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International Joint Conference on Neural networks (IJCNN)

作者： Wenyue Hu Zhenyu Yang Yan Huang Zhibo Zhang Baojie Xu Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong 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)9798350359312

ISBN: (纸本)9798350359329

The objective of sequential recommendation is to predict user preferences for items based on historical interaction sequences. This process often leads to a phenomenon known as popularity bias, where popular items are excessively recommended. Conformity, the tendency of users to follow popular items, is a significant factor contributing to this issue. Previous methods have not adequately disentangled conformity and interest, failing to accurately model users’ true intent. To address this, we propose a novel Disentangled Interest and Conformity Sequential Recommendation method (DICSRec) to mitigate the popularity bias. Specifically, we first design an Intent Encoding Module (IEM), which includes two independent encoders for conformity and interest to model their representations. To better disentangle these two factors, we design a disentangling task with proxy-based self-supervised learning and orthogonal regularization. Furthermore, to provide the Intent Encoding Module with more global information, we design a Global Conformity-aware Module (GCM), which supplies item popularity information and aids in enhancing user conformity representation. Lastly, recognizing the varying significance of user conformity and interest, we propose an adaptive Fusion Prediction Module (FPM) that adaptively aggregates user conformity and interest representations for final prediction. Experiments on four real-world datasets consistently demonstrate the superiority of our method over advanced sequential recommendation models. Code implementation is available at: https://***/lyra0611/DICSRec.

关键词： Adaptation models Codes Fuses Aggregates Neural networks Self-supervised learning Multitasking

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CSLP: Collaborative Solution to Long-Tail Problem and Popularity Bias in Sequential Recommendation

CSLP: Collaborative Solution to Long-Tail Problem and Popula...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yan Huang Zhenyu Yang Wenyue Hu Baojie Xu Zhibo Zhang The 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

Sequential Recommender Systems (SRS), leveraging the temporal information from users' behaviors, have noticeably improved user experience against traditional systems. However, these behaviors often follow long-tail distribution, making the systems biased towards popular items (i.e., popularity bias). Moreover, popularity bias would amplify the neglect of long-tail recommendations, thereby sharpening the long-tail problem. Previous researches usually address these challenges independently, focusing on reducing the over-recommendation of popular items or enhancing the representation quality of tail items. Indeed, it is possible to incorporate their merits to achieve the best of both worlds. Thus, we propose a novel and unified framework, named Collaborative Solution to Long tailed problem and Popularity bias (CSLP), to tackle both the long-tail problem and popularity bias simultaneously. To achieve this, we first introduce a representation enhancement module featuring dual generators to enhance user and item representations, particularly for those in the tail. On the other hand, a debiasing module incorporating an Inverse Propensity Score (IPS) with a clipping strategy is introduced to further alleviate the popularity bias. Specifically, this clipping strategy demonstrates a clear decrease in the original IPS method's variance, effectively improving the recommendation for stability and accuracy. Experiments on three widely-used datasets show CSLP's effectiveness in solving both issues. CSLP surpasses all baselines (traditional, popularity bias, and long-tail problem) in overall performance, significantly enhancing recommendation accuracy for both tail users and items, and achieving a more balanced ratio of recommendations between popular and tail items. Code is available at https://***/Echohuangyan/CSLP.

关键词： Hands Heavily-tailed distribution Accuracy Collaboration Tail Linear programming Generators User experience IP networks Recommender systems

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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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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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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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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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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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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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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