48 research outputs found
Mining complex trees for hidden fruit : a graphâbased computational solution to detect latent criminal networks : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Technology at Massey University, Albany, New Zealand.
The detection of crime is a complex and difficult endeavour. Public and private organisations â focusing on law enforcement, intelligence, and compliance â commonly apply the rational isolated actor approach premised on observability and materiality. This is manifested largely as conducting entity-level risk management sourcing âleadsâ from reactive covert human intelligence sources and/or proactive sources by applying simple rules-based models. Focusing on discrete observable and material actors simply ignores that criminal activity exists within a complex system deriving its fundamental structural fabric from the complex interactions between actors - with those most unobservable likely to be both criminally proficient and influential. The graph-based computational solution developed to detect latent criminal networks is a response to the inadequacy of the rational isolated actor approach that ignores the connectedness and complexity of criminality.
The core computational solution, written in the R language, consists of novel entity resolution, link discovery, and knowledge discovery technology. Entity resolution enables the fusion of multiple datasets with high accuracy (mean F-measure of 0.986 versus competitors 0.872), generating a graph-based expressive view of the problem. Link discovery is comprised of link prediction and link inference, enabling the high-performance detection (accuracy of ~0.8 versus relevant published models ~0.45) of unobserved relationships such as identity fraud. Knowledge discovery uses the fused graph generated and applies the âGraphExtractâ algorithm to create a set of subgraphs representing latent functional criminal groups, and a mesoscopic graph representing how this set of criminal groups are interconnected. Latent knowledge is generated from a range of metrics including the âSuper-brokerâ metric and attitude prediction.
The computational solution has been evaluated on a range of datasets that mimic an applied setting, demonstrating a scalable (tested on ~18 million node graphs) and performant (~33 hours runtime on a non-distributed platform) solution that successfully detects relevant latent functional criminal groups in around 90% of cases sampled and enables the contextual understanding of the broader criminal system through the mesoscopic graph and associated metadata. The augmented data assets generated provide a multi-perspective systems view of criminal activity that enable advanced informed decision making across the microscopic mesoscopic macroscopic spectrum
Linked Open Data - Creating Knowledge Out of Interlinked Data: Results of the LOD2 Project
Database Management; Artificial Intelligence (incl. Robotics); Information Systems and Communication Servic
Human Fatigue Predictions in Complex Aviation Crew Operational Impact Conditions
In this last decade, several regulatory frameworks across the world in all modes of transportation had brought fatigue and its risk management in operations to the forefront. Of all transportation modes air travel has been the safest means of transportation. Still as part of continuous improvement efforts, regulators are insisting the operators to adopt strong fatigue science and its foundational principles to reinforce safety risk assessment and management. Fatigue risk management is a data driven system that finds a realistic balance between safety and productivity in an organization. This work discusses the effects of mathematical modeling of fatigue and its quantification in the context of fatigue risk management for complex global logistics operations. A new concept called Duty DNA is designed within the system that helps to predict and forecast sleep, duty deformations and fatigue. The need for a robust structure of elements to house the components to measure and manage fatigue risk in operations is also debated. By operating on the principles of fatigue management, new science-based predictive, proactive and reactive approaches were designed for an industry leading fatigue risk management program
Accurately predicting sleep is very critical to predicting fatigue and alertness. Mathematical models are being developed to track the biological processes quantitatively and predicting temporal profile of fatigue given a personâs sleep history, planned work schedule including night and day exposure. As these models are being continuously worked to improve, a new limited deep learning machine learning based approach is attempted to predict fatigue for a duty in isolation without knowing much of work schedule history. The model within also predicts the duty disruptions and predicted fatigue at the end state of duty
Real-Time Event Analysis and Spatial Information Extraction From Text Using Social Media Data
Since the advent of websites that enable users to participate and interact with each other by sharing content in different forms, a plethora of possibly relevant information is at scientists\u27 fingertips. Consequently, this thesis elaborates on two distinct approaches to extract valuable information from social media data and sketches out the potential joint use case in the domain of natural disasters
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Geographic Knowledge Graph Summarization
Geographic knowledge graphs play a significant role in the geospatial semantics paradigm for fulfilling the interoperability, the accessibility, and the conceptualization demands in geographic information science. However, due to the immense quantity of information accompanying and the enormous diversity of geographic knowledge graphs, there are many challenges that hinder the applicability and mass adoption of such useful structured knowledge. In order to tackle these challenges, this dissertation focuses on devising ways in which geographic knowledge graphs can be digested and summarized. Such a summarization task, on the one hand lifts the burden of information overload for end users, on the other hand facilitates the reduction of data storage, speeds up queries, and helps eliminate noise. The main contribution of this dissertation is that it introduces the general concept of geospatial inductive bias and explains different ways this idea can be used in the geographic knowledge graph summarization task. By decomposing the task into separate but related components, this dissertation is based upon three peer-reviewed articles which focus on the hierarchical place type structure, multimedia leaf nodes, and general relation and entity components respectively. A spatial knowledge map interface that illustrates the effectiveness of summarizing geographic knowledge graphs is presented. Throughout the dissertation, top-down knowledge engineering and bottom-up knowledge learning methods are integrated. We hope this dissertation would promote the awareness of this fascinating area and motivate researchers to investigate related questions
Methodology and algorithms for Urdu language processing in a conversational agent
This thesis presents the research and development of a novel text based goal-orientated conversational agent (CA) for the Urdu language called UMAIR (Urdu Machine for Artificially Intelligent Recourse). A CA is a computer program that emulates a human in order to facilitate a conversation with the user. The aim is investigate the Urdu language and its lexical and grammatical features in order to, design a novel engine to handle the language unique features of Urdu. The weakness in current Conversational Agent (CA) engines is that they are not suited to be implemented in other languages which have grammar rules and structure totally different to English. From a historical perspective CAâs including the design of scripting engines, scripting methodologies, resources and implementation procedures have been implemented for the most part in English and other Western languages (i.e. German and Spanish). The development of an Urdu conversational agent has required the research and development of new CA framework which incorporates methodologies and components in order overcome the language unique features of Urdu such as free word order, inconsistent use of space, diacritical marks and spelling. The new CA framework was utilised to implement UMAIR. UMAIR is a customer service agent for National Database and Registration Authority (NADRA) designed to answer user queries related to ID card and Passport applications. UMAIR is able to answer user queries related to the domain through discourse with the user by leading the conversation using questions and offering appropriate advice with the intention of leading the discourse to a pre-determined goal. The research and development of UMAIR led to the creation of several novel CA components, namely a new rule based Urdu CA engine which combines pattern matching and sentence/string similarity techniques along with new algorithms to process user utterances. Furthermore, a CA evaluation framework has been researched and tested which addresses the gap in research to develop the evaluation of natural language systems in general. Empirical end user evaluation has validated the new algorithms and components implemented in UMAIR. The results show that UMAIR is effective as an Urdu CA, with the majority of conversations leading to the goal of the conversation. Moreover the results also revealed that the components of the framework work well to mitigate the challenges of free word order and inconsistent word segmentation
The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications
The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data