We present a method that avoids the problem of a large vocabulary recognition system missing keywords due to pruning errors or degraded speech. The method, called white listing, assures that all tokens of all of the keywords are found by the recognizer, albeit with a low score. We show that this method far outperforms methods that attempt to increase recall by using subword models. In addition,...

This paper addresses energy-aware application mapping for large-scale Network-on-chip (NoC). The increasing number of intellectual property (IP) cores in multi-processor system-on-chips (MPSoCs) makes NoC application mapping more challenging to find optimum core-to-topology mapping. This paper proposes an application mapping technique that incorporates domain knowledge into genetic algorithm (G...

Recent years have seen significant advances in automatic identification of the Q-matrix necessary for cognitive diagnostic assessment. As data-driven approaches are introduced to identify latent knowledge components (KC) based on observed student performance, it becomes crucial to describe and interpret these latent KCs. We address the problem of naming knowledge components using keyword automa...

Keyword extraction attracts much attention for its significant role in various natural language processing tasks. While some existing methods for keyword extraction have considered using single type of semantic relatedness between words or inherent attributes of words, almost all of them ignore two important issues: 1) how to fuse multiple types of semantic relations between words into a unifor...

The purpose of this research was to develop a mapping program for online-based teaching, using the instructional learning design based on ICT. Developing consisted three stages: analysis stage, stage and development where each step had an output that supported process content media. This is expected support online Financial Accounting course during COVID-19 Pandemic.
 Keywords: Accounting,...

In recent years, many private corporations and government organizations have digitized corpuses of legacy paper documents. Often, these organizations hope to take advantage of digital representations to transform costly manual tasks associated with paper archives into less-costly computer-assisted tasks. The most common approach toward automated information extraction is through inverted indexi...