Reviewerly/exHarmony huggingface dataset:
The spatial distribution of China's solar energy resources and the optimum tilt angle and power generation potential of PV systems

This study aims at filling the gaps and completing the missing global solar radiation and diffuse solar radiation data considering the limited radiation data of 85 meteorological observation stations and the routine meteorological data of 2001 meteorological observation stations. Employing the Inverse Distance Weighing model, the annual and seasonal spatial distributions of global solar radiation and diffuse solar radiation are obtained. A calculation method of the optimum tilt angle of photovoltaic systems for optimal utilization is established using the Collares-Pereira and Rabl hourly solar radiation model and the solar radiation on sloped surfaces model. The spatial distribution of the optimum tilt angle is obtained using the proposed method. The simplified calculation method of the optimum tilt angle based on latitude and altitude is established by introducing two factors of latitude and altitude. In addition, the annual and seasonal photovoltaic power of China is calculated, and the spatial distribution of China's solar resource utilization potential is obtained using the calculated optimum tilt angle, solar radiation data on sloped surfaces, and the photovoltaic power model. The results show that the annual global solar radiation in China is in the range of 3097–7311 MJm−2, and the annual diffuse solar radiation value ranges from 495 to 3036 MJm−2. Moreover, the optimum tilt angle value in different regions of China is found to range between 14.5°and 49.1°, and the theoretical optimum tilt angle value generally aligns with the law of angle increasing with latitude. The annual photovoltaic power generation is between 117 kWhm−2 and 483 kWhm−2. Compared with the solar energy utilization potential of a PV placed on the horizontal surface, the annual average power generation of a PV panel placed at the optimum tilt angle can increase by up to 144.76 kWhm−2, with an average increase of 10.41%.
A Systematic Review of Transformer-Based Pre-Trained Language Models through Self-Supervised Learning

Transfer learning is a technique utilized in deep learning applications to transmit learned inference to a different target domain. The approach is mainly to solve the problem of a few training datasets resulting in model overfitting, which affects model performance. The study was carried out on publications retrieved from various digital libraries such as SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, and Google Scholar, which formed the Primary studies. Secondary studies were retrieved from Primary articles using the backward and forward snowballing approach. Based on set inclusion and exclusion parameters, relevant publications were selected for review. The study focused on transfer learning pretrained NLP models based on the deep transformer network. BERT and GPT were the two elite pretrained models trained to classify global and local representations based on larger unlabeled text datasets through self-supervised learning. Pretrained transformer models offer numerous advantages to natural language processing models, such as knowledge transfer to downstream tasks that deal with drawbacks associated with training a model from scratch. This review gives a comprehensive view of transformer architecture, self-supervised learning and pretraining concepts in language models, and their adaptation to downstream tasks. Finally, we present future directions to further improvement in pretrained transformer-based language models.
Sentiment and Context-Aware Hybrid DNN With Attention for Text Sentiment Classification

A massive volume of unstructured data in the form of comments, opinions, and other sorts of data is generated in real-time with the growth of web 2.0.Due to the unstructured nature of the data, building an accurate predictive model for sentiment analysis remains a challenging task.While various DNN architectures have been applied to sentiment analysis with encouraging results, they suffer from high dimensional feature space and consider various features equally.State-of-the-art methods cannot properly leverage semantic and sentiment knowledge to extract meaningful relevant contextual sentiment features.This paper proposes a sentiment and context-aware hybrid DNN model with an attention mechanism that {intelligently learns and highlights salient features of relevant sentiment context in the text.We first use integrated wide coverage sentiment lexicons to identify text sentiment features then leverage bidirectional encoder representation from transformers to produce sentiment-enhanced word embeddings for text semantic extraction.After that, the proposed approach adapts the BiLSTM to capture both word order/contextual text semantic information and the long-dependency relation in the word sequence.Our model also employs an attention mechanism to assign weight to features and give greater significance to salient features in the word sequence.Finally, CNN is utilized to reduce the dimensionality of feature space and extract the local key features for sentiment analysis.The effectiveness of the proposed model is evaluated on real-world benchmark datasets demonstrating that the proposed model significantly improves the accuracy of existing text sentiment classification.
Blockchain-Based New Business Models: A Systematic Review

The role of blockchain in new business model development requires greater focus because the technology is still in its infancy. Thus, there has been little research on the effects of the various blockchain networks (such as public, private, and consortium). This finding prompted a thorough investigation of new blockchain-based business models created between 2012 and 2022 to close this gap. This review’s focus is on journals, and duplicate articles have been removed. Works based on interviews, articles in press, non-English articles, reviews, conferences, book chapters, dissertations, and monographs are also not included. Seventy-five papers from the past ten years are included in this evaluation. This study examines the current state of new blockchain-based business models. Additionally, the implications and applications in the related literature have been investigated. These findings highlight numerous open research questions and promising new directions for investigation, which will likely be helpful to academics and professionals. The business strategies built on blockchain are currently on a path with a rapid upward trajectory. Blockchain technology offers businesses numerous chances to modify and develop new company models. By changing the conventional framework, blockchain innovation leads to the development of new methods for developing company models. The supportive potential of blockchain technologies such as NFT and P2E is increasingly being coupled with the development of new corporate projects and the modification of current business models. Since this field of study is still fairly new, researchers will have fresh opportunities to analyze its characteristics.
Enhancing Image Encryption with the Kronecker xor Product, the Hill Cipher, and the Sigmoid Logistic Map

In today’s digital age, it is crucial to secure the flow of information to protect data and information from being hacked during transmission or storage. To address this need, we present a new image encryption technique that combines the Kronecker xor product, Hill cipher, and sigmoid logistic Map. Our proposed algorithm begins by shifting the values in each row of the state matrix to the left by a predetermined number of positions, then encrypting the resulting image using the Hill Cipher. The top value of each odd or even column is used to perform an xor operation with all values in the corresponding even or odd column, excluding the top value. The resulting image is then diffused using a sigmoid logistic map and subjected to the Kronecker xor product operation among the pixels to create a secure image. The image is then diffused again with other keys from the sigmoid logistic map for the final product. We compared our proposed method to recent work and found it to be safe and efficient in terms of performance after conducting statistical analysis, differential attack analysis, brute force attack analysis, and information entropy analysis. The results demonstrate that our proposed method is robust, lightweight, and fast in performance, meets the requirements for encryption and decryption, and is resistant to various attacks.
Technical Intercession of Mixed Reality 4.0: Upholding Senior Citizens Health

In today’s era, where youth are linked with cutting-edge innovation, an unexpected category of early investors is already emerging: the senior citizens. Ageing population all around the planet are using virtual reality (VR) to improve their lifestyles by eliminating solitude, enhancing their psychological health, and taking them to far-off locations without having to depart the warmth of their place. This particular study focused on the mixed reality involvement for the health and well-being of elderly people employs a mix of competitiveness, social contact, and entertaining video material to encourage and promote physical exercise among seniors. After examining the high-quality articles, we create that methodology how virtual reality and augmented reality helps in the monitoring the health of elderly. At the end, article is concluded with way forward in domain of mixed reality intervention for maintaining the health of elderly.
Set-based integer-coded fuzzy granular evolutionary algorithms for high-dimensional feature selection

Feature selection plays a pivotal role in handling today's high-dimensional databases by keeping only the most valuable features, leading to less computation, improved performance, and higher transparency in decision-making processes. Despite the considerable advances in combinatorial optimization, this data-preprocessing step is computationally NP-hard and continues to pose critical challenges, particularly for very high-dimensional (VHD) databases. Here, we propose integer coding and fuzzy granulation (FG) as an integral part of evolutionary wrapper-based feature selection. Based on this integer coding, we further propose crossover and mutation operators that employ set operations such as 'union,' 'intersection,' and 'complement' for higher transparency in their evolutionary explorative and exploitative search processes. In addition to its common use as a surrogate technique to avoid unnecessary computations by recognizing similarities, the fuzzy granulation concept also operates as a repulsive strategy that searches for dissimilarities in the elitist and population initialization routines to reach higher population diversity. An ablation study is implemented to discover the role of individual components of this multi-prong approach. The results are then compared on 22 benchmark problems, ranging from 64 to 138672 attributes, with nine competing methods. Superior performance is shown for the proposed approach in terms of accuracy (in 15 of 22 cases) and achieving a substantially smaller (as much as six times less) feature set with considerably less computational cost (by an average of 30 percent), particularly for VHD feature selection
What is Virtual Reality? A healthcare-focused systematic review of definitions

There has been significant advancement in virtual reality (VR) technology since its conception in 1960, and this evolution has particularly accelerated in recent years. Alongside this, we are seeing an expansion of research interest within which the definitions and nomenclature can be complex and lead to potential misunderstanding or confusion. We present a systematic review of definitions of the term VR as reported within the medical literature with the aim to establish the terminology used to define VR, the differences that exist through the literature, and if they have changed over time. By reporting according to the PRISMA guidelines, we present a systematic review of VR definitions in the English language medical literature. The databases Medline, PubMed, Web of Science, and Scopus were searched using the search terms ‘virtual reality’, ‘definition’, ‘defined’, or ‘define’. Articles were included if they were peer-reviewed, within the medical literature, published between 22nd December 2001 and 22nd December 2021, and offered either an original or cited definition for the term VR. Following data extraction, quantitative analysis of terminology over time and term density maps have been created. Eighty-eight studies were included offering 105 definitions of the term VR. Of these articles, 58 were published within the last 5 years. Common terms when defining VR included ‘computer’, ‘environment’, ‘user’, ‘interactive’ and ‘simulation’. In recent years, a novel term ‘head mounted display’ has emerged which was not previously featured in healthcare literature. This systematic review highlights that the published literature in the field of VR is rapidly expanding. With the growth in technology we can see a complex network of terminology emerge with little homogeneity. Definitions of VR are numerable and high variability exists. We recommend the requirement for consensus in order to urgently unify terminology within the immersive technology field, and whilst waiting for agreement, an evidence-based definition for VR has been suggested. A systematic review of the literature has been performed to better understand the terminology that academic authors have used to define what virtual reality technology is. This review concluded that a wide range of definitions have been used in the last 20 years. Throughout these definitions a large number of individual terms are being used with very little agreement on their appropriate use. With rapidly expanding technology and increasing complexity within the terminology, future research is at risk of misrepresentation until the academic community agree on the most appropriate terminology to be used when defining VR.
A Survey on Technical Challenges of Assistive Robotics for Elder People in Domestic Environments: The ASPiDA Concept

In recent decades, modern developed societies have experienced a significant increase in life expectancy, resulting in a significant degree of aging in the population. This demographic shift has had a significant impact on the daily needs and habits of the population. According to recent demographic projections, the elderly population is expected to triple and reach 2 billion people worldwide by 2050. However, future societies around the world are not adequately prepared to face the potential challenges arising from population aging. Elderly people tend to spend more time at home, but the functionalities and ergonomics of current home equipment and appliances do not fully satisfy their needs. Moreover, the absence of familiarity with new, smarter, and automated domestic technologies creates a significant gap that affects the acceptance and adoption of these technologies by the elderly. In this work, we present the most important technical challenges for the integration of assisted living technologies into a Mobile Robotic Platform (MRP). Additionally, we present the ASPiDA concept, a project proposing a holistic system to support elderly people in their domicile environment.
A Deep Learning Ensemble With Data Resampling for Credit Card Fraud Detection

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase in credit card fraud. Machine learning (ML) algorithms have been utilized for credit card fraud detection. However, the dynamic shopping patterns of credit card holders and the class imbalance problem have made it difficult for ML classifiers to achieve optimal performance. In order to solve this problem, this paper proposes a robust deep-learning approach that consists of long short-term memory (LSTM) and gated recurrent unit (GRU) neural networks as base learners in a stacking ensemble framework, with a multilayer perceptron (MLP) as the meta-learner. Meanwhile, the hybrid synthetic minority oversampling technique and edited nearest neighbor (SMOTE-ENN) method is employed to balance the class distribution in the dataset. The experimental results showed that combining the proposed deep learning ensemble with the SMOTE-ENN method achieved a sensitivity and specificity of 1.000 and 0.997, respectively, which is superior to other widely used ML classifiers and methods in the literature.
A bibliometric analysis of artificial intelligence chatbots in educational contexts

Purpose The application of artificial intelligence chatbots is an emerging trend in educational technology studies for its multi-faceted advantages. However, the existing studies rarely take a perspective of educational technology application to evaluate the application of chatbots to educational contexts. This study aims to bridge the research gap by taking an educational perspective to review the existing literature on artificial intelligence chatbots. Design/methodology/approach This study combines bibliometric analysis and citation network analysis: a bibliometric analysis through visualization of keyword, authors, organizations and countries and a citation network analysis based on literature clustering. Findings Educational applications of chatbots are still rising in post-COVID-19 learning environments. Popular research issues on this topic include technological advancements, students’ perception of chatbots and effectiveness of chatbots in different educational contexts. Originating from similar technological and theoretical foundations, chatbots are primarily applied to language education, educational services (such as information counseling and automated grading), health-care education and medical training. Diversifying application contexts demonstrate specific purposes for using chatbots in education but are confronted with some common challenges. Multi-faceted factors can influence the effectiveness and acceptance of chatbots in education. This study provides an extended framework to facilitate extending artificial intelligence chatbot applications in education. Research limitations/implications The authors have to acknowledge that this study is subjected to some limitations. First, the literature search was based on the core collection on Web of Science, which did not include some existing studies. Second, this bibliometric analysis only included studies published in English. Third, due to the limitation in technological expertise, the authors could not comprehensively interpret the implications of some studies reporting technological advancements. However, this study intended to establish its research significance by summarizing and evaluating the effectiveness of artificial intelligence chatbots from an educational perspective. Originality/value This study identifies the publication trends of artificial intelligence chatbots in educational contexts. It bridges the research gap caused by previous neglection of treating educational contexts as an interconnected whole which can demonstrate its characteristics. It identifies the major application contexts of artificial intelligence chatbots in education and encouraged further extending of applications. It also proposes an extended framework to consider that covers three critical components of technological integration in education when future researchers and instructors apply artificial intelligence chatbots to new educational contexts.
Image encryption based on 8D hyperchaotic system using Fibonacci Q-Matrix

One of the key tenets of the enormous and still-expanding digital realm is the security of images. In recent years, the interest in image encryption has increased among researchers. This paper proposes an image encryption scheme using an 8D hyperchaotic system with Fibonacci Q-Matrix (FQ-matrix). The strength of the proposed image encryption algorithm is estimated by checking the statistical attacks (Histogram analysis, correlation coefficient, and information entropy) and differential attacks (NPCR, UACI).
A novel contrastive adversarial network for minor-class data augmentation: Applications to pipeline fault diagnosis

In recent years, deep learning techniques have achieved significant progress in a variety of tasks. However, existing techniques might not perform well in the presence of data scarcity and imbalance, which is a problem commonly encountered in practice. In this paper, we propose a novel contrastive adversarial network (CAN) that aims to augment imperfect data with satisfactory quality and desired diversity. Specifically, we first propose a new distance metric, called class-aware mean discrepancy, to excavate features associated with operating conditions and generate data with improved compactness (within the same class) as well as enhanced discrimination (for different classes). Furthermore, a dynamic fault-semantic embedding scheme is developed to capture structural priors from real time-series data, which contributes to the comprehensive characterization of the context information of the generated data. Experimental results indicate that the proposed CAN outperforms some state-of-the-art augmentation approaches in terms of quality and diversity. Moreover, the proposed CAN is applied to the pipeline fault diagnosis problem with better diagnostic accuracy than that from the existing algorithms, which demonstrates the applicability of our research results in real-world scenarios.
Overlap in meaning is a stronger predictor of semantic activation in GPT-3 than in humans

Modern large language models generate texts that are virtually indistinguishable from those written by humans and achieve near-human performance in comprehension and reasoning tests. Yet, their complexity makes it difficult to explain and predict their functioning. We examined a state-of-the-art language model (GPT-3) using lexical decision tasks widely used to study the structure of semantic memory in humans. The results of four analyses showed that GPT-3's patterns of semantic activation are broadly similar to those observed in humans, showing significantly higher semantic activation in related (e.g., "lime-lemon") word pairs than in other-related (e.g., "sour-lemon") or unrelated (e.g., "tourist-lemon") word pairs. However, there are also significant differences between GPT-3 and humans. GPT-3's semantic activation is better predicted by similarity in words' meaning (i.e., semantic similarity) rather than their co-occurrence in the language (i.e., associative similarity). This suggests that GPT-3's semantic network is organized around word meaning rather than their co-occurrence in text.