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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Chengyu Zhang Lijuan Duan Bian Ma Zhi Gong Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing China

ISBN: (数字)9798350351699

ISBN: (纸本)9798350351705

Sleep staging plays a significant role in diagnosing sleep-related diseases and assessing sleep quality. Recent years have witnessed a remarkable advancement in deep learning for automatic sleep staging. However, the classification accuracy for certain stages remains unsatisfactory due to imbalanced sleep data. Moreover, the sleep stages are easily confused with each other, further leading to low classification accuracy. To address the issues, we propose a multi-task-based feature-reconstruction sleep staging method, comprising a feature extraction module and a multi-task module. In the multi-task module, specifically, the main task branch encodes the features extracted by the feature extraction module for sleep staging, while the auxiliary task branch randomly masks the features before performing the same encoding. Subsequently, based on the classification results of the main task, a dimensional reconstruction is employed to reconstruct the original features from the encoded features for confused sleep stages. The overall loss, formed by combining the classification loss and the reconstruction loss, is utilized to constrain the model. We evaluate the performance of our proposed model using three public datasets, and the results demonstrate the effectiveness of the method in solving the problem of low accuracy in confused sleep stages classification.

关键词： Deep learning Accuracy Force Multitasking Feature extraction Encoding Task analysis

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Integrating Global Context Contrast and Local Sensitivity for Blind Image Quality Assessment 41

Integrating Global Context Contrast and Local Sensitivity fo...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Xudong Hu, Runze Zheng, Jingyuan Zhang, Yan Zhang, Shengchuan Zheng, Xiawu Li, Ke Shen, Yunhang Liu, Yutao Dai, Pingyang Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China School of Information and Electronics Beijing Institute of Technology Beijing100080 China School of Medicine Xiamen University China Tencent Youtu Lab China School of Computer Science and Technology Ocean University of China China

Blind Image Quality Assessment (BIQA) mirrors subjective made by human observers. Generally, humans favor comparing relative qualities over predicting absolute qualities directly. However, current BIQA models focus on mining the "local" context, i.e., the relationship between information among individual images and the absolute quality of the image, ignoring the "global" context of the relative quality contrast among different images in the training data. In this paper, we present the Perceptual Context and Sensitivity BIQA (CSIQA), a novel contrastive learning paradigm that seamlessly integrates "global" and "local" perspectives into the BIQA. Specifically, the CSIQA comprises two primary components: 1) A Quality Context Contrastive Learning module, which is equipped with different contrastive learning strategies to effectively capture potential quality correlations in the global context of the dataset. 2) A Quality-aware Mask Attention Module, which employs the random mask to ensure the consistency with visual local sensitivity, thereby improving the model's perception of local distortions. Extensive experiments on eight standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods. Copyright 2024 by the author(s)

关键词： Contrastive Learning

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Multi-attribute Context-Aware Item Recommendation Method Based on Deep Learning 5

Multi-attribute Context-Aware Item Recommendation Method Bas...

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5th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Li, Shuxin Duan, Lijuan Zhang, Wenbo Wang, Wenjian Beijing University of Technology Beijing Key Laboratory of Trusted Computing Faculty of Information Technology Beijing China Beijing University of Technology China Natl. Eng. Laboratory for Critical Technologies of Information Security Classified Protection Faculty of Information Technology Beijing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

With the wide application of deep learning in the field of recommendation, item recommendation has gradually become a new hotspot. The existing item recommendation methods ignore the context relationship between multiple attributes and fail to make full use of the potential structural interaction relationship. This paper proposes a new multi-attribute context-aware item recommendation method (MCIR) based on deep learning to recommend a series of suitable items for characters in the match. The attribute attention encoder (AAE) module proposed in this method mainly decouples the local and global features of multiple attributes, and the global multiple aggregation (GMA) module integrates the obtained context features in a graph structure. Extensive experiments on the kaggle public game dataset show that our method significantly outperforms previous methods in terms of Precision, F1 and MAP compared to other existing methods. © 2022 IEEE.

关键词： Deep learning

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Adaptive Feature Selection for No-Reference Image Quality Assessment by Mitigating Semantic Noise Sensitivity 41

Adaptive Feature Selection for No-Reference Image Quality As...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Xudong Gao, Timin Hu, Runze Zhang, Yan Zhang, Shengchuan Zheng, Xiawu Zheng, Jingyuan Shen, Yunhang Li, Ke Liu, Yutao Dai, Pingyang Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University 361005 China School of Information and Electronics Beijing Institute of Technology Beijing100080 China School of Medicine Xiamen University China Tencent Youtu Lab China School of Computer Science and Technology Ocean University of China China

The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation that not all features are beneficial, and some may even be harmful, necessitating careful selection. Empirically, we find that many image pairs with small feature spatial distances can have vastly different quality scores, indicating that the extracted features may contain quality-irrelevant noise. To address this issue, we propose a Quality-Aware Feature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective to remove harmful semantic noise features from the upstream task. Specifically, QFM-IQM enhances the semantic noise distinguish capabilities by matching image pairs with similar quality scores but varying semantic features as adversarial semantic noise and adaptively adjusting the upstream task's features by reducing sensitivity to adversarial noise perturbation. Furthermore, we utilize a distillation framework to expand the dataset and improve the model's generalization ability. Extensive experiments conducted on eight standard IQA datasets have demonstrated the effectiveness of our proposed QFM-IQM. Copyright 2024 by the author(s)

关键词： Feature Selection

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A research framework for constructing the knowledge database of public security information

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International Journal of Wireless and Mobile computing 2021年第3期21卷 191-197页

作者： Zhong, Han Zhang, Shiqiang Liu, Jianli College of Information Technology and Network Security People’s Public Security University of China Xicheng Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Trusted Computing Beijing China

At present, the public security organs in China have accumulated a great deal of public security data. These data have broad sources, complex structures, large and increasing scales. How to effectively integrate, manage and mine these data has become a new problem faced by all public security organs. This paper proposes a research framework for constructing the knowledge database of public security information. Based on this multi-dimension framework, data features can be effectively extracted and modelled for improving the management and utilisation of public safety data. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Information management

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Participant attack on the deterministic measurement-device-independent quantum secret sharing protocol

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Science China(Physics,Mechanics & Astronomy) 2021年第6期64卷 121-124页

作者： Yu-Guang Yang Yue-Chao Wang Yong-Li Yang Xiu-Bo Chen Dan Li Yi-Hua Zhou Wei-Min Shi Faculty of Information Technology Beijing University of TechnologyBeijing 100124China Beijing Key Laboralory of Trusted Computing Beijing 100124China Guangxi Key Laboratory of Crptography and Information Security Guilin 541004China Information Security Center State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 211106China

In a recent article[Gao et al.,***.63,120311(2020)],a two-receiver measurement-deviceindependent quantum secret sharing(MDI-QSS)protocol was *** was proven to be secure against eavesdropping and generalized to the multireceiver ***,the participant attack is a fatal threat to QSS ***,we highlight that a dishonest participant can obtain a sender’s secret message alone without introducing any detectable error,evidencing the vulnerability of the MDI-QSS protocol to the participant attack.

关键词： quantum cryptography quantum secret sharing participant attack

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Boosting CLIP Adaptation for Image Quality Assessment via Meta-Prompt Learning and Gradient Regularization

arXiv

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arXiv 2024年

作者： Li, Xudong Huang, Zihao Hu, Runze Zhang, Yan Cao, Liujuan Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China School of Information and Electronics Beijing Institute of Technology China

Image Quality Assessment (IQA) remains an unresolved challenge in the field of computer vision, due to complex distortion conditions, diverse image content, and limited data availability. The existing Blind IQA (BIQA) methods heavily rely on extensive human annotations to train models, which is both labor-intensive and costly due to the demanding nature of creating IQA datasets. To mitigate the dependence on labeled samples, this paper introduces a novel Gradient-Regulated Meta-Prompt IQA Framework (GRMP-IQA). This framework aims to fast adapt the powerful visual-language pre-trained model, CLIP, to downstream IQA tasks, significantly improving accuracy in scenarios with limited data. Specifically, the GRMP-IQA comprises two key modules: Meta-Prompt Pre-training Module and Quality-Aware Gradient Regularization. The Meta Prompt Pre-training Module leverages a meta-learning paradigm to pre-train soft prompts with shared meta-knowledge across different distortions, enabling rapid adaptation to various IQA tasks. On the other hand, the Quality-Aware Gradient Regularization is designed to adjust the update gradients during fine-tuning, focusing the model's attention on quality-relevant features and preventing overfitting to semantic information. Extensive experiments on five standard BIQA datasets demonstrate the superior performance to the state-of-the-art BIQA methods under limited data setting, i.e., achieving SRCC values of 0.836 (↑ 7.6% vs. 0.760 on LIVEC) and 0.853 (↑ 4.1% vs. 0.812 on KonIQ). Notably, utilizing just 20% of the training data, our GRMP-IQA outperforms most existing fully supervised BIQA methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

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Dmscts: Dynamic Measurement Scheme for the Containers-Hybrid-Deployment Based on trusted Subsystem

SSRN

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SSRN 2024年

作者： Han, Yufei Li, Chao Jianbiao, Zhang Wang, Yifan Yu, Lehao Cao, Yihao Shen, Hong Hou, Weixing Luo, Hailin College of Computer Science Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China Ant Group China The School of Engineering and Technology Central Queensland University Australia

The hybrid deployment of containers with different kernel types offers a novel solution for cloud service providers. While extensive research has been conducted on shared kernel containers, the security risks associated with diverse kernel types in hybrid deployment scenarios present more complex security and trust challenges. Establishing trusted relationships from hardware to containers in hybrid deployment has become a primary concern. Additional challenges include the lack of measurement and communication methods for independent kernel containers and insufficient dynamic measurement capabilities for containers. To address these issues, we propose a new solution called DMSCTS. We define container trust as comprising three layers: the container infrastructure is trusted, the container application environment is trusted, and the container runtime is trusted. We propose corresponding measurement schemes for each trust layer. Through functional verification and performance evaluation, we demonstrate that our architecture exhibits improved feasibility and effectiveness. © 2024, The Authors. All rights reserved.

关键词： Containers

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A federated sleep staging method based on adaptive re-aggregation and double prototype-contrastive using single-channel electroencephalogram

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Engineering Applications of Artificial Intelligence 2025年 156卷

作者： Ma, Bian Duan, Lijuan Huang, Zhaoyang Qiao, Yuanhua College of Computer Science Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing Beijing100124 China China National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China Department of Neurology Xuanwu Hospital Capital Medical University Beijing100053 China Beijing Key Laboratory of Neuromodulation Beijing100053 China School of Mathematics Statistics and Mechanics Beijing University of Technology Beijing100124 China

The limited availability of sleep data collected by individual institutions poses a significant challenge in developing automatic sleep staging models, especially given that most existing models rely heavily on data-driven approaches. The advent of federated learning has introduced an innovative and reliable paradigm for inter-institutional collaboration, facilitating task knowledge sharing and implicit data augmentation while preserving privacy. However, in traditional federated learning methodologies, local models are typically initialized with the global model at the onset of each iteration. This global-knowledge-centric approach often results in catastrophic forgetting during local training and inadequately adapts to the diversity of local data, compromising the overall generalization performance of the model. In this paper, we introduce a novel re-aggregation strategy for local model initialization that synergizes the global model with historical local models, thereby mitigating the undue influence of global knowledge and preserving local task-specific information. The re-aggregation weights are adaptively determined based on the confidence of the global and historical models on local datasets. Furthermore, to address the inherent ambiguity among sleep stages, a dual prototype-contrastive learning module is proposed, comprising Prototype-Consistency Contrastive (PCC) and Prototype-Distinguishability Contrastive (PDC) components. Specifically, the PCC component is designed to ensure consistency between local prototypes and unbiased prototypes derived from re-aggregation, enhancing intra-class knowledge coherence. The goal of the PDC component is to strengthen the discriminability among various sleep stages, improving inter-class differentiation. Comprehensive experiments conducted on two public datasets demonstrate the effectiveness, superiority, and flexibility of the proposed method. © 2025 Elsevier Ltd

关键词： Federated learning

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Multi-Granular Multimodal Clue Fusion for Meme Understanding 39

Multi-Granular Multimodal Clue Fusion for Meme Understanding

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zheng, Li Fei, Hao Dai, Ting Peng, Zuquan Li, Fei Ma, Huisheng Teng, Chong Ji, Donghong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University Wuhan China National University of Singapore Singapore Singapore Laboratory for Advanced Computing and Intelligence Engineering Wuxi China North China Institute of Computing Technology beijing China

ISBN: (纸本)157735897X

With the continuous emergence of various social media platforms frequently used in daily life, the multimodal meme understanding (MMU) task has been garnering increasing attention. MMU aims to explore and comprehend the meanings of memes from various perspectives by performing tasks such as metaphor recognition, sentiment analysis, intention detection, and offensiveness detection. Despite making progress, limitations persist due to the loss of fine-grained metaphorical visual clue and the neglect of multimodal text-image weak correlation. To overcome these limitations, we propose a multi-granular multimodal clue fusion model (MGMCF) to advance MMU. Firstly, we design an object-level semantic mining module to extract object-level image feature clues, achieving fine-grained feature clue extraction and enhancing the model’s ability to capture metaphorical details and semantics. Secondly, we propose a brand-new global-local cross-modal interaction model to address the weak correlation between text and images. This model facilitates effective interaction between global multimodal contextual clues and local unimodal feature clues, strengthening their representations through a bidirectional cross-modal attention mechanism. Finally, we devise a dual-semantic guided training strategy to enhance the model’s understanding and alignment of multimodal representations in the semantic space. Experiments conducted on the widely-used MET-MEME bilingual dataset demonstrate significant improvements over state-of-the-art baselines. Specifically, there is an 8.14% increase in precision for offensiveness detection task, and respective accuracy enhancements of 3.53%, 3.89%, and 3.52% for metaphor recognition, sentiment analysis, and intention detection tasks. These results, underpinned by in-depth analyses, underscore the effectiveness and potential of our approach for advancing MMU. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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