Clothing production is an important sector within manufacturing.It includes the sewing, knitting and leather industries.It is important for a manufacturer to organize the production process well.This organization includes personnel allocation, machine loading, and material allocation.The goal is to complete the given order in the shortest time and, if possible, at the lowest cost.This paper addresses the task of producing knitted shirts.An ant colony optimization algorithm has been proposed to solve the problem.The objective is to complete the order in the shortest possible time.

The correct organization of medical assistance after the occurrence of a major disaster is very important for saving the lives of the victims.Earthquakes are natural phenomena/disasters in which there are many victims.The timely provision of medical assistance to the injured is an important element of their service.It is good to divide them into types of injuries and severity of injuries.Thus, the medical teams will be prepared for how many people need outpatient treatment and how many need hospital treatment.Rapid distribution of victims to hospitals according to their injuries can reduce the number of deaths and people with serious consequences.In this article, we present a breakdown of the injured by hospitals and medical facilities near the earthquake site.The type of injuries and the capacity and equipment of hospital facilities are taken into account.

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.

Production organization plays a key role in the success of any enterprise.Optimizing workforce planning can improve the overall organization of production.The main goal is to minimize the assignment cost of the workers who will perform the planned work.The problem is known to be NPhard, therefore we will apply methods from the field of artificial intelligence.For this reason, most of the existing methods hardly find feasible solutions.We propose Ant Colony Optimization Algorithm with hybridization, combination with local search procedures.We compare and analyze their performance.

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.

Cereals contribute significantly to humanity's livelihood.They are a source of more food energy worldwide than any other group of crops.Their production contributes considerably to the total global anthropogenic greenhouse gas (GHGs) emissions.In this study we propose a basic bio-economic farm model (BEFM) solved with the help of Ant Colony Optimization (ACO) methodology.We aim to assess farm profits and risks considering various types of policy incentives and adverse weather events.The proposed model can be applied to any annual crop.

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.

The local search procedure is a method for hybridization and improvement of the main algorithm, when complex problems are solved.It helps to avoid local optimums and to find faster the global one.In this paper we apply InterCriteria analysis (ICrA) on hybrid Ant Colony Optimization (ACO) algorithm for Multiple Knapsack Problem (MKP).The aim is to study the hybrid algorithm behavior comparing with traditional ACO algorithm.Based on the obtained numerical results and on the ICrA approach the efficiency and effectiveness of the proposed hybrid ACO, combined with appropriate local search procedure are confirmed.

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.

Public transport plays an important role in our live.It is very important to have a reliable service.Up to 1000 km, trains and buses play the main role in the public transport.The number of the people and which kind of transport they prefer is important information for transport operators.In this paper is proposed algorithm for transport modeling and passenger flow, based on Ant Colony Optimization method.The problem is described as multi-objective optimization problem.There are two optimization purposes: minimal transportation time and minimal price.Some fuzzy element is included.When the price is in a predefined interval it is considered the same.Similar for the starting traveling time.The aim is to show how many passengers will prefer train and how many will prefer buses according their preferences, the price or the time.

Optimization of the production process is important for every factory or organization.The better organization can be done by optimization of the workforce planing.The main goal is decreasing the assignment cost of the workers with the help of which, the work will be done.The problem is NP-hard, therefore it can be solved with algorithms coming from artificial intelligence.The problem is to select employers and to assign them to the jobs to be performed.The constraints of this problem are very strong and for the algorithms is difficult to find feasible solutions.We apply Ant Colony Optimization Algorithm to solve the problem.We investigate the algorithm performance according evaporation parameter.The aim is to find the best parameter setting.

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.

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.

International audience

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