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Neural network Driven by Density and Parallel Features for Field-Road Mode Mining

Neural Network Driven by Density and Parallel Features for F...

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International Conference on Computer Supported Cooperative Work in Design

作者： Fengqi Hao Xiyuan Zhao Cunxiang Bian Jinqiang Bai Qingyan Ding Faculty of Data Science City University of Macau Macau 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) 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

Field-road mode mining (FRMM) has gained increasing attention because of its crucial role in machinery management. As the Global Navigation Satellite System (GNSS) is widely applied in agricultural machinery, many methods based on motion and spatial-temporal features of GNSS trajectory data have been proposed for FRMM. However, these methods ignore the density and parallel features of GNSS points. The two features are useful for FRMM because field points usually have a higher density and more parallel points than road points. Therefore, a neural network driven by density and parallel features is proposed for accurate FRMM. Firstly, a statistical method is designed to extract the density feature (i.e., the number of its neighbor points) and parallel feature (i.e., the number of approximately parallel points in its neighbors) of each point. Then, the two features and eight motion features (e.g., speed, direction, etc.) are fed into a neural network to extract valuable latent features. Finally, a linear classifier is used to identify the field and road categories of GNSS points based on the latent features. Experimental results show that our method outperforms state-of-the-art methods and achieves the accuracy of 91.69% and 86.44% on public Wheat and Paddy datasets, respectively.

关键词： Global navigation satellite system Accuracy Statistical analysis Federated learning Roads Neural networks Time series analysis Feature extraction Trajectory Data mining

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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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Multi-Label Text Classification for Judicial Texts via Dual Graph and Label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

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Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu 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 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 Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head Label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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Unsupervised Anomalous Sound Detection for Mechanical Equipment Based on Domain Generalization Techniques

Unsupervised Anomalous Sound Detection for Mechanical Equipm...

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International Conference on Computer Supported Cooperative Work in Design

作者： Shumei Yang Kun Wang Kun Ma Fuqiang Wang Peng 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 Qingdao Branch of China E-Port Data Center Qingdao China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Monitoring mechanical equipment is crucial for industrial efficiency and safety. Anomalous sound detection faces challenges due to limited labeled data and varying acoustic conditions. Supervised methods require extensive labeled data, which is often scarce. Unsupervised methods, which model the distributions of normal data, are more suitable when normal data is abundant but anomalies are rare. In this paper, we present SAM-AE-GAN, an unsupervised anomaly detection model that combines self-attention, autoencoders, GANs, and spectral perturbation for data augmentation. The self-attention mechanism captures long-range dependencies, autoencoders enable efficient data representation, and GANs enhance generative performance. Spectral perturbation enhances adaptability, improving robustness and accuracy. Experimental results demonstrate the effectiveness of SAM-AE-GAN across five machine types in the 2022 Challenge TASK 2 dataset, validating its capability in complex industrial environments. This model provides an effective solution for anomaly detection and predictive maintenance, even in scenarios with limited labeled data.

关键词： Adaptation models Accuracy Perturbation methods Autoencoders Data augmentation Generative adversarial networks Data models Robustness Anomaly detection Unsupervised learning

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EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CMR Segmentation

EML-SAM: Incorporating Multi-Scale Fusion in SAM for Cine-CM...

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International Conference on Computer Supported Cooperative Work in Design

作者： Huijuan Hao Wenpeng Wang Qingyan Ding Haibo Cheng Fengqi Hao Hu Zhang Jinqiang Bai 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 Faculty of Data Science City University of Macau Macau China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Accurate and reproducible assessment of myocardial conditions is essential for the diagnosis of prior infarctions, cardiomyopathies, and inflammatory diseases. Although cardiac magnetic resonance imaging (CMR) is regarded as the gold standard for evaluating myocardial anatomy and function, manual segmentation remains labor-intensive and susceptible to variability. The demand for high-resolution images, dense predictions, and comprehensive segmentation across all phases of the cardiac cycle introduces significant challenges for Cine-CMR analysis. To address these issues, we present EML-SAM, an innovative interactive segmentation model based on the Segment Anything Model (SAM). EML-SAM incorporates an early-mid-late (EML) multi-scale fusion strategy, effectively mitigating the premature dilution of interaction information, thereby reducing segmentation errors and enhancing feature utilization. Furthermore, the model utilizes a multi-scale linear attention (MLA) transformer to establish an efficient global receptive field. Comprising 12 Transformer layers, the model integrates multi-scale attention with multi-scale perceptrons, striking a balance between computational efficiency and capacity. The proposed methodology offers a robust and efficient solution for high-resolution Cine-CMR segmentation, delivering accurate and real-time segmentation performance throughout the various stages of temporal changes within the cardiac cycle.

关键词： Image segmentation Accuracy Computational modeling Refining Myocardium Transformers Real-time systems Reproducibility of results Reliability Diseases

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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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DAGCL: Diversified Recommendation with Graph-Augmented Contrastive Learning

DAGCL: Diversified Recommendation with Graph-Augmented Contr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Haozhi Xia Zhenyu Yang Ziyu Chen Xiaoyang Wang Xueli Chang 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

In personalized recommendation systems, Graph Neural networks (GNNs) have gained attention for their ability to effectively model complex user-item interactions. The core concept of GNNs is to leverage ample and high-quality training data, recursively performing message passing along user-item interaction edges to progressively refine embedding representations. However, the highly imbalanced nature of user interaction data often leads traditional GNN-based recommendation systems to experience information redundancy, concentrating recommendations on a few popular items. Additionally, the long-tail problem further limits the visibility of less popular items, reducing recommendation diversity and impairing the user experience. In this paper, we propose a novel Diversity Augmented Graph Contrastive Learning method (DAGCL), which aims to increase recommendation diversity by improving the embedding generation process, while maintaining a balance between accuracy and diversity. Specifically, we design two graph augmentation methods, including a diversified subset selection module based on Deterministic Point Process (DPP) and a long-tail item interaction augmentation module, to generate diversified embeddings. Moreover, DAGCL incorporates contrastive learning, leveraging augmented views to optimize the recommendation process, alleviate data sparsity issue and capture potential user-item relationships. Experiments on two real-world datasets demonstrate that DAGCL significantly improves diversity while preserving recommendation accuracy, achieving an effective balance between these two aspects and validating its effectiveness. The code is available on https://***/XiaHaoZhi/DAGCL.

关键词： Accuracy Heavily-tailed distribution Message passing Redundancy Training data Contrastive learning User experience Graph neural networks Recommender systems Optimization

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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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An Efficient Energy Consumption Prediction Framework for High Performance computing Cluster Jobs

An Efficient Energy Consumption Prediction Framework for Hig...

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International Conference on Computer Engineering and Applications (ICCEA)

作者： Yantao Lou Jibin Wang Shoupeng Feng Xian Yu 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) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Public Security of Shandong Province Shandong Intelligent Public Security Technology Innovation Center Jinan China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Energy consumption prediction is a crucial approach to enhance the operational efficiency of HPC (High Performance computing) clusters. Existing predicting methods for the energy consumption mainly rely on time series mode, focusing on the instantaneous power during job execution. However, in practical applications, due to the large scale of HPC clusters, the prediction models need to process a vast amount of data, often resulting in poor performance. Moreover, it's challenging to provide effective predictions for jobs with significant power fluctuations. Thus we propose a more efficient energy consumption prediction framework for HPC cluster jobs, achieving two main objectives: On one hand, it uses a data encoding module to pre-encode the sampled data of energy consumption related indicators, simplifying the data that the time series prediction model needs to process and improving prediction efficiency. On the other hand, the prediction target is set to the power distribution range in a future period of time, rather than the instantaneous power at some specific time points, which improves the usability of the prediction results. Test results show that the proposed prediction framework can achieve more markable performance improvement while ensuring the effectiveness of the predictions.

关键词： Energy consumption Computational modeling High performance computing Time series analysis Power distribution Predictive models Feature extraction

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Cell-YOLOv8: A Novel Automated Method for Multi-Scale Cervical Cell Detection

Cell-YOLOv8: A Novel Automated Method for Multi-Scale Cervic...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hai Xu Maoyu Zhang Fanfan Yan Haoran Ding Meng Guo 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 Jinan Institute of Supercomputing Technology Jinan Shandong P. R. China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

Early screening for cervical cancer is a common preventive strategy that can significantly reduce the risk of the disease through regular examinations. In microscopic images of cervical cells, the changes in cellular characteristics are often subtle, and considerable variability exists among individuals, leading to challenges such as false positives and missed detections. Addressing the issue of detecting multi-scale cells is therefore a complex task. To tackle this challenge, we propose the Cell-YOLOv8 object detection model for effective and accurate detection of multi-scale cervical cells. In this study, we first introduce and design the Convolutional Attention Aggregation Module (CAAM). Second, we incorporate the CARAFE upsam-pling operator and reparameterize its design to reconstruct the backbone and neck networks of Cell-YOLOv8. Finally, we conduct comparisons with state-of-the-art detection methods on two distinct datasets (PQCC and Herlev). The experimental results demonstrate that the Cell-YOLOv8 model outperforms existing methods in the domain of multi-scale cervical cell detection, achieving an average precision (mAP) of 80.2%. This approach provides valuable insights for the development of automated cervical cell detection methods.

关键词： Computational modeling Microscopy Refining Morphology Object detection Neck Image restoration Image reconstruction Cervical cancer Optimization

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