Showing papers presented at "Spring Simulation Multiconference in 2020"
18 May 2020
TL;DR: A Blockchain simulator is presented that evaluates the consensus algorithms in a realistic and configurable network environment and provides the ability to evaluate the impact of the consensus and network layer that will inform practitioners on the appropriate choice of consensus algorithms and theimpact of network layer events in congested or contested scenarios in IoT.
Abstract: The massive scale, heterogeneity and distributed nature of Internet-of-Things (IoT) presents challenges in realizing a practical and effective security solution. Blockchain empowered platforms and technologies have been proposed to address aspects of this challenge. In order to realize a practical Blockchain deployment for IoT, there is a need for a testing and evaluation platform to evaluate performance and security of Blockchain applications and systems. In this paper, we present a Blockchain simulator that evaluates the consensus algorithms in a realistic and configurable network environment. Though, there are several Blockchain evaluation platforms, they are either wedded to a specific consensus protocol and do not allow evaluation in a configurable and realistic network environment. In our proposed simulator, we provide the ability to evaluate the impact of the consensus and network layer that will inform practitioners on the appropriate choice of consensus algorithms and the impact of network layer events in congested or contested scenarios in IoT. To accomplish this a generalized representation for consensus methods is proposed. The Blockchain simulator uses a discrete event simulation engine for fidelity and increased scalability. We evaluate the performance of the simulator by varying the number of peer nodes and number of messages required to find consensus.
26 citations
1 Jan 2020
TL;DR: The existing toolchain used to implement DEMES, ECD-Boost, has some shortcomings that are addressed and improved in Real-Time Cadmium (RT-Cadmium); which is a real-Time (RT) DEVS kernel developed on top of the cadmium DEVS Simulator.
Abstract: With the rise of Internet of Things devices, there is an increasing demand for embedded control systems. Discrete-Event Modeling of Embedded Systems (DEMES) is a Discrete Event System Specification (DEVS) based model driven development methodology that increases reliability and improves time to market by simplifying the development and testing of embedded systems. The existing toolchain used to implement DEMES, ECD-Boost, has some shortcomings that are addressed and improved in Real-Time Cadmium (RT-Cadmium); which is a Real-Time (RT) DEVS kernel developed on top of the Cadmium DEVS Simulator. RT-Cadmium allows users to switch between simulating and deploying their models with ease. RT-Cadmium is portable between target platforms and it already supports MBed Enabled ARM microprocessors and Linux based systems. RT-Cadmium can also handle asynchronous events, which are important for embedded system design and do not exist in standard DEVS simulators.
15 citations
18 May 2020
TL;DR: A new framework for scalable object detection, tracking and pattern recognition of moving objects that relies on dimensionality reduction with edge computing architecture is proposed and a scalable object Detection and tracking method based on You Only Look Once (YOLO) method is proposed.
Abstract: The Internet of Things (IoT) devices such as sensors and video cameras have small memories and less computational power. The video analytics of traditional approaches for detection, tracking and pattern recognition of moving objects used only in the cloud. This approach suffered from high latency and more network bandwidth to transfer data into the cloud. We address this problem by using edge computing devices between IoT devices and the cloud. We propose a new framework for scalable object detection, tracking and pattern recognition of moving objects that relies on dimensionality reduction with edge computing architecture. We also propose a scalable object detection and tracking method based on You Only Look Once (YOLO) method. The experiment demonstrates that our proposed method will save network bandwidth and processing time. The performance of object detection and tracking model is greater than 96%. This shows that our method has greater performance than existing models.
13 citations
1 May 2020
TL;DR: It is found that developers of artificial societies code the decision-making of their agents from scratch in every model, despite the existence of several libraries that could be used as building blocks.
Abstract: Many guidelines have been developed for simulations in general or for agent-based models which support artificial societies. When applying such guidelines to examine existing practices, assessment studies are limited by the artifacts released by modelers. Although code is the final product defining an artificial society, 90% of the code produced is not released hence previous assessments necessarily focused on higher-level items such as conceptual design or validation. We address this gap by collecting 338 artificial societies from two hosting platforms, CoMSES/OpenABM and GitHub. An innovation of our approach is the use of software engineering techniques to automatically examine the models with respect to items such as commenting the code, using libraries, or dividing code into functions. We found that developers of artificial societies code the decision-making of their agents from scratch in every model, despite the existence of several libraries that could be used as building blocks.
12 citations
1 May 2020
TL;DR: In this article, the authors introduce norm perception errors in simple agent-based models of social influence and examine whether an increase in perceptual uncertainty changed the outcome of the models reported by their authors.
Abstract: Simple Agent-Based Models (ABMs) of social influence assume that agents can perfectly discriminate between the actions of their peers or implicitly agree on a population-wide definition of states such as ‘healthy’ or ‘unhealthy’. In reality, mistakes are a part of being human and incorrectly estimating the state of a peer is no exception. For instance, experimental studies show that whether we find a food ‘healthy’ also depends on the body size of those who eat it. In this paper, we introduce norm perception errors in models from common application areas of ABMs, namely computational sociology and public health. Using simulations, we examined whether an increase in perceptual uncertainty changed the outcome of the models reported by their authors. We found that the effect depends on the model: even frequent incorrect judgments by the agents had no effect in an eating model whereas correlations were statistically significant in a segregation model.
11 citations
18 May 2020
TL;DR: By handling imbalanced classifications with imbalance datasets, one can minimize the number of false alarms, reduce damages to financial institutions and individuals, increase accurate for fraud detection, and decrease the occurrence of fraud cases using machine learning techniques.
Abstract: Credit card fraud is one of the critical issues due to its significant losses to both financial institutions and individuals in the society. The accurate detection and prevention of fraudulent activities are necessary to protect financial institutions and individuals. This paper performs a comparative experimental study to detect credit card frauds, as well as to tackle the imbalance classification problem by applying different machine learning algorithms for handling imbalanced datasets. Our study shows that there is no need to process imbalance dataset by applying resampling techniques to measure the performance of our classifiers and it is sufficient to measure the performance through the three-performance measurements (Accuracy, Sensitivity, and Area Under Precision/Recall Curve (PRC)) to prove the accuracy of the prediction of classification. Finally, by handling imbalanced classifications with imbalance datasets, one can minimize the number of false alarms, reduce damages to financial institutions and individuals, increase accurate for fraud detection, and decrease the occurrence of fraud cases using machine learning techniques.
11 citations
1 May 2020
TL;DR: An "artificial social ethics" approach to addressing ethical concerns is proposed and illustrated in relation to three agent-based models implemented within the Artificial Society Analytics Platform.
Abstract: In recent years advances in computational modeling and social simulation technologies have enabled scientists to identify some of the conditions under which – and the mechanisms by which – conflict and cooperation within and across human cultures are likely to emerge. There are significant ethical concerns surrounding the increased capacity and growing use of such computer tools to guide public policy discussions. The purpose of this paper is to propose and promote an "artificial social ethics" approach to addressing these concerns and illustrate its application in relation to three agent-based models implemented within the Artificial Society Analytics Platform. We conclude with a brief discussion of next steps in future work, and an invitation to join the ethical conversation around the development and deployment of artificial societies designed to address societal challenges resulting from climate change and other crises.
10 citations
1 May 2020
TL;DR: This paper proposes an approach that aids the digital twin developer by finding the optimal instrumentation rate for a set of parameters that are to be instrumented on a hard real-time controller, taking into account the developer’s preferences with regards to the instrumented parameters.
Abstract: One of the challenges with digital twins is data collection from the physical systems. Data collection for digital twins in Cyber-Physical Systems, where the controllers are often hard real-time, is a challenging task, as the functional system behavior may be impacted by the instrumentation. An often applied solution is the use of additional monitoring controllers, which passively monitor the system. However, this solution introduces additional points of failure, and is costly as more hardware is required. Therefore, in this paper, we propose an approach that aids the digital twin developer by finding the optimal instrumentation rate for a set of parameters that are to be instrumented on a hard real-time controller. The optimization ensures that the hard real-time constraints are guaranteed, while also taking into account the developer’s preferences with regards to the instrumented parameters. We apply this process on two cases: an exploratory example and an adaptive cruise controller.
9 citations
1 Jan 2020
TL;DR: This paper proposes a generic sensor fusion framework to facilitate the implementation of sensor fusion algorithms using the Discrete-Event Modeling of Embedded Systems (DEMES) which is a model-based development methodology for cyber-physical systems based on discrete-event systems specifications (DEVS).
Abstract: Sensor fusion is a technique used to combine sensor data and improve the description of the physical property measured by these sensors. Sensors working alone could provide data that is erroneous, incomplete and uncertain. Several algorithms have been developed for sensor fusion, some of which are complex to understand and difficult to implement. The extensive list of algorithms often leaves designers confused about what choices to make as regards which algorithm is best and this discourages them from implementing sensor fusion. In this paper, we propose a generic sensor fusion framework to facilitate the implementation of sensor fusion algorithms using the Discrete-Event Modeling of Embedded Systems (DEMES) which is a model-based development methodology for cyber-physical systems based on discrete-event systems specifications (DEVS).
9 citations
1 May 2020
TL;DR: Analysis of social interactions in Twitter to studying the influence blocking maximization to minimize the propagation of fake information in social media by discovering influential users and their impact to spread fake/false information among their users/followers shows that the influence of spreading fake or false information increases with the popularity of user through his/her followers and retweets.
Abstract: Influence can be used to propagate the (fake or true) information in social media where a set of influential nodes (individuals) in social media can leverage their connections (e.g., followers in Tweeter) to impact others. Lately, most of the social interactions take place on-line where followers/members can get the information directly from their following accounts. Influential users can be used to propagate fake or false information to their followers. This paper analyzes social interactions in Twitter to studying the influence blocking maximization to minimize the propagation of fake information in social media by discovering influential users and their impact to spread fake/false information among their users/followers. Specifically, Greedy algorithm is studied to discover influence of spreading false/fake information among users in Twitter where malicious users could exploit influential users to gain more exposure to their malicious data. Results show that the influence of spreading fake or false information increases with the popularity of user through his/her followers and retweets. Furthermore, attributes such as number of followers, number of likes, retweets, etc. have huge impact on the influence for propagating the information in social media.
6 citations
1 May 2020
TL;DR: This work uses machine learning to predict the results of a simulation and its computational resources and demonstrates that machine learning can build such surrogate and performance models with low errors for five previously developed models of HIV, while outperforming interpolation methods commonly used for surrogate modeling.
Abstract: Accurately simulating viral dynamics within a human body is often computationally intensive, thus requiring dedicated computing infrastructures. This limits the use of simulations in personalized medicine since patients may not all have access to such infrastructures or the possibility of waiting weeks for predictions. Lowering the computational burden by simplifying the models is a challenge since simulation results must remain accurate in order to safely support the decision-making activities of patients. In this work, we use machine learning to predict the results of a simulation (i.e. surrogate model) and its computational resources (i.e. performance model). This allows patients to use machine learning as a computationally light proxy to simulations and to know how long an accurate simulation would take. We demonstrate that machine learning can build such surrogate and performance models with low errors for five previously developed models of HIV, while outperforming interpolation methods commonly used for surrogate modeling.
18 May 2020
TL;DR: This proposed framework shows understanding the water-energy system nexus is advantageous using interaction modeling, and is acceptable compared with the tightly WEAP-LEAP system.
Abstract: It is useful to model the interactions between the water and energy models separately. A coupled water and energy model using an interaction model lends itself to developing flexible, yet rigorous predictive simulation studies. A WEAP-LEAP interaction model is developed based on the Knowledge Interchange Broker modeling approach. A RESTful simulation framework is developed for coupling the Componentized-WEAP and Componentized-LEAP systems. Every interaction model is defined to have a set of modules where each module has its input and output ports with data transformation schemes. The input and output ports of the Componentized-WEAP and Componentized-LEAP models are coupled with those of the interaction model. A cyclic, synchronous execution protocol is defined for concurrent, bidirectional data transformations. The computational efficiency of this loosely coupled water-energy framework is acceptable compared with the tightly WEAP-LEAP system. This proposed framework shows understanding the water-energy system nexus is advantageous using interaction modeling.
1 Jan 2020
TL;DR: The design and implementation of a parallel version of the Cadmium DEVS simulator is presented and empirical evaluation for executing the protocol in multithreading architectures is conducted.
Abstract: Complex models in science and engineering need better techniques to execute simulations efficiently. The Discrete-Event System Specification (DEVS) formalism, a well-known technique for modeling and simulation, has been enhanced by executing in parallel computers. Here, we present the design and implementation of a parallel version of the Cadmium DEVS simulator. We conducted empirical evaluation for executing the protocol in multithreading architectures and discuss performance by applying these techniques in real problems.
1 May 2020
TL;DR: This paper illustrates how model-driven approaches and technologies have been investigated and applied to achieve the HRAF Pilot 3 goals, along with the related implementation and validation issues.
Abstract: The development of complex autonomous robotic systems is critical for building future planetary exploration missions at the European Space Agency (ESA). In this context, ESA started the Harwell Robotics and Autonomy Facility (HRAF) project, whose main objective is to provide an effective support for the integration, validation and verification of autonomy components. The HRAF architecture is founded on both HLA (High Level Architecture) as a distributed simulation middleware, and the Distributed Simulation Engineering and Execution Process (DSEEP) as the adopted reference process for building HLA-based simulations. The current Pilot 3 phase of the HRAF development plan deals with the development and validation of a model-driven framework for supporting the semi-automated generation of HLA-based simulations. In this respect, this paper illustrates how model-driven approaches and technologies have been investigated and applied to achieve the HRAF Pilot 3 goals, along with the related implementation and validation issues.
18 May 2020
TL;DR: An agent-based model (ABM) is built that simulates migrant decision-making of which border to choose, to test the effects of social influence on an agent’s migration strategy, as well as the effect of policy changes on migrant behavior.
Abstract: Migration to the United States (U.S.) has been an ongoing topic in the political discourse. In the specific case of migration using the United States-Mexico border, it is difficult to understand the patterns and uncertainty that comes from trying to capture the behavior of individuals and groups. Migrants using the U.S.-Mexico border present features and migration patterns that are unique, particularly in the case of group migrations, structured social networks, and caravans that travel towards the border together. We build an agent-based model (ABM) that simulates migrant decision-making of which border to choose, to test the effects of social influence on an agent’s migration strategy, as well as the effects of policy changes on migrant behavior. Deciphering these emerging patterns of migration and social phenomena can help provide researchers and policymakers with an added dimension of knowledge on how to better utilize resources at the U.S. ports of entry.
1 May 2020
TL;DR: The use of FCM for agent’s design in social simulation as compared to previous research using different cognitive models, and a system architecture consisting of 8 components that combine FCM and ABM for simulation are provided.
Abstract: Agent Based Social Simulation (ABSS) has been used to create simulations with agents that contain cognitive models to achieve more realistic and explainable results by mimicking human cognition. However when using this technique the decision model used by agents is often a black box. Fuzzy Cognitive Maps (FCMs) have been used to provide clear and transparent representations of cognition and could be used as models for agents in simulations to achieve results with better transparency. Utilizing FCMs to represent agents’ cognitions in social and organizational modeling is still an emerging field with limited work done to show how FCM and ABM can be combined for greater model interpretability. This paper provides 2 major contributions: (1) the use of FCM for agent’s design in social simulation as compared to previous research using different cognitive models, and (2) a system architecture consisting of 8 components that combine FCM and ABM for simulation.
18 May 2020
TL;DR: This paper investigates the scalability of LIDAR-type sensor simulation in ROS-supported Gazebo, an open-source multi-robot simulator and shows that the use of GPU helps improve performance, providing better speedups in the complex scene.
Abstract: Autonomous mobile robots depend heavily on sensors for interpreting the external environment and planning their movement. Given the complexity of even simple environments, the use of simulation is necessary to test sensory perception and movement strategies efficiently. Accurate and efficient sensor simulation is key to the successful development and fielding of autonomous mobile robots. In this paper, we investigate the scalability of LIDAR-type sensor simulation in ROS-supported Gazebo, an open-source multi-robot simulator. Specifically, the performance and scalability of the simulator are assessed with and without support from GPU in two virtual worlds, one highly-complex in terms of the number of collision targets and one not-complex. In both cases, we increase the number of sensors and evaluate system performance. The results show that the use of GPU helps improve performance, providing better speedups in the complex scene. However, the number of sensors that can be used is limited when using GPU.
1 May 2020
TL;DR: An input/output mod-ular discrete-event cellular automata framework based on the Discrete Event System Specification (DEVS) is proposed, adapted from a Composable Cellular Automata (CCA) formalism that is defined in terms of a multicomponent discrete time system specification.
Abstract: The concept of cellular automata is one of the cornerstones of scientific and engineering pursuit with wide applicability in various domains. The rise of system complexity points to developing frameworks that can aid in developing composable models at multiple spatiotemporal levels. Toward this goal, an input/output mod-ular discrete-event cellular automata framework based on the Discrete Event System Specification (DEVS) is proposed. It is adapted from a Composable Cellular Automata (CCA) formalism that is defined in terms of a multicomponent discrete time system specification. The resulting Composable Cellular Automata DEVS (CCA-DEVS) framework is built using the Cellular Automata DEVS that is a part of the DEVS-Suite simulator. The approach and realization of the proposed CCA-DEVS framework are detailed. The framework is used to develop an example model composed of CCA-DEVS agent and partial differential equation models.
18 May 2020
TL;DR: A general framework for bridging the worlds of simulation and RL is proposed by placing "interpreters" between the simulation and the RL agent that translate information into a form that is coherent to each entity.
Abstract: Reinforcement learning (RL) is used for sequential decision making such as operating and maintaining manufacturing systems. In RL, the system is modeled as a Markov decision process with states, actions, rewards, and policies. A policy is learned through repeated interaction with the environment. When a RL agent cannot interact with the real system due to time and cost constraints, a simulation of the system may be used in its place. Unfortunately, most simulations are not built for the purpose of interacting with a RL agent. Simulations built to function as the environment are often structured only according to the defined state, action, and reward and can lack fidelity, detail, and accuracy. We propose a general framework for bridging the worlds of simulation and RL. This is accomplished by placing "interpreters" between the simulation and the RL agent that translate information into a form that is coherent to each entity.
1 May 2020
TL;DR: Simulation is demonstrated how simulation can be used to investigate macro level effects of online social network link recommendations, such as whether these technologies may be fragmenting or bridging communities of individuals.
Abstract: The vast majority of recommender system research has focused on improving performance accuracy, while limited work has explored their societal, network level effects. This paper demonstrates how simulation can be used to investigate macro level effects of online social network link recommendations, such as whether these technologies may be fragmenting or bridging communities of individuals. An agent-based model is presented that generates stylized online social networks with different percentages of real world contacts and link recommendations. Results show that networks with higher percentages of recommendation-based links produce more clustered, distinct, and dispersed communities, suggesting that these technologies could fragment society. Furthermore, scale-free network properties diminished with higher percentages of recommendations, suggesting that these technologies could be contributing to recent findings that social networks are at most ‘weakly’ scale-free. Building upon this research, further simulation work could inform the design of link recommendation algorithms that help connect both individuals and communities.
1 May 2020
TL;DR: This paper presents and programs two very important categories of synthetic networks with known community structures and challenges several well-known community detection algorithms to discover communities in these networks.
Abstract: One of the most significant attributes of networks is their community structure. One can define a quality criterion for a community of a network in such a way that a high-quality community refers to a group of vertices that contains more highly-connected edges between its members than between its members and the rest of the network. Various algorithms have been developed to discover communities of networks, although there is not a straightforward solution to see which algorithms are good and how they are good. However, generating synthetic graphs, which are similar to real-world networks and also have a built-in community structure (known community structure), can be used in evaluating accurately different community detection algorithms. This paper presents and programs two very important categories of synthetic networks with known community structures. Afterward, it challenges several well-known community detection algorithms to discover communities in these networks.
1 May 2020
TL;DR: A use case in which a regional Department of Veterans Affairs healthcare organization is expanding its telehealth capabilities to meet growing demand for Post-Traumatic Stress Disorder treatment and is using telehealth to deliver services to Veterans, which saves them from traveling long distances is model.
Abstract: We propose a framework, Modeling HealthCARE: Capacity, Access, and Resource Evaluation, that uses hybrid simulation to help healthcare organizations evaluate supply and demand as they implement alternative healthcare services. The focus of this tool is on patients and their access to care. We integrate agent-based and discrete event simulation to model the patient as an agent adopting and requesting new services, following a discrete-event-driven process based on the existing and new healthcare journey. We model a use case in which a regional Department of Veterans Affairs healthcare organization is expanding its telehealth capabilities to meet growing demand for Post-Traumatic Stress Disorder treatment and is using telehealth to deliver services to Veterans, which saves them from traveling long distances. Results of our proof of concept model show that as adoption of telehealth increases, wait times and travel distances decrease, as service capacity and resources are configured to meet demand.
1 May 2020
TL;DR: This research aims to investigate if different patterns of cyberattacks could be identified with speed using simulation and machine learning algorithms and design a simulation model that can help better defend against cyber threats.
Abstract: The North American bulk power system is one of the most vital infrastructures in modern society as it accounts for virtually all the electricity supplied to the United States, Canada, and a portion of Baja Notre California, Mexico. Cyberattacks, of all forms, are becoming increasingly prominent within power networks and other infrastructures whereas their resolution can consume a significant deal of time and monetary resources. This can be further worsened if there are subsequent physical attacks in the wake of a cyberattack, as the system downtime leaves the government, military, and other critical infrastructures incredibly vulnerable This research aims to investigate if different patterns of cyberattacks could be identified with speed using simulation and machine learning algorithms. More specifically, we design a simulation model that can help better defend against cyber threats.
18 May 2020
TL;DR: A new method of quantitative inquiry is outlined—experimental wargaming—as a means to bridge the complexity-scarcity gap that offers human-generated, empirical data to inform a variety of model and simulation tasks (model building, calibration, testing, and validation).
Abstract: National security decisions are driven by complex, interconnected contextual, individual, and strategic variables. Modeling and simulation tools are often used to identify relevant patterns, which can then be shaped through policy remedies. In the paper to follow, however, we argue that models of these scenarios may be prone to the complexity-scarcity gap, in which relevant scenarios are too complex to model from first principles and data from historical scenarios are too sparse—making it difficult to draw representative conclusions. The result are models that are either too simple or are unduly biased by the assumptions of the analyst. We outline a new method of quantitative inquiry—experimental wargaming—as a means to bridge the complexity-scarcity gap that offers human-generated, empirical data to inform a variety of model and simulation tasks (model building, calibration, testing, and validation). Below, we briefly describe SIGNAL—our first-of-a-kind experimental wargame designed to study strategic stability in conflict settings with nuclear weapons. We then highlight the potential utility of this data for modeling and simulation efforts in the future using this data.
18 May 2020
TL;DR: An agent-based model stylized on spatially explicit data of Detroit Tri-county area examines how micro-level housing trades impact urban shrinkage by capturing interactions between sellers and buyers within different sub-housing markets.
Abstract: While the world’s total urban population continues to grow, this growth is not equal. Some cities are declining, resulting in urban shrinkage which is now a global phenomenon. Many problems emerge due to urban shrinkage including population loss, economic depression, vacant properties and the contraction of housing markets. To explore this issue, this paper presents an agent-based model stylized on spatially explicit data of Detroit Tri-county area, an area witnessing urban shrinkage. Specifically, the model examines how micro-level housing trades impact urban shrinkage by capturing interactions between sellers and buyers within different sub-housing markets. The stylized model results highlight not only how we can simulate housing transactions but the aggregate market conditions relating to urban shrinkage (i.e., the contraction of housing markets). To this end, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue.
18 May 2020
TL;DR: The model running failure of beginner-level users may suggest a trial and error approach to building a model rather than an established process, and a critical line of inquiry is opened on user engagement instead of process establishment.
Abstract: This paper presents our novel efforts on automatically capturing and analyzing user data from a discrete-event simulation environment. We collected action data such as adding/removing blocks and running a model that enable creating calculated data fields and examining their relations across expertise groups. We found that beginner-level users use more blocks/edges and make more build errors compared to intermediate-level users. When examining the users with higher expertise, we note differences related to time spent in the tool, which could be linked to user engagement. The model running failure of beginner-level users may suggest a trial and error approach to building a model rather than an established process. Our study opens a critical line of inquiry focused on user engagement instead of process establishment, which is the current focus in the community. In addition to these findings, we report other potential uses of such user action data and lessons learned.
1 May 2020
TL;DR: This paper sets up a theoretical framework for future research and development of brain and other cyber-physical models by employing the finite state regular language framework as a basis for models of neuronal structure and behavior aimed at closing the gap between circuits and cognitive behavior.
Abstract: This paper extends the theory of iterative systems specification for application to a class of cyber-physical systems. We employ the finite state regular language framework as a basis for models of neuronal structure and behavior aimed at closing the gap between circuits and cognitive behavior. Although the biological brain is rarely considered in the context of cyber-physical systems, we draw insight from parallels between the physical and cognitive faculties of humans with those of such engineered systems. We show how hybrid iterative systems specification offers an abstraction that is more fundamental than DEVS that supports development of simulation models of cyber-physical systems. Computational models of dynamic brain activity are needed to understand the complex patterns of signaling and communication within and between brain networks. This paper sets up a theoretical framework for future research and development of brain and other cyber-physical models.
1 May 2020
TL;DR: The preliminary results show that the Alert and Recommender system could have a positive impact on graduation rates, which helps to save time and cost.
Abstract: This paper presents the simulated results of four different scenarios from an ABM to understand the effect of utilization of Alert and Recommender systems on graduation rate. Academic advising help students to enhance their academic performance, and it is also one of the critical aspects of judging institutional effectiveness. For evaluating the effectiveness, college graduation rates are calculated for every scenario, and then they are compared with the baseline scenario. This objective is achieved by using details of SAT scores and core courses of the Department of Physics and Astronomy at a large public university. The prime focus of the paper is to model the impact of the system, which, in reality, helps to save time and cost. The preliminary results show that the Alert and Recommender system could have a positive impact on graduation rates.
1 May 2020
TL;DR: It is found that running simulations with auto_diff takes less than 4 times as long as simulations with hand-written differentiation code, and caters to an important need within the numerical Python community.
Abstract: We present auto_diff, a package that performs automatic differentiation of numerical Python code. auto_diff overrides Python’s NumPy package’s functions, augmenting them with seamless automatic differentiation capabilities. Notably, auto_diff is non-intrusive, i.e., the code to be differentiated does not require auto_diff-specific alterations. We illustrate auto_diff on electronic devices, a circuit simulation, and a mechanical system simulation. In our evaluations so far, we found that running simulations with auto_diff takes less than 4 times as long as simulations with hand-written differentiation code. We believe that auto_diff, which was written after attempts to use existing automatic differentiation packages on our applications ran into difficulties, caters to an important need within the numerical Python community. We have attempted to write this paper in a tutorial style to make it accessible to those without prior background in automatic differentiation techniques and packages. We have released auto_diff as open source on GitHub.
1 May 2020
TL;DR: A methodology to ensure data security throughout the medical device life cycle, ranging from data initiation to data processing and data transmission, is proposed and an Adaptive Mode Selection (AMS) scheme to investigate security threats amongst system functions is proposed.
Abstract: Data is a valuable currency that our modern world thrives upon. Security and privacy for data are becoming critical concerns, especially in healthcare where sensitive information is exchanged amongst the healthcare stakeholders. The protection of medical data is considerably more apparent and significant with guidelines such as HIPAA and FDA regulations in place. In this paper, we propose a methodology to ensure data security throughout the medical device life cycle, ranging from data initiation to data processing and data transmission. The goal is to facilitate communication between patients and doctors in a fast and secure manner. We propose an Adaptive Mode Selection (AMS) scheme to investigate security threats amongst system functions. A Priority-Queue Based (PQB) process is established to improve data management and data isolation within medical devices. Further, we propose an Adaptive Protocol Selection (APS) to enhance data transmission over the most appropriate communication protocol based on risk values identified by AMS. The combination of AMS, PQB and APS contributes towards delivering health services with continual secured data feeds and reduction in time of medical intervention.