate their security, and evaluate their performance through an experimental study. We conclude by discussing how these algorithms can be scaled to permissionless blockchain settings.
Blockchain Technology: An Overview

Blockchain Technology has acquired notoriety in both scholar and industry due to its decentralized, versatile, security and irrefutable characteristics. The blockchain is a disseminated data set that holds record of exchanges that are conveyed among members in its most essential structure. Manufactured exchanges can't pass aggregate certification since every exchange requires the understanding of various individuals. A record can never be changed or erased after it has been made and recognized by the blockchain. After the Internet, blockchain innovation is presently viewed as the main development. The previous could tackle the trust issue utilizing shared systems administration and public-key cryptography assuming the last option interfaces individuals to all the more likely comprehend online business processes. The objective of this paper is to take a gander at the critical instances of blockchain innovation's comprehensive effect and see it as an inseparable part of our daily existences.
Remote Sensing Big Data Collection Challenges and Cyberinfrastructure and Sensor Web Solutions

The Chapter introduces major Remote Sensing Big Data Collection Challenges—5Vs (Volume, Variety, Velocity, Veracity, and Value) and other remote-sensing-data-collection-specific challenges: identification, storage, distribution, representation, fusion, and visualization. Cyberinfrastructure is introduced as one of the platforms to enable the collection and management of remote sensing big data. Three major cyberinfrastructures for remote sensing big data were briefed, that is, Global Earth Observation System of Systems (GEOSS), NASA Earth Observing System (EOS) Data and Information System (EOSDIS), and ESA Federated Earth Observation (FedEO). Sensor web is introduced as a solution to enable lively collect data from sensors. A framework for sensor web cyberinfrastructure, Self-adaptive Earth Predictive System (SEPS), is briefed. Example applications of SEPS as a cyberinfrastructure building framework were discussed, including areas of climate, weather, disasters, and agriculture.
Automatic Breast Cancer Detection with Mammography Approach Using Deep Learning Algorithm

Breast cancer is a significant health concern for women worldwide, and early detection is crucial for successful treatment. In recent years, deep learning techniques, including convolutional neural networks (CNNs), have shown promising results in detecting breast cancer from medical images, particularly mammograms. Among the popular CNN architectures, VGG16 has been widely used for various image classification tasks, including breast cancer detection. In this abstract, we present a literature review of recent studies on breast cancer detection using CNN and VGG16. The studies demonstrate that CNN and VGG16-based models achieve high accuracy and sensitivity in detecting breast cancer from mammography images. The results suggest that these models have great potential to improve the accuracy and speed of breast cancer detection, which can help reduce the mortality rate and improve the quality of life for women with breast cancer. However, further research is needed to validate these models on more extensive and diverse datasets and to investigate their generalizability and interpretability. In conclusion, CNN and VGG16-based models are promising for breast cancer detection, and can contribute to advancing personalized medicine and precision oncology.
Using Natural Language Processing for Biometric Identification Optimizatoin

The use of natural language processing (NLP) methods for assessing employee performance is examined in this research article. The paper suggests a technique for extracting pertinent information on work performance from employee-generated text data, such as emails, chats, and feedback surveys. The suggested approach makes use of NLP methods including topic modelling, sentiment analysis, and text classification to extract characteristics that may be utilized as performance indicators for employees. The study's findings imply that the suggested approach, when compared to more conventional assessment techniques, may provide a more comprehensive perspective of employee performance, and assist managers in reaching more informed conclusions about that performance.
Fuzzy Based Image Edge Detection Using Improved Artificial Bee Colony Optimization Algorithm

The goal of evolutionary algorithms (EAs) is to eliminate solutions with the least fitness in order to identify the best one. This study offers an evolution-based edge identification method as a consequence. A training dataset made up of straightforward images and the ideal edge characteristics associated with them is used to determine the appropriate filter coefficients and thresholding method. An efficient technique based on bees’ foraging habits is the artificial bee colony algorithm. An enhanced version of the artificial bee colony method is suggested in this research, which will increase the capacity to search for many threshold values for edge detection. The picture is segmented using an effective fuzzy logic approach that uses fuzzy membership functions. The examination of the suggested model demonstrates the importance in multiple images.
Ethereum Decentralized peer-to-peer Contracts Mechanism

With its dedicated money Ether, Ethereum offers regionalized Ethernet simulated computers to manage as an open-source public blockchain, peer-to-peer agreementsability to perform clever contracts. And since Ethereum is the second largest blockchain, transaction data volumes are growing rapidly. However, at this point, most analyses of Ethereum data are focused on the statistical characteristics of Ethereum nodes and do not examine how transactions between them behave. In this research, leverage transaction data from existing blocks to categorize users and smart contracts into groups and Applies machine learning to Ethereum analytics for the first time. The identification data of the accessible Ethereum users and smart contracts is used to examine the clustering findings.
A Parallelized Genitor II Implementation For Training Semiring Neural Networks

Neural networks are a common and powerful tool to approximate highly complex non-linear functions. Most typically, the connection weights of these structures are adapted during training by employing gradient descent techniques. As this is not possible for so-called semiring neural networks, we propose to use a genetic algorithm for training this type of network and present a modern extendable C++-based parallelized implementation of the genetic algorithm Genitor II for this task. We compare our parallelized implementation with a more conventional implementation of the Genitor II algorithm and demonstrate that this novel approach is efficient. As a consequence, this novel implementation allows for the first time efficient and practical training of semiring neural networks even for relatively large network sizes of several hundred units. The source code is publicly available under the MIT license: https://git.informatik.uni-leipzig.de/ml-group/semiring-nn/parallelized-c-implementation.
Attention Guided Human Fall Detection for Elderly Patient Monitoring

Fall has been a significant threat to life if not attended to immediately. Elderly patients have problems with their motor activities, and patients with Alzheimer’s, Dementia, and Autism often suffer from falls. This paper presents a novel video-based fall detection approach using a deep neural network. We have designed an attention-guided CNN-LSTM neural network that detects falls in a video sequence. The convolutional neural network (CNN) is used to extract the spatial features of input data. In contrast, long short-term memory (LSTM) is used to learn the temporal relations between the frames. A multiplicative attention mechanism has been employed after the CNN layer that will extract the enhanced and focused features of input data. After that, an attention map is created based on the output of the context vector and fed to the LSTM layer for temporal feature learning. The experiments are carried out on the UR Fall detection dataset to validate the efficacy of the proposed algorithm.
Detection of Parkinson disease for handwriting dataset using deep learning algorithms

The most common neurodegenerative diseases are Parkinson's disease. People who have reached the age of 70 or older are affected. Parkinson's disease is the second incurable illness after Almeria's disease. The numerous characteristics that distinguish between people with Parkinson's disease and those who are healthy, and earlier diagnosis of the disease results in reduced disease severity. These are the typical signs of Parkinson's disease, which include both motor and non-motor deficiency in patients. The handwritten data set, which includes spiral and wave drawings, was retrieved from UCI equipment. EfficientnetB3 provides high accuracy in comparison to the current CNN model. To increase efficiency, EfflcientnetB3 shrinks the parameter size and equally scales each dimension using a predetermined set of scaling dimensions. The proposed model Adam –EfficientNet achieves 98.3 % which is the best accuracy compared to all other existing CNN models.