Suchergebnisse - (( (statny OR state) python code analysis ) OR ( (statnet OR statnet) python code analysis ))*

Quantifying cross-language code reuse via function-level clone detection

Autoren: Yi Rong Yan Zhou

Quelle: Journal of King Saud University: Computer and Information Sciences, Vol 37, Iss 10, Pp 1-20 (2025)

Schlagwörter: Natural language processing, Code reuse detection, Function-level clone analysis, Java and python source code, Academic plagiarism detection, Electronic computers. Computer science, QA75.5-76.95

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1319-1578; https://doaj.org/toc/2213-1248

Zugangs-URL: https://doaj.org/article/0e8d85656442482785bc981b326c25a4

GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries.

Autoren: Fachada, Nuno Fernandes, Daniel Fernandes, Carlos M. et al.

Quelle: Future Internet. Sep2025, Vol. 17 Issue 9, p412. 28p.

Schlagwörter: *Experimental design, *Quantitative research, Language models, Python programming language, Generative pre-trained transformers, Benchmark problems (Computer science), Code generators

Programming models to support data science workflows

Autoren: Ramón-Cortés Vilarrodona, Cristián

Weitere Verfasser: University/Department: Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors

Thesis Advisors: Badia Sala, Rosa M. (Rosa Maria), Ejarque Artigas, Jorge

Quelle: TDX (Tesis Doctorals en Xarxa)

Schlagwörter: Distributed computing, High-performance computing, Data science pipelines, Task-ba workflows, Dataflows, Containers (Computer sicience), COMPSs, PyCOMPSs, AutoParallel, Docker, Àrees temàtiques de la UPC::Informàtica

Dateibeschreibung: application/pdf

Zugangs-URL: http://hdl.handle.net/10803/669728
https://dx.doi.org/10.5821/dissertation-2117-330142

Smart Compiler Assistant: An AST based Python Code Analysis

Autoren: Aneesh, Ch Saumik, Gembali Varun, Kvv et al.

Quelle: 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) ; page 1-6

Verfügbarkeit: https://doi.org/10.1109/icccnt61001.2024.10724220
http://xplorestaging.ieee.org/ielx8/10723818/10723316/10724220.pdf?arnumber=10724220

Numerical Analyses of a SAW Gas Sensor in a Non-Steady State

Autoren: T. Hejczyk J. Wrotniak P. Powroźnik et al.

Quelle: International Journal of Electronics and Telecommunications, Vol vol. 71, Iss No 4 (2025)

Schlagwörter: gas sensors, numerical modeling, ingebrigtsen’s formula, dmmp, (rr)-p3ht, acoustoelectric analysis (naa), Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Telecommunication, TK5101-6720

Dateibeschreibung: electronic resource

Relation: https://journals.pan.pl/Content/136909/6-5168-Hejczyk_sk.pdf; https://doaj.org/toc/2081-8491; https://doaj.org/toc/2300-1933

Zugangs-URL: https://doaj.org/article/c9d170a1144c4d9f86026066ca8dbfea

Percolation Theory Using Python

Autoren: Malthe-Sørenssen, Anders

Schlagwörter: fractal models, critical phenomena in statistical physics, disordered systems, scaling theory, anomalous diffusion, random media textbook, Statistical physics, Condensed matter physics (liquid state and solid state physics), Cybernetics and systems theory, Engineering applications of electronic, magnetic, optical materials, Mathematical physics, Geophysics

Dateibeschreibung: application/pdf

Relation: Lecture Notes in Physics

Zugangs-URL: https://library.oapen.org/handle/20.500.12657/92326

ChatGPT for Univariate Statistics: Validation of AI-Assisted Data Analysis in Healthcare Research.

Autoren: Ruta, Michael R Gaidici, Tony Irwin, Chase et al.

Quelle: Journal of Medical Internet Research. 2025, Vol. 27, p1-9. 9p.

Schlagwörter: ChatGPT, Inferential statistics, Medical care research, Statistical software, Univariate analysis, Descriptive statistics, Data analysis, Data management

Firma/Körperschaft: OpenAI Inc.

Exploring Methods for Quantifying Expressibility and Entangling Capability of Parameterized Quantum Circuits

Autoren: Mjaaseth, Jørgen

Verfügbarkeit: https://hdl.handle.net/11250/3145867

Travel times and distances to health services in Australia

Autoren: Michaela Filipcikova Louisa Jorm Sebastiano Barbieri

Quelle: Scientific Data, Vol 13, Iss 1, Pp 1-6 (2025)

Schlagwörter: Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2052-4463

Zugangs-URL: https://doaj.org/article/3f04c331bd41458c8c42e566f2961448

CLAVE: A deep learning model for source code authorship verification with contrastive learning and transformer encoders.

Autoren: Álvarez-Fidalgo, David Ortin, Francisco

Quelle: Information Processing & Management. May2025, Vol. 62 Issue 3, pN.PAG-N.PAG. 1p.

Schlagwörter: *Transformer models, Language models, Source code, Dispute resolution, Python programming language

Transforming Digital Phenotyping Raw Data Into Actionable Biomarkers, Quality Metrics, and Data Visualizations Using Cortex Software Package: Tutorial.

Autoren: Burns, James Chen, Kelly Flathers, Matthew et al.

Quelle: Journal of Medical Internet Research. 2024, Vol. 26, p1-27. 27p.

Schlagwörter: *Electronic data processing, *Data visualization, *Data quality, Cell phones, Integrated software

User guide for Social Determinants of Health Survey data in the All of Us Research Program.

Autoren: Koleck, Theresa A Dreisbach, Caitlin Zhang, Chen et al.

Quelle: Journal of the American Medical Informatics Association. Dec2024, Vol. 31 Issue 12, p3032-3041. 10p.

Schlagwörter: Social determinants of health, Health equity, Research personnel, Sociometry, Source code

All of whom? Limitations encountered using All of Us Researcher Workbench in a Primary Care residents secondary data analysis research training block.

Autoren: LaPolla, Fred Willie Zametkin Grossi, Marco Barber Chen, Sharon et al.

Quelle: Journal of the American Medical Informatics Association. Dec2024, Vol. 31 Issue 12, p3008-3012. 5p.

Schlagwörter: Secondary analysis, Graduate medical education, Research personnel, Residents (Medicine), Research questions

GE-IA-NAM: gene–environment interaction analysis via imaging-assisted neural additive model.

Autoren: Li, Jingmao Xu, Yaqing Ma, Shuangge et al.

Quelle: Bioinformatics. Sep2025, Vol. 41 Issue 9, p1-9. 9p.

Schlagwörter: *CANCER research, *GENOTYPE-environment interaction, *DIAGNOSTIC imaging, *DEEP learning, *EMPIRICAL research, *COMPUTER simulation

HABiC: an algorithm based on the exact computation of the Kantorovich-Rubinstein optimizer for binary classification in transcriptomics.

Autoren: Cordier, Chiara Jézéquel, Pascal Campone, Mario et al.

Quelle: Bioinformatics. Jun2025, Vol. 41 Issue 6, p1-13. 13p.

Schlagwörter: *PREDICTION algorithms, *TRANSCRIPTOMES, *DEGREES of freedom, *INDIVIDUALIZED medicine, *MACHINE learning

State of the interactomes: an evaluation of molecular networks for generating biological insights.

Autoren: Wright, Sarah N Colton, Scott Schaffer, Leah V et al.

Quelle: Molecular Systems Biology. Jan2025, Vol. 21 Issue 1, p1-29. 29p.

Schlagwörter: *SYSTEMS biology, *LIFE sciences, *REGULATOR genes, *BIOLOGICAL systems, *BIOLOGICAL networks

An algorithm for automated extraction of resonance parameters from the stabilization method.

Autoren: Langner, Johanna Sadhukhan, Anjan Saha, Jayanta K. et al.

Quelle: Computer Physics Communications. Nov2025, Vol. 316, pN.PAG-N.PAG. 1p.

Schlagwörter: *RESONANCE, *DENSITY of states, *STATISTICAL accuracy, *EIGENANALYSIS, *PYTHON programming language, *ALGORITHMS, *DATA extraction

Quantifying the nuclear localization of fluorescently tagged proteins.

Autoren: Hurbain, Julien Wolde, Pieter Rein ten Swain, Peter S

Quelle: Bioinformatics Advances. 2025, Vol. 5 Issue 1, p1-8. 8p.

A Study of Large Language Models in Detecting Python Code Violations

Autoren: Hekar A. Mohammed Salih Qusay I. Sarhan

Quelle: ARO-The Scientific Journal of Koya University, Vol 13, Iss 2 (2025)

Schlagwörter: Large language models, Software metrics, Software quality, Static code analysis, Technology, Science

Dateibeschreibung: electronic resource

Relation: https://aro.koyauniversity.org/index.php/aro/article/view/2395; https://doaj.org/toc/2410-9355; https://doaj.org/toc/2307-549X

Zugangs-URL: https://doaj.org/article/a66cf05353b54e19a03b46c0b089d1fc

NFEmbed: modeling nitrogenase activity via classification and regression with pretrained protein embeddings.

Autoren: Nafi, Md Muhaiminul Islam Mohaimin, Abdullah Al

Quelle: Bioinformatics Advances. 2025, Vol. 5 Issue 1, p1-17. 17p.