Computer Sciences Commons^™

9th Annual Postdoctoral Science Symposium, University Of Texas Md Anderson Cancer Center Postdoctoral Association

Annual Postdoctoral Science Symposium Abstracts

The mission of the Annual Postdoctoral Science Symposium (APSS) is to provide a platform for talented postdoctoral fellows throughout the Texas Medical Center to present their work to a wider audience. The MD Anderson Postdoctoral Association convened its inaugural Annual Postdoctoral Science Symposium (APSS) on August 4, 2011.

The APSS provides a professional venue for postdoctoral scientists to develop, clarify, and refine their research as a result of formal reviews and critiques of faculty and other postdoctoral scientists. Additionally, attendees discuss current research on a broad range of subjects while promoting academic interactions and enrichment and developing new collaborations.

The Future Of Cybercrime Prevention Strategies: Human Factors And A Holistic Approach To Cyber Intelligence, Sinchul Back, Jennifer Laprade

International Journal of Cybersecurity Intelligence & Cybercrime

New technology is rapidly emerging to fight increasing cybercrime threats, however, there is one important component of a cybercrime that technology cannot always impact and that is human behavior. Unfortunately, humans can be vulnerable and easily deceived making technological advances alone inadequate in the cybercrime fight. Instead, we must take a more holistic approach by using technology and better understanding the human factors that make cybercrime possible. In this issue of the International Journal of Cybersecurity Intelligence and Cybercrime, three studies contribute to our knowledge of human factors and emerging cybercrime technology so that more effective comprehensive cybercrime prevention strategies …

A Test Of Structural Model For Fear Of Crime In Social Networking Sites, Seong-Sik Lee, Kyung-Shick Choi, Sinyong Choi, Elizabeth Englander

International Journal of Cybersecurity Intelligence & Cybercrime

This study constructed a structural model which consists of social demographic factors, experience of victimization, opportunity factors, and social context factors to explain the public’s fear of crime on social networking sites (SNS). The model is based on the risk interpretation model, which predicts that these factors influence users’ fear of crime victimization. Using data from 486 university students in South Korea, an empirically-tested model suggests that sex and age have direct and significant effects on fear of victimization, supporting the vulnerability hypothesis. Among opportunity factors, the level of personal information and the number of offending peers have significant effects …

An Exploratory Perception Analysis Of Consensual And Nonconsensual Image Sharing, Jin Ree Lee, Steven Downing

International Journal of Cybersecurity Intelligence & Cybercrime

Limited research has considered individual perceptions of moral distinctions between consensual and nonconsensual intimate image sharing, as well as decision making parameters around why others might engage in such behavior. The current study conducted a perception analysis using mixed-methods online surveys administered to 63 participants, inquiring into their perceptions of why individuals engage in certain behaviors surrounding the sending of intimate images from friends and partners. The study found that respondents favored the concepts of (1) sharing images with romantic partners over peers; (2) sharing non-intimate images over intimate images; and (3) sharing images with consent rather than without it. …

Blockchain Security: Situational Crime Prevention Theory And Distributed Cyber Systems, Nicholas J. Blasco, Nicholas A. Fett

International Journal of Cybersecurity Intelligence & Cybercrime

The authors laid the groundwork for analyzing the crypto-economic incentives of interconnected blockchain networks and utilize situational crime prevention theory to explain how more secure systems can be developed. Blockchain networks utilize smaller blockchains (often called sidechains) to increase throughput in larger networks. Identified are several disadvantages to using sidechains that create critical exposures to the assets locked on them. Without security being provided by the mainchain in the form of validated exits, sidechains or statechannels which have a bridge or mainchain asset representations are at significant risk of attack. The inability to have a sufficiently high cost to attack …

Technical Report 2019-01: Pupil Labs Eye Tracking User Guide, Joan D. Gannon, Augustine Ubah, Chris Dancy

Other Faculty Research and Publications

No abstract provided.

Do It Like A Syntactician: Using Binary Gramaticality Judgements To Train Sentence Encoders And Assess Their Sensitivity To Syntactic Structure, Pablo Gonzalez Martinez

Dissertations, Theses, and Capstone Projects

The binary nature of grammaticality judgments and their use to access the structure of syntax are a staple of modern linguistics. However, computational models of natural language rarely make use of grammaticality in their training or application. Furthermore, developments in modern neural NLP have produced a myriad of methods that push the baselines in many complex tasks, but those methods are typically not evaluated from a linguistic perspective. In this dissertation I use grammaticality judgements with artificially generated ungrammatical sentences to assess the performance of several neural encoders and propose them as a suitable training target to make models learn …

Applied Deep Learning In Intelligent Transportation Systems And Embedding Exploration, Xiaoyuan Liang

Dissertations

Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization …

Study On The Development Of Mass Based On Safety, Qiyu Yu

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Cooperation In Maritime Search And Rescue Between Democratic People’S Republic Of Korea, The People’S Republic Of China And The Russian Federation, Kwangmyong Ri

Maritime Safety & Environment Management Dissertations (Dalian)

No abstract provided.

Document Images And Machine Learning: A Collaboratory Between The Library Of Congress And The Image Analysis For Archival Discovery (Aida) Lab At The University Of Nebraska, Lincoln, Ne, Yi Liu, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang

School of Computing: Conference and Workshop Papers

This presentation summarized and presented preliminary results from the first weeks of work conducted by the Aida research team in response to Library of Congress funding notice ID 030ADV19Q0274, “The Library of Congress – Pre-processing Pilot.” It includes overviews of projects on historic document segmentation, document classification, document quality assessment, figure and graph extraction from historic documents, text-line extraction from figures, subject and objective quality assesments, and digitization type differentiation.

Anticipating Widespread Augmented Reality: Insights From The 2018 Ar Visioning Workshop, Gregory F. Welch, Gerd Bruder, Peter Squire, Ryan Schubert

Faculty Scholarship and Creative Works

In August of 2018 a group of academic, government, and industry experts in the field of Augmented Reality gathered for four days to consider potential technological and societal issues and opportunities that could accompany a future where AR is pervasive in location and duration of use. This report is intended to summarize some of the most novel and potentially impactful insights and opportunities identified by the group.

Our target audience includes AR researchers, government leaders, and thought leaders in general. It is our intent to share some compelling technological and societal questions that we believe are unique to AR, and …

Map My Murder: A Digital Forensic Study Of Mobile Health And Fitness Applications, Courtney Hassenfeldt, Shabana Baig, Ibrahim Baggili, Xiaolu Zhang

Electrical & Computer Engineering and Computer Science Faculty Publications

The ongoing popularity of health and fitness applications catalyzes

the need for exploring forensic artifacts produced by them. Sensitive

Personal Identifiable Information (PII) is requested by the applications

during account creation. Augmenting that with ongoing

user activities, such as the user’s walking paths, could potentially

create exculpatory or inculpatory digital evidence. We conducted

extensive manual analysis and explored forensic artifacts produced

by (n = 13) popular Android mobile health and fitness applications.

We also developed and implemented a tool that aided in the timely

acquisition and identification of artifacts from the examined applications.

Additionally, our work explored the type of …

A Multimodal Approach To Sarcasm Detection On Social Media, Dipto Das

Graduate Theses/Dissertations

In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly …

Publication And Evaluation Challenges In Games & Interactive Media, Elizabeth L. Lawley

Presentations and other scholarship

Faculty in the fields of games and interactive media face significant challenges in publishing and documenting their scholarly work for evaluation in the tenure and promotion process. These challenges include selecting appropriate publication venues and assigning authorship for works spanning multiple disciplines; archiving and accurately citing collaborative digital projects; and redefining “peer review,” impact, and dissemination in the context of creative digital works. In this paper I describe many of these challenges, and suggest several potential solutions.

Blocks' Network: Redesign Architecture Based On Blockchain Technology, Moataz Hanif

Doctoral Dissertations and Master's Theses

The Internet is a global network that uses communication protocols. It is considered the most important system reached by humanity, which no one can abandon. However, this technology has become a weapon that threatens the privacy of users, especially in the client-server model, where data is stored and managed privately. Additionally, users have no power over their data that store in a private server, which means users’ data may interrupt by government or might be sold via service provider for-profit purposes. Furthermore, blockchain is a technology that we can rely on to solve issues related to client-server model if appropriately …

Analysis Of Social Unrest Events Using Spatio-Temporal Data Clustering And Agent-Based Modelling, Sudeep Basnet

School of Computing: Dissertations, Theses, and Student Research

Social unrest such as appeals, protests, conflicts, fights and mass violence can result from a wide ranging of diverse factors making the analysis of causal relationships challenging, with high complexity and uncertainty. Unrest events can result in significant changes in a society ranging from new policies and regulations to regime change. Widespread unrest often arises through a process of feedback and cascading of a collection of past events over time, in regions that are close to each other. Understanding the dynamics of these social events and extrapolating their future growth will enable analysts to detect or forecast major societal events. …

Law Library Blog (August 2019): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Evaluating Vulnerability To Fake News In Social Networks: A Community Health Assessment Model, Bhavtosh Rath, Wei Gao, Jaideep Srivastava

Research Collection School Of Computing and Information Systems

Understanding the spread of false information in social networks has gained a lot of recent attention. In this paper, we explore the role community structures play in determining how people get exposed to fake news. Inspired by approaches in epidemiology, we propose a novel Community Health Assessment model, whose goal is to understand the vulnerability of communities to fake news spread. We define the concepts of neighbor, boundary and core nodes of a community and propose appropriate metrics to quantify the vulnerability of nodes (individual-level) and communities (group-level) to spreading fake news. We evaluate our model on communities identified using …

Iot Ignorance Is Digital Forensics Research Bliss: A Survey To Understand Iot Forensics Definitions, Challenges And Future Research Directions, Tina Wu, Frank Breitinger, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

Interactions with IoT devices generates vast amounts of personal data that can be used as a source of evidence in digital investigations. Currently, there are many challenges in IoT forensics such as the difficulty in acquiring and analysing IoT data/devices and the lack IoT forensic tools. Besides technical challenges, there are many concepts in IoT forensics that have yet to be explored such as definitions, experience and capability in the analysis of IoT data/devices and current/future challenges. A deeper understanding of these various concepts will help progress the field. To achieve this goal, we conducted a survey which received 70 …

Cybersecurity Education: The Quest To Building Bridge Skills, Andy Igonor, Raymond L. Forbes, Jonathan Mccombs

All Faculty and Staff Scholarship

Today's employers differ in what skills and abilities they believe make for a competent cybersecurity professional; however, they concur on the importance of technical and soft skills, which we collectively refer to as "bridge skills" - in other words, skills needed to bridge employer needs and what higher education teaches. Higher education, on the other hand favors producing a holistic and rounded graduate, with soft skills incorporated into the first one or two years of study. Somewhere between these two dichotomies is a missing link which currently manifests as higher education not meeting the needs of industry relative to cybersecurity …

Industry 4.0: Challenges And Opportunities In Different Countries, Keng Siau, Yingrui Xi, Cui Zou

Research Collection School Of Computing and Information Systems

Along with the rapid development of artificial intelligence (AI), cyber-physical systems (CPSs), big data analytics, and cloud computing, Industry 4.0 — a subset of the fourth Industrial Revolution — has started to emerge and take root in many countries. Many expect that Industry 4.0 will be transformative and revolutionary for multiple industries and countries. Its impact will be much more significant than those of Industry 1.0, 2.0, and 3.0. Most studies and papers on Industry 4.0 have examined its impact on various industries, jobs, and organizations. In this article, we investigate the impact of Industry 4.0 on countries and groups …

Designing The Arriving Refugee Informatics Surveillance And Epidemiology (Arive) System: A Web-Based Electronic Database For Epidemiological Surveillance, William A. Mattingly, Ruth M. Carrico, Timothy L. Wiemken, Robert R. Kelley, Rebecca A. Ford, Rahel Bosson, Kimberley A. Buckner, Julio A. Ramirez

Journal of Refugee & Global Health

Objectives: We design and implement the Arriving Refugee Informatics surVeillance and Epidemiology (ARIVE) system to improve the health of refugees undergoing resettlement and enhance existing health surveillance networks.

Materials and Methods: Using the REDCap electronic data capture software as a basis we create a refugee health database incorporating data from the Center for Disease Control and Prevention’s Electronic Disease Notification (EDN) system and domestic screening data from refugee health care providers.

Results: Domestic screening and EDN refugee health data have been integrated for 13,824 refugees resettled from 35 different countries into the state of Kentucky from the years 2013-2016.

Discussion: …

Synthetic, Yet Natural: Properties Of Wordnet Random Walk Corpora And The Impact Of Rare Words On Embedding Performance, Filip Klubicka, Alfredo Maldonado, Abhijit Mahalunkar, John D. Kelleher

Conference papers

Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find …

The Design And Implementation Of Aida: Ancient Inscription Database And Analytics System, M. Parvez Rashid

School of Computing: Dissertations, Theses, and Student Research

AIDA, the Ancient Inscription Database and Analytic system can be used to translate and analyze ancient Minoan language. The AIDA system currently stores three types of ancient Minoan inscriptions: Linear A, Cretan Hieroglyph and Phaistos Disk inscriptions. In addition, AIDA provides candidate syllabic values and translations of Minoan words and inscriptions into English. The AIDA system allows the users to change these candidate phonetic assignments to the Linear A, Cretan Hieroglyph and Phaistos symbols. Hence the AIDA system provides for various scholars not only a convenient online resource to browse Minoan inscriptions but also provides an analysis tool to explore …

Iamhappy: Towards An Iot Knowledge-Based Cross-Domain Well-Being Recommendation System For Everyday Happiness, Amelia Gyrard, Amit Sheth

Kno.e.sis Publications

Nowadays, healthy lifestyle, fitness, and diet habits have become central applications in our daily life. Positive psychology such as well-being and happiness is the ultimate dream of everyday people’s feelings (even without being aware of it). Wearable devices are being increasingly employed to support well-being and fitness. Those devices produce physiological signals that are analyzed by machines to understand emotions and physical state. The Internetof Things (IoT) technology connects (wearable) devices to the Internet to easily access and process data, even using Web technologies (aka Web of Things).

We design IAMHAPPY, an innovative IoT-based well-being recommendation system to encourage every …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data, Biraj Adhikari

Civil & Environmental Engineering Theses & Dissertations

A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident …

Aggregating Private And Public Web Archives Using The Mementity Framework, Matthew R. Kelly

Computer Science Theses & Dissertations

Web archives preserve the live Web for posterity, but the content on the Web one cares about may not be preserved. The ability to access this content in the future requires the assurance that those sites will continue to exist on the Web until the content is requested and that the content will remain accessible. It is ultimately the responsibility of the individual to preserve this content, but attempting to replay personally preserved pages segregates archived pages by individuals and organizations of personal, private, and public Web content. This is misrepresentative of the Web as it was. While the Memento …

Searching Activity Trajectories With Semantics, Li-Hua Yin, Huiwen Liu

PhD Student’s Publications Collection

With the widespread use of smart phones and mobile Internet, social network users have generated massive geo-tagged tweets, photos and videos to form lots of informative trajectories which reveal not only their spatio-temporal dynamics, but also their activities in the physical world. Existing spatial trajectory query studies mainly focus on analyzing the spatio-temporal properties of the users’ trajectories, while leaving the understanding of their activities largely untouched. In this paper, we incorporate the semantics of the activity information embedded in trajectories into query modelling and processing, with the aim of providing end users more informative and meaningful results. To this …