Reviewerly/exHarmony huggingface dataset:
Circular Subaperture Stitching Interferometry Based on Polarization Grating and Virtual–Real Combination Interferometer

This paper presents a polarization grating based circular subaperture stitching interferometer. The system can be used for small F/# concave surface tests with a large F/# transmission sphere, where F/# is the ratio of focal length to aperture. A polarization grating was employed to deflect the incident beam for subaperture scanning by its axial rotation instead of a multi-axis motion-control system. Compared with the traditional subaperture stitching interferometric system, the system proposed in this paper is smaller in size and reduces the measurement error introduced by mechanical adjustment. Using a virtual interferometer model and a virtual-real combination algorithm to remove the retrace error, the full-aperture figure error can be directly obtained without the need for a complex stitching algorithm. The feasibility of the algorithm was verified, and the measurement error caused by the modeling error was analyzed by simulation. The capability of the polarization grating to scan subapertures was experimentally confirmed, and possible solutions to some engineering challenges were pointed out. The research in this paper has pioneering and guiding significance for the application of polarization grating in interferometry.
FAIR Principles for data and AI models in high energy physics research and education

In recent years, digital object management practices to support findability, accessibility, interoperability, and reusability (FAIR) have begun to be adopted across a number of data-intensive scientific disciplines.These digital objects include datasets, AI models, software, notebooks, workflows, documentation, etc.With the collective dataset at the Large Hadron Collider scheduled to reach the zettabyte scale by the end of 2032, the experimental particle physics community is looking at unprecedented data management challenges.It is expected that these grand challenges may be addressed by creating end-to-end AI frameworks that combine FAIR and AI-ready datasets, advances in AI, modern computing environments, and scientific data infrastructure.In this work, the FAIR4HEP collaboration explores the interpretation of FAIR principles in the context of data and AI models for experimental high energy physics research.We investigate metrics to quantify the FAIRness of experimental datasets and AI models, and provide open source notebooks to guide new users on the use of FAIR principles in practice.
Crystal-modeler: A tool for geometric analysis and three-dimensional modeling of crystal forms based on rectangular coordinates in space

Various classification approaches have been developed for geometric form of crystals. One method involves the generation of three-dimensional (3D) models of crystals, mainly through graphical representations. However, traditional graphical representations mainly depend on symmetrical operations of crystal planes and the application of polar stereographic projections. There is still very few geometric analysis or mathematical expression for the rectangular coordinate system, implying the limited 3D visualization of crystal forms. In this paper, we present a newly geometric analysis that allows 3D visualization analysis of crystal forms and further identifies geometric relations between crystal forms. In detail, crystal models are divided in a 3D rectangular coordinate system within 47 types of geometric form into the tetrahedron, polygon (polyhedron), and cube classes. What’s more, we determine the vertex space coordinates of the 47 types of crystal form through geometric analysis and coordinate calculation of the crystal forms. Last but not least, in order to obtain 3D models of every geometric form under the automorphism of crystals, it is the convex envelope geometric polyhedron algorithm created to calculate the edges and triangular faces by WebGL technology, which makes it possible to accomplish an online interactive 3D crystallographic study system in a web-browser.
Effect of repetitive motion intervention on self-avatar on the sense of self-individuality

In recent years, the human Digital Twin has been discussed as new technology. When we discuss a world in which one’s self-avatar autonomously performs social activities in cyberspace, the questions arise whether or not the behavior of the avatars feels like one’s own, and whether or not we can approve of the self-avatars’ social activities on behalf of ourselves. We define such feeling as the sense of self-individuality. In this study, we focused on the situation in which self-avatars perform presentations on behalf of ourselves to investigate the effect of the modification experience on the presentation motions by self-avatars on the sense of self-individuality. We conducted VR-based experiments in which the motion modification intervention was performed on self-avatars over eight weeks by 24 experiment participants. As a result, we found that the sense of self-individuality was improved as the number of modifications and interventions increased. However, we found that the intensity of motion modification did not correlate with the improvement of the sense of self-individuality in this experiment condition. We also found that the sense of self-individuality was reduced when others intervened in the motion. From these results, we clarified that the experience of motion modification on self-avatars is significant when designing the behavior of avatars acting on behalf of ourselves in human Digital Twin. Further investigation is required to clarify the effect of the long-term intervention on behavior to distinguish between the mere exposure effect.
Multilevel Authentication Employing Watermarking based on QR Codes, Mobile OTP, and Hadamard Transformation

High-speed internet technology has made it possible for users to transact with banks more quickly thanks to online banking services. The increased connectivity also creates numerous online security holes like phishing and pharming. By confirming the user's legitimacy, this mobile banking problem may be resolved. Here, we suggest a hybrid system that makes use of QR codes, One-Time Passwords (OTPs), and digital watermarking—a method of data concealment. The bank generates QR codes using OTP as the key to the watermark sequence. Here, OTP can be utilised for the initial authentication verification. The sequence for the second level of verification is concealed throughout the watermarking process using the Hadamard transformation. Online transactions may be made with maximum security thanks to the OTP and watermark combination. One benefit of this strategy is that the QR code may be created within a limited design window with a suitable real-time extraction method. Performance may be assessed by utilising an android application, including the real-time extraction using a mobile device.
Multi-Objective Load Balancing in Cloud Computing: A Meta-Heuristic Approach

Workload balancing in cloud computing continues to be a significant topic, notably within the Infrastructure as a Service (IaaS) paradigm. Site or web service being overcrowded or underloaded impairs the waiting times or culminates in a system malfunction, and it is indeed an issue that need not happen with cloud storage. To overcome such issues, a suitable access schedule should be devised, allowing the organization to spread work and overall resources available, a process known as load balancing. As a response, a unique multi-objective load balancing framework is presented in this research for optimum load balancing in the cloud. The suggested multi-objective load balancing paradigm considers power usage, bandwidth consumption, migration costs, memory usage, and load balancing parameters (Response time, Turnaround time, Server load). A novel hybrid optimization technique-Mouse Customized Golden Eagle Optimization (MCGEO) model is developed for optimal load balancing, which is conceptual combination of traditional Golden Eagle Optimizer (GEO) and Cat and Mouse-Based Optimizer (CMBO). Therefore, the newly developed hybrid optimization model improves convergence and addresses the optimization problems in load balancing. The proposed MCGEO achieved a throughput value of ∼281.6255, at 150 tasks for the cloud environment-2 this proves the superiority of the proposed approach.
Humans trade off search costs and accuracy in a combined visual search and perceptual task

To interact with one's environment, relevant objects have to be selected as targets for saccadic eye movements. Previous studies have demonstrated that factors such as visual saliency and reward influence saccade target selection, and that humans can dynamically trade off these factors to maximize expected value during visual search. However, expected value in everyday situations not only depends on saliency and reward, but also on the required time to find objects, and the likelihood of a successful object-interaction after search. Here we studied whether search costs and the accuracy to discriminate an object feature can be traded off to maximize expected value. We designed a combined visual search and perceptual discrimination task, where participants chose whether to search for an easy- or difficult-to-discriminate target in search displays populated by distractors that shared features with either the easy or the difficult target. Participants received a monetary reward for correct discriminations and were given limited time to complete as many trials as they could. We found that participants considered their discrimination performance and the search costs when choosing targets and, by this, maximized expected value. However, the accumulated reward was constrained by noise in both the choice of which target to search for, and which elements to fixate during search. We conclude that humans take into account the prospective search time and the likelihood of successful a object-interaction, when deciding what to search for. However, search performance is constrained by noise in decisions about what to search for and how to search for it.
Formation of Cross-cultural Competence of Prospective English Teachers

The article substantiates the importance of forming prospective English teachers’ cross-cultural competence. Nowadays, education focuses on cooperation and establishing cross-cultural relations between the representatives of diverse cultural societies. Therefore, it concentrates on forming a personality capable of absorbing and reproducing verbal and non-verbal information during dialogical communication with foreign negotiators. This paper aims to validate the category “cross-cultural competence of a prospective English teacher,” develop its structure and experimentally verify its effectiveness. The study establishes that cross-cultural competence covers linguistic, sociocultural, activity, and axiologically-emotive components. The authors used the following theoretical research methods: analysis of philosophical, lingua didactic literature and scientific studies, synthesis, comparison, concretization; empiric methods: questionnaire-diagnostic (survey, testing, summative assessment, interview); analysis of speech activity products (spoken replies); lecture attendance and analysis; pedagogical experiment; methods of statistical treatment and experimental results analysis. The research on the training peculiarities of future English teachers based on cross-cultural grounds allowed us to conclude that the experimentally-verified methodology is effective. The practical data analysis points out the rise of forming cross-cultural competence levels in the experimental group. The reason is the cultural enrichment of linguistic material and the revelation of contrastive aspects.
ABN: Anti-Blur Neural Networks for Multi-Stage Deformable Image Registration

Deformable image registration, i.e., the task of aligning multiple images into one coordinate system by nonlinear transformation, serves as an essential preprocessing step for neuroimaging data.Recent research on deformable image registration is mainly focused on improving the registration accuracy using multi-stage alignment methods, where the source image is repeatedly deformed in stages by a same neural network until it is well-aligned with the target image.Conventional methods for multi-stage registration can often blur the source image as the pixel/voxel values are repeatedly interpolated from the image generated by the previous stage.However, maintaining image quality such as sharpness during image registration is crucial to medical data analysis.In this paper, we study the problem of anti-blur deformable image registration and propose a novel solution, called Anti-Blur Network (ABN), for multistage image registration.Specifically, we use a pair of shortterm registration and long-term memory networks to learn the nonlinear deformations at each stage, where the short-term registration network learns how to improve the registration accuracy incrementally and the long-term memory network combines all the previous deformations to allow an interpolation to perform on the raw image directly and preserve image sharpness.Extensive experiments on both natural and medical image datasets demonstrated that ABN can accurately register images while preserving their sharpness.
The formulation of irrigation and nitrogen application strategies under multi-dimensional soil fertility targets based on preference neural network

With the aim of improving soil fertility, it is of great significance to put forward optimal irrigation and nitrogen fertilizer application strategies for improving land productivity and alleviating non-point source pollution effects. To overcome this task, a 6-hidden layer neural network with a preference mechanism, namely Preference Neural network (PNN), has been developed in this study based on the field data from 2018 to 2020. PNN takes soil total nitrogen, organic matter, total salt, pH, irrigation time and target soil depth as input, and irrigation amount and nitrogen application rate (N rate) as output, and the prior preference matrix was used to adjust the learning of weight matrix of each layer. The outcomes indicated that the predictive accuracy of PNN for irrigation amount were (R2 = 0.913, MAE = 0.018, RMSE = 0.022), and for N rate were (R2 = 0.943, MAE = 0.009, RMSE = 0.011). The R2 predicted by PNN at the irrigation amount and N rate were 40.03% to more than 99% and 40.33% to more than 99% higher than those obtained using support vector regression (SVR), linear regression (LR), logistic regression (LOR) and traditional back propagation neural network (BPNN), respectively. In addition, compared with the neural network (Reverse Multilayer Perceptron, RMLP) with the same structure but no preference structure, the R2 of the predicted irrigation amount and N rate by PNN increased by 25.81% and 27.99%, respectively. The results showed that, through the irrigation of 93 to 102, 92 to 98 and 92 to 98 mm, along with nitrogen applications of 65 to 71, 64 to 73 and 72 to 81 kg/hm2 at 17, 59 and 87 days after sowing, respectively, the organic matter, total nitrogen, total salt content and pH of the soil would reach high fertility levels simultaneously.
Aerial Traffic Statistics Based on YOLOv5+DeepSORT

Traffic flow statistics, as an important part of intelligent transportation system, usually requires manual statistics, which is time-consuming and labor-intensive. In order to save manual labor and improve the statistical efficiency, this paper is based on the strategy of YOLOv5+DeepSORT to count the aerial traffic flow by UAV, and the results show that the statistical accuracy of this method is close to that of manual statistics, which has high practical value.
Chinese Text Sentiment Classification Based on ERNIE and BiLSTM-AT

Chinese text sentiment classification is a sub-task of natural language processing. However, when text representation is carried out, the polysemy of a word cannot be processed when using the traditional language model to construct the word vector, and the long-distance text information cannot be fully extracted when extracting text features. To solve this problem, this paper proposes a text sentiment classification model combining ERNIE and BiLSTM-AT. First, the pre-training model ERNIE is used to obtain the word vector representation of the fused statement context. Then, the bidirectional long-short-term memory neural network is used to extract the context information and depth semantic information of the text. Then, the attention mechanism is used to assign the corresponding weights to the hidden layer vectors of each time step output by the BiLSTM layer, and the weighted summation is integrated into the sentence features. Finally, the softmax function is used to calculate the probability distribution of the emotional category of the text in the output layer. The results show that the proposed model can achieve high accuracy on both hotel reviews and takeaway reviews. Based on the pre-training model, adding bidirectional long-term and short-term memory network and attention mechanism is beneficial to improve the classification effect of the model, and has certain practicability in text sentiment classification tasks.
Fidelity optimized multiparty quantum teleportation protocol with quantum Shor codes and OSPF method in imperfect local operations

Abstract Relying on the probabilistic nature and entanglement property of quantum state, quantum communication will support the theoretically unconditional security and computational efficiency beyond that of classical communication. In this article, we present a fidelity optimized quantum teleportation protocol for multiparty quantum network communication. Firstly, a special transmission mechanism ensures the secure establishment of the channel between the communicators. Secondly, the quantum information is encoded by Shor codes to resist channel noise. Thirdly, the Open Shortest Path First (OSPF) method is of help for establishing the channel between non-adjacent communicators. In this protocol, the special transmission mechanism guarantees the security of quantum communication. The Shor codes improve the fidelity of quantum information. The OSPF method ensures efficient transmission. The effectiveness of the protocol is verified by experiments. Experimental results show that this protocol provides effective qubit fidelity improvements within the range of noise thresholds of <?CDATA $2.322 \times {10^{-2}}$ ?> . Compared with existing quantum communication protocols, this protocol has better reliability and fidelity performance.
CUDM: A Combined UAV Detection Model Based on Video Abnormal Behavior

The widespread use of unmanned aerial vehicles (UAVs) has brought many benefits, particularly for military and civil applications. For example, UAVs can be used in communication, ecological surveys, agriculture, and logistics to improve efficiency and reduce the required workforce. However, the malicious use of UAVs can significantly endanger public safety and pose many challenges to society. Therefore, detecting malicious UAVs is an important and urgent issue that needs to be addressed. In this study, a combined UAV detection model (CUDM) based on analyzing video abnormal behavior is proposed. CUDM uses abnormal behavior detection models to improve the traditional object detection process. The work of CUDM can be divided into two stages. In the first stage, our model cuts the video into images and uses the abnormal behavior detection model to remove a large number of useless images, improving the efficiency and real-time detection of suspicious targets. In the second stage, CUDM works to identify whether the suspicious target is a UAV or not. Besides, CUDM relies only on ordinary equipment such as surveillance cameras, avoiding the use of expensive equipment such as radars. A self-made UAV dataset was constructed to verify the reliability of CUDM. The results show that CUDM not only maintains the same accuracy as state-of-the-art object detection models but also reduces the workload by 32%. Moreover, it can detect malicious UAVs in real-time.
Piston Sensing for Golay-6 Sparse Aperture System with Double-Defocused Sharpness Metrics via ResNet-34

In pursuit of high imaging quality, optical sparse aperture systems must correct piston errors quickly within a small range. In this paper, we modified the existing deep-learning piston detection method for the Golay-6 array, by using a more powerful single convolutional neural network based on ResNet-34 for feature extraction; another fully connected layer was added, on the basis of this network, to obtain the best results. The Double-defocused Sharpness Metric (DSM) was selected first, as a feature vector to enhance the model performance; the average RMSE of the five sub-apertures for valid detection in our study was only 0.015λ (9 nm). This modified method has higher detecting precision, and requires fewer training datasets with less training time. Compared to the conventional approach, this technique is more suitable for the piston sensing of complex configurations.
A Mask-Based Adversarial Defense Scheme

Adversarial attacks hamper the functionality and accuracy of deep neural networks (DNNs) by meddling with subtle perturbations to their inputs. In this work, we propose a new mask-based adversarial defense scheme (MAD) for DNNs to mitigate the negative effect from adversarial attacks. Our method preprocesses multiple copies of a potential adversarial image by applying random masking, before the outputs of the DNN on all the randomly masked images are combined. As a result, the combined final output becomes more tolerant to minor perturbations on the original input. Compared with existing adversarial defense techniques, our method does not need any additional denoising structure or any change to a DNN’s architectural design. We have tested this approach on a collection of DNN models for a variety of datasets, and the experimental results confirm that the proposed method can effectively improve the defense abilities of the DNNs against all of the tested adversarial attack methods. In certain scenarios, the DNN models trained with MAD can improve classification accuracy by as much as 90% compared to the original models when given adversarial inputs.
Application Research of User Co-creation in Intelligent Cockpit Solutions

The method of user co-creation originated in the Internet industry and gradually penetrated into other industries. In the era of "user experience as the core", the definition of intelligent cockpit functions needs to be closely centered on the user's use scenarios and needs. It refers to the user co creation workshop to obtain the user's experience, mine the user's needs, and through the participation of real users, automotive industry experts and automotive designers, Provide innovative solutions for intelligent cockpit through co creation, and carry out functional definition and engineering development.