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Found 8 papers, 2 papers with code
Large Language Models Based Automatic Synthesis of Software Specifications
As software systems grow in complexity and scale, the number of configurations and associated specifications required to ensure the correct operation can become large and prohibitively difficult to manipulate manually.
Using Sentence Embeddings and Semantic Similarity for Seeking Consensus when Assessing Trustworthy AI
Therefore, a core challenge of the assessment process is to identify when experts from different disciplines talk about the same problem but use different terminologies.
A New Parallel Algorithm for Sinkhorn Word-Movers Distance and Its Performance on PIUMA and Xeon CPU
Fortunately, WMD can be framed as an Earth Mover's Distance (EMD) for which the algorithmic complexity can be reduced to $O(V^2)$ by adding an entropy penalty to the optimization problem and solving it using the Sinkhorn-Knopp algorithm.
(Updated submission 11/20/2020) MISIM: A Novel Code Similarity System
First, MISIM uses a novel context-aware semantic structure (CASS), which is designed to aid in lifting semantic meaning from code syntax.
MISIM: A Neural Code Semantics Similarity System Using the Context-Aware Semantics Structure
Code semantics similarity can be used for many tasks such as code recommendation, automated software defect correction, and clone detection.
An Efficient Shared-memory Parallel Sinkhorn-Knopp Algorithm to Compute the Word Mover's Distance
Additionally, the computation can be made highly parallel by computing WMD of a single query document against multiple target documents at once (e. g., finding whether a given tweet is similar to any other tweets happened in a day).
Online and Real-time Object Tracking Algorithm with Extremely Small Matrices
Online and Real-time Object Tracking is an interesting workload that can be used to track objects (e. g., car, human, animal) in a series of video sequences in real-time.
Context-Aware Parse Trees
The simplified parse tree (SPT) presented in Aroma, a state-of-the-art code recommendation system, is a tree-structured representation used to infer code semantics by capturing program \emph{structure} rather than program \emph{syntax}.
