Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Other Computer Engineering (3)
- Artificial Intelligence and Robotics (1)
- Arts and Humanities (1)
- Biological Engineering (1)
- Biomedical Engineering and Bioengineering (1)
-
- Computational Engineering (1)
- Computer Sciences (1)
- Data Science (1)
- Data Storage Systems (1)
- Digital Communications and Networking (1)
- Digital Humanities (1)
- Other Engineering (1)
- Other Psychology (1)
- Other Public Affairs, Public Policy and Public Administration (1)
- Other Sociology (1)
- Physical Sciences and Mathematics (1)
- Psychology (1)
- Public Affairs, Public Policy and Public Administration (1)
- Public Policy (1)
- Quantitative, Qualitative, Comparative, and Historical Methodologies (1)
- Social Psychology (1)
- Social Statistics (1)
- Social and Behavioral Sciences (1)
- Sociology (1)
- Work, Economy and Organizations (1)
Articles 1 - 6 of 6
Full-Text Articles in Computer Engineering
Deception Detection Across Domains, Languages And Modalities, Subhadarshi Panda
Deception Detection Across Domains, Languages And Modalities, Subhadarshi Panda
Dissertations, Theses, and Capstone Projects
With the increase of deception and misinformation especially in social media, it has become crucial to develop machine learning methods to automatically identify deception. In this dissertation, we identify key challenges underlying text-based deception detection in a cross-domain setting, where we do not have training data in the target domain. We analyze the differences between domains and as a result develop methods to improve cross-domain deception detection. We additionally develop approaches that take advantage of cross-lingual properties to support deception detection across languages. This involves the usage of either multilingual NLP models or translation models. Finally, to better understand multi-modal …
Coded Matrix Multiplication, Xiaodi Fan
Coded Matrix Multiplication, Xiaodi Fan
Dissertations, Theses, and Capstone Projects
Matrix multiplication is a fundamental building block in many machine learning models. As the input matrices may be too large to be multiplied on a single server, it is common to split input matrices into multiple sub-matrices and execute the multiplications on different servers. However, in a distributed infrastructure, it is common to observe stragglers whose performance is significantly lower than other servers at some time. Compared to replicating each task on multiple servers, coded matrix multiplication, i.e., a combination of coding theoretic techniques and distributed matrix multiplication, can tolerate the same number of stragglers with much fewer servers. The …
Bitrdf: Extending Rdf For Bitemporal Data, Di Wu
Bitrdf: Extending Rdf For Bitemporal Data, Di Wu
Dissertations, Theses, and Capstone Projects
The Internet is not only a platform for communication, transactions, and cloud storage, but it is also a large knowledge store where people as well as machines can create, manipulate, infer, and make use of data and knowledge. The Semantic Web was developed for this purpose. It aims to help machines understand the meaning of data and knowledge so that machines can use the data and knowledge in decision making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web which is organized as the Semantic Web Layer Cake. RDF is limited and can only express a binary …
Happiness And Policy Implications: A Sociological View, Sarah M. Kahl
Happiness And Policy Implications: A Sociological View, Sarah M. Kahl
Dissertations, Theses, and Capstone Projects
The World Happiness Report is released every year, ranking each country by who is “happier” and explaining the variables and data they have used. This project attempts to build from that base and create a machine learning algorithm that can predict if a country will be in a “happy” or “could be happier” category. Findings show that taking a broader scope of variables can better help predict happiness. Policy implications are discussed in using both big data and considering social indicators to make better and lasting policies.
A Machine Learning Approach To Predicting The Onset Of Type Ii Diabetes In A Sample Of Pima Indian Women, Meriem Benarbia
A Machine Learning Approach To Predicting The Onset Of Type Ii Diabetes In A Sample Of Pima Indian Women, Meriem Benarbia
Dissertations, Theses, and Capstone Projects
Type II diabetes is a disease that affects how the body regulates and uses sugar (glucose) as a fuel. This chronic disease results in too much sugar circulating in the bloodstream. High blood sugar levels can lead to circulatory, nervous, and immune systems disorders. Machine learning (ML) techniques have proven their strength in diabetes diagnosis. In this paper, we aimed to contribute to the literature on the use of ML methods by examining the value of a number of supervised machine learning algorithms such as logistic regression, decision tree classifiers, random forest classifiers, and support vector classifiers to identify factors …
Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed
Representation Learning For Chemical Activity Predictions, Mohamed S. Ayed
Dissertations, Theses, and Capstone Projects
Computational prediction of a phenotypic response upon the chemical perturbation on a biological system plays an important role in drug discovery and many other applications. Chemical fingerprints derived from chemical structures are a widely used feature to build machine learning models. However, the fingerprints ignore the biological context, thus, they suffer from several problems such as the activity cliff and curse of dimensionality. Fundamentally, the chemical modulation of biological activities is a multi-scale process. It is the genome-wide chemical-target interactions that modulate chemical phenotypic responses. Thus, the genome-scale chemical-target interaction profile will more directly correlate with in vitro and in …