Reviewerly/exHarmony huggingface dataset:
Dual-resolution transformer combined with multi-layer separable convolution fusion network for real-time semantic segmentation

Environmental perception is crucial for unmanned mobile platforms such as autonomous vehicles and robots. Precise and fast semantic segmentation of the surrounding scene is a key task to enhance this capability. Existing real-time semantic segmentation networks are typically based on convolutional neural networks (CNNs), which have achieved good results, but they still lack control over global context features. In recent years, the Transformer architecture has achieved significant success in capturing global context, which is beneficial for improving segmentation accuracy. However, Transformers tend to ignore local connections, and their computational complexity makes real-time segmentation challenging. We propose a lightweight real-time semantic segmentation network called DTMC-Net, which combines the advantages of CNNs and Transformers. We design a special residual convolution module called the Lightweight Multi-layer Separable Convolution Attention module (LMSCA) to reduce the parameter count and perform multi-scale feature fusion to capture local features effectively. We introduce the Simple Dual-Resolution Transformer (SDR Transformer) that utilizes lightweight attention mechanisms and residual feed forward networks to capture and maintain features, with multiple bilateral fusions between two branches to exchange information. The proposed Anti-artifact Aggregation Pyramid Pooling Module (AAPPM) optimizes the upsampling process, refines features, and performs multi-scale feature fusion again. DTMC-Net only contains 4.2M parameters and achieves good performance on multiple public datasets with different scenarios.
Frequency-aware network for low-light image enhancement

Low-light images often suffer from severe visual degradation, affecting both human perception and high-level computer vision tasks. Most existing methods process images in the spatial domain, making it challenging to simultaneously improve brightness while suppressing noise. In this paper, we present a novel perspective to enhance images based on frequency domain characteristics. Specifically, we reveal that the low-frequency components are closely related to luminance and color, whereas the high-frequency components are not. Based on this observation, we propose the Frequency-aware Network (FaNet) for low-light image enhancement. By selectively adjusting low-frequency components, FaNet preserves more high-frequency details while achieving low-light image enhancement. Additionally, we employ a multi-scale framework and selective fusion for effective feature learning and image reconstruction. Experimental results demonstrate the superiority of the proposed method.
Accurate road user localization in aerial images captured by unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) have become increasingly popular for traffic data collection. However, the depth relief of road users and the perspective distortion of the onboard camera induce nonnegligible measurement errors while exploiting UAVs to localize road users. To address this issue, this paper presents a method for accurate road user location estimation in aerial images. First, a deep-learning-based method was employed to detect road users in aerial images using oriented bounding boxes. Then, the localization error induced by the depth relief and perspective distortion was examined and modeled, based on which an error compensation scheme was developed to offset the localization error for each road user so that higher localization accuracy is attainable. Field experiments were conducted to evaluate the proposed method's performance. The results demonstrated a promising accuracy in estimating the location of road users, signifying the method's potential to improve the credibility of UAVs in traffic applications.
Artificial Intelligence and the Future of Chinese Language Pedagogy

This chapter explores the function of cutting-edge technologies including artificial intelligence, augmented reality, and the metaverse in teaching Chinese as a foreign language. It's an example of how AI is being put to use in tailor-made programs for mastering Chinese and all its subtleties. The chapter covers how to use augmented reality and how it can help you have more meaningful conversations with others. The potential of ChatGPT and the metaverse as interactive learning environments is also discussed in this chapter. The effects on educational policy and teacher preparation, as well as an examination of students' independence in technologically advanced classrooms, are discussed in the last section.
Measuring near-field wave elevation with monocular vision using self-supervised learning techniques

Ocean waves are among the most influential factors affecting marine mechanical systems. The existing methods for measuring wide-area wave-field data have limitations and require high maintenance costs. Therefore, it is crucial to develop a reliable and efficient field measurement method for wide-area wave fields. This study proposes a monocular vision-based approach for wave field measurement, where the depth of each pixel is equalised to summation of the depth of the mean water surface and the depth variation induced by wave elevation. A deep network was trained using a self-supervised algorithm to approximate the depth variation induced by wave elevation. A loss of representative features was proposed to improve the performance of deep networks. The images of an open-source dataset were used to train and validate the proposed model. By using the proposed method, the images were utilised to reconstruct the three-dimensional wave field of the test set. The time history and wave spectrum of the wave fields were compared with those calculated by existing method and measured by wave gauge. The influence of the training set on the measurement results was also investigated. The results indicate that the proposed method is capable of performing effective near-field wave measurements by monocular images, effectively reducing the device requirements, and increasing update frequency for ocean wave measurement.
Multiple-instance Learning from Triplet Comparison Bags

Multiple-instance learning (MIL) solves the problem where training instances are grouped in bags, and a binary (positive or negative) label is provided for each bag. Most of the existing MIL studies need fully labeled bags for training an effective classifier, while it could be quite hard to collect such data in many real-world scenarios, due to the high cost of data labeling process. Fortunately, unlike fully labeled data, triplet comparison data can be collected in a more accurate and human-friendly way. Therefore, in this article, we for the first time investigate MIL from only triplet comparison bags , where a triplet (X a , X b , X c ) contains the weak supervision information that bag X a is more similar to X b than to X c . To solve this problem, we propose to train a bag-level classifier by the empirical risk minimization framework and theoretically provide a generalization error bound. We also show that a convex formulation can be obtained only when specific convex binary losses such as the square loss and the double hinge loss are used. Extensive experiments validate that our proposed method significantly outperforms other baselines.
High-low level task combination for object detection in foggy weather conditions

For the object detection task in foggy weather conditions, image dehazing network is often used as preprocessing method to get a clear input. However, there is not strictly a strong positive correlation between image dehazing task and object detection task. Moreover, the preprocessing module can increase the inference time of the whole model to a certain extent. To alleviate these problems, we propose a novel High-Low level task combination network (HLNet) based on multitask learning, which can learn both high-level and low-level tasks. Specially, instead of restoring the features to clear pixel-wise feature space like common image dehazing method, we opt to perform a restoration in feature level to mitigate the influence of the Batch Normalization (BN) layer of encoder on dehazing task. HLNet jointly learn dehazing task and detection task in an end-to-end fashion, which ensures that the weather-specific information in latent feature space is suppressed. Moreover, we applied the HLNet framework on three different object detection networks, including RetinaNnet, YOLOv3 and YOLOv5s network, and achieved improvements of 1.7 percent, 2.3 percent, and 1.2 percent in mAP respectively. The experimental results demonstrate the effectiveness and generalization ability of our proposed HLNet framework in real foggy scenarios.
SPGAN: Siamese projection Generative Adversarial Networks

Noise-to-image synthesis continues to be challenging, despite the application of the advanced loss functions in Generative Adversarial Networks (GANs). The main issue lies in the fact that discriminators employ hard margin classification, which is susceptible to misclassification. Moreover, the features of real image distribution learned by the generator are limited during adversarial training, thus the generated images are visually inferior to the real images. To tackle these challenges, a GAN method based on Siamese projection network (abbreviated as SPGAN) is proposed to learn a similarity measurement of features for image synthesis. Then, the similarity measurement is incorporated into the loss functions of the generator and discriminator, forming a similar feature adversarial learning. Through similar feature adversarial learning, SPGAN encourages the discriminator to maximize the dissimilarity between the features of real and generated images during recognition process. Simultaneously, it encourages the generator to synthesize images that contain more features resembling those of real images. Furthermore, we extend the SPGAN method by rewriting five representative loss functions, showcasing its compatibility with different loss functions. Experimental results demonstrate that the performance of SPGAN outperforms the advanced loss functions.
Three-dimensional mineral prospectivity mapping Considering structural restoration in the Dayingezhuang gold Deposit, eastern china

In the era of big data and the rapid advancements in machine learning, particularly deep learning, three-dimensional mineral prospectivity mapping (3D MPM) has emerged as a powerful tool for accurately deciphering the complex nonlinear relationships between ore-controlling factors hidden within geoscientific datasets and known mineralization. However, post-mineralization structural deformations can significantly distort the structures and mineralization distribution. This distortion introduces a substantial amount of error into 3D MPM, thereby diminishing its effectiveness in mineral resource exploration. In response to this challenge, our study introduces an innovative 3D MPM approach incorporating structural restoration. This approach has been applied to the Dayingezhuang gold deposit, where the Zhaoping fault, experienced disruption due to the post-mineralization NW-trending Dayingezhuang fault. The existing 3D model of the Zhaoping fault was first constructed using the observation data. A 3D geometric transformation technique was then employed to restore the original structures and mineralization distribution during the mineralization period. Subsequently, morphological analysis and prospectivity modeling were performed using deep learning algorithms including the Bayesian decomposition model for regression and multilayer perceptron model for classification. The results indicated several crucial findings: (1) structural restoration effectively eliminated the spatial distortions (250 m to 500 m) within the Zhaoping fault and rectified the discontinuities in mineralization zones; (2) the post-mineralization ore-controlling features exhibited smaller dips and undulations, with the main mineralization concentrated in areas transitioning from steep to gentle dips (-10°/100 m to 2°/100 m); (3) the post-restoration regression and classification models showed higher accuracy and reliability compared to the pre-restoration models, with an improvement of approximately 27 % and 10 %, respectively. As per our research findings, structural restoration effectively eliminates distortions in both fault and mineralization induced by post-ore structural deformations. Consequently, this method significantly enhances the reliability of 3D MPM. In the context of structural deformations, it underscores the necessity and effectiveness of structural restoration in conducting 3D MPM on a deposit scale.
Enhancing Heterogeneous Knowledge Graph Completion with a Novel GAT-based Approach

Knowledge graphs (KGs) play a vital role in enhancing search results and recommendation systems. With the rapid increase in the size of KGs, they are becoming inaccurate and incomplete. This problem can be solved by the KG completion methods, of which graph attention network (GAT)-based methods stand out because of their superior performance. However, existing GAT-based KG completion methods often suffer from overfitting issues when dealing with heterogeneous KGs, primarily due to the unbalanced number of samples. Additionally, these methods demonstrate poor performance in predicting the tail (head) entity that shares the same relation and head (tail) entity with others. To solve these problems, we propose GATH, a novel GAT -based method designed for H eterogeneous KGs. GATH incorporates two separate attention network modules that work synergistically to predict the missing entities. We also introduce novel encoding and feature transformation approaches, enabling the robust performance of GATH in scenarios with imbalanced samples. Comprehensive experiments are conducted to evaluate GATH’s performance. Compared with the existing state-of-the-art GAT-based model on Hits@10 and MRR metrics, our model improves performance by 5.2% and 5.2% on the FB15K-237 dataset and by 4.5% and 14.6% on the WN18RR dataset, respectively.
REQA: Coarse-to-fine assessment of image quality to alleviate the range effect

Blind image quality assessment (BIQA) of User Generated Content (UGC) suffers from the range effect, which indicates that on the overall quality range, mean opinion score (MOS) and predicted MOS (pMOS) are well correlated, but when focusing on a particular narrow range, the correlation is lower. To tackle this problem, a novel method is proposed from coarse-grained metric to fine-grained prediction. Concretely, we utilize global context features and local detailed features for the multi-scale distortion perception. Then, to further boost the ability of fine-grained assessment, we introduce the feedback mechanism, which is in accord with Human Vision System (HVS), to perceive detailed distortions gradually. Also, two coarse-to-fine loss functions are proposed to facilitate the feedback perception progress: a rank-and-gradient loss for coarse-grained metric keeps the assessment rank and gradient consistency between pMOS and MOS; a multi-level tolerance loss following the curriculum learning strategy is proposed to make a fine-grained prediction. Both coarse-grained and fine-grained experiments demonstrate that the proposed method outperforms the state-of-the-art ones, which validates that our method effectively alleviates the range effect. The codes are available at https://github.com/huofushuo/REQA.
DAHGN: Degree-Aware Heterogeneous Graph Neural Network

In recent years, Graph Neural Networks (GNNs), an emerging technology for learning from graph-structured data, have attracted much attention. Despite the widespread applications of GNNs across diverse domains, recent studies have shown that insufficient information aggregation for low-degree nodes (i.e., tail nodes) leads to inferior performance of GNNs. While numerous recent studies have aimed to address the issue of degree bias in homogeneous graphs, there are limited studies on analyzing such unfairness for heterogeneous information networks. In this article, we propose a novel semi-supervised contrastive learning framework named Degree-Aware Heterogeneous Graph Neural Network (DAHGN), which achieves promising performance by alleviating the degree bias. In brief, we first construct two contrastive graph views, one from a heterogeneous graph that focuses on capturing complementary information to enhance the node representations and one from an extracted homogeneous subgraph that focuses on applying different strategies to low- and high- degree nodes to eliminate the degree bias. We then leverage the label learned from the heterogeneous view to reconstruct the positive node pairs in the contrastive learning framework. Finally, we combine the task loss (semi-supervised) and the contrastive loss to optimize the model performance. We conduct extensive experiments on benchmark datasets to validate the effectiveness of our model, and the results demonstrate that the DAHGN outperforms a wide range of state-of-the-art methods in the semi-supervised node classification problem.
Leveraging zero knowledge proofs for blockchain-based identity sharing: A survey of advancements, challenges and opportunities

Identity sharing systems, regardless of their architectural models, share common vulnerabilities. These systems compel users to divulge personal information and furnish proof of identity for accessing services, leaving them susceptible to data breaches that can culminate in identity theft and jeopardize online data security. While blockchain technology offers a potential remedy, delivering enhanced security, immutability, and traceability, it simultaneously raises pertinent concerns surrounding privacy and transparency. The integration of zero-knowledge proof (ZKP) technology has emerged as a promising solution, particularly in enhancing privacy within the transparent blockchain ecosystem. Our paper conducts an exhaustive survey of the existing literature, with a particular focus on the assimilation of ZKP technology into blockchain for the secure sharing of user identities. We undertake a critical evaluation of the advancements achieved in this domain, pinpoint the formidable challenges that must be confronted, and uncover nascent opportunities for further exploration. Our contribution transcends the realms of mere summarization and analysis; we go a step further by offering recommendations drawn from real-world case studies and delineating future research directions.
Integrative measurement method for tensile test based on DIC using modified second-order shape function

Conventional tensile tests still require significant manual intervention for assessment. Therefore, the digital image correlation (DIC) technique is introduced into the field of conventional tensile test. Flexible chain, chain subset-based DIC (C-DIC) technique and modified second-order shape function (MSSF) are proposed for methodology and implementation. Tensile tests of several standard metal specimens were carried out to validate estimation accuracy and effectiveness of the proposed method. Various material properties (e.g. necking effect, elastic modulus, ultimate elongation and necking stress–strain curve) have been automatically measured by the proposed method. The relative errors of different material properties measured by the proposed method are generally less than 3% compared to that of conventional methods. The efficiency and accuracy of the proposed method, validated by experiments, demonstrate its capability to achieve automatic measurements for tensile tests. These results suggest that the method has the potential for widespread industrial adoption in the field of tensile testing.
A Novel Cross-Domain Recommendation with Evolution Learning

In this “info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activities and e-commerce. Several techniques have been widely applied for recommendation systems, but the cold-start and sparsity problems remain a major challenge. The cold-start problem occurs when generating recommendations for new users and items without sufficient information. Sparsity refers to the problem of having a large amount of users and items but with few transactions or interactions. In this article, a novel cross-domain recommendation model, Cross-Domain Evolution Learning Recommendation (abbreviated as CD-ELR), is developed to communicate the information from different domains in order to tackle the cold-start and sparsity issues by integrating matrix factorization and recurrent neural network. We introduce an evolutionary concept to describe the preference variation of users over time. Furthermore, several optimization methods are developed for combining the domain features for precision recommendation. Experimental results show that CD-ELR outperforms existing state-of-the-art recommendation baselines. Finally, we conduct experiments on several real-world datasets to demonstrate the practicability of the proposed CD-ELR.
Cybersecurity Risk and Audit Pricing—A Machine Learning-Based Analysis

ABSTRACT Cybersecurity risk represents a growing business threat. However, little attention has been paid to its assessment. This study proposes a machine learning algorithm that considers firm cybersecurity risk disclosure, information technology governance, external monitoring by financial analysts and auditors, and general firm characteristics to estimate cybersecurity risk (i.e., the likelihood of a firm experiencing data breaches during a year). This measure outperforms the measure produced by logistic regression models, is higher in industries more prone to cyberattacks, and effectively predicts future data breaches and firm use of cybersecurity insurance policies. I also examine whether auditors consider firm cybersecurity risk in the engagement planning process, finding that, on average, a one-percentage-point increase in cybersecurity risk is associated with a 1.15 percent increase in audit fees. In addition, auditors charge a fee premium after a data breach only if the client has heightened cybersecurity risk. Data Availability: Data are available from the public sources cited in the text.
Grid neighbourhood based three way clustering (3WC)

The key idea behind three-way clustering (3WC) is to group items into three regions to reveal patterns that might not be apparent using two-way clustering. Three-way clustering, contrasting with two-way, considers data across three regions instead of two regions. The three regions are referred to as inside, outside, and partial regions, contrary to the two-way clustering that uses inside and outside regions. The objects in the inside region are members of a cluster, whereas those in the outside region are not, and partial objects may be members of a cluster. Three-way clustering encounters two challenges: 1) Investigation of sophisticated evaluation functions. 2) Finding an optimized pair of thresholds used to identify three regions for a cluster. In the context of Three-Way Clustering (3WC), applying conditional probability can be beneficial for assessing the likelihood of patterns or relationships within each region. In this study, the first challenge is addressed by employing a grid-based approach for a sophisticated evaluation function based on conditional probability. Whereas the second challenge is addressed by employing the elbow-based method to determine a pair of thresholds within a grid-based histogram. Further, two approaches namely the OGN3 and RGN3 are introduced to discover clear and crisp clusters. Experimental results on UCI and Kaggle datasets indicate improvements for commonly used F1 measures for OGN3 and RGN3 up to 4.97% and 12.88%, respectively