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arXiv 2025年

作者： Xie, Yiping Zhao, Bo Dai, Mingtong Zhou, Jian-Ping Sun, Yue Tan, Tao Xie, Weicheng Shen, Linlin Yu, Zitong Shenzhen University China Great Bay University China National Engineering Laboratory for Big Data System Computing Technology China Dongguan Key Laboratory for Intelligence and Information Technology China Guangdong Medical University China Southern Medical University Dongguan People’s Hospital China Macao Polytechnic University China

Remote photoplethysmography (rPPG) enables noncontact physiological measurement but remains highly susceptible to illumination changes, motion artifacts, and limited temporal modeling. Large Language Models (LLMs) excel at capturing long-range dependencies, offering a potential solution but struggle with the continuous, noise-sensitive nature of rPPG signals due to their text-centric design. To bridge this gap, we introduce PhysLLM, a collaborative optimization framework that synergizes LLMs with domain-specific rPPG components. Specifically, the Text Prototype Guidance (TPG) strategy is proposed to establish cross-modal alignment by projecting hemodynamic features into LLM-interpretable semantic space, effectively bridging the representational gap between physiological signals and linguistic tokens. Besides, a novel Dual-Domain Stationary (DDS) Algorithm is proposed for resolving signal instability through adaptive time-frequency domain feature re-weighting. Finally, rPPG task-specific cues systematically inject physiological priors through physiological statistics, environmental contextual answering, and task description, leveraging cross-modal learning to integrate both visual and textual information, enabling dynamic adaptation to challenging scenarios like variable illumination and subject movements. Evaluation on four benchmark datasets, PhysLLM achieves state-of-the-art accuracy and robustness, demonstrating superior generalization across lighting variations and motion scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Physiological models

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Detecting Data-flow Errors Based on Petri Nets With Data Operations

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 251-260页

作者： Dongming Xiang Guanjun Liu Chungang Yan Changjun Jiang Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai 201804 China IEEE

In order to guarantee the correctness of business processes, not only control-flow errors but also data-flow errors should be considered. The control-flow errors mainly focus on deadlock, livelock, soundness, and so on. However, there are not too many methods for detecting data-flow errors. This paper defines Petri nets with data operations(PN-DO) that can model the operations on data such as read, write and delete. Based on PN-DO, we define some data-flow errors in this paper. We construct a reachability graph with data operations for each PN-DO, and then propose a method to reduce the reachability graph. Based on the reduced reachability graph, data-flow errors can be detected rapidly. A case study is given to illustrate the effectiveness of our methods.

关键词： Business process modeling data-flow errors Petri nets reachability graph

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Structural controllability of optimized networks with onion-like topologies

Structural controllability of optimized networks with onion-...

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Chinese Intelligent systems Conference, CISC 2018

作者： Li, Manli Sun, Shiwen Wu, Yafang Xia, Chengyi Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin300384 China Key Laboratory of Computer Vision and System Tianjin University of Technology Tianjin300384 China

ISBN: (纸本)9789811322877

Recently, an optimization method has been proposed to increase the ability of complex networks to resist intentional attacks on hub nodes. The finally optimized networks exhibit a novel type of "onion-like" structure. At the same time, structural controllability of complex networks also has been a hot research topic in recent years. Thus, structural controllability of "onion-like" networks deserves sufficient discussion. In this study, we explored the relationship between the attack robustness and structural controllability of scale-free networks before and after optimization. After implementing large quantity of numerical simulations, it has been found that the optimized scale-free networks have both increased robustness and enhanced structural controllability. Current research results can shed some light on the deep understanding of structural complexity and dynamical properties of real-world networked systems. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Topology

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Corrigendum to "Event-centric Multi-modal Fusion Method for Dense Video Captioning" [Neural Networks 146 (2022) 120-129]

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Neural networks : the official journal of the International Neural Network Society 2022年 152卷 527页

作者： Zhi Chang Dexin Zhao Huilin Chen Jingdan Li Pengfei Liu Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin 300384 China Key Laboratory of Computer Vision and System Ministry of Education China. Electronic address: zhaodexin@***.

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IGTP: A next point-of-interest recommendation method that integrates geospatial and temporal preferences

Research Square

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Research Square 2021年

作者： Jiao, Xu Xiao, Yingyuan Zheng, Wenguang Zhu, Ke School of General Education Tianjin Foreign Studies University Tianjin China Engineering Research Center of Learning-Based Intelligent System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

With the rapid development of location-based social networks(LBSNs), point-of-interest(POI) recommendation has become an important way to meet the personalized needs of users. The purpose of POI recommendation is to provide personalized POI recommendation services for users. However, general POI recommendations cannot meet the individual needs of users. This is mainly because the decision-making process for users to choose POIs is very complicated and will be affected by various user contexts such as time, location, etc. This paper proposes a next POI recommendation method that integrates geospatial and temporal preferences, called IGTP. Compared with general POI recommendation, IGTP can provide more personalized recommendations for users according to their context information. First, IGTP uses users’ preferences information to model users’ check-in histories to effectively overcome the challenge of extremely sparse check-in data. Secondly, IGTP takes into account the geographic distance and density factors that affect people’s choice of POIs, and limits POIs to be recommended to the potential activitive area centered on the current loca- tion of the target user. Finally, IGTP integrates geospatial and users’ temporal preferences information into a unified recommendation process. Compared with six advanced baseline methods, the experimental results demonstrate that IGTP achieves much better performance. © 2021, CC BY.

关键词： Location

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Long-term Dependency for 3D Reconstruction of Freehand Ultrasound Without External Tracker

arXiv

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arXiv 2023年

作者： Li, Qi Shen, Ziyi Li, Qian Barratt, Dean C. Dowrick, Thomas Clarkson, Matthew J. Vercauteren, Tom Hu, Yipeng The UCL Centre for Medical Image Computing The Wellcome/EPSRC Centre for Interventional and Surgical Sciences Department of Medical Physics and Biomedical Engineering University College London LondonWC1E 6BT United Kingdom State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China School of Biomedical Engineering & Imaging Sciences King’s College London LondonWC2R 2LS United Kingdom

Objective: Reconstructing freehand ultrasound in 3D without any external tracker has been a longstanding challenge in ultrasound-assisted procedures. We aim to define new ways of parameterising long-term dependencies, and evaluate the performance. Methods: First, long-term dependency is encoded by transformation positions within a frame sequence. This is achieved by combining a sequence model with a multi-transformation prediction. Second, two dependency factors are proposed, anatomical image content and scanning protocol, for contributing towards accurate reconstruction. Each factor is quantified experimentally by reducing respective training variances. Results: 1) The added long-term dependency up to 400 frames at 20 frames per second (fps) indeed improved reconstruction, with an up to 82.4% lowered accumulated error, compared with the baseline performance. The improvement was found to be dependent on sequence length, transformation interval and scanning protocol and, unexpectedly, not on the use of recurrent networks with long-short term modules;2) Decreasing either anatomical or protocol variance in training led to poorer reconstruction accuracy. Interestingly, greater performance was gained from representative protocol patterns, than from representative anatomical features. Conclusion: The proposed algorithm uses hyperparameter tuning to effectively utilise long-term dependency. The proposed dependency factors are of practical significance in collecting diverse training data, regulating scanning protocols and developing efficient networks. Significance: The proposed new methodology with publicly available volunteer data and code1 for parametersing the long-term dependency, experimentally shown to be valid sources of performance improvement, which could potentially lead to better model development and practical optimisation of the reconstruction application. © 2023, CC BY.

关键词： Ultrasonics

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Noncontact Doppler Radar-based Heart Rate Detection on the SVD and ANC

Noncontact Doppler Radar-based Heart Rate Detection on the S...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiuxia Shi Bin Hu Fuze Tian Qinglin Zhao Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University School of Computer and Communication Lanzhou University of Technology Ministry of Education Key Laboratory of Special Functional Materials and Structural Design (Lanzhou University) Lanzhou China CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of Semiconductors Chinese Academy of Sciences Engineering Research Center of Open Source Software and Real-Time System(Lanzhou University) Ministry of Education Key Laboratory of Special Functional Materials and Structural Design Ministry of Education Lanzhou University Lanzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

In the Doppler biological radar-based applications of noncontact measurement of vital signs, effectively extracting heartbeat information from weak thoracic mechanical motion is an important problem to be solved. This study is aimed at extracting heartbeat signal via the technology combined with Short Time Fourier Transform (STFT), Singular Value Decomposition (SVD) and Adaptive Noise Canceller (ANC) from radar recording. The simulated data and the data collected by Doppler radar biosensor realized in laboratory are employed to validate the proposed method. The results show that the proposed method has the ability of detection for the heart rate and heart rate variability indexes in rest state, it has certain advantages in time-consuming and detection accuracy. Therefore, the current method provides another way to process vital sign signals recorded by Doppler radar.

关键词： Biomedical measurement Noise cancellation Time measurement Doppler radar Recording Doppler effect Biosensors

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Multi-level Directed Fuzzing for Detecting Use-after-Free Vulnerabilities

Multi-level Directed Fuzzing for Detecting Use-after-Free Vu...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yuntao Zhang Zhongru Wang Weiqiang Yu Binxing Fang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Cyberspace Studies Beijing China Beijing DigApis Technology Co. Ltd Beijing China

ISBN: (纸本)9781665416597

Greybox fuzzing has been widely used in vulnerabilities detection. Most greybox fuzzing tools are coverage-based, which usually use basic block transition to gain code coverage and focus on improving it to trigger more bugs. However, only increasing code coverage is insufficient to find some heap-based vulnerabilities such as use-after-free (UAF) and double-free (DF). This is because, to trigger these vulnerabilities, one needs not only to cover more code, but also to execute special heap operations to satisfy a particular temporal constraint (i.e., allocating heap memory, free memory, and accessing the heap memory). In this paper, we propose an approach, namely MDFuzz, to detect heap-based vulnerabilities adopting multi-level directed greybox fuzzing. The key idea is identifying different targets to guide the fuzzing process to cover specific heap operations without wasting resources exploring unrelated program components. We first perform a static analysis to automatically recognize three critical targets related to heap operations and then calculate each basic block's distance to the targets. Moreover, we propose a probability-based multi-level seed queue and a novel seed selection strategy to augment the guidance of directed fuzzing. To evaluate MDFuzz, we have performed an evaluation on 7 real-world applications. The experimental results demonstrate that MDFuzz significantly outperforms the state-of-the-art fuzzers, including AFL, AFLFast and VUzzer, in terms of the time consumed to discover heap-based vulnerabilities. Moreover, MD-Fuzz found 4 previously unknown vulnerabilities in real-world programs.

关键词： Privacy Target recognition Conferences Computer bugs Static analysis Binary codes Fuzzing

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Finedge: A Dynamic Cost-Efficient Edge Resource Management Platform for NFV Network

Finedge: A Dynamic Cost-Efficient Edge Resource Management P...

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International Workshop on Quality of service

作者： Miao Li Qixia Zhang Fangming Liu National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

With the evolution of network function virtualization (NFV) and edge computing, software-based network functions (NFs) can be deployed on closer-to-end-user edge servers to support a broad range of new services with high bandwidth and low latency. However, due to the resource limitation, strict QoS requirements and real-time flow fluctuations in edge network, existing cloud-based resource management strategy in NFV platforms is inefficient to be applied to the edge. Thus, we propose Finedge, a dynamic, fine-grained and cost-efficient edge resource management platform for NFV network. First, we conduct empirical experiments to find out the effect of NFs' resource allocation and their flow-level characteristics on performance. Then, by jointly considering these factors and QoS requirements (e.g., latency and packet loss rate), Finedge can automatically assign the most suitable CPU core and tune the most cost-efficient CPU quota to each NF. Finedge is also implemented with some key strategies including real-time flow monitoring, elastic resource scaling up and down, and also flexible NF migration among cores. Through extensive evaluations, we validate that Finedge can efficiently handle heterogeneous flows with the lowest CPU quota and the highest SLA satisfaction rate as compared with the default OS scheduler and other state-of-the-art resource management schemes.

关键词： Resource management Noise measurement Packet loss Servers Throughput Quality of service

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Chinese named entity recognition method for the field of network security based on RoBERTa

Chinese named entity recognition method for the field of net...

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International Conference on Networking and Network Applications (NaNA)

作者： Xiaoyan Zhu Yu Zhang Lei Zhu Xinhong Hei Yichuan Wang Feixiong Hu Yanni Yao School of Computer Science and Engineering Xi’an Jiaotong University Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Computer Science and Technology Xi’an University of technology Xi’an China Shenzhen Tencent Computer System Co. Ltd Shenzhen China

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively.

关键词： Text recognition Cellular phones Semantics Neural networks Network security Feature extraction Internet

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