Reviewerly/exHarmony huggingface dataset:
Digital volume correlation for meso/micro in-situ damage analysis in carbon fiber reinforced composites

Recently, in-situ X-ray Computed Tomography (CT) has shown its potential for 3D damage analysis in composite materials. However, the characterization of damage in X-ray tomograms is not always straightforward, and it requires post-processing of the 3D images. In this study, we explore the potential of Digital Volume Correlation (DVC) for the detection and characterization of damage in fiber-reinforced composites, where fibers provide the required 3D speckle pattern. Preliminary analysis via “digital deformation” of 3D images is performed to verify the applicability of DVC for quantification of deformation and damage in a carbon/epoxy laminate and to estimate the measurement errors. Then, real-deformation images, acquired with synchrotron CT during in-situ tensile loading of the laminate, are analyzed with DVC to detect different damage mechanisms. A rough analysis is performed at the mesoscale using subset-based DVC, followed by a more detailed investigation at the microscale via finite-element-based DVC. Damage appears in the DVC strain fields as local strain magnification. Crack opening displacement can be estimated reliably via the jumps in displacement fields. DVC proves to be a promising tool for damage characterization in X-ray tomograms of fiber-reinforced composites, especially when simple methods such as grayscale thresholding are not adequate.
Modified Flower Pollination Algorithm for Global Optimization

In this paper, a modified flower pollination algorithm (MFPA) is proposed to improve the performance of the classical algorithm and to tackle the nonlinear equation systems widely used in engineering and science fields. In addition, the differential evolution (DE) is integrated with MFPA to strengthen its exploration operator in a new variant called HFPA. Those two algorithms were assessed using 23 well-known mathematical unimodal and multimodal test functions and 27 well-known nonlinear equation systems, and the obtained outcomes were extensively compared with those of eight well-known metaheuristic algorithms under various statistical analyses and the convergence curve. The experimental findings show that both MFPA and HFPA are competitive together and, compared to the others, they could be superior and competitive for most test cases.
Optimal Location Privacy Preserving and Service Quality Guaranteed Task Allocation in Vehicle-Based Crowdsensing Networks

With increasing popularity of related applications of mobile crowdsensing, especially in the field of Internet of Vehicles (IoV), task allocation has attracted wide attention. How to select appropriate participants is a key problem in vehicle-based crowdsensing networks. Some traditional methods choose participants based on minimizing distance, which requires participants to submit their current locations. In this case, participants' location privacy is violated, which influences disclosure of participants' sensitive information. Many privacy preserving task allocation mechanisms have been proposed to encourage users to participate in mobile crowdsensing. However, most of them assume that different participants' task completion quality is the same, which is not reasonable in reality. In this paper, we propose an optimal location privacy preserving and service quality guaranteed task allocation in vehicle-based crowdsensing networks. Specifically, we utilize differential privacy to preserve participants' location privacy, where every participant can submit the obfuscated location to the platform instead of the real one. Based on the obfuscated locations, we design an optimal problem to minimize the moving distance and maximize the task completion quality simultaneously. In order to solve this problem, we decompose it into two linear optimization problems. We conduct extensive experiments to demonstrate the effectiveness of our proposed mechanism.
ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose Estimation

Active Shape Model (ASM) is a statistical model of object shapes that represents a target structure. ASM can guide machine learning algorithms to fit a set of points representing an object (e.g., face) onto an image. This paper presents a lightweight Convolutional Neural Network (CNN) architecture with a loss function being assisted by ASM for face alignment and estimating head pose in the wild. We use ASM to first guide the network towards learning a smoother distribution of the facial landmark points. Inspired by transfer learning, during the training process, we gradually harden the regression problem and guide the network towards learning the original landmark points distribution. We define multi-tasks in our loss function that are responsible for detecting facial landmark points as well as estimating the face pose. Learning multiple correlated tasks simultaneously builds synergy and improves the performance of individual tasks. We compare the performance of our proposed model called ASMNet with MobileNetV2 (which is about 2 times bigger than ASMNet) in both the face alignment and pose estimation tasks. Experimental results on challenging datasets show that by using the proposed ASM assisted loss function, the ASMNet performance is comparable with MobileNetV2 in the face alignment task. In addition, for face pose estimation, ASMNet performs much better than MobileNetV2. ASMNet achieves an acceptable performance for facial landmark points detection and pose estimation while having a significantly smaller number of parameters and floating-point operations compared to many CNN-based models.
Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks

Malaria is a serious disease responsible for thousands of deaths each year. Many efforts have been made to aid in the diagnosis of malaria using machine learning techniques, but to date, the presence of other elements that may interfere with the recognition of malaria has not been considered. We have developed the first deep learning model using convolutional neural networks capable of differentiating malaria-infected red blood cells from not only normal erythrocytes but also erythrocytes with other types of inclusions. 6415 images of red blood cells were segmented from digital images of 53 peripheral blood smears using thresholding and watershed transformation techniques. These images were used to train a VGG-16 architecture using transfer learning. Using an independent test set of 23 smears, this model was 99.5% accurate in classifying malaria parasites and other red blood cell inclusions. This model also exhibited sensitivity and specificity values of 100% and 91.7%, respectively, classifying a complete smear as infected or not infected. Our model represents a promising advance for automation in the identification of malaria-infected patients. The differentiation between malaria parasites and other red blood cell inclusions demonstrates the potential utility of our model in a real work environment.
LiDAR-Aug: A General Rendering-based Augmentation Framework for 3D Object Detection

Annotating the LiDAR point cloud is crucial for deep learning-based 3D object detection tasks. Due to expensive labeling costs, data augmentation has been taken as a necessary module and plays an important role in training the neural network. "Copy" and "paste" (i.e., GT-Aug) is the most commonly used data augmentation strategy, however, the occlusion between objects has not been taken into consideration. To handle the above limitation, we propose a rendering-based LiDAR augmentation frame-work (i.e., LiDAR-Aug) to enrich the training data and boost the performance of LiDAR-based 3D object detectors. The proposed LiDAR-Aug is a plug-and-play module that can be easily integrated into different types of 3D object detection frameworks. Compared to the traditional object augmentation methods, LiDAR-Aug is more realistic and effective. Finally, we verify the proposed framework on the public KITTI dataset with different 3D object detectors. The experimental results show the superiority of our method compared to other data augmentation strategies. We plan to make our data and code public to help other researchers reproduce our results.
A new approach to determine the optimum tilt angle and orientation of solar collectors in mountainous areas with high altitude

In mountainous areas with high altitude, abundant sunshine, and low cloud cover presence, the complex terrain is the key factor affecting the spatial and temporal distribution of solar energy. However, at present, when seeking the optimal installation angles of solar collectors in mountainous areas, the terrain shading effect is not fully considered, which seriously restricts the solar energy application. To accurately calculate the optimal angles of solar collectors in mountainous areas with high altitude, the concept of viewshed is introduced in the study to analyze the terrain shading effect quantitatively. By double integrating the solar irradiance over the viewshed, the potential solar irradiation under the terrain shading is predicted to detect the solar collectors’ optimal tilt angle and orientation. Then, validation of the method is performed by comparing with the meteorological data of a typical meteorological year and the prediction values of existing radiation models. Besides, Batang county is selected as a case to demonstrate the application of the method. Compared with the recommended angles of the current standard, after considering the terrain shading effect, the optimized annual and monthly optimal angles can increase the solar energy collection by 238.72 kWh/m2 (9.73%) and 398.33 kWh/m2 (16.24%) per year respectively.
Context-aware co-supervision for accurate object detection

State-of-the-art object detection approaches are often composed of two stages, namely, proposing a number of regions on an image and classifying each of them into one class. Both stages share a network backbone which builds visual features in a bottom-up manner. In this paper, we advocate the importance of equipping two-stage detectors with top-down signals, in order to which provides high-level contextual cues to complement low-level features. In practice, this is implemented by adding a side path in the detection head to predict all object classes in the image, which is co-supervised by image-level semantics and requires little extra overheads. Our approach is easily applied to two popular object detection algorithms, and achieves consistent performance gain in the MS-COCO dataset.
Privacy-Preserving Incentive Mechanism Design for Federated Cloud-Edge Learning

To avoid the original private data uploading in cloud-edgecomputing, the federated learning (FL) scheme is recently proposed which enhances the privacy preservation. However, the attacks against the uploaded model updates in FL can still cause private data leakage which demotivates the privacy-sensitive participating edge devices. To address this issue, we aim to design a privacy-preserving incentive mechanism for the federated cloud-edge learning (PFCEL) system such that 1) the privacy-sensitive edge devices are motivated to contribute to the local training and model uploading, 2) a trade-off between the private data leakage and the model accuracy is achieved. We first model the data leakage quantitatively from an adversarial perspective, and then formulate the incentive design problem as a three-layer Stackelberg game, where the interaction between the edge servers and edge devices is further formulated as an optimal contract design problem. Extensive theoretical analysis and numerical evaluations demonstrate the effectiveness of our designed mechanism in terms of privacy preservation and system utility.
Disentangled Feature Learning Network and a Comprehensive Benchmark for Vehicle Re-Identification

Vehicle Re-Identification (ReID) is of great significance for public security and intelligent transportation. Large and comprehensive datasets are crucial for the development of vehicle ReID in model training and evaluation. However, existing datasets in this field have limitations in many aspects, including the constrained capture conditions, limited variation of vehicle appearances, and small scale of training and test set, etc. Hence, a new, large, and challenging benchmark for vehicle ReID is urgently needed. In this paper, we propose a large vehicle ReID dataset, called VERI-Wild 2.0, containing 825,042 images. It is captured using a city-scale surveillance camera system, consisting of 274 cameras covering a very large area over 200 km2. Specifically, the samples in our dataset present very rich appearance diversities thanks to the long time span collecting settings, unconstrained capturing viewpoints, various illumination conditions, diversified background environments, and different weather conditions. Furthermore, to facilitate more practical benchmarking, we define a challenging and large test set containing about 400K vehicle images that do not have any camera overlap with the training set. VERI-Wild 2.0 is expected to be able to facilitate the design, adaptation, development, and evaluation of different types of learning models for vehicle ReID. Besides, we also design a new method for vehicle ReID. We observe that orientation is a crucial factor for feature matching in vehicle ReID. To match vehicle pairs captured from similar orientations, the learned features are expected to capture specific detailed differential information for discriminating the visually similar yet different vehicles. In contrast, features are desired to capture the orientation invariant common information when matching samples captured from different orientations. Thus a novel disentangled feature learning network (DFNet) is proposed. It explicitly considers the orientation information for vehicle ReID, and concurrently learns the orientation specific and orientation common features that thus can be adaptively exploited via an adaptive matching scheme when dealing with matching pairs from similar or different orientations. The comprehensive experimental results show the effectiveness of our proposed method.
Informing cybersecurity strategic commitment through top management perceptions: The role of institutional pressures

Given the financial consequences of security breaches, security risk management has gained more attention in board rooms and garnered more involvement from top management. We undertake a study to understand the top managers’ role in cybersecurity strategy, specifically with cyberinsurance. This study draws from institutional and upper echelons theories to explain how top managers’ values and perceptions mediate the impact of external institutional pressures on the commitment to use cyberinsurance as a risk management strategy. We empirically test proposed hypotheses using data collected from executive-level managers of various firms and perform semi-structured interviews of six case sites as post hoc analysis. The results suggest that institutional pressures positively affect top managers’ perceptions of job security, breach risk, financial risk, transaction cost, and regulatory oversight. In turn, these perceptions influence their commitment to cyberinsurance. We find that values and perceptions of personal relevance have a significant impact on their strategic decisions. The findings emphasize the critical role that top management plays in mediating the influence of institutional pressures on cybersecurity strategy. Implications for research and practice, along with limitations and future directions, are discussed.
IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos

Most current action recognition methods heavily rely on appearance information by taking an RGB sequence of entire image regions as input. While being effective in exploiting contextual information around humans, e.g., human appearance and scene category, they are easily fooled by out-of-context action videos where the contexts do not exactly match with target actions. In contrast, pose-based methods, which take a sequence of human skeletons only as input, suffer from inaccurate pose estimation or ambiguity of human pose per se. Integrating these two approaches has turned out to be non-trivial; training a model with both appearance and pose ends up with a strong bias towards appearance and does not generalize well to unseen videos. To address this problem, we propose to learn pose-driven feature integration that dynamically combines appearance and pose streams by observing pose features on the fly. The main idea is to let the pose stream decide how much and which appearance information is used in integration based on whether the given pose information is reliable or not. We show that the proposed IntegralAction achieves highly robust performance across in-context and out-of-context action video datasets. The codes are available in here.
Denoising of MR and CT images using cascaded multi-supervision convolutional neural networks with progressive training

As MR Rician noise and CT low-dose perfusion noise have a complicated distribution, it is still a challenging task to automatically and accurately remove the noises existing in MR and CT images. Recently, convolutional neural networks have shown favorable performance on image denoising tasks. However, existing methods ignored the hierarchical features extracted from multi-supervision inner layers and estimated the denoised image just by the last single layer, which can not adequately reserve the details of the image. In this paper, we propose a cascaded multi-supervision convolutional neural network named CMSNet to remove the low-dose perfusion noise in CT images and the Rician noise exist in MR images. The CMSNet consists of a multi-supervision network (MSNet) followed with a Refinement network. MSNet is presented to predict the noise constrained by the supervisions from last three convolution layers, which can help acquire more accurate noise prediction and thus obtain the noise-free image. Refinement network is introduced to relief the details lost problem caused by the denoising operation. We employ a progressive training strategy, i.e., MSNet is first trained independently to predict the preliminary noise and then jointly trained with Refinement network for more accurate noise estimating, which can boost the network performance. Experiments are conducted on clinic abdominal MR and CT images, and the results show that our proposed model achieved a promising performance in terms of unknown noise level, a specific noise level on peak signal to noise ratio (PSNR) and global structure similarity index measurement (SSIM).
ARID: A New Dataset for Recognizing Action in the Dark

The task of action recognition in dark videos is useful in various scenarios, e.g., night surveillance and self-driving at night. Though progress has been made in action recognition task for videos in normal illumination, few have studied action recognition in the dark, partly due to the lack of sufficient datasets for such a task. In this paper, we explored the task of action recognition in dark videos. We bridge the gap of the lack of data by collecting a new dataset: the Action Recognition in the Dark (ARID) dataset. It consists of 3,784 video clips with 11 action categories. To the best of our knowledge, it is the first dataset focused on human actions in dark videos. To gain further understanding of our ARID dataset, we analyze our dataset in detail and showed its necessity over synthetic dark videos. Additionally, we benchmark the performance of current action recognition models on our dataset and explored potential methods for increasing their performances. We show that current action recognition models and frame enhancement methods may not be effective solutions for the task of action recognition in dark videos (data available at https://xuyu0010.github.io/arid).
Combating hate speech using an adaptive ensemble learning model with a case study on COVID-19

Social media platforms generate an enormous amount of data every day. Millions of users engage themselves with the posts circulated on these platforms. Despite the social regulations and protocols imposed by these platforms, it is difficult to restrict some objectionable posts carrying hateful content. Automatic hate speech detection on social media platforms is an essential task that has not been solved efficiently despite multiple attempts by various researchers. It is a challenging task that involves identifying hateful content from social media posts. These posts may reveal hate outrageously, or they may be subjective to the user or a community. Relying on manual inspection delays the process, and the hateful content may remain available online for a long time. The current state-of-the-art methods for tackling hate speech perform well when tested on the same dataset but fail miserably on cross-datasets. Therefore, we propose an ensemble learning-based adaptive model for automatic hate speech detection, improving the cross-dataset generalization. The proposed expert model for hate speech detection works towards overcoming the strong user-bias present in the available annotated datasets. We conduct our experiments under various experimental setups and demonstrate the proposed model's efficacy on the latest issues such as COVID-19 and US presidential elections. In particular, the loss in performance observed under cross-dataset evaluation is the least among all the models. Also, while restricting the maximum number of tweets per user, we incur no drop in performance.
Training Robust Neural Networks Using Lipschitz Bounds

Due to their susceptibility to adversarial perturbations, neural networks (NNs) are hardly used in safety-critical applications. One measure of robustness to such perturbations in the input is the Lipschitz constant of the input-output map defined by an NN. In this letter, we propose a framework to train multi-layer NNs while at the same time encouraging robustness by keeping their Lipschitz constant small, thus addressing the robustness issue. More specifically, we design an optimization scheme based on the Alternating Direction Method of Multipliers that minimizes not only the training loss of an NN but also its Lipschitz constant resulting in a semidefinite programming based training procedure that promotes robustness. We design two versions of this training procedure. The first one includes a regularizer that penalizes an accurate upper bound on the Lipschitz constant. The second one allows to enforce a desired Lipschitz bound on the NN at all times during training. Finally, we provide two examples to show that the proposed framework successfully increases the robustness of NNs.
Variational optimization based single image dehazing

In this paper, we present a new approach for single image dehazing based on the proposed variational optimization. A hazy image captures the information about haze in terms of the transmission map and object details present in it. We propose to estimate the initial transmission map by performing the structure-aware smoothing of the hazy image. Further, we formulated a variational optimization for the estimation of final transmission, which refines the initial transmission of a hazy image. Atmospheric light can be considered to be constant throughout the scene for practical purposes. The uniform atmospheric light is computed from the dark channel of a hazy image. The exhaustive experimentation shows that the performance of the proposed method is comparable or better.