Reviewerly/exHarmony huggingface dataset:
WORKSHOP: Spatial Vis™: A sketching app with automatic grading to teach spatial visualization skills to introductory engineering students.

Abstract Objectives: Spatial visualization (SV) refers to the ability to manipulate geometric shapes in one’s mind and is important in many STEM fields. Studies have shown that SV skills are learnable, and SV training can increase GPAs and graduate rates in engineering. Additionally, sketching has been shown to be an important part of increasing 3D visualization skills, which is key for creativity and team communication. However, few engineering curricula include SV training, and adding new content to an already full program can be difficult. The Spatial Vis™ App, developed by eGrove Education, was designed with the potential to teach SV training outside of scheduled class time. It allows for freehand sketches of isometric and orthographic assignments to be graded automatically and for hints to be provided when needed. The app has been tested in a number of trials at universities across the country and has been shown to be an engaging and effective tool in improving SV skills. The app is currently available on Android and Apple smartphones and tablets, as well as advanced touchscreen Chromebooks. Features of the app include automatic grading, personalized hints for students when stuck, teacher data for tracking student progress, gradual increase in difficulty of assignments, a gamification start system to encourage persistence, and several test questions the end of each lesson where the Hint and Peek questions have been disabled. Results from trials using the technology in a number of university classroom settings, which have demonstrated the efficacy of the app based on a pre- and post- standardized spatial visualization test, will be discussed. Targeted audience: First year engineering or engineering graphics college or community college instructors; K-12 pre-engineering teachers and administrators. Activities Planned: This workshop will provide an overview of the benefits and best practices of using the Spatial Vis app in your classroom. Participants will be given a license to the app to use on their own smartphone devices to engage in interactive spatial visualization (SV) activities. Learning Outcomes: Instructors will learn 1) spatial visualization skills such as 2D rotations, 3D isometric views, and 2D orthographic projections; 2) how to use the app including the algorithm that automatically grades students’ sketches and provides guidance when requested; and 3) best practices for how to implement the SV training in their curriculum. You won’t want to miss this interactive workshop! Approximate length: 2 hours Required supplies: Android or Apple touchscreen device such as a smartphone or tablet.
Visual Relationship Detection for Workplace Safety Applications

Applications of object and visual relationship detection for safety and security applications are in its infancy. State-of-the-art computer vision research is largely focused on improving mean Average Precision and mean Average Recall performance on standard, general datasets such as the Verbs in COmmon objects in COntext and the Visual Genome dataset and rarely mention the potential of such models in safety and security scenarios. We propose to train and develop an object and visual relationship detection neural network to be used as part of the back-end model for a decision support system. We use a naive Bayesian Network to determine scenarios where our proposed object and visual relationship detection network is error-prone. We also release a graphical user interface which demonstrates how our back-end neural network and naive Bayesian Network can be used for hazardous workplace safety and security applications. All models and source code can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/th-truong/rd-gui</uri> for future research and applications of object and visual relationship detection models in the safety and security sector.
Margin-Aware Adaptive-Weighted-Loss for Deep Learning Based Imbalanced Data Classification

In supervised learning algorithms, the class imbalance problem often leads to generate results biased towards the majority classes. Present methods used to deal with the class imbalance problem ignore a principal aspect of separating the overlapping classes. This is the reason why most of these methods are prone to overfit on the training data. To this end, we propose a novel loss function, namely Margin-Aware Adaptive-Weighted (MAAW) loss. Here, we first use the large margin softmax to leverage intra-class compactness and inter-class separability. Further to learn an unbiased representation of the classes, we put forward a dynamically weighted loss for imbalanced data classification. This weight dynamically adapts on every minibatch based on the inverse class frequencies. In addition, it takes care of the hard-to-train samples by using the confidence scores to learn discriminative hidden representations of the data. The overall framework is found to be effective when evaluated on two widely used datasets, namely CIFAR-10 and Fashion-MNIST. Additional experiments on HAM10000 and APTOS-2019 BD datasets prove the robustness of our methodology. The source codes of the proposed methodology is provided under the GitHub repository <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/rishavpramanik/MAAW</uri>
Unsupervised Domain Adaptation for Medical Image Segmentation Using Transformer With Meta Attention

Image segmentation is essential to medical image analysis as it provides the labeled regions of interest for the subsequent diagnosis and treatment. However, fully-supervised segmentation methods require high-quality annotations produced by experts, which is laborious and expensive. In addition, when performing segmentation on another unlabeled image modality, the segmentation performance will be adversely affected due to the domain shift. Unsupervised domain adaptation (UDA) is an effective way to tackle these problems, but the performance of the existing methods is still desired to improve. Also, despite the effectiveness of recent Transformer-based methods in medical image segmentation, the adaptability of Transformers is rarely investigated. In this paper, we present a novel UDA framework using a Transformer for building a cross-modality segmentation method with the advantages of learning long-range dependencies and transferring attentive information. To fully utilize the attention learned by the Transformer in UDA, we propose Meta Attention (MA) and use it to perform a fully attention-based alignment scheme, which can learn the hierarchical consistencies of attention and transfer more discriminative information between two modalities. We have conducted extensive experiments on cross-modality segmentation using three datasets, including a whole heart segmentation dataset (MMWHS), an abdominal organ segmentation dataset, and a brain tumor segmentation dataset. The promising results show that our method can significantly improve performance compared with the state-of-the-art UDA methods.
Sensor Data Protection Through Integration of Blockchain and Camouflaged Encryption in Cyber-Physical Manufacturing Systems

Abstract The advancement of sensing technology enables efficient data collection from manufacturing systems for monitoring and control. Furthermore, with the rapid development of the Internet of Things (IoT) and information technologies, more and more manufacturing systems become cyber-enabled, facilitating real-time data sharing and information exchange, which significantly improves the flexibility and efficiency of manufacturing systems. However, the cyber-enabled environment may pose the collected sensor data with high risks of cyber-physical attacks during the data and information sharing. Specifically, cyber-physical attacks could target the manufacturing process and/or the data transmission process to maliciously tamper the sensor data, resulting in false alarms or failures in anomaly detection in monitoring. In addition, cyber-physical attacks may also enable illegal data access without authorization and cause the leakage of key product/process information. Therefore, it becomes critical to develop an effective approach to protect data from these attacks so that the cyber-physical security of the manufacturing systems can be assured in the cyber-enabled environment. To achieve this goal, this paper proposes an integrative blockchain-enabled data protection method by leveraging camouflaged asymmetry encryption. A real-world case study that protects the cyber-physical security of collected sensor data in additive manufacturing is presented to demonstrate the effectiveness of the proposed method. The results demonstrate that malicious tampering could be detected in a relatively short time (less than 0.05 ms), and the risk of unauthorized data access is significantly reduced as well.
sEMG-Based Inter-Session Hand Gesture Recognition via Domain Adaptation with Locality Preserving and Maximum Margin

Surface electromyography (sEMG)-based gesture recognition can achieve high intra-session performance. However, the inter-session performance of gesture recognition decreases sharply due to the shift in data distribution. Therefore, developing a robust model to minimize the data distribution difference is crucial to improving the user experience. In this work, based on the inter-session gesture recognition task, we propose a novel algorithm called locality preserving and maximum margin criterion (LPMM). The LPMM algorithm integrates three main modules, including domain alignment, pseudo-label selection, and iteration result selection. Domain alignment is designed to preserve the neighborhood structure of the feature and minimize the overlap of different classes. The pseudo-label selection and iteration result selection can avoid the decrease in accuracy caused by mislabeled samples. The proposed algorithm was evaluated on two of the most widely used EMG databases. It achieves a mean accuracy of 98.46% and 71.64%, respectively, which is superior to state-of-the-art domain adaptation methods.
Deep Kernel Principal Component Analysis for multi-level feature learning

Principal Component Analysis (PCA) and its nonlinear extension Kernel PCA (KPCA) are widely used across science and industry for data analysis and dimensionality reduction. Modern deep learning tools have achieved great empirical success, but a framework for deep principal component analysis is still lacking. Here we develop a deep kernel PCA methodology (DKPCA) to extract multiple levels of the most informative components of the data. Our scheme can effectively identify new hierarchical variables, called deep principal components, capturing the main characteristics of high-dimensional data through a simple and interpretable numerical optimization. We couple the principal components of multiple KPCA levels, theoretically showing that DKPCA creates both forward and backward dependency across levels, which has not been explored in kernel methods and yet is crucial to extract more informative features. Various experimental evaluations on multiple data types show that DKPCA finds more efficient and disentangled representations with higher explained variance in fewer principal components, compared to the shallow KPCA. We demonstrate that our method allows for effective hierarchical data exploration, with the ability to separate the key generative factors of the input data both for large datasets and when few training samples are available. Overall, DKPCA can facilitate the extraction of useful patterns from high-dimensional data by learning more informative features organized in different levels, giving diversified aspects to explore the variation factors in the data, while maintaining a simple mathematical formulation.
Double Transformer Super-Resolution for Breast Cancer ADC Images

Diffusion-weighted imaging (DWI) has been extensively explored in guiding the clinic management of patients with breast cancer. However, due to the limited resolution, accurately characterizing tumors using DWI and the corresponding apparent diffusion coefficient (ADC) is still a challenging problem. In this paper, we aim to address the issue of super-resolution (SR) of ADC images and evaluate the clinical utility of SR-ADC images through radiomics analysis. To this end, we propose a novel double transformer-based network (DTformer) to enhance the resolution of ADC images. More specifically, we propose a symmetric U-shaped encoder-decoder network with two different types of transformer blocks, named as UTNet, to extract deep features for super-resolution. The basic backbone of UTNet is composed of a locally-enhanced Swin transformer block (LeSwin-T) and a convolutional transformer block (Conv-T), which are responsible for capturing long-range dependencies and local spatial information, respectively. Additionally, we introduce a residual upsampling network (RUpNet) to expand image resolution by leveraging initial residual information from the original low-resolution (LR) images. Extensive experiments show that DTformer achieves superior SR performance. Moreover, radiomics analysis reveals that improving the resolution of ADC images is beneficial for tumor characteristic prediction, such as histological grade and human epidermal growth factor receptor 2 (HER2) status.
WBC YOLO-ViT: 2 Way - 2 stage white blood cell detection and classification with a combination of YOLOv5 and vision transformer

Accurate detection and classification of white blood cells, otherwise known as leukocytes, play a critical role in diagnosing and monitoring various illnesses. However, conventional methods, such as manual classification by trained professionals, must be revised in terms of accuracy, efficiency, and potential bias. Moreover, applying deep learning techniques to detect and classify white blood cells using microscopic images is challenging owing to limited data, resolution noise, irregular shapes, and varying colors from different sources. This study presents a novel approach integrating object detection and classification for numerous type-white blood cell. We designed a 2-way approach to use two types of images: WBC and nucleus. YOLO (fast object detection) and ViT (powerful image representation capabilities) are effectively integrated into 16 classes. The proposed model demonstrates an exceptional 96.449% accuracy rate in classification.
Automatic patient-level recognition of four <i>Plasmodium</i> species on thin blood smear by a real-time detection transformer (RT-DETR) object detection algorithm: a proof-of-concept and evaluation

Malaria remains a global health problem, with 247 million cases and 619,000 deaths in 2021. Diagnosis of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis for accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a real-time detection transformer (RT-DETR) object detection algorithm to discriminate Plasmodium species on thin blood smear images. The algorithm was trained and validated on a data set consisting in 24,720 images from 475 thin blood smears corresponding to 2,002,597 labels. Performances were calculated with a test data set of 4,508 images from 170 smears corresponding to 358,825 labels coming from six French university hospitals. At the patient level, the RT-DETR algorithm exhibited an overall accuracy of 79.4% (135/170) with a recall of 74% (40/54) and 81.9% (95/116) for negative and positive smears, respectively. Among Plasmodium-positive smears, the global accuracy was 82.7% (91/110) with a recall of 90% (38/42), 81.8% (18/22), and 76.1% (35/46) for P. falciparum, P. malariae, and P. ovale/vivax, respectively. The RT-DETR model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices such as a smartphone and could be suitable for deployment in low-resource setting areas lacking microscopy experts.IMPORTANCEMalaria remains a global health problem, with 247 million cases and 619,000 deaths in 2021. Diagnosis of Plasmodium species is important for administering the appropriate treatment. The gold-standard diagnosis for accurate species identification remains the thin blood smear. Nevertheless, this method is time-consuming and requires highly skilled and trained microscopists. To overcome these issues, new diagnostic tools based on deep learning are emerging. This study aimed to evaluate the performances of a real-time detection transformer (RT-DETR) object detection algorithm to discriminate Plasmodium species on thin blood smear images. Performances were calculated with a test data set of 4,508 images from 170 smears coming from six French university hospitals. The RT-DETR model achieved a World Health Organization (WHO) competence level 2 for species identification. Besides, the RT-DETR algorithm may be run in real-time on low-cost devices and could be suitable for deployment in low-resource setting areas.
Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.
Counterfactual formulation of patient-specific root causes of disease

Root causes of disease intuitively correspond to root vertices of a causal model that increase the likelihood of a diagnosis. This description of a root cause nevertheless lacks the rigorous mathematical formulation needed for the development of computer algorithms designed to automatically detect root causes from data. We seek a definition of patient-specific root causes of disease that models the intuitive procedure routinely utilized by physicians to uncover root causes in the clinic.We use structural equation models, interventional counterfactuals and the recently developed mathematical formalization of backtracking counterfactuals to propose a counterfactual formulation of patient-specific root causes of disease matching clinical intuition.We introduce a definition of patient-specific root causes of disease that climbs to the third rung of Pearl's Ladder of Causation and matches clinical intuition given factual patient data and a working causal model. We then show how to assign a root causal contribution score to each variable using Shapley values from explainable artificial intelligence.The proposed counterfactual formulation of patient-specific root causes of disease accounts for noisy labels, adapts to disease prevalence and admits fast computation without the need for counterfactual simulation.
Optical ReLU-like activation function based on a semiconductor laser with optical injection

Artificial neural networks usually consist of successive linear multiply–accumulate operations and nonlinear activation functions. However, most optical neural networks only achieve the linear operation in the optical domain, while the optical implementation of activation function remains challenging. Here we present an optical ReLU-like activation function (with 180° rotation) based on a semiconductor laser subject to the optical injection in an experiment. The ReLU-like function is achieved in a broad regime above the Hopf bifurcation of the injection-locking diagram and is operated in the continuous-wave mode. In particular, the slope of the activation function is reconfigurable by tuning the frequency difference between the master laser and the slave laser.
A Dual Stream Fusion Network for Underwater Image Enhancement of Multi-scale Turbidity Restoration and Multi-path Color Correction

Underwater images frequently experience color distortion and blurred details due to the absorption and scattering of light, which can hinder underwater visual tasks. To address these challenges, we propose a dual-stream fusion network for enhancing underwater images. Our multi-scale turbidity restoration module (MTRM) adopts a two-stage dehazing process from coarse to fine, while employing the SOS boosting strategy and frequency-based dense connections to further improve the performance of the U-Net. The multi-path color correction module (MCCM) utilizes the multi-path residual block as the basic unit to construct RGB enhancement paths. It selectively establishes inter-color channels through attention-based cross connections, which efficiently harness the distinctive features from various color channels. Additionally, non-local spatial and channel attention provide essential correlation information for the final fusion stage. Qualitative and quantitative evaluations conducted on various underwater datasets have demonstrated the excellent performance of our method.
MTIE-Net: Multi-technology fusion of low-light image enhancement network

Images obtained in low-light scenes are often accompanied by problems such as low visibility, blurred details, and color distortion, enhancing them can effectively improve the visual effect and provide favorable conditions for advanced visual tasks. In this study, we propose a Multi-Technology Fusion of Low-light Image Enhancement Network (MTIE-Net) that modularizes the enhancement task. MTIE-Net consists of a residual dense decomposition network (RDD-Net) based on Retinex theory, an encoder-decoder denoising network (EDD-Net), and a parallel mixed attention-based self-calibrated illumination enhancement network (PCE-Net). The low-light image is first decomposed by RDD-Net into a lighting map and reflectance map; EDD-Net is used to process noise in the reflectance map; Finally, the lighting map is fused with the denoised reflectance map as an input to PCE-Net, using the Fourier transform for illumination enhancement and detail recovery in the frequency domain. Numerous experimental results show that MTIE-Net outperforms the comparison methods in terms of image visual quality enhancement improvement, denoising, and detail recovery. The application in nighttime face detection also fully demonstrates its promise as a pre-processing means in practical applications.