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IEEE International Conference on Systems, Man and Cybernetics

作者： Xitong Ma Yuansen Kuang Lin Yuan Cheng Tian Yijie Zeng 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

Radiology report generation is an essential task in the medical field, which aims to automate the generation of medical terminology descriptions of radiology images. However, this task currently suffers from several problems: 1) existing methods need to manually build knowledge graphs or templates (consuming time and effort) to introduce medical or prior knowledge to assist in report generation; 2) previous models cannot handle the problem of data bias well (anomaly reports and anomaly descriptions make up only a tiny portion of the dataset), causing the models to ignore the learning of anomaly descriptions easily; 3) existing approaches cannot robustly supervise the model, resulting in incomplete and inconsistent reports being generated. To address these issues, we propose a cross-modal interactive memory network based on fine-grained medical feature extraction. In our model, we design a cross-modal interactive memory network to automatically store and remember the required medical text knowledge and use this medical knowledge to help generate reports. Furthermore, we design an abnormal medical knowledge enhancement module to enhance the learning of abnormal fine-grained knowledge through the interaction of disease topics and their states to interact with text features. In addition, we design a cross-modal joint semantic loss unit to reduce semantic differences between different features and improve the visual representation ability of the model. We experimented and evaluated our model on MIMIC-CXR and IU-Xray datasets to compare with other baseline models.

关键词： Knowledge engineering Visualization Terminology Semantics MIMICs Memory management Radiology Feature extraction Data models Biomedical imaging

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A Collaborative Heterogeneous Graph Neural Network for Personalized News Recommendation

A Collaborative Heterogeneous Graph Neural Network for Perso...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Chenglong Shi Haibin Geng Wenfeng Jiang Jinbao Li Chao Wu 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

Personalized news recommendation is the process of predicting the relevance of news to users and recommending news to user to fulfill their information needs. However, existing news recommendation methods extract semantic information from users and candidate news respectively, ignoring semantic interaction information between users and candidate news. Furthermore, previous models only use same node types for message passing, ignoring different characteristics and topology between different node types. In addition, existing methods learn news representations through text representations, ignoring semantic correlation information between entity relationships and texts. To solve these problems, we propose a personalized news recommendation model named CoHG. In our model, we design a collaborative fusion module to obtain semantic interaction information through interacting user history news with candidate news. Furthermore, we design a heterogeneous gated graph neural network that maps different node types into a same space to extract higher-order information in user graphs for message passing. Moreover, we design an enhanced relevant attention module to enhance semantic correlation information of text content by aggregating text representation and entity representation into a unified representation. Finally, we conducted experiments on MIND and Adressa datasets to compare with other baseline models.

关键词： Correlation Message passing Semantics Collaboration Logic gates Graph neural networks Topology History Data mining Cybernetics

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Removing Scattered Light in Biomedical Images via an Unsupervised Deep Neural Network

Removing Scattered Light in Biomedical Images via an Unsuper...

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Data science and Engineering in Healthcare, Medicine and Biology, IEEE EMBS Special Topic Conference on

作者： Yuanhao Gong Meng Xu Yawei Li Michele Magno Faculty of Electronics and Information Engineering Shenzhen University China School of Computer Science Queen Mary University of London U.K. Computer Vision Lab ETH Zurich Switzerland Center for Project-based Learning ETH Zurich Switzerland

Scattered light is unavoidable during biomedical imaging, leading to downgraded images. Removing such scattered light, however, is challenging, because the imaging objects are complex and the light path is complicated. In this paper, we propose to use deep learning methods to computationally remove such scattered light. Inspired by the dehazing methods for natural images, we first developed a novel mathematical model for biomedical images. Then, we developed a deep neural network method to solve this model. Our network simultaneously estimates the clear image and the scattered light. Several experiments are conducted to confirm the network’s effectiveness and efficiency.

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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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Application of a hybrid EfficientNet-SVM model to medical image classification

Application of a hybrid EfficientNet-SVM model to medical im...

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International Conference on Intelligent Systems: Theories and Applications (SITA)

作者： Imane Bouslihim Walid Cherif Mohamed Kissi School of Information Sciences LYRICA Laboratory Rabat Morocco Laboratory LIM Department of Computer Science Faculty of Sciences and Technology University Hassan II Casablanca Mohammedia Morocco

Within the realm of medical science, imaging stands out as the most potent diagnostic and therapeutic tool. Convolutional Neural Networks (CNN) have revolutionized this field due to their ability to extract complex features and learn intricate patterns from raw image data. However, due to the complexity of medical image data and the growing demand for greater precision, CNNs struggle to operate at their best within realistic computing timeframes. In response to these challenges, the hybridization of CNN with classification algorithms such as SVM has ameliorated its performance. However, additional improvements are required to bring it into compliance with the requirements of medical image analysis. This study introduces an innovative methodology that leverages a contemporary metaheuristic algorithm to augment the efficiency and efficacy of CNN-SVM within medical image analysis tasks, employing an EfficientNet architecture. Brain Tumor Database (BTD); an MRI-associated dataset was used in this research. In the suggested approach, automatic feature extraction from images is done using CNN, and the final classification is done with Support Vector Machines (SVM). The evaluation of the suggested model entails two categories of statistical assessment metrics: Accuracy and F-measure. The overall accuracy achieved by the proposed model stands at 95.45% for the Brain Tumor Database (BTD).

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Stackelberg Game-based and Broker-assisted Computation Offloading in MEC Networks

Stackelberg Game-based and Broker-assisted Computation Offlo...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Deng Meng Jianmeng Guo Liang Zhao Huan Zhou Shouzhi Xu College of Economics and Management China Three Gorges University Yichang China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering College of Computer and Information Technology China Three Gorges University Yichang China School of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Mobile Edge Computing (MEC) can effectively speed up data processing and improve Quality of Service (QoS) by offloading Mobile Users' (MUs') tasks to nearby Edge Servers (ESs). However, due to the individual rationality of entities (i.e., ESs and MUs) in MEC networks, they may be reluctant to participate in the computation offloading process without reasonable resource pricing or compensation. To address the challenge, we propose a Two-stage Stackelberg game-based computation Offloading and Resource Pricing mechanism (TORP). Specifically, we first introduce a broker in MEC, which rents computation resources from ESs and provides services to MUs. Next, we formulate the interactions among the broker, MUs, and ESs as a two-stage Stackelberg game, aiming to maximize their respective utilities. Then, we propose a Gradient-Ascent-Based Dynamic Iterative Search Algorithm (GADISA) and an Alternating Iteration-Based Resource Pricing and Task Offloading Algorithm (AIPOA) to solve the optimization problem. Finally, simulations show that TORP greatly outperforms other benchmarks in improving the utilities of three entities.

关键词： Heuristic algorithms Simulation Pricing Games Quality of service Benchmark testing Search problems

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An intelligent solar flare prediction model based on X-ray flux curves using Long Short-Term Memory

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Astronomical Techniques and Instruments 2025年第2期2卷 65-72页

作者： Yan Gao Li Zhang Long Xu College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang 550025China Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China

Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth,potentially causing disruption of the ionosphere and interference with the geomagnetic field,thus causing magnetic ***,it is very important to accurately predict the time period of solar *** paper proposes a flare prediction model,based on physical images of active solar *** employ X-ray flux curves recorded directly by the Geostationary Operational Environmental Satellite,used as input data for the model,allowing us to largely avoid the influence of accidental errors,effectively improving the model prediction efficiency.A model based on the X-ray flux curve can predict whether there will be a flare event within 24 *** reverse can also be verified by the peak of the X-ray flux curve to see if a flare has occurred within the past 24 *** True Positive Rate and False Positive Rate of the prediction model,based on physical images of active regions are 0.6070 and 0.2410 respectively,and the accuracy and True Skill Statistics are 0.7590 and *** model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records,providing a simple method for solar flare prediction.

关键词： Neural Network Long Short-Term Memory Solar flare prediction X-ray flux curve

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A Planar Substrate-Integrated Phased Array With an Over-One-Octave Bandwidth

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IEEE Transactions on Antennas and Propagation 2025年

作者： Sun, Liying Lu, Yunlong Wang, Kai Xu, Jun You, Yang Qu, Shi-Wei Ningbo University Faculty of Electrical Engineering and Computer Sciences Zhejiang Ningbo315211 China Pengcheng Laboratory Shenzhen518500 China Southeast University State Key Laboratory of Millimeter Waves School of Information Science and Engineering Nanjing210096 China School of Electronic Science and Engineering Chengdu611731 China

A new wideband planar substrate-integrated phased array is presented in this paper. The array element is based on an open slot excited by a coaxial probe. A T-shaped stripline with shorting pins is employed in each element to achieve a smooth mode conversion from quasi-coaxial to quasi-parallel plate waveguide in a compact manner. In this case, the element supports a full quasi-TEM mode for transmission and radiation. Additionally, the air-filled holes and metal pins are incorporated into the radiating and feeding structures to shift the undesirable resonance to a higher frequency. By combining these techniques, the impedance bandwidth can be extended to more than one octave. The scanning blindness caused by unexpected guided-wave mode during beam scanning along the H-plane is further analyzed in detail, and removed from the desired frequency range by adjusting the corresponding element period and manipulating the guided-wave characteristics. A 10×12-element phased array prototype covering 8-18 GHz (76.9%) is fabricated and tested. Experimental results demonstrate that the proposed phased array achieves a beam scanning of ± 60° in the E-, H- and D-planes, with no scanning blindness occurring across the operating band. © 1963-2012 IEEE.

关键词： Antenna phased arrays

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MDEC: Mamba-based Debiased Extended Contrast Learning in Sequential Recommendation

MDEC: Mamba-based Debiased Extended Contrast Learning in Seq...

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International Conference on computer Supported Cooperative Work in Design

作者： Yan Huang Zhenyu Yang Baojie Xu Wenyue Hu Zhibo 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 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Recommender systems are critical for mitigating information overload, assisting users in uncovering their latent interests, and enhancing their overall experience. Sequential recommendation leverages users' historical interaction sequences to predict dynamic interests more effectively than traditional rec-ommendation approaches. However, existing models-including RNN-based and Transformer-based methods-face significant limitations. RNNs struggle with vanishing gradients and long-term dependency capture, while Transformers, though effective for long-range relationships, suffer from computational inefficiency due to their quadratic attention complexity. Recent advancements have employed contrastive learning for sequential recommendation, aiming to enhance the consistency between augmented views and improve self-supervised learning signals. Despite their promise, these methods often lack diversity in data augmentation strategies, which restricts their capacity for bias mitigation, resulting in augmented data that still retains inherent biases. To address these challenges, we propose MDEC, a novel sequential modeling framework that leverages State Space Models (SSM) combined with unbiased contrastive learning. MDEC utilizes Mamba to efficiently model user preferences as an alternative to Transformer-based models. Additionally, it integrates graph-based information, including item transition and co-interaction data, to improve data augmentation comprehensively. Finally, we introduce adaptive anchor-enhanced contrastive learning, which adaptively utilizes augmented samples to improve representation quality and bias mitigation. Extensive experiments on multiple datasets demonstrate that MDEC significantly out-performs existing models, showcasing improved efficiency, better mitigation of biases, and enhanced recommendation quality. Code is available at https://***/Echohuangyan/CSLP.

关键词： Adaptation models Federated learning Prevention and mitigation Computational modeling Data visualization Contrastive learning Diversity methods Transformers Data augmentation Recommender systems

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Multilayer Feature Fusion and Joint Loss Optimization for Emotion Recognition in Conversations

Multilayer Feature Fusion and Joint Loss Optimization for Em...

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International Conference on computer Supported Cooperative Work in Design

作者： Zhibo Zhang Zhenyu Yang Yan Huang Baojie Xu Wenyue Hu 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

The goal of Emotion Recognition in Conversations (ERC) is to accurately identify the emotions expressed in each utterance within a dialogue. Despite advancements made by current ERC methods, particularly those using RNN-based and GCN-based models to capture emotional dynamics and model speaker relationships, there remain two primary limitations: first, an insufficient integration of multiple feature representations and commonsense knowledge, which hampers the model's ability for deep emotional understanding; and second, the reliance on a single cross-entropy loss for classification optimization, which restricts the accuracy and robustness of emotion recognition. To address these issues, we propose a method for ERC using Multilayer Feature Fusion and Joint Loss Optimization (MFFJL). This approach combines contextual information, speaker dependency, and commonsense knowledge features by extracting feature vectors through RoBERTa and COMET, utilizing bidirectional LSTM and attention mechanisms to capture conversational context, and applying a cross-fusion module to deeply integrate various features, thus enhancing comprehension of complex emotional expressions. Additionally, the feature classification module incorporates joint cross-entropy and KL divergence optimization, further improving classification accuracy and consistency. Experimental results demonstrate the effectiveness of our method, as evidenced by superior performance on the IEMOCAP and MELD datasets. Our code is available at https://***/r/MFFJL-D9D8.

关键词： Emotion recognition Accuracy Federated learning Computational modeling Feature extraction Nonhomogeneous media Vectors Robustness Optimization Commonsense reasoning

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