PeerJ Computer Science Preprints: Autonomous Systemshttps://peerj.com/preprints/index.atom?journal=cs&subject=8400Autonomous Systems articles published in PeerJ Computer Science PreprintsMachine 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.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.Event-driven industrial robot control architecture for the Adept V+ platformhttps://peerj.com/preprints/275522019-02-272019-02-27Oleksandr SemeniutaPetter Falkman
Modern industrial robotic systems are highly interconnected. They operate in a distributed environment and communicate with sensors, computer vision systems, mechatronic devices, and computational components. On the fundamental level, communication and coordination between all parties in such distributed system are characterized by discrete event behavior. The latter is largely attributed to the specifics of communication over the network, which, in terms, facilitates asynchronous programming and explicit event handling. In addition, on the conceptual level, events are an important building block for realizing reactivity and coordination. Event-driven architecture has manifested its effectiveness for building loosely-coupled systems based on publish-subscribe middleware, either general-purpose or robotic-oriented. Despite all the advances in middleware, industrial robots remain difficult to program in context of distributed systems, to a large extent due to the limitation of the native robot platforms. This paper proposes an architecture for flexible event-based control of industrial robots based on the Adept V+ platform. The architecture is based on the robot controller providing a TCP/IP server and a collection of robot skills, and a high-level control module deployed to a dedicated computing device. The control module possesses bidirectional communication with the robot controller and publish/subscribe messaging with external systems. It is programmed in asynchronous style using pyadept, a Python library based on Python coroutines, AsyncIO event loop and ZeroMQ middleware. The proposed solution facilitates integration of Adept robots into distributed environments and building more flexible robotic solutions with event-based logic.
Modern industrial robotic systems are highly interconnected. They operate in a distributed environment and communicate with sensors, computer vision systems, mechatronic devices, and computational components. On the fundamental level, communication and coordination between all parties in such distributed system are characterized by discrete event behavior. The latter is largely attributed to the specifics of communication over the network, which, in terms, facilitates asynchronous programming and explicit event handling. In addition, on the conceptual level, events are an important building block for realizing reactivity and coordination. Event-driven architecture has manifested its effectiveness for building loosely-coupled systems based on publish-subscribe middleware, either general-purpose or robotic-oriented. Despite all the advances in middleware, industrial robots remain difficult to program in context of distributed systems, to a large extent due to the limitation of the native robot platforms. This paper proposes an architecture for flexible event-based control of industrial robots based on the Adept V+ platform. The architecture is based on the robot controller providing a TCP/IP server and a collection of robot skills, and a high-level control module deployed to a dedicated computing device. The control module possesses bidirectional communication with the robot controller and publish/subscribe messaging with external systems. It is programmed in asynchronous style using pyadept, a Python library based on Python coroutines, AsyncIO event loop and ZeroMQ middleware. The proposed solution facilitates integration of Adept robots into distributed environments and building more flexible robotic solutions with event-based logic.Implementation and validity of the long jump knowledge-based system: Case of the approach run phasehttps://peerj.com/preprints/275242019-02-082019-02-08Teerawat KamnardsiriWorawit JanchaiPattaraporn KhuwuthyakornWacharee Rittiwat
This study aimed to propose the method of implementation of the Knowledge-Based System (KBS) in the case of approach-run phase. The proposed method was implemented for improving the long jump performance of athletes in the approach-run phase. Moreover, this study aimed to examine KBS concurrent validity in distinguishing between professional and amateur populations and then KBS convergent validity against a Tracker video analysis tool. Seven running professionals aged 19 to 42 years and five amateurs aged 18 to 38 years had captured with ten conditions of different movements (C1 to C10) using a standard video camera (60 fps, 10 mm lens). The camera was fixed on the tripod. The results showing an age-related difference in a speed measurement of ten conditions were evidently using the KBS. Good associations were found between KBS and Tracker 4.94 video analysis tool across various conditions of three variables that were the starting position (r=0.926 and 0.963), the maximum velocity (r=0.972 and 0.995) and the location of maximum velocity (r=0.574 and 0.919). In conclusion, the proposed method is a reliable tool for measuring the starting position, maximum speed and position of maximum speed. Furthermore, the proposed method can also distinguish speed performance between professional and amateur across multiple movement conditions.
This study aimed to propose the method of implementation of the Knowledge-Based System (KBS) in the case of approach-run phase. The proposed method was implemented for improving the long jump performance of athletes in the approach-run phase. Moreover, this study aimed to examine KBS concurrent validity in distinguishing between professional and amateur populations and then KBS convergent validity against a Tracker video analysis tool. Seven running professionals aged 19 to 42 years and five amateurs aged 18 to 38 years had captured with ten conditions of different movements (C1 to C10) using a standard video camera (60 fps, 10 mm lens). The camera was fixed on the tripod. The results showing an age-related difference in a speed measurement of ten conditions were evidently using the KBS. Good associations were found between KBS and Tracker 4.94 video analysis tool across various conditions of three variables that were the starting position (r=0.926 and 0.963), the maximum velocity (r=0.972 and 0.995) and the location of maximum velocity (r=0.574 and 0.919). In conclusion, the proposed method is a reliable tool for measuring the starting position, maximum speed and position of maximum speed. Furthermore, the proposed method can also distinguish speed performance between professional and amateur across multiple movement conditions.Ulyxes: an open source project for automation in engineering surveyinghttps://peerj.com/preprints/272262018-09-202018-09-20Zoltan SikiBence TakácsCsaba Égető
Ulyxes is an open source project to drive robotic total stations as well as other sensors, collect their measurements in database and finally publish the results for authorized users on the web. On special requests the results are also presented with web based maps in the background. This project is like an instant coffee: three in one (coffee, sugar and milk). The coffee and the strongest part is the research and coding. The sugar is the application of the program in industrial environment and the milk on the top is the educational usage. The software development started in 2008 connected to a monitoring task in the Hungarian Nuclear Power Plant. Since then the development has been extended from total stations to different positioning capable sensors. In 2012 the development of a new Python based object oriented framework started. The code is based on the results of some other open source projects, Python, PySerial, GNUGama, SQLite, OpenCV, etc. After connecting to the international Geo4All network in 2014, Ulyxes became a project of our Geo4All Lab. The project has its own home page (http://www.agt.bme.hu/ulyxes) and the source code is available on the GitHub portal (https://github.com/zsiki/ulyxes). The code is maintained by the colleagues at the Department of Geodesy and Surveying at the Budapest University of Technology, volunteers from all over the World are welcome. BSc and MSc students are also involved in the development and testing. More theses were connected to this project in the recent five years. In the curriculum of an MSc subject called Surveying Automation, Ulyxes is used to demonstrate automatized tasks in engineering surveying. The system has been applied for several projects during the last 10+ years. Typical applications are the load tests of bridges and other engineering structures and on the other hand Ulyxes can be used to monitor the movements of buildings in the nearby of constructional works, like metro stations, underground garage and other buildings as well. Raspberry Pi small, single board computers are used with Raspbian operating system during on-site works. The source code is divided into three parts. The first one is the Ulyxes API which is the core of the system. The second one, Ulyxes Apps is a collection of applications based upon the API. Some of them were developed by our students. The third part is the server side scripts to publish observation results through the Internet. Moreover it is also planned to implement SOS standard using IstSOS. Our Geo4All Lab maintains another open source software, called GeoEasy to process observation data in engineering and land surveying. A closer cooperation is also planned between our two open source projects. In this paper the most important features of Ulyxes will be presented with examples, an actual monitoring project in Budapest and test loads of bridges and overpasses.
Ulyxes is an open source project to drive robotic total stations as well as other sensors, collect their measurements in database and finally publish the results for authorized users on the web. On special requests the results are also presented with web based maps in the background. This project is like an instant coffee: three in one (coffee, sugar and milk). The coffee and the strongest part is the research and coding. The sugar is the application of the program in industrial environment and the milk on the top is the educational usage. The software development started in 2008 connected to a monitoring task in the Hungarian Nuclear Power Plant. Since then the development has been extended from total stations to different positioning capable sensors. In 2012 the development of a new Python based object oriented framework started. The code is based on the results of some other open source projects, Python, PySerial, GNUGama, SQLite, OpenCV, etc. After connecting to the international Geo4All network in 2014, Ulyxes became a project of our Geo4All Lab. The project has its own home page (http://www.agt.bme.hu/ulyxes) and the source code is available on the GitHub portal (https://github.com/zsiki/ulyxes). The code is maintained by the colleagues at the Department of Geodesy and Surveying at the Budapest University of Technology, volunteers from all over the World are welcome. BSc and MSc students are also involved in the development and testing. More theses were connected to this project in the recent five years. In the curriculum of an MSc subject called Surveying Automation, Ulyxes is used to demonstrate automatized tasks in engineering surveying. The system has been applied for several projects during the last 10+ years. Typical applications are the load tests of bridges and other engineering structures and on the other hand Ulyxes can be used to monitor the movements of buildings in the nearby of constructional works, like metro stations, underground garage and other buildings as well. Raspberry Pi small, single board computers are used with Raspbian operating system during on-site works. The source code is divided into three parts. The first one is the Ulyxes API which is the core of the system. The second one, Ulyxes Apps is a collection of applications based upon the API. Some of them were developed by our students. The third part is the server side scripts to publish observation results through the Internet. Moreover it is also planned to implement SOS standard using IstSOS. Our Geo4All Lab maintains another open source software, called GeoEasy to process observation data in engineering and land surveying. A closer cooperation is also planned between our two open source projects. In this paper the most important features of Ulyxes will be presented with examples, an actual monitoring project in Budapest and test loads of bridges and overpasses.Resolving the optimal selection of a natural reserve using the particle swarm optimisation by applying transfer functionshttps://peerj.com/preprints/269412018-05-292018-05-29Boris Almonacid
The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.
The optimal selection of a natural reserve (OSRN) is an optimisation problem with a binary domain. To solve this problem the metaheuristic algorithm Particle Swarm Optimization (PSO) has been chosen. The PSO algorithm has been designed to solve problems in real domains. Therefore, a transfer method has been applied that converts the equations with real domains of the PSO algorithm into binary results that are compatible with the OSRN problem. Four transfer functions have been tested in four case studies to solve the OSRN problem. According to the tests carried out, it is concluded that two of the four transfer functions are apt to solve the problem of optimal selection of a natural reserve.