361 to 370 of 3,568 Results
Apr 28, 2025 -
eSPARQL Implementation
Java Source Code - 247 B -
MD5: f1196658a10f9f45475f5eca8e4a8a42
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Apr 28, 2025 -
eSPARQL Implementation
Java Source Code - 247 B -
MD5: f1196658a10f9f45475f5eca8e4a8a42
|
Apr 28, 2025 -
eSPARQL Implementation
Java Source Code - 247 B -
MD5: f1196658a10f9f45475f5eca8e4a8a42
|
Apr 28, 2025 -
eSPARQL Implementation
Java Source Code - 247 B -
MD5: f1196658a10f9f45475f5eca8e4a8a42
|
Apr 28, 2025 -
eSPARQL Implementation
Turtle RDF - 1.7 KB -
MD5: e2145957dc8116812072e211d47ef08f
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Apr 28, 2025 -
eSPARQL Implementation
Turtle RDF - 1.3 KB -
MD5: 7bc56a7e20d120def4404c18cf5d18e7
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Apr 28, 2025 -
eSPARQL Implementation
Turtle RDF - 414 B -
MD5: fd7fe09942ffac9637ed8ee240bba53a
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Apr 28, 2025 -
eSPARQL Implementation
Turtle RDF - 541 B -
MD5: 9283c6780f94e573beec1b5cb0c33501
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Apr 28, 2025 -
eSPARQL Implementation
Turtle RDF - 483 B -
MD5: 14f72da7aaf6a63ea209a83c39c281a3
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Feb 28, 2025 - Analytic Computing
Fathallah, Nadeen, 2025, "Code for Improving Video Caption Accuracy with LLMs", https://doi.org/10.18419/DARUS-4776, DaRUS, V1
As part of the IKILeUS project at the University of Stuttgart, research was conducted to explore how Large Language Models (LLMs) can enhance the accuracy and contextual relevance of automatic speech recognition (ASR)-generated captions. While ASR tools provide a foundation for accessibility, they often produce grammatical errors, misinterpret homo... |
