Fireground location understanding by semantic linking of visual objects and building information models

This paper presents an outline for improved localization and situational awareness in fire emergency situations based on semantic technology and computer vision techniques. The novelty of our methodology lies in the semantic linking of video object recognition results from visual and thermal cameras with Building Information Models (BIM). The current limitations and possibilities of certain building information streams in the context of fire safety or fire incident management are addressed in this paper. Furthermore, our data management tools match higher-level semantic metadata descriptors of BIM and deep-learning based visual object recognition and classification networks. Based on these matches, estimations can be generated of camera, objects and event positions in the BIM model, transforming it from a static source of information into a rich, dynamic data provider. Previous work has already investigated the possibilities to link BIM and low-cost point sensors for fireground understanding, but these approaches did not take into account the benefits of video analysis and recent developments in semantics and feature learning research. Finally, the strengths of the proposed approach compared to the state-of-the-art is its (semi -)automatic workflow, generic and modular setup and multi-modal strategy, which allows to automatically create situational awareness, to improve localization and to facilitate the overall fire understanding

A Survey of Requirements Engineering Methods for Pervasive Services

Designing and deploying ubiquitous computing systems, such as those delivering large-scale mobile services, still requires large-scale investments in both development effort as well as infrastructure costs. Therefore, in order to develop the right system, the design process merits a thorough investigation of the wishes of the foreseen user base. Such investigations are studied in the area of requirements engineering (RE). In this report, we describe and compare three requirements engineering methods that belong to one specific form of RE, namely Goal-Oriented Requirements Engineering. By mapping these methods to a common framework, we assess their applicability in the field of ubiquitous computing systems

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

Learning to like or dislike : revealing similarities and differences between evaluative learning effects

Researchers study phenomena such as the mere-exposure effect, evaluative conditioning, and persuasion to learn more about the ways in which likes and dislikes can be formed and changed. Often, these phenomena are studied in isolation. Here, we review and integrate conceptual analyses that highlight ways to relate these different phenomena and that reveal new avenues for research on evaluative learning. At the core of these analyses lies the idea that evaluative learning can be defined as changes in liking that are due to regularities in the environment. We discuss how this definition allows one to distinguish different types of evaluative learning on the basis of the nature of regularities (e.g., in the presence of one stimulus vs. in the presence of two stimuli) and the function of regularities (i.e., symbolic vs. nonsymbolic)

Integration of Ontological Scene Representation and Logic-Based Reasoning for Context-Aware Driver Assistance Systems

Co-operative driver assistance systems share information about their surrounding with each other, thus enhancing their knowledge and their performance. For successful information exchange and interpretation, a common domain understanding is needed. This paper first presents an ontology-based context-model for driving scene description, including next to spatio-temporal components also additional context information like traffic signs, state of the driver and the own-vehicle. For traffic rules, we integrate the ontological scene description with a logic programming environment, to enable complex and powerful reasoning on the given information. The proposed ontology is discussed with respect to a set of validation criteria. For integration with logic programming a prototypical development of an overtaking assistant is shown to demonstrate the feasibility of the approach