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A Multi-Layer Cyclic Predictive Reversible Hiding Scheme for Data Blind Extraction of Compressed Domain

Jisuanji Xuebao/Chinese Journal of Computers 2025年第4期48卷 910-926页

作者： Wen, Wen-Ying Yang, Yu-Heng Luo, Xin-Yu Zhang, Yu-Shu Fang, Yu-Ming School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang330013 China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Nanchang330013 China

Reversible data hiding in compressed sensing (CS) domains has been extensively studied in recent years. The proposal and development of reversible data hiding schemes in this domain have made it possible to hide large-capacity data in CS signals. However, existing reversible data hiding schemes in the CS domain face several challenges and limitations while realizing high-capacity data hiding. First, existing schemes usually lack directly usable measurements, which prevents the secret-carrying measurements from being directly used to reconstruct the original image, and thus can be easily detected during the transmission of the secret information. Second, some schemes generate auxiliary information when hiding the secret information, and these auxiliary information require an additional secure channel for transmission to ensure the correct extraction of the secret information, thus these schemes are unable to realize the blind extraction of the secret information. To address these problems, this paper proposes a new scheme, i. e., multi-layer cyclic predictive reversible hiding scheme for data blind-extraction of compressed domain. This new scheme utilizes the asymptotic recovery property of CS and the prediction error expansion technique to hide and extract data through multi-layer cyclic prediction. Specifically, this scheme performs cyclic prediction of CS measurements by CS multilayer cyclic prediction embedding technique and generates sharp histograms of prediction errors, which provide a large-capacity space for secret data embedding. Then, the additional information is embedded in the prediction error and then superimposed on the measurements by employing the histogram shifting method, and finally the auxiliary information is hidden in the lowest significant bit of the measurements in the specified region. In this way, blind extraction of information can also be realized when extracting information, and the lossless restoration of measurement values can be guar

关键词： Data encapsulation

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Learning discriminative features via deep metric learning for video-based person re-identification

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Expert Systems with Applications 2025年 286卷

作者： Wang, Jiahe Gao, Xizhan Niu, Sijie Zhao, Hui Feng, Guang Lin, Jiaxin School of Information Science and Engineering Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing University of Jinan Jinan250022 China

Video-based person re-identification (Video-Re-ID) is a crucial application in practical scenarios and has gradually emerged as one of the most popular research topics in the field of computer vision. Although many efforts have been made, it still remains a challenge due to substantial variations among person videos and even within each video. For Video-Re-ID, we propose a synchronous intrA-video and intEr-video distance metric learning approach based on temporal ViT architecture, termed as TAE-ViT. The TAE-ViT model, in particular consists of a View Guide Patch Embedding (VGPE) module, a Spatial-Temporal Attention (STA) module and an Intra and Inter-video Distance Metric Learning (IIDML) module. The VGPE module is used to utilize diverse view information and extract discriminative features with perspective invariance. The STA module alternately learns the spatial and temporal information by using the spatial and temporal multi-head attention operation, respectively. The IIDML module simultaneously captures intra-video and inter-video distance metrics from the training videos. Specifically, the intra-video distance metric aims to compact each video representation, while the inter-video distance metric ensures that truly matched videos are closer in distance compared to incorrectly matched ones. Experimental results show that our method achieves the best mAP (86.7 %, 96.3 %, 97.3 %) on three public Video-ReID datasets and achieves the best Rank-1 (93.3 %, 96.6 %) on two datasets, reducing the error of state-of-the-art methods by 1.48-45.16 % on mAP and by 2.86 % on Rank-1. Its robust performance highlights its potential for real-world applications like intelligent surveillance and public safety. Our code will be available at https://***/JingShenZhuangTaiYiChang/TAE-ViT. © 2025 Elsevier Ltd

关键词： Deep learning

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Multi-scale CNN-Swin transformer network with boundary supervision for multiclass biomarker segmentation in retinal OCT images

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Biocybernetics and Biomedical Engineering 2025年第3期45卷 340-356页

作者： Fan, Zhanpeng Liu, Xiaoming Zhang, Ying Zhang, Jia School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430070 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430070 China Wuhan Aier Eye Hospital of Wuhan University Wuhan 430064 China

Retinal biomarker morphology is closely associated with a variety of chronic ophthalmic diseases, in which biomarker localization and segmentation in optical coherence tomography (OCT) play a key role in the diagnosis of retina-related diseases. Although great progress has been made in deep learning based OCT biomarker segmentation, several challenges still exist. Due to issues such as image noise or class imbalance, retinal biomarkers affect the model's recognition of other biomarkers. Moreover, small biomarkers are prone to lose accuracy during downsampling. And most existing methods rely on convolutional neural networks, which make it challenging to obtain the global context due to locality of convolution. Benefiting from the Swin Transformer with powerful modeling capabilities, we propose MSCS-Net (Multi-scale CNN-Swin Network), a network for OCT biomarker segmentation, which effectively combines CNN and Swin Transformer and integrates them in parallel into a dual-encoder structure. Specifically, an edge detection path is added alongside to enhance the localization of biomarkers at the edges. For the Swin Transformer branch, considering the irregular distribution of most OCT biomarkers, a new windowing partition is performed in the Swin Transformer to capture the features more efficiently. Meanwhile, we design a Feature Dimensionality Reduction Module to extensively collect the information of small-scale biomarkers. To effectively integrate information from two scales, we design a Transformer Cross Fusion Module to finely fuse the global and local feature information from the two-branch encoders. We validate the proposed approach on local and public datasets, and the experimental results demonstrate the effectiveness of the proposed framework. © 2025 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences

关键词： Convolutional Neural Network Multi-class Segmentation Optical Coherence Tomography (OCT) Retinal Biomarker Segmentation

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Multi-scale Traffic Camera Image Detection Network Based on Improved YOLOv8 21st

Multi-scale Traffic Camera Image Detection Network Based on...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Peng, Zhihao Qi, Xinyuan Wu, Sheng Shang, Jianga Yang, Linquan China University of Geosciences Wuhan430074 China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan430074 China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences Wuhan430074 China

ISBN: (纸本)9789819601240

With the extensive deployment of traffic cameras across cities, utilizing these devices for vehicle detection has become essential for efficient traffic management. However, variations in vehicle sizes and distances between vehicles and cameras lead to inconsistent scaling of vehicles in the images, which presents a challenge for feature extraction. Furthermore, conventional object detection algorithms fail to fully utilize the rich feature information across multiple layers. To solve these issues, we develop a novel and advanced multi-scale traffic YOLO (MT-YOLO), based on the YOLOv8 model. Firstly, we propose the spatial multi-scale (SMS) attention mechanism to improve the extraction capability of spatial multi-scale vehicle features and aggregate these features for dynamic spatial modulation. Secondly, we develop the multi-scale dilation transposition CARAFE (MDT-CARAFE) upsampling method, which utilizes dilated convolutions to enhance the receptive field at different scales during the upsampling process. Lastly, we augment the network with a detection head and replace the original detection head with the adaptively spatial feature fusion(ASFF) detection head, which dynamically selects and fuses multi-layer feature maps, enabling the network to better capture multi-scale information. Our experiments demonstrate that the MT-YOLO model greatly improves results on the traffic camera detection dataset., achieving an mAP@0.5 of 80.9%, with a precision of 92% and recall of 93%. These results surpass the performance of leading detection algorithms. In addition, the model takes only 12.5 milliseconds to complete an inference, which makes it highly deployable and valuable for real-world applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image enhancement

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Pencil beam based on the computational holography method 2

Pencil beam based on the computational holography method

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2nd International Academic Conference on Optics and Photonics, IACOP 2024

作者： Luo, Tianlei Lv, Anzhi Liu, Minghao Dong, Jiaqing Liu, Guolin Li, Zilong Song, Xianlin School of Information Engineering Nanchang University Nanchang330031 China School of Advanced Manufacture Nanchang University Nanchang330031 China Jiangxi Provincial Key Laboratory of Advanced Signal Processing and Intelligent Communications Nanchang University Nanchang330031 China

ISBN: (数字)9781510688827

ISBN: (纸本)9781510688810

This paper presents a novel pencil beam generation method based on computational holography. The method generates intensity maps through computer simulation, obtains a computed hologram using a computational holographic method, and simulates near-field diffraction using computer simulation to simulate the role of a spatial light modulator (SLM) in a real holographic system. The theoretical effects of the computed hologram are simulated using computer simulation, and finally, a pencil-shaped beam with a focus position that can be flexibly adjusted at all depths is generated. Through experimental simulations, the feasibility of adjusting the focus position of the beam within a certain depth range is verified, showing its potential application value in fluorescence excitation and biomedical fields. © 2025 SPIE.

关键词： Holograms

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Research on Multi-Agent Question Answering Based on Large Language Models 25

Research on Multi-Agent Question Answering Based on Large La...

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Proceedings of the 2025 3rd International Conference on Communication Networks and Machine Learning

作者： Xinpeng Lei Fucheng Wan Key Laboratory of Linguistic and Cultural Computing Ministry of Education Northwest Minzu University Lanzhou Gansu China Key Laboratory of Minzu Languages and Cultures Intelligent Information Processing Northwest Minzu University Lanzhou Gansu China

ISBN: (纸本)9798400713231

The continuous development of large language models has led to tremendous performance improvement and has made them increasingly more important. More and more researchers are starting to explore the use of these large language models in developing artificial intelligence agents. The goal of this research is to develop a question-answering system based on a multi-agent setup with the open-source large language model Qwen2.5. In this multi-agent setup, the two agents include a student and a teacher where the student solves issues with guidance from the teacher. We evaluate accuracy difference between a single-agent and a multi-agent setup with the Chinese evaluation data, C-Eval. This is a departure from other approaches like fine-tuning large models or utilizing methods like chain of thought (CoT) and n-shot learning that are tailor-made to improve model performance. Rather, it is a more general approach aimed at further testing the abilities of large language models and thereby improve their logical reasoning capabilities. The experimental results demonstrate a significant improvement in accuracy in multi-agent systems compared to single-agent setup. In particular, improvement in accuracy over various subjects is around 2.5%. In addition, it has been found that improvement is directly proportional to model parameter size; for example, a model with a parameter size of 14 billion has a 22.18% improvement in accuracy for hard cases and reflects a significant improvement in performance.

关键词： Multi-agent

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Nonlinear Interference-Aware Routing,Wavelength and Power Allocation in C+L+S Multi-Band Optical Networks

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China Communications 2025年第4期22卷 129-142页

作者： Zhang Xu Xie Wang Feng Chuan Zeng Hankun Zhou Shanshan Zhang Fan Gong Xiaoxue School of Communications and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing 400065China

Multi-band optical networks are a potential technology for increasing network ***,the strong interference and non-uniformity between wavelengths in multi-band optical networks have become a bottleneck restricting the transmission capacity of multi-band optical *** overcome these challenges,it is particularly important to implement optical power optimization targeting wavelength ***,based on the generalized Gaussian noise model,we first formulate an optimization model for the problems of routing,modulation format,wavelength,and power allocation in C+L+S multi-band optical *** objective function is to maximize the average link capacity of the network while ensuring that the Optical Signal-to-Noise(OSNR)threshold of the service request is not ***,we propose a NonLinear Interferenceaware(NLI-aware)routing,modulation format,wavelength,and power allocation ***,we conduct simulations under different test *** simulation results indicate that our algorithm can effectively reduce the blocking probability by 23.5%and improve the average link capacity by 3.78%in C+L+S multi-band optical networks.

关键词： multiband optical communications multiband optical networks power allocation wavelength assignment

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TCMS: A Multi-Sequence Log Parsing Method Based on Token Conversion

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 3028-3045页

作者： Wei, Mingkuan Wen, Jigang He, Shiming Xie, Kun Liang, Wei Xie, Gaogang Li, Kenli Zhu, Ziyu Hunan University Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" College of Computer Science and Electronics Engineering Changsha410012 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Changsha University of Science and Technology School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China Beijing100045 China Columbia University Electrical Engineering New YorkNY10027 United States

Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from low parsing accuracy and slow efficiency due to the ignorance of variable characteristics and the use of costly pairwise comparison methods. In this paper, we propose a TCMS framework to parse logs, consisting of two main technologies. First, by studying 16 public log datasets, we find that most log variable tokens are structured variable tokens. Based on this discovery, we propose a token conversion algorithm to improve parsing accuracy. This algorithm converts the changed parts in structured variable tokens into wildcards (‘’), preventing these tokens from being directly identified as constant tokens. Second, to improve efficiency, we propose the LogMLCS algorithm, which intelligently constructs a graph to facilitate the extraction of common parts from multiple log messages at once, instead of using pairwise comparisons. Comprehensive experiments conducted on 16 log datasets reveal that our TCMS outperforms seven other parsing methods, achieving the highest parsing accuracy at the fastest speed. Furthermore, experimental results from running a log anomaly detection algorithm in conjunction with different log parsing methods demonstrate that TCMS significantly boosts detection accuracy. For instance, on the OpenStack dataset, our TCMS-facilitated log anomaly detection algorithm achieves a perfect F1-score, precision, and recall of 100% each, surpassing the best peer method by 32.2, 0.8, and 19.5 percentage points, respectively. © 2004-2012 IEEE.

关键词： Accuracy Anomaly Detection Clustering Algorithms Peer To Peer computing Electronic Mail Source Coding Particle Separators Codes Tokenization Itemsets Log Parsing Log Analysis Token Conversion Log MLCS Log Parsing Pairwise Comparisons Low Accuracy Public Datasets Anomaly Detection Common Part Multiple Messages Long Short Term Memory Point Source Target Domain Logarithm Of The Number Captive Animals Multi Objective Optimization Problem Regular Expressions Biological Sequences Rule Based Methods Point In The Graph Kinds Of Datasets Template Generation Number Of Templates Similar Template Parse Tree Syntax Errors Harmonic Average Of Precision Log Length Earliest Methods Common Subsequence Precision And Recall Leaf Node Part Of The Message

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A gaze prediction model based on Vision Transformer and VGG-16

A gaze prediction model based on Vision Transformer and VGG-...

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2025 2nd International Conference on Algorithms, Software Engineering and Network Security (ASENS)

作者： Yidong Hu Li Tong Ying Zeng Yuanlong Gao Bin Yan Zhongrui Li Henan Key Laboratory of Imaging and Intelligent Processing PLA Cyberspace Force Information Engineering University Zhengzhou China

ISBN: (数字)9798331520298

ISBN: (纸本)9798331520304

Currently, most saliency prediction models are stimulus-driven, and the regions they highlight are the salient areas from the machine vision. However, intelligent agents represented by humans can flexibly adjust their visual focus according to task requirements and only pay attention to task-related targets. Eye gaze information, as a concrete manifestation of human visual focus under specific task-driven conditions, can effectively improve the performance of models in corresponding tasks. In this paper, we integrate gaze information and propose a gaze prediction model based on Vision Transformer and VGG-16. This model is designed to predict the human gaze regions in the cross-view matching task from street-view images to aerial-view images. This model uses Vision Transformer to extract the low-level visual features of images and VGG-16 to extract the high-level semantic features of images respectively. It adopts a multi-level feature fusion strategy to strengthen feature representation. In addition, through gaze metric guidance and residual connections, task-related information is effectively introduced to optimize the model performance. Experimental results show that this model achieves competitive results on both self-constructed datasets and public datasets.

关键词： Visualization Computer vision Semantics Software algorithms Predictive models Network security Feature extraction Transformers Prediction algorithms Software engineering

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keypoints and Action Units Jointly Drive Talking Head Generation for Video Conferencing

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Shi, Wuzhen Xue, Zibang Wen, Yang Shenzhen University Shenzhen Key Laboratory of Digital Creative Technology Guangdong Province Engineering Laboratory for Digital Creative Technology Guangdong Provincial Key Laboratory of Intelligent Information Processing College of Electronics and Information Engineering Shenzhen518060 China

This paper introduces a high-quality talking head generation method that is jointly driven by keypoints and action units, aiming to strike a balance between low-bandwidth transmission and high-quality generation in video conference scenarios. Existing methods for talking head generation often face limitations: they either require an excessive amount of driving information or struggle with accuracy and quality when adapted to low-bandwidth conditions. To address this, we decompose the talking head generation task into two components: a driving task, focused on information-limited control, and an enhancement task, aimed at achieving high-quality, high-definition output. Our proposed method innovatively incorporates the joint driving of keypoints and action units, improving the accuracy of pose and expression generation while remaining suitable for low-bandwidth environments. Furthermore, we implement a multistep video quality enhancement process, targeting both the entire frame and key regions, while incorporating temporal consistency constraints. By leveraging attention mechanisms, we enhance the realism of the challenging-to-generate mouth regions and mitigate background jitter through background fusion. Finally, a prior-driven super-resolution network is employed to achieve high-quality display. Extensive experiments demonstrate that our method effectively supports low-resolution recording, low-bandwidth transmission, and high-definition display. © 1991-2012 IEEE.

关键词： Bandwidth

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