Reviewerly/exHarmony huggingface dataset:
A Fast Registration Method for Optical and SAR Images Based on SRAWG Feature Description

Due to differences in synthetic aperture radar (SAR) and optical imaging modes, there is a considerable degree of nonlinear intensity difference (NID) and geometric difference between the two images. The SAR image is also accompanied by strong multiplicative speckle noise. These phenomena lead to what is known as a challenging task to register optical and SAR images. With the development of remote sensing technology, both optical and SAR images equipped with sensor positioning parameters can be roughly registered according to geographic coordinates in advance. However, due to the inaccuracy of sensor parameters, the relative positioning accuracy is still as high as tens or even hundreds of pixels. This paper proposes a fast co-registration method including 3D dense feature description based on a single-scale Sobel and the ratio of exponentially weighted averages (ROEWA) combined with the angle-weighted gradient (SRAWG), overlapping template merging, and non-maxima suppressed template search. In order to more accurately describe the structural features of the image, the single-scale Sobel and ROEWA operators are used to calculate the gradients of optical and SAR images, respectively. On this basis, the 3 × 3 neighborhood angle-weighted gradients of each pixel are fused to form a pixel-wise 3D dense feature description. Aiming at the repeated feature description in the overlapping template and the multi-peak problem on the search surface, this paper adopts the template search strategy of overlapping template merging and non-maximum suppression. The registration results obtained on seven pairs of test images show that the proposed method has significant advantages over state-of-the-art methods in terms of comprehensive registration accuracy and efficiency.
Multi-Channel Attention Selection GANs for Guided Image-to-Image Translation

We propose a novel model named Multi-Channel Attention Selection Generative Adversarial Network (SelectionGAN) for guided image-to-image translation, where we translate an input image into another while respecting an external semantic guidance. The proposed SelectionGAN explicitly utilizes the semantic guidance information and consists of two stages. In the first stage, the input image and the conditional semantic guidance are fed into a cycled semantic-guided generation network to produce initial coarse results. In the second stage, we refine the initial results by using the proposed multi-scale spatial pooling & channel selection module and the multi-channel attention selection module. Moreover, uncertainty maps automatically learned from attention maps are used to guide the pixel loss for better network optimization. Exhaustive experiments on four challenging guided image-to-image translation tasks (face, hand, body, and street view) demonstrate that our SelectionGAN is able to generate significantly better results than the state-of-the-art methods. Meanwhile, the proposed framework and modules are unified solutions and can be applied to solve other generation tasks such as semantic image synthesis. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Ha0Tang/SelectionGAN</uri> .
CSTGAN: Cycle Swin Transformer GAN for Unpaired Infrared Image Colorization

Infrared images can be captured in harsh conditions such as low light and foggy weather, which provides an effective solution for image capture throughout the day. However, the low contrast and blurred object boundaries of infrared images hinder human interpretation and the application of computer vision algorithms. Colorizing infrared images is a significant and effective method to promote infrared image understanding. Image-to-image translation methods based on generative adversarial networks are currently the main methods for colorizing infrared images. The generative adversarial network designed by Transformer overcomes the disadvantage of weak global information capture ability caused by the convolutional generative adversarial network product focusing on local features. This paper proposed a new method called Cycle Swin Transformer Generative Adversarial Networks (CSTGAN) based on Cycle-Consistent Generative Adversarial Networks. The proposed method redesigns the generator with Swin Transformer and convolution layers, and modified the discriminator and loss function. The proposed method combines the advantages of convolution and Transformer to obtain accurate mapping between infrared image domain and visible light image domain, which reduces the artifacts and distortions caused by the existing infrared image colorization methods. In addition, we collected and produced a near-infrared image colorization dataset named NIR2RGB. Extensive experimental results show that the proposed method outperforms the previous methods on the FID and KID metrics on the public datasets RGB-NIR Scene and MFNet as well as produced NIR2RGB.
Bayesian Learning of Graph Substructures

Graphical models provide a powerful methodology for learning the conditional independence structure in multivariate data. Inference is often focused on estimating individual edges in the latent graph. Nonetheless, there is increasing interest in inferring more complex structures, such as communities, for multiple reasons, including more effective information retrieval and better interpretability. Stochastic blockmodels offer a powerful tool to detect such structure in a network. We thus propose to exploit advances in random graph theory and embed them within the graphical models framework. A consequence of this approach is the propagation of the uncertainty in graph estimation to large-scale structure learning. We consider Bayesian nonparametric stochastic blockmodels as priors on the graph. We extend such models to consider clique-based blocks and to multiple graph settings introducing a novel prior process based on a Dependent Dirichlet process. Moreover, we devise a tailored computation strategy of Bayes factors for block structure based on the Savage-Dickey ratio to test for presence of larger structure in a graph. We demonstrate our approach in simulations as well as on real data applications in finance and transcriptomics.
Building Natural Language Processing Applications with EasyNLP

The successful application of Pre-Trained Models (PTMs) has revolutionized the development of Natural Language Processing (NLP) by large-scale self-supervised pre-training. However, it is not easy to obtain high-performing models in domain-specific applications and deploy them online with strict QPS (Query Per Second) requirements for industrial practitioners. To solve these issues, the EasyNLP toolkit is designed for building PTM-based NLP applications with ease, which supports a comprehensive suite of NLP algorithms and is suitable for meeting the inference requirements in industry. It features knowledge-enhanced pre-training that captures rich domain knowledge to better support domain-specific applications. In addition, the knowledge distillation and prompt-based few-shot learning functionalities are provided to improve the performance of large-scale PTMs with little training data available, and to distill models to smaller ones that are suitable for online deployment. EasyNLP provides a unified framework of model training, inference and deployment for real-world applications, using simple high-level APIs or command-line tools. Currently, EasyNLP has powered over ten business units within Alibaba Group and is seamlessly integrated to the Platform of AI (PAI) products on Alibaba Cloud. EasyNLP is also beneficial for academia, as it integrates state-of-the-art methods and models to make it easy for researchers to benchmark and develop their own algorithms. We have released EasyNLP to public at GitHub (https://github.com/alibaba/EasyNLP).
CFS-MTL: A Causal Feature Selection Mechanism for Multi-task Learning via Pseudo-intervention

Multi-task learning (MTL) has been successfully applied to a wide range of real-world applications. However, MTL models often suffer from performance degradation with negative transfer due to sharing all features without distinguishing their helpfulness for all tasks. To this end, many works on feature selection for multi-task learning (FS-MTL) have been proposed to alleviate negative transfer between tasks by learning features selectively for each specific task. However, due to latent confounders between features and task targets, the correlations captured by the feature selection modules proposed in these works may fail to reflect the actual effect of the features on the targets. This paper explains negative transfer in FS-MTL from a causal perspective and presents a novel architecture called Causal Feature Selection for Multi-task Learning(CFS-MTL). This method incorporates the idea of causal inference into feature selection for multi-task learning via pseudo-intervention. It aims to select features with more stable causal effects rather than spurious correlations for each task by regularizing the distance between feature ITEs and feature importance. We conduct extensive experiments based on three real-world datasets to demonstrate that our proposed CFS-MTL outperforms state-of-the-art MTL models significantly in the AUC metric.
Design of Distributed Database System Based on Improved DES Algorithm

With the development of information technology, distributed storage, as an advanced and efficient data processing technology, plays an important role in information management system.This paper designs a visualization system based on DES algorithm.This system manages and allocates resources to users.First, analyze the requirements from the database, and then classify them into users, administrators and other roles according to the actual situation and establish the corresponding structure to realize the control of role permissions.Finally, through the establishment of the corresponding model, the function of the distributed database system is improved, and the security of the system is tested through the simulation experiment.The experimental results show that the distributed database system based on the improved DES algorithm can read the requested data within 10s, and the success rate of intercepting and decrypting operations is more than 90%.This shows that the algorithm performs well in the security of the protection system.
A High-Capacity Steganography Algorithm Based on Adaptive Frequency Channel Attention Networks

Deep learning has become an essential technique in image steganography. Most of the current deep-learning-based steganographic methods process digital images in the spatial domain. There are problems such as limited embedding capacity and unsatisfactory visual quality. To improve capacity-distortion performance, we develop a steganographic method from the frequency-domain perspective. We propose a module called the adaptive frequency-domain channel attention network (AFcaNet), which makes full use of the frequency features in each channel by a fine-grained manner of assigning weights. We apply this module to the state-of-the-art SteganoGAN, forming an Adaptive Frequency High-capacity Steganography Generative Adversarial Network (AFHS-GAN). The proposed neural network enhances the ability of high-dimensional feature extraction through overlaying densely connected convolutional blocks. In addition to this, a low-frequency loss function is introduced as an evaluation metric to guide the training of the network and thus reduces the modification of low-frequency regions of the image. Experimental results on the Div2K dataset show that our method has a better generalization capability compared to the SteganoGAN, with substantial improvement in both embedding capacity and stego-image quality. Furthermore, the embedding distribution of our method in the DCT domain is more similar to that of the traditional method, which is consistent with the prior knowledge of image steganography.
HSI: Human Saliency Imitator for Benchmarking Saliency-Based Model Explanations

Model explanations are generated by XAI (explainable AI) methods to help people understand and interpret machine learning models. To study XAI methods from the human perspective, we propose a human-based benchmark dataset, i.e., human saliency benchmark (HSB), for evaluating saliency-based XAI methods. Different from existing human saliency annotations where class-related features are manually and subjectively labeled, this benchmark collects more objective human attention on vision information with a precise eye-tracking device and a novel crowdsourcing experiment. Taking the labor cost of human experiment into consideration, we further explore the potential of utilizing a prediction model trained on HSB to mimic saliency annotating by humans. Hence, a dense prediction problem is formulated, and we propose an encoder-decoder architecture which combines multi-modal and multi-scale features to produce the human saliency maps. Accordingly, a pretraining-finetuning method is designed to address the model training problem. Finally, we arrive at a model trained on HSB named human saliency imitator (HSI). We show, through an extensive evaluation, that HSI can successfully predict human saliency on our HSB dataset, and the HSI-generated human saliency dataset on ImageNet showcases the ability of benchmarking XAI methods both qualitatively and quantitatively.
GoTreeScape: Navigate and Explore the Tree Visualization Design Space

Declarative grammar is becoming an increasingly important technique for understanding visualization design spaces. The GoTreeScape system presented in the paper allows users to navigate and explore the vast design space implied by GoTree, a declarative grammar for visualizing tree structures. To provide an overview of the design space, GoTreeScape, which is based on an encoder-decoder architecture, projects the tree visualizations onto a 2D landscape. Significantly, this landscape takes the relationships between different design features into account. GoTreeScape also includes an exploratory framework that allows top-down, bottom-up, and hybrid modes of exploration to support the inherently undirected nature of exploratory searches. Two case studies demonstrate the diversity with which GoTreeScape expands the universe of designed tree visualizations for users. The source code associated with GoTreeScape is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/bitvis2021/gotreescape.</uri>
Model NOx, SO2 Emissions Concentration and Thermal Efficiency of CFBB Based on a Hyper-Parameter Self-Optimized Broad Learning System

At present, establishing a multidimensional characteristic model of a boiler combustion system plays an important role in realizing its dynamic optimization and real-time control, so as to achieve the purpose of reducing environmental pollution and saving coal resources. However, the complexity of the boiler combustion process makes it difficult to model it using traditional mathematical methods. In this paper, a kind of hyper-parameter self-optimized broad learning system by a sparrow search algorithm is proposed to model the NOx, SO2 emissions concentration and thermal efficiency of a circulation fluidized bed boiler (CFBB). A broad learning system (BLS) is a novel neural network algorithm, which shows good performance in multidimensional feature learning. However, the BLS has several hyper-parameters to be set in a wide range, so that the optimal combination between hyper-parameters is difficult to determine. This paper uses a sparrow search algorithm (SSA) to select the optimal hyper-parameters combination of the broad learning system, namely as SSA-BLS. To verify the effectiveness of SSA-BLS, ten benchmark regression datasets are applied. Experimental results show that SSA-BLS obtains good regression accuracy and model stability. Additionally, the proposed SSA-BLS is applied to model the combustion process parameters of a 330 MW circulating fluidized bed boiler. Experimental results reveal that SSA-BLS can establish the accurate prediction models for thermal efficiency, NOx emission concentration and SO2 emission concentration, separately. Altogether, SSA-BLS is an effective modelling method.
Real-Time Vibration Visualization Using GPU-Based High-Speed Vision

In this study, we developed a real-time vibration visualization system that can estimate and display vibration distributions at all frequencies in real time through parallel implementation of subpixel digital image correlation (DIC) computations with short-time Fourier transforms on a GPU-based high-speed vision platform. To help operators intuitively monitor high-speed motion, we introduced a two-step framework of high-speed video processing to obtain vibration distributions at hundreds of hertz and video conversion processing for the visualization of vibration distribution at dozens of hertz. The proposed system can estimate the full-field vibration displacements of 1920 × 1080 images in real time at 1000 fps and display their frequency responses in the range of 0–500 Hz on a computer at dozens of frames per second by accelerating phase-only DICs for full-field displacement measurement and video conversion. The effectiveness of this system for real-time vibration monitoring and visualization was demonstrated by conducting experiments on objects vibrating at dozens or hundreds of hertz.
Super resolution reconstruction of digital core image based on transfer learning

The resolution of a digital core image obtained by CT scanning is inversely proportional to the core size. The sample size of the core plunger is large; therefore, the core image has low-resolution and loses microstructure information, which is difficult to meet the needs of follow-up research on core pores, fractures, rock skeleton and so on. By selecting a suitable position on the plunger sample, drilling one or more small-size subsamples for scanning to obtain the high-resolution image, the plunger core subsample image has both high-resolution and low-resolution. Due to the limited core image data of small-size plug samples, it cannot meet the demand for the number of training samples in super-resolution reconstruction based on depth learning. To solve the above problems, this paper first uses the low-resolution plunger sample image and its down sampled image to form the training data set, and then uses the SRResNet algorithm for model pretraining. Secondly, all the feature extraction layer parameters in the pre-trained model are frozen, and the model is retrained with the measured high-resolution plunger sample images and the corresponding low-resolution image in the plunger samples. The experimental results show that after 100 epochs of training, the loss function value of the SRResNet algorithm based on transfer learning is close to 0, and the PSNR value reaches 34. And the algorithm converges very fast, after about 30 epochs of training, the PSNR value is close to 34. Based on the transfer learning and SRResNet algorithm, the details such as small pores and textures that cannot be clearly displayed in the original image can be reconstructed, which can effectively realize the super-resolution reconstruction of digital core images.
<i>PHISHWEB</i>

We propose PHISHWEB, a novel approach to website phishing detection, which detects and categorizes malicious websites through a progressive, multi-layered analysis. PHISHWEB combines and extends different detection approaches proposed in the literature, adding robustness to the identification and visibility into the particular type of deception technique employed by the attacker. We present preliminary results on the application of PHISHWEB to multiple open domain-name datasets, showing precision and recall results above 90% for the specific case of lexicographic-based analysis, improving state-of-the-art detection by more than 60% for Domain Generated Algorithms-driven attacks.
Research Mapping in the Use of Technology for Fake News Detection: Bibliometric Analysis from 2011 to 2021

This study aims to (1) determine research developments, (2) produce research distribution maps based on co-authorship analysis, (3) produce research distribution maps based on citation analysis, (4) produce research distribution maps based on co-citation analysis, (5) produce a research distribution map based on co-occurrence analysis, and (6) find out the state-of-the-art research in the use of technology for fake news detection. This study uses a quantitative method with a descriptive approach in bibliometric analysis from 2011 to 2021. From the research results obtained (1) the development of research publications in the field of the use of technology for fake news detection shows an increase, (2) the interrelationships between countries of authors, organizations affiliated with the authors, and among authors, (3) distribution map of citation analysis based on units of countries, sources, and authors, (4) distribution map of co-citation analysis based on unit cited sources and cited authors, (5) connection of keyword in the use of technology for fake news detection publications is related to literacy skills such as media literacy and information literacy, and (6) several research trends that researchers have widely used in the last 2 years including COVID-19, media literacy, and cyber deception. The research results can be used to determine the development of research o in the use of technology for fake news detection and as a reference for developing further research in the use of technology for fake news detection.
Algebraic Persistent Fault Analysis of SKINNY_64 Based on S_Box Decomposition

Algebraic persistent fault analysis (APFA), which combines algebraic analysis with persistent fault attacks, brings new challenges to the security of lightweight block ciphers and has received widespread attention since its introduction. Threshold Implementation (TI) is one of the most widely used countermeasures for side channel attacks. Inspired by this method, the SKINNY block cipher adopts the S_box decomposition to reduce the number of variables in the set of algebraic equations and the number of Conjunctive Normal Form (CNF) equations in this paper, thus speeding up the algebraic persistent fault analysis and reducing the number of fault ciphertexts. In our study, we firstly establish algebraic equations for full-round faulty encryption, and then analyze the relationship between the number of fault ciphertexts required and the solving time in different scenarios (decomposed S_boxes and original S_box). By comparing the two sets of experimental results, the success rate and the efficiency of the attack are greatly improved by using S_box decomposition. In this paper, We can recover the master key in a minimum of 2000s using 11 pairs of plaintext and fault ciphertext, while the key recovery cannot be done in effective time using the original S_box expression equations. At the same time, we apply S_box decomposition to another kind of algebraic persistent fault analysis, and the experimental results show that using S_box decomposition can effectively reduce the solving time and solving success rate under the same conditions.
How Quantum Computing Can Tackle Climate and Energy Challenges

The day is coming when quantum computers, once the stuff of science fiction, will help scientists solve complex, real-world problems that are proving intractable to classical computing.