Reviewerly/exHarmony huggingface dataset:
The Shape of International Art Purchasing—The Shape of Things to Come

This article is about the role of cryptocurrencies, for example, decentralized autonomous organisations (DAOs) and non-fungible tokens (NFTs), in the international art market. These are cryptocurrencies which can be used to work with local governments to deliver non-state-funded consultancy in, for example, funding bid writing or community risk assessment. Self-polycentric and cause-based DAOs typically focus on actively listening to their token owners, utilizing the group’s skills under a transparent incentive structure fostering trust. This article delivers a critical evaluation of DAOs as an organisational management structure and business operations vehicle. This evaluation considers DAOs’ utility in supplying goods and services, through the critical lens of facilitating the international art market. The objective of this article is to raise wider awareness and understanding of DAOs as a legal entity. This paper acts to introduce the uninitiated to the business, societal value and legal uncertainties of DAOs and NFTs. DAOs are internet-based organisations built upon a set of instructions presented in and controlled by a computer programme, i.e., a smart contract. Effectively, DAOs are an artificial, electronic, online, digital technology entity, with no physical form.
Hypergraph-Clustering Method Based on an Improved Apriori Algorithm

With the complexity and variability of data structures and dimensions, traditional clustering algorithms face various challenges. The integration of network science and clustering has become a popular field of exploration. One of the main challenges is how to handle large-scale and complex high-dimensional data effectively. Hypergraphs can accurately represent multidimensional heterogeneous data, making them important for improving clustering performance. In this paper, we propose a hypergraph-clustering method dubbed the “high-dimensional data clustering method” based on hypergraph partitioning using an improved Apriori algorithm (HDHPA). First, the method constructs a hypergraph based on the improved Apriori association rule algorithm, where frequent itemsets existing in high-dimensional data are treated as hyperedges. Then, different frequent itemsets are mined in parallel to obtain hyperedges with corresponding ranks, avoiding the generation of redundant rules and improving mining efficiency. Next, we use the dense subgraph partition (DSP) algorithm to divide the hypergraph into multiple subclusters. Finally, we merge the subclusters through dense sub-hypergraphs to obtain the clustering results. The advantage of this method lies in its use of the hypergraph model to discretize the association between data in space, which further enhances the effectiveness and accuracy of clustering. We comprehensively compare the proposed HDHPA method with several advanced hypergraph-clustering methods using seven different types of high-dimensional datasets and then compare their running times. The results show that the clustering evaluation index values of the HDHPA method are generally superior to all other methods. The maximum ARI value can reach 0.834, an increase of 42%, and the average running time is lower than other methods. All in all, HDHPA exhibits an excellent comparable performance on multiple real networks. The research results of this paper provide an effective solution for processing and analyzing large-scale network datasets and are also conducive to broadening the application range of clustering techniques.
Blind super-resolution model based on unsupervised degenerate indication learning

Blind super-resolution of images based on contrast learning has achieved better performance, which can distinguish between different degraded information. However, there is no significant improvement in the processing of image super-resolution. To further improve the performance of image blind super-resolution, a new degenerate higher-order attention network (DHAN) is designed for image super-resolution, in which a higher-order channel attention mechanism is introduced to the framework. Meanwhile, it also introduces normalized variance and global covariance pooling, which can provide higher-order feature information and more discriminative feature information. Comparing the experimental results, it can be found that the proposed higher-order attention network (DHAN) is able to distinguish various complex degradation patterns and thus obtain accurate degradation information. Numerous experiments have shown that higher-order attention network (DHAN) achieves good visual results in the image blind super-resolution task.
Empirical Study of ESG Score Prediction through Machine Learning—A Case of Non-Financial Companies in Taiwan

In recent years, ESG (Environmental, Social, and Governance) has become a critical indicator for evaluating sustainable companies. However, the actual logic used for ESG score calculation remains exclusive to rating agencies. Therefore, with the advancement of AI, using machine learning to establish a reliable ESG score prediction model is a topic worth exploring. This study aims to build ESG score prediction models for the non-financial industry in Taiwan using random forest (RF), Extreme Learning Machines (ELM), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) and investigates whether the COVID-19 pandemic has affected the accuracy of these models. The dependent variable is the Taiwan ESG Sustainable Development Index, while the independent variables are 27 financial metrics and corporate governance indicators with three parts: pre-pandemic, pandemic, and the entire period (2018–2021). RMSE, MAE, MAPE, and r2 are conducted to evaluate these models. The results demonstrate the four supervised models perform well during all three periods. ELM, XGBoost, and SVM exhibit excellent performance, while RF demonstrates good accuracy but relatively lower than the others. XGBoost’s r2 shows inconsistency with RMSE, MAPE, and MAE. This study concludes the predictive performance of RF and XGBoost is inferior to that of other models.
Remote Sensing Image Haze Removal Based on Superpixel

The presence of haze significantly degrades the quality of remote sensing images, resulting in issues such as color distortion, reduced contrast, loss of texture, and blurred image edges, which can ultimately lead to the failure of remote sensing application systems. In this paper, we propose a superpixel-based visible remote sensing image dehazing algorithm, namely SRD. To begin, the remote sensing haze images are divided into content-aware patches using superpixels, which cluster adjacent pixels considering their similarities in color and brightness. We assume that each superpixel region shares the same atmospheric light and transmission properties. Subsequently, methods to estimate local atmospheric light and transmission within each superpixel are proposed. Unlike existing dehazing algorithms that assume a globally constant atmospheric light, our approach considers the global heterogeneous distribution of the atmospheric ambient light, which allows us to model it as a global non-uniform variable. Furthermore, we introduce an effective atmospheric light estimation method inspired by the maximum reflectance prior. Moreover, recognizing the wavelength-dependent nature of light transmission, we independently estimate the transmittance for each RGB channel of the input image. The quantitative and qualitative evaluation results of comprehensive experiments on synthetic datasets and real-world samples demonstrate the superior performance of the proposed algorithm compared to state-of-the-art methods for remote sensing image dehazing.
Beyond Human Detection: A Benchmark for Detecting Common Human Posture

Human detection is the task of locating all instances of human beings present in an image, which has a wide range of applications across various fields, including search and rescue, surveillance, and autonomous driving. The rapid advancement of computer vision and deep learning technologies has brought significant improvements in human detection. However, for more advanced applications like healthcare, human-computer interaction, and scene understanding, it is crucial to obtain information beyond just the localization of humans. These applications require a deeper understanding of human behavior and state to enable effective and safe interactions with humans and the environment. This study presents a comprehensive benchmark, the Common Human Postures (CHP) dataset, aimed at promoting a more informative and more encouraging task beyond mere human detection. The benchmark dataset comprises a diverse collection of images, featuring individuals in different environments, clothing, and occlusions, performing a wide range of postures and activities. The benchmark aims to enhance research in this challenging task by designing novel and precise methods specifically for it. The CHP dataset consists of 5250 human images collected from different scenes, annotated with bounding boxes for seven common human poses. Using this well-annotated dataset, we have developed two baseline detectors, namely CHP-YOLOF and CHP-YOLOX, building upon two identity-preserved human posture detectors: IPH-YOLOF and IPH-YOLOX. We evaluate the performance of these baseline detectors through extensive experiments. The results demonstrate that these baseline detectors effectively detect human postures on the CHP dataset. By releasing the CHP dataset, we aim to facilitate further research on human pose estimation and to attract more researchers to focus on this challenging task.
Enhancing the Automatic Recognition Accuracy of Imprinted Ship Characters by Using Machine Learning

In this paper, we address the challenge of ensuring safe operations and rescue efforts in emergency situations, for the sake of a sustainable marine environment. Our focus is on character recognition, specifically on deciphering characters present on the surface of aged and corroded ships, where the markings may have faded or become unclear over time, in contrast to vessels with clearly visible letters. Imprinted ship characters encompassing engraved, embroidered, and other variants found on ship components serve as vital markers for ship identification, maintenance, and safety in marine technology. The accurate recognition of these characters is essential for ensuring efficient operations and effective decision making. This study presents a machine-learning-based method that markedly improves the recognition accuracy of imprinted ship numbers and characters. This improvement is achieved by enhancing data classification accuracy through data augmentation. The effectiveness of the proposed method was validated by comparing it to State-of-the-Art classification technologies within the imprinted ship character dataset. We started with the originally sourced dataset and then systematically increased the dataset size, using the most suitable generative adversarial networks for our dataset. We compared the effectiveness of classic and convolutional neural network (CNN)-based classifiers to our classifier, a CNN-based classifier for imprinted ship characters (CNN-ISC). Notably, on the augmented dataset, our CNN-ISC model achieved impressive maximum recognition accuracy of 99.85% and 99.7% on alphabet and digit recognition, respectively. Overall, data augmentation markedly improved the recognition accuracy of ship digits and alphabets, with the proposed classification model outperforming other methods.
Estimating the prevalence of osteoporosis using ranked-based methodologies and Manitoba's population-based BMD registry

AbstractOsteoporosis is a metabolic bone disorder that is characterized by reduced bone mineral density (BMD) and deterioration of bone microarchitecture. Osteoporosis is highly prevalent among women over 50, leading to skeletal fragility and risk of fracture. Early diagnosis and treatment of those at high risk for fracture is very important in order to avoid morbidity, mortality and economic burden from preventable fractures. The province of Manitoba established a BMD testing program in 1997. The Manitoba BMD registry is now the largest population-based BMD registry in the world, and has detailed information on fracture outcomes and other covariates for over 160,000 BMD assessments. In this paper, we develop a number of methodologies based on ranked-set type sampling designs to estimate the prevalence of osteoporosis among women of age 50 and older in the province of Manitoba. We use a parametric approach based on finite mixture models, as well as the usual approaches using simple random and stratified sampling designs. Results are obtained under perfect and imperfect ranking scenarios while the sampling and ranking costs are incorporated into the study. We observe that rank-based methodologies can be used as cost-efficient methods to monitor the prevalence of osteoporosis.Keywords: Bone mineral densityEM algorithmfinite mixture modelosteoporosisstratified samplingunbalanced ranked set sampling Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingMohammad Jafari Jozani gratefully acknowledges the research support of the Natural Sciences and Engineering Research Council of Canada (NSERC). We express our gratitude to two anonymous reviewers and an associate editor for their valuable and constructive comments
Viewpoint-Agnostic Taekwondo Action Recognition Using Synthesized Two-Dimensional Skeletal Datasets

Issues of fairness and consistency in Taekwondo poomsae evaluation have often occurred due to the lack of an objective evaluation method. This study proposes a three-dimensional (3D) convolutional neural network-based action recognition model for an objective evaluation of Taekwondo poomsae. The model exhibits robust recognition performance regardless of variations in the viewpoints by reducing the discrepancy between the training and test images. It uses 3D skeletons of poomsae unit actions collected using a full-body motion-capture suit to generate synthesized two-dimensional (2D) skeletons from desired viewpoints. The 2D skeletons obtained from diverse viewpoints form the training dataset, on which the model is trained to ensure consistent recognition performance regardless of the viewpoint. The performance of the model was evaluated against various test datasets, including projected 2D skeletons and RGB images captured from diverse viewpoints. Comparison of the performance of the proposed model with those of previously reported action recognition models demonstrated the superiority of the proposed model, underscoring its effectiveness in recognizing and classifying Taekwondo poomsae actions.
Smart Grid Outlier Detection Based on the Minorization–Maximization Algorithm

Outliers can be generated in the power system due to aging system equipment, faulty sensors, incorrect line connections, etc. The existence of these outliers will pose a threat to the safe operation of the power system, reduce the quality of the data, affect the completeness and accuracy of the data, and thus affect the monitoring analysis and control of the power system. Therefore, timely identification and treatment of outliers are essential to ensure stable and reliable operation of the power system. In this paper, we consider the problem of detecting and localizing outliers in power systems. The paper proposes a Minorization-Maximization (MM) algorithm for outlier detection and localization and an estimation of unknown parameters of the Gaussian mixture model (GMM). To verify the performance of the method, we conduct simulation experiments by simulating different test scenarios in the IEEE 14-bus system. Numerical examples show that in the presence of outliers, the MM algorithm can detect outliers better than the traditional algorithm and can accurately locate outliers with a probability of more than 95%. Therefore, the algorithm provides an effective method for the handling of outliers in the power system, which helps to improve the monitoring analyzing and controlling ability of the power system and to ensure the stable and reliable operation of the power system.
Adversarial Robust Aerial Image Recognition Based on Reactive-Proactive Defense Framework with Deep Ensembles

As a safety-related application, visual systems based on deep neural networks (DNNs) in modern unmanned aerial vehicles (UAVs) show adversarial vulnerability when performing real-time inference. Recently, deep ensembles with various defensive strategies against adversarial samples have drawn much attention due to the increased diversity and reduced variance for their members. Aimed at the recognition task of remote sensing images (RSIs), this paper proposes to use a reactive-proactive ensemble defense framework to solve the security problem. In reactive defense, we fuse scoring functions of several classical detection algorithms with the hidden features and average output confidences from sub-models as a second fusion. In terms of proactive defense, we attempt two strategies, including enhancing the robustness of each sub-model and limiting the transferability among sub-models. In practical applications, the real-time RSIs are first input to the reactive defense part, which can detect and reject the adversarial RSIs. The accepted ones are then passed to robust recognition with a proactive defense. We conduct extensive experiments on three benchmark RSI datasets (i.e., UCM, AID, and FGSC-23). The experimental results show that the deep ensemble method of reactive and proactive defense performs very well in gradient-based attacks. The analysis of the applicable attack scenarios for each proactive ensemble defense is also helpful for this field. We also perform a case study with the whole framework in the black-box scenario, and the highest detection rate reaches 93.25%. Most of the adversarial RSIs can be rejected in advance or correctly recognized by the enhanced deep ensemble. This article is the first one to combine reactive and proactive defenses with a deep ensemble against adversarial attacks in the context of RSI recognition for DNN-based UAVs.
SCDA: A Style and Content Domain Adaptive Semantic Segmentation Method for Remote Sensing Images

Due to the differences in imaging methods and acquisition areas, remote sensing datasets can exhibit significant variations in both image style and content. In addition, the ground objects can be quite different in scale even within the same remote sensing image. These differences should be considered in remote sensing image segmentation tasks. Inspired by the recently developed domain generalization model WildNet, we propose a domain adaption framework named “Style and Content Domain Adaptation” (SCDA) for semantic segmentation tasks involving multiple remote sensing datasets with different data distributions. SCDA uses residual style feature transfer (RSFT) in the shallow layer of the baseline network model to enable source domain images to obtain style features from the target domain and reduce the loss of source domain content information. Considering the scale difference of different ground objects in remote sensing images, SCDA uses the projection of the source domain images, the style-transferred source domain images, and the target domain images to construct a multiscale content adaptation learning (MCAL) loss. This enables the model to capture multiscale target domain content information. Experiments show that the proposed method has obvious domain adaptability in remote sensing image segmentation. When performing cross-domain segmentation tasks from VaihingenIRRG to PotsdamIRRG, mIOU is 48.64%, and the F1 is 63.11%, marking improvements of 1.21% and 0.45%, respectively, compared with state-of-the-art methods. When performing cross-domain segmentation tasks from VaihingenIRRG to PotsdamRGB, the mIOU is 44.38%, an improvement of 0.77% over the most advanced methods. In summary, SCDA improves the semantic segmentation of remote sensing images through domain adaptation for both style and content. It fully utilizes multiple innovative modules and strategies to enhance the performance and the stability of the model.
Orbital Uncertainty Propagation Based on Adaptive Gaussian Mixture Model under Generalized Equinoctial Orbital Elements

The number of resident space objects (RSOs) has been steadily increasing over time, posing significant risks to the safe operation of on-orbit assets. The accurate prediction of potential collision events and implementation of effective and nonredundant avoidance maneuvers require the precise estimation of the orbit positions of objects of interest and propagation of their associated uncertainties. Previous research mainly focuses on striking a balance between accurate propagation and efficient computation. A recently proposed approach that integrates uncertainty propagation with different coordinate representations has the potential to achieve such a balance. This paper proposes combining the generalized equinoctial orbital elements (GEqOE) representation with an adaptive Gaussian mixture model (GMM) for uncertainty propagation. Specifically, we implement a reformulation for the orbital dynamics so that the underlying state and the moment feature of the GMM are propagated under the GEqOE coordinates. Starting from an initial Gaussian probability distribution function (PDF), the algorithm iteratively propagates the uncertainty distribution using a detection-splitting module. A differential entropy-based nonlinear detector and a splitting library are utilized to adjust the number of GMM components dynamically. Component splitting is triggered when a predefined threshold of differential entropy is violated, generating several GMM components. The final probability density function (PDF) is obtained by a weighted summation of the component distributions at the target time. Benefiting from the nonlinearity reduction caused by the GEqOE representation, the number of triggered events largely decreases, causing the necessary number of components to maintain uncertainty realism also to decrease, which enables the proposed approach to achieve good performance with much more efficiency. As demonstrated by the results of propagation in three scenarios with different degrees of complexity, compared with the Cartesian-based approach, the proposed approach achieves comparable accuracy to the Monte Carlo method while largely reducing the number of components generated during propagation. Our results confirm that a judicious choice of coordinate representation can significantly improve the performance of uncertainty propagation methods in terms of accuracy and computational efficiency.
Shallow Marine High-Resolution Optical Mosaics Based on Underwater Scooter-Borne Camera

Optical cameras equipped with an underwater scooter can perform efficient shallow marine mapping. In this paper, an underwater image stitching method is proposed for detailed large scene awareness based on a scooter-borne camera, including preprocessing, image registration and post-processing. An underwater image enhancement algorithm based on the inherent underwater optical attenuation characteristics and dark channel prior algorithm is presented to improve underwater feature matching. Furthermore, an optimal seam algorithm is utilized to generate a shape-preserving seam-line in the superpixel-restricted area. The experimental results show the effectiveness of the proposed method for different underwater environments and the ability to generate natural underwater mosaics with few artifacts or visible seams.