Digital Commons Network^™

Greatness In Now - The Class Of 2019 Remembers A Classmate, Richard F. Roadcap Dds

Virginia Dental Journal

No abstract provided.

Remembering Former Vda President - Dr Michael Abbott

Virginia Dental Journal

No abstract provided.

Strength In Numbers - Members Descend On Washington, Laura Givens

Virginia Dental Journal

No abstract provided.

Dental Safety Net, Ally Singer Wright

Virginia Dental Journal

No abstract provided.

What's Up Pac?, Laura Givens

Virginia Dental Journal

No abstract provided.

Fraud Protection - How To Protect Your Practice From Financial Fraud, Jennifer Nieto

Virginia Dental Journal

No abstract provided.

Double Standard - Diy And Ethics, Elizabeth C. Stapleston Leiner Dds

Virginia Dental Journal

No abstract provided.

A Balanced Portfolio - Understanding Various Mutal Fund Options, Mona Jain

Virginia Dental Journal

No abstract provided.

Brush Up On Marketing, Kelsey Leavey

Virginia Dental Journal

No abstract provided.

Dental Benefits Expert - Giving You A Fighting Chance, Laura Givens

Virginia Dental Journal

No abstract provided.

Businessperson Or Practitioner?, James R. Schroeder Dds

Virginia Dental Journal

No abstract provided.

Pathology Puzzler, John A. Svirsky Dds

Virginia Dental Journal

No abstract provided.

Board Of Dentistry Notes June 21, 2019, Ursula Klostermyer Dds, Phd

Virginia Dental Journal

No abstract provided.

Two New Endorsements, Elise Rupinski

Virginia Dental Journal

No abstract provided.

Estranged?, Richard F. Roadcap Dds

Virginia Dental Journal

No abstract provided.

How Dirty Is Your Office's Bathroom?, Henry M. Botuck Dds

Virginia Dental Journal

No abstract provided.

No Longer Tooth Doctors, Marvin E. Pizer Dds, Ms, Ma, Ficd

Virginia Dental Journal

No abstract provided.

A Little Perspective, Kirk Norbo Dmd

Virginia Dental Journal

No abstract provided.

Take Action Like A Superhero, Ryan Dunn

Virginia Dental Journal

No abstract provided.

Va Dent J July, August And September 2019

Virginia Dental Journal

No abstract provided.

At The Table, Sam W. Galstan Dds, Mph

Virginia Dental Journal

No abstract provided.

Hand Gesture Control For A Di2erential Drive Robot, Seyoung Kan, Laray Lopez, Zachary Hinnen, Alfredo Weitzenfeld

Bellini College REU Symposium (BCREUS)

As technology becomes increasingly integrated into daily life, seamless and multimodal human-robot interaction has become essential. This project presents a lightweight, real-time human-following system implemented on a differential-drive robot (Ham-Bot) using a Raspberry Pi, motivated by the growing need for robotic companionship among elderly and mobility-impaired individuals. The system integrates HSV-based color segmentation, MediaPipe hand gesture recognition, LiDAR-based distance regulation, and a six-state finite state machine to achieve robust human-robot interaction without relying on heavy machine learning models for body detection. A socket-based communication architecture streams camera frames from the robot to a MacOS client, where perception is performed and …

Hardware Cluster For Key Security And Education Experiments, Michelle Mcaveety, Rylan Pietras, Ahmed Ghoneim, Robert Karam

Bellini College REU Symposium (BCREUS)

In response to increased cyberattacks against digital infrastructures, significant contributions have been made to the education of software-based cybersecurity principles through online resources, coursework, and other means. However, there is a lack of accessible platforms dedicated to the learning of hardware-based cybersecurity principles. Specifically, topics such as side-channel attacks or fault injection attacks can be challenging to study without expensive hardware resources. To address this need, we propose the Hardware Cluster for Key Security and Education Experiments (HaCKSEE) platform. HaCKSEE is an experiment-oriented platform designed to democratize hardware security education. The proposed platform allows students to conduct configurable hardware security …

Neurons Of Bias: Power-Mediated Compliance And Mitigation In Large Language Models, Anubhav Gupta, Otavio Rosa, Mahammed Kamruzzaman, Gene Kim

Bellini College REU Symposium (BCREUS)

Introduction & Problem Statement

Large language models (LLMs) are increasingly deployed in sociotechnical systems involving power, authority, and compliance, raising important questions about fairness, consistency, and reliability across diverse user populations. As these systems are integrated into hiring tools, customer service systems, assistive technologies, and decision-support pipelines, they are expected to operate consistently regardless of the identity they are prompted to adopt. However, when demographic attributes are introduced into prompts, model behavior may shift even when identity does not change the underlying task.

In power-mediated settings, such variation is especially important because differences in compliance, refusal, or loophole-seeking behavior may …

Misty: An Llm-Driven Recipe Recommender Robot, Alina Pineda, Stuti Goyal, Jingjing Li, Zhao Han

Bellini College REU Symposium (BCREUS)

Introduction & Problem Statement

Meal planning requires balancing nutritional needs, available ingredients, and user preferences, which can be time-consuming and cognitively demanding. While mobile recipe applications provide personalized recommendations, they lack interactive and socially engaging communication. In contrast, social robots such as Misty enable expressive interaction through speech, gestures, and emotional cues, but typically rely on rigid, pre-programmed behaviors and lack domain-specific knowledge for tasks such as recipe recommendation.

This gap highlights the need for a system that combines the strengths of both technologies. Recipe recommendation is inherently multimodal, requiring both social interaction for engagement and visual presentation for detailed …

Strider: Human Re-Identification Using Gait Recognition, Mark Calvelo, John Pham, Aleksandre Papunashvili, Mauricio Pamplona Segundo

Bellini College REU Symposium (BCREUS)

Gait Recognition pipelines have shown promise in recent years with modern models such as SkeletonGait [1] and GPGait [2] demonstrating decent performance on known benchmarks. These Gait Recognition models utilize resource and computationally expensive pipelines for full end-to-end Gait Recognition. This overhead can prove to be a problem for real-time tasks such as on-device recognition. In this project, we aim to reduce one of the most important phases of the Gait Recognition pipeline which is Skeleton and Silhouette generation. We build off of the YOLOv11 architecture [3] simplifying aspects of the existing model to create a model that can efficiently …

Last-Mile Autonomous Delivery Robot, Lucas Araujo Bianco, Clement Joseph, Chance Hamilton, Alfredo Weitzenfeld

Bellini College REU Symposium (BCREUS)

Autonomous delivery robots require robust, real-time navigation systems to safely traverse dynamic outdoor environments like college campuses. This project presents an edge-optimized vision pipeline deployed on a resource-constrained Jetson Nano-based robot to accurately identify drivable surfaces and detect path intersections. We employ a lightweight semantic segmentation Convolutional Neural Network (LRASPP MobileNetV3-Large), initially pretrained on the Cityscapes dataset. By applying transfer learning to a manually refined, locally labeled dataset of our campus, we significantly improved feature extraction for urban textures and adapted the model to our specific use case. To ensure efficient edge deployment, the network is optimized using TensorRT [1], …

Microarchitectural Vulnerabilities And Performance Tradeoffs In Shared Scientific Computing, Zyad Abd-Elrahman, Santosh Pandey

Bellini College REU Symposium (BCREUS)

Introduction & Problem Statement

Shared scientific computing platforms are built for speed and utilization, but they create a security problem that isolation alone can't fully solve. Even with TEEs, containers, or a privilege separation in place, the hardware underneath remains a shared resource. Speculative execution, cache contention, interconnect timing, and similar microarchitectural effects can leak information or create integrity risks across protection boundaries without any malicious code involved. For CI operators running graph workloads, HPC kernels, or ML pipelines in co-tenanted systems, the threat is inherent in normal execution. The real question isn't whether these vulnerabilities exist, but it's what …

Optimizing Machine Learning Workloads Performance In Ml Accelerators, Chowdhury Nafiun Nuru, Santosh Panday

Bellini College REU Symposium (BCREUS)

This project explores how to improve the performance of machine learning systems at the kernel level. As models grow in size, efficient execution depends on reducing data movement and using limited on-chip memory effectively.

Techniques such as tiling and optimized execution order are used to improve data locality and reduce memory overhead. These approaches enable better use of fast on-chip memory and minimize costly data transfers.

Using benchmark workloads, improved scheduling strategies are shown to reduce latency by avoiding suboptimal local decisions and enabling better global execution patterns.

Overall, this work demonstrates that kernel-level optimization can significantly improve performance and …

Autonomous Indoor Mapping And Navigation Using Ros2-Based Slam On A Jetson-Powered Mobile Robot, Daniel Schagen, Mark Halim, Chance Hamilton, Alfredo Weitzenfeld

Bellini College REU Symposium (BCREUS)

This project presents the design, implementation, and evaluation of an autonomous indoor mobile robot capable of simultaneous localization and mapping (SLAM) using a ROS 2-based architecture on resource-constrained embedded hardware. The system is built on a Yahboom ROSMASTER X3 platform equipped with a Slamtec RPLidar A1, IMU, and wheel odometry, running ROS 2 Foxy on an NVIDIA Jetson Nano 4GB inside a Docker container. The primary objective is to enable the robot to autonomously map an indoor hallway environment while navigating without human intervention.

The mapping pipeline integrates slam_toolbox for occupancy grid generation, with loop closure enabled and parameters tuned …