Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot

This study proposed a strategy for a quick fault recovery response when an actuator failure problem occurred while a humanoid robot with 7-DOF anthropomorphic arms was performing a task with upper body motion. The objective of this study was to develop an algorithm for joint reconfiguration of the receptionist robot called Namo so that the robot can still perform a set of emblematic gestures if an actuator fails or is damaged. We proposed a gesture similarity measurement to be used as an objective function and used bio-inspired artificial intelligence methods, including a genetic algorithm, a bacteria foraging optimization algorithm, and an artificial bee colony, to determine good solutions for joint reconfiguration. When an actuator fails, the failed joint will be locked at the average angle calculated from all emblematic gestures. We used grid search to determine suitable parameter sets for each method before making a comparison of their performance. The results showed that bio-inspired artificial intelligence methods could successfully suggest reconfigured gestures after joint motor failure within 1 s. After 100 repetitions, BFOA and ABC returned the best-reconfigured gestures; there was no statistical difference. However, ABC yielded more reliable reconfigured gestures; there was significantly less interquartile range among the results than BFOA. The joint reconfiguration method was demonstrated for all possible joint failure conditions. The results showed that the proposed method could determine good reconfigured gestures under given time constraints; hence, it could be used for joint failure recovery in real applications.

Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

The university curriculum is a systematic and organic study complex with some immediate associated steps; the initial learning of each semester’s course is crucial, and significantly impacts the learning process of subsequent courses and further studies. However, the low teacher–student ratio makes it difficult for teachers to consistently follow up on the detail-oriented learning situation of individual students. The extant learning early warning system is committed to automatically detecting whether students have potential difficulties—or even the risk of failing, or non-pass reports—before starting the course. Previous related research has the following three problems: first of all, it mainly focused on e-learning platforms and relied on online activity data, which was not suitable for traditional teaching scenarios; secondly, most current methods can only proffer predictions when the course is in progress, or even approaching the end; thirdly, few studies have focused on the feature redundancy in these learning data. Aiming at the traditional classroom teaching scenario, this paper transforms the pre-class student performance prediction problem into a multi-label learning model, and uses the attribute reduction method to scientifically streamline the characteristic information of the courses taken and explore the important relationship between the characteristics of the previously learned courses and the attributes of the courses to be taken, in order to detect high-risk students in each course before the course begins. Extensive experiments were conducted on 10 real-world datasets, and the results proved that the proposed approach achieves better performance than most other advanced methods in multi-label classification evaluation metrics.