Reviewerly/exHarmony huggingface dataset:
High-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision

To resolve the disparity between accuracy and speed in the current surface flatness detection of medium and thick steel plates, which predominantly relies on the single-line laser vision method, we propose a high-efficiency flatness detection method for medium-thick steel plates based on multiline parallel laser vision. On the basis of building a flatness detection system, we present the adaptive cropping algorithm and improved variable threshold Otsu thresholding segmentation algorithm (IVT-Otsu) for image preprocessing. Then, the centerline of subpixel-level laser stripes is accurately extracted using a weighted quadratic grayscale center of gravity method. Meanwhile, a pruning algorithm-based multiline laser stripe center extraction method is proposed to address the phenomenon of spotting and branching in the center of multiline parallel laser, to efficiently obtain the surface depth information and flatness index of medium-thick steel plate. Results of the experiment indicate that the proposed method better fulfills the accuracy and efficiency requirements for online inspection when extracting the laser centers and calculating the flatness.
Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen's kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson's correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00).The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.Our study confirms the feasibility and validity of an automated process to gather data from the EHR.
Adopting artificial intelligence in sustainable business

Artificial intelligence (AI) in sustainable businesses has attracted interest in various industries. However, research on the sustainable adoption of AI is scarce. The present research contributes to this gap by investigating the benefits of AI for sustainable businesses. Qualitative content analysis was used to investigate twenty-five of the largest Finnish enterprises. In the years 2017–2021, AI was employed in twenty of these enterprises, sixteen of which relied on AI to obtain benefits associated with sustainable business practices. Based on the sample, AI can be perceived as a generalizing technology in Finland. Additionally, the strategic significance of AI for enterprises has been discovered to be increasing. The research results indicate that AI deployment was driven primarily by two interlinked aims: 1) optimization and 2) the pursuit of benefits in various dimensions of sustainable business.
Toward Effective Semi-supervised Node Classification with Hybrid Curriculum Pseudo-labeling

Semi-supervised node classification is a crucial challenge in relational data mining and has attracted increasing interest in research on graph neural networks (GNNs). However, previous approaches merely utilize labeled nodes to supervise the overall optimization, but fail to sufficiently explore the information of their underlying label distribution. Even worse, they often overlook the robustness of models, which may cause instability of network outputs to random perturbations. To address the aforementioned shortcomings, we develop a novel framework termed Hybrid Curriculum Pseudo-Labeling (HCPL) for efficient semi-supervised node classification. Technically, HCPL iteratively annotates unlabeled nodes by training a GNN model on the labeled samples and any previously pseudo-labeled samples, and repeatedly conducts this process. To improve the model robustness, we introduce a hybrid pseudo-labeling strategy that incorporates both prediction confidence and uncertainty under random perturbations, therefore mitigating the influence of erroneous pseudo-labels. Finally, we leverage the idea of curriculum learning to start from annotating easy samples, and gradually explore hard samples as the iteration grows. Extensive experiments on a number of benchmarks demonstrate that our HCPL beats various state-of-the-art baselines in diverse settings.
Nonlinear optical feature generator for machine learning

Modern machine learning models use an ever-increasing number of parameters to train (175 × 109 parameters for GPT-3) with large datasets to achieve better performance. Optical computing has been rediscovered as a potential solution for large-scale data processing, taking advantage of linear optical accelerators that perform operations at lower power consumption. However, to achieve efficient computing with light, it remains a challenge to create and control nonlinearity optically rather than electronically. In this study, a reservoir computing approach (RC) is investigated using a 14-mm waveguide in LiNbO3 on an insulator as an optical processor to validate the benefit of optical nonlinearity. Data are encoded on the spectrum of a femtosecond pulse, which is launched into the waveguide. The output of the waveguide is a nonlinear transform of the input, enabled by optical nonlinearities. We show experimentally that a simple digital linear classifier using the output spectrum of the waveguide increases the classification accuracy of several databases by ∼10% compared to untransformed data. In comparison, a digital neural network (NN) with tens of thousands of parameters was required to achieve similar accuracy. With the ability to reduce the number of parameters by a factor of at least 20, an integrated optical RC approach can attain a performance on a par with a digital NN.
Evaluating an Immersive Virtual Classroom as an Augmented Reality Platform in Synchronous Remote Learning

Previous research has explored different models of synchronous remote learning environments supported by videoconferencing and virtual reality platforms. However, few studies have evaluated the preference and acceptance of synchronous remote learning in a course streamed in an immersive or augmented reality platform. This case study uses ANOVA analysis to examine engineering students’ preferences for receiving instruction during the COVID-19 pandemic in three classroom types: face-to-face, conventional virtual (mediated by videoconferencing) and an immersive virtual classroom (IVC). Likewise, structural equation modeling was used to analyze the acceptance of the IVC perceived by students, this includes four latent factors: ease of receiving a class, perceived usefulness, attitude towards IVC and IVC use. The findings showed that the IVC used in synchronous remote learning has a similar level of preference to the face-to-face classroom and a higher level than the conventional virtual one. Despite the high preference for receiving remote instruction in IVC, aspects such as audio delays that affect interaction still need to be resolved. On the other hand, a key aspect for a good performance of these environments is the dynamics associated with the teaching–learning processes and the instructor’ qualities.
ML Pro: digital assistance system for interactive machine learning in production

Abstract The application of machine learning promises great growth potential for industrial production. The development process of a machine learning solution for industrial use cases requires multi-layered, sophisticated decision-making processes along the pipeline that can only be accomplished by subject matter experts with knowledge of statistical mathematics, coding, and engineering process knowledge. By having humans and computers work together in a digital assistance system, the special characteristics of human and artificial intelligence can be used synergistically. This paper presents the development of a digital human-centered assistance system for employees in the production and development departments of industrial manufacturing companies. This assistance system enables users to apply production-specific data mining and machine learning techniques without programming to typical tabular production data, which is often inherently high-dimensional, nonstationary, and highly imbalanced data streams. Through tight interactive process guidance that considers the dependencies between machine learning process modules, users are empowered to build and optimize predictive models. Compared to existing commercial and academic tools with similar objectives, the digital assistance system offers the added value that both classical shallow and deep learning as well as generative and oversampling methods can be interactively applied to all feature table use cases for different user modes without programming.
BlueVR: Design and Evaluation of a Virtual Reality Serious Game for Promoting Understanding towards People with Color Vision Deficiency

People with color vision deficiency (CVD) often encounter color-related challenges in their daily life, which are difficult for those with non-CVD to comprehend fully. Therefore, we designed a Virtual Reality (VR) serious game, BlueVR , to simulate challenging scenarios encountered by people with CVD and facilitate understanding from people with non-CVD. We conducted an empirical study with thirty participants with non-CVD and six participants with CVD to evaluate the opportunities and challenges of BlueVR . Our findings suggest that BlueVR increased people with non-CVD’s understanding, awareness, and perspective-taking abilities towards people with CVD. Moreover, interviews with participants with CVD revealed that BlueVR accurately depicts their real-life discomforts and meets their expectations to improve potential social awareness. This research contributes valuable insights into the mechanisms underlying the effectiveness of VR serious games in promoting understanding and design implications for future game development.
An Improved Parallel Particle Swarm Optimization

Abstract In the area of global optimization, a variety of techniques have been developed to find the global minimum. These techniques, in most cases, require a significant amount of computational resources and time to complete and therefore there is a need to develop parallel techniques. In addition, the wide spread of parallel architectures in recent years greatly facilitates the implementation of such techniques. Among the most widely used global optimization techniques is the particle swarm optimization technique. In this work, a series of modifications are proposed in the direction of efficient parallelization for particle swarm optimization. These modifications include an innovative velocity calculation mechanism that has also been successfully used in the serial version of the method, mechanisms for propagating the best particles between parallel computing units, but also a process termination mechanism, which has been properly configured for efficient execution in parallel computing environments. The proposed technique was applied to a multitude of computational problems from the relevant literature and the results were more than promising, since it was found that increasing the computational threads can significantly reduce the required number of function calls to find the global minimum. The proposed technique is at rate of 50–70% of the required number of function calls compared to other optimization techniques. This reduction is visible even if one to two parallel processing units are used. In addition, with the increase in parallel processing units, a drastic reduction in the number of calls is observed and therefore a reduction in the required computing time, which can reach up to 70%.
Enabling Binary Neural Network Training on the Edge

The ever-growing computational demands of increasingly complex machine learning models frequently necessitate the use of powerful cloud-based infrastructure for their training. Binary neural networks are known to be promising candidates for on-device inference due to their extreme compute and memory savings over higher-precision alternatives. However, their existing training methods require the concurrent storage of high-precision activations for all layers, generally making learning on memory-constrained devices infeasible. In this article, we demonstrate that the backward propagation operations needed for binary neural network training are strongly robust to quantization, thereby making on-the-edge learning with modern models a practical proposition. We introduce a low-cost binary neural network training strategy exhibiting sizable memory footprint reductions while inducing little to no accuracy loss vs Courbariaux &amp; Bengio’s standard approach. These decreases are primarily enabled through the retention of activations exclusively in binary format. Against the latter algorithm, our drop-in replacement sees memory requirement reductions of 3–5×, while reaching similar test accuracy (± 2 pp) in comparable time, across a range of small-scale models trained to classify popular datasets. We also demonstrate from-scratch ImageNet training of binarized ResNet-18, achieving a 3.78× memory reduction. Our work is open-source, and includes the Raspberry Pi-targeted prototype we used to verify our modeled memory decreases and capture the associated energy drops. Such savings will allow for unnecessary cloud offloading to be avoided, reducing latency, increasing energy efficiency, and safeguarding end-user privacy.
Combining R and Earth Engine

Abstract The purpose of this chapter is to introduce rgee , a non-official API for Earth Engine. You will explore the main features available in rgee and how to set up an environment that integrates rgee with third-party R and Python packages. After this chapter, you will be able to combine R, Python, and JavaScript in the same workflow.
"Connected to the people": Social Inclusion &amp; Cohesion in Action through a Cultural Heritage Digital Tool

Current cultural policies are evolving from social inclusion (removing barriers and promoting equality for participation in culture) to social cohesion (fostering solid bonds between groups despite their differences). Digital interventions can create spaces that promote social inclusion and cohesion. In this paper, we report on the design and evaluation of a cultural heritage and digital storytelling application supporting a participatory approach to culture and hosting society. We evaluate our intervention in three marginalized communities with different social-cultural contexts: migrant women in Barcelona, a community living in a priority neighbourhood in Paris and second and third-generation migrants in Lisbon. Through an analysis of their application use, our findings point at their needs and desires, highlighting how the app can support social inclusion as the first step towards cohesion, but that these are heterogeneous concepts susceptible to nuanced appropriations by the different communities.
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanations

The local explanation provides heatmaps on images to explain how Convolutional Neural Networks (CNNs) derive their output. Due to its visual straightforwardness, the method has been one of the most popular explainable AI (XAI) methods for diagnosing CNNs. Through our formative study (S1), however, we captured ML engineers' ambivalent perspective about the local explanation as a valuable and indispensable envision in building CNNs versus the process that exhausts them due to the heuristic nature of detecting vulnerability. Moreover, steering the CNNs based on the vulnerability learned from the diagnosis seemed highly challenging. To mitigate the gap, we designed DeepFuse, the first interactive design that realizes the direct feedback loop between a user and CNNs in diagnosing and revising CNN's vulnerability using local explanations. DeepFuse helps CNN engineers to systemically search "unreasonable" local explanations and annotate the new boundaries for those identified as unreasonable in a labor-efficient manner. Next, it steers the model based on the given annotation such that the model doesn't introduce similar mistakes. We conducted a two-day study (S2) with 12 experienced CNN engineers. Using DeepFuse, participants made a more accurate and "reasonable" model than the current state-of-the-art. Also, participants found the way DeepFuse guides case-based reasoning can practically improve their current practice. We provide implications for design that explain how future HCI-driven design can move our practice forward to make XAI-driven insights more actionable.
A Comparison of Machine Learning Models with Data Augmentation Techniques for Skeleton-based Human Action Recognition

3D skeleton motion recognition plays a crucial role in the field of human action recognition (HAR) due to its efficiency and reliability. This paper introduces data augmentation techniques applied to skeleton data with the aim of improving the accuracy of machine learning models and examining the effectiveness of different augmentation methods for different activities. Spatial transformations are applied to generate augmented samples from the original 3D skeleton sequences, while temporal augmentation techniques are employed to capture the temporal differences in motion. We evaluated the effects of spatial and temporal data augmentation at different levels on the MSR Daily Activity 3D dataset using SVM, CNN, LSTM, and CNNLSTM models. The results show that temporal augmentation significantly improves model performance, while spatial augmentation has only a limited effect on model performance. Simultaneously applying both spatial and temporal augmentation further improved model performance, highlighting the importance of temporal information in action sequence data.
ROI-YOLOv8-Based Far-Distance Face-Recognition

This paper presents a model for far-distance face recognition using ROI-YOLOv8. We achieve this by training YOLOv8 for 3 target faces on our custom datasets: (i) The first dataset is the original dataset with training and validation images of 2412 and 229, respectively. (ii) The second dataset augments the more pixelated images from the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> one with training and validation images of 4824 and 458, respectively. (iii) The third dataset considers various exposure, noise, and blur from the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> one with training and validation images of 14,272 and 459, respectively. To enhance the far-distance recognition, a two-stage recognition is considered. At first, a pre-trained YOLOv8 model for human detection is achieved. A Region-of-Interest (ROI) including detected humans is segmented as the size of 640 × 640 pixels for the input of another YOLOv8, i.e., ROI-YOLOv8-FR. A computer with Intel i5-12400F, 16GB RAM, and NVIDIA RTX 3080Ti with 12GB VRAM is used as computing platform. The trained times for these 3 datasets are 50, 95, and 219 minutes, respectively. Their mAP50s are 99.5% and mAP50-95s are slightly different as 88.112%, 87.962%, and 88.103% respectively. More important, the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> trained model can successfully recognize a face at 30 and 35m with the confidences of 65.1% and 75.6%.
A Comparative Analysis of Explainable AI Techniques for Enhanced Model Interpretability

Machine learning models have become increasingly complex, making it difficult to understand how they make predictions. Explainable AI (XAI) techniques have been developed to enhance model interpretability, thereby improving model transparency, trust, and accountability. In this paper, we present a comparative analysis of several XAI techniques to enhance the interpretability of machine learning models. We evaluate the performance of these techniques on a dataset commonly used for regression or classification tasks. The XAI techniques include SHAP, LIME, PDP, and GAM. We compare the effectiveness of these techniques in terms of their ability to explain model predictions and identify the most important features in the dataset. Our results indicate that XAI techniques significantly improve model interpretability, with SHAP and LIME being the most effective in identifying important features in the dataset. Our study provides insights into the strengths and limitations of different XAI techniques and their implications for the development and deployment of machine learning models. We conclude that XAI techniques have the potential to significantly enhance model interpretability and promote trust and accountability in the use of machine learning models. The paper emphasizes the importance of interpretability in medical applications of machine learning and highlights the significance of XAI techniques in developing accurate and reliable models for medical applications.
Uncertainty quantification for sparse spectral variational approximations in Gaussian process regression

We investigate the frequentist guarantees of the variational sparse Gaussian process regression model. In the theoretical analysis, we focus on the variational approach with spectral features as inducing variables. We derive guarantees and limitations for the frequentist coverage of the resulting variational credible sets. We also derive sufficient and necessary lower bounds for the number of inducing variables required to achieve minimax posterior contraction rates. The implications of these results are demonstrated for different choices of priors. In a numerical analysis we consider a wider range of inducing variable methods and observe similar phenomena beyond the scope of our theoretical findings.