Reviewerly/exHarmony huggingface dataset:
Skeleton Graph-Based Ultrasound-CT Non-Rigid Registration

Autonomous ultrasound (US) scanning has attracted increased attention, and it has been seen as a potential solution to overcome the limitations of conventional US examinations, such as inter-operator variations. However, it is still challenging to autonomously and accurately transfer a planned scan trajectory on a generic atlas to the current setup for different patients, particularly for thorax applications with limited acoustic windows. To address this challenge, we proposed a skeleton graph-based non-rigid registration to adapt patient-specific properties using subcutaneous bone surface features rather than the skin surface. To this end, the self-organization mapping is successively used twice to unify the input point cloud and extract the key points, respectively. Afterward, the minimal spanning tree is employed to generate a tree graph to connect all extracted key points. To appropriately characterize the rib cartilage outline to match the source and target point cloud, the path extracted from the tree graph is optimized by maximally maintaining continuity throughout each rib. To validate the proposed approach, we manually extract the US cartilage point cloud from one volunteer and seven CT cartilage point clouds from different patients. The results demonstrate that the proposed graph-based registration is more effective and robust in adapting to the inter-patient variations than the ICP (distance error mean <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\pm$</tex-math></inline-formula> SD: <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5.0\pm \text{1.9}\,\text{mm}$</tex-math></inline-formula> vs <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$8.6\pm \text{6.7}\,\text{mm}$</tex-math></inline-formula> on seven CTs).
Enhanced crow search algorithm with multi-stage search integration for global optimization problems

Crow search algorithm (CSA), as a new swarm intelligence algorithm that simulates the crows’ behaviors of hiding and tracking food in nature, performs well in solving many optimization problems. However, while handling complex and high-dimensional global optimization problems, CSA is apt to fall into evolutionary stagnation and has slow convergence speed, low accuracy, and weak robustness. This is mainly because it only utilizes a single search stage, where position updating relies on random following among individuals or arbitrary flight of individuals. To address these deficiencies, a CSA with multi-stage search integration (MSCSA) is presented. Chaos and multiple opposition-based learning techniques are first introduced to improve original population quality and ergodicity. The free foraging stage based on normal random distribution and Lévy flight is designed to conduct local search for enhancing the solution accuracy. And the following stage using mixed guiding individuals is presented to perform global search for expanding the search space through tracing each other among individuals. Finally, the large-scale migration stage based on the best individual and mixed guiding individuals concentrates on increasing the population diversity and helping the population jump out of local optima by moving the population to a promising area. All of these strategies form multi-level and multi-granularity balances between global exploration and local exploitation throughout the evolution. The proposed MSCSA is compared with a range of other algorithms, including original CSA, three outstanding variants of CSA, two classical meta-heuristics, and six state-of-the-art meta-heuristics covering different categories. The experiments are conducted based on the complex and high-dimensional benchmark functions CEC 2017 and CEC 2010, respectively. The experimental and statistical results demonstrate that MSCSA is competitive for tackling large-scale complicated problems, and is significantly superior to the competitors.
Recognition of bird nests on transmission lines based on YOLOv5 and DETR using small samples

The safety and reliability of transmission line operations are critical to the delivery of electricity. Bird nests are one of the frequent potential factors affecting the safety of transmission lines. The main objective of this paper is to propose a small-sample learning method to detect bird nests on transmission lines. The method combines the two latest deep learning models, YOLOv5 and detection transformer (DETR). Inspired by biological vision, this method transfers the learning of bird nests in daily scenes to the recognition of bird nests on transmission lines. The proposed method is evaluated by two public datasets. The test on the first one presents a recognition rate of 95.50%, whereas the training set only contains ten homologous data and 80 non-homologous data. The second test shows that 85.54% of the samples are recognized by generalization without homologous training data. The results show that our method provides a way to identify bird nests on transmission lines with the help of bird nests of daily scenes under small sample conditions.
Mobile authentication of copy detection patterns

In the recent years, the copy detection patterns (CDP) attracted a lot of attention as a link between the physical and digital worlds, which is of great interest for the internet of things and brand protection applications. However, the security of CDP in terms of their reproducibility by unauthorized parties or clonability remains largely unexplored. In this respect, this paper addresses a problem of anti-counterfeiting of physical objects and aims at investigating the authentication aspects and the resistances to illegal copying of the modern CDP from machine learning perspectives. A special attention is paid to a reliable authentication under the real-life verification conditions when the codes are printed on an industrial printer and enrolled via modern mobile phones under regular light conditions. The theoretical and empirical investigation of authentication aspects of CDP is performed with respect to four types of copy fakes from the point of view of (i) multi-class supervised classification as a baseline approach and (ii) one-class classification as a real-life application case. The obtained results show that the modern machine-learning approaches and the technical capacities of modern mobile phones allow to reliably authenticate CDP on end-user mobile phones under the considered classes of fakes.
Underwater Small Target Detection Based on YOLOX Combined with MobileViT and Double Coordinate Attention

The underwater imaging environment is complex, and the application of conventional target detection algorithms to the underwater environment has yet to provide satisfactory results. Therefore, underwater optical image target detection remains one of the most challenging tasks involved with neighborhood-based techniques in the field of computer vision. Small underwater targets, dispersion, and sources of distortion (such as sediment and particles) often render neighborhood-based techniques insufficient, as existing target detection algorithms primarily focus on improving detection accuracy and enhancing algorithm complexity and computing power. However, excessive extraction of deep-level features leads to the loss of small targets and decrease in detection accuracy. Moreover, most underwater optical image target detection is performed by underwater unmanned platforms, which have a high demand of algorithm lightweight requirements due to the limited computing power of the underwater unmanned platform with the mobile vision processing platform. In order to meet the lightweight requirements of the underwater unmanned platform without affecting the detection accuracy of the target, we propose an underwater target detection model based on mobile vision transformer (MobileViT) and YOLOX, and we design a new coordinate attention (CA) mechanism named a double CA (DCA) mechanism. This model utilizes MobileViT as the algorithm backbone network, improving the global feature extraction ability of the algorithm and reducing the amount of algorithm parameters. The double CA (DCA) mechanism can improve the extraction of shallow features as well as the detection accuracy, even for difficult targets, using a minimum of parameters. Research validated in the Underwater Robot Professional Contest 2020 (URPC2020) dataset revealed that this method has an average accuracy rate of 72.00%. In addition, YOLOX’s ability to compress the model parameters by 49.6% efficiently achieves a balance between underwater optical image detection accuracy and parameter quantity. Compared with the existing algorithm, the proposed algorithm can carry on the underwater unmanned platform better.
Prediction Calibration for Generalized Few-Shot Semantic Segmentation

Generalized Few-shot Semantic Segmentation (GFSS) aims to segment each image pixel into either base classes with abundant training examples or novel classes with only a handful of (e.g., 1-5) training images per class. Compared to the widely studied Few-shot Semantic Segmentation FSS, which is limited to segmenting novel classes only, GFSS is much under-studied despite being more practical. Existing approach to GFSS is based on classifier parameter fusion whereby a newly trained novel class classifier and a pre-trained base class classifier are combined to form a new classifier. As the training data is dominated by base classes, this approach is inevitably biased towards the base classes. In this work, we propose a novel Prediction Calibration Network PCN to address this problem. Instead of fusing the classifier parameters, we fuse the scores produced separately by the base and novel classifiers. To ensure that the fused scores are not biased to either the base or novel classes, a new Transformer-based calibration module is introduced. It is known that the lower-level features are useful of detecting edge information in an input image than higher-level features. Thus, we build a cross-attention module that guides the classifier's final prediction using the fused multi-level features. However, transformers are computationally demanding. Crucially, to make the proposed cross-attention module training tractable at the pixel level, this module is designed based on feature-score cross-covariance and episodically trained to be generalizable at inference time. Extensive experiments on PASCAL-$5^{i}$ and COCO-$20^{i}$ show that our PCN outperforms the state-the-the-art alternatives by large margins.
Integrating BERT Embeddings and BiLSTM for Emotion Analysis of Dialogue

Dialogue system is an important application of natural language processing in human-computer interaction. Emotion analysis of dialogue aims to classify the emotion of each utterance in dialogue, which is crucially important to dialogue system. In dialogue system, emotion analysis is helpful to the semantic understanding and response generation and is great significance to the practical application of customer service quality inspection, intelligent customer service system, chatbots, and so on. However, it is challenging to solve the problems of short text, synonyms, neologisms, and reversed word order for emotion analysis in dialogue. In this paper, we analyze that the feature modeling of different dimensions of dialogue utterances is helpful to achieve more accurate sentiment analysis. Based on this, we propose the BERT (bidirectional encoder representation from transformers) model that is used to generate word-level and sentence-level vectors, and then, word-level vectors are combined with BiLSTM (bidirectional long short-term memory) that can better capture bidirectional semantic dependencies, and word-level and sentence-level vectors are connected and inputted to linear layer to determine emotions in dialogue. The experimental results on two real dialogue datasets show that the proposed method significantly outperforms the baselines.
A Comprehensive Survey of Blockchain-Powered Cybersecurity- A survey

Text mining was used to examine the academic publications on blockchain technology and cybersecurity that were published in huge digital libraries. Automated text mining methods like topic modeling and key word extraction are utilized in this literature study to extract topics from a vast corpus of literature. This article emphasizes the cross-disciplinary character of blockchain technology in the context of cybersecurity. The outcomes also show the risks and security holes that emerge as blockchain technology matures. Nevertheless, the investigation this article also outlines research gaps in the area of computer security and recommends research directions for future work that will be necessary to create safe blockchain platforms.
Blockchain Enabled Intelligent Surveillance System Model with AI and IoT

In the New Year, Internet of Things (IoT) is industrializing in a few certifiable applications, for example, clever transportation, brilliant city to make human existence reliable. Extreme amounts of detecting information are being delivered from various sensors devices in the Modern IoT as a result of the growing industrialization of IoT. Understanding the constraints of flow innovations and the potential for future exploration techniques is necessary to the establishment of a secure and dependable information environment. A dishonest individual can connect with a trusted person through the blockchain, a decentralized digital ledger that tracks the conversation from start to finish. This report urges the medical care industry to deploy blockchain-coordinated digital protection in order to preserve clinically relevant things in light of the use of artificial intelligence in clinical settings. Applications based on blockchain may be able to precisely pinpoint the most serious, potentially dangerous mistakes in the medical sector. Blockchain-based decentralized information insurance safeguards patient health records from data theft.
An intelligent feature selection method using binary teaching-learning based optimization algorithm and ANN

The most challenging issue in dealing with big datasets is the large number of their dimensions. Feature selection is a technique for reducing the dimensionality of datasets by removing irrelevant and useless features from the dataset, finally selecting the most crucial features in the dataset, will ultimately increase the algorithm's efficiency. This paper presents a novel feature selection procedure that includes a binary-modified teaching learning-based optimization algorithm (BMTLBO). One of the most effective and popular methods is the TLBO algorithm. Although this algorithm has a good convergence rate, there is a chance to get stuck in a local optimum. Our goal is to balance between exploration and exploitation capabilities. The proposed method consists of two parts: First, the BMTLBO algorithm, which includes the improved binary version of the basic algorithm, is used for the feature selection problem. The idea of a pool is used in this algorithm that boosts the population's diversity while enhancing the algorithm's precision and convergence rate. Second, we are used from improved TLBO algorithm with the self-learning phase (SLTLBO) for neural network training to show the application of the classification problem to evaluate the performance of the procedures of the method. We are tested the proposed method on 14 datasets in the term of classification accuracy and the number of features. Additionally, a different chemical data set named Biodegradable is used to assess the proposed method. The evaluation results demonstrate that, in terms of accuracy, convergence rate, and success in achieving promising solutions, the proposed algorithm outperforms all other optimization algorithms compared. The results are very promising and close to optimal.
DSC-MVSNet: attention aware cost volume regularization based on depthwise separable convolution for multi-view stereo

Abstract Deep learning has recently been proven to deliver excellent performance in multi-view stereo (MVS). However, it is difficult for deep learning-based MVS approaches to balance their efficiency and effectiveness. Towards this end, we propose the DSC-MVSNet, a novel coarse-to-fine and end-to-end framework for more efficient and more accurate depth estimation in MVS. In particular, we propose an attention aware 3D UNet-shape network, which first uses the depthwise separable convolutions for cost volume regularization. This mechanism enables effective aggregation of information and significantly reduces the model parameters and computation by transforming the ordinary convolution on cost volume as depthwise convolution and pointwise convolution. Besides, a 3D-Attention module is proposed to alleviate the feature mismatching problem in cost volume regularization and aggregate the important information of cost volume in three dimensions (i.e. channel, space, and depth). Moreover, we propose an efficient Feature Transfer Module to upsample the low-resolution (LR) depth map to a high-resolution (HR) depth map to achieve higher accuracy. With extensive experiments on two benchmark datasets, i.e. DTU and Tanks &amp; Temples, we demonstrate that the parameters of our model are significantly reduced to $$25\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>25</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> of the state-of-the-art model MVSNet. Besides, our method outperforms or maintains on par accuracy with the state-of-the-art models. Our source code is available at https://github.com/zs670980918/DSC-MVSNet .
Rethinking Boundary Detection in Deep Learning Models for Medical Image Segmentation

Medical image segmentation is a fundamental task in the community of medical image analysis. In this paper, a novel network architecture, referred to as Convolution, Transformer, and Operator (CTO), is proposed. CTO employs a combination of Convolutional Neural Networks (CNNs), Vision Transformer (ViT), and an explicit boundary detection operator to achieve high recognition accuracy while maintaining an optimal balance between accuracy and efficiency. The proposed CTO follows the standard encoder-decoder segmentation paradigm, where the encoder network incorporates a popular CNN backbone for capturing local semantic information, and a lightweight ViT assistant for integrating long-range dependencies. To enhance the learning capacity on boundary, a boundary-guided decoder network is proposed that uses a boundary mask obtained from a dedicated boundary detection operator as explicit supervision to guide the decoding learning process. The performance of the proposed method is evaluated on six challenging medical image segmentation datasets, demonstrating that CTO achieves state-of-the-art accuracy with a competitive model complexity.
blob loss: Instance Imbalance Aware Loss Functions for Semantic Segmentation

Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can tackle class imbalance, however, it does not recognize instance imbalance within a class. As a result, a large foreground instance can dominate minor instances and still produce a satisfactory DSC. Nevertheless, detecting tiny instances is crucial for many applications, such as disease monitoring. For example, it is imperative to locate and surveil small-scale lesions in the follow-up of multiple sclerosis patients. We propose a novel family of loss functions, blob loss, primarily aimed at maximizing instance-level detection metrics, such as F1 score and sensitivity. Blob loss is designed for semantic segmentation problems where detecting multiple instances matters. We extensively evaluate a DSC-based blob loss in five complex 3D semantic segmentation tasks featuring pronounced instance heterogeneity in terms of texture and morphology. Compared to soft Dice loss, we achieve 5% improvement for MS lesions, 3% improvement for liver tumor, and an average 2% improvement for microscopy segmentation tasks considering F1 score.
An Intelligent Fault Detection Framework for FW-UAV Based on Hybrid Deep Domain Adaptation Networks and the Hampel Filter

Fixed-wing unmanned aerial vehicles (FW-UAVs) play an essential role in many fields, but the faults of FW-UAV components lead to severe accidents frequently; so, there is a need to continuously explore more intelligent fault detection methods to improve the safety and reliability of FW-UAVs. Deep learning provides advanced solution ideas for future UAV fault detection, but the current lack of UAV monitoring data limits the advantages of deep learning in UAV fault detection, which are both a challenge and an opportunity. In this paper, we mainly consider the data availability of deep learning under various practical flight conditions of FW-UAVs and propose a fault detection framework based on hybrid deep domain adaptation BiLSTM networks and the Hampel filter (HDBNH), the main purpose of which is to learn the knowledge of acquired data for detecting FW-UAV faults in other unknown operating conditions. HDBNH consists of three modules: feature extractor, domain adaptor, and fault detector. The feature extractor is two BiLSTM networks constructed to extract the past and future state features from the time-series flight data. The discrepancy of feature distribution between different domains is effectively reduced in the domain adaptor by a hybrid adversarial and the maximum mean discrepancy (MMD) domain adaptation method. The fault detector consists of a fault classification module and a Hampel filter. According to the continuous and dynamic characteristics of FW-UAV state changes, the Hampel filter is used to detect and correct the predicted values of the fault classification module. Meanwhile, a new state sample preparation strategy is proposed to support the work of HDBNH better. Finally, the effectiveness of HDBNH is confirmed by conducting extensive experiments in real FW-UAV flight data.
Towards AI forensics: Did the artificial intelligence system do it?

Artificial intelligence (AI) makes decisions impacting our daily lives in an increasingly autonomous manner. Their actions might cause accidents, harm, or, more generally, violate regulations. Determining whether an AI caused a specific event and, if so, what triggered the AI’s action, are key forensic questions. We provide a conceptualization of the problems and strategies for forensic investigation. We focus on AI that is potentially “malicious by design” and gray box analysis. Our evaluation using convolutional neural networks illustrates challenges and ideas for identifying malicious AI.
A Century-Long Challenge in Teletraffic Theory: Blocking Probability Evaluation for Overflow Loss Systems With Mutual Overflow

In this review, we describe historical and recent developments towards tackling a centurylong challenge in teletraffic theory, namely the evaluation of blocking probability in overflow systems with mutual overflow.Such systems have many applications in a variety of telecommunications and service systems, including wireless communications, cloud computing, intensive care, and emergency services, and various methods have been developed over the past century to address this challenge.In particular, the recent development of the Information Exchange Surrogate Approximation (IESA) (Wong et al., Sep. 2013; Chan and Wong, 2018) provides significantly increased accuracy and robustness compared to previous approximation methods of its kind while also providing high computational efficiency not available via simulation or exact analysis.To the best of our knowledge, IESA is the first analytical method to combine high levels of accuracy, robustness, and computational efficiency when evaluating blocking probability in overflow systems with mutual overflow, and thus forms a major breakthrough in this century-long effort.