Reviewerly/exHarmony huggingface dataset:
Hyperledger, Ethereum and Blockchain Technology: A Short Overview

Blockchain is a tamper-proof distributed ledger for tracking public or private pair transactions in pair networks that can not be retroactively changed without modifying all corresponding network blocks. The Consensus Protocol upgrades a blockchain, which guarantees a sequential, unambiguous transaction ordering. Blocks ensure that the blockchain is integral and uniform across a network of distributed nodes. Different blockchain implementations use different consensus protocols. This paper offers a brief overview of the most important discrepancies between the Hyperledger Fabric and Ethereum distributed ledger technologies (DLT).
Differentiable Patch Selection for Image Recognition

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand. We propose a method based on a differentiable Top-K operator to select the most relevant parts of the input to efficiently process high resolution images. Our method may be interfaced with any downstream neural network, is able to aggregate information from different patches in a flexible way, and allows the whole model to be trained end-to-end using backpropagation. We show results for traffic sign recognition, inter-patch relationship reasoning, and fine-grained recognition without using object/part bounding box annotations during training.
Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility, and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing. However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performance. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution, namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Furthermore, we highlight relevant directions for future research.
Training Robust Deep Neural Networks via Adversarial Noise Propagation

In practice, deep neural networks have been found to be vulnerable to various types of noise, such as adversarial examples and corruption. Various adversarial defense methods have accordingly been developed to improve adversarial robustness for deep models. However, simply training on data mixed with adversarial examples, most of these models still fail to defend against the generalized types of noise. Motivated by the fact that hidden layers play a highly important role in maintaining a robust model, this paper proposes a simple yet powerful training algorithm, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Adversarial Noise Propagation</i> (ANP), which injects noise into the hidden layers in a layer-wise manner. ANP can be implemented efficiently by exploiting the nature of the backward-forward training style. Through thorough investigations, we determine that different hidden layers make different contributions to model robustness and clean accuracy, while shallow layers are comparatively more critical than deep layers. Moreover, our framework can be easily combined with other adversarial training methods to further improve model robustness by exploiting the potential of hidden layers. Extensive experiments on MNIST, CIFAR-10, CIFAR-10-C, CIFAR-10-P, and ImageNet demonstrate that ANP enables the strong robustness for deep models against both adversarial and corrupted ones, and also significantly outperforms various adversarial defense methods.
Spatio-temporal Contrastive Domain Adaptation for Action Recognition

Compared with image-based UDA, video-based UDA is comprehensive to bridge the domain shift on both spatial representation and temporal dynamics. Most previous works focus on short-term modeling and alignment with frame-level or clip-level features, which is not discriminative sufficiently for video-based UDA tasks. To address these problems, in this paper we propose to establish the cross-modal domain alignment via self-supervised contrastive framework, i.e., spatio-temporal contrastive domain adaptation (STCDA), to learn the joint clip-level and video-level representation alignment. Since the effective representation is modeled from unlabeled data by self-supervised learning (SSL), spatio-temporal contrastive learning (STCL) is proposed to explore the useful long-term feature representation for classification, using self-supervision setting trained from the contrastive clip/video pairs with positive or negative properties. Besides, we involve a novel domain metric scheme, i.e., video-based contrastive alignment (VCA), to optimize the category-aware video-level alignment and generalization between source and target. The proposed STCDA achieves stat-of-the-art results on several UDA benchmarks for action recognition.
ANR: Articulated Neural Rendering for Virtual Avatars

The combination of traditional rendering with neural networks in Deferred Neural Rendering (DNR) [38] provides a compelling balance between computational complexity and realism of the resulting images. Using skinned meshes for rendering articulating objects is a natural extension for the DNR framework and would open it up to a plethora of applications. However, in this case the neural shading step must account for deformations that are possibly not captured in the mesh, as well as alignment in-accuracies and dynamics—which can confound the DNR pipeline. We present Articulated Neural Rendering (ANR), a novel framework based on DNR which explicitly addresses its limitations for virtual human avatars. We show the superiority of ANR not only with respect to DNR but also with methods specialized for avatar creation and animation. In two user studies, we observe a clear preference for our avatar model and we demonstrate state-of-the-art performance on quantitative evaluation metrics. Perceptually, we observe better temporal stability, level of detail and plausibility. More results are available at our project page: https://anr-avatars.github.io.
Enabling Cross-Chain Transactions: A Decentralized Cryptocurrency Exchange Protocol

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve secure trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on an Ethereum blockchain and deployed on an Ethereum test network. In addition to implementing transactions between individual users, our scheme also allows transactions among multiple users. The experimental result proves that the cost of our scheme is acceptable.
PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important roles in scene flow estimation. To tackle this problem, we present point-voxel correlation fields, which capture both local and long-range dependencies of point pairs. To capture point-based correlations, we adopt the K-Nearest Neighbors search that preserves fine-grained information in the local region. By voxelizing point clouds in a multi-scale manner, we construct pyramid correlation voxels to model long-range correspondences. Integrating these two types of correlations, our PV-RAFT makes use of all-pairs relations to handle both small and large displacements. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Experimental results show that PV-RAFT outperforms state-of-the-art methods by remarkable margins.
Research and Applied Perspective to Blockchain Technology: A Comprehensive Survey

Blockchain being a leading technology in the 21st century is revolutionizing each sector of life. Services are being provided and upgraded using its salient features and fruitful characteristics. Businesses are being enhanced by using this technology. Countries are shifting towards digital currencies i.e., an initial application of blockchain application. It omits the need of central authority by its distributed ledger functionality. This distributed ledger is achieved by using a consensus mechanism in blockchain. A consensus algorithm plays a core role in the implementation of blockchain. Any application implementing blockchain uses consensus algorithms to achieve its desired task. In this paper, we focus on provisioning of a comparative analysis of blockchain’s consensus algorithms with respect to the type of application. Furthermore, we discuss the development platforms as well as technologies of blockchain. The aim of the paper is to provide knowledge from basic to extensive from blockchain architecture to consensus methods, from applications to development platform, from challenges and issues to blockchain research gaps in various areas.
Spatial-Temporal Correlation and Topology Learning for Person Re-Identification in Videos

Video-based person re-identification aims to match pedestrians from video sequences across non-overlapping camera views. The key factor for video person re-identification is to effectively exploit both spatial and temporal clues from video sequences. In this work, we propose a novel Spatial-Temporal Correlation and Topology Learning framework (CTL) to pursue discriminative and robust representation by modeling cross-scale spatial-temporal correlation. Specifically, CTL utilizes a CNN backbone and a key-points estimator to extract semantic local features from human body at multiple granularities as graph nodes. It explores a context-reinforced topology to construct multi-scale graphs by considering both global contextual information and physical connections of human body. Moreover, a 3D graph convolution and a cross-scale graph convolution are designed, which facilitate direct cross-spacetime and cross-scale information propagation for capturing hierarchical spatial-temporal dependencies and structural information. By jointly performing the two convolutions, CTL effectively mines comprehensive clues that are complementary with appearance information to enhance representational capacity. Extensive experiments on two video benchmarks have demonstrated the effectiveness of the proposed method and the state-of-the-art performance.
Are Labels Always Necessary for Classifier Accuracy Evaluation?

To calculate the model accuracy on a computer vision task, e.g., object recognition, we usually require a test set composing of test samples and their ground truth labels. Whilst standard usage cases satisfy this requirement, many real-world scenarios involve unlabeled test data, rendering common model evaluation methods infeasible. We investigate this important and under-explored problem, Automatic model Evaluation (AutoEval). Specifically, given a labeled training set and a classifier, we aim to estimate the classification accuracy on unlabeled test datasets. We construct a meta-dataset: a dataset comprised of datasets generated from the original images via various transformations such as rotation, background substitution, foreground scaling, etc. As the classification accuracy of the model on each sample (dataset) is known from the original dataset labels, our task can be solved via regression. Using the feature statistics to represent the distribution of a sample dataset, we can train regression models (e.g., a regression neural network) to predict model performance. Using synthetic meta-dataset and real-world datasets in training and testing, respectively, we report a reasonable and promising prediction of the model accuracy. We also provide insights into the application scope, limitation, and potential future direction of AutoEval.
Three Ways to Improve Semantic Segmentation with Self-Supervised Depth Estimation

Training deep networks for semantic segmentation requires large amounts of labeled training data, which presents a major challenge in practice, as labeling segmentation masks is a highly labor-intensive process. To address this issue, we present a framework for semi-supervised semantic segmentation, which is enhanced by self-supervised monocular depth estimation from unlabeled image sequences. In particular, we propose three key contributions: (1) We transfer knowledge from features learned during self-supervised depth estimation to semantic segmentation, (2) we implement a strong data augmentation by blending images and labels using the geometry of the scene, and (3) we utilize the depth feature diversity as well as the level of difficulty of learning depth in a student-teacher framework to select the most useful samples to be annotated for semantic segmentation. We validate the proposed model on the Cityscapes dataset, where all three modules demonstrate significant performance gains, and we achieve state-of-the-art results for semi-supervised semantic segmentation. The implementation is available at https://github.com/lhoyer/improving_segmentation_with_selfsupervised_depth.
Extreme learning machine for reference crop evapotranspiration estimation: Model optimization and spatiotemporal assessment across different climates in China

Accurate estimation of reference crop evapotranspiration (ETo) is critical for quantifying crop water requirements and irrigation schedule design. This study proposed two optimization methods, i.e., particle swarm optimization (PSO) and genetic algorithm (GA), to determine the input weights and hidden biases of extreme learning machine (ELM), and developed two novel hybrid GA-ELM and PSO-ELM models for ETo estimations with limited input data. A temporal and spatial scanning strategy that can avoid misleading or partially valid results was applied to train and test the models, using daily climatic data during 1994–2016 from 96 meteorological stations across various climates of China. The results revealed that GA-ELM and PSO-ELM could quantify ETo on daily, monthly, and annual time scales, with GA-ELM (with model efficiency ranging from 0.80 to 0.97) performing better than PSO-ELM (with model efficiency ranging from 0.70 to 0.93) in all climates. The temperature-based GA-ELM only with air temperature data as forcing input has relatively accurate ETo estimates across different environments, and can be recommended as an alternative method when radiation data are not available. Spatial assessment reveals that the machine learning models trained by external data also offer accurate ETo estimates, confirming the applicability of models for ETo estimations elsewhere without the model training or when local data for model training are unavailable. Overall, the GA-ELM model provides accurate ETo estimates on various time scales and exhibits superior performance than the PSO-ELM model, and thus can be recommended for ETo estimations using limited data as model forcing in different climates of China. Our work presents a novel method for accurate ETo estimations with limited data, which has practical implications in regional agricultural water management.
IEPSBP: A Cost-Efficient Image Encryption Algorithm Based on Parallel Chaotic System for Green IoT

With the fast development of the Internet of Things (IoT) technologies, more IoT devices are currently connected with the Internet, resulting in more exchange of information. However, data privacy and security threats have become emerging challenges of the IoT. In this paper, we are concerned about the security of image transmission in green IoT. Image encryption algorithms for green IoT are faced with two challenges: 1) To save the cost, devices in green Internet of Things (IoT) always have very low computing power, so they cannot support high-precision computing and 2) The algorithm deployed on the device should be efficient to guarantee the device to run for a long time. To solve the problem of precision limited, we propose a parallel chaotic system named PSBP. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup>1</sup></xref> The PSBP is composed of Piecewise Linear Chaotic Map (PWLCM), Skew Tent Map (STM) and Bernoulli map in parallel model with the limited precision of 16-bit, which can generate good-performance key matrix for encryption. Then, we introduce a cost-efficient image encryption algorithm based on PSBP (named IEPSBP) which addresses the second challenge. Instead of bits or bytes, we use one row or one column as the basic unit to realize permutation and diffusion. The security analysis and performance experiments indicate that IEPSBP is secure and cost-efficient for green IoT.
<scp>3D</scp> reconstruction based on photoelastic fringes

Summary A three‐dimensional (3D) reconstruction method of structured light based on photoelastic fringes was proposed in this research. The photoelastic fringes are produced by both simulation and a polycarbonate disk under diametric compression load. Six fringes are projected onto an object by using the six‐step phase shifting technique. Therefore, the isochromatic phase image is calculated. After phase unwrapping, the isochromatic phase image can be used for 3D reconstruction. In order to verify the effectiveness of this method, two experiment devices were built by using projector and photoelastic instrument, respectively. The results show that the fringe pattern based on photoelasticity can be used for 3D reconstruction as a structured light pattern. Compared to the simulation results, the fringes produced by load are more blurred. In order to obtain a better reconstruction result, a large load should be applied to produce dense fringes.
Machine Learning Prediction of Quartz Forming‐Environments

Abstract Trace elements of quartz document the physical‐chemical evolutions of quartz growth, which has been a great and most applied tool in the study of geological settings in quartz‐forming environments. A classic method is using graphic diagram plots visualizing the quartz trace element discriminations and trends, examples including the Al‐Ti diagram (Rusk, 2012, https://doi.org/10.1007/978-3-642-22161-3_14 ) and the Ti‐Al‐Ge diagram (Schrön et al., 1988, https://www.researchgate.net/publication/236149159_Geochemische_Untersuchungen_an_Pegmatitquarzen ). However, those diagrams are limited to two dimensions and cannot show the information in a higher dimension. In the study, we thus used a machine learning‐based approach to evaluate quartz trace elements, and visualized them for the first time in the high‐dimensional diagrams. We revisited 1,626 quartz samples from nine geological environments from previous studies, and applied a support vector machine to characterize values of the contained trace elements, including Al, Ti, Li, Ge, and Sr. We demonstrate that support vector machines can identify the crystallization environment of quartz with a significantly higher accuracy than the traditional plotting methods. Our work can massively improve the confidence on distinguishing quartz origin from different geological environments with a high efficiency. The method may also be applicable for other minerals, and we anticipate our research is a starting point for investigating mineral trace elements with machine learning techniques. Our quartz classifier can be accessed via https://quartz-classifier.herokuapp.com .
Face Forgery Detection by 3D Decomposition

Detecting digital face manipulation has attracted extensive attention due to fake media’s potential harms to the public. However, recent advances have been able to reduce the forgery signals to a low magnitude. Decomposition, which reversibly decomposes an image into several constituent elements, is a promising way to highlight the hidden forgery details. In this paper, we consider a face image as the production of the intervention of the underlying 3D geometry and the lighting environment, and decompose it in a computer graphics view. Specifically, by disentangling the face image into 3D shape, common texture, identity texture, ambient light, and direct light, we find the devil lies in the direct light and the identity texture. Based on this observation, we propose to utilize facial detail, which is the combination of direct light and identity texture, as the clue to detect the subtle forgery patterns. Besides, we highlight the manipulated region with a supervised attention mechanism and introduce a two-stream structure to exploit both face image and facial detail together as a multi-modality task. Extensive experiments indicate the effectiveness of the extra features extracted from the facial detail, and our method achieves the state-of-the-art performance.
A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images

Ship detection is a challenging task for synthetic aperture radar (SAR) images. Ships have arbitrary directionality and multiple scales in SAR images. Furthermore, there is a lot of clutter near the ships. Traditional detection algorithms are not robust to these situations and easily cause redundancy in the detection area. With the continuous improvement in resolution, the traditional algorithms cannot achieve high-precision ship detection in SAR images. An increasing number of deep learning algorithms have been applied to SAR ship detection. In this study, a new ship detection network, known as the instance segmentation assisted ship detection network (ISASDNet), is presented. ISASDNet is a two-stage detection network with two branches. A branch is called an object branch and can extract object-level information to obtain positioning bounding boxes and classification results. Another branch called the pixel branch can be utilized for instance segmentation. In the pixel branch, the designed global relational inference layer maps the features to interaction space to learn the relationship between ship and background. The global reasoning module (GRM) based on global relational inference layers can better extract the instance segmentation results of ships. A mask assisted ship detection module (MASDM) is behind the two branches. The MASDM can improve detection results by interacting with the outputs of the two branches. In addition, a strategy is designed to extract the mask of SAR ships, which enables ISASDNet to perform object detection training and instance segmentation training at the same time. Experiments carried out two different datasets demonstrated the superiority of ISASDNet over other networks.