Geospatial Narratives and their Spatio-Temporal Dynamics: Commonsense Reasoning for High-level Analyses in Geographic Information Systems

The modelling, analysis, and visualisation of dynamic geospatial phenomena has been identified as a key developmental challenge for next-generation Geographic Information Systems (GIS). In this context, the envisaged paradigmatic extensions to contemporary foundational GIS technology raises fundamental questions concerning the ontological, formal representational, and (analytical) computational methods that would underlie their spatial information theoretic underpinnings. We present the conceptual overview and architecture for the development of high-level semantic and qualitative analytical capabilities for dynamic geospatial domains. Building on formal methods in the areas of commonsense reasoning, qualitative reasoning, spatial and temporal representation and reasoning, reasoning about actions and change, and computational models of narrative, we identify concrete theoretical and practical challenges that accrue in the context of formal reasoning about `space, events, actions, and change'. With this as a basis, and within the backdrop of an illustrated scenario involving the spatio-temporal dynamics of urban narratives, we address specific problems and solutions techniques chiefly involving `qualitative abstraction', `data integration and spatial consistency', and `practical geospatial abduction'. From a broad topical viewpoint, we propose that next-generation dynamic GIS technology demands a transdisciplinary scientific perspective that brings together Geography, Artificial Intelligence, and Cognitive Science. Keywords: artificial intelligence; cognitive systems; human-computer interaction; geographic information systems; spatio-temporal dynamics; computational models of narrative; geospatial analysis; geospatial modelling; ontology; qualitative spatial modelling and reasoning; spatial assistance systemsComment: ISPRS International Journal of Geo-Information (ISSN 2220-9964); Special Issue on: Geospatial Monitoring and Modelling of Environmental Change}. IJGI. Editor: Duccio Rocchini. (pre-print of article in press

A hybrid approach for arabic semantic relation extraction

Information retrieval applications are essential tools to manage the huge amount of information in the Web. Ontologies have great importance in these applications. The idea here is that several data belonging to a domain of interest are represented and related semantically in the ontology, which can help to navigate, manage and reuse these data. Despite of the growing need of ontology, only few works were interested in Arabic language. Indeed, arabic texts are highly ambiguous, especially when diacritics are absent. Besides, existent works does not cover all the types of se-mantic relations, which are useful to structure Arabic ontol-ogies. A lot of work has been done on cooccurrence- based techniques, which lead to over-generation. In this paper, we propose a new approach for Arabic se-mantic relation extraction. We use vocalized texts to reduce ambiguities and propose a new distributional approach for similarity calculus, which is compared to cooccurrence. We discuss our contribution through experimental results and propose some perspectives for future research

Modeling and Solving the Rush Hour puzzle

We introduce the physical puzzle Rush Hour and its generalization. We briefly survey its complexity limits, then we model and solve it using declarative paradigms. In particular, we provide a constraint programming encoding in MiniZinc and a model in Answer Set Programming and we report and compare experimental results. Although this is simply a game, the kind of reasoning involved is the same that autonomous vehicles should do for exiting a garage. This shows the potential of logic programming for problems concerning transport problems and self-driving cars

Applied Deep Learning: Case Studies in Computer Vision and Natural Language Processing

Deep learning has proved to be successful for many computer vision and natural language processing applications. In this dissertation, three studies have been conducted to show the efficacy of deep learning models for computer vision and natural language processing. In the first study, an efficient deep learning model was proposed for seagrass scar detection in multispectral images which produced robust, accurate scars mappings. In the second study, an arithmetic deep learning model was developed to fuse multi-spectral images collected at different times with different resolutions to generate high-resolution images for downstream tasks including change detection, object detection, and land cover classification. In addition, a super-resolution deep model was implemented to further enhance remote sensing images. In the third study, a deep learning-based framework was proposed for fact-checking on social media to spot fake scientific news. The framework leveraged deep learning, information retrieval, and natural language processing techniques to retrieve pertinent scholarly papers for given scientific news and evaluate the credibility of the news

A Comparative Analysis of Machine Learning Techniques For Foreclosure Prediction

The current decline in the U.S. economy was accompanied by an increase in foreclosure rates starting in 2007. Though the earliest figures for 2009 - 2010 indicate a significant decrease, foreclosure of homes in the U.S. is still at an alarming level (Gutierrez, 2009a). Recent research at the University of Michigan suggested that many foreclosures could have been averted had there been a predictive system that did not only rely on credit scores and loan-to-value ratios (DeGroat, 2009). Furthermore, Grover, Smith & Todd (2008) contend that foreclosure prediction can enhance the efficiency of foreclosure mitigation by facilitating the allocation of resources to areas where predicted foreclosure rates will be high. The primary goal of this dissertation was to develop a foreclosure prediction model that builds upon established bankruptcy and credit scoring models. The study utilized and compared the predictive accuracy of three supervised machine learning (ML) techniques when applied to mortgage data. The selected ML techniques were: ML1. Classification Trees ML2. Support Vector Machines (SVM) ML3. Genetic Programming The data used for the study is comprised of mortgage data, demographic metrics and certain macro-economic indicators that are available at the time of the inception of the loan. The hypothesis of the study was based on the assumption that foreclosure rates, and associated actions, are dependent on critical demographic (age, gender), economic (per capita income, inflation) and regional variables (predatory lending, unemployment index). The task of the machine learning techniques was to identify a function that well approximates the relationship between these explanatory variables and the binary outcome of interest (mortgage status in +3 years from inception). The predictive accuracy of ML1 through ML3 was significantly better than expected given the size of the recordset (1000) and the number of input variables (~110). Each ML technique achieved classification accuracy better than 75%, with ML3 scoring in the upper 90s. Given such high scores, it was concluded that the hypothesis was satisfied and that ML techniques are suitable for prediction tasks in this problem domain

Air Force Institute of Technology Research Report 2009

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics