Reviewerly/exHarmony huggingface dataset:
Blockchain adoption for sustainable development in developing countries: Challenges and opportunities in the banking sector

Blockchain technology has been gaining relevance in every walk of life. Keeping this relevance in the banking sector, the current study aims to synthesize the body of knowledge on blockchain adoption for sustainability in developing countries while encapsulating challenges and opportunities using a systematic literature review. It offers a review of scholarly published articles published between 2010 and 2022 in EBSCO, Web of Science, Scopus, and Google Scholar databases. The findings of the study revealed that scalability, the lack of operability among the blockchain service providers, and the absence of favorable regulatory laws and generally accepted rules were the major hurdles in the way of blockchain adoption in developing countries. Implantation of smart contracts, risk management, risk mitigation and faster payment are the opportunities for the banking sectors of developing countries to increase trust and transparency in their financial systems as well as formalization of their economy. However, the studies concluded that upon the adoption of blockchain technologies, there will be ease in financial transactions and transfers of money for financial institutions. Other challenges highlighted by the studies included threats of cybercrimes and privacy breaches.
Metaverse Banking Service: Are We Ready to Adopt? A Deep Learning-Based Dual-Stage SEM-ANN Analysis

Metaverse banking service is the transformation from online banking to a metaverse environment that allows customers to access banking services and interact with representatives in a virtual environment. The metaverse refers to a virtual realm that integrates physical reality with digital environments, enabling users to interact, socialize, and participate in a wide range of activities through the use of avatars and immersive technologies. While there are advantages to adopt the metaverse, the metaverse adoption researches are scarce and primarily focus on the game, education, and sport, providing limited attention to banking services. Furthermore, most adoption research using standard information technology/information system models has focused primarily on organizational context and adopted compulsorily. Metaverse banking service is mainly adopted voluntarily by users and for personal purposes. Thus, this leads to the difficulty in drawing meaningful conclusions toward metaverse adoption. The study addresses these issues by proposing a new unified theory of acceptance and use of the metaverse technology model (UTAUMT), which consists of metaverse performance expectancy (MPE), metaverse facilitating conditions (MFC), metaverse effort expectancy (MEE), and metaverse social influence (MSI) to determine metaverse banking service adoption. Moreover, metaverse trust (MET) and metaverse financial resources (MEF) are also incorporated to investigate complexity in the metaverse environment. The integrated model has been developed and validated through a pretest (face validity and content validity) and pilot test before applying to 491 metaverses-experienced users in Vietnam through a questionnaire approach. Partial least squares structural equation modelling-artificial neural network (PLS-SEM-ANN) has provided a comprehensive result as it can capture both linear and nonlinear relationships. The results from the model showed that only one of the proposed hypotheses between metaverse financial resources (MEF) and behavioural intention to use metaverse banking services (BIM) was not supported in this study and thus needed further investigation. The study contributes to the academic literature by proposing new constructs to assess users’ likelihood of adopting metaverse banking services. The result also assists bank managers in understanding metaverse banking adoption and makes them realize the metaverse banking services’ growth opportunity to pursue. Given the limited scope of the study focusing solely on Vietnam, it would be advantageous for future research on the cultural variations among users of mobile social commerce to incorporate a comparative analysis across multiple countries, with a particular emphasis on Asian nations.
Experimental demonstration of multiparty quantum secret sharing and conference key agreement

Abstract Quantum secret sharing (QSS) and conference key agreement (CKA) provide efficient encryption approaches for realizing multi-party secure communication, which are essential components of quantum networks. In this work, a practical, scalable, verifiable ( k , n ) threshold continuous variable QSS protocol secure against eavesdroppers and dishonest players are proposed and demonstrated. The protocol does not require preparing the laser source by each player and phase locking of independent lasers. The parameter evaluation and key extraction can be accomplished by only the dealer and the corresponding player. By using the multiple sideband modulation, a single heterodyne detector can extract the information of multiple players. The practical security of the system is considered. The system is versatile, it can support the CKA protocol by only modifying the classic post-processing and requiring no changes to the underlying hardware architecture. By implementing the QSS and CKA protocols with five parties over 25 km (55 km) single-mode fibers, a key rate of 0.0061 (7.14 × 10 −4 ) bits per pulse is observed. The results significantly reduces the system complexity and paves the way for the practical applications of QSS and CKA with efficient utilization of resources and telecom technologies.
PI-ELM: Reinforcement learning-based adaptable policy improvement for dynamical system

Behavioral cloning of imitation learning is theoretically sound that can capture and generate the motor skills from expert demonstrations, but they suffer poor adaptability with a small dataset in a new environment. This study improves adaptability, by proposing a novel reinforcement learning strategy for low-level behavioral learning with a small number of expert demonstrations. Specifically, the policy improvement-based reinforcement learning framework is divided into two phases: the low level is based on supervised learning using extreme learning machine (ELM) to clone the behavior from demonstrations, which can further be represented as a dynamical system with policy parameters; and the high level reinforcement learning improves the adaptability of ELM in new tasks. In this paper, we bridge the gap between machine learning and stochastic optimal control systems and propose the improved path integral-based reinforcement learning PI-ELM strategy to learn the policy parameters from low-level ELM. The proposed framework's performance and effectiveness are illustrated through several task experiments. The results indicate that our method can significantly improve the adaptability of imitation learning in new scenarios, including single task obstacle avoidance, via-points, antidisturbance, or hybrid tasks.
Augmented Reality: Survey

An Augmented Reality (AR) system is a technology that overlays digital information, such as images, sounds, or text, onto a user’s view of the real world, providing an enriched and interactive experience of the surrounding environment. It has evolved into a potent instrument for improving human perception and decision-making across various domains, including industrial, automotive, healthcare, and urban planning. This systematic literature review aims to offer a comprehensive understanding of AR technology, its limitations, and implementation challenges in the most significant areas of application in engineering and beyond. The review will explore the state-of-the-art AR techniques, their potential use cases, and the barriers to widespread adoption, while also identifying future research directions and opportunities for innovation in the rapidly evolving field of augmented reality. This study works as a compilation of the existing technologies in the subject, especially useful for beginners in AR or as a starting point for developers who seek to innovate or implement new technologies, thus knowing the limitations and current challenges that could arise.
Metaverse in services marketing: an overview and future research directions

Although it is still at the inception stage, the Metaverse is likely to revolutionize service marketing and management by disrupting existing business strategies, consumer norms, and marketing practices. However, most of the existing research focuses on the co-creation of metaverstic experiences in terms of interaction, but not on the co-creation of the purchase experience process of the actual products and services. This study proposes a conceptual framework that explains how and why the Metaverse will have significant impacts on creation and delivery of service experiences, the marketing of those experiences, and the co-creation of the purchase experience process through the provision of functional and hedonic benefits to various stakeholders. This study also discusses the potential of Metaverse in mitigating decision risks attributed to uncertainties associated with service experience offerings, information overload, and confusion in the marketing ecosystems and customers' service experience journey. Since the adoption of the Metaverse will have significant implications for all stakeholders while presenting challenges, implications of the Metaverse and the associated challenges are discussed. This study also provides a research agenda to investigate the possible impacts of the Metaverse on service industries.
Adaptive Weighted Multi-view Evidential Clustering

Multi-view clustering, which integrates information from different views for better performance, has gained widespread attention. However, the existing clustering methods cannot represent the uncertainty and imprecision in cluster assignment caused by the tendency of clusters to overlap and the diversity among views. To surmount this issue, in this paper, we propose an adaptive weighted multi-view evidential clustering (WMVWC) method based on the framework of belief functions. The proposed WMVEC can be viewed as a multi-view version of conventional evidential c-means clustering. We construct the objective function of WMVEC by integrating the learning of view weights and credal partition into a unified framework, and design an optimization scheme to obtain the optimal results of WMVEC. Specifically, the view weight can measure the contribution of each view in clustering. The credal partition can provide a deeper understanding of the data structure by allowing samples to belong not only to singleton clusters, but also to a union of different singleton clusters, called meta-cluster. Experiment results demonstrate the effectiveness of the proposed WMVEC with respect to other state-of-the-art methods on real-world datasets. The code can be available at link .
Enhancing gland segmentation in colon histology images using an instance-aware diffusion model

In pathological image analysis, determination of gland morphology in histology images of the colon is essential to determine the grade of colon cancer. However, manual segmentation of glands is extremely challenging and there is a need to develop automatic methods for segmenting gland instances. Recently, due to the powerful noise-to-image denoising pipeline, the diffusion model has become one of the hot spots in computer vision research and has been explored in the field of image segmentation. In this paper, we propose an instance segmentation method based on the diffusion model that can perform automatic gland instance segmentation. Firstly, we model the instance segmentation process for colon histology images as a denoising process based on a diffusion model. Secondly, to recover details lost during denoising, we use Instance Aware Filters and multi-scale Mask Branch to construct global mask instead of predicting only local masks. Thirdly, to improve the distinction between the object and the background, we apply Conditional Encoding to enhance the intermediate features with the original image encoding. To objectively validate the proposed method, we compared several state-of-the-art deep learning models on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset (165 images), the Colorectal Adenocarcinoma Glands (CRAG) dataset (213 images) and the RINGS dataset (1500 images). Our proposed method obtains significantly improved results for CRAG (Object F1 0.853 ± 0.054, Object Dice 0.906 ± 0.043), GlaS Test A (Object F1 0.941 ± 0.039, Object Dice 0.939 ± 0.060), GlaS Test B (Object F1 0.893 ± 0.073, Object Dice 0.889 ± 0.069), and RINGS dataset (Precision 0.893 ± 0.096, Dice 0.904 ± 0.091). The experimental results show that our method significantly improves the segmentation accuracy, and the experiment results demonstrate the efficacy of the method.
Cryptocurrencies and global sustainability: do blockchained sectors have distinctive effects?

This study aims to present evidence for the relationship between blockchained business models and global sustainability. To that end, thirteen sectorial cryptocurrency indices are investigated by proxying global sustainability through the Solactive Global Sustainability Leaders Performance Index (GSI). Empirical analyses are conducted using DCC-FIGARCH, FIGARCH and Quantile VAR models with consideration for results of various long memory (Hurst exponent) tests. The FIGARCH modeling for mean and variance equations indicates that five cryptocurrency sectors (Logistics, Entertainment, Gaming, Metaverse and NFT) possess a relatively weak but consistent relationship with global sustainability. These sectors appear to be positively and negatively impacting the returns and volatilities of global sustainability, respectively. Additionally, the QVAR model indicates that global sustainability and respective cryptocurrency indices mainly interact during upward market trends. According to the results, utilizing blockchain technology in traditional markets may offer promising outcomes for global sustainability. The findings are encouraging for the future practices of this technology in traditional sectors.
Practicing Listening Comprehension Skills in High-Immersion Virtual Reality

Holding learners’ attention is challenging, especially when they are asked to listen to long passages. High-immersion virtual reality (VR) can immerse learners in listening tasks, even in such complex languages as Chinese. This empirical study examined the effect of VR on 43 Chinese language learners’ listening comprehension, enjoyment, sense of presence, and cognitive load. The convenience sample was assigned to two groups. The experimental group (n1 = 23) experienced an interactive multimedia story in VR, and the comparison group (n2 = 20) watched a screencast recording of the same story. The analysis of the multivariate analysis of variance (MANOVA) and Wilcoxon rank tests yielded four findings indicating that VR has a positive effect on the development of listening skills. Compared with the video group, the VR group had significantly higher listening comprehension scores, reported significantly more enjoyment and sense of presence, and experienced significantly less cognitive load. Thus, VR could be used as a pedagogical tool to enhance foreign language listening skills.
Deterministic photon source interfaced with a programmable silicon-nitride integrated circuit

Abstract We develop a quantum photonic platform that interconnects a high-quality quantum dot single-photon source and a low-loss photonic integrated circuit made in silicon nitride. The platform is characterized and programmed to demonstrate various multiphoton applications, including bosonic suppression laws and photonic entanglement generation. The results show a promising technological route forward to scale-up photonic quantum hardware.
A survey of Transformer applications for histopathological image analysis: New developments and future directions

Abstract Transformers have been widely used in many computer vision challenges and have shown the capability of producing better results than convolutional neural networks (CNNs). Taking advantage of capturing long-range contextual information and learning more complex relations in the image data, Transformers have been used and applied to histopathological image processing tasks. In this survey, we make an effort to present a thorough analysis of the uses of Transformers in histopathological image analysis, covering several topics, from the newly built Transformer models to unresolved challenges. To be more precise, we first begin by outlining the fundamental principles of the attention mechanism included in Transformer models and other key frameworks. Second, we analyze Transformer-based applications in the histopathological imaging domain and provide a thorough evaluation of more than 100 research publications across different downstream tasks to cover the most recent innovations, including survival analysis and prediction, segmentation, classification, detection, and representation. Within this survey work, we also compare the performance of CNN-based techniques to Transformers based on recently published papers, highlight major challenges, and provide interesting future research directions. Despite the outstanding performance of the Transformer-based architectures in a number of papers reviewed in this survey, we anticipate that further improvements and exploration of Transformers in the histopathological imaging domain are still required in the future. We hope that this survey paper will give readers in this field of study a thorough understanding of Transformer-based techniques in histopathological image analysis, and an up-to-date paper list summary will be provided at https://github.com/S-domain/Survey-Paper .
A mixing algorithm of ACO and ABC for solving path planning of mobile robot

Path planning is involved in many applications such as trajectory planning, mobile robotics, pipeline layout, etc. Researchers use artificial intelligence algorithms to solve path planning efficiently, among which the ant colony algorithm (ACO) is one of the common intelligent algorithms to solve path planning problems. However, the traditional ACO has defects such as low early search efficiency and easy to fall into local optimum, while the artificial bee colony algorithm (ABC) has high search efficiency. Therefore, an improved ant colony optimization-artificial bee colony algorithm (IACO-IABC) is proposed in this study. IACO-IABC contains three mechanisms. First, the heuristic mechanism with directional information for the ACO is improved to enhance the efficiency of steering towards the target direction. Secondly, the novel neighborhood search mechanism of the employed bee and the onlooker bee in the ABC is presented to enhance the exploitation of optimal solutions. Then, the path optimization mechanism is introduced further to reduce the number of turn times in the planned path. To verify the performance of the IACO-IABC, a series of experiments are conducted with 10 different maps. The experiments compare nine variants of ACO and eight commonly used intelligent search algorithms, and the results show the advantages of the IACO-IABC in reducing the number of turn times and path lengths and enhancing the convergence speed of the algorithm. Compared to the best results of other algorithms, the average improvement percentages of the proposed algorithm in terms of the path turn times are 375%, 258.33%, 483.33%, 186.67%, 166.77% and 255.33%, further demonstrating the ability of IACO-IABC to obtain high-quality path planning result.
Cochran’s Q test for analyzing categorical data under uncertainty

Abstract Motivation The Cochran test, also known as Cochran’s Q test, is a statistical procedure used to assess the consistency of proportions across multiple groups in a dichotomous dataset Description This paper introduces a modified version of Cochran’s Q test using neutrosophic statistics to handle uncertainty in practical situations. The neutrosophic Cochran’s Q test determines whether the proportions of a specific outcome are consistent across different groups, considering both determinate and indeterminate parts. Results An application of the proposed test is presented using production data to assess the capabilities of machines during different days of the week. The comparative study demonstrates the advantages of the proposed test over the classical Cochran’s Q test, providing insights into the degree of indeterminacy and enhancing decision-making in uncertain scenarios. Conclusion This study introduces a modified version of the Cochran test, utilizing neutrosophic statistics to address uncertainty in practical scenarios. The neutrosophic Cochran’s Q test effectively assesses the consistency of outcome proportions across various groups, accounting for both determinate and indeterminate factors. The application of this novel approach to machine capabilities assessment, based on production data collected over different days of the week, unveils its superiority over the traditional Cochran’s Q test. This superiority is reflected in the insights it offers into the degree of indeterminacy, thereby enhancing decision-making in contexts marked by uncertainty. The simulation study further underscores the critical role of indeterminacy in affecting test statistics and decision outcomes, highlighting the significance of the proposed method in capturing real-world complexities. In essence, the neutrosophic Cochran’s Q test presents a refined and pragmatic tool for addressing the uncertainties inherent in diverse datasets, rendering it invaluable in practical decision-making scenarios.
Determining factors related to artificial intelligence (AI) adoption among Malaysia's small and medium-sized businesses

The purpose of the study is to examine the relationship between Competitive Pressure (CP), Top Management Commitment (TMC), Employee Adaptability (EA), External Support (ES), Organisation Readiness (OR) and Artificial Intelligence Adoption (AIA) among SMES operating in Sabah, Malaysia. By employing judgemental sampling a total of 196 respondents were involved (i.e. owners or managers) in varied SME sectors such as services, manufacturing, construction, agriculture, and mining & quarrying. A survey questionnaire was used for data collection and analysed using Smart PLS 4. The results revealed that top management commitment and organisation readiness have a significant relationship with AI adoption. However, competitive pressure, employee adaptability, and external support have an insignificant impact on AI adoption. This suggests that SME organizations may benefit from focusing on and enhancing TMC and OR practices to improve Al outcomes. Overall, these findings can guide decision-making and resource allocation, emphasizing the importance of OR and TMC in driving desired outcomes related to Al and highlighting areas where efforts may not yield significant effects. Based on present technological demands, practical implications and future research directions are also highlighted.