Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.
About 10,000 papers for “Machine code”
Graham Lee · 1982
Before SDC’s machine-code programs can be executed, they must first be placed, or loaded, into the store. This can be done using a systems-software program called a loader, illustrated in figure 14.1. Sometimes loaders are implemented in hardware.
Cited by 0
Paperswithcode · Zenodo (CERN European Organization for Nuclear Research) · 2022
This dataset contains data from Paperswithcode.com obtained on January of 2022. The first file, 'Papers_with_abstracts' includes information about different research papers like their title, abstract, authors, etc. 'Links_between_papers_and_code' includes each paper connection to their corresponding github repository. Finally, 'Methods' includes a categorization of the aforementioned papers done by the community of Paperswithcode in different areas. These files were used in the Master Thesis Topic Modeling for Research Software done by María Ayuso in Universidad Politécnica de Madrid.
Cited by 0Open access
MTC1, Earthmoving Machinery · 1985
<div class="section abstract"> <div class="htmlview paragraph">This SAE Standard applies to off-road self-propelled construction, forestry and industrial machines as listed in categories 1, 2, and 4 of SAE J1116.</div> </div>
Cited by 0
2014
Cited by 0
Shawn Graham · 1985
Abstract : A compiler construction tool has been designed and built that automates much of the case analysis necessary to exploit special purpose instructions on a target machine. Given a suitable description of the target machine, the analysis identifies instruction sequences that are equivalent to single instructions. During code generation, these equivalences can be used to avoid inefficient sequences in favor of more efficient instructions. A working prototype of the instruction set analyzer needed in the framework outlined by (Giegerich 83) is presented. In contrast to the work presented in (Davidson and Fraser 80, 84), machine descriptions are analyzed entirely during compiler construction (i.e., once per compiler), rather than during code generation (i.e. , each time the compiler is used). (R Kessler 84) describes such a system for discovering equivalent instructions for instruction sequences of length 2. The techniques presented here can identify instruction sequences of arbitrary length that are equivalent to single instructions. This analysis has been applied to the descriptions of two machines, and the results have been used to replace hand-written case analysis routines in an otherwise table driven code generator. The translation of a programming language onto a target architecture requires analyses of both the language and the architecture. This paper describes a novel technique for identifying three kinds of idioms: set idioms, binding idioms, and composite idioms.
Cited by 1
2018
Cited by 0
1997
Cited by 0
2015
Cited by 0
Jenni, Raphael · HSR (HSR University of Applied Sciences Rapperswil) · 2022
Using machine learning for images, text, or audio has become popular and relatively mainstream. On the other hand, using machine learning for code is a rather new field. Only a few commercial products are available, and the research is still in its early stages. When trying to join this field, many different topics need to be explored. This paper aims to bring a software engineer or a programming language researcher up to speed on the current state of machine learning and show the possibilities of such technologies in respect to code. It covers code2vec, code2seq, CuBERT, CoCluBERT, CodeBERT, TreeBERT, and DeepBugs AST Context Representations, with their respective backgrounds and list tools and points out available further research. It also covers a few use cases and gives a practical example that leads through the whole paper.
Cited by 0Open access
Daniel R. Perkins, Richard L. Sites · 1979
A variant of PASCAL pseudo-code which is suitable for optimization is presented. This new language, Universal pseudo-code, is designed to be easily extended to meet the needs of a variety of target machines. The language is further designed such that only one optimizer need be written for it. This approach lends itself well to the portable software spirit of PASCAL. The problems and opportunities associated with optimization at this level are discussed.
Cited by 22
AHSAN JAMIL · Figshare · 2023
Results for all the Four MLP-NN, RF, XGBR, and RF algorithms for each of geophysical array`s and machine learning python code is provided.
Cited by 0Open access
Alan Tootill & David Barrow · Bulletin of Miscellaneous Information (Royal Gardens Kew) · 1984
6502 Machine Code for Humans Machine code is the language of the processor at the heart of your computer system. As its name suggests, it is a language designed for a machine to use. But the language and the machine are both products of human minds and machine code is just as much a language for humans.
Cited by 0
Héctor Lasluisa-Naranjo, David Rivas-Lalaleo, Joaquín Vaquero et al. · 2024
G-codes are essential in CNC systems, providing crucial instructions for controlling machine parameters and operations in manufacturing, including 3D printing. They may contain errors affecting product quality and increasing resource consumption. This research applies the K-means machine learning clustering algorithm to optimize G-code parameters such as extruder and heated bed temperature, deposition speed, and flow control. The objective is to reduce manufacturing time and material usage while maintaining surface quality. A line-by-line analysis and rewriting of the G-code resulted in an average 24.36% reduction in time and 5% in material use, with minimal impact on quality, validated with the Taguchi method.
Cited by 2Open access
2018
Cited by 0
2007
Cited by 0
Gokilavani, M. · 2025
Cited by 0Open access
Alturaief, Nouf, HASSINE, Jameleddine · Figshare · 2025
This is the code created and used in the paper: "Data Leakage Detection in Machine Learning Code: Transfer Learning, Active Learning, or Low-shot Prompting?"Transfer learning and Active learning approaches are found in AL4Code folder. Low-shot prompting approach can be found in GPT4Code folder.PrerequisitesPython 3.8.16numpy 1.21.2pandas 1.5.3torch 1.11.0scikit-learn 1.0.1transformers 4.27.4sentence_transformers 2.2.2openai < 1.0.0Demopython demo.py \--n_query 50 \--n_init_labeled 50 \--strategy_name LeastConfidence \--active_learning True \--with_augmentation True \--patient 5
Cited by 0Open access
Alturayeif, Nouf, HASSINE, Jameleddine · Figshare · 2025
This is the code created and used in the paper: "Data Leakage Detection in Machine Learning Code: Transfer Learning, Active Learning, or Low-shot Prompting?"Transfer learning and Active learning approaches are found in AL4Code folder. Low-shot prompting approach can be found in GPT4Code folder.PrerequisitesPython 3.8.16numpy 1.21.2pandas 1.5.3torch 1.11.0scikit-learn 1.0.1transformers 4.27.4sentence_transformers 2.2.2openai < 1.0.0Demopython demo.py \--n_query 50 \--n_init_labeled 50 \--strategy_name LeastConfidence \--active_learning True \--with_augmentation True \--patient 5
Cited by 0Open access
João Dias, Norman F. Ramsey · Lecture notes in computer science · 2006
Cited by 12Open access
Michele, Magrini, Marrocco, Francesco, Laurenti, Laura et al. · Zenodo (CERN European Organization for Nuclear Research) · 2025
This codebase supports the research presented in the paper "Explainable Machine Learning for Earthquakes: SHAP Interpretation of CNNs to Distinguish Seismic Spectrograms of Foreshocks and Aftershocks." It provides the complete pipeline for preprocessing seismic waveform data, training convolutional neural networks (CNNs) on RGB spectrograms, and interpreting model decisions using SHAP (SHapley Additive exPlanations). The pipeline is designed to work with the Norcia 2016 seismic sequence dataset and enables transparent, reproducible experiments focused on understanding how machine learning models distinguish between foreshocks and aftershocks. The code is modular and includes notebooks for each stage of the workflow, from spectrogram generation to explainability analysis. SHAP values are used to highlight the time-frequency regions most relevant to classification decisions, offering insight into underlying physical processes such as fault healing and wave attenuation. This codebase may be used to: Train and evaluate CNNs on 3-component RGB spectrograms Compute SHAP tensors for model interpretation Visualize and analyze time-frequency relevance patterns in seismic data Reproduce the figures and results presented in the associated paper Contents: preprocessing/: Scripts to convert raw waveforms into RGB spectrograms training/: Notebooks to train the CNN and select samples for SHAP explainability/: SHAP tensor generation and visualization Conference Paper (ICIAP 2025):👉 https://doi.org/10.1007/978-3-032-10185-3_25 Associated Zenodo dataset: 👉 https://doi.org/10.5281/zenodo.15683047 Also check the GitHub repository: 👉 https://github.com/mich1803/CNN-Explainability-on-Earthquakes
Cited by 0Open access