Reviewerly/exHarmony huggingface dataset:
Intelligent Moving Machine (IMM) Based on Battery for an Efficient Regenerative Braking System

Modern-day electric vehicle technology is based on batteries and semiconductor devices; these systems have complementary features which can be successfully used in an electrical energy storage system for intelligent moving machine which is known as IMM. The utilization of the electrical energy storage system in electrical moving machine provides more advantages, like efficient regenerative braking energy, life span of battery, and improved acceleration of IMM. In this research paper, a new regenerative braking system (RBS) is developed for electric vehicle technology using electrical energy storage system and run by brushless DC (BLDC) motor. During the runtime of IMM when it takes brake, the BLDC acts as a generator. Hence, by using an appropriate artificial neural network technique and switching algorithm, the DC link voltage is boosted, and the energy is transferred to the superconductor devices and the battery through the inverter. To provide a reliable and smooth braking regeneration system, the braking force distribution is realized through a soft computing technique. Checking the performance of the regenerative braking system, different modeling and simulations performance test and experiments are carried out in indoor and outdoor environment. The IMM experiment result confirms high capacity of the future RBS technology.
Research on Interactive Advertisement User Experience Based on VR Technology

Based on the continuous development of VR technology and its innovative application in various fields, this paper takes interactive advertisement as the starting point to further consider the impact of VR (virtual reality) technology on user experience.Through the study of relevant literature and practical application cases, interactive advertisement under VR technology is divided into three categories: Internet-based, mobile media and offline entities.Based on the sensory experience, interactive experience, emotional experience and other dimensions of user experience, the evaluation model is constructed and empirical research is carried out to evaluate, by combining Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation method, user experience of interactive advertisement under VR technology.The results show that users attach most importance, when experiencing VR interactive advertisements, to the design of the process of “interactive experience”, including interactive content, interactive system, interactive function, interactive interface and so on.
A Comparative study on Securing Electronic Health Records (EHR) in Cloud Computing

The Personal Health Record (PHR) framework converts a general clinical history of paper into a web-based electronic record. In this framework, a patient could share, oversee and control his PHR with approved clients, for example, family members, companions and wellbeing care [1]. EHR is a source that focuses on capturing clinical history and treatment of patient health information. These important patient-centered records can be successfully accessed by all authorized medical service providers (research institutes, authorities etc.) [2]. The method empowers an individual to collect, store, oversee, and share his state of being information inescapably. But with the advantages of these PHR systems there are so many flaws related to security. Different cryptographic techniques are used to encrypt the data and make it secure. After applying different techniques, the data on the cloud is now much more secure than before.
Transport Distances on Random Vectors of Measures: Recent Advances in Bayesian Nonparametrics

Random vectors of measures are at the core of many recent developments in Bayesian nonparametrics. For a deep understanding of these infinite-dimensional discrete random structures and their impact on the inferential and theoretical properties of the induced models, we consider a class of transport distances based on the Wasserstein distance. The geometrical definition makes it ideal for measuring similarity between distributions with possibly different supports. Moreover, when applied to random vectors of measures with independent increments (completely random vectors), the interesting theoretical properties are coupled with analytical tractability. This leads to a new measure of dependence for completely random vectors and the quantification of the impact of hyperparameters in notable models for exchangeable time-to-event data.
Model Selection of Kernel Ridge Regression for Extrapolation

Model selection of the kernel ridge regression is discussed in this paper. The cross-validation approach is one of popular and powerful model selection techniques for many machine learning methods including the kernel ridge regression. However, the cross-validation approach is not suitable for extrapolation scenarios due to its principle. In this paper, we propose a novel model selection criterion for the kernel ridge regression which is applicable to extrapolation scenarios. The key idea of the proposed criterion is direct evaluation of the generalization error, defined in a certain reproducing kernel Hilbert spaces, which is feasible under a certain assumption on a set of kernel candidates.
The Effect of Classes through Google Classroom due to COVID-19 on Nursing Education

This study was conducted to develop and evaluate the impact of education through Google Classroom on nursing education such as critical thinking, problem-solving ability, self-regulated learning ability and academic major satisfaction with the face-to-face instructional closure due to COVID-19. The methodology used in this research is a quasi-experimental study of a group of pretest-posttest design, which was designed to check for differences before and after the intervention through Google Classroom to the treatment group. For a total of six weeks from March 16, 2020 to April 24, 2020, H University, located in J city, has taught eight nursing courses in the first, second, third and fourth grades through Google Classroom. The collected data were analyzed with descriptive statistics and paired t-test using the SPSS WIN 23.0 program. The effects of education with Google Classroom on critical thinking ability, problem-solving ability, self-regulated ability and academic major satisfaction were statistically significant as follows; critical thinking ability (t =,-3.48 p =).000), self-regulating learning ability (t = -3.35, p = .001), problem-solving ability (t = -3.49, p = .001), and academic major satisfaction (t = -2.81, p = .016). The above findings confirmed that applying Google Classroom to nursing education can have a positive impact on critical thinking, problem-solving ability and self-regulated learning ability among nursing students, and increase academic major satisfaction.
An Encoder-decoder based approach for generating Faces using Facial Attributes using CNN

This paper addresses the challenge of generating faces using facial attributes. Although there are researches the address the problem of generating faces, they do so by using a facial image as a base and changing the required attributes. To solve this problem, we make CNN models to learn a classifier that can predict these features (1 feature per model) and output their labels. Labels are the enumerated value each attribute can take. Then these models are combined into one model to generate a dataset that maps the above 6 facial features to each image. This prepared dataset is then used to train the final CNN model that learns to generate a 200 × 200 × 3 matrix using a 6 × 1 matrix as input. The output matrix represents the resolution of the image with 3 channels namely, Red, Green and Blue. This 3D array when plotted gives the desired image. The 6 × 1 matrix represents the six labels. To improve the output, the final CNN model is changed and an Auto-encoder and decoder are used. Also, instead of 6 × 1 input array, 55 × 1 input array is used. This is first trained to regenerate images from an input image. The decoder from this trained model is then used for transfer learning. The decoder is retrained to learn the features specified by the 55 × 1 input matrix. Finally, this decoder is used to generate the desired images of size 150 × 150 × 3 using the 55 × 1 input matrix.
The Interaction of Participants in the Payment Systems in the Digital Environment

The article deals with payment system of the Bank of Russia and its evolution for implementation budgetary and monetary policy. The interaction of economic actors in the digital environment is concerned. Likewise, the system of treasury payment and its transformation is revealed. The article analyzes the process of improving the technologies and managing the liquidity. The paper touches upon the issue of the centralization process of financial capacity of banks. The interaction of participants in the national payments in the digital environment for efficiency of the payment turnover is estimated. Constant changes in the financial sphere are accompanied by adaptation particular components of national payment system. In conclusion, authors point out that expanding possibility of participants in the financial market and solving the issues of ensuring financial stability.
Thumbs up and down: The cultural technique of thumb-typing

This paper explores thumb-typing as a cultural technique stemming from the mutual development of typing interfaces and practices. Focusing on the work of the typing fingers, it examines how the assignment of thumbs to be the primary writing digits is an innovation that correlates—and in some respects causes—textual and social changes that are central to digital culture. It argues that thumb-typing embodies recursive relations between behavioral patterns, technological infrastructure, and textual creation. The analysis shows how the invention of the typewriter keyboard introduced the fingers to typing, and how developments of digital media refined the finger-work in interacting with the device, resulting in thumb-typing. The new functionality of the thumb as an executing rather than supporting finger, promotes a novel equivalency and interchangeability in finger employment to typing. This, I propose, problematizes traditional concepts of textuality, its performance, and authorship.
A Survey of Word Embedding Algorithms for Textual Data Information Extraction

Unlike other popular data types, such as images, textual data cannot be easily converted into a numerical form that machine learning algorithms can process. Therefore, text must be embedded into a vector space using embedding algorithms. These algorithms attempt to encapsulate as much information as possible from the text into a resulting vector space. Natural language is complex and contains numerous layers of information. Information can be obtained from a sequence of characters or subword units that make up the word. It can also be derived from the context in which a word occurs. For this reason, a variety of word embedding algorithms have been developed over time, which use different pieces of information in different ways. In this paper, the currently available word embedding algorithms are described and it is shown what kind of information these algorithms use. After analyzing these algorithms, we discuss how it can be advantageous to use combinations of different types of information in different research and application areas.
Efficient Recommender System for Over-the-Top Media Service

Recommender System’s main idea is to decide the most suitable products for the customers. It enhances the relation between the users and the products. There are various applications of Recommender Systems. One such application that is most needed as well as helpful for users is the Movie Recommender System. A Movie Recommender System helps the users to find the movies those are more appropriate for them and which they may like the recommender system considers the user preferences to recommend movies to the users. There are various factors that can be considered to recommend a movie to the users. They are actors, genre and language of the movies. It will also consider the history of the movies watched by a particular user to recommend movies to them. The dataset that will be used for this project is Netflix prize dataset and hotstar dataset. Two models will be developed in this project namely Collaborative Filtering Algorithm and Pearson’s R Correlation algorithm. The outcome of this recommender system will be a customized list of top-rated movies from Netflix and Hotstar respectively. The future scope of this system is to recommend top rated movies from various OTT platforms which will help the user to identify his/her favorites in single application.
Exploring an Extensible Children Game Framework based on Augmented Reality Building Blocks

Playing is an essential way for preschoolers to learn. There are three types of games for preschoolers: functional play, constructive play, and symbolic play. However, existing works/games can only work for one specific type of children's play. Therefore, we designed and implemented an extensible children's game framework based on Augmented Reality building blocks. We implement an AR prototype from scratch that achieves up to 8 blocks with 81% detection rate and overhead of 21ms (46 FPS) on average.
Efficiently Training On-Policy Actor-Critic Networks in Robotic Deep Reinforcement Learning with Demonstration-like Sampled Exploration

In complex environments with high dimension, training a reinforcement learning (RL) model from scratch often suffers from lengthy and tedious collection of agent-environment interactions. Instead, leveraging expert demonstration to guide RL agent can boost sample efficiency and improve final convergence. In order to better integrate expert prior with on-policy RL models, we propose a generic framework for Learning from Demonstration (LfD) based on actor-critic algorithms. Technically, we first employ K-Means clustering to evaluate the similarity of sampled exploration with demonstration data. Then we increase the likelihood of actions in similar frames by modifying the gradient update strategy to leverage demonstration. We conduct experiments on 4 standard benchmark environments in Mujoco and 2 self-designed robotic environments. Results show that, under certain condition, our algorithm can improve sample efficiency by 20%~ 40%. By combining our framework with on-policy algorithms, RL models can accelerate convergence and obtain better final mean episode rewards especially in complex robotic context where interactions are expensive.
Quaternary Reed – Muller codes and their minimum weight bases

We prove that the families of quaternary Reed – Muller codes obtained by the BQ-Plotkin construction 2009 have bases of minimum weight codewords. In 2020 we found that the quaternary Reed – Muller codes constructed by the quaternary Plotkin approach have the minimum weight bases. Combining these two constructions we prove that all known quaternary linear Reed – Muller codes have bases of minimum weight codewords. The bases are obtained iteratively.
Changes and Challenges: Application of Artificial Intelligence Technology in College English Teaching

With the rapid development of artificial intelligence technology, it has exerted a great influence in the fields of data, communication and computing. It is not only the inevitable trend of future education development but also the internal demand of education to apply artificial skills and technology into education. In this context, college English teaching is also facing changes and challenges. On the one hand, artificial intelligence technology promotes the realization of personalized and precise education. On the other hand, there are still many problems in the application of artificial intelligence technology in teaching. Schools, teachers and students should make joint efforts to explore a scientific and reasonable model of applying artificial intelligence to college English teaching.
A bit-object-based method for mining maximum frequent patterns in intensive cloud computing data

In order to overcome the problems of poor timeliness and low accuracy of mining existing in traditional methods, this paper designs a bit-object based maximum frequent pattern mining method for intensive cloud computing data. After judging the support number according to the bit object of the maximum frequent pattern, the intensive cloud computing data is accurately collected according to the difference between the load value of cloud data and the true value of load, so as to improve the accuracy of subsequent mining results, and then the maximum frequent pattern of data is accurately mined by combining the bit object. Experimental results show that the maximum time to generate mining results is only 4.6 s, the maximum bit error rate of output results is only 7%, and the maximum memory occupancy is only 3.90%. The above results show that this method is more suitable for practical excavation.
On the rational method for solving the hydrodynamic dispersion equation in radial filtering flows

One of the methods for solving boundary value problems for some special cases of the hydrodynamic dispersion equation is considered. The method is based on the representation of solutions of differential equations of parabolic type in the form of series with generalized powers of Bers. The equations that allow the application of this method are obtained from the general equation of hydrodynamic dispersion in radial filtering flows. An example of solving a specific boundary value problem for one of the equations is given. The phenomenon of hydrodynamic dispersion is a complex type of mass transfer that depends on many factors. The proposed method allows us to significantly reduce computational difficulties because, in the end, it is reduced to the construction of generalized Bers degrees representable through integrals of combinations of exponential and power functions. In addition, the analytical expressions of these functions can be used to solve inverse problems, i.e., to determine the mass transfer coefficients.
Motivation and Interest in Learning in Online Learning during the Covid-19 Pandemic in Yapis 02 Elementary School Manokwari

This study aims to determine the motivation and interest in learning in online learning during the Covid-19 pandemic in students of SD Yapis 02 Manokwari. The research method used is descriptive qualitative research method because the problem is not clear, complex and dynamic and full of meaning, so qualitative data is more appropriate to use. The instrument in this research is the researcher himself. Data collection techniques by means of observation, in-depth interviews, and documentation. In the interview, the participants who will be involved are students of SD Yapis 02 Manokwari along with their parents, teachers, all of whom were selected based on certain criteria (purposive and snowball sampling). The student criteria include grade 4, 5 and 6 students who since the beginning of the pandemic have participated in online learning, with the consideration that they have understood the context of the questions asked in the interview. In addition to these criteria, students are also selected based on the ability to describe their thoughts and opinions. Data analysis techniques are carried out in line with the data collection process, carried out interactively through data reduction, data display, and verification. To maintain the credibility of the data, it is necessary to extend observations, increase persistence in research, triangulate sources, place and time, analyze negative cases and use reference materials.
Application of Wolf Pack Algorithm Based on Probabilistic Strategy in Unmanned Equipment Coordination

The purpose of building the model of Combat Effectiveness Coordination for the unmanned Equipment is to improve the overall efficiency through reasonable allocation, so that the Combat Effectiveness can be optimized. According to the characteristics of high requirement in attacking target for elite equipment, normal targets are only controlled. This paper modifies the model and introduces Probabilistic Estimation of Nonuniform Chaotic Map Settings in algorithm initialization stage based on WPA, the Probabilistic Evaluation is used to increase the exploratory repair strategy in the updating stage so as to maintain the diversity of the population and speed up the convergence rate of the algorithm. The simulation results show that the improved algorithm is suitable for the actual needs of the model which can provide a reasonable and effective optimization scheme for the Collaborative Configuration of Combat Effectiveness Coordination for the Unmanned Equipment.
Bibliometric Analysis of Technology Blockchain with IoT in Vehicle

This research focuses exclusively on analyzing articles related to innovation in blockchain technology with IoT in vehicles and automotive. This study has the main objective of identifying and analyzing the literature on this topic scientifically in the past, present, and future. Bibliometric analysis approach using publish and perish software and VOS viewer. The bibliometric analysis used in this study to visualize topic areas, The number of keywords that appear frequently, the number of publications per year between 2016 – 2020, the most published papers on the topic of blockchain with IoT in automotive, the most frequent trends, and words topics. In conclusion, Blockchain with IoT in vehicles and automotive makes a recently developed topic and currently has great relevance for practitioners and academics. However, the interpretation of the results presented and discussed above has several limitations to address. First, this research is based on samples of articles published in journals and conferences indexed by Scopus. Second, the resulting keywords include blockchain, Internet of things, Internet of vehicle, smart contract, security, privacy. Third, the publication of scientific articles related to this research topic from 2016 has 20 articles, 2017 does not exist, 2018 has 6 articles, 2019 has 34 articles, 2020 has 36 articles, and 2021 has 1 article. Therefore, the topics used to allow us to have an evident view of the trends in the discussion can generate ideas and knowledge for future research.
Research on Microseismic Triggered Image Acquisition System for Landslide Monotoring

The previous landslide image acquisition system adopts a continuous image acquisition and transmission method, which has high consumption and produces many redundant data. Therefore, a new landslide image acquisition system is developed in this paper. The system adopts seismic geophone to gain landslide motion data, and then the dual limit comparator filters out the landslide motion data and forms a trigger signal to wake up the system. In the end, after being awakened, the embedded micro-controller collects and transmits the image. With the intermittent image acquisition and transmission method, the equipment keeps in a low-power consumption for a long time when the landslide movement is not severe. The equipment will be awakened only when the landslide movement is violent. It turns out that compared with the continuous acquisition method, the power consumption of the system can be reduced by about 66%, and the data amount can be reduced by about 60KB when the system is triggered only once in a minute.