Reviewerly/exHarmony huggingface dataset:
Developing VILLE 5.0 (Virtual Language Laboratory based on Education 5.0) as an Innovative Learning Model

Learning English in the Education 5.0 era encourages massive changes, especially in the learning models and media. Researchers developed an Innovative learning model that plays a very important role, namely VILLE 5.0 (Virtual Language Laboratory based on Education 5.0). The subjects in this research were 93 students of the 11th grade Vocational High School SMK Perdana Semarang. The instruments used questionnaires and expert validation tests. Data was processed by analyzing validator response data with a Likert scale. The syntax VILLE 5.0 learning model consist of providing the problem, making the group, doing inquiry through virtual media, constructing the media, simulating the solution, and presenting the result on virtual media. The results of VILLE 5.0 validity test are very valid level, while the feasibility aspects from students’ view were 96% and from the teachers’ view were 92 %. The score for practicality test were 95% from the teachers and 92% score from the students. The conclusion based on the results that VILLE 5.0 are valid, feasible and practical. VILLE 5.0 can be used as an innovative learning model for English Subject. VILLE 5.0 is very comprehensive to be implemented as an Education 5.0 learning model era because the class can optimize the technology and
A Literature Review on Virtual Reality in Teaching Education

In recent years, virtual reality technology has been progressively integrated into the classroom teaching process with the aim of enhancing academic performance and increasing students' interest and enthusiasm for learning. Virtual reality has become a crucial component of modern educational technology. Therefore, the objective of this study is to summarize the findings in the literature through a comprehensive review. Specifically, this review focuses on literature published between 2018 and 2022 that explores the effectiveness of virtual reality technology in education. Throughout the study, research articles published during the specified timeframe were analyzed using predetermined criteria. The analysis encompassed research objectives, methodologies, topics, and outcomes of each article. Studies have demonstrated that the incorporation of virtual reality into instruction not only has a positive impact on students, but also increases students' interest and motivation. Consequently, in the more educators are likely to consider utilizing virtual reality in art classrooms to enhance students' skills, improve instructional strategies, and enhance teaching outcomes and students' learning capabilities. The findings of this research provide valuable insights and methods for educators.
Designing Immersive Experiences in Virtual Reality for Training and Working With Digital Artifacts in Design for Additive Manufacturing Processes

Abstract Adopting additive manufacturing (AM) in product design is a challenge for organizations due to a shortage of skilled designers who can effectively use AM to address emerging engineering problems. The development of institutional talent in AM and design for AM (DfAM) is crucial for organizations looking to leverage AM capabilities. Therefore, designers need meaningful active experiences that highlight the advantages and limitations of AM technologies to enable informed artifact generation during the design process. However, limited access to active in-person learning experiences with industrial AM systems and knowledge to support the design of AM artifacts is a major hurdle to AM talent development. Digital modalities of AM education, such as computer-aided (CAx) and virtual reality (VR) tools, can therefore increase access to experiential learning to cultivate DfAM intuition. There is a gap in the literature, however, that investigates and compares immersive and non-immersive modalities in AM and studies their effectiveness in training and artifact generation. Additionally, there is a scarcity of information on the design process behind creating immersive experiences that can support these research endeavors. It is essential to investigate design guidelines and heuristics for creating immersive experiences that are meaningful and effective for AM and DfAM training. This research is thus motivated to explore the design and application of immersive and non-immersive digital modalities in AM and DfAM. The presented work here investigates the design of immersive VR user experiences for this purpose with support from the literature and findings in previously published work.
ANDROID BASED ADVERTISING REMINDER SYSTEM

The aim of this research is to introduce an Android-based reminder system that automatically notifies Surya Media Advertising employees of expired leases. Android is a "Linux-based operating system used on mobile devices such as smartphones and tablet computers (PDAs)"[8]. The method used in system development is the SDLC method which is the waterfall model. B. The design step must wait for the completion of the previous step, namely the requirements step. Visual Studio Code and Android Studio are used as software for this research [12]. The results of this study are; 1) Reminder system can help capture rentals for locations faster and easier; 2) The notification function makes it easy to know when the rental period has expired.
Comparative Evaluation of Metaheuristic Algorithms for Hyperparameter Selection in Short-Term Weather Forecasting

Weather forecasting plays a vital role in numerous sectors, but accurately capturing the complex dynamics of weather systems remains a challenge for traditional statistical models.Apart from Auto Regressive time forecasting models like ARIMA, deep learning techniques (Vanilla ANNs, LSTM and GRU networks), have shown promise in improving forecasting accuracy by capturing temporal dependencies.This paper explores the application of metaheuristic algorithms, namely Genetic Algorithm (GA), Differential Evolution (DE), and Particle Swarm Optimization (PSO), to automate the search for optimal hyperparameters in these model architectures.Metaheuristic algorithms excel in global optimization, offering robustness, versatility, and scalability in handling non-linear problems.We present a comparative analysis of different model architectures integrated with metaheuristic optimization, evaluating their performance in weather forecasting based on metrics such as Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE).The results demonstrate the potential of metaheuristic algorithms in enhancing weather forecasting accuracy & helps in determining the optimal set of hyper-parameters for each model.The paper underscores the importance of harnessing advanced optimization techniques to select the most suitable metaheuristic algorithm for the given weather forecasting task.
Blockchain-Enhanced IoHT: A Patient-Centric Internet of Healthcare Things Platform with Smart Contract-Driven Data Management

The Internet of Things (IoT) has seen rapid adoption across various sectors, with healthcare being one of the most significant. The IoHT-MBA platform, an Internet of Healthcare Things (IoHT) system based on microservice and brokerless architecture, has proven to be highly effective in addressing critical challenges such as data collection, user and device management, and remote device control. However, to further enhance patient data privacy and control, an additional layer of decentralization and self-sovereign data management is necessary. In this work, we introduce a blockchain-enhanced IoHT-MBA platform - a new system that incorporates a distributed ledger technology, specifically Hyperledger Fabric, to provide a secure, transparent, and patient-controlled data management system. Patients can manage their edge local devices at the edge layer, contributing to a distributed storage service. All requests for user data must comply with the patient’s privacy conditions, defined through smart contracts, adding an extra layer of security and transparency. We further evaluate our platform using the microservice and brokerless architecture theories (Round Trip Time and Broken Connection Test Cases), in addition to data creation tests in Hyperledger Caliper, demonstrating the robustness and efficiency of our blockchain-enhanced system.
Revolutionizing Real Estate: A Blockchain, NFT, and IPFS Multi-platform Approach

Traditional real estate management faces challenges like lack of transparency, inefficiency, and fraud. Blockchain has been proposed as a solution, but its full potential remains untapped. Our paper introduces a sophisticated approach, combining Non-Fungible Tokens (NFTs), InterPlanetary File System (IPFS), and blockchain technology. Using Ethereum’s ERC721 standard, we create unique NFTs for real estate assets, guaranteeing ownership and transferability. IPFS, integrated via Node.js, ensures permanent data storage for each NFT, preserving asset information integrity. What sets our model apart is its multi-platform deployment on Fantom, Polygon, Binance Smart Chain, and Celo networks, enhancing resilience and accessibility. This robust integration offers a transformative tool for real estate management, enabling transparent, secure, and efficient transactions, promoting market efficiency, and bolstering investor confidence.
Creating Animations with Powerpoint for Kindergarten Teachers in Dumoga Central District and Bolaang Mongondow District

The ability of kindergarten teachers to create animations is still far from "can," and they only create animations to meet specific requirements or tasks. In fact, compiling animations that were thought to be simple was not as simple as one might think. Creating animation appears to be the same as creating teaching materials in general. In fact, if the animation is organized according to the correct rules and regulations, compiling the animation becomes the most "difficult" research to complete. This is demonstrated by the situation of teachers who have been unable to correctly arrange animations. The community service program, which takes the form of learning media development training for kindergarten teachers in the Dumoga Tengah district, is expected to increase knowledge, skills, and confidence in their profession. Later on, teachers will be more motivated to develop themselves. The outcomes of this training will benefit schools by making the teaching and learning process more interesting by utilizing a wider range of learning media. Furthermore, the availability of training in the development of learning media will improve teachers' abilities to prepare their learning tools. Keywords: creating animation, PowerPoint
Pandoc resources

These are the resources that I’d want to have if I was to learn about pandoc (the universal document converter) all over again: Installing latest release The GitHub release page has installers for Windows, macOS, and Linux. Docker images There are three types of pandoc Docker images: minimal – very small, just the bare pandoc binary; core – includes pandoc-crossref and helpers programs, e.g. those used by pandoc for SVG image conversion;
Enhanced Residue Prediction for Lossless Coding of Multimodal Image Pairs Based on Image-to-Image Translation

Multimodal medical imaging combine data obtained from multiple techniques simultaneously, yielding more detailed information about the content, which is a clear advantage over independent acquisition techniques. As these images are acquired using different imaging modalities and sometimes even in different dimensions, they commonly require a geometrical registration process. However, when they are encoded using standard image codecs the prediction methods do not exploit the redundancies related to the multimodal acquisition. In this paper, a novel lossless multimodal prediction module is introduced. The proposed method employs a deep learning-based approach with Image-to-Image translation for the purpose of joint coding of Positron Emission Tomography (PET) and Computed Tomography (CT) image pairs. Prior to the coding stage, a Generative Adversarial Network (GAN) is used for multimodal image translation. Then, a weighted estimated image is utilised as the I-frame, while the weighted sum of the original and synthesised image from the same modality serves as the P-frame for inter prediction. By employing weighted frames, the predictive frame approximates the reference frame more accurately, enhancing the overall performance of the prediction process. The experimental results, on a publicly available PET-CT dataset, demonstrate that the proposed prediction scheme outperformed the previously proposed method, and attains coding gains up to 13.20% when compared with the single modality intra coding of the Versatile Video Coding (VVC) lossless standard.
Extracting Clinical Information From Japanese Radiology Reports Using a 2-Stage Deep Learning Approach: Algorithm Development and Validation

Abstract Background Radiology reports are usually written in a free-text format, which makes it challenging to reuse the reports. Objective For secondary use, we developed a 2-stage deep learning system for extracting clinical information and converting it into a structured format. Methods Our system mainly consists of 2 deep learning modules: entity extraction and relation extraction. For each module, state-of-the-art deep learning models were applied. We trained and evaluated the models using 1040 in-house Japanese computed tomography (CT) reports annotated by medical experts. We also evaluated the performance of the entire pipeline of our system. In addition, the ratio of annotated entities in the reports was measured to validate the coverage of the clinical information with our information model. Results The microaveraged F 1 -scores of our best-performing model for entity extraction and relation extraction were 96.1% and 97.4%, respectively. The microaveraged F 1 -score of the 2-stage system, which is a measure of the performance of the entire pipeline of our system, was 91.9%. Our system showed encouraging results for the conversion of free-text radiology reports into a structured format. The coverage of clinical information in the reports was 96.2% (6595/6853). Conclusions Our 2-stage deep system can extract clinical information from chest and abdomen CT reports accurately and comprehensively.
QUANTITATIVE STUDIES OF DEEP REINFORCEMENT LEARNING IN GAMING, ROBOTICS, AND REAL-WORLD CONTROL SYSTEMS

Deep Reinforcement Learning (DRL) has emerged as a transformative paradigm with profound implications for gaming, robotics, real-world control systems, and beyond. This quantitative analysis delves into the applications of DRL across these domains, assessing its capabilities, challenges, and potential. In the gaming realm, we showcase DRL's prowess through significant score improvements in benchmark games, with DQN and PPO leading the way. A3C underscores its adaptability through strong generalization within the gaming domain. While specific robotics and real-world control results are not presented here, their promise in enhancing task completion and precision is evident. Sample efficiency and safety strategies address critical concerns, demonstrating DRL's capacity to optimize resource utilization and ensure robustness. Generalization and transfer learning underscore DRL's adaptability to new scenarios. While these findings are not empirical but illustrative, they emphasize DRL's versatility and highlight the need for continued research to unlock its full potential in addressing complex real-world challenges.
PENGARUH MANAJEMEN KELAS DAN DISIPLIN BELAJAR TERHADAP MOTIVASI BELAJAR SISWA KELAS V DI SEKOLAH DASAR

The purpose of this study was to analyze a) the effect of classroom management on the learning motivation of fifth grade students at SDN 01 Luwunggede. b) the influence of learning discipline on the learning motivation of fifth grade students at SDN 01 Luwunggede. c) the influence of class management and learning discipline on the learning motivation of fifth grade students at SDN 01 Luwunggede. This study uses a quantitative approach using explanatory research, namely research that aims to explain the relationship between two or more variables with the aim of obtaining valid data. The research data used are a) questionnaires in the form of written question data which are then distributed to respondents to be filled out. b) documentation in the form of pictures of the research process c) literature study containing literature references that support research. The data analysis technique used is with the help of the SPSS version 25 application. The results of this study show that classroom management has a positive effect of 17.20% on the learning motivation of fifth grade students at SDN 01 Luwunggede, Tanjung sub-district, learning discipline has a positive influence of 32.20% on the learning motivation of fifth grade students at SDN 01 Luwunggede Tanjung sub-district, class management and student learning discipline have a simultaneous positive influence of 42.90% on the learning motivation of fifth grade students at SDN 01 Luwunggede Tanjung sub-district. &#x0D;
An Augmented Reality-Assisted Disassembly Approach for End-of-Life Vehicle Power Batteries

The rapid expansion of the global electric vehicle industry has presented significant challenges in the management of end-of-life power batteries. Retired power batteries contain valuable resources, such as lithium, cobalt, nickel, and other metals, which can be recycled and reused in various applications. The existing disassembly processes rely on manual operations that are time-consuming, labour-intensive, and prone to errors. This research proposes an intelligent augmented reality (AR)-assisted disassembly approach that aims to increase disassembly efficiency by providing scene awareness and visual guidance to operators in real-time. The approach starts by employing a deep learning-based instance segmentation method to process the Red-Green-Blue-Dept (RGB-D) data of the disassembly scene. The segmentation method segments the disassembly object instances and reconstructs their point cloud representation, given the corresponding depth information obtained from the instance masks. In addition, to estimate the pose of the disassembly target in the scene and assess their disassembly status, an iterative closed point algorithm is used to align the segmented point cloud instances with the actual disassembly objects. The acquired information is then utilised for the generation of AR instructions, decreasing the need for frequent user interaction during the disassembly processes. To verify the feasibility of the AR-assisted disassembly system, experiments were conducted on end-of-life vehicle power batteries. The results demonstrated that this approach significantly enhanced disassembly efficiency and decreased the frequency of disassembly errors. Consequently, the findings indicate that the proposed approach is effective and holds promise for large-scale industrial recycling and disassembly operations.
A new fractional-order 5D memristive chaotic system with special extreme multistability and its application to image encryption

Abstract Introducing memristor into the chaotic system can enrich the dynamic behaviors of the chaotic system. We propose a symbolic function memristor model and introduce it into a chaotic system to construct a fractional-order 5D memristor chaotic system. Through dynamic analysis of equilibrium point, Lyapunov exponents, phase diagram and bifurcation diagram, it is found that the system has abundant dynamic behaviors, for example, the change of equilibrium point type with parameters, transient chaos, offset-boosting and a special kind of extreme multistability. And with the change of parameters, the attractor state and shape will appear rich changes. Then the correctness of the system is verified by circuit simulation. The chaotic system is introduced into the process of image encryption, and an encryption system is constructed, which is composed of Zigzag scrambling, Hilbert curve scrambling, DNA encryption and GF257 diffusion algorithm. Finally, through a variety of security verification, the results show that the encryption system has good security and can resist many kinds of attacks effectively.
Robot path planning based on improved A* algorithm

Abstract To address the issues of excessive polyline paths and the increased number of search nodes in the A* algorithm, a weighted WA* algorithm is proposed based on the A* algorithm. Firstly, a new distance function is introduced to reduce computational resource usage and improve search efficiency. Then, the heuristic function is improved by searching all feasible paths to find the shortest path. When the lengths of feasible paths are very similar, an additional value is added to the heuristic function along with weights to minimize frequent searches. Lastly, Bezier curves are introduced to enhance the smoothness of the global path. Simulation results show that the improved WA* algorithm reduces the number of turns, time consumption, search space, and turning angles by 25.0%, 65.4%, 53.5%, and 18.7%, respectively, compared to the A* algorithm. Compared to the bidirectional A* algorithm, these reductions are 7.6%, 34.4%, 43.5%, and 7.1%, respectively. In addition, the WA* algorithm uses Bezier curves to curve-fit the generated path points, making the path trajectory smoother.
Construction of RDF Knowledge Graph with MongoDB

Resource Description Framework (RDF) is widely used in semantic extraction, unified organization, and intelligent processing of large amounts of data because of its machine intelligibility. For example, knowledge graph based on RDF is commonly used in intelligent search, recommendation system, and smart medical treatment. And RDF is used to express the relationship between entities and process the semantics of data. Many efforts have been made to convert various data (such as relational database, XML, and JSON) into RDF. Yet, the effective generation of usable RDF data is still an urgent problem to be solved. With the wide use of NoSQL database, massive data is stored in NoSQL database, but the research on generating RDF from NoSQL database is not emphasized. We put forward a formal definition of MongoDB, and according to this definition, we propose a method of automatically extracting data from MongoDB and building corresponding RDF. Based on this method, we have also implemented a prototype system named M2R to validate method performance. The experimental results show that our approach is feasible and efficient.
SEAG: A novel dynamic security risk assessment method for industrial control systems with consideration of social engineering

The development of information and communication technology and its wide application in industrial control systems (ICSs) has brought a growing number of security risks to ICSs. Quantifying and dynamically assessing the security risks of ICSs is of great significance to protect ICSs from cyber attacks. Current risk assessment methods, however, do not take into account social engineering (SE) attacks and the potential cyber-to-physical risks associated with cyber attacks. To address these issues, we propose a novel method for the dynamic security risk assessment of ICSs, called SEAG. Specifically, the scheme first extends and modifies three metrics in the common vulnerability scoring system to four metrics for objectively calculating the exploit probability of SE. Then, we construct the attack graph by relying on the knowledge graph that integrates three kinds of knowledge including SE knowledge, common vulnerability knowledge, and control system knowledge. In addition, we combine real time attack data that causes system performance loss with the industrial protocol function code attack detected by the intrusion detection system to accurately quantify the potential cyber-to-physical risks associated with cyber attacks. This method allows us to dynamically assess the security risks of ICSs in real time. Finally, the method is verified by one simulation testbed, which shows the effectiveness and accuracy of the proposed method for dynamic quantitative evaluating security risks of ICSs.