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

作者： Aimei Dong Jingyuan Xu Long Wang Guohua Lv Guixin Zhao Jinyong Cheng 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 Faculty of Computer Science and Technology Qilu University of Technology(Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal image fusion aims to merge features from different modalities to create a comprehensively representative image. However, existing medical image fusion methods often struggle to handle noise generated during image acquisition, significantly diminishing their impact on visual quality. To address these challenges, we propose a semantically text-guided medical image fusion model, named TDMF. Specifically, TDMF guides classical image fusion through textual semantics and effectively coordinates the resolution of degradation and interaction issues during the fusion process. By integrating text encoders and interactive fusion modules, TDMF establishes a unified framework for denoising and interactive fusion of medical images. Extensive experiments have demonstrated that our proposed text-guided image fusion strategy offers significant advantages over state-of-the-art methods in medical image fusion performance.

关键词： Visualization Image resolution Semantics Noise reduction Noise Pipelines Noise measurement Speech processing Image fusion Biomedical imaging

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ACOD: An Asynchronous Clustering Framework Based on Outlier Detection for Data Lakehouse 25

ACOD: An Asynchronous Clustering Framework Based on Outlier ...

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Proceedings of the 2025 8th International Conference on Data Storage and Data Engineering

作者： Xi Wang Ying Guo Hu Zhang Jibin Wang Jing Chen Xudong Meng Qilu University of Technology (Shandong Academy of Sciences) & Shandong Computer Science Center (National Supercomputer Center in Jinan) & Key Laboratory of Computing Power Network and Information Security Ministry of Education Jinan Shandong China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400710391

With the development of big data technology, the amount of data available to us is growing by leaps and bounds, and Data Lakehouse technology has become a mainstream solution. It is important to note that Data Lakehouse provides an important data clustering mechanism, but this also brings problems of cluster resource waste, unsupported concurrent updates, and unfriendly incremental queries. To address these issues, we propose an asynchronous clustering framework based on outlier detection. It uses improved outlier detection algorithms and metadata information to analyze and determine tasks that may be anomalous and then optimizes the data layout through the data clustering mechanism. From the experimental results, the improved outlier detection algorithm performs better, which can control the number of times the clustering operation is executed and mitigate the problems of unsupported concurrent updates, unfriendly incremental queries, and wasted cluster resources brought by the clustering mechanism.

关键词： Data Lakehouse

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An Approach to New Bearing Fault Diagnosis Utilizing Deep Transfer Learning

An Approach to New Bearing Fault Diagnosis Utilizing Deep Tr...

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Smart World Congress (SWC), IEEE

作者： HuiJuan Hao Yue Feng HongGe Zhao SiJian Zhu 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 Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

Bearings, serving as crucial components in mechanical equipment, exert a direct influence on the overall performance of the system. Deep transfer learning has garnered significant attention in the realm of intelligent fault diagnosis for machines. Nevertheless, extant deep transfer learning algorithms encounter challenges when detecting newly emerging faults within the target domain. To address this limitation, this study introduces a novel bearing fault diagnosis approach for newly emerging faults, leveraging deep transfer learning principles. Initially, a multi-grained hierarchical feature extraction network is constructed to effectively capture intricate data characteristics. Subsequently, these extracted features are employed to train a fault classifier, thereby establishing a fault diagnosis network grounded in deep transfer learning. This network is capable of discriminating unlabeled new faults from labeled known faults with remarkable precision. Furthermore, an adaptive hierarchical clustering algorithm, informed by the elbow method, is incorporated to categorize the newly emerging faults. The clustered samples are then utilized to train the online sequential extreme learning machine (OS-ELM), facilitating the construction of a new fault diagnosis network tailored for diagnosing these faults in the target domain. The efficacy of the proposed methodology was rigorously verified through experiments executed on both the CWRU and XJTU-SY datasets. The outcomes conclusively demonstrated an accuracy of $\mathbf{9 8. 7 5 \%}$ on the CWRU dataset and $\mathbf{9 7. 9 2 \%}$ on the XJTU-SY dataset. This model portends immense potential in tackling fault diagnosis transfer scenarios characterized by the emergence of multiple novel faults within the target domain, thereby markedly broadening the applicability of deep transfer learning.

关键词： Fault diagnosis Accuracy Adaptive systems Extreme learning machines Transfer learning Clustering algorithms Feature extraction Classification algorithms Data mining Elbow

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TSF-Net:Abnormal Video Behavior Detection Framework Based on Two-Stream Appearance Motion Semantic Fusion 23

TSF-Net:Abnormal Video Behavior Detection Framework Based on...

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Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence

作者： Bonan Li Rui Cao Xingang Wang Hong Zhang Jinyan Zhou China Radio and Television Shandong Network Co Ltd China 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) China

ISBN: (纸本)9798400716157

Video anomalous behavior detection aims to detect rare and random anomalous events, which usually deviate from expectations. Existing detection methods do not consider the appearance semantics and motion semantics of the events in conjunction, so they lead to poor detection results. Since the appearance semantics and motion semantics of normal events are highly consistent and correspond, while the semantic consistency of abnormal events is low. Based on this, this paper proposes a two-stream fusion hybrid network for abnormal behavior detection. The appearance semantics and motion semantics of events are recorded separately using a multi-scale fused self-encoder to generate corresponding reconstructed appearance frames and reconstructed motion frames. Afterwards, the two frames are fused to generate the final prediction frame. We introduce temporal attention to enhance the model's ability to represent motion semantics and capture normal behavior with effective regular motion. Experiments on three standard public datasets show that the method proposed in this paper can accurately detect anomalous events with a performance comparable to state-of-the-art methods.

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SRIF: Data-Free Knowledge Distillation via Stable Regulation and Input Filtering

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Zherui Xu, Rongtao Wang, Changwei Xu, Shibiao Zhou, Jie Xu, Wenhao Gao, Longxiang Xu, Wenbo Guo, Li Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Qilu University of Technology Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

Data-free knowledge distillation (DFKD) enables knowledge transfer from a pre-trained teacher to a student network without accessing the real dataset. However, generator-based DFKD methods struggle to ensure that the synthetic images accurately reflect the real dataset distribution. The update of the generator network relies heavily on teacher category guidance, but varying teacher prediction accuracy across categories leads to inconsistent synthetic image quality. Such variations introduce a distribution shift between synthetic and real datasets, negatively impacting student network performance during knowledge distillation. To address this challenge, we propose the SRIF, comprising two components: Student-Driven Flexible Filtering (SDFF) and Re-weighting for Independent Regularization (RIR). SDFF filters out synthetic images affected by the category distribution shift during data generation, producing a more reliable dataset. RIR, applied during distillation, encourages the student to learn stable causal relationships through sample reweighting. Both components flexibly integrate into existing DFKD frameworks, improving performance while reducing training costs. © 1999-2012 IEEE.

关键词： Photointerpretation

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FedSimRep: A Federated Learning Method Based on Data Representation Selection 25

FedSimRep: A Federated Learning Method Based on Data Represe...

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Proceedings of the 2025 3rd International Conference on Communication networks and Machine Learning

作者： Falu Zhai Lu Wu Chunxiao Wang Tongyang Hao Shucang Zang 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 Shandong China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan Shandong China

ISBN: (纸本)9798400713231

As an emerging distributed technology, Federated learning enables model training while protecting privacy without sharing local data. However, the heterogeneity of geographic locations and devices leads to response delays from stragglers. Client grouping in federated learning can tolerate stragglers from a global perspective, but differences in data distribution within groups may result in local performance bias. To address this issue, we propose a federated learning method called FedSimRep, which selects representative clients based on data distribution similarity. FedSimRep performs group training by collecting readiness signals from topological communications and maintains synchronization tables and graphs to track training progress. Additionally, it selects representative clients by leveraging the relationship between local optimization similarity and data distribution similarity to optimize the global model. Experiments and theoretical analyses on public datasets such as MNIST, CIFAR-10, and UEU-Fed demonstrate that FedSimRep achieves significant advantages in terms of accuracy and efficiency.

关键词： Client Grouping

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Symmetry-Regularized Transformer for Solving Traveling Salesman Problems

Symmetry-Regularized Transformer for Solving Traveling Sales...

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International Conference on Computer Supported Cooperative Work in Design

作者： Shuyun Li Maoli Wang Zixin Liu Huan Ma Zhihui Wang Xubing Dou Xinchang Zhang 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 Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China College of Cyber Security Qufu Normal University Jining China Shandong Normal University Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The Traveling Salesman Problem (TSP), a well-known NP-hard problem, is widely applied across manufacturing, biology, transportation, and other fields. In recent years, deep reinforcement learning (DRL), particularly DRL based on Transformer architectures, has emerged as a popular approach to solving TSP. However, the quadratic computational and space complexities of Transformer models pose challenges for handling larger-scale TSP instances. This paper introduces Sym-Favformer, an end-to-end DRL method based on symmetry regularization in the Transformer. In this approach, the encoder incorporates a Fast Attention Via positive Orthogonal Random features (FAVOR+) mechanism with linear complexity, while the decoder adopts a distance-based dynamic selection mechanism, and employs the Lion optimizer to reduce training time and memory consumption. To enhance performance and generalization, Sym-Favformer applies a random transformation data augmentation technique within the encoder and introduces enhanced context embedding in the decoder. Subsequently, the model is trained using a symmetry-regularized REINFORCE method. Experimental results show that Sym- Favformer significantly outperforms existing end-to-end DRL methods on both randomly generated benchmark instances and TSPLIB datasets, demonstrating strong generalization to larger-scale TSP instances.

关键词： Training Federated learning Memory management Transportation Traveling salesman problems Transformers Deep reinforcement learning Decoding Space exploration Manufacturing

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CroPrompt: Cross-task Interactive Prompting for Zero-shot Spoken Language Understanding

arXiv

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arXiv 2024年

作者： Qin, Libo Wei, Fuxuan Chen, Qiguang Zhou, Jingxuan Huang, Shijue Si, Jiasheng Lu, Wenpeng Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China Harbin Institute of Technology Shenzhen China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology Shandong Academy of Sciences China

Slot filling and intent detection are two highly correlated tasks in spoken language understanding (SLU). Recent SLU research attempts to explore zero-shot prompting techniques in large language models to alleviate the data scarcity problem. Nevertheless, the existing prompting work ignores the cross-task interaction information for SLU, which leads to sub-optimal performance. To solve this problem, we present the pioneering work of Cross-task Interactive Prompting (CroPrompt) for SLU, which enables the model to interactively leverage the information exchange across the correlated tasks in SLU. Additionally, we further introduce a multi-task self-consistency mechanism to mitigate the error propagation caused by the intent information injection. We conduct extensive experiments on the standard SLU benchmark and the results reveal that CroPrompt consistently outperforms the existing prompting approaches. In addition, the multi-task self-consistency mechanism can effectively ease the error propagation issue, thereby enhancing the performance. We hope this work can inspire more research on cross-task prompting for SLU. © 2024, CC BY-NC-SA.

关键词： Speech recognition

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A Routing Algorithm for Ensuring the Schedulability of Time-Sensitive Flows

A Routing Algorithm for Ensuring the Schedulability of Time-...

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International Conference on Mobile Ad-hoc and Sensor networks, MSN

作者： Yiwei Zhang Xiaolong Wang Wei Zhang Cheng Wang Huiling Shi 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 Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331516024

ISBN: (纸本)9798331516031

While current Time-Sensitive network (TSN) research focuses on reducing end-to-end delay through scheduling optimization, it often overlooks routing impacts. Traditional methods like Shortest Path First (SPF) struggle with new flow schedulability and existing flow disruption. This paper presents a TSN routing algorithm based on network calculus, calculating worst-case delays to ensure deadline compliance and balance link loads. A new flow prioritization method further optimizes scheduling. Experiments show the proposed algorithm improves scheduling success, keeps average delay under 70 μs, reduces maximum link load by 18.5% compared to wt-ECMP, and achieves a tenfold runtime reduction.

关键词： Schedules Runtime network topology Heuristic algorithms Routing Scheduling Calculus Delays Sensors Optimization

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DAC-UNet: Dual Attention CNN-Enhanced CswinUnet for Gastric Cancer Pathological Image Segmentation

DAC-UNet: Dual Attention CNN-Enhanced CswinUnet for Gastric ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Tao Hu Jingshan Pan Na Li Tangbin Tian Shengyu Liu Lu Zhang Yujiao Han Jiwei Xu 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 Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Jinan China

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

Gastric cancer is one of the most lethal cancers. Currently, clinical diagnosis primarily relies on gastroscopic biopsies and the manual experience of doctors to assess the development of lesions in tissue sections. However, pathological screening requires a significant amount of time from specialized pathologists. Due to variations in clinical experience and limitations in medical resources, the detection of gastric cancer still faces challenges. Traditional methods for segmenting gastric cancer images often overlook the hierarchical structure of features and the spatial details, both local and global, in the lesion images. To address the above issues, this paper proposes a hybrid and efficient pathological image segmentation network named DAC-UNet, designed for precise pathological image segmentation tasks. DAC-UNet uses Cswin Transformer blocks to build a hierarchical structure, allowing it to capture global information with less computational complexity. The network also includes multi-scale dual attention skip connections between the encoder and decoder to preserve local spatial details and improve global interactions across different scales. We conducted a thorough theoretical analysis of this method and validated it on a gastric slice dataset. The experimental results demonstrate that our proposed method achieves outstanding performance on both our gastric slice dataset and the MoNuSeg dataset. Specifically, on our gastric slice dataset, we obtained a Dice similarity coefficient (DSC) of 89.42% and an HD95 of 47.24, while on the MoNuSeg dataset, we achieved a DSC of 79.28% and a 95th percentile Hausdorff distance (HD95) of 19.38.

关键词： Image segmentation Pathology Semantics Manuals Transformers Decoding Lesions Medical diagnostic imaging Faces Cancer

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