Knowledge-rich Image Gist Understanding Beyond Literal Meaning

We investigate the problem of understanding the message (gist) conveyed by images and their captions as found, for instance, on websites or news articles. To this end, we propose a methodology to capture the meaning of image-caption pairs on the basis of large amounts of machine-readable knowledge that has previously been shown to be highly effective for text understanding. Our method identifies the connotation of objects beyond their denotation: where most approaches to image understanding focus on the denotation of objects, i.e., their literal meaning, our work addresses the identification of connotations, i.e., iconic meanings of objects, to understand the message of images. We view image understanding as the task of representing an image-caption pair on the basis of a wide-coverage vocabulary of concepts such as the one provided by Wikipedia, and cast gist detection as a concept-ranking problem with image-caption pairs as queries. To enable a thorough investigation of the problem of gist understanding, we produce a gold standard of over 300 image-caption pairs and over 8,000 gist annotations covering a wide variety of topics at different levels of abstraction. We use this dataset to experimentally benchmark the contribution of signals from heterogeneous sources, namely image and text. The best result with a Mean Average Precision (MAP) of 0.69 indicate that by combining both dimensions we are able to better understand the meaning of our image-caption pairs than when using language or vision information alone. We test the robustness of our gist detection approach when receiving automatically generated input, i.e., using automatically generated image tags or generated captions, and prove the feasibility of an end-to-end automated process

Knowledge hubs and knowledge clusters: Designing a knowledge architecture for development.

With globalisation and knowledge-based production, firms may cooperate on a global scale, outsource parts of their administrative or productive units and negate location altogether. The extremely low transaction costs of data, information and knowledge seem to invalidate the theory of agglomeration and the spatial clustering of firms, going back to the classical work by Alfred Weber (1868-1958) and Alfred Marshall (1842-1924), who emphasized the microeconomic benefits of industrial collocation. This paper will argue against this view and show why the growth of knowledge societies will rather increase than decrease the relevance of location by creating knowledge clusters and knowledge hubs. A knowledge cluster is a local innovation system organized around universities, research institutions and firms which successfully drive innovations and create new industries. Knowledge hubs are localities with high internal and external networking and knowledge sharing capabilities. Both form a new knowledge architecture within an epistemic landscape of knowledge creation and dissemination, structured by knowledge gaps and areas of low knowledge intensity. The paper will focus on the internal dynamics of knowledge clusters and knowledge hubs and show why clustering takes place despite globalisation and the rapid growth of ICT. The basic argument that firms and their delivery chains attempt to reduce transport (transaction) costs by choosing the same location is still valid for most industrial economies, but knowledge hubs have different dynamics relating to externalities produced from knowledge sharing and research and development outputs. The paper draws on empirical data derived from ongoing research in the Lee Kong Chian School of Business, Singapore Management University and in the Center for Development Research (ZEF), University of Bonn, supported by the German Aeronautics and Space Agency (DLR).

Knowledge hubs and knowledge clusters: Designing a knowledge architecture for development.

With globalisation and knowledge-based production, firms may cooperate on a global scale, outsource parts of their administrative or productive units and negate location altogether. The extremely low transaction costs of data, information and knowledge seem to invalidate the theory of agglomeration and the spatial clustering of firms, going back to the classical work by Alfred Weber (1868-1958) and Alfred Marshall (1842-1924), who emphasized the microeconomic benefits of industrial collocation. This paper will argue against this view and show why the growth of knowledge societies will rather increase than decrease the relevance of location by creating knowledge clusters and knowledge hubs. A knowledge cluster is a local innovation system organized around universities, research institutions and firms which successfully drive innovations and create new industries. Knowledge hubs are localities with high internal and external networking and knowledge sharing capabilities. Both form a new knowledge architecture within an epistemic landscape of knowledge creation and dissemination, structured by knowledge gaps and areas of low knowledge intensity. The paper will focus on the internal dynamics of knowledge clusters and knowledge hubs and show why clustering takes place despite globalisation and the rapid growth of ICT. The basic argument that firms and their delivery chains attempt to reduce transport (transaction) costs by choosing the same location is still valid for most industrial economies, but knowledge hubs have different dynamics relating to externalities produced from knowledge sharing and research and development outputs. The paper draws on empirical data derived from ongoing research in the Lee Kong Chian School of Business, Singapore Management University and in the Center for Development Research (ZEF), University of Bonn, supported by the German Aeronautics and Space Agency (DLR).knowledge; knowledge and development; industrial agglomeration; knowledge hub; cluster; space

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Extracting Computational Representations of Place with Social Sensing

Place-based GIS are at the forefront of GIScience research and characterized by textual descriptions, human conceptualizations as well as the spatial-semantic relationships among places. The concepts of places are difficult to handle in geographic information science and systems because of their intrinsic vagueness. They arise from the complex interaction of individuals, society, and the environment. The exact delineation of vague regions is challenging as their borders are vague and the membership within a region varies non-monotonically and as a function of context. Consequently, vague regions are difficult to handle computationally, e.g., in spatial analysis, cartography, geographic information retrieval, and GIS workflows in general. The emergence of big data brings new opportunities for us to understand the place semantics from large-scale volunteered geographic information and data streams, such as geotags, texts, activity streams, and GPS trajectories. The term "social sensing" describes such individual-level big geospatial data and the associated analysis methods. In this dissertation, I present a generalizable, data-driven framework that complements classical top-down approaches by extracting the representations of vague cognitive regions and function regions from bottom-up approaches using spatial statistics and machine learning techniques with various social sensing sources. I demonstrate how to derive crisp boundaries for cognitive and functional regions from points of interest data, and show how natural language processing techniques can enrich our understanding of places and form a foundation for the semantic characterization of place types and the generalization of regions. This work makes contributions to the development of computational methodologies for extracting vague cognitive regions and functional regions using data-driven approaches as well as the novel semantic generalization processing technique