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Whistle blowing is an act aimed at unveiling or exposing gross misdeed associated with services rendered in public or private institutions. These acts which constitutes diverse dimensions of crimes popularly referred to as corruption has obviously eaten deep into the fabrics of virtually every sector of our economy including university education system. It is pathetic but frankly to remark that this imbroglio characterizing the society has remained unabated due to absence of secure reporting line. Hence, the development of Whistle-Blowing System with secure electronic avenue for reporting every act or conduct that contradicts and compromises ethical provisions. It creates seamless link to enroll prospective whistle-blowers, accepts documented reports with verifiable evidence, allows communication with investigators, protects whistle-blower’s identity and interfaces them for due rewards. The system development process adopted the object-oriented methodology with its tools such as use-case diagram, sequence diagram and flowcharts. Implementation protocol utilized a combination of Javascript/php/HTML at the front-end while MySQL was engaged at the back-end for appropriate data documentation. It is suited with enormous prospects for assisting management trap down most of the sharp practices and inconsequentialities inherent in most Nigerian universities by accepting user identity, the reports and processes same to specifications., {"references":["Ahmed, S., Smith, G. M. and Ismail, Z. Internal whistle-blowing intentions: A study of demographic and individual factors. Journal of Modern Accounting and Auditing, 8(11), 2012, 1632-1645","Ahmed, S., Smith, G. M. and Ismail, Z. Internal whistle-blowing intentions: A study of demographic and individual factors. Journal of Modern Accounting and Auditing, 8(11), 2012, 1632-1645","Vadera, A. K., Aguilera, R. V., and Caza, B. B. Making sense of whistle-blowing's antecedents: Learning from research on identity and ethics programs. Business Ethics Quarterly, 19(4), 2009, 553-58","Gobert, J. and Punch, M. 2000. Whistle-blowers, The Public Interest, and the Public Interest Disclosure Act 1998. The Modern Law Review, 63(1), 25-54","Ayers, S., and Kaplan, S. E. Wrongdoing by Consultants: An Examination of Employees' Reporting Intentions. Journal of Business Ethics, 57(2), 2005, 121-137.","Brennan, N. and Kelly, J. 2007. A Study of Whistle-Blowing Among Trainee Auditors. The British Accounting Review, 39(1), Pp61-87"]}

This topic presents the development of an intelligent rule-based system for diagnosing multiple Eye diseases. The motivation behind this work was due to the inflexibility of the existing systems that are only able to diagnose a single type of eye disease. An expert system like the CASNET/GLAUCOMA or PADI-HAA was used to diagnose glaucoma. The stages used to develop our system includes modelling, Knowledge representation, Knowledge acquisition, Inference and System evaluation. This system was developed using HTML, CSS and JavaScript for the frontend and Node.JS for the backend. This system can diagnose eleven different eye diseases thereby solving the issue of the inflexibility of other expert systems.

relying on a unique integrated database, this work explores the relationship between labour productivity, on one side; intensity and characteristics of companies’ skills need and degree of skill mismatch, on the other. the analysis focuses on a representative sample of italian limited liability companies observed during the years 2012, 2014 and 2017. skill gap, mismatch, and the dynamics of italian companies’ productivity lucrezia fanti dario guarascio matteo tubiana

Engineering and Applied Science Research, 49, 4, 545-561

Background Despite effectiveness of action and coping planning in digital health interventions to promote physical activity (PA), attrition rates remain high. Indeed, support to make plans is often abstract and similar for each individual. Nevertheless, people are different, and context varies. Tailored support at the content level, involving suggestions of specific plans that are personalized to the individual, may reduce attrition and improve outcomes in digital health interventions. The aim of this study was to investigate whether user information relates toward specific action and coping plans using a clustering method. In doing so, we demonstrate how knowledge can be acquired in order to develop a knowledge-base, which might provide personalized suggestions in a later phase. Methods To establish proof-of-concept for this approach, data of 65 healthy adults, including 222 action plans and 204 coping plans, were used and were collected as part of the digital health intervention MyPlan 2.0 to promote PA. As a first step, clusters of action plans, clusters of coping plans and clusters of combinations of action plans and barriers of coping plans were identified using hierarchical clustering. As a second step, relations with user information (i.e. gender, motivational stage, ...) were examined using anova’s and chi2–tests. Results First, three clusters of action plans, eight clusters of coping plans and eight clusters of the combination of action and coping plans were identified. Second, relating these clusters to user information was possible for action plans: 1) Users with a higher BMI related more to outdoor leisure activities (F = 13.40, P 2 = 16.92, P 2 = 20.34, P 2 = 10.79, P = .004; respectively), 3) Younger users related more to active transport and different sports activities (F = 14.40, P Conclusions The approach used in this study might be a feasible approach to acquire input for a knowledge-base, however more data (i.e. contextual and dynamic user information) from possible end users should be acquired in future research. This might result in a first type of context-aware personalized suggestions on the content level. Trial registration The digital health intervention MyPlan 2.0 was preregistered as a clinical trial (ID:NCT03274271). Release date: 6-September-2017.

The research deals with improvement of methods and systems of controlling integrated power systems (IPSs) on the basis of intellectualization of decision-making support. Complex analysis of large-scale accidents at power facilities is performed, and their causes and damages are determined. There is substantiated topicality of building condition knowledge-bases as the foundation for developing decision-support systems in power engineering. The top priorities of the research include developing methods of building a knowledge base based on intensity models of control actions influencing the parameters of power system conditions and introducing the smart system into information contours of the automated dispatch control system (ADCS), as well as assessing practical results of the research. To achieve these goals, the authors apply methods of experiment planning, artificial intelligence, knowledge presentation, mathematical simulation, and mathematical statistics as well as methods of power systems studying. The basic research results include regression models of a power system sensitivity to control actions, methods of building a knowledge base based on the models of sensitivity matrices, a structure of the smart decision-support system, a scheme of introducing the decision-support system into the operating ADCS environment. The problem of building a knowledge base of the dispatch decision-support system on the basis of empirical data resulted from calculating experiments on the system diagram has been solved. The research specifies practical efficiency of the suggested approaches and developed models.

Work email is one of the organisations most critical tool today. It`s have become a standard way to communicate internally and externally. It can also affect our well-being. Email overload has become a well-known issue for many people. With interviews, follow up interviews, and a workshop, three persons from an accounting firm prioritise pre-define emails needs. And identified several other email needs that were added to the priority list. A thematic analysis and summarizing of a Likert scale was conducted to identify underlying work email needs and work email needs that are not apparent. Three work email needs were selected and using scenario-based methods and the elements of PACT to investigating how the characteristics of a chatbot can help solve the identified work email overload issue? The result shows that email overload is percept different from individual to individual. The choice of how email is handled and email activities indicate how email overload feeling is experienced. The result shows a need to get a sense of the email content quickly, fast collect financial information and information from Swedish authorities, and repetitive, time-consuming tasks. Suggestions on how this problem can be solved have been put forward for many years, and how to use machine learning to help reduce email overload. However, many of these proposed solutions have not yet been implemented on a full scale. One conclusion may be that since email overload is not experienced in the same way, individuals have different needs - One solution does not fit all. With the help of the character of a chatbot, many problems can be solved. And with a technological character of a chatbot that can learn individuals' email patterns, suggest email task to the user and performing tasks to reducing the email overload perception. Using keyword for email intents to get a sense of the email content faster and produce quick links where to find information about the identified subject. And to work preventive give the user remainder and perform repetitive tasks on specific dates.

Peer-teaching and interprofessional education can help students define individual healthcare roles, establish improved interprofessional relationships, and improve overall knowledge-base. Peer-teaching was provided by student pharmacists completing Advanced Pharmacy Practice Experiences (APPEs) for nine weeks to undergraduate student nurses. 4 student pharmacists and 25 student nurses participated in this study. Knowledge-base was measured with quizzes that were pre and post educational intervention. Confidence, self-awareness, and interprofessional perception were assessed using a modified ISVS-9A. Data were analyzed using a paired t-test. The mean difference between pre and post-knowledge-based tests averaged an improvement of 3.15 points (out of an average of 10.8 points per test) showing an overall improvement (p

Data analytics technologies are rapidly expanding across all industry sectors as organizations try to make analytics an integral part of their everyday decision making. Although there are many software tools and libraries to assist analysts and software engineers, organizations are looking for flexible analytics platforms that can align with their specific objectives, existing IT infrastructures and reuse resources and knowledge already accumulated in the organization. We identified that a knowledge engineering approach that utilizes semantic web technologies might help organizations to address these needs, as semantic web technologies are proven to provide rich information models that can integrate knowledge and support flexible software design. We conducted a literature survey to understand state-of-art research that uses semantic web technologies for DAS engineering and identified five key research gaps to address in this thesis. They are, the knowledge silos problem, lack of knowledge and resource reuse, lack of frameworks that support end-to-end platform engineering, analytics intents are not considered in the design process, and the usability issues in existing analytics platforms. To address these limitations, this thesis proposes the DASE (Data Analytics Solution Engineering) Framework, a novel framework to guide data analytics related knowledge management and requirements-driven platform development based on a knowledge-base. The DASE framework comprises four components: two semantic models to integrate organizational analytics knowledge and represent analytics requirements, a set of use-cases for DAS platform engineering and an architecture to execute these use-cases. Utilizing the DASE framework, organizations can accumulate their analytics knowledge and use that to develop usable and flexible DAS platforms in model-driven fashion, supported by service-oriented and workflow-based enterprise architecture. To evaluate the outcome, we conduct two real-world case studies related to house price prediction and time-series data analytics, using a prototype implementation of the proposed DASE architecture. The results show that the DASE framework can be used to successfully manage organizational knowledge and engineer usable and easy-to-maintain DAS platforms that reflect organizational analytics requirements. We also identify existing limitations in adapting DASE framework for operational context and discuss possible future work to address them.

The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we discuss how analytical games can be exploited as knowledge acquisition techniques in order to collect information useful to intelligent systems design. More specifically, we introduce a recently developed method, called the MARISA (MARItime Surveillance knowledge Acquisition) Game. The aim of this game is to ease the elicitation from domain experts of a considerable amount of conditional probabilities to be encoded into a maritime behavioral analysis service based on a multi-source dynamic Bayesian network. The game has been deployed in two experiments. The main objectives of such experiments are the validation of the network structure, the acquisition of the conditional probabilities for the network, and the overall validation of the game method. The results of the experiment show that the objectives have been met and that the MARISA Game proved to be an effective and efficient approach.

To systematically test a program, one needs good inputs—inputs that are valid such that they are not rejected by the program, and inputs that cover as much of the input space as possible in order to reach a maximum of functionality. We present recent techniques to systematically cover input structure. Our k-path algorithm for grammar production [HZ19] systematically covers syntactic elements of the input as well as their combinations. We show how to learn such input structures from graphical user interfaces, notably their interaction language [DBZ19]. Finally, we demonstrate that knowledge bases such as DBPedia can be a reliable source of semantically coherent inputs [Wa20]. All these techniques result in a significantly higher code coverage than state of the art.

Relying on a unique integrated database, this work explores the relationship between labour productivity, on one side; intensity and characteristics of companies’ skills need and degree of skill mismatch, on the other. The analysis focuses on a representative sample of Italian limited liability companies observed during the years 2012, 2014 and 2017. First, companies acknowledging the need to update their knowledge base display a higher productivity vis-à-vis other firms. Second, when it comes to the skill need distinguished by competence/knowledge domains (management, STEM, social and soft skills, technical operatives and humanities) it emerges that companies looking for technical operative and social skills show lower labour productivity as compared to other firms. On the contrary, companies characterized by a need in managerial, STEM or humanities-related skills show higher productivity. Third, the ability to match the skill need via new hiring is always positively correlated with firms’ productivity. This result is confirmed across all the adopted specifications.

Submitted by Kátia Marques Da Silva (azeretolum@outlook.com) on 2018-04-23T23:48:25Z No. of bitstreams: 1 Tese - Katia Marques.pdf: 4561081 bytes, checksum: df3b1f8ff238748716a20d44b5315c27 (MD5) Approved for entry into archive by Elza Mitiko Sato null (elzasato@ibilce.unesp.br) on 2018-04-24T15:54:07Z (GMT) No. of bitstreams: 1 silva_km_dr_sjrp.pdf: 4561081 bytes, checksum: df3b1f8ff238748716a20d44b5315c27 (MD5) Made available in DSpace on 2018-04-24T15:54:07Z (GMT). No. of bitstreams: 1 silva_km_dr_sjrp.pdf: 4561081 bytes, checksum: df3b1f8ff238748716a20d44b5315c27 (MD5) Previous issue date: 2018-03-09 Esta pesquisa, um estudo de caso etnográfico, teve por objetivo investigar, segundo os princípios da perspectiva sociocultural de formação de professores de línguas, apoiada na psicologia sócio-histórica de Vygotsky (1978, 1988, 1987, 1986, 1981, 1992, 1990) e em Johnson e Golombek (2016, 2013, 2011, 2011a, 2011b, 2002, 2003), Johnson (2015, 2009) e Golombek (2011, 2015), a relação da ensinagem de uma professora formadora de Metodologia de Ensino de Língua Estrangeira - Inglês com a ensinagem de um aluno-professor de um Curso de Letras nas aulas de Metodologia e no Estágio Supervisionado. Para atingir esse objetivo principal, foram investigadas as possíveis ocorrências de (co)construção de conceitos nas atividades teórico-práticas desenvolvidas. Os dados foram gerados a partir de diários, desenho pictórico, gravações em áudio, entrevistas, questionários e documentos (planos de aula) produzidos pelo participante da pesquisa. A análise dos dados, com base em conceitos advindos da perspectiva sociocultural, permitiu identificar e selecionar, no curso desses diferentes momentos de observações teórico-práticas, a construção, a coconstrução e a reconstrução de conceitos cotidianos e científicos nas ações do aluno-professor de língua inglesa em formação e a relação destes com a ensinagem da professora formadora. Estes conceitos, chamados de acadêmicos e cotidianos (JOHNSON; GOLOMBEK, 2016), são necessários para o desenvolvimento dialógico do professor em formação inicial, por serem parte de um processo único de construção do conhecimento (VYGOTSKY, 1978). This study research, an ethnographic case study, aims at investigating the relation of the teaching of a teacher educator of the subject course Metodologia de Ensino de Língua Estrangeira - Inglês to the teaching of a student-teacher in the classes of methodology and practicum of a Curso de Letras of a federal university of the interior of the state of Minas Gerais, based on the principles of a sociocultural perspective of second language teacher education, supported by Vygotsky (1978, 1988, 1987, 1986, 1981, 1992, 1990), and by Johnson and Golombek (2016, 2013, 2011, 2011a, 2011b, 2002, 2003), Johnson (2015, 2009) and Golombek (2011, 2015). Therefore, theoretical-practical activities developed during the second semester of 2014 and the first semester of 2015 were observed and monitored. The data were collected from diaries, drawings, audio recordings, interviews, questionnaires, and also from documents (lesson plans) produced by the participant of this research. The analysis of the data, oriented by the concepts of a sociocultural perspective, contributed to the identification and selection in different moments of theoretical-practical observations, the construction, coconstruction, and reconstruction of academic and everyday concepts in the action of the student-teacher in preservice education in relation to the teaching of the teacher educator. These concepts, named academic and everyday concepts (JOHNSON; GOLOMBEK, 2016), are necessary to the student-teacher’s dialogical development, as they are part of a unique process of construction of knowledge (VYGOTSKY, 1978).

This thesis is a result of our research efforts in building a multi-modal knowledgebase for the specific case of Carnatic music. Besides making use of metadata and symbolic notations, we process natural language text and audio data to extract culturally relevant and musically meaningful information and structuring it with formal knowledge representations. This process broadly consists of two parts. In the first part, we analyze the audio recordings for intonation description of pitches used in the performances. We conduct a thorough survey and evaluation of the previously proposed pitch distribution based approaches on a common dataset, outlining their merits and limitations. We propose a new data model to describe pitches to overcome the shortcomings identified. This expands the perspective of the note model in-vogue to cater to the conceptualization of melodic space in Carnatic music. We put forward three different approaches to retrieve compact description of pitches used in a given recording employing our data model. We qualitatively evaluate our approaches comparing the representations of pitched obtained from our approach with those from a manually labeled dataset, showing that our data model and approaches have resulted in representations that are very similar to the latter. Further, in a raaga classification task on the largest Carnatic music dataset so far, two of our approaches are shown to outperform the state-of-the-art by a statistically significant margin. In the second part, we develop knowledge representations for various concepts in Carnatic music, with a particular emphasis on the melodic framework. We discuss the limitations of the current semantic web technologies in expressing the order in sequential data that curtails the application of logical inference. We present our use of rule languages to overcome this limitation to a certain extent. We then use open information extraction systems to retrieve concepts, entities and their relationships from natural language text concerning Carnatic music. We evaluate these systems using the concepts and relations from knowledge representations we have developed, and groundtruth curated using Wikipedia data. Thematic domains like Carnatic music have limited volume of data available online. Considering that these systems are built forweb-scale data where repetitions are taken advantage of, we compare their performances qualitatively and quantitatively, emphasizing characteristics desired for cases such as this. The retrieved concepts and entities are mapped to those in the metadata. In the final step, using the knowledge representations developed, we publish and integrate the information obtained from different modalities to a knowledge-base. On this resource, we demonstrate how linking information from different modalities allows us to deduce conclusions which otherwise would not have been possible., Esta tesis es resultado de nuestro trabajo de investigación para construir una base de conocimiento multimodal para el caso específico de la música carnática. Además de hacer uso de metadatos y notación simbólica, procesamos texto de lenguaje natural y datos de audio para extraer información culturalmente relevante y musicalmente significativa, y estructurarla con representaciones formales de conocimiento. En líneas generales, este proceso consiste en dos partes. En la primera parte, analizamos grabaciones de audio para describir la entonación de las alturas usadas en las interpretaciones. Llevamos a cabo un exhaustivo análisis y evaluación de los métodos basados en distribución de altura propuestos anteriormente, señalando sus ventajas y limitaciones. Proponemos un nuevo modelo de datos para la descripción de alturas con el fin de superar las limitaciones identificadas. Esto amplía la perspectiva del modelo actual de nota para contribuir a la conceptualización del espacio melódico en música carnática. Ofrecemos tres propuestas diferentes para la extracción de una descripción compacta de las alturas usadas en una grabación dada utilizando nuestro modelo de datos. Evaluamos cualitativamente nuestras propuestas comparando las representaciones de alturas obtenidas según nuestro método con aquellas procedentes de un conjunto de datos anotado manualmente, con lo que mostramos que nuestro modelo de datos y nuestras propuestas resultan en representaciones muy similares a estas últimas. Además, en una tarea de clasificación de raagas en el mayor conjunto de datos de música carnática hasta la fecha, dos de nuestras propuestas muestran mejor rendimiento que el estado del arte con un margen estadístico significativo. En la segunda parte, desarrollamos representaciones de conocimiento para varios conceptos en música carnática, con un particular énfasis en el marco melódico. Discutimos las limitaciones de las tecnologías de web semántica actuales para expresar el orden de datos secuenciales, lo que restringe la aplicación de inferencia lógica. Presentamos nuestro uso de lenguajes de reglas para superar hasta cierto punto esta limitación. A continuación utilizamos sistemas abiertos de extracción de información para extraer conceptos, entidades y sus relaciones a partir de texto de lenguaje natural relacionado con música carnática. Evaluamos estos sistemas usando los conceptos y las relaciones de las representaciones de conocimiento que hemos desarrollado, así como información de referencia contrastada con datos de Wikipedia. Dominios temáticos como el de música carnática tienen un volumen limitado de datos disponibles en internet. Considerando que estos sistemas están construidos para datos a escala de la web, en la que es posible beneficiarse de las repeticiones, comparamos sus rendimientos cualitativa y cuantitativamente, enfatizando las características deseadas para casos como este. Los conceptos y entidades extraídas son mapeadas a aquellos existentes en los metadatos. En el paso final, usando las representaciones de conocimiento desarrolladas, publicamos e integramos la información obtenida por diferentes modalidades en una base de conocimiento. Con este recurso demostramos como la conexión de información de diferentes modalidades nos permite deducir conclusiones que de otra manera no habrían sido posibles., Aquesta tesi és el resultat de la nostra investigació per a construir una base de coneixement multimodal per a la música Carnàtica. A part d’utilitzar metadades i representacions simbòliques musicals, també processem text en llenguatge natural i l’àudio mateix per tal d’extreure informació que sigui rellevant tant des d’un punt de vista cultural com musical i que puguem estructurar amb representacions formals de coneixement. El procés que seguim està compost principalment de dues parts. En la primera part analitzem les gravacions d’àudio per descriure’n l’entonació de les altures tonals utilitzades. Comparem i avaluem aproximacions existents basades en histogrames d’altures tonals utilitzant una base de dades comuna de referència i en subratllem els avantatges i les limitacions. Proposem un nou model de dades per descriure l’altura tonal de les notes i superar les limitacions prèviament identificades. Aquest model va més enllà dels ja establerts i permet acomodar la conceptualització de l’espai melòdic en la música Carnàtica. Utilitzant el nostre model de dades proposem tres mètodes diferents per extreure descripcions compactes de les altures tonals de les notes d’una gravació. Fem una avaluació qualitativa a través de la comparació de descripcions generades amb els mètodes proposats i descripcions generades manualment, i comprovem que els nostres mètodes generen descripcions molt semblants a les generades manualment. També comprovem com els nostres mètodes són útils per a la classificació de raga avaluant amb la base de dades més gran de música Carnàtica que s’ha creat fins al dia d’avui. Dos dels nostres mètodes obtenen puntuacions més altes que els millors mètodes existents, amb marges de millora estadísticament significatius. En la segona part de la nostra investigació desenvolupem representacions de coneixement sobre diversos conceptes de la música Carnàtica, posant un èmfasi especial en aspectes melòdics. Parlem sobre les limitacions de les tecnologies de la web semàntica pel que fa a la representació del concepte d’ordre en dades seqüencials, fet que limita les possibilitats d’inferències lògiques. Proposem l’ús de llenguatges de normes per, fins a cert punt, superar aquestes limitacions. Després utilitzem sistemes d’extracció d’informació per recuperar conceptes, entitats i les seves relacions a partir de l’anàlisi de text natural sobre música Carnàtica. Avaluem aquests sistemes utilitzant conceptes i relacions extretes de representacions de coneixement que nosaltres mateixos hem desenvolupat i també utilitzant dades curades provinents de la Wikipedia. Per temàtiques com la música Carnàtica hi ha un volum de dades limitat accessible en línia. Tenint en compte que aquests sistemes estan pensats per funcionar amb grans volums de dades on les repeticions són importants, en fem una comparació qualitativa i quantitativa emfatitzant aquelles característiques més rellevants per casos amb volums de dades limitats. Els conceptes i entitats recuperades són emparellats amb conceptes i entitats presents a les nostres metadades. Finalment, utilitzant les representacions de coneixement desenvolupades, integrem les informacions obtingues de les diferents modalitats i les publiquem en una base de coneixement. Utilitzant aquesta base de coneixement demostrem com el fet de combinar informacions provinents de diferents modalitats ens permet arribar a conclusions que d’una altra manera no haurien estat possibles.

We propose a novel fully-automated approach towards inducing multilingual taxonomies from Wikipedia. Given an English taxonomy, our approach first leverages the interlanguage links of Wikipedia to automatically construct training datasets for the isa relation in the target language. Character-level classifiers are trained on the constructed datasets, and used in an optimal path discovery framework to induce high-precision, high-coverage taxonomies in other languages. Through experiments, we demonstrate that our approach significantly outperforms the state-of-the-art, heuristics-heavy approaches for six languages. As a consequence of our work, we release presumably the largest and the most accurate multilingual taxonomic resource spanning over 280 languages.

Wageningen Economic Research has developed Global-Detector, a knowledge-based Geographic Information System that aims to detect the worldwide potential for production, demand and market strategies. At any spot in the world Global-Detector can show the values from a large amount of indicators, such as climate, infrastructure, and land characteristics. A large set of indicators is readily available for use without any GIS-processing, and the model builder together with the expert can instantly start building the knowledge base for the concerning research aim. Knowledge from experts is applied to combine relevant indicators to create new indicators. The concept of Global-Detector and 10 applications developed by this tool are described. As a generic tool with increased flexibility, Global-Detector has many application possibilities in a wide variety of fields.

While flood risk is evolving as one of the most imminent natural hazards and the shift from a reactive decision environment to a proactive one sets the basis of the latest thinking in flood management, the need to equip decision makers with necessary tools to think about and intelligently select options and strategies for flood management is becoming ever more pressing. Within this context, the Preparing for Extreme and Rare Events in Coastal Regions (PEARL) intelligent knowledge-base (PEARL KB) of resilience strategies is presented here as an environment that allows end-users to navigate from their observed problem to a selection of possible options and interventions worth considering within an intuitive visual web interface assisting advanced interactivity. Incorporation of real case studies within the PEARL KB enables the extraction of (evidence-based) lessons from all over the word, while the KB’s collection of methods and tools directly supports the optimal selection of suitable interventions. The Knowledge-Base also gives access to the PEARL KB Flood Resilience Index (FRI) tool, which is an online tool for resilience assessment at a city level available to authorities and citizens. We argue that the PEARL KB equips authorities with tangible and operational tools that can improve strategic and operational flood risk management by assessing and eventually increasing resilience, while building towards the strengthening of risk governance. The online tools that the PEARL KB gives access to were demonstrated and tested in the city of Rethymno, Greece.

International audience; This paper tackles the problem of the semantic gap between a document and a query within an ad-hoc information retrieval task. In this context, knowledge bases (KBs) have already been acknowledged as valuable means since they allow the representation of explicit relations between entities. However, they do not necessarily represent implicit relations that could be hidden in a corpora. This latter issue is tackled by recent works dealing with deep representation learning of texts. With this in mind, we argue that embedding KBs within deep neural architectures supporting document-query matching would give rise to fine-grained latent representations of both words and their semantic relations. In this paper, we review the main approaches of neural-based document ranking as well as those approaches for latent representation of entities and relations via KBs. We then propose some avenues to incorporate KBs in deep neural approaches for document ranking. More particularly, this paper advocates that KBs can be used either to support enhanced latent representations of queries and documents based on both distributional and relational semantics or to serve as a semantic translator between their latent distributional representations.

Research in the domain of 'Innovation Studies' has been claimed to allow for the study of how technology will develop in the future. Some suggest that the National and Sectoral Innovation Systems literature has become bogged down, however, into case studies of how specific institutions affect innovation in a specific country. A useful notion for policy makers in particular, Balzat and Hanusch (2004 Recent trends in the research on national innovation systems Journal of Evolutionary Economics 14(2): 197-210) argued that there is a need for NIS studies to develop complementary and also quantitative methods in order to generate new insights that are comparable across national borders. We use data for patents granted by the World Intellectual Property Organization (WIPO) to map innovation systems. Groupings of patents into primary and secondary classes (co-classification) can be used as relational indicators. Knowledge from one class may be more easily used in another class when a co-classification relation exists. Using social network analysis, we map the co-classification of patents among classes and thus indicate what characterizes an innovation system. A main contribution of this paper is methodological, adding to the repertoire of methods NIS studies use and using information from patents in a different way. Policy makers may also find benefits in the social network analysis of the complete set of patents granted by the WIPO to firms and individuals in a country. Social network analysis indicates what innovation activity occurs in a countries and which fields of technology are likely to give rise to innovative products in the near future. We offer such analysis for the Dutch and Indian Innovation Systems. This social network analysis could also be done for a sector innovation system, and we do so for nanotech to determine empirically the knowledge field relevant for this emerging scientific domain.

Research in the domain of 'Innovation Studies' has been claimed to allow for the study of how technology will develop in the future. Some suggest that the National and Sectoral Innovation Systems literature has become bogged down, however, into case studies of how specific institutions affect innovation in a specific country. A useful notion for policy makers in particular, Balzat and Hanusch (2004 Recent trends in the research on national innovation systems Journal of Evolutionary Economics 14(2): 197-210) argued that there is a need for NIS studies to develop complementary and also quantitative methods in order to generate new insights that are comparable across national borders. We use data for patents granted by the World Intellectual Property Organization (WIPO) to map innovation systems. Groupings of patents into primary and secondary classes (co-classification) can be used as relational indicators. Knowledge from one class may be more easily used in another class when a co-classification relation exists. Using social network analysis, we map the co-classification of patents among classes and thus indicate what characterizes an innovation system.A main contribution of this paper is methodological, adding to the repertoire of methods NIS studies use and using information from patents in a different way. Policy makers may also find benefits in the social network analysis of the complete set of patents granted by the WIPO to firms and individuals in a country. Social network analysis indicates what innovation activity occurs in a countries and which fields of technology are likely to give rise to innovative products in the near future. We offer such analysis for the Dutch and Indian Innovation Systems. This social network analysis could also be done for a sector innovation system, and we do so for nanotech to determine empirically the knowledge field relevant for this emerging scientific domain.

BACKGROUND: Although genetic studies have reported a number of loci associated with cutaneous melanoma (CM) risk, a comprehensive synopsis of genetic association studies published in the field and systematic meta-analysis for all eligible polymorphisms have not been reported. METHODS: We systematically annotated data from all genetic association studies published in the CM field (n = 145), including data from genome-wide association studies (GWAS), and performed random-effects meta-analyses across all eligible polymorphisms on the basis of four or more independent case-control datasets in the main analyses. Supplementary analyses of three available datasets derived from GWAS and GWAS-replication studies were also done. Nominally statistically significant associations between polymorphisms and CM were graded for the strength of epidemiological evidence on the basis of the Human Genome Epidemiology Network Venice criteria. All statistical tests were two-sided. RESULTS: Forty-two polymorphisms across 18 independent loci evaluated in four or more datasets including candidate gene studies and available GWAS data were subjected to meta-analysis. Eight loci were identified in the main meta-analyses as being associated with a risk of CM (P < .05) of which four loci showed a genome-wide statistically significant association (P < 1 x 10(-7)), including 16q24.3 (MC1R), 20q11.22 (MYH7B/PIGU/ASIP), 11q14.3 (TYR), and 5p13.2 (SLC45A2). Grading of the cumulative evidence by the Venice criteria suggested strong epidemiological credibility for all four loci with genome-wide statistical significance and one additional gene at 9p23 (TYRP1). In the supplementary meta-analyses, a locus at 9p21.3 (CDKN2A/MTAP) reached genome-wide statistical significance with CM and had strong epidemiological credibility. CONCLUSIONS: To the best of our knowledge, this is the first comprehensive field synopsis and systematic meta-analysis to identify genes associated with an increased susceptibility to CM. J Natl Cancer Inst

In a globalizing economy, competitiveness means information and know-how rather than capital and physical assets. The function of knowledge management is therefore to allow organizations to leverage their information resources and knowledge assets by remembering and applying experience. An organization’s ability to compete on the market is increasingly seen as depending on the skills and knowledge of its employees, regarded as intellectual capital, and on its capacity to preserve and use as much as possible of this knowledge in knowledge-bases and expert-systems. However, knowledge evolves rapidly, due to the continuous changes of the business environment, and the useful life span of the organizational skills is decreasing, which means the survival and competitiveness of an organization is linked to its ability to produce and use knowledge as well as to include the results of the learning process in organizational competences and virtual products.

The choice of a proper computer system is not an easy task for a decider. One reason could be the present market development of computer systems applied in business. The big number of the Romanian market players determines a big number of computerized products, with a multitude of various properties. Our proposal tries to optimize and facilitate this decisional process within an e-shop where are sold IT packets applied in business, building an online decisional assistant, a special component conceived to facilitate the decision making needed for the selection of the pertinent IT package that fits the requirements of one certain business, described by the decider. The user interacts with the system as an online buyer that visit an e-shop where are sold IT package applied in economy.

Many international standards exist in the field of IT security. This research is based on the ISO/IEC 15408, 15446, 19791, 13335 and 17799 standards. In this paper, we propose a knowledge base comprising a threat countermeasure model based on international standards for identifying and specifying threats which affect IT environments. In addition, the proposed knowledge base system aims at fusing similar security control policies and objectives in order to create effective security guidelines for specific IT environments. As a result, a knowledge base of security objectives was developed on the basis of the relationships inside the standards as well as the relationships between different standards. In addition, a web application was developed which displays details about the most common threats to information systems, and for each threat presents a set of related security control policies from different international standards, including ISO/IEC 27002.

International audience; Multilingual document classification is often addressed by approaches that rely on language-specific resources (e.g., bilingual dictionaries and machine translation tools) to evaluate cross-lingual document similarities. However, the required transformations may alter the original document semantics, raising additional issues to the known difficulty of obtaining high-quality labeled datasets. To overcome such issues we propose a new framework for multilingual document classification under a transductive learning setting. We exploit a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. We resort to a state-of-the-art transductive learner to produce the document classification. Results on two real-world multilingual corpora have highlighted the effectiveness of the proposed document model w.r.t. document representations usually involved in multilingual and cross-lingual analysis, and the robustness of the transductive setting for multilingual document classification.

Background: The study was carried out in two primary schools in Lagos State, Nigeria. One hundred students were from Pacelli School for the Blind and one hundred and sixty-four students were from Onitolo Primary School, a main stream school. The study compared the knowledge base of the two groups on malaria prevention and treatment options. Approval for the study was granted by the Ethics Committee of the Lagos University Teaching Hospital, Nigeria and consent was sought from parents/guardians of the students and from the principals of the two schools. Method: The instrument of data collection was a close ended questionnaire. The average time it took to fill the questionnaire was twenty minutes. Analysis of categorical variables was done by Chi-Square Analysis. Results: The respondents from Pacelli School for the Blind were older than the respondents from Onitolo Primary School when matched by class in primary school (P < 0.05). The majority of the respondents were Yorubas, 178 (70.5%). In Pacelli School, the number of males that were visually-impaired was more than the number of females (P < 0.05). There was a statistically significant difference on their knowledge base on the cause of malaria (P < 0.05). The students from Pacelli School 92 (92%) knew that malaria was caused by mosquitoes and only 75 (45.7%) of the respondents from Onitolo knew that malaria was caused by mosquitoes. The age at which respondents from Pacelli School became visually impaired was not statistically related to the correct knowledge that malaria was caused by mosquito (P < 0.05). The number of respondents that preferred the parenteral route for treatment of malaria was fewer in Pacelli School than in Onitolo School (P < 0.05). A greater number of respondents from Pacelli School did not like to take malaria tablets because the malaria tablets were bitter (P < 0.05). The major source of information about malaria from both schools was from their doctors and other health personnel 116 (43.9 %). When the responses from both groups were combined, the least source of information about malaria in both groups was from teacher/school 27 (10.2%). Two hundred and twenty-seven (85%) of the respondents would choose a doctor as their health-care provider if they had malaria (P > 0.05). In Pacelli School, 44 (44%) of the respondents had had malaria in the past twelve months. In Onitolo School, 49 (29.9%) of the respondents had had malaria in the past twelve months. There was no statistically significant difference in the number of times both groups had had malaria in the past twelve months. Conclusion: There were more visually-impaired males in Pacelli School than females. There was a statistically significant difference on their knowledge base on the cause of malaria. More students from Pacelli School had the correct knowledge on the cause of malaria. The major source of information about malaria from both schools was from their doctors and other health personnel. Teacher/School was the least source of information about malaria when responses from both schools were combined. Health Education on preventable diseases like malaria should be strengthened in both schools. More schools for the visually-impaired should be replicated across the states in Nigeria and more schools for the visually-impaired should be replicated in Lagos State. Virginia Polytechnic Institute and State University’s Open Access Subvention Fund (OASF)

Složeni inteligentni sustavi za uspješno rješavanje problema iz stvarnog svijeta zahtijevaju veliku količinu znanja i učinkovite postupke zaključivanja. Znanje o problemima iz stvarnog svijeta je neizrazito, nejasno i dvosmisleno. Za rješenje navedenih problema predlaže se biološki, neurološki i psihološki inspirirana arhitektura hijerarhijske heterogene baze znanja. Arhitektura omogućava izgradnju složenih inteligentnih sustava prikladnih za razna područja primjene. Baza znanja ima tri razine: asocijativnu, semantičku i razinu s pravilima. Za bazu znanja razvijeni su ulančani paralelni neizraziti postupci zaključivanja koji koristite znanje pohranjeno na trima razinama koje koriste različite sheme za predstavljanje znanja. Za učinkovito paralelno raspodijeljeno pohranjivanje i pretraživanje velikog broja pojmova u asocijativnoj razini, na temelju sličnosti opisane neizrazitom lingvističkom varijablom, razvijena je prikladna arhitektura asocijativne memorije. Complex intelligent systems require a huge amount of knowledge, as well as some efficacious mechanisms for manipulating this knowledge, in order to perceive, organize and summarize stimuli obtained from the real world. They also require a large amount of information from a problem domain and efficient reasoning procedures to support a useful degree of problem-solving ability which is limited by three major obstacles. The first obstacle is complexity of world knowledge which humans acquire through experience, communication and education. The second obstacle centres on the concept of relevance which is very complex. The third obstacle is deduction from perception-based information which is fuzzy, uncertain and ambiguous. Architecture of a hierarchical heterogeneous knowledge base suitable for solving these problems is proposed in this dissertation. The architecture enables development of complex intelligent systems suitable for various problem domains. Motivation and aim of the research is to develop a knowledge base model capable for efficient reasoning with human knowledge about real or abstract concepts obtained from the real world that are fuzzy, uncertain, vague and ambiguous. The model is inspired by biological, neurological and psychological models obtained by analyzing how human and animal brains abstract, process and store knowledge from the interaction with the environment. A brain has hierarchical heterogeneous structure. It consists of distinct layers of neuron cells which are hierarchically stacked in brain’s tissues and interconnected with forward and backward connections. These layers are formed from characteristic cell types of neurons. Biological and neurological models of the human and animal brains show clear distinction between processing of familiar well known stimuli and novel stimuli during an interaction with the environment. The integration within and among specialized areas is mediated by forward, backward and lateral connections and is very complex. The important anatomical and functional distinctions between forward and backward connections are: forward connections are less divergent, and transmit known stimuli directly to higher levels, and backward connections are more divergent and they are used when processing unknown stimuli. Familiar environmental stimuli travel fast by means of the forward connections from layers of neuron cells in sensors to top layers of neuron cells in a brain’s cognitive system. Novel stimuli at one layer of neurons create an “unknown” neuron activity patterns that are unable to activate neurons at higher layers with forward connections (i.e., input stimuli are not “recognized”). In this case backward connections are used to activate large areas of a lower layer in the hierarchy of the layers in the brain. Consequent neural activity patterns at the lower layer result with multiple activations of forward connections corresponding to some similar known stimuli that are connected in the experience and have similar neuron activity patterns with the stimulus that is unknown at the upper layer. This process of activation is based on the maximum overlap of subsets of neurons that encode known and unknown stimuli. A minimum overlap which is required to recognize an incoming stimulus as a known, already learned, concept is not exactly known but it has to be significantly larger than an overlap between two random stimuli. This process enables the brain to process novel stimuli based on similarity estimations to already known concepts obtained from lower levels. In short, novel unknown stimuli at one layer are substituted with most similar stimuli obtained from a lower layer. Similarity between stimuli is automatically established based on an overlap between corresponding neural activity patterns. If some of the obtained similar stimuli are known at the upper layer than cognitive reasoning is able to continue, if not, than the procedure is recursively repeated with similar concepts retrieved in the previous step. This forward and backward connections multilayer interplay is recursive with many cycles and can last very long time for novel situations and difficult cognitive problems (i.e., seconds, days, or longer). Familiar situations and well learned stimuli can be successfully interpreted just after few milliseconds. This can explain why familiar faces are recognized in milliseconds due to dominant use of forward connections but recognition of faces of distant friends or relatives can take minutes, or even longer, due to use of the backward connections recursively in many cycles. This dissertation describes the knowledge base model inspired by the above described organization of a brain, as well as the underlining reasoning processes. Stimuli learned through the interaction with the entities from the environment are modelled as concepts. Entity is something that exists in the real or an abstract world separately from other things and has its own identity. The entities are perceived with percepts. Percepts in general are basic component in the formation of a concept. Entities are represented as concepts. A concept is an idea or a principle that is connected with an entity. A concept is described with one unique term (a word or linguistic expression) and with one or more percepts (pictures, sounds, or any other perception-based representations). Each percept can be characterised with its modality (visual, auditory, infrared, radar, ultrasound, ...) and with its specific type (shape, colour, video, face image, iris image, pitch, tone, voice sequence, song, ... ). The proposed architecture of the hierarchical heterogeneous knowledge base has three levels: an associative level, a semantic level and a rule-based level. Architecture of an associative memory suitable for efficient parallel distributed storing and searching a large number of concepts at the associative level, based on similarities expressed with values of a fuzzy linguistic variable, is developed. The associative level is composed of several modality modules, each of which is composed of several percept modules. The levels use different knowledge representation schemes which support reasoning procedures similar to cognitive processes in humans and animals. The levels in the knowledge base model are hierarchically organized and connected with forward and backward connections. Initialization of reasoning procedures performed at the levels in the model is equivalent to neuron activations mediated by forward connections in a brain. Neuron activations mediated by backward connections are modelled with an activation of reasoning procedures at a lower level. Neuron activations mediated by lateral connections at neuron layers are equivalent to reasoning processes performed at the three levels in the model. Cycles of forward and backward connections activations in a brain are modelled as chaining of reasoning procedures. The model is able to replicate cognitive processes.

With the ever growing complexity of aircrafts, new tools and eventually methods to use these tools are needed in aircraft conceptual design. To reduce the development cost, an enhancement in the conceptual design is needed. This thesis presents a knowledge-based aircraft geometry design tool RAPID and the methodology applied in realizing the design. The parameters used to create a geometry need to be exchange between different tools. This is achieved by using a centralized database or onedata concept. One-database will enable creating a less number of cross connections between different tools to exchange data with one another. Different types of aircraft configurations can be obtained with less effort. As RAPID is developed based on relational design, any changes made to the geometric model will update automatically. The geometry model is carefully defined to carry over to the preliminary design. The validation of RAPID is done by implementing it in different aircraft design courses at Linköping University. In the aircraft project course, RAPID was effectively used and new features were added to the obtained desired design. Knowledge-base is used to realize the design performance for the geometry with an integrated database approach for a multidisciplinary aircraft conceptual design.

The information systems and technologies (IS/IT) are a decisive platform in the response capacity of organizations to the new management imperatives through new solutions that permit improvement in their competitive position. As information management is a highly differentiated area, the present study compares several enterprises of different sectors, located (head office or branch) in the Algarve region, with the aim of systematising the main differentiating issues related with adoption, restructuring and implementation of information systems and supportive technologies. Every organization should define na accurate and timely plan which, in case of adopting and implementing information systems, has to analyse results within the same plan on a yearly basis of the system’s activity, ensuring that it is always aligned with the business strategy. Given the potential of the powerful tools that guide customer-oriented understanding, greater attention is required to their performance on sustainable business dynamics.

Barbara Wąsikowska

The paper explains a system to assess automatically vulnerability due to landslide on socio-economy of a region by categorizing landslide hazard using spatial as well as temporal causative factors. The expert system has input, understanding, expert & output modules & uses digital spatial data of causative factors of landslide. Input accepts thematic images of contributing factors for landslides, Understanding module interprets to extract relevant information as required by expert module consisting of a Knowledge Base & Inference strategy categorizing region into different susceptibilities of landslide. Overlaid on socio-economic parameters in output module for vulnerability maps of landslide on population, forestry, urban, rural, agriculture separately to ascertain the impact of landslide on socio-economy of the Tehri-Garhwal region lower Himalayas, India.

Research in the domain of 'Innovation Studies' has been claimed to allow for the study of how technology will develop in the future. Some suggest that the National and Sectoral Innovation Systems literature has become bogged down, however, into case studies of how specific institutions affect innovation in a specific country. A useful notion for policy makers in particular, Balzat and Hanusch (2004 Recent trends in the research on national innovation systems Journal of Evolutionary Economics 14(2): 197-210) argued that there is a need for NIS studies to develop complementary and also quantitative methods in order to generate new insights that are comparable across national borders.We use data for patents granted by the World Intellectual Property Organization (WIPO) to map innovation systems. Groupings of patents into primary and secondary classes (co-classification) can be used as relational indicators. Knowledge from one class may be more easily used in another class when a co-classification relation exists. Using social network analysis, we map the co-classification of patents among classes and thus indicate what characterizes an innovation system.A main contribution of this paper is methodological, adding to the repertoire of methods NIS studies use and using information from patents in a different way. Policy makers may also find benefits in the social network analysis of the complete set of patents granted by the WIPO to firms and individuals in a country. Social network analysis indicates what innovation activity occurs in a countries and which fields of technology are likely to give rise to innovative products in the near future. We offer such analysis for the Dutch and Indian Innovation Systems. This social network analysis could also be done for a sector innovation system, and we do so for nanotech to determine empirically the knowledge field relevant for this emerging scientific domain.

The 2DECIDE project has developed an ITS Toolkit to support decision makers and planners in utilizing ITS to solve their problems and to reach their objectives. The foundation of the toolkit is its knowledge base, which is built on results and experiences from research and studies on the impacts, socio-economic profitability, user acceptance as well as technical, legal, institutional and financial feasibility of different ITS services either individually or in a bundle of several services. At the same time, the knowledge base contains basic information of the usual deployment issues of different ITS services and applications. At the core of the toolkit is the inference engine, which is designed to provide the user the most relevant ITS solutions for his/her own context as well as their expected impacts, benefits, costs, user acceptance and feasibility. The paper describes the design framework and the process undertaken to build up the knowledge base and inference engine as well as the different problems encountered during the process and our solutions to these problems

Verification of knowledge-bases is a critical step to ensure the quality of a knowledge-based system. The success of these systems depends heavily on how qualitative the knowledge is. Manual verification is however cumbersome and error prone, especially for large knowledge-bases. This thesis provides a design theory, based upon the suggested framework by Gregor and Jones (2007). The theory proposes a general design of automated verification tools, which have the abilities of verifying heuristic knowledge in rule-based systems utilizing certainty factors. Included is a verification of completeness and consistency technique customized to this class of knowledge-based systems. The design theory is instantiated in a real-world verification tool development project at Uppsala University. Considerable attention is given to the design and implementation of this artifact – uncovering issues and considerations involved in the development process. For the knowledge management practitioner, this thesis offers guidance and recommendations for automated verification tool development projects. For the IS research community, the thesis contributes with extensions of existing design theory, and reveals some of the complexity involved with verification of a specific rule-based system utilizing certainty factors.

The 2DECIDE project has developed an ITS Toolkit to support decision makers and planners in utilizing ITS to solve their problems and to reach their objectives. The foundation of the toolkit is its knowledge base, which is built on results and experiences from research and studies on the impacts, socio-economic profitability, user acceptance as well as technical, legal, institutional and financial feasibility of different ITS services either individually or in a bundle of several services. At the same time, the knowledge base contains basic information of the usual deployment issues of different ITS services and applications. At the core of the toolkit is the inference engine, which is designed to provide the user the most relevant ITS solutions for his/her own context as well as their expected impacts, benefits, costs, user acceptance and feasibility. The paper describes the design framework and the process undertaken to build up the knowledge base and inference engine as well as the different problems encountered during the process and our solutions to these problems

Introduction: This paper describes the design, implementation, and potential use of a comparative anatomy information system (CAIS) for querying on similarities and differences between homologous anatomical structures across species, the knowledge base it operates upon, the method it uses for determining the answers to the queries, and the user interface it employs to present the results. The relevant informatics contributions of our work include (1) the development and application of the structural difference method, a formalism for symbolically representing anatomical similarities and differences across species; (2) the design of the structure of a mapping between the anatomical models of two different species and its application to information about specific structures in humans, mice, and rats; and (3) the design of the internal syntax and semantics of the query language. These contributions provide the foundation for the development of a working system that allows users to submit queries about the similarities and differences between mouse, rat, and human anatomy; delivers result sets that describe those similarities and differences in symbolic terms; and serves as a prototype for the extension of the knowledge base to any number of species. Additionally, we expanded the domain knowledge by identifying medically relevant structural questions for the human, the mouse, and the rat, and made an initial foray into the validation of the application and its content by means of user questionnaires, software testing, and other feedback. Methods: The anatomical structures of the species to be compared, as well as the mappings between species, are modeled on templates from the Foundational Model of Anatomy knowledge base, and compared using graph-matching techniques. A graphical user interface allows users to issue queries that retrieve information concerning similarities and differences between structures in the species being examined. Queries from diverse information sources, including domain experts, peer-reviewed articles, and reference books, have been used to test the system and to illustrate its potential use in comparative anatomy studies. Results: 157 test queries were submitted to the CAIS system, and all of them were correctly answered. The interface was evaluated in terms of clarity and ease of use. This testing determined that the application works well, and is fairly intuitive to use, but users want to see more clarification of the meaning of the different types of possible queries. Some of the interface issues will naturally be resolved as we refine our conceptual model to deal with partial and complex homologies in the content. Conclusions: The CAIS system and its associated methods are expected to be useful to biologists and translational medicine researchers. Possible applications range from supporting theoretical work in clarifying and modeling ontogenetic, physiological, pathological, and evolutionary transformations, to concrete techniques for improving the analysis of genotype-phenotype relationships among various animal models in support of a wide array of clinical and scientific initiatives.

International audience; At first, the three current questions on the ‘transmission of knowledge’, what, how and why to transmit, are reviewed recalling the emergence and evolution of the concepts of ‘cellulose’ and ‘lignin’, devised more than one century ago as ‘chemical compounds’ and now currently perceived as ‘bio-polymer-structures’ or ‘macromolecular self-organized systems’. Avoiding any contemporary controversy, it is then suggested that such challenging questions would result from both 1/ the difficulty to explain ‘new’ specific concepts by using ‘old’ generic ‘words’ as illustrated with reference to recent ‘modern’ papers, as well as from 2/ the lack of extended studies of the ‘recent and old literature’, mainly due to the difficulty of extensively explore, in a limited period of time, an ever increasing number of more and more specialized publications in different thematic domains-journals. Finally, some principles and practices of organizing and editing specialized ‘data- and knowledge-bases’ are thus recalled and discussed in relation to the new possibilities, trusts and fears, of 'electronic edition' based on assembling electronic files as 'e-base', according to an original procedure of Documentary Recollection, ‘ReDoc’, very recently adapted at INRA.

This paper focuses on a CBS module of Knowledge-Based Tutoring System for Software Reuse Practices. The paper presents a public CBS using a validated Software Reuse Reference Model (SKRM). A CBS-SRRM allows the software engineer to improve reuse practice by being tutored with selected course material based on the user profile. This material is combined with actual practice-based knowledge derived from different case studies from software development organizations' reuse practices. A CBS-SRRM provides software engineers with a way to be tutored using positive lessons learned by other organizations. Our research focuses on achieving more effective means for software development organizations to find alternative educational (training) solutions to problems in software reuse practices.

We have developed a database named BRITE, which contains knowledge of interacting molecules and/or genes concering cell cycle and early development. Here, we report an overview of the database and the method of automatic search for functionally common sub-pathways between two biological pathways in BRITE.

Over the past two decades, the impact of disasters has been devastating, affecting 4.4 billion people, resulted in 1.3 million causalities and $2 trillion in economic losses. Post-disaster reconstruction and rehabilitation is a complex process with several dimensions. Government, nongovernmental, and international organizations have their own stakes in disaster recovery programs, and links must be established among them as well as with the community. Concerning post-disaster reconstruction scenario, the most significant factor is prompt decision making based on best possible information available. Effective sustainable post-disaster response is crucial and lies at the heart of disaster management agencies in almost every cautious country around the globe. Development is a dynamic process and disasters provide the opportunities to vitalize and/or revitalize this process, especially to generate local economies, and to upgrade livelihood and living condition. The success of the reconstruction phases, i.e., rescue, relief, and rehabilitation, is mainly dependent on the availability of efficient project teams and timely information to make informed decision. By having the knowledge-based system to make well-informed decisions, combined with the efficiency of a project team and strong coordination, project success should increase. This paper presents a theoretical framework of a knowledge-based approach for enhancing prompt and effective sustainable disaster management. The conceptual model consists of two main IT based components of knowledge- based system, i.e., a knowledge-base and a decision support shell for making more informed decisions for effective, timely and sustainable response in post-disaster reconstruction scenarios. The system is expected to assist in improving reconstruction project processes, coordination, and team building process because the most likely areas on which to focus can be identified during the early stage of the post-disaster scenario. Tapping into the past experiences of post-disaster scenarios, the knowledge-based system provides a wealth of pertinent and useful information for decision makers and will eventually enhance collaborative ventures for sustainable disaster management. The system would be helpful for emergency response management teams to take proactive measures by learning from past similar experiences, making informed decisions related to team building and project coordination processes undertaken by disaster management agencies. Professionals need to work in close cooperation with each other to give rise to a better and more efficient system for sustainable disaster management. Hence, the study is valuable for all professionals involved with research and development of sustainable disaster management strategies.

Health Information technology (HIT) played important role in wide range of healthcare settings. The strategy of HIT is to reduce cost and increase efficiency. The aim of cardiac implantable devices is to reduce the effect and risk of cardiovascular diseases. The number of patients that benefits from these devices are growing rapidly. Cardiac rehabilitation program improves the ability of cardiac patient to restore his health to an optimal level and reduce his stress and depression, especially after the cardiovascular surgeries. The need for the use of ontologies in cardiac rehabilitation domain has emerged over the last years, in order to improve the standardization of cardiac rehabilitation's practice worldwide. In this study, ontology-based system for managing the cardiac rehabilitation program has been developed. The ontology presents the required functionalities to implement the program management that helps to enhance the quality of cardiac rehabilitation program. Moreover, proposed ontology contributes to enhance the understanding, reusability, sharing and transferring knowledge in the domain of cardiac rehabilitation.

Discovering new reactions, optimizing their performance, and extending the synthetically accessible chemical space are critical drivers for major technological advances and more sustainable processes. The current wave of machine intelligence is revolutionizing all data-rich disciplines. Machine intelligence has emerged as a potential game-changer for chemical reaction space exploration and the synthesis of novel molecules and materials. Herein, we will address the recent development of data-driven technologies for chemical reaction tasks, including forward reaction prediction, retrosynthesis, reaction optimization, catalysts design, inference of experimental procedures, and reaction classification. Accurate predictions of chemical reactivity are changing the R&D processes and, at the same time, promoting an accelerated discovery scheme both in academia and across chemical and pharmaceutical industries. This work will help to clarify the key contributions in the fields and the open challenges that remain to be addressed. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning Data Science > Computer Algorithms and Programming Data Science > Chemoinformatics, Wiley Interdisciplinary Reviews. Computational Molecular Science, 12 (5), ISSN:1759-0876, ISSN:1759-0884

The increasing accessibility and availability of online data provides a valuable knowledge source for information analysis and decision-making processes. In this paper we argue that extracting information from this data is better guided by domain knowledge of the targeted use-case and investigate the integration of a knowledge-driven approach with Machine Learning techniques in order to improve the quality of the Relation Extraction process. Targeting the financial domain, we use Semantic Web Technologies to build the domain Knowledgebase, which is in turn exploited to collect distant supervision training data from semantic linked datasets such as DBPedia and Freebase. We conducted a serious of experiments that utilise the number of Machine Learning algorithms to report on the favourable implementations/configuration for successful Information Extraction for our targeted domain. © 2015 by SCITEPRESS - Science and Technology Publications, Lda.

In this paper, we propose an expert system for daylighting in architecture which is used to guide a goal-oriented, user-interactive design process. This system is supported by a knowledge-base which has been populated using a set of previously completed simulations using the Design of Experiments methodology. The knowledge-base contains information regarding the effects of a variety of design conditions on resultant daylighting performance. Eighty different design conditions are considered, encompassing two different values of ten design variables on four facades. Daylighting effects are considered for three times of day, for three seasons, and for five zones. The use of the knowledge-base as both a stand-alone resource and as a component of the expert system is considered. Within the expert system, the knowledge-base provides customized information based on user inputs and guides an iterative process which improves daylighting performance of the user’s original design.

International audience; Multilingual document classification is often addressed by approaches that rely on language-specific resources (e.g., bilingual dictionaries and machine translation tools) to evaluate cross-lingual document similarities. However, the required transformations may alter the original document semantics, raising additional issues to the known difficulty of obtaining high-quality labeled datasets. To overcome such issues we propose a new framework for multilingual document classification under a transductive learning setting. We exploit a large-scale multilingual knowledge base, BabelNet, to support the modeling of different language-written documents into a common conceptual space, without requiring any language translation process. We resort to a state-of-the-art transductive learner to produce the document classification. Results on two real-world multilingual corpora have highlighted the effectiveness of the proposed document model w.r.t. document representations usually involved in multilingual and cross-lingual analysis, and the robustness of the transductive setting for multilingual document classification.