Reviewerly/exHarmony huggingface dataset:
Thickness and surface estimation of thin porous media based on 3D image data

Authors:
raw_author_name: Florian Frank
raw_affiliation_string: Math2Markt GmbH
raw_author_name: Erik Glatt
raw_affiliation_string: Math2Markt GmbH
raw_author_name: Andreas Wiegmann
raw_affiliation_string: Math2Markt GmbH

Abstract: Abstract Measuring the thickness of thin porous materials provides valuable insights into their structure, properties, and performance, including key properties such as porosity and permeability, and is highly beneficial for a range of industrial applications, particularly for ensuring effective quality control processes. A novel approach for estimating the thickness of porous media and their surfaces is proposed based on voxel sets of 3D images, such as 3D scans and segmented scan data. Initially, the solid volume fraction (SVF) is computed for each voxel layer perpendicular to the through direction. Then, fitting functions consisting of piecewise linear segments are chosen to ensure an accurate representation of the layer data. Each function is associated with various thickness regions of the medium, including the medium itself and its surface. An optimization problem is then solved to find the best-fitting function based on the squared area between the SVF and the fitting function. The thickness of the medium and its surfaces is determined based on the identified optimal fit. This robust, reliable, and fast approach aims to provide not only a non-intrusive method for thickness estimation of porous media represented by voxel sets but also a precise alternative to existing methodologies.
Multi-image optical information hiding algorithm based on Fourier transform without hidden key transmission

Authors:
raw_author_name: yuan guo
raw_author_name: Ping Zhai
raw_affiliation_string: Qiqihar University
raw_author_name: Wenpeng Li
raw_affiliation_string: Qiqihar University
raw_author_name: Xuewen Wang
raw_affiliation_string: Qiqihar University

Abstract: Existing optical information hiding algorithms for multiple images require generating hidden keys for embedded images, resulting in the transmission of numerous keys. This challenge undermines the usage of these algorithms in low-quality networks. To address this issue and enhance transmission efficiency, we present a multi-image optical information hiding algorithm based on Fourier transformation principles, which is employed to generate hidden frequency maps and carrier frequency maps. Specific low-frequency information zones are extracted within these hidden frequency maps. A chaotic system integrates a phase mask, modulated with the low-frequency regions, positioned in the carrier frequency map’s high-frequency sector. The final stego image is obtained by subjecting the carrier frequency map to inverse Fourier transformation. Experimental analysis shows that concealing three images takes only 0.0089 s, with extraction requiring 0.0658 s. Post-extraction PSNR values for hidden images exceed 32 dB. Robustness and anti-attack experiments were done to prove the security of this algorithm. The compared experiments between the proposed method and other state-of-the-art algorithms affirm the algorithm’s attributes of simplicity, ease of implementation, robust security, and high efficiency. Importantly, the restoration process eliminates the necessity of transmitting hidden keys, reducing network burdens and enhancing both concealment and extraction efficiencies significantly.
Trace element geochemistry of high-temperature geothermal waters in the Yunnan-Tibet geothermal province, Southwest China

Authors:
raw_author_name: Mingliang Liu
raw_affiliation_string: Hubei Key Laboratory of Petroleum Geochemistry and Environment & College of Resources and Environment, Yangtze University, Wuhan 430100, China
raw_author_name: Yuanyuan Cao
raw_affiliation_string: Geophysical and Geochemical Exploration Institute of Ningxia Hui Autonomous Region, Yinchuan 750001, China
raw_author_name: Xing Wei
raw_affiliation_string: Hubei Key Laboratory of Petroleum Geochemistry and Environment & College of Resources and Environment, Yangtze University, Wuhan 430100, China

Abstract: To investigate the origins of the trace elements and related geochemical processes, seven representative geothermal systems in southern Tibet and one in western Yunnan were selected. The geothermal waters were divided into acidic and neutral-to-alkaline in magmatic geothermal systems and neutral-to-alkaline in non-magmatic geothermal systems. Acidic geothermal waters exhibit low concentrations of most trace elements. In the neutral-to-alkaline geothermal waters in magmatic geothermal systems, the composition of most trace elements is determined by the water–rock interactions, along with the solubility of secondary minerals, with magmatic fluids contributing to other trace elements (e.g., B, As, Sb, Mo and W). As for the geothermal waters of non-magmatic geothermal systems, trace elements are primarily derived from carbonate reservoir rock dissolution. Trace elements of magmatic origin are more abundant in the Tibetan geothermal waters than in those of Yunnan, which results from the varied chemical composition of their underlying magma chambers. Our findings indicate that the trace element geochemistry has the potential to identify the genesis of geothermal systems.
Multiuser medical image encryption algorithm using phase-only CGH in the gyrator domain

Authors:
raw_author_name: Raman Yadav
raw_affiliation_string: Department of Mathematics, Central University of Haryana, Mahendergarh 123031, India
raw_author_name: Sachin Sachin
raw_affiliation_string: Department of Mathematics, IIHS, Kurukshetra Universityhttps://ror.org/019bzvf55, Kurukshetra, Haryana 136119, India
raw_author_name: Phool Singh
raw_affiliation_string: Department of Mathematics, SOET, Central University of Haryana, Mahendergarh 123031, India

Abstract: In this paper, a multiuser medical image encryption algorithm is proposed. The proposed algorithm utilizes polar decomposition, which enables multiuser features in the proposed algorithm. A computer-generated hologram (CGH) improves the security of the proposed algorithm in the gyrator domain. The phase-only CGH-based multiuser algorithm offers advantages such as storing a large amount of information in a compact space, resistance to counterfeiting, and enhanced security. The proposed method is validated with various statistical metrics, such as information entropy, mean squared error, correlation coefficient, histogram, and mesh plots. Results confirm that the proposed algorithm is secure and robust against potential attacks, such as plaintext attacks, iterative attacks, and contamination attacks. The proposed method has a large keyspace, which makes it very difficult to be breached in real-time with existing computational power.
Light field camera calibration and point spread function calculation based on differentiable ray tracing

Authors:
raw_author_name: Di Mei
raw_affiliation_string: Information Engineering University, Zhengzhou 450001, China
raw_author_name: Yinsen Luan
raw_affiliation_string: Information Engineering University, Zhengzhou 450001, China; School of Mechanical Engineering, Shanghai Jiao Tong Universityhttps://ror.org/0220qvk04, Shanghai 200240, China
raw_author_name: XiJiang Wu
raw_affiliation_string: SAIC Motor R&D Innovation Headquarters, Shanghai 200030, China

Abstract: The imaging process of the light field (LF) camera with a micro-lens array (MLA) may suffer from multiple aberrations. It is thus difficult to precisely calibrate the intrinsic hardware parameters and calculate the corresponding point spread function (PSF). To build an aberration-aware solution with better generalization, we propose an end-to-end imaging model based on the differentiable ray tracing. The input end is the point source location, and the output end is the rendered LF image, namely, PSF. Specially, a projection method is incorporated into the imaging model, eliminating the huge memory overhead induced by a large array of periodic elements. Taking captured PSF images as the ground truth, the LF camera is calibrated with the genetic algorithm initially and then the gradient-based optimization. This method is promising to be used in various LF camera applications, especially in challenging imaging conditions with severe aberrations.
Exploring EFL learners’ acceptance and cognitive absorption at VR-Based language learning: A survey and experimental study

Authors:
raw_author_name: Liwei Hsu
raw_affiliation_string: Professor, Graduate Institute of Hospitality Management National Kaohsiung University of Hospitality and Tourism, No. 1, Song-he Rd., Hsiao-kang District, Kaohsiung City, Taiwan.

Abstract: <h2>Abstract</h2> This study aimed to explore the applicability of VR-based language learning in an EFL context. An online survey was conducted to understand the structural relationship between EFL learners' cognitive absorption, behavioral intention to use VR for English learning, and perceptions regarding the sense of immersion created by VR. The hedonic motivation system adoption model (HMSAM) was adopted, and 230 valid responses were retrieved for statistical analyses. The results showed that most constructs of HMSAM, namely, perceived ease of use, perceived usefulness, curiosity, joy, control, behavioral intention to use, and immersion, were significantly associated with other constructs. VR's immersion had a positive significant influence on the behavioral intention of EFL learners to engage in VR-based language learning. It was revealed that curiosity was not a significant predictor for immersion. Moreover, a within-subject neurophysiological experiment was conducted with 33 EFL learners who experienced both VR-based and non-VR-based settings to examine the influence of VR technologies on their cognitive absorption and learning outcomes. Results demonstrated that VR did increase the participants' cognitive absorption; furthermore, participants had better retention about the learned contents in VR-based setting. The findings have practical and theoretical implications based on the findings of the survey and experiment.
Time-aware forecasting of search volume categories and actual purchase

Authors:
raw_author_name: Dana A. Al-Qudah
raw_affiliation_string: King Abdullah II School of Information Technology, The University of Jordan, Jordan
raw_author_name: Bilal Abu-Salih
raw_affiliation_string: King Abdullah II School of Information Technology, The University of Jordan, Jordan

Abstract: The new e-commerce field has attracted businesses of all sizes, retailers, and individuals. Consequently, there is an ongoing necessity for applications that can offer predictions on trending products and optimal selling time. This research suggests aiding businesses in forecasting demand for various product categories by employing data mining algorithms on multivariate time series data. To ensure the most recent information, real-time data was gathered through APIs to build the first block in this research. While search volume was derived from the Keywords Everywhere tool, Amazon's search volume was derived from the Helium 10 tool and external features about actual purchased data. The harvested raw datasets went through multiple processes to generate the dataset and were validated. The models XGBoost, Linear Regression, Random Forest, long-short-term memory, and K-nearest neighbor were employed to predict the trends, and the performance is demonstrated using evaluation metrics, namely Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2). Overall, Linear Regression outperformed, especially at a correlation coefficient of 0.9, with R2 = 90.688, MAE = 0.038, MSE = 0.003, and RMSE = 0.057. KNN outperformed on correlation coefficient of 0.7, R2 = 85.129, MAE = 0.045, MSE = 0.005, and RMSE = 0.068. XGBoost produced the best results with a correlation coefficient of 0.9, yielding R2 = 85.89, MAE = 0.042, MSE = 0.004, and RMSE = 0.062. Random Forest, on the other hand, achieves peak metrics with a correlation coefficient of 0.6, R2 = 84.854, MAE = 0.041, MSE = 0.004, and RMSE = 0.066.
Real-time binocular visual localization system based on the improved BGNet stereo matching framework

Authors:
raw_author_name: Zanxi Qu
raw_affiliation_string: Key Laboratory of Photoelectronic Imaging Technology and System, Beijing Institute of Technologyhttps://ror.org/01skt4w74, Beijing 100081, China
raw_author_name: Weiqi Jin
raw_affiliation_string: Key Laboratory of Photoelectronic Imaging Technology and System, Beijing Institute of Technologyhttps://ror.org/01skt4w74, Beijing 100081, China
raw_author_name: Ye Yang
raw_affiliation_string: Key Laboratory of Low Light Night Vision Technology, Shanxi, Xian 710065, China

Abstract: Binocular vision technology is widely used to acquire three-dimensional information of images because of its low cost. In recent years, the use of deep learning for stereo matching has shown promising results in improving the measurement stability of binocular vision systems, but the real-time performance in high-precision networks is typically poor. Therefore, this study constructed a deep-learning-based stereo matching binocular vision system based on the BGLGA-Net, which combines the advantages of past networks. Experiments showed that the ability to detect the edges of foreground objects was enhanced. The network was used to build a system on the Xavier NX. The measurement accuracy and stability were better than those of traditional algorithms.
CMBEE: A constraint-based multi-task learning framework for biomedical event extraction

Authors:
raw_author_name: Jingyue Hu
raw_affiliation_string: Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China
raw_author_name: Buzhou Tang
raw_affiliation_string: Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China; Peng Cheng Laboratory, Shenzhen, China
raw_author_name: Ying Xiong
raw_affiliation_string: Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China

Abstract: Event extraction plays a crucial role in natural language processing. However, in the biomedical domain, the presence of nested events adds complexity to event extraction compared to single events, and these events usually have strong semantic relationships and constraints. Previous approaches ignored the binding connections between these complex nested events. This study aims to develop a unified framework based on event constraint information that jointly extract biomedical event triggers and arguments and enhance the performance of nested biomedical event extraction. We propose a multi-task learning framework based on constraint information called CMBEE for the task of biomedical event extraction. The N-tuple form of event patterns is used to represent the constrained information, which is integrated into role detection and event type classification tasks. The framework use attention mechanism and gating mechanism to explore the fusion of multiple tuple information, as well as local and global constrained information fusion methods to dig further into the connections between events. Experimental results demonstrate that our proposed method achieves the highest F1 score on a multilevel event extraction biomedical (MLEE) corpus and performs favorably on the biomedical natural language processing shared task 2013 Genia event corpus (GE 13). The experimental results indicate that modeling event patterns and constraints for multi-event extraction tasks is effective for complex biomedical event extraction. The fusion strategy proposed in this study, which incorporates different constraint information, helps to better express semantic information.
Decision support framework for IT project manager recruitment

Authors:
raw_author_name: Jana Gerlach
raw_affiliation_string: Leibniz Universität Hannover, Königsworther Platz 1, 30167, Hannover, Germany.
raw_author_name: Michael H. Breitner
raw_affiliation_string: Leibniz Universität Hannover, Königsworther Platz 1, 30167, Hannover, Germany.

Abstract: <h2>Abstract</h2> Information technology project managers (IT PM) have a critical influence on IT project success while attracting and selecting the right IT PM is challenging. We followed a four-level research design and firstly developed a taxonomy as an input for a cluster analysis to identify patterns in IT PM job advertisements. Based hereon, we developed a decision support framework for IT PM recruitment. We evaluated our findings in an online survey. We identified multiple design elements for IT PM job advertisements within five perspectives and deduced five IT PM archetypes. The decision support framework uses five questions to assist IT PM recruitment. We expand the knowledge base and consider not only IT PM requirements but also benefits. Our decision support framework is the first to holistically support IT PM recruitment, supports recruitment managers in structuring job interviews, identifies potential matches between applicants and recruiters, and assists in the final selection.
Loom Pedals: Retooling Jacquard Weaving for Improvisational Design Workflows

Authors:
raw_author_name: Shanel Wu
raw_affiliation_string: ATLAS Institute, University of Colorado Boulder, United States
raw_author_name: Sasha De Koninck
raw_affiliation_string: Intermedia Art Writing and Performance, University of Colorado Boulder, United States
raw_author_name: Laura Devendorf
raw_affiliation_string: ATLAS Institute &amp; Information Science, University of Colorado Boulder, United States

Abstract: We present the Loom Pedals, an open-source hardware/software interface for enhancing a weaver's ability to create on-the-fly, improvised designs in Jacquard weaving. Learning from traditional handweaving and our own weaving experiences, we describe our process of designing, implementing, and using the prototype Loom Pedals system with a TC2 Digital Jacquard loom. The Loom Pedals include a set of modular, reconfigurable foot pedals which can be mapped to parametric Operations that generate and transform digital woven designs. Our novel interface integrates design and loom control, providing a customizable workflow for playful, improvisational Jacquard weaving. We conducted a formative evaluation of the prototype through autobiographical methods and collaboratively developed future Loom Pedals features. We contribute our prototype, design process, and conceptual reflections on weaving as a human-machine dialog between a weaver, the loom, and many other agents.
Discrete residual diffusion model for high-resolution prostate MRI synthesis

Authors:
raw_author_name: Zhitao Han
raw_affiliation_string: School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, People's Republic of China

Abstract: Objective.High-resolution magnetic resonance imaging (HR MRI) is an effective tool for diagnosing PCa, but it requires patients to remain immobile for extended periods, increasing chances of image distortion due to motion. One solution is to utilize super-resolution (SR) techniques to process low-resolution (LR) images and create a higher-resolution version. However, existing medical SR models suffer from issues such as excessive smoothness and mode collapse. In this paper, we propose a novel generative model avoiding the problems of existing models, called discrete residual diffusion model (DR-DM).Approach.First, the forward process of DR-DM gradually disrupts the input via a fixed Markov chain, producing a sequence of latent variables with increasing noise. The backward process learns the conditional transit distribution and gradually match the target data distribution. By optimizing a variant of the variational lower bound, training diffusion models effectively address the issue of mode collapse. Second, to focus DR-DM on recovering high-frequency details, we synthesize residual images instead of synthesizing HR MRI directly. The residual image represents the difference between the HR and LR up-sampled MR image, and we convert residual image into discrete image tokens with a shorter sequence length by a vector quantized variational autoencoder (VQ-VAE), which reduced the computational complexity. Third, transformer architecture is integrated to model the relationship between LR MRI and residual image, which can capture the long-range dependencies between LR MRI and the synthesized imaging and improve the fidelity of reconstructed images.Main results.Extensive experimental validations have been performed on two popular yet challenging magnetic resonance image super-resolution tasks and compared to five state-of-the-art methods.Significance.Our experiments on the Prostate-Diagnosis and PROSTATEx datasets demonstrate that the DR-DM model significantly improves the signal-to-noise ratio of MRI for prostate cancer, resulting in greater clarity and improved diagnostic accuracy for patients.
High-performance reconstruction method combining total variation with a video denoiser for compressed ultrafast imaging

Authors:
raw_author_name: David Li
raw_affiliation_string: Faculty of Engineering, University of Strathclyde, Glasgow G4 0RE, Scotland, UK
raw_author_name: Chengzhi Jin
raw_affiliation_string: State Key Laboratory of Precision Spectroscopy, School of Physics and Materials Science, East China Normal University, 3663 North Zhongshan Road, Shanghai 200062, China

Abstract: Compressed ultrafast photography (CUP) is a novel two-dimensional (2D) imaging technique to capture ultrafast dynamic scenes. Effective image reconstruction is essential in CUP systems. However, existing reconstruction algorithms mostly rely on image priors and complex parameter spaces. Therefore, in general, they are time-consuming and result in poor imaging quality, which limits their practical applications. In this paper, we propose a novel reconstruction algorithm, to the best of our knowledge, named plug-in-plug-fast deep video denoising net-total variation (PnP-TV-FastDVDnet), which exploits an image's spatial features and correlation features in the temporal dimension. Therefore, it offers higher-quality images than those in previously reported methods. First, we built a forward mathematical model of the CUP, and the closed-form solution of the three suboptimization problems was derived according to plug-in and plug-out frames. Secondly, we used an advanced video denoising algorithm based on a neural network named FastDVDnet to solve the denoising problem. The peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) are improved on actual CUP data compared with traditional algorithms. On benchmark and real CUP datasets, the proposed method shows the comparable visual results while reducing the running time by 96% over state-of-the-art algorithms.