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arXiv 2024年

作者： Fa, Zhan Li, Shumeng Zhang, Jian Qi, Lei Yu, Qian Shi, Yinghuan State Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China Southeast University Nanjing China School of Data and Computer Science Shandong Women’s University Jinan China

Semi-supervised learning (SSL) techniques address the high labeling costs in 3D medical image segmentation, with the teacher-student model being a common approach. However, using an exponential moving average (EMA) in single-teacher models may cause coupling issue, where the weights of the student and teacher models become similar, limiting the teacher’s ability to provide additional knowledge for the student. Dual-teacher models were introduced to address this problem but often neglected the importance of maintaining teacher model diversity, leading to coupling issue among teachers. To address the coupling issue, we incorporate a double-copy-paste (DCP) technique to enhance the diversity among the teachers. Additionally, we introduce the Staged Selective Ensemble (SSE) module, which selects different ensemble methods based on the characteristics of the samples and enables more accurate segmentation of label boundaries, thereby improving the quality of pseudo-labels. Experimental results demonstrate the effectiveness of our proposed method in 3D medical image segmentation tasks. Here is the code link: https://***/Fazhan-cs/DCPMSC Codes 68T05 © 2024, CC BY.

关键词： Students

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A Hybrid Dual-View Constraint Framework for Single-Source Domain Generalization in Medical Image Segmentation

SSRN

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SSRN 2024年

作者： Wang, Ruofan Guo, Jintao Zhang, Jian Qi, Lei Yu, Qian Shi, Yinghuan State Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China Ministry of Education Nanjing China School of Data and Computer Science Shandong Women’s University Jinan China

Single-source domain generalization in medical image segmentation exhibits promising prospects for clinical applications, yet suffers from domain shifts. Learning domain-invariant representations is crucial for resisting domain shifts, which current augmentation and domain-invariant feature learning strive for implicitly or explicitly. Despite their common successes, sufficient and explicit representation learning incorporating properties of medical images still deserves exploration. Motivated by observations of limited stylized variation, small inter-class difference, and similar anatomical structure in medical images, a hybrid Dual-view Constraint Framework (DCON) is proposed for effective domain-invariant representation learning. Our dualview asymmetric augmentation utilizes diverse image-level and feature-level augmentation, including a proposed global-local stylized augmentation with controllability. Bilevel contrastive learning performs two constraints: object-focused consistency between dual views and cross-individual constraint across images simultaneously to capture invariant semantic features. DCON outperforms existing advanced methods on three datasets for single-source domain generalization. Our source code is available at https://***/wrfnj/DCON. © 2024, The Authors. All rights reserved.

关键词： Medical imaging

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Uplink Control Information Based Clustered Access and Discontinuous Reception Management for mMTC

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IEEE Communications Letters 2025年第6期29卷 1380-1384页

作者： Wang, Shao-Di Wang, Chang-Long Wang, Hui-Ming Zhou, Feng Leung, Victor C. M. Xidian University Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education Xi’an710071 China Xi’an Jiaotong University School of Information and Communication Engineering Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an710049 China Shenzhen MSU BIT University Artificial Intelligence Research Institute Shenzhen518172 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

Uplink control information (UCI) and discontinuous reception (DRX) play important roles for massive machine type communication (mMTC). Despite their standalone significance, a conspicuous gap exists in comprehensively exploring the synergy between UCI and DRX concerning both energy conservation and latency reduction for mMTC. In this letter, we propose a UCI-based clustered access and DRX management (UCADM) scheme that strikes a balance between these two critical performance metrics, reducing energy usage while preserving low latency in uplink-downlink interaction for mMTC. Specifically, we present a strictly feasible solution for the proposed UCADM scheme by using the spectral radius and the constraint projection, which can effectively solve DRX switching operations as indicated by scheduling information from UCI. Simulation results confirm the effectiveness of our scheme. © 1997-2012 IEEE.

关键词： Information management

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Scenario Generation for Cooling,Heating,and Power Loads Using Generative Moment Matching Networks

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CSEE Journal of Power and Energy Systems 2022年第6期8卷 1730-1740页

作者： Wenlong Liao Yusen Wang Yuelong Wang Kody Powell Qi Liu Zhe Yang Department of Energy Technology Aalborg UniversityAalborg 9220Denmark School of Electrical Engineering and Computer Science KTH Royal Institute of TechnologyStockholmSE-10044Sweden State Grid Tianjin Chengxi Electric Power Supply Branch Tianjin 300100China Department of Chemical Engineering University of UtahUT 84112America Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjin 300072China

Scenario generations of cooling,heating,and power loads are of great significance for the economic operation and stability analysis of integrated energy *** this paper,a novel deep generative network is proposed to model cooling,heating,and power load curves based on generative moment matching networks(GMMNs)where an auto-encoder transforms highdimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real *** training the model,the new scenarios are generated by feeding Gaussian noises to the scenario generator of the *** the explicit density models,the proposed GMMN does not need to artificially assume the probability distribution of the load curves,which leads to stronger *** simulation results show that the GMMN not only fits the probability distribution of multiclass load curves very well,but also accurately captures the shape(e.g.,large peaks,fast ramps,and fluctuation),frequency-domain characteristics,and temporal-spatial correlations of cooling,heating,and power ***,the energy consumption of generated samples closely resembles that of real samples.

关键词： Deep learning generative moment matching networks integrated energy systems scenario generations

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Dual-Teacher Ensemble Models with Double-Copy-Paste for 3d Semi-Supervised Medical Image Segmentation

SSRN

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SSRN 2024年

关键词： Students

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Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models

arXiv

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arXiv 2025年

作者： Zhang, Quanjun Fang, Chunrong Zheng, Yi Zhang, Yaxin Zhao, Yuan Huang, Rubing Zhou, Jianyi Yang, Yun Zheng, Tao Chen, Zhenyu Department of Computing Technologies Swinburne University of Technology Melbourne Australia State Key Laboratory for Novel Software Technology Nanjing University Jiangsu Nanjing China School of Computer Science and Engineering Macau University of Science and Technology Jiangsu Nanjing China Huawei Cloud Computing Technologies Co. Ltd. Beijing China Shenzhen Research Institute of Nanjing University Guangdong Shenzhen China

Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to generate test assertions automatically, including deep learning (DL)-based, retrieval-based, and integration-based ones. Among them, recent integration-based approaches inherit from both DL-based and retrieval-based approaches and are considered state-of-the-art. Despite being promising, such integration-based approaches suffer from inherent limitations, such as retrieving assertions with lexical matching while ignoring meaningful code semantics, and generating assertions with a limited training corpus. In this paper, we propose a novel Retrieval-Augmented Deep Assertion Generation approach, namely RetriGen, based on a hybrid assertion retriever and a pre-trained language model (PLM)-based assertion generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant test-assert pair from external codebases. The retrieval process takes both lexical similarity and semantical similarity into account via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion with historical test-assert pairs and the retrieved external assertion. Although our concept is general and can be adapted to various off-the-shelf encoder-decoder PLMs, we implement RetriGen to facilitate assertion generation based on the recent CodeT5 model. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The experimental results demonstrate that RetriGen achieves 57.66% and 73.24% in terms of accuracy and CodeBLEU, outperforming all baselines with an average improvement of 50.66

关键词： Semantics

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A BiLSTM cardinality estimator in complex database systems based on attention mechanism

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CAAI Transactions on Intelligence technology 2022年第3期7卷 537-546页

作者： Qiang Zhou Guoping Yang Haiquan Song Jin Guo Yadong Zhang Shengjie Wei Lulu Qu Louis Alberto Gutierrez Shaojie Qiao School of Information Science and Technology Southwest Jiaotong UniversityChengduChina School of Software Engineering Chengdu University of Information TechnologyChengduChina School of Energy Power and Mechanical Engineering North China Electric Power UniversityBaodingChina Digital Media Art Key Laboratory of Sichuan ProvinceSichuan Conservatory of MusicChengduChina Department of Computer Science Rensselaer Polytechnic InstituteNew YorkNew YorkUSA

An excellent cardinality estimation can make the query optimiser produce a good execution *** there are some studies on cardinality estimation,the prediction results of existing cardinality estimators are inaccurate and the query efficiency cannot be guaranteed as *** particular,they are difficult to accurately obtain the complex relationships between multiple tables in complex database *** dealing with complex queries,the existing cardinality estimators cannot achieve good *** this study,a novel cardinality estimator is *** uses the core techniques with the BiLSTM network structure and adds the attention ***,the columns involved in the query statements in the training set are sampled and compressed into ***,the Word2vec model is used to embed the word vectors about the query ***,the BiLSTM network and attention mechanism are employed to deal with word *** proposed model takes into consideration not only the correlation between tables but also the processing of complex *** experiments and the evaluation of BiLSTM-Attention Cardinality Estimator(BACE)on the IMDB datasets are *** results show that the deep learning model can significantly improve the quality of cardinality estimation,which is a vital role in query optimisation for complex databases.

关键词： attention BiLSTM cardinality estimation complex database systems query optimiser Word2vec

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Privacy-preserving cloud-fog–based traceable road condition monitoring in VANET

Privacy-preserving cloud-fog–based traceable road condition...

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作者： Wang, Wei Wu, Lei Qu, Wenlei Liu, Zhaoman Wang, Hao School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China Shandong Provincial Key Laboratory for Software Engineering Jinan China

With the development of intelligent vehicles, the research on road condition monitoring has attracted much attention in the vehicular ad hoc network (VANET). The combination of VANET, cloud computing, and fog computing provides on-demand computational resources while creating a lot of new challenges. In this paper, we propose an efficient privacy-preserving cloud-fog–based traceable road surface condition monitoring scheme. We use certificateless aggregate signcryption technology to implement multiple messages aggregation verification, which greatly saves computing resources and bandwidth. Moreover, we also use a traceable vehicle pseudo identity generated by a trace authority (TRA) to achieve identity privacy protection. To ensure the privacy of the fog and cloud server, the road condition information is reported in ciphertext format. In addition, the cloud server can perform ciphertext equivalence test operation to distinguish different road condition information of the same area without compromising the confidentiality. The ciphertext, which exceeds the set threshold, is uploaded to the blockchain. It can be stored permanently and never be tampered with or deleted. Finally, we demonstrate the correctness of the proposed scheme and show that the proposed scheme has higher efficiency. © 2020 John Wiley & Sons, Ltd.

关键词： Fog

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Spatial-information Guided Adaptive Context-aware Network for Efficient RGB-D Semantic Segmentation

arXiv

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arXiv 2023年

作者： Zhang, Yang Xiong, Chenyun Liu, Junjie Ye, Xuhui Sun, Guodong The School of Mechanical Engineering Hubei University of Technology Wuhan430068 China The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing210023 China

Efficient RGB-D semantic segmentation has received considerable attention in mobile robots, which plays a vital role in analyzing and recognizing environmental information. According to previous studies, depth information can provide corresponding geometric relationships for objects and scenes, but actual depth data usually exist as noise. To avoid unfavorable effects on segmentation accuracy and computation, it is necessary to design an efficient framework to leverage cross-modal correlations and complementary cues. In this paper, we propose an efficient lightweight encoder-decoder network that reduces the computational parameters and guarantees the robustness of the algorithm. Working with channel and spatial fusion attention modules, our network effectively captures multi-level RGB-D features. A globally guided local affinity context module is proposed to obtain sufficient high-level context information. The decoder utilizes a lightweight residual unit that combines short- and long-distance information with a few redundant computations. Experimental results on NYUv2, SUN RGB-D, and Cityscapes datasets show that our method achieves a better trade-off among segmentation accuracy, inference time, and parameters than the state-of-the-art methods. © 2023, CC BY-NC-ND.

关键词： Semantic Segmentation

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