Reviewerly/exHarmony huggingface dataset:
An ensemble machine learning-based solar power prediction of meteorological variability conditions to improve accuracy in forecasting

ABSTRACTThe increasing energy demand has significantly improved solar photovoltaic (SPV) systems as a distributed energy source. Real-time control of SPV performance is vital for accurate solar power (SP) prediction. The article proposes an ensemble Machine Learning Approach (MLA) called Random Forest Algorithm-Based Regression Model (RFARM) for hourly forecasting of SP. The approach selectively analyzes meteorological and solar irradiance data (SI) to enhance short-term solar panel prediction. It focuses on employing a correlation-based approach using an RFA with regression to achieve improved SP prediction accuracy. The study compares the PV power generated at Thiagarajar College of Engineering (TCE), Madurai, using four prediction techniques: Artificial Neural Networks (ANN), Random Forest (RF), K-Nearest Neighbours (KNN), and Support Vector Machine (SVM) along with a proposed RFARM for different meteorological weather conditions over a 24-hour time horizon. The proposed RFARM method achieves high prediction accuracy by selecting significant parameters, avoiding artificial filtering, and minimizing errors, particularly in predicting solar output during cloud shading. The RFARM model outperforms conventional methods in predicting the daily curve of solar power performance. It achieves an RMSE of 1.52, MAE of 14, and R-squared of 98%. Feature selection further improves accuracy, reducing RMSE by 12.5% and MAE by 17.2% respectively.CO EDITOR-IN-CHIEF: Kuo, Cheng-ChienASSOCIATE EDITOR: Kuo, Cheng-ChienKEYWORDS: Solar photovoltaic (SPV)predictionmachine learning (MLA)ANNRFARMRFKNNSVMfeature selective technique Nomenclature AC=Alternating currentANFIS=Adaptive Neuro-Fuzzy Inference SystemANN=Artificial Neural NetworksAT=Atmosphere TemperatureBI=Beam Irradiance (W/m2)CT=Cell Temperature (C)D=DayDC=Direct currentDI=Diffuse Irradiance (W/m2)DL=Deep LearningDPP=Data Pre-ProcessingDR=Demand responseDSM=Demand Side ManagementH=HourKNN=K-Nearest NeighboursLRM=Linear Regression ModelLsSVR=Least square Support Vector RegressionM=MonthMAE=Mean Absolute ErrorMAPE=Mean Absolute Percentage ErrorMLA=Machine Learning AlgorithmMSE=Mean Squared ErrorMVR=Multivariate regression modelPA=Plane of Array Irradiance (W/m2)PV=PhotovoltaicRF=Random ForestRFARM=Random Forest Algorithm-Based Regression ModelRMSE=Root Mean Square ErrorSI=Solar irradianceSP=Solar PowerSVM=Support Vector MachineTCE=Thiagarajar College Of EngineeringVIF=Variance Inflation FactorWS=Wind Speed (m/s)AcknowledgmentsThis research has been made possible by the Department of Electrical and Electronics Engineering, TCE, Madurai, which has provided all the necessary data, and I wish to express my sincere thanks to the administration and the staff.Disclosure statementNo potential conflict of interest was reported by the author(s).Correction StatementThis article has been corrected with minor changes. These changes do not impact the academic content of the article.
Contextual and Semantic Novelty in Text

Novelty, anomaly, or out-of-distribution (OOD) detection has been an active research area for decades, since the 1960s [1, 2], due to its widespread applications in various domains [3, 4], such as financial surveillance, health and medical risk, AI safety, network intrusion detection, etc. Recently, this research has also received increased attention in the Natural Language Processing (NLP) domain. For example, many researchers have studied the problem of text classification [5–9], but, as opposed to classical text classification that works under the closed-world assumption, novelty or OOD detection works under the open-world setting, which allows the class of a test instance to be unknown. However, the existing text classification methods that perform novelty or OOD detection are mainly coarse-grained and topic-based. Given a text document, their goal is to detect whether the text belongs to a known topic or an unknown topic. Some examples include detecting product reviews of unknown product categories [7], news articles of unknown topics [6], and natural language commands of unknown intents/domains [9]. This chapter is based on two papers published in 2021 and 2022 [10, 11]. They introduce a new novelty detection problem in the NLP field—fine-grained semantic novelty detection in texts, which we also call contextual novelty detection. In this chapter, we want to present a learning-based agent for semantic novelty detection in text. The agent does not actively interact with the world. The perceptual operators are Deep Neural Networks (DNN) built on text features created from large language models and external knowledge bases.
Research on the Development Path of Professional Teachers from the Perspective of Undergraduate Vocational Education

In view of the opportunities and challenges brought by vocational education at the undergraduate level to professional teachers in higher vocational schools, combined with the current situation of teacher development in our school, we will explore the development path of professional teachers from the perspective of front-line teachers. Firstly, it analyses the hierarchical structure of professional teacher development, and analyses the stages that are easier to break through in the hierarchical structure. Then, it establishes the triggering system for professional teacher development, and demonstrates the necessity of triggering the internal driving force of teacher development from the perspective of classroom, emotion and environment. Finally, a professional teacher development accumulation matrix is established by taking engineering measurement technology professional teachers as an example, in order to learn from and inspire other vocational teachers in the period of rapid development, and keep in mind the original intention and mission together.
Comparison of K-Nearest Neighbor and Decision Tree Methods using Principal Component Analysis Technique in Heart Disease Classification

Heart disease has become a global health issue that can threaten anyone, regardless of age. Numerous research efforts have been made to develop classification methods that can aid in diagnosing heart disease. In this study, we compared two classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree, by applying Principal Component Analysis (PCA) technique to the heart disease classification. The dataset used contains relevant clinical attributes. After analyzing the dataset and performing data preprocessing, we applied PCA to reduce the dataset's dimensions. PCA models with KNN and Decision Tree were implemented and evaluated using performance metrics such as Confusion Matrix, F1 Score, and Accuracy. The analysis results showed that the PCA model with Decision Tree outperformed the PCA model with KNN in terms of accuracy. The Decision Tree model successfully classified all data correctly, while KNN had some misclassifications. This research recommends using the PCA model with Decision Tree for heart disease classification with the best performance. However, further research with larger datasets is needed for a deeper understanding
Visual measurement of the bearing diameter based on the homography matrix and partial area effect

A measurement method based on the homography matrix and partial area effect is studied to improve the precision of the visual measurement of the bearing size. Firstly, the camera is calibrated to obtain its distortion coefficient, and the image is corrected for distortion. Secondly, the relationship between the pixel plane and the bearing imaging plane is determined by solving the homography matrix with a chessboard. Finally, the sub-pixel edge contour of the bearing is obtained through the improved partial area effect, and the pixel coordinates are mapped to the world coordinates according to the homography matrix. The feature size is obtained by fitting the mapping points. Experimental results showed that the transformation of the homography matrix is more accurate than the pixel equivalent in the process of converting the pixel size to the actual size. The improved partial area effect sub-pixel edge detection results are better than the partial area effect and Zernike moment. The measurement error for the bearing outer diameter is less than 0.05 mm and is within 0.5 pixel.
Error Correction for Discrete Tomography

Discrete tomography focuses on the reconstruction of functions from their line sums in a finite number d of directions. In this paper we consider functions f : A → R where A is a finite subset of ℤ2 and R an integral domain. Several reconstruction methods have been introduced in the literature. Recently Ceko, Pagani and Tijdeman developed a fast method to reconstruct a function with the same line sums as f. Up to here we assumed that the line sums are exact. Some authors have developed methods to recover the function f under suitable conditions by using the redundancy of data. In this paper we investigate the case where a small number of line sums are incorrect as may happen when discrete tomography is applied for data storage or transmission. We show how less than d/2 errors can be corrected and that this bound is the best possible. Moreover, we prove that if it is known that the line sums in k given directions are correct, then the line sums in every other direction can be corrected provided that the number of wrong line sums in that direction is less than k/2.
Revisiting Reverse <i>Eikos</i>: Dialectical Evaluation of a Rhetorical Argument

ABSTRACT Reverse eikos (plausibility) arguments are notorious for reversing a reason that supports an accusation into a reason that denies this accusation. This article offers new insights on their analysis and evaluation, by reconstructing a reverse eikos argument’s line of reasoning as an argumentative pattern. The pattern reveals that this type of argument centers not only on the arguer’s claim that by doing the act of which they have been accused, they would risk becoming the likely suspect, but also on the connected reasoning that they would not want to risk this since that would be stupid and they are not stupid. The proposed analysis, which is illustrated with classic and modern examples of reverse eikos arguments, shows that the evaluation of these arguments boils down to estimating the arguer’s calculation of the costs and benefits of taking the risk, while taking into account the arguer’s character, intellect, and circumstances.
An Essay on the Common Nature of Writing - Possibilities of Writing to Organize a Community of Friendship -

Today writing represents a professional and professional ability. Entering the 2000s, the Creative Writing Department separated from the Korean Literature Department institutionalized this professionalism as a study. Making a nice settlement as one of the compulsory liberal arts courses, writing has managed a standardized curriculum to cultivate critical and logical writing skills. Under the relative evaluation system, writing is close to an intellectual “skill” for students to demonstrate “themselves” as modern subjects rather than an expression of their intense and natural “internal impulses” or a conversation open to others.BR This study investigated unprofessional writing programs by independent bookstores as a base of community-based humanities to explore the possibilities of a non-identical community as a new political plan rather than the suppressive and closed traditional community. Writing programs of independent stores have just entered their starting stage. Participants in such programs choose “personal writing” to write about almost “my” stories freely, submit their works completed based on feedback from invited writers that published a book, and issue a collection of works by the group. In most cases, such programs remain at the level of holding irregular meetings by the head of a bookstore due to the lack of a budget to invite an instructor. Despite this limitation, participants, who are strangers to one another at their first meeting, share friendship, welcome each other, and cherish the significance of being together in the process of writing together. Checked through “writing for myself,” “communication” among participants that have an age gap between them like parents and their children seems like a venue where the “Inoperative Community” mentioned by Jean-Luc Nancy and Maurice Blanchot is realized. Participants open themselves to the otherness of others who are writing with the otherness of themselves in the past, writing about their stories regardless of the level of their outcomes.BR This raises a need for instructors to discard an attitude of teaching writing skills professionally and accompany their students as a “facilitator” at such meetings. This might not be an easy position for instructors that have defined themselves as writing “professionals,” but the Commons Theory argues that the artistic talent of human beings is not a personal acquisition, but a “gift from a god” inherent in the human nature. Following this perspective, a writing community will be possible through “social writing” in the future. Oral literature is a gift of commons for this.
Analysis of AR Application for Traditional Culture Teaching

Music is the common language of the world, and also one of the important ways of cultural inheritance. Traditional Chinese instrument is the essence of Chinese culture and an important part of Chinese national culture. With the development of the society, the innovation and inheritance of folk music have become an important task of our generation. This learning platform integrates traditional Chinese instrument and AR technology into primary and secondary school classes, develops and innovates new modes of audio-visual teaching, provides teachers with new educational ideas, keeps students actively involved in class, mobilizes students' multiple intelligences, stimulates learning interest and enthusiasm so as to improves learning efficiency.
Rural Education Empowerment Village Rejuvenation: Based on the Results of Rural Education Research Results in Limin Village Central School, Peace Town, Wu Dalian Chi City, Heilongjiang Province

Rural education is the foundation of rural revitalization. As a typical school representing the development of rural education, Peace Town Limin Village Central School can provide a beneficial benefit for rural education to empower rural education through in depth research and analysis. Learn and inspiration. Through combing and analyzing the research results of Limin Village Central School in Peace Town, this paper discusses the current situation and problems of rural education, and puts forward countermeasures and suggestions to improve the quality and efficiency of rural education, which plays an important role in promoting rural revitalization and realizing educational equity.
Metadata Matters: Identifying Research Communities and Estimating Author Influence in Networks

This work illustrates how clustering of author communities can be improved by using metadata about the authors (such as subject area or institutional affiliation) to supplement network structure based on co-authorship or citation relationships. Using such metadata can improve clustering significantly, both in terms of human interpretability and by allowing detection of smaller groups – but weighting metadata too highly in relation to network structure can be detrimental. The author communities uncovered may subsequently be used to better elucidate the effect of each author on their peers, and so help distinguish between influence and popularity.
Towards Unconstrained Audio Splicing Detection and Localization with Neural Networks

Freely available and easy-to-use audio editing tools make it straightforward to perform audio splicing. Convincing forgeries can be created by combining various speech samples from the same person. Detection of such splices is important both in the public sector when considering misinformation, and in a legal context to verify the integrity of evidence. Unfortunately, most existing detection algorithms for audio splicing use handcrafted features and make specific assumptions. However, criminal investigators are often faced with audio samples from unconstrained sources with unknown characteristics, which raises the need for more generally applicable methods. With this work, we aim to take a first step towards unconstrained audio splicing detection to address this need. We simulate various attack scenarios in the form of post-processing operations that may disguise splicing. We propose a Transformer sequence-to-sequence (seq2seq) network for splicing detection and localization. Our extensive evaluation shows that the proposed method outperforms existing dedicated approaches for splicing detection [3, 10] as well as the general-purpose networks EfficientNet [28] and RegNet [25]. Our source code is available at: https://www.cs1.tf.fau.de/research/multimedia-security/code
Single Image Inpainting Method Using Wasserstein Generative Adversarial Networks and Self-attention

Due to various factors, some parts of images can be lost. Recovering the damaged regions of images is essential. In this paper, a single image inpainting method using Wasserstein Generative Adversarial Networks (WGAN) and self-attention is proposed. The global consistency of the inpainting region is established and the Wasserstein distance is used to measure the similarity of the two distributions. Finally, self-attention is embedded to exploit the self-similarity of local features. The experiments confirm that the proposed method can recover the global correlation of corrupted images better than similar methods.
Linear Blur Direction Estimation Using a Convolutional Neural Network

The paper is focused on the estimation of the parameters of motion blur produced by unintended camera motion. We consider a scenario when a photo is taken using a handheld camera with relatively fast shutter speed, and motion vectors have little time to change significantly. We use linear model for the motion and propose a deep learning approach for linear blur parameter estimation at patch level using a convolutional neural network. We also introduce an aperture criterion and propose a method for constructing the training dataset by choosing the patches that meet this criterion.
Image Quality Assessment Metric Fusing Traditional and Dempster-Shafer Theory

Image analysis is being applied in many applications including industrial automation with the Industrial Internet of Things and machine vision. The images captured by cameras, from the outdoor environment are impacted by various parameters such as lens blur, dirty lens and lens distortion (barrel distortion). There exist many approaches that assess the impact of camera parameters on the quality of the images. However, most of these techniques do not use important quality assessment metrics such as Oriented FAST and Rotated BRIEF and Structural Content. None of these techniques objectively evaluate the impact of barrel distortion on the image quality using quality assessment metrics such as Mean Square Error, Peak signal-to-noise ratio, Structural Content, Oriented FAST and Rotated BRIEF and Structural Similarity Index. In this paper, besides lens dirtiness and blurring, we also examine the impact of barrel distortion using various types of dataset having different levels of barrel distortion. Analysis shows none of the existing metrics produces quality values consistent with intuitively defined impact levels for lens blur, dirtiness and barrel distortion. To address the loopholes of existing metrics and make the quality assessment metric more reliable, we present two new image quality assessment metrics. For our combined metric, results show that the maximum values of impact level created by barrel distortion, blurriness and dirtiness are 66.6%, 87.9% and 94.4%, respectively. These results demonstrate the effectiveness of our metric to assess the impact level more accurately. The second approach fuses the quality values obtained from different metrics using a decision fusion technique known as the Dempster-Shafer (DS) theory. Our metric produces quality values that are more consistent and conform with the perceptually defined camera parameter impact levels. For all above-mentioned camera impacts, our metric DS exhibits $$100\%$$ assessment reliability, which includes an enormous improvement over other metrics.
Toward Smart Doors: A Position Paper

Conventional automatic doors cannot distinguish between people wishing to pass through the door and people passing by the door, so they often open unnecessarily. This leads to the need to adopt new systems in both commercial and non-commercial environments: smart doors. In particular, a smart door system predicts the intention of people near the door based on the social context of the surrounding environment and then makes rational decisions about whether or not to open the door. This work proposes the first position paper related to smart doors, without bells and whistles. We first point out that the problem not only concerns reliability, climate control, safety, and mode of operation. Indeed, a system to predict the intention of people near the door also involves a deeper understanding of the social context of the scene through a complex combined analysis of proxemics and scene reasoning. Furthermore, we conduct an exhaustive literature review about automatic doors, providing a novel system formulation. Also, we present an analysis of the possible future application of smart doors, a description of the ethical shortcomings, and legislative issues.
Analogs of Image Analysis Tools in the Search of Latent Regularities in Applied Data

The problem of detecting hidden latent regularities in large-scale applied data is investigated. A new approach is presented, where the possibilities of using multidimensional analogs of image processing and understanding methods adapted for higher dimensions are studied. Several promising options for combining practical competencies while solving applied problems from these positions are presented, and some prospects for further development of the approach are outlined.
IL PERCORSO EVOLUTIVO DEI TEMPI COMPOSTI CON L’AUSILIARE „AVERE“ NEI TESTI ITALIANI DEL XIII E DEL XIV SECOLO

Complex changes actualized during the linguistic transformation of Vulgar Latin into modern Italian are often explained in terms of replacement of the synthetic structure of the former by the analytic form of the latter. Although defining any linguistic system as entirely analytic or synthetic could easily be deemed an oversimplification, it is reasonable to claim that the introduction of analytic structures into a system that had previously been void of them possesses the power to significantly modify the parameters of its morphosyntactic functioning. The genesis of innovative forms of Italian complex verb tenses has substantially transformed the primary aspects of phenomena such as word order and organization of clause constituents. This research aims to analyze the examples of complex tenses present in prose texts dating from the earliest developmental phase of the Italian system, in order to illustrate how the grammaticalization process which resulted in analytic verbal tenses influenced a wide array of changes in the Italian language system.