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Research Square 2021年

作者： Meng, Lubin Huang, Jian Zeng, Zhigang Jiang, Xue Yu, Shan Jung, Tzyy-Ping Lin, Chin-Teng Chavarriaga, Ricardo Wu, Dongrui Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Brainnetome Center and National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China La Jolla CA United States Center for Advanced Neurological Engineering Institute of Engineering in Medicine UCSD La Jolla CA United States Centre of Artificial Intelligence Faculty of Engineering and Information Technology University of Technology Sydney Australia ZHAW DataLab Zürich University of Applied Sciences Winterthur8401 Switzerland

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to the wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks, e.g., the attacker can add tiny adversarial perturbations to a test sample to fool the model, or poison the training data to insert a secret backdoor. Previous research has shown that adversarial attacks are also possible for EEG-based BCIs. However, only adversarial perturbations have been considered, and the approaches are theoretically sound but very difficult to implement in practice. This article proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which is more feasible in practice and has never been considered before. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs. © 2021, CC BY.

关键词： Electroencephalography

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A model-free four component scattering power decomposition for polarimetric SAR data

TechRxiv

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TechRxiv 2020年

作者： Dey, Subhadip Bhattacharya, Avik Frery, Alejandro C. López-Martínez, Carlos The Microwave Remote Sensing Lab Center of Studies in Resources Engineering Indian Institute of Technology Bombay India The School of Mathematics and Statistics Victoria University of Wellington New Zealand The Key Lab of Intelligent Perception and Image Understanding of the Ministry of Education Xidian University Xi’an China Barcelona Spain

Target decomposition methods of polarimetric Synthetic Aperture Radar (PolSAR) data explain scattering information from a target. In this regard, several conventional model-based methods utilize scattering power components to analyze polarimetric SAR data. However, the typical hierarchical process to enumerate power components uses various branching conditions, leading to several limitations. These techniques assume ad hoc scattering models within a radar resolution cell. Therefore, the use of several models makes the computation of scattering powers ambiguous. Some common issues of model-based decompositions are related to the compensation of the orientation angle about the radar line of sight and the negative power components’ occurrence. We propose a model-free four-component scattering power decomposition that alleviates these issues. In the proposed approach, we use the non-conventional 3D Barakat degree of polarization to obtain the scattered electromagnetic wave’s polarization state. The degree of polarization is used to obtain the even-bounce, odd-bounce, and diffused scattering power components. Along with this, a measure of target scattering asymmetry is also proposed, which is then suitably utilized to obtain the helicity power. All the power components are roll-invariant, nonnegative and unambiguous. In addition to this, we propose an unsupervised clustering technique that preserves the dominance of the scattering power components for different targets. This clustering technique assists in understanding the importance of diverse scattering mechanisms based on target characteristics. The technique adequately captures the clusters’ variations from one target to another according to their physical and geometrical properties. This study utilized two dual-frequency (i.e., C- and L-bands) polarimetric SAR data. These two data sets are used to show the decomposition powers’ effectiveness and the apparent interpretability of the clustering results. The code is a

关键词： Synthetic aperture radar

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Parity space-based model mismatch detection for linear discrete time-invariant systems with unknown disturbances

Parity space-based model mismatch detection for linear discr...

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Chinese control Conference (CCC)

作者： Yi Tang Dan Ling Hong Zhang Yanewei Wang Ying Zheng the Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical and Information Engineering Zhengzhou University of Light Industry Zhengzhou China School of Mechanical & Electrical Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9789881563903

ISBN: (纸本)9781728165233

Model mismatch is one of the main factors of control performance degradation. In this paper, a new model mismatch detection approach with parity space-based methods is proposed for linear discrete time-invariant (LDTI) systems. This paper establishes a parity space-based residual evaluation function for the detection of model mismatch. The problem of parity space-based model mismatch is formulated on a state space model of a LDTI system. A standard evaluation function and its thresholds for the detection of model mismatch are proposed by providing the parity model-based residuals. It is shown the proposed approach results in an optimal fault detectability without any assumptions on the probability distribution of unknown disturbances. The effectiveness of the proposed methods is demonstrated by the simulation study of the paper machine head box control example.

关键词： Generators System performance Benchmark testing Sensitivity Degradation Fault detection Aerospace electronics

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Software Test Resource Allocation Based on Adaptive Operator Selection

Software Test Resource Allocation Based on Adaptive Operator...

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2018 2nd International Conference on Electrical engineering and Automation（ICEEA2018）

作者： Wenjie Chang Baolong Guo Institute of Intelligent Control and Image Engineering of Xidian University

With the rapid development of computer software, making the complexity of the software system increased dramatically, the inevitable cost of testing resources is also increasing. Under the conditions of limited resources, how to better find the balance between resource consumption and reliability obtains more and more people's attention. Optimization of test resource allocation(OTRAPs) involves finding the optimal reliability, cost, etc. Therefore, the traditional test resource allocation optimization problem is a multi-objective optimization problem. In recent years, many effective algorithms, such as MOEA/D[15] and other famous algorithms, and achieved good results. However, the shortcomings of these algorithms are the use of a single operator and fixed neighborhood size, although the operator is not adapted to each search stage and small neighborhood size can accelerate convergence and big neighborhood size can get rid of the local optimal solution, The algorithm(DS-MAB-MOEA/D) we proposed based on the Multiarmed Bandit principle to adaptively select the excellent operator and neighborhood size in the pool during the different stages of evolution. Considering the retention of Pareto set's diversity, this paper embeded distance sorting algorithm in DS-MAB-MOEA/D algorithm and applies it to 16-module system, Experiments show that the algorithm is better than the MOEA/D algorithm.

关键词： computer software software test resource allocation multi-objective optimization multi-armed bandit moea/d

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Research on Reliability Analysis of Component-Based Software

Research on Reliability Analysis of Component-Based Software

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2018 2nd International Conference on Electrical engineering and Automation（ICEEA2018）

作者： Wenna Ren Baolong Guo Institute of Intelligent Control and Image Engineering of Xidian University

Component-based software development method has gradually become the mainstream software development method, with the rapid development of component technology, the software development method of complex software system with component design is more and more mature, but the software reliability analysis technology based on this technology is comparatively backward. At present, the component software reliability model does not consider the influence of component importance and complexity factors on software reliability, and presents the complexity analysis model of component software and the reliability evaluation model of important analysis model. In component-based software system, the granularity of component is usually less than the module, so based on the structure of component software, the complexity measure model with functional granularity is established, and the important measurement model is established according to the level, on the basis of which the state transition process between components is described as Markov process, This method calculates the reliability system-level reliability activity of software system by integrating component-level reliability evaluation. The feasibility of this method is validated by real time software, which is of great significance to the reliability evaluation of software development and the prediction of software reliability in the later stage.

关键词： component reliability evaluation markov process state transfer

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Adversarial Attacks and Defenses in Physiological Computing: A Systematic Review

arXiv

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arXiv 2021年

作者： Wu, Dongrui Xu, Jiaxin Fang, Weili Zhang, Yi Yang, Liuqing Xu, Xiaodong Luo, Hanbin Yu, Xiang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China The School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China The College of Public Administration Huazhong University of Science and Technology Wuhan430074 China The University of Michigan Ann ArborMI48109 United States The School of Management and Sino European Institute for Intellectual Property Huazhong University of Science and Technology Wuhan430074 China

Physiological computing uses human physiological data as system inputs in real time. It includes, or significantly overlaps with, brain-computer interfaces, affective computing, adaptive automation, health informatics, and physiological signal based biometrics. Physiological computing increases the communication bandwidth from the user to the computer, but is also subject to various types of adversarial attacks, in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output, leading to possible user confusion, frustration, injury, or even death. However, the vulnerability of physiological computing systems has not been paid enough attention to, and there does not exist a comprehensive review on adversarial attacks to them. This paper fills this gap, by providing a systematic review on the main research areas of physiological computing, different types of adversarial attacks and their applications to physiological computing, and the corresponding defense strategies. We hope this review will attract more research interests on the vulnerability of physiological computing systems, and more importantly, defense strategies to make them more secure. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Dual sentence representation model integrating prior knowledge for bio-text-mining

Dual sentence representation model integrating prior knowled...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhijing Li Yangyang Lan Saikat Chatterjee Pargorn Puttapirat Xiangrong Zhang Chen Li School of Computer Science and Technology Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech Xi’an Jiaotong University Xi’an China Communication Theory Laboratory School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi’an China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Data mining, especially the extraction of the relationship between genes and proteins, plays an important role in the biomedical field. Several related models have been proposed for data mining in the biomedical domain. Furthermore, manually curated biomedical knowledge bases, which could assist the task, have been used to enhance the data-mining model. However, due to the limitation of methods, much prior knowledge information is not be fully exploited. In this work, we propose a novel method that reasonably applied the curated prior knowledge for biomedical text mining by dual sentence representation models; one model is for the experimental data and the other one is for the prior knowledge information sentence. We evaluated our method on two community-supported datasets; BioNLP and BioCreative corpora. The experimental results demonstrate that the dual sentence representation model can successfully utilize external prior knowledge information to extract relationship from biomedical text. Our method can achieve state-of-art results and it could be an application of biomedical relation extraction in the future.

关键词： Biological system modeling Task analysis Proteins Semantics Knowledge engineering Data models Biology

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NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM image Processing

NiuEM: A Nested-iterative Unsupervised Learning Model for Si...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Rui Hu Jiaming Cai Wangjie Zheng Yang Yang Hong-Bin Shen Shanghai Jiao Tong University and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728162157

ISBN: (纸本)9781728162164

Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges is the high noise in the images. A common method is to cluster the images with close projecting angles to get mean images, which are used for 3D reconstruction. However, due to the extremely low signal-to-noise-ratio, common clustering methods often fail to obtain high-quality mean images, leading to poorly reconstructed structures. In this study, we present a new unsupervised learning framework, called NiuEM, to discriminate images captured from different angles and yield cluster-mean images. NiuEM first generates pseudo-labels and then exploits both contrastive loss and cross-entropy loss for training convolutional layers to learn feature representations. Moreover, the pseudo-labels are updated iteratively to enhance the reliability of labels. We assess the performance of NiuEM on four data sets via both visualized and quantitative experiments. Especially, two kinds of metrics are adopted to measure the performance, regarding the clustering quality and the resolution of reconstructed 3D models, respectively. The experimental results show that NiuEM achieves very competitive clustering accuracy in the comparison with the state-of-the-art image clustering methods. Moreover, the cluster mean images yielded by NiuEM lead to better initial 3D models compared with the mainstream reconstruction tools.

关键词： Three-dimensional displays Feature extraction image reconstruction Task analysis Solid modeling Electron microscopy Convolutional neural networks

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Transfer Learning for EEG-Based Brain-Computer Interfaces: A Review of Progress Made Since 2016

arXiv

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arXiv 2020年

作者： Wu, Dongrui Xu, Yifan Lu, Bao-Liang The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Qing Yuan Research Institute Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. The most common non-invasive BCI modality, electroencephalogram (EEG), is sensitive to noise/artifact and suffers between-subject/within-subject non-stationarity. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time-consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications - motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks - are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain computer interface

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Refined recognition and intelligent smart interference of radar signal

Refined recognition and intelligent smart interference of ra...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Jianquan Yuan Yunong Bu Shuyuan Yang Qingxi Chi Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-Mechanical Engineering Institute Beijing China Beijing Electro-Mechanical Engineering Institute Beijing China Xidian University Xi’an China

ISBN: (纸本)9781728137933

Facing the complex electromagnetic environment on the battlefield, how to effectively identify important radar signals by jammers and how to implement strong and effective electromagnetic interference to the enemy's huge radar detection system under the condition of limited jamming resources will be the difficult problems that need to be solved continuously in the electronic warfare field. The solution of these problems will be the premise of effective interference for wartime jammers. In this paper, the technical gap of traditional jammers is analyzed. For radar signal perception, a refined perception and recognition method is proposed, forming the algorithm idea of multi-scale feature extraction and recognition based on radar fingerprint and multi-channel integrated multi-label recognition and classification based on radar radiation source. For interference implementation, the idea of smart interference based on deep GAN is put forward, laying the ground for further research on cognitive electronic warfare.

关键词： Cognitive electronic warfare Smart interference Wartime application RADAR SIGNALS jammers Recognition (Psychology) Electromagnetic environments Electromagnetic fields electronic warfare PROBLEM SOLUTION PREMISES interference interference Battlefields

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