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作者： Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Hu, Chunhua Chen, Jinjun State Key Laboratory for Novel Software Technology Nanjing University China Faculty of Engineering and IT University of Technology Sydney NSW 2007 Australia Department of Computer Science and Technology Nanjing University China Machine Learning Research Group NICTA Victoria Australia School of Computer and Information Engineering Hunan University of Commerce China

Traffic hotline discovery is necessary for rational and scientific urban transportation planning in the new living quarters and economic zones. Cloud of Things (CoT) is a newly emerging concept, involving with two advanced technologies, that is, Cloud Computing and Internet of Things (IoT). CoT provides promising opportunities for traffic hotline discovery. However, it is still a challenge to discover the traffic hotlines over CoT. In view of this challenge, a hotline discovery method over CoT by using big taxi GPS data is proposed in this paper. Specifically, a traffic hotline discovery principle is presented to provide a reference standard for the generalized traffic spots that need planning, and a corresponding hotline discovery method is proposed for traffic hotspot identification and hotline selection. To improve the scalability and efficiency of the proposed method in "Big Data" environment, the SAP HANA cloud is applied to implement the proposed method under two application scenarios. Finally, the experimental results demonstrate that the proposed method is both effective and efficient. software—Practice and Experience. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Taxicabs

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Applying H-index within 5-year Citations Window

Applying H-index within 5-year Citations Window

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2017 IEEE 8th International Conference on software Engineering and Service science

作者： Chuanqi Wang Yanhui Li State Key Laboratory for Novel Software Technology Nanjing University Department of Computer Science and Technology Nanjing University

There are a number of indexes proposed in many studies to evaluate the quality of journals. Among them, H-index is a recently proposed index as a useful supplement to journal impact ***, current H-index is based on long journal *** changes of citations of journals are often ignored by H-index. In this paper, we propose 5-year H-index to solve this problem. 5-year H-index evaluates the immediate citation performance(in this year) of a journal's papers published in the last 5 years. We can measure journals by the recent citations with 5-year H-index, which can be a new way to determine the value of journals.

关键词： Applying H-index within 5-year Citations Window

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Deeply supervised depth map super-resolution as novel view synthesis

arXiv

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arXiv 2018年

作者： Song, Xibin Dai, Yuchao Qin, Xueying School of Computer Science and Technology Shandong University China Baidu Research Beijing China National Engineering Laboratory of Deep Learning Technology and Application China Shaanxi Key Lab of Information Acquisition and Processing School of Electronics and Information Northwestern Polytechnical University School of Software Shandong University China

Deep convolutional neural network (DCNN) has been successfully applied to depth map super-resolution and outperforms existing methods by a wide margin. However, there still exist two major issues with these DCNN based depth map super-resolution methods that hinder the performance: i) The low-resolution depth maps either need to be up-sampled before feeding into the network or substantial deconvolution has to be used;and ii) The supervision (high-resolution depth maps) is only applied at the end of the network, thus it is difficult to handle large up-sampling factors, such as ×8;×16. In this paper, we propose a new framework to tackle the above problems. First, we propose to represent the task of depth map super-resolution as a series of novel view synthesis sub-tasks. The novel view synthesis sub-task aims at generating (synthesizing) a depth map from different camera pose, which could be learned in parallel. Second, to handle large up-sampling factors, we present a deeply supervised network structure to enforce strong supervision in each stage of the network. Third, a multi-scale fusion strategy is proposed to effectively exploit the feature maps at different scales and handle the blocking effect. In this way, our proposed framework could deal with challenging depth map super-resolution efficiently under large up-sampling factors (e.g.×8;×16). Our method only uses the low-resolution depth map as input, and the support of color image is not needed, which greatly reduces the restriction of our method. Extensive experiments on various benchmarking datasets demonstrate the superiority of our method over current state-of-the-art depth map super-resolution methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Optical resolving power

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Recognizing keystrokes Using WiFi Devices

Recognizing Keystrokes Using WiFi Devices

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作者： Ali, Kamran Liu, Alex X. Wang, Wei Shahzad, Muhammad Department of Computer Science and Engineering Michigan State University East LansingMI48823 United States State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science North Carolina State University RaleighNC27695 United States

keystroke privacy is critical for ensuring the security of computer systems and the privacy of human users as what is being typed could be passwords or privacy sensitive information. In this paper, we show for the first time that WiFi signals can also be exploited to recognize keystrokes. The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. In this paper, we propose a WiFi signal-based keystroke recognition system called Wikey. Wikey consists of two commercial off-the-shelf WiFi devices, a sender (such as a router) and a receiver (such as a laptop). The sender continuously emits signals and the receiver continuously receives signals. When a human subject types on a keyboard, Wikey recognizes the typed keys based on how the CSI values at the WiFi signal receiver end. We implemented the Wikey system using a TP-Link TL-WR1043ND WiFi router and a Lenovo X200 laptop. Wikey achieves over 97.5% detection rate for detecting the keystroke and 96.4% recognition accuracy for classifying single keys. In real-world experiments, Wikey can recognize keystrokes in a continuously typed sentence with an accuracy of 93.5%. Wikey can also recognize complete words inside a sentence with over 85% accuracy. © 2017 IEEE.

关键词： Channel state information

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Underwater photogrammetry in very shallow waters: Main challenges and caustics effect removal

Underwater photogrammetry in very shallow waters: Main chall...

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2018 ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020"

作者： Agrafiotis, P. Skarlatos, D. Forbes, T. Poullis, C. Skamantzari, M. Georgopoulos, A. Cyprus University of Technology Civil Engineering and Geomatics Dept. Lab of Photogrammetric Vision 2-8 Saripolou str. Limassol3036 Cyprus Immersive and Creative Technologies Lab. Department of Computer Science and Software Engineering Concordia University QuebecH3G 1M8 Canada National Technical University of Athens School of Rural and Surveying Engineering Lab. of Photogrammetry Zografou Campus 9 Heroon Polytechniou str. Zografou Athens15780 Greece

In this paper, main challenges of underwater photogrammetry in shallow waters are described and analysed. The very short camera to object distance in such cases, as well as buoyancy issues, wave effects and turbidity of the waters are challenges to be resolved. Additionally, the major challenge of all, caustics, is addressed by a new approach for caustics removal (Forbes et al., 2018) which is applied in order to investigate its performance in terms of SfM-MVS and 3D reconstruction results. In the proposed approach the complex problem of removing caustics effects is addressed by classifying and then removing them from the images. We propose and test a novel solution based on two small and easily trainable Convolutional Neural Networks (CNNs). Real ground truth for caustics is not easily available. We show how a small set of synthetic data can be used to train the network and later transfer the le arning to real data with robustness to intra-class variation. The proposed solution results in caustic-free images which can be further used for other tasks as may be needed. © Authors 2018.

关键词： Underwater photogrammetry

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Does manual delineation only provide the side information in CT prostate segmentation? 20th

Does manual delineation only provide the side information in...

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20th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Shi, Yinghuan Yang, Wanqi Gao, Yang Shen, Dinggang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Science Nanjing Normal University Nanjing China Department of Radiology and BRIC UNC Chapel Hill Chapel Hill United States

ISBN: (纸本)9783319661780

Prostate segmentation, for accurate prostate localization in CT images, is regarded as a crucial yet challenging task. Nevertheless, due to the inevitable factors (e.g., low contrast, large appearance and shape changes), the most important problem is how to learn the informative feature representation to distinguish the prostate from non-prostate regions. We address this challenging feature learning by leveraging the manual delineation as guidance: the manual delineation does not only indicate the category of patches, but also helps enhance the appearance of prostate. This is realized by the proposed cascaded deep domain adaptation (CDDA) model. Specifically, CDDA constructs several consecutive source domains by employing a mask of manual delineation to overlay on the original CT images with different mask ratios. Upon these source domains, convnet will guide better transferrable feature learning until to the target domain. Particularly, we implement two typical methods: patch-to-scalar (CDDA-CNN) and patch-to-patch (CDDA-FCN). Also, we theoretically analyze the generalization error bound of CDDA. Experimental results show the promising results of our method. © Springer International Publishing AG 2017.

关键词： computerized tomography

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Device-Free Human Activity Recognition Using Commercial WiFi Devices

Device-Free Human Activity Recognition Using Commercial WiFi...

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作者： Wang, Wei Liu, Alex X. Shahzad, Muhammad Ling, Kang Lu, Sanglu State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Engineering Michigan State University East LansingMI48824 United States Department of Computer Science North Carolina State University RaleighNC27695 United States

Since human bodies are good reflectors of wireless signals, human activities can be recognized by monitoring changes in WiFi signals. However, existing WiFi-based human activity recognition systems do not build models that can quantify the correlation between WiFi signal dynamics and human activities. In this paper, we propose a Channel State Information (CSI)-based human Activity Recognition and Monitoring system (CARM). CARM is based on two theoretical models. First, we propose a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds. Second, we propose a CSI-activity model that quantifies the relation between human movement speeds and human activities. Based on these two models, we implemented the CARM on commercial WiFi devices. Our experimental results show that the CARM achieves recognition accuracy of 96% and is robust to environmental changes. © 2017 IEEE.

关键词： Channel state information

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Sentiment Analysis with the Exploration of Overall Opinion Sentences

Sentiment Analysis with the Exploration of Overall Opinion S...

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International Joint Conference on Neural Networks

作者： Xiaojia Pu Gangshan Wu Chunfeng Yuan State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210023 China

ISBN: (纸本)9781509061839

With the rapid growth of opinionated contents, e.g. product reviews, sentiment analysis has drawn much attention from the researchers. The most fundamental task of sentiment analysis is document sentiment classification which aims to predict the overall sentiment (e.g. positive or negative) towards the opinion target in a review. There are usually various opinion sentences towards different aspects with different sentiments. Among them, the overall opinion towards the whole target should be more deterministic in document sentiment prediction. However, most existing methods treat all the sentences equally, thus, they may encounter difficulty especially when the sentiments of most aspect opinion sentences differ from the overall sentiment. To address this, we propose a novel method for document sentiment classification which adequately explores the effect of overall opinion sentences. The method is extended from structural SVM, and the overall opinion sentences are taken as the hidden variables for document sentiment. Experiments on several standard product review datasets show the effectiveness of our method.

关键词： Support vector machines Pragmatics Cameras Sentiment analysis Learning systems Predictive models Probability

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A flexible I/O arbitration framework for netCDF-based big data processing workflows on high-end supercomputers

A flexible I/O arbitration framework for netCDF-based big da...

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作者： Liao, Jianwei Gerofi, Balazs Lien, Guo-Yuan Miyoshi, Takemasa Nishizawa, Seiya Tomita, Hirofumi Liao, Wei-Keng Choudhary, Alok Ishikawa, Yutaka College of Computer and Information Science Southwest University of China Chongqing China State Key Laboratory for Novel Software Technology Nanjing University Jiangsu China RIKEN Advanced Institute for Computational Science Kobe Japan Department of Electrical Engineering and Computer Science Northwestern University EvanstonIL United States

On the verge of the convergence between high-performance computing and Big Data processing, it has become increasingly prevalent to deploy large-scale data analytics workloads on high-end supercomputers. Such applications often come in the form of complex workflows with various different components, assimilating data from scientific simulations as well as from measurements streamed from sensor networks, such as radars and satellites. For example, as part of the Flagship 2020 (post-K) supercomputer project of Japan, RIKEN is investigating the feasibility of a highly accurate weather forecasting system that would provide a real-time outlook for severe guerrilla rainstorms. One of the main performance bottlenecks of this application is the lack of efficient communication among workflow components, which currently takes place over the parallel file *** this paper, we present an initial study of a direct communication framework designed for complex workflows that eliminates unnecessary file I/O among components. Specifically, we propose an I/O arbitration layer that provides direct parallel data transfer (both synchronous and asynchronous) among job components that rely on the netCDF interface for performing I/O operations. Our solution requires only minimal modifications to application code. Moreover, we propose a configuration file–based approach that allows users to specify the desired data transfer pattern among workflow components, offering a general solution for different application contexts. We present a preliminary evaluation of the proposed framework on the K computer (running on up to 4800 compute nodes) using RIKEN's experimental weather forecasting workflow as a case study. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Supercomputers

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An energy-aware virtual machine scheduling method for service QoS enhancement in clouds over big data

An energy-aware virtual machine scheduling method for servic...

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作者： Dou, Wanchun Xu, Xiaolong Meng, Shunmei Zhang, Xuyun Hu, Chunhua Yu, Shui Yang, Jian State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China Department of Electrical & Computer Engineering The University of Auckland Auckland New Zealand School of Computer and Information Engineering Hunan University of Commerce Hunan China School of Information Technology Deakin University Melbourne Australia Jiangsu Second Normal University Nanjing China

Because of the strong demands of physical resources of big data, it is an effective and efficient way to store and process big data in clouds, as cloud computing allows on-demand resource provisioning. With the increasing requirements for the resources provisioned by cloud platforms, the Quality of Service (QoS) of cloud services for big data management is becoming significantly important. Big data has the character of sparseness, which leads to frequent data accessing and processing, and thereby causes huge amount of energy consumption. Energy cost plays a key role in determining the price of a service and should be treated as a first-class citizen as other QoS metrics, because energy saving services can achieve cheaper service prices and environmentally friendly solutions. However, it is still a challenge to efficiently schedule Virtual Machines (VMs) for service QoS enhancement in an energy-aware manner. In this paper, we propose an energy-aware dynamic VM scheduling method for QoS enhancement in clouds over big data to address the above challenge. Specifically, the method consists of two main VM migration phases where computation tasks are migrated to servers with lower energy consumption or higher performance to reduce service prices and execution time. Extensive experimental evaluation demonstrates the effectiveness and efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Quality of service

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