Automatic Macro Mining from Interaction Traces at Scale

Macros are building block tasks of our everyday smartphone activity (e.g., "login", or "booking a flight"). Effectively extracting macros is important for understanding mobile interaction and enabling task automation. These macros are however difficult to extract at scale as they can be comprised of multiple steps yet hidden within programmatic components of the app. In this paper, we introduce a novel approach based on Large Language Models (LLMs) to automatically extract semantically meaningful macros from both random and user-curated mobile interaction traces. The macros produced by our approach are automatically tagged with natural language descriptions and are fully executable. To examine the quality of extraction, we conduct multiple studies, including user evaluation, comparative analysis against human-curated tasks, and automatic execution of these macros. These experiments and analyses show the effectiveness of our approach and the usefulness of extracted macros in various downstream applications.

PLay: Parametrically Conditioned Layout Generation using Latent Diffusion

Layout design is an important task in various design fields, including user interfaces, document, and graphic design. As this task requires tedious manual effort by designers, prior works have attempted to automate this process using generative models, but commonly fell short of providing intuitive user controls and achieving design objectives. In this paper, we build a conditional latent diffusion model, PLay, that generates parametrically conditioned layouts in vector graphic space from user-specified guidelines, which are commonly used by designers for representing their design intents in current practices. Our method outperforms prior works across three datasets on metrics including FID and FD-VG, and in user test. Moreover, it brings a novel and interactive experience to professional layout design processes.

Sketch-based Creativity Support Tools using Deep Learning

Sketching is a natural and effective visual communication medium commonly used in creative processes. Recent developments in deep-learning models drastically improved machines' ability in understanding and generating visual content. An exciting area of development explores deep-learning approaches used to model human sketches, opening opportunities for creative applications. This chapter describes three fundamental steps in developing deep-learning-driven creativity support tools that consumes and generates sketches: 1) a data collection effort that generated a new paired dataset between sketches and mobile user interfaces; 2) a sketch-based user interface retrieval system adapted from state-of-the-art computer vision techniques; and, 3) a conversational sketching system that supports the novel interaction of a natural-language-based sketch/critique authoring process. In this chapter, we survey relevant prior work in both the deep-learning and human-computer-interaction communities, document the data collection process and the systems' architectures in detail, present qualitative and quantitative results, and paint the landscape of several future research directions in this exciting area.

Creating User Interface Mock-ups from High-Level Text Descriptions with Deep-Learning Models

The design process of user interfaces (UIs) often begins with articulating high-level design goals. Translating these high-level design goals into concrete design mock-ups, however, requires extensive effort and UI design expertise. To facilitate this process for app designers and developers, we introduce three deep-learning techniques to create low-fidelity UI mock-ups from a natural language phrase that describes the high-level design goal (e.g. "pop up displaying an image and other options"). In particular, we contribute two retrieval-based methods and one generative method, as well as pre-processing and post-processing techniques to ensure the quality of the created UI mock-ups. We quantitatively and qualitatively compare and contrast each method's ability in suggesting coherent, diverse and relevant UI design mock-ups. We further evaluate these methods with 15 professional UI designers and practitioners to understand each method's advantages and disadvantages. The designers responded positively to the potential of these methods for assisting the design process.

Scones: Towards Conversational Authoring of Sketches

Iteratively refining and critiquing sketches are crucial steps to developing effective designs. We introduce Scones, a mixed-initiative, machine-learning-driven system that enables users to iteratively author sketches from text instructions. Scones is a novel deep-learning-based system that iteratively generates scenes of sketched objects composed with semantic specifications from natural language. Scones exceeds state-of-the-art performance on a text-based scene modification task, and introduces a mask-conditioned sketching model that can generate sketches with poses specified by high-level scene information. In an exploratory user evaluation of Scones, participants reported enjoying an iterative drawing task with Scones, and suggested additional features for further applications. We believe Scones is an early step towards automated, intelligent systems that support human-in-the-loop applications for communicating ideas through sketching in art and design.

Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications

Sketching and natural languages are effective communication media for interactive applications. We introduce Sketchforme, the first neural-network-based system that can generate sketches based on text descriptions specified by users. Sketchforme is capable of gaining high-level and low-level understanding of multi-object sketched scenes without being trained on sketched scene datasets annotated with text descriptions. The sketches composed by Sketchforme are expressive and realistic: we show in our user study that these sketches convey descriptions better than human-generated sketches in multiple cases, and 36.5% of those sketches are considered to be human-generated. We develop multiple interactive applications using these generated sketches, and show that Sketchforme can significantly improve language learning applications and support intelligent language-based sketching assistants.

t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data

Modern datasets and models are notoriously difficult to explore and analyze due to their inherent high dimensionality and massive numbers of samples. Existing visualization methods which employ dimensionality reduction to two or three dimensions are often inefficient and/or ineffective for these datasets. This paper introduces t-SNE-CUDA, a GPU-accelerated implementation of t-distributed Symmetric Neighbor Embedding (t-SNE) for visualizing datasets and models. t-SNE-CUDA significantly outperforms current implementations with 50-700x speedups on the CIFAR-10 and MNIST datasets. These speedups enable, for the first time, visualization of the neural network activations on the entire ImageNet dataset - a feat that was previously computationally intractable. We also demonstrate visualization performance in the NLP domain by visualizing the GloVe embedding vectors. From these visualizations, we can draw interesting conclusions about using the L2 metric in these embedding spaces. t-SNE-CUDA is publicly available at https://github.com/CannyLab/tsne-cuda