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arXiv 2019年

作者： Zhou, Yanning Onder, Omer Fahri Dou, Qi Tsougenis, Efstratios Chen, Hao Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Hong Kong Imsight Medical Technology Co. Ltd. Hong Kong Hong Kong Department of Computing Imperial College London London United Kingdom Department of Computer Science and Engineering Chinese University of Hong Kong Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Accurate segmenting nuclei instances is a crucial step in computer-aided image analysis to extract rich features for cellular estimation and following diagnosis as well as treatment. While it still remains challenging because the wide existence of nuclei clusters, along with the large morphological variances among different organs make nuclei instance segmentation susceptible to over-/under-segmentation. Additionally, the inevitably subjective annotating and mislabeling prevent the network learning from reliable samples and eventually reduce the generalization capability for robustly segmenting unseen organ nuclei. To address these issues, we propose a novel deep neural network, namely Contour-aware Informative Aggregation Network (CIA-Net) with multilevel information aggregation module between two task-specific decoders. Rather than independent decoders, it leverages the merit of spatial and texture dependencies between nuclei and contour by bi-directionally aggregating task-specific features. Furthermore, we proposed a novel smooth truncated loss that modulates losses to reduce the perturbation from outliers. Consequently, the network can focus on learning from reliable and informative samples, which inherently improves the generalization capability. Experiments on the 2018 MICCAI challenge of Multi-Organ-Nuclei-Segmentation validated the effectiveness of our proposed method, surpassing all the other 35 competitive teams by a significant margin. Copyright © 2019, The Authors. All rights reserved.

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Modeling detailed cloud scene from multi-source images 26

Modeling detailed cloud scene from multi-source images

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26th Pacific Conference on computer Graphics and Applications, Pacific Graphics 2018

作者： Cen, Yunchi Liang, Xiaohui Chen, Junping Yang, Bailin Li, Frederick W.B. State Key Laboratory of Virtual Reality Technology and System Beihang University Beijing100191 China School of Computer Science and Information Engineering Zhejiang Gongshang University Hangzhou310018 China Department of Computer Science University of Durham DurhamDH1 3LE United Kingdom

ISBN: (纸本)9783038680734

Realistic cloud is essential for enhancing the quality of computer graphics applications, such as flight simulation. Data-driven method is an effective way in cloud modeling, but existing methods typically only utilize one data source as input. For example, natural images are usually used to model small-scale cloud with details, and satellite images and WRF data are used to model large scale cloud without details. To construct large-scale cloud scene with details, we propose a novel method to extract relevant cloud information from both satellite and natural images. Experiments show our method can produce more detailed cloud scene comparing with existing methods. © 2018 The Author(s).

关键词： Flight simulators

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Integrating LDA into the weighted average method for semantic friend recommendation 6th

Integrating LDA into the weighted average method for semanti...

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6th CCF Academic Conference on Big Data, CCF Big Data 2018

作者： Gong, Jibing Chen, Shuai Gao, Xiaoxia Song, Yanqing Wang, Shuli School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao066004 China Key Laboratory for Software Engineering of Hebei Province Yanshan University Qinhuangdao066004 China The Department of Applied Mathematics Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9789811329210

Friend recommendation is a fundamental service for both social networks and practical applications. The majority of existing friend-recommendation methods utilize user profiles, social relationships, or static post content data, but rarely consider the semantic intentions and dynamic behaviors of users. In this paper, we propose FRec++, a friend recommendation method based on semantic and dynamic information. In FRec++, the first plus stands for new friend recommendation using a semantic model based on LDA while the next plus stands for the use of dynamic user attributes (e.g., behaviors or positions). More specifically, we first use the LDA method to generate semantic topics for user interests, and then compute the topic similarities between the target user and candidate friends. Next, we calculate the similarities of dynamic behaviors (i.e., forwarding, making comments, liking, and replying) and investigate the static attributes of users to measure the relevance of preferences. Finally, the weighted average method is used to integrate the above factors. We conducted experiments on the Weibo dataset and the results show that FRec++ outperforms several existing methods. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Semantics

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CAS-YNU multi-modal cross-view human action dataset

CAS-YNU multi-modal cross-view human action dataset

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2018 IEEE International Conference on Information and Automation, ICIA 2018

作者： Zhao, Qingsong Cheng, Jun Tao, Dapeng Ji, Xiaopeng Wang, Lei Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences China Chinese University of Hong Kong Hong Kong School of Information Science and Engineering Yunnan University China

ISBN: (纸本)9781538680698

Human action recognition is a hot research topic in computer vision, which has extensive applications including human-computer interaction, robot and surveillance. Some outstanding human action datasets have been published. However, they still have some defects, such as the limited modality types, camera views, complexity of scene and action categories. Furthermore, most of them are devised for a subset of the action recognition, such as single-modal recognition and single-view recognition. In this paper, we propose a multi-modal cross-view human action dataset (named CAS-YNU MCHAD). It consists of five synchronous data modalities, including RGB image, depth maps, human segment maps, skeleton data and inertial data. Our dataset contains 14782 action samples from 50 subjects and 10 actions in two perspectives. Extensive benchmark experiments on this dataset are conducted with the state-of-the-art recognition approaches. Experimental results illustrate that it is challenging on cross-view action recognition. We also provide the baseline for the assessment of existing recognition methods. © 2018 IEEE.

关键词： Human computer interaction

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Deep learning based joint resource scheduling algorithms for hybrid MEC networks

arXiv

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arXiv 2019年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Xu, Wei Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Technology and Business Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom NCRL Southeast University Nanjing China School of Computer Technology and Engineering Changchun Institute of Technology Changchun China School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom

In this paper, we consider a hybrid mobile edge computing (H-MEC) platform, which includes ground stations (GSs), ground vehicles (GVs) and unmanned aerial vehicle (UAVs), all with mobile edge cloud installed to enable user equipments (UEs) or Internet of thing (IoT) devices with intensive computing tasks to offload. Our objective is to obtain an online offloading algorithm to minimize the energy consumption of all the UEs, by jointly optimizing the positions of GVs and UAVs, user association and resource allocation in real-time, while considering the dynamic environment. To this end, we propose a hybrid deep learning based online offloading (H2O) framework where a large-scale path-loss fuzzy c-means (LSFCM) algorithm is first proposed and used to predict the optimal positions of GVs and UAVs. Secondly, a fuzzy membership matrix U-based particle swarm optimization (U-PSO) algorithm is applied to solve the mixed integer nonlinear programming (MINLP) problems and generate the sample datasets for the deep neural network (DNN) where the fuzzy membership matrix can capture the small-scale fading effects and the information of mutual interference. Thirdly, a DNN with the scheduling layer is introduced to provide user association and computing resource allocation under the practical latency requirement of the tasks and limited available computing resource of H-MEC. In addition, different from traditional DNN predictor, we only input one UEs information to the DNN at one time, which will be suitable for the scenarios where the number of UE is varying and avoid the curse of dimensionality in DNN. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Single Image Super-Resolution Based on Improved WGAN

Single Image Super-Resolution Based on Improved WGAN

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2018 International Conference on Advanced Control, Automation and Artificial Intelligence (ACAAI2018)

作者： Lei Yu Xiang Long Chao Tong School of Computer Science and Engineering Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang University

ISBN: (纸本)9781510859432

SRGAN has successfully applied the Generative Adversarial Network to the single image super-resolution reconstruction, which has achieved good results. But the loss function based on feature space in SRGAN objectively sacrifices the pursuit of high peak signal-to-noise-ratio(PSNR), which is the result of a tradeoff. At the same time, Improved Training of Wasserstein GANs makes the training process more stable. We redesign the SRGAN, using VGG16 network for feature extraction,setting discriminator network's working space as feature space,and adding the loss function based on the mean square error of pixel space, then gain more details and high PSNR in the reconstruction at the same time. We use the design of WGAN-GP for reference to make the training more stable.

关键词： Super-resolution WGAN-GP VGG

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Gedetector: Early detection of gathering events based on cluster containment join in trajectory streams 21

Gedetector: Early detection of gathering events based on clu...

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21st International Conference on Extending Database technology, EDBT 2018

作者： Zhao, Bin Yu, Zhaoyuan Ji, Genlin Liu, Xintao Yang, Yu Mi, Ningfang School of Computer Science and Technology Nanjing Normal University China Key Laboratory of Virtual Geographic Environment Ministry of Education Nanjing Normal University China Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic University Hong Kong Electrical and Computer Engineering Department Northeastern University BostonMA United States

ISBN: (纸本)9783893180783

Existing trajectory patterns, such as flock, convoy, swarm, and gathering, are to detect moving clusters staying or travelling together for a certain time period. But these patterns model group movement behaviors after moving objects' gathering together. This may result in loosing golden opportunities to detect emergency incidents earlier, such as traffic congestion and serious stampedes. In this work, we propose a novel group pattern, called converging, which can model converging behaviors of moving objects. As a proof-of-concept, we implemented a visual analytic system GEDetector based on trajectory streams to detect gathering events as early as possible. A user-friendly interface is designed to help users gain insights into gathering events from spatial and temporal aspects. Finally, we demonstrate the effectiveness and efficiency of our system by using a real world dataset. © 2018 Copyright held by the owner/author(s)

关键词： Traffic congestion

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Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number

Word2Cluster: A New Multi-Label Text Clustering Algorithm wi...

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2019 IEEE Global Communications Conference (GLOBECOM)

作者： Kaili Mao Jianwei Niu Xuefeng Liu Shui Yu Longbo Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University School of Computer Science and Cyber Engineering Guangzhou University Guangdong China China aerospace science and industry corporation China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.

关键词： Clustering algorithms Biological system modeling Merging Task analysis computer science Technological innovation Natural language processing

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Multisource multisink optimal evacuation routing with dynamic network changes: A geometric algebra approach

Multisource multisink optimal evacuation routing with dynami...

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作者： Yu, Zhaoyuan Li, Dongshuang Zhu, Shuai Luo, Wen Hu, Yong Yuan, Lingwang Key Laboratory of Virtual Geographic Environment MOE Nanjing Normal University Nanjing210023 China Nanjing210023 China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing China Department of Computer Science and Technology Nanjing Normal University Nanjing210023 China

In this paper, we introduced geometric algebra to develop a new multisource multisink optimal evacuation route planning method. A dynamically updatable data structure and a matrix-based greedy searching algorithm were developed to support the dynamic evacuation route searching for multiple evacuees. Unlike most existing methods, which iteratively search the optimal path for each evacuee, our method can search all the possible evacuation routes synchronously. The dynamic updating of the network topography, weights, and constraints during the route searching is direct and flexible, thus can support the evacuation in dynamic cases. The method is demonstrated and tested by an evacuation case in the city of Changzhou, China. The simulation experiments suggest that the method can well support the dynamic route searching in large scales with dynamic weight changes. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Iterative methods

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Effective Free Field of View Scene Exploration in VR and AR

Effective Free Field of View Scene Exploration in VR and AR

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IEEE International Symposium on Mixed and Augmented Reality Workshops (ISMARW)

作者： Lili Wang Antong Cao Zhichao Li Xuefeng Yang Voicu Popescu Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Computer Science Purdue University West Lafayette US

ISBN: (纸本)9781538675939;9781538675922

We propose to improve virtual reality (VR) and optical see-through augmented reality (AR) head-mounted display scene exploration efficiency by allowing the user to adapt the field of view interactively. This way the user can zoom in to examine parts of the scene in more detail without having to translate the viewpoint forward, as would be required in conventional fixed field of view scene exploration. The user can also zoom out, to gain a more comprehensive view of the scene and to examine distant parts of the scene in parallel, without the need to translate the viewpoint backward. Zooming in is supported with a focus+context visualization approach that integrates a distortion-free magnified focus region seamlessly into context. For AR, the higher resolution focus region is resampled from the video feed acquired by a head-mounted high-resolution camera. We demonstrate the benefits of our free field of view scene exploration in the context of VR and AR tasks, where it brings a substantial reduction of viewpoint translation, view direction rotation, and task completion time.

关键词： Visualization Cameras Resists Distortion Augmented reality Legged locomotion

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