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Yuting Chen, Pengjun Zhao, Yi Lin, Yushi Sun, Rui Chen, Ling Yu and Yu Liu
Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in ge...
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Md Easin Hasan and Amy Wagler
Neuroimaging experts in biotech industries can benefit from using cutting-edge artificial intelligence techniques for Alzheimer?s disease (AD)- and dementia-stage prediction, even though it is difficult to anticipate the precise stage of dementia and AD....
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Ruitao Wu, Xiang Zhang, Runtao Wang and Haipeng Wang
Protein and peptide identification based on tandem mass spectrometry is a pillar technology in proteomics research. In recent years, increasing numbers of researchers have utilized deep learning to tackle challenges in proteomics. For example, catalyzed ...
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Zhuangzhuang Yang, Chengxin Pang and Xinhua Zeng
Predicting the future trajectories of multiple agents is essential for various applications in real life, such as surveillance systems, autonomous driving, and social robots. The trajectory prediction task is influenced by many factors, including individ...
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Lei Ma, Stefan Seipel, Sven Anders Brandt and Ding Ma
Examining the complexity of urban form may help to understand human behavior in urban spaces, thereby improving the conditions for sustainable design of future cities. Metrics, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) inde...
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Chenfang Zhang, Yong Gan and Ruisen Yang
The main steps in a graph neural network are message propagation and aggregation between nodes. Message propagation allows messages from distant nodes in the graph to be transmitted to the central node, while feature aggregation allows the central node t...
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Michael Hopwood, Phuong Pho and Alexander V. Mantzaris
Sampling is an important step in the machine learning process because it prioritizes samples that help the model best summarize the important concepts required for the task at hand. The process of determining the best sampling method has been rarely stud...
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Dmitry Pavlyuk
Transfer learning is a modern concept that focuses on the application of ideas, models, and algorithms, developed in one applied area, for solving a similar problem in another area. In this paper, we identify links between methodologies in two fields: vi...
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Fabíola Martins Campos de Oliveira and Edson Borin
Billions of devices will compose the IoT system in the next few years, generating a huge amount of data. We can use fog computing to process these data, considering that there is the possibility of overloading the network towards the cloud. In this conte...
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Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo and Francesco Camastra
A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep lea...
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Bin Li, Yunlong Fan, Yikemaiti Sataer, Zhiqiang Gao and Yaocheng Gui
Semantic dependency parsing could be applied in many downstream tasks of natural language processing, including named entity recognition, information extraction, machine translation, sentiment analysis, question generation, question answering, etc.
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Petros Brimos, Areti Karamanou, Evangelos Kalampokis and Konstantinos Tarabanis
Traffic forecasting has been an important area of research for several decades, with significant implications for urban traffic planning, management, and control. In recent years, deep-learning models, such as graph neural networks (GNN), have shown grea...
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Zhihao Zhang, Yong Han, Tongxin Peng, Zhenxin Li and Ge Chen
Accurate subway passenger flow prediction is crucial to operation management and line scheduling. It can also promote the construction of intelligent transportation systems (ITS). Due to the complex spatial features and time-varying traffic patterns of s...
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Dahye Kim, YoungJin Kim and Young-Seob Jeong
We make daily comments on online platforms (e.g., social networks), and such natural language texts often contain sentiment (e.g., positive and negative) for certain aspects (e.g., food and service). If we can automatically extract the aspect-based senti...
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Ammar Yahya Daeef, Ali Al-Naji, Ali K. Nahar and Javaan Chahl
Malware is the primary attack vector against the modern enterprise. Therefore, it is crucial for businesses to exclude malware from their computer systems. The most responsive solution to this issue would operate in real time at the edge of the IT system...
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Zhenxin Li, Yong Han, Zhenyu Xu, Zhihao Zhang, Zhixian Sun and Ge Chen
Traffic forecasting has always been an important part of intelligent transportation systems. At present, spatiotemporal graph neural networks are widely used to capture spatiotemporal dependencies. However, most spatiotemporal graph neural networks use a...
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Jiawei Kang, Shangwen Yang, Xiaoxuan Shan, Jie Bao and Zhao Yang
Exploring the delay causality between airports and comparing the delay propagation patterns across different airport networks is critical to better understand delay propagation mechanisms and provide effective delay mitigation strategies. A novel attenti...
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Jingjing Liu, Xinli Yang, Denghui Zhang, Ping Xu, Zhuolin Li and Fengjun Hu
Multi-node wind speed forecasting is greatly important for offshore wind power. It is a challenging task due to unknown complex spatial dependencies. Recently, graph neural networks (GNN) have been applied to wind forecasting because of their capability ...
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Zhengyan Cui, Junjun Zhang, Giseop Noh and Hyun Jun Park
Traffic prediction is a popular research topic in the field of Intelligent Transportation System (ITS), as it can allocate resources more reasonably, relieve traffic congestion, and improve road traffic efficiency. Graph neural networks are widely used i...
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Junwei Zhou, Xizhong Qin, Kun Yu, Zhenhong Jia and Yan Du
Accurate urban traffic flow prediction plays a vital role in Intelligent Transportation System (ITS). The complex long-term and long-range spatiotemporal correlations of traffic flow pose a significant challenge to the prediction task. Most current resea...
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