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Spatiotemporal Attacks for Embodied Agents 1

16th European Conference on Computer Vision, ECCV 2020

作者： Liu, Aishan Huang, Tairan Liu, Xianglong Xu, Yitao Ma, Yuqing Chen, Xinyun Maybank, Stephen J. Tao, Dacheng State Key Laboratory of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China UC Berkeley London United States Birkbeck University of London London United Kingdom UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney Sydney Australia

ISBN: (数字)9783030585204

ISBN: (纸本)9783030585198

Adversarial attacks are valuable for providing insights into the blind-spots of deep learning models and help improve their robustness. Existing work on adversarial attacks have mainly focused on static scenes;however, it remains unclear whether such attacks are effective against embodied agents, which could navigate and interact with a dynamic environment. In this work, we take the first step to study adversarial attacks for embodied agents. In particular, we generate spatiotemporal perturbations to form 3D adversarial examples, which exploit the interaction history in both the temporal and spatial dimensions. Regarding the temporal dimension, since agents make predictions based on historical observations, we develop a trajectory attention module to explore scene view contributions, which further help localize 3D objects appeared with highest stimuli. By conciliating with clues from the temporal dimension, along the spatial dimension, we adversarially perturb the physical properties (e.g., texture and 3D shape) of the contextual objects that appeared in the most important scene views. Extensive experiments on the EQA-v1 dataset for several embodied tasks in both the white-box and black-box settings have been conducted, which demonstrate that our perturbations have strong attack and generalization abilities (Our code can be found at https://***/liuaishan/SpatiotemporalAttack). © 2020, Springer Nature Switzerland AG.

关键词： Textures

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HI-Net: Hyperdense inception 3D UNet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Qamar, Saqib Ahmad, Parvez Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional neural networks (CNNs), mainly fully convolutional networks, have shown promising brain tumor segmentation. This paper further boosts the performance of brain tumor segmentation by proposing hyperdense inception 3D UNet (HI-Net), which captures multi-scale information by stacking factorization of 3D weighted convolutional layers in the residual inception block. We use hyper dense connections among factorized convolutional layers to extract more contexual information, with the help of features reusability. We use a dice loss function to cope with class imbalances. We validate the proposed architecture on the multi-modal brain tumor segmentation challenges (BRATS) 2020 testing dataset. Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0.79457, 0.87494, and 0.83712, respectively. © 2020, CC BY.

关键词： Convolution

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Consistent and Complementary Graph Regularized Multi-view Subspace Clustering

arXiv

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arXiv 2020年

作者： Zheng, Qinghai Zhu, Jihua Li, Zhongyu Pang, Shanmin Wang, Jun Chen, Lei School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing210023 China

This study investigates the problem of multi-view clustering, where multiple views contain consistent information and each view also includes complementary information. Exploration of all information is crucial for good multi-view clustering. However, most traditional methods blindly or crudely combine multiple views for clustering and are unable to fully exploit the valuable information. Therefore, we propose a method that involves consistent and complementary graph-regularized multi-view subspace clustering (GRMSC), which simultaneously integrates a consistent graph regularizer with a complementary graph regularizer into the objective function. In particular, the consistent graph regularizer learns the intrinsic affinity relationship of data points shared by all views. The complementary graph regularizer investigates the specific information of multiple views. It is noteworthy that the consistent and complementary regularizers are formulated by two different graphs constructed from the first-order proximity and second-order proximity of multiple views, respectively. The objective function is optimized by the augmented Lagrangian multiplier method in order to achieve multi-view clustering. Extensive experiments on six benchmark datasets serve to validate the effectiveness of the proposed method over other state-of-the-art multi-view clustering methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Optimally Combining Classifiers for Semi-Supervised Learning

arXiv

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arXiv 2020年

作者： Wang, Zhiguo Yang, Liusha Yin, Feng Lin, Ke Shi, Qingjiang Luo, Zhi-Quan School of Science & Engineering Chinese University of Hong Kong Shenzhen518172 China University of Science and Technology of China China Shenzhen Research Institute of Big Data Shenzhen518172 China Huawei Technologies Co. Ltd School of Software Engineering Tongji University Shanghai200092 China

This paper considers semi-supervised learning for tabular data. It is widely known that Xgboost based on tree model works well on the heterogeneous features while transductive support vector machine can exploit the low density separation assumption. However, little work has been done to combine them together for the end-to-end semi-supervised learning. In this paper, we find these two methods have complementary properties and larger diversity, which motivates us to propose a new semi-supervised learning method that is able to adaptively combine the strengths of Xgboost and transductive support vector machine. Instead of the majority vote rule, an optimization problem in terms of ensemble weight is established, which helps to obtain more accurate pseudo labels for unlabeled data. The experimental results on the UCI data sets and real commercial data set demonstrate the superior classification performance of our method over the five state-of-the-art algorithms improving test accuracy by about 3% − 4%. The code can be found at https://***/hav-cam-mit/CTO. Copyright © 2020, The Authors. All rights reserved.

关键词： Support vector machines

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Modeling and Simulation of Infectious Propagation Based on Cellular Automata

Modeling and Simulation of Infectious Propagation Based on C...

引用

IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability (ECBIOS)

作者： Shuang Zhou Sheng Bin Gengxin Sun Qingdao University School of Data Science and Software Engineering Qingdao Shandong China

Based on cellular automaton, combine compartment model, proposed SEIDRS model that can reflect the actual propagation process of infectious diseases. This model combined with factors such as population density, sexual proportion and age structure, rules the evolution rules of the model. Using Matlab to simulate the model, the simulation results show that the larger population density, the infectious disease propagation faster; There are differences in some aspects in sexual proportion, sexual have larger number and high impact factor, make more obvious effects on the spread of infectious diseases; There are differences in age structure, the city have more aged and light people have lower defense capability to infectious diseases, the appropriate age distribution play a more active role to resist the spread of infectious diseases.

关键词： Infectious diseases Mathematical model Sociology Statistics Automata Biological system modeling

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On PID control for synchronization of complex dynamical network with delayed nodes

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science China(Technological sciences) 2019年第8期62卷 1412-1422页

作者： GU HaiBo Lv JinHu LIN ZongLi Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing 100049 China School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University Beijing 100083 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 USA

Over the past two decades, synchronization, as an interesting collective behavior of complex dynamical networks, has been attracting much attention. To reveal and analyze the inherent mechanism of synchronization in complex dynamical networks with time delays in nodes, this paper attempts to use PD and PI control protocols to achieve synchronization. Based on a classical network model, we investigate the PD and PI control for synchronization of complex dynamical networks with delayed nodes and obtain some sufficient conditions. By using Lyapunov functions and appropriate state transformations, we prove that global synchronization can be achieved via the above control protocols. Finally, some simulation examples are illustrated to validate the effectiveness of the proposed theoretical results.

关键词： synchronization PID control complex dynamical network networked control system delayed node

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Simplified Deep Forest Model based Just-In-Time Defect Prediction for Android Mobile Apps

Simplified Deep Forest Model based Just-In-Time Defect Predi...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Kunsong Zhao Zhou Xu Tao Zhang Yutian Tang School of Computer Science Wuhan University Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China Faculty of Information Technology Macau University of Science and Technology Macao China The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728189130

ISBN: (纸本)9781728189147

The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-In-Time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can provide timely feedback by determining whether a new code commit will introduce defects into the apps. As defect prediction performance usually relies on the quality of the data representation and the used classification model, in this work, we modify a state-of-the-art model, called Simplified Deep Forest (SDF) to conduct JIT defect prediction for Android mobile apps. This method uses a cascade structure with ensemble forests for representation learning and classification. We conduct experiments on 10 Android mobile apps and experimental results show that SDF performs significantly better than comparative methods in terms of three performance indicators.

关键词： Forestry software quality Predictive models data models Mobile applications software reliability Security

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MIPI 2022 Challenge on Under-Display Camera Image Restoration: Methods and Results

arXiv

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arXiv 2022年

作者： Zhu, Yurui Wang, Xi Fu, Xueyang Hu, Xiaowei Hu, Jinfan Liu, Xina Chen, Xiangyu Dong, Chao Zhang, Dafeng Huang, Feiyu Liu, Shizhuo Wang, Xiaobing Jin, Zhezhu Jiang, Xuhao Shao, Guangqi Wang, Xiaotao Lei, Lei Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Huang, Tao Fang, Zhenxuan Huang, Mengluan Xu, Junwei Zhang, Yong Yang, Yuechi Shu, Qidi Yang, Zhiwen Li, Shaocong Yao, Mingde Xu, Ruikang Guan, Yuanshen Huang, Jie Xiong, Zhiwei Zhu, Hangyan Liu, Ming Liu, Shaohui Zuo, Wangmeng Jia, Zhuang Song, Binbin Song, Ziqi Mao, Guiting Hou, Ben Liu, Zhimou Ke, Yi Ouyang, Dengpei Han, Dekui Zhang, Jinghao Zhu, Qi Zheng, Naishan Zhao, Feng Jin, Wu Conde, Marcos Nathan, Sabari Timofte, Radu Xu, Tianyi Xu, Jun Hrishikesh, P.S. Puthussery, Densen Jiji, C.V. Biao, Jiang Yuhan, Ding Zhang, Li Wan Xiaoyue, Feng Sijing, Chen Tianheng, Zhong Lu, Jiyang Chen, Hongming Fan, Zhentao Chen, Xiang University of Science and Technology of China China Shanghai Ai Laboratory China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Macau China Samsung Research China Beijing China Xiaomi Beijing China Hefei University of Technology China School of Artificial Intelligence Xidian University China School of Remote Sensing and Information Engineering Wuhan University China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University China Harbin Institute of Technology China Changsha Research Institute of Mining and Metallurgy China Tianjin University China University of Wurzburg Computer Vision Lab Germany Couger Inc. Japan School of Statistics and Data Science Nankai University China Founding Minds Software India Department of Electronics and Communication Srm University Fudan University China Shenyang Aerospace University China Nanjing University of Science and Technology China

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). To bridge the gap, we introduce the first MIPI challenge including five tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Under-Display Camera (UDC) Image Restoration track on MIPI 2022. In total, 167 participants were successfully registered, and 19 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Under-Display Camera Image Restoration. A detailed description of all models developed in this challenge is provided in this paper. More details of this challenge and the link to the dataset can be found at https://***/mipi-challenge/MIPI2022. Copyright © 2022, The Authors. All rights reserved.

关键词： Image reconstruction

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Matrix Classifier On Dynamic Functional Connectivity For Mci Identification

Matrix Classifier On Dynamic Functional Connectivity For Mci...

引用

IEEE International Conference on Image Processing

作者： Lei Zhou Liang Zhang Xiao Bai Jun Zhou School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Software Development Environment Beihang University Beijing School of Information and Communication Technology Griffith University Nathan

ISBN: (数字)9781728163956

ISBN: (纸本)9781728163963

One of the most popular method for Alzheimer's disease (AD) diagnosis is exploring the Brain functional connectivity (FC) from resting-state functional magnetic resonance imaging (RS-fMRI). To early prevent AD, it is crucial to distinguish AD and and its preclinical stage, mild cognitive impairment (MCI) and early MCI (eMCI). In many existing works, dynamic functional connectivity (dFC) which contains rich spatiotemporal information has been exploited for the MCI and eMCI identification. However, most of these dFC based methods only consider the correlation between discrete brain status while ignore the valuable spatiotemporal information contained in dFC. To overcome this limitation, we propose a matrix classifier based method on the dFC signal for MCI and eMCI identification. Specifically, we first represent the dFC correlations by matrix features which contain rich spatiotemporal information and then learn the support matrix machines (SMM) to classify AD and its preclinical stage. Experiments on 600 real people data provide by the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed matrix classifier based method outperforms other FC and dFC based methods for both normal controls (NC)/MCI identification and NC/eMCI identification.

关键词： Feature extraction Correlation Dementia Microsoft Windows Spatiotemporal phenomena Training

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Deep Reinforcement Learning for loT Networks: Age of Information and Energy Cost Tradeoff

Deep Reinforcement Learning for loT Networks: Age of Informa...

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GLOBECOM 2020 - 2020 IEEE Global Communications Conference

作者： Xiongwei Wu Xiuhua Li Jun Li P. C. Ching H. Vincent Poor Princeton University Princeton USA School of Big Data & Software Engineering Chongqing University Chongqing China School of Electronic & Optical Engineering Nanjing University of Science and Technology Nanjing China The Chinese University of Hong Kong Shatin Hong Kong SAR China

ISBN: (数字)9781728182988

ISBN: (纸本)9781728182995

In most Internet of Things (IoT) networks, edge nodes are commonly used as to relays to cache sensing data generated by IoT sensors as well as provide communication services for data consumers. However, a critical issue of IoT sensing is that data are usually transient, which necessitates temporal updates of caching content items while frequent cache updates could lead to considerable energy cost and challenge the lifetime of IoT sensors. To address this issue, we adopt the Age of Information (AoI) to quantity data freshness and propose an online cache update scheme to obtain an effective tradeoff between the average AoI and energy cost. Specifically, we first develop a characterization of transmission energy consumption at IoT sensors by incorporating a successful transmission condition. Then, we model cache updating as a Markov decision process to minimize average weighted cost with judicious definitions of state, action, and reward. Since user preference towards content items is usually unknown and often temporally evolving, we therefore develop a deep reinforcement learning (DRL) algorithm to enable intelligent cache updates. Through trial-and-error explorations, an effective caching policy can be learned without requiring exact knowledge of content popularity. Simulation results demonstrate the superiority of the proposed framework.

关键词： Sensors Internet of Things Energy consumption Transient analysis Wireless sensor networks Signal to noise ratio Intelligent sensors

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