Reviewerly/exHarmony huggingface dataset:
Game Application as Teaching Tool to Assist Mastering Installation of Three-Phase Motor Control Topic: Expert Perception

The learning and teaching process using conventional methods might cause students to be unable to highlight their true potential. Besides, it can be more difficult for teachers to choose the types of games that suit the curriculum and students' needs. Hence, this study aims to develop a game application as a teaching assistant tool for a three-phase direct online starter topic in Electrical Technology Malaysian Vocational Certificate subject for Vocational College students using the Android platform. Learning materials and games are developed using multimedia and interactive elements to attract students to be active in using the learning medium based on game applications as 21st-century learning tools. The development purpose of the game application is to give early exposure to students to the installation of three-phase motor control before going through the laboratory session. The behaviorism learning theory and Game development Life Cycle (GDLC) model, were implemented in the game application development in this research. The researcher used Unity 3D software is online software such as Canva and Microsoft PowerPoint design tools interactively to make a game-based learning application for the three-phase motor control installation subject. Based on the analysis of the findings, the experts agreed that the functionality of this three-phase Motor Control game application can be used well and is suitable to be used as a teaching aid. In addition, these research results can be used by teachers to increase students' interest and further make the learning process more interesting to improve student understanding.
An Authentication-Based Image Scrambling Algorithm Using Hybrid Chaotic Maps and SHA

Digital mode of communication is widely used for exchanging various kinds of data through internet. The data that is being transmitted may be used in sensitive applications. Thus, those data have to be secured from various kinds of attacks and must ensure basic security services as well. One such kind of data is Image data which is used in medical imaging and some other sensitive fields as well. There are many algorithms to protect and secure the images during transmission but the algorithm and their security has to be modified constantly to cope up with the technological advancements. So, in this enhanced work, an authentication-based image scrambling algorithm using hybrid chaotic maps and SHA is proposed. RSA provides the initial-values for the chaotic-map. Improved LS will be used to generate random values for XOR operation. SBOX will be generated by using both Improved LS and Henon map. Authentication message will be embedded into the image and it will be XORed and scrambled thrice (SBOX, Arnold and Sub-Image based). Receiver will decrypt and verify the message. This algorithm is expected to be faster, provide good security and resist several attacks.
Using Pattern Analysis and Machine Learning to Categorise users of Online Directories based on their Surfing Habits

The major goal is to group users together, including in student organizations. Assume that the notion is required to cluster consumers in educational institutions. A cluster of websites or web pages organized by user interest makes up an online use directory. Web mining is the process of using strategies to extract websites from the web proxy log file. We are employing the Apriori Algorithm and the Fuzzy Clustering technique for it. Typically, data cleaning, a preprocessing step, is followed by aggregation of the data after cleanliness in web use mining. We do a pattern analysis following the data's classification. The outcomes of the studies are contrasted with another intriguing technique that is made public. In comparison to other efforts, the outcomes produced by the suggested system exhibit better performance. The technique we utilised, called WDUM or web directories user mining, produces better results than earlier techniques. We compare user growth and penetration performance to the threshold values. Thus, after preprocessing, the log files are retrieved, and the Apriori Algorithm and fuzzy logic classification are applied. These outcomes demonstrate the greatest methods for capturing user interest. Finally, the projected user clicked the prediction findings using machine learning techniques.
Cryptographic Approach for Cloud Data Security

The objective is to securely store information in the cloud by dividing the encryption between multiple methods and storing it on the cloud. The aim is to maintain the confidentiality, integrity, and availability of the data. In the IT sector, many businesses are adopting cloud computing at a rapid pace, which reduces the cost of new software. Cloud computing offers the advantage of easily accessing data at a minimal cost. It is a cost-effective and convenient way to access data over the Internet. However, ensuring the integrity of cloud computing is crucial, especially since cloud storage providers may not always be trustworthy. This is particularly challenging when users store sensitive data with unreliable cloud storage providers. Therefore, securely exchanging data and maintaining data integrity remains a complex problem. This approach involves utilizing AES, DES, and RC2 algorithms to store data in a single cloud, ensuring the security and privacy of client-sensitive information.
High Accuracy Curve Reconstruction Based on Twisted Multicore Fiber and Twist Bias Calibration

A high accuracy curve reconstruction method based on twisted multicore fiber and twisted bias calibration is proposed. The reconstruction error is optimized from 7.43 mm to 1.41 mm after twist bias calibration.
A Control Pipeline for Robust Lane Keeping in Model Cars

Reliable lane keeping is a fundamental requirement of autonomous model cars. This paper presents a sophisticated image processing pipeline in combination with the Stanley steering controller to reliably steer model cars on their lane at high speeds. We show that the pipeline significantly improves robustness compared to a naive steering implementation. The results ensure reliable lane keeping and hence enable testing of higher-level algorithms for e.g. overtaking or merging scenarios.
An Efficient Approach for Image Forgery Detection Using Deep Convolutional Neural Network

The advent of digital platforms revolutionized sharing of personal images and videos on social media. Intruders extract and modify these images to gain popularity or favor. There is freely available software that changes these images very quickly. If the image is forged, the algorithm should detect it and stop to publish on social media. Copy-move and splicing forgery are prevalent methods of image forgery. To combat these forgeries, a real-time deep learning-based approach has been developed, providing enhanced social security in today's society. This approach can effectively detect and identify counterfeit images shared on various social media platforms. The real-time forgery detection algorithms are developed using supervised learning. This algorithm is tested on publicly available datasets such as DVMM, CMFD, and Colombia.
IL-YOLOv5(Improved Loss in YOLOv5): An Improved Multi-Object Detection Model Focusing on Snowy Weather Conditions in Mixed Driving Environments for Self-Driving Cars

This paper presents a modified YOLOv5 algorithm named IL-YOLOv5 (i.e., YOLOv5 with Improved Loss) for object detection specifically in challenging weather conditions, specifically focusing on snowy weather conditions in autonomous vehicles. Object detection is a critical component of autonomous vehicle technology, but it can be challenging in adverse weather conditions due to limited visibility and changes in object appearance. The proposed modified YOLOv5 algorithm includes the modified loss function i.e. Focal- <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\alpha$</tex> EIoU Loss instead of CIoU Loss. The algorithm is evaluated on a dataset of snowy road scenes, and the results show a significant improvement in object detection performance compared to the native YOLOv5 algorithm. The bounding box regression process in the model is sped up and the loss is decreased by the IL-YOLOv5 algorithm. Test results showed that the YOLOv5 algorithm is outperformed by the IL-YOLOv5 method based on convergence speed, mAP enhancement by 4.1, and detection speed by a factor of 3.
Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks

Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks. However, most state-of-the-art CNNs are large, which results in high inference latency. Depth-wise separable convolution has been proposed for image recognition tasks on platforms with limited computation power, such as robots and self-driving cars. Any regular deep CNN has a depth-wise separable counterpart, which is faster, but less accurate, when equally trained. In this paper, we propose a novel decomposition approach based on SVD, namely depth-wise decomposition, for converting regular convolutions into depth-wise separable convolutions post-training, while maintaining high accuracy. We show that our approach generalizes to the multi-channel and multi-layer cases, by applying Generalized Singular Value Decomposition (GSVD). We conduct thorough experiments with the ShuffleNet V2 model on a large-scale image recognition dataset: ImageNet. Our approach outperforms the baseline, channel decomposition. Moreover, our approach improves the Top-1 accuracy of ShuffleNet V2 by ∼2%.
A Journey Towards the Most Efficient State Database For Hyperledger Fabric

Hyperledger Fabric is a leading permissioned blockchain platform known for its flexibility and customization. A crucial yet often overlooked component is its state database, which records the current state of blockchain applications. While the platform currently supports LevelDB and CouchDB, this study argues that there is an unmet need for exploring alternative databases to enhance performance and scalability. We evaluate RocksDB, Boltdb, and BadgerDB under various workloads, focusing on memory and CPU utilization. Our findings reveal that each alternative outperforms the existing options: RocksDB excels in throughput and latency, Boltdb minimizes CPU usage, and BadgerDB is most memory-efficient. This research not only provides a roadmap for integrating new state databases into Hyperledger Fabric but also offers critical insights for those aiming to optimize enterprise blockchain systems. The study underscores the significant gains in scalability and performance that can be achieved by reconsidering the choice of state database.
The application of artificial intelligence in computer network technology in the data age

With the rapid progress of China's social, political and economic development, the level of science and technology is also continuously improving, and China has also entered the era of big data. In the context of this era of big data, the rapid development of China's economy has also promoted the progress of artificial intelligence technology. The integration of artificial intelligence into China's computer network technology is conducive to the healthy growth of China's network technology. Based on this, this paper briefly analyzes the application of artificial intelligence in computer network technology in the era of big data.
Machine learning: Training model with the case study

In recent decades, making machines learn from a massive dataset has become a prevalent task. It is significant to build a suitable deep learning model which would make its own decisions after training. This project aims to deal with a massive dataset with the trained deep learning model. More specifically, this project applies a Convolutional Neural Network (CNN) model to classify massive text data. It applies Generative Adversarial Network (GAN) model to regress image data. Furthermore, we start by building the model and enhance the model with necessary modifications for satisfying performance. Then we train and test the model to verify that the model achieves its aim. While the whole process implements based on Python and the Tensor Flow environment.
Optimizing the Waste Bank Mapping Management Information System in Batam City

Geographic Information Systems (GIS) have been widely applied in environmental health, including determining the locations of waste banks. Currently, in Batam City, there is no mapping-based system available to track the distribution of waste banks, posing challenges for residents to access information about nearby waste bank locations. Population growth is correlated with rising household demands, including the need for food, clothing, and housing. People need their basic necessities to survive. The industries of manufacturing, transportation, healthcare, and communication have all seen significant advancements since the Industrial Revolution. With the advent of information and technology (IT), all of those areas' development in the 19th and 20th centuries was larger and faster. Hence, this study introduces a management information system with mapping technology for waste banks in Batam City. Employing a descriptive analysis approach, the software development follows the waterfall method, and Unified Modeling Language (UML) serves as the design tool. The resulting system proves effective in providing information on Waste Bank locations and assisting administrators in managing Waste Bank location data.
The Difference in Performance and Motivation by Focus Types on the Outcome / Process and Device in Games

With the advent of virtual reality (VR) games, many people are using them appropriately in various fields to fulfill their own interests. Among them, gaming field showed great interest toward VR upon its initial appearance. A study was carried out to find out the difference compared to the existing game device, Computer screen (CS). An experiment was conducted using an actual game to determine how both devices (VR, CS) and the type of focus induced (result focus, process focus) affect motivation and performance of the game player. It was expected that motivation and performance would be high when the type of focus and the characteristics of each device align, that is, when playing games using each device, the information or value provided while playing the game matched the focus. The results of the study showed that people who played games on VR with a focus on the process performed better than those who focused on the outcome. Additionally, when asked about their level of performance, process-focused participants were less accurate on VR than on CS. Nevertheless, better performance was shown on VR with lower accuracy on level of performance. Hence, these results suggest that the type of the device and psychological state of the player should be considered altogether.
A study on cultural adaptation of Chinese Students in Indonesia

As closer cooperation in education between Guangxi and Indonesia, the cross-cultural adaptation of foreign students in target countries has become a very important issue. In terms of society, psychology, learning and culture, the article studies the adaptation of international students who come from Guangxi University of Foreign Language in Indonesia by means of questionnaires and interviews. Their cultural adaptation is generally good, but there are many differences in gender, length of study abroad, cultural experience, etc..This thesis will analyze the factors that affect cross-cultural adaptation and the reasons for the differences, and provide relevant suggestions for further improving at training program.
Perancangan Sistem Pengenalan Organ Tubuh Manusia Menggunakan Diagram Activity

The human body organ recognition information system is one of the fields of medical science that studies the structure of the human body. As humans, we really need to know our own organs. The reason is, by knowing and understanding every part of our body, we can know a healthy lifestyle and care more about maintaining a healthy body. The problem here is that the introduction of human organs done in 2D is less attractive so that students get bored studying the parts of the human body. The purpose of this research is to improve the learning system for the introduction of human organs by designing an information system using activity diagrams which will later be continued towards Android-based software and in its manufacture using the Unified Modeling Language (UML) method, using the StarUML application. The result of this research is that a system for recognizing human organs made in activity diagrams using StarUML software has been completed and is very well made.
Multimedia Collaboration and Structured Worksheets on Synchronous and Asynchronous Distance Training Scientific Publications at Jakarta Religious Education and Training Center

Scientific Writing is an element of scientific publication that is difficult for teachers to produce to fulfil credit points for promotion. For the Distance Training (PJJ) learning process, which consists of synchronous and asynchronous stages, the teacher must create a strategy using multimedia and structured worksheets. The problem examined in this research is so that training participants understand written work, are motivated and can produce as many products in PJJ Scientific Publications as possible in a short time. The research aims to see the increase in competency of training participants and the products produced as well as the motivation of training participants by collaborating between multimedia and structured worksheets in synchronous and asynchronous PJJ Scientific Publications for MA teachers. The research design is descriptive analysis and the data analyzed are assignment scores, formative test results and questionnaire results in the form of training participant responses regarding the PJJ process with multimedia collaboration and structured worksheets both synchronously and asynchronously. Training course products: Presentations at Scientific Forums, Research Results or Scientific Ideas in the Field of Formal Education, Research Results or Teacher's Manuals were rated very competent. The questionnaire results in the form of training participants' responses regarding the use of structured worksheets and the use of multimedia on average, who answered that they were very efficient, helpful, and satisfying. The impact that participants feel after participating in PJJ is very positive. The use of multimedia and structured worksheets should be maximized in every training, both synchronous and asynchronous, improving the IT competency of students on an ongoing basis and increasing innovative and creative training implementation strategies.
Digitalisasi Prasasti dan Pelinggih Desa Baturan Gianyar Berbasis Augmented Reality Based Marker

Digitalization of the inscriptions and shrines of Batuan Gianyar Village based on Augmented Reality (AR) Markers is an innovative project that aims to preserve, promote and revive the cultural and historical heritage of Baturan Village, Gianyar, Indonesia, through the application of Augmented Reality technology. Baturan Village is known to have inscriptions and pelinggih which have high historical value. This research uses AR Marker technology to connect the physical world with digital content. The inscriptions and shrines of Baturan Village are marked with AR markers which allow users to access additional information about these objects via mobile devices. This AR application provides an immersive and interactive experience, allowing users to explore the history and meaning of inscriptions and shrines. The method used in developing this AR application is R &amp; D. The results of this research after testing loading time using four different smartphones, Xiaomi Note8 Pro and iPhone 13, have a faster average loading time response compared to other smartphones.
INTRODUCTION LEARNING VIDEO DEVELOPMENT MICROSOFT WORD FOR ELEMENTARY SCHOOL STUDENTS

&lt;p&gt;This study aims to (1) produce a learning video product for the introduction of &lt;em&gt;microsoft word &lt;/em&gt;; and (2) describe the effectiveness of the introduction of &lt;em&gt;microsoft word learning videos &lt;/em&gt;for elementary school students. This development research uses the ADDIE design model with five steps, namely: (1) Analysis, (2) Design, (3) Development, (4) Implementation, and (5) Evaluation. The design in this study was validated by two instrument validators, two design validators, two media validators and two material validators with the overall product validation results showing very good results. The research data was obtained from the questionnaire instrument and the value of students' knowledge. This study involved 27 third grade students of SD Marie Joseph Pontianak with the results on the one-on-one test, small group test and overall field test which could be interpreted very well. The results of the one-on-one trial obtained an overall average of 3.31 (good), the results of the small group trial obtained an overall average of 3.15 (good), the results of the large group trial obtained an overall average of 3.08 (good ). The results of the test of the effectiveness of the &lt;em&gt;microsoft word &lt;/em&gt;introduction learning video product for elementary school students, the results were obtained from the &lt;em&gt;pretest &lt;/em&gt;and &lt;em&gt;posttest data &lt;/em&gt;that had been collected. It is known that the average &lt;em&gt;pretest score &lt;/em&gt;is 72.5 with 11 students achieving the KKM and 9 students not achieving the KKM. Then the average posttest score was 85 with the number of students who reached the KKM there were 17 people and those who did not reach the KKM were 3 people. Through these results it can also be concluded if there is an increase in the average value of 12.5. These data indicate that there is a significant completeness.&lt;/p&gt;
Results on the Empirical Design of a Residual Binary Multilayer Perceptron Architecture

Binary neural networks have emerged as an efficient solution for resource-constrained devices due to their reduced computational, memory, and storage requirements. However, binary neural networks often suffer from decreased accuracy compared to floating-point models. In this study, we propose a binary residual multilayer perceptron architecture that mitigates the degradation caused by binarization through the incorporation of normalization layers and residual connections. By leveraging design recommendations from state-of-the-art binary architectures, we aim to create a user-friendly model that can be easily implemented without requiring extensive neural network design expertise. This paper presents the empirical results of our proposed architecture, demonstrating its effectiveness in reducing degradation and improving performance for hardware-constrained devices.