Digital Commons Network^™

Achievement And Friends: Key Factors Of Player Retention Vary Across Player Levels In Online Multiplayer Games, Korea Advanced Institute Of Science & Technology, Qatar Computing Research Institute, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Retaining players over an extended period of time is a long-standing challenge in game industry. Significant effort has been paid to understanding what motivates players enjoy games. While individuals may have varying reasons to play or abandon a game at different stages within the game, previous studies have looked at the retention problem from a snapshot view. This study, by analyzing in-game logs of 51,104 distinct individuals in an online multiplayer game, uniquely offers a multifaceted view of the retention problem over the players' virtual life phases. We find that key indicators of longevity change with the game level. Achievement …

What’S In An “X”? An Exchange About The Politics Of “Latinx”, Catalina M. De Onis

Environmental Studies Faculty Publications

'From its origins in queer community conversations online, the term "Latinx" continues to gain wider circulation in various publics. This scholarly exchange examines the language and other politics of choosing to employ or to reject the "x" signifier. To engage a variety of perspectives on this topic, we invited five scholars with expertise in language, sexuality, gender, and latinidad from the continental United States and Puerto Rico to participate in an online exchange about what the "x" linguistic marker enables and constrains. Contributors do not always agree, and the tensions that arise point to broader discussions and strains unfolding beyond …

Modeling Topics And Behavior Of Microbloggers: An Integrated Approach, Tuan Anh Hoang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content …

Assessing The Language Of Chat For Teamwork Dialogue, Antonette Shibani, Elizabeth Koh, Vivian Lai, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

In technology enhanced language learning, many pedagogical activities involve students in online discussion such as synchronous chat, in order to help them practice their language skills. Besides developing the language competency of students, it is also crucial to nurture their teamwork competencies for today's global and complex environment. Language communication is an important glue of teamwork. In order to assess the language of chat for teamwork dimensions, several text mining methods are pos sible. However, difficulties arise such as pre-processing being a black box and classification approaches and algorithms being dependent on the context. To address these issues, the study …

Clustering Classes In Packages For Program Comprehension, Xiaobing Sun, Xiangyue Liu, Bin Li, Bixin Li, David Lo, Lingzhi Liao

Research Collection School Of Computing and Information Systems

During software maintenance and evolution, one of the important tasks faced by developers is to understand a system quickly and accurately. With the increasing size and complexity of an evolving system, program comprehension becomes an increasingly difficult activity. Given a target system for comprehension, developers may first focus on the package comprehension. The packages in the system are of different sizes. For small-sized packages in the system, developers can easily comprehend them. However, for large-sized packages, they are difficult to understand. In this article, we focus on understanding these large-sized packages and propose a novel program comprehension approach for large-sized …

Knowing How: A Computational Approach, Joseph A. Roman

Student Publications

With advances in Artificial Intelligences being achieved through the use of Artificial Neural Networks, we are now at the point where computers are able to do tasks that were previously only able to be accomplished by humans. These advancements must cause us to reconsider our previous understanding of how people come to know how to do a particular task. In order to unpack this question, I will first look to an account of knowing how presented by Jason Stanley in his book Know How. I will then look towards criticisms of this view before using evidence presented by the existence …

Aspect Extraction From Product Reviews Using Category Hierarchy Information, Yifeng Yang, Chen Cen, Minghui Qiu, Forrest Sheng Bao

Research Collection School Of Computing and Information Systems

Aspect extraction is a task to abstract the common properties of objects from corpora discussing them, such as reviews of products. Recent work on aspect extraction is leveraging the hierarchical relationship between products and their categories. However, such effort focuses on the aspects of child categories but ignores those from parent categories. Hence, we propose an LDA-based generative topic model inducing the two-layer categorical information (CAT-LDA), to balance the aspects of both a parent category and its child categories. Our hypothesis is that child categories inherit aspects from parent categories, controlled by the hierarchy between them. Experimental results on 5 …

Language Relations On Twitter: A Network Science Approach, Priya Saha

Theses and Dissertations

Online social networks (e.g., Twitter and Facebook) play a vital role in the spreading of information in today’s world. Interestingly, the spread of information is enabled by the existence of an underlying connectivity of the users. One factor influencing the online connectivity, which only recently has been receiving attention, is the language used by the user in his or her activities. The understanding of information propagation from the perspective of languages is of particular interest because we live in a world with a very diverse set of languages. Using Network Science approaches, we demonstrate that Twitter users have a strong …

Statistical Learning Methods For Mining Marketing And Biological Data, Jie Zhang

Dissertations

Nowadays, the value of data has been broadly recognized and emphasized. More and more decisions are made based on data and analysis rather than solely on experience and intuition. With the fast development of networking, data storage, and data collection capacity, data have increased dramatically in industry, science and engineering domains, which brings both great opportunities and challenges. To take advantage of the data flood, new computational methods are in demand to process, analyze and understand these datasets.

This dissertation focuses on the development of statistical learning methods for online advertising and bioinformatics to model real world data with temporal …

A Compare-Aggregate Model For Matching Text Sequences, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a general "compare-aggregate" framework that performs word-level matching followed by aggregation using Convolutional Neural Networks. We particularly focus on the different comparison functions we can use to match two vectors. We use four different datasets to evaluate the model. We find that some simple comparison functions based on element-wise operations can work better than standard neural network and neural tensor network.

Machine Comprehension Using Match-Lstm And Answer Pointer, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Machine comprehension of text is an important problem in natural language processing. A recently released dataset, the Stanford Question Answering Dataset (SQuAD), offers a large number of real questions and their answers created by humans through crowdsourcing. SQuAD provides a challenging testbed for evaluating machine comprehension algorithms, partly because compared with previous datasets, in SQuAD the answers do not come from a small set of candidate answers and they have variable lengths. We propose an end-to-end neural architecture for the task. The architecture is based on match-LSTM, a model we proposed previously for textual entailment, and Pointer Net, a sequence-to-sequence …

Mixed-Initiative Personal Assistants, Joshua W. Buck, Saverio Perugini

Computer Science Faculty Publications

Specification and implementation of flexible human-computer dialogs is challenging because of the complexity involved in rendering the dialog responsive to a vast number of varied paths through which users might desire to complete the dialog. To address this problem, we developed a toolkit for modeling and implementing task-based, mixed-initiative dialogs based on metaphors from lambda calculus. Our toolkit can automatically operationalize a dialog that involves multiple prompts and/or sub-dialogs, given a high-level dialog specification of it. Our current research entails incorporating the use of natural language to make the flexibility in communicating user utterances commensurate with that in dialog completion …

Inferring User Consumption Preferences From Social Media, Yang Li, Jing Jiang, Ting Liu

Research Collection School Of Computing and Information Systems

Social Media has already become a new arena of our lives and involved different aspects of our social presence. Users' personal information and activities on social media presumably reveal their personal interests, which offer great opportunities for many e-commerce applications. In this paper, we propose a principled latent variable model to infer user consumption preferences at the category level (e.g. inferring what categories of products a user would like to buy). Our model naturally links users' published content and following relations on microblogs with their consumption behaviors on e-commerce websites. Experimental results show our model outperforms the state-of-the-art methods significantly …

Badiou’S Logics: Math, Metaphor, And (Almost) Everything, Vladimir Tasic

Journal of Humanistic Mathematics

Mathematics plays a central role in the philosophical system of Alain Badiou. The aim of this essay is to situate this appeal to mathematics in the broader context of his work, including its literary and political elements.

Synthesis Of Complex/Multifunctional Metal (Hydr)Oxide/Graphite Oxide/Aunps Or Agnps Adsorbents And Analysis Of Their Interactions With Chemical Warfare Agents, Dimitrios A. Giannakoudakis

Dissertations, Theses, and Capstone Projects

One of the most widely used chemical warfare agent (CWA) is mustard gas, which is a highly toxic blistering agent. In this study we investigated the interactions of mustard gas surrogates’ vapors on zinc and zirconium hydroxides and their composites with graphite oxide (GO) and/or Au or Ag nanoparticles. Even though a small quantity of reports about the detoxification of CWAs on metal oxides is reported in the literature, almost all of these studies are performed in the liquid phase. Both herein reported Zn(OH)₂ and Zr(OH)₄ showed a good adsorption performance, with the former being photoactive under visible …

Tandem 2.0: Image And Text Data Generation Application, Christopher J. Vitale

Dissertations, Theses, and Capstone Projects

First created as part of the Digital Humanities Praxis course in the spring of 2012 at the CUNY Graduate Center, Tandem explores the generation of datasets comprised of text and image data by leveraging Optical Character Recognition (OCR), Natural Language Processing (NLP) and Computer Vision (CV). This project builds upon that earlier work in a new programming framework. While other developers and digital humanities scholars have created similar tools specifically geared toward NLP (e.g. Voyant-Tools), as well as algorithms for image processing and feature extraction on the CV side, Tandem explores the process of developing a more robust and user-friendly …

The Proscriptive Principle And Logics Of Analytic Implication, Thomas M. Ferguson

Dissertations, Theses, and Capstone Projects

The analogy between inference and mereological containment goes at least back to Aristotle, whose discussion in the Prior Analytics motivates the validity of the syllogism by way of talk of parts and wholes. On this picture, the application of syllogistic is merely the analysis of concepts, a term that presupposes—through the root ἀνά + λύω —a mereological background.

In the 1930s, such considerations led William T. Parry to attempt to codify this notion of logical containment in his system of analytic implication AI. Parry’s original system AI was later expanded to the system PAI. The hallmark of Parry’s systems—and of …

Recurrent Neural Networks With Auxiliary Labels For Cross-Domain Opinion Target Extraction, Ying Ding, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Opinion target extraction is a fundamental task in opinion mining. In recent years, neural network based supervised learning methods have achieved competitive performance on this task. However, as with any supervised learning method, neural network based methods for this task cannot work well when the training data comes from a different domain than the test data. On the other hand, some rule-based unsupervised methods have shown to be robust when applied to different domains. In this work, we use rule-based unsupervised methods to create auxiliary labels and use neural network models to learn a hidden representation that works well for …

Implementing Semantic Document Search Using A Bounded Random Walk In A Probabilistic Graph, Lubomir Stanchev

Computer Science and Software Engineering

Given a set of documents and an input query that is expressed using natural language, the problem of document search is retrieving all relevant documents ordered by the degree of relevance. Semantic document search fetches not only documents that contain words from the input query, but also documents that are semantically relevant. For example, the query "friendly pets" will consider documents that contain the words "dog" and "cat", among others. One way to implement semantic search is to use a probabilistic graph in which the input query is connected to the documents through paths that contain semantically similar words and …

Tsu Faculty Research Database-Jan 2017, David Owerbach

Office of Research Institutional Research and Scholarship

Research interests and selected publications from 230 Texas Southern University faculty have been updated in Jan 2017. Faculty from Public Affairs, the College of Science, Engineering and Technology, the College of Pharmacy and Health Sciences, the College of Education, the College of Business, the College of Liberal Arts and Behavioral Sciences, the Law School and the School of Communications are included.

Extracting Geography From Datasets In Social Sciences, Yuke Li, Tianhao Wu, Nicholas Marshall, Stefan Steinerberger

Yale Day of Data

No abstract provided.

Robot Perception Errors And Human Resolution Strategies In Situated Human-Robot Dialogue, Niels Schütte, Brian Mac Namee, John D. Kelleher

Articles

Errors in visual perception may cause problems in situated dialogues. We investigated this problem through an experiment in which human participants interacted through a natural language dialogue interface with a simulated robot.We introduced errors into the robot’s perception, and observed the resulting problems in the dialogues and their resolutions.We then introduced different methods for the user to request information about the robot’s understanding of the environment. We quantify the impact of perception errors on the dialogues, and investigate resolution attempts by users at a structural level and at the level of referring expressions.

Visualizing Incongruity: Visual Data Mining Strategies For Modeling Humor In Text, Andrew Smigaj

All Graduate Projects

The goal of this project is to investigate the use of visual data mining to model verbal humor. We explored various means of text visualization to identify key featrues of garden path jokes as compared with non jokes. With garden path jokes one interpretation is established in the setup but new information indicating some alternative interpretation triggers some resolution process leading to a new interpretation. For this project we visualize text in three novel ways, assisted by some web mining to build an informal ontology, that allow us to see the differences between garden path jokes and non jokes of …

Culturally Induced Range Infilling Of Eastern Redcedar: A Problem In Ecology, An Ecological Problem, Or Both?, Aubrey Streit Krug, Daniel R. Uden, Craig R. Allen, Dirac L. Twidwell Jr

School of Natural Resources: Faculty Publications

The philosopher John Passmore distinguished between (1) “problems in ecology,” or what we might call problems in scientific understanding of ecological change, and (2) “ecological problems,” or what we might call problems faced by societies due to ecological change. The spread of eastern redcedar (Juniperus virginiana) and conversion of the central and southern Great Plains of North America to juniper woodland might be categorized as a problem in ecology, an ecological problem, or both. Here, we integrate and apply two interdisciplinary approaches to problem-solving—social-ecological systems thinking and ecocriticism—to understand the role of human culture in recognizing, driving, and …

Finding Street Gang Member Profiles On Twitter, Lakshika Balasuriya

Browse all Theses and Dissertations

The crime and violence street gangs introduce into neighborhoods is a growing epidemic in cities around the world. Today, over 1.4 million people, belonging to more than 33,000 gangs, are active in the United States, of which 88% identify themselves as being members of a street gang. With the recent popularity of social media, street gang members have established online presences coinciding with their physical occupation of neighborhoods. Recent studies report that approximately 45% of gang members participate in online offending activities such as threatening, harassing individuals, posting violent videos or attacking someone on the street for something they said …

Examining The Interaction Of Mathematical Abilities And Mathematical Memory: A Study Of Problem-Solving Activity Of High-Achieving Swedish Upper Secondary Students, Attila Szabo, Paul Andrews

The Mathematics Enthusiast

In this paper we investigate the abilities that six high-achieving Swedish upper secondary students demonstrate when solving challenging, non-routine mathematical problems. Data, which were derived from clinical interviews, were analysed against an adaptation of the framework developed by the Soviet psychologist Vadim Krutetskii (1976). Analyses showed that when solving problems students pass through three phases, here called orientation, processing and checking, during which students exhibited particular forms of ability. In particular, the mathematical memory was principally observed in the orientation phase, playing a crucial role in the ways in which students’ selected their problem-solving methods; where these methods failed to …

Review Of Trends In Health Social Media Analysis, Liliya Akhtyamova, Mikhail Alexandrov, John Cardiff

Conference Papers

This paper surveys recent publications (2008-2017) on using social media data to study public health. The survey describes the main topics being discussed in forums and presents short information about methods and tools used for analysis health social media. We put especial attention on adverse drug reaction detection problem (ADR).

Adverse Drug Extraction In Twitter Data Using Convolutional Neural Network, Liliya Akhtyamova, John Cardiff, Mikhail Alexandrov

Conference Papers

The study of health-related topics on social media has become a useful tool for the early detection of the different adverse medical conditions. In particular, it concerns cases related to the treatment of mental diseases, as the effects of medications here often prove to be unpredictable. In our research, we use convolutional neural networks (CNN) with word2vec embedding to classify user comments on Twitter. The aim of the classification is to reveal adverse drug reactions of users. The results obtained are highly promising, showing the overall usefulness of neural network algorithms in this kind of tasks.

Impact Of Reviewer Social Interaction On Online Consumer Review Fraud Detection, Kunal Goswami, Younghee Park, Chungsik Song

Faculty Publications

Background Online consumer reviews have become a baseline for new consumers to try out a business or a new product. The reviews provide a quick look into the application and experience of the business/product and market it to new customers. However, some businesses or reviewers use these reviews to spread fake information about the business/product. The fake information can be used to promote a relatively average product/business or can be used to malign their competition. This activity is known as reviewer fraud or opinion spam. The paper proposes a feature set, capturing the user social interaction behavior to identify fraud. …

Topic Supervised Non-Negative Matrix Factorization, K. Macmillan, James D. Wilson

Mathematics

Topic models have been extensively used to organize and interpret the contents of large, unstructured corpora of text documents. Although topic models often perform well on traditional training vs. test set evaluations, it is often the case that the results of a topic model do not align with human interpretation. This interpretability fallacy is largely due to the unsupervised nature of topic models, which prohibits any user guidance on the results of a model. In this paper, we introduce a semi-supervised method called topic supervised non-negative matrix factorization (TS-NMF) that enables the user to provide labeled example documents to promote …