Computer Sciences Commons^™

Uc-079 - Linked In Alumni Data | It4983 Section W01 | Group 2, Timothy Maroosis, Devin Javier, Niyoosha Peymani, Celina Harris, Rumsha Karim

C-Day Computing Showcase

No abstract provided.

Uc-085 - Universe Language Crm App, Kara Stanley, Khalid Diab, Gabriel A. Rodriguez Serrano, Sammantha King, Carolina Martinez, Taren Bridges

C-Day Computing Showcase

No abstract provided.

Uc-110 - Habermas V0.1, Anthony Grant, Chloe E. Raymond, Sean M. Shields, Mark T. Mcvay

C-Day Computing Showcase

No abstract provided.

Gc-123 - Service Delivery Enhancements Using Live Chats And Virtual Agents, Patrick R. Wadkins, Peter Twene, Rachel Williamson, Tamar Mcdowell, Naomi Degraft

C-Day Computing Showcase

No abstract provided.

Gc-074 - Statistical And Machine Learning Approaches To Investigate Adverse Effects Of Covid-19 Vaccinations, Aleema Dyer, Azadeh Khorsandi, Carter Mauro, Francelene Carmichael, Lillian Mccrary, Martin Martinelli

C-Day Computing Showcase

No abstract provided.

Uc-089 - 3d Information Extraction From Physics Exercise Videos, Benjamin M. Miller, Khoa Ho, Mickael D. Yongo

C-Day Computing Showcase

No abstract provided.

Uc-092 - Wireless Information And Power Transfer Using Beam Forming, Shikirah B. Johnson, Orion Williams, Aaron Benentt, Braden Tucker

C-Day Computing Showcase

No abstract provided.

Uc-112 - Telemedicine Web Application - Cs4850/03 - Group 5a, Cade T. Lanktree, Michael L. Wessels, Yash Shidhaye, Kevin J. Crews, Estephany Bonilla, Jodi-Ann Patterson, Will Paris

C-Day Computing Showcase

No abstract provided.

Uc-141 - Georgia Schools, Maxwell Trivers, Bailey Knezevich, Samson Oso, Mo Khan

C-Day Computing Showcase

No abstract provided.

Uc-134 - Georgia School Grades Map, Wiliam T. Duren, Joe Richardson, Amanda Doige, Tylor Anderson, Gamada Fayiso

C-Day Computing Showcase

No abstract provided.

Gc-106 - Emotion Recognition Using Wireless Signals, Jui Mhatre

C-Day Computing Showcase

No abstract provided.

Gc-113 - Nlp Sentiment Analysis On Amazon Reviews, Sushma Aladhalli Shivakumar, Swetha Pailla, Sireesha Hasti

C-Day Computing Showcase

No abstract provided.

Gc-132 - Microsoft Azure Sentinel To Connectwise Integration, Christine Neal, Miseker Birega, Ryan James, Charul Patel, L. Renee Davis Townsend, Matthew Parker

C-Day Computing Showcase

No abstract provided.

Uc-072 - Authenticating Middleware Implementing Dns-Based Identity, Brooklyn Crowe, Jade Godwin, Marilyn Marcos, Aaron Moore, Ricardo Rojo, Noah Starr

C-Day Computing Showcase

No abstract provided.

Uc-080 - It Deployment Aoa, Trexell T. Bailey, Elijah Deputy, Sabiha Iqbal, James Diaz, Evan Dillon, Kathryn S. Highers

C-Day Computing Showcase

No abstract provided.

Gc-125 - Covid-19 Prediction Using Symtpoms, Yagna Gurjala, Chalamayya Batchu, Shruthi Pethe

C-Day Computing Showcase

No abstract provided.

Uc-082 - Remote Presence Robot In The Classroom, Noah Trinite, Anna Furrow, Christopher Lyons, Joshua Odeyemi, George Tatge, James Perdue, Dylan Sloan

C-Day Computing Showcase

No abstract provided.

Uc-087 - Hibernate Funds: Back Office Portals For Fundraisers, Phillip Blackwell, Kasey L. Merritt, Patrick A. Krouba, Daniel Duran, Diana Calixto, Logan Miller

C-Day Computing Showcase

No abstract provided.

Uc-138 - 3d Extraction From Physics Exercise Videos, Zachary Matthews, Lhakpa N. Sherpa, Francisco J. Alvarez, Sam Guan, Philip Mcfarland, Cole Bruton

C-Day Computing Showcase

No abstract provided.

Uc-116 - Security Solution For A Small Business, Jessica Casasola, Craig Englert, Phuc Nguyen, Charles Pegram, James Vesper

C-Day Computing Showcase

No abstract provided.

Developing A Practice In Remote Sensing For Next-Generation Human Rights Researchers, Theresa Harris, Jonathan Drake, Umesh K. Haritashya, Wumi Asubiaro Dada, Fredy Cumes

Biennial Conference: The Social Practice of Human Rights

Remote sensing is increasingly recognized as an important tool for documenting human rights abuses. When used alongside interviews, case studies, surveys, forensic science, and other well-established research methods in human rights and humanitarian practice, remotely sensed data can effectively geolocate and establish chronologies for mass graves, forced displacement, destruction of cultural heritage sites, and other violations. But as a highly technical field of science that relies on ever-changing technologies, remote sensing and geospatial analysis are not readily accessible for human rights and humanitarian practitioners. The community of practice grew out of innovative work by practitioners at NGOs and specialized inter-governmental …

Ready, Willing, And Able, Gerry Boyle

Colby Magazine

So what gives? How, after four years on Mayflower Hill, do these Colby alumni have an outsized impact in a fintech company that is focused on, for example, changing the way municipal bonds are traded? What makes them able to dive in and figure it out? “That’s part of the liberal arts education,” said Associate Professor of History John Turner, who taught Tagg Martin ’13, history major turned MarketAxess go-to analyst. “You’re always learning. … You are always going to be mastering something, as opposed to having mastered.”

Ow-Detr: Open-World Detection Transformer, Akshita Gupta, Sanath Narayan, K.J. Joseph, Salman Khan, Fahad Shahbaz Khan, Mubarak Shah

Computer Vision Faculty Publications

Open-world object detection (OWOD) is a challenging computer vision problem, where the task is to detect a known set of object categories while simultaneously identifying unknown objects. Additionally, the model must incrementally learn new classes that become known in the next training episodes. Distinct from standard object detection, the OWOD setting poses significant challenges for generating quality candidate proposals on potentially unknown objects, separating the unknown objects from the background and detecting diverse unknown objects. Here, we introduce a novel end-to-end transformer-based framework, OW-DETR, for open-world object detection. The proposed OW-DETR comprises three dedicated components namely, attention-driven pseudo-labeling, novelty classification …

Analyze And Examine Wildfire Events In California, Aleena Hoodith, Sakim Zaman, Safoan Hossain, Jiehao Huang

Publications and Research

•A wildfire is an unplanned, unwanted, uncontrolled fire in an area of combustible vegetation starting in rural areas and urban areas. •Recent studies have shown that the effect of anthropogenic climate change has fueled the wildfire events, leading to an increase in the annual burned areas and number of events. •California is one of the places having the most deadliest and destructive wildfire seasons. With the global warming effect of 1°C since 1850, the 20 largest wildfires events that have occurred in California, 8 of them were in 2017. (Center For Climate And Energy Solutions) •Climate change is primarily caused …

Semantically Meaningful Sentence Embeddings, Rojina Deuja

School of Computing: Dissertations, Theses, and Student Research

Text embedding is an approach used in Natural Language Processing (NLP) to represent words, phrases, sentences, and documents. It is the process of obtaining numeric representations of text to feed into machine learning models as vectors (arrays of numbers). One of the biggest challenges in text embedding is representing longer text segments like sentences. These representations should capture the meaning of the segment and the semantic relationship between its constituents. Such representations are known as semantically meaningful embeddings. In this thesis, we seek to improve upon the quality of sentence embeddings that capture semantic information.

The current state-of-the-art models are …

Hsva: Hierarchical Semantic-Visual Adaptation For Zero-Shot Learning, Shiming Chen, Guo Sen Xie, Yang Liu, Qinmu Peng, Baigui Sun, Hao Li, Xinge You, Ling Shao

Machine Learning Faculty Publications

Zero-shot learning (ZSL) tackles the unseen class recognition problem, transferring semantic knowledge from seen classes to unseen ones. Typically, to guarantee desirable knowledge transfer, a common (latent) space is adopted for associating the visual and semantic domains in ZSL. However, existing common space learning methods align the semantic and visual domains by merely mitigating distribution disagreement through one-step adaptation. This strategy is usually ineffective due to the heterogeneous nature of the feature representations in the two domains, which intrinsically contain both distribution and structure variations. To address this and advance ZSL, we propose a novel hierarchical semantic-visual adaptation (HSVA) framework. …

Automated Discovery And Interpretation Of Ada-Compliant Door Placards, John J. Feilmeier

Computer Science and Information Technology Faculty

A familiar difficulty to any new student on campus is making one’s way from classroom A to classroom B. Facilities with different wings, multiple floors, and irregular floorplans can magnify this challenge, while students with vision impairments are impacted even more by the challenge of identifying the destination. This thesis explored different methods of discovering Americans with Disabilities Act (ADA)- compliant room identifying placards (“plaques”) and identifying the text on the sign. The plaque detection was accomplished with both standard image manipulation techniques and a Histogram of Oriented Gradients (HOG) (Dalal & Triggs, 2005) object detector. The text reading utilized …

Contrastive Learning For Unsupervised Auditory Texture Models, Christina Trexler

Computer Science and Computer Engineering Undergraduate Honors Theses

Sounds with a high level of stationarity, also known as sound textures, have perceptually relevant features which can be captured by stimulus-computable models. This makes texture-like sounds, such as those made by rain, wind, and fire, an appealing test case for understanding the underlying mechanisms of auditory recognition. Previous auditory texture models typically measured statistics from auditory filter bank representations, and the statistics they used were somewhat ad-hoc, hand-engineered through a process of trial and error. Here, we investigate whether a better auditory texture representation can be obtained via contrastive learning, taking advantage of the stationarity of auditory textures to …

Winect: 3d Human Pose Tracking For Free-Form Activity Using Commodity Wifi, Y. Ren, Z. Wang, Sheng Tan, Y. Chen, J. Yang

Computer Science Faculty Research

WiFi human sensing has become increasingly attractive in enabling emerging human-computer interaction applications. The corresponding technique has gradually evolved from the classification of multiple activity types to more fine-grained tracking of 3D human poses. However, existing WiFi-based 3D human pose tracking is limited to a set of predefined activities. In this work, we present Winect, a 3D human pose tracking system for free-form activity using commodity WiFi devices. Our system tracks free-form activity by estimating a 3D skeleton pose that consists of a set of joints of the human body. In particular, we combine signal separation and joint movement modeling …

Negations Of Probability Distributions: A Survey, Ildar Z. Baryrshin, Nailya I. Kubysheva, Venera R. Bayrasheva, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In recent years many papers have been devoted to the analysis and applications of negations of finite probability distributions (PD), first considered by Ronald Yager. This paper gives a brief overview of some formal results on the definition and properties of negations of PD. Negations of PD are generated by negators of probability values transforming element-by-element PD into a negation of PD. Negators are non-increasing functions of probability values. There are two types of negators: PD-independent and PD-dependent negators. Yager's negator is fundamental in the characterization of linear PD-independent negators as a convex combination of Yager's negator and uniform negator. …