tag:joss.theoj.org,2005:/papers/tagged/xAIJournal of Open Source Software2024-01-09T15:42:20ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/38102024-01-09T15:42:20Z2024-01-10T00:01:06ZScarlet: Scalable Anytime Algorithms for Learning Fragments of Linear Temporal Logicacceptedv0.0.12022-08-10 18:27:58 UTC932024-01-09 15:42:20 UTC920245052RitamRahaUniversity of Antwerp, Antwerp, Belgium, CNRS, LaBRI and Université de Bordeaux, France0000-0003-1467-1182RajarshiRoyMax Planck Institute for Software Systems, Kaiserslautern, Germany0000-0002-0202-1169NathanaëlFijalkowCNRS, LaBRI and Université de Bordeaux, France0000-0002-6576-4680DanielNeiderTU Dortmund University, Dortmund, Germany, Center for Trustworthy Data Science and Security, University Alliance Ruhr, Germany0000-0001-9276-634210.21105/joss.05052https://doi.org/10.5281/zenodo.10419514Pythonhttps://joss.theoj.org/papers/10.21105/joss.05052.pdflinear temporal logic (LTL), Explainable AI (XAI), specification mining, Formal Methodstag:joss.theoj.org,2005:Paper/45102023-10-23T16:10:44Z2023-10-24T00:01:04ZPASCal Python: A Principal Axis Strain Calculatoracceptedv1.0.02023-05-30 14:09:21 UTC902023-10-23 16:10:44 UTC820235556MonthakanLertkiattrakulSchool of Chemistry, University Park, Nottingham, NG7 2RD, United KingdomMatthewL.EvansInstitut de la Matière Condensée et des Nanosciences, Université catholique de Louvain, Chemin des Étoiles 8, Louvain-la-Neuve 1348, Belgium0000-0002-1182-9098MatthewJ.CliffeSchool of Chemistry, University Park, Nottingham, NG7 2RD, United Kingdom0000-0002-0408-764710.21105/joss.05556https://doi.org/10.5281/zenodo.10004384Pythonhttps://joss.theoj.org/papers/10.21105/joss.05556.pdfcrystallography, non-ambient, thermal expansion, compressibility, electrochemical straintag:joss.theoj.org,2005:Paper/40282023-09-29T15:46:58Z2023-09-30T07:21:05ZTrash AI: A Web GUI for Serverless Computer Vision Analysis of Images of Trashaccepted1.02022-11-28 09:06:07 UTC892023-09-29 15:46:58 UTC820235136WinCowgerMoore Institute for Plastic Pollution Research, USA0000-0001-9226-3104StevenHollingsworthCode for Sacramento and Open Fresno, USADayFeyCode for Sacramento and Open Fresno, USAMaryC.NorrisCode for Sacramento and Open Fresno, USAWalterYuCalifornia Department of Transportation, USAKristiinaKergeLet's Do It Foundation, EstoniaKrisHaamerLet's Do It Foundation, EstoniaGinaDuranteCode for Sacramento and Open Fresno, USABriandaHernandezCode for Sacramento and Open Fresno, USA10.21105/joss.05136https://doi.org/10.5281/zenodo.8384126Python, JavaScripthttps://joss.theoj.org/papers/10.21105/joss.05136.pdftensorflow.js, IndexDB, Plastic Pollution, Trash, Litter, AI, Image Classification, Serverless, vue, vuetify, vite, piniatag:joss.theoj.org,2005:Paper/41902023-05-17T14:31:41Z2023-05-18T00:01:24ZXGI: A Python package for higher-order interaction networksacceptedv0.5.42023-02-03 15:17:14 UTC852023-05-17 14:31:41 UTC820235162NicholasW.LandryVermont Complex Systems Center, University of Vermont, USA, Department of Mathematics and Statistics, University of Vermont, USA0000-0003-1270-4980MaximeLucasCENTAI Institute, Italy0000-0001-8087-2981IacopoIacopiniDepartment of Network and Data Science, Central European University, Austria0000-0001-8794-6410GiovanniPetriCENTAI Institute, Italy0000-0003-1847-5031AliceSchwarzeDepartment of Mathematics, Dartmouth College, USA0000-0002-9146-8068AlicePataniaVermont Complex Systems Center, University of Vermont, USA, Department of Mathematics and Statistics, University of Vermont, USA0000-0002-3047-4376LeoTorresMax Planck Institute for Mathematics in the Sciences, Germany0000-0002-2675-277510.21105/joss.05162https://doi.org/10.5281/zenodo.7939055Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.05162.pdfpython, higher-order, hypergraph, simplicial complextag:joss.theoj.org,2005:Paper/34332022-12-15T20:02:32Z2022-12-16T00:01:07ZDIANNA: Deep Insight And Neural Network Analysisacceptedv.0.4.02022-03-22 16:05:29 UTC802022-12-15 20:02:32 UTC720224493ElenaRanguelovaNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-9834-1756ChristiaanMeijerNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-5529-5761LeonOostrumNetherlands eScience Center, Amsterdam, the Netherlands0000-0001-8724-8372YangLiuNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-1966-8460PatrickBosNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-6033-960XGiuliaCrocioniNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-0823-0121MatthieuLaneuvilleSURF, Amsterdam, the Netherlands0000-0001-6022-0046BryanCardenasGuevaraSURF, Amsterdam, the Netherlands0000-0001-9793-910XRenaBakhshiNetherlands eScience Center, Amsterdam, the Netherlands0000-0002-2932-3028DamianPodareanuSURF, Amsterdam, the Netherlands0000-0002-4207-872510.21105/joss.04493https://doi.org/10.5281/zenodo.7387004Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.04493.pdfexplainable AI, Deep Neural Networks, ONNX, benchmark datasetstag:joss.theoj.org,2005:Paper/38192022-10-14T16:13:22Z2022-10-15T00:00:36ZGSAreport: Easy to Use Global Sensitivity Reportingacceptedv1.0.02022-08-15 10:46:50 UTC782022-10-14 16:13:22 UTC720224721VanStein,BasLIACS, Leiden University, The Netherlands0000-0002-0013-7969ElenaRaponiTechnical University of Munich, Germany0000-0001-6841-740910.21105/joss.04721https://doi.org/10.5281/zenodo.7191341Python, Jupyter Notebookhttps://joss.theoj.org/papers/10.21105/joss.04721.pdfglobal sensitivity analysis, explainable aitag:joss.theoj.org,2005:Paper/31042022-01-29T01:00:18Z2022-01-30T19:09:58ZDelve: Neural Network Feature Variance Analysisacceptedv0.1.482021-10-08 17:58:31 UTC692022-01-29 01:00:18 UTC720223992JustinShenkVisioLab, Berlin, Germany, Institute of Cognitive Science, University of Osnabrueck, Osnabrueck, Germany0000-0002-0664-7337MatsL.RichterInstitute of Cognitive Science, University of Osnabrueck, Osnabrueck, Germany0000-0002-9525-9730WolfByttnerRapid Health, London, England, United Kingdom0000-0002-9525-973010.21105/joss.03992https://doi.org/10.5281/zenodo.5865465Pythonhttps://joss.theoj.org/papers/10.21105/joss.03992.pdfdeep learning, machine learning, saturation, pytorch, AItag:joss.theoj.org,2005:Paper/28182021-12-21T10:35:27Z2021-12-22T00:00:19ZdaiR: an R package for OCR with Google Document AIacceptedv.0.9.12021-06-24 12:22:13 UTC682021-12-21 10:35:27 UTC620213538ThomasHegghammerSenior Research Fellow, Norwegian Defence Research Establishment (FFI)0000-0001-6253-151810.21105/joss.03538https://doi.org/10.5281/zenodo.5792037Rhttps://joss.theoj.org/papers/10.21105/joss.03538.pdfoptical character recognition, cloud computing, text mining, natural language processingtag:joss.theoj.org,2005:Paper/27002021-10-21T13:30:14Z2021-10-22T00:01:23ZfishRman: A Shiny R Dashboard improving Global Fishing Watch data availabilityacceptedv1.0.02021-06-01 07:04:09 UTC662021-10-21 13:30:14 UTC620213467PasqualeBuonomoOpen-Source for Marine and Ocean Sciences (OSMOS)0000-0002-1848-931310.21105/joss.03467https://doi.org/10.5281/zenodo.5582567Rhttps://joss.theoj.org/papers/10.21105/joss.03467.pdffisheries, marine biology, global fishing watch, AIS data, dashboard, shiny, spatial analysistag:joss.theoj.org,2005:Paper/13972020-02-05T18:14:54Z2021-02-15T11:31:16Zshapr: An R-package for explaining machine learning models with dependence-aware Shapley valuesacceptedv0.1.02019-12-10 14:11:31 UTC462020-02-05 18:14:54 UTC520192027NikolaiSellereiteNorwegian Computing Center0000-0002-4671-0337MartinJullumNorwegian Computing Center0000-0003-3908-515510.21105/joss.02027https://doi.org/10.5281/zenodo.3641831R, Python, C++https://joss.theoj.org/papers/10.21105/joss.02027.pdfexplainable AI, interpretable machine learning, shapley values, feature dependence