Edge agents, represented by socially-aware robots and autonomous vehicles, have gradually been integrated into human society. The safety navigation system in interactive scenes is of great importance to them. The key of this system is that the edge agent has the ability to predict the pedestrian trajectory in the dynamic scene, so as to avoid collision. However, predicting pedestrian trajectories in dynamic scenes is not an easy task, because it is necessary to comprehensively consider the spatial-temporal structure of human-environment interaction, visual attention, and the multi-modal behavior of human walking. In this paper, a scalable spatial-temporal graph generation adversarial network architecture (STG-GAN) is introduced, which can comprehensively consider the influence of human-environment interaction and generate a reasonable multi-modal prediction trajectory. First, we use LSTM nodes to flexibly transform the spatial-temporal graph of human-environment interactions into feed-forward differentiable feature coding, and innovatively propose the global node to integrate scene context information. Then, we capture the relative importance of global interactions on pedestrian trajectories through scaled dot product attention, and use recurrent sequence modeling and generative adversarial network architecture for common training, so as to generate reasonable pedestrian future trajectory distributions based on rich mixed features. Experiments on public data sets show that STG-GAN is superior to previous work in terms of accuracy, reasoning speed and rationality of trajectory prediction.

With the rapid development of the Internet of Things and Big Data, smart cities have received increasing attention. Predicting air quality accurately and efficiently is an important part of building a smart city. However, air quality prediction is very challenging because it is affected by many complex factors, such as dynamic spatial correlation between air quality detection sensors, dynamic temporal correlation, and external factors (such as road networks and points of interest). Therefore, this paper proposes a long short-term memory (LSTM) air quality prediction model based on a spatiotemporal attention mechanism (STA-LSTM). The model uses an encoder-decoder structure to model spatiotemporal features. A spatial attention mechanism is introduced in the encoder to capture the relative influence of surrounding sites on the prediction area. A temporal attention mechanism is introduced in the decoder to capture the time dependence of air quality. In addition, for spatial data such as point of interest (POI) and road networks, this paper uses the LINE graph embedding method to obtain a low-dimensional vector representation of spatial data to obtain abundant spatial features. This paper evaluates STA-LSTM on the Beijing dataset, and the root mean square error (RMSE) and R-squared ( R 2 ) indicators are used to compare with six benchmarks. The experimental results show that the model proposed in this paper can achieve better performance than the performances of other benchmarks.

Predicting and understanding pedestrian intentions is crucial for autonomous vehicles and mobile robots to navigate in a crowd. However, the movement of pedestrian is random. Pedestrian trajectory modeling needs to consider not only the past movement of pedestrians, the interaction between different pedestrians, the constraints of static obstacles in the scene, but also multi-modal of the human trajectory, which brings challenges to pedestrian trajectory prediction. Most of the existing trajectory prediction methods only consider the interaction between pedestrians in the scene, ignoring the static obstacles in the scene can also have impacts on the trajectory of pedestrian. In this paper, a scalable relative interactive spatial-temporal graph generation adversarial network architecture (RISTG-GAN) is proposed to generate a reasonable multi-modal prediction trajectory by considering the interaction effects of all agents in the scene. Our method extends recent work on trajectory prediction. First, LSTM nodes are flexibly used to model the spatial-temporal graph of human-environment interactions, and the spatial-temporal graph is converted into feed-forward differentiable feature coding, and the time attention module is proposed to capture the trajectory information in time domain and learn the time dependence in long time range. Then, we capture the relative importance of the interaction of all agents in the scene on the pedestrian trajectory through the improved relative scaled dot product attention and use the generative adversarial network architecture for training to generate reasonable pedestrian future trajectory distribution. Experiments on five commonly used real public datasets show that RISTG-GAN is better than previous work in terms of reasoning speed, accuracy and the rationality of trajectory prediction.INDEX TERMS Pedestrian trajectory prediction, spatial-temporal graph, time attention, relative scaled dot product attention, generative adversarial network.

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Exploring drug-target interactions has always been an important step in drug development. However, exploring drug-target interaction is time-consuming and laborious. A large number of studies try to use artificial intelligence methods to predict possible drug-target interactions to reduce the workload of the wet-lab identification experiments. However, the accuracy of existing methods is still limited. This paper proposes an ensemble learning-based drug-target interaction prediction method (ELDTIP in short). First, the multiple similarity matrices of drugs or proteins are integrated by singular value decomposition (SVD) to obtain their low-dimensional feature vectors. After that, by concatenating the low-dimensional feature vectors of specific drugs and targets, the feature vector of a drug-target pair are obtained. An ensemble learning model based on gradient boosting decision tree (GBDT) was constructed to predict whether this pair of drug-target can interact with each other. The main contributions of ELDTIP are as follows: (1): ELDTIP uses SVD to integrate multiple similarity matrices, which can retain more valuable information of the original feature. (2): ELDTIP uses the ensemble learning-based model, GBDT, which can make full use of the unknown DTIs in the dataset and mitigate the influence of class imbalance. Experimental results show that the performance of ELDTIP is higher than that of several state-of-the-art DTI prediction methods.