Reviewerly/exHarmony huggingface dataset:
A feature transformation and extraction approach-based artificial neural network for an improved production prediction of grid-connected solar photovoltaic systems

The grid operators face challenges due to the fluctuations in the energy production from Photovoltaic (PV) power plants. Therefore, reliable prediction models are critical as they aid grid operators in optimizing operational planning and lowering operating costs. One of the most challenging components of production prediction accuracy is determining a proper set of features that affect the models’ prediction accuracy. In this work, an accurate data-driven prediction model has been developed based on new timestamps and weather condition features that affect the PV system. Specifically, standard PV physics-based models have been employed for feature transformation and extraction from the traditional available historical features. The new features are then utilized to build an Artificial Neural Network (ANN), whose production predictions are compared to those obtained using the old features and other features extracted using the principal component analysis (PCA). The suggested approach’s efficacy is demonstrated using a 264 kWp PV system in Jordan. The proposed approach has shown superior with performance gain in accuracy reaches up to 21% and 64% compared to the models that employ old and PCA features, respectively. Additionally, the proposed approach reduces the computational cost by 20% and 7% compared to old and PCA models, respectively.
Quantum network security dependent on the connection density between trusted nodes

Besides true quantum repeaters, a trusted node paradigm seems to be inevitable for practical implementations (at least in the short term), and one should consider configurations of trusted nodes as the basis for global quantum networks. In this paper, we estimate how the introduction of additional connections between trusted nodes through one, two, etc., nodes (i.e., connection density) to a quantum network with serial connections of trusted nodes affects its security. We provide proper scaling of the failure probability of authentication and quantum key distribution protocols to the level of the whole quantum network. Expressions of the failure probability dependent on the total number of connected nodes between users and the connection density for the given mean failure probability of each element are derived. The result provides an explicit trade-off between an increase of key transport security and a consequent increase of spent resources. We believe that the obtained result may be useful for both the design of future networks and optimization of existing ones.
PENINGKATAN KEMAMPUAN BERHITUNG 1-10 MELALUI VIDEO ANIMASI PADA KELOMPOK A DI RA AR ROHMAH PEKUKUHAN KECAMATAN MOJOSARI KABUPATEN MOJOKERTO

This study discusses efforts to improve early numeracy skills through animated video media. This study is motivated by the low ability to count from 1-10 in children due to the lack of learning media used by teachers. This study aims to see the effectiveness of the use of animated video media in increasing the ability to count from 1-10 in group A RA Ar-Rohmah children.In the implementation of this research, it was precisely carried out in the first semester with 2 cycles, the results of the study could be obtained by looking at the observation sheets of children who increasingly showed an increase in the development of counting 1-10 seen in the first cycle of 58% while in the second cycle it reached 83%. it can be concluded that this study was successful because it achieved an increase of up to 25%. By using animated video media, children can know the concept of numbers, recognize number symbols, count objects by playing while learning in a fun atmosphere. The use of animated videos also influences the development of children's numeracy skills with the support of activities that support them, namely creative and innovative activities.
Semi-supervised structure attentive temporal mixup coherence for medical image segmentation

Deep convolutional neural networks have shown eminent performance in medical image segmentation in supervised learning. However, this success is predicated on the availability of large volumes of pixel-level labeled data, making these approaches impractical when labeled data is scarce. On the other hand, semi-supervised learning utilizes pertinent information from unlabeled data along with minimal labeled data, alleviating the demand for labeled data. In this paper, we leverage the mixup-based risk minimization operator in a student–teacher-based semi-supervised paradigm along with structure-aware constraints to enforce consistency coherence among the student predictions for unlabeled samples and the teacher predictions for the corresponding mixup sample by significantly diminishing the need for labeled data. Besides, due to the intrinsic simplicity of the linear combination operation used for generating mixup samples, the proposed method stands at a computational advantage over existing consistency regularization-based SSL methods. We experimentally validate the performance of the proposed model on two public benchmark datasets, namely the Left Atrial (LA) and Automatic Cardiac Diagnosis Challenge (ACDC) datasets. Notably, on the LA dataset’s lowest labeled data set-up (5%), the proposed method significantly improved the Dice Similarity Coefficient and the Jaccard Similarity Coefficient by 1.08% and 1.46%, respectively. Furthermore, we demonstrate the efficacy of the proposed method with a consistent improvement across various labeled data proportions on the aforementioned datasets.
Opinion texts summarization based on texts concepts with multi-objective pruning approach

Considering the huge volume of opinion texts published on various social networks, it is extremely difficult to peruse and use these texts. The automatic creation of summaries can be a significant help for the users of such texts. The current paper employs manifold learning to mitigate the challenges of the complexity and high dimensionality of opinion texts and the K-Means algorithm for clustering. Furthermore, summarization based on the concepts of the texts can improve the performance of the summarization system. The proposed method is unsupervised extractive, and summarization is performed based on the concepts of the texts using the multi-objective pruning approach. The main parameters utilized to perform multi-objective pruning include relevancy, redundancy, and coverage. The simulation results show that the proposed method outperformed the MOOTweetSumm method while providing an improvement of 11% in terms of the ROGUE-1 measure and an improvement of 9% in terms of the ROGUE-L measure.
Towards super-resolution via iterative multi-exposure coaddition

In this article, we provide an alternative up-sampling and PSF deconvolution method for the iterative multi-exposure coaddition. Different from the previous works, the new method has a ratio-correction term, which allows the iterations to converge more rapidly to an accurate representation of the underlying image than those with difference-correction terms. By employing this method, one can coadd the under-sampled multi-exposures to a super-resolution and obtain a higher peak signal-to-noise ratio. A set of simulations show that we can take many advantages of the new method, e.g. in the signal-to-noise ratio, the average deviation of all source fluxes, super-resolution, and source distortion ratio, etc., which are friendly to astronomical photometry and morphology, and benefits faint source detection and shear measurement of weak gravitational lensing. It provides an improvement in fidelity over the previous works tested in this paper.
FSOLAP: A fuzzy logic-based spatial OLAP framework for effective predictive analytics

Nowadays, with the rise in sensor technology, the amount of spatial and temporal data increases day by day. Fast, effective, and accurate analysis and prediction of collected data have become more essential than ever. Spatial Online Analytical Processing (SOLAP) emerged to perform data mining on spatial and temporal data that naturally contains the hierarchical structure used in many complex applications. In addition, uncertainty and fuzziness are inherently essential elements of data in many complex data applications, particularly in spatial–temporal database applications. In this study, FSOLAP is proposed as a new fuzzy SOLAP-based framework to compose the benefits of fuzzy logic and SOLAP concepts and is extended with inference capability to the framework to support predictive analytics. The predictive accuracy and resource utilization performance of FSOLAP are compared using real data with some well-known machine learning techniques such as Support Vector Machine, Random Forest, and Fuzzy Random Forest. The extensive experimental results show that the FSOLAP framework for the predictive analytics of various spatiotemporal events in big meteorological databases is considerably more accurate and scalable than using conventional machine learning techniques.
Consolidation of structure of high noise data by a new noise index and reinforcement learning

Data denoising is an essential issue in machine learning and computer vision. However, most existing denoising methods can handle only low-noise data, because it is difficult for these methods to obtain the true structures of high-noise data. An existing partial solution is to consolidate the structures of data by moving each sample to its near high density region. It actually regards all non-noisy samples as noisy ones, but non-noisy samples are important for constructing the structures of high-noise data so they should not be moved. To address this problem, we propose a new denoising method called Denoising by a new Noise index and Reinforcement learning (DNR). Firstly, it detects each noisy sample by its density and the distance between this sample and the center of its neighbors. A noisy sample usually has a low density and most neighbors of this sample will be on the same side of it, leading to the center of its neighbors far from it, especially for high-noise data. Secondly, for each detected noisy sample, DNR models its movement as a Markov decision process to store the experience in this movement. Finally, NDR learns a policy to iteratively move each detected noisy sample to its near high density region by learns the experience of this movement in reinforcement learning. The learned experience can effectively help the movement adapt to the high-noise in real-world cases. In DNR, the structures of high-noise data can be well consolidated by our detection and movement of noisy samples. To prove the rationality of DNR, we theoretically analyze its convergence. Then, we perform the experiment to illustrate that DNR can better denoise high-noise data than existing denoising methods. The source code can be downloaded from https://github.com/TianyiHuang2022.
Streaming learning with Move-to-Data approach for image classification

In Deep Neural Network training, the availability of a large amount of representative training data is the sine qua non-condition for a good generalization capacity of the model. In many real-world applications, data is not available at a glance, but coming on the fly. If a pre-trained model is fine-tuned on the new data, then catastrophic forgetting happens mostly. Incremental learning mechanisms propose ways to overcome catastrophic forgetting. Streaming learning is a type of incremental learning where models learn from new data instances as soon as they become available in a single training pass. In this work, we conduct an experimental study, on a large dataset, of an incremental/streaming learning method Move-to-Data we previously proposed, and propose an updated approach by ”re-targeting” with gradient descent which is faster than the popular streaming learning method ExStream. The method achieves better performances and computational efficiency compared to ExStream. Move-to-Data with gradient is on average 3.5 times faster than ExStream and has a similar accuracy, with 0.5% improvement compared to ExStream.
Research on Intelligent Organization and Application of Multi-source Heterogeneous Knowledge Resources for Energy Internet

ABSTRACT To improve the informationization and intelligence of the energy Internet industry and enhance the capability of knowledge services, it is necessary to organize the energy Internet body of knowledge from existing knowledge resources of the State Grid, which have the characteristics of large scale, multiple sources, and heterogeneity. At the same time, the business fields of State Grid cover a wide range. There are many sub-fields under each business field, and the relationship between fields is diverse and complex. The key to establishing the energy Internet body of knowledge is how to fuse the heterogeneous knowledge resources from multiple sources, extract the knowledge contents from them, and organize the different relationships. This paper considers transforming the original knowledge resources of State Grid into a unified and well-organized knowledge system described in OWL language to meet the requirements of heterogeneous resource integration, multi-source resource organization, and knowledge service provision. For the State Grid knowledge resources mainly in XML format, this paper proposes a Knowledge Automatic Fusion and Organization idea and method based on XSD Directed Graph. According to the method, the XML corresponding XSD documents are transformed into a directed graph in the first stage during which the graph neural network detects hidden knowledge inside the structure to add semantic information to the graph. In the second stage, for other structured knowledge resources (e.g., databases, spreadsheets), the knowledge contents and the relationships are analyzed manually to establish the mappings from structured resources to graph structures, using which the original knowledge resources are transformed into graph structures, and merged with the directed graphs obtained in the first stage to achieve the fusion of heterogeneous knowledge resources. And expert knowledge is introduced for heterogeneous knowledge fusion to further extend the directed graph. And in the third stage, the expanded directed graph is converted to the body of knowledge in the form of OWL. This paper takes the knowledge resources in the field of human resources of the State Grid as an example, to establish the ontology of the human resources training field in a unified manner, initially demonstrating the effectiveness of the proposed method.
Trusted Data Sharing Mechanism Based on Blockchain and Federated Learning in Space-Air-Ground Integrated Networks

Network data is distributed data on electricity, with the explosive growth of network data, and it has become an inevitable trend of network development to synergized and shared crossdomain scattered data and enhances the value transmission of network data. Federated learning, as a technology that combines data value delivery and data privacy security, is widely concerned in the process of data sharing. However, currently federated learning is used within a single business system. In the process of crossdomain data sharing, how to ensure the data trust, model trust, and result trust of federated learning is still an urgent problem to be solved. To this end, we designed to use blockchain structure to record each behavior of data sharing. Based on its tamper-proof and traceability, combined with cryptography technology, we constructed an endogenous trusted architecture for crossdomain data sharing. In addition, a reverse auction node incentive mechanism based on high credit preference is designed to solve the common problems in data sharing, such as low enthusiasm of users in sharing, unstable data quality of contributions, and unreasonable distribution of data sharing benefits. Through theoretical analysis and experimental verification, it can be seen that the incentive mechanism designed in this paper can meet the authenticity, user rationality, and budget feasibility. On this basis, it can motivate users to participate in data sharing, improve the average quality of data shared by users, and ensure security and trustworthiness and resist malicious attacks to a certain extent.
Asymmetric reference-frame-independent measurement-device-independent quantum key distribution

Reference-frame-independent measurement-device-independent quantum key distribution is a promising candidate for building star-type quantum secure networks because it does not require reference alignment and removes all detector-side-channel attacks. However, prior works considered only a symmetric case in which the channels of both users have the same loss. In a realistic quantum secure network, the losses of various channels are likely to be different owing to their geographical locations. In this study, we present an asymmetric protocol for scalable reference-frame-independent measurement-device-independent quantum key distribution networks. By allowing independent adjustments of signal intensities of both users, our protocol provides a higher key rate than previous symmetric protocols in a realistic quantum secure network. The simulation results demonstrate that our protocol works well under realistic experimental conditions and obtains a key rate that is approximately one order of magnitude higher than that of previous methods. Our study paves the way for high-rate quantum secure communication network development.
Application Analysis of Block Technology Based on Block Chain Computer Model in the False Identification of the Catalog List of the Financial Department of Multinational Companies

The financial data of some listed companies is not transparent enough, especially in financial-related management and program planning. In addition to legal and ethical constraints, with the continuous upgrading of blockchain technology, it is possible to explore the use of blockchain technology to lower the threshold for a certain number of investors to participate in corporate financial governance. Jointly supervising the capital flow of listed companies can ensure the safety of funds and improve the efficiency of capital use, thereby solving the problem of financial fraud of listed companies to a certain extent. The four core features of blockchain technology include decentralized distributed ledgers, consensus mechanisms, asymmetric encryption, and smart contracts. Exploring the feasibility and embedding path of chain technology in the capital supervision of listed companies will help to solve this problem. The integrated application of blockchain technology plays an important role in new technology innovation and industrial transformation. According to the unique technical characteristics of blockchain technology such as decentralization, nontampering, and traceability, combined with its application scenarios in the accounting field, it is hoped to use blockchain technology to solve problems such as information asymmetry and easy tampering of financial amounts from the source. Therefore, effective analysis can reduce the occurrence of financial fraud and fraud in listed companies, and blockchain technology has become a new way to prevent financial fraud in listed companies. This paper analyzes the classification and basic characteristics of blockchain, and studies the access mechanism and voting rules of candidate nodes based on the private chain architecture of the Raft distributed protocol to ensure the authenticity and real-time of financial information of listed companies and financial decisions. Improving the difficulty of financial fraud can provide good social and economic benefits.
Comparison of Knowledge Change in a Virtual Reality Simulation Across Four Platform Technologies

Introduction There has been an increase in the delivery of educational and training content using computer-based simulations across a range of platforms. Minimal studies have compared the effectiveness of multiple digital delivery platforms with one another using the same simulation. Objective This research investigated differences in knowledge change due to the platform used for delivery of a stand-alone learning simulation. Methods A pretest–posttest design was used to evaluate 127 participants. Participants were randomly assigned to one of four conditions: desktop, tablet, mobile VR (Google Cardboard), and virtual reality headset. A pretest was given prior to participants’ completion of a learning simulation. Upon completion of the learning simulation, participants completed a posttest. Results This study indicated that all participants improved knowledge scores across the four platforms used to deliver the learning simulation. All participants positively increased their overall scores from the pretest to the posttest. There were no statistically significant differences in knowledge change between the four groups. Conclusion All participants positively increased their overall scores from the pretest to the posttest regardless of platform technology used to deliver the virtual reality learning simulation. This indicates that it is possible to effectively deliver the same learning simulation across different platforms that include a mix of 2D and immersive technologies.
On the Hardware–Software Integration in Cryptographic Accelerators for Industrial IoT

Industrial Internet of Things (IIoT) applies IoT technologies on industrial automation systems with the aims of providing remote sensing, remote control, self-organization and self-maintenance. Since IIoT systems often constitute a critical infrastructure, cybersecurity risks have rapidly increased over the last years. To address cybersecurity requirements, we need to deploy cryptographic processing components which are particularly efficient, considering also that many IIoT systems have real-time constraints. Hardware acceleration can greatly improve the efficiency of cryptographic functions, but the speed-up could be jeopardized by a bad hardware–software integration, which is an aspect often underrated by the literature. Considering that modern IIoT devices often mount an operating system to fulfill their complex tasks, software influence on efficiency cannot be neglected. In this paper, we develop a software–hardware integration of various cryptographic accelerators with a Linux operating system, and we test its performance with two typical IIoT reference applications. We also discuss our design choices and the lessons learned during the development process.