2674 Slices
Medium 9781601323118

Session - Gene Expression and Representation, Microarray, Sequencing, Alignment, and Related Bioinformatics Issues

Hamid R. Arabnia, Quoc-Nam Tran, Mary Q. Yang, George Jandieri, Ashu M. G. Solo, Fernando G. Tinetti CSREA Press PDF
Medium 9781601323101

Using Influence for Navigation in Online Social Networks

Hamid R. Arabnia, Mary Q. Yang, George Jandieri, James J. (Jong Hyuk) Park, Ashu M. G. Solo, and Fernando G. Tinetti CSREA Press PDF

44

Int'l Conf. on Advances in Big Data Analytics | ABDA'14 |

Using Influence for Navigation in Online Social

Networks

Bastien Lebayle1 , Mehran Asadi2 and Afrand Agah1

(Corresponding author: Afrand Agah)

(Email: aagah@wcupa.edu)

Department of Computer Science, West Chester University1

West Chester, PA 19383

Department of Business and Entrepreneurial Studies, The Lincoln University2

Lincoln University, PA 19352

Abstract

If influence is the capacity to have an effect on someone, then who are the influential people in an Online

Social Network?

In this paper, we investigate the role of influential people in an Online Social Network. Then we present an algorithm that take advantage of influential people to reach a target in the network. Our navigation algorithm returns a path between two nodes in an average of 10% less iterations, with a maximum of 83% less iterations, and only relies on public attributes of a node in the network.

1

Here we are interested in connectedness at the level of behavior - the fact that each individual’s actions have implicit consequences on the outcomes of everyone in the system [4]. We investigate prediction of peoples behavior and influences in online social networks. Our focus is on how different nodes can play distinct roles in information flow through an online social network.

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Medium 9781601322548

Efficient Sparse Representation Classification Using Adaptive Clustering

Hamid R. Arabnia, Leonidas Deligiannidis, Joan Lu, Fernando G. Tinetti, Jane You, George Jandieri, Gerald Schaefer, Ashu M. G. Solo, Vladimir Volkov CSREA Press PDF

Int'l Conf. IP, Comp. Vision, and Pattern Recognition | IPCV'13 |

693

Efficient Sparse Representation Classification Using Adaptive

Clustering

Soheil Shafiee, Farhad Kamangar, Vassilis Athitsos, and Junzhou Huang

Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX, USA

Abstract— This paper is presenting a method for an efficient face recognition algorithm based on sparse representation classification (SRC) using an adaptive K-means clustering.

In the context of face recognition, SRC is implemented based on the assumption that a face image from a particular subject can be represented as a linear combination of other face images from the same subject. SRC uses a set of extracted features from the original face images as columns of a training matrix. This training matrix is used to form an under-determined system of linear equations. An unknown face image can be classified by finding the sparse solution of this linear system using an l1 -norm optimization which has a quadratic time complexity. In practice using all the training face images for a large population increases computational and memory requirements which may not be feasible to be used in mobile devices. The proposed method reduces the size of the training matrix by using adaptive K-means clustering along with SRC method which lowers computational requirements of the overall recognition system. Experimental results on a face dataset with 14,794 training face images from 100 classes show that the new proposed method reduces the running time of the classification algorithm compared to

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Medium 9781601323101

Exploring big-data analysis using integrative systems biology approaches for kidney renal clear cell carcinoma studies

Hamid R. Arabnia, Mary Q. Yang, George Jandieri, James J. (Jong Hyuk) Park, Ashu M. G. Solo, and Fernando G. Tinetti CSREA Press PDF

248

Int'l Conf. on Advances in Big Data Analytics | ABDA'14 |

Exploring big-data analysis using integrative systems biology approaches for kidney renal clear cell carcinoma studies

William Yang and Kenji Yoshigoe

Andrzej Niemierko and Jack Y. Yang

Department of Computer Science, University of Arkansas

Little Rock College of Engineering and Information

Technology, Little Rock, Arkansas 72204 USA wxyang1@ualr.edu and kxyoshigoe@ualr.edu

Division of Biostatistics and Biomathematics, Department of

Radiation Oncology, Massachusetts General Hospital and

Harvard Medical School, Boston, MA 02140 USA aniemieko@partners.org & jyang@hadron.mgh.harvard.edu

Xiang Qin

Yunlong Liu

Human Genome Sequencing Center

Baylor College of Medicine

Houston, Texas 77345 USA xqin@bcm.edu

Center for Computational Biology and Bioinformatics

Indiana University School of Medicine

Indianapolis, Indiana 46202 USA yunliu@indiana.edu

Jun S. Liu

Department of Statistics, Harvard University,

Cambridge, MA 02138, USA jliu@stat.harvard.edu

Zhongxue Chen

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Medium 9781601322401

The Correlation of Speech and Hand Gestures for Multimodal Web Interaction

Hamid R. Arabnia, Azita Bahrami, Fernando G. Tinetti, Leonidas Deligiannidis, George Jandieri, and Ashu M. G. Solo CSREA Press PDF

258

Int'l Conf. e-Learning, e-Bus., EIS, and e-Gov. | EEE'13 |

The Correlation of Speech and Hand Gestures for

Multimodal Web Interaction

Jing Liu1, Manolya Kavakli2

Department of Computing, Macquarie University, Sydney, NSW, Australia

Abstract - With the development of Multimodal Interfaces

(MMI) in Human Computer Interaction (HCI), there is an increasing interest at applying this technology to multimodal web interaction. Multimodal web interfaces can provide endusers with a natural, flexible and non-invasive interface that allow graphical, vocal and gestural interaction with web.

Integration of speech and gestures in an MMI framework is now the focus of the researchers in this area. In order to combine speech and gestures in multimodal web interaction, it is essential to know the correlations between speech and associated gestures. This paper presents an empirical study aimed at studying the correlations between speech and hand gestures from a cognitive aspect. The methodology used in this paper is the video analysis to investigate the cognitive actions of speakers in the descriptions of objects using speech and hand gestures. The speakers' cognitive actions are analyzed using a cognitive scheme and protocol analysis method. Our initial findings suggest that speech is highly correlated with co-verbal hand gestures perceptually and semantically, regardless of the age, gender, background of the speakers, or the speed of speech and gesticulation.

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