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Artykuły w czasopismach na temat „Temporal Graph Processing”

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Data publikacji: 6 września 2023

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1

Christensen, Andrew J., Ananya Sen Gupta, and Ivars Kirsteins. "Sonar target feature representation using temporal graph networks." Journal of the Acoustical Society of America 151, no. 4 (2022): A102. http://dx.doi.org/10.1121/10.0010791.

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Autonomous sonar target recognition suffers from uncertainty caused by waveguide distortions to signal, unknown target geometry, and morphing target features. Typical “black-box” neural networks do not produce physically interpretable features and, therefore, are not effective in meeting these challenges. The primary objective of our work is to harness signal processing with machine learning to extract braided features that allow such physical interpretation by a domain expert. In this work, we introduce a feature extraction method using graph neural networks (GNNs) that seeks to discover brai

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Choi, Jeongwhan, Hwangyong Choi, Jeehyun Hwang, and Noseong Park. "Graph Neural Controlled Differential Equations for Traffic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6367–74. http://dx.doi.org/10.1609/aaai.v36i6.20587.

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Traffic forecasting is one of the most popular spatio-temporal tasks in the field of machine learning. A prevalent approach in the field is to combine graph convolutional networks and recurrent neural networks for the spatio-temporal processing. There has been fierce competition and many novel methods have been proposed. In this paper, we present the method of spatio-temporal graph neural controlled differential equation (STG-NCDE). Neural controlled differential equations (NCDEs) are a breakthrough concept for processing sequential data. We extend the concept and design two NCDEs: one for the

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Zhao, Xiaojuan, Aiping Li, Rong Jiang, Kai Chen, and Zhichao Peng. "Householder Transformation-Based Temporal Knowledge Graph Reasoning." Electronics 12, no. 9 (2023): 2001. http://dx.doi.org/10.3390/electronics12092001.

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Knowledge graphs’ reasoning is of great significance for the further development of artificial intelligence and information retrieval, especially for reasoning over temporal knowledge graphs. The rotation-based method has been shown to be effective at modeling entities and relations on a knowledge graph. However, due to the lack of temporal information representation capability, existing approaches can only model partial relational patterns and they cannot handle temporal combination reasoning. In this regard, we propose HTTR: Householder Transformation-based Temporal knowledge graph Reasoning

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Liu, Jun. "Motion Action Analysis at Basketball Sports Scene Based on Image Processing." Scientific Programming 2022 (March 7, 2022): 1–11. http://dx.doi.org/10.1155/2022/7349548.

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To solve the problems of low accuracy and high time cost in manual recording and statistics of basketball data, an automatic analysis method of motion action under the basketball sports scene based on the spatial temporal graph convolutional neural network is proposed. By using the graph structure in the data structure to model the joints and limbs of the human body, and using the spatial temporal graph structure to model the posture action, the extraction and estimation of human body posture in basketball sports scenes are realized. Then, training combined with transfer learning, the recognit

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Li, Jing, Wenyue Guo, Haiyan Liu, Xin Chen, Anzhu Yu, and Jia Li. "Predicting User Activity Intensity Using Geographic Interactions Based on Social Media Check-In Data." ISPRS International Journal of Geo-Information 10, no. 8 (2021): 555. http://dx.doi.org/10.3390/ijgi10080555.

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Predicting user activity intensity is crucial for various applications. However, existing studies have two main problems. First, as user activity intensity is nonstationary and nonlinear, traditional methods can hardly fit the nonlinear spatio-temporal relationships that characterize user mobility. Second, user movements between different areas are valuable, but have not been utilized for the construction of spatial relationships. Therefore, we propose a deep learning model, the geographical interactions-weighted graph convolutional network-gated recurrent unit (GGCN-GRU), which is good at fit

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Ke, Xiangyu, Arijit Khan, and Francesco Bonchi. "Multi-relation Graph Summarization." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (2022): 1–30. http://dx.doi.org/10.1145/3494561.

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Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this article, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive mo

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7

Zhang, Guoxing, Haixiao Wang, and Yuanpu Yin. "Multi-type Parameter Prediction of Traffic Flow Based on Time-space Attention Graph Convolutional Network." International Journal of Circuits, Systems and Signal Processing 15 (August 11, 2021): 902–12. http://dx.doi.org/10.46300/9106.2021.15.97.

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Graph Convolutional Neural Networks are more and more widely used in traffic flow parameter prediction tasks by virtue of their excellent non-Euclidean spatial feature extraction capabilities. However, most graph convolutional neural networks are only used to predict one type of traffic flow parameter. This means that the proposed graph convolutional neural network may only be effective for specific parameters of specific travel modes. In order to improve the universality of graph convolutional neural networks. By embedding time feature and spatio-temporal attention layer, we propose a spatio-

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8

Zheng, Xiaolong, Dongdong Guan, Bangjie Li, Zhengsheng Chen, and Lefei Pan. "Global and Local Graph-Based Difference Image Enhancement for Change Detection." Remote Sensing 15, no. 5 (2023): 1194. http://dx.doi.org/10.3390/rs15051194.

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Change detection (CD) is an important research topic in remote sensing, which has been applied in many fields. In the paper, we focus on the post-processing of difference images (DIs), i.e., how to further improve the quality of a DI after the initial DI is obtained. The importance of DIs for CD problems cannot be overstated, however few methods have been investigated so far for re-processing DIs after their acquisition. In order to improve the DI quality, we propose a global and local graph-based DI-enhancement method (GLGDE) specifically for CD problems; this is a plug-and-play method that c

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9

Steinbauer, Matthias, and Gabriele Anderst Kotsis. "DynamoGraph: extending the Pregel paradigm for large-scale temporal graph processing." International Journal of Grid and Utility Computing 7, no. 2 (2016): 141. http://dx.doi.org/10.1504/ijguc.2016.077491.

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Chen, Yaosen, Bing Guo, Yan Shen, Wei Wang, Weichen Lu, and Xinhua Suo. "Boundary graph convolutional network for temporal action detection." Image and Vision Computing 109 (May 2021): 104144. http://dx.doi.org/10.1016/j.imavis.2021.104144.

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Sun, Linhui, Yifan Zhang, Jian Cheng, and Hanqing Lu. "Asynchronous Event Processing with Local-Shift Graph Convolutional Network." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 2 (2023): 2402–10. http://dx.doi.org/10.1609/aaai.v37i2.25336.

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Event cameras are bio-inspired sensors that produce sparse and asynchronous event streams instead of frame-based images at a high-rate. Recent works utilizing graph convolutional networks (GCNs) have achieved remarkable performance in recognition tasks, which model event stream as spatio-temporal graph. However, the computational mechanism of graph convolution introduces redundant computation when aggregating neighbor features, which limits the low-latency nature of the events. And they perform a synchronous inference process, which can not achieve a fast response to the asynchronous event sig

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12

Zeng, Hui, Chaojie Jiang, Yuanchun Lan, Xiaohui Huang, Junyang Wang, and Xinhua Yuan. "Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting." Electronics 12, no. 1 (2023): 238. http://dx.doi.org/10.3390/electronics12010238.

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Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously captu

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13

GLAVAŠ, GORAN, and JAN ŠNAJDER. "Construction and evaluation of event graphs." Natural Language Engineering 21, no. 4 (2014): 607–52. http://dx.doi.org/10.1017/s1351324914000060.

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AbstractEvents play an important role in natural language processing and information retrieval due to numerous event-oriented texts and information needs. Many natural language processing and information retrieval applications could benefit from a structured event-oriented document representation. In this paper, we proposeevent graphsas a novel way of structuring event-based information from text. Nodes in event graphs represent the individual mentions of events, whereas edges represent the temporal and coreference relations between mentions. Contrary to previous natural language processing re

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14

Fang, Junhua, Jiafeng Ding, Pengpeng Zhao, Jiajie Xu, An Liu, and Zhixu Li. "Distributed and parallel processing for real-time and dynamic spatio-temporal graph." World Wide Web 23, no. 2 (2019): 905–26. http://dx.doi.org/10.1007/s11280-019-00741-6.

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15

Körner, Christof, Margit Höfler, Barbara Tröbinger, and Iain D. Gilchrist. "Eye Movements Indicate the Temporal Organisation of Information Processing in Graph Comprehension." Applied Cognitive Psychology 28, no. 3 (2014): 360–73. http://dx.doi.org/10.1002/acp.3006.

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16

Wang, Xiaojuan, Ziliang Gan, Lei Jin, Yabo Xiao, and Mingshu He. "Adaptive Multi-Scale Difference Graph Convolution Network for Skeleton-Based Action Recognition." Electronics 12, no. 13 (2023): 2852. http://dx.doi.org/10.3390/electronics12132852.

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Graph convolutional networks (GCNs) have obtained remarkable performance in skeleton-based action recognition. However, previous approaches fail to capture the implicit correlations between joints and handle actions across varying time intervals. To address these problems, we propose an adaptive multi-scale difference graph convolution Network (AMD-GCN), which comprises an adaptive spatial graph convolution module (ASGC) and a multi-scale temporal difference convolution module (MTDC). The first module is capable of acquiring data-dependent and channel-wise graphs that are adaptable to both sam

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17

Kerracher, Natalie, Jessie Kennedy, and Kevin Chalmers. "A Task Taxonomy for Temporal Graph Visualisation." IEEE Transactions on Visualization and Computer Graphics 21, no. 10 (2015): 1160–72. http://dx.doi.org/10.1109/tvcg.2015.2424889.

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El rai, Marwa Chendeb, Muna Darweesh, and Mina Al-Saad. "Semi-Supervised Segmentation of Echocardiography Videos Using Graph Signal Processing." Electronics 11, no. 21 (2022): 3462. http://dx.doi.org/10.3390/electronics11213462.

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Machine learning and computer vision algorithms can provide a precise and automated interpretation of medical videos. The segmentation of the left ventricle of echocardiography videos plays an essential role in cardiology for carrying out clinical cardiac diagnosis and monitoring the patient’s condition. Most of the developed deep learning algorithms for video segmentation require an enormous amount of labeled data to generate accurate results. Thus, there is a need to develop new semi-supervised segmentation methods due to the scarcity and costly labeled data. In recent research, semi-supervi

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19

Xue, Jizhong, Zaohui Kang, Chun Sing Lai, Yu Wang, Fangyuan Xu, and Haoliang Yuan. "Distributed Generation Forecasting Based on Rolling Graph Neural Network (ROLL-GNN)." Energies 16, no. 11 (2023): 4436. http://dx.doi.org/10.3390/en16114436.

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The future power grid will have more distributed energy sources, and the widespread access of distributed energy sources has the potential to improve the energy efficiency, resilience, and sustainability of the system. However, distributed energy, mainly wind power generation and photovoltaic power generation, has the characteristics of intermittency and strong randomness, which will bring challenges to the safe operation of the power grid. Accurate prediction of solar power generation with high spatial and temporal resolution is very important for the normal operation of the power grid. In or

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Liu, Zhi, Jixin Bian, Deju Zhang, Yang Chen, Guojiang Shen, and Xiangjie Kong. "Dynamic Multi-View Coupled Graph Convolution Network for Urban Travel Demand Forecasting." Electronics 11, no. 16 (2022): 2620. http://dx.doi.org/10.3390/electronics11162620.

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Accurate urban travel demand forecasting can help organize traffic flow, improve traffic utilization, reduce passenger waiting time, etc. It plays an important role in intelligent transportation systems. Most of the existing research methods construct static graphs from a single perspective or two perspectives, without considering the dynamic impact of time changes and various factors on traffic demand. Moreover, travel demand is also affected by regional functions such as weather, etc. To address these issues, we propose an urban travel demand prediction framework based on dynamic multi-view

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Li, Tingwei, Ruiwen Zhang, and Qing Li. "A Novel Graph Representation for Skeleton-based Action Recognition." Signal & Image Processing : An International Journal 11, no. 6 (2020): 65–73. http://dx.doi.org/10.5121/sipij.2020.11605.

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Graph convolutional networks (GCNs) have been proven to be effective for processing structured data, so that it can effectively capture the features of related nodes and improve the performance of model. More attention is paid to employing GCN in Skeleton-Based action recognition. But there are some challenges with the existing methods based on GCNs. First, the consistency of temporal and spatial features is ignored due to extracting features node by node and frame by frame. We design a generic representation of skeleton sequences for action recognition and propose a novel model called Tempora

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22

Li, Chaoyue, Lian Zou, Cien Fan, Hao Jiang, and Yifeng Liu. "Multi-Stage Attention-Enhanced Sparse Graph Convolutional Network for Skeleton-Based Action Recognition." Electronics 10, no. 18 (2021): 2198. http://dx.doi.org/10.3390/electronics10182198.

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Graph convolutional networks (GCNs), which model human actions as a series of spatial-temporal graphs, have recently achieved superior performance in skeleton-based action recognition. However, the existing methods mostly use the physical connections of joints to construct a spatial graph, resulting in limited topological information of the human skeleton. In addition, the action features in the time domain have not been fully explored. To better extract spatial-temporal features, we propose a multi-stage attention-enhanced sparse graph convolutional network (MS-ASGCN) for skeleton-based actio

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23

Binsfeld Gonçalves, Laurent, Ivan Nesic, Marko Obradovic, Bram Stieltjes, Thomas Weikert, and Jens Bremerich. "Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame." JMIR Medical Informatics 10, no. 12 (2022): e40534. http://dx.doi.org/10.2196/40534.

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Background A concise visualization framework of related reports would increase readability and improve patient management. To this end, temporal referrals to prior comparative exams are an essential connection to previous exams in written reports. Due to unstructured narrative texts' variable structure and content, their extraction is hampered by poor computer readability. Natural language processing (NLP) permits the extraction of structured information from unstructured texts automatically and can serve as an essential input for such a novel visualization framework. Objective This study prop

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Tomei, Matteo, Lorenzo Baraldi, Simone Calderara, Simone Bronzin, and Rita Cucchiara. "Video action detection by learning graph-based spatio-temporal interactions." Computer Vision and Image Understanding 206 (May 2021): 103187. http://dx.doi.org/10.1016/j.cviu.2021.103187.

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Wang, Daocheng, Chao Chen, Chong Di, and Minglei Shu. "Exploring Behavior Patterns for Next-POI Recommendation via Graph Self-Supervised Learning." Electronics 12, no. 8 (2023): 1939. http://dx.doi.org/10.3390/electronics12081939.

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Next-point-of-interest (POI) recommendation is a crucial part of location-based social applications. Existing works have attempted to learn behavior representation through a sequence model combined with spatial-temporal-interval context. However, these approaches ignore the impact of implicit behavior patterns contained in the visit trajectory on user decision making. In this paper, we propose a novel graph self-supervised behavior pattern learning model (GSBPL) for the next-POI recommendation. GSBPL applies two graph data augmentation operations to generate augmented trajectory graphs to mode

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Li, Huiyong, Xiaofeng Wu, and Yanhong Wang. "Dynamic Performance Analysis of STEP System in Internet of Vehicles Based on Queuing Theory." Computational Intelligence and Neuroscience 2022 (April 10, 2022): 1–13. http://dx.doi.org/10.1155/2022/8322029.

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The Internet of vehicles (IoV) is an important research area of the intelligent transportation systems using Internet of things theory. The complex event processing technology is a basic issue for processing the data stream in IoV. In recent years, many researchers process the temporal and spatial data flow by complex event processing technology. Spatial Temporal Event Processing (STEP) is a complex event query language focusing on the temporal and spatial data flow in Internet of vehicles. There are four processing models of the event stream processing system based on the complex event query

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Karuza, Elisabeth A., Ari E. Kahn, and Danielle S. Bassett. "Human Sensitivity to Community Structure Is Robust to Topological Variation." Complexity 2019 (February 11, 2019): 1–8. http://dx.doi.org/10.1155/2019/8379321.

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Despite mounting evidence that human learners are sensitive to community structure underpinning temporal sequences, this phenomenon has been studied using an extremely narrow set of network ensembles. The extent to which behavioral signatures of learning are robust to changes in community size and number is the focus of the present work. Here we present adult participants with a continuous stream of novel objects generated by a random walk along graphs of 1, 2, 3, 4, or 6 communities comprised of N = 24, 12, 8, 6, and 4 nodes, respectively. Nodes of the graph correspond to a unique object and

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Bayram, Ulya, Runia Roy, Aqil Assalil, and Lamia BenHiba. "The unknown knowns: a graph-based approach for temporal COVID-19 literature mining." Online Information Review 45, no. 4 (2021): 687–708. http://dx.doi.org/10.1108/oir-12-2020-0562.

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PurposeThe COVID-19 pandemic has sparked a remarkable volume of research literature, and scientists are increasingly in need of intelligent tools to cut through the noise and uncover relevant research directions. As a response, the authors propose a novel framework. In this framework, the authors develop a novel weighted semantic graph model to compress the research studies efficiently. Also, the authors present two analyses on this graph to propose alternative ways to uncover additional aspects of COVID-19 research.Design/methodology/approachThe authors construct the semantic graph using stat

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Carrillo, Rafael E., Martin Leblanc, Baptiste Schubnel, Renaud Langou, Cyril Topfel, and Pierre-Jean Alet. "High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution." Energies 13, no. 21 (2020): 5763. http://dx.doi.org/10.3390/en13215763.

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Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art techniques combine numerical weather predictions with statistical post-processing, but their resolution is too coarse for applications such as local congestion management. In this paper we introduce computing methods for multi-site PV forecasting, which exploit the intuition that PV systems provide a dense network of simple weather stations. These methods rely entirely on production data and address the real-life challenges

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Aqil, Marco, Selen Atasoy, Morten L. Kringelbach, and Rikkert Hindriks. "Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome." PLOS Computational Biology 17, no. 1 (2021): e1008310. http://dx.doi.org/10.1371/journal.pcbi.1008310.

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Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed “connectome harmonics”, have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph La

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Guda, Vanitha, and SureshKumar Sanampudi. "Event Time Relationship in Natural Language Text." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 7, no. 3 (2019): 4. http://dx.doi.org/10.3991/ijes.v7i3.10985.

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<p>Due to the numerous information needs, retrieval of events from a given natural language text is inevitable. In natural language processing (NLP) perspective, "Events" are situations, occurrences, real-world entities or facts. Extraction of events and arranging them on a timeline is helpful in various NLP application like building the summary of news articles, processing health records, and Question Answering System (QA) systems. This paper presents a framework for identifying the events and times from a given document and representing them using a graph data structure. As a result, a

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Huang, Xiaohui, Yuanchun Lan, Yuming Ye, Junyang Wang, and Yuan Jiang. "Traffic Flow Prediction Based on Multi-Mode Spatial-Temporal Convolution of Mixed Hop Diffuse ODE." Electronics 11, no. 19 (2022): 3012. http://dx.doi.org/10.3390/electronics11193012.

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In recent years, traffic flow forecasting has attracted the great attention of many researchers with increasing traffic congestion in metropolises. As a hot topic in the field of intelligent city computing, traffic flow forecasting plays a vital role, since predicting the changes in traffic flow can timely alleviate traffic congestion and reduce the occurrence of accidents by vehicle scheduling. The most difficult challenges of traffic flow prediction are the temporal feature extraction and the spatial correlation extraction of nodes. At the same time, graph neural networks (GNNs) show an exce

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Ghosh, Dipon Kumar, Amitabha Chakrabarty, Hyeonjoon Moon, and M. Jalil Piran. "A Spatio-Temporal Graph Convolutional Network Model for Internet of Medical Things (IoMT)." Sensors 22, no. 21 (2022): 8438. http://dx.doi.org/10.3390/s22218438.

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In order to provide intelligent and efficient healthcare services in the Internet of Medical Things (IoMT), human action recognition (HAR) can play a crucial role. As a result of their stringent requirements, such as high computational complexity and memory efficiency, classical HAR techniques are not applicable to modern and intelligent healthcare services, e.g., IoMT. To address these issues, we present in this paper a novel HAR technique for healthcare services in IoMT. This model, referred to as the spatio-temporal graph convolutional network (STGCN), primarily aims at skeleton-based human

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He, Jiatong, Jia Cui, Gaobo Zhang, Mingrui Xue, Dengyu Chu, and Yanna Zhao. "Spatial–temporal seizure detection with graph attention network and bi-directional LSTM architecture." Biomedical Signal Processing and Control 78 (September 2022): 103908. http://dx.doi.org/10.1016/j.bspc.2022.103908.

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Zhao, Mengyao, Zhengping Hu, Shufang Li, Shuai Bi, and Zhe Sun. "Two-stream graph convolutional neural network fusion for weakly supervised temporal action detection." Signal, Image and Video Processing 16, no. 4 (2021): 947–54. http://dx.doi.org/10.1007/s11760-021-02039-5.

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Shuai, Wenjing, Fenlong Jiang, Hanhong Zheng, and Jianzhao Li. "MSGATN: A Superpixel-Based Multi-Scale Siamese Graph Attention Network for Change Detection in Remote Sensing Images." Applied Sciences 12, no. 10 (2022): 5158. http://dx.doi.org/10.3390/app12105158.

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With the rapid development of Earth observation technology, how to effectively and efficiently detect changes in multi-temporal images has become an important but challenging problem. Relying on the advantages of high performance and robustness, object-based change detection (CD) has become increasingly popular. By analyzing the similarity of local pixels, object-based CD aggregates similar pixels into one object and takes it as the basic processing unit. However, object-based approaches often have difficulty capturing discriminative features, as irregular objects make processing difficult. To

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Wu, Lei, Yong Tang, Pei Zhang, and Ying Zhou. "Spatio-Temporal Heterogeneous Graph Neural Networks for Estimating Time of Travel." Electronics 12, no. 6 (2023): 1293. http://dx.doi.org/10.3390/electronics12061293.

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Estimating Time of Travel (ETT) is a crucial element of intelligent transportation systems. In most previous studies, time of travel is estimated by identifying the spatio-temporal features of road segments or intersections independently. However, due to continuous changes in road segments and intersections in a path, dynamic features should be coupled and interactive. Therefore, employing only road segment or intersection features is inadequate for improving the accuracy of ETT. To address this issue, we proposed a novel deep learning framework for ETT based on a spatio-temporal heterogeneous

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Cao, Yibo, Lu Liu, and Yuhan Dong. "Convolutional Long Short-Term Memory Two-Dimensional Bidirectional Graph Convolutional Network for Taxi Demand Prediction." Sustainability 15, no. 10 (2023): 7903. http://dx.doi.org/10.3390/su15107903.

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With the rise of the online ride-hailing market, taxi demand prediction has received more and more research interest in intelligent transportation. However, most traditional research methods mainly focused on the demand based on the original point and ignored the intention of the passenger’s destination. At the same time, many forecasting methods need sufficient investigation and data processing, which undoubtedly increases the complexity and operability of forecasting problems. Therefore, we regard the current taxi demand prediction as an origin–destination problem in order to provide more ac

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Huang, Wanrong, Xiaodong Yi, Yichun Sun, Yingwen Liu, Shuai Ye, and Hengzhu Liu. "Scalable Parallel Distributed Coprocessor System for Graph Searching Problems with Massive Data." Scientific Programming 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1496104.

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The Internet applications, such as network searching, electronic commerce, and modern medical applications, produce and process massive data. Considerable data parallelism exists in computation processes of data-intensive applications. A traversal algorithm, breadth-first search (BFS), is fundamental in many graph processing applications and metrics when a graph grows in scale. A variety of scientific programming methods have been proposed for accelerating and parallelizing BFS because of the poor temporal and spatial locality caused by inherent irregular memory access patterns. However, new p

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Rozhdestvenskaya, К. N. "Temporal analysis of a control system in a data processing network." Information and Control Systems, no. 1 (February 19, 2019): 32–39. http://dx.doi.org/10.31799/1684-8853-2019-1-32-39.

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Introduction:A control system for a data processing network interacts with the network by sending commands and receiving responses. Such a control system is responsible for the network viability, and therefore should be analyzed, in particular, in terms of behavior over time, without exhaustive search for possible control options.Purpose:Studying and analyzing the behavior of a control system in a data processing network using mathematical modeling based on finite automata theory, and performing computer simulation of the obtained theoretical positions.Results:A finite state machine is constru

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Pang, Shiyan, Xiangyun Hu, Mi Zhang, Zhongliang Cai, and Fengzhu Liu. "Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images." Remote Sensing 11, no. 6 (2019): 729. http://dx.doi.org/10.3390/rs11060729.

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Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpi

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Do, M., and S. Kambhampati. "SAPA: A Multi-objective Metric Temporal Planner." Journal of Artificial Intelligence Research 20 (December 1, 2003): 155–94. http://dx.doi.org/10.1613/jair.1156.

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SAPA is a domain-independent heuristic forward chaining planner that can handle durative actions, metric resource constraints, and deadline goals. It is designed to be capable of handling the multi-objective nature of metric temporal planning. Our technical contributions include (i) planning-graph based methods for deriving heuristics that are sensitive to both cost and makespan (ii) techniques for adjusting the heuristic estimates to take action interactions and metric resource limitations into account and (iii) a linear time greedy post-processing technique to improve execution flexibility o

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Sighencea, Bogdan Ilie, Ion Rareș Stanciu, and Cătălin Daniel Căleanu. "D-STGCN: Dynamic Pedestrian Trajectory Prediction Using Spatio-Temporal Graph Convolutional Networks." Electronics 12, no. 3 (2023): 611. http://dx.doi.org/10.3390/electronics12030611.

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Predicting pedestrian trajectories in urban scenarios is a challenging task that has a wide range of applications, from video surveillance to autonomous driving. The task is difficult since pedestrian behavior is affected by both their individual path’s history, their interactions with others, and with the environment. For predicting pedestrian trajectories, an attention-based interaction-aware spatio-temporal graph neural network is introduced. This paper introduces an approach based on two components: a spatial graph neural network (SGNN) for interaction-modeling and a temporal graph neural

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Weghenkel, Björn, and Laurenz Wiskott. "Slowness as a Proxy for Temporal Predictability: An Empirical Comparison." Neural Computation 30, no. 5 (2018): 1151–79. http://dx.doi.org/10.1162/neco_a_01070.

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The computational principles of slowness and predictability have been proposed to describe aspects of information processing in the visual system. From the perspective of slowness being a limited special case of predictability we investigate the relationship between these two principles empirically. On a collection of real-world data sets we compare the features extracted by slow feature analysis (SFA) to the features of three recently proposed methods for predictable feature extraction: forecastable component analysis, predictable feature analysis, and graph-based predictable feature analysis

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Shi, Yong, Yang Xiao, Pei Quan, MingLong Lei, and Lingfeng Niu. "Document-level relation extraction via graph transformer networks and temporal convolutional networks." Pattern Recognition Letters 149 (September 2021): 150–56. http://dx.doi.org/10.1016/j.patrec.2021.06.012.

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Pan, Chengsheng, Jiang Zhu, Zhixiang Kong, Huaifeng Shi, and Wensheng Yang. "DC-STGCN: Dual-Channel Based Graph Convolutional Networks for Network Traffic Forecasting." Electronics 10, no. 9 (2021): 1014. http://dx.doi.org/10.3390/electronics10091014.

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Network traffic forecasting is essential for efficient network management and planning. Accurate long-term forecasting models are also essential for proactive control of upcoming congestion events. Due to the complex spatial-temporal dependencies between traffic flows, traditional time series forecasting models are often unable to fully extract the spatial-temporal characteristics between the traffic flows. To address this issue, we propose a novel dual-channel based graph convolutional network (DC-STGCN) model. The proposed model consists of two temporal components that characterize the daily

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Feng, Yongliang. "Air Quality Prediction Model Using Deep Learning in Internet of Things Environmental Monitoring System." Mobile Information Systems 2022 (September 29, 2022): 1–9. http://dx.doi.org/10.1155/2022/7221157.

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In order to realize the accurate prediction of spatial-temporal air quality index, this paper constructs a STAQI prediction model based on deep learning, including data processing, spatial feature acquisition, temporal feature acquisition, and STAQI prediction. Firstly, the spatial interpolation method is used to optimize the sample data set to provide reliable data; the improved graph convolutional network and the improved long short-term memory are used to effectively extract the spatial and temporal distribution characteristics of AQI data; and then, the extreme learning machine model is us

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Hu, Zhiqiu, Fengjing Shao, and Rencheng Sun. "A New Perspective on Traffic Flow Prediction: A Graph Spatial-Temporal Network with Complex Network Information." Electronics 11, no. 15 (2022): 2432. http://dx.doi.org/10.3390/electronics11152432.

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Traffic flow prediction provides support for travel management, vehicle scheduling, and intelligent transportation system construction. In this work, a graph space–time network (GSTNCNI), incorporating complex network feature information, is proposed to predict future highway traffic flow time series. Firstly, a traffic complex network model using traffic big data is established, the topological features of traffic road networks are then analyzed using complex network theory, and finally, the topological features are combined with graph neural networks to explore the roles played by the topolo

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Zhu, Qilin, Hongmin Deng, and Kaixuan Wang. "Skeleton Action Recognition Based on Temporal Gated Unit and Adaptive Graph Convolution." Electronics 11, no. 18 (2022): 2973. http://dx.doi.org/10.3390/electronics11182973.

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In recent years, great progress has been made in the recognition of skeletal behaviors based on graph convolutional networks (GCNs). In most existing methods, however, the fixed adjacency matrix and fixed graph structure are used for skeleton data feature extraction in the spatial dimension, which usually leads to weak spatial modeling ability, unsatisfactory generalization performance, and an excessive number of model parameters. Most of these methods follow the ST-GCN approach in the temporal dimension, which inevitably leads to a number of non-key frames, increasing the cost of feature extr

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Zhong, Yang Jun, and Qian Cai. "A Novel Registration Approach for Mammograms Based on SIFT and Graph Transformation." Applied Mechanics and Materials 157-158 (February 2012): 1313–19. http://dx.doi.org/10.4028/www.scientific.net/amm.157-158.1313.

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Mammogram registration is an important step in the processing of automatic detection of breast cancer. It provides aid to better visualization correspondence on temporal pairs of mammograms. This paper presents a novel algorithm based on SIFT feature and Graph Transformation methods for mammogram registration. First, features are extracted from the mammogram images by scale invariant feature transform (SIFT) method. Second, we use graph transformation matching (GTM) approach to obtain more accurate image information. At last, we registered a pair of mammograms using Thin-Plate spline (TPS) int

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