Reviewerly/exHarmony huggingface dataset:
Robustness of machine learning to color, size change, normalization, and image enhancement on micrograph datasets with large sample differences

Appropriate image preprocessing could improve machine learning performance, but the robustness of machine learning to preprocessing methods in micrograph datasets with significant sample differences remains unexplored. Here we collected hundreds of optical micrographs varied in color, contrast, size and brightness and tensile strength from published literature. After preprocessing including color transformation, size adjustment, normalization and image enhancement, classification model of whether it was texture or not and tensile strength prediction model were established using transfer learning and VGG16 model. Our results showed comparable accuracy between classification models using grayscale and color micrographs. Based on grayscale images, classification model utilizing center cropping achieved an average accuracy 1.09% higher than model employing scaling, while coefficient of determination (R2) of regression model showed an average increase of 0.0404. The combination of cropping and dividing by 255 was preferable for classification model, yielding an accuracy of 90.79%. Similarly, the combination of cropping and min–max normalization had a better effect on regression model, yielding R2 of 0.2167. Histogram equalization emerged as the optimal technique for improving classification model, yielding 2.63% increase in accuracy compared to models without image enhancement. For regression models, gamma correction exhibited the highest improvement effect, with an R2 of 0.2681.
Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods

This paper investigates the applications of explainable AI (XAI) in healthcare, which aims to provide transparency, fairness, accuracy, generality, and comprehensibility to the results obtained from AI and ML algorithms in decision-making systems. The black box nature of AI and ML systems has remained a challenge in healthcare, and interpretable AI and ML techniques can potentially address this issue. Here we critically review previous studies related to the interpretability of ML and AI methods in medical systems. Descriptions of various types of XAI methods such as layer-wise relevance propagation (LRP), Uniform Manifold Approximation and Projection (UMAP), Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), ANCHOR, contextual importance and utility (CIU), Training calibration-based explainers (TraCE), Gradient-weighted Class Activation Mapping (Grad-CAM), t-distributed Stochastic Neighbor Embedding (t-SNE), NeuroXAI, Explainable Cumulative Fuzzy Class Membership Criterion (X-CFCMC) along with the diseases which can be explained through these methods are provided throughout the paper. The paper also discusses how AI and ML technologies can transform healthcare services. The usability and reliability of the presented methods are summarized, including studies on the usability and reliability of XGBoost for mediastinal cysts and tumors, a 3D brain tumor segmentation network, and the TraCE method for medical image analysis. Overall, this paper aims to contribute to the growing field of XAI in healthcare and provide insights for researchers, practitioners, and decision-makers in the healthcare industry. Finally, we discuss the performance of XAI methods applied in medical health care systems. It is also needed to mention that a brief implemented method is provided in the methodology section.
Explainable AI is Dead, Long Live Explainable AI!

In this paper, we argue for a paradigm shift from the current model of explainable artificial intelligence (XAI), which may be counter-productive to better human decision making. In early decision support systems, we assumed that we could give people recommendations and that they would consider them, and then follow them when required. However, research found that people often ignore recommendations because they do not trust them; or perhaps even worse, people follow them blindly, even when the recommendations are wrong. Explainable artificial intelligence mitigates this by helping people to understand how and why models give certain recommendations. However, recent research shows that people do not always engage with explainability tools enough to help improve decision making. The assumption that people will engage with recommendations and explanations has proven to be unfounded. We argue this is because we have failed to account for two things. First, recommendations (and their explanations) take control from human decision makers, limiting their agency. Second, giving recommendations and explanations does not align with the cognitive processes employed by people making decisions. This position paper proposes a new conceptual framework called Evaluative AI for explainable decision support. This is a machine-in-the-loop paradigm in which decision support tools provide evidence for and against decisions made by people, rather than provide recommendations to accept or reject. We argue that this mitigates issues of over- and under-reliance on decision support tools, and better leverages human expertise in decision making.
The Effect of Blockchain Technology on Supply Chain Collaboration: A Case Study of Lenovo

Blockchain technology, as a revolutionary technology that has emerged in recent years, holds significant potential for application in supply chain operations. This paper provides a systematic review of blockchain-based supply chain case studies. The existing literature primarily focuses on the food, agriculture, and pharmaceutical sectors, highlighting the advantages of blockchain technology in terms of traceability and transparency. However, there is a limited number of studies addressing the improvement of collaboration efficiency in supply chains, particularly within the realm of information technology enterprises. By conducting semi-structured interviews, we present a case study of Lenovo, a leading enterprise utilizing blockchain technology, to elucidate the advantages of using blockchain technology. Subsequently, it proposes a conceptual model for a blockchain-based information collaboration system and discusses the potential applications of blockchain technology in supply chain collaboration. Our study contributes to the existing work on blockchain applications to enhance supply chain collaboration.
A Universal Question-Answering Platform for Knowledge Graphs

Knowledge from diverse application domains is organized as knowledge graphs (KGs) that are stored in RDF engines accessible in the web via SPARQL endpoints. Expressing a well-formed SPARQL query requires information about the graph structure and the exact URIs of its components, which is impractical for the average user. Question answering (QA) systems assist by translating natural language questions to SPARQL. Existing QA systems are typically based on application-specific human-curated rules, or require prior information, expensive pre-processing and model adaptation for each targeted KG. Therefore, they are hard to generalize to a broad set of applications and KGs. In this paper, we propose KGQAn, a universal QA system that does not need to be tailored to each target KG. Instead of curated rules, KGQAn introduces a novel formalization of question understanding as a text generation problem to convert a question into an intermediate abstract representation via a neural sequence-to-sequence model. We also develop a just-in-time linker that maps at query time the abstract representation to a SPARQL query for a specific KG, using only the publicly accessible APIs and the existing indices of the RDF store, without requiring any pre-processing. Our experiments with several real KGs demonstrate that KGQAn is easily deployed and outperforms by a large margin the state-of-the-art in terms of quality of answers and processing time, especially for arbitrary KGs, unseen during the training.
Chiral and nonreciprocal single-photon scattering in a chiral-giant-molecule waveguide-QED system

We study chiral and nonreciprocal single-photon scattering in a chiral-giant-molecule waveguide-QED system. Here, the giant molecule consists of two coupled giant atoms, which interact with two linear waveguides, forming a four-port quantum device. We obtain the exact analytical expressions of the four scattering amplitudes using a real-space method. Under the Markovian limit, we find that the single-photon scattering behavior is determined by the coupling strength between the giant atoms and the waveguides, the coupling strength between the two giant atoms, and the nondipole effect caused by the phase accumulation of photons travelling between the coupling points. It is also found that chiral and nonreciprocal single-photon scattering can be realized by introducing the chiral coupling to break the symmetry in the coupling configuration between the giant molecule and the waveguides. In addition, an ideal chiral emitter-waveguide coupling enables a directional single-photon routing. In the non-Markovian regime, the scattering spectra are characterized by more abundant structures with multiple peaks and dips. In particular, we demonstrate that the non-Markovian retarded effect can induce the nonreciprocal single-photon scattering. Our results have potential applications in the design of optical quantum devices involving giant atoms, which can provide an efficient platform for studying chiral quantum optics.
Telepresence in live-stream shopping: An experimental study comparing Instagram and the metaverse

Abstract Advances in digital platforms allow influencers to use live shows to showcase products, which has given rise to live-stream shopping. Moreover, new communication forms have emerged, such as the metaverse. The social interactions that occur in these immersive environments foster influencer-follower interactions. This research compares the impact of live-stream shopping on followers’ experiences and behaviors on two communication channels (live shows on Instagram and in the metaverse) and analyses the moderating role of content involvement in telepresence. Data were collected in a between-subjects quasi-field experiment. Two scenarios that differed in degree of telepresence were created, a live show on Instagram and a live show in the metaverse. The results showed that telepresence is an important part of the user experience and in the success of live shows. Telepresence was seen to have a major influence on experiential value, but not on instrumental value. Both values provided followers with satisfaction with the experience. This satisfaction generated stickiness intention and intention to recommend the influencers’ live shows. The effect of telepresence on experiential value was greater for followers less involved with the influencer’s content. This research is pioneering in comparing an established platform, Instagram, with the metaverse. Despite the greater experiential value provided by the metaverse, to increase its instrumental value metaverse spaces needs to be further developed.
Breast cancer diagnosis from histopathology images using deep neural network and XGBoost

Globally, breast cancer is one of the most common diseases among women. As a result of the disadvantages of manual analysis, computer-aided diagnosis (CAD) systems are being used to detect images because of their time-consuming and trustworthy capability. With deep learning techniques based on image analysis and classification, CAD systems can efficiently classify images. This paper proposes methodologies for enhancing the speed and precision of histopathological image classification, which is a challenge for therapeutic measures. We assess three different classifiers and six pre-trained networks. A pre-trained model is used to extract features from images and then feed those extracted features into the extreme gradient boosting (XGBoost) method, which is selected as the final classifier. Our methodology is based on transfer learning and uses histopathological images as input. To evaluate the performance of the proposed method, we use the BreakHis dataset, which presents histopathology images in four magnification levels, i.e., 40X, 100X, 200X, and 400X. The accuracies achieved by the proposed method in 40X, 100X, 200X, and 400X magnifications are 93.6%, 91.3%, 93.8%, and 89.1%, respectively. After analyzing the accuracy achieved in this study, the final method proposed combines the DenseNet201 model as a feature extractor with XGBoost as a classifier.
Orders of magnitude increased accuracy for quantum many-body problems on quantum computers via an exact transcorrelated method

Transcorrelated methods provide an efficient way of partially transferring the description of electronic correlations from the ground-state wave function directly into the underlying Hamiltonian. In particular, Dobrautz et al. [Phys. Rev. B 99, 075119 (2019)] have demonstrated that the use of momentum-space representation, combined with a nonunitary similarity transformation, results in a Hubbard Hamiltonian that possesses a significantly more ``compact'' ground-state wave function, dominated by a single Slater determinant. This compactness/single-reference character greatly facilitates electronic structure calculations. As a consequence, however, the Hamiltonian becomes non-Hermitian, posing problems for quantum algorithms based on the variational principle. We overcome these limitations with the Ansatz-based quantum imaginary-time evolution algorithm and apply the transcorrelated method in the context of digital quantum computing. We demonstrate that this approach enables up to four orders of magnitude more accurate and compact solutions in various instances of the Hubbard model at intermediate interaction strength ($U/t=4$), enabling the use of shallower quantum circuits for wave-function Ans\"atzes. In addition, we propose a more efficient implementation of the quantum imaginary-time evolution algorithm in quantum circuits that is tailored to non-Hermitian problems. To validate our approach, we perform hardware experiments on the ibmq_lima quantum computer. Our work paves the way for the use of exact transcorrelated methods for the simulations of ab initio systems on quantum computers.
A strategic roadmap to overcome blockchain technology barriers for sustainable construction: A deep learning-based dual-stage SEM-ANN approach

The adoption of blockchain technology in the sustainable construction industry in India is slow. Existing literature in this area has primarily focused on the potential applications of blockchain technology in construction but little attention is given to the barriers, impeding its adoption. This study fills this research gap by identifying the most significant barriers to adopting blockchain technology for India's sustainable construction industry. The collected data were analyzed via a two-stage PLS-SEM-artificial-neural-network (ANN) predictive analytical approach. In total, 722 construction stakeholder surveys were conducted, and the final model of barriers to adopting blockchain technology for sustainable construction was statistically validated. This study's findings suggest that significant organizational, technological, cultural, legal, security and government barriers limit the adoption of blockchain technology for sustainable construction in India. These findings also imply that effective legislative processes and economic incentives are crucial for ensuring blockchain technology integration into sustainable construction and for efficient implementation of practices. As a further step, a roadmap is developed to support decision-makers in overcoming these barriers in the short, medium, and long terms. Finally, multi-tiered strategies that construction mapping, sustainability, and integration should be adopted to ensure the successful integration of BT into sustainable construction practices in India.
Hyperchaotic Maps and the Single Neuron Model: A Novel Framework for Chaos-Based Image Encryption

With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need for and significance of the development of novel image encryption algorithms are unprecedented. In this research work, we propose a novel framework for image encryption that is based on two hyperchaotic maps utilized in conjunction with the single neuron model (SNM). The framework entails three successive stages, where in every stage a substitution box (S-box) is applied, then XORing with an encryption key is carried out. The S-boxes and the encryption keys are generated from the numerical solutions of the hyperchaotic maps and the SNM. The performance of the proposed framework is gauged through a number of metrics, reflecting superior performance and complete asymmetry between the plain images and their encrypted versions. The main advantages of this work are (1) vast key space and (2) high encryption efficiency. The superior key space of 22551 is the result of employing the two hyperchaotic maps, while the improved efficiency, resulting in an average encryption rate of 8.54 Mbps, is the result of using the SNM as well as the employment of optimized parallel processing techniques. In addition, the proposed encryption framework is shown to output encrypted images that pass the NIST SP 800 suite. Average achieved values for the metrics include MSE of 9626, PSNR of 8.3 dB, MAE of 80.99, entropy of 7.999, NPCR of 99.6% and UACI of 31.49%.
Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation

Semi-supervised learning has greatly advanced medical image segmentation since it effectively alleviates the need of acquiring abundant annotations from experts, wherein the mean-teacher model, known as a milestone of perturbed consistency learning, commonly serves as a standard and simple baseline. Inherently, learning from consistency can be regarded as learning from stability under perturbations. Recent improvement leans toward more complex consistency learning frameworks, yet, little attention is paid to the consistency target selection. Considering that the ambiguous regions from unlabeled data contain more informative complementary clues, in this paper, we improve the mean-teacher model to a novel ambiguity-consensus mean-teacher (AC-MT) model. Particularly, we comprehensively introduce and benchmark a family of plug-and-play strategies for ambiguous target selection from the perspectives of entropy, model uncertainty and label noise self-identification, respectively. Then, the estimated ambiguity map is incorporated into the consistency loss to encourage consensus between the two models' predictions in these informative regions. In essence, our AC-MT aims to find out the most worthwhile voxel-wise targets from the unlabeled data, and the model especially learns from the perturbed stability of these informative regions. The proposed methods are extensively evaluated on left atrium segmentation and brain tumor segmentation. Encouragingly, our strategies bring substantial improvement over recent state-of-the-art methods. The ablation study further demonstrates our hypothesis and shows impressive results under various extreme annotation conditions.
A comparative review on multi-modal sensors fusion based on deep learning

The wide deployment of multi-modal sensors in various areas generates vast amounts of data with characteristics of high volume, wide variety, and high integrity. However, traditional data fusion methods face immense challenges when dealing with multi-modal data containing abundant intermodality and cross-modality information. Deep learning has the ability to automatically extract and understand the potential association of multi-modal information. Despite this, there is a lack of a comprehensive review of the inherent inference mechanisms of deep learning for multi-modal sensor fusion. This work investigates up-to-date developments in multi-modal sensor fusion via deep learning to provide a broad picture of data fusion needs and technologies. It compares the characteristics of multi-modal data for various sensors, summarizes background concepts about data fusion and deep learning, and carefully reviews a large number of investigations in four inference mechanisms: adaptive learning, deep generative, deep discriminative, and algorithms unrolling. The pros and cons of the above methodologies are presented, and several popular application domains are discussed, including medical imaging, autonomous driving, remote sensing, and robotics. A large collection of multi-modal datasets published in recent years is presented, and several tables that quantitatively compare and summarize the performance of fusion algorithms are provided. Finally, by acknowledging the limitations of current research, we establish potential open challenges and future directions as guidance for deep learning-based multi-sensor fusion.
Metaverse marketing and consumer research: theoretical framework and future research agenda in tourism and hospitality industry

ABSTRACTABSTRACTWhile research on metaverse (MV) has expanded, the domain of real-time multisensory-social-interactions (RTMSIs) in metaverse-tourism has not been recognized so far in travel, tourism and hospitality marketing. This research note develops a theoretical-framework that recommends nine set of important research propositions in MV-tourism. An extensive literature review was conducted to examine the conceptual developments on virtual reality (VR) and metaverse in tourism, hospitality, marketing and psychology. The proposed framework suggests if RTMSIs in MV, once accessed via VR-headsets can produce more significance for consumers/tourists relating to interaction-based outcomes (e.g. performance/emotions/evaluation) than 2D-internet through intermediate-conditions (e.g. social presence, engagement, immersion and exhaustion). As such, this article recommends theoretical/management implications to offer an important future research agenda on metaverse and consumer research in tourism, hospitality, branding and marketing contexts.KEYWORDS: Metaverse tourism marketingreal-time multisensory-social-interactionsvirtual realityconsumer researchtourism and hospitalityfuture research agenda Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRaouf Ahmad RatherRaouf Ahmad Rather is a Scientific Independent Researcher, Jammu & Kashmir, India. He is listed in Stanford's Top 2% of Scientists, and his research interests centre on customer/tourist engagement, experience, virtual reality and social media technology. His work has appeared in the Journal of Travel Research, Current Issues in Tourism, among others. He also serves as Editorial Board Member at Current Issues in Tourism, among others.