Reviewerly/exHarmony huggingface dataset:
A Hybrid Improved Whale Optimization Algorithm with Support Vector Machine for Short-Term Photovoltaic Power Prediction

Presently, the grid-connected scale from photovoltaic (PV) system is getting higher among renewable power generations. However, the PV output power can be affected by different meteorological conditions due to PV randomness and volatility. Accordingly, reasonable generation plans can be well arranged using accurate PV power prediction among various types of energy sources, thus reducing the effect of PV system on the grid. To resolve this problem, a PV output power prediction model, namely IMWOASVM, is proposed based on the combination of improved whale optimization algorithm (IMWOA) and support vector machine (SVM). The IMWOA is used to optimize the kernel function parameter and penalty coefficient in SVM. The optimal parameter and coefficient values can then be input to SVM for enhancing the PV prediction. The performance results verify that the coefficient of determination using the IMWOA model can reach beyond 99% in both sunny and cloudy days. Simultaneously, the mean absolute errors on sunny and cloudy days are 0.0251 and 0.0705, respectively. The root mean square errors in sunny and cloudy days are 2.17% and 1.03%, respectively. The results confirm that the proposed model effectively increases the accuracy of the PV output power prediction and is superior to existing methods.
Clustering with Missing Features: A Density-Based Approach

Density clustering has been widely used in many research disciplines to determine the structure of real-world datasets. Existing density clustering algorithms only work well on complete datasets. In real-world datasets, however, there may be missing feature values due to technical limitations. Many imputation methods used for density clustering cause the aggregation phenomenon. To solve this problem, a two-stage novel density peak clustering approach with missing features is proposed: First, the density peak clustering algorithm is used for the data with complete features, while the labeled core points that can represent the whole data distribution are used to train the classifier. Second, we calculate a symmetrical FWPD distance matrix for incomplete data points, then the incomplete data are imputed by the symmetrical FWPD distance matrix and classified by the classifier. The experimental results show that the proposed approach performs well on both synthetic datasets and real datasets.
A Secure Softwarized Blockchain-based Federated Health Alliance for Next Generation IoT Networks

Medical health care centres are seen as a potential paradigm for dealing with large amounts of data utilizing artificial intelligence (AI). The fast growth in the massive volume of data created by connected devices in the health paradigm brings up new opportunities for data sharing to improve the quality of service for developing applications. Traditional AI approaches frequently need centralized data gathering and model training within a single company, which is a significant flaw owing to the lack of privacy and security of raw data transfer through mobile networks. The leakage of sensitive data can result in substantial consequences for the providers, in addition to financial loss. For that, we first build a blockchain-enabled safe data sharing architecture for distributed multiple parties in this paper. Then, by adding suggested privacy-preserved federated learning, we turn the data-sharing problem into a machine-learning challenge. Recommendations are based on the user’s previous search values, and data privacy is protected by providing the data model rather than the raw figures. Finally, we include federated learning into the permissioned blockchain consensus process, allowing the consensus computing effort to be used for federated training. According to numerical findings generated from data sets, the suggested data sharing method delivers good accuracy, high efficiency, and increased security.
Colorectal multi-class image classification using deep learning models

Colorectal image classification is a novel application area in medical image processing. Colorectal images are one of the most prevalent malignant tumour disease type in the world. However, due to the complexity of histopathological imaging, the most accurate and effective classification still needs to be addressed. In this work we proposed a novel architecture of convolution neural network with deep learning models for the multiclass classification of histopathology images. We achieved the findings using three deep learning models, including the vgg16 with 96.16% and a modified version of Resnet50 with 97.08%, however the proposed Adaptive Resnet152 model generated the best accuracy of 98.38%. The colorectal image multiclass dataset is publicly available which has 5000 images with 8 classes. In this study we have increased all classes equally, total 15000 images have been generated using image augmentation technique. This dataset consists of 60% training images and 40% testing images. The suggested method in this paper produced better results than the existing histopathology image categorization methods with the lowest error rate. For histopathological image categorization, it is a straightforward, effective, and efficient method. We were able to attain state-of-the-art outcomes by efficiently utilizing the resourced dataset.
A New Image Encryption Algorithm Based on Multi Chaotic System

In recent years, encryption technology has been developed rapidly and many image encryption methods have been put forward. The chaos-based image encryption technique is a modern encryption system for images. To encrypt images, it uses random sequence chaos, which is an efficient way to solve the intractable problem of simple and highly protected image encryption. There are, however, some shortcomings in the technique of chaos-based image encryption, such limited accuracy issue. The approach focused on the chaotic system in this paper is to construct a dynamic IP permutation and S-Box substitution by following steps. First of all, use of a new IP table for more diffusion of all image pixels based on a 1D logistic map to build IP table. Secondly, a new S-Box based on 2D-Henon chaos was created using more confusion to replace G-channel image data. Finally, design of a modern image encryption approach. This approach uses the key process confusion and diffusion operation and depend on IP and S-Box proposals in the encryption process and several shuffling operations using the 3D- Lornez chaos theory. Theoretical research and simulation suggest that starting sensitivity value of this method is high, has high protection, and encryption speed. Moreover, it also holds the value of the neighboring RGB close to zero. The studies show that the information security capabilities would be both safer and more efficient, as a result of our image quality assessment study. Number of Differential Pixel Rate Change Attacks (NPSR), Unified Average Altered Intensity (UACI), are quality and strength of encryption processing are proved by pixel correlation, Entropy to be good results.
Sense, Transform &amp; Send for the Internet of Things (STS4IoT): UML profile for data-centric IoT applications

The Internet of Things is currently one of the most representative sources of Big Data. It can acquire real-time data from multiple spatially distributed points, allowing for the extraction of valuable insights. However, an appropriate integration, processing, and analysis of these data depends on several factors starting from the correct definition of the information systems. This paper introduces STS4IoT, a UML profile and automatic code-generation tool for model-driven IoT, to address this issue. STS4IoT allows designing and implementing an IoT application from the required data only, bridging the gaps between the IoT and database design worlds. The IoT data design includes both different in-network transformations and the join of streams from multiple sources. Besides, it follows the Model-Driven Architecture (MDA) guidelines to provide abstraction levels oriented to the different roles participating in the application design. The STS4IoT validation shows it has an excellent structure and is highly understandable. Its instance models are well-formed, highly abstract and readable. And the automatic implementation tool can generate complete code for complex real-world applications involving multiple IoT devices. Then, STS4IoT simplifies the definition and development of IoT applications and their integration into other information systems, such as stream data warehouses.
Comparison of GAN Deep Learning Methods for Underwater Optical Image Enhancement

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.
Comparison and Performance Evaluation of Boundary Fill and Flood Fill Algorithm

In computer graphics, there are many polygon filling algorithms available like as inside and outside test, scan line method, boundary fill, flood fill, edge fill and fence fill algorithm. In this paper we focus on the flood fill and boundary fill algorithms for both four connected as well as eight connected pixels. This research paper shows the comparison and performance evaluation of boundary fill and flood fill algorithms with consideration of running time in C-language. And also shows the how stack affects the performance of the system due to overflow of buffer later on JAVA implementation with queue improves the system performance for large polygon. It also shows the literature review of various papers use the fill algorithm on different applications, some proposed the new and enhanced polygon fill algorithms.
Quantum Algorithms for Some Strings Problems Based on Quantum String Comparator

We study algorithms for solving three problems on strings. These are sorting of n strings of length k, “the Most Frequent String Search Problem”, and “searching intersection of two sequences of strings”. We construct quantum algorithms that are faster than classical (randomized or deterministic) counterparts for each of these problems. The quantum algorithms are based on the quantum procedure for comparing two strings of length k in O(k) queries. The first problem is sorting n strings of length k. We show that classical complexity of the problem is Θ(nk) for constant size alphabet, but our quantum algorithm has O˜(nk) complexity. The second one is searching the most frequent string among n strings of length k. We show that the classical complexity of the problem is Θ(nk), but our quantum algorithm has O˜(nk) complexity. The third problem is searching for an intersection of two sequences of strings. All strings have the same length k. The size of the first set is n, and the size of the second set is m. We show that the classical complexity of the problem is Θ((n+m)k), but our quantum algorithm has O˜((n+m)k) complexity.
Teacher Autonomy, Motivation, and Job Satisfaction: Perceptions of Elementary School Teachers According to Self-Determination Theory

Research has long established a connection between teacher autonomy and motivation and job satisfaction.Teacher autonomy in the context of motivation and job satisfaction includes making independent choices, having control, and being part of shared decision-making processes.Self-Determination Theory posits that autonomy does not exist by itself, but rather, is inextricably connected with competence and relatedness.Similarly, research on self-efficacy suggests a relationship between the control and capability a person feels to their goals and motivation.These constructs make sense, however, in the context of the United States public-school system, they belie the encouragement of teacher autonomy considering its historically hierarchical, top-down model.Conversely, countries, namely Finland, have instituted models that encourage and support teacher autonomy.Teachers in Finland are encouraged to be creative and autonomous, behaviors that have yielded both a high retention rate among its teachers and high achievement scores among its students.This paper examines teacher autonomy and suggests that its significance may impact school policy, teacher motivation, job satisfaction, and self-efficacy.In a study of 165 elementary teachers, the author administered The Basic Need Satisfaction at Work instrument to assess for teacher autonomy (Self-Determiniation Theory, 2000).Results supported the notion that autonomy is linked to competence and relatedness and job satisfaction.The study also suggests that teacher autonomy may be influenced by the leadership within the school and recommends greater exploration of teacher autonomy within the U.S. public-school system.
Research on Agricultural Product Traceability Technology (Economic Value) Based on Information Supervision and Cloud Computing

Traditional agricultural product traceability system adopts centralized storage, and the traceability process is solidified, which results in the low reliability of traceability results and the poor flexibility of the system. Aiming to solve this problem, blockchain technology is applied to supply chain traceability, and a supply chain traceability system based on sidechain technology is proposed. Goods management, information sharing, and product traceability in supply chain are realized through Ethereum smart contract. The sidechain technology is adopted to expand Ethereum so that it can meet the needs of practical applications. The experiment results show that the proposed system has a transaction function and information sharing function. Compared with similar trading systems, the proposed system has more advantages in throughput and security.
Advances in Evolutionary Optimization of Quantum Operators

A comparative study is presented regarding the evolutionary design of quantum operators in the form of unitary matrices.A comparative study is presented regarding the evolutionary design of quantum operators in the form of unitary matrices. Three existing techniques (representations) which allow generating unitary matrices are used in various evolutionary algorithms in order to optimize their coefficients. The objective is to obtain as precise quantum operators (the resulting unitary matrices) as possible for given quantum transformations. Ordinary evolution strategy, self-adaptive evolution strategy and differential evolution are applied with various settings as the optimization algorithms for the quantum operators. These algorithms are evaluated on the tasks of designing quantum operators for the 3-qubit and 4-qubit maximum amplitude detector and a solver of a logic function of three variables in conjunctive normal form. These tasks require unitary matrices of various sizes. It will be demonstrated that the self-adaptive evolution strategy and differential evolution are able to produce remarkably better results than the ordinary evolution strategy. Moreover, the results can be improved by selecting a proper settings for the evolution as presented by a comparative evaluation.
Autonomous navigation of ships by combining optimal trajectory planning with informed graph search

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Security scheduling and transaction mechanism of virtual power plants based on dual blockchains

Abstract Aiming at the data authenticity and storage problems in the current coordinated scheduling of virtual power plants, as well as the opaque information and high transaction costs, a dual blockchains security mechanism is proposed to solve above problems. In the process of security scheduling, a hybrid attribute proxy re-encryption algorithm based on ciphertext strategy is designed. The algorithm is composed of an identity encryption algorithm and an attribute proxy re-encryption algorithm with ciphertext strategy. Combining blockchain, tamper-proof smart metering equipment can effectively solve data authenticity and confidentiality in the information transmission process of distributed energy. In the research process of the trading mechanism, a continuous double auction mechanism based on reputation is proposed. In order to create a favorable trading atmosphere, reputation-based market segmentation mechanisms are integrated, and participants are divided according to reputation value. Depending on the properties of the stored information, they are divided into the private blockchain (with coordination scheduling information) and the consortium blockchain (with transaction information). The system analysis shows the reliability of the dual blockchains architecture. The communication and calculation costs of the proxy re-encryption algorithm verify the practicability of the proposed scheme. The case analysis of the auction mechanism declares that the mechanism can operate effective in the electricity trading market.
CMSEA: Compound Model Scaling With Efficient Attention for Fine-Grained Image Classification

The purpose of fine-grained image classification is to distinguish subcategories belonging to the same basic-level category, for example, two hundred subcategories belonging to birds. It has been a challenging topic in the field of computer vision in recent years due to the small inter-class variance among different subcategories (e.g., color and texture) and the large intra-class variance in the same subcategory (e.g., pose and viewpoint). In this paper, we propose a Compound Model Scaling with Efficient Attention (CMSEA) for fine-grained image classification, which carefully balances the various dimensions of width, depth, and image resolution in model scaling. Furthermore, the proposed method utilizes an additional computational low attention module to efficiently learn subtler features from discriminative regions. In addition, regularization and data augmentation were employed to improve accuracy in the training. Extensive experiments demonstrate that CMSEA achieves 90.63%, 94.51%, and 95.19% accuracy on CUB-200-2011, FGVC-Aircraft, and Stanford Cars datasets, respectively. In particular, CMSEA on CUB-200-2011 obtains 2.3% higher accuracy with 18% fewer network parameters than the original approach. Consequently, our method has better accuracy and parameter efficiency compared to most existing methods.
AI-Based Equipment Optimization of the Design on Intelligent Education Curriculum System

With the rapid development of artificial intelligence-related technologies, especially the use of big data, an intelligent world is coming. In the era of intelligence, the traditional trading teaching work model is no longer adaptable. If it wants to survive the new wave of technological development, it must carry out a self-revolution in science and technology. This article aims to study the improvement and optimization of the current college education curriculum system by artificial intelligence equipment under the use of big data technology. To this end, this paper proposes a clustering algorithm for data analysis. Through the improvement of the clustering algorithm and using it in the reform of the education system of colleges and universities, the relevant education data is calculated with high performance and fed back to the teacher to improve the teaching method. At the same time, experiments are designed to analyze the performance of the algorithm and the feasibility of the teaching mode. The experimental analysis results in this paper show that the improved data analysis clustering algorithm has improved the data analysis ability in the teaching process by 37%, and the use of big data has increased the teaching quality score of colleges and universities by nearly 1 point. It can well promote the popularization of education informatization in the country and the improvement of teaching quality.
Academic Motivation of University Students and the Factors that Influence it in an E-Learning Environment

One of the most important factors in successful e- learning is the motivation of the student. This article examines the very essence of student motivation in the context of e-learning and identifies the factors that influence it. The investigation involved 123 five-year students of the Elabuga Institute of Kazan Federal University and the I.M. Sechenov First Moscow State Medical University aged from 21 to 23 (M = 22.6 SD = 0.03). Determination of specific factors affecting students’ motivation was performed by means of a specially designed questionnaire. Motivation analysis was done by using the Academic Motivation Scale (AMS) adapted for Russian students. The link between the reviewed factors of influence and academic motivation was established by using correlation analysis. In general, the results of the experiment indicate the absence of statistically significant variations in motivation levels across experimental (studying online) and control (studying traditionally) group participants (t = 0.721, p &gt; 0.05). The significance of this research stems from the fact that it provides an idea of how to increase student engagement in e-learning and limit the effect of determinants reducing student motivation. That aside, they may be taken advantage of when planning and carrying out future research on the influence of e-learning on student motivation.