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A Generative Image Steganography Based on Disentangled Attribute Feature Transformation and Invertible Mapping Rule

computers, Materials & Continua 2025年第4期83卷 1149-1171页

作者： Xiang Zhang Shenyan Han Wenbin Huang Daoyong Fu School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China Engineering Research Center of Digital Forensics Nanjing University of Information Science and TechnologyMinistry of EducationNanjing210044China

Generative image steganography is a technique that directly generates stego images from secret *** traditional methods,it theoretically resists steganalysis because there is no cover ***,the existing generative image steganography methods generally have good steganography performance,but there is still potential room for enhancing both the quality of stego images and the accuracy of secret information ***,this paper proposes a generative image steganography algorithm based on attribute feature transformation and invertible mapping ***,the reference image is disentangled by a content and an attribute encoder to obtain content features and attribute features,***,a mean mapping rule is introduced to map the binary secret information into a noise vector,conforming to the distribution of attribute *** noise vector is input into the generator to produce the attribute transformed stego image with the content feature of the reference ***,we design an adversarial loss,a reconstruction loss,and an image diversity loss to train the proposed *** results demonstrate that the stego images generated by the proposed method are of high quality,with an average extraction accuracy of 99.4%for the hidden ***,since the stego image has a uniform distribution similar to the attribute-transformed image without secret information,it effectively resists both subjective and objective steganalysis.

关键词： Image information hiding generative information hiding disentangled attribute feature transformation invertible mapping rule steganalysis resistance

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CBIR: a novel identification approach for college students in need based on consumer behavior psychology theory

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Neural Computing and Applications 2025年第6期37卷 4663-4677页

作者： Liu, Xinze Liu, Shixi Hu, Xiaojing Zhang, Yudong Fang, Xianwen School of Computer and Information Engineering Chuzhou University Chuzhou239000 China School of Computer Science and Engineering Southeast University Nanjing210096 China School of Mathematics and Big Data Anhui University of Science and Technology Huainan232001 China

The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying students in need rely on manual operations that include manually extracting consumption behavior information, statistical consumption characteristics and principal component analysis. However, this issue may lead to low prediction accuracy and inefficiency in identifying students in need. We design a three-stage framework to accurately identify college students in need from the perspective of consumer behavior psychology. The consumption behavior information is first obtained from the student consumption records using the consumption behavior clustering approach. The consumption behavior matrix is then built by extracting consumption and spatiotemporal information in different periods. Finally, a novel consumption behavior identification ResNeSt (CBIR) model is proposed to identify college students in need accurately. The experimental results on real datasets show that the CBIR model has higher prediction accuracy than the baseline models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Social psychology

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Domain generalization with semi-supervised learning for people-centric activity recognition

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science China(information sciences) 2025年第1期68卷 171-188页

作者： Jing LIU Wei ZHU Di LI Xing HU Liang SONG Academy for Engineering & Technology Fudan University Shanghai East-bund Research Institute on Networking Systems of AI School of Optoelectronic Information and Computer Engineering University of Shanghai for Science & Technology

People-centric activity recognition is one of the most critical technologies in a wide range of real-world applications,including intelligent transportation systems, healthcare services, and brain-computer interfaces. Large-scale data collection and annotation make the application of machine learning algorithms prohibitively expensive when adapting to new tasks. One way of circumventing this limitation is to train the model in a semi-supervised learning manner that utilizes a percentage of unlabeled data to reduce the labeling burden in prediction tasks. Despite their appeal, these models often assume that labeled and unlabeled data come from similar distributions, which leads to the domain shift problem caused by the presence of distribution gaps. To address these limitations, we propose herein a novel method for people-centric activity recognition,called domain generalization with semi-supervised learning(DGSSL), that effectively enhances the representation learning and domain alignment capabilities of a model. We first design a new autoregressive discriminator for adversarial training between unlabeled and labeled source domains, extracting domain-specific features to reduce the distribution gaps. Second, we introduce two reconstruction tasks to capture the task-specific features to avoid losing information related to representation learning while maintaining task-specific consistency. Finally, benefiting from the collaborative optimization of these two tasks, the model can accurately predict both the domain and category labels of the source domains for the classification task. We conduct extensive experiments on three real-world sensing datasets. The experimental results show that DGSSL surpasses the three state-of-the-art methods with better performance and generalization.

关键词： activity recognition deep learning domain generalization semi-supervised learning adversarial training

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OCRBench: on the hidden mystery of OCR in large multimodal models

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science China(information sciences) 2024年第12期67卷 23-35页

作者： Yuliang LIU Zhang LI Mingxin HUANG Biao YANG Wenwen YU Chunyuan LI Xu-Cheng YIN Cheng-Lin LIU Lianwen JIN Xiang BAI School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Electronic and Information Engineering South China University of Technology Microsoft Research School of Computer & Communication Engineering University of Science and Technology Beijing Institute of Automation Chinese Academy of Sciences School of Software Engineering Huazhong University of Science and Technology

Large models have recently played a dominant role in natural language processing and multimodal vision-language learning. However, their effectiveness in text-related visual tasks remains relatively unexplored. In this paper, we conducted a comprehensive evaluation of large multimodal models, such as GPT4V and Gemini, in various text-related visual tasks including text recognition, scene text-centric visual question answering(VQA), document-oriented VQA, key information extraction(KIE), and handwritten mathematical expression recognition(HMER). To facilitate the assessment of optical character recognition(OCR) capabilities in large multimodal models, we propose OCRBench, a comprehensive evaluation benchmark. OCRBench contains 29 datasets, making it the most comprehensive OCR evaluation benchmark available. Furthermore, our study reveals both the strengths and weaknesses of these models, particularly in handling multilingual text, handwritten text, non-semantic text, and mathematical expression *** importantly, the baseline results presented in this study could provide a foundational framework for the conception and assessment of innovative strategies targeted at enhancing zero-shot multimodal *** evaluation pipeline and benchmark are available at https://***/Yuliang-Liu/Multimodal OCR.

关键词： large multimodal model OCR text recognition scene text-centric VQA document-oriented VQA key information extraction handwritten mathematical expression recognition

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A Novel Trustworthiness Measurement Model Based on Weight and User Feedback

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Chinese Journal of Electronics 2022年第4期31卷 612-625页

作者： ZHOU Wei MA Yanfang PAN Haiyu School of Computer Science and Technology Huaibei Normal University School of Computer Science and Information Engineering Changzhou Institute of Technology School of Computer Science and Information Security Guilin University of Electronic Technology

Software trustworthiness is an essential criterion for evaluating software quality. In componentbased software, different components play different roles and different users give different grades of trustworthiness after using the software. The two elements will both affect the trustworthiness of software. When the software quality is evaluated comprehensively, it is necessary to consider the weight of component and user feedback. According to different construction of components, the different trustworthiness measurement models are established based on the weight of components and user feedback. Algorithms of these trustworthiness measurement models are designed in order to obtain the corresponding trustworthiness measurement value automatically. The feasibility of these trustworthiness measurement models is demonstrated by a train ticket purchase system.

关键词： Component-based software Trustworthiness Weight User feedback Measurement

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A Compact Filtering Antenna System with Wide-Angle Scanning Capability for V2I Communication

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Chinese Journal of Electronics 2024年第2期33卷 516-526页

作者： Chuang HAN Tong LI Zhaolin ZHANG Ling WANG Guangwei YANG School of Electronics and Information Northwestern Polytechnical University School of Electronic Engineering and Computer Science Queen Mary University of London

A compact filtering antenna system with wide-angle scanning is proposed for vehicle to infrastructure(V2I) communication which would handle complex communication scenarios. In this work, a wide beam filtering antenna is realized by using some inductive resistance structures such as metal pins and pillars, and capacitive structures such as slots, parasitical patches to produce the radiation nulls at two sides of the operating frequency band and improve the impedance matching in the passband. Meanwhile, the wide beam capability is also realized by the above structure. Furthermore, two H-and E-plane linear arrays are designed for the beam scanning capability with filtering characteristics based on the proposed antenna. To verify the proposed design concept, a prototype is fabricated and measured. The measurement and simulation agree well, demonstrating an excellent filtering characteristic with the operating frequency band from 3.18 to 3.45 GHz(about 8.1%), the high total efficiency of about 88%, and 3-d B-beamwidth of more than 100° and 120° in the above two arrays, respectively. Additionally, the proposed arrays can realize the beam scanning up to the coverage of 112° and 120° with a lower gain reduction and a good filtering characteristic, respectively.

关键词： Filtering antenna Wide beam Beam scanning Beam steering Phased array

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Classifying distinct emotions from parents of ASD child using EEG source data by combining Bernoulli–Laplace Prior and graph neural networks

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Neural Computing and Applications 2025年第12期37卷 7877-7895页

作者： ArulDass, Stephen Dass Jayagopal, Prabhu School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore632014 India

Emotion recognition using biological brain signals needs to be reliable to attain effective signal processing and feature extraction techniques. The impact of emotions in interpretations, conversations, and decision-making, has made automatic emotion recognition and examination of a significant feature in the field of psychiatric disease treatment and cure. The problem arises from the limited spatial resolution of EEG recorders. Predetermined quantities of electroencephalography (EEG) channels are used by existing algorithms, which combine several methods to extract significant data. The major intention of this study was to focus on enhancing the efficiency of recognizing emotions using signals from the brain through an experimental, adaptive selective channel selection approach that recognizes that brain function shows distinctive behaviors that vary from one individual to another individual and from one state of emotions to another. We apply a Bernoulli–Laplace-based Bayesian model to map each emotion from the scalp senses to brain sources to resolve this issue of emotion mapping. The standard low-resolution electromagnetic tomography (sLORETA) technique is employed to instantiate the source signals. We employed a progressive graph convolutional neural network (PG-CNN) to identify the sources of the suggested localization model and the emotional EEG as the main graph nodes. In this study, the proposed framework uses a PG-CNN adjacency matrix to express the connectivity between the EEG source signals and the matrix. Research on an EEG dataset of parents of an ASD (autism spectrum disorder) child has been utilized to investigate the ways of parenting of the child's mother and father. We engage with identifying the personality of parental behaviors when regulating the child and supervising his or her daily activities. These recorded datasets incorporated by the proposed method identify five emotions from brain source modeling, which significantly improves the accurac

关键词： Electroencephalography

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Enhancing Fabric Defect Detection with Attention Mechanisms and Optimized YOLOv8 Framework

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IEEE Access 2025年 13卷 96767-96781页

作者： Mao, Yonghua Wang, Guowen Ma, Yingcang Gui, Xiaolin Xi’an Polytechnic University School of Computer Science School of Science Xi’an710048 China Xi’an Jiaotong University School of Electronic and Information Engineering Xi’an710049 China

Fabric defect detection is a critical task in the textile industry, requiring high precision and recall to ensure effective quality control. This study presents an enhanced YOLOv8-based framework that integrates novel attention mechanisms and advanced architectural modules to improve detection accuracy and robustness. The framework incorporates the SimAM attention mechanism within the SPPF module and adopts an optimized Dilation-wise Residual (DWR) structure in the backbone. Comprehensive ablation studies and comparisons with state-of-the-art methods validate the effectiveness of the proposed approach. The enhanced model achieves a mAP50-95 of 74.3%, outperforming the baseline by 4.7 percentage points, with marked improvements in detecting challenging defect categories. While the framework demonstrates significant advancements, limitations in dataset diversity and computational efficiency are acknowledged. Future work will focus on resource optimization, dataset augmentation, and extending the framework’s applicability to other domains. © 2013 IEEE.

关键词： Textile industry

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Semi-supervised Domain Adaptation for Semantic Segmentation via Active Learning with Feature- and Semantic-Level Alignments

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-11页

作者： Wen, Lu Xu, Yuanyuan Feng, Zhenghao Zhou, Jiliu Zhou, Luping Wang, Yan School of Computer Science Sichuan University China School of Electrical and Information Engineering The University of Sydney Sydney NSW Australia

Unsupervised domain adaptation (UDA) is a popular technique to reduce the manual annotation cost in semantic segmentation. However, due to the absence of strong supervision in the target domain, UDA is prone to biasing the decision boundary towards the source domain. To alleviate this issue, this paper proposes a more effective semi-supervised domain adaptation (SSDA) method for semantic segmentation via active learning with feature- and semantic-level alignments. Specifically, active learning is utilized to select those samples with high diversity and uncertainty from the target domain for labeling. These selected data could provide reliable clues for domain transfer since they reveal the intrinsic distribution of the target domain as well as including hard samples at boundaries. Moreover, to better adapt the segmentation model from the source data to the labeled target data selected above, we propose a scheme based on both feature- and semantic-level domain alignments. The feature-level domain alignment imposes the distribution consistency between the Transformer features of the two domains by adversarial learning, which is a global alignment. In contrast, the semantic-level domain alignment optimizes the affinity and divergence of the semantic representations across domains via contrastive learning, which is a local alignment. These two alignments jointly bridge the domain gap from both the global and the local views, respectively. In addition, the pseudo labels of the unlabeled data are generated to expand the labeled data and further strengthen the cross-domain segmentation in a self-training manner. Extensive experiments on segmentation benchmarks demonstrate the effectiveness of our proposed method. IEEE

关键词： Semantics

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Gaining Technological Autonomy and Social-emotional Support: A Case Study of How and Why Chinese Older Adults Engage with a Semi-acquaintance Online Community

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Proceedings of the ACM on Human-computer Interaction 2024年第CSCW2期8卷 1-35页

作者： Qian, Zhigu Fu, Jiaojiao Zhou, Yangfan School of Computer Science Fudan University Shanghai Key Laboratory of Intelligent Information Processing Shanghai China School of Information Science and Engineering East China University of Science and Technology Shanghai China

Older adults are often underserved and marginalized in technology engagement due to their reluctance and the barriers they face in adopting and engaging with mainstream technology. However, Pinxiaoquan, a social feature of an e-commerce platform in China, has gained a large number of older users. This work investigates how and why Chinese older adults use Pinxiaoquan, aiming to unveil the underlying logic and inspire technology-inclusive design for older adults. To this end, we conducted a mixed-methods qualitative study over two years, which included online observation, and semi-interview. We found that Pinxiaoquan’s success among Chinese older adults is mainly due to its ability to inspire technological autonomy and provide social-emotional support. Rather than simply lowering technical barriers or asking them to seek assistance outside the platform, Pinxiaoquan builds a semi-acquaintance online community based on location and social ties that allows older adults to realize technical support mutually. Pinxiaoquan also fulfills their social-emotional needs, such as free online expression, memory creation and preservation, relationship expansion and maintenance, and a sense of value. Our research contributes to the HCI community by highlighting the importance of improving older adults’ technology autonomy following their specific social and cultural background and social-emotional needs. This work also provides unique insights and implications for building inclusive technology for the growing aging population. © 2024 Copyright held by the owner/author(s).

关键词： Marketplaces

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