machine acm

Target-guided Emotion-aware Chat Machine | ACM

Wu Y Zhao M (2024) Emotional Adjustment and Interaction in Virtual Health Assistant: Combining Seq2Seq-GAN Technology Proceedings of the 2024 International Conference on Machine Intelligence and Digital Applications 10.1145/3662739.3672179 (785-789) Online publication date: 30-May-2024

A Systematic Review on Imbalanced Data Challenges in Machine

In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. In Proceedings of the 23rd International Conference on Machine Learning. ACM, 233--240. Digital Library. Google Scholar [30] Sauptik Dhar and Vladimir Cherkassky. 2015. Development and evaluation

Machine Learning for Detecting Data Exfiltration: A Review: ACM

Context: Research at the intersection of cybersecurity, Machine Learning (ML), and Software Engineering (SE) has recently taken significant steps in proposing countermeasures for detecting sophisticated data exfiltration attacks.It is important to systematically review and synthesize the ML-based data exfiltration countermeasures for

About ACM - Association for Computing Machinery

1 ACM is a volunteer-driven organization. Our volunteer leaders choose the directions for ACM and oversee our many activities and initiatives. ACM’s elected officers have overall responsibility for ACM. [Learn more...ACM Council members function as our board of directors. [Learn more...ACM’s Board and Council Chairs are responsible for particular

Evolutionary Machine Learning: A Survey | ACM Computing

EC algorithms have recently been used to improve the performance of Machine Learning (ML) models and the quality of their results. Jonathan Katzy, Jeroen Kloppenburg, Tim Verbelen, and Jan S. Rellermeyer. 2020. A survey on distributed machine learning. ACM Comput. Surv. 53, 2 (2020), 1–33. Digital Library. Google Scholar [178]

The Seven Tools of Causal Inference, with Reflections on Machine

Another obstacle is “explainability,” or that “machine learning models remain mostly black boxes” 26 unable to explain the reasons behind their predictions or recommendations, thus eroding users’ trust and impeding diagnosis and repair; see Hutson 8 and Marcus. 11 A third obstacle concerns the lack of understanding of cause-effect

Machine learning in automated text categorization | ACM

Text categorization for multiple users based on semantic features from a machine-readable dictionary. ACM Trans. Inform. Syst. 12, 3, 278-295.]] Crossref. Google Scholar [90] LIERE,R.AND TADEPALLI, P. 1997. Active learning with committees for text categorization. In Proceedings of AAAI-97, 14th Conference of the American Association

Triton: an intermediate language and compiler for - ACM

We present Triton, a language and compiler centered around the concept of tile, i.e., statically shaped multi-dimensional sub-arrays.Our approach revolves around (1) a C-based language and an LLVM-based intermediate representation (IR) for expressing tensor programs in terms of operations on parametric tile variables and (2) a set of novel

Machine Unlearning: A Survey | ACM Computing Surveys

In recent years, machine learning has seen remarkable progress and wide exploration across every field of artificial intelligence (AI) [].However, as AI becomes increasingly data-dependent, more and more factors, such as privacy concerns, regulations, and laws, are leading to a new type of request—to delete information.

Federated Machine Learning: Concept and Applications: ACM

Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical secure aggregation for privacy-preserving machine learning. In Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS’17).

The Role of Autonomous Machine Computing in Shaping

Liu, S. Growth Of The Autonomous Machine Computing Ecosystem Driven By Semiconductor Innovations, Forbes, August 2022. Liu, S. and Gaudiot, J.L., 2023, International Roadmap for Devices and Systems (IRDS) 2023 Autonomous Machine Computing, IEEE. Shaoshan Liu’s background is a unique combination of technology,

DoH Insight | Proceedings of the 15th - ACM Digital Library

The aim is to evaluate what information (if any) can be gained from HTTPS extended IP flow data using machine learning. We evaluated five popular ML methods to find the best DoH classifiers. The experiments show that the accuracy of DoH recognition is over 99.9 %. Additionally, it is also possible to identify the application that was used for