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Next activity prediction of business processes (BPs) provides valid execution information of ongoing (i.e., unfinished) process instances, which enables process executors to rationally allocate resources and detect process deviations in advance. Current researches on next activity prediction, however, concentrate mostly on model construction without in‐depth analysis of historical event logs. In this article, we are dedicated to proposing an approach to forecast the next activity effectively in BPs. After in‐depth analysis of historical event logs, three types of candidate activity attributes are defined and calculated as additional input for the prediction based on three essential elements, that is, frequent activity patterns, trace similarity and position information. Furthermore, we construct an effective hybrid prediction model combining the popular convolutional neural network (CNN) and bidirectional long short‐term memory (Bi‐LSTM) with self‐attention mechanism. Specifically, CNN is used to extract the temporal features before importing into Bi‐LSTM for accurate prediction, and self‐attention mechanism is applied to strengthen features that have decisive effects on the prediction results. Comparison experiments on four real‐life datasets demonstrate that our hybrid model with selected attributes achieves better performance on next activity prediction than single models, and improves the prediction accuracy by 2.98%, 6.05%, 2.70% and 5.26% on Helpdesk, Sepsis, BPIC2013 Incidents and BPIC2012O datasets than the state‐of‐the‐art methods, respectively. [ABSTRACT FROM AUTHOR]

As a new software design paradigm, microservices structure an application as a collection of services that are independently deployable and loosely coupled. A key step of migrating non‐microservices‐based systems to microservices‐based systems is the identification of microservices in the target application. Traditional approaches to identify microservices, however, usually suffer from lack of full automation and low effectiveness. This paper puts forward an expert system to identify microservices automatically from legacy systems by leveraging the similarity of RESTful APIs. The system consists of three major parts. The first part calculates the candidate topic similarity and the response message similarity of APIs, and the overall similarity is obtained through their combination. Afterwards, the second part constructs a graph of API similarities with API as the node and the overall similarity as the weight. The third part employs a graph‐based clustering algorithm to identify candidate microservices from the API similarity graph. Experiments conducted on open‐source projects demonstrate the effectiveness of our system. [ABSTRACT FROM AUTHOR]

Traffic prediction aims to predict the future traffic state by mining features from history traffic information, and it is a crucial component for the intelligent transportation system. However, most existing traffic prediction methods focus on road segment prediction while ignore the fine-grainedlane-level traffic prediction. From observations, we found that different lanes on the same road segment have similar but not identical patterns of variation. Lane-level traffic prediction can provide more accurate prediction results for humans or autonomous driving systems to make appropriate and efficient decisions. In traffic prediction, the mining of spatial features is an important step and graph-based methods are effective methods. While most existing graph-based methods construct a static adjacent matrix, these methods are difficult to respond to spatio-temporal changes in time. In this paper, we propose a deep learning model for lane-level traffic prediction. Specifically, we take advantage of the graph convolutional network (GCN) with a data-driven adjacent matrix for spatial feature modeling and treat different lanes of the same road segment as different nodes. The data-driven adjacent matrix consists of the fundamental distance-based adjacent matrix and the dynamic lane correlation matrix. The temporal features are extracted with the gated recurrent unit (GRU). Then, we adaptively fuse spatial and temporal features with the gating mechanism to get the final spatio-temporal features for lane-level traffic prediction. Extensive experiments on a real-world dataset validate the effectiveness of our model., Author(s): Bao Li [1]; Quan Yang [2]; Jianjiang Chen [3]; Dongjin Yu [3]; Dongjing Wang (corresponding author) [3]; Feng Wan [3] 1. Introduction Intelligent transportation systems (ITS) include driving behaviour [...]

Many studies have shown that antibiotic resistance genes (ARGs) can be facilitated by a variety of antibacterial substances. Cyanobacteria are photosynthetic bacteria that are widely distributed in the ocean. Some extracellular substances produced by marine cyanobacteria have been found to possess antibacterial activity. However, the impact of these extracellular substances on ARGs is unclear. Therefore, we established groups of seawater microcosms that contained different concentrations (1000, 100, 10, 1, 0.1, 0.01, and 0 μg mL−1) of cyanobacterial extracellular substances (CES), and tracked the changes of 17 types of ARGs, the integron gene (intI1), as well as the bacterial community at different time points. The results showed that CES could enrich most ARGs (15/17) in the initial stage, particularly at low concentrations (10 and 100 μg mL−1). The correlation analysis showed a positive correlation between several ARGs and intI1. It is suggested that the abundance of intI1 increased with CES may contribute to the changes of these ARGs, and co-resistance of CES may be the underlying reason for the similar variation pattern of some ARGs. Moreover, the results of qPCR and high-throughput sequencing of 16S rRNA showed that CES had an inhibitory impact on the growth of bacterial communities. High concentrations of CES were found to alter the structure of bacterial communities. Co-occurrence networks showed that bacteria elevated in the high concentration group of CES and might serve as the potential hosts for a variety of ARGs. In general, marine cyanobacteria could play an important role in the global dissemination of ARGs and antibiotic-resistant bacteria (ARBs). [ABSTRACT FROM AUTHOR]

Code vulnerabilities can have serious consequences such as system attacks and data leakage, making it crucial to perform code vulnerability detection during the software development phase. Deep learning is an emerging approach for vulnerability detection tasks. Existing deep learning‐based code vulnerability detection methods are usually based on word2vec embedding of linear sequences of source code, followed by code vulnerability detection through RNNs network. However, such methods can only capture the superficial structural or syntactic information of the source code text, which is not suitable for modeling the complex control flow and data flow and miss edge information in the graph structure constructed by the source code, with limited effect of neural network model. To solve the above problems, this article proposes a code vulnerability detection method, named VulGraB, which is based on graph embedding and bidirectional gated graph neural networks. VulGraB uses node2vec to convert the program‐dependent graphs into graph embeddings of the code, which contain rich structure information of the source code, improving the ability of features to express nonlinear information to a certain extent. Then the BiGGNN is used for training, and finally the accuracy of the detection results is evaluated using target program. The bi‐directional gated neural network utilizes a bi‐directional recurrent structure, which is beneficial to global information aggregation. The experimental results show that the accuracy of VulGraB is significantly improved over the baseline models on two datasets, with F1 scores of 85.89% and 97.24% being the highest, demonstrating that VulGraB consistently outperforms other effective vulnerability detection models. [ABSTRACT FROM AUTHOR]

Action recognition techniques based on skeleton data are receiving more and more attention in the field of computer vision due to their ability to adapt to dynamic environments and complex backgrounds. Topologizing human skeleton data as spatial-temporal graphs and processing them using graph convolutional networks (GCNs) has been shown to produce good recognition results. However, with existing GCN methods, a fixed-size convolution kernel is often used to extract time-domain features, which may not be very suitable for multi-level model structures. Equal proportion fusion of different streams in a multi-stream network may ignore the difference in recognition ability of different streams, and these will affect the final recognition result. In this paper, we are proposing (1) a multi-scale dilated temporal graph convolution layer (MDTGCL) and (2) a multi-branch feature fusion (MFF) structure. The MDTGCL utilizes multiple convolution kernels and dilated convolution to better adapt to the multi-layer structure of the GCN model and to obtain longer periods of contextual spatial-temporal information, resulting in richer behavioural features. MFF entails weighted fusion based on the results of multi-stream outputs, and this is used to obtain the final recognition results. As higher-order skeleton data are highly discriminative and more conducive to human action recognition, we used spatial information on joints and bones and their multiple motion, as well as angle information pertaining to bones, to model together in this study. By combining the above, we designed a multi-stream, multi-scale dilated spatial-temporal graph convolutional network (2M-STGCN) model and conducted extensive experiments with two large datasets (NTU RGB+D 60 and Kinetics Skeleton 400), which showed that our model performs at SOTA level. [ABSTRACT FROM AUTHOR]

Graph convolutional networks are widely used in skeleton-based action recognition because of their good fitting ability to non-Euclidean data. While conventional multi-scale temporal convolution uses several fixed-size convolution kernels or dilation rates at each layer of the network, we argue that different layers and datasets require different receptive fields. We use multi-scale adaptive convolution kernels and dilation rates to optimize traditional multi-scale temporal convolution with a simple and effective self attention mechanism, allowing different network layers to adaptively select convolution kernels of different sizes and dilation rates instead of being fixed and unchanged. Besides, the effective receptive field of the simple residual connection is not large, and there is a great deal of redundancy in the deep residual network, which will lead to the loss of context when aggregating spatio-temporal information. This article introduces a feature fusion mechanism that replaces the residual connection between initial features and temporal module outputs, effectively solving the problems of context aggregation and initial feature fusion. We propose a multi-modality adaptive feature fusion framework (MMAFF) to simultaneously increase the receptive field in both spatial and temporal dimensions. Concretely, we input the features extracted by the spatial module into the adaptive temporal fusion module to simultaneously extract multi-scale skeleton features in both spatial and temporal parts. In addition, based on the current multi-stream approach, we use the limb stream to uniformly process correlated data from multiple modalities. Extensive experiments show that our model obtains competitive results with state-of-the-art methods on the NTU-RGB+D 60 and NTU-RGB+D 120 datasets. [ABSTRACT FROM AUTHOR]