Computer Sciences Commons^™

Disease Gene Classification With Metagraph Representations, Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

Protein-protein interaction (PPI) networks play an important role in studying the functional roles of proteins, including their association with diseases. However, protein interaction networks are not sufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To complement and enrich PPI networks, we propose to exploit biological properties of individual proteins. More specifically, we integrate keywords describing protein properties into the PPI network, and construct a novel PPI-Keywords (PPIK) network consisting of both proteins and keywords as two different types of nodes. As disease proteins tend to have a similar topological characteristics …

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 …

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 …

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 …

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 …

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 …

An Integrated Framework For Modeling And Predicting Spatiotemporal Phenomena In Urban Environments, Tuc Viet Le

Dissertations and Theses Collection (Open Access)

This thesis proposes a general solution framework that integrates methods in machine learning in creative ways to solve a diverse set of problems arising in urban environments. It particularly focuses on modeling spatiotemporal data for the purpose of predicting urban phenomena. Concretely, the framework is applied to solve three specific real-world problems: human mobility prediction, trac speed prediction and incident prediction. For human mobility prediction, I use visitor trajectories collected a large theme park in Singapore as a simplified microcosm of an urban area. A trajectory is an ordered sequence of attraction visits and corresponding timestamps produced by a visitor. …

Scalable Online Kernel Learning, Jing Lu

Dissertations and Theses Collection (Open Access)

One critical deficiency of traditional online kernel learning methods is their increasing and unbounded number of support vectors (SV’s), making them inefficient and non-scalable for large-scale applications. Recent studies on budget online learning have attempted to overcome this shortcoming by bounding the number of SV’s. Despite being extensively studied, budget algorithms usually suffer from several drawbacks.
First of all, although existing algorithms attempt to bound the number of SV’s at each iteration, most of them fail to bound the number of SV’s for the final averaged classifier, which is commonly used for online-to-batch conversion. To solve this problem, we propose …

Eeg-Based Emotion Recognition Via Fast And Robust Feature Smoothing, Cheng Tang, Di Wang, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

Electroencephalograph (EEG) signals reveal much of our brain states and have been widely used in emotion recognition. However, the recognition accuracy is hardly ideal mainly due to the following reasons: (i) the features extracted from EEG signals may not solely reflect one’s emotional patterns and their quality is easily affected by noise; and (ii) increasing feature dimension may enhance the recognition accuracy, but it often requires extra computation time. In this paper, we propose a feature smoothing method to alleviate the aforementioned problems. Specifically, we extract six statistical features from raw EEG signals and apply a simple yet cost-effective feature …

Vireo @ Trecvid 2017: Video-To-Text, Ad-Hoc Video Search And Video Hyperlinking, Phuong Anh Nguyen, Qing Li, Zhi-Qi Cheng, Yi-Jie Lu, Hao Zhang, Xiao Wu, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

In this paper, we describe the systems developed for Video-to-Text (VTT), Ad-hoc Video Search (AVS) and Video Hyper-linking (LNK) tasks at TRECVID 2017 [1] and the achieved results.

Leveraging Social Analytics Data For Identifying Customer Segments For Online News Media, Jansen, Bernard J, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Haewoon Kwak

Research Collection School Of Computing and Information Systems

In this work, we describe a methodology for leveraging large amounts of customer interaction data with online content from major social media platforms in order to isolate meaningful customer segments. The methodology is robust in that it can rapidly identify diverse customer segments using solely online behaviors and then associate these behavioral customer segments with the related distinct demographic segments, presenting a holistic picture of the customer base of an organization. We validate our methodology via the implementation of a working system that rapidly and in near real-time processes tens of millions of online customer interactions with content posted on …

Color-Sketch Simulator: A Guide For Color-Based Visual Known-Item Search, Jakub Lokoč, Anh Nguyen Phuong, Marta Vomlelová, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

In order to evaluate the effectiveness of a color-sketch retrieval system for a given multimedia database, tedious evaluations involving real users are required as users are in the center of query sketch formulation. However, without any prior knowledge about the bottlenecks of the underlying sketch-based retrieval model, the evaluations may focus on wrong settings and thus miss the desired effect. Furthermore, users have usually no clues or recommendations to draw color-sketches effectively. In this paper, we aim at a preliminary analysis to identify potential bottlenecks of a flexible color-sketch retrieval model. We present a formal framework based on position-color feature …

Unsupervised Topic Hypergraph Hashing For Efficient Mobile Image Retrieval, Lei Zhu, Jialie Shen, Liang Xie, Zhiyong Cheng

Research Collection School Of Computing and Information Systems

Hashing compresses high-dimensional features into compact binary codes. It is one of the promising techniques to support efficient mobile image retrieval, due to its low data transmission cost and fast retrieval response. However, most of existing hashing strategies simply rely on low-level features. Thus, they may generate hashing codes with limited discriminative capability. Moreover, many of them fail to exploit complex and high-order semantic correlations that inherently exist among images. Motivated by these observations, we propose a novel unsupervised hashing scheme, called topic hypergraph hashing (THH), to address the limitations. THH effectively mitigates the semantic shortage of hashing codes by …

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 …

A Fast Trajectory Outlier Detection Approach Via Driving Behavior Modeling, Hao Wu, Weiwei Sun, Baihua Zheng

Research Collection School Of Computing and Information Systems

Trajectory outlier detection is a fundamental building block for many location-based service (LBS) applications, with a large application base. We dedicate this paper on detecting the outliers from vehicle trajectories efficiently and effectively. In addition, we want our solution to be able to issue an alarm early when an outlier trajectory is only partially observed (i.e., the trajectory has not yet reached the destination). Most existing works study the problem on general Euclidean trajectories and require accesses to the historical trajectory database or computations on the distance metric that are very expensive. Furthermore, few of existing works consider some specific …

Collaborative Topic Regression With Denoising Autoencoder For Content And Community Co-Representation, Trong T. Nguyen, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Personalized recommendation of items frequently faces scenarios where we have sparse observations on users' adoption of items. In the literature, there are two promising directions. One is to connect sparse items through similarity in content. The other is to connect sparse users through similarity in social relations. We seek to integrate both types of information, in addition to the adoption information, within a single integrated model. Our proposed method models item content via a topic model, and user communities via an autoencoder model, while bridging a user's community-based preference to her topic-based preference. Experiments on public real-life data showcase the …

Answerbot: Automated Generation Of Answer Summary To Developers’ Technical Questions, Bowen Xu, Zhenchang Xing, Xin Xia, David Lo

Research Collection School Of Computing and Information Systems

The prevalence of questions and answers on domain-specific Q&A sites like Stack Overflow constitutes a core knowledge asset for software engineering domain. Although search engines can return a list of questions relevant to a user query of some technical question, the abundance of relevant posts and the sheer amount of information in them makes it difficult for developers to digest them and find the most needed answers to their questions. In this work, we aim to help developers who want to quickly capture the key points of several answer posts relevant to a technical question before they read the details …

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 …

Tweet Geolocation: Leveraging Location, User And Peer Signals, Wen-Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Which venue is a tweet posted from? We referred this as fine-grained geolocation. To solve this problem effectively, we develop novel techniques to exploit each posting user's content history. This is motivated by our finding that most users do not share their visitation history, but have ample content history from tweet posts. We formulate fine-grained geolocation as a ranking problem whereby given a test tweet, we rank candidate venues. We propose several models that leverage on three types of signals from locations, users and peers. Firstly, the location signals are words that are indicative of venues. We propose a location-indicative …

Modeling Check-In Behavior With Geographical Neighborhood Influence Of Venues, Thanh Nam Doan, Ee Peng Lim

Research Collection School Of Computing and Information Systems

With many users adopting location-based social networks (LBSNs) to share their daily activities, LBSNs become a gold mine for researchers to study human check-in behavior. Modeling such behavior can benefit many useful applications such as urban planning and location-aware recommender systems. Unlike previous studies [4,6,12,17] that focus on the effect of distance on users checking in venues, we consider two venue-specific effects of geographical neighborhood influence, namely, spatial homophily and neighborhood competition. The former refers to the fact that venues share more common features with their spatial neighbors, while the latter captures the rivalry of a venue and its nearby …

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: …

Interactive Social Recommendation, Xin Wang, Steven C. H. Hoi, Chenghao Liu, Martin Ester

Research Collection School Of Computing and Information Systems

Social recommendation has been an active research topic over the last decade, based on the assumption that social information from friendship networks is beneficial for improving recommendation accuracy, especially when dealing with cold-start users who lack sufficient past behavior information for accurate recommendation. However, it is nontrivial to use such information, since some of a person's friends may share similar preferences in certain aspects, but others may be totally irrelevant for recommendations. Thus one challenge is to explore and exploit the extend to which a user trusts his/her friends when utilizing social information to improve recommendations. On the other hand, …

On Analyzing Job Hop Behavior And Talent Flow Networks, Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

Analyzing job hopping behavior is important for theunderstanding of job preference and career progression of working individuals.When analyzed at the workforce population level, job hop analysis helps to gaininsights of talent flow and organization competition. Traditionally, surveysare conducted on job seekers and employers to study job behavior. While surveysare good at getting direct user input to specially designed questions, they areoften not scalable and timely enough to cope with fast-changing job landscape.In this paper, we present a data science approach to analyze job hops performedby about 490,000 working professionals located in a city using their publiclyshared profiles. We develop several …

Second-Order Online Active Learning And Its Applications, Shuji Hao, Jing Lu, Peilin Zhao, Chi Zhang, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

The goal of online active learning is to learn predictive models from a sequence of unlabeled data given limited label querybudget. Unlike conventional online learning tasks, online active learning is considerably more challenging because of two reasons.Firstly, it is difficult to design an effective query strategy to decide when is appropriate to query the label of an incoming instance givenlimited query budget. Secondly, it is also challenging to decide how to update the predictive models effectively whenever the true labelof an instance is queried. Most existing approaches for online active learning are often based on a family of first-order online …

Highly Efficient Mining Of Overlapping Clusters In Signed Weighted Networks, Tuan-Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In many practical contexts, networks are weighted as their links are assigned numerical weights representing relationship strengths or intensities of inter-node interaction. Moreover, the links' weight can be positive or negative, depending on the relationship or interaction between the connected nodes. The existing methods for network clustering however are not ideal for handling very large signed weighted networks. In this paper, we present a novel method called LPOCSIN (short for "Linear Programming based Overlapping Clustering on Signed Weighted Networks") for efficient mining of overlapping clusters in signed weighted networks. Different from existing methods that rely on computationally expensive cluster cohesiveness …