A typical contract would necessitate the use of an intermediary, make a payment to them, then wait for the record to return. Through a smart contract, though, it is as easy as putting the bitcoin into the vending machine, and the products would be published instantly. Smart contracts are Blockchain-based autonomous software. On Ethereum, a vast number of smart contracts have been deployed. Meanwhile, transaction security vulnerabilities have resulted in significant financial damages and have harmed the contract layer’s ecological integrity on Blockchain. As a result, detecting contract bugs reliably and efficiently is a new yet critical problem. Currently, available identification approaches, such as Oyente and Securify, depend primarily on symbolic execution or analysis. Since symbolic execution necessitates the discovery of all executable routes or the study of dependence diagrams in a contract, these approaches are time-consuming. In this paper, we suggest SmartPol, a machine learning-based method for detecting vulnerabilities in smart contracts. First, we use a data pre-processor to clean up the data before extracting features and classifying them. Second, we present a PSOGSA-based method for data optimisation and function extraction and a TSVM-based approach for semi-supervised learning classification models to identify smart contract vulnerabilities automatically. On EtherScan, SmartPol was used to test 49512 real-world smart contracts. The experimental findings show SmartPol’s reliability and efficacy. When we use PSOGSA for function extraction and TSVM classification sets, SmartPol’s predictive precision and accuracy are over 96%, and the average prediction time is 4 seconds on each smart contract.
In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and energy to defense, big data has given new insights into the huge volume of data accumulated over the past few decades that is helping reshape our knowledge and understanding of these sectors. Unfortunately, the same cannot be said about the state of big data in the developing world, where investments in IT infrastructure are dangerously low, keeping huge proportions of the population offline. This chapter discussed the challenges that exist in developing countries, which affect the smooth take-off of big data and data science as well as recommendations as to how countries and companies in the developing world can overcome these challenges to harness the benefits and opportunities presented by this technology.
Cognitive robots, exhibiting cognitive characteristics and synthesizing knowledge to perform tasks and interacting with humans in both industrial and social settings, have become a big part of modern societies. In this chapter, the authors review the processes and approaches to knowledge management in cognitive robot agents for effective human robot interaction. They present the current state of the art in current robotics technology and human-robot interaction. They state current requirements of cognitive robot agents in human-robot interaction and examine the role of knowledge in human-robot interaction. They finally propose a knowledge management framework for cognitive robots that consist of three main stages: knowledge acquisition and grounding, knowledge representation and knowledge integration, and instantiation into robot architectures.
Although information technology has positively influenced operations in the Ghanaian financial sector, there is still a high operational cost in performing KYC procedures due to duplication of efforts during clients' onboarding. The decentralized nature of blockchains makes them ideal for addressing these challenges. In this paper, the blockchain maturity model was used to assess the maturity and readiness of Ghanaian banks to adopt blockchain technology for KYC processes. Using primary data obtained via questionnaires and interviews, the individual components of the blockchain maturity model were assessed. The results indicate that the network, hardware, and software components are at repeatable, defined, and managed stages, respectively, while the people component lags in the initial stage due to a lack of adequate staff training. Finally, security and privacy are at the defined stage, whereas policy and regulations are at the initial stage.
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