LEADER 13628nam 22009615 450 001 996465973703316 005 20200707021651.0 010 $a3-642-25832-8 024 7 $a10.1007/978-3-642-25832-9 035 $a(CKB)3390000000021474 035 $a(SSID)ssj0000609124 035 $a(PQKBManifestationID)11433912 035 $a(PQKBTitleCode)TC0000609124 035 $a(PQKBWorkID)10609763 035 $a(PQKB)10913221 035 $a(DE-He213)978-3-642-25832-9 035 $a(MiAaPQ)EBC6287833 035 $a(MiAaPQ)EBC5585294 035 $a(Au-PeEL)EBL5585294 035 $a(OCoLC)1083462016 035 $a(PPN)157513017 035 $a(EXLCZ)993390000000021474 100 $a20111201d2011 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aAI 2011: Advances in Artificial Intelligence$b[electronic resource] $e24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011, Proceedings /$fedited by Dianhui Wang, Mark Reynolds 205 $a1st ed. 2011. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2011. 215 $a1 online resource (XVII, 821 p.) 225 1 $aLecture Notes in Artificial Intelligence ;$v7106 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-25831-X 327 $aIntro -- Title page -- Preface -- Organization -- Table of Contents -- Session 1: Data Mining and Knowledge Discovery -- Guided Rule Discovery in XCS for High-Dimensional Classification Problems -- Introduction -- Background -- XCS Overview -- Related Work -- Model -- Experiments -- Data Sets -- Parameters -- Results -- Conclusions -- References -- Motif-Based Method for Initialization the K-Means Clustering for Time Series Data -- Introduction -- Background -- Dimensionality Reduction -- Clustering for Time Series Data -- locations are often termed the seeds for the k-Means algorithm.2.3 Time Series Motifs and the Brute-Force Algorithm for Finding Motifs -- The Proposed Clustering Method for Time Series Data -- How to Speed Up the Brute-Force Algorithm for Finding 1-Motifs -- How to Derive Initial Centers from Results of K-Means Clustering on 1-Motifs -- Experimental Evaluation -- Conclusions -- References -- Semi-Supervised Classification Using Tree-Based Self-Organizing Maps -- Introduction -- The Tree-Based Topology Oriented SOM -- The TTOSOM-Based Classifier -- Experimental Setup -- Results -- Conclusions -- References -- The Discovery and Use of Ordinal Information on Attribute Values in Classifier Learning -- Introduction -- Value of Ordinal Information -- Testing -- Results -- Discovering Orders -- Developing Methods -- Testing Order Discovery -- Random Orders for Ensemble Classifiers -- Conclusions and Further Work -- References -- Beyond Trees: Adopting MITI to Learn Rules and Ensemble Classifiers for Multi-Instance Data -- Introduction -- The MITI Algorithm -- Experimental Results -- MIRI: Using MITI to Learn Rule Sets -- Experimental Results -- Building Ensemble Classifiers -- Experimental Results -- Conclusions -- References -- Automatically Measuring the Quality of User Generated Content in Forums -- Introduction. 327 $aProblem Definition -- UGCQ Assessment Model -- Experiment -- Datasets -- Feature Selection -- Performance Evaluation -- Post Quality Classification -- Results -- Friedman Test -- Nemenyi Test -- Discussion -- Related Work -- Conclusion -- References -- Genetically Enhanced Feature Selection of Discriminative Planetary Crater Image Features -- Introduction -- Related Work -- Genetically Enhanced Feature Selection -- Genetic Representation -- Wrapped Classifier Fitness Function -- Random Crossover -- Mutation -- Highest Fitness (Greedy) Selection -- Weighted Random Selection -- Weighted Random Selection with Simulated Annealing -- Complexity -- Experimental Results -- Conclusion -- References -- Penalized Least Squares for Smoothing Financial Time Series -- Introduction -- Current Methods -- Our Proposed Method -- Experiment Description -- Data -- Smoothness (Noise) Function -- Lag Function -- Cross Validation -- Results -- Conclusions -- References -- Logistic Regression with the Nonnegative Garrote -- Introduction -- Nonnegative Garrote -- Simulation -- Simulated Data -- Path Consistency -- Initial Estimates for the NNG -- Real Data -- Discussion and Recommendations -- References -- Identification of Breast Cancer Subtypes Using Multiple Gene Expression Microarray Datasets -- Introduction -- The Consensus Clustering Problem -- Objective Function -- The Genetic Algorithm -- The Breast Cancer Datasets -- Results -- Comparison with Existing Subtypes -- Conclusion -- References -- Combining Instantaneous and Time-Delayed Interactions between Genes - A Two Phase Algorithm Based on Information Theory -- Introduction -- Background -- Bayesian Network (BN) -- Dynamic Bayesian Network (DBN) -- Information Theoretic Quantities -- The Method -- The Framework for Representation -- Finding the Appropriate Search Strategy -- Finding the Intra-slice Arc Directions. 327 $aSimulation and Results -- Synthetic Network -- Real-Life Biological Data -- Conclusion -- References -- A Sparse-Grid-Based Out-of-Sample Extension for Dimensionality Reduction and Clustering with Laplacian Eigenmaps -- Introduction -- Laplacian Eigenmaps and Spectral Clustering -- Sparse Grids -- Sparse-Grid-Based Out-of-Sample Extension -- Experiments -- Conclusion -- References -- Distribution Based Data Filtering for Financial Time Series Forecasting -- Introduction -- Related Work -- Distribution Based Samples Removing Algorithm -- Distance Value - Threshold Based Decision -- Distance Value - Percentage Based Decision -- Datasets -- Experiments -- Conclusion -- References -- Sequential Feature Selection for Classification -- Introduction -- Framework -- General Idea -- Sequential Classification -- Action Selection without Replacement -- Solving the POMDP -- Experiments and Discussion -- Handwritten MNIST Digit Classification -- Diabetes Dataset with Naive Bayes Classification -- Discussion -- Conclusion -- References -- Long-Tail Recommendation Based on Reflective Indexing -- Introduction -- Novelty as an Important Value of Long-Tail Recommendations -- Methodological Assumptions -- Contribution of the Paper -- Algebraic Model for PRI -- Modeling User-Item Dependencies as a Probability Space -- Reflective Data Processing -- The PRI Algorithm -- Evaluation -- Data Sets -- Recommendation Quality Evaluation -- Conclusions -- References -- Author Name Disambiguation for Ranking and Clustering PubMed Data Using NetClus -- Introduction -- Related Work -- The Challenges of Author Name Disambiguation on PubMed -- Related Work on Disambiguation of PubMed Authors -- A Multi-evidence Author Disambiguation System -- Disambiguation Using Organisation Names and Addresses -- Disambiguation Using Co-author Network -- Evaluation of the Disambiguation Technique. 327 $aAccuracy of the Proposed Disambiguation Technique -- Evaluation of NetClus Results -- Conclusion and Future Work -- References -- Self-Organizing Maps for Translating Health Care Knowledge: A Case Study in Diabetes Management -- Introduction -- Background: Mining Diabetic Patient Data -- Application -- Chronic Disease Management (CDM) -- Chronic Disease Management Network (cdmNet) -- Chronic Disease Management Network - Business Intelligence (cdmNet-BI) Module -- The Self-Organizing Map (SOM) -- The Growing Self-Organizing Map (GSOM) -- Patterns in Diabetes Management -- Analysis of Features Common to Any Individual -- Analysis of Common Features with Diabetes Specific Medical Features -- Patterns Recognised from Diabetes Data: Outcomes -- Conclusions and Future Work -- References -- Distance-Based Feature Selection on Classification of Uncertain Objects -- Introduction -- Related Work -- Problem Definition -- UK-Means -- Supervised UK-Means -- Algorithms -- Averaging Approach -- Distribution-Based Approach -- Experimental Results -- Data Sets -- Performance Evaluation -- Conclusion -- References -- Session 2: Machine Learning -- Closure Spaces of Isotone Galois Connections and Their Morphisms -- Introduction -- Preliminaries: Matrices, Decompositions, Concept Lattices -- Closure Spaces Induced by (^,V) -- Morphisms of c-Closure Spaces -- Isomorphic c-Closure Spaces -- Conclusions -- References -- Ensemble Learning and Pruning in Multi-Objective Genetic Programming for Classification with Unbalanced Data -- Introduction -- Related Work: Ensemble Learning for Class Imbalance -- Multi-Objective GP (MOGP) for Evolving Ensembles -- GP Framework for Classification -- MOGP Fitness -- MOGP Search -- MOGP Ensemble Performance -- Evolutionary Parameters and Unbalanced Data Sets -- MOGP Ensemble Results -- Ensemble Pruning -- Fitness-Based Pruning. 327 $aGP for Evolving Composite Voting Trees -- Performance of Ensembles Using Puning Methods -- Conclusions -- References -- Compiling Bayesian Networks for Parameter Learning Based on Shared BDDs -- Introduction -- Preliminary -- Bayesian Networks -- Parameter Learning Problem for BNs -- Proposed Method -- Encoding and Compiling -- Learning -- Experiments -- Conclusion and Related Work -- References -- An Empirical Study of Bagging Predictors for Imbalanced Data with Different Levels of Class Distribution -- Introduction -- Designed Framework -- Evaluation Metrics -- Experimental Setting -- Selection of Base Learners -- Data-Sets -- Experimental Results Analysis -- Statistical Test -- Comparison between Bagging and Single Learners -- Comparison between Bagging Predictors -- Conclusion -- References -- A Simple Bayesian Algorithm for Feature Ranking in High Dimensional Regression Problems -- Introduction -- Bayesian Feature Ranking (BFR) Algorithm -- Discussion and Results -- Simulated Data -- Real Data -- Conclusion -- References -- Semi-random Model Tree Ensembles: An Effective and Scalable Regression Method -- Introduction -- Random Model Trees -- Experiments -- Linear Regression -- Gaussian Process Regression -- Additive Groves -- Random Model Trees -- Results -- Relative Mean Absolute Error -- Conclusions -- References -- Supervised Subspace Learning with Multi-class Lagrangian SVM on the Grassmann Manifold -- Introduction -- Proposed Method -- Multi-class Lagrangian SVM -- Learning the Projection -- Experiments -- Conclusion -- References -- Bagging Ensemble Selection -- Introduction -- Bagging Ensemble Selection -- Experimental Results -- Comparison of Bagging Ensemble Selection Algorithms to the Forward Ensemble Selection Algorithms -- Comparison of Bagging Ensemble Selection Algorithms to Other Ensemble Learning Algorithms -- Conclusions. 327 $aReferences. 330 $aThis book constitutes the refereed proceedings of the 24th Australasian Joint Conference on Artificial Intelligence, AI 2011, held in Perth, Australia, in December 2011. The 82 revised full papers presented were carefully reviewed and selected from 193 submissions. The papers are organized in topical sections on data mining and knowledge discovery, machine learning, evolutionary computation and optimization, intelligent agent systems, logic and reasoning, vision and graphics, image processing, natural language processing, cognitive modeling and simulation technology, and AI applications. 410 0$aLecture Notes in Artificial Intelligence ;$v7106 606 $aArtificial intelligence 606 $aAlgorithms 606 $aApplication software 606 $aComputers 606 $aInformation storage and retrieval 606 $aData mining 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 608 $aKongress$zPerth (Westaustralien)$y2011. 608 $aConference papers and proceedings.$2fast 610 1 $aAI 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aApplication software. 615 0$aComputers. 615 0$aInformation storage and retrieval. 615 0$aData mining. 615 14$aArtificial Intelligence. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aComputation by Abstract Devices. 615 24$aInformation Storage and Retrieval. 615 24$aData Mining and Knowledge Discovery. 676 $a006.3 686 $aSS 4800$2rvk 686 $a004$2sdnb 686 $aDAT 700f$2stub 702 $aWang$b Dianhui$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aReynolds$b Mark$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465973703316 996 $aAI 2011: Advances in Artificial Intelligence$92830628 997 $aUNISA LEADER 12673nam 22008415 450 001 9910886991403321 005 20240909131029.0 010 $a9783031686061 010 $a3031686063 024 7 $a10.1007/978-3-031-68606-1 035 $a(MiAaPQ)EBC31652780 035 $a(Au-PeEL)EBL31652780 035 $a(CKB)34909803900041 035 $a(DE-He213)978-3-031-68606-1 035 $a(OCoLC)1456141463 035 $a(EXLCZ)9934909803900041 100 $a20240909d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aComputer Safety, Reliability, and Security $e43rd International Conference, SAFECOMP 2024, Florence, Italy, September 18?20, 2024, Proceedings /$fedited by Andrea Ceccarelli, Mario Trapp, Andrea Bondavalli, Friedemann Bitsch 205 $a1st ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (325 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14988 311 08$a9783031686054 311 08$a3031686055 327 $aIntro -- Preface -- Organization -- Contents -- Fault Injection and Tolerance -- In-Memory Zero-Space Floating-Point-Based CNN Protection Using Non-significant and Invariant Bits -- 1 Introduction -- 2 Background -- 3 Proposed Methodology -- 3.1 Identification of non-Significant bits -- 3.2 Location of invariant bits -- 4 Case Study: LeNet-5 and GoogLeNet -- 4.1 Locating Non-Significant Bits -- 4.2 Locating Invariant Bits -- 4.3 Proposed Error Correcting Codes -- 4.4 Overhead Estimation of the Proposed ECCs -- 5 Discussion -- 6 Conclusions -- References -- A Failure Model Library for Simulation-Based Validation of Functional Safety -- 1 Introduction -- 2 Related Work -- 3 A Failure Model Library for Simulation-Based FI -- 3.1 Failure Model Collection -- 3.2 Failure Model Semantics -- 4 Case Study -- 5 Limitations -- 6 Conclusion -- References -- Strategic Resilience Evaluation of Neural Networks Within Autonomous Vehicle Software -- 1 Introduction -- 2 Autonomous Driving Frameworks -- 2.1 L4 System: LBC -- 2.2 L2 System: OpenPilot -- 2.3 Driving Simulator: CARLA -- 3 Methodology -- 3.1 Vulnerable Weights: Taylor Guided Fault Injection (TGFI) -- 3.2 Experimental Campaigns -- 4 Resilience Evaluation -- 4.1 Resilience of L4 LBC -- 4.2 Resilience of L2 OpenPilot -- 5 Mitigation -- 5.1 L4 LBC: Ranger -- 5.2 L2 OpenPilot: Driver Intervention -- 6 Case Studies and Discussion -- 6.1 Importance of Layer Depth for Resilience -- 6.2 Sensitivity to Single and Multi-bit Faults -- 6.3 Lessons Learned from L4 LBC and L2 OpenPilot -- 7 Related Work -- 8 Conclusions -- References -- System and Software Safety Assurance -- Reconciling Safety Measurement and Dynamic Assurance -- 1 Introduction -- 2 Conceptual Background -- 3 Motivating Example -- 3.1 Baseline Safety -- 3.2 Practical Drift -- 4 Framework -- 4.1 Defining Safety Metrics and Indicators. 327 $a4.2 Updating and Revising the Operational Risk Assessment -- 4.3 Characterizing the Change to Safety Risk -- 4.4 Numerical Examples -- 5 Towards Formal Foundations -- 6 Concluding Remarks -- References -- Safety Invariant Engineering for Interlocking Verification -- 1 Introduction -- 2 Safety Invariants -- 2.1 Requirements -- 2.2 Current Solutions -- 3 Property Engineering -- 3.1 Eliciting Safety Properties from Standards -- 3.2 Reducing the Number of False Positives -- 3.3 Redefining What the Property Violation Is -- 3.4 Regression Testing of Developed Safety Invariants -- 4 Discussion and Conclusions -- References -- Assurance Case Synthesis from a Curated Semantic Triplestore -- 1 Introduction -- 2 The Rapid Assurance Curation Kit (RACK) -- 3 Automated GSN Synthesis from RACK -- 3.1 The GSN Ontology -- 3.2 Project-Specific GSN Pattern Example -- 3.3 Automated Synthesis of GSN Fragments Using Patterns -- 3.4 GUI Support for GSN Generation and Analysis -- 4 Related Work -- 5 Conclusion -- References -- CyberDS: Auditable Monitoring in the Cloud -- 1 Introduction -- 2 Motivating Example -- 3 Monitoring Approach -- 3.1 Specification Language -- 3.2 Security Monitor -- 3.3 Tamper-Proof Claim Database and Auditability -- 4 Claim Revision Control -- 4.1 Revision Model -- 4.2 Monitoring with Revisions -- 5 Implementation and Experimental Results -- 6 Discussion -- 7 Related Work -- 7.1 Runtime Monitoring -- 7.2 Auditability of System Events -- 8 Conclusion -- References -- Automated Driving Systems -- Anatomy of a Robotaxi Crash: Lessons from the Cruise Pedestrian Dragging Mishap -- 1 Introduction -- 2 Background -- 2.1 Terminology -- 2.2 Crash Context and Overview -- 3 Crash Details -- 3.1 Crash Timeline -- 3.2 Crash Analysis -- 3.3 Potential Lessons -- 4 The Immediate Response -- 4.1 Immediate Response Timeline -- 4.2 Post-Crash Analysis. 327 $a4.3 Potential Lessons -- 5 Organizational Response -- 5.1 Organizational Response Analysis -- 5.2 Potential Lessons -- 6 Conclusions -- References -- Comprehensive Change Impact Analysis Applied to Advanced Automotive Systems -- 1 Introduction -- 2 Background on CIA for Incremental Safety Assurance -- 3 Running Example -- 4 Original Vehicle Family Models Stage 0 -- 4.1 HARA Metamodels -- 4.2 HAZOP Metamodels -- 4.3 Verification Metamodel -- 4.4 Instance Models -- 4.5 Instances for Stage 0 -- 5 CIA After a Change to the Battery Management System -- 5.1 Identifying Direct Changes - Stage 1 -- 5.2 Identifying Potential Impacts in the System - Stage 2 -- 5.3 Confirming Actual Impacts in the System - Stage 3 -- 5.4 Identifying Potential Impacts in the Assurance Case - Stage 4 -- 5.5 Confirming Actual Impacts in the Assurance - Stage 5 -- 5.6 Incremental Assurance - Stages 6 and 7 -- 6 Discussion -- 7 Conclusion -- References -- A Case Study of Continuous Assurance Argument for Level 4 Automated Driving -- 1 Introduction -- 2 Related Work -- 3 A Case Study in a Local City -- 3.1 Top Level of Assurance Case for Level 4 Automated Driving -- 3.2 GSN Module M2 for Identification of Risk and Hazard -- 3.3 GSN Module M4 for Evaluation and Validation -- 4 A Toolchain of an Assurance Case Tool and a Monitoring System -- 5 Lessons Obtained from the Case Study -- 6 Concluding Remarks -- References -- Security of Safety-Critical Systems -- TitanSSL: Towards Accelerating OpenSSL in a Full RISC-V Architecture Using OpenTitan Root-of-Trust -- 1 Introduction -- 2 Background and Related Works -- 3 Hardware Architecture -- 4 TitanSSL Software Architecture -- 4.1 Application Processor -- 4.2 Security Controller -- 5 Experimental Results -- 5.1 Comparison with Software Implementation -- 5.2 OpenTitan Firmware Analysis -- 6 Security Assumptions and Implications. 327 $a7 Conclusion -- References -- A Lightweight and Responsive On-Line IDS Towards Intelligent Connected Vehicles System -- 1 Introduction -- 2 Methodology -- 2.1 System Overview -- 2.2 Threat Model -- 2.3 ML-BF Model -- 2.4 Feature Engineering -- 2.5 Blacklist Filter -- 3 Implementation -- 3.1 Testbed Setting -- 3.2 Dataset Selection -- 3.3 Data Pre-processing -- 3.4 Machine Learning Approaches Adoption -- 3.5 Model Training -- 4 Experimental Results -- 4.1 Evaluation Metrics -- 4.2 Detection Performance -- 4.3 Computational Consumption -- 4.4 Analysis and Discussion -- 5 Related Work -- 5.1 Machine Learning for Intrusion Detection in ICV -- 5.2 Lightweight IDS in ICV -- 5.3 Responsive IDS in ICV -- 6 Conclusion -- References -- Evaluating the Vulnerability Detection Efficacy of Smart Contracts Analysis Tools -- 1 Introduction -- 2 Background -- 3 Related Works -- 4 Experimental Study Methodology -- 5 Experimental Study Results -- 5.1 RQ1: Contests Versus Vulnerabilities -- 5.2 RQ2: Tools Versus Vulnerabilities -- 5.3 RQ3: Tools in Theory Versus Tools in Practice -- 5.4 RQ4: Analysis Complexity Versus Tool Efficacy -- 6 Conclusions -- References -- Safety-Security Analysis via Attack-Fault-Defense Trees: Semantics and Cut Set Metrics -- 1 Introduction -- 2 Related Work -- 3 Case Study: Gridshield -- 4 Background -- 5 Attack-Fault-Defense Trees -- 5.1 Formal Definition of AFDT -- 5.2 Gridshield AFDT -- 6 Qualitative Analysis of AFDT -- 7 Safety and Security Dependencies via MCS -- 8 Conclusion and Future Work -- References -- Safety Verification -- Coyan: Fault Tree Analysis - Exact and Scalable -- 1 Introduction -- 2 Preliminaries -- 3 Computing Unreliability Values -- 4 Unreliability Through WMC of Tseitin Transformation -- 5 Implementation -- 6 Experimentation -- 6.1 Benchmarks -- 6.2 Results -- 6.3 Industrial Benchmarks -- 7 Concluding Remarks. 327 $aReferences -- Safety Argumentation for Machinery Assembly Control Software -- 1 Introduction -- 2 Background -- 2.1 EN ISO 13849:2023 -- 2.2 Assurance Cases -- 2.3 Contract-Based Design -- 3 SAMACS: Safety Argumentation for Machinery Assembly Control Software -- 4 Case Study -- 4.1 Establishment of Software Responsibility -- 4.2 Definition of Software Safety Goals -- 4.3 Identification of Software Safety Requirements -- 4.4 Definition of Contracts -- 4.5 Identification of Verification Techniques and Evidence Provision -- 4.6 Composing the Safety Case Arguments -- 5 Discussion -- 6 Related Work -- 7 Conclusions and Future Work -- References -- Sound Non-interference Analysis for C/C++ -- 1 Introduction -- 2 Sound Static Source Code Analysis -- 3 Data and Control Flow Errors -- 4 Data and Control Flow Analysis -- 5 Taint Analysis -- 5.1 Modeling Interference -- 5.2 Signal Flow Analysis -- 5.3 Freedom of Interference Between Software Components -- 6 Experimental Results -- 7 Related Work -- 8 Conclusion -- References -- Autonomous Systems -- A Dynamic Assurance Framework for an Autonomous Survey Drone -- 1 Introduction -- 2 Related Literature -- 3 Case Study and Monitor Examples -- 3.1 Monitoring Battery State and Required Power Consumption -- 3.2 Monitoring and Predicting Task Scheduling and Executing Times -- 3.3 Reconfigurations Calculation -- 4 Dynamic Assurance Case Approach -- 4.1 Battery Monitor Assurance Case -- 4.2 Discussion -- 4.3 Online Monitoring Interface -- 5 Conclusions -- References -- Redefining Safety for Autonomous Vehicles -- 1 Introduction -- 2 Existing Safety Definitions -- 2.1 ISO 26262 -- 2.2 ISO 21448 -- 2.3 ANSI/UL 4600 -- 2.4 Other Safety Definitions -- 3 Examples of AV Safety Problems -- 4 What is Missing from Safety Definitions -- 4.1 Open World Environment -- 4.2 Self-enforcement of Operational Limitations. 327 $a4.3 Ad Hoc Systems of Systems. 330 $aThis book constitutes the refereed proceedings of the 43rd International Conference on Computer Safety, Reliability and Security, SAFECOMP 2024, held in Florence, Italy, in September 2024. The 19 full papers included in this volume were carefully reviewed and selected from 80 submissions. They have been organized in topical sections as follows: Fault Injection and Tolerance; System and Software Safety Assurance; Automated Driving Systems; Security of safety-critical systems; Safety Verification; and Autonomous Systems. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14988 606 $aComputer networks 606 $aSoftware engineering 606 $aInformation technology$xManagement 606 $aRobotics 606 $aMicroprogramming 606 $aComputer networks$xSecurity measures 606 $aComputer Communication Networks 606 $aSoftware Engineering 606 $aComputer Application in Administrative Data Processing 606 $aRobotics 606 $aControl Structures and Microprogramming 606 $aMobile and Network Security 615 0$aComputer networks. 615 0$aSoftware engineering. 615 0$aInformation technology$xManagement. 615 0$aRobotics. 615 0$aMicroprogramming. 615 0$aComputer networks$xSecurity measures. 615 14$aComputer Communication Networks. 615 24$aSoftware Engineering. 615 24$aComputer Application in Administrative Data Processing. 615 24$aRobotics. 615 24$aControl Structures and Microprogramming. 615 24$aMobile and Network Security. 676 $a004.6 700 $aCeccarelli$b Andrea$01768495 701 $aTrapp$b Mario$01768496 701 $aBondavalli$b Andrea$0742432 701 $aBitsch$b Friedemann$01427978 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910886991403321 996 $aComputer Safety, Reliability, and Security$94229466 997 $aUNINA