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The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. YU, Grace ALOINA, Panca JODIAWAN, Aldy GUNAWAN, Tsung-C. HUANG 2023 Singapore Management University

The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang

Research Collection School Of Computing and Information Systems

This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and ...


Learning Comprehensive Global Features In Person Re-Identification: Ensuring Discriminativeness Of More Local Regions, Jiali XIA, Jianqiang HUANG, Shibao ZHENG, Qin ZHOU, Bernt SCHIELE, Xian-Sheng HUA, Qianru SUN 2023 Singapore Management University

Learning Comprehensive Global Features In Person Re-Identification: Ensuring Discriminativeness Of More Local Regions, Jiali Xia, Jianqiang Huang, Shibao Zheng, Qin Zhou, Bernt Schiele, Xian-Sheng Hua, Qianru Sun

Research Collection School Of Computing and Information Systems

Person re-identification (Re-ID) aims to retrieve person images from a large gallery given a query image of a person of interest. Global information and fine-grained local features are both essential for the representation. However, global embedding learned by naive classification model tends to be trapped in the most discriminative local region, leading to poor evaluation performance. To address the issue, we propose a novel baseline network that learns strong global feature termed as Comprehensive Global Embedding (CGE), ensuring more local regions of global feature maps to be discriminative. In this work, two key modules are proposed including Non-parameterized Local Classifier ...


Online Hyperparameter Optimization For Class-Incremental Learning, Yaoyao LIU, Yingying LI, Bernt SCHIELE, Qianru SUN 2023 Singapore Management University

Online Hyperparameter Optimization For Class-Incremental Learning, Yaoyao Liu, Yingying Li, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Class-incremental learning (CIL) aims to train a classification model while the number of classes increases phase-by-phase. An inherent challenge of CIL is the stability-plasticity tradeoff, i.e., CIL models should keep stable to retain old knowledge and keep plastic to absorb new knowledge. However, none of the existing CIL models can achieve the optimal tradeoff in different data-receiving settings—where typically the training-from-half (TFH) setting needs more stability, but the training-from-scratch (TFS) needs more plasticity. To this end, we design an online learning method that can adaptively optimize the tradeoff without knowing the setting as a priori. Specifically, we first ...


Observing Human Mobility Internationally During Covid-19, Shane Allcroft, Mohammed Metwaly, Zachery Berg, Isha Ghodgaonkar, Fischer Bordwell, XinXin Zhao, Xinglei Liu, Jiahao Xu, Subhankar Chakraborty, Vishnu Banna, Akhil Chinnakotla, Abhinav Goel, Caleb Tung, Gore Kao, Wei Zakharov, David A. Shoham, George K. Thiruvathukal, Yung-Hsiang Lu 2023 Purdue University

Observing Human Mobility Internationally During Covid-19, Shane Allcroft, Mohammed Metwaly, Zachery Berg, Isha Ghodgaonkar, Fischer Bordwell, Xinxin Zhao, Xinglei Liu, Jiahao Xu, Subhankar Chakraborty, Vishnu Banna, Akhil Chinnakotla, Abhinav Goel, Caleb Tung, Gore Kao, Wei Zakharov, David A. Shoham, George K. Thiruvathukal, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

This article analyzes visual data captured from five countries and three U.S. states to evaluate the effectiveness of lockdown policies for reducing the spread of COVID-19. The main challenge is the scale: nearly six million images are analyzed to observe how people respond to the policy changes.


Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn 2023 University of Vermont

Leveraging A Machine Learning Based Predictive Framework To Study Brain-Phenotype Relationships, Sage Hahn

Graduate College Dissertations and Theses

An immense collective effort has been put towards the development of methods forquantifying brain activity and structure. In parallel, a similar effort has focused on collecting experimental data, resulting in ever-growing data banks of complex human in vivo neuroimaging data. Machine learning, a broad set of powerful and effective tools for identifying multivariate relationships in high-dimensional problem spaces, has proven to be a promising approach toward better understanding the relationships between the brain and different phenotypes of interest. However, applied machine learning within a predictive framework for the study of neuroimaging data introduces several domain-specific problems and considerations, leaving the ...


The Interaction Of Normalisation And Clustering In Sub-Domain Definition For Multi-Source Transfer Learning Based Time Series Anomaly Detection, Matthew Nicholson, Rahul Agrahari, Clare Conran, Haythem Assem, John D. Kelleher 2022 ADAPT Centre, Trinity College Dublin

The Interaction Of Normalisation And Clustering In Sub-Domain Definition For Multi-Source Transfer Learning Based Time Series Anomaly Detection, Matthew Nicholson, Rahul Agrahari, Clare Conran, Haythem Assem, John D. Kelleher

Articles

This paper examines how data normalisation and clustering interact in the definition of sub-domains within multi-source transfer learning systems for time series anomaly detection. The paper introduces a distinction between (i) clustering as a primary/direct method for anomaly detection, and (ii) clustering as a method for identifying sub-domains within the source or target datasets. Reporting the results of three sets of experiments, we find that normalisation after feature extraction and before clustering results in the best performance for anomaly detection. Interestingly, we find that in the multi-source transfer learning scenario clustering on the target dataset and identifying subdomains in ...


Algorithms For Compression Of Electrocardiogram Signals, Yuliyan Velchev 2022 Technical University of Sofia

Algorithms For Compression Of Electrocardiogram Signals, Yuliyan Velchev

Books

The study is dedicated to modern methods and algorithms for compression of electrocardiogram (ECG) signals. In its original part, two lossy compression algorithms based on a combination of linear transforms are proposed. These algorithms are with relatively low computational complexity, making them applicable for implementation in low power designs such as mobile devices or embedded systems. Since the algorithms do not provide perfect signal reconstruction, they would find application in ECG monitoring systems rather than those intended for precision medical diagnosis.

This monograph consists of abstract, preface, five chapters and conclusion. The chapters are as follows: Chapter 1 — Introduction to ...


Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli 2022 Army Cyber Institute, U.S. Military Academy

Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli

ACI Journal Articles

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach ...


Uc-263 It4983 Cybersecurity Capstone, Jordan White, Stephen C. Woodman, Jenny Owens, Hector Gomez, Aaron Scott 2022 Kennesaw State University

Uc-263 It4983 Cybersecurity Capstone, Jordan White, Stephen C. Woodman, Jenny Owens, Hector Gomez, Aaron Scott

C-Day Computing Showcase

For the Cybersecurity capstone project, our team was given a webserver and website for the company Akwaaba. We were tasked to fix all vulnerabilities, keep the server up to date, and help maintain the site with uptime being the priority. After the first two milestones were complete, we were to attempt to hack another team’s server while they attempted to do the same to us. When the attack phase began on Wednesday 10/26, our team discovered the other team had not changed any of their original passwords, so we took control within thirty minutes and took it down ...


Uc-270 Red Pepper It: It Web Management Application And Content Management System, Eun Kim, Bukky Adekunle, Caitlin Washington, Neha Dedani, Ivana George 2022 Kennesaw State University

Uc-270 Red Pepper It: It Web Management Application And Content Management System, Eun Kim, Bukky Adekunle, Caitlin Washington, Neha Dedani, Ivana George

C-Day Computing Showcase

Red Pepper IT is an application developed for Georgia Tech Research Institute. The conception of this project arose from the lack of IT management tools that can address all the needs of an IT department. Furthermore, with the currently available tools, they are either poorly designed or have a high licensing cost. This project seeks to solve the need for such applications by building an open-source application that can be deployed internally by any business seeking a management tool for its IT department.


Uc-275 Network Simulation Software Analysis Of Alternatives, Justin McCannon, Nidhi Marsonia, Michael McInnis, Tiffany Nguyen, Azm Uddin 2022 Kennesaw State University

Uc-275 Network Simulation Software Analysis Of Alternatives, Justin Mccannon, Nidhi Marsonia, Michael Mcinnis, Tiffany Nguyen, Azm Uddin

C-Day Computing Showcase

Data networking is a complex field. Designing networks is a complex task. Simulators have been developed to create hypothetical designs with configuration settings for evaluating architectures and settings. At least 2 simulators are freely available to IT professionals and students. This project involves researching the landscape of free network design simulators to determine how many there are, then downloading, testing, evaluating, and documenting the features of each by designing, on each, a network with multiple routers, switches, and host devices using IPv4 and IPv6, defining features appropriate for classroom use in a university, and finally determining which solution fits the ...


Gc-250 Object Detection And Tracking: Deep Learning-Based Framework With Euclidean Distance, Iou, And Hungarian Algorithm, Md Jobair Hossain Faruk 2022 Kennesaw State University

Gc-250 Object Detection And Tracking: Deep Learning-Based Framework With Euclidean Distance, Iou, And Hungarian Algorithm, Md Jobair Hossain Faruk

C-Day Computing Showcase

Object tracking is an important basis for the logistics industry where multiple packages are moved on conveyor belts at a time. Accurate datasets and efficient benchmarks are a few of the several problems for both object detection and tracking for training the deep learning-based framework. Preparing 100% accurate correspondence between objects throughout different frames by assigning human annotated unique_attributes to train framework efficiently over ground truth data. In this research, we develop an (i) OpenCV-based framework that allows the user to assign human-annotated identification between objects and (ii) a novel application for object detection and tracking. We utilize the assigned ...


Gc-258 Heart Disease Prediction Using Machine Learning, Devin Jackson, Richard Stupka, Trinadh Chigurupati, Demontae Moore 2022 Kennesaw State University

Gc-258 Heart Disease Prediction Using Machine Learning, Devin Jackson, Richard Stupka, Trinadh Chigurupati, Demontae Moore

C-Day Computing Showcase

Research has shown that the early detection of Heart Disease is critical to treating and understanding the causes. Through the use of advanced machine learning models and com- prehensive data sets collected on patients of varying backgrounds and health statuses, this research shows the listed correlations between attributes of data points and positive identification of the disease. This research uses 1026 unique records and 14 attributes including the classifier of Heart Disease. These attributes range from simple (cholesterol level) to more complex and subjective (chest pain type) but each attribute presents an opportunity to improve each of the analyzed models ...


Gc-311 Singsingmarketplace.Com: E-Commerce Marketplace For Remote Vendors, Daniel K. Tor, Ebikela Ogegbene-Ise 2022 Kennesaw State University

Gc-311 Singsingmarketplace.Com: E-Commerce Marketplace For Remote Vendors, Daniel K. Tor, Ebikela Ogegbene-Ise

C-Day Computing Showcase

American residents in the (Nigerian, Liberian, Indian, Ghanaian, etc.) Diaspora have strong ties back home and as such support loved ones, back home, on a regular basis, by sending cash remittances through Western Union, MoneyGram, etc. Remittances are expensive. Remitters have no control over how funds are spent once received. Remitters cannot send small amounts because the fees cannot be justified. We built a marketplace platform that allows the Diaspora to remit goods and services, instead of sending money to relatives back home in. The objectives are to remove or greatly reduce the cost of remittances, give more control to ...


Gr-245 Parsimonics: Achieving High Classification Accuracy Even With High Dimensional Image Reduction, Joshua Owens 2022 Kennesaw State University

Gr-245 Parsimonics: Achieving High Classification Accuracy Even With High Dimensional Image Reduction, Joshua Owens

C-Day Computing Showcase

The asl-alphabet dataset, hosted by Kaggle, is a collection of 87000 color images sized 200x200x3, grouped into 29 classes of 3000 images apiece (dataset A). The classes consist of the 26 English letters plus three classes for space, delete and nothing. As a proof of concept, the dataset was first truncated by deleting 2700 images from each class, leaving only the first 300 images per class and totaling 8700 images, or 10% of the original number of images (dataset B). Then, transfer learning was applied to dataset B using Alexnet with Imagenet weights. >99% accuracy with dataset B was readily ...


Gr-241 On Training Explainable Neurons, Lance Kennedy 2022 Kennesaw State University

Gr-241 On Training Explainable Neurons, Lance Kennedy

C-Day Computing Showcase

Neural networks have become increasingly powerful and commonplace tools for guiding decision-making. However, due to the black-box nature of many of these networks, it is often difficult to understand exactly what guides them to a certain prediction, making them dangerous to use for sensitive decision making, and making it difficult to ensure confidence in their output. For instance, a network which classifies images of dogs and cats may turn out to be flawed with little consequence, but a neural network that diagnoses the presence of diseases should be assured to make sound predictions. By understanding why a network makes the ...


Gr-284 Automated Vulnerability Detection In Source Code Using Deep Neural Networks, Mst Shapna Akter 2022 Kennesaw State University

Gr-284 Automated Vulnerability Detection In Source Code Using Deep Neural Networks, Mst Shapna Akter

C-Day Computing Showcase

One of the most important challenges in the field of a software code audit is the presence of vulnerabilities in software source code. Every year, more and more software flaws are found, either internally in proprietary code or revealed publicly. These flaws are highly likely exploited and lead to system compromise, data leakage, or denial of service. C and C++ open-source code are now available in order to create a large-scale, machine-learning system for function-level vulnerability identification. We assembled a sizable dataset of millions of open-source functions that point to potential exploits. We created an efficient and scalable vulnerability detection ...


Uc-238 Sp4-Dogem, Chloe Chung, Zane Atkinson, Melissa Iniestra, Deo Intshakal A Nzeng, Joshua K. Willson 2022 Kennesaw State University

Uc-238 Sp4-Dogem, Chloe Chung, Zane Atkinson, Melissa Iniestra, Deo Intshakal A Nzeng, Joshua K. Willson

C-Day Computing Showcase

The DogEm project's overarching objective is to produce a functional cross-platform mobile app that reliably sends a specific contact a series of calls, emails, and texts until the contact responds. To accomplish this goal, we have compiled preliminary research, produced a series of prototypes, and started the development process. Our preliminary research consists of research pertaining to our tech stack, our user base, our app's requirements, possibilities for messaging and calling features, UX/UI research, reading through React Native documentation, and constraints on the messaging and calling features on iOS vs. Android. Each member produced a DogEm app ...


Uc-240 Gone Fishin' Vr, Donovan E. Lott, Richard Halbert, Tanner M. Peters, Joseph B. Hancock, Anthony L. Polidura 2022 Kennesaw State University

Uc-240 Gone Fishin' Vr, Donovan E. Lott, Richard Halbert, Tanner M. Peters, Joseph B. Hancock, Anthony L. Polidura

C-Day Computing Showcase

Gone Fishing is a VR game that allows the player to fish from the comfort of their own home. This take on a fishing simulator has creative and playful designs that are sure to surprise the players. With this game, we intend to invoke different comedic aspects found in other games such as designs, descriptions, and possible voiceovers in order to give the players a good time. This isn’t the average fishing simulator.


Uc-249 Hemorrhage, Daniel M. Respess, Antonio S. Brewer, Kenny Tran, Sandy Li, Rick B. Watson 2022 Kennesaw State University

Uc-249 Hemorrhage, Daniel M. Respess, Antonio S. Brewer, Kenny Tran, Sandy Li, Rick B. Watson

C-Day Computing Showcase

Hemorrhage is a fast-paced FPS action game with a focus on risky gameplay and dodging enemy attacks. Fight your way through hordes of grotesque creatures and make it to the end! The player starts with limited health but can steal more from killing enemies. Then, you can unleash this stored-up health to deal massive damage to your foes! Will you choose to be an unkillable tank? Or a brutal glass cannon?


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