Real-time recognition of driver emotions can greatly improve traffic safety. With the rapid development of communication technology, it becomes possible to process large amounts of video data and identify the driver's emotions in real time. To effectively recognize driver's emotions, this paper proposes a new deep learning framework called Convolution Bidirectional Long Short-term Memory Neural Network (CBLNN). This method predicts the driver's emotion based on the geometric features extracted from facial skin information and the heart rate extracted from changes in RGB components. The facial geometry features obtained by using Convolutional Neural Network (CNN) are intermediate variables for the heart rate analysis of Bidirectional Long Short Term Memory (Bi-LSTM). Subsequently, the output of Bi-LSTM is used as input to the CNN module to extract the hear rate features. CBLNN uses Multi-modal factorized bilinear pooling (MFB) to fuse the extracted information and classifies it into five common emotions: happiness, anger, sadness, fear and neutrality. Our emotion recognition method was tested, proving that it can be used to quickly and steadily recognize emotions in real time.

Knowledge tracing (KT) is the problem of predicting students’ future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components1 (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed AT-DKT to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., question tagging (QT) prediction task and individualized prior knowledge (IK) prediction task. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students’ global historical performance by progressively predicting student-level prior knowledge that is hidden in students’ historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that AT-DKT outperforms all sequential models with more than 0.9% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of AT-DKT. To encourage reproducible research, we make our data and code publicly available at https://github.com/pykt-team/pykt-toolkit 2.

Joint angle errors of robotic arm have great impacts on the accuracy of the end-effector, which is critical in industrial applications. Therefore, an online cognitive joint angle error compensation method based on incremental learning is proposed to reduce joint angle errors. The proposed method consists of a joint angle error solver and a compensation module, which ensure that the robot can obtain effective joint angle compensation in various situations. The compensation module uses self-feedback incremental fuzzy neural network (SFIFN) to predict and update the compensation value in real time. Specifically, two inertial measurement units mounted at the ends of links are used to measure pose changes of the ends of corresponding links. Both the simulated and the real experiments show that the proposed method yields good compensations to joint angle error and its potentials for smart manipulation.

To allow more rapid and accurate accesses in the virtual reality browsing interfaces, an amphitheater layout with varying egocentric distance-based item sizing (EDIS) is presented, and items at different egocentric distances with sizes proportional to the distances are placed. The effects of EDIS variations on the retrieval and recall performance depend on the distance configurations. Experiments 1, 2, and 3 showed that small and medium EDIS variations gave efficient trial completions in near-field distance perception for small and medium item sets. For large-item sets, the further items in the amphitheater layout may lie beyond the near-field distance perception. We therefore adopt additional 3D visual landmarks in the amphitheater layout (Experiment 4); while both the location-fixed and user-defined 3D pins greatly improved completion and accuracy performance, they showed no significant difference between them, indicating that location-fixed landmarks would be suffice. These findings suggest the use of spatial and visual landmarks for effective VR browsing interfaces.