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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Xiang, Jinhai Zhang, Yan Yu, JiaKun He, Xinwei Huazhong Agricultural University College of Informatics Wuhan430070 China Huazhong Agricultural University Agricultural Bioinformatics Key Laboratory of Hubei Province Wuhan430070 China Huazhong Agricultural University Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan430070 China

ISBN: (纸本)9798350386226

Semi-supervised semantic segmentation (SSS), which allows for learning a better model with a small fraction of labeled samples and a large amount of unlabeled ones, is valuable yet challenging in medical image analysis. Recent works (e.g., UniMatch) have found that weak-to-strong consistency via augmentation is especially conducive to SSS training. However, they inadvertently introduce cognitive biases for unlabeled images, making it difficult to segment accurately near the target's edge regions. In this paper we present a lightweight bias-correct module to self-correct these mistakes between the strong perturbations. Based on it, we design a new framework named BcMatch by plugging it into UniMatch to reduce such cognitive biases stemming from incorrect pseudo-labels for unlabeled images. Moreover, we also introduce a bias correction loss, which works in tandem with the consistency loss to guide the model learning, focusing more on the edge regions of the targets. Experiments on the representative semi-supervised segmentation dataset, ACDC, demonstrate our BcMatch surpasses UniMatch by a large margin, attaining new state-of-the-art performance. The code is at https://***/zhangyan498/BcMatch. © 2024 IEEE.

关键词： Semantic Segmentation

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Improving Cross-Domain Named Entity Recognition from the Perspective of Representation 28th

Improving Cross-Domain Named Entity Recognition from the P...

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28th International Conference on database Systems for Advanced Applications, DASFAA 2023

作者： Xu, Jingyun Cai, Yi School of Software Engineering South China University of Technology Guangzhou510650 China The Key Laboratory of Big Data and Intelligent Robots South China University of Technology and the Pazhou Lab Guangzhou510335 China

ISBN: (纸本)9783031306778

Recently, cross-domain named entity recognition (cross-domain NER), which can reduce the high data annotation costs faced by fully-supervised methods, has drawn attention. Most competitive approaches mainly rely on pre-trained language models like BERT to represent words. As such, the original chaotic representations may bring challenges (e.g., entity span detection errors and entity type misclassification) for them. Motivated by this, this proposal proposes to improve cross-domain NER by refining the original representations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Contrastive Learning Cross-domain Named Entity Recognition Representation

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Multivariate Time Series Co-evolution Shapelet Learning Method 6

Multivariate Time Series Co-evolution Shapelet Learning Meth...

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6th International Conference on Machine Learning and Natural Language Processing, MLNLP 2023

作者： Wang, Ling Chu, Zhongkun Liu, Wei School of Automation and Electrical Engineering University of Science and Technology of Beijing Key Laboratory of Knowledge Automation for Industrial Process Ministry of Education Beijing China Shunde Innovation School University of Science and Technology Beijing Guangdong528399 China

ISBN: (纸本)9798400709241

Shapelets are discriminative subsequences in time series that effectively capture local shape characteristics. However, the time complexity of the existing shapelet extraction process remains high. To address this issue, this paper proposes an unsupervised shapelet extraction algorithm. The algorithm utilizes the coherence of multiple wavelets and constraints on multivariate collaborative evolution intervals to constrain the generation range of shapelets, thereby improving the efficiency of shapelet learning while preserving their significance. The superiority of the proposed algorithm is validated through experiments conducted on the UEA dataset. © 2023 ACM.

关键词： Extraction

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Adaptive Detr: A Framework with Dynamic Sampling Points and Feature-Guided Adaptive Attention Updates

SSRN

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SSRN 2024年

作者： Li, Botao Yang, Huguang Xia, Chenglong Zheng, Han Zhang, Taohong Wulamu, Aziguli Beijing100083 China Guangxi546300 China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083 China

In recent years, models based on DETR (Detection Transformer) have made significant progress in the field of object detection. However, the original DETR model lacks adaptability in its encoder, performing well only on single datasets, which limits its performance in complex scenarios. To address this issue, we propose a new framework—Adaptive DETR—that features dynamic adjustment of the number of sampling points and feature-guided adaptive attention *** main innovations include: First, we introduce a new attention update strategy that dynamically calculates weights based on the features of the output image, determining the frequency of attention updates on the features, thereby enhancing detection accuracy. Second, to tackle the fixed sampling point issue in the deformable attention module of the DETR model, we implement dynamic adjustment of the number of sampling points, improving the model's generalization ability in complex scenes. Finally, we optimize the DETR decoder by performing attention calculations between queries responsible for bounding boxes and those responsible for semantic information during the query initialization phase, effectively injecting semantic information to enhance localization *** experimental results demonstrate that Adaptive DETR achieves the best performance on the UAVDT, VisDrone, and RSOD datasets, showcasing its efficiency and generalization capability. © 2024, The Authors. All rights reserved.

关键词： Object detection

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IDIDNG: A Domain Generalization Remaining Useful Life Prediction Method of Unknown Bearings

IDIDNG: A Domain Generalization Remaining Useful Life Predic...

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Sensing, Measurement & data Analytics in the era of Artificial Intelligence (ICSMD), International Conference on

作者： Juan Xu Zhen Xu Key Laboratory of Knowledge Engineering with Big Data School of Computer and Information Hefei University of Technology Hefei China

Domain adaptation (DA) -based RUL prediction methods have achieved great success for the adaptation ability of the distribution discrepancy between the source and target domains. However, DA methods are powerless when the target domain data are not available for training. To solve this problem, we propose an inter-domain intra-domain normalized generalization (IDIDNG) network, which consists of three modules, respectively, the pre-processing module, the feature transformation module, and the RUL prediction module. First, we design the pre-processing module to process the bearing vibration data with peak-to-peak and Z-score. Finally, it is connected into a four-dimensional array. In the feature transformation module, via intra-domain and inter-domain normalization as well as mean-variance cross-swap, we transform the data distribution expressions of invariant features of bearings from the perspective of different bearings and different degradation stage of one bearing, such that the model enables to learn the intra-domain and inter-domain discrepancy. Further we design four adaptable weighting parameters into the intra-domain normalization to learn the appropriate normalized mean and variance via the model training. Finally, we design the GRU-based RUL prediction module to predict the unknown bearings. We conducted experiments under the PHM2012 dataset, experimental results show that our method achieves satisfactory prediction accuracy in the unknown bearings.

关键词： Training Vibrations Degradation Deep learning Adaptation models Transforms Predictive models

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EdgeAuth: An intelligent token-based collaborative authentication scheme

EdgeAuth: An intelligent token-based collaborative authentic...

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作者： Jiang, Xutong Dou, Ruihan He, Qiang Zhang, Xuyun Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Mathematics University of Waterloo Waterloo Canada Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University China

Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply-chain supervision and collaboration, Internet of Vehicles, real-time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token-based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man-in-the-middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes. © 2023 John Wiley & Sons Ltd.

关键词： Authentication

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An Energy-Aware UAVs Path Coverage for Critical Infrastructure Inspections 12

An Energy-Aware UAVs Path Coverage for Critical Infrastructu...

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12th International Conference on Advanced Cloud and Big data, CBD 2024

作者： Wang, Yifan Zeng, Wei Li, Guanyu Xiong, Chenglong Wang, Zicheng Mao, Yingchi College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Lancang River Hydropower Co. CHINA HUANENG Group Kunming China Power China Kunming Engineering Co. Yunnan China

ISBN: (纸本)9798331511074

Critical infrastructures increasingly rely on unmanned aerial vehicles (UAVs) for inspection tasks. The significance of different components within these infrastructures is subject to variability and can be influenced by external factors. The principal aim of routine UAV inspections is to ensure differentiated coverage of pivotal sections with minimal energy consumption. However, extant research on UAV path coverage fails to fully account for the variability and dynamic shifts in regional significance and their impact on coverage efficacy. This paper presents an energy-aware collaborative coverage policy for UAVs, designated as ***-MATD3 employs a dynamic weight region partitioning method tailored to real-world environments and addresses the action selection challenge for UAVs using a discrete Partially Observable Markov Decision Process (Dec-POMDP). By amalgamating MATD3 with stacked LSTM, this approach mitigates redundant path overlaps and unnecessary action replication across multiple agents, thus optimizing coverage and diminishing energy usage. Simulation outcomes demonstrate that EA-MATD3 reduces energy consumption by an average of 9.65% relative to the Greedy, MADDPG, and MATD3 algorithms while sustaining a superior coverage rate. © 2024 IEEE.

关键词： Markov processes

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LESaET: Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER

LESaET: Low-Dimensional Embedding Method for Link Prediction...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ding, Lichao Zhao, Jing Lu, Kai Hao, Zenghao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781665410205

knowledge graphs (KGs) provide a structured representation of the real world through entity-relation triples. However, current KGs are often incomplete, typically containing only a small fraction of all possible facts. This involves inferring missing content from existing information, a task known as link prediction. Existing methods in the field of link prediction struggle with controlling the dimensionality of embedding vectors or suffer from overly complex models. In order to tackle these challenges, we introduce a method in this paper, named Low-Dimensional Embedding Method for Link Prediction Combining Self-Attention and Enhanced-TuckER (LESaET). LESaET leverages both self-attention mechanisms and tensor factorization to learn expressive contextual-enhanced representations of KGs. Specifically, LESaET employs the multi-head self-attention mechanism of Transformer as an encoder to capture the mutual information between entities and relationships, and utilizes Enhanced-TuckER as a decoder, ultimately achieving expressive low-dimensional embeddings for link prediction tasks. LESaET demonstrates competitive performance compared to advanced methods on standard datasets. © 2024 IEEE.

关键词： Prediction models

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Research on water extraction method from remote sensing images of lakes in cold and arid regions based on deep learning 3

Research on water extraction method from remote sensing imag...

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3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Xin Fu, Xueliang Hu, Hua Li, Honghui College of Computer and Information Engineering Inner Mongolia Agricultural University Hohhot China Inner Mongolia Autonomous Region Key Laboratory of Big Data Research Application of Agriculture and Animal Husbandry Hohhot China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

Semantic segmentation technology is an important research direction in the field of computer vision, with the continuous improvement of computer GPU and other hardware arithmetic power, as well as the more clear and accurate remote sensing images. In recent years, deep learning technology has developed rapidly in the field of semantic segmentation, and the level of semantic segmentation technology has improved significantly. Based on unsupervised, automated, end-to-end deep learning networks have gradually become the object of research in scene object segmentation, pre-person background segmentation, face recognition and other related fields. In this paper, we conduct research on deep learning semantic segmentation algorithms for water body extraction, focusing on specific analysis of typical problems such as long freezing period of lakes in cold and arid regions, low precipitation, complex seasonal changes of lakes, and fragile water bodies. The cold and arid zone lakes distributed in Inner Mongolia region of China are selected as the research objects, and the water body extraction experiments are conducted on the remote sensing images of Hulun Lake and Wuoliangsuhai Lake. Propose a SER34AUnet model, the model was improved based on the Unet network model of Encode-Decode architecture, and finally achieved good results in the water extraction experiments. The main work of this paper is as follows: (1) Establishing image segmentation dataset: remote sensing images of several lakes in cold and arid regions in different seasons, different satellites and various remote sensing images with specific representative features typical of cold and arid regions are used to enhance the generalization ability of the dataset. (2) Improving the Unet model: Firstly, the Resnet34 network, which is more capable of extracting image features, is used in the Encode part. Secondly, the SE (Queeze and-Excitation) module based on the channel attention mechanism is added to the BasicBlock mo

关键词： Lakes

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Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid Model 14th

Prediction of Ionospheric TEC Based on BLS-LSTM-GRU Hybrid M...

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14th China Satellite Navigation Conference, CSNC 2024

作者： Li, Dengao Zhang, Xinfang Zhao, Jumin Wu, Fanming Feng, Ran Zhao, Jinhua Shi, Danyang College of Data Science Taiyuan University of Technology Taiyuan030024 China College of Information and Computer Taiyuan University of Technology Taiyuan030024 China Key Laboratory of Big Data Fusion Analysis and Application of Shanxi Province Taiyuan030024 China Intelligent Perception Engineering Technology Center of Shanxi Taiyuan030024 China Shanxi Province Engineering Technology Research Center of Spatial Information Network Taiyuan030024 China

ISBN: (纸本)9789819969272

In satellite communications, global navigation satellite systems(GNSS) and other important space activities, the value of total electron content (TEC) in the ionosphere directly affects the size of ionospheric delay. Achieving accurate prediction of ionospheric total electron content through modeling can effectively improve the reliability and accuracy of GNSS positioning. Considering the high prediction accuracy of long short-term memory neural network (LSTM) and the fast training speed of gated recurrent unit network (GRU), but it is easy to fall into the local optimum, while the width learning system(BLS) can effectively avoid this drawback. Therefore, this paper proposes a model for predicting the total electron content of the ionosphere, which consists of a broad learning system, an LSTM network and a GRU networks. Six parameters of TEC, geomagnetic index Dst, Kp, Ap, solar activity index F10.7, and hour (HD) are selected as input features from the observed data to predict the variation of the total ionospheric electron content. The results generate prediction maps of the total ionospheric electron content and are compared with empirical models and conventional neural network models at different times and solar activity conditions. The experimental results show that the root mean square error (RMSE) of the prediction results of this BLS-LSTM-GRU model is decreased by 23.07%, 17.48% and 9.46% compared with the IRI-2016, NeQuick and CNN-LSTM prediction models, respectively, with higher prediction accuracy. © 2024, Aerospace Information Research Institute.

关键词： Long short-term memory

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