Reviewerly/exHarmony huggingface dataset:
Niching Grey Wolf Optimizer for Multimodal Optimization Problems

Metaheuristic algorithms are widely used for optimization in both research and the industrial community for simplicity, flexibility, and robustness. However, multi-modal optimization is a difficult task, even for metaheuristic algorithms. Two important issues that need to be handled for solving multi-modal problems are (a) to categorize multiple local/global optima and (b) to uphold these optima till the ending. Besides, a robust local search ability is also a prerequisite to reach the exact global optima. Grey Wolf Optimizer (GWO) is a recently developed nature-inspired metaheuristic algorithm that requires less parameter tuning. However, the GWO suffers from premature convergence and fails to maintain the balance between exploration and exploitation for solving multi-modal problems. This study proposes a niching GWO (NGWO) that incorporates personal best features of PSO and a local search technique to address these issues. The proposed algorithm has been tested for 23 benchmark functions and three engineering cases. The NGWO outperformed all other considered algorithms in most of the test functions compared to state-of-the-art metaheuristics such as PSO, GSA, GWO, Jaya and two improved variants of GWO, and niching CSA. Statistical analysis and Friedman tests have been conducted to compare the performance of these algorithms thoroughly.
Stereo Digital Image Correlation in MATLAB

Digital Image Correlation (DIC) has found widespread use in measuring full-field displacements and deformations experienced by a body from images captured of it. Stereo-DIC has received significantly more attention than two-dimensional (2D) DIC since it can account for out-of-plane displacements. Although many aspects of Stereo-DIC that are shared in common with 2D DIC are well documented, there is a lack of resources that cover the theory of Stereo-DIC. Furthermore, publications which do detail aspects of the theory do not detail its implementation in practice. This literature gap makes it difficult for newcomers to the field of DIC to gain a deep understanding of the Stereo-DIC process, although this knowledge is necessary to contribute to the development of the field by either furthering its capabilities or adapting it for novel applications. This gap in literature acts as a barrier thereby limiting the development rate of Stereo-DIC. This paper attempts to address this by presenting the theory of a subset-based Stereo-DIC framework that is predominantly consistent with the current state-of-the-art. The framework is implemented in practice as a 202 line MATLAB code. Validation of the framework shows that it performs on par with well-established Stereo-DIC algorithms, indicating it is sufficiently reliable for practical use. Although the framework is designed to serve as an educational resource, its modularity and validation make it attractive as a means to further the capabilities of DIC.
Behavior change through wearables: the interplay between self-leadership and IT-based leadership

Abstract Physical inactivity is a global public health problem that poses health risks to individuals and imposes financial burdens on already strained healthcare systems. Wearables that promote regular physical activity and a healthy diet bear great potential to meet these challenges and are increasingly integrated into the healthcare system. However, extant research shows ambivalent results regarding the effectiveness of wearables in improving users’ health behavior. Specifically important is understanding users’ systematic behavior change through wearables. Constructive digitalization of the healthcare system requires a deeper understanding of why some users change their behavior and others do not. Based on self-leadership theory and our analysis of narrative interviews with 50 long-term wearable users, we identify four wearable use patterns that bring about different behavioral outcomes: following, ignoring, combining, and self-leading. Our study contributes to self-leadership theory and research on individual health information systems and has practical implications for wearable and healthcare providers.
An Ensemble of U-Net Models for Kidney Tumor Segmentation with CT images

We present here the Arkansas AI-Campus solution method for the 2019 Kidney Tumor Segmentation Challenge (KiTS19). Our Arkansas AI-Campus team participated the KiTS19 Challenge for four months, from March to July of 2019. This paper provides a summary of our methods, training, testing and validation results for this grand challenge in biomedical imaging analysis. Our deep learning model is an ensemble of U-Net models developed after testing many model variations. Our model has consistent performance on the local test dataset and the final competition independent test dataset. The model achieved local test Dice scores of 0.949 for kidney and tumor segmentation, and 0.601 for tumor segmentation, and the final competition test earned Dice scores 0.9470 and 0.6099 respectively. The Arkansas AI-Campus team solution with a composite DICE score of 0.7784 has achieved a final ranking of top fifty worldwide, and top five among the United States teams in the KiTS19 Competition.
Expertise in Tool Use Promotes Tool Embodiment

Body representations are known to be dynamically modulated or extended through tool use. Here, we review findings that demonstrate the importance of a user's tool experience or expertise for successful tool embodiment. Examining expert tool users, such as individuals who use tools in professional sports, people who use chopsticks at every meal, or spinal injury patients who use a wheelchair daily, offers new insights into the role of expertise in tool embodiment: Not only does tool embodiment differ between novices and experts, but experts may experience enhanced changes to their body representation when interacting with their own, personal tool. The findings reviewed herein reveal the importance of assessing tool skill in future studies of tool embodiment.
DeepScribble: Interactive Pathology Image Segmentation Using Deep Neural Networks with Scribbles

Tumor segmentation is a challenging but crucial task in digital pathology for accurate diagnosis. Recent studies on deep neural networks have shown promising results in various image segmentation problems. However, unlike several medical imaging modalities (such as computerized tomography, CT and magnetic resonance imaging, MRI), the boundary between the normal and the tumor area in pathology images is usually fuzzy and ambiguous, making it difficult to adapt conventional image segmentation methods to these images. In this paper, we propose an interactive segmentation method that corrects the segmented boundaries from deep neural networks using user interaction. The proposed method functions in two stages; the first network initially generates the best prediction of the tumor boundary; the second network then refines the segmentation iteratively by exploiting the user scribble annotations on-the-fly. Our approach leverages the feature learning aspects of deep neural networks in the correction step to reduce user effort. We demonstrate the efficacy of the proposed method on real pathology images.
PSPSO: A package for parameters selection using particle swarm optimization

This paper reports a high-level python package for selecting machine learning algorithms and ensembles of machine learning algorithms parameters by using the particle swarm optimization (PSO) algorithm named PSPSO. The first version of PSPSO supports four algorithms: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost) and Gradient Boosting Decision Trees (GBDT). PSPSO provides an easy framework for building machine learning algorithms using PSO and a new platform for researchers to investigate their selection methods. In addition, it provides a basis for establishing new selection ideas and can be easily extended to support other algorithms.
Spatiotemporal module for video saliency prediction based on self-attention

Considering that the existing video saliency prediction methods still have limitations in spatiotemporal correlation learning between features and saliency regions, this paper proposes a spatiotemporal module for video saliency prediction based on self-attention. The proposed model emphasizes three essential problems as follows. First, we raise a multi-scale feature-fusion network (MFN) for effective feature integration. The framework can extract and fuse features from four scales at low memory cost. Second, we view the task as a global evaluation of the correlation on pixel level to predict human visual attention in task-driven scenes more accurately. An adapted transformer encoder is designed for spatiotemporal correlation learning. Finally, we introduce DConvLSTM to learn the context in videos. Experimental results show that the proposed model achieves state-of-the-art performance on both driving scenes and natural scenes with multi-motion information. And our model also achieves very comparable performance especially in natural scenes with multi-category objects. It proves our method is practicable in both data-driven and task-driven conditions.
Deep importance sampling based on regression for model inversion and emulation

Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been widely used as powerful tools for numerical inference and optimization. They require the choice of a suitable proposal density that is crucial for their performance. For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature. We here present an AIS framework called Regression-based Adaptive Deep Importance Sampling (RADIS). In RADIS, the key idea is the adaptive construction via regression of a non-parametric proposal density (i.e., an emulator), which mimics the posterior distribution and hence minimizes the mismatch between proposal and target densities. RADIS is based on a deep architecture of two (or more) nested IS schemes, in order to draw samples from the constructed emulator. The algorithm is highly efficient since employs the posterior approximation as proposal density, which can be improved adding more support points. As a consequence, RADIS asymptotically converges to an exact sampler under mild conditions. Additionally, the emulator produced by RADIS can be in turn used as a cheap surrogate model for further studies. We introduce two specific RADIS implementations that use Gaussian Processes (GPs) and Nearest Neighbors (NN) for constructing the emulator. Several numerical experiments and comparisons show the benefits of the proposed schemes. A real-world application in remote sensing model inversion and emulation confirms the validity of the approach.
Spatially Constrained Context-Aware Hierarchical Deep Correlation Filters for Nucleus Detection in Histology Images

Nucleus detection in histology images is a fundamental step for cellular-level analysis in computational pathology. In clinical practice, quantitative nuclear morphology can be used for diagnostic decision making, prognostic stratification, and treatment outcome prediction. Nucleus detection is a challenging task because of large variations in the shape of different types of nucleus such as nuclear clutter, heterogeneous chromatin distribution, and irregular and fuzzy boundaries. To address these challenges, we aim to accurately detect nuclei using spatially constrained context-aware correlation filters using hierarchical deep features extracted from multiple layers of a pre-trained network. During training, we extract contextual patches around each nucleus which are used as negative examples while the actual nucleus patch is used as a positive example. In order to spatially constrain the correlation filters, we propose to construct a spatial structural graph across different nucleus components encoding pairwise similarities. The correlation filters are constrained to act as eigenvectors of the Laplacian of the spatial graphs enforcing these to capture the nucleus structure. A novel objective function is proposed by embedding graph-based structural information as well as the contextual information within the discriminative correlation filter framework. The learned filters are constrained to be orthogonal to both the contextual patches and the spatial graph-Laplacian basis to improve the localization and discriminative performance. The proposed objective function trains a hierarchy of correlation filters on different deep feature layers to capture the heterogeneity in nuclear shape and texture. The proposed algorithm is evaluated on three publicly available datasets and compared with 15 current state-of-the-art methods demonstrating competitive performance in terms of accuracy, speed, and generalization.
Using Blockchain in Autonomous Vehicles

Autonomous vehicles have the potential to revolutionize the automotive industry and are gaining immense attention from academia as well as industry. However, facets of autonomous vehicle systems related to the interconnection of independent components pose vulnerabilities to the system. These vulnerabilities aren’t guaranteed to be solved by traditional security methods. Blockchain technology is a powerful tool that can aid in improving trust and reliability in such systems. This paper provides a survey on how blockchain can help improve not only security but also other aspects of the AV systems, focussing on the two major blockchain ecosystems as of this writing - Ethereum and Bitcoin. Our survey found that blockchain technology can assist in different use cases related to AVs, such as providing shared storage, enhancing security, optimizing vehicular functionalities, and enhancing related industries. This paper suggests directions for improvement in the sectors of Autonomous Vehicles (AV), which can be achieved with the incorporation of blockchain into Intelligent Transport Systems (ITS) or individual vehicular units.
A Holographic Augmented Reality Interface for Visualizing of MRI Data and Planning of Neurosurgical Procedures

The recent introduction of wireless head-mounted displays (HMD) promises to enhance 3D image visualization by immersing the user into 3D morphology. This work introduces a prototype holographic augmented reality (HAR) interface for the 3D visualization of magnetic resonance imaging (MRI) data for the purpose of planning neurosurgical procedures. The computational platform generates a HAR scene that fuses pre-operative MRI sets, segmented anatomical structures, and a tubular tool for planning an access path to the targeted pathology. The operator can manipulate the presented images and segmented structures and perform path-planning using voice and gestures. On-the-fly, the software uses defined forbidden-regions to prevent the operator from harming vital structures. In silico studies using the platform with a HoloLens HMD assessed its functionality and the computational load and memory for different tasks. A preliminary qualitative evaluation revealed that holographic visualization of high-resolution 3D MRI data offers an intuitive and interactive perspective of the complex brain vasculature and anatomical structures. This initial work suggests that immersive experiences may be an unparalleled tool for planning neurosurgical procedures.
Potential of Bayesian additive regression trees for predicting daily global and diffuse solar radiation in arid and humid areas

This study aims to evaluate the potential of Bayesian additive regression trees (BART) for predicting global and diffuse solar radiation. Long-term daily weather data were collected at four stations in arid and humid areas. Models with different input combinations were created. The default parameters within R language package of BART were used. Model accuracy was assessed with Pearson correlation coefficient (R), root mean square error (RMSE), mean absolute error (MAE), relative root mean square error (RRMSE), and Nash-Sutcliffe efficiency coefficient (NSE). Taylor diagram was applied to illustrate model performance. On average, the model with sunshine duration, theoretical sunshine duration, mean temperature, maximum temperature, minimum temperature, relative humidity, and rainfall performed best for predicting global solar radiation, with mean R of 0.973, RMSE of 1.685 MJ/m2d, NSE of 0.944, RRMSE of 0.124, and MAE of 1.265 MJ/m2d. The model with sunshine duration, theoretical sunshine duration, global solar radiation, extraterrestrial solar radiation, and day of year outperformed others for predicting diffuse solar radiation, with mean R of 0.912, RMSE of 1.291 MJ/m2d, NSE of 0.827, RRMSE of 0.214, and MAE of 0.933 MJ/m2d. The results showed that BART was a suitable method for predicting global and diffuse solar radiation using climatic variables.
Gaze+Hold: Eyes-only Direct Manipulation with Continuous Gaze Modulated by Closure of One Eye

The eyes are coupled in their gaze function and therefore usually treated as a single input channel, limiting the range of interactions. However, people are able to open and close one eye while still gazing with the other. We introduce Gaze+Hold as an eyes-only technique that builds on this ability to leverage the eyes as separate input channels, with one eye modulating the state of interaction while the other provides continuous input. Gaze+Hold enables direct manipulation beyond pointing which we explore through the design of Gaze+Hold techniques for a range of user interface tasks. In a user study, we evaluated performance, usability and user's spontaneous choice of eye for modulation of input. The results show that users are effective with Gaze+Hold. The choice of dominant versus non-dominant eye had no effect on performance, perceived usability and workload. This is significant for the utility of Gaze+Hold as it affords flexibility for mapping of either eye in different configurations.
A Baseline for Cross-Database 3D Human Pose Estimation

Vision-based 3D human pose estimation approaches are typically evaluated on datasets that are limited in diversity regarding many factors, e.g., subjects, poses, cameras, and lighting. However, for real-life applications, it would be desirable to create systems that work under arbitrary conditions (“in-the-wild”). To advance towards this goal, we investigated the commonly used datasets HumanEva-I, Human3.6M, and Panoptic Studio, discussed their biases (that is, their limitations in diversity), and illustrated them in cross-database experiments (for which we used a surrogate for roughly estimating in-the-wild performance). For this purpose, we first harmonized the differing skeleton joint definitions of the datasets, reducing the biases and systematic test errors in cross-database experiments. We further proposed a scale normalization method that significantly improved generalization across camera viewpoints, subjects, and datasets. In additional experiments, we investigated the effect of using more or less cameras, training with multiple datasets, applying a proposed anatomy-based pose validation step, and using OpenPose as the basis for the 3D pose estimation. The experimental results showed the usefulness of the joint harmonization, of the scale normalization, and of augmenting virtual cameras to significantly improve cross-database and in-database generalization. At the same time, the experiments showed that there were dataset biases that could not be compensated and call for new datasets covering more diversity. We discussed our results and promising directions for future work.
Deep Pre-trained Models for Computer Vision Applications: Traffic sign recognition

Objects detection and Recognition are an important task for computer vision field and intelligent transportation systems. Generally, these tasks remain challenging for the artificial machines due to the need of pre-learning phase in which the machine acquires an intelligent brain. Some researchers have shown that deep learning tools work well in computer vision, image processing, and pattern recognition. To solve such tasks, this paper focuses on deep Convolutional Neural Network (CNN) and its architectures, such as, VGG16, VGG19, AlexNet, and Resnet50. An overview for the techniques and schemes used for computer vision applications such as Road Sign Recognition will be introduced. Then by customizing the hyperparameters for each pre-trained models, we re-implement these models for the traffic sign recognition application. In the experiments, these pre-trained CNN classifiers are trained and tested with the German Traffic Sign Recognition Benchmark dataset (GTSRB). Experimental results show that the proposed scheme achieved a good performance results in terms of evaluations metrics of traffic signs recognition. A performance comparison analysis between the selected pre-trained models for traffic sign recognition confirmed that the AlexNet model outperforms all other implemented models.
Round-Optimal Verifiable Oblivious Pseudorandom Functions from Ideal Lattices

Verifiable Oblivious Pseudorandom Functions (VOPRFs) are protocols that allow a client to learn verifiable pseudorandom function (PRF) evaluations on inputs of their choice. The PRF evaluations are computed by a server using their own secret key. The security of the protocol prevents both the server from learning anything about the client's input, and likewise the client from learning anything about the server's key. VOPRFs have many applications including password-based authentication, secret-sharing, anonymous authentication and efficient private set intersection. In this work, we construct the first round-optimal (online) VOPRF protocol that retains security from well-known subexponential lattice hardness assumptions. Our protocol requires constructions of non-interactive zero-knowledge arguments of knowledge (NIZKAoK). Using recent developments in the area of post-quantum zero-knowledge arguments of knowledge, we show that our VOPRF may be securely instantiated in the quantum random oracle model. We construct such arguments as extensions of prior work in the area of lattice-based zero-knowledge proof systems.
FIN-GAN: Face illumination normalization via retinex-based self-supervised learning and conditional generative adversarial network

Illumination normalization is one of the most challenging issues for facial analysis. To be specific, the variation of environmental illumination influences the visual quality of an image and drastically degrades the performance of face recognition, detection, landmark and other related applications. Retinex theory provides an important concept for processing illumination variation, which supposes that a face image can be decomposed into [an invariant reflectance component and a variant illumination component. Enlighten by this theory, in this paper, we put forward a novel deep learning approach which combines self-supervised learning and adversarial training for face illumination normalization (FIN-GAN). The proposed FIN-GAN framework can be implemented by two steps. Firstly, self-supervised learning is employed to decompose the original face image into the illumination component and the illumination-invariant component with Retinex constraint. Then, we employ the conditional generative adversarial network for face image reconstruction. For network optimization, we design the combined loss to ensure visual quality and preserve identity information. Experiments are performed on Extended-YaleB, CAS-PEAL, CMU-PIE and Multi-PIE datasets. Through multiple quantitative criteria, we demonstrate that the proposed FIN-GAN obtains promising performance in face illumination normalization.