Reviewerly/exHarmony huggingface dataset:
Comparative Study of Solar PV System Performance under Partial Shaded Condition Utilizing Different Control Approaches

Objectives: To improve the reduction of photovoltaic system's power output under various resistance load.Additionally, partial shaded conditions (PSCs) lead to several peaks on photovoltaic (PV) curves, which decrease conventional techniques' efficiency and in these (PSCs), standard equations might not be implemented entirely, therefore, the mathematical model of PV array is compulsory to modify and re-establish as well as it is compulsory to apply some methods based on artificial intelligence to develop the performance of traditional techniques.Methods: This work has modified and re-established the mathematical model of PV array under (PSCs) which are recognized and verified using MATLAB/Simulink environment.Also, heuristic algorithms (Cuckoo Search Algorithm (CSA) and Modified Particle Swarm Optimization (MPSO)) have been suggested and applied with PV system to promote output power under various resistance load, varying weather conditions and (PSCs).Moreover, these suggested algorithms can improve the dynamic response and steady-state PV systems' performance simultaneously and effectively comparing to the Modified Perturb and Observe (MP&O) and Artificial Neural Network (ANN) methods.Findings: The proposed methods are examined under various resistance load, several scenarios for (PSCs) and non-uniform irradiation levels to investigate its effectiveness.The results ensure that proposed tracker based on Cuckoo Search Algorithm (CSA) can distinguish between the global and local maximum peaks of PV system effectively with efficiency of 99% comparing to other MPPT approaches.So, all approaches mentioned above are implemented to improve the output power of PV system in Yemen.Novelty: Modified and re-established the mathematical model of PV array under (PSCs) and also, proposed a heuristic algorithms (CSA) and (MPSO)) https://www.indjst.org/
Framing constructicography

Abstract Constructicography can be defined as a blend between Construction Grammar and Practical Lexicography, which aims at developing constructicons: repositories of form and function pairings in a language. In this paper, we present a comprehensive overview of this emerging field by (i) tracking the origins of both Frame Semantics and Construction Grammar and the repercussions of their intertwined developments to Computational Lexicography and Constructicography; (ii) comparing the impacts of the different degrees of interconnection between constructicons and framenets and (iii) discussing the possible applications of these resources. Also, we argue that Constructicography, while obviously building on the accumulated knowledge compiled by numerous Construction Grammar approaches to language, also contributes to its mother theory, since the effort to build coherent formalized computational resources forces constructionist analysis to go beyond describing families of constructions into the enterprise of describing a coherent construction grammar of a language.
Nanotechnology and mobile learning: perspectives and opportunities in young children's education

The adoption of digital technologies in early childhood settings attracts the attention of an increasing number of researchers and scholars throughout the globe. Despite the proliferation of investigations focusing on learning through digital technologies in preschool and early-primary education, there are fields of knowledge in which the impact of digital technologies has yet to be explored. A typical example is that of Nano-Science and Nano-Technology (NST). NST is a new interdisciplinary field with products and applications (apps) that utilise the cutting-edge technology and is increasingly penetrating into today's everyday life, promising to solve global challenges. The objectives of this paper are to (a) examine, based on relevant literature, whether digital technologies could enhance the teaching of concepts related to NST in early childhood settings (b) present the perspectives of mobile devices and their educational apps in young children's learning procedure. The study concludes with a theoretical analysis of the research findings and a brief proposal for future research.
Classification of Anemia with Digital Images of Nails and Palms using the Naive Bayes Method

Purpose: Early detection of anemia based on nails and palms images by applying the Naive Bayes method, as well as to measure the level of accuracy in detecting anemia.Design/methodology/approach: Using the Naive Bayes method. System development uses the waterfall method.Findings/result: Based on the results of the tests that have been carried out, the resulting accuracy is 87.5% with varying light intensities and is 92.3% by using a light intensity of 5362 Lux.Originality/value/state of the art: The difference between this study and previous research is in the image pre-processing method and classification method. In this study, the images of the nails and palms were converted to the YCbCr color space to be segmented and color features extracted. Then the color features will be classified using the Naive Bayes classification method. The output of this system is the result of the input image classification, whether normal or anemic.
Optimised and dynamic KYC system based on blockchain technology

Systems that use blockchain technology to improve the know-your-customer (KYC) process have only been proposed at a conceptual level and all share certain attributes that make their adoption very difficult. We propose and program a blockchain-based system that reduces and shares out among the financial institutions (FIs) that work with a customer the costs of the KYC process and also makes it possible for FIs to dynamically update information related to customers and disseminates this information among participating FIs. Additionally, our system addresses some of the attributes that hinder the adoption of previously proposed solutions by FIs. The result is a stand-alone solution that reduces the cost of the KYC process without requiring any central instance to store the customer's data, and in which FIs share the initial costs of the KYC process as well as the running costs of keeping the information about customers up to date.
A hybrid K-means algorithm improving low-density map-based medical image segmentation with density modification

Segmentation is grouping of a set of pixels, which are mapped from the structures inside the prostate and the background image. The main aim of this research is to provide a better segmentation technique for medical images by solving the drawbacks that currently exist in the density map-based discriminability of feature values. In this paper, we have proposed a method for image segmentation-based density map segmentation properties medical image. The accurateness of the resultant value possibly not up to the level of anticipation while the dimension of the dataset is high because we cannot say that the dataset chosen are free from noises and faults. The kernel change, i.e., segmentation is made by using hybrid K-means clustering algorithm. Thus this method is used to provide the segmentation processing information as well as also be noise free output in an efficient way. Hence, the developed model is implemented in the working platform of MATLAB and the output is compared with the existing techniques such as FCM, K-means to evaluate the performance of our proposed system.
A Semantic Web Solution for Enhancing The Interoperability of E-learning Systems by Using Next Generation of SCORM Specifications

E-learning is a very dynamic field, constantly growing, which refers to an educa-tional content or learning experiences provided or made by means of digital tech-nologies. The development of this area has a direct impact on the quality of edu-cation and cost reduction.&#x0D; E-learning is now dominated by learning management systems such as Moo-dle, to improve the interoperability of these systems; the use of data / resources is a major requirement, this need to push the e-Learning community to seek new approaches to improve this interoperability of systems. On the other hand, se-mantic Web technologies offers standards like RDF allows to describe Web re-sources with their metadata. We propose in this work an approach based on Se-mantic Web technology to improve the interoperability of e-Learning systems like Moodle.
Multiple criteria decision making based on bipolar picture fuzzy sets and extended TOPSIS

The notion of bipolar fuzzy sets (B p FSs) has got much attention from the experts or decision-makers (DMs).B p FSs have ample information in the form of two degrees called the positive belonging degree (P v BD) and a negative belonging degree (N v BD).In this article, we introduced the concept of bipolar picture fuzzy sets (BP c FSs) by connecting the concepts of B p FSs and picture fuzzy sets (P c FSs).Firstly, we presented the concept, operational rules, score, and accuracy functions of BP c FSs. Secondly, a distance measure is formulated for the BP c FSs and then implemented for the extension of TOPSIS.Thirdly, a multiple criteria decision making (MCDM) model is proposed to handle the uncertain MCDM problems.Lastly, a practical example related to the sum of money's investment is exemplified to validate and effectiveness of the proposed model.
A Comparison of Machine Learning Tools for Early Prediction of Sepsis from ICU Data

We explore the efficacy of modern machine learning methods for the task of modeling sepsis progression.We applied a novel imputation and feature selection scheme based on signal processing technology and our medical expertise.We compared the performance of several approaches including neural networks, sparse quantile regression, and baseline classification algorithms such as random forest and SVMs.Among all the experimented methods, CNN-LSTM neural network performed the best with the full test utility score of the challenge being 0.076.We conclude that the application of neural network, random forest, sparse quantile regression, neighborhood algorithms, and naive Bayes classifiers yields superior performance with respect to accuracy, sensitivity, and specificity.
Effect of partial distinguishability on quantum supremacy in Gaussian Boson sampling

Abstract Gaussian boson sampling (GBS) allows for a way to demonstrate quantum supremacy with the relatively modest experimental resources of squeezed light sources, linear optics, and photon detection. In a realistic experimental setting, numerous effects can modify the complexity of the sampling, in particular loss, partial distinguishability of the photons, and the use of threshold detectors rather than photon counting detectors. In this paper, we investigate GBS with partial distinguishability using an approach based on virtual modes and indistinguishability efficiency. We develop a model using these concepts and derive the probabilities of measuring a specific output pattern from partially distinguishable and lossy GBS for both types of detectors. In the case of threshold detectors, the probability as calculated by the Torontonian is a special case under our framework. By analyzing the expressions of these probabilities we propose an efficient classical simulation algorithm which can be used to calculate the probabilities. Our model and algorithm provide foundations for an approximate method for calculating probabilities. It also allows for a way to design sampling algorithms that are not only compatible with existing algorithms for ideal GBS, but can also reduce their complexity exponentially, depending on the indistinguishability efficiency. Using this we show how the boundary of quantum supremacy in GBS can be affected by partial distinguishability.
Wearing a Mask Shapes Interpersonal Space during COVID-19 Pandemic

Social distancing norms have been promoted after the COVID-19 pandemic. In this work, we tested interpersonal space (IPS) in 107 subjects through a reaching-comfort distance estimation task. In the main experiment, subjects had to estimate the comfort and reach space between an avatar wearing or not wearing a face mask. We found that IPS was greater between avatars not wearing a mask with respect to stimuli with the mask on, while reaching space was not modulated. IPS increment in the NoMask condition with respect to the Mask condition correlated with anxiety traits, as shown with the State-Trait Anxiety Inventory, rather than with transient aspects related to the pandemic situation. In the control experiment, the avatars with a mask were removed to further explore the conditioning effect provided by the presence of the facial protection in the main experiment. We found a significant difference comparing this condition with the same condition of the main experiment, namely, the distances kept between avatars not wearing a mask in the main experiment were greater than those between the same stimuli in the control experiment. This showed a contextual adaptation of IPS when elements related to the actual pandemic situation were relevant. Additionally, no significant differences were found between the control experiment and the Mask condition of the main experiment, suggesting that participants had internalized social distancing norms and wearing a mask has become the new normal. Our results highlight the tendency of people in underestimating the risk of contagion when in the presence of someone wearing a mask.
A quantum system control method based on enhanced reinforcement learning

Traditional quantum system control methods often face different constraints, and are easy to cause both leakage and stochastic control errors under the condition of limited resources. Reinforcement learning has been proved as an efficient way to complete the quantum system control task. To learn a satisfactory control strategy under the condition of limited resources, a quantum system control method based on enhanced reinforcement learning (QSC-ERL) is proposed. The states and actions in reinforcement learning are mapped to quantum states and control operations in quantum systems. By using new enhanced neural networks, reinforcement learning can quickly achieve the maximization of long-term cumulative rewards, and a quantum state can be evolved accurately from an initial state to a target state. According to the number of candidate unitary operations, the three-switch control is used for simulation experiments. Compared with other methods, the QSC-ERL achieves close to 1 fidelity learning control of quantum systems, and takes fewer episodes to quantum state evolution under the condition of limited resources.
Understanding the foremost challenges in the transition to online teaching and learning during COVID-19 pandemic: A systematic literature review

The study aims to understand the foremost challenges in the transition to online teaching and learning during the COVID-19 pandemic. The study adopts the PRISMA approach to screening the selection of journal articles and review papers according to the research aims and the inclusion criteria. The journal articles and review papers were extracted and stored in Microsoft Excel and Google Scholar, Academic. Microsoft, Semantic Scholar, Elsevier, and Emerald Insight databases searched relevant documents using formulated keywords. A statistical technique was applied using the M.S. Excel analysis tool (PivotTable and an independent t-Test) to analyze data and determine the differences between teachers and students. The review revealed the evidence that the majority of the studies were primarily focused on the individual developing countries and results from other developing countries were not considered. In addition, the foremost challenges in the transition to online teaching and learning during the covid-19 pandemic were inadequate skills and training, inadequate Internet/Infrastructure, lack of supporting resources and lack of online student engagement and feedback. Finally, the independent t-test reveals there is no statistically significant difference in challenges in the transition to online teaching and learning during the covid-19 pandemic. Both teachers and students encounter similar challenges. The systematic review raised concerns that higher learning needs to effectively implement long term strategies and support teachers and students in getting into online teaching and learning.
Non-fungible Token, Market Development, Trading Models, and Impact in China

Many people, including rapper Snoop Dogg, actor William Shatner, Paris Hilton, Lindsay Lohan, Grimes, and Emily Ratajkowski, are enthusiastic about how Non-fungible tokens (NFTs) will bring value to create many beautiful arts for easy collection and trade in the dynamics of the art market. According to the OpenSea market estimates, the NFT projects available in the different blockchain is worth roughly 840 million US$. NFTs recently received enormous attention from both cryptocurrency investors and the media; even billionaire Elon Mask posted a Meme on his Twitter account and bought the sale of meme NFT for $19800. The biggest economic power that banned cryptocurrency last year, China, made a formal supportive statement to back NFT booming in the whole country. Suddenly, the red power realized the transaction order of the meta-verse system; the tokenized economy is an undoubted benefit for the living style of the current generation. Although the concept of NFTs is very new, trading activities are moving extremely fast in China. An exhibition like “Virtual Niche” can take up to three years to organize in the rest of the world, but it took just a few months in the second biggest superpower China, Beijing. Eventually, this new form of entity will make it easier and more capacitive for the Chinese style of politics, economy, and culture to stand strong in the world for thousands of years. This thesis will introduce the current NFT market development, trading models, and impact in China. With no hesitation, this time, China has entered the era of "everything can be NFT".
An effective up-sampling approach for breast cancer prediction with imbalanced data: A machine learning model-based comparative analysis

Early detection of breast cancer plays a critical role in successful treatment that saves thousands of lives of patients every year. Despite massive clinical data have been collected and stored by healthcare organizations, only a small portion of the data has been used to support decision-making for treatments. In this study, we proposed an engineered up-sampling method (ENUS) for handling imbalanced data to improve predictive performance of machine learning models. Our experiment results showed that when the ratio of the minority to the majority class is less than 20%, training models with ENUS improved the balanced accuracy 3.74%, sensitivity 8.36% and F1 score 3.83%. Our study also identified that XGBoost Tree ( XGBTree ) using ENUS achieved the best performance with an average balanced accuracy of 97.47% (min = 93%, max = 100%), sensitivity of 97.88% (min = 89% and max = 100%), and F1 score of 96.20% (min = 89.5%, max = 100%) in the validation dataset. Furthermore, our ensemble algorithm identified Cell_Shape and Nuclei as the most important attributes in predicting breast cancer. The finding re-affirms the previous knowledge of the relationship between Cell_Shape , Nuclei , and the grades of breast cancer using a data-driven approach. Finally, our experiment showed that Random Forest and Neural Network models had the least training time. Our study provided a comprehensive comparison of a wide range of machine learning methods in predicting breast cancer risk. It can be used as a tool for healthcare practitioners to effectively detect and treat breast cancer.
Multiple-Attention Mechanism Network for Semantic Segmentation

Contextual information and the dependencies between dimensions is vital in image semantic segmentation. In this paper, we propose a multiple-attention mechanism network (MANet) for semantic segmentation in a very effective and efficient way. Concretely, the contributions are as follows: (1) a novel dual-attention mechanism for capturing feature dependencies in spatial and channel dimensions, where the adjacent position attention captures the dependencies between pixels well; (2) a new cross-dimensional interactive attention feature fusion module, which strengthens the fusion of fine location structure information in low-level features and category semantic information in high-level features. We conduct extensive experiments on semantic segmentation benchmarks including PASCAL VOC 2012 and Cityscapes datasets. Our MANet achieves the mIoU scores of 75.5% and 72.8% on PASCAL VOC 2012 and Cityscapes datasets, respectively. The effectiveness of the network is higher than the previous popular semantic segmentation networks under the same conditions.
Prior to Implementation of Digital Pathology—Assessment of Expectations among Staff by Means of Normalization Process Theory

The Region of Southern Denmark is the first in Denmark to implement digital pathology (DIPA), starting at the end of 2020. The DIPA process involves changes in workflow, and the pathologist will have to diagnose based on digital whole slide imaging instead of through the traditional use of the conventional light microscope and glass slides. In addition, in the laboratory, the employees will have to implement one more step to their workflow-scanning of tissue. The aim of our study was to assess the expectations and readiness among employees and management towards the implementation of DIPA, including their thoughts and motivations for starting to use DIPA. We used a mixed-method approach. Based on the findings derived from 18 semi-structured interviews with employees from the region's departments of pathology, we designed a questionnaire, including questions from the normalization measure development tool. The questionnaires were e-mailed to 181 employees. Of these employees, 131 responded to the survey. Overall, they reported feeling sufficiently tech-savvy to be able to use DIPA, and they had high expectations as well as motivation and readiness for the upcoming changes. However, the employees were skeptical regarding the allocation of resources, and few were aware of reports about the effects of DIPA. Based on the findings, it seems to be important to provide not only a thorough introduction to the new intervention and the changes it will entail, but also to continue to ensure that the staff know how it works and why it is necessary to implement.
Deep learning with transfer learning in pathology. Case study: classification of basal cell carcinoma

Establishing basal cell carcinoma (BCC) subtype is sometimes challenging for pathologists. Deep-learning (DL) algorithms are an emerging approach in image classification due to their performance, accompanied by a new concept - transfer learning, which implies replacing the final layers of a trained network and retraining it for a new task, while keeping the weights from the imported layers. A DL convolution-based software, capable of classifying 10 subtypes of BCC, was designed. Transfer learning from three general-purpose image classification networks (AlexNet, GoogLeNet, and ResNet-18) was used. Three pathologists independently labeled 2249 patches. Ninety percent of data was used for training and 10% for testing on 100 independent training sequences. Each of the resulted networks independently labeled the whole dataset. Mean and standard deviation (SD) accuracy (ACC) [%]∕sensitivity (SN) [%]∕specificity (SP) [%]∕area under the curve (AUC) for all the networks was 82.53±2.63∕72.52±3.63∕97.94±0.3/0.99. The software was validated on another 50-image dataset, and its results are comparable with the result of three pathologists in terms of agreement. All networks had similar classification accuracies, which demonstrated that they reached a maximum classification rate on the dataset. The software shows promising results, and with further development can be successfully used on histological images, assisting pathologists' diagnosis and teaching.