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1
Zhang, Zijian, Xiangyu Zhao, Hao Miao, Chunxu Zhang, Hongwei Zhao, and Junbo Zhang. "AutoSTL: Automated Spatio-Temporal Multi-Task Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4902–10. http://dx.doi.org/10.1609/aaai.v37i4.25616.
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Spatio-temporal prediction plays a critical role in smart city construction. Jointly modeling multiple spatio-temporal tasks can further promote an intelligent city life by integrating their inseparable relationship. However, existing studies fail to address this joint learning problem well, which generally solve tasks individually or a fixed task combination. The challenges lie in the tangled relation between different properties, the demand for supporting flexible combinations of tasks and the complex spatio-temporal dependency. To cope with the problems above, we propose an Automated Spatio-Temporal multi-task Learning (AutoSTL) method to handle multiple spatio-temporal tasks jointly. Firstly, we propose a scalable architecture consisting of advanced spatio-temporal operations to exploit the complicated dependency. Shared modules and feature fusion mechanism are incorporated to further capture the intrinsic relationship between tasks. Furthermore, our model automatically allocates the operations and fusion weight. Extensive experiments on benchmark datasets verified that our model achieves state-of-the-art performance. As we can know, AutoSTL is the first automated spatio-temporal multi-task learning method.
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Zhang, Yitao, Ruiqing Xu, Wangping Lu, Wolfgang Mayer, Da Ning, Yucong Duan, Xi Zeng, and Zaiwen Feng. "Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management." Applied Sciences 13, no. 16 (August 18, 2023): 9393. http://dx.doi.org/10.3390/app13169393.
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In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.
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Hu, W., S. Winter, and K. Khoshelham. "OPTIMIZING URBAN MONITORING BETWEEN STATIONARY, OPPORTUNISTIC VEHICULAR, AND HYBRID SENSING." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W3-2022 (October 14, 2022): 65–72. http://dx.doi.org/10.5194/isprs-annals-x-4-w3-2022-65-2022.
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Abstract. Urban monitoring based on wireless sensor networks is a recent paradigm that exploits a large number of low-cost sensors deployed in certain places or/and on mobile devices to collect data ubiquitously at a large scale. In this study, we explore and compare the coverage of stationary and opportunistic vehicular sensing methods with respect to the requirements of a task at hand. We distinguish spatial granularity, temporal granularity, and budget constraints. First we compare the spatio-temporal coverage of stationary sensing and opportunistic vehicular sensing for various tasks, which demonstrates that these two sensing methods are suitable for different tasks. Then we propose a hybrid sensing deployment framework integrating a genetic algorithm to achieve the maximum spatio-temporal coverage for specific tasks. Experiments with a real-world vehicle trajectory dataset demonstrate that the proposed hybrid sensing framework achieves the maximum spatio-temporal coverage in various tasks. Our results provide fundamental guidelines on network planning for urban monitoring applications.
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Li, X. M., W. X. Wang, S. J. Tang, J. Z. Xia, Z. G. Zhao, Y. Li, Y. Zheng, and R. Z. Guo. "A NEW CLOUD-EDGE-TERMINAL RESOURCES COLLABORATIVE SCHEDULING FRAMEWORK FOR MULTI-LEVEL VISUALIZATION TASKS OF LARGE-SCALE SPATIO-TEMPORAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 25, 2020): 477–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-477-2020.
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Abstract. To address the multi-modal spatio-temporal data efficient scheduling problem of the diverse and highly concurrent visualization applications in cloud-edge-terminal environment, this paper systematically studies the cloud-edge-terminal integrated scheduling model of multi-level visualization tasks of multi-modal spatio-temporal data. By accurately defining the hierarchical semantic mapping relationship between the diverse visual application requirements of different terminals and scheduling tasks, we propose a multi-level task-driven cloud-edge-terminal multi-granularity storage-computing-rendering resource collaborative scheduling method. Based on the workflow, the flexible allocation strategy of cloud-edge-terminal scheduling service chain that consider the characteristics of spatio-temporal task is constructed. Finally, we established a cloud-edge-terminal scheduling adaptive optimization mechanism based on the service quality evaluation model, and developed a prototype system. Experiments are conducted with the urban construction and construction management, the results show that the new method breaks through the bottleneck of traditional spatio-temporal data visualization scheduling, and it can provide theoretical and methodological support for the visualization and scheduling of spatio-temporal big data.
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Feng, Bin, Qing Zhu, Mingwei Liu, Yun Li, Junxiao Zhang, Xiao Fu, Yan Zhou, Maosu Li, Huagui He, and Weijun Yang. "An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization." ISPRS International Journal of Geo-Information 7, no. 9 (September 8, 2018): 371. http://dx.doi.org/10.3390/ijgi7090371.
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Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.
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Zhang, Yujia, Lai-Man Po, Xuyuan Xu, Mengyang Liu, Yexin Wang, Weifeng Ou, Yuzhi Zhao, and Wing-Yin Yu. "Contrastive Spatio-Temporal Pretext Learning for Self-Supervised Video Representation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 3380–89. http://dx.doi.org/10.1609/aaai.v36i3.20248.
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Spatio-temporal representation learning is critical for video self-supervised representation. Recent approaches mainly use contrastive learning and pretext tasks. However, these approaches learn representation by discriminating sampled instances via feature similarity in the latent space while ignoring the intermediate state of the learned representations, which limits the overall performance. In this work, taking into account the degree of similarity of sampled instances as the intermediate state, we propose a novel pretext task - spatio-temporal overlap rate (STOR) prediction. It stems from the observation that humans are capable of discriminating the overlap rates of videos in space and time. This task encourages the model to discriminate the STOR of two generated samples to learn the representations. Moreover, we employ a joint optimization combining pretext tasks with contrastive learning to further enhance the spatio-temporal representation learning. We also study the mutual influence of each component in the proposed scheme. Extensive experiments demonstrate that our proposed STOR task can favor both contrastive learning and pretext tasks and the joint optimization scheme can significantly improve the spatio-temporal representation in video understanding. The code is available at https://github.com/Katou2/CSTP.
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Baek, Seung-Hwan, and Felix Heide. "Polarimetric spatio-temporal light transport probing." ACM Transactions on Graphics 40, no. 6 (December 2021): 1–18. http://dx.doi.org/10.1145/3478513.3480517.
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Light emitted from a source into a scene can undergo complex interactions with multiple scene surfaces of different material types before being reflected towards a detector. During this transport, every surface reflection and propagation is encoded in the properties of the photons that ultimately reach the detector, including travel time, direction, intensity, wavelength and polarization. Conventional imaging systems capture intensity by integrating over all other dimensions of the incident light into a single quantity, hiding this rich scene information in these aggregate measurements. Existing methods are capable of untangling these measurements into their spatial and temporal dimensions, fueling geometric scene understanding tasks. However, examining polarimetric material properties jointly with geometric properties is an open challenge that could enable unprecedented capabilities beyond geometric scene understanding, allowing for material-dependent scene understanding and imaging through complex transport, such as macroscopic scattering. In this work, we close this gap, and propose a computational light transport imaging method that captures the spatially- and temporally-resolved complete polarimetric response of a scene, which encodes rich material properties. Our method hinges on a novel 7D tensor theory of light transport. We discover low-rank structure in the polarimetric tensor dimension and propose a data-driven rotating ellipsometry method that learns to exploit redundancy of polarimetric structure. We instantiate our theory with two imaging prototypes: spatio-polarimetric imaging and coaxial temporal-polarimetric imaging. This allows us, for the first time, to decompose scene light transport into temporal, spatial, and complete polarimetric dimensions that unveil scene properties hidden to conventional methods. We validate the applicability of our method on diverse tasks, including shape reconstruction with subsurface scattering, seeing through scattering media, untangling multi-bounce light transport, breaking metamerism with polarization, and spatio-polarimetric decomposition of crystals.
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Ikura, Mikihiro, Sarthak Pathak, Jun Younes Louhi Kasahara, Atsushi Yamashita, and Hajime Asama. "AdjustSense: Adaptive 3D Sensing System with Adjustable Spatio-Temporal Resolution and Measurement Range Using High-Speed Omnidirectional Camera and Direct Drive Motor." Sensors 21, no. 21 (October 21, 2021): 6975. http://dx.doi.org/10.3390/s21216975.
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Many types of 3D sensing devices are commercially available and were utilized in various technical fields. In most conventional systems with a 3D sensing device, the spatio-temporal resolution and the measurement range are constant during operation. Consequently, it is necessary to select an appropriate sensing system according to the measurement task. Moreover, such conventional systems have difficulties dealing with several measurement targets simultaneously due to the aforementioned constants. This issue can hardly be solved by integrating several individual sensing systems into one. Here, we propose a single 3D sensing system that adaptively adjusts the spatio-temporal resolution and the measurement range to switch between multiple measurement tasks. We named the proposed adaptive 3D sensing system “AdjustSense.” In AdjustSense, as a means for the adaptive adjustment of the spatio-temporal resolution and measurement range, we aimed to achieve low-latency visual feedback for the adjustment by integrating not only a high-speed camera, which is a high-speed sensor, but also a direct drive motor, which is a high-speed actuator. This low-latency visual feedback can enable a large range of 3D sensing tasks simultaneously. We demonstrated the behavior of AdjustSense when the positions of the measured targets in the surroundings were changed. Furthermore, we quantitatively evaluated the spatio-temporal resolution and measurement range from the 3D points obtained. Through two experiments, we showed that AdjustSense could realize multiple measurement tasks: 360∘ 3D sensing, 3D sensing at a high spatial resolution around multiple targets, and local 3D sensing at a high spatio-temporal resolution around a single object.
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Li, He, Xuejiao Li, Liangcai Su, Duo Jin, Jianbin Huang, and Deshuang Huang. "Deep Spatio-temporal Adaptive 3D Convolutional Neural Networks for Traffic Flow Prediction." ACM Transactions on Intelligent Systems and Technology 13, no. 2 (April 30, 2022): 1–21. http://dx.doi.org/10.1145/3510829.
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Traffic flow prediction is the upstream problem of path planning, intelligent transportation system, and other tasks. Many studies have been carried out on the traffic flow prediction of the spatio-temporal network, but the effects of spatio-temporal flexibility (historical data of the same type of time intervals in the same location will change flexibly) and spatio-temporal correlation (different road conditions have different effects at different times) have not been considered at the same time. We propose the Deep Spatio-temporal Adaptive 3D Convolution Neural Network (ST-A3DNet), which is a new scheme to solve both spatio-temporal correlation and flexibility, and consider spatio-temporal complexity (complex external factors, such as weather and holidays). Different from other traffic forecasting models, ST-A3DNet captures the spatio-temporal relationship at the same time through the Adaptive 3D convolution module, assigns different weights flexibly according to the influence of historical data, and obtains the impact of external factors on the flow through the ex-mask module. Considering the holidays and weather conditions, we train our model for experiments in Xi’an and Chengdu. We evaluate the ST-A3DNet and the results show that we have better results than the other 11 baselines.
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Chen, Nanyu, Anran Yang, Luo Chen, Wei Xiong, and Ning Jing. "STO2Vec: A Multiscale Spatio-Temporal Object Representation Method for Association Analysis." ISPRS International Journal of Geo-Information 12, no. 5 (May 21, 2023): 207. http://dx.doi.org/10.3390/ijgi12050207.
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Spatio-temporal association analysis has attracted attention in various fields, such as urban computing and crime analysis. The proliferation of positioning technology and location-based services has facilitated the expansion of association analysis across spatio-temporal scales. However, existing methods inadequately consider the scale differences among spatio-temporal objects during analysis, leading to suboptimal precision in association analysis results. To remedy this issue, we propose a multiscale spatio-temporal object representation method, STO2Vec, for association analysis. This method comprises of two parts: graph construction and embedding. For graph construction, we introduce an adaptive hierarchical discretization method to distinguish the varying scales of local features. Then, we merge the embedding method for spatio-temporal objects with that for discrete units, establishing a heterogeneous graph. For embedding, to enhance embedding quality for homogeneous and heterogeneous data, we use biased sampling and unsupervised models to capture the association strengths between spatio-temporal objects. Empirical results using real-world open-source datasets show that STO2Vec outperforms other models, improving accuracy by 16.25% on average across diverse applications. Further case studies indicate STO2Vec effectively detects association relationships between spatio-temporal objects in a range of scenarios and is applicable to tasks such as moving object behavior pattern mining and trajectory semantic annotation.
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Schutera, Mark, Stefan Elser, Jochen Abhau, Ralf Mikut, and Markus Reischl. "Strategies for supplementing recurrent neural network training for spatio-temporal prediction." at - Automatisierungstechnik 67, no. 7 (July 26, 2019): 545–56. http://dx.doi.org/10.1515/auto-2018-0124.
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Abstract In autonomous driving, prediction tasks address complex spatio-temporal data. This article describes the examination of Recurrent Neural Networks (RNNs) for object trajectory prediction in the image space. The proposed methods enhance the performance and spatio-temporal prediction capabilities of Recurrent Neural Networks. Two different data augmentation strategies and a hyperparameter search are implemented for this purpose. A conventional data augmentation strategy and a Generative Adversarial Network (GAN) based strategy are analyzed with respect to their ability to close the generalization gap of Recurrent Neural Networks. The results are then discussed using single-object tracklets provided by the KITTI Tracking Dataset. This work demonstrates the benefits of augmenting spatio-temporal data with GANs.
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Du, Zexu, Guoliang Zhang, Weijiang Lu, Ting Zhao, and Peng Wu. "Spatio-Temporal Transformer for Online Video Understanding." Journal of Physics: Conference Series 2171, no. 1 (January 1, 2022): 012020. http://dx.doi.org/10.1088/1742-6596/2171/1/012020.
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Abstract Leading methods in the field of online video understanding try to extract useful information from the spatial and temporal dimensions of an input video. But they are suffering from two problems: (1) These methods can only extract local video information, and cannot relate to the important features of the temporal context in the video. (2) Although some methods can quickly process the information of each frame in the video, the processing efficiency of the whole video is not good, so this type of method cannot be applied to online video understanding tasks. This article introduces a Transformer-based network, which considers spatial and temporal content, and can quickly process each video at the same time. Our approach can efficiently handle up to 170 videos with hundreds of frames per second for action classification. Our method achieve 10 to 90 times faster than existing methods on the action classification datasets.
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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 (June 28, 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 temporal processing and the other for the spatial processing. After that, we combine them into a single framework. We conduct experiments with 6 benchmark datasets and 20 baselines. STG-NCDE shows the best accuracy in all cases, outperforming all those 20 baselines by non-trivial margins.
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Gohel, Bakul, and Yong Jeong. "Sensory modality-specific spatio-temporal dynamics in response to counting tasks." Neuroscience Letters 581 (October 2014): 20–25. http://dx.doi.org/10.1016/j.neulet.2014.08.015.
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Grundmann, Jens, Sebastian Hörning, and András Bárdossy. "Stochastic reconstruction of spatio-temporal rainfall patterns by inverse hydrologic modelling." Hydrology and Earth System Sciences 23, no. 1 (January 16, 2019): 225–37. http://dx.doi.org/10.5194/hess-23-225-2019.
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Abstract. Knowledge of spatio-temporal rainfall patterns is required as input for distributed hydrologic models used for tasks such as flood runoff estimation and modelling. Normally, these patterns are generated from point observations on the ground using spatial interpolation methods. However, such methods fail in reproducing the true spatio-temporal rainfall pattern, especially in data-scarce regions with poorly gauged catchments, or for highly dynamic, small-scale rainstorms which are not well recorded by existing monitoring networks. Consequently, uncertainties arise in distributed rainfall–runoff modelling if poorly identified spatio-temporal rainfall patterns are used, since the amount of rainfall received by a catchment as well as the dynamics of the runoff generation of flood waves is underestimated. To address this problem we propose an inverse hydrologic modelling approach for stochastic reconstruction of spatio-temporal rainfall patterns. The methodology combines the stochastic random field simulator Random Mixing and a distributed rainfall–runoff model in a Monte Carlo framework. The simulated spatio-temporal rainfall patterns are conditioned on point rainfall data from ground-based monitoring networks and the observed hydrograph at the catchment outlet and aim to explain measured data at best. Since we infer a three-dimensional input variable from an integral catchment response, several candidates for spatio-temporal rainfall patterns are feasible and allow for an analysis of their uncertainty. The methodology is tested on a synthetic rainfall–runoff event on sub-daily time steps and spatial resolution of 1 km2 for a catchment partly covered by rainfall. A set of plausible spatio-temporal rainfall patterns can be obtained by applying this inverse approach. Furthermore, results of a real-world study for a flash flood event in a mountainous arid region are presented. They underline that knowledge about the spatio-temporal rainfall pattern is crucial for flash flood modelling even in small catchments and arid and semiarid environments.
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Trăscău, Mihai, Mihai Nan, and Adina Florea. "Spatio-Temporal Features in Action Recognition Using 3D Skeletal Joints." Sensors 19, no. 2 (January 21, 2019): 423. http://dx.doi.org/10.3390/s19020423.
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Robust action recognition methods lie at the cornerstone of Ambient Assisted Living (AAL) systems employing optical devices. Using 3D skeleton joints extracted from depth images taken with time-of-flight (ToF) cameras has been a popular solution for accomplishing these tasks. Though seemingly scarce in terms of information availability compared to its RGB or depth image counterparts, the skeletal representation has proven to be effective in the task of action recognition. This paper explores different interpretations of both the spatial and the temporal dimensions of a sequence of frames describing an action. We show that rather intuitive approaches, often borrowed from other computer vision tasks, can improve accuracy. We report results based on these modifications and propose an architecture that uses temporal convolutions with results comparable to the state of the art.
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Govender, Divina, and Jules-Raymond Tapamo. "Spatio-Temporal Scale Coded Bag-of-Words." Sensors 20, no. 21 (November 9, 2020): 6380. http://dx.doi.org/10.3390/s20216380.
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The Bag-of-Words (BoW) framework has been widely used in action recognition tasks due to its compact and efficient feature representation. Various modifications have been made to this framework to increase its classification power. This often results in an increased complexity and reduced efficiency. Inspired by the success of image-based scale coded BoW representations, we propose a spatio-temporal scale coded BoW (SC-BoW) for video-based recognition. This involves encoding extracted multi-scale information into BoW representations by partitioning spatio-temporal features into sub-groups based on the spatial scale from which they were extracted. We evaluate SC-BoW in two experimental setups. We first present a general pipeline to perform real-time action recognition with SC-BoW. Secondly, we apply SC-BoW onto the popular Dense Trajectory feature set. Results showed SC-BoW representations to successfully improve performance by 2–7% with low added computational cost. Notably, SC-BoW on Dense Trajectories outperformed more complex deep learning approaches. Thus, scale coding is a low-cost and low-level encoding scheme that increases classification power of the standard BoW without compromising efficiency.
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Mutis, Ivan, and Pavlo Antonenko. "Unmanned aerial vehicles as educational technology systems in construction engineering education." Journal of Information Technology in Construction 27 (March 28, 2022): 273–89. http://dx.doi.org/10.36680/j.itcon.2022.014.
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Integrating complex spatio-temporal cognitive tasks such as in-situ planning and trade coordination of job site activities is a continuous challenge to learners in Construction Engineering (CE) courses. Spatial information in this context addresses how physical resources are related to one another at a job site, whereas temporal information defines work sequences and hierarchies that transform physical resources. This paper discusses the impacts of using an innovative learning environment for supporting spatio-temporal cognition in CE education using aerial visualizations from Unmanned Aerial Vehicles (UAVs). Learners experience a unique, ‘birds-eye view’ of the spatio-temporal dynamics of a job site. The effects were on improved abilities to apply, analyze, and synthesize any form of design representation to situations and physical contexts. Our findings demonstrate that participants in the intervention group outperformed the control group on measures of learning and motivation, which underscores the potential of UAVs as an educational technology system in CE education.
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Kim, Dahun, Donghyeon Cho, and In So Kweon. "Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8545–52. http://dx.doi.org/10.1609/aaai.v33i01.33018545.
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Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have been utilized for visual representation learning for still images, when the number of labeled images is limited or absent at all. Recently, this worthwhile stream of study extends to video domain where the cost of human labeling is even more expensive. However, the most of existing methods are still based on 2D CNN architectures that can not directly capture spatio-temporal information for video applications. In this paper, we introduce a new self-supervised task called as Space-Time Cubic Puzzles to train 3D CNNs using large scale video dataset. This task requires a network to arrange permuted 3D spatio-temporal crops. By completing Space-Time Cubic Puzzles, the network learns both spatial appearance and temporal relation of video frames, which is our final goal. In experiments, we demonstrate that our learned 3D representation is well transferred to action recognition tasks, and outperforms state-of-the-art 2D CNN-based competitors on UCF101 and HMDB51 datasets.
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Zheng, Hanle, Yujie Wu, Lei Deng, Yifan Hu, and Guoqi Li. "Going Deeper With Directly-Trained Larger Spiking Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (May 18, 2021): 11062–70. http://dx.doi.org/10.1609/aaai.v35i12.17320.
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Spiking neural networks (SNNs) are promising in a bio-plausible coding for spatio-temporal information and event-driven signal processing, which is very suited for energy-efficient implementation in neuromorphic hardware. However, the unique working mode of SNNs makes them more difficult to train than traditional networks. Currently, there are two main routes to explore the training of deep SNNs with high performance. The first is to convert a pre-trained ANN model to its SNN version, which usually requires a long coding window for convergence and cannot exploit the spatio-temporal features during training for solving temporal tasks. The other is to directly train SNNs in the spatio-temporal domain. But due to the binary spike activity of the firing function and the problem of gradient vanishing or explosion, current methods are restricted to shallow architectures and thereby difficult in harnessing large-scale datasets (e.g. ImageNet). To this end, we propose a threshold-dependent batch normalization (tdBN) method based on the emerging spatio-temporal backpropagation, termed “STBP-tdBN”, enabling direct training of a very deep SNN and the efficient implementation of its inference on neuromorphic hardware. With the proposed method and elaborated shortcut connection, we significantly extend directly-trained SNNs from a shallow structure (
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Saha, Sangeet, Arnab Sarkar, and Amlan Chakrabarti. "Spatio-Temporal Scheduling of Preemptive Real-Time Tasks on Partially Reconfigurable Systems." ACM Transactions on Design Automation of Electronic Systems 22, no. 4 (July 22, 2017): 1–26. http://dx.doi.org/10.1145/3056561.
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Ji, Jiahao, Jingyuan Wang, Chao Huang, Junjie Wu, Boren Xu, Zhenhe Wu, Junbo Zhang, and Yu Zheng. "Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4356–64. http://dx.doi.org/10.1609/aaai.v37i4.25555.
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Robust prediction of citywide traffic flows at different time periods plays a crucial role in intelligent transportation systems. While previous work has made great efforts to model spatio-temporal correlations, existing methods still suffer from two key limitations: i) Most models collectively predict all regions' flows without accounting for spatial heterogeneity, i.e., different regions may have skewed traffic flow distributions. ii) These models fail to capture the temporal heterogeneity induced by time-varying traffic patterns, as they typically model temporal correlations with a shared parameterized space for all time periods. To tackle these challenges, we propose a novel Spatio-Temporal Self-Supervised Learning (ST-SSL) traffic prediction framework which enhances the traffic pattern representations to be reflective of both spatial and temporal heterogeneity, with auxiliary self-supervised learning paradigms. Specifically, our ST-SSL is built over an integrated module with temporal and spatial convolutions for encoding the information across space and time. To achieve the adaptive spatio-temporal self-supervised learning, our ST-SSL first performs the adaptive augmentation over the traffic flow graph data at both attribute- and structure-levels. On top of the augmented traffic graph, two SSL auxiliary tasks are constructed to supplement the main traffic prediction task with spatial and temporal heterogeneity-aware augmentation. Experiments on four benchmark datasets demonstrate that ST-SSL consistently outperforms various state-of-the-art baselines. Since spatio-temporal heterogeneity widely exists in practical datasets, the proposed framework may also cast light on other spatial-temporal applications. Model implementation is available at https://github.com/Echo-Ji/ST-SSL.
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Gao, Yuyang, Liang Zhao, Lingfei Wu, Yanfang Ye, Hui Xiong, and Chaowei Yang. "Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3638–46. http://dx.doi.org/10.1609/aaai.v33i01.33013638.
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Due to the potentially significant benefits for society, forecasting spatio-temporal societal events is currently attracting considerable attention from researchers. Beyond merely predicting the occurrence of future events, practitioners are now looking for information about specific subtypes of future events in order to allocate appropriate amounts and types of resources to manage such events and any associated social risks. However, forecasting event subtypes is far more complex than merely extending binary prediction to cover multiple classes, as 1) different locations require different models to handle their characteristic event subtype patterns due to spatial heterogeneity; 2) historically, many locations have only experienced a incomplete set of event subtypes, thus limiting the local model’s ability to predict previously “unseen” subtypes; and 3) the subtle discrepancy among different event subtypes requires more discriminative and profound representations of societal events. In order to address all these challenges concurrently, we propose a Spatial Incomplete Multi-task Deep leArning (SIMDA) framework that is capable of effectively forecasting the subtypes of future events. The new framework formulates spatial locations into tasks to handle spatial heterogeneity in event subtypes, and learns a joint deep representation of subtypes across tasks. Furthermore, based on the “first law of geography”, spatiallyclosed tasks share similar event subtype patterns such that adjacent tasks can share knowledge with each other effectively. Optimizing the proposed model amounts to a new nonconvex and strongly-coupled problem, we propose a new algorithm based on Alternating Direction Method of Multipliers (ADMM) that can decompose the complex problem into subproblems that can be solved efficiently. Extensive experiments on six real-world datasets demonstrate the effectiveness and efficiency of the proposed model.
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Haalman, Iris, and Eilon Vaadia. "Emergence of Spatio-Temporal Patterns in Neuronal Activity." Zeitschrift für Naturforschung C 53, no. 7-8 (August 1, 1998): 657–69. http://dx.doi.org/10.1515/znc-1998-7-818.
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Abstract Neuronal Activity, Emergence of Spatio-Temporal Patterns This paper explores if dynamic modulation of coherent firing serves cortical functions. We recorded neuronal activity in the frontal cortex of behaving monkeys and found that tempo ral coincidences of spikes firing of different neurons can emerge within a fraction of a second in relation to the animal behavior. The temporal patterns of the correlation could not be predicted from the modulations of the neurons firing rate and finally, the patterns of correla tion depend on the distance between neurons. These findings call for a revision of prevailing models of neural coding that solely rely on firing rates. The findings suggest that modification of neuronal interactions can serve as a mechanism by which neurons associate rapidly into a functional group in order to perform a specific computational task. Increased correlation between members of the groups, and decreased or negative correlation with others, enhance the ability to dissociate one group from concurrently activated competing groups. Such modu lation of neuronal interactions allows each neuron to become a member of several different groups and participate in different computational tasks.
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Liu, Zhexiong, Licheng Liu, Yiqun Xie, Zhenong Jin, and Xiaowei Jia. "Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14365–73. http://dx.doi.org/10.1609/aaai.v37i12.26680.
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Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data often exhibit complex patterns and significant data variability across different locations. The labels in many real-world applications can also be limited, which makes it difficult to separately train independent models for different locations. Although meta learning has shown promise in model adaptation with small samples, existing meta learning methods remain limited in handling a large number of heterogeneous tasks, e.g., a large number of locations with varying data patterns. To bridge the gap, we propose task-adaptive formulations and a model-agnostic meta-learning framework that transforms regionally heterogeneous data into location-sensitive meta tasks. We conduct task adaptation following an easy-to-hard task hierarchy in which different meta models are adapted to tasks of different difficulty levels. One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks. It also enhances the model generalization by automatically adapting the meta model of the corresponding difficulty level to any new tasks. We demonstrate the superiority of our proposed framework over a diverse set of baselines and state-of-the-art meta-learning frameworks. Our extensive experiments on real crop yield data show the effectiveness of the proposed method in handling spatial-related heterogeneous tasks in real societal applications.
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Moroz, V. M., M. V. Yoltukhivskyy, O. V. Vlasenko, G. S. Moskovko, O. V. Bogomaz, I. L. Rokunets, I. V. Tyshchenko, L. V. Kostyuk, and K. V. Suprunov. "Age-related features of walking with cognitive tasks." Biomedical and Biosocial Anthropology, no. 34 (February 28, 2019): 68–76. http://dx.doi.org/10.31393/bba34-2019-10.
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Human walking is considered as a complex cognitive act. The research purpose is an analysis of age-related features of spatio-temporal parameters of human walking and directions of their changes at walking with dual (cognitive) tasks. The walking spatio-temporal indexes were studied in 608 individuals of both sexes aged 12-43 years by GAITRite® (CIR Systems Inc.,Clifton, NJ) under normal walking at individually comfortable velocity and under additional cognitive tasks: 1) sequentially pronounce aloud any known animals; 2) starting from a number 100, subtract 7 and pronounce the result aloud. The statistical processing of the got results was carried out in the licensed software “STATISTICA 5.5”. At performing the first, simpler, task, the spatial parameters had no significant changes in all age groups. Most of the temporal parameters changed: cycle time, swing time, single support time, and double support time increased. Therefore, equilibrium maintaining at walking with naming animals is realized with a longer overall support period, reducing the walking cadence and velocity. The constant width of the support base and the angle of the feet turn indicate that the magnitudes of the functional support base and angle of the feet turn at normal walking is sufficient to maintain posture and balance at walking with simultaneous performance of the cognitive task, as well as more rigid mechanisms of regulation of these two parameters. The walking temporal parameters are more labile than spatial parameters. With age, the percentage of the integral index of walking quality (FAP) decreases especially in females: in girls by 15.3 %, in young women by 14.4 %, in middle-aged women by 7.4 %. At performing the second, more complex, arithmetic task, in young men and young girls support base, toe-in-out, step length difference had no significant changes only. The mean velocity, cadence, step length, stride length, step extremity ratio decreased. The count of steps, all temporal parameters, and stance percentage increased. FAP declined critically by 30.4 % in young men and 33.4 % in young women, indicating a decrease in balance and body stability under walking with cognitive task and increasing the risk of falls. Therefore, a significant reduction in FAP can be used as a diagnostic criterion in neurological practice.
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Kopylova, Oksana A. "INFORMATIVE SIGNS IN TASKS OF RECOGNITION OF TRANSPORT OBJECTS BY NOISE." Interexpo GEO-Siberia 4, no. 1 (July 8, 2020): 57–65. http://dx.doi.org/10.33764/2618-981x-2020-4-1-57-65.
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The study relates to the problem of assessing and preventing geoecological risks arising from exposure to traffic noise. This paper explores the noise of large vehicles. Spectral, spatio-temporal characteristics of noise of tracked and wheeled heavy equipment, railway transport were obtained. The patterns of attenuation of the low-frequency transport noise level by distance are determined, the main modes of heavy equipment noise are highlighted.
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Luo, Dezhao, Chang Liu, Yu Zhou, Dongbao Yang, Can Ma, Qixiang Ye, and Weiping Wang. "Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (April 3, 2020): 11701–8. http://dx.doi.org/10.1609/aaai.v34i07.6840.
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We propose a novel self-supervised method, referred to as Video Cloze Procedure (VCP), to learn rich spatial-temporal representations. VCP first generates “blanks” by withholding video clips and then creates “options” by applying spatio-temporal operations on the withheld clips. Finally, it fills the blanks with “options” and learns representations by predicting the categories of operations applied on the clips. VCP can act as either a proxy task or a target task in self-supervised learning. As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning. As a target task, it can assess learned representation models in a uniform and interpretable manner. With VCP, we train spatial-temporal representation models (3D-CNNs) and apply such models on action recognition and video retrieval tasks. Experiments on commonly used benchmarks show that the trained models outperform the state-of-the-art self-supervised models with significant margins.
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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-temporal attention graph convolutional neural network based on the attention mechanism of the neural network. Through experiments on passenger flow data and vehicle speed data of two different travel modes (Hangzhou Metro Data and California Highway Data), it is verified that the proposed spatio-temporal attention graph convolutional neural network can be used to predict passenger flow and vehicle speed simultaneously. Meanwhile, the error distribution range of the proposed model is minimum, and the overall level of prediction results is more accurate.
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Ahmed, Irfan, Indika Kumara, Vahideh Reshadat, A. S. M. Kayes, Willem-Jan van den Heuvel, and Damian A. Tamburri. "Travel Time Prediction and Explanation with Spatio-Temporal Features: A Comparative Study." Electronics 11, no. 1 (December 29, 2021): 106. http://dx.doi.org/10.3390/electronics11010106.
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Travel time information is used as input or auxiliary data for tasks such as dynamic navigation, infrastructure planning, congestion control, and accident detection. Various data-driven Travel Time Prediction (TTP) methods have been proposed in recent years. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm. The existing studies that empirically compare different TTP models only use a few models with specific features. Moreover, there is a lack of research on explaining TTPs made by black-box models. Such explanations can help to tune and apply TTP methods successfully. To fill these gaps in the current TTP literature, using three data sets, we compare three types of TTP methods (ensemble tree-based learning, deep neural networks, and hybrid models) and ten different prediction algorithms overall. Furthermore, we apply XAI (Explainable Artificial Intelligence) methods (SHAP and LIME) to understand and interpret models’ predictions. The prediction accuracy and reliability for all models are evaluated and compared. We observed that the ensemble learning methods, i.e., XGBoost and LightGBM, are the best performing models over the three data sets, and XAI methods can adequately explain how various spatial and temporal features influence travel time.
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Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.
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Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.
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Chen, Bo, Tianyi Hu, Zishen Huang, and Chunhui Fang. "A spatio-temporal clustering and diagnosis method for concrete arch dams using deformation monitoring data." Structural Health Monitoring 18, no. 5-6 (September 26, 2018): 1355–71. http://dx.doi.org/10.1177/1475921718797949.
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The timely analysis of deformation monitoring data and reasonable diagnosis of the structural health are key tasks in dam health monitoring studies. This article presents a spatio-temporal clustering and health diagnosis method for super-high concrete arch dams that uses deformation monitoring data obtained from plumb meters. The spatio-temporal expression of the deformation monitoring data is proposed first by upgrading a punctuated time series to a curved panel time series, including cross-sectional, dam axial, and temporal changing directions. Second, a comprehensive similarity indicator on three aspects, namely, the absolute distance, incremental distance, and growth rate distance, is constructed after a deep discussion on deformation similarity characteristics both temporally and spatially. Next, the temporal clustering method is proposed by keeping the key features, namely, extreme points and turning points, while eliminating extraneous details, namely, noise points. Finally, the optimal spatio-temporal clustering of dam deformation is achieved by designing a multi-scale fuzzy C-means method of data mining and its iterative algorithm. The proposed method is applied to the Jinping-I hydraulic structure, which is the highest concrete arch dam in the world. The clustering results is quite sensitive in different weight coefficients of the comprehensive similarity indicator and clustering numbers of fuzzy C-means method. The dam deformation behaviors on high-water-level, water-falling, and low-water-level periods are analyzed and diagnosed. The advanced version of proposed methods is verified by comparative analysis on dam health diagnosis results obtained from ordinary deformation distribution figures and the spatio-temporal clustering figures. The proposed method will facilitate the recognition of abnormal deformation areas and associated safety diagnoses.
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Wu, Jiangnan, Yongmei Zhao, Hongmei Zhang, Gang Hu, Hang Zeng, and Song Li. "Spatio-Temporal Traffic Data Tensor Restoration Method Based on Direction Weighting and P-Shrinkage Norm." Mathematical Problems in Engineering 2022 (October 13, 2022): 1–17. http://dx.doi.org/10.1155/2022/3304677.
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Due to the influence of data collection methods and external complex factors, missing traffic data is inevitable. However, complete traffic information is a necessary input for route planning and forecasting tasks. To reduce the impact of missing data problems, this paper uses the low-rank tensor completion framework based on T-SVD to complete the missing spatio-temporal traffic data, the aim is to recover a low-rank tensor from a tensor with partial observation terms, and the WLRTC-P model is proposed. We use the idea of direction weighting to solve the dependence of the original model on the data input direction, extract each direction correlation information of the tensor spatio-temporal traffic data, and use the p-shrinkage norm to replace the tensor average rank minimization problem, and the study shows that the p-shrinkage norm is tighter than the tensor nuclear norm and, finally, uses the alternating direction method of multipliers to solve this model. Experiments on two publicly available spatio-temporal traffic datasets verified the conjecture of data input direction’s influence on the completion accuracy, and compared with the existing classical model methods, WLRTC-P has high precision and generalization ability.
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Liu, Hao, Qiyu Wu, Fuzhen Zhuang, Xinjiang Lu, Dejing Dou, and Hui Xiong. "Community-Aware Multi-Task Transportation Demand Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (May 18, 2021): 320–27. http://dx.doi.org/10.1609/aaai.v35i1.16107.
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Transportation demand prediction is of great importance to urban governance and has become an essential function in many online applications. While many efforts have been made for regional transportation demand prediction, predicting the diversified transportation demand for different communities (e.g., the aged, the juveniles) remains an unexplored problem. However, this task is challenging because of the joint influence of spatio-temporal correlation among regions and implicit correlation among different communities. To this end, in this paper, we propose the Multi-task Spatio-Temporal Network with Mutually-supervised Adaptive task grouping (Ada-MSTNet) for community-aware transportation demand prediction. Specifically, we first construct a sequence of multi-view graphs from both spatial and community perspectives, and devise a spatio-temporal neural network to simultaneously capture the sophisticated correlations between regions and communities, respectively. Then, we propose an adaptively clustered multi-task learning module, where the prediction of each region-community specific transportation demand is regarded as distinct task. Moreover, a mutually supervised adaptive task grouping strategy is introduced to softly cluster each task into different task groups, by leveraging the supervision signal from one another graph view. In such a way, Ada-MSTNet is not only able to share common knowledge among highly related communities and regions, but also shield the noise from unrelated tasks in an end-to-end fashion. Finally, extensive experiments on two real-world datasets demonstrate the effectiveness of our approach compared with seven baselines.
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Fischer, Colin, Monika Sester, and Steffen Schön. "Spatio-Temporal Research Data Infrastructure in the Context of Autonomous Driving." ISPRS International Journal of Geo-Information 9, no. 11 (October 25, 2020): 626. http://dx.doi.org/10.3390/ijgi9110626.
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In this paper, we present an implementation of a research data management system that features structured data storage for spatio-temporal experimental data (environmental perception and navigation in the framework of autonomous driving), including metadata management and interfaces for visualization and parallel processing. The demands of the research environment, the design of the system, the organization of the data storage, and computational hardware as well as structures and processes related to data collection, preparation, annotation, and storage are described in detail. We provide examples for the handling of datasets, explaining the required data preparation steps for data storage as well as benefits when using the data in the context of scientific tasks.
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Omidvarborna, Hamid, Prashant Kumar, Joe Hayward, Manik Gupta, and Erick Giovani Sperandio Nascimento. "Low-Cost Air Quality Sensing towards Smart Homes." Atmosphere 12, no. 4 (April 2, 2021): 453. http://dx.doi.org/10.3390/atmos12040453.
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The evolution of low-cost sensors (LCSs) has made the spatio-temporal mapping of indoor air quality (IAQ) possible in real-time but the availability of a diverse set of LCSs make their selection challenging. Converting individual sensors into a sensing network requires the knowledge of diverse research disciplines, which we aim to bring together by making IAQ an advanced feature of smart homes. The aim of this review is to discuss the advanced home automation technologies for the monitoring and control of IAQ through networked air pollution LCSs. The key steps that can allow transforming conventional homes into smart homes are sensor selection, deployment strategies, data processing, and development of predictive models. A detailed synthesis of air pollution LCSs allowed us to summarise their advantages and drawbacks for spatio-temporal mapping of IAQ. We concluded that the performance evaluation of LCSs under controlled laboratory conditions prior to deployment is recommended for quality assurance/control (QA/QC), however, routine calibration or implementing statistical techniques during operational times, especially during long-term monitoring, is required for a network of sensors. The deployment height of sensors could vary purposefully as per location and exposure height of the occupants inside home environments for a spatio-temporal mapping. Appropriate data processing tools are needed to handle a huge amount of multivariate data to automate pre-/post-processing tasks, leading to more scalable, reliable and adaptable solutions. The review also showed the potential of using machine learning technique for predicting spatio-temporal IAQ in LCS networked-systems.
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Zhao, Shanchun, and Xu Li. "An Attention Encoder-Decoder Dual Graph Convolutional Network with Time Series Correlation for Multi-Step Traffic Flow Prediction." Journal of Advanced Transportation 2022 (April 9, 2022): 1–17. http://dx.doi.org/10.1155/2022/7682274.
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Accurate traffic prediction is a powerful factor of intelligent transportation systems to make assisted decisions. However, existing methods are deficient in modeling long series spatio-temporal characteristics. Due to the complex and nonlinear nature of traffic flow time series, traditional methods of prediction tasks tend to ignore the heterogeneity and long series dependencies of spatio-temporal data. In this paper, we propose an attentional encoder-decoder dual graph convolution model with time-series correlation (AED-DGCN-TSC) for solving the spatio-temporal sequence prediction problem in the traffic domain. First, the time-series correlation module calculates the sequence similarity by fast Fourier transform and inverse fast Fourier transform, while obtaining multiple possible lengths as possible solutions for the sequence period length. Then, K possible periods fetches are selected and the corresponding sequences are weighted and aggregated to the target sequence. Then, the gated dual graph convolution recurrent unit uses the graph convolution operation, which combines the ideas of node embedding, and dual graph, as an operation inside the gated recurrent structure to capture the spatio-temporal heterogeneity relationship of long sequences. The gated decomposition recurrent module decomposes the time series into the period and trend terms, which are modelled by convolutional gated recurrent unit (ConvGRU) and then fused with features, respectively, and output after graph convolution. Finally, multi-step prediction of future traffic flow is performed in the form of encoder-decoder. Experimental evaluations are conducted on two real traffic datasets, and the results demonstrate the effectiveness of the proposed model.
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Zimmer, Hubert D., Harry R. Speiser, and Beate Seidler. "Spatio-temporal working-memory and short-term object-location tasks use different memory mechanisms." Acta Psychologica 114, no. 1 (September 2003): 41–65. http://dx.doi.org/10.1016/s0001-6918(03)00049-0.
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Gratton, Gabriele, Emily Wee, Elena I. Rykhlevskaia, Echo E. Leaver, and Monica Fabiani. "Does White Matter Matter? Spatio-temporal Dynamics of Task Switching in Aging." Journal of Cognitive Neuroscience 21, no. 7 (July 2009): 1380–95. http://dx.doi.org/10.1162/jocn.2009.21093.
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Older adults often encounter difficulties in switching between tasks, perhaps because of age-related decreases in executive function. Executive function may largely depend on connections between brain areas—connections that may become structurally and functionally weaker in aging. Here we investigated functional and structural age-related changes in switching between a spatial and a verbal task. These tasks were chosen because they are expected to differentially use the two hemispheres. Brain measures included anatomical information about anterior corpus callosum size (CC; the major commissure linking the left and right hemisphere), and the event-related optical signal (EROS). Behavioral results indicated that older adults had greater task-switching difficulties, which, however, were largely restricted to switching to the spatial task and to individuals with smaller anterior CCs. The EROS data showed both general switching-related activity in the left middle frontal gyrus (with approximately 300-msec latency) and task-specific activity in the inferior frontal gyrus, lateralized to the left for the switch-to-verbal condition and to the right for the switch-to-spatial condition. This lateralization was most evident in younger adults. In older adults, activity in the switch-to-spatial condition was lateralized to the right hemisphere in individuals with large CC, and to the left in individuals with small CC. These data suggest that (a) task switching may involve both task-general and task-specific processes; and (b) white matter changes may underlie some of the age-related problems in switching. These effects are discussed in terms of the hypothesis that aging involves some degree of cortical disconnection, both functional and anatomical.
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Franzheva, Olena D. "SPATIAL SYNCHRONIZATION OF CELLULAR AUTOMATA IN EVOLUTIONARY PROCESSES SIMULATION TASKS." Herald of Advanced Information Technology 3, no. 4 (November 20, 2020): 217–25. http://dx.doi.org/10.15276/hait.04.2020.1.
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Many applied tasks are simulated by difference equations that describe the vector of system states evolution in time. However it is required to take into account the spatial structure of simulated processes or systems in some tasks. In paper the possibility of a spatio-temporal processes simulation by cellular automata is considered. The brief review of two-dimensional cellular automata properties is provided. The principle of the most famous two-dimensional cellular automata “Game of Life” is described. Also the general way to set these automata in an analytical form by Reaction-Diffusion equation is considered. Concrete forms of the Reaction equation and Diffusion equation are constructed and invariant sets for this system are defined. The generalization of analytical cellular automata representation in total is provided. As an example, the model of population development is considered. It utilizes the classic Ferhulst equation, in which the spatial structure is taken into account having form of the cumulative neighbors’ impact on population changes rate. As per using of analytical form of cellular automata, different schemas of system spatio-temporal characteristics control are suggested. These schemas are based on feedback: delayed feedback (that is one that uses previous system states) and predictive feedback (that is one that uses predicted system states). As a result there is managed to synchronize spatial configuration of cellular automata and it can be interpreted as stable population development. Particularly, cellular automata could work in cycle with cycle length set earlier. For cellular automata evolution visualization the algorithms and their computer implementation are developed. Discrepancy function is suggested, due to which it is possible to evaluate the synchronization accuracy. Research results and examples of received configurations are presented.
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Clarke, Alex, Kirsten I. Taylor, and Lorraine K. Tyler. "The Evolution of Meaning: Spatio-temporal Dynamics of Visual Object Recognition." Journal of Cognitive Neuroscience 23, no. 8 (August 2011): 1887–99. http://dx.doi.org/10.1162/jocn.2010.21544.
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Research on the spatio-temporal dynamics of visual object recognition suggests a recurrent, interactive model whereby an initial feedforward sweep through the ventral stream to prefrontal cortex is followed by recurrent interactions. However, critical questions remain regarding the factors that mediate the degree of recurrent interactions necessary for meaningful object recognition. The novel prediction we test here is that recurrent interactivity is driven by increasing semantic integration demands as defined by the complexity of semantic information required by the task and driven by the stimuli. To test this prediction, we recorded magnetoencephalography data while participants named living and nonliving objects during two naming tasks. We found that the spatio-temporal dynamics of neural activity were modulated by the level of semantic integration required. Specifically, source reconstructed time courses and phase synchronization measures showed increased recurrent interactions as a function of semantic integration demands. These findings demonstrate that the cortical dynamics of object processing are modulated by the complexity of semantic information required from the visual input.
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Vanmechelen, Inti, Saranda Bekteshi, Marco Konings, Hilde Feys, Kaat Desloovere, Jean-Marie Aerts, and Elegast Monbaliu. "Psychometric properties of upper limb kinematics during functional tasks in children and adolescents with dyskinetic cerebral palsy." PLOS ONE 17, no. 9 (September 23, 2022): e0266294. http://dx.doi.org/10.1371/journal.pone.0266294.
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Dyskinetic cerebral palsy (DCP) is characterised by involuntary movements, and the movement patterns of children with DCP have not been extensively studied during upper limb tasks. The aim of this study is to evaluate psychometric properties of upper limb kinematics in participants with DCP and typically developing (TD) participants. In current repeatability and validity study, forty individuals with typical development (n = 20) and DCP (n = 20) performed a reach forward/sideways and a reach and grasp task during motion analysis on two occasions. Joint angles at point of task achievement (PTA) and spatio-temporal parameters were evaluated within-and between-sessions using intra-class correlation coefficients (ICC) and standard error of measurement (SEM). Independent t-tests/Mann-Whitney-U tests were used to compare parameters between groups. Within-session ICC values ranged from 0.45 to 1.0 for all parameters for both groups. Within-session SEM values ranged from 1.1° to 11.7° for TD participants and from 1.9° to 13.0° for participants with DCP. Eight within-session repetitions resulted in the smallest change in ICC and SEM values for both groups. Within-session variability was higher for participants with DCP in comparison with the TD group for the majority of the joint angles and spatio-temporal parameters. Intrinsic variability over time was small for all angles and spatio-temporal parameters, whereas extrinsic variability was higher for elbow and scapula angles. Between-group differences revealed lower shoulder adduction and higher elbow flexion, pronation and wrist flexion, as well as higher trajectory deviation and a lower maximal velocity for participants with DCP. This is the first study to assess the psychometric properties of upper limb kinematics in children and adolescents with DCP, showing that children with DCP show higher variability during task execution, requiring a minimum of eight repetitions. However, their variable movement pattern can be reliably captured within-and between-sessions, confirming the potential of three-dimensional motion analysis for assessment of rehabilitation interventions in DCP.
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Russo, Sofia, Giulia Calignano, Marco Dispaldro, and Eloisa Valenza. "An Integrated Perspective on Spatio-Temporal Attention and Infant Language Acquisition." International Journal of Environmental Research and Public Health 18, no. 4 (February 8, 2021): 1592. http://dx.doi.org/10.3390/ijerph18041592.
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Efficiency in the early ability to switch attention toward competing visual stimuli (spatial attention) may be linked to future ability to detect rapid acoustic changes in linguistic stimuli (temporal attention). To test this hypothesis, we compared individual performances in the same cohort of Italian-learning infants in two separate tasks: (i) an overlap task, measuring disengagement efficiency for visual stimuli at 4 months (Experiment 1), and (ii) an auditory discrimination task for trochaic syllabic sequences at 7 months (Experiment 2). Our results indicate that an infant’s efficiency in processing competing information in the visual field (i.e., visuospatial attention; Exp. 1) correlates with the subsequent ability to orient temporal attention toward relevant acoustic changes in the speech signal (i.e., temporal attention; Exp. 2). These results point out the involvement of domain-general attentional processes (not specific to language or the sensorial domain) playing a pivotal role in the development of early language skills in infancy.
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Wu, Bo, Xun Liang, Xiangping Zheng, and Jun Wang. "Enhancing Dynamic GCN for Node Attribute Forecasting with Meta Spatial-Temporal Learning (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 16360–61. http://dx.doi.org/10.1609/aaai.v37i13.27040.
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Node attribute forecasting has recently attracted considerable attention. Recent attempts have thus far utilize dynamic graph convolutional network (GCN) to predict future node attributes. However, few prior works have notice that the complex spatial and temporal interaction between nodes, which will hamper the performance of dynamic GCN. In this paper, we propose a new dynamic GCN model named meta-DGCN, leveraging meta spatial-temporal tasks to enhance the ability of dynamic GCN for better capturing node attributes in the future. Experiments show that meta-DGCN effectively modeling comprehensive spatio-temporal correlations between nodes and outperforms state-of-the-art baselines on various real-world datasets.
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Reyes Leiva, Karla Miriam, Miguel Ángel Cuba Gato, and José Javier Serrano Olmedo. "Estimation of Spatio-Temporal Parameters of Gait and Posture of Visually Impaired People Using Wearable Sensors." Sensors 23, no. 12 (June 14, 2023): 5564. http://dx.doi.org/10.3390/s23125564.
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In rehabilitating orientation and mobility (O&M) for visually impaired people (VIP), the measurement of spatio-temporal gait and postural parameters is of specific interest for rehabilitators to assess performance and improvements in independent mobility. In the current practice of rehabilitation worldwide, this assessment is carried out in people with estimates made visually. The objective of this research was to propose a simple architecture based on the use of wearable inertial sensors for quantitative estimation of distance traveled, step detection, gait velocity, step length and postural stability. These parameters were calculated using absolute orientation angles. Two different sensing architectures were tested for gait according to a selected biomechanical model. The validation tests included five different walking tasks. There were nine visually impaired volunteers in real-time acquisitions, where the volunteers walked indoor and outdoor distances at different gait velocities in their residences. The ground truth gait characteristics of the volunteers in five walking tasks and an assessment of the natural posture during the walking tasks are also presented in this article. One of the proposed methods was selected for presenting the lowest absolute error of the calculated parameters in all of the traveling experimentations: 45 walking tasks between 7 and 45 m representing a total of 1039 m walked and 2068 steps; the step length measurement was 4.6 ± 6.7 cm with a mean of 56 cm (11.59 Std) and 1.5 ± 1.6 relative error in step count, which compromised the distance traveled and gait velocity measurements, presenting an absolute error of 1.78 ± 1.80 m and 7.1 ± 7.2 cm/s, respectively. The results suggest that the proposed method and its architecture could be used as a tool for assistive technology designed for O&M training to assess gait parameters and/or navigation, and that a sensor placed in the dorsal area is sufficient to detect noticeable postural changes that compromise heading, inclinations and balancing in walking tasks.
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Li, Jianing, Xiao Wang, Lin Zhu, Jia Li, Tiejun Huang, and Yonghong Tian. "Retinomorphic Object Detection in Asynchronous Visual Streams." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 1332–40. http://dx.doi.org/10.1609/aaai.v36i2.20021.
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Due to high-speed motion blur and challenging illumination, conventional frame-based cameras have encountered an important challenge in object detection tasks. Neuromorphic cameras that output asynchronous visual streams instead of intensity frames, by taking the advantage of high temporal resolution and high dynamic range, have brought a new perspective to address the challenge. In this paper, we propose a novel problem setting, retinomorphic object detection, which is the first trial that integrates foveal-like and peripheral-like visual streams. Technically, we first build a large-scale multimodal neuromorphic object detection dataset (i.e., PKU-Vidar-DVS) over 215.5k spatio-temporal synchronized labels. Then, we design temporal aggregation representations to preserve the spatio-temporal information from asynchronous visual streams. Finally, we present a novel bio-inspired unifying framework to fuse two sensing modalities via a dynamic interaction mechanism. Our experimental evaluation shows that our approach has significant improvements over the state-of-the-art methods with the single-modality, especially in high-speed motion and low-light scenarios. We hope that our work will attract further research into this newly identified, yet crucial research direction. Our dataset can be available at https://www.pkuml.org/resources/pku-vidar-dvs.html.
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Wang, Yong, Guoliang Li, Kaiyu Li, and Haitao Yuan. "A Deep Generative Model for Trajectory Modeling and Utilization." Proceedings of the VLDB Endowment 16, no. 4 (December 2022): 973–85. http://dx.doi.org/10.14778/3574245.3574277.
Full textAbstract:
Modern location-based systems have stimulated explosive growth of urban trajectory data and promoted many real-world applications, e.g. , trajectory prediction. However, heavy big data processing overhead and privacy concerns hinder trajectory acquisition and utilization. Inspired by regular trajectory distribution on transportation road networks, we propose to model trajectory data privately with a deep generative model and leverage the model to generate representative trajectories for downstream tasks or directly support these tasks ( e.g. , popularity ranking), rather than acquiring and processing the original big trajectory data. Nevertheless, it is rather challenging to model high-dimensional trajectories with time-varying yet skewed distribution. To address this problem, we model and generate trajectory sequence with judiciously encoded spatio-temporal features over skewed distribution by leveraging an important factor neglected by the literature - the underlying road properties ( e.g. , road types and directions), which are closely related to trajectory distribution. Specifically, we decompose trajectory into map-matched road sequence with temporal information and embed them to encode spatio-temporal features. Then, we enhance trajectory representation by encoding inherent route planning patterns from the underlying road properties. Later, we encode spatial correlations among edges and daily and weekly temporal periodicity information. Next, we employ a meta-learning module to generate trajectory sequence step by step by learning generalized trajectory distribution patterns from skewed trajectory data based on the well-encoded trajectory prefix. Last but not least, we preserve trajectory privacy by learning the model differential privately with clipping gradients. Experiments on real-world datasets show that our method significantly outperforms existing methods.
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Cherian, Anoop, Chiori Hori, Tim K. Marks, and Jonathan Le Roux. "(2.5+1)D Spatio-Temporal Scene Graphs for Video Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 444–53. http://dx.doi.org/10.1609/aaai.v36i1.19922.
Full textAbstract:
Spatio-temporal scene-graph approaches to video-based reasoning tasks, such as video question-answering (QA), typically construct such graphs for every video frame. These approaches often ignore the fact that videos are essentially sequences of 2D ``views'' of events happening in a 3D space, and that the semantics of the 3D scene can thus be carried over from frame to frame. Leveraging this insight, we propose a (2.5+1)D scene graph representation to better capture the spatio-temporal information flows inside the videos. Specifically, we first create a 2.5D (pseudo-3D) scene graph by transforming every 2D frame to have an inferred 3D structure using an off-the-shelf 2D-to-3D transformation module, following which we register the video frames into a shared (2.5+1)D spatio-temporal space and ground each 2D scene graph within it. Such a (2.5+1)D graph is then segregated into a static sub-graph and a dynamic sub-graph, corresponding to whether the objects within them usually move in the world. The nodes in the dynamic graph are enriched with motion features capturing their interactions with other graph nodes. Next, for the video QA task, we present a novel transformer-based reasoning pipeline that embeds the (2.5+1)D graph into a spatio-temporal hierarchical latent space, where the sub-graphs and their interactions are captured at varied granularity. To demonstrate the effectiveness of our approach, we present experiments on the NExT-QA and AVSD-QA datasets. Our results show that our proposed (2.5+1)D representation leads to faster training and inference, while our hierarchical model showcases superior performance on the video QA task versus the state of the art.
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Zheng, Chuanpan, Xiaoliang Fan, Cheng Wang, and Jianzhong Qi. "GMAN: A Graph Multi-Attention Network for Traffic Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 1234–41. http://dx.doi.org/10.1609/aaai.v34i01.5477.
Full textAbstract:
Long-term traffic prediction is highly challenging due to the complexity of traffic systems and the constantly changing nature of many impacting factors. In this paper, we focus on the spatio-temporal factors, and propose a graph multi-attention network (GMAN) to predict traffic conditions for time steps ahead at different locations on a road network graph. GMAN adapts an encoder-decoder architecture, where both the encoder and the decoder consist of multiple spatio-temporal attention blocks to model the impact of the spatio-temporal factors on traffic conditions. The encoder encodes the input traffic features and the decoder predicts the output sequence. Between the encoder and the decoder, a transform attention layer is applied to convert the encoded traffic features to generate the sequence representations of future time steps as the input of the decoder. The transform attention mechanism models the direct relationships between historical and future time steps that helps to alleviate the error propagation problem among prediction time steps. Experimental results on two real-world traffic prediction tasks (i.e., traffic volume prediction and traffic speed prediction) demonstrate the superiority of GMAN. In particular, in the 1 hour ahead prediction, GMAN outperforms state-of-the-art methods by up to 4% improvement in MAE measure. The source code is available at https://github.com/zhengchuanpan/GMAN.
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Zhang, Jing, Qian Ding, Biao Li, and Xiucai Ye. "Bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing." PeerJ Computer Science 9 (February 20, 2023): e1244. http://dx.doi.org/10.7717/peerj-cs.1244.
Full textAbstract:
Spatial crowdsourcing refers to the allocation of crowdsourcing workers to each task based on location information. K-nearest neighbor technology has been widely applied in crowdsourcing applications for crowdsourcing allocation. However, there are still several issues need to be stressed. Most of the existing spatial crowdsourcing allocation schemes operate on a centralized framework, resulting in low efficiency of crowdsourcing allocation. In addition, these spatial crowdsourcing allocation schemes are one-way allocation, that is, the suitable matching objects for each task can be queried from the set of crowdsourcing workers, but cannot query in reverse. In this article, a bidirectional k-nearest neighbor spatial crowdsourcing allocation protocol based on edge computing (BKNN-CAP) is proposed. Firstly, a spatial crowdsourcing task allocation framework based on edge computing (SCTAFEC) is established, which can offload all tasks to edge nodes in edge computing layer to realize parallel processing of spatio-temporal queries. Secondly, the positive k-nearest neighbor spatio-temporal query algorithm (PKNN) and reverse k-nearest neighbor spatio-temporal query algorithm (RKNN) are proposed to make the task publishers and crowdsourcing workers conduct two-way query. In addition, a road network distance calculation method is proposed to improve the accuracy of Euclidean distance in spatial query scenarios. Experimental results show that the proposed protocol has less time cost and higher matching success rate compared with other ones.