Image classification tasks in scientific research often struggle with limited data, particularly when few images are available per class, necessitating effective data augmentation strategies. For snakes and other string-shaped objects, traditional augmentation methods such as flipping, adding noise, or altering colour prove inadequate in enhancing classification accuracy. This paper introduces KGOT (Keypoint-Guided Ophidian Transformation), a novel image augmentation technique designed to generate realistic, non-linear transformations of string-shaped objects. KGOT first straightens the snake image, then warps it according to a reference curve defined by key points, effectively inverting the straightening process. By utilizing advanced curve interpolation, pixel mapping, and post-processing techniques, KGOT ensures smooth, biologically plausible deformations while preserving important individual-specific features. Our experiments demonstrate that KGOT-augmented data increase accuracy compared to the traditional augmentation method using geographic transformation techniques. Although some limitations persist in scenarios where snake bodies overlap, the success of KGOT augmentation provides valuable information on factors that improve classification performance. This approach holds promise not only for snake individual identification but also for a broader range of applications involving string-shaped object classification tasks.

Large Language Models (LLMs) have demonstrated remarkable success in tasks like the Winograd Schema Challenge (WSC), showcasing advanced textual common-sense reasoning.However, applying this reasoning to multimodal domains, where understanding text and images together is essential, remains a substantial challenge.To address this, we introduce WINOVIS, a novel dataset specifically designed to probe text-to-image models on pronoun disambiguation within multimodal contexts.Utilizing GPT-4 for prompt generation and Diffusion Attentive Attribution Maps (DAAM) for heatmap analysis, we propose a novel evaluation framework that isolates the models' ability in pronoun disambiguation from other visual processing challenges.Evaluation of successive model versions reveals that, despite incremental advancements, Stable Diffusion 2.0 achieves a precision of 56.7% on WINOVIS, showing minimal improvement from past iterations and only marginally surpassing random guessing.Further error analysis identifies important areas for future research aimed at advancing text-to-image models in their ability to interpret and interact with the complex visual world.

Detecting performance issues and identifying their root causes in the runtime is a challenging task. Typically, developers use methods such as logging and tracing to identify bottlenecks. These solutions are, however, not ideal as they are time-consuming and require manual effort. In this paper, we propose a method to automate the task of detecting latency outliers using system-level traces and then comparing them to identify the root cause(s). Our method makes use of dependency graphs to show internal interactions between threads and system resources. With these graphs, one can pinpoint where performance issues occur. However, a single trace can be composed of a large number of requests, each generating one graph. To automate the task of identifying outliers within the dataset, we use machine learning density-based models and statistical calculations such as$Z$-score. Our evaluation shows an accuracy greater than 97 % on outlier detection, making them appropriate for in-production servers and industry-level use cases.