Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

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”

A machine that knows its own code

Alexander, Samuel · Figshare · 2018

We construct a machine that knows its own code, at the price of not knowing its own factivity.

Cited by 0Open access

Neural Machine Based Mobile Applications Code Translation

Mohammed Hassan, Omar Mahmoud, Omar Mohammed et al. · 2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES) · 2020

Although many cross platform mobile development software used a trans-compiler-based approach, it was very difficult to generalize it to work in both directions. For example, to convert between Java for Android Development and Swift for iOS development and vice versa. This is due to the need of writing a specific parser for each source language, and a specific code generator for each destination language. Neural network-based models are used successfully to translate between natural languages, including English, French, German any many others by providing enough datasets and without the need of adding language specific code for understanding and generation. In this paper, a source code converter based on the Neural Machine Translation Transformer Model that can translate from Java to Swift and vice versa is introduced. A synthesized dataset is used to train the model, the pipeline used for the translation as well as the code synthesis procedure throughout the work are illustrated. Initial results are promising and give motivation to further enhance the proposed tool.

Cited by 6

Master statistics machine learning: intuition, math, code

Bulletin of Miscellaneous Information (Royal Gardens Kew)

A rigorous and engaging deep-dive into statistics and machine-learning, with hands-on applications in Python and MATLAB. What you'll learn Descriptive statistics (mean, variance, etc) Inferential statistics T-tests, correlation, ANOVA, regression, clustering The math behind the "black box" statistical methods How to implement statistical methods in code How to interpret statistics correctly and avoid common misunderstandings Coding techniques in Python and MATLAB/Octave Machine learning methods like clustering, predictive analysis, classification, and data cleaning Requirements Good work ethic and motivation to learn. Previous background in statistics or machine learning is not necessary. Python -OR- MATLAB with the Statistics toolbox (or Octave). Some coding familiarity for the optional code exercises. No textbooks necessary! All materials are provided inside the course. Description Statistics and probability control your life. I don't just mean What YouTube's algorithm recommends you to watch next, and I don't just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms,

Cited by 0

Generation of Virtual Machine Code at Startup

Robert Griesemer Sun, Sun Microsystems Inc · 2007

Performance-critical components of virtual machines are often implemented in assembly language. Traditionally, this code is compiled with an assembler and linked statically with the rest of the system. Generating this code at startup time has significant engineering advantages over the conventional approach: The generated machine code is much safer, easier to maintain, and has better performance. The virtual machine is easier to build and more portable. Introduction Ideally, a virtual machine (VM) is written entirely in a high-level language (e.g., C++ or Java) in order to be machine-independent and easily portable. For performance reasons however, many VMs make direct use of machine code. In the following we look at the architectural implications caused by the direct use of machine code. In particular we are concerned about static VM code that is written in assembly language. We will refer to this code as VM machine code. We will refer to all other VM code as VM high-level code. Thi...

Cited by 0

Code Generation from UML State Machine Description

Ľuboš Píš · Digital Repository (National Repository of Grey Literature) · 2012

This paper discusses the implementation of a suitable algorithm for code generation from UML state machine diagrams. The work includes analysis of state machines described in UML, followed by a description of the input fi le format of the proposed design of the generator and the generator itself. The generator was fully implemented in the work along with other functional requirements. At the end of this thesis is a description of the resulting implementation.

Cited by 0

Converting copyright legislation into machine-executable code

Alice Witt, Anna Huggins, Guido Governatori et al. · Griffith Research Online (Griffith University, Queensland, Australia) · 2021

A critical challenge in "Rules as Code" ("RaC") initiatives is enhancing legal accuracy. In this paper, we present the preliminary results of a two-week, first of its kind experiment that aims to shed light on how different legally trained people interpret and convert Australian Commonwealth legislation into machine-executable code. We find that coders collaboratively agreeing on key legal terms, or atoms, before commencing independent coding work can significantly increase the similarity of their encoded rules. Participants nonetheless made a range of divergent interpretive choices, which we argue are most likely due to: (1) the complexity of statutory interpretation, (2) encoded provisions having varying levels of granularity, and (3) the functionality of our coding language. Based on these findings, we draw an important distinction between processes for technical validation of encoded rules, which focus on ensuring rules adhere to select coding languages and conventions, and processes of legal alignment, which we conceptualise as enhancing congruence between the encoded provisions and the true meaning of the statutory text in line with the modern approach to statutory interpretation. We argue that these processes are distinct but both critically important in enhancing the accuracy of encoded rules. We conclude by emphasising the need in RaC initiatives for multi-disciplinary expertise across specific legal subject matters, statutory interpretation and technical programming.

Cited by 8Open access

Malicious QR Code Detection Using Machine Learning

Subhagata Sardar, Shyamal K. Saha, H. Ray et al. · INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS · 2025

UGC-CARE, UGC CARE, UGC CARE list, ugc approved journal, UGC CARE, UGC CARE list, UGC CARE list of Journal, UGCCARE, care journal list, UGC-CARE list, New UGC-CARE Reference List, New ugc care journal list, Research Journal, Research Journal Publication, Research Paper, Low cost research journal, Free of cost paper publication in Research Journal, High impact factor journal, Journal, Research paper journal, UGC CARE journal, UGC CARE Journals, ugc care list of journal, ugc approved list, ugc approved list of journal, ugc approved journal,ugc care journal, ugc care approved journal,Journal publication, ISSN approved, Research journal, research paper, research paper publication, research journal publication, high impact factor, free publication, index journal, publish paper, publish Research paper, low cost publication, ugc approved journal, UGC CARE, follow norms ugc approved journal, ugc approved list of journal, ugc care journal, care journal, UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list 2020, ugc care approved journal, ugc care list 2020, new ugc approved journal in 2020, IJCRT - international Research Journal, INTERNATIONAL JOURNAL OF CREATIVE RESEARCH THOUGHTS , Low cost research journal, Online international research journal, Peer-reviewed, and Refereed Journals, ISSN approved, low cost journal, Research journal, research paper, research paper publication, research journal publication, high impact factor, free publication, index journal, Regarding Approval and Equivalence of - ugc approved journal, UGC CARE, ugc approved list of journal, ugc care journal, UGC CARE list, UGCCARE, care journal, UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list 2020, ugc care approved journal, ugc care list 2020, new ugc approved journal in 2020, ugc care list 2021, ugc approved journal in 2021 : ugc approved journal in 18 before care approved journal and Valid as per new UGC Gazette regulations for UGC regulations on minimum qualifications for appointment of teachers and other academic staff in universities and colleges Peer-reviewed, and Refereed Journal is valid and point in Publication. IJCRT Followed Norms, guidelines, requirement, listing criteria and methodology of score/point from: ugc approved journal, UGC CARE, ugc approved list of journal, ugc care journal, UGC CARE list, UGCCARE, care journal, UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list 2020, ugc care approved journal, ugc care list 2020, new ugc approved journal in 2020, ugc care list 2021, ugc approved journal in 2021, Scopus, web of Science , scholarly journals, UGC Approved journal, ugc approved,ugc, ugc certify, publish free of cost, free publication, UGC and ISSN Approved , International Peer Reviewed, Open Access Journal , ISSN: 2320-2882 Impact Factor : 7.97 , E- journal, Low Cost INR 500, Free Publication

Cited by 0Open access

Synthesizing correct code for machine learning programs

Joshua Gisi · 2020

Success using machine learning (ML) in numerous fields has created a new class of users, who are not experts in the data science domain but want to use ML as a means to solve their inference problems. Various automatic machine learning (AutoML) approaches attempt to make ML solutions accessible to such users. In this work, we present a system that automatically synthesizes correct code within the context of the user’s data using sketching. In sketching, insight is determined through a partial program; a sketch expresses the high-level structure of implementation but leaves holes in place of the low-level details. We use meta-learning on meta-features to approximately solve holes. We observe that the sketch-based approach is more expressive, easier to implement, and easier to optimize than existing AutoML frameworks. Our initial results are very promising. Our approach uses fewer resources and still produces comparable results to existing techniques.

Cited by 0

Learning Code Transformations Via Neural Machine Translation

Michele Tufano · W&M Publish (College of William & Mary) · 2019

Source code evolves – inevitably – to remain useful, secure, correct, readable, and efficient. Developers perform software evolution and maintenance activities by transforming existing source code via corrective, adaptive, perfective, and preventive changes. These code changes are usually managed and stored by a variety of tools and infrastructures such as version control, issue trackers, and code review systems. Software Evolution and Maintenance researchers have been mining these code archives in order to distill useful insights on the nature of such developers’ activities. One of the long-lasting goal of Software Engineering research is to better support and automate different types of code changes performed by developers. In this thesis we depart from classic manually crafted rule- or heuristic-based approaches, and propose a novel technique to learn code transformations by leveraging the vast amount of publicly available code changes performed by developers. We rely on Deep Learning, and in particular on Neural Machine Translation (NMT), to train models able to learn code change patterns and apply them to novel, unseen, source code. First, we tackle the problem of generating source code mutants for Mutation Testing. In contrast with classic approaches, which rely on handcrafted mutation operators, we propose to automatically learn how to mutate source code by observing real faults. We mine millions of bug fixing commits from GitHub, process and abstract their source code. This data is used to train and evaluate an NMT model to translate fixed code into buggy code (i.e., the mutated code). In the second project, we rely on the same dataset of bug-fixes to learn code transformations for the purpose of Automated Program Repair (APR). This represents one of the most challenging research problem in Software Engineering, whose goal is to automatically fix bugs without developers’ intervention. We train a model to translate buggy code into fixed code (i.e., learning patches) and, in conjunction with Beam Search, generate many different potential patches for a given buggy method. In our empirical investigation we found that such a model is able to fix thousands of unique buggy methods in the wild.Finally, in our third project we push our novel technique to the limits and enlarge the scope to consider not only bug-fixing activities, but any type of meaningful code changes performed by developers. We focus on accepted and merged code changes that undergone a Pull Request (PR) process. We quantitatively and qualitatively investigate the code transformations learned by the model to build a taxonomy. The taxonomy shows that NMT can replicate a wide variety of meaningful code changes, especially refactorings and bug-fixing activities. In this dissertation we illustrate and evaluate the proposed techniques, which represent a significant departure from earlier approaches in the literature. The promising results corroborate the potential applicability of learning techniques, such as NMT, to a variety of Software Engineering tasks.

Cited by 0Open access

Machine learning based multi-scale remodelling code

Chi Wu · Zenodo (CERN European Organization for Nuclear Research) · 2021

This instruction illustrates a machine learning-based multi-scale model to predict bone formation in tissue scaffolds. This code uses neural networks to predict bone formation in synthetic scaffolds. We are sorry that the code is a little bit messy as we are not good at coding. The code can be used to predict bone remodelling results in synthetic scaffolds in a multi-level way. Therefore, it enables to inversely identification of the bone remodelling related parameters from clinical data. In order to run the machine learning-based multiscale bone remodelling program. The following platforms are what you need: Abaqus v2016/v614 Matlab R2020b An Abaqus plugin tool which can be downloaded from https://github.com/mhogg/pyvxray.git Jupyter notebook with Python 3. <strong>Here is a detailed description of the program</strong>. <strong>demo_example and demo_pearson_opt. </strong>This document provides instructions on running a demo example and a demo example calculating Pearson’s coefficient. The necessary functions for running the machine learning-based algorithm are located in the folder “demo_example”. In "demo_example", Multiscale_boneRemodelling_ML is the main function to start the program. “macro_umat” is the user subroutine to pass the homogenized material properties to Abaqus. “read_macro_1423” is a post-process file to obtain necessary stress/strain data information. “Sheep_macro_1423_C3D4.inp” is the input file of the sheep tibia scaffold model. Before start, please change line 49 and line 58 of “macro_umat” file to your current work directory. In “results” file, the results are obtained by running the demo_program. In “demo_pearson_opt” file, there are inversely-identified virtual X-ray images and <em>in-vivo </em>X-ray images. The python code "sheep3_6_9_8roi.ipynb" can be found to calculate the Pearson’s coefficient between the virtual X-ray images and in-vivo X-ray images. Jupyter notebook is required to run “sheep3_6_9_8roi.ipynb” to calculate the Pearson’s coefficient in "demo_pearson_opt". “image_opt” is the calculated Pearson’s coefficient based on the inverse-identified case. <strong>Micro_samples</strong> contains the files for the generation of micro RVE samples. “microRVE.inp” is the input file of the micro RVE for Abaqus. “Micro_USDF1.for” is the Fortran file that is used as a user subroutine in Abaqus to consider the adaptive bone density change in the bone regeneration area. “read_microRVE” is the post-process file to obtain strain and stress information. <strong>Neural_network_training</strong> contains the files for the training of the 1<sup>st</sup> neural network for calculating the elastic tensor and the 2<sup>nd</sup> series of neural networks for calculating the unit SED components. In <strong>1<sup>st</sup>_neural_network</strong> file, a Matlab code is for the training of the neural network based on Matlab R2020b. The training data of the 1<sup>st</sup> round and the 2<sup>nd</sup> round are provided in the dataset file. In <strong>2<sup>nd</sup>_neural_network</strong> file, a Matlab code “Train3D_SED” is used for the training of 21 independent neural networks for predicting 21 unit SED components. “dataset” contains the training data of unit SED components. <strong>ML_approach </strong>contains the files of the proposed machine learning-based approach and the trained neural networks. The Matlab code “Multiscale_boneRemodelling _ML” is the program for the proposed approach, which will call Abaqus for the finite element analysis at the macroscopic level. “sheep_macro_1423_C3D4.inp” is the input file of the in-silico model, including sheep tibia, bone fixation plate, screws and scaffold. “macro_umat” is the user subroutine file. “read_macro_1423” is used for post-process of FE data. “Trained_model” file includes all the trained neural networks. <strong>Inverse_identification</strong> contains files for the inverse problem. “image_analysis” contains the 5000 virtual images generated by the proposed ML approach (“postresults_imageupdate_5000). “sheep3_6_9_8roi.ipynb” is the python-based code for the images analysis and calculates the Pearson’s coefficient. “NN_pear” is the trained neural network to output Pearson’s coefficient. “Pea_opt” is the code to find the optimal bone remodelling parameters by multi-objective genetic algorithm. “in-vivo X_ray” includes the clinical X-ray images taken at different time points. “inverse_identified_results” includes the final inverse identified results.

Cited by 0Open access

INFERRING UML STATE MACHINE FROM JAVA CODE

My Hafid Aabidi, El Mahi Bouziane, Chafik Baidada et al. · PONTE International Scientific Researchs Journal · 2018

Software documentation is essential to understand the structure and behavior of an application. Soft-ware development starts with sound documentation and design but during the course of time, design document or other software artifacts are neither updated or nor well maintained. It is very essential to understand the existing system to maintain and to evolve it with new requirements. Reverse engineer-ing extracts and retrieves the missing software documentation in the form of abstract UML model from existing source code. This enables developers to gain better understanding of the existing source code of system. Reverse engineering of behavioral models consists in extracting high-level models that help to understand the behavior of existing software systems. In this paper we present a new approach on extracting UML2 state machine diagram from Object-Oriented Java source code using Nested choice patterns, motivated by the popularity of state machine models in software systems.

Cited by 0