Reviewerly/exHarmony huggingface dataset:
Consumers’ adoption of artificial intelligence and robotics in hospitality and tourism sector: literature review and future research agenda

Purpose The purpose of this study, a current systematic literature review, is to synthesize the extant literature on consumers’ adoption of artificial intelligence and robotics (AIR) in the context of the hospitality and tourism sector (HATS) to gain a comprehensive understanding of it. This study also outlines insights for academia, practitioners, AI marketers, developers, designers and policymakers. Design/methodology/approach This study used a content analysis approach to conduct a systematic literature review for the period of 10 years (2011–2020) of the various published studies themed around consumer’s adoption of AIR in HATS. Findings The synthesis draws upon various factors affecting the adoption of AIR, such as individual factors, service factors, technical and performance factors, social and cultural factors and infrastructural factors. Additionally, the authors identified four major barriers, namely, psychological, social, financial, technical and functional that hinder the consumer’s adoption of artificial intelligence and robots in the hospitality and tourism industry. Originality/value To the best of the author’s/authors’ knowledge, this study is a first attempt to synthesize the factors that drive consumers’ adoption of artificial intelligence and robots in the hospitality and tourism industry. The present work also advances the tourism and consumer behavior literature by offering an integrated antecedent-outcome framework. Visual abstract Figure 2 The objective of the current systematic literature review is to synthesize the extant literature on consumer’s adoption of artificial intelligence and robotics (AIR) in the context of the hospitality and tourism sector (HATS) to gain a comprehensive understanding of it. For that purpose, authors conducted content analysis of extant literature on consumer’s adoption of AIR in HATS from 2011 to 2020. Authors presented an integrated antecedent outcome framework of the factors that drive consumer’s adoption of artificial intelligence and robots in the hospitality and tourism industry.
Probabilistic Modeling for Human Mesh Recovery

This paper focuses on the problem of 3D human reconstruction from 2D evidence. Although this is an inherently ambiguous problem, the majority of recent works avoid the uncertainty modeling and typically regress a single estimate for a given input. In contrast to that, in this work, we propose to embrace the reconstruction ambiguity and we recast the problem as learning a mapping from the input to a distribution of plausible 3D poses. Our approach is based on the normalizing flows model and offers a series of advantages. For conventional applications, where a single 3D estimate is required, our formulation allows for efficient mode computation. Using the mode leads to performance that is comparable with the state of the art among deterministic unimodal regression models. Simultaneously, since we have access to the likelihood of each sample, we demonstrate that our model is useful in a series of downstream tasks, where we leverage the probabilistic nature of the prediction as a tool for more accurate estimation. These tasks include reconstruction from multiple uncalibrated views, as well as human model fitting, where our model acts as a powerful image-based prior for mesh recovery. Our results validate the importance of probabilistic modeling, and indicate state-of-the-art performance across a variety of settings. Code and models are available at: https://www.seas.upenn.edu/~nkolot/projects/prohmr.
EPPDA: An Efficient Privacy-Preserving Data Aggregation Federated Learning Scheme

Federated learning (FL) is a kind of privacy-awaremachine learning, in which the machine learning models are trained on the users’ side and then the model updates are transmitted to the server for aggregating. As the data owners need not upload their data, FL is a privacy-persevering machine learning model. However, FL is weak as it suffers from a reverse attack, in which an adversary can get users’ data by analyzing the user uploaded model. Motivated by this, in this paper, based on the secret sharing, we design, an efficient privacy-preserving data aggregation mechanism for FL, to resist the reverse attack, which can aggregate users’ trained models secretly without leaking the user’s model. Moreover, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EPPDA</i> has efficient fault tolerance for the user disconnection. Even if a large number of users are disconnected when the protocol runs, EPPDA will execute normally. Analysis shows that the EPPDA can provide a sum of locally trained models to the server without leaking any single user’s model. Moreover, adversary can not get any non-public information from the communication channel. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Efficiency verification</i> proves that the EPPDA not only protects users’ privacy but also needs fewer computing and communication resources.
An adaptive decoding biased random key genetic algorithm for cloud workflow scheduling

With the ever-growing data and computing requirements, more and more scientific and business applications represented by workflows have been moved or are in active transition to cloud platforms. Therefore, the cloud workflow scheduling has become a hot topic. As a well-known NP-hard problem, many heuristic or metaheuristic algorithms/methods have been proposed. However, the heuristic method is problem-dependent which fits only a particular of problems, while the metaheuristic method has the problems of incomplete search space or low search efficiency in the complete space. To fill these gaps, a novel adaptive decoding biased random key genetic algorithm for cloud workflow scheduling is proposed. In this algorithm, the improved real number coding based on random key with limited value range is employed, and some novel schemes such as the population initialization based on level and heuristics including dynamic heterogeneous earliest finish time, the dynamic adaptive decoding, the load balance with communication avoidance and iterative forward–backward scheduling are designed for population initialization, chromosome decoding and improvement. To evaluate the performance, extensive experiments have been conducted on various real and random workflow applications, which demonstrates that the proposed algorithm outperforms the conventional approaches.
UTAUT in Metaverse: An “Ifland” Case

Since 2021, big tech companies have been interested in metaverse platforms and services. Metaverse is the permanent, immersive mixed-reality world where people and people and people and objects can synchronously interact, collaborate, and live beyond the limitations of time and space, using avatars and the immersion-supporting devices, platforms, and infrastructures. On metaverse platforms, people can merge the real world and the virtual world. Because the metaverse has only recently begun to be studied, there are only dozens of studies on the metaverse published in qualified academic journals. There are few empirical studies on the extent to which metaverse platforms will be accommodated in the lives of information system users from an integrated perspective. Therefore, this paper aimed to empirically verify user acceptance of metaverse platforms by referring the unified theory of acceptance and use of technology (UTAUT). This study was conducted in two stages. (1) The concept and research trends of the metaverse platform were examined, and (2) the UTAUT model was introduced in “Ifland”, one of the metaverse platforms, to verify its acceptance of information system users. I conducted a laboratory experiment while complying with the quarantine rules. Participants were required to watch a 15 min lecture on artificial intelligence on the metaverse platform “Ifland” for a sufficient time, then they discussed the impacts of artificial intelligence with others in the lecture. A total of 120 valid data points, excluding insincere responses, were collected, and hypotheses were verified through PLS analysis. Results indicate that performance expectancy, effort expectancy, and social influence of the metaverse platform significantly increased satisfaction, usage intention, purchase intention, and word-of-mouth intention. Facilitating conditions had no significant impact on satisfaction. The results of this study provide implications for how the metaverse platform should be designed and what factors should be emphasized to increase user acceptance of metaverse platforms.
A novel unsupervised ensemble framework using concept-based linguistic methods and machine learning for twitter sentiment analysis

Concept-based sentiment analysis (CBSA) methods have gained prominence in natural language processing in recent years. These methods consider the underlying semantic meanings of text to perform different tasks such as Twitter sentiment analysis (assigning positive, negative, or neutral sentiment to Tweets). CBSA is superior to traditional statistical methods for accurately discovering sentiment labels. Due to a limited knowledge base, these methods are unable to identify the sentiment polarity of all kinds of text. Therefore, supervised learning techniques are mostly ensembled with CBSA methods to classify the whole text. These techniques require labeled data. It is a tedious and time-consuming task due to the manually labeling of large datasets (Such as Twitter datasets). Therefore, an unsupervised learning mechanism can be a better alternative to solve this problem. In this paper, a novel unsupervised learning framework based on Concept-based and hierarchical clustering is proposed for Twitter sentiment analysis. Popular hierarchical clustering methods including single linkage, complete linkage, and average linkage algorithms are ensembled serially. Two different feature representation methods including Boolean and Term frequency-inverse document frequency (TF-IDF) are investigated. We have also experimented with Well-known classifiers (Naive Bayes, Neural Network) for a fair comparison. Accuracy measure (proportion of correct predictions) is used to evaluate the performance of understudied techniques. It is empirically shown that the performance of unsupervised learning techniques is comparable with supervised learning techniques.
Multi-Modal Remote Sensing Image Matching Considering Co-Occurrence Filter

Traditional image feature matching methods cannot obtain satisfactory results for multi-modal remote sensing images (MRSIs) in most cases because different imaging mechanisms bring significant nonlinear radiation distortion differences (NRD) and complicated geometric distortion. The key to MRSI matching is trying to weakening or eliminating the NRD and extract more edge features. This paper introduces a new robust MRSI matching method based on co-occurrence filter (CoF) space matching (CoFSM). Our algorithm has three steps: (1) a new co-occurrence scale space based on CoF is constructed, and the feature points in the new scale space are extracted by the optimized image gradient; (2) the gradient location and orientation histogram algorithm is used to construct a 152-dimensional log-polar descriptor, which makes the multi-modal image description more robust; and (3) a position-optimized Euclidean distance function is established, which is used to calculate the displacement error of the feature points in the horizontal and vertical directions to optimize the matching distance function. The optimization results then are rematched, and the outliers are eliminated using a fast sample consensus algorithm. We performed comparison experiments on our CoFSM method with the scale-invariant feature transform (SIFT), upright-SIFT, PSO-SIFT, and radiation-variation insensitive feature transform (RIFT) methods using a multi-modal image dataset. The algorithms of each method were comprehensively evaluated both qualitatively and quantitatively. Our experimental results show that our proposed CoFSM method can obtain satisfactory results both in the number of corresponding points and the accuracy of its root mean square error. The average number of obtained matches is namely 489.52 of CoFSM, and 412.52 of RIFT. As mentioned earlier, the matching effect of the proposed method was significantly greater than the three state-of-art methods. Our proposed CoFSM method achieved good effectiveness and robustness. Executable programs of CoFSM and MRSI datasets are published: https://skyearth.org/publication/project/CoFSM/.
Global-Local Transformer for Brain Age Estimation

Deep learning can provide rapid brain age estimation based on brain magnetic resonance imaging (MRI). However, most studies use one neural network to extract the global information from the whole input image, ignoring the local fine-grained details. In this paper, we propose a global-local transformer, which consists of a global-pathway to extract the global-context information from the whole input image and a local-pathway to extract the local fine-grained details from local patches. The fine-grained information from the local patches are fused with the global-context information by the attention mechanism, inspired by the transformer, to estimate the brain age. We evaluate the proposed method on 8 public datasets with 8,379 healthy brain MRIs with the age range of 0-97 years. 6 datasets are used for cross-validation and 2 datasets are used for evaluating the generality. Comparing with other state-of-the-art methods, the proposed global-local transformer reduces the mean absolute error of the estimated ages to 2.70 years and increases the correlation coefficient of the estimated age and the chronological age to 0.9853. In addition, our proposed method provides regional information of which local patches are most informative for brain age estimation. Our source code is available on: https://github.com/shengfly/global-local-transformer.
PhiDMA – A phishing detection model with multi-filter approach

Phishing remains a basic security issue in the cyberspace. In phishing, assailants steal sensitive information from victims by providing a fake site which looks like the visual clone of a legitimate site. Phishing shall be handled using various approaches. It is established that single filter methods would be insufficient to detect different categories of phishing attempts. This paper provides a multilayer model to detect phishing, titled as PhiDMA(Phishing Detection using Multi-filter Approach). The PhiDMA model incorporates five layers: Auto upgrade whitelist layer, URL features layer, Lexical signature layer, String matching layer and Accessibility Score comparison layer. A prototype implementation of the proposed PhiDMA model is built with an accessible interface so that persons with visual impairments shall access it without any barrier. The result from the experiment shows that the model is capable to detect phishing sites with an accuracy of 92.72%.
An iterative boosting-based ensemble for streaming data classification

Among the many issues related to data stream applications, those involved in predictive tasks such as classification and regression, play a significant role in Machine Learning (ML). The so-called ensemble-based approaches have characteristics that can be appealing to data stream applications, such as easy updating and high flexibility. In spite of that, some of the current approaches consider unsuitable ways of updating the ensemble along with the continuous stream processing, such as growing it indefinitely or deleting all its base learners when trying to overcome a concept drift. Such inadequate actions interfere with two inherent characteristics of data streams namely, its possible infinite length and its need for prompt responses. In this paper, a new ensemble-based algorithm, suitable for classification tasks, is proposed. It relies on applying boosting to new batches of data aiming at maintaining the ensemble by adding a certain number of base learners, which is established as a function of the current ensemble accuracy rate. The updating mechanism enhances the model flexibility, allowing the ensemble to gather knowledge fast to quickly overcome high error rates, due to concept drift, while maintaining satisfactory results by slowing down the updating rate in stable concepts. Results comparing the proposed ensemble-based algorithm against eight other ensembles found in the literature show that the proposed algorithm is very competitive when dealing with data stream classification.
Discrete Probability Distribution Prediction of Image Emotions with Shared Sparse Learning

Computationally modelling the affective content of images has been extensively studied recently because of its wide applications in entertainment, advertisement, and education. Significant progress has been made on designing discriminative features to bridge the affective gap. Assuming that viewers can reach a consensus on the emotion of images, most existing works focused on assigning the dominant emotion category or the average dimension values to an image. However, the image emotions perceived by viewers are subjective by nature with the influence of personal and situational factors. In this paper, we propose a novel machine learning approach that characterizes the categorical image emotions as a discrete probability distribution (DPD). To associate emotion with the visual features extracted from images, we present shared sparse learning to learn the combination coefficients, with which the DPD of an unseen image is predicted by linearly combining the DPDs of the training images. Furthermore, we extend our method to the setup where multi-features are available and learn the optimal weights for each feature to reflect the importance of different features. Extensive experiments are carried out on Abstract, Emotion6 and IESN datasets and the results demonstrate the superiority of the proposed method, as compared to the state-of-the-art approaches.
Membership-dependent stability conditions for type-1 and interval type-2 T–S fuzzy systems

This paper presents an idea to simplify and relax the stability conditions of Takagi–Sugeno (T–S) fuzzy systems based on the membership function extrema1. By considering the distribution of membership functions in a unified membership space, a graphical approach is provided to analyze the conservativeness of membership-dependent stability conditions. Membership function extrema are used to construct a simple and tighter convex polyhedron that encloses the membership trajectory and produces less conservative linear matrix inequality (LMI) conditions. The cases of both type-1 and interval type-2 T–S fuzzy systems are considered, and comparison with existing methods is made in the proposed membership vector framework.
Demonstration of entanglement purification and swapping protocol to design quantum repeater in IBM quantum computer

Quantum communication is a secure way to transfer quantum information and to communicate with legitimate parties over distant places in a network. Although communication over a long distance has already been attained, technical problem arises due to unavoidable loss of information through the transmission channel. Quantum repeaters can extend the distance scale using entanglement swapping and purification scheme. Here we demonstrate the working of a quantum repeater by the above two processes. We use IBM's real quantum processor 'ibmqx4' to create two pair of entangled qubits and design an equivalent quantum circuit which consequently swaps the entanglement between the two pairs. We then develop a novel purification protocol which enhances the degree of entanglement in a noisy channel that includes combined errors of bit-flip, phase-flip and phase-change error. We perform quantum state tomography to verify the entanglement swapping between the two pairs of qubits and working of the purification protocol.
A Probabilistic and Multi-Objective Analysis of Lexicase Selection and ε-Lexicase Selection

Lexicase selection is a parent selection method that considers training cases individually, rather than in aggregate, when performing parent selection. Whereas previous work has demonstrated the ability of lexicase selection to solve difficult problems in program synthesis and symbolic regression, the central goal of this article is to develop the theoretical underpinnings that explain its performance. To this end, we derive an analytical formula that gives the expected probabilities of selection under lexicase selection, given a population and its behavior. In addition, we expand upon the relation of lexicase selection to many-objective optimization methods to describe the behavior of lexicase selection, which is to select individuals on the boundaries of Pareto fronts in high-dimensional space. We show analytically why lexicase selection performs more poorly for certain sizes of population and training cases, and show why it has been shown to perform more poorly in continuous error spaces. To address this last concern, we propose new variants of ε-lexicase selection, a method that modifies the pass condition in lexicase selection to allow near-elite individuals to pass cases, thereby improving selection performance with continuous errors. We show that ε-lexicase outperforms several diversity-maintenance strategies on a number of real-world and synthetic regression problems.
A novel deep model with multi-loss and efficient training for person re-identification

The purpose of Person re-identification (PReID) is to identify the same individual from the non-overlapping cameras, the task has been greatly promoted by the deep learning system. In this study, we review two widely-used CNN frameworks in the PReID community: identification model and triplet model. We provide a comprehensive overview of the advantages and limitations of the two models and present a hybrid model that combines the advantages of both identification and triplet models. Specifically, the proposed model employs triplet loss, identification loss and center loss to simultaneously train the carefully designed network. Furthermore, the dropout scheme is adopted by its identification subnetwork. Given a triplet unit images, the model can output the identities of the three input images and force the Euclidean distance between the mismatched pairs to be larger than those between the matched pairs as well as reduce the variance of the same class at the same time. Extensive comparative experiments on three PReID benchmark datasets (CUHK01, CUHK03, Market-1501) show that our proposed architecture outperforms many state of the art methods in most cases.
On the Cost of Computing Isogenies Between Supersingular Elliptic Curves

The security of the Jao-De Feo Supersingular Isogeny Diffie-Hellman (SIDH) key agreement scheme is based on the intractability of the Computational Supersingular Isogeny (CSSI) problem—computing $${\mathbb F}_{p^2}$$ -rational isogenies of degrees $$2^e$$ and $$3^e$$ between certain supersingular elliptic curves defined over $${\mathbb F}_{p^2}$$ . The classical meet-in-the-middle attack on CSSI has an expected running time of $$O(p^{1/4})$$ , but also has $$O(p^{1/4})$$ storage requirements. In this paper, we demonstrate that the van Oorschot-Wiener golden collision finding algorithm has a lower cost (but higher running time) for solving CSSI, and thus should be used instead of the meet-in-the-middle attack to assess the security of SIDH against classical attacks. The smaller parameter p brings significantly improved performance for SIDH.