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Practical asynchronous measurement-device-independent quantum key distribution with advantage distillation

Physical Review A 2024年第2期110卷 022605-022605页

作者： Di Luo Xin Liu Kaibiao Qin Zhenrong Zhang Kejin Wei Guangxi Key Laboratory for Relativistic Astrophysics School of Physical Science and Technology Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information

The advantage distillation (AD) method has proven effective in improving the performance of quantum key distribution (QKD). In this paper we introduce the AD method into a recently proposed asynchronous measurement-device-independent (AMDI) QKD protocol, taking finite-key effects into account. Simulation results show that the AD method significantly enhances AMDI QKD, e.g., extending the transmission distance by 16 km with a total pulse count of N=7.24×1013, and enables AMDI QKD, previously unable to generate keys, to generate keys with a misalignment error rate of 10%. As the AD method can be directly integrated into the current system through refined postprocessing, our results facilitate the practical implementation of AMDI QKD in various applications, particularly in scenarios with high channel losses and misalignment errors.

关键词： Quantum cryptography Quantum information processing Quantum information theory

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Time Series Anomaly Detection Based on Normalized Flow and Bayesian networks 9

Time Series Anomaly Detection Based on Normalized Flow and B...

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9th IEEE International Conference on Data Science in Cyberspace, DSC 2024

作者： Yao, Honglei Liu, Donglan Chang, Yingxian Liu, Xin Tang, Chaofan State Grid Shandong Electric Power Research Institute Jinan China State Grid Shandong Electric Power Company Jinan China Qilu University of Technology Shandong Academy of Sciences Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Jinan China

ISBN: (纸本)9798350391367

Time series anomaly detection often faces significant challenges due to the high dimensionality, interdependence, and label sparsity of the data. Traditional methods struggle to model the intricate relationships between multiple time series components and often fail to accurately detect anomalies due to insufficient data dependence mining. To address these issues, we introduce GCRNF, a novel Graph-Enhanced Standardized Flow Model that incorporates a Bayesian network for causality modeling. This model decomposes joint probabilities into conditional probabilities and utilizes a graph dependency encoder with hybrid CNN-RNN models and Graph Convolutional networks (GCNs). Our experimental results demonstrate that GCRNF outperforms existing methods in density estimation, anomaly detection, and drift identification within time series distributions. Specifically, GCRNF showed a significant improvement in detection accuracy and robustness compared to traditional methods, providing a more effective solution for identifying anomalies in complex multivariate time series data. © 2024 IEEE.

关键词： Markov processes

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A Deep Learning-based Data Usability Enhancement Scheme for Differential Privacy 9

A Deep Learning-based Data Usability Enhancement Scheme for ...

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9th IEEE Smart World Congress, SWC 2023

作者： Yan, Haonan Li, Xiaoguang Zheng, Gewei Li, Hui Li, Fenghua Lin, Xiaodong Xidian University University of Guelph China Xidian University State Key Laboratory of Isn School of Cyber Engineering Xi'an China University of Cas State Key Laboratory of Information Security Institute of Information Engineering Cas School of Cyber Security Beijing China University of Guelph School of Computer Science Guelph Canada

ISBN: (纸本)9798350319804

While differential privacy (DP) is widely used to ensure privacy, it can also significantly reduce data accuracy. Current research attempts to improve accuracy by leveraging post-processing techniques, but these methods are sub-optimal and only applicable to specific data types. To address the issue, in this work, we propose a novel deep learning-based data usability enhancement method for differential privacy that is data-type independent. By using image denoising technology, the proposed scheme reduces the mean square error (MSE) of DP-perturbed data and theoretically justifies the relationship between data noise reduction and image noise reduction. The effectiveness of the proposed scheme is demonstrated through a comprehensive evaluation. © 2023 IEEE.

关键词： Image denoising

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A personalized test paper generation model for English examination based on knowledge tracking 5

A personalized test paper generation model for English exami...

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5th International Conference on Artificial Intelligence and computer Science, AICS 2023

作者： Zhou, Ya Shao, Xiuli Huang, Guimin Guo, Qingkai Deng, Nanxiao Lin, Jianxing School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

In recent years, personalized paper grouping is a hot research topic in the field of intelligence education. In this paper, we propose an optimized deep knowledge tracking model Mul-MAKT combined with a genetic algorithm for personalized paper grouping with English practice as the target. Firstly, we use students’ records (exercise labels, response results, learning behaviors, exercise attributes) as input to the knowledge tracking model to predict students’ performance at the next moment and also to obtain students’ mastery level of knowledge points. Based on the students’ mastery levels, we adjust the students’ ability in each type of English exercise and calculate the students’ weaknesses in each type of question, so as to extract the questions from the test bank with the difficulty matching the students’ ability and containing the students’ weaknesses. © 2023 SPIE.

关键词： Students

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Multimodal emotion recognition based on multilevel acoustic and textual information 5

Multimodal emotion recognition based on multilevel acoustic ...

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5th International Conference on Artificial Intelligence and computer Science, AICS 2023

作者： Zhou, Ya Xing, Yuyi Huang, Guimin Guo, Qingkai Deng, Nanxiao School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guangxi Guilin541004 China

ISBN: (纸本)9781510668621

The study and application of multimodal emotion recognition have gained significant popularity in recent years, representing one of the challenging tasks in the field of affective computing. We propose a multimodal speech emotion recognition model that utilizes multiple acoustic and textual information layers. This model incorporates transcribed textual data to complement speech data and enable accurate emotion recognition. In the unimodal model, we employ AlexNet, BiGRU, and HuBERT to extract multi-layer acoustic feature information from speech, and the RoBERTa encoder to extract text features. Additionally, we perform fusion between speech and text by utilizing the co-attentive mechanism to extract complementary information across modalities and eliminate inter-modality noise. This process ultimately enhances the emotional representation of the target modality. Finally, the fused features are utilized to predict the emotion category. Our model achieved a weighted sentiment recognition accuracy of 77.41% and an unweighted accuracy of 78.66%. © 2023 SPIE.

关键词： Emotion Recognition

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MAGRET: Machine-generated Text Detection with Rewritten Texts 31

MAGRET: Machine-generated Text Detection with Rewritten Text...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Huang, Yifei Cao, Jingxin Luo, Hanyu Guan, Xin Liu, Bo School of Cyber Science and Engineering Southeast University Nanjing211189 China Key Laboratory of Computer Network and Information of Ministry of Education of China Nanjing211189 China School of Computer Science and Engineering Southeast University Nanjing211189 China

ISBN: (纸本)9798891761964

With the quick advancement in text generation ability of Large Language Model(LLM), concerns about the misuse of machine-generated text(MGT) have grown, raising potential violations of legal and ethical standards. Some existing studies concentrate on detecting machine-generated text in open-source models using in-model features, but their performance on closed-source large models is limited. This limitation occurs because, in the closed-source model detection, the only reference that can be obtained is the texts, which may differ significantly due to random sampling. In this paper, we demonstrate that texts generated by the same model can align both semantically and statistically under similar prompts, facilitating effective detection and traceability. Specifically, we fine-tune a BERT encoder through contrastive learning to achieve semantic alignment in randomly generated texts from the same model. Then, we propose a method called Machine-Generated Text Detection with Rewritten Texts, which designed several prompt refactoring methods and used them to request rewritten text from LLMs. Semantic and statistical relationships between rewritten and original texts provide a basis for detection and traceability. Finally, we expanded the text dataset with multi-parameter random sampling and verified the performance of MAGRET on three text-generated datasets. Experimental results show that previous methods struggle with closed-source model detection, while our approach significantly outperforms baseline methods in this regard. It also shows MAGRET's stable performance in detection and tracing tasks across various randomly sampled texts. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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MMLN: Multi-directional and Multi-constraint Learning network for Remote Sensing Imagery Semantic Segmentation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2024年 1-16页

作者： Sun, Hang Xie, Yongchang Ren, Dong Wen, Fangqing Tong, Lyuyang Chang, Laibin Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges University Yichang China Institute of Vehicle Information Control and Network Technology Hubei University of Automotive Technology Shiyan China School of Computer Science Wuhan University Wuhan China

Recent advances in satellite remote sensing technology and computer technology have significantly impacted practical applications in remote sensing image segmentation. However, the prevalent hybrid segmentation models that combine Convolutional Neural networks (CNNs) and Transformers, often overlook the critical exploration of local and global feature correlations across various scales. This exploration is essential for learning more representative features and strengthening context modeling capabilities. Additionally, the decoding layers of these models do not effectively exploit the pixel-level semantic relationships within cross-layer feature maps, thereby limiting the models' ability to discern small object features. To address these challenges, this paper introduces a Multi-directional and Multi-constraint Learning network (MMLN) designed for semantic segmentation of remote sensing imagery. This network features a Multi-directional Dynamic Complement Decoder (MDCD), which enhances the interaction between local and global features in the feature space, and subsequently improves the feature discrimination within the segmentation network. Moreover, a Multi-constraint Saliency Boundary-adaptive Module (MSBM) is implemented to reinforce the spatial constraints on saliency at the edge regions and ensure semantic consistency along the mask boundaries. This, in turn, augments the segmentation model's capability to detect small objects. The evaluation on four datasets reveals that the MMLN outperforms the existing state-of-the-art methods in remote sensing imagery segmentation. The code is available at https://***/zhongyas/MMLN. Authors

关键词： Semantics

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FairCoRe: Fairness-Aware Recommendation Through Counterfactual Representation Learning

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IEEE Transactions on Knowledge and Data Engineering 2025年第7期37卷 4049-4062页

作者： Bin, Chenzhong Liu, Wenqiang Zhang, Feng Chang, Liang Gu, Tianlong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology School of Business Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Jinan University College of Cyber Security Engineering Research Center of Trustworthy AI Ministry of Education Guangzhou510632 China

Eliminating bias from data representations is crucial to ensure fairness in recommendation. Existing studies primarily focus on weakening the correlation between data representations and sensitive attributes, yet may inadvertently steer the user representations toward another potential bias direction of the target attribute. Furthermore, they often overlook the impact of user preferences on capturing sensitive information, incurring inadequate bias elimination. In this paper, we propose a Fair Counterfactual Representations (FairCoRe) learning framework, which aims to ensure the neutrality of representations among all bias directions. First, we intervene on sensitive attributes to construct a counterfactual scenario. Then, two opposing attribute prediction tasks are respectively performed in ground-truth and counterfactual scenarios to encode sensitive information along different bias directions. Second, we design a bias-aware enhancement learning method that quantifies the respective correlation of user preferences and sensitive attributes to enhance sensitive information encoding. Finally, we introduce two mutual information optimization methods that optimize the representations to capture users’ interests and disentangle sensitive factors. Moreover, we propose an attribute neutralization strategy that refines the learned representations, ensuring sensitive attribute neutrality. Extensive experiments demonstrate that our method achieves the optimal fairness and competitive accuracy compared to state-of-the-art methods. © 1989-2012 IEEE.

关键词： Accuracy Mutual information Correlation Data Visualization Medical Services Training Measurement Disentangled Representation Learning Semantics Recommender Systems Recommender System Recommendation Fairness Representation Learning Counterfactual Representations Potential Bias Mutual information User Preferences Sensitive Attributes Counterfactual Scenario User Representation Time Complexity Generative Adversarial networks Learning Objectives Demographic Groups Baseline Methods Recommender Systems Minimum Method Prediction Loss Preference information Graph Convolution User Side Recommendation Model Counterfactual Thinking Maximum Mutual information Representation Learning Methods Maximization Method Recommendation Accuracy Recommendation Method Recommendation Task Notions Of Fairness Negative Samples Mutual information Estimation

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Blockchain-Based Ciphertext Policy-Hiding Access Control Scheme 1

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18th EAI International Conference on security and Privacy in Communication networks, SecureComm 2022

作者： Du, Ruizhong Zhang, Tianhe School of Cyber Security and Computer Hebei University Baoding071002 China Hebei Provincial Key Laboratory of High Credibility Information System Baoding071000 China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

Ciphertext policy attribute encryption(CP-ABE) can realize one-to-many fine-grained access control, which can effectively solve the security problem of shared data. However, most existing CP-ABE protocols exhibit a single point of failure and access policy privacy disclosure problems. To resolve these issues, a blockchain-based ciphertext policy-hiding access control scheme is proposed. First, we propose a vector generation algorithm using vector compression techniques that can improve the efficiency of our scheme. Then, the privacy policy is protected by combining CP-ABE and inner product encryption. We then solve the user attribute revocation problem and single point of failure in the cloud-based access control models using ethereum. Finally, security analysis, the theoretical results, and experimental results show that the proposed solution is secure and efficient. © 2023, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Access control

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Cloud Data Deduplication Scheme Based on Blockchain

Cloud Data Deduplication Scheme Based on Blockchain

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2023 International Conference on networking and network Applications, NaNA 2023

作者： Shi, Pengliang Hao, Dongning Cyber Security and Computer Science Hebei University Key Laboratory on High Trusted Information System Baoding China International College of Hebei University Baoding China

ISBN: (纸本)9798350327380

To address the problem of untrustworthiness among entities faced in the process of data deduplication in cloud storage environment, this paper proposes a cloud data deduplication scheme based on blockchain. Firstly, to ensure the trustworthiness of data ownership proof in the data deduplication process, a data ownership verification algorithm based on random location sampling is designed with the help of blockchain technology and Merkle hash tree;Secondly, an oblivious pseudo-random protocol is used to obtain convergent encryption key to facilitate deduplication by cloud servers provides. Finally, the scheme security is proved by conducting security analysis, while simulation experiments are conducted to verify the effectiveness of the scheme. © 2023 IEEE.

关键词： Cryptography

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