Enhancing E-learning platforms with social networks mining

Social Networks appeared as an Internet application that offers several tools to create a personal virtual profile, add other users as friends, and interact with them through messages. These networks quickly evolved and won particular importance in people lives. Now, everyday, people use social networks to share news, interests, and discuss topics that in some way are important to them. Together with social networks, e-learning platforms and related technologies have evolved in the recent years. Both platforms and technologies (social networks and e-learning) enable access to specific information and are able to redirect specific content to an individual person. This dissertation is motivated on social networks data mining over e-learning platforms. It considers the following four social networks: Facebook, Twitter, Google Plus, and Delicious. In order to acquire, analyze, and make a correct and precise implementation of data, two different approaches were followed: enhancement of a current e-learning platform and improvement of search engines. The first approach proposes and elaborates a recommendation tool for Web documents using, as main criterion, social information to support a custom Learning Management System (LMS). In order to create the proposed system, three distinct applications (the Crawler, the SocialRank, and the Recommender) were proposed. Such data will be then incorporated into an LMS system, such as the Personal Learning Environment Box (PLEBOX). PLEBOX is a custom platform based on operating systems layout, and also, provides a software development kit (SDK), a group of tools, to create and manage modules. The results of recommendation tool about ten course units are presented. The second part presents an approach to improve a search engine based on social networks content. Subsequently, a depth analysis to justify the abovementioned procedures in order to create the SocialRank is presented. Finally, the results are presented and validated together with a custom search engine. Then, a solution to integrate and offer an order improvement of Web contents in a search engine was proposed, created, demonstrated, and validated, and it is ready for use.As redes sociais surgiram como um serviço Web com funcionalidades de criação de perfil, criação e interação de amigos. Estas redes evoluíram rapidamente e ganharam uma determinada importância na vida das pessoas. Agora, todos os dias, as pessoas usam as redes sociais para partilhar notícias, interesses e discutir temas que de alguma forma são importantes para elas. Juntamente com as redes sociais, as plataformas de aprendizagem baseadas em tecnologias, conhecidas como plataformas E-learning têm evoluído muito nos últimos anos. Ambas as plataformas e tecnologias (redes sociais e E-learning) fornecem acesso a informações específicas e são capazes de redirecionar determinado conteúdo para um ou vários indivíduos (personalização). O tema desta dissertação é motivado pela mineração do conteúdo das redes sociais em plataformas E-learning. Neste sentido, foram selecionadas quatro redes sociais, Facebook, Twitter, Google Plus, e Delicious para servir de estudo de caso à solução proposta. A fim de adquirir, analisar e concretizar uma aplicação correta e precisa dos dados, duas abordagens diferentes foram seguidas: enriquecimento de uma plataforma E-learning atual e melhoria dos motores de busca. A primeira abordagem propõe e elaboração de uma ferramenta de recomendação de documentos Web usando, como principal critério, a informação social para apoiar um sistema de gestão de aprendizagem (LMS). Desta forma, foram construídas três aplicações distintas, designadas por Crawler, SocialRank e Recommender. As informações extraídas serão incorporadas num sistema E-learning, tendo sido escolhida a PLEBOX (Personal Learning Environment Box). A PLEBOX é uma plataforma personalizada baseada numa interface inspirada nos sistemas operativos, fornecendo um conjunto de ferramentas (os conhecidos SDK - software development kit), para a criação e gestão de módulos. Dez unidades curriculares foram avaliadas e os resultados do sistema de recomendação são apresentados. A segunda abordagem apresenta uma proposta para melhorar um motor de busca com base no conteúdo das redes sociais. Subsequentemente, uma análise profunda é apresentada, justificando os procedimentos de avaliação, afim de criar o ranking de resultados (o SocialRank). Por último, os resultados são apresentados e validados em conjunto com um motor de busca. Assim, foi proposta, construída, demonstrada e avaliada uma solução para integrar e oferecer uma melhoria na ordenação de conteúdos Web dentro de um motor de busca. A solução está pronta para ser utilizad

Anthropology with algorithms?

Based on a study of more than twenty thousand reports on drug experiences from the online drug education portal Erowid, this article argues that the integration of ethnographic methods with computational methods and digital data analysis, including so-called big data, is not only possible but highly rewarding. The analysis of ‘natively’ digital data from sites like Facebook, message boards, and web archives can offer glimpses into worlds of practice and meaning, introduce anthropologists to user-based semantics, provide greater context, help to re-evaluate hypotheses, facilitate access to difficult fields, and point to new research questions. This case study generated important insights into the social and political entanglements of drug consumption, drug phenomenology, and harm reduction. We argue here that deep ethnographic knowledge, what we term ‘field groundedness’, is indispensable for thoroughly making sense of the resulting visualizations, and we advocate for seeing ethnography and digital data analysis in a symbiotic relationship

A crowdsourcing recommendation model for image annotations in cultural heritage platforms

Cultural heritage is one of many fields that has seen a significant digital transformation in the form of digitization and asset annotations for heritage preservation, inheritance, and dissemination. However, a lack of accurate and descriptive metadata in this field has an impact on the usability and discoverability of digital content, affecting cultural heritage platform visitors and resulting in an unsatisfactory user experience as well as limiting processing capabilities to add new functionalities. Over time, cultural heritage institutions were responsible for providing metadata for their collection items with the help of professionals, which is expensive and requires significant effort and time. In this sense, crowdsourcing can play a significant role in digital transformation or massive data processing, which can be useful for leveraging the crowd and enriching the metadata quality of digital cultural content. This paper focuses on a very important challenge faced by cultural heritage crowdsourcing platforms, which is how to attract users and make such activities enjoyable for them in order to achieve higher-quality annotations. One way to address this is to offer personalized interesting items based on each user preference, rather than making the user experience random and demanding. Thus, we present an image annotation recommendation system for users of cultural heritage platforms. The recommendation system design incorporates various technologies intending to help users in selecting the best matching images for annotations based on their interests and characteristics. Different classification methods were implemented to validate the accuracy of our work on Egyptian heritage.Agencia Estatal de Investigación | Ref. TIN2017-87604-RXunta de Galicia | Ref. ED431B 2020/3

Development of service-oriented architectures using model-driven development : a mapping study

Context: Model-Driven Development (MDD) and Service-Oriented Architecture (SOA) are two challenging research areas in software engineering. MDD is about improving software development whilst SOA is a service-based conceptual development style, therefore investigating the available proposals in the literature to use MDD when developing SOA may be insightful. However, no studies have been found with this purpose. Objective: This work aims at assessing the state of the art in MDD for SOA systems. It mainly focuses on: what are the characteristics of MDD approaches that support SOA; what types of SOA are supported; how do they handle non-functional requirements. Method: We conducted a mapping study following a rigorous protocol. We identified the representative set of venues that should be included in the study. We applied a search string over the set of selected venues. As result, 129 papers were selected and analysed (both frequency analysis and correlation analysis) with respect to the defined classification criteria derived from the research questions. Threats to validity were identified and mitigated whenever possible. Results: The analysis allows us to answer the research questions. We highlight: (1) predominance of papers from Europe and written by researchers only; (2) predominance of top-down transformation in software development activities; (3) inexistence of consolidated methods; (4) significant percentage of works without tool support; (5) SOA systems and service compositions more targeted than single services and SOA enterprise systems; (6) limited use of metamodels; (7) very limited use of NFRs; and (8) limited application in real cases. Conclusion: This mapping study does not just provide the state of the art in the topic, but also identifies several issues that deserve investigation in the future, for instance the need of methods for activities other than software development (e.g., migration) or the need of conducting more real case studies.Peer ReviewedPostprint (author's final draft

Non-Visual Representation of Complex Documents for Use in Digital Talking Books

Essential written information such as text books, bills, and catalogues needs to be accessible by everyone. However, access is not always available to vision-impaired people. As they require electronic documents to be available in specific formats. In order to address the accessibility issues of electronic documents, this research aims to design an affordable, portable, standalone and simple to use complete reading system that will convert and describe complex components in electronic documents to print disabled users

Crowdsourcing based local Web search engine

Web sisältää valtavan määrän tietoa, jolla on usein maantieteellinen luonne. Samaan aikaan käyttäjät myös etsivät Webistä yhä useammin tietoa omasta ympäristöstään. Vaikka Web-dokumentit sisältävät suoria viittauksia maantieteellisiin sijainteihin, eivät nykyiset paikallishakukoneet pysty mallintamaan Webin maantieteellistä luonnetta tehokkaasti. Tästä johtuen Webin monipuolisen tiedon etsiminen maantieteellisen sijainnin perusteella ei ole mahdollista. Tässä diplomityössä kuvataan esimerkkitoteutus joukkoistamiseen pohjautuvasta paikallishakukoneesta. Joukkoistamalla on mahdollista kerätä Web-dokumentteihin liitettävää paikkatietoa suuressa mittakaavassa. Käyttäjiltä kerätty paikkatieto myös kuvastaa ennen kaikkea juuri käyttäjien näkemystä Web-dokumenttien ja maantieteellisten sijaintien yhteydestä. Joukkoistamisen haasteena on kuitenkin verkkoyhteisön luominen ja käyttäjien tuottaman paikkatiedon laatu. Kuten hyvin tiedetään, kaikki käyttäjät eivät aina tuota laadukasta sisältöä ja joukkoon mahtuu myös käyttäjiä, jotka pyrkivät tietoisesti käyttämään järjestelmää hyväkseen. Työssä kuvatun esimerkkitoteutuksen pohjalta on toteutettu yksinkertainen prototyyppi, jonka avulla on kokeiltu käytännössä joukkoistamiseen pohjautuvan paikallishakukoneen toimivuutta. Prototyyppi ei sisällä kaikkia esimerkkitoteutuksessa kuvattuja ominaisuuksia. Sen perusteella voidaan kuitenkin todeta, että joukkoistaminen voi tarjota ratkaisun yleiskäyttöiseen Web-paikallishakukoneeseen, mutta ratkaisun toimivuuden toteaminen vaatii vielä lisätutkimusta

Development of traceability solution for furniture components

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáIn the contemporary context, characterized by intensified global competition and the constant evolution of the globalization landscape, it becomes imperative for industries, including Small and Medium Enterprises (SMEs), to undertake efforts to enhance their operational processes, often through digital technological adaptation. The present study falls within the scope of the project named “Wood Work 4.0,” which aims to infuse innovation into the wood furniture manufacturing industry through process optimization and the adoption of digital technologies. This project received funding from the European Union Development Fund, in collaboration with the North 2020 Regional Program, and was carried out at the Carpintaria Mofreita company, located in Macedo de Cavaleiros, Portugal. In this regard, this study introduces a software architecture that supports the traceability of projects in the wood furniture industry and simultaneously employs a system to identify and manage material leftovers, aiming for more efficient waste management. For the development of this software architecture, an approach that integrates the Fiware platform, specialized in systems for the Internet of Things (IoT), with an Application Programming Interface (API) specifically created to manage information about users, projects, and associated media files, was adopted. The material leftovers identification system employs image processing techniques to extract geometric characteristics of the materials. Additionally, these data are integrated into the company’s database. In this way, it was possible to develop an architecture that allows not only the capturing of project information but also its effective management. In the case of material leftovers identification, the system was able to establish, with a satisfactory degree of accuracy, the dimensions of the materials, enabling the insertion of these data into the company’s database for resource management and optimization.No contexto contemporâneo, marcado por uma competição global intensificada e pela constante evolução do cenário de globalização, torna-se imperativo para as indústrias, incluindo as Pequenas e Médias Empresas (PMEs), empreender esforços para aprimorar seus processos operacionais, frequentemente pela via da adaptação tecnológica digital. O presente estudo insere-se dentro do escopo do projeto denominado “Wood Work 4.0”, cujo propósito é infundir inovação na indústria de fabricação de móveis de madeira por meio da otimização de processos e da adoção de tecnologias digitais. Este projeto obteve financiamento do Fundo de Desenvolvimento da União Europeia, em colaboração com o programa Regional do Norte 2020 e foi realizado na empresa Carpintaria Mofreita, localizada em Macedo de Cavaleiros, Portugal. Nesse sentido, este estudo introduz uma arquitetura de software que oferece suporte à rastreabilidade de projetos na indústria de móveis de madeira, e simultaneamente emprega um sistema para identificar e gerenciar sobras de material, objetivando uma gestão de resíduos mais eficiente. Para o desenvolvimento dessa arquitetura de software, adotou-se uma abordagem que integra a plataforma Fiware, especializada em sistemas para a Internet das Coisas (IoT), com uma Interface de Programação de Aplicações (API) criada especificamente para gerenciar informações de usuários, projetos, e arquivos de mídia associados. O sistema de identificação de sobras de material emprega técnicas de processamento de imagem para extrair características geométricas dos materiais. Adicionalmente, esses dados são integrados ao banco de dados da empresa. Desta forma, foi possível desenvolver uma arquitetura que permite não só capturar informações de projetos, mas também gerenciá-las de forma eficaz. No caso da identificação de sobras de material, o sistema foi capaz de estabelecer, com um grau de precisão satisfatório, as dimensões dos materiais, possibilitando a inserção desses dados no banco de dados da empresa para gestão e otimização do uso de recursos