Reviewerly/exHarmony huggingface dataset:
Utilisation of Service Robots in Accommodation Businesses From the Perspective of the Technology Acceptance Model

The advancement of technology and increased sales of service robots have led to discussions about their usage. Recently, the use of service robots in accommodation businesses has become a topic of examination. This study aims to explore the behavioural intentions of accommodation business managers towards using service robots from the perspective of the technology acceptance model. As part of this project-supported study, the 'Sanbot Elf' service robot was rented for 14 days and demonstrated in six accommodation businesses in Kastamonu. Semi-structured interviews were conducted with managers and unit supervisors after the demonstration, and the process was also monitored through participant observation. The data were analysed using content analysis and participant observation methods. It was determined that participants' opinions on service robots were generally positive. Participants stated that personalised service presentations could be provided with service robots and that these robots could be used in different departments and marketing processes. The study concluded that service robots could positively impact service quality, are an exciting technology for accommodation businesses and the use of service robots could increase in the future. Participants' attitudes towards using service robots in accommodation businesses were positive in this context.
Blockchain aware decentralized identity management and access control system

Identity Management Systems (IMS) manage individual profiles, user authorization, authentication, and access privileges. Paper-based profiles and defacement are vulnerable to spoofing, loss, and theft. Digital platforms with efficient IMS boost administrative processes and bring transparency and interoperability among establishments. The traditional paper-based identity systems are slow and rigid. Conventional centralized IMS lack transparency and are vulnerable to single-point failure. This paper presents a blockchain-aware decentralized identity management and access control (BADIMAC) model that mitigates these barriers. BADIMAC allows users to keep their digital identity within their control (i.e., self-sovereign identity). On the other hand, with the help of BADIMAC, service providers across enterprise boundaries can verify their identity without depending on any central authority. BADIMAC offers transparency and better security. The decentralized consensus-based design of BADIMAC enables the verifier to validate claims and establish proof of digital identity with process effectiveness.
Learning a Holistic-Specific color transformer with Couple Contrastive constraints for underwater image enhancement and beyond

Underwater images suffer from different types of degradation due to medium characteristics and interfere with underwater tasks. While deep learning methods based on the Convolutional Neural Network (CNN) excel at detection tasks, they have inherent limitations when it comes to handling long-range dependencies. The enhanced images generated by these methods often have problems such as color cast, artificial traces and insufficient contrast. To address these limitations, we present a novel Holistic-Specific attention (HSA) mechanism based on the Vision Transformer (ViT). This mechanism allows us to capture global information in finer detail and perform initial enhancements on underwater images. Notably, even when combined with ViT, CNNs do not always approach the ideal state of image enhancement, as reference images themselves may involve human intervention. To tackle this, we design a loss function that incorporates contrastive learning, using the source image as a negative example. This approach guides the enhancement results to be closer to the ideal enhancement state while keeping away from the degraded state, not just closer to the reference. Additionally, we introduce patch-based contrastive learning to address the shortcomings of image-based methods in fine-detail correction. Our extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art techniques.
Continuous-variable quantum key distribution robust against environmental disturbances

Continuous variable quantum key distribution (CV-QKD) can guarantee that two parties share secure keys even in the presence of an eavesdropper. However, the polarization direction of the coherent state transmitted in CV-QKD is susceptible to environmental disturbances during channel transmission, making it difficult to share keys consistently over long periods of time. Therefore, a CV-QKD system that can resist environmental disturbance is very urgent. In this paper, we propose a new optical architecture for CV-QKD based on the Faraday-Michelson interference (FMI) structure, and finally form an all-single-mode (SM) fiber-based stable CV-QKD system which employs transmitted local oscillator (TLO) scheme and discrete modulation coherent state (DMCS) protocol. Specifically, since the Faraday mirror rotates the polarization direction of light by 90 o , the birefringence effect of light can be effectively dealt with, thus ensuring the same polarization state of light before and after reflection. The final simulation results show that the theoretical secret key rate of this scheme can reach 139 kbps at 70 km, which can further improve the stability and robustness of CV-QKD in the real environment, and provide technical support for the next-generation high-stability QKD system.
Exploring a context-gated network for effective image deraining

The existing deraining methods have obtained noteworthy improvements, but it is a challenging problem to extend the methods for complicated rain conditions where rain streaks exhibit different distribution densities, sizes, shapes, etc. The main challenges are the ability to fully explore and utilize the multi-scale context information of rain streaks that maintain both global structure completeness and local detail accurateness. To this end, this paper proposes an Exploring Context-Gated Network, known as ECGNet. To adequately explore the richer context information, the proposed method consists of two key elements: context-enhanced feature block (CEFB) and multi-scale-gated aggregation block (MGAB). Specifically, the various scale features can be captured by CEFB with the multi-scale operation, to better remove the rain streaks and effectively restore the local detail textures. Subsequently, the captured features from different spaces are sent to MGAB, to aggregate and transmit these different scale features from the encoder to the decoder and reduce the transmission loss of information. Massive experiments on the commonly used benchmarks have demonstrated that the proposed method obtains more appealing performances against other competitive methods.
Reality Technologies (AR, VR, MR, XR) in Tourism

The change in the technological environment within the macro-environment factors in recent years affects states, businesses, societies and individuals and concerns not only technology-based sectors but also many fields. In particular, trends such as artificial intelligence, metaverse, robotics, advanced connectivity, the Internet of Things, big data, small data, blockchain, cloud technologies and reality technologies, which are called new technology, are developing very quickly compared to the past and expanding their global usage areas. Creating strategies and policies without considering these factors creates problems in many areas. These problems are marketing, competition, cost, efficiency and productivity.Reality technologies, which are the research area in this chapter and enable users to interact with the digital world, have a wide application area in the tourism industry. With technological tools such as smartphones and virtual reality (VR) glasses; personalisation, interactive experience, information gathering and decision-making; many different solutions are produced in areas such as education, service and security. Along with its many advantages, the disadvantages of reality technologies and the negative outputs of this transformation are significant for the understanding and future of the subject.
Service Robots in the Gastronomy Industry

The gastronomy industry is one of the industries in which consumers use technology most frequently, such as robotics, artificial intelligence and automation systems. Service robots are generally defined as online programs that can act independently or under the control of a human that can carry human characteristics such as speaking, understanding and acting. This study aims to examine and evaluate the studies on service robots in the gastronomy industry regarding quantity or quality, to understand the phenomenon more comprehensively, and to determine a holistic perspective. To this end, a qualitative research approach was adopted. Document technique was used to collect data within a systematic literature review framework, and the data were analysed by descriptive analysis. According to the findings, the papers on service robots in the gastronomy industry are still in their infancy, and these studies have increasingly gained speed in the last five years.
Voice Control and Voice Searching

Voice command and voice search are becoming increasingly popular in the hospitality and tourism industry, with many hotels and travel companies investing in voice-enabled technology to improve guest experiences and streamline operations. This technology allows travellers to manage their travel plans, request services and get information through natural voice commands on any voice-enabled device. Voice assistants are also multilingual, allowing hotels to customise responses to guests who do not speak the local language. Angie, a multilingual, in-room voice assistant, is an example of this technology. It can fulfil guest requests, answer common questions about the property and create streamlined access to a wide range of hotel amenities, such as ordering room service or requesting extra towels. Hotels can control questions and responses to assist stretched staff and provide upsell and advertising revenue through digital promotions or recommended onsite amenities or discounts. In addition, voice command technology can be used to book travel and find things to do at a destination. Google Assistant can help with travel plans like booking a hotel, checking flight status and finding things to do at a destination. In conclusion, voice command and voice search technology are transforming the hospitality and tourism industry by improving guest experiences, reducing operational costs and increasing revenue.
Low-Order Automatic Domain Splitting Approach for Nonlinear Uncertainty Mapping

This paper introduces a novel method for the automatic detection and handling of nonlinearities in a generic transformation. A nonlinearity index that exploits second-order Taylor expansions and polynomial bounding techniques is first introduced to estimate the Jacobian variation of a nonlinear transformation. This index is then embedded into a low-order automatic domain splitting algorithm that accurately describes the mapping of an initial uncertainty set through a generic nonlinear transformation by splitting the domain whenever nonlinearities grow above a predefined threshold. The algorithm is illustrated in the critical case of orbital uncertainty propagation, and it is coupled with a tailored merging process that limits the growth of the domains in time by recombining them when nonlinearities decrease. The low-order automatic domain splitting algorithm is then combined with Gaussian mixture models to accurately describe the propagation of a probability density function.
Visual Observer-Based State-Feedback Autonomous Motion Control on SE(3)

This paper introduces a visual observer-based state-feedback control tailored for autonomous motion on SE(3) to address challenges posed by modeling uncertainties and measurement noise. The proposed approach unfolds in two fundamental phases. In the initial stage, the visual observer, based on modeling information, visual sensors, and pre-deployed landmarks, along with the state-feedback controller, are developed independently, underpinning their individual semi-global practical asymptotic (SPA) stability. This modular approach ensures a robust foundation for the subsequent synthesis. The latter stage applies the well-established Small Gain Principle to regulate observer and controller parameters to guarantee the SPA stability of the closed-loop system with the visual-observer based state feedback control. The effectiveness of the proposed method is validated through simulation and experiments.
FRPNet: An improved Faster-ResNet with PASPP for real-time semantic segmentation in the unstructured field scene

The agricultural environment has numerous unstructured scenes, like back roads, alameda, and farmland. Existing semantic segmentation approaches of structured roads cannot meet the real-time and accuracy requirements when encountering unstructured scenes, thus hindering the autonomous operation of the intelligent agents in these scenes. To address the gordian problems, FRPNet is proposed to conduct real-time unstructured semantic segmentation in the field scene. Specifically, the semantic contexts are accurately extracted by introducing customized residual connections into the lightweight FasterNet-based encoder. Afterward, a modified partial Atrous spatial pyramid pooling (PASPP) is proposed to extract multi-scale features from the high-level semantic embedding, which improves the recognition of irregular boundaries and confused classes. Finally, a decoder whose structure is symmetric with the encoder is proposed to segment unstructured scenes by decoding the multi-scale semantic embedding. Additionally, a niche-targeting loss function called Ohd-Loss is proposed to optimize FRPNet. It enhances the model's focus on small-sample classes and addresses the issues of imbalanced class distribution and loss of scene details. Quantitative evaluations show that MIoU of FRPNet reaches 55.10% and 53.17% in the RUGD and RELLIS test sets, respectively. Meanwhile, FLOPs and Params are reduced to 5.27G and 9.74 M, which indicates that FRPNet effectively improves the segmentation accuracy in unstructured field scenes while satisfying real-time requirements. Qualitative evaluations of the self-developed unmanned vehicle running on the back roads verify the generalization performance of FRPNet. In a nutshell, FRPNet endows autonomous agents to perceive the surrounding field scenes with low-cost RGB cameras in real-time, facilitating the subsequent decision-making process. The code will be released at https://github.com/beautifulgirl11/FRPNet.
Temporal relation extraction with contrastive prototypical sampling

Temporal relation extraction aims to infer the temporal order of either two events in the document. Because of the nature of events in real life, severe imbalanced temporal relation classes exist in the temporal relation extraction task. Even though various methods have been proposed to improve the overall performance, the accuracy of these methods on minority temporal classes is limited. In this work, we present a contrastive prototypical learning architecture to address this problem, which explicitly models the spatial similarity between instances in the embedding space so that instances from minority classes can be distinguished from the large classes. To make it compatible with current temporal relation extraction settings, we propose a novel sampling memory queue-based method so that the architecture can be applied to a limited batch size scenario. We further design a context encoding layer that incorporates both contextualized information and linguistic features such as tense information and dependency. Our extensive experiments on TimeBank-Dense, TDDiscourse, and MATRES datasets demonstrate that our model can significantly improve the performance of minority relation classes and, therefore increase the overall learning ability.
In situ three-dimensional reconstruction of laser powder bed fusion parts by light field camera

The assessment of output quality in the laser powder bed fusion process entails the analysis of the three-dimensional morphology of the manufactured parts, which plays a crucial role in quality control to prevent metallurgical defects. This study proposes a novel approach of three-dimensional reconstruction for laser powder bed fusion parts using a light field camera. Initially, a consistency method for high-precision camera calibration was explored to determine the virtual camera pixel size and coefficient of effective focal length of light field camera. Subsequently, the model based light field epipolar-plane image Unet is designed to obtain disparity information without any data augmentation techniques. By leveraging both binocular and light field optical paths, a robust mapping relationship of the disparity, depth, and visually coherent three-dimensional contour was established. Finally, the three-dimensional contours of laser powder bed fusion parts were reconstructed by the established mapping with a single exposure of the light field camera. The proposed method enables three-dimensional contour visualization of laser powder bed fusion parts from multi-view perspectives, which facilitates in situ monitoring of the spattering distance, three-dimensional spatial points and lines of the printed layers and contours of printed parts. With a calibration error of 50 μm and resolution of 180 μm for the light field camera (Lytro illum camera), the maximum relative and absolute errors between reconstructed three-dimensional spatial points were 4.33 % and 260 μm respectively when compared with theoretical dimension, and 0.5 % and 210 μm respectively when compared with manufactured dimension. The maximum root-mean-square error of reconstructed three-dimensional contour surfaces was found to be 1.55 mm. This study represents the first application of a light field camera for in situ three-dimensional reconstruction of laser powder bed fusion parts, which notably can be obtained from only a single round of calibration and exposure, enabling seamless monitoring of their surface morphology and facilitating online quality control. These advancements have vast potential for enhancing high-end additive manufacturing.
The world in a grain of sand: Condensing the string vacuum degeneracy

The proliferation of vacuum solutions to string theory highly deter the search for the Standard Model. We propose a novel approach to this problem by finding an efficient measure of similarity of vacua. Using one million concrete examples, the paradigm of few-shot machine-learning represents them as points in Euclidean three-space with similar points clustered together. We thereby compress the search space for desired physics to within one percent of the original. Our analysis provides an explicit method for machine-learning the landscape and finding 'typicality', even with minuscule data.
Lightweight Whole-Body Human Pose Estimation With Two-Stage Refinement Training Strategy

Human whole-body pose estimation is a challenging task since the model needs to learn more keypoints than the body-only case. To meet the needs of real-time performance while maintaining accuracy is also a hard issue in whole-body pose estimation due to the learning capability of lightweight networks. In order to solve the above problems to a large extent, we propose a light whole-body pose estimation method with an optimized training strategy. The model is designed based on bottom-up architecture as a base network followed by a refinement network. We propose a two-stage training process, which learns rough features in the first stage and then improves estimation precision in the second stage. An online data augmentation procedure is proposed in the second stage to improve refinement performance. We also introduce a separate learning refinement structure that fine-tunes for body, foot, and hand part independently. Experimental results show that our method improves over 8%–10% average precision compared with other lightweight state-of-the-art approaches in the whole-body pose estimation task, with nearly a quarter (25%) size of model parameters saved.
A combined deep CNN with a chimp optimization approach for lung cancer diagnosis

The most common type of disease that is normal among women is lung cancer. It is one of the main reasons among women, despite great efforts to prevent it through trackers. An automatic disease detection system helps doctors identify and provide accurate results, thus minimizing the mortality rate. Computer Aided Diagnosis (CAD) has minimal human intervention and produces more accurate results than humans. It will be a difficult and lengthy task that depends on the experience of the pathologists. Deep learning methods have been shown to give better results when correlated with ML and extract the best highlights from images. The main objective of this article is to propose a deep learning technique in combination with a convolution neural network (CNN) with a chimpanzee optimization algorithm to diagnose lung cancer. Here, CNN is used for feature extraction and used for extracted feature detection. Experimental results show that the proposed system achieves 100% accuracy, 99% sensitivity, 99% recall, and 98% F1 score compared to other traditional models. As the system achieved correct results, it can help doctors easily investigate lung cancer.
Blending of a novel all sky imager model with persistence and a satellite based model for high-resolution irradiance nowcasting

High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts). In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy. ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10km around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods. Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT≤11min ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5-13% over the respective optimal individual method.