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Object detection using convolutional neural networks addresses the recognition problem solely in terms of feature extraction and disregards knowledge and experience to explore higher-level relationships between objects. This paper proposed a knowledge graph network based on a graph convolution network to improve the accuracy of baseline detectors. This network can be integrated into any object detection framework. First, this paper created an experience memory module to store information about categories in the database. When inputting the image to the database, an experience vector for it was obtained. The experience data graph was then constructed by counting the co-occurrences of labels in the dataset. Finally, a graph convolutional neural network was used to extract the relationship between the experience vector and the data graph matrix. This relational pattern can help the baseline detector perform better. Several classical object detectors were then evaluated using the COCO, VOC, and KITTI datasets. The results indicated a significant increase for the baseline detector in mAP using the knowledge graph network. [ABSTRACT FROM AUTHOR]

• This is a comprehensive review of Transformer for Skeleton-based Action Recognition. • This paper proposes a new taxonomy of transformer-style techniques. • This survey aims to help researchers systematically select promising future directions. Skeleton-based action recognition has rapidly become one of the most popular and essential research topics in computer vision. The task is to analyze the characteristics of human joints and accurately classify their behaviors through deep learning technology. Skeleton provides numerous unique advantages over other data modalities, such as robustness, compactness, noise immunity, etc. In particular, the skeleton modality is extremely lightweight, which is especially beneficial for deep learning research in low-resource environments. Due to the non-European nature of skeleton data, Graph Convolution Network (GCN) has become mainstream in the past few years, leveraging the benefits of processing topological information. However, with the explosive development of transformer methods in natural language processing and computer vision, many works have applied transformer into the field of skeleton action recognition, breaking the accuracy monopoly of GCN. Therefore, we conduct a survey using transformer method for skeleton-based action recognition, forming of a taxonomy on existing works. This paper gives a comprehensive overview of the recent transformer techniques for skeleton action recognition, proposes a taxonomy of transformer-style techniques for action recognition, conducts a detailed study on benchmark datasets, compares the algorithm accuracy of standard methods, and finally discusses the future research directions and trends. To the best of our knowledge, this study is the first to describe skeleton-based action recognition techniques in the style of transformers and to suggest novel recognition taxonomies in a review. We are confident that Transformer-based action recognition technology will become mainstream in the near future, so this survey aims to help researchers systematically learn core tasks, select appropriate datasets, understand current challenges, and select promising future directions. [ABSTRACT FROM AUTHOR]

In order to analyze the content contained in the text more accurately and provide suggestions for people' s production and life, a multi-label classification model that combines multi-granularity features and label semantics on the basis of the traditional multilabel classification method based on deep learning was proposed. Bidirectional long short-term memory network(Bi-LSTM) was used in this model to extract multi-grained text features and obtain text features at different levels. The label diagram was constructed by calculating pmi, and the graph convolution network(GCN) was used to extract hidden relationships of labels and obtain text representation with label information. Finally, multi-granularity text features were fused to carry out multi-label text classification. Experiments were conducted on AAPD and news data sets. The results show that the Micro-F1 values of the proposed model are up to 0. 704 and 0. 729, respectively, which verifies the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a single predefined matrix or a single self-generated matrix. It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph convolutional network (PMGCN), which includes spatiotemporal attention, multi-graph convolution, and multi-scale convolution modules. Specifically, we use a new spatiotemporal attention multi-graph convolution that can extract extensive and comprehensive dynamic spatial dependence between nodes, in which multiple graph convolutions adopt progressive connections and spatiotemporal attention dynamically adjusts each item of the Chebyshev polynomial in graph convolutions. In addition, multi-scale time convolution was added to obtain an extensive and comprehensive dynamic time dependence from multiple receptive field features. We used real datasets to predict traffic speed and traffic flow, and the results were compared with a variety of typical prediction models. PMGCN has the smallest Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) results under different horizons (H = 15 min, 30 min, 60 min), which shows the superiority of the proposed model. [ABSTRACT FROM AUTHOR]

The malware detection methods based on data flow graph only focused on the data flow information caused by API calls, which ignored the sequence information among API calls. To address this issue, this paper integrated the sequence information of API calls into data flow graph, put forward the concept of dual data flow graph combined with sequence information, gave the method of constructing event logs from the API call sequence, and proposed the mining method of dual data flow graph combined with sequence information. Finally, this paper proposed an improved graph convolution network to classify dual data flow graph combined with sequential information. \The classification result could be used to detect and classify malwares. The experimental results show that the proposed method has better detection accuracy than traditional methods based on data flow graphs. [ABSTRACT FROM AUTHOR]

Social recommendation systems leverage the abundant social information of users existing in the current Internet to mitigate the problem of data sparsity, ultimately enhancing recommendation performance. However, most existing recommendation systems that introduce social information ignore the negative messages passed by high-order neighbor nodes and aggregate messages without filtering, which results in a decline in the performance of the recommendation system. Considering this problem, we propose a novel social recommendation model based on graph neural networks (GNNs) called the preference-aware light graph convolutional network (PLGCN), which contains a subgraph construction module using unsupervised learning to classify users according to their embeddings and then assign users with similar preferences to a subgraph to filter useless or even negative messages from users with different preferences to attain even better recommendation performance. We also designed a feature aggregation module to better combine user embeddings with social and interaction information. In addition, we employ a lightweight GNN framework to aggregate messages from neighbors, removing nonlinear activation and feature transformation operations to alleviate the overfitting problem. Finally, we carried out comprehensive experiments using two publicly available datasets, and the results indicate that PLGCN outperforms the current state-of-the-art (SOTA) method, especially in dealing with the problem of cold start. The proposed model has the potential for practical applications in online recommendation systems, such as e-commerce, social media, and content recommendation. [ABSTRACT FROM AUTHOR]

Cybersecurity has become the most significant research area in the domain of the Internet of Things (IoT) owing to the ever-increasing number of cyberattacks. The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process. Furthermore, Android malware is increasing on a daily basis. So, precise malware detection analytical techniques need a large number of hardware resources that are signifi- cantly resource-limited for mobile devices. In this research article, an optimal Graph Convolutional Neural Network-based Malware Detection and classification (OGCNN-MDC) model is introduced for an IoT-cloud environment. The proposed OGCNN-MDC model aims to recognize and categorize malware occurrences in IoT-enabled cloud platforms. The presented OGCNN-MDC model has three stages in total, such as data pre-processing, malware detection and parameter tuning. To detect and classify the malware, the GCNN model is exploited in this work. In order to enhance the overall efficiency of the GCNN model, the Group Mean-based Optimizer (GMBO) algorithm is utilized to appropriately adjust the GCNN parameters, and this phenomenon shows the novelty of the current study. A widespread experimental analysis was conducted to establish the superiority of the proposed OGCNN-MDC model. A comprehensive comparison study was conducted, and the outcomes highlighted the supreme performance of the proposed OGCNN-MDC model over other recent approaches. [ABSTRACT FROM AUTHOR]

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Graph neural network-based representation models have shown extraordinary potential in numerous recommender system applications. Previous studies mainly considered high-order connectivity information in a single view from an interaction graph but ignored the individualized information of the users or items, making them vulnerable to over-smoothing problems. In this study, we proposed a dynamic multi-view fusion-based graph convolution network model for recommendation systems. Multiple views were generated for learning on the basis of latent user interest topics from the decomposed matrix, and a continuous awareness mechanism was proposed to maintain the model's focus on the individualized features of the nodes. During the graph learning process, a dynamic aggregation mechanism was designed to adjust the fusion weight of different propagation layers. Lastly, the different features from multiple views were dynamically fused through an attention mechanism and a principal component control mechanism to predict the similarities between users and items. Experimental results of three popular datasets of recommendation systems demonstrated that our method could effectively alleviate the over-smoothing problem and achieved better performance than four state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

Tuberculosis (TB) has long been recognized as a significant health concern worldwide. Recent advancements in noninvasive wearable devices and machine learning (ML) techniques have enabled rapid and cost-effective testing for the real-time detection of TB. However, small datasets are often encountered in biomedical and chemical engineering domains, which can hinder the success of ML models and result in overfitting issues. To address this challenge, we propose various data preprocessing methods and ML approaches, including long short-term memory (LSTM), convolutional neural network (CNN), Gramian angular field-CNN (GAF-CNN), and multivariate time series with MinCutPool (MT-MinCutPool), for classifying a small TB dataset consisting of multivariate time series (MTS) sensor signals. Our proposed methods are compared with state-of-the-art models commonly used in MTS classification (MTSC) tasks. We find that lightweight models are more appropriate for small-dataset problems. Our experimental results demonstrate that the average performance of our proposed models outperformed the baseline methods in all aspects. Specifically, the GAF-CNN model achieved the highest accuracy of 0.639 and the highest specificity of 0.777, indicating its superior effectiveness for MTSC tasks. Furthermore, our proposed MT-MinCutPool model surpassed the baseline MTPool model in all evaluation metrics, demonstrating its viability for MTSC tasks. [ABSTRACT FROM AUTHOR]

Targeting the challenge of variable working conditions in bearing fault diagnosis, most of the fault diagnosis methods based on transfer learning focus on the transfer of knowledge, resulting in a poor diagnosis effect in the target domain. To solve the problem of transfer performance degradation, a multi-perception graph convolution transfer network (MPGCTN) is proposed. The MPGCTN is composed of a graph generation module, graph perception module, and domain discrimination module. In the graph generation module, a one-dimensional convolution neural network (1-D CNN) is used to extract features from the input, and then the structural features of samples are mined in the graph generation layer to construct the sample graph. In the following graph perception module, a multi-perception graph convolution network is designed to model the sample graph and learn the data structure information of the sample. Finally, in the domain discrimination module, the method is used to align the structural differences of the case graphs in different domains. Experimental results from experiments on Case Western Reserve University (CWRU) and Paderborn University (PU) bearing datasets show that the proposed method is effective and superior.

Traffic flow prediction is considered to be one of the fundamental technologies in intelligent transportation systems (ITSs) with a tremendous application prospect. Unlike traditional time series analysis tasks, the key challenge in traffic flow prediction lies in effectively modelling the highly complex and dynamic spatiotemporal dependencies within the traffic data. In recent years, researchers have proposed various methods to enhance the accuracy of traffic flow prediction, but certain issues still persist. For instance, some methods rely on specific static assumptions, failing to adequately simulate the dynamic changes in the data, thus limiting their modelling capacity. On the other hand, some approaches inadequately capture the spatiotemporal dependencies, resulting in the omission of crucial information and leading to unsatisfactory prediction outcomes. To address these challenges, this paper proposes a model called the Dynamic Spatial–Temporal Self-Attention Network (DSTSAN). Firstly, this research enhances the interaction between different dimension features in the traffic data through a feature augmentation module, thereby improving the model’s representational capacity. Subsequently, the current investigation introduces two masking matrices: one captures local spatial dependencies and the other captures global spatial dependencies, based on the spatial self-attention module. Finally, the methodology employs a temporal self-attention module to capture and integrate the dynamic temporal dependencies of traffic data. We designed experiments using historical data from the previous hour to predict traffic flow conditions in the hour ahead, and the experiments were extensively compared to the DSTSAN model, with 11 baseline methods using four real-world datasets. The results demonstrate the effectiveness and superiority of the proposed approach.

Digital health apps have become a staple in daily life, promoting awareness and providing motivation for a healthier lifestyle. With an already overwhelmed healthcare system, digital therapies offer relief to both patient and physician alike. One such planned digital therapy application is the incorporation of an emotion recognition model as a tool for therapeutic interventions for people with autism spectrum disorder (ASD). Diagnoses of ASD have increased relatively rapidly in recent years. To ensure effective recognition of expressions, a system is designed to analyze and classify different emotions from facial landmarks. Facial landmarks combined with a corresponding mesh have the potential of bypassing hurdles of model robustness commonly affecting emotion recognition from images. Landmarks are extracted from facial images using the Mediapipe framework, after which a custom mesh is constructed from the detected landmarks and used as input to a graph convolution network (GCN) model for emotion classification. The GCN makes use of the relations formed from the mesh along with the special distance features extracted. A weighted loss approach is also utilized to reduce the effects of an imbalanced dataset. The model was trained and evaluated with the Aff-Wild2 database. The results yielded a 58.76% mean accuracy on the selected validation set. The proposed approach shows the potential and limitations of using GCNs for emotion recognition in real-world scenarios.

Given the escalating frequency and severity of global forest fires, it is imperative to develop advanced detection and segmentation technologies to mitigate their impact. To address the challenges of these technologies, the development of deep learning-based forest fire surveillance has significantly accelerated. Nevertheless, the integration of graph convolutional networks (GCNs) in forest fire detection remains relatively underexplored. In this context, we introduce a novel superpixel-based graph convolutional network (SCGCN) for forest fire image segmentation. Our proposed method utilizes superpixels to transform images into a graph structure, thereby reinterpreting the image segmentation challenge as a node classification task. Additionally, we transition the spatial graph convolution operation to a GraphSAGE graph convolution mechanism, mitigating the class imbalance issue and enhancing the network’s versatility. We incorporate an innovative loss function to contend with the inconsistencies in pixel dimensions within superpixel clusters. The efficacy of our technique is validated on two different forest fire datasets, demonstrating superior performance compared to four alternative segmentation methodologies.

Predicting photovoltaic (PV) power generation is a crucial task in the field of clean energy. Achieving high-accuracy PV power prediction requires addressing two challenges in current deep learning methods: (1) In photovoltaic power generation prediction, traditional deep learning methods often generate predictions for long sequences one by one, significantly impacting the efficiency of model predictions. As the scale of photovoltaic power stations expands and the demand for predictions increases, this sequential prediction approach may lead to slow prediction speeds, making it difficult to meet real-time prediction requirements. (2) Feature extraction is a crucial step in photovoltaic power generation prediction. However, traditional feature extraction methods often focus solely on surface features, and fail to capture the inherent relationships between various influencing factors in photovoltaic power generation data, such as light intensity, temperature, and more. To overcome these limitations, this paper proposes a mid-term PV power prediction model that combines Graph Convolutional Network (GCN) and Informer models. This fusion model leverages the multi-output capability of the Informer model to ensure the timely generation of predictions for long sequences. Additionally, it harnesses the feature extraction ability of the GCN model from nodes, utilizing graph convolutional modules to extract feature information from the ‘query’ and ‘key’ components within the attention mechanism. This approach provides more reliable feature information for mid-term PV power prediction, thereby ensuring the accuracy of long sequence predictions. Results demonstrate that the GCN–Informer model significantly reduces prediction errors while improving the precision of power generation forecasting compared to the original Informer model. Overall, this research enhances the prediction accuracy of PV power generation and contributes to advancing the field of clean energy.

Human behavior recognition has always been a hot spot for research in computer vision. In this paper, we propose a novel video behavior recognition method based on Actional-Structural Graph Convolution and a Temporal Extension Module under the framework of a Spatio-Temporal Graph Convolution Neural Network, which can optimize the spatial and temporal features simultaneously. The basic network framework of our method consists of three parts: spatial graph convolution module, temporal extension module and attention mechanism module. In the spatial dimension, the action graph convolution is utilized to obtain abundant spatial features by capturing the correlations of distant joint features, and the structural graph convolution expands the existing skeleton graph to acquire the spatial features of adjacent joints. In the time dimension, the sampling range of the temporal graph is expanded for extracting the same and adjacent joints of adjacent frames. Furthermore, attention mechanisms are introduced to improve the performance of our method. In order to verify the effectiveness and accuracy of our method, a large number of experiments were carried out on two standard behavior recognition datasets: NTU-RGB+D and Kinetics. Comparative experiment results show that our proposed method can achieve better performance.

The technology of change detection from a pair of registered satellite remote sensing images is of great importance for the study of landform changes and thus has attracted much attention. Recently, convolutional neural networks have been favoured in many applications due to their excellent feature extraction performance. However, for high-resolution satellite remote sensing images, previous methods are difficult to obtain finer details of change detection. There are many complex scenery and objects in satellite remote sensing images, which leads to the decline of the performance of the network model. In order to better solve the above problems, we propose a graph convolution-based multi-scale network with a channel attention mechanism. Specifically, we first project the initial features at different levels as vertex features, and there is a mutual relationship between the vertices. After that, we learn the correlations between these vertices through a graph reasoning. We then reproject the newly obtained vertex features to the original pixel space to obtain fine feature maps of the same size and fuse fine features of different scales through the attention fusion module. Finally, we realize the change detection of the input bi-temporal image through the feature difference module of the decoder. We separately conduct extensive experiments on three different high-resolution datasets to demonstrate that our method is effective. [ABSTRACT FROM AUTHOR]

Parkinson's disease (PD) is a neurodegenerative disorder that causes gait abnormalities. Early and accurate recognition of PD gait is crucial for effective treatment. Recently, deep learning techniques have shown promising results in PD gait analysis. However, most existing methods focus on severity estimation and frozen gait detection, while the recognition of Parkinsonian gait and normal gait from the forward video has not been reported. In this paper, we propose a novel spatiotemporal modeling method for PD gait recognition, named WM–STGCN, which utilizes a Weighted adjacency matrix with virtual connection and Multi-scale temporal convolution in a Spatiotemporal Graph Convolution Network. The weighted matrix enables different intensities to be assigned to different spatial features, including virtual connections, while the multi-scale temporal convolution helps to effectively capture the temporal features at different scales. Moreover, we employ various approaches to augment skeleton data. Experimental results show that our proposed method achieved the best accuracy of 87.1% and an F1 score of 92.85%, outperforming Long short-term memory (LSTM), K-nearest neighbors (KNN), Decision tree, AdaBoost, and ST–GCN models. Our proposed WM–STGCN provides an effective spatiotemporal modeling method for PD gait recognition that outperforms existing methods. It has the potential for clinical application in PD diagnosis and treatment. [ABSTRACT FROM AUTHOR]

Current food recommender systems tend to prioritize either the user's dietary preferences or the healthiness of the food, without considering the importance of personalized health requirements. To address this issue, we propose a novel approach to healthy food recommendations that takes into account the user's personalized health requirements, in addition to their dietary preferences. Our work comprises three perspectives. Firstly, we propose a collaborative recipe knowledge graph ( C R K G ) with millions of triplets, containing user–recipe interactions, recipe–ingredient associations, and other food-related information. Secondly, we define a score-based method for evaluating the healthiness match between recipes and user preferences. Based on these two prior perspectives, we develop a novel health-aware food recommendation model ( F K G M ) using knowledge graph embedding and multi-task learning. F K G M employs a knowledge-aware attention graph convolutional neural network to capture the semantic associations between users and recipes on the collaborative knowledge graph and learns the user's requirements in both preference and health by fusing the losses of these two learning tasks. We conducted experiments to demonstrate that F K G M outperformed four competing baseline models in integrating users' dietary preferences and personalized health requirements in food recommendations and performed best on the health task. [ABSTRACT FROM AUTHOR]

Accurate traffic forecasting is a critical function of intelligent transportation systems, which remains challenging due to the complex spatial and temporal dependence of traffic data. GNN-based traffic forecasting models typically utilize predefined graphical structures based on prior knowledge and do not adapt well to dynamically changing traffic characteristics, which may limit their performance. The transformer is a compelling architecture with an innate global self-attention mechanism, but cannot capture low-level detail very well. In this paper, we propose a novel Spatial-Temporal Gated Hybrid Transformer Network (STGHTN), which leverages local features from temporal gated convolution, spatial gated graph convolution respectively and global features by transformer to further improve the traffic flow forecasting results. First, in the temporal dimension, we take full advantage of the local properties of temporal gated convolution and the global properties of transformer to effectively fuse short-term and long-term temporal dependence. Second, we mutually integrate two modules to complement each representation by utilizing spatial gated graph convolution to extract local spatial dependence and transformer to extract global spatial dependence. Furthermore, we propose a multi-graph model that constructs a road connection graph, a similarity graph, and an adaptive dynamic graph to exploit the static and dynamic associations between road networks. Experiments on four real datasets confirm the proposed method's state-of-the-art performance. Our implementation of the STGHTN code via PyTorch is available at https://github.com/JianSoL/STGHTN. [ABSTRACT FROM AUTHOR]

Most of the existing graph convolutional network-based action recognition methods use an adaptive mechanism to learn action features from a skeleton sequence. Although this mechanism improves the recognition accuracy to some extent, its performance is still limited by the initial skeleton topology, which uses a natural human connection approach to connect skeletal joints. In addition, the semantic information of skeletal joints is naturally informative and discriminative for action recognition tasks, but its inclusion has rarely been investigated in the existing methods. To solve these problems, in this work, we propose a novel multistream-based effective skeleton topology and semantically guided adaptive graph convolution network for action recognition. By comparing several different topological graphs, we design an elbow- and knee-centric topology structure that forms the input to the adaptive graph convolutional network. Moreover, we explicitly embed the high-level semantic skeletal information into this network to enhance the feature representation capabilities. Finally, we study the positional relationships between different joints and the center of gravity in the same frame to generate relative position data. They are combined with the joint data, bone data and their corresponding motion information by a multistream network to further improve the action recognition accuracy. Extensive experiments show that the proposed method achieves state-of-the-art performance levels. [ABSTRACT FROM AUTHOR]

• We propose a new graph convolution network with subgraph embedding. • It extracts local information with subgraphs partitioned by Girvan Newman algorithm. • Experiments confirm the superiority of predicting mutagenicity in aromatic hydrocarbons. • The accuracy is verified with comparison of nine SOTA deep learning models. • The proposed method prevents about 15 %p of GCN's information dilution. An aromatic hydrocarbon refers to an organic material having a carbon ring such as benzene and a functional group in the carbon ring. As the industry develops, natural pollution becomes harsh, new compounds emerge, and the exposure to aromatic hydrocarbons is continuously increasing. Predicting mutagenicity is one of the crucial issues in reducing the risk because these organisms may have properties that penetrate the DNA of living things to cause mutations. Recently, the accuracy of mutation prediction has improved due to the power of deep learning. However, most conventional methods do not consider the characteristics of molecular aromatic hydrocarbons, which dilutes local information and results in a severe deterioration of the prediction performance. In this paper, we propose a method of exploiting subgraph convolution neural networks that enables the extraction of local information of a graph by partitioning it to maintain the detailed information. For extracting the features of molecules, we use the Girvan Newman algorithm to partition the graph according to the carbon ring and functional group and obtain the embedding vectors of the subgraphs as well as the original graph with graph convolution network (GCN). The embedding vectors are combined to represent the whole graph information and predict mutagenicity. Experiments with MUTAG, NCI1 and NCI109, datasets for predicting mutagenicity of molecules in graph structure, confirm that we successfully segment carbon rings and functional groups from molecular graphs and predict mutations using the partitioned graphs, leading to a 2 %p performance improvement. In addition, the proposed method has prevented about 15 %p of information dilution in GCN, and an analysis of the latent space of graphs reveals that the subgraphs extracted maintain the local information appropriately. [ABSTRACT FROM AUTHOR]

Traditional convolutional neural networks (CNNs) are limited to be directly applied to 3D graph data due to their inherent grid structure. And most of graph-based learning methods use local-to-global hierarchical structure learning, and often ignore the global context. To overcome these issues, we propose two strategies: one is topological learning with 3D offset convolution, which provides learnable parameters in local graph construction, effectively expands the sampling space and improves the perception ability of diverse local structures. The other is geometrical learning with an adaptive spec-graph convolution network (AsGCN), which establishes a joint learning mechanism of local geometry in spatial domain and global structure in feature domain, and generates informative deep features through spectral filtering and weighting. Extensive experiments demonstrate that our deep features have strong discerning ability and robustness to non-rigid transformed graph data, incomplete mesh data, and better performance can be obtained compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Objectives: Individual location prediction is significant in applications such as precise prevention, control of infectious diseases and scientific planning of public facilities. Existing location prediction algorithms mainly focus on mining and modeling individual longitudinal historical trajectory characteristics, and realize location prediction, and less consider the regular characteristics of users with horizontal similarity. Therefore, based on the framework of graph convolution network and long short-term memory (LSTM) model, this paper proposes an individual location prediction algorithm considering the characteristics of horizontally similar user trajectories and the characteristics of vertical historical regularity. Methods: First, we construct a user trajectory similarity algorithm, and select screen users with high similarity. Second,we use the graph convolution model to extract user trajectory features with high similarity. Finally, we use the LSTM model framework to extract historical trajectory features and integrate similar user trajectory features, so as to achieve individual location prediction. Results and Conclusions: Based on the data of more than 80 000 users in a city for 4 consecutive working days, the results show that the accuracy of the proposed algorithm decreases with the increase of the prediction time step, and the accuracy of night prediction is significantly higher than that of the day. Compared to the previous models, our proposed algorithm has an improvement of more than 10%. When 15 minutes is used as the prediction time step, the model accuracy rate reaches 80.45%. [ABSTRACT FROM AUTHOR]

The goal of the knowledge representation is to embed entities and relationships in the facts into consecutive low-dimensional dense vectors. Although shallow embedding methods can directly map entities or relations into vectors, they lose information about the structure of the knowledge graph network during the learning process. Alternatively, deep embedding methods can be used to learn rich structural information. As a practical matter, existing deep embedding methods rely too heavily on simple logical operations, such as subtraction and multiplication, between entities and relations. This paper proposes a method for deep embedding, RRGCN. Unlike the traditional method of knowledge graph convolution, this method does not rely on logical transformations to determine aggregation information. In RRGCN, aggregation information is defined as a mapping projection of neighboring features on a unique hyperplane that corresponds to the relation. Furthermore, RRGCN constructs a residual neural network between two graph convolution layers in order to reduce the amount of information loss as a consequence of superimposing graph convolution layers on top of each other. Results from experiments show that RRGCN is capable of performing well on the publicly available benchmark datasets FB15k-237 and WN18RR in the knowledge graph link prediction task, which outerforms the state-of-the-art relevent models. [ABSTRACT FROM AUTHOR]

The extant event detection models, which rely on dependency parsing, have exhibited commendable efficacy. However, for some long sentences with more words, the results of dependency parsing are more complex, because each word corresponds to a directed edge with a dependency parsing label. These edges do not all provide guidance for the event detection model, and the accuracy of dependency parsing tools decreases with the increase in sentence length, resulting in error propagation. To solve these problems, we developed an event detection model that uses a self-constructed dependency and graph convolution network. First, we statistically analyzed the ACE2005 corpus to prune the dependency parsing tree, and combined the named entity features in the sentence to generate an undirected graph. Second, we implemented an enhanced graph convolution network using the multi-head attention mechanism to understand the representation of nodes in the graph. Finally, a gating mechanism combined the semantic and structural dependency information of the sentence, enabling us to accomplish the event detection task. A series of experiments conducted on the ACE2005 corpus demonstrates that the proposed method enhances the performance of the event detection model. [ABSTRACT FROM AUTHOR]

The accurate diagnosis of autism spectrum disorder (ASD) is of great practical significance in clinical practice. The spontaneous hemodynamic fluctuations were collected by functional near-infrared spectroscopy (fNIRS) from the bilateral frontal and temporal cortices of typically developing (TD) children and children with ASD. Since traditional machine learning and deep learning methods cannot make full use of the potential spatial dependence between variable pairs, and require a long time series to diagnose ASD. Therefore, we use adaptive spatiotemporal graph convolution network (ASGCN) and short time series to classify ASD and TD. To capture spatial and temporal features of fNIRS multivariable time series without the pre-defined graph, we combined the improved adaptive graph convolution network (GCN) and gated recurrent units (GRU).We conducted a series of experiments on the fNIRS dataset, and found that only using 2.1 s short time series could achieve high precision classification, with an accuracy of 95.4%. This suggests that our approach may have the potential to detect pathological signals in autism patients within 2.1 s. In different brain regions, the left frontal lobe has the best classification effect, and the abnormalities occur more frequently in left hemisphere and frontal lobe region. Moreover, we also found that there were correlations between multiple channels, which had different degrees of influence on the classification of ASD. From this, we can also generalize to a wider range, there may be potential correlations between different brain regions. This may help to better understand the cortical mechanism of ASD. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Literature classification is a fundamental task in library and information service, which is of great value for information resource management, and literature retrieval and acquisition. Deep learning-based literature classification methods are the current mainstream methods in text classification, which employ neural networks to model and use the textual content for literature classification. This approach only utilizes the information of the literature itself, but ignores the knowledge of the association between the literature. By observing the data, we found that literature in the same category tends to share more keyword information. The literature can build association networks through keywords to form structural relationships between literature. We attempt to utilize this structural in-formation to improve the performance of literature classification. [Methods/Process] This paper proposes a method that can model the structural representation of the literature and employ this representation to enhance traditional literature classification methods. Specifi-cally, we first constructed a large-scale keyword dictionary based on the collected data from about 930,000 documents. Second, we extracted the keyword set from the titles and abstracts of papers by a two-way maximum matching algorithm and constructed the keyword-literature graph data with the literature and keywords as nodes and the inclusion relationship between the documents and keywords as edges. The literature was connected with each other by keywords. Furthermore, we employed graph convolutional neural network to model the literature graph and learn the representation of literature and keywords in the keyword-literature graph. The literature representation generated by graph neural network contained the structural relationships between the literature. In addition, we employed Bert+BiLSTM to model the textual content representation of literature. Finally, the structural and textual representations of the literature were concatenated, and the classification of the literature was performed based on this representation. [Results/Conclusions] We constructed a literature classification dataset containing 423 classes and divided the training set, validation set and test set according to the ratio of 8:1:1. We conducted literature classification experiments on this dataset. The experimental results show that the structural information of literature can effectively enhance the performance of traditional literature classification methods. The results of the stripping experiments also show that the structural information alone is insufficient for the literature classification task. Through detailed analysis of the error data, we found that the model still has problems in handling some less frequent keywords and concepts. In the future, we plan to use small-sample learning methods to solve the classification problem for literature categories with less data. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) has become a solution for real-time monitoring environments and is widely used in various fields. A substantial number of sensors in WSNs are prone to succumb to failures due to faulty attributes, complex working environments, and their hardware, resulting in transmission error data. To resolve the existing problem of fault detection in WSN, this paper presents a WSN node fault detection method based on ant colony optimization-graph convolutional network (ACO-GCN) models, which consists of an input layer, a space-time processing layer, and an output layer. First, the users apply the random search algorithm and the search strategy of the ant colony algorithm (ACO) to find the optimal path and locate the WSN node failures to grasp the overall situation. Then, the WSN fault node information obtained by the GCN model is learned. During the data training process, where the WSN fault node is used for error prediction, the weights and thresholds of the network are further adjusted to increase the accuracy of fault diagnosis. To evaluate the performance of the ACO-GCN model, the results show that the ACO-GCN model significantly improves the fault detection rate and reduces the false alarm rate compared with the benchmark algorithms. Moreover, the proposed ACO-GCN fusion algorithm can identify fault sensors more effectively, improve the service quality of WSN and enhance the stability of the system. [ABSTRACT FROM AUTHOR]

The dynamic urban link travel speed estimation (DU-LSE) problem has been studied extensively with approaches ranging from model to data driven since it benefits multiple applications in transport mobility, especially in dense cities. However, with drawbacks such as heavy assumption in model-driven and not being capable for big city network in data-driven, there has not been a consensus on the most effective method. This study aims to develop a Sequential Three Step framework to solve the DU-LSE problem using only the passively collected taxi trip data. The framework makes use of two deep learning models namely Traffic Graph Convolution (TGCN) and its recurrent variant TGCNlstm to capture both spatial and temporal correlation between road segments. The proposed framework has three advantages over similar approaches: (1) it uses only the affordable taxi data and overcomes the data's incompleteness both in spatial (full GPS trajectory is not available) and temporal (incomplete historic time-series) domain, (2) it is specifically designed to preserve the directionality nature of traffic flow, and (3) it is capable for large networks. The model results and validations suggest the framework can achieve high enough accuracy and will provide valuable mobility data for cities especially those without traffic sensing infrastructure already in place. [ABSTRACT FROM AUTHOR]

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In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the information of risk unlabeled data, which will degrade the performance of semi-supervised learning. Therefore, we propose a Safe GCN framework (Safe-GCN) to improve the learning performance. In the Safe-GCN, we design an iterative process to label the unlabeled data. In each iteration, a GCN and its supervised version (S-GCN) are learned to find the unlabeled data with high confidence. The high-confidence unlabeled data and their pseudo labels are then added to the label set. Finally, both added unlabeled data and labeled ones are used to train a S-GCN which can achieve the safe exploration of the risk unlabeled data and enable safe use of large numbers of unlabeled data. The performance of Safe-GCN is evaluated on three well-known citation network datasets and the obtained results demonstrate the effectiveness of the proposed framework over several graph-based semi-supervised learning methods.

Abstract Background MircoRNAs (miRNAs) play a central role in diverse biological processes of Camellia sinensis var.assamica (CSA) through their associations with target mRNAs, including CSA growth, development and stress response. However, although the experiment methods of CSA miRNA-target identifications are costly and time-consuming, few computational methods have been developed to tackle the CSA miRNA-target association prediction problem. Results In this paper, we constructed a heterogeneous network for CSA miRNA and targets by integrating rich biological information, including a miRNA similarity network, a target similarity network, and a miRNA-target association network. We then proposed a deep learning framework of graph convolution networks with layer attention mechanism, named MTAGCN. In particular, MTAGCN uses the attention mechanism to combine embeddings of multiple graph convolution layers, employing the integrated embedding to score the unobserved CSA miRNA-target associations. Discussion Comprehensive experiment results on two tasks (balanced task and unbalanced task) demonstrated that our proposed model achieved better performance than the classic machine learning and existing graph convolution network-based methods. The analysis of these results could offer valuable information for understanding complex CSA miRNA-target association mechanisms and would make a contribution to precision plant breeding.

• A DFGCN of transmission system is proposed from a multi-sensor-multi-condition view. • Multi-branch parallel GCN realizes the internal correlation learning. • MR-Softmax is designed to obtain multi-representation characteristics. • The entropy weighting is used to improve the evidence theory. Power transmission reliability of drivelines guarantees fast maneuverability of heavy vehicles. During health monitoring, multi-sensor data fusion technology has been widely used in the improvement of fault diagnosis accuracy of long-link multi-structure drivelines. However, in multi-sensor fusion and joint fault diagnosis scenarios where multiple conditions coexist, it is still challenging to fuse multi-sensor data and extract generalized fault intrinsic features for any combination of operating conditions under multi-sensor monitoring. In this paper, a dual fusion graph convolutional network (DFGCN) is proposed for multi-sensor-multi-condition fault diagnosis of the transmission system. First, considering the data structure and the correlation of different sensors, DFGCN constructs multi-sensor intrinsic links synchronously from the data and feature levels by using multi-branch parallel GCN. Second, considering the susceptibility to over-confidence when the feature space of multi-condition data is inconsistent, an entropy-weighted multi-representation Dempster-Shafer (EWMR-DS) evidence theory fusion strategy is designed to extract the condition-shared features by increasing the label space diversity. Finally, an end-to-end lightweight diagnosis framework is scalable to multi-sensor and multi-working conditions in engineering practice, and the dual information fusion improves the fusion efficiency of fine-grained features with distributional differences. Using experimental datasets collected from two typical transmission fault test benches, the effectiveness of the proposed DFGCN method in multi-sensor-multi-condition scenarios is verified. The results indicate that DFGCN achieves an average diagnostic accuracy of more than 99.7% and superior noise resistance under different degrees of environmental noise. [ABSTRACT FROM AUTHOR]

Multimodal emotion recognition in conversation (MERC) has attracted increasing attention for its wide applications. While previous methods have focused on extracting and merging features from various modalities, they often do not sufficiently consider interactions within and across modalities, as well as the semantic and modal correlations of utterances within a dialogue. To address this gap, we propose a dual-level constraint-based distributed GCN model. This model constructs a graph convolution network by simultaneously incorporating multiple modal-central graphs to capture the multimodal features of utterances. Additionally, we adopt a dual-level constraint strategy to identify the semantic and modal correlation of each speaker's utterances in a dialogue. By comprehensively capturing the utterance features, our model significantly enhances emotion classification performances. The comparative experiment results show that our model outperforms the state-of-the-art models on the benchmark datasets IEMOCAP, MELD and M3ED. In addition, we verify the effectiveness of each component of our model by ablation experiments. [ABSTRACT FROM AUTHOR]

A continuous real-time video monitoring for preterm infants that suffer from fragile condition attracts increasing attention due to its significance in the early screening of diseases. Specially, the limb movement recognition of preterm infants in neonatal intensive care units (NICUs) plays an important role for assessing the health status of preterm infants. The main challenges of the preterm infant limb movement recognition are arisen from the irregular movement speed and the multi-limb classification. To address these issues, a novel convolution graph recognition network (CGRN) is proposed by integrating three-dimensional (3D) convolution neural networks (CNNs) with graph convolution networks (GCNs). Particularly, the spatial–temporal frequency characteristics of different channels in 3D CNN generally have poor adaptability to characterize fast limb movement. Thus, GCN is merged to enhance the channel connection and improve the capabilities of representation learning. The node filtering and topological connection are learned by GCN to establish dependencies among different channels. A fusion multi-label loss function consisting of the Binary Cross Entropy (BCE) and the Multi-label Soft-Margin (MSM) loss is further developed to train the proposed CGRN. Experiments on the preterm Infant Activity Dataset (IAD) are conducted to demonstrate the effectiveness of the proposed CGRN algorithm. The ablations studies indicate that the structure optimization process and the auxiliary performance of Laplace matrices are worked effectively, and the proposed CGRN obtains the average accuracy of 94.1%, the CF1 value of 88.4%, and the OF1 score of 91.7%. • A novel CGRN is developed for limb movement recognition of premature infants. • The CNN is used to reduce GCN's dependence on formatted data. • The Laplacian matrix is used to represent the relationship of feature channels. • Model fusion addresses CNN channel independence and GCN data restrictions. • Unlike the fully connected layer, the GCN layer uses Laplacian matrix weighting. [ABSTRACT FROM AUTHOR]