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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dianqing Liu Yi Liu Junbo Guo Zhendong Mao School of Cyberspace Security University of Science and Technology of China Hefei China State Key Laboratory of Communication Content Cognition People’s Daily Online Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Influenced by context such as tone, emotion and demographic, pre-trained language models may generate harmful text, which limits their widespread application. While detoxifying language models seeks to reduce the likelihood of generating harmful content. There are two categories of detoxification strategies: fine-tuning language models and constraining outputs during inference. Neither category of methods achieved a proper balance between detoxification efficacy, the amount of annotated data, and inference efficiency. In this paper, we introduce a lightweight detoxification approach aiming at guiding the probability distribution of generated tokens towards the opposite direction of toxification, which relies on the language model itself and the contrastive pairs of contexts in the inference phase, without training. Experiments show that our method has state-of-the-art performance in detoxification effect while it has an edge in both fluency and speed of text generation.

关键词： Training Toxicology Signal processing Probability distribution Acoustics Speech processing Context modeling

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Improving key rates by tighter information reconciliation leakage estimation for quantum key distribution

arXiv

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arXiv 2025年

作者： Mao, Hao-Kun Yang, Bo Qiao, Yu-Cheng Yan, Bing-Ze Zhao, Qiang Xu, Bing-Jie Li, Qiong School of Cyberspace Science Faculty of Computing Harbin Institute of Technology Harbin150080 China Guangxi Key Lab Cryptography and Information Security Guilin University of Electronic Technology Guangxi Guilin541004 China School of Information Engineering Zhejiang Ocean University Zhoushan316022 China Science and Technology on Security Communication Laboratory Institute of Southwestern Communication Chengdu610041 China

Previous research has aimed to precisely estimate information leakage to improve the secure key rate (SKR) and maximum transmission distance in quantum key distribution (QKD). However, existing methods repeatedly considerd the information of the multi-photon pulses known to Eve before and after information reconciliation, resulting in an overestimation of the leakage amount. We propose a novel approach that considers the quantum part’s effect on post-processing, providing a more accurate estimation of information reconciliation leakage to improve the SKR. Theoretical analysis shows that our method more accurately estimates the information reconciliation leakage, significantly improving the SKR at any distance as well as the maximum transmission distance. It is worth mentioning that previous studies treat information leakage of the error correction as Shannon bound Nh(e), and our method can estimate it more tightly. Simulation results for decoy-BB84 and measurement-device-independent (MDI) protocols using Cascade are consistent with the theoretical analysis. The farther the transmission distance, the greater the growth rate of SKR. When the error rate is 33%, compared with the original method, the SKR growth rate of decoy-BB84 at 100KM is 100.4%, and the transmission distance of MDI increases by 22KM. Copyright © 2025, The Authors. All rights reserved.

关键词： Information leakage

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A Novel Vulnerability-Detection Method Based on the Semantic Features of Source Code and the LLVM Intermediate Representation

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Journal of Software: Evolution and Process 2025年第5期37卷

作者： Chen, Jinfu Zhou, Jiapeng Lin, Wei Towey, Dave Cai, Saihua Chen, Haibo Chen, Jingyi Yin, Yemin School of Computer Science and Communication Engineering Jiangsu University Zhenjiang China Jiangsu Key Laboratory of Security Technology for Industrial Cyberspace Jiangsu University Zhenjiang China School of Computer Science University of Nottingham Ningbo China Ningbo China

With the increasingly frequent attacks on software systems, software security is an issue that must be addressed. Within software security, automated detection of software vulnerabilities is an important subject. Most existing vulnerability detectors rely on the features of a single code type (e.g., source code or intermediate representation [IR]), which may lead to both the global features of the code slices and the memory operation information not being captured or considered. In particular, vulnerability detection based on source-code features cannot usually include some macro or type definition content. In this paper, we propose a vulnerability-detection method that combines the semantic features of source code and the low level virtual machine (LLVM) IR. Our proposed approach starts by slicing (C/C++) source files using improved slicing techniques to cover more comprehensive code information. It then extracts semantic information from the LLVM IR based on the executable source code. This can enrich the features fed to the artificial neural network (ANN) model for learning. We conducted an experimental evaluation using a publicly-available dataset of 11,381 C/C++ programs. The experimental results show the vulnerability-detection accuracy of our proposed method to reach over 96% for code slices generated according to four different slicing criteria. This outperforms most other compared detection methods. © 2025 John Wiley & Sons Ltd.

关键词： C (programming language)

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A Fast Framework for Efficiently Constructing Valuable Cubes 26th

A Fast Framework for Efficiently Constructing Valuable Cubes

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26th International Conference on Information and communications security, ICICS 2024

作者： Li, Jingtao Gao, Bo Wan, Jianxiong Li, Leixiao Zhang, Jiaxiang School of Data Science and Application Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Hohhot China School of Information Engineering Beijing Institute of Graphic Communication Beijing China Research Center of Large-Scale Energy Storage Technologies Ministry of Education of the People’s Republic of China Beijing China

ISBN: (纸本)9789819787975

The cube attack is a powerful technique for attacking symmetric ciphers, of which one of the core parts is the search for good cubes. The construction of candidate cubes targeting linear superpolies, proposed at Asiacrypt 2021 [20], is the dominant strategy for searching good cubes. However, the time spent on constructing candidate cubes increases significantly with initialization rounds on Trivium. To address this issue, we propose a novel framework called fast framework (The source code and data for this paper are available at https://***/Elpsys/Fast-Framework.), which enhances the efficiency of constructing candidates and discovers better cubes. The integration of numeric mapping and division property is the core of our framework;they can improve efficiency while guaranteeing accuracy. The proposed method is successfully applied to 830-and 832-round Trivium, both identifying more than 20 valuable cubes whose superpolies contain linear-independent terms and have the potential to be used in further practical key-recovery attacks. The experimental results demonstrated that the efficiency of constructing the mother cube is improved by at least a factor of 9 compared to conventional methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Geometry

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Multi-channel transmission meta-holograms enabled by a single-layer complex-amplitude metasurface

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Optics Letters 2025年第8期50卷 2542-2545页

作者： Zhu, Lei Wang, Yiya Dong, Liang Shang, Guanyu Burokur, Shah Nawaz Ding, Xumin School of Computer and Control Engineering Qiqihar University Qiqihar161006 China Communication and Electronics Engineering Institute Qiqihar University Qiqihar161006 China Key Laboratory of Big Data Network Security Detection and Analysis in Heilongjiang Qiqihar University Qiqihar161006 China Innovation Photonics and Imaging Center Instrumentation Science and Engineering Harbin Institute of Technology Harbin150001 China LEME UPL Univ. Paris Nanterre Ville d'AvrayF92410 France Advanced Microscopy and Instrumentation Research Center Harbin Institute of Technology Harbin150080 China

With an increasing demand for wireless communications, it is difficult for traditional communication technologies to achieve high-quality multi-channel wireless transmissions. Complex-amplitude metasurfaces provide an effective method to deal with this issue by jointly controlling the amplitude and phase of electromagnetic (EM) waves. However, the limitations of single-layer transmission efficiency and amplitude-phase coupling make it challenging to achieve multi-channel high-quality transmission and system integration. Here, a single-layer complex-amplitude metasurface design strategy for multi-channel high-quality transmission is proposed. The amplitude and phase of each transmission channel can be concurrently controlled by adjusting the structural parameters and rotation angles of the resonators. The metasurface successfully reconstructs different holograms in five transmission channels. All experimental results align well with the calculated and simulated results, demonstrating an innovative solution for multi-channel high-quality information transmission, with significant potential applications in the fields of wireless communications. © 2025 Optica Publishing Group.

关键词： Holograms

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Personalized Federated Learning with State-Adaptive IoT Device Scheduling in Mobile Edge Computing

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Mai, Jun Cao, Kun Wei, Tongquan Jinan University College of Cyber Security Guangzhou510632 China Southeast University National Mobile Communication Research Laboratory Nanjing211189 China East China Normal University School of Computer Science and Technology Shanghai200062 China

Federated learning (FL) is envisioned as a pioneering framework for the distributed training of artificial intelligence models in mobile edge computing (MEC) environments. Traditional MEC-empowered FL approaches commonly neglect the inherent competition for computation resources between uncertain user-own tasks and FL training activities on individual Internet-of-Things (IoT) devices. Meanwhile, these approaches fail to address the personalized reward perception that dominates the active participation of IoT devices in FL training. As a result, both the resource utilization on IoT devices and the overall performance of FL models are significantly degraded in practical MEC systems. To address these challenges, this paper investigates the personalized FL with state-adaptive IoT device scheduling in MEC scenarios. We first develop a collaborative device-state estimation method to effectively capture the uncertainty in future states of FL candidates. Subsequently, we design a user-personality inspired degree-of-satisfaction (DoS) prediction scheme to quantify the impact of computation resource competition on the satisfaction levels of personalized FL participants. Building on these efforts, we propose a state-adaptive IoT device scheduling technique to optimize the accuracy of FL models at the offline stage. An FL runtime management policy is also designed to deal with the timing failures of unsuccessful return of local training results at the online stage. Evaluations show that our approach enhances the accuracy of WideResNet FL model by up to 35.96% on CIFAR-10 and EuroSAT datasets. Our source code is available at https://***/superguymj/ACE. © 2025 IEEE.

关键词： Federated learning

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Scloud+: An Efficient LWE-Based KEM Without Ring/Module Structure 9th

Scloud+: An Efficient LWE-Based KEM Without Ring/Module Stru...

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9th International Conference on security Standardisation Research, SSR 2024

作者： Wang, Anyu Zheng, Zhongxiang Zhao, Chunhuan Qiu, Zhiyuan Zeng, Guang Yuan, Ye Mu, Changchun Wang, Xiaoyun Institute for Advanced Study BNRist Tsinghua University Beijing China School of Computer and Cyber Sciences Communication University of China Beijing China Shield Lab Huawei Technologies Beijing China Shandong Institute of Blockchain Jinan China Digital Currency Institute the People’s Bank of China China Zhongguancun Laboratory Beijing China National Financial Cryptography Research Center Beijing China Key Laboratory of Cryptologic Technology and Information Security School of Cyber Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031875403

We present Scloud+, an LWE-based key encapsulation mechanism (KEM). The key feature of Scloud+ is its use of the unstructured-LWE problem (i.e., without algebraic structures such as rings or modules) and its incorporation of ternary secrets and lattice coding to enhance performance. A notable advantage of the unstructured-LWE problem is its resistance to potential attacks exploiting algebraic structures, making it a conservative choice for constructing high-security schemes. However, a key disadvantage of such schemes is their limited computational and communication efficiency. Scloud+ utilizes ternary secrets and BW32 lattice codes to enhance noise control and ensure robust error correction during decryption, enabling smaller parameters while maintaining low decryption failure probabilities. Equipped with these techniques, Scloud+ exhibits a significant improvement in efficiency. When compared with FrodoKEM for parameter sets targeting 128, 192, and 256 bits of security respectively, Scloud+ achieves practical performance with a public key size approximately 0.71∼0.87x and a ciphertext size approximately 0.56∼0.78x that of FrodoKEM. The encapsulation plus decapsulation time is approximately 0.74∼0.84x that of FrodoKEM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum cryptography

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FL-based NN in 30.4-km MMW Transmission using Full-Photonic Conversion and TWT Amplifier

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IEEE Photonics technology Letters 2025年

作者： Zhao, Junhao Dong, Boyu Liu, Yinjun Ping, Dianyuan Tao, Li He, Shuhong Liu, Shishuo Zi, Zhangxiong Lu, Qichao Li, Yaxuan Jia, Junlian Li, Zhongya Yan, An Shi, Jianyang Chi, Nan Zhang, Junwen Shanghai Engineering Research Center of LEO Satellite Communication and Applications Shanghai Collaborative Innovation Center of LEO Satellite Communication Technology Department of Communication Science and Engineering Shanghai200433 China Shanghai Innovation Institute Shanghai200433 China CSDDC National Key Laboratory of Electromagnetic Effect and Security on Marine Equipment China Ship Development and Design Center Wuhan430000 China Beijing Vacuum Electronics Research Institute National Key Laboratory of Science and Technology on Vacuum Electronics Beijing100015 China

This letter presents a focal loss (FL)-based neural network soft de-mapping method for 30.4-km millimeter-wave (MMW) transmission, utilizing full-photonic up- and down-conversion. A traveling wave tube (TWT) is employed to improve power budget to realize over 50-km equivalent distance. The proposed method is shown to effectively reduce the bit error rate (BER) in MMW transmission, particularly when the model parameters are small, highlighting its potential to lower complexity of receiver. The performance of the FL function is validated using convolutional neural networks (CNN), recurrent neural networks (RNN), and residual networks (ResNet) architectures, all of which lead to a significant reduction in BER, with ResNet achieving the best results. The field trial of 30.4-km MMW transmission and targeted over 50.0-km equivalent distance have been demonstrated successfully. Notably, a 10.43-Gb/s line rate is achieved over the 30.4-km near-sea surface wireless link at 0-dBm received optical power (ROP) of transmitter photodiode. © 2025 IEEE.

关键词： Budget control

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M-procedures robust to structural changes detection under strong mixing heavy-tailed time series models

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Journal of Statistical Planning and Inference 2025年 239卷

作者： Jin, Hao Hu, Jiating Zhu, Ling Tian, Shiyu Zhang, Si School of Computer Science and Technology Xi'an University of Science and Technology Xi'an 710054 China School of Sciences Xi'an University of Science and Technology Xi'an 710054 China Chongqing College of Mobile Communication Chongqing Key Laboratory of Public Big Data Security Technology Chongqing 401420 China

Many tests of change points resort to least squares estimation method, but it can lead to bias if these observations are heavy-tailed processes. The aim of this paper is to construct a ratio-typed test based on M-estimation, which avoids the long-range variance estimation and is robust to structural change detection under strong mixing series with heavy-tailed. The proposed test consisting of M-procedures has more utility in that it allows processes in the domain of attraction of a stable law with index κ∈(0,2), not limited to (1,2). Under some regular conditions, asymptotic distribution under the null hypothesis of no change is functional of a Brownian motion, and the divergent rate under the alternative hypothesis is also provided. Furthermore, the convergence rate of a ratio-typed change point estimator is established. Simulation study illustrates there is no distortion in empirical sizes, and empirical powers have satisfactory performance. Finally, two practical applications to real examples are presented as well. © 2025 Elsevier B.V.

关键词： Asymptotic distribution Heavy-tailed M-procedures Ratio-typed tests Structural change

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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus

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Computers in Biology and Medicine 2025年 186卷 109708-109708页

作者： Ejiyi, Chukwuebuka Joseph Qin, Zhen Agbesi, Victor K. Ejiyi, Makuachukwu Bennedith Chikwendu, Ijeoma A. Bamisile, Oluwatoyosi F. Onyekwere, Favour Ezinne Bamisile, Olusola O. College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Pharmacy Department University of Nigeria Nsukka Enugu State Nsukka Nigeria School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China

In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can significantly impact model performance. Plain and standard skip connections, widely used in most models, often fall short of maintaining high segmentation accuracy across different modalities, because essential spatial information transferred from the encoder to the decoder is lost. Inspired by these limitations, the Attention-Embedded Deep UNet (ATEDU-Net) is presented here, an innovative framework designed and tested on diverse medical image modalities, including X-ray, breast ultrasound, and retinal fundus images. ATEDU-Net features a unique architecture that combines progressive context refinement modules (PCRM) and global context modules (GCM) within a U-shaped network structure with Convgroup blocks which facilitate the integration of convolutional operations. This allows ATEDU-Net to autonomously capture rich spatial details and crucial contextual information from input images. The GCM captures essential contextual information for informed decision-making, while the PCRM enhances feature attention, resulting in precise and robust segmentation. The experimentation and analysis demonstrate that ATEDU-Net promises to be a powerful tool for medical professionals, aiding in the early detection of chest-related diseases, accurate localization of breast tumors, and early identification of eye diseases. This, in turn, contributes significantly to the formulation of optimal therapeutic strategies, enhancing patient care and outcomes. The versatility of ATEDU-Net is further highlighted by its ability to analyze medical images across various modalities, making it well-suited for the complex task of medical image analysis in diverse clinical scenarios. © 2025 Elsevier Ltd

关键词： Image segmentation

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