On learning context-free and context-sensitive languages

The long short-term memory (LSTM) is not the only neural network which learns a context sensitive language. Second-order sequential cascaded networks (SCNs) are able to induce means from a finite fragment of a context-sensitive language for processing strings outside the training set. The dynamical behavior of the SCN is qualitatively distinct from that observed in LSTM networks. Differences in performance and dynamics are discussed.

Knowledge, attitude and practice of mammography among women users of public health services

OBJECTIVE: To evaluate knowledge, attitude and practice related to mammography among women users of local health services, identifying barriers to its performance. METHODS: A total of 663 women were interviewed at 13 local health centers in a city of Southeastern Brazil, in 2001. Interviewees were randomly selected at each center and they were representative from different socioeconomic conditions. The number of interviewees at each center was proportional to monthly mean appointments. For data analysis, answers were described as knowledge, attitude, practice and their respective adequacies and then they were correlated with control variables through the chi-square test. RESULTS: Only 7.4% of the interviewees had adequate knowledge on mammography, while 97.1% of women had an adequate attitude. The same was seen for the practice of mammography that was adequate in 35.7% of the cases. The main barrier to mammography was lack of referral by physicians working at the health center (81.8%). There was an association between adequacy of attitude and five years or more of education and being married. There was also an association between adequacy of mammography practice and being employed and family income up to four minimum wages. CONCLUSIONS: Women users of local health services had no adequate knowledge and practice related to mammography despite having an adequate attitude about this exam.

Introduction: digital methods and tools for historical research

As an introduction to a series of articles focused on the exploration of particular tools and/or methods to bring together digital technology and historical research, the aim of this paper is mainly to highlight and discuss in what measure those methodological approaches can contribute to improve analytical and interpretative capabilities available to historians. In a moment when the digital world present us with an ever-increasing variety of tools to perform extraction, analysis and visualization of large amounts of text, we thought it would be relevant to bring the digital closer to the vast historical academic community. More than repeating an idea of digital revolution introduced in the historical research, something recurring in the literature since the 1980s, the aim was to show the validity and usefulness of using digital tools and methods, as another set of highly relevant tools that the historians should consider. For this several case studies were used, combining the exploration of specific themes of historical knowledge and the development or discussion of digital methodologies, in order to highlight some changes and challenges that, in our opinion, are already affecting the historians' work, such as a greater focus given to interdisciplinarity and collaborative work, and a need for the form of communication of historical knowledge to become more interactive.

Efficient paraconsistent reasoning with rules and ontologies for the semantic web

Ontologies formalized by means of Description Logics (DLs) and rules in the form of Logic Programs (LPs) are two prominent formalisms in the field of Knowledge Representation and Reasoning. While DLs adhere to the OpenWorld Assumption and are suited for taxonomic reasoning, LPs implement reasoning under the Closed World Assumption, so that default knowledge can be expressed. However, for many applications it is useful to have a means that allows reasoning over an open domain and expressing rules with exceptions at the same time. Hybrid MKNF knowledge bases make such a means available by formalizing DLs and LPs in a common logic, the Logic of Minimal Knowledge and Negation as Failure (MKNF). Since rules and ontologies are used in open environments such as the Semantic Web, inconsistencies cannot always be avoided. This poses a problem due to the Principle of Explosion, which holds in classical logics. Paraconsistent Logics offer a solution to this issue by assigning meaningful models even to contradictory sets of formulas. Consequently, paraconsistent semantics for DLs and LPs have been investigated intensively. Our goal is to apply the paraconsistent approach to the combination of DLs and LPs in hybrid MKNF knowledge bases. In this thesis, a new six-valued semantics for hybrid MKNF knowledge bases is introduced, extending the three-valued approach by Knorr et al., which is based on the wellfounded semantics for logic programs. Additionally, a procedural way of computing paraconsistent well-founded models for hybrid MKNF knowledge bases by means of an alternating fixpoint construction is presented and it is proven that the algorithm is sound and complete w.r.t. the model-theoretic characterization of the semantics. Moreover, it is shown that the new semantics is faithful w.r.t. well-studied paraconsistent semantics for DLs and LPs, respectively, and maintains the efficiency of the approach it extends.

Património Cultural Imaterial: MEMORIAMEDIA e-Museu - métodos, técnicas e práticas / Intagible Cultural Heritage: MEMORIAMEDIA e-Museum - methods, techniques and practices

O projeto MEMORIAMEDIA tem como objetivos o estudo, a inventariação e divulgação de manifestações do património cultural imaterial: expressões orais; práticas performativas; celebrações; o saber-fazer de artes e ofícios e as práticas e conhecimentos relacionados com a natureza e o universo. O MEMORIAMEDIA iniciou em 2006, em pleno debate nacional e internacional das questões do património cultural imaterial. Este livro cruza essas discussões teóricas, metodológicas e técnicas com a caracterização do MEMORIAMEDIA. Os resultados do projeto, organizados num inventário nacional, estão publicados no site www.memoriamedia.net, onde se encontram disponíveis para consulta e partilha. Filomena Sousa é investigadora de pós-doutoramento em antropologia (FCSH/UNL) e doutorada em sociologia (ISCTE-IUL). Membro integrado no Instituto de Estudos de Literatura e Tradição - patrimónios, artes e culturas (IELT) da FCSH/UNL e consultora da Memória Imaterial CRL – organização não-governamental autora e gestora do projeto MEMORIAMEDIA. Desenvolve investigação no âmbito das políticas e instrumentos de identificação, documentação e salvaguarda do património cultural imaterial e realizou vários documentários sobre expressões culturais.

Exploring Literary Landscapes: From Texts to Spatiotemporal Analysis through Collaborative Work and GIS

This article argues that the study of literary representations of landscapes can be aided and enriched by the application of digital geographic technologies. As an example, the article focuses on the methods and preliminary findings of LITESCAPE.PT—Atlas of Literary Landscapes of Mainland Portugal, an on-going project that aims to study literary representations of mainland Portugal and to explore their connections with social and environmental realities both in the past and in the present. LITESCAPE.PT integrates traditional reading practices and ‘distant reading’ approaches, along with collaborative work, relational databases, and geographic information systems (GIS) in order to classify and analyse excerpts from 350 works of Portuguese literature according to a set of ecological, socioeconomic, temporal and cultural themes. As we argue herein this combination of qualitative and quantitative methods—itself a response to the difficulty of obtaining external funding—can lead to (a) increased productivity, (b) the pursuit of new research goals, and (c) the creation of new knowledge about natural and cultural history. As proof of concept, the article presents two initial outcomes of the LITESCAPE.PT project: a case study documenting the evolving literary geography of Lisbon and a case study exploring the representation of wolves in Portuguese literature.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Supporting knowledge creation and sharing in the early phases of the strategic innovation process

The driving forces of technology and globalization continuously transform the business landscape in a way which undermines the existing strategies and innovations of organizations. The challenge for organizations is to establish such conditions where they are able to create new knowledge for innovative business ideas in interaction between other organizations and individuals. Innovation processes continuously need new external stimulations and seek new ideas, new information and knowledge locating more and more outside traditional organizational boundaries. In several studies, the early phases of the innovation process have been considered as the most critical ones. During these phases, the innovation process can emerge or conclude. External knowledge acquirement and utilization are noticed to be important at this stage of the innovation process giving information about the development of future markets and needs for new innovative businessideas. To make it possible, new methods and approaches to manage proactive knowledge creation and sharing activities are needed. In this study, knowledge creation and sharing in the early phases of the innovation process has been studied, and the understanding of knowledge management in the innovation process in an open and collaborative context advanced. Furthermore, the innovation management methods in this study are combined in a novel way to establish an open innovation process and tested in real-life cases. For these purposes two complementary and sequentially applied group work methods - the heuristic scenario method and the idea generation process - are examined by focusing the research on the support of the open knowledge creation and sharing process. The research objective of this thesis concerns two doctrines: the innovation management including the knowledge management, and the futures research concerning the scenario paradigm. This thesis also applies the group decision support system (GDSS) in the idea generation process to utilize the converged knowledge during the scenario process.

Knowledge creation and transfer in an iterative project environment

This Master’s Thesis examines knowledge creation and transfer processes in an iterative project environment. The aim is to understand how knowledge is created and transferred during an actual iterative implementation project which takes place in International Business Machines (IBM). The second aim is to create and develop new working methods that support more effective knowledge creation and transfer for future iterative implementation projects. The research methodology in this thesis is qualitative. Using focus group interviews as a research method provides qualitative information and introduces the experiences of the individuals participating in the project. This study found that the following factors affect knowledge creation and transfer in an iterative, multinational, and multi-organizational implementation project: shared vision and common goal, trust, open communication, social capital, and network density. All of these received both theoretical and empirical support. As for future projects, strengthening these factors was found to be the key for more effective knowledge creation and transfer.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.

Marketing culture of Finnish research libraries : an analysis of marketing attitude, knowledge and behaviour

The aim of the present dissertation is to investigate the marketing culture of research libraries in Finland and to understand the awareness of the knowledge base of library management concerning modern marketing theories and practices. The study was based onthe notion that a leader in an organisation can have large impact on its culture. Therefore, it was considered important to learn about the market orientation that initiates at the top management and flows throughout the whole organisationthus resulting in a particular kind of library culture. The study attempts to examine the marketing culture of libraries by analysing the marketing attitudes, knowledge (underlying beliefs, values and assumptions), behaviour (market orientation), operational policies and activities, and their service performance (customer satisfaction). The research was based on the assumption that if the top management of libraries has market oriented behaviour, then their marketing attitudes, knowledge, operational policies and activities and service performance should also be in accordance. The dissertation attempts to connect all these theoretical threads of marketing culture. It investigates thirty three academic and special libraries in the south of Finland. The library director and three to ten customers from each library participated as respondents in this study. An integrated methodological approach of qualitative as well as quantitative methods was used to gain knowledge on the pertinent issues lying behind the marketing culture of research libraries. The analysis of the whole dissertation reveals that the concept of marketing has very varied status in the Finnish research libraries. Based on the entire findings, three kinds of marketing cultures were emerged: the strong- the high fliers; the medium- the brisk runners; and the weak- the slow walkers. The high fliers appeared to be modern marketing believers as their marketing approach was customer oriented and found to be closer to the emerging notions of contemporary relational marketing. The brisk runners were found to be traditional marketing advocates as their marketing approach is more `library centred¿than customer defined and thus is in line of `product orientation¿ i.e. traditional marketing. `Let the interested customers come to the library¿ was appeared to be the hallmark of the slow walkers. Application of conscious market orientation is not reflected in the library activities of the slow walkers. Instead their values, ideology and approach to serving the library customers is more in tuneof `usual service oriented Finnish way¿. The implication of the research is that it pays to be market oriented which results in higher customer satisfaction oflibraries. Moreover, it is emphasised that the traditional user based service philosophy of Finnish research libraries should not be abandoned but it needs to be further developed by building a relational based marketing system which will help the libraries to become more efficient and effective from the customers¿ viewpoint. The contribution of the dissertation lies in the framework showing the linkages between the critical components of the marketing culture of a library: antecedents, market orientation, facilitators and consequences. The dissertationdelineates the significant underlying dimensions of market-oriented behaviour of libraries which are namely customer philosophy, inter-functional coordination,strategic orientation, responsiveness, pricing orientation and competition orientation. The dissertation also showed the extent to which marketing attitudes, behaviour, knowledge were related and impact of market orientation on the serviceperformance of libraries. A strong positive association was found to exist between market orientation and marketing attitudes and knowledge. Moreover, it also shows that a higher market orientation is positively connected with the service performance of libraries, the ultimate result being higher customer satisfaction. The analysis shows that a genuine marketing culture represents a synthesis of certain marketing attitudes, knowledge and of selective practices. This finding is particularly significant in the sense that it manifests that marketing culture consists of a certain sets of beliefs and knowledge (which form a specific attitude towards marketing) and implementation of a certain set of activities that actually materialize the attitude of marketing into practice (market orientation) leading to superior service performance of libraries.

Application and development of numerical methods for the modelling of innovative gas cooled fission reactors

Innovative gas cooled reactors, such as the pebble bed reactor (PBR) and the gas cooled fast reactor (GFR) offer higher efficiency and new application areas for nuclear energy. Numerical methods were applied and developed to analyse the specific features of these reactor types with fully three dimensional calculation models. In the first part of this thesis, discrete element method (DEM) was used for a physically realistic modelling of the packing of fuel pebbles in PBR geometries and methods were developed for utilising the DEM results in subsequent reactor physics and thermal-hydraulics calculations. In the second part, the flow and heat transfer for a single gas cooled fuel rod of a GFR were investigated with computational fluid dynamics (CFD) methods. An in-house DEM implementation was validated and used for packing simulations, in which the effect of several parameters on the resulting average packing density was investigated. The restitution coefficient was found out to have the most significant effect. The results can be utilised in further work to obtain a pebble bed with a specific packing density. The packing structures of selected pebble beds were also analysed in detail and local variations in the packing density were observed, which should be taken into account especially in the reactor core thermal-hydraulic analyses. Two open source DEM codes were used to produce stochastic pebble bed configurations to add realism and improve the accuracy of criticality calculations performed with the Monte Carlo reactor physics code Serpent. Russian ASTRA criticality experiments were calculated. Pebble beds corresponding to the experimental specifications within measurement uncertainties were produced in DEM simulations and successfully exported into the subsequent reactor physics analysis. With the developed approach, two typical issues in Monte Carlo reactor physics calculations of pebble bed geometries were avoided. A novel method was developed and implemented as a MATLAB code to calculate porosities in the cells of a CFD calculation mesh constructed over a pebble bed obtained from DEM simulations. The code was further developed to distribute power and temperature data accurately between discrete based reactor physics and continuum based thermal-hydraulics models to enable coupled reactor core calculations. The developed method was also found useful for analysing sphere packings in general. CFD calculations were performed to investigate the pressure losses and heat transfer in three dimensional air cooled smooth and rib roughened rod geometries, housed inside a hexagonal flow channel representing a sub-channel of a single fuel rod of a GFR. The CFD geometry represented the test section of the L-STAR experimental facility at Karlsruhe Institute of Technology and the calculation results were compared to the corresponding experimental results. Knowledge was gained of the adequacy of various turbulence models and of the modelling requirements and issues related to the specific application. The obtained pressure loss results were in a relatively good agreement with the experimental data. Heat transfer in the smooth rod geometry was somewhat under predicted, which can partly be explained by unaccounted heat losses and uncertainties. In the rib roughened geometry heat transfer was severely under predicted by the used realisable k − epsilon turbulence model. An additional calculation with a v2 − f turbulence model showed significant improvement in the heat transfer results, which is most likely due to the better performance of the model in separated flow problems. Further investigations are suggested before using CFD to make conclusions of the heat transfer performance of rib roughened GFR fuel rod geometries. It is suggested that the viewpoints of numerical modelling are included in the planning of experiments to ease the challenging model construction and simulations and to avoid introducing additional sources of uncertainties. To facilitate the use of advanced calculation approaches, multi-physical aspects in experiments should also be considered and documented in a reasonable detail.

Semantic and fuzzy modelling for human behaviour recognition in smart spaces : a case study on ambient assisted living.

Human activity recognition in everyday environments is a critical, but challenging task in Ambient Intelligence applications to achieve proper Ambient Assisted Living, and key challenges still remain to be dealt with to realize robust methods. One of the major limitations of the Ambient Intelligence systems today is the lack of semantic models of those activities on the environment, so that the system can recognize the speci c activity being performed by the user(s) and act accordingly. In this context, this thesis addresses the general problem of knowledge representation in Smart Spaces. The main objective is to develop knowledge-based models, equipped with semantics to learn, infer and monitor human behaviours in Smart Spaces. Moreover, it is easy to recognize that some aspects of this problem have a high degree of uncertainty, and therefore, the developed models must be equipped with mechanisms to manage this type of information. A fuzzy ontology and a semantic hybrid system are presented to allow modelling and recognition of a set of complex real-life scenarios where vagueness and uncertainty are inherent to the human nature of the users that perform it. The handling of uncertain, incomplete and vague data (i.e., missing sensor readings and activity execution variations, since human behaviour is non-deterministic) is approached for the rst time through a fuzzy ontology validated on real-time settings within a hybrid data-driven and knowledgebased architecture. The semantics of activities, sub-activities and real-time object interaction are taken into consideration. The proposed framework consists of two main modules: the low-level sub-activity recognizer and the high-level activity recognizer. The rst module detects sub-activities (i.e., actions or basic activities) that take input data directly from a depth sensor (Kinect). The main contribution of this thesis tackles the second component of the hybrid system, which lays on top of the previous one, in a superior level of abstraction, and acquires the input data from the rst module's output, and executes ontological inference to provide users, activities and their in uence in the environment, with semantics. This component is thus knowledge-based, and a fuzzy ontology was designed to model the high-level activities. Since activity recognition requires context-awareness and the ability to discriminate among activities in di erent environments, the semantic framework allows for modelling common-sense knowledge in the form of a rule-based system that supports expressions close to natural language in the form of fuzzy linguistic labels. The framework advantages have been evaluated with a challenging and new public dataset, CAD-120, achieving an accuracy of 90.1% and 91.1% respectively for low and high-level activities. This entails an improvement over both, entirely data-driven approaches, and merely ontology-based approaches. As an added value, for the system to be su ciently simple and exible to be managed by non-expert users, and thus, facilitate the transfer of research to industry, a development framework composed by a programming toolbox, a hybrid crisp and fuzzy architecture, and graphical models to represent and con gure human behaviour in Smart Spaces, were developed in order to provide the framework with more usability in the nal application. As a result, human behaviour recognition can help assisting people with special needs such as in healthcare, independent elderly living, in remote rehabilitation monitoring, industrial process guideline control, and many other cases. This thesis shows use cases in these areas.

Sustainability, Fashion Design, and Consumerism: Undergraduate Entry Level Fashion Students' Knowledge, Attitudes, and Habits

This qualitative inquiry explored 7 undergraduate students' attitudes, habits, and knowledge of consumerism, fashion design, and sustainability. The postmodern study employed crystallization as its methodological framework to gain insight into how participants' knowledge is manifested in their daily habits, and used 4 methods of data gathering: semistructured interviews, visual exercises, journal entries, and the researcher's own reflections. Four major themes emerged: Knowledge-Concepts Linked and Fragmented; Dissonance Between Knowledge Versus Attitudes and Consumer Habits; Surrendering to the Unsustainable Structures; Design Process and Caring Attitude. Findings indicate that participants possessed some knowledge of sustainability but lacked a well-rounded understanding of environmental and humanitarian implications of Western consumer society. Findings also reveal a dissonance between participants' knowledge and attitudes-affecting how their knowledge influences their behaviour-and how reflection, creative thinking, and drawing initiate change in participants' underlying attitudes. Recommendations are made to merge a variety of theoretical frameworks into the educational system in order to create curricula that offer a holistic overview and unique insights into sustainability challenges, particularly in specialized areas of the fashion industry.