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The power of system call traces: predicting the software energy consumption impact of changes., , , , and . CASCON, page 219-233. IBM / ACM, (2014)Optimizing the Linear Fascicle Evaluation Algorithm for Multi-Core and Many-Core Systems., , , and . CoRR, (2019)A system-call based model of software energy consumption without hardware instrumentation., , , , , , , and . IGSC, page 1-6. IEEE Computer Society, (2015)Adversarial Unsupervised Representation Learning for Activity Time-Series., , , and . CoRR, (2018)GreenAdvisor: A tool for analyzing the impact of software evolution on energy consumption., , and . ICSME, page 311-320. IEEE Computer Society, (2015)Temporal Proximity induces Attributes Similarity., , and . CoRR, (2018)Optimizing the linear fascicle evaluation algorithm for many-core systems., and . ICS, page 425-437. ACM, (2019)Adversarial Unsupervised Representation Learning for Activity Time-Series., , , and . AAAI, page 834-841. AAAI Press, (2019)Domain adaptation in display advertising: an application for partner cold-start., , and . RecSys, page 178-186. ACM, (2019)A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging., , , , and . BigData, page 1318-1327. IEEE, (2018)