Optimising user experience with: conversational Interfaces

Dissertação de Mestrado em Engenharia InformáticaUser Experience is one of the main aspects that maintain a customer loyal to cloud based solutions or SaaS (Software as a Service). With the rise of the natural language processing techniques, the industry is looking at automated chatbot solutions to boost and expand their services. This thesis presents a practical case study of the implementation of a chatbot solution to complement a CRM (Customer Relationship Management) software called FOXAIO, and then quantify, following the most appropriate guides and solutions available, the User Experience (UX) optimisation. In order to create a robust and scalable solution based on the constraints created by the company in the case, we reviewed the current deep learning techniques, tools and libraries available to help the development process. The most proven techniques in the field of Natural Language Processing (NLP) will be introduced. To achieve the goals of this solution without "reinventing the wheel", we present possible architectures to use at the top of some open source and available tools on the market, with a special relief in the framework RASA. Also we discussed some of possible techniques to create the intent classifier, where we detail the better performance in the top of the rasa tensorflow embedding pipeline for this particular case. The conversational system, also, required a channel to interact with the final user. To achieve that, we also implemented a basic chat interface created on the top of the socket protocol, which communicate with the conversation system. In any case, it would be possible to extend to the other channel’s available on the market, like messenger, slack, telegram. Finally, we detail with a few use cases, that’s hypothetically possible to improve the user experience of an existing software system (FOXAIO) using a conversational interface on the top of that. Also, we achieved some highlights about the preference to use a conversational interface because of his simplicity, defended by a better score in the SUS scale, 70 against 58 to the traditional UI, and good indicatives by the HEART framework.O User Experience é possivelmente um dos principais aspetos para fidelizar um cliente numa solução cloud, as chamadas soluções SaaS (Software as a Service). O crescimento acentuado deste tipo de soluções aquece a rivalidade entre competidores e cada vez mais pretende-se oferecer as formas mais revolucionárias para premiar a qualidade de um serviço. Com o crescimento acentuado das técnicas na área do NLP (Natural Language Processing) a indústria começa a olhar para os chatbots como uma possível solução de automatizar, impulsionar e expandir as suas ofertas. A presente tese visa a apresentar uma implementação prática de um chatbot sobre um software com semelhanças de um CRM (Customer Relationship Management) existente intitulado por FOXAIO. Com o objetivo de desenvolver uma solução robusta e escalável tendo em atenção as condições elaboradas pela empresa em questão, um longo e detalhado estudo foi elaborado sobre as mais diversas técnicas de deep learning usadas no ramo de Processamento de Linguagem Natural (NLP). Atribuindo um particular ênfase às redes neurais recorrentes (RNN) e com a devida extensão Long Short Term Memory (LSTM) que juntas, formam e trabalham muito bem na resolução dos problemas de um sistema de inteligência artificial, como é o caso. Para a sua implementação sobre um software já existente, foi necessário o desenvolvimento de uma pequena interface conversacional com o objetivo de mais tarde a complementar sobre a interface do utilizador do mesmo. Para esse efeito, foi implementado um canal sobre o sistema conversacional de comunicação em protocolo de socket, criando uma classe para o efeito que mais tarde seria útil para gerar logs de análise. Durante a implementação do sistema conversacional foram feitas várias comparações sobre as variantes dos seus módulos desde o Dialog Management (DM) ao Intent Classifier onde várias arquiteturas foram expostas e comparadas com o intuito de corresponder à melhor solução possível para um chatbot de língua portuguesa em primeira instância, foi optado pela escolha de um Dialog Management híbrido face ao domínio e à existência de conversas contextuais contínuas onde, por exemplo, se torna bastante difícil de desenvolver sobre outros paradigmas. Quanto ao Intent Classifier, foi usada a técnica rasa tensorflow embedding, esta técnica (que treina palavras do princípio) usada obteve melhores resultados para o particular caso estudado na presente tese (CRM), do que por exemplo o uso um modelo de dados com palavras já treinadas. Finalmente, conseguimos apresentar hipoteticamente, possíveis melhorias do UX no uso de uma interface conversacional sobre uma interface tradicional, usando as várias ferramentas de análise disponíveis, onde por exemplo com o auxílio da framework HEART (criada pelo Google), conseguimos obter indicativos bastante satisfatórios por 34 pessoas que fizeram os primeiros testes no chatbot desenvolvido. Examinando o feedback desses mesmos utilizadores em ambiente de teste, conseguimos obter um resultado na escala de SUS (System Usability Scale) com um valor de 70, enquanto a interface tradicional arrecadou 58, notando então que as pessoas se sentiram mais capazes no uso do sistema conversacional

Practical techniques building on encryption for protecting and managing data in the Cloud

Companies as well as individual users are adopting cloud solutions at an over-increasing rate for storing data and making them accessible to others. While migrating data to the cloud brings undeniable benefits in terms of data availability, scalability, and reliability, data protection is still one of the biggest concerns faced by data owners. Guaranteeing data protection means ensuring confidentiality and integrity of data and computations over them, and ensuring data availability to legitimate users. In this chapter, we survey some approaches for protecting data in the cloud that apply basic cryptographic techniques, possibly complementing them with additional controls, to the aim of producing efficient and effective solutions that can be used in practice

Secure and Reliable Routing Protocol for Transmission Data in Wireless Sensor Mesh Networks

Abstract Sensor nodes collect data from the physical world then exchange it until it reaches the intended destination. This information can be sensitive, such as battlefield surveillance. Therefore, providing secure and continuous data transmissions among sensor nodes in wireless network environments is crucial. Wireless sensor networks (WSN) have limited resources, limited computation capabilities, and the exchange of data through the air and deployment in accessible areas makes the energy, security, and routing major concerns in WSN. In this research we are looking at security issues for the above reasons. WSN is susceptible to malicious activities such as hacking and physical attacks. In general, security threats are classified depending on the layers. Physical, Transport, Network, Data link, and the Application layer. Sensor nodes can be placed in an unfriendly environments and it has lower power energy, computation and bandwidth, are exposed to a failure, and the WSN topology dynamically unstable. The recent wireless sensor protocols are intended for data communication transmission energy consumption. Therefore, many do not consider the security in WSN as much as they should and it might be vulnerable to attacks. Standard crypto systems methods aim to protect the authentication and integrity of data packets during the transmission stage between senders and receivers. In this dissertation we present Adel which is a novel routing protocol for exchanging data through wireless sensor mesh networks using Ant Colony Optimization (ACO) algorithm. Adel enhances security level during data transmission between sender party and receiver party in wireless network environment. Once the sensor nodes are deployed in a network, they need to inform their location and their data related to the security for the further communication in the network. For that purpose, ii an efficient mechanism is implemented in order to perform better communication among sensor nodes. Adel generates dynamic routing table using ACO algorithm with all the necessary information from network nodes after being deployed. Adel works with minimum routing restrictions and exploits the advantages of the three multicast routing styles, unicast, path, and mesh based. Since it takes a routing decision with a minimum number of nodes using the shortest path between the sender and the receiver nodes, Adel is applicable in static networks. Four essential performance metrics in mesh networks, network security analysis, network latency time, network packets drop, network delivery ratio, and network throughput are evaluated. Adel routing protocol has met the most important security requirements such as authorization, authentication, confidentiality, and integrity. It also grantees the absence of the cycle path problem in the network.This research reports the implementation and the performance of the proposed protocol using network simulator NS-2. The seven main parameters are considered for evaluation all experiments are security trust, packets drop, energy consumption, throughput, end to end delay and packet delivery ratio. The results show that the proposed system can significantly enhance the network security and connectivity level compared to other routing protocols. Yet, as expected, it did not do so well in energy consumption since our main goal was to provide higher level of security and connectivit

LightBox: Full-stack Protected Stateful Middlebox at Lightning Speed

Running off-site software middleboxes at third-party service providers has been a popular practice. However, routing large volumes of raw traffic, which may carry sensitive information, to a remote site for processing raises severe security concerns. Prior solutions often abstract away important factors pertinent to real-world deployment. In particular, they overlook the significance of metadata protection and stateful processing. Unprotected traffic metadata like low-level headers, size and count, can be exploited to learn supposedly encrypted application contents. Meanwhile, tracking the states of 100,000s of flows concurrently is often indispensable in production-level middleboxes deployed at real networks. We present LightBox, the first system that can drive off-site middleboxes at near-native speed with stateful processing and the most comprehensive protection to date. Built upon commodity trusted hardware, Intel SGX, LightBox is the product of our systematic investigation of how to overcome the inherent limitations of secure enclaves using domain knowledge and customization. First, we introduce an elegant virtual network interface that allows convenient access to fully protected packets at line rate without leaving the enclave, as if from the trusted source network. Second, we provide complete flow state management for efficient stateful processing, by tailoring a set of data structures and algorithms optimized for the highly constrained enclave space. Extensive evaluations demonstrate that LightBox, with all security benefits, can achieve 10Gbps packet I/O, and that with case studies on three stateful middleboxes, it can operate at near-native speed.Comment: Accepted at ACM CCS 201

Hypatiamat - I want to solve questions about...

Dissertação de mestrado em Informatics EngineeringHypatiamat is a Portuguese project comprised of several applications that aim to develop the Math skills of students from the 1st through 9th grades (Basic Education). The ingraining of mental calculation strategies, numbering systems, and logical operations lead to a better success rate in this subject in later years. One of the project’s components is the online platform (https://www.hypatiamat.com), which aims to foster autonomous learning through more interactive practices due to the current ease of technological access in this age group, by trying to appropriate teaching to everyday life. Several tools are made available, such as videos, tutorials, explanations, questions, etc. on various Math topics that students can easily access at any time. Teachers that aim to enhance their students’ learning process using this digital approach can exercise it in multiple applications provided by the platform, where the interactions are carried out and controlled through these means. The monolithic architecture (written in PHP) has received contributions from multiple developers over the years in order to address the scalability issues introduced with this platform’s growing popularity, which thus far demanded manual efforts for maintenance and content insertion. As such, there has been an incremental process of modernization, turning the various constituent applications into distinct microservices. "I Want to Solve Questions About..." is one of these applications where students are provided with a large selection of questions in the form of mini-games (multiple choice, true or false, ...), regarding the themes mentioned above. The first objective of the dissertation is to develop a back-office that allows the teachers in charge of the project to manage existing questions as well as add new ones for the students, since the current process requires updating the database manually. The second one is the modernization of the application’s interface at the technological level, by making use of adequate frameworks and programming languages and at the user level, by making an effort to maintain the intuitive workflow that led to its popularity but with a modernized design, in order to be consistent with other online tools.O Hypatiamat é um projeto português constituído por várias aplicações que visa desenvolver as aptidões, na disciplina de Matemática, de alunos do 1º ao 9º ano de escolaridade (Educação Básica). O enraizamento de estratégias de cálculo mental, sistemas de numeração e operações lógicas originam uma melhor taxa de sucesso nesta disciplina em anos posteriores. Uma das componentes deste projeto é a plataforma online (https://www.hypatiamat.com), cujo propósito é fomentar a aprendizagem autónoma através de práticas mais interativas, devido à facilidade de acesso tecnológico atual desta faixa etária, tentando apropriar o ensino ao quotidiano. São disponibilizadas várias ferramentas, tais como vídeos, tutoriais, explicações, questões, etc sobre os vários temas da Matemática (Ensino Básico) que os alunos podem facilmente aceder a qualquer momento. Professores que pretendam enriquecer a aprendizagem dos seus alunos com esta metodologia digital podem exercê-lo nas várias aplicações que a plataforma disponibiliza, onde a interação é realizada e controlada através destes meios. A arquitetura monolítica (escrita em PHP) tem recebido contribuições de vários desenvolvedores ao longo dos anos de modo a colmatar os problemas de escalabilidade introduzidos com a popularidade crescente desta plataforma, que até agora exigia esforço manual para manutenção e inserção de conteúdo. Assim, tem existido um processo incremental de modernização, tornando as várias aplicações constituintes em microsserviços distintos. A "Quero resolver questões de..." é uma destas aplicações, onde são disponibilizadas aos alunos várias questões, sob a forma de mini-jogos (escolha múltipla, verdadeiro ou falso, ...), relativas aos temas mencionados anteriormente. O primeiro objetivo da dissertação é o desenvolvimento de um backoffice que permita aos professores responsáveis gerirem as questões existentes assim como adicionarem novas para os alunos, visto que o processo atual obriga a atualização manual na base de dados. O segundo é a modernização da interface da aplicação ao nível: tecnológico, utilizando frameworks e linguagens de programação adequadas ao problema; do utilizador, de modo a manter o fluxo intuitivo que gerou a sua popularidade mas tendo em conta um design mais atualizado para manter a consistência com outras ferramentas online

Applied Formal Methods in Wireless Sensor Networks

This work covers the application of formal methods to the world of wireless sensor networks. Mainly two different perspectives are analyzed through mathematical models which can be distinct for example into qualitative statements like "Is the system error free?" From the perspective of quantitative propositions we investigate protocol optimal parameter settings for an energy efficient operation

API diversity for microservices in the domain of connected vehicles

Web services in the domain of connected vehicles are subject to various requirements including high availability and large workloads. Microservices are an architectural style which can fulfill those requirements by fostering the independence and decoupling of software components as reusable services. To achieve this independence, microservices have to implement all aspects of providing the services themselves, including different API technologies for heterogeneous consumers and supporting features like authentication. In this work, we examine the use of a service proxy that externalizes these concerns into a sidecar that provides multiple APIs and common service functionality in a platform-independent manner. We look at how different kinds of API styles and technologies solve selected classes of problems and how we can translate between API technologies. We design and implement a framework for building gateways that enables the creation and composition of reusable components, in the fashion of Lego bricks, to maximize flexibility, while reducing the effort for building gateway components. We design and implement selected components of common and reusable API functionality enabling us to build a reference setup with a service proxy as a sidecar using our framework. Finally, we evaluate the proposed solution to identify benefits and drawbacks of the approach of using our framework as a service proxy. We conclude that the examined approach provides benefits for the development of many polyglot microservices, but splitting one service into two components adds additional complexity that has to be managed.Web Services für vernetzte Fahrzeuge unterliegen unterschiedlichen Anforderungen, unter anderem einer hohen Verfügbarkeit und einem großen Datendurchsatz. Microservices sind ein Architekturstil, der diesen Anforderungen gerecht werden kann, indem er die Unabhängigkeit und Entkopplung von Softwarekomponenten als wiederverwendbare Services fördert. Zum Erreichen der Unabhängigkeit implementieren Microservices alle Aspekte der Servicebereitstellung eigenständig. Dazu gehört verschiedene API Technologien für heterogene Clients bereitzustellen und unterstützende Funktionalität wie Authentifizierung zu implementieren. In dieser Arbeit wird die Verwendung einer Proxy Komponente vor einem Service untersucht, durch welche die Bereitstellung verschiedener API Technologien und allgemeiner unterstützender Funktionalität aus dem Service extrahiert wird. Die Lösungen verschiedener API Technologien und Stile für ausgewählte Klassen an Problemen werden verglichen und mögliche Umwandlungen der verschiedenen API Technologien werden untersucht. Es wird ein Framework konzeptioniert und implementiert, das die Erstellung von Gateways durch Kombination von wiederverwendbaren Komponenten, wie das Zusammensetzen von Legosteinen, ermöglicht. Dieses Framework sorgt für eine hohe Flexibilität, während es den Aufwand bei der Erstellung von Gateways gering hält. Es werden ausgewählte wiederverwendbare Komponenten entworfen, um eine Referenzimplementierung des Ansatzes umzusetzen, bei der allgemeine Funktionalität in einen parallel laufenden Proxy ausgelagert wird. Dieser Ansatz wird evaluiert, indem Vor- und Nachteile anhand eines mit dem Framework erstellten Proxys identifiziert werden. Das Fazit dieser Arbeit ist, dass dieser Ansatz bei Systemen mit vielen Microservices mit unterschiedlichen Programmiersprachen Vorteile bringt, aber die Trennung eines Services in zwei Komponenten eine nicht unerhebliche Komplexität einführt

Deep Learning Techniques for Automated Analysis and Processing of High Resolution Medical Imaging

Programa Oficial de Doutoramento en Computación . 5009V01[Abstract] Medical imaging plays a prominent role in modern clinical practice for numerous medical specialties. For instance, in ophthalmology, different imaging techniques are commonly used to visualize and study the eye fundus. In this context, automated image analysis methods are key towards facilitating the early diagnosis and adequate treatment of several diseases. Nowadays, deep learning algorithms have already demonstrated a remarkable performance for different image analysis tasks. However, these approaches typically require large amounts of annotated data for the training of deep neural networks. This complicates the adoption of deep learning approaches, especially in areas where large scale annotated datasets are harder to obtain, such as in medical imaging. This thesis aims to explore novel approaches for the automated analysis of medical images, particularly in ophthalmology. In this regard, the main focus is on the development of novel deep learning-based approaches that do not require large amounts of annotated training data and can be applied to high resolution images. For that purpose, we have presented a novel paradigm that allows to take advantage of unlabeled complementary image modalities for the training of deep neural networks. Additionally, we have also developed novel approaches for the detailed analysis of eye fundus images. In that regard, this thesis explores the analysis of relevant retinal structures as well as the diagnosis of different retinal diseases. In general, the developed algorithms provide satisfactory results for the analysis of the eye fundus, even when limited annotated training data is available.[Resumen] Las técnicas de imagen tienen un papel destacado en la práctica clínica moderna de numerosas especialidades médicas. Por ejemplo, en oftalmología es común el uso de diferentes técnicas de imagen para visualizar y estudiar el fondo de ojo. En este contexto, los métodos automáticos de análisis de imagen son clave para facilitar el diagnóstico precoz y el tratamiento adecuado de diversas enfermedades. En la actualidad, los algoritmos de aprendizaje profundo ya han demostrado un notable rendimiento en diferentes tareas de análisis de imagen. Sin embargo, estos métodos suelen necesitar grandes cantidades de datos etiquetados para el entrenamiento de las redes neuronales profundas. Esto complica la adopción de los métodos de aprendizaje profundo, especialmente en áreas donde los conjuntos masivos de datos etiquetados son más difíciles de obtener, como es el caso de la imagen médica. Esta tesis tiene como objetivo explorar nuevos métodos para el análisis automático de imagen médica, concretamente en oftalmología. En este sentido, el foco principal es el desarrollo de nuevos métodos basados en aprendizaje profundo que no requieran grandes cantidades de datos etiquetados para el entrenamiento y puedan aplicarse a imágenes de alta resolución. Para ello, hemos presentado un nuevo paradigma que permite aprovechar modalidades de imagen complementarias no etiquetadas para el entrenamiento de redes neuronales profundas. Además, también hemos desarrollado nuevos métodos para el análisis en detalle de las imágenes del fondo de ojo. En este sentido, esta tesis explora el análisis de estructuras retinianas relevantes, así como el diagnóstico de diferentes enfermedades de la retina. En general, los algoritmos desarrollados proporcionan resultados satisfactorios para el análisis de las imágenes de fondo de ojo, incluso cuando la disponibilidad de datos de entrenamiento etiquetados es limitada.[Resumo] As técnicas de imaxe teñen un papel destacado na práctica clínica moderna de numerosas especialidades médicas. Por exemplo, en oftalmoloxía é común o uso de diferentes técnicas de imaxe para visualizar e estudar o fondo de ollo. Neste contexto, os métodos automáticos de análises de imaxe son clave para facilitar o diagn ostico precoz e o tratamento adecuado de diversas enfermidades. Na actualidade, os algoritmos de aprendizaxe profunda xa demostraron un notable rendemento en diferentes tarefas de análises de imaxe. Con todo, estes métodos adoitan necesitar grandes cantidades de datos etiquetos para o adestramento das redes neuronais profundas. Isto complica a adopción dos métodos de aprendizaxe profunda, especialmente en áreas onde os conxuntos masivos de datos etiquetados son máis difíciles de obter, como é o caso da imaxe médica. Esta tese ten como obxectivo explorar novos métodos para a análise automática de imaxe médica, concretamente en oftalmoloxía. Neste sentido, o foco principal é o desenvolvemento de novos métodos baseados en aprendizaxe profunda que non requiran grandes cantidades de datos etiquetados para o adestramento e poidan aplicarse a imaxes de alta resolución. Para iso, presentamos un novo paradigma que permite aproveitar modalidades de imaxe complementarias non etiquetadas para o adestramento de redes neuronais profundas. Ademais, tamén desenvolvemos novos métodos para a análise en detalle das imaxes do fondo de ollo. Neste sentido, esta tese explora a análise de estruturas retinianas relevantes, así como o diagnóstico de diferentes enfermidades da retina. En xeral, os algoritmos desenvolvidos proporcionan resultados satisfactorios para a análise das imaxes de fondo de ollo, mesmo cando a dispoñibilidade de datos de adestramento etiquetados é limitada