Intrusion Detection With Unsupervised Techniques for Network Management Protocols Over Smart Grids

[Abstract] The present research work focuses on overcoming cybersecurity problems in the Smart Grid. Smart Grids must have feasible data capture and communications infrastructure to be able to manage the huge amounts of data coming from sensors. To ensure the proper operation of next-generation electricity grids, the captured data must be reliable and protected against vulnerabilities and possible attacks. The contribution of this paper to the state of the art lies in the identification of cyberattacks that produce anomalous behaviour in network management protocols. A novel neural projectionist technique (Beta Hebbian Learning, BHL) has been employed to get a general visual representation of the traffic of a network, making it possible to identify any abnormal behaviours and patterns, indicative of a cyberattack. This novel approach has been validated on 3 different datasets, demonstrating the ability of BHL to detect different types of attacks, more effectively than other state-of-the-art methods

Spatial CPU-GPU data structures for interactive rendering of large particle data

In this work, I investigate the interactive visualization of arbitrarily large particle data sets which ft into system memory, but not into GPU memory. With conventional rendering techniques, interactivity of visualizations is drastically reduced when rendering tens- or hundreds of millions of objects. At the same time, graphics hardware memory capabilities limit the size of data sets which can be placed in GPU memory for rendering. To circumvent these obstacles, a progressive rendering approach is employed, which gradually streams and renders all particle data to the GPU without reducing or altering the particle data itself. The particle data is rendered according to a visibility sorting derived from occlusion relations between different parts of the data set, leading to a rendering order of scene contents guided by importance for the rendered image. I analyze and compare possible implementation choices for rendering particles as opaque spheres in OpenGL, which forms the basis of the particle rendering application developed within this work. The application utilizes a multi-threaded architecture, where data preprocessing on a CPU-thread and a rendering algorithm on a GPU-thread ensure that the user can interact with the application at any time. In particular it is guaranteed that the user can explore the particle data interactively, by ensuring minimal latency from user input to seeing the effects of that input. This is achieved by favoring user inputs over completeness of the rendered image at all stages during rendering. At the same time the user is provided with an immediate feedback about interactions by re-projecting all currently visible particles to the next rendered image. The re-projection is realized with an on-GPU particle-cache of visible particles that is built during particle data streaming and rendering, and drawn upon user interaction using the most recent camera confguration according to user inputs. The combination of the developed techniques allows interactive exploration of particle data sets with up to 1.5 billion particles on a commodity computer.In dieser Arbeit wird die interaktive Visualisierung beliebig großer Partikeldaten untersucht, wobei die Partikeldaten im Arbeitsspeicher hinterlegt sind, aber nicht zwangsläufig in den Grafikspeicher passen. Mit üblichen Rendering Methoden büßen Visualisierungen drastisch an Interaktivität ein, wenn mehrere zehn- bis hunderte Millionen Objekte dargestellt werden. Gleichzeitig ist die Größe möglicher zu visualisierender Datensätze begrenzt durch den Videospeicher von Grafikkarten, auf dem zu visualisierende Daten vorliegen müssen. Um diese Einschränkungen zu umgehen, wird in dieser Arbeit ein progressiver Rendering Ansatz verfolgt, der sukzessive alle Partikeldaten zur Grafikkarte hochlädt und rendert, ohne die Partikeldaten zu reduzieren oder anderweitig zu verändern. Die Partikeldaten werden entsprechend einer vorgenommenen Sichtbarkeitssortierung gerendert, die aus gegenseitigen Verdeckungen verschiedener Teile des Partikeldatensatzes berechnet wird. Dies führt dazu, dass Teile der Szene nach ihrer Wichtigkeit für das aktuelle Bild sortiert und dargestellt werden. Es werden verschiedene Möglichkeiten analysiert und verglichen, Partikel als opake Kugeln in OpenGL zu rendern. Dies formt die Grundlage für die Partikel-Rendering Software, die in dieser Arbeit entwickelt wurde. Die Architektur der Rendering-Software benutzt mehrere Threads, sodass durch eine Daten-Vorverarbeitung auf einem CPUThread und durch Rendering-Algorithmen auf einem GPU-Thread sichergestellt ist, dass der Benutzer mit der Software jederzeit interagieren kann. Insbesondere ist sichergestellt, dass der Benutzer die Partikeldaten interaktiv untersuchen kann, indem die Latenz zwischen Benutzereingaben und dem Anzeigen der daraus resultierenden Veränderungen minimal gehalten wird. Dies wird erreicht indem der Verarbeitung von Benutzereingaben an allen Stellen des Rendering-Prozesses höhere Priorität eingeräumt wird als der Vollständigkeit des gerenderten Bildes. Gleichzeitig wird dem Benutzer eine sofortige Rückmeldung über getätigte Benutzereingaben gegeben, indem alle sichtbaren Partikel in das nächste gerenderte Bild neu projeziert werden. Diese Neu-Projektion wird durch einen GPU-seitigen Partikel-Cache aller aktuell sichtbaren Partikel realisiert, der während des sukzessiven Partikelstreamings und -renderns aufgebaut wird. Sobald der Benutzer eine Eingabe tätigt, wird der auf der GPU liegende Partikel-Cache unter der aktuellsten benutzerdefinierten Kameraposition neu gerendert. Die Kombination dieser entwickelten Methoden erlaubt ein interaktives Betrachten von Partikeldaten mit bis zu 1,5 Milliarden Partikeln auf einem handelsüblichen Computer

Close and Distant Reading Visualizations for the Comparative Analysis of Digital Humanities Data

Traditionally, humanities scholars carrying out research on a specific or on multiple literary work(s) are interested in the analysis of related texts or text passages. But the digital age has opened possibilities for scholars to enhance their traditional workflows. Enabled by digitization projects, humanities scholars can nowadays reach a large number of digitized texts through web portals such as Google Books or Internet Archive. Digital editions exist also for ancient texts; notable examples are PHI Latin Texts and the Perseus Digital Library. This shift from reading a single book “on paper” to the possibility of browsing many digital texts is one of the origins and principal pillars of the digital humanities domain, which helps developing solutions to handle vast amounts of cultural heritage data – text being the main data type. In contrast to the traditional methods, the digital humanities allow to pose new research questions on cultural heritage datasets. Some of these questions can be answered with existent algorithms and tools provided by the computer science domain, but for other humanities questions scholars need to formulate new methods in collaboration with computer scientists. Developed in the late 1980s, the digital humanities primarily focused on designing standards to represent cultural heritage data such as the Text Encoding Initiative (TEI) for texts, and to aggregate, digitize and deliver data. In the last years, visualization techniques have gained more and more importance when it comes to analyzing data. For example, Saito introduced her 2010 digital humanities conference paper with: “In recent years, people have tended to be overwhelmed by a vast amount of information in various contexts. Therefore, arguments about ’Information Visualization’ as a method to make information easy to comprehend are more than understandable.” A major impulse for this trend was given by Franco Moretti. In 2005, he published the book “Graphs, Maps, Trees”, in which he proposes so-called distant reading approaches for textual data that steer the traditional way of approaching literature towards a completely new direction. Instead of reading texts in the traditional way – so-called close reading –, he invites to count, to graph and to map them. In other words, to visualize them. This dissertation presents novel close and distant reading visualization techniques for hitherto unsolved problems. Appropriate visualization techniques have been applied to support basic tasks, e.g., visualizing geospatial metadata to analyze the geographical distribution of cultural heritage data items or using tag clouds to illustrate textual statistics of a historical corpus. In contrast, this dissertation focuses on developing information visualization and visual analytics methods that support investigating research questions that require the comparative analysis of various digital humanities datasets. We first take a look at the state-of-the-art of existing close and distant reading visualizations that have been developed to support humanities scholars working with literary texts. We thereby provide a taxonomy of visualization methods applied to show various aspects of the underlying digital humanities data. We point out open challenges and we present our visualizations designed to support humanities scholars in comparatively analyzing historical datasets. In short, we present (1) GeoTemCo for the comparative visualization of geospatial-temporal data, (2) the two tag cloud designs TagPies and TagSpheres that comparatively visualize faceted textual summaries, (3) TextReuseGrid and TextReuseBrowser to explore re-used text passages among the texts of a corpus, (4) TRAViz for the visualization of textual variation between multiple text editions, and (5) the visual analytics system MusikerProfiling to detect similar musicians to a given musician of interest. Finally, we summarize our and the collaboration experiences of other visualization researchers to emphasize the ingredients required for a successful project in the digital humanities, and we take a look at future challenges in that research field

Cultural Heritage Storytelling, Engagement and Management in the Era of Big Data and the Semantic Web

The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production–consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today’s ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities – smart cities;Smart systems for cultural heritage sustainability

Reinventing the Social Scientist and Humanist in the Era of Big Data

This book explores the big data evolution by interrogating the notion that big data is a disruptive innovation that appears to be challenging existing epistemologies in the humanities and social sciences. Exploring various (controversial) facets of big data such as ethics, data power, and data justice, the book attempts to clarify the trajectory of the epistemology of (big) data-driven science in the humanities and social sciences

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI