Reviewerly/exHarmony huggingface dataset:
Recycling nonlocality in quantum star networks

The nonlocality of an entangled qubit pair can be recycled for several Bell experiments. Here, we go beyond standard Bell scenarios and investigate the recycling of nonlocal resources in a quantum network. First, we show that the ability to recycle nonlocality can be independent of the size of the network. Then, we realize a photonic quantum three-branch star network in which three sources of entangled pairs independently connect three outer parties with a central node. After measuring, each outer party respectively relays their system to an independent secondary measuring party. We experimentally demonstrate that the outer parties can perform unsharp measurements that are strong enough to violate a network Bell inequality with the central party, but weak enough to maintain sufficient entanglement in the network to allow the three secondary parties to do the same. Moreover, the violations are strong enough to exclude any model based on standard projective measurements on the Einstein-Podolsky-Rosen pairs emitted in the network. Our experiment brings together the research program of recycling quantum resources with that of Bell nonlocality in networks.
Interactive webcam travel: supporting wildlife tourism and conservation during COVID-19 lockdowns

Abstract Using the conceptual frameworks and theories of virtual tourism, telepresence and para-social interactions, this exploratory study investigates an innovative campaign employed by a nature-based wildlife tourism operator as a response to the COVID-19 lockdowns and travel restrictions of 2020/21. Insights are provided into a unique model of webcam livestreaming that is scheduled, hosted and interactive. Over 73,000 social media comments and 590 survey responses from webcam viewers were analysed and indicate that watching the livestream had positive impacts for tourism recovery and conservation action. Research findings suggest that interactive webcam travel can affect travel behaviour and conservation awareness and action in part through building and engaging online communities and supporting a sense of connection with nature. This study contributes new knowledge to the emerging research on webcam livestreaming in tourism. As a subset of virtual tourism, interactive webcam travel emerges as an alternative to more costly forms of virtual reality for industry practitioners and stakeholders to engage new and old audiences, especially in the context of tourism recovery initiatives after disasters and crises that prevent or limit physical visitation.
A Novel Ensemble Based Reduced Overfitting Model with Convolutional Neural Network for Traffic Sign Recognition System

The ELVD (Ensemble-based Lenet VGGNet and DropoutNet) model is used in this paper to examine hypothetical principles and theoretical identification of a real-time image classification and object, tracking, and recognition device running on board a vehicle. Initially, we obtained the dataset from Kaggle. After loading the images, they were converted into 4D tensors and then into a grid. The model has to set the training to 70% training and 30% testing. The ELVD model uses 39,209 32 × 32-pixel color images for preparation, and 12,630 images specifically for research, in the GTSD (German Traffic Sign Detection) dataset. Each picture is a photograph of a traffic sign that corresponds to one of the 43 classes. The picture is a 32 × 32 × 3 sequence of pixel quality values in the RGB color region, defined as pixel values. The image’s class is hidden as a numerical value from 0 to 42. The image collection is somewhat unbalanced, and a few classes are represented significantly better than in the alternative model. The contrast and brightness of the images also differ significantly. The proposed model was created with CNN with Keras and applied with ensemble-based combined LeNet, VGGNet, and DropoutNet pooling layer for tuning the information. The proposed model compares the predicted class with the correct class for all input images and time calculation for predicting different road sign detection images. Underfitting is shown by a standard of low accuracy on the training and testing sets. For a small dataset, the trained model achieved a 98% accuracy level which implied that overfitting the model with the best results on classification accuracy, tested with 15 epochs, resulted in a loss of information of 0.059% and test accuracy of 98%, respectively. Next, the ELVD proposed models trained and validated with different class presents, dataset 2 achieved 93% training accuracy and testing accuracy predicted with 91%. Finally, the ELVD proposed model predicted the test results of unseen class information measured with the 60/km ph, which predicted 99% accuracy. The proposed model predicted noisy as well as unseen multiclass information with fast-moving vehicles. The usage of convolutional layer filter with ensemble-based VGGNet, DropouNet, and LeNet trained and predicted with a high classification accuracy of more than 99% combined ELVD model with fastest time calculation also the high accuracy prediction of selected image dataset labels that enables these models to be used in real-time applications. The ELVD model was also compared with other traditional models of VGGNet, LeNet, and DropoutNet; its detection time outperformed the other models, and it achieved a 98% detection label set of information. In the ELVD model, closure to various human abilities on a related responsibility differs from 97.3% to 99.5%; consequently, the ELVD model performs better than an average human.
The Praxis of User Experience (UX) in the Design of Undergraduate Online Classes: Framing the Perceptions of Engineering and Social Sciences Students

A sustainable educational design that derives from a user experience (UX) frame does not only represent the praxis of online class design(s), but also empowers students to take an active part in their educational journey. The purpose of the study is to promote user experience (UX) measurements for the design of online classes. An online survey based on the criteria of user experience, UX, derived from the literature is administered in two higher educational contexts in Saudi Arabia and the USA with 890 participants from both genders and diverse fields of study. User experience is identified within usability, educational context, and the emotional or hedonic aspects of the experience, which are measured from thirty-six items from the survey constituting the independent variables. Reframing of students’ perceptions into UX offers a sustainable model of technology design that ensures a student-centered model by filling the gap between theoretical use of students’ perceptions and the praxis of online class design. The findings reveal that a working educational model should be centered around human values in addition to usability. This model can be replicated in various educational online contexts, but it has to be an ongoing process accompanying online design where student satisfaction is the outcome from variable measurement.
Lightweight Deep Neural Network Embedded with Stochastic Variational Inference Loss Function for Fast Detection of Human Postures

Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed. This technique was then applied in fast human posture identification. The integer-arithmetic-only algorithm and the feature pyramid network were adopted to reduce the computational complexity in training and to capture features of small objects, respectively. Features of sequential human motion frames (i.e., the centroid coordinates of bounding boxes) were extracted by the self-attention mechanism. With the techniques of Bayesian neural network and stochastic variational inference, human postures could be promptly classified by fast resolving of the Gaussian mixture model for human posture classification. The model took instant centroid features as inputs and indicated possible human postures in the probabilistic maps. Our model had better overall performance than the baseline model ResNet in mean average precision (32.5 vs. 34.6), inference speed (27 vs. 48 milliseconds), and model size (46.2 vs. 227.8 MB). The model could also alert a suspected human falling event about 0.66 s in advance.
A Federated Learning Multi-Task Scheduling Mechanism Based on Trusted Computing Sandbox

At present, some studies have combined federated learning with blockchain, so that participants can conduct federated learning tasks under decentralized conditions, sharing and aggregating model parameters. However, these schemes do not take into account the trusted supervision of federated learning and the case of malicious node attacks. This paper introduces the concept of a trusted computing sandbox to solve this problem. A federated learning multi-task scheduling mechanism based on a trusted computing sandbox is designed and a decentralized trusted computing sandbox composed of computing resources provided by each participant is constructed as a state channel. The training process of the model is carried out in the channel and the malicious behavior is supervised by the smart contract, ensuring the data privacy of the participant node and the reliability of the calculation during the training process. In addition, considering the resource heterogeneity of participant nodes, the deep reinforcement learning method was used in this paper to solve the resource scheduling optimization problem in the process of constructing the state channel. The proposed algorithm aims to minimize the completion time of the system and improve the efficiency of the system while meeting the requirements of tasks on service quality as much as possible. Experimental results show that the proposed algorithm has better performance than the traditional heuristic algorithm and meta-heuristic algorithm.
Study on Automatic Electric Vehicle Charging Socket Detection Using ZED 2i Depth Sensor

This article introduces the utilization of the ZED 2i depth sensor in a robot-based automatic electric vehicle charging application. The employment of a stereo depth sensor is a significant aspect in robotic applications, since it is both the initial and the fundamental step in a series of robotic operations, where the intent is to detect and extract the charging socket on the vehicle’s body surface. The ZED 2i depth sensor was utilized for scene recording with artificial illumination. Later, the socket detection and extraction were accomplished using both simple image processing and morphological operations in an object extraction algorithm with tilt angles and centroid coordinates determination of the charging socket itself. The aim was to use well-known, simple, and proven image processing techniques in the proposed method to ensure both reliable and smooth functioning of the robot’s vision system in an industrial environment. The experiments demonstrated that the deployed algorithm both extracts the charging socket and determines the slope angles and socket coordinates successfully under various depth assessment conditions, with a detection rate of 94%.
Development and Prospect of Hyperspectral Imager and Its Application

地表形态和目标特性是空间对地遥感的两个重要问题.经过近40年的发展,高光谱遥感技术已取得了巨大的进展,从数据获取、处理到应用都积累了大量成果.作为涉及多学科交叉的年轻学科,高光谱遥感技术的发展经历了一个以需求为导向,在发展中又不断探索完善需求的历程.高光谱成像仪作为获取目标图谱数据的重要仪器,其发展对于整个高光谱遥感的发展至关重要.本文分析了国内外不同类型高光谱成像仪的成像体制及应用案例,得出了近40年来高光谱成像仪发展历程所展现出的技术特点,结合当前发展现状,对未来高光谱成像仪的发展趋势,尤其是核心指标的提升和技术体制拓展进行了综合展望,以期为该领域的后续研究提供一些思路,同时为相关领域的科技规划提供参考.
Energy vehicle user demand mining method based on fusion of online reviews and complaint information

Understanding user demand is the basis for enterprises to improve their product quality and services. Online reviews have become a critical reflection of user demand. In particular, complaint information usually indicates defects in products or services. This study proposes a user demand mining method that integrates online reviews and complaint information. The term frequency-inverse document frequency (TF–IDF) method and expert consultations were adopted to generate a product attribute set. The latent Dirichlet allocation (LDA) method was also used to add more attributes to the product attribute set. This study analysed the aspect-level sentiment of each product attribute using the fine-tuning bidirectional encoder representation from transformers (BERT) model. The labelled dataset was used to train the BERT classification model to classify the product complaint information. Finally, the sentiment analysis results of online reviews and the classification results of complaint information were comprehensively analysed to obtain more accurate and comprehensive user demand elements. An example of a car product verified the effectiveness of the proposed method in the current paper. The results reveal that the method proposed in this study can integrate online reviews and complaint information to improve the accuracy and comprehensiveness of data mining results, thereby playing an auxiliary decision-making role for enterprises to accurately obtain user demand information and formulate product development strategies.
DivGAN: A diversity enforcing generative adversarial network for mode collapse reduction

Generative Adversarial Networks (GANs) are one of the most efficient generative models to generate data. They have made breakthroughs in many computer vision tasks. However, the generic GAN suffers from a mode collapse problem. To alleviate this problem, the present work proposes a new GAN framework called diversified GAN (DivGAN). DivGAN can be incorporated into any existing GAN. It includes a new network called DivNet that aims at enforcing the GANs to produce diverse data. One advantage of the proposed network is that it does not alter the architectures of the GANs and hence can be easily incorporated into them. Extensive experiments on synthetic and real datasets show that the proposed framework significantly contributes to mode collapse reduction and performs better than recent state-of-the-art GANs.
Distributed H∞ fusion filtering for multi-sensor networked systems with DoS attacks and sensor saturations

The design problem of a distributed H∞ fusion filter is investigated for multi-sensor networked systems subject to denial of service (DoS) attacks and sensor saturations. The prediction compensation strategy is adopted to solve the problem of packet losses caused by DoS attacks. The local H∞ filter based on single-sensor measurements is obtained via a linear matrix inequality (LMI) method such that the local filtering error system is exponentially stable in the mean square sense and possesses a specified H∞ performance. According to the different H∞ performance levels of local filters, a distributed scalar-weighted fusion filter is constructed. The proposed distributed fusion filter is low complex in computation and has better H∞ performance than the average of all local filters. Simulation studies verify the effectiveness of the proposed algorithms.
Mapping of trace elements in topsoil of arid areas and assessment of ecological and human health risks in Qatar

Soil is the incubator of human activities. Mapping of soil contaminants needs to be constantly updated. It is fragile in arid regions, especially if it accompanies dramatic and successive industrial and urban activities in addition to the climate change. Contaminants affecting soil are changing due to natural and anthropogenic influences. Sources, transport and impacts of trace elements including toxic heavy metals need continuous investigations. We sampled soil in accessible sites in the State of Qatar. An inductively coupled plasma-optical emission spectrometry (ICP-OES) and an inductively coupled plasma-mass spectrometry (ICP-MS) were used to determine the concentrations of Ag, Al, As, Ba, C, Ca, Ce, Cd, Co, Cr, Cu, Dy, Er, Eu, Fe, Gd, Ho, K, La, Lu, Mg, Mn, Mo, Na, Nd, Ni, Pb, Pr, S, Se, Sm, Sr, Tb, Tm, U, V, Yb and Zn. The study also presents new maps for the spatial distribution of these elements using the World Geodetic System 1984 (projected on UTM Zone 39N) which is based on socio-economic development and land use planning. The study assessed the ecological risks and human health risks of these elements in soil. The calculations showed no ecological risks associated with the tested elements in soil. However, the contamination factor (CF) for Sr (CF > 6) in two sampling locations calls for further investigations. More important, human health risks were not detected for population living in Qatar and the results were within the acceptable range of the international standards (hazard quotient HQ < 1 and Cancer risk between 10-5 and 10-6). Soil remains a critical component with water and food nexus. In Qatar and arid regions, fresh water is absent and soil is very poor. Our findings enhance the establishment of scientific strategies for investigating soil pollution and potential risks to achieve food security.
2D DIC from single moving camera by homographies compensation

The use of unmanned aerial vehicles in combination with vision-based measurement techniques has increasing interest in structural health monitoring. When displacement fields must be measured, Digital Image Correlation is one of the most popular techniques for image analysis. However, when images are taken from a single camera mounted on a drone, DIC gives incorrect results, since the camera movement creates apparent displacements and deformations. In the paper a technique for the compensation of these perspective distortions is proposed for planar surface case. The technique uses homographies estimated on a limited region of the measurement target assumed undeformed. Tests on numerically generated images have been performed to validate the proposed algorithm and to study the effect of the non-complete compliance with theoretical assumptions. Model for compensation error prediction is also proposed. Finally, some experimental tests considering real targets have been performed, obtaining good performances for the crack aperture case study.
Sec-Health: A Blockchain-Based Protocol for Securing Health Records

Storing and sharing health records through electronic systems pose security risks.To address them, several countries' regulations have established that healthcare information systems must fulfill security properties (confidentiality, access control, integrity, revocation and anonymity) and complementary ones (emergency access and interoperability).Upon tackling these issues, several proposals present security limitations and/or address specific properties only.We propose Sec-Health, a blockchain-based protocol that secures health records, addressing all of the main security and complementary properties defined in current regulations.We show that Sec-Health is a suitable solution by analyzing it under several attack scenarios and describing how it overcomes the problems of existing solutions.Furthermore, we evaluate a Sec-Health Proof of Concept, showing that it can reduce from 26% up to 90% the time to access health records, and reduce up to 50% client-side memory overhead, compared to related work.
Privacy-Preserving Hierarchical Federated Recommendation Systems

With growing concern on data privacy, traditional Recommendation System (RS) raises the risk of privacy disclosure since it needs to collect a large amount of personal data. To tackle this problem, implementing RS in a federated learning (FL) manner is proposed as an efficient approach. Although various solutions have been proposed to improve privacy of federated RS models, related works ignore the communication efficiency. Moreover, most of related works merely consider one server to coordination with all users, which might not suitable for large-scale networks. To protect privacy and reduce communication overhead, we propose a privacy-preserving hierarchical federated collaborative filtering scheme for the RS. Finally, we provide the simulation results to evaluate our proposed scheme, which show that our scheme can maintain good recommendation accuracy, preserve data privacy and reduce communication overhead.
Neighborhood evolutionary sampling with dynamic repulsion for expensive multimodal optimization

Many real-world applications can be categorized as expensive multimodal optimization problems which not only have multiple global optima, but also their objective functions are time-consuming. To address them, the article proposed a neighborhood evolutionary sampling framework with dynamic repulsion, termed DR-NESO, which consists of two main parts. On one hand, DR-NESO uses neighborhood evolutionary sampling strategies to generate candidate solutions for real fitness evaluation, balance exploration and exploitation, and accelerate population rapidly converge to multiple global optima. On the other hand, when potential global optimal solutions are identified and archived during optimization, DR-NESO dynamically sets taboo areas based on them. Individuals located in taboo areas are moved to other promising areas by a surrogate-assisted elite restart strategy. Based on this framework, expensive multimodal optimization problems can be solved well in a limited real fitness evaluation budget. We compared the approach with state-of-the-art expensive multimodal and multimodal algorithms on the CEC2013 benchmark. The result shows DR-NESO has the best performance.
HDIEA: high dimensional color image encryption architecture using five-dimensional Gauss-logistic and Lorenz system

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Quantum identity authentication protocol based on flexible quantum homomorphic encryption with qubit rotation

Quantum identity authentication (QIA) ensures that entities of quantum communication will not be impersonated by attackers. As an auxiliary protocol, an ideal QIA protocol cannot take up too many quantum resources and needs to be embedded in the original protocol to enhance security. In this paper, we propose a flexible ternary quantum homomorphic encryption (QHE) protocol using qubit rotation and indicate that the QHE framework can be used to design QIA protocols. The ternary QIA protocol based on QHE can be embedded in the original protocol because both protocols use the same quantum resources. The proposed protocol uses different vouchers from previous protocols, allowing it to prevent various types of attacks and performs well in terms of communication efficiency. The QHE framework can benefit other quantum cryptographic fields.
The Innovative Use of Intelligent Chatbot for Sustainable Health Education Admission Process: Learnt Lessons and Good Practices

This article presents the methodology of creation of an innovative used by intelligent chatbots which support the admission process in universities. The lifecycle of the ontology, unlike the classical lifecycles, has six stages: conceptualization, formalization, development, testing, production and maintenance. This leads to sustainability of the chatbot, called Ana, which has been implemented at the “Iuliu Hatieganu” University of Medicine and Pharmacy from Cluj-Napoca during the admission session throughout July–September 2022, for international candidates. The sustainability of the chatbot comes from the continuous maintenance and updates of the ontology, based on candidates’ interraction with the system and updates of the admission procedures. Over time, the chatbot is able to answer the questions according to the present situation of the admission and the real needs of the candidates. Ana had a huge impact, succeeding to resolve a number of 5173 applicants requests, and only 809 messages was transferred to the human operators, statistics which show a high cost-benefit improvement in terms of reducing the travel expenses for the candidates and also for the university. The article also summarizes the good practices in developing and use of such an intelligent chatbot.