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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Yao Liu, Chunfeng Zhao, Zhao Qu, Wenyu College of Intelligence and Computing Tianjin University Tianjin300072 China Tianjin Key Laboratory of Advance Network Technology and Application Tianjin China

ISBN: (纸本)9781665405270

This paper focuses on the anti-interference transmission strategy for underwater acoustic sensor networks(UASNs). The interference existing between nodes communicating in a shared channel may significantly decrease the communication quality and increase the energy consumption of nodes. However, existing researches on transmission strategies for UASNs either do not consider inter-node interference or ignore the effect of acoustic channels. To solve the above problems, we propose to characterize the interference communication problem of nodes as an ordinal potential game model. Furthermore, a deep reinforcement learning (DRL)- based algorithm is designed to solve the problem, which selects the transmission power for nodes by learning historical information of signal-to-interference-plus-noise ratio(SINR) to minimize network interference. Finally, we verify that the DRL-based anti-interference transmission strategy proposed in this paper can obtain the optimal transmission strategy from variable underwater environment through extensive simulations, and show the feasibility of the algorithm under large-scale networks. © 2022 IEEE.

关键词： Reinforcement learning

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Class Semantic Prompts Enhanced Prototypical Fusion Method for Few-shot Named Entity Recognition

Class Semantic Prompts Enhanced Prototypical Fusion Method f...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Yu, Mei Tao, Yuang Zhao, Mankun Xu, Tianyi Meng, Zechen Zhang, Wenbin Yu, Jian School of Future Technology Tianjin University Tianjin300350 China College of Intelligence and Computing Tianjin University Tianjin300350 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China Tianjin Key Laboratory of Advanced Networking Tianjin300350 China Information and Network Center Tianjin University Tianjin300350 China

ISBN: (纸本)9798350368741

Few-shot named entity recognition is to identify named entities in scenarios where labeled data is scarce. Existing prototype building methods ignore the use of class semantic and it is difficult to obtain accurate prototype representations only by relying on few support samples. In this paper, we propose a class semantic prompts enhanced prototypical fusion method (CSFP). Specifically, we design a class-semantic prototype that adapts to current task through prompts. In order to add intra-class similarity to the existing prototype and obtain a more accurate and stable prototype representation, we consider the samples distribution and fuse class-semantic prototype with existing prototype through a weighted strategy. Experimental results on two few-shot NER benchmarks show that our method outperforms previous SOTA methods. The analysis further verifies the effectiveness of our method. © 2025 IEEE.

关键词： class semantic few-shot NER prompt learning prototype

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LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices 35

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

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35th IEEE International Symposium on Software Reliability Engineering, ISSRE 2024

作者： Zhang, Shenglin Che, Zeyu Pan, Zhongjie Nie, Xiaohui Sun, Yongqian Pan, Lemeng Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Chinese Academy of Sciences Computer Network Information Center China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei China Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350353884

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-Automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service's failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F1-score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators' labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-Automatic trace labeling tool capable of achieving efficient and accurate trace labeling. © 2024 IEEE.

关键词： Labeled data

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EAFNet: Feature Enhancement and Self-Adaptive Guided Feature Fusion for Object Detection in Haze Conditions 3

EAFNet: Feature Enhancement and Self-Adaptive Guided Feature...

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3rd International Conference on Electronic Information Engineering and Computer, EIECT 2023

作者： Hu, Xinxin Zhou, Yuanbo Kuang, Xu Chen, Hao Lin, Xiaoyong Liu, Fang Liu, Hongjun Wang, Xiaofei Tong, Tong Gao, Qinquan Fuzhou University Fuzhou China Xiamen University of Technology Fujian Provincial Key Laboratory for Network Audiovisual Application Innovationand Fujian China Chongqing Dadukou District Archives Chongqing China Imperial Vision Technology FuzhouFujian China

ISBN: (纸本)9798350357707

Recently, there is a significant success in the current object detection task. However, existing research has demonstrated that detectors trained on regular images experience a significant decrease when facing to wild scences such as haze scence. Although enhancing detection performance is possible through multi-stage adaptive parameter-guided image enhancement, this method fails to consistently enhance downstream object detection performance. To address this issue, we propose an end-To-end object detection framework that utilizes weakly supervised parameter-guided feature fusion to improve detection performance. Specifically, we introduce the Self-Adaptive Feature Fusion Module (SAFFM) that dynamically guides feature fusion in the detection network through the haze image information encoded by the Prior Knowledge (PK) components. By the joint learning of the PK components and the detection network in an end-To-end manner, we ensure that the PK components weakly supervise the feature fusion process to enhance detection performance. Additionally, we propose a Downsampling Feature Enhancement Module (DFEM) that utilizes multibranch down-sampling operators and reparameterization to enhance the feature extraction capability of the network, allowing it to capture more latent information. Comprehensive experiments on synthetic datasets and real datasets, demonstrated that the detection performance of the proposed network significantly outperforms other state-of-The-Art detection methods. © 2023 IEEE.

关键词： Object detection

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Microbiologically Influenced Corrosion Behavior of Fe_(40)(CoCrMnNi)_(60) and Fe_(60)(CoCrMnNi)_(40) Medium Entropy Alloys in the Presence of Pseudomonas Aeruginosa

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Acta Metallurgica Sinica(English Letters) 2023年第3期36卷 379-390页

作者： Weiwei Chang Yangyang Li Huaibei Zheng Hongchang Qian Dawei Guo Shuyuan Zhang Yuntian Lou Chi Tat Kwok Lap Mou Tam Dawei Zhang National Materials Corrosion and Protection Data Center Institute for Advanced Materials and TechnologyUniversity of Science and Technology BeijingBeijing 100083China BRI Southeast Asia Network for Corrosion and Protection(MOE) Shunde Graduate School of University of Science and Technology BeijingFoshan 528399China State Key Laboratory of Metal Material for Marine Equipment and Application Anshan 114002China Department of Electromechanical Engineering University of MacaoMacao 999078China Institute for the Development and Quality Macao 999078China

In this work,the microbiologically influenced corrosion(MIC)of Fe_(40)(CoCrMnNi)_(60) and Fe_(60)(CoCrMnNi)_(40) medium entropy alloys(MEAs)induced by Pseudomonas aeruginosa(***)was *** behaviors during 14 days of immersion in sterile and ***-inoculated culture media are *** sterile conditions,both MEAs exhibited good corrosion resistance against the culture medium *** the presence of ***,the pitting corrosion of MEAs was *** results of inductively coupled plasma‒mass spectrometry(ICP‒MS)and potentiodynamic polarization tests showed that the presence of *** promoted the selective dissolution of passive film and accelerated the corrosion of *** results of X-ray photoelectron spectroscopy(XPS)and Mott-Schottky measurements further demonstrated the degradation effect of *** on the passive *** with Fe_(60)(CoCrMnNi)_(40),Fe_(40)(CoCrMnNi)_(60) manifested better resistance to the MIC caused by ***,which may be attributed to more Cr oxides and fewer Fe oxides of the passive film.

关键词： Microbiologically influenced corrosion Medium entropy alloys Pseudomonas aeruginosa

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A Dual-View Fusion network for Automatic Spinal keypoint Detection in Biplane X-ray Images

A Dual-View Fusion Network for Automatic Spinal Keypoint Det...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhou, Dandan Guo, Lijun Zhang, Rong He, Xiuchao Wang, Qiang Wang, Jianhua Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

Accurate keypoint detection in medical images of the spine is critical for the assessment, diagnosis, treatment planning, and clinical investigation of spinal deformities. However, due to severe occlusions of spinal structures in lateral X-ray images, accurate keypoint detection can be hardly achieved in lateral X-ray images based on single-view information. Thus, methods based on both the anterior-posterior (AP) and lateral (LAT) X-ray image views have been proposed to alleviate occlusion problems and achieve better keypoint detection performance. Although some progress has been made with these dual-view methods, they do not effectively exploit a priori knowledge of the spine and hence cannot adequately account for the structural correlation of the vertebrae across views. In this paper, a new dual-view fusion network (DVFNet) framework is proposed for keypoint detection in spinal X-ray images. This framework obtains structural correlations between AP and LAT views of the spine based on a priori spine knowledge represented by high-level semantic features. Meanwhile, the proposed framework combines local and global features extracted respectively by a local subnetwork and a global subnetwork. On the one hand, the local subnetwork is constructed as an enhanced codec structure based on both the AP and LAT views. This subnetwork is trained to output local features that contain both joint semantic features of the two views and independent fine-grained features of each individual view. This scheme leads to accurate keypoint estimation locally. On the other hand, the global subnetwork utilizes a self-attention mechanism to extract view-specific global features based on either the AP view or the LAT view in order to eliminate ambiguity, and reduce confusion on keypoint locations. Further, we propose a weighted feature fusion (WFF) module for adaptive fusion of the local and global features. We evaluated the DVFNet model on a private dataset and found that our proposed metho

关键词： biplane X-ray images dual-view feature fusion spinal keypoint detection

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IIESC-Net: Incorporating Implicit and Explicit Structural Constraints for Hip Joint Landmark Detection in pelvic X-ray

IIESC-Net: Incorporating Implicit and Explicit Structural Co...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Jing Guo, Lijun Ni, Lixin He, Xiuchao Wang, Jianhua Zhang, Rong Ningbo University The Faculty of Electrical Engineering and Computer Science Ningbo China The Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China The Affiliated Hospital of Medicine School of Ningbo University The Department of Radiology Ningbo China

ISBN: (纸本)9798350337488

The hip joint, a vital weight-bearing joint in the human body, is susceptible to various hip-related diseases. The accurate identification of anatomical landmarks in the hip joint is essential for both disease diagnosis and surgical planning. However, these landmarks are often inconspicuous in X-ray images, where irrelevant background interference increases detection difficulty. In this study, we proposed an IIESC-Net model that combined local and global features, enabling a hierarchical understanding of the implicit structural characteristics of the hip joint. Additionally, drawing from domain expertise based on the explicit physiological structure of the hip joint, we designed a Hip Morphology-Aware loss function to constrain large landmark errors through the application of high-confidence landmarks with robust distinctive identification, thereby achieving accuracy in automatic detection. Furthermore, we constructed a dataset comprising of 843 pelvic X-ray images. The experimental results demonstrated a substantial enhancement in hip joint landmark detection accuracy attributed to the proposed IIESC-Net. This innovation established state-of-the-art performance, notably excelling in attaining heightened successful detection rates under stringent error tolerance. This achievement has profound practical implications for clinical applications. © 2023 IEEE.

关键词： Global Features Hip joint Hip Morphology-Aware Loss Landmark Detection Local Features

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Breaking Speaker Recognition with Paddingback

Breaking Speaker Recognition with Paddingback

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhe Ye Diqun Yan Li Dong Kailai Shen Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology China

Machine Learning as a Service (MLaaS) has gained popularity due to advancements in Deep Neural networks (DNNs). However, untrusted third-party platforms have raised concerns about AI security, particularly in backdoor attacks. Recent research has shown that speech backdoors can utilize transformations as triggers, similar to image backdoors. However, human ears can easily be aware of these transformations, leading to suspicion. In this paper, we propose PaddingBack, an inaudible backdoor attack that utilizes malicious operations to generate poisoned samples, rendering them indistinguishable from clean ones. Instead of using external perturbations as triggers, we exploit the widely-used speech signal operation, padding, to break speaker recognition systems. Experimental results demonstrate the effectiveness of our method, achieving a significant attack success rate while retaining benign accuracy. Furthermore, Padding-Back demonstrates the ability to resist defense methods and maintain its stealthiness against human perception.

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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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IEEE Workshop on Automatic Speech Recognition and Understanding

作者： Kailai Shen Diqun Yan Li Dong Ying Ren Xiaoxun Wu Jing Hu Faculty of Electrical Engineering and Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge’s emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method.

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Gap-free genome assemblies of two Pyrus bretschneideri cultivars and GWAS analyses identify a CCCH zinc finger protein as a key regulator of stone cell formation in pear fruit

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Plant Communications 2025年第3期6卷 246-262页

作者： Yunpeng Cao Xiaofeng Feng Baopeng Ding Heqiang Huo Muhammad Abdullah Jiayi Hong Lan Jiang Han Wang Risheng Li Yongping Cai Xiaoxu Li Zhichao Xia Rajeev K.Varshney Haifei Hu Mengfei Lin Fei Shen CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture Wuhan Botanical GardenChinese Academy of SciencesWuhan 430074China College of Life Sciences Anhui Agricultural UniversityHefei 230036China Beijing Life Science Academy Beijing 102209China Key Laboratory of Non-coding RNA Transformation Research of Anhui Higher Education Institution Yijishan Hospital of Wannan Medical CollegeWuhu 241000China Key Laboratory of Horticultural Crop Germplasm Innovation and Utilization(Co-construction by Ministry and Province) Institute of HorticultureAnhui Academy of Agricultural SciencesHefei 230000China School of Forestry&Landscape Architecture Anhui Agricultural UniversityHefei 230036China Rice Research Institute Guangdong Academy of Agricultural Sciences&Key Laboratory of Genetics and Breeding of High-Quality Rice in Southern China Co-construction by Ministry and Province) Ministry of Agriculture and Rural Affairs&Guangdong Key Laboratory of New Technology in Rice Breeding&Guangdong Rice Engineering LaboratoryGuangzhou 510640China Jiangxi Provincial Key Laboratory of Plantation and High Valued Utilization of Specialty Fruit Tree and Tea Institute of Biological ResourcesJiangxi Academy of SciencesNanchangJiangxiChina Institute of Biotechnology Beijing Academy of Agriculture and Forestry SciencesBeijing 100097China 10Engineering Research Center of Coal-Based Ecological Carbon Sequestration Technology of the Ministry of Education and Key Laboratory of National Forest and Grass Administration for the Application of Graphene in ForestryShanxi Datong UniversityDatong 037009China Department of Environmental Horticulture Mid-Florida Research and Education CenterUniversity of FloridaApopkaFL 32703USA Queensland Alliance for Agriculture and Food Innovation University of Queensland7 BrisbaneBrisbaneQLDAustralia Centre for Crop and Food Innovation WA State Agricultural Biotechnology CentreFood Futures InstituteMurdoch UniversityMurdochWAAustralia

The Chinese white pear(Pyrus bretschneideri)is an economically significant fruit crop *** versions of the *** genome assembly contain numerous gaps and unanchored genetic ***,we generated two high-quality,gap-free genome assemblies for‘Dangshansu’(DS;503.92 Mb)and‘Lianglizaosu’(ZS;509.01 Mb),each anchored to 17 chromosomes,achieving a benchmarking universal single-copy ortholog completeness score of nearly 99.0%.Our genome-wide association studies explored the associations between genetic variations and stone cell traits,revealing a significant association peak on DS chromosome 3 and identifying a novel non-tandem CCCH-type zinc finger gene,designated *** genetic transformation,we verified the pivotal role of PbdsZF in regulation of both lignin biosynthesis and stone cell formation,as it transcriptionally activates multiple genes involved in these *** binding to the CT-rich motifs CT1(CTTTTTTCT)and CT2(CTCTTTTT),PbdsZF significantly influences the transcription of genes essential for lignin production,underscoring its regulatory importance in plant lignin *** study illuminates the complex biology of fruit development and delineates the gene regulatory networks that influence stone cell and lignocellulose formation,thereby enriching genetic resources and laying the groundwork for the molecular breeding of perennial trees.

关键词： Pyrus bretschneideri gap-free genome stone cell formation GWAS transcriptional regulation

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