Iot Health Devices: Exploring Security Risks In The Connected Landscape,
2023
University of Tartu
Iot Health Devices: Exploring Security Risks In The Connected Landscape, Abasi-Amefon Obot Affia, Hilary Finch, Woosub Jung, Issah Abubakari Samori, Lucas Potter, Xavier-Lewis Palmer
School of Cybersecurity Faculty Publications
The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of …
Sprout: Using A Garden Metaphor To Visualize And Support Customizable And Collaborative Health Tracking,
2023
Dartmouth College
Sprout: Using A Garden Metaphor To Visualize And Support Customizable And Collaborative Health Tracking, Pape Sow Traoré
Dartmouth College Master’s Theses
Self-tracking tools have become increasingly popular, especially with the advent of wearable technology and smartphone applications. However, traditional tracking tools often display data in a quantitative format that can be overwhelming and cause users to abandon their tracking efforts. Additionally, these tools typically provide a generic user experience and are designed from a single-user perspective, lacking external support. To overcome these limitations, we develop Sprout, a mobile data-tracking application that offers a more qualitative, customizable, and collaborative experience for health monitoring and management. Sprout uses a garden metaphor to visually represent health information and allows users to tailor their …
Liloc: Enabling Precise 3d Localization In Dynamic Indoor Environments Using Lidars,
2023
Singapore Management University
Liloc: Enabling Precise 3d Localization In Dynamic Indoor Environments Using Lidars, Darshana Rathnayake, Meera Radhakrishnan, Inseok Hwang, Archan Misra
Research Collection School Of Computing and Information Systems
We present LiLoc, a system for precise 3D localization and tracking of mobile IoT devices (e.g., robots) in indoor environments using multi-perspective LiDAR sensing. The key differentiators in our work are: (a) First, unlike traditional localization approaches, our approach is robust to dynamically changing environmental conditions (e.g., varying crowd levels, object placement/layout changes); (b) Second, unlike prior work on visual and 3D SLAM, LiLoc is not dependent on a pre-built static map of the environment and instead works by utilizing dynamically updated point clouds captured from both infrastructural-mounted LiDARs and LiDARs equipped on individual mobile IoT devices. To achieve fine-grained, …
Compositional Prompt Tuning With Motion Cues For Open-Vocabulary Video Relation Detection,
2023
Singapore Management University
Compositional Prompt Tuning With Motion Cues For Open-Vocabulary Video Relation Detection, Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun
Research Collection School Of Computing and Information Systems
Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary prediction trained on limited base categories, e.g., object classification and detection. In this paper, we propose compositional prompt tuning with motion cues: an extended prompt tuning paradigm for compositional predictions of video data. In particular, we present Relation Prompt (RePro) for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where conventional prompt tuning is easily biased to certain subject-object combinations and motion patterns. To this end, RePro addresses the two technical challenges of Open-VidVRD: 1) the prompt tokens should respect the two different semantic roles of subject and object, and 2) the tuning …
Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection,
2023
Singapore Management University
Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudendo, Thanh-Nam Doan, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Smart contracts in blockchains have been increasingly used for high-value business applications. It is essential to check smart contracts' reliability before and after deployment. Although various program analysis and deep learning techniques have been proposed to detect vulnerabilities in either Ethereum smart contract source code or bytecode, their detection accuracy and scalability are still limited. This paper presents a novel framework named MANDO-HGT for detecting smart contract vulnerabilities. Given Ethereum smart contracts, either in source code or bytecode form, and vulnerable or clean, MANDO-HGT custom-builds heterogeneous contract graphs (HCGs) to represent control-flow and/or function-call information of the code. It then …
Chronos: Time-Aware Zero-Shot Identification Of Libraries From Vulnerability Reports,
2023
Singapore Management University
Chronos: Time-Aware Zero-Shot Identification Of Libraries From Vulnerability Reports, Yunbo Lyu, Thanh Le Cong, Hong Jin Kang, Ratnadira Widyasari, Zhipeng Zhao, Xuan-Bach Dinh Le, Ming Li, David Lo
Research Collection School Of Computing and Information Systems
Tools that alert developers about library vulnerabilities depend on accurate, up-to-date vulnerability databases which are maintained by security researchers. These databases record the libraries related to each vulnerability. However, the vulnerability reports may not explicitly list every library and human analysis is required to determine all the relevant libraries. Human analysis may be slow and expensive, which motivates the need for automated approaches. Researchers and practitioners have proposed to automatically identify libraries from vulnerability reports using extreme multi-label learning (XML). While state-of-the-art XML techniques showed promising performance, their experimental settings do not practically fit what happens in reality. Previous studies …
Diffseer: Difference-Based Dynamic Weighted Graph Visualization,
2023
Singapore Management University
Diffseer: Difference-Based Dynamic Weighted Graph Visualization, Xiaolin Wen, Yong Wang, Meixuan Wu, Fengjie Wang, Xuanwu Yue, Qiaomu Shen, Yuxin Ma, Min Zhu
Research Collection School Of Computing and Information Systems
Existing dynamic weighted graph visualization approaches rely on users’ mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based …
Lpt: Long-Tailed Prompt Tuning For Image Classification,
2023
Singapore Management University
Lpt: Long-Tailed Prompt Tuning For Image Classification, Bowen Dong, Pan Zhou, Shuicheng Yan, Wangmeng Zuo
Research Collection School Of Computing and Information Systems
For long-tailed classification tasks, most works often pretrain a big model on a large-scale (unlabeled) dataset, and then fine-tune the whole pretrained model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization capability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning (LPT) method for long-tailed classification tasks. LPT introduces several trainable prompts into a frozen pretrained model to adapt it to long-tailed data. For better …
Towards Understanding Why Mask Reconstruction Pretraining Helps In Downstream Tasks,
2023
Singapore Management University
Towards Understanding Why Mask Reconstruction Pretraining Helps In Downstream Tasks, Jiachun Pan, Pan Zhou, Shuicheng Yan
Research Collection School Of Computing and Information Systems
For unsupervised pretraining, mask-reconstruction pretraining (MRP) approaches, e.g. MAE (He et al., 2021) and data2vec (Baevski et al., 2022), randomly mask input patches and then reconstruct the pixels or semantic features of these masked patches via an auto-encoder. Then for a downstream task, supervised fine-tuning the pretrained encoder remarkably surpasses the conventional “supervised learning" (SL) trained from scratch. However, it is still unclear 1) how MRP performs semantic feature learning in the pretraining phase and 2) why it helps in downstream tasks. To solve these problems, we first theoretically show that on an auto-encoder of a two/one-layered convolution encoder/decoder, MRP …
Graph Neural Point Process For Temporal Interaction Prediction,
2023
Singapore Management University
Graph Neural Point Process For Temporal Interaction Prediction, Wenwen Xia, Yuchen Li, Shengdong Li
Research Collection School Of Computing and Information Systems
Temporal graphs are ubiquitous data structures in many scenarios, including social networks, user-item interaction networks, etc. In this paper, we focus on predicting the exact time of the next interaction, given a node pair on a temporal graph. This novel problem can support interesting applications, such as time-sensitive items recommendation, congestion prediction on road networks, and many others. We present Graph Neural Point Process (GNPP) to tackle this problem. GNPP relies on the graph neural message passing and the temporal point process framework. Most previous graph neural models only utilize the chronological order of observed events and ignore exact timestamps. …
Procedural City Generation With Combined Architectures For Real-Time Visualization,
2023
Clemson University
Procedural City Generation With Combined Architectures For Real-Time Visualization, Griffin Poyck
All Theses
The work and research of this paper sought to build upon traditional city generation and simulation in creating a tool that both realistically simulates cities and their prominent features and also creates aesthetic and artistically rich cities using assets that combine several contemporary or near contemporary architectural styles. The major city features simulated are the surrounding terrain, road networks, individual buildings, and building placement. The tools used to both create and integrate these features were created in Houdini with Unreal Engine 5 as the intended final destination. This research was influenced by the city, town, and road networking of Ghost …
Using Immersive Technology To Improve Mechanical Design: Use Cases, A Review Of Current Technology, And An Experiment In Requirement Elicitation In Virtual Reality,
2023
Clemson University
Using Immersive Technology To Improve Mechanical Design: Use Cases, A Review Of Current Technology, And An Experiment In Requirement Elicitation In Virtual Reality, William Hawthorne
All Theses
There has been a growing trend in the use of immersive technologies within engineering design. This research is focused on understanding how Virtual Reality (VR) technologies support design reviews. Modern tools including virtual reality hardware and software were tested for current capabilities and challenges. In a review of literature, two key gaps are identified: the rapid advancements of VR and AR technology and limited formal studies to determine the costs and benefits of immersive technology within engineering design reviews. This research has resulted in three key outcomes. First, use cases of immersive reality technologies are identified, through the lens of …
Blind Fighter – A Video Game For The Visually Impaired,
2023
Arkansas Tech University
Blind Fighter – A Video Game For The Visually Impaired, Avery Wayne Harrah
ATU Scholars Symposium
Blind Fighter is a video game made to be playable by anyone, regardless of any visual impairments the player may have. The game relies on auditory queues to allow players to understand what is happening in the game without ever having to see the screen. The project’s goal is to serve as a proof of concept that video games can be made inclusive with a few additions during development, without sacrificing overall quality. To do this, the game features full graphics in addition to testing many strategies for visually impaired players, including direction-based audio, unique sound effects for each game …
The Ozark Getaway,
2023
Arkansas Tech University
The Ozark Getaway, Houston Barber, Marcus Gasca, Evan Reece Matlock, Avery Wayne Harrah
ATU Scholars Symposium
Website designed for usage of renting AirBnb houses outside of the original website under the specific owner.
Creating And Evaluating Dimensional Analysis Software For University Students,
2023
Southern Adventist University
Creating And Evaluating Dimensional Analysis Software For University Students, Ac Hýbl
Campus Research Month
Most scientific disciplines depend on mathematics to varying degrees. Real-world problems often include quantifiable measurements with units. For example, chemistry, physics, and pharmacology require flawless unit conversions and dimensional homogeneity to obtain acceptable results. Students often choose to ignore units until the end of the problem-solving process, but this introduces errors and robs students of a deep understanding of units. Current tools for teaching dimensional analysis are limited both in scope and accessibility. Unit Playground addresses this issue by providing an interactive interface to experiment with units and their relationships.
A Study Of A Collaborative Task Management Application Built On React Native Using The Basic Ux Framework,
2023
Southern Adventist University
A Study Of A Collaborative Task Management Application Built On React Native Using The Basic Ux Framework, Andrei Modiga
Campus Research Month
Many times it can be difficult to accomplish all this is proposed in a meeting. This project aimed to build a simple planner application using React Native that allows groups of people to collaborate and stay organized. The application was built using the BASIC Framework as a guide, and featured a collaboration feature that enabled users to share tasks, projects, and communicate with one another in order to stay coordinated and productive. The user interface was designed for easy use, allowing for quick and efficient task management within a group. The goal of the application was to provide a useful …
Visualizing Literary Narratives With A Graph-Centered Approach.,
2023
Southern Adventist University
Visualizing Literary Narratives With A Graph-Centered Approach., Meg Ermer
Campus Research Month
The art of storytelling is multifaceted and nonlinear, involving multiple characters, themes, and symbols while often jumping between the present and past. While media forms such as novels can encapsulate these complexities, it is often difficult to visualize a narrative in an easy-to-understand format. Our contribution is a graph-based system to let users organize and visualize those narratives. Events and characters are represented as nodes and their relationships are represented as edges. Neo4J is used as a database management system to store the graph and to run queries on it, and Streamlit and Pyvis are used to represent the database …
Counterventions: A Reparative Reflection On Interventionist Hci,
2023
Purdue University
Counterventions: A Reparative Reflection On Interventionist Hci, Rua Mae Williams, Louanne E. Boyd, Juan E. Gilbert
Engineering Faculty Articles and Research
Research in HCI applied to clinical interventions relies on normative assumptions about which bodies and minds are healthy, valuable, and desirable. To disrupt this normalizing drive in HCI, we define a “counterventional approach” to intervention technology design informed by critical scholarship and community perspectives. This approach is meant to unsettle normative assumptions of intervention as urgent, necessary, and curative. We begin with a historical overview of intervention in HCI and its critics. Then, through reparative readings of past HCI projects in autism intervention, we illustrate the emergent principles of a counterventional approach and how it may manifest research outcomes that …
Supporting Novices Author Audio Descriptions Via Automatic Feedback,
2023
Singapore Management University
Supporting Novices Author Audio Descriptions Via Automatic Feedback, Rosiana Natalie, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara
Research Collection School Of Computing and Information Systems
Audio descriptions (AD) make videos accessible to those who cannot see them. But many videos lack AD and remain inaccessible as traditional approaches involve expensive professional production. We aim to lower production costs by involving novices in this process. We present an AD authoring system that supports novices to write scene descriptions (SD)—textual descriptions of video scenes—and convert them into AD via text-to-speech. The system combines video scene recognition and natural language processing to review novice-written SD and feeds back what to mention automatically. To assess the effectiveness of this automatic feedback in supporting novices, we recruited 60 participants to …
Open-Set Domain Adaptation By Deconfounding Domain Gaps,
2023
Jilin University
Open-Set Domain Adaptation By Deconfounding Domain Gaps, Xin Zhao, Shengsheng Wang, Qianru Sun
Research Collection School Of Computing and Information Systems
Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any distractions caused by open-set classes, i.e., the classes “unknown” to the source model. Compared to standard DA, the key of OSDA lies in the separation between known and unknown classes. Existing OSDA methods often fail the separation because of overlooking the confounders (i.e., the domain gaps), which means their recognition of “unknown classes” is not because of class semantics but domain difference (e.g., styles and contexts). We address this issue by explicitly deconfounding domain gaps …
