2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)最新文献_第3页

Brain Tumor Segmention Based on Dilated Convolution Refine Networks 基于扩展卷积优化网络的脑肿瘤分割

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477213

Di Liu, H. Zhang, Mingming Zhao, Xiaojuan Yu, Shaowen Yao, Wei Zhou

{"title":"Brain Tumor Segmention Based on Dilated Convolution Refine Networks","authors":"Di Liu, H. Zhang, Mingming Zhao, Xiaojuan Yu, Shaowen Yao, Wei Zhou","doi":"10.1109/SERA.2018.8477213","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477213","url":null,"abstract":"A brain tumor is a growth of abnormal cells in the tissues of the brain, which is difficult for treatment and severely affects patients' cognitive ability. Recent year magnetic resonance imaging (MRI) has been widely used imaging technique to assess brain tumors. However manual segmentation and artificial extracting features block MRI's practice when facing with the huge amount of data produced by MRI. An efficient and automatic image segmentation of brain tumor is still needed. In this paper, a novel automatic segmentation framework of brain tumors, which have 5 parts and resnet-50 use as a backbone, is proposed based on convolutional neural network. A dilated convolution refine (DCR) structure is introduced to extract the local features and global features. After investigating different parameters of our framework, it is proved that DCR is an efficient and robust method in Brain Tumor Segmentation. The experiments are evaluated by Multimodal Brain Tumor Image Segmentation (BRATS 2015) dataset. The results show that our framework in complete tumor segmentation achieved excellent results with a DEC score of 0.87 and a PPV score of 0.92. (GitHub: https://github.com/wei-lab/DCR)","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133693479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Global Shuffle Grouping (GSG): A Load Balancing Strategy for Continuous Range Queries on Storm 全局Shuffle分组(GSG): Storm上连续范围查询的负载均衡策略

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477194

Yuqi Zhang, Botao Wang, Jianpeng Zhou, Hanhui Zhong, Xiao Tian

{"title":"Global Shuffle Grouping (GSG): A Load Balancing Strategy for Continuous Range Queries on Storm","authors":"Yuqi Zhang, Botao Wang, Jianpeng Zhou, Hanhui Zhong, Xiao Tian","doi":"10.1109/SERA.2018.8477194","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477194","url":null,"abstract":"Apache Storm is a distributed stream processing framework to support real-time processing of big data. Even if many stream grouping strategies have been implemented in Storm to partition stream data in order to maximize usability of resources, but they cannot efficiently support continuous range query. It is the basis of location based services, in which both queries and objects are moving. The reason is that the spatial semantics of the query (range and data distribution) cannot be expressed by those strategies, and this is easy to result in load imbalance. For this problem, we propose a load-balancing strategy called global shuffle grouping (GSG) to support efficient continuous range queries on Storm. There the cost of the query is estimated based on the range and density of moving objects. The continuous range queries are grouped according to their costs by the way of round-robin. For the queries belonging to the same group, they are distributed according to a counter array by another round-robin. Double round-robins ensure that the load distributions to multiple downstream bolts are balanced. We implemented continuous range query topology with GSG into Storm. Compared with the most practicable built-in grouping strategy shuffle grouping, our proposed grouping is able to reduce load imbalance degree and load standard deviation by 2–3 times and reduce load fluctuation by 1–2 times. The throughput can be improved up to nearly 20%.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123928649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Motion Deblurring via Using Generative Adversarial Networks for Space-Based Imaging 基于生成对抗网络的空间成像运动去模糊

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477191

Yi Chen, Fengge Wu, Junsuo Zhao

引用次数: 2

Extraction of Interactions of Genes2Genes Related to Breast Cancer 乳腺癌相关基因的相互作用提取

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477190

Lejun Gong, Daoyu Huang, Shixin Sun, Z. Gao, Chuandi Pan, R. Yang, Yongmin Li, Geng Yang

引用次数: 1

The Research of a Lightweight Distributed Crawling System 一种轻量级分布式爬行系统的研究

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477212

Feng Ye, Zongfei Jing, Qian Huang, Yong Chen

{"title":"The Research of a Lightweight Distributed Crawling System","authors":"Feng Ye, Zongfei Jing, Qian Huang, Yong Chen","doi":"10.1109/SERA.2018.8477212","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477212","url":null,"abstract":"Nowadays, information on the Internet is growing at an explosive rate. The ability of the stand-alone web crawling system has come to its bottleneck, so more and more companies turn to distributed web crawling techniques. However, existing distributed web crawling systems have some shortcomings. Thread management modules for solving thread synchronization and resource competition are usually designed by using pure multithread asynchronous methods, but the execution of this kind of modules observably reduces the performance. Moreover, the deduplication algorithms lead to low efficiency in dealing with large data sets or the problem of occupying large storage space. To solve the problems mentioned above, this paper proposes a lightweight and practical distributed crawling system, which combines Docker and distributed computing techniques. It can make full use of the computing resources of the cluster and improve the efficiency of the crawling system effectively. Taking the data of Netease news page as an example, the experimental results show that the distributed crawler proposed has higher execution efficiency.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127607104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Network Traffic Anomaly Detection Based on Wavelet Analysis 基于小波分析的网络流量异常检测

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477230

Zhen-bin Du, Lipeng Ma, Huakang Li, Qun Li, Guozi Sun, Zichang Liu

{"title":"Network Traffic Anomaly Detection Based on Wavelet Analysis","authors":"Zhen-bin Du, Lipeng Ma, Huakang Li, Qun Li, Guozi Sun, Zichang Liu","doi":"10.1109/SERA.2018.8477230","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477230","url":null,"abstract":"Network traffic anomaly detection is an important research content in the field of network and security management. By analyzing network traffic, the health of the network environment can be intuitively evaluated. In particular, analyzing network traffic provides practical and effective guidance for identification and classification of anomaly. This paper proposes a network traffic anomaly detection method based on wavelet analysis for pcap files contain two different delay injections. The wavelet analysis can effectively extract information from the signal and is suitable for the detection of anomaly. Firstly, wavelet analysis is used to extract the waveform features, and then the support vector machine is used for classification. In particular, packet lengths in the pcap files is parsed out to form a sequence of packet lengths in chronological order. Then followed by the wavelet analysis based packet length sequence feature extraction and feature selection methods, the resulting eigenvectors are used as input features to support vector machine for training the classifier. Thus to differentiate the two types of anomaly in the mixed traffic with both normal and abnormal traffic. The qualitative and quantitative experimental results show that our approach achieves good classification results.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127429260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

Least-Squares Support Vector Machine for Semi-Supervised Multi-Tasking 半监督多任务的最小二乘支持向量机

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477214

Xuekuo Jia, Shipu Wang, Yun Yang

{"title":"Least-Squares Support Vector Machine for Semi-Supervised Multi-Tasking","authors":"Xuekuo Jia, Shipu Wang, Yun Yang","doi":"10.1109/SERA.2018.8477214","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477214","url":null,"abstract":"The semi-supervised multi-tasking using least-squares support vector machine can further improve performance by using related information of related tasks, and it inherits the advantages of high training speed and high efficiency of the least square support vector machine. Standard support vector machine is based on supervised learning, and it is necessary to manually mark large amounts of data for obtaining sufficient training data, which is costly and inefficient. In this paper, we apply least squares support vector machine based on semi-supervised learning to the multi-tasks and propose a semi-supervised multi-tasking approach using least-squares support vector machine. Based on related tasks learning simultaneously, multi-task least-squares support vector machine is used to train both labeled and unlabeled samples, overcoming the limitation of slow training, and using the useful information among related tasks to improve the efficiency of all tasks. In the training process, the regional tagging and the tag reset methods are used to reduce the number of iterations to achieve convergence and increases the fault tolerance rate. The experiment on the actual dataset shows the effectiveness of the approach.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125589054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Software Project Estimation Using Improved Use Case Point 使用改进用例点的软件项目评估

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477225

S. Bagheri, Alireza Shameli-Sendi

{"title":"Software Project Estimation Using Improved Use Case Point","authors":"S. Bagheri, Alireza Shameli-Sendi","doi":"10.1109/SERA.2018.8477225","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477225","url":null,"abstract":"Estimating metrics, such as effort, schedule and cost, needed for a software to be created and launched into market have significant economical effects. One of the most extensively utilized method for such estimation is a technique called Use Case Points. It is based on the use case modeling which is a popular and widely used technique for capturing and describing the functional requirements of a software system. In this paper multitude number of techniques have been proposed as the basis for improving estimation of the effort, schedule, and costs of software projects. These terms are conceptually similar but utilize different parameter values and metrics. Moreover, different versions of use case points have been proposed. This method suffers some limitations such as less accuracy, failure to consider software risks, failure to consider software quality aspects, failure to consider different levels of software security, and so on. The aim of this paper is to propose a new approach for cost estimation, based on use case points method, by considering all the existing risks related to software projects. The results indicate that the new estimation approach can produce relatively accurate estimates and also declare various aspects of project risks during project estimation. Our results also provide guidance for organizations that want to develop a software project.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"397 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123236241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

A Method of Channel Capacity Optimization Based on Dynamically Adjusted Inertia Weight Acceleration Factor in Cognitive Sensing Network 基于动态调整惯性权重加速度因子的认知感知网络信道容量优化方法

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477198

Yanjun Hu, Dongdong Wei

{"title":"A Method of Channel Capacity Optimization Based on Dynamically Adjusted Inertia Weight Acceleration Factor in Cognitive Sensing Network","authors":"Yanjun Hu, Dongdong Wei","doi":"10.1109/SERA.2018.8477198","DOIUrl":"https://doi.org/10.1109/SERA.2018.8477198","url":null,"abstract":"The optimization of channel capacity in cognitive sensor networks is a complicated optimization problem. The traditional gradient search method based on the analysis has more restrictions on the objective function, and high complexity, and can not determine the convergence. Aiming at the inherent problems of the traditional gradient search algorithm, the particle swarm optimization(PSO) with simple and easy to implement, distributed computing and fast convergence speed can be used to solve the problem of channel capacity optimization. It is difficult to balance the global search with the local search by adopting a standard particle swarm algorithm with fixed algorithm parameters, which can not solve the premature convergence problem that may occur. The specific meaning of each parameter of the algorithm is analyzed in this paper, and an improved particle swarm optimization algorithm based on dynamic adjustment of inertia weight acceleration factor(DWAPSO) is proposed, and the improved particle swarm optimization algorithm is applied to the optimization of channel capacity in cognitive sensor networks. The simulation results show that the improved channel capacity optimization algorithm(DWAPSO-CA) can speed up the convergence rate, increase the system capacity and get a lower bit error rate.","PeriodicalId":161568,"journal":{"name":"2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133716871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time Series Clustering via NMF in Networks 网络中基于NMF的时间序列聚类

2018 IEEE 16th International Conference on Software Engineering Research, Management and Applications (SERA) Pub Date : 2018-06-01 DOI: 10.1109/SERA.2018.8477221

Guowang Du, Lihua Zhou, Yuan Fang, Ming Yang

引用次数: 0