Computer Sciences Commons^™

Controlling For Confounding Network Properties In Hypothesis Testing And Anomaly Detection, Timothy La Fond

Open Access Dissertations

An important task in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they could be examples of malicious activity or network malfunction. Hypothesis testing using network statistics to summarize the behavior of the network provides a robust framework for the anomaly detection decision process. Unfortunately, choosing network statistics that are dependent on confounding factors like the total number of nodes or edges can lead to incorrect conclusions (e.g., false positives and false negatives). In this dissertation we describe the challenges that face …

Detection Of Cyberbullying In Sms Messaging, Bryan W. Bradley

Computer Science Summer Fellows

Cyberbullying is a type of bullying that uses technology such as cell phones to harass or malign another person. To detect acts of cyberbullying, we are developing an algorithm that will detect cyberbullying in SMS (text) messages. Over 80,000 text messages have been collected by software installed on cell phones carried by participants in our study. This paper describes the development of the algorithm to detect cyberbullying messages, using the cell phone data collected previously. The algorithm works by first separating the messages into conversations in an automated way. The algorithm then analyzes the conversations and scores the severity and …

Robust Median Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Hoi, Steven C. H., Shuigeng Zhou

Research Collection School Of Computing and Information Systems

On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal portfolios based …

Machine Learning Methods For Medical And Biological Image Computing, Rongjian Li

Computer Science Theses & Dissertations

Medical and biological imaging technologies provide valuable visualization information of structure and function for an organ from the level of individual molecules to the whole object. Brain is the most complex organ in body, and it increasingly attracts intense research attentions with the rapid development of medical and bio-logical imaging technologies. A massive amount of high-dimensional brain imaging data being generated makes the design of computational methods for efficient analysis on those images highly demanded. The current study of computational methods using hand-crafted features does not scale with the increasing number of brain images, hindering the pace of scientific discoveries …

Exploring Data Mining Techniques For Tree Species Classification Using Co-Registered Lidar And Hyperspectral Data, Julia K. Marrs

Theses and Dissertations

NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level.

Comparison Of Machine Learning Algorithms In Suggesting Candidate Edges To Construct A Query On Heterogeneous Graphs, Rohit Ravi Kumar Bhoopalam

Computer Science and Engineering Theses - Archive

Querying graph data can be difficult as it requires the user to have knowledge of the underlying schema and the query language. Visual query builders allow users to formulate the intended query by drawing nodes and edges of the query graph, which can be translated into a database query. Visual query builders help users formulate the query without requiring the user to have knowledge of the query language and the underlying schema. To the best of our knowledge, none of the currently available visual query builders suggest users what nodes/edges to include into their query graph. We provide suggestions to …

Spatio-Temporal Patterns Of Gps Trajectories Using Association Rule Mining, Vivek Kumar Sharma

Computer Science and Engineering Theses - Archive

The availability of location-tracking devices such as GPS, Cellular Networks and other devices provides the facility to log a person or device locations automatically. This creates spatio-temporal datasets of user's movement with features like latitude,longitude of a particular location on a specific day and time. With the help of these features different patterns of user movement can be collected,queues and analyzed.In this research work, we are focused on user's movement patterns and frequent movements of users on a particular place,day or time interval. To achieve this we used Association Rule mining concept based on Apriori algorithm to find interesting movement …

Euclidean Co-Embedding Of Ordinal Data For Multi-Type Visualization, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Embedding deals with reducing the high-dimensional representation of data into a low-dimensional representation. Previous work mostly focuses on preserving similarities among objects. Here, not only do we explicitly recognize multiple types of objects, but we also focus on the ordinal relationships across types. Collaborative Ordinal Embedding or COE is based on generative modelling of ordinal triples. Experiments show that COE outperforms the baselines on objective metrics, revealing its capacity for information preservation for ordinal data.

Mining And Clustering Mobility Evolution Patterns From Social Media For Urban Informatics, Chien-Cheng Chen, Meng-Fen Chiang, Wen-Chih Peng

Research Collection School Of Computing and Information Systems

In this paper, given a set of check-in data, we aim at discovering representative daily movement behavior of users in a city. For example, daily movement behavior on a weekday may show users moving from one to another spatial region associated with time information. Since check-in data contain both spatial and temporal information, we propose a mobility evolution pattern to capture the daily movement behavior of users in a city. Furthermore, given a set of daily mobility evolution patterns, we formulate their similarity distances and then discover representative mobility evolution patterns via the clustering process. Representative mobility evolution patterns are …

Identifying Terrorist Affiliations Through Social Network Analysis Using Data Mining Techniques, Govand A. Ali

Information Technology Master Theses

In a technologically enabled world, local ideologically inspired warfare becomes global all too quickly, specifically terrorist groups like Al Quaeda and ISIS (Daesh) have successfully used modern computing technology and social networking environments to broadcast their message, recruit new members, and plot attacks. This is especially true for such platforms as Twitter and encrypted mobile apps like Telegram or the clandestine Alrawi. As early detection of such activity is crucial to attack prevention data mining techniques have become increasingly important in the fight against the spread of global terrorist activity. This study employs data mining tools to mine Twitter for …

Unsupervised Learning Framework For Large-Scale Flight Data Analysis Of Cockpit Human Machine Interaction Issues, Abhishek B. Vaidya

Open Access Theses

As the level of automation within an aircraft increases, the interactions between the pilot and autopilot play a crucial role in its proper operation. Issues with human machine interactions (HMI) have been cited as one of the main causes behind many aviation accidents. Due to the complexity of such interactions, it is challenging to identify all possible situations and develop the necessary contingencies. In this thesis, we propose a data-driven analysis tool to identify potential HMI issues in large-scale Flight Operational Quality Assurance (FOQA) dataset. The proposed tool is developed using a multi-level clustering framework, where a set of basic …

Novel Machine Learning Methods For Modeling Time-To-Event Data, Bhanukiran Vinzamuri

Wayne State University Dissertations

Predicting time-to-event from longitudinal data where different events occur at different time points is an extremely important problem in several domains such as healthcare, economics, social networks and seismology, to name a few. A unique challenge in this problem involves building predictive models from right censored data (also called as survival data). This is a phenomenon where instances whose event of interest are not yet observed within a given observation time window and are considered to be right censored. Effective models for predicting time-to-event labels from such right censored data with good accuracy can have a significant impact in these …

A Cloud-Based Framework For Smart Permit System For Buildings, Magdalini Eirinaki, Subhankar Dhar, Shishir Mathur

Faculty Publications

In this paper we propose a novel cloud-based platform for building permit system that is efficient, user-friendly, transparent, and has quick turn-around time for homeowners. Compared to the existing permit systems, the proposed smart city permit framework provides a pre-permitting decision workflow, and incorporates a data analytics and mining module that enables the continuous improvement of a) the end user experience, by analyzing explicit and implicit user feedback, and b) the permitting and urban planning process, allowing a gleaning of key insights for real estate development and city planning purposes, by analyzing how users interact with the system depending on …

An Improved Smote Algorithm Based On Genetic Algorithm For Imbalanced Data Collection, Qiong Gu, Xian-Ming Wang, Zhao Wu, Bing Ning, Chun-Sheng Xin

Electrical & Computer Engineering Faculty Publications

Classification of imbalanced data has been recognized as a crucial problem in machine learning and data mining. In an imbalanced dataset, minority class instances are likely to be misclassified. When the synthetic minority over-sampling technique (SMOTE) is applied in imbalanced dataset classification, the same sampling rate is set for all samples of the minority class in the process of synthesizing new samples, this scenario involves blindness. To overcome this problem, an improved SMOTE algorithm based on genetic algorithm (GA), namely, GASMOTE was proposed. First, GASMOTE set different sampling rates for different minority class samples. A combination of the sampling rates …

Novel Dynamic Partial Reconfiguration Implementations Of The Support Vector Machine Classifier On Fpga, Hanaa Hussain, Khaled Benkrid, Hüseyi̇n Şeker

Turkish Journal of Electrical Engineering and Computer Sciences

The support vector machine (SVM) is one of the highly powerful classifiers that have been shown to be capable of dealing with high-dimensional data. However, its complexity increases requirements of computational power. Recent technologies including the postgenome data of high-dimensional nature add further complexity to the construction of SVM classifiers. In order to overcome this problem, hardware implementations of the SVM classifier have been proposed to benefit from parallelism to accelerate the SVM. On the other hand, those implementations offer limited flexibility in terms of changing parameters and require the reconfiguration of the whole device. The latter interrupts the operation …

Iot+Small Data: Transforming In-Store Shopping Analytics And Services, Meera Radhakrishnan, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Balan

Research Collection School Of Computing and Information Systems

We espouse a vision of small data-based immersive retail analytics, where a combination of sensor data, from personal wearable-devices and store-deployed sensors & IoT devices, is used to create real-time, individualized services for in-store shoppers. Key challenges include (a) appropriate joint mining of sensor & wearable data to capture a shopper’s product level interactions, and (b) judicious triggering of power-hungry wearable sensors (e.g., camera) to capture only relevant portions of a shopper’s in-store activities. To explore the feasibility of our vision, we conducted experiments with 5 smartwatch-wearing users who interacted with objects placed on cupboard racks in our lab (to …

Speaker Identification In Live Events Using Twitter, Minumol Joseph

Computer Science and Engineering Theses - Archive

The prevalence of social media has given rise to a new research area. Data from social media is now being used in research to gather deeper insights into many different fields. Twitter is one of the most popular microblogging websites. Users express themselves on a variety of different topics in 140 characters or less. Oftentimes, users “tweet” about issues and subjects that are gaining in popularity, a great example being politics. Any development in politics frequently results in a tweet of some form. The research which follows focuses on identifying a speaker’s name at a live event by collecting and …

Capstone Projects Mining System For Insights And Recommendations, Melvrivk Aik Chun Goh, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

In this paper, we present a classification based system to discover knowledge and trends in higher education students’ projects. Essentially, the educational capstone projects provide an opportunity for students to apply what they have learned and prepare themselves for industry needs. Therefore mining such projects gives insights of students’ experiences as well as industry project requirements and trends. In particular, we mine capstone projects executed by Information Systems students to discover patterns and insights related to people, organization, domain, industry needs and time. We build a capstone projects mining system (CPMS) based on classification models that leverage text mining, natural …

Intelligshop: Enabling Intelligent Shopping In Malls Through Location-Based Augmented Reality, Aditi Adhikari, Vincent W. Zheng, Hong Cao, Miao Lin, Yuan Fang, Kevin Chen-Chuan Chang

Research Collection School Of Computing and Information Systems

Shopping experience is important for both citizens and tourists. We present IntelligShop, a novel location-based augmented reality application that supports intelligent shopping experience in malls. As the key functionality, IntelligShop provides an augmented reality interface-people can simply use ubiquitous smartphones to face mall retailers, then IntelligShop will automatically recognize the retailers and fetch their online reviews from various sources (including blogs, forums and publicly accessible social media) to display on the phones. Technically, IntelligShop addresses two challenging data mining problems, including robust feature learning to support heterogeneous smartphones in localization and learning to query for automatically gathering the retailer content …

The Importance Of Being Isolated: An Empirical Study On Chromium Reviews, Subhajit Datta, Devarshi Bhatt, Manish Jain, Proshanta Sarkar, Santonu Sarkar

Research Collection School Of Computing and Information Systems

As large scale software development has become more collaborative, and software teams more globally distributed, several studies have explored how developer interaction influences software development outcomes. The emphasis so far has been largely on outcomes like defect count, the time to close modification requests etc. In the paper, we examine data from the Chromium project to understand how different aspects of developer discussion relate to the closure time of reviews. On the basis of analyzing reviews discussed by 2000+ developers, our results indicate that quicker closure of reviews owned by a developer relates to higher reception of information and insights …

Exploratory Data Modeling Of Traumatic Brain Injury, Martin Zwick

Complex Systems Faculty Publications and Presentations

A short presentation of an analysis of data from Dr. Megan Preece on traumatic brain injury, the first in a series of planned secondary analyses of multiple TBI data sets. The analysis employs the systems methodology of reconstructability analysis (RA), utilizing both variable- and state-based and both neutral and directed models. The presentation explains RA and illustrates the results it can obtain. Unlike the confirmatory approach standard to most data analyses, this methodology is designed for exploratory modeling. It thus allows the discovery of unanticipated associations among variables, including multi-variable interaction effects of unknown form. It offers the opportunity for …

Author Topic Model-Based Collaborative Filtering For Personalized Poi Recommendations, Shuhui Jiang, Xueming Qian, Jialie Shen, Yun Fu, Tao Mei

Research Collection School Of Computing and Information Systems

From social media has emerged continuous needs for automatic travel recommendations. Collaborative filtering (CF) is the most well-known approach. However, existing approaches generally suffer from various weaknesses. For example, sparsity can significantly degrade the performance of traditional CF. If a user only visits very few locations, accurate similar user identification becomes very challenging due to lack of sufficient information for effective inference. Moreover, existing recommendation approaches often ignore rich user information like textual descriptions of photos which can reflect users' travel preferences. The topic model (TM) method is an effective way to solve the "sparsity problem," but is still far …

Should We Use The Sample? Analyzing Datasets Sampled From Twitter's Stream Api, Yazhe Wang, Jamie Callan, Baihua Zheng

Research Collection School Of Computing and Information Systems

Researchers have begun studying content obtained from microblogging services such as Twitter to address a variety of technological, social, and commercial research questions. The large number of Twitter users and even larger volume of tweets often make it impractical to collect and maintain a complete record of activity; therefore, most research and some commercial software applications rely on samples, often relatively small samples, of Twitter data. For the most part, sample sizes have been based on availability and practical considerations. Relatively little attention has been paid to how well these samples represent the underlying stream of Twitter data. To fill …

Data Mining In Computational Proteomics And Genomics, Yang Song

Dissertations

This dissertation addresses data mining in bioinformatics by investigating two important problems, namely peak detection and structure matching. Peak detection is useful for biological pattern discovery while structure matching finds many applications in clustering and classification.

The first part of this dissertation focuses on elastic peak detection in 2D liquid chromatographic mass spectrometry (LC-MS) data used in proteomics research. These data can be modeled as a time series, in which the X-axis represents time points and the Y-axis represents intensity values. A peak occurs in a set of 2D LC-MS data when the sum of the intensity values in a …

Data Mining Temporal Work Patterns Of Programming Student Populations, Dale E. Parson, Lori Bogumil, Allison Seidel

Computer Science and Information Technology Faculty

This paper reports the second stage of a study of the correlations between the temporal work patterns of computer programming students and their success or failure as measured by programming project assignment grades and related metrics. The first stage confirmed the importance for most students of getting an early start on a programming project, and it also uncovered the fact that some student groups perform well with late starts, suggesting the likelihood that they engage in the productive practice of active procrastination. The second most important factor for success is the average length of assignment work sessions. Session lengths from …

Topic Analysis And Application Using Nonnegative Matrix Factorizations (Nmf), Xin Wang

Legacy Theses & Dissertations (2009 - 2024)

Managing large and growing amount of information is a central goal of modern computer science. Data repositories of texts, images and videos have become widely accessible, thus necessitating good methods of retrieval, organization and exploration.

A Theory Of Name Resolution, Pierre Néron, Andrew Tolmach, Eelco Visser, Guido Wachsmuth

Computer Science Faculty Publications and Presentations

We describe a language-independent theory for name binding and resolution, suitable for programming languages with complex scoping rules including both lexical scoping and modules. We formulate name resolution as a two-stage problem. First a language-independent scope graph is constructed using language-specific rules from an abstract syntax tree. Then references in the scope graph are resolved to corresponding declarations using a language-independent resolution process. We introduce a resolution calculus as a concise, declarative, and language- independent specification of name resolution. We develop a resolution algorithm that is sound and complete with respect to the calculus. Based on the resolution calculus we …

Novel Computational Methods For Transcript Reconstruction And Quantification Using Rna-Seq Data, Yan Huang

Theses and Dissertations--Computer Science

The advent of RNA-seq technologies provides an unprecedented opportunity to precisely profile the mRNA transcriptome of a specific cell population. It helps reveal the characteristics of the cell under the particular condition such as a disease. It is now possible to discover mRNA transcripts not cataloged in existing database, in addition to assessing the identities and quantities of the known transcripts in a given sample or cell. However, the sequence reads obtained from an RNA-seq experiment is only a short fragment of the original transcript. How to recapitulate the mRNA transcriptome from short RNA-seq reads remains a challenging problem. We …

Pattern Mining And Events Discovery In Molecular Dynamics Simulations Data, Shobhit Sandesh Shakya

LSU Doctoral Dissertations

Molecular dynamics simulation method is widely used to calculate and understand a wide range of properties of materials. A lot of research efforts have been focused on simulation techniques but relatively fewer works are done on methods for analyzing the simulation results. Large-scale simulations usually generate massive amounts of data, which make manual analysis infeasible, particularly when it is necessary to look into the details of the simulation results. In this dissertation, we propose a system that uses computational method to automatically perform analysis of simulation data, which represent atomic position-time series. The system identifies, in an automated fashion, the …

Demonstrating "Data Near Here": Scientific Data Search, Veronika Margaret Megler, David Maier

Computer Science Faculty Publications and Presentations

Prior work proposed "Data Near Here" (DNH), a data search engine for scientific archives that is modeled on Internet search engines. DNH performs a periodic, asynchronous scan of each dataset in an archive, extracting lightweight features that are combined to form a dataset summary. During a search, DNH assesses the similarity of the search terms to the summary features and returns to the user, at interactive timescales, a ranked list of datasets for further exploration and analysis. We will demonstrate the search capabilities and ancillary metadata-browsing features for an archive of observational oceanographic data. While comparing search terms to complete …