Complex Adaptive On-line Learning System “Theory of Automatic Control"

The controlled from distance teaching (DT) in the system of technical education has a row of features: complication of informative content, necessity of development of simulation models and trainers for conducting of practical and laboratory employments, conducting of knowledge diagnostics on the basis of mathematical-based algorithms, organization of execution collective projects of the applied setting. For development of the process of teaching bases of fundamental discipline control system Theory of automatic control (TAC) the combined approach of optimum combination of existent programmatic instruments of support was chosen DT and own developments. The system DT TAC included: controlled from distance course (DC) of TAC, site of virtual laboratory practical works in LAB.TAC and students knowledge remote diagnostic system d-tester.

An automated tool for semantic accessing to formal software models

Many software engineers have found that it is difficult to understand, incorporate and use different formal models consistently in the process of software developments, especially for large and complex software systems. This is mainly due to the complex mathematical nature of the formal methods and the lack of tool support. It is highly desirable to have software models and their related software artefacts systematically connected and used collaboratively, rather than in isolation. The success of the Semantic Web, as the next generation of Web technology, can have profound impact on the environment for formal software development. It allows both the software engineers and machines to understand the content of formal models and supports more effective software design in terms of understanding, sharing and reusing in a distributed manner. To realise the full potential of the Semantic Web in formal software development, effectively creating proper semantic metadata for formal software models and their related software artefacts is crucial. This paper proposed a framework that allows users to interconnect the knowledge about formal software models and other related documents using the semantic technology. We first propose a methodology with tool support is proposed to automatically derive ontological metadata from formal software models and semantically describe them. We then develop a Semantic Web environment for representing and sharing formal Z/OZ models. A method with prototype tool is presented to enhance semantic query to software models and other artefacts. © 2014.

Mathematical Models of Domain Ontologies

* This paper was made according to the program No 14 of fundamental scientific research of the Presidium of the Russian Academy of Sciences, the project "Intellectual Systems Based on Multilevel Domain Models".

Logic Based Pattern Recognition - Ontology Content (1)

* The research is supported partly by INTAS: 04-77-7173 project, http://www.intas.be

New Knowledge Obtaining in Structural-predicate Models of Knowledge

An effective mathematical method of new knowledge obtaining on the structure of complex objects with required properties is developed. The method comprehensively takes into account information on the properties and relations of primary objects, composing the complex objects. It is based on measurement of distances between the predicate groups with some interpretation of them. The optimal measure for measurement of these distances with the maximal discernibleness of different groups of predicates is constructed. The method is tested on solution of the problem of obtaining of new compound with electro-optical properties.

Linked knowledge sources for topic classification of microposts:a semantic graph-based approach

Short text messages a.k.a Microposts (e.g. Tweets) have proven to be an effective channel for revealing information about trends and events, ranging from those related to Disaster (e.g. hurricane Sandy) to those related to Violence (e.g. Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of Microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of Microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to Microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of Microposts with features extracted only from the Microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of Microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen Microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and Microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures. © 2014 Elsevier B.V. All rights reserved.

Survey of Existing Services in the Mathematical Digital Libraries and Repositories in the EuDML Project

This paper presents a survey of the existing services provided by the digital libraries and repositories on mathematics of the content provider partners in the EuDML project. The purpose is to support the development of the concepts, criteria and methods for the continuous evaluation of these and new relevant existing services. The work was concentrated on the classification of the relevant services in order to specify a common evaluating structure.

Reliability modelling and verification of manufacturing processes based on process knowledge management

Reliability modelling and verification is indispensable in modern manufacturing, especially for product development risk reduction. Based on the discussion of the deficiencies of traditional reliability modelling methods for process reliability, a novel modelling method is presented herein that draws upon a knowledge network of process scenarios based on the analytic network process (ANP). An integration framework of manufacturing process reliability and product quality is presented together with a product development and reliability verification process. According to the roles of key characteristics (KCs) in manufacturing processes, KCs are organised into four clusters, that is, product KCs, material KCs, operation KCs and equipment KCs, which represent the process knowledge network of manufacturing processes. A mathematical model and algorithm is developed for calculating the reliability requirements of KCs with respect to different manufacturing process scenarios. A case study on valve-sleeve component manufacturing is provided as an application example of the new reliability modelling and verification procedure. This methodology is applied in the valve-sleeve component manufacturing processes to manage and deploy production resources.

Bulgarian Digital Mathematical Library BulDML and Czech Digital Mathematical Library DML-CZ as parts of the European Digital Mathematics Library EuDML

The paper presents in brief the Bulgarian Digital Mathematical Library BulDML and the Czech Digital Mathematical Library DML-CZ. Both libraries use the open source software DSpace and both are partners in the European Digital Mathematics Library EuDML. We describe their content and metadata schemas; outline the architecture system and overview the statistics of its use.

Decision-making in product quality based on failure knowledge

Decision-making in product quality is an indispensable stage in product development, in order to reduce product development risk. Based on the identification of the deficiencies of quality function deployment (QFD) and failure modes and effects analysis (FMEA), a novel decision-making method is presented that draws upon a knowledge network of failure scenarios. An ontological expression of failure scenarios is presented together with a framework of failure knowledge network (FKN). According to the roles of quality characteristics (QCs) in failure processing, QCs are set into three categories namely perceptible QCs, restrictive QCs, and controllable QCs, which present the monitor targets, control targets and improvement targets respectively for quality management. A mathematical model and algorithms based on the analytic network process (ANP) is introduced for calculating the priority of QCs with respect to different development scenarios. A case study is provided according to the proposed decision-making procedure based on FKN. This methodology is applied in the propeller design process to solve the problem of prioritising QCs. This paper provides a practical approach for decision-making in product quality. Copyright © 2011 Inderscience Enterprises Ltd.

Towards Wider Sharing of Iconographical Art Content

Information and multimedia technologies that have been developed during the past couple of years provided new e-tools to memory institutions (viz. museum, libraries, galleries, etc.), reviving the valuable treasure made by generation of people. Digital libraries (DLs) are such powerful contemporary tools for cultural heritage presentation, preservation and archiving. However, DLs power will in-crease significantly if they use mechanisms for ubiquitous sharing of their e-artefacts and they distribute attractive content in the social networks, reflecting community demands and needs. This paper presents a service for automatic sharing of iconographical artefacts and full collections from the Bulgarian Iconographical Digital Library to selected Facebook communities. In this case the service will be used for widely promotion of knowledge about East-Christian Iconographical Art and Culture, but I could be used not only for this and not only in this domain.

Discovering knowledge structures in mind maps of mental health risks

This thesis addressed the problem of risk analysis in mental healthcare, with respect to the GRiST project at Aston University. That project provides a risk-screening tool based on the knowledge of 46 experts, captured as mind maps that describe relationships between risks and patterns of behavioural cues. Mind mapping, though, fails to impose control over content, and is not considered to formally represent knowledge. In contrast, this thesis treated GRiSTs mind maps as a rich knowledge base in need of refinement; that process drew on existing techniques for designing databases and knowledge bases. Identifying well-defined mind map concepts, though, was hindered by spelling mistakes, and by ambiguity and lack of coverage in the tools used for researching words. A novel use of the Edit Distance overcame those problems, by assessing similarities between mind map texts, and between spelling mistakes and suggested corrections. That algorithm further identified stems, the shortest text string found in related word-forms. As opposed to existing approaches’ reliance on built-in linguistic knowledge, this thesis devised a novel, more flexible text-based technique. An additional tool, Correspondence Analysis, found patterns in word usage that allowed machines to determine likely intended meanings for ambiguous words. Correspondence Analysis further produced clusters of related concepts, which in turn drove the automatic generation of novel mind maps. Such maps underpinned adjuncts to the mind mapping software used by GRiST; one such new facility generated novel mind maps, to reflect the collected expert knowledge on any specified concept. Mind maps from GRiST are stored as XML, which suggested storing them in an XML database. In fact, the entire approach here is ”XML-centric”, in that all stages rely on XML as far as possible. A XML-based query language allows user to retrieve information from the mind map knowledge base. The approach, it was concluded, will prove valuable to mind mapping in general, and to detecting patterns in any type of digital information.

Knowledge representation and evaluation an ontology-based knowledge management approach

Competition between Higher Education Institutions is increasing at an alarming rate, while changes of the surrounding environment and demands of labour market are frequent and substantial. Universities must meet the requirements of both the national and European legislation environment. The Bologna Declaration aims at providing guidelines and solutions for these problems and challenges of European Higher Education. One of its main goals is the introduction of a common framework of transparent and comparable degrees that ensures the recognition of knowledge and qualifications of citizens all across the European Union. This paper will discuss a knowledge management approach that highlights the importance of such knowledge representation tools as ontologies. The discussed ontology-based model supports the creation of transparent curricula content (Educational Ontology) and the promotion of reliable knowledge testing (Adaptive Knowledge Testing System).

Examination of the role of Mycoplasma bovis in bovine pneumonia and a mathematical model for its evaluation

The authors screened 34 large cattle herds for the presence of Mycoplasma bovis infection by examining slaughtered cattle for macroscopic lung lesions, by culturing M. bovis from lung lesions and at the same time by testing sera for the presence of antibodies against M. bovis. Among the 595 cattle examined, 33.9% had pneumonic lesions, mycoplasmas were isolated from 59.9% of pneumonic lung samples, and 10.9% of sera from those animals contained antibodies to M.bovis. In 25.2% of the cases M. bovis was isolated from lungs with no macroscopic lesions. The proportion of seropositive herds was 64.7%. The average seropositivity rate of individuals was 11.3% but in certain herds it exceeded 50%. A probability model was developed for examining the relationship among the occurrence of pneumonia, the isolation of M. bovis from the lungs and the presence of M. bovis specific antibodies in sera.

How market information is transformed into marketing knowledge?

In the future, competitors will have more and more opportunities to buy the same information; therefore the companies’ competitiveness will not primarily depend on how much information they possess, but rather on how they can “translate” it to their own language. This study aims to examine those factors that have the most significant impact on the degree to which market studies are utilised by companies. Most of the work in this area has studied the use of information in strategic decisions a priori. This paper — while reflecting on the findings of research on organisational theories of information processing — aims to bridge this gap. It proposes and tests a new conceptual framework that examines the use of managerial market research information in decision-making and knowledge creation within one single model. Collected survey data, including all the top-income business enterprises in Hungary indicate that market research findings are efficiently incorporated into the marketing information system only if the marketing manager has trust in the researcher, and believes that the market study is of high quality. Decision-makers are more likely to learn from market studies facilitating the resolution of some specific problem than descriptive studies of a more general nature.