3,440 research outputs found
Recommended from our members
Knowledge Cartography: Software tools and mapping techniques
Knowledge Cartography is the discipline of mapping intellectual landscapes.The focus of this book is on the process by which manually crafting interactive, hypertextual maps clarifies oneâs own understanding, as well as communicating it.The authors see mapping software as a set of visual tools for reading and writing in a networked age. In an information ocean, the primary challenge is to find meaningful patterns around which we can weave plausible narratives. Maps of concepts, discussions and arguments make the connections between ideas tangible and disputable.
With 17 chapters from the leading researchers and practitioners, the reader will find the current stateâof-the-art in the field. Part 1 focuses on educational applications in schools and universities, before Part 2 turns to applications in professional communitie
Context-Specific Preference Learning of One Dimensional Quantitative Geospatial Attributes Using a Neuro-Fuzzy Approach
Change detection is a topic of great importance for modern geospatial information systems. Digital aerial imagery provides an excellent medium to capture geospatial information. Rapidly evolving environments, and the availability of increasing amounts of diverse, multiresolutional imagery bring forward the need for frequent updates of these datasets. Analysis and query of spatial data using potentially outdated data may yield results that are sometimes invalid. Due to measurement errors (systematic, random) and incomplete knowledge of information (uncertainty) it is ambiguous if a change in a spatial dataset has really occurred. Therefore we need to develop reliable, fast, and automated procedures that will effectively report, based on information from a new image, if a change has actually occurred or this change is simply the result of uncertainty. This thesis introduces a novel methodology for change detection in spatial objects using aerial digital imagery. The uncertainty of the extraction is used as a quality estimate in order to determine whether change has occurred. For this goal, we develop a fuzzy-logic system to estimate uncertainty values fiom the results of automated object extraction using active contour models (a.k.a. snakes). The differential snakes change detection algorithm is an extension of traditional snakes that incorporates previous information (i.e., shape of object and uncertainty of extraction) as energy functionals. This process is followed by a procedure in which we examine the improvement of the uncertainty at the absence of change (versioning). Also, we introduce a post-extraction method for improving the object extraction accuracy. In addition to linear objects, in this thesis we extend differential snakes to track deformations of areal objects (e.g., lake flooding, oil spills). From the polygonal description of a spatial object we can track its trajectory and areal changes. Differential snakes can also be used as the basis for similarity indices for areal objects. These indices are based on areal moments that are invariant under general affine transformation. Experimental results of the differential snakes change detection algorithm demonstrate their performance. More specifically, we show that the differential snakes minimize the false positives in change detection and track reliably object deformations
A Robust Information Source Estimator with Sparse Observations
In this paper, we consider the problem of locating the information source
with sparse observations. We assume that a piece of information spreads in a
network following a heterogeneous susceptible-infected-recovered (SIR) model
and that a small subset of infected nodes are reported, from which we need to
find the source of the information. We adopt the sample path based estimator
developed in [1], and prove that on infinite trees, the sample path based
estimator is a Jordan infection center with respect to the set of observed
infected nodes. In other words, the sample path based estimator minimizes the
maximum distance to observed infected nodes. We further prove that the distance
between the estimator and the actual source is upper bounded by a constant
independent of the number of infected nodes with a high probability on infinite
trees. Our simulations on tree networks and real world networks show that the
sample path based estimator is closer to the actual source than several other
algorithms
Exploiting data semantics to discover, extract, and model web sources
We describe DEIMOS, a system that automatically discovers and models new sources of information. The system exploits four core technologies developed by our group that makes an end-to-end solution to this problem possible. First, given an example source, DEIMOS finds other similar sources online. Second, it invokes and extracts data from these sources. Third, given the syntactic structure of a source, DEIMOS maps its inputs and outputs to semantic types. Finally, it infers the sourceâs semantic definition, i.e., the function that maps the inputs to the outputs. DEIMOS is able to successfully automate these steps by exploiting a combination of background knowledge and data semantics. We describe the challenges in integrating separate components into a unified approach to discovering, extracting and modeling new online sources. We provide an end-toend validation of the system in two information domains to show that it can successfully discover and model new data sources in those domains. 1
Data Driven Inference in Populations of Agents
abstract: In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive.  More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand the different types of reasonings which can lead to better data-driven inference. Â
This dissertation has laid the groundwork for studying these relationships and applying them to three real-world problems. In criminal modeling, inductive and deductive reasonings are applied to early prediction of violent criminal gang members. To address this problem the features derived from the co-arrestee social network as well as geographical and temporal features are leveraged. Then, a data-driven variant of geospatial abductive inference is studied in missing person problem to locate the missing person. Finally, induction and abduction reasonings are studied for identifying pathogenic accounts of a cascade in social networks.Dissertation/ThesisDoctoral Dissertation Computer Science 201
- âŠ