Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications

We present an overview and evaluation of a new, systematic approach for generation of highly realistic, annotated synthetic data for training of deep neural networks in computer vision tasks. The main contribution is a procedural world modeling approach enabling high variability coupled with physically accurate image synthesis, and is a departure from the hand-modeled virtual worlds and approximate image synthesis methods used in real-time applications. The benefits of our approach include flexible, physically accurate and scalable image synthesis, implicit wide coverage of classes and features, and complete data introspection for annotations, which all contribute to quality and cost efficiency. To evaluate our approach and the efficacy of the resulting data, we use semantic segmentation for autonomous vehicles and robotic navigation as the main application, and we train multiple deep learning architectures using synthetic data with and without fine tuning on organic (i.e. real-world) data. The evaluation shows that our approach improves the neural network's performance and that even modest implementation efforts produce state-of-the-art results.Comment: The project web page at http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the paper with high-resolution images as well as additional materia

Leveraging Semantic Web Technologies for Managing Resources in a Multi-Domain Infrastructure-as-a-Service Environment

This paper reports on experience with using semantically-enabled network resource models to construct an operational multi-domain networked infrastructure-as-a-service (NIaaS) testbed called ExoGENI, recently funded through NSF's GENI project. A defining property of NIaaS is the deep integration of network provisioning functions alongside the more common storage and computation provisioning functions. Resource provider topologies and user requests can be described using network resource models with common base classes for fundamental cyber-resources (links, nodes, interfaces) specialized via virtualization and adaptations between networking layers to specific technologies. This problem space gives rise to a number of application areas where semantic web technologies become highly useful - common information models and resource class hierarchies simplify resource descriptions from multiple providers, pathfinding and topology embedding algorithms rely on query abstractions as building blocks. The paper describes how the semantic resource description models enable ExoGENI to autonomously instantiate on-demand virtual topologies of virtual machines provisioned from cloud providers and are linked by on-demand virtual connections acquired from multiple autonomous network providers to serve a variety of applications ranging from distributed system experiments to high-performance computing

Learning from AI : new trends in database technology

Recently some researchers in the areas of database data modelling and knowledge representations in artificial intelligence have recognized that they share many common goals. In this survey paper we show the relationship between database and artificial intelligence research. We show that there has been a tendency for data models to incorporate more modelling techniques developed for knowledge representations in artificial intelligence as the desire to incorporate more application oriented semantics, user friendliness, and flexibility has increased. Increasing the semantics of the representation is the key to capturing the "reality" of the database environment, increasing user friendliness, and facilitating the support of multiple, possibly conflicting, user views of the information contained in a database

Didactic Networks: A proposal for e-learning content generation

The Didactic Networks proposed in this paper are based on previous publications in the field of the RSR (Rhetorical-Semantic Relations). The RSR is a set of primitive relations used for building a specific kind of semantic networks for artificial intelligence applications on the web: the RSN (Rhetorical-Semantic Networks). We bring into focus the RSR application in the field of elearning, by defining Didactic Networks as a new set of semantic patterns oriented to the development of eleaming applications. The different lines we offer in our research Jail mainly into three levels: • The most basic one is in the field of computational linguistics and related to Logical Operations on RSR (RSR Inverses and plurals. RSR combinations, etc), once they have been created. The application of Walter Bosma 's results regarding rhetorical distance application and treatment as semantic weighted networks is one of the important issues here. • In parallel, we have been working on the creation of a knowledge representation and storage model and data architecture capable of supporting the definition of knowledge networks based on RSR. • The third strategic line is in the meso-level, the formulation of a molecular structure of knowledge based on the most frequently used patterns. The main contribution at this level is the set of Fundamental Cognitive Networks (FCN) as an application of Novak's mental maps proposal. This paper is part of this third intermediate level, and the Fundamental Didactic Networks (FDN) are the result of the application of rhetorical theoiy procedures to the instructional theory. We have formulated a general set of RSR capable of building discourse, making it possible to express any concept, procedure or principle in terms of knowledge nodes and RSRs. The instructional knowledge can then be elaborated in the same way. This network structure expressing the instructional knowledge in terms of RSR makes the objective of developing web-learning lessons semi-automutkally possible, as well as any other type of utilities oriented towards the exploitation of semantic structure, such as the automatic question answering systems