Databases and Information Systems Commons^™

Integrating Motivated Learning And K-Winner-Take-All To Coordinate Multi-Agent Reinforcement Learning, Teck-Hou Teng, Ah-Hwee Tan, Janusz Starzyk, Yuan-Sin Tan, Loo-Nin Teow

Research Collection School Of Computing and Information Systems

This work addresses the coordination issue in distributed optimization problem (DOP) where multiple distinct and time-critical tasks are performed to satisfy a global objective function. The performance of these tasks has to be coordinated due to the sharing of consumable resources and the dependency on non-consumable resources. Knowing that it can be sub-optimal to predefine the performance of the tasks for large DOPs, the multi-agent reinforcement learning (MARL) framework is adopted wherein an agent is used to learn the performance of each distinct task using reinforcement learning. To coordinate MARL, we propose a novel coordination strategy integrating Motivated Learning (ML) …

Ranking Model Selection And Fusion For Effective Microblog Search, Zhongyu Wei, Wei Gao, Tarek El-Ganainy, Walid Magdy, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Re-ranking was shown to have positive impact on the effectiveness for microblog search. Yet existing approaches mostly focused on using a single ranker to learn some better ranking function with respect to various relevance features. Given various available rank learners (such as learning to rank algorithms), in this work, we mainly study an orthogonal problem where multiple learned ranking models form an ensemble for re-ranking the retrieved tweets than just using a single ranking model in order to achieve higher search effectiveness. We explore the use of query-sensitive model selection and rank fusion methods based on the result lists produced …

Administrative Support - Task Management System, Ramesh Avula

All Capstone Projects

Administrative Support - Task Management System (ASTMS) is a web application by which any university can manage tasks among its employees. This project has various small parts like commenting on task, upload and download files, task forwarding, editing existing and creating new project, task, employee, user etc . ASTMS is an automation system, which is used to store the Work tasks information of a university.

The task management system eliminates manual request and assignment of work. Faculty can request any work tasks online. Administrative staff can review the incoming work tasks and assign them to appropriate personnel Development process of …

Multi-Cost And Upgradable Spatial Network Databases, Yimin Lin

Dissertations and Theses Collection (Open Access)

In this dissertation, we first consider data processing problems in multi-cost networks and in upgradable networks. These network types are motivated by real-life situations, which do not fall under the standard spatial network formulation and have not received much attention from database researchers. In a multi-cost network (MCN), each edge is associated with more than one weight type that may affect the user-specific perception of distance. We study two query types on MCNs, namely, the MCN skyline and the MCN top-k query. In an upgradable network, a subset of the edges are amenable to weight reduction, at a cost (e.g., …

Structure Preserving Large Imagery Reconstruction, Ju Shen, Jianjun Yang, Sami Taha Abu Sneineh, Bryson Payne, Markus Hitz

Computer Science Faculty Publications

With the explosive growth of web-based cameras and mobile devices, billions of photographs are uploaded to the internet. We can trivially collect a huge number of photo streams for various goals, such as image clustering, 3D scene reconstruction, and other big data applications. However, such tasks are not easy due to the fact the retrieved photos can have large variations in their view perspectives, resolutions, lighting, noises, and distortions. Furthermore, with the occlusion of unexpected objects like people, vehicles, it is even more challenging to find feature correspondences and reconstruct realistic scenes. In this paper, we propose a structure-based image …

Systems For Delivering Electric Vehicle Data Analytics, Vamshi Krishna Bolly

Open Access Theses

n the recent times, advances in scientific research related to electric vehicles led to generation of large amounts of data. This data is majorly logger data collected from various sensors in the vehicle. It is predominantly unstructured and non-relational in nature, also called Big Data. Analysis of such data needs a high performance information technology infrastructure that provides superior computational efficiency and storage capacity. It should be scalable to accommodate the growing data and ensure its security over a network. This research proposes an architecture built over Hadoop to effectively support distributed data management over a network for real-time data …

A Model-Based Approach To System-Of-Systems Engineering Via The Systems Modeling Language, Kevin Hughes Bonanne

Open Access Theses

In the field of Systems Engineering, a movement is underway to capture the aspects of a system in a centralized model format instead of various documents. This is the basis of Model Based Systems Engineering (MBSE). In order to better formalize this change, the Systems Modeling Language (SysML) was developed to characterize an ontology for MBSE. Despite the growth of both MBSE practices and SysML tools, they have yet to be rigorously analyzed as to their applicability to the field of System-of-Systems (SoS). This thesis applies SysML to a methodology for System-of-Systems Engineering (SoSE) known as the Wave Model, which …

The Use Of Business Intelligence Techniques In Supply Chain Performance, Jue Gu

Open Access Theses

Who likes data? Businesses are always loyal data followers. Companies analyze various forms of data to maintain businesses and identify their current performance in different areas so they can find business opportunities to improve and obtain more market share in advance (Qrunfleh & Tarafdar, 2012). When Big Data comes to businesses, companies who can take advantage of data the best tend to regularly get more business and customers (Waller & Fawcett, 2013). Collecting, analyzing, and demonstrating data could be essential to a single business, a company's supply chain performance and its sustainability. As an intelligent data processing product in terms …

A Quantitative Framework Of Skill Evaluation Of It Workforce, O Hyun Hwang

Open Access Theses

Every employee has different abilities as well as experience, leading to different results in terms of skills and job performance. In order to improve employee IT skills, organizations should evaluate their employees' skills to understand the current levels of skills and knowledge and to figure out areas where skills are currently lacking.

This study used a different approach than other existing methods to evaluate the IT skills of employees. The research used quantitative empirical data that represents the work experience of employees based on their task history and job positions. The suggested method defines the relationship between IT skills and …

Yar Consulting Marketing Website And Extranet Site, John Suarez

Masters Theses & Doctoral Dissertations

This is a hybrid project. As a new Information Systems consultancy the need for a website to market to potential clients was evident. The decision to add an extranet site was made after specific needs for Ace Hardware were made evident to Yar’s founders. Ace Hardware Corporation is a cooperative with over 4,400 members in over 100 nations worldwide. Ace corporate rolled out a B2B pilot program in the fall of 2013 to help Ace independently owned retail stores learn how to sell to other businesses. One of YAR’s founders is currently working with Ace corporate as a program consultant …

Unraveling The Digital Literacy Paradox: How Higher Education Fails At The Fourth Literacy., Meg Coffin Murray, Jorge Perez

Faculty Articles

Governments around the globe are recognizing the economic ramifications of a digitally literate citizenry and implementing systemic strategies to advance digital literacy. Awareness of the growing importance of digital literacy in today’s workplace coexists paradoxically with apparent foot-dragging on the part of many universities in assessment and amplification of these important competencies. This paper makes a case for digital literacy, presents models of the complex construct, and presents the results of a digital literacy assessment administered to students enrolled in a senior seminar course at a regional university in the United States. Reflection on the study results evoked our mantra …

Assessing The Fit Between Child Welfare Information Systems And Frontline Workers: Development Of A Task-Technology Fit Instrument, Kurt William Heisler

Health Services Research Dissertations

States and the federal government continue to invest heavily in child welfare information systems (CWIS) to improve caseworkers' performance, but the extent to which these systems meet caseworkers' needs is unclear. In the field of child welfare there are no reliable user-evaluation measures states can use to assess the degree to which a CWIS meets caseworkers' needs, and identify which specific features of the CWIS most need improvement. The study developed such a measure based on the task-technology fit (TTF) framework, which posits that users will evaluate the usefulness of a technology based on how well it meets their tasks …

Ultimate Codes: Near-Optimal Mds Array Codes For Raid-6, Zhijie Huang, Hong Jiang, Chong Wang, Ke Zhou, Yuhong Zhao

School of Computing: Technical Reports

As modern storage systems have grown in size and complexity, RAID-6 is poised to replace RAID-5 as the dominant form of RAID architectures due to its ability to protect against double disk failures. Many excellent erasure codes specially designed for RAID-6 have emerged in recent years. However, all of them have limitations. In this paper, we present a class of near perfect erasure codes for RAID-6, called the Ultimate codes. These codes encode, update and decode either optimally or nearly optimally, regardless of what the code length is. This implies that utilizing these codes we can build highly efficient and …

User Daily Activity Pattern Learning: A Multi-Memory Modeling Approach, Shan Gao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

In this paper, we propose a multi-memory model, ADLART model, to discover the daily activity pattern of a sensor monitored user from his/her activities of daily living (ADL). The proposed model mimics the human multiple memory system which comprises a working memory, an episodic memory, and a semantic memory. Through encoding user's daily activities patterns in episodic memory and extracting the regularities of activity routines in semantic memory, the ADLART system is able to learn, recognize, compare, and retrieve daily ADL patterns of the user. Experiments are presented to show the performance of the ADLART model using different parameter settings …

Mobile Humanoid Agent With Mood Awareness For Elderly Care, Di Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Human, especially elderly, require frequent attention, continuous companionship, and deep understanding from the others. To provide more specific and appropriate tender care to the elderly, knowing their affective states is a great advantage. Recent work on human emotion recognition shows promising results that the expressive emotion can be successfully captured through visual, audio, and keyboard or touchpad stroke pattern signals. Furthermore, human activities are shown to be accurately recognizable with context by nonintrusive sensors within or connected to the smartphones. In this paper, we propose a computational model to characterize the affective states of the elderly based on the recognizable …

Influences Of Influential Users: An Empirical Study Of Music Social Network, Jing Ren, Zhiyong Cheng, Jialie Shen, Feida Zhu

Research Collection School Of Computing and Information Systems

Influential user can play a crucial role in online social networks. This paper documents an empirical study aiming at exploring the effects of influential users in the context of music social network. To achieve this goal, music diffusion graph is developed to model how information propagates over network. We also propose a heuristic method to measure users' influences. Using the real data from Last. fm, our empirical test demonstrates key effects of influential users and reveals limitations of existing influence identification/characterization schemes.

Manifold Learning For Jointly Modeling Topic And Visualization, Tuan Minh Van Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Classical approaches to visualization directly reduce a document's high-dimensional representation into visualizable two or three dimensions, using techniques such as multidimensional scaling. More recent approaches consider an intermediate representation in topic space, between word space and visualization space, which preserves the semantics by topic modeling. We call the latter semantic visualization problem, as it seeks to jointly model topic and visualization. While previous approaches aim to preserve the global consistency, they do not consider the local consistency in terms of the intrinsic geometric structure of the document manifold. We therefore propose an unsupervised probabilistic model, called Semafore, which aims to …

Soml: Sparse Online Metric Learning With Application To Image Retrieval, Xingyu Gao, Steven C. H. Hoi, Yongdong Zhang, Ji Wan, Jintao Li

Research Collection School Of Computing and Information Systems

Image similarity search plays a key role in many multimedia applications, where multimedia data (such as images and videos) are usually represented in high-dimensional feature space. In this paper, we propose a novel Sparse Online Metric Learning (SOML) scheme for learning sparse distance functions from large-scale high-dimensional data and explore its application to image retrieval. In contrast to many existing distance metric learning algorithms that are often designed for low-dimensional data, the proposed algorithms are able to learn sparse distance metrics from high-dimensional data in an efficient and scalable manner. Our experimental results show that the proposed method achieves better …

Learning Relative Similarity By Stochastic Dual Coordinate Ascent, Pengcheng Wu, Ding Yi, Peilin Zhao, Chunyan Miao, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Learning relative similarity from pairwise instances is an important problem in machine learning and has a wide range of applications. Despite being studied for years, some existing methods solved by Stochastic Gradient Descent (SGD) techniques generally suffer from slow convergence. In this paper, we investigate the application of Stochastic Dual Coordinate Ascent (SDCA) technique to tackle the optimization task of relative similarity learning by extending from vector to matrix parameters. Theoretically, we prove the optimal linear convergence rate for the proposed SDCA algorithm, beating the well-known sublinear convergence rate by the previous best metric learning algorithms. Empirically, we conduct extensive …

Predicting The Popularity Of Web 2.0 Items Based On User Comments, Xiangnan He, Ming Gao, Min-Yen Kan, Yiqun Liu, Kazunari Sugiyama

Research Collection School Of Computing and Information Systems

In the current Web 2.0 era, the popularity of Web resources fluctuates ephemerally, based on trends and social interest. As a result, content-based relevance signals are insufficient to meet users' constantly evolving information needs in searching for Web 2.0 items. Incorporating future popularity into ranking is one way to counter this. However, predicting popularity as a third party (as in the case of general search engines) is difficult in practice, due to their limited access to item view histories. To enable popularity prediction externally without excessive crawling, we propose an alternative solution by leveraging user comments, which are more accessible …