KN
K.R. Nanhekhan
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Amidst the rampant spread of misinformation, fact-checking of diverse claims made on the internet has become a pertinent task to mitigate this problem. Manual fact-checking cannot scale up with this demand and is very cumbersome, therefore instead automated fact-checking can be used. However, existing work has primarily focused on the fact-verification part rather than evidence retrieval for large data collections, leading to scalability issues for practical applications. In this study, we address this gap by exploring various methods for indexing a succinct set of supporting facts extracted from large data collections and enhancing the retrieval phase of the fact-checking pipeline. Our evaluation, consisting of measuring the performance and efficiency, is performed on the state-of-the-art claim datasets HoVer and WiCE, where we utilised the English Wikipedia as a large evidence data collection. Overall our results underscore the effectiveness of integrating supporting facts and advanced retrieval techniques for fact-checking pipelines in practical applications. We achieve, through a combination of indexing supporting facts together with Dense retrieval and Index compression, a massive improvement over the original fact-checking pipeline. This is up to a 10.0x speedup using a CPU-based approach and up to a 20.0x speedup using a GPU-based approach, while only incurring a modest loss of less than 6 points in accuracy.
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Amidst the rampant spread of misinformation, fact-checking of diverse claims made on the internet has become a pertinent task to mitigate this problem. Manual fact-checking cannot scale up with this demand and is very cumbersome, therefore instead automated fact-checking can be used. However, existing work has primarily focused on the fact-verification part rather than evidence retrieval for large data collections, leading to scalability issues for practical applications. In this study, we address this gap by exploring various methods for indexing a succinct set of supporting facts extracted from large data collections and enhancing the retrieval phase of the fact-checking pipeline. Our evaluation, consisting of measuring the performance and efficiency, is performed on the state-of-the-art claim datasets HoVer and WiCE, where we utilised the English Wikipedia as a large evidence data collection. Overall our results underscore the effectiveness of integrating supporting facts and advanced retrieval techniques for fact-checking pipelines in practical applications. We achieve, through a combination of indexing supporting facts together with Dense retrieval and Index compression, a massive improvement over the original fact-checking pipeline. This is up to a 10.0x speedup using a CPU-based approach and up to a 20.0x speedup using a GPU-based approach, while only incurring a modest loss of less than 6 points in accuracy.
Smart contracts play an important role within the blockchain by ensuring that valid transactions are being recorded. However, there are critical concerns regarding the security and privacy of data within these blockchain applications. This research provides information on how the integration of the Trusted Platform Module can achieve more security in a blockchain application built on Hyperledger Fabric. Despite the implemented prototype and Trusted Platform Module functions working separately, combining the two into one working prototype was not successful. Tests analysis has been performed showing that the prototype performs worse due to higher latencies and has no security vulnerabilities. However, these are not conclusive statements, as not only the prototype did not work fully but the analysis tools were lacking. Recommendations have been made to use the described process of this research for future research and also the development of more analysis tools.
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Smart contracts play an important role within the blockchain by ensuring that valid transactions are being recorded. However, there are critical concerns regarding the security and privacy of data within these blockchain applications. This research provides information on how the integration of the Trusted Platform Module can achieve more security in a blockchain application built on Hyperledger Fabric. Despite the implemented prototype and Trusted Platform Module functions working separately, combining the two into one working prototype was not successful. Tests analysis has been performed showing that the prototype performs worse due to higher latencies and has no security vulnerabilities. However, these are not conclusive statements, as not only the prototype did not work fully but the analysis tools were lacking. Recommendations have been made to use the described process of this research for future research and also the development of more analysis tools.