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Privacy-Preserving and Efficient Aggregation Based on Blockchain for Power Grid Communications in Smart Communities
A privacy-preserving and efficient data aggregation scheme that divides users into different groups, and each group has a private blockchain to record its members' data to preserve the inner privacy within a group.
Semantic oriented ontology cohesion metrics for ontology-based systems
APPA: An anonymous and privacy preserving data aggregation scheme for fog-enhanced IoT
Stable cohesion metrics for evolving ontologies
- Yinglong Ma, Haijiang Wu, X. Ma, Beihong Jin, Tao Huang, Jun Wei
- Computer Science, PhilosophyJ. Softw. Maintenance Res. Pract.
- 1 August 2011
Four ontology cohesion metrics are proposed, which fully consider the implicitly expressed semantic information and are defined based on ontological semantics rather than ontology structure, and can be reasonably used as a cogent complementarity of existing ontology metrics.
A Graph Derivation Based Approach for Measuring and Comparing Structural Semantics of Ontologies
- Yinglong Ma, Ling Liu, K. Lu, Beihong Jin, Xiang-jun Liu
- Computer ScienceIEEE Transactions on Knowledge and Data…
- 1 May 2014
This paper presents a graph derivation representation based approach (GDR) for stable semantic measurement, which captures structural semantics of ontology and addresses those problems that cause unstable measurement of ontologies.
Utility-Privacy Tradeoff Based on Random Data Obfuscation in Internet of Energy
- Zhitao Guan, Guanlin Si, Jun Wu, Liehuang Zhu, Zijian Zhang, Yinglong Ma
- Computer ScienceIEEE Access
- 2 February 2017
The proposed scheme adopts random data-obfuscation to mask the real-time data and realize the fault-tolerance during data aggregation, and the random obfuscation value obeys the Laplace distribution, and it has better performance than that of other popular methods.
Combining Domain Knowledge Extraction With Graph Long Short-Term Memory for Learning Classification of Chinese Legal Documents
A method for learning Chinese legal document classification using Graph LSTM (Long Short-Term Memory) combined with domain knowledge extraction is proposed, which shows that compared with the traditional classification methods of support vector machine (SVM) and LSTm, Graph L STM has higher classification accuracy and better classification performance.
Measuring ontology information by rules based transformation
Hybrid embedding-based text representation for hierarchical multi-label text classification
A Timing Analysis Model for Ontology Evolutions Based on Distributed Environments
A timing analysis model for ontology evolution management with more expressive time constraints in a distributed environment is proposed and a prototype system called TEAM is developed that can perform the timing analysis task of distributed ontology evolutions.