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Chatbots are becoming increasingly popular in various industries, and thereare many options available for businesses and organisations. Several studieshave investigated open-source chatbots and identified their core strengths,implementation, and integration capabilities however few have investigatedopen-source chatbot frameworks and libraries in a specific use case such asmedicine. The project's objective was to evaluate a selection of chatbots ormore specifically two frameworks: Botkit and Rasa, and two libraries:ChatterBot, and Natural which was utilised together with Botkit and alanguage model which is DialoGPT. The evaluation focuses specifically onaccuracy, consistency, and response time. Frequently asked questions fromthe World Health Organization and COVID-19 related Dialogue Datasetfrom GitHub were utilised to test the chatbots' abilities in handling differentqueries and accuracy was measured through metrics like Jaccard similarity,bilingual evaluation understudy (BLEU), and recall oriented gistingevaluation (ROUGE) scores, consistency through Jaccard similarity betweenthe generated responses and response time was taken to be the average timefor a response in seconds. The analysis revealed unique strengths andlimitations for each chatbot model. Rasa displayed robust performance inaccuracy, consistency, and customisation capabilities if the chatbot works ina particular topic with acceptable response times. DialoGPT demonstratedstrong conversational abilities and contextually relevant responses withtrade-offs in consistency. ChatterBot showed consistency, though sometimesstruggled with advanced queries, and Botkit with Natural stood out for itsquick response times, albeit with limitations in accuracy and scalability.Despite implementation challenges, these open-source frameworks, libraries,and models offer promising solutions for organisations intending to harnessconversational agents' technology. The study suggests encouraging furtherexploration and refinemen

The growing complexity of cloud native applications has necessitated the intro- duction of operators to the container orchestration tools’ suite of components. Operators affords developers the ability to encode domain knowledge and make fine-grained controllers for their Kubernetes clusters, radically extending the range of feasible applications to host. Operators are however vast software entities and places large requirements on its developing party, almost forcing the use of a software framework. This master’s thesis explores the evaluation and comparison of Kubernetes operator frameworks. To address the challenge, a novel solution design is proposed, presenting an evaluation model that categorizes and assesses the frameworks using predefined attributes and accompanying metrics. The attributes were related to one of two overarching categories deemed appropriate for by the performed framework study, open source health status or operator capability. Profile assignment is done through the concordance non-discordance principle, inspired by multi-criteria decision theory. This research contributes to the understanding of evaluating Kubernetes operator frameworks and offers valuable insights for developers and decision-makers in selecting appropriate tools for managing cloud-based applications., In today's interconnected world, online services have become an integral part of our lives. This has seen the demand for scalable, reliable, and efficient infrastructure skyrocket. In the wake of this demand cloud services and Kubernetes have taken great strides to become industry standards. However, large parts of Kubernetes's facilitating technologies are still unexplored. Cloud services have enjoyed great success on the premise of cloud native applications. Such applications are what facilitates sought-after qualities such as scalability and reliability. However, cloud services and their applications are prone to difficulties in hosting due to their micro-service-based architecture approach. This has spurred the uprising of orchestration tools, such as Kubernetes, which serve as a central hub for management of micro-services. Kubernetes was introduced by Google in 2014 and offers a set of building blocks that collectively provides mechanisms to deploy and maintain cloud native applications. These building blocks are heavily standardized, and for good reason. Kubernetes delves into very complex areas and developers would rather focus on their application's logic than the intricacies of microservices and orchestration. Such standardization may however prove problematic, something that is made obvious in the cases of more elaborate and multi-faceted applications. Cloud native applications are often divided on the condition of either being stateful or stateless, meaning it either keeps persistent data or it doesn't. Applications of the stateful kind requires knowledge and techniques that the native Kubernetes building blocks have difficulties providing. To rectify this gap of capability Kubernetes recently introduced the operator pattern into its suits of components. Once implemented, operators can extend Kubernetes with encoded domain knowledge, enabling developers to provide Kubernetes with whatever capability they find missing. As with most things orchestration re

Chatbots are becoming increasingly popular in various industries, and thereare many options available for businesses and organisations. Several studieshave investigated open-source chatbots and identified their core strengths,implementation, and integration capabilities however few have investigatedopen-source chatbot frameworks and libraries in a specific use case such asmedicine. The project's objective was to evaluate a selection of chatbots ormore specifically two frameworks: Botkit and Rasa, and two libraries:ChatterBot, and Natural which was utilised together with Botkit and alanguage model which is DialoGPT. The evaluation focuses specifically onaccuracy, consistency, and response time. Frequently asked questions fromthe World Health Organization and COVID-19 related Dialogue Datasetfrom GitHub were utilised to test the chatbots' abilities in handling differentqueries and accuracy was measured through metrics like Jaccard similarity,bilingual evaluation understudy (BLEU), and recall oriented gistingevaluation (ROUGE) scores, consistency through Jaccard similarity betweenthe generated responses and response time was taken to be the average timefor a response in seconds. The analysis revealed unique strengths andlimitations for each chatbot model. Rasa displayed robust performance inaccuracy, consistency, and customisation capabilities if the chatbot works ina particular topic with acceptable response times. DialoGPT demonstratedstrong conversational abilities and contextually relevant responses withtrade-offs in consistency. ChatterBot showed consistency, though sometimesstruggled with advanced queries, and Botkit with Natural stood out for itsquick response times, albeit with limitations in accuracy and scalability.Despite implementation challenges, these open-source frameworks, libraries,and models offer promising solutions for organisations intending to harnessconversational agents' technology. The study suggests encouraging furtherexploration and refinemen

Graph neural networks (GNNs) have been demonstrated to be a powerful algorithmic model in broad application fields for their effectiveness in learning over graphs. To scale GNN training up for large-scale and ever-growing graphs, the most promising solution is distributed training that distributes the workload of training across multiple computing nodes. At present, the volume of related research on distributed GNN training is exceptionally vast, accompanied by an extraordinarily rapid pace of publication. Moreover, the approaches reported in these studies exhibit significant divergence. This situation poses a considerable challenge for newcomers, hindering their ability to grasp a comprehensive understanding of the workflows, computational patterns, communication strategies, and optimization techniques employed in distributed GNN training. As a result, there is a pressing need for a survey to provide correct recognition, analysis, and comparisons in this field. In this article, we provide a comprehensive survey of distributed GNN training by investigating various optimization techniques used in distributed GNN training. First, distributed GNN training is classified into several categories according to their workflows. In addition, their computational patterns and communication patterns, as well as the optimization techniques proposed by recent work, are introduced. Second, the software frameworks and hardware platforms of distributed GNN training are also introduced for a deeper understanding. Third, distributed GNN training is compared with distributed training of deep neural networks (DNNs), emphasizing the uniqueness of distributed GNN training. Finally, interesting issues and opportunities in this field are discussed. © 1963-2012 IEEE.

Multimodal journey planners are used worldwide to support travelers in planning and executing their journeys. Generated travel plans usually involve local mobility service providers, consider some travelers' preferences, and provide travelers information about the routes' current status and expected delays. However, those planners cannot fully consider the special situations of individual cities when providing travel planning services. Specifically, authorities of different cities might define customizable regulations or constraints of movements in the cities (e.g., due to construction works or pandemics). Moreover, with the transformation of traditional cities into smart cities, travel planners could leverage advanced monitoring features. Finally, most planners do not consider relevant information impacting travel plans, for instance, information that might be provided by travelers (e.g., a crowded square) or by mobility service providers (e.g., changing the timetable of a bus). To address the aforementioned shortcomings, in this paper, we propose ROUTE, a framework for customizable smart mobility planners that better serve the needs of travelers, local authorities, and mobility service providers in the dynamic ecosystem of smart cities. ROUTE is composed of an architecture, a process, and a prototype developed to validate the feasibility of the framework. Experiments' results show that the framework scales well in both centralized and distributed deployment settings.

In the paper, the main steps involved in the development of an object-oriented computational framework for the 3D bending and free vibration analysis of multilayer plates are presented. The mathematical formulation for layered finite elements is based on Reddy’s plate theory for laminated composites. The analysis model has been implemented into Matlab, and the pre- and post-processing phases are performed using GiD. The proposed solver is characterized by a fast assembly procedure of sparse matrices using matrix vectorization, and a novel algorithm for the evaluation of interlaminar stresses satisfying continuity at layer interfaces. The performance, efficiency and accuracy of the computational framework are demonstrated through a number of validation examples by comparing the obtained results against the exact solution. Results from both static and dynamic analyses of multilayer panels are shown.

Monitoring a large and complex system such as a data center generates many time series of metric data, which are often stored using a database system specifically designed for managing time series data. Different, possibly distributed, databases may be used to collect data representing different aspects of the system, which complicates matters when, for example, developing data analytics applications that require integrating data from two or more of these. From the developer’s point of view, it would be highly convenient if all of the required data were available in a single database, but it may well be that the different databases do not even implement the same query language. To address this problem, we propose using an ontology to capture the semantic similarities among different time series database systems and to hide their syntactic differences. Alongside the ontology, we have developed a Python software framework that enables the developer to build and execute queries using classes and properties defined by the ontology. The ontology thus effectively specifies a semantic query language that can be used to retrieve data from any of the supported database systems, and the Python framework can be set up to treat the different databases as a single data store that can be queried using this semantic language. This is demonstrated by presenting an application involving predictive analytics on resource usage and electricity consumption metrics gathered from a Kubernetes cluster, stored in Prometheus and KairosDB databases, but the framework can be extended in various ways and adapted to different use cases, enabling machine learning research using distributed heterogeneous data sources.

In the paper, the main steps involved in the development of an object-oriented computational framework for the 3D bending and free vibration analysis of multilayer plates are presented. The mathematical formulation for layered finite elements is based on Reddy’s plate theory for laminated composites. The analysis model has been implemented into Matlab, and the pre- and post-processing phases are performed using GiD. The proposed solver is characterized by a fast assembly procedure of sparse matrices using matrix vectorization, and a novel algorithm for the evaluation of interlaminar stresses satisfying continuity at layer interfaces. The performance, efficiency and accuracy of the computational framework are demonstrated through a number of validation examples by comparing the obtained results against the exact solution. Results from both static and dynamic analyses of multilayer panels are shown.

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems.

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems.

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems.

An emerging area with unique security challenges is the area of automated diagnosis (autodiagnosis) in teleradiology. In teleradiology, patients’ scans and associated electronic medical records(EMR) are transmitted to a remote location (rural-urban or urban-urban) for image analysis, classification, and diagnosis. The major challenge with this approach is that these scans and EMR are often fragmented and sent out to different users, such as requesting hospitals, independent specialists, patients, external artificial intelligence(AI) systems, and image archives. This occurrence makes it difficult to control the security and privacy of these health information. Therefore, new methods for tamper detection on the image and secrecy preservation of patient’s health records are now necessary in this new setting. Steganography and digital watermarking, collectively known as information hiding (IH) techniques, are among the methods of providing robust security for multimedia (image, video, audio, and text) data. In particular, Spread Spectrum(SS) Steganography and watermarking are hiding techniques that provide secret and robust information hiding, respectively, by using secret keys that are known only to the authorized parties. However, due to the non-standardisation of IH techniques, coupled with the issues of diagnostic quality after data hiding in medical images, the adoption of IH methods in medical practice is currently low. Hence, we are also faced with the challenges of validation and adoption of IH-based algorithms for practical use. Therefore, we are faced with two major challenges in this thesis: (i)how to improve tamper detection and data hiding capacity of spread spectrum steganography while retaining its robustness and secrecy and(ii)how to increase the adoption of data hiding security techniques in teleradiology for autodiagnosis. The goal of solving these challenges is to improve global healthcare with maximum security but at a low cost. The quest to achieve

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems.

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems.

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems.

A number of streaming technologies have appeared in the last years as a result of the rising of Big Data applications. Nowadays, deciding which technology to adopt is not an easy task due not only to the number of available data streaming processing projects, but also because they are continuously evolving. In this paper, we focus on how these issues have affected jMetalSP, a framework for dynamic multi-objective optimization that incorporates streaming features. jMetalSP allows the development of three tier optimization workflows where the central component is an optimizer that is continuously solving a dynamic multi-objective optimization problem. This problem can change as a consequence of the analysis of data streams carried out by components that use the Apache Spark streaming engine. A third kind of components receive and process the Pareto front approximations being yielded by the optimization algorithm. However, all jMetalSP elements are tightly coupled and linked to Spark, making it difficult to use a different streaming system. To overcome this issue, we have redesigned the jMetalSP architecture to make it flexible enough to avoid the dependence of any particular streaming system. This way, popular Apache projects such as Spark Structured Streaming, Kafka Streams, or Flink can be used without requiring to change the rest of components of the application. Furthermore, Kafka can be used for inter-process communication, what enables the execution of components in different nodes of a cluster, independently of their implementation languages thanks to the serialization of data streams with Apache Avro. We show how the embraced solution provides a high degree of flexibility that enhances the usability of jMetalSP. To this end, a representative case study based on a transport problem is conducted that focuses on data representation and performance evaluation of the Spark, Flink, and Kafka systems.

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a di erent programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy o ers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.

In this viewpoint we describe the architecture of, and design rationale for, a new software platform designed to support the conduct of digital phenotyping research studies. These studies seek to collect passive and active sensor signals from participants' smartphones for the purposes of modelling and predicting health outcomes, with a specific focus on mental health. We also highlight features of the current research landscape that recommend the coordinated development of such platforms, including the significant technical and resource costs of development, and we identify specific considerations relevant to the design of platforms for digital phenotyping. In addition, we describe trade-offs relating to data quality and completeness versus the experience for patients and public users who consent to their devices being used to collect data. We summarize distinctive features of the resulting platform, InSTIL (Intelligent Sensing to Inform and Learn), which includes universal (ie, cross-platform) support for both iOS and Android devices and privacy-preserving mechanisms which, by default, collect only anonymized participant data. We conclude with a discussion of recommendations for future work arising from learning during the development of the platform. The development of the InSTIL platform is a key step towards our research vision of a population-scale, international, digital phenotyping bank. With suitable adoption, the platform will aggregate signals from large numbers of participants and large numbers of research studies to support modelling and machine learning analyses focused on the prediction of mental illness onset and disease trajectories.

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a di erent programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy o ers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a di erent programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy o ers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a di erent programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy o ers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.

This paper describes jMetalPy, an object-oriented Python-based framework for multi-objective optimization with metaheuristic techniques. Building upon our experiences with the well-known jMetal framework, we have developed a new multi-objective optimization software platform aiming not only at replicating the former one in a di erent programming language, but also at taking advantage of the full feature set of Python, including its facilities for fast prototyping and the large amount of available libraries for data processing, data analysis, data visualization, and high-performance computing. As a result, jMetalPy provides an environment for solving multi-objective optimization problems focused not only on traditional metaheuristics, but also on techniques supporting preference articulation and dynamic problems, along with a rich set of features related to the automatic generation of statistical data from the results generated, as well as the real-time and interactive visualization of the Pareto front approximations produced by the algorithms. jMetalPy o ers additionally support for parallel computing in multicore and cluster systems. We include some use cases to explore the main features of jMetalPy and to illustrate how to work with it.

The subject of this paper is development of software framework for real-time management of intelligent devices. The framework enables intelligent management of IoT devices in cyber-physical systems using models based on recurrence relations and differential equations. The platform was developed using Python programming language, Django framework and wide corpus of modules and libraries that supports continuous simulation. The software framework incorporates application programming interface as well, for specification of system behaviour, transmission of input parameters and output results, sending control actions via web services to the IoT system.

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.

Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes.

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.

Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes.

Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes.

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.

Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes.

The Internet of Things vision proposes a global platform in an unforeseen scale for distributed applications that rely on data provided by interconnected resource-constrained things. In such large-scale systems, centralized control of system operation by a single component through vertical interactions becomes unfeasible. Ideally, decentralized control in the proximity of things enables to take into account the local dynamic resource availability and environmental characteristics that are used to optimize the application execution. To realize decentralization, capabilities for horizontal interactions, that complement the vertical interactions, and for opportunistic participation of things are needed. This thesis explores mobile agent technology to implement distributed Internet of Things applications that benefit from both vertical and horizontal interactions of the application components. First, a resource-oriented reactive mobile agent architecture and a software framework are presented. The framework facilitates RESTful interactions between agents and other system components and provides a REST-based interface to build opportunistic agent-based applications. Two agent platforms are presented that integrate resource-constrained things into the framework as mobile agent hosts. Second, mobile agent based mobile crowdsensing and edge computing applications are evaluated with large-scale simulations and real-world experiments. The results show that energy consumption is decreased in the participating things, in comparison with the existing approaches, by agent-based in-network data processing and control of the thing operation. This thesis makes a valuable contribution by enabling mobile agent operations in a heterogeneous set of resource-constrained things. The presented empirical evidence shows how mobile agent technology increases energy efficiency in distributed application execution. The presented mobile agent architecture and software framework potentia, Tiivistelmä Esineiden Internet liittää resurssirajoitteiset sulautetut laitteet laajamittaisesti Internet-verkkoon, jossa niiden tuottamaa tietoa hyödynnetään hajautettujen järjestelmien sovelluksissa. Esineiden Internetin järjestelmien odotetaan skaalautuvan niin laajoiksi, ettei keskitetty, vertikaaliseen vuorovaikutukseen perustuva järjestelmähallinta ole enää käyttökelpoinen ratkaisu. Hajautettu hallinta lähellä tietoa tuottavia laitteita tuo etuja, kun paikallisesti sovelluksen suorituksessa otetaan huomioon toimintaympäristön tila ja voidaan reagoida dynaamisesti resurssien saatavuuteen. Hajautus Esineiden Internetin sovellusalustoilla edellyttää menetelmiä sekä laitteiden vertikaaliseen ja horisontaaliseen vuorovaikutukseen, että niiden dynaamisen osallistumisen mahdollistamiseksi sovelluksen suorittamisessa. Tässä työssä tutkittiin mobiiliagentti-teknologiaa hajautettujen sovellusten toteuttamiseen Esineiden Internet-ympäristössä. Työssä esitettiin resurssisuuntautunut arkkitehtuuri reaktiivisille mobiiliagenteille sekä ohjelmistokehys, joita käyttäen voidaan toteuttaa agenttipohjaisia hajautettuja sovelluksia. Ohjelmistokehys perustuu REST-arkkitehtuurimalliin, jonka pohjalta esitettiin yhdenmukainen rajapinta agenttien ja järjestelmäkomponenttien väliseen vuorovaikutukseen. Ohjelmistokehyksen osana toteutettiin kaksi mobiiliagentti-ohjelmistoalustaa resurssirajoitteisille sulautetuille laitteille. Työssä tarkasteltiin mobiiliagentti-pohjaisten Esineiden Internet-sovellusten energiatehokkuutta simuloinneilla sekä tosielämän kokeilla. Tarkastelun kohteeksi valittiin joukkoistettu mobiilihavainnointi sekä reunalaskennan ulottaminen resurssirajoitteisiin laitteisiin. Saadut tulokset osoittavat, että laitteiden energiankulutusta voidaan pienentää verrattuna aiemmin esitettyihin ratkaisuihin hyödyntämällä mobiiliagentteja tiedonkäsittelyyn laitteessa sekä laitteen toiminnan ohjaamiseen. Työssä esitetty resurssisuuntautunut mobiiliagenttiarkkitehtuuri sekä ohjelm

Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes.

Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes.

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.

Multi-objective optimization with metaheuristics is an active and popular research field which is supported by the availability of software frameworks providing algorithms, benchmark problems, quality indicators and other related components. Most of these tools follow a monolithic architecture that frequently leads to a lack of flexibility when a user intends to add new features to the included algorithms. In this paper, we explore a different approach by designing a component-based architecture for a multi-objective optimization framework based on the observer pattern. In this architecture, most of the algorithmic components are observable entities that naturally allows to register a number of observers. This way, a metaheuristic is composed of a set of observable and observer elements, which can be easily extended without requiring to modify the algorithm. We have developed a prototype of this architecture and implemented the NSGA-II evolutionary algorithm on top of it as a case study. Our analysis confirms the improvement of flexibility using this architecture, pointing out the requirements it imposes and how performance is affected when adopting it.

Multi-objective metaheuristics have become popular techniques for dealing with complex optimization problems composed of a number of conflicting functions. Nowadays, we are in the Big Data era, so metaheuristics must be able to solve dynamic problems that may vary over time due to the processing and analysis of several streaming data sources. As this is a new field, there is a need for software platforms to solve dynamic multi-objective Big Data optimization problems. In this paper, we present jMetalSP, which combines the multi-objective optimization features of the jMetal framework with the streaming facilities of the Apache Spark cluster computing system. Thus, existing state-of-the-art multi-objective metaheuristics can be easily adapted to deal with dynamic optimization problems that are fed by multiple streaming data sources. Moreover, these algorithms can take advantage of the parallel computing features of Spark. We describe the architecture of jMetalSP and show how it can be used to solve a dynamic bi-objective instance of the Traveling Salesman Problem (TSP) based on New York City's real-time traffic data. We have also carried out an experimental study to assess the performance of the resultant jMetalSP application in a Hadoop cluster composed of 100 nodes.

This paper presents a platform for airborne sensor applications using low-cost, open-source components carried by an easy-to-fly unmanned aircraft vehicle (UAV). The system, available in open-source, is designed for researchers, students and makers for a broad range of exploration and data-collection needs. The main contribution is the extensible architecture for modularized airborne sensor deployment and real-time data visualisation. Our open-source Android application provides data collection, flight path definition and map tools. Total cost of the system is below 800 dollars. The flexibility of the system is illustrated by mapping the location of Bluetooth beacons (iBeacons) on a ground field and by measuring water temperature in a lake.

We describe our software system enabling a tight integration between vision and control modules on complex, high-DOF humanoid robots. This is demonstrated with the iCub humanoid robot performing visual object detection and reaching and grasping actions. A key capability of this system is reactive avoidance of obstacle objects detected from the video stream while carrying out reach-and-grasp tasks. The subsystems of our architecture can independently be improved and updated, for example, we show that by using machine learning techniques we can improve visual perception by collecting images during the robot's interaction with the environment. We describe the task and software design constraints that led to the layered modular system architecture.

As the world population ages, the healthcare system is facing new challenges in treating more patients at a lower cost than today. One trend in addressing this problem is to increase the opportunities of in-home care. To achieve this there is a need for safe and cost-effective monitoring systems. Biosignal PI is an ongoing open-source project created to develop a flexible and affordable platform for development of stand-alone devices able to measure and process physiological signals. This master thesis project, performed at the department of Medical Sensors, Signals and System at the School of Technology and Health, aimed at further develop the Biosignal PI software by constructing a new flexible software framework architecture that could be used for measurement and processing of different types of biosignals. The project also aimed at implementing features for Heart Rate Variability(HRV) Analysis in the Biosignal PI software as well as developing a graphical user interface(GUI) for the Raspberry PI hardware module PiFace Control and Display. The project developed a new flexible abstract software framework for the Biosignal PI. The new framework was constructed to abstract all hardware specifics into smaller interchangeable modules, with the idea of the modules being independent in handling their specific task making it possible to make changes in the Biosignal PI software without having to rewrite all of the core. The new developed Biosignal PI software framework was implemented into the existing hardware setup consisting of an Raspberry PI, a small and affordable single-board computer, connected to ADAS1000, a low power analog front end capable of recording an Electrocardiography(ECG). To control the Biosignal PI software two different GUIs were implemented. One GUI extending the original software GUI with the added feature of making it able to perform HRV-Analysis on the Raspberry PI. This GUI requires a mouse and computer screen to function. To be able to contro, Allt eftersom världens befolkning åldras, ställs sjukvården inför nya utmaningar i att behandla fler patienter till en lägre kostnad än idag. En trend för att lösa detta problem är att utöka möjligheterna till vård i hemmet.För att kunna göra detta finns det ett ökande behov av säkra och kostnadseffektiva patientövervakningssystem. Biosignal PI är ett pågående projekt med öppen källkod som skapats för att utveckla en flexibel och prisvärd plattform för utveckling av fristående enheter som kan mäta och bearbeta olika fysiologiska signaler. Detta examensarbete genomfördes vid institutionen för medicinska sensorer, signaler och system vid Skolan för Teknik och Hälsa. Projektet syftade till att vidareutveckla den befintliga mjukvaran för Biosignal PI genom att skapa ett nytt flexibelt mjukvaruramverk som kan användas för mätning och bearbetning av olika typer av biosignaler.Projektet syftade också till att utvidga mjukvaran och lägga till funktioner för att kunna genomföra hjärtfrekvensvariabilitets(HRV) analys i Biosignal PIs mjukvara, samt att utveckla ett grafiskt användargränssnitt(GUI) för hårdvarumodulen PiFace Control and Display. Projektet har utvecklat ett nytt flexibelt mjukvaruramverk för Biosignal PI. Det nya ramverket konstruerades för att abstrahera alla hårdvaruspecifika delar in i mindre utbytbara moduler, med tanken att modulerna ska vara oberoende i hur de hanterar sin specifika uppgift. På så sätt ska det vara möjligt att göra ändringar i Biosignal PIs programvara utan att behöva skriva om hela mjukvaran.Det nyutvecklade Biosignal PI ramverket implementerades i det befintliga hårdvaru systemet, som består av en Raspberry PI, liten och prisvärd enkortsdator, ansluten till ADAS1000, en analog hårdvarumodul med möjlighet att registrera ett elektrokardiografi(EKG/ECG). För att kontrollera Biosignal PI programmet har två olika grafiska användargränssnitt skapats.Det ena gränssnitt är en utvidgning av original programvaran med tillagd funktionalitet för att

As the world population ages, the healthcare system is facing new challenges in treating more patients at a lower cost than today. One trend in addressing this problem is to increase the opportunities of in-home care. To achieve this there is a need for safe and cost-effective monitoring systems. Biosignal PI is an ongoing open-source project created to develop a flexible and affordable platform for development of stand-alone devices able to measure and process physiological signals. This master thesis project, performed at the department of Medical Sensors, Signals and System at the School of Technology and Health, aimed at further develop the Biosignal PI software by constructing a new flexible software framework architecture that could be used for measurement and processing of different types of biosignals. The project also aimed at implementing features for Heart Rate Variability(HRV) Analysis in the Biosignal PI software as well as developing a graphical user interface(GUI) for the Raspberry PI hardware module PiFace Control and Display. The project developed a new flexible abstract software framework for the Biosignal PI. The new framework was constructed to abstract all hardware specifics into smaller interchangeable modules, with the idea of the modules being independent in handling their specific task making it possible to make changes in the Biosignal PI software without having to rewrite all of the core. The new developed Biosignal PI software framework was implemented into the existing hardware setup consisting of an Raspberry PI, a small and affordable single-board computer, connected to ADAS1000, a low power analog front end capable of recording an Electrocardiography(ECG). To control the Biosignal PI software two different GUIs were implemented. One GUI extending the original software GUI with the added feature of making it able to perform HRV-Analysis on the Raspberry PI. This GUI requires a mouse and computer screen to function. To be able to contro, Allt eftersom världens befolkning åldras, ställs sjukvården inför nya utmaningar i att behandla fler patienter till en lägre kostnad än idag. En trend för att lösa detta problem är att utöka möjligheterna till vård i hemmet.För att kunna göra detta finns det ett ökande behov av säkra och kostnadseffektiva patientövervakningssystem. Biosignal PI är ett pågående projekt med öppen källkod som skapats för att utveckla en flexibel och prisvärd plattform för utveckling av fristående enheter som kan mäta och bearbeta olika fysiologiska signaler. Detta examensarbete genomfördes vid institutionen för medicinska sensorer, signaler och system vid Skolan för Teknik och Hälsa. Projektet syftade till att vidareutveckla den befintliga mjukvaran för Biosignal PI genom att skapa ett nytt flexibelt mjukvaruramverk som kan användas för mätning och bearbetning av olika typer av biosignaler.Projektet syftade också till att utvidga mjukvaran och lägga till funktioner för att kunna genomföra hjärtfrekvensvariabilitets(HRV) analys i Biosignal PIs mjukvara, samt att utveckla ett grafiskt användargränssnitt(GUI) för hårdvarumodulen PiFace Control and Display. Projektet har utvecklat ett nytt flexibelt mjukvaruramverk för Biosignal PI. Det nya ramverket konstruerades för att abstrahera alla hårdvaruspecifika delar in i mindre utbytbara moduler, med tanken att modulerna ska vara oberoende i hur de hanterar sin specifika uppgift. På så sätt ska det vara möjligt att göra ändringar i Biosignal PIs programvara utan att behöva skriva om hela mjukvaran.Det nyutvecklade Biosignal PI ramverket implementerades i det befintliga hårdvaru systemet, som består av en Raspberry PI, liten och prisvärd enkortsdator, ansluten till ADAS1000, en analog hårdvarumodul med möjlighet att registrera ett elektrokardiografi(EKG/ECG). För att kontrollera Biosignal PI programmet har två olika grafiska användargränssnitt skapats.Det ena gränssnitt är en utvidgning av original programvaran med tillagd funktionalitet för att

Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection - based AR. The developed framework is suitable and has been tested in AR applications using camera - projector pairs, for both fixed and nomadic setups., Peer Reviewed, Postprint (author's final draft)

This paper presents an overview of coordinated project MISSION-SICUVA, and the results achieved at its recent completion. A prototype of UUV has been built with an orientation to oceanographic research and test of new control algorithms. It consist of an underwater vehicle towing a surface buoy, with applications such as monitoring water quality, high resolution bathymetry of the seabed and its map projection. New biological inspired navigation algorithms have been implemented using a comprehensive component based development framewor

This paper presents an overview of coordinated project MISSION-SICUVA, and the results achieved at its recent completion. A prototype of UUV has been built with an orientation to oceanographic research and test of new control algorithms. It consist of an underwater vehicle towing a surface buoy, with applications such as monitoring water quality, high resolution bathymetry of the seabed and its map projection. New biological inspired navigation algorithms have been implemented using a comprehensive component based development framework.

There is no established standard for how Internet of Things devices are communicating with each other, every manufacturer uses their own proprietary software and protocols. This makes it difficult to ensure the best possible user experience. There are several projects that can become a standard for how devices discovering, communicating, networking etc. The goal for this thesis work was to compare such software frameworks in some areas and investigate how Inteno’s operating system Iopsys OS can be complemented by implement one of these frameworks. A literature study gave two candidates for the comparison, AllJoyn and Bonjour. The result of the comparison showed that AllJoyn was the most appropriate choice for Inteno to implement into their OS. AllJoyn was chosen because it has a potential to become an established standard and includes tools for easy implementation. To make a proof of concept, an AllJoyn application was created. The application together with a JavaScript web page, can show and control options for an AllJoyn Wi-Fi manager application and AllJoyn enabled lamps., Det finns ingen etablerad standard för hur enheter inom Internet of Things kommunicerar med varandra. När alla tillverkare använder sina egna programvaror och protokoll, försvårar det möjligheten att skapa bästa möjliga användarvänlighet. Det finns flera projekt som utvecklar mjukvaruramverk, flera av dessa har möjligheten att bli en standard för hur enheter upptäcker, kommunicerar mm. Målet med examensarbete var att jämföra sådana mjukvaruramverk inom vissa områden samt att undersöka hur Intenos operativsystem Iopsys OS kan förbättras genom att implementera ett av dessa ramverk. En litteraturstudie gav två kandidater till jämförelsen, AllJoyn och Bonjour. Resultatet av jämförelsen visade att AllJoyn var det lämpligaste valet för Inteno att implementera i sitt operativsystem. AllJoyn valdes eftersom den har potential att bli en etablerad standard och innehåller verktyg för enkel implementering. För att bevisa konceptet, skapades ett AllJoyn-program. Programmet kan tillsammans med JavaScript generera en webbsida där användaren kan styra Wi-Fi inställningar och styra lampor via AllJoyn.

The role of the PELARS Learning Analytics System (LAS) system is to collect information from students performing project-based tasks, reason on such information and provide visualization to teachers and students, that is usable for understanding the learning process. The information collected by the LAS comprises pieces of information collected directly by the Students, and other collected by the System automatically. In this work we will provide a comprehensive description of the framework and the motivations behind the various decisions. The software framework will be described starting from the broad vision of the context and then the different components will be described in detail.

There is no established standard for how Internet of Things devices are communicating with each other, every manufacturer uses their own proprietary software and protocols. This makes it difficult to ensure the best possible user experience. There are several projects that can become a standard for how devices discovering, communicating, networking etc. The goal for this thesis work was to compare such software frameworks in some areas and investigate how Inteno’s operating system Iopsys OS can be complemented by implement one of these frameworks. A literature study gave two candidates for the comparison, AllJoyn and Bonjour. The result of the comparison showed that AllJoyn was the most appropriate choice for Inteno to implement into their OS. AllJoyn was chosen because it has a potential to become an established standard and includes tools for easy implementation. To make a proof of concept, an AllJoyn application was created. The application together with a JavaScript web page, can show and control options for an AllJoyn Wi-Fi manager application and AllJoyn enabled lamps., Det finns ingen etablerad standard för hur enheter inom Internet of Things kommunicerar med varandra. När alla tillverkare använder sina egna programvaror och protokoll, försvårar det möjligheten att skapa bästa möjliga användarvänlighet. Det finns flera projekt som utvecklar mjukvaruramverk, flera av dessa har möjligheten att bli en standard för hur enheter upptäcker, kommunicerar mm. Målet med examensarbete var att jämföra sådana mjukvaruramverk inom vissa områden samt att undersöka hur Intenos operativsystem Iopsys OS kan förbättras genom att implementera ett av dessa ramverk. En litteraturstudie gav två kandidater till jämförelsen, AllJoyn och Bonjour. Resultatet av jämförelsen visade att AllJoyn var det lämpligaste valet för Inteno att implementera i sitt operativsystem. AllJoyn valdes eftersom den har potential att bli en etablerad standard och innehåller verktyg för enkel implementering. För att bevisa konceptet, skapades ett AllJoyn-program. Programmet kan tillsammans med JavaScript generera en webbsida där användaren kan styra Wi-Fi inställningar och styra lampor via AllJoyn.

Rapid prototyping environments can speed up the research of visual control algorithms. We have designed and implemented a software framework for fast prototyping of visual control algorithms for Micro Aerial Vehicles (MAV). We have applied a combination of a proxy-based network communication architecture and a custom Application Programming Interface. This allows multiple experimental configurations, like drone swarms or distributed processing of a drone's video stream. Currently, the framework supports a low-cost MAV: the Parrot AR.Drone. Real tests have been performed on this platform and the results show comparatively low figures of the extra communication delay introduced by the framework, while adding new functionalities and flexibility to the selected drone. This implementation is open-source and can be downloaded from www.vision4uav.com/?q=VC4MAV-FW

U ovoj disertaciji istražena je primena savremenih metoda softverskog inženjerstva u razvoju softvera otvorenog koda u oblasti inteligentnih sistema. Razvijen je opšti model softverskog frejmvorka i date su preporuke za vođenje projekata otvorenog koda u toj oblasti. Model frejmvorka i preporuke za vođenje projekata razvijeni su, i potvrđeni u praksi kroz razvoj dodatnih funkcionalnosti jednog izabranog softverskog frejmvorka za neuronske mreže. Izabrani softverki frejmvork za neuronske mreže je trenutno jedan od vodećih softvera u toj oblasti u svetu. Predložene metode razvoja softvera su opšteg karaktera, primenljive su i u drugim oblastima inteligentnih sistema, i odgovaraju na aktuelne probleme i specifičnosti razvoja softvera u tim oblastima. Problemi i specifičnosti se javljaju kao posledica toga što se često radi o softveru istraživačke namene koji treba da bude primenljiv i za praktične probleme, i kao posledica složenosti softvera koji istovremeno treba da bude jednostavan za korišćenje i modifikaciju. Predložene metode razvoja odnose se na specifikaciju zahteva, planiranje razvoja, projektovanje, implementaciju i održavanje, i date su u vidu preporuka koje su se pokazale kao dobra praksa. Pored ovog teorijskog i metodološkog doprinosa, kroz disertaciju su razvijena i konkretna unapređenja izabranog frejmvorka iz oblasti inteligentnih sistema, koja značajno unapređuju njegovu praktičnu primenu..., This dissertation survey the application of modern software engineering methods for open source software development in the field of intelligent systems. Main results are a general model of software framework and recommendations for managing open source projects in that field. Framework model and recommendations for the project management were developed and validated in practice through the development of additional features of a selected software framework for neural networks. Selected neural network framework is one of the leading Java open source neural network frameworks. Suggested software development methods are also applicable in other fields of intelligent systems, and they are response to problems and specific requirements in this field. Most problems are due the fact that it is often software for research purposes, that should also be applicable for practical problems, and complexity of the software that also needs to be easy to use and modify. Proposed development methods are related to requirements specification, planning, design, implementation and maintenance. They are given in the form of recommendations that have been proven as a good practice. In addition to these theoretical and methodological contributions, this dissertation also provides practical contribution in the form of new features for one selected framework in the field of intelligent systems, which greatly enhance its practical application...

Rapid prototyping environments can speed up the research of visual control algorithms. We have designed and implemented a software framework for fast prototyping of visual control algorithms for Micro Aerial Vehicles (MAV). We have applied a combination of a proxy-based network communication architecture and a custom Application Programming Interface. This allows multiple experimental configurations, like drone swarms or distributed processing of a drones video stream. Currently, the framework supports a low-cost MAV: the Parrot AR.Drone. Real tests have been performed on this platform and the results show comparatively low figures of the extra communication delay introduced by the framework, while adding new functionalities and flexibility to the selected drone. This implementation is open-source and can be downloaded from www.vision4uav.com/?q=VC4MAV-FW