Reviewerly/exHarmony huggingface dataset:
An Improved UKF and Its Application in Maneuvering Target Tracking

UKF (unscented Kalman filter) is a widely used nonlinear Kalman filtering algorithm, but it needs accurate system model and noise to give good filtering results. Aiming at this problem, the paper puts forward an improved UKF filtering algorithm (IMP-UKF). Firstly based on the traditional UKF, the system state noise adaptive estimation equation of the SAGE-HUSA algorithm is introduced. Secondly, memory exponential decay weighting and covariance matching criterion are introduced into the system state noise adaptive estimation equation. Finally, tracking simulations are performed for maneuvering targets. The simulation results show that the IMP-UKF algorithm is able to adaptively adjust the state noise and its covariance matrix to fit the motion state of the target better under the conditions of inaccurate system state model and state noise. And it is almost not affected by the sudden change of the target maneuver. It maintained a good track of maneuvering target.
Hierarchical Multi-label Classifier Based on Transformer Encoder for Grassroots Social Network Governance

Hierarchical multi-label classification (HMC) involves assigning objects to multiple paths in a hierarchical label structure. This makes HMC a unique classification task. HMC approach can be leveraged to improve the grassroots social network governance. This paper discusses the problems associated with existing methods for solving HMC problems and proposes a hierarchical multi-label classifier based on Transformer encoder (HMC-TE) to address these issues. HMC-TE is a hybrid classifier with both local and global information. It is mainly implemented by Transformer-Encoder. Existing hybrid methods fail to incorporate the effect of word position information on the text, which makes it difficult to capture the semantics and positional information of words in the sequence. To address these limitations, the model integrates positional variables to capture sequential data and improve the hierarchical multi-label classification problem. The proposed model, HMC-TE, was compared to state-of-the-art baseline algorithms in experiments conducted on 8 real-world datasets. HMC-TE has been shown to significantly enhance the performance of hierarchical multi-label classification tasks.
Deep Learning-Based Face Mask detection in the Working Scenario of Nuclear Power Plant: Under the Omicron Pandemic

COVID-19 is a great threat to mankind. At present, the world is trying to reduce the spread of the virus. Omicron virus emerged in 2021 and it is much more contagious. This makes it more difficult for humans to stop the spread of the virus, but wearing a mask is a good way to effectively control the Omicron epidemic. According to China’s data, wearing masks has greatly reduced the spread of Omicron. As a national strategic facility, the personnel of the nuclear power plant should pay more attention to protective measures, but it is not easy to ensure that all personnel wear face masks. In order to prevent Omicron from spreading on a large scale in the nuclear power plant and threatening national security. This paper proposes a face mask detection system based on deep learning instead of manual detection, which can detect whether the personnel in the nuclear power plant wear masks as required. At the same time, we developed a new face mask dataset, MASK-N. Our algorithm achieved 0.951 precision on the MASK-N dataset, outperforming all traditional algorithms.
PGN: A High Quality Proposal Generation Network for Object Detection

In the filed of object detection, a two-stage approach, which includes both region proposal step and object detector, is widely used due to its high detection precision. And the quality of proposals is crucial to the detection result, which should be optimized. However, little attention is paid to the proposal part. In this paper, we put forward a high quality proposal generation network (PGN) to obtain high quality proposals. Specifically, PGN, made up of cascade anchor refinement modules, uses the output of the current module to train the next. The reason is that in single anchor refinement module the quality of output proposals, estimated by IoU distribution, surpasses the input of it. The quality of final proposals in PGN outperforms the predefined anchors after several cascade operations. Then we propose a novel training scheme to further optimize the network. The proposed training scheme employs increasing Intersection Over Union (IoU) thresholds to distinguish positive/negative examples because the regression is relative to the distribution of them. In this way, the former module with a smaller IoU threshold ensures not to omit the positive anchors and the latter module with a larger IoU threshold can make sure that the module regresses more accurately. In addition, we find that cascade operation leads to misalignment between anchor boxes and convolutional features, and also leads to degradation in precision. In order to tackle the mentioned problem, we propose a feature adjustment module, which can better match the refined proposals and their features. Experimental results on MS COCO dataset show that our PGN achieves 65% AR with ResNet-50 backbone, surpassing the region proposal network(RPN) by 8 points. When integrated into the Faster R-CNN and Cascade R-CNN detector, it can boost the performance by 2.2 and 1.3 points respectively.
A Data Selection Method for Domain-specific Machine Translation

Neural machine translation performs well in general domains with large-scale bilingual corpora, but it is not very effective in specific domains with only a small amount of bilingual corpora. Data selection technology is one of the main methods to alleviate the problem of bilingual data scarcity in specific domains by selecting corpus that is close to the domain corpus from large-scale general domain corpora for data expansion. Due to the large scale of general data, traditional dimension reduction methods are inefficient when dealing with massive data. This paper proposes a two-stage data selection strategy, which improves the efficiency of data selection and the data quality at the same time. First, the Inverted File System Product Quantization (IVFPQ) method is used to preliminarily select data, which improves the computational efficiency and the quality of data selection. In order to obtain data with higher quality, on this basis, this paper trains a domain classifier to fully consider the domain characteristics, and further selects higher quality data. Experimental results show that compared with the baseline method, a better domain-specific machine translation model can be trained using the expanded data obtained by this method.
Nested perceptron model and its learning algorithm

Perceptron model has been studied and extended from many different aspects deeply. The most typical one among them is multilayer perceptron, which can complete multi-classification or complex tasks. Obviously, by nested using of two-classification perceptron model, it can also complete the multi-classification tasks. But this is not a concrete multi-classification model, which is not conducive to the interpretation of the model because of lacking of analytic expression. Therefore, basing on the concept of linear separable set and the classical perceptron theory of two-classification, it proposes the concept of multilinear separable set and the concrete nested perceptron model as well as its learning algorithm. Furthermore, it proves the convergence of learning algorithm of nested perceptron on multilinear separable set. Subsequently, it illustrates the nested perceptron model and its learning algorithm with the help of a series of detailed operation steps of a small data experiment. The nested perceptron could realize the multiple categories classifier, which is not unique, to distinguish the multilinear separable set correctly.
A kidney staining transfer model based on CycleGan using Spade as the normalization layer

Masson staining is crucial for accurately identifying fibrotic areas in kidney tissue. The CycleGan model can convert HE-stained kidney pathology images into Masson-stained kidney section images, aiding doctors in identifying fibrotic regions. Prior models had limited generalization performance, causing inaccuracies in simulated images when there was a color shift between training and validation sets. This paper introduces SPCGan, a CycleGan-based staining translation model. SPCGan incorporates a color enhancement algorithm during training to increase the number of HE-stained kidney pathology image samples. Additionally, the normalization layer in CycleGan is replaced with the Spade layer to prevent the loss of spatial morphology semantic information when image features pass through the normalization layer. After validation on a non-public dataset, SPCGan improved staining accuracy for StainGan by 0.034 and 0.051 for CNGan, with FID scores improving by 1.47 and 10.26, respectively. This enhanced generalization performance and improved the similarity between simulated Masson staining results and authentic images, making significant progress in histopathological image staining translation.
An Adaptive Load Balancing Strategy for Distributed Machine Learning

Within a distributed deep learning training system, variances in performance among computing nodes, as well as the influence of external environmental factors, can result in training interruptions or reduced convergence speed. As such, this paper presents an approach to address this issue by proposing a dynamic task allocation strategy among nodes, aimed at mitigating the impact of performance discrepancies on the training efficiency of distributed deep learning systems. This proposed approach is referred to as the “Auto weight-based load balancing strategy” (Auto-WLBS) and involves dynamically adjusting the task allocation among computing nodes based on their performance characteristics. In order to maximize computing power while minimizing the lag effect on the training of the entire system, Auto-WLBS partitions and alters the errands by introductory division and halfway alteration. Combined with LSP model, AW-LSP model is proposed. Finally, a comparative experiment was conducted on the cifar10 and cifar100 datasets, and Auto-WLBS was experimentally verified from the perspective of model training loss function changes, model accuracy, and training time. The experimental findings demonstrate that compared to the BSP model, SSP model, and LSP model, the AW-LSP model has a smaller communication overhead, and the communication overhead can be reduced by up to 23.70%. Compared with the SSP model and the LSP model, the model accuracy can be improved by up to 14.5% and 8.6%, respectively.
A Fine-Grained Word-level Translation Quality Estimation Method based on Deep Learning

Translation quality estimation (QE) technology aims to evaluate the quality of machine translations without reference translations. Currently, research on word-level QE mainly focuses on discriminating the correctness of word translations in sentences. In order to further identify the types of errors in the translation, this paper proposes a fine-grained word-level QE method. A new translation error taxonomy and a method to automatically generate labelled training corpus are proposed. Two methods based on recurrent neural network BiLSTM and pre-trained model XLM-R are adopted to build a model on the automatically generated corpus, and weighted cross-entropy loss function is used to alleviate the problem of label imbalance. Experimental results show that the proposed methods can effectively identify the fine-grained errors in the translation, which provides more information for post-editors.
Vector-Quantized Latent Flows for Medical Image Synthesis and Out-Of-Distribution Detection

We present an innovative method that allows for simultaneous out-of-distribution detection and image generation by encoding images in the latent space of a vector-quantized autoencoder and using normalizing flow models. The technique is demonstrated on a medical dataset of knee radiographs and can be used to relieve clinical radiologists of tedious tasks of quality control while simultaneously guiding radiologic technologists to improved and standardized image quality during image acquisition.
PCT-GAN: A Real CT Image Super-Resolution Model for Trabecular Bone Restoration

We consider the real-world super-resolution (SR) problem for medical images with two distinct imaging modalities. Most previous attempts either focused on image enhancement between similar imaging modalities or used the down-sampled or simulated low-resolution images as input to train their model, which reduces the clinical significance of the work. We, therefore, collect a dataset with paired clinical computed tomography (CT) and microcomputed tomography (µCT) of vertebra specimens and develop the progressive CT generative adversarial network (PCT-GAN) model to generate high-resolution images. PCT-GAN is designed using the progressive strategy by first denoising the input clinical CT image and then increasing its spatial resolution. To encourage our model to generate trabecular bone details from clinical CTs, we propose to combine wavelet and edge losses with the L1 and adversarial losses for model training. Experimental results show that the proposed method outperforms the state-of-the-art methods in terms of quantitative measurements and visual results.
Generalization Properties of Geometric 3D Deep Learning Models for Medical Segmentation

Recent advances in medical Deep Learning (DL) have enabled the significant reduction in time required to extract anatomical segmentations from 3-Dimensional images in an unprecedented manner. Among these methods, supervised segmentation-based approaches using variations of the UNet architecture remain extremely popular. However, these methods remain tied to the input images’ resolution, and their generalisation performance relies heavily on the data distribution over the training dataset. Recently, a new family of approaches based on 3D geometric DL has emerged. These approaches encompass both implicit and explicit surface representation methods and promises to represent a 3D volume using a continuous representation of its surface whilst conserving its topological properties. It has been conjectured that these geometrical methods are more robust to out-of-distribution data and have increased generalisation properties. In this paper, we test these hypotheses for the challenging task of cortical surface reconstruction (CSR) using recently proposed architectures.
TriFormer: A Multi-modal Transformer Framework For Mild Cognitive Impairment Conversion Prediction

The prediction of mild cognitive impairment (MCI) conversion to Alzheimer’s disease (AD) is important for early treatment to prevent or slow the progression of AD. To accurately predict the MCI conversion to stable MCI or progressive MCI, we propose TriFormer, a novel transformer-based framework with three specialized transformers to incorporate multi-modal data. TriFormer uses I) an image transformer to extract multi-view image features from medical scans, II) a clinical transformer to embed and correlate multi-modal clinical data, and III) a modality fusion transformer that produces an accurate prediction based on fusing the outputs from the image and clinical transformers. Triformer is evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 1 and ADNI2 datasets and outperforms previous state-of-the-art single and multi-modal methods.
Multi-Task Curriculum Learning for Partially Labeled Data

Incomplete labels are common in multi-task learning for biomedical applications due to several practical difficulties, e.g., expensive annotation efforts by experts, limit of data collection, different sources of data. A naive approach to enable joint learning for partially labeled data is adding self-supervised learning for tasks without ground truths by augmenting an input image and forcing the multi-task model to return the same outputs for both the input and augmented images. However, the partially labeled setting can result in imbalanced learning of tasks since not all tasks are trainable with ground truth supervisions for each data sample. In this work, we propose a multi-task curriculum learning method tailored for partially labeled data. For balanced learning of tasks, our multitask curriculum prioritizes less performing tasks during training by setting different supervised learning frequencies for each task. We demonstrate that our method outperforms standard approaches on one biomedical and two natural image datasets. Furthermore, our learning method with partially labeled data performs better than the standard multi-task learning methods with fully labeled data for the same number of annotations.
Self-Supervised Learning Guided Transformer for Survival Prediction of Lung Cancer Using Pathological Images

Accurate overall survival (OS) prediction for lung cancer patients is of great significance, and the histopathology slides are considered the gold standard for cancer diagnosis and prognosis. However, the current methods usually lack extracting effective features and ignore the utilization of spatial information. To address these challenges, we propose a self-supervised learning guided transformer framework (SET) for OS prediction with whole slide images (WSIs). We introduce self-supervised learning to exploit the characteristics of pathological images and thus capture domain-specific contextual representations. Furthermore, we design a dual-stream position embedding architecture to facilitate aggregating global spatial information. The experimental results on the lung cancer dataset of stage III-N2 demonstrate that our proposed algorithm can achieve a better concordance index compared with state-of-the-art methods. Moreover, the proposed method can significantly divide patients into high-risk group and low-risk group to assist the personalized treatment.
Automated Coronary Arteries Labeling Via Geometric Deep Learning

Automatic labelling of anatomical structures, such as coronary arteries, is critical for diagnosis, yet existing (non-deep learning) methods are limited by a reliance on prior topological knowledge of the expected tree-like structures. As the structure such vascular systems is often difficult to conceptualize, graph-based representations have become popular due to their ability to capture the geometric and topological properties of the morphology in an orientation-independent and abstract manner. However, graph-based learning for automated labeling of tree-like anatomical structures has received limited attention in the literature. The majority of prior studies have limitations in the entity graph construction, are dependent on topological structures, and have limited accuracy due to the anatomical variability between subjects.In this paper, we propose an intuitive graph representation method, well suited to use with 3D coordinate data obtained from angiography scans. We subsequently seek to analyze subject-specific graphs using geometric deep learning. The proposed models leverage expert annotated labels from 141 patients to learn representations of each coronary segment, while capturing the effects of anatomical variability within the training data. We investigate different variants of so-called message passing neural networks. Through extensive evaluations, our pipeline achieves a promising weighted F1-score of 0.805 for labeling coronary artery (13 classes) for a fivefold cross-validation. Considering the ability of graph models in dealing with irregular data, and their scalability for data segmentation, this work highlights the potential of such methods to provide quantitative evidence to support the decisions of medical experts.
Improved HER2 Tumor Segmentation with Subtype Balancing Using Deep Generative Networks

Tumor segmentation in histopathology images is often complicated by its composition of different histological subtypes and class imbalance. Oversampling subtypes with low prevalence features is not a satisfactory solution since it eventually leads to overfitting. We propose to create synthetic images with semantically-conditioned deep generative networks and to combine subtype-balanced synthetic images with the original dataset to achieve better segmentation performance. We show the suitability of Generative Adversarial Networks (GANs) and especially diffusion models to create realistic images based on subtype-conditioning for the use case of HER2-stained histopathology. Additionally, we show the capability of diffusion models to conditionally inpaint HER2 tumor areas with modified subtypes. Combining the original dataset with the same amount of diffusion-generated images increased the tumor Dice score from 0.833 to 0.854 and almost halved the variance between the HER2 subtype recalls. These results create the basis for more reliable automatic HER2 analysis with lower performance variance between individual HER2 subtypes.
An Accountable Anonymous Voting Scheme Based on One-time Ring Signature

Electronic voting plays an increasingly important role in both economic life and social activities. The electronic voting scheme based on one-time ring signature and blockchain solves the problems of voter anonymity, double voting, and public verification, while achieving the openness and transparency of voting. However, it is not applicable to the multi-round voting scenario where the voting group is relatively fixed, nor does it consider the need for anonymous accountability and differential privacy protection. In this paper, based on one-time ring signature, stealth address, blacklist and whitelist, an improved accountability anonymous voting scheme is proposed to enhance the practicability of the scheme. The security analysis shows that the scheme confirms to the basic security standards of an electronic voting system.
SFCNEXT: A Simple Fully Convolutional Network for Effective Brain Age Estimation with Small Sample Size

Deep neural networks (DNN) have been designed to predict the chronological age of a healthy brain from T1-weighted magnetic resonance images (T1 MRIs), and the predicted brain age could serve as a valuable biomarker for the early detection of development-related or aging-related disorders. Recent DNN models for brain age estimations usually rely too much on large sample sizes and complex network structures for multi-stage feature refinement. However, in clinical application scenarios, researchers usually cannot obtain thousands or tens of thousands of MRIs in each data center for thorough training of these complex models. This paper proposes a simple fully convolutional network (SFCNeXt) for brain age estimation in small-sized cohorts with biased age distributions. The SFCNeXt consists of Single Pathway Encoded ConvNeXt (SPEC) and Hybrid Ranking Loss (HRL), aiming to estimate brain ages in a lightweight way with a sufficient exploration of MRI, age, and ranking features of each batch of subjects. Experimental results demonstrate the superiority and efficiency of our approach.
Medical Shape Pattern Analysis with MeshCNN

Accurate recognition and knowledge of anatomical shapes are important in both medical diagnosis and treatment planning. Improving techniques for 3D shape analysis would help gain clinical knowledge, be useful for efficient treatment delivery, and minimise side effects. In this paper, a new approach was proposed for shape variation analysis using MeshCNN. The classification network of the MeshCNN framework was used along with the density visualisation approach for feature extraction. The approach was applied on a hippocampus dataset with both normal control and Alzheimer’s disease (AD) groups to identify the localised shape variations between the two groups. The result highlighted the subiculum subfield as the region with highest local shape variation which was consistent with previous study on AD’s effect on hippocampus shape. The proposed approach showed ability to identify regions of clinical interest and to extract non-linear features of 3D shapes.