Association for the Advancement of Artificial Intelligence: AAAI Publications
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Data Attribution: A Data-Centric Approach for Trustworthy AI Development
No full textData plays an increasingly crucial role in both the performance and the safety of AI models. Data attribution is an emerging family of techniques aimed at quantifying the impact of individual training data points on a model trained on them, which has found data-centric applications such as instance-based explanation, unsafe training data detection, and copyright compensation. In this talk, I will comprehensively review our work contributing to the applications, methods, and open-source benchmarks of data attribution, and discuss open challenges in this field
Automating the Expansion of Instrument Typicals in Piping and Instrumentation Diagrams (P&IDs)
No full textWithin the Engineering, Procurement, and Construction (EPC) industry, engineers manually create documents based on engineering drawings, which can be time-consuming and prone to human error. For example, the expansion of typical assemblies of instrument items (Instrument Typicals) in Piping and Instrumentation Diagrams (P&IDs) is a labor-intensive task. Each Instrument Typical assembly is depicted in the P&IDs via a simplified representation showing only a subset of the utilized instruments. The expansion activity involves recording all utilized instruments to create an instrument item list document based on the P&IDs for a particular EPC project. Fortunately, Artificial Intelligence (AI) could help to automate this process.
In this paper, we propose the first method for automating the process of Instrument Typical expansion in P&IDs. The method utilizes computer vision techniques and domain knowledge rules to extract information about the Instrument Typicals from a project's P&IDs and legend sheets. Subsequently, the extracted information is used to automatically generate the listing of all utilized instruments. The effectiveness of our method is evaluated on P&IDs from large industrial EPC projects, resulting in precision rates exceeding 98% and recall rates surpassing 99%. These results demonstrate the suitability of our method for industrial deployment. The successful application of our method has the potential to reduce engineering costs and increase the efficiency of EPC projects. Furthermore, the method could be adapted for additional applications in the EPC industry, which highlights the method's industrial value
Empowering Educators in AI: Insights from Co-Designing an AI Microcredential with and for K-12 Educators
No full textThis paper examines the co-design process for a foundational AI microcredential course targeting K-12 teachers' knowledge, agency, and effectiveness in integrating AI into their classrooms. We collaborated with six K-12 teachers and instructional coaches to ensure the course's relevance and practicality. Using conjecture mapping and memoing, we systematically captured and analyzed insights from the collaborative process. These methods helped us pinpoint essential themes and requirements for effective professional development (PD) that meets the unique challenges and opportunities of teaching about and using AI in K-12 classrooms. Themes included concerns about in-class monitoring for unethical impacts of AI integration and the desire for empowerment in evaluating and selecting AI tools that they can best leverage to meet state and national standards. Educator requirements centered on the creation of quick, easily accessible, and asynchronous learning activities. In addition, educators requested just-in-time AI integration resources and learning opportunities that can be leveraged throughout the year, rather than being limited to PD sessions. This study contributes to AI education by providing a framework for designing teacher professional development programs that are responsive to the evolving educational landscape and the specific needs of K-12 teachers
Towards Addressing Frontiers in Graph Generation
No full textThis doctoral dissertation establishes and addresses frontiers in graph generation. I first apply a Graph Neural Network (GNN) model on social network data, a new domain, to establish what frontiers exist for graph generators. I establish that GNN models are currently limited in the diversity of feature sets that they can produce, the variety of graph structure types they can generate, and highly limited in the size of generated graphs. Further, I find that the quality metrics available for graph generation are aggregate-based and un-expressive. To address the issue of scale I propose Hierarchical Generation of Graphs (HiGGs), a framework for producing graphs orders of magnitude larger than is possible with a single model. As a step towards more expressive metrics I develop Topology only Pre-training (ToP), a pre-training framework for graph models that is capable of representing multiple domains of graphs simultaneously, without relying on tertiary models in downstream applications. The next stage of research will adapt ToP as a model based metric for graph generators
Tracking and Identifying International Propaganda and Influence Networks Online
No full textMisinformation and propaganda undermine trust in institutions, spread falsehoods, and sometimes incite violence. However, recent advancements in transformer-based AI models can help combat the proliferation of disinformation globally and in real time. In this work, I propose and develop a system using these models to scalably identify, track, and analyze the spread of narratives from over 40,000 international news websites. First, by employing novel multilingual Matryoshka embeddings and hierarchical level-wise clustering, my proposed system identifies news stories, topics, and themes across these thousands of news websites. Second, by utilizing multilingual stance detection, my system assesses the biases and factual inconsistencies in news articles, enabling the identification of websites that spread propaganda or misinformation. Finally, through network inference methods, my system uncovers connections among websites disseminating slanted or false content. My approach illustrates how AI can be utilized to mitigate the global spread of harmful misinformation and propaganda
Hyper-Spectral Image Generation from Frequency Spectrums
No full textMy thesis primarily focuses on hyper-spectral image generation from frequency spectrums for downstream computer vision tasks. Hyper-spectral images are images with more than three channels commonly created by special hyper-spectral cameras or from frequency spectrums of various sensing applications such as radargrams or distributed acoustic sensing (DAS) systems. The range of frequencies considered in a frequency spectrum is typically too large to map one frequency to one image channel, i.e. we generally consider a frequency spectrum of 2500 Hz. Frequencies need to be binned together in frequency bands where each band forms one image channel. Usually, frequency bands are created either by expert knowledge or trial-and-error.
I research how filters can be trained to automatically select frequencies and bin them into frequency bands. My aim is to represent a variety of signal information and decrease noise. Signal representation is optimised for object detection on time-sequenced images with a set number of image channels. The object detection task consists of localising and classifying events in the generated hyper-spectral images. Events are typically types of intrusions, structural changes, or defined actions and structures, e.g. someone climbing a fence. Events and noise often share at least some frequencies and vary between application types
Towards Autonomous Network Management: AI-Driven Framework for Intelligent Log Analysis, Troubleshooting and Documentation
No full textAs modern network management grows increasingly complex, administrators are tasked with navigating vast volumes of log data, often resulting in inefficiencies, errors, and operational challenges. My doctoral research addresses these pressing issues by leveraging advanced AI techniques to minimize human intervention and pave the way for fully automated network operations. I propose a novel AI-driven framework that integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and a human-in-the-loop process to effectively automate key network management tasks, including log analysis, troubleshooting recommendations, and documentation generation. By enhancing the accuracy and efficiency of these tasks, this study aims to improve network reliability, reduce operational complexity, and contribute to the evolution of self-running networks
Hotspotter: A Generalizable Pipeline for Automated Detection of Subtle Volcanic Thermal Features in Satellite Images
No full textGeologists seek to understand the relationship between volcanic unrest and eruptions by identifying subtle Volcanic Thermal Features (VTFs) in high-resolution satellite imagery. This analysis requires the careful curation of large databases of relevant volcanic thermal information. However, volcanic unrest is characterized by highly subtle thermal anomalies. Manual identification on a global scale is highly labor- and time-intensive. We propose Hotspotter: an end-to-end system to automatically detect subtle volcanic thermal anomalies in satellite images and derive relevant thermal statistics. Previous solutions for automated VTF detection have limited data size and geographic diversity. To accommodate an unprecedentedly large and diverse volcanic dataset, we propose an automated pipeline combining unsupervised anomaly detection with supervised classification to filter anomalous regions. Hotspotter gives 90% anomaly detection accuracy and robust generalization to new volcanoes. Our automated approach can accelerate scientists' search for VTFs to help identify relevant thermal precursors and enable more precise forecasts of global volcanic eruptions
A Hybrid Approach for Visual Recognition of Object States
No full textThe basic objective of my research work is to address the challenging problem of recognizing object states in a visual context by integrating data-driven and symbolic approaches. In particular, I focus on the Zero-shot variation of this task. The contributions made so far include the development of novel methods that exhibit state-of-the-art (SOTA) performance, the creation of a new object states dataset, the formulation of novel problems, the successful integration of low-level and high-level approaches, and comprehensive analyses that highlight the specific challenges posed by the problem
Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks
No full textPretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on NLP tasks, but their black-box nature, which leads to a lack of interpretability, has been a major concern. My dissertation focuses on developing intrinsically interpretable models when using LMs as encoders while maintaining their superior performance via prototypical networks. I initiated my research by investigating enhancements in performance for interpretable models of sarcasm detection. My proposed approach focuses on capturing sentiment incongruity to enhance accuracy while offering instance-based explanations for the classification decisions. Later, we develop a novel white-box multi-head graph attention-based prototypical framework designed to explain the decisions of text classification models without sacrificing the accuracy of the original black-box LMs. In addition, I am working on extending the attention-based prototypical framework with contrastive learning to redesign an interpretable graph neural network for document classification, aiming to enhance both the interpretability and performance of the model in document classification