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EARLY BRONZE SNAKE MOTIFS ON POTTERY VESSELS AND THEIR SYMBOLISM IN SOUTHERN LEVANT

Autor: Al-Ajlouny, F., Khrisat, B., Al-Masri, E., Alyassin, H., Mayyas, A., Kraishan, D.

Snake symbolism is prevalent throughout recorded history and is seen in cultures vastly separated by distance and time such as from Ancient Egypt to modern Jordan. In the Southern Levant (present day Jordan and Palestine), many objects with snake app

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::65afcf4d337c7da45b13300778e36d4a

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Akademický článek

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An exploratory analysis on Agritech policies, innovations and funding for climate change mitigation

Autor: Hernandez Serrano, Pedro, Altenburg, Lina, Kumar, Parveen, Wu, XT, Jermaine, C, Xiong, L, Hu, XH, Kotevska, O, Lu, SY, Xu, WJ, Aluru, S, Zhai, CX, Al-Masri, E, Chen, ZY, Saltz, J

Publikováno v: IEEE BigData
2020 IEEE International Conference on Big Data (Big Data), 2365-2370
STARTPAGE=2365;ENDPAGE=2370;TITLE=2020 IEEE International Conference on Big Data (Big Data)

Climate change mitigation technologies (CCMT) have significantly been developed for the last decade to mitigate the consequences of mega global challenges, such as climate risk and threatened food security. International agreements such as the Kyoto

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3290020cdad018c2fc619a593ed75d1
https://doi.org/10.1109/bigdata50022.2020.9378314

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Q-learning for Statically Scheduling DAGs

Autor: Roeder, J., Rouxel, B., Grelck, C., Wu, X., Jermaine, C., Xiong, L., Hu, X., Kotevska, O., Lu, S., Xu, W., Aluru, S., Zhai, C., Al-Masri, E., Chen, Z., Saltz, J.

Publikováno v: 2020 IEEE International Conference on Big Data (Big Data)
2020 IEEE International Conference on Big Data: proceedings : Dec 10-Dec 13, 2020, virtual event, 5813-5815
STARTPAGE=5813;ENDPAGE=5815;TITLE=2020 IEEE International Conference on Big Data
IEEE BigData

Data parallel frameworks (e.g. Hive, Spark or Tez) can be used to execute complex data analyses consisting of many dependent tasks represented by a Directed Acylical Graph (DAG). Minimising the job completion time (i.e. makespan) is still an open pro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c36e4f12a5065d1c1154523eefce45f2
https://doi.org/10.1109/BigData50022.2020.9378062

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EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming Events

Autor: Antaris, Stefanos, Rafailidis, Dimitrios, Girdzijauskas, Sarunas, Wu, XT, Jermaine, C, Xiong, L, Hu, XH, Kotevska, O, Lu, SY, Xu, WJ, Aluru, S, Zhai, CX, Al-Masri, E, Chen, ZY, Saltz, J

Publikováno v: IEEE BigData
2020 IEEE International Conference on Big Data (Big Data), 1455-1464
STARTPAGE=1455;ENDPAGE=1464;TITLE=2020 IEEE International Conference on Big Data (Big Data)

In this study, we present a dynamic graph representation learning model on weighted graphs to accurately predict the network capacity of connections between viewers in a live video streaming event. We propose EGAD, a neural network architecture to ca

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3712d33a3748bde17cc269a3ed49bf76

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