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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Yin, Xiaotian Zhang, Chao Wang, Wei School of Computer Science and Technology Anhui University Anhui Province Key Laboratory of Multimodal Cognitive Computation Hefei230601 China Xiaomi Inc. Beijing100085 China

ISBN: (纸本)9798350300482

About 70 million people around the world are suffering from autism, which is about one in every 160 children. The causes of autism are complex, and there is no specific drug treatment. However, for an individual with autism, the earlier the age of treatment, the greater the improvement. In this paper, we collected an autism speech dataset and conducted a study on speech feature classification of autistic and normal children. We built a deep neural network, using Convolutional Recurrent Neural Network as the front-end encoder, and added Convolutional Block Attention Module to it. Integrate local features using recurrent neural networks. To prevent overfitting, we add Connectionist Temporal Classification based speech recognition auxiliary task during training. After introducing the loss function in the field of face recognition, the best classification accuracy reached 94.76%. © 2023 IEEE.

关键词： Diseases

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Trace-based Microservice Anomaly Detection through Deep Learning 2

Trace-based Microservice Anomaly Detection through Deep Lear...

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2nd International Conference on Electronic Information Engineering, Big Data, and Computer Technology, EIBDCT 2023

作者： Bai, Laigang Zhang, Cheng School of Computer Science and Technology Anhui University Hefei230601 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei230601 China Nanjing210093 China

ISBN: (纸本)9781510664968

Over the years, microservices have become increasingly popular and are being adopted more and more, microservice architecture is gradually replacing monolithic applications as the mainstream architecture. Anomaly detection for microservices is a hot topic of current research, which is important to ensure the security and performance of the application. This paper proposes a deep learning based anomaly detection method for microservices, which first serializes trace data, then trains a model combining AE and LSTM to detect anomalies, and also detects performance anomalies by executing time series. We evaluated our approach on a widely adopted microservice system and showed that our approach outperformed current trace-based approaches. © 2023 SPIE.

关键词： Anomaly detection

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Dual-channel Feature Extraction Network for Camouflaged Small Object Detection 3

Dual-channel Feature Extraction Network for Camouflaged Smal...

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3rd International Conference on Optics and Machine Vision, ICOMV 2024

作者： Wang, Futian Huang, Mengyu Chen, Yitao Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China AHU-IAI AI Joint Laboratory Anhui University Hefei China

ISBN: (纸本)9781510680319

Small object detection is a challenging problem in object detection, in practice, we found a special kind of small object detection results are very poor, we call it camouflaged small object, the color of the object itself is similar to the surrounding environment, that is, the contrast between the object and the surrounding environment is low. Therefore, we propose a method to stretch the grayscale map to directly increase the contrast between the object and the surrounding environment. Then, as to how to effectively fuse the feature of object in grayscale map with the feature of object in original RGB image, we propose a dual-channel feature fusion module DCF, which allows the features extracted from the grayscale map to enhance the features extracted from the original RGB image. We also constructed a camouflaged small object detection dataset SSD, which is a smoking object detection dataset with only one smoking object, containing 4349 images and 4705 object instances. Our proposed DCF framework can be easily embedded into existing object detection frameworks. We experimented on a number of different excellent detectors, Faster R-CNN, RetinaNet, and YOLOv8, and the results show that our method is effective in all of these detectors. © 2024 SPIE.

关键词： Object detection

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3D Reconstruction and Texture Optimization Based on Differentiable Rendering 8

3D Reconstruction and Texture Optimization Based on Differen...

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8th International Conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Liu, Yongxin Zhao, Haifeng Zhang, Shaojie Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350302455

Due to noise and quantization errors inherent in depth cameras, RGB-D based 3D reconstruction algorithms inevitably suffer from defects such as texture blurring and ghosting. However, current texture optimization methods tend to only focus on optimizing one aspect, such as camera pose, geometry, or texture, without considering the highly coupled relationship between them. As a result, it is difficult to achieve global optimality. To address this issue, we propose a joint optimization framework based on differentiable rendering that simultaneously optimizes camera pose, geometry, and texture. By fully exploiting the complementary relationship between these aspects, we are able to reconstruct textures with high fidelity while recovering fine geometric details. First, we propose an optimized geometry model combined with adaptive subdivision, where we selectively subdivide the model only in texture-rich regions to increase the number of vertex facets. Then, we perform geometry optimization to recover high-frequency geometric details evenly in the presence of large geometric errors without incurring computational costs. After the pose correction and geometric model optimization, we use pixel regeneration pipelines to generate clear texture atlases. Comparative evaluations with state-of-the-art texture optimization methods on a public dataset demonstrate the effectiveness of our proposed approach in reducing errors in the reconstruction of 3D models, generating clear textures, and recovering high-frequency geometric details. © 2023 IEEE.

关键词： Textures

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Selective Detail Enhancement Algorithm for Finger Vein Images 16th

Selective Detail Enhancement Algorithm for Finger Vein Image...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Sun, Mingze Li, Jiahao Wang, Huabin Xu, Yujie Tao, Liang Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

ISBN: (纸本)9783031202322

In near-infrared finger vein images, the contrast between vein texture and skin is low, and traditional algorithms based on enhancement filters and grayscale stretching are difficult to effectively enhance the vein structure, and it is easy to introduce noise. Therefore, a selective enhancement algorithm for finger vein images based on adaptive guided filtering is proposed. Firstly, a gradient operator weighted guided filter with better edge-preserving ability is proposed to decompose the vein structure of finger vein images, and obtain smooth layer and detail layer. Secondly, a selective weighting factor is designed for vein detail layer enhancement while reducing noise interference. Finally, the enhanced detail layer is fused with the smooth layer to obtain an enhanced image of finger veins. The experimental results show that the proposed algorithm can effectively improve the quality of finger vein images. In six popular image quality evaluation indicators such as information entropy (IE) and peak signal-to-noise ratio, the image recognition rate after enhancement is also improved due to mainstream algorithms. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Image recognition

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Multi-branch Attention Fusion Network for Aspect Sentiment Classification 8

Multi-branch Attention Fusion Network for Aspect Sentiment C...

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8th International Conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Zhao, HaiFeng Yan, Kun Cao, MingWei Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350302455

For specific aspects (aspect words) of the object described in a given sentence, the research goal of aspect sentiment classification is to predict the sentiment polarity expressed by different aspect words. At present, sentiment classification methods based on attention mechanism and graph neural networks use attention models to capture contextual information related to aspect words, but ignore the global information of the sentence where aspect words are located. The classification accuracy in dealing with complex sentences still needs to be further improved. To solve the above problems, we propose a novel syntax-aware method - Multi-branch Attention Fusion Network (MAFNet) for aspect sentiment classification. Firstly, we use graph attention networks to capture contextual information related to aspect words, while using the mean-pooling layer to capture global information of sentences. Secondly, we propose a novel attention fusion mechanism to integrate contextual information related to aspect words and global information of sentences, which can enrich the semantic relationship between aspect words and opinion words in the sentence. Then, we use orthogonal and differential regularization to constrain the attention weight of contextual information, which can accurately capture the semantic correlation between aspect words and opinion words in the sentence. Finally, we verify the effectiveness of the algorithm on three public datasets. The experimental results prove that our method can correctly establish the semantic relationship between aspect words and opinion words, which can achieve better classification accuracy than traditional methods, attention-based methods, and graph neural network-based methods. © 2023 IEEE.

关键词： Semantics

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Improving the Liquid Sensing Resolution via Multiple Transceiver Pairs 2

Improving the Liquid Sensing Resolution via Multiple Transce...

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2nd International Conference on Electronic Information Technology, EIT 2023

作者： Dai, Zhengtao Li, Ping Zhu, Biaokai Dong, Chong Lv, Zhao Anhui Province Key Laboratory of Multimodal Cognitive Computation Anhui University An Hui He Fei China Shanxi Police College China School of Electrical and Information Engineering Anhui University of Technology China

ISBN: (数字)9781510666627

ISBN: (纸本)9781510666610

Many studies have shown that wireless sensing would be a promising method for liquid identification, but existing methods still have limitations for fine-grained liquid sensing. In this paper, we propose a liquid identification method based on multiple transceiver pairs, which effectively improves the sensing resolution of liquid identification. We implement our method with a commercially FMCW millimeter wave radar and evaluate its performance. Our result shows that for concentrations as low as 0.5% in alcohol solutions, our method can achieve an accuracy of more than 95%. © 2023 SPIE.

关键词： Millimeter waves

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Traumatic Brain Injury Classification Based on Slice Feature Selection and Transformer 8

Traumatic Brain Injury Classification Based on Slice Feature...

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8th International Conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Liu, Jinyu Ye, Tianhui Huang, Lili Li, Chuanfu Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China Anhui University Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Hefei China The First Affiliated Hospital of Anhui University of Chinese Medicine Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China

ISBN: (纸本)9798350302455

Traumatic brain injury (TBI) usually originates from primary injury, has a high rate of death and disability, and can lead to temporary or permanent neurological deficit in severe cases. Therefore, it is very important to make an accurate imaging diagnosis as early as possible. Deep learning is widely used in medical image analysis, and the use of convolutional neural networks for initial diagnosis of traumatic brain trauma has been proven effective. Most of the traumatic brain injury recognition work in recent years is to classify a single image. We propose a new approach for diagnosing traumatic brain injuries from CT images that takes advantage of the sequence relationship between slices. Unlike natural images, medical images require doctors to synthesize information from multiple slices to make an accurate diagnosis. To address this, we combine convolutional neural networks (CNN) and Transformers to extract complex features and classify brain CT images by leveraging the correlation between slices. We also introduce an embeddable slice feature selection module that further improves accuracy. We evaluated our approach on a hospital-provided dataset and achieved superior performance compared to existing methods. © 2023 IEEE.

关键词： Convolutional neural networks

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multimodal Fusion Induced Attention Network for Industrial VOCs Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6385-6398页

作者： Kang, Yu Shi, Kehao Tan, Jifang Cao, Yang Zhao, Lijun Xu, Zhenyi Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation Hefei230601 China Yangtze River Delta Hart Robot Industry Technology Research Institute Wuhu241000 China University of Science and Technology of China Anhui Province Key Laboratory of Intelligent Low-Carbon Information Technology and Equipment Hefei230088 China University of Science and Technology of China Department of Automation Hefei230026 China Anhui University AHU-IAI AI Joint Laboratory Hefei230601 China University of Science and Technology of China Institute of Advanced Technology Hefei230088 China

Industrial volatile organic compounds (VOCs) emissions and leakage have caused serious problems to the environment and public safety. Traditional VOCs monitoring systems require professionals to carry gas sensors into the emission area to collect VOCs, which might cause secondary hazards. VOCs infrared (IR) imaging visual inspection technology is a convenient and low-cost method. However, current visual detection methods with VOCs IR imaging are limited due to blurred imaging and indeterminate gas shapes. Moreover, major works pay attention to only IR modality for VOCs emissions detection, which would neglect semantic expressions of VOCs. To this end, we propose a dual-stream fusion detection framework to deal with visible and IR features of VOCs. Additionally, a multimodal fusion induced attention (MFIA) module is designed to realize feature fusion across modalities. Specifically, MFIA uses the spatial attention fusion module (SAFM) to mine association among modalities in terms of spatial location and generates fused features by spatial location weighting. Then, the modality adapter (MA) and induced attention module (IAM) are proposed to weight latent VOCs regions in IR features, which alleviates the problem of noise interference and degradation of VOCs characterization caused by fusion. Finally, comprehensive experiments are carried out on the challenging VOCs dataset, and the mAP@0.5 and F1-score of the proposed model are 0.527 and 0.601, which outperforms the state-of-the-art methods by 3.3% and 3.4%, respectively. © 2024 IEEE.

关键词： Volatile organic compounds

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AlignFuse: Multi-feature Alignment and Fusion for Medical Image Segmentation 8

AlignFuse: Multi-feature Alignment and Fusion for Medical Im...

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8th International Conference on Intelligent computing and Signal Processing, ICSP 2023

作者： Chao, Shengyang Liu, Zijun Huang, Lili Fu, Yanping Anhui University Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Anhui Hefei230088 China

ISBN: (纸本)9798350302455

U-Net, which is a U-shaped network, has made great progress on numerous medical image segmentation tracks. However, even if convolutional neural network (CNN) has a talent for extracting local information, it falls short in capturing global information due to the inherent drawback of the convolution operation. In contrast, whereby the design of tokenized images and self-attention mechanism, Transformer has a brilliant performance in modeling global contextual information but has a poor performance in grasping low-level details on account of the patch division which leads to ignorance of local correlation in each patch. In this paper, we develop a hybrid network structure, AlignFuse, which is in possession of the advantages of both CNN and Transformer. AlignFuse fits CNN and Transformer together in a parallel style, where Transformer leverages the high-level features and CNN pays more attention to details. Moreover, owing to the downsampling operation in CNNs, traditional context-free feature aggregation operations lead to misalignment issues between feature maps. To alleviate this problem, we propose to use Feature Aligned Fusion Module (FAFM) to effectively align and fuse the feature maps from CNN and Transformer in each decoder stage. We conduct experiments on Gland, breast cancer, and automatic cardiac segmentation, with competitive results obtained. © 2023 IEEE.

关键词： Convolution

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