Reviewerly/exHarmony huggingface dataset:
In situ calibration of shear ratio forquadriwave lateral shearing interferometryusing double-slit interference

A new method, to the best of our knowledge, based on double-slit (DS) interference is proposed to accurately estimate the shear ratio of the system, with plane wave or spherical wave incidence. Existing shear ratio calibration methods, designed primarily for lateral shearing interferometry (LSI) with plane wave incidence, are not applicable to LSIs directly testing divergent or convergent spherical waves. Equations for calculating the shear ratio using the fringe spacing of the DS interferogram and the NA of the incident spherical wave are derived in this paper. The simulation result shows that the relative error of the shear ratio value is about 0.3%, when the shear ratio is 0.1. In the experiment, the quadriwave LSI is designed with a plug-in feature. The shear ratio at integer multiples of 1/6 Talbot distance from the modified Hartmann mask was calibrated using a DS, and the results were in good agreement with theoretical values, confirming the accuracy of the method. Subsequently, with the assistance of an inductance micrometer, the shear ratio was calibrated at intervals of 0.5 mm, and the results closely matched the theoretical variation of the shear ratio caused by displacement, confirming the high precision of the method.
Revealing the Denoising Principle of Zero-Shot N2N-Based Algorithm from 1D Spectrum to 2D Image

Denoising is a necessary step in image analysis to extract weak signals, especially those hardly identified by the naked eye. Unlike the data-driven deep-learning denoising algorithms relying on a clean image as the reference, Noise2Noise (N2N) was able to denoise the noise image, providing sufficiently noise images with the same subject but randomly distributed noise. Further, by introducing data augmentation to create a big data set and regularization to prevent model overfitting, zero-shot N2N-based denoising was proposed in which only a single noisy image was needed. Although various N2N-based denoising algorithms have been developed with high performance, their complicated black box operation prevented the lightweight. Therefore, to reveal the working function of the zero-shot N2N-based algorithm, we proposed a lightweight Peak2Peak algorithm (P2P) and qualitatively and quantitatively analyzed its denoising behavior on the 1D spectrum and 2D image. We found that the high-performance denoising originates from the trade-off balance between the loss function and regularization in the denoising module, where regularization is the switch of denoising. Meanwhile, the signal extraction is mainly from the self-supervised characteristic learning in the data augmentation module. Further, the lightweight P2P improved the denoising speed by at least ten times but with little performance loss, compared with that of the current N2N-based algorithms. In general, the visualization of P2P provides a reference for revealing the working function of zero-shot N2N-based algorithms, which would pave the way for the application of these algorithms toward real-time (in situ, in vivo, and operando) research improving both temporal and spatial resolutions. The P2P is open-source at https://github.com/3331822w/Peak2Peakand will be accessible online access at https://ramancloud.xmu.edu.cn/tutorial.
Multispectral image defogging based onwavelength-dependent extinction coefficientmodel in fog

Most of the state-of-the-art defogging models presented in the literature assume that the attenuation coefficient of all spectral channels is constant, which inevitably leads to spectral distortion and information bias. To address this issue, this paper proposes a defogging method that takes into account the difference between the extinction coefficients of multispectral channels of light traveling through fog. Then the spatially distributed transmission map of each spectral channel is reconstructed to restore the fog-degraded images. The experimental results of various realistic complex scenes show that the proposed method has more outstanding advantages in restoring lost detail, compensating for degraded spectral information, and recognizing more targets hidden in uniform ground fog than state-of-the-art technologies. In addition, this work provides a method to characterize the intrinsic property of fog expressed as multispectral relative extinction coefficients, which act as a fundament for further reconstruction of multispectral information.
Privacy-Preserving Face Recognition Method Based on Randomization and Local Feature Learning

Personal privacy protection has been extensively investigated. The privacy protection of face recognition applications combines face privacy protection with face recognition. Traditional face privacy-protection methods encrypt or perturb facial images for protection. However, the original facial images or parameters need to be restored during recognition. In this paper, it is found that faces can still be recognized correctly when only some of the high-order and local feature information from faces is retained, while the rest of the information is fuzzed. Based on this, a privacy-preserving face recognition method combining random convolution and self-learning batch normalization is proposed. This method generates a privacy-preserved scrambled facial image and an image fuzzy degree that is close to an encryption of the image. The server directly recognizes the scrambled facial image, and the recognition accuracy is equivalent to that of the normal facial image. The method ensures the revocability and irreversibility of the privacy preserving of faces at the same time. In this experiment, the proposed method is tested on the LFW, Celeba, and self-collected face datasets. On the three datasets, the proposed method outperforms the existing face privacy-preserving recognition methods in terms of face visual information elimination and recognition accuracy. The recognition accuracy is >99%, and the visual information elimination is close to an encryption effect.
Response to “Antisocial personality traits relationship with behaviours and beliefs on COVID‐19 containment measures: Investigation in a large Brazilian sample”

Personality and Mental HealthEarly View RESPONSE Response to “Antisocial personality traits relationship with behaviours and beliefs on COVID-19 containment measures: Investigation in a large Brazilian sample” Lien-Chung Wei, Corresponding Author Lien-Chung Wei [email protected] orcid.org/0000-0002-7383-7309 Department of Addiction Psychiatry, Taoyuan Psychiatric Center, Ministry of Health and Welfare, Republic of China (Taiwan), Taoyuan, Taiwan Correspondence Lien-Chung Wei, Department of Addiction Psychiatry, Taoyuan Psychiatric Center, Ministry of Health and Welfare, Republic of China (Taiwan), 71 Longshou Street, Taoyuan District, Taoyuan 330, Taiwan. Email: [email protected]Search for more papers by this author Lien-Chung Wei, Corresponding Author Lien-Chung Wei [email protected] orcid.org/0000-0002-7383-7309 Department of Addiction Psychiatry, Taoyuan Psychiatric Center, Ministry of Health and Welfare, Republic of China (Taiwan), Taoyuan, Taiwan Correspondence Lien-Chung Wei, Department of Addiction Psychiatry, Taoyuan Psychiatric Center, Ministry of Health and Welfare, Republic of China (Taiwan), 71 Longshou Street, Taoyuan District, Taoyuan 330, Taiwan. Email: [email protected]Search for more papers by this author First published: 27 February 2024 https://doi.org/10.1002/pmh.1608Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. REFERENCES De Coninck, D., Duque, M., & Schwartz, S. J. (2023). Dark triad personality traits and realistic and symbolic COVID-19 threat: The role of conspiracy mentality. Scandinavian Journal of Psychology. https://doi.org/10.1111/sjop.12980 10.1111/sjop.12980 PubMedGoogle Scholar de Francisco Carvalho, L., Koich Miguel, F., Machado, G. M., de Oliveira, C., & Pianowski, G. (2023). Antisocial personality traits relationship with behaviours and beliefs on COVID-19 containment measures: Investigation in a large Brazilian sample. Personality and Mental Health. https://doi.org/10.1002/pmh.1601 10.1002/pmh.1601 PubMedGoogle Scholar Lisa, E., & Valachova, M. (2023). Dispositional employability and self-regulation in antisocial and prosocial personalities: Different contributions to employability. BMC Psychology, 11(1), 7. https://doi.org/10.1186/s40359-023-01037-1 10.1186/s40359-023-01037-1 PubMedGoogle Scholar Early ViewOnline Version of Record before inclusion in an issue ReferencesRelatedInformation
NANOSCALE SURFACE METROLOGY WITH LIQUID CRYSTAL BASED PHASE SHIFTING ANGULAR SHEARING INTERFEROMETER

In this Letter, a phase-shifting angular shearing interferometer has been proposed for the application in optical surface metrology (SM) by using a combination of a wedge-shaped liquid crystal (LC) cell and a polarization phase shifter. The demonstration of this angular shearing interferometer for step height measurement is accomplished with the help of a phase-shifting technique. Four phase-shifted interferograms produced by a geometrical phase shifter are subjected to a simplified Wiener deconvolution method, which resembles a simple analysis technique for shearing interferograms in comparison to alternative approaches. A simulation study has been conducted to validate the proposed technique. The experimental results show an accuracy of 5.56% for determining the step height, which also agrees with the results obtained by atomic force microscopy. Owing to the tunability of birefringence, the proposed LC-based angular shearing interferometry technique will be useful to control spatial resolution in optical metrology.
Blockchain-secure patient Digital Twin in healthcare using smart contracts

Modern healthcare has a sharp focus on data aggregation and processing technologies. Consequently, from a data perspective, a patient may be regarded as a timestamped list of medical conditions and their corresponding corrective interventions. Technologies to securely aggregate and access data for individual patients in the quest for precision medicine have led to the adoption of Digital Twins in healthcare. Digital Twins are used in manufacturing and engineering to produce digital models of physical objects that capture the essence of device operation to enable and drive optimization. Thus, a patient's Digital Twin can significantly improve health data sharing. However, creating the Digital Twin from multiple data sources, such as the patient's electronic medical records (EMR) and personal health records (PHR) from wearable devices, presents some risks to the security of the model and the patient. The constituent data for the Digital Twin should be accessible only with permission from relevant entities and thus requires authentication, privacy, and provable provenance. This paper proposes a blockchain-secure patient Digital Twin that relies on smart contracts to automate the updating and communication processes that maintain the Digital Twin. The smart contracts govern the response the Digital Twin provides when queried, based on policies created for each patient. We highlight four research points: access control, interaction, privacy, and security of the Digital Twin and we evaluate the Digital Twin in terms of latency in the network, smart contract execution times, and data storage costs.
Application of Artificial Intelligence (AI) System in Opportunistic Screening and Diagnostic Population in a Middle-income Nation

Objective:: This study evaluates the effectiveness of artificial intelligence (AI) in mammography in a diverse population from a middle-income nation and compares it to traditional methods. Methods:: A retrospective study was conducted on 543 mammograms of 467 Malays, 48 Chinese, and 28 Indians in a middle-income nation. Three breast radiologists interpreted the examinations independently in two reading sessions (with and without AI support). Breast density and BI-RADS categories were assessed, comparing the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) results. Results:: Of 543 mammograms, 69.2% had lesions detected. Biopsies were performed on 25%(n=136), with 66(48.5%) benign and 70(51.5%) malignant. Substantial agreement in density assessment between the radiologist and AI software (κ =0.606, p &lt; 0.001) and the BI-RADS category with and without AI (κ =0.74, p &lt; 0.001). The performance of the AI software was comparable to the traditional methods. The sensitivity, specificity, PPV, and NPV or radiologists alone, radiologist + AI, and AI alone were 81.9%,90.4%,56.0%, and 97.1%; 81.0%, 93.1%,55.5%, and 97.0%; and 90.0%,76.5%,36.2%, and 98.1%, respectively. AI software enhances the accuracy of lesion diagnosis and reduces unnecessary biopsies, particularly for BI-RADS 4 lesions. The AI software results for synthetic were almost similar to the original 2D mammography, with AUC of 0.925 and 0.871, respectively. Conclusion:: AI software may assist in the accurate diagnosis of breast lesions, enhancing the efficiency of breast lesion diagnosis in a mixed population of opportunistic screening and diagnostic patients. Key Messages:: • The use of artificial intelligence (AI) in mammography for population-based breast cancer screening has been validated in high-income nations, with reported improved diagnostic performance. Our study evaluated the usage of an AI tool in an opportunistic screening setting in a multi-ethnic and middle-income nation. • The application of AI in mammography enhances diagnostic accuracy, potentially leading to reduced unnecessary biopsies. • AI integration into the workflow did not disrupt the performance of trained breast radiologists, as there is a substantial inter-reader agreement for BI-RADS category assessment and breast density.
Toward Improving the Generation Quality of Autoregressive Slot VAEs

Abstract Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric representations (“slots”) from images, unconditional generation of scenes from slots has received less attention. This is primarily because learning the multiobject relations necessary to imagine coherent scenes is difficult. We hypothesize that most existing slot-based models have a limited ability to learn object correlations. We propose two improvements that strengthen object correlation learning. The first is to condition the slots on a global, scene-level variable that captures higher-order correlations between slots. Second, we address the fundamental lack of a canonical order for objects in images by proposing to learn a consistent order to use for the autoregressive generation of scene objects. Specifically, we train an autoregressive slot prior to sequentially generate scene objects following a learned order. Ordered slot inference entails first estimating a randomly ordered set of slots using existing approaches for extracting slots from images, then aligning those slots to ordered slots generated autoregressively with the slot prior. Our experiments across three multiobject environments demonstrate clear gains in unconditional scene generation quality. Detailed ablation studies are also provided that validate the two proposed improvements.
Noise Removal from the Image Using Convolutional Neural Networks-Based Denoising Auto Encoder

With the exponential growth in the volume of digital images captured daily, there is an escalating demand for elevating image quality to achieve both accuracy and visual appeal. Addressing this need, the development of techniques for reducing image noise while preserving crucial features, such as edges, corners, and sharp structures, has become imperative. This paper delves into the significance of image denoising and introduces a novel approach utilizing a denoising autoencoder based on convolutional neural networks (CNNs). The proposed method adopts a meticulous two-step process to effectively eliminate noise. Initially, input images are segregated into training and testing sets. Subsequently, a denoising autoencoder model is trained using the designated training data. This model is then further refined through training on a CNN, enhancing its noise reduction capabilities. The evaluation of the system's performance is conducted using testing data to gauge its effectiveness. The study employs the MATLAB programming language for implementation and evaluation. Results, measured through RMSE (Root Mean Square Error) and PSNR (Peak Signal-to-Noise Ratio) criteria on two distinct datasets—the Covid19-radiography-database and SIIM-medical-images—reveal that our proposed method outperforms existing approaches significantly. This approach is particularly promising for applications demanding enhanced image quality, such as the resolution enhancement of medical images. The study contributes to the ongoing efforts in noise reduction research, offering a robust solution for improving visual perception in diverse image processing applications.
VIRTUAL REALITY IN THE PHILIPPINES: FACILITATORS AND PERCEIVED BARRIERS OF VIRTUAL REALITY AS A THERAPEUTIC TOOL IN THE CLINICAL SETTING

Introduction: Virtual Reality (VR) begins to emerge in the Physical Therapy Practice here in the Philippines. However, it goes through a process of acceptance before it can be used as a part of the mainstream treatment modality in the current PT practice. Study aims to explore the perceived barriers and limitations of a handheld virtual reality as a therapeutic tool. Methods: The study employed a qualitative phenomenological study a semi-structured interview conducted via Zoom platform. A snowball sampling was utilized to gather informants’ opinions. Coding was conducted through a meticulous review of respondents by the researchers. The codes were analyzed using Collaizi Method through QDA Miner Lite. Theoretical thematic analysis was utilized to support the themes from the answers of the informants. Results: Twelve informants were initially invited to reach the number of subjects required. However, only ten agreed and consented to participate. Data saturation was met during the sixth interview. The following themes emerged: Barriers (Expensiveness, Atypical Practice), Limitations (Short-Term Functional Effect, Level of Comprehension Required to Understand VR), and Advantages (Entertaining and Usefulness to Neurological and Musculoskeletal cases). Discussion: This paper gathered information from ten registered physical therapists (PTRP) in the country who experienced the use of handheld VR (Nintendo Wii, Xbox, etc.) and their perception of their exposure to the emerging therapeutic tool. Nine out of ten subjects concluded that the cost and expense of the console and the setup of the whole handheld VR room is the main barrier as to why it is still not being used in majority of the rehabilitative clinics nowadays. As an atypical practice, it also plays a role in the perceived barriers of the informants. Most of the subjects believed that current practice in the country needs to work with technology more – in order to keep up with the ever-growing technology in healthcare.
Science Learning Game (SLG) Based on Augmented Reality Enhances Science Literacy and Critical Thinking Students Skills

Science literacy is the ability of individuals to understand, evaluate, and apply scientific knowledge in everyday problem-solving, while critical thinking is the ability to analyze, evaluate, and make decisions based on evidence and logic. This research is based on several issues in SDN Sentul2 Kota Blitar in grade 6 post-Covid 19. This research aims to produce a product used as a learning tool in Grade 6, namely Science Learning Game (SLG) Media based on Augmented Reality to Improve Science Literacy and Critical Thinking Skills in Grade 6 Students in Kota Blitar. This research and development (R&amp;D) study uses the ADDIE (analysis, design, development, implementation, and evaluation) model. The instrument used is a questionnaire. The research data analysis results show that the developed media can improve critical thinking skills, as evidenced by comparing the Pre-test results at 72.21% with the Post-test results at 93.57%. This media can also enhance science literacy, as indicated by the increase between the Pre-test results at 74.28% and the Post-test results at 92.14%. The implication is that the science literacy and critical thinking abilities of students in Indonesia still need improvement, which can be achieved by considering the use of instructional media. With positive results in pre-test and post-test scores, this media has the potential to enhance science literacy and critical thinking skills in students.
Hybrid Android Interface Practicum Mode to Develop Students' Science Process Skills

Science Process Skills are abilities that students must possess because they are applicable not only to science learning but also to daily life. To be familiar with scientific method activities, students must have direct experience in order to be trained in Science Process Skills. This study aims to enhance students' Science Process Skills through hybrid android interface mode practicum learning. The research data was collected using description questions previously validated by subject matter experts. This study conducted quasi-experiments using the Nonquivalent (pretest-posttest) Control Group Design method. The research participants included control and experimental groups. The research results show an increase in Science Process Skills in the experiment class as evidenced by the achievement of aspects of observation 71.25%, interpretation 78.25%, classification 86.12%, prediction 84.45%, conducting experiments or investigations 83.34%, communicating 63.33%, hypothesizing 79.63%, applying concepts 88.34%, and asking questions 82.43%. It shows that the practicum mode of the hybrid Android interface can develop Science Process Skills
Remote Rescue Vehicle

Search and rescue operations in collapsed buildings provide crucial challenges due to the complexity of the environment and the need for information about trapped individuals. This document presents a versatile system equipped with various sensors, including LiDAR, temperature, GPS, GSM, and Wi-Fi modules, designed to assist rescue teams. The system data collection capabilities include high-quality video, audio, and image formats that supplement situational awareness. The LiDAR sensor is used to create detailed 3D models and identify potential hazards and obstacles. The thermal sensor helps in the detection of temperature signatures and enables the identification of surviving or overheated machines. Additionally, the integration of the YOLO algorithm enables real-time object classification, providing critical insights for faster decision-making. The GSM module enables communication with the system. The Wi-Fi module enables high-speed data transmission to ensure that the collected information is quickly transmitted to the rescue team. High-quality video, audio, and image formats offer comprehensive situational awareness, support decision-making, and improve overall operational efficiency.
CONTENT CLASSIFICATION OF LETTERS

This article talks about the content classification of the letters found in the artistic work. The previous classifications were discussed and the reaction to them was expressed. Types of letters are proved by examples.