Extending pythonQA with knowledge from stackOverflow

Question and Answering (QA) Systems provide a platform where users can ask questions in natural language to a system and get answers retrieved from a knowledge base. The work proposed in PythonQA create a Question and Answer System for the Python Programming Language. The knowledge is built from the Python Frequent Answered Questions (PyFAQ). In this paper, we extend the PythonQA system by enhancing the Knowledge Base with Question-Answer pairs from the StackExchange Python Question Answering Community Site. Some tests were performed to analyze the impact of a richer Knowledge Base on the PythonQA system, increasing the number of answer candidates.info:eu-repo/semantics/publishedVersio

Ferramenta para monitoramento de redes wireless com foco em amenizar a aglomeração de usuário em pontos de acesso.

By monitoring the network, you can improve the quality of service in order to increase efficiency and productivity. This work describes a tool developed called CTIWifi, which used PHP libraries together with information collections from the network, the tool that aims to assist in wireless network monitoring, presenting easy-to-read graphics of infrastructure information.O monitoramento da rede, pode-se melhorar a qualidade do servic¸o, tendo em vista aumentar a eficiencia e produtividade. Este trabalho descreve ˆ uma ferramenta desenvolvida denominada CTIWifi, a qual utilizado bibliotecas PHP junto com coletas de informac¸oes da rede, a ferramenta que tem como ˜ objetivo auxiliar no monitoramento da rede sem fio apresentando graficos de ´ facil leitura das informac¸ ´ oes da infraestrutura

Development of Q&A systems using AcQA

In order to help the user to search for relevant information, Question and Answering (Q&A) Systems provide the possibility to formulate the question freely in a natural language, retrieving the most appropriate and concise answers. These systems interpret the user question to understand his information needs and return him the more adequate replies in a semantic sense; they do not perform a statistical word search like happens in the existing search engines. There are several approaches to developing and deploying Q&A Systems, making it hard to choose the best way to build the system. To turn easier this process, we are proposing a way to automatically create Q&A Systems (AcQA) based on DSLs, thus allowing the setup and the validation of the Q&A System independent of the implementation techniques. With our proposal (AcQA language), we want the developers to focus on the data and contents, instead of implementation details. We conducted an experiment to assess the feasibility of using AcQA. The study was carried out with people from the computer science field and shows that our language simplifies the development of a Q&A System.info:eu-repo/semantics/publishedVersio

Topic maps constraint languages : understanding and comparing

Topic map constraint language (TMCL) provides a means to express constraints on topic maps conforming to ISO/IEC 13250. In this article, we will use a test suite and show, step-by-step, the way we handled several kinds of topic maps constraints in many different instances in order to answer questions like: Do they do the same job? Are there some kinds of topic maps constraints that are easier to specify with one of them? Do you need different background to use the tools? Is it possible to use them in similar situations (the same topic maps instances)? May we use them to produce an equal result? How do AsTMa!, OSL, Toma and XTche relate to TMCL? What kind of constraints each one of these three cannot specify? We will conclude this paper with a summary of the comparisons accomplished between those topic maps constraint languages over the use case proposed.(undefined

Topic maps constraint languages: understanding and comparing

Topic Map Constraint Language (TMCL) provides a means to express constraints on topic maps conforming to ISO/IEC 13250. In this article, we will use a test suite and show, step-by-step, the way we handled several kinds of Topic Maps constraints in many d

Topic maps constraint specification languages : comparing AsTMa!, OSL, and XTche

Topic maps are an ISO standard for the representation and interchange of knowledge, with an emphasis on the findability of information. A topic map can represent information using topics (representing any concept), associations (which represent the relationships between them), and occurrences (which represent relationships between topics and information resources relevant to them). They are thus similar to semantic networks and both concept and mind maps in many respects. According to Topic Map Data Model (TMDM), Topic Maps are abstract structures that can encode knowledge and connect this encoded knowledge to relevant information resources. In order to cope with a broad range of scenarios, a topic is a very wide concept. On one hand, this makes Topic Maps a convenient model for knowledge representation; but on the other hand, this can also put in risk the topic map consistency. A set of semantic constraints must be imposed to the topic map in order to grant its consistency. Currently, we can find three approaches to constrain Topic Maps -- AsTMa!, OSL, and XTche -- that allow us to specify constraints and to validate the instances of a family of topic maps against that set of rules. With these resemblances it is easy to conclude that they are quite similar. However they differ in some fundamental concepts. These three Topic Maps constraint specification languages were hardly tested and benchmarked with a huge test suite. The most significant results will be discussed in this paper. In this article, we will use that test suite and show, step-by-step, the way we handled several kinds of Topic Maps constraints in many different instances in order to answer questions like: Do they do the same job? Are there some kind of Topic Maps constraints that are easier to specify with one of them? Do you need different background to use the tools? Is it possible to use them in similar situations (the same topic maps instances)? May we use them to produce an equal result? How do AsTMa!, OSL, and XTche relate to Topic Maps Constraint Language (TMCL)? What kind of constraints each one of these three can not specify? What is the intersection area of these three? What kind of constraints each one of these three is able to specify? We will conclude this paper with a summary of the comparisons accomplished between those Topic Maps constraint languages over the use case proposed

Navegando na rede semântica dos topic maps com o Ulisses

A norma ISO-IEC 13250 Topic Maps, composta principalmente de tópicos interligados através de associações, define uma rede semântica estruturada sobre um sistema de informação, criando uma ponte entre a gestão de informação e os domínios de representação de conhecimento. A estrutura de dados que um topic map representa é um grafo; portanto, uma das melhores maneiras de visualizar um grafo é percebendo sua estrutura de vértices (tópicos) e arestas (associações). Assim sendo, este artigo tem por objetivo a visualização eficiente desta camada semântica através de navegadores Web, permitindo que todos os vértices (tópicos) do grafo topic map sejam acessados em uma navegação conceitual, seja por ligações HTML (links), seja navegando no próprio grafo visualmente. Para apresentar essa abordagem de navegação em Topic Maps, divide-se o artigo em 3 partes: primeiro, apresentam-se os conceitos básicos de Topic Maps, para então, descrever algumas técnicas de sua visualização. Por fim, descrever-se-á a ferramenta de visualização desenvolvida, caracterizando as técnicas empregadas em seu desenvolvimento

Comparing topic maps constraint specification languages

Topic Map Constraint Language (TMCL) provides a means to express constraints on topic maps conforming to ISO/IEC 13250. In this article, we will use a test suite and show, step-by-step, the way we handled several kinds of Topic Maps constraints in many different instances in order to answer questions like: Do they do the same job? Are there some kinds of Topic Maps constraints that are easier to specify with one of them? Do you need different background to use the tools? Is it possible to use them in similar situations (the same topic maps instances)? May we use them to produce an equal result? How do AsTMa!, OSL, Toma, and XTche relate to Topic Maps Constraint Language (TMCL)? What kind of constraints each one of these three can not specify? We will conclude this paper with a summary of the comparisons accomplished between those Topic Maps constraint languages over the use case proposed

Implementing TMCL: XTche : a topic map schema and constraint specification language

In this paper we present a Topic Maps Validation System – XTche constraint language and its processor. We started with our strong motivation to check a topic map for syntactic and semantic correctness – as a notation to describe an ontology that supports a sophisticated computer system where its validation is crucial! Then we assume XTM and TMCL as starting points and we used our background in compilers and XML validation to come up with our proposal. XTche complies with all requirements stated for TMCL but it is an XML Schema oriented language. This idea brings two benefits: on one hand it allows for the syntactic specification of Topic Maps (not only the constraints), eliminating the need for two separated specifications (schema and constraints); and on the other hand it enables the use of an XML Schema editor (like XMLSpy) to provide a graphical interface and the basic syntactic checker. With XTche,atopicmapdesignerdefinesasetofrestrictionsthatguaranteethataparticulartopicmapissemanticallyvalid

Network traffic analysis - a different approach using incoming and outgoing traffic differences

The network traffic analysis is a fundamental area on network management because the network anomalies may affect the network quality of service. However, the data network traffic anomalies are still a critical issue. On last years signal processing methods like wavelet-based ones have been used to detect anomalies on network traffic, specially because wavelet transforms allow the selection of signal characteristics via a combined time-frequency representation. This paper explores a simple and fast wavelet transform for analyzing the network flow, considering the difference between incoming and outgoing traffic data, for improving identification of deny of service attacks