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About 10,000 papers for “Machine code”

Fast Machine Code for Modular Multiplication

Scott, Michael · 1995

Cited by 0Open access

COCLUBERT: Clustering Machine Learning Source Code

Marcus Hagglund, Francisco J. Ribadas, Sepideh Pashami et al. · 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) · 2021

Nowadays, we can find machine learning (ML) applications in nearly every aspect of modern life, and we see that more developers are engaged in the field than ever. In order to facilitate the development of new ML applications, it would be beneficial to provide services that enable developers to share, access, and search for source code easily. A step towards making such a service is to cluster source code by functionality. In this work, we present COCLUBERT, a BERT-based model for source code embedding based on their functionality and clustering them accordingly. We build COCLUBERT using CuBERT, a variant of BERT pre-trained on source code, and present three ways to fine-tune it for the clustering task. In the experiments, we compare COCLUBERT with a baseline model, where we cluster source code using CuBERT embedding without fine-tuning. We show that COCLUBERT significantly outperforms the baseline model by increasing the Dunn Index metric by a factor of 141, the Silhouette Score metric by a factor of two, and the Adjusted Rand Index metric by a factor of 11.

Cited by 4

Secure Computation of MIPS Machine Code

Xiao Wang, Steven Gordon, Allen McIntosh et al. · Lecture notes in computer science · 2016

Cited by 33

Source Code of Machine Learning Algorithm

Yang Hu · Data Archiving and Networked Services (DANS) · 2022

Machine learning was used to model associations between exercise performance and metabolite responses. Model variables included metabolite response to exercise expressed as log2([post-exercise]/[pre-exercise]) (independent variable), VO2max, and RQ. Due to small sample size, samples were randomly sampled without replacement for 5 unique training and testing sets of combined RT and CT (80/20 split). Gradient boosting regression tree (GBRT) was used for machine learning analyses (https://scikit-learn.org/stable/modules/ensemble.html#gradient-tree-boosting). For each training/testing set split, the lambda parameter was tuned based on the training set only using 10-fold cross-validation. All correlation analyses with model predictions are based on testing set predicted values. Predictive performance was evaluated based on the Pearson correlation coefficient between the predicted values and actual values. The SHAP (SHapley Additive exPlanations) feature values provided multiple explanation for the models, and were calculated to evaluate the interpretation of top 20 metabolites contributed significantly to the predicted models. The Force plot algorithm was used to calculate the coefficients of potential markable contributors to predict VO2max or RQ. All these calculations and analyses were performed in Python using Scikit-learn database (https://scikit-learn.org).

Cited by 0Open access

Thwarting reverse engineering of machine code

Sašo Pajntar · 2011

Reverse engineering is a process in which we try to discover the details of software, while we do not have access to its source code. In the diploma thesis we present reverse engineering process on Microsoft Windows operating system. First, we describe PE format and structure of x86 assembler. Then we describe tools, which are used in process of reverse engineering. The focus is on debuggers, dissasemblers and system monitors. We describe functionality of such tools to detect and thwart reverse engineering process. We focus on the use of junk and self-modifying code, since the goal of the thesis was to develope a tool, which will use these methods to code and decode machine instructions. Finaly, we describe the use of the tool, its weaknesses and possible improvements for better protection of machine code.

Cited by 0

Machine learning code snippets semantic classification

Valeriy Berezovskiy, Anastasia Gorodilova, Е. В. Трофимова et al. · PeerJ Computer Science · 2023

Program code has recently become a valuable active data source for training various data science models, from code classification to controlled code synthesis. Annotating code snippets play an essential role in such tasks. This article presents a novel approach that leverages CodeBERT, a powerful transformer-based model, to classify code snippets extracted from Code4ML automatically. Code4ML is a comprehensive machine learning code corpus compiled from Kaggle, a renowned data science competition platform. The corpus includes code snippets and information about the respective kernels and competitions, but it is limited in the quality of the tagged data, which is ~0.2%. Our method addresses the lack of labeled snippets for supervised model training by exploiting the internal ambiguity in particular labeled snippets where multiple class labels are combined. Using a specially designed algorithm, we effectively separate these ambiguous fragments, thereby expanding the pool of training data. This data augmentation approach greatly increases the amount of labeled data and improves the overall quality of the trained models. The experimental results demonstrate the prowess of the proposed code classifier, achieving an impressive F1 test score of ~89%. This achievement not only enhances the practicality of CodeBERT for classifying code snippets but also highlights the importance of enriching large-scale annotated machine learning code datasets such as Code4ML. With a significant increase in accurately annotated code snippets, Code4ML is becoming an even more valuable resource for learning and improving various data processing models.

Cited by 1Open access

hmCodeTrans: Human–Machine Interactive Code Translation

Jiaqi Liu, Fengming Zhang, Xin Zhang et al. · IEEE Transactions on Software Engineering · 2024

Code translation, i.e., translating one kind of code language to another, plays an important role in scenarios such as application modernization and multi-language versions of applications on different platforms. Even the most advanced machine-based code translation methods can not guarantee an error-free result. Therefore, the participance of software engineer is necessary. Considering both accuracy and efficiency, it is suggested to work in a human-machine collaborative way. However, in many realistic scenarios, human and machine collaborate ineffectively - model translates first and then human makes further editing, without any interaction. To solve this problem, we propose hmCodeTrans, a novel method that achieves code translation in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">interactive human-machine collaborative way. It can (1) save the human effort by introducing two novel human-machine collaboration patterns: prefix-based and segment-based ones, which feed the software engineer's sequential or scattered editing back to model and thus enabling the model to make a better retranslation; (2) reduce the response time based on two proposed modules: attention cache module that avoids duplicate prefix inference with cached attention information, and suffix splicing module that reduces invalid suffix inference by splicing a predefined suffix. The experiments are conducted on two real datasets. Results show that compared with the baselines, our approach can effectively save the human effort and reduce the response time. Last but not least, a user study involving five real software engineers is given, which validates that the proposed approach owns the lowest human effort and shows the users’ satisfaction towards the approach.

Cited by 3

Soil data and machine learning code

Sun · Figshare · 2025

This is a publicly available dataset containing soil data and random forest model code for research.

Cited by 0Open access

vmc machine g code list pdf

pvugcjth · Zenodo (CERN European Organization for Nuclear Research) · 2024

&lt;pre&gt;&lt;code&gt;\n&lt;p&gt;&lt;strong&gt;vmc machine g code list pdf&lt;/strong&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;Rating: 4.3 / 5 (3007 votes)&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;Downloads: 9667&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n &lt;p&gt;= = = = = \n&lt;strong&gt;&lt;a href="https://calendario2023.es/21Nr9y?keyword=vmc machine g code list pdf" target="_blank"&gt;CLICK HERE TO DOWNLOAD&lt;/a&gt;&lt;/strong&gt;\n = = = = = &lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;br&gt;&lt;/p&gt;\n&lt;p&gt;&lt;br&gt;&lt;br&gt;Complete GCode Simulator. Mill G Codes. Fanuc Alarm Code List. Details 2nd reference position return was performed before 1st reference position return has been completed C G &amp; M Codes. Complete g code list with m-codes for milling in the Fanuc, LinuxCNC, GRBL, and Haas dialects. We give a quick definition for all g code commands along with a link to tutorials and examples of how to use it Reset the alarm with reset, and move the machine in the reverse direction. G CODES For VMC and HMCFree download as Word Doc.doc /.docx), PDF File.pdf), Text File.txt) or read online for free. Details 2nd reference position return was performed before 1st reference Complete list of cnc machine G codes and M codes. M Codes. Click the g-code link (if any) to visit a tutorial from our popuplar Online Free G-Code Training – G-Code and M-Code in CNC Lathe Machine in Hindi, अगर आपको ये जानकारी अच्छी लगे तो इस पोस्ट को सेयर करें। और जो कुछ न समझ में आया हो आप कमेंट करके पूछ सकते हैं। GRapid Positioning Motion (X,Y,Z,A,B) GLinear Interpolation Motion (X,Y,Z,A,B,F) GCircular Interpolation Motion CW (X,Y,Z,A,I,J,K,R,F) GCircular Interpolation Motion CCW (X,Y,Z,A,I,J,K,R,F) GDwell (P) (P=Seconds′′." GCode Simulator. Formatting Overview: Menus, options, icons, fields, and text boxes on the screen Vmc Machine G Code List CNC Machining Handbook: Building, Programming, and Implementation Alan Overby A Practical Guide to CNC Machining Get a 2 Stop Codes (T)Reset the alarm with reset, and move the machine in the reverse direction. CNC G-codes &amp; M Codes for milling and turning with explanations, syntax and program examples. (Full list and guide) G CODES For VMC and HMCFree download as Word Doc.doc /.docx), PDF File.pdf), Text File.txt) or read online for free. We give a quick definition for all g code commands along with a link to tutorials and examples of how to use it. NO; NT05; NGAXZF S; NM05; What does each letter in the program mean? This book explains various G-code that has many variants, is the common name for the most widely used numerical control (NC) programming language Every G code command sorted by how often they are used and organized by code type to make learning G code quick and simple Complete g code list with m-codes for milling in the Fanuc, LinuxCNC, GRBL, and Haas dialects. Fanuc Alarm Code List. We give a quick definition for all g 6 P a g e G &amp; M Code G Preparatory function, G followed by a numerical code, specifies machining modes and functions M Miscellaneous function, M followed by a numerical code, defines program flow and can control auxiliary functions such as coolant. Complete g code list with m-codes for milling in the Fanuc, LinuxCNC, GRBL, and Haas dialects. "G" stands for Geometry; A computerized numerical control (CNC) machine controls the tool with a computer and is programmed with a machine code system that enables it to operate with repeatability G AND M CODE/30/ R This manual covers the G and M-codes used in Mach4 Lathe. Can also perform machine specific functions and macros user or builder * frgh 0hdqlqj ri * frgh &amp;rppdqg eorfn * 5dslg 3rvlwlrqlqj wr 5htxluhg ; &lt; = 9doxhv * ; &lt; * /lqhdu pryhphqw ; &lt; dqg = d[lv zlwk phqwlrqhg)hhgudwh List of M-codes. This book explains various G-code that has many * frgh 0hdqlqj ri * frgh &amp;rppdqg eorfn * 5dslg 3rvlwlrqlqj wr 5htxluhg ; Every G code command sorted by how often they are used and organized by code type to make learning G code quick and simple Sample short program. This G code and M code list can be used as reference while programming or learning cnc machine programming.&lt;/p&gt;&lt;/code&gt;&lt;/pre&gt;

Cited by 0Open access

MCDASH: Refinement-based . . . for Machine Code

Akash Lal, Junghee Lim, Thomas Reps · 2009

This paper presents MCDASH, a refinement-based model checker for machine code. While model checkers such as SLAM, BLAST, and DASH have each made significant contributions in the field of verification/flaw-detection, their use has been restricted to programs for which source code is available. This paper discusses several challenges that arise when working with machine code, and explains how they are addressed in MCDASH. Unlike previous model checkers, MCDASH does not require the usual preprocessing steps of (a) building control-flow graphs, and (b) performing points-to analysis (or alias analysis); nor does MCDASH require type information to be supplied. The paper also describes how we extended MCDASH to check properties of self-modifying code. MCDASH is built using language-independent meta-tools that generate the implementations of the required analysis components from descriptions of an instruction set’s syntax and semantics. It has been instantiated for Intel x86 and PowerPC.

Cited by 0

R code for machine learning analysis

Marmolejo-Ramos, Fernando · Figshare · 2021

This R code performs a machine learning analysis on the data.

Cited by 0Open access

Explainable Machine Learning for Earthquakes - code

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 1Open access

R code for machine learning analysis

Marmolejo-Ramos, Fernando · Figshare · 2021

This R code performs a machine learning analysis on the data.

Cited by 0Open access

Modeling code quality using machine intelligence

Malmström, Arvid, Kindt, Erik · Lund University Publications Student Papers (Lund University) · 2022

For any company with a software development branch, one of the most important aspects is to write maintainable, understandable, high-quality source code. This will result in fewer work hours to refactor the code if changes are needed. Therefore it’s expensive to keep working with source code of poor quality. The question is, how to measure the quality of source code? The writers of this thesis approached the problem using machine learn- ing. Firstly two literature studies were conducted, one concerning usable software metrics and one concerning usable machine learning algorithms. Secondly, a large, external, labeled data base was set up and the models were trained on said data. There were different final models set up. Two to focus on large projects consisting of hundreds of files and two to focus on singular stand-alone files. After examining the models, the ones with the lowest RMSEs on the test set were used to compare the predictions from the models with the opinions of experienced developers. The final models were built using the algorithms artificial neural network and random forest which both gave similar results. The models for specific files were tested for both front end and back end files where four files in each were ranked according to the final score from the model. The rank was then compared to a ranking of the same files by experienced software developers. The results showed for front end files, that the model agreed with the developers ranking to a large extent. For back end files, there was a wider discrepancy between the opinions of the developers and the predictions made by the model. The final models were also able to evaluate a large project over time, to evaluate the overall code quality, which also was one thought application of the model. When a project that was rewritten a year ago was continuously evaluated, the code quality improved which was the expected result according to developers in the project.

Cited by 0

Better Code Representation for Machine Learning

Jenni, Raphael · HSR (HSR University of Applied Sciences Rapperswil) · 2022

Using machine learning for code becomes more and more common. Different approaches based on paths or BERT are available. This paper focuses on improving parts of the input vector by creating a more compact embedding. Furthermore, it explores and discusses ways to reduce the amount of data inserted into a model when working with code changes. The results presented in this paper show that it is possible to reduce the input data into a latent space, cutting it to half the input data size, representing differences and similarities between code paths in a very compact way while still maintaining an accuracy of 99%. Moreover, it is shown that with proper preprocessing, it is possible to reduce the amount of data inserted into a code changes model by around 84%.

Cited by 1Open access

Combining ACSL Specifications and Machine Code

P.A. Putro, P.A. Putro · Proceedings of the Institute for System Programming of RAS · 2018

When developing programs in high-level languages, developers have to make assumptions about the correctness of the compiler. However, this may be unacceptable for critical systems. As long as there are no full-fledged formally verified compilers, the author proposes to solve this problem by proving the correctness of the generated machine code by deductive verification. To achieve this goal, it is required to combine the pre- and postcondition specifications with the machine code behavior model. The paper presents an approach how to combine them for the case of C functions without loops. The essence of the approach is to build models, both machine code and its specifications in a single logical language, and use target processor ABI to bind machine registers with the parameters of the high-level function. For the successful implementation of this approach, you have to take a number of measures to ensure the compatibility of the high-level specification model with the machine code behavior model. Such measures include the use of a register type in the high-level specifications and the translation of the pre- and postconditions into the abstract predicates. Also in the paper the choice of logical language for building models is made and justified, the most suitable tools for implementing the approach of merging specifications are selected and the evaluation of the system of deductive verification of machine code built on the basis of the proposed approach is made using test examples obtained by compiling C programs without loops.

Cited by 2Open access

The Scientific Byte Code Virtual Machine

Brian Vinter, Rasmus Fonnesbæk Andersen · Research at the University of Copenhagen (University of Copenhagen) · 2008

Cited by 0

Machine code instructions and addressing modes

2012

Cited by 0

Hoare Logic for ARM Machine Code

Magnus O. Myreen, Anthony Fox, Michael J. C. Gordon · Lecture notes in computer science · 2007

Cited by 9

Synthesis of machine code from semantics

Venkatesh Srinivasan, Thomas Reps · ACM SIGPLAN Notices · 2015

In this paper, we present a technique to synthesize machine-code instructions from a semantic specification, given as a Quantifier-Free Bit-Vector (QFBV) logic formula. Our technique uses an instantiation of the Counter-Example Guided Inductive Synthesis (CEGIS) framework, in combination with search-space pruning heuristics to synthesize instruction-sequences. To counter the exponential cost inherent in enumerative synthesis, our technique uses a divide-and-conquer strategy to break the input QFBV formula into independent sub-formulas, and synthesize instructions for the sub-formulas. Synthesizers created by our technique could be used to create semantics-based binary rewriting tools such as optimizers, partial evaluators, program obfuscators/de-obfuscators, etc. Our experiments for Intel's IA-32 instruction set show that, in comparison to our baseline algorithm, our search-space pruning heuristics reduce the synthesis time by a factor of 473, and our divide-and-conquer strategy reduces the synthesis time by a further 3 to 5 orders of magnitude.

Cited by 5