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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xiling Cao Yan Li Hong Liu School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Inspur (Shandong) Computer Technology Co Ltd. Jinan China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Predicting how pedestrians will move is a valuable and challenging task. It can be applied to various domains, such as crowd evacuation and autonomous driving. However, in practice, scene information and pedestrian interaction make it difficult to accurately predict pedestrian trajectories. Therefore, more effective processing of information affecting pedestrian trajectories is particularly important. Extraction of pedestrian motion features is insufficient in current research in this area. To address this problem, a neural network trajectory prediction method, PMC-CNN, is proposed. This method incorporates pedestrian motion features and stores the heterogeneous information required for prediction within a knowledge graph. Furthermore, it combines this information with the proposed IN-EX pedestrian motion model, resulting in an improvement in prediction accuracy. In addition, this research applies the prediction framework to a crowd evacuation scenario to improve the efficiency of crowd evacuation. This verifies the effectiveness and practicality of our proposed model.

关键词： Pedestrians Accuracy Urban planning Neural networks Safety management Knowledge graphs Predictive models Feature extraction Real-time systems Trajectory

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Analysis of vocational education dynamics evolution based on topic modeling

Analysis of vocational education dynamics evolution based on...

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IEEE International Symposium on Information (IT) in Medicine and Education, ITME

作者： Xinhua Wang Xiaomei Yu Yunmeng Jiang Xiangwei Zheng Wei Li School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China Shandong Normal University Library Shandong Normal University Jinan China

ISBN: (数字)9798350356670

ISBN: (纸本)9798350356687

Clarifying the focus of vocational education dynamics is crucial for understanding the current situation and future development trends. However, comprehensive exploration on vocational education dynamics is inadequate presently. In this paper, textual information on vocational education is analyzed with Latent Dirichlet Allocation, BERTopic, and the Dynamic Topic Model. Two key issues are thoroughly investigated as follows: (1) What are the main aspects of the current development of vocational education; (2) How to learn the embeddings of vocational education text and further to promote the formulation of policy. The experiments are conducted and the experimental results show that LDA and BERTopic model achieve effective performance in clustering the texts of vocational education dynamics, and the DTM captures the topics trend of vocational education, which facilitate the government to guide the formulation and implementation of policies.

关键词： Analytical models Education Government Market research Dynamic scheduling Resource management Information technology

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A Swin transformer encoder-based StyleGAN for unbalanced endoscopic image enhancement

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computers in Biology and Medicine 2024年 175卷 108472-108472页

作者： Deng, Bo Zheng, Xiangwei Chen, Xuanchi Zhang, Mingzhe School of Information Science and Engineering Shandong Normal University Jinan250352 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250352 China State Key Laboratory of High–end Server & Storage Technology Jinan250101 China

With the rapid development of artificial intelligence, automated endoscopy-assisted diagnostic systems have become an effective tool for reducing the diagnostic costs and shortening the treatment cycle of patients. Typically, the performance of these systems depends on deep learning models which are pre-trained with large-scale labeled data, for example, early gastric cancer based on endoscopic images. However, the expensive annotation and the subjectivity of the annotators lead to an insufficient and class-imbalanced endoscopic image dataset, and these datasets are detrimental to the training of deep learning models. Therefore, we proposed a Swin Transformer encoder-based StyleGAN (STE-StyleGAN) for unbalanced endoscopic image enhancement, which is composed of an adversarial learning encoder and generator. Firstly, a pre-trained Swin Transformer is introduced into the encoder to extract multi-scale features layer by layer from endoscopic images. The features are subsequently fed into a mapping block for aggregation and recombination. Secondly, a self-attention mechanism is applied to the generator, which adds detailed information of the image layer by layer through recoded features, enabling the generator to autonomously learn the coupling between different image regions. Finally, we conducted extensive experiments on a private intestinal metaplasia grading dataset from a Grade-A tertiary hospital. The experimental results show that the images generated by STE-StyleGAN are closer to the initial image distribution, achieving a Fréchet Inception Distance (FID) value of 100.4. Then, these generated images are used to enhance the initial dataset to improve the robustness of the classification model, and achieved a top accuracy of 86 %. © 2024 Elsevier Ltd

关键词： Image enhancement

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Multi-Stage Guided Code Generation for Large Language Models

SSRN

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SSRN 2024年

作者： Han, Mr Yewei Lyu, Chen Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology School of Information Science and Engineering Shandong Normal University University Road 1 Jinan250014 China

Currently, although Large Language Models have shown significant performance in the field of code generation, their effectiveness in handling complex programming tasks remains limited. This is primarily due to the substantial distance between the problem description and the correct code, making it difficult to ensure accuracy when directly generating code. Human programmers, when faced with a complex programming problem, usually use multiple stages to solve it in order to reduce the difficulty of development. First, they analyze the problem and think about a solution plan, then they design a code architecture based on that plan, and finally they finish writing the detailed code. Based on this, we propose a multi-stage guided code generation strategy that aims to gradually shorten the transformation distance between the problem description and the correct code, thus improving the accuracy of code generation. Specifically, the approach consists of three stages: planning, design and implementation. In the planning phase, the Large Language Model generates a solution plan based on the problem description;in the design phase, the code architecture is further designed based on the solution plan;and in the implementation phase, the previous solution plan and code architecture are utilized to guide the Large Language Model in generating the final code. Additionally, we found that existing competition-level code generation benchmarks may overlap with the training data of the Chat Generative Pre-trained Transformer, posing a risk of data leakage. To validate the above findings and circumvent this risk, we created a competition-level code generation dataset. © 2024, The Authors. All rights reserved.

关键词： Architecture

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DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

arXiv

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arXiv 2025年

作者： Liu, Guanlin Yu, Xiaomei Liu, Zihao Li, Xue Fan, Xingxu Zheng, Xiangwei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China State Key Laboratory of High-end Server and Storage Technology Jinan250300 China

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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TFSG: A Self-Supervised Guided Graph Learning Method for Traffic Flow Prediction

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IEEE Internet of Things Journal 2025年

作者： Zheng, Yingran Luo, Chao Shao, Rui Shandong Normal University School of Information Science and Engineering Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China China Mobile Shandong Co. Ltd center of network optimization Jinan250014 China

With the rapid development of the Internet of Things, a large amount of real-time traffic data has been collected. How to fully utilize these traffic data for rational planning of intelligent transportation systems is an important issue. Graph learning has proved to be potential tools for modeling complex spatiotemporal relationships in addressing traffic flow prediction problems. Existing methods usually relied on the graph structure constructed from predefined distance information between different regions. However, affected by factors such as road capacity, traffic rules etc., distance bias is non-trivial to affect traffic flow in reality. Besides, the distance based static transportation network structure is also difficult to describe the dynamic and uncertainty of road traffic in complex situations. To address the aforementioned issues, this article proposes an enhanced graph generation approach for traffic flow prediction, which is based on Temporal-Frequency Self-Supervised Guided Graph Learning (TFSG). The method progressively guides the feature latent representation to approach the target latent representation in both the time domain and frequency domain. Meanwhile, a multi-head self-attention mechanism is introduced to capture dependencies among different time series patches, constructing an enhanced graph structure for downstream prediction tasks. Experimental results first independently evaluated the qualities of the generated graphs. Subsequently, by using five public traffic datasets, the feasibility and effectiveness of the proposed method are further verified. © 2014 IEEE.

关键词： Self-supervised learning

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ST-Tree with Interpretability for Multivariate Time Series Classification

arXiv

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arXiv 2024年

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

Multivariate time series classification is of great importance in practical applications and is a challenging task. However, deep neural network models such as Transformers exhibit high accuracy in multivariate time series classification but lack interpretability and fail to provide insights into the decision-making process. On the other hand, traditional approaches based on decision tree classifiers offer clear decision processes but relatively lower accuracy. Swin Transformer (ST) addresses these issues by leveraging self-attention mechanisms to capture both fine-grained local patterns and global patterns. It can also model multi-scale feature representation learning, thereby providing a more comprehensive representation of time series features. To tackle the aforementioned challenges, we propose ST-Tree with interpretability for multivariate time series classification. Specifically, the ST-Tree model combines ST as the backbone network with an additional neural tree model. This integration allows us to fully leverage the advantages of ST in learning time series context while providing interpretable decision processes through the neural tree. This enables researchers to gain clear insights into the model’s decision-making process and extract meaningful interpretations. Through experimental evaluations on 10 UEA datasets, we demonstrate that the ST-Tree model improves accuracy in multivariate time series classification tasks and provides interpretability through visualizing the decision-making process across different datasets. © 2024, CC BY.

关键词： Deep neural networks

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Privacy-preserving cloud-fog–based traceable road condition monitoring in VANET

Privacy-preserving cloud-fog–based traceable road condition...

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作者： Wang, Wei Wu, Lei Qu, Wenlei Liu, Zhaoman Wang, Hao School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China Shandong Provincial Key Laboratory for Software Engineering Jinan China

With the development of intelligent vehicles, the research on road condition monitoring has attracted much attention in the vehicular ad hoc network (VANET). The combination of VANET, cloud computing, and fog computing provides on-demand computational resources while creating a lot of new challenges. In this paper, we propose an efficient privacy-preserving cloud-fog–based traceable road surface condition monitoring scheme. We use certificateless aggregate signcryption technology to implement multiple messages aggregation verification, which greatly saves computing resources and bandwidth. Moreover, we also use a traceable vehicle pseudo identity generated by a trace authority (TRA) to achieve identity privacy protection. To ensure the privacy of the fog and cloud server, the road condition information is reported in ciphertext format. In addition, the cloud server can perform ciphertext equivalence test operation to distinguish different road condition information of the same area without compromising the confidentiality. The ciphertext, which exceeds the set threshold, is uploaded to the blockchain. It can be stored permanently and never be tampered with or deleted. Finally, we demonstrate the correctness of the proposed scheme and show that the proposed scheme has higher efficiency. © 2020 John Wiley & Sons, Ltd.

关键词： Fog

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A Pervasive Multi-physiological Signal-Based Emotion Classification with Shapelet Transformation and Decision Fusion 17th

A Pervasive Multi-physiological Signal-Based Emotion Classif...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Zhang, Shichao Zheng, Xiangwei Zhang, Mingzhe Guo, Gengyuan Ji, Cun School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan China

ISBN: (纸本)9783030926342

Emotion classification is a hot pot at present. Since physiological signals are objective and difficult to hide, physiological signals are commonly used in emotion classification methods. However, traditional emotional classification based on physiological signals faced the following challenges: low accuracy and low interpretability. For this, this paper proposes a pervasive multi-physiological signal-based emotion classification with shapelet transformation and decision fusion (PMSEC). In PMSEC, the shapelet transformation and feature extraction are carried out for ECG, GSR, and RA, respectively. Following by, six sub-classifiers are constructed for different physiological signals. Lastly, decision-level fusion is implemented to obtain final emotion results. Experimental results show that the proposed PMSEC is not only highly competitive but also has a broad application prospect compared with EEG-based classification method. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Electrocardiograms

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The application of PAD teaching method in computer network course 8

The application of PAD teaching method in computer network c...

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8th Annual International Conference on Geo-Spatial Knowledge and Intelligence, GSKI 2020

作者： Liu, Lining Zhang, Yongsheng Zhang, Penglong Sun, Wenqian School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology China

As a new teaching mode and method, PAD has been developed in recent years. This teaching mode has been applied in the teaching of universities, ordinary primary and secondary schools and vocational colleges. It has achieved good teaching results. In order to improve teaching effect, improve the students' autonomy and enthusiasm and cultivate the independent innovative talents needed by the society, more and more computer teachers have also begun to apply PAD to the computer classroom. "computer Network" is a basic and compulsory course for learning computers. "transmission media" in this course is selected as the specific content, and PAD is used for teaching design. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Teaching

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