Databases and Information Systems Commons^™

Unsupervised Multi-Graph Cross-Modal Hashing For Large-Scale Multimedia Retrieval, Liang Xie, Lei Zhu, Guoqi Chen

Research Collection School Of Computing and Information Systems

With the advance of internet and multimedia technologies, large-scale multi-modal representation techniques such as cross-modal hashing, are increasingly demanded for multimedia retrieval. In cross-modal hashing, three essential problems should be seriously considered. The first is that effective cross-modal relationship should be learned from training data with scarce label information. The second is that appropriate weights should be assigned for different modalities to reflect their importance. The last is the scalability of training process which is usually ignored by previous methods. In this paper, we propose Multi-graph Cross-modal Hashing (MGCMH) by comprehensively considering these three points. MGCMH is unsupervised method which …

Probabilistic Robust Route Recovery With Spatio-Temporal Dynamics, Hao Wu, Jiangyun Mao, Weiwei Sun, Baihua Zheng, Hanyuan Zhang, Ziyang Chen, Wei Wang

Research Collection School Of Computing and Information Systems

Vehicle trajectories are one of the most important data in location-based services. The quality of trajectories directly affects the services. However, in the real applications, trajectory data are not always sampled densely. In this paper, we study the problem of recovering the entire route between two distant consecutive locations in a trajectory. Most existing works solve the problem without using those informative historical data or solve it in an empirical way. We claim that a data-driven and probabilistic approach is actually more suitable as long as data sparsity can be well handled. We propose a novel route recovery system in …

Intermediaries Vs Peer-To-Peer: A Study Of Lenders’ Incentive On A Donation-Based Crowdfunding Platform, Ling Ge, Zhiling Guo, Xuechen Luo

Research Collection School Of Computing and Information Systems

Donation-based crowdfunding platform Kiva seems to hold the promise of peer-to-peer lending with zero interest rate to help the poor. However, it is actually intermediated by microfinance institutions, which raise funds from Kiva lenders, disburse the funds to borrowers and collect high interest. Later Kiva launched another platform Kiva Zip that implements interest-free loans directly from lenders to borrowers. This unique setup enables us to examine how lenders choose between Kiva and Kiva Zip, i.e. a platform with intermediaries and a real P2P platform. We develop a theoretical model and explicate that the lenders trade-off is between the sustainability of …

Understanding Patient Portal Use Intentions: Enablers And Inhibitors Of It Use, M. Moqbel, Fiona Fui-Hoon Nah, V. Bartelt, R. O’Dell

Research Collection School Of Computing and Information Systems

This research explores factors that influence patient’s intentions to use a hospital’s patient portal. Specifically, we investigate patient portal use intentions using two different perspectives: enablers of IT use (patient need for healthcare empowerment and healthcare professional encouragement) and inhibitors of IT use (privacy and security concerns). Drawing on theories of privacy calculus and protection motivation, we propose a research model to assess the relationships between the enablers and inhibitors of IT use as well as their effects on patient portal adoption. We will administer a survey questionnaire to existing patients of a major hospital in the Midwest and employ …

Latent Semantic Indexing In The Discovery Of Cyber-Bullying In Online Text, Jacob L. Bigelow

Computer Science Summer Fellows

The rise in the use of social media and particularly the rise of adolescent use has led to a new means of bullying. Cyber-bullying has proven consequential to youth internet users causing a need for a response. In order to effectively stop this problem we need a verified method of detecting cyber-bullying in online text; we aim to find that method. For this project we look at thirteen thousand labeled posts from Formspring and create a bank of words used in the posts. First the posts are cleaned up by taking out punctuation, normalizing emoticons, and removing high and low …

Detection Of Cyberbullying In Sms Messaging, Bryan W. Bradley

Computer Science Summer Fellows

Cyberbullying is a type of bullying that uses technology such as cell phones to harass or malign another person. To detect acts of cyberbullying, we are developing an algorithm that will detect cyberbullying in SMS (text) messages. Over 80,000 text messages have been collected by software installed on cell phones carried by participants in our study. This paper describes the development of the algorithm to detect cyberbullying messages, using the cell phone data collected previously. The algorithm works by first separating the messages into conversations in an automated way. The algorithm then analyzes the conversations and scores the severity and …

A Framework For Collecting, Extracting And Managing Event Identity Information From Textual Content In Social Media, Debanjan Mahata

Theses and Dissertations

With the popularity of social media platforms such as Facebook, Twitter and Google Plus, there has been voluminous growth in the digital footprints of real-life events on the Internet. The user-generated colloquial and concise textual content related to different types of real-life events, available in these websites, acts as an extremely useful source for researchers and organizations for extracting valuable and insightful information. There has been significant improvement in natural language processing techniques for mining formal and long textual content commonly found in newspapers. It is still a challenging task to mine textual information from the social media channels producing …

Analyzing Clustered Web Concepts With Homology, Eric Nam

Master's Projects

As data is being mined more and more from the Internet today, Data Science has become an important field of computing to make that data useful. Data Science allows people to turn all of that data into structured knowledge that is easily utilized, validated, and understandable. There are many known theories to analyze data, but this project will focus on a recently introduced method: analyzing text data with homology from mathematics to understand relationships between keyword-sets.

Using structures of algebraic topology as a starting point, keyword-sets in the text are represented by simplexes based on what they are and what …

On Understanding Preference For Agile Methods Among Software Developers, David Brian Bishop, Amit V. Deokar, Surendra Sarnikar

Research & Publications

Agile methods are gaining widespread use in industry. Although management is keen on adopting agile, not all developers exhibit preference for agile methods. The literature is sparse in regard to why developers may show preference for agile. Understanding the factors informing the preference for agile can lead to more effective formation of teams, better training approaches, and optimizing software development efforts by focusing on key desirable components of agile. This study, using a grounded theory methodology, finds a variety of categories of factors that influence software developer preference for agile methods including self-efficacy, affective response, interpersonal response, external contingencies, and …

What Motivates High School Students To Take Precautions Against The Spread Of Influenza? A Data Science Approach To Latent Modeling Of Compliance With Preventative Practice, William L. Romine, Tanvi Banerjee, William R. Folk, Lloyd H. Barrow

Kno.e.sis Publications

– This study focuses on a central question: What key behavioral factors influence high school students’ compliance with preventative measures against the transmission of influenza? We use multilevel logistic regression to equate logit measures for eight precautions to students’ latent compliance levels on a common scale. Using linear regression, we explore the efficacy of knowledge of influenza, affective perceptions about influenza and its prevention, prior illness, and gender in predicting compliance. Hand washing and respiratory etiquette are the easiest precautions for students, and hand sanitizer use and keeping the hands away from the face are the most difficult. Perceptions of …

Introduction To Gis Using Open Source Software, 7th Ed, Frank Donnelly

Open Educational Resources

This tutorial was created to accompany the GIS Practicum, a day-long workshop offered by the Newman Library at Baruch College CUNY that introduces participants to geographic information systems (GIS) using the open source software QGIS. The practicum introduces GIS as a concept for envisioning information and as a tool for conducting geographic analyses and creating maps. Participants learn how to navigate a GIS interface, how to prepare layers and conduct a basic geographic analysis, and how to create thematic maps. This tutorial was written using QGIS version 2.14 "Essen", a cross-platform (Windows, Mac, Linux) desktop GIS software package.

Fine-Grained Detection Of Programming Students’ Frustration Using Keystrokes, Mouse Clicks And Interaction Logs, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Prolonged frustration leads to loss of confidence and eventual disinterest in the learning itself. The modelling of frustration in learning is thus important as it informs on the appropriate time to intervene to sustain the interest and motivation of students. To automatically detect learner’s frustration in a naturalistic learning environment, the novel use of keystrokes, mouse clicks and interaction patterns of students captured within the context of a tutoring system was proposed. The modelling approach was described and a comparison was made between the proposed model using Bayesian Network and the baseline Naïve Bayes model. With the formulation of an …

Mhealth Support System For Researchers And Participants, Taskina Fayezeen

Master's Theses (2009 -)

With the proliferation of mobile technologies, there is a significant increase of research using mobile devices in the medical and public health area. Mobile technology has improved the efficiency of healthcare delivery effectively. Mobile Health or mHealth is an interdisciplinary research area which has been active for more than a decade. Much research has been conducted and many software research tools (mHealth Support System) have been developed. Despite the time length, there is a significant gap in the mHealth research area regarding software research tools. Individual research groups are developing their own software research tool though there is a significant …

Build Emotion Lexicon From The Mood Of Crowd Via Topic-Assisted Joint Non-Negative Matrix Factorization, Kaisong Song, Wei Gao, Ling Chen, Shi Feng, Daling Wang, Chengqi Zhang

Research Collection School Of Computing and Information Systems

Kaisong Song, Wei Gao, Ling Chen, Shi Feng, Daling Wang, and Chengqi Zhang. (2016). . In , pages 773–776, Pisa, Italy. ACM Press. https://doi.org/10.1145/2911451.2914759

Ordinal Text Quantification, Giovanni Da San Martino, Wei Gao, Fabrizio Sebastiani

Research Collection School Of Computing and Information Systems

In recent years there has been a growing interest in text quantification, a supervised learning task where the goal is to accurately estimate, in an unlabelled set of items, the prevalence (or "relative frequency") of each class c in a predefined set C. Text quantification has several applications, and is a dominant concern in fields such as market research, the social sciences, political science, and epidemiology. In this paper we tackle, for the first time, the problem of ordinal text quantification, defined as the task of performing text quantification when a total order is defined on the set of classes; …

Detecting Rumors From Microblogs With Recurrent Neural Networks, Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, Meeyoung Cha

Research Collection School Of Computing and Information Systems

Microblogging platforms are an ideal place for spreading rumors and automatically debunking rumors is a crucial problem. To detect rumors, existing approaches have relied on hand-crafted features for employing machine learning algorithms that require daunting manual effort. Upon facing a dubious claim, people dispute its truthfulness by posting various cues over time, which generates long-distance dependencies of evidence. This paper presents a novel method that learns continuous representations of microblog events for identifying rumors. The proposed model is based on recurrent neural networks (RNN) for learning the hidden representations that capture the variation of contextual information of relevant posts over …

Three Strategies To Success: Learning Adversary Models In Security Games, Nika Haghtalab, Fei Fang, Thanh Hong Nguyen, Arunesh Sinha, Ariel D. Procaccia, Milind Tambe

Research Collection School Of Computing and Information Systems

State-of-the-art applications of Stackelberg security games -- including wildlife protection -- offer a wealth of data, which can be used to learn the behavior of the adversary. But existing approaches either make strong assumptions about the structure of the data, or gather new data through online algorithms that are likely to play severely suboptimal strategies. We develop a new approach to learning the parameters of the behavioral model of a bounded rational attacker (thereby pinpointing a near optimal strategy), by observing how the attacker responds to only three defender strategies. We also validate our approach using experiments on real and …

On Effective Personalized Music Retrieval By Exploring Online User Behaviors, Zhiyong Cheng, Jialie Shen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

In this paper, we study the problem of personalized text based music retrieval which takes users’ music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel DualLayer Music Preference Topic Model (DL-MPTM) is proposed to construct latent music interest space and characterize the correlations among (user, song, term). Based on the DL-MPTM, we further develop an effective personalized music retrieval system. To evaluate the system’s performance, extensive experimental studies have been conducted over two test collections to compare the proposed method with the state-of-the-art music retrieval methods. The results …

Word Clouds With Latent Variable Analysis For Visual Comparison Of Documents, Tuan M. V. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Word cloud is a visualization form for text that is recognized for its aesthetic, social, and analytical values. Here, we are concerned with deepening its analytical value for visual comparison of documents. To aid comparative analysis of two or more documents, users need to be able to perceive similarities and differences among documents through their word clouds. However, as we are dealing with text, approaches that treat words independently may impede accurate discernment of similarities among word clouds containing different words of related meanings. We therefore motivate the principle of displaying related words in a coherent manner, and propose to …

Robust Median Reversion Strategy For Online Portfolio Selection, Dingjiang Huang, Junlong Zhou, Bin Li, Hoi, Steven C. H., Shuigeng Zhou

Research Collection School Of Computing and Information Systems

On-line portfolio selection has been attracting increasing interests from artificial intelligence community in recent decades. Mean reversion, as one most frequent pattern in financial markets, plays an important role in some state-of-the-art strategies. Though successful in certain datasets, existing mean reversion strategies do not fully consider noises and outliers in the data, leading to estimation error and thus non-optimal portfolios, which results in poor performance in practice. To overcome the limitation, we propose to exploit the reversion phenomenon by robust L1-median estimator, and design a novel on-line portfolio selection strategy named "Robust Median Reversion" (RMR), which makes optimal portfolios based …