Digital Commons Network^™

Ai As Your Ally: The Effects Of Ai-Assisted Venting On Negative Affect And Perceived Social Support, Meilan Hu, Xavier Cheng Wee Chua, Shu Fen Diong, K. T. A. Sandeeshwara Kasturiratna, Nadyanna M. Majeed, Andree Hartanto

Research Collection School of Social Sciences

In recent years, artificial intelligence (AI) chatbots have made significant strides in generating human-like conversations. With AI's expanding capabilities in mimicking human interactions, its affordability and accessibility underscore the potential of AI chatbots to facilitate negative emotional disclosure or venting. The study's primary objective is to highlight the potential benefits of AI-assisted venting by comparing its effectiveness to venting through a traditional journaling platform in reducing negative affect and increasing perceived social support. We conducted a pre-registered within-subject experiment involving 150 participants who completed both traditional venting and AI-assisted venting conditions with counterbalancing and a wash-out period of 1-week between …

Data And Digitalization In Energy Efficiency Policy Design: The Case Of Singapore, Ishani Mukherjee, Diandrea Ho

Research Collection School of Social Sciences

Overarching and broad policy goals for enhancing energy efficiency have existed globally over the last 50 years as a response to rising energy demands, heightening costs, and construction levels of residential and commercial buildings, and the associated rises in greenhouse gas emissions from energy use (Levine et al., 2007; World Bank, 2010). Buildings, in particular, have been widely recognized as offering the greatest potential for reducing energy use and related greenhouse gas emissions, followed by reduced energy consumption in manufacturing, appliances, electronic goods, and end-users of electricity (Levine, 2007; IEA, 2010). And while significant technological strides have been made globally …

Towards The Next Generation Of Geospatial Artificial Intelligence, Gengchen Mai, Yiqun Xie, Xiaowei Jia, Ni Lao, Jinmeng Rao, Qing Zhu, Zeping Liu, Yao-Yi Chiang, Jiao, Junfeng

Research Collection College of Integrative Studies

Geospatial Artificial Intelligence (GeoAI), as the integration of geospatial studies and AI, has become one of the fastest-developing research directions in spatial data science and geography. This rapid change in the field calls for a deeper understanding of the recent developments and envision where the field is going in the near future. In this work, we provide a quantitative analysis of the GeoAI literature from the spatial, temporal, and semantic aspects. We briefly discuss the history of AI and GeoAI by highlighting some pioneering work. Then we discuss the current landscape of GeoAI by selecting five representative subdomains including remote …

Exploring Key Factors Influencing Depressive Symptoms Among Middle-Aged And Elderly Adult Population: A Machine Learning-Based Method, Ngoc Doan Thu Tran, Yi Zhen Tan, Sapphire Lin, Fang Zhao, Yee Sien Ng, Dong Ma, Jeonggil Ko, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

Objective: This paper aims to investigate the key factors, including demographics, socioeconomics, physical wellbeing, lifestyle, daily activities and loneliness that can impact depressive symptoms in the middle-aged and elderly population using machine learning techniques. By identifying the most important predictors of depressive symptoms through the analysis, the findings can have important implications for early depression detection and intervention. Participants: For our cross-sectional study, we recruited a total of 976 volunteers, with a specific focus on individuals aged 50 and above. Each participant was requested to provide their demographic, socioeconomic information and undergo several physical health tests. Additionally, they were asked …

A Causality-Aware Paradigm For Evaluating Creativity Of Multimodal Large Language Models, Zhongzhan Huang, Shanshan Zhong, Pan Zhou, Shanghua Gao, Marink Zitnik, Liang Lin

Research Collection School Of Computing and Information Systems

Recently, numerous benchmarks have been developed to evaluate the logical reasoning abilities of large language models (LLMs). However, assessing the equally important creative capabilities of LLMs is challenging due to the subjective, diverse, and data-scarce nature of creativity, especially in multimodal scenarios. In this paper, we consider the comprehensive pipeline for evaluating the creativity of multimodal LLMs, with a focus on suitable evaluation platforms and methodologies. First, we find the Oogiri game—a creativity-driven task requiring humor, associative thinking, and the ability to produce unexpected responses to text, images, or both. This game aligns well with the input-output structure of modern …

Towards Resource-Efficient Reactive And Proactive Auto-Scaling For Microservice Architectures, Hussain Ahmad, Christoph Treude, Markus Wagner, Claudia Szabo

Research Collection School Of Computing and Information Systems

Microservice architectures have become increasingly popular in both academia and industry, providing enhanced agility, elasticity, and maintainability in software development and deployment. To simplify scaling operations in microservice architectures, container orchestration platforms such as Kubernetes feature Horizontal Pod Auto-scalers (HPAs) designed to adjust the resources of microservices to accommodate fluctuating workloads. However, existing HPAs are not suitable for resource-constrained environments, as they make scaling decisions based on the individual resource capacities of microservices, leading to service unavailability, resource mismanagement, and financial losses. Furthermore, the inherent delay in initializing and terminating microservice pods hinders HPAs from timely responding to workload fluctuations, …

Seven Hci Grand Challenges Revisited: Five-Year Progress, Constantine Stephanidis, Gavriel Salvendy, Margherita Antona, Vincent G Duffy, Qin Gao, Waldemar Karwowski, Fiona Nah, Stavroula Ntoa, Pei-Luen Patrick Rau, Keng Siau, Jia Zhou

Research Collection School Of Computing and Information Systems

Motivated by the rapid technological advancements achieved in the last five years, and the pervasiveness of Artificial Intelligence, the paper investigates the evolving role of Human-Computer Interaction and revisits the seven grand challenges outlined in 2019: human-technology symbiosis, human-environment interactions, ethics, privacy and security, well-being, health and eudaimonia, accessibility and universal access, learning and creativity, and social organization and democracy. Through literature analysis, the paper reevaluates the status of each challenge and highlights emerging requirements. Key findings reveal the widespread impact of Artificial Intelligence across all domains and emphasize the need for improved AI transparency, alignment with human values, and …

Hotpatching On The Fly: Mitigating Drone Incidents Arising From Incorrect Configuration, Ruidong Han, Juanru Li, Zhuo Ma, David Lo, Arash Shaghaghi, Jianfeng Ma, Siqi Ma

Research Collection School Of Computing and Information Systems

Manufacturers offer adjustable control parameters for flight control systems to accommodate diverse environments and missions. To ensure flight safety, they also develop established boundaries, i.e., range specifications for parameter values. However, even when the configuration parameters fall within the prescribed manufacturer range, they could still lead to instability or even severe incidents like crashes, which are referred to as Range Specification Bugs. Prior research has suggested shrinking the range of parameter values to protect drones from the adverse effects of such bugs. However, narrowing the range of parameters may only reduce the probability of errors and could potentially limit the …

Scrutinizer: Towards Secure Forensics On Compromised Trustzone, Yiming Zhang, Fengwei Zhang, Xiapu Luo, Rui Hou, Xuhua Ding, Zhenkai Liang, Shoumeng Yan, Tao We, Zhengyu He

Research Collection School Of Computing and Information Systems

The number of vulnerabilities exploited in Arm TrustZone systems has been increasing recently. The absence of digital forensics tools prevents platform owners from incident response or periodic security scans. However, the area of secure forensics for compromised TrustZone remains unexplored and presents unresolved challenges. Traditional out-of-TrustZone forensics are inherently hindered by TrustZone protection, rendering them infeasible. In-TrustZone approaches are susceptible to attacks from privileged adversaries, undermining their security. To fill these gaps, we introduce SCRUTINIZER, the first secure forensics solution for compromised TrustZone systems. SCRUTINIZER utilizes the highest privilege domain of the recent Arm Confidential Computing Architecture (CCA), called the …

Human-Ai Synergy In Survey Development: Implications From Large Language Models In Business And Research, Ping Fan Ke, Ka Chung Ng

Research Collection School Of Computing and Information Systems

This study examines the novel integration of Large Language Models (LLMs) into the survey development process in business and research through the development and evaluation of the Behavioral Research ASSistant (BRASS) Bot. We first analyzed the traditional scale development process to identify tasks suitable for LLM integration, including both human-in-the-loop and automated LLM data collection methods. Following this analysis, we developed the details of BRASS Bot, incorporating design principles of falsifiability and reproducibility. We then conducted a comprehensive evaluation of the BRASS Bot across a diverse set of LLMs, including GPT, Claude, Gemini, and Llama, to assess its usability, validity, …

The Role Of Surprisal In Issue Trackers, James Caddy, Christoph Treude, Markus Wagner, Earl T. Barr

Research Collection School Of Computing and Information Systems

Context: Software development creates and relies on a large volume of information, yet the volume of this information can make it challenging for developers to maintain an overview of all goings-on that a team and external actors contribute to a project. We posit that unexpected or “surprising” events could serve as important signposts amidst this information overload. These unexpected events may indicate underlying anomalies or emergent situations that require immediate attention. To explore this premise, our study leverages the concept of ‘surprisal’ from information theory to identify and quantify these unusual occurrences from the issues and pull requests of popular …

Wf-Ppg: A Wrist-Finger Dual-Channel Dataset For Studying The Impact Of Contact Pressure On Ppg Morphology, Matthew Yiwen Ho, Hung Manh Pham, Aaqib Saeed, Dong Ma

Research Collection School Of Computing and Information Systems

Photoplethysmography (PPG) is a simple optical technique widely used in wearable devices for continuous cardiac health monitoring. However, the quality of PPG signals, particularly their morphology, is influenced by the contact pressure between the skin and the sensor. This variability in signal quality complicates complex tasks that rely on high-quality signals, such as blood pressure and heart rate variability estimation, making them less reliable or even impossible. To address this issue, we present a novel dataset (termed WF-PPG) comprising PPG signals from the wrist measured under varying contact pressures, along with high-quality PPG signals from the fingertip captured simultaneously. Data …

Density Boosts Everything: A One-Stop Strategy For Improving Performance, Robustness, And Sustainability Of Malware Detectors, Jianwen Tian, Wei Kong, Debin Gao, Tong Wang, Taotao Gu, Kefan Qiu, Zhi Wang, Xiaohui Kuang

Research Collection School Of Computing and Information Systems

In the contemporary landscape of cybersecurity, AI-driven detectors have emerged as pivotal in the realm of malware detection. However, existing AI-driven detectors encounter a myriad of challenges, including poisoning attacks, evasion attacks, and concept drift, which stem from the inherent characteristics of AI methodologies. While numerous solutions have been proposed to address these issues, they often concentrate on isolated problems, neglecting the broader implications for other facets of malware detection. This paper diverges from the conventional approach by not targeting a singular issue but instead identifying one of the fundamental causes of these challenges, sparsity. Sparsity refers to a scenario …

Learning An Interpretable Stylized Subspace For 3d-Aware Animatable Artforms, Chenxi Zheng, Bangzhen Liu, Xuemiao Xu, Huaidong Zhang, Shengfeng He

Research Collection School Of Computing and Information Systems

Throughout history, static paintings have captivated viewers within display frames, yet the possibility of making these masterpieces vividly interactive remains intriguing. This research paper introduces 3DArtmator, a novel approach that aims to represent artforms in a highly interpretable stylized space, enabling 3D-aware animatable reconstruction and editing. Our rationale is to transfer the interpretability and 3D controllability of the latent space in a 3D-aware GAN to a stylized sub-space of a customized GAN, revitalizing the original artforms. To this end, the proposed two-stage optimization framework of 3DArtmator begins with discovering an anchor in the original latent space that accurately mimics the …

Negotiating With Gpt-4: Digital Doormat Or Skilful Counterpart?, Dorcas Quek Anderson

Research Collection Yong Pung How School Of Law

Large language models (LLMs) such as GPT-4 have been creatively harnessed in the conflict resolution arena as dialogue agents interacting with humans within negotiations, due to their capacity for in-context learning and giving human-like responses. In light of the burgeoning use of LLMs in conflict resolution training, a pilot study was conducted to ascertain the desirability of using dialogue agents built on GPT-4 in conducting simulations for students learning negotiation skills. This article discusses insights gained from the study on the reliability of LLM agents in following prompts for negotiation simulations; notable negotiation behaviour of the LLM agent; the degree …

Chatgpt Didn’T Write This: Evaluating The Impact Of Llms With A Case Study In Grading Cuny Language Immersion Program Student Essays, Benjamin Inbar

Dissertations, Theses, and Capstone Projects

This study evaluates the capabilities and limitations of large language models (LLMs), specifically OpenAI’s ChatGPT-4o, in grading essays from students in the City University of New York’s Language Immersion Program. The program serves English language learners with diverse linguistic and demographic backgrounds, offering intensive language instruction to prepare students for academic success in college. Using a dataset of 30 pre- and post-program essays scored by program instructors and ChatGPT-4o under three paradigms, this research explores the alignment between human and AI-generated scores across five rubric-based competency areas. Findings reveal that ChatGPT-4o aligns moderately with human grading, with the strongest agreement …

3bb: Loss And Remembrance In The Anthropocene, Amanda J. Woolsey

Dissertations, Theses, and Capstone Projects

Undisputedly, we are living in a time of tremendous and unprecedented ecological loss. Various forms of life on earth are disappearing at a rate unheard of in human history, with a growing amount of evidence suggesting that we are in a period of mass biodiversity loss and extinction. This extinction event is marked by a series of environmental catastrophes that vary in acceleration, intensity, and impact, such as climate-change driven weather events wiping out an endemic island species, to habitat loss and fragmentation leading to the slow death of a once-common species. One such piece of supporting evidence is Rosenberg …

Translation Of: Dupin'sche Hyperflächen In E^4, Manuscripta Math By Ulrich Pinkall, Thomas E. Cecil

Mathematics and Computer Science Department Faculty Scholarship

This is an English translation of the article "Dupin'sche Hyperflächen in E⁴" by Ulrich Pinkall, which was originally published in manuscripta math. 51 (1985), 89-119.

A note from Thomas E. Cecil, translator: This is an unofficial translation of the original paper which was written in German. All references should be made to the original paper.

Pilot Study: Initial Investigation Suggests Differences In Emt-Associated Gene Expression In Breast Tumor Regions, Kylie L. King, Hamed Abdollahi, Zoe Dinkel, Alannah Akins, Homayoun Valafar, Heather Dunn

Faculty Publications

Triple negative breast cancer (TNBC) is the most aggressive subtype and disproportionately affects African American women. The development of breast cancer is highly associated with interactions between tumor cells and the extracellular matrix (ECM), and recent research suggests that cellular components of the ECM vary between racial groups. This pilot study aimed to evaluate gene expression in TNBC samples from patients who identified as African American and Caucasian using traditional statistical methods and emerging Machine Learning (ML) approaches. ML enables the analysis of complex datasets and the extraction of useful information from small datasets. We selected four regions of interest …

Electronic Component Authenticity Identification System And Related Methods, Yunghsiao Chung, Feng Yu, Stephen Edward Saddow, Junjie Xiong

Computer Science Faculty Research & Creative Works

A method and a system for identifying authenticity of an electronic component is disclosed. The method may include obtaining chip data of an electronic component; extracting feature information of the chip data for reducing noise of the chip data; providing the feature information of the chip data to a trained deep learning model; and providing a user with an authenticity indication for the electronic component based on an output of the deep learning model. Other aspects, embodiments, and features are also claimed and described.

Deep Learning-Based Auto-Segmentation For Liver Yttrium-90 Selective Internal Radiation Therapy, Jun Li, Wookjin Choi, Rani Anne

Department of Radiation Oncology Faculty Papers

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, …

Exploring School Teachers' Cyber Security Awareness, Experiences, And Practices In The Digital Age, R Ravichandran, Sonam Singh, P Sasikala

Journal of Cybersecurity Education, Research and Practice

This study investigates the awareness and practices of cyber security among school teachers, exploring their understanding of cyber threats, online behaviours, and response mechanisms to cyber incidents. A structured questionnaire was administered to gather data on demographic information, cyber security training, online practices, and experiences with cybercrime. The findings reveal varying levels of awareness among teachers, with many reporting limited knowledge of prevalent cyber threats such as phishing and identity theft. Despite the increasing reliance on digital tools for teaching, a significant number of respondents indicated a lack of formal training in cyber security. The study highlights the necessity for …

Playing The Digital Dialectic Game: Writing Pedagogy With Generative Ai, Rebekah Shultz Colby

University Writing Program: Faculty Scholarship

This article explores teaching writing with generative AI as critical play where students and teachers engage in an ethically dialectical and aleatory game with generative AI. I qualitatively surveyed 24 writing teachers about how they teach writing with generative AI as well as its advantages and disadvantages. I discovered that teachers used generative AI to teach about the ethics of generative AI's design and rhetorical use to avoid plagiarism. Teachers also critically played with generative AI to teach the writing process of invention, drafting, revision, and editing. Specifically, the critical, dialectical interplay of human and machine invents in aleatory and …

Identifying Cyberbullying Roles In Social Media, Manuel Sandoval, Mohammed Abuhamad, Patrick Furman, Mujtaba Nazari, Deborah Hall, Yasin N. Silva

Computer Science: Faculty Publications and Other Works

Social media has revolutionized communication, allowing people worldwide to connect and interact instantly. However, it has also led to increases in cyberbullying, which poses a significant threat to children and adolescents globally, affecting their mental health and well-being. It is critical to accurately detect the roles of individuals involved in cyberbullying incidents to effectively address the issue on a large scale. This study explores the use of machine learning models to detect the roles involved in cyberbullying interactions. After examining the AMiCA dataset and addressing class imbalance issues, we evaluate the performance of various models built with four underlying LLMs …

Incorporating Visual Information Into Natural Language Processing, Maxwell Mbabilla Aladago

Dartmouth College Ph.D Dissertations

Natural language describes entities in the world, some real and some abstract. It is also common practice to complement human learning of natural language with visual cues. This is evident in the heavily graphical nature of children’s literature which underscores the importance of visual cues in language acquisition. Similarly, the notion of “visual learners” is well recognized, reflecting the understanding that visual signals such as illustrations, gestures, and depictions effectively supplement language. In machine learning, two primary paradigms have emerged for training systems involving natural language. The first paradigm encompasses setups where pre-training and downstream tasks are exclusively in natural …

Feasibility And Acceptability Of The Mazi Umntanakho Digital Tool In South African Settings: A Qualitative Evaluation, Catherine E. Draper, Caylee J. Cook, Elizabeth A. Ankrah, Jesus A. Beltran, Franceli L. Cibrian, Kimberley D. Lakes, Hanna Mofid, Lucretia Williams, Gillian R. Hayes

Engineering Faculty Articles and Research

To address the need for interventions targeting social emotional development and mental health of young children in South Africa, the Mazi Umntanakho (‘know your child’) digital tool was co-designed, and piloted with caregivers and 3–5-year-old children involved in home visiting programmes promoting early childhood development. The aim of this study was to qualitatively evaluate the feasibility and acceptability of this tool in four urban and four rural low-income communities, from the perspective of home visitors and caregivers. Focus groups were conducted with home visitors (n = 117) and caregivers (n = 72). Issues relating to the feasibility of …

Mathematics In Everyday Life: Exploring Practical Applications And Real-World Impact, Priyant Banerjee, Arshad Bhat

Himalayan Research Papers Archive

Mathematics is an essential part of daily life and influences decisions and problem-solving in various aspects of life. This study explores how mathematical concepts are embedded in daily activities such as financial management, cooking, travel planning, and technological interactions. We will show how arithmetic, algebra, geometry, and statistics are applied in real life to improve decision-making, efficiency, and productivity. Findings indicate that people with higher mathematical literacy solve problems more efficiently, especially in budgeting, as accurate calculations minimize financial mistakes and facilitate long-term financial planning. In cooking, proportional reasoning ensures the accuracy of recipes, thus providing consistent culinary results. Travel …

Collision-Free Exploration By Mobile Agents Using Pebbles, Sajal K. Das, Amit Kumar Dhar, Barun Gorain, Madhuri Mahawar

Computer Science Faculty Research & Creative Works

In this paper, we study collision-free graph exploration in an anonymous network. The network is modeled as a graph G = (V, E) where the nodes of the graph are unlabeled, and each edge incident to a node v has a unique label, called the port number, in {0, 1, ⋯, d - 1}, where d is the degree of the node v. Two identical mobile agents, starting from different nodes in G have to explore the nodes of G in such a way that for every node v in G, at least one mobile agent visits v and no …

Smevca: Stable Matching-Based Ev Charging Assignment In Subscription-Based Models, Arindam Khanda, Anurag Satpathy, Anusha Vangala, Sajal K. Das

Computer Science Faculty Research & Creative Works

The rapid shift from internal combustion engine vehicles to battery-powered electric vehicles (EVs) presents considerable challenges, such as limited charging points (CPs), unpredictable wait times for charging, and difficulty in selecting appropriate CPs for EVs. To address these challenges, we propose a novel end-to-end framework, called Stable Matching based EV Charging Assignment (SMEVCA) that efficiently assigns charge-seeking EVs to CPs with the assistance of roadside units (RSUs). The proposed framework operates within a subscription-based model, ensuring that the subscribed EVs complete their charging within a predefined time limit enforced by a service level agreement (SLA). The framework SMEVCA employs a …

Adaptive Workload Management For Enhanced Function Performance In Serverless Computing, Priyanka Ashok Birajdar, V. Harsha, Anurag Satpathy, Sourav Kanti Addya

Computer Science Faculty Research & Creative Works

Serverless computing streamlines application deployment by removing the need for infrastructure management, but fluctuating workloads make resource allocation challenging. To solve this, we propose an adaptive workload manager that intelligently balances workloads, optimizes resource use, and adapts to changes with auto-scaling, ensuring efficient and reliable serverless performance. Preliminary experiments demonstrate an ≈ 0.6X% and 2X% improvement in execution time and resource utilization compared to the First-Come-First Serve (FCFS) scheduling algorithm.