Anh H. N. Nguyen

Trong-Thang Pham, Loc Nguyen, Anh Nguyen et al.

Generative diffusion models are increasingly used for medical imaging data augmentation, but text prompting cannot produce causal training data. Re-prompting rerolls the entire generation trajectory, altering anatomy, texture, and background. Inversion-based editing methods introduce reconstruction error that causes structural drift. We propose MedSteer, a training-free activation-steering framework for endoscopic synthesis. MedSteer identifies a pathology vector for each contrastive prompt pair in the cross-attention layers of a diffusion transformer. At inference time, it steers image activations along this vector, generating counterfactual pairs from scratch where the only difference is the steered concept. All other structure is preserved by construction. We evaluate MedSteer across three experiments on Kvasir v3 and HyperKvasir. On counterfactual generation across three clinical concept pairs, MedSteer achieves flip rates of 0.800, 0.925, and 0.950, outperforming the best inversion-based baseline in both concept flip rate and structural preservation. On dye disentanglement, MedSteer achieves 75% dye removal against 20% (PnP) and 10% (h-Edit). On downstream polyp detection, augmenting with MedSteer counterfactual pairs achieves ViT AUC of 0.9755 versus 0.9083 for quantity-matched re-prompting, confirming that counterfactual structure drives the gain. Code is at link https://github.com/phamtrongthang123/medsteer

Jack Le, Anh H. N. Nguyen, Tien N. Nguyen

While code obfuscation impairs human code comprehension, it remains unclear if large language models share these failure modes. Building directly on a recent human study of program comprehension under code obfuscation, we evaluate whether large language models share the failure modes that obfuscation induces in human programmers. Evaluating several LLMs with five obfuscation tiers using the Block Model, we localize comprehension failures at the atom, block, relational, and macro levels. We find that reasoning-tuned models demonstrate significant alignment with human difficulty patterns across experience levels, whereas instruction and coder-tuned models show near-zero correlation. Chain-of-Thought trace length tracks task difficulty across tasks. Results indicate that performance under control-flow flattening degrades in proportion to state-space complexity, while adversarial identifier renaming disrupts comprehension through the interaction of semantic displacement and identifier-level interference. These findings suggest that reasoning-tuned LLMs approximate human sensitivity to code complexity more effectively than instruction-tuned variants.

Hochul Hwang, Soowan Yang, Anh N. H. Nguyen et al.

Tactile Walking Surface Indicators (TWSIs) are safety-critical landmarks that blind and low-vision (BLV) pedestrians use to locate crossings and hazard zones. From our observation sessions with BLV guide dog handlers, trainers, and an O&M specialist, we confirmed the critical importance of reliable and accurate TWSI segmentation for navigation assistance of BLV individuals. Achieving such reliability requires large-scale annotated data. However, TWSIs are severely underrepresented in existing urban perception datasets, and even existing dedicated paving datasets are limited: they lack robot-relevant viewpoints (e.g., egocentric or top-down) and are geographically biased toward East Asian directional bars - raised parallel strips used for continuous guidance along sidewalks. This narrow focus overlooks truncated domes - rows of round bumps used primarily in North America and Europe as detectable warnings at curbs, crossings, and platform edges. As a result, models trained only on bar-centric data struggle to generalize to dome-based warnings, leading to missed detections and false stops in safety-critical environments.

Hoang Hiep Ly, Cong-Nhat Nguyen, Doan-Quang Tran et al.

Grasping objects with diverse mechanical properties, such as heavy, slippery, or fragile items, remains a significant challenge in robotics. Conventional rigid grippers typically rely on increasing the normal forces to secure an object, however, this can cause damage to fragile objects due to excessive force. To address this limitation, we propose a soft rigid hybrid gripper finger that combines rigid structural shells with soft, inflatable silicone pockets, which could be integrated into a conventional gripper. The hybrid gripper can actively modulate its surface friction by varying the internal air pressure of the silicone pockets, enabling the gripper to securely grasp objects without increasing the gripping force. This is demonstrated by fundamental experimental results, in which an increase in internal pressure leads to a proportional increase in the effective coefficient of friction. The gripping experiments also show that the integrated gripper can stably lift heavy and slippery objects or fragile, deformable objects, such as eggs, tofu, fruits, and paper cups, with minimal damage by increasing friction rather than applying high force.

Anh H. N. Nguyen, Jack Le, Ilse Lahnstein Coronado et al.

We investigate how code obfuscation influences human understanding of programs through an output-prediction task. To study this effect, we construct multiple levels of obfuscation, ranging from unobfuscated code to transformations involving identifier renaming, adversarially misleading identifiers, control-flow modifications, and combinations of these techniques. These transformations are applied to function-level programs written in Python and JavaScript. Participants were asked to predict program outputs while we recorded correctness, response time, and self-reported programming experience. Our results show that obfuscation generally increases the time required to reason about code and tends to reduce prediction accuracy. However, the relationship between obfuscation strength and performance is not strictly monotonic and varies across programming languages. JavaScript exhibits the expected pattern of increasing difficulty with stronger obfuscation, whereas Python displays a more complex trend in which certain renaming transformations can perform comparably to, or occasionally better than, the unobfuscated baseline. Response-time analyses further suggest that obfuscation shifts participants away from rapid, heuristic reasoning toward slower and more deliberate reasoning processes. Performance appears highest within a moderate range of response times, indicating that careful deliberation can improve accuracy, while extremely long response times often correspond to confusion. Finally, programming experience predicts performance primarily within a given language, with limited transfer across languages, suggesting that obfuscation challenges language-specific familiarity more than general programming ability.