scholarly journals DAG-FL: Direct Acyclic Graph-based Blockchain Empowers On-Device Federated Learning

2021 ◽  
Author(s):  
Mingrui Cao ◽  
Bin Cao ◽  
Wei Hong ◽  
Zhongyuan Zhao ◽  
Xiang Bai ◽  
...  
Keyword(s):  
Acyclic Graph ◽  
Direct Acyclic Graph

Retiming DAGs [direct acyclic graph]

1998 ◽  
Vol 17 (12) ◽  
pp. 1319-1325 ◽  
Author(s):  
P.Y. Calland ◽  
A. Mignotte ◽  
O. Peyran ◽  
Y. Robert ◽  
F. Vivien
Keyword(s):  
Acyclic Graph ◽  
Direct Acyclic Graph

scholarly journals Direct Acyclic Graph-Based Ledger for Internet of Things: Performance and Security Analysis

2020 ◽  
Vol 28 (4) ◽  
pp. 1643-1656 ◽  
Author(s):  
Yixin Li ◽  
Bin Cao ◽  
Mugen Peng ◽  
Long Zhang ◽  
Lei Zhang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Security Analysis ◽  
Acyclic Graph ◽  
Direct Acyclic Graph

Leaf Recognition for Plant Classification Using Direct Acyclic Graph Based Multi-Class Least Squares Twin Support Vector Machine

2016 ◽  
Vol 16 (03) ◽  
pp. 1650012 ◽  
Author(s):  
Divya Tomar ◽  
Sonali Agarwal
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Plant Species ◽  
Recognition System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Individual Plant ◽  
Species Classification ◽  
Acyclic Graph ◽  
Direct Acyclic Graph

As most of the plant species are at the risk of extinction, the task of plant identification has become a challenging process and an active area of research. In this paper, we propose a leaf recognition system for plant species classification using leaf image data through a novel direct acyclic graph based multi-class least squares twin support vector machine (DAG-MLSTSVM) classifier. Hybrid feature selection (HFS) approach is used to obtain the best discriminant features for the recognition of individual plant species. Leaves are recognized on the basis of shape and texture features. The experimental results indicate that the proposed DAG-MLSTSVM based plant leaf recognition system is highly accurate and having faster processing speed as compared to artificial neural network and direct acyclic graph based support vector machine.

Toward a Mixed Tangle-Blockchain Architecture

2020 ◽  
Author(s):  
Motaz Ben Hassine ◽  
Mourad Kmimech ◽  
Hussein Hellani ◽  
Layth Sliman
Keyword(s):  
Poisson Distribution ◽  
Scale Up ◽  
Mathematical Foundation ◽  
Validation Process ◽  
Acyclic Graph ◽  
Direct Acyclic Graph ◽  
Industrial Enterprises ◽  
Design And Implementation ◽  
Transaction Fees ◽  
Industrial Context

This paper presents the design and implementation of a new platform that takes into consideration the requirements and constraints resulting from the industrial context based on IoT. This platform combines the “Tangle” and “Blockchain” techniques. Tangle is primarily designed to address scale-up issues and the relatively high cost (time and resource) of transactions in a traditional blockchain-based platform. Unlike the “blockchain” structure, it consists of a solid mathematical foundation called DAG (Direct Acyclic Graph). It uses a validation process in which transactions are entered into the distributed registry after authenticating two other randomly selected transactions according to a Poisson distribution (thus, the locations of the new transactions are chosen using random runs in the graph). Therefore, it is an easily scalable system that does not require mining or transaction fees. We aim to study the integration of Tangle and Blockchain techniques to improve the performance and scalability of distributed registry-based platforms to be adapted in industrial enterprises whose processes incorporate or are based on IoT.

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