Twitter And Disasters: A Social Resilience Fingerprint,
2019
Purdue University
Twitter And Disasters: A Social Resilience Fingerprint, Benjamin A. Rachunok, Jackson B. Bennett, Roshanak Nateghi
Purdue University Libraries Open Access Publishing Fund
Understanding the resilience of a community facing a crisis event is critical to improving its adaptive capacity. Community resilience has been conceptualized as a function of the resilience of components of a community such as ecological, infrastructure, economic, and social systems, etc. In this paper, we introduce the concept of a “resilience fingerprint” and propose a multi-dimensional method for analyzing components of community resilience by leveraging existing definitions of community resilience with data from the social network Twitter. Twitter data from 14 events are analyzed and their resulting resilience fingerprints computed. We compare the fingerprints between events and show that …
Environmental Monitoring Using A Drone-Enabled Wireless Sensor Network,
2019
James Madison University
Environmental Monitoring Using A Drone-Enabled Wireless Sensor Network, Gina Valentino, Laura Yates, Brooke Potter
Senior Honors Projects, 2010-2019
Water quality monitoring traditionally occurs via resource intensive field surveys, such as when a researcher manually collects data in a stream. Limiting factors such as time, money, and accessibility often result in less oversight of impaired water bodies, significantly threatening ecosystemic health and related ecosystem services. According to the United States Environmental Protection Agency, 84% of rivers and streams within the United States remain unassessed, resulting in significant lapses in available data. Such lapses prohibit efficient and effective monitoring, restoration, and conservation efforts throughout the United States. The objective of this project was to employ an unmanned aerial vehicle to …
The Future Robo-Advisor,
2019
Singapore Management University
The Future Robo-Advisor, Catalin Burlacu
MITB Thought Leadership Series
The accelerated digitalisation of both people and business around the world today is having a huge impact on the investment management and advisory space. The addition of new and vastly larger data sets, as well as exponentially more sophisticated analytical tools to turn that data into usable information is constantly changing the way investments are decided on, made and managed.
Different Approaches To Blurring Digital Images And Their Effect On Facial Detection,
2019
University of Arkansas, Fayetteville
Different Approaches To Blurring Digital Images And Their Effect On Facial Detection, Erich-Matthew Pulfer
Computer Science and Computer Engineering Undergraduate Honors Theses
The purpose of this thesis is to analyze the usage of multiple image blurring techniques and determine their effectiveness in combatting facial detection algorithms. This type of analysis is anticipated to reveal potential flaws in the privacy expected from blurring images or, rather, portions of images. Three different blurring algorithms were designed and implemented: a box blurring method, a Gaussian blurring method, and a differential privacy-based pixilation method. Datasets of images were collected from multiple sources, including the AT&T Database of Faces. Each of these three methods were implemented via their own original method, but, because of how common they …
Identifying Fake News Using Emotion Analysis,
2019
University of Arkansas, Fayetteville
Identifying Fake News Using Emotion Analysis, Brady Gilleran
Computer Science and Computer Engineering Undergraduate Honors Theses
This paper presents research applying Emotional Analysis to “Fake News” and “Real News” articles to investigate whether or not there is a difference in the emotion used in these two types of news articles. The paper reports on a dataset for Fake and Real News that we created, and the natural language processing techniques employed to process the collected text. We use a lexicon that includes predefined words for eight emotions (anger, anticipation, disgust, fear, surprise, sadness, joy, trust) to measure the emotional impact in each of these eight dimensions. The results of the emotion analysis are used as features …
Applications Of Fog Computing In Video Streaming,
2019
University of Arkansas, Fayetteville
Applications Of Fog Computing In Video Streaming, Kyle Smith
Computer Science and Computer Engineering Undergraduate Honors Theses
The purpose of this paper is to show the viability of fog computing in the area of video streaming in vehicles. With the rise of autonomous vehicles, there needs to be a viable entertainment option for users. The cloud fails to address these options due to latency problems experienced during high internet traffic. To improve video streaming speeds, fog computing seems to be the best option. Fog computing brings the cloud closer to the user through the use of intermediary devices known as fog nodes. It does not attempt to replace the cloud but improve the cloud by allowing faster …
Prototyping A Capacitive Sensing Device For Gesture Recognition,
2019
University of Arkansas, Fayetteville
Prototyping A Capacitive Sensing Device For Gesture Recognition, Chenglong Lin
Computer Science and Computer Engineering Undergraduate Honors Theses
Capacitive sensing is a technology that can detect proximity and touch. It can also be utilized to measure position and acceleration of gesture motions. This technology has many applications, such as replacing mechanical buttons in a gaming device interface, detecting respiration rate without direct contact with the skin, and providing gesture sensing capability for rehabilitation devices. In this thesis, an approach to prototype a capacitive gesture sensing device using the Eagle PCB design software is demonstrated. In addition, this paper tested and evaluated the resulting prototype device, validating the effectiveness of the approach.
Normally-Off Computing Design Methodology Using Spintronics: From Devices To Architectures,
2019
University of Central Florida
Normally-Off Computing Design Methodology Using Spintronics: From Devices To Architectures, Arman Roohi
Electronic Theses and Dissertations
Energy-harvesting-powered computing offers intriguing and vast opportunities to dramatically transform the landscape of Internet of Things (IoT) devices and wireless sensor networks by utilizing ambient sources of light, thermal, kinetic, and electromagnetic energy to achieve battery-free computing. In order to operate within the restricted energy capacity and intermittency profile of battery-free operation, it is proposed to innovate Elastic Intermittent Computation (EIC) as a new duty-cycle-variable computing approach leveraging the non-volatility inherent in post-CMOS switching devices. The foundations of EIC will be advanced from the ground up by extending Spin Hall Effect Magnetic Tunnel Junction (SHE-MTJ) device models to realize SHE-MTJ-based …
Optimization Of Ultra-Low Power Application-Specific Asynchronous Deep Learning Integrated Circuit Design,
2019
University of Arkansas, Fayetteville
Optimization Of Ultra-Low Power Application-Specific Asynchronous Deep Learning Integrated Circuit Design, Cole Sherrill
Computer Science and Computer Engineering Undergraduate Honors Theses
The Internet of Things (IoT) consists of all devices connected to the internet, including battery-powered devices like surveillance cameras and smart watches. IoT devices are often idle, making leakage power a crucial design constraint. Currently, there are only a few low-power application-specific processors for deep learning. Recently, the Trustable Logic Circuit Design (TruLogic) laboratory at the UofA designed an asynchronous Convolutional Neural Network (CNN) system. However, the original design suffered from delay-sensitivity issues undermining its reliable operation. The aim of this thesis research is to modify the existing CNN circuit to achieve increased reliability and to optimize the improved design …
An Explainable Sequence-Based Deep Learning Predictor With Applications To Song Recommendation And Text Classification.,
2019
University of Louisville
An Explainable Sequence-Based Deep Learning Predictor With Applications To Song Recommendation And Text Classification., Khalil Damak
Electronic Theses and Dissertations
Streaming applications are now the predominant tools for listening to music. What makes the success of such software is the availability of songs and especially their ability to provide users with relevant personalized recommendations. State of the art music recommender systems mainly rely on either Matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based filtering or predict the next user interaction (listening to a song) using a memory-based deep learning structure that learns from temporal sequences of user actions. Despite advances in deep learning models for song recommendation systems, none has taken …
Smart Home Audio Assistant,
2019
Washington University in St. Louis
Smart Home Audio Assistant, Xipeng Wang
All Computer Science and Engineering Research
This report introduces an audio processing algorithm. It provides a way to access smart devices using audio. Although there are many audio assistants already on the market, most of them will not be able to control the smart devices. Therefore, this new system presented in this report will provide a way to analysis the customer’s questions. Then the algorithm will be able to query smart device information, modify the schedule or provide the reason for some arrangement.
Debiasing The Human-Recommender System Feedback Loop In Collaborative Filtering,
2019
University of Louisville
Debiasing The Human-Recommender System Feedback Loop In Collaborative Filtering, Wenlong Sun, Sami Khenissi, Olfa Nasraoui, Patrick Shafto
Faculty and Staff Scholarship
Recommender Systems (RSs) are widely used to help online users discover products, books, news, music, movies, courses, restaurants,etc. Because a traditional recommendation strategy always shows the most relevant items (thus with highest predicted rating), traditional RS’s are expected to make popular items become even more popular and non-popular items become even less popular which in turn further divides the haves (popular) from the have-nots (un-popular). Therefore, a major problem with RSs is that they may introduce biases affecting the exposure of items, thus creating a popularity divide of items during the feedback loop that occurs with users, and this may …
Crude Oil Price Prediction With Decision Tree Based Regression Approach,
2019
Fairfield University
Crude Oil Price Prediction With Decision Tree Based Regression Approach, Engu Chen, Xin James He
Journal of International Technology and Information Management
Crude oil is an essential commodity for industry and the prediction of its price is crucial for many business entities and government organizations. While there have been quite a few conventional statistical models to forecast oil prices, we find that there is not much research using decision tree models to predict crude oil prices. In this research, we develop decision tree models to forecast crude oil prices. In addition to historical crude oil price time series data, we also use some predictor variables that would potentially affect crude oil prices, including crude oil demand and supply, and monthly GDP and …
Ipv6 Diffusion Milestones: Assessing The Quantity And Quality Of Adoption,
2019
East Carolina University
Ipv6 Diffusion Milestones: Assessing The Quantity And Quality Of Adoption, John Pickard, Mark Angolia, Dale Drummond
Journal of International Technology and Information Management
There are currently two versions of Internet Protocol (IP) in use today, IP version 4 (IPv4) and IP version 6 (IPv6). The original version, IPv4, was standardized in the early 1980s as part of the Defense Advanced Research Project Agency Internet program and became the official Internet protocol in 1983 (Kleinrock, 2010). IPv6 was standardized in 1995 as its successor to provide enhanced capabilities and address IPv4 technological limitations, most notable of which was the anticipated exhaustion of address space (Deering & Hinden, 1995). While the two protocols have some functional similarities, they are distinct and not backward compatible; IPv4-only …
The Role Of Emotional Expression In Accessing Social Networks: The Case Of Newcomers' Blogs,
2019
Carleton University
The Role Of Emotional Expression In Accessing Social Networks: The Case Of Newcomers' Blogs, Michael J. Hine, Luciara Nardon, Daniel Gulanowski
Journal of International Technology and Information Management
Research has established the critical role of social networks in facilitating adjustment to foreign environments. Increasingly, social interactions are happening through computer mediated technology. This paper explores the role of emotional expression in newcomers’ blogs in developing and interacting with social networks in a new country. This research uses a dictionary-based text analytics approach to detect emotional expression in newcomers’ blog posts and their associated discussions. Blog posts with more emotional expression had more associated responses; discussions tended to be more positive than posts; and the relative amount of negative emotion in the discussions increases as posts become more negative. …
The Most Powerful Thing,
2019
Andrews University
Community Discovery In Heterogeneous Social Networks,
2019
Nanyang Technological University
Community Discovery In Heterogeneous Social Networks, Lei Meng, Ah-Hwee Tan, Donald C. Wunsch
Research Collection School Of Computing and Information Systems
Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a priori, do not address the weighting problem for fusing heterogeneous types of links, and have a heavy computational cost. This chapter studies the commonly used social links of users and explores the feasibility of the proposed heterogeneous data co-clustering algorithm GHF-ART, as introduced in Sect. 3.6, for discovering user communities in social networks. Contrary to the existing algorithms proposed for this task, GHF-ART performs real-time matching of patterns and one-pass learning, which guarantees …
A Data-Driven Approach For Detecting Stress In Plants Using Hyperspectral Imagery,
2019
University of Nebraska-Lincoln
A Data-Driven Approach For Detecting Stress In Plants Using Hyperspectral Imagery, Suraj Gampa
School of Computing: Dissertations, Theses, and Student Research
A phenotype is an observable characteristic of an individual and is a function of its genotype and its growth environment. Individuals with different genotypes are impacted differently by exposure to the same environment. Therefore, phenotypes are often used to understand morphological and physiological changes in plants as a function of genotype and biotic and abiotic stress conditions. Phenotypes that measure the level of stress can help mitigate the adverse impacts on the growth cycle of the plant. Image-based plant phenotyping has the potential for early stress detection by means of computing responsive phenotypes in a non-intrusive manner. A large number …
A Survey Of Prediction And Classification Techniques In Multicore Processor Systems,
2019
Marquette University
A Survey Of Prediction And Classification Techniques In Multicore Processor Systems, Cristinel Ababei, Milad Ghorbani Moghaddam
Electrical and Computer Engineering Faculty Research and Publications
In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application's behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated …
Operating Systems Laboratory, Emt 2390l, Course Outline,
2019
CUNY New York City College of Technology
Operating Systems Laboratory, Emt 2390l, Course Outline, Jose Reyes Alamo
Open Educational Resources
Students study the characteristics of operating systems used in IBM systems. They learn how to install the operating systems and set it up to control the computer hardware. They also learn to use the features of the operating systems to manage the computer system.
