Data Storage Systems Commons^™

Deep Air Learning: Interpolation, Prediction, And Feature Analysis Of Fine-Grained Air Quality, Zhongang Qi, Tianchun Wang, Guojie Song, Weisong Hu, Xi Li, Zhongfei Mark Zhang

Research Collection School Of Information Systems

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep ...

Delegation Application, Erik Matthew Phillips

Computer Science

Delegation is a cross-platform application to provide smart task distribution to users. In a team environment, the assignment of tasks can be tedious and difficult for management or for users needing to discover a starting place for where to begin with accomplishing tasks. Within a specific team, members possess individual skills within different areas of the team’s responsibilities and specialties, and certain members will be better suited to tackle specific tasks. This project provides a solution, consisting of a smart cross-platform application that allows for teams and individuals to quickly coordinate and delegate tasks assigned to them.

Combining Algorithms For More General Ai, Mark Robert Musil

Undergraduate Research & Mentoring Program

Two decades since the first convolutional neural network was introduced the AI sub-domains of classification, regression and prediction still rely heavily on a few ML architectures despite their flaws of being hungry for data, time, and high-end hardware while still lacking generality. In order to achieve more general intelligence that can perform one-shot learning, create internal representations, and recognize subtle patterns it is necessary to look for new ML system frameworks. Research on the interface between neuroscience and computational statistics/machine learning has suggested that combined algorithms may increase AI robustness in the same way that separate brain regions specialize ...

An Embarrassment Of Riches: Data Integration In Vr Pompeii, Adam Schoelz

Computer Science and Computer Engineering Undergraduate Honors Theses

It is fair to say that Pompeii is the most studied archaeological site in the world. Beyond the extensive remains of the city itself, the timing of its rediscovery and excavation place it in a unique historiographical position. The city has been continuously studied since the 18th century, with historians and archaeologists constantly reevaluating older sources as our knowledge of the ancient world expands. While several studies have approached the city from a data driven perspective, no studies of the city have taken a quantitative holistic approach on the scale of the VR Pompeii project. Hyper-specificity has been the order ...

Library Awesome Sauce Undergraduate Research, Jeremy Evert, Phillip Joe Fitzsimmons, Hector Lucas

Faculty Articles & Research

Library Awesome Sauce Undergraduate Research was a presentation at the 2018 CADRE Conference in Stillwater, OK. The presenters discussed their collaboration on a video project to film interviews of students giving progress reports about their software engineering projects. The videos were posted on the institutional repository.

The speakers discussed Student-Led research and the role that academic libraries play in facilitating student and faculty research and publishing for all disciplines on campus.

Strategic Implications Of Blockchain, William R. Adams

Undergraduate Honors Theses

This thesis introduces blockchain, the underlying technology of cryptocurrencies such as Bitcoin, and discusses how best to conceptualize it relative to other technologies. Following an explanation of the fundamentals of blockchain, also known as the distributed ledger, I identify the characteristics of the technology. Building upon blockchain’s inherent strengths and limitations, I explore potential business applications of blockchain. Finally, I recommend that leaders continue to track the development and adoption of blockchain technology, even if they decide that implementing it does not align with their organization’s strategy at present.

Image Processing Applications In Real Life: 2d Fragmented Image And Document Reassembly And Frequency Division Multiplexed Imaging, Houman Kamran Habibkhani, Houman Kamran Habibkhani

LSU Doctoral Dissertations

In this era of modern technology, image processing is one the most studied disciplines of signal processing and its applications can be found in every aspect of our daily life. In this work three main applications for image processing has been studied.

In chapter 1, frequency division multiplexed imaging (FDMI), a novel idea in the field of computational photography, has been introduced. Using FDMI, multiple images are captured simultaneously in a single shot and can later be extracted from the multiplexed image. This is achieved by spatially modulating the images so that they are placed at different locations in the ...

Scaling Human Activity Recognition Via Deep Learning-Based Domain Adaptation, Md Abdullah Hafiz Khan, Nirmalya Roy, Archan Misra

Research Collection School Of Information Systems

We investigate the problem of making human activityrecognition (AR) scalable–i.e., allowing AR classifiers trainedin one context to be readily adapted to a different contextualdomain. This is important because AR technologies can achievehigh accuracy if the classifiers are trained for a specific individualor device, but show significant degradation when the sameclassifier is applied context–e.g., to a different device located ata different on-body position. To allow such adaptation withoutrequiring the onerous step of collecting large volumes of labeledtraining data in the target domain, we proposed a transductivetransfer learning model that is specifically tuned to the propertiesof convolutional neural ...

Administrative Cost Reimbursement Online, Luciano Mogorovic

Honors Research Projects

Administrative Cost Reimbursement (ACR) is a program funded by the state of Ohio that reimburses non-public elementary and high schools for having employees perform a series of mandated activities. ACR Consultants is a company that collects data from schools that contains the amount of time each teacher spent on any of the mandated activities. The president of ACR Consultants is a neighbor and longtime family friend. Currently, to collect the time data, the company goes to each school it services and gives each teacher a large paper spreadsheet for them to fill in with the amount of time they spend ...

Improving The Performance And Energy Efficiency Of Emerging Memory Systems, Yuhua Guo

Theses and Dissertations

Modern main memory is primarily built using dynamic random access memory (DRAM) chips. As DRAM chip scales to higher density, there are mainly three problems that impede DRAM scalability and performance improvement. First, DRAM refresh overhead grows from negligible to severe, which limits DRAM scalability and causes performance degradation. Second, although memory capacity has increased dramatically in past decade, memory bandwidth has not kept pace with CPU performance scaling, which has led to the memory wall problem. Third, DRAM dissipates considerable power and has been reported to account for as much as 40% of the total system energy and this ...

Variance-Optimal Offline And Streaming Stratified Random Sampling, Trong Duc Nguyen, Ming-Hung Shih, At&T Labs–Research, Srikanta Tirthapura, Bojian Xu

Electrical and Computer Engineering Publications

Stratified random sampling (SRS) is a fundamental sampling technique that provides accurate estimates for aggregate queries using a small size sample, and has been used widely for approximate query processing. A key question in SRS is how to partition a target sample size among different strata. While Neyman's allocation provides a solution that minimizes the variance of an estimate using this sample, it works under the assumption that each stratum is abundant, i.e. has a large number of data points to choose from. This assumption may not hold in general: one or more strata may be bounded, and ...

Chronic Risk And Disease Management Model Using Structured Query Language And Predictive Analysis, Mamata Ojha

Electronic Theses and Dissertations

Individuals with chronic conditions are the ones who use health care most frequently and more than 50% of top ten causes of death are chronic diseases in United States and these members always have health high risk scores. In the field of population health management, identifying high risk members is very important in terms of patient health care, disease management and cost management. Disease management program is very effective way of monitoring and preventing chronic disease and health related complications and risk management allows physicians and healthcare companies to reduce patient’s health risk, help identifying members for care/disease ...

International Journal Of Information Privacy, Security And Integrity, James Stewart, Maurice Dawson

Maurice Dawson

Data Protection In Nigeria: Addressing The Multifarious Challenges Of A Deficient Legal System, Roland Akindele

Journal of International Technology and Information Management

This paper provides an overview of the current state of privacy and data protection policies and regulations in Nigeria. The paper contends that the extant legal regime in Nigeria is patently inadequate to effectively protect individuals against abuse resulting from the processing of their personal data. The view is based on the critical analysis of the current legal regime in Nigeria vis-à-vis the review of some vital data privacy issues. The paper makes some recommendations for the reform of the law.

Table Of Contents Jitim Vol 26 Issue 4, 2017

Journal of International Technology and Information Management

Table of Contents

Process Models Discovery And Traces Classification: A Fuzzy-Bpmn Mining Approach., Kingsley Okoye Dr, Usman Naeem Dr, Syed Islam Dr, Abdel-Rahman H. Tawil Dr, Elyes Lamine Dr

Journal of International Technology and Information Management

The discovery of useful or worthwhile process models must be performed with due regards to the transformation that needs to be achieved. The blend of the data representations (i.e data mining) and process modelling methods, often allied to the field of Process Mining (PM), has proven to be effective in the process analysis of the event logs readily available in many organisations information systems. Moreover, the Process Discovery has been lately seen as the most important and most visible intellectual challenge related to the process mining. The method involves automatic construction of process models from event logs about any ...

Privacy Risks And Security Threats In Mhealth Apps, Brinda Hansraj Sampat, Bala Prabhakar

Journal of International Technology and Information Management

mHealth (Mobile Health) applications (apps) have transformed the doctor-patient relationship. They help users with varied functionalities such as monitoring their health, understanding specific health conditions, consulting doctors online and achieving fitness goals. Whilst these apps provide an option of equitable and convenient access to healthcare, a lot of personal and sensitive data about users is collected, stored and shared to achieve these functionalities. Little is known about the privacy and security concerns these apps address. Based on literature review, this paper identifies the privacy risks and security features for evaluating thirty apps in the Medical category across two app distribution ...

Proactive Sequential Resource (Re)Distribution For Improving Efficiency In Urban Environments, Supriyo Ghosh

Dissertations and Theses Collection (Open Access)

Due to the increasing population and lack of coordination, there is a mismatch in supply and demand of common resources (e.g., shared bikes, ambulances, taxis) in urban environments, which has deteriorated a wide variety of quality of life metrics such as success rate in issuing shared bikes, response times for emergency needs, waiting times in queues etc. Thus, in my thesis, I propose efficient algorithms that optimise the quality of life metrics by proactively redistributing the resources using intelligent operational (day-to-day) and strategic (long-term) decisions in the context of urban transportation and health & safety. For urban transportation, Bike Sharing System (BSS) is adopted as the motivating domain. Operational decisions are crucial for BSS, because the stations of BSS are often not balanced due to uncoordinated movements of resources (i.e., bikes) by customers. The imbalanced stations lead to significant loss in demand and increase the usage of private transportation and therefore, defeat the primary objective of BSS which is to reduce carbon footprint. In order to reduce the carbon footprint, I contribute three operational decision making approaches for sequential redistribution of bikes: (i) Optimising lost demand through dynamic redistribution; (ii) Optimising lost demand through robust ...

Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu

Research Collection School Of Information Systems

Data fusion is a fundamental research problem of identifyingtrue values of data items of interest from conflicting multi-sourceddata. Although considerable research efforts have been conducted on thistopic, existing approaches generally assume every data item has exactlyone true value, which fails to reflect the real world where data items withmultiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items.SourceVote models the endorsement relations among sources by quantifyingtheir two-sided inter-source agreements. In particular, two graphs areconstructed to model inter-source relations. Then two aspects of sourcereliability are derived from these ...

Sourcevote: Fusing Multi-Valued Data Via Inter-Source Agreements, Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Mahmoud Barhamgi, Lina Yao, Anne H.H. Ngu

Research Collection School Of Information Systems

Data fusion is a fundamental research problem of identifyingtrue values of data items of interest from conflicting multi-sourceddata. Although considerable research efforts have been conducted on thistopic, existing approaches generally assume every data item has exactlyone true value, which fails to reflect the real world where data items withmultiple true values widely exist. In this paper, we propose a novel approach,SourceVote, to estimate value veracity for multi-valued data items.SourceVote models the endorsement relations among sources by quantifyingtheir two-sided inter-source agreements. In particular, two graphs areconstructed to model inter-source relations. Then two aspects of sourcereliability are derived from these ...