Measuring and analyzing color and texture information in anatomical leaf cross sections: An approach using computer vision to aid plant species identification

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Architecture for peer-to-peer databases with routing queries using ant colony algorithm and semantic support

In a peer-to-peer network, the nodes interact with each other by sharing resources, services and information. Many applications have been developed using such networks, being a class of such applications are peer-to-peer databases. The peer-to-peer databases systems allow the sharing of unstructured data, being able to integrate data from several sources, without the need of large investments, because they are used existing repositories. However, the high flexibility and dynamicity of networks the network, as well as the absence of a centralized management of information, becomes complex the process of locating information among various participants in the network. In this context, this paper presents original contributions by a proposed architecture for a routing system that uses the Ant Colony algorithm to optimize the search for desired information supported by ontologies to add semantics to shared data, enabling integration among heterogeneous databases and the while seeking to reduce the message traffic on the network without causing losses in the amount of responses, confirmed by the improve of 22.5% in this amount. © 2011 IEEE.

Information access law and disclosing of information by joint-stock companies in Brazil: open data and Extensible Business Reporting Language (XBRL) standard

The strategic management of information plays a fundamental role in the organizational management process since the decision-making process depend on the need for survival in a highly competitive market. Companies are constantly concerned about information transparency and good practices of corporate governance (CG) which, in turn, directs relations between the controlling power of the company and investors. In this context, this article presents the relationship between the disclosing of information of joint-stock companies by means of using XBRL, the open data model adopted by the Brazilian government, a model that boosted the publication of Information Access Law (Lei de Acesso à Informação), nº 12,527 of 18 November 2011. Information access should be permeated by a mediation policy in order to subsidize the knowledge construction and decision-making of investors. The XBRL is the main model for the publishing of financial information. The use of XBRL by means of new semantic standard created for Linked Data, strengthens the information dissemination, as well as creates analysis mechanisms and cross-referencing of data with different open databases available on the Internet, providing added value to the data/information accessed by civil society.

Challenges and opportunities for knowledge organization at the intersection with information technologies

In this paper we study the intersection of Knowledge Organization with Information Technologies and the challenges and opportunities for Knowledge Organization experts that, in our view, are important to be studied and for them to be aware of. We start by giving some definitions necessary for providing the context for our work. Then we review the history of the Web, beginning with the Internet and continuing with the World Wide Web, the Semantic Web, problems of Artificial Intelligence, Web 2.0, and Linked Data. Finally, we conclude our paper with IT applications for Knowledge Organization in libraries, such as FRBR, BIBFRAME, and several OCLC initiatives, as well as with some of the challenges and opportunities in which Knowledge Organization experts and researchers might play a key role in relation to the Semantic Web.

The neuroimmune changes induced by cohabitation with an Ehrlich tumor-bearing cage mate rely on olfactory information

Cohabitation for 14 days with Ehrlich tumor-bearing mice was shown to increase locomotor activity, to decrease hypothalamic noradrenaline (NA) levels, to increase NA turnover and to decrease innate immune responses and decrease the animals' resistance to tumor growth. Cage mates of a B16F10 melanoma-bearer mice were also reported to show neuroimmune changes. Chemosignals released by Ehrlich tumor-bearing mice have been reported to be relevant for the neutrophil activity changes induced by cohabitation. The present experiment was designed to further analyze the effects of odor cues on neuroimmune changes induced by cohabitation with a sick cage mate. Specifically, the relevance of chemosignals released by an Ehrlich tumor-bearing mouse was assessed on the following: behavior (open-field and plus maze); hypothalamic NA levels and turnover; adrenaline (A) and NA plasmatic levels; and host resistance induced by tumor growth. To comply with such objectives, devices specifically constructed to analyze the influence of chemosignals released from tumor-bearing mice were employed. The results show that deprivation of odor cues released by Ehrlich tumor-bearing mice reversed the behavioral, neurochemical and immune changes induced by cohabitation. Mice use scents for intraspecies communication in many social contexts. Tumors produce volatile organic compounds released into the atmosphere through breath, sweat, and urine. Our results strongly suggest that volatile compounds released by Ehrlich tumor-injected mice are perceived by their conspecifics, inducing the neuroimmune changes reported for cohabitation with a sick companion. (C) 2011 Elsevier Inc. All rights reserved.

Antenatal depression strongly predicts postnatal depression in primary health care

Objective: To estimate the association between antenatal and postnatal depression and to examine the role of socioeconomic conditions in the risk of postnatal depression. Methods: A prospective cohort study, conducted between May 2005 and January 2006, with 831 pregnant women recruited from primary care clinics in the public sector in the city of Sao Paulo, Brazil. The presence of antenatal and postnatal depression was measured with the Self Report Questionnaire (SRQ-20). Sociodemographic and socioeconomic characteristics and obstetric information were obtained through a questionnaire. Crude and adjusted risk ratios (RR), with 95% CI, were calculated using a Poisson regression. Results: The prevalence of postnatal depressive symptoms was 31.2% (95% CI: 27.8-34.8%). Among the 219 mothers who had depressive symptoms, nearly 50% had already shown depressive symptoms during pregnancy. Women who had antenatal depression were 2.4 times more likely to present with postnatal depression than were women who did not have such symptoms during pregnancy. In the multivariate analysis, higher scores for assets (RR: 0.76, 95% CI 0.61-0.96), higher education (RR: 0.75 95% CI 0.59-0.96), daily contact with neighbors (RR: 0.68, 95% CI 0.51-0.90) and antenatal depression (RR: 2.44, 95% CI 1.93-3.08) remained independently associated with postnatal depression. Conclusions: Antenatal and postnatal depression are highly prevalent in the primary care setting.

Semantic integration of gene expression analysis tools and data sources using software connectors

Abstract Background The study and analysis of gene expression measurements is the primary focus of functional genomics. Once expression data is available, biologists are faced with the task of extracting (new) knowledge associated to the underlying biological phenomenon. Most often, in order to perform this task, biologists execute a number of analysis activities on the available gene expression dataset rather than a single analysis activity. The integration of heteregeneous tools and data sources to create an integrated analysis environment represents a challenging and error-prone task. Semantic integration enables the assignment of unambiguous meanings to data shared among different applications in an integrated environment, allowing the exchange of data in a semantically consistent and meaningful way. This work aims at developing an ontology-based methodology for the semantic integration of gene expression analysis tools and data sources. The proposed methodology relies on software connectors to support not only the access to heterogeneous data sources but also the definition of transformation rules on exchanged data. Results We have studied the different challenges involved in the integration of computer systems and the role software connectors play in this task. We have also studied a number of gene expression technologies, analysis tools and related ontologies in order to devise basic integration scenarios and propose a reference ontology for the gene expression domain. Then, we have defined a number of activities and associated guidelines to prescribe how the development of connectors should be carried out. Finally, we have applied the proposed methodology in the construction of three different integration scenarios involving the use of different tools for the analysis of different types of gene expression data. Conclusions The proposed methodology facilitates the development of connectors capable of semantically integrating different gene expression analysis tools and data sources. The methodology can be used in the development of connectors supporting both simple and nontrivial processing requirements, thus assuring accurate data exchange and information interpretation from exchanged data.

A Fuzzy Ontology-Driven Approach to Semantic Interoperability in e-Government Big Data

With the increasing production of information from e-government initiatives, there is also the need to transform a large volume of unstructured data into useful information for society. All this information should be easily accessible and made available in a meaningful and effective way in order to achieve semantic interoperability in electronic government services, which is a challenge to be pursued by governments round the world. Our aim is to discuss the context of e-Government Big Data and to present a framework to promote semantic interoperability through automatic generation of ontologies from unstructured information found in the Internet. We propose the use of fuzzy mechanisms to deal with natural language terms and present some related works found in this area. The results achieved in this study are based on the architectural definition and major components and requirements in order to compose the proposed framework. With this, it is possible to take advantage of the large volume of information generated from e-Government initiatives and use it to benefit society.

People semantic description and re-identification from point cloud geometry

The automatic extraction of biometric descriptors of anonymous people is a challenging scenario in camera networks. This task is typically accomplished making use of visual information. Calibrated RGBD sensors make possible the extraction of point cloud information. We present a novel approach for people semantic description and re-identification using the individual point cloud information. The proposal combines the use of simple geometric features with point cloud features based on surface normals.

Semantic-based middleware solutions to support context-aware service provisioning in pervasive environments

The dynamicity and heterogeneity that characterize pervasive environments raise new challenges in the design of mobile middleware. Pervasive environments are characterized by a significant degree of heterogeneity, variability, and dynamicity that conventional middleware solutions are not able to adequately manage. Originally designed for use in a relatively static context, such middleware systems tend to hide low-level details to provide applications with a transparent view on the underlying execution platform. In mobile environments, however, the context is extremely dynamic and cannot be managed by a priori assumptions. Novel middleware should therefore support mobile computing applications in the task of adapting their behavior to frequent changes in the execution context, that is, it should become context-aware. In particular, this thesis has identified the following key requirements for novel context-aware middleware that existing solutions do not fulfil yet. (i) Middleware solutions should support interoperability between possibly unknown entities by providing expressive representation models that allow to describe interacting entities, their operating conditions and the surrounding world, i.e., their context, according to an unambiguous semantics. (ii) Middleware solutions should support distributed applications in the task of reconfiguring and adapting their behavior/results to ongoing context changes. (iii) Context-aware middleware support should be deployed on heterogeneous devices under variable operating conditions, such as different user needs, application requirements, available connectivity and device computational capabilities, as well as changing environmental conditions. Our main claim is that the adoption of semantic metadata to represent context information and context-dependent adaptation strategies allows to build context-aware middleware suitable for all dynamically available portable devices. Semantic metadata provide powerful knowledge representation means to model even complex context information, and allow to perform automated reasoning to infer additional and/or more complex knowledge from available context data. In addition, we suggest that, by adopting proper configuration and deployment strategies, semantic support features can be provided to differentiated users and devices according to their specific needs and current context. This thesis has investigated novel design guidelines and implementation options for semantic-based context-aware middleware solutions targeted to pervasive environments. These guidelines have been applied to different application areas within pervasive computing that would particularly benefit from the exploitation of context. Common to all applications is the key role of context in enabling mobile users to personalize applications based on their needs and current situation. The main contributions of this thesis are (i) the definition of a metadata model to represent and reason about context, (ii) the definition of a model for the design and development of context-aware middleware based on semantic metadata, (iii) the design of three novel middleware architectures and the development of a prototypal implementation for each of these architectures, and (iv) the proposal of a viable approach to portability issues raised by the adoption of semantic support services in pervasive applications.

Semantic coordination through programmable Tuple spaces

Two of the main features of today complex software systems like pervasive computing systems and Internet-based applications are distribution and openness. Distribution revolves around three orthogonal dimensions: (i) distribution of control|systems are characterised by several independent computational entities and devices, each representing an autonomous and proactive locus of control; (ii) spatial distribution|entities and devices are physically distributed and connected in a global (such as the Internet) or local network; and (iii) temporal distribution|interacting system components come and go over time, and are not required to be available for interaction at the same time. Openness deals with the heterogeneity and dynamism of system components: complex computational systems are open to the integration of diverse components, heterogeneous in terms of architecture and technology, and are dynamic since they allow components to be updated, added, or removed while the system is running. The engineering of open and distributed computational systems mandates for the adoption of a software infrastructure whose underlying model and technology could provide the required level of uncoupling among system components. This is the main motivation behind current research trends in the area of coordination middleware to exploit tuple-based coordination models in the engineering of complex software systems, since they intrinsically provide coordinated components with communication uncoupling and further details in the references therein. An additional daunting challenge for tuple-based models comes from knowledge-intensive application scenarios, namely, scenarios where most of the activities are based on knowledge in some form|and where knowledge becomes the prominent means by which systems get coordinated. Handling knowledge in tuple-based systems induces problems in terms of syntax - e.g., two tuples containing the same data may not match due to differences in the tuple structure - and (mostly) of semantics|e.g., two tuples representing the same information may not match based on a dierent syntax adopted. Till now, the problem has been faced by exploiting tuple-based coordination within a middleware for knowledge intensive environments: e.g., experiments with tuple-based coordination within a Semantic Web middleware (surveys analogous approaches). However, they appear to be designed to tackle the design of coordination for specic application contexts like Semantic Web and Semantic Web Services, and they result in a rather involved extension of the tuple space model. The main goal of this thesis was to conceive a more general approach to semantic coordination. In particular, it was developed the model and technology of semantic tuple centres. It is adopted the tuple centre model as main coordination abstraction to manage system interactions. A tuple centre can be seen as a programmable tuple space, i.e. an extension of a Linda tuple space, where the behaviour of the tuple space can be programmed so as to react to interaction events. By encapsulating coordination laws within coordination media, tuple centres promote coordination uncoupling among coordinated components. Then, the tuple centre model was semantically enriched: a main design choice in this work was to try not to completely redesign the existing syntactic tuple space model, but rather provide a smooth extension that { although supporting semantic reasoning { keep the simplicity of tuple and tuple matching as easier as possible. By encapsulating the semantic representation of the domain of discourse within coordination media, semantic tuple centres promote semantic uncoupling among coordinated components. The main contributions of the thesis are: (i) the design of the semantic tuple centre model; (ii) the implementation and evaluation of the model based on an existent coordination infrastructure; (iii) a view of the application scenarios in which semantic tuple centres seem to be suitable as coordination media.

Supporting semantic web technologies in the pervasive service ecosystems middleware

Semantic Web technologies are strategic in order to fulfill the openness requirement of Self-Aware Pervasive Service Ecosystems. In fact they provide agents with the ability to cope with distributed data, using RDF to represent information, ontologies to describe relations between concepts from any domain (e.g. equivalence, specialization/extension, and so on) and reasoners to extract implicit knowledge. The aim of this thesis is to study these technologies and design an extension of a pervasive service ecosystems middleware capable of exploiting semantic power, and deepening performance implications.

Semantic Service Architectures for Smart Environments

Many industries and academic institutions share the vision that an appropriate use of information originated from the environment may add value to services in multiple domains and may help humans in dealing with the growing information overload which often seems to jeopardize our life. It is also clear that information sharing and mutual understanding between software agents may impact complex processes where many actors (humans and machines) are involved, leading to relevant socioeconomic benefits. Starting from these two input, architectural and technological solutions to enable “environment-related cooperative digital services” are here explored. The proposed analysis starts from the consideration that our environment is physical space and here diversity is a major value. On the other side diversity is detrimental to common technological solutions, and it is an obstacle to mutual understanding. An appropriate environment abstraction and a shared information model are needed to provide the required levels of interoperability in our heterogeneous habitat. This thesis reviews several approaches to support environment related applications and intends to demonstrate that smart-space-based, ontology-driven, information-sharing platforms may become a flexible and powerful solution to support interoperable services in virtually any domain and even in cross-domain scenarios. It also shows that semantic technologies can be fruitfully applied not only to represent application domain knowledge. For example semantic modeling of Human-Computer Interaction may support interaction interoperability and transformation of interaction primitives into actions, and the thesis shows how smart-space-based platforms driven by an interaction ontology may enable natural ad flexible ways of accessing resources and services, e.g, with gestures. An ontology for computational flow execution has also been built to represent abstract computation, with the goal of exploring new ways of scheduling computation flows with smart-space-based semantic platforms.

Semantic under-determinacy, Content and Context

The aim of the thesis is to investigate the topic of semantic under-determinacy, i.e. the failure of the semantic content of certain expressions to determine a truth-evaluable utterance content. In the first part of the thesis, I engage with the problem of setting apart semantic under-determinacy as opposed to other phenomena such as ambiguity, vagueness, indexicality. As I will argue, the feature that distinguishes semantic under-determinacy from these phenomena is its being explainable solely in terms of under-articulation. In the second part of the thesis, I discuss the topic of how communication is possible, despite the semantic under-determinacy of language. I discuss a number of answers that have been offered: (i) the Radical Contextualist explanation which emphasises the role of pragmatic processes in utterance comprehension; (ii) the Indexicalist explanation in terms of hidden syntactic positions; (iii) the Relativist account, which regards sentences as true or false relative to extra coordinates in the circumstances of evaluation (besides possible worlds). In the final chapter, I propose an account of the comprehension of utterances of semantically under-determined sentences in terms of conceptual constraints, i.e. ways of organising information which regulate thought and discourse on certain matters. Conceptual constraints help the hearer to work out the truth-conditions of an utterance of a semantically under-determined sentence. Their role is clearly semantic, in that they contribute to “what is said” (rather than to “what is implied”); however, they do not respond to any syntactic constraint. The view I propose therefore differs, on the one hand, from Radical Contextualism, because it stresses the role of semantic-governed processes as opposed to pragmatics-governed processes; on the other hand, it differs from Indexicalism in its not endorsing any commitment as to hidden syntactic positions; and it differs from Relativism in that it maintains a monadic notion if truth.

A Semantic-based Framework for Digital Survey of Architectural Heritage

The research aims at developing a framework for semantic-based digital survey of architectural heritage. Rooted in knowledge-based modeling which extracts mathematical constraints of geometry from architectural treatises, as-built information of architecture obtained from image-based modeling is integrated with the ideal model in BIM platform. The knowledge-based modeling transforms the geometry and parametric relation of architectural components from 2D printings to 3D digital models, and create large amount variations based on shape grammar in real time thanks to parametric modeling. It also provides prior knowledge for semantically segmenting unorganized survey data. The emergence of SfM (Structure from Motion) provides access to reconstruct large complex architectural scenes with high flexibility, low cost and full automation, but low reliability of metric accuracy. We solve this problem by combing photogrammetric approaches which consists of camera configuration, image enhancement, and bundle adjustment, etc. Experiments show the accuracy of image-based modeling following our workflow is comparable to that from range-based modeling. We also demonstrate positive results of our optimized approach in digital reconstruction of portico where low-texture-vault and dramatical transition of illumination bring huge difficulties in the workflow without optimization. Once the as-built model is obtained, it is integrated with the ideal model in BIM platform which allows multiple data enrichment. In spite of its promising prospect in AEC industry, BIM is developed with limited consideration of reverse-engineering from survey data. Besides representing the architectural heritage in parallel ways (ideal model and as-built model) and comparing their difference, we concern how to create as-built model in BIM software which is still an open area to be addressed. The research is supposed to be fundamental for research of architectural history, documentation and conservation of architectural heritage, and renovation of existing buildings.