Temat: data networks - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Enriching WSN environment with context information
Autorzy:: Konieczny, M.
Powiązania:: https://bibliotekanauki.pl/articles/305246.pdf
Data publikacji:: 2012
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: wireless sensor networks
context-aware systems
data-centric architectures
Opis:: Currently the Internet of Things environment suffer from a variety of unique problems such as low throughput, inadequate support for mobility and notcompatible (and often close) application environments. However, characteristic of physical environment often provides opportunities to address these problems. This paper explores concepts of enriching the WNS environment with context-based information. By context we consider all data available in sensors environment, not only data processed by them. Currently this data are not utilized by sensors, but we think that they can improve overall functionality of the sensor network. We also discuss the practical challenges facing the integration of such an approach into the sensor application stack.
Źródło:: Computer Science; 2012, 13 (4); 101-114
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Towards textual data augmentation for neural networks: synonyms and maximum loss
Autorzy:: Jungiewicz, Michał
Smywiński-Pohl, Aleksander
Powiązania:: https://bibliotekanauki.pl/articles/305750.pdf
Data publikacji:: 2019
Wydawca:: Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
Tematy:: deep learning
data augmentation
neural networks
natural language processing
sentence classification
Opis:: Data augmentation is one of the ways to deal with labeled data scarcity and overfitting. Both of these problems are crucial for modern deep-learning algorithms, which require massive amounts of data. The problem is better explored in the context of image analysis than for text; this work is a step forward to help close this gap. We propose a method for augmenting textual data when training convolutional neural networks for sentence classification. The augmentation is based on the substitution of words using a thesaurus as well as Princeton University's WordNet. Our method improves upon the baseline in most of the cases. In terms of accuracy, the best of the variants is 1.2% (pp.) better than the baseline.
Źródło:: Computer Science; 2019, 20 (1); 57-83
1508-2806
2300-7036
Pojawia się w:: Computer Science
Dostawca treści:: Biblioteka Nauki

Artykuł

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