Reviewerly/exHarmony huggingface dataset:
Space-Time Crop &amp; Attend: Improving Cross-modal Video Representation Learning

The quality of the image representations obtained from self-supervised learning depends strongly on the type of data augmentations used in the learning formulation. Recent papers have ported these methods from still images to videos and found that leveraging both audio and video signals yields strong gains; however, they did not find that spatial augmentations such as cropping, which are very important for still images, work as well for videos. In this paper, we improve these formulations in two ways unique to the spatio-temporal aspect of videos. First, for space, we show that spatial augmentations such as cropping do work well for videos too, but that previous implementations, due to the high processing and memory cost, could not do this at a scale sufficient for it to work well. To address this issue, we first introduce Feature Crop, a method to simulate such augmentations much more efficiently directly in feature space. Second, we show that as opposed to naïve average pooling, the use of transformer-based attention improves performance significantly, and is well suited for processing feature crops. Combining both of our discoveries into a new method, Space-Time Crop & Attend (STiCA) we achieve state-of-the-art performance across multiple video-representation learning benchmarks. In particular, we achieve new state-of-the-art accuracies of 67.0% on HMDB-51 and 93.1% on UCF-101 when pre-training on Kinetics-400. Code and pretrained models are available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
VariTex: Variational Neural Face Textures

Deep generative models can synthesize photorealistic images of human faces with novel identities. However, a key challenge to the wide applicability of such techniques is to provide independent control over semantically meaningful parameters: appearance, head pose, face shape, and facial expressions. In this paper, we propose VariTex - to the best of our knowledge the first method that learns a variational latent feature space of neural face textures, which allows sampling of novel identities. We combine this generative model with a parametric face model and gain explicit control over head pose and facial expressions. To generate complete images of human heads, we propose an additive decoder that adds plausible details such as hair. A novel training scheme enforces a pose-independent latent space and in consequence, allows learning a one-to-many mapping between latent codes and pose-conditioned exterior regions. The resulting method can generate geometrically consistent images of novel identities under fine-grained control over head pose, face shape, and facial expressions. This facilitates a broad range of downstream tasks, like sampling novel identities, changing the head pose, expression transfer, and more.
Visual attention prediction improves performance of autonomous drone racing agents

Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable of imitating human eye gaze behavior and attention can improve neural networks' performance for the challenging task of vision-based autonomous drone racing. We hypothesize that gaze-based attention prediction can be an efficient mechanism for visual information selection and decision making in a simulator-based drone racing task. We test this hypothesis using eye gaze and flight trajectory data from 18 human drone pilots to train a visual attention prediction model. We then use this visual attention prediction model to train an end-to-end controller for vision-based autonomous drone racing using imitation learning. We compare the drone racing performance of the attention-prediction controller to those using raw image inputs and image-based abstractions (i.e., feature tracks). Comparing success rates for completing a challenging race track by autonomous flight, our results show that the attention-prediction based controller (88% success rate) outperforms the RGB-image (61% success rate) and feature-tracks (55% success rate) controller baselines. Furthermore, visual attention-prediction and feature-track based models showed better generalization performance than image-based models when evaluated on hold-out reference trajectories. Our results demonstrate that human visual attention prediction improves the performance of autonomous vision-based drone racing agents and provides an essential step towards vision-based, fast, and agile autonomous flight that eventually can reach and even exceed human performances.
A systematic review on recent developments in nonlocal and variational methods for SAR image despeckling

Speckle is a granular deformity that frequently appears in images acquired through coherent imaging sensors such as synthetic aperture radar (SAR). The existence of such noise in the image significantly restricts the perception of visual data resulting in low contrast and loss of singular structures. Among various speckle reduction methods, the nonlocal means (NLMs) and variational models (VMs) have shown promising performance. The NLMs restore the underlying image by selecting similar pixels through patches. For VMs, it is achieved by minimizing the objective function (combination of the forward and prior models) using a suitable optimization algorithm. A review of some major state-of-the-art speckle filtering algorithms for aforesaid methods available in the literature is presented in this manuscript. The different multiplicative noise models in SAR images along with their characteristics are explained. Brief descriptions regarding NLMs and VMs are presented. Different algorithms are listed by clearly stating their operating norms, strengths, and drawbacks.
Kernel Methods in Hyperbolic Spaces

Embedding data in hyperbolic spaces has proven beneficial for many advanced machine learning applications such as image classification and word embeddings. However, working in hyperbolic spaces is not without difficulties as a result of its curved geometry (e.g., computing the Frechet mean of a set of points requires an iterative algorithm). Furthermore, in Euclidean spaces, one can resort to kernel machines that not only enjoy rich theoretical properties but that can also lead to superior representational power (e.g., infinite-width neural networks). In this paper, we introduce positive definite kernel functions for hyperbolic spaces. This brings in two major advantages, 1. kernelization will pave the way to seamlessly benefit from kernel machines in conjunction with hyperbolic embeddings, and 2. the rich structure of the Hilbert spaces associated with kernel machines enables us to simplify various operations involving hyperbolic data. That said, identifying valid kernel functions on curved spaces is not straightforward and is indeed considered an open problem in the learning community. Our work addresses this gap and develops several valid positive definite kernels in hyperbolic spaces, including the universal ones (e.g., RBF). We comprehensively study the proposed kernels on a variety of challenging tasks including few-shot learning, zero-shot learning, person reidentification and knowledge distillation, showing the superiority of the kernelization for hyperbolic representations.
The effect of loss function on conditional generative adversarial networks

Conditional Generative Adversarial Network (cGAN) is a general purpose approach for many image-to-image translation tasks, which aims to translate images from one form to another resulting in high-quality translated images. In this paper, the loss function of the cGAN model is modified by combining the adversarial loss of state-of-the-art Generative Adversarial Network (GAN) models with a new combination of non-adversarial loss functions to enhance model performance and generate more realistic images. Specifically, the effect of the Wasserstein GAN (WGAN), the WGAN with Gradient Penalty (WGAN-GP), and least Squared GAN (lsGAN) adversarial loss functions are explored. Several comparisons are performed to select an optimized combination of L1 with structure, gradient, content-based, Kullback-Leibler divergence, and softmax non-adversarial loss functions. For experimentation purposes, the Facades dataset is used in case of image-to-image translation task. Peak-signal-to-noise-ratio (PSNR), Structural Similarity Index (SSIM), Universal Quality Index (UQI), and Visual Information Fidelity (VIF) are used to quantitatively evaluate the translated images. Based on our experimental results, the best combination of the loss functions for image-to-image translation on facade dataset is (WGAN) adversarial loss with (L1 and content) non-adversarial loss functions. The model generates fine structure images, and captures both high and low frequency details of translated images. Image in-painting and lesion segmentation is investigated to demonstrate practicality of proposed work.
A novel fuzzy clustering based method for image segmentation in RGB-D images

Automatic image segmentation is a challenging task in computer vision applications, especially in the presence of occluded objects, varying color, and different lighting conditions. The advancement of depth-sensing technologies has introduced RGB-Depth cameras which are capable to generate RGB-Depth images and brought significant changes in computer vision applications. However, the segmentation of RGB-Depth images is a difficult task. Therefore, in this paper, a new segmentation method for RGB-Depth images has been introduced and named as random Henry gas solubility optimization-fuzzy clustering method. Firstly, a random Henry gas solubility optimization algorithm has been developed. Next, the proposed optimization algorithm has been employed to obtain optimal fuzzy clusters which are finally merged through segmentation by aggregating superpixels. The standard NYU depth V2 RGB-Depth indoor image dataset is used for performance evaluation. The proposed segmentation approach has been compared with five different methods namely, kmeans, fuzzy c-means, Henry gas solubility optimization algorithm, chaotic gravitational search algorithm, and J-Segmentation in terms of qualitative and quantitative measures. The result analysis shows that the proposed RGB-D segmentation method outperforms the other considered methods.
Identity-Quantity Harmonic Multi-Object Tracking

The data association problem of multi-object tracking (MOT) aims to assign IDentity (ID) labels to detections and infer a complete trajectory for each target. Most existing methods assume that each detection corresponds to a unique target and thus cannot handle situations when multiple targets occur in a single detection due to detection failure in crowded scenes. To relax this strong assumption for practical applications, we formulate the MOT as a Maximizing An Identity-Quantity Posterior (MAIQP) problem on the basis of associating each detection with an identity and a quantity characteristic and then provide solutions to tackle two key problems arising. Firstly, a local target quantification module is introduced to count the number of targets within one detection. Secondly, we propose an identity-quantity harmony mechanism to reconcile the two characteristics. On this basis, we develop a novel Identity-Quantity HArmonic Tracking (IQHAT) framework that allows assigning multiple ID labels to detections containing several targets. Through extensive experimental evaluations on five benchmark datasets, we demonstrate the superiority of the proposed method.
MapReduce Preprocess of Big Graphs for Rapid Connected Components Detection

Paramount and vast applications such as social networks deal with big graphs. For this reason, big graph analysis and processing is currently a necessity. Detection of connected components is one of the analysis of graphs which is utilized as a sub-part in many graph algorithms, such as clustering. The goal of this paper is to propose a parallel preprocess algorithm with MapReduce to decrease graph volume for rapid detection of connected components. Suggested method is able to lessen the volume up to more than 99% quickly by just two rounds of MapReduce. Our evaluation shows that the combination of the preprocess with detection of connected components has a significant impact on: reduction of execution time up to 7 times, decrease in data transmission of processing nodes in network and MapReduce rounds.
“Saving Precious Seconds”—A Novel Approach to Implementing a Low-Cost Earthquake Early Warning System with Node-Level Detection and Alert Generation

This paper presents findings from ongoing research that explores the ability to use Micro-Electromechanical Systems (MEMS)-based technologies and various digital communication protocols for earthquake early warning (EEW). The paper proposes a step-by-step guide to developing a unique EEW network architecture driven by a Software-Defined Wide Area Network (SD-WAN)-based hole-punching technology consisting of MEMS-based, low-cost accelerometers hosted by the general public. In contrast with most centralised cloud-based approaches, a node-level decentralised data-processing is used to generate warnings with the support of a modified Propagation of Local Undamped Motion (PLUM)-based EEW algorithm. With several hypothetical earthquake scenarios, experiments were conducted to evaluate the system latencies of the proposed decentralised EEW architecture and its performance was compared with traditional centralised EEW architecture. The results from sixty simulations show that the SD-WAN-based hole-punching architecture supported by the Transmission Control Protocol (TCP) creates the optimum alerting conditions. Furthermore, the results provide clear evidence to show that the decentralised EEW system architecture can outperform the centralised EEW architecture and can save valuable seconds when generating EEW, leading to a longer warning time for the end-user. This paper contributes to the EEW literature by proposing a novel EEW network architecture.
PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm

Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-based tasks. However, as mainstream GNNs are designed based on the neural message passing mechanism, they do not scale well to data size and message passing steps. Although there has been an emerging interest in the design of scalable GNNs, current researches focus on specific GNN design, rather than the general design space, limiting the discovery of potential scalable GNN models. This paper proposes PasCa, a new paradigm and system that offers a principled approach to systemically construct and explore the design space for scalable GNNs, rather than studying individual designs. Through deconstructing the message passing mechanism, PasCa presents a novel Scalable Graph Neural Architecture Paradigm (SGAP), together with a general architecture design space consisting of 150k different designs. Following the paradigm, we implement an auto-search engine that can automatically search well-performing and scalable GNN architectures to balance the trade-off between multiple criteria (e.g., accuracy and efficiency) via multi-objective optimization. Empirical studies on ten benchmark datasets demonstrate that the representative instances (i.e., PasCa-V1, V2, and V3) discovered by our system achieve consistent performance among competitive baselines. Concretely, PasCa-V3 outperforms the state-of-the-art GNN method JK-Net by 0.4\% in terms of predictive accuracy on our large industry dataset while achieving up to $28.3\times$ training speedups.
A welding seam positioning method based on polarization 3D reconstruction and linear structured light imaging

Aiming at the current welding seam tracking problems such as susceptibility to arc light interference, low work efficiency, and poor real-time performance, a single-frame large-field welding seam positioning method combining polarization three-dimensional (3D) reconstruction and linear structured light imaging is proposed. Calculate the surface polarization state of the welding area through the polarization intensity images in four directions, use the degree of polarization and polarization angle to calculate the surface normal vector and integrate to obtain the relative depth value. Design a centerline extraction method based on skeleton extraction and Random Sample Consensus (RANSAC) to obtain the three-dimensional coordinates of the laser stripe. The three-dimensional coordinates of the laser fringe and the relative depth value of the polarization three-dimensional reconstruction are combined to calculate the real three-dimensional point cloud of the weld and the welded part. With this method, only a single frame of image data is acquired prior to welding and the coordinates of the 3D scene within the large field of view of the polarisation camera can be calculated, so there is no interference from arc light. For the weld within 100 mm, the 3D positioning accuracy is 0.2502 mm, which meets the requirements of weld positioning.
Security breaches and organization response strategy: Exploring consumers’ threat and coping appraisals

We address a long-standing lacuna in the Information Management literature on the relationships among security breaches, organization response strategy as well as consumers' threat and coping appraisal. Security breaches can involve the leak of sensitive data, and potentially lead to negative consumer reactions. It is, thus, timely and critical to theorize and empirically investigate the ways in which organization can respond effectively to security breaches and how consumers' threat and coping appraisals vary according to the different response strategies. Our study addresses this lacuna by developing a conceptual model of i) security breach, ii) organization response strategies, and iii) consumer appraisal, grounded on the risk theory and protection motivation theory. We use the principal and agent perspectives to portray the breached organization as the agent providing the coping strategy, and consumers as the principal actors who evaluate the strategy. We incorporated a vignette-based survey to test the model with empirical data. We identify that the variations in the response strategy of organization after a security breach can lead to significantly different consumers' reactions. We discuss the implications of our findings for theory and practice and delineate an agenda for future research.
Indicators to assess preservice teachers’ digital competence in security: A systematic review

The goal of this review is to analyse the state of inquiry in the field of digital competence in security in initial teacher education, via indicators to assess preservice teachers' digital competence in security, in order to help find opportunities to improve their competence level. Following the parameters defined in the PRISMA declaration, the review uses a bibliographic research methodology to explore the WoS, Scopus and ERIC databases. After a search identifying a sample of 31 scholarly articles published between 2010 and 2021, we analyse the information obtained using descriptive statistics and content analysis. The results show a predominance of empirical research in the European context. These studies are quantitative and tend to use questionnaires. Our conclusion proposes the need to train preservice teachers in data protection and privacy, searching for and using Internet images with authorship screening, use of open software programs, and respect for online communication norms, as well as ethical and responsible technology use. All of these issues are implicitly and transversally linked to the area of digital competence in security.
A methodology for an optimal design of ground-mounted photovoltaic power plants

A methodology for estimating the optimal distribution of photovoltaic modules with a fixed tilt angle in ground-mounted photovoltaic power plants has been described. It uses Geographic Information System, available in the public domain, to estimate Universal Transverse Mercator coordinates of the area which has been selected for the installation of the photovoltaic plant. An open-source geographic information system software, QGIS, has been used. The estimation of the solar irradiance takes into account the variations in the local cloud cover distribution. The optimization process is considered to maximize the amount of energy absorbed by the photovoltaic plant using a packing algorithm (in Mathematica™ software). This packing algorithm calculates the shading between photovoltaic modules. This methodology can be applied to any photovoltaic plant. Different rack configurations and tilt angles are incorporated in the study to account for the characteristics of the irregular shape of the land. The most used rack configurations in photovoltaic plants are the 2V×12 configuration (2 vertically modules in each row and 12 modules per row) and the 3V×8 configuration (3 vertically consecutive modules in each row and 8 modules per row). Codes and standards have been used for the structural analysis of these rack configurations. For this purpose, the wind loads, the snow loads, the weight of the structure, the weight of the photovoltaic modules, and combinations thereof have been calculated. This analysis has been performed with AutoDesk Robot Structural Analysis software for the different rack configurations. A detailed cost analysis of the most used rack configurations in photovoltaic plants has been presented. The levelized cost of the produced electricity efficiency is calculated for each rack configuration. The methodology has been applied in Sigena I photovoltaic plant located in Northeast of Spain. The current rack configuration used in this photovoltaic plant is the 2V×12 configuration with a tilt angle of 30 (°). The configurations 3V×8 configuration with a tilt angle of 14 (°) and 2V×12 configuration with a tilt angle of 22 (°) are the best options proposed by the optimization algorithm. The results show that the 3V×8 configuration with a tilt angle of 14 (°) increases the amount of energy captured by up to 32.45% in relation to the current configuration of Sigena I photovoltaic plant with a levelized cost of the produced electricity efficiency of 1.10. In the other hand, the 3V×8 configuration increases the amount of energy captured by up to 19.52% in relation to the 2V×12 configuration with a tilt angle of 22 (°) with a levelized cost of the produced electricity efficiency of 1.05. The 3V×8 configuration is the one which has the lowest cost for the same number of photovoltaic modules. The 2V×12 configuration with a tilt angle of 30 (°) increases the cost by up to 32.48% in relation to a 3V×8 configuration with a tilt angle of 14 (°).