Search Results - ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.8: Performance/D.4.8.0: Measurements

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1. American Heart Association. (2021). Heart disease and stroke statistics—2021 update. Circulation, 143(8), e254-e743. 2. Rahman, M., Al Amin, M., Hasan, R., Hossain, S. T., Rahman, M. H., & Rashed, R. A. M. (2025). A Predictive AI Framework for Cardiovascular Disease Screening in the US: Integrating EHR Data with Machine and Deep Learning Models. British Journal of Nursing Studies, 5(2), 40-48. 3. ZakirHossain, M., Khan, M. M., Thapa, S., Uddin, R., Meem, E. J., Niloy, S. K., ... & Bhavani, G. D. (2025, February). Advanced Deep Learning Techniques for Precision Diagnosis of Tea Leaf Diseases. In 2025 IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) (pp. 1-6). IEEE. 4. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 785-794). ACM. 5. Damen, J. A., Hooft, L., Schuit, E., Debray, T. P., Collins, G. S., Tzoulaki, I., Lassale, C. M., Siontis, G. C., Chiocchia, V., Roberts, C., Schlüssel, M. M., Gerry, S., Black, J. A., Heus, P., van der Schouw, Y. T., Peelen, L. M., & Moons, K. G. (2016). Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ, 353, i2416. 6. Framingham Heart Study. (1948). Framingham Heart Study cohort research data. National Heart, Lung, and Blood Institute. 7. Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035. 8. Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 69(21), 2657-2664. 9. Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (NIPS 2017) (pp. 4765-4774). 10. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, É. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. 11. Shameer, K., Johnson, K. W., Glicksberg, B. S., Dudley, J. T., & Sengupta, P. P. (2018). Machine learning in cardiovascular medicine: are we there yet? Heart, 104(14), 1156-1164. 12. Steyerberg, E. W., Vergouwe, Y., & van Calster, B. (2019). Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal, 40(15), 1255–1264. 13. Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLOS Medicine, 12(3), e1001779. 14. Weng, S. F., Reps, J., Kai, J., Garibaldi, J. M., & Qureshi, N. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944. 15. World Health Organization. (2021). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds) 16. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., ... Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16) (pp. 265–283). 17. Chollet, F. (2015). Keras (Version 2.4.0) [Computer software]. https://github.com/fchollet/keras

Authors: Okunola, Abiodun

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Tagging Latency Estimator: A Standalone Software for Estimating Latency of Event-Related Potentials in P300-based Brain-Computer Interfaces ; Tagging Latency Estimator: Un logiciel pour estimer la latence des potentiels évoqués cognitifs pour les interfaces cerveau-ordinateurs.

Authors: Cattan, Grégoire Mendoza, Cesar IBM et al.

Contributors: Cattan, Grégoire Mendoza, Cesar IBM et al.

Source: ISSN: 2475-9066 ; Journal of Open Source Software ; https://hal.science/hal-03932533 ; Journal of Open Source Software, 2023, 8 (81), pp.4114. ⟨10.21105/joss.04114⟩.

Subject Terms: Unity, C#, Event-Related Potential (ERP), P300, Brain-Computer Interface BCI, Event related Potentials ERP, Potentiel Evoqué Cognitif PEC, Interface Cerveau-Ordinateur ICO, Interface Cerveau-Machine ICM, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.2: Design Tools and Techniques/D.2.2.12: User interfaces, ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.8: Performance/D.4.8.0: Measurements, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-HC]Computer Science [cs]/Human-Computer Interaction [cs.HC], [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], [SCCO.NEUR]Cognitive science/Neuroscience, [SCCO.PSYC]Cognitive science/Psychology

Relation: hal-03932533; https://hal.science/hal-03932533; https://hal.science/hal-03932533/document; https://hal.science/hal-03932533/file/10.21105.joss.04114.pdf

Availability: https://hal.science/hal-03932533
https://hal.science/hal-03932533/document
https://hal.science/hal-03932533/file/10.21105.joss.04114.pdf
https://doi.org/10.21105/joss.04114

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3

acm forum.

Authors: Ousterhout, John K. Parnas, David Lorge Sheppard, Sylvia B. et al.

Source: Communications of the ACM; Aug1981, Vol. 24 Issue 8, p546-549, 4p

Subject Terms: LETTERS to the editor, COMPUTER science, COMPUTER operating systems, COMPUTER software, INFORMATION technology, COMPUTER programming

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Software and Hardware Requirements and Trade-Offs in Operating Systems for Wearables: A Tool to Improve Devices' Performance.

Authors: J. P. Amorim, Vicente C. Silva, Mateus A. R. Oliveira, Ricardo

Source: Sensors (14248220); Apr2019, Vol. 19 Issue 8, p1904-1904, 1p

Subject Terms: WEARABLE technology, COMPUTER input-output equipment, COMPUTER software, COMPUTER operating systems, DATA mining

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5

EMP: execution time measurement protocol for compute-bound programs.

Authors: Suh, Young ‐ Kyoon Snodgrass, Richard T. Kececioglu, John D. et al.

Source: Software: Practice & Experience; Apr2017, Vol. 47 Issue 4, p559-597, 39p

Subject Terms: COMPUTER software execution, TIME measurements, COMPUTER programming, COMPUTER network protocols, PERFORMANCE evaluation, LINUX operating systems

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Coordinating multiple administration loops using discrete control

Authors: Gueye, Soguy Mak-Karé de Palma, Noël Rutten, Eric et al.

Contributors: Gueye, Soguy Mak-Karé de Palma, Noël Rutten, Eric et al.

Source: ACM SIGOPS Operating Systems Review. 47:18-25

Subject Terms: [INFO.INFO-DC]Computer Science [cs]/Distributed, coordination, reconfiguration, reactive programming, Autonomic managers, administration loops, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.11: Software Architectures/D.2.11.1: Domain-specific architectures, discrete controller synthesis, 02 engineering and technology, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.2: Design Tools and Techniques, Parallel, components, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], and Cluster Computing [cs.DC], [INFO.INFO-OS] Computer Science [cs]/Operating Systems [cs.OS], [INFO.INFO-PF] Computer Science [cs]/Performance [cs.PF], [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], 0202 electrical engineering, electronic engineering, information engineering, [INFO.INFO-OS]Computer Science [cs]/Operating Systems [cs.OS], model-based approach, ACM: K.: Computing Milieux/K.6: MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS/K.6.4: System Management

Access URL: https://dl.acm.org/doi/10.1145/2553070.2553074
https://hal.inria.fr/hal-00949555

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7

Accelerating and benchmarking operating system functions in a "soft" system.

Authors: Molnár, Péter Kicsák, Ádám Végh, János

Source: Carpathian Journal of Electronic & Computer Engineering; 2015, Vol. 8 Issue 1, p3-6, 4p

Subject Terms: COMPUTER operating systems, COMPUTER software, COMPUTER engineering, REAL-time computing, COPROCESSORS, PERFORMANCE evaluation

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EnosLib: A Library for Experiment-Driven Research in Distributed Computing

Authors: Ronan-Alexandre Cherrueau Marie Delavergne Alexandre van Kempen et al.

Contributors: Ronan-Alexandre Cherrueau Marie Delavergne Alexandre van Kempen et al.

Source: IEEE Transactions on Parallel and Distributed Systems. 33:1464-1477

Subject Terms: [INFO.INFO-DC]Computer Science [cs]/Distributed, [INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI], ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.7: Organization and Design/D.4.7.1: Distributed systems, [INFO.INFO-OH]Computer Science [cs]/Other [cs.OH], Experiment-driven research, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.5: Testing and Debugging, 02 engineering and technology, Distributed computing experimentation library, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS, Parallel, [INFO.INFO-OH] Computer Science [cs]/Other [cs.OH], [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], and Cluster Computing [cs.DC], ACM: K.: Computing Milieux/K.6: MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS/K.6.2: Installation Management/K.6.2.3: Performance and usage measurement, Performance evaluation, [INFO.INFO-DC] Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], 0202 electrical engineering, electronic engineering, information engineering, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.8: Metrics/D.2.8.1: Performance measures, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.3: Performance evaluation (efficiency and effectiveness)

File Description: application/pdf

Access URL: https://hal.inria.fr/hal-03324177/file/tpds-enoslib-preprint.pdf
http://ieeexplore.ieee.org/document/9534688
https://hal.inria.fr/hal-03324177
https://www.computer.org/csdl/journal/td/5555/01/09534688/1wLblAxTkxq
https://ieeexplore.ieee.org/document/9534688
https://hal.inria.fr/hal-03324177/document
https://hal.archives-ouvertes.fr/hal-03324177v1

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Operating System Performance.

Authors: Lynch, W. C.

Source: Communications of the ACM; Jul1972, Vol. 15 Issue 7, p579-585, 7p

Subject Terms: COMPUTER operating systems, SYSTEM analysis, COMPUTER systems, SYSTEMS development, COMPUTER industry, SYSTEMS design, COMPUTER software, TECHNICAL specifications, MULTIMEDIA systems

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Achieving Efficiency and Portability in Systems Software: A Case Study on POSIX-Compliant Multithreaded Programs.

Authors: Shinjo, Yasushi Pu, Calton

Source: IEEE Transactions on Software Engineering; Sep2005, Vol. 31 Issue 9, p785-800, 16p, 22 Diagrams, 4 Charts, 7 Graphs

Subject Terms: SYSTEMS software, PARALLEL processing, POSIX (Computer software standard), MICROPROCESSORS, COMPUTER multitasking, COMPUTER operating system standards

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Going beyond mean and median programs performances

Authors: Worms, Julien Touati, Sid Laboratoire de Mathématiques de Versailles (LMV) et al.

Contributors: Worms, Julien Touati, Sid Laboratoire de Mathématiques de Versailles (LMV) et al.

Source: IEEE MCSoC 2016 : IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip ; https://inria.hal.science/hal-01371467 ; IEEE MCSoC 2016 : IEEE 10th International Symposium on Embedded Multicore/Many-core Systems-on-Chip, Sep 2016, Lyon, France. ⟨10.1109/MCSoC.2016.14⟩

Subject Terms: parametric statistics, programs performances variability, gaussians mixture, programs performances modelling, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.8: Metrics/D.2.8.1: Performance measures, ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.8: Performance, ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software/H.3.4.3: Performance evaluation (efficiency and effectiveness), ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.4: Software/Program Verification/D.2.4.7: Statistical methods, [SCCO.COMP]Cognitive science/Computer science, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], [STAT.AP]Statistics [stat]/Applications [stat.AP]

Subject Geographic: Lyon, France

Availability: https://inria.hal.science/hal-01371467
https://inria.hal.science/hal-01371467v1/document
https://inria.hal.science/hal-01371467v1/file/MCSoC-16-Worms-Touati.pdf
https://doi.org/10.1109/MCSoC.2016.14

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12

MPTCP Is Not Pareto-Optimal: Performance Issues and a Possible Solution.

Authors: Khalili, Ramin Gast, Nicolas Popovic, Miroslav et al.

Source: IEEE/ACM Transactions on Networking; Oct2013, Vol. 21 Issue 5, p1651-1665, 15p

Subject Terms: APPLICATION software, TCP/IP, COMPUTER performance, ALGORITHMS, KERNEL operating systems, LINUX operating systems

Company/Entity: INSTITUTE of Electrical & Electronics Engineers

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Papers from the Third ACM Symposium on Operating Systems Principles.

Authors: Denning, Peter J. Randell, Brian

Source: Communications of the ACM; Mar1972, Vol. 15 Issue 3, p133-134, 2p

Subject Terms: COMPUTER operating systems, RESEARCH, CONFERENCES & conventions, COMPUTER software, SYSTEMS software

Company/Entity: ASSOCIATION for Computing Machinery

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Atomic Object Factory: Efficient, Consistent and Dependable Support for Distributed Objects

Authors: Sutra, Pierre Rivière, Etienne Cotes, Cristian et al.

Contributors: Sutra, Pierre Rivière, Etienne Cotes, Cristian et al.

Source: [Research Report] Télécom SudParis; Université de Neuchâtel; Universitat Rovira i Virgili; Red Hat. 2016

Subject Terms: ACM: H.: Information Systems/H.3: INFORMATION STORAGE AND RETRIEVAL/H.3.4: Systems and Software, Distributed Objects, ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS/C.2.4: Distributed Systems, [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL], [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], Dependability, ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.5: Reliability, [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF], Composability, Language support, [INFO]Computer Science [cs], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], Listenable Key-Value Store

Access URL: https://inria.hal.science/hal-01318706v1/document
https://inria.hal.science/hal-01318706
https://inria.hal.science/hal-01318706v1/file/paper.pdf

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15

Automated Analysis of Impact of Scheduling on Performance of Self-Stabilizing Protocols

Authors: Aflaki, Saba Bonakdarpour, Borzoo Tixeuil, Sébastien et al.

Contributors: Aflaki, Saba Bonakdarpour, Borzoo Tixeuil, Sébastien et al.

Source: The International Conference on Stabilization, Safety, and Security in Distributed Systems (SSS 2015) ; https://hal.science/hal-01213273 ; The International Conference on Stabilization, Safety, and Security in Distributed Systems (SSS 2015), Aug 2015, Edmonton, Canada. pp.156-170, ⟨10.1007/978-3-319-21741-3_11⟩

Subject Terms: ACM: D.: Software/D.1: PROGRAMMING TECHNIQUES/D.1.3: Concurrent Programming, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.2: Design Tools and Techniques, ACM: D.: Software/D.2: SOFTWARE ENGINEERING/D.2.4: Software/Program Verification, ACM: D.: Software/D.4: OPERATING SYSTEMS/D.4.5: Reliability/D.4.5.2: Fault-tolerance, [INFO]Computer Science [cs], [INFO.INFO-CC]Computer Science [cs]/Computational Complexity [cs.CC], [INFO.INFO-DC]Computer Science [cs]/Distributed, Parallel, and Cluster Computing [cs.DC], [INFO.INFO-IA]Computer Science [cs]/Computer Aided Engineering, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], [INFO.INFO-PF]Computer Science [cs]/Performance [cs.PF]