Reviewerly/exHarmony huggingface dataset:
Pengembangan Media Pembelajaran Menggambar Teknik Untuk Mahasiswa Berbasis Android

This research aims to develop Engineering drawing learning media for students based on Android using Adobe Flash Professional CS6 software. The development method used in this research is Research and Development (R&amp;D) with the ADDIE development model, The stages are: Analysis, Design, Development, Implementation, and Evaluation. The method used in data collection is using a questionnaire. In the material validation trial, the learning media got a percentage score of 80% and was included in the "Valid" category. Furthermore, in the media validation trial, the learning media got a percentage score of 70.67% and was included in the "Valid" category. Then the results of small group trials involving 5 students got a percentage score of 92.53% and were included in the "Very Practical" category. While the results of field trials involving 20 students got a percentage score of 91.40% and were included in the "Very Practical" category. Based on these data, thus the android-based engineering drawing learning media is valid and feasible to be used as a learning media for mechanical engineering education students.
DGECN++: A Depth-Guided Edge Convolutional Network for End-to-end 6D Pose Estimation via Attention Mechanism

Monocular object 6D pose estimation is a fundamental yet challenging task in computer vision. Recently, deep learning has been proven to be capable of predicting remarkable results in this task. Existing works often adopt a two-stage pipeline with establishing 2D-3D correspondences and utilizing a PnP/RANSAC or differentiable PnP algorithm to recover 6 degrees-of-freedom (6DoF) pose parameters. However, most of them hardly consider the geometric features in 3D space, and ignore the topological cues when performing differentiable PnP algorithms. To this end, we present an improved end-to-end monocular 6D pose estimation method (DGECN++) that incorporates depth estimation and a geometric-aware learnable PnP network. Our method is based on keypoints. First we detect the 2D keypoints that correspond to the 3D model. We then integrate differentiable PnP/RANSAC algorithm to create an end-to-end pipeline for 6D pose estimation. We focuses on the following three key aspects: 1) We utilize the estimated depth information to guide the process of extracting 2D-3D correspondences and refine the results using a cascaded differentiable PnP/RANSAC algorithm that incorporates geometric information. 2) We leverage the uncertainty of the estimated depth map to enhance the accuracy and robustness of the predicted 6D pose. 3) We propose a differentiable Perspective-n-Point (PnP) algorithm based on edge convolution and self-attention to explore the topological relationships between 2D-3D correspondences. Experimental results demonstrate that our proposed network surpasses existing methods in terms of both effectiveness and efficiency.
Long-Arm Jurisdiction in Cross-Border Data Flows: EU-US Gaming and Balancing

In the era of digital economy, the power struggle and legal challenges arising from cross-border data flows have become increasingly prominent, making data governance a focal topic in the international community. Countries are competing and cooperating in formulating rules on cross-border data flows, as this phenomenon becomes more significant with the global popularity and rapid development of big data-based digital products like ChatGPT. As traditional powers in this field, the United States and the European Union have established long-arm jurisdiction systems with unique characteristics and have engaged in multiple rounds of fierce gamesmanship over dominance in global data flow governance standards, while constantly seeking power balance in bilateral cooperation. Through historical research, comparative analysis, and case studies, this paper analyzes and summarizes the governance principles, regulatory models, and competitive and compromising processes of transatlantic data flows, leading to the conclusion that Chinas data governance strategy should balance data security and data freedom, while transforming its approach to regulating data outflows and emphasizing the importance of efficiency values in maintaining data sovereignty. By expanding international cooperation and promoting the signing of regional data flow agreements, China can enhance its voice in the global data governance system.
Waterscape in Hjari Veraldar The 'Last Habitable Edge of the Earth'

Iceland is a geologically active land and home to some of the world’s most active volcanoes and the largest glaciers in Europe. Due to its harsh environmental circumstances, its inhabitants’ living conditions are unpredictable. Icelanders live in a sort of ‘rescue mood’, ready to face a variety of natural disasters, such as avalanches, flooding, heavy storms, eruptions, landslides and earthquakes. In this northern sea island, water is the most influential element in culture, language, economy, leisure, landscape, planning strategy, and islanders’ everyday life. Water comes in all forms, both as a provider and a threat. Based on this premise, this commentary is a synthesis image of the forms of water from an outlander point of view, reading an alien landscape in which the forms of water are one of the most enchanting interpretation experiences in the landscape. This anecdote is a narration of the forms of water concerning urbanisation, land and human in different subtle layers of Icelandic culture, economy and landscape design
Errata for “Theory of Communication Efficient Quantum Secret Sharing”

Here we give the errata for the paper “Theory of Communication Efficient Quantum Secret Sharing” [1]. As one of the results in this paper, we proposed a construction for universal CE-QTS schemes. However, we recently found an error in this construction. We found that the Vandermonde matrix used for the encoding in this construction leads to a failure in secret recovery. Here we shortly describe why this error occurs and provide a rectification. By replacing the Vandermonde matrix with a Cauchy matrix, the secret recovery in the construction goes through. However, the construction now needs a higher field size. With this rectification, the construction is now correct. The remaining results in the paper remain unaffected.
Efficient Realization of an SC Circuit with Feedback and Its Applications

Stochastic Computing (SC) allows additions and multiplications to be realized with lower power than the conventional binary operations if we admit some errors. However, for many complex functions which cannot be realized by only additions and multiplications, we do not know a generic efficient method to calculate a function by using an SC circuit; it is necessary to realize an SC circuit by using a generic method such as polynomial approximation methods for such a function, which may lose the advantage of SC. Thus, there have been many researches to consider efficient SC realization for specific functions; an efficient SC square root circuit with a feedback circuit was proposed by D. Wu et al. recently. This paper generalizes the SC square root circuit with a feedback circuit; we identify a situation when we can implement a function efficiently by an SC circuit with a feedback circuit. As examples of our generalization, we propose SC circuits to calculate the n-th root calculation and division. We also show our analysis on the accuracy of our SC circuits and the hardware costs; our results show the effectiveness of our method compared to the conventional SC designs; our framework may be able to implement a SC circuit that is better than the existing methods in terms of the hardware cost or the calculation error.
The Effectiveness of Using Genre Based Approach to Enhance Reading Comprehension in Narrative Text for Young Learners

This study focuses on the effectiveness of using Genre Based Approach (GBA) to improve reading comprehension of narrative texts in young learners who have an age range of 11-12 years and aims to determine the level of students' reading ability in the form of narrative text. The classroom Action Research is a research method with pre-test, cycle 1 and cycle II. The researcher finds a positive side from using GBA to enhance reading comprehension in narrative text for young learners. This is evidenced by the significant increase that occurred. Previously, the average score was 63.8, now it has increased to 76.9 in cycle 1. The increase from pre-cycle to cycle I was 13.1 points. Then, in cycle II pupils obtained an average score of 81.9. Furthermore, the increase in students' abilities from cycle I to cycle II increased by 5 points. Thus, students experienced an increase from pre-cycle to cycle II of 18.1 points. Therefore, the results of this study can be used as a reference for educators so they don't hesitate in using GBA as a medium to improve the reading ability of English young learners in the range pf ages 11-12 years. Because besides increasing interest in reading, this research also increase the level of students' critical thinking in analysing readings and finding the main ideas in all elements of narrative text.
NEW STUDENT REGISTRATION INFORMATION SYSTEM AT THE WAISUMAR STATE ELEMENTARY SCHOOL MODEL SCHOOL

Registration carried out by the system automatically provides announcements of registration results carried out online. Prospective new students can find out announcements easily using the internet. The implementation of online admissions for new students is considered to have its own advantages and benefits. Apart from being more efficient, online acceptance of new students is more transparent and open. This includes facilitating monitoring both by students or parents and the community as well as by departments and related parties. The method used in this research is a data collection technique where there are observations, interviews and literature studies. This new student admissions information system was built using the PHP programming language and utilizes the MySQL database as the database server. It is hoped that this research can help prospective new students and new student admissions officers in the process of registering new students at the Waisumar State Elementary School.
Forensics Analysis of Virtual Reality Social Community Applications on Oculus Quest 2

As the popularity of virtual reality (VR) applications increases, there is a growing concern for the forensic aspect of privacy and security of user data. This paper aims to investigate the extent to which forensically relevant user data can be recovered from Oculus Quest 2, specifically, from four social community applications: Horizon Worlds, Multiverse, Rec Room, and VRChat. By performing a forensic analysis on Oculus Quest 2, we were able to determine to what extent forensically relevant user data could be recovered from four social-community applications. Existing efforts in this field are limited. Thus, this study adds to the growing body of research on the forensic analysis of VR applications.
Conceptual Frontiers in the Understanding of Hate Speech

Chapter 1 establishes the context of our project and defends its theoretical and practical importance. Section 1.2 outlines the basic conceptual framework employed in the book, including the distinction between two concepts of hate speech and our twin-track approach to analysing them. We also highlight some of the pay-offs that flow from this conceptual framework. Section 1.3 explains what we mean by ‘grey areas of hate speech’ including identifying three underlying reasons or explanations why certain phenomena might end up falling into these areas, namely moral, semantic, and conceptual. We also try to motivate the significance and value of working to clear up the grey areas. Finally, Section 1.4 introduces and attempts to respond to the sceptical challenge that says, because the term ‘hate speech’ is linked to conceptual ambiguities, misleading connotations, an explosion of applications, and politicisation, it would be better to dispense with both the term and its concepts. We critically examine five main ways of responding to this sceptical challenge: rehabilitation, downsizing, abandonment, replacement, and enhanced understanding. We defend the final response as being the most promising and the overarching goal of the book.
Judicial Determination of the Provision of VPN Behavior

The issue of a judicial determination of the act of providing Virtual Private Network software is currently a hot topic of discussion in the judicial practice as well as theoretical urgent, and research has found that many VPN providers have engaged in many illegal acts by taking advantage of loopholes in legislation and justice. The offshore network activities have been categorized as one major national security concern by many countries and VPN providers are surely among those potential criminals. Since the use of offshore networks may involve infringement of national security as well as national ideology, China has implemented restrictions on access to specific foreign networks. However, the reason behind its formation is that there is not yet a uniform interpretation and determination standard for the application of the relevant issues. Therefore, this article will propose a set of unified standards as well as legislative improvement suggestions through the analysis of the crimes involved.
The Use of Capcut Application in Improving Students’ Creativity in Social Studies Learning at Junior High School

According to 21st Century Skills, creativity is one of the competencies needed in all aspects of life. In addition, the 21st century requires all aspects to be based on technology, especially in education. However, in reality students' creativity skills are still low and the use of technology is still limited. Based on this problem, the purpose of a research in this paper is to describe of students’ creativity skills after using an application Capcut. This research design is classroom action research (CAR). This research result that there was an increase in each cycle. The observation results of the students’ creativity in the first cycle got a percentage 50% which means quite good category, in the second cycle got a percentage 75% which means good category, and in third cycle got a percentage 95% which means good category. Based on this research, it was found that the use of Capcut application can improve students’ creativity skills in social studies learning.
Role-Based Access Control in Private Blockchain for IoT Integrated Smart Contract

Role-based access control (RBAC) is a mechanism that controls access to resources within an organization based on the roles of individual users. This RBAC can be used in the context of an IoT-integrated smart contract for a private blockchain to govern access to smart contract functions and data based on the responsibilities of the system’s participants. By preventing unauthorized access to vital functions and data, RBAC can help assure the security and integrity of an IoT-integrated smart contract. In this study, we investigate novel methods to devise a smart contract process that enables data sharing among stakeholders for IoT-based applications to provide complete access control implementation in a private blockchain environment. Here, we have developed and verified our proposed access control mechanism with an added layer of machine learning-based security for an Ethereum-based private blockchain to securely handle IoT-based application data.
A deep learning approach to real-time video analytics for people and passenger counting

Recent advances in computer vision and deep learning are driving unprecedented interest in the use of video analytics as a key component in IoT solutions for intelligent transport systems and smart city infrastructure. In particular, the development of real-time video analytics algorithms to support the analysis of passenger counting and patronage analysis, deployable "on the edge" and capable of supporting robust and accurate performance under varying environmental conditions, is emerging as a key area of focus. This chapter presents a state-of-the-art review of real-time video analytics for people and passenger counting, highlighting the latest developments and future directions for this research. Focus is given to the emergence and recent developments in deep learning techniques for the analysis of video, and in particular those designed for deployment on the edge. Further, case studies are presented showing how this research is being applied in the context of real-world, public transport passenger analytics problems, specifically to support demand prediction for and analysis of rail replacement bus patronage, and for acquiring real-time public space patronage statistics. These case studies demonstrate how robust, fast and accurate video analytics are being achieved on low-cost edge computing hardware to support real-time patronage analytics. The chapter elucidates key methodological and practical challenges to consider when deploying and evaluating such systems in the field.
Variational Inference Over Graph: Knowledge Representation for Deep Process Data Analytics

With the advent of the industrial Big Data era, accurate estimation of product quality and monitoring of working conditions from historical data have become crucial in the process industry. However, the majority of data-driven approaches predominantly rely on observational data, overlooking the valuable empirical knowledge derived from experience or underlying mechanisms. In order to leverage this knowledge, researchers employ variousgraphneuralnetwork-basedmethodswhichintroduceconnectionsamongprocess variables for feature extraction. Nevertheless, it is imperative to recognize that process knowledge undergoes changes due to internal or external concept drift. To address this challenge, we propose a novel deep learning module called “variational inference over graph” to effectively harness shifting knowledge. Building upon the self-attention mechanism, we design a probabilistic self-attention mechanism for encoding and reconciling prior knowledge. Instead of directly encoding the prior knowledge through graph neural network edges, we incorporate it as regularization term within the variational inference framework that accounts for knowledge shift. Furthermore, we introduce reparameterization estimator to control the variance resulting from knowledge uncertainty. To showcase the capability of our proposed method, we conduct various experiments on quality prediction tasks in real industrial process.
Cloud management of pumping systems using digital twins supported by augmented reality

Smart pumping systems of tomorrow will feature pumps and drives that respond to real-time changes downstream to keep operations at high efficiency and meet growing performance demand. A key component of a smart pumping system is its digital twin, an exact 3D digital copy of the facility. A digital twin enhanced with Augmented Reality (AR) encompasses as-built facility data captured with 3D scanning devices, as well as precise measurement data collected on the actual rigs with high precision instruments. An interactive model based on augmented reality allows the autonomous and efficient use of pumping systems. It provides clear instructions for the step-by-step management of the system. In addition, it shows relevant information with the exploded views of the components for a better understanding of the operation of the equipment. This research is about the interconnection of the digital twin of pumping systems with the real-world using automation and augmented reality systems. In this project, a local area network is configured to exert control and monitoring on an industrial PLC. This PLC controls a test bench with two centrifugal pumps by means of a web page. An augmented reality application is also developed in Unity 3D with the Vuphoria SDK integration.
A Tour of Adiabatic Quantum Computing

Different types and approaches for using quantum mechanical events for computation have developed quickly with the quantum technology revolution. One of these, adiabatic quantum computing, uses the adiabatic theorem as a paradigm for computation. According to the adiabatic theorem, if conditions gradually change and a system is permitted to adjust its configuration (changing the probability density), the associated eigenstate will be of the final Hamiltonian if the system starts in an eigenstate of its initial Hamiltonian. Simply put, a quantum mechanical system that is subjected to rapidly changing and varying conditions, there isn’t enough time for a function to adapt which leaves the spatial probability density unchanged. A closely related optimization process that utilizes the adiabatic theorem known as quantum annealing, finds a global minimum over a candidate set of solutions by processing quantum fluctuations. Quantum annealing is used primarily in combinatorial optimization problems where the search space is large and discrete, containing multiple local minima.
The ensemble learning process for short-term prediction of traffic state on rural roads

Rural road traffic data including, speed, volume, density, travel time, and traffic state are considered "Big Data" due to high volume, speed of data generation, and varieties including videos, texts, and quantitative data. The analysis and prediction of this data for the short-term future provide real-time information for road operators and passengers to better plan their trips. Among traffic parameters, speed, volume, density, and travel time are quantitative, and traffic state is qualitative–usually classified into light, semi-heavy, and heavy states. In this chapter, traffic data from rural roads covering a period of five years is applied to the calibration of statistical time series and machine learning algorithms to explore influential factors on a real-time traffic state. First, the traffic database is shown with calendar data including season, month, week, day, hour, holiday, sequence of holidays, and weather data obtained from meteorological stations. Among the aforementioned variables, several variables are defined in cyclical form, and others are converted to dummy variables. In the second step, time series regression, long short-term memory (LSTM), random forest (RF), support vector machine (SVM), and K-nearest neighbours (KNN) are trained by using the first three years of data as a training dataset, and their performance evaluated using the remaining two years as a test dataset. In terms of accuracy, the RF model showed superiority over other models (RF accuracy, 76.9%). In terms of balanced accuracy, SVM was more accurate than RF to predict light traffic state (SVM accuracy, 78.7%). The maximum balanced accuracy for predicting semi-heavy and heavy states was achieved by RF (69.9% and 53.8%, respectively). There was no single model with the highest accuracy for each month of a year. These two reasons were motivations to use the ordered logit (OL) model in the ensemble learning process. The input of this process is the output of the base models. Combining the outputs of single models provides a single output that is expected to be more accurate than the base models. After calibrating the OL model using the predictions from the base models for the first year of the test data set, all models were evaluated on data from the second year. The highest accuracy was obtained using the OL model in the ensemble learning process (an accuracy of 82.2%).