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Models of complicated systems can be represented in different ways - in scientific papers, they are represented using natural language text as well as equations. But to be of real use, they must also be implemented as software, thus making code a third form of representing models. We introduce the AutoMATES project, which aims to build semantically-rich unified representations of models from scientific code and publications to facilitate the integration of computational models from different domains and allow for modeling large, complicated systems that span multiple domains and levels of abstraction.
In this paper, we introduce new methods and discuss results of text-based LSTM (Long Short-Term Memory) networks for automatic music composition. The proposed network is designed to learn relationships within text documents that represent chord progr
Graph Neural Networks (GNNs) have recently shown to be powerful tools for representing and analyzing graph data. So far GNNs is becoming an increasingly critical role in software engineering including program analysis, type inference, and code repres
As one of the fundamental tasks in text analysis, phrase mining aims at extracting quality phrases from a text corpus. Phrase mining is important in various tasks such as information extraction/retrieval, taxonomy construction, and topic modeling. Mo
In this paper, we address the text-to-audio grounding issue, namely, grounding the segments of the sound event described by a natural language query in the untrimmed audio. This is a newly proposed but challenging audio-language task, since it requir
Process mining studies ways to derive value from process executions recorded in event logs of IT-systems, with process discovery the task of inferring a process model for an event log emitted by some unknown system. One quality criterion for discover