A Collection of Industry-Developed Blockchain-based Applications

A set of blockchain-based applications (DApps) consists of 400 public, private, and hybrid DApps. They are manually selected between September 2019 and February 2020 from nine blockchain platforms: Blockstack, Corda, Ethereum, EOS, Hive, Hyperledger Fabric, Klaytn, POA, and Steem

A Collection of Event Logs of Blockchain-based Applications

A set of event logs of 101 blockchain-based applications (DApps). For each DApp, there are two event log files. The first one is a raw version where data is encoded by blockchain. The second file is a decoded version where data is decoded into a human-readable format. If a DApp has multiple versions on different blockchain networks, then there are two event log files (encoded and decoded) for each version.  In addition, the event registry file includes a comprehensive list of event names and their corresponding signatures obtained from contract ABIs of the 101 DApps

Extracting conceptual models from user stories with Visual Narrator

Extracting conceptual models from natural language requirements can help identify dependencies, redundancies, and conflicts between requirements via a holistic and easy-to-understand view that is generated from lengthy textual specifications. Unfortunately, existing approaches never gained traction in practice, because they either require substantial human involvement or they deliver too low accuracy. In this paper, we propose an automated approach called Visual Narrator based on natural language processing that extracts conceptual models from user story requirements. We choose this notation because of its popularity among (agile) practitioners and its focus on the essential components of a requirement: Who? What? Why? Coupled with a careful selection and tuning of heuristics, we show how Visual Narrator enables generating conceptual models from user stories with high accuracy. Visual Narrator is part of the holistic Grimm method for user story collaboration that ranges from elicitation to the interactive visualization and analysis of requirements

Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature

Abstract Background Automatic identification of term variants or acceptable alternative free-text terms for gene and protein names from the millions of biomedical publications is a challenging task. Ontologies, such as the Cardiovascular Disease Ontology (CVDO), capture domain knowledge in a computational form and can provide context for gene/protein names as written in the literature. This study investigates: 1) if word embeddings from Deep Learning algorithms can provide a list of term variants for a given gene/protein of interest; and 2) if biological knowledge from the CVDO can improve such a list without modifying the word embeddings created. Methods We have manually annotated 105 gene/protein names from 25 PubMed titles/abstracts and mapped them to 79 unique UniProtKB entries corresponding to gene and protein classes from the CVDO. Using more than 14 M PubMed articles (titles and available abstracts), word embeddings were generated with CBOW and Skip-gram. We setup two experiments for a synonym detection task, each with four raters, and 3672 pairs of terms (target term and candidate term) from the word embeddings created. For Experiment I, the target terms for 64 UniProtKB entries were those that appear in the titles/abstracts; Experiment II involves 63 UniProtKB entries and the target terms are a combination of terms from PubMed titles/abstracts with terms (i.e. increased context) from the CVDO protein class expressions and labels. Results In Experiment I, Skip-gram finds term variants (full and/or partial) for 89% of the 64 UniProtKB entries, while CBOW finds term variants for 67%. In Experiment II (with the aid of the CVDO), Skip-gram finds term variants for 95% of the 63 UniProtKB entries, while CBOW finds term variants for 78%. Combining the results of both experiments, Skip-gram finds term variants for 97% of the 79 UniProtKB entries, while CBOW finds term variants for 81%. Conclusions This study shows performance improvements for both CBOW and Skip-gram on a gene/protein synonym detection task by adding knowledge formalised in the CVDO and without modifying the word embeddings created. Hence, the CVDO supplies context that is effective in inducing term variability for both CBOW and Skip-gram while reducing ambiguity. Skip-gram outperforms CBOW and finds more pertinent term variants for gene/protein names annotated from the scientific literature

Evaluation of Software-Based Early Leak-Warning System in Gulf of Mexico Subsea Flowlines

Early detection of subsea pipeline leaks is a very serious and ongoing issue for the oil and gas industry with limited successful cases reported. For example, aerial surveillance of pipelines can be applied only for relatively shallow and concentrated areas, and an advanced technology such as fiber-optic cable can be considered at the significant expense of time and cost for installation and equipment. The objective of this study is to evaluate a software-based leak-detection technique through complex multiphase flow mechanics. More specifically, this study investigates (i) how leak-detection problems can be formulated from a fluid-mechanics viewpoint and (ii) how reliable such a technique can be under conditions resembling the deepwater Gulf of Mexico (GOM). In examining a wide range of scenarios, this study proves that software-based techniques have a potential for playing a key role in the future. First, this study defines a base case selected from the literature review of deepwater GOM flowlines in terms of pressure and temperature conditions, fluid properties, reservoir properties, and flowline characteristics that allows a steady-state flow in pipeline to be determined with no leak present. Next, leaks with certain opening sizes (dleak) at different longitudinal locations (xD ¼ x/L) are positioned, and new steady states in the presence of leaks are calculated. By comparing the two steady-state responses (with and without leak), finally, the changes in two leak-detection indicators [i.e., change in upstream pressure (DPin) and change in downstream total flow rate (Dqt out)] can be calculated in a wide range of input parameters. This study presents the results in the form of contour plots for pressure and flow responses. The major finding of this study is that, theoretically, it is possible to estimate both size and longitudinal location of the leak with the two leak-detection indicators in the software-based leak-detection method. The results from various subsea flowline conditions [such as different gas/oil ratios (GORs) and fluid types, water depths, pressures at the receiving facilities, inclination angles, pipe diameters, water cuts, and so on] show that the reliability of this technique is improved when the sink term (i.e., amount of leaking fluid) is more dominant, which, in turn, means that leaks positioned farther upstream, with larger opening size, and occurring at higher pressure inside pipe are relatively easier to detect. In many of the scenarios considered, Dqt out as a leak-detection indicator shows more than a 10% change in the presence of a leak with dleak\u3e1 in., allowing relatively easier activation of a leak-warning system, which demonstrates the robustness of this technique. Other scenarios in which the indicators are less than a few percent changes, however, may be challenging—in those cases, additional responses from other methods (hardware-based or transient simulation) will be helpful