Reviewerly/exHarmony huggingface dataset:
(Retracted) Evaluation of bioinspired algorithms for image optimization

Steganography is a technique for concealing sensitive information behind a specific media source, such as an image, audio, or video file, in such a way that the concealed data are invisible to everyone. Many algorithms have been developed to optimize this process for better output. We aim to identify the different optimization algorithms used in image steganography after embedding the data to improve the resilience, visibility, and payload carrying capacity. Additionally, we highlight several bioinspired algorithms, including particle swarm optimization, ant colony optimization, firefly optimization, and artificial bee colony optimization, and evaluate through performance measures such as peak signal-to-noise ratio (PSNR) and mean square error (MSE). The performance metrics generated from the collected data indicate that the firefly method produced a higher PSNR and a lower MSE, namely 72.42 dB and 0.13, respectively. The methods are evaluated in terms of their ability for data embedding, robustness, and imperceptibility.
Low-Rankness Guided Group Sparse Representation for Image Restoration

As a spotlighted nonlocal image representation model, group sparse representation (GSR) has demonstrated a great potential in diverse image restoration tasks. Most of the existing GSR-based image restoration approaches exploit the nonlocal self-similarity (NSS) prior by clustering similar patches into groups and imposing sparsity to each group coefficient, which can effectively preserve image texture information. However, these methods have imposed only plain sparsity over each individual patch of the group, while neglecting other beneficial image properties, e.g., low-rankness (LR), leads to degraded image restoration results. In this article, we propose a novel low-rankness guided group sparse representation (LGSR) model for highly effective image restoration applications. The proposed LGSR jointly utilizes the sparsity and LR priors of each group of similar patches under a unified framework. The two priors serve as the complementary priors in LGSR for effectively preserving the texture and structure information of natural images. Moreover, we apply an alternating minimization algorithm with an adaptively adjusted parameter scheme to solve the proposed LGSR-based image restoration problem. Extensive experiments are conducted to demonstrate that the proposed LGSR achieves superior results compared with many popular or state-of-the-art algorithms in various image restoration tasks, including denoising, inpainting, and compressive sensing (CS).
Blockchain-based trust management and authentication of devices in smart grid

The digitalization of the power grid and advancement in intelligent technologies have enabled the service provider to convert the existing electrical grid into a smart grid. The transformation of the grid will help in integrating cleaner energy technologies with energy management to improve power network efficiency. Internet of things (IoT) and various network components need to be deployed to harness the full potential of the smart grid. Also, integrating intermittent renewable energy sources, energy storage, intelligent control of selected power-intensive loads, etc will improve energy efficiency. But deployment of this information and communication technologies will make the grid more vulnerable to cyber attacks from hackers. In this work, blockchain-based self-sovereign identification and authentication technique is presented to avert identity theft and masquerading. The proposed approach can minimize the chances of identity-based security breaches in the smart grid. This paper provides an overview of the model of identification and authentication of IoT devices in Smart Grid based on Blockchain technology. The Blockchain based implementation of identification and authentication of devices is proposed to validate the model in the distributed electrical energy network. The model is able to authenticate the device using Blockchain in a trusted model. The system works according to plan validating the authenticity of transaction in a node in log(n) time, which justifies presented result.
MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction

This paper presents MuCGEC, a multireference multi-source evaluation dataset for Chinese Grammatical Error Correction (CGEC), consisting of 7,063 sentences collected from three Chinese-as-a-Second-Language (CSL) learner sources.Each sentence is corrected by three annotators, and their corrections are carefully reviewed by a senior annotator, resulting in 2.3 references per sentence.We conduct experiments with two mainstream CGEC models, i.e., the sequenceto-sequence model and the sequence-to-edit model, both enhanced with large pretrained language models, achieving competitive benchmark performance on previous and our datasets.We also discuss CGEC evaluation methodologies, including the effect of multiple references and using a char-based metric.Our annotation guidelines, data, and code are available at https:
Patient-Chain: Patient-centered Healthcare System a Blockchain-based Technology in Dealing with Emergencies

Medical healthcare currently plays a vital role for humans in society. For each patient, personal health records are critical and sensitive assets, so how to manage them effectively is becoming exciting research to solve. Many types of research in managing and operating personal health records have been introduced; however, dealing with patients’ data in emergency cases remains an uncertain issue. When emergencies happen in reality, using a traditional access system is challenging for patients to consent medical staff, i.e., nurses or doctors, to access their data. Besides, there is no secured record management of patient’ data, which reveals highly confidential personal information, such as what happened, when, and who has access to such information. Thus, this paper proposes a control and management system regarding emergency access to protect the patients’ data called the Patient-Chain platform: a patient-centred healthcare system, a Blockchain-based technology in dealing with emergencies. The Patient-Chain system is built based on permitted Blockchain Hyperledger Fabric, defines several rules and regulations by using smart contracts and time duration to deal with emergencies. The patients also restrict the time to access the data in such urgent cases-several algorithms representing how the system works are also provided to make readers understand the proposed management system.
Multiscale Network Traffic Prediction Method Based on Deep Echo-State Network for Internet of Things

As a typical Internet of Things application, network traffic prediction (NTP) plays a decisive role in congestion control, resource allocation, and anomaly detection. The trend of network traffic is different at different scales, so multiscale is an important characteristic of network traffic. In addition, the network traffic is nonlinear on each scale and dependent between scales. The existing NTP methods cannot comprehensively consider these characteristics, which limits their performance. In view of the characteristics of network traffic, such as multiscale, nonlinearity, and scale dependence, this article proposes a new multiscale NTP method based on a deep echo-state network (ESN). First, a multiscale parallel layered structure based on deep ESN is designed to fully consider the influence of each scale on the prediction result and then reduce the prediction error. Second, a feature extraction algorithm is proposed to improve the nonlinear approximation ability by extracting more abundant dynamic features with multiple reservoirs. Third, an NTP model based on scale dependence is proposed to reduce the influence from partial scale missing and then improve the prediction accuracy. Finally, simulation results demonstrate that compared with the state-of-the-art NTP methods, the proposed method significantly improves the prediction performance of network traffic with a slight increase in running time.
Educational applications of artificial intelligence in simulation-based learning: A systematic mapping review

The field of education has experienced a transformation as artificial intelligence (AI) becomes increasingly applicable for learning purposes. AI has the potential to transform the social interactions in educational contexts among learners, teachers, and technologies. In this systematic mapping review, we focus on mapping and framing trends for educational applications of AI in simulation-based learning. Fifty-nine studies met the inclusion and exclusion criteria. We coded and analyzed six mapped categories in this literature review: (1) the year-of-study trend, (2) methods, (3) AI technologies, (4) simulation, (5) study trends, and (6) learning principles and theories. To provide nuanced details from the included literature, we also synthesized three thematic trends: (1) AI built in virtual agents for simulation-based learning, (2) AI infused in simulation-based learning with affective computing, and (3) AI leveraged in simulation-based learning for assessments. Trend One builds on a general acknowledgement of virtual agents as a guide for situated learning. Trend Two posits the role of affective states in learning trajectories and suggests the related machine learning approaches. Trend Three discusses machine learning techniques and multimodal computing used for assessment and feedback. The paper concludes with implications and suggestions for research and practice in AI in education using simulation-based learning.
AAP-MIT: Attentive Atrous Pyramid Network and Memory Incorporated Transformer for Multisentence Video Description

Generating multi-sentence descriptions for video is considered to be the most complex task in computer vision and natural language understanding due to the intricate nature of video-text data. With the recent advances in deep learning approaches, the multi-sentence video description has achieved an impressive progress. However, learning rich temporal context representation of visual sequences and modelling long-term dependencies of natural language descriptions is still a challenging problem. Towards this goal, we propose an Attentive Atrous Pyramid network and Memory Incorporated Transformer (AAP-MIT) for multi-sentence video description. The proposed AAP-MIT incorporates the effective representation of visual scene by distilling the most informative and discriminative spatio-temporal features of video data at multiple granularities and further generates the highly summarized descriptions. Profoundly, we construct AAP-MIT with three major components: i) a temporal pyramid network, which builds the temporal feature hierarchy at multiple scales by convolving the local features at temporal space, ii) a temporal correlation attention to learn the relations among various temporal video segments, and iii) the memory incorporated transformer, which augments the new memory block in language transformer to generate highly descriptive natural language sentences. Finally, the extensive experiments on ActivityNet Captions and YouCookII datasets demonstrate the substantial superiority of AAP-MIT over the existing approaches.
30 years of artificial intelligence (AI) research relating to the hospitality and tourism industry

Purpose This study aims to review the progress of research on artificial intelligence (AI) relating to the hospitality and tourism industry, focusing on the content, focal points, key terms and trends of AI research. Design/methodology/approach A total of 491 referred papers are selected from the Web of Science core collection database. These papers, published in the past 30 years (1991–2021), are analyzed by using Gephi and VOSviewer software. Findings AI research shows a growing trend since 1991, and the number of publications and citations increased significantly since 2018, indicating that AI became a focus for researchers. AI studies are grouped into four clusters, namely, AI technology, technology acceptance, customers’ perception and future trends. The research focus changed from AI technology in the early stage to customers’ attitudes toward and willingness to accept AI. Research limitations/implications The findings contribute to advance knowledge development, identify research gaps and shed light on future research. The results offer practical enlightenment for governments, tourism destinations and hospitality organization. Practical implications The results offer practical enlightenment for governments, tourism destinations and hospitality organization. Originality/value This study is the initial attempt to provide a systematic review of AI research relating to the tourism and hospitality fields.
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translation

Unpaired image-to-image translation has broad applications in art, design, and scientific simulations. One early breakthrough was CycleGAN that emphasizes one-to-one mappings between two unpaired image domains via generative-adversarial networks (GAN) coupled with the cycle-consistency constraint, while more recent works promote one-to-many mapping to boost diversity of the translated images. Motivated by scientific simulation and one-to-one needs, this work revisits the classic CycleGAN framework and boosts its performance to outperform more contemporary models without relaxing the cycle-consistency constraint. To achieve this, we equip the generator with a Vision Transformer (ViT) and employ necessary training and regularization techniques. Compared to previous best-performing models, our model performs better and retains a strong correlation between the original and translated image. An accompanying ablation study shows that both the gradient penalty and self-supervised pre-training are crucial to the improvement. To promote reproducibility and open science, the source code, hyperparameter configurations, and pre-trained model are available at https://github.com/LS4GAN/uvcgan.
Exploring non‐immersive virtual reality experiences in tourism: Empirical evidence from a world heritage site

Abstract With the popularization of the concept of the metaverse, non‐immersive virtual reality (VR) has become a focus of tourism destination marketing. Drawing upon telepresence theory, this study identifies the antecedents and consequences of telepresence to help interpret tourist behavior in the context of non‐immersive VR experience. Results revealed that mental imagery and vividness positively predict tourist telepresence and satisfaction; Telepresence positively predicts satisfaction and intention during non‐immersive VR experiences. Our work enriches telepresence theory by identifying the antecedents and consequences of telepresence in the context of non‐immersive VR, and provides meaningful practical insights for similar attractions that adopt the technology.
Research data management and research data literacy in Slovenian science

Purpose The purpose of this paper is to investigate the differences between scientific disciplines (SDs) in Slovenia in research data literacy (RDL) and research data management (RDM) to form recommendations regarding how to move things forward on the institutional and national level. Design/methodology/approach Purposive sample of active researchers was used from widest possible range of SD. Data were collected from April 21 to August 7, 2017, using 24-question online survey (5 demographic, 19 content questions (single/multiple choice and Likert scale type). Bivariate (ANOVA) and multivariate methods (clustering) were used. Findings The authors identified three perception-related and four behavior-related connections; this gave three clusters per area. First, perceptions – skeptical group, mainly social (SocS) and natural sciences (NatS): no clear RDM and ethical issues standpoints, do not agree that every university needs a data management plan (DMP). Careful group, again including mainly SocS and NatS: RDM is problematic and linked to ethical dilemmas, positive toward institutional DMPs. Convinced group, mainly from humanities (HUM), NatS, engineering (ENG) and medicine and health sciences (MedHeS): no problems regarding RDM, agrees this is an ethical question, is positive toward institutional DMP’s. Second, behaviors – sparse group, mainly from MedHeS, NatS and HUM, some agricultural scientists (AgS), and some SocS and ENG: do not tag data sets with metadata, do not use file-naming conventions/standards. Frequent group – many ENG, SocS, moderate numbers of NatS, very few AgS and only a few MedHeS and HUM: often use file-naming conventions/standards, version-control systems, have experience with public-domain data, are reluctant to use metadata with their RD. Slender group, mainly from AgS and NatS, moderate numbers of ENG, SocS and HUM, but no MedHeS: often use public-domain data, other three activities are rare. Research limitations/implications Research could be expanded to a wider population, include other stakeholders and use qualitative methods. Practical implications Results are useful for international comparisons but also give foundations and recommendations on institutional and national RDM and RDL policies, implementations, and how to bring academic libraries into the picture. Identified differences suggest that different educational, awareness-raising and participatory approaches are needed for each group. Originality/value The findings offer valuable insight into RDM and RDL of Slovenian scientists, which have not yet been investigated in Slovenia.
Unsupervised Discovery of Parts, Structure, and Dynamics

Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos. Our Parts, Structure, and Dynamics (PSD) model learns to, first, recognize the object parts via a layered image representation; second, predict hierarchy via a structural descriptor that composes low-level concepts into a hierarchical structure; and third, model the system dynamics by predicting the future. Experiments on multiple real and synthetic datasets demonstrate that our PSD model works well on all three tasks: segmenting object parts, building their hierarchical structure, and capturing their motion distributions.
Accuracy Measurement of Pyranometer vs Reference cell for PV resource assessment

To accelerate the PV integration, assessment, with high accuracy, of the technology performances before installation is crucial. This can be performed by measuring the solar irradiance at ground level in the selected areas. A variety of irradiance sensors is available in the market, however, the selection of the most suitable and adaptable one for the local conditions is a hard task. The aims of this study is to evaluate the difference between the existing two devices used in the PV market such as thermopile pyranometer and reference cell. We carry out the experiment campaign at Green Energy Park in Benguerir. The SR11 pyranometer and the ISET reference cell was the chosen devices, installed and monitored side by side at the selected site. The results show, that for two months of the period tests with different climate conditions (June represent the summer season, and December that represent the cold season), the daily absolute deviation between the plan of array irradiance measurements of the chosen devices can reach 30 W/m² in June and it range between -6 W/m² and 2 W/m² in December. The monthly average deviation was found to be -4.8% and -0.7% in June and December respectively. The present results underline that to guarantee good resource assessment, the measurements of solar irradiance need to be corrected such that the reference cell underestimate the irradiations values.
Design of Student Capability Evaluation System Merging Blockchain Technology

This article mainly introduces the application program of blockchain technology in the evaluation of students' professional ability. Through K-means clustering algorithm, students' academic performance and academic achievements in school, training, competitions, practice and other activities outside school are analyzed, in order to objectively and effectively evaluate the students' professional ability, which provides reasonable advice for student employment. In the system design, the blockchain technology is applied to the student ability evaluation system. The blockchain technology provides distributed storage and is nondestructive and traceability, so as to achieve the overall monitoring of students' professional ability evaluation process, student assessment data can be traced and student learning credit can be increased anytime anywhere. This paper presents a design scheme of the student ability evaluation system based on blockchain technology, which includes a student ability analysis method based on clustering algorithm and an overall system architecture diagram based on blockchain technology, which provides possibilities for creating an ecosystem of student ability evaluation for the future.
Spatial Resolution Enhancement of Satellite Microwave Radiometer Data with Deep Residual Convolutional Neural Network

Satellite microwave radiometer data is affected by many degradation factors during the imaging process, such as the sampling interval, antenna pattern and scan mode, etc., leading to spatial resolution reduction. In this paper, a deep residual convolutional neural network (CNN) is proposed to solve these degradation problems by learning the end-to-end mapping between low-and high-resolution images. Unlike traditional methods that handle each degradation factor separately, our network jointly learns both the sampling interval limitation and the comprehensive degeneration factors, including the antenna pattern, receiver sensitivity and scan mode, during the training process. Moreover, due to the powerful mapping capability of the deep residual CNN, our method achieves better resolution enhancement results both quantitatively and qualitatively than the methods in literature. The microwave radiation imager (MWRI) data from the Fengyun-3C (FY-3C) satellite has been used to demonstrate the validity and the effectiveness of the method.
Light field calibration and 3D shape measurement based on epipolar-space

Light field camera calibration is much more complicated by the fact that a single point in the 3D scene appears many times in the image plane. Compared to the previous geometrical models of light field camera, which describe the relationship between 3D point in the scene and 4D light field, we proposed an epipolar-space (EPS) based geometrical model in this paper, which determines the relationship between 3D point in the scene and 3-parameter vector in the EPS. Moreover, a close-form solution for the 3D shape measurement based on the geometrical model is accomplished. Our calibration method includes an initial linear solution and nonlinear optimization with the Levenberg-Marquardt algorithm. The light field model is validated with the commercially available light field camera Lytro iIIum, and the performance of 3D shape measurement is verified by both real scene data and the data set on the internet.