Content-based Information Retrieval via Nearest Neighbor Search

Content-based information retrieval (CBIR) has attracted significant interest in the past few years. When given a search query, the search engine will compare the query with all the stored information in the database through nearest neighbor search. Finally, the system will return the most similar items. We contribute to the CBIR research the following: firstly, Distance Metric Learning (DML) is studied to improve retrieval accuracy of nearest neighbor search. Additionally, Hash Function Learning (HFL) is considered to accelerate the retrieval process. On one hand, a new local metric learning framework is proposed - Reduced-Rank Local Metric Learning (R2LML). By considering a conical combination of Mahalanobis metrics, the proposed method is able to better capture information like data\u27s similarity and location. A regularization to suppress the noise and avoid over-fitting is also incorporated into the formulation. Based on the different methods to infer the weights for the local metric, we considered two frameworks: Transductive Reduced-Rank Local Metric Learning (T-R2LML), which utilizes transductive learning, while Efficient Reduced-Rank Local Metric Learning (E-R2LML)employs a simpler and faster approximated method. Besides, we study the convergence property of the proposed block coordinate descent algorithms for both our frameworks. The extensive experiments show the superiority of our approaches. On the other hand, *Supervised Hash Learning (*SHL), which could be used in supervised, semi-supervised and unsupervised learning scenarios, was proposed in the dissertation. By considering several codewords which could be learned from the data, the proposed method naturally derives to several Support Vector Machine (SVM) problems. After providing an efficient training algorithm, we also study the theoretical generalization bound of the new hashing framework. In the final experiments, *SHL outperforms many other popular hash function learning methods. Additionally, in order to cope with large data sets, we also conducted experiments running on big data using a parallel computing software package, namely LIBSKYLARK

Brute - Force Sentence Pattern Extortion from Harmful Messages for Cyberbullying Detection

Cyberbullying, or humiliating people using the Internet, has existed almost since the beginning ofInternet communication.The relatively recent introduction of smartphones and tablet computers has caused cyberbullying to evolve into a serious social problem. In Japan, members of a parent-teacher association (PTA)attempted to address the problem by scanning the Internet for cyber bullying entries. To help these PTA members and other interested parties confront this difficult task we propose a novel method for automatic detection of malicious Internet content. This method is based on a combinatorial approach resembling brute-force search algorithms, but applied in language classification. The method extracts sophisticated patterns from sentences and uses them in classification. The experiments performed on actual cyberbullying data reveal an advantage of our method vis-à-visprevious methods. Next, we implemented the method into an application forAndroid smartphones to automatically detect possible harmful content in messages. The method performed well in the Android environment, but still needs to be optimized for time efficiency in order to be used in practic

Automatic Population of Structured Reports from Narrative Pathology Reports

There are a number of advantages for the use of structured pathology reports: they can ensure the accuracy and completeness of pathology reporting; it is easier for the referring doctors to glean pertinent information from them. The goal of this thesis is to extract pertinent information from free-text pathology reports and automatically populate structured reports for cancer diseases and identify the commonalities and differences in processing principles to obtain maximum accuracy. Three pathology corpora were annotated with entities and relationships between the entities in this study, namely the melanoma corpus, the colorectal cancer corpus and the lymphoma corpus. A supervised machine-learning based-approach, utilising conditional random fields learners, was developed to recognise medical entities from the corpora. By feature engineering, the best feature configurations were attained, which boosted the F-scores significantly from 4.2% to 6.8% on the training sets. Without proper negation and uncertainty detection, the quality of the structured reports will be diminished. The negation and uncertainty detection modules were built to handle this problem. The modules obtained overall F-scores ranging from 76.6% to 91.0% on the test sets. A relation extraction system was presented to extract four relations from the lymphoma corpus. The system achieved very good performance on the training set, with 100% F-score obtained by the rule-based module and 97.2% F-score attained by the support vector machines classifier. Rule-based approaches were used to generate the structured outputs and populate them to predefined templates. The rule-based system attained over 97% F-scores on the training sets. A pipeline system was implemented with an assembly of all the components described above. It achieved promising results in the end-to-end evaluations, with 86.5%, 84.2% and 78.9% F-scores on the melanoma, colorectal cancer and lymphoma test sets respectively

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

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

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data

Gaining Insight into Determinants of Physical Activity using Bayesian Network Learning

Contains fulltext : 228326pre.pdf (preprint version ) (Open Access) Contains fulltext : 228326pub.pdf (publisher's version ) (Open Access)BNAIC/BeneLearn 202

Mining the Medical and Patent Literature to Support Healthcare and Pharmacovigilance

Recent advancements in healthcare practices and the increasing use of information technology in the medical domain has lead to the rapid generation of free-text data in forms of scientific articles, e-health records, patents, and document inventories. This has urged the development of sophisticated information retrieval and information extraction technologies. A fundamental requirement for the automatic processing of biomedical text is the identification of information carrying units such as the concepts or named entities. In this context, this work focuses on the identification of medical disorders (such as diseases and adverse effects) which denote an important category of concepts in the medical text. Two methodologies were investigated in this regard and they are dictionary-based and machine learning-based approaches. Futhermore, the capabilities of the concept recognition techniques were systematically exploited to build a semantic search platform for the retrieval of e-health records and patents. The system facilitates conventional text search as well as semantic and ontological searches. Performance of the adapted retrieval platform for e-health records and patents was evaluated within open assessment challenges (i.e. TRECMED and TRECCHEM respectively) wherein the system was best rated in comparison to several other competing information retrieval platforms. Finally, from the medico-pharma perspective, a strategy for the identification of adverse drug events from medical case reports was developed. Qualitative evaluation as well as an expert validation of the developed system's performance showed robust results. In conclusion, this thesis presents approaches for efficient information retrieval and information extraction from various biomedical literature sources in the support of healthcare and pharmacovigilance. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. The applied strategies have potential to enhance the literature-searches performed by biomedical, healthcare, and patent professionals. This can promote the literature-based knowledge discovery, improve the safety and effectiveness of medical practices, and drive the research and development in medical and healthcare arena

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others