Reviewerly/exHarmony huggingface dataset:
Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications.Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field.To this end, we offer an in-depth review of MKG in this work.Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development.Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed.A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG.In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types.Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.
Assessing engagement decisions in NFT Metaverse based on the Theory of Planned Behavior (TPB)

Non-Fungible Tokens (NFTs) have reached enormous levels of interest all over the world; The attraction was huge besides buying or creating an NFT. However, the actual use requires consideration of many aspects and sources to make decisions and engage in the NFT market. Moving forward and selecting what to create or where to buy, it is necessary to assess the NFT and whether it is worth investing in or not. On the other hand, Metaverse has become a trending topic and market interest has increased dramatically. However, NFT is crucial to enable the Metaverse; NFT approved its essential to the success of Metaverse adoption. In conclusion, this choice is complicated, especially when it involves extensive knowledge and information from different sources and perspectives (Engineering and Social Science). Some aspects are related to the customer experience and trust, and others are more likely to be asset-related. As we look at the blockchain market, we distinguish a variety of platforms and applications that can be used conveniently, presenting numerous options and possibilities. That brings to mind some ideas and asks some questions to assist the engagement in the NFT metaverse; When is the best moment to engage in the NFT metaverse? Is trading or engaging in the NFT metaverse trustworthy? Is it legal to use and trade NFTs, is there any need for governments to get involved? What NFT piece should create or what price to buy? What is the best and most secure platform to use? Assessing the NFTs requires deep research into different sources of controversial information and data. However, no such reference or study covers these considerations in detail; the lack of trusted resources and knowledge impacts the user experience and engagement. This paper contributes to social science studies by performing a multi-factor (comprehensive) analysis that includes the NFT Metaverse engagement decision and user behavior. We are using an extended model of the theory of planned behavior (TPB), proposing a model which includes external factors to help identify the variables that influence the engagement with NFTs in the metaverse. This comprehensive study has a multi-perspective approach; customer perspective, social perspective, technology perspective, legal perspective, and market perspective. We present extensive knowledge and information to be a helpful piece of awareness for everyone who intends to buy, invest, or create an NFT. This study uses a quantitative analysis method and provides meaningful results explaining this dilemma and the reasons behind the massive adoption of NFT and its fluctuation worldwide. This work helps the decision-makers, creators, and investors consider new development perspectives in the NFT metaverse. The methodology used primary survey data and analyzed with Smart-PLS 4 to determine variance-based structural equation modeling (SEM) using the partial least squares path modeling (PLS) method.
3D Medical image segmentation using parallel transformers

Most recent 3D medical image segmentation methods adopt convolutional neural networks (CNNs) that rely on deep feature representation and achieve adequate performance. However, due to the convolutional architectures having limited receptive fields, they cannot explicitly model the long-range dependencies in the medical image. Recently, Transformer can benefit from global dependencies using self-attention mechanisms and learn highly expressive representations. Some works were designed based on the Transformers, but the existing Transformers suffer from extreme computational and memories, and they cannot take full advantage of the powerful feature representations in 3D medical image segmentation. In this paper, we aim to connect the different resolution streams in parallel and propose a novel network, named Transformer based High Resolution Network (TransHRNet), with an Effective Transformer (EffTrans) block, which has sufficient feature representation even at high feature resolutions. Given a 3D image, the encoder first utilizes CNN to extract the feature representations to capture the local information, and then the different feature maps are reshaped elaborately for tokens that are fed into each Transformer stream in parallel to learn the global information and repeatedly exchange the information across streams. Unfortunately, the proposed framework based on the standard Transformer needs a huge amount of computation, thus we introduce a deep and effective Transformer to deliver better performance with fewer parameters. The proposed TransHRNet is evaluated on the Multi-Atlas Labeling Beyond the Cranial Vault (BCV) dataset that consists of 11 major human organs and the Medical Segmentation Decathlon (MSD) dataset for brain tumor and spleen segmentation tasks. Experimental results show that it performs better than the convolutional and other related Transformer-based methods on the 3D multi-organ segmentation tasks. Code is available at https://github.com/duweidai/TransHRNet.
Class-overlap undersampling based on Schur decomposition for Class-imbalance problems

The class-imbalance problem is an important area that plagues machine learning and data mining researchers. It is ubiquitous in all areas of the real world. At present, many methods have been developed to deal with the class-imbalance problem. For the class-imbalance problem, many researchers believe that the class distribution imbalance is not the main factor affecting the performance of the classification model. When the class distribution imbalance coexists with problems such as class overlap, small disjuncts, and noise, the model performance will be severely affected. For the problem of class-distribution imbalance and class-overlap, the existing methods mainly use nearest neighbors to obtain the local similarity of instances in the local domain, and find the overlapping domains in the data set. To the best of our knowledge, no researchers have considered global similarity. In this regard, to find the global similarity of datasets, a novel Schur decomposition class-overlap undersampling method (SDCU) is proposed. SDCU attempts to obtain potentially overlapping instances on global similarity, and is the first to use matrix decomposition to deal with the problem of class-overlap on class-imbalanced data. We conduct comparative experiments on 46 publicly available real datasets. The experimental results show that when using AUC as the performance evaluation metric, the performance of SDCU has obvious advantages compared with other state-of-the-art methods on three different types of classifiers: SVM, CART, and 3NN. In addition, the test results of Friedman ranking and Holm’s post-hoc test also confirmed the conclusions obtained by the experiments.
How blockchain adoption affects supply chain sustainability in the fashion industry: a systematic review and case studies

Abstract The fashion industry is the fourth biggest industry globally, with a value of $3 trillion and a workforce of 3384 million. However, the rapid development of the fashion industry negatively impacts the environment. With industrial practice, blockchain technology is found to be efficient and effective in helping fashion supply chain members cope with sustainability issues with accessibility, security, traceability, and transparency. This paper makes a systematic literature review and in‐depth exploration of adopting blockchain technology to facilitate sustainable supply chain practices with a particular case study on the fashion industry in coastal areas and a discussion of the application of blockchain technology in the global fashion industry. Based on the combination of the literature review, the case study, and the discussion, managerial insights are generated concerning blockchain adoption for boosting sustainability in the fashion industry in various domains. Future research opportunities are also discussed.
A Reinforcement Learning Paradigm of Configuring Visual Enhancement for Object Detection in Underwater Scenes

This article investigates the problem of enhancing underwater visual observations for the purpose of accurate underwater object detection. Most existing underwater visual enhancement algorithms tend to follow human vision preference but do not necessarily favor the effectiveness of an object detection algorithm. We observe that it should not be the human vision preference but the object detection algorithm that knows what underwater visual enhancement configuration is most beneficial to the detection tasks. In light of this observation, we propose a reinforcement learning paradigm of configuring visual enhancement for object detection in underwater scenes. Specifically, we use underwater image features as states and object detection score increments as rewards. We set up a collection of extensible actions that consist of multiple visual enhancement algorithms. The optimal policy is learned in the form of an action sequence, which characterizes a stepwise process of visual enhancement. Experimental results validate that the sequence of visual enhancement algorithms configured with respect to the object detection algorithm is in favor of improving the detection results.
Integrated identity and auditing management using blockchain mechanism

An integrated identity is a centralized identifier that makes it possible for customers to get access to a variety of business services from a single network. The risks and assaults include identity leaks, centralized management, auditing restrictions, and lengthy breach investigation procedures. The article presents a technique for creating a blockchain-based, integrated identification system in a marketplace by automating and decentralizing the creation and auditing of strong and secure attributes. When individuals engage in market transactions, they act as nodes in a distributed blockchain network, contributing to the development of federated identities. Using a single federated identity, members of this network are able to use any of the participating companies' services. In this, IoT sensors and wearables can automatically log real-time data for you while identifying patterns and flagging problems. The total transparency of all blockchain transactions provides participants the ability to see which services they're using and their users the ability to track the usage of their identities. To test the proposed architecture, implementation done on a public blockchain and a permissioned blockchain (Ethereum and Hyperledger Fabric).
Near-term quantum computing techniques: Variational quantum algorithms, error mitigation, circuit compilation, benchmarking and classical simulation

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.
Whether to trust chatbots: Applying the event-related approach to understand consumers’ emotional experiences in interactions with chatbots in e-commerce

Chatbots can be used in marketing services to substantially improve the consumer experience. Based on cognitive appraisal theory, this study applied an event-related potential (ERP) approach to investigate consumers' emotional experiences and consumer trust in passive interaction with chatbots versus humans, taking into account objective or subjective tasks in e-commerce. The results showed that chatbot (vs. human) service interactions automatically drew more consumer attention at the subconscious stage (i.e., a larger P2); consumers purposefully allocated more resources to regulate the negative emotions elicited by chatbots at the conscious stage (i.e., a larger LPP); and there was a lower trust in chatbots than in humans. Moreover, under subjective tasks, the differences between chatbots and human agents in emotional experience (as reflected by LPP) and trust were amplified. The findings will encourage e-retailers to improve the emotional service experience of their chatbots and prioritize the application of chatbots for objective tasks in customer service.
Dual-Path Rare Content Enhancement Network for Image and Text Matching

Image and text matching plays a crucial role in bridging the cross-modal gap between vision and language, and has achieved great progress due to the deep learning. However, the existing methods still suffer from the long-tail problem, where only a small proportion contains highly frequent semantics and a long tail proportion is constructed by rare semantics. In this paper, we propose a novel Dual-path Rare Content Enhancement Network (DRCE) to tackle the long-tail issue. Specifically, the Cross-modal Representation Enhancement (CRE) and Cross-modal Association Enhancement (CAE) are proposed to construct dual-path structure to enhance rare content representation and association with the benefit of cross-modal prior knowledge. This structure can effectively exploit the complementary cross-modal relation from different aspects and fuse these information in an adaptively manner by the proposed Adaptive Fusion Strategy (AFS). Moreover, we also propose an alternative re-ranking strategy (ARR) to explore the reciprocal contextual information to refine image-text matching results, which can further suppress the negative effect of long-tail effect. Extensive experiments on two large-scale datasets show the significant improvements and validate the superiority of our method.
Development of the Natural Survivor Method (NSM) for designing an updating mechanism in metaheuristic search algorithms

Meta-heuristic search algorithms (MHSs) are methods that take their inspiration from nature. However, the fitness value information used in the design of the update mechanism in MHSs is insufficient to represent the concept of adaptation to the environment and the ability to survive in nature. This causes problems in the selection of survivors and the premature convergence in the search process. This article introduces the Natural Survivor Method (NSM), developed as a design for population management as it occurs in nature, depending on analytical relationships and environmental factors. In the NSM, scores representing the adaptability of individuals to nature are calculated in order to determine the survivors. In this proposed method, the update mechanism is designed using NSM scores instead of fitness values. The NSM is the first study presented to the literature on this subject since the 1980s, when the meta-heuristics was introduced. The NSM was used for survivor selection by applying it to three different types of MHS algorithms based on physics (SFS), biology (TLABC), and evolution (LSHADE-SPACMA). Thirty-nine global optimization problems in the IEEE CEC 2017/2020 benchmark suites and ten constrained real-world engineering problems were used in the experimental studies. Data obtained from experimental studies were analyzed by using non-parametric statistical test methods. Among the 25 competing algorithms according to Friedman scores, the rankings of the three algorithms with NSM and their original versions are as follows: While TLABC is 18th, NSM-TLABC is 9th, SFS is 10th, NSM-SFS is 6th, LSHADE-SPACMA is 3rd, NSM-LSHADE-SPACMA is 1st. According to the results of the Wilcoxon pairwise comparison test between the original and NSM versions of the algorithms, the NSM versions have a clear advantage in finding optimal solutions. However, the drawback of the proposed method is that it increases the computational complexity of the algorithms. The source codes of the NSM (NSM-LSHADE_SPACMA, NSM-TLABC and NSM-SFS) can be accessed at this link: https://www.mathworks.com/matlabcentral/fileexchange/126050-natural-survivor-method-nsm-for-metaheuristic-algorithms.
A Survey on Role of Blockchain for IoT: Applications and Technical Aspects

In recent times, IoT has emerged as a new paradigm for the interconnection of heterogeneous, resource-constrained, and communication-capable smart devices. It has been anticipated as a key enabler for various domains of applications such as health care, automotive, agriculture, industrial operations, automation, energy, and the next generation of living. However, the current IoT applications face significant challenges in terms of the huge amount of collected data, intensive data exchange, security, privacy, centralized processing, and interoperability. To mitigate many of these issues, blockchain has been identified as a promising innovative technology. Blockchain, in conjunction with smart contracts, has received significant attention both from the industry and academia and offers features such as irreversibility, non-repudiation, proof of provenance, fault tolerance, pseudonymity, decentralized operations and decision-making, and distributed ledger. The integration of blockchain with IoT requires essential insights concerning the application areas, scalability, security, privacy, data college and storage, performance, and governance. Thus, this paper intends to expound on the opportunities and key aspects of using blockchain in the IoT landscape. Specifically, this paper surveys the utilization of blockchain for various IoT applications. Besides, the paper distinguishes different technical aspects and presents the associated research challenges. At last, future research directions are discussed depending on the lessons learned.
Generalizable biomarker prediction from cancer pathology slides with self-supervised deep learning: A retrospective multi-centric study

Deep learning (DL) can predict microsatellite instability (MSI) from routine histopathology slides of colorectal cancer (CRC). However, it is unclear whether DL can also predict other biomarkers with high performance and whether DL predictions generalize to external patient populations. Here, we acquire CRC tissue samples from two large multi-centric studies. We systematically compare six different state-of-the-art DL architectures to predict biomarkers from pathology slides, including MSI and mutations in BRAF, KRAS, NRAS, and PIK3CA. Using a large external validation cohort to provide a realistic evaluation setting, we show that models using self-supervised, attention-based multiple-instance learning consistently outperform previous approaches while offering explainable visualizations of the indicative regions and morphologies. While the prediction of MSI and BRAF mutations reaches a clinical-grade performance, mutation prediction of PIK3CA, KRAS, and NRAS was clinically insufficient.
Continuous-mode quantum key distribution with digital signal processing

Continuous-variable quantum key distribution (CVQKD) offers the specific advantage of sharing keys remotely by the use of standard telecom components, thereby promoting cost-effective and high-performance metropolitan applications. Nevertheless, the introduction of high-rate spectrum broadening has pushed CVQKD from a single-mode to a continuous-mode region, resulting in the adoption of modern digital signal processing (DSP) technologies to recover quadrature information from continuous-mode quantum states. However, the security proof of DSP involving multi-point processing is a missing step. Here, we propose a generalized method of analyzing continuous-mode state processing by linear DSP via temporal-modes theory. The construction of temporal modes is key in reducing the security proof to single-mode scenarios. The proposed practicality oriented security analysis method paves the way for building classical compatible digital CVQKD.
The blockchain technology selection in the logistics industry using a novel MCDM framework based on Fermatean fuzzy sets and Dombi aggregation

Logistics is a sophisticated system involving third-party logistics (3PL) providers, freight forwarders, warehousing companies, and transport service producers in various transport modes, such as road, rail, air, maritime, and multimodal transportation. Furthermore, it is possible to add customs clearance agencies, insurance companies, banks, and relevant institutions and organizations to the system. Effective logistics systems must sustainably provide customers with quality and satisfactory logistics services using data shared over advanced technologies. Nevertheless, data and information on logistics are usually challenging to collect, process, and understand, as they are primarily unstructured, unreachable, and unstandardized. Blockchain is a new and advanced technology promising to eliminate or mitigate the adverse effects of these difficulties. However, blockchain technology practices in the logistics industry are extraordinarily scarce; and a few blockchain platforms have attempted to produce solutions for a few large-scale and global logistics firms. Hence, the digital transformation process involving blockchain technology for a logistics company encounters the challenge of selecting an appropriate blockchain platform for the logistics industry's needs. Although studies have been carried out in the relevant literature to select a suitable blockchain platform for various industries, few of these studies dealt with selecting the best blockchain platform for the logistics industry. Hence, experimental studies on choosing the proper blockchain platform in the logistics industry that tries to manage outstandingly complicated relations and linkages among stakeholders are currently inadequate. The current study presents a novel, robust, practical, and powerful decision-making tool that can also overcome highly complex uncertainties to identify the most feasible blockchain technology for the logistics industry. The robustness of the study's findings is validated with comprehensive sensitivity and comparative analyses.