Computer Sciences Commons^™

Fake News Detection On Social Media: A Word Embedding-Based Approach, Muammer Eren Sahin, Chunyang Tang, Mohammad A. Al-Ramahi

Computer Information Systems Faculty Publications

The rapid development of social media, together with the large number of user-generated content on them, has not only connected an unprecedented number of people together to do good stuff, but also has provided convenient platforms to spread misleading pieces of information such as fake news. Existing research has attempted to leverage machine learning to automatically classify fake news. In this paper, we extend such literature by proposing an approach that utilize word embedding and Long Short-Term Memory (LSTM) neural network algorithm. Unlike existing studies, we used two publicly available datasets of news articles to evaluate the proposed model. The ...

Weighted Incremental–Decremental Support Vector Machines For Concept Drift With Shifting Window, Honorius Gâlmeanu, Răzvan Andonie

Computer Science Faculty Scholarship

We study the problem of learning the data samples’ distribution as it changes in time. This change, known as concept drift, complicates the task of training a model, as the predictions become less and less accurate. It is known that Support Vector Machines (SVMs) can learn weighted input instances and that they can also be trained online (incremental–decremental learning). Combining these two SVM properties, the open problem is to define an online SVM concept drift model with shifting weighted window. The classic SVM model should be retrained from scratch after each window shift. We introduce the Weighted Incremental–Decremental ...

"Design For Co-Design" In A Computer Science Curriculum Research-Practice Partnership, Victor R. Lee, Jody Clarke-Midura, Jessica F. Shumway, Mimi Recker

Publications

This paper reports on a study of the dynamics of a Research-Practice Partnership (RPP) oriented around design, specifically the co-design model. The RPP is focused on supporting elementary school computer science (CS) instruction by involving paraprofessional educators and teachers in curricular co-design. A problem of practice addressed is that few elementary educators have backgrounds in teaching CS and have limited available instructional time and budget for CS. The co-design strategy entailed highlighting CS concepts in the mathematics curriculum during classroom instruction and designing computer lab lessons that explored related ideas through programming. Analyses focused on tensions within RPP interaction dynamics ...

Understanding The Assumptions Of An Seir Compartmental Model Using Agentization And A Complexity Hierarchy, Elizabeth Hunter, John D. Kelleher

Articles

Equation-based and agent-based models are popular methods in understanding disease dynamics. Although there are many types of equation-based models, the most common is the SIR compartmental model that assumes homogeneous mixing and populations. One way to understand the effects of these assumptions is by agentization. Equation-based models can be agentized by creating a simple agent-based model that replicates the results of the equationbased model, then by adding complexity to these agentized models it is possible to break the assumptions of homogeneous mixing and populations and test how breaking these assumptions results in different outputs. We report a set of experiments ...

Medical Image Segmentation With Deep Convolutional Neural Networks, Chuanbo Wang

Theses and Dissertations

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and ...

Data Collection And Machine Learning Methods For Automated Pedestrian Facility Detection And Mensuration, Joseph Bailey Luttrell Iv

Dissertations

Large-scale collection of pedestrian facility (crosswalks, sidewalks, etc.) presence data is vital to the success of efforts to improve pedestrian facility management, safety analysis, and road network planning. However, this kind of data is typically not available on a large scale due to the high labor and time costs that are the result of relying on manual data collection methods. Therefore, methods for automating this process using techniques such as machine learning are currently being explored by researchers. In our work, we mainly focus on machine learning methods for the detection of crosswalks and sidewalks from both aerial and street-view ...

Syntax Exercises And Their Effect On Computational Thinking, Marina Johnson

All Graduate Theses and Dissertations

Abstract—Job opportunities and the need for programmers are increasing. Companies are looking for new hires who have the ability to learn how to learn, who have computational thinking skills. Student dropout rate in computer science is the highest among college majors. Educators are striving to find a way to teach efficiently and effectively the technical and the problem solving skills students need. In this paper we will be studying the effects of syntax exercises on a subject’s ability to think computationally and precisely. We tested our process on professionals and students. Half of the professionals were in the ...

Submodularity And Local Search Approaches For Maximum Capture Problems Under Generalized Extreme Value Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai

Research Collection School Of Computing and Information Systems

We study the maximum capture problem in facility location under random utility models, i.e., the problem of seeking to locate new facilities in a competitive market such that the captured user demand is maximized, assuming that each customer chooses among all available facilities according to a random utility maximization model. We employ the generalized extreme value (GEV) family of discrete choice models and show that the objective function in this context is monotonic and submodular. This finding implies that a simple greedy heuristic can always guarantee a (1−1/e) approximation solution. We further develop a new algorithm combining ...

Extract Human Mobility Patterns Powered By City Semantic Diagram, Zhangqing Shan, Weiwei Shan, Baihua Zheng

Research Collection School Of Computing and Information Systems

With widespread deployment of GPS devices, massive spatiotemporal trajectories became more accessible. This booming trend paved the solid data ground for researchers to discover the regularities or patterns of human mobility. However, there are still three challenges in semantic pattern extraction including semantic absence, semantic bias and semantic complexity. In this paper, we invent and apply a novel data structure namely City Semantic Diagram to overcome above three challenges. First, our approach resolves semantic absence by exactly identifying semantic behaviours from raw trajectories. Second, the delicate design of semantic purification helps us to detect semantic complexity from human mobility. Third ...

Reduced Fuel Emissions Through Connected Vehicles And Truck Platooning, Paul D. Brummitt

Electronic Theses and Dissertations

Vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication enable the sharing, in real time, of vehicular locations and speeds with other vehicles, traffic signals, and traffic control centers. This shared information can help traffic to better traverse intersections, road segments, and congested neighborhoods, thereby reducing travel times, increasing driver safety, generating data for traffic planning, and reducing vehicular pollution. This study, which focuses on vehicular pollution, used an analysis of data from NREL, BTS, and the EPA to determine that the widespread use of V2V-based truck platooning—the convoying of trucks in close proximity to one another so as to reduce air ...

Deep Learning Edge Detection In Image Inpainting, Zheng Zheng

Electronic Theses, Projects, and Dissertations

In recent years, deep learning has grown rapidly, and it has been creatively implemented for various applications. In 2019, deep learning based EdgeConnect image inpainting algorithm came out and occupied a place in the image inpainting field. Unlike traditional image inpainting methods which mainly read and use the color information of the remaining part of the image to fill the missing regions of the image, EdgeConnect uses the innovative edge-first and color-next approach. It uses an edge detector to generate an edge map of an image with missing regions, then the missing edges are completed by an edge model, finally ...

Programming Process, Patterns And Behaviors: Insights From Keystroke Analysis Of Cs1 Students, Raj Shrestha

All Graduate Theses and Dissertations

With all the experiences and knowledge, I take programming as granted. But learning to program is still difficult for a lot of introductory programming students. This is also one of the major reasons for a high attrition rate in CS1 courses. If instructors were able to identify struggling students then effective interventions can be taken to help them. This thesis is a research done on programming process data that can be collected non-intrusively from CS1 students when they are programming. The data and their findings can be leveraged in understanding students’ thought process, detecting patterns and identifying behaviors that could ...

Academic Hats And Ice Cream: Two Optimization Problems, Valery F. Ochkov, Yulia V. Chudova

Journal of Humanistic Mathematics

This article describes the use of computer software to optimize the design of an academic hat and an ice cream cone!

Simulating Sub-Threshold Communication Channels Through Neurons, Richard Maina

Computer Science and Engineering: Theses, Dissertations, and Student Research

Molecular Communication is an emerging paradigm with the potential to revolutionize the technology behind wearable and implantable devices and the broad range of functions they support, from tracking physical activity to medical diagnostics. This can be achieved through intra-body communication networks that take advantage of natural biological processes as a means of transmitting, propagating and receiving information. In this thesis we focus particularly on using the neuron as a means to facilitate information transfer for interconnected wearable or implantable devices through a technique known as sub-threshold electrical stimulation. We develop upon a prior work by introducing a linear model of ...

Addressing Ethical Issues In The Design Of Smart Home Technology For Older Adults And People With Disabilities., Jonathan Turner, Dympna O'Sullivan, Damian Gordon, Yannis Stavrakakis, Brian Keegan, Emma Murphy

Articles

Unique ethical, privacy and safety implications arise for people who are reliant on home-based smart technology due to health conditions or disabilities. In this paper we highlight a need for a reflective, inclusive ethical framework that encompasses the life cycle of smart home technology. We present key ethical considerations for smart home technology for older adults and people with disabilities and argue for ethical frameworks which combine these key considerations with existing models of design and development.

2-Dimensional String Problems: Data Structures And Quantum Algorithms, Dhrumilkumar Patel

LSU Master's Theses

The field of stringology studies algorithms and data structures used for processing strings efficiently. The goal of this thesis is to investigate 2-dimensional (2D) variants of some fundamental string problems, including \textit{Exact Pattern Matching} and \textit{Longest Common Substring}.

In the 2D pattern matching problem, we are given a matrix $\M[1\dd n,1\dd n]$ that consists of $N = n \times n$ symbols drawn from an alphabet $\Sigma$ of size $\sigma$. The query consists of a $ m \times m$ square matrix $\PP[1\dd m, 1\dd m]$ drawn from the same alphabet, and the task is ...

Developing Artificial Intelligence And Machine Learning To Support Primary Care Research And Practice, Jacqueline K. Kueper

Electronic Thesis and Dissertation Repository

This thesis was motivated by the potential to use "everyday data", especially that collected in electronic health records (EHRs) as part of healthcare delivery, to improve primary care for clients facing complex clinical and/or social situations. Artificial intelligence (AI) techniques can identify patterns or make predictions with these data, producing information to learn about and inform care delivery. Our first objective was to understand and critique the body of literature on AI and primary care. This was achieved through a scoping review wherein we found the field was at an early stage of maturity, primarily focused on clinical decision ...

Computational Models To Detect Radiation In Urban Environments: An Application Of Signal Processing Techniques And Neural Networks To Radiation Data Analysis, Jose Nicolas Gachancipa

Beyond: Undergraduate Research Journal

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This article presents a computational ...

Scheduling Many-Task Computing Applications For A Hybrid Cloud, Shifat Perveen Mithila

LSU Doctoral Dissertations

A centralized scheduler can become a bottleneck for placing the tasks of a many-task application on heterogeneous cloud resources. Previously, it was demonstrated that a decentralized vector scheduling approach based on performance measurements can be used successfully for this task placement scenario. In this dissertation, we extend this approach to task placement based on latency measurements. Each node collects performance metrics from its neighbors on an overlay graph, measures the communication latency, and then makes local decisions on where to move tasks. We present a decentralized and a centralized algorithm for configuring the overlay graph based on latency measurements and ...

A Nature-Inspired Approach For Scenario-Based Validation Of Autonomous Systems, Quentin Goss, Mustafa Akbas

Beyond: Undergraduate Research Journal

Scenario-based approaches are cost and time effective solutions to autonomous cyber-physical system testing to identify bugs before costly methods such as physical testing in a controlled or uncontrolled environment. Every bug in an autonomous cyber-physical system is a potential safety risk. This paper presents a scenario-based method for finding bugs and estimating boundaries of the bug profile. The method utilizes a nature-inspired approach adapting low discrepancy sampling with local search. Extensive simulations demonstrate the performance of the approach with various adaptations.