Long-tailed class distributions in camera-trap datasets hinder automated wildlife monitoring because many species appear in only a handful of training images. We test whether self-supervised visual representations from DINOv2 can improve classification of these low-frequency classes compared with standard supervised models. Using a fully decoupled approach with frozen backbones and similar linear heads, we compared DINOv2 against four convolutional ResNet variants (18/34/50/101) and a supervised Vision Transformer on the iWild-Cam 2022 dataset (201399 images, 204 classes). A location-based train/validation split mimics deployment in unseen environments, and results are reported by class-frequency segments (head, medium, tail). DINOv2 achieves a macro-F1 of 36.8%, surpassing the best supervised baseline (ResNet-18) by 6.2 percentage points, with the largest gains in medium-frequency classes and consistent improvements for tail classes. These findings suggest that self-supervised features, combined with lightweight linear classifiers, constitute a promising approach to improve classification tasks in data-scarce scenarios.

Pseudocode is a valuable resource used in programming education, software development, and scientific reports for designing algorithmic solutions as it is easy to write, understand, and modify. Since pseudocode is lacking in its ability to be tested, it is difficult to determine whether a pseudocode solution is correct or not. Software tools are specially required to reach this goal, e.g., helping professors find race conditions, deadlocks, or starvation issues while grading students’ concurrent pseudocode. Although there are various tools to work with sequential pseudocode, there is a lack of tools to work with concurrent pseudocode. This shortage motivated us to determine the state-of-the-art in notations and tools for testing concurrent and distributed pseudocode. We conducted a systematic literature review and found only a few related publications, confirming that this topic is understudied. We found and report about five software tools capable of interpreting concurrent or distributed pseudocode, and two software tools capable of verifying its correctness. As another result, no other literature review was found about this topic, conferring novelty to the contributions of this work.