Reviewerly/exHarmony huggingface dataset:
A buzzword, a phase or the next chapter for the Internet? The status and possibilities of the metaverse for tourism

Purpose Growing recognition of the metaverse has implied its far-reaching impacts on the tourism and hospitality industry. This paper sets out to detail the status of metaverse-related research in tourism and hospitality, propose intriguing directions for future studies and highlight multiple areas that call for immediate attention from practitioners in navigating the metaverse phenomenon. Design/methodology/approach This viewpoint paper referenced the extant academic discussion on the metaverse, based on which timely suggestions for academia and practices are proposed. Findings This viewpoint paper presents an account of the metaverse and discusses the status of metaverse-related research in hospitality and tourism. It then proposes intriguing avenues for future research around the topics of marketing, reconceptualizing service quality, attitude and behaviors, electronic customer-to-customer interactions, transformative impacts on the society well-being and research methodology. Multiple areas that call for immediate attention from practitioners in navigating the metaverse phenomenon are also highlighted. Both scholars and industry organizations are called upon to assume some responsibility for mapping out protocols to guide the appropriate development, use and governance of metaverse worlds. Governments and policymakers are further encouraged to consider the ramifications of metaverse development for individuals and society and to devise proactive mitigation strategies. Practical implications This viewpoint paper proposes several directions for future business practices in the areas of co-creation, experiential consumption, and emerging critical issues in healthcare, human resources, and social media services. It expects to inspire more discussion about the potential impacts of metaverse on the wider society. Its practical significance will further expand the theoretical foundation of the metaverse research and makes this viewpoint paper an intriguing prospect. Originality/value The nascent stage of academic discussion intended to guide the development of metaverse is noteworthy, which forms a notable contrast with the growing recognition of its potential of co-creating transformational experiences in hospitality and tourism. This viewpoint paper joins the current academic conversations acknowledging this phenomenon in hospitality and tourism. Provided the notable topicality and empirical relevance, the expanded scope and rich content the present viewpoint paper provides for metaverse will offer a fruitful ground for future research to tap further into currently underrepresented areas.
New Algorithms for the Deuring Correspondence

The Deuring correspondence defines a bijection between isogenies of supersingular elliptic curves and ideals of maximal orders in a quaternion algebra. We present a new algorithm to translate ideals of prime-power norm to their corresponding isogenies — a central task of the effective Deuring correspondence. The new method improves upon the algorithm introduced in 2021 by De Feo, Kohel, Leroux, Petit and Wesolowski as a building-block of the SQISign signature scheme. SQISign is the most compact post-quantum signature scheme currently known, but is several orders of magnitude slower than competitors, the main bottleneck of the computation being the ideal-to-isogeny translation. We implement the new algorithm and apply it to SQISign, achieving a more than two-fold speedup in key generation and signing with a new choice of parameter. Moreover, after adapting the state-of-the-art $$\mathbb {F}_{p^2}$$ multiplication algorithms by Longa to implement SQISign’s underlying extension field arithmetic and adding various improvements, we push the total speedups to over three times for signing and four times for verification. In a second part of the article, we advance cryptanalysis by showing a very simple distinguisher against one of the assumptions used in SQISign. We present a way to impede the distinguisher through a few changes to the generic KLPT algorithm. We formulate a new assumption capturing these changes, and provide an analysis together with experimental evidence for its validity.
Robust regression under the general framework of bounded loss functions

Conventional regression methods often fail when encountering noise. The application of a bounded loss function is an effective means to enhance regressor robustness. However, most bounded loss functions exist in Ramp-style forms, losing some inherent properties of the original function due to hard truncation. Besides, there is currently no unified framework on how to design bounded loss functions. In response to the above two issues, this paper proposes a general framework that can smoothly and adaptively bound any non-negative function. It can not only degenerate to the original function, but also inherit its elegant properties, including symmetry, differentiability and smoothness. Under this framework, a robust regressor called bounded least squares support vector regression (BLSSVR) is proposed to mitigate the effects of noise and outliers by limiting the maximum loss. With appropriate parameters, the bounded least squares loss grows faster than its unbounded form in the initial stage, which facilitates BLSSVR to assign larger weights to non-outlier points. Meanwhile, the Nesterov accelerated gradient (NAG) algorithm is employed to optimize BLSSVR. Extensive experiments on synthetic and real-world datasets profoundly demonstrate the superiority of BLSSVR over benchmark methods.
Breast cancer classification using Deep Q Learning (DQL) and gorilla troops optimization (GTO)

Breast cancer (BC) is a primary reason for death among the female population around the world. Early identification can aid in decreasing the mortality rates associated with this disease all over the world. The application of big data in healthcare not only saves lives but also saves time and money. Hospital records, patient medical information, and medical exam results are big data sources in the healthcare sector. In this research, a big data-based two-class (i.e., Benign or Cancer) BC classification model is developed using the Deep Reinforcement Learning (DRL) method. The model stages are big data collection, preprocessing, feature selection, classification, and explanations. In the preprocessing step, the data is normalized, and the missing values are replaced. The gorilla troops optimization (GTO) algorithm is employed for the feature selections. Deep reinforcement learning-based Deep Q learning (DQL) is used for the classification, and LIME is used to explain the predicted output. Three different datasets such as WBCD, WDBC, and WPBC from the UCI repository, are used for evaluation. We verify the proposed model against the radial basis function ensemble boosting learning method (RBF-ELB), the Particle Swarm Optimization Multilayer Perceptron (PSO-MLP), and the Genetic Algorithm Multilayer Perceptron (GA-MLP). The results of the experiments show that our method outperforms the traditional methods. The proposed GTO-DQL model achieves 98.90% accuracy for the WBCD dataset, 99.02% for WDBC, and 98.88% for the WPBC dataset, respectively.
Developments of Digital Technologies Related to Supply Chain Management

Supply chain management aims to optimize profits and keep a firm ahead of the competition by coordinating activities between suppliers and consumers. It exemplifies the logistics sector's dedication to streamlined operations. The digital supply chain strategy aims to maximize choices concerning supply and demand, as well as the interdependencies among them, via cutting-edge technology and a deeper knowledge of the responsibilities performed by all stakeholders along the supply chain. It is undeniable that the advent of digital technology has profoundly impacted the workings of businesses, the habits of consumers, and the relationships between different parts of the economy. Consequently, the mobile Internet has contributed to the always-online mentality and the hyperconnectivity of people, businesses, and machines in the global digital economy. Social networks amass a mountain of information on their users. Artificial intelligence provides decision-making algorithms and process management automation. Big data technologies allow for the analysis of massive volumes of organized and unstructured data and predictive analytics. The cloud facilitates the sharing of complex data and knowledge across all actors in an ecosystem. When it comes to some tasks, intelligent robots can do away with the need for human workers altogether. Utilizing Augmented Reality (AR) provides a more all-encompassing and in-depth understanding of business processes. The Internet of Things allows for the remote administration of systems and processes, and the blockchain ensures the legitimacy of all involved parties and transactions.
Region coverage-aware path planning for unmanned aerial vehicles: A systematic review

The use of unmanned aerial vehicles (UAVs) to carry out remote aerial surveys has become prominent in recent years. The UAV-based survey faces several operational issues, such as complicated terrain, limited UAV resources, obstacles, sensor limitations, and other environmental factors. In addition, the coverage plan includes numerous objectives, such as lowering path length, maximizing coverage, and limiting survey time, necessitating multi-objective optimization. UAVs require effective coverage path planning (CPP) to generate the ideal route. It involves determining the path which encompasses every point inside the required region under different constraints. The process automates the process of route determination for autonomous operation by considering various environmental constraints. In this paper, we explore and analyze the existing research on the various techniques used in coverage route planning for UAVs. It provides an overview of the current state-of-the-art CPP methods for UAVs. The study discusses the key challenges and requirements of CPP for UAVs and presents various approaches proposed in the literature to tackle these challenges. We explore a variety of geometric flight patterns for the area of interest having UAV deployment. It also features multi-UAV and multi-region coverage strategies, providing a new dimension to UAV-based operations. The energy consumption of UAVs during CPP is an essential factor, as it influences their flight length and mission duration. The design of the CPP algorithm is determined by the unique requirements of the UAV application, such as the size and form of the region to be mapped, the existence of obstacles, and the desired coverage resolution. Path planning strategies in a three-dimensional environment and dynamic coverage are also included in the study. Moreover, we compare the existing strategies using different performance metrics to evaluate the success of covering missions. Finally, the problems and unresolved concerns related to UAV coverage path planning are examined to provide valuable insights to the readers.
Cyber-Physical System Security Based on Human Activity Recognition through IoT Cloud Computing

Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen cyber-physical security (CPS) with minimal human intervention. It is an approach based on human activity recognition (HAR), where GoogleNet–BiLSTM network hybridization has been used to recognize suspicious activities in the cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates machine vision at the IoT edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and the GoogleNet–BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security at only USD 4.29±0.29 per month.
RelTR: Relation Transformer for Scene Graph Generation

Different objects in the same scene are more or less related to each other, but only a limited number of these relationships are noteworthy. Inspired by Detection Transformer, which excels in object detection, we view scene graph generation as a set prediction problem. In this article, we propose an end-to-end scene graph generation model Relation Transformer (RelTR), which has an encoder-decoder architecture. The encoder reasons about the visual feature context while the decoder infers a fixed-size set of triplets subject-predicate-object using different types of attention mechanisms with coupled subject and object queries. We design a set prediction loss performing the matching between the ground truth and predicted triplets for the end-to-end training. In contrast to most existing scene graph generation methods, RelTR is a one-stage method that predicts sparse scene graphs directly only using visual appearance without combining entities and labeling all possible predicates. Extensive experiments on the Visual Genome, Open Images V6, and VRD datasets demonstrate the superior performance and fast inference of our model.
Interactions between investors’ fear and greed sentiment and Bitcoin prices

This paper examines the relationship between investor fear in the cryptocurrency market and Bitcoin prices by considering the potential effects of the ongoing COVID-19 pandemic during the period of May 5, 2018 and December 10, 2020. The existence of structural changes in the time series for the full sample reveals a non-constant causality between fear sentiment and Bitcoin prices, which leads us to apply a bootstrap rolling window Granger causality test. Our results show that both negative and positive interactions between fear sentiment and Bitcoin prices occur during several subperiods. The nature of these interactions changes significantly before and during the pandemic. Thus, we contribute to the fast-growing literature on the financial effects of the COVID-19 global pandemic, as well as to the debate on whether to classify Bitcoin as a new asset, speculative investment, currency, or safe haven asset.
A Secure Blockchain-Assisted Access Control Scheme for Smart Healthcare System in Fog Computing

IoT-based smart healthcare system (SHS) allows doctors to monitor and diagnose patients remotely, which can greatly ease overcrowding in the hospitals and disequilibrium of medical resources, especially during the rage of COVID-19. However, the SHS generates enormous data which contains sensitive personal information. To protect patients’ privacy, we propose a secure blockchain (BC)-assisted access control scheme for SHS in fog computing. All the operations of users are recorded on the BC by smart contract in order to ensure transparency and reliability of the system. We present a BC-assisted multiauthority attribute-based encryption (MA-ABE) scheme with keyword search to ensure the confidentiality of the data, avoid single point of failure and implement fine-grained access control of the system. IoT devices are limited in resources, therefore it is not practical to apply the BC-assisted MA-ABE scheme directly. To reduce the burdens of IoT devices, We outsource most of the computational tasks to fog nodes. Finally, the security and performance analysis demonstrate that the proposed system is reliable, practical, and efficient.
Why airdrop cryptocurrency tokens?

A cryptocurrency token airdrop is a novel means of distributing rights over a blockchain project to a community of users and owners for free. The market value of these airdrop giveaways is often upwards of hundreds of millions of dollars. This paper considers why projects might choose this unusual and costly means of token distribution. It considers a selection of high-profile airdrops as case studies between 2014 and 2022. This is the first comprehensive analysis of the rationales and mechanisms of Web3 token airdrops. We find that two primary rationales for airdrops are marketing (to attract new users and to maintain a community) and decentralisation of ownership and control of a project (building community, providing regulatory protection, and enhancing security). The paper contributes to an understanding of business practice and strategy in the emerging cryptocurrency and blockchain industry.
Acceptance of service robots: a meta-analysis in the hospitality and tourism industry

ABSTRACTABSTRACTRobot services have become an essential competitive differentiator in the hospitality and tourism industry. Seminal studies indicate the need for more profound knowledge about the factors that can increase the acceptance of service robots. This paper investigates the relationship between informational cues and acceptance of service robots through a meta-analysis. Our study also analyzes the acceptance of service robots through contextual characteristics moderators. The results showed that perceived robot trust influences the relationship between informational cues (anthropomorphism and social influence) and acceptance of service robots. By analyzing previous studies, our framework demonstrates that acceptance of service robots has increased more after the COVID pandemic than before the COVID pandemic. Furthermore, the relationship between attitude toward robots and robot usage intention tends to be greater in humanoid service robots (than social robots) and analytical intelligence (than mechanical intelligence).机器人服务已经成为酒店业和旅游业的一个重要竞争优势. 研讨会研究表明,需要对提高服务机器人接受度的因素有更深入的了解. 本文通过荟萃分析研究了信息线索与服务机器人接受度之间的关系. 我们的研究还通过情境特征调节因子分析了服务机器人的接受程度. 结果表明,感知机器人信任影响信息线索(拟人化和社会影响)与服务机器人接受度之间的关系. 通过分析之前的研究,我们的框架表明,在新冠肺炎疫情之后,服务机器人的接受度比新冠肺炎之前增加了更多. 此外,在人形服务机器人(比社交机器人)和分析智能(比机械智能)中,对机器人的态度与机器人使用意愿之间的关系往往更大.KEYWORDS: Acceptance of service robotsinformational cuesperceived robot trustand AI technology Disclosure statementNo potential conflict of interest was reported by the author(s).
Partially Blended Realities: Aligning Dissimilar Spaces for Distributed Mixed Reality Meetings

Mixed Reality allows for distributed meetings where people's local physical spaces are virtually aligned into blended interaction spaces. In many cases, people's physical rooms are dissimilar, making it challenging to design a coherent blended space. We introduce the concept of Partially Blended Realities (PBR) — using Mixed Reality to support remote collaborators in partially aligning their physical spaces. As physical surfaces are central in collaborative work, PBR supports users in transitioning between different configurations of tables and whiteboard surfaces. In this paper, we 1) describe the design space of PBR, 2) present RealityBlender to explore interaction techniques for how users may configure and transition between blended spaces, and 3) provide insights from a study on how users experience transitions in a remote collaboration task. With this work, we demonstrate new potential for using partial solutions to tackle the alignment problem of dissimilar spaces in distributed Mixed Reality meetings.
Feminist Posthumanist Design of Menstrual Care for More-than-Human Bodies

Social stigma and human exceptionalism have contributed to unsustainable menstrual products and a neglect for the nutrients in menstrual blood that can enrich soil. In a Research-through-Design project, we explored how menstrual care can extend to caring for non-human species and the environment. We describe our design process and insights from three workshops with 20 participants, where we designed tools and technologies and worked with biomaterials to create biodegradable menstrual artifacts that can be composted and bring the nutrients in menstrual blood into soil. By drawing on feminist HCI’s quality of ecology and bringing more-than-human design into the domain of intimate care, our research affirms the fertile relations between feminist HCI and posthumanist HCI through the concept of more-than-human bodies. We discuss how our work contributes to inclusive understandings of technology, and to a feminist posthumanist design methodology that centers more-than-human bodies in intimate care.
Multi-party semiquantum private comparison of size relationship with d-dimensional Bell states

Abstract In this paper, we utilize d -dimensional Bell states to construct a multi-party semiquantum private comparison (MSQPC) protocol with two supervisors, which can determine the size relationship of more than two classical users’ private inputs under the control of two supervisors within one round implementation. The two supervisors, i.e., one quantum third party (TP) and one classical TP, are both semi-honest, which means that they can misbehave at their own wishes but are not permitted to conspire with anyone else. Neither quantum entanglement swapping nor unitary operations are required in the proposed MSQPC protocol. The security analysis certifies that the proposed MSQPC protocol can overcome both the outside attacks and the participant attacks.
Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers

Abstract Conversational agents (CAs) have come a long way from their first appearance in the 1960s to today’s generative models. Continuous technological advancements such as statistical computing and large language models allow for an increasingly natural and effortless interaction, as well as domain-agnostic deployment opportunities. Ultimately, this evolution begs multiple questions: How have technical capabilities developed? How is the nature of work changed through humans’ interaction with conversational agents? How has research framed dominant perceptions and depictions of such agents? And what is the path forward? To address these questions, we conducted a bibliometric study including over 5000 research articles on CAs. Based on a systematic analysis of keywords, topics, and author networks, we derive “five waves of CA research” that describe the past, present, and potential future of research on CAs. Our results highlight fundamental technical evolutions and theoretical paradigms in CA research. Therefore, we discuss the moderating role of big technologies, and novel technological advancements like OpenAI GPT or BLOOM NLU that mark the next frontier of CA research. We contribute to theory by laying out central research streams in CA research, and offer practical implications by highlighting the design and deployment opportunities of CAs.