Intelligent Robotic Perception Systems

Robotic perception is related to many applications in robotics where sensory data and artificial intelligence/machine learning (AI/ML) techniques are involved. Examples of such applications are object detection, environment representation, scene understanding, human/pedestrian detection, activity recognition, semantic place classification, object modeling, among others. Robotic perception, in the scope of this chapter, encompasses the ML algorithms and techniques that empower robots to learn from sensory data and, based on learned models, to react and take decisions accordingly. The recent developments in machine learning, namely deep-learning approaches, are evident and, consequently, robotic perception systems are evolving in a way that new applications and tasks are becoming a reality. Recent advances in human-robot interaction, complex robotic tasks, intelligent reasoning, and decision-making are, at some extent, the results of the notorious evolution and success of ML algorithms. This chapter will cover recent and emerging topics and use-cases related to intelligent perception systems in robotics

Graph Mining for Cybersecurity: A Survey

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. In recent years, with the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance. It is imperative to summarize existing graph-based cybersecurity solutions to provide a guide for future studies. Therefore, as a key contribution of this paper, we provide a comprehensive review of graph mining for cybersecurity, including an overview of cybersecurity tasks, the typical graph mining techniques, and the general process of applying them to cybersecurity, as well as various solutions for different cybersecurity tasks. For each task, we probe into relevant methods and highlight the graph types, graph approaches, and task levels in their modeling. Furthermore, we collect open datasets and toolkits for graph-based cybersecurity. Finally, we outlook the potential directions of this field for future research

Towards accurate multi-person pose estimation in the wild

In this thesis we are concerned with the problem of articulated human pose estimation and pose tracking in images and video sequences. Human pose estimation is a task of localising major joints of a human skeleton in natural images and is one of the most important visual recognition tasks in the scenes containing humans with numerous applications in robotics, virtual and augmented reality, gaming and healthcare among others. Articulated human pose tracking requires tracking multiple persons in the video sequence while simultaneously estimating full body poses. This task is important for analysing surveillance footage, activity recognition, sports analytics, etc. Most of the prior work focused on the pose estimation of single pre-localised humans whereas here we address a case with multiple people in real world images which entails several challenges such as person-person overlaps in highly crowded scenes, unknown number of people or people entering and leaving video sequences. The first contribution is a multi-person pose estimation algorithm based on the bottom-up detection-by-grouping paradigm. Unlike the widespread top-down approaches our method detects body joints and pairwise relations between them in a single forward pass of a convolutional neural network. Multi-person parsing is performed by optimizing a joint objective based on a multicut graph partitioning framework. Secondly, we extend our pose estimation approach to articulated multi-person pose tracking in videos. Our approach performs multi-target tracking and pose estimation in a holistic manner by optimising a single objective. We further simplify and refine the formulation which allows us to reach close to the real-time performance. Thirdly, we propose a large scale dataset and a benchmark for articulated multi-person tracking. It is the first dataset of video sequences comprising complex multi-person scenes and fully annotated tracks with 2D keypoints. Our fourth contribution is a method for estimating 3D body pose using on-body wearable cameras. Our approach uses a pair of downward facing, head-mounted cameras and captures an entire body. This egocentric approach is free of limitations of traditional setups with external cameras and can estimate body poses in very crowded environments. Our final contribution goes beyond human pose estimation and is in the field of deep learning of 3D object shapes. In particular, we address the case of reconstructing 3D objects from weak supervision. Our approach represents objects as 3D point clouds and is able to learn them with 2D supervision only and without requiring camera pose information at training time. We design a differentiable renderer of point clouds as well as a novel loss formulation for dealing with camera pose ambiguity.In dieser Arbeit behandeln wir das Problem der Schätzung und Verfolgung artikulierter menschlicher Posen in Bildern und Video-Sequenzen. Die Schätzung menschlicher Posen besteht darin die Hauptgelenke des menschlichen Skeletts in natürlichen Bildern zu lokalisieren und ist eine der wichtigsten Aufgaben der visuellen Erkennung in Szenen, die Menschen beinhalten. Sie hat zahlreiche Anwendungen in der Robotik, virtueller und erweiterter Realität, in Videospielen, in der Medizin und weiteren Bereichen. Die Verfolgung artikulierter menschlicher Posen erfordert die Verfolgung mehrerer Personen in einer Videosequenz bei gleichzeitiger Schätzung vollständiger Körperhaltungen. Diese Aufgabe ist besonders wichtig für die Analyse von Video-Überwachungsaufnahmen, Aktivitätenerkennung, digitale Sportanalyse etc. Die meisten vorherigen Arbeiten sind auf die Schätzung einzelner Posen vorlokalisierter Menschen fokussiert, wohingegen wir den Fall mehrerer Personen in natürlichen Aufnahmen betrachten. Dies bringt einige Herausforderungen mit sich, wie die Überlappung verschiedener Personen in dicht gedrängten Szenen, eine unbekannte Anzahl an Personen oder Personen die das Sichtfeld der Video-Sequenz verlassen oder betreten. Der erste Beitrag ist ein Algorithmus zur Schätzung der Posen mehrerer Personen, welcher auf dem Paradigma der Erkennung durch Gruppierung aufbaut. Im Gegensatz zu den verbreiteten Verfeinerungs-Ansätzen erkennt unsere Methode Körpergelenke and paarweise Beziehungen zwischen ihnen in einer einzelnen Vorwärtsrechnung eines faltenden neuronalen Netzwerkes. Die Gliederung in mehrere Personen erfolgt durch Optimierung einer gemeinsamen Zielfunktion, die auf dem Mehrfachschnitt-Problem in der Graphenzerlegung basiert. Zweitens erweitern wir unseren Ansatz zur Posen-Bestimmung auf das Verfolgen mehrerer Personen und deren Artikulation in Videos. Unser Ansatz führt eine Verfolgung mehrerer Ziele und die Schätzung der zugehörigen Posen in ganzheitlicher Weise durch, indem eine einzelne Zielfunktion optimiert wird. Desweiteren vereinfachen und verfeinern wir die Formulierung, was unsere Methode nah an Echtzeit-Leistung bringt. Drittens schlagen wir einen großen Datensatz und einen Bewertungsmaßstab für die Verfolgung mehrerer artikulierter Personen vor. Dies ist der erste Datensatz der Video-Sequenzen von komplexen Szenen mit mehreren Personen beinhaltet und deren Spuren komplett mit zwei-dimensionalen Markierungen der Schlüsselpunkte versehen sind. Unser vierter Beitrag ist eine Methode zur Schätzung von drei-dimensionalen Körperhaltungen mittels am Körper tragbarer Kameras. Unser Ansatz verwendet ein Paar nach unten gerichteter, am Kopf befestigter Kameras und erfasst den gesamten Körper. Dieser egozentrische Ansatz ist frei von jeglichen Limitierungen traditioneller Konfigurationen mit externen Kameras und kann Körperhaltungen in sehr dicht gedrängten Umgebungen bestimmen. Unser letzter Beitrag geht über die Schätzung menschlicher Posen hinaus in den Bereich des tiefen Lernens der Gestalt von drei-dimensionalen Objekten. Insbesondere befassen wir uns mit dem Fall drei-dimensionale Objekte unter schwacher Überwachung zu rekonstruieren. Unser Ansatz repräsentiert Objekte als drei-dimensionale Punktwolken and ist im Stande diese nur mittels zwei-dimensionaler Überwachung und ohne Informationen über die Kamera-Ausrichtung zur Trainingszeit zu lernen. Wir entwerfen einen differenzierbaren Renderer für Punktwolken sowie eine neue Formulierung um mit uneindeutigen Kamera-Ausrichtungen umzugehen

Exploring the topical structure of short text through probability models : from tasks to fundamentals

Recent technological advances have radically changed the way we communicate. Today’s communication has become ubiquitous and it has fostered the need for information that is easier to create, spread and consume. As a consequence, we have experienced the shortening of text messages in mediums ranging from electronic mailing, instant messaging to microblogging. Moreover, the ubiquity and fast-paced nature of these mediums have promoted their use for unthinkable tasks. For instance, reporting real-world events was classically carried out by news reporters, but, nowadays, most interesting events are first disclosed on social networks like Twitter by eyewitness through short text messages. As a result, the exploitation of the thematic content in short text has captured the interest of both research and industry. Topic models are a type of probability models that have traditionally been used to explore this thematic content, a.k.a. topics, in regular text. Most popular topic models fall into the sub-class of LVMs (Latent Variable Models), which include several latent variables at the corpus, document and word levels to summarise the topics at each level. However, classical LVM-based topic models struggle to learn semantically meaningful topics in short text because the lack of co-occurring words within a document hampers the estimation of the local latent variables at the document level. To overcome this limitation, pooling and hierarchical Bayesian strategies that leverage on contextual information have been essential to improve the quality of topics in short text. In this thesis, we study the problem of learning semantically meaningful and predictive representations of text in two distinct phases: • In the first phase, Part I, we investigate the use of LVM-based topic models for the specific task of event detection in Twitter. In this situation, the use of contextual information to pool tweets together comes naturally. Thus, we first extend an existing clustering algorithm for event detection to use the topics learned from pooled tweets. Then, we propose a probability model that integrates topic modelling and clustering to enable the flow of information between both components. • In the second phase, Part II and Part III, we challenge the use of local latent variables in LVMs, specially when the context of short messages is not available. First of all, we study the evaluation of the generalization capabilities of LVMs like PFA (Poisson Factor Analysis) and propose unbiased estimation methods to approximate it. With the most accurate method, we compare the generalization of chordal models without latent variables to that of PFA topic models in short and regular text collections. In summary, we demonstrate that by integrating clustering and topic modelling, the performance of event detection techniques in Twitter is improved due to the interaction between both components. Moreover, we develop several unbiased likelihood estimation methods for assessing the generalization of PFA and we empirically validate their accuracy in different document collections. Finally, we show that we can learn chordal models without latent variables in text through Chordalysis, and that they can be a competitive alternative to classical topic models, specially in short text.Els avenços tecnològics han canviat radicalment la forma que ens comuniquem. Avui en dia, la comunicació és ubiqua, la qual cosa fomenta l’ús de informació fàcil de crear, difondre i consumir. Com a resultat, hem experimentat l’escurçament dels missatges de text en diferents medis de comunicació, des del correu electrònic, a la missatgeria instantània, al microblogging. A més de la ubiqüitat, la naturalesa accelerada d’aquests medis ha promogut el seu ús per tasques fins ara inimaginables. Per exemple, el relat d’esdeveniments era clàssicament dut a terme per periodistes a peu de carrer, però, en l’actualitat, el successos més interessants es publiquen directament en xarxes socials com Twitter a través de missatges curts. Conseqüentment, l’explotació de la informació temàtica del text curt ha atret l'interès tant de la recerca com de la indústria. Els models temàtics (o topic models) són un tipus de models de probabilitat que tradicionalment s’han utilitzat per explotar la informació temàtica en documents de text. Els models més populars pertanyen al subgrup de models amb variables latents, els quals incorporen varies variables a nivell de corpus, document i paraula amb la finalitat de descriure el contingut temàtic a cada nivell. Tanmateix, aquests models tenen dificultats per aprendre la semàntica en documents curts degut a la manca de coocurrència en les paraules d’un mateix document, la qual cosa impedeix una correcta estimació de les variables locals. Per tal de solucionar aquesta limitació, l’agregació de missatges segons el context i l’ús d’estratègies jeràrquiques Bayesianes són essencials per millorar la qualitat dels temes apresos. En aquesta tesi, estudiem en dos fases el problema d’aprenentatge d’estructures semàntiques i predictives en documents de text: En la primera fase, Part I, investiguem l’ús de models temàtics amb variables latents per la detecció d’esdeveniments a Twitter. En aquest escenari, l’ús del context per agregar tweets sorgeix de forma natural. Per això, primer estenem un algorisme de clustering per detectar esdeveniments a partir dels temes apresos en els tweets agregats. I seguidament, proposem un nou model de probabilitat que integra el model temàtic i el de clustering per tal que la informació flueixi entre ambdós components. En la segona fase, Part II i Part III, qüestionem l’ús de variables latents locals en models per a text curt sense context. Primer de tot, estudiem com avaluar la capacitat de generalització d’un model amb variables latents com el PFA (Poisson Factor Analysis) a través del càlcul de la likelihood. Atès que aquest càlcul és computacionalment intractable, proposem diferents mètodes d estimació. Amb el mètode més acurat, comparem la generalització de models chordals sense variables latents amb la del models PFA, tant en text curt com estàndard. En resum, demostrem que integrant clustering i models temàtics, el rendiment de les tècniques de detecció d’esdeveniments a Twitter millora degut a la interacció entre ambdós components. A més a més, desenvolupem diferents mètodes d’estimació per avaluar la capacitat generalizadora dels models PFA i validem empíricament la seva exactitud en diverses col·leccions de text. Finalment, mostrem que podem aprendre models chordals sense variables latents en text a través de Chordalysis i que aquests models poden ser una bona alternativa als models temàtics clàssics, especialment en text curt.Postprint (published version

09491 Abstracts Collection -- Graph Search Engineering

From the 29th November to the 4th December 2009, the Dagstuhl Seminar 09491 ``Graph Search Engineering \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available