The possibilities of visual spatial data analysis methods on human migration data.

The theme of this Master’s thesis is to research the possible methods for visual analysis of human migration. Precedents of analysis methods are considered as viable techniques that would successfully achieve a rewarding result if conducted on Finland´s migration database. Human migration phenomenon can be analyzed with the visualization of spatial data. Spatial data visualization components such as spatial data, maps and methods are discussed with focus on human migration. Human migration data are a type of spatial data that are georeferenced so as to give a context in relation to a location in real world. Maps are geographical presentation interfaces used for visual analysis of migration data. Special purpose and thematic maps or the combination of both are deemed suitable for this task as they are able to portray the information desired. Spatial data visualization techniques such as flow lines, flowstrates, map animation and space time cube are researched as tools for fully analyzing migration data. Flowstrates is a specialized visualization method that analyses the spatial and temporal dimensions of migration data by utilizing a combination technique of flow lines, timelines, and origin-destination matrices for the visualization of migration data. With this novel method, detailed information can be visualized on individual migration flow/route. The result of this thesis are the examples of the most fitting visual analysis methods for migration data. These examples serve as the possible methods for analyzing actual migration data in Finland

Educational ecosystems for Information Science: the case of the University of Pisa

Interdisciplinarity is becoming increasingly important in education. With the rapidly evolving job market, an interdisciplinary education can prepare students for the flexibility and broad knowledge base required to adapt. At the University of Pisa, we recognized the value of an interdisciplinary educational environment during our participation in the European project EINFOSE, where we harmonized the entry requirements for master programs in Information Science. Prior to this project, we had been building study programs in Digital Humanities and Data Science, whose intersection organically nurtured a diverse learning space. Through this lens, we will reflect on the obstacles constituted by disciplinary barriers and stress the importance of a flexible and open ‘ecosystem’ for education. These conclusions will be supported by data analysis on the careers of our students over the last eight years

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

Structural cognitive training with immersive virtual reality

Dissertação de mestrado em Informatics EngineeringIn this thesis, a systematic review was conducted on the study of the use of VRSs. VR is an immersive technology capable of simulating real life events through image, sound and headed mounted devices or technologies such as windows kinnect. These technologies can be used to evaluate the performance and evolution of IADLs in older adults. An electronic data search was conducted, during January 2022. The final analysis includes 12 studies with 285 participants in total. The use of VRSs is an innovative and feasible technique to support and improve the functional autonomy of older adults living in the community compared to conventional treatment. Between 20% to 25% of community-dwelling people over 75 years old have limitations in the ability to perform ADLs. The ability to perform ADLs is extremely important as it enables individuals to have a good quality of life by creating a sense of competence, self-esteem, confidence, identify and realisation. In this thesis we present the concept of structural cognitive training, in which cognitive training tasks (executive functions and cognitive abilities) are combined with training of instrumental activities of daily living. The methodology adequacy is assessed by the design of a digital game to train older adults to conduct IADLs.Nesta tese, foi realizada uma revisão sistemática sobre o estudo do uso de sistemas de realidade virtual. A realidade virtual é uma tecnologia imersiva capaz de simular eventos da vida real através de imagem, som e head-mounted devices ou tecnologias como o windows kinnect. Estas tecnologias podem ser utilizadas para avaliar o desempenho e a evolução dos IADLs em adultos mais velhos. Foi realizada uma pesquisa electrónica de dados, durante o mês de Janeiro de 2022. A análise final inclui 12 estudos com 285 participantes no total. A utilização de sistemas de realidade virtual é uma técnica inovadora e viável para apoiar e melhorar a autonomia funcional dos adultos mais velhos que vivem na comunidade, em comparação com o tratamento convencional. Entre 20% a 25% dos habitantes da comunidade com mais de 75 anos de idade têm limitações na capacidade de realizar ADLs. A capacidade de realizar ADLs é extrema mente importante, pois permite que os indivíduos tenham uma boa qualidade de vida, criando um sentido de competência, auto-estima, confiança, identificação e realização. Nesta tese apresentamos o conceito de treino cognitivo estrutural, na qual as tarefas de treino cognitivo (funções executivas e capacidades cognitivas) são combinadas com o treino de actividades instrumentais da vida quotidiana. A adequação da metodologia é avaliada através da concepção de um jogo digital para treinar os adultos mais velhos a realizar actividades instrumentais da vida quotidiana

The space physics environment data analysis system (SPEDAS)

With the advent of the Heliophysics/Geospace System Observatory (H/GSO), a complement of multi-spacecraft missions and ground-based observatories to study the space environment, data retrieval, analysis, and visualization of space physics data can be daunting. The Space Physics Environment Data Analysis System (SPEDAS), a grass-roots software development platform (www.spedas.org), is now officially supported by NASA Heliophysics as part of its data environment infrastructure. It serves more than a dozen space missions and ground observatories and can integrate the full complement of past and upcoming space physics missions with minimal resources, following clear, simple, and well-proven guidelines. Free, modular and configurable to the needs of individual missions, it works in both command-line (ideal for experienced users) and Graphical User Interface (GUI) mode (reducing the learning curve for first-time users). Both options have “crib-sheets,” user-command sequences in ASCII format that can facilitate record-and-repeat actions, especially for complex operations and plotting. Crib-sheets enhance scientific interactions, as users can move rapidly and accurately from exchanges of technical information on data processing to efficient discussions regarding data interpretation and science. SPEDAS can readily query and ingest all International Solar Terrestrial Physics (ISTP)-compatible products from the Space Physics Data Facility (SPDF), enabling access to a vast collection of historic and current mission data. The planned incorporation of Heliophysics Application Programmer’s Interface (HAPI) standards will facilitate data ingestion from distributed datasets that adhere to these standards. Although SPEDAS is currently Interactive Data Language (IDL)-based (and interfaces to Java-based tools such as Autoplot), efforts are under-way to expand it further to work with python (first as an interface tool and potentially even receiving an under-the-hood replacement). We review the SPEDAS development history, goals, and current implementation. We explain its “modes of use” with examples geared for users and outline its technical implementation and requirements with software developers in mind. We also describe SPEDAS personnel and software management, interfaces with other organizations, resources and support structure available to the community, and future development plans.Published versio