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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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PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing

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ACM Transactions on software engineering and Methodology 1000年

作者： Yuwei Zhang Zhi Jin Ying Xing Ge Li Fang Liu Jiaxin Zhu Wensheng Dou Jun Wei Key Laboratory of System Software (Chinese Academy of Sciences) Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education School of Computer Science Peking University School of Computer Science Wuhan University China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications China School of Computer Science Peking University China State Key Laboratory of Complex & Critical Software Environment School of Computer Science and Engineering Beihang University China

Bug fixing holds significant importance in software development and maintenance. Recent research has made substantial strides in exploring the potential of large language models (LLMs) for automatically resolving software bugs. However, a noticeable gap in existing approaches lies in the oversight of collaborative facets intrinsic to bug resolution, treating the process as a single-stage endeavor. Moreover, most approaches solely take the buggy code snippet as input for LLMs during the patch generation stage. To mitigate the aforementioned limitations, we introduce a novel stage-wise framework named PATCH. Specifically, we first augment the buggy code snippet with corresponding dependence context and intent information to better guide LLMs in generating the correct candidate patches. Additionally, by taking inspiration from bug management practices, we decompose the bug-fixing task into four distinct stages: bug reporting, bug diagnosis, patch generation, and patch verification. These stages are performed interactively by LLMs, aiming to simulate the collaborative behavior of programmers during the resolution of software bugs. By harnessing these collective contributions, PATCH effectively enhances the bug-fixing capability of LLMs. We implement PATCH by employing the powerful dialogue-based LLM ChatGPT. Our evaluation on the widely used bug-fixing benchmark BFP demonstrates that PATCH has achieved better performance than state-of-the-art LLMs.

关键词： Bug Fixing Large Language Model Bug Management Multi-Agent Collaboration

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Analysis and improvement of ZG off-line TTP non-repudiation protocol

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Tongxin Xuebao/Journal on Communications 2009年第11 A期30卷 44-51页

作者： Liu, Dong-Mei Qing, Si-Han Li, Peng-Fei Li, Shang-Jie National Engineering Research Center for Fundamental Software Institute of Software Chinese Academy of Sciences Beijing 100080 China Engineering Research Center for Information Security Technology Institute of Software Chinese Academy of Sciences Beijing 100080 China Graduate School Chinese Academy of Sciences Beijing 100039 China National Key Laboratory of Integrated Information System Technology Chinese Academy of Sciences Beijing 100080 China

Based on the weakness of ZG off-line TTP(trusted third party) non-repudiation protocol because of using protocol deadline, the improvement of the ZG off-line TTP non-repudiation protocol was given, which eliminated the delaying resolve request attack and ensured non-repudiation and fairness. Therefore, two kinds of improvement of ZG off-line TTP non-repudiation protocol were given, which had different point of focus. Among them, the first one could eliminate delaying message attack in ZG protocol;the second one also had the property of preventing the abuse of sub-protocol besides eliminating delaying message attack. The first one is suitable for electronic payment and certified email, but the second one is suitable for digital contract signature.

关键词： Fair non-repudiation protocol Fairness Non-repudiation Timeliness

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Vertical Transport of Ultrafine Particles and Turbulence Evolution Impact on New Particle Formation at the Surface & Canton Tower

SSRN

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SSRN 2023年

作者： Wu, Hao Li, Zhanqing Hai, Shangfei Gao, Yang Jiang, Jingkun Zhao, Bin Cribb, Maureen Zhang, Dongmei Pu, Dongyang Liu, Mengqi Wang, Chunling Lan, Jing Wang, Yuying Key Laboratory of China Meteorological Administration Atmospheric Sounding School of Electrical Engineering Chengdu University of Information Technology Chengdu610225 China Department of Atmospheric and Oceanic Science University of Maryland College ParkMD20742 United States College of Oceanic and Atmospheric Sciences Ocean University of China Qingdao266100 China Frontiers Science Center for Deep Ocean Multispheres and Earth System Key Laboratory of Marine Environmental Science and Ecology Ministry of Education Ocean University of China Qingdao266100 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing100084 China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing100084 China College of Global Change and Earth System Science Beijing Normal University Beijing100875 China Guangzhou Climate and Agrometeorology Center Guangzhou511430 China Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration Nanjing University of Information Science & Technology Nanjing210044 China

New particle formation (NPF) is a key process occurring in the planetary boundary layer (PBL). Newly formed particles are an important source of aerosols and cloud condensation nuclei (CCN) that influence clouds and climate, while the distribution of these new particles at different altitudes has rarely been studied. In-situ measurements of ultrafine particles (UFP) observed at the ground and at the top of the Canton Tower (454 m) located in downtown Guangzhou in southern China were analyzed using the measurements of multiple meteorological and physicochemical quantities, as both observed during a field campaign and simulated with the WRF-chem model. We found that turbulence and NPF characters vary considerably with heights, with UFP concentration diminishing by half from the surface to the tower top. This indicates the UFP transports upward from the ground in the lower boundary layer. A consistent relationship is established between the occurrences of NPF and the evolution of turbulence. The correlation between the exchange ratio at the tower top has correlated well with nucleation growth, suggesting that turbulence can play an important role in the episodes of NPF growth, whose growth rate is closely related to the turbulence exchange ratio, effectively dictating the ultrafine particle concentration before and during the lockdown period. A new mechanism is thus hypothesized: NPF happens eailer near the surface and grows faster at the upper PBL, attributed to condensable vapors being transported by turbulent vertical mixing in the boundary layer. Model simulations using the WRF-Chem model reveal that the exchange ratio changed the NPF parameters, supporting the proposed mechanism that the evolution of the PBL variation has a significant impact on NPF, which should not be omitted in the NPF research, since this physical factor could be a dominant one in the NPF mechanism. © 2023, The Authors. All rights reserved.

关键词： Turbulence

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Integral Inequalities for the Analysis of Distributed Parameter systems: A Complete Characterization via the Least-Squares Principle

Integral Inequalities for the Analysis of Distributed Parame...

引用

American Control Conference (ACC)

作者： Qian Feng Alexandre Seuret Sing Kiong Nguang Feng Xiao School of Control and Computer Engineering North China Electric Power University Beijing China Dpto de Ingeniería de Sistemas y Automática Universidad de Sevilla Camino de los Descubrimientos Sevilla Spain Department of Electrical Computer and Software Engineering The University of Auckland New Zealand State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

A wide variety of integral inequalities (IIs) have been developed and studied for the stability analysis of distributed parameter systems using the Lyapunov functional approach. However, no unified mathematical framework has been proposed that could characterize the similarity and connection between these IIs, as most of them was introduced in a dispersed manner for the analysis of specific types of systems. Additionally, the extent to which the generality of these IIs can be expanded and the optimality of their lower bounds (LBs) remains open questions. In this study, we introduce two general classes of IIs that can generalize nearly all IIs in the literature. The integral kernels of the LBs of our IIs can contain an unlimited number of weighted £2 functions that are linearly independent in a Lebesgue sense. Moreover, we not only estalish the equivalence relations between the LBs of our IIs, but also demonstrate that these LBs are guaranteed by the least squares principle, implying asymptotic convergence to the upper bound when the kernels functions constitutes a Schauder basis of the underlying Hilbert space. Owing to their general structures, our IIs are applicable in a variety of contexts, such as the stability analysis of coupled PDE-ODE systems or cybernetic systems with delay structures.

关键词： Upper bound Integral equations Stability analysis Hilbert space Linear matrix inequalities Delays Kernel

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Wide-Area Damping Control Enhanced by Non-Invasive Data Verification Against False Data Injection Attacks

SSRN

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SSRN 2024年

作者： Sun, Zhenglong Long, Hao Li, Zewei Gao, Bo Wei, Xiaoqiang Belikov, Juri Levron, Yoash Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy State Grid Jiashan County Power Supply Company Jiaxing China State Grid Heilongjiang Electric Power Company Limited Heilongjiang China The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa Israel Department of Software Science Tallinn University of Technology Tallinn Estonia Northeast Electric Power University China

The increasing interconnectivity of power systems and the integration of renewable energy sources into the grid have exacerbated the low-frequency oscillation problem, which directly impacts the stability of the power grid. The performance of wide-area damping control (WADC), which is an effective oscillation suppression technique , is susceptible to false data injection attacks (FDIA). This study proposes a non-intrusive data verification method using an adaptive variational autoencoder (AVAE) combined with a deep convolutional neural network (DCNN) to enhance WADC’s resistance to FDIA. The method integrates data dimensionality reduction of AVAE and reconstruction capabilities with DCNN’s feature extraction advantages. Itemploys adaptive thresholds to dynamically track grid data fluctuations, accurately distinguishing between normal and abnormal measurements and improving attack detection and performance discrimination. In addition, the study proposes an intelligent repair process based on latent representations for anomalous data in the repair module, using hierarchical clustering and Gaussian mixture models post-Bayesian inference to repair data. AVAE then reconstructs the repaired data, mitigating attack impacts. Simulations on IEEE 39-Bus, 68-Bus, and 118-bus systems demonstrate that this method effectively detects network attacks and restores manipulated signals with minimal reconstruction errors, thus ensuring grid stability and reliability. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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A new quantum machine learning algorithm: split hidden quantum Markov model inspired by quantum conditional master equation

arXiv

引用

arXiv 2023年

作者： Li, Xiao-Yu Zhu, Qin-Sheng Hu, Yong Wu, Hao Yang, Guo-Wu Yu, Lian-Hui Chen, Geng School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China School of Physics University of Electronic Science and Technology of China Chengdu610054 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610103 China Institute of Electronics and Information Industry Technology of Kash Kash 844000 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610054 China

The Hidden Quantum Markov Model (HQMM) has significant potential for analyzing time-series data and studying stochastic processes in the quantum domain as an upgrading option with potential advantages over classical Markov models. In this paper, we introduced the split HQMM (SHQMM) for implementing the hidden quantum Markov process, utilizing the conditional master equation with a fine balance condition to demonstrate the interconnections among the internal states of the quantum system. The experimental results suggest that our model outperforms previous models in terms of scope of applications and robustness. Additionally, we establish a new learning algorithm to solve parameters in HQMM by relating the quantum conditional master equation to the HQMM. Finally, our study provides clear evidence that the quantum transport system can be considered a physical representation of HQMM. The SHQMM with accompanying algorithms present a novel method to analyze quantum systems and time series grounded in physical implementation. © 2023, CC BY-NC-SA.

关键词： Stochastic systems

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Integral Inequalities for the Analysis of Distributed Parameter systems: A Complete Characterization via the Least-Squares Principle

arXiv

引用

arXiv 2023年

作者： Feng, Qian Seuret, Alexandre Nguang, Sing Kiong Xiao, Feng School of Control and Computer Engineering North China Electric Power University Beijing China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China Dpto de Ingeniería de Sistemas y Automática Universidad de Sevilla Camino de los Descubrimientos Sevilla Spain Department of Electrical Computer and Software Engineering The University of Auckland New Zealand

A wide variety of integral inequalities (IIs) have been developed and studied for the stability analysis of distributed parameter systems using the Lyapunov functional approach. However, no unified mathematical framework has been proposed that could characterize the similarity and connection between these IIs, as most of them was introduced in a dispersed manner for the analysis of specific types of systems. Additionally, the extent to which the generality of these IIs can be expanded and the optimality of their lower bounds (LBs) remains open questions. In this study, we introduce two general classes of IIs that can generalize nearly all IIs in the literature. The integral kernels of the LBs of our IIs can contain an unlimited number of weighted L2 functions that are linearly independent in a Lebesgue sense. Moreover, we not only estalish the equivalence relations between the LBs of our IIs, but also demonstrate that these LBs are guaranteed by the least squares principle, implying asymptotic convergence to the upper bound when the kernels functions constitutes a Schauder basis of the underlying Hilbert space. Owing to their general structures, our IIs are applicable in a variety of contexts, such as the stability analysis of coupled PDE-ODE systems or cybernetic systems with delay structures. Copyright © 2023, The Authors. All rights reserved.

关键词： Least squares approximations

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Towards Task Sampler Learning for Meta-Learning

arXiv

引用

arXiv 2023年

作者： Wang, Jingyao Qiang, Wenwen Su, Xingzhe Zheng, Changwen Sun, Fuchun Xiong, Hui National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Technology Tsinghua University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing task diversity will enhance the generalization ability of meta-learning models. However, this paper challenges this view through empirical and theoretical analysis. We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models;(ii) over-constraining task diversity may incur the risk of under-fitting or overfitting during training;and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty. Based on this insight, we design a novel task sampler, called Adaptive Sampler (ASr). ASr is a plug-and-play module that can be integrated into any meta-learning framework. It dynamically adjusts task weights according to task diversity, task entropy, and task difficulty, thereby obtaining the optimal probability distribution for meta-training tasks. Finally, we conduct experiments on a series of benchmark datasets across various scenarios, and the results demonstrate that ASr has clear advantages. The code is publicly available at https://***/WangJingyao07/AdaptiveSampler. Copyright © 2023, The Authors. All rights reserved.

关键词： Entropy

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A more compact object detector head network with feature enhancement and relational reasoning

arXiv

引用

arXiv 2021年

作者： Zhang, Wenchao Fu, Chong Chang, Xiangshi Zhao, Tengfei Li, Xiang Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Baidu Smart Life Group NO. 10 Xibeiwang East Street Ke Ji Yuan Haidian District Beijing100193 China School of Computer Science University of Auckland New Zealand

Modeling implicit feature interaction patterns is of significant importance to object detection tasks. However, in the two-stage detectors, due to the excessive use of hand-crafted components, it is very difficult to reason about the implicit relationship of the instance features. To tackle this problem, we analyze three different levels of feature interaction relationships, namely, the dependency relationship between the cropped local features and global features, the feature autocorrelation within the instance, and the cross-correlation relationship between the instances. To this end, we propose a more compact object detector head network (CODH), which can not only preserve global context information and condense the information density, but also allows instance-wise feature enhancement and relational reasoning in a larger matrix space. Without bells and whistles, our method can effectively improve the detection performance while significantly reducing the parameters of the model, e.g., with our method, the parameters of the head network is 0.6× smaller than the state-of-the-art Cascade R-CNN, yet the performance boost is 1.3% on COCO test-dev. Without losing generality, we can also build a more lighter head network for other multi-stage detectors by assembling our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

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