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Frontiers of computers and Communication Engineering (FCCE), International Conference on

作者： Zhu Tang Jingjing Zhao Hu Li Tianhang Guo Quan Wang Hongyan Chen Baokang Zhao College of Computer National University of Defense Technology Changsha China National Key Laboratory of Science and Technology on Information System Security Beijing China

Recently, mega satellite constellations are very popular for global instant messaging, and inter-satellite links are planned for delay reduction, leading to satellite network architecture finally. Although there are many satellite orbit simulation tools (e.g. STK, Savi) and network simulation tool (e.g. NS2/3, OPNET, Mininet, SNS3), none of them gives good consideration to both. In this paper, a satellite network and orbit integrated simulation platform (Mini-Savi) is proposed for large scale satellite constellation, which is based on the combination of network simulation tool Mininet and orbit simulation tool Savi. Finally, a typical satellite constellation Iridium system is employed for simulation verification.

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Low Energy Consumption and Time Deterministic Energy-saving Workflow Task Scheduling Algorithm based on DVFS 9

Low Energy Consumption and Time Deterministic Energy-saving ...

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9th IEEE International Conference on Cyber security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Liu, Xun Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

With the development of streaming media, soft real-time system in today's life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task. © 2022 IEEE.

关键词： Scheduling

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GAN-DIRNet:A Novel Deformable Image Registration Approach for Multimodal Histological Images

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computers, Materials & Continua 2024年第7期80卷 487-506页

作者： Haiyue Li Jing Xie Jing Ke Ye Yuan Xiaoyong Pan Hongyi Xin Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai200240China Department of Pathology Ruijin HospitalShanghai Jiao Tong UniversitySchool of MedicineShanghai200025China Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai200240China Global Institute of Future Technology Shanghai Jiao Tong UniversityShanghai200240China

Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of *** neural network(CNN)and generative adversarial network(GAN)are pivotal inmedical image ***,existing methods often struggle with severe interference and deformation,as seen in histological images of conditions like Cushing’s *** argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator *** this study,we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image *** begin with,the discriminators of two GANs are embedded as a new dual parallel feature extraction module into the unsupervised registration networks,characterized by implicitly extracting feature descriptors of specific ***,modal feature description layers and registration layers collaborate in unsupervised optimization,facilitating faster convergence and more precise ***,experiments and evaluations were conducted on the registration of the Mixed National Institute of Standards and technology database(MNIST),eight publicly available datasets of histological sections and the Clustering-Registration-Classification-Segmentation(CRCS)dataset on the Cushing’s *** results demonstrate that our proposed GAN-DIRNet method surpasses existing approaches like DIRNet in terms of both registration accuracy and time efficiency,while also exhibiting robustness across different image types.

关键词： Histological images registration deformable registration generative adversarial network cushing’s disease machine learning computer vision

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Adaptive focusing optical coherence tomography for corneal imaging

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Optics Letters 2025年第3期50卷 896-899页

作者： Shi, Yejiong He, Bin Yin, Zichen Chen, Zhengyu Hu, Zhangwei Xue, Ruizhi Yang, Panqi Zhang, Xiao Zhang, Ning Meng, Zhe Ying, Yuzhe Jing, Linkai Wang, Guihuai Xue, Ping State Key Laboratory of Low-dimensional Quantum Physics Department of Physics Tsinghua University Beijing China Frontier Science Center for Quantum Information Beijing China Beijing Institute of Technology Beijing China Institute of Forensic Science Ministry of Public Security Beijing China Department of Neurosurgery Beijing Tsinghua Changgung Hospital School of Clinical Medicine Institute of Precision Medicine Tsinghua University Beijing China

Optical coherence tomography (OCT) plays a crucial role in diagnosing corneal diseases due to its capacity to provide high-resolution three-dimensional imaging. However, the convex shape of the cornea and the inherent tradeoff between depth of field (DOF) and lateral resolution in OCT systems often result in defocusing issues, leading to reduced lateral resolution and sensitivity in single-shot high-resolution imaging. Traditional methods typically involve multiple focusing at different depths followed by image stitching to achieve full-depth high-resolution imaging of the cornea, which increases imaging times and introduces potential stitching artifacts. To address these limitations, we propose a novel adaptive focusing OCT system. By leveraging the symmetry of the corneal structure and the periodic focusing stability of an electrically tunable lens, our system can achieve full-depth high-resolution imaging of the entire cornea in a single scan, covering an 11 mm imaging range and 1.5 mm depth variation with a lateral resolution of 10 µm. This approach not only halves the imaging time but also eliminates the need for image stitching. Imaging of curved tape samples and excised porcine corneas demonstrates the potential of this novel technique for high-resolution and fast corneal imaging. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Optical coherence tomography

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Membership information Leakage in Well-Generalized Auto Speech Recognition systems

Membership Information Leakage in Well-Generalized Auto Spee...

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2023 International Conference on Data science and Network security, ICDSNS 2023

作者： Li, Honglei Zhao, Xinlong School of Cyberspace Security School of Cryptology Hainan University Haikou China Key Laboratory of Internet Information Retrieval of Hainan Province Haikou China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9798350301595

Membership inference attack (MIA) attempts to determine whether a particular data sample has participated in the training process of the target model, and is regarded as basic form of privacy breach in machine learning. This paper provides a label-only membership inference attack to well-generalized automatic speech recognition (ASR) systems, while only being granted black-box access permissions for the target system. Initially, we develop shadow models as substitution of the target ASR model. Subsequently, data augmentation techniques are employed to generate additional speech samples for the member and non-member sets elements of the shadow models. These augmentation techniques include modifying speech speed, cropping, adding noise, and so on. We query the shadow models with all the samples above, and we can derive a series of features from the model's output. All the features and their corresponding membership labels are then utilized to train an audit model. This audit model will be used to judge the membership of a given sample. The experimental results indicate that the performance metrics of this attack are significantly higher when the adversary has knowledge about the data distribution of target model's training set and the target model's architecture. However, if the adversary is lack of this knowledge, the probability of successful MIA is comparable to random guessing. The main contribution is that we investigate the membership leakage in well-generalized auto speech recognition system, and propose a data augmentation-based membership inference attack. We also discuss the transferability of the proposed scheme under various attack uncertainty. © 2023 IEEE.

关键词： Speech recognition

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Vulnerability knowledge extraction method based on joint extraction model 9

Vulnerability knowledge extraction method based on joint ext...

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9th International Conference on Advanced Cloud and Big Data, CBD 2021

作者： Liu, Zhen Di, XiaoQiang Song, Wei Ren, WeiWu Jilin Key Laboratory of Network and Information Security Changchun University of Science and Tecchnology Changchun China School of Computer Science and Technology Changchun University of Science of Technology Changchun China Information Center Changchun University of Science and Technology Changchun China

ISBN: (纸本)9781665407458

information extraction is an important semantic processing task to construct network security knowledge graph. Extracting entities and relationships in vulnerability description from public data sets will inevitably lead to waste of manpower and difficulty in accurate positioning. Another challenge is that there are multiple relationships among vulnerable descriptors. This paper proposes a framework for the common vulnerabilities and exposures (CVE) analysis, which consists of entity annotation algorithm and relational classification model. In particular, we apply the model to CVE dataset to solve the problem of information extraction and relationship classification in the CVE vulnerability analysis. Moreover, the predicted relationship is used to construct vulnerability security knowledge graph. The experimental results show that the framework can deal with the CVE vulnerability description effectively, and has good relationship classification performance. © 2022 IEEE.

关键词： Network security

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Pyramid Pooling-based Local Profiles for Graph Classification

Pyramid Pooling-based Local Profiles for Graph Classificatio...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Wu, Chengpei Lou, Yang Li, Junli College of Computer Science Sichuan Normal University Chengdu610066 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan Graduate School of Information Science and Technology Osaka University Osaka565-0871 Japan

ISBN: (纸本)9798350337020

Many natural and engineering systems can be modeled and represented in the forms of graph data, and then studied using graph theory and network analysis tools. Graph representation learning aims at generating lower-dimensional representations from higher-dimensional graph data, which is a crucial step that facilitates the follow-up tasks, such as node and graph classifications. In this paper, we present a simple but effective graph representation learning method, namely the pyramid pooling-based local profile (PPLP), which enables local nodal profiles to be transformed into a graph representation, with multi-scale features extracted. PPLP can be either embedded into a graph neural network as the readout layer, or perform independently as a graph embedding algorithm. The resultant representations of PPLP are for graph-level tasks. PPLP is experimentally tested by performing graph classification tasks on ten representative datasets, either as the readout layer of different graph neural networks, or as an independent graph embedding algorithm. Experimental results demonstrate that: 1) when embedded into graph neural networks, PPLP outperforms the widely-used global pooling-based readout methods;2) as an independent graph embedding algorithm, PPLP performs fairly good, especially on the social network datasets. The investigation confirms PPLP as a simple but promising method for graph-level tasks. © 2023 IEEE.

关键词： graph classification graph neural network Graph representation learning pyramid pooling

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MineLoRa: Markov Transition Fields and Adaptive Neural Networks for LoRa Device Radio Frequency Fingerprint Identification

MineLoRa: Markov Transition Fields and Adaptive Neural Netwo...

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2024 International Conference on Artificial Intelligence of Things and systems, AIoTSys 2024

作者： Zhu, Biaokai Jia, Yupeng Wang, Zhonghao Wen, Jiawei Liu, Xinle Zhao, Shijia Xu, Qiongyue Song, Jie Li, Ping Liu, Sanman Li, Feng Zhao, Jumin Shanxi Police College Taiyuan University of Technology Taiyuan China Shanxi Police College Cyber Security Department Taiyuan China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China School of Computer Science and Technology Anhui University Hefei China Taiyuan University of Technology Taiyuan China College of Information and Computer Taiyuan University of Technology Jinzhong China

ISBN: (纸本)9798331507886

With the development of LPWAN technology, LoRa, one of the typical representatives, is widely used in various communication scenarios. At the same time, it is also faced with problems such as difficulty in identifying the terminal equipment and the easy mistake and confusion. The industry has widely valued LoRa RF fingerprint recognition technology, which has become a security focus that cannot be ignored. Most recent research on terminal recognition has been carried out by multi-class feature fusion. Still, it needs help with high computational complexity, significant noise and other external interference factors, and harrowing feature extraction. We propose a time-frequency feature analysis method based on GAF and MTF. The time-domain features are transformed into two-dimensional images using the Gram Angle field and Markov transfer field. The spectral graph has both time-domain and frequency-domain characteristics, and the feature probability distribution in the images is intuitively analyzed. Finally, we design a deep neural network model based on CNN improvement. It can accurately extract and classify the features contained in multi-Time frequency. Our experiments show that a 97% recognition rate can be achieved in an indoor environment, and a 90% recognition accuracy can be achieved in an outdoor environment in time. © 2024 IEEE.

关键词： Frequency domain analysis

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Unidirectional Identity-Based Proxy Re-Signature with key Insulation in EHR Sharing system

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computer Modeling in Engineering & sciences 2022年第6期131卷 1497-1513页

作者： Yanan Chen Ting Yao Haiping Ren Zehao Gan School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu610054China Basic Course Teaching Department Jiangxi University of Science and TechnologyNanchang330013China Institute of Electronic and Information Engineering University of Electronic Science and Technology of ChinaDongguan523808China Network and Data Security Key Laboratory of Sichuan Province Unversity of Electronic Science and Technology of ChinaChengdu610054China

The introduction of the electronic medical record(EHR)sharing system has made a great contribution to the management and sharing of healthcare *** referral treatment for patients,the original signature needs to be converted into a re-signature that can be verified by the new *** re-signature(PRS)can be applied to this scenario so that authenticity and nonrepudiation can still be insured for ***,the existing PRS schemes cannot realize forward and backward ***,this paper proposes the first PRS scheme that can provide key-insulated property,which can guarantee both the forward and backward security of the *** the leakage of the private key occurs at a certain moment,the forward and backward key will not be ***,the purpose of key insulation is ***’s more,it can update different corresponding private keys in infinite time periods without changing the identity information of the user as the public ***,the unforgeability of our scheme is proved based on the extended Computational Diffie-Hellman assumption in the random oracle ***,the experimental simulation demonstrates that our scheme is feasible and in possession of promising properties.

关键词： Proxy re-signature key insulation electronic medical record(EHR) random oracle model

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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation systems

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IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (AI) to the network edge, which enables AI applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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