Reviewerly/exHarmony huggingface dataset:
Attention-guided multi-scale learning network for automatic prostate and tumor segmentation on MRI

Image-guided clinical diagnosis can be achieved by automatically and accurately segmenting prostate and prostatic cancer in male pelvic magnetic resonance imaging (MRI) images. For accurate tumor removal, the location, number, and size of prostate cancer are crucial, especially in surgical patients. The morphological differences between the prostate and tumor regions are small, the size of the tumor is uncertain, the boundary between the tumor and surrounding tissue is blurred, and the classification that separates the normal region from the tumor is uneven. Therefore, segmenting prostate and tumor on MRI images is challenging.This study offers a new prostate and prostatic cancer segmentation network based on double branch attention driven multi-scale learning for MRI. To begin, the dual branch structure provides two input images with different scales for feature coding, as well as a multi-scale attention module that collects details from different scales. The features of the double branch structure are then entered into the built feature fusion module to get more complete context information. Finally, to give a more precise learning representation, each stage is built using a deep supervision mechanism.The results of our proposed network's prostate and tumor segmentation on a variety of male pelvic MRI data sets show that it outperforms existing techniques. For prostate and prostatic cancer MRI segmentation, the dice similarity coefficient (DSC) values were 91.65% and 84.39%, respectively.Our method maintains high correlation and consistency between automatic segmentation results and expert manual segmentation results. Accurate automatic segmentation of prostate and prostate cancer has important clinical significance.
Method of Insulator Detection Based on Improved Faster R-CNN

Contrapose the problem that the accuracy of insulator detection by the feature extraction network of Faster R-CNN called VGG16 is low, a insulator detection method of employing ResNet50 + FPN feature extraction network was proposed for instead of VGG16 feature extraction network of Faster R-CNN and optimized anchor’s scale of RPN network, increased the number of anchor and Soft-NMS was used to screen candidate box, which reduced the regression error of bounding box, reduced the missed detection rate of insulators, and improved the recall rate of the model; the IoU threshold cascading structure was added to avoid the occurrence of mismatched prediction results; Meanwhile, Focal Loss was used to improve the classification loss function in Faster R-CNN; Eventually, The joint training of RPN Loss + Faster R-CNN Loss was introduced to detecting insulator. The experiment compared to YOLOv3 network, SSD network, with VGG16 network of Faster R-CNN respectively. The results indicated that the detection precision of the improved Faster R-CNN achieved 98.67%, the precision of insulator detection has enhanced.
Detail R-CNN: Insulator Detection Based on Detail Feature Enhancement and Metric Learning

Insulators need to be regularly inspected to ensure the normal operation of the power system. Currently, the detection of insulators in power distribution network poses greater challenges due to the wider variety of types and smaller differences between classes. Furthermore, most of distribution network insulators are small targets, background will generate significant interference. To enhance detail in feature maps, a detail feature enhancement module is proposed. In the proposed module, shallow features are integrated into deep features, and object regions of the feature maps are adjusted by attention mechanism. Additionally, to enhance the discriminant ability of classification features, an auxiliary classification module is introduced. Typical insulators are used as examples and category score of the detected insulators are adjusted, thereby improving the ability to distinguish similar insulators. Finally, the proposed auxiliary classification module is employed to construct an insulator detection algorithm based on Faster R-CNN. Notably, the experimental results conducted on a distribution network insulator dataset show that the proposed algorithm can achieve a mean average precision of 96.6%, which can accurately identify the majority of insulators.
AI and Blockchain Synergy in Aerospace Engineering: An Impact Survey on Operational Efficiency and Technological Challenges

This paper presents an exhaustive investigation into the potential of integrating Blockchain and Artificial Intelligence (AI) technologies within aerospace engineering, explicitly emphasizing supply chain management and operational efficiency.Given the decentralized nature of Blockchain, it has the potential to enhance diverse facets of an aircraft's lifecycle management significantly.At the same time, AI stands to revolutionize predictive supply chain models and structural fault detection.This paper provides a comprehensive overview of the current state, potential applications, challenges, and future research directions in this field based on an analysis of previous relevant literature.Further, it compares blockchain technology against traditional record management systems, underlining its data storage, security, transparency, and traceability advantages.Although these technologies promise significant advancements, many legal, regulatory, and technological readiness issues need addressing for broader acceptance within the industry.The findings highlight the importance of targeted research and development to unfold an array of new applications, driving innovation in aerospace engineering.This paper serves as a comprehensive survey for researchers, practitioners, policymakers, and industry stakeholders, illustrating the transformative potential of AI and Blockchain in the aerospace sector.
ESD-YOLOv5: A Full-Surface Defect Detection Network for Bearing Collars

To address the different forms and sizes of bearing collar surface defects, uneven distribution of defect positions, and complex backgrounds, we propose ESD-YOLOv5, an improved algorithm for bearing collar full-surface defect detection. First, a hybrid attention module, ECCA, was constructed by combining an efficient channel attention (ECA) mechanism and a coordinate attention (CA) mechanism, which was introduced into the YOLOv5 backbone network to enhance the localization ability of object features by the network. Second, the original neck was replaced by the constructed Slim-neck, which reduces the model’s parameters and computational complexity without sacrificing accuracy for object detection. Furthermore, the original head was replaced by the decoupled head from YOLOX, which separates the classification and regression tasks for object detection. Last, we constructed a dataset of defective bearing collars using images collected from industrial sites and conducted extensive experiments. The results demonstrate that our proposed ESD-YOLOv5 detection model achieved an mAP of 98.6% on our self-built dataset, which is a 2.3% improvement over the YOLOv5 base model. Moreover, it outperformed mainstream one-stage object detection algorithms. Additionally, the bearing collar surface defect detection system developed based on our proposed method has been successfully applied in the industrial domain for bearing collar inspection.
Universal Basic Income in a Blockchain-Based Community Currency

Recent advancements of blockchain technologies ensure security and trustability of Community Currency Systems (CCSs), enabling their increasingly widespread adoption. These systems aim at empowering the local economies by virtue of a medium of exchange whose governance and circulation are local. Smart contracts enable the enforcement of token economy policies, which facilitate the experimentation of radically new economic models. Recent studies investigated blockchain-based CCSs. Still, to the best of our knowledge, this is the first study analyzing a CCS providing a token-based Universal Basic Income (UBI). We evaluate the Circles UBI decentralised application utility in delivering an unconditional income to its users, focusing on its main pilot project running in Berlin. We analyse the structural changes in the network, especially in relation to a subsidy program, involving local businesses. We also identify prominent users based on centrality measures, and investigate how the UBI was effectively spent. We adopt a method agnostic to the economic context to identify optimal aggregation windows for the temporal network of CCS transactions based on the Causal Fidelity (CF) index. This aims to provide static representations as accurate as possible in terms of sequential order of edges, which aspect was not considered in previous research on CCSs. Our findings suggest that the pilot project sustained the expansion of the economic network and the system facilitated trade in urban communities in Berlin. Future research is needed to identify methods to ensure sustainability of self-organised CCSs adopting a UBI issuance scheme and to further decentralise their governance.
Towards efficient privacy and trust in decentralized blockchain-based peer-to-peer renewable energy marketplace

Renewable energy sources are becoming increasingly important as a substitute for fossil energy production. However, distributed renewable energy production faces several challenges regarding trading and management, such as inflexible pricing models and inaccurate green consumption information. A decentralized peer-to-peer (P2P) electricity marketplace may address these challenges. It enables prosumers to market their self-produced electricity. However, such a marketplace needs to guarantee that the transactions follow market rules and government regulations, cannot be manipulated, and are consistent with the generated electricity. One of the ways to provide these guarantees is to leverage blockchain technology. This work describes a decentralized blockchain-based P2P energy marketplace addressing privacy, trust, and governance issues. It uses a private permissioned blockchain Hyperledger Fabric (HF) and its smart contracts to perform energy trading settlements. The suggested P2P marketplace includes a particular regulator actor acting as a governmental representative overseeing marketplace operations. In this way, the suggested P2P marketplace can address the governance issues needed in electricity marketplaces. Further, the proposed marketplace ensures actors’ data privacy by employing HF’s private data collections while preserving the integrity and auditability of all operations. We present an in-depth performance evaluation and provide insights into the security and privacy challenges emerging from such a marketplace. The results demonstrate that partial centralization by the applied regulator does not limit the P2P energy trade settlement execution. Blockchain technology allows for automated marketplace operations enabling better incentives for prosumer electricity production. Finally, the suggested marketplace preserves the user’s privacy when P2P energy trade settlements are conducted.
From Plane to Hierarchy: Deformable Transformer for Remote Sensing Image Captioning

With the growth of remote sensing images, un-derstanding image content automatically has attracted many researchers' interests in deep learning for remote sensing image. Inspired from the natural image captioning, the model with CNN-RNN as the backbone and supplemented by attention has been widely used in remote sensing image captioning. However, it is inefficient for the current attention layer to simultaneously mine hidden foreground from the background of remote sensing image and perform feature interactive learning. Meanwhile, the new mainstream language model has recently surpassed the traditional LSTM in sentence generation. For solving the above problems, in this paper, we proposed a novel thought to make the flat remote sensing images stereoscopic by separating the fore- and background. Based on hierarchical image informa-tion, we designed a novel Deformable Transformer equipped with deformable scaled dot-product attention to learn multi-scale feature from fore- and background through the powerful interactive learning ability. Evaluations are conducted on Four classic remote sensing image captioning datasets. Compared with the state-of-the-art methods, our Transformer variant achieves higher captioning accuracy.
Continuation of Events Detection with Global Long-Period Seismic Data: An Analysis from 2010 to 2022

Abstract We develop an algorithm to detect and locate sources of long-period (25–100 s) seismic signals. Our method is based on the analysis of seismological data recorded at global networks, plus all available stations at latitude larger (smaller) than 60° (−60°). We use a delay and stack method to identify coherent Rayleigh waves generated at the free surface of the Earth. The application of our approach to 13 yr of continuous data permitted us to detect more than 36,000 events. After a precise analysis to classify known events and to remove spurious detections, we report more than 1700 previously unidentified source of long-period seismic signals. Each source is characterized by its location (with associated uncertainty) and moment magnitude. This new catalog mainly reveals sources in the polar regions likely associated with glaciers dynamics but also volcanic activity, landslides, and regular earthquakes located in remote areas of the planet. Our study reveals the importance of maintaining global seismic networks and exploring the recorded data, beyond providing new observations that can be the basis to future studies to better characterize physical processes occurring at the free surface of our planet.
MDFF-Net: A multi-dimensional feature fusion network for breast histopathology image classification

Breast cancer is a common malignancy and early detection and treatment of it is crucial. Computer-aided diagnosis (CAD) based on deep learning has significantly advanced medical diagnostics, enhancing accuracy and efficiency in recent years. Despite the convenience, this technology also has certain limitations. When the morphological characteristics of the patient's pathological section are not evident or complex, certain small lesions or cells deep within the lesion cannot be recognized, and misdiagnosis is prone to occur. As a result, MDFF-Net, a CNN-based multidimensional feature fusion network, is proposed. The model consists of a one-dimensional feature extraction network, a two-dimensional feature extraction network, and a feature fusion classification network. The basic part of the two-dimensional feature extraction network is stacked by modules integrated with multi-scale channel shuffling networks and channel attention modules. Furthermore, inspired by natural language processing, this model integrates a one-dimensional feature extraction network to extract detailed information in the image to avoid misdiagnosis caused by insufficient information extraction such as cell morphological characteristics and differentiation degree. Finally, the extracted one-dimensional and two-dimensional features are fused in the feature fusion network and employed for the final classification. The effectiveness of MDFF-Net and classical classification models were evaluated on the BreakHis and the BACH datasets. According to experimental results, MDFF-Net achieves an accuracy of 98.86% on the BreakHis and 86.25% on the BACH dataset. Furthermore, to further assess the effectiveness of the model in other classification tasks, the colon cancer and the lung cancer datasets were employed for additional experiments, achieving a classification accuracy of 100% in both cases.
Frequency inception based graph neural network for relation prediction in knowledge graphs

Recently, knowledge graphs have been broadly studied using approaches such as translation-based models and convolutional neural networks. Although such approaches can express powerful and rich embeddings, graphs are naturally suitable for irregular data. Thus, they can model knowledge graphs with structured data and aggregate considerable information. However, graphs are subject to incompleteness, leading to unconnected knowledge graphs. To address this problem, we propose a framework called frequency-inception-based graph neural network (FiGNN) for relation prediction in knowledge graphs. It exploits a graph to aggregate the most beneficial information through mathematical analysis. Specifically, combined with the relations and inception parameters, each channel of a node and its neighbours dynamically contribute to next-layer channel information, which can extract underlying signals of different channels and neighbouring nodes. Moreover, the representation ability of the nodes is enhanced and over-smoothing is alleviated. We validate the effectiveness of the proposed network on various benchmark datasets. The experimental results demonstrate that our network achieves substantial improvements and substantially outperforms state-of-the-art methods.
Graph-based robot optimal path planning with bio-inspired algorithms

Recently, bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps. However, these approaches endure performance degradation as problem complexity increases, often resulting in lengthy search times to find an optimal solution. This limitation is particularly critical for real-world applications like autonomous off-road vehicles, where high-quality path computation is essential for energy efficiency. To address these challenges, this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm, improved seagull optimization algorithm (iSOA) for rapid path planning of autonomous robots. A modified Douglas-Peucker (mDP) algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains. The resulting mDP-derived graph is then modeled using a Maklink graph theory. By applying the iSOA approach, the trajectory of an autonomous robot in the workspace is optimized. Additionally, a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints. The proposed model is validated through simulated experiments undertaken in various real-world settings, and its performance is compared with state-of-the-art algorithms. The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.
3D face recognition: A comprehensive survey in 2022

Abstract In the past ten years, research on face recognition has shifted to using 3D facial surfaces, as 3D geometric information provides more discriminative features. This comprehensive survey reviews 3D face recognition techniques developed in the past decade, both conventional methods and deep learning methods. These methods are evaluated with detailed descriptions of selected representative works. Their advantages and disadvantages are summarized in terms of accuracy, complexity, and robustness to facial variations (expression, pose, occlusion, etc.). A review of 3D face databases is also provided, and a discussion of future research challenges and directions of the topic.
Adaptive filters in Graph Convolutional Neural Networks

Over the last few years, we have witnessed the availability of an increasing data generated from non-Euclidean domains, which are usually represented as graphs with complex relationships, and Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data. In particular, there is a strong interest in exploring the possibilities in performing convolution on graphs using an extension of the GNN architecture, generally referred to as Graph Convolutional Neural Networks (ConvGNN). Convolution on graphs has been achieved mainly in two forms: spectral and spatial convolutions. Due to the higher flexibility in exploring and exploiting the graph structure of data, there is recently an increasing interest in investigating the possibilities that the spatial approach can offer. The idea of finding a way to adapt the network behaviour to the inputs they process to maximize the total performances has aroused much interest in the neural networks literature over the years. This paper presents a novel method to adapt the behaviour of a ConvGNN to the input proposing a method to perform spatial convolution on graphs using input-specific filters, which are dynamically generated from nodes feature vectors. The experimental assessment confirms the capabilities of the proposed approach, which achieves satisfying results using a low number of filters.
Inverse Extended Kalman Filter—Part I: Fundamentals

Recent advances in counter-adversarial systems have garnered significant research attention to inverse filtering from a Bayesian perspective. For example, interest in estimating the adversary’s Kalman filter tracked estimate with the purpose of predicting the adversary’s future steps has led to recent formulations of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inverse Kalman filter</i> (I-KF). In this context of inverse filtering, we address the key challenges of non-linear process dynamics and unknown input to the forward filter by proposing an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inverse extended Kalman filter</i> (I-EKF). The purpose of this paper and the companion paper (Part II) is to develop the theory of I-EKF in detail. In this paper, we assume perfect system model information and derive I-EKF with and without an unknown input when both forward and inverse state-space models are non-linear. In the process, I-KF-with-unknown-input is also obtained. We then provide theoretical stability guarantees using both bounded non-linearity and unknown matrix approaches and prove the I-EKF’s consistency. Numerical experiments validate our methods for various proposed inverse filters using the recursive Cram´er-Rao lower bound as a benchmark. In the companion paper (Part II), we further generalize these formulations to highly non-linear models and propose reproducing kernel Hilbert spacebased EKF to handle incomplete system model information.