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Nowadays, natural language processing (NLP) is one of the most popular areas of, broadly understood, artificial intelligence. Therefore, every day, new research contributions are posted, for instance, to the arXiv repository. Hence, it is rather difficult to capture the current "state of the field" and thus, to enter it. This brought the idea of applying state-of-the-art NLP techniques to analyse the NLP-focused literature. As a result, (1) meta-level knowledge, concerning the current state of NLP has been captured, and (2) a guide to use of basic NLP tools is provided. It should be noted that all the tools and the dataset described in this contribution are publicly available. Furthermore, the originality of this review lies in its full automation. This allows easy reproducibility and continuation and updating of this research in the future as new researches emerge in the field of NLP.

Current software projects require continuous integration during their whole lifetime. In this context, different approaches regarding introduction of DevOps and DevSecOps strategies have been proposed in the literature. While DevOps proposes an agile methodology for the development and instantiation of software platforms with minimal impact in any kind of operations environment, this contribution proposes the introduction of DevOps methodology for Next Generation IoT deployments. Moreover, novelty of the proposed approach lies in leveraging DevSecOps in different stages and layers of the architecture. In particular, the present work describes the different DevSecOps methodology tasks, and how the security is included on pre-design activities such as planning, creation or adaptation, the design and implementation, as well as on post-implementation activities such as detection, response. Without proper consideration of security and privacy best practices identified in this article, the continuous delivery of services using DevOps methodologies may create risks and introduce different vulnerabilities for Next Generation IoT deployments.

Eight years ago we have designed and implemented an initial prototype of an agent-based glider pilot support system (the GliderAgent). Our aim was to validate correctness of the initial assumption that an agent-based system, combined with sensor data, can help pilots in various situations that occur during a flight. For instance, the GliderAgent was capable of detecting certain dangers, warn the pilot and, autonomously, send notification(s) to the ground station. Due to the continuous and rapid development of mobile technologies and sensors, our initial prototype has evolved. First, we moved the system, from an emulated environment, to real devices. Second, a semantic-rule-based decision making system was integrated with the GliderAgent, to analyze feeds from sensors (altitude, temperature, blood pressure, etc.) and, based on received information, to trigger appropriate behaviors. The result of our work provided a foundations for development of a general-purpose, cyber-physical system framework, which will be described in this paper. Furthermore, the developed system illustrates an interesting approach to integration of heterogeneous IoT devices (potentially also IoT platforms).

The disruptive potentials of the Internet of Things (IoT) entails multifaceted requirements and development issues (large scale deployments, heterogeneity, cyberphysicality, interoperability, distributed smartness, self-management, etc.). To adequately tackle them and to comprehensively support the development of the IoT ecosystem, the Agent-Based Computing (ABC) represents a proper and solid modeling, programming and simulation paradigm. Indeed, abstractions, design methods, technology and frameworks related to the ABC have been widely exploited, possibly jointly with other wellestablished/emerging computing paradigms, to actually develop advanced IoT ecosystem. This survey, an extension of our previous work, reports most relevant contemporary contributions in the field, aiming at assessing suitability of the ABC paradigm for the (current and future) IoT development. (C) 2019 Elsevier B.V. All rights reserved.

Next Generation Internet of Things (NGIoT) addresses the deployment of complex, novel IoT ecosystems. These ecosystems are related to different technologies and initiatives, such as 5G/6G, AI, cybersecurity, and data science. The interaction with these disciplines requires addressing complex challenges related with the implementation of flexible solutions that mix heterogeneous software and hardware, while providing high levels of customisability and manageability, creating the need for a blueprint reference architecture (RA) independent of particular existing vertical markets (e.g., energy, automotive, or smart cities). Different initiatives have partially dealt with the requirements of the architecture. However, the first complete, consolidated NGIoT RA, covering the hardware and software building blocks, and needed for the advent of NGIoT, has been designed in the ASSIST-IoT project. The ASSIST-IoT RA delivers a layered and modular design that divides the edge-cloud continuum into independent functions and cross-cutting capabilities. This contribution discusses practical aspects of implementation of the proposed architecture within the context of real-world applications. In particular, it is shown how use of cloud-native concepts (microservices and applications, containerisation, and orchestration) applied to the edge-cloud continuum IoT systems results in bringing the ASSIST-IoT concepts to reality. The description of how the design elements can be implemented in practice is presented in the context of an ecosystem, where independent software packages are deployed and run at the selected points in the hardware environment. Both implementation aspects and functionality of selected groups of virtual artefacts (micro-applications called enablers) are described, along with the hardware and software contexts in which they run.

Personal and home sensors generate valuable information that could be used in Smart Cities. Unfortunately, typically, this data is locked out and used only by application/system developer. While vendors are to blame, one should consider also the "binary nature" of data access. Specifically, either owner has full control over her data (e.g. in a "closed system"), or she completely looses control, when the data is "opened". In this context, we propose, a semantic technologies-based, authorization and privacy control framework that enables user to maintain flexible, yet manageable data access control policies. The proposed approach is described in detail, including implementation and testing., in IEEE Transactions on Emerging Topics in Computing (Early Access), 2020

This Handbook is written in the framework of WP2 – Project Coordination and Management of ASSIST-IoT project under Grant Agreement No. 957258. The document is a guide to the working procedures, governance structures and available tools to be employed towards the management and implementation of the ASSIST-IoT project. Its purpose is to briefly describe the management and coordination framework and the procedural guidelines of the project towards a successful execution. It aims at complementing the information that can be found both in Consortium Agreement and in Grant Agreement.

In multiple areas of image processing, such as Computed Tomography, in which data acquisition is based on counting particles that hit a detector surface, Poisson noise occurs. Using variance-stabilizing transformations, the Poisson noise can be approximated by a Gaussian one, for which classical denoising filters can be used. This paper presents an experimental performance study of a parallel implementation of the Poissonian image restoration algorithm, introduced in Harizanov etźal. (2013). Hybrid parallelization based on MPI and OpenMP standards is investigated. The convergence rate of the algorithm heavily depends on both the image size and the choice of input parameters ( ź , ź ) , thus maximizing its parallel efficiency is vital for real-life applications. The implementation is tested for high-resolution radiographic images, on Linux clusters with Intel processors and on an IBM supercomputer.

Over time, two counteracting trends have been observed in the “world of computing”. One of them was a push from centralized towards decentralized solutions. The first wave of this process can be associated with the introduction of the Internet and personal computers and took place in early 1980th. The second was the move in the opposite direction. Here, the first wave of centralization can be associated with the ascent of cloud computing. These two seem to be similar to the thesis and antithesis, in Hegel’s philosophy. Interestingly, similarly to Hegel’s synthesis, we are approaching a unified model of edge-fog-cloud continuum. My talk will reflect on the journey and outline the proposed way forward.

In combinatorial optimization, the goal is to find the optimal object from a finite set. Since such problems are hard to be solved, usually some metaheuristics is applied. One of the most successful techniques for a number of classes of problems is Ant Colony Optimization (ACO). Some start strategies can be applied, to the ACO algorithms, to improve their performance. Here, the InterCriteria Analysis (ICrA) is applied to the ACO algorithm. On the basis of the ICrA, we examine and analyse the ACO performance according to the different start strategies.

Every organization and factory optimize their production process with a help of workforce planing. The aim is minimization of the assignment costs of the workers, who will do the jobs. The problem is very complex and needs exponential number of calculations, therefore special algorithms are developed to be solved. The problem is to select employers and to assign them to the jobs to be performed. This problem has very strong constraints and it is difficult to find feasible solutions. The objective is to fulfil the requirements and to minimize the assignment cost. We propose a hybrid Ant Colony Optimization (ACO) algorithm to solve the workforce problem, which is a combination between ACO and an appropriate local search procedure.

The workforce planning helps organizations to optimize the production process with aim to minimize the assigning costs. A workforce planning problem is very complex and needs special algorithms to be solved. The problem is to select set of employers from a set of available workers and to assign this staff to the jobs to be performed. Each job requires a time to be completed. For efficiency, a worker must performs a minimum number of hours of any assigned job. There is a maximum number of jobs that can be assigned and a maximum number of workers that can be assigned. There is a set of jobs that shows the jobs on which the worker is qualified. The objective is to minimize the costs associated to the human resources needed to fulfill the work requirements. On this work we propose a variant of Ant Colony Optimization (ACO) algorithm to solve workforce optimization problem. The algorithm is tested on a set of 20 test problems. Achieved solutions are compared with other methods, as scatter search and genetic algorithm. Obtained results show that ACO algorithm performs better than other two algorithms.

Recent years have seen growing interest in collecting and processing sensor data, in distributed mobile environments. In this context, two, somewhat contradictory, trends have emerged: (1) growing popularity of crowdsourcing-type mechanisms, for (sensor) data collection, and (2) collecting sensed data in data “silos”, which are not only unavailable to “outsiders”, but most often incompatible, thus reducing their usability for data mining. Given these limitations in data accessibility, and compatibility, enormous potential for knowledge discovery is lost. To counter this trend, we propose a generic, adaptive, system that will allow voluntary participation in arbitrary crowdsensing initiatives, with the output stored in a standard data format. The system utilizes a rule-based multiagent approach to instructing sensors when to make readings and how to, if necessary, preprocess them, before sharing the data with user-selected initiatives. The initial version of the system has been implemented, and tested in a...

The Internet of Things (IoT) idea, explored across the globe, brings about an important issue: how to achieve interoperability among multiple existing (and constantly created) IoT platforms. In this context, in January 2016, the European Commission has funded seven projects that are to deal with various aspects of interoperability in the Internet of Things. Among them, the INTER-IoT project is aiming at the design and implementation of, and experimentation with, an open cross-layer framework and associated methodology to provide voluntary interoperability among heterogeneous IoT platforms. While the project considers interoperability across all layers of the software stack, we are particularly interested in answering the question: how ontologies and semantic data processing can be harnessed to facilitate interoperability across the IoT landscape. Henceforth, we have engaged in a “fact finding mission” to establish what is currently at our disposal when semantic interoperability is concerned. Since the INTER-IoT project is initially driven by two use cases originating from (i) ( e/m ) Health and (ii) transportation and logistics , these two application domains were used to provide context for our search. The paper summarizes our findings and provides foundation for developing methods and tools for supporting semantic interoperability in the INTER-IoT project (and beyond).

The chapter includes a hybrid concept combining bee colony optimization with the application of Ordered Fuzzy Numbers. This is another research, after the OFNAnt method, prepared in AIRlab - Artificial Intelligence and Robotics Laboratory at Kazimierz Wielki University in Bydgoszcz, in which authors enriched metaheuristics by implementing the arithmetics of Ordered Fuzzy Numbers (OFNs). Applied fuzzy observation enabled very faithful modeling of the navigation mechanism used by bees when orienting with reference to the position of the sun. Experiments aimed at verification of the developed concept have been carried out on a set of several commonly known benchmarks. The preliminary results of experiments allow us to nurture grounded hope that further modifications of the metaheuristics using OFN arithmetics shall enable smooth control of the optimization criteria of the tested phenomena.

Internet of Things (IoT), as a new paradigm for information management requires a number of novel solutions. The aim of this note is to consider methods and approaches needed to facilitate autonomous resource access. The access policies have not only to become capable of dealing with device-to-device interactions, but have to be flexible enough to deal with enormous heterogeneity of entities that are to interact in the IoT. Specifically, we will briefly summarize the existing approaches to access management and outline our approach to provisioning of the needed functionality.

In this paper we describe an early stage prototype of a system for information resource management in an agent-based virtual organization. We focus our work on support for human resource adaptability (e.g. knowledge, skills) as a tool for assisting project managers. In the proposed approach, a virtual organization is functionalized in terms of roles played by agents, while organization structure and information flow are represented in terms of agent-agent and agent-human interactions. Finally, all resources (e.g. workers, skills, training resources, etc.) are ontologically demarcated, and information is semantically processed. Discussion of organizational needs is followed by the outline of the system. Finally, basic capabilities of the implemented prototype are illustrated.

Monitoring resource utilization in distributed systems remains of importance. This is especially the case in LAN-based distributed systems (and, in particular, in global Grid systems), where individual nodes can be (may need to be) added to and/or removed from the system at ?random? moments. The aim of this paper is to report initial results of the project that aims at using Erlang-based software agents as a robust and flexible resource monitoring infrastructure. The implemented prototype is capable not only of collecting performance data, but can also detect certain network problems. Furthermore, an assessment of the eXAT agent platform, based on experiences gathered during prototype implementation, is included.

The total number of popular search engines has decreased over time from its peak of the late 1990s. However, when combining the three remaining major ones (Yahoo!, Google, MS Live) with large repositories of information (e.g. BBC.com, NYT.com, Gazeta.pl, etc.), the total number of important information sources can be seen as slowly increasing. Focusing on utilization of search engines only, it is easy to observe that the same query issued to each one of them results in different ''suggestions.'' The question thus arrives, is it possible (and worthy) to combine responses obtained from each one of them into a single answer set. In this paper, we look into three approaches of achieving this goal, which are based on: Game theory, Auction and Consensus methods, while our focus is to study (and compare) their ''performance.''

Internet of Things (IoT) requires novel solutions to facilitate autonomous, though controlled, resource access. Access policies have to facilitate interactions between heterogeneous entities (devices and humans). Here, we focus our attention on access control in eHealth. We propose an approach based on enriching policies, based on well-known and widely-used eXtensible Access Control Markup Language, with semantics. In the paper we describe an implementation of a Policy Information Point integrated with the HL7 Security and Privacy Ontology.

The physical model of Water Capsule Flight is relatively simple but analytically unsolvable. The input data includes the mass of the capsule, velocity, altitude, aerodynamic coefficients of the capsule, and horizontal and vertical winds. The ABC optimization is focused on those attributes. This article is a part of the series dedicated to Inspired by Nature Methods of AI and their implementation in the mechatronic systems. A bag filled with water is an excellent source of explosion-produced water spray which can be used for extinguishing large fires or for other purposes. The paper presents theoretical models of flight of a bag filled with water, dropped from an aircraft moving horizontally. Results of numerical computations based on this model are compared with results of measurements for the trajectory of a bag dropped from a helicopter. A description of the experimental and numerical setup for this experiment are also discussed.

In our previous work we have studied the performance of a parallel algorithm, based on a direction splitting approach, for solving of time dependent Stokes equation. We used a rectangular uniform mesh, combined with a central difference scheme for the second derivatives. Hence, the proposed algorithm required only solution of tridiagonal linear systems.In our work, we are targeting massively parallel computers, as well as clusters of multi-core nodes. The somehow slower (experimentally-established) performance of the proposed approach was observed when using all cores on a single node of a cluster. To remedy this problem, we tried to use LAPACK subroutines from the multi-threaded layer library, but the parallel performance of the code (while improved) was still not satisfactory on a single (multi-core) node.Our current work considers hybrid parallelization based on the MPI and OpenMP standards. It is motivated by the need to maximize the parallel efficiency of our implementation of the proposed algorithm. Essential improvements of the parallel algorithm are achieved by introducing two levels of parallelism: (i) between-node parallelism based on the MPI and (ii) inside-node parallelism based on the OpenMP. The implementation was tested on Linux clusters with Intel processors and on the IBM supercomputer.

The aim of our work is to design a decision support system based on ontological representation of domain(s) and semantic technologies. Specifically, we consider the case when Grid / Cloud user describes his/her requirements regarding a “resource” as a class expression from an ontology, while the instances of (the same) ontology represent available resources. The goal is to help the user to find the best option with respect to his/her requirements, while remembering that user’s knowledge may be “limited.” In this context, we discuss multiple approaches based on semantic data processing, which involve different “forms” of user interaction with the system. Specifically, we consider: (a) ontological matchmaking based on SPARQL queries and class expression, (b) graph-based semantic closeness of instances representing user requirements (constructed from the class expression) and available resources, and (c) multicriterial analysis based on the AHP method, which utilizes expert domain knowledge (also ontologically represented).

A rapid growth of available geospatial data requires development of systems capable of autonomous data retrieval, integration and validation. Mobile agents may provide the suitable framework for developing such systems since this technology, in a natural way, can deal with the distributed heterogeneous nature of such data. In this paper, we evaluate SDIAGENT our, recently introduced, multi-agent architecture for geospatial data integration and conflation, and compare its model performance with that of client/server and single-agent approaches. Experimental results for several realistic scenarios, under varying conditions, are presented for these three system architectures. We analyze the performance alteration for various numbers of participating nodes, varying amount of database accesses, processing loads, and network loads.

In this paper, an investigation of the influence of the population size on the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) performance for a model parameter identification problem, is considered. The mathematical model of an E. coli fed-batch cultivation process is studied. The three model parameters—maximum specific growth rate (\(\mu _{max}\)), saturation constant (\(k_{S}\)) and yield coefficient (\(Y_{S/X}\)) are estimated using different population sizes. Population sizes between 5 and 200 chromosomes and 5 and 100 ants in the population are tested with constant number of generations. In order to obtain meaningful information about the influence of the population size a considerable number of independent runs of the GA are performed. The observed results show that the optimal population size is 100 chromosomes for GA and 70 ants for ACO for 200 generations. In this case accurate model parameters values are obtained in reasonable computational time. Further increase of the population size, above 100 chromosomes for GA and 70 ants for ACO, does not improve the solution accuracy. Moreover, the computational time is increased significantly.

Vector Product Format (VPF) based databases store geographical data in a relational framework, where individual VPF files are arranged hierarchically in a directory tree structure. Access and update of the VPF data can become difficult due to fragmentation of data among multiple tables. This paper presents an object-oriented model for the management of a VPF database, which provides easy access and automatic update for the VPF data, and is compatible with ESRI ArcView. This model has been successfully implemented in Java, for Digital Nautical Charts (DNC).

Agent oriented programming is often claimed to become the next breakthrough in development and implementation of large-scale complex software systems. At the same time it is rather difficult to find successful applications of agent technology, in particular when large-scale systems are considered. The aim of this paper is to investigate if one of the possible limits could be the scalability of existing agent environments. For this purpose we have selected JADE agent platform and investigated its performance in a number of test-scenarios. Results of our experiments are presented and discussed.

Maria Ganzha, Marcin Paprzycki, Costin Badica and Rainer Unlandd,∗ aSystems Research Institute, Polish Academy of Sciences, Warsaw and University of Gdansk, Warsaw, Poland bSystems Research Institute, Polish Academy of Sciences, Warsaw and Management Academy, Warsaw, Poland cSoftware Engineering Department, Faculty of Automatics, Computers and Electronics, University of Craiova, Bvd.Decebal, Craiova, Romania dUniversity of Duisburg-Essen, Institute for Computer Science and Business Information Systems (ICB), Practical Computer Science, Data Management Systems and Knowledge Representation, Essen, Germany

In this paper, a hybrid scheme, to solve optimization problems, using a Genetic Algorithm (GA) and an Ant Colony Optimization (ACO) is introduced. In the hybrid GA-ACO approach, the GA is used to find a feasible solutions to the considered optimization problem. Next, the ACO exploits the information gathered by the GA. This process obtains a solution, which is at least as good as—but usually better than—the best solution devised by the GA. To demonstrate the usefulness of the presented approach, the hybrid scheme is applied to the parameter identification problem in the E. coli MC4110 fed-batch fermentation process model. Moreover, a comparison with both the conventional GA and the stand-alone ACO is presented. The results show that the hybrid GA-ACO takes the advantages of both the GA and the ACO, thus enhancing the overall search ability and computational efficiency of the solution method.

Since the beginning of the 21st century, we observe rapid changes in the area of, broadly understood, computational sciences. One of interesting effects of these changes is the need for reevaluation of the role of dense matrix multiplication. The aim of this paper is two-fold. First, to summarize developments that point toward a need for reconsidering usefulness of matrix multiplication generalized on the basis of the theory of algebraic semirings. Second, to propose generalized matrix-matrix multiply-and-update (MMU) operation and its object oriented model.

Recently, a novel method for image scrambling (and unscrambling) has been proposed. This method is based on a linear transformation involving the Kroneker-delta function. However, while quite interesting, the way it was introduced, leaves some open issues concerning its actual usability for information hiding. Therefore, in this paper, we extend the original proposal and show how it can be used to securely pass image-like information between the users.

Recently, work has started to apply the agent-semantic infrastructure, developed within the scope of the Agents in Grid project, to the resource management needed in tsunami modeling. The original proposal was based on the perceived simplicity, versatility and flexibility of the agent-based approach that makes it easier to deploy than the standard grid middlewares. The aim of this paper is to report on the progress in implementing and deploying the proposed system at the University of Aizu.

Grid computing has, in recent history, become an invaluable tool for scientific research. As grid middleware has matured, considerations have extended beyond the core functionality, towards greater usability. The aim of this paper is to consider how resources that are available to the users across the Queens gate Grid (QGG) at the University of Huddersfield (UoH), could be accessed with the help of an ontology-driven interface. The interface is a part of the Agent in Grid (AiG) project under development at the Systems Research Institute Polish Academy of Sciences (SRIPAS). It is to be customized and integrated with the UoH computing environment. The overarching goal is to help users of the grid infrastructure. The secondary goals are: (i) to improve the performance of the system, and (ii) to equalize the distribution of work among resources. Results presented in this paper include the new ontology that is being developed for the grid at the UoH, and the description of issues encountered during the development of a scenario when user searches for an appropriate resource within the Unicore grid middleware and submits job to be executed on such resource.