Shortest Path Computation with No Information Leakage

Shortest path computation is one of the most common queries in location-based services (LBSs). Although particularly useful, such queries raise serious privacy concerns. Exposing to a (potentially untrusted) LBS the client's position and her destination may reveal personal information, such as social habits, health condition, shopping preferences, lifestyle choices, etc. The only existing method for privacy-preserving shortest path computation follows the obfuscation paradigm; it prevents the LBS from inferring the source and destination of the query with a probability higher than a threshold. This implies, however, that the LBS still deduces some information (albeit not exact) about the client's location and her destination. In this paper we aim at strong privacy, where the adversary learns nothing about the shortest path query. We achieve this via established private information retrieval techniques, which we treat as black-box building blocks. Experiments on real, large-scale road networks assess the practicality of our schemes.Comment: VLDB201

Context-sensitive interpretation of natural language location descriptions : a thesis submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy in Information Technology at Massey University, Auckland, New Zealand

People frequently describe the locations of objects using natural language. Location descriptions may be either structured, such as 26 Victoria Street, Auckland, or unstructured. Relative location descriptions (e.g., building near Sky Tower) are a common form of unstructured location description, and use qualitative terms to describe the location of one object relative to another (e.g., near, close to, in, next to). Understanding the meaning of these terms is easy for humans, but much more difficult for machines since the terms are inherently vague and context sensitive. In this thesis, we study the semantics (or meaning) of qualitative, geospatial relation terms, specifically geospatial prepositions. Prepositions are one of the most common forms of geospatial relation term, and they are commonly used to describe the location of objects in the geographic (geospatial) environment, such as rivers, mountains, buildings, and towns. A thorough understanding of the semantics of geospatial relation terms is important because it enables more accurate automated georeferencing of text location descriptions than use of place names only. Location descriptions that use geospatial prepositions are found in social media, web sites, blogs, and academic reports, and georeferencing can allow mapping of health, disaster and biological data that is currently inaccessible to the public. Such descriptions have unstructured format, so, their analysis is not straightforward. The specific research questions that we address are: RQ1. Which geospatial prepositions (or groups of prepositions) and senses are semantically similar? RQ2. Is the role of context important in the interpretation of location descriptions? RQ3. Is the object distance associated with geospatial prepositions across a range of geospatial scenes and scales accurately predictable using machine learning methods? RQ4. Is human annotation a reliable form of annotation for the analysis of location descriptions? To address RQ1, we determine the nature and degree of similarity among geospatial prepositions by analysing data collected with a human subjects experiment, using clustering, extensional mapping and t-stochastic neighbour embedding (t-SNE) plots to form a semantic similarity matrix. In addition to calculating similarity scores among prepositions, we identify the senses of three groups of geospatial prepositions using Venn diagrams, t-sne plots and density-based clustering, and define the relationships between the senses. Furthermore, we use two text mining approaches to identify the degree of similarity among geospatial prepositions: bag of words and GloVe embeddings. By using these methods and further analysis, we identify semantically similar groups of geospatial prepositions including: 1- beside, close to, near, next to, outside and adjacent to; 2- across, over and through and 3- beyond, past, by and off. The prepositions within these groups also share senses. Through is recognised as a specialisation of both across and over. Proximity and adjacency prepositions also have similar senses that express orientation and overlapping relations. Past, off and by share a proximal sense but beyond has a different sense from these, representing on the other side. Another finding is the more frequent use of the preposition close to for pairs of linear objects than near, which is used more frequently for non-linear ones. Also, next to is used to describe proximity more than touching (in contrast to other prepositions like adjacent to). Our application of text mining to identify semantically similar prepositions confirms that a geospatial corpus (NCGL) provides a better representation of the semantics of geospatial prepositions than a general corpus. Also, we found that GloVe embeddings provide adequate semantic similarity measures for more specialised geospatial prepositions, but less so for those that have more generalised applications and multiple senses. We explore the role of context (RQ2) by studying three sites that vary in size, nature, and context in London: Trafalgar Square, Buckingham Palace, and Hyde Park. We use the Google search engine to extract location descriptions that contain these three sites with 9 different geospatial prepositions (in, on, at, next to, close to, adjacent to, near, beside, outside) and calculate their acceptance profiles (the profile of the use of a preposition at different distances from the reference object) and acceptance thresholds (maximum distance from a reference object at which a preposition can acceptably be used). We use these to compare prepositions, and to explore the influence of different contexts. Our results show that near, in and outside are used for larger distances, while beside, adjacent to and at are used for smaller distances. Also, the acceptance threshold for close to is higher than for other proximity/adjacency prepositions such as next to, adjacent to and beside. The acceptance threshold of next to is larger than adjacent to, which confirms the findings in ‎Chapter 2 which identifies next to describing a proximity rather than touching spatial relation. We also found that relatum characteristics such as image schema affect the use of prepositions such as in, on and at. We address RQ3 by developing a machine learning regression model (using the SMOReg algorithm) to predict the distance associated with use of geospatial prepositions in specific expressions. We incorporate a wide range of input variables including the similarity matrix of geospatial prepositions (RQ1); preposition senses; semantic information in the form of embeddings; characteristics of the located and reference objects in the expression including their liquidity/solidity, scale and geometry type and contextual factors such as the density of features of different types in the surrounding area. We evaluate the model on two different datasets with 25% improvement against the best baseline respectively. Finally, we consider the importance of annotation of geospatial location descriptions (RQ4). As annotated data is essential for the successful study of automated interpretation of natural language descriptions, we study the impact and accuracy of human annotation on different geospatial elements. Agreement scores show that human annotators can annotate geospatial relation terms (e.g., geospatial prepositions) with higher agreement than other geospatial elements. This thesis advances understanding of the semantics of geospatial prepositions, particularly considering their semantic similarity and the impact of context on their interpretation. We quantify the semantic similarity of a set of 24 geospatial prepositions; identify senses and the relationships among them for 13 geospatial prepositions; compare the acceptance thresholds of 9 geospatial prepositions and describe the influence of context on them; and demonstrate that richer semantic and contextual information can be incorporated in predictive models to interpret relative geospatial location descriptions more accurately

A novel visualisation paradigm for three-dimensional map-based mobile services

Estágio realizado na NDrive Navigation Systems, S. A.Tese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Special Libraries, May-June 1977

Volume 68, Issue 5-6https://scholarworks.sjsu.edu/sla_sl_1977/1004/thumbnail.jp

The representation of spatial mental models in long-term memory

This thesis is concerned with how people understand and remember spatial information derived from verbal descriptions. The thesis distinguishes between three different ways of representing spatial information in working memory. The first way is to represent the surface form of the source from which the spatial information is derived (the language of a description). The second is to represent the structure of the situation derived from that source (a spatial mental model). The third is to represent the perceptual characteristics of the situation from a particular perspective (a visual image). Considerable evidence exists that people construct and manipulate spatial mental models in working memory. The purpose of this thesis is to investigate the claim that mental models are represented in long-term memory. An outline of the spatial mental modeling processes required to understand a simple spatial description is proposed. It is proposed that spatial mental modeling is comprised of three processing stages. Firstly, comprehension processes are required to access the linguistic meaning of information presented in spatial description. Secondly, construction processes are required to build up a representation of the spatial structure of the situation derived from the language of the description. Thirdly, consultation processes are required to monitor construction and to access information from the spatial mental model. Nine experiments are reported which investigate evidence for and against the view that people remember the construction and consultation of a spatial mental model. In the final chapter this evidence is reviewed and a 'sketch' of a processing theory of memory for spatial descriptions is proposed. It is argued that memory for a spatial mental model is a product of the interaction between construction and consultation processes over a period of time rather than a simple 'copy' of a completed working memory spatial mental model

Identifying related landmark tags in urban scenes using spatial and semantic clustering

There is considerable interest in developing landmark saliency models as a basis for describing urban landscapes, and in constructing wayfinding instructions, for text and spoken dialogue based systems. The challenge lies in knowing the truthfulness of such models; is what the model considers salient the same as what is perceived by the user? This paper presents a web based experiment in which users were asked to tag and label the most salient features from urban images for the purposes of navigation and exploration. In order to rank landmark popularity in each scene it was necessary to determine which tags related to the same object (e.g. tags relating to a particular café). Existing clustering techniques did not perform well for this task, and it was therefore necessary to develop a new spatial-semantic clustering method which considered the proximity of nearby tags and the similarity of their label content. The annotation similarity was initially calculated using trigrams in conjunction with a synonym list, generating a set of networks formed from the links between related tags. These networks were used to build related word lists encapsulating conceptual connections (e.g. church tower related to clock) so that during a secondary pass of the data related network segments could be merged. This approach gives interesting insight into the partonomic relationships between the constituent parts of landmarks and the range and frequency of terms used to describe them. The knowledge gained from this will be used to help calibrate a landmark saliency model, and to gain a deeper understanding of the terms typically associated with different types of landmarks

The National Transport Data Framework

Report by Professor Peter Landshoff (Cambridge University) and Professor John Polak (Imperial College London) on a project for the Department for Transport. emails: [email protected] [email protected] NTDF is designed to be a resource for data owners to deposit descriptions into a central catalogue, so that people can search for data and find data and understand their characteristics. The value of this is to individuals, to commercial organizations, and to public bodies. For example, services that provide better information to travellers will help to make their journey less stressful and persuade them to make more use of public transport. Transport operators need very diverse information to help them plan developments to their services: demographic, geographical, economic etc. And policy makers need a similar range of information to help them decide how to divide their budget and afterwards to evaluate how valuable it has been.This work was supported by the Department for Transport (DfT)

DYNAMICS OF COLLABORATIVE NAVIGATION AND APPLYING DATA DRIVEN METHODS TO IMPROVE PEDESTRIAN NAVIGATION INSTRUCTIONS AT DECISION POINTS FOR PEOPLE OF VARYING SPATIAL APTITUDES

Cognitive Geography seeks to understand individual decision-making variations based on fundamental cognitive differences between people of varying spatial aptitudes. Understanding fundamental behavioral discrepancies among individuals is an important step to improve navigation algorithms and the overall travel experience. Contemporary navigation aids, although helpful in providing turn-by-turn directions, lack important capabilities to distinguish decision points for their features and importance. Existing systems lack the ability to generate landmark or decision point based instructions using real-time or crowd sourced data. Systems cannot customize personalized instructions for individuals based on inherent spatial ability, travel history, or situations. This dissertation presents a novel experimental setup to examine simultaneous wayfinding behavior for people of varying spatial abilities. This study reveals discrepancies in the information processing, landmark preference and spatial information communication among groups possessing differing abilities. Empirical data is used to validate computational salience techniques that endeavor to predict the difficulty of decision point use from the structure of the routes. Outlink score and outflux score, two meta-algorithms that derive secondary scores from existing metrics of network analysis, are explored. These two algorithms approximate human cognitive variation in navigation by analyzing neighboring and directional effect properties of decision point nodes within a routing network. The results are validated by a human wayfinding experiment, results show that these metrics generally improve the prediction of errors. In addition, a model of personalized weighting for users\u27 characteristics is derived from a SVMrank machine learning method. Such a system can effectively rank decision point difficulty based on user behavior and derive weighted models for navigators that reflect their individual tendencies. The weights reflect certain characteristics of groups. Such models can serve as personal travel profiles, and potentially be used to complement sense-of-direction surveys in classifying wayfinders. A prototype with augmented instructions for pedestrian navigation is created and tested, with particular focus on investigating how augmented instructions at particular decision points affect spatial learning. The results demonstrate that survey knowledge acquisition is improved for people with low spatial ability while decreased for people of high spatial ability. Finally, contributions are summarized, conclusions are provided, and future implications are discussed

Terrestrial applications: An intelligent Earth-sensing information system

For Abstract see A82-2214