LEADER 03024nam 2200613Ia 450 001 9910452060303321 005 20200520144314.0 010 $a0-7914-8609-5 010 $a1-4175-3873-2 035 $a(CKB)1000000000448702 035 $a(EBL)3408524 035 $a(SSID)ssj0000160533 035 $a(PQKBManifestationID)11159349 035 $a(PQKBTitleCode)TC0000160533 035 $a(PQKBWorkID)10190407 035 $a(PQKB)11628670 035 $a(MiAaPQ)EBC3408524 035 $a(OCoLC)56418905 035 $a(MdBmJHUP)muse6072 035 $a(Au-PeEL)EBL3408524 035 $a(CaPaEBR)ebr10594852 035 $a(EXLCZ)991000000000448702 100 $a20030716d2003 ub 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aGalileo's pendulum$b[electronic resource] $escience, sexuality, and the body-instrument link /$fDus?an I. Bjelic? 210 $aAlbany $cState University of New York Press$dc2003 215 $a1 online resource (222 p.) 225 0$aSUNY series in science, technology, and society 300 $aDescription based upon print version of record. 311 $a0-7914-5881-4 320 $aIncludes bibliographical references (p. 161-198) and index. 327 $a""GALILEOa???S PENDULUM""; ""Contents""; ""Foreword by Michael Lynch""; ""Acknowledgments""; ""Introduction""; ""PART ONE: Pleasure""; ""1. Time, Pleasure, and Knowledge""; ""2. The Perversion of Objectivity and the Objectivity of Perversion""; ""3. The Jesuitsa??? Homosocial Ties and the Experiments with Galileoa???s Pendulum""; ""PART TWO: Pedagogy""; ""4. Thea??? Body-Instrument Linka??? and the Prism: A Case Study""; ""5. The Formal Structure of Galileoa???s Pendulum""; ""6. The Respecification of Galileoa???s Pendulum""; ""Conclusion""; ""Notes""; ""FOREWORD""; ""INTRODUCTION"" 327 $a""1. TIME, PLEASURE, AND KNOWLEDGE""""2. THE PERVERSION OF OBJECTIVITY AND THE OBJECTIVITY OF PERVERSION""; ""3. THE JESUITSa??? HOMOSOCIAL TIES AND THE EXPERIMENTS WITH GALILEOa???S PENDULUM""; ""4. THE a???BODY-INSTRUMENT LINKa??? AND THE PRISM: A CASE STUDY""; ""5. THE FORMAL STRUCTURE OF GALILEOa???S PENDULUM""; ""6. THE RESPECIFICATION OF GALILEOa???S PENDULUM""; ""CONCLUSION""; ""Index""; ""A""; ""B""; ""C""; ""D""; ""E""; ""F""; ""G""; ""H""; ""I""; ""J""; ""K""; ""L""; ""M""; ""N""; ""O""; ""P""; ""Q""; ""R""; ""S""; ""T""; ""V""; ""W""; ""X"" 410 0$aSUNY Series in Science, Technology, and Society 606 $aScience$xMethodology 606 $aEthnomethodology 606 $aPendulum 608 $aElectronic books. 615 0$aScience$xMethodology. 615 0$aEthnomethodology. 615 0$aPendulum. 676 $a501 700 $aBjelic?$b Dus?an I$0781384 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910452060303321 996 $aGalileo's pendulum$92449767 997 $aUNINA LEADER 05033nam 22006975 450 001 9910882893203321 005 20251113205645.0 010 $a9783031667053$b(electronic bk.) 010 $z9783031667046 024 7 $a10.1007/978-3-031-66705-3 035 $a(MiAaPQ)EBC31608967 035 $a(Au-PeEL)EBL31608967 035 $a(CKB)34118602400041 035 $a(DE-He213)978-3-031-66705-3 035 $a(EXLCZ)9934118602400041 100 $a20240821d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDeep Learning Theory and Applications $e5th International Conference, DeLTA 2024, Dijon, France, July 10?11, 2024, Proceedings, Part II /$fedited by Ana Fred, Allel Hadjali, Oleg Gusikhin, Carlo Sansone 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (404 pages) 225 1 $aCommunications in Computer and Information Science,$x1865-0937 ;$v2172 311 08$aPrint version: Fred, Ana Deep Learning Theory and Applications Cham : Springer,c2024 9783031667046 327 $aGeometrical Realization for Time Series Forecasting -- Brains over Brawn: Small AI Labs in the Age of Datacenter-Scale Compute -- Time Series Prediction for Anomalies Detection in Concentrating Solar Power Plants Using Long Short-Term Memory N Networks -- Bayes Classification Using an Approximation to the Joint Probability Distribution of the Attributes -- Pollutant Source Localization Based on Siamese Neural Network Similarity Measure -- Automatic Emotion Analysis in Movies: Matteo Garrone?s Dogman as a Case Study -- Empowering Cybersecurity: CyberShield AI Advanced Integration of Machine Learning and Deep Learning for Dynamic Ransomware Detection -- Empirical Performance of Deep Learning Models with Class Imbalance for Crop Disease Classification -- Automating the Conducting of Surveys Using Large Language Models -- Computer Vision Based Monitoring System for Flotation in Mining Industry 4.0 -- Self-Supervised Learning for Robust Surface Defect Detection -- Efficient Deep Neural Network Verification with QAP-Based zkSNARK -- Version 8 of YOLO for Wildfire Detection -- Investigating a Semantic Similarity Loss Function for the Parallel Training of Abstractive and Extractive Scientific Document Summarizers -- Deep Learning-Based Preprocessing Tools for Turkish Natural Language Processing -- Skin Cancer Classification: A Comparison of CNN-Backbones for Feature-Extraction -- Multilingual Detection of Cyberbullying on Social Networks Using a Fine-Tuned GPT-3.5 Model -- Detecting Big-5 Personality Dimensions from Text Based on Large Language Models -- ME-ODAL: Mixture-of-Experts Ensemble of CNN Models for 3D Object Detection from Automotive LiDAR Point Clouds -- BitNet b1.58 Reloaded: State-of-the-Art Performance Also on Smaller Networks -- Deep Learning for Cattle Face Identification -- OBBabyFace: Oriented Bounding Box for Infant Face Detection -- EEG-Based Patient Independent Epileptic Seizure Detection Using GCN-BRF -- Predicting Components of a Target Value Versus Predicting the Target Value Directly. 330 $aThe two-volume set CCIS 2171 and 2172 constitutes the refereed papers from the 5th INternational Conference on Deep Learning Theory and Applications, DeLTA 2024, which took place in Dijon, France, during July 10-11, 2024. The 44 papers included in these proceedings were carefully reviewed and selected from a total of 70 submissions. They focus on topics such as deep learning and big data analytics; machine-learning and artificial intelligence, etc. . 410 0$aCommunications in Computer and Information Science,$x1865-0937 ;$v2172 606 $aArtificial intelligence 606 $aMachine learning 606 $aApplication software 606 $aData mining 606 $aNatural language processing (Computer science) 606 $aArtificial Intelligence 606 $aMachine Learning 606 $aComputer and Information Systems Applications 606 $aData Mining and Knowledge Discovery 606 $aNatural Language Processing (NLP) 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 0$aApplication software. 615 0$aData mining. 615 0$aNatural language processing (Computer science) 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 615 24$aComputer and Information Systems Applications. 615 24$aData Mining and Knowledge Discovery. 615 24$aNatural Language Processing (NLP). 676 $a006.3 700 $aFred$b Ana$01372767 701 $aHadjali$b Allel$01372591 701 $aGusikhin$b Oleg$01073423 701 $aSansone$b Carlo$01372864 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910882893203321 996 $aDeep Learning Theory and Applications$94207477 997 $aUNINA