Reviewerly/exHarmony huggingface dataset:
Solar and Wind Quantity 24 h—Series Prediction Using PDE-Modular Models Gradually Developed according to Spatial Pattern Similarity

The design and implementation of efficient photovoltaic (PV) plants and wind farms require a precise analysis and definition of specifics in the region of interest. Reliable Artificial Intelligence (AI) models can recognize long-term spatial and temporal variability, including anomalies in solar and wind patterns, which are necessary to estimate the generation capacity and configuration parameters of PV panels and wind turbines. The proposed 24 h planning of renewable energy (RE) production involves an initial reassessment of the optimal day data records based on the spatial pattern similarity in the latest hours and their follow-up statistical AI learning. Conventional measurements comprise a larger territory to allow the development of robust models representing unsettled meteorological situations and their significant changes from a comprehensive aspect, which becomes essential in middle-term time horizons. Differential learning is a new unconventionally designed neurocomputing strategy that combines differentiated modules composed of selected binomial network nodes as the output sum. This approach, based on solutions of partial differential equations (PDEs) defined in selected nodes, enables us to comprise high uncertainty in nonlinear chaotic patterns, contingent upon RE local potential, without an undesirable reduction in data dimensionality. The form of back-produced modular compounds in PDE models is directly related to the complexity of large-scale data patterns used in training to avoid problem simplification. The preidentified day-sample series are reassessed secondary to the training applicability, one by one, to better characterize pattern progress. Applicable phase or frequency parameters (e.g., azimuth, temperature, radiation, etc.) are related to the amplitudes at each time to determine and solve particular node PDEs in a complex form of the periodic sine/cosine components. The proposed improvements contribute to better performance of the AI modular concept of PDE models, a cable to represent the dynamics of complex systems. The results are compared with the recent deep learning strategy. Both methods show a high approximation ability in radiation ramping events, often in PV power supply; moreover, differential learning provides more stable wind gust predictions without undesirable alterations in day errors, namely in over-break frontal fluctuations. Their day average percentage approximation of similarity correlation on real data is 87.8 and 88.1% in global radiation day-cycles and 46.7 and 36.3% in wind speed 24 h. series. A parametric C++ executable program with complete spatial metadata records for one month is available for free to enable another comparative evaluation of the conducted experiments.
Multi-Source Interactive Stair Attention for Remote Sensing Image Captioning

The aim of remote sensing image captioning (RSIC) is to describe a given remote sensing image (RSI) using coherent sentences. Most existing attention-based methods model the coherence through an LSTM-based decoder, which dynamically infers a word vector from preceding sentences. However, these methods are indirectly guided through the confusion of attentive regions, as (1) the weighted average in the attention mechanism distracts the word vector from capturing pertinent visual regions and (2) there are few constraints or rewards for learning long-range transitions. In this paper, we propose a multi-source interactive stair attention mechanism that separately models the semantics of preceding sentences and visual regions of interest. Specifically, the multi-source interaction takes previous semantic vectors as queries and applies an attention mechanism on regional features to acquire the next word vector, which reduces immediate hesitation by considering linguistics. The stair attention divides the attentive weights into three levels—that is, the core region, the surrounding region, and other regions—and all regions in the search scope are focused on differently. Then, a CIDEr-based reward reinforcement learning is devised, in order to enhance the quality of the generated sentences. Comprehensive experiments on widely used benchmarks (i.e., the Sydney-Captions, UCM-Captions, and RSICD data sets) demonstrate the superiority of the proposed model over state-of-the-art models, in terms of its coherence, while maintaining high accuracy.
BERT-Based Joint Model for Aspect Term Extraction and Aspect Polarity Detection in Arabic Text

Aspect-based sentiment analysis (ABSA) is a method used to identify the aspects discussed in a given text and determine the sentiment expressed towards each aspect. This can help provide a more fine-grained understanding of the opinions expressed in the text. The majority of Arabic ABSA techniques in use today significantly rely on repeated pre-processing and feature-engineering operations, as well as the use of outside resources (e.g., lexicons). In essence, there is a significant research gap in NLP with regard to the use of transfer learning (TL) techniques and language models for aspect term extraction (ATE) and aspect polarity detection (APD) in Arabic text. While TL has proven to be an effective approach for a variety of NLP tasks in other languages, its use in the context of Arabic has been relatively under-explored. This paper aims to address this gap by presenting a TL-based approach for ATE and APD in Arabic, leveraging the knowledge and capabilities of previously trained language models. The Arabic base (Arabic version) of the BERT model serves as the foundation for the suggested models. Different BERT implementations are also contrasted. A reference ABSA dataset was used for the experiments (HAAD dataset). The experimental results demonstrate that our models surpass the baseline model and previously proposed approaches.
Sentiment enhanced answer generation and information fusing for product-related question answering

The reviews written by users in E-commerce platform have been fully exploited by product-related question answering systems, which ignore the product descriptions with valuable information to improve the system performance, especially for objective questions. In this paper, we propose a model named as Sentiment-enhanced Answer Generation and product Descriptions Fusing (SAGDF) for Product-related Question Answering task. The valid information from user reviews and product descriptions are learned jointly for answer generation. Importantly, the sentiment analysis task is performed initially to make the model judge the sentiment tendency correctly for answer accuracy. The review encoder of the model is fine-tuned on the large-scale Amazon review dataset to implement the sentiment analysis task. In addition, we build the dataset named AmazonQARD and it contains both reviews and corresponding product descriptions. The proposed model is trained on AmazonQARD and it processes user reviews individually and integrates product descriptions to constantly correct the generated answers iteratively. Experimental results demonstrate that SAGDF significantly outperforms the other baseline systems.
Design and Evaluation for Immersive Virtual Reality Learning Environment: A Systematic Literature Review

This systematic review broadly attempted to synthesize all relevant evidence residing in the Scopus, IEEE Xplore and MDPI databases, in order to inform the related Research Questions of this work. More precisely, the review protocol includes a broad and comprehensive search for eligible data sets from the Scopus, IEEE Xplore and MDPI databases, published from January 2017 to December 2022 by using inclusion/exclusion search criteria. Medical Education Research Study Quality Instrument (MERSQI) was commissioned to assess and analyze the quality of 69 quantitative studies. The findings generally received positive feedback and there was a discussion about the results. This work was an original contribution guided by pedagogical theory and the validity of the evaluation constitutes a proposal for future improvement.
<scp>Metaverse‐Based</scp> Learning Through Children's School Space Design

Abstract Metaverse is understood with technological terms such as augmented reality, lifelogging, mirrored worlds, and virtual worlds, and using a metaverse platform is considered an emerging form of learning. Yet, its pedagogical features, such as self‐learning, collaborative learning, and learning‐by‐doing, are identical to those of online learning. In this study, we argue that metaverse‐based learning refers to a new reality learners create by assuming that the existence of reality is different in the metaverse. We extracted six educational values in epistemological and ontological senses, reviewing the four realities of the metaverse, including virtual reality, augmented reality, mixed reality, and extended reality, which is a way to avoid technological determinists'; approach to education. Based on the values – bottom‐up, collaboration, authorship, ownership, interconnectivity, and community, primary school children's (year 5 &amp; 6) groups ( n = 20) redesigned 20 assigned school spaces such as classroom teacher office, science room, and music room on their chosen metaverse platforms. Throughout the thematic analysis of design artifacts, activity scenarios, and reflection &amp;amp; presentation, we discovered four themes: collaborative learning with co‐ownership and co‐authorship; being interconnected with all living and non‐living things; co‐participants with different roles; and transdisciplinary research‐driven learning, which is used to articulate a framework for metaverse‐based learning, which is different from online learning.
Cognitive and motivational benefits of a theory-based immersive virtual reality design in science learning

This study investigated the effects of an immersive virtual reality (IVR) nature-trail tour on participants' science learning, self-efficacy, cognitive load, perceived enjoyment, and perceived usefulness, as compared to actual walking tours. The IVR tour was designed based on the Cognitive Theory of Multimedia Learning. In a between-subjects quasi-experiment, participants learned environmental science topics in one of three types of nature-trail tours, including an IVR tour, a business-as-usual walking tour, and an enhanced walking tour. Results of analyses of covariance indicated that the theory-based IVR design was effective in improving participants' science learning and their self-efficacy perceptions. At the same time, the IVR tour was found to be as enjoyable as the walking tours and did not pose an unnecessary cognitive load during the learning process. The results have implications for designing IVR environments to (1) enhance cognitive and motivational outcomes in science learning and (2) increase the accessibility of nature-based sites.
New quantum codes from CSS codes

Abstract We present a new propagation rule for CSS codes. Starting with a CSS code $$[\![n,k,d]\!]_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>[</mml:mo> <mml:mspace /> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>,</mml:mo> <mml:mi>d</mml:mi> <mml:mo>]</mml:mo> </mml:mrow> <mml:mspace /> <mml:mo>]</mml:mo> </mml:mrow> <mml:mi>q</mml:mi> </mml:msub> </mml:math> , we construct a CSS code with parameters $$[\![n-2,k,d-1]\!]_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>[</mml:mo> <mml:mspace /> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>,</mml:mo> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>1</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mspace /> <mml:mo>]</mml:mo> </mml:mrow> <mml:mi>q</mml:mi> </mml:msub> </mml:math> . In general, one would only obtain a code with parameters $$[\![n-2,k,d-2]\!]_q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow> <mml:mo>[</mml:mo> <mml:mspace /> <mml:mrow> <mml:mo>[</mml:mo> <mml:mi>n</mml:mi> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> <mml:mo>,</mml:mo> <mml:mi>k</mml:mi> <mml:mo>,</mml:mo> <mml:mi>d</mml:mi> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> <mml:mo>]</mml:mo> </mml:mrow> <mml:mspace /> <mml:mo>]</mml:mo> </mml:mrow> <mml:mi>q</mml:mi> </mml:msub> </mml:math> . The construction applies to asymmetric quantum codes from the CSS construction as well.
Computer-Aided Diagnosis System for Blood Diseases Using EfficientNet-B3 Based on a Dynamic Learning Algorithm

The immune system's overproduction of white blood cells (WBCs) results in the most common blood cancer, leukemia. It accounts for about 25% of childhood cancers and is one of the primary causes of death worldwide. The most well-known type of leukemia found in the human bone marrow is acute lymphoblastic leukemia (ALL). It is a disease that affects the bone marrow and kills white blood cells. Better treatment and a higher likelihood of survival can be helped by early and precise cancer detection. As a result, doctors can use computer-aided diagnostic (CAD) models to detect early leukemia effectively. In this research, we proposed a classification model based on the EfficientNet-B3 convolutional neural network (CNN) model to distinguish ALL as an automated model that automatically changes the learning rate (LR). We set up a custom LR that compared the loss value and training accuracy at the beginning of each epoch. We evaluated the proposed model on the C-NMC_Leukemia dataset. The dataset was pre-processed with normalization and balancing. The proposed model was evaluated and compared with recent classifiers. The proposed model's average precision, recall, specificity, accuracy, and Disc similarity coefficient (DSC) were 98.29%, 97.83%, 97.82%, 98.31%, and 98.05%, respectively. Moreover, the proposed model was used to examine microscopic images of the blood to identify the malaria parasite. Our proposed model's average precision, recall, specificity, accuracy, and DSC were 97.69%, 97.68%, 97.67%, 97.68%, and 97.68%, respectively. Therefore, the evaluation of the proposed model showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing models.
A Secure and Lightweight Multi-Party Private Intersection-Sum Scheme over a Symmetric Cryptosystem

A private intersection-sum (PIS) scheme considers the private computing problem of how parties jointly compute the sum of associated values in the set intersection. In scenarios such as electronic voting, corporate credit investigation, and ad conversions, private data are held by different parties. However, despite two-party PIS being well-developed in many previous works, its extended version, multi-party PIS, has rarely been discussed thus far. This is because, depending on the existing works, directly initiating multiple two-party PIS instances is considered to be a straightforward way to achieve multi-party PIS; however, by using this approach, the intersection-sum results of the two parties and the data only belonging to the two-party intersection will be leaked. Therefore, achieving secure multi-party PIS is still a challenge. In this paper, we propose a secure and lightweight multi-party private intersection-sum scheme called SLMP-PIS. We maintain data privacy based on zero sharing and oblivious pseudorandom functions to compute the multi-party intersection and consider the privacy of associated values using arithmetic sharing and symmetric encryption. The security analysis results show that our protocol is proven to be secure in the standard semi-honest security model. In addition, the experiment results demonstrate that our scheme is efficient and feasible in practice. Specifically, when the number of participants is five, the efficiency can be increased by 22.98%.
Scale in Scale for SAR Ship Instance Segmentation

Ship instance segmentation in synthetic aperture radar (SAR) images can provide more detailed location information and shape information, which is of great significance for port ship scheduling and traffic management. However, there is little research work on SAR ship instance segmentation, and the general accuracy is low because the characteristics of target SAR ship task, such as multi-scale, ship aspect ratio, and noise interference, are not considered. In order to solve these problems, we propose an idea of scale in scale (SIS) for SAR ship instance segmentation. Its essence is to establish multi-scale modes in a single scale. In consideration of the characteristic of the targeted SAR ship instance segmentation task, SIS is equipped with four tentative modes in this paper, i.e., an input mode, a backbone mode, an RPN mode (region proposal network), and an ROI mode (region of interest). The input mode establishes multi-scale inputs in a single scale. The backbone mode enhances the ability to extract multi-scale features. The RPN mode makes bounding boxes better accord with ship aspect ratios. The ROI mode expands the receptive field. Combined with them, a SIS network (SISNet) is reported, dedicated to high-quality SAR ship instance segmentation on the basis of the prevailing Mask R-CNN framework. For Mask R-CNN, we also redesign (1) its feature pyramid network (FPN) for better small ship detection and (2) its detection head (DH) for a more refined box regression. We conduct extensive experiments to verify the effectiveness of SISNet on the open SSDD and HRSID datasets. The experimental results reveal that SISNet surpasses the other nine competitive models. Specifically, the segmentation average precision (AP) index is superior to the suboptimal model by 4.4% on SSDD and 2.5% on HRSID.
Use of virtual reality compared to the role-playing methodology in basic life support training: a two-arm pilot community-based randomised trial

Virtual reality (VR) is a technology that allows us to replace our real environment with one created with digital media. This technology is increasingly used in the training of healthcare professionals, and previous studies show that the involvement and motivation of students who participate in activities that use VR increases compared to those who undergo training with the traditional methodology. The main aim of the study is to evaluate the learning curve of the students using a VR environment, to evaluate the satisfaction with the training activity and the cost, and to compare them with training that uses role-playing methodology.Two-arm community-based randomised trial. The control arm will base the training on the usual role-playing methodology. The second arm or intervention arm will base the Basic Life Support (BLS) training on a VR programme.Statistically significant differences are observed in the percentage of correct answers in favour of the group that used VR as a learning methodology in the test taken at the end of the course. These differences disappear when comparing the results of the test performed at six months. The satisfaction rating of the role-playing training activity has a score of 9.37 out of a total of 10 and satisfaction with the VR methodology has a score of 9.72. The cost analysis shows that the cost of training a student by role-playing is 32.5 euros and, if trained by VR, it is 41.6 euros.VR is a tool that allows the consolidation of a greater amount of knowledge in the short term and can be used for situations such as pandemics, where traditional formats are not available. In relation to student satisfaction with the training activity, the rating in both groups is very high and the differences are minimal. The results will be directly applicable to the decision making of BLS training in Central Catalonia in relation to the scheduling of training activities that use the VR methodology in an uncertain environment.
The role of perspective-taking on empowering the empathetic behavior of educators in VR-based training sessions: An experimental evaluation

VR-based training approaches can be used to empower the empathetic behavior of educators, raising their awareness and understanding of their learners, particularly in an era of social isolation and distancing. This paper presents research related to the use of VR as a tool for the practical training of educators aiming to empower their empathy skills through perspective-taking. Educator trainees participated in an experimental evaluation that involved exposure and interaction in a virtual classroom environment. Participants were divided into three groups; members of the first group experienced only the educator's perspective while members of the other two groups experienced both the educator's perspective and the perspective of students facing issues that may affect their academic performance and/or their integration in the school community. A thorough statistical analysis of the pre and post-experiment questionnaire responses, using ANOVA, indicated a significant impact of the VR intervention only for the two groups that experienced both the educator and student perspective. The results indicate the high added value of using VR with perspective-changing to strengthen empathetic behavior during the educator's training sessions.
Improving mathematics assessment readability: Do large language models help?

Abstract Background Readability metrics provide us with an objective and efficient way to assess the quality of educational texts. We can use the readability measures for finding assessment items that are difficult to read for a given grade level. Hard‐to‐read math word problems can put some students at a disadvantage if they are behind in their literacy learning. Despite their math abilities, these students can perform poorly on difficult‐to‐read word problems because of their poor reading skills. Less readable math tests can create equity issues for students who are relatively new to the language of assessment. Less readable test items can also affect the assessment's construct validity by partially measuring reading comprehension. Objectives This study shows how large language models help us improve the readability of math assessment items. Methods We analysed 250 test items from grades 3 to 5 of EngageNY, an open‐source curriculum. We used the GPT‐3 AI system to simplify the text of these math word problems. We used text prompts and the few‐shot learning method for the simplification task. Results and Conclusions On average, GPT‐3 AI produced output passages that showed improvements in readability metrics, but the outputs had a large amount of noise and were often unrelated to the input. We used thresholds over text similarity metrics and changes in readability measures to filter out the noise. We found meaningful simplifications that can be given to item authors as suggestions for improvement. Takeaways GPT‐3 AI is capable of simplifying hard‐to‐read math word problems. The model generates noisy simplifications using text prompts or few‐shot learning methods. The noise can be filtered using text similarity and readability measures. The meaningful simplifications AI produces are sound but not ready to be used as a direct replacement for the original items. To improve test quality, simplifications can be suggested to item authors at the time of digital question authoring.