Reviewerly/exHarmony huggingface dataset:
Research trends on guest experience with service robots in the hospitality industry: a bibliometric analysis

Purpose The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry. Design/methodology/approach In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were. Findings On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies. Research limitations/implications In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs). Originality/value In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.
Geometry-assisted multi-representation view reconstruction network for Light Field image angular super-resolution

Light Field (LF) imaging enables many attractive applications since scene angular and spatial information can be captured simultaneously. However, the limited angular resolution hinders its development. To alleviate this problem, in this paper, we put forward a geometry-assisted multi-representation view reconstruction network for LF angular super-resolution. There are two stages in our method, namely multi-representation view reconstruction and geometry-assisted refinement. The former fully utilizes the LF lenslet image, sub-aperture image array, and pseudo video sequence representations to synthesize a high-angular-resolution dense LF image stack by adequately exploring the structural characteristics of such three representations. The latter aims to exploit the intra-LF spatial–angular information and inter-LF geometry information contained in the derived dense LF image stack to further promote the reconstruction performance. Specifically, a bidirectional view stack structure is constituted first, which is then fed into the proposed geometry-aware refinement network to allow adequate interactions between the intra-LF spatial–angular information and inter-LF geometry information. Comprehensive angular reconstruction experiments on both real-world and synthetic LF scenes validate the effectiveness of the proposed method. Furthermore, extensible applications on scene depth estimation also demonstrate that the proposed method can recover more texture details.
Text-based neural networks for question intent recognition

An effective organization of their knowledge bases is pivotal in keeping social networks vibrant, namely in designing successful personalization and contextualization strategies. This way, enhancing dedicated displays and encouraging the production of better content. Particularly for question answering communities, splitting archived material according to their intent is essential to reuse their knowledge and social capital. Recently, deep neural networks have shown breakthrough capabilities on multiple tasks related to language understanding. Thus the main contribution of this work is a thorough comparison of assorted architectures applied to the detection of question intents (i.e., informational and conversational). Evaluated on two collections, DEBERTA and RoBERTa demonstrated to be the best options by finishing with an accuracy of 71.19% and 74.10%, respectively. As for conventional neural networks, RCNNs proven to be the most effective technique. Overall, best models signal the usefulness of both question titles and bodies, and that fusing diverse learning strategies hold promise since they focus on learning different discriminative patterns.
<scp>AutoCRW</scp>: Learning based robust watermarking for smart city applications

Abstract Deep learning has become a promising model in the industry due to its superior learning accuracy and efficiency. In addition to conventional applications, such as fraud detection, natural language processing, image classification and reconstruction, object detection and segmentation this model can be widely used for data hiding, that is, watermarking. Existing transformed‐domain‐based watermarking provided better robustness toward attacks. In this article, an interesting autoencoder convolutional neural network (CNN)‐based watermarking technique, AutoCRW, is proposed, which can prevent intellectual property theft of digital images. First, the autoencoder functionality of CNN generates two versions of the same image, namely positive and negative version of the images, which decompose by a transformed domain scheme. Then, watermark information is embedded into the output images, which can be extracted to realize copyright protection and ownership verification. Finally, a denoising convolutional neural network (DnCNN) is employed over the extracted mark to ensure the robustness of the watermarking system. Extensive experiments demonstrate that the proposed algorithm has high invisibility and good robustness against several attacks.
A family of separability criteria and lower bounds of concurrence

The problem on detecting the entanglement of a bipartite state is significant in quantum information theory. In this article, we apply the Ky Fan norm to the revised realignment matrix of a bipartite state. We consider a family of separable criteria for bipartite states and present when the density matrix corresponds to a state is real, the criterion is equivalent to the enhanced realignment criterion. Moreover, we present analytical lower bounds of concurrence and the convex-roof extended negativity for arbitrary dimensional systems.
Efficient and secure content-based image retrieval with deep neural networks in the mobile cloud computing

Smart devices offer a variety of more convenient forms to help us record our lives and generate a large amount of data in this process. Limited by the local storage capacity, many users outsource their image data directly to the cloud server. However, images stored in plaintext on the cloud server are very insecure, resulting in image privacy information can be easily leaked. Therefore, users will encrypt the images and outsource them to the cloud server, but the encrypted images cannot be retrieved. Therefore, we proposed a secure and efficient ciphertext image retrieval scheme based on image content retrieval (CBIR) and approximate homomorphic encryption (HE). First, we used approximate homomorphic encryption to encrypt images after resizing and uploaded the ciphertext images to the cloud for feature extraction of ciphertext. At the same time, the large images (size, dimension, and resolution) would generate data inflation after using homomorphic encryption. Therefore, the original images are encrypted using the chaotic image encryption scheme to reduce ciphertext size and computation costs. Second, we proposed two deepening network depth optimization strategies that address the problem of insufficient neural network depth. Finally, reducing the dimensionality of the ciphertext feature vector using locally sensitive hashing (LSH) can accelerate the retrieval of ciphertext images. Compared with other literature, our ciphertext image retrieval scheme can significantly reduce the rounds of user-server communication.
Automatic diagnostic system for segmentation of 3D/2D brain MRI images based on a hardware architecture

Magnetic resonance imaging is among the advanced diagnostic testing tools for brain health issues. This method captures a series of detailed head images. These images are then printed and diagnosed by a specialist doctor to demonstrate differences in the brain tissue. Accordingly, additional diagnostic information can be given to determine the extent of the damage and the appropriate treatment methods. In this paper, and in order to facilitate the work of the specialist doctor and help him, we propose an automated hardware architecture for 3D/2D segmentation on MRI images to diagnose differences in brain tissue. For this, we used the metaheuristic technique based on Particle Swarm Optimization (PSO); for which we proposed improvements both for the velocity and position equations and for the fitness function. The goal of the work is to develop a real time automatic system for MRI images segmentation with improved metrics such as accuracy, sensitivity, specificity, dice metrics, execution time and resources utilization. The proposed hardware architecture was synthetized and then co-simulated using Matlab-Vivado System (VSM) for Field Programmable Gate Array (FPGA). Results show that our 3D segmentation method benefited from 2D segmentation with 95.39% accuracy rate and 87.97% DSC similarity (for 5-level segmentation) with 4.57 ms execution time for the case of BraTS 2013 dataset of brain MRI Images.
Deep generative image priors for semantic face manipulation

Previous works on generative adversarial networks (GANs) mainly focus on how to synthesize high-fidelity images. In this paper, we present a framework to leverage the knowledge learned by GANs for semantic face manipulation. In particular, we propose to control the semantics of synthesized faces by adapting the latent codes with an attribute prediction model. Moreover, in order to achieve a more accurate estimation of different facial attributes, we propose to pretrain the attribute prediction model by inverting the synthesized face images back to the GAN latent space. As a result, our method explicitly considers the semantics encoded in the latent space of a pretrained GAN and is able to faithfully edit various attributes like eyeglasses, smiling, bald, age, mustache and gender for high-resolution face images. Extensive experiments show that our method has superior performance compared to state of the art for both face attribute prediction and semantic face manipulation.
A lightweight object detection network in low-light conditions based on depthwise separable pyramid network and attention mechanism on embedded platforms

Deep learning-based object detection algorithms have been widely used and are effective in autonomous driving. But their performance degrades dramatically under low-light conditions. The existing mitigation approach for low-light object detection uses image enhancement preprocessing to improve detection performance with limited success. In addition, this preprocessing approach incurs extra computation costs, consumes more resources, and makes it more challenging to implement the algorithm on embedded platforms. This paper proposes a novel object detection network in low-light conditions for embedded platforms. The new system consists of two innovative modules, including a lightweight low-light enhancement network DS-PyLENet and an anchor-free lightweight object detector CFEDet. DS-PyLENet is a three-level pyramid network configured with depthwise separable convolution residual blocks (DSCRBs) and multiscale DSCRBs. The CFEDet is configured with an improved EfficientNet backbone network with Coordinate Attention (CA) and vanilla MBconv, i.e., CA-fused EfficientNet, an improved Path Aggregation Network (PAN) fusion module, and an anchor-free Global Focal Loss (GFL) detection head. Two evaluation metrics, relative recall ratios, are proposed to depict the effectiveness of image enhancement on object detection more intuitively. The new model is demonstrated to be superior to the assembled model of EnlightenGAN and YOLOv5n in terms of detection accuracy and speed on GPU and embedded platforms. The testing results of the new model reveal that 83.5% mAP is achieved on the Exdark dataset and 67.4% mAP on the DarkFace dataset. The running rates of GTX 1080Ti and NVIDIA Jetson Xavier NX are 22 FPS and 2.6 FPS, respectively. The recall of CFEDet with DS-PyLENet increased to 22.1% and significantly improved over 18% with Zero-DCE and 17.2% with EnlightenGAN.
Scalable flux controllers using adiabatic superconductor logic for quantum processors

Quantum processors have the potential to accelerate specific computing tasks but are difficult to scale up due to engineering limitations, such as the number of available cables and cooling power for dilution refrigerators for quantum bits (qubits). Hence, it is very important to develop scalable, energy-efficient interface circuits that can control many qubits via a few control lines inside a dilution refrigerator. One of the most important interface circuits is the flux controller (FC), which generates arbitrary dc flux bias to adjust the characteristics of component devices such as qubits. In this paper, we propose and demonstrate FCs using an energy-efficient superconductor logic family, adiabatic quantum-flux-parametron (AQFP) logic. We develop two types of FCs: the AQFP FC and the AQFP/single-flux-quantum (AQFP/SFQ) FC. Both FCs require only a few control lines and have extremely small power dissipation, thus exhibiting high scalability. Furthermore, the AQFP/SFQ FC can control flux bias using ballistic SFQ transmission, which is crucial for integration with qubits. As a proof of concept, we demonstrate AQFP and AQFP/SFQ FCs at 4.2 K, fabricated by the AIST high-speed standard process. Our results indicate that AQFP logic is highly suitable for use as qubit interface circuits for very large-scale quantum processors, especially from the viewpoint of the control line count, power dissipation, and amount of supply currents.
Actuator fault detection and isolation on multi-rotor UAV using extreme learning neuro-fuzzy systems

Undetected partial actuator faults on multi-rotor UAVs can lead to system failures and uncontrolled crashes, necessitating the development of accurate and efficient fault detection and isolation (FDI) strategy. This paper proposes a hybrid FDI model for a quadrotor UAV that integrates an extreme learning neuro-fuzzy algorithm with a model-based extended Kalman filter (EKF). Three FDI models using Fuzzy-ELM, R-EL-ANFIS, and EL-ANFIS are compared based on training, validation performances, and sensitivity to weaker and shorter actuator faults. They are also tested online for linear and nonlinear incipient faults by measuring their isolation time delays and accuracies. The results show that the Fuzzy-ELM FDI model exhibits greater efficiency and sensitivity, while Fuzzy-ELM and R-EL-ANFIS FDI models demonstrate better performance than a conventional neuro-fuzzy algorithm, ANFIS.
On Cohesive Fuzzy Sets, Operations and Properties with Applications in Electromagnetic Signals and Solar Activities

In the present communication, a new concept of a cohesive fuzzy set (CHFS) has been proposed as a synchronized generalization of complex fuzzy sets and hesitant fuzzy sets in a systematic way. The novelty of the introduced notion lies in the selection of the best alternative among the available multiple favorable situations, where the possibility of its range is in the extended unit circle of the complex plane. We study the relationship between the CHFS and the complex intuitionistic fuzzy set (CIFS), along with validation of the obtained results. On the basis of the proposed notion, various properties, operations, and identities have been established with their necessary proof. The applications of CHFS in the process of filtering the signals for obtaining the reference signal using the necessary Fourier cosine transform or inverse Fourier cosine transform and identifying the maximum number of sunspots in a particular interval of solar activity have been suitably discussed with illustrative numerical examples. Some advantages of incorporating the proposed notion have also been tabulated for better understanding.
Acute Leukemia Diagnosis Based on Images of Lymphocytes and Monocytes Using Type-II Fuzzy Deep Network

A cancer diagnosis is one of the most difficult medical challenges. Leukemia is a type of cancer that affects the bone marrow and/or blood and accounts for approximately 8% of all cancers. Understanding the epidemiology and trends of leukemia is critical for planning. Specialists diagnose leukemia using morphological analysis, but there is a possibility of error in diagnosis. Since leukemia is so difficult to diagnose, intelligent methods of diagnosis are required. The primary goal of this study is to develop a novel method for extracting features hierarchically and accurately, in order to diagnose various types of acute leukemia. This method distinguishes between acute leukemia types, namely Acute Lymphocytic Leukemia (ALL) and Acute Myeloid Leukemia (AML), by distinguishing lymphocytes from monocytes. The images used in this study are obtained from the Shahid Ghazi Tabatabai Oncology Center in Tabriz. A type-II fuzzy deep network is designed for this purpose. The proposed model has an accuracy of 98.8% and an F1-score of 98.9%, respectively. The results show that the proposed method has a high diagnostic performance. Furthermore, the proposed method has the ability to generalize more satisfactorily and has a stronger learning performance than other methods.
Development of Autonomous Driving and Motion Control System for a Patient Transfer Robot

In this study, an autonomous driving system of a patient-transfer robot is developed. The developed autonomous driving system has a path-planning module and a motion-control module. Since the developed autonomous driving system is applied to medical robots, such as patient-transfer robots, the main purpose of this study is to generate an optimal path for the robot’s movement and to ensure the patient on board moves comfortably in the PTR. In particular, for the patient’s comfortable movement, a lower controller is needed to minimize the sway angle of the patient. In this paper, we propose a hybrid path-planning algorithm that combines the A-STAR algorithm as a global path-planning method and the AHP (Analytic Hierarchy Process)-based path-planning algorithm as a local path-planning method. In addition, model-based controllers are designed to move patient-transport robots along planned paths. In particular, the LQR controller with the Kalman filter is designed to be robust to the uncertainty and disturbance of the model including the patient. The optimal path generation and patient shaking angle reduction performance of the proposed autonomous driving system have been demonstrated via a simulation on a map that mimics a hospital environment.
A Multi-Label Based Physical Activity Recognition via Cascade Classifier

Physical activity recognition is a field that infers human activities used in machine learning techniques through wearable devices and embedded inertial sensors of smartphones. It has gained much research significance and promising prospects in the fields of medical rehabilitation and fitness management. Generally, datasets with different wearable sensors and activity labels are used to train machine learning models, and most research has achieved satisfactory performance for these datasets. However, most of the methods are incapable of recognizing the complex physical activity of free living. To address the issue, we propose a cascade classifier structure for sensor-based physical activity recognition from a multi-dimensional perspective, with two types of labels that work together to represent an exact type of activity. This approach employed the cascade classifier structure based on a multi-label system (Cascade Classifier on Multi-label, CCM). The labels reflecting the activity intensity would be classified first. Then, the data flow is divided into the corresponding activity type classifier according to the output of the pre-layer prediction. The dataset of 110 participants has been collected for the experiment on PA recognition. Compared with the typical machine learning algorithms of Random Forest (RF), Sequential Minimal Optimization (SMO) and K Nearest Neighbors (KNN), the proposed method greatly improves the overall recognition accuracy of ten physical activities. The results show that the RF-CCM classifier has achieved 93.94% higher accuracy than the 87.93% obtained from the non-CCM system, which could obtain better generalization performance. The comparison results reveal that the novel CCM system proposed is more effective and stable in physical activity recognition than the conventional classification methods.
Detection of Inappropriate Tweets Linked to Fake Accounts on Twitter

It is obvious that one of the most significant challenges posed by Twitter is the proliferation of fraudulent and fake accounts, as well as the challenge of identifying these accounts. As a result, the primary focus of this paper is on the identification of fraudulent accounts, fake information, and fake accounts on Twitter, in addition to the flow of content that these accounts post. The research utilized a design science methodological approach and developed a bot account referred to as “Fake Account Detector” that assists with the detection of inappropriate posts that are associated with fake accounts. To develop this detector, previously published tweets serve as the datasets for the training session. This data comes from Twitter and are obtained through the REST API. The technique of machine learning with random forest (RF) is then used to train the data. The high levels of accuracy (99.4%) obtained from the RF detection results served as the foundation for the development of the bot account. This detector tool, developed using this model, can be utilized by individuals, businesses, and government agencies to assist in the detection and prevention of Twitter problems related to fake news and fake accounts.