Development of a context knowledge system for mobile conversational agents

Un agente conversacional móvil o chatbot es un software que puede realizar tareas o servicios para un usuario o grupo en concreto. El objetivo principal de este Trabajo de Fin de Grado es desarrollar un sistema de conocimiento de contexto para agentes móviles, así como proporcionarle herramientas para que pueda adaptarse dinámicamente. Este sistema permitirá al usuario recibir sugerencias personalizadas de acciones basadas en su contexto y preferencias. Este proyecto se desarrolla en la modalidad A, que significa que está asociado a un departamento universitario. En este caso, este proyecto está vinculado al departamento de Grupo de Ingeniería del Software y de los Servicios (GESSI) de la Facultad de Informática de Barcelona, Universitat Politècnica de Catalunya. Este sistema expondrá integraciones de funciones entre diferentes aplicaciones de un dispositivo móvil, permitiendo al usuario realizar acciones en una aplicación y recibir sugerencias de acciones posibles para ser ejecutadas en otra, permitiéndole completar esa acción sin tener que abrir explícitamente la aplicación en cuestión.A mobile conversational agent or chatbot is software that can perform tasks or services for a particular user or group. The main goal of this Final Degree Project is to develop a context knowledge system for mobile agents, as well as provide it with tools that allow it to be adapted dynamically. This system will allow the user to receive personalised suggestions of actions based on their context and preferences. This project is developed in the A modality, which means it is associated with a university department. In this case, this project is linked to the Software and Service Engineering Group (GESSI) department from the Barcelona School of Informatics, Universitat Politècnica de Catalunya. This system will expose feature integrations between different applications of a mobile device, allowing the user to perform actions in one application and receive suggestions of possible actions to be executed in another application, letting them complete that suggestion without having to explicitly open the application

Actas da 10ª Conferência sobre Redes de Computadores

Universidade do MinhoCCTCCentro AlgoritmiCisco SystemsIEEE Portugal Sectio

Fortschritte im unüberwachten Lernen und Anwendungsbereiche: Subspace Clustering mit Hintergrundwissen, semantisches Passworterraten und erlernte Indexstrukturen

Over the past few years, advances in data science, machine learning and, in particular, unsupervised learning have enabled significant progress in many scientific fields and even in everyday life. Unsupervised learning methods are usually successful whenever they can be tailored to specific applications using appropriate requirements based on domain expertise. This dissertation shows how purely theoretical research can lead to circumstances that favor overly optimistic results, and the advantages of application-oriented research based on specific background knowledge. These observations apply to traditional unsupervised learning problems such as clustering, anomaly detection and dimensionality reduction. Therefore, this thesis presents extensions of these classical problems, such as subspace clustering and principal component analysis, as well as several specific applications with relevant interfaces to machine learning. Examples include password guessing using semantic word embeddings and learning spatial index structures using statistical models. In essence, this thesis shows that application-oriented research has many advantages for current and future research.In den letzten Jahren haben Fortschritte in der Data Science, im maschinellen Lernen und insbesondere im unüberwachten Lernen zu erheblichen Fortentwicklungen in vielen Bereichen der Wissenschaft und des täglichen Lebens geführt. Methoden des unüberwachten Lernens sind in der Regel dann erfolgreich, wenn sie durch geeignete, auf Expertenwissen basierende Anforderungen an spezifische Anwendungen angepasst werden können. Diese Dissertation zeigt, wie rein theoretische Forschung zu Umständen führen kann, die allzu optimistische Ergebnisse begünstigen, und welche Vorteile anwendungsorientierte Forschung hat, die auf spezifischem Hintergrundwissen basiert. Diese Beobachtungen gelten für traditionelle unüberwachte Lernprobleme wie Clustering, Anomalieerkennung und Dimensionalitätsreduktion. Daher werden in diesem Beitrag Erweiterungen dieser klassischen Probleme, wie Subspace Clustering und Hauptkomponentenanalyse, sowie einige spezifische Anwendungen mit relevanten Schnittstellen zum maschinellen Lernen vorgestellt. Beispiele sind das Erraten von Passwörtern mit Hilfe semantischer Worteinbettungen und das Lernen von räumlichen Indexstrukturen mit Hilfe statistischer Modelle. Im Wesentlichen zeigt diese Arbeit, dass anwendungsorientierte Forschung viele Vorteile für die aktuelle und zukünftige Forschung hat

Utilization of Big Data Analysis Through Public Video, Virus Data Cooperation, and Social Media as the Surveillance to COVID-19 in Indonesia

This article discusses Big Data's use as a surveillance tool for the spread of Corona Virus Disease 2019 (COVID-19), both in Indonesia and the world. In Indonesia, the range of COVID-19 is increasingly sporadic, causing mass panic and Indonesia's geographical characteristics, which will be difficult when this spread could not control quickly. Researchers are conducting several studies to overcome this pandemic, including supervision, features, handling, mobility, patient interaction, treatment evaluation, and the biological structure. These studies become data and lead to Big Data. This article explores how to use Big Data analysis to monitor the spread of COVID-19 as a communication process that reflects mediated communication as a form of mobility and spatial relationships in communication practices. The method used in this article is a literature review and uses meta-synthesis techniques as its analysis. The literature sources used are articles in highly reputable international journals. Based on the reports, various ways to monitor the virus's spread, through public video data, GPS, and social media tracking, trace the patient's movement. Big Data can also provide data collaboration for viruses and pathogens for further research as digital mediated communication is anchored by the diversity of places and the mobility of people, data, and objects

Department of Computer Science Activity 1998-2004

This report summarizes much of the research and teaching activity of the Department of Computer Science at Dartmouth College between late 1998 and late 2004. The material for this report was collected as part of the final report for NSF Institutional Infrastructure award EIA-9802068, which funded equipment and technical staff during that six-year period. This equipment and staff supported essentially all of the department\u27s research activity during that period

Just-in-time hypermedia

Many analytical applications, especially legacy systems, create documents and display screens in response to user queries dynamically or in real time . These documents and displays do not exist in advance, and thus hypermedia must be generated \u27just in time -automatically and dynamically. This dissertation details the idea of \u27just-in-time hypermedia and discusses challenges encountered in this research area. A fully detailed literature review about the research issues and related research work is given. A framework for the \u27just-in-time hypermedia compares virtual documents with static documents, as well as dynamic with static hypermedia functionality. Conceptual \u27just-in-time hypermedia architecture is proposed in terms of requirements and logical components. The \u27just-in-time hypermedia engine is described in terms of architecture, functional components, information flow, and implementation details. Then test results are described and evaluated. Lastly, contributions, limitations, and future work are discussed

SPADE: SPKI/SDSI for Attribute Release Policies in a Distributed Environment

Shibboleth is a federated administrated system that supports inter-institutional authentication and authorization for sharing of resources. SPKI/SDSI is a public key infrastructure whose creation was motivated by the perception that X.509 is too complex and flawed. This thesis addresses the problem of how users that are part of a Public Key Infrastructure in a distributed computing system can effectively specify, create, and disseminate their Attribute Release Policies for Shibboleth using SPKI/SDSI. This thesis explores existing privacy mechanims, as well as distributed trust management and policy based systems. My work describes the prototype for a Trust Management Framework called SPADE (SPKI/SDSI for Attribute Release Policies in a Distributed Environment) that I have designed, developed and implemented. The principal result of this research has been the demonstration that SPKI/SDSI is a viable approach for trust management and privacy policy specification, especially for minimalistic policies in a distributed environment

A Cooperative Approach for Composite Ontology Matching

Ontologies have proven to be an essential element in a range of applications in which knowl-edge plays a key role. Resolving the semantic heterogeneity problem is crucial to allow the interoperability between ontology-based systems. This makes automatic ontology matching, as an anticipated solution to semantic heterogeneity, an important, research issue. Many dif-ferent approaches to the matching problem have emerged from the literature. An important issue of ontology matching is to find effective ways of choosing among many techniques and their variations, and then combining their results. An innovative and promising option is to formalize the combination of matching techniques using agent-based approaches, such as cooperative negotiation and argumentation. In this thesis, the formalization of the on-tology matching problem following an agent-based approach is proposed. Such proposal is evaluated using state-of-the-art data sets. The results show that the consensus obtained by negotiation and argumentation represent intermediary values which are closer to the best matcher. As the best matcher may vary depending on specific differences of multiple data sets, cooperative approaches are an advantage. *** RESUMO - Ontologias são elementos essenciais em sistemas baseados em conhecimento. Resolver o problema de heterogeneidade semântica é fundamental para permitira interoperabilidade entre sistemas baseados em ontologias. Mapeamento automático de ontologias pode ser visto como uma solução para esse problema. Diferentes e complementares abordagens para o problema são propostas na literatura. Um aspecto importante em mapeamento consiste em selecionar o conjunto adequado de abordagens e suas variações, e então combinar seus resultados. Uma opção promissora envolve formalizara combinação de técnicas de ma-peamento usando abordagens baseadas em agentes cooperativos, tais como negociação e argumentação. Nesta tese, a formalização do problema de combinação de técnicas de ma-peamento usando tais abordagens é proposta e avaliada. A avaliação, que envolve conjuntos de testes sugeridos pela comunidade científica, permite concluir que o consenso obtido pela negociação e pela argumentação não é exatamente a melhoria de todos os resultados individuais, mas representa os valores intermediários que são próximo da melhor técnica. Considerando que a melhor técnica pode variar dependendo de diferencas específicas de múltiplas bases de dados, abordagens cooperativas são uma vantagem