Wearable devices running Human Activity Recognition (HAR) on Inertial Measurement Units (IMUs) waste energy by performing continuous classification for each window, even during long periods of unchanged activity. We address this with a lightweight change-detection gate: a non-parametric algorithm based on dynamic template matching that runs continuously at only approximately 16 kFLOPs per step, requires no offline training, and does not need prior definition of target activity classes. The gate invokes the full HAR network only when it detects an activity change, reducing the computational load by over 67% in realistic monitoring settings. The algorithm is evaluated on smart glasses, smartwatch, and smartphone data, requiring only a brief device-specific calibration phase. The gate achieves 98% sensitivity on UCA-EHAR, ensuring no genuine activity transition is missed, while 75 % specificity keeps unnecessary HAR invocations low. Results on WISDM are 97% sensitivity and 76% specificity, demonstrating robustness and flexibility to various settings.

Semantic eye segmentation has proven useful in different fields, including biometrics, eye-tracking, and physiological signal extraction. For this reason, identifying the areas of an image regarding the three main parts of the eye surface (sclera, iris, pupil) is of utmost importance, as it allows one to extract various meaningful information. Most studies found in the literature focus on the development of custom Deep Learning models, with an architecture tailored specifically for eye segmentation. Moreover, for tasks such as eye-tracking, the iris and the pupil are often modeled as ellipses, thus including sections of the skin in the segmented area. In this paper, we propose a method for eye segmentation based on You Only Look Once (YOLO) models, generally employed for object detection, solving the previously described issues. First, a large version of YOLOv11 (YOLOv11L-seg) was used to perform a semiautomatic labeling of the dataset, starting from a reduced number of manually generated masks. The so-obtained dataset was then used to train two different versions of YOLO (YOLOv8 nano and YOLOv11 nano), much more compact than the model used for semi-automated labeling. The two models achieved a mean Intersection Over Union (mIOU) over the three classes of 93% and 92%, respectively, while also presenting high generalization capabilities over data acquired with different hardware compared to the current state-of-the-art models.

Large Language Models (LLMs) have the potential to transform humanities and social science research, yet their history knowledge and comprehension at a graduate level remains untested. Benchmarking LLMs in history is particularly challenging, given that human knowledge of history is inherently unbalanced, with more information available on Western history and recent periods. We introduce the History Seshat Test for LLMs (HiST-LLM), based on a subset of the Seshat Global History Databank, which provides a structured representation of human historical knowledge, containing 36,000 data points across 600 historical societies and over 2,700 scholarly references. This dataset covers every major world region from the Neolithic period to the Industrial Revolution and includes information reviewed and assembled by history experts and graduate research assistants. Using this dataset, we benchmark a total of seven models from the Gemini, OpenAI, and Llama families. We find that, in a four-choice format, LLMs have a balanced accuracy ranging from 33.6% (Llama-3.1-8B) to 46% (GPT-4-Turbo), outperforming random guessing (25%) but falling short of expert comprehension. LLMs perform better on earlier historical periods. Regionally, performance is more even but still better for the Americas and lowest in Oceania and Sub-Saharan Africa for the more advanced models. Our benchmark shows that while LLMs possess some expert-level historical knowledge, there is considerable room for improvement.