Digital Commons Network^™

Towards A More Representative Definition Of Cyber Security, Daniel Schatz, Rabih Bashroush, Julie Wall

Journal of Digital Forensics, Security and Law

In recent years, ‘Cyber Security’ has emerged as a widely-used term with increased adoption by practitioners and politicians alike. However, as with many fashionable jargon, there seems to be very little understanding of what the term really entails. Although this is may not be an issue when the term is used in an informal context, it can potentially cause considerable problems in context of organizational strategy, business objectives, or international agreements. In this work, we study the existing literature to identify the main definitions provided for the term ‘Cyber Security’ by authoritative sources. We then conduct various lexical and semantic …

Question Type Recognition Using Natural Language Input, Aishwarya Soni

Master's Projects

Recently, numerous specialists are concentrating on the utilization of Natural Language Processing (NLP) systems in various domains, for example, data extraction and content mining. One of the difficulties with these innovations is building up a precise Question and Answering (QA) System. Question type recognition is the most significant task in a QA system, for example, chat bots. Organization such as National Institute of Standards (NIST) hosts a conference series called as Text REtrieval Conference (TREC) series which keeps a competition every year to encourage and improve the technique of information retrieval from a large corpus of text. When a user …

Back To The Future: Logic And Machine Learning, Simon Dobnik, John D. Kelleher

Conference papers

In this paper we argue that since the beginning of the natural language processing or computational linguistics there has been a strong connection between logic and machine learning. First of all, there is something logical about language or linguistic about logic. Secondly, we argue that rather than distinguishing between logic and machine learning, a more useful distinction is between top-down approaches and data-driven approaches. Examining some recent approaches in deep learning we argue that they incorporate both properties and this is the reason for their very successful adoption to solve several problems within language technology.

Axioms: Mathematical And Spiritual: What Says The Parable?, Melvin Royer

ACMS Conference Proceedings 2017

Relational structure A is compact provided for any structure Jffi of the same signature, if every finite substructure of Jffi has a homomorphism to A then so does Jffi. The Constraint Satisfaction Problem (CSP) for A is the computational problem of determining whether finite structures have homomorphisms into A. We explore a connection between the hierarchy of logical axioms and the complexity hierarchy of CSPs: It appears that the complexity of CSP for A corresponds to the strength of the axiom "A is compact". At the top, the statement "K3 is compacts" is logically equivalent to the compactness theorem. Thus …

The R Journal (June 2017) 9(1): Complete Issue, The R Foundation

The R Journal

Editorial, Roger Bivand

Contributed Research Articles

iotools: High-Performance I/O Tools for R, Taylor Arnold, Michael J. Kane, and Simon Urbanek

IsoGeneGUI: Multiple Approaches for Dose-Response Analysis of Microarray Data Using R, Martin Otava, Rudradev Sengupta, Ziv Shkedy, Dan Lin, Setia Pramana, Tobias Verbeke, Philippe Haldermans, Ludwig A. Hothorn, Daniel Gerhard, Rebecca M. Kuiper, Florian Klinglmueller, and Adetayo Kasim

Network Visualization with ggplot2, Sam Tyner, François Briatte, and Heike Hofmann

OrthoPanels: An R Package for Estimating a Dynamic Panel Model with Fixed Effects Using the Orthogonal Reparameterization Approach, Mark Pickup, Paul Gustafson, Davor Cubranic, and Geoffrey Evans

The mosaic Package: Helping …

Multilabel Classification With R Package Mlr, Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl

The R Journal

We implemented several multilabel classification algorithms in the machine learning package mlr. The implemented methods are binary relevance, classifier chains, nested stacking, dependent binary relevance and stacking, which can be used with any base learner that is accessible in mlr. Moreover, there is access to the multilabel classification versions of random ForestSRC and rFerns. All these methods can be easily compared by different implemented multilabel performance measures and resampling methods in the standardized mlr framework. In a benchmark experiment with several multilabel datasets, the performance of the different methods is evaluated.

Comparing Grounded Theory And Topic Modeling: Extreme Divergence Or Unlikely Convergence?, Eric P.S. Baumer, David Mimno, Shion Guha, Emily Quan, Geri K. Gay

Mathematics, Statistics and Computer Science Faculty Research and Publications

Researchers in information science and related areas have developed various methods for analyzing textual data, such as survey responses. This article describes the application of analysis methods from two distinct fields, one method from interpretive social science and one method from statistical machine learning, to the same survey data. The results show that the two analyses produce some similar and some complementary insights about the phenomenon of interest, in this case, nonuse of social media. We compare both the processes of conducting these analyses and the results they produce to derive insights about each method's unique advantages and drawbacks, as …

Bug Characteristics In Blockchain Systems: A Large-Scale Empirical Study, Zhiyuan Wan, David Lo, Xin Xia, Liang Cai

Research Collection School Of Computing and Information Systems

Bugs severely hurt blockchain system dependability. A thorough understanding of blockchain bug characteristics is required to design effective tools for preventing, detecting and mitigating bugs. We perform an empirical study on bug characteristics in eight representative open source blockchain systems. First, we manually examine 1,108 bug reports to understand the nature of the reported bugs. Second, we leverage card sorting to label the bug reports, and obtain ten bug categories in blockchain systems. We further investigate the frequency distribution of bug categories across projects and programming languages. Finally, we study the relationship between bug categories and bug fixing time. The …

Aliner: An R Package For Optimizing Feature-Weighted Alignments And Linguistic Distances, Sean S. Downey, Guowei Sun, Peter Norquest

The R Journal

Linguistic distance measurements are commonly used in anthropology and biology when quantitative and statistical comparisons between words are needed. This is common, for example, when analyzing linguistic and genetic data. Such comparisons can provide insight into historical population patterns and evolutionary processes. However, the most commonly used linguistic distances are derived from edit distances, which do not weight phonetic features that may, for example, represent smaller-scale patterns in linguistic evolution. Thus, computational methods for calculating feature-weighted linguistic distances are needed for linguistic, biological, and evolutionary applications; additionally, the linguistic distances presented here are generic and may have broader applications in …

An Exploratory Study Of Functionality And Learning Resources Of Web Apis On Programmableweb, Yuan Tian, Pavneet Singh Kochhar, David Lo

Research Collection School Of Computing and Information Systems

Web APIs provide various functionalities that can be leveraged by developers in building their applications. ProgrammableWeb, which is the largest and most active web API and mashup collection, provides a record of thousands of web APIs and mashups. However, important properties about these large number of web APIs, such as their functionality and support/resources for learning, have never been studied by the existing research work. In this study, we perform an exploratory analysis on functionality and learning resources of 9,883 web APIs and 4,315 mashups listed on ProgrammableWeb, and find that: (1) web APIs provide a wide range of functionalities …

Employing Smartwatch For Enhanced Password Authentication, Bing Chang, Ximing Liu, Yingjiu Li, Pingjian Wang, Wen-Tao Zhu, Zhan Wang

Research Collection School Of Computing and Information Systems

This paper presents an enhanced password authentication scheme by systematically exploiting the motion sensors in a smartwatch. We extract unique features from the sensor data when a smartwatch bearer types his/her password (or PIN), and train certain machine learning classifiers using these features. We then implement smartwatch-aided password authentication using the classifiers. Our scheme is user-friendly since it does not require users to perform any additional actions when typing passwords or PINs other than wearing smartwatches. We conduct a user study involving 51 participants on the developed prototype so as to evaluate its feasibility and performance. Experimental results show that …

Document Classification Using Machine Learning, Ankit Basarkar

Master's Projects

To perform document classification algorithmically, documents need to be represented such that it is understandable to the machine learning classifier. The report discusses the different types of feature vectors through which document can be represented and later classified. The project aims at comparing the Binary, Count and TfIdf feature vectors and their impact on document classification. To test how well each of the three mentioned feature vectors perform, we used the 20-newsgroup dataset and converted the documents to all the three feature vectors. For each feature vector representation, we trained the Naïve Bayes classifier and then tested the generated classifier …

Mining Frequency Of Drug Side Effects Over A Large Twitter Dataset Using Apache Spark, Dennis Hsu

Master's Projects

Despite clinical trials by pharmaceutical companies as well as current FDA reporting systems, there are still drug side effects that have not been caught. To find a larger sample of reports, a possible way is to mine online social media. With its current widespread use, social media such as Twitter has given rise to massive amounts of data, which can be used as reports for drug side effects. To process these large datasets, Apache Spark has become popular for fast, distributed batch processing. In this work, we have improved on previous pipelines in sentimental analysis-based mining, processing, and extracting tweets …

Named Entity Recognition And Classification For Natural Language Inputs At Scale, Shreeraj Dabholkar

Master's Projects

Natural language processing (NLP) is a technique by which computers can analyze, understand, and derive meaning from human language. Phrases in a body of natural text that represent names, such as those of persons, organizations or locations are referred to as named entities. Identifying and categorizing these named entities is still a challenging task, research on which, has been carried out for many years. In this project, we build a supervised learning based classifier which can perform named entity recognition and classification (NERC) on input text and implement it as part of a chatbot application. The implementation is then scaled …

Headline Generation Using Deep Neural Networks, Dhruven Vora

Master's Projects

News headline generation is one of the important text summarization tasks. Human generated news headlines are generally intended to catch the eye rather than provide useful information. There have been many approaches to generate meaningful headlines by either using neural networks or using linguistic features. In this report, we are proposing a novel approach based on integrating Hedge Trimmer, which is a grammar based extractive summarization system with a deep neural network abstractive summarization system to generate meaningful headlines. We analyze the results against current recurrent neural network based headline generation system.

Detecting Deception In Asynchronous Text, Fletcher Glancy

Annual ADFSL Conference on Digital Forensics, Security and Law

Glancy and Yadav (2010) developed a computational fraud detection model (CFDM) that successfully detected financial reporting fraud in the text of the management’s discussion and analysis (MDA) portion of annual filings with the United States Securities and Exchange Commission (SEC). This work extends the use of the CFDM to additional genres, demonstrates the generalizability of the CFDM and the use of text mining for quantitatively detecting deception in asynchronous text. It also demonstrates that writers committing fraud use words differently from truth tellers.

Toward Measuring Network Aesthetics Based On Symmetry, Zengqiang Chen, Matthias Dehmer, Frank Emmert-Streib, Abbe Mowshowitz, Yongtang Shi

Publications and Research

In this exploratory paper, we discuss quantitative graph-theoretical measures of network aesthetics. Related work in this area has typically focused on geometrical features (e.g., line crossings or edge bendiness) of drawings or visual representations of graphs which purportedly affect an observer’s perception. Here we take a very different approach, abandoning reliance on geometrical properties, and apply information-theoretic measures to abstract graphs and networks directly (rather than to their visual representaions) as a means of capturing classical appreciation of structural symmetry. Examples are used solely to motivate the approach to measurement, and to elucidate our symmetry-based mathematical theory of network aesthetics.

Enhancing The Learning Experience- Use Of Video Game Technology For Teaching Japanese Language, Craig A. Decampli

Senior Honors Projects, 2010-2019

The Japanese language is challenging to learn, especially for native speakers of Indo- European languages. The three components of written Japanese -- Hiragana, Katakana, and Kanji – include 2,136 Kanji characters, and 46 each for Katakana and Hiragana. Teaching Japanese – generally through repetition – can lead to student boredom and affect success. Research shows that video games can at least provide a more enjoyable learning experience. Despite this fact, there are a lack of video games for teaching Japanese characters. Using the Unity game engine and the C# programming language, a video game for enhancing the learning of students …

Digital Hegemonies: The Localness Of Search Engine Results, Andrea Ballatore, Mark Graham, Shilad Sen

Faculty Publications

Every day, billions of Internet users rely on search engines to find information about places to make decisions about tourism, shopping, and countless other economic activities. In an opaque process, search engines assemble digital content produced in a variety of locations around the world and make it available to large cohorts of consumers. Although these representations of place are increasingly important and consequential, little is known about their characteristics and possible biases. Analyzing a corpus of Google search results generated for 188 capital cities, this article investigates the geographic dimension of search results, focusing on searches such as “Lagos” and …

A Comparative Study Of Cognitive Systems For Learning, Praneetha Mandava

Theses and Dissertations

Learning is the modification of a behavioral tendency by experience. Memory and reasoning are the most important aspects for learning in humans; information is temporarily stored in the short-term memory and processed, compared with existing memories and stored in long-term memory, and can be re-used when needed. One way to describe an organized pattern of thought or behavior and the categories of information along with their relationships is by using schemas. A cognitive script is one form of a schema that evolves over multiple exposures to the same set of stimuli and/or repeated enactment of a particular behavior. This research …

Choosing An Nlp Library For Analyzing Software Documentation: A Systematic Literature Review And A Series Of Experiments, Fouad N. A. Al Omran, Christoph Treude

Research Collection School Of Computing and Information Systems

To uncover interesting and actionable information from natural language documents authored by software developers, many researchers rely on "out-of-the-box" NLP libraries. However, software artifacts written in natural language are different from other textual documents due to the technical language used. In this paper, we first analyze the state of the art through a systematic literature review in which we find that only a small minority of papers justify their choice of an NLP library. We then report on a series of experiments in which we applied four state-of-the-art NLP libraries to publicly available software artifacts from three different sources. Our …

Predicting Friendship Strength In Facebook, Nitish Dhakal

Boise State University Theses and Dissertations

Effective friend classification in Online Social Networks (OSN) has many benefits in privacy. Anything posted by the user in social networks like Facebook is distributed among all their friends. Although the user can select the manual option for their post-dissemination, it is not feasible every time. Since not all friends are the same in a social network, the visibility access for the post should be different for different strengths of friendship for privacy. We propose a model with 24 features for finding friendship strength in a social network like Facebook. Previous works in finding friendship strength in social networks have …

Deep Learning Implementation For Comparison Of User Reviews And Ratings, Nishit Shrestha

UNLV Theses, Dissertations, Professional Papers, and Capstones

Sentiment Analysis is the task of identifying and classifying the sentiment expressed in a piece of text as of positive or negative sentiment and has wide application in E-Commerce. In present time, most e-commerce websites have product review sections, which can be used to identify customer satisfaction/dissatisfaction for their product. In E-COMMERCE websites such as Amazon.com, E-bay.com etc, consumers can submit their reviews along with a specific polarity rating (e.g. 1 to 5 stars at Amazon.com). There is a possibility of mismatch between review submitted and polarity of rating. For Amazon.com, a customer can submit a strongly positive review but …

Lexicons In Sentiment Analytics, B. Yuan, Keng Siau

Research Collection School Of Computing and Information Systems

With the increasing amount of text data, sentiment analytics (SA) is becoming an important tool for text miners. An automated approach is needed to parse the online reviews and comments, and analyze their sentiments. Since lexicon is the most important component in SA, enhancing the quality of lexicons will improve the efficiency and accuracy of sentiment analysis. In this research, we study the effect of coupling a general lexicon with a specialized lexicon (for a specific domain) and its impact on sentiment analysis. Two special domains and one general domain were used. The two special domains are the petroleum domain …

Tango: A Spanish-Based Programming Language, Ashley M. Zegiestowsky

Butler Journal of Undergraduate Research

The first part of this article deals with the creation of my own Spanish-based programming language, Tango, using Spanish key words (instead of English key words). The second part relates to the design and implementation of a compiler that follows the grammar rules outlined in the Tango language in order to successfully lexically analyze, parse, semantically analyze, and generate code for Tango. This article begins with a description of the specific goals achieved in the Tango language, an explanation and brief examples of the Tango Grammar, a high-level overview of the compiler design and data structures used, and concludes with …

Network Exploration Of Correlated Multivariate Protein Data For Alzheimer's Disease Association, Matthew J. Lane

Theses

Alzheimer Disease (AD) is difficult to diagnose by using genetic testing or other traditional methods. Unlike diseases with simple genetic risk components, there exists no single marker determining as to whether someone will develop AD. Furthermore, AD is highly heterogeneous and different subgroups of individuals develop the disease due to differing factors. Traditional diagnostic methods using perceivable cognitive deficiencies are often too little too late due to the brain having suffered damage from decades of disease progression. In order to observe AD at early stages prior to the observation of cognitive deficiencies, biomarkers with greater accuracy are required. By using …

Quantitative Criticism Of Literary Relationships, Joseph P. Dexter, Theodore Katz, Nilesh Tripuraneni, Tathagata Dasgupta, Ajay Kannan, James Brofos, Jorge A. Bonilla Lopez, Lea Schroeder

Dartmouth Scholarship

Authors often convey meaning by referring to or imitating prior works of literature, a process that creates complex networks of literary relationships (“intertextuality”) and contributes to cultural evolution. In this paper, we use techniques from stylometry and machine learning to address subjective literary critical questions about Latin literature, a corpus marked by an extraordinary concentration of intertextuality. Our work, which we term “quantitative criticism,” focuses on case studies involving two influential Roman authors, the playwright Seneca and the historian Livy. We find that four plays related to but distinct from Seneca’s main writings are differentiated from the rest of the …

Eassistant: Cognitive Assistance For Identification And Auto-Triage Of Actionable Conversations, Hamid R. Motahari Nezhad, Kalpa Gunaratna, Juan Cappi

Kno.e.sis Publications

The browser and screen have been the main user interfaces of the Web and mobile apps. The notification mechanism is an evolution in the user interaction paradigm by keeping users updated without checking applications. Conversational agents are posed to be the next revolution in user interaction paradigms. However, without intelligence on the triage of content served by the interaction and content differentiation in applications, interaction paradigms may still place the burden of information overload on users. In this paper, we focus on the problem of intelligent identification of actionable information in the content served by applications, and in particular in …

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 …

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 …