Skeleton-based action recognition has drawn much attention recently. Previous methods mainly focus on using RNNs or CNNs to process skeletons. But they ignore the topological structure of the skeleton which is very im...
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ISBN:
(纸本)9783030341206;9783030341190
Skeleton-based action recognition has drawn much attention recently. Previous methods mainly focus on using RNNs or CNNs to process skeletons. But they ignore the topological structure of the skeleton which is very important for action recognition. Recently, graphconvolutionalnetworks (GCNs) achieve remarkable performance in modeling non-Euclidean structures. However, current graphconvolutionalnetworks lack the capacity of modeling hierarchical information, which may be sub-optimal for classifying actions which are performed in a hierarchical way. In this work, a novel hierarchical graph convolutional network (HiGCN) is proposed to deal with these problems. The proposed model includes several hierarchicalgraphconvolutional Layers (HiGCLs). Each layer consists of an attention block and a hierarchicalgraphconvolutional block, which are used for salient feature enhancement and hierarchical representation learning, respectively. To represent hierarchical information of human actions, we propose a graph pooling method, which is differentiable and can be plugged into GCN in an end-to-end manner. Extensive experiments on two benchmark datasets show the state-of-the-art performance of our method.
Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. The goal of KT is to provide personalized learning paths for learners b...
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ISBN:
(纸本)9781450387323
Knowledge tracing (KT) which aims at predicting learner's knowledge mastery plays an important role in the computer-aided educational system. The goal of KT is to provide personalized learning paths for learners by diagnosing the mastery of each knowledge, thus improving the learning efficiency. In recent years, many deep learning models have been applied to tackle the KT task, which has shown promising results. However, most existing methods simplify the exercising records as knowledge sequences, which fail to explore the rich information that existed in exercises. Besides, the existing diagnosis results of knowledge tracing are not convincing enough since they neglect hierarchical relations between exercises. To solve the above problems, we propose a hierarchicalgraph knowledge tracing model called HGKT to explore the latent complex relations between exercises. Specifically, we introduce the concept of problem schema to construct a hierarchical exercise graph that could model the exercise learning dependencies. Moreover, we employ two attention mechanisms to highlight important historical states of learners. In the testing stage, we present a knowledge&schema diagnosis matrix that could trace the transition of mastery of knowledge and problem schema, which can be more easily applied to different applications. Extensive experiments show the effectiveness and interpretability of our proposed model.
Understanding the fatigue behavior and accurately predicting the fatigue life of laser powder bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms, time-consuming tests, and limited f...
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Understanding the fatigue behavior and accurately predicting the fatigue life of laser powder bed fusion (L-PBF) parts remain a pressing challenge due to complex failure mechanisms, time-consuming tests, and limited fatigue data. This study proposes a physics-informed data-driven framework, a multimodal transfer learning (MMTL) framework, to understand process-defect-fatigue relationships in L-PBF by integrating various modalities of fatigue performance, including process parameters, XCT-inspected defects, and fatigue test conditions. It aims to leverage a pre-trained model with abundant process and defect data in the source task to predict fatigue life nondestructively with limited fatigue test data in the target task. MMTL employs a hierarchical graph convolutional network (HGCN) to classify defects in the source task by representing process parameters and defect features in graphs, thereby enhancing its interpretability. The feature embedding learned from HGCN is then transferred to fatigue life modeling in neural network layers, enabling fatigue life prediction for L-PBF parts with limited data. MMTL validation through a numerical simulation and real-case study demonstrates its effectiveness, achieving an F1-score of 0.9593 in defect classification and a mean absolute percentage log error of 0.0425 in fatigue life prediction. MMTL can be extended to other applications with multiple modalities and limited data.
In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning(HMARL), the diversi...
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ISBN:
(数字)9789887581581
ISBN:
(纸本)9798350366907
In practical applications of multi-agent systems, agents are often heterogeneous, and each type of them typically has different task objectives. For heterogeneous multi-agent reinforcement learning(HMARL), the diversity of agent types and the unbalanced agent number of each type can lead to the curse of dimensionality and non-stationary. Moreover, the increase in the number of heterogeneous agents may result in slow convergence during training. This paper proposes a graph-based selection-activation reinforcement learning(GSARL) method for training heterogenous multi-agent collaboration strategies. It first constructs agents based on their types, then extracts the global adjacency matrices and the ally adjacency matrices from the agents' observations, and calculates the global feature matrices. Afterwards, GSARL utilizes hierarchical graph convolutional network to sequentially convolve the global information and ally information, obtaining action logits based on agent types. By using the neural topology graph and the selection-activation method, the optimal multi-agent collaboration configuration is obtained through combinatorial optimization. Experiments are conducted in an adversarial combat simulation environment involving collaborative Unmanned Aerial Vehicles(UAVs) and Unmanned Ground Vehicles(UGVs). Simulation results show that the proposed method can accelerate convergence while allowing that each type of heterogeneous agents can leverage its unique advantages.
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