Reviewerly/exHarmony huggingface dataset:
A Control Process for Active Solar-Tracking Systems for Photovoltaic Technology and the Circuit Layout Necessary for the Implementation of the Method

What basically determines how much energy is generated by a photovoltaic (PV) system is the amount of solar irradiation that is absorbed by its PV modules. One of the technical solutions to boost this quantity, and thusly also maximize the return on PV investments, is solar tracking, which makes the following of the sun on its daily and annual journey in the sky possible and also takes changes in cloud conditions into consideration. The solar-tracking solutions that PV systems are most frequently equipped with deploy active sensor technologies, while passive ones are less common in present-day practice. However, even the popular solutions of today have their limitations. Their active sensor-tracking algorithms leave room for improvement for at least three major reasons, as they do not prevent the unnecessary operation of the motors in cloudy weather, they do not make the modules assume an appropriate position after nightfall, and they do not make sure that the structure and the electronics of the PV systems are protected from rain and the strong winds in the event of storms. This paper introduces a new active sensor-tracking algorithm, which has not only been tested but it is also in the process of patenting (patent ID: p2100209). By their contribution, the authors endeavor to propose a solution that can solve all three of the issues mentioned above. The concept is based on two fundamental findings. According to the first one, periodic movement can not only considerably decrease motor movement but also increase system lifetime, while the second one simply suggests that moving the modules into an almost horizontal position facing the equator at low light levels is conducive to the prevention of damages caused by storms and fast reaction to the increase in the amount of light at daybreak. A positive feature of the new system for PV power plant operators is that it performs the tracking of the sun practically without any decrease in power compared to the focal point position, since it works with an average inaccuracy of 1.9°.
Review of Semantic Importance and Role of using Ontologies in Web Information Retrieval Techniques

The Web contains an enormous amount of information, which is managed to accumulate, researched, and regularly used by many users. The nature of the Web is multilingual and growing very fast with its diverse nature of data including unstructured or semi-structured data such as Websites, texts, journals, and files. Obtaining critical relevant data from such vast data with its diverse nature has been a monotonous and challenging task. Simple key phrase data gathering systems rely heavily on statistics, resulting in a word incompatibility problem related to a specific word's inescapable semantic and situation variants. As a result, there is an urgent need to arrange such colossal data systematically to find out the relevant information that can be quickly analyzed and fulfill the users' needs in the relevant context. Over the years ontologies are widely used in the semantic Web to contain unorganized information systematic and structured manner. Still, they have also significantly enhanced the efficiency of various information recovery approaches. Ontological information gathering systems recover files focused on the semantic relation of the search request and the searchable information. This paper examines contemporary ontology-based information extraction techniques for texts, interactive media, and multilingual data types. Moreover, the study tried to compare and classify the most significant developments utilized in the search and retrieval techniques and their major disadvantages and benefits.
Video Analysis and System Construction of Basketball Game by Lightweight Deep Learning under the Internet of Things

With the explosive growth of sports video data on the internet platform, how to scientifically manage this information has become a major challenge in the current big data era. In this context, a new lightweight player segmentation algorithm is proposed to realize the automatic analysis of basketball game video. Firstly, semantic events are expressed by extracting group and global motion features. A complete basketball game video is divided into three stages, and a basketball event classification method integrating global group motion patterns and domain knowledge is proposed. Secondly, a player segmentation algorithm based on lightweight deep learning is proposed to detect basketball players, segment the players, and finally extract players' spatial features based on deep learning to realize players' pose estimation. As the experimental results indicate, when a proposed 2-stage classification algorithm is used to classify the videos, the accuracy of identifying layup, the shooting, and other 2-pointers are improved by 21.26% and 6.41%, respectively. And the accuracy of average events sees an improvement of 2.74%. The results imply that the 2-stage classification based on event-occ is effective. After comparing the four methods of classifying players, it is found that there is no significant difference among these four methods about the accuracy of segmenting. Nevertheless, when judged with the time that these methods take separately, FCN-CNN (Fully Convolutional Network-Convolutional Neural Network) based on superpixels has overwhelming advantages. The event analysis method of basketball game video proposed here can realize the automatic analysis of basketball video, which is beneficial to promoting the rapid development of basketball and even sports.
Cloud Computing Load Balancing Techniques: Retrospect and Recommendations

Load balancing is a research area that seeks to improve the quality of services provided to various clients in cloud computing environments. As cloud users increase around the world, cloud service providers are challenged to develop strategies for distributing tasks to machines for processing at cloud data centers. This work collected and undertook a thorough review of various load balancing techniques, uncovering the key limitations of existing strategies. The publications were chosen from peer-reviewed papers on Google Scholar. Cloud computing, cloud load balancing techniques, approaches to cloud load balancing, and big-data cloud computing systems were among the terms used in the search. Out of 201 studies, 39 met the criteria for inclusion. 5 of the research focused on cloud computing, 6 on cloud load balancing, 7 on resource scheduling in cloud, 16 on techniques for balancing cloud load, and 5 on big-data cloud computing environments. The study identified some research gaps and recommended a throughput-maximization based central-distributive load balancing architecture as a solution to maximize throughput, minimize response time and processing cost, and optimize load balancing architecture. Keywords— Centralized, cloud-computing, distributive, load-balancing.
Problems in teaching and learning English online during the Covid-19 pandemic

The coronaviruses (Covid-19) pandemic has spread rapidly worldwide, including the education sector. The policy toward the study from home during the Covid-19 outbreak caused a new problem in the teaching and learning system. The objectives of the research were to find out and describe the problem faced by the teachers in conducting online learning during the Covid-19 pandemic. This is a qualitative method approach. The research subjects are three English teachers. The data was gathered through observation and interviews from three English teachers at SMA Negeri 4 Banda Aceh. Some of the problems emerged during online learning, nine problems. The arisen of teaching problems were: internet access, disability in accessing technology, difficulty in explaining the materials, flexibility in controlling and managing students' activities, indiscipline, time-consuming, lack students' motivation, students absent in online class, and the last parents support system. The findings showed that there are two solutions to overcome the problems by teachers, namely, applying online chat of WhatsApp and keeping the interaction with the students' parents to support the student activities and explain the lesson plans while online learning teaching and learning process.
Introducing a Novel Model-Free Multivariable Adaptive Neural Network Controller for Square MIMO Systems

In this study, a novel Multivariable Adaptive Neural Network Controller (MANNC) is developed for coupled model-free n-input n-output systems. The learning algorithm of the proposed controller does not rely on the model of a system and uses only the history of the system inputs and outputs. The system is considered as a ‘black box’ with no pre-knowledge of its internal structure. By online monitoring and possessing the system inputs and outputs, the parameters of the controller are adjusted. Using the accumulated gradient of the system error along with the Lyapunov stability analysis, the weights’ adjustment convergence of the controller can be observed, and an optimal training number of the controller can be selected. The Lyapunov stability of the system is checked during the entire weight training process to enable the controller to handle any possible nonlinearities of the system. The effectiveness of the MANNC in controlling nonlinear square multiple-input multiple-output (MIMO) systems is demonstrated via three simulation studies covering the cases of a time-invariant nonlinear MIMO system, a time-variant nonlinear MIMO system, and a hybrid MIMO system, respectively. In each case, the performance of the MANNC is compared with that of a properly selected existing counterpart. Simulation results demonstrate that the proposed MANNC is capable of controlling various types of square MIMO systems with much improved performance over its existing counterpart. The unique properties of the MANNC will make it a suitable candidate for many industrial applications.
Development of Art Education as a Basis for Sustainable Development of Society

The article considers questions about the role and significance of art education in the context of ensuring sustainable development of society. The features of the development of art education in the postmodern era, its functions and specific features (individuality, creativity, understanding of a person as the highest value, etc.) are analyzed. The interdependence of arts education and sustainable development are characterized. The exceptional importance for ensuring sustainable development of an appropriately oriented and updated arts education was emphasized. In particular, it seems possible to ensure a change in stereotypes, ways of thinking and actions of people, the formation of appropriate motivation and public opinion in the context of applied nature through the influence of art. Ways to improve art education with the aim of ensuring sustainable development are proposed: integration of formal and non-formal art education, ensuring innovativeness, interdisciplinarity and multiculturalism of art education, creating an inclusive environment in higher educational institutions. We see prospects for further scientific research in the creation of a model for training specialists with certain properties and substantiation of the features of its implementation in real conditions of the educational process in higher education institutions of the artistic direction.
Application of Neural Network Based on Multisource Information Fusion in Production Cost Prediction

Production cost forecasting is an important basis for cost accounting, cost decision-making, and cost planning. It is the scale necessary to reduce product costs and an important way to enhance enterprise competitiveness and improve system benefits. A neural network based on multisource information fusion is a manifestation of integrated internal knowledge. By learning to integrate multiple sources of information, it is easier to understand cognitive thinking and integrate the complex relationships of uncertain regions into regular signals. Fusion prediction does not need to understand the specific mechanism of the process but can fully approximate various nonlinear functional relationships determined by input and output with the continuous update of its internal weights. This paper mainly studies the application of neural network based on multisource information fusion in production cost prediction, analyzes the technology of multisource information fusion, and proposes a method of applying multisource information fusion theory to BP neural network and RBF network. Experiments have proved that through the comparison of the results of the BP neural network and the RBF network, for the six cost categories, compared with the BP neural network, the prediction results of the RBF network are closer to the true value, and they all show higher prediction capabilities. Among them, the error of the RBF network in predicting the total salary of the current month is 0.01004. The performance of the RBF network model is better than that of the BP neural network model.
Hand-written Character Recognition with Neural Network

Abstract: Various handwriting styles are unique in this manner, making it challenging to identify characters that were written by hand. Handwritten character recognition has become the subject of exploration over the last few decades through an exploration of neural networks. Languages written from left-to-right, such as Hindi, are read from start-to-finish design. To recognize these types of writing, we present a Deep Learning-based handwritten Hindi character recognition system utilizing deep learning techniques such as Convolutional Neural Networks (CNN) with Optimizer Adaptive Moment Estimation (Adam) and Deep Neural Networks (DNN) in this paper. The suggested system was trained on samples from a large number of database images and then evaluated on images from a user-defined data set, yielding extremely high accuracy rates. Keywords: Deep learning, CNN, Adam Optimizer, Handwritten character recognition, Accuracy, Training Time.
Criterion for a state to be distillable via stochastic incoherent operations

Coherence distillation is a basic information-theoretic task in the resource theory of coherence. In this paper, we present the necessary and sufficient conditions under which a mixed state can be distilled into a pure coherent state via stochastic incoherent operations (sIOs). With the help of this result, we further show the following: (i) Any two-dimensional coherent state is distillable via sIOs if and only if it is a pure coherent state; (ii) a state $\ensuremath{\rho}$ is $n$-distillable via sIOs if and only if it is 1-distillable; and (iii) the set of distillable states via stochastic maximally incoherent operations is identical to the set of distillable states via sIOs. Finally, we analyze the reason why sIO is stronger than stochastic strictly incoherent operations when we use them to distill a coherent state.
Mudanças no processo de compra e consumo de alimentos orgânicos durante a pandemia do COVID-19

Changes on organic food consuming during the covid-19 pandemic. Inserted in an increasing production and consumption of organic food context, the world faced to a great adversity, the COVID-19 pandemic, causing a great impact on the lives of people worldwide, also including shopping and consumption process. Thus, the study aimed to identify the changes that occurred in the process of purchase and consumption of organic foods consumers, as well as the changes that occurred in the influences of this process during the COVID-19 pandemic. A qualitative research was conducted in which twelve consumers of organic products were interviewed through online interviews. The main results found in the study were changes regarding to situational influences due to the situation of the COVID-19 pandemic. The study also shows that regarding to the stages of the purchasing decision-making process, it was identified that changes occurred in all stages, and the stage in which the most changed occurred was the purchase and consumption stage.
Big Data Analysis and Prediction System Based on Improved Convolutional Neural Network

This paper presents a big data analysis and prediction system based on convolutional neural networks. Continuous template matching technology is used to analyze the distributed data structure of big data, and the information fusion processing of cloud service combination big data is combined with matching related detection methods, frequent item detection, and association rule feature extraction of high-dimensional fusion data. A clustering method is adopted to realize the classification and mining of cloud service portfolio big data. The hardware equipment of the car to detect the surrounding environment is complicated, and the combination of the convolutional neural network and the camera to detect the surrounding environment has become a research hotspot. However, simply using the convolutional neural network to process the camera data to control the turning angle of the car has the problems of long training time and low accuracy. An improved convolutional neural network is proposed. The experimental results show that the accuracy of data mining by this method is 12.43% and 21.76% higher than that of traditional methods, and the number of iteration steps is shorter, indicating that the timeliness of mining is higher. This network structure can effectively improve the training speed of the network and improve the accuracy of the network. It is proven that the convolutional neural network has faster training speed and higher accuracy.
Intelligent Obstacle Avoidance Algorithm for Mobile Robots in Uncertain Environment

The application of mobile robots and artificial intelligence technology has shown great application prospects in many fields. The ability of intelligent obstacle avoidance is the basis for the deep application of mobile robots. However, there are often more or less uncertain factors in the actual operating environment of the robot, such as people or objects that are not updated in time or temporarily appear. Therefore, it is an important step to complete the automatic learning of obstacle avoidance for mobile robots. In a nondeterministic environment, a mobile robot intelligent obstacle avoidance algorithm based on an improved fuzzy neural network with self-learning is firstly proposed. The mobile robot intelligent obstacle avoidance system is constructed through the reaction layer, the deliberation layer, and the supervision layer. Through the analysis of sensor performance, model accuracy, path obstacle avoidance optimization, and obstacle avoidance simulation, the following conclusions are drawn. First, through network training, the accuracy rate of the test set is stable at 98%, and the loss of the function value has also been reduced from the original 0.79 to 0.08, which is 10 times smaller. Second, the traditional single sensor cannot meet the obstacle avoidance requirements of robots, and mobile robots must combine multipurpose technology. Third, the algorithm in this paper encounters the following. When there are obstacles, the path is dominated by straight lines, obstacle avoidance planning is optimal, and the distance is shorter. Fourth, the larger N : M, the larger the solution space, indicating that this algorithm gradually improves the search efficiency to the greatest extent and can handle any form of medium and large scale task allocation problem.
Non-repudiation and privacy-preserving sharing of electronic health records

In this work, we present a framework that facilitates the sharing of EHRs among the community of health-care providers (HCPs). However, the sharing might be obstructed by patients’ privacy and the controlling legislation. Nevertheless, our sharing scheme of EHRs strives to respect patients’ privacy and comply with relevant legislator guidelines, e.g., HIPPA. The proposed work introduces two services while sharing the EHRs: privacy and non-repudiation. To this end, we introduce the partners and the role of each during the course of exchanging of EHRs. The principle of sharing EHRs among HCPs has to be reinforced to save patients’ lives while cryptographic primitives have to be employed to serve this purpose. In this paper, we are motivated to introduce the notion of non-repudiation private membership test (NR-PMT). In NR-PMT, we help patients receive medical services with great flexibility while maintaining their privacy and thwarting all possible threats to disclose their identities. In addition, a formal security analysis based on Kailar’s accountability framework has been used to analyze the proposed framework. Also, a complexity analysis has been conducted.
A Game Theory approach for communication security and safety assurance in cyber-physical systems with Reputation and Trust-based mechanisms

Cyber-physical systems' security and safety assurance is a challenging research problem for Smart City concept development.Technical faults or malicious attacks over communication between its elements can jeopardize the whole system and its users.Reputation systems implementation is an effective measure to detect such malicious agents.Each agent in the group has its indicator, which reflects how trustworthy it is to the other agents.However, in the scenario when it is not possible to calculate the Reputation indicator based on objective characteristics, malicious or defective agents can negatively affect the system's performance.In this paper, we propose an approach based on Game Theory to address the Reputation and Trust initial values calculation challenge.We introduced a mixed strategies game concept and a probability indicator.The possible outcomes of using different strategies by the system agents are represented with a payoff matrix.To evaluate the approach effectiveness, an empirical study using a software simulation environment was conducted.As a Cyber-physical system implementation scenario, we considered an intersection management system with a group of unmanned autonomous vehicles, the aim of which is to perform conflict-free optimal intersection traversal.To simulate the attack scenario, some vehicles were able to transmit incorrect data to other traffic participants.The obtained results showed that the Game Theory approach allowed us to increase the number of detected intruders compared to the conventional Reputation and Trust model.
A Unique Multi-Agent-Based Approach for Enhanced QoS Resource Allocation in Multi Cloud Environment while Maintaining Minimized Energy and Maximize Revenue

The use of the multi-cloud data storage in one heterogeneous service is a polynimbus cloud strategy. Cloud computing uses a pay-as-you-go model to deliver services to a variety of end users. Customers can outsource daunting tasks to cloud data centres for processing and producing results, thanks to cloud computing. Cloud computing becomes the popular IT brand that provides various on-demand services over the internet. This technology is devoted to distributing computer and software resources. The proven usefulness of workflows to enforce relevant scientific achievements is the availability of data from advanced scientific tools. Scheduling algorithms are essential in order to automate these strenuous workflows efficiently. A number of new heuristics based on a Cloud resource model have been developed. The majority of these heuristic - based address QoS issues in one or two dimensions. The cloud computing technology offers a decentralised pool of services and resources with various models that are provided to the customers across the Internet in an on-demand, continuously distributed, and pay-per-use model. The key challenge we address in this paper is to maximise revenue while maintaining a minimum consumption of energy with an enhanced QoS for resource allocation. The obtained results from proposed method when compared with the existing state of art methods observed to be novel and better.