LEADER 01491nas 2200469-a 450 001 9910678531003321 005 20241221111145.0 011 $a2161-7376 035 $a(CKB)110992357345392 035 $a(CONSER)--2002202182 035 $a(MiAaPQ)26783 035 $a(DE-599)ZDB3066759-8 035 $a(MiFhGG)0BQQ 035 $a(EXLCZ)99110992357345392 100 $a20010709a20019999 --- a 101 0 $aeng 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInternational railway journal $eIRJ 210 $aNew York, NY $cSimmons-Boardman Pub. Corp.$dc2001- 215 $a1 online resource 300 $aSome issues accompanied by CD-ROM. 311 08$aPrint version: International railway journal (New York, N.Y. : 2001 : Print) 0744-5326 (DLC) 2002202182 (OCoLC)47259848 517 3 $aIRJ 531 $aINTERNATIONAL RAILWAY JOURNAL 531 $aINTERNATIONAL RAILWAY JOURNAL & RAPID TRANSIT REVIEW 606 $aRailroads$vPeriodicals 606 $aLocal transit$vPeriodicals 606 $aLocal transit$2fast$3(OCoLC)fst01001523 606 $aRailroads$2fast$3(OCoLC)fst01088711 608 $aPeriodicals.$2fast 615 0$aRailroads 615 0$aLocal transit 615 7$aLocal transit. 615 7$aRailroads. 676 $a385/.05 906 $aJOURNAL 912 $a9910678531003321 920 $aexl_impl conversion 996 $aInternational railway journal$92379951 997 $aUNINA LEADER 04353nam 22007455 450 001 9911047709303321 005 20250831130226.0 010 $a3-031-97822-6 024 7 $a10.1007/978-3-031-97822-7 035 $a(CKB)40851778000041 035 $a(MiAaPQ)EBC32276206 035 $a(Au-PeEL)EBL32276206 035 $a(DE-He213)978-3-031-97822-7 035 $a(EXLCZ)9940851778000041 100 $a20250831d2026 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aReproducible Research in Pattern Recognition $eFifth International Workshop, RRPR 2024, Kolkata, India, December 1, 2024, Revised Selected Papers /$fedited by Bertrand Kerautret, Federico Bolelli, Miguel Colom, Daniel Lopresti 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (230 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v15705 311 08$a3-031-97821-8 327 $a -- Reproducible Research Framework and Results. -- A Benchmark for Automated Vickers Hardness Testing. -- Scratch Assay Assessment Benchmark. -- Reducing Run-to-Run Variability in Neural Networks: A Comparative Study of Weight Optimization Methods. -- A Minimal Neural Network for Reproducible Gesture Recognition on Knitted Capacitive Touch Sensors. -- Unified Anomaly Detection methods on Edge Device using Knowledge Distillation and Quantization. -- BarBeR - Barcode Benchmark Repository:Implementation and Reproducibility Notes. -- Research Reproducibility Paper: Learning Neural Networks forMulti-label Medical Image Retrieval Using Hamming Distance Fabricated with Jaccard Similarity Coefficient. -- Resolution-Robust Medical Image Registration Method Based on Fourier Neural Operator: Implementation and Reproducibility Aspects. -- MeDiANet Implementation and Reproducibility Details. -- On Reproducibility of Graph Neural Network for Facial Palsy and Paresis Assessment: Effects of Pose Variability in Dataset. -- Implementatipn and Reproducibility Notes on GolfSwing Dataset and GolfPose Models. -- Implementation and Reproducibility Notes on EMPATH: Enhancing Word-Level Sign Language Recognition. -- Exploring the Impact of Model Parameters and Components on Video Saliency Prediction with Foundation Models. 330 $aThis book constitutes the thoroughly refereed post-workshop proceedings of the 5th International Workshop on Reproducible Research in Pattern Recognition, RRPR 2024, held in Kolkata, India, on December 1, 2024. The 5 full papers and 8 short papers included in this book were carefully reviewed and selected from 20 submissions. RRPR 2024 book cover advances in platforms on reproducibility and new reproducible research results. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v15705 606 $aApplication software 606 $aComputer engineering 606 $aComputer networks 606 $aComputers 606 $aArtificial intelligence 606 $aComputers, Special purpose 606 $aComputer and Information Systems Applications 606 $aComputer Engineering and Networks 606 $aComputing Milieux 606 $aArtificial Intelligence 606 $aComputer Communication Networks 606 $aSpecial Purpose and Application-Based Systems 615 0$aApplication software. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aComputers. 615 0$aArtificial intelligence. 615 0$aComputers, Special purpose. 615 14$aComputer and Information Systems Applications. 615 24$aComputer Engineering and Networks. 615 24$aComputing Milieux. 615 24$aArtificial Intelligence. 615 24$aComputer Communication Networks. 615 24$aSpecial Purpose and Application-Based Systems. 676 $a005.3 700 $aKerautret$b Bertrand$01423672 701 $aBolelli$b Federico$01845098 701 $aColom$b Miguel$01423673 701 $aLopresti$b Daniel$0899206 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911047709303321 996 $aReproducible Research in Pattern Recognition$94428589 997 $aUNINA LEADER 03697nam 22007095 450 001 9910863143603321 005 20251230063955.0 010 $a3-030-62746-2 024 7 $a10.1007/978-3-030-62746-1 035 $a(CKB)4100000011558813 035 $a(MiAaPQ)EBC6384943 035 $a(DE-He213)978-3-030-62746-1 035 $a(PPN)252504496 035 $a(EXLCZ)994100000011558813 100 $a20201104d2021 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy $eSPIoT-2020, Volume 2 /$fedited by John MacIntyre, Jinghua Zhao, Xiaomeng Ma 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XXXII, 863 p. 222 illus., 128 illus. in color.) 225 1 $aAdvances in Intelligent Systems and Computing,$x2194-5365 ;$v1283 300 $aIncludes index. 311 08$a3-030-62745-4 311 08$a3-030-62745-4 330 $aThis book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. 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