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A robust tensor watermarking algorithm for diffusion-tensor images

Frontiers of Information technology & Electronic Engineering 2024年第3期25卷 384-397页

作者： Chengmeng LIU Zhi LI Guomei WANG Long ZHENG State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China

Watermarking algorithms that use convolution neural networks have exhibited good robustness in studies of deep learning ***,after embedding watermark signals by convolution,the feature fusion eficiency of convolution is relatively low;this can easily lead to distortion in the embedded *** distortion occurs in medical images,especially in diffusion tensor images(DTIs),the clinical value of the DTI is *** address this issue,a robust watermarking algorithm for DTIs implemented by fusing convolution with a Transformer is proposed to ensure the robustness of the watermark and the consistency of sampling distance,which enhances the quality of the reconstructed image of the watermarked DTIs after embedding the watermark *** the watermark-embedding network,Ti-weighted(Tlw)images are used as prior *** correlation between T1w images and the original DTI is proposed to calculate the most significant features from the T1w images by using the Transformer *** maximum of the correlation is used as the most significant feature weight to improve the quality of the reconstructed *** the watermark extraction network,the most significant watermark features from the watermarked DTI are adequately learned by the Transformer to robustly extract the watermark signals from the watermark *** results show that the average peak signal-to-noise ratio of the watermarked DTI reaches 50.47 dB,the diffusion characteristics such as mean diffusivity and fractional anisotropy remain unchanged,and the main axis deflection angleαAc is close to *** proposed algorithm can effectively protect the copyright of the DTI and barely affects the clinical diagnosis.

关键词： Robust watermarking algorithm Transformer Image reconstruction Diffusion tensor images Soft attention Hard attention Tl-weighted images

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MTSC-Net:A Semi-Supervised Counting Network for Estimating the Number of Slash pine New Shoots

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植物表型组学（英文） 2024年第4期6卷 953-966页

作者： Zhaoxu Zhang Yanjie Li Yue Cao Yu Wang Xuchao Guo Xia Hao College of Information Science and Engineering Shandong Agricultural UniversityTaian271018Shandong ProvinceChina Research Institute of Subtropical Forestry Chinese Academy of ForestryHangzhou 311400Zhejiang ProvinceChina School of Big Data Taishan College of Science and TechnologyTaian271034Shandong ProvinceChina

The new shoot density of slash pine serves as a vital indicator for assessing its growth and photosynthetic capacity,while the number of new shoots offers an intuitive reflection of this *** deep learning methods becoming increasingly popular,automated counting of new shoots has greatly improved in recent years but is still limited by tedious and expensive data collection and *** resolve these issues,this paper proposes a semi-supervised counting network(MTSC-Net)for estimating the number of slash pine new ***,based on the mean-teacher framework,we introduce the improved VGG19 to extract multiscale new shoot ***,to connect local new shoot feature information with global channel features,attention feature fusion module is introduced to achieve effective feature ***,the new shoot density map and density probability distribution are processed in a fine-grained manner through multiscale dilated convolution of the regression head and classification *** addition,a masked image modeling strategy is introduced to encourage the contextual understanding of global new shoot features and improve the counting *** experimental results show that MTSC-Net outperforms other semi-supervised counting models with labeled percentages ranging from 5％to 50％.When the labeled percentage is 5％,the mean absolute error and root mean square error are 17.71 and 25.49,*** findings demonstrate that our work can be used as an efficient semi-supervised counting method to provide automated support for tree breeding and genetic utilization.

关键词： network counting estimating mtsc-net number pine semi-supervised shoots slash

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Resolution-Adaptive Representation Learning via Eliminating Abnormal Features for Cross-Resolution Person Re-Identification 14

Resolution-Adaptive Representation Learning via Eliminating ...

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14th International Conference on Information technology in Medicine and Education, ITME 2024

作者： Lu, Xing Lai, Fengshan Cao, Zhixiang Xia, Daoxun Guizhou Normal University School of Big Data and Computer Science Guiyang China Guizhou Normal University Key Laboratory of Information and Computing Science Guizhou Province Guiyang China Guizhou Normal University Engineering Laboratory for Applied Technology of Big Data in Education Guiyang China

ISBN: (纸本)9798350356670

Cross-resolution person re-identification(CR-ReID) seeks to overcome the challenge of retrieving and matching specific person images across cameras with varying resolutions. Numerous existing studies utilize established CNNs and ViTs models to resize captured low-resolution (LR) images and align them with high-resolution (HR) image features. However, these methods overlook the potential feature connections between LR and HR images. Consequently, CNNs or ViTs tend to highlight outliers rather than the intended characteristics in LR image attention maps. In this work, we propose the abnormal feature elimination and reconfiguration transformer (ART) network, a novel network architecture, for robust cross-resolution person re-identification tasks. Our proposed method uses a resolution feature discriminator to learn resolution invariant features and outputs feature matrices of images with different resolutions. We utilize output feature matrices to model LR and HR image interactions through cross-resolution landmark agent attention. We aim to mitigate abnormal image features and prioritize the attention on the target person by learning representations from input images of various resolutions. Extensive evaluations on three real-world datasets confirm the excellent results of our approach. © 2024 IEEE.

关键词： Contrastive Learning

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Seeing Through the Lies: A Vision Transformer-Based Solution 3rd

Seeing Through the Lies: A Vision Transformer-Based Solution

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3rd International Conference on Artificial-Business Analytics, Quantum and Machine Learning: Trends, Perspectives, and Prospects, Com-IT-Con 2023

作者： Rasool, Aale Katarya, Rahul Big Data Analytics and Web Intelligence Laboratory Department of Computer Science Delhi Technological University New Delhi India

ISBN: (纸本)9789819725076

Deepfake technology has become increasingly sophisticated and poses a growing threat to society, as it can be used to create convincing fake videos for malicious purposes. Therefore, detecting deepfakes has become crucial to ensure the authenticity of visual content. This paper proposes a novel deepfake detection approach that utilizes InceptionResNetV2 for feature extraction and Vision Transformer (ViT) with Nyström Attention mechanism for classification. Our model achieves high accuracy, AUC, precision, and recall on the CelebDFv2 dataset and outperforms state-of-the-art techniques like EfficientNet, XceptionNet, and ResNet. We also perform evaluations on the CelebDFv1 and DFDC datasets. Furthermore, we conduct a comprehensive literature survey to highlight the existing research in deepfake detection. We provide a roadmap for future work in this field, highlighting potential research directions that can further improve the effectiveness and applicability of deepfake detection models. The proposed model shows great potential for real-world applications, and the suggested future work can help to address some of the remaining challenges in this area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Face recognition

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Formal Specification and Verification of API Security Based on Design by Contract 6

Formal Specification and Verification of API Security Based ...

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6th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2024

作者： Tang, Qing Zhou, Zetong Bu, Zhaoxiong Ma, Li Zhong, Yong Guangdong Chankong Big Data Technology Co. Ltd. Foshan China School of Management Guangdong University of Technology Foshan China School of Electronic and Information Engineering Foshan University Foshan China School of Electronic and Information Engineering Foshan University Zhixin Urban Big Data Technology Research Institute Foshan China School of Electronic and Information Engineering Foshan University Zhixin Urban Big Data Technology Research Institute Foshan Data Science Association Foshan China

ISBN: (纸本)9798350374315

The exploitation and hijacking of API vulnerabilities are becoming increasingly prominent issues in software supply chain security. This paper proposes an Object-Z based formal verification method for APIs in the supply chain, aiming to address the security risks associated with API applications. By incorporating the concept of design by contract and using Object-Z for formal modeling, a dynamic defense against API threats is implemented. Finally, based on a classification analysis of API threats in the supply chain, this paper presents a ase study through modeling and application analysis. © 2024 IEEE.

关键词： Formal specification

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IGED:Towards Intelligent DDoS Detection Model Using Improved Generalized Entropy and DNN

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Computers, Materials & Continua 2024年第8期80卷 1851-1866页

作者： Yanhua Liu Yuting Han HuiChen Baokang Zhao XiaofengWang Ximeng Liu College of Computer and Data Science Fuzhou UniversityFuzhou350108China Engineering Research Center of Big Data Intelligence Ministry of EducationFuzhou350108China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350108China College of Computer National University of Defense TechnologyChangsha410073China

As the scale of the networks continually expands,the detection of distributed denial of service(DDoS)attacks has become increasingly *** propose an intelligent detection model named IGED by using improved generalized entropy and deep neural network(DNN).The initial detection is based on improved generalized entropy to filter out as much normal traffic as possible,thereby reducing data *** the fine detection is based on DNN to perform precise DDoS detection on the filtered suspicious traffic,enhancing the neural network’s generalization *** results show that the proposed method can efficiently distinguish normal traffic from DDoS *** with the benchmark methods,our method reaches 99.9%on low-rate DDoS(LDDoS),flooded DDoS and CICDDoS2019 datasets in terms of both accuracy and efficiency in identifying attack flows while reducing the time by 17%,31%and 8%.

关键词： DDoS real-time improved generalized entropy DNN

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Study on the improvement of medical service quality in Beijing's Tianqiao Community Health Service Centre

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Engineering Management in Production and Services 2022年第4期14卷 61-76页

作者： He, Qiong Li, Qixiao Chen, Jindong Beijing Information Science and Technology University China Laboratory of Big Data Decision Making for Green Development China

Within the context of the COVID-19 pandemic, community-level medical institutions as health service centres have been gaining importance in the medical reform expansion. As prior research has not fully addressed how to index and evaluate the quality of medical service, this article proposes a framework based on the service quality gap theory and the three-faceted "structure-process-outcome"quality evaluation theory. The study took the medical services at Beijing's Tianqiao Community Health Service Centre as an example to construct an index system for medical service quality evaluations. data was collected from 211 people, and SPSS software was used for data processing and analysis. Due to the COVID-19 pandemic, patients without serious diseases tend to choose community hospitals to reduce their infection risk. As a result, they have growing requirements for clinics to have more departments and specialists. The studied community health service centre has encountered difficulties connected to low patient expectations, a poor medical environment, outdated hardware and equipment, and a low level of medical services. Some suggestions have been made to add specialised departments and consider the convenience of medical treatment for the elderly. © 2022 Qiong He et al., published by Sciendo.

关键词： COVID-19

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A Length-Adaptive Non-Dominated Sorting Genetic Algorithm for Bi-Objective High-Dimensional Feature Selection

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1834-1844页

作者： Yanlu Gong Junhai Zhou Quanwang Wu MengChu Zhou Junhao Wen College of Computer Science Chongqing UniversityChongqing 400044China IEEE Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA College of Big Data and Software Engineering Chongqing UniversityChongqing 400044China

As a crucial data preprocessing method in data mining,feature selection(FS)can be regarded as a bi-objective optimization problem that aims to maximize classification accuracy and minimize the number of selected *** computing(EC)is promising for FS owing to its powerful search ***,in traditional EC-based methods,feature subsets are represented via a length-fixed individual *** is ineffective for high-dimensional data,because it results in a huge search space and prohibitive training *** work proposes a length-adaptive non-dominated sorting genetic algorithm(LA-NSGA)with a length-variable individual encoding and a length-adaptive evolution mechanism for bi-objective highdimensional *** LA-NSGA,an initialization method based on correlation and redundancy is devised to initialize individuals of diverse lengths,and a Pareto dominance-based length change operator is introduced to guide individuals to explore in promising search space ***,a dominance-based local search method is employed for further *** experimental results based on 12 high-dimensional gene datasets show that the Pareto front of feature subsets produced by LA-NSGA is superior to those of existing algorithms.

关键词： Bi-objective optimization feature selection(FS) genetic algorithm high-dimensional data length-adaptive

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Fine extraction of multi-crop planting area based on deep learning with Sentinel- 2 time-series data

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Environmental science and Pollution Research 2025年第19期32卷 11931-11949页

作者： Jiang, Jingmin Zhang, Jiahua Wang, Xue Zhang, Shichao Kong, Delong Wang, Xiaopeng Ali, Shawkat Ullah, Hidayat Space Information and Big Earth Data Research Center School of Computer Science and Technology Qingdao University Qingdao266071 China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areas based on high-resolution time-series data and deep learning still faces challenges. Therefore, this study aims to provide an effective model for multi-crop classification using high-resolution remote sensing time-series data. We designed two deep learning models based on convolutional neural network-long short-term memory (CNN-LSTM) and bidirectional long short-term memory (Bi-LSTM). The monthly synthetic time series of the normalized difference vegetation index (NDVI) from Sentinel-2 data will be used as input features to extract the multi-crop planting area in Shandong province’s northwestern, southwestern, and eastern regions. The results showed that deep learning models achieved higher accuracy compared to the random forest (RF) and extreme gradient boosting (XGBoost) models, with CNN-LSTM achieving the highest overall accuracy of 96.48%. At the county level, the coefficients of determination (R2) for the CNN-LSTM model were 0.91 for wheat, 0.88 for maize, and 0.73 for spring cotton. This study demonstrates that the CNN-LSTM model combined with monthly synthetic time-series NDVI provides a feasible approach for accurately mapping high-resolution multi-crop planting areas and also contributes significantly to decision support and resource management in agricultural production. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Deep learning High-resolution Monthly synthetic time-series NDVI Multi-crop classification Remote sensing Sentinel- 2

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Primordial magnetic non-Gaussianity with generic vacua and detection prospects in CMB spectral distortions

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Physical Review D 2024年第4期110卷 043511-043511页

作者： Arko Bhaumik Supratik Pal Physics and Applied Mathematics Unit Technology Innovation Hub on Data Science Big Data Analytics and Data Curation

Assuming a slow-roll inflationary model where conformal invariance of the Maxwell action is broken via a nonminimal kinetic coupling term, we investigate the non-Gaussian three-point cross-correlation function between the primordial curvature perturbation and the primordial magnetic field, under a fairly general choice of initial vacua for both the scalar and the gauge field sectors. Among the possible triangular configurations of the resulting cross-bispectrum, we find that the squeezed limit leads to local-type non-Gaussianity allowing a product form decomposition in terms of the scalar and magnetic power spectra, which is a generic result independent of any specific choice of the initial states. We subsequently explore its detection prospects in the cosmic microwave background (CMB) via correlations between prerecombination μ-type spectral distortions and temperature anisotropies, sourced by such a primordial cross-correlation. Our analysis with several proposed next-generation CMB missions forecasts a low value of the signal-to-noise ratio (SNR) for the μT spectrum if both the vacua are assumed to be pure Bunch-Davies. On the contrary, the SNR may be enhanced significantly for non-Bunch-Davies initial states for the magnetic sector within allowed bounds from current CMB data.

关键词： Cosmic microwave background Inflation Primordial magnetic fields

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