Publications

Do LLMs Meet the Needs of Software Tutorial Writers? Opportunities and Design Implications
Avinash Bhat
Disha Shrivastava
Jin L.C. Guo
Creating software tutorials involves developing accurate code examples and explanatory text that engages and informs the reader. Large Langu… (voir plus)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.
2024-06-30
Conference on Designing Interactive Systems (publié)
doi.org
A logistics provider’s profit maximization facility location problem with random utility maximizing followers
David Pinzon Ulloa
Emma Frejinger
Bernard Gendron
2024-06-30
Computers & Operations Research (publié)
doi.org
arxiv.org
Motivating Users to Attend to Privacy: A Theory-Driven Design Study
Varun Shiri
Maggie Xiong
Jinghui Cheng
Jin L.C. Guo
In modern technology environments, raising users’ privacy awareness is crucial. Existing efforts largely focused on privacy policy present… (voir plus)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.
2024-06-30
Designing Interactive Systems Conference (publié)
doi.org
arxiv.org
One-shot Learning for MIPs with SOS1 Constraints
Charly Robinson La Rocca
Jean-François Cordeau
Emma Frejinger
2024-06-30
Operations Research Forum (publié)
doi.org
arxiv.org
Overcoming Boundaries: Interdisciplinary Challenges and Opportunities in Cognitive Neuroscience
Arnaud Brignol
Anita Paas
Luis Sotelo-Castro
David St-Onge
Giovanni Beltrame
Emily B.J. Coffey
2024-06-30
Neuropsychologia (publié)
doi.org
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning
Georg Pichler
Marco Romanelli
Leonardo Rey Vega
Pablo Piantanida
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data i… (voir plus)s ever exchanged either between the clients or between the clients and the central server. In this paper, we challenge this claim by introducing a simple but still very effective membership inference attack algorithm, which relies only on a single training step. In contrast to the popular honest-but-curious model, we investigate a framework with a dishonest central server. Our strategy is applicable to models with ReLU activations and uses the properties of this activation function to achieve perfect accuracy. Empirical evaluation on visual classification tasks with MNIST, CIFAR10, CIFAR100 and CelebA datasets show that our method provides perfect accuracy in identifying one sample in a training set with thousands of samples. Occasional failures of our method lead us to discover duplicate images in the CIFAR100 and CelebA datasets.
2024-06-30
IEEE Transactions on Dependable and Secure Computing (publié)
doi.org
arxiv.org
Surface water temperature observations and ice phenology estimations for 1.4 million lakes globally
Maartje C. Korver
Bernhard Lehner
Jeffrey A. Cardille
Laura Carrea
2024-06-30
Remote Sensing of Environment (publié)
doi.org
Survey on Explainable AI: Techniques, challenges and open issues
Adel Abusitta
Miles Q. Li
Benjamin C. M. Fung
2024-06-30
Expert systems with applications (publié)
doi.org
The Position Dependence of Electron Beam Induced Effects in 2D Materials with Deep Neural Networks
Kevin M. Roccapriore
Max Schwarzer
Joshua Greaves
Jesse Farebrother
Riccardo Torsi
Rishabh Agarwal
Colton Bishop
Igor Mordatch
Ekin D. Cubuk
Aaron Courville
Bellemare Marc-Emmanuel
Joshua Robinson
Pablo Samuel Castro
Sergei V Kalinin
2024-06-30
Microscopy and Microanalysis (inconnu)
doi.org
Mirror Descent Algorithms with Nearly Dimension-Independent Rates for Differentially-Private Stochastic Saddle-Point Problems extended abstract
Tomas Gonzalez
Cristobal Guzman
Courtney Paquette
2024-06-29
Proceedings of Thirty Seventh Conference on Learning Theory (publié)
proceedings.mlr.press
proceedings.mlr.press
Variable Time Step Reinforcement Learning for Robotic Applications
Yong Wang
Giovanni Beltrame
Traditional reinforcement learning (RL) generates discrete control policies, assigning one action per cycle. These policies are usually impl… (voir plus)emented as in a fixed-frequency control loop. This rigidity presents challenges as optimal control frequency is task-dependent; suboptimal frequencies increase computational demands and reduce exploration efficiency. Variable Time Step Reinforcement Learning (VTS-RL) addresses these issues with adaptive control frequencies, executing actions only when necessary, thus reducing computational load and extending the action space to include action durations. In this paper we introduce the Multi-Objective Soft Elastic Actor-Critic (MOSEAC) method to perform VTS-RL, validating it through theoretical analysis and experimentation in simulation and on real robots. Results show faster convergence, better training results, and reduced energy consumption with respect to other variable- or fixed-frequency approaches.
2024-06-28
ArXiv (prépublication)
doi.org
arxiv.org
Adversarial Training with Synthesized Data: A Path to Robust and Generalizable Neural Networks
Reza Bayat
Irina Rish
Adversarial Training (AT) is a well-known framework designed to mitigate adversarial vulnerabilities in neural networks. Recent research ind… (voir plus)icates that incorporating adversarial examples (AEs) in training can enhance models' generalization capabilities. To understand the impact of AEs on learning dynamics, we study AT through the lens of sample difficulty methodologies. Our findings show that AT leads to more stable learning dynamics compared to Natural Training (NT), resulting in gradual performance improvements and less overconfident predictions. This suggests that AT steers training away from learning easy, perturbable spurious features toward more resilient and generalizable ones. However, a trade-off exists between adversarial robustness and generalization gains, due to robust overfitting, limiting practical deployment. To address this, we propose using synthesized data to bridge this gap. Our results demonstrate that AT benefits significantly from synthesized data, whereas NT does not, enhancing generalization without compromising robustness and offering new avenues for developing robust and generalizable models.
2024-06-27
ICML.cc/2024/Workshop/NextGenAISafety (poster)
openreview.net
openreview.net