Reviewerly/exHarmony huggingface dataset:
Understanding travellers’ reactions to robotic services: a multiple case study approach of robotic hotels

Purpose The purpose of this paper is to investigate travellers’ behaviour and examine their reactions to high-tech hotels offering robotic services to customers. Design/methodology/approach Data were gathered from user-generated content within the context of a qualitative research method by analysing the online narratives of travellers at TripAdvisor who visited robotic hotels. Data analysis was realised through content analysis, which revealed various themes and categories of traveller behaviour and reactions to technology. Findings Results show that robotic services significantly improve the quality of service offered to travellers, while positively affecting travellers’ intention to revisit robotic hotels within the context of customer engagement behaviours. Research limitations/implications The results of the present research reveal that the introduction of new technologies in the service industry, such as the robotic butler, can have considerable effects on guest behaviour and attitudes. This field has emerged as a new sub-dimension of customer engagement. Practical implications The use of robots will most likely enhance experiences through interaction between customers and robots. Additionally, in cases where social distancing is required, the use of robots in the hospitality and tourism industry may increase the mobility of people wishing to travel by applying social distancing through use of robots in services. Originality/value The study contributes to the extant literature by identifying the concept of adoption as a sub-dimension deriving from human–robot interaction, thus generating the novelty of the research.
Feasible measurement-based entanglement purification in linear optics

Entanglement purification is used to distill high quality entangled states from several noisy low quality entangled states, and it plays a key role in quantum repeater. The measurement-based entanglement purification protocol (MB-EPP) does not require local two-qubit gates or single-particle measurements on the noisy pairs and may offer significant advantages compared with the gate-based EPPs. We present an alternative MB-EPP in linear optics. Subsequently, we provide a detailed analysis on the realization of this MB-EPP using spontaneous parametric down conversion (SPDC) sources. By delicately designing the optical circuits, the double-pair emission noise caused by SPDC sources can be eliminated automatically. Combined with suitable quantum memory and entanglement swapping, this MB-EPP may have application potential in the implementation of a practical measurement-based quantum repeater.
Text to Image Synthesis for Improved Image Captioning

Generating textual descriptions of images has been an important topic in computer vision and natural language processing.A number of techniques based on deep learning have been proposed on this topic.These techniques use human-annotated images for training and testing the models.These models require a large number of training data to perform at their full potential.Collecting human generated images with associative captions is expensive and time-consuming.In this paper, we propose an image captioning method that uses both real and synthetic data for training and testing the model.We use a Generative Adversarial Network (GAN) based text to image generator to generate synthetic images.We use an attention-based image captioning method trained on both real and synthetic images to generate the captions.We demonstrate the results of our models using both qualitative and quantitative analysis on popularly used evaluation metrics.We show that our experimental results achieve two fold benefits of our proposed work: i) it demonstrates the effectiveness of image captioning for synthetic images, and ii) it further improves the quality of the generated captions for real images, understandably because we use additional images for training.
COVID-19 and cyber fraud: emerging threats during the pandemic

Purpose The emergence of the novel coronavirus (COVID-19) has threatened physical and mental health, and changed the behaviour and decision-making processes of individuals, organisations, and institutions worldwide. As many services move online due to the pandemic, COVID-19-themed cyber fraud is also growing. This article explores cyber fraud victimization and cyber security threats during COVID-19 using psychological and traditional criminological theories. It also provides a COVID-19-themed cyber fraud typology using empirical evidence from institutional and agency reports. Through organizing COVID-19-themed cyber fraud into four different categorizations, we aim to offer classification insights to researchers and industry professionals so that stakeholders can effectively manage emerging cyber fraud risks in our current pandemic. Design/methodology/approach The approach the study take for this conceptual paper is typology.
Anonymous and Verifiable Reputation System for E-Commerce Platforms Based on Blockchain

E-commerce platforms incorporate reputation systems that allow customers to rate suppliers following financial transactions. Existing reputation systems cannot defend the centralized server against arbitrarily tampering with the supplier’s reputation. Furthermore, they do not offer reputation access across platforms. Rates are faced with privacy leakages because rating activities are correlated with privacy (e.g., identity and rating). Meanwhile, raters could be malicious and initiate multiple rating attacks and abnormal rating attacks. Determining how to address these issues have both research and practical value. In this paper, we propose a blockchain-based privacy-preserving reputation system for e-commerce platforms named RepChain; our system allows cross-platform reputation access and anonymous and private ratings. Using RepChain, all e-commerce platforms collaborate and share users’ reputations by co-constructing a consortium blockchain and modeling the rating process as a finite state machine. In particular, we facilitate one-show anonymous credentials constructed from two-move blind signatures to protect customers’ identities and resist multiple rating attacks, leverage zero-knowledge range proof to verify the correctness of ratings and defend against abnormal rating attacks, design a secure sum computation protocol among nodes to update reputations, and verify ratings via batch processing and consensus hashes. Finally, we demonstrate the security and privacy of RepChain via a formal analysis and evaluate its performance based on Ethereum test network.
Are we ready for a new paradigm shift? A survey on visual deep MLP

Recently, the proposed deep multilayer perceptron (MLP) models have stirred up a lot of interest in the vision community. Historically, the availability of larger datasets combined with increased computing capacity led to paradigm shifts. This review provides detailed discussions on whether MLPs can be a new paradigm for computer vision. We compare the intrinsic connections and differences between convolution, self-attention mechanism, and token-mixing MLP in detail. Advantages and limitations of token-mixing MLP are provided, followed by careful analysis of recent MLP-like variants, from module design to network architecture, and their applications. In the graphics processing unit era, the locally and globally weighted summations are the current mainstreams, represented by the convolution and self-attention mechanism, as well as MLPs. We suggest the further development of the paradigm to be considered alongside the next-generation computing devices.
<i>omg</i> blueprint for trapped ion quantum computing with metastable states

Quantum computers, much like their classical counterparts, will likely benefit from flexible qubit encodings that can be matched to different tasks. For trapped ion quantum processors, a common way to access multiple encodings is to use multiple, co-trapped atomic species. Here, we outline an alternative approach that allows flexible encoding capabilities in single-species systems through the use of long-lived metastable states as an effective, programmable second species. We describe the set of additional trapped ion primitives needed to enable this protocol and show that they are compatible with large-scale systems that are already in operation.
Semi-supervised Software Defect Prediction Model Based on Tri-training

Aiming at the problem of software defect prediction difficulty caused by insufficient software defect marker samples and unbalanced classification, a semi-supervised software defect prediction model based on a tri-training algorithm was proposed by combining feature normalization, over-sampling technology, and a Tri-training algorithm.First, the feature normalization method is used to smooth the feature data to eliminate the influence of too large or too small feature values on the model's classification performance.Secondly, the oversampling method is used to expand and sample the data, which solves the unbalanced classification of labelled samples.Finally, the Tri-training algorithm performs machine learning on the training samples and establishes a defect prediction model.The novelty of this model is that it can effectively combine feature normalization, oversampling techniques, and the Tri-training algorithm to solve both the under-labelled sample and class imbalance problems.Simulation experiments using the NASA software defect prediction dataset show that the proposed method outperforms four existing supervised and semi-supervised learning in terms of Precision, Recall, and F-Measure values.
The Dependent Dirichlet Process and Related Models

Standard regression approaches assume that some finite number of the response distribution characteristics, such as location and scale, change as a (parametric or nonparametric) function of predictors. However, it is not always appropriate to assume a location/scale representation, where the error distribution has unchanging shape over the predictor space. In fact, it often happens in applied research that the distribution of responses under study changes with predictors in ways that cannot be reasonably represented by a finite dimensional functional form. This can seriously affect the answers to the scientific questions of interest, and therefore more general approaches are indeed needed. This gives rise to the study of fully nonparametric regression models. We review some of the main Bayesian approaches that have been employed to define probability models where the complete response distribution may vary flexibly with predictors. We focus on developments based on modifications of the Dirichlet process, historically termed dependent Dirichlet processes, and some of the extensions that have been proposed to tackle this general problem using nonparametric approaches.
Blockchain Adoption in Agricultural Supply Chain for Better Sustainability: A Game Theory Perspective

Within the context of the rise of the Internet of Things, blockchain, and other new technologies, telecommunications operators are committed to applying technologies to promote business transformation and upgrading. The government also actively applies technologies to traditional fields to promote social progress. In agriculture, the agricultural supply chain has a low information level and low degree of digitization. The application of blockchain technology in agriculture offers exceptional advantages because of its decentralization, openness, and transparency. Based on the application of blockchain in an agricultural scenario, an evolutionary game model made up of governments, telecom operators, and agricultural enterprises was established to analyze the model’s equilibrium stability and evolutionary stable strategy. Then, numerical simulation was carried out to study the influence of the initial green level, equipment deployment cost, technology operation cost, and other core factors on the tripartite evolution behaviour. The results show that each factor influences the behaviour of a third party in different ways. Finally, according to the simulation results, this paper puts forward practical suggestions, explores the long-term impact of the application cost and sustainable income of blockchain technology on cooperation, and provides new ideas for the governance of China’s traditional fields from the perspective of new technology application.
An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more abstract representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models.
UFPMP-Det:Toward Accurate and Efficient Object Detection on Drone Imagery

This paper proposes a novel approach to object detection on drone imagery, namely Multi-Proxy Detection Network with Unified Foreground Packing (UFPMP-Det). To deal with the numerous instances of very small scales, different from the common solution that divides the high-resolution input image into quite a number of chips with low foreground ratios to perform detection on them each, the Unified Foreground Packing (UFP) module is designed, where the sub-regions given by a coarse detector are initially merged through clustering to suppress background and the resulting ones are subsequently packed into a mosaic for a single inference, thus significantly reducing overall time cost. Furthermore, to address the more serious confusion between inter-class similarities and intra-class variations of instances, which deteriorates detection performance but is rarely discussed, the Multi-Proxy Detection Network (MP-Det) is presented to model object distributions in a fine-grained manner by employing multiple proxy learning, and the proxies are enforced to be diverse by minimizing a Bag-of-Instance-Words (BoIW) guided optimal transport loss. By such means, UFPMP-Det largely promotes both the detection accuracy and efficiency. Extensive experiments are carried out on the widely used VisDrone and UAVDT datasets, and UFPMP-Det reports new state-of-the-art scores at a much higher speed, highlighting its advantages. The code is available at https://github.com/PuAnysh/UFPMP-Det.
Integration of Industry 4.0 technologies with Education 4.0: advantages for improvements in learning

Purpose The purpose of this study is to identify the uses of Industry 4.0 technologies in the area of education and how they contribute to learning in addition to highlighting at what educational level they are used. Design/methodology/approach A systematic literature review was carried out in the Scopus (Elsevier), Web of Science and ScienceDirect (Elsevier) databases, starting in 2011. In total, 51 articles were selected for a quantitative analysis, and 23 of them were read to answer the questions of the research from a qualitative analysis. Findings The results show a greater use of augmented reality, simulation, Internet of Things and virtual reality. The level of education at which they are most present is higher education. They collaborate to increase immersion in content, student engagement, interpersonal interaction, reduce costs and risks, simulate real work scenarios, expand study possibilities, without limited time and space, develop soft skills and learn about technologies, their uses and modes of operation. It is concluded that the technologies of Industry 4.0 support the entire learning process, but they are not used as much as they should. They are still largely restricted to universities and courses related to manufacturing. It is hoped that this work can contribute to the development of Education 4.0 at all stages of teaching. Originality/value Reviews have already been carried out on the use of technologies in teaching. The originality of this work is in the fact that it focuses on Industry 4.0 technologies in the Education 4.0 scenario.
An approach to the acquisition of tacit knowledge based on an ontological model

Our knowledge includes irreducible tacit elements which are related to the individual’s personal nature that go beyond what we can express, which makes it very difficult to formalize, communicate and share. As this tacit knowledge consists of either actions or personal attitudes, we propose an approach to acquisition of tacit knowledge based on an ontological model. The ontology is built top down by changing the actors’ cognitive focus from the focal to the subsidiary, or from the aim of an action to its detailed objectives. We also use explicitation interviews and self-confrontation techniques to identify the tacit elements in actors’ activities, such as the concepts of situation, know-how and know-that, which constitute our ontology for knowledge acquisition.
Towards Personalized Review Summarization via User-Aware Sequence Network

We address personalized review summarization, which generates a condensed summary for a user’s review, accounting for his preference on different aspects or his writing style. We propose a novel personalized review summarization model named User-aware Sequence Network (USN) to consider the aforementioned users’ characteristics when generating summaries, which contains a user-aware encoder and a useraware decoder. Specifically, the user-aware encoder adopts a user-based selective mechanism to select the important information of a review, and the user-aware decoder incorporates user characteristic and user-specific word-using habits into word prediction process to generate personalized summaries. To validate our model, we collected a new dataset Trip, comprising 536,255 reviews from 19,400 users. With quantitative and human evaluation, we show that USN achieves state-ofthe-art performance on personalized review summarization.
A Robust DCT-SVD Based Video Watermarking Using Zigzag Scanning

In this paper, a hybridMeenakshi, K. non-blind video watermarkingSwaraja, K. based on discrete cosine transform (DCT) and singular value decompositionKora, Padmavathi (SVD) is proposed. The DCT coefficients of each frame in the host video are reordered in a zigzag fashion and mapped into four blocks. These four blocks represent four frequency bands of low–low (LL), low–high (LH), high–low (HL), and high–high (HH) bands. Later, SVD is individually applied to each block. The singular values in each block are then modified by the singular values of the DCT transformed visual watermark to get watermarked video. This algorithm computes robustness in terms of Normalized Cross Correlation (NCC) between original and extracted watermarks from four bands. The proposed algorithm is compared with recent works. and the experimental results confirm that the proposed method is more resilient to attacks and is transparent.
Development of a modular, high-speed plenoptic-camera for 3D flow-measurement

This paper describes the development of a Modular Plenoptic Adaptor (MPA) for rapid and reversible conversion of high-speed cameras into plenoptic imaging systems, with the primary goal of enabling single-camera, time-resolved 3D flow-measurements. The MPA consists of a regular imaging lens, a microlens array, a tilt-adjustable microlens mount and an optical relay, which are collectively installed onto a high-speed camera through a standard lens mount. Each component within the system is swappable to optimize for specific imaging applications. In this study, multiple configurations of the MPA were tested and they demonstrated the ability to refocus and shift perspectives within high-speed scenes after capture. Additionally, the MPA demonstrated 3D reconstruction of captured scenes with <1% spatial error across a volume spanning approximately 50×30×50mm3. Finally, the MPA also demonstrated reconstruction of a 3D droplets-field with sufficient quality to support qualitatively accurate plenoptic particle image velocimetry (PPIV) calculations.
An Intelligent Scheme for Continuous Authentication of Smartphone Using Deep Auto Encoder and Softmax Regression Model Easy for User Brain

The smartphones are becoming more popular these days because of having more technologies in a smaller and slim-sized device. The people can get Internet connectivity and some other facilities, such as communication and storing data, with these smaller devices. It is necessary for securing personal data on smartphone devices with authentication techniques. In recent years, most of the static authentication techniques, such as password, patterns, and fingerprint, were used for securing smartphones. Most of the studies are focusing on a new method called continuous authentication for improving the security of smartphones. The continuous authentication techniques differ from other static authentication techniques by authenticating the smartphone user periodically. It uses behavioral features of users for authenticating the user. Continuous authentication is easy for the user's brain, as a user need not to memorize anything. In this paper, a continuous authentication technique is designed with the Deep Auto Encoder and Softmax Regression techniques (DAE-SR). The DAE-SR achieved 0.950% and 0.970% of accuracy on predicting the users on different states (walking and sitting) of users.
Identity-based and anonymous key agreement protocol for fog computing resistant in the Canetti–Krawczyk security model

Abstract Fog computing allows to connect the edge of the network, consisting of low cost Internet of Things devices, with high end cloud servers. Fog devices can perform data processing, which can significantly reduce the delay for the application. Moreover, data aggregation can be carried out by fog devices which decrease the bandwidth needed being very important for the wireless part of the communication with the cloud servers. The edge-fog-cloud architecture is currently being rolled out for several applications in the field of connected cars, health care monitoring, etc. In this paper, we propose an identity-based, mutual authenticated key agreement protocol for this fog architecture, in which end device and fog are able to establish a secure communication without leakage of their identities. Only the cloud server is able to control the identities of device and fog. We formally prove that the session keys are also protected in the Canetti–Krawczyk security model, in which adversaries are considered to have access to session state specific information, previous session keys, or long-term private keys. The scheme is very efficient as it only utilises elliptic curve operations and basic symmetric key operations.