Reviewerly/exHarmony huggingface dataset:
Formation of Readiness for Creative Activity in the Field of Audiovisual Art and Production

The purpose of the study is to investigate and analyze the criteria and indicators of readiness for creative activity in the field of audiovisual art and production, namely: motivational and value, cognitive and cognitive, and activity and creative. In accordance with these criteria, the work aims to prove the professional competencies that an audiovisual art and production specialist should have, which will enable a creative approach to professional activity. The research methodology consists in the application of the following methods: empirical research level – the method of observation, rating and self-evaluation, collective consultation, experiment, content analysis, generalization of practical experience, expert evaluation; theoretical level of research – experimental verification of new creative projects, solving integrated tasks, analytical method, deductive, inductive, modelling, comparison, abstraction and concretization, analysis and synthesis, thought (imaginary) experiment, generalization of the studied material. Scientific novelty. For the first time in the theory and practice of audiovisual art and production, criteria and indicators of readiness for creative activity in the field of audiovisual art and production have been proposed and experimentally verified. Conclusions. The formation of readiness for creative activity in the field of audiovisual art and production is a complex and lengthy process, the effectiveness of which is influenced by certain conditions, factors and causes. This approach identifies criteria and indicators for the formation of readiness for creative activity in the field of audiovisual art and production, as well as professional competencies that form a holistic structure in which all elements are interdependent and interrelated.
A Sample Approach to Validating the Use of Publication Metadata to Track Swedish Researcher Mobility

We examine the extent to which metadata for publications indexed in Scopus reflect the career placements of researchers at universities and other organisations. Our study focuses on a sample of researchers affiliated to three Swedish universities to explore the amenability of this data for tracking researcher mobility patterns. Using a desk-research approach, we locate publicly available details of employment and then compare those organisations and timeframes to the affiliation names and dates listed on publications, across 30 researchers connected to Swedish universities. Although the sample size is small, our findings demonstrate that the use of publication metadata is broadly reliable for tracking the mobility of researchers as they move between organisations: more than seven out of ten universities and other organisations do appear in the publication metadata. In a smaller majority of cases, the publication metadata was also accurate in terms of depicting the timing of career placements, although lags are common.
A Study on Improved Pet Behavior Monitoring Through Data Imputation Using Multiple Data

In multiple-data-based pet behavior monitoring-related studies, data may be frequently missing because the area where data can be collected simultaneously is limited. This paper is a study on pet monitoring to supplement this. From the image data, the features of the joint movement of the pet are extracted through DeepLabCut-based learning, and from the sensor data, features according to time such as the direction and speed of the actual movement are extracted. When a pet is out of the shooting area and missing in the image data, the missing data are imputed by augmenting generative adversarial network (GAN)-based behavior data using sensor data. Finally, the behavior is monitored by recognizing the behavior of pets through a convolutional neural network (CNN) and long short-term memory (LSTM)-based network model. The proposed method is meaningful in that it is augmentation between behavioral data, and performance improvement for multiple-data-based behavior monitoring can be expected.
Interactive piecewise planar building reconstruction from a single image based on geometric priors

The development of methods to reconstruct the piecewise planar structures of buildings from a single image is an important and challenging problem in the field of computer vision. Although most prior works have considered fully automatic reconstruction solutions, these methods typically perform poorly for complex building structures. To address this problem, we propose an interactive single-image 3D building reconstruction method to integrate automatic plane inference with interactive plane refinement based on geometric priors. In this method, based on geometric priors (e.g., the orientation of the plane and the intersection angle between planes) learned using convolutional neural networks, building regions are first partitioned into multiple polygonal regions using click interactions. Then, the planes corresponding to these polygonal regions are automatically and progressively inferred by taking full advantage of the constraints constructed using the geometric priors. Finally, under the guidance of interactive plane refinement based on click correlations, the resulting planes are globally optimized by jointly utilizing image cues and geometric priors. The results of an experimental evaluation on three datasets demonstrate that the proposed method achieved better performance than existing methods in terms of both qualitative and quantitative measures.
Equatorial ice

A flickering glow in the sky seems to accompany some earthquakes. Could this point to a way of predicting these disasters? Nathaniel Scharping investigates
Improved diffractive-imaging based image encryption scheme in 3D space

Abstract In this paper, we present an improved diffractive-imaging-based image encryption scheme in 3D space. In this cryptosystem, the plaintext is divided into several sub-images by binary amplitude masks and then diffused into 3D space at different axial positions for encryption, which confuses the relation between the plaintext and the ciphertext to make the chosen-plaintext attack no longer work. For the decryption, we develop a modified iterative retrieval algorithm to retrieve a decrypted image with high quality. Additionally, this cryptosystem obtains higher security without increasing the amount of the ciphertext. Compared with previous diffractive-imaging-based image encryption schemes, the key space is enlarged so greatly that this cryptosystem can resist the brute force attack. Furthermore, this scheme can significantly reduce the computation and retrieve a decrypted image with high-quality.
Accelerating Inference of 3D-CNN on ARMMany-core CPU via Hierarchical Model Partition

Many applications such as biomedical analysis and scientific data analysis involve analyzing volumetric data. This spawns huge demand for 3D CNN. Although accelerators such as GPU may provide higher throughput on deep learning applications, they may not be available in all scenarios. CPU, especially many-core CPU, remains an attractive choice for deep learning in many scenarios. In this paper, we propose a inference solution that targets on the emerging ARM many-core CPU platform. A hierarchical partition approach is claimed to accelerate 3D-CNN inference by exploiting characteristics of memory and cache on ARM many-core CPU.
RankSearch: An Automatic Rank Search Towards Optimal Tensor Compression for Video LSTM Networks on Edge

Various industrial and domestic applications call for optimized lightweight video LSTM network models on edge. The recent tensor-train method can transform space-time features into tensors, which can be further decomposed into low-rank network models for lightweight video analysis on edge. The rank selection of tensor is however manually performed with no optimization. This paper formulates a rank search algorithm to automatically decide tensor ranks with consideration of the trade-off between network accuracy and complexity. A fast rank search method, called RankSearch, is developed to find optimized low-rank video LSTM network models on edge. Results from experiments show that RankSearch achieves a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$4.84 &gt;$</tex> reduction in model complexity, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1.96\times$</tex> speed-up in run time while delivering a 3.86% accuracy improvement compared with the manual-ranked models.
Emdedded Large Scale Face Recognition in the Wild

Cameras have emerged as one of the most important tools to bring ubiquity to the Internet of Things (IoT) since its beginning and can be used to improve contextual accuracy substantially by using effective face recognition technology. The recent literature suggests that there is a large accuracy gap between today’s publicly available techniques and state-of-the-art private face recognition systems. This paper aims at bridging this gap by presenting the results of utilizing the OpenFace public library to perform face recognition in the wild. The key focus of this paper is to present a mechanism that guarantees higher accuracy with low training and less prediction time. In order to avoid the problem of reduced accuracy with the increase in the number of samples, this paper presents a strategy to keep up with low training time and high accuracy in comparison with a range of different recognition/classification techniques. A collection of large data samples of 245 different human faces was utilized to conduct this study, where the training was performed gradually starting from 5 classes to 245 distinct classes, incorporating a total of 7273 images to achieve reasonable accuracy. This paper also includes the details on how the classification was performed on live stream using webcams, where training and classification were done using the NVIDIA Jetson nano platform.
A Novel Association Rules Mining Based on Improved Fusion Particle Swarm Optimization Algorithm

Association rule learning is to mine the correlation between items and objects from certain data or other information carriers, and the results can reveal the hidden association patterns in the data. This paper firstly analyzes the advantages and disadvantages of the traditional particle swarm optimization algorithm, and finds the existing problems. Aiming at these problems, this paper improves the particle swarm optimization algorithm based on the random search theory of complex groups. In this paper, the improved fusion particle swarm optimization algorithm is used in the association rule mining model, and more effective rules can be obtained. Experiments show that the improved fusion particle swarm optimization algorithm proposed in this paper is feasible and efficient in mining association rules.
Creating an Augmented Reality Game

Now that you’ve developed a few AR effects, you’ve accumulated a set of foundational techniques that can be combined to make even more sophisticated projects. Previously, we’ve explored using lightweight interactivity in our effects, such as an effect responding to the user opening their mouth. In this project, we will be constantly using our user’s actions to power the effect, thus making a more fully interactive game.
Getting Started with Meta Spark Studio

Now that we have an idea of some tools used for AR, let’s get started by installing Meta Spark Studio.
Creating Your First Effect

Now that you’ve familiarized yourself with Meta Spark Studio’s interface and capabilities, let’s get hands on with creating a complete AR project—also called an “effect”—from scratch!
Blockchain for Smart Healthcare: A SWOT Analysis from the Patient Perspective

Smart healthcare lies in the technologies that enable physicians and care providers to cooperate in the best interest of the patients. The expansion of smart healthcare services raises relevant challenges to keep the patient at the heart of a cooperative, trusted, and datafied healthcare system. From the patient perspective, the purpose of this contribution is to highlight the relevance of blockchains and related technologies for smart healthcare. Recent blockchains are programmable, decentralized, and trust technologies with suitable properties for intelligent, personalized, preventive, and participatory healthcare. Through a Strengths, Weaknesses, Opportunities, and Threats analysis, the key factors of blockchain’s relevance have been grouped and analyzed to build the four quadrants of a patient-driven SWOT matrix. Decentralized by design, the recent blockchains are promising extensions to the current centralized healthcare systems. Subject to the identified weaknesses and threats, blockchain might be the next patient-centric trust technology that enables trustworthy infrastructures for smart healthcare services.
Basic Statistics and Functions Using R

Data science represents a multifaceted discipline, since it requires knowledge from statistics to understand the data, knowledge from programming to manipulate the data, and the know-how to explain the data, which is often best done with one or more visualizations. Beyond statistics as the general subject matter within the branch of mathematics, the word “statistics” carries a second definition referring to the numeric values that summarize a sample, such as the mean, median, standard deviation, and variance. The R programming language signifies a preferred choice among statisticians and data scientists to easily manipulate data and provide useful statistics on that data. R has many popular plots, but here we will focus on three of the most basic ones, which are necessary for the study of linear regression.
Deployment and Scaling

This chapter focuses on designing and building scalable and resilient architectures on Google Cloud Platform (GCP). The chapter begins by introducing the principles of scalable and resilient design, including fault tolerance, elasticity, and scalability. You will learn about the key components of scalable and resilient architectures on GCP, including load balancing, autoscaling, and distributed databases. The chapter also covers best practices for building scalable and resilient architectures on GCP, including using managed services, designing for failure, and monitoring and alerting. Overall, this chapter provides a comprehensive guide to building scalable and resilient architectures on GCP, with practical examples and real-world use cases.
Google App Engine

In this chapter, you will learn about Google App Engine (GAE), a fully managed platform for developing and deploying web applications and services. GAE allows developers to focus on writing code without worrying about the underlying infrastructure and about scaling the application as traffic increases. The chapter covers the key features of GAE, including the programming languages and frameworks supported, storage options, and deployment methods. You will also learn about scaling and optimizing GAE applications to handle changes in traffic and improve performance. Finally, the chapter covers security considerations and best practices for deploying and managing GAE applications. By the end of this chapter, you will have a comprehensive understanding of Google App Engine and how to use it to develop and deploy web applications.
Design of e-government platform based on cloud computing in the era of big data

The governments at various levels shall conduct e-commerce activities, can produce large quantities of government affairs information and citizen privacy information such as content, due to the information security requirement is higher, so in order to avoid illegal access and information leakage, under the background in the era of big data, based on the concept of cloud computing technology research scholars e-commerce platform design system is presented. In implementing e-government service, cloud computing platform is bound to face large data processing, storage and sharing of the multipolar localized problem, so the research scholars in the construction of a cloud computing platform at the same time, according to the future demand for e-government service, strengthening the construction of e-government security platform model, based on the order from e-government security requirements. Therefore, on the basis of understanding the application status of cloud computing technology in the era of big data, this paper explores how to build an e-commerce platform with cloud computing as the core according to the current situation of e-government services. The final experimental results show that although the security problems of e-commerce are becoming more and more obvious, the reasonable use of emerging cloud computing technology can guarantee the security of e-government from the foundation, and relevant research topics have practical significance for the development of technology.
The application of artificial intelligence technology in the field of mechanical and electronic engineering reflects

In the social economic sustained growth, our technology manufacturing industry has been a rapid development, the practice of machinery manufacturing level has been improved, which has made a great contribution to the economic development of our country, and mastered more valuable data theory technology in the practice of inquiry. Especially after entering the background of big data era, the field of mechanical and electronic engineering in China began to develop toward intelligent in practice, and the overall degree of automation and production efficiency have been significantly improved. In recent years, in order to improve the competitiveness of our production, countries have given great attention to the development of mechanical and electronic technology. As a concrete manifestation of scientific and technological progress, artificial intelligence is a successful attempt of multi-disciplinary integration and development, while mechatronics is the transformation of traditional energy into information connection. Therefore, on the basis of understanding the concepts of artificial intelligence and mechanical electronic engineering and according to the development trend of the field of mechanical electronic engineering in recent years, this paper deeply discusses how to reasonably use artificial intelligence technology and improve the comprehensive level of our machinery industry.
Obstacle avoidance flight control method of UAV formation based on neural network adaptive PID

According to the analysis of the research results of UAV formation must-rise path planning and intelligent control technology in recent years, scholars around the world began to use artificial intelligence technology theory for optimization and innovation, put forward a variety of scientific research and exploration topics, and achieved excellent research results. In this paper, based on the understanding of neural network adaptive PID and UAV cluster track planning, according to the accumulated experience of scientific research and technology achievements in recent years, in-depth discussion of the UAV formation obstacle avoidance flight control method based on neural network adaptive PID algorithm. The final experimental results show that the neural network adaptive PID algorithm can effectively control the UAV formation and truly realize the basic functions.
Analysis of key technologies of cloud computing based on openstack cloud platform

With the rapid development of cloud computing technology, the problem of high energy consumption in data centers is becoming more and more obvious. Researchers pay more attention to how to guarantee the quality of service and improve the efficiency of resource application in their research. According to the analysis of the application of open source infrastructure service platform OpenStack proposed by experimental research in recent years, it is clear that the key technology points of cloud computing can provide an effective basis for the development of modern technology. Therefore, on the basis of understanding the research status of OpenStack cloud platform and cloud computing key technologies, this paper mainly discusses the agile elastic scaling technology with load prediction or feedback as the core. The final experimental results show that the proposed algorithm can use less disk space, improve the efficiency of resource application and service quality.
Research on the new model of "Internet + education" based on artificial intelligence

China's "Internet + education" respects the essence of education at the same time, based on the Internet mode of thinking and behavior, re-create the teaching model and teaching methods, it does not belong to a simple online education, but the Internet into the whole process of teaching. Therefore, in the context of the new era, after introducing the concept of artificial intelligence technology into the field of education, the new information technology with artificial intelligence as the core can not only balance the processing of educational resources, gradually improve the teaching efficiency, but also present students with personalized and autonomous learning mode. In this article, therefore, artificial intelligence and Internet + education development model, on the basis of deeply discussed with artificial intelligence as the core "Internet + education" of the new mode, and connecting with the new era under the trend of the development of artificial intelligence, has been clear about the Internet + education the main content of the new model, in order to optimize the comprehensive level of education in our country.
Research on preschool education management information system based on big data algorithm

At present, our preschool education information management system way is relatively backward, the various departments do not form the information cooperative management relationship, not with the new era of information development trend, it is difficult to form effective coordination and education regulator. From the perspective of preschool education management, the information system can help supervisors better assume the responsibility of education supervision and administration, and promote the development and innovation of preschool education management. Therefore, on the basis of understanding the current research status of big data algorithm, according to the application status of preschool education management information system, this paper deeply discusses the structure of preschool education management information system with big data algorithm as the core. The final experimental results prove that this system design can truly achieve the first phase of preschool education information management, solve the independence of information transmission and information submission in traditional preschool education institutions, and further improve the efficiency and practicability of the overall system management.
Optimal allocation of innovation and entrepreneurship education resources for local normal education majors based on PSO algorithm

Under the background of modern education innovation, in order to ensure the university innovation entrepreneurship education resources get reasonable configuration, practice application to maximize benefits, the current research scholars to build the education resources input and output of the evaluation index system, and proposed the corresponding function model, need to use particle swarm optimization algorithm for the simulation analysis. The final experimental results show that the allocation analysis using particle swarm optimization algorithm can further improve the application efficiency of innovation and entrepreneurship education resources in colleges and universities, ensure the results of resource allocation, and provide an effective basis for the innovation of modern college education.