01158nam a2200265 i 450099100008634970753620020503113824.0000530s|||| it ||| | ita 8815076107b1002671x-39ule_instocm00000367ExLDip.to Beni CulturaliitaCammelli, Marco24915La nuova disciplina dei Beni culturali e ambientali :testo unico approvato con il decreto legislativo 29 ottobre 1999, n. 490 /a cura di Marco CammelliBologna :Il Mulino,2000XV, 623 p. ;21 cmIn app. contiene testo integraleBeni culturaliItaliaLegislazione.b1002671x22-05-0831-05-02991000086349707536LE001 BC 1812001000008544le001-E0.00-l- 00000.i1003008631-05-02LE008 FL.M. (TR.P.) I B 17612008000365738le008-E0.00-l- 00000.i1476151822-05-08Nuova disciplina dei Beni culturali e ambientali178198UNISALENTOle001le00801-01-00ma -itait 3102328oam 2200421Ka 450 991069652070332120080513111409.0(CKB)5470000002379648(OCoLC)207069313(EXLCZ)99547000000237964820080225d2008 ua 0engtxtrdacontentcrdamediacrrdacarrierDetermination of sugars, byproducts, and degradation products in liquid fraction process samples[electronic resource] laboratory analytical procedure (LAP) : issue date, 12/08/2006 /A. Sluiter ... [and others]Golden, Colo. :National Renewable Energy Laboratory,[2008]11 pages digital, PDF fileTechnical report ;NREL/TP-510-42623Title from title screen (viewed on May 9, 2008)."January 2008."Carbohydrates make up a major portion of biomass samples. These carbohydrates are polysaccharides constructed primarily of glucose, xylose, arabinose, galactose, and mannose monomeric subunits. During certain pretreatments of biomass, a portion of these polysaccharides are hydrolyzed and soluble sugars are released into the liquid stream. This method is used to quantify the total amount of soluble carbohydrates released into solution as well as the amount of monomeric sugars released into solution. The soluble sugars in the liquid fraction of process samples can be quantified by HPLC with refractive index detection. If the sugars are present in oligomeric form further processing into their monomeric units is required prior to HPLC analysis.Determination of sugars, byproducts, and degradation products in liquid fraction process samples BiomassLaboratory manualsBiomass conversionLaboratory manualsSugarInversionBiomassBiomass conversionSugarInversion.Sluiter A1388888National Renewable Energy Laboratory (U.S.)SOESOEGPOBOOK9910696520703321Determination of sugars, byproducts, and degradation products in liquid fraction process samples3543601UNINA04272nam 22005295 450 991029916450332120200629195912.03-319-95092-410.1007/978-3-319-95092-1(CKB)4100000005820400(DE-He213)978-3-319-95092-1(MiAaPQ)EBC5494745(PPN)229918174(EXLCZ)99410000000582040020180817d2018 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierData Science Thinking The Next Scientific, Technological and Economic Revolution /by Longbing Cao1st ed. 2018.Cham :Springer International Publishing :Imprint: Springer,2018.1 online resource (XX, 390 p. 62 illus., 61 illus. in color.) Data Analytics,2520-18593-319-95091-6 1 The Data Science Era -- 2 What is Data Science -- 3 Data Science Thinking -- 4 Data Science Challenges -- 5 Data Science Discipline -- 6 Data Science Foundations -- 7 Data Science Techniques -- 8 Data Economy and Industrialization -- 9 Data Science Applications -- 10 Data Profession -- 11 Data Science Education -- 12 Prospects and Opportunities in Data Science.This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective. The topics cover an extremely wide spectrum of essential and relevant aspects of data science, spanning its evolution, concepts, thinking, challenges, discipline, and foundation, all the way to industrialization, profession, education, and the vast array of opportunities that data science offers. The book's three parts each detail layers of these different aspects. The book is intended for decision-makers, data managers (e.g., analytics portfolio managers, business analytics managers, chief data analytics officers, chief data scientists, and chief data officers), policy makers, management and decision strategists, research leaders, and educators who are responsible for pursuing new scientific, innovation, and industrial transformation agendas, enterprise strategic planning, a next-generation profession-oriented course development, as well as those who are involved in data science, technology, and economy from an advanced perspective. Research students in data science-related courses and disciplines will find the book useful for positing their innovative scientific journey, planning their unique and promising career, and competing within and being ready for the next generation of science, technology, and economy.Data Analytics,2520-1859Data miningBig dataArtificial intelligenceData Mining and Knowledge Discoveryhttps://scigraph.springernature.com/ontologies/product-market-codes/I18030Big Data/Analyticshttps://scigraph.springernature.com/ontologies/product-market-codes/522070Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Data mining.Big data.Artificial intelligence.Data Mining and Knowledge Discovery.Big Data/Analytics.Artificial Intelligence.006.312Cao Longbingauthttp://id.loc.gov/vocabulary/relators/aut921499BOOK9910299164503321Data Science Thinking2067145UNINA12670nam 22008175 450 991088698970332120240905130738.03-031-70903-910.1007/978-3-031-70903-6(MiAaPQ)EBC31648844(Au-PeEL)EBL31648844(CKB)34825489000041(DE-He213)978-3-031-70903-6(OCoLC)1455111061(EXLCZ)993482548900004120240905d2024 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierComputer Security – ESORICS 2024 29th European Symposium on Research in Computer Security, Bydgoszcz, Poland, September 16–20, 2024, Proceedings, Part IV /edited by Joaquin Garcia-Alfaro, Rafał Kozik, Michał Choraś, Sokratis Katsikas1st ed. 2024.Cham :Springer Nature Switzerland :Imprint: Springer,2024.1 online resource (495 pages)Lecture Notes in Computer Science,1611-3349 ;149853-031-70902-0 Intro -- Preface -- Organization -- Contents - Part IV -- Attacks and Defenses -- Cips: The Cache Intrusion Prevention System -- 1 Introduction -- 2 Background -- 3 Cache Attack Detection Overview and Open Challenges -- 4 CIPS in a Nutshell -- 4.1 Attacker Model -- 4.2 Attack Detection -- 4.3 Attack Prevention -- 5 Evaluation -- 5.1 Evaluation Setup -- 5.2 Security Analysis -- 5.3 Performance -- 5.4 Hardware Implementation -- 6 Conclusion -- A Comparison to Related Work -- References -- ReminISCence: Trusted Monitoring Against Privileged Preemption Side-Channel Attacks -- 1 Introduction -- 2 Background -- 2.1 Privileged Side-Channel Attacks -- 2.2 Hardware Performance Monitor -- 2.3 RISC-V Infrastructures -- 3 System Design -- 3.1 Threat Model -- 3.2 ReminISCence Overview -- 4 Implementation -- 4.1 ReminISCing over Side-Channel Vectors on RISC-V -- 4.2 Sampling Facility -- 4.3 Trusted Scheduling -- 5 Evaluation -- 5.1 Monitoring Preemption Attacks -- 5.2 Overhead -- 5.3 Security Discussion -- 6 Related Work -- 7 Conclusion -- References -- A Plug-and-Play Long-Range Defense System for Proof-of-Stake Blockchains -- 1 Introduction -- 2 Preliminaries -- 3 Protocol Description -- 4 Construction of InPoSW -- 4.1 Challenges of Constructing InPoSW -- 4.2 Construction Overview -- 5 Construction of Bootstrap Against Long-Range Attacks -- 5.1 Security -- 6 Performance Estimation with Concrete Parameters -- 7 Related Works -- A Formal Proofs -- References -- Leveraging Hierarchies: HMCAT for Efficiently Mapping CTI to Attack Techniques -- 1 Introduction -- 2 Related Work -- 2.1 Cyber Threat Intelligence -- 2.2 Mapping of Cyber Threat Intelligence -- 3 Method -- 3.1 Processing Step -- 3.2 Hierarchical Mapping of CTI -- 4 Results and Discussion -- 4.1 Main Results -- 4.2 Contribution of Components -- 5 Limitations -- 6 Conclusions and Future Work.A The Comparison of Dataset Distributions -- B Experimental Setup -- B.1 Datasets and Evaluation Metrics -- B.2 Implementation Details -- References -- Duplication-Based Fault Tolerance for RISC-V Embedded Software -- 1 Introduction -- 2 Related Work -- 3 Protection by Fault Injection Emulation -- 4 Debugger-Driven FI Testing -- 5 Debug Specification Extension -- 6 Code Hardening Tool -- 7 Implementation -- 8 Evaluation -- 9 Conclusion -- References -- Similar Data is Powerful: Enhancing Inference Attacks on SSE with Volume Leakages -- 1 Introduction -- 2 The Proposed Attacks -- 2.1 Intuition -- 2.2 VolScore -- 2.3 RefVolScore -- 2.4 ClusterVolScore -- 3 Experimental Evaluation -- 3.1 Methodology -- 3.2 Results -- 4 Conclusion -- References -- SAEG: Stateful Automatic Exploit Generation -- 1 Introduction -- 1.1 Challenges from Modern Protection Mechanisms -- 1.2 Our Solutions -- 2 Background -- 3 Design -- 3.1 Methodology -- 3.2 Architecture -- 3.3 Example -- 4 Implementation -- 5 Evaluation -- 6 Discussion -- 7 Related Works -- 7.1 AEG -- 7.2 Path Exploration -- 8 Conclusion -- References -- IntentObfuscator: A Jailbreaking Method via Confusing LLM with Prompts -- 1 Introduction -- 1.1 Our Contributions -- 2 Related Work -- 3 Problem Definition -- 3.1 Definition of Successful Prompt Attack -- 3.2 Assumptions on LLM Vulnerability to Query Obfuscation -- 4 Methodology -- 4.1 Obscure Intention -- 4.2 Create Ambiguity -- 5 Experiments and Analysis -- 5.1 Experiment Environment -- 5.2 Datasets Preparation -- 5.3 Evaluation Metrics -- 5.4 Results Analysis of Jailbreak Attack -- 6 Possible Mitigation Strategies for Prompt Injection Attacks -- 7 Conclusion -- References -- Breaking Through the Diversity: Encrypted Video Identification Attack Based on QUIC Features -- 1 Introduction -- 2 Related Work -- 3 Threat Model and Challenges -- 3.1 Threat Model.3.2 Challenges -- 4 Methodology -- 4.1 Constructing the Key-Value Structured Real Fingerprint Database -- 4.2 Obtaining Accurate Transmission Fingerprints -- 4.3 Implementing Efficient Video Identification -- 5 Evaluation -- 5.1 Dataset -- 5.2 Experimental Setup -- 5.3 Closed-World Analysis -- 5.4 Open-World Analysis -- 5.5 Comparison with Relevant Studies -- 6 Mitigation -- 7 Conclusion -- A Impact of the QUIC-Based Correction -- B Experimental Setup -- B.1 Correction Parameters , , and p -- B.2 HMM Probability Matrix A and B -- C Open-World Thresholds -- References -- Patronum: In-network Volumetric DDoS Detection and Mitigation with Programmable Switches -- 1 Introduction -- 2 Background and Motivation -- 2.1 Programmable Switches and Count-Min Sketch -- 2.2 Motivating Patronum -- 3 Design of Patronum -- 3.1 Overview -- 3.2 High Frequency Periodic In-Network Measurement -- 3.3 Entropy Difference Based DDoS Detection -- 3.4 In-Network Source-Based Bandwidth Monitor -- 4 Implementation and Evaluation -- 4.1 Methodology -- 4.2 EDM Approximation Accuracy and Micro Benchmarks -- 4.3 Many-to-Few Attacks -- 4.4 Few-to-Few Attacks -- 5 Discussion -- 6 Related Work -- 7 Conclusion -- A Derivation of Entropy Reformulation -- References -- Wherever I May Roam: Stealthy Interception and Injection Attacks Through Roaming Agreements -- 1 Introduction -- 2 Background -- 2.1 Lawful Interception Interfaces and Regulations -- 2.2 Roaming in 5G -- 3 Attacker Model -- 4 Attacks on 5G Roaming -- 4.1 Exploiting the System -- 4.2 Network Name Displayed on UE -- 4.3 Authentication Vector Abuse -- 4.4 Network Traffic Rerouting -- 5 Mitigations -- 5.1 Mitigating the Root Cause -- 5.2 Trust Chain Visibility -- 5.3 Proof of Location -- 5.4 Indicators of Roaming Abuse -- 5.5 Responsible Disclosure -- 6 Related Work -- 7 Conclusion -- A Appendix -- References.It is Time To Steer: A Scalable Framework for Analysis-Driven Attack Graph Generation -- 1 Introduction -- 2 Preliminaries -- 3 Overview of Our Approach -- 4 StatAG: Statistically Significant Generation -- 4.1 StatAG Validation -- 5 SteerAG: Steered Generation and Analysis -- 5.1 SteerAG Validation -- 6 Case Study Evaluation -- 6.1 Application to Large Real Networks -- 6.2 Coverage of Attack Path Analyses -- 7 Related Work -- 8 Discussion and Concluding Remarks -- A Query Stringency Analysis -- References -- Resilience to Chain-Quality Attacks in Fair Separability -- 1 Introduction -- 2 Related Work -- 3 Model -- 3.1 Processes and Network -- 3.2 Cryptography -- 3.3 Secure Broadcast -- 3.4 Byzantine Agreement -- 3.5 State Machine Replication -- 3.6 Fair Separability -- 3.7 Notations -- 4 Safe Implementation -- 4.1 Overview -- 4.2 Ordering Step -- 4.3 Consensus Step -- 4.4 Delivery Step -- 5 Fixing Liveness -- 5.1 Issue with Previous Protocol -- 5.2 Fixing Liveness -- 6 Protocol Analysis -- 6.1 State Machine Replication -- 6.2 Fair Separability -- 6.3 Discussion -- 7 Conclusion -- References -- Leveraging Transformer Architecture for Effective Trajectory-User Linking (TUL) Attack and Its Mitigation -- 1 Introduction -- 2 Related Work -- 2.1 Trajectory-User Linking (TUL) -- 2.2 Location Privacy-Preserving Mechanisms (LPPM) -- 3 TUL-STEO and Priv-STEO -- 3.1 Problem Statement and Adversary Model -- 3.2 Overview of the Approach -- 3.3 Preprocessing Steps -- 3.4 Trajectory Representation Learning -- 3.5 Spatio-Temporal Encoder-Only (STEO) -- 3.6 Training Procedure -- 4 Experimental Evaluation -- 5 Conclusion and Future Work -- A Multi-resolution Vocabulary Construction -- References -- VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification -- 1 Introduction -- 2 Preliminaries -- 2.1 Vertical Federated Learning.2.2 Backdoor Attacks in VFL -- 2.3 Threat Model -- 3 Method -- 3.1 MAE Training -- 3.2 VFLIP Mechanism -- 4 Experiments -- 4.1 Experiments Setup -- 4.2 Main Results -- 4.3 Multiple Attackers -- 4.4 Anomaly Score Distribution -- 4.5 Ablation Study -- 5 Adaptive Attack -- 6 Conclusion -- A Appendix -- A.1 VFL Backdoor Attacks -- A.2 Attack Settings -- A.3 Results for Label Inference Attacks -- A.4 Impact of Bottom Model Architecture -- A.5 Impact of the MAE Training Strategies -- References -- How to Better Fit Reinforcement Learning for Pentesting: A New Hierarchical Approach -- 1 Introduction -- 2 Background and Related Work -- 3 Problem Statement -- 4 Model Definition -- 5 Experimental Setup -- 5.1 Modified CybORG -- 5.2 Experimental Scenarios -- 6 Results -- 7 Conclusion -- A Reduction of Action Space -- B Configuration of Hyperparamters -- C Rewards Definition -- References -- Revoke: Mitigating Ransomware Attacks Against Ethereum Validators -- 1 Introduction -- 2 Background and Motivation -- 3 Revoke Design -- 3.1 Decentralised Key Revocation -- 3.2 Threat Model -- 3.3 Revocation Overview -- 4 Revocation Algorithms -- 4.1 Chain Level -- 4.2 View Level -- 4.3 Ethereum Implementation -- 5 Correctness -- 5.1 Preliminaries -- 5.2 Revoke Definitions -- 5.3 Safety -- 5.4 Liveness -- 6 Revocation Incentives -- 7 Related Work -- 8 Conclusions -- A Appendix -- A.1 Safety -- A.2 Liveness -- References -- Exploiting Layerwise Feature Representation Similarity For Backdoor Defence in Federated Learning -- 1 Introduction -- 2 Background -- 2.1 Centered Kernel Alignment -- 3 FedAvgCKA Design -- 3.1 Design Challenges -- 3.2 Implementation -- 4 Experimental Setup -- 5 Experimental Results -- 6 Related Work -- 7 Conclusion -- A Appendix A: FedAvgCKA Algorithm -- References -- Miscellaneous.Automatic Verification of Cryptographic Block Function Implementations with Logical Equivalence Checking.This four-volume set LNCS 14982-14985 constitutes the refereed proceedings of the 29th European Symposium on Research in Computer Security, ESORICS 2024, held in Bydgoszcz, Poland, during September 16–20, 2024. The 86 full papers presented in these proceedings were carefully reviewed and selected from 535 submissions. They were organized in topical sections as follows: Part I: Security and Machine Learning. Part II: Network, Web, Hardware and Cloud; Privacy and Personal Datat Protection. Part III: Software and Systems Security; Applied Cryptopgraphy. Part IV: Attacks and Defenses; Miscellaneous.Lecture Notes in Computer Science,1611-3349 ;14985Data protectionCryptographyData encryption (Computer science)Computer networksSecurity measuresComputer networksComputer systemsData and Information SecurityCryptologySecurity ServicesMobile and Network SecurityComputer Communication NetworksComputer System ImplementationData protection.Cryptography.Data encryption (Computer science)Computer networksSecurity measures.Computer networks.Computer systems.Data and Information Security.Cryptology.Security Services.Mobile and Network Security.Computer Communication Networks.Computer System Implementation.005.8Garcia-Alfaro Joaquin1731903Kozik Rafał1453541Choraś Michał1453540Katsikas Sokratis597791MiAaPQMiAaPQMiAaPQBOOK9910886989703321Computer Security – ESORICS 20244229457UNINA01267nlm 2200265Ia 450 99664017010331620250129101807.020040903e20041999 uy 0engUSdrcnuListening inradio and the American imaginationSusan J. DouglasMinneapolis, Minn.LondonUniversity of Minnesota Press2004Testo elettronico (PDF) (448 p.)Base dati testualeIn Listening In, Susan Douglas esplora come l'ascolto abbia alterato le nostre esperienze quotidiane e le nostre identità generazionali, coltivando diverse modalità di ascolto in epoche diverse; come la radio ha plasmato la nostra visione della razza, dei ruoli di genere, delle barriere etniche, delle dinamiche familiari, della leadership e del divario generazionale. Con il suo spirito distintivo, Douglas ha creato una storia culturale della radio estremamente leggibileTrasmissioni radiofonichePubblicoStati Uniti d'AmericaSociologiaBNCF302.3044DOUGLAS,Susan Jeanne1950-1739115ITcbaREICAT996640170103316EBERListening in4310774UNISA