Reviewerly/exHarmony huggingface dataset:
Promoting students' learning achievement and self‐efficacy: A mobile chatbot approach for nursing training

Abstract The aims of nursing training include not only mastering skills but also fostering the competence to make decisions for problem solving. In prenatal education, cultivating nurses' knowledge and competence of vaccine administration is a crucial issue for protecting pregnant women and newborns from infection. Therefore, obstetric vaccination knowledge has become a basic and essential training program for nursing students. However, most of these training programs are given via the lecture‐based teaching approach with skills practice, providing students with few opportunities to think deeply about the relevant issues owing to the lack of interaction and context. This could have a negative impact on their learning effectiveness and clinical judgment. To address this problem, a mobile chatbot‐based learning approach is proposed in this study to enable students to learn and think deeply in the contexts of handling obstetric vaccine cases via interacting with the chatbot. In order to verify the effectiveness of the proposed approach, an experiment was implemented. Two classes of 36 students from a university in northern Taiwan were recruited as participants. One class was the experimental group learning with the proposed approach, while the other class was the control group learning with the conventional approach (ie, giving lectures to explain the instructional content and training cases). The results indicate that applying a mobile chatbot for learning can enhance nursing students' learning achievement and self‐efficacy. In addition, based on the analysis of the interview results, students generally believed that learning through the mobile chatbot was able to promote their self‐efficacy as well as their learning engagement and performance. Practitioner notes What is already known about this topic Issues relevant to AI technology in education have been extensively discussed and explored around the world. Among the various AI systems, the potential of chatbots has been highlighted by researchers owing to the user‐friendly interface developed using the natural language processing (NLP) technology. Few studies using AI chatbots in professional training have been conducted. What this paper adds In this study, a mobile chatbot was used in a nursing training program to enhance students' learning achievement and self‐efficacy for handling vaccine cases. The mobile chatbot significantly improved the students' learning achievement and self‐efficacy in comparison with the conventional learning approach in the vaccine training program. From the interview results, it was found that the students generally believed that the mobile chatbot was able to promote their self‐efficacy as well as learning engagement and performances in the vaccine training program. Implications for practice and/or policy Mobile chatbots have great potential for professional training owing to their convenient and user‐friendly features. It would be worth applying mobile chatbots as well as other NLP‐based applications to other professional training programs in the future.
A Survey on Zero-Knowledge Proof in Blockchain

Blockchain, which is usually regarded as a public, decentralized and distributed ledger, has attracted significant attention recently. In the environment of blockchain, all historical transaction data are recorded and stored. However, because blockchain is open and transparent, a malicious user may illegally access private transaction data, including transaction amount, account address, and account balance. As a cryptographic technique, zero-knowledge proof (ZKP) can be used to verify whether the prover has enough transaction amount in the environment of blockchain without leaking any private transaction data. This article provides a comprehensive survey on ZKP in the environment of blockchain with the aim of highlighting security problems and challenges. It first discusses the framework, models and applications of ZKP. Next, it provides an introduction of blockchain, and proposes a framework of ZKP in the environment of blockchain. Then, it highlights the current state of ZKP in the environment of blockchain. Finally, it identifies some potential problems and future research directions.
A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting

In this study, a novel deep solar forecasting approach is proposed based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous wavelet transform (CWT), feature extraction networks, RReliefF feature selection, and extreme learning machine (ELM). The global solar radiation is decomposed into mode functions with the CEEMDAN method. The CWT reconstructs one-dimensional data into two-dimensional scalogram images to include both frequency and the time of the daily and hourly correlations. For the feature extraction process, a cascade convolutional neural network architecture, which consists of AlexNet and GoogLeNet, was designed to extract distinctive deep features. As the high-performance features provide a high level of forecasting accuracy, these are concatenated as the subset feature vector and RReliefF utilized to rank and select the most distinctive features from the subset. The designed ELM is then trained with the selected features and the fully-trained ELM model is then used to evaluate the forecast performance. In the experiments, root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of the proposed method were observed as 0.0642, 0.0241, and 0.1201 for one-step ahead, 0.0686, 0.0285, and 0.1279 for two-step ahead, and 0.0724, 0.0315, and 0.1317 for three-step ahead, respectively. The obtained results show that the proposed method exhibits accurate and robust forecasting performance and outperforms conventional regression models.
FREE: Feature Refinement for Generalized Zero-Shot Learning

Generalized zero-shot learning (GZSL) has achieved significant progress, with many efforts dedicated to over-coming the problems of visual-semantic domain gap and seen-unseen bias. However, most existing methods directly use feature extraction models trained on ImageNet alone, ignoring the cross-dataset bias between ImageNet and GZSL benchmarks. Such a bias inevitably results in poor-quality visual features for GZSL tasks, which potentially limits the recognition performance on both seen and unseen classes. In this paper, we propose a simple yet effective GZSL method, termed feature refinement for generalized zero-shot learning (FREE), to tackle the above problem. FREE employs a feature refinement (FR) module that in-corporates semantic→visual mapping into a unified generative model to refine the visual features of seen and unseen class samples. Furthermore, we propose a self-adaptive margin center loss (SAMC-loss) that cooperates with a semantic cycle-consistency loss to guide FR to learn class- and semantically-relevant representations, and concatenate the features in FR to extract the fully refined features. Extensive experiments on five benchmark datasets demonstrate the significant performance gain of FREE over its baseline and current state-of-the-art methods. The code is available at https://github.com/shiming-chen/FREE.
AA-RMVSNet: Adaptive Aggregation Recurrent Multi-view Stereo Network

In this paper, we present a novel recurrent multi-view stereo network based on long short-term memory (LSTM) with adaptive aggregation, namely AA-RMVSNet. We firstly introduce an intra-view aggregation module to adaptively extract image features by using context-aware convolution and multi-scale aggregation, which efficiently improves the performance on challenging regions, such as thin objects and large low-textured surfaces. To overcome the difficulty of varying occlusion in complex scenes, we propose an interview cost volume aggregation module for adaptive pixel-wise view aggregation, which is able to preserve better-matched pairs among all views. The two proposed adaptive aggregation modules are lightweight, effective and complementary regarding improving the accuracy and completeness of 3D reconstruction. Instead of conventional 3D CNNs, we utilize a hybrid network with recurrent structure for cost volume regularization, which allows high-resolution reconstruction and finer hypothetical plane sweep. The proposed network is trained end-to-end and achieves excellent performance on various datasets. It ranks 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> among all submissions on Tanks and Temples benchmark and achieves competitive results on DTU dataset, which exhibits strong generalizability and robustness. Implementation of our method is available at https://github.com/QT-Zhu/AA-RMVSNet.
Quantum Models as Kernel Methods

This chapter is an adapted version of the preprint article “Quantum machine learning models are kernel methods” by Maria Schuld [].
Observing Product Touch: The Vicarious Haptic Effect in Digital Marketing and Virtual Reality

Retail is rapidly evolving to construct virtual environments for consumers. Online product images, videos, and virtual reality (VR) interfaces enliven consumer experiences and are a source of product information. Because consumers are unable to physically touch products in these digital environments, this research examines vicarious touch, or the observation of a hand in physical contact with a product in a digital environment. Across eight studies, the authors use images, GIFs, and VR to show that vicarious touch affects consumers’ psychological ownership and product valuation due to the active nature of product touch, which results in a felt sense of body ownership of the virtual hand. This is termed the “vicarious haptic effect.” Results demonstrate that it is not enough to show a hand in an advertisement; the hand must be touching a product. The vicarious haptic effect is strongest for people who become highly stimulated by an immersive VR experience (i.e., measured via the elevation in heart rate). The vicarious haptic effect is attenuated if the viewed interaction does not represent a diagnostic hand movement. The authors discuss theoretical and managerial implications for digital product presentation to encourage feelings of product ownership and valuation.
A suitability mapping for the PV solar farms in Egypt based on GIS-AHP to optimize multi-criteria feasibility

Nowadays, photovoltaic (PV) are considered a promising alternative solution as a clean energy source. The power output from the solar PV module is proportional to the amount of solar radiation received by its surface. Hence, the determination of the most convenient locations of PV systems is crucial to maximize their output power. In this paper, the most suitable sites of PV farms in Egypt are identified based on the multi-criteria decision-making (MCDM) technique using the geographic information system (GIS) software. Four categories have been taken into acount; location, environment, meteorology, and climatology, to select the suitable locations. Based on experts’ knowledge ten criteria belong to the main categiories have been weighted using the analytical hierarchy process (AHP) and combined in PV land suitabliltiy index which maped using GIS techniques. The conducted suitability map could be used to guiding installation of renewable energy projects in the future.
Chinese named entity recognition: The state of the art

Named Entity Recognition(NER), one of the most fundamental problems in natural language processing, seeks to identify the boundaries and types of entities with specific meanings in natural language text. As an important international language, Chinese has uniqueness in many aspects, and Chinese NER (CNER) is receiving increasing attention. In this paper, we give a comprehensive survey of recent advances in CNER. We first introduce some preliminary knowledge, including the common datasets, tag schemes, evaluation metrics and difficulties of CNER. Then, we separately describe recent advances in traditional research and deep learning research of CNER, in which the CNER with deep learning is our focus. We summarize related works in a basic three-layer architecture, including character representation, context encoder, and context encoder and tag decoder. Meanwhile, the attention mechanism and adversarial-transfer learning methods based on this architecture are introduced. Finally, we present the future research trends and challenges of CNER.
Revolutionizing Membrane Design Using Machine Learning-Bayesian Optimization

Polymeric membrane design is a multidimensional process involving selection of membrane materials and optimization of fabrication conditions from an infinite candidate space. It is impossible to explore the entire space by trial-and-error experimentation. Here, we present a membrane design strategy utilizing machine learning-based Bayesian optimization to precisely identify the optimal combinations of unexplored monomers and their fabrication conditions from an infinite space. We developed ML models to accurately predict water permeability and salt rejection from membrane monomer types (represented by the Morgan fingerprint) and fabrication conditions. We applied Bayesian optimization on the built ML model to inversely identify sets of monomer/fabrication condition combinations with the potential to break the upper bound for water/salt selectivity and permeability. We fabricated eight membranes under the identified combinations and found that they exceeded the present upper bound. Our findings demonstrate that ML-based Bayesian optimization represents a paradigm shift for next-generation separation membrane design.
Binary Horse herd optimization algorithm with crossover operators for feature selection

This paper proposes a binary version of Horse herd Optimization Algorithm (HOA) to tackle Feature Selection (FS) problems. This algorithm mimics the conduct of a pack of horses when they are trying to survive. To build a Binary version of HOA, or referred to as BHOA, twofold of adjustments were made: i) Three transfer functions, namely S-shape, V-shape and U-shape, are utilized to transform the continues domain into a binary one. Four configurations of each transfer function are also well studied to yield four alternatives. ii) Three crossover operators: one-point, two-point and uniform are also suggested to ensure the efficiency of the proposed method for FS domain. The performance of the proposed fifteen BHOA versions is examined using 24 real-world FS datasets. A set of six metric measures was used to evaluate the outcome of the optimization methods: accuracy, number of features selected, fitness values, sensitivity, specificity and computational time. The best-formed version of the proposed versions is BHOA with S-shape and one-point crossover. The comparative evaluation was also accomplished against 21 state-of-the-art methods. The proposed method is able to find very competitive results where some of them are the best-recorded. Due to the viability of the proposed method, it can be further considered in other areas of machine learning.
Channel Attention Image Steganography With Generative Adversarial Networks

Recently, extensive research has revealed the enormous potential of deep learning in the application of image steganography. However, some defects still exist in previous studies on deep learning-based steganography. In this paper, we propose a novel end-to-end network architecture for image steganography with channel attention mechanisms based on generative adversarial networks, which can yield perceptually indistinguishable stego images at various capacities. Three subnetworks constitute our model, where a generator embeds the payload into cover images, an extractor extracts it from stego images, and a powerful steganalyzer acts as a discriminator to enhance steganographic security. We design a specific channel attention module, which tunes channel-wise features in the deep representation of images dynamically by exploiting channel interdependencies. The experimental results demonstrate that the channel attention strategy is conducive to improving the quality of generated stego images and the accuracy of message extraction. To tackle the inevitable issue of extraction errors, we resort to error correction codes, with which our model achieves the maximum effective embedding rates over 4 bits per pixel. Finally, we verify that the proposed model outperforms current GAN-based steganographic schemes on two datasets and the undetectability is superior to traditional algorithms when the steganalyst cannot access model hyperparameters.
Blockchain, climate damage, and death: Policy interventions to reduce the carbon emissions, mortality, and net-zero implications of non-fungible tokens and Bitcoin

The art industry has commercialised and popularised non-fungible tokens (NFTs), with the volume and value of NFT transactions rapidly growing to US$ 10.7 billion in Q3 2021. The increase in NFT transactions has drawn the attention of the art market to the consequent carbon emissions resulting from verifying transactions in proof-of-work blockchains supporting NFT transactions. With CO2-related deaths attributable to NFT transactions, social pressure from the art market has helped to progress the switch away from the deliberately polluting proof-of-work blockchains to more sustainable consensus protocols. Nonetheless, many popular types of blockchain have resisted the pressure to decrease their environmental impact, including Bitcoin, whose attributed 2021 annual emissions will produce emissions responsible for around 19,000 future deaths. In response, recent global policy interventions have employed legal and fiscal tools to reduce the carbon impact of some or all types of blockchains. Linking the damage caused by proof-of-work blockchains to climate change and human mortality, this study examines the recent policy interventions designed to motivate a shift in blockchain consensus protocols and promote miners' energy efficiency to mitigate environmental damage. This article further explores available policy intervention options that are currently not utilised.
GGWO: Gaze cues learning-based grey wolf optimizer and its applications for solving engineering problems

In this article, an improved variant of the grey wolf optimizer (GWO) named gaze cues learning-based grey wolf optimizer (GGWO) is proposed. The main intentions are to reduce the existing high selective pressure and low diversification of the GWO algorithm, which results in premature convergence, local optima trapping, and stagnation problems. The GGWO algorithm benefits from two new search strategies: neighbor gaze cues learning (NGCL) and random gaze cues learning (RGCL) inspired by the gaze cueing behavior in wolves. The NGCL strategy enhances the exploitation ability and local optima avoidance. The RGCL, however, boosts the population diversity and balance between exploration and exploitation. The cooperation among three search strategies GWO, NGCL, and RGCL, improves diversification, exploration, and exploitation. The GGWO algorithm performance was evaluated by conducting CEC'18 test functions. Furthermore, the results of GGWO were compared with nine metaheuristic algorithms KH, iwPSO, WOA, GWO, GWO-EPD, HGWOSCA, EEGWO, BOA, and VAGWO. Moreover, the experimental results were statistically analyzed by the Wilcoxon signed-rank and Friedman tests. Additionally, four real engineering design problems and two problems of optimal power flow (OPF) for the IEEE 30-bus and IEEE 118-bus are optimized to verify the applicability of the GGWO in practice. The results show that the GGWO algorithm has been able to provide competitive and superior results to the compared algorithms, and it is capable of solving engineering problems.
Hypercorrelation Squeeze for Few-Shot Segmenation

Few-shot semantic segmentation aims at learning to segment a target object from a query image using only a few annotated support images of the target class. This challenging task requires to understand diverse levels of visual cues and analyze fine-grained correspondence relations between the query and the support images. To address the problem, we propose Hypercorrelation Squeeze Networks (HSNet) that leverages multi-level feature correlation and efficient 4D convolutions. It extracts diverse features from different levels of intermediate convolutional layers and constructs a collection of 4D correlation tensors, i.e., hypercorrelations. Using efficient center-pivot 4D convolutions in a pyramidal architecture, the method gradually squeezes high-level semantic and low-level geometric cues of the hypercorrelation into precise segmentation masks in coarse-to-fine manner. The significant performance improvements on standard few-shot segmentation benchmarks of PASCAL-5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> , COCO-20 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sup> , and FSS-1000 verify the efficacy of the proposed method.
DMDE: Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization

DE algorithms have outstanding performance in solving complex problems. However, they also have highlighted the need for an effective approach to alleviating the risk of premature convergence and loss of population diversity, particularly for non-decomposition large-scale global optimization (N-LSGO) problems. In this study, first, the multi-trial vector (MTV) approach is improved by adding a population diversity component, then, a diversity-maintained multi-trial differential evolution (DMDE) algorithm is proposed using the improved approach for N-LSGO problems. The DMDE algorithm produces solutions during six phases: initializing, subpopulation constructing, movement, evaluating and updating, archiving, and diversity maintaining. In initializing, the population is located in the search space and partitioned into three subpopulations using a success-rate based policy. Then, the subpopulations are independently evolved using the proposed meaningful search strategies to decrease the risk of stagnation. The DMDE algorithm introduces an archiving mechanism to maintain population diversity, which prevents premature convergence. Finally, the individuals' behavior is analyzed using the diversity maintaining phase, and those dimensions that have lost their diversity will be enriched. The effectiveness and scalability of the DMDE algorithm are evaluated using benchmark functions CEC 2018 with dimensions 30, 50, and 100 and CEC 2013 with dimension 1000 as LSGO problems. Moreover, the performance of the proposed algorithm was statistically analyzed using Wilcoxon signed-rank sum, ANOVA, and the Friedman tests. In addition, the applicability of the DMDE was assessed by solving seven well-known real-world problems including large-scale problems selected mostly from test-suite CEC 2020. In all experiments and tests, several well-known and N-LSGO algorithms have competed with DMDE through which the comparison results prove that the proposed DMDE algorithm is superior to competitor algorithms.