We introduce the Open Sequential Repetitive Action Counting (OSRAC) task, which aims to count all repetitions and locate transition boundaries of sequential actions from large-scale video data, without relying on predefined action categories. Unlike the Repetitive Action Counting (RAC) task that focuses on a single-action assumption, OSRAC handles diverse and alternating repetitive action sequences in real-world scenarios, which is fundamentally more challenging. To this end, we propose UniCount, a universal system capable of counting multiple sequential repetitive actions from video data. Specifically, UniCount designs three primary modules: the Universal Repetitive Pattern Learner (URPL) to capture general repetitive patterns in alternating actions, Temporal Action Boundary Discriminator (TABD) to locate the action transition boundaries, and Dual Density Map Estimator (DDME) to achieve action counting and repetition segmentation. We also design a novel actionness loss to improve the detection of action transitions. To support this task, we conduct in-depth data analysis on existing RAC datasets and construct several OSRAC benchmarks (i.e., MUCFRep, MRepCount, and MInfiniteRep) by developing a pipeline on data processing and mining. We further perform comprehensive experiments to evaluate the effectiveness of UniCount. On MInfiniteRep, UniCount substantially improves the Off-By-One Accuracy (OBOA) from 0.39 to 0.78 and decreases the Mean Absolute Error (MAE) from 0.29 to 0.14 compared to counterparts. UniCount also achieves superior performance in open-set data, showcasing its universality.

The modern university computer lab and kindergarden through 12th grade classrooms require a centralized solution to efficiently manage a large number of desktops. The existing solutions either bring virtualization overhead in runtime or requires loading a large image over 30 GB leading to an unacceptable network latency. In this work, we propose Troy which takes advantage of the differencing virtual hard disk techniques in Windows systems. As such, Troy only loads the modifications made on one machine to all other machines. Troy consists of two modules that are responsible to generate an initial image and merge a differencing image with its parent image, respectively. Specifically, we identify the key fields in the virtual hard disk image that links the differencing image and the parent image and find the modified blocks in the differencing images that should be used to replace the blocks in the parent image. We further design a lazy copy solution to reduce the I/O burden in image merging. We have implemented Troy on bare metal machines. The evaluation results show that the performance of Troy is comparable to the native implementation in Windows, without requiring the Windows environment.