Special Issue Editorial: Cognitively-Inspired Computing for Knowledge Discovery
Cognitive Computation
Special Issue Editorial: Cognitively-Inspired Computing for Knowledge Discovery
Kaizhu Huang 0 1 2
Rui Zhang 0 1 2
Xiaobo Jin 0 1 2
Amir Hussain 0 1 2
Kaizhu Huang 0 1 2
Amir Hussain 0 1 2
0 Henan University of Technology , Lianhua street 100, Gaoxin District, Zhengzhou, 450001 , China
1 Department of Mathematical Sciences, Xi'an Jiaotong- Liverpool University , SIP, Suzhou, 215123 , China
2 Division of Computing Science and Maths, University of Stirling , Stirling FK9 4LA, Scotland , UK
Knowledge discovery is an emerging topic in many domains addressing a variety of methodologies for extracting useful knowledge from data. In an era of explosive data growth, together with wide-spreading powerful distributive and parallel computing, we are faced with an urgent demand for research and development of more efficient, effective and smart methodologies. On the other hand, it is also crucially challenging to extract, summarize, and even generate knowledge due to the large-scale, noisy, heterogeneous nature of big data. To this end, significant efforts have been reported in the literature on social networks, computer vision, data science, machine learning, data mining, statistical analysis, and fast computing. A number of successful models have recently emerged and led to great impact in the field. Interestingly, despite the diverse research topics and applications, these works recognize that cognitivelyinspired mechanisms should be investigated in order to make the algorithms more intelligent, powerful, and effective in extracting insightful knowledge, from huge amounts of heterogeneous Big data.
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Department of Electrical and Electronic Engineering, Xi’an
Jiaotong-Liverpool University, SIP, Suzhou, 215123, China
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Model framework (SSDM2) based on a
cognitivelyinspired model, Sequential Dependence Model (SSDM)
to answer biomedical questions with relevant snippets
in academic papers. The whole system achieves
better performance against the state-of-the-art algorithm in
biomedical question answering.
Knowledge-inspired document and image analysis.
When reading documents or watching images or videos,
humans can exploit certain habits or mechanisms
to extract and integrate knowledge. Wang et al.
investigate a human reading, knowledge-inspired text
line extraction approach. They build a directed graph
upon the candidate characters from left to right,
inspired by human reading habits. The directed graph
can automatically construct a relationship to eliminate
the disorder of character components. Plugging the
information with the directed graph, the text extraction
performance is shown to be greatly improved. On the
other hand, Yan et al. study a cognitive fusion of
thermal and visible imagery for pedestrian detection
and tracking. Inspired by human visual perception,
they combine various cognitive models for effective
pedestrian detection. This model is shown to be the best
when compared with several state-of-the-art techniques.
Cognitively-inspired applications. In this final Special
Issue topic, Luo et al. discuss a semi-blind model to
estimate building temperatures. Inspired by human
perception of the real world, the proposed model does
not rely on data-driven models that require a large
amount of data for training. Instead, the proposed
semiblind model is able to accurately estimate the
temperature based only on the first several days’ data.
Specifically, the authors propose a highly integrated
parameter identification method in conjunction with
self-adaptive algorithms and the grey prediction
technique for parameters estimation. Experimental results
indicate that the proposed cognitively-inspired
semiblind method attains greater improvement over previous
methods.
Concluding the editorial, we extend our sincere thanks to
authors, reviewers, and the management team of Cognitive
Computation journal, all of whom enabled the publication
of this special issue. All papers have been through a rigorous
review process of two to four rounds, in order to generate a
high quality special issue.
Funding Information The paper was supported by the National
Science Foundation of China (NSFC 61473236), Natural science fund
for colleges and universities in Jiangsu Province under grant no.
17KJD520010, Suzhou Science and Technology Program under grant
no. SYG201712, SZS201613 and the UK Engineering and Physical
Sciences Research Council (EPSRC) grant no. EP/M026981/1.
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