Spatio-temporal Attention Network for Student Action Recognition in Classroom Teaching Videos

Inspired by the wide application of transformer in computer vision and its excellent ability in temporal feature learning. This paper proposes a novel and efficient spatio-temporal residual attention network for student action recognition in classroom teaching video. It first fuses 2D spatial convolution and 1D temporal convolution to study spatio-temporal feature, then combines the powerful Reformer to better study the deeper spatio-temporal characteristics with visual significance of student classroom action. Based on the spatio-temporal residual attention network, a single person action recognition model in classroom teaching video is proposed. Considering that there are often multiple students in the classroom video scene, on the basis of single person action recognition, combined with object detection and tracking technology, the association of temporal and spatial characteristics of the same student targets is established, so as to realize the multi-student action recognition in classroom video scene. The experimental results on classroom teaching video dataset and public video dataset show that the proposed model achieves higher action recognition performance than the existing excellent models and methods.

Time-Aware Cross-Platform IoT Service Recommendation with Privacy Preservation

IoT service recommendation techniques can help a user select appropriate IoT services efficiently. Aiming at improving the recommendation efficiency and preserving the data privacy, the locality-sensitive hashing (LSH) technique is adopted in service recommendation. However, existing LSH-based service recommendation methods ignore the intrinsic temporal feature of IoT services. In light of this challenge, we integrate the temporal feature into the conventional LSH-based method and present a time-aware approach with the capability of privacy preservation for IoT service recommendation across multiple platforms. Experiments on a real-world dataset are conducted to validate the advantage of our proposed approach in terms of accuracy and efficiency in recommendation.

Temporal feature analysis method describing for automata models of computation systems

The paper reported the temporal feature analysis method describing for automata models of computation systems. The method is based on from source algorithm to modified algorithm expanding makes it possible to take characteristics of work process related to execution time measurement. The paper considers the transition method from source algorithm representation to modification a final state machine with additional states providing registration of temporal features. The paper demonstrates method usage example on computer system authorization algorithm, approves an algorithm complexity for features registration O(n) for n-states automata algorithm. The method can be used for a large class algorithm, but is recommended to apply it to an algorithm, separated on procedure thus mean of commands amount should be more than 500 for more time measurement accuracy.