In this contribution, the numerical solution of the spectral fractional elliptic equation with power α ∈ (1, 2) is studied.After discretization, the problem is reduced to solving a system of linear algebraic equations.We apply the Best Uniform Rational Approximation (BURA) method and focus on the numerical aspects, related to its implementation.An extensive experimental study is conducted to evaluate the accuracy of the proposed approach.The numerical results confirm the theoretical analysis and demonstrate the effectiveness of using the BURA method for the fractional super-diffusion problems.

There is great potential in leveraging Artificial Intelligence (AI) systems to optimize complex infrastructures, automate difficult tasks, or support autonomy and coordination between networked devices. However, advances in state-of-the-art AI often neglect features and/or requirements that businesses care deeply about, namely traceability and explainability. While majority of research concerning Explainable AI remains focused on weight modelling and timid gray-box approaches, the state-of-the-art has not explored much the deployment of semi-physical architectures combining fuzzy rule-based systems with more opaque models to improve explainability. This contribution aims to explore and make the case for a middle ground of mixed AI architectures that combine the performance of black-box AI models with a more explainable overall architecture, enabling operators to use them, while still retaining the core aspects of explainability, when compared to full black-box AI systems. This work contextualizes a potential application of such approach to the problem of Service Level Agreement compliance, in a case of microservice allocation decision over cloud (and cloud-like) infrastructures.

In recent years, there has been an increase in interest in carbon-aware computing. Here, the Green Edge Processing project inspired the development of the Extended Green Cloud Simulator (EGCS), which serves as an agent-based digital twin, facilitating the simulation of cloud infrastructure powered, in part, by renewable energy sources. Given that cloud systems must operate efficiently for a large number of clients, the following contribution briefly describes the design of the EGCS and discusses the results of the experimental assessment of its scalability.

It was shown that block-circulant preconditioners, applied to a conjugate gradient method, used to solve structured sparse linear systems, arising from 2D or 3D elliptic problems, have very good numerical properties and a potential for good parallel efficiency.In this contribution, hybrid parallelization based on MPI and OpenMP standards is experimentally investigated.Specifically, the aim of this work is to analyze parallel performance of the implemented algorithms on a supercomputer consisting of Intel Xeon processors and Intel Xeon Phi coprocessors.While obtained results confirm the positive outlook of the proposed approach, important open issues are also identified.

While straightforward machine translation got significant improvements in the last 10 years with the arrival of encoder-decoder neural networks and transformers architecture, controllable machine translation still remains a difficult task, which requires lots of research.Existing methods like tagging provide very limited control over model results or they require to support multiple models at once, like domain fine-tuning approach.In this paper, we propose a method to control translation results style by transferring features from a set of texts with target structure and wording.Our solution consists of new modifications for the encoder-decoder networks, where we can add feature descriptors to each token embedding to decode input text into the translation with the proposed domain.In conducted experiments with English-Ukrainian translation and a set of 4 domains our proposed model gives more options to influence the result than some existing approaches to solve the controllability model.

This paper presents the Multitask, Multilingual, Multimodal Language Generation COST Action – Multi3Generation (CA18231), an interdisciplinary network of research groups working on different aspects of language generation. This “meta-paper” will serve as reference for citations of the Action in future publications. It presents the objectives, challenges and a the links for the achieved outcomes.

The aim of this contribution is to analyse practical aspects of the use of REST APIs and gRPC to realize communication tasks in applications in microservice-based ecosystems. On the basis of performed experiments, classes of communication tasks, for which given technology performs data transfer more efficiently, have been established. This, in turn, allows formulation of criteria for the selection of appropriate communication methods for communication tasks to be performed in an application using microservices-based architecture.

New requirements, posed by the Next Generation IoT, demand design of novel reference architectures, providing foundation for implementation of Internet of Things (IoT) ecosystems. Building on cloud-native concepts (e.g. microservices, virtualisation, and containerization), a flexible architecture that answers requirements present in recent IoT deployments is introduced. A general description of components of the architecture (grouped in horizontal planes and vertical capabilities) is provided, together with formal definition of architectural views. Moreover, ground is laid for upcoming validation in real-world-anchored scenarios. Functional, node, deployment and data views are presented, each of them addressing concerns of different stakeholder groups, typically involved in an IoT deployments.

In this work, the development of virtual reality software for “industrial applications” is considered. It is argued that, in this context, the vast experience from the development of computer games cannot be used directly. Especially, the specific nature of solutions dedicated to industrial applications requires taking into account their specificities, needs, and limitations of VR. This brings about the formulation of a domain-specific approach to creation of VR-related courses for the industry. As an example, the development of course concerning the operation of a professional 3D printer, which is used in industry, as well as in prototyping, is discussed. In particular, it is used to illustrate implementation of UI and UX aspects of the developed VR-environment.

Reusing ontologies in practice is still very challenging, especially when multiple ontologies are (jointly) involved. Moreover, despite recent advances, the realization of systematic ontology quality assurance remains a difficult problem. In this work, the quality of thirty biomedical ontologies, and the Computer Science Ontology are investigated, from the perspective of a practical use case. Special scrutiny is given to cross-ontology references, which are vital for combining ontologies. Diverse methods to detect potential issues are proposed, including natural language processing and network analysis. Moreover, several suggestions for improving ontologies and their quality assurance processes are presented. It is argued that while the advancing automatic tools for ontology quality assurance are crucial for ontology improvement, they will not solve the problem entirely. It is ontology reuse that is the ultimate method for continuously verifying and improving ontology quality, as well as for guiding its future development. Specifically, multiple issues can be found and fixed primarily through practical and diverse ontology reuse scenarios.

Reasoning about, and debugging, hierarchical control plane systems is hard. Moreover, OpenTracing, the industry adopted tracing model, has problems with tracing activities in presence of coalescing effects, which materialize, among others, in cloud platforms and build systems. In our earlier work we have proposed a novel approach to distributed systems tracing, based on an extension of OpenTracing. The aim of this contribution is to outline how the proposed approach can be implemented.

Summary In this article, we present an experimental performance study of a parallel implementation of two Poissonian image restoration algorithms. Hybrid parallelization, based on MPI and OpenMP standards, is investigated. The implementation is tested for high‐resolution radiographic images, on a supercomputer based on Intel Xeon processors, combined with Intel Xeon Phi coprocessors. The experimental results show an essential improvement in the execution times, when running experiments for a variety of problem sizes, and number of threads.

We have recently proposed a mathematical model of collective profitability, in semi-competitive intermediation networks. In this work, we are interested in determining optimal pricing strategies of network participants. The optimization criterion is defined using the Nash social welfare function. We provide theoretical results of existence of such strategies, as well as computational experimental results, based on nonlinear convex mathematical optimization.

Lack of interoperability of Internet of Things (IoT) platforms / systems / applications (artifacts) is being recognized as an important issue that prevents faster development of IoT ecosystems (including Smart Grids / Homes / Neighborhoods, office buildings, etc.). While interoperability can be considered on many levels, in the presentation I will focus on semantic interoperability. Moreover, while originating from IoT scenarios, the proposed approach can be naturally applied outside of the Internet of Things.

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.

From the general SOA architectural pattern, through distributed computing based on Grids and Clouds, to the Internet of Things, the idea of collaboration between software entities, independent from their vendors and technologies, attracts much attention. This brings about a question: how to achieve interoperability among multiple (existing and upcoming) platforms/systems/applications. The context for the presented research is provided by the INTER-IoT project, which deals with different aspects of interoperability in the Internet of Things (IoT). It aims at the design and implementation of an open framework and associated methodology to provide interoperability among heterogeneous IoT platforms, across a software stack (devices, network, middleware, application services, data and semantics). We focus on the data and semantics layer. Specifically, the role of ontologies and semantic data processing, as means of achieving interoperability. However, since the vision of the Semantic Web remains mostly unfulfilled, semantics remains implicitly “hidden” data and in exchanged messages. Therefore, we are particularly interested in establishing what methods and tools exist to create OWL ontologies from implicitly expressed semantics. We focus on popular data formats i.e. XML, JSON, RDF, Relational Databases and NoSQL Databases.

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.

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.

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 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 fedbatch 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.

In our work, an agent-based system supporting workers in an organization is centered around utilization of ontologically demarcated data. In this system, ontological matchmaking, understood as a way of establishing closeness between resources, is one

Currently, we are developing an agent-team based infrastructure for resource brokering and management in Grids. In this note we consider how team is supervised and how mirroring can be applied to improve chances of its long-term persistence.

Recently we have proposed a novel approach to utilizing agent teams as resource brokers and managers in the grid. Thus far we have presented an overview of the proposed approach discussed how to efficiently implement the information center, where agent teams advertise their needs and resources. In this paper we focus our attention on the way that user selects agent team that will execute its job. Details of initial implementation are presented and discussed

In our work we have proposed an agent-based model e-commerce system. In this system buyer agents negotiate prices with seller agents. Thus far our attention was devoted "one sided" price negotiation mechanisms, i.e. mechanisms in which one side is active, while the other side is passive (possibly other than initiating the process). Examples of such negotiations are, among others, English and Dutch Auctions, as well as a number of sealed-bid auctions. Here, we focus our attention on auctions in which both sides take active part. We discuss how these auctions can be incorporated into our system and present their UML formalizations.

Whole genome comparison consists of comparing or aligning genome sequences with a goal of finding similarities between them. Previously we have shown how SIMD extensions used in Intel processors can be used to efficiently implement the, genome comparing, Smith-Waterman algorithm. Here we present distributed version of that algorithm. We show that on somewhat outdated hardware we can achieve speeds upwards of 8000 MCUPS; one of the fastest implementations of the Smith-Waterman algorithm.

Recently, we have proposed a comprehensive agent-based e-commerce system. While UML formalized, it lacked details how basic functions - e.g. user request to purchase a given product - are to be implemented. Furthermore, the "airline ticket reservation model" used in the system involves time management issues that have not been addressed. The aim of the paper is to discuss the way in which the information flow and data transformations involved in it are to be implemented; assuming that information about products is to be ontologically represented. Furthermore, a simple way in which time information can be successfully managed to support the proposed product reservation approach will be discussed

We consider the construction and management of user profiles for an agent-based travel support system, with the goal of providing personalized content for individual users of the system. Profiles consist of statements about the "world of travel." Conditional probabilities for each statement model the strengths of user preferences. These probabilities are derived from implicit and explicit observations of user behavior.

Online travel support systems have often been cited as an ideal proving ground for agent-based architectures, yet no working systems have materialized. Over the last few years we have been considering various aspects of the design of such travel support systems. The aim of this note is to present the current state of our comprehensive framework for delivering personalized travel services using agent infrastructure