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Reputation systems have been one method of solving the unique challenges that face distributed networks of independent operators. Fundamentally, historical performance must be considered in a way that attempts to predict future behavior, optimize present functionality, and provide some measure of immutable recording. In this paper, a three-part system, MnemoSys, is proposed to solve this diverse set of problems. First, historical performance is dynamically weighted and scored using geometrically expanding time windows. Second, a quorum is abstracted as a restricted Boltzmann machine to produce a conditional probability estimate of log-normal likelihood of good-faith behavior. Third, all rewards and punishments are recorded on an immutable, decentralized ledger. Our experimentation shows that when applied iteratively to an entire network, consistently underperforming nodes are removed, network stability is maintained even with high percentages of simulated error, and global network parameters are optimized in the long-term. 

Bitcoin transactions are pseudonymous, which means that even when addresses or addresses can be connected to one another, it is really hard to connect them with outside entities. On top of that, there has been a proliferation of bitcoins mixing sites in recent years. These sites operate mostly on the dark web, and their main mission is to launder bitcoins by making vast amounts of complicated transactions with them and to make their association with a single owner even harder. Our mission is to make that distinction easier. In this paper, we plan to introduce two novel heuristics. Our OI heuristic is designed to parse blockchain data in a way where we only receive information we deem is of interest. We introduce HOLO, which aims to taint bitcoin addresses in reference to a fixed output. We also visualize the blockchain and our heuristic in an easily digestible manner. 

Researchers collaborating from different locations need a method to capture and store scientific workflow provenance that guarantees provenance integrity and reproducibility. As modern science is moving towards greater data accessibility, researchers also need a platform for open access data sharing. We propose SciLedger, a blockchain-based platform that provides secure, trustworthy storage for scientific workflow provenance to reduce research fabrication and falsification. SciLedger utilizes a novel invalidation mechanism that only invalidates necessary provenance records. SciLedger also allows for workflows with complex structures to be stored on a single blockchain so that researchers can utilize existing data in their scientific workflows by branching from and merging existing workflows. Our experimental results show that SciLedger provides an able solution for maintaining academic integrity and research flexibility within scientific workflows.