Reviewerly/exHarmony huggingface dataset:
Prediction of thermal currents for autonomous drone flight with Artificial Intelligence

This work presents the proposal for a method of prediction of thermal currents by means of neural networks, to facilitate decision-making in the flight of autonomous drones that use this form of energy to fulfill their missions. It is based on the experimental measurement during the flight of environmental parameters, geographical location, time and speed.
Markerless Registration for Augmented-Reality Surgical Navigation System Based on Monocular Depth Estimation

In augmented reality(AR) surgical navigation system, the depth estimation using RGBD camera has limitation in obtaining the dense depth necessary to increase the registration accuracy. Recently, deep learning based monocular depth estimation has showed remarkable performance. In this study, we developed a markerless registration method using the monocular depth estimation, and applied it to AR surgical navigation system. The accuracy of our method of monocular depth estimation was 2.47 ± 1.15mm, while that of the method of the RGBD camera was 2.33 ± 1.24mm. There was no significant difference by paired T-test. Furthermore, the monocular depth estimation was able to acquire denser depth than the RGBD camera.
Research on Resource Allocation and Optimization of Community Intelligent Sports Service for the Elderly Based on Group Intelligence

Objective. The mainstream development trend in the era of intelligent sports. At present, with the rapid development of science and technology, it is absolutely wise to combine group intelligence with community intelligent sports services for the elderly. Group intelligence has opened a new era of intelligent sports service. Group intelligence has become an important factor in the development and growth of community intelligent sports service for the elderly and has become a hot topic at present. However, intelligence has encountered difficulties on the road of development. At present, the aging of the population is getting worse and worse, and the elderly have higher and higher requirements for fitness and leisure services, which leads to the need for sports services to be continuously strengthened. The distribution of resources is uneven, the data is not clear enough, and the swarm intelligence algorithm is not perfect. With the adaptation of the elderly to intelligence, more intelligent, concise, and personalized services need to be developed. The most important method is to optimize the swarm intelligence algorithm continuously. In this paper, PSO algorithm is optimized and HCSSPSO algorithm is proposed. HCSSPSO algorithm is a combination of PSO algorithm and clonal selection strategy, and test simulation experiments, PSO algorithm, CLPSO algorithm, and HCSSPSO algorithm for comparison. From the experimental results, HCSSPSO algorithm has better convergence speed and stability, whether it is data or comparison graph. The data optimized by HCSSPSO algorithm is higher than the original data and the other two algorithms in terms of satisfaction and resource allocation.
Contrastive Explanations for Machine Learning

Contrastive learning is a newer approach to finding similar and dissimilar candidate objects in a machine learning pipeline. The contrastive explanation aims to find the similarity between two features to help the prediction of a class. Typical black box models trained on different types of hyper parameters and a bunch of parameters optimized on various epochs and learning rates are very hard to interpret and even harder to reason why the model predicted Class A vs. Class B. The need to generate more explanations so business users can understand the predictions has attracted many developers to create innovative frameworks that can generate value. The contrastive explanation focuses on explaining why the model predicted Class A and not Class B. The reason finding helps the business user understand the behavior of the model. In this chapter, you are going to use the Alibi library for an image classification task using a TensorFlow-based framework.
Video Game Art Styles

The article examines the graphic styles that have arisen in the video game industry due to the technical features and development of this media. These styles are widely used and starting to go beyond the industry into the field of contemporary art. Despite the fact that pixel art is mainly used in the creation of video games, it has already become an independent form of visual style. Contemporary artists such as the Russian digital artist and designer under the pseudonym Uno Morales and the artist Natalya Struchkova turn to the pixel style when creating their works. Like the pixel art, voxel graphics has moved into the category of the game visual style, which employs an impressive community of digital artists. Low рoly graphics have modified from the main graphics of three-dimensional games, which look technically imperfect, into the category of an artistic style that forms a recognizable, attractive and unique geometric aesthetics of the image. We can trace the transformation of video game graphics, which have arisen as a result of technical constraints, into separate art styles: pixel art, voxel art, low рoly style, the minimalist style of the first classic video games. These styles are gradually becoming an independent visual unit that does not depend on the video game product as a whole.
Study of the chaotic behaviour of temperature time series processes

The results of the nonlinearity analysis of the temperature time series with different sampling steps are presented. The values of statistical functions describing the amount of information contained in one time series relative to another are calculated. The graphs of the Lyapunov exponent plotted. The nonlinearity of the studied time series was checked using White's test and Terasvirt's test. In both tests, Fisher's criteria and chi-square's criteria reject the null hypothesis, i.e. confirm the nonlinearity nature of the time series under study.
An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection

Medical prediagnosis systems are now available online to give users quick and preliminary diagnosis information. The need for such a system has become particularly evident in areas with insufficient health professionals. Due to the privacy of patient medical information and the sensitivity of cloud diagnosis models, it is necessary to protect the security of data, models, and communications. These existing diagnosis systems can hardly provide a satisfied diagnosis accuracy while ensuring comprehensive security and high efficiency. In order to solve these problems, we proposed Relief- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> minimum Wasserstein distance (Relief- <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>k</mi> </math> MW) classification method, which combined data encryption and BLS signature to form a privacy-preserving efficient online multiparty interactive medical prediagnostic scheme (OMPD). Theoretical analysis shows our OMPD effectively provides high-precision prediagnosis services. Extensive experimental results demonstrate that OMPD not only greatly improves the diagnostic accuracy but also reduces the computational and communication overhead.
ToSSecC: Two-Stage Solution for Securing Cloud Data Using Game Theory

The use of the cloud for storing data for online applications has become a popular technology. It has become a paradigm of the next generation. With the widespread of smartphones and popular applications like Facebook, WhatsApp, etc., individuals are much acquainted using cloud storage, even without understanding the same. However, the security of data in the cloud is still a big issue irrespective of the user and cloud provider. Applications are implemented in various domains to achieve data security, privacy, and data transparency in cloud-based applications. The evaluation of smart contracts, run on the blockchain framework, is used nowadays in diverse sectors of business for achieving data security. However, in real-life implementation, it is found that the blockchain itself is suffering from different security threats. In this paper, a methodology, ToSSecC is proposed towards enhancing the security features of cloud data by addressing the issues of spam mails in the admission system of a large university in India. The work has been divided into two steps: 1) Deployment of in the cloud-based application, by incorporating smart contract and 2) Enhancement of the security of the smart contract by proper validation using game theory.
Analysis of Encrypted Image Data with Deep Learning Models

While various encryption algorithms ensure data security, it is essential to determine the accuracy and loss values and performance status in the analyzes made to determine encrypted data by deep learning. In this research, the analysis steps made by applying deep learning methods to encrypted cifar10 picture data are presented practically. The data was tried to be estimated by training with VGG16, VGG19, ResNet50 deep learning models. During this period, the network&#x2019;s performance was tried to be measured, and the accuracy and loss values in these calculations were shown graphically.
Improved Simplified Swarm Optimization for UAVs path planning

We intend in this paper to present an improvement of the simplified swarm optimization algorithm for multi UAVs path planning under environmental and collision avoidance constraints. The improvements we proposed to the SSO algorithm are chaos-based initialization, acceleration adjustment, and adoption of linearly varying inertia weight. They serve to make the planning faster and choose the simplest path. We conducted several speed and path tests and compared with the standard SSO algorithm and we obtained promising results.
Andrews Skolemization May Shorten Resolution Proofs Non-elementarily

In this paper we construct a sequence of formulas $$F_1, F_2, \ldots $$ with resolution proofs $$\pi _1, \pi _2, \ldots $$ of these formulas after Andrews Skolemization, such that there is no elementary bound in the complexity of $$\pi _1, \pi _2, \ldots $$ of resolution proofs $$\pi '_1, \pi '_2, \ldots $$ after structural Skolemization. The proofs are based on the elementary relation of resolution derivations with Andrews Skolemization to cut-free $$\mathbf{LK}^{+}$$ -derivations and of resolution derivations with structural Skolemization to cut-free $$\mathbf{LK}$$ -derivations. Therefore, this paper develops an application of the concept of only globally sound calculi to automated theorem proving.
Dampak Pembelajaran Online Bagi Siswa Sekolah Dasar

The purpose of this study is to analyze the impact of online learning on elementary school students 1 Barongan Kudus.The research method used is qualitative. Research data collection is done through the provision of questionnaires, observations, interviews, and documentation. The research was conducted in the odd semester of the 2021/2021 academic year. The research data were collected using observation and interview techniques. Research sources include teachers, students, and parents of elementary school students 1 Barongan Kudus. Data checking was carried out using triangulation to increase the degree of confidence and accuracy of the data. Triangulation is done with three strategies, namely source triangulation, method triangulation, and time triangulation. The data analysis technique used is the Miles and Huberman analysis technique.The results showed that teachers, students and parents were unable to communicate directly and online learning required all parties to communicate via WhatsApp..
Symbolic Learning using Brain Programming for the Recognition of Leukemia Images

In this work, we propose an approach of symbolic learning for the recognition of leukemia images. Image recognition for cancer detection is often a subjective problem due to different interpretations by experts of the medical area. Feature extraction is acritical step in image recognition, and current automatic approaches are unintelligible since they need to be adapted to different image domains. We propose the paradigm of brain programming as a symbolic learning approach to address aspects involved in the derivation of knowledge that allows us to recognize subtypes of leukemia in color images. Experimental results provide evidence that the multi-class recognition task is achieved through the solutions discovered from multiples runs of the bioinspired model.
Event Causality

A crucial aspect of understanding and reconstructing narratives is identifying the underlying causal chains, which explain why certain things happened and make a coherent story. To build such causal chains, we need to identify causal links between events in the story, which may be expressed explicitly as well as understood implicitly using commonsense knowledge.
Artificial intelligence

AI, being a new advanced technology, has been widely examined, tested, and applied in various industries, including the creative industries. Those musical works ‘created’ by machines designed by various technology companies do not only predict the future of the digital music industry but also bring conceptual conflicts in the interpretations of different terms, such as ‘authors’ and ‘composers’, in the traditional copyright context. The existing copyright framework is reluctant to recognise non-human creations. It is arguable that AI systems can “create” music by themselves based on human users’ needs. In this case, who are the author/beneficiaries of AI-produced music, together with a further question – whether copyright law is appropriate to protect AI-produced music raises concerns from both national and international perspectives. Besides, there are also other surrounding issues about AI-produced music, such as the infringement issues of training data and the possible misappropriation of folk music of indigenous/ethnic minority communities. However, to address these issues, identifying who is the author/beneficiary of AI-produced music is the starting point. The chapter first analyses how AI re-shapes the music industry by reviewing the impact of technologies on the music industry in the past and comparing such impact with the predicted impact that AI brings to this industry; then the chapter explores the definition of AI and several types of AI research trying to identify the creative capabilities of AI. Furthermore, the chapter evaluates to what extent existing copyright systems leave room for a piece of AI-produced music to be a copyrightable work from both legislative and practice perspectives. Finally, the chapter ends by foreseeing uncertainties with AI-produced music in the music industry.
Gate-Based Rules for Extracting Attribute Values

Automated ontology population is intended toenrich ontologies. The main entities of an ontologicalmodel are classes, subclasses, attributes—datatypeproperties—, relationships—object properties—, andinstances. Class instances are important to thescientific community, since some work is devoted toautomatically populating ontologies by using statisticalmethods, information extraction, and natural languageprocessing, among others. The problem is focused onidentifying and extracting attribute values of instances.Commonly, such values have a predefined type likenumeric, string, boolean, etc. The difficulty arises whenyou want to know which instance belongs to such values.In this paper we propose an approach based on NaturalLanguage Processing (NLP) and Information Extraction(IE) technologies for extracting attribute values. We usesyntactic patterns implemented on the GATE (GeneralArchitecture for Text Engineering) tool. The results areindependent of the application domain and they exhibitpromising values of recall, precision, and F-measure.
A Fast Real-time Facial Expression Classifier Deep Learning-based for Human-robot Interaction

Human-robot interaction drives the need for vision technology to recognize user expressions. Convolutional Neural Networks (CNN) has been introduced as a robust facial feature extractor and can overcome classification task. However, it is not supported by efficient computation for real-time applications. The work proposes an efficient CNN architecture to recognize human facial expressions that consist of five stages containing a combination of lightweight convolution operations. It introduces the efficient contextual extractor with a partial transfer module to suppress computational compression. This technique is applied to the mid and high-level features by separating the channel-based input features into two parts. Then it applies sequential convolution to only one part and combines it with the previous separated part. A shuffle channel group is used to exchange the information extracted. The structure of the entire network generates less than a million parameters. The CK+ and KDEF datasets are used as training and test sets to evaluate the performance of the proposed architecture. As a result, the proposed classifier obtains an accuracy that is competitive with other methods. In addition, the efficiency of the classifier has strongly suitable for implementation to edge devices by achieving 43 FPS on a Jetson Nano.
Design of a Soft Gripper Using Genetic Algorithms

In this paper, we present an artificial intelligence-assisted design of a soft robotic gripper. First, we formulate the design of the soft gripper as an optimization problem. Then, we design and configure a genetic algorithm (GA) method to solve the problem under design constraints. Lastly, we implement the whole system in co-simulation between the GA and a computer-aided design software that evaluates the candidate solutions using finite element analysis. A network-attached storage server connecting multiple nodes runs the GA method in parallel, to accelerate the process. After experimentation, we present simulation results to validate our approach.
Simulation of Two‐Qubit Gates with a Superconducting Qudit

Herein, a two‐qubit system is experimentally simulated through a four‐level superconducting circuit. By mapping the energy levels of the two‐qubit system to a noncomposite system, some two‐qubit gates with designed microwave pulses, such as Hadamard and CNOT gates, are implemented. Furthermore, the Shannon entropy of the simulated two‐qubit system is measured and the log 2 uncertainty relation is verified, which describes that the noncomposite single‐qudit system possesses hidden quantum correlations.
A Deep Learning-based Part Classification Method for Ship Part Pairing in Nesting Problems

With the rapid development of artificial intelligence technology, deep learning-based classification techniques have had enough reliability to be applied to industrial sites. However, while the study of the object classification on data acquired with 3D scanners or cameras has made remarkable progress, research activity based on geometric data sets is still in its infancy. In particular, in order to improve the classification performance of ship parts based on deep learning in the nesting problem to increase productivity in shipbuilding, the study of the construction of part datasets and data pre-processing is necessary. In this paper, we introduce a method to apply the artificial neural network technology of deep learning to the nesting algorithm for shipbuilding. Labeled with histogram-based shape contexts for constructing a dataset for classifying ship parts using Convolutional Neural Networks (CNNs). In addition, we introduce the preprocessing method of the geometric information of the ship parts for learning and the no-fit polygon (NFP) method for classified parts to pair up. To train the classification model for the 23,201 ship parts, a data set of 842 classes was constructed through the shape matching algorithm. The trained CNN model was able to classify those parts with an accuracy of 85.13%.
Account of a Foretold Death: Analysing the Response to the Pandemic in Spanish Schools

The COVID-19 pandemic has had a profound impact on education, not only due to its dramatic interruption of the school year (2019-2020) but also its potential effect on many aspects regarding teaching in the future. In particular, focusing on Spain, this year’s events have also highlighted the lack of digital resources and preparedness of the schools, which has resulted in some difficulties when adapting to the new circumstances. In this paper, the author will start by explaining how the digital competence has developed into an indispensable competence for learning in the past decade, which the current global pandemic has emphasised even more. As a consequence of this pandemic, schools and education centres remained closed from March 2020 to the end of the academic year in Spain. This article focuses on the importance of technology accessibility and digital competence in language learning, as well as the way it was overlooked in practice. The immediate response from the Ministry of Education will be analysed and compared to the way teachers and schools in the Valencian region (Spain) supported the students during the enforced lockdown in the last trimester of the academic year in primary school. For this, teachers in four schools in Castellon (Valencian region) were interviewed, and drawing from their replies, the current reality of the use of technology in primary school will be examined and compared to the national guidelines and frameworks provided for educators. Questionnaires were used and analysed using a qualitative approach, while comparing the current situation to the expected response according to the educative guidelines.
NATURE OF COGNITIVE EXPERIENCE: UNITY OF PERCEPTION, THINKING, BODY AND LANGUAGE

The article analyzes the nature of cognitive experience in the context of non-classical epistemology and critically interprets the dogmatic concept of cognitive process as a special form of reality reflection. Borrowing his arguments from neurobiology, cognitive science and cross-cultural studies, the author makes the following conclusions: the modular architecture of perceptual systems and embodied cognition impose certain restrictions on the way of presenting perceptual data, determining what stimulus diversity should be reduced to in order for it to be perceived and comprehended. At the same time, perception involves cooperation with other cognitive abilities – attention, memory, and thinking. In this regard, the perception of sensory stimuli depends on the subject’s set of categories, anticipatory schemes, and linguistic frameworks. All these means provide procedures for the selection and categorization of information. As a result, sensory data receive objective meanings, and perception turns out to be irreducible to the passive «copying» of reality.