PeerJ Computer Science Preprints: Adaptive and Self-Organizing Systemshttps://peerj.com/preprints/index.atom?journal=cs&subject=8000Adaptive and Self-Organizing Systems articles published in PeerJ Computer Science PreprintsTowards a quantitative model of epidemics during conflictshttps://peerj.com/preprints/276512019-08-042019-08-04Soumya Banerjee
Epidemics may both contribute to and arise as a result of conflict. The effects of conflict on infectious diseases are complex and there have been confounding observations of both increase and decrease in disease outbreaks during and after conflicts. However there is no unified mathematical model that explains all these counter-intuitive observations. There is an urgent need for a quantitative framework for modelling conflicts and epidemics. We introduce a set of mathematical models to understand the role of conflicts in epidemics. Our mathematical framework has the potential to explain the counterintuitive observations and the complex role of human conflicts in epidemics. Our work suggests that aid and peacekeeping organizations should take an integrated approach that combines public health measures, socio-economic development, and peacekeeping in the conflict zone. Our approach exemplifies the role of non-linear thinking in complex systems like human societies. We view our work as a step towards a quantitative model of disease spread in conflicts.
Epidemics may both contribute to and arise as a result of conflict. The effects of conflict on infectious diseases are complex and there have been confounding observations of both increase and decrease in disease outbreaks during and after conflicts. However there is no unified mathematical model that explains all these counter-intuitive observations. There is an urgent need for a quantitative framework for modelling conflicts and epidemics. We introduce a set of mathematical models to understand the role of conflicts in epidemics. Our mathematical framework has the potential to explain the counterintuitive observations and the complex role of human conflicts in epidemics. Our work suggests that aid and peacekeeping organizations should take an integrated approach that combines public health measures, socio-economic development, and peacekeeping in the conflict zone. Our approach exemplifies the role of non-linear thinking in complex systems like human societies. We view our work as a step towards a quantitative model of disease spread in conflicts.Machine learning approach for automated defense against network intrusionshttps://peerj.com/preprints/277772019-06-032019-06-03Farhaan Noor HamdaniFarheen Siddiqui
With the advent of the internet, there is a major concern regarding the growing number of attacks, where the attacker can target any computing or network resource remotely Also, the exponential shift towards the use of smart-end technology devices, results in various security related concerns, which include detection of anomalous data traffic on the internet. Unravelling legitimate traffic from malignant traffic is a complex task itself. Many attacks affect system resources thereby degenerating their computing performance. In this paper we propose a framework of supervised model implemented using machine learning algorithms which can enhance or aid the existing intrusion detection systems, for detection of variety of attacks. Here KDD (knowledge data and discovery) dataset is used as a benchmark. In accordance with detective abilities, we also analyze their performance, accuracy, alerts-logs and compute their overall detection rate.
These machine learning algorithms are validated and tested in terms of accuracy, precision, true-false positives and negatives. Experimental results show that these methods are effective, generating low false positives and can be operative in building a defense line against network intrusions. Further, we compare these algorithms in terms of various functional parameters
With the advent of the internet, there is a major concern regarding the growing number of attacks, where the attacker can target any computing or network resource remotely Also, the exponential shift towards the use of smart-end technology devices, results in various security related concerns, which include detection of anomalous data traffic on the internet. Unravelling legitimate traffic from malignant traffic is a complex task itself. Many attacks affect system resources thereby degenerating their computing performance. In this paper we propose a framework of supervised model implemented using machine learning algorithms which can enhance or aid the existing intrusion detection systems, for detection of variety of attacks. Here KDD (knowledge data and discovery) dataset is used as a benchmark. In accordance with detective abilities, we also analyze their performance, accuracy, alerts-logs and compute their overall detection rate.These machine learning algorithms are validated and tested in terms of accuracy, precision, true-false positives and negatives. Experimental results show that these methods are effective, generating low false positives and can be operative in building a defense line against network intrusions. Further, we compare these algorithms in terms of various functional parametersAn architecture for context-aware reactive systems based on run-time semantic modelshttps://peerj.com/preprints/277022019-05-042019-05-04Ester GiallonardoFrancesco PoggiDavide RossiEugenio Zimeo
In recent years, new classes of highly dynamic, complex systems are gaining momentum. These systems are characterized by the need to express behaviors driven by external and/or internal changes, i.e. they are reactive and context-aware. These classes include, but are not limited to IoT, smart cities, cyber-physical systems and sensor networks.
An important design feature of these systems should be the ability of adapting their behavior to environment changes. This requires handling a runtime representation of the context enriched with variation points that relate different behaviors to possible changes of the representation.
In this paper, we present a reference architecture for reactive, context-aware systems able to handle contextual knowledge (that defines what the system perceives) by means of virtual sensors and able to react to environment changes by means of virtual actuators, both represented in a declarative manner through semantic web technologies. To improve the ability to react with a proper behavior to context changes (e.g. faults) that may influence the ability of the system to observe the environment, we allow the definition of logical sensors and actuators through an extension of the SSN ontology (a W3C standard). In our reference architecture a knowledge base of sensors and actuators (hosted by an RDF triple store) is bound to real world by grounding semantic elements to physical devices via REST APIs.
The proposed architecture along with the defined ontology try to address the main problems of dynamically reconfigurable systems by exploiting a declarative, queryable approach to enable runtime reconfiguration with the help of (a) semantics to support discovery in heterogeneous environment, (b) composition logic to define alternative behaviors for variation points, (c) bi-causal connection life-cycle to avoid dangling links with the external environment. The proposal is validated in a case study aimed at designing an edge node for smart buildings dedicated to cultural heritage preservation.
In recent years, new classes of highly dynamic, complex systems are gaining momentum. These systems are characterized by the need to express behaviors driven by external and/or internal changes, i.e. they are reactive and context-aware. These classes include, but are not limited to IoT, smart cities, cyber-physical systems and sensor networks.An important design feature of these systems should be the ability of adapting their behavior to environment changes. This requires handling a runtime representation of the context enriched with variation points that relate different behaviors to possible changes of the representation.In this paper, we present a reference architecture for reactive, context-aware systems able to handle contextual knowledge (that defines what the system perceives) by means of virtual sensors and able to react to environment changes by means of virtual actuators, both represented in a declarative manner through semantic web technologies. To improve the ability to react with a proper behavior to context changes (e.g. faults) that may influence the ability of the system to observe the environment, we allow the definition of logical sensors and actuators through an extension of the SSN ontology (a W3C standard). In our reference architecture a knowledge base of sensors and actuators (hosted by an RDF triple store) is bound to real world by grounding semantic elements to physical devices via REST APIs.The proposed architecture along with the defined ontology try to address the main problems of dynamically reconfigurable systems by exploiting a declarative, queryable approach to enable runtime reconfiguration with the help of (a) semantics to support discovery in heterogeneous environment, (b) composition logic to define alternative behaviors for variation points, (c) bi-causal connection life-cycle to avoid dangling links with the external environment. The proposal is validated in a case study aimed at designing an edge node for smart buildings dedicated to cultural heritage preservation.Resolve the cell formation problem in a set of three manufacturing cellshttps://peerj.com/preprints/276922019-04-292019-04-29Boris Almonacid
The problem of cell formation is an NP-Hard problem, which consists of organising a group of machines and pieces in several cells. The machines are arranged in a fixed way inside the cells, and each machine has some manufacturing operation that applies in different pieces or parts. The idea of the problem is to be able to minimise the movements made by the pieces to reach the machines in the cells. For this problem, a data set has been organised using three manufacturing cells. Through the data set an experiment has been carried out that focuses on obtaining the best solution using a global search solution within 6 days for each instance. The experimental results have been able to obtain the general optimum value for a set of test instances.
The problem of cell formation is an NP-Hard problem, which consists of organising a group of machines and pieces in several cells. The machines are arranged in a fixed way inside the cells, and each machine has some manufacturing operation that applies in different pieces or parts. The idea of the problem is to be able to minimise the movements made by the pieces to reach the machines in the cells. For this problem, a data set has been organised using three manufacturing cells. Through the data set an experiment has been carried out that focuses on obtaining the best solution using a global search solution within 6 days for each instance. The experimental results have been able to obtain the general optimum value for a set of test instances.Preliminary experiments with the Andean Condor Algorithm to solve problems of Continuous Domainshttps://peerj.com/preprints/276782019-04-242019-04-24Boris L Almonacid
In this article a preliminary experiment is carried out in which a set of elements and procedures are described to be able to solve problems of continuous domains integrated in the Andean Condor Algorithm. The Andean Condor Algorithm is a metaheuristic algorithm of swarm intelligence inspired by the movement pattern of the Andean condor when searching for its food. An experiment focused on solving the problem of the function 1st De Jong's \(f(x_1 \cdots x_n) = \sum_{i=1}^n x_i^2,~ -100 \leq x_i \leq 100\). According to the results obtained, solutions have been obtained close to the overall optimum value of the problem.
In this article a preliminary experiment is carried out in which a set of elements and procedures are described to be able to solve problems of continuous domains integrated in the Andean Condor Algorithm. The Andean Condor Algorithm is a metaheuristic algorithm of swarm intelligence inspired by the movement pattern of the Andean condor when searching for its food. An experiment focused on solving the problem of the function 1st De Jong's \(f(x_1 \cdots x_n) = \sum_{i=1}^n x_i^2,~ -100 \leq x_i \leq 100\). According to the results obtained, solutions have been obtained close to the overall optimum value of the problem.Lymph node inspired computing: immune system inspired architectures for human-engineered complex systemshttps://peerj.com/preprints/31502019-04-122019-04-12Soumya Banerjee
The immune system is a distributed decentralized system that functions without any centralized control. The immune system has millions of cells that function somewhat independently and can detect and respond to pathogens with considerable speed and efficiency. Lymph nodes are physical anatomical structures that allow the immune system to rapidly detect pathogens and mobilize cells to respond to it. Lymph nodes function as: 1) information processing centers, and 2) a distributed detection and response network. We introduce biologically inspired computing that uses lymph nodes as inspiration. We outline applications to diverse domains like mobile robots, distributed computing clusters, peer-to-peer networks and online social networks. We argue that lymph node inspired computing systems provide powerful metaphors for distributed computing and complement existing artificial immune systems. We view our work as a first step towards holistic simulations of the immune system that would capture all the complexities and the power of a complex adaptive system like the immune system. Ultimately this would lead to holistic immune system inspired computing that captures all the complexities and power of the immune system in human-engineered complex systems.
The immune system is a distributed decentralized system that functions without any centralized control. The immune system has millions of cells that function somewhat independently and can detect and respond to pathogens with considerable speed and efficiency. Lymph nodes are physical anatomical structures that allow the immune system to rapidly detect pathogens and mobilize cells to respond to it. Lymph nodes function as: 1) information processing centers, and 2) a distributed detection and response network. We introduce biologically inspired computing that uses lymph nodes as inspiration. We outline applications to diverse domains like mobile robots, distributed computing clusters, peer-to-peer networks and online social networks. We argue that lymph node inspired computing systems provide powerful metaphors for distributed computing and complement existing artificial immune systems. We view our work as a first step towards holistic simulations of the immune system that would capture all the complexities and the power of a complex adaptive system like the immune system. Ultimately this would lead to holistic immune system inspired computing that captures all the complexities and power of the immune system in human-engineered complex systems.Software-defined networks: A walkthrough guide from occurrence To data plane fault tolerancehttps://peerj.com/preprints/276242019-04-012019-04-01Ali MalikBenjamin AzizAli Al-HajMo Adda
In recent years, the emerging paradigm of software-defined networking has become a hot and thriving topic that grabbed the attention of industry sector as well as the academic research community. The decoupling between the network control and data planes means that software-defined networking architecture is programmable, adjustable and dynamically re-configurable. As a result, a large number of leading companies across the world have latterly launched software-defined solutions in their data centers and it is expected that most of the service providers will do so in the near future due to the new opportunities enabled by software-defined architectures. Nonetheless, each emerging technology is accompanied by new issues and concerns, and fault tolerance and recovery is one such issue that faces software-defined networking. Although there have been numerous studies that have discussed this issue, gaps still exist and need to be highlighted. In this paper, we start by tracing the evolution of networking systems from the mid 1990's until the emergence of programmable networks and software-defined networking, and then define a taxonomy for software-defined networking dependability by means of fault tolerance of data plane to cover all aspects, challenges and factors that need to be considered in future solutions. We discuss in a detailed manner current state-of-the-art literature in this area. Finally, we analyse the current gaps in current research and propose possible directions for future work.
In recent years, the emerging paradigm of software-defined networking has become a hot and thriving topic that grabbed the attention of industry sector as well as the academic research community. The decoupling between the network control and data planes means that software-defined networking architecture is programmable, adjustable and dynamically re-configurable. As a result, a large number of leading companies across the world have latterly launched software-defined solutions in their data centers and it is expected that most of the service providers will do so in the near future due to the new opportunities enabled by software-defined architectures. Nonetheless, each emerging technology is accompanied by new issues and concerns, and fault tolerance and recovery is one such issue that faces software-defined networking. Although there have been numerous studies that have discussed this issue, gaps still exist and need to be highlighted. In this paper, we start by tracing the evolution of networking systems from the mid 1990's until the emergence of programmable networks and software-defined networking, and then define a taxonomy for software-defined networking dependability by means of fault tolerance of data plane to cover all aspects, challenges and factors that need to be considered in future solutions. We discuss in a detailed manner current state-of-the-art literature in this area. Finally, we analyse the current gaps in current research and propose possible directions for future work.Online transfer learning and organic computing for deep space research and astronomyhttps://peerj.com/preprints/275812019-03-122019-03-12Sadanandan Natarajan
Deep space exploration is the pillars within the field of outer space analysis and physical science. The amount of knowledge from numerous space vehicle and satellites orbiting the world of study are increasing day by day. This information collected from numerous experiences of the advanced space missions is huge. These information helps us to enhance current space knowledge and the experiences can be converted and transformed into segregated knowledge which helps us to explore and understand the realms of the deep space.. Online Transfer Learning (OTL) is a machine learning concept in which the knowledge gets transferred between the source domain and target domain in real time, in order to help train a classifier of the target domain. Online transfer learning can be an efficient method for transferring experiences and data gained from the space analysis data to a new learning task and can also routinely update the knowledge as the task evolves.
Deep space exploration is the pillars within the field of outer space analysis and physical science. The amount of knowledge from numerous space vehicle and satellites orbiting the world of study are increasing day by day. This information collected from numerous experiences of the advanced space missions is huge. These information helps us to enhance current space knowledge and the experiences can be converted and transformed into segregated knowledge which helps us to explore and understand the realms of the deep space.. Online Transfer Learning (OTL) is a machine learning concept in which the knowledge gets transferred between the source domain and target domain in real time, in order to help train a classifier of the target domain. Online transfer learning can be an efficient method for transferring experiences and data gained from the space analysis data to a new learning task and can also routinely update the knowledge as the task evolves.Data based intervention approach for Complexity-Causality measurehttps://peerj.com/preprints/274162018-12-072018-12-07Aditi KathpaliaNithin Nagaraj
Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful as the underlying model generating the data is often unknown. However, existing model-free measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of ‘cause’ and ‘effect’ between well separated samples. In real-world processes, often ‘cause’ and ‘effect’ are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression- Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.
Causality testing methods are being widely used in various disciplines of science. Model-free methods for causality estimation are very useful as the underlying model generating the data is often unknown. However, existing model-free measures assume separability of cause and effect at the level of individual samples of measurements and unlike model-based methods do not perform any intervention to learn causal relationships. These measures can thus only capture causality which is by the associational occurrence of ‘cause’ and ‘effect’ between well separated samples. In real-world processes, often ‘cause’ and ‘effect’ are inherently inseparable or become inseparable in the acquired measurements. We propose a novel measure that uses an adaptive interventional scheme to capture causality which is not merely associational. The scheme is based on characterizing complexities associated with the dynamical evolution of processes on short windows of measurements. The formulated measure, Compression- Complexity Causality is rigorously tested on simulated and real datasets and its performance is compared with that of existing measures such as Granger Causality and Transfer Entropy. The proposed measure is robust to presence of noise, long-term memory, filtering and decimation, low temporal resolution (including aliasing), non-uniform sampling, finite length signals and presence of common driving variables. Our measure outperforms existing state-of-the-art measures, establishing itself as an effective tool for causality testing in real world applications.Ecological theory provides insights about evolutionary computationhttps://peerj.com/preprints/273152018-11-022018-11-02Emily L DolsonWolfgang BanzhafCharles Ofria
Evolutionary algorithms often incorporate ecological concepts to help maintain diverse populations and drive continued innovation. However, while there is strong evidence for the value of ecological dynamics, a lack of overarching theoretical framework renders the precise mechanisms behind these results unclear. These gaps in our understanding make it challenging to predict which approaches will be most appropriate for a given problem. Biologists have been developing ecological theory for decades, but the resulting body of work has yet to be translated into an evolutionary computation context. This paper lays the groundwork for such a translation by applying ecological theory to three different selection mechanisms in evolutionary computation: fitness sharing, lexicase selection, and Eco-EA. First, we use ecological ideas to establish a framework that clarifies how these selection schemes are alike and how they differ. We then build upon this framework by using metrics from ecology to gather empirical data about the underlying differences in the population dynamics that these approaches produce. Specifically, we measure interaction networks and phylogenetic diversity within the population to explore long-term stable coexistence. Notably, we find that selection methods affect phylogenetic diversity differently than phenotypic diversity. These results can inform parameter selection, choice of selection scheme, and the development of new selection schemes.
Evolutionary algorithms often incorporate ecological concepts to help maintain diverse populations and drive continued innovation. However, while there is strong evidence for the value of ecological dynamics, a lack of overarching theoretical framework renders the precise mechanisms behind these results unclear. These gaps in our understanding make it challenging to predict which approaches will be most appropriate for a given problem. Biologists have been developing ecological theory for decades, but the resulting body of work has yet to be translated into an evolutionary computation context. This paper lays the groundwork for such a translation by applying ecological theory to three different selection mechanisms in evolutionary computation: fitness sharing, lexicase selection, and Eco-EA. First, we use ecological ideas to establish a framework that clarifies how these selection schemes are alike and how they differ. We then build upon this framework by using metrics from ecology to gather empirical data about the underlying differences in the population dynamics that these approaches produce. Specifically, we measure interaction networks and phylogenetic diversity within the population to explore long-term stable coexistence. Notably, we find that selection methods affect phylogenetic diversity differently than phenotypic diversity. These results can inform parameter selection, choice of selection scheme, and the development of new selection schemes.