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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Siyang Wang Weigang Wu Zhou Zhi School of Data and Computer Science Sun Yet-Sen University Guangzhou China Guangdong Province Key Lab. of Big Data Analysis and Processing Sun Yet-Sen University Guangzhou China Sun Yet-Sen University Guangzhou China

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Recently, Named data Networking (NDN) has been used for vehicular networks to improve the efficiency of data forwarding. However, existing solutions for vehicular NDN (V-NDN) rarely consider the priority of data packets. In this paper, we propose a priority-based data packet forwarding algorithm. We observed that different data types may have different priorities and the importance of a data packet decreases as the distance between data packet location and data source location increases. Our algorithm handles data packets differently according to their priorities. It first assigns data packets into three priority levels: high, medium and low. Then, when transmitting a data packet, the node uses either push or pull-based methods depending on the data packet's priority. For high-priority data packets, the push-based forwarding algorithm is adopted; for medium-and low-priority data packets, the pull-based forwarding algorithm is employed. With our design, the push-based forwarding algorithm is used in the local area to improve the hit ratio of high priority data; the pull-based forwarding algorithm is used to disseminate data packets to interested nodes and to reduce network overhead. Our algorithm satisfies the vehicle's need for different priority. Compared with existing works, our algorithm has a higher hit ratio than pure pull-based NDN and costs less overhead than traditional IP architectures. Simulations via SUMO, OpenStreetMap and ndnSIM verify the advantages of our work.

关键词： Roads Vehicular ad hoc networks Wireless communication Sun IP networks Routing protocols

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Design of fine management system for civil aviation airspace resources based on spatiotemporal grid model 1

Design of fine management system for civil aviation airspace...

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1st IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2019

作者： Zheng, Yinger Liu, Juqing Li, Jun Xu, Yubin Huang, Qian Zhu, Yan Pei, Yanqiu Big Data Application Center China Academy of Civil Aviation Science and Technology Beijing China College of Geoscience and Surveying Engineering China University of Mining and Technology Beijing China Research and Development Center China Academy of Civil Aviation Science and Technology Beijing China Huawei Research Institute Huawei Technology Co. Ltd Service Lab Beijing China

ISBN: (纸本)9781728125985

Along with the rapid development of aviation industry, airspace demand is increasing. However, the current airspace resources are tight and the utilization efficiency is low. Aiming at addressing this problem, this paper proposes a fine management system for civil aviation airspace resources based on spatiotemporal grid model. Relying on massive, multisource, multi-scale and multi-dimensional aviation big data, this study expands the time dimension of global discrete grid technology and establishes a global unified spatiotemporal grid model. On this basis, the calculation and analysis method of the spatiotemporal grid model is designed to carry out route management, conduct flight collision detection, allocate airspace resources, and then realize efficient and intelligent management, allocation and intelligent application of airspace resources. Finally, the scientificity and feasibility of the management system is verified through the practical application of civil aviation ADS-B data, meteorological data and route data. © 2019 IEEE.

关键词： Civil aviation

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Generalized independent noise condition for estimating latent variable causal graphs 20

Generalized independent noise condition for estimating laten...

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Feng Xie Ruichu Cai Biwei Huang Clark Glymour Zeng Hao Kun Zhang School of Computer Science Guangdong University of Technology Guangzhou China and School of Mathematical Sciences Peking University Beijing China School of Computer Science Guangdong University of Technology Guangzhou China and Pazhou Lab Guangzhou China Department of Philosophy Carnegie Mellon University Pittsburgh School of Computer Science Guangdong University of Technology Guangzhou China and School of Mathematics and Big Data Foshan University Foshan China

ISBN: (纸本)9781713829546

Causal discovery aims to recover causal structures or models underlying the observed data. Despite its success in certain domains, most existing methods focus on causal relations between observed variables, while in many scenarios the observed ones may not be the underlying causal variables (e.g., image pixels), but are generated by latent causal variables or confounders that are causally related. To this end, in this paper, we consider Linear, Non-Gaussian Latent variable Models (LiNGLaMs), in which latent confounders are also causally related, and propose a Generalized Independent Noise (GIN) condition to estimate such latent variable graphs. Specifically, for two observed random vectors Y and Z, GIN holds if and only if ΩT Y and Z are statistically independent, where Ω is a parameter vector characterized from the cross-covariance between Y and Z. From the graphical view, roughly speaking, GIN implies that causally earlier latent common causes of variables in Y d-separate Y from Z. Interestingly, we find that the independent noise condition, i.e., if there is no confounder, causes are independent from the error of regressing the effect on the causes, can be seen as a special case of GIN. Moreover, we show that GIN helps locate latent variables and identify their causal structure, including causal directions. We further develop a recursive learning algorithm to achieve these goals. Experimental results on synthetic and real-world data demonstrate the effectiveness of our method.

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A Markov Traffic Model for Signalized Traffic Networks Based on Bayesian Estimation

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IFAC-PapersOnLine 2020年第2期53卷 15029-15034页

作者： S.Y. Liu S. Lin Y.B. Wang B. De Schutter W.H.K. Lam School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100089 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences College of Civil Engineering and Architecture Zhejiang University PR China Delft Center for Systems and Control Delft University of Technology The Netherlands Department of Civil and Structural Engineering The Hong Kong Polytechnic University Hong Kong SAR China

In order to better understand the stochastic dynamic features of signalized traffic networks, we propose a Markov traffic model to simulate the dynamics of traffic link flow density for signalized urban traffic networks with demand uncertainty. In this model, we have four different state modes for the link according to different congestion levels of the link. Each link can only be in one of the four link state modes at any time, and the transition probability from one state to the other state is estimated by Bayesian estimation based on the distributions of the dynamic traffic flow densities, and the posterior probabilities. Therefore, we use a first-order Markov Chain Model to describe the dynamics of the traffic flow evolution process. We illustrate our approach for a small traffic network. Compared with the data from the microscopic traffic simulator SUMO, the proposed model can estimate the link traffic densities accurately and can give a reliable estimation of the uncertainties in the dynamic process of signalized traffic networks.

关键词： Markov traffic model Traffic signals Bayesian Urban traffic network

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Real-time EEG classification via coresets for BCI applications

arXiv

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arXiv 2019年

作者： Netzer, Eitan Frid, Alex Feldman, Dan Robotics and Big Data Lab Computer Science department University of Haifa Haifa Israel Laboratory of Clinical Neurophysiology Faculty of Medicine Technion IIT Haifa Israel

A brain-computer interface (BCI) based on the motor imagery (MI) paradigm translates one’s motor intention into a control signal by classifying the Electroencephalogram (EEG) signal of different tasks. However, most existing systems either (i) use a high-quality algorithm to train the data off-line and run only classification in real-time, since the off-line algorithm is too slow, or (ii) use low-quality heuristics that are sufficiently fast for real-time training but introduces relatively large classification error. In this work, we propose a novel processing pipeline that allows real-time and parallel learning of EEG signals using high-quality but possibly inefficient algorithms. This is done by forging a link between BCI and core-sets, a technique that originated in computational geometry for handling streaming data via data summarization. We suggest an algorithm that maintains the representation such coreset tailored to handle the EEG signal which enables: (i) real time and continuous computation of the Common Spatial Pattern (CSP) feature extraction method on a coreset representation of the signal (instead on the signal itself), (ii) improvement of the CSP algorithm efficiency with provable guarantees by applying CSP algorithm on the coreset, and (iii) real time addition of the data trials (EEG data windows) to the coreset. For simplicity, we focus on the CSP algorithm, which is a classic algorithm. Nevertheless, we expect that our coreset will be extended to other algorithms in future papers. In the experimental results we show that our system can indeed learn EEG signals in real-time for example a 64 channels setup with hundreds of time samples per second. Full open source is provided to reproduce the experiment and in the hope that it will be used and extended to more coresets and BCI applications in the future. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

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A study on the uncertainty of convolutional layers in deep neural networks

arXiv

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arXiv 2020年

作者： Shen, Haojing Chen, Sihong Wang, Ran Big Data Institute College of Computer Science and Software Engineering Guangdong Key Lab. of Intelligent Information Processing Shenzhen University ShenzhenGuangdong518060 China College of Mathematics and Statistics Shenzhen University Shenzhen518060 China Shenzhen Key Laboratory of Advanced Machine Learning and Applications Shenzhen University Shenzhen518060 China

This paper shows a Min-Max property existing in the connection weights of the convolutional layers in a neural network structure, i.e., the LeNet. Specifically, the Min-Max property means that, during the back propagation-based training for LeNet, the weights of the convolutional layers will become far away from their centers of intervals, i.e., decreasing to their minimum or increasing to their maximum. From the perspective of uncertainty, we demonstrate that the Min-Max property corresponds to minimizing the fuzziness of the model parameters through a simplified formulation of convolution. It is experimentally confirmed that the model with the Min-Max property has a stronger adversarial robustness, thus this property can be incorporated into the design of loss function. This paper points out a changing tendency of uncertainty in the convolutional layers of LeNet structure, and gives some insights to the interpretability of convolution. © 2020, CC-BY.

关键词： Convolution

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KPNet: Towards Minimal Face Detector

arXiv

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arXiv 2020年

作者： Song, Guanglu Liu, Yu Zang, Yuhang Wang, Xiaogang Leng, Biao Yuan, Qingsheng SenseTime X-Lab Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 National Computer network Emergency Response technical Team/Coordination Center of China

The small receptive field and capacity of minimal neural networks limit their performance when using them to be the backbone of detectors. In this work, we find that the appearance feature of a generic face is discriminative enough for a tiny and shallow neural network to verify from the background. And the essential barriers behind us are 1) the vague definition of the face bounding box and 2) tricky design of anchor-boxes or receptive field. Unlike most top-down methods for joint face detection and alignment, the proposed KPNet detects small facial keypoints instead of the whole face by in the bottom-up manner. It first predicts the facial landmarks from a low-resolution image via the well-designed fine-grained scale approximation and scale adaptive soft-argmax operator. Finally, the precise face bounding boxes, no matter how we define it, can be inferred from the keypoints. Without any complex head architecture or meticulous network designing, the KPNet achieves state-of-the-art accuracy on generic face detection and alignment benchmarks with only ∼ 1M parameters, which runs at 1000fps on GPU and is easy to perform real-time on most modern front-end chips. Copyright © 2020, The Authors. All rights reserved.

关键词： Face recognition

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ZSTAD: Zero-Shot Temporal Activity Detection

ZSTAD: Zero-Shot Temporal Activity Detection

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Lingling Zhang Xiaojun Chang Jun Liu Minnan Luo Sen Wang Zongyuan Ge Alexander Hauptmann School of Computer Science and Technology Xi'an Jiaotong University Xian China Ministry of Education Key Lab For Intelligent Networks and Network Security Xian China Faculty of Information Technology Monash University Australia National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xian China School of Information Technology and Electrical Engineering The University of Queensland Australia School of Computer Science Carnegie Mellon University USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of temporal activity detection are based on deep learning, and they typically perform very well with large scale annotated videos for training. However, these methods are limited in real applications due to the unavailable videos about certain activity classes and the time-consuming data annotation. To solve this challenging problem, we propose a novel task setting called zero-shot temporal activity detection (ZSTAD), where activities that have never been seen in training can still be detected. We design an end-to-end deep network based on R-C3D as the architecture for this solution. The proposed network is optimized with an innovative loss function that considers the embeddings of activity labels and their super-classes while learning the common semantics of seen and unseen activities. Experiments on both the THUMOS'14 and the Charades datasets show promising performance in terms of detecting unseen activities.

关键词： Semantics Proposals Training Task analysis Feature extraction Microsoft Windows Three-dimensional displays

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Knowledge-driven SPARQL Query Construction Based on Feedback Mechanism

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Journal of Physics: Conference Series 2020年第1期1646卷

作者： Jing Hu Mei Zhang Zhi Zhang College of Computer Science and Technology Big Data Lab Wuhan University of Science and Technology Wuhan 430065 China and Technology China

According to the survey, the performance of the existing knowledge graph answering system in entity linking, relation linking and SPARQL query construction in terms of execution time and accuracy cannot meet the requirements of knowledge graph answering system. For this challenge, a new feedback mechanism based Knowledge-Driven query construction method is proposed. This method takes the entity set and predicate set in the problem as input, and constructs SPARQL query statements in a knowledge-driven way to solve simple and complex problems, and further proposes heuristic ideas to deal with implicit entity problems. At the same time, the method also proposes to feed back the query results of the knowledge graph to the entity link and relationship connection steps, so that the SPARQL query statement is optimized again. The evaluation results of the LC_QuAD data show that this method outperform the existing state of the art in precision and recall rate.

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