scholarly journals A fabric architecture towards block chain application using hyper ledger

2018 ◽  
Vol 7 (2.20) ◽  
pp. 83 ◽  
Author(s):  
Md Haseeb ◽  
K Raja Sekhar ◽  
Y V. Spandana ◽  
M Syam
Keyword(s):  
Business Models ◽  
State Machine ◽  
The State ◽  
Distributed Ledger ◽  
Block Chain ◽  
Fabric Architecture ◽  
State Machine Replication ◽  
Main Factor

Block chain is a trust which can be best understood by the state machine replication, It is decentralized distributed ledger which is along all peers in the network connected through nodes over Internet. Every Node in the chain have equal stake and also the main factor is every node which have greater CPU cycles has a chance to operate node by spending those CPU cycles and also show Proof Of Work. Hence when the block chain is used in Business Models it loses its Private transactions and Confidential Contracts.  

scholarly journals Lodestone: An Efficient Byzantine Fault-Tolerant Protocol in Consortium Blockchains

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Chen Shan ◽  
Lei Fan
Keyword(s):  
Communication Complexity ◽  
Fault Tolerant ◽  
State Of The Art ◽  
State Machine ◽  
The State ◽  
Byzantine Fault ◽  
Two Phases ◽  
A Chain ◽  
Partial Synchrony ◽  
State Machine Replication

We present Lodestone, a chain-based Byzantine fault-tolerant (BFT) state machine replication (SMR) protocol under partial synchrony. Lodestone enables replicas to achieve consensus with two phases of voting and enjoys (1) optimistic responsiveness and (2) linear communication complexity on average. Similar to the state-of-the-art chain-based BFT protocols, Lodestone can be optimized with a pipelining idea elegantly. We implement pipelined Lodestone and deploy experiments to evaluate its performance. The evaluation results demonstrate that Lodestone has a lower latency than HotStuff under various workloads.

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Yongsen Ma ◽  
Sheheryar Arshad ◽  
Swetha Muniraju ◽  
Eric Torkildson ◽  
Enrico Rantala ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Activity Recognition ◽  
Deep Neural Networks ◽  
State Machine ◽  
Recognition Algorithm ◽  
The State ◽  
Neural Architecture ◽  
Learning Agent

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition. In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi. The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.

scholarly journals Better state pictures facilitating state machine characteristic conjecture

2021 ◽  
Author(s):  
Dang Duy Bui ◽  
Kazuhiro Ogata
Keyword(s):  
Virtual Machines ◽  
Mutual Exclusion ◽  
State Machine ◽  
Formal Proof ◽  
The State ◽  
Lessons Learned ◽  
Java Virtual Machines ◽  
Core Part ◽  
Proximity Principle

AbstractThe mutual exclusion protocol invented by Mellor-Crummey and Scott (called MCS protocol) is used to exemplify that state picture designs based on which the state machine graphical animation (SMGA) tool produces graphical animations should be better visualized. Variants of MCS protocol have been used in Java virtual machines and therefore the 2006 Edsger W. Dijkstra Prize in Distributed Computing went to their paper on MCS protocol. The new state picture design of a state machine formalizing MCS protocol is assessed based on Gestalt principles, more specifically proximity principle and similarity principle. We report on a core part of a formal verification case study in which the new state picture design and the SMGA tool largely contributed to the successful completion of the formal proof that MCS protocol enjoys the mutual exclusion property. The lessons learned acquired through our experiments are summarized as two groups of tips. The first group is some new tips on how to make state picture designs. The second one is some tips on how to conjecture state machine characteristics by using the SMGA tool. We also report on one more case study in which the state picture design has been made for the mutual exclusion protocol invented by Anderson (called Anderson protocol) and some characteristics of the protocol have been discovered based on the tips.

scholarly journals State-machine replication for planet-scale systems

2020 ◽  
Author(s):  
Vitor Enes ◽  
Carlos Baquero ◽  
Tuanir França Rezende ◽  
Alexey Gotsman ◽  
Matthieu Perrin ◽  
...  
Keyword(s):  
State Machine ◽  
State Machine Replication
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