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IEEE International Conference on Systems, Man and Cybernetics

作者： Lin Lu Qingzhi Zou Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Due to the exceptional performance of Transform-ers in 2 $D$ medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%.

关键词： Image segmentation Solid modeling Visualization Three-dimensional displays Current transformers Multilayer perceptrons Convolutional neural networks Kernel Synapses Biomedical imaging

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Cross-Domain Retinopathy Classification with OCT Images via Disentangling Representation and Adaptation networks

Cross-Domain Retinopathy Classification with OCT Images via ...

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Optoelectronics Global Conference (OGC)

作者： Wickramaarachchi Minidu Thiranjaya Yuan Li Lei Tao Yuemei Luo Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Provincial Key Laboratory of Intelligent Medical Image Computing (iMIC) School of Future Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350391466

ISBN: (纸本)9798350391473

Deep learning methods have shown significant potential in retinopathy classification using optical coherence tomography (OCT) images. However, substantial challenges arise due to domain shift issues, which are attributable to heterogeneity in data collection devices, variations in subject populations, and discrepancies in imaging protocols and parameters. To address these, we propose a novel approach leveraging Disentangling Representation and Adaptation networks (DRANet) to enhance the robustness and generalization of retinopathy classification models across different OCT datasets. Our methodology involves developing DRANet, a specialized network architecture designed to disentangle representations of data and facilitate domain adaptation. DRANet includes components for learning invariant representations across domains through adversarial training, ensuring robustness against domain shifts. DRANet encodes individual representations of content and style from both source and target images. Then, it adapts the domain by incorporating the transferred style factor into the content factor, along with learnable weights specified for each domain. This learning framework allows bidirectional domain. After conducting extensive experiments conducted on three public datasets achieved accuracies of $\mathrm{8 8 \%, ~} \mathrm{9 0 \%}$, and $\mathrm{9 2 \%}$ under three cross-domain scenarios, demonstrating the efficacy of our approach in mitigating domain shift effects and improving retinopathy classification performance.

关键词： Training Deep learning Protocols Retinopathy Optical coherence tomography Imaging Coherence Network architecture Data collection Robustness

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Scene-based Graph Convolutional networks for Federated Multi-Label Classification

Scene-based Graph Convolutional Networks for Federated Multi...

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International Joint Conference on Neural networks (IJCNN)

作者： Shaocong Xue Wenjian Luo Yongkang Luo Zeping Yin Jiahao Gu Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China School of Science China University of Mining & Technology-Beijing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Federated multi-label learning can collaboratively train multi-label classification models without compromising user privacy. Compared to multi-class learning, one of the most critical issues of multi-label learning is how to capture the correlations between labels, which is often ignored by existing research on federated multi-label learning. In this paper, a scene-based federated multi-label learning framework is proposed, which effectively utilizes the dependencies among labels for model training on the client-side and aggregates diverse client information on the server-side. Specifically, in the local training phase, a scene recognition module is employed to detect the scene for each image and the corresponding label co-occurrence matrix is used to guide the propagation of image features on the label graph. In the aggregation phase, a scene-aware aggregation method is adopted to enrich the scene-label co-occurrence information of each client. Experiments on PASCAL VOC 2007 and MS-COCO show that our proposed method can significantly improve the accuracy of federated multi-label image classification.

关键词： Training Privacy Data privacy Correlation Image recognition Graph convolutional networks Aggregates

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G2PL: Lexicon Enhanced Chinese Polyphone Disambiguation Using Bert Adapter with a New Dataset 48

G2PL: Lexicon Enhanced Chinese Polyphone Disambiguation Usin...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Zhao, Haifeng Wan, Hongzhi Huang, Lili Cao, Mingwei Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Anhui Hefei230601 China Imsl Shenzhen Key Lab PKU-HKUST Shenzhen Hong Kong Institute Hong Kong Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

ISBN: (纸本)9781728163277

Polyphone disambiguation is the core of grapheme-to-phoneme(G2P) module for the Chinese speech synthesis system. However, there is a lack of datasets and only one public for polyphone disambiguation. Moreover, due to the double long-tail distribution of polyphones, the ratio of pronunciation data for most polyphones is extremely unbalanced after sampling. To solve these problems, we propose a new dataset with 57,000 sentences from various domains by a new strategy for sampling. In addition, we propose the G2PL, which integrates word features into the bottom of BERT to assist in predicting the correct pronunciation of polyphone. In the experiment, we train the G2PL model to outperform other methods on our and public datasets. Our dataset, codes and user-friendly package are freely available. © 2023 IEEE.

关键词： BERT Grapheme to phonemes Polyphone disambiguation Speech synthesis Word features

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Supervisory Scheme for Blockchain Privacy Protection Technique Based on Group Signature

Supervisory Scheme for Blockchain Privacy Protection Techniq...

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2021 ACM International Conference on Intelligent Computing and its Emerging Applications, ICEA 2021

作者： Fu, Kunlun Wang, Lianhai Shao, Wei Wang, Wei Zhang, Shuhui Xu, Shujiang Hu, Shanshan Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan China

ISBN: (纸本)9781450391603

As research on blockchain deepens, privacy protection technologies of blockchain continue to develop. However, the use of these privacy protection technologies may cause supervisory problems since tracing and censoring malicious users and their misbehaviors become difficult. In order to solve the supervisory problems, we propose a supervisory scheme based on the Linkable Spontaneous Anonymous Group Signature (LSAG) schemes. In this paper, we first proposed the overall supervisory scheme based on LSAG to achieve effective tracing of blockchain users. To prevent tracing power abuses, we then redesigned the key generation algorithm and its corresponding negotiative decryption algorithm in the scheme to allow decentralized traceability. Only when all supervisory nodes agree can a transaction be decrypted and linked to its sender. Experiments show that our scheme can effectively realize transaction accountability and also prevent the abuse of this supervisory power. © 2021 ACM.

关键词： Blockchain

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Towards Robust Video Anomaly Detection in 6G networks: A Scene-Adaptive Framework

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IEEE Network 2025年

作者： Zhang, Sijia Yu, Yong Tao, Xiaoling Wang, Lianhai Shaanxi Normal University School of Computer Science China Guilin University of Electronic Technology Guangxi Cooperative Innovation Center of Cloud Computing and Big Data China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing China

With the rapid advancement of 5th Generation (5G) networks, their low latency and high bandwidth facilitate the development of smart city systems. Among the smart city systems, anomaly detection in surveillance videos has been an essential part due to its importance to public safety and security. With the transition to 6G technology, the practical applications of video anomaly detection are expanding to encompass a variety of scenarios, such as sidewalks, shopping malls and campuses, which exhibit significant differences. However, most existing anomaly detection methods deployed in the cloud typically rely on the fixed datasets for training and apply the trained uniform model to the edge services across different scenarios. The distinct environmental conditions may lead to varying performance of a uniform anomaly detection model. To alleviate these issues, we propose a scene-adaptive video anomaly detection framework in 6G-based applications. The proposed framework leverages the advantages of 6G networks to facilitate localized processing and rapid adaptation to diverse environments. To enable the anomaly detection model to adapt more robustly to local environments, the proposed framework integrates a pre-trained model and simulates anomalies to train the anomaly detection model in a denoising manner. By integrating pre-trained teacher models with edge computing nodes, the proposed method facilitates accurate anomaly detection while maintaining robust performance across varying scenarios. The framework utilizes both original data and simulated anomalous data to fine-tune the edge model, ensuring it aligns with the unique characteristics of each environment. Experimental results demonstrate the effectiveness of the proposed method across different scenarios, enabling smarter and more responsive surveillance systems in the 6G era. © 2025 IEEE.

关键词： Video analysis

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Identity-Based Matchmaking Encryption with Stronger Security and Instantiation on Lattices

SSRN

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SSRN 2023年

作者： Wang, Yuejun Wang, Baocang Lai, Qiqi Zhan, Yu State Key Laboratory of Integrated Services Networks Xidian University Xi’an China Henan Key Laboratory of Network Cryptography Technology Henan China School of Computer Science Shaanxi Normal University Xi’an China

An identity-based matchmaking encryption (IB-ME) scheme proposed at JOC 2021 supports anonymous but authenticated communications in a way that communication parties can both specify the senders or receivers on the fly. IB-ME is easy to be used in several network applications requiring privacy-preserving for its efficient implementation and special syntax. In the literature, IB-ME schemes are built from the variants of Diffie-Hellman assumption and all fail to retain security for quantum attackers. Despite the rigorous security proofs in previous security models, the existing schemes are still possibly vulnerable to some potential neglected attacks. Aiming at the above problems, we provide a stronger security definition of authenticity considering new attacks to fit real-world scenarios and then propose a generic construction of IB-ME satisfying the new model. Inspired by the prior IB-ME construction of Chen et al., the proposed scheme is constructed by combining 2-level anonymous hierarchical IBE (HIBE) and identity-based signature (IBS) schemes. In order to upgrade lattice-based IB-ME with better efficiency, we additionally improve a lattice IBS, as an independent technical contribution, to shorten its signature and thus reduce the final IB-ME ciphertext size. By combining the improved IBS and any 2-level adaptively-secure lattice-based HIBE with anonymity, we finally obtain the first lattice-based construction that implements IB-ME directly. © 2023, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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Causal and Local Correlations Based Network for Multivariate Time Series Classification

arXiv

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arXiv 2024年

作者： Du, Mingsen Wei, Yanxuan Zheng, Xiangwei Ji, Cun School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

Recently, time series classification has attracted the attention of a large number of researchers, and hundreds of methods have been proposed. However, these methods often ignore the spatial correlations among dimensions and the local correlations among features. To address this issue, the causal and local correlations based network (CaLoNet) is proposed in this study for multivariate time series classification. First, pairwise spatial correlations between dimensions are modeled using causality modeling to obtain the graph structure. Then, a relationship extraction network is used to fuse local correlations to obtain long-term dependency features. Finally, the graph structure and long-term dependency features are integrated into the graph neural network. Experiments on the UEA datasets show that CaLoNet can obtain competitive performance compared with state-of-the-art methods. © 2024, CC BY.

关键词： Graph neural networks

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Data-Driven Active Power Dispatch for Wind Farms

Data-Driven Active Power Dispatch for Wind Farms

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Data Driven Control and Learning Systems (DDCLS)

作者： Xuguo Jiao Daoyuan Zhang Xin Wang Zhaoxing Ma Zhenyong Zhang Wenfeng Liu School of Information and Control Engineering Qingdao University of Technology Qingdao China State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School of Civil Engineering Qingdao University of Technology Qingdao China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Active power dispatch of wind farms plays an important role in power grid scheduling. In this paper, a data-driven active power dispatch strategy for wind farms is proposed, which uses the key point of minimizing the standard deviation of wind turbines' output power series to reduce their fluctuations. First, to build the optimization problem, the historical active power output data of each wind turbine is collected from the Supervisory Control And Data Acquisition (SCADA) system. Then, the objective function is developed based on the idea of minimizing the standard deviation of the output power time series. Meanwhile, considering the constraints of wind turbines' output power and active power balance of the grid, the feasible domain of the cost function is constructed. Further, to solve the proposed nonlinear optimization problem with equality and inequality constraints, the trust region interior point method (TRIPM) is employed to explore optimal solution within the feasible domain. Finally, actual data collected from wind farms is used to test effectiveness of the designed approach and experimental results show that our method can achieve better output power smoothing performance than conventional Proportional Dispatch (PD) method.

关键词： Fluctuations Time series analysis SCADA systems Power smoothing Wind farms Linear programming Power grids

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An Entity Relation Extraction Framework Based on Large Language Model and Multi-Tasks Iterative Prompt Engineering

An Entity Relation Extraction Framework Based on Large Langu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Haibin Geng Chenglong Shi Xuesong Jiang Zan Kong Song Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models.

关键词： Large language models Semantics Noise Merging Knowledge graphs Multitasking Natural language processing Iterative methods Prompt engineering Data mining

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