1  15
 唐代书画理论 : 以李嗣真, 张怀瓘, 窦氏兄弟三家为中心 = Tangdai shuhua lilun : yi Li Sizhen, Zhang Huaiguan, Doushi xiong di san jia wei zhongxin
 Shao, Jun, author.
 邵军, author.
 Di 1 ban. 第1版.  Beijing : Gao deng jiao yu chu ban she, 2014. 北京 : 高等教育出版社, 2014.
 Description
 Book — 16, 431 pages : color illustrations, facsimiles ; 29 cm
 Summary

本书全面清理了其理论成就及其审美观念, 并将之置于唐代文艺理论发展的历史环境中, 对其在书画理论史上的意义, 价值予以了重新审视; 本书还以此作为切入点, 全面观照了有唐一代书画理论的发展脉络和历史 成就, 对唐代书画理论的发展予以了重新认识. 本书全面清理了其理论成就及其审美观念, 并将之置于唐代文艺理论发展的历史环境中, 对其在书画理论史上的意义, 价值予以了重新审视; 本书还以此作为切入点, 全面观照了有唐一代书画理论的发展脉络和历史 成就, 对唐代书画理论的发展予以了重新认识.
 Online
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request (opens in new tab) 
NK3634 .A2 S536 2014  Available 
3. Mathematical Statistics [2008]
 Shao, Jun.
 2nd ed.  New York : Springer, 2008.
 Description
 Book — 1 online resource (607 pages)
 Summary

 Probability Theory * Fundamentals of Statistics * Unbiased Estimation * Estimation in Parametric Models * Estimation in Nonparametric Models * Hypothesis Tests * Confidence Sets.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
4. Gang yi zong si [2006]
 刚逸纵肆
 Shao, Jun.
 邵军.
 Di 1 ban. 第1版.  Shanghai Shi : Shanghai shu hua chu ban she, 2006. 上海市 : 上海书画出版社, 2006.
 Description
 Book — 127 p. : col. ill. ; 23 cm.
 Online
SAL3 (offcampus storage)
SAL3 (offcampus storage)  Status 

Stacks  Request (opens in new tab) 
ND1366.7 .Z47 2006 V.15  Available 
 Shao, Jun.
 New York : Springer, c2005.
 Description
 Book — xxviii, 359 p. : ill.
 Summary

 Probability Theory. Fundamentals of Statistics. Unbiased Estimation. Estimation in Parametric Models. Estimation in Nonparametric Models. Hypothesis Tests. Confidence Sets.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
The exercises are grouped into seven chapters with titles matching those in the author's Mathematical Statistics. Can also be used as a standalone because exercises and solutions are comprehensible independently of their source, and notation and terminology are explained in the front of the book. Suitable for selfstudy for a statistics Ph.D. qualifying exam.
(source: Nielsen Book Data)
 Shao, Jun.
 New York ; London : Springer, 2005.
 Description
 Book — 1 online resource (xxviii, 359 pages) : illustrations Digital: text file.PDF.
 Summary

 Probability Theory. Fundamentals of Statistics. Unbiased Estimation. Estimation in Parametric Models. Estimation in Nonparametric Models. Hypothesis Tests. Confidence Sets.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
The exercises are grouped into seven chapters with titles matching those in the author's Mathematical Statistics. Can also be used as a standalone because exercises and solutions are comprehensible independently of their source, and notation and terminology are explained in the front of the book. Suitable for selfstudy for a statistics Ph.D. qualifying exam.
(source: Nielsen Book Data)
7. Mathematical statistics [2003]
 Shao, Jun.
 2nd ed.  New York : Springer, ©2003.
 Description
 Book — 1 online resource Digital: text file.PDF.
 Summary

 Probability Theory
 Fundamentals of Statistics
 Unbiased Estimation
 Estimation in Parametric Models
 Estimation in Nonparametric Models
 Hypothesis Tests
 Confidence Sets.
8. Mathematical statistics [2003]
 Shao, Jun.
 2nd ed.  New York : Springer, c2003.
 Description
 Book — xvi, 591 p. : ill. ; 24 cm.
 Summary

 Probability Theory * Fundamentals of Statistics * Unbiased Estimation * Estimation in Parametric Models * Estimation in Nonparametric Models * Hypothesis Tests * Confidence Sets.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 Online
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA276 .S458 2003  Unknown 
9. Mathematical statistics [1999]
 Shao, Jun.
 New York : Springer, c1999.
 Description
 Book — xiv, 529 p. : ill. ; 25 cm.
 Summary

 Probability Theory. Fundamentals of Statistics. Unbiased Estimation. Estimation in Parametric Models. Estimation in Nonparametric Models. Hypothesis Tests. Confidence Sets.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
Science Library (Li and Ma)
Science Library (Li and Ma)  Status 

Stacks  
QA276 .S458 1999  Unknown Request 
 Shao, Jun.
 New York : Springer, c1999.
 Description
 Book — xiv, 529 p.
 Summary

 INTRODUCTION TO PROBABILITY Random Experiments Probability Measures Conditional Probability and Independence Random Variables Expected Values RANDOM VECTORS AND JOINT DISTRIBUTIONS Introduction Discrete and Continuous Random Vectors Conditional Distributions Normal Distributions Poisson Processes Generating Random Variables CONVERGENCE OF RANDOM VARIABLES Introduction Convergence in Probability and Distribution WLLN Proving Convergence in Distribution CLT Some Applications Convergence with Probability 1 PRINCIPLES OF POINT ESTIMATION Introduction Statistical Models Sufficiency Point Estimation Substitution Principle Influence Curves Standard Errors Relative Efficiency The Jackknife LIKELIHOODBASED ESTIMATION Introduction The Likelihood Function The Likelihood Principle Asymptotics for MLEs Misspecified Models Nonparametric Maximum Likelihood Estimation Numerical Computation Bayesian Estimation OPTIMAL ESTIMATION Decision Theory UMVUEs The CramerRao Lower Bound Asymptotic Efficiency INTERVAL ESTIMATION AND HYPOTHESIS TESTING Confidence Intervals and Regions Highest Posterior Density Regions Hypothesis Testing Likelihood Ratio Tests Other Issues LINEAR AND GENERALIZED LINEAR MODELS Linear Models Estimation Testing NonNormal Errors Generalized Linear Models QuasiLikelihood Models GOODNESS OF FIT Introduction Tests Based on the Multinomial Distribution Smooth Goodness of Fit Tests REFERENCES Each chapter also contains a Problems and Complements section.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
This graduate textbook covers topics in statistical theory essential for graduate students preparing for work on a Ph.D. degree in statistics. The first chapter provides a quick overview of concepts and results in measuretheoretic probability theory that are useful in statistics. The second chapter introduces some fundamental concepts in statistical decision theory and inference. Chapters 37 contain detailed studies on some important topics: unbiased estimation, parametric estimation, nonparametric estimation, hypothesis testing, and confidence sets. A large number of exercises in each chapter provide not only practice problems for students, but also many additional results. In addition to the classical results that are typically covered in a textbook of a similar level, this book introduces some topics in modern statistical theory that have been developed in recent years, such as Markov chain Monte Carlo, quasilikelihoods, empirical likelihoods, statistical functionals, generalized estimation equations, the jackknife, and the bootstrap. Jun Shao is Professor of Statistics at the University of Wisconsin, Madison.
(source: Nielsen Book Data)
11. The Jackknife and Bootstrap [1995]
 Shao, Jun.
 New York, NY : Springer New York, 1995.
 Description
 Book — 1 online resource (xvii, 517 pages) Digital: text file.PDF.
 Summary

 1. Introduction. 1.1 Statistics and Their Sampling Distributions. 1.2 The Traditional Approach. 1.3 The Jackknife. 1.4 The Bootstrap. 1.5 Extensions to Complex Problems. 1.6 Scope of Our Studies.
 2. Theory for the Jackknife. 2.1 Variance Estimation for Functions of Means. 2.1.1 Consistency. 2.1.2 Other properties. 2.1.3 Discussions and examples. 2.2 Variance Estimation for Functionals. 2.2.1 Differentiability and consistency. 2.2.2 Examples. 2.2.3 Convergence rate. 2.2.4 Other differential approaches. 2.3 The Deleted Jackknife. 2.3.1 Variance estimation. 2.3.2 Jackknife histograms. 2.4 Other Applications. 2.4.1 Bias estimation. 2.4.2 Bias reduction. 2.4.3 Miscellaneous results. 2.5 Conclusions and Discussions.
 3. Theory for the Bootstrap. 3.1 Techniques in Proving Consistency. 3.1.1 Bootstrap distribution estimators. 3.1.2 Mallows' distance. 3.1.3 BerryEsseen
 '1. Introduction. 1.1 Statistics and Their Sampling Distributions. 1.2 The Traditional Approach. 1.3 The Jackknife. 1.4 The Bootstrap. 1.5 Extensions to Complex Problems. 1.6 Scope of Our Studies.
 2. Theory for the Jackknife. 2.1 Variance Estimation for Functions of Means. 2.1.1 Consistency. 2.1.2 Other properties. 2.1.3 Discussions and examples. 2.2 Variance Estimation for Functionals. 2.2.1 Differentiability and consistency. 2.2.2 Examples. 2.2.3 Convergence rate. 2.2.4 Other differential approaches. 2.3 The Deleted Jackknife. 2.3.1 Variance estimation. 2.3.2 Jackknife histograms. 2.4 Other Applications. 2.4.1 Bias estimation. 2.4.2 Bias reduction. 2.4.3 Miscellaneous results. 2.5 Conclusions and Discussions.
 3. Theory for the Bootstrap. 3.1 Techniques in Proving Consistency. 3.1.1 Bootstrap distribution estimators. 3.1.2 Mallows' distance. 3.1.3 BerryEsseen's inequality. 3.1.4 Imitation. 3.1.5 Linearization. 3.1.6 Convergence in moments. 3.2 Consistency: Some Major Results. 3.2.1 Distribution estimators. 3.2.2 Variance estimators. 3.3 Accuracy and Asymptotic Comparisons. 3.3.1 Convergence rate. 3.3.2 Asymptotic minimaxity. 3.3.3 Asymptotic mean squared error. 3.3.4 Asymptotic relative error. 3.3.5 Conclusions. 3.4 Fixed Sample Performance. 3.4.1 Moment estimators. 3.4.2 Distribution estimators. 3.4.3 Conclusions. 3.5 Smoothed Bootstrap. 3.5.1 Empirical evidences and examples. 3.5.2 Sample quantiles. 3.5.3 Remarks. 3.6 Nonregular Cases. 3.7 Conclusions and Discussions.
 4. Bootstrap Confidence Sets and Hypothesis Tests. 4.1 Bootstrap Confidence Sets. 4.1.1 The bootstrapt. 4.1.2 The bootstrap percentile. 4.1.3 The bootstrap biascorrected percentile. 4.1.4 The bootstrap accelerated biascorrected percentile. 4.1.5 The hybrid bootstrap. 4.2 Asymptotic Theory. 4.2.1 Consistency. 4.2.2 Accuracy. 4.2.3 Other asymptotic comparisons. 4.3 The Iterative Bootstrap and Other Methods. 4.3.1 The iterative bootstrap. 4.3.2 Bootstrap calibrating. 4.3.3 The automatic percentile and variance stabilizing. 4.3.4 Fixed width bootstrap confidence intervals. 4.3.5 Likelihood based bootstrap confidence sets. 4.4 Empirical Comparisons. 4.4.1 The bootstrapt, percentile, BC, and BCa. 4.4.2 The bootstrap and other asymptotic methods. 4.4.3 The iterative bootstrap and bootstrap calibration. 4.4.4 Summary. 4.5 Bootstrap Hypothesis Tests. 4.5.1 General description. 4.5.2 Twosided hypotheses with nuisance parameters. 4.5.3 Bootstrap distance tests. 4.5.4 Other results and discussions. 4.6 Conclusions and Discussions.
 5. Computational Methods. 5.1 The Delete1 Jackknife. 5.1.1 The onestep jackknife. 5.1.2 Grouping and random subsampling. 5.2 The Deleted Jackknife. 5.2.1 Balanced subsampling. 5.2.2 Random subsampling. 5.3 Analytic Approaches for the Bootstrap. 5.3.1 The delta method. 5.3.2 Jackknife approximations. 5.3.3 Saddle point approximations. 5.3.4 Remarks. 5.4 Simulation Approaches for the Bootstrap. 5.4.1 The simple Monte Carlo method. 5.4.2 Balanced bootstrap resampling. 5.4.3 Centering after Monte Carlo. 5.4.4 The linear bootstrap. 5.4.5 Antithetic bootstrap resampling. 5.4.6 Importance bootstrap resampling. 5.4.7 The onestep bootstrap. 5.5 Conclusions and Discussions.
 6. Applications to Sample Surveys. 6.1 Sampling Designs and Estimates. 6.2 Resampling Methods. 6.2.1 The jackknife. 6.2.2 The balanced repeated replication. 6.2.3 Approximated BRR methods. 6.2.4 The bootstrap. 6.3 Comparisons by Simulation. 6.4 Asymptotic Results. 6.4.1 Assumptions. 6.4.2 The jackknife and BRR for functions of averages. 6.4.3 The RGBRR and RSBRR for functions of averages. 6.4.4 The bootstrap for functions of averages. 6.4.5 The BRR and bootstrap for sample quantiles. 6.5 Resampling Under Imputation. 6.5.1 Hot deck imputation. 6.5.2 An adjusted jackknife. 6.5.3 Multiple bootstrap hot deck imputation. 6.5.4 Bootstrapping under imputation. 6.6 Conclusions and Discussions.
 7. Applications to Linear Models. 7.1 Linear Models and Regression Estimates. 7.2 Variance and Bias Estimation. 7.2.1 Weighted and unweighted jackknives. 7.2.2 Three types of bootstraps. 7.2.3 Robustness and efficiency. 7.3 Inference and Prediction Using the Bootstrap. 7.3.1 Confidence sets. 7.3.2 Simultaneous confidence intervals. 7.3.3 Hypothesis tests. 7.3.4 Prediction. 7.4 Model Selection. 7.4.1 Crossvalidation. 7.4.2 The bootstrap. 7.5 Asymptotic Theory. 7.5.1 Variance estimators. 7.5.2 Bias estimators. 7.5.3 Bootstrap distribution estimators. 7.5.4 Inference and prediction. 7.5.5 Model selection. 7.6 Conclusions and Discussions.
 8. Applications to Nonlinear, Nonparametric, and Multivariate Models. 8.1 Nonlinear Regression. 8.1.1 Jackknife variance estimators. 8.1.2 Bootstrap distributions and confidence sets. 8.1.3 Crossvalidation for model selection. 8.2 Generalized Linear Models. 8.2.1 Jackknife variance estimators. 8.2.2 Bootstrap procedures. 8.2.3 Model selection by bootstrapping. 8.3 Cox's Regression Models. 8.3.1 Jackknife variance estimators. 8.3.2 Bootstrap procedures. 8.4 Kernel Density Estimation. 8.4.1 Bandwidth selection by crossvalidation. 8.4.2 Bandwidth selection by bootstrapping. 8.4.3 Bootstrap confidence sets. 8.5 Nonparametric Regression. 8.5.1 Kernel estimates for fixed design. 8.5.2 Kernel estimates for random regressor. 8.5.3 Nearest neighbor estimates. 8.5.4 Smoothing splines. 8.6 Multivariate Analysis. 8.6.1 Analysis of covariance matrix. 8.6.2 Multivariate linear models. 8.6.3 Discriminant analysis. 8.6.4 Factor analysis and clustering. 8.7 Conclusions and Discussions.
 9. Applications to Time Series and Other Dependent Data. 9.1 mDependent Data. 9.2 Markov Chains. 9.3 Autoregressive Time Series. 9.3.1 Bootstrapping residuals. 9.3.2 Model selection. 9.4 Other Time Series. 9.4.1 ARMA(p, q) models. 9.4.2 Linear regression with time series errors. 9.4.3 Dynamical linear regression. 9.5 Stationary Processes. 9.5.1 Moving block and circular block. 9.5.2 Consistency of the bootstrap. 9.5.3 Accuracy of the bootstrap. 9.5.4 Remarks. 9.6 Conclusions and Discussions.
 10. Bayesian Bootstrap and Random Weighting. 10.1 Bayesian Bootstrap. 10.1.1 Bayesian bootstrap with a noninformative prior. 10.1.2 Bayesian bootstrap using prior information. 10.1.3 The weighted likelihood bootstrap. 10.1.4 Some remarks. 10.2 Random Weighting. 10.2.1 Motivation. 10.2.2 Consistency. 10.2.3 Asymptotic accuracy. 10.3 Random Weighting for Functional and Linear Models. 10.3.1 Statistical functionals. 10.3.2 Linear models. 10.4 Empirical Results for Random Weighting. 10.5 Conclusions and Discussions. Appendix A. Asymptotic Results. A.1 Modes of Convergence. A.2 Convergence of Transformations. A.4 The BorelCantelli Lemma. A.5 The Law of Large Numbers. A.6 The Law of the Iterated Logarithm. A.7 Uniform Integrability. A.8 The Central Limit Theorem. A.9 The BerryEsseen Theorem. A.10 Edgeworth Expansions. A.11 CornishFisher Expansions. Appendix B. Notation. References. Author Index.
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 儒家统治的时代 : 宋的转型 = The age of Confucian rule : the Song transformation of China
 Age of Confucian rule. Chinese
 Kuhn, Dieter, 1946 author.
 Di 1 ban. 第1版.  Beijing : Zhong xin chu ban she, 2016. 北京 : 中信出版社, 2016.
 Description
 Book — 17, 364 pages : illustrations, maps ; 24 cm.
 Online
East Asia Library
East Asia Library  Status 

Find it Chinese collection  
DS735 .K84 2016  Unknown 
 Third edition.  Boca Raton : Taylor & Francis, 2017.
 Description
 Book — 1 online resource
 Summary

 Introduction. Considerations Prior to Sample Size Calculation. Comparing Means. Large Sample Tests for Proportions. Exact Tests for Proportions. Tests for GoodnessofFit and Contingency Tables. Comparing TimetoEvent Data. Group Sequential Methods. Comparing Variabilities. Bioequivalence Testing. Dose Resonse Studies. Microarray Studies. Bayesian Sample Size Calculation. Nonparametrics. Cluster Randomized Design. Sample Size Calculation for TwoStage Adaptive Design. Sample Size for Clinical Trials with Extremely Low Incidence Rates. Clinical Trial Simulation for Sample Size Estimation. Sample Size Calculation in Other Areas. .
 (source: Nielsen Book Data)
(source: Nielsen Book Data)
 ACNS (Conference) (20th : 2022 : Rome, Italy)
 Cham : Springer, [2022]
 Description
 Book — 1 online resource (xx, 622 pages) : illustrations (chiefly color).
 Summary

 AIBlock – Application Intelligence and Blockchain Security
 Universal Physical Adversarial Attack via Background Image
 Efficient Verifiable Boolean Range Query for Light Clients on Blockchain Database
 SuppliedTrust: A Trusted Blockchain Architecture for Supply Chains
 Towards Interpreting Vulnerability of Object Detection Models via Adver[1]sarial Distillation
 Vulnerability Detection for Smart Contract via Backward Bayesian Active Learning
 A MultiAgent Deep Reinforcement LearningBased Collaborative
 Hybrid Isolation Model for Device Application Sandboxing Deployment in Zero Trust Architecture
 AIHWS – Artificial Intelligence in Hardware Security
 On the Effect of Clock Frequency on Voltage and Electromagnetic Fault Injection
 Sbox Pooling: Towards More Efficient SideChannel Security Evaluations
 Deep Learningbased Sidechannel Analysis against AES Inner Rounds
 A sidechannel based disassembler for the ARMCortex M0
 Towards Isolated AI Accelerators with OPTEE on SoCFPGAs
 Order Vs. Chaos: Multitrunk classifier for sidechannel attack
 AIoTS – Artificial Intelligence and Industrial IoT Security
 Framework for Calculating Residual Cybersecurity Risk of Threats to Road Vehicles in Alignment with ISO/SAE 21434
 Output Prediction Attacks on Block Ciphers using Deep Learning
 HolA: Holistic and Autonomous Attestation for IoT Networks
 CIMSS – Critical Infrastructure and Manufacturing System Security
 The Etiology of Cybersecurity
 Outsider Key Compromise Impersonation Attack on a MultiFactor Authenticated Key Exchange Protocol
 Toward Safe Integration of Legacy SCADA Systems in the Smart Grid
 Cloud S&P – Cloud Security and Privacy
 RATLS: Integrating Transport Layer Security with Remote Attestation
 DLPFS: The Data Leakage Prevention FileSystem
 Privacypreserving record linkage using local sensitive hash and private set intersection
 SCI – Secure Cryptographic Implementation
 UniqueChain: Achieving (Near) Optimal Transaction Settlement Time via Single Leader Election
 PEPEC: Precomputed ECC Points Embedded in Certificates and Verified by CT Log Servers
 Efficient Software Implementation of GMT672 and GMT8542 PairingFriendly Curves for a 128bit Security Level
 SecMT – Security in Mobile Technologies
 Leaky Blinders: Information Leakage in Mobile VPNs
 Instrumentation Blueprints: Towards Combining Several Android Instrumentation Tools
 SiMLA – Security in Machine Learning and its Applications
 A Siamese Neural Network for scalable Behavioral Biometrics Authentication
 A methodology for training homomorphic encryption friendly neural networks
 Scalable and Secure HTML5 CanvasBased User Authentication
 Android Malware Detection Using BERT
 POSTERS
 POSTER: A Transparent Remote Quantum Random Number Generator Over a QuantumSafe Link
 POSTER: Enabling UserAccountable Mechanisms in Decision Systems
 POSTER: Key Generation Scheme Based on Physical Layer
 POSTER: ODABE: Outsourced Decentralized CPABE in Internet of Things
 POSTER: Ransomware detection mechanism – current state of the project.
 Cham : Springer, 2023.
 Description
 Book — 1 online resource (xiii, 729 pages) : illustrations (chiefly color).
 Summary

 ADSC  Automated Methods and Datadriven Techniques in Symmetrickey Cryptanalysis
 Automatic Search Model for RelatedTweakey Impossible Differential Cryptanalysis
 Comprehensive Preimage Security Evaluations on Rijndaelbased Hashing
 Conditional Cube Key Recovery Attack on RoundReduced Xoodyak
 AIBlock  Application Intelligence and Blockchain Security Smart Contractbased EVoting System Using Homomorphic Encryption and Zeroknowledge Proof
 Preventing Content Cloning in NFT Collections
 NFT Trades in Bitcoin with Offchain Receipts
 AIHWS  Artificial Intelligence in Hardware Security A Comparison of Multitask learning and Singletask learning Approaches
 Hide and Seek: Using Occlusion Techniques for SideChannel Leakage Attribution in CNNs
 Secret Key Recovery Attack on Masked and Shuffed Implementations of CRYSTALSKyber and Saber
 SoK: Assisted Fault Simulation Existing Challenges and Opportunities Offered by AI
 Using Model Optimization as Countermeasure against Model Recovery Attacks
 AIoTS  Artificial Intelligence and Industrial IoT Security
 Blockchainenabled Data Sharing in Connected Autonomous Vehicles for Heterogeneous Networks
 A Security Policy Engine for Building Energy Management Systems
 EARIC: Exploiting ADC Registers in IoT and Control Systems
 CIMSS  Critical Infrastructure and Manufacturing System Security RoundEffcient Security Authentication Protocol for 5G Network
 A Framework for TLS Implementation Vulnerability Testing in 5G
 Safety Watermark: A Defense Tool for RealTime Digital Forensic Incident Response in Industrial Control Systems
 Leveraging Semantic Relationships to Prioritise Indicators of Compromise in Additive Manufacturing Systems
 WiP: Towards Zero Trust Authentication in Critical Industrial Infrastructures with PRISM
 Cloud S&P  Cloud Security and Privacy slytHErin: An Agile Framework for Encrypted Deep Neural Network Inference
 Trust Management Framework for Containerized Workloads  Applications to 5G Networks
 SCI  Secure Cryptographic Implementation
 cPSIR: Circuitbased Private Stateful Information Retrieval for Private Media Consumption
 A DeepLearning Approach for Predicting Round Obfuscation in WhiteBox Block Ciphers
 Effcient Arithmetic for Polynomial Multiplication in PostQuantum Latticebased Cryptosystem on RISCV Platform
 Generic Constructions of ServerAided Revocable ABE with Verifiable Transformation
 Hybrid PostQuantum Signatures in Hardware Security Keys
 MultiArmed SPHINCS+
 SpanL: Creating Algorithms for Automatic API Misuse Detection with Program Analysis Compositions
 ZKBdf: A ZKBoobased QuantumSecure Verifiable Delay Function with Proversecret
 SecMT  Security in Mobile Technologies
 If you're scanning this, it's too late! A QR Codebased Fuzzing Methodology to Identify Input Vulnerabilities In Mobile Apps
 Enabling Lightweight Privilege Separation in Applications with MicroGuards
 SiMLA  Security in Machine Learning and its Applications
 Eliminating Adversarial Perturbations Using ImagetoImage Translation Method
 Federated Learning Approach for Distributed Ransomware Analysis
 Forensic Identification of Android Trojans Using Stacked Ensemble of Deep Neural Networks
 POSTERS
 Ransomware detection mechanism  Project status at the beginning of 2023
 AuthZit: MultiModal Authentication with VisualSpatial and Text Secrets
 Integration of EndtoEnd Security and LightweightSSL for Enhancing Security and Effciency of MQTT
 Stopping Runtime Countermeasures in Cryptographic Primitives
 Swarmbased IoT Network Penetration Testing by IoT Devices
 Advancing Federated Edge Computing with Continual Learning for Secure and Effcient Performance
 A FineGrained Metric for Evaluating the Performance of Adversarial Attacks and Defenses
 Integrating Quantum Key Distribution into Hybrid QuantumClassical Networks
 Adaptive Moving Target Defense: Enhancing Dynamic Perturbation through Voltage Sensitivity Analysis in Power Systems
 PriAuct: Privacy Preserving Auction Mechanism
 Using Verifiable Credentials for Authentication of UAVs in Logistics
 A cardbased protocol that lets you know how close two parties are in their opinions (agree/disagree) by using a fourpoint Likert scale
 Collaborative AuthorityBased Searchable Encryption Using Access Control Encryption.
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