Reviewerly/exHarmony huggingface dataset:
The internet of things and corporate business models: A systematic literature review

The paper proposes a systematic literature review emphasising scholars’ view on the role of IoT in shaping and developing business models of companies. Providing a deep understanding of the state of the art, the paper drafts implications and valuable insights to address future research. A two-decade study of the connection of Internet of Things and business models has been investigated using Scopus and ISI web of science databases as leading sources to get access to the articles. The diffusion of Internet of things needs to be investigated along its implications for corporate business models, such as modifications in the production process, interaction with customers and in the identification of the corporate building blocks. This study provides for the first time a systematic connection among IoT and Business models towards the lens of business, management and accounting field, providing valuable implications, insights and emerging issues.
AI-assisted superresolution cosmological simulations

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in Artificial Intelligence (specifically Deep Learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data, and then make accurate super-resolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore our results can be viewed as new simulation realizations themselves rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations, and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to $16\,h^{-1}\mathrm{Mpc}$, and the HR halo mass function to within $10 \%$ down to $10^{11} \, M_\odot$. We successfully deploy the model in a box 1000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy formation physics in large cosmological volumes.
F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation

In order to generate images for a given category, existing deep generative models generally rely on abundant training images. However, extensive data acquisition is expensive and fast learning ability from limited data is necessarily required in real-world applications. Also, these existing methods are not well-suited for fast adaptation to a new category. Few-shot image generation, aiming to generate images from only a few images for a new category, has attracted some research interest. In this paper, we propose a Fusing-and-Filling Generative Adversarial Network (F2GAN) to generate realistic and diverse images for a new category with only a few images. In our F2GAN, a fusion generator is designed to fuse the high-level features of conditional images with random interpolation coefficients, and then fills in attended low-level details with non-local attention module to produce a new image. Moreover, our discriminator can ensure the diversity of generated images by a mode seeking loss and an interpolation regression loss. Extensive experiments on five datasets demonstrate the effectiveness of our proposed method for few-shot image generation.
Adoption of AI-empowered industrial robots in auto component manufacturing companies

The usage of AI-empowered Industrial Robots (InRos) is booming in the Auto Component Manufacturing Companies (ACMCs) across the globe. Based on a model leveraging the Technology, Organisation, and Environment (TOE) framework, this work examines the adoption of InRos in ACMCs in the context of an emerging economy. This research scrutinises the adoption intention and potential use of InRos in ACMCs through a survey of 460 senior managers and owners of ACMCs in India. The findings indicate that perceived compatibility, external pressure, perceived benefits and support from vendors are critical predictors of InRos adoption intention. Interestingly, the study also reveals that IT infrastructure and government support do not influence InRos adoption intention. Furthermore, the analysis suggests that perceived cost issues negatively moderate the relationship between the adoption intention and potential use of InRos in ACMCs. This study offers a theoretical contribution as it deploys the traditional TOE framework and discovers counter-intuitively that IT resources are not a major driver of technology adoption: as such, it suggests that a more comprehensive framework than the traditional RBV should be adopted. The work provides managerial recommendations for managers, shedding light on the antecedents of adoption intention and potential use of InRos at ACMCs in a country where the adoption of InRos is in a nascent stage.
A Blockchain Platform for User Data Sharing Ensuring User Control and Incentives

We propose a new platform for user modelling with blockchains that allows users to share data without losing control and ownership of it and applied it to the domain of travel booking. Our platform provides solution to three important problems: ensuring privacy and user control, and incentives for sharing. It tracks who shared what, with whom, when, by what means and for what purposes in a verifiable fashion. The paper presents a case study of applying the framework for a hotel reservation system as one of the enterprise nodes of Multichain which collects users’ profile data and allows users to receive rewards while sharing their data with other travel service providers according to their privacy preferences expressed in smart contracts. The user data from the repository is converted into an open data format and shared via stream in the blockchain so that other nodes can efficiently process and use the data. The smart contract verifies and executes the agreed terms of use of the data and transfers digital tokens as a reward to the user. The smart contract imposes double deposit collateral to ensure that all participants act honestly. The paper also presents a performance evaluation of the platform by analyzing latency and memory consumption with selected three test-scenarios and measuring the transaction cost for smart contracts deployment. The results show that the node responded quickly in all our cases with a befitting transaction cost.
Focus on Blockchain: A Comprehensive Survey on Academic and Application

As a decentralized distributed ledger, blockchain has developed rapidly since its birth and has been highly valued by governments, academia, and industry. Start in the financial field, blockchain technology has been rapidly applied to various fields such as the Internet of Things, supply chain, and healthcare. Blockchain technology has made sufficient development and innovation, but it also faces many challenges, such as security, scalability, and waste of resources. This paper starts with the development history of blockchain and combines the characteristics and technical principles of it to analyze the current academic research status and application scenarios. Meanwhile, this paper analyzes the existing outstanding blockchain projects and elaborates on their key advantages and current challenges. With discussing the ongoing development trend of blockchain, the development direction, and the research trend of blockchain in the future, this paper provides a useful reference for related research.
Prediction of breast cancer distant recurrence using natural language processing and knowledge-guided convolutional neural network

Distant recurrence of breast cancer results in high lifetime risks and low 5-year survival rates. Early prediction of distant recurrent breast cancer could facilitate intervention and improve patients' life quality. In this study, we designed an EHR-based predictive model to estimate the distant recurrent probability of breast cancer patients. We studied the pathology reports and progress notes of 6,447 patients who were diagnosed with breast cancer at Northwestern Memorial Hospital between 2001 and 2015. Clinical notes were mapped to Concept unified identifiers (CUI) using natural language processing tools. Bag-of-words and pre-trained embedding were employed to vectorize words and CUI sequences. These features integrated with clinical features from structured data were downstreamed to conventional machine learning classifiers and Knowledge-guided Convolutional Neural Network (K-CNN). The best configuration of our model yielded an AUC of 0.888 and an F1-score of 0.5. Our work provides an automated method to predict breast cancer distant recurrence using natural language processing and deep learning approaches. We expect that through advanced feature engineering, better predictive performance could be achieved.
Falling Asleep Together: What Makes Activities in Social Virtual Reality Meaningful to Users

The increasingly popular social virtual reality (VR) is dramatically transforming how people meet, interact, and socialize online. These immersive digital spaces blend aspects of the online and offline worlds, creating new opportunities for online interactivity. Yet, questions still remain about what individuals actually do in social VR and how they envision and reflect on the design of social VR technologies. In this paper, we report our findings of an interview study (N=30)that investigated what made activities on these platforms meaningful to users as well as users' recommendations for designing social VR to better support such activities. Our findings expand current literature on social dynamics and sociality in online digital spaces by focusing on social VR. We also inform future design directions to create more socially supportive and satisfying social VR space.
Energy-Efficient Resource Allocation for Blockchain-Enabled Industrial Internet of Things With Deep Reinforcement Learning

Industrial Internet of Things (IIoT) has emerged with the developments of various communication technologies. In order to guarantee the security and privacy of massive IIoT data, blockchain is widely considered as a promising technology and applied into IIoT. However, there are still several issues in the existing blockchain-enabled IIoT: 1) unbearable energy consumption for computation tasks; 2) poor efficiency of consensus mechanism in blockchain; and 3) serious computation overhead of network systems. To handle the above issues and challenges, in this article, we integrate mobile-edge computing (MEC) into blockchain-enabled IIoT systems to promote the computation capability of IIoT devices and improve the efficiency of the consensus process. Meanwhile, the weighted system cost, including the energy consumption and the computation overhead, are jointly considered. Moreover, we propose an optimization framework for blockchain-enabled IIoT systems to decrease consumption, and formulate the proposed problem as a Markov decision process (MDP). The master controller, offloading decision, block size, and computing server can be dynamically selected and adjusted to optimize the devices energy allocation and reduce the weighted system cost. Accordingly, due to the high-dynamic and large-dimensional characteristics, deep reinforcement learning (DRL) is introduced to solve the formulated problem. Simulation results demonstrate that our proposed scheme can improve system performance significantly compared to other existing schemes.
Maps Around Me

Visual exploration of maps often requires a contextual understanding at multiple scales and locations. Multiview map layouts, which present a hierarchy of multiple views to reveal detail at various scales and locations, have been shown to support better performance than traditional single-view exploration on desktop displays. This paper investigates the extension of such layouts of 2D maps into 3D immersive spaces, which are not limited by the real-estate barrier of physical screens and support sensemaking through spatial interaction. Based on our initial implementation of immersive multiview maps, we conduct an exploratory study with 16 participants aimed at understanding how people place and view such maps in immersive space. We observe the layouts produced by users performing map exploration search, comparison and route-planning tasks. Our qualitative analysis identifies patterns in layoutgeometry (spherical, spherical cap, planar),overview-detail relationship (central window, occluding, coordinated) andinteraction strategy. Based on these observations, along with qualitative feedback from a user walkthrough session, we identify implications and recommend features for immersive multiview map systems. Our main findings are that participants tend to prefer and arrange multiview maps in a spherical cap layout around them and that they often rearrange the views during tasks.
Explainable Artificial Intelligence for Predictive Maintenance Applications

This paper presents and provides a realistic, yet synthetic, predictive maintenance dataset for use in this paper and by the community. An explainable model and an explanatory interface are described, trained using the dataset, and their explanatory performance evaluated and compared.
Ethical Issues in COVID-19 Communication to Mitigate the Pandemic: Dilemmas and Practical Implications

Communication plays a critical role in all stages of a pandemic. From the moment it is officially declared governments and public health organizations aim to inform the public about the risk from the disease and to encourage people to adopt mitigation practices. The purpose of this article is to call attention to the multiple types and the complexity of ethical challenges in COVID-19 communication. Different types of ethical issues in COVID-19 communication are presented in four main sections. The first deals with ethical issues in informing the public about the risk of the pandemic and dilemmas regarding communicating uncertainty, using threats and scare tactics, and framing the pandemic as a war. The second concerns unintended consequences that relate to increasing inequities, stigmatization, ageism, and delaying medical care. The third raises ethical issues in communicating about specific mitigation practices: contact tracing, wearing face masks, spatial (also referred to as social) distancing, and handwashing or sanitizing. The fourth concerns appealing to positive social values associated with solidarity and personal responsibility, and ethical challenges when using these appeals. The article concludes with a list of practical implications and the importance of identifying ethical concerns, which necessitate interdisciplinary knowledge, cross-disciplinary collaborations, public discourse and advocacy.
Blockchain Technology for Smart Tourism Destinations

This conceptual paper discusses the potential of blockchain technology for Smart Tourism Destinations. The main focus is placed on Smart Tourism Destinations’ four major goals that can be achieved by using blockchain technology, namely: enhancing tourism experience, rewarding sustainable behaviour, ensuring benefits for local communities, and reducing privacy concerns. The paper also outlines the major challenges that need to be overcome to successfully implement this innovative technology. This paper attempts to further advance the current knowledge about the possible implications of blockchain technology within the smart tourism domain, and especially Smart Tourism Destinations.
Measurement-induced steering of quantum systems

We set out a general protocol for steering the state of a quantum system from an arbitrary initial state towards a chosen target state by coupling it to auxiliary quantum degrees of freedom. The protocol requires multiple repetitions of an elementary step: during each step the system evolves for a fixed time while coupled to auxiliary degrees of freedom (which we term 'detector qubits') that have been prepared in a specified initial state. The detectors are discarded at the end of the step, or equivalently, their state is determined by a projective measurement with an unbiased average over all outcomes. The steering harnesses back-action of the detector qubits on the system, arising from entanglement generated during the coupled evolution. We establish principles for the design of the system-detector coupling that ensure steering of a desired form. We illustrate our general ideas using both few-body examples (including a pair of spins-1/2 steered to the singlet state) and a many-body example (a spin-1 chain steered to the Affleck-Kennedy-Lieb-Tasaki state). We study the continuous time limit in our approach and discuss similarities to (and differences from) drive-and-dissipation protocols for quantum state engineering. Our protocols are amenable to implementations using present-day technology. Obvious extensions of our analysis include engineering of other many-body phases in one and higher spatial dimensions, adiabatic manipulations of the target states, and the incorporation of active error correction steps.
AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a high-contact service industry

Abstract The restaurant technology market is rapidly evolving and is transforming the restaurant business as a significant sector of tourism and hospitality. Enabled by artificial intelligence (AI), mobile apps, kiosks and chatbots revolutionize the guest experience and robots automate restaurant operations. Despite the increasing interest, the use of AI and robotics in restaurants is still in its early stage and restaurant managers are seeking guidance to leverage these technologies for service excellence. In this high-contact service sector, emotional skills need to be balanced with the possible automation potentials. The present research analyzes the current state of AI and robotics in the restaurant sector and proposes a systematic identification of process innovation potentials. For this purpose, a market analysis of the European AI and robotics market for restaurant operations is conducted, which yields a first knowledge base for future research and conceptual work. Besides detailed empirical data, a reference process is developed for leveraging new technologies for process innovation.
Hypergraph Learning: Methods and Practices

Hypergraph learning is a technique for conducting learning on a hypergraph structure. In recent years, hypergraph learning has attracted increasing attention due to its flexibility and capability in modeling complex data correlation. In this paper, we first systematically review existing literature regarding hypergraph generation, including distance-based, representation-based, attribute-based, and network-based approaches. Then, we introduce the existing learning methods on a hypergraph, including transductive hypergraph learning, inductive hypergraph learning, hypergraph structure updating, and multi-modal hypergraph learning. After that, we present a tensor-based dynamic hypergraph representation and learning framework that can effectively describe high-order correlation in a hypergraph. To study the effectiveness and efficiency of hypergraph generation and learning methods, we conduct comprehensive evaluations on several typical applications, including object and action recognition, Microblog sentiment prediction, and clustering. In addition, we contribute a hypergraph learning development toolkit called THU-HyperG.
Error-corrected gates on an encoded qubit

To solve classically hard problems, quantum computers need to be resilient to the influence of noise and decoherence. In such a fault-tolerant quantum computer, noise-induced errors must be detected and corrected in real-time to prevent them from propagating between components. This requirement is especially pertinent while applying quantum gates, when the interaction between components can cause errors to quickly spread throughout the system. However, the large overhead involved in most fault-tolerant architectures makes implementing these systems a daunting task, which motivates the search for hardware-efficient alternatives. Here, we present a gate enacted by a multilevel ancilla transmon on a cavity-encoded logical qubit that is fault-tolerant with respect to decoherence in both the ancilla and the encoded qubit. We maintain the purity of the encoded qubit in the presence of ancilla errors by detecting those errors in real-time, and applying the appropriate corrections. We show a reduction of the logical gate error by a factor of two in the presence of naturally occurring decoherence, and demonstrate resilience against ancilla bit-flips and phase-flips by observing a sixfold suppression of the gate error with increased energy relaxation, and a fourfold suppression with increased dephasing noise. The results demonstrate that bosonic logical qubits can be controlled by error-prone ancilla qubits without inheriting the ancilla's inferior performance. As such, error-corrected ancilla-enabled gates are an important step towards fully fault-tolerant processing of bosonic qubits.
tsDetect: an open source test smells detection tool

The test code, just like production source code, is subject to bad design and programming practices, also known as smells. The presence of test smells in a software project may affect the quality, maintainability, and extendability of test suites making them less effective in finding potential faults and quality issues in the project's production code. In this paper, we introduce tsDetect, an automated test smell detection tool for Java software systems that uses a set of detection rules to locate existing test smells in test code. We evaluate the effectiveness of tsDetect on a benchmark of 65 unit test files containing instances of 19 test smell types. Results show that tsDetect achieves a high detection accuracy with an average precision score of 96% and an average recall score of 97%. tsDetect is publicly available, with a demo video, at: https://testsmells.github.io/
Temporal Complementary Learning for Video Person Re-identification

This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification. Firstly, we introduce a Temporal Saliency Erasing (TSE) module including a saliency erasing operation and a series of ordered learners. Specifically, for a specific frame of a video, the saliency erasing operation drives the specific learner to mine new and complementary parts by erasing the parts activated by previous frames. Such that the diverse visual features can be discovered for consecutive frames and finally form an integral characteristic of the target identity. Furthermore, a Temporal Saliency Boosting (TSB) module is designed to propagate the salient information among video frames to enhance the salient feature. It is complementary to TSE by effectively alleviating the information loss caused by the erasing operation of TSE. Extensive experiments show our method performs favorably against state-of-the-arts. The source code is available at https://github.com/blue-blue272/VideoReID-TCLNet .