ConDyGNN: A Context-Aware Dynamic Graph Neural Network for Predicting Developer Churn in Open Source Communities

Temporal graph convolutional network for multi-agent reinforcement learning of action detection

This monograph presents studies concerning the challenging topic of Human Action Recognition, which has recently become of increasing importance to those working in the field of Computer Vision. It contains an overview of the research literature on tennis movements recognition based on video, images, motion capture data, and data registered using various sensors. In this monograph, three novel methods of tennis movements recognition are presented in detail with success, achieving high performance of the proposed classifiers. They all utilize Graph Convolutional Neural networks (GCN) solutions. Two methods are also applied using attention modules which improve feature extraction and thus make the classification better. The Spatial- -Temporal Graph Convolutional Network, the Attention-Temporal Graph Convolutional Network and the Dual Attention Temporal Graph Convolutional Network are applied for recognition of the most frequently used tennis moves, such as: forehand, backhand, volley forehand, and volley backhand. Moreover, the forehand and backhand are further divided into two phases: 1) the preparation, starting from the player’s positioning until the moment just before the ball makes contact with the racket, 2) the hit together with the racket swinging until the move is finished. These separations allow detailed analysis. For the purpose of this study, a 3DTennisDS dataset was created that contains motion capture data of the forehand, backhand, volley forehand, and volley backhand. They were registered via the Vicon motion capture system. Three proposed neural networks methods were verified based on the gathered dataset. In this monograph, the interpretability aspect of GCN was also presented. This issue is important and was examined by presenting feature maps and heatmaps obtained after selected convolutional operations. These results showed how individual filters in GCNs extract features that are relevant for recognizing tennis movements.

Graph convolutional networks (GCNs) can effectively learn graph data features and are widely used to predict the bearing remaining useful life (RUL). However, most existing GCN-based methods are based on single-node relationships to construct the graph structure while ignoring potential complex relationships between nodes. To enrich graph feature information and improve bearing RUL prediction accuracy, a novel bearing RUL prediction method is proposed in this article, which use spatial–temporal information fusion graph convolution network. The proposed method mines implicit temporal dependence and spatial correlation in graph samples by constructing PathGraph and RadiusGraph, which effectively improves the feature diversity of graph samples. In addition, to efficiently extract the spatial–temporal features of graph samples and alleviate the over-smoothing problem in GCNs, a feature extraction module combining graph attention network (GAT) and graph convolution network–temporal convolution network (GCN-TCN) is designed, where GAT dynamically assigns edge weights to capture spatial dependencies, while GCN-TCN enhances global temporal dependency capture. The experimental validation results show that the proposed method effectively improves bearing RUL prediction accuracy.

Cloud‐assisted physical education teaching is an important direction for the development of smart education. However, the data processing and transmission of videos severely restrict the real‐time performance of action analysis. To this end, this paper proposes an efficient edge cloud‐assisted student movement recognition framework based on the graph convolutional network and human skeleton data. On the edge server, we use the YOLO‐pose algorithm to generate robust human skeleton sequences and design an improved spatial–temporal dual stream graph convolutional neural network with an early fusion structure, which introduces the node weight module and the dynamic graph module to exploit long‐distance dependency relationships of nodes. In the cloud server, we use a federated learning framework based on the density clustering mechanism to collaboratively train and aggregate parameters of models scattered across edge nodes. The experimental results show that our proposed model achieves excellent recognition accuracy on the self‐built sports action dataset, providing an effective solution for intelligent and real‐time feedback in outdoor sports teaching.

The main core purpose of artificial emotional intelligence is to recognize human emotions. Technologies such as facial, semantic, or brainwave recognition applications have been widely proposed. However, the abovementioned recognition techniques for emotional features require a large number of training samples to obtain high accuracy. Human behaviour pattern can be trained and recognized by the continuous movement of the Spatial Temporal Graph Convolution Network (ST-GCN). However, this technology does not distinguish between the speed of delicate emotions, and the speed of human behaviour and delicate changes of emotions cannot be effectively distinguished. This research paper proposes Spatial Temporal Variation Convolutional Network training for human emotion recognition, using skeleton detection technology to calculate the degree of skeleton point change between consecutive actions and using the nearest neighbour algorithm to classify speed levels and train the ST-GCN recognition model to obtain the emotional state. Application of the speed change recognition ability of the Spatial Temporal Variation Graph Convolution Network (STV-GCN) to artificial emotional intelligence calculation makes it possible to efficiently recognize the delicate actions of happy, sad, fear, and angry in human behaviour. The STV-GCN technology proposed in this paper is compared with ST-GCN and can effectively improve the recognition accuracy by more than 50%.

Detecting anomalous traffic behaviors with seasonal deep Kalman filter graph convolutional neural networks

Embedded industries have invested a lot in the introduction of software product lines in their software development. In addition, using open source software appears to be a profitable way to obtain good software. This is also applicable for organizations doing product line engineering. On the other hand, because of the diverse use of open source software, product line development is an attractive way of working in open source communities. In fact, the configuration mechanisms used in open source communities may be applicable within software product lines as well. In addition, product line organizations are usually involved in distributed development, which works very efficiently within open source communities. However, at present, there is limited interaction between the open source and product line development communities. Our aim is to explore what the two communities can learn from each other and to develop a better understanding of how the two communities can benefit from each other.

Current forecasting methods for complex geographic scenes often fail to adequately account for both intrinsic factors and external environmental variables, which limits their predictive accuracy. This study proposes Scene-GCN, a Graph Convolutional Network (GCN)-based model that utilizes a spatio-temporal knowledge graph (ST-KG) to integrate static and dynamic environmental factors with the scene’s spatio-temporal characteristics to predict trends. First, variations at each monitoring target point are abstracted as geo-processes, and instantaneous monitoring values are represented as geographic states, forming a coupled “process-state” ST-KG model tailored for monitoring networks in complex geographic scenes. Environmental knowledge, both dynamic and static, is then embedded within the nodes of the ST-KG, capturing historical and real-time contextual influences through feature computation. Next, a temporal graph convolutional network (T-GCN) cell is developed to quantify the relationships between spatio-temporal features, environmental knowledge, and target predictions. Experimental evaluation on a real-world time-series dataset for monitoring surface deformation in a tailings dam shows that our method outperforms baseline models. Ablation studies further show that the integration of the ST-KG enhances forecasting performance, reducing root mean square error (RMSE) by 49.2% compared to a T-GCN model without ST-KG. Additionally, leveraging a graph neural network framework improves the model’s interpretability over traditional time-series forecasting methods.

Traffic speed prediction is difficult because of the complicated dynamic spatiotemporal correlations. Recent studies in spatiotemporal models have achieved impressive outcomes for traffic speed prediction. But many studies use graphs in graph convolutional networks to learn spatial features that are often static. Additionally, effectively modeling long-range temporal features is crucial for prediction accuracy. In order to overcome these challenges, a Spatial–Temporal Adaptive Graph Convolutional Network with Transformer (ST_AGCNT) is designed in this paper. Specifically, an adaptive graph convolution network (AGCN) is designed to extract spatial dependency. An adaptive graph that fuses predefined matrices and learnable matrix is proposed to learn the correlations between nodes. The predefined matrices provide the model with richer prior information, while the learnable matrix can extract the dynamic nature of the nodes. And a temporal transformer (TT) is proposed to extract the long-range temporal dependency. In addition, to learn more information to achieve better results, different historical segments are modeled. Experiments conducted on a real-world traffic dataset confirm the effectiveness of the proposed model when compared to other baseline models. This model demonstrated excellent performance in prediction tasks across different time steps, effectively accomplishing traffic speed forecasting. It provides data support for improving traffic efficiency and reducing resource waste, contributing to the sustainable development of traffic management.

A Method of Vehicle Interactive Information Drive Speed Prediction Based on Temporal Dynamic Graph Convolutional Neural Network

Multi-channel speech enhancement aims at utilizing spatial relationships between signals captured from a microphone array along with temporal-spectral information efficiently to estimate the clean target. An emerging approach is to design deep learning-based end-to-end architectures. In this work, we provide a new way to process latent multi-channel representations. We introduce a novel end-to-end system called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal graph convolutional network</i> , which views the embedding space of multi-channel signals as a graph and leverages the power of graph neural networks (GNNs) to analyze spatial correlations as well as temporal-spectral information simultaneously. To be specific, graph convolutional networks (GCNs), a popular GNN variant, are integrated into a complex convolutional encoder-decoder structure to compute a complex ideal ratio mask. The estimated mask is subsequently multiplied with the reference microphone spectrogram to get enhanced speech. We demonstrate the superiority of our approach by comparing it to state-of-the-art methods on ConferencingSpeech 2021 Challenge data. Our results and analyses prove that GCN is a novel yet promising mechanism for speech enhancement systems, providing an interesting alternative for recent deep learning-based approaches and inspiration for future research.

In autonomous driving scenarios, pedestrian trajectory prediction is an important research direction. Based on the spatio-temporal graph convolutional neural network, we propose a new pedestrian trajectory prediction algorithm. The new algorithm constructs a series of new models around pedestrian intention estimation. The construction of the estimation algorithm considers the following aspects: the contextual information of pedestrians and the surrounding environment, the “pedestrian ego-vehicle” interaction combined with the vehicle speed estimation, the pedestrian’s own skeletal structure information and body language estimation, which includes head joints and the relative structural relationship of the torso joints, including whether it is out of the same plane, is rotated, and so on. Skeleton information feature extraction and construction adopts the method of graph convolutional neural network to structure pedestrians into joints in the form of graphs in non-Euclidean space, and further adopts spatial temporal graph convolutional network for feature extraction and learning. The new method is named a “head-torso”-based spatial temporal graph convolutional network (HT-STGCN). On the dataset PID, the novel method achieves substantial improvements over mainstream methods. Experimental results show that combining HT-STGCN with observed action can improve trajectory prediction.

Sea Surface Temperature (SST) prediction is a hot topic that has received tremendous popularity in recent years. Existing methods for SST prediction usually select one sea area of interest and conduct SST prediction by learning the spatial and temporal dependencies and patterns in historical SST data. However, global SST is a unified system of high regionality, and the SST in different sea areas shows different changing patterns due to the influence of various factors, e.g., geographic location, ocean currents and sea depth. Without a good understanding of such regionality of SST, we cannot quantitatively integrate the regionality information of SST into SST prediction models to make them adaptive to different SST patterns around the world and improve the prediction accuracy. To address this issue, we proposed the Multi-Stage Spatio–Temporal Clustering (MuSTC) method to quantitatively identify sea areas with similar SST patterns. First, MuSTC sequentially learns the representation of long-term SST with a deep temporal encoder and calculates the spatial correlation scores between grid ocean regions with self-attention. Then, MuSTC clusters grid ocean regions based on the original SST data, encoded long-term SST representation and spatial correlation scores, respectively, to obtain the sea areas with similar SST patterns from different perspectives. According to the experiments in three ocean areas, i.e., the North Pacific Ocean (NPO), the South Atlantic Ocean (SAO) and the North Atlantic Ocean (NAO), the clustering results generally match the distribution of ocean currents, which demonstrates the effectiveness of our MuSTC method. In addition, we integrate the clustering results into two representative spatio–temporal prediction models, i.e., Spatio–Temporal Graph Convolutional Networks (STGCN) and Adaptive Graph Convolutional Recurrent Network (AGCRN), to conduct SST prediction. According to the results of experiments, the integration of regionality information leads to the reduction of Root Mean Square Error (RMSE) by 1.95%, 1.39% and 1.28% in NPO, SAO and NAO, respectively, using the STGCN model, and the reduction of RMSE by 4.94%, 0.74% and 1.43% by using the AGCRN model. Such results indicate that the integration of regionality information could notably improve the prediction accuracy of SST.

A novel structural deformation prediction method based on graph convolutional network during shield tunnel construction

Spatial-temporal dual-channel adaptive graph convolutional network for remaining useful life prediction with multi-sensor information fusion