Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552067
Kenneth B. Pasiah, L. Deng, Dale Bowman, Ching-Chi Yang
Pseudo-random numbers (PRNs) are the basis for almost any statistical simulation and this depends largely on the quality of the pseudo-random number generator (PRNG) used. In this study, we used some results from number theory to propose an efficient method to accelerate the computer search of super-order maximum-period multiple recursive generators (MRGs). We conduct efficient computer searches and identify many efficient and portable MRGs of super-orders, 40751, 50551, and 50873; which respectively have equi-distribution property up to 40751, 50551, and 50873 dimensions, and period lengths of approximately 10380278.1, 10471730.6, and 10474729.3. Using the generalized Mersenne prime algorithm, we extend some known results of some efficient, portable and maximum-period MRGs. In particular, the DX/DL/DS/DT large-order generators are extended to super-order generators. An extensive empirical evaluation shows that these generators behave well when tested with the stringent Small Crush and Crush batteries of the TestU01 package.
{"title":"Searching for Large-Order Multiple Recursive Generators","authors":"Kenneth B. Pasiah, L. Deng, Dale Bowman, Ching-Chi Yang","doi":"10.23919/ANNSIM52504.2021.9552067","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552067","url":null,"abstract":"Pseudo-random numbers (PRNs) are the basis for almost any statistical simulation and this depends largely on the quality of the pseudo-random number generator (PRNG) used. In this study, we used some results from number theory to propose an efficient method to accelerate the computer search of super-order maximum-period multiple recursive generators (MRGs). We conduct efficient computer searches and identify many efficient and portable MRGs of super-orders, 40751, 50551, and 50873; which respectively have equi-distribution property up to 40751, 50551, and 50873 dimensions, and period lengths of approximately 10380278.1, 10471730.6, and 10474729.3. Using the generalized Mersenne prime algorithm, we extend some known results of some efficient, portable and maximum-period MRGs. In particular, the DX/DL/DS/DT large-order generators are extended to super-order generators. An extensive empirical evaluation shows that these generators behave well when tested with the stringent Small Crush and Crush batteries of the TestU01 package.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"21 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75327906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552047
Minsik Hong, J. Rozenblit, A. Allen, U. Nair, S. Allen
Postpartum relapse to cigarette smoking (PRS) rate has not substantially improved for more than two decades. Over 55% of women successfully quit smoking during pregnancy; however, half (50%) return to smoking within three months of childbirth and 90% relapse within a year. The identification of effective PRS prevention interventions are needed, especially since factors related to PRS risk factors vary by person, time, and context. In this paper, a prototype risk estimation system using daily ecological momentary assessment data is proposed to develop an adaptive intervention system which will consider multiple risk factors. The risk estimator is designed using a hierarchical fuzzy inference system design scheme to capture human knowledge. A particle swarm optimization scheme is also applied. The simulation results show the feasibility of the proposed estimator for the PRS prevention intervention system.
{"title":"A Risk Estimation System to Predict Postpartum Cigarette Smoking Relapse","authors":"Minsik Hong, J. Rozenblit, A. Allen, U. Nair, S. Allen","doi":"10.23919/ANNSIM52504.2021.9552047","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552047","url":null,"abstract":"Postpartum relapse to cigarette smoking (PRS) rate has not substantially improved for more than two decades. Over 55% of women successfully quit smoking during pregnancy; however, half (50%) return to smoking within three months of childbirth and 90% relapse within a year. The identification of effective PRS prevention interventions are needed, especially since factors related to PRS risk factors vary by person, time, and context. In this paper, a prototype risk estimation system using daily ecological momentary assessment data is proposed to develop an adaptive intervention system which will consider multiple risk factors. The risk estimator is designed using a hierarchical fuzzy inference system design scheme to capture human knowledge. A particle swarm optimization scheme is also applied. The simulation results show the feasibility of the proposed estimator for the PRS prevention intervention system.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"1 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74445294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552097
Fabrizio Angaroni, C. Damiani, Giulia Ramunni, M. Antoniotti
Preparedness for any future epidemic has become an urgent need. Epidemic modeling and simulation are at the core of the healthcare efforts that are underway to assert some level of control over the spreading and the treatment of a pathogen. In this milieu, this paper describes a stochastic dynamic model to simulate the spreading of infectious diseases. We present the equations that describe the system dynamics, their adjoint systems, and their optimal control characterization by means of the discrete-system extension of Pontryagin's Maximum Principle. This derivation is presented in two different cases: a vaccination policy and a combined vaccination-treatment approach. We show the behavior of such models via numerical simulations using the forward-backward sweep procedure. While somewhat speculative, this paper provides insights into how to evaluate different theoretical optimal healthcare policies during an epidemic, either at the individual or metapopulation resolution level.
{"title":"Optimal Control of a Discrete Time Stochastic Model of an Epidemic Spreading in Arbitrary Networks","authors":"Fabrizio Angaroni, C. Damiani, Giulia Ramunni, M. Antoniotti","doi":"10.23919/ANNSIM52504.2021.9552097","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552097","url":null,"abstract":"Preparedness for any future epidemic has become an urgent need. Epidemic modeling and simulation are at the core of the healthcare efforts that are underway to assert some level of control over the spreading and the treatment of a pathogen. In this milieu, this paper describes a stochastic dynamic model to simulate the spreading of infectious diseases. We present the equations that describe the system dynamics, their adjoint systems, and their optimal control characterization by means of the discrete-system extension of Pontryagin's Maximum Principle. This derivation is presented in two different cases: a vaccination policy and a combined vaccination-treatment approach. We show the behavior of such models via numerical simulations using the forward-backward sweep procedure. While somewhat speculative, this paper provides insights into how to evaluate different theoretical optimal healthcare policies during an epidemic, either at the individual or metapopulation resolution level.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"26 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84692356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552138
Soroosh Gholami, H. Sarjoughian
Properties represent the state of a system at any instance of time or for a period of time. We consider properties as a common concept for Experimental Frame (EF) that can be used for simulation and modeling checking. This affords to define experimental frames that can evaluate the dynamics of models of systems purposed for both validation and verification. We show this approach through simulation of Parallel DEVS models as well as model checking of Constrained-DEVS models. We develop experiments for simulating and model checking a prototypical Network-on-Chip (NoC) system. The models and experiments are developed and executed using the DEVS-Suite tool. New capabilities of this tool include support for defining experimental frames that stimulate and monitor executions of models. The proposed approach with the developed execution engine affords both simulation validation and model checking verification.
{"title":"Unified Property Evaluations of Constrained-DEVS Models for Simulation and Model Checking","authors":"Soroosh Gholami, H. Sarjoughian","doi":"10.23919/ANNSIM52504.2021.9552138","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552138","url":null,"abstract":"Properties represent the state of a system at any instance of time or for a period of time. We consider properties as a common concept for Experimental Frame (EF) that can be used for simulation and modeling checking. This affords to define experimental frames that can evaluate the dynamics of models of systems purposed for both validation and verification. We show this approach through simulation of Parallel DEVS models as well as model checking of Constrained-DEVS models. We develop experiments for simulating and model checking a prototypical Network-on-Chip (NoC) system. The models and experiments are developed and executed using the DEVS-Suite tool. New capabilities of this tool include support for defining experimental frames that stimulate and monitor executions of models. The proposed approach with the developed execution engine affords both simulation validation and model checking verification.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"8 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79806189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552036
Christopher B. Lutz, P. Giabbanelli, Andrew Fisher, Vijay K. Mago
Computer simulations are used in precision medicine to assist in adapting treatment plans for varying patient characteristics, especially for diseases like HIV where these characteristics have a major impact on disease trajectory. However, simulations are computationally intensive, which can be prohibitive at scale. Meta-models for HIV progression have been developed previously to approximate these simulation results more efficiently, but we are interested in determining how much data is required to build an accurate meta-model. Using many different amounts of data from two HIV simulation models, we build machine learning classification meta-models to predict if an HIV patient is at risk for AIDS based on treatment parameters. Our findings indicate that the amount required to achieve high meta-model accuracy varies for different computer simulations. We are able to achieve near-perfect accuracy with one of our models using limited data, while the other model requires more extensive data to achieve stable accuracy.
{"title":"How Many Costly Simulations Do we Need to Create Accurate Metamodels? A Case Study on Predicting HIV Viral Load in Response to Clinically Relevant Intervention Scenarios","authors":"Christopher B. Lutz, P. Giabbanelli, Andrew Fisher, Vijay K. Mago","doi":"10.23919/ANNSIM52504.2021.9552036","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552036","url":null,"abstract":"Computer simulations are used in precision medicine to assist in adapting treatment plans for varying patient characteristics, especially for diseases like HIV where these characteristics have a major impact on disease trajectory. However, simulations are computationally intensive, which can be prohibitive at scale. Meta-models for HIV progression have been developed previously to approximate these simulation results more efficiently, but we are interested in determining how much data is required to build an accurate meta-model. Using many different amounts of data from two HIV simulation models, we build machine learning classification meta-models to predict if an HIV patient is at risk for AIDS based on treatment parameters. Our findings indicate that the amount required to achieve high meta-model accuracy varies for different computer simulations. We are able to achieve near-perfect accuracy with one of our models using limited data, while the other model requires more extensive data to achieve stable accuracy.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"2015 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86107218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552116
Maura Lapoff, H. Kavak
We present a model verification and validation (V&V) framework to evaluate COVID-19 forecasting models on their report of eight V&V-related components: (1) Conceptual Model, (2) Code and Calculation Verification, (3) Data Validation, (4) Parameter Estimation, (5) Initialization, (6) Uncertainty Estimation, (7) Output Validation, and (8) Model-to-Model Comparison. The framework provides a structured method to evaluate these models based on their reported V&V practices qualitatively. We applied this framework as a checklist for nine models included in the COVID-19 Forecast Hub. One model got the highest score by supporting seven components, while the lowest-ranked model got only two. This framework can serve as part of a larger framework to qualitatively and quantitatively examine COVID-19 models' V&V reported practices and provide credibility for those models that not only perform well but also robust in model construction.
{"title":"Towards a Verification and Validation Framework for COVID-19 Forecast Models","authors":"Maura Lapoff, H. Kavak","doi":"10.23919/ANNSIM52504.2021.9552116","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552116","url":null,"abstract":"We present a model verification and validation (V&V) framework to evaluate COVID-19 forecasting models on their report of eight V&V-related components: (1) Conceptual Model, (2) Code and Calculation Verification, (3) Data Validation, (4) Parameter Estimation, (5) Initialization, (6) Uncertainty Estimation, (7) Output Validation, and (8) Model-to-Model Comparison. The framework provides a structured method to evaluate these models based on their reported V&V practices qualitatively. We applied this framework as a checklist for nine models included in the COVID-19 Forecast Hub. One model got the highest score by supporting seven components, while the lowest-ranked model got only two. This framework can serve as part of a larger framework to qualitatively and quantitatively examine COVID-19 models' V&V reported practices and provide credibility for those models that not only perform well but also robust in model construction.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"2 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84215761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552046
Román Cárdenas, Alonso Inostrosa-Psijas, Gabriel A. Wainer
The COVID-19 outbreak has shown that Modeling and Simulation (M&S) methodologies are an important aspect to study the spread of the disease and assess the effect of different measures to diminish its negative effect. Although traditional models have been widely used, there is a need to build new, highly configurable disease models to explore multiple scenarios quickly. We present an M&S framework to perform rapid prototyping of pandemic spread using the Cell-DEVS space-based discrete-event modeling approach. This method supports age segmentation of the population, hospital-capacity-dependent deaths, and enforcing mobility restriction policies. This method is useful for studying the spread of the disease, as well as combining the simulation results with different visualization tools.
{"title":"A Modeling and Simulation Platform for Space-Based Compartmental Modeling of Pandemic Spread","authors":"Román Cárdenas, Alonso Inostrosa-Psijas, Gabriel A. Wainer","doi":"10.23919/ANNSIM52504.2021.9552046","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552046","url":null,"abstract":"The COVID-19 outbreak has shown that Modeling and Simulation (M&S) methodologies are an important aspect to study the spread of the disease and assess the effect of different measures to diminish its negative effect. Although traditional models have been widely used, there is a need to build new, highly configurable disease models to explore multiple scenarios quickly. We present an M&S framework to perform rapid prototyping of pandemic spread using the Cell-DEVS space-based discrete-event modeling approach. This method supports age segmentation of the population, hospital-capacity-dependent deaths, and enforcing mobility restriction policies. This method is useful for studying the spread of the disease, as well as combining the simulation results with different visualization tools.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"43 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89708486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552161
M. Oxley, R. Hartman
This paper introduces the Service member Veteran Risk Profile (SVRP), a mathematical process/solution to quantitatively represent transitioning Service member (TSM) and/or Veteran quality of life risks by integrating clinical and social determinant data into an individual risk profile. The SVRP creates, for the first time, a mechanism for the Department of Defense (DoD) and Department of Veterans Affairs (VA) to holistically represent the challenges of military members transitioning into civilian life that can lead to negative outcomes and proactively identify transitioning Service members and Veterans at risk. More importantly, the SVRP supports clinical and non-clinical modalities to reduce the negative impacts of transition and beyond for TSM and Veterans. Lastly, the SVRP can be displayed through user-friendly visualizations so DoD/VA policymakers and decision-makers can make more informed policy and resource decisions to improve TSM/Veteran overall quality of life.
{"title":"Combining Clinical and Social Determinants to Improve DoD/Veteran Well-Being: The Service Member Veteran Risk Profile","authors":"M. Oxley, R. Hartman","doi":"10.23919/ANNSIM52504.2021.9552161","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552161","url":null,"abstract":"This paper introduces the Service member Veteran Risk Profile (SVRP), a mathematical process/solution to quantitatively represent transitioning Service member (TSM) and/or Veteran quality of life risks by integrating clinical and social determinant data into an individual risk profile. The SVRP creates, for the first time, a mechanism for the Department of Defense (DoD) and Department of Veterans Affairs (VA) to holistically represent the challenges of military members transitioning into civilian life that can lead to negative outcomes and proactively identify transitioning Service members and Veterans at risk. More importantly, the SVRP supports clinical and non-clinical modalities to reduce the negative impacts of transition and beyond for TSM and Veterans. Lastly, the SVRP can be displayed through user-friendly visualizations so DoD/VA policymakers and decision-makers can make more informed policy and resource decisions to improve TSM/Veteran overall quality of life.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"74 1","pages":"1-11"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90978223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552154
Stefano Guarino, D. Torre, M. Bernaschi, Alessandro Celestini, Marco Cianfriglia, Enrico Mastrostefano, L. Zastrow
The COVID-19 pandemic triggered a global research effort to define and assess timely and effective containment policies. Understanding the role that specific venues play in the dynamics of epidemic spread is critical to guide the implementation of fine-grained non-pharmaceutical interventions (NPIs). In this paper, we present a new model of context-dependent interactions that integrates information about the surrounding territory and the social fabric. Building on this model, we developed an open-source data-driven simulator of the patterns of fruition of specific gathering places that can be easily configured to project and compare multiple scenarios. We focused on the greatest park of the City of Florence, Italy, to provide experimental evidence that our simulator produces contact graphs with unique, realistic features, and that gaining control of the mechanisms that govern interactions at the local scale allows to unveil and possibly control non-trivial aspects of the epidemic.
{"title":"Data-Driven Simulation of Contagions in Public Venues","authors":"Stefano Guarino, D. Torre, M. Bernaschi, Alessandro Celestini, Marco Cianfriglia, Enrico Mastrostefano, L. Zastrow","doi":"10.23919/ANNSIM52504.2021.9552154","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552154","url":null,"abstract":"The COVID-19 pandemic triggered a global research effort to define and assess timely and effective containment policies. Understanding the role that specific venues play in the dynamics of epidemic spread is critical to guide the implementation of fine-grained non-pharmaceutical interventions (NPIs). In this paper, we present a new model of context-dependent interactions that integrates information about the surrounding territory and the social fabric. Building on this model, we developed an open-source data-driven simulator of the patterns of fruition of specific gathering places that can be easily configured to project and compare multiple scenarios. We focused on the greatest park of the City of Florence, Italy, to provide experimental evidence that our simulator produces contact graphs with unique, realistic features, and that gaining control of the mechanisms that govern interactions at the local scale allows to unveil and possibly control non-trivial aspects of the epidemic.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"66 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75795686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-19DOI: 10.23919/ANNSIM52504.2021.9552088
L. Deng, D. Bowman, Ching-Chi Yang, Henry Horng-Shing Lu
We consider a general framework for constructing non-linear generators by adding a (32-bit or larger) pseudo-random number generator (PRNG) as a baseline generator to the basic RC4 design, in which an index-selection scheme similar to RC4 is used. We refer to the proposed design as the eRC (enhanced/extended RC4) design. We discuss several advantages of adding a good baseline generator to the RC4 design, including new updating schemes for the auxiliary table. We consider some popular PRNGs with the nice properties of high-dimensional equi-distribution, efficiency, long period, and portability as the baseline generator. We demonstrate that eRC generators are very efficient via extensive empirical testing on some eRC generators. We also show that eRC is flexible enough to choose minimal design parameters for eRC generators and yet the resulting eRC generators still pass stringent empirical tests, which makes them suitable for both software and hardware implementations.
{"title":"Extending RC4 to Construct Secure Random Number Generators","authors":"L. Deng, D. Bowman, Ching-Chi Yang, Henry Horng-Shing Lu","doi":"10.23919/ANNSIM52504.2021.9552088","DOIUrl":"https://doi.org/10.23919/ANNSIM52504.2021.9552088","url":null,"abstract":"We consider a general framework for constructing non-linear generators by adding a (32-bit or larger) pseudo-random number generator (PRNG) as a baseline generator to the basic RC4 design, in which an index-selection scheme similar to RC4 is used. We refer to the proposed design as the eRC (enhanced/extended RC4) design. We discuss several advantages of adding a good baseline generator to the RC4 design, including new updating schemes for the auxiliary table. We consider some popular PRNGs with the nice properties of high-dimensional equi-distribution, efficiency, long period, and portability as the baseline generator. We demonstrate that eRC generators are very efficient via extensive empirical testing on some eRC generators. We also show that eRC is flexible enough to choose minimal design parameters for eRC generators and yet the resulting eRC generators still pass stringent empirical tests, which makes them suitable for both software and hardware implementations.","PeriodicalId":6782,"journal":{"name":"2021 Annual Modeling and Simulation Conference (ANNSIM)","volume":"32 1","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2021-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80713690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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