Reviewerly/exHarmony huggingface dataset:
Spicing up hospitality service encounters: the case of Pepper™

Purpose This paper aims to explore the implications of integrating humanoid service robots into hospitality service encounters by evaluating two service prototypes using Softbank Robotics’ popular service robot Pepper™: to provide information (akin to a receptionist) and to facilitate order-taking (akin to a server). Drawing both studies together, the paper puts forward novel, theory-informed yet context-rooted design principles for humanoid robot adoption in hospitality service encounters. Design/methodology/approach Adopting a multiple method qualitative approach, two service prototypes are evaluated with hospitality and tourism experts ( N = 30, Prototype 1) and frontline hospitality employees ( N = 18, Prototype 2) using participant observation, in situ feedback, semi-structured interviews and photo-elicitation. Findings The adoption of humanoid service robots in hospitality is influenced by the following four layers of determinants: contextual, social, interactional and psychological factors, as well as extrinsic and intrinsic drivers of adoption. These empirical findings both confirm and extend previous conceptualizations of human-robot interaction (HRI) in hospitality service. Research limitations/implications Despite using photo-elicitation to evoke insight regarding the use of different types of service robots in hospitality, the paper mostly focuses on anthropomorphized service robots such as Pepper™. Practical implications Adopting humanoid service robots will transform hospitality operations, whereby the most routine, unpleasant tasks such as taking repeat orders or dealing with complaints may be delegated to service robots or human-robot teams. Social implications Working with and receiving service from Pepper™ changes the service encounter from direct practical, technical considerations to more nuanced social and psychological implications, particularly around feelings of self-esteem, social pressure and social judgment. Originality/value This paper presents one of the first empirical studies on HRI in hospitality service encounters using Softbank Robotics’ Pepper™. In doing so, the paper presents a novel framework for service robot adoption rooted in first-hand user interaction as opposed to previous, theory-driven conceptualizations of behavior or empirical studies exploring behavioral intention.
MARVIS: Combining Mobile Devices and Augmented Reality for Visual Data Analysis

We present Marvis, a conceptual framework that combines mobile devices and head-mounted Augmented Reality (AR) for visual data analysis. We propose novel concepts and techniques addressing visualization-specific challenges. By showing additional 2D and 3D information around and above displays, we extend their limited screen space. AR views between displays as well as linking and brushing are also supported, making relationships between separated visualizations plausible. We introduce the design process and rationale for our techniques. To validate Marvis’ concepts and show their versatility and widespread applicability, we describe six implemented example use cases. Finally, we discuss insights from expert hands-on reviews. As a result, we contribute to a better understanding of how the combination of one or more mobile devices with AR can benefit visual data analysis. By exploring this new type of visualization environment, we hope to provide a foundation and inspiration for future mobile data visualizations.
Antecedents and consequences of chatbot initial trust

Purpose Artificial intelligence chatbots are shifting the nature of online services by revolutionizing the interactions of service providers with consumers. Thus, this study aims to explore the antecedents (e.g. compatibility, perceived ease of use, performance expectancy and social influence) and consequences (e.g. chatbot usage intention and customer engagement) of chatbot initial trust. Design/methodology/approach A sample of 184 responses was collected in Lebanon using a questionnaire and analyzed using structural equation modeling (SEM) by AMOS 24. Findings The results revealed that except for performance expectancy, all the other three factors (compatibility, perceived ease of use and social influence) significantly boost customers’ initial trust toward chatbots. Further, initial trust in chatbots enhances the intention to use chatbots and encourages customer engagement. Research limitations/implications The study provides insights into some variables influencing initial chatbot trust. Future studies could extend the model by adding other variables (e.g. customer experience and attitude), in addition to exploring the dark side of artificial intelligence chatbots. Practical implications This study suggests key insights for marketing managers on how to build chatbot initial trust, which, in turn, will lead to an increase in customers’ interactions with the brand. Originality/value The current study marks substantial contributions to the artificial intelligence marketing literature by proposing and testing a novel conceptual model that examines for the first time the factors that impact chatbot initial trust and the key outcomes of the latter.
A Critical Review of Blockchain Acceptance Models—Blockchain Technology Adoption Frameworks and Applications

Blockchain is a promising breakthrough technology that is highly applicable in manifold sectors. The adoption of blockchain technology is accompanied by a range of issues and challenges that make its implementation complicated. To facilitate the successful implementation of blockchain technology, several blockchain adoption frameworks have been developed. However, selecting the appropriate framework based on the conformity of its features with the business sector may be challenging for decision-makers. This study aims to provide a systematic literature review to introduce the adoption frameworks that are most used to assess blockchain adoption and realize business sectors that these models have been applied. Thus, the blockchain adoption models in 56 articles are reviewed and the results of the studies are summarized by categorizing the articles into five main sections including supply chain, industries, financial sector, cryptocurrencies, and other articles (excluded from the former fields). The findings of the study show that the models based on the technology acceptance model (TAM), technology–organization–environment (TOE), and new conceptual frameworks were the focus of the majority of selected articles. Most of the articles have focused on blockchain adoption in different industry fields and supply chain areas.
Development and Transformation in Digital Marketing and Branding with Artificial Intelligence and Digital Technologies Dynamics in the Metaverse Universe

The Metaverse will have a variety of effects on marketing. Businesses need to maintain their identities in the Metaverse realm. Generation Z and Generation alpha will have an easier time adjusting to virtual realms. The augmented reality environment allows buyers to interact with products without leaving the comfort of their own homes. Realization of in-store experiences is possible in the universe of the Metaverse. In addition, there will be an increase in the number of options for branding in the Metaverse. Virtual billboards and the virtual clothing consumers choose to wear will also influence brand awareness. Additionally, Non-Fungible Tokens (NFTs) will be used to produce branded virtual content for end users.&#x0D; Examining the influence that technologies powered by artificial intelligence have had on digital marketing and branding will be the primary focus of this research project. In addition, research will be conducted into the applications of the Metaverse, artificial intelligence, and other digital technologies in the marketing field and studies about these subject areas. The research investigated several digital technologies, including the Metaverse, artificial intelligence, blockchain, virtual reality, and augmented reality. It is of the utmost importance for businesses to be able to compete in digital and virtual environments within the context of digital transformation to thrive in an increasingly competitive world. Companies need to invest in the Metaverse, artificial intelligence, and various other forms of digital technology to expand their marketing awareness in virtual environments, expand their customer portfolios, and become brands to take the lead in their respective markets.
What is the metaverse? Definitions, technologies and the community of inquiry

The term metaverse appeared for the first time in a novel published in 1992. Since the early 2000s, researchers have started to use this term to refer to digital technologies for learners to interact with other users with avatars. The term came to prominence in around 2020 due to the rebranding of Facebook. However, there was no consensus on what kind of technologies should belong to the metaverse and how to conceptualise the term. As such, this paper presents an exploratory review for conceptualising the metaverse based on 19 articles from the Web of Science database. This review focuses on the metaverse trend, how researchers in the past and present conceptualizing the term, and key technologies identified in the metaverse world. The findings identify the major types of technologies used in the metaverse studies and offers a sound theoretical foundation in terms of cognitive, social and teacher presence to understand what future potential of these technologies could bring to online learning. Five major types of technologies are identified which could map to four key elements of the metaverse (i.e., immersion, advanced computing, socialisation, decentralisation). At the end, a model is proposed to connect the key elements of the metaverse and its three presences in the community of inquiry that enhance students’ learning outcomes in the metaverse learning environment. Implications for practice or policy: Educators and researchers could rethink what types of technologies belong to the metaverse and how it has the potential to influence the education sector. Instructional designers could create meaningful learning experiences through the four key elements of the metaverse – immersion, advanced computing, socialisation and decentralisation. Policy-makers and educators could refer to the model of metaverse learning environment to guide their future policy and practices.
Machine Learning for Microcontroller-Class Hardware: A Review

The advancements in machine learning (ML) opened a new opportunity to bring intelligence to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML deployment has high memory and computes footprint hindering their direct deployment on ultraresource-constrained microcontrollers. This article highlights the unique requirements of enabling onboard ML for microcontroller-class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure that the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of ML model development for microcontroller-class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.
Two-dimensional topological quantum walks in the momentum space of structured light

Quantum walks are powerful tools for quantum applications and for designing topological systems. Although they are simulated in a variety of platforms, genuine two-dimensional realizations are still challenging. Here we present an innovative approach to the photonic simulation of a quantum walk in two dimensions, where walker positions are encoded in the transverse wavevector components of a single light beam. The desired dynamics is obtained by means of a sequence of liquid-crystal devices, which apply polarization-dependent transverse "kicks" to the photons in the beam. We engineer our quantum walk so that it realizes a periodically-driven Chern insulator, and we probe its topological features by detecting the anomalous displacement of the photonic wavepacket under the effect of a constant force. Our compact, versatile platform offers exciting prospects for the photonic simulation of two-dimensional quantum dynamics and topological systems.
Architecture of next-generation e-commerce platform

E-commerce, driven by computer and internet technology, has experienced a significant growth in almost all fields during the past two decades. E-commerce has significantly changed the rules of business. Numerous research institutions and enterprises have made e-commerce more intelligent and convenient. Here, we propose a novel prototype of next-generation e-commerce platform with an architecture framework and theoretical models. Each subject, including the individual, enterprise, and administrative department, has his/her personalized portal to complete the subject information synchronization, supply release, demand satisfaction, and social contact. By using the personalized portal, instead of the traditional trading platform, the consumers and suppliers can complete intelligent matching transactions without intermediate traders. Moreover, the overall transaction process can be reviewed, making the transaction safer, more transparent, and more interesting. Moreover, the interconnected personalized portals solve the isolated islands of information, and the counterparts support parallel processing. Thus, this may improve the operating efficiency of the entire society.
A modulator-free quantum key distribution transmitter chip

Quantum key distribution (QKD) has convincingly been proven compatible with real life applications. Its wide-scale deployment in optical networks will benefit from an optical platform that allows miniature devices capable of encoding the necessarily complex signals at high rates and with low power consumption. While photonic integration is the ideal route toward miniaturisation, an efficient route to high-speed encoding of the quantum phase states on chip is still missing. Consequently, current devices rely on bulky and high power demanding phase modulation elements which hinder the sought-after scalability and energy efficiency. Here we exploit a novel approach to high-speed phase encoding and demonstrate a compact, scalable and power efficient integrated quantum transmitter. We encode cryptographic keys on-demand in high repetition rate pulse streams using injection-locking with deterministic phase control at the seed laser. We demonstrate record secure-key-rates under multi-protocol operation. Our modulator-free transmitters enable the development of high-bit rate quantum communications devices, which will be essential for the practical integration of quantum key distribution in high connectivity networks.
Disjoint Label Space Transfer Learning with Common Factorised Space

In this paper, a unified approach is presented to transfer learning that addresses several source and target domain labelspace and annotation assumptions with a single model. It is particularly effective in handling a challenging case, where source and target label-spaces are disjoint, and outperforms alternatives in both unsupervised and semi-supervised settings. The key ingredient is a common representation termed Common Factorised Space. It is shared between source and target domains, and trained with an unsupervised factorisation loss and a graph-based loss. With a wide range of experiments, we demonstrate the flexibility, relevance and efficacy of our method, both in the challenging cases with disjoint label spaces, and in the more conventional cases such as unsupervised domain adaptation, where the source and target domains share the same label-sets.
Knowledge Aware Semantic Concept Expansion for Image-Text Matching

Image-text matching is a vital cross-modality task in artificial intelligence and has attracted increasing attention in recent years. Existing works have shown that learning semantic concepts is useful to enhance image representation and can significantly improve the performance of both image-to-text and text-to-image retrieval. However, existing models simply detect semantic concepts from a given image, which are less likely to deal with long-tail and occlusion concepts. Frequently co-occurred concepts in the same scene, e.g. bedroom and bed, can provide common-sense knowledge to discover other semantic-related concepts. In this paper, we develop a Scene Concept Graph (SCG) by aggregating image scene graphs and extracting frequently co-occurred concept pairs as scene common-sense knowledge. Moreover, we propose a novel model to incorporate this knowledge to improve image-text matching. Specifically, semantic concepts are detected from images and then expanded by the SCG. After learning to select relevant contextual concepts, we fuse their representations with the image embedding feature to feed into the matching module. Extensive experiments are conducted on Flickr30K and MSCOCO datasets, and prove that our model achieves state-of-the-art results due to the effectiveness of incorporating the external SCG.
Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning

In settings with related prediction tasks, integrated multi-task learning models can often improve performance relative to independent single-task models. However, even when the average task performance improves, individual tasks may experience negative transfer in which the multi-task model’s predictions are worse than the single-task model’s. We show the prevalence of negative transfer in a computational chemistry case study with 128 tasks and introduce a framework that provides a foundation for reducing negative transfer in multitask models. Our Loss-Balanced Task Weighting approach dynamically updates task weights during model training to control the influence of individual tasks.
Is bitcoin a safe haven or a hedging asset? Evidence from China

Based on daily data about Bitcoin and six other major financial assets (stocks, commodity futures (commodities), gold, foreign exchange (FX), monetary assets, and bonds) in China from 2013 to 2017, we use a VAR-GARCH-BEKK model to investigate mean and volatility spillover effects between Bitcoin and other major assets and explore whether Bitcoin can be used either as a hedging asset or a safe haven. Our empirical results show that (i) only the monetary market, i.e., the Shanghai Interbank Offered Rate (SHIIBOR) has a mean spillover effect on Bitcoin and (ii) gold, monetary, and bond markets have volatility spillover effects on Bitcoin, while Bitcoin has a volatility spillover effect only on the gold market. We further find that Bitcoin can be hedged against stocks, bonds and SHIBOR and is a safe haven when extreme price changes occur in the monetary market. Our findings provide useful information for investors and portfolio risk managers who have invested or hedged with Bitcoin.
Scalable quantum computing with qudits on a graph

We show a significant reduction of the number of quantum operations and the improvement of the circuit depth for the realization of the Toffoli gate by using qudits. This is done by establishing a general relation between the dimensionality of qudits and their topology of connections for a scalable multiqudit processor, where higher qudit levels are used for substituting ancillas. The suggested model is of importance for the realization of quantum algorithms and as a method of quantum error correction codes for single-qubit operations.
Local Features and Visual Words Emerge in Activations

We propose a novel method of deep spatial matching (DSM) for image retrieval. Initial ranking is based on image descriptors extracted from convolutional neural network activations by global pooling, as in recent state-of-the-art work. However, the same sparse 3D activation tensor is also approximated by a collection of local features. These local features are then robustly matched to approximate the optimal alignment of the tensors. This happens without any network modification, additional layers or training. No local feature detection happens on the original image. No local feature descriptors and no visual vocabulary are needed throughout the whole process. We experimentally show that the proposed method achieves the state-of-the-art performance on standard benchmarks across different network architectures and different global pooling methods. The highest gain in performance is achieved when diffusion on the nearest-neighbor graph of global descriptors is initiated from spatially verified images.
Enhancing online rapport experience via augmented reality

Purpose The purpose of this study is to draws on self-determination and self-evaluation theories to examine the psychological factors impacted by augmented reality (AR) services, an augmented reality try-on system. This study highlights three characteristics of modality, synchronous sense or ownership and re-processability within an AR try-on experiences as well as the moderating effects of consumers’ body surveillance and fashion consciousness. Design/methodology/approach Using a scenario survey approach, this study designs characteristics of an AR try-on system to examine the research model and the hypotheses. A total of 207 responses are collected and analysed using the SmartPLS 3 statistical software. Findings The results show that modality, synchronous sense of ownership control and re-processability of AR try-on system positively affect consumer’s rapport experience. Both body surveillance and fashion consciousness significantly moderate the effects of AR try-on service system characteristics on consumer rapport experience. Research limitations/implications This study highlights the importance of understanding the implications of the evolution of cyborg consumerism where consumer technology interface systems such as AR, as a source of technologically mediated modality, become part of the consumer’s body, an extension of their body if you will. Practical implications Based on the study findings, marketing managers can understand how to better use AR to implement digital promotional strategies for various body-involvement products. Originality/value Using immersive technologies, this study shows that AR allows a consumer see an authentic self and tangible extension of their physical self in an online shopping setting, thus enhancing a consumer’s online shopping experience.
On-Edge Multi-Task Transfer Learning: Model and Practice With Data-Driven Task Allocation

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes a heavy computation burden to resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify \emph{task importance}. We then show that task allocation with task importance for MTL (TATIM) is a variant of the NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a Data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study that bridges model and practice via a new architecture and main components design within the AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4\% energy consumption compared with the state-of-the-art when solving TATIM.