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Articles 1 - 21 of 21
Full-Text Articles in Databases and Information Systems
Open Source Foundations For Spatial Decision Support Systems, Jochen Albrecht
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
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
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
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
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 …
Exact Processing Of Uncertain Top-K Queries In Multi-Criteria Settings, Kyriakos Mouratidis, Bo Tang
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, …
Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson
Evaluation Criteria For Selecting Nosql Databases In A Single Box Environment, Ryan D. Engle, Brent T. Langhals, Michael R. Grimaila, Douglas D. Hodson
Faculty Publications
In recent years, NoSQL database systems have become increasingly popular, especially for big data, commercial applications. These systems were designed to overcome the scaling and flexibility limitations plaguing traditional relational database management systems (RDBMSs). Given NoSQL database systems have been typically implemented in large-scale distributed environments serving large numbers of simultaneous users across potentially thousands of geographically separated devices, little consideration has been given to evaluating their value within single-box environments. It is postulated some of the inherent traits of each NoSQL database type may be useful, perhaps even preferable, regardless of scale. Thus, this paper proposes criteria conceived to …
Learning Representations Of Ultrahigh-Dimensional Data For Random Distance-Based Outlier Detection, Guansong Pang, Longbing Cao, Ling Chen, Defu Lian, Huan Liu
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 …
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
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 …
Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Research Collection School Of Computing and Information Systems
Two fundamental challenges in local search based metaheuristics are how to determine parameter configurations and design the underlying Local Search (LS) procedure. In this paper, we propose a framework in order to handle both challenges, called ADaptive OPeraTor Ordering (ADOPT). In this paper, The ADOPT framework is applied to two metaheuristics, namely Iterated Local Search (ILS) and a hybridization of Simulated Annealing and ILS (SAILS) for solving two variants of the Orienteering Problem: the Team Dependent Orienteering Problem (TDOP) and the Team Orienteering Problem with Time Windows (TOPTW). This framework consists of two main processes. The Design of Experiment (DOE) …
Assessing The Accuracy Of Four Popular Face Recognition Tools For Inferring Gender, Age, And Race, Soon-Gyu Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Assessing The Accuracy Of Four Popular Face Recognition Tools For Inferring Gender, Age, And Race, Soon-Gyu Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
In this research, we evaluate four widely used face detection tools, which are Face++, IBM Bluemix Visual Recognition, AWS Rekognition, and Microsoft Azure Face API, using multiple datasets to determine their accuracy in inferring user attributes, including gender, race, and age. Results show that the tools are generally proficient at determining gender, with accuracy rates greater than 90%, except for IBM Bluemix. Concerning race, only one of the four tools provides this capability, Face++, with an accuracy rate of greater than 90%, although the evaluation was performed on a high-quality dataset. Inferring age appears to be a challenging problem, as …
User-Centric Privacy Preservation In Mobile And Location-Aware Applications, Mingming Guo
User-Centric Privacy Preservation In Mobile And Location-Aware Applications, Mingming Guo
FIU Electronic Theses and Dissertations
The mobile and wireless community has brought a significant growth of location-aware devices including smart phones, connected vehicles and IoT devices. The combination of location-aware sensing, data processing and wireless communication in these devices leads to the rapid development of mobile and location-aware applications. Meanwhile, user privacy is becoming an indispensable concern. These mobile and location-aware applications, which collect data from mobile sensors carried by users or vehicles, return valuable data collection services (e.g., health condition monitoring, traffic monitoring, and natural disaster forecasting) in real time. The sequential spatial-temporal data queries sent by users provide their location trajectory information. The …
Continuous Top-K Monitoring On Document Streams (Extended Abstract), Leong Hou U, Junjie Zhang, Kyriakos Mouratidis, Ye Li
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, …
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
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 …
Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein
Integrated Cooperation And Competition In Multi-Agent Decision-Making, Kyle Hollins Wray, Akshat Kumar, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Observing that many real-world sequential decision problems are not purely cooperative or purely competitive, we propose a new model—cooperative-competitive process (CCP)—that can simultaneously encapsulate both cooperation and competition.First, we discuss how the CCP model bridges the gap between cooperative and competitive models. Next, we investigate a specific class of group-dominant CCPs, in which agents cooperate to achieve a common goal as their primary objective, while also pursuing individual goals as a secondary objective. We provide an approximate solution for this class of problems that leverages stochastic finite-state controllers.The model is grounded in two multi-robot meeting and box pushing domains that …
Lowering Legal Barriers To Rpki Adoption, Christopher S. Yoo, David A. Wishnick
Lowering Legal Barriers To Rpki Adoption, Christopher S. Yoo, David A. Wishnick
All Faculty Scholarship
Across the Internet, mistaken and malicious routing announcements impose significant costs on users and network operators. To make routing announcements more reliable and secure, Internet coordination bodies have encouraged network operators to adopt the Resource Public Key Infrastructure (“RPKI”) framework. Despite this encouragement, RPKI’s adoption rates are low, especially in North America.
This report presents the results of a year-long investigation into the hypothesis—widespread within the network operator community—that legal issues pose barriers to RPKI adoption and are one cause of the disparities between North America and other regions of the world. On the basis of interviews and analysis of …
Recommender Systems For Large-Scale Social Networks: A Review Of Challenges And Solutions, Magdalini Eirinaki, Jerry Gao, Iraklis Varlamis, Konstantinos Tserpes
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 …
Interoperable Ocean Observing Using Archetypes: A Use-Case Based Evaluation, Paul Stacey, Damon Berry
Interoperable Ocean Observing Using Archetypes: A Use-Case Based Evaluation, Paul Stacey, Damon Berry
Conference papers
This paper presents a use-case based evaluation of the impact of two-level modeling on the automatic federation of ocean observational data. The goal of the work is to increase the interoperability and data quality of aggregated ocean observations to support convenient discovery and consumption by applications. An assessment of the interoperability of served data flows from publicly available ocean observing spatial data infrastructures was performed. Barriers to consumption of existing standards-compliant ocean-observing data streams were examined, including the impact of adherence to agreed data standards. Historical data flows were mapped to a set of archetypes and a backward integration experiment …
Ransomware Behavioural Analysis On Windows Platforms, Nikolai Hampton, Zubair A. Baig, Sherali Zeadally
Ransomware Behavioural Analysis On Windows Platforms, Nikolai Hampton, Zubair A. Baig, Sherali Zeadally
Research outputs 2014 to 2021
Ransomware infections have grown exponentially during the recent past to cause major disruption in operations across a range of industries including the government. Through this research, we present an analysis of 14 strains of ransomware that infect Windows platforms, and we do a comparison of Windows Application Programming Interface (API) calls made through ransomware processes with baselines of normal operating system behaviour. The study identifies and reports salient features of ransomware as referred through the frequencies of API calls
Cyber Security And Risk Society: Estonian Discourse On Cyber Risk And Security Strategy, Lauren Kook
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 …
An Attribute Agreement Method For Hfacs Inter-Rater Reliability Assessment, Teddy Steven Cotter, Veysel Yesilbas
An Attribute Agreement Method For Hfacs Inter-Rater Reliability Assessment, Teddy Steven Cotter, Veysel Yesilbas
Engineering Management & Systems Engineering Faculty Publications
Inter-rater reliability can be regarded as the degree of agreement among raters on a given item or a circumstance. Multiple approaches have been taken to estimate and improve inter-rater reliability of the United States Department of Defense Human Factors Analysis and Classification System used by trained accident investigators. In this study, three trained instructor pilots used the DoD-HFACS to classify 347 U.S. Air Force Accident Investigation Board (AIB) Class-A reports between the years of 2000 and 2013. The overall method consisted of four steps: (1) train on HFACS definitions, (2) verify rating reliability, (3) rate HFACS reports, and (4) random …