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1. Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

   Moritz, Dominik; Wang, Chenglong; Nelson, Greg L.; Lin, Halden; Smith, Adam M.; Howe, Bill; Heer, Jeffrey (April 2019, InfoVis)

   There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments. 

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2. HADA: Hardware Assertion through Data Augmentation

   Fu, Weimin; Wang, Yiting; Lu, Zelin; Guo, Xiaolong; Qu, Gang (June 2025, IEEE Design Automation Conference (DAC) 2025)

   Hardware security verification in hardware design has been identified as a significant bottleneck due to complexity and time-to-market constraints. Assertion-Based Verification is a recognized solution to this challenge; however, assertion generation relies on expertise and labor. While LLMs show promise as automated tools, existing approaches often rely on complex prompt engineering, requiring expert validation. The challenge lies in identifying effective methods for constructing training datasets that enhance LLMs' hardware performance. We introduce HADA (Hardware Assertion through Data Augmentation), a novel framework to train hardware debug specific expert LLM by leveraging its ability to integrate knowledge from formal verification tools, hardware security knowledge database, and version control system. Our results demonstrate that integrating multi-source data significantly enhances the effectiveness of hardware security verification, with each addressing the limitations of the others. 

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3. Dziban: Balancing Agency & Automation in Visualization Design via Anchored Recommendations

   https://doi.org/10.1145/3313831.3376880  

   Lin, Halden; Moritz, Dominik; Heer, Jeffrey (April 2020, Human Factors in Computing Systems)

   Visualization recommender systems attempt to automate design decisions spanning choices of selected data, transformations, and visual encodings. However, across invocations such recommenders may lack the context of prior results, producing unstable outputs that override earlier design choices. To better balance automated suggestions with user intent, we contribute Dziban, a visualization API that supports both ambiguous specification and a novel anchoring mechanism for conveying desired context. Dziban uses the Draco knowledge base to automatically complete partial specifications and suggest appropriate visualizations. In addition, it extends Draco with chart similarity logic, enabling recommendations that also remain perceptually similar to a provided “anchor” chart. Existing APIs for exploratory visualization, such as ggplot2 and Vega-Lite, require fully specified chart definitions. In contrast, Dziban provides a more concise and flexible authoring experience through automated design, while preserving predictability and control through anchored recommendations. 

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4. Classifying Component Function in Product Assemblies With Graph Neural Networks

   https://doi.org/10.1115/1.4052720  

   Ferrero, Vincenzo; DuPont, Bryony; Hassani, Kaveh; Grandi, Daniele (February 2022, Journal of Mechanical Design)

   Abstract Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design, where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Function-based design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learning from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F1-score of 0.617 for tier 1 (broad), 0.624 for tier 2, and 0.415 for tier 3 (specific) functions. Given the imbalance of data features and the subjectivity in the definition of product function, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems and Design-for-X consideration in function-based design. 

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5. Knowledge-Based Design Analytics for Authoring Courses with Smart Learning Content

   https://doi.org/10.1007/s40593-021-00253-3  

   Albó, Laia; Barria-Pineda, Jordan; Brusilovsky, Peter; Hernández-Leo, Davinia (May 2021, International Journal of Artificial Intelligence in Education)

   null (Ed.)

   Over the last 10 years, learning analytics have provided educators with both dashboards and tools to understand student behaviors within specific technological environments. However, there is a lack of work to support educators in making data-informed design decisions when designing a blended course and planning appropriate learning activities. In this paper, we introduce knowledge-based design analytics that uncover facets of the learning activities that are being created. A knowledge-based visualization is integrated into edCrumble, a (blended) learning design authoring tool. This new approach is explored in the context of a higher education programming course, where instructors design labs and home practice sessions with online smart learning content on a weekly basis. We performed a within-subjects user study to compare the use of the design tool both with and without visualization. We studied the differences in terms of cognitive load, controllability, confidence and ease of choice, design outcomes, and user actions within the system to compare both conditions with the objective of evaluating the impact of using design analytics during the decision-making phase of course design. Our results indicate that the use of a knowledge-based visualization allows the teachers to reduce the cognitive load (especially in terms of mental demand) and that it facilitates the choice of the most appropriate activities without affecting the overall design time. In conclusion, the use of knowledge-based design analytics improves the overall learning design quality and helps teachers avoid committing design errors. 

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