2017 IEEE International Conference on Information Reuse and Integration (IRI)最新文献_第2页

Using Weather and Playing Surface to Predict the Occurrence of Injury in Major League Soccer Games: A Case Study 利用天气和场地预测美国职业足球大联盟比赛中受伤的发生:个案研究

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.86

Sara Landset, M. Bergeron, T. Khoshgoftaar

{"title":"Using Weather and Playing Surface to Predict the Occurrence of Injury in Major League Soccer Games: A Case Study","authors":"Sara Landset, M. Bergeron, T. Khoshgoftaar","doi":"10.1109/IRI.2017.86","DOIUrl":"https://doi.org/10.1109/IRI.2017.86","url":null,"abstract":"Injuries in professional soccer games are very common and can greatly impact players, teams, and leagues. The ability to predict conditions under which injuries are likely to occur would help to mitigate competitive and financial losses. This paper presents a case study in which we look at injuries during 713 Major League Soccer games spanning the 2015 and 2016 seasons. Our dataset consists of 713 regular season games, 548 of which recorded at least one injury. In total, our dataset includes information on 1,238 separate in-game injuries. In this paper, we compare the performance of nine different classifiers for predicting occurrence of injury based on local weather and playing surface. We find that Support Vector Machine (SVM) works best with this dataset, while three of the other classifiers also yield comparable performance. Further analysis shows that our results are statistically significant. We are presenting this study as a proof of concept in which we are able to confirm the efficacy of using available weather and surface data points to predict likelihood of injury and identify a path forward for future research.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132004635","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: 4

Species Distribution Modeling with Scalability: The Case Study of P-GARP, a Parallel Genetic Algorithm for Rule-Set Production 具有可扩展性的物种分布建模:基于规则集生成的并行遗传算法P-GARP的案例研究

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.93

F. Santana, C. Pariente, A. Saraiva

引用次数: 2

An Architecture for the Internet of Things and the Use of Big Data Techniques in the Analysis of Carbon Monoxide 物联网架构及大数据技术在一氧化碳分析中的应用

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.76

Marco Aurélio Borges, P. B. Lopes, L. A. Silva, M. Igarashi, G. Correia

{"title":"An Architecture for the Internet of Things and the Use of Big Data Techniques in the Analysis of Carbon Monoxide","authors":"Marco Aurélio Borges, P. B. Lopes, L. A. Silva, M. Igarashi, G. Correia","doi":"10.1109/IRI.2017.76","DOIUrl":"https://doi.org/10.1109/IRI.2017.76","url":null,"abstract":"The use of sensors for the monitoring of a given environment allied to the Internet as a means of communication is popularly known as Internet of Things (IoT). The amount of information generated in this environment has led to an unprecedented increase in data collection. One of the major challenges for its development lies in the storage and the processing of this huge volume of data into acceptable measurement and analysis parameters. This research takes up this challenge by storing and compiling data from different sensors, and by carrying out an exploratory analysis of the information gathered. In this research, sensors that collect data from a specific Sao Paulo's Metropolitan Area (SMA) have been analysed. These sensors are capable of measuring carbon monoxide (CO) levels. This research aims to analyse the main architectures for both batch and stream sensor processing and to use one of them for the construction of a Big Data environment. Big Data tools were used for IoT storage, processing and visualization data. During the experiments, carbon monoxide sensors (MQ7), were analysed. They were connected through a microcontroller unit that supports the Transmission Control Protocol/Internet Protocol (TCP/IP). This project highlights the necessary tools to execute and analyse the data in a dynamic manner. The data collected by the sensors show that the avarage levels of carbon monoxide are well above the international standards set by the World Health Organization (WHO).","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124443455","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

A Survey of Online Social Networks: Challenges and Opportunities 在线社交网络调查:挑战与机遇

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.74

Fabio Persia, D. D’Auria

引用次数: 22

A Cyber-Provenance Infrastructure for Sensor-Based Data-Intensive Applications 基于传感器的数据密集型应用的网络溯源基础设施

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.91

E. Bertino, Murat Kantarcioglu

引用次数: 2

Effective Information Spreading in Social Networks 社会网络中的有效信息传播

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.71

Nunzio Cassavia, E. Masciari, D. Saccá

引用次数: 0

Accelerated Sparse Representation for Human Activity Recognition 人类活动识别的加速稀疏表示

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.22

Long Cheng, Yani Guan, Kecheng Zhu, Yiyang Li, Ruokun Xu

{"title":"Accelerated Sparse Representation for Human Activity Recognition","authors":"Long Cheng, Yani Guan, Kecheng Zhu, Yiyang Li, Ruokun Xu","doi":"10.1109/IRI.2017.22","DOIUrl":"https://doi.org/10.1109/IRI.2017.22","url":null,"abstract":"Human activity recognition using wearable sensors plays a significant role in many applications. How to accurately and quickly recognize various activities based on wearable sensors draws more and more attentions. This paper proposes an accelerated sparse representation classification method based on random projection and k-nearest neighbor for human activity recognition. Random projection is first applied to reduce the dimensionality of the activity signal sampled from each wearable sensor. To optimally reconstruct an activity test sample, some nearest neighbor training samples from a few near neighbor classes of the test sample are selected to form a reduced training sample set, based on which the test sample can be sparsely represented. And then the activity class of the test sample is determined by solving an L1 minimization problem. The effectiveness of our method is experimentally validated on an open Wearable Action Recognition Database by recognizing nine common human activities performed by 20 subjects. Our method achieves the highest average recognition rate (92.56%), which beats the traditional sparse representation classification method and the conventional near neighbor algorithm. Meanwhile, the runtime of our method is significantly reduced compared to the traditional sparse representation classification method.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130717744","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: 13

Business Objects plus (BO+): An Approach to Enhance Service Reuse and Integration in Cross-Domain SOA Compounds 业务对象加(BO+):一种在跨域SOA组合中增强服务重用和集成的方法

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.28

J. Königsberger, B. Mitschang

{"title":"Business Objects plus (BO+): An Approach to Enhance Service Reuse and Integration in Cross-Domain SOA Compounds","authors":"J. Königsberger, B. Mitschang","doi":"10.1109/IRI.2017.28","DOIUrl":"https://doi.org/10.1109/IRI.2017.28","url":null,"abstract":"With globalization and rising competition, innovation cycles have shortened in recent years. Thus, the need for flexible and quickly changeable IT systems motivated companies to adopt new technologies and IT architectures, leading to novel concepts like the Industrial Internet of Things or Industrie 4.0. These concepts make it necessary to propagate information through multiple software systems and integrate data along the entire product life cycle and across different business domains. Companies often already rely on service-oriented Architectures to realize and solve integration problems. Due to the fact that service interfaces are mostly designed from a technical perspective, there still exist two important problems in everyday use: (1) property names of services oftentimes do not match established terms within a company, leading to communication problems between business and IT departments (semantic ambiguity) and (2) because of each service being developed separately, they do not follow naming conventions, leading to a format mismatch that has to be resolved for each communication path between two systems. In this paper, we present our concept for Business Objects plus (BO+) as well as the governance processes necessary to introduce them into and to manage them within a company. These Business Objects plus aim to remedy the problems described above, by defining small, self-contained data objects that can be used in service interface definitions to create a common semantic base for services and applications. The BO+ are motivated from a business perspective, supporting multiple hierarchy levels and therefore also aim to enhance reuse on the service interface level throughout a company.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125090558","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

Modified Convolution Neural Network for Highly Effective Parallel Processing 基于改进卷积神经网络的高效并行处理

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.37

Sang-Soo Park, Jung-Hyun Hong, Ki-Seok Chung

{"title":"Modified Convolution Neural Network for Highly Effective Parallel Processing","authors":"Sang-Soo Park, Jung-Hyun Hong, Ki-Seok Chung","doi":"10.1109/IRI.2017.37","DOIUrl":"https://doi.org/10.1109/IRI.2017.37","url":null,"abstract":"Today, Convolutional Neural Network (CNN) is adopted in a lot of areas such as computer vision and natural language processing. By employing hardware accelerators such as graphic processing unit (GPU), a significant amount of speedup can be achieved in CNN and many studies have proposed such acceleration methods. However, it is not straightforward to parallelize the CNN on a hardware accelerator because there are irregular characteristics of generating output feature maps. In this paper, we propose a modified CNN for efficient parallel processing. A well-known CNN architecture called Lenet-5 has an inefficient convolution combination. The proposed method of this paper improves the efficiency by utilizing a special operation called dummy operation. The proposed method is capable of maximizing the utilization of GPU by modifying Lenet-5's convolution combination. Its improved efficiency is validated on a platform that integrates a CPU and a GPU in the same die. Our OpenCL implementation of the proposed method has achieved an average peak performance of 115.66 GFLOPS which is an improvement of 37.26 times in execution time. Further, a reduction of 26.40 times in energy consumption is achieved.","PeriodicalId":254330,"journal":{"name":"2017 IEEE International Conference on Information Reuse and Integration (IRI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128047472","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: 1

Fresh-Phish: A Framework for Auto-Detection of Phishing Websites Fresh-Phish:一个自动检测网络钓鱼网站的框架

2017 IEEE International Conference on Information Reuse and Integration (IRI) Pub Date : 2017-08-01 DOI: 10.1109/IRI.2017.40

H. Shirazi, Kyle Haefner, I. Ray

引用次数: 21