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Marie-Therese Charlotte Evans, Majid Latifi, Mominul Ahsan and Julfikar Haider
Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent ...
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Yuexing Zhang, Yiping Li, Shuo Li, Junbao Zeng, Yiqun Wang and Shuxue Yan
This paper proposes a centralized MTT method based on a state-of-the-art multi-sensor labeled multi-Bernoulli (LMB) filter in underwater multi-static networks with autonomous underwater vehicles (AUVs). The LMB filter can accurately extract the number of...
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Jonathan Ponniah and Or D. Dantsker
A system is considered in which agents (UAVs) must cooperatively discover interest-points (i.e., burning trees, geographical features) evolving over a grid. The objective is to locate as many interest-points as possible in the shortest possible time fram...
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Amedeo Buonanno, Antonio Nogarotto, Giuseppe Cacace, Giovanni Di Gennaro, Francesco A. N. Palmieri, Maria Valenti and Giorgio Graditi
In this work, we investigate an Information Fusion architecture based on a Factor Graph in Reduced Normal Form. This paradigm permits to describe the fusion in a completely probabilistic framework and the information related to the different features are...
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Andrea Ruggieri, Francesco Stranieri, Fabio Stella and Marco Scutari
Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations. BN parameter learning from incomplete da...
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Yue Zhang and Fangai Liu
A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (...
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Jianzhuo Yan, Ya Gao, Yongchuan Yu, Hongxia Xu and Zongbao Xu
Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it is p...
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Jun Lin, Lei Su, Yingjie Yan, Gehao Sheng, Da Xie and Xiuchen Jiang
It is of great significance to accurately get the running state of power transformers and timely detect the existence of potential transformer faults. This paper presents a prediction method of transformer running state based on LSTM_DBN network. Firstly...
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David Gamarnik, Devavrat Shah, and Yehua Wei
Pág. 410 - 428
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Komodakis, N.; Tziritas, G.
Pág. 2649 - 2661
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