Databases and Information Systems Commons^™

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 …

Online Learning With Nonlinear Models, Doyen Sahoo

Dissertations and Theses Collection (Open Access)

Recent years have witnessed the success of two broad categories of machine learning algorithms: (i) Online Learning; and (ii) Learning with nonlinear models. Typical machine learning algorithms assume that the entire data is available prior to the training task. This is often not the case in the real world, where data often arrives sequentially in a stream, or is too large to be stored in memory. To address these challenges, Online Learning techniques evolved as a promising solution to having highly scalable and efficient learning methodologies which could learn from data arriving sequentially. Next, as the real world data exhibited …

Policy Analytics For Environmental Sustainability: Household Hazardous Waste And Water Impacts Of Carbon Pollution Standards, Kustini

Dissertations and Theses Collection (Open Access)

Policy analytics are essential in supporting more informed policy-making in environmental management. This dissertation employs a fusion of machine methods and explanatory empiricism that involves data analytics, math programming, optimization, econometrics, geospatial and spatiotemporal analysis, and other approaches for assessing and evaluating current and future environmental policies.
Essay 1 discusses household informedness and its impact on the collection and recycling of household hazardous waste (HHW). Household informedness is the degree to which households have the necessary information to make utility-maximizing decisions about the handling of their waste. Such informedness seems to be influenced by HHW public education and environmental quality …

Who Are Your Users? Comparing Media Professionals' Preconception Of Users To Data-Driven Personas, Lene Nielsen, Soon-Gyu Jung, Jisun An, Joni Salminen, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

One of the reasons for using personas is to align user understandings across project teams and sites. As part of a larger persona study, at Al Jazeera English (AJE), we conducted 16 qualitative interviews with media producers, the end users of persona descriptions. We asked the participants about their understanding of a typical AJE media consumer, and the variety of answers shows that the understandings are not aligned and are built on a mix of own experiences, own self, assumptions, and data given by the company. The answers are sometimes aligned with the data-driven personas and sometimes not. The end …

Enterprise Social Media Use And Impact On Performance: The Role Of Workplace Integration And Positive Emotions, Murad Moqbel, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

Organizations struggle to find ways to improve employees’ performance. To date, little research has empirically examined the relationship between enterprise social media use and knowledge workers’ performance. Using social capital theory and the broaden-and-build theory of positive emotions as our theoretical framework, we investigate the relationship between enterprise social media use and knowledge workers’ performance. We tested our research model by collecting data from employees working for a large information technology firm in the Midwestern United States and analyzing the data using a structural equation modeling approach. The results suggest that enterprise social media use can increase workplace integration, which …

Analyzing The E-Learning Video Environment Requirements Of Generation Z Students Using Echo360 Platform, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

As with any other generational cohort,Generation Z students have their own unique characteristics that influencetheir approach to learning process. They are the future workforce and severalefforts are undertaken by Government and education institutes to consider thecharacteristics of Gen-Z in developing the curriculum and teaching environmentsuitable for these students. E-learning plays a key role in students learningprocess and has been widely adopted by many education institutions. Inparticular, videos play a major role in the learning process of Gen-Zstudents. The purpose of this paper isto focus the on requirements of Gen-Z students and to provide suggestions forhow to create a e-learning video …

D-Watch: Embracing “Bad” Multipaths For Device-Free Localization With Cots Rfid Devices, Ju Wang, Jie Xiong, Hongbo Jiang, Xiaojiang Chen, Dingyi Fang

Research Collection School Of Computing and Information Systems

Device-free localization, which does not require any device attached to the target, is playing a critical role in many applications, such as intrusion detection, elderly monitoring and so on. This paper introduces D-Watch, a device-free system built on the top of low cost commodity-off-the-shelf RFID hardware. Unlike previous works which consider multipaths detrimental, D-Watch leverages the ''bad'' multipaths to provide a decimeter-level localization accuracy without offline training. D-Watch harnesses the angle-of-arrival information from the RFID tags' backscatter signals. The key intuition is that whenever a target blocks a signal's propagation path, the signal power experiences a drop which can be …

Leveraging Auxiliary Tasks For Document-Level Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study domain adaptationwith a state-of-the-art hierarchicalneural network for document-level sentimentclassification. We first design a newauxiliary task based on sentiment scoresof domain-independent words. We thenpropose two neural network architecturesto respectively induce document embeddingsand sentence embeddings that workwell for different domains. When thesedocument and sentence embeddings areused for sentiment classification, we findthat with both pseudo and external sentimentlexicons, our proposed methods canperform similarly to or better than severalhighly competitive domain adaptationmethods on a benchmark dataset of productreviews.

Btci: A New Framework For Identifying Congestion Cascades Using Bus Trajectory Data, Meng-Fen Chiang, Ee Peng Lim, Wang-Chien Lee, Agus Trisnajaya Kwee

Research Collection School Of Computing and Information Systems

The knowledge of traffic health status is essential to the general public and urban traffic management. To identify congestion cascades, an important phenomenon of traffic health, we propose a Bus Trajectory based Congestion Identification (BTCI) framework that explores the anomalous traffic health status and structure properties of congestion cascades using bus trajectory data. BTCI consists of two main steps, congested segment extraction and congestion cascades identification. The former constructs path speed models from historical vehicle transitions and design a non-parametric Kernel Density Estimation (KDE) function to derive a measure of congestion score. The latter aggregates congested segments (i.e., those with …

Inferring Social Media Users’ Demographics From Profile Pictures: A Face++ Analysis On Twitter Users, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ …

A Novel Density Peak Clustering Algorithm Based On Squared Residual Error, Milan Parmar, Di Wang, Ah-Hwee Tan, Chunyan Miao, Jianhua Jiang, You Zhou

Research Collection School Of Computing and Information Systems

The density peak clustering (DPC) algorithm is designed to quickly identify intricate-shaped clusters with high dimensionality by finding high-density peaks in a non-iterative manner and using only one threshold parameter. However, DPC has certain limitations in processing low-density data points because it only takes the global data density distribution into account. As such, DPC may confine in forming low-density data clusters, or in other words, DPC may fail in detecting anomalies and borderline points. In this paper, we analyze the limitations of DPC and propose a novel density peak clustering algorithm to better handle low-density clustering tasks. Specifically, our algorithm …

Robust Human Activity Recognition Using Lesser Number Of Wearable Sensors, Di Wang, Edwin Candinegara, Junhui Hou, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

In recent years, research on the recognition of human physical activities solely using wearable sensors has received more and more attention. Compared to other types of sensory devices such as surveillance cameras, wearable sensors are preferred in most activity recognition applications mainly due to their non-intrusiveness and pervasiveness. However, many existing activity recognition applications or experiments using wearable sensors were conducted in the confined laboratory settings using specifically developed gadgets. These gadgets may be useful for a small group of people in certain specific scenarios, but probably will not gain their popularity because they introduce additional costs and they are …

Using Data Analytics For Discovering Library Resource Insights: Case From Singapore Management University, Ning Lu, Rui Song, Dina Li Gwek Heng, Swapna Gottipati, Aaron Tay

Research Collection School Of Computing and Information Systems

Library resources are critical in supporting teaching, research and learning processes. Several universities have employed online platforms and infrastructure for enabling the online services to students, faculty and staff. To provide efficient services by understanding and predicting user needs libraries are looking into the area of data analytics. Library analytics in Singapore Management University is the project committed to provide an interface for data-intensive project collaboration, while supporting one of the library’s key pillars on its commitment to collaborate on initiatives with SMU Communities and external groups. In this paper, we study the transaction logs for user behavior analysis that …

The Graph Database: Jack Of All Trades Or Just Not Sql?, George F. Hurlburt, Maria R. Lee, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

This special issue of IT Professional focuses on the graph database. The graph database, a relatively new phenomenon, is well suited to the burgeoning information era in which we are increasingly becoming immersed. Here, the guest editors briefly explain how a graph database works, its relation to the relational database management system (RDBMS), and its quantitative and qualitative pros and cons, including how graph databases can be harnessed in a hybrid environment. They also survey the excellent articles submitted for this special issue.

Multi-Step Tokenization Of Automated Clearing House Payment Transactions, Privin Alexander

USF Tampa Graduate Theses and Dissertations

Since its beginnings in 1974, the Automated Clearing House (ACH) network has grown into one of the largest, safest, and most efficient payment systems in the world. An ACH transaction is an electronic funds transfer between bank accounts using a batch processing system.

Currently, the ACH Network moves almost $43 trillion and 25 billion electronic financial transactions each year. With the increasing movement toward an electronic, interconnected and mobile infrastructure, it is critical that electronic payments work safely and efficiently for all users. ACH transactions carry sensitive data, such as a consumer's name, account number, tax identification number, account holder …

Constructing A Clinical Research Data Management System, Michael C. Quintero

USF Tampa Graduate Theses and Dissertations

Clinical study data is usually collected without knowing what kind of data is going to be collected in advance. In addition, all of the possible data points that can apply to a patient in any given clinical study is almost always a superset of the data points that are actually recorded for a given patient. As a result of this, clinical data resembles a set of sparse data with an evolving data schema. To help researchers at the Moffitt Cancer Center better manage clinical data, a tool was developed called GURU that uses the Entity Attribute Value model to handle …

A Study On The Practical Use Of Operations Research And Vessels Big Data In Benefit Of Efficient Ports Utilization In Panama, Gabriel Fuentes Lezcano

World Maritime University Dissertations

No abstract provided.

Uncovering User-Triggered Privacy Leaks In Mobile Applications And Their Utility In Privacy Protection, Joo Keng Joseph Chan

Dissertations and Theses Collection

Mobile applications are increasingly popular, and help mobile users in many aspects of their lifestyle. Applications have access to a wealth of information about the user through powerful developer APIs. It is known that most applications, even popular and highly regarded ones, utilize and leak privacy data to the network. It is also common for applications to over-access privacy data that does not fit the functionality profile of the application. Although there are available privacy detection tools, they might not provide sufficient context to help users better understand the privacy behaviours of their applications. In this dissertation, I present the …

Semvis: Semantic Visualization For Interactive Topical Analysis, Le Van Minh Tuan, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Exploratory analysis of a text corpus is an important task that can be aided by informative visualization. One spatially-oriented form of document visualization is a scatterplot, whereby every document is associated with a coordinate, and relationships among documents can be perceived through their spatial distances. Semantic visualization further infuses the visualization space with latent semantics, by incorporating a topic model that has a representation in the visualization space, allowing users to also perceive relationships between documents and topics spatially. We illustrate how a semantic visualization system called SemVis could be used to navigate a text corpus interactively and topically via …

Large Scale Kernel Methods For Online Auc Maximization, Yi Ding, Chenghao Liu, Peilin Zhao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Learning to optimize AUC performance for classifying label imbalanced data in online scenarios has been extensively studied in recent years. Most of the existing work has attempted to address the problem directly in the original feature space, which may not suitable for non-linearly separable datasets. To solve this issue, some kernel-based learning methods are proposed for non-linearly separable datasets. However, such kernel approaches have been shown to be inefficient and failed to scale well on large scale datasets in practice. Taking this cue, in this work, we explore the use of scalable kernel-based learning techniques as surrogates to existing approaches: …