Reviewerly/exHarmony huggingface dataset:
MHGCN: Multiview highway graph convolutional network for cross-lingual entity alignment

Knowledge graphs (KGs) provide a wealth of prior knowledge for the research on social networks. Cross-lingual entity alignment aims at integrating complementary KGs from different languages and thus benefits various knowledge-driven social network studies. Recent entity alignment methods often take an embedding-based approach to model the entity and relation embedding of KGs. However, these studies mostly focus on the information of the entity itself and its structural features but ignore the influence of multiple types of data in KGs. In this paper, we propose a new embedding-based framework named multiview highway graph convolutional network (MHGCN), which considers the entity alignment from the views of entity semantic, relation semantic, and entity attribute. To learn the structural features of an entity, the MHGCN employs a highway graph convolutional network (GCN) for entity embedding in each view. In addition, the MHGCN weights and fuses the multiple views according to the importance of the embedding from each view to obtain a better entity embedding. The alignment entities are identified based on the similarity of entity embeddings. The experimental results show that the MHGCN consistently outperforms the state-of-the-art alignment methods. The research also will benefit knowledge fusion through cross-lingual KG entity alignment.
Disease-Specific Imaging Utilizing Support Vector Machine Classification of H-Scan Parameters: Assessment of Steatosis in a Rat Model

In medical imaging, quantitative measurements have shown promise in identifying diseases by classifying normal versus pathological parameters from tissues. The support vector machine (SVM) has shown promise as a supervised classification algorithm and has been widely used. However, the classification results typically identify a category of abnormal tissues but do not necessarily differentiate progressive stages of a disease. Moreover, the classification result is typically provided independently as a supplement to medical images, which contributes to an overload of information sources in the clinic. Hence, we propose a new imaging method utilizing the SVM to integrate classification results into medical images. This framework is called disease-specific imaging (DSI) that produces a color overlaid highlight on B-mode ultrasound images indicating the type, location, and severity of pathology from different conditions. In this article, the SVM training was performed to construct hyperplanes that can differentiate normal, fibrosis, steatosis, and pancreatic ductal adenocarcinoma (PDAC) metastases in livers based on ultrasound echoes. Also, cluster centroids for specific diseases define unique disease axes, and the inner product between measured features and any disease axis selected by the SVM quantifies the disease progression. The features were measured from 2794 ultrasound frames using the H-scan analysis, attenuation estimation, and B-mode image analysis. The performance of our proposed DSI method was evaluated for a preclinical model of steatosis (<inline-formula> <tex-math notation="LaTeX">${n} =400$ </tex-math></inline-formula> frames). The contribution of each feature was assessed, and the results were compared with ground truth from histology. Moreover, the images generated by our DSI were compared with earlier imaging methods of B-mode, H-scan, and histology. The comparisons demonstrate that DSI images yield higher sensitivity to monitor progressive steatosis than B-mode and H-scan and provide a comparable performance with the histology. For the parameter comparison, DSI and H-scan resulted in similar correlation with histology (<inline-formula> <tex-math notation="LaTeX">${r}_{s} =0.83$ </tex-math></inline-formula>) but higher than attenuation (<inline-formula> <tex-math notation="LaTeX">${r}_{s} =0.73$ </tex-math></inline-formula>) and B-mode (<inline-formula> <tex-math notation="LaTeX">${r}_{s} =0.47$ </tex-math></inline-formula>). Therefore, we conclude that DSI utilizing the SVM applied to steatosis can visually represent the classification results with color highlighting, which can simplify the interpretation of classification compared to the traditional SVM result. We expect that the proposed DSI can be used for any medical imaging modality that can estimate multiple quantitative parameters at high resolution.
3MRS: An Effective Coarse-to-Fine Matching Method for Multimodal Remote Sensing Imagery

The fusion of image data from multiple sensors is crucial for many applications. However, there are significant nonlinear intensity deformations between images from different kinds of sensors, leading to matching failure. To address this need, this paper proposes an effective coarse-to-fine matching method for multimodal remote sensing images (3MRS). In the coarse matching stage, feature points are first detected on a maximum moment map calculated with a phase congruency model. Then, feature description is conducted using an index map constructed by finding the index of the maximum value in all orientations of convolved images obtained using a set of log-Gabor filters. At last, several matches are built through image matching and outlier removal, which can be used to estimate a reliable affine transformation model between the images. In the stage of fine matching, we develop a novel template matching method based on the log-Gabor convolution image sequence and match the template features with a 3D phase correlation matching strategy, given that the initial correspondences are achieved with the estimated transformation. Results show that compared with SIFT, and three state-of-the-art methods designed for multimodal image matching, PSO-SIFT, HAPCG, and RIFT, only 3MRS successfully matched all six types of multimodal remote sensing image pairs: optical–optical, optical–infrared, optical–depth, optical–map, optical–SAR, and day–night, with each including ten different image pairs. On average, the number of correct matches (NCM) of 3MRS was 164.47, 123.91, 4.88, and 4.33 times that of SIFT, PSO-SIFT, HAPCG, and RIFT for the successfully matched image pairs of each method. In terms of accuracy, the root-mean-square error of correct matches for 3MRS, SIFT, PSO-SIFT, HAPCG, and RIFT are 1.47, 1.98, 1.79, 2.83, and 2.45 pixels, respectively, revealing that 3MRS got the highest accuracy. Even though the total running time of 3MRS was the longest, the efficiency for obtaining one correct match is the highest considering the most significant number of matches. The source code of 3MRS and the experimental datasets and detailed results are publicly available.
Underwater Image Enhancement Using Improved CNN Based Defogging

Due to refraction, absorption, and scattering of light by suspended particles in water, underwater images are characterized by low contrast, blurred details, and color distortion. In this paper, a fusion algorithm to restore and enhance underwater images is proposed. It consists of a color restoration module, an end-to-end defogging module and a brightness equalization module. In the color restoration module, a color balance algorithm based on CIE Lab color model is proposed to alleviate the effect of color deviation in underwater images. In the end-to-end defogging module, one end is the input image and the other end is the output image. A CNN network is proposed to connect these two ends and to improve the contrast of the underwater images. In the CNN network, a sub-network is used to reduce the depth of the network that needs to be designed to obtain the same features. Several depth separable convolutions are used to reduce the amount of calculation parameters required during network training. The basic attention module is introduced to highlight some important areas in the image. In order to improve the defogging network’s ability to extract overall information, a cross-layer connection and pooling pyramid module are added. In the brightness equalization module, a contrast limited adaptive histogram equalization method is used to coordinate the overall brightness. The proposed fusion algorithm for underwater image restoration and enhancement is verified by experiments and comparison with previous deep learning models and traditional methods. Comparison results show that the color correction and detail enhancement by the proposed method are superior.
Feature Matching and Position Matching Between Optical and SAR With Local Deep Feature Descriptor

Image matching between the optical and synthetic aperture radar (SAR) is one of the most fundamental problems for earth observation. In recent years, many researchers have used hand-made descriptors with their expertise to find matches between optical and SAR images. However, due to the large nonlinear radiation difference between optical images and SAR images, the image matching becomes very difficult. To deal with the problems, the article proposes an efficient feature matching and position matching algorithm (MatchosNet) based on local deep feature descriptor. First, A new dataset is presented by collecting a large number of corresponding SAR images and optical images. Then a deep convolutional network with dense blocks and cross stage partial networks is designed to generate deep feature descriptors. Next, the hard L2 loss function and ARCpatch loss function are designed to improve matching effect. In addition, on the basis of feature matching, the two-dimensional (2-D) Gaussian function voting algorithm is designed to further match the position of optical images and SAR images of different sizes. Finally, a large number of quantitative experiments show that MatchosNet has a excellent matching effect in feature matching and position matching. The code will be released at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/LiaoYun0x0/Feature-Matching-and-Position-Matching-between-Optical-and-SAR</uri> .
Inter-Hour Forecast of Solar Radiation Based on Long Short-Term Memory with Attention Mechanism and Genetic Algorithm

The installed capacity of photovoltaic power generation occupies an increasing proportion in the power system, and its stability is greatly affected by the fluctuation of solar radiation. Accurate prediction of solar radiation is an important prerequisite for ensuring power grid security and electricity market transactions. The current mainstream solar radiation prediction method is the deep learning method, and the structure design and data selection of the deep learning method determine the prediction accuracy and speed of the network. In this paper, we propose a novel long short-term memory (LSTM) model based on the attention mechanism and genetic algorithm (AGA-LSTM). The attention mechanism is used to assign different weights to each feature, so that the model can focus more attention on the key features. Meanwhile, the structure and data selection parameters of the model are optimized through genetic algorithms, and the time series memory and processing capabilities of LSTM are used to predict the global horizontal irradiance and direct normal irradiance after 5, 10, and 15 min. The proposed AGA-LSTM model was trained and tested with two years of data from the public database Solar Radiation Research Laboratory site of the National Renewable Energy Laboratory. The experimental results show that under the three prediction scales, the prediction performance of the AGA-LSTM model is below 20%, which effectively improves the prediction accuracy compared with the continuous model and some public methods.
Dynamic analysis of a fractional-order hyperchaotic system and its application in image encryption

Abstract Compared with integer order chaotic systems, fractional order chaotic systems can reflect natural phenomena more accurately, which are more suitable for chaotic cryptosystems. In order to explore the application of fractional order chaotic system in cryptography, a novel fractional order hyperchaotic system is constructed and implemented on DSP platform. More progressively, based on Adomian decomposition method, the dynamic behavior is studied by phase diagram, bifurcation diagram, Lyapunov exponent spectrum and spectral entropy (SE) complexity. It is found that each parameter and order have a large range of intervals that can keep the system in a hyperchaotic state. Therefore, the hyperchaotic sequences generated by the constructed fractional order hyperchaotic system have sufficient randomness and are well suited for applications in secure communications. In addition, a color image encryption scheme is designed based on the fractional order hyperchaotic system and DNA dynamic coding. Firstly, the improved Arnold algorithm is used to scramble the original image, then the column cyclic shift method is applied for secondary scrambling, and finally the pixel value is diffused by DNA sequence operation. The security analysis results indicate that the designed encryption algorithm can not only encrypt images effectively, but also has high security and can resist various common attacks.
Asynchronous Federated Learning System Based on Permissioned Blockchains

The existing federated learning framework is based on the centralized model coordinator, which still faces serious security challenges such as device differentiated computing power, single point of failure, poor privacy, and lack of Byzantine fault tolerance. In this paper, we propose an asynchronous federated learning system based on permissioned blockchains, using permissioned blockchains as the federated learning server, which is composed of a main-blockchain and multiple sub-blockchains, with each sub-blockchain responsible for partial model parameter updates and the main-blockchain responsible for global model parameter updates. Based on this architecture, a federated learning asynchronous aggregation protocol based on permissioned blockchain is proposed that can effectively alleviate the synchronous federated learning algorithm by integrating the learned model into the blockchain and performing two-order aggregation calculations. Therefore, the overhead of synchronization problems and the reliability of shared data is also guaranteed. We conducted some simulation experiments and the experimental results showed that the proposed architecture could maintain good training performances when dealing with a small number of malicious nodes and differentiated data quality, which has good fault tolerance, and can be applied to edge computing scenarios.
Improved YOLOv4 Based on Attention Mechanism for Ship Detection in SAR Images

Ship detection in synthetic aperture radar (SAR) images is an important and challenging work in the field of image processing.Traditional detection algorithms usually rely on handmade features or predefined thresholds, the different performance is obtained with varying degrees of prior knowledge, and it is difficult to take advantage of big data.Recently, deep learning algorithms have found wide applications in ship detection from SAR images.However, due to the complex backgrounds and multiscale ships, it is hard for deep networks to extract representative target features, which limits the ship detection performance to a certain extent.In order to tackle the above problems, we propose an improved YOLOv4 (ImYOLOv4) based on attention mechanism.Firstly, to achieve the best trade-off between detection accuracy and speed, we adopt the off-the-shelf YOLOv4 as our basic framework because of its fast detection speed.Secondly, a thresholding attention module (TAM) is introduced to suppress the adverse effect of complex backgrounds and noises.Besides, we embed channel attention module (CAM) into improved BiFPN as the feature pyramid network (FPN) to better enhance the discrimination of the multiscale target features.Finally, the decoupled head with two parallel branches improves the performance of classification and regression.The proposed method is evaluated on public SAR dataset and the experimental results demonstrate that it has higher efficiency and feasibility than other mainstream methods, yielding the accuracy of 94.16% at intersection over union of 0.5 and 58.19% at intersection over union of 0.75.
Modified Yolov3 for Ship Detection with Visible and Infrared Images

As the demands for international marine transportation increase rapidly, effective port management has become an important issue. Automatic ship recognition can facilitate the realization of smart ports, and improve the efficiency of port operation and management. In order to take into account the processing efficiency and detection accuracy at the same time, the study presented an improved deep-learning network based on You only look once version 3 (Yolov3) for all-day ship detection with visible and infrared images. Yolov3 network can simultaneously improve the recognition ability of large and small objects through multiscale feature-extraction architecture. Considering reducing computational time and network complexity with relatively competitive detection accuracy, the study modified the architecture of Yolov3 by choosing an appropriate input image size, fewer convolution filters, and detection scales. In addition, the reduced Yolov3 was further modified with the spatial pyramid pooling (SPP) module to improve the network performance in feature extraction. Therefore, the proposed modified network can achieve the purpose of multi-scale, multi-type, and multi-resolution ship detection. In the study, a common self-built data set was introduced, aiming to conduct all-day and real-time ship detection. The data set included a total of 5557 infrared and visible light images from six common ship types in northern Taiwan ports. The experimental results on the data set showed that the proposed modified network architecture achieved acceptable performance in ship detection, with the mean average precision (mAP) of 93.2%, processing 104 frames per second (FPS), and 29.2 billion floating point operations (BFLOPs). Compared with the original Yolov3, the proposed method can increase mAP and FPS by about 5.8% and 8%, respectively, while reducing BFLOPs by about 47.5%. Furthermore, the computational efficiency and detection performance of the proposed approach have been verified in the comparative experiments with some existing convolutional neural networks (CNNs). In conclusion, the proposed method can achieve high detection accuracy with lower computational costs compared to other networks.
Stateful Serverless Computing with <scp>Crucial</scp>

Serverless computing greatly simplifies the use of cloud resources. In particular, Function-as-a-Service (FaaS) platforms enable programmers to develop applications as individual functions that can run and scale independently. Unfortunately, applications that require fine-grained support for mutable state and synchronization, such as machine learning (ML) and scientific computing, are notoriously hard to build with this new paradigm. In this work, we aim at bridging this gap. We present Crucial , a system to program highly-parallel stateful serverless applications. Crucial retains the simplicity of serverless computing. It is built upon the key insight that FaaS resembles to concurrent programming at the scale of a datacenter. Accordingly, a distributed shared memory layer is the natural answer to the needs for fine-grained state management and synchronization. Crucial allows to port effortlessly a multi-threaded code base to serverless, where it can benefit from the scalability and pay-per-use model of FaaS platforms. We validate Crucial with the help of micro-benchmarks and by considering various stateful applications. Beyond classical parallel tasks (e.g., a Monte Carlo simulation), these applications include representative ML algorithms such as k -means and logistic regression. Our evaluation shows that Crucial obtains superior or comparable performance to Apache Spark at similar cost (18%–40% faster). We also use Crucial to port (part of) a state-of-the-art multi-threaded ML library to serverless. The ported application is up to 30% faster than with a dedicated high-end server. Finally, we attest that Crucial can rival in performance with a single-machine, multi-threaded implementation of a complex coordination problem. Overall, Crucial delivers all these benefits with less than 6% of changes in the code bases of the evaluated applications.
Deep Learning model and Classification Explainability of Renewable energy-driven Membrane Desalination System using Evaporative Cooler

Recently, the scientific community has become more interested in solar-driven steam materials and systems for desalination and disinfection. Solar thermal energy for membrane distillation desalination provides a green and sustainable option for building settings where there is a strong connection between water constraint and high solar irradiation. Artificial intelligence (AI) is rapidly being used to optimize water treatments and saltwater desalination because of its high precision and accuracy. Explainable AI (XAI) enables people to better understand and trust a model's predictions and to detect and rectify inaccurate AI predictions. This study analyses recent advances in solar-driven steam materials engineering and the significant technological constraints that impede its wide-scale deployment. Using local interpretable model-agnostic explanations (LIME), our study provides an interpretable solution (in addition to the binary classification result of the developed black-box deep learning (DL) model) so that experts can understand why the machine thinks this way, providing critical insights for the decision-making process. The proposed XAI-DL model is based on a DL network consisting of three cascaded convolutional blocks for processing tabular data. Therefore, the XAI-DL classification model achieves a cooling quality accuracy of 82.64% during the validation stage, supporting the explaining capability. During the testing, the [inlet-cooling-water-temperature] pushes the model lower, whereas the [ambient-temperature], [feed-water-flow-rate], and the [inlet-feed-water-temperature] pushes the model higher. The LIME explanation result is consistent with the statistical analysis of the data. Consequently, the proposed explainer assists non-experts in comparing and improving the untrustworthy model through and clarifies the importance of each feature and its relationship to other features and its relationship to the class. Finally, the XAI-DL fits and supports the different manufacturers of membrane desalination system(s) to inspect cooling quality in their designed system and consistency of interpretability and trust.
Graph Neural Transport Networks with Non-local Attentions for Recommender Systems

Graph Neural Networks (GNNs) have emerged as powerful tools for collaborative filtering. A key challenge of recommendations is to distill long-range collaborative signals from user-item graphs. Typically, GNNs generate embeddings of users/items by propagating and aggregating the messages between local neighbors. Thus, the ability of GNNs to capture long-range dependencies heavily depends on their depths. However, simply training deep GNNs has several bottleneck effects, e.g., over-fitting & over-smoothing, which may lead to unexpected results if GNNs are not well regularized.