Reviewerly/exHarmony huggingface dataset:
Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group

With the increasing proportion of distributed photovoltaic (DPV) installations in county-level power grids, to improve the centralized operation and maintenance of the stations and to meet the needs of power grid dispatching, the output of the county-level regional DPV stations group needs to be predicted. In this paper, the weather prediction information is used to predict the output based on the model input average strategy. To eliminate the effect of the selected non-optimal training sample collection period on the prediction accuracy, an ensemble prediction method based on the minimum redundancy maximum relevance criterion and TabNet model is carried out. To reduce the influence of weather prediction errors on the power output prediction, a modified model based on error prediction is proposed. The ensemble prediction model is used to predict the day-ahead output, and a combination prediction model based on the proposed ensemble prediction model and the proposed modified model is established to predict the hour-ahead output. The experimental results verify the effectiveness of the proposed models. Compared with the corresponding reference models, the proposed ensemble prediction method reduces the normalized mean absolute errors (nMAEs) and the normalized root mean square errors (nRMSEs) of the day-ahead output prediction results by 2.86% and 5.51%, respectively. The combination prediction model reduces the nMAE and nRMSE of the hour-ahead output prediction results by 3.05% and 3.05%, respectively. Therefore, the prediction accuracy can be improved by the proposed models.
Deep learning approach and cover image transportation: a multi-security adaptive image steganography scheme

In this era of information security and communication, a major priority is the achievement of a robust and secure steganography system when thinking about information concealment. The development of such an information-hiding scheme demands that the scheme be able to hide a secret message within the cover media. The most vexing issues in existing steganography protocols are imperceptibility, security, and capacity, and researchers have frequently emphasized a trade-off between these issues. Scholars have consistently ignored the balance between security and payload because resolving one problem has been shown to have an impact on the other, and vice versa. To overcome these problems, an effective method known as the Conventional Neural Network based Edge Detection Method (CNN-EDM) has been presented for image steganography in this study. The CNN-EDM is used to improve the contributions of the proposed scheme. Four main stages were used to achieve the objectives in this research, beginning with the cover image and secret image preparation, followed by embedding, and culminating in extraction. The last stage is the evaluation stage, which employs several evaluations to benchmark the obtained results. A standard database from the Signal and Image Processing Institute (SIPI) containing color and grayscale images with 512 × 512 pixels was utilized in this study. Different parameters were used to test the performance of the suggested scheme based on security and imperceptibility (image quality). The image quality was evaluated using three important metrics: histogram analysis, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM). Furthermore, two metrics were used to evaluate the security properties of the proposed system: the Human Visual System (HVS) and Chi-square (X2) attacks. The evaluations showed that the proposed scheme can enhance the capacity, invisibility, and security features and address the already existing problems in this domain.
Digital Design: The Inevitability of Production

The purpose of the study is to study the impact of digital design on the communicative interaction between the consumer and the manufacturer. The study involved two groups of respondents. The first group included 47 digital design professionals (26 men and 21 women) involved in websites development. The second group included 49 consumers of these goods and services (25 men and 24 women). For the survey of respondents, a validated questionnaire was developed that included characteristics of the main digital design tools used to develop websites and aimed at attracting consumers of goods. The research has shown that the main factors of digital site design, affecting the communication between a manufacturer and consumers, are loading speed, security, usability, and the possibility of feedback. At the same time, designers prioritize the technical parameters of the site and the presence of the target audience, while consumers prioritize user-friendliness, site design, and information security.
A robust class of nonlinear autoregressive models with regression function and dependent innovations using semiparametric kernel estimation

In this study, researchers examine a nonlinear autoregressive (NLAR) time-series model with regression function and dependent innovations in which the errors of the model follow the two-piece scale mixtures of normal (TP-SMN) distributions. Robustness and atypical forms of the proposed class of two-piece distributions along with the flexibility of the nonlinear autoregressive model develop desirable properties which can be applied to several types of datasets. The nonlinear regression function part of the autoregressive time-series model is estimated via the semiparametric and nonparametric curve estimation based on the conditional least square method and nonparametric kernel approach. The maximum likelihood (ML) estimates of the model parameters, using a suitable hierarchical representation of the TP-SMN family on the model are obtained via an expectation maximization (EM)-type algorithm. Performances and usefulness of the proposed model and estimates are shown via simulation studies and a real dataset.
Neural Network-Based Bilingual Lexicon Induction for Indonesian Ethnic Languages

Indonesia has a variety of ethnic languages, most of which belong to the same language family: the Austronesian languages. Due to the shared language family, words in Indonesian ethnic languages are very similar. However, previous research suggests that these Indonesian ethnic languages are endangered. Thus, to prevent that, we propose the creation of a bilingual dictionary between ethnic languages, using a neural network approach to extract transformation rules, employing character-level embedding and the Bi-LSTM method in a sequence-to-sequence model. The model has an encoder and decoder. The encoder reads the input sequence character by character, generates context, and then extracts a summary of the input. The decoder produces an output sequence wherein each character at each timestep, as well as the subsequent character output, are influenced by the previous character. The first experiment focuses on Indonesian and Minangkabau languages with 10,277 word pairs. To evaluate the model’s performance, five-fold cross-validation was used. The character-level seq2seq method (Bi-LSTM as an encoder and LSTM as a decoder) with an average precision of 83.92% outperformed the SentencePiece byte pair encoding (vocab size of 33) with an average precision of 79.56%. Furthermore, to evaluate the performance of the neural network model in finding the pattern, a rule-based approach was conducted as the baseline. The neural network approach obtained 542 more correct translations compared to the baseline. We implemented the best setting (character-level embedding with Bi-LSTM as the encoder and LSTM as the decoder) for four other Indonesian ethnic languages: Malay, Palembang, Javanese, and Sundanese. These have half the size of input dictionaries. The average precision scores for these languages are 65.08%, 62.52%, 59.69%, and 58.46%, respectively. This shows that the neural network approach can identify transformation patterns of the Indonesian language to closely related languages (such as Malay and Palembang) better than distantly related languages (such as Javanese and Sundanese).
Study of Root Canal Length Estimations by 3D Spatial Reproduction with Stereoscopic Vision

Extended Reality (XR) applications are considered useful for skill acquisition in dental education. In this study, we examined the functionality and usefulness of an application called “SR View for Endo” that measures root canal length using a Spatial Reality Display (SRD) capable of naked-eye stereoscopic viewing. Three-dimensional computer graphics (3DCG) data of dental models were obtained and output to both the SRD and conventional 2D display devices. Forty dentists working at the Kanagawa Dental University Hospital measured root canal length using both types of devices and provided feedback through a questionnaire. Statistical analysis using one-way analysis of variance evaluated the measurement values and time, while multivariate analysis assessed the relationship between questionnaire responses and measurement time. There was no significant difference in the measurement values between the 2D device and SRD, but there was a significant difference in measurement time. Furthermore, a negative correlation was observed between the frequency of device usage and the extended measurement time of the 2D device. Measurements using the SRD demonstrated higher accuracy and shorter measurement times compared to the 2D device, increasing expectations for clinical practice in dental education and clinical education for clinical applications. However, a certain percentage of participants experienced symptoms resembling motion sickness associated with virtual reality (VR).
A Novel Robust Metric Distance Optimization-Driven Manifold Learning Framework for Semi-Supervised Pattern Classification

In this work, we address the problem of improving the classification performance of machine learning models, especially in the presence of noisy and outlier data. To this end, we first innovatively design a generalized adaptive robust loss function called Vθ(x). Intuitively, Vθ(x) can improve the robustness of the model by selecting different robust loss functions for different learning tasks during the learning process via the adaptive parameter θ. Compared with other robust loss functions, Vθ(x) has some desirable salient properties, such as symmetry, boundedness, robustness, nonconvexity, and adaptivity, making it suitable for a wide range of machine learning applications. Secondly, a new robust semi-supervised learning framework for pattern classification is proposed. In this learning framework, the proposed robust loss function Vθ(x) and capped L2,p-norm robust distance metric are introduced to improve the robustness and generalization performance of the model, especially when the outliers are far from the normal data distributions. Simultaneously, based on this learning framework, the Welsch manifold robust twin bounded support vector machine (WMRTBSVM) and its least-squares version are developed. Finally, two effective iterative optimization algorithms are designed, their convergence is proved, and their complexity is calculated. Experimental results on several datasets with different noise settings and different evaluation criteria show that our methods have better classification performance and robustness. With the Cancer dataset, when there is no noise, the classification accuracy of our proposed methods is 94.17% and 95.62%, respectively. When the Gaussian noise is 50%, the classification accuracy of our proposed methods is 91.76% and 90.59%, respectively, demonstrating that our method has satisfactory classification performance and robustness.
Design of Control Elements in Virtual Reality—Investigation of Factors Influencing Operating Efficiency, User Experience, Presence, and Workload

Virtual reality (VR) enables prototypes of devices to be evaluated in a simulation of a future usage environment. A disadvantage is the insufficient feedback design during the interaction with prototypes. In this study, we investigated how virtual control elements in VR must be designed to compensate for the lack of feedback. Therefore, 35 participants performed VR tasks using a virtual joystick and virtual rotary control. According to the design of experiments method, 12 factors, such as haptic feedback, sensitivity, size, and shape, were systematically varied. The control accuracy, task time, user experience, presence, and mental workload were recorded. The effect of a factor on the recorded parameters was examined using multifactorial ANOVA. Linear regression was used to calculate the mathematical models between the factors and parameters. These models were used to calculate the optimal design of the control elements in the VR. For rotary control, eight factors had a significant influence on the recorded parameters. There were seven factors for the joystick. With mathematical models, optimized control element designs for VR were calculated for the first time. These findings can help to better adapt prototypes and human–machine interfaces to different modalities in VR.
MDAR: A Knowledge-Graph-Enhanced Multi-Task Recommendation System Based on a DeepAFM and a Relation-Fused Multi-Gead Graph Attention Network

In recent years, MKR has attracted increasing attention due to its ability to enhance the accuracy of recommendation systems through cooperation between the RS tasks and the KGE tasks, allowing for complementarity of the information. However, there are still three challenging issues: historical behavior preferences, missing data, and knowledge graph completion. To tackle these challenging problems, we propose MDAR, a multi-task learning approach that combines DeepFM with an attention mechanism (DeepAFM) and a relation-fused multi-head graph attention network (RMGAT). Firstly, we propose to leverage the attention mechanism in the DeepAFM to distinguish the importance of different features for target prediction by assigning different weights to different interaction features of the user and the item, which solves the first problem. Secondly, we introduce deep neural networks (DNNs) to extract the deep semantic information in the cross-compressed units by obtaining the high-dimensional features of the interactions between the RS task and the KG task to solve the second problem. Lastly, we design a multi-head graph attention network for relationship fusion (RMGAT) in the KGE task, which learns entity representations through the different contributions of the neighbors by aggregating the relationships into the attention network of the knowledge graph and by obtaining information about the neighbors with different importance for different relationships, effectively solving the third problem. Through experimenting on real-world public datasets, we demonstrate that MDAR obtained substantial results over state-of-the-art baselines for recommendations for movie, book, and music datasets. Our results underscore the effectiveness of MDAR and its potential to advance recommendation systems in various domains.
Neotocite textures as a clue to the exploration of Red Bed type sediment-hosted stratabound copper (SSC) deposits – evidence from Ravar–Tabas–Eshghabad copper belt, Central Iran

ABSTRACTABSTRACTRed Bed type sediment-hosted stratabound copper (SSC) deposits consist of replaced, disseminated to veinlets copper sulphides, hosted by thick, haematite-stained sequences of sandstone, conglomerate, and siltstone. More than 120 sediment-hosted stratabound copper (SSC) deposits and occurrences have been reported in Iran. Ravar–Tabas–Eshghabad is the major Red Bed type SSC belt in Iran, with a cumulative tonnage of more than 400 Mt ore at ~1–2% Cu. These deposits are hosted in Garedu Red Bed Formation (including reddish conglomerates and micro-conglomerates, red siltstones and sandstones), and copper sulphides (chalcocite, with minor chalcopyrite and bornite) are concentrated in local reducing conditions that are achieved around plant fossil accumulations, within grey paleochannels in the upper part of the Garedu Red Bed Formation. These deposits are usually explored using geochemical, geophysical, and remote sensing methods. However, the host layers usually extend laterally up to tens of kilometres, and the critical point is to find the paleochannels in the host horizons where organic matter and plant fossils accumulate, especially in underground mining and boreholes. The lateral extension of ore-bearing lenses is usually 50–150 m, so it is not easy to find them within the reduced horizons. Recognizing neotocite textures (including diffusion, radial DLA (diffusion-limited aggregation), cat footprint, and spotty), and distribution in the host rocks of the Red Bed type SSC deposits can be used as a clue to find the ore lenses, organic matter, and plant fossil accumulations.GRAPHICAL ABSTRACTKEYWORDS: Neotocite migrationRavar–Tabas–Eshghabadcopper mineralizationradial DLA (diffusion-limited aggregation)spotty texture AcknowledgmentsThe clarity of the paper was substantially improved by the constructive reviews of Dr H. Kouhestani and two anonymous reviewers, and the editorial suggestions of Prof. R. Stern.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThe author(s) reported that there is no funding associated with the work featured in this article.
Web-Based And Whatsapp Gateway Tuition Payment Information System (Case Study Of SMK Antartika 2 Sidoarjo)

The tuition paymant system at Vocational High School Antartika 2 Sidoarjo is still carried out semi- manually, meaning that transactions are already using a computer but data recaps are still recorded in books manually. This can slow down the payment process causing some problems. The method used is an object-oriented method using the Unified Modeling Language (UML). While the data collection techniques used are by way of observation, interviews, and documentation. This journal will discuss the design of a web-based tuition paymant information system and Whatsapp gateway using Whatsapp messages to be sent to each student's guardian. This plan aims to facilitate the administration of the finance department in recording tuition paymant data and making reports, as well as making it easier for students' parents to provide payment information via Whatsapp messages. The author hopes that with this application planning it is hoped that it will run more effectively and efficiently both in conducting tuition paymant transactions, as well as in making reports so that it can help the school and parents of students also easily get information on tuition payments.
Giving Students the Tools: Looking at Teaching and Learning using Corpora

This article discusses a pilot project aimed at giving tertiary students a wider repertoire of resources to use in language learning, with a particular focus on Italian. This project responds to the exponential increase in and access to online data and the potential value such data represent for students studying additional languages at tertiary level. By examining whether current language students are aware of online resources, such as linguistic corpora and other potential applications of big data, we aim to provide an insight into the possible uses of corpus-assisted learning in the language classroom. In this paper, we detail a project undertaken in 2017 with undergraduate students of Italian in a major metropolitan university. Our project directed students to complete a translation task using corpora-based resources and assessed their experience through a post-assessment survey. Subsequently, we present our initial findings in relation to the possibilities of a corpus-based approach to language teaching and learning. While today’s students are already predisposed to relying on online resources as part of their language studies, our results suggest students are not aware of emerging online resources such as corpora. Moreover, even when these resources are presented to students, the complex nature of the software programs used to interrogate corpora often results in their underutilisation.
The Role of Decision-Making Styles in Shaping Cybersecurity Compliance Behavior

The growing number of attacks on crucial cyber networks is one of humanity’s most significant security threats. Combining technological solutions with the convenient cybersecurity behavior of the human factor is necessary to reduce the effects of these attacks. In this study, the impact of individual decision-making styles on cybersecurity compliance behavior was examined. A conceptual framework was developed based on the protective motivation theory, and it was tested with a data set representing the survey results of 668 employees and students with work experience. Regression analyses were performed to evaluate the impacts of individuals’ decision-making styles on cybersecurity compliance behaviors. Results showed that the decision-making styles of individuals affected their cybersecurity compliance behaviors, and these effects showed significant differences according to their decision-making styles. The study also confirmed that security awareness was another important indicator of cybersecurity compliance behavior. Our research provided new insight into the effects of individual decision-making styles on cybersecurity compliance behavior.
A Novel Multiple Role Evaluation Fusion-Based Trust Management Framework in Blockchain-Enabled 6G Network

Six-generation (6G) networks will contain a higher density of users, base stations, and communication equipment, which poses a significant challenge to secure communications and collaborations due to the complex network and environment as well as the number of resource-constraint devices used. Trust evaluation is the basis for secure communications and collaborations, providing an access criterion for interconnecting different nodes. Without a trust evaluation mechanism, the risk of cyberattacks on 6G networks will be greatly increased, which will eventually lead to the failure of network collaboration. For the sake of performing a comprehensive evaluation of nodes, this paper proposes a novel multiple role fusion trust evaluation framework that integrates multiple role fusion trust calculation and blockchain-based trust management. In order to take advantage of fused trust values for trust prediction, a neural network fitting method is utilized in the paper. This work further optimizes the traditional trust management framework and utilizes the optimized model for node trust prediction to better increase the security of communication systems. The results show that multiple role fusion has better stability than a single role evaluation network and better performance in anomaly detection and evaluation accuracy.