Ontonuts: Reusable Semantic Components for Multi-agent Systems

The volumes of data in information systems are growing drastically. The systems become increasingly complex in trying to handle heterogeneity of ubiquitous components, standards, data formats, etc. According to the vision of Autonomic Computing, the complexity can be handled by introducing self-manageable components able to "run themselves." Agent Technology fits this vision, whereas interoperability among autonomic components can be tackled by Semantic Technologies. The problem of efficient heterogeneous data sharing, exchange and reuse within such systems plays a key role. We present an approach of constructing semantic capabilities (self-descriptive functional components) for software ag…

Using UDDI for Publishing Metadata of the Semantic Web

Although UDDI does not provide support for semantic search, retrieval and storage, it is already accepted as an industrial standard and a huge number of services already store their service specifications in UDDI. Objective of this paper is to analyze possibilities and ways to use UDDI registry to allow utilization of meta-data encoded according to Semantic Web standards for semantic-based description, discovery and integration of web resources in the context of needs of two research projects: “Adaptive Services Grid” and “SmartResource”. We present an approach of mapping RDFS upper concepts to UDDI data model using tModel structure, which makes possible to store semantically annotated reso…

Large Scale Knowledge Matching with Balanced Efficiency-Effectiveness Using LSH Forest

Evolving Knowledge Ecosystems were proposed to approach the Big Data challenge, following the hypothesis that knowledge evolves in a way similar to biological systems. Therefore, the inner working of the knowledge ecosystem can be spotted from natural evolution. An evolving knowledge ecosystem consists of Knowledge Organisms, which form a representation of the knowledge, and the environment in which they reside. The environment consists of contexts, which are composed of so-called knowledge tokens. These tokens are ontological fragments extracted from information tokens, in turn, which originate from the streams of information flowing into the ecosystem. In this article we investigate the u…

Responsible cognitive digital clones as decision-makers: A design science research study

This study uses a design science research methodology to develop and evaluate the Pi-Mind agent, an information technology artefact that acts as a responsible, resilient, ubiquitous cognitive clone – or a digital copy – and an autonomous representative of a human decision-maker. Pi-Mind agents can learn the decision-making capabilities of their “donors” in a specific training environment based on generative adversarial networks. A trained clone can be used by a decision-maker as an additional resource for one’s own cognitive enhancement, as an autonomous representative, or even as a replacement when appropriate. The assumption regarding this approach is as follows: when someone was forced t…

Large Scale Knowledge Matching with Balanced Efficiency-Effectiveness Using LSH Forest

Evolving Knowledge Ecosystems were proposed to approach the Big Data challenge, following the hypothesis that knowledge evolves in a way similar to biological systems. Therefore, the inner working of the knowledge ecosystem can be spotted from natural evolution. An evolving knowledge ecosystem consists of Knowledge Organisms, which form a representation of the knowledge, and the environment in which they reside. The environment consists of contexts, which are composed of so-called knowledge tokens. These tokens are ontological fragments extracted from information tokens, in turn, which originate from the streams of information flowing into the ecosystem. In this article we investigate the u…

Artificial General Intelligence vs. Industry 4.0 : Do They Need Each Other?

Artificial Intelligence (AI) is known to be a driving force behind the Industry 4.0. Nowadays the current hype on development and industrial adoption of the AI systems is mostly associated with the deep learning, i.e., with the abilities of the AI to perform various specific cognitive activities better than humans do. However, what about the Artificial General Intelligence (AGI), associated with the generic ability of a machine to perform consciously any task that a human can? Do we have many samples of the AGI research adopted by Industry 4.0 and used for smart manufacturing? In this paper, we report the systematic mapping study regarding the AGI-related papers (published during the five-y…

An introduction to knowledge computing

This paper deals with the challenges related to self-management and evolution of massive knowledge collections. We can assume that a self-managed knowledge graph needs a kind of a hybrid of: an explicit declarative self-knowledge (as knowledge about own properties and capabilities) and an explicit procedural self-knowledge (as knowledge on how to utilize own properties and the capabilities for the self-management).We offer an extension to a traditional RDF model of describing knowledge graphs according to the Semantic Web standards so that it will also allow to a knowledge entity to autonomously perform or query from remote services different computational executions needed. We also introdu…

Ignorance-Aware Approaches and Algorithms for Prototype Selection in Machine Learning

Operating with ignorance is an important concern of the Machine Learning research, especially when the objective is to discover knowledge from the imperfect data. Data mining (driven by appropriate knowledge discovery tools) is about processing available (observed, known and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples, which are not yet observed, known or understood. These tools traditionally take samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach and we suggest considering the things the other way around. What if the task would be as follows: how to learn a mode…

Bridging human and machine learning for the needs of collective intelligence development

There are no doubts that artificial and human intelligence enhance and complement each other. They are stronger together as a team of Collective (Collaborative) Intelligence. Both require training for personal development and high performance. However, the approaches to training (human vs. machine learning) are traditionally very different. If one needs efficient hybrid collective intelligence team, e.g. for managing processes within the Industry 4.0, then all the team members have to learn together. In this paper we point out the need for bridging the gap between the human and machine learning, so that some approaches used in machine learning will be useful for humans and vice-versa, some …

Semantic Agent Programming Language (S-APL): A Middleware Platform for the Semantic Web

The agent-based approach is an effective one for building middleware interconnecting distributed heterogeneous resources and providing semantic interoperability among them. On the other hand, agents need the semantic Web technologies for flexible yet effective coordination among them with a particular issue of enabling agents to communicate not only about the domain but also about their own abilities, goals, and present and intended actions. This paper describes semantic agent programming language (SAPL)intended to be a core middleware language for the semantic Web. S-APL integrates the semantic description of the domain resources with the semantic prescription of the agents' behaviors. Add…

The role of Ukrainian universities in the development of the global information society

This paper presents an observation of the positive experience obtained by Kharkov State Technical University in the area of education specialists for the development of an information society programme. Economic problems postponed the beginning of the information society programme in Ukraine. Nevertheless, Ukraine now has good possibilities to apply the best experience of western universities and speed up the process of transferring existing education to the European level. The paper also presents an analysis of the main trends that are taking place in that education. The paper uses the example of one of the leading Ukrainian technical universities to show the possible ways of positive chan…

Causality-Aware Convolutional Neural Networks for Advanced Image Classification and Generation

Smart manufacturing uses emerging deep learning models, and particularly Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), for different industrial diagnostics tasks, e.g., classification, detection, recognition, prediction, synthetic data generation, security, etc., on the basis of image data. In spite of being efficient for these objectives, the majority of current deep learning models lack interpretability and explainability. They can discover features hidden within input data together with their mutual co-occurrence. However, they are weak at discovering and making explicit hidden causalities between the features, which could be the reason behind the parti…

A Similarity Evaluation Technique for Cooperative Problem Solving with a Group of Agents

Evaluation of distance or similarity is very important in cooperative problem solving with a group of agents. Distance between problems is used by agents to recognize nearest solved problems for a new problem, distance between solutions is necessary to compare and evaluate the solutions made by different agents, and distance between agents is useful to evaluate weights of the agents to be able to integrate them by weighted voting. The goal of this paper is to develop a similarity evaluation technique to be used for cooperative problem solving with a group of agents. Virtual training environment used for this goal is represented by predicates that define relationships within three sets: prob…

Evolutionary cloud for cooperative UAV coordination

Global Understanding Environment: Towards Self-managed Web of Everything

Current Web grows rapidly to several directions (from the Web of Documents to the Webs of Humans, Things, Services, Knowledge, Intelligence, etc.). Consequently the recent and future Web-based applications, systems and frameworks (like, e.g., Social and Ubiquitous Computing, SOA and Cloud Computing, etc.) should take into account challenges related to extremely high heterogeneity of components and exponentially increased complexity of a business logic connecting and making them interoperable. Enabling self-management enhanced with semantic technology seems to be an only option to handle that. We suggest adding a "virtual representative" to every resource in the Web to solve the global inter…

Semantic Web Services for Smart Devices in a “Global Understanding Environment”

Various Web resources and services are usually assumed to be used and accessed by human users (current Web) or by software agents on behalf of human users (emerging Semantic Web). However industry emerges also a new group of “users”, which are smart industrial devices, robots or any other objects, which can be adapted to the (Semantic) Web environment. They would need special services for e.g. online condition monitoring, information provisioning, remote diagnostics, maintenance support, etc. The goal of this paper is to specify main requirements to Web services that automatically follow up and predict the performance and maintenance needs of field devices. Semantic Web enabled services for…

Taxonomy of generative adversarial networks for digital immunity of Industry 4.0 systems

Abstract Industry 4.0 systems are extensively using artificial intelligence (AI) to enable smartness, automation and flexibility within variety of processes. Due to the importance of the systems, they are potential targets for attackers trying to take control over the critical processes. Attackers use various vulnerabilities of such systems including specific vulnerabilities of AI components. It is important to make sure that inappropriate adversarial content will not break the security walls and will not harm the decision logic of critical systems. We believe that the corresponding security toolset must be organized as a trainable self-protection mechanism similar to immunity. We found cer…

Agile Deep Learning UAVs Operating in Smart Spaces: Collective Intelligence Versus “Mission-Impossible”

The environments, in which we all live, are known to be complex and unpredictable. The complete discovery of these environments aiming to take full control over them is a “mission-impossible”, however, still in our common agenda. People intend to make their living spaces smarter utilizing innovations from the Internet of Things and Artificial Intelligence. Unmanned aerial vehicles (UAVs) as very dynamic, autonomous and intelligent things capable to discover and control large areas are becoming important “inhabitants” within existing and future smart cities. Our concern in this paper is to challenge the potential of UAVs in situations, which are evolving fast in a way unseen before, e.g., em…

The decision support system for telemedicine based on multiple expertise

This paper discusses the application of artificial intelligence in telemedicine and some of our research results in this area. The main goal of our research is to develop methods and systems to collect, analyse, distribute and use medical diagnostics knowledge from multiple knowledge sources and areas of expertise. Use of modern communication tools enable a physician to collect and analyse information obtained from experts worldwide with the help of a decision support medical system. In this paper we discuss a multilevel representation and processing of medical data using a system which evaluates and exploits knowledge about the behaviour of statistical diagnostics methods. The presented te…

Decision support system for telemedicine based on multiple expertise

This paper discusses results of the research in the area of artificial intelligence applications in telemedicine. The main goal of research is to manage multiple expertise obtained from experts-physicians in different countries to develop decision support medical system of broad earmarking based on telecommunication tools. The multilevel representation of medical data is discussed based on the apparatus of metastatistics. The technique is able to acquire semantically essential information from complex dynamics of quasi-periodical medical signals by applying recursively ordinary statistical tools. The voting-type technique is used to find consensus among medical experts in their description …

TB-Structure : Collective Intelligence for Exploratory Keyword Search

In this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker’s intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in the form of merged query trails – sequences of queries approaching iteratively a seeker’s goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker’s i…

ПРЕМЬЕР: Проактивная экосистема для глобальной интеграции корпоративных ресурсов

We present PRIME software ecosystem, which connects heterogeneous resources from different layers of the Internet of Things and capable of handling complex interoperability scenarios involving: hardware devices, software-based systems and humans

Semantic Web Services for Smart Devices Based on Mobile Agents

Among traditional users of Web resources, industry has a growing set of smart industrial devices with embedded intelligence. Just like humans, they need online services (i.e., for condition monitoring, remote diagnostics, maintenance, etc.). In this paper, we present one possible implementation framework for such Web services. Such services should be Semantic Web enabled and form a Service Network based on internal and external agents’ platforms, which can host heterogeneous mobile agents and coordinate them to perform needed tasks. The concept of a “mobile service component†assumes not only exchanging queries and service responses, but also delivering and composition of a service pro…

Reasoning with Multilevel Contexts in Semantic Metanetworks

It is generally accepted that knowledge has a contextual component. Acquisition, representation, and exploitation of knowledge in context would have a major contribution in knowledge representation, knowledge acquisition, and explanation, as Brezillon and Abu-Hakima supposed in [Brezillon and Abu-Hakima, 1995]. Among the advantages of the use of contexts in knowledge representation and reasoning Akman and Surav [Akman and Surav, 1996] mentioned the following: economy of representation, more competent reasoning, allowance for inconsistent knowledge bases, resolving of lexical ambiguity and flexible entailment. Brezillon and Cases noticed however in [Brezillon and Cases, 1995] that knowledge-…

From Deep Learning to Deep University: Cognitive Development of Intelligent Systems

Search is not only an instrument to find intended information. Ability to search is a basic cognitive skill helping people to explore the world. It is largely based on personal intuition and creativity. However, due to the emerged big data challenge, people require new forms of training to develop or improve this ability. Current developments within Cognitive Computing and Deep Learning enable artificial systems to learn and gain human-like cognitive abilities. This means that the skill how to search efficiently and creatively within huge data spaces becomes one of the most important ones for the cognitive systems aiming at autonomy. This skill cannot be pre-programmed, it requires learning…

Ontosmartresource: an industrial resource generation in semantic web

Semantic Web is a logical evolution of the existing Web. It was meant to serve for machines as today's Web does for humans. The term "machines" according to the existing semantic Web's vocabulary mostly means "computers". However industry needs such applications, which consider machines also as embedded computational entities within field devices, personal devices, microwave ovens, etc. In other words, now we should involve the real (industrial) world objects as resources into semantic Web. Still the main object of such a world will be a human, which becoming a resource (not just a user of resources) in the distributed environment. In this paper we introduce an extension of the semantic Web…

Semantics of Voids within Data: Ignorance-Aware Machine Learning

Operating with ignorance is an important concern of geographical information science when the objective is to discover knowledge from the imperfect spatial data. Data mining (driven by knowledge discovery tools) is about processing available (observed, known, and understood) samples of data aiming to build a model (e.g., a classifier) to handle data samples that are not yet observed, known, or understood. These tools traditionally take semantically labeled samples of the available data (known facts) as an input for learning. We want to challenge the indispensability of this approach, and we suggest considering the things the other way around. What if the task would be as follows: how to buil…

Towards digital cognitive clones for the decision-makers: adversarial training experiments

Abstract There can be many reasons for anyone to make a digital copy (clone) of own decision-making behavior. This enables virtual presence of a professional decision-maker simultaneously in many places and processes of Industry 4.0. Such clone can be used as one’s responsible representative when the human is not available. Pi-Mind (“Patented Intelligence”) is a technology, which enables “cloning” cognitive skills of humans using adversarial machine learning. In this paper, we present a cyber-physical environment as an adversarial learning ecosystem for cloning image classification skills. The physical component of the environment is provided by the logistic laboratory with camera-surveilla…

Semantic Web Enabled Web Services: State-of-Art and Industrial Challenges

Semantic Web technology has a vision to define and link Web data in a way that it can be understood and used by machines for automation, integration and reuse of data across various applications. Ontological definition of every resource as it is assumed in Semantic Web, along with new techniques for semantics processing and new vision Intelligent Web Services is expected to bring Web on its new level. At present, Web Services technology is stressed by the search of a right way for further development. Combination of Semantic Web and Web Services concepts may address many of difficulties of existing technology. It is not a question of whether Semantic Web is coming or not, but a question of …

Learning Bayesian Metanetworks from Data with Multilevel Uncertainty

Managing knowledge by maintaining it according to dynamic context is among the basic abilities of a knowledge-based system. The two main challenges in managing context in Bayesian networks are the introduction of contextual (in)dependence and Bayesian multinets. We are presenting one possible implementation of a context sensitive Bayesian multinet-the Bayesian Metanetwork, which implies that interoperability between component Bayesian networks (valid in different contexts) can be also modelled by another Bayesian network. The general concepts and two kinds of such Metanetwork models are considered. The main focus of this paper is learning procedure for Bayesian Metanetworks.

Bayesian Metanetwork for Context-Sensitive Feature Relevance

Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of appropriate conditional dependency. However, depending on task and context, many attributes of the model might not be relevant. If a network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on a “relevance” of the predictive attributes towards tar…

Challenges of the "Global Understanding Environment" Based on Agent Mobility

Among traditional users of Web resources, industry also has a growing set of smart industrial devices with embedded intelligence. Just as humans do, smart industrial devices need online services—for example, for condition monitoring, remote diagnostics, maintenance, and so on. In this chapter, we present one possible implementation framework for such Web services. Assume that such services should be Semantic-Web-enabled and form a service network based on internal and external agents’ platforms, which can host heterogeneous mobile agents and coordinate them to perform needed tasks. The concept of a “mobile-service component” assumes not only the exchange of queries and service responses but…

Emotional Business Intelligence : Enabling experience-centric business with the FeelingsExplorer

The domain of Emotional Business Intelligence (EBI) aims to support business-relevant emotional and emotionaware decisions in addition to rational decision making. EBI originates from three root domains: Emotional Business, Emotional Intelligence and Business Intelligence (BI). In this paper we emphasize emotional empowerment of the traditional BI function; outline its main characteristics as a business working model of an emotionally smart, continuously learning organization; and introduce a first candidate of the EBI Toolkit, the FeelingsExplorer (FE). FE is a mash-up browser based on 4i (“ForEye”) technology, capable of visualizing objects in an emotional semantic space and thereby suppo…

Strategic industrial alliances in paper industry

Purpose – To identify cases related to design of ICT platforms for industrial alliances, where the use of Ontology‐driven architectures based on Semantic web standards is more advantageous than application of conventional modeling together with XML standards.Design/methodology/approach – A comparative analysis of the two latest and the most obvious use cases (NASA and Nordic Process Industry Data Exchange Alliance) concerned with development of an environment for integration and collaboration of industrial partners, has been used as a basis for the research results. Additionally, dynamics of changes in a domain data model and their consequences have been analyzed on a couple of typical use …

Predictive and Contextual Feature Separation for Bayesian Metanetworks

Bayesian Networks are proven to be a comprehensive model to describe causal relationships among domain attributes with probabilistic measure of conditional dependency. However, depending on a context, many attributes of the model might not be relevant. If a Bayesian Network has been learned across multiple contexts then all uncovered conditional dependencies are averaged over all contexts and cannot guarantee high predictive accuracy when applied to a concrete case. We are considering a context as a set of contextual attributes, which are not directly effect probability distribution of the target attributes, but they effect on "relevance" of the predictive attributes towards target attribut…

<title>Pilot clinical evaluation of prototype system for visually reporting the results of ultrasound examination</title>

A dedicated optic-mechanical device, attachable to an ultrasound scanner, has been developed that allows visual documenting of ultrasound examination by recording multiple gray-scale images, i.e. ultrasound tomography (UST), to be performed routinely and at low cost. The device is operated by one hand without interrupting the examination. Each page of UST report is composed by deliberate positioning multiple images within the 2X4 framework and recorded on a 35-mm microfilm. If necessary, graphic reconstructions were composed from standard graphic components and interposed between the original images on the same frame. UST report is communicated to a patient and/or a referring doctor on a co…

Semantic Approach to Dynamic Coordination in Autonomous Systems

In open systems where the components, i.e. the agents and the resources, may be unknown at design time, or in dynamic and self-organizing systems evolving with time, there is a need to enable the agents to communicate their intentions with respect to future activities and resource utilization to resolve coordination issues dynamically. Ideally, we would like to allow ad-hoc interaction, where two standalone independently-designed systems are able to coordinate whenever a need arises. The Semantic Web based approach presented in this paper aims at enabling agents to coordinate without assuming any design-time ontological alignment of them. An agent can express an action intention using own v…

Balanced Large Scale Knowledge Matching Using LSH Forest

Evolving Knowledge Ecosystems were proposed recently to approach the Big Data challenge, following the hypothesis that knowledge evolves in a way similar to biological systems. Therefore, the inner working of the knowledge ecosystem can be spotted from natural evolution. An evolving knowledge ecosystem consists of Knowledge Organisms, which form a representation of the knowledge, and the environment in which they reside. The environment consists of contexts, which are composed of so-called knowledge tokens. These tokens are ontological fragments extracted from information tokens, in turn, which originate from the streams of information flowing into the ecosystem. In this article we investig…

Emotional Business Intelligence

The domain of Emotional Business Intelligence (EBI) aims to support business-relevant emotional and emotion-aware decisions in addition to rational decision making. EBI originates from three root domains: Emotional Business, Emotional Intelligence and Business Intelligence (BI). In this paper we emphasize emotional empowerment of the traditional BI function; outline its main characteristics as a business working model of an emotionally smart, continuously learning organization; and introduce a first candidate of the EBI Toolkit, the FeelingsExplorer (FE). FE is a mash-up browser based on 4i (“ForEye”) technology, capable of visualizing objects in an emotional semantic space and thereby supp…

Anonymization as homeomorphic data space transformation for privacy-preserving deep learning

Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation),…

Industry 4.0 Intelligence under Attack : From Cognitive Hack to Data Poisoning

Artificial intelligence is an unavoidable asset of Industry 4.0. Artificial actors participate in real-time decision-making and problem solving in various industrial processes, including planning, production, and management. Their efficiency, as well as intelligent and autonomous behavior is highly dependent on the ability to learn from examples, which creates new vulnerabilities exploited by security threats. Today's disruptive attacks of hackers go beyond system's infrastructures targeting not only hard-coded software or hardware, but foremost data and trained decision models, in order to approach system's intelligence and compromise its work. This paper intends to reveal security threats…

<title>Expanding context against weighted voting of classifiers</title>

In the paper we propose a new method to integrate the predictions of multiple classifiers for Data Mining and Machine Learning tasks. The method assumes that each classifier stands in it's own context, and the contexts are partially ordered. The order is defined by monotonous quality function that maps each context to the value from the interval [0,1]. The classifier that has the context with better quality is supposed to predict better than the classifier from worse quality. The objective is to generate the opinion of `virtual' classifier that stands in the context with quality equal to 1. This virtual classifier must have the best accuracy of predictions due to the best context. To do thi…

Hybrid Threats against Industry 4.0 : Adversarial Training of Resilience

Industry 4.0 and Smart Manufacturing are associated with the Cyber-Physical-Social Systems populated and controlled by the Collective Intelligence (human and artificial). They are an important component of Critical Infrastructure and they are essential for the functioning of a society and economy. Hybrid Threats nowadays target critical infrastructure and particularly vulnerabilities associated with both human and artificial intelligence. This article summarizes some latest studies of WARN: “Academic Response to Hybrid Threats” (the Erasmus+ project), which aim for the resilience (regarding hybrid threats) of various Industry 4.0 architectures and, especially, of the human and artificial de…

An Observation Framework for Multi-agent Systems

Existing middleware platforms for multi-agent systems (MAS) do not provide general support for observation. On the other hand, observation is considered to be an important mechanism needed for realizing effective and efficient coordination of agents. This paper describes a framework called Agent Observable Environment (AOE) for observation-based interaction in MAS. The framework provides 1) possibility to model MAS components with RDF-based observable soft-bodies, 2) support for both query and publish/subscribe style ontology-driven observation, and 3) ability to restrict the visibility of observable information using observation rules. Additionally, we report on an implementation of the fr…

A Security Framework for Smart Ubiquitous Industrial Resources

Conventional approaches to manage and control security seem to have reached their limits in new complex environments. These environments are open, dynamic, heterogeneous, distributed, self-managing, collaborative, international, nomadic, and ubiquitous. We are currently working on a middleware platform focused on the industrial needs, UBIWARE. UBIWARE integrates Ubiquitous Computing with Semantic Web, Distributed AI, Security and Privacy, and Enterprise Application Integration. In this paper, we describe our long-term vision for the security and privacy management in complex multi-agent systems like UBIWARE, SURPAS. The security infrastructure has to become pervasive, interoperable and inte…

The Truth is Out There : Focusing on Smaller to Guess Bigger in Image Classification

In Artificial Intelligence (AI) in general and in Machine Learning (ML) in particular, which are important and integral components of modern Industry 4.0, we often deal with uncertainty, e.g., lack of complete information about the objects we are classifying, recognizing, diagnosing, etc. Traditionally, uncertainty is considered to be a problem especially in the responsible use of AI and ML tools in the smart manufacturing domain. However, in this study, we aim not to fight with but rather to benefit from the uncertainty to improve the classification performance in supervised ML. Our objective is a kind of uncertainty-driven technique to improve the performance of Convolutional Neural Netwo…

Agile Deep Learning UAVs Operating in Smart Spaces : Collective Intelligence Versus “Mission-Impossible”

The environments, in which we all live, are known to be complex and unpredictable. The complete discovery of these environments aiming to take full control over them is a “mission-impossible”, however, still in our common agenda. People intend to make their living spaces smarter utilizing innovations from the Internet of Things and Artificial Intelligence. Unmanned aerial vehicles (UAVs) as very dynamic, autonomous and intelligent things capable to discover and control large areas are becoming important “inhabitants” within existing and future smart cities. Our concern in this paper is to challenge the potential of UAVs in situations, which are evolving fast in a way unseen before, e.g., em…

General Adaption Framework

Integration of heterogeneous applications and data sources into an interoperable system is one of the most relevant challenges for many knowledge-based corporations nowadays. Development of a global environment that would support knowledge transfer from human experts to automated Web services, which are able to learn, is a very profit-promising and challenging task. The domain of industrial maintenance is not an exception. This paper outlines in detail an approach for adaptation of heterogeneous Web resources into a unified environment as a first step toward interoperability of smart industrial resources, where distributed human experts and learning Web services are utilized by various devi…

Arbiter Meta-Learning with Dynamic Selection of Classifiers and its Experimental Investigation

In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classification result. Recently promising approaches using parallel and distributed computing have been presented. In this paper, we consider an approach that uses classifiers trained on a number of data subsets in parallel as in the arbiter meta-learning technique. We suggest that information is collected during the learning phase about the performance of the included base classifiers and arbiters and that this information is used during the application phase to select the best classifier dynamically. We evaluate our techn…

Towards a Framework for Agent-Enabled Semantic Web Service Composition

The article presents the framework for agent-enabled dynamic Web service composition. The core of the methodology is the new understanding of a Web service as an agent capability having proper ontological description. It is demonstrated how diverse Web services may be composed and mediated by dynamic coalitions of software agents collaboratively performing tasks for service requestors. Middle Agent Layer is introduced to conduct service request to task transformation, agent-enabled cooperative task decomposition and performance. Discussed are the formal means to arrange agents’ negotiation, to represent the semantic structure of the task-activity-service hierarchy and to assess fellow-agent…

<title>Dynamic integration of multiple data mining techniques in a knowledge discovery management system</title>

One of the most important directions in improvement of data mining and knowledge discovery, is the integration of multiple classification techniques of an ensemble of classifiers. An integration technique should be able to estimate and select the most appropriate component classifiers from the ensemble. We present two variations of an advanced dynamic integration technique with two distance metrics. The technique is one variation of the stacked generalization method, with an assumption that each of the component classifiers is the best one, inside a certain sub area of the entire domain area. Our technique includes two phases: the learning phase and the application phase. During the learnin…

Generative adversarial networks with bio-inspired primary visual cortex for Industry 4.0

Biologicalization (biological transformation) is an emerging trend in Industry 4.0 affecting digitization of manufacturing and related processes. It brings up the next generation of manufacturing technology and systems that extensively use biological and bio-inspired principles, materials, functions, structures and resources. This research is a contribution to the further convergence of computer and human vision for more robust and accurate automated object recognition and image generation. We present VOneGANs, a novel class of generative adversarial networks (GANs) with the qualitatively updated discriminative component. The new model incorporates a biologically constrained digital primary…

Patented intelligence: Cloning human decision models for Industry 4.0

Industry 4.0 is a trend related to smart factories, which are cyber-physical spaces populated and controlled by the collective intelligence for the autonomous and highly flexible manufacturing purposes. Artificial Intelligence (AI) embedded into various planning, production, and management processes in Industry 4.0 must take the initiative and responsibility for making necessary real-time decisions in many cases. In this paper, we suggest the Pi-Mind technology as a compromise between completely human-expert-driven decision-making and AI-driven decision-making. Pi-Mind enables capturing, cloning and patenting essential parameters of the decision models from a particular human expert making …

Querying Dynamic and Context-Sensitive Metadata in Semantic Web

RDF (core Semantic Web standard) is not originally appropriate for context representation, because of its initial focus on the ordinary Web resources, such as web pages, files, databases, services, etc., which structure and content are more or less stable. However, on the other hand, emerging industrial applications consider e.g. machines, processes, personnel, services for condition monitoring, remote diagnostics and maintenance, etc. to be specific classes of Web resources and thus a subject for semantic annotation. Such resources are naturally dynamic, not only from the point of view of changing values for some attributes (state of resource), but also from the point of view of changing “…

Transaction management for m-commerce at a mobile terminal

Although there has been a lot of discussion of "transactions" in mobile e-commerce (m-commerce), very little attention has been paid for distributed transactional properties of the computations facilitating m-commerce. In this paper, we first present a requirement analysis and then present a wireless terminal-based transaction manager (TM) architecture. This architecture is based on the assumption that there is an application that supports certain business transaction(s) and that it uses the TM to store transactional state information and retrieve it after a communication link, application, or terminal crash. We present the design of such a TM, including the application interface, modules a…

Explainable AI for Industry 4.0 : Semantic Representation of Deep Learning Models

Artificial Intelligence is an important asset of Industry 4.0. Current discoveries within machine learning and particularly in deep learning enable qualitative change within the industrial processes, applications, systems and products. However, there is an important challenge related to explainability of (and, therefore, trust to) the decisions made by the deep learning models (aka black-boxes) and their poor capacity for being integrated with each other. Explainable artificial intelligence is needed instead but without loss of effectiveness of the deep learning models. In this paper we present the transformation technique between black-box models and explainable (as well as interoperable) …

SmartResource Platform and Semantic Agent Programming Language (S-APL)

Although the flexibility of agent interactions has many advantages when it comes to engineering a complex system, the downside is that it leads to certain unpredictability of the run-time system. Literature sketches two major directions for search for a solution: social-level characterization of agent systems and ontological approaches to inter-agent coordination. Especially the latter direction is not yet studied much by the scientific community. This paper describes our vision and the present state of the SmartResource Platform. The main distinctive features of the platform are externalization of behavior prescriptions, i.e. agents access them from organizational repositories, and utiliza…

Temporal and spatial analysis to personalise an agent's dynamic belief, desire, and intention profiles

The paper addresses the dynamic belief, desire and intention profiles that can be made of an agent following a particular route, for example through a city. It assumes that location of an agent has effects on his beliefs desires and intentions and that the history of agent’s mobility and observed states in different locations can be used to predict his future states if the location is being permanently observed. A formal spatial route language is introduced. Formal relationships between the intentional notions, and the spatial behaviour of an agent are defined. As an application an information agent architecture for reasoning about the intentions of the customers of a mobile location-based …

The ‘magic square’: A roadmap towards emotional business intelligence

Emotions are known to be an important driver in human behaviour and decision-making. In the business world, there is a growing belief that emotions are not an obstacle but rather an enabler for a successful business. Business intelligence (by providing analytical processing and convenient presentation of a business data) traditionally supports rational decision-making. However, opposite to former opinion that all decisions should be ‘cleansed’ of emotions, there are more and more indicators of the need for solutions supporting also emotional decision-making. The domain of emotional business intelligence, suggested in this paper, concerns emotional and emotion-aware decisions, intuition, inn…

Linked Data in Enterprise Integration

RgbDF: Resource Goal and Behaviour Description Framework

Agent-oriented approach has proven to be very efficient in engineering complex distributed software environments with dynamically changing conditions. The efficiency of underlying modelling framework for this domain is undoubtedly of a crucial importance. Currently, a model-driven architecture has been the most popular and developed for purposes of modelling different aspects of multi-agent systems, including behaviour of individual agents. UML is utilized as a basis for this modelling approach and variety of existing UML-based modelling tools after slight extension are reused. This paper proposes an ontology-driven approach to modelling agent behaviour as an emerging paradigm that originat…

Knowledge Acquisition Based on Semantic Balance of Internal and External Knowledge

This paper presents a strategy to handle incomplete knowledge during acquisition process. The goal of this research is to develop formal tools that benefit the law of semantic balance. The assumption is used that a situation inside the object’s boundary in some world should be in balance with a situation outside it. It means that continuous cognition of an object aspires to a complete knowledge about it and knowledge about internal structure of the object will be in balance with knowledge about relationships of the object with other objects in its environment. It is supposed that one way to discover incompleteness of knowledge about some object is to measure and compare knowledge about its …

<title>Distance functions in dynamic integration of data mining techniques</title>

One of the most important directions in the improvement of data mining and knowledge discovery is the integration of multiple data mining techniques. An integration method needs to be able either to evaluate and select the most appropriate data mining technique or to combine two or more techniques efficiently. A recent integration method for the dynamic integration of multiple data mining techniques is based on the assumption that each of the data mining techniques is the best one inside a certain subarea of the whole domain area. This method uses an instance-based learning approach to collect information about the competence areas of the mining techniques and applies a distance function to…

Semantic Portal as a Tool for Structural Reform of the Ukrainian Educational System

Education is recognized as a fundamental enabler of human development. The adoption of information and communications technologies (ICTs) by education (especially in developing countries) contributes to educational system reforms, in addition to the traditional advantages, such as social openness and accessibility. Yet the academic community has not studied sufficiently the challenging context in which ICTs are used as instruments for the reform of inefficient, and sometimes even corrupted, educational systems rather than just as means for smarter classrooms, remote access, or content management. The object of this study is Ukrainian higher education (HE) and its quality assurance (QA) syst…

TB-Structure: Collective Intelligence for Exploratory Keyword Search

In this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker’s intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in the form of merged query trails – sequences of queries approaching iteratively a seeker’s goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker’s i…

Knowledge Acquisition from Multiple Experts Based on Semantics of Concepts

This paper presents one approach to acquire knowledge from multiple experts. The experts are grouped into a multilevel hierarchical structure, according to the type of knowledge acquired. The first level consists of experts who have knowledge about the basic objects and their relationships. The second level of experts includes those who have knowledge about the relationships of the experts at the first level and each higher level accordingly. We show how to derive the most supported opinion among the experts at each level. This is used to order the experts into categories of their competence defined as the support they get from their colleagues.

Cloning and training collective intelligence with generative adversarial networks

Industry 4.0 and highly automated critical infrastructure can be seen as cyber‐physical‐social systems controlled by the Collective Intelligence. Such systems are essential for the functioning of the society and economy. On one hand, they have flexible infrastructure of heterogeneous systems and assets. On the other hand, they are social systems, which include collaborating humans and artificial decision makers. Such (human plus machine) resources must be pre‐trained to perform their mission with high efficiency. Both human and machine learning approaches must be bridged to enable such training. The importance of these systems requires the anticipation of the potential and previously unknow…

Personalized distance learning based on multiagent ontological system

The paper presents architecture of a personalized distance learning system based on multiagent technology and ontological modelling of students' profiles. Delocalization of a student data in the system is achieved by software agents, which assumed to be distributed at different platforms. These platforms operate as separate Web services and use the ACL (agent communication language) for the data transfer. In this paper the algorithm is proposed, according to which the multiagent ontological system for personalized distance learning (MOSPDL) solves the tasks of distant learning process automation, which assume utilization of the ontological models of students' and learning resources' profile…

Industry 4.0 vs. Industry 5.0 : Co-existence, Transition, or a Hybrid

Smart manufacturing is being shaped nowadays by two different paradigms: Industry 4.0 proclaims transition to digitalization and automation of processes while emerging Industry 5.0 emphasizes human centricity. This turn can be explained by unprecedented challenges being faced recently by societies, such as, global climate change, pandemics, hybrid and conventional warfare, refugee crises. Sustainable and resilient processes require humans to get back into the loop of organizational decision-making. In this paper, we argue that the most reasonable way to marry the two extremes of automation and value-based human-driven processes is to create an Industry 4.0 + Industry 5.0 hybrid, which inher…

A dynamic integration algorithm for an ensemble of classifiers

Numerous data mining methods have recently been developed, and there is often a need to select the most appropriate data mining method or methods. The method selection can be done statically or dynamically. Dynamic selection takes into account characteristics of a new instance and usually results in higher classification accuracy. We discuss a dynamic integration algorithm for an ensemble of classifiers. Our algorithm is a new variation of the stacked generalization method and is based on the basic assumption that each basic classifier is best inside certain subareas of the application domain. The algorithm includes two main phases: a learning phase, which collects information about the qua…

Global Understanding Environment

Industry pushes a new type of Internet characterized as the Internet of Things, which represents a fusion of the physical and digital worlds. The technology of the Internet of Things opens new horizons for industrial automation, that is, automated monitoring, control, maintenance planning, and so forth, of industrial resources and processes. Internet of Things definitely needs explicit semantics, even more than the traditional Web—for automatic discovery and interoperability among heterogeneous devices and also to facilitate the behavioral coordination of the components of complex physical-digital systems. In this chapter, the authors describe their work towards the Global Understanding Env…

Proactively Composing Web Services as Tasks by Semantic Web Agents

This chapter presents the framework for agent-enabled dynamic composition of Semantic Web services. The approach and the framework have been developed in several research and development projects by ISRG and IOG. The core of the methodology is the new understanding of a Semantic Web service as a capability of an intelligent software agent supplied with the proper ontological description. It is demonstrated how diverse Web services may be composed and mediated by dynamic coalitions of software agents collaboratively performing tasks for service requestors. Middle agent layer is introduced to conduct the transformation of a Web service request to the corresponding task, agent-enabled cooperat…

Hyper-flexible Convolutional Neural Networks based on Generalized Lehmer and Power Means

Convolutional Neural Network is one of the famous members of the deep learning family of neural network architectures, which is used for many purposes, including image classification. In spite of the wide adoption, such networks are known to be highly tuned to the training data (samples representing a particular problem), and they are poorly reusable to address new problems. One way to change this would be, in addition to trainable weights, to apply trainable parameters of the mathematical functions, which simulate various neural computations within such networks. In this way, we may distinguish between the narrowly focused task-specific parameters (weights) and more generic capability-spec…

Encryption and Generation of Images for Privacy-Preserving Machine Learning in Smart Manufacturing

Current advances in machine (deep) learning and the exponential growth of data collected by and shared between smart manufacturing processes give a unique opportunity to get extra value from that data. The use of public machine learning services actualizes the issue of data privacy. Ordinary encryption protects the data but could make it useless for the machine learning objectives. Therefore, “privacy of data vs. value from data” is the major dilemma within the privacy preserving machine learning activity. Special encryption techniques or synthetic data generation are being in focus to address the issue. In this paper, we discuss a complex hybrid protection algorithm, which assumes sequenti…

A framework for context-sensitive metadata description

Expectations regarding the new generation of Web depend on the success of Semantic Web technology. Resource Description Framework (RDF) is a basis for explicit and machine-readable representation of semantics. However RDF is not suitable for describing dynamic and context-sensitive resources (eg. processes). We present the Context Description Framework (CDF) as an extension of the RDF by adding a 'TrueInContext' component to the basic RDF triple ('subject-predicate-object'), and consider contextual value as a container of RDF statements. We also add a probabilistic component, which allows multilevel contextual dependence descriptions as well as presumes possibility for Bayesian reasoning wi…

Bayesian metanetworks for modelling user preferences in mobile environment

The problem of profiling and filtering is important particularly for mobile information systems where wireless network traffic and mobile terminal’s size are limited comparing to the Internet access from the PC. Dealing with uncertainty in this area is crucial and many researchers apply various probabilistic models. The main challenge of this paper is the multilevel probabilistic model (the Bayesian Metanetwork), which is an extension of traditional Bayesian networks. The extra level(s) in the Metanetwork is used to select the appropriate substructure from the basic network level based on contextual features from user’s profile (e.g. user’s location). Two models of the Metanetwork are consi…

UbiRoad: Semantic Middleware for Context-Aware Smart Road Environments

A smart road environment is such a traffic environment that is equipped with all necessary facilities to enable seamless mobile service provisioning to the users. However, advanced sensors and network architectures deployed within the traffic environment are insufficient to make mobile service provisioning autonomous and proactive, thus minimizing drivers’ distraction during their presence in the environment. For that, an Intelligent Transportation System, which is operating on top of numerous sensor and access networks and governing the process of mobile services provisioning to the users in self-managed and proactive way, must be deployed. Specifically, such system should provide solution…

Proactive Future Internet: Smart Semantic Middleware for Overlay Architecture

Some initiatives towards Future Internet, e.g., GENI, DARPA's Active Networks, argue the need for programmability of the network components. Some other initiatives extend this with argumentation for declarative networking, where the behavior of a network component is specified using some high-level declarative language, with a software-based engine implementing the behavior based on that specification. Our Proactive Future Internet (PROFI) vision follows these initiatives targeting also the following two problems: interoperability of the network elements programmed by different organizations, and the need for flexible cooperation among network elements, including coordination, conflict reso…