Reviewerly/exHarmony huggingface dataset:
DuoDIC: 3D Digital Image Correlation in MATLAB

Three-dimensional Digital Image Correlation (3D-DIC) is a non-contact optical-numerical technique for measuring the 3D shape and full-field displacement, deformation, and strain, from stereo digital images of the surface of an object.3D-DIC is useful in numerous applications, such as characterizing the mechanical behavior of materials and structures, quantifying material parameters, and validating numerical simulations.DuoDIC is a freely available open source MATLAB toolbox for 2-camera stereo 3D-DIC, which can be used either as a standalone package or as functions in custom scripts.DuoDIC receives two series of synchronized images taken from two cameras: (1) images of a flat checkerboard target, which are used to calibrate the stereo camera pair; and (2) images of a speckled test object, which may undergo movement and deformation.The toolbox processes the image series and integrates several camera calibration algorithms with the 2D subset-based DIC software Ncorr (Blaber et al., 2015), to transform matching image points into 3D points, and outputs a dynamic point cloud, meshed surfaces, rigid body motion, and full-field displacement, deformation, and strain measures.Furthermore, DuoDIC offers advanced functions to visualize various measures on the 3D meshes and overlaid on the original images.
Is More Realistic Better? A Comparison of Game Engine and GAN-based Avatars for Investigative Interviews of Children

The success of investigative interviews with maltreated children is often defined by the interviewer's ability to elicit a reliable and coherent account of the alleged incident from the child. Research shows that a child avatar mimicking a maltreated child can improve interviewers' performance in conducting these interviews. The realism of such a child avatar is considered one of the most critical factors. Based on this, the current study aims to generate realistic child avatars in real-time that utilize multimodal data and different components from artificial intelligence. This paper discusses the subjective findings of a study of two types of child avatar videos; animated avatars created using the Unity game engine and photorealism talking-head avatars using Generative adversarial networks (GANs). The results show that although the state-of-the-art GAN-generated avatars are significantly more realistic, they do not necessarily create a better experience, as most of the participants prefer talking to animated avatars.
SketchMaker: Sketch Extraction and Reuse for Interactive Scene Sketch Composition

Sketching is an intuitive and simple way to depict sciences with various object form and appearance characteristics. In the past few years, widely available touchscreen devices have increasingly made sketch-based human-AI co-creation applications popular. One key issue of sketch-oriented interaction is to prepare input sketches efficiently by non-professionals because it is usually difficult and time-consuming to draw an ideal sketch with appropriate outlines and rich details, especially for novice users with no sketching skills. Thus, sketching brings great obstacles for sketch applications in daily life. On the other hand, hand-drawn sketches are scarce and hard to collect. Given the fact that there are several large-scale sketch datasets providing sketch data resources, but they usually have a limited number of objects and categories in sketch, and do not support users to collect new sketch materials according to their personal preferences. In addition, few sketch-related applications support the reuse of existing sketch elements. Thus, knowing how to extract sketches from existing drawings and effectively re-use them in interactive scene sketch composition will provide an elegant way for sketch-based image retrieval (SBIR) applications, which are widely used in various touch screen devices. In this study, we first conduct a study on current SBIR to better understand the main requirements and challenges in sketch-oriented applications. Then we develop the SketchMaker as an interactive sketch extraction and composition system to help users generate scene sketches via reusing object sketches in existing scene sketches with minimal manual intervention. Moreover, we demonstrate how SBIR improves from composited scene sketches to verify the performance of our interactive sketch processing system. We also include a sketch-based video localization task as an alternative application of our sketch composition scheme. Our pilot study shows that our system is effective and efficient, and provides a way to promote practical applications of sketches.
GAN-BElectra: Enhanced Multi-class Sentiment Analysis with Limited Labeled Data

Performing sentiment analysis with high accuracy using machine-learning techniques requires a large quantity of training data. However, getting access to such a large quantity of labeled data for specific domains can be expensive and time-consuming. These warrant developing more efficient techniques that can perform sentiment analysis with high accuracy with a few labeled training data. In this paper, we aim to address this problem with our proposed novel sentiment analysis technique, named GAN-BElectra. With rigorous experiments, we demonstrate that GAN-BElectra outperforms its baseline technique in terms of multiclass sentiment analysis accuracy with a few labeled data while maintaining an architecture with reduced complexity compared to its predecessor.
Joint feedback and recurrent deraining network with ensemble learning

Rainy images typically contain heterogeneous rain distributions; however, many existing methods perform well in simple homogeneous rain and fail to handle complex heterogeneous rain effectively. In this paper, we try to solve this problem by fully exploiting the complementary contextual information in the manner of a Joint Feedback and Recurrent deraining scheme with Ensemble Learning (JFREL). First, the proposed JFREL is built on a recurrent multistage architecture, and the output of each stage is fused automatically via ensemble learning. Second, the feedback mechanism is utilized to refine information from inter- and intra-stages. Third, at each stage the residual dilated aggregation attention module is recursively adopted to adequately characterize complementary high-level contextual information in multiple receptive fields and adaptively aggregate beneficial details to achieve feature compensation. Extensive experiments demonstrate that the proposed JFREL can achieve a competitive performance over the state-of-the-art methods on both synthetic and real-world datasets.
Solar Wind Speed Prediction via Graph Attention Network

Abstract The solar wind is a plasma flow formed by the expansion of the high‐temperature corona and propagates in the interplanetary space with speeds between 200 km/s and 900 km/s. Accurate solar wind speed prediction and longer lead time will help mitigate the impact of solar storms on aerospace equipment and the Earth's magnetic field. Recently, most approaches do not explicitly capture the relationships between different solar wind features, and the prediction accuracy of 96‐hr is still not good enough. This paper elaborately designs an end‐to‐end model: Graph‐Temporal‐AR model (GTA) for solar wind speed prediction. First, our framework considers each feature as the node to construct the graph structure and adopts the graph attention module to learn the complex dependencies among features. Second, our approach employs the dilated causal convolution to extend the receptive field and prolong the prediction time. Furthermore, we leverage the autoregressive model to solve the scale insensitive problem of the neural network, making our model more robust. Specifically, we combine the OMNI data measured at Lagrangian Point 1 (L1) with the extreme ultraviolet images observed by the Solar Dynamics Observatory satellite to predict the solar wind speed at L1. Compared with the baseline models, GTA obtains significant performance improvements. Through visualization, we find GTA excavates the relationships between multiply variables without domain prior knowledge, which may help us find other unknown associations in heliophysics data sets. The data and code are available from https://github.com/syrGitHub/GTA .
Analysis of Renewable Energy Policies through Decision Trees

This paper presents an alternative way of making predictions on the effectiveness and efficacy of Renewable Energy (RE) policies using Decision Trees (DT). As a data-driven process for decision-making, the analysis uses the Renewable Energy (RE) target achievement, predicting whether or not a RE target will likely be achieved (efficacy) and to what degree (effectiveness), depending on the different criteria, including geographical context, characterizing concerns, and policy characteristics. The results suggest different criteria that could help policymakers in designing policies with a higher propensity to achieve the desired goal. Using this tool, the policy decision-makers can better test/predict whether the target will be achieved and to what degree. The novelty in the present paper is the application of Machine Learning methods (through the Decision Trees) for energy policy analysis. Machine learning methodologies present an alternative way to pilot RE policies before spending lots of time, money, and other resources. We also find that using Machine Learning techniques underscores the importance of data availability. A general summary for policymakers has been included.
Application of Blockchain Technology in an IoT-Integrated Framework

The IoT is a current technology that has the ability to interconnect the embedded devices and merge the sensors and wireless networks to communicate and exchange data with one another via the internet. Building and automation, healthcare, monitoring, agriculture, etc. are applications of IoT. Currently, with the rapid increase of IoT applications, we created an environment for device-to-device interconnections, new business models, where these interconnected devices accumulate, process, and share the data with each other. Data integrity or ownership issues, cyber-attacks, single point of failure, etc. are some of the limitations of current IoT solutions. Blockchain technology or a distributed ledger technology has the potential to greatly enhance the security aspect, privacy, and reliability concern of the data. High efficiency, transparency, low cost, and no third-party interventions make this technology a one-stop solution for limitations to integrated IoT devices.
Five components of social design: A unified framework to support research and practice

Although the term social design is being widely adopted around the world, conceptual clarity is still lacking. The way social design is understood and executed can vary greatly. Without a shared conceptual foundation, knowledge exchange and development are restricted, and so is the professionalisation of practice. This paper brings forward a framework that unites all social design around an overarching objective to foster the common good but argues that design activities to do so may be driven by different values and focus on different outcomes. We identify five components of social design: (1) care-driven design activities for the wellbeing of underprivileged people, (2) responsiveness-driven design activities for good governance, (3) political progress-driven design activities for empowered citizens, (4) social capital-driven design activities for beneficial communities, and, (5) resilience-driven design activities for sustainable future systems. We describe how the framework helps explain, discuss and systematically study social design.
SO-softmax loss for discriminable embedding learning in CNNs

Convolutional neural networks (CNNs)-based classifiers, trained with the softmax cross-entropy loss, have achieved remarkable success in learning embeddings for pattern recognition. The cosine similarity-based softmax variants further improve the performance by focusing on optimizing the angles between embeddings and class weights. However, embeddings learned by these variants still have significant intra-class variances since these methods only optimize the relative differences between intra- and inter-class cosine similarities. To simultaneously optimize intra- and inter-class cosine similarities, this paper proposes a cosine Similarity Optimization-based softmax (SO-softmax) loss, which is based on a generalized softmax loss formulation that combines both similarities. The proposed loss constrains the intra-class (positive) and inter-class (negative) cosine similarity by quadratic transformations, thus making the embedding representation more compact within classes and more distinguishable between classes. It is verified theoretically that SO-softmax loss can optimize both the similarities simultaneously. Thorough experiments are conducted on typical audio classification, image classification, face verification, image retrieval, and person re-identification tasks, and the results show that SO-softmax loss outperforms the state-of-the-art loss functions in CNNs-based frameworks.
A Chaotic Antlion Optimization Algorithm for Text Feature Selection

Abstract Text classification is one of the important technologies in the field of text data mining. Feature selection, as a key step in processing text classification tasks, is used to process high-dimensional feature sets, which directly affects the final classification performance. At present, the most widely used text feature selection methods in academia are to calculate the importance of each feature for classification through an evaluation function, and then select the most important feature subsets that meet the quantitative requirements in turn. However, ignoring the correlation between the features and the effect of their mutual combination in this way may not guarantee the best classification effect. Therefore, this paper proposes a chaotic antlion feature selection algorithm (CAFSA) to solve this problem. The main contributions include: (1) Propose a chaotic antlion algorithm (CAA) based on quasi-opposition learning mechanism and chaos strategy, and compare it with the other four algorithms on 11 benchmark functions. The algorithm has achieved a higher convergence speed and the highest optimization accuracy. (2) Study the performance of CAFSA using CAA for feature selection when using different learning models, including decision tree, Naive Bayes, and SVM classifier. (3) The performance of CAFSA is compared with that of eight other feature selection methods on three Chinese datasets. The experimental results show that using CAFSA can reduce the number of features and improve the classification accuracy of the classifier, which has a better classification effect than other feature selection methods.
An empirical analysis of keystroke dynamics in passwords: A longitudinal study

The use of keystroke timings as a behavioural biometric in fixed-text authentication mechanisms has been extensively studied. Previous research has investigated in isolation the effect of password length, character substitution, and participant repetition. These studies have used publicly available datasets, containing a small number of passwords with timings acquired from different experiments. Multiple experiments have also used the participant's first and last name as the password; however, this is not realistic of a password system. Not only is the user's name considered a weak password, but their familiarity with typing the phrase minimises variation in acquired samples as they become more familiar with the new password. Furthermore, no study has considered the combined impact of length, substitution, and repetition using the same participant pool. This is explored in this work, where the authors collected timings for 65 participants, when typing 40 passwords with varying characteristics, 4 times per week for 8 weeks. A total of 81,920 timing samples were processed using an instance-based distance and threshold matching approach. Results of this study provide empirical insight into how a password policy should be created to maximise the accuracy of the biometric system when considering substitution type and longitudinal effects.
Image and video dehazing based on transmission estimation and refinement using Jaya algorithm

Due to its in-built ill-posed nature, image and video dehazing is an immensely thought-provoking problem, and it is an essential pre-processing step for various applications such as ADAS (automatic driver-assistance systems), security and surveillance, remote sensing, and long-range imaging. We proposed a novel single image and video dehazing method, called JA-dehaze, by using Jaya Algorithm (JA) to address this problem. The technique consists of three factors: Discrete Haar Wavelet Transform (DHWT) for sub-bands, local atmospheric light estimation and transmission map estimation and refinement using Jaya Algorithm. We formulated haze removal as an optimization problem, and the fitness function for the single image is taken from the information loss term, distance and the haze density term. The JA-dehaze method improves the gradient and preserves the information optimally by minimizing the fitness function. Furthermore, we extended the single image dehazing process to real-time video dehazing. Experimental results demonstrate that the proposed JA-dehaze method can successfully improve the quality of a foggy image without sacrificing color fidelity and can retain image details sufficiently.
Global finite-time control for image-based visual servoing of quadrotor using backstepping method

The main objective of this paper is to use a novel finite-time control method to solve the global finite-time convergence problem of image-based visual servoing of a quadrotor Unmanned Aerial Vehicle (QUAV). The effects of external wind resistance and system uncertainty are considered in the QUAV dynamics, and a disturbance observer is used to compensate for these effects. For the problem of obtaining target feature depth information, a novel nonlinear finite-time linear velocity observer is proposed by using the backstepping method. Based on the above two observers, we use the backstepping method to design the global finite-time controller of the system. The system is proved global finite-time stable using the Lyapunov method. Finally, numerical simulation and ROS gazebo simulation results demonstrate the effectiveness of the proposed control scheme.
Orientation aware weapons detection in visual data: a benchmark dataset

Automatic detection of weapons is significant for improving security and well being of individuals, nonetheless, it is a difficult task due to large variety of size, shape and appearance of weapons. View point variations and occlusion also are reasons which makes this task more difficult. Further, the current object detection algorithms process rectangular areas, however a slender and long rifle may really cover just a little portion of area and the rest may contain unessential details. To overcome these problem, we propose a CNN architecture for Orientation Aware Weapons Detection, which provides oriented bounding box with improved weapons detection performance. The proposed model provides orientation not only using angle as classification problem by dividing angle into eight classes but also angle as regression problem. For training our model for weapon detection a new dataset comprising of total 6400 weapons images is gathered from the web and then manually annotated with position oriented bounding boxes. Our dataset provides not only oriented bounding box as ground truth but also horizontal bounding box. We also provide our dataset in multiple formats of modern object detectors for further research in this area. The proposed model is evaluated on this dataset, and the comparative analysis with off-the shelf object detectors yields superior performance of proposed model, measured with standard evaluation strategies. The dataset and the model implementation are made publicly available at this link: https://bit.ly/2TyZICF.
Alleviating the estimation bias of deep deterministic policy gradient via co-regularization

The overestimation in Deep Deterministic Policy Gradients (DDPG) caused by value approximation error may result in unstable policy training. Twin Delayed Deep Deterministic Policy Gradient (TD3) addresses the overestimation but suffers from the underestimation. In this paper, we propose a Co-Regularization based Deep Deterministic (CoD2) policy gradient method to mitigate the estimation bias. Two learners characterized by overestimated and underestimated biases are trained with Co-regularization to achieve this goal. The overestimated and underestimated values are updated conservatively in CoD2 for policy evaluation. Experimental results show that our method achieves comparable performance compared with other methods.
Multi-Level Wavelet-Based Network Embedded with Edge Enhancement Information for Underwater Image Enhancement

As an image processing method, underwater image enhancement (UIE) plays an important role in the field of underwater resource detection and engineering research. Currently, the convolutional neural network (CNN)- and Transformer-based methods are the mainstream methods for UIE. However, CNNs usually use pooling to expand the receptive field, which may lead to information loss that is not conducive to feature extraction and analysis. At the same time, edge blurring can easily occur in enhanced images obtained by the existing methods. To address this issue, this paper proposes a framework that combines CNN and Transformer, employs the wavelet transform and inverse wavelet transform for encoding and decoding, and progressively embeds the edge information on the raw image in the encoding process. Specifically, first, features of the raw image and its edge detection image are extracted step by step using the convolution module and the residual dense attention module, respectively, to obtain mixed feature maps of different resolutions. Next, the residual structure Swin Transformer group is used to extract global features. Then, the resulting feature map and the encoder’s hybrid feature map are used for high-resolution feature map reconstruction by the decoder. The experimental results show that the proposed method can achieve an excellent effect in edge information protection and visual reconstruction of images. In addition, the effectiveness of each component of the proposed model is verified by ablation experiments.
Projector Calibration in a Two-Layer Flat Refractive Geometry for Underwater Imaging

In underwater imaging we observe objects in the water through a flat glass interface which defines a two-layer flat refractive geometry. Using structured light for underwater 3D imaging requires that both camera and projector are calibrated, which is a difficult task. In this paper we discuss how to calibrate a projector for underwater imaging under a two-layer flat refractive geometry. We propose to model the projector as an inverse camera which enables the use of known procedures for camera calibration in flat refractive geometry. We discuss how to efficiently collect the calibration data using only a planar calibration board and an uncalibrated camera. Performed experiments show that the results of projector calibration are comparable to the results of camera calibration making it highly applicable in practice.