Reviewerly/exHarmony huggingface dataset:
Utility-Scale Solar Power Forecasting: Single-Step vs. Multi-Step Feed Forward Neural Networks

Photovoltaic (PV) power forecasting serves as an important tool to manage the intermittent nature of PV-system power supply. In recent years, machine learning based forecast models have proven to be an effective solution to reduce the uncertainties associated with PV power. Typically, from a time-series forecasting perspective, practitioners either deploy a single-step or multi-step forecasting methodology. Single-step forecast models predict a value for a single point in time, whereas multi-step models predict multiple future values at once for a range of time. From literature, it is not evident which of these two approaches are more advantageous for an intra-day (1–6 h) PV power forecasting scenario. Given the high computational cost of single-step forecasting solutions, compared to multi-step solutions, it is important to understand the difference in performance of these two approaches. Results are generated for a real-world forecasting scenario, where the well-established Feedforward Neural Network (FFNN) model is used to deliver forecasts for a 75 MW rated PV system. From the overall results, both single-step and multi-step FFNNs delivered forecasting errors within 2 % of one another, with single-step models proven to have an improved ability to capture the complex PV system dynamics. However, from an engineering perspective, it is concluded that multi-step forecasting is a more effective forecasting solution when the available processing power and time is limited. The findings of this study have implications for energy producers and practitioners in the field of solar PV forecasting.
A Contrastive Model with Local Factor Clustering for Semi-Supervised Few-Shot Learning

Learning novel classes with a few samples per class is a very challenging task in deep learning. To mitigate this issue, previous studies have utilized an additional dataset with extensively labeled samples to realize transfer learning. Alternatively, many studies have used unlabeled samples that originated from the novel dataset to achieve few-shot learning, i.e., semi-supervised few-shot learning. In this paper, an easy but efficient semi-supervised few-shot learning model is proposed to address the embeddings mismatch problem that results from inconsistent data distributions between the novel and base datasets, where samples with the same label approach each other while samples with different labels separate from each other in the feature space. This model emphasizes pseudo-labeling guided contrastive learning. We also develop a novel local factor clustering module to improve the ability to obtain pseudo-labels from unlabeled samples, and this module fuses the local feature information of labeled and unlabeled samples. We report our experimental results on the mini-ImageNet and tiered-ImageNet datasets for both five-way one-shot and five-way five-shot settings and achieve better performance than previous models. In particular, the classification accuracy of our model is improved by approximately 11.53% and 14.87% compared to the most advanced semi-supervised few-shot learning model we know in the five-way one-shot scenario. Moreover, ablation experiments in this paper show that our proposed clustering strategy demonstrates accuracy improvements of about 4.00% in the five-way one-shot and five-way five-shot scenarios compared to two popular clustering methods.
WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering

Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations, these methods however implicitly deprioritize the modeling of user-wise similarity in the embedding space; consequently, identifying similar users is underperforming, and additional processing schemes are usually required otherwise. To avoid thorough model re-training, we propose WSFE, a model-agnostic and training-free representation encoder, to be flexibly employed on the fly for effective user segmentation. Underpinned by the optimal transport theory, the encoded representations from WSFE present a matched user-wise similarity/distance measurement between the realistic and embedding space. We incorporate WSFE into six state-of-the-art recommender models and conduct extensive experiments on six real-world datasets. The empirical analyses well demonstrate the superiority and generality of WSFE to fuel multiple downstream tasks with diverse underlying targets in recommendation.
Autoencoders for Anomaly Detection in Electricity and District Heating Consumption: A Case Study in School Buildings in Sweden

Real-time anomaly detection in real-time energy consumption helps identify the technical and infrastructure issues and events that result in significant energy waste and provides the end users feedback to address the issues, which reduces drift operation costs and saves energy in the building. This study proposes deep learning reconstruction models to detect anomalies in daily energy consumption data for nine school buildings. We evaluated the performance of three proposed models, stacked RNN-LSTM autoencoder, CNN-LSTM autoencoder, and LSTM Variational Autoencoder (VAE), to learn the features of normal consumption in an unsupervised manner and detect anomalies based on reconstruction error. We used Exponential Moving Average (EMA) and static threshold to detect local and global anomalies. The experimental results demonstrate that the local CNN-LSTM autoencoder performs better than the local Stacked Autoencoder(AE), with RMSE values ranging between 8-13% for electricity and 11-19% for district heating compared to 12-17% and 15-34% resulting from AE model, respectively. Local LSTM-Variational Autoencoder (VAE) outperformed both methods, with RMSE 4-6% for electricity and 5-7% for district heating. LSTM-VAE trained model on grouped training datasets of schools with similar energy consumption and building profiles has improved the local model by lowering RMSE values to 2-3% for electricity and 3-4% in district heating.
User-Dependent Learning to Debias for Recommendation

In recommender systems (RSs), inverse propensity score (IPS) has been a key technique to mitigate popularity bias by decreasing the contribution of popular items in modeling user-item interactions. However, conventional IPS treats all users equally, which tends to over-debias the popularity-insensitive (PI) users and under-debias the popularity-sensitive (PS) users. Furthermore, in such a treatment, IPS only performs slightly well on the debiased test while does not work on the normal biased test. To this end, we propose a user-dependent IPS (UDIPS in short) method, which adaptively conducts propensity estimation for each user-item pair based on the user's sensitivity to item popularity. Like IPS, our theoretical analysis validates the unbiasedness of UDIPS. Remarkably, our solution is model-agnostic and can be easily used to upgrade current unbiased recommenders. We implemented it in four state-of-the-art models for unbiased recommendation, and experimental results on two benchmark datasets demonstrate the effectiveness of our method in both unbiased and normal biased test.
SynthStrip: skull stripping for any brain image

The removal of non-brain signal from MR data is an integral component of neuroimaging streams. However, popular skull-stripping utilities are typically tailored to isotropic T1-weighted scans and tend to fail, sometimes catastrophically, on images with other MRI contrasts or stack-of-slices acquisitions that are common in the clinic. We propose SynthStrip , a flexible tool that produces highly accurate brain masks across a landscape of neuroimaging data with widely varying contrast and resolution. We implement our method by leveraging anatomical label maps to synthesize a broad set of training images, optimizing a robust convolutional network agnostic to MRI contrast and acquisition scheme.
IntME: Combined Improving Feature Interactions and Matrix Multiplication for Convolution-Based Knowledge Graph Embedding

Knowledge graph embedding is a method to predict missing links in knowledge graphs by learning the interactions between embedded entities and relations in a continuous low-dimensional space. Current research on convolution-based models tends to provide sufficient interactions for extracting potential knowledge. However, sufficient interactions do not mean that they are reasonable. Our studies find that reasonable interactions can further stimulate knowledge extraction capability. Reasonable interactions need to ensure that the elements participating in interactions are disordered and in a reasonable number. To model reasonable interactions that cannot be specifically quantified, we propose a concise and effective model IntME to address this challenge. In detail, we utilize checked feature reshaping and disordered matrix multiplication to form two different types of feature maps to ensure the disorder of the interacting elements and control the number of elements before feature fusion by the shapes of the feature maps after channel scaling reshaping. In feature fusion, we employ large convolution filters and pointwise filters for the deep and shallow linear fusion of feature interactions, which can take into account both explicit and implicit knowledge extraction capability. The evaluations of four benchmark datasets show that IntME has a powerful performance in convolution-based models and a lower training cost, and also demonstrate that our proposed approaches based on reasonable interactions can effectively improve knowledge discovery capability.
Centrifugal Navigation-Based Emotion Computation Framework of Bilingual Short Texts with Emoji Symbols

Heterogeneous corpora including Chinese, English, and emoji symbols are increasing on platforms. Previous sentiment analysis models are unable to calculate emotional scores of heterogeneous corpora. They also struggle to effectively fuse emotional tendencies of these corpora with the emotional fluctuation, generating low accuracy of tendency prediction and score calculation. For these problems, this paper proposes a Centrifugal Navigation-Based Emotional Computation framework (CNEC). CNEC adopts Emotional Orientation of Related Words (EORW) to calculate scores of unknown Chinese/English words and emoji symbols. In EORW, t neighbor words of the predicted sample from one element in the short text are selected from a sentiment dictionary according to spatial distance, and related words are extracted using the emotional dominance principle from the t neighbor words. Emotional scores of related words are fused to calculate scores of the predicted sample. Furthermore, CNEC utilizes Centrifugal Navigation-Based Emotional Fusion (CNEF) to achieve the emotional fusion of heterogeneous corpora. In CNEF, how the emotional fluctuation occurs is illustrated by the trigger angle of centrifugal motion in physical theory. In light of the corresponding relationship between the trigger angle and conditions of the emotional fluctuation, the fluctuation position is determined. Lastly, emotional fusion with emotional fluctuation is carried out by a CNEF function, which considers the fluctuation position as a significant position. Experiments demonstrate that the proposed CNEC effectively computes emotional scores for bilingual short texts with emojis on the Weibo dataset collected.
THE INFLUENCE OF PERCEPTION ON LEARNING TECHNIQUES AND VOCABULARY MASTERY TOWARD ENGLISH WRITING ABILITY (Survey on Students of Public Vocational High School in Central Jakarta)

&lt;p align="justify"&gt;&lt;strong&gt;Abstract: The Influence of Perceptions &lt;/strong&gt;&lt;strong&gt;on&lt;/strong&gt;&lt;strong&gt; Learning Techniques and Vocabulary Mastery &lt;/strong&gt;&lt;strong&gt;toward&lt;/strong&gt;&lt;strong&gt; English Writing Ability (Survey on &lt;/strong&gt;&lt;strong&gt;Students&lt;/strong&gt;&lt;strong&gt; of &lt;/strong&gt;&lt;strong&gt;Public &lt;/strong&gt;&lt;strong&gt;Vocational &lt;/strong&gt;&lt;strong&gt;High School in Central Jakarta).&lt;/strong&gt; The purpose of this study was to determine the effect of learning technique and vocabulary mastery on the English writing ability of students of XI grade of Public Vocational High School (SMK Negeri) in Jakarta Pusata. The research method used is quantitative research with correlational survey research techniques. The analysis technique used in this research is Path Analysis. The population in this study was students of XI grade of Public Vocational High School (SMK Negeri) in Central Jakarta. The sample in this study was 110 students. The results of the study prove that: (1) there is an effect of perception of learning technique on English writing ability as indicated by the t-count = 1.865 (2) there is an effect of perception on vocabulary mastery on English writing ability as indicated by the t-count value = 16,733 (3) there is an effect of perception on learning technique and vocabulary mastery together on the ability to write English as indicated by the value of F-table = 161.202. The results of this study can provide input to vocational level teachers, especially English teachers to improve their competence in order to develop learning techniques and improve students’ vocabulary mastery, so as to optimize students' English writing ability.&lt;/p&gt;&lt;p align="justify"&gt; &lt;/p&gt;&lt;p&gt;Keywords: learning techniques, vocabulary mastery, and writing ability&lt;/p&gt;
A Multi-Scale Hybrid Attention Network for Sentence Segmentation Line Detection in Dongba Scripture

Dongba scripture sentence segmentation is an important and basic work in the digitization and machine translation of Dongba scripture. Dongba scripture sentence segmentation line detection (DS-SSLD) as a core technology of Dongba scripture sentence segmentation is a challenging task due to its own distinctiveness, such as high inherent noise interference and nonstandard sentence segmentation lines. Recently, projection-based methods have been adopted. However, these methods are difficult when dealing with the following two problems. The first is the noisy problem, where a large number of noise in the Dongba scripture image interference detection results. The second is the Dongba scripture inherent characteristics, where many vertical lines in Dongba hieroglyphs are easily confused with the vertical sentence segmentation lines. Therefore, this paper aims to propose a module based on the convolutional neural network (CNN) to improve the accuracy of DS-SSLD. To achieve this, we first construct a tagged dataset for training and testing DS-SSLD, including 2504 real images collected from Dongba scripture books and sentence segmentation targets. Then, we propose a multi-scale hybrid attention network (Multi-HAN) based on YOLOv5s, where a multiple hybrid attention unit (MHAU) is used to enhance the distinction between important features and redundant features, and the multi-scale cross-stage partial unit (Multi-CSPU) is used to realize multi-scale and richer feature representation. The experiment is carried out on the Dongba scripture sentence segmentation dataset we built. The experimental results show that the proposed method exhibits excellent detection performance and outperforms several state-of-the-art methods.
MOTIVE AND CHARACTER EDUCATIONAL VALUES IN NOVEL THE INVENTION OF HUGO CABRET BY BRIAN SELZNICK

&lt;p&gt;This is a descriptive qualitative research that uses a psychological approach. The data source of this research is the novel The Invention of Hugo Cabret by Brian Selznick, publisher of Scholastic Press, New York (original version) in 2007, Mizan Fantasi (Indonesian version) in 2012. The research data are in the form of words, phrases and sentences that related with the main character's motives and the character education values in the novel. Data obtained by reading and note taking techniques. This objective of this study to analyze the existence of the main character's action motives and the value of character education in the novel The Invention of Hugo Cabret by Brian Selznick. The results of the analysis obtained the following conclusions: 1. Hugo's motives as the main character there are 20 quotes, namely as follows: a) motives for physical needs there are 3 quotations, b) motives for security and safety needs are 4 quotations, c) motives for trust and compassion consist from 5 quotations, d) the motive for self-esteem needs there are 3 quotations, e). The motives for self-actualization needs are 6 quotations. The prominent action motive is the motive for self-actualization needs. 2. The value of character education contained in Brian's novel The Invention of Hugo Cabret, consisting of; a) religious 2 quotes, b) honest 4 quotes, c) discipline 3 quotes, d) hard work 7 quotes, e) creative 2 quotes, f) independent 1 quote, g) curiosity 4 quotes, h) appreciate achievement 2 quotes, i) friends 3 quotes, j) peace love 1 quote, k) likes to read 2 quotes, l) social care 4 quotes, m) responsibility 2 quotes.&lt;/p&gt;
Hybrid supervised and self-supervised deep learning for quantitative mapping from weighted images using low-resolution labels

Deep learning methods have been developed to estimate quantitative maps from conventional weighted images, which has the potential to improve the availability and clinical impact of quantitative MRI. However, high-resolution labels required for network training are not commonly available in practice. In this work, a hybrid supervised and physics-informed self-supervised loss function was proposed to train parameter estimation networks when only limited low-resolution labels are accessible. By taking advantage of high-resolution information from the input weighted images, the proposed method generated sharp quantitative maps and had improved performance over the supervised training method purely relying on the low-resolution labels.
The Potential of AI-Based Clinical Text Mining to Improve Patient Safety: the Case of Implant Terms and Patient Journals

It is important for radiologists to know in advance if a patient has an implant, since MR-scanning is incompatible with some implants. At present, the unbiased process to ascertain whether a patient could be at risk is manual and not entirely reliable. We argue that this process can be enhanced and accelerated using AI-based clinical text-mining. We therefore investigated the automatic discovery of medical implant terms in electronic-medical-records (EMRs) written in Swedish using an AI-based text mining algorithm called BERT. BERT is a state-of-the-art language model trained using a deep learning algorithm based on transformers. Results are promising.
UniRoVE: Unified Robot Virtual Environment Framework

With robotics applications playing an increasingly significant role in our daily lives, it is crucial to develop effective methods for teaching and understanding their behavior. However, limited access to physical robots in educational institutions and companies poses a significant obstacle for many individuals. To overcome this barrier, a novel framework that combines realistic robot simulation and intuitive control mechanisms within a virtual reality environment is presented. By accurately emulating the physical characteristics and behaviors of various robots, this framework offers an immersive and authentic learning experience. Through an intuitive control interface, users can interact naturally with virtual robots, facilitating the acquisition of practical robotics skills. In this study, a qualitative assessment to evaluate the effectiveness and user satisfaction with the framework is conducted. The results highlighted its usability, realism, and educational value. Specifically, the framework bridges the gap between theoretical knowledge and practical application in robotics, enabling users to gain hands-on experience and develop a deeper understanding of robot behavior and control strategies. Compared to existing approaches, the framework provides a more accessible and effective alternative for interacting with robots, particularly for individuals with limited physical access to such devices. In conclusion, the study presents a comprehensive framework that leverages virtual reality technology to enhance the learning and training process in robotics. By combining realistic simulations and intuitive controls, this framework represents a significant advancement in providing an immersive and effective learning environment. The positive user feedback obtained from the study reinforces the value and potential of the framework in facilitating the acquisition of essential robotics skills. Ultimately, this work contributes to flattening the robotics learning curve and promoting broader access to robotics education.
Distance Metric Optimization-Driven Neural Network Learning Framework for Pattern Classification

As a novel neural network learning framework, Twin Extreme Learning Machine (TELM) has received extensive attention and research in the field of machine learning. However, TELM is affected by noise or outliers in practical applications so that its generalization performance is reduced compared to robust learning algorithms. In this paper, we propose two novel distance metric optimization-driven robust twin extreme learning machine learning frameworks for pattern classification, namely, CWTELM and FCWTELM. By introducing the robust Welsch loss function and capped L2,p-distance metric, our methods reduce the effect of outliers and improve the generalization performance of the model compared to TELM. In addition, two efficient iterative algorithms are designed to solve the challenges brought by the non-convex optimization problems CWTELM and FCWTELM, and we theoretically guarantee their convergence, local optimality, and computational complexity. Then, the proposed algorithms are compared with five other classical algorithms under different noise and different datasets, and the statistical detection analysis is implemented. Finally, we conclude that our algorithm has excellent robustness and classification performance.
Optical Characterization of Materials for Precision Reference Spheres for Use with Structured Light Sensors

Traditionally, 3D digitizing sensors have been based on contact measurement. Given the disadvantages of this type of measurement, non-contact sensors such as structured light sensors have gained the attention of many sectors in recent years. The fact that their metrological performance is affected by the optical properties of the digitized material, together with the lack of standards, makes it necessary to develop characterization work to validate materials and calibration artifacts for the qualification and calibration of these sensors. This work compares and optically characterizes different materials and surface finishes of reference spheres used in the calibration of two structured light sensors with different fields of application, with the aim to determine the most suitable sphere material–sensor combination in each case. The contact measurement system of a CMM is used as a reference and, for the processing of the information from the sensors, the application of two different filters is analyzed. The results achieved point to sandblasted stainless steel spheres as the best choice for calibrating or qualifying these sensors, as well as for use as registration targets in digitizing. Tungsten carbide spheres and zirconium are unsuitable for this purpose.