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Vertex Weighted Spectral Clustering, Mohammad Masum 2017 East Tennessee State University

Vertex Weighted Spectral Clustering, Mohammad Masum

Electronic Theses and Dissertations

Spectral clustering is often used to partition a data set into a specified number of clusters. Both the unweighted and the vertex-weighted approaches use eigenvectors of the Laplacian matrix of a graph. Our focus is on using vertex-weighted methods to refine clustering of observations. An eigenvector corresponding with the second smallest eigenvalue of the Laplacian matrix of a graph is called a Fiedler vector. Coefficients of a Fiedler vector are used to partition vertices of a given graph into two clusters. A vertex of a graph is classified as unassociated if the Fiedler coefficient of the vertex is close to ...


Community Detection In Social Networks, Ketki Kulkarni 2017 San Jose State University

Community Detection In Social Networks, Ketki Kulkarni

Master's Projects

The rise of the Internet has brought people closer. The number of interactions between people across the globe has gone substantially up due to social awareness, the advancements of the technology, and digital interaction. Social networking sites have built societies, communities virtually. Often these societies are displayed as a network of nodes depicting people and edges depicting relationships, links. This is a good and e cient way to store, model and represent systems which have a complex and rich information. Towards that goal we need to nd e ective, quick methods to analyze social networks. One of the possible solution ...


Influence Detection And Spread Estimation In Social Networks, Madhura Kaple 2017 San Jose State University

Influence Detection And Spread Estimation In Social Networks, Madhura Kaple

Master's Projects

A social network is an online platform, where people communicate and share information with each other. Popular social network features, which make them di erent from traditional communication platforms, are: following a user, re-tweeting a post, liking and commenting on a post etc. Many companies use various social networking platforms extensively as a medium for marketing their products. A xed amount of budget is alloted by the companies to maximize the positive in uence of their product. Every social network consists of a set of users (people) with connections between them. Each user has the potential to extend its in ...


An Improved Algorithm For Learning To Perform Exception-Tolerant Abduction, Mengxue Zhang 2017 Washington University in St. Louis

An Improved Algorithm For Learning To Perform Exception-Tolerant Abduction, Mengxue Zhang

Engineering and Applied Science Theses & Dissertations

Abstract

Inference from an observed or hypothesized condition to a plausible cause or explanation for this condition is known as abduction. For many tasks, the acquisition of the necessary knowledge by machine learning has been widely found to be highly effective. However, the semantics of learned knowledge are weaker than the usual classical semantics, and this necessitates new formulations of many tasks. We focus on a recently introduced formulation of the abductive inference task that is thus adapted to the semantics of machine learning. A key problem is that we cannot expect that our causes or explanations will be perfect ...


Solving Capacitated Data Storage Placement Problems In Sensor Networks, Zhenfei Wu 2017 The University of Western Ontario

Solving Capacitated Data Storage Placement Problems In Sensor Networks, Zhenfei Wu

Electronic Thesis and Dissertation Repository

Data storage is an important issue in sensor networks as the large amount of data collected by the sensors in such networks needs to be archived for future processing. In this thesis we consider sensor networks in which the information produced by the sensors needs to be collected by storage nodes where the information is compressed and then sent to a central storage node called the sink. We study the problem of selecting k sensors to be used as storage nodes so as to minimize the total cost of sending information from the sensors to the storage nodes and from ...


Music Feature Matching Using Computer Vision Algorithms, Mason Hollis 2017 University of Arkansas, Fayetteville

Music Feature Matching Using Computer Vision Algorithms, Mason Hollis

Computer Science and Computer Engineering Undergraduate Honors Theses

This paper seeks to establish the validity and potential benefits of using existing computer vision techniques on audio samples rather than traditional images in order to consistently and accurately identify a song of origin from a short audio clip of potentially noisy sound. To do this, the audio sample is first converted to a spectrogram image, which is used to generate SURF features. These features are compared against a database of features, which have been previously generated in a similar fashion, in order to find the best match. This algorithm has been implemented in a system that can run as ...


Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw CHONG, Ee-peng LIM 2017 Singapore Management University

Exploiting Contextual Information For Fine-Grained Tweet Geolocation, Wen Haw Chong, Ee-Peng Lim

Research Collection School Of Information Systems

The problem of fine-grained tweet geolocation is to link tweets to their posting venues. We solve this in a learning to rank framework by ranking candidate venues given a test tweet. The problem is challenging as tweets are short and the vast majority are non-geocoded, meaning information is sparse for building models. Nonetheless, although only a small fraction of tweets are geocoded, we find that they are posted by a substantial proportion of users. Essentially, such users have location history data. Along with tweet posting time, these serve as additional contextual information for geolocation. In designing our geolocation models, we ...


Ds-Pso: Particle Swarm Optimization With Dynamic And Static Topologies, Dominick Sanchez 2017 Bowdoin College

Ds-Pso: Particle Swarm Optimization With Dynamic And Static Topologies, Dominick Sanchez

Honors Projects

Particle Swarm Optimization (PSO) is often used for optimization problems due to its speed and relative simplicity. Unfortunately, like many optimization algorithms, PSO may potentially converge too early on local optima. Using multiple neighborhoods alleviates this problem to a certain extent, although premature convergence is still a concern. Using dynamic topologies, as opposed to static neighborhoods, can encourage exploration of the search space at the cost of exploitation. We propose a new version of PSO, Dynamic-Static PSO (DS-PSO) that assigns multiple neighborhoods to each particle. By using both dynamic and static topologies, DS-PSO encourages exploration, while also exploiting existing knowledge ...


Electrodynamical Modeling For Light Transport Simulation, Michael G. Saunders 2017 East Tennessee State University

Electrodynamical Modeling For Light Transport Simulation, Michael G. Saunders

Undergraduate Honors Theses

Modernity in the computer graphics community is characterized by a burgeoning interest in physically based rendering techniques. That is to say that mathematical reasoning from first principles is widely preferred to ad hoc, approximate reasoning in blind pursuit of photorealism. Thereby, the purpose of our research is to investigate the efficacy of explicit electrodynamical modeling by means of the generalized Jones vector given by Azzam [1] and the generalized Jones matrix given by Ortega-Quijano & Arce-Diego [2] in the context of stochastic light transport simulation for computer graphics. To augment the status quo path tracing framework with such a modeling technique ...


Socket Golf - Building A Google Cardboard Game In Unity, Caleb P. Carlson 2017 University of Wyoming

Socket Golf - Building A Google Cardboard Game In Unity, Caleb P. Carlson

Honors Theses AY 16/17

Virtual reality is a new and emerging technology in the field of computer science designed to immerse the consumer into the product. To study and learn more about this technology, a four-person team of graduating seniors set out to build a mobile game for Google Cardboard. The game that was created uses the Unity game engine along with Unity multiplayer servers for the development tools. The application was designed to be run using both a Google Cardboard headset and an android controller to allow the user to control the game without removing themselves from the immersive experience.

The idea for ...


Improving Discovery And Patron Experience Through Data Mining, Boyuan Guan, Jamie Rogers 2017 Florida International University

Improving Discovery And Patron Experience Through Data Mining, Boyuan Guan, Jamie Rogers

Works of the FIU Libraries

As information professionals, we know simple database searches are imperfect. With rich and expansive digital collections, patrons may not find content that is buried in a long list of results. So, how do we improve discovery of pertinent materials and offer serendipitous experience? Following the example of recommendation functionality in online applications like Netflix, we have developed a recommendation function for our digital library system that provides relevant content beyond the narrow scope of patrons' original search parameters. This session will outline the reasoning, methodology, and design of the recommendation system as well as preliminary results from implementation.


On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei LEE, Tuan Anh HOANG, Ee-peng LIM 2017 Singapore Management University

On Analyzing User Topic-Specific Platform Preferences Across Multiple Social Media Sites, Roy Ka Wei Lee, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Information Systems

Topic modeling has traditionally been studied for single text collections and applied to social media data represented in the form of text documents. With the emergence of many social media platforms, users find themselves using different social media for posting content and for social interaction. While many topics may be shared across social media platforms, users typically show preferences of certain social media platform(s) over others for certain topics. Such platform preferences may even be found at the individual level. To model social media topics as well as platform preferences of users, we propose a new topic model known ...


Learning Personalized Preference Of Strong And Weak Ties For Social Recommendation, Xin WANG, HOI, Steven C. H., Martin ESTER, Jiajun BU, Chun CHEN 2017 Singapore Management University

Learning Personalized Preference Of Strong And Weak Ties For Social Recommendation, Xin Wang, Hoi, Steven C. H., Martin Ester, Jiajun Bu, Chun Chen

Research Collection School Of Information Systems

Recent years have seen a surge of research on social recommendation techniques for improving recommender systems due to the growing influence of social networks to our daily life. The intuition of social recommendation is that users tend to show affinities with items favored by their social ties due to social influence. Despite the extensive studies, no existing work has attempted to distinguish and learn the personalized preferences between strong and weak ties, two important terms widely used in social sciences, for each individual in social recommendation. In this paper, we first highlight the importance of different types of ties in ...


Optimizing Campus Mobility With A Focus On Sustainability: A Graph Theory Approach To Intra-Campus Transportation Networks, Quinn M. Nelson 2017 Quinn Nelson

Optimizing Campus Mobility With A Focus On Sustainability: A Graph Theory Approach To Intra-Campus Transportation Networks, Quinn M. Nelson

Student Research and Creative Activity Fair

The idea of public transportation is supported by most in theory but often heavily criticized by users when put into application. There are common tensions that are related to public transportation, as described by frequent users: unreliable, too crowded, and slow. The University of Nebraska-Omaha (UNO) is a growing metropolitan institution that uses a shuttle system to transport students among their three campuses daily. As of 2015, the current total student enrollment is approximately 16,000; UNO plans to enroll 20,000 students by 2020. The expected student growth is also reflected by the current construction of new buildings and ...


Metric Similarity Joins Using Mapreduce, Yunjun GAO, Keyu YANG, Lu CHEN, Baihua ZHENG, Gang CHEN, Chun CHEN 2017 Zhejiang University

Metric Similarity Joins Using Mapreduce, Yunjun Gao, Keyu Yang, Lu Chen, Baihua Zheng, Gang Chen, Chun Chen

Research Collection School Of Information Systems

Given two object sets Q and O , a metric similarity join finds similar object pairs according to a certain criterion. This operation has a wide variety of applications in data cleaning, data mining, to name but a few. However, the rapidly growing volume of data nowadays challenges traditional metric similarity join methods, and thus, a distributed method is required. In this paper, we adopt a popular distributed framework, namely, MapReduce, to support scalable metric similarity joins. To ensure the load balancing, we present two sampling based partition methods. One utilizes the pivot and the space-filling curve mappings to cluster the ...


Learning Convolutional Neural Network To Maximize Pos@Top Performance Measure, 2017 Selected Works

Learning Convolutional Neural Network To Maximize Pos@Top Performance Measure

KEPING WU

In the machine learning problems, the performance measure is used to evaluate the machine learning models. Recently, the number positive data points ranked at the top positions (Pos@Top) has been a popular performance measure in the machine learning community. In this paper, we propose to learn a convolutional neural network (CNN) model to maximize the Pos@Top performance measure. The CNN model is used to represent the multi-instance data point, and a classifier function is used to predict the label from the its CNN representation. We propose to minimize the loss function of Pos@Top over a training set ...


Alternating Direction Method Of Multipliers For Penalized Zero-Variance Discriminant Analysis, Brendan P.W. Ames, Mingyi Hong 2017 University of Alabama - Tuscaloosa

Alternating Direction Method Of Multipliers For Penalized Zero-Variance Discriminant Analysis, Brendan P.W. Ames, Mingyi Hong

Mingyi Hong

We consider the task of classification in the high dimensional setting where the number of features of the given data is significantly greater than the number of observations. To accomplish this task, we propose a heuristic, called sparse zero-variance discriminant analysis, for simultaneously performing linear discriminant analysis and feature selection on high dimensional data. This method combines classical zero-variance discriminant analysis, where discriminant vectors are identified in the null space of the sample within-class covariance matrix, with penalization applied to induce sparse structures in the resulting vectors. To approximately solve the resulting nonconvex problem, we develop a simple algorithm based ...


Discovering Historic Traffic-Tolerant Paths In Road Networks, Pui Hang LI, Man Lung YIU, Kyriakos MOURATIDIS 2017 Singapore Management University

Discovering Historic Traffic-Tolerant Paths In Road Networks, Pui Hang Li, Man Lung Yiu, Kyriakos Mouratidis

Research Collection School Of Information Systems

Historic traffic information is valuable in transportation analysis and planning, e.g., evaluating the reliability of routes for representative source-destination pairs. Also, it can be utilized to provide efficient and effective route-search services. In view of these applications, we propose the k traffic-tolerant paths (TTP) problem on road networks, which takes a source-destination pair and historic traffic information as input, and returns k paths that minimize the aggregate (historic) travel time. Unlike the shortest path problem, the TTP problem has a combinatorial search space that renders the optimal solution expensive to find. First, we propose an exact algorithm with effective ...


Exploring Representativeness And Informativeness For Active Learning, Bo DU, Zengmao WANG, Lefei ZHANG 2017 Singapore Management University

Exploring Representativeness And Informativeness For Active Learning, Bo Du, Zengmao Wang, Lefei Zhang

Research Collection School Of Information Systems

How can we find a general way to choose the most suitable samples for training a classifier? Even with very limited prior information? Active learning, which can be regarded as an iterative optimization procedure, plays a key role to construct a refined training set to improve the classification performance in a variety of applications, such as text analysis, image recognition, social network modeling, etc. Although combining representativeness and informativeness of samples has been proven promising for active sampling, state-of-the-art methods perform well under certain data structures. Then can we find a way to fuse the two active sampling criteria without ...


Network Modeling Of Infectious Disease: Transmission, Control And Prevention, Christina M. Chandler 2017 Georgia Southern University

Network Modeling Of Infectious Disease: Transmission, Control And Prevention, Christina M. Chandler

University Honors Program Theses

Many factors come into play when it comes to the transmission of infectious diseases. In disease control and prevention, it is inevitable to consider the general population and the relationships between individuals as a whole, which calls for advanced mathematical modeling approaches.

We will use the concept of network flow and the modified Ford-Fulkerson algorithm to demonstrate the transmission of infectious diseases over a given period of time. Through our model one can observe what possible measures should be taken or improved upon in the case of an epidemic. We identify key nodes and edges in the resulted network, which ...


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