Databases and Information Systems Commons^™

Cure: Flexible Categorical Data Representation By Hierarchical Coupling Learning, Songlei Jian, Guansong Pang, Longbing Cao, Kai Lu, Hang Gao

Research Collection School Of Computing and Information Systems

The representation of categorical data with hierarchical value coupling relationships (i.e., various value-to-value cluster interactions) is very critical yet challenging for capturing complex data characteristics in learning tasks. This paper proposes a novel and flexible coupled unsupervised categorical data representation (CURE) framework, which not only captures the hierarchical couplings but is also flexible enough to be instantiated for contrastive learning tasks. CURE first learns the value clusters of different granularities based on multiple value coupling functions and then learns the value representation from the couplings between the obtained value clusters. With two complementary value coupling functions, CURE is instantiated into …

Modeling Sequential And Basket-Oriented Associations For Top-K Recommendation, Duc-Trong Le Duc Trong

Dissertations and Theses Collection (Open Access)

Top-K recommendation is a typical task in Recommender Systems. In traditional approaches, it mainly relies on the modeling of user-item associations, which emphasizes the user-specific factor or personalization. Here, we investigate another direction that models item-item associations, especially with the notions of sequence-aware and basket-level adoptions . Sequences are created by sorting item adoptions chronologically. The associations between items along sequences, referred to as “sequential associations”, indicate the influence of the preceding adoptions on the following adoptions. Considering a basket of items consumed at the same time step (e.g., a session, a day), “basket-oriented associations” imply correlative dependencies among these …

A Coordination Framework For Multi-Agent Persuasion And Adviser Systems, Budhitama Subagdja, Ah-Hwee Tan, Yilin Kang

Research Collection School Of Computing and Information Systems

Assistive agents have been used to give advices to the users regarding activities in daily lives. Although adviser bots are getting smarter and gaining more popularity these days they are usually developed and deployed independent from each other. When several agents operate together in the same context, their advices may no longer be effective since they may instead overwhelm or confuse the user if not properly arranged. Only little attentions have been paid to coordinating different agents to give different advices to a user within the same environment. However, aligning the advices on-the-fly with the appropriate presentation timing at the …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Examination Of Adoption Theory On The Devops Practice Of Continuous Delivery, Andrew John Anderson

Walden Dissertations and Doctoral Studies

Many organizations have difficulty adopting advanced software development practices. Some software development project managers in large organizations are not aligned with the relationship between performance expectancy, effort expectancy, social influence, and facilitating conditions, as moderated by experience, with intent to adopt the DevOps practice of continuous delivery. The purpose of this study was to examine the statistical relationships between the independent variables—performance expectancy, effort expectancy, social influence, and facilitating conditions, as moderated by experience—and the dependent variable of behavioral intent to adopt a continuous delivery system. Venkatesh, Morris, Davis, and Davis's unified theory of acceptance and use of technology provided …

Procure-To-Pay Software In The Digital Age: An Exploration And Analysis Of Efficiency Gains And Cybersecurity Risks In Modern Procurement Systems, Drew Lane

MPA/MPP/MPFM Capstone Projects

Procure-to-Pay (P2P) softwares are an integral part of the payment and procurement processing functions at large-scale governmental institutions. These softwares house all of the financial functions related to procurement, accounts payable, and often human resources, helping to facilitate and automate the process from initiation of a payment or purchase, to the actual disbursal of funds. Often, these softwares contain budgeting and financial reporting tools as part of the offering. As such an integral part of the financial process, these softwares obviously come at an immense cost from a set of reputable vendors. In the case of government, these vendors mainly …

Big Data Investment And Knowledge Integration In Academic Libraries, Saher Manaseer, Afnan R. Alawneh, Dua Asoudi

Copyright, Fair Use, Scholarly Communication, etc.

Recently, big data investment has become important for organizations, especially with the fast growth of data following the huge expansion in the usage of social media applications, and websites. Many organizations depend on extracting and reaching the needed reports and statistics. As the investments on big data and its storage have become major challenges for organizations, many technologies and methods have been developed to tackle those challenges.

One of such technologies is Hadoop, a framework that is used to divide big data into packages and distribute those packages through nodes to be processed, consuming less cost than the traditional storage …

The Global Disinformation Order: 2019 Global Inventory Of Organised Social Media Manipulation, Samantha Bradshaw, Philip N. Howard

Copyright, Fair Use, Scholarly Communication, etc.

Executive Summary

Over the past three years, we have monitored the global organization of social media manipulation by governments and political parties. Our 2019 report analyses the trends of computational propaganda and the evolving tools, capacities, strategies, and resources.

1. Evidence of organized social media manipulation campaigns which have taken place in 70 countries, up from 48 countries in 2018 and 28 countries in 2017. In each country, there is at least one political party or government agency using social media to shape public attitudes domestically.

2.Social media has become co-opted by many authoritarian regimes. In 26 countries, computational propaganda …

Open Source Foundations For Spatial Decision Support Systems, Jochen Albrecht

Publications and Research

Spatial Decision Support Systems (SDSS) were a hot topic in the 1990s, when researchers tried to imbue GIS with additional decision support features. Successful practical developments such as HAZUS or CommunityViz have since been built, based on commercial desktop software and without much heed for theory other than what underlies their process models. Others, like UrbanSim, have been completely overhauled twice but without much external scrutiny. Both the practical and the theoretical foundations of decision support systems have developed considerably over the past 20 years. This article presents an overview of these developments and then looks at what corresponding tools …

Deep Unsupervised Pixelization, Chu Han, Qiang Wen, Shengfeng He, Qianshu Zhu, Yinjie Tan, Guoqiang Han, Tien-Tsin Wong

Research Collection School Of Computing and Information Systems

In this paper, we present a novel unsupervised learning method for pixelization. Due to the difficulty in creating pixel art, preparing the paired training data for supervised learning is impractical. Instead, we propose an unsupervised learning framework to circumvent such difficulty. We leverage the dual nature of the pixelization and depixelization, and model these two tasks in the same network in a bi-directional manner with the input itself as training supervision. These two tasks are modeled as a cascaded network which consists of three stages for different purposes. GridNet transfers the input image into multi-scale grid-structured images with different aliasing …

Is There Space For Violence?: A Data-Driven Approach To The Exploration Of Spatial-Temporal Dimensions Of Conflict, Tin Seong Kam, Vincent Zhi

Research Collection School Of Computing and Information Systems

With recent increases in incidences of political violence globally, the world has now become more uncertain and less predictable. Of particular concern is the case of violence against civilians, who are often caught in the crossfire between armed state or non-state actors. Classical methods of studying political violence and international relations need to be updated. Adopting the use of data analytic tools and techniques of studying big data would enable academics and policy makers to make sense of a rapidly changing world.

An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings, Di Wang, Xiaolin Qian, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Xiaofeng Zhang, Geok See Ng, You Zhou

Research Collection School Of Computing and Information Systems

Due to their aptitude in both accurate data processing and human comprehensible reasoning, neural fuzzy inference systems have been widely adopted in various application domains as decision support systems. Especially in real-world scenarios such as decision making in financial transactions, the human experts may be more interested in knowing the comprehensive reasons of certain advices provided by a decision support system in addition to how confident the system is on such advices. In this paper, we apply an integrated autonomous computational model termed genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS) to predict underpricing in initial public …

Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …

Sort Vs. Hash Join On Knights Landing Architecture, Victor L. Pan, Felix Lin

The Summer Undergraduate Research Fellowship (SURF) Symposium

With the increasing amount of information stored, there is a need for efficient database algorithms. One of the most important database operations is “join”. This involves combining columns from two tables and grouping common values in the same row in order to minimize redundant data. The two main algorithms used are hash join and sort merge join. Hash join builds a hash table to allow for faster searching. Sort merge join first sorts the two tables to make it more efficient when comparing values. There has been a lot of debate over which approach is superior. At first, hash join …

Customer Segmentation Using Online Platforms: Isolating Behavioral And Demographic Segments For Persona Creation Via Aggregated User Data, Jisun An, Haewoon Kwak, Soon‑Gyo Jung, Joni Salminen, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

We propose a novel approach for isolating customer segments using online customer data for products that are distributed via online social media platforms. We use non-negative matrix factorization to first identify behavioral customer segments and then to identify demographic customer segments. We employ a methodology for linking the two segments to present integrated and holistic customer segments, also known as personas. Behavioral segments are generated from customer interactions with online content. Demographic segments are generated using the gender, age, and location of these customers. In addition to evaluating our approach, we demonstrate its practicality via a system leveraging these customer …

Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang

Research Collection School Of Computing and Information Systems

Traditional rank-aware processing assumes a dataset that contains available options to cover a specific need (e.g., restaurants, hotels, etc) and users who browse that dataset via top-k queries with linear scoring functions, i.e., by ranking the options according to the weighted sum of their attributes, for a set of given weights. In practice, however, user preferences (weights) may only be estimated with bounded accuracy, or may be inherently uncertain due to the inability of a human user to specify exact weight values with absolute accuracy. Motivated by this, we introduce the uncertain top-k query (UTK). Given uncertain preferences, that is, …

Learning Representations Of Ultrahigh-Dimensional Data For Random Distance-Based Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu

Research Collection School Of Computing and Information Systems

Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods to address the curse of dimensionality. However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can result in suboptimal and unstable performance of detecting irregularities (i.e., outliers).This paper introduces a ranking model-based framework, called RAMODO, to address this issue. RAMODO unifies representation learning and outlier detection to learn low-dimensional representations that are tailored for a state-of-the-art outlier detection approach - the random …

Augmented Keyword Search On Spatial Entity Databases, Dongxiang Zhang, Yuchen Li, Xin Cao, Jie Shao, Heng Tao Shen

Research Collection School Of Computing and Information Systems

In this paper, we propose a new type of query that augments the spatial keyword search with an additional boolean expression constraint. The query is issued against a corpus of structured or semi-structured spatial entities and is very useful in applications like mobile search and targeted location-aware advertising. We devise three types of indexing and filtering strategies. First, we utilize the hybrid IR2" role="presentation" style="display: inline; line-height: normal; letter-spacing: normal; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border-width: 0px; border-style: initial; position: relative;">IR2IR2-tree and propose a novel hashing …

Continuous Top-K Monitoring On Document Streams (Extended Abstract), Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li

Research Collection School Of Computing and Information Systems

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top-k documents that are most relevant to her keywords. The objective is to support large numbers of users and high stream rates, while refreshing the topk results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach, …

Keep It Simple, Keep It Safe - Research On The Impacts Of Increasing Complexity Of Modern Enterprise Applications, Shawn Ware, David Phillips

UNO Student Research and Creative Activity Fair

As the Cybersecurity program within UNO continues to adapt to the ever-changing world of information systems and information security, the Cybersecurity Capstone has recently become an active, community-involvement project, where real-world organizations can receive valuable, useful research and information from students on their way towards a degree. This presentation encompasses two such projects from the Cybersecurity Capstone, looking at how modern, more complex systems can often increase system vulnerability.

Sparse Modeling-Based Sequential Ensemble Learning For Effective Outlier Detection In High-Dimensional Numeric Data, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu

Research Collection School Of Computing and Information Systems

The large proportion of irrelevant or noisy features in reallife high-dimensional data presents a significant challenge to subspace/feature selection-based high-dimensional outlier detection (a.k.a. outlier scoring) methods. These methods often perform the two dependent tasks: relevant feature subset search and outlier scoring independently, consequently retaining features/subspaces irrelevant to the scoring method and downgrading the detection performance. This paper introduces a novel sequential ensemble-based framework SEMSE and its instance CINFO to address this issue. SEMSE learns the sequential ensembles to mutually refine feature selection and outlier scoring by iterative sparse modeling with outlier scores as the pseudo target feature. CINFO instantiates SEMSE …

Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes

Faculty Publications

Social networks have become very important for networking, communications, and content sharing. Social networking applications generate a huge amount of data on a daily basis and social networks constitute a growing field of research, because of the heterogeneity of data and structures formed in them, and their size and dynamics. When this wealth of data is leveraged by recommender systems, the resulting coupling can help address interesting problems related to social engagement, member recruitment, and friend recommendations.In this work we review the various facets of large-scale social recommender systems, summarizing the challenges and interesting problems and discussing some of the …

Cyber Security And Risk Society: Estonian Discourse On Cyber Risk And Security Strategy, Lauren Kook

Copyright, Fair Use, Scholarly Communication, etc.

The main aim of this thesis is to call for a new analysis of cyber security which departs from the traditional security theory. I argue that the cyber domain is inherently different in nature, in that it is lacking in traditional boundaries and is reflexive in nature. Policy-makers are aware of these characteristics, and in turn this awareness changes the way that national cyber security strategy is handled and understood. These changes cannot be adequately understood through traditional understanding of security, as they often are, without missing significant details. Rather, examining these changes through the lens of Ulrich Beck’s risk …

Perceptions Of Female Cybersecurity Professionals Toward Factors That Encourage Females To The Cybersecurity Field, Kembley Kay Lingelbach

CCAC Theses and Dissertations

Despite multiple national, educational, and industry initiatives, women continue to be underrepresented in the cybersecurity field. Only 11% of cybersecurity professionals, globally, are female. This contributes to the growing overall shortage of workers in the field. This research addressed the significant underrepresentation of females in the cybersecurity workforce. There are many practitioner and industry studies that suggest self-efficacy, discrimination and organizational culture play important roles in the low rate of women in the cybersecurity field. A limited number of scholarly studies identify causal factors; however, there is not a general consensus or framework to explain the problem thoroughly. Moreover, there …

Breadcrumbs: Privacy As A Privilege, Prachi Bhardwaj

Capstones

Breadcrumbs: Privacy as a Privilege Abstract

By: Prachi Bhardwaj

In 2017, the world saw more data breaches than in any year prior. The count was more than the all-time high record in 2016, which was 40 percent more than the year before that.

That’s because consumer data is incredibly valuable today. In the last three decades, data storage has gone from being stored physically to being stored almost entirely digitally, which means consumer data is more accessible and applicable to business strategies. As a result, companies are gathering data in ways previously unknown to the average consumer, and hackers are …

Design And Implementation Of A Stand-Alone Tool For Metabolic Simulations, Milad Ghiasi Rad

School of Computing: Dissertations, Theses, and Student Research

In this thesis, we present the design and implementation of a stand-alone tool for metabolic simulations. This system is able to integrate custom-built SBML models along with external user’s input information and produces the estimation of any reactants participating in the chain of the reactions in the provided model, e.g., ATP, Glucose, Insulin, for the given duration using numerical analysis and simulations. This tool offers the food intake arguments in the calculations to consider the personalized metabolic characteristics in the simulations. The tool has also been generalized to take into consideration of temporal genomic information and be flexible for simulation …

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 …

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 Computing and Information Systems

Data fusion is a fundamental research problem of identifying true values of data items of interest from conflicting multi-sourced data. Although considerable research efforts have been conducted on this topic, existing approaches generally assume every data item has exactly one true value, which fails to reflect the real world where data items with multiple 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 quantifying their two-sided inter-source agreements. In particular, two graphs are constructed to model inter-source relations. Then two aspects …

Selective Value Coupling Learning For Detecting Outliers In High-Dimensional Categorical Data, Guansong Pang, Hongzuo Xu, Cao Longbing, Wentao Zhao

Research Collection School Of Computing and Information Systems

This paper introduces a novel framework, namely SelectVC and its instance POP, for learning selective value couplings (i.e., interactions between the full value set and a set of outlying values) to identify outliers in high-dimensional categorical data. Existing outlier detection methods work on a full data space or feature subspaces that are identified independently from subsequent outlier scoring. As a result, they are significantly challenged by overwhelming irrelevant features in high-dimensional data due to the noise brought by the irrelevant features and its huge search space. In contrast, SelectVC works on a clean and condensed data space spanned by selective …

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 Computing and 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 graphs and …