User and Machine Authentication and Authorization Infrastructure for Distributed Wireless Sensor Network Testbeds

The intention of an authentication and authorization infrastructure (AAI) is to simplify and unify access to different web resources. With a single login, a user can access web applications at multiple organizations. The Shibboleth authentication and authorization infrastructure is a standards-based, open source software package for web single sign-on (SSO) across or within organizational boundaries. It allows service providers to make fine-grained authorization decisions for individual access of protected online resources. The Shibboleth system is a widely used AAI, but only supports protection of browser-based web resources. We have implemented a Shibboleth AAI extension to protect web services using Simple Object Access Protocol (SOAP). Besides user authentication for browser-based web resources, this extension also provides user and machine authentication for web service-based resources. Although implemented for a Shibboleth AAI, the architecture can be easily adapted to other AAIs.

Knowledge representation through graphs

Due to the increasing amount of data, knowledge aggregation, representation and reasoning are highly important for companies. In this paper, knowledge aggregation is presented as the first step. In the sequel, successful knowledge representation, for instance through graphs, enables knowledge-based reasoning. There exist various forms of knowledge representation through graphs; some of which allow to handle uncertainty and imprecision by invoking the technology of fuzzy sets. The paper provides an overview of different types of graphs stressing their relationships and their essential features.

Fuzzy knowledge representation in cognitive cities

This paper gives an insight into cognitive computing for smart cities, resulting in cognitive cities. Cognitive cities and cognitive computing research with the underlying concepts of knowledge graphs and fuzzy cognitive maps are presented and supported by existing tools (i.e., IBM Watson and Google Now) and intended tools (meta-app). The paper illustrates FCM as a suiting instrument to represent information/knowledge in a city environment driven by human-technology interaction, enforcing the concept of cognitive cities. A proposed paper prototype combines the findings of the paper and shows the next step in the implementation of the proposed meta-app.

Shallow Dialogue Processing Using Machine Learning Algorithms (or not)

This paper presents a shallow dialogue analysis model, aimed at human-human dialogues in the context of staff or business meetings. Four components of the model are defined, and several machine learning techniques are used to extract features from dialogue transcripts: maximum entropy classifiers for dialogue acts, latent semantic analysis for topic segmentation, or decision tree classifiers for discourse markers. A rule-based approach is proposed for solving cross-modal references to meeting documents. The methods are trained and evaluated thanks to a common data set and annotation format. The integration of the components into an automated shallow dialogue parser opens the way to multimodal meeting processing and retrieval applications.

Environmental policy and knowledge. Copying with complexity through inclusive management

Scholars agree that governance of the public environment entails cooperation between science, policy and society. This requires the active role of public managers as catalysts of knowledge co-production, addressing participatory arenas in relation to knowledge integration and social learning. This paper deals with the question of whether public managers acknowledge and take on this task. A survey accessing Directors of Environmental Offices (EOs) of 64 municipalities was carried out in parallel for two regions - Tuscany (Italy) and Porto Alegre Metropolitan Region (Brazil). The survey data were analysed using the multiple correspondence method. Results showed that, regarding policy practices, EOs do not play the role of knowledge co-production catalysts, since when making environmental decisions they only use technical knowledge. We conclude that there is a gap between theory and practice, and identify some factors that may hinder local environmental managers in acting as catalyst of knowledge co-production, raising a further question for future research.

Knowledge Management and Leadership in the Higher Education: A First Approach

Today, it is more and more important to develop competences in the learning process of the university students (that is to say, to acquire knowledge but also skills, abilities, attitudes and values). This is because professional practice requires that the future graduates design and market products, defend the interests of their clients, be introduced in the Administration or, even, in the Politics. Universities must form professionals that become social and opinion leaders, consultants, advisory, entrepreneurs and, in short, people with capacity to solve problems. This paper offers a tool to evaluate the application for the professor of different styles of management in the process of the student’s learning. Its main contribution consists on advancing toward the setting in practice of a model that overcomes the limitations of the traditional practices based on the masterful class, and that it has been applied in Portugal and Spain.

Microarray analysis of autoimmune diseases by machine learning procedures

—Microarray-based global gene expression profiling, with the use of sophisticated statistical algorithms is providing new insights into the pathogenesis of autoimmune diseases. We have applied a novel statistical technique for gene selection based on machine learning approaches to analyze microarray expression data gathered from patients with systemic lupus erythematosus (SLE) and primary antiphospholipid syndrome (PAPS), two autoimmune diseases of unknown genetic origin that share many common features. The methodology included a combination of three data discretization policies, a consensus gene selection method, and a multivariate correlation measurement. A set of 150 genes was found to discriminate SLE and PAPS patients from healthy individuals. Statistical validations demonstrate the relevance of this gene set from an univariate and multivariate perspective. Moreover, functional characterization of these genes identified an interferon-regulated gene signature, consistent with previous reports. It also revealed the existence of other regulatory pathways, including those regulated by PTEN, TNF, and BCL-2, which are altered in SLE and PAPS. Remarkably, a significant number of these genes carry E2F binding motifs in their promoters, projecting a role for E2F in the regulation of autoimmunity.

Knowledge representation tool for cognitiveprocesses modeling

In the last decades, neuropsychological theories tend to consider cognitive functions as a result of the whole brainwork and not as individual local areas of its cortex. Studies based on neuroimaging techniques have increased in the last years, promoting an exponential growth of the body of knowledge about relations between cognitive functions and brain structures [1]. However, so fast evolution make complicated to integrate them in verifiable theories and, even more, translated in to cognitive rehabilitation. The aim of this research work is to develop a cognitive process-modeling tool. The purpose of this system is, in the first term, to represent multidimensional data, from structural and functional connectivity, neuroimaging, data from lesion studies and derived data from clinical intervention [2][3]. This will allow to identify consolidated knowledge, hypothesis, experimental designs, new data from ongoing studies and emerging results from clinical interventions. In the second term, we pursuit to use Artificial Intelligence to assist in decision making allowing to advance towards evidence based and personalized treatments in cognitive rehabilitation. This work presents the knowledge base design of the knowledge representation tool. It is compound of two different taxonomies (structure and function) and a set of tags linking both taxonomies at different levels of structural and functional organization. The remainder of the abstract is organized as follows: Section 2 presents the web application used for gathering necessary information for generating the knowledge base, Section 3 describes knowledge base structure and finally Section 4 expounds reached conclusions.

Terminological Importation for Adapting Reusable Knowledge Representation Components in the KSM Environment

This paper describes the adaptation approach of reusable knowledge representation components used in the KSM environment for the formulation and operationalisation of structured knowledge models. Reusable knowledge representation components in KSM are called primitives of representation. A primitive of representation provides: (1) a knowledge representation formalism (2) a set of tasks that use this knowledge together with several problem-solving methods to carry out these tasks (3) a knowledge acquisition module that provides different services to acquire and validate this knowledge (4) an abstract terminology about the linguistic categories included in the representation language associated to the primitive. Primitives of representation usually are domain independent. A primitive of representation can be adapted to support knowledge in a given domain by importing concepts from this domain. The paper describes how this activity can be carried out by mean of a terminological importation. Informally, a terminological importation partially populates an abstract terminology with concepts taken from a given domain. The information provided by the importation can be used by the acquisition and validation facilities to constraint the classes of knowledge that can be described using the representation formalism according to the domain knowledge. KSM provides the LINK-S language to specify terminological importation from a domain terminology to an abstract one. These terminologies are described in KSM by mean of the CONCEL language. Terminological importation is used to adapt reusable primitives of representation in order to increase the usability degree of such components in these domains. In addition, two primitives of representation can share a common vocabulary by importing common domain CONCEL terminologies (conceptual vocabularies). It is a necessary condition to make possible the interoperability between different, heterogeneous knowledge representation components in the framework of complex knowledge - based architectures.

Generation of human computational models with machine learning

Services in smart environments pursue to increase the quality of people?s lives. The most important issues when developing this kind of environments is testing and validating such services. These tasks usually imply high costs and annoying or unfeasible real-world testing. In such cases, artificial societies may be used to simulate the smart environment (i.e. physical environment, equipment and humans). With this aim, the CHROMUBE methodology guides test engineers when modeling human beings. Such models reproduce behaviors which are highly similar to the real ones. Originally, these models are based on automata whose transitions are governed by random variables. Automaton?s structure and the probability distribution functions of each random variable are determined by a manual test and error process. In this paper, it is presented an alternative extension of this methodology which avoids the said manual process. It is based on learning human behavior patterns automatically from sensor data by using machine learning techniques. The presented approach has been tested on a real scenario, where this extension has given highly accurate human behavior models,

Fast 2D to 3D conversion using a clustering-based hierarchical search in a machine learning framework

Automatic 2D-to-3D conversion is an important application for filling the gap between the increasing number of 3D displays and the still scant 3D content. However, existing approaches have an excessive computational cost that complicates its practical application. In this paper, a fast automatic 2D-to-3D conversion technique is proposed, which uses a machine learning framework to infer the 3D structure of a query color image from a training database with color and depth images. Assuming that photometrically similar images have analogous 3D structures, a depth map is estimated by searching the most similar color images in the database, and fusing the corresponding depth maps. Large databases are desirable to achieve better results, but the computational cost also increases. A clustering-based hierarchical search using compact SURF descriptors to characterize images is proposed to drastically reduce search times. A significant computational time improvement has been obtained regarding other state-of-the-art approaches, maintaining the quality results.

Paired structures in knowledge representation.

In this position paper we propose a consistent and unifying view to all those basic knowledge representation models that are based on the existence of two somehow opposite fuzzy concepts. A number of these basic models can be found in fuzzy logic and multi-valued logic literature. Here it is claimed that it is the semantic relationship between two paired concepts what determines the emergence of different types of neutrality, namely indeterminacy, ambivalence and conflict, widely used under different frameworks (possibly under different names). It will be shown the potential relevance of paired structures, generated from two paired concepts together with their associated neutrality, all of them to be modeled as fuzzy sets. In this way, paired structures can be viewed as a standard basic model from which different models arise. This unifying view should therefore allow a deeper analysis of the relationships between several existing knowledge representation formalisms, providing a basis from which more expressive models can be later developed.

Machine learning techniques for automatic opinion detection in non-traditional textual genres

This paper presents a preliminary study in which Machine Learning experiments applied to Opinion Mining in blogs have been carried out. We created and annotated a blog corpus in Spanish using EmotiBlog. We evaluated the utility of the features labelled firstly carrying out experiments with combinations of them and secondly using the feature selection techniques, we also deal with several problems, such as the noisy character of the input texts, the small size of the training set, the granularity of the annotation scheme and the language object of our study, Spanish, with less resource than English. We obtained promising results considering that it is a preliminary study.

The aid of machine learning to overcome the classification of real health discharge reports written in Spanish

Hospitals attached to the Spanish Ministry of Health are currently using the International Classification of Diseases 9 Clinical Modification (ICD9-CM) to classify health discharge records. Nowadays, this work is manually done by experts. This paper tackles the automatic classification of real Discharge Records in Spanish following the ICD9-CM standard. The challenge is that the Discharge Records are written in spontaneous language. We explore several machine learning techniques to deal with the classification problem. Random Forest resulted in the most competitive one, achieving an F-measure of 0.876.

Sustainable Development and Collective Learning: Theory and a European Case Study. Bruges European Economic Policy (BEEP) Briefing 7/2004

Network governance of collective learning processes is an essential approach to sustainable development. The first section of the article briefly refers to recent theories about both market and government failures that express scepticism about the way framework conditions for market actors are set. For this reason, the development of networks for collective learning processes seems advantageous if new solutions are to be developed in policy areas concerned with long-term changes and a stepwise internalisation of externalities. With regard to corporate actors’ interests, the article shows recent insights from theories about the knowledge-based firm, where the creation of new knowledge is based on the absorption of societal views. This concept shifts the focus towards knowledge generation as an essential element in the evolution of sustainable markets. This involves at the same time the development of new policies. In this context innovation-inducing regulation is suggested and discussed. The evolution of the Swedish, German and Dutch wind turbine industries are analysed based on the approach of governance put forward in this article. We conclude that these coevolutionary mechanisms may take for granted some of the stabilising and orientating functions previously exercised by basic regulatory activities of the state. In this context, the main function of the governments is to facilitate learning processes that depart from the government functions suggested by welfare economics.