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Roman Rybka, Yury Davydov, Danila Vlasov, Alexey Serenko, Alexander Sboev and Vyacheslav Ilyin
Developing a spiking neural network architecture that could prospectively be trained on energy-efficient neuromorphic hardware to solve various data analysis tasks requires satisfying the limitations of prospective analog or digital hardware, i.e., local...
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Song Xue, Jingyan Chen, Sheng Li and Huaai Huang
Early warning of safety risks downstream of small reservoirs is directly related to the safety of people?s lives and property and the economic and social development of the region. The lack of data and low collaboration in downstream safety management of...
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Jun Li, Chenyang Zhang, Jianyi Zhang and Yanhua Shao
To address the challenge of balancing privacy protection with regulatory oversight in blockchain transactions, we propose a regulatable privacy protection scheme for blockchain transactions. Our scheme utilizes probabilistic public-key encryption to obsc...
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Khrystyna Zub, Pavlo Zhezhnych and Christine Strauss
In this paper, we investigate the methods used to evaluate the admission chances of higher education institutions? (HEI) entrants as a crucial factor that directly influences the admission efficiency, quality of education results, and future students? li...
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Fábio Mendonça, Sheikh Shanawaz Mostafa, Fernando Morgado-Dias and Antonio G. Ravelo-García
This study presents a novel approach for kernel selection based on Kullback?Leibler divergence in variational autoencoders using features generated by the convolutional encoder. The proposed methodology focuses on identifying the most relevant subset of ...
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Ning Jin, Linlin Song, Gabriel Jing Huang and Ke Yan
Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive infor...
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Wenyu Cao, Benbo Sun and Pengxiao Wang
Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the applicat...
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Marino Marrocu and Luca Massidda
Rainfall forecasting plays a key role in mitigating environmental risks in urban areas, which are subject to increasing hydrogeological risk due to transformations in the urban landscape. We present a new technique for probabilistic precipitation nowcast...
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Haoxiang Xu, Tongyao Ren, Zhuangda Mo and Xiaohui Yang
Since the classification methods mentioned in previous studies are currently unable to meet the accuracy requirements for fault diagnosis in large-scale chemical industries, these methods are gradually being eliminated and rarely used. This research offe...
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Wenyi Zhou, Hongguang Fan, Jihong Zhu, Hui Wen and Ying Xie
This paper first studies the generalization ability of the convolutional layer as a feature mapper (CFM) for extracting image features and the classification ability of the multilayer perception (MLP) in a CNN. Then, a novel generalized hybrid probabilit...
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