Computer Sciences Commons^™

Artificial Intelligence And Role-Reversible Judgment, Kiel Brennan-Marquez, Stephen E. Henderson

Faculty Articles

As intelligent machines begin more generally outperforming human experts, why should humans remain ‘in the loop’ of decision-making? One common answer focuses on outcomes: relying on intuition and experience, humans are capable of identifying interpretive errors—sometimes disastrous errors—that elude machines. Though plausible today, this argument will wear thin as technology evolves. Here, we seek out sturdier ground: a defense of human judgment that focuses on the normative integrity of decision-making. Specifically, we propose an account of democratic equality as ‘role-reversibility.’ In a democracy, those tasked with making decisions should be susceptible, reciprocally, to the impact of decisions; there ought to …

Speech Interfaces And Pilot Performance: A Meta-Analysis, Kenneth A. Ward

International Journal of Aviation, Aeronautics, and Aerospace

As the aviation industry modernizes, new technology and interfaces must support growing aircraft complexity without increasing pilot workload. Natural language processing presents just such a simple and intuitive interface, yet the performance implications for use by pilots remain unknown. A meta-analysis was conducted to understand performance effects of using speech and voice interfaces in a series of pilot task analogs. The inclusion criteria selected studies that involved participants performing a demanding primary task, such as driving, while interacting with a vehicle system to enter numbers, dial radios, or enter a navigation destination. Compared to manual system interfaces, voice interfaces reduced …

Hierarchical Cluster Analysis: A New Type Of Ranking Criteria Based On Arwu Ranking Data, Zhengshuo Li

Dissertations

The advent of big data leads to many applications of Machine Learning techniques. University rankings is one of the applicable domains, which is currently playing a crucial role in the assessment of the universities' performance. Currently, the rankings are usually carried out by some authoritative ranking institutions by means of weighting techniques and the results are conveyed in numerical rankings. Three of the most famous university ranking institutions have been introduced from a technical perspective. However, these institutions have been proven to be subjective in relation to their data selection and weighting method.

Using Supervised Learning To Predict English Premier League Match Results From Starting Line-Up Player Data, Runzuo Yang

Dissertations

Soccer is one of the most popular sports around the world. Many people, whether they are a fan of a soccer team, a player of online soccer games or even the professional coach of a soccer team, will attempt to use some relevant data to predict the result of a match. Many of these kinds of prediction models are built based on data from the match itself, such as the overall number of shots, yellow or red cards, fouls committed, etc. of the home and away teams. However, this research attempted to predict soccer game results (win, draw or loss) …

Predicting Violent Crime Reports From Geospatial And Temporal Attributes Of Us 911 Emergency Call Data, Vincent Corcoran

Dissertations

The aim of this study is to create a model to predict which 911 calls will result in crime reports of a violent nature. Such a prediction model could be used by the police to prioritise calls which are most likely to lead to violent crime reports. The model will use geospatial and temporal attributes of the call to predict whether a crime report will be generated. To create this model, a dataset of characteristics relating to the neighbourhood where the 911 call originated will be created and combined with characteristics related to the time of the 911 call. Geospatial …

Enhancing Partially Labelled Data: Self Learning And Word Vectors In Natural Language Processing, Eamon Mcentee

Dissertations

There has been an explosion in unstructured text data in recent years with services like Twitter, Facebook and WhatsApp helping drive this growth. Many of these companies are facing pressure to monitor the content on their platforms and as such Natural Language Processing (NLP) techniques are more important than ever. There are many applications of NLP ranging from spam filtering, sentiment analysis of social media, automatic text summarisation and document classification.

Performance Comparison Of Hybrid Cnn-Svm And Cnn-Xgboost Models In Concrete Crack Detection, Sahana Thiyagarajan

Dissertations

Detection of cracks mainly has been a sort of essential step in visual inspection involved in construction engineering as it is the commonly used building material and cracks in them is an early sign of de-basement. It is hard to find cracks by a visual check for the massive structures. So, the development of crack detecting systems generally has been a critical issue. The utilization of contextual image processing in crack detection is constrained, as image data usually taken under real-world situations vary widely and also includes the complex modelling of cracks and the extraction of handcrafted features. Therefore the …

Speech Enhancement Algorithm Based On Super-Gaussian Modeling And Orthogonal Polynomials, Basheera M. Mahmmod, Abd Rahman Ramli, Thar Baker, Feras Al-Obeidat, Sadiq H. Abdulhussain, Wissam A. Jassim

All Works

© 2020 Lippincott Williams and Wilkins. All rights reserved. Different types of noise from the surrounding always interfere with speech and produce annoying signals for the human auditory system. To exchange speech information in a noisy environment, speech quality and intelligibility must be maintained, which is a challenging task. In most speech enhancement algorithms, the speech signal is characterized by Gaussian or super-Gaussian models, and noise is characterized by a Gaussian prior. However, these assumptions do not always hold in real-life situations, thereby negatively affecting the estimation, and eventually, the performance of the enhancement algorithm. Accordingly, this paper focuses on …

A Novel Quality And Reliability-Based Approach For Participants' Selection In Mobile Crowdsensing, May El Barachi, Assane Lo, Sujith Samuel Mathew, Kiyan Afsari

All Works

© 2013 IEEE. With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants' mobility, render the task of participants' selection, a difficult task. In this paper, we propose a comprehensive and practical …

Applying Text Analytics To Derive Value From Blog Posts, Ivan Tang

Honors College Theses

No abstract provided.

Spectre: Attack And Defense, Rae Harris

Scripps Senior Theses

Modern processors use architecture like caches, branch predictors, and speculative execution in order to maximize computation throughput. For instance, recently accessed memory can be stored in a cache so that subsequent accesses take less time. Unfortunately microarchitecture-based side channel attacks can utilize this cache property to enable unauthorized memory accesses. The Spectre attack is a recent example of this attack.

The Spectre attack is particularly dangerous because the vulnerabilities that it exploits are found in microprocessors used in billions of current systems. It involves the attacker inducing a victim’s process to speculatively execute code with a malicious input and store …

Walking With A Robotic Exoskeleton Does Not Mimic Natural Gait: A Within-Subjects Study, Chad Swank, Sharon Wang-Price, Fan Gao, Sattam Almutairi

Kinesiology and Health Promotion Faculty Publications

Background: Robotic exoskeleton devices enable individuals with lower extremity weakness to stand up and walk over ground with full weight-bearing and reciprocal gait. Limited information is available on how a robotic exoskeleton affects gait characteristics.

Objective: The purpose of this study was to examine whether wearing a robotic exoskeleton affects temporospatial parameters, kinematics, and muscle activity during gait.

Methods: The study was completed by 15 healthy adults (mean age 26.2 [SD 8.3] years; 6 males, 9 females). Each participant performed walking under 2 conditions: with and without wearing a robotic exoskeleton (EKSO). A 10-camera motion analysis system synchronized with 6 …

Contextualizing Sexual Assault Data Collection On College Campuses: A Socio-Technical Approach, Anushikha Sharma

Honors Theses

Sexual assault is a rampant issue on college campuses in the United States. Colleges and universities use a variety of survey instruments to collect data regarding sexual assault as a means to improve campus culture, policies, and resources. These instruments contain a wealth of associated information in the form of metadata, that is, data about data.

This project takes a human-centered socio-technical approach to understanding the data collection processes associated with sexual assault, specifically, on the campus of Bucknell University. By identifying the underlying metadata within the data collection processes, this research contextualizes and critiques the process of data collection, …

Bridging Act-R And Project Malmo, Developing Models Of Behavior In Complex Environments, David M. Schwartz

Honors Theses

Cognitive architectures such as ACT-R provide a system for simulating the mind and human behavior. On their own they model decision making of an isolated agent. However, applying a cognitive architecture to a complex environment yields more interesting results about how people make decisions in more realistic scenarios. Furthermore, cognitive architectures enable researchers to study human behavior in dangerous tasks which cannot be tested because they would harm participants. Nonetheless, these architectures aren’t commonly applied to such environments as they don’t come with one. It is left to the researcher to develop a task environment for their model. The difficulty …

A Deep Learning Framework For Predicting Cyber Attacks Rates, Xing Fang, Maochao Xu, Shouhuai Xu, Peng Zhao

Faculty Publications - Information Technology

Like how useful weather forecasting is, the capability of forecasting or predicting cyber threats can never be overestimated. Previous investigations show that cyber attack data exhibits interesting phenomena, such as long-range dependence and high nonlinearity, which impose a particular challenge on modeling and predicting cyber attack rates. Deviating from the statistical approach that is utilized in the literature, in this paper we develop a deep learning framework by utilizing the bi-directional recurrent neural networks with long short-term memory, dubbed BRNN-LSTM. Empirical study shows that BRNN-LSTM achieves a significantly higher prediction accuracy when compared with the statistical approach.

Cidf: A Clustering-Based Interaction-Driven Friending Algorithm For The Next-Generation Social Networks, Aadil Alshammari, Abdelmounaam Rezgui

Faculty Publications - Information Technology

Online social networks, such as Facebook, have been massively growing over the past decade. Recommender algorithms are a key factor that contributes to the success of social networks. These algorithms, such as friendship recommendation algorithms, are used to suggest connections within social networks. Current friending algorithms are built to generate new friendship recommendations that are most likely to be accepted. Yet, most of them are weak connections as they do not lead to any interactions. Facebook is well known for its Friends-of-Friends approach which recommends familiar people. This approach has a higher acceptance rate but the strength of the connections, …

Medical Data Visual Synchronization And Information Interaction Using Internet-Based Graphics Rendering And Message-Oriented Streaming, Qi Zhang

Faculty Publications - Information Technology

The rapid technology advances in medical devices make possible the generation of vast amounts of data, which contain massive quantities of diagnostic information. Interactively accessing and sharing the acquired data on the Internet is critically important in telemedicine. However, due to the lack of efficient algorithms and high computational cost, collaborative medical data exploration on the Internet is still a challenging task in clinical settings. Therefore, we develop a web-based medical image rendering and visual synchronization software platform, in which novel algorithms are created for parallel data computing and image feature enhancement, where Node.js and Socket.IO libraries are utilized to …

Characterizations Of Certain Recently Introduced Discrete Distributions, Gholamhossein G. Hamedani

Mathematics, Statistics and Computer Science Faculty Research and Publications

Characterizations of certain recently introduced discrete distributions are presented to complete, in some way, the works cited in the References.

Predicting Adhd Using Eye Gaze Metrics Indexing Working Memory Capacity, Anne M.P. Michalek, Gavindya Jayawardena, Sampath Jayarathna

Communication Disorders & Special Education Faculty Publications

ADHD is being recognized as a diagnosis that persists into adulthood impacting educational and economic outcomes. There is an increased need to accurately diagnose this population through the development of reliable and valid outcome measures reflecting core diagnostic criteria. For example, adults with ADHD have reduced working memory capacity (WMC) when compared to their peers. A reduction in WMC indicates attention control deficits which align with many symptoms outlined on behavioral checklists used to diagnose ADHD. Using computational methods, such as machine learning, to generate a relationship between ADHD and measures of WMC would be useful to advancing our understanding …

Evolved Parameterized Selection For Evolutionary Algorithms, Samuel Nathan Richter

Masters Theses

"Selection functions enable Evolutionary Algorithms (EAs) to apply selection pressure to a population of individuals, by regulating the probability that an individual's genes survive, typically based on fitness. Various conventional fitness based selection functions exist, each providing a unique method of selecting individuals based on their fitness, fitness ranking within the population, and/or various other factors. However, the full space of selection algorithms is only limited by max algorithm size, and each possible selection algorithm is optimal for some EA configuration applied to a particular problem class. Therefore, improved performance is likely to be obtained by tuning an EA's selection …

Impact Of Framing And Base Size Of Computer Security Risk Information On User Behavior, Xinhui Zhan

Masters Theses

"This research examines the impact of framing and base size of computer security risk information on users' risk perceptions and behavior (i.e., download intention and download decision). It also examines individual differences (i.e., demographic factors, computer security awareness, Internet structural assurance, self-efficacy, and general risk-taking tendencies) associated with users' computer security risk perceptions. This research draws on Prospect Theory, which is a theory in behavioral economics that addresses risky decision-making, to generate hypotheses related to users' decision-making in the computer security context. A 2 x 3 mixed factorial experimental design (N = 178) was conducted to assess the effect of …

Advanced Techniques For Improving Canonical Genetic Programming, Adam Tyler Harter

Masters Theses

"Genetic Programming (GP) is a type of Evolutionary Algorithm (EA) commonly employed for automated program generation and model identification. Despite this, GP, as most forms of EA's, is plagued by long evaluation times, and is thus generally reserved for highly complex problems. Two major impacting factors for the runtime are the heterogeneous evaluation time for the individuals and the choice of algorithmic primitives. The first paper in this thesis utilizes Asynchronous Parallel Evolutionary Algorithms (APEA) for reducing the runtime by eliminating the need to wait for an entire generation to be evaluated before continuing the search. APEA is applied to …

Design And Implementation Of Applications Over Delay Tolerant Networks For Disaster And Battlefield Environment, Karthikeyan Sachidanandam

Masters Theses

"In disaster/battlefield applications, there may not be any centralized network that provides a mechanism for different nodes to connect with each other to share important data. In such cases, we can take advantage of an opportunistic network involving a substantial number of mobile devices that can communicate with each other using Bluetooth and Google Nearby Connections API(it uses Bluetooth, Bluetooth Low Energy (BLE), and Wi-Fi hotspots) when they are close to each other. These devices referred to as nodes form a Delay Tolerant Network (DTN), also known as an opportunistic network. As suggested by its name, DTN can tolerate delays …

Controlled Switching In Kalman Filtering And Iterative Learning Controls, He Li

Masters Theses

“Switching is not an uncommon phenomenon in practical systems and processes, for examples, power switches opening and closing, transmissions lifting from low gear to high gear, and air planes crossing different layers in air. Switching can be a disaster to a system since frequent switching between two asymptotically stable subsystems may result in unstable dynamics. On the contrary, switching can be a benefit to a system since controlled switching is sometimes imposed by the designers to achieve desired performance. This encourages the study of system dynamics and performance when undesired switching occurs or controlled switching is imposed. In this research, …

Strategies That Mitigate It Infrastructure Demands Produced By Student Byod Usa, Martha Dunne

Walden Dissertations and Doctoral Studies

The use of bring your own devices (BYOD) is a global phenomenon, and nowhere is it more evident than on a college campus. The use of BYOD on academic campuses has grown and evolved through time. The purpose of this qualitative multiple case study was to identify the successful strategies used by chief information officers (CIOs) to mitigate information technology infrastructure demands produced by student BYOD usage. The diffusion of innovation model served as the conceptual framework. The population consisted of CIOs from community colleges within North Carolina. The data collection process included semistructured, in-depth face-to-face interviews with 9 CIOs …

Probabilistic Algorithms, Lean Methodology Techniques, And Cell Optimization Results, Michael Mccurrey

Walden Dissertations and Doctoral Studies

There is a significant technology deficiency within the U.S. manufacturing industry compared to other countries. To adequately compete in the global market, lean manufacturing organizations in the United States need to look beyond their traditional methods of evaluating their processes to optimize their assembly cells for efficiency. Utilizing the task-technology fit theory this quantitative correlational study examined the relationships among software using probabilistic algorithms, lean methodology techniques, and manufacturer cell optimization results. Participants consisted of individuals performing the role of the systems analyst within a manufacturing organization using lean methodologies in the Southwestern United States. Data were collected from 118 …

If The Legislature Had Been Serious About Data Privacy..., Fred H. Cate, Christopher Kuner, Orla Lynskey, Christopher Millard, Nora Ni Loideain, Dan Jerker B. Svantesson

Articles by Maurer Faculty

No abstract provided.

Hard: A Heterogeneity-Aware Replica Deletion For Hdfs, Hilmi Egemen Ciritoglu, John Murphy, Christina Thorpe

Articles

The Hadoop distributed fle system (HDFS) is responsible for storing very large datasets reliably on clusters of commodity machines. The HDFS takes advantage of replication to serve data requested by clients with high throughput. Data replication is a trade-of between better data availability and higher disk usage. Recent studies propose diferent data replication management frameworks that alter the replication factor of fles dynamically in response to the popularity of the data, keeping more replicas for in-demand data to enhance the overall performance of the system. When data gets less popular, these schemes reduce the replication factor, which changes the data …

Bigger Versus Similar: Selecting A Background Corpus For First Story Detection Based On Distributional Similarity, Fei Wang, Robert J. Ross, John D. Kelleher

Articles

The current state of the art for First Story Detection (FSD) are nearest neighbourbased models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus. Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution. However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale …

Cs1: How Will They Do? How Can We Help? A Decade Of Research And Practice, Keith Quille, Susan Bergin

Articles

Background and Context: Computer Science attrition rates (in the western world) are very concerning, with a large number of students failing to progress each year. It is well acknowledged that a significant factor of this attrition, is the students’ difficulty to master the introductory programming module, often referred to as CS1.

Objective: The objective of this article is to describe the evolution of a prediction model named PreSS (Predict Student Success) over a 13-year period (2005–2018).

Method: This article ties together, the PreSS prediction model; pilot studies; a longitudinal, multi-institutional re-validation and replication …