a.SCatch: semantic structure for architectural floor plan retrieval

Architects’ daily routine involves working with drawings. They use either a pen or a computer to sketch out their ideas or to do a drawing to scale. We therefore propose the use of a sketch-based approach when using the floor plan repository for queries. This enables the user of the system to sketch a schematic abstraction of a floor plan and search for floor plans that are structurally similar. We also propose the use of a visual query language, and a semantic structure as put forward by Langenhan. An algorithm extracts the semantic structure sketched by the architect on DFKI’s Touch& Write table and compares the structure of the sketch with that of those from the floor plan repository. The a.SCatch system enables the user to access knowledge from past projects easily. Based on CBR strategies and shape detection technologies, a sketch-based retrieval gives access to a semantic floor plan repository. Furthermore, details of a prototypical application which allows semantic structure to be extracted from image data and put into the repository semi-automatically are provided

Towards Intelligent Assistance for a Data Mining Process:-

A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data-mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and non-trivial interactions, both novices and data-mining specialists need assistance in composing and selecting DM processes. Extending notions developed for statistical expert systems we present a prototype Intelligent Discovery Assistant (IDA), which provides users with (i) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use the prototype to show that an IDA can indeed provide useful enumerations and effective rankings in the context of simple classification processes. We discuss how an IDA could be an important tool for knowledge sharing among a team of data miners. Finally, we illustrate the claims with a comprehensive demonstration of cost-sensitive classification using a more involved process and data from the 1998 KDDCUP competition.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Intelligent Assistance for the Data Mining Process: An Ontology-based Approach

A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data-mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and non-trivial interactions, both novices and data-mining specialists need assistance in composing and selecting DM processes. We present the concept of Intelligent Discovery Assistants (IDAs), which provide users with (i) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use a prototype to show that an IDA can indeed provide useful enumerations and effective rankings. We discuss how an IDA is an important tool for knowledge sharing among a team of data miners. Finally, we illustrate all the claims with a comprehensive demonstration using a more involved process and data from the 1998 KDDCUP competition.Information Systems Working Papers Serie

Intelligent Assistance for the Data Mining Process: An Ontology-based Approach

A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data-mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and non-trivial interactions, both novices and data-mining specialists need assistance in composing and selecting DM processes. We present the concept of Intelligent Discovery Assistants (IDAs), which provide users with (i) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and (ii) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use a prototype to show that an IDA can indeed provide useful enumerations and effective rankings. We discuss how an IDA is an important tool for knowledge sharing among a team of data miners. Finally, we illustrate all the claims with a comprehensive demonstration using a more involved process and data from the 1998 KDDCUP competition.Information Systems Working Papers Serie

Industrialising Software Development in Systems Integration

Compared to other disciplines, software engineering as of today is still dependent on craftsmanship of highly-skilled workers. However, with constantly increasing complexity and efforts, existing software engineering approaches appear more and more inefficient. A paradigm shift towards industrial production methods seems inevitable. Recent advances in academia and practice have lead to the availability of industrial key principles in software development as well. Specialization is represented in software product lines, standardization and systematic reuse are available with component-based development, and automation has become accessible through model-driven engineering. While each of the above is well researched in theory, only few cases of successful implementation in the industry are known. This becomes even more evident in specialized areas of software engineering such as systems integration. Today’s IT systems need to quickly adapt to new business requirements due to mergers and acquisitions and cooperations between enterprises. This certainly leads to integration efforts, i.e. joining different subsystems into a cohesive whole in order to provide new functionality. In such an environment. the application of industrial methods for software development seems even more important. Unfortunately, software development in this field is a highly complex and heterogeneous undertaking, as IT environments differ from customer to customer. In such settings, existing industrialization concepts would never break even due to one-time projects and thus insufficient economies of scale and scope. This present thesis, therefore, describes a novel approach for a more efficient implementation of prior key principles while considering the characteristics of software development for systems integration. After identifying the characteristics of the field and their affects on currently-known industrialization concepts, an organizational model for industrialized systems integration has thus been developed. It takes software product lines and adapts them in a way feasible for a systems integrator active in several business domains. The result is a three-tiered model consolidating recurring activities and reducing the efforts for individual product lines. For the implementation of component-based development, the present thesis assesses current component approaches and applies an integration metamodel to the most suitable one. This ensures a common understanding of systems integration across different product lines and thus alleviates component reuse, even across product line boundaries. The approach is furthermore aligned with the organizational model to depict in which way component-based development may be applied in industrialized systems integration. Automating software development in systems integration with model-driven engineering was found to be insufficient in its current state. The reason herefore lies in insufficient tool chains and a lack of modelling standards. As an alternative, an XML-based configuration of products within a software product line has been developed. It models a product line and its products with the help of a domain-specific language and utilizes stylesheet transformations to generate compliable artefacts. The approach has been tested for its feasibility within an exemplarily implementation following a real-world scenario. As not all aspects of industrialized systems integration could be simulated in a laboratory environment, the concept was furthermore validated during several expert interviews with industry representatives. Here, it was also possible to assess cultural and economic aspects. The thesis concludes with a detailed summary of the contributions to the field and suggests further areas of research in the context of industrialized systems integration

Toward Intelligent Assistance for a Data Mining Process: An Ontology-Based Approach for Cost-Sensitive Classification

A data mining (DM) process involves multiple stages. A simple, but typical, process might include preprocessing data, applying a data mining algorithm, and postprocessing the mining results. There are many possible choices for each stage, and only some combinations are valid. Because of the large space and nontrivial interactions, both novices and data mining specialists need assistance in composing and selecting DM processes. Extending notions developed for statistical expert systems we present a prototype intelligent discovery assistant (IDA), which provides users with 1) systematic enumerations of valid DM processes, in order that important, potentially fruitful options are not overlooked, and 2) effective rankings of these valid processes by different criteria, to facilitate the choice of DM processes to execute. We use the prototype to show that an IDA can indeed provide useful enumerations and effective rankings in the context of simple classification processes. We discuss how an IDA could be an important tool for knowledge sharing among a team of data miners. Finally, we illustrate the claims with a demonstration of cost-sensitive classification using a more complicated process and data from the 1998 KDDCUP competition

Cannabidiol antidepressant-like effect in the lipopolysaccharide model in mice: Modulation of inflammatory pathways

Major Depression is a severe psychiatric condition with a still poorly understood etiology. In the last years, evidence supporting the neuroinflammatory hypothesis of depression has increased. In the current clinical scenario, in which the available treatments for depression is far from optimal, there is an urgent need to develop fast-acting drugs with fewer side effects. In this regard, recent pieces of evidence suggest that cannabidiol (CBD), the major non-psychotropic component of Cannabis sativa with anti-inflammatory properties, appears as a drug with antidepressant properties. In this work, CBD 30 mg/kg was administered systemically to mice 30 min before lipopolysaccharide (LPS; 0.83 mg/kg) administration as a neuroinflammatory model, and behavioral tests for depressive-, anhedonic- and anxious-like behavior were performed. NF-?B, I?B? and PPAR? levels were analyzed by western blot in nuclear and cytosolic fractions of cortical samples. IL-6 and TNF? levels were determined in plasma and prefrontal cortex using ELISA and qPCR techniques, respectively. The precursor tryptophan (TRP), and its metabolites kynurenine (KYN) and serotonin (5-HT) were measured in hippocampus and cortex by HPLC. The ratios KYN/TRP and KYN/5-HT were used to estimate indoleamine 2,3-dioxygenase (IDO) activity and the balance of both metabolic pathways, respectively. CBD reduced the immobility time in the tail suspension test and increased sucrose preference in the LPS model, without affecting locomotion and central activity in the open-field test. CBD diminished cortical NF-?B activation, IL-6 levels in plasma and brain, and the increased KYN/TRP and KYN/5-HT ratios in hippocampus and cortex in the LPS model. Our results demonstrate that CBD produced antidepressant-like effects in the LPS neuroinflammatory model, associated to a reduction in the kynurenine pathway activation, IL-6 levels and NF-?B activation. As CBD stands out as a promising antidepressant drug, more research is needed to completely understand its mechanisms of action in depression linked to inflammation.FUNDING AND ACKNOWLEDGMENTS: This research was supported by the Ministerio de Economía y Competitividad (SAF2015-67457-R MINECO/FEDER), the Ministerio de Ciencia, Innovación y Universidades (RTI2018-097534-B-I00), the Instituto de Salud Carlos III (PI19/00170), and Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM). E F-Z was supported by a predoctoral fellowship from the Universidad de Cantabria (Spain). We acknowledge the technical assistance of Annamaria Architravo and Deborah Vasturzo, and Dr Rebeca Vidal for her scientific advice

Component-Based Development Using UML

Component-based software development (CBD) is a potential breakthrough for software engineering. Unified Modeling Language (UML) can potentially facilitate CBD design and modeling. Although many research projects concentrate on the conceptual interrelation of UML and CBD, few incorporate actual component frameworks into the discussion, which is critical for real-world software system design and modeling. This paper reviews component-based development, including the use of UML for modeling CBD. The paper then discusses the means by which UML extension mechanisms can be used to better support the popular component framework -- CORBA. Two other important component frameworks, DCOM and Web Services, are also discussed