High-level Information Research Articles

Panoramic radiograph quality assessment: Database and algorithm

High-quality panoramic radiographs are crucial for providing accurate diagnosis and appropriate treatment to the patients, which have been widely employed in dentist clinic. Unfortunately, panoramic radiographs can be bothered with various distortions during the capture and imaging process, which can further negatively affect the judgment of dentists. Therefore, to deal with the challenge of panoramic radiographs quality assessment, we first carry out a large scale panoramic radiograph quality assessment database, which contains 2009 images labeled by experienced dentists. Then we propose a novel objective quality assessment method based on the convolutional neural network (CNN). Specifically, the multi-scale features from the different stages of the CNN are employed to make full use of both low-level and high-level information. Moreover, weight-sharing auxiliary networks are utilized to improve the quality understanding of the proposed method. The experimental results show that the proposed method outperforms all the compared methods for panoramic radiographs quality assessment tasks and is more capable of handling the practical medical diagnosis situations. The proposed method can also be used as preliminary screening tool and provide useful guidelines for the dentists.

An adversarial contrastive autoencoder for robust multivariate time series anomaly detection

Multivariate time series (MTS), whose patterns change dynamically, often have complex temporal and dimensional dependence. Most existing reconstruction-based MTS anomaly detection methods only learn the point-wise information while ignoring the overall trend of time series, resulting in their incompetence in extracting high-level semantic information. Although a few contrastive learning-based approaches have been proposed recently to solve this problem, they forcibly increase the difference between the features of normal data, leading to the loss of useful information. This paper proposes an adversarial contrastive autoencoder (ACAE) for MTS anomaly detection. ACAE conducts feature combination and decomposition as the contrastive learning proxy task, which introduces adversarial training to learn the transformation-invariant representation of data, achieving a robust representation of MTS. Firstly, ACAE constructs positive and negative sample pairs through the multi-scale timestamp mask and random sampling. Secondly, the features of the original samples are combined with those of the positive and negative samples to generate the positive and negative composite features. Finally, ACAE trains the encoder and discriminator to decompose the negative composite features cooperatively to decrease the similarity between the features of negative pairs. In contrast, it adversarially decomposes the positive composite features to increase the similarity between the features of positive pairs. Experimental results show that ACAE outperforms 14 state-of-the-art baselines on five real-world datasets from different fields.

EDF-YOLOv5: An Improved Algorithm for Power Transmission Line Defect Detection Based on YOLOv5

Detecting defects in power transmission lines through unmanned aerial inspection images is crucial for evaluating the operational status of outdoor transmission equipment. This paper presents a defect recognition method called EDF-YOLOv5, which is based on the YOLOv5s, to enhance detection accuracy. Firstly, the EN-SPPFCSPC module is designed to improve the algorithm’s ability to extract information, thereby enhancing the detection performance for small target defects. Secondly, the algorithm incorporates a high-level semantic feature information extraction network, DCNv3C3, which improves its ability to generalize to defects of different shapes. Lastly, a new bounding box loss function, Focal-CIoU, is introduced to enhance the contribution of high-quality samples during training. The experimental results demonstrate that the enhanced algorithm achieves a 2.3% increase in mean average precision (mAP@.5) for power transmission line defect detection, a 0.9% improvement in F1-score, and operates at a detection speed of 117 frames per second. These findings highlight the superior performance of EDF-YOLOv5 in detecting power transmission line defects.

MSDANet: A multi-scale dilation attention network for medical image segmentation

Deep learning shows excellent performance in medical image segmentation. However, a pooling operation in its encoding stage leads to feature loss and the ability of multi-scale contextual information extraction is insufficient. In addition, there is a semantic gap between low-level features and high-level features in the encoder–decoder. Inspired by an U-shaped network (UNet), this work proposes a multi-scale dilation attention network (MSDANet) for medical image segmentation. Specifically, it is mainly composed of a parallel dilation pooling module (PD), a multi-scale channel attention mechanism (MSCA), a multilayer perceptron squeeze and excitation module (MSE), a big kernel convolution module (BC), and a skip feature pyramid module (SFP). The proposed PD reduces the loss of subtle features during downsampling. In order to improve the extraction of effective features, MSCA, MSE and BC are used in encoding and decoding stages. In the skip connection stage, SPF is used to reduce the difference between the low-level and high-level semantic information to accelerate the network learning. We validate the proposed model on two publicly available medical image datasets. The results show that MSDANet has superior performance on several evaluation metrics compared to state-of-the-art methods. Code is available at https://github.com/1999luan/MSDANet.

Semantic perceptive infrared and visible image fusion Transformer

Deep learning based fusion mechanisms have achieved sophisticated performance in the field of image fusion. However, most existing approaches focus on learning global and local features but seldom consider to modeling semantic information, which might result in inadequate source information preservation. In this work, we propose a semantic perceptive infrared and visible image fusion Transformer (SePT). The proposed SePT extracts local feature through convolutional neural network (CNN) based module and learns long-range dependency through Transformer based modules, and meanwhile designs two semantic modeling modules based on Transformer architecture to manage high-level semantic information. One semantic modeling module maps the shallow features of source images into deep semantic, the other learns the deep semantic information in different receptive fields. The final fused results are recovered from the combination of local feature, long-range dependency and semantic feature. Extensive comparison experiments demonstrate the superiority of SePT compare to other advanced fusion approaches.

CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification.

The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagnose multiple diseases that occurred in a single X-Ray image since multiple levels of features are generated in the images, and create different features as in single label detection. Various works were developed to address this challenge with proposed deep learning architectures to improve classification performance and enrich diagnosis results with multi-probability disease detection. The objective is to create an accurate result and a faster inference system to support a quick diagnosis in the medical system. To contribute to this state-of-the-art, we designed a fusion architecture, CheXNet and Feature Pyramid Network (FPN), to classify and discriminate multiple thoracic diseases from chest X-Rays. This concept enables the model to extract while creating a pyramid of feature maps with different spatial resolutions that capture low-level and high-level semantic information to encounter multiple features. The model's effectiveness is evaluated using the NIH ChestXray14 dataset, with the Area Under Curve (AUC) and accuracy metrics used to compare the results against other cutting-edge approaches. The overall results demonstrate that our method outperforms other approaches and has become promising for multilabel disease classification in chest X-Rays, with potential applications in clinical practice. The result demonstrated that we achieved an average AUC of 0.846 and an accuracy of 0.914. Further, our proposed architecture diagnoses images in 0.013s, faster than the latest approaches.

EFDCNet: Encoding fusion and decoding correction network for RGB-D indoor semantic segmentation

Semantic segmentation is a crucial task in vision measurement systems that involves understanding and segmenting different objects and regions within an image. Over the years, numerous RGB-D semantic segmentation methods have been developed, leveraging the encoder-decoder architecture to achieve outstanding performance. However, existing methods have two main problems that constrain further performance improvement. Firstly, in the encoding stage, existing methods have a weak ability to fuse cross-modal information, and low-quality depth maps can easily lead to poor feature representation. Secondly, in the decoding stage, the upsampling of high-level semantic information may cause the loss of contextual information, and low-level features from the encoder may bring noises to the decoder through skip connections. To solve these issues, we propose a novel Encoding Fusion and Decoding Correction Network (EFDCNet) for RGB-D indoor semantic segmentation. First, in the encoding stage of EFDCNet, we focus on extracting valuable information from low-quality depth maps, and employ a channel-wise filter to select informative depth features. Additionally, we establish the global dependencies between RGB and depth features via the self-attention mechanism to enhance the cross-modal feature interactions, extracting discriminant and powerful features. Then, in the decoding stage of EFDCNet, we use the highest-level information as semantic guidance to compensate for the upsampling information and filter out noise from the low-level encoder features propagated through the skip connections to the decoder. Extensive experiments conducted on two widely-used RGB-D indoor semantic segmentation datasets demonstrate that the proposed EFDCNet surpasses the performance of relevant state-of-the-art methods. The code is available at https://github.com/ Mark9010/EFDCNet

Большой город как предпосылка развития альтернативного школьного образования

A large city 'gives birth' to groups of carriers of certain ideas, values, and attitudes, who seek each other out. Parents choose schools that appear closer to their own values, where the methods of transmitting these values and the forms of classes seem more preferable and coherent. Schools 'attract' those parents whose basic life attitudes align and correlate with the school's mission, values, and strategy. The study aimed to identify factors influencing the development of alternative education in large cities. To achieve this, information was collected and analyzed on 667 private alternative educational projects in the five largest cities of Russia (Moscow, Saint Petersburg, Novosibirsk, Yekaterinburg, Kazan). In each city, five leaders of private alternative educational projects were identified and interviewed in-depth. Interviews and focus groups were also conducted with experts in alternative education. The study revealed that the concentration of maximum professional, infrastructural, economic, cultural, religious, political-ideological, and social diversity in large cities is a precondition for the development of alternative school education. Stimulating factors for the development of alternative education in large cities include overcrowded schools and the quality of education not meeting parents' expectations. The factor of parents plays a significant role: their age, involvement in their children's education, high level of information, etc. The lifestyle of city dwellers is a formative factor. Here, two strategies can be identified: the 'all inclusive' strategy and the strategy of education connoisseurs. At the same time, in a large city, there are also limiting factors for the development of alternative education: a complex transport system and logistical component, expensive rent and/or cost of buildings.

CREATING A DATA PROCESSING SYSTEM FOR THERAPY DEPARTMENT

Over the past few decades, medical ideas and technologies that were previously considered science fiction have rapidly burst into basic and clinical medicine, allowing it to make a transition from standardized treatment methods to individual selection of medicines, taking into account the genetic characteristics of each individual patient. In addition, the generation of ideas at the intersection of medicine and information technology is changing the usual approaches to health monitoring and treatment. In this context, considering medicine as a complex and specific field of activity, which is characterized by a very high level of heterogeneous medical and economic information, two tasks are updated - optimization of the clinical and economic activities of a medical institution. The ultimate goal of these tasks is to optimize the performance of the healthcare system in general and any medical institution in particular. The introducing of automatic systems for processing and analyzing large volumes of data is relevant not only in clinical practice, but also in the healthcare system as a whole for planning the financing and management of medical organizations, monitoring the circulation of medicines and pricing throughout the country, taking into account the characteristics of each region, for achieving better efficiency. The article is devoted to the creation of functional circuit medical department disease, data processing, definition of technological schemes of the department,describe the history of the disease formatting. Keywords: database (DB), IS (information systems), EJ (extract journal)

Hierarchical attention-guided multiscale aggregation network for infrared small target detection

All man-made flying objects in the sky, ships in the ocean can be regarded as small infrared targets, and the method of tracking them has been received widespread attention in recent years. In search of a further efficient method for infrared small target recognition, we propose a hierarchical attention-guided multiscale aggregation network (HAMANet) in this thesis. The proposed HAMANet mainly consists of a compound guide multilayer perceptron (CG-MLP) block embedded in the backbone net, a spatial-interactive attention module (SiAM), a pixel-interactive attention module (PiAM) and a contextual fusion module (CFM). The CG-MLP marked the width-axis, height-axis, and channel-axis, which can result in a better segmentation effect while reducing computational complexity. SiAM improves global semantic information exchange by increasing the connections between different channels, while PiAM changes the extraction of local key information features by enhancing information exchange at the pixel level. CFM fuses low-level positional information and high-level channel information of the target through coding to improve network stability and target feature utilization. Compared with other state-of-the-art methods on public infrared small target datasets, the results show that our proposed HAMANet has high detection accuracy and a low false-alarm rate.

How Do Uncertainties Affect Supply-Chain Resilience? The Moderating Role of Information Sharing for Sustainable Supply-Chain Management

Uncertainties caused by many internal and external factors can lead to supply-chain disruptions, increasing the vulnerability and cost of operations. In particular, the COVID-19 pandemic, whose worldwide emergence was not foreseen, has become a major threat to supply-chain resilience and has caused the disruption of global network connections. The purpose of this study is to examine in depth the impact of uncertainty on supply-chain resilience and to determine whether information sharing has a moderating effect on this interaction. The relationships proposed in the research model are tested through empirical analyses in SEM applied to 244 survey data points from internationally operating manufacturing firms in Turkey. The findings reveal several key insights. First, it is concluded that all dimensions of uncertainty, except technological uncertainty, negatively affect supply-chain resilience. Second, although no direct effect of technological uncertainty on supply-chain resilience is found, technological uncertainty has a negative effect on resilience when the moderating role of internal and supplier information sharing is taken into account. Low-level information sharing, as opposed to high-level, creates variation in the severity of supply-chain resilience at different levels of technological uncertainty. In addition, it is worth noting that a high level of information sharing with suppliers under high-uncertainty conditions negatively affects supply-chain resilience. The results of this study, conducted within the framework of the Contingent Resource-Based Theory, demonstrate compatibility with the theory. Based on all the findings, this study suggests that managers should adopt proactive strategies to maintain high supply-chain resilience, considering today’s highly uncertain conditions.

GaFL: Geometric-aware Feature Learning for universal 3D models recognition

Existing methods of 3D model recognition mainly depend on deep learning algorithms. However, the extracted deep feature lacks the geometric information of the models. Thus, these methods fail to identify rigid and non-rigid 3D models simultaneously, i.e., universal 3D models recognition. In this work, we propose a novel method, Geometric-aware Feature Learning (GaFL), to further investigate the combination mechanism of geometric feature and deep learning in universal 3D models recognition. In GaFL, we design the Layer-Projected and Ray-Projected feature extraction policies to obtain depth values, which contain rich geometric information. Furthermore, the sphere convolution is proposed to guarantee the continuity and integrity of the ray feature when feeding into a deep network and the feature inactivation fusion module is designed to achieve the complementarity between layer and ray feature. Finally, the final merged feature vector contains enough geometric information as well as high-level semantic information, which are critical to universal 3D models recognition. In the experiments, GaFL achieves 95.0% and 95.2% classification accuracy in the rigid 3D models dataset ModelNet40 and the non-rigid 3D models dataset SHREC16, respectively, indicating that GaFL is powerful in universal 3D models recognition. Moreover, its significant advantage over state-of-the-art methods has also been validated on three other datasets, i.e., ShapeNet Core55, ScanObjectNN and SHREC15.

BLIND: A privacy preserving truth discovery system for mobile crowdsensing

Nowadays, an increasing number of applications exploit users who act as intelligent sensors and can quickly provide high-level information. These users generate valuable data that, if mishandled, could potentially reveal sensitive information. Protecting user privacy is thus of paramount importance for crowdsensing systems. In this paper, we propose BLIND, an innovative open-source truth discovery system designed to improve the quality of information (QoI) through the use of privacy-preserving computation techniques in mobile crowdsensing scenarios. The uniqueness of BLIND lies in its ability to preserve user privacy by ensuring that none of the parties involved are able to identify the source of the information provided. The system uses homomorphic encryption to implement a novel privacy-preserving version of the well-known K-Means clustering algorithm, which directly groups encrypted user data. Outliers are then removed privately without revealing any useful information to the parties involved. We extensively evaluate the proposed system for both server-side and client-side scalability, as well as truth discovery accuracy, using a real-world dataset and a synthetic one, to test the system under challenging conditions. Comparisons with four state-of-the-art approaches show that BLIND optimizes QoI by effectively mitigating the impact of four different security attacks, with higher accuracy and lower communication overhead than its competitors. With the optimizations proposed in this paper, BLIND is up to three times faster than the baseline system, and the obtained Root Mean Squared Error (RMSE) values are up to 42% lower than other state-of-the-art approaches.

MS-RAFT+: High Resolution Multi-Scale RAFT

AbstractHierarchical concepts have proven useful in many classical and learning-based optical flow methods regarding both accuracy and robustness. In this paper we show that such concepts are still useful in the context of recent neural networks that follow RAFT’s paradigm refraining from hierarchical strategies by relying on recurrent updates based on a single-scale all-pairs transform. To this end, we introduce MS-RAFT+: a novel recurrent multi-scale architecture based on RAFT that unifies several successful hierarchical concepts. It employs a coarse-to-fine estimation to enable the use of finer resolutions by useful initializations from coarser scales. Moreover, it relies on RAFT’s correlation pyramid that allows to consider non-local cost information during the matching process. Furthermore, it makes use of advanced multi-scale features that incorporate high-level information from coarser scales. And finally, our method is trained subject to a sample-wise robust multi-scale multi-iteration loss that closely supervises each iteration on each scale, while allowing to discard particularly difficult samples. In combination with an appropriate mixed-dataset training strategy, our method performs favorably. It not only yields highly accurate results on the four major benchmarks (KITTI 2015, MPI Sintel, Middlebury and VIPER), it also allows to achieve these results with a single model and a single parameter setting. Our trained model and code are available at https://github.com/cv-stuttgart/MS_RAFT_plus.

An Adaptive Atrous Spatial Pyramid Pooling Network for Hyperspectral Classification

Hyperspectral imaging (HSI) offers rich spectral and spatial data, beneficial for a variety of applications. However, challenges persist in HSI classification due to spectral variability, non-linearity, limited samples, and a dearth of spatial information in conventional spectral classifiers. While various spectral–spatial classifiers and dimension reduction techniques have been developed to mitigate these issues, they are often constrained by the utilization of handcrafted features. Deep learning has been introduced to HSI classification, with pixel- and patch-level deep learning (DL) classifiers gaining substantial attention. Yet, existing patch-level DL classifiers encounter difficulties in concentrating on long-distance dependencies and managing category areas of diverse sizes. The proposed Self-Adaptive 3D atrous spatial pyramid pooling (ASPP) Multi-Scale Feature Fusion Network (SAAFN) addresses these challenges by simultaneously preserving high-resolution spatial detail data and high-level semantic information. This method integrates a modified hyperspectral superpixel segmentation technique, a multi-scale 3D ASPP convolution block, and an end-to-end framework to extract and fuse multi-scale features at a self-adaptive rate for HSI classification. This method significantly enhances the classification accuracy of HSI with limited samples.

Rail Internal Defect Detection Method Based on Enhanced Network Structure and Module Design Using Ultrasonic Images

Improving the detection accuracy of rail internal defects and the generalization ability of detection models are not only the main problems in the field of defect detection but also the key to ensuring the safe operation of high-speed trains. For this reason, a rail internal defect detection method based on an enhanced network structure and module design using ultrasonic images is proposed in this paper. First, a data augmentation method was used to extend the existing image dataset to obtain appropriate image samples. Second, an enhanced network structure was designed to make full use of the high-level and low-level feature information in the image, which improved the accuracy of defect detection. Subsequently, to optimize the detection performance of the proposed model, the Mish activation function was used to design the block module of the feature extraction network. Finally, the proposed rail defect detection model was trained. The experimental results showed that the precision rate and F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} score of the proposed method were as high as 98%, while the model’s recall rate reached 99%. Specifically, good detection results were achieved for different types of defects, which provides a reference for the engineering application of internal defect detection. Experimental results verified the effectiveness of the proposed method.

TOWARDS DETECTING SPEED BUMPS FROM MLS POINT CLOUDS DATA

Abstract. High definition maps contain a high level of information about the road and its features. This includes traffic signs, speed limits and lane markings, that are not generally included on regular maps. However, they may still lack critical information, such as the locations of speed bumps. Without the information about the whereabouts of speed bumps the passengers’ comfort and safety, as well as the condition of the car may be jeopardized. There are currently methods for detecting speed bumps that use changes in acceleration or 3D cameras. However, these approaches are susceptible to external influences interfering with the recorded data. Hence, it is desirable to have this information stored and available through HD maps. In this work, we tested two deep learning-based approaches (PointNet++ and PointCNN) and compared the results with conventional region growing method, in order to find out pros and cons of the modern deep learning-based methods. For our test, we used MLS (mobile laser scanning) point clouds data in Trondheim, Norway.

A three-path network with multi-scale selective feature fusion, edge-inspiring and edge-guiding for liver tumor segmentation

Automatic liver tumor segmentation is one of the most important tasks in computer-aided diagnosis and treatment. Deep learning techniques have gained increasing popularity for medical image segmentation in recent years. However, due to the various shapes, sizes, and obscure boundaries of tumors, it is still difficult to automatically extract tumor regions from CT images. Based on the complementarity of edge detection and region segmentation, a three-path structure with multi-scale selective feature fusion (MSFF) module, multi-channel feature fusion (MFF) module, edge-inspiring (EI) module, and edge-guiding (EG) module is proposed in this paper. The MSFF module includes the process of generation, fusion, and selection of multi-scale features, which can adaptively correct the response weights in multiple branches to filter redundant information. The MFF module integrates richer hierarchical features to capture targets at different scales. The EI module aggregates high-level semantic information at different levels to obtain fine edge semantics, which is injected into the EG module for representation learning of segmentation features. Experiments on the LiTs2017 dataset show that our proposed method achieves a Dice index of 85.55% and a Jaccard index of 81.11%, which are higher than what can be obtained by the current state-of-the-art methods. Cross-dataset validation experiments conducted on 3Dircadb and Clinical datasets show the generalization and robustness of the proposed method by achieving dice indices of 80.14% and 81.68%, respectively.

IMSF-Net:An improved multi-scale information fusion network for PPG-based blood pressure estimation

Recently, deep learning (DL) architectures have been widely used for PPG-based blood pressure (BP) monitoring due to their powerful feature extraction ability. However, the DL methods still suffer from limitations. First, the multi-scale time dependency characteristics of PPG require the DL-based methods to have the ability of sufficient feature representation at various scales. Second, the lack of DL model interpretability can lead to concerns about the reliability and generalizability of prediction. In this study, a new PPG-based end-to-end deep learning method, referred to as the IMSF-Net, is proposed for BP estimation, and its advantages are as follows: (1) the improved multi-scale fusion (IMSF) block is designed to extract different-scale feature in a two-channel way, which can obtain the abstract high-level information while avoiding low-level information loss simultaneously; (2) Permutation importance algorithm is firstly adopted to determine what parts of PPG are most useful for automatic BP estimation. On the University of California, Irvine database, the proposed IMSF-Net achieves mean absolute error (MAE) ± standard deviation (STD) of 2.07 ± 2.32 mmHg for systolic blood pressure (SBP) and 1.21 ± 1.51 mmHg for diastolic blood pressure (DBP). On the healthy database, referred to as the VDB, the method achieves MAE ± STD of 1.75 ± 3.98 mmHg for SBP and 1.29 ± 2.3 mmHg for DBP. Both of the performances outperform related previous works. The model interpretation results show that feature grids comprising of Diastolic 1–3, and Systolic 5, which generally cover the waveform from the peak point to the dicrotic notch, have a strong correlation with BP estimation.

Pregnant women with diabetes and their clinician's experience of participating in a pilot randomised controlled trial of corticosteroid administration in late pregnancy: A qualitative study.

Little research exists to support the administration of corticosteroids to pregnant women with diabetes. Pregnant women are often excluded from clinical trials due to concerns of harm to the foetus. This study aimed to understand the experiences of women and clinicians of participating in the Prevention of neonatal Respiratory distress with antenatal corticosteroids before Elective Caesarean section in women with Diabetes pilot randomised controlled trial to determine the acceptability of the study protocol. Women and clinicians participating in the pilot trial were invited to complete a telephone interview regarding their experiences of participating. Qualitative data were collected and subsequently analysed using thematic analysis. A total of 13 women and nine clinicians were recruited between June 2020 and May 2022 for a telephone interview. Participating in the study was deemed acceptable by women and clinicians. Women chose to participate in the study due to the perceived low risk of harm associated with the intervention and for altruistic reasons. The high level of clinical support and information provided for the duration of the pilot trial was valued by women and clinicians. All clinicians highlighted the importance of conducting the trial to inform evidence-based practice. Pregnant women are more likely to participate in clinical trials when perceived risks are low and they are well-informed during decision-making. Clinicians will support clinical trials when they perceive a benefit to practice and feel assured that women receive extensive monitoring and support. Incorporating these factors into study protocols is more likely to be successful in recruiting pregnant women and maintaining the engagement of clinical staff for the duration of clinical trials. Patients were invited to be participants in this study. A consumer has been included in the planning and oversite of the large multicentre trial.