Distributed Data Collaborative Fusion Method for Industry-University-Research Cooperation Innovation System Based on Machine Learning

2021 ◽  
pp. 251-261
Author(s):  
Wen Li ◽  
Hai-li Xia ◽  
Wen-hao Guo
Keyword(s):  
Machine Learning ◽  
Innovation System ◽  
University Research ◽  
Distributed Data ◽  
Fusion Method ◽  
Research Cooperation

scholarly journals Swarm Learning for decentralized and confidential clinical machine learning

Nature ◽  
2021 ◽  
Author(s):  
Stefanie Warnat-Herresthal ◽  
◽  
Hartmut Schultze ◽  
Krishnaprasad Lingadahalli Shastry ◽  
Sathyanarayanan Manamohan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Precision Medicine ◽  
Distributed Data ◽  
X Ray ◽  
Uniform Distributions ◽  
Privacy Laws ◽  
Data Owner ◽  
Machine Learning Approach ◽  
Chest X Ray ◽  
Privacy Legislation

AbstractFast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.

Research on Innovation Efficiency and Technology Gap in China Economic Development

2017 ◽  
Vol 34 (02) ◽  
pp. 1750005 ◽  
Author(s):  
Jian-Wen Fang ◽  
Yung-ho Chiu
Keyword(s):  
Economic Development ◽  
Fixed Effect Model ◽  
University Research ◽  
Quadrant Analysis ◽  
Eastern Region ◽  
Network Dea ◽  
Research Cooperation ◽  
Effect Model ◽  
Technology Gaps ◽  
Technology Level

In this paper, we use the meta-frontier network DEA approach to evaluate the innovation efficiency of 30 provinces in China from 2009 to 2011. These provinces have been classified into two groups based on their levels of economic development. The first group comprises provinces in the Eastern region, while the second group comprises provinces in the Central and Western regions. First, we use the meta-frontier network DEA method to estimate the technology gaps of innovation efficiency between different operating types. Second, the quadrant analysis method explores the reasons for efficiency losses. Finally, we take the fixed effect model to examine whether industry–university–research cooperation influences technology efficiency. The empirical results indicate (i) the Eastern region has significantly higher innovation efficiency than the Central and Western regions. (ii) Some Eastern provinces have a high technology level, yet their resource allocation capabilities still need to be improved. (iii) Industry–university–research cooperation is an effective way to improve innovation performance.

scholarly journals An Evolutionary Game Analysis of Synergetic Innovation behaviour in regional innovation networks

2021 ◽  
Vol 235 ◽  
pp. 03032
Author(s):  
Dong Li ◽  
Chunyan Liu
Keyword(s):  
Research Collaboration ◽  
Software Evolution ◽  
Evolutionary Game ◽  
Innovation System ◽  
University Research ◽  
Regional Innovation ◽  
Related Factors ◽  
Independent Innovation ◽  
Coordination Costs ◽  
The Government

Regional innovation network has become an important way to improve national independent innovation ability and promote regional and social development. This paper studies the evolution of the synergetic innovation system of industry-university-Research Collaboration under the government behaviour, constructs an asymmetric evolutionary game model between the government and industry-university Research collaboration, and analyses the influence of parameter changes on synergetic innovation decisionmaking behaviour. The results showed that the government’s regulatory costs, subsidies from the government, the production, will take the profit resulting from the synergetic innovation behavior, coordination costs and government punishment, and one party to the cooperative behaviour decision to bring the other side of the loss factors such as size, to participate in Innovation decision-making behaviour of the subject of dynamic evolution path and the results have important influence to the use of MATLAB software evolution paths of related factors under different conditions and the trend of simulation test and verify.

Research on Knowledge Creation and Knowledge Diffusion Mechanism of Industry University Research Cooperation Model —Take the Tobacco Industry ASAN Example

2021 ◽  
Vol 7 (6) ◽  
pp. 5304-5317
Author(s):  
Zhang Hongyan ◽  
Luo Xiaoguang
Keyword(s):  
Knowledge Creation ◽  
Tobacco Industry ◽  
Double Helix ◽  
Diffusion Mechanism ◽  
Knowledge Diffusion ◽  
University Research ◽  
Research Cooperation ◽  
Innovative Products ◽  
Cooperation Model ◽  
Level Of Knowledge

Knowledge creation is an important basis of national innovation strategy, and industry university research cooperation is an important way of knowledge creation. Exploring the knowledge diffusion mechanism of different industry university research cooperation modes is conducive to knowledge creation. A good knowledge creation mechanism can improve the level of knowledge creation. Taking the tobacco industry as an example, this paper improves the knowledge creation level of the tobacco industry by establishing the knowledge creation mechanism of the tobacco industry, so as to solve the technical bottleneck of the tobacco industry and increase the number of innovative products in the tobacco industry. Through combing the research of scholars, it is found that the research on the promotion of knowledge creation by industry university research cooperation mode is relatively rich, but the research from the perspective of the relationship between knowledge diffusion mechanism and knowledge creation mechanism is slightly insufficient. In order to improve the knowledge creation level of industry university research cooperation, this paper obtains the knowledge creation mechanism of different industry university research cooperation modes by using SECI knowledge creation model. Innovatively put forward the "double helix" relationship between knowledge creation and knowledge diffusion in industry university research cooperation, that is, the knowledge diffusion mechanism in different industry university research cooperation modes will affect the level of knowledge creation, which provides a theoretical basis for the main subject of industry university research cooperation to find a way to improve the level of industry university research cooperation.

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