Impresso Inspect and Compare. Visual Comparison of Semantically Enriched Historical Newspaper Articles

The automated enrichment of mass-digitised document collections using techniques such as text mining is becoming increasingly popular. Enriched collections offer new opportunities for interface design to allow data-driven and visualisation-based search, exploration and interpretation. Most such interfaces integrate close and distant reading and represent semantic, spatial, social or temporal relations, but often lack contrastive views. Inspect and Compare (I\&C) contributes to the current state of the art in interface design for historical newspapers with highly versatile side-by-side comparisons of query results and curated article sets based on metadata and semantic enrichments. I\&C takes search queries and pre-curated article sets as inputs and allows comparisons based on the distributions of newspaper titles, publication dates and automatically generated enrichments, such as language, article types, topics and named entities. Contrastive views of such data reveal patterns, help humanities scholars to improve search strategies and to facilitate a critical assessment of the overall data quality. I\&C is part of the impresso interface for the exploration of digitised and semantically enriched historical newspapers

Scientific History of Incipit in the period 2010-2016

Historial de la actividad científica y técnica del Instituto de Ciencias del Patrimonio (Incipit) del CSIC, basado en Santiago de Compostela, desde su fecha de creación (2010) hasta el año 2016. Se presentan la misión y las líneas de investigación del Incipit, centradas principalmente en el estudio de los procesos de patrimonialización y de valorización social del patrimonio cultural realizadas con una perspectiva transdisciplinar. Se relacionan las publicaciones, proyectos de investigación, actividades de ciencia pública, eventos de comunicación y productos de divulgación que su personal investigador ha producido a lo largo de estos años.General introduction to the Incipit. Presentation of the Research Line: Cultural Heritage Studies: Sub-Theme: Landscape Archaeology and Cultural Landscapes, Sub-theme: Heritagization Processes: Memory, Power and Ethnicity, Sub-theme: Socioeconomics of Cultural Heritage, Sub-theme: Archaeology of the Contemporary Past, Sub-theme: Material culture and formalization processes of cultural heritage. Scientific Contributions. Transfer of Knowledge. International Activities. Other Activities and Results. Scientific DisseminationN

Educational Technology and Related Education Conferences for June to December 2015

The 33rd edition of the conference list covers selected events that primarily focus on the use of technology in educational settings and on teaching, learning, and educational administration. Only listings until December 2015 are complete as dates, locations, or Internet addresses (URLs) were not available for a number of events held from January 2016 onward. In order to protect the privacy of individuals, only URLs are used in the listing as this enables readers of the list to obtain event information without submitting their e-mail addresses to anyone. A significant challenge during the assembly of this list is incomplete or conflicting information on websites and the lack of a link between conference websites from one year to the next

Faithful visualization and dimensionality reduction on graphics processing unit

Information visualization is a process of transforming data, information and knowledge to the geometric representation in order to see unseen information. Dimensionality reduction (DR) is one of the strategies used to visualize high-dimensional data sets by projecting them onto low-dimensional space where they can be visualized directly. The problem of DR is that the straightforward relationship between the original highdimensional data sets and low-dimensional space is lost, which causes the colours of visualization to have no meaning. A new nonlinear DR method which is called faithful stochastic proximity embedding (FSPE) is proposed in this thesis to visualize more complex data sets. The proposed method depends on the low-dimensional space rather than the high-dimensional data sets to overcome the main shortcomings of the DR by overcoming the false neighbour points, and preserving the neighbourhood relation to the true neighbours. The visualization by our proposed method displays the faithful, useful and meaningful colours, where the objects of the image can be easily distinguished. The experiments that were conducted indicated that the FSPE is higher in accuracy than many dimension reduction methods because it prevents as much as possible the false neighbourhood errors to occur in the results. In addition, in the results of other methods, we have demonstrated that the FSPE has an important role in enhancing the low-dimensional space which are carried by other DR methods. Choosing the worst efficient points to update the rest of the points has helped in improving the visualization information. The results showed the proposed method has an impacting role in increasing the trustworthiness of the visualization by retrieving most of the local neighbourhood points, which they missed during the projection process. The sequential dimensionality reduction (SDR) method is the second proposed method in this thesis. It redefines the problem of DR as a sequence of multiple DR problems, each of which reduces the dimensionality by a small amount. It maintains and preserves the relations among neighbour points in low-dimensional space. The results showed the accuracy of the proposed SDR, which leads to a better visualization with minimum false colours compared to the direct projection of the DR method, where those results are confirmed by comparing our method with 21 other methods. Although there are many measurement metrics, our proposed point-wise correlation metric is the better. In this metric, we evaluate the efficiency of each point in the visualization to generate a grey-scale efficiency image. This type of image gives more details instead of representing the evaluation in one single value. The user can recognize the location of both the false and the true points. We compared the results of our proposed methods (FSPE and SDR) and many other dimension reduction methods when applied to four scenarios: (1) the unfolding curved cylinder data sets; (2) projecting a human face data sets into two dimensions; (3) classifing connected networks and (4) visualizing a remote sensing imagery data sets. The results showed that our methods are able to produce good visualization by preserving the corresponding colour distances between the visualization and the original data sets. The proposed methods are implemented on the graphic processing unit (GPU) to visualize different data sets. The benefit of a parallel implementation is to obtain the results in as short a time as possible. The results showed that compute unified device architecture (CUDA) implementation of FSPE and SDR are faster than their sequential codes on the central processing unit (CPU) in calculating floating-point operations, especially for a large data sets. The GPU is also more suited to the implementation of the metric measurement methods because they do a large computation. We illustrated that this massive speed-up requires a parallel structure to be suitable for running on a GPU

Visual analytics of big data from distributed systems

Distributed Systems are challenging to debug because the temporal order of events and distributed states are hard to track. The high complexity of distributed systems make fully automatic reasoning difficult to apply. Domain experts are often required to reason about the behavior of a system based on log files from various sources. This situation presents a good opportunity for visual analytics. Data from multiple sources can be preprocessed and visualized, and then domain experts can conduct exploratory analysis to accelerate the identification of issues. The goal of this master thesis was to create such a visual analytics tool to help domain experts explore data collected from distributed systems more efficiently and assist in identifying bugs and anomalies. The system was used by domain experts and helped to identify issues in a distributed system, showing that visual analytics can be a useful tool to assist domain experts in their daily work.Fehlersuche in verteilten Systemen ist eine Herausforderung, da es schwierig ist, die zeitliche Ordnung von Ereignissen sowie verteilte Zustände im Auge zu behalten. Die hohe Komplexität von verteilten Systemen macht es schwierig, vollautomatisch Schlussfolgerungen zu ziehen. Domänenexperten müssen oft Rückschlüsse über ein komplexes, verteiltes System auf Grundlage von Logdateien aus verschiedenen Quellen ziehen. Diese Situation bietet eine gute Möglichkeit, Visual Analytics anzuwenden. Daten aus diversen Quellen können vorverarbeitet und visualisiert werden, woraufhin Domänenexperten explorative Analyse zur Beschleunigung der Fehlersuche betreiben können. Das Ziel dieser Masterarbeit war es, solch ein Visual Analytics-Werkzeug zu erschaffen, um Domänenexperten das Erkunden von Daten von verteilten Systemen zu erleichtern und bei der Identifizierung von Fehlern und Anomalien zu helfen. Das System wurde von Domänenexperten verwendet und half bei der Identifizierung von Problemen in einem verteilten System, was zeigt, dass Visual Analytics ein nützliches Werkzeug ist, um Domänenexperten bei ihrer täglichen Arbeit zu unterstützen

Comparative Uncertainty Visualization for High-Level Analysis of Scalar- and Vector-Valued Ensembles

With this thesis, I contribute to the research field of uncertainty visualization, considering parameter dependencies in multi valued fields and the uncertainty of automated data analysis. Like uncertainty visualization in general, both of these fields are becoming more and more important due to increasing computational power, growing importance and availability of complex models and collected data, and progress in artificial intelligence. I contribute in the following application areas: Uncertain Topology of Scalar Field Ensembles. The generalization of topology-based visualizations to multi valued data involves many challenges. An example is the comparative visualization of multiple contour trees, complicated by the random nature of prevalent contour tree layout algorithms. I present a novel approach for the comparative visualization of contour trees - the Fuzzy Contour Tree. Uncertain Topological Features in Time-Dependent Scalar Fields. Tracking features in time-dependent scalar fields is an active field of research, where most approaches rely on the comparison of consecutive time steps. I created a more holistic visualization for time-varying scalar field topology by adapting Fuzzy Contour Trees to the time-dependent setting. Uncertain Trajectories in Vector Field Ensembles. Visitation maps are an intuitive and well-known visualization of uncertain trajectories in vector field ensembles. For large ensembles, visitation maps are not applicable, or only with extensive time requirements. I developed Visitation Graphs, a new representation and data reduction method for vector field ensembles that can be calculated in situ and is an optimal basis for the efficient generation of visitation maps. This is accomplished by bringing forward calculation times to the pre-processing. Visually Supported Anomaly Detection in Cyber Security. Numerous cyber attacks and the increasing complexity of networks and their protection necessitate the application of automated data analysis in cyber security. Due to uncertainty in automated anomaly detection, the results need to be communicated to analysts to ensure appropriate reactions. I introduce a visualization system combining device readings and anomaly detection results: the Security in Process System. To further support analysts I developed an application agnostic framework that supports the integration of knowledge assistance and applied it to the Security in Process System. I present this Knowledge Rocks Framework, its application and the results of evaluations for both, the original and the knowledge assisted Security in Process System. For all presented systems, I provide implementation details, illustrations and applications

Saliency Prediction in the Data Visualization Design Process

L'abstract è presente nell'allegato / the abstract is in the attachmen

ICSEA 2022: the seventeenth international conference on software engineering advances

The Seventeenth International Conference on Software Engineering Advances (ICSEA 2022), held between October 16th and October 20th, 2022, continued a series of events covering a broad spectrum of software-related topics. The conference covered fundamentals on designing, implementing, testing, validating and maintaining various kinds of software. Several tracks were proposed to treat the topics from theory to practice, in terms of methodologies, design, implementation, testing, use cases, tools, and lessons learned. The conference topics covered classical and advanced methodologies, open source, agile software, as well as software deployment and software economics and education. Other advanced aspects are related to on-time practical aspects, such as run-time vulnerability checking, rejuvenation process, updates partial or temporary feature deprecation, software deployment and configuration, and on-line software updates. These aspects trigger implications related to patenting, licensing, engineering education, new ways for software adoption and improvement, and ultimately, to software knowledge management. There are many advanced applications requiring robust, safe, and secure software: disaster recovery applications, vehicular systems, biomedical-related software, biometrics related software, mission critical software, E-health related software, crisis-situation software. These applications require appropriate software engineering techniques, metrics and formalisms, such as, software reuse, appropriate software quality metrics, composition and integration, consistency checking, model checking, provers and reasoning. The nature of research in software varies slightly with the specific discipline researchers work in, yet there is much common ground and room for a sharing of best practice, frameworks, tools, languages and methodologies. Despite the number of experts we have available, little work is done at the meta level, that is examining how we go about our research, and how this process can be improved. There are questions related to the choice of programming language, IDEs and documentation styles and standard. Reuse can be of great benefit to research projects yet reuse of prior research projects introduces special problems that need to be mitigated. The research environment is a mix of creativity and systematic approach which leads to a creative tension that needs to be managed or at least monitored. Much of the coding in any university is undertaken by research students or young researchers. Issues of skills training, development and quality control can have significant effects on an entire department. In an industrial research setting, the environment is not quite that of industry as a whole, nor does it follow the pattern set by the university. The unique approaches and issues of industrial research may hold lessons for researchers in other domains. We take here the opportunity to warmly thank all the members of the ICSEA 2022 technical program committee, as well as all the reviewers. The creation of such a high-quality conference program would not have been possible without their involvement. We also kindly thank all the authors who dedicated much of their time and effort to contribute to ICSEA 2022. We truly believe that, thanks to all these efforts, the final conference program consisted of top-quality contributions. We also thank the members of the ICSEA 2022 organizing committee for their help in handling the logistics of this event. We hope that ICSEA 2022 was a successful international forum for the exchange of ideas and results between academia and industry and for the promotion of progress in software engineering advances

Application des cartes combinatoires à la modélisation géométrique et sémantique des bâtiments

3D building models are widely used in the civil engineering industry. While the models are needed by several applications, such as architectural representations and simulation processes, they often lack of information that are of major importance for the consistency of the calculations. The original models are then often rebuilt in the way that fits better to the intended applications. To overcome this drawback, we introduce a framework allowing to enrich a 3D model of a building presenting just a geometry, in a way more interoperable model, by adding to it topological and semantic information. A cellular subdivision of the building space is first performed relying on its geometry, then the topological relationships between the cells are explicitely defined. Semantic labels are then attributed to the identified components based on the topology and defined heuristic rules. A 3D combinatorial map data structure (3-map) is used to handle the reconstructed information. From the enriched model we show how to extract applications-driven information allowing to perform acoustic simulation and indoor ray tracing navigation. The approach stands as a bridge between the modeling approaches and the applications in building analysis using the model. It is fully automatic and present interesting results on several types of building modelsLes modèles 3D de bâtiment sont largement utilisés dans l'industrie de la construction et sont nécessités par plusieurs applications telles que la représentation architecturale et les processus de simulation. Malheureusement, ces modèles manquent souvent d'informations d'une importance majeure pour permettre d'effectuer des opérations d'analyse et de calcul. Les modèles originaux sont alors souvent reconstruits par les différents acteurs qui les utilisent afin de les rendre plus adaptés à leur besoins. Dans le but de pallier ce problème, nous introduisons une approche permettant d'enrichir un modèle 3D de bâtiment et le rendre beaucoup plus interopérable. À partir de l'information géométrique seulement, nous rajoutons au modèle des informations topologiques et sémantiques. Une subdivision cellulaire de l'espace occupé par le bâtiment est d'abord effectuée en se basant sur sa géométrie, puis les relations topologiques entre les cellules sont reconstruites et explicitement définies. Des étiquettes sémantiques sont ensuite attribuées aux composants identifiés du bâtiment à l'aide de la topologie reconstruite et des règles heuristiques prédéfinies. Une structure de données topologique appelée carte combinatoire 3D (3-carte) est utilisée comme une base solide pour la mise au point des opération de reconstruction et le traitement des informations reconstruites. À partir du modèle enrichi, nous montrons comment extraire des données pour des applications dédiées, par exemple la simulation acoustique et lancer de rayon pour la navigation intérieure. Notre méthode se présente comme un pont entre les approches de modélisation et les applications d'analyse du bâtiment qui utilisent ces modèles. Il est entièrement automatique et présente des résultats intéressants sur plusieurs types de modèle