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Specifying and verifying contract-driven service compositions using commitments and model checking.

, , , and . Expert Syst. Appl., (2017)

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Toward monetizing personal data: A two-sided market analysis., , , and . Future Gener. Comput. Syst., (2020)A Trust and Energy-Aware Double Deep Reinforcement Learning Scheduling Strategy for Federated Learning on IoT Devices., , , and . ICSOC, volume 12571 of Lecture Notes in Computer Science, page 319-333. Springer, (2020)Using model checking for Trivial File Transfer Protocol validation., , , and . COMNET, page 1-7. IEEE, (2014)Monetizing Personal Data: A Two-Sided Market Approach., , , and . ANT/SEIT, volume 83 of Procedia Computer Science, page 472-479. Elsevier, (2016)Deep Smart Scheduling: A Deep Learning Approach for Automated Big Data Scheduling Over the Cloud., , , and . FiCloud, page 189-196. IEEE, (2019)Improving Autonomous Vehicles Safety in Snow Weather Using Federated YOLO CNN Learning., , , and . MobiWIS, volume 12814 of Lecture Notes in Computer Science, page 121-134. Springer, (2021)Cloud as platform for monetizing complementary data for AI-driven services: A two-sided cooperative game., , , , and . SCC, page 443-449. IEEE, (2021)Deep and reinforcement learning for automated task scheduling in large-scale cloud computing systems., , , and . Concurr. Comput. Pract. Exp., (2021)Cloud Computing as a Platform for Monetizing Data Services: A Two-Sided Game Business Model., , , , , and . IEEE Trans. Netw. Serv. Manag., 19 (2): 1336-1350 (2022)A Game-Based Secure Trading of Big Data and IoT Services: Blockchain as a Two-Sided Market., , , , and . ICSOC, volume 12571 of Lecture Notes in Computer Science, page 85-100. Springer, (2020)