Following the design of more efficient blockchain consensus algorithms, the execution layer has emerged as the new performance bottleneck of blockchains, especially under high contention. Current parallel execution frameworks either rely on optimistic concurrency control (OCC) or on pessimistic concurrency control (PCC), both of which see their performance decrease when workloads are highly contended, albeit for different reasons. In this work, we present NEMO, a new blockchain execution engine that combines OCC with the object data model to address this challenge. NEMO introduces four core innovations: (i) a greedy commit rule for transactions using only owned objects; (ii) refined handling of dependencies to reduce re-executions; (iii) the use of incomplete but statically derivable read/write hints to guide execution; and (iv) a priority-based scheduler that favors transactions that unblock others. Through simulated execution experiments, we demonstrate that NEMO significantly reduces redundant computation and achieves higher throughput than representative approaches. For example, with 16 workers when running on the Delft High Performance Center (DHPC) supercomputer, NEMO's throughput is up to 42% higher than the one of Block-STM, the state-of-the-art OCC approach, and 61% higher than the PCC baseline used.

Common sense knowledge (CSK) comes naturally to humans, but is very hard for computers to comprehend. However it is critical for machines to behave intelligently, and as such collecting CSK has become a prevalent field of research. Whilst a lot of research has been done to develop CSK acquisition methods, not much work has been done to survey the literature that already exists. Furthermore the surveys that have been done are outdated, and as such there is a clear gap in the literature. This paper will survey the different approaches to CSK acquisition and evaluate their effectiveness, as a way of gauging their real life applicability. It will also compare the current \textit{state of the art} methods, to some previous work to illustrate the progress that has been made and project that into the future. Furthermore this paper will also create a taxonomy categorizing the surveyed literature in order give a better overview of existing methods. Finally, from the literature surveyed it is clear that these methods have made a lot of progress, but aren't quite yet at the same level as human performance. Nevertheless they have become robust enough to be deployed in real applications.