Reviewerly/exHarmony huggingface dataset:
Ancient mural segmentation based on multiscale feature fusion and dual attention enhancement

Authors:
raw_author_name: Jianfang Cao
raw_affiliation_string: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China
raw_author_name: Zhen Cao
raw_affiliation_string: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China
raw_author_name: Zhiqiang Chen
raw_affiliation_string: Information Technology, SEGi University, Kota Damansara, Petaling Jaya, Selangor, Malaysia
raw_author_name: Fang Wang
raw_affiliation_string: Department of Computer Science and Technology, Xinzhou Normal University, No. 1 Dunqi East Street, Xinzhou, 034000, China
raw_author_name: Xianhui Wang
raw_affiliation_string: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China
raw_author_name: Zhuolin Yang
raw_affiliation_string: School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China

Abstract: Abstract To address the fuzzy segmentation boundaries, missing details, small target losses and low efficiency of traditional segmentation methods in ancient mural image segmentation scenarios, this paper proposes a mural segmentation model based on multiscale feature fusion and a dual attention-augmented segmentation model (MFAM). The model uses the MobileViT network, which integrates a coordinate attention mechanism, as the feature extraction backbone network. It attains global and local expression capabilities through self-attention, class convolution, and coordinate attention and focuses on location information to expand the receptive field and achieve improved feature extraction efficiency. An A_R_ASPP feature enhancement module is proposed for the attention-optimized residual atrous spatial pyramid pooling module. The module uses residual connections to solve the small target loss problem in murals caused by the excessive sampling rate of atrous convolution and uses a feature attention mechanism to adaptively adjust the feature map weight according to the channel importance levels. A dual attention-enhanced feature fusion module is proposed for multiscale decoder feature fusion to improve the mural segmentation effect. This module uses a cross-level aggregation strategy and an attention mechanism to weight the importance of different feature levels to obtain multilevel semantic feature representations. The model improves the mean intersection over union (MIoU) by 3.06% and the MPA by 1.81% on a mural dataset compared with other models. The model is proven to be effective at improving the segmentation details, efficiency and small target segmentation results produced for mural images, and a new method is proposed for segmenting ancient mural images.
Use of noisy labels as weak learners to identify incompletely ascertainable outcomes: A Feasibility study with opioid-induced respiratory depression

Authors:
raw_author_name: Daniel Fabbri
raw_affiliation_string: Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
raw_author_name: Ruth M. Reeves
raw_affiliation_string: Department of Biomedical Informatics, Vanderbilt University Medical Center, Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA
raw_author_name: Michael E. Matheny
raw_affiliation_string: Department of Biomedical Informatics, Vanderbilt University Medical Center, Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN, USA

Abstract: <h2>Abstract</h2><h3>Objective</h3> Assigning outcome labels to large observational data sets in a timely and accurate manner, particularly when outcomes are rare or not directly ascertainable, remains a significant challenge within biomedical informatics. We examined whether noisy labels generated from subject matter experts' heuristics using heterogenous data types within a data programming paradigm could provide outcomes labels to a large, observational data set. We chose the clinical condition of opioid-induced respiratory depression for our use case because it is rare, has no administrative codes to easily identify the condition, and typically requires at least some unstructured text to ascertain its presence. <h3>Materials and methods</h3> Using de-identified electronic health records of 52,861 post-operative encounters, we applied a data programming paradigm (implemented in the Snorkel software) for the development of a machine learning classifier for opioid-induced respiratory depression. Our approach included subject matter experts creating 14 labeling functions that served as noisy labels for developing a probabilistic Generative model. We used probabilistic labels from the Generative model as outcome labels for training a Discriminative model on the source data. We evaluated performance of the Discriminative model with a hold-out test set of 599 independently-reviewed patient records. <h3>Results</h3> The final Discriminative classification model achieved an accuracy of 0.977, an F1 score of 0.417, a sensitivity of 1.0, and an AUC of 0.988 in the hold-out test set with a prevalence of 0.83% (5/599). <h3>Discussion</h3> All of the confirmed Cases were identified by the classifier. For rare outcomes, this finding is encouraging because it reduces the number of manual reviews needed by excluding visits/patients with low probabilities. <h3>Conclusion</h3> Application of a data programming paradigm with expert-informed labeling functions might have utility for phenotyping clinical phenomena that are not easily ascertainable from highly-structured data.
Enhancing Camera Calibration for Traffic Surveillance with an Integrated Approach of Genetic Algorithm and Particle Swarm Optimization

Authors:
raw_author_name: Hwan-Sik Yoon
raw_affiliation_string: Department of Mechanical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA

Abstract: Recent advancements in sensor technologies, coupled with signal processing and machine learning, have enabled real-time traffic control systems to effectively adapt to changing traffic conditions. Cameras, as sensors, offer a cost-effective means to determine the number, location, type, and speed of vehicles, aiding decision-making at traffic intersections. However, the effective use of cameras for traffic surveillance requires proper calibration. This paper proposes a new optimization-based method for camera calibration. In this approach, initial calibration parameters are established using the Direct Linear Transformation (DLT) method. Then, optimization algorithms are applied to further refine the calibration parameters for the correction of nonlinear lens distortions. A significant enhancement in the optimization process is achieved through the integration of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) into a combined Integrated GA and PSO (IGAPSO) technique. The effectiveness of this method is demonstrated through the calibration of eleven roadside cameras at three different intersections. The experimental results show that when compared to the baseline DLT method, the vehicle localization error is reduced by 22.30% with GA, 22.31% with PSO, and 25.51% with IGAPSO.
Polarization-Selective Enhancement of Telecom Wavelength Quantum Dot Transitions in an Elliptical Bullseye Resonator

Authors:
raw_author_name: Ginny Shooter
raw_affiliation_string: Toshiba Europe Limited, 208 Science Park, Milton Road, Cambridge CB4 0GZ, United Kingdom
raw_author_name: Tina Müller
raw_affiliation_string: Toshiba Europe Limited, 208 Science Park, Milton Road, Cambridge CB4 0GZ, United Kingdom
raw_author_name: Joanna Skiba-Szymanska
raw_affiliation_string: Toshiba Europe Limited, 208 Science Park, Milton Road, Cambridge CB4 0GZ, United Kingdom
raw_author_name: R. Mark Stevenson
raw_affiliation_string: Toshiba Europe Limited, 208 Science Park, Milton Road, Cambridge CB4 0GZ, United Kingdom
raw_author_name: David A. Ritchie
raw_affiliation_string: Cavendish Laboratory, University of Cambridge, Madingley Road, Cambridge CB3 0HE, United Kingdom
raw_author_name: Andrew J. Shields
raw_affiliation_string: Toshiba Europe Limited, 208 Science Park, Milton Road, Cambridge CB4 0GZ, United Kingdom

Abstract: Semiconductor quantum dots are promising candidates for the generation of nonclassical light. Coupling a quantum dot to a device capable of providing polarization-selective enhancement of optical transitions is highly beneficial for advanced functionalities, such as efficient resonant driving schemes or applications based on optical cyclicity. Here, we demonstrate broadband polarization-selective enhancement by coupling a quantum dot emitting in the telecom O-band to an elliptical bullseye resonator. We report bright single-photon emission with a degree of linear polarization of 96%, Purcell factor of 3.9 ± 0.6, and count rates up to 3 MHz. Furthermore, we present a measurement of two-photon interference without any external polarization filtering. Finally, we demonstrate compatibility with compact Stirling cryocoolers by operating the device at temperatures up to 40 K. These results represent an important step toward practical integration of optimal quantum dot photon sources in deployment-ready setups.
Correction: Guo et al. A Siamese Transformer Network for Zero-Shot Ancient Coin Classification. J. Imaging 2023, 9, 107

Authors:
raw_author_name: Zhongliang Guo
raw_affiliation_string: School of Computer Science, University of St Andrews, Scotland KY16 9AJ, UK
raw_author_name: Ognjen Arandjelović
raw_affiliation_string: School of Computer Science, University of St Andrews, Scotland KY16 9AJ, UK
raw_author_name: David Reid
raw_affiliation_string: School of Computer Science, University of St Andrews, Scotland KY16 9AJ, UK
raw_author_name: Yaxiong Lei
raw_affiliation_string: School of Computer Science, University of St Andrews, Scotland KY16 9AJ, UK
raw_author_name: Jochen Büttner
raw_affiliation_string: Max Planck Institute for the History of Science, Boltzmannstraße 22, 14195 Berlin, Germany

Abstract: Jochen Büttner was not included as an author in the original publication [...].
Mapping words to the world: Adults, but not children, understand how mismatching descriptions refer.

Authors:
raw_author_name: Gabor Brody
raw_affiliation_string: Department of Cognitive, Linguistic and Psychological Sciences, Brown University.

Abstract: How do children learn to connect expressions (e.g., "that red apple") to the real-world objects they refer to? The dominant view in developmental psychology is that children rely on descriptive information, "red" and "apple." In contrast, linguistic theories of the adult language attribute primacy to grammatical elements: words such as "that" or "another" first establish the status of potential referents within the discourse context (old or new) before descriptions can factor in. These theories predict that reference can succeed even when the description does not match the referent. We explored this novel prediction in adults and children. Over four experiments, we found that (a) adults relied on the articles to identify the referent, even when the description did not fit, consistent with grammar-first accounts; (b) consistent with description-first accounts, and unlike adults, 3- to 5-year-old children prioritized the descriptions provided by nouns and adjectives, despite being sensitive to grammatical information. This suggests that children connect expressions to referents differently from adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
B-SAFE: Blockchain-Enabled Security Architecture for Connected Vehicle Fog Environment

Authors:
raw_author_name: Priyanka Gaba
raw_affiliation_string: School of Computer Science Engineering & Technology, Bennett University, Greater Noida 201310, India
raw_author_name: Ram Shringar Raw
raw_affiliation_string: School of Computing Science & Engineering, Netaji Subhas University of Technology, New Delhi 110031, India
raw_author_name: Omprakash Kaiwartya
raw_affiliation_string: Computing and Informatics Research Centre, Nottingham Trent University, Nottingham NG11 8NS, UK; Department of Computer Science, Nottingham Trent University, Nottingham NG11 8NS, UK
raw_author_name: Mohammad Aljaidi
raw_affiliation_string: Computer Science Department, Faculty of Information Technology, Zarqa University, Zarqa 13110, Jordan

Abstract: Vehicles are no longer stand-alone mechanical entities due to the advancements in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication-centric Internet of Connected Vehicles (IoV) frameworks. However, the advancement in connected vehicles leads to another serious security threat, online vehicle hijacking, where the steering control of vehicles can be hacked online. The feasibility of traditional security solutions in IoV environments is very limited, considering the intermittent network connectivity to cloud servers and vehicle-centric computing capability constraints. In this context, this paper presents a Blockchain-enabled Security Architecture for a connected vehicular Fog networking Environment (B-SAFE). Firstly, blockchain security and vehicular fog networking are introduced as preliminaries of the framework. Secondly, a three-layer architecture of B-SAFE is presented, focusing on vehicular communication, blockchain at fog nodes, and the cloud as trust and reward management for vehicles. Thirdly, details of the blockchain implementation at fog nodes is presented, along with a flowchart and algorithm. The performance of the evaluation of the proposed framework B-SAFE attests to the benefits in terms of trust, reward points, and threshold calculation.
In situ calibration of shear ratio forquadriwave lateral shearing interferometryusing double-slit interference

Authors:
raw_author_name: Xiaotian Zhang
raw_author_name: Tao Wang
raw_author_name: Hui Zhong
raw_author_name: Shuhao Liu
raw_author_name: Yanqiu Li

Abstract: 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

Authors:
raw_author_name: Yi Xie
raw_affiliation_string: Fujian Key Laboratory of Sensing and Computing for Smart City, School of Information Science and Engineering, Xiamen University, Xiamen, Fujian 361005, China; Shenzhen Research Institute of Xiamen University, Xiamen University, Shenzhen 518000, China
raw_author_name: Bin Ren
raw_affiliation_string: Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China; State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
raw_author_name: Zhong-Qun Tian
raw_affiliation_string: Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361005, China; State Key Laboratory for Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China

Abstract: 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

Authors:
raw_author_name: feng huang
raw_author_name: Chaozhen Ke
raw_author_name: Xianyu Wu
raw_author_name: Cuixia Guo
raw_author_name: Yu Liu

Abstract: 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

Authors:
raw_author_name: Yanhua Huang
raw_affiliation_string: School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
raw_author_name: Zhendong Wu
raw_affiliation_string: School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
raw_author_name: Juan Chen
raw_affiliation_string: School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China
raw_author_name: Hui Xiang
raw_affiliation_string: School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China

Abstract: 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.