Computer Sciences Commons^™

Utilizing A Modular Approach To Gamification To Improve Nutrition And Fitness In Children, Chad Richards

Graduate Theses and Dissertations

Obesity is a worldwide epidemic that affects adults and children, impacts over 30% of the population in several states of the U.S., damages national economies, and is a factor in four out of the six of the leading causes of death, including diabetes and heart disease. Obesity is preventable: solution approaches include better education, more exercise, better nutrition, and changing eating habits. Still, it is difficult for many people to remain interested enough to educate themselves and to learn new behaviors to change their eating and exercise habits.

Gamification is a relatively new research area that involves using video game …

Part-Structured Inkball Models For One-Shot Handwritten Word Spotting, Nicholas Howe

Computer Science: Faculty Publications

Many document collections of historical interest are handwritten and lack transcripts. Scholars need tools for high-quality information retrieval in such environments, preferably without the burden of extensive system training. This paper presents a novel approach to word spotting designed for manuscripts or degraded print that requires minimal initial training. It can infer a generative word appearance model from a single instance, and then use the model to retrieve similar words from arbitrary documents. An approximation to the retrieval statistic runs efficiently on graphics processing hardware. Tested on two standard data sets, the method compares favorably with prior results.

On Fuzzy Soft Matrix Based On Reference Function, Florentin Smarandache, Said Broumi, Mamoni Dhar

Branch Mathematics and Statistics Faculty and Staff Publications

In this paper we study fuzzy soft matrix based on reference function. Firstly, we define some new operations such as fuzzy soft complement matrix and trace of fuzzy soft matrix based on reference function. Then, we introduced some related properties, and some examples are given. Lastly, we define a new fuzzy soft matrix decision method based on reference function.

Characterizations Of Distributions Of Ratio Of Certain Independent Random Variables, Gholamhossein Hamedani

Mathematics, Statistics and Computer Science Faculty Research and Publications

Various characterizations of the distributions of the ratio of two independent gamma and exponential random variables as well as that of two independent Weibull random variables are presented. These characterizations are based, on a simple relationship between two truncated moments ; on hazard function ; and on functions of order statistics.

On Generalized Gamma Convolution Distributions, Gholamhossein Hamedani

Mathematics, Statistics and Computer Science Faculty Research and Publications

We present here characterizations of certain families of generalized gamma convolution distributions of L. Bondesson based on a simple relationship between two truncated moments. We also present a list of well-known random variables whose distributions or the distributions of certain functions of them belong to the class of generalized gamma convolutions.

The Pitman Inequality For Exchangeable Random Vectors, J. Behboodian, Naveen K. Bansal, Gholamhossein Hamedani, Hans Volkmer

Mathematics, Statistics and Computer Science Faculty Research and Publications

In this short article the following inequality called the “Pitman inequality” is proved for the exchangeable random vector (X₁,X₂,…,X_n) without the assumption of continuity and symmetry for each component X_i:

P(|1n∑i=1nXi|≤|∑i=1nαiXi|)≥12 ,

where all αi≥0 are special weights with ∑i=1nαi=1.

Segmentation And Model Generation For Large-Scale Cyber Attacks, Steven E. Strapp

Theses

Raw Cyber attack traffic can present more questions than answers to security analysts. Especially with large-scale observables it is difficult to identify which packets are relevant and what attack behaviors are present. Many existing works in Host or Flow Clustering attempt to group similar behaviors to expedite analysis; these works often phrase the problem directly as offline unsupervised machine learning. This work proposes online processing to simultaneously model coordinating actors and segment traffic that is relevant to a target of interest, all while it is being received. The goal is not just to aggregate similar attack behaviors, but to provide …

Probabilistic Model And Algorithms Design For Motif Detection, Yuan Xue

Theses and Dissertations - UTB/UTPA

In this thesis, a natural probabilistic model has been used to test the quality of motif discovery programs. In this model, there are k background sequences, in which each character is a random character from Σ. Motif is a string G = g1g2 . . . gm. Each background sequence is implanted a probabilistically generated approximate copy of G. For each copy b1b2 . . . bm of G, every character bi is probabilistically generated such that the probability for bi 6= gi is at most α. Based on this model, two randomized algorithms ,one deterministic algorithm and one enumerative …

Algorithms In Abstract Dna Self Assembly, Xingsi Zhong

Theses and Dissertations - UTB/UTPA

For the past two years, I have always been working on the topic of Abstract DNA Tiles Self-Assembly. This is a very new area, driven by the interest of DNA molecules. The feature that the system composed by DNA molecules can be a highly parallelized system, make it much more powerful when comparing with the traditional methods. This thesis will introduce the concept of abstract DNA self-assembly models as well as some interesting problems and their solutions.

Incremental And Accuracy-Aware Personalized Pagerank Through Scheduled Approximation, Fanwei Zhu, Yuan Fang, Kevin Chen-Chuan Chang, Jing Ying

Research Collection School Of Computing and Information Systems

As Personalized PageRank has been widely leveraged for ranking on a graph, the efficient computation of Personalized PageRank Vector (PPV) becomes a prominent issue. In this paper, we propose FastPPV, an approximate PPV computation algorithm that is incremental and accuracy-aware. Our approach hinges on a novel paradigm of scheduled approximation: the computation is partitioned and scheduled for processing in an "organized" way, such that we can gradually improve our PPV estimation in an incremental manner, and quantify the accuracy of our approximation at query time. Guided by this principle, we develop an efficient hub based realization, where we adopt the …

Segmenting Tables Via Indexing Of Value Cells By Table Headers, Sharad C. Seth, George Nagy

School of Computing: Conference and Workshop Papers

Correct segmentation of a web table into its component regions is the essential first step to understanding tabular data. Our algorithmic solution to the segmentation problem relies on the property that strings defining row and column header paths uniquely index each data cell in the table. We segment the table using only “logical layout analysis” without resorting to any appearance features or natural language understanding. We start with a CSV table that preserves the 2- dimensional structure and contents of the original source table (e.g., an HTML table) but not font size, font weight, and color. The indexing property of …

Near-Duplicate Video Retrieval: Current Research And Future Trends, Jiajun Liu, Zi Huang, Hongyun Cai, Heng Tao Shen, Chong-Wah Ngo, Wei Wang

Research Collection School Of Computing and Information Systems

The exponential growth of online videos, along with increasing user involvement in video-related activities, has been observed as a constant phenomenon during the last decade. User's time spent on video capturing, editing, uploading, searching, and viewing has boosted to an unprecedented level. The massive publishing and sharing of videos has given rise to the existence of an already large amount of near-duplicate content. This imposes urgent demands on near-duplicate video retrieval as a key role in novel tasks such as video search, video copyright protection, video recommendation, and many more. Driven by its significance, near-duplicate video retrieval has recently attracted …

Utilizing Big Data In Identification And Correction Of Ocr Errors, Shivam Agarwal

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this thesis, we report on our experiments for detection and correction of OCR errors with web data. More specifically, we utilize Google search to access the big data resources available to identify possible candidates for correction. We then use a combination of the Longest Common Subsequences (LCS) and Bayesian estimates to automatically pick the proper candidate.

Our experimental results on a small set of historical newspaper data show a recall and precision of 51% and 100%, respectively. The work in this thesis further provides a detailed classification and analysis of all errors. In particular, we point out the shortcomings …

Verifying R Code And Visualizing Power Grid Data: Signature Quality Metrics And Gui Development, Emmanuel C. Herrera, Brett Amidan

STAR Program Research Presentations

Verifying R code and visualizing Power Grid data:

Signature Quality Metrics and GUI development

Author: Emmanuel Herrera

Mentors: Brett Amidan and Landon Sego

There are many bioforensic signatures produced by analytical instruments that are expensive to produce and maintain accuracy. SQM is an R package which will provide subject matter experts with tools that will help them assess the specific quality of signatures and determine their accuracy, utility and cost by simple function calls. Many published academic papers were surveyed on the kinds of metrics already being implemented. Once understood how some metrics measured accuracy, the SQM package was duplicated …

Renal Cryoablation: Investigation Of Periprocedural Visualization To Ols And Treatment Response Quantification, Katherine L. Dextraze

Dissertations and Theses (Open Access)

Cryoablation for small renal tumors has demonstrated sufficient clinical efficacy over the past decade as a non-surgical nephron-sparing approach for treating renal masses for patients who are not surgical candidates. Minimally invasive percutaneous cryoablations have been performed with image guidance from CT, ultrasound, and MRI. During the MRI-guided cryoablation procedure, the interventional radiologist visually compares the iceball size on monitoring images with respect to the original tumor on separate planning images. The comparisons made during the monitoring step are time consuming, inefficient and sometimes lack the precision needed for decision making, requiring the radiologist to make further changes later in …

Development Of Map/Reduce Based Microarray Analysis Tools, Guangyu Yang

All Theses

High density oligonucleotide array (microarray) from the Affymetrix GeneChip¨ system has been widely used for the measurements of gene expressions. Currently, public data repositories, such as Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information (NCBI), have accumulated very large amount of microarray data. For example, there are 84389 human and 9654 Arabidopsis microarray experiments in GEO database. Efficiently integrative analysis large amount of microarray data will provide more knowledge about the biological systems. Traditional microarray analysis tools all implemented sequential algorithms and can only be run on single processor. They are not able to handle very large …

Engineering Specifications And Mathematics For Verified Software, Hampton Smith

All Dissertations

Developing a verifying compiler---a compiler that proves that components are correct with respect to their specifications---is a grand challenge for the computing community. The prevailing view of the software engineering community is that this problem is necessarily difficult because, to-date, the resultant verification conditions necessary to prove software correctness have required powerful automated provers and deep mathematical insight to dispatch. This seems counter-intuitive, however, since human programmers only rarely resort to deep mathematics to assure themselves that their code works.
In this work, we show that well-specified and well-engineered software supported by a flexible, extensible mathematical system can yield verification …

Gesture-Based Robot Path Shaping, Paul Yanik

All Dissertations

For many individuals, aging is frequently associated with diminished mobility and dexterity. Such decreases may be accompanied by a loss of independence, increased burden to caregivers, or institutionalization. It is foreseen that the ability to retain independence and quality of life as one ages will increasingly depend on environmental sensing and robotics which facilitate aging in place. The development of ubiquitous sensing strategies in the home underpins the promise of adaptive services, assistive robotics, and architectural design which would support a person's ability to live independently as they age. Instrumentation (sensors and processing) which is capable of recognizing the actions …

Applying Search In An Automatic Contract-Based Testing Tool, Alexey Kolesnichenko, Christopher M. Poskitt, Bertrand Meyer

Research Collection School Of Computing and Information Systems

Automated random testing has been shown to be effective at finding faults in a variety of contexts and is deployed in several testing frameworks. AutoTest is one such framework, targeting programs written in Eiffel, an object-oriented language natively supporting executable pre- and postconditions; these respectively serving as test filters and test oracles. In this paper, we propose the integration of search-based techniques—along the lines of Tracey—to try and guide the tool towards input data that leads to violations of the postconditions present in the code; input data that random testing alone might miss, or take longer to find. Furthermore, we …

Riskvis: Supply Chain Visualization With Risk Management And Real-Time Monitoring, Rick S. M. Goh, Zhaoxia Wang, Xiaofeng Yin, Xiuju Fu, Loganathan Ponnanbalam, Sifei Lu, Xiaorong Li

Research Collection School Of Computing and Information Systems

With increased complexity, supply chain networks (SCNs) of modern era face higher risks and lower efficiency due to limited visibility. Hence, there is an immediate need to provide end-to-end supply chain visibility for efficient management of complex supply chains. This paper proposes a visualization scheme based on multi-hierarchical modular design and develops a supply chain visualization platform with risk management and real-time monitoring, named RiskVis, for realizing better Supply Chain Risk Management (SCRM). A Supply Chain Visualizer (SCV) with a graphical visualization platform is mounted as a part of a SCRM management decision-making dashboard and it provides senior management a …

Improving Traffic Prediction With Tweet Semantics, Jingrui He, Wei Shen, Phani Divakaruni, Laura Wynter, Rick Lawrence

Research Collection School Of Computing and Information Systems

Road traffic prediction is a critical component in modern smart transportation systems. It provides the basis for traffic management agencies to generate proactive traffic operation strategies for alleviating congestion. Existing work on near-term traffic prediction (forecasting horizons in the range of 5 minutes to 1 hour) relies on the past and current traffic conditions. However, once the forecasting horizon is beyond 1 hour, i.e., in longer-term traffic prediction, these techniques do not work well since additional factors other than the past and current traffic conditions start to play important roles.To address this problem, in this paper, for the first time, …

Open Source Software Development Process Model – A Grounded Theory Approach, Keng Siau, Y. Tian

Research Collection School Of Computing and Information Systems

The open source movement has provided software users with more choices, lower software acquisition cost, more flexible software customization, and possibly higher quality software product. Although the development of open source software is dynamic and it encourages innovations, the process can be chaotic. An Open Source Software Development (OSSD) process model to enhance the survivability of OSSD projects is needed. This research uses the grounded theory approach to derive a Phase-Role-Skill-Responsibility (PRSR) OSSD process model. The three OSSD process phases -- Launch Stage, Before the First Release, and Between Releases -- address the characteristics of the OSSD process as well …

Scalable Randomized Patrolling For Securing Rapid Transit Networks, Pradeep Varakantham, Hoong Chuin Lau, Zhi Yuan

Research Collection School Of Computing and Information Systems

Mass Rapid Transit using rail is a popular mode of transport employed by millions of people in many urban cities across the world. Typically, these networks are massive, used by many and thus, can be a soft target for criminals. In this paper, we consider the problem of scheduling randomised patrols for improving security of such rail networks. Similar to existing work in randomised patrols for protecting critical infrastructure, we also employ Stackelberg Games to represent the problem. In solving the Stackelberg games for massive rail networks, we make two key contributions. Firstly, we provide an approach called RaPtoR for …

A Multi-Objective Memetic Algorithm For Vehicle Resource Allocation In Sustainable Transportation Planning, Hoong Chuin Lau, Lucas Agussurja, Shih-Fen Cheng, Pang Jin Tan

Research Collection School Of Computing and Information Systems

Sustainable supply chain management has been an increasingly important topic of research in recent years. At the strategic level, there are computational models which study supply and distribution networks with environmental considerations. At the operational level, there are, for example, routing and scheduling models which are constrained by carbon emissions. Our paper explores work in tactical planning with regards to vehicle resource allocation from distribution centers to customer locations in a multi-echelon logistics network. We formulate the bi-objective optimization problem exactly and design a memetic algorithm to efficiently derive an approximate Pareto front. We illustrate the applicability of our approach …

Best Upgrade Plans For Large Road Networks, Yimin Lin, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

In this paper, we consider a new problem in the context of road network databases, named Resource Constrained Best Upgrade Plan computation (BUP, for short). Consider a transportation network (weighted graph) G where a subset of the edges are upgradable, i.e., for each such edge there is a cost, which if spent, the weight of the edge can be reduced to a specific new value. Given a source and a destination in G, and a budget (resource constraint) B, the BUP problem is to identify which upgradable edges should be upgraded so that the shortest path distance between source and …

Interacting Knapsack Problem In Designing Resource Bundles, Truong Huy D. Nguyen, Pradeep Reddy Varakantham, Hoong Chuin Lau, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

In many real-life businesses, the service provider/seller keeps a log of the visitors’ behavior as a way to assess the efficiency of the current business/operation model and find room for improvement. For example, by tracking when visitors entering attractions in a theme park, theme park owners can detect when and where congestion may occur, thus having contingency plans to reroute the visitors accordingly. Similarly, a Cable TV service provider can track channel switching events at each household to identify uninteresting channels. Subsequently, the repertoire of channels up for subscription can evolve over time to better serve the entertainment demand of …

Integration Of Social Media Technologies With Erp:A Prototype Implementation, Venky Shankararaman, Eng Kit Lum

Research Collection School Of Computing and Information Systems

Many organizations use social media to enhance collaboration both internally among employees and externally with customers and partners. However, it is not sufficient to just create a presence in some key social environments such as Facebook or YouTube. In order to fully leverage the value of social media, organizations must integrate social media with their business processes, which means new solutions have to be developed that extend existing enterprise systems. In this paper, we present a prototype solution that demonstrates the integration of social media with the order processing process. The implemented solution brings together the embedded best practice processes …

Robust Median Reversion Strategy For On-Line Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Steven Hoi, 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 …

Learning To Name Faces: A Multimodal Learning Scheme For Search-Based Face Annotation, Dayong Wang, Steven C. H. Hoi, Pengcheng Wu, Jianke Zhu, Ying He, Chunyan Miao

Research Collection School Of Computing and Information Systems

Automated face annotation aims to automatically detect human faces from a photo and further name the faces with the corresponding human names. In this paper, we tackle this open problem by investigating a search-based face annotation (SBFA) paradigm for mining large amounts of web facial images freely available on the WWW. Given a query facial image for annotation, the idea of SBFA is to first search for top-n similar facial images from a web facial image database and then exploit these top-ranked similar facial images and their weak labels for naming the query facial image. To fully mine those information, …

Parameter Learning For Latent Network Diffusion, Xiaojian Wu, Akshat Kumar, Daniel Sheldon, Shlomo Zilberstein

Research Collection School Of Computing and Information Systems

Diffusion processes in networks are increasingly used to model dynamic phenomena such as the spread of information, wildlife, or social influence. Our work addresses the problem of learning the underlying parameters that govern such a diffusion process by observing the time at which nodes become active. A key advantage of our approach is that, unlike previous work, it can tolerate missing observations for some nodes in the diffusion process. Having incomplete observations is characteristic of offline networks used to model the spread of wildlife. We develop an EM algorithm to address parameter learning in such settings. Since both the E …