Institute for Architecture for Application Systems

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41 to 50 of 2,454 Results

unitaries.zip Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" ZIP Archive - 43.6 KB - MD5: 364ec719013af5ad6c6fc07d236211ad Unitary matrices (as pytorch files) that were used as target operators in the experiments.
utils.py Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements" Python Source Code - 5.2 KB - MD5: a5623fd0ce92b1547522e599c9fc5ec9 General utility functions.
Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing Oct 7, 2024 - Architectures and Middleware @IAAS Pesl, Robin D.; Mombrey, Carolin; Klein, Kevin; Georgievski, Ilche; Becker, Steffen; Herzwurm, Georg; Aiello, Marco, 2024, "Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing", https://doi.org/10.18419/DARUS-4497, DaRUS, V1 A classic, central Service-Oriented Computing (SOC) challenge is the service composition problem. It concerns solving a user-defined task by selecting a suitable set of services, possibly found at runtime, determining an invocation order, and handling request and response parameters. The solutions proposed in the past two decades mostly resort to a...
chat.py Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing Python Source Code - 857 B - MD5: 59cd746b143ee1293e59219c31945cde
code_extraction.py Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing Python Source Code - 251 B - MD5: 0f4fbdf56add8c4f75dd3f0bc35a050e
compare.py Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing Python Source Code - 3.1 KB - MD5: 350886b84b6feff7ec69dba8952e39f3
comparison.json Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing JSON - 4.0 MB - MD5: c921aaef4f00080518c973d79821b578
comparison.json Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing JSON - 4.0 MB - MD5: 44e13e98ecbf1dad4fc334b3a3590960
comparison.json Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing JSON - 4.0 MB - MD5: 8d4a9ee69c425a907705577eb7b25cff
comparison.json Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing JSON - 4.0 MB - MD5: 20c5617b5b916c3d271f20aa146e679f

unitaries.zip

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

ZIP Archive - 43.6 KB -

Unitary matrices (as pytorch files) that were used as target operators in the experiments.

utils.py

Oct 8, 2024 - Data repository for "Minimial-Risk Training Samples for QNN Training from Measurements"

Python Source Code - 5.2 KB -

General utility functions.

Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing

Oct 7, 2024 - Architectures and Middleware @IAAS

Pesl, Robin D.; Mombrey, Carolin; Klein, Kevin; Georgievski, Ilche; Becker, Steffen; Herzwurm, Georg; Aiello, Marco, 2024, "Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing", https://doi.org/10.18419/DARUS-4497, DaRUS, V1

A classic, central Service-Oriented Computing (SOC) challenge is the service composition problem. It concerns solving a user-defined task by selecting a suitable set of services, possibly found at runtime, determining an invocation order, and handling request and response parameters. The solutions proposed in the past two decades mostly resort to a...

chat.py

Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing

Python Source Code - 857 B -

code_extraction.py

Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing

Python Source Code - 251 B -

compare.py

Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing

Python Source Code - 3.1 KB -

comparison.json

Oct 7, 2024 - Replication Data for: Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing

JSON - 4.0 MB -