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2023 International Conference on Optoelectronic Materials and Devices, ICOMD 2023

作者： Guan, Nianci Han, Qizhuo Institute of Biomedical Materials & Engineering Northwestern Polytechnical University Xi'an710072 China The School of Computer Science National Engineering Laboratory for Integrated Aero-Space-Ground- Ocean Big Data Application Technology Shaanxi Provincial Key Laboratory of Speech and Image Information Processing Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9781510674509

Efficient and stable Perovskite solar cells (PSCs) have been successfully produced using a dual-layer modified (DLM) perovskite thin film. This method simultaneously modifies both the bottom (SnO2/perovskite) and top (perovskite/SpiroOMeTAD) interfaces of the perovskite. The crystallinity of the perovskite film is enhanced after the DLM treatment. Additionally, thin barrier layers are formed on both its bottom and top surfaces, which reduce surface defect density and non-radiative recombination of carriers. The bottom and top modifier layers prevent direct contact between the hole transport layer and the electron transport layer, forming a high-energy barrier that promotes carrier transmission in PSCs. Consequently, the methylammonium acetate (MAAc)-PSC achieves an efficiency of 21.96%. © 2024 SPIE. All rights reserved.

关键词： Perovskite solar cells

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A review of enhancement techniques based on flight parameters time series data 24

A review of enhancement techniques based on flight parameter...

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4th International Conference on Artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Wang, Hui Sun, Qianhui Liu, Wei Zhang, Zhen Shandong Dingdong electronic Technology Group Co. Ltd. Jinan Shandong China Standard Institute of Emerging Technologies and Innovations Shandong Computer Science Center National Supercomputer Center in Jinan Jinan Shandong China Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Jinan Shandong China

ISBN: (纸本)9798400710247

With the rapid development of the aviation field, the importance of flight data has become increasingly prominent. It usually records the working status of key components and the control behavior of the crew, playing a significant role in aircraft maintenance, accident investigation, and flight quality assessment. The multi-dimensional flight parameter timing data possesses characteristics such as relevance, difference, and diversity;thus obtaining this type of data is challenging. Overcoming this obstacle by utilizing existing technology to expand flight parameter timing data is a major focus in current research. This paper summarizes the time series data enhancement techniques and the current research progress while also highlighting limitations in existing literature. Furthermore, it conducts experiments using real-time flight parameter data to analyze key issues related to time series data enhancement before finally describing future trends in enhancing flight parameter time series data. © 2024 Copyright held by the owner/author(s).

关键词： Aircraft accidents

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Dual-branch adjacent connection and channel mixing network for video crowd counting

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Pattern Recognition 2025年 167卷

作者： Ling, Miaogen Chen, Jixuan Liu, Yongwen Fang, Wei Geng, Xin School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Computer Science and Engineering Southeast University Nanjing210096 China

This paper focuses on the problem of video crowd counting, which usually uses the spatial and temporal correlations of the consecutive frames to achieve better performance than the single-image crowd counting methods. However, most of the current video crowd counting methods either use only two or three frames for optical flow or frame-difference feature extraction or construct a single-branch network to extract spatiotemporal correlated features. The interactions of features for multiple adjacent frames, which can effectively prevent disturbances caused by background noise, are mostly overlooked. Considering the above problems, we propose a dual-branch adjacent connection and channel mixing network for multi-frame video crowd counting. For the upper branch, an adjacent layer connection method is proposed to capture the multi-scaled spatiotemporal correlations among multiple consecutive frames instead of the traditional dense connections in decomposed 3D convolutional blocks. It achieves better performance and low computation cost. For the lower branch, adaptive temporal channel mixing blocks are proposed to exchange partial channel information among the adjacent frames for feature interaction. The partial channel transpose operation is first proposed to exchange information. It is parameter-free and flexible to achieve interactions among features of any number of consecutive frames. The proposed method outperforms the current image-based and video-based crowd counting models, achieving state-of-the-art performance on six publicly available datasets. The code is available at: https://***/aaaabbbbcccccjxzxj/mfvcc. © 2025 Elsevier Ltd

关键词： Feature extraction

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KAN-Face: Efficient Resource Usage and Precision Lip-Sync in Talking Head Generation

KAN-Face: Efficient Resource Usage and Precision Lip-Sync in...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Feng, Guanwen Jin, Siyu Qian, Zhihao Li, Yunan Miao, Qiguang School of Computer Science and Technology Xidian University Shaanxi Xi'an710071 China Xi'an Key Laboratory of Big Data and Intelligent Vision Xidian University Shaanxi Xi'an710071 China Key Laboratory of Collaborative Intelligence Systems Ministry of Education Xidian University Xi'an710071 China

ISBN: (纸本)9798350368741

Despite significant progress in NeRF-based talking head generation, problems like poor lip synchronization and inefficient resource usage remain. To solve these, we propose KAN-Face, a lightweight framework. In preprocessing, we introduce a Lip-Sync Enhancement Module that uses Wav2Lip to extract high-resolution audio features and map them to an explicit intermediate representation, ensuring precise lip movement alignment with the speaker's identity. These predicted lip-sync features are combined with fundamental audio-extracted lip features and injected into the rendering module to improve synchronization. For rendering, we introduce FastKAN to map spatial points to color values. As a variant of KAN, FastKAN's sensitivity to 3D scenes and efficient structure enable precise, fast color prediction. Our framework reduces resource consumption while enhancing lip-sync accuracy and facial reconstruction, making it ideal for talking head generation tasks in resource-limited settings. Project: https://***/KAN-Face/. © 2025 IEEE.

关键词： Face Animation KAN Network Low Resource Neural Radiance Fields Talking Head Generation

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SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation for Histopathology Image

SS-WSSS: Small-Scale Weakly Supervised Semantic Segmentation...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Jiatai Han, Guoqiang Zhou, Jingxuan Shi, Zhenwei Liu, Zaiyi Han, Chu South China University of Technology Guangzhou China Shenzhen China South China University of Technology School of Computer Science and Engineering Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou China

ISBN: (纸本)9798350386226

Semantic segmentation for histopathology images is one of the fundamental tasks in computational pathology. Due to the high cost of pixel-level annotation acquisition, the weakly supervised semantic segmentation (WSSS) attempts to achieve information-intensive segmentation task for histopathology images to reduce the labeling effort of pathologists by leveraging image-level labels. However, traditional WSSS requires a large-scale training set with image-level labels, which still imposes considerable labeling costs on pathologists. To this end, this work proposes a Small-Scale Weakly Supervised Semantic Segmentation (SS-WSSS) approach to achieve the comparable performance only with small-scale weakly-labeled data to further reduce pathologist's labeling effort. Since histopathology images can easily generate massive unlabeled data, our SS-WSSS aims to learn with the unlabeled data to bridge the information gap. First, we propose a Single-to-Multi Prototype Similarity (S2M-PS) method to generate reliable pseudo-labels for unlabeled data by measuring the similarity between single-label prototypes and multi-label feature maps. Then, we introduce a Cross-Task CoTraining (CT2) method for pseudo-supervision of models with pseudo-labels self-refinement to avoid overfitting to noisy labels. We conduct the experiment on two public datasets to demonstrate the effectiveness of our SS-WSSS. In the experiment, our method achieves comparable performance with SOTA methods only using 30% labeled data. © 2024 IEEE.

关键词： Semantic Segmentation

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Verifiable searchable symmetric encryption for conjunctive keyword queries in cloud storage

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Frontiers of computer science 2022年第6期16卷 103-121页

作者： Qingqing GAN Joseph K.LIU Xiaoming WANG Xingliang YUAN Shi-Feng SUN Daxin HUANG Cong ZUO Jianfeng WANG Guangzhou Key Laboratory of Multilingual Intelligent Processing School of Information Science and Technology/School of Cyber SecurityGuangdong University of Foreign StudiesGuangzhou510006China Department of Computer Science Jinan UniversityGuangzhou510632China Faculty of Information Technology Monash UniversityClayton3168Australia State Key Laboratory of Integrated Service Networks(ISN) Xidian UniversityXi’an710071China

Searchable symmetric encryption(SSE)has been introduced for secure outsourcing the encrypted database to cloud storage,while maintaining searchable *** various SSE schemes,most of them assume the server is honest but curious,while the server may be trustless in the real *** a malicious server not honestly performing the queries,verifiable SSE(VSSE)schemes are constructed to ensure the verifiability of the search ***,existing VSSE constructions only focus on single-keyword search or incur heavy computational cost during *** address this challenge,we present an efficient VSSE scheme,built on OXT protocol(Cash et al.,CRYPTO 2013),for conjunctive keyword queries with sublinear search *** proposed VSSE scheme is based on a privacy-preserving hash-based accumulator,by leveraging a well-established cryptographic primitive,Symmetric Hidden Vector Encryption(SHVE).Our VSSE scheme enables both correctness and completeness verifiability for the result without pairing operations,thus greatly reducing the computational cost in the verification ***,the proposed VSSE scheme can still provide a proof when the search result is ***,the security analysis and experimental evaluation are given to demonstrate the security and practicality of the proposed scheme.

关键词： searchable symmetric encryption verifiability conjunctive keyword queries hash-based accumulator cloud storage

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Uncertainty-guided Mutual Consistency Training for Semi-supervised Biomedical Relation Extraction

Uncertainty-guided Mutual Consistency Training for Semi-supe...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Mao, Bing Jia, Chang Huang, Yucheng He, Kai Wu, Jialun Gong, Tieliang Li, Chen Xi'an Jiaotong University School of Computer Science and Technology Xi'an China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering Xi'an China

ISBN: (纸本)9781665468190

Biomedical relation extraction seeks to automatically extract biomedical relations from biomedical text, which plays an important role in biomedical studies. However, constructing high-quality biomedical annotation data is not only time-consuming but also requires a high level of knowledge in the biomedical field. To alleviate this problem, Semi-supervised Biomedical Relation Extraction aims to extract relation facts from the limited labeled data and the more readily available unlabeled samples. Existing works can be roughly categorized as self-training methods and self-ensembling methods. The former aims to generate pseudo labels, which may lead to the gradual drift problem. The latter aims to encourage the output of one model to be consistent with the other model, where the acquisition of the model is tedious. To alleviate these issues, we propose a novel Uncertainty-Guided Mutual Consistency Training framework(UG-MCT) for semi-supervised Biomedical relation extraction. Specifically, our framework consists of two models with the same structure, which differ only when updating their weights, and then an intersecting pseudo-label mechanism is designed to convert the prediction discrepancies of the two models into mutual consistency training loss, thus promoting the consistency of model predictions. In addition, we utilize uncertainty as guided information to assist the model in focusing on the confident pseudo labels and mitigate the noise of inaccurate pseudo labeling during training. Thus, our model is very simple and efficient while mitigating the noise introduced by pseudo-labels. UG-MCT is evaluated on multiple datasets in different settings and the experimental results demonstrate that our method is highly effective in semi-supervised biomedical relation extraction compared to the state-of-the-art. © 2022 IEEE.

关键词： Extraction

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Prototype Transferred Diverse Features for Day-night Cross-domain Facial Expression Recognition 16

Prototype Transferred Diverse Features for Day-night Cross-d...

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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Ma, Teng Qi, Yuhan Wang, ZhuoRan Li, YiPei Wang, Jiaxiang State Grid Corporation of Shanxi Province Shanxi 030025 China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Beijing Institute of Technology China Dublin International College Ireland

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

Facial Expression Recognition (FER) encounters significant challenges due to the limited sensitivity of visible light images in low light conditions. Most existing cross-domain emotion recognition studies have focused on domain adaptation for different visible light datasets, often neglecting the problem of emotion recognition under varying lighting conditions. To address this, we introduce the first cross-domain emotion recognition study for day and night environments, emphasizing the modal migration between normal visible light and low-light emotion maps. Considering the limited number of emotion maps and the reduced emotional detail in nighttime imaging, we designed an attention-switching diversified feature capture module to focus on facial expressions and extract more local emotional features that aid in migration. Given the significant domain shifts between samples in different lighting environments, we further developed a prototype feature transfer module to learn modality-independent category features and reduce domain differences between visible and low-light features. To tackle the challenge of numerous low sentiment value samples in the dataset, we introduce a high-confidence blending module to filter informative visible and infrared samples for fusion, producing features that combine the styles of both domains. Compared to state-of-the-art methods in domain adaptation and cross-domain emotion recognition, our approach demonstrates superior performance and validates its effectiveness. © 2025 SPIE.

关键词： Emotion Recognition

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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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EEG based Parkinson Detection through Supervised Information Enhanced Contrastive Learning

EEG based Parkinson Detection through Supervised Information...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Song, Jian Li, Xiang Jiang, Wenjing Wang, Chunxiao Zhao, Zhigang Lv, Jialiang Hu, Bin Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China Qilu Hospital of Shandong University Department of Geriatric Neurology Key Laboratory of Cardiovascular Proteomics of Shandong Province Jinan China Beijing Institute of Technology Institute of Engineering Medicine Beijing China

ISBN: (纸本)9798350337488

This study presents a novel Supervised Information Enhanced Contrastive Learning Algorithm for Parkinson's Disease Detection (SI-CLAPD) based on Electroencephalography (EEG). SI-CLAPD performs contrastive learning in a multi-granularity manner on enhanced contextual views to achieve robust contextual representations for EEG, thus could contribute to improving the effectiveness of PD detection. Unlike existing methods for constructing PD detection models guided solely by classification loss, we propose a joint learning model that combines self-supervised contrastive learning with supervised classification learning. This model is optimized using both contrastive loss and classification loss, allowing it to capture subtle differences between EEG signals and representations that are specific to PD detection. Through extensive experimental evaluations, we demonstrate that SI-CLAPD achieves robust and high accuracy in PD detection tasks on three benchmark datasets. To the best of our knowledge, this study represents the first effort in validating the effectiveness of contrastive learning for the detection of PD. Besides, within the realm of contrastive learning research in EEG, it also represents the first endeavor to fuse supervised learning with self-supervised contrastive learning for EEG classification. This investigation unveils an universally applicable approach to EEG signal processing, with the potential to confer advantages to a multitude of EEG classification tasks. © 2023 IEEE.

关键词： Contrastive learning Deep Learning Electroencephalogram Joint training Parkinson's disease

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