Reviewerly/exHarmony huggingface dataset:
Modified beluga whale optimization with multi-strategies for solving engineering problems

Abstract The beluga whale optimization (BWO) algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation, and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization (MBWO) with a multi-strategy. It was inspired by beluga whales’ two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group aggregation strategy (GAs) and a migration strategy (Ms). The GAs can improve the local development ability of the algorithm and accelerate the overall rate of convergence through the group aggregation fine search; the Ms randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO’s ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems.
Study of communication modalities to support teaching tool manipulation skills in a shared immersive environment

This work investigates the potential benefits of using a shared immersive environment for training purposes. Such an environment provides a safe space for teachers to impart their knowledge and expertise to trainees, especially when teaching technical skills that require proper tool manipulation. Our research focuses on exploring different communication modalities that can be used to teach movement amplitude during tool manipulation tasks. Specifically, we examine the effectiveness of haptic, visual, and verbal modalities in enhancing the learning process. Our user study results reveal that trainees were able to replicate movements more accurately when given instructions using the visual modality, and they were able to replicate movements faster when given instructions using the haptic modality. While verbal instructions increased the sense of copresence with the teacher, it was the least preferred modality. These findings suggest that multimodality could be the most appropriate approach to enhance the teaching of movement amplitude skills. Our study provides insights for improving the design of immersive shared systems. It opens up new avenues for further research on the effectiveness of shared immersive virtual environments in supporting the teaching of technical skills.
The use of IoT-based wearable devices to ensure secure lightweight payments in FinTech applications

Daily digital payments in Financial Technology (FinTech) are growing exponentially. A huge demand is for developing secure, lightweight cryptography protocols for wearable IoT-based devices. The devices hold the consumer information and transit functions in a secure environment to provide authentication and confidentiality using contactless Near-Field Communication (NFC) or Bluetooth technologies. On the other hand, Security breaches have been observed in various dimensions, especially in wearable payment technologies. In this paper, we developed a threat model in the proposed framework and how to mitigate these attacks. This study accepts the three-authentication factor, as biometrics is one of the user’s most vital authentication mechanisms. The scheme uses an “Elliptic Curve Integrated Encryption Scheme (ECIES)”, “Elliptic Curve Digital Signature Algorithm (ECDSA)” and “Advanced Encryption Standard (AES)” to encrypt the messages between the entities to ensure higher security. The security analysis of the proposed scheme is demonstrated through the Real-or-Random oracle model (RoR) and Scyther’s widely accepted model-checking tools. Finally, we present a comparative summary based on security features, communication cost, and computation overhead of existing methods, specifying that the proposed framework is secure and efficient for all kinds of remote and proximity payments, such as mini, macro, and micro-payments, using wearable devices.
Leveraging generative artificial intelligence based on large language models for collaborative learning

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).
Interpretable machine learning assessment

With the surge of machine learning in AI and data science, there remains an urgent need to not only compare the performance of different methods across diverse datasets but also to analyze machine learning behaviors with sensitivity using an explainable approach. In this study, we introduce a uniquely designed diagnostic index: d-index to tackle this challenge. This tool integrates classification effectiveness from multiple dimensions, delivering a transparent and comprehensive assessment that transcends the limitations of traditional evaluation methods in classification. We propose two innovative concepts: breakeven states and imbalanced points in this study. Integrated with the d-index, these concepts afford a more profound understanding of the learning behaviors across different machine learning models compared to the existing classification metrics. Significantly, the d-index excels as a powerful tool, identifying learning singularity problems (LSPs) that remain elusive to most current machine learning models and imbalanced learning techniques. Furthermore, leveraging the d-index, we unravel the mechanisms behind imbalanced point generation in binary and multiclass classification. We also put forth a novel technique: identifying a priori informative kernels to optimize support vector machine learning, ensuring outstanding d-index values with the fewest necessary support vectors. Moreover, we address a seldom-discussed state of overfitting in deep learning, where overfitting occurs despite the training and testing loss curves exhibiting favorable trends throughout the epochs. To the best of our knowledge, this work represents a pioneering stride in the realm of explainable machine learning assessments and will inspire further studies in this area.
A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis

Abstract Cervical cancer is one of the most common cancers in daily life. Early detection and diagnosis can effectively help facilitate subsequent clinical treatment and management. With the growing advancement of artificial intelligence (AI) and deep learning (DL) techniques, an increasing number of computer-aided diagnosis (CAD) methods based on deep learning have been applied in cervical cytology screening. In this paper, we survey more than 80 publications since 2016 to provide a systematic and comprehensive review of DL-based cervical cytology screening. First, we provide a concise summary of the medical and biological knowledge pertaining to cervical cytology, since we hold a firm belief that a comprehensive biomedical understanding can significantly contribute to the development of CAD systems. Then, we collect a wide range of public cervical cytology datasets. Besides, image analysis approaches and applications including cervical cell identification, abnormal cell or area detection, cell region segmentation and cervical whole slide image diagnosis are summarized. Finally, we discuss the present obstacles and promising directions for future research in automated cervical cytology screening.
Quantum image encryption based on Baker map and DNA circular shift operation

Abstract Quantum computation can improve the efficiency of image processing by utilizing parallelism and superposition characteristics. In this paper, a quantum cryptosystem based on Baker map and deoxyribonucleic acid (DNA) circular shift operation is designed. Firstly, the digital image to be encrypted is firstly represented with a novel enhanced quantum representation model. Then quantum Baker map is used to permutate the position information of the prepared quantum image. Next, the designed DNA circular shift operation is performed to preliminarily change the color information of the scrambled image. Finally, the ciphertext image is obtained by implementing the exclusive OR (XOR) operation between the pseudo-random sequence generated by a four-dimensional (4D) hyper-chaotic system and the DNA shift operated image. Numerical experimental results and security analysis verify that the proposed scheme has good performance at securing information storage and transmission as the histograms of ciphertexts are uniform, the correlation coefficient values of three directions are very close to 0, the average information entropy is 7.9972, the key space is more than 10 15×4 to resist brute-force attack. Furthermore, it has lower computational complexity compared with classical algorithms.
Physical entanglement between localized orbitals

Abstract The goal of the present work is to guide the development of quantum technologies in the context of fermionic systems. For this, we first elucidate the process of entanglement swapping in electron systems such as atoms, molecules or solid bodies. This demonstrates the significance of the number-parity superselection rule and highlights the relevance of localized few-orbital subsystems for quantum information processing tasks. Then, we explore and quantify the entanglement between localized orbitals in two systems, a tight-binding model of non-interacting electrons and the hydrogen ring. For this, we apply the first closed formula of a faithful entanglement measure, derived in (arXiv: 2207.03377 ) as an extension of the von Neumann entropy to genuinely correlated many-orbital systems. For both systems, long-distance entanglement is found at low and high densities η , whereas for medium densities, <?CDATA $\eta \approx \frac{1}{2}$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>η</mml:mi> <mml:mo>≈</mml:mo> <mml:mfrac> <mml:mn>1</mml:mn> <mml:mn>2</mml:mn> </mml:mfrac> </mml:math> , practically only neighboring orbitals are entangled. The Coulomb interaction does not change the entanglement pattern qualitatively except for low and high densities where the entanglement increases as function of the distance between both orbitals.
Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image Segmentation

Clinically deployed deep learning-based segmentation models are known to fail on data outside of their training distributions While clinicians review the segmentations, these models do tend to perform well in most instances, which could exacerbate automation bias. Therefore, it is critical to detect out-of-distribution images at inference to warn the clinicians that the model likely failed. This work applies the Mahalanobis distance post hoc to the bottleneck features of a Swin UNETR model that segments the liver on T1-weighted magnetic resonance imaging. By reducing the dimensions of the bottleneck features with principal component analysis, images the model failed on were detected with high performance and minimal computational load. Specifically, the proposed technique achieved 92% area under the receiver operating characteristic curve and 94% area under the precision-recall curve and can run in seconds on a central processing unit.
Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization

Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.
A systematic review of the use of virtual reality in teaching Chinese as a foreign language

Abstract The purpose of this review is to provide a thorough analysis of existing research on the implementation of virtual reality (VR) in teaching Chinese as a foreign language (TCFL). In recent years, there has been a growing interest in the potential benefits of VR for language learning, and multiple studies have explored its application in TCFL. However, to date, no systematic reviews on the specific uses of VR in TCFL have been conducted. This study aims to bridge this gap by conducting a comprehensive review of empirical articles on the topic, identified from the Scopus and Web of Science databases. The search terms include “VR”, “virtual reality”, and “Chinese language”. The findings of this review shed light on the current trends in VR-related publications in the field of TCFL, the research settings where VR is used to support learning, and the potential benefits and effectiveness of VR in enhancing Chinese language learning, such as linguistic growth, communication skills, motivation, and immersive and authentic learning contexts. The review also highlights the challenges and limitations of using this technology in TCFL. The significance of this review lies in its potential to inform educators, researchers, and practitioners interested in using VR in TCFL about the current state of research and its implications for language learning. It can also contribute to the development of best practices for the effective use of VR in TCFL, ultimately leading to improved language learning outcomes for students.
Metadata for Efficient Management of Digital News Articles in Multilingual News Archives

The digital news preservation and management of low-resource languages are challenging tasks, especially in vast collections. Unique identification of individual digital objects is possible with well-defined attributes to assure efficient management, such as access, retrieval, preservation, usability, and transformability. The metadata element set is required to maximize the available attributes related to the digital objects. To create a comprehensive metadata set that contains all the necessary attributes and data about the digital news objects. It is more challenging and complicated when the archive contains articles from low-resourced and morphologically complex languages like Urdu and Arabic, which is difficult for machines to understand. The study presents challenges in low-resource languages (LRL) and research challenges. This metadata will help to link news articles based on similarity with other news articles stored in the digital news stories archive (DNSA) and ensures accessibility. In this study, we introduced 38 metadata elements set for the digital news stories preservation (DNSP) framework, of which 16 are explicit and 12 are implicit metadata elements. The paper presents how the digital news stories archive (DNSA) is enhanced to a multilingual archive and discusses the digital news stories extractor, which addresses major issues in implementing low-resource languages and facilitates normalized format migration. The extraction results are presented in detail for high-resource languages, that is, English, and low-resource languages (HRL), that is, Urdu and Arabic. The LRL encountered a high error rate during preservation compared to HRL, 10%, and 03%, respectively. The metadata extraction results show that HRL sources support all metadata elements as compared to LRL. The LRL has good support for explicit meta elements and many implicit meta elements with low extraction percentages. The LRL needs a more detailed study for accurate news content extraction and archiving for future access.
The Impact of Virtual Reality (VR) Tour Experience on Tourists’ Intention to Visit

Drawing on media richness theory, this study investigates the effect of rich media, such as virtual reality (VR), on visit intentions for a specific destination. Specifically, this research employs a mixed-method approach, using abductive theorization to explore and confirm the dimensions of the VR visit experience, notably those related to telepresence, a key concept in tourism through VR. Furthermore, the study aims to elucidate how telepresence influences mental imagery, attitudes towards tourist destinations, and actual visit intentions. To do this, qualitative data were gathered between February and June 2022 from 34 semi-structured interviews with respondents who viewed a VR video of the destination. A second study collected quantitative data from 400 participants through face-to-face questionnaires after a VR video view between June and August 2022. The findings reveal that telepresence comprises three dimensions: realism of the virtual environment, immersion, and the sense of presence in the virtual environment. Telepresence, in turn, both directly and indirectly affects actual visit intentions, with mental imagery and attitude toward tourist destinations partially mediating those relationships. This study provides methodological, theoretical, and tourism management implications to enhance our comprehension of telepresence’s facets, its measurement, and the process by which VR influences real visit intentions.
A semantic framework for condition monitoring in Industry 4.0 based on evolving knowledge bases

In Industry 4.0, factory assets and machines are equipped with sensors that collect data for effective condition monitoring. This is a difficult task since it requires the integration and processing of heterogeneous data from different sources, with different temporal resolutions and underlying meanings. Ontologies have emerged as a pertinent method to deal with data integration and to represent manufacturing knowledge in a machine-interpretable way through the construction of semantic models. Ontologies are used to structure knowledge in knowledge bases, which also contain instances and information about these data. Thus, a knowledge base provides a sort of virtual representation of the different elements involved in a manufacturing process. Moreover, the monitoring of industrial processes depends on the dynamic context of their execution. Under these circumstances, the semantic model must provide a way to represent this evolution in order to represent in which situation(s) a resource is in during the execution of its tasks to support decision making. This paper proposes a semantic framework to address the evolution of knowledge bases for condition monitoring in Industry 4.0. To this end, firstly we propose a semantic model (the COInd4 ontology) for the manufacturing domain that represents the resources and processes that are part of a factory, with special emphasis on the context of these resources and processes. Relevant situations that combine sensor observations with domain knowledge are also represented in the model. Secondly, an approach that uses stream reasoning to detect these situations that lead to potential failures is introduced. This approach enriches data collected from sensors with contextual information using the proposed semantic model. The use of stream reasoning facilitates the integration of data from different data sources, different temporal resolutions as well as the processing of these data in real time. This allows to derive high-level situations from lower-level context and sensor information. Detecting situations can trigger actions to adapt the process behavior, and in turn, this change in behavior can lead to the generation of new contexts leading to new situations. These situations can have different levels of severity, and can be nested in different ways. Dealing with the rich relations among situations requires an efficient approach to organize them. Therefore, we propose a method to build a lattice, ordering those situations depending on the constraints they rely on. This lattice represents a road-map of all the situations that can be reached from a given one, normal or abnormal. This helps in decision support, by allowing the identification of the actions that can be taken to correct the abnormality avoiding in this way the interruption of the manufacturing processes. Finally, an industrial application scenario for the proposed approach is described.
MERGE: A model for multi-input biomedical federated learning

Driven by the deep learning (DL) revolution, artificial intelligence (AI) has become a fundamental tool for many biomedical tasks, including analyzing and classifying diagnostic images. Imaging, however, is not the only source of information. Tabular data, such as personal and genomic data and blood test results, are routinely collected but rarely considered in DL pipelines. Nevertheless, DL requires large datasets that often must be pooled from different institutions, raising non-trivial privacy concerns. Federated learning (FL) is a cooperative learning paradigm that aims to address these issues by moving models instead of data across different institutions. Here, we present a federated multi-input architecture using images and tabular data as a methodology to enhance model performance while preserving data privacy. We evaluated it on two showcases: the prognosis of COVID-19 and patients' stratification in Alzheimer's disease, providing evidence of enhanced accuracy and F1 scores against single-input models and improved generalizability against non-federated models.