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Computer-Aided System for the Detection of Multicategory Pulmonary Tuberculosis in Radiographs

Journal of Healthcare Engineering 2020年第1期2020卷

作者： Yilin Xie Zhuoyue Wu Xin Han Hongyu Wang Yifan Wu Lei Cui Jun Feng Zhaohui Zhu Zhongyuanlong Chen Department of Information Science and Technology Northwest University Xi’an Shaanxi 710127 *** School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an University of Posts and Telecommunications Xi’an Shaanxi 710121 *** State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University Xi’an 710127 Shaanxi *** Chest Hospital of Xinjiang Uyghur Autonomous Region of the PRC Urumqi Xinjiang Uygur Autonomous Region 830049 China

The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional Network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.

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Context-aware video recommendation by mining users' view preferences based on access points 27

Context-aware video recommendation by mining users' view pre...

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27th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2017

作者： Zhang, Jiaming Zhou, Yipeng Wu, Di Yang, Chunfeng School of Data and Computer Science Sun Yat-sen University Guangzhou Guangdong China Institute for Telecommunications Research University of South Australia Australia Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Guangzhou Guangdong China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China Tencent Inc. China

ISBN: (纸本)9781450350037

With the astronomical growth of online video population, the video recommendation system is crucial for users to locate videos fitting their interests. The collaborative filtering (CF) realizing personalized recommendation by analyzing users' historical view records is currently the most prevalent algorithm adopted by existing systems. Nevertheless, video recommendation performance can be improved for most cases, particularly the one with cold-start problem, if additional information is involved. In this article, we propose an AP-based Context-Aware (APCA) recommendation scheme on top of the traditional factor-based CF algorithm by utilizing the information of access points which has not been explored yet by existing works. The underlying principle is that users' view preferences are expected to be different with different contexts, e.g., hotel, home, public areas, which can be inferred by mining the information of access points via which users launch video requests. With the data collected from Tencent Video, a leading online video provider in China, we present a measurement study to show user view preferences in different contexts before our APCA algorithm is introduced. Experiments are executed driven by the trace data collected from Tencent Video to validate the effectiveness of our scheme in improving recommendation performance. © 2017 ACM.

关键词： Collaborative filtering

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Unveiling latent behaviors of video viewers with cross-platform information 27

Unveiling latent behaviors of video viewers with cross-platf...

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27th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2017

作者： Gu, Xuhong Zhou, Yipeng Wu, Di Chan, Terence H. Chen, Min School of Data and Computer Science Sun Yat-sen University China Institute for Telecommunications Research University of South Australia Australia Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University China Collaborative Innovation Center of High Performance Computing National University of Defense Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781450350037

The online video streaming service is of huge market values with billions of worldwide users. For online video providers, e.g., Net-flix, Youku, the crucial question is how to understand users' view behaviors and preferences because this knowledge is important for their business operation. Existing solutions mainly rely on analyzing users' historical view records, which however are not always available, especially for new videos and unprovided videos. Different from existing solutions, we propose to infer user behaviors and preferences by jointly analyzing data collected from multiple platforms (e.g., video streaming systems, video databases, etc.). In particular, we use the movie data crawled from a leading video streaming system (i.e., Youku), and a well-known video database in China (i.e, Douban) for this study. Our investigation points out that movie quality (evaluated in terms of Douban scores) and release date jointly influence viewers' preferences. In addition, we reveal a series of user behaviors, e.g., users are reluctant to post comments or ratings for movies they do not like, and user eyeballs are heavily captured by new movies. Understanding of these user behaviors covered by this study is essential for video recommendation and video popularity prediction which can benefit video procurement and advertisement campaign. © 2017 ACM.

关键词： Video streaming

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Enabling QoE-aware Mobile Cloud Video Recording over Roadside Vehicular Networks

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China Communications 2016年第8期13卷 63-73页

作者： Tongtong Jiang Zhaobin Deng Weiwei Huang Guoqing Zhang Department of Computer Science Sun Yat-sen University Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Collaborative Innovation Center of High Performance Computing National University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences

Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/Wi Fi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.

关键词： mobile cloud video vehicular networks quality-of-experience

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EasyHPC: An online programming platform for learning high performance computing

EasyHPC: An online programming platform for learning high pe...

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IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Zhepeng Zou Yuxiao Zhang Jiang Li Xiaojun Hei Yunfei Du Di Wu Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangzhou China National Supercomputer Center in Guangzhou Guangzhou China School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan China

Lacking of appropriate learning facilities, the traditional approach to learn high-performance computing (HPC) is commonly theory-oriented without sufficient hands-on programming experiences. To improve the hands-on experiences of HPC learners, we design and implement a flexible and adaptive online HPC learning platform in this paper, called EasyHPC. This platform contains various online course modules such as quiz bank, interactive community, and virtual laboratory. In our system, various HPC theoretical and experimental learning activities can be conducted online, such as assigning HPC parallel programming tasks, and creating HPC questions and collecting students' submissions. It is convenient for students to study HPC-related courses in our platform, submit course assignments, exchange ideas, and complete HPC programming tasks. Our preliminary learning trials have shown that our system can effectively improve the hands-on experience of our students by providing an integrated HPC learning and programming environment. Our students are able to achieve various HPC capstone projects in our platform to develop their system capability.

关键词： Programming profession Servers High performance computing Education Supercomputers

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Topology analysis system for vehicular Ad Hoc network 17

Topology analysis system for vehicular Ad Hoc network

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17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016

作者： Dong, Baihong Deng, Jian Wu, Weigang Meng, Tianyu 1. School of Data and Computer Science Sun Yat-Sen Univ. China Guangdong Province Key Lab. of Big Data Analysis and Processing China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou China

ISBN: (纸本)9781509050819

With the development of the technology, Vehicular Ad-hoc Network is developing rapidly and continuously. And many new algorithms in VANET were put forward. For example, many people apply the Named data Networks or Software Defined Networking6 to the VANET. However, few researches are looking into the analysis of the topology of the VANET, among most of which are based on simple situations and calculation of the track of vehicles. And therefore, these analysis are inadequate for analyzing the topology under complex situations. This paper is going to put forward a new method for analyzing the change of the topology of VANET, which is capable to quantitatively analyze the change under different and complex situations. In the real experiment, when compared with traditional analysis methods, this new method can analyze more precisely the impact of some possible wireless communication problem on the change of the topology, for example, the impact of hidden node on the vehicular wireless communication and whether the number or the density of vehicles makes the topology of VANET more stable. And this new method can provide a numerical result instead of purely qualitative analysis. © 2016 IEEE.

关键词： Topology

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DTC: A Dynamic Transaction Chopping Technique for Geo-replicated Storage Systems

DTC: A Dynamic Transaction Chopping Technique for Geo-replic...

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Reliable Distributed Systems

作者： Ning Huang Lihui Wu Weigang Wu Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Data and Computer Science Sun Yat-Sen University

ISBN: (纸本)9781509035144

Large Web applications usually require replicating data across geo-distributed datacenters to achieve high locality, durability and availability. However, maintaining strong consistency in geo-replicated systems usually suffers from long latency due to costly coordination across datacenters. Among others, transaction chopping is an effective and efficient approach to cope with such a challenge. In this paper, we propose DTC (Dynamic Transaction Chopping), a novel technique that chops transactions and checks their conflicts in a dynamic and automatic way, during application execution. DTC mainly consists of two parts: a dynamic chopper that chops transaction dynamically according to data partition scheme, and a conflict detection algorithm for determining the safety of the dynamic chopping. Compared with existing transaction chopping technique for geo-replicated systems, DTC has several advantages, including transparency to programmers, flexibility in conflict analysis, high degree of piecewise execution, and adaptability to dynamic partition schemes. We implement our DTC technique and conduct experiments to examine the correctness of DTC and evaluate its performance. The experiment results show that our DTC technique can achieve much more piecewise execution than the existing chopping approach does, and reduce execution time obviously.

关键词： Servers databases Heuristic algorithms Safety Concurrency control Choppers (circuits) Memory

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Improving artificial bee colony algorithm with historical archive 11th

Improving artificial bee colony algorithm with historical ar...

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11th International Conference on Bio-inspired Computing – Theories and Applications, BIC-TA 2016

作者： Zhou, Yalan Wang, Jiahai Gao, Shangce Yang, Xing Yin, Jian College of Information Guangdong University of Finance & Economics Guangzhou510320 China Department of Computer Science Sun Yat-sen University Guangzhou510006 China Guangdong Province Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China Faculty of Engineering University of Toyama Toyama-shi930-8555 Japan

ISBN: (纸本)9789811036132

In this study, an artificial bee colony with historical archive (HAABC) is proposed to help ABC escape from stagnation situation. The proposed framework keeps track of the search history and stores excellent successful solutions into an archive. Once stagnation is detected in scout bees phase of ABC, a new individual is generated by utilizing the historical archive. Experimental results on 28 benchmark functions show that the proposed framework significantly improves the performance of basic ABC and five state-of-the-art ABC algorithms. © Springer Nature Singapore Pte Ltd. 2016.

关键词： Benchmarking

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