01944nas a2200145 4500008004100000245004000041210004000081520152200121100001801643700001501661700002001676700002101696700002301717856005801740 2022 eng d00aMaking Blockchain Validators Honest0 aMaking Blockchain Validators Honest3 a

The importance of honesty among blockchain validators can not be overemphasized, especially as blockchain is used by many as an underlying technology for the development of various Industry 4.0 solutions. In the blockchain consensus process, validators validate the correctness of transactions and propose new blocks for addition to the blockchain. They are rewarded for this task with the blockchain native token (e.g., ETH on the Ethereum blockchain). This reward is often distributed among validators with respect to their staked amount. An increasing number of validators joining the network leads to a decreasing chance of a validator being chosen for the validation task and thus a reduction in the validation reward. This situation results in some form of competition among the validators, leading them to carry out various malicious actions to influence the blockchain validator selection protocol in order to be chosen. In this paper, we examine the competitive interactions between validators in a blockchain consensus process and propose a model using an infinitely repeated game model that ensures that the validators are deterred from behaving maliciously while also encouraging a self-policing notion due to the extra incentive mechanism of an improved reputation score when a validator can verifiably report malicious activities by others. Further, we discuss the factors that can incentivize or disincentivize a validator to either continue to behave honestly or switch to dishonest behavior.

1 aSalau, Abiola1 aDantu, Ram1 aMorozov, Kirill1 aBadruddoja, Syed1 aUpadhyay, Kritagya uhttps://ieeexplore.ieee.org/abstract/document/992195201784nas a2200157 4500008004100000245004500041210004500086520130700131100002101438700001501459700001501474700001901489700001801508700002301526856007701549 2022 eng d00aMaking Smart Contracts Predict and Scale0 aMaking Smart Contracts Predict and Scale3 a

The machine learning algorithms can predict the events based on the trained models and datasets. However, a reliable prediction requires the model to be trusted and tamper-resistant. Blockchain technology provides trusted output with consensus-based transactions and an immutable distributed ledger. The machine learning algorithms can be trained on blockchain smart contracts to produce trusted models for reliable prediction. But most smart contracts in the blockchain do not support floating-point data type, limiting computations for classification, which can affect the prediction accuracy. In this work, we propose a novel method to produce floating-point equivalent probability estimation to classify labels on-chain with a Naive Bayes algorithm. We derive a mathematical model with Taylor series expansion to compute the ratio of the posterior probability of classes to classify labels using integers. Moreover, we implemented our solution in Ethereum blockchain smart-contract with the Solidity programming language, where we achieved a prediction accuracy comparable to the scikit-learn library in Python. Our derived method is platform-agnostic and can be supported in any blockchain network. Furthermore, machine learning and deep-learning algorithms can borrow the derived method.

1 aBadruddoja, Syed1 aDantu, Ram1 aHe, Yanyan1 aThompson, Mark1 aSalau, Abiola1 aUpadhyay, Kritagya uhttps://nsl.cse.unt.edu/content/making-smart-contracts-predict-and-scale02393nas a2200301 4500008004100000020002200041245007000063210006900133260002700202520152700229653001401756653001501770653001201785653001501797653001301812653001601825653002001841653002801861653000801889653001901897653002501916100002301941700001501964700001501979700001801994700002102012856005802033 2021 eng d a978-1-6654-1623-800aMake Consumers Happy by Defuzzifying the Service Level Agreements0 aMake Consumers Happy by Defuzzifying the Service Level Agreement aAtlanta, GA, USAbIEEE3 a

A Service Level Agreement (SLA) is a special kind of legal contract that binds a vendor to its customers where the vendor commits to provide certain services in exchange for certain payments from the customers. However, when customers do not get the services that they have subscribed for, it becomes a laborious job for customers to contact or visit the company and claim the correct amount of compensation or service credits. On the other hand, a Smart Contract is a contract that is a computer program that also binds multiple parties into given agreements but is a set of precise rules and is self-enforceable and self-executable. In this paper, we have introduced a novel work where we use fuzzy logic inside the Ethereum-based smart contract for two significant objectives. The first objective is to make the claim of the compensation easier and faster for customers by translating the SLA into a smart contract. The second objective is to make the smart contract even smarter and intelligent by implementing fuzzy logic so that customers who have a hard time understanding the legal jargon and ambiguities of the legal contract and SLA to find out if the compensation amount they are getting when the service is poor is good enough. Since fuzzy logic models semantics of linguistic expressions by capturing vagueness in the fuzzy sets, it becomes easier to solve the problem of contractual ambiguities and expedite the process of claiming compensation when implemented in a Blockchain-based smart contract.

10aambiguity10ablockchain10aclauses10acomplexity10aEthereum10aFuzzy logic10ainterpretations10aservice level agreement10aSLA10asmart contract10asmart legal contract1 aUpadhyay, Kritagya1 aDantu, Ram1 aHe, Yanyan1 aSalau, Abiola1 aBadruddoja, Syed uhttps://ieeexplore.ieee.org/abstract/document/975025801646nas a2200241 4500008004100000020002200041245003500063210003500098260002800133520098300161653002801144653001501172653000901187653002101196653001601217653001901233100002101252700001501273700001501288700002401303700001901327856005801346 2021 eng d a978-1-6654-3578-900aMaking Smart Contracts Smarter0 aMaking Smart Contracts Smarter aSydney, AustraliabIEEE3 a

Blockchain technology develops static smart contracts for decentralized business transactions, lacks dynamic decision-making capabilities that limit the possibilities of ever-increasing demands of modern business applications. Artificial intelligence, a computational prediction platform provides intelligent predictions, actions, and recognition that lacks the ability to hold on to the integrity of the prediction result and requires the help of external authorities to secure the system. Blockchain-based AI prediction can cover the gaps of individual technologies and can mutually benefit from one another to develop a decentralized machine learning architecture that promises to yield better security, automation, and dynamism of the application. This paper proposes a Naive Bayes prediction algorithm to perform prediction with inside blockchain smart contracts that promises to open up more opportunities in the field of Blockchain-AI decentralized applications.

10aartificial intelligence10ablockchain10aDApp10amachine learning10aNaive Bayes10asmart contract1 aBadruddoja, Syed1 aDantu, Ram1 aHe, Yanyan1 aUpadhayay, Kritagya1 aThompson, Mark uhttps://ieeexplore.ieee.org/abstract/document/9461148