Computer Sciences Commons^™

Effects Of Mindfulness And Emotion Regulation On Aesthetics: A Theoretical Model From Hedonic Perspective Of Processing Fluency, Geng-Bao Lin, Fiona Fui-Hoon Nah, Choon Ling Sia

Research Collection School Of Computing and Information Systems

Research has shown that processing fluency positively impacts perceived aesthetics, with pleasure mediating the relationship. Considering the important role of pleasure, we propose studying the role of emotion regulation in moderating the mediated relationship from processing fluency to perceived aesthetics. Based on our hypotheses, individuals’ emotion regulation strategies are expected to have moderating effects on the relationship between processing fluency and perceived aesthetics such that cognitive reappraisal positively moderates the relationship from processing fluency to pleasure, and expressive suppression negatively moderates the relationship from pleasure to perceived aesthetics. Trait mindfulness is also expected to influence perceived aesthetics through emotion regulation …

Towards Dynamic Context Detection From Voice Commands And Conversations With Smart Assistants In Smart Homes, Jeniya Sultana

Graduate Theses/Dissertations

Voice-enabled interactions have become increasingly popular with the rise of voice assistants. Identifying contexts or meanings from voice commands and conversations with smart assistants can contribute to the autonomous control of smart home devices and appliances. To improve automation, there is a growing need for efficient context detection that eliminates the need to memorize voice commands. To address this need, I followed a two-step approach in my research. In the first step, I developed a unique context recognition model using a transformer, an attention mechanism, and a fully connected neural network. I trained this model on a conversational dataset of …

Scene Understanding And Spatial Analysis Using Scene Graph Enhanced By Hall's Proxemics Zones In Smart Homes, Debaleen Das Spandan

Graduate Theses/Dissertations

Voice-controlled smart assistants have received widespread popularity. It plays a pivotal role in smart homes by providing a natural and convenient interface for interacting with smart devices. However, these assistants are unable to serve persons with physical disabilities and speech impairments. Therefore, non-verbal communication methods, such as eye tracking, gesture recognition, and context awareness can complement and overcome some of these limitations to enhance user experience in smart homes. To address this issue, I am investigating non-verbal communication methods to make smart home technology more accessible and intuitive. In this research, I focus on proxemics, i.e., the study of distance …

Trust: The Feature That Vending Machines And Atms Share, But Simplygo Lacks, Sun Sun Lim

Research Collection College of Integrative Studies

The article discussed the intricacies of trust in the SimplyGo debacle and highlighted how the design of physical interfaces like vending machines and ATMs and digital interfaces from apps like Grab, Parking.sg and ShopBack have critical features to instil trust. People need to be reassured that their transactions have proceeded as they should, and thay have not been short-changed.

Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have become the de facto standard for representation learning on topological graphs, which usually derive effective node representations via message passing from neighborhoods. Although GNNs have achieved great success, previous models are mostly confined to static and homogeneous graphs. However, there are multiple dynamic interactions between different-typed nodes in real-world scenarios like academic networks and e-commerce platforms, forming temporal heterogeneous graphs (THGs). Limited work has been done for representation learning on THGs and the challenges are in two aspects. First, there are abundant dynamic semantics between nodes while traditional techniques like meta-paths can only capture static …

Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao

Research Collection School Of Computing and Information Systems

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …

Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to …

All In One Place: Ensuring Usable Access To Online Shopping Items For Blind Users, Yash Prakash, Akshay Kolgar Nayak, Mohan Sunkara, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Perusing web data items such as shopping products is a core online user activity. To prevent information overload, the content associated with data items is typically dispersed across multiple webpage sections over multiple web pages. However, such content distribution manifests an unintended side effect of significantly increasing the interaction burden for blind users, since navigating to-and-fro between different sections in different pages is tedious and cumbersome with their screen readers. While existing works have proposed methods for the context of a single webpage, solutions enabling usable access to content distributed across multiple webpages are few and far between. In this …

Enhancing Research Productivity: Seamless Integration Of Personal Devices And Hpc Resources With The Cybershuttle Notebook Gateway, Yasith Jayawardana, Dimuthu Wannipurage, Eroma Abeysinghe, Suresh Marru

Computer Science Faculty Publications

Scientists often utilize personal laptops and workstations for initial research stages and turn to high-performance computing (HPC) supercomputers for compute-intensive tasks. However, seamless transitions between these environments are vital for enhancing productivity and accelerating research progress. Our paper presents the Cybershuttle Notebook Gateway, an open-source framework crafted to streamline this transition, optimize resource utilization, and reduce time-to-science for researchers. Leveraging JupyterLab, the framework extends kernel mechanics for seamless provisioning and connection to remote HPC cluster kernels. We delve into its architecture, which separates user authentication, kernel provisioning, and remote file system access. Additionally, we highlight practical capabilities like analyzing network …

Improving Usability Of Data Charts In Multimodal Documents For Low Vision Users, Yash Prakash, Akshay Kolgar Nayak, Shoaib Mohammed Alyaan, Pathan Aseef Khan, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Data chart visualizations and text are often paired in news articles, online blogs, and academic publications to present complex data. While chart visualizations offer graphical summaries of the data, the accompanying text provides essential context and explanation. Associating information from text and charts is straightforward for sighted users but presents significant challenges for individuals with low vision, especially on small-screen devices such as smartphones. The visual nature of charts coupled with the layout of the text inherently makes it difficult for low vision users to mentally associate chart data with text and comprehend the content due to their dependence on …

A-Disetrac Advanced Analytic Dashboard For Distributed Eye Tracking, Yasasi Abeysinghe, Bhanuka Mahanama, Gavindya Jayawardena, Yasith Jayawardena, Mohan Sunkara, Andrew T. Duchowski, Vikas Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Understanding how individuals focus and perform visual searches during collaborative tasks can help improve user engagement. Eye tracking measures provide informative cues for such understanding. This article presents A-DisETrac, an advanced analytic dashboard for distributed eye tracking. It uses off-the-shelf eye trackers to monitor multiple users in parallel, compute both traditional and advanced gaze measures in real-time, and display them on an interactive dashboard. Using two pilot studies, the system was evaluated in terms of user experience and utility, and compared with existing work. Moreover, the system was used to study how advanced gaze measures such as ambient-focal coefficient K …

Bias-Aware Gaze Uniformity Assessment In Group Images, Omkar Kulkarni

Electronic Theses & Dissertations (2024 - present)

Today, more than 5 billion photos are captured every day, with smartphones generating over 94\% of these images. However, despite advancements in technology, achieving aesthetically pleasing group photos remains challenging, especially when it comes to aligning the direction of everyone’s gaze. While current methods focus on facial features, they often fail to ensure consistent gaze direction. The introduction of the iPhone's Live mode, which captures a 1.5-second video snippet along with still images, complicates the selection of the best key photo due to its subjective nature and a lack of publicly available data, especially during the pandemic.

To address these …

Consistent Monte Carlo Methods For Non-Linear Applications In Light Transport, Zackary T. Misso

Dartmouth College Ph.D Dissertations

The study of light transport focuses on describing the propagation of light from emitters to sensors through accurately describing the interactions light can undergo with everything in between. Physically-based rendering is the process of applying the laws of light transport to formulate practical algorithms which simulate the flow of light for the purpose of synthesizing images of virtual environments.

Unfortunately, there are very few interesting scene configurations which can be computed analytically. Instead, modern solutions predominantly rely on Monte Carlo integration to stochastically estimate the transfer of light since the process is both unbiased and consistent. Meaning, it is expected …

Random Walk Methods For Geometry Representation Agnostic Transport, Dario R. Seyb

Dartmouth College Ph.D Dissertations

In computer graphics, we use geometry representations to model a wide range of virtual scenes—from the fantastical worlds shown in animated movies to intricate mechanical parts.
These representations provide the context for transport problems—light transport is used to produce images of virtual scenes and diffusive transport to simulate distributions of quantities like heat.

There are many types of representations each with their own advantages.
For example, explicit ones make it easy to directly manipulate surfaces, while implicit representations allow for intuitive modeling by non-technical users and straightforward integration into machine learning systems.
Unfortunately, many algorithms that work on these digital …

Garbage In ≠ Garbage Out: Exploring Gan Resilience To Image Training Set Degradations, Nicholas Crino, Bruce A. Cox, Nathan B. Gaw

Faculty Publications

Generative Adversarial Networks (GANs) have received immense attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. This manuscript focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to empirically determine the effects of 10 fundamental image degradation modes, applied to the training image dataset, on the Fréchet inception distance …

An Analysis Of Precision: Occlusion And Perspective Geometry’S Role In 6d Pose Estimation, Jeffrey Choate, Derek Worth, Scott Nykl, Clark N. Taylor, Brett J. Borghetti, Christine M. Schubert Kabban

Faculty Publications

Achieving precise 6 degrees of freedom (6D) pose estimation of rigid objects from color images is a critical challenge with wide-ranging applications in robotics and close-contact aircraft operations. This study investigates key techniques in the application of YOLOv5 object detection convolutional neural network (CNN) for 6D pose localization of aircraft using only color imagery. Traditional object detection labeling methods suffer from inaccuracies due to perspective geometry and being limited to visible key points. This research demonstrates that with precise labeling, a CNN can predict object features with near-pixel accuracy, effectively learning the distinct appearance of the object due to perspective …

Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He

Research Collection School Of Computing and Information Systems

Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with highcontrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate goal is to assign appropriate …

Demonstrating Canvas-Based Processing Of Multiple Camera Streams At The Edge, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

We demonstrate criticality-aware canvas-based processing of multiple concurrent camera streams at the resource constrained edge to show substantial improvement in the accuracy-throughput trade-off. The proposed system focuses the available computation resources on select Regions of Interest (RoI) across all the camera streams by (i) extracting RoI from the input camera stream (ii) 2D bin packing the RoI on a canvas frame and (iii) batching and inferring upon these constructed composite canvas frames with a YOLOv5 object detection model. Our experiments show that such canvas-based processing can (i) sustain real-time processing throughput of 23 FPS per camera across 6 concurrent input …

Breaking Down Computer Networking Instructional Videos: Automatic Summarization With Video Attributes And Language Models, Totok Sukardiyono, Muhammad Irfan Luthfi, Nisa Dwi Septiyanti

Elinvo (Electronics, Informatics, and Vocational Education)

Instructional videos have become a popular tool for teaching complex topics in computer networking. However, these videos can often be lengthy and time-consuming, making it difficult for learners to obtain the key information they need. In this study, we propose an approach that leverages automatic summarization and language models to generate concise and informative summaries of instructional videos. To enhance the performance of the summarization algorithm, we also incorporate video attributes that provide contextual information about the video content. Using a dataset of computer networking tutorials, we evaluate the effectiveness of the proposed method and show that it significantly improves …

Differences In Software Usability Level Based On User Background, Abdur Rohman Sholeh, Agung Fatwanto

Elinvo (Electronics, Informatics, and Vocational Education)

The development of software must consider usability as one of its key success indicators. Relatively few studies discuss the factors influencing usability, including users' backgrounds. The purpose of this research was to investigate the impact of user background, specifically gender, class (year of college admission), and frequency of use on the rating of usability. This research utilized a descriptive quantitative method with instruments: the usability matrix of the Computer System Usability Questionnaire (CSUQ), the System Usability Scale (SUS), the Usability Metric for User Experience (UMUX), and the Net Promoter Score (NPS). The research object was the Learning Management System (LMS) …

Usability Of Mobile Application For Implementing Genetic Counselling Intervention Among Thalassemia Patients And Caregivers: A Case Study Of Cyber Gen, Henri Setiawan, Nur Hidayat, Atun Farihatun, Marlina Indriastuti, Rudi Kurniawan, Andan Firmansyah, Esti Andarini, Yudisa Diaz Lutfi Sandi

Elinvo (Electronics, Informatics, and Vocational Education)

Utilization of communication and information technology has been widely used in the health sector, especially nursing. As one of the nursing interventions for thalassemia patients and caregivers, genetic counseling is not only done face to face but can use android-based telenursing facilities through the complete features available in the Cyber Gen application. This study aims to measure the usability level of Cyber Gen application as an indirect genetic counseling medium for thalassemia patients. This application was developed with four main services: basic information about diseases, consultation rooms, social support, and direct surveys. This application is built using the Flutter Framework, …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Deep Learning Image Analysis To Isolate And Characterize Different Stages Of S-Phase In Human Cells, Kevin A. Boyd, Rudranil Mitra, John Santerre, Christopher L. Sansam

SMU Data Science Review

Abstract. This research used deep learning for image analysis by isolating and characterizing distinct DNA replication patterns in human cells. By leveraging high-resolution microscopy images of multiple cells stained with 5-Ethynyl-2′-deoxyuridine (EdU), a replication marker, this analysis utilized Convolutional Neural Networks (CNNs) to perform image segmentation and to provide robust and reliable classification results. First multiple cells in a field of focus were identified using a pretrained CNN called Cellpose. After identifying the location of each cell in the image a python script was created to crop out each cell into individual .tif files. After careful annotation, a CNN was …

Adaptable Object And Animation System For Game Development, Isaiah Turner

Masters Theses & Specialist Projects

In contemporary times, video games have swiftly evolved into a prominent medium, excelling in both entertainment and narrative delivery, positioning themselves as significant rivals to traditional forms such as film and theater. The burgeoning popularity of gaming has led to a surge in aspiring game developers seeking to craft their own creations, driven by both commercial aspirations and personal passion. However, a common challenge faced by these individuals involves the considerable time investment required to acquire essential skills and establish a foundational framework for their projects. Accessible game development engines that offer a diverse range of fundamental features play a …

Mermaid: A Dataset And Framework For Multimodal Meme Semantic Understanding, Shaun Toh, Adriel Kuek, Wen Haw Chong, Roy Ka Wei Lee

Research Collection School Of Computing and Information Systems

Memes are widely used to convey cultural and societal issues and have a significant impact on public opinion. However, little work has been done on understanding and explaining the semantics expressed in multimodal memes. To fill this research gap, we introduce MERMAID, a dataset consisting of 3,633 memes annotated with their entities and relations, and propose a novel MERF pipeline that extracts entities and their relationships in memes. Our framework combines state-of-the-art techniques from natural language processing and computer vision to extract text and image features and infer relationships between entities in memes. We evaluate the proposed framework on a …

Triple Helix: Ai-Artist-Audience Collaboration In A Performative Art Experience, Xuedan Zou

Dartmouth College Master’s Theses

Imagine an art exhibition that morphs its content according to the audience’s experience like a chameleon, reflecting the audience’s mind and culture and turning the artist’s exhibition into the viewer’s. But when the viewers leave, the work fades back to the creator’s original work and waits for the next audience. In this project, my team introduced an interactive exhibition called "Triple Helix," where audience members were provided the opportunity to alter the artworks created by the artist, thus imbuing them with their own perspectives. This interactive exhibition was held at three physical-locations and online, and a comprehensive user study was …

Data-Centric Image Super-Resolution In Magnetic Resonance Imaging: Challenges And Opportunities, Mamata Shrestha

Graduate Theses and Dissertations

Super-resolution has emerged as a crucial research topic in the field of Magnetic Resonance Imaging (MRI) where it plays an important role in understanding and analysis of complex, qualitative, and quantitative characteristics of tissues at high resolutions. Deep learning techniques have been successful in achieving state-of-the-art results for super-resolution. These deep learning-based methods heavily rely on a substantial amount of data. Additionally, they require a pair of low-resolution and high-resolution images for supervised training which is often unavailable. Particularly in MRI super-resolution, it is often impossible to have low-resolution and high-resolution training image pairs. To overcome this, existing methods for …

Enhanced Privacy-Enabled Face Recognition Using Κ-Identity Optimization, Ryan Karl

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Facial recognition is becoming more and more prevalent in the daily lives of the common person. Law enforcement utilizes facial recognition to find and track suspects. The newest smartphones have the ability to unlock using the user's face. Some door locks utilize facial recognition to allow correct users to enter restricted spaces. The list of applications that use facial recognition will only increase as hardware becomes more cost-effective and more computationally powerful. As this technology becomes more prevalent in our lives, it is important to understand and protect the data provided to these companies. Any data transmitted should be encrypted …

Mrim: Lightweight Saliency-Based Mixed-Resolution Imaging For Low-Power Pervasive Vision, Jiyan Wu, Vithurson Subasharan, Minh Anh Tuan Tran, Kasun Pramuditha Gamlath, Archan Misra

Research Collection School Of Computing and Information Systems

While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes …

Clueless: Revolutionizing Sustainable Fashion And Combating Overconsumption, Tanya Ravichandran

Graphic Communication

“Clueless” revolutionizes sustainable fashion by combating wardrobe overconsumption and the industry’s carbon footprint, using AI to suggest personalized outfits from existing wardrobes tailored to weather and wear history. It enhances user engagement through features like outfit ‘shuffle’ and provides insights into wardrobe utilization and carbon impact.

It’s more than an app; it’s a step towards a greener wardrobe and a healthier planet.