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Combing data filter and data sampling for cross- Company defect prediction: An empricial study 29

Combing data filter and data sampling for cross- Company def...

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29th International Conference on software Engineering and Knowledge Engineering, SEKE 2017

作者： Yu, Xiao Wu, Man Zhang, Yan Fu, Mandi State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China School of Computer Science and Information Engineering HuBei University Wuhan China Educational Informationalization Engineering Research Center of HuBei Province Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)1891706411

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a prediction model with high performance. On the other hand, the CC data has the highly imbalanced nature between the defectiveprone and non-defective classes, which will degrade the performance of CCDP. To address such issues, this paper proposes an approach, in which data sampling is combined with data filter, to overcome these problems. Data sampling seeks a more balanced dataset through the addition or removal of inst.nces, while data filter is a process of filtering out the irrelevant CC data so that the performance of CCDP models can be improved. We employ two data filtering methods called NN filter and DBSCAN filter combined with SMOTE (Synthetic Minority Oversampling Technique) and RUS (Random Under- Sampling). Eight different approaches would be produced when combing these four techniques: 1- NN filter performed prior to RUS;2- NN filter performed after RUS;3- NN filter performed prior to SMOTE;4- NN filter performed after SMOTE;5- DBSCAN filter performed prior to RUS;6- DBSCAN filter performed after RUS;7- DBSCAN filter performed prior to SMOTE;8- DBSCAN filter performed after SMOTE. The empirical study was carried out on 15 publicly availab.e project datasets. The experimental results demonstrate that NN filter performed prior to RUS (Approach 1) performs better than the other seven approaches.

关键词： Forecasting

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Using class imbalance learning for cross-company defect prediction 29

Using class imbalance learning for cross-company defect pred...

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29th International Conference on software Engineering and Knowledge Engineering, SEKE 2017

作者： Yu, Xiao Zhou, Mingsong Chen, Xu Deng, Lijun Wang, Lu State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Department of Information Science and Technology Beijing Normal University Beijing China College of Business City University of Hong Kong Kowloon Tong Hong Kong School of Computer Science and Information Engineering HuBei University Wuhan China

ISBN: (纸本)1891706411

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, the performance of such CCDP models is susceptible to the high imbalanced nature between the defect-prone and non-defect classes of CC data. Class imbalance learning is applied to alleviate this issue. Because many class imbalance learning methods have been proposed, there is an imperative need to analyze and compare the performance of these methods for CCDP. Although prior empirical studies have proven *** algorithm achieves the best performance for defect prediction. This observation leads us to conduct a careful empirical study the issues of if and how class imbalance learning methods can benefit cross-company defect prediction. We investigate different types of class imbalance learning methods, including under-sampling technique, over-sampling technique and over sampling followed by under-sampling technique on the cross-company defect prediction performance over 15 publicly availab.e datasets. Experimental results show that under-sampling technique achieves the best overall performance in terms of the gmeasure among those methods we studied.1.

关键词： Forecasting

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A cloud-based taxi trace mining framework for smart city

A cloud-based taxi trace mining framework for smart city

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作者： Liu, Jin Yu, Xiao Xu, Zheng Choo, Kim-Kwang Raymond Hong, Liang Cui, Xiaohui State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Third Research Institute of Ministry of Public Security Shanghai China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States School of Information Technology and Mathematical Science University of South Australia AdelaideSA Australia School of Information Management Wuhan University Wuhan China International School of Software Wuhan University Wuhan China

As a well-known field of big data applications, smart city takes advantage of massive data analysis to achieve efficient management and sustainable development in the current worldwide urbanization process. An important problem in smart city is how to discover frequent trajectory sequence pattern and cluster trajectory. To solve this problem, this paper proposes a cloud-based taxi trajectory pattern mining and trajectory clustering framework for smart city. Our work mainly includes (1) preprocessing raw Global Positioning System trace by calling the Baidu API Geocoding;(2) proposing a distributed trajectory pattern mining (DTPM) algorithm based on Spark;and (3) proposing a distributed trajectory clustering (DTC) algorithm based on Spark. The proposed DTPM algorithm and DTC algorithm can overcome the high input/output overhead and communication overhead by adopting in-memory computation. In addition, the proposed DTPM algorithm can avoid generating redundant local trajectory patterns to significantly improve the overall performance. The proposed DTC algorithm can enhance the performance of trajectory similarity computation by transforming the trajectory similarity calculation into AND and OR operators. Experimental results indicate that DTPM algorithm and DTC algorithm can significantly improve the overall performance and scalab.lity of trajectory pattern mining and trajectory clustering on massive taxi trace data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Clustering algorithms

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Constructing Canonical Regions for Fast and Effective View Selection

Constructing Canonical Regions for Fast and Effective View S...

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IEEE Conference on computer Vision and Pattern Recognition

作者： Wencheng Wang Tianhao Gao State Key Lab. of Comput. Sci. Inst. of Software Beijing China

ISBN: (纸本)9781467388528

In view selection, little work has been done for optimizing the search process;views must be densely distributed and checked individually. Thus, evaluating poor views wastes much time, and a poor view may even be misidentified as a best one. In this paper, we propose a search strategy by identifying the regions that are very likely to contain best views, referred to as canonical regions. It is by decomposing the model under investigation into meaningful parts, and using the canonical views of these parts to generate canonical regions. Applying existing view selection methods in the canonical regions can not only accelerate the search process but also guarantee the quality of obtained views. As a result, when our canonical regions are used for searching N-best views during comprehensive model analysis, we can attain greater search speed and reduce the number of views required. Experimental results show the effectiveness of our method.

关键词： Visualization Computational modeling Acceleration Analytical models Semantics Color Feature extraction

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A multi-source tradaboost approach for cross-company defect prediction 28

A multi-source tradaboost approach for cross-company defect ...

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28th International Conference on software Engineering and Knowledge Engineering, SEKE 2016

作者： Yu, Xiao Liu, Jin Fu, Mandi Ma, Chuanxiang Nie, Guoping Chen, Xu State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Educational Informationalization Engineering Research Center of HuBei Province Wuhan China School of Mathematics Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)189170639X

Cross-company defect prediction (CCDP) is a practical way that trains a prediction model by exploiting one or multiple projects of a source company and then applies the model to target company. Unfortunately, larger irrelevant crosscompany (CC) data usually makes it difficult to build a prediction model with high performance. On the other hand, brute force leveraging of CC data poorly related to withincompany (WC) data may decrease the prediction model performance. To address such issues, this paper introduces Multi-Source TrAdaBoost algorithm, an effective transfer learning approach to perform CCDP. The core idea of our approach is that: 1) employ limited amount of lab.led WC data to weaken the impact of irrelevant CC data;2) import knowledge not from one but from multiple sources to avoid negative transfer. The experimental results indicate that: 1) our proposed approach achieves the best overall performance among all tested CCDP approaches;2) only 10% lab.led WC data is enough to achieve good performance of CCDP by using our proposed approach. Copyright © 2016 by KSI Research Inc. and Knowledge Systems inst.tute Graduate School.

关键词： Forecasting

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HAND DORSAL VEIN RECOGNITION BY MATCHING WIDTH SKELETON MODELS

HAND DORSAL VEIN RECOGNITION BY MATCHING WIDTH SKELETON MODE...

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IEEE International Conference on Image Processing

作者： Xiaoxia Li Di Huang Renke Zhang Yunhong Wang Xianbo Xie State Key Lab. of Software Dev. Environ. Beihang Univ. IRIP Lab. School of Computer Science and Engineering Beihang Univ.

ISBN: (纸本)9781467399623

This paper proposes a novel and efficient shape-based approach for hand dorsal vein recognition. A coarse-to-fine segmentation method is first introduced to precisely detect the boundaries of the vein areas. A generalized graph model, namely Width Skeleton Model (WSM), is built then, which takes both the topology of the vein network and the width of the vessel into account, thereby achieving more comprehensive geometric representation and conveying more discriminative cues for identification. The models of different samples are further efficiently compared through a new matching scheme for similarity measurement, based on which the identity of the individual is finally decided. We evaluate the proposed approach on the NCUT database, and the rank-one recognition rate reaches 99.31%, which is superior to the state of the arts, clearly illustrating its competency.

关键词： Biometrics Hand dorsal vein recognition Width Skeleton Model Biometrics Veins Skeletons state of the art matching scheme similar measure mineral dyke Graphical models Hand competence pressure vessels topology

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Real-time Individual 3D Facial Animation by Combining Parameterized Model and Muscular Model

Real-time Individual 3D Facial Animation by Combining Parame...

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第35届中国控制会议

作者： Jun Yu Lingyan Li Jie Zou Department of Automation University of Science and Technology of China State Key Lab. for Novel Software Technology Nanjing University Department of Computer Science and Technology Wuhan Technology and Business University

ISBN: (纸本)9781509009107

A real-time individual 3D facial animation system is ***,facial regions and features are localized on a frontal facial image based on the combination of AdaBoost,CamShift and Active Appearance ***,an individual3 D facial mesh model,including appearance and internal articulators,is *** individual appearance mesh model is obtained by adapting an universal 3D facial mesh model to the facial features,and the individual internal articulatory mesh is obtained by integrating with the individual appearance mesh model at the same ***,to compromise between performance and realism,the parameterized model and muscular model are combined to produce facial animation based on a facial action area splitting *** results demonstrate the effectiveness and efficiency of the proposed system.

关键词： Facial model adaptation facial animation parameterized model muscular model

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Recommendations Based on LDA Topic Model in Android Applications

Recommendations Based on LDA Topic Model in Android Applicat...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Tianhao Pan Weifeng Zhang Ziyuan Wang Lei Xu School of Computer Science and Technology Nanjing University of Posts and Telecommunications Nanjing China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

ISBN: (纸本)9781509037148

With the popularity of smart phones, mobile applications have become an essential element in people's lives. Wherever you are, the Android market can allow people to download these applications. When you open Android mobile phone market in a mobile application program interface, we can see not only the application content presentation but also other information. The user can also see the scoring for this application, comments and so on. The text describes the application content information and user reviews, no more than 200 words, the contents of which are closely related to mobile applications. How to use these text data to obtain valuable information for the users has been an important research field of data mining, it is also the focus of this study. The topics of different mobile applications are the summary of their contents. This generalization to some extent reflects the different mobile application content core idea. Therefore mobile applications relating to mining user interest analysis has important significance, the results relating to mining can provide data support for topic-based personalized recommendation applications. This article extracts the contents and users' descriptions from a real Android Market dataset, and builds a new topic model called combineLDA to analyze different topics of each mobile application. By combineLDA model we can analyze the topic probability distribution of each mobile application, and then we can calculate the similarity and recommend to users with high similarity applications.

关键词： Conferences software quality software reliability Security

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Efficient Snapshot KNN Join Processing for Large Data Using MapReduce

Efficient Snapshot KNN Join Processing for Large Data Using ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yupeng Hu Chong Yang Cun Ji Yang Xu Xueqing Li State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Computer Science and Technology Shandong University P.R. China

ISBN: (纸本)9781509053827

The kNN join problem, denoted by R × KNN S, is to find the k nearest neighbors from a given dataset S for each point in the query set R. It is an operation required by many big data applications. As large volume of data are continuously generated in more and more real-life cases, we address the problem of monitoring kNN join results on data streams. Specifically, we are concerned with answering kNN join periodically at each snapshot which is called snapshot kNN join. Existing kNN join solutions mainly solve the problem on static datasets, or on a single centralized machine, which are difficult to scale to large data on data streams. In this paper, we propose to incrementally calculate the kNN join results of time t i from the results of snapshot t i-1 . Typically, for the data continuously generated on the data stream, we can get S i = S i-1 + ΔS i for the valid datasets of adjacent snapshots, where ΔS i denotes the updated points between time t i-1 and t i . Our basic idea is to first find the queries in R whose kNN results can be affected by the updated points in ΔS i , and then update the kNN results of these small part of queries respectively. In this way, we can avoid calculating the kNN join results on the whole dataset S i in time t i . We propose an implementation of searching for affected query points in MapReduce to scale to large volume of data. In brief, the mappers partition the datasets into groups, and the reducers search for affected queries separately on each group of points. Furthermore, we present the enhanced strategies of data partitioning and grouping to reduce the shuffling cost and computational cost. Extensive experiments on real-world datasets demonstrate that proposed methods are efficient, robust, and scalab.e.

关键词： Measurement Distributed databases Computational efficiency Robustness Servers Indexes Algorithm design and analysis

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Where to Fix Temporal Violations: A Novel Handling Point Selection Strategy for Business Cloud Workflows

Where to Fix Temporal Violations: A Novel Handling Point Sel...

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IEEE International Conference on Services Computing (SCC)

作者： Haoyu Luo Xiao Liu Jin Liu Futian Wang State Key Lab. of Software Engineering Wuhan University Wuhan China School of Information Technology Deakin University Melbourne Australia School of Computer Science and Technology Anhui University Hefei China

On-time completion is an important QoS (Quality of Service) dimension for business cloud workflow systems. Due to the dynamic and uncertain system performance, failures of on-time completion, namely temporal violations, often take place during workflow runtime and thus temporal verification and temporal violation handling are required. Currently, workflow throughput is employed by temporal verification for a large number of parallel workflow inst.nces as the detector for temporal violations in a collective way. However, based on workflow throughput, it is impossible to locate the exact workflow activities and cloud services where temporal violations occurred. This is a critical problem as to increase workflow throughput of the whole system is very expensive if we do not know where to fix local temporal violations. To address such a problem, based on the results of a throughput based temporal checkpoint selection strategy, this paper proposes a novel temporal violation handling point selection strategy which further utilizes the average response time of a cloud service as the key detector to determine whether temporal violation handling needs to be implemented or not. Comprehensive experimental results demonstrate that while achieving the same on-time completion rate, our strategy is much more cost effective as it can precisely localize temporal violations and hence require a much smaller number of times for temporal violation handling compared with other strategies.

关键词： Throughput Business Quality of service Cloud computing Time factors Runtime Monitoring

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