Reviewerly/exHarmony huggingface dataset:
Neural Network Pattern Recognition Experiments Toward a Fully Automatic Detection of Anomalies in InSAR Time Series of Surface Deformation

We present a neural network-based method to detect anomalies in time-dependent surface deformation fields given a set of geodetic images of displacements collected from multiple viewing geometries. The presented methodology is based on a supervised classification approach using combinations of line of sight multitemporal, multi-geometry interferometric synthetic aperture radar (InSAR) time series of displacements. We demonstrate this method with a set of 170 million time series of surface deformation generated for the entire Italian territory and derived from ERS, ENVISAT, and COSMO-SkyMed Synthetic Aperture Radar satellite constellations. We create a training dataset that has been compared with independently validated data and current state-of-the-art classification techniques. Compared to state-of-the-art algorithms, the presented framework provides increased detection accuracy, precision, recall, and reduced processing times for critical infrastructure and landslide monitoring. This study highlights how the proposed approach can accelerate the anomalous points identification step by up to 147 times compared to analytical and other artificial intelligence methods and can be theoretically extended to other geodetic measurements such as GPS, leveling data, or extensometers. Our results indicate that the proposed approach would make the anomaly identification post-processing times negligible when compared to the InSAR time-series processing.
Crisis management, surveillance, and digital ethics in the COVID‐19 era

Abstract In this special issue, we reflect on the global coronavirus disease 2019 (COVID‐19) crisis and the containment measures put in place by formal authorities, combining both theoretically and empirically three different fields of study: crisis management, surveillance studies, and digital ethics. The special issue shows how the intersection of these fields provides a great opportunity to better understand challenges that are of critical importance to today's societies, as well as opening up new avenues for innovation. The focus of this special issue is to unpack and understand the debate on crisis management measures, surveillance, and ethical consequences during the ongoing, enduring COVID‐19 crisis. Building on crisis management literature, surveillance studies, and digital ethics research the articles included in this special issue reflect on issues of governance, space, as well as moral and ethical considerations, which were often overlooked in the public discourse in relation to the COVID‐19 pandemic. The special issue provides a deeper and clearer understanding of intended and unintended ethical and political consequences of crisis management practices, such as a politics of visibility that makes the operation of power invisible and fails to combat inequality, whilst ignoring the potential positive power of digital data and surveillance for empowerment and resilience
Virtual reality induces symptoms of depersonalization and derealization: A longitudinal randomised control trial

Trusting reports on internet forums, a substantial number of people have developed unpleasant symptoms of depersonalization (DP) and derealization (DR) after virtual reality (VR) consumption. Likewise, one case series study indicates that even after one single VR session, transient DPDR experiences may occur. Despite these indications, little is otherwise known about the risk of developing DPDR from VR consumption. Therefore, we carried out the present longitudinal randomised controlled trial (N = 80), which examined, whether VR-gaming induces higher DPDR effects than classical PC gaming, and if so, how long DPDR effects persist. DPDR effects were assessed immediately before gaming (T0), immediately after gaming (T1), one day after gaming (T2) and one week after gaming (T3). Moreover, given evidence for an emotional hyporeagibility under DPDR, the participants' emotional and physiological (EDA, HRV) reagibility towards emotional pictures was assessed immediately after gaming. Likewise, to study whether certain personality traits might be associated with the occurrence of VR-induced DPDR effects, we administered a personality inventory to our participants. Results reveal that, after VR gaming, a significantly stronger DPDR experience was reported in the VR group than PC group at T1, while at T2 and T3, no significant DPDR group differences were detectable. Furthermore, no significant group differences were found in respect to emotional and physiological reagibility, nor were any correlations between personality traits and DPDR symptoms found. In summary, although our study provides further evidence that VR consumption can transiently induce DPDR-like symptoms, we find no evidence that these DPDR effects persist in the long term.
Image-text bidirectional learning network based cross-modal retrieval

The problem of cross-modal retrieval has attracted significant attention in the cross-media retrieval community. One key challenge of cross-modal retrieval is to eliminate the heterogeneous gap between different patterns. The existing numerous cross-modal retrieval approaches tend to jointly construct a common subspace, while these methods fail to consider mutual influence between modalities sufficiently during the whole training process. In this paper, we propose a novel image-text Bidirectional Learning Network (BLN) based cross-modal retrieval method. The method constructs a common representation space and directly measures the similarity of heterogeneous data. More specifically, a multi-layer supervision network is proposed to learn the cross-modal relevance of the generated representations. Moreover, a bidirectional crisscross loss function is proposed to preserve the modal invariance with the bidirectional learning strategy in the common representation space. The loss functions of discriminant consistency and the bidirectional crisscross loss are integrated into an objective function which aims to minimize the intra-class distance and maximize the inter-class distance. Comprehensive experimental results on four widely-used databases show that the proposed method is effective and superior to the existing cross-modal retrieval methods.
AIFS-DATASET for Few-Shot Aerial Image Scene Classification

Few-shot learning (FSL), which aims to rapidly recognize unseen categories with limited samples, has attracted wide attention in aerial image scene classification. However, the existing methods generally train and evaluate the model within a dataset, and changing the dataset requires retraining and evaluation, which only realizes the generalization of intra-dataset. Considering meta-learning, this brings in a natural assumption: FSL should learn meta-knowledge from cross-domain heterogeneous tasks and then can generalize to new data distributions (e.g., datasets) with few samples. To this end, we propose a new benchmark, dubbed aerial image few-shot dataset (AIFS-DATASET), which is composed of diverse datasets and can provide more realistic heterogeneous task distributions. On AIFS-DATASET, we use many heterogeneous tasks, across multi-domains without any aerial image category, to train the model, achieving “see more.” Then we transfer the learned knowledge to new tasks in aerial images to evaluate the generalization performance of the model, thus acquiring a “well-informed” few-shot aerial image scene classification model. Moreover, the challenges of inter-class similarity and intra-class discrepancy in aerial images still exist. We also develop a dual constrained distance metric learning (DC-DML) framework to deal with the variable learning tasks adaptively and to achieve compact data distribution within a class and clear distribution gaps between classes from the perspective of metric learning. DC-DML mainly uses a task-adapted feature extractor while devising a novel distance metric with a cross-class bias penalty. By conducting experiments on AIFS-DATASET, we observed that DC-DML outperforms the current prevailing FSL approaches by a large margin.
Student at a distance: exploring the potential and prerequisites of using telepresence robots in schools

'Homebound' children are unable to attend school for illness-related reasons. To lessen their predicament, schools have begun experimenting with 'telepresence robots' that can enable remote participation. While promising, we know little about the use of telepresence robots in practice. To begin to redress this, we draw on 159 semi-structured interviews to explore the experiences of 37 child users of the robot 'AV1' in Norwegian schools. The children's experiences varied, with some benefitting greatly and others not getting any benefit from using the robot. To explain these variations, we reconstruct the robot's critical component structure – that is, the assembly of sociomaterial elements that determines whether and how the robot works in practice. We also explore the benefits of using the robot when these critical components align. In so doing, we provide in-depth knowledge about the potential and prerequisites of using telepresence robots in schools – to the benefit of users, producers and scholars of telepresence technology.
Adversarial Mixup Ratio Confusion for Unsupervised Domain Adaptation

Multimedia applications often involve knowledge transfer across domains, e.g., from images to texts, where Unsupervised Domain Adaptation (UDA) can be used to reduce the domain shifts. Most of the UDA methods are based on adversarial learning. However, previous adversarial domain adaptation methods may suffer from three issues. First, although the features learned by previous methods could fool the domain classifier to make false classification predictions, they may not be domain-invariant. Second, the limited number of training samples make the latent space of features not smooth and continuous enough. Third, the target domain features may lack discriminability. In this paper, we propose a novel adversarial domain adaptation method named Adversarial Mixup Ratio Confusion (AMRC) to alleviate all the above issues. Specifically, we propose a new adversarial training pattern that uses mixup to generate multiple features with different mixup ratios, which represent different intermediate states between the source and target domain. Then, on one hand, we train an estimator to estimate the mixup ratio as accurately as possible. On the other hand, we train a generator to make the estimator be uncertain about the mixup ratio. In this way, our method could learn a continuous and domain-invariant latent space. Furthermore, we apply the intra-domain and cross-domain mixup regularizations to ensure the smoothness and continuity of the latent space, while making the classifier behave more linearly on in-between samples. At last, we exploit the sharpened pseudo-labels of the target samples for self-supervised learning to enhance the discriminability of the target features. The experimental results on 3 benchmarks verify the effectiveness of our method.
Image retrieval using contrastive weight aggregation histograms

Aggregating deep convolutional features for image retrieval has obtained excellent results in recent years; however, exploiting the several advantages of deep convolutional feature maps remains challenging. To address this problem, we propose a novel weighting method called the contrastive weight aggregation histogram to distinguish the slightly distinguishable and highly distinguishable features in deep convolutional features maps for image retrieval. The main highlights of this paper are as follows: (1) a contrastive weighting is proposed to represent the differential contribution of the slightly and highly distinguishable features. It can enhance the distinguishable information and further improve the representative power of deep convolutional features. (2) A novel method is introduced to generate contrastive weighting by comparing the slightly and highly distinguishable feature aggregation. It has the ability to exploit the several advantages of deep convolutional feature maps in terms of differentiating. Experiments demonstrated that the proposed contrastive weighting method outperforms methods that use the deep convolutional feature aggregation on five benchmark datasets.
Conditional Generative Adversarial Networks with Adversarial Attack and Defense for Generative Data Augmentation

Developing deep neural network (DNN) models for computer vision applications for construction is challenging due to the shortage of training data. To address this issue, we proposed a novel data augmentation method that integrates a conditional generative adversarial networks (GANs) framework with a target classifier. The integrated architecture enables adversarial attack and defense during end-to-end training, thereby making it possible to generate effective images for the target classifier's training. We trained and tested two image classification DNNs with and without data augmentation, where we confirmed the effectiveness of the proposed method: with the data augmentation, the classification accuracy improved by 4.2 percentage points, from 71.24% to 75.46%, with qualitatively improved feature extraction more focused on the target object. Given that the application areas of our method are open-ended, the result is noteworthy. The proposed method can help construction researchers offset the data insufficiency, which will contribute to having more accurate and scalable DNN-powered vision models in construction applications.
EC-ECC: Accelerating Elliptic Curve Cryptography for Edge Computing on Embedded GPU TX2

Driven by artificial intelligence and computer vision industries, Graphics Processing Units (GPUs) are now rapidly achieving extraordinary computing power. In particular, the NVIDIA Tegra K1/X1/X2 embedded GPU platforms, which are also treated as edge computing devices, are now widely used in embedded environments such as mobile phones, game consoles, and vehicle-mounted systems to support high-dimension display, auto-pilot, and so on. Meanwhile, with the rise of the Internet of Things (IoT), the demand for cryptographic operations for secure communications and authentications between edge computing nodes and IoT devices is also expanding. In this contribution, instead of the conventional implementations based on FPGA, ASIC, and ARM CPUs, we provide an alternative solution for cryptographic implementation on embedded GPU devices. Targeting the new cipher suite added in TLS 1.3, we implement Edwards25519/448 and Curve25519/448 on an edge computing platform, embedded GPU NVIDIA Tegra X2, where various performance optimizations are customized for the target platform, including a novel parallel method for the register-limited embedded GPUs. With about 15 W of power consumption, it can provide 210k/31k ops/s of Curve25519/448 scalar multiplication, 834k/123k ops/s of fixed-point Edwards25519/448 scalar multiplication, and 150k/22k ops/s of unknown-point one, which are respectively the primitives and main workloads of key agreement, signature generation, and verification of the TLS 1.3 protocol. Our implementations achieve 8 to 26 times speedup of OpenSSL running in the very powerful ARM CPU of the same platform and outperform the state-of-the-art implementations in FPGA by a wide margin with better power efficiency.
Opposition-based spiral dynamic algorithm with an application to optimize type-2 fuzzy control for an inverted pendulum system

This paper presents two variants of the Opposition-based Spiral Dynamic Algorithm (ObSDA) for an application to optimize a type-2 fuzzy logic controller for an inverted pendulum system. Spiral Dynamic Algorithm (SDA) is a group-based optimization algorithm formulated based on the concept of a natural spiral phenomenon on earth. It has the theory of diversification and intensification in its strategy, which allows the algorithm to present itself as a good deterministic type of optimization tool to solve various engineering problems. Despite the good concept and strategy, the algorithm still suffers from getting trapped in a local optima solution. This is due to the limitation of the deterministic strategy that prevents the search agents from sufficiently exploring the whole feasible search space. The search operation only occurs within the area covered by the search agents, and thus there is a low opportunity to thoroughly diverse outside the covered area. Quasi-reflected and Quasi opposition-based strategies were incorporated into the SDA to overcome the exploration problem of the search agents. It helped the search agents to explore the opposite location of the current location of the agents. The opposition strategy also offered varying step sizes to the agents during the movement. The proposed QR-ObSDA and Q-ObSDA were tested on various benchmark functions comprising multimodal and unimodal fitness landscapes. They are also applied to optimize a type-2 fuzzy logic controller for an inverted pendulum system in comparison to SDA, Spotted Hyena Optimizer, Tunicate Swarm Algorithm, and Sooty Tern Optimization Algorithm. A statistical analysis on the accuracy achievement was conducted using Friedman and Wilcoxon Sign Rank methods. The result had shown that the proposed ObSDA variants had outperformed the original SDA in locating the theoretical optima solution of the benchmark functions. Application of the control problem had shown the accuracy performance of ObSDA variants had significantly improved compared to the existing SDA variants and outperformed the other three optimization algorithms.
Semantic scene segmentation for robotics

Comprehensive scene understanding is a critical enabler of robot autonomy. Semantic segmentation is one of the key scene understanding tasks, which is pivotal for several robotics applications including autonomous driving, domestic service robotics, last mile delivery, among many others. Semantic segmentation is a dense prediction task that aims to provide a scene representation in which each pixel of an image is assigned a semantic class label. Therefore semantic segmentation considers the full scene context, incorporating the object category, location, and shape of all the scene elements, including the background. Numerous algorithms have been proposed for semantic segmentation over the years. However, the recent advances in deep learning combined with the boost in the computational capacity and the availability of large-scale labeled data sets have led to significant advances in semantic segmentation. In this chapter, we introduce the task of semantic segmentation and present the deep learning techniques that have been proposed to address this task over the years. We first define the task of semantic segmentation and contrast it with other closely related scene understanding problems. We detail different algorithms and architectures for semantic segmentation and the commonly employed loss functions. Furthermore, we present an overview of data sets, benchmarks, and metrics that are used in semantic segmentation. We conclude the chapter with a discussion of challenges and opportunities for further research in this area.
Efficient Lightweight Attention Network for Face Recognition

Although face recognition has achieved great success due to deep learning, many factors may affect the quality of faces in the wild, such as pose changes, age variations, and light changes, which can seriously affect the performance of face recognition. In this work, an effective approach called Efficient Lightweight Attention Networks (ELANet) is proposed to address the challenge brought by the impacts of poses and ages on face recognition performance. First, similar local patches are particularly important when the geometry and appearance of a face change drastically. To alleviate this challenge, spatial attention is used to capture important locally similar patches and channel attention is employed to focus on features with different levels of importance. Furthermore, Efficient Fusion Attention (EFA) module is designed to achieve better performance, which can alleviate the computational effort required by fusing spatial and channel attention. Second, multi-scale features learning is necessary because pose or large expression changes can cause similar recognition regions to appear at different scales. For this purpose, pyramid multi-scale module is presented, which constructs a series of features at different scales via pooling operations. Third, to unite low-level local detail information with high-level semantic information, the features of different layers are fused by Adaptively Spatial Feature Fusion (ASFF) instead of simply utilizing addition or concatenation. Compared to recent lightweight networks, the ELANet improved performance by 1.83% and 2.17% on the CPLFW and VGG2_FP datasets, respectively, and by 0.92% on the CALFW dataset. The ELANet addresses the challenge regarding the impacts of poses and ages on face recognition performance with few parameters and computational effort and is suitable for embedded and mobile devices.
Disaggregating Civility: Politeness, Public-Mindedness and Their Connection

Abstract Calls for civility feature prominently in public discourse, and the concept has received growing attention by political philosophers recently. But what does it mean to be civil? The existing literature distinguishes between two main understandings of civility: civility as politeness and civility as public-mindedness. The objective of this article is to show that these conceptions and the different normative claims associated with them can all fit together. We argue that civility and incivility should be disaggregated in order to uncover fruitful connections between different aspects of the concept. We introduce a distinction between two dimensions of civility as public-mindedness (moral and justificatory), as well as a new distinction between the means and ends of civility. We examine the complex connections between the different dimensions of (in)civility and show that the disaggregation of civility and incivility tells us what kind of (in)civility matters, as well as when and why.
Evaluation of MERRA-2 hourly surface solar radiation across China

Accurate global horizontal irradiance (GHI) is key to solar energy resource assessment and prediction. The GHI provided by MERRA-2 (Version 2 of the Modern-Era Retrospective Analysis for Research and Applications) still needs to be evaluated over China. In this study, GHI observed by 37 ground stations in 2018 are used to systematically assess the MERRA-2 GHI performance for all-sky, clear-sky and cloudy days. Moreover, aerosol and cloud products from MERRA-2 are compared with satellite counterparts to determine the dominant factor affecting MERRA-2 GHI. The results show that MERRA-2 overestimates the hourly and daily mean GHI by 69.35 W/m2 and 27.54 W/m2 on all-sky days, respectively. The mean bias error and root mean square error of hourly GHI between MERRA-2 and CERN are 4.98 W/m2 and 124.85 W/m2 on clear-sky days and 107.16 W/m2 and 215.24 W/m2 on cloudy days, respectively. These values indicate a much greater overestimation of MERRA-2 GHI on cloudy days than on clear-sky days. By comparing cloud fraction (CF) and aerosol optical thickness (AOD) between the Moderate-resolution Imaging Spectroradiometer (MODIS) and MERRA-2, we find that the overestimation of MERRA-2 GHI in most areas is induced by the substantial underestimation of MERRA-2 CF. MERRA-2 underestimates AOD in North China and further enhances the overestimation of GHI caused by the underestimation of MERRA-2 CF, whereas the overestimation of AOD in the Sichuan Basin and Southeastern China partly counterbalances the overestimation of GHI caused by the underestimation of MERRA-2 CF.