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Publications
Deep learning based vessel arrivals monitoring via autoregressive statistical control charts
Creating software tutorials involves developing accurate code examples and explanatory text that engages and informs the reader. Large Langu… (see more)age Models (LLMs) demonstrate a strong capacity to generate both text and code, but their potential to assist tutorial writing is unknown. By interviewing and observing seven experienced writers using OpenAI playground as an exploration environment, we uncover design opportunities for leveraging LLMs in software tutorial writing. Our findings reveal background research, resource creation, and maintaining quality standards as critical areas where LLMs could significantly assist writers. We observe how tutorial writers generated tutorial content while exploring LLMs’ capabilities, formulating prompts, verifying LLM outputs, and reflecting on interaction goals and strategies. Our observation highlights that the unpredictability of LLM outputs and unintuitive interface design contributed to skepticism about LLM’s utility. Informed by these results, we contribute recommendations for designing LLM-based tutorial writing tools to mitigate usability challenges and harness LLMs’ full potential.
2024-07-01
Conference on Designing Interactive Systems (published)
In modern technology environments, raising users’ privacy awareness is crucial. Existing efforts largely focused on privacy policy present… (see more)ation and failed to systematically address a radical challenge of user motivation for initiating privacy awareness. Leveraging the Protection Motivation Theory (PMT), we proposed design ideas and categories dedicated to motivating users to engage with privacy-related information. Using these design ideas, we created a conceptual prototype, enhancing the current App Store product page. Results from an online experiment and follow-up interviews showed that our design effectively motivated participants to attend to privacy issues, raising both the threat appraisal and coping appraisal, two main factors in PMT. Our work indicated that effective design should consider combining PMT components, calibrating information content, and integrating other design elements, such as visual cues and user familiarity. Overall, our study contributes valuable design considerations driven by the PMT to amplify the motivational aspect of privacy communication.
2024-07-01
Designing Interactive Systems Conference (published)
Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature, with seve… (see more)ral works highlighting diverse benefits such as improving loss landscape conditioning and combatting overestimation bias. However, normalization brings with it a subtle but important side effect: an equivalence between growth in the norm of the network parameters and decay in the effective learning rate. This becomes problematic in continual learning settings, where the resulting effective learning rate schedule may decay to near zero too quickly relative to the timescale of the learning problem. We propose to make the learning rate schedule explicit with a simple re-parameterization which we call Normalize-and-Project (NaP), which couples the insertion of normalization layers with weight projection, ensuring that the effective learning rate remains constant throughout training. This technique reveals itself as a powerful analytical tool to better understand learning rate schedules in deep reinforcement learning, and as a means of improving robustness to nonstationarity in synthetic plasticity loss benchmarks along with both the single-task and sequential variants of the Arcade Learning Environment. We also show that our approach can be easily applied to popular architectures such as ResNets and transformers while recovering and in some cases even slightly improving the performance of the base model in common stationary benchmarks.
Normalization layers have recently experienced a renaissance in the deep reinforcement learning and continual learning literature, with seve… (see more)ral works highlighting diverse benefits such as improving loss landscape conditioning and combatting overestimation bias. However, normalization brings with it a subtle but important side effect: an equivalence between growth in the norm of the network parameters and decay in the effective learning rate. This becomes problematic in continual learning settings, where the resulting effective learning rate schedule may decay to near zero too quickly relative to the timescale of the learning problem. We propose to make the learning rate schedule explicit with a simple re-parameterization which we call Normalize-and-Project (NaP), which couples the insertion of normalization layers with weight projection, ensuring that the effective learning rate remains constant throughout training. This technique reveals itself as a powerful analytical tool to better understand learning rate schedules in deep reinforcement learning, and as a means of improving robustness to nonstationarity in synthetic plasticity loss benchmarks along with both the single-task and sequential variants of the Arcade Learning Environment. We also show that our approach can be easily applied to popular architectures such as ResNets and transformers while recovering and in some cases even slightly improving the performance of the base model in common stationary benchmarks.
