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IEEE Conference on Computer Communications Workshops, INFOCOM Wksps

作者： Zhiqiang Wang Pingchuan Ma Xiaoxiang Zou Jianyi Zhang Tao Yang Beijing Electronic Science and Technology Institute State Information Center National Computer Network Emergency Response Technical Team/Coordination Center of China Key Lab of Information Network Security Ministry of Public Security

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186962

Recent years have witnessed a boom of connected medical devices, which brings security issues in the meantime. Medical imaging devices, an essential part of medical cyber-physical systems, play a vital role in modern hospitals and are often life-critical. However, security and privacy issues in these medical cyber-physical systems are sometimes ignored. We perform an empirical study on imaging devices to analyse the security of medical cyber-physical systems. To be precise, we design a threat model and propose prospective attack techniques for medical imaging devices. To tackle potential cyber threats, we introduce protection mechanisms, evaluate the effectiveness and efficiency of protection mechanisms as well as its interplay with attack techniques. To scoring security, we design a hierarchical system that provides actionable suggestions for imaging devices in different scenarios. We investigate 15 devices from 9 manufacturers to demonstrate empirical comprehension and real-world security issues.

关键词： Security Virtual private networks Runtime Hospitals DICOM

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Exact Decomposition of Joint Low Rankness and Local Smoothness Plus Sparse Matrices

arXiv

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arXiv 2022年

作者： Peng, Jiangjun Wang, Yao Zhang, Hongying Wang, Jianjun Meng, Deyu School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi'an Jiaotong University Shaan'xi Xi’an710049 China The Center for Intelligent Decision-making and Machine Learning School of Management Xian Jiaotong University Shaan'xi Xi’an China The College of Artificial Intelligence Southwest University Chongqing400715 China Macau Institute of Systems Engineering Macau University of Science and Technology Taipa China

It is known that the decomposition in low-rank and sparse matrices (L+S for short) can be achieved by several Robust PCA techniques. Besides the low rankness, the local smoothness (LSS) is a vitally essential prior for many real-world matrix data such as hyperspectral images and surveillance videos, which makes such matrices have low-rankness and local smoothness property at the same time. This poses an interesting question: Can we make a matrix decomposition in terms of L&LSS +S form exactly? To address this issue, we propose in this paper a new RPCA model based on three-dimensional correlated total variation regularization (3DCTV-RPCA for short) by fully exploiting and encoding the prior expression underlying such joint low-rank and local smoothness matrices. Specifically, using a modification of Golfing scheme, we prove that under some mild assumptions, the proposed 3DCTV-RPCA model can decompose both components exactly, which should be the first theoretical guarantee among all such related methods combining low rankness and local smoothness. In addition, by utilizing Fast Fourier Transform (FFT), we propose an efficient ADMM algorithm with a solid convergence guarantee for solving the resulting optimization problem. Finally, a series of experiments on both simulations and real applications are carried out to demonstrate the general validity of the proposed 3DCTV-RPCA model. Copyright © 2022, The Authors. All rights reserved.

关键词： Fast Fourier transforms

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A conditional-probability zone transformation coding method for categorical features 19

A conditional-probability zone transformation coding method ...

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2019 ACM Turing Celebration Conference - China, ACM TURC 2019

作者： He, Liang Shen, Chao Li, Yun National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu Sichuan China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Shaanxi Xi'an China

ISBN: (纸本)9781450371582

It has been a key issue for solving problems efficiently by machine learning models with code categorical features. The state-of-the-art one-hot coding is a widely accepted method to convert the categorical features into numerical values. However, it attracts a sparse space and meaningless value after coding. We come up with a novel coding method based on conditional probability after dividing the features into zones, which is called Conditional-probability-based Zone Transformation (CZT) coding. CZT coding calculates the conditional probability of each feature, then divides the features into several zones according to the probability and finally codes the features in each zone. We mathematically prove that compared with the state-of-the-art method, CZT coding reduces the code length by at least the mean of feature space and the issue becomes into an easier one after CZT coding for the following machine learning model. Finally, using the same neuron network as the classifier, we compare the performance of CZT coding and one-hot coding by using the titanic dataset, where most of the features are categorical, and the result is that CZT coding makes the classifier performs better both on the accuracy and steadiness. © 2019 Association for Computing Machinery.

关键词： Probability

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Optimization from Structured Samples for Coverage Functions

arXiv

引用

arXiv 2020年

作者： Chen, Wei Sun, Xiaoming Zhang, Jialin Zhang, Zhijie Microsoft Research Asia Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

We revisit the optimization from samples (OPS) model, which studies the problem of optimizing objective functions directly from the sample data. Previous results showed that we cannot obtain a constant approximation ratio for the maximum coverage problem using polynomially many independent samples of the form {Si, f (Si)}ti=1 (Balkanski et al., 2017), even if coverage functions are (1 − ǫ)-PMAC learnable using these samples (Badanidiyuru et al., 2012), which means most of the function values can be approximately learned very well with high probability. In this work, to circumvent the impossibility result of OPS, we propose a stronger model called optimization from structured samples (OPSS) for coverage functions, where the data samples encode the structural information of the functions. We show that under three general assumptions on the sample distributions, we can design efficient OPSS algorithms that achieve a constant approximation for the maximum coverage problem. We further prove a constant lower bound under these assumptions, which is tight when not considering computational efficiency. Moreover, we also show that if we remove any one of the three assumptions, OPSS for the maximum coverage problem has no constant approximation. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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YOLO＿v3-Based Pulmonary Nodules Recognition System

YOLO＿v3-Based Pulmonary Nodules Recognition System

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作者： Wenhao Deng Zhiqiang Wang Xiaorui Ren Xusheng Zhang Bing Wang Tao Yang Guangdong Hangyu Satellite Technology Co. Ltd. State Information Center Beijing Electronic Science and Technology Institute Key Lab of Information Network Security Ministry of Public Security

The incidence of pneumonia and lung cancer is high in *** CT images are widely used in the screening and adjuvant treatment of lung diseases due to their advantages of a thin layer, high definition, and low *** manual film-reading method has strong subjectivity, depends on the doctor's experience, and now hospitals produce a large number of lung CT images every day. The manual film-reading method is inefficient. The machine can help doctors to do lesion screening, auxiliary diagnosis, and treatment. Conventional machine learning methods have been applied to the recognition of pulmonary nodules in CT images. Still, these methods need to manually extract features that are not comprehensive or proper, resulting in misdiagnosis and missed diagnosis. These methods enable to rapidly detect lung nodules, which is terrible for patients to receive treatment in a timely manner. With the development of deep learning technology, Convolutional Neural networks（CNNs） have been widely used in recognizing images, such as facial identification, character recognition,car plate recognition, etc. Its ability to solve computer vision problems has won the approval in some natural scene tasks. In this paper, the YOLO3 is used for feature extraction and classification, and residual network（ResNet）, one of the classical CNN models, is used to decrease the false-positive rate, which improves the detection accuracy of pulmonary nodules in CT images.

关键词： YOLO ResNet Medical imaging analysis Pulmonary nodules

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Climatic and Biotic Controls of Evapotranspiration Across Grassland Ecosystems on Tibetan Plateau

SSRN

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SSRN 2024年

作者： Peng, Siyuan Yang, Yue Luo, Dengnan Zeng, Xiang Liang, Minqi Tao, Long Zhang, Guangru Li, Pan Liao, Weijie Guo, Qun Cao, Ruochen Li, Yuzhe Zhang, Weirong Hu, Zhongmin School of Geography South China Normal University Shipai Campus Guangzhou510631 China Hainan Baoting Tropical Rainforest Ecosystem Observation and Research Station Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation School of Ecology and Environment Hainan University Haikou570228 China Center for Eco-Environment Restoration Engineering of Hainan Province Hainan University Haikou570228 China Facility of Geographical Science Beijing Normal University Beijing100875 China Zhuhai Branch of State Key Laboratory of Earth Surface Processes and Resource Ecology Beijing Normal University Zhuhai519087 China School of Atmospheric Sciences Guangdong Province Data Center of Terrestrial and Marine Ecosystems Carbon Cycle Sun Yat-sen University Guangdong Zhuhai510245 China Key Lab of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing100101 China College of Resources and Environment University of Chinese Academy of Sciences Beijing101408 China International Institute for Earth System Sciences Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing University Jiangsu210023 China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources School of Geography and Ocean Science Nanjing University Jiangsu210023 China Key Laboratory of Land Surface Pattern and Simulation Institute of Geographical Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China

Understanding the spatial variations of evapotranspiration (ET) and its components on Tibetan Plateau (TP) is pivotal for grasping the regional water cycle and energy balance dynamics. Yet it is unclear how the changes of the intricate synergy among diverse environment factors significantly impacts the patterns of ET and its transformation state. Addressing this gap, this study employed observational data from 14 sites, utilizing the Shuttleworth-Wallace-Hu (SWH) model to investigate the spatial relationships between ET, its components, and influencing factors. Model parameters of SWH were optimized by Monte Carlo method, achieving a commendable simulation performance with a mean R2 value of 0.75. Our analysis reveals that in alpine meadow, the ratio of transpiration (T) to ET (T/ET) is generally higher (0.55) compared to alpine steppe (0.39). The T/ET values for hover around or below 0.5, indicating a balanced contribution of various ET components. Spatially, both PPT and LAI have an impact on ET and T/ET. PPT emerges as the primary driver of interannual spatial variations in ET, while LAI plays a crucial role in regulating T/ET. Our results further demonstrate that a transition from water-dominated to energy-dominated systems in TP grassland ecosystem at an approximately aridity index of 0.73. This is noteworthy considering the general application of an aridity index of 0.65 to delineate arid from humid regions. It implies that TP grasslands, within the 0.65-0.73 range, remain primarily water-driven. This insight holds substantial implications for comprehending water-energy dynamics in similar ecosystems, challenging the conventional binary categorization of arid and humid areas. Such findings are of immense significance in enhancing understanding of the intricate interactions of the water cycle between land and the atmosphere. © 2024, The Authors. All rights reserved.

关键词： Water supply

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GraphWGAN: Graph Representation Learning with Wasserstein Generative Adversarial networks

GraphWGAN: Graph Representation Learning with Wasserstein Ge...

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International Conference on Big Data and Smart Computing (BIGCOMP)

作者： Rong Yan Huawei Shen Cao Qi Keting Cen Li Wang College of Big Data Taiyuan University Of Technology Taiyuan China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China Institute of Computing Technology Beijing China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Graph representation learning aims to represent vertices as low-dimensional and real-valued vectors to facilitate subsequent downstream tasks, i.e., node classification, link predictions. Recently, some novel graph representation learning frameworks, which try to approximate the underlying true connectivity distribution of the vertices, show their superiority. These methods characterize the distance between the true connectivity distribution and generated connectivity distribution by Kullback-Leibler or Jensen-Shannon divergence. However, since these divergences are not continuous with respect to the generator's parameters, such methods easily lead to unstable training and poor convergence. In contrast, Wasserstein distance is continuous and differentiable almost everywhere, which means it can produce more reliable gradient, allowing the training more stable and more convergent. In this paper, we utilize Wasserstein distance to characterize the distance between the underlying true connectivity distribution and generated distribution in graph representation learning. Experimental results show that the accuracy of our method exceeds existing baselines in tasks of both node classification and link prediction.

关键词： Hafnium Conferences Big Data

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Sharding for Blockchain based Mobile Edge Computing System: A Deep Reinforcement Learning Approach

Sharding for Blockchain based Mobile Edge Computing System: ...

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2021 IEEE Global Communications Conference (GLOBECOM)

作者： Shijing Yuan Jie Li Jinghao Liang Yuxuan Zhu Xiang Yu Jianping Chen Chentao Wu Shanghai Jiao Tong University China Zhejiang Lab Research Center for Intelligent Network China University of Michigan-Shanghai Jiao Tong University Joint Institute Shanghai Jiao Tong University China Jiangsu Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and Technology China

ISBN: (纸本)9781728181059

With the growth of data scale in the mobile edge computing (MEC) network, data security of the MEC network has become a burning concern. The application of blockchain technology in MEC enhances data security and privacy protection. However, throughput becomes the bottleneck of the blockchain-enabled MEC system. Hence, this paper proposes a novel hierarchical and partitioned blockchain framework to improve scalability while guaranteeing the security of partitions. Next, we model the joint optimization of throughput and security as a Markov decision process (MDP). After that, we adopt deep reinforcement learning (DRL) based algorithms to obtain the number of partitions, the size of micro blocks and the large block generation interval. Finally, we analyze the security and throughput performance of proposed schemes. Simulation results demonstrate that proposed schemes can improve throughput while ensuring the security of partitions.

关键词： Multi-access edge computing Simulation Data security Scalability Reinforcement learning Markov processes Throughput

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Ranking enhanced dialogue generation

arXiv

引用

arXiv 2020年

作者： Hao, Changying Pang, Liang Lan, Yanyan Sun, Fei Guo, Jiafeng Cheng, Xueqi CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China

How to effectively utilize the dialogue history is a crucial problem in multi-turn dialogue generation. Previous works usually employ various neural network architectures (e.g., recurrent neural networks, attention mechanisms, and hierarchical structures) to model the history. However, a recent empirical study by Sankar et al. has shown that these architectures lack the ability of understanding and modeling the dynamics of the dialogue history. For example, the widely used architectures are insensitive to perturbations of the dialogue history, such as words shuffling, utterances missing, and utterances reordering. To tackle this problem, we propose a Ranking Enhanced Dialogue generation framework in this paper. Despite the traditional representation encoder and response generation modules, an additional ranking module is introduced to model the ranking relation between the former utterance and consecutive utterances. Specifically, the former utterance and consecutive utterances are treated as query and corresponding documents, and both local and global ranking losses are designed in the learning process. In this way, the dynamics in the dialogue history can be explicitly captured. To evaluate our proposed models, we conduct extensive experiments on three public datasets, i.e., bAbI, PersonaChat, and JDC. Experimental results show that our models produce better responses in terms of both quantitative measures and human judgments, as compared with the state-of-the-art dialogue generation models. Furthermore, we give some detailed experimental analysis to show where and how the improvements come from. Copyright © 2020, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Device-independent quantum secure direct communication against collective attacks

引用

science Bulletin 2020年第1期65卷 12-20,M0003页

作者： Lan Zhou Yu-Bo Sheng Gui-Lu Long School of Science Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China State Key Laboratory of Low-dimensional Quantum Physics Beijing 100084China Department of Physics Tsinghua UniversityBeijing 100084China

"Device-independent"not only represents a relaxation of the security assumptions about the internal working of the quantum devices,but also can enhance the security of the quantum *** the paper,we put forward the first device-independent quantum secure direct communication(DIQSDC)protocol and analyze its security and communication efficiency against collective *** practical noisy quantum channel condition,the photon transmission loss and photon state decoherence would reduce DI-QSDC’s communication quality and threaten its absolute *** solving the photon transmission loss and decoherence problems,we adopt noiseless linear amplification(NLA)protocol and entanglement purification protocol(EPP)to modify the DI-QSDC *** the help of the NLA and EPP,we can guarantee DI-QSDC’s absolute security and effectively improve its communication quality.

关键词： Quantum cryptography Device-independent quantum secure direct communication Information security Communication efficiency

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