Reviewerly/exHarmony huggingface dataset:
How word semantics and phonology affect handwriting of Alzheimer’s patients: A machine learning based analysis

Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.
Health-aware food recommendation system with dual attention in heterogeneous graphs

Recommender systems (RS) have been increasingly applied to food and health. However, challenges still remain, including the effective incorporation of heterogeneous information and the discovery of meaningful relationships among entities in the context of food and health recommendations. To address these challenges, we propose a novel framework, the Health-aware Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA), for unsupervised representation learning on heterogeneous graph-structured data. HFRS-DA utilizes an attention technique to reconstruct node features and edges and employs a dual hierarchical attention mechanism for enhanced unsupervised learning of attributed graph representations. HFRS-DA addresses the challenge of effectively leveraging the heterogeneous information in the graph and discovering meaningful semantic relationships between entities. The framework analyzes recipe components and their neighbours in the heterogeneous graph and can discover popular and healthy recipes, thereby promoting healthy eating habits. We compare HFRS-DA using the Allrecipes dataset and find that it outperforms all the related methods from the literature. Our study demonstrates that HFRS-DA enhances the unsupervised learning of attributed graph representations, which is important in scenarios where labelled data is scarce or unavailable. HFRS-DA can generate node embeddings for unused data effectively, enabling both inductive and transductive learning.
Learning with centered reproducing kernels

Kernel-based learning algorithms have been extensively studied over the past two decades for their successful applications in scientific research and industrial problem-solving. In classical kernel methods, such as kernel ridge regression and support vector machines, an unregularized offset term naturally appears. While its importance can be defended in some situations, it is arguable in others. However, it is commonly agreed that the offset term introduces essential challenges to the optimization and theoretical analysis of the algorithms. In this paper, we demonstrate that Kernel Ridge Regression (KRR) with an offset is closely connected to regularization schemes involving centered reproducing kernels. With the aid of this connection and the theory of centered reproducing kernels, we will establish generalization error bounds for KRR with an offset. These bounds indicate that the algorithm can achieve minimax optimal rates.
Accurate weight coefficient estimation of multi-camera light field PIV through backpropagation neural network

Volumetric velocity measurement through multi-camera light field particle image velocimetry (LF-PIV) requires an accurate estimation of the weight coefficient (WC) of three-dimensional (3D) tracer particle distribution reconstruction. To achieve that, this study proposes a calibration method based on a backpropagation neural network (BP-NN) for the WC estimation of the multi-camera LF-PIV. The BP-NN model establishes a mapping relationship between the spatial voxels and pixels of the multi-cameras. The proposed method is compared with the direct ray tracing (DRT) method and it shows that the proposed method provides an accurate estimation of the WC. It also does not depend on the prior knowledge of angle separations of the multi-cameras as is required for the DRT method. The proposed method is initially evaluated by conducting synthetic tests of ring vortex field reconstruction and further verified by conducting experiments on a low-swirl injector (LSI) flow. Results show that the root mean square error of the ring vortex displacement field can be reduced from 0.71 voxels to 0.35 voxels by the proposed method. The relative errors of LSI flow axial and radial velocity components are smaller than 10%. Therefore, it demonstrates that the 3D flow velocity can be measured accurately by the multi-camera LF-PIV by incorporating the proposed BP-NN calibration method.
Thickness and surface estimation of thin porous media based on 3D image data

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

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

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

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

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

<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

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.
Efficient and accurate personalized product recommendations through frequent item set mining fusion algorithm

In the realm of personalized product recommendation, addressing the challenges of sparse data and "cold start" has been the primary focus. However, filtering invalid information amidst the overwhelming data on e-commerce platforms remains an underexplored issue. This paper proposes a fusion recommendation algorithm based on frequent item set mining to tackle this problem by compressing the commodity data set and identifying the frequent commodity set. The algorithm not only improves time efficiency by reducing the number of candidate frequent item sets but also generates more accurate recommendations by calculating user-commodity interest rankings and recommending similar products. We first present the existing problems in fusion recommendation algorithms based on frequent item set mining, such as redundant rules, low recommendation accuracy, and the inability to explore deep connections between users and products. Next, we introduce our proposed algorithm, which involves filtering the commodity data set, calculating user-commodity interest rankings, and defining similar product recommendation rules. The algorithm's effectiveness is demonstrated by its ability to adapt to users' dynamic preferences and capture their changing interests in real-time. A comparative analysis using our algorithm and other data mining algorithms reveals a reduction in the number of frequent commodity data sets and weighted frequent item sets, leading to decreased algorithm operation time. This research contributes to the development of more efficient and accurate personalized product recommendation algorithms, enhancing user experience on e-commerce platforms.
The Formal Solutions of Diophantine Equation ag = bx + c

We develop a novel method to completely solve the 3-term partial exponential Diophantine equation that represents a generalization of the standard discrete logarithm problem. Our method not only reveals the internal structure of the equation's solution and yields a numerical algorithm to solve it systematically, but also provides an alternative approach to the discrete logarithm problem.
Real-time binocular visual localization system based on the improved BGNet stereo matching framework

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

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

<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

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.