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学者姓名：赵铁松
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Abstract ：
With the emergence of Artificial Intelligence (AI), smart education has become an attractive topic. In a smart education system, automated classrooms and examination rooms could help reduce the economic cost of teaching, and thus improve teaching efficiency. However, existing AI algorithms suffer from low surveillance accuracies and high computational costs, which affect their practicability in real-word scenarios. To address this issue, we propose an AI-driven anomaly detection framework for smart proctoring. The proposed method, namely, Smart Exam (SmartEx), consists of two artificial neural networks: an object recognition network to locate invigilators and examinees, and a behavior analytics network to detect anomalies of examinees during the exam. To validate the performance of our method, we construct a dataset by annotating 6,429 invigilator instances, 34,074 examinee instances and 8 types of behaviors with 267,888 instances. Comprehensive experiments on the dataset show the superiority of our SmartEx method, with a superior proctoring performance and a relatively low computational cost. Besides, we also examine the pre-trained SmartEx in an examination room in our university, which shows high robustness to identify diversified anomalies in real-world scenarios.
Keyword ：
Examination Examination Smart education Smart education Smart proctoring Smart proctoring
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| GB/T 7714 | Wang, Pu , Lin, Yifeng , Zhao, Tiesong . Smart proctoring with automated anomaly detection [J]. | EDUCATION AND INFORMATION TECHNOLOGIES , 2024 . |
| MLA | Wang, Pu 等. "Smart proctoring with automated anomaly detection" . | EDUCATION AND INFORMATION TECHNOLOGIES (2024) . |
| APA | Wang, Pu , Lin, Yifeng , Zhao, Tiesong . Smart proctoring with automated anomaly detection . | EDUCATION AND INFORMATION TECHNOLOGIES , 2024 . |
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在"中国制造2025"的国家需求及福建省海西地方经济和产业升级需求的背景下,传统的信号与信息处理专业的培养方式对未来所需的人才品质存在不适应性.通过分析信号与信息处理专业教学体系现状,以福州大学为例,研究人工智能时代的信号专业教育教学改革机制,分别从学位点建设、课程建设、培养方案、培养目标、课程体系等方面探讨了教学改革机制,从而为高等院校培养信号与信息处理方向的综合型创新人才提供参考.
Keyword ：
5G 5G 人工智能 人工智能 信号与信息处理专业 信号与信息处理专业 教学改革 教学改革 课程思政 课程思政
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| GB/T 7714 | 陈炜玲 , 林丽群 , 赵铁松 . "5G+人工智能"时代的教学新挑战 [J]. | 教育教学论坛 , 2024 , (40) : 42-46 . |
| MLA | 陈炜玲 等. ""5G+人工智能"时代的教学新挑战" . | 教育教学论坛 40 (2024) : 42-46 . |
| APA | 陈炜玲 , 林丽群 , 赵铁松 . "5G+人工智能"时代的教学新挑战 . | 教育教学论坛 , 2024 , (40) , 42-46 . |
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With the rise of immersive media applications such as digital museums, virtual reality, and interactive exhibitions, point clouds, as a three-dimensional data storage format, have gained increasingly widespread attention. The massive data volume of point clouds imposes extremely high requirements on transmission bandwidth in the above applications, gradually becoming a bottleneck for immersive media applications. Although existing learning-based point cloud compression methods have achieved specific successes in compression efficiency by mining the spatial redundancy of their local structural features, these methods often overlook the intrinsic connections between point cloud data and other modality data (such as image modality), thereby limiting further improvements in compression efficiency. To address the limitation, we innovatively propose a view-guided learned point cloud geometry compression scheme, namely ViewPCGC. We adopt a novel self-attention mechanism and cross-modality attention mechanism based on sparse convolution to align the modality features of the point cloud and the view image, removing view redundancy through Modality Redundancy Removal Module (MRRM). Simultaneously, side information of the view image is introduced into the Conditional Checkboard Entropy Model (CCEM), significantly enhancing the accuracy of the probability density function estimation for point cloud geometry. In addition, we design a View-Guided Quality Enhancement Module (VG-QEM) in the decoder, utilizing the contour information of the point cloud in the view image to supplement reconstruction details. The superior experimental performance demonstrates the effectiveness of our method. Compared to the state-of-the-art point cloud geometry compression methods, ViewPCGC exhibits an average performance gain exceeding 10% on D1-PSNR metric. © 2024 ACM.
Keyword ：
Deep learning Deep learning Ferroelectric RAM Ferroelectric RAM Health risks Health risks Image compression Image compression Network security Network security Redundancy Redundancy Risk assessment Risk assessment Risk perception Risk perception Virtual storage Virtual storage
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| GB/T 7714 | Zheng, Huiming , Gao, Wei , Yu, Zhuozhen et al. ViewPCGC: View-Guided Learned Point Cloud Geometry Compression [C] . 2024 : 7152-7161 . |
| MLA | Zheng, Huiming et al. "ViewPCGC: View-Guided Learned Point Cloud Geometry Compression" . (2024) : 7152-7161 . |
| APA | Zheng, Huiming , Gao, Wei , Yu, Zhuozhen , Zhao, Tiesong , Li, Ge . ViewPCGC: View-Guided Learned Point Cloud Geometry Compression . (2024) : 7152-7161 . |
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Within the domain of multimodal communication, the compression of audio, image, and video information is well-established, but compressing haptic signals, including vibrotactile signals, remains challenging. Particularly with the enhancement of haptic signal sampling rate and degrees of freedom, there is a substantial increase in data volume. While existing algorithms have made progress in vibrotactile codecs, there remains significant room for improvement in compression ratios. We propose an innovative Nbeats Network-based Vibrotactile Codec (NNVC) that leverages the statistical characteristics of vibrotactile data. This advanced codec integrates the Nbeats network for precise vibrotactile prediction, residual quantization, efficient Run-Length Encoding, and Huffman coding. The algorithm not only captures the intricate details of vibrotactile signals but also ensures high-efficiency data compression. It exhibits robust overall performance in terms of Signal-to-Noise Ratio (SNR) and Peak Signal-to-Noise Ratio (PSNR), significantly surpassing the state-of-the-art.
Keyword ：
Codecs Codecs Databases Databases Decoding Decoding Encoding Encoding Haptic interfaces Haptic interfaces haptics haptics Huffman coding Huffman coding Long short term memory Long short term memory Multimodal communication Multimodal communication PSNR PSNR Quantization (signal) Quantization (signal) signal compression signal compression Training Training vibrotactile vibrotactile
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| GB/T 7714 | Xu, Yiwen , Chen, Dongfang , Fang, Ying et al. Efficient Vibrotactile Codec Based on Nbeats Network [J]. | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 : 2845-2849 . |
| MLA | Xu, Yiwen et al. "Efficient Vibrotactile Codec Based on Nbeats Network" . | IEEE SIGNAL PROCESSING LETTERS 31 (2024) : 2845-2849 . |
| APA | Xu, Yiwen , Chen, Dongfang , Fang, Ying , Lu, Yang , Zhao, Tiesong . Efficient Vibrotactile Codec Based on Nbeats Network . | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 , 2845-2849 . |
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Integrating haptic feedback with audio and video not only expands the perceptual dimensions of multimedia applications but also enhances user engagement and experience. However, higher signal sampling rates and multi-degree-freedom in haptic interaction increase data significantly. For low-latency and reliable transmission of haptic signal (i.e. tactile and kinesthetic signals), efficient haptic coding is crucial. Existing algorithms overlook haptic signal characteristics, leaving room for improvement. We analyze the statistical characteristics of kinesthetic signals in-depth. Based on the local linear characteristics of position and velocity signals, and the sparse distribution of force signal, we propose an improved kinesthetic coding algorithm by combining dead-zone coding with segmented linear prediction. Extensive experiments on the standard datasets of the IEEE P1918.1.1 Haptic Codecs Task Group demonstrate the superior performance compared to state-of-the-art methods, achieving a more than halved reduction in data transmission rates with high signal-to-noise ratios and structural similarity IEEE
Keyword ：
Data compression Data compression Encoding Encoding Force Force Haptic interfaces Haptic interfaces Haptics Haptics Kinesthetic coding Kinesthetic coding Linear regression Linear regression Mathematical models Mathematical models Prediction algorithms Prediction algorithms Regression algorithm Regression algorithm Signal processing algorithms Signal processing algorithms
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| GB/T 7714 | Xu, Y. , Huang, Q. , Zheng, Q. et al. Perception-Based Prediction for Efficient Kinesthetic Coding [J]. | IEEE Signal Processing Letters , 2024 , 31 : 1-5 . |
| MLA | Xu, Y. et al. "Perception-Based Prediction for Efficient Kinesthetic Coding" . | IEEE Signal Processing Letters 31 (2024) : 1-5 . |
| APA | Xu, Y. , Huang, Q. , Zheng, Q. , Fang, Y. , Zhao, T. . Perception-Based Prediction for Efficient Kinesthetic Coding . | IEEE Signal Processing Letters , 2024 , 31 , 1-5 . |
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Owing to the rapid development of emerging 360(degrees)panoramic imaging techniques, indoor 360(degrees)depth estimation has aroused extensive attention in the community. Due to the lack of available ground truth depth data, it is extremely urgent to model indoor 360(degrees)depth estimation in self-supervised mode. However, self-supervised 360 degrees depth estimation suffers from two major limitations. One is the distortion and network training problems caused by Equirectangular projection (ERP), and the other is that texture-less regions are quite difficult to back-propagate in self-supervised mode. Hence, to address the above issues, we introduce spherical view synthesis for learning self-supervised 360(degrees)depthestimation. Specifically, to alleviate the ERP-related problems, we first propose a dual-branch distortion-aware network to produce the coarse depth map, including a distortion-aware module and a hybrid projection fusion module. Subsequently, the coarse depth map is utilized for spherical view synthesis, in which a spherically weighted loss function for view reconstruction and depth smoothing is investigated to optimize the projection distribution problem of360(degrees)images. In addition, two structural regularities of indoor360(degrees)scenes are devised as two additional supervisory signals to efficiently optimize our self-supervised 360(degrees)depth estimation model, containing the principal-direction normal constraint and the co-planar depth constraint. The principal-direction normal constraint is designed to align the normal of the 360(degrees)imagewith the direction of the vanishing points. Meanwhile, we employ the co-planar depth constraint to fit the estimated depth of each pixel through its 3D plane. Finally, a depth map is obtained for the 360(degrees)image. Experimental results illustrate that our proposed method achieves superior performance than the current advanced depth estimation methods on four publicly available datasets
Keyword ：
360(degrees) image 360(degrees) image depth estimation depth estimation Distortion Distortion Estimation Estimation Feature extraction Feature extraction Image reconstruction Image reconstruction self-supervised learning self-supervised learning Self-supervised learning Self-supervised learning structural regularity structural regularity Task analysis Task analysis Training Training
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| GB/T 7714 | Wang, Xu , Kong, Weifeng , Zhang, Qiudan et al. Distortion-Aware Self-Supervised Indoor 360°Depth Estimation via Hybrid Projection Fusion and Structural Regularities [J]. | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 : 3998-4011 . |
| MLA | Wang, Xu et al. "Distortion-Aware Self-Supervised Indoor 360°Depth Estimation via Hybrid Projection Fusion and Structural Regularities" . | IEEE TRANSACTIONS ON MULTIMEDIA 26 (2024) : 3998-4011 . |
| APA | Wang, Xu , Kong, Weifeng , Zhang, Qiudan , Yang, You , Zhao, Tiesong , Jiang, Jianmin . Distortion-Aware Self-Supervised Indoor 360°Depth Estimation via Hybrid Projection Fusion and Structural Regularities . | IEEE TRANSACTIONS ON MULTIMEDIA , 2024 , 26 , 3998-4011 . |
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低照度条件下拍摄的照片往往存在亮度低、颜色失真、噪声高、细节退化等多重耦合问题，因此低照度图像增强是一个具有挑战性的任务. 现有基于深度学习的低照度图像增强方法通常聚焦于对亮度和色彩的提升，导致增强图像中仍然存在噪声等缺陷. 针对上述问题，本文提出了一种基于任务解耦的低照度图像增强方法，根据低照度图像增强任务对高层和低层特征的不同需求，将该任务解耦为亮度与色彩增强和细节重构两组任务，进而构建双分支低照度图像增强网络模型（Two-Branch Low-light Image Enhancement Network，TBLIEN）. 其中，亮度与色彩增强分支采用带全局特征的U-Net结构，提取深层语义信息改善亮度与色彩；细节重构分支采用保持原始分辨率的全卷积网络实现细节复原和噪声去除. 此外，在细节重构分支中，本文提出一种半双重注意力残差模块，能在保留上下文特征的同时通过空间和通道注意力强化特征，从而实现更精细的细节重构. 在合成和真实数据集上的广泛实验表明，本文模型的性能超越了当前先进的低照度图像增强方法，并具有更好的泛化能力，且可适用于水下图像增强等其他图像增强任务.
Keyword ：
任务解耦 任务解耦 低照度图像增强 低照度图像增强 双分支网络模型 双分支网络模型 对比学习 对比学习 残差网络 残差网络
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| GB/T 7714 | 牛玉贞 , 陈铭铭 , 李悦洲 et al. 基于任务解耦的低照度图像增强方法 [J]. | 电子学报 , 2024 . |
| MLA | 牛玉贞 et al. "基于任务解耦的低照度图像增强方法" . | 电子学报 (2024) . |
| APA | 牛玉贞 , 陈铭铭 , 李悦洲 , 赵铁松 . 基于任务解耦的低照度图像增强方法 . | 电子学报 , 2024 . |
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Abstract ：
由于网络环境的多变性，视频播放过程中容易出现卡顿、比特率波动等情况，严重影响了终端用户的体验质量. 为优化网络资源分配并提升用户观看体验，准确评估视频质量至关重要. 现有的视频质量评价方法主要针对短视频，普遍关注人眼视觉感知特性，较少考虑人类记忆特性对视觉信息的存储和表达能力，以及视觉感知和记忆特性之间的相互作用. 而用户观看长视频的时候，其质量评价需要动态评价，除了考虑感知要素外，还要引入记忆要素.为了更好地衡量长视频的质量评价，本文引入深度网络模型，深入探讨了视频感知和记忆特性对用户观看体验的影响，并基于两者特性提出长视频的动态质量评价模型. 首先，本文设计主观实验，探究在不同视频播放模式下，视觉感知特性和人类记忆特性对用户体验质量的影响，构建了基于用户感知和记忆的视频质量数据库（Video Quality Database with Perception and Memory，PAM-VQD）；其次，基于 PAM-VQD 数据库，采用深度学习的方法，结合视觉注意力机制，提取视频的深层感知特征，以精准评估感知对用户体验质量的影响；最后，将前端网络输出的感知质量分数、播放状态以及自卡顿间隔作为三个特征输入长短期记忆网络，以建立视觉感知和记忆特性之间的时间依赖关系. 实验结果表明，所提出的质量评估模型在不同视频播放模式下均能准确预测用户体验质量，且泛化性能良好.
Keyword ：
体验质量 体验质量 注意力机制 注意力机制 深度学习 深度学习 视觉感知特性 视觉感知特性 记忆效应 记忆效应
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| GB/T 7714 | 林丽群 , 暨书逸 , 何嘉晨 et al. 基于感知和记忆的视频动态质量评价 [J]. | 电子学报 , 2024 . |
| MLA | 林丽群 et al. "基于感知和记忆的视频动态质量评价" . | 电子学报 (2024) . |
| APA | 林丽群 , 暨书逸 , 何嘉晨 , 赵铁松 , 陈炜玲 , 郭宗明 . 基于感知和记忆的视频动态质量评价 . | 电子学报 , 2024 . |
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Photos captured under low-light conditions suffer from multiple coupling problems, i.e., low brightness, color distortion, heavy noise, and detail degradation, making low-light image enhancement a challenging task. Existing deep learning-based low-light image enhancement methods typically focus on improving the illumination and color while neglecting the noise in the enhanced image. To solve the above problems, this paper proposes a low-light image enhancement method based on task decoupling. According to the different requirements for high-level and low-level features, the low-light image enhancement task is decoupled into two subtasks: illumination and color enhancement and detail reconstruction. Based on the task decoupling, we propose a two-branch low-light image enhancement network (TBLIEN). The illumination and color enhancement branch is built as a U-Net structure with global feature extraction, which exploits deep semantic information for illumination and color improvement. The detail reconstruction branch uses a fully convolutional network that preserves the original resolution while performing detail restoration and noise removal. In addition, for the detail reconstruction branch, we design a half-dual attention residual module. Our module enhances features through spatial and channel attention mechanisms while preserving their context, allowing precise detail reconstruction. Extensive experiments on real and synthetic datasets show that our model outperforms other state-of-the-art methods, and has better generalization capability. Our method is also applicable to other image enhancement tasks, i.e., underwater image enhancement. © 2024 Chinese Institute of Electronics. All rights reserved.
Keyword ：
Color Color Deep learning Deep learning Image enhancement Image enhancement Semantics Semantics
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| GB/T 7714 | Niu, Yu-Zhen , Chen, Ming-Ming , Li, Yue-Zhou et al. Task Decoupling Guided Low-Light Image Enhancement [J]. | Acta Electronica Sinica , 2024 , 52 (1) : 34-45 . |
| MLA | Niu, Yu-Zhen et al. "Task Decoupling Guided Low-Light Image Enhancement" . | Acta Electronica Sinica 52 . 1 (2024) : 34-45 . |
| APA | Niu, Yu-Zhen , Chen, Ming-Ming , Li, Yue-Zhou , Zhao, Tie-Song . Task Decoupling Guided Low-Light Image Enhancement . | Acta Electronica Sinica , 2024 , 52 (1) , 34-45 . |
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Video compression leads to compression artifacts, among which Perceivable Encoding Artifacts (PEAs) degrade user perception. Most of existing state-of-the-art Video Compression Artifact Removal (VCAR) methods indiscriminately process all artifacts, thus leading to over-enhancement in non-PEA regions. Therefore, accurate detection and location of PEAs is crucial. In this paper, we propose the largest-ever Fine-grained PEA database (FPEA). First, we employ the popular video codecs, VVC and AVS3, as well as their common test settings, to generate four types of spatial PEAs (blurring, blocking, ringing and color bleeding) and two types of temporal PEAs (flickering and floating). Second, we design a labeling platform and recruit sufficient subjects to manually locate all the above types of PEAs. Third, we propose a voting mechanism and feature matching to synthesize all subjective labels to obtain the final PEA labels with fine-grained locations. Besides, we also provide Mean Opinion Score (MOS) values of all compressed video sequences. Experimental results show the effectiveness of FPEA database on both VCAR and compressed Video Quality Assessment (VQA). We envision that FPEA database will benefit the future development of VCAR, VQA and perception-aware video encoders. The FPEA database has been made publicly available. IEEE
Keyword ：
Perceivable encoding artifact Perceivable encoding artifact video compression video compression video compression artifact removal video compression artifact removal video quality assessment video quality assessment
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| GB/T 7714 | Lin, L. , Wang, M. , Yang, J. et al. Toward Efficient Video Compression Artifact Detection and Removal: A Benchmark Dataset [J]. | IEEE Transactions on Multimedia , 2024 , 26 : 1-12 . |
| MLA | Lin, L. et al. "Toward Efficient Video Compression Artifact Detection and Removal: A Benchmark Dataset" . | IEEE Transactions on Multimedia 26 (2024) : 1-12 . |
| APA | Lin, L. , Wang, M. , Yang, J. , Zhang, K. , Zhao, T. . Toward Efficient Video Compression Artifact Detection and Removal: A Benchmark Dataset . | IEEE Transactions on Multimedia , 2024 , 26 , 1-12 . |
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