Course recommender systems can assist students in identifying suitable or appealing courses by leveraging user interaction data. However, a prevalent issue with existing course recommender systems is their tendency to prioritize accuracy over explainability. To address this limitation, we propose a novel Knowledge Entity-Aware Model for course recommendation called KEAM, which supports explicit user profile generation based on detailed information from a knowledge graph to enhance comprehension of the students. Specifically, we exploit the information within knowledge graphs using neural networks. Then, KEAM captures students' preferences and creates profiles for explainable recommendations. Comprehensive experiments are conducted on two datasets to verify the effectiveness and explainability of KEAM.

Completing a motion sequence based on sparse key-frames remains a challenging task. The limited grasp of the human skeleton's spatial structure and the complexity of handling sparsely distributed motion sequences pose challenges for traditional interpolation algorithms, hindering their ability to generate authentic and smooth results. Recent progress utilizes Graph Convolutional Networks (GCN) to analyze human skeleton data, yielding promising results. In this paper, we introduce an improved Attention-Based Graph Convolutional Network framework for motion completion that tackles two key challenges: modeling correlations across indirectly connected joints and modeling correlations across frames in motion sequences with diverse sparsity. The method is designed to augment the learning capability of the GCN-based model without being constrained by the inherent human skeleton structure. Furthermore, this design can be concurrently applied to scenarios involving the completion of both single-person and multi-person motions. Experimental results on public human action datasets NTU RGB-D affirm the Spatio-Temporal Attention-Based Graph Convolutional Network's ability to generate smooth and authentic motion results.

In recent years, there has been a growing interest in the application of Graph Convolutional Networks (GCNs) for classifying or generating human skeleton-based action sequences. Despite the progress in this field, there exists a relative dearth of research on the underlying mechanisms of how these network structures learn and represent the information features of the human skeleton. This paper proposes a novel GCN-based reconstruction network ST-ATGCN that utilizes spatial and temporal attention mechanisms for analyzing the extraction and reconstruction patterns of human action sequences. This versatile network can be effectively employed in a wide array of applications, including data compression, noise reduction, and interpolation. Experimental results on a public dataset demonstrate that ST-ATGCN network outperforms most of the currently prevailing GCN-based methods. This indicates the efficacy of the proposed network architecture in accurately extracting and reconstructing human skeleton information. Moreover, the reconstruction network exhibits proficiency in effectively restoring noisy action sequences.

The increasing autonomy and connectivity have been transitioning automobiles to complex and open architectures that are vulnerable to malicious attacks beyond conventional cyber attacks. Attackers may non-invasively compromise sensors and spoof the controller to perform unsafe actions. This concern emphasizes the need to validate sensor data before acting on them. Unlike existing works, this paper exploits inherent redundancy among heterogeneous sensors for detecting anomalous sensor measurements. The redundancy is that multiple sensors simultaneously respond to the same physical phenomenon in a related fashion. Embedding the redundancy into a deep autoencoder, we propose an anomaly detector that learns a consistent pattern from vehicle sensor data in normal states and utilizes it as the nominal behavior for the detection. The proposed method is independent of the scarcity of anomalous data for training and the intensive calculation of pairwise correlation among senors as in existing works. Using a real-world data set collected from tens of vehicle sensors, we demonstrate the feasibility and efficacy of the proposed method.