Reviewerly/exHarmony huggingface dataset:
Validitas Perancangan Game Edukasi Berbasis Android pada Mata Pelajaran Informatika

This study aims to describe the validity of Android-based educational game learning media in the Informatics subject at SMK Negeri 9 Padang. This research is a Research and Development study conducted in class X at SMK Negeri 9 Padang. The test subjects in this study were class X students majoring in Culinary and Hospitality. The instrument used is a media validation sheet which will be validated by material experts, media experts and linguists. The results of the assessment of the three validators obtained a validity test of 88.81%, so that the validity level can be interpreted as very valid to use. In conclusion, based on the assessment along with expert input as well as the results of field trials of Android-based educational games, the feasibility, excellence, and can be used in the learning process in Informatics class X SMK N 9 Padang
Mining Top-k Frequent Patterns in Large Geosocial Networks: A Mnie-Based Extension Approach

Frequent pattern mining (FPM) has played an important role in many graph domains, such as bioinformatics and social networks. In this paper, we focus on geo-social graphs, a kind of social network augmented by geographical information. However, in addition to the exponential time complexity of the problem, we face the challenge of efficient subgraph retrieval since we are interested in patterns in a specific region in such a network. For this reason, we formulate the top-k FPM problem in large geo-social networks. Specifically, we devise a novel framework for subgraph retrieval and FPM mining with a series of optimizations. First, we propose a neighboring-aware R-tree (NaR-Tree) index structure to alleviate the challenge of retrieving subgraphs from a large graph. NaR-Tree is a variant of R-tree in which each nonleaf tree node further maintains some edge statistics information for the rectangle related to it. Second, we define the concept of minimum image-based support of edges (MNIE). With the help of the NaR-Tree and MNIE-based pattern extension approach, a mining algorithm that addresses the problem of exponential candidate patterns is proposed. We also present a lazy retrieval strategy to reduce the frequency of subgraph retrieval. Finally, we adopt an edge sampling approach to further accelerate the mining process. Extensive experiments on real-world and synthesized datasets are conducted to demonstrate the effectiveness and efficiency of our solution.
Improving the Efficiency of 3D Monocular Object Detection and Tracking for Road and Railway Smart Mobility

Three-dimensional (3D) real-time object detection and tracking is an important task in the case of autonomous vehicles and road and railway smart mobility, in order to allow them to analyze their environment for navigation and obstacle avoidance purposes. In this paper, we improve the efficiency of 3D monocular object detection by using dataset combination and knowledge distillation, and by creating a lightweight model. Firstly, we combine real and synthetic datasets to increase the diversity and richness of the training data. Then, we use knowledge distillation to transfer the knowledge from a large, pre-trained model to a smaller, lightweight model. Finally, we create a lightweight model by selecting the combinations of width, depth &amp; resolution in order to reach a target complexity and computation time. Our experiments showed that using each method improves either the accuracy or the efficiency of our model with no significant drawbacks. Using all these approaches is especially useful for resource-constrained environments, such as self-driving cars and railway systems.
An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System

Visual acuity measurement is an important visual function test to perform in infancy and childhood. However, accurate visual acuity measurement in infants is difficult because of deficiencies in their communication ability. This paper presents a novel automated method to assess visual acuity in children (5-36 months old). This method, the automated acuity card procedure (AACP), uses a webcam for eye tracking and recognizes children's watching behaviors automatically. A two-choice preferential-looking test is performed when the tested child watches the visual stimuli shown on a high-resolution digital display screen. When the tested child watches the stimuli, their facial pictures are recorded by the webcam. These pictures are used by the set computer program to analyze their watching behavior. With this procedure, the child's eye movement responses to different stimuli are measured, and their visual acuity is assessed without communication. By comparing the results with grating acuity obtained by Teller Acuity Cards (TACs), AACP performance is deemed comparable to that of TACs.
Neural Network based automated hot water mixture

In the present day and age, most residential spaces comprise a shower system and generally a conventional system of hot water showers. Throughout history, showering has developed as an essential need in a person’s life. Nevertheless, a typical hot water shower system comprises delays in hot water mixing and usually requi res an average of 2 to 4 minutes to mix the cold and hot water to deliver the appropriate shower temperature. The delay in mixing provides less comfort and poor satisfaction affecting people’s lifestyles. Due to these disadvantages, a system incorporating artificial Intelligence can be utilized to enhance the performance of mixing which can offer an automated hot water mixture system with improved efficiency and effectiveness. Recently, significant research has been focused on utilizing deep learning technology due to its multiple breakthroughs in fabricating a broad range of automated novel applications since Neural Networks comprise the capacity to learn from data to offer efficient and accurate systems. In this research project, the hot water mixture is employed by an Artificial Neural Network model integrated with the combination of an embedded system of the proposed system of hot water mixture. Furthermore, the proposed system comprises temperature and flow sensors along with controllable flow valves. The tested system indicated acceptable accuracy between the actual and desired output flow rate and temperature. KEYWORDS: neural networks, deep learning, hot water mixture, embedded system
Similarity of graphic features in family, professional and accidental handwritten records

This publication concerns the problem of similarities appearing in the records of different persons, and it presents this issue in practical terms based on examples covering handwriting samples taken from members of the same family, persons pursuing the same profession, or unrelated persons. Its objective is not only to emphasise the existence of this phenomenon and its importance for opinion-making in a handwriting expert opinion process regarding the author of a handwritten transcript, but also an attempt to identify the handwriting features in which these analogies occur.
Research on Online and Offline Hybrid Teaching of Introduction to Artificial Intelligence

In view of the current teaching situation and challenges faced by the Introduction to Artificial Intelligence course, this paper analyzes the core teaching objectives of the course, and explores the online and offline hybrid teaching methods of it. This paper analyzes the advantages of online and offline hybrid teaching, and probes into the development mode of online and offline hybrid teaching of Introduction to Artificial Intelligence from the aspects of teaching content, teaching mode, teaching effect, examination method, etc.
Ontology-Based Solution for Handling Safety and Cybersecurity Interdependency in Safety-Critical Systems

In case, safety-critical systems face an anomaly (either intentional or not), safety and cybersecurity impact humans and environment. Thus, they affect each other and so they are considered as interdependent. An ontology-based solution for safety is needed to handle this interdependency. We propose a new safety ontology for Network Function Virtualization (NFV) framework which is able to cover reliability, availability, maintainability, and integrity-related breakdown types, since they interact and influence safety according to ENISA. Our ontology allows us to have a uniformized representation of the potential anomalies that a system and its elements can face. Based on this representation, a decision-making process takes place to avoid potential conflicts between safety and cybersecurity in order to best handle their interdependency. The results of our implementation show that our ontology handles the safety and cybersecurity interdependency and has little impact on decision-making time, which makes it an effective methodology for NFV framework.
An Investigation of English Sound Production Among Nande Bilingual Students in the Democratic Republic of Congo : A Case of Bilingual Christian University of Congo

This article is based on a study that was conducted to examine the different areas of English sound mispronunciation by Nande bilingual students of the Bilingual Christian University of Congo (UCBC) in order to point out the instances where the phonetic system of Kinande causes difficulty for the learner of English resulting in communication and intelligibility breakdown. A total of 50 Nande students were randomly selected from the five faculties organized at UCBC and were involved in the study. The study adopted a qualitative design and used the ethnography method in which the researcher was involved in observing, listening, and asking a few questions as he was immersed in different activities (debate, presentation, dramatization, expressive reading , asking riddles, singing song, prayer, English teaching and teaching some content courses). After data analysis, it was revealed that bilingual Nande students of UCBC acquired their mother language (Kinande) before they began to acquire and to develop the English language. Moreover, the phonological system of Kinande has some sounds that are absent in English, and English has certain sounds that are absent in Kinande, which complicates the learning of those sounds and thus impede smooth communication and mutual intelligibility. Furthermore, Kinande and English have some letters that share the same spelling but they are pronounced differently. Also, teachers of English minimize the teaching of English pronunciations and thus reduce students’ chance to practice pronunciation. Adding to this, teachers of English medium courses do not play their role of language teachers. They seem not to be concerned with language related items and think only TESOL faculties should address challenges related to language.
Metzler/Zeta Correspondence

We present a formula for determinants on the Metzler matrices of digraphs. Furthermore, we introduce a walk-type zeta function with respect to this Metzler matrix of a finite torus graph and express its limit formula by using the integral expression.
Image denoising method based on double-branch hole residual convolutional neural network

Relying on powerful learning and representation capabilities, Convolutional Neural Networks(CNN) have better denoising performance than traditional methods in image denoising tasks. However, these methods generally have the disadvantages of excessive smoothing and large amount of parameters. In order to overcome this shortcoming, an image denoising method based on double-branch hole residual convolution neural network is proposed in this paper. This method designs a novel network structure. By designing different voids on the branches, the detailed information at different levels of the image is fully mined. By reducing the number of convolution kerns on the branches, the number of parameters in the whole network is reduced. Compared with Denoising Convolutional Neural Network(DnCNN), the parameters are reduced by 19.6%, and the residual mechanism further accelerates the training process. On the test datasets, the proposed method not only achieved excellent performance in the two quantitative evaluation indicators of peak signal-to-noise ratio and structural similarity, but also subjectively denoised images contained richer texture details.
Research on Image Semantic Segmentation Method Based on Combination of DeepLabV3+ and Structure Reparameterization

Aiming at the problems of incomplete segmentation of small objects when the sample is not balanced and low accuracy in the current commonly used semantic segmentation models, an improved DeepLabV3+ network is proposed in this paper. By using the structural reparameterization method, this paper replaces the normal convolution in the partial structure of the network with DBB module, which allows the network to have a simple structure while maintaining the high segmentation performance that comes with complex multi-branch network. In addition, the network is trained by using a hybrid loss function consisting of cross entropy and dice loss function to further improve the segmentation accuracy. The proposed algorithm is verified on the Cityscapes dataset and the obtained mIoU value is 78.84% and MPA is 89.49%, which are 3.69% and 3.94% higher than the original model, respectively. Experiments show that the improved segmentation algorithm proposed in this paper can effectively improve the segmentation accuracy and achieve better segmentation effectiveness.
An overview of the current state of science library services. A look from within.

The process of moving from traditional forms of reader service to new ones begins with an analysis of the current state of library services, determining the degree of relevance of the services provided against the backdrop of changing tasks and responsibilities that librarians face in connection with new roles. The purpose of this study was to study the real state of library, information, methodological services, which are carried out by the departments of the network of the Library for Natural Sciences of the Russian Academy of Sciences, located in research institutes. Based on the results of the survey, a range of services has been identified that has already been introduced into work (access to local and remote electronic resources, workstations, electronic document delivery, bibliometric research, etc.) and is planned to be included in the future (organization of virtual exhibitions, checking scientific papers for correctness), borrowings in the Anti-Plagiarism system, provision of patent information, translation services, etc.). The employees themselves attribute the slow introduction of new forms of service to the lack of material, technical and human resources in the departments of the Network, the lack of skills to work with new technologies; and they see further development in improving the material base of the library, expanding services due to customer focus, establishing feedback with readers, and improving their own skills.
A Security Perspective of Blockchain Technology in the Financial Sector

The paper provides a snapshot of blockchain security technology by examining its data structure, achieving basic security requirements and some primary challenges.Blockchain qualifies as a disruptive technology.Although it started in 2008, it is now one of the popular technologies, which continues to transverse different industries.The peer-to-peer approach to sharing information, sophisticated data structure and features such as immutability, can offer solutions to identity theft, among other online-based frauds in the financial sector.Although it is a promising model, issues such as privacy concerns, regulatory uncertainty, slow adoption and lack of trust among stakeholders, will likely slow down its adoption in the financial sector.
The Feasibility of Grid Trading Approach for Bitcoin Based on Backtesting

Contemporarily, cryptocurrency represented by Bitcoin has attracted extensive attention from investors.The nature of Bitcoin is different from fiat currency or traditional financial assets, and the high volatility of bitcoin leads to greater potential financial risks in the bitcoin trading market.Therefore, the research on the volatility of the bitcoin market is increasing of practical significance.This paper investigates the feasibility and effectiveness of applying grid transactions for the high volatility trend like Bitcoin.Based on analyzing the daily transaction data of Bitcoin from July 2019 to July 2021, we conclude that grid transaction is feasible in the bitcoin exchange market.On this basis, this paper discusses the impact of initial position price, grid price upper limit, grid price lower limit, the number of the upper grid, the number of the lower grid, and other factors on the yield of bitcoin grid transactions based on backtesting.According to the result, when the parameters: initial position price is 35000, grid price upper limit is 0000, grid price lower limit is 6000, the number of upper grids is 20, and the number of lower grids is 1 or 2, grid trading has the maximum yield.These results shed light for further study of the investment strategy of grid transactions in the cryptocurrency market.
Integrating a simulation model as an architectural component of a game

Many computer games involve the simulation of real-world phenomena, mechanisms, and behavior of beings. Examples vary from simple Physics found in the platform style to characters that learn and adapt according to user choices. Another field that uses computer models is criminology, a theme that is also a starting point for many games. This paper studies the integration of a (serious) criminal simulation model and a computer game, keeping the two perspectives separated; the model functions as an add-on that reshapes parts of the game.
The Failure of Hedging Practice in Terms of Model Design: A Case Study of MGRM

Metallgesellschaft Refining and Marketing (MGRM) reported enormous losses on its positions in energy futures and swaps.The failure of the hiding practice leads to a case study of MGRM in order to figure out the attribution behind the hedging model that incurs such loss.The case study is based on secondary information retrieval and literature analysis.Specifically, the paper will look at the role of the "stack-and-roll" strategy in its hedging model design, analyze the required assumptions for the model to be effective, and the significance of backwardation in the model's revenue streaming, as well as evaluate its performances in different scenarios.Based on the analysis, the disconnect of the company market, as well as the breakdown of communication between both companies, led to its failure.Finally, the causal relationship between the model's faulty design and its collapse in market fluctuation has also been investigated in terms of cash flow risk, counterparty risk, market-to-market risk.A possible direction of future research is to conduct quantitative research on primary data on the evaluation of the variance hedging path.These results and findings of this paper revealed the faulty elements in MGRM's hedging model, which can be used as a guideline for the betterment of future financial operations.
Fundamentals of Designing and Building Cloud Environments

This following study is based on the fundamentals of designing and building cloud environments and all types of valid data has been collected to evaluate study in a proper manner. At the beginning of the study, an introduction of the cloud environment has been depicted in proper manner and in this section; the concept has been clarified by collecting valid and reliable sources. Also, several types of data have been gathered which are related with the use of cloud environments in IoT devices and several types of business organizations. The data which has been collected for this study is secondary by nature and the data has been collected by following a qualitative approach. Also inductive research design has been followed in this following study. For this following study, three themes have been developed and first theme is based on the concept of the cloud environment and then the importance of cloud environment in IoT devices and business organizations have been showcased in a proper manner. Also, the use of cloud computing has been discussed with elaboration and the fundamentals of designing and building cloud environments has been depicted with valid insights. Keyword : Cloud environment, cloud computing, IoT devices, business organization
Evaluating Present and Future Challenges in IoT Security

The internet of things is presently faced with huge challenges which presently create a major effect on the present and future organizational development. IoT proceeds with high concentrated security and thus is mainly confirmed with the major development due to maintaining data security. Providing IoT security has been faced many challenges such as: in healthcare service, data analytics, information technology and agricultural issues. The government body has taken better chances under the construction of carelessness and various fraud activities. The strong passwords and security codes should be provided at the time of better processing. Lack of scalability, lack of government support, interoperability, security and personal privacy, lack of treating patients, design base challenges are major issues faced by managing IoT security. There is high cultural effectiveness and huge drastic changes have been faced by the organization as technical upgradation has not been done in any sector. Hence, security personnel and many important human resources should have a better impact on future development. The application of advanced technicians can have a better impact on future growth. Keyword : Development, information, Internet of things, IoT security.
Impact of digital transformation on knowledge-sharing activities of accounting students at universities in VietNam

The fourth industrial revolution is a journey with the breakthrough of digital technologies leading to the intelligence of all aspects of society. Digital transformation is the way to go in the development of the 4.0 industrial revolution and an invaluable opportunity to develop the country. In today's social era, we are almost no stranger to the phrase “digital transformation”. With the success of the fourth industrial revolution, digital transformation is now an inevitable trend, a matter of survival for countries, organizations, businesses, and consumers around the world. This is how to apply digital technology logically and effectively in all aspects of life, from management, production, business, etc. Or the process of digital transformation is to move our activities from the real world to the virtual world in cyberspace. In the world, many countries have been implementing digital transformation strategies such as in the UK, Australia, Denmark, Estonia... The content of digital transformation is very wide and diverse, but it has some main content in common, including digital government (such as online public services, open data), digital economy (such as digital finance, e-commerce), digital society (such as education, health care, culture), and digital transformation in key industries (such as agriculture, tourism, electricity, transportation).