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4th International Conference on Signal Image Processing and Communication, ICSIPC 2024

作者： Liu, Rong Yang, Xiyu Yang, Tingting School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781510682467

Based on the characteristics of synthetic aperture radar (SAR) images, this paper designs a lightweight generative adversarial network (GAN) model that uses dual-channel mode to simultaneously read two SAR images from different angles and generate new images containing multi-angle image information to achieve data expansion. This model uses an attention mechanism module to enhance the expressiveness of key features and improve the quality of generated images. Images synthesized by this method incorporate information from multiple angle views and contain more detailed target features. Finally, experiments show that the fused image obtained by this method has high information entropy and is highly similar to the source image. Additional, the effectiveness of the data expansion is proved by using the single shot multibox detection (SSD) framework. © 2024 SPIE.

关键词： Generative adversarial networks

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Non-revisiting Stochastic Search for Automatic Graph Learning 20th

Non-revisiting Stochastic Search for Automatic Graph Learni...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Bu, Chenyang Lu, Hezhen School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9789819970216

In recent years, automatic graph learning (AutoGL) has been widely concerned by academia and industry because it can significantly reduce the threshold and labor cost of graph learning. It has shown powerful functions in hyper-parameter optimization, model selection, graph neural architecture search, and feature engineering. With the development of the network structure, the time and computing resources consumed by the AutoGL process are increasing. AutoGL can be viewed as a bilevel optimization problem encompassing inner and outer optimization. The inner optimization focuses on optimizing the model parameters through techniques like stochastic gradient descent, aiming to minimize the loss function and enhance model performance. On the other hand, the outer optimization aims to identify the best configuration settings for hyperparameters and neural network structures. To address the computational cost associated with heuristic algorithms in bilevel optimization for AutoGL, the non-revisiting idea is proposed to keep a record of all previously evaluated individuals to avoid redundant search. Experiments on multiple search algorithms demonstrate that non-revisiting can improve the time performance of AutoGL. The time performance of the search algorithm with non-revisiting is improved by 40 % to 80 % compared with that without non-revisiting under the condition that the experimental accuracy remains unchanged. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Heuristic algorithms

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On the Performance of Advanced Modulation Formats for Extended Single-Span Links

On the Performance of Advanced Modulation Formats for Extend...

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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on information Photonics and Optical Communications, IPOC 2024

作者： Xu, Jiwei Chen, Bin Zhao, Can Sheng, Yajie Lei, Yi School of Computer Science and Information Engineering Hefei University of Technology Hefei China

Six modulation formats referring the 400ZR standard are compared, revealing that PCS-16QAM and 4D-64PRS may offer optimal performance at different transmission distances, enhancing high-capacity optical fiber communic... 详细信息

ISBN: (纸本)9798350379266

关键词： Signal modulation

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DAT: Dialogue-Aware Transformer with Modality-Group Fusion for Human Engagement Estimation 24

DAT: Dialogue-Aware Transformer with Modality-Group Fusion f...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Jia Yu, Yangchen Chen, Yin Zhang, Yu Jia, Peng Xu, Yunbo Li, Ziqiang Wang, Meng Hong, Richang School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798400706868

Engagement estimation plays a crucial role in understanding human social behaviors, attracting increasing research interests in fields such as affective computing and human-computer interaction. In this paper, we propose a Dialogue-Aware Transformer framework (DAT) with Modality-Group Fusion (MGF), which relies solely on audio-visual input and is language-independent, for estimating human engagement in conversations. Specifically, our method employs a modality-group fusion strategy that independently fuses audio and visual features within each modality for each person before inferring the entire audio-visual content. This strategy significantly enhances the model's performance and robustness. Additionally, to better estimate the target participant's engagement levels, the introduced Dialogue-Aware Transformer considers both the participant's behavior and cues from their conversational partners. Our method was rigorously tested in the Multi-Domain Engagement Estimation Challenge held by MultiMediate'24, demonstrating notable improvements in engagement-level regression precision over the baseline model. Notably, our approach achieves a CCC score of 0.76 on the NoXi Base test set and an average CCC of 0.64 across the NoXi Base, NoXi-Add, and MPIIGI test sets. The source code will be available at https://***/MSA-LMC/DAT. © 2024 ACM.

关键词： engagement estimation

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A new framework for GNSS spoofing detection based on signal phase and structure 4

A new framework for GNSS spoofing detection based on signal ...

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4th International Conference on Electronic information engineering and computer Communication, EIECC 2024

作者： Xia, Na Li, Jiacheng Du, Huazheng Jing, Shuhao Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798331534622

Spoofing interference casts significant threat to the GNSS receivers making the enhancement of spoofing detection capability a crucial issue. Detection performance of traditional SQM method encounters evident degradation in interference process. Utilizing phase feature and specific structure of spoofing signal, a new spoofing detection framework has been developed to determine the existence of interference. Preliminary experimental results in TEXBAT indicate that this method owns great potential in spoofing detection, maintaining stability and accuracy in different stages of interference. © 2024 IEEE.

关键词： Global positioning system

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CauImputation: Utilizing Structural Causal Model in Data Imputation

CauImputation: Utilizing Structural Causal Model in Data Imp...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yin, Yue Yang, Jiaoyun An, Ning Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350386226

Missing data pose significant obstacles in data analysis. Many imputation methods, operating under the assumption that similar instances exhibit similar feature values, often overlook the essential role of the feature containing the missing value. These methods tend to apply a uniform strategy to all missing data within a single instance. This study proposes selecting neighbor instances for imputation based on both instance and missing feature information. To support this approach, a Structural Causal Model (SCM) delineates the relationships among missing data, features, and instances. The model reveals that the feature with the target missing value acts as a confounder, influencing both the selection of neighbor instances and the prediction of the missing value. Building upon these findings, this study introduces a new imputation method called the Causal Imputation strategy (CauImputation). CauImputation utilizes both feature and instance information to select neighbor instances and then applies causal interventions to compute the missing value. This strategy effectively leverages feature information while mitigating confounding effects. Tests on the National Alzheimer's Coordinating Center (NACC) dataset and the National Center for Biotechnology information (NCBI) microarray datasets demonstrate that CauImputation outperforms nine established methods, achieving a 3% to 15% increase in imputation accuracy and reducing root mean squared errors by 0.05 to 0.2. By utilizing the Structural Causal Model, this research introduces a novel perspective on data imputation, enhancing the robustness and reliability of biomedical data analyses. © 2024 IEEE.

关键词： Data accuracy

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Lithium battery SOH and RUL prediction based on time-frequency multi-feature extraction and fusion networks 5

Lithium battery SOH and RUL prediction based on time-frequen...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Wang, Xiaohua Dai, Ke Hu, Min Ni, Nanbing Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350373820

Accurate prediction of battery state of health (SOH) and remaining useful life (RUL) is crucial for reducing the risk of lithium battery failure and intelligent management of energy storage power plants. Currently, most of the existing research methods only take the features between charge/discharge cycles as model inputs, ignoring the interconnectivity of feature data within the battery cycle. A model based on time-frequency multi-feature extraction and fusion (TFMEFNet) is proposed to improve the accuracy of lithium-ion battery SOH and RUL prediction. The time-frequency multi-feature extraction module is used to extract features of time and frequency domains respectively from the data within the discharge cycle, and then the global, time-domain and frequency-domain features are fused by the feature fusion module. Long-term dependencies in continuous data then captured by a TCN-GRU (Temporal Convolutional Network-Gated Recurrent Unit) module. A Temporal Attention module is used to better represent the decaying trend of the capacity series, weighted by attention, resulting in more accurate predictions. Extensive experiments are conducted on NASA datasets and compared with existing methods. The experimental results show that the proposed model can predict the SOH and RUL of Li-ion batteries more accurately. The root mean square error (RMSE) on the NASA dataset is less than 0.786%. © 2024 IEEE.

关键词： Feature extraction

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Video Segment Time-Channel Frequency Attention Network for Remote Photoplethysmography 5

Video Segment Time-Channel Frequency Attention Network for R...

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5th International Conference on computer Vision, Image and Deep Learning, CVIDL 2024

作者： Hu, Min Gao, Dongming Wang, Xiaohua Tang, Yiming Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350373820

Remote Photoplethysmography (rPPG) is a technique for remotely measuring physiological signals such as (HR). Compared to traditional contact-based measurements, HR estimation based on video recording of human face offers significant convenience and wide-ranging application prospects. This paper proposes an end-to-end model named VSTCFANet. Firstly, a video segment attention module (VSA) is constructed to mitigate redundancy in facial videos. The facial videos are divided into multiple subsegments and passed through corresponding subpaths to extract spatial and temporal information. Secondly, a time-channel frequency attention module (TCFA) is designed to extract features in the channel frequency domain. The channel frequency domain features of the video segments are aggregated to enhance the model's robustness, thereby reducing the effection caused by changes in distance and head movements. Additionally, an emotion recognition module is built to utilize the obtained rPPG signals to identify the emotion of experimental subjects. Lastly, two loss functions are employed to mitigate the interference from other physiological signals on the rPPG signals. Extensive experiments are conducted on the PURE, COHFACE, and MAHNOB-HCI datasets to validate the effectiveness of the proposed model, the experimental results show that the proposed model has made significantly better performances in measurement accuracy than the state-of-the-art baselines. © 2024 IEEE.

关键词： Emotion Recognition

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A Multi-scale Feature Embedding Extension Network for RGB-D Cross-modal Person Re-identification 7

A Multi-scale Feature Embedding Extension Network for RGB-D ...

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7th World Conference on Computing and Communication Technologies, WCCCT 2024

作者： Dai, Xuesong Li, Xiaohong Li, Kunyuan Liu, Xueliang Wang, Jing Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350372328

The RGB-D cross-modal person re-identification task is a branch of the cross-modal person re-identification task, aiming to match pedestrian images between RGB and depth modalities. Its main challenge lies in the significant modality difference between RGB and depth images. Due to the limited research attention on this task, there is a lack of large-scale, high-quality training data, making existing methods struggle to capture effective correlational information between RGB and depth modalities. This paper proposes a novel feature embedding expansion network for RGB-D cross-modal person re-identification. The network introduces perturbations to the embedding of intermediate-layer features to generate multiple diversified embedding features, effectively alleviating the problem of limited training data for RGB-D tasks. Simultaneously, through the incorporation of deformable convolution operations and depth-wise separable convolution operations during the embedding feature generation, the model is encouraged to learn richer scale and channel information from the limited training data. Meanwhile, by controlling the distance between expanded feature embeddings and original feature embeddings for different modalities, we mitigate the significant modality differences. Experimental results on the public benchmark datasets BIWI and RobotPKU demonstrate that our approach outperforms state-of-the-art methods. © 2024 IEEE.

关键词： Convolution

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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