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An attention-based cascade R-CNN model for sternum fracture detection in X-ray images

CAAI Transactions on Intelligence Technology 2022年第4期7卷 658-670页

作者： Yang Jia Haijuan Wang Weiguang Chen Yagang Wang Bin Yang School of Computer Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina Shaanxi Key Laboratory of Network Data Intelligent Processing Xi'an University of Posts and TelecommunicationsXi'anShaanxiChina Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'anShaanxiChina Department of Radiology Xi'an Honghui HospitalXi'anChina

Fracture is one of the most common and unexpected *** not treated in time,it may cause serious consequences such as joint stiffness,traumatic arthritis,and nerve *** computer vision technology to detect fractures can reduce the workload and misdiagnosis of fractures and also improve the fracture detection ***,there are still some problems in sternum fracture detection,such as the low detection rate of small and occult *** this work,the authors have constructed a dataset with 1227 labelled X-ray images for sternum fracture *** authors designed a fully automatic fracture detection model based on a deep convolution neural network(CNN).The authors used cascade R-CNN,attention mechanism,and atrous convolution to optimise the detection of small fractures in a large X-ray image with big local *** authors compared the detection results of YOLOv5 model,cascade R-CNN and other state-of-the-art *** authors found that the convolution neural network based on cascade and attention mechanism models has a better detection effect and arrives at an mAP of 0.71,which is much better than using the YOLOv5 model(mAP=0.44)and cascade R-CNN(mAP=0.55).

关键词： attention mechanism cascade R-CNN fracture detection X-ray image

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Gradient-Based Differential kWTA Network With Application to Competitive Coordination of Multiple Robots

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IEEE/CAA Journal of Automatica Sinica 2022年第8期9卷 1452-1463页

作者： Mei Liu Xiaoyan Zhang Mingsheng Shang Long Jin School of Information Science and Engineering Lanzhou UniversityLanzhou 730000 State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining 810016China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Shenzhen Research Institute Lanzhou UniversityShenzhen 518063China

Aiming at the k-winners-take-all(kWTA)operation,this paper proposes a gradient-based differential kWTA(GDk WTA)*** obtaining the network,theorems and related proofs are provided to guarantee the exponential convergence and noise resistance of the proposed GD-kWTA ***,numerical simulations are conducted to substantiate the preferable performance of the proposed network as compared with the traditional ***,the GD-k WTA network,backed with a consensus filter,is utilized as a robust control scheme for modeling the competition behavior in the multi-robot coordination,thereby further demonstrating its effectiveness and feasibility.

关键词： Consensus filter k-winners-take-all(kWTA) multirobot coordination noise resistance

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IF-Font: Ideographic Description Sequence-Following Font Generation 38

IF-Font: Ideographic Description Sequence-Following Font Gen...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xinping Ke, Xiao Guo, Wenzhong Fujian Provincial Key Laboratory of Networking Computing and Intelligent Information Processing College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou350116 China

Few-shot font generation (FFG) aims to learn the target style from a limited number of reference glyphs and generate the remaining glyphs in the target font. Previous works focus on disentangling the content and style features of glyphs, combining the content features of the source glyph with the style features of the reference glyph to generate new glyphs. However, the disentanglement is challenging due to the complexity of glyphs, often resulting in glyphs that are influenced by the style of the source glyph and prone to artifacts. We propose IF-Font, a novel paradigm which incorporates Ideographic Description Sequence (IDS) instead of the source glyph to control the semantics of generated glyphs. To achieve this, we quantize the reference glyphs into tokens, and model the token distribution of target glyphs using corresponding IDS and reference tokens. The proposed method excels in synthesizing glyphs with neat and correct strokes, and enables the creation of new glyphs based on provided IDS. Extensive experiments demonstrate that our method greatly outperforms state-of-the-art methods in both one-shot and few-shot settings, particularly when the target styles differ significantly from the training font styles. The code is available at https://***/Stareven233/IF-Font. © 2024 Neural information processing systems foundation. All rights reserved.

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keypoint-based Progressive Chain-of-Thought Distillation for LLMs 41

Keypoint-based Progressive Chain-of-Thought Distillation for...

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41st International Conference on Machine Learning, ICML 2024

作者： Feng, Kaituo Li, Changsheng Zhang, Xiaolu Zhou, Jun Yuan, Ye Wang, Guoren Beijing Institute of Technology China Ant Group China Hebei Province Key Laboratory of Big Data Science and Intelligent Technology China

Chain-of-thought distillation is a powerful technique for transferring reasoning abilities from large language models (LLMs) to smaller student models. Previous methods typically require the student to mimic the step-by-step rationale produced by LLMs, often facing the following challenges: (i) Tokens within a rationale vary in significance, and treating them equally may fail to accurately mimic keypoint tokens, leading to reasoning errors. (ii) They usually distill knowledge by consistently predicting all the steps in a rationale, which falls short in distinguishing the learning order of step generation. This diverges from the human cognitive progression of starting with easy tasks and advancing to harder ones, resulting in sub-optimal outcomes. To this end, we propose a unified framework, called KPOD, to address these issues. Specifically, we propose a token weighting module utilizing mask learning to encourage accurate mimicry of keypoint tokens by the student during distillation. Besides, we develop an in-rationale progressive distillation strategy, starting with training the student to generate the final reasoning steps and gradually extending to cover the entire rationale. To accomplish this, a weighted token generation loss is proposed to assess step reasoning difficulty, and a value function is devised to schedule the progressive distillation by considering both step difficulty and question diversity. Extensive experiments on four reasoning benchmarks illustrate our KPOD outperforms previous methods by a large margin. Copyright 2024 by the author(s)

关键词： Students

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A Unified Framework Integrating Knowledge and data for Collaborative Root Cause Identification 5

A Unified Framework Integrating Knowledge and Data for Colla...

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5th International Conference on Sensing, Measurement and data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Yu, Jiefei Cao, Zicheng He, Siyi Gu, Zuyi Xu, Yingcheng Zhong, Kai Institutes of Physical Science and Information Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Anhui China School of Big Data and Statistics Anhui University Anhui China

ISBN: (纸本)9798331529192

Capturing the root cause and propagation path of the fault is critical to ensuring the safety and efficiency of industrial processes, especially those that inadequately utilize process knowledge and data. To address this issue, a unified framework integrating knowledge and data for collaborative root cause identification is proposed. First, the knowledge causal graph (KCG) is constructed using expert knowledge and industrial flow charts, providing a preliminary reference for subsequent causality analysis. Next, by replacing the traditional vector autoregression (VAR) model in Granger Causality (GC) with the gated recurrent unit (GRU), a more reliable causal relationship between variables is obtained. Additionally, a causality fusion propagation path identification method (CF-PPI) is designed to identify the root cause and propagation path of the fault, so that the obtained fault propagation path has less redundancy and higher accuracy. Finally, the method is validated using data from the ASHRAE RP-1043 centrifugal chiller. © 2024 IEEE.

关键词： Knowledge graph

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Semi-supervised PolSAR image classification method based on contrastive learning 6

Semi-supervised PolSAR image classification method based on ...

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6th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Hua, Wenqiang Sun, Nan Liu, Lin The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China School of Computer Technology Xi'an University of Posts and Telecommunications Xi'an China

ISBN: (纸本)9798400707674

Polarimetric synthetic aperture radar (PolSAR) image classification has important application value and a wide range of application scenarios in many fields. Supervised classification methods, which need to use a large amount of labeled data to improve classification accuracy, have achieved satisfactory classification performance. However, it is difficult and expensive to obtain sufficient labeled data for PolSAR images. The problem to be solved is how to obtain high classification accuracy with a limited number of labeled samples. Semi-supervised PolSAR image classification method based on contrastive learning is proposed. Firstly, a supervised PolSAR image classification method based on meta-learning is introduced. Prototypical network based on meta-learning ideas is used as the basic classification model, and a prototypical representation is created for each category. Classification is performed by computing the similarity between the prototypical representation and samples to be classified. Secondly, a method of training feature extractor based on contrastive learning is designed. By maintaining the consistency of features between the same data and distinguishing the differences between different data after data augmentation, the method enables the feature extractor to obtain accurate feature representation. By co-training with meta-learning, so as to improve the classification performance. Finally, experiments on real PolSAR data prove the effectiveness of the proposed method. © 2023 ACM.

关键词： Image classification

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Federated Dynamic Client Selection for Fairness Guarantee in Heterogeneous Edge computing

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Journal of Computer Science & Technology 2024年第1期39卷 139-158页

作者：毛莺池沈莉娟吴俊平萍吴杰 Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai UniversityNanjing 211100China College of Computer and Information Hohai UniversityNanjing 211100China Center of Networked Computing Temple UniversityPhiladelphiaPA 19122-6096U.S.A

Federated learning has emerged as a distributed learning paradigm by training at each client and aggregat-ing at a parameter *** heterogeneity hinders stragglers from responding to the server in time with huge com-munication *** client grouping in federated learning can solve the straggler problem,the stochastic selection strategy in client grouping neglects the impact of data distribution within each ***,current client grouping ap-proaches make clients suffer unfair participation,leading to biased performances for different *** order to guaran-tee the fairness of client participation and mitigate biased local performances,we propose a federated dynamic client selec-tion method based on data representativity(FedSDR).FedSDR clusters clients into groups correlated with their own lo-cal computational *** estimate the significance of client datasets,we design a novel data representativity evalua-tion scheme based on local data ***,the two most representative clients in each group are selected to optimize the global ***,the DYNAMIC-SELECT algorithm updates local computational efficiency and data representativity states to regroup clients after periodic average *** on real datasets show that FedS-DR improves client participation by 27.4%,37.9%,and 23.3%compared with FedAvg,TiFL,and FedSS,respectively,tak-ing fairness into account in federated *** addition,FedSDR surpasses FedAvg,FedGS,and FedMS by 21.32%,20.4%,and 6.90%,respectively,in local test accuracy variance,balancing the performance bias of the global model across clients.

关键词： federated learning fairness computational efficiency data distribution client selection client grouping

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Conditional selection with CNN augmented transformer for multimodal affective analysis

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 917-931页

作者： Jianwen Wang Shiping Wang Shunxin Xiao Renjie Lin Mianxiong Dong Wenzhong Guo College of Computer and Data Science Fuzhou UniversityFuzhouChina College of Computer and Cyber Security Fujian Normal UniversityFuzhouChina Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhouChina Digital Fujian Institute of Big Data Security Technology Fujian Normal UniversityFuzhouChina Department of Sciences and Informatics Muroran Institute of TechnologyMuroranJapan

Attention mechanism has been a successful method for multimodal affective analysis in recent years. Despite the advances, several significant challenges remain in fusing language and its nonverbal context information. One is to generate sparse attention coefficients associated with acoustic and visual modalities, which helps locate critical emotional se-mantics. The other is fusing complementary cross‐modal representation to construct optimal salient feature combinations of multiple modalities. A Conditional Transformer Fusion Network is proposed to handle these problems. Firstly, the authors equip the transformer module with CNN layers to enhance the detection of subtle signal patterns in nonverbal sequences. Secondly, sentiment words are utilised as context conditions to guide the computation of cross‐modal attention. As a result, the located nonverbal fea-tures are not only salient but also complementary to sentiment words directly. Experi-mental results show that the authors’ method achieves state‐of‐the‐art performance on several multimodal affective analysis datasets.

关键词： affective computing data fusion information fusion multimodal approaches

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Multi-scale cyclical similarity prototype refinement for few-shot breast ultrasound image segmentation 17

Multi-scale cyclical similarity prototype refinement for few...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Ou, Yingfeng Yang, Xing Zhang, Jian Jian, Caiqing Wang, Lihui College of Computer Science and Technology Guizhou University Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data Engineering Research Center of Text Computing Ministry of Education Guiyang China

ISBN: (纸本)9798350387384

Few-shot learning based methods can address the reliance on large-scale labeled samples in current breast tumor segmentation. However, previous methods typically rely on a few support samples to extract abstract, coarse-grained semantic prototypes, without considering the interaction between support and query data. In this study, we aim to provide each query image with relevant and information-rich support information. By integrating the difficult point selection module and the multi-scale cyclical prototype refinement module, we progressively refine coarse-grained support prototypes into more informative, fine-grained class prototypes, thereby better matching the highly variable breast ultrasound query data. Experiments on two datasets demonstrate that our proposed multi-scale cyclical similarity prototype refinement network (CSPRNet) achieves remarkable improvements compared to previous state-of-the-art methods. Specifically, using ResNet50 as the backbone, we achieve 57.93%/57.92% and 57.58%/59.24% mIoU for 1-shot and 5-shot segmentation, respectively, on public and private datasets, outperforming the suboptimal method by 8.18%/2.55% and 2.77%/4.48%. © 2024 IEEE.

关键词： Ultrasonic imaging

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Towards privacy-preserving and efficient word vector learning for lightweight IoT devices

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Digital Communications and Networks 2024年第4期10卷 895-903页

作者： Nan Jia Shaojing Fu Guangquan Xu Kai Huang Ming Xu National University of Defense Technology ChangshaChina Big Data School Qingdao Huanghai UniversityQingdaoChina Tianjin Key Laboratory of Advanced Networking College of Intelligence and ComputingTianjin UniversityTianjinChina College of Joint Operations National Defense UniversityShijiazhuangChina

Nowadays,Internet of Things(IoT)is widely deployed and brings great opportunities to change people's daily *** realize more effective human-computer interaction in the IoT applications,the Question Answering(QA)systems implanted in the IoT services are supposed to improve the ability to understand natural ***,the distributed representation of words,which contains more semantic or syntactic information,has been playing a more and more important role in the QA ***,learning high-quality distributed word vectors requires lots of storage and computing resources,hence it cannot be deployed on the resource-constrained IoT *** is a good choice to outsource the data and computation to the cloud ***,it could cause privacy risks to directly upload private data to the untrusted ***,realizing the word vector learning process over untrusted cloud servers without privacy leakage is an urgent and challenging *** this paper,we present a novel efficient word vector learning scheme over encrypted *** first design a series of arithmetic computation *** we use two non-colluding cloud servers to implement high-quality word vectors learning over encrypted *** proposed scheme allows us to perform training word vectors on the remote cloud servers while protecting *** analysis and experiments over real data sets demonstrate that our scheme is more secure and efficient than existing privacy-preserving word vector learning schemes.

关键词： Privacy-preserving Word vector learning Secret sharing Internet of things

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