Artificial Intelligence and Robotics Commons^™

Generative Ai For Pull Request Descriptions: Adoption, Impact, And Developer Interventions, Tao Xiao, Hideaki Hata, Christoph Treude, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

GitHub's Copilot for Pull Requests (PRs) is a promising service aiming to automate various developer tasks related to PRs, such as generating summaries of changes or providing complete walkthroughs with links to the relevant code. As this innovative technology gains traction in the Open Source Software (OSS) community, it is crucial to examine its early adoption and its impact on the development process. Additionally, it offers a unique opportunity to observe how developers respond when they disagree with the generated content. In our study, we employ a mixed-methods approach, blending quantitative analysis with qualitative insights, to examine 18,256 PRs in …

Jigsaw: Edge-Based Streaming Perception Over Spatially Overlapped Multi-Camera Deployments, Ila Gokarn, Yigong Hu, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

We present JIGSAW, a novel system that performs edge-based streaming perception over multiple video streams, while additionally factoring in the redundancy offered by the spatial overlap often exhibited in urban, multi-camera deployments. To assure high streaming throughput, JIGSAW extracts and spatially multiplexes multiple regions-of-interest from different camera frames into a smaller canvas frame. Moreover, to ensure that perception stays abreast of evolving object kinematics, JIGSAW includes a utility-based weighted scheduler to preferentially prioritize and even skip object-specific tiles extracted from an incoming stream of camera frames. Using the CityflowV2 traffic surveillance dataset, we show that JIGSAW can simultaneously process 25 …

Reinforcement Learning For Strategic Airport Slot Scheduling: Analysis Of State Observations And Reward Designs, Anh Nguyen-Duy, Duc-Thinh Pham, Jian-Yi Lye, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Due to the NP-hard nature, the strategic airport slot scheduling problem is calling for exploring sub-optimal approaches, such as heuristics and learning-based approaches. Moreover, the continuous increase in air traffic demand requires approaches that can work well in new scenarios. While heuristics rely on a fixed set of rules, which limits the ability to explore new solutions, Reinforcement Learning offers a versatile framework to automate the search and generalize to unseen scenarios. Finding a suitable state observation and reward structure design is essential in using Reinforcement Learning. In this paper, we investigate the impact of providing the Reinforcement Learning agent …

Label-Free Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms In Pathogenic Microbial Identification: Current Trends, Challenges, And Perspectives, Jia Wei Tang, Quan Yuan, Xin Ru Wen, Muhammad Usman, Alfred Chin Yen Tay, Liang Wang

Research outputs 2022 to 2026

Infectious diseases caused by microbial pathogens remain a primary contributor to global health burdens. Prompt control and effective prevention of these pathogens are critical for public health and medical diagnostics. Conventional microbial detection methods suffer from high complexity, low sensitivity, and poor selectivity. Therefore, developing rapid and reliable methods for microbial pathogen detection has become imperative. Surface-enhanced Raman Spectroscopy (SERS), as an innovative non-invasive diagnostic technique, holds significant promise in pathogenic microorganism detection due to its rapid, reliable, and cost-effective advantages. This review comprehensively outlines the fundamental theories of Raman Spectroscopy (RS) with a focus on label-free SERS strategy, reporting …

Development Of A Rule-Based Monitoring System For Autonomous Heavy Equipment Safety, Amirpooya Shirazi

Department of Construction Engineering and Management: Dissertations, Theses, and Student Research

Roadway construction work zones are constantly exposed to interactions among construction equipment, workers, and vehicles. Furthermore, ensuring safety in these areas is considered a challenging task due to the complexity of the environment. As shown in the rising trend of fatal accidents in roadway work zones, current OSHA regulations in construction safety are insufficient in effectively detecting unsafe situations and mitigating the risks. Furthermore, best practices, such as internal traffic control planning (ITCP), exhibit critical limitations requiring continuous monitoring of active work zones as well as adjustments to the site coordination plans due to the dynamic nature of work zone …

My Ai Companion: An Examination Of The Removal Of Erotic Role Play From Replika Through User Discussion On Reddit, Chelsee M. Allen

Department of Sociology: Dissertations, Theses, and Student Research

The development of artificial intelligence (AI) software has expanded rapidly in recent years, and thus has emerged the importance of exploring human relationships with AI chatbots. Replika, an app which uses AI to mimic human conversation, removed a function called Erotic Role Play (ERP) that allowed for sexual conversation with users’ customizable chatbots in February of 2023. This exploratory qualitative study examines the aftermath of ERP’s removal through an analysis of user interactions on Reddit. Five overarching themes emerged through the analysis of top posts to a Replika-specific subreddit, encompassing topics around mental health, stigma, coping, sex work and gendered …

Cognitive Technologies, Tom Davenport

Asian Management Insights

AI and the revolution of work.

Professor Tom Davenport, the President’s Distinguished Professor of Information Technology and Management at Babson College, speaks about how companies can integrate generative Artificial Intelligence (GenAI) into their operations while ensuring workforce adaptation and skills development.

Unveiling The Dynamics Of Ai Applications: A Review Of Reviews Using Scientometrics And Bertopic Modeling, Raghu Raman, Debidutta Pattnaik, Laurie Hughes, Prema Nedungadi

Research outputs 2022 to 2026

In a world that has rapidly transformed through the advent of artificial intelligence (AI), our systematic review, guided by the PRISMA protocol, investigates a decade of AI research, revealing insights into its evolution and impact. Our study, examining 3,767 articles, has drawn considerable attention, as evidenced by an impressive 63,577 citations, underscoring the scholarly community's profound engagement. Our study reveals a collaborative landscape with 18,189 contributing authors, reflecting a robust network of researchers advancing AI and machine learning applications. Review categories focus on systematic reviews and bibliometric analyses, indicating an increasing emphasis on comprehensive literature synthesis and quantitative analysis. The …

Application Of Multilayer Perceptron Artificial Neural Network (Mlp-Ann) Algorithm For Pm2.5 Mass Concentration Estimation During Open Biomass Burning Episodes In Thailand, P. Paluang, W. Thavorntam, W. Phairuang

Research outputs 2022 to 2026

Open biomass burning (OBB) is the main cause of air pollution in Northern Thailand, where PM2.5 concentrations exceed Thailand's air quality standards annually during the January–April (dry season). The air emissions from databases that detail the pollutants discharged into the atmosphere from specific sources of air pollution are crucial for monitoring air pollution. However, this data has been poorly studied in Thailand. This study estimated ground-level PM2.5 concentration in Northern Thailand using the Multilayer Perceptron Artificial Neural Networks (MLP-ANN) model, integrating the in-depth data as input variables. The 10-fold cross-validation approach was applied to validate the model's performance. The meteorological …

Integrating Remote Sensing And Machine Learning To Determine Past, Current And Future Crop Water Use From The Nubian Sandstone Aquifer System, Moaz Ishag

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

The agriculture sector is a significant consumer of water, and sustainable water use begins with monitoring irrigated land. Delineating irrigated land supports decision-makers and promotes the sustainable use of this crucial resource. This study focuses on the Nubian Sandstone Aquifer System (NSAS), the largest aquifers in the world, which spans Egypt, Sudan, Libya, and Chad. The study aims to: 1) quantify the increase in irrigated hectares (both pivot and non-pivot) from 2000-2001 to 2023-2024; 2) identify major irrigated crop types and their water requirements; and 3) quantify groundwater crop water use from the NSAS using remote sensing via the Google …

Essays On Artificial Intelligence (Ai) In Management, Bowen Zhou

Dissertations and Theses Collection (Open Access)

This dissertation comprises three essays that investigate the transformative potential of Artificial Intelligence (AI) in business.

Chapter 1 investigates the fundamental issue of how integrating AI within R&D activities influences a firm’s market value. We developed an "AI Index" using patent data and textual analysis. Interestingly, empirical results indicate a negative correlation between AI integration and market value. However, this does not suggest that AI is an unviable avenue for exploration. Further analysis of the boundary conditions reveals that complementary assets are crucial for successful commercialisation, highlighting that while AI adoption is costly, these assets significantly enhance its market value. …

Decentralized Consensus And Governance For Collaborative Intelligence, Huiwen Liu

Dissertations and Theses Collection (Open Access)

The data economy today is becoming increasingly collaborative in nature. Take business intelligence, for example. To unleash the full potential of big data, it is essential to integrate multi-source data depicting entities from a multi-faceted and multi-modal perspective, which, not surprisingly, is not achievable by any company alone. In collaborative intelligence, there are two core issues, namely "trust" and "incentive". The core mechanisms to solve these two problems are consensus and tokenization separately.

To solve the trust problem more effectively, we propose a systematic consensus evaluation framework to investigate whether existing consensus algorithms can do so. After a lot of …

Explainable Artificial Intelligence: Methods And Evaluation, Gayane Grigoryan

Engineering Management & Systems Engineering Theses & Dissertations

A wide array of techniques within explainable artificial intelligence (XAI) have been developed to measure the importance of features in machine learning models. A notable portion of these methods draws upon principles of cooperative game theory (CGT), with the Shapley value emerging as a widely used solution concept. Despite the rising prominence of the Shapley value, other promising solutions from cooperative game theory—such as the Nucleolus, Banzhaf power index, Shapley-Shubik power index, and solutions to conflicting claims problems—have been comparatively overlooked, even though they hold significant potential. In this dissertation, multiple XAI methods based on these other CGT solutions are …

Who Wrote The Scientific News? Improving The Discernibility Of Llms To Human-Written Scientific News, Dominik Soós

Computer Science Theses & Dissertations

Large Language Models (LLMs) have rapidly advanced the field of Natural Language Processing and become powerful tools for generating and evaluating scientific text. Although LLMs have demonstrated promising as evaluators for certain text generation tasks, there is still a gap until they are used as reliable text evaluators for general purposes. In this thesis project, I attempted to fill this gap by examining the discernibility of LLMs from human-written and LLM-generated scientific news. This research demonstrated that although it was relatively straightforward for humans to discern scientific news written by humans from scientific news generated by GPT-3.5 using basic prompts, …

Privacy-Preserving Deep Learning Framework For Iot Malware Detection, Sabbir Ahmed Khan

Computer Science Theses & Dissertations

Cyberattacks on IoT devices are accelerating at an unprecedented rate, largely driven by IoT malware activities. The IoT malware attacks typically comprise three stages: intrusion, infection, and monetization. Existing IoT malware detection methods fail to identify malicious activities at the intrusion and infection stages and thus cannot stop potential attacks timely. In our research, we have leveraged power side-channel information as input to our deep learning model to identify malware at early stages of intrusion on IoT devices. But, deploying a resource-intensive deep learning model on highly resource-constrained IoT devices is a significant challenge. Consequently, utilizing a Machine Learning as …

Mesostructure Reconstruction Of Prepreg Platelet Molded Composite With Artificial Intelligence, Richard Larson

Mechanical & Aerospace Engineering Theses & Dissertations

Prepreg platelet molded composites (PPMC) are long, discontinuous fiber reinforced polymer materials. PPMC are an important subcategory of composite materials as they are processible into geometrically complex structures and can be produced via high-throughput manufacturing processes, however they have higher stiffness and strength as compared to traditional discontinuous fiber reinforced polymers. However, there is inherent randomness in the structure of PPMCs and as such, PPMC parts frequently require per part testing that is cost prohibitive.

Herein, a method using artificial intelligence is proposed as a more cost-effective method of inspecting PPMC parts. Different artificial intelligence (AI) architectures are explored to …

Deep Learning In Reproducing Kernel Banach Spaces, Mingsong Yan

Mathematics & Statistics Theses & Dissertations

Deep learning has achieved immense success in the past decade. The goal of this dissertation is to understand deep learning through the framework of reproducing kernel Banach spaces (RKBSs), which were originally proposed for promoting sparse solutions. We begin by considering learning problems in a general functional setting, and establishing explicit and data-dependent representer theorems for both minimal norm interpolation (MNI) problems and regularization problems. These theorems provide a crucial foundation for the subsequent results derived for both sparse learning and deep learning. Next, we investigate the essential properties of RKBSs capable of encouraging sparsity in learning solutions. With the …

Harnessing Social Media For Disaster Response: Intelligent Identification Of Reliable Rescue Requests During Hurricanes, Wael Khallouli

Engineering Management & Systems Engineering Theses & Dissertations

Hurricanes pose a significant threat to both human lives and infrastructure. Decision-makers face substantial challenges during such events, as they must act quickly to address victims’ needs. Social media platforms provide a valuable source for quick and real-time information. Recent hurricane events have shown that people turn to social media to call for help when official communication channels, such as 911, are overwhelmed. However, extracting actionable information from the massive number of messages posted on social media is challenging. Furthermore, verifying social media messages posted by the public is a critical concern for disaster response practitioners, making them hesitant to …

Configurable Mirror Descent : Towards A Unification Of Decision Making, Pengdeng Li, Shuxin Li, Chang Yang, Xinrun Wang, Hau Chan, Bo An

Research Collection School Of Computing and Information Systems

Decision-making problems, categorized as single-agent, e.g., Atari, cooperative multi-agent, e.g., Hanabi, competitive multi-agent, e.g., Hold’em poker, and mixed cooperative and competitive, e.g., football, are ubiquitous in the real world. Although various methods have been proposed to address the specific decision-making categories, these methods typically evolve independently and cannot generalize to other categories. Therefore, a fundamental question for decision-making is: Can we develop a single algorithm to tackle ALL categories of decision-making problems? There are several main challenges to address this question: i) different decision-making categories involve different numbers of agents and different relationships between agents, ii) different categories have different …

Topic Modeling On Document Networks With Dirichlet Optimal Transport Barycenter (Extended Abstract), Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Texts are often interconnected in a network structure, e.g., academic papers via citations. On the one hand, though Graph Neural Networks (GNNs) have shown promising ability to derive effective embeddings for networked documents, they do not assume latent topics, resulting in uninterpretahle embeddings. On the other hand, topic models can infer interpretable document representations. However, most topic models focus on plain text and fail to leverage network structure across documents. In this paper, we propose a GNN-based topic model that both captures network connection and derives semantically interpretable text representations. For network modeling, we build our model with Optimal Transport …