Journal Articles
Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915
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Item Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach(Elsevier Inc, 2018-03-29) Perez-Acle T; Fuenzalida I; Martin AJM; Santibañez R; Avaria R; Bernardin A; Bustos AM; Garrido D; Dushoff J; Liu JHComputational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems.Item A label noise filtering and label missing supplement framework based on game theory(Elsevier B.V. on behalf of KeAi Communications Co Ltd for the Chongqing University of Posts and Telecommunications, 2023-08-31) Liu Y; Yao R; Jia S; Wang F; Wang R; Ma R; Qi LLabeled data is widely used in various classification tasks. However, there is a huge challenge that labels are often added artificially. Wrong labels added by malicious users will affect the training effect of the model. The unreliability of labeled data has hindered the research. In order to solve the above problems, we propose a framework of Label Noise Filtering and Missing Label Supplement (LNFS). And we take location labels in Location-Based Social Networks (LBSN) as an example to implement our framework. For the problem of label noise filtering, we first use FastText to transform the restaurant's labels into vectors, and then based on the assumption that the label most similar to all other labels in the location is most representative. We use cosine similarity to judge and select the most representative label. For the problem of label missing, we use simple common word similarity to judge the similarity of users' comments, and then use the label of the similar restaurant to supplement the missing labels. To optimize the performance of the model, we introduce game theory into our model to simulate the game between the malicious users and the model to improve the reliability of the model. Finally, a case study is given to illustrate the effectiveness and reliability of LNFS.