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4th Asia-Pacific Artificial Intelligence and big data Forum, AIBDF 2024

作者： Zhao, Feng Fan, Wenxuan Han, Zhongwei Chen, Hongyu School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong Yantai China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China Information Engineering College Yantai Institute of Technology Shandong Yantai China Shandong Technology and Business University Shandong Yantai China

ISBN: (纸本)9798400710865

Functional connectivity networks (FCNs), particularly those utilizing the phase lag index (PLI) method, have been instrumental in elucidating the pathological features of Major Depressive Disorder (MDD) by assessing the synchrony and lag of electroencephalogram (EEG) signal waveforms. Currently, the more prevalent methods are traditional FCNs that quantify phase synchronization as scalar values and dynamic FCNs (D-FCNs) that employ sliding window approaches to capture the instantaneous variations in functional connectivity. However, these two methods merely capture the low-level functional connections between paired regions of interest (ROIs) from both the static and dynamic perspectives, and fail to reflect the high-level functional connections involving multiple ROIs. Moreover, the feature dimension of the dynamic FCN is excessively high, and there exists a phase mismatch issue between different samples, which renders it impossible to be directly applied to the classification task, and no better solution is available at present. To mitigate these issues, we proposed a comprehensive framework that includes segmenting EEG sequences based on PLI to construct dynamic FCNs;employing seventh-order central moments for feature extraction to form low-order FCNs (Lo-FCNs);and establishing high-order FCNs (Ho-FCNs) based on Pearson correlation theory derived from Lo-FCNs. We conducted classification experiments across four distinct EEG frequency bands using our proposed framework while analyzing relevant features through graph theory and brain topological mapping techniques. The experiments showed that the proposed framework has a high classification accuracy rate in the classification experiments of MDD and NC, and proved the importance of high-order features in classification experiments, indicating that they may serve as physiological markers for MDD diagnosis. © 2024 Copyright held by the owner/author(s).

关键词： Electroencephalography

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A PERSONALIZED MODEL BASED ON SIMPLE RECURRENT UNIT FOR ACCURATE PREDICTION OF STOCK PRICE TREND

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International Journal of Innovative computing, Information and Control 2025年第3期21卷 791-812页

作者： Bai, Wenjiang Yuan, Shuai Lu, Yan Xu, Mingyan Chen, Shuoru Zhao, Feng Kang, Feng Intelligence and Information Engineering Department Taiyuan University No. 7 Fendong Street Tanghuai Industrial Park Taiyuan030032 China Information Engineering College Yantai Institute of Technology No. 100 Gangcheng East Street Laishan Yantai264005 China School of Statistics Computing Shandong Technology and Business University No. 191 Haibin Middle Road Laishan Yantai264005 China School of Computer Science and Technology Computing Shandong Technology and Business University No. 191 Haibin Middle Road Laishan Yantai264005 China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong Technology and Business University No. 191 Haibin Middle Road Laishan Yantai264005 China

Stock is a vital component of the financial market, and how to accurately predict stock price trend is a popular topic and open problem. In order to achieve higher prediction accuracy, we propose a personalized model based on simple recurrent unit (PSRU), which can effectively eliminate the impact of noise and diversity of stock price series. Specifically, we firstly introduce variational mode decomposition (VMD) in terms of parameter determination for denoising parameters and perform denoising on the time series, effectively reducing the noise in the data and improving the prediction accuracy. Then, to better capture the characteristics of diverse time series, we cluster the time series into multiple classes, and each class fits an optimal prediction model. Especially, to improve the clustering, we introduce the dynamic multi-perspective personalized similarity measurement (DMPSM), which can help avoid singularities, time shifts and warping in time series. Finally, SRU is adopted as the prediction model which can achieve high prediction accuracy with excellent mapping and parallel processing capabilities. The experimental results show that PSRU outperforms other commonly used time series forecasting methods on multiple performance metrics, demonstrating the effectiveness and superiority of PSRU in stock price prediction, especially in dealing with nonlinear and non-stationary time series data. © 2025, ICIC International. All rights reserved.

关键词： Prediction models

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Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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5th International Conference on intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism 5

Image Denoising Method Based on Channel Compression and Spli...

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5th International Conference on Computer Engineering and intelligent Control, ICCEIC 2024

作者： Sun, Hao Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Shandong Yantai China

ISBN: (纸本)9798331507992

In the image restoration task, how to make full use of spatial and channel feature information to improve the reconstruction quality of the model without significantly increasing the computational complexity is an important research issue. To address this challenge, this paper proposes a CS module (Channel Compression and Split-Attention) specifically for optimizing image denoising tasks. The module first compresses the features through a preliminary convolution operation, then introduces the split attention convolution mechanism to split the features by channel and dynamically weight them, thereby enhancing the model's adaptive attention to different feature channels and improving feature expression capabilities. Through experiments on multiple image denoising datasets, the results show that the CS module significantly improves the image reconstruction quality, especially in terms of peak signal-to-noise ratio (PSNR). At the same time, the introduction of the CS module does not lead to a significant increase in computational complexity, maintaining a high computational efficiency, which verifies the practicality and superiority of the module in image denoising tasks. © 2024 IEEE.

关键词： Restoration

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Practical Applications of ChatGPT and BERT: Long Text Summarization and Model Feature Ablation Study for Stock Comment Topic Identification 24

Practical Applications of ChatGPT and BERT: Long Text Summar...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Xu, Mingyan Chen, Hongyu Deng, Fuwen Liu, Yuan Zhao, Feng Zhang, Yong School of Statistics Shandong Technology and Business University Yantai264000 China School of Hospital Shandong Technology and Business University Yantai264000 China School of Computer Science and Technology Shandong Technology and Business University Yantai264000 China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing School of Computer Science and Technology Shandong Technology and Business University Yantai China School of Information and Electrotic Shandong Technology and Business University Yantai264000 China

ISBN: (纸本)9798400717147

Topic identification of expert stock comments is an important task in the field of finance, which helps investors understand the market dynamics and expert opinions so that they can make more informed investment decisions. The Bidirectional Encoder Representations from Transformers (BERT) model is one of the widely used methods for text topic identification. To improve the identification accuracy and overcome the limitations of the BERT model, two strategies are proposed in this study. First, given the BERT model's input length limitation when dealing with long texts, we used the summaries created by Chat Generative Pre-training Transformer (ChatGPT) as inputs to the model. The generative summarization capability of ChatGPT not only reduces the length of the text effectively but also preserves the key information of the original text, which enables the model to better understand and deal with long texts. Second, we perform multi-layer feature ablation on the BERT model to find the most representative features for text topic identification. The multi-layer features of the BERT model can be more effectively utilized to improve the accuracy of text topic identification. Experiments show that our proposed strategies exhibit excellent performance and provide effective paths to improve the accuracy of stock comment topic identification. © 2024 ACM.

关键词： Ablation

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Task Separation and Knowledge Sharing for Class Incremental Learning

Task Separation and Knowledge Sharing for Class Incremental ...

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intelligent computing and Human-Computer Interaction (ICHCI), International Conference on

作者： Jiali Zhang Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center School of Computer Science and Technology Shandong Technology and Business University Yantai Shandong China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center School of Mathematics and Information Science Shandong Technology and Business University Yantai Shandong China

ISBN: (数字)9798350368284

ISBN: (纸本)9798350368291

关键词： Knowledge engineering Human computer interaction Degradation Incremental learning Computational modeling Memory management Information sharing Network architecture Feature extraction Knowledge transfer

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Image Denoising Method Based on Channel Compression and Split-Attention Mechanism

Image Denoising Method Based on Channel Compression and Spli...

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Computer Engineering and intelligent Control (ICCEIC) International Conference on

作者： Hao Sun Xiaoyan Qiao Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion School of Computer Science and Technology Shandong Technology and Business University Technology and Evaluation Shandong Engineering Research Center Yantai Shandong

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

关键词： Image coding PSNR Convolution Computational modeling Refining Image restoration Computational efficiency Computational complexity Image reconstruction Image denoising

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Linn: A Lightweight Image Hiding Method Based on Invertible Neural Network

SSRN

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SSRN 2024年

作者： Cheng, Dapeng Zhu, Minghui Mao, Yanyan Gao, Xiaolian Jing, Wanting Kong, Yue Li, Jinjiang School of Computer Science and Technology Shandong Technology and Business University Shandong Yantai264005 China Yantai Key Laboratory of Big Data Modeling and Intelligent Computingy Shandong Yantai264005 China School of Information and Electronic Engineering Shandong Technology and Business University Shandong Yantai264005 China

Image steganography aims to conceal a secret image within a cover image, allowing only those who are informed can recover it. Compared to ordinary steganography, image steganography not only demands the recovered secret image to closely resemble the original image while keeping the stego image well concealed, but also requires a larger capacity. Existing image hiding methods based on invertible neural network have met this requirement, but they generally have a large number of parameters, the complexity of the model is high. In this paper, we propose a lightweight image hiding framework based on invertible neural network called LINN. This framework incorporates the idea of residuals, aiming to minimize the network depth while ensuring image quality. Additionally, it incorporates a spatial attention module, enabling adaptive identiffcation of regions more suitable for concealing secret images. We also try to ensure that the spatial domain and frequency domain of the original image and the image processed by the model are consistent through loss function. Experimental results demonstrate that the method proposed in this paper has stronger generalization ability and a smaller number of parameters, and the image quality generated is better than that generated by other existing methods, while having higher security. © 2024, The Authors. All rights reserved.

关键词： Image quality

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Improving Incremental Learning: A Closer Look at the Softmax Function

SSRN

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SSRN 2024年

作者： Zhai, Zheng Zhang, Jiali Wang, Haiyu Wu, Mingxin Yang, Keshun Qiao, Xiaoyan Sun, Qiang Beijing Normal University No.18 Jinfeng Road Guangdong Zhuhai519087 China Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong China School of Mathematics Sichuan University Chengdu China College of Liberal Arts and Sciences University of Illinois Urbana-Champaign IL United States Department of Statistical Sciences University of Toronto ON Canada Department of Computer Science University of Toronto ON Canada Department of Statistics and Data Science MBZUAI Abu Dhabi United Arab Emirates

This paper investigates the limitations of the widely adopted softmax cross-entropy loss in incremental learning problems. Specifically, we highlight how the shift-invariant property of this loss function can lead to multiple optimal solutions and imbalanced weights across different tasks, which exacerbates catastrophic forgetting. To address these issues, we propose two key modifications to the objective function: (1) regularizing or replacing the original joint distillation loss with a summation of task-specific distillation losses, and/or (2) utilizing a shift-sensitive prediction loss function, such as the binary cross-entropy loss. Numerical experiments validate the effectiveness of these proposals, demonstrating improved prediction accuracy and a significant reduction in forgetting. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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