Graph-based ship traffic partitioning for intelligent maritime surveillance in complex port waters

Maritime Situational Awareness (MSA) is a critical component of intelligent maritime traffic surveillance. However, it becomes increasingly challenging to gain MSA accurately given the growing complexity of ship traffic patterns due to multi-ship interactions possibly involving classical manned ships and emerging autonomous ships. This study proposes a new traffic partitioning methodology to realise the optimal maritime traffic partition in complex waters. The methodology combines conflict criticality and spatial distance to generate conflict-connected and spatially compact traffic clusters, thereby improving the interpretability of traffic patterns and supporting ship anti-collision risk management. First, a composite similarity measure is designed using a probabilistic conflict detection approach and a newly formulated maritime traffic route network learned through maritime knowledge mining. Then, an extended graph-based clustering framework is used to produce balanced traffic clusters with high intra-connections but low inter-connections. The proposed methodology is thoroughly demonstrated and tested using Automatic Identification System (AIS) trajectory data in the Ningbo-Zhoushan Port. The experimental results show that the proposed methodology 1) has effective performance in decomposing the traffic complexity, 2) can assist in identifying high-risk/density traffic clusters, and 3) is sufficiently generic to handle various traffic scenarios in complex geographical waters. Therefore, this study makes significant contributions to intelligent maritime surveillance and provides a theoretical foundation for promoting maritime anti-collision risk management for the future mixed traffic of both manned and autonomous ships

Security and Privacy in Mobile Computing: Challenges and Solutions

abstract: Mobile devices are penetrating everyday life. According to a recent Cisco report [10], the number of mobile connected devices such as smartphones, tablets, laptops, eReaders, and Machine-to-Machine (M2M) modules will hit 11.6 billion by 2021, exceeding the world's projected population at that time (7.8 billion). The rapid development of mobile devices has brought a number of emerging security and privacy issues in mobile computing. This dissertation aims to address a number of challenging security and privacy issues in mobile computing. This dissertation makes fivefold contributions. The first and second parts study the security and privacy issues in Device-to-Device communications. Specifically, the first part develops a novel scheme to enable a new way of trust relationship called spatiotemporal matching in a privacy-preserving and efficient fashion. To enhance the secure communication among mobile users, the second part proposes a game-theoretical framework to stimulate the cooperative shared secret key generation among mobile users. The third and fourth parts investigate the security and privacy issues in mobile crowdsourcing. In particular, the third part presents a secure and privacy-preserving mobile crowdsourcing system which strikes a good balance among object security, user privacy, and system efficiency. The fourth part demonstrates a differentially private distributed stream monitoring system via mobile crowdsourcing. Finally, the fifth part proposes VISIBLE, a novel video-assisted keystroke inference framework that allows an attacker to infer a tablet user's typed inputs on the touchscreen by recording and analyzing the video of the tablet backside during the user's input process. Besides, some potential countermeasures to this attack are also discussed. This dissertation sheds the light on the state-of-the-art security and privacy issues in mobile computing.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Using topological analysis to support event-guided exploration in urban data

The explosion in the volume of data about urban environments has opened up opportunities to inform both policy and administration and thereby help governments improve the lives of their citizens, increase the efficiency of public services, and reduce the environmental harms of development. However, cities are complex systems and exploring the data they generate is challenging. The interaction between the various components in a city creates complex dynamics where interesting facts occur at multiple scales, requiring users to inspect a large number of data slices over time and space. Manual exploration of these slices is ineffective, time consuming, and in many cases impractical. In this paper, we propose a technique that supports event-guided exploration of large, spatio-temporal urban data. We model the data as time-varying scalar functions and use computational topology to automatically identify events in different data slices. To handle a potentially large number of events, we develop an algorithm to group and index them, thus allowing users to interactively explore and query event patterns on the fly. A visual exploration interface helps guide users towards data slices that display interesting events and trends. We demonstrate the effectiveness of our technique on two different data sets from New York City (NYC): data about taxi trips and subway service. We also report on the feedback we received from analysts at different NYC agencies

Large-scale and Deep Spatiotemporal Point-Process Models

Many accurate spatiotemporal data sets have recently become available for research. Real-world applications create strong demands for a better multivariate point-process modeling. In this thesis, we develop new multivariate models with generalization ability and scalability. The first two chapters provide a research background, real-world problems and a mathematical introduction to point-process models. In chapter 3, we develop a nonparametric method for multivariate spatiotemporal Hawkes processes with applications on network reconstruction. In contrast to prior work, which has often focused on exclusively temporal information, our approach uses spatiotemporal information and does not assume a specific parametric form. Our results demonstrate that, in comparison to using only temporal data, our approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis---such as examinations of community structure and motifs---of the reconstructed networks. In chapter 4, we present a fast and accurate estimation method for multivariate Hawkes processes. Our method, with guaranteed consistency, combines two estimation approaches. Extensive numerical experiments, with synthetic data and real-world social network data, show that our method improves the accuracy, scalability and computational efficiency of prevailing estimation approaches. Moreover, it greatly boosts the performance of Hawkes process-based models on social network reconstruction and helps to understand the spatiotemporal triggering dynamics over social media.In chapter 5, we focus on multivariate spatial point processes, which can describe heterotopic data over space. However, highly multivariate intensities are computationally challenging due to the curse of dimensionality. To bridge this gap, we introduce a declustering-based hidden-variable model that leads to an efficient inference via a variational autoencoder (VAE). We also prove that this model is a generalization of the VAE-based model for collaborative filtering. This leads to an interesting application of spatial point-process models to recommender systems. Experimental results show the method's utility on both synthetic data and real-world data. Finally, in chapter 6, we show how multivariate point processes can be applied to opioid overdose events and real-time prediction of the hourly crime rate. In chapter 7, we discuss future directions and conclude the thesis

Disruption analytics in urban metro systems with large-scale automated data

Urban metro systems are frequently affected by disruptions such as infrastructure malfunctions, rolling stock breakdowns and accidents. Such disruptions give rise to delays, congestion and inconvenience for public transport users, which in turn, lead to a wider range of negative impacts on the social economy and wellbeing. This PhD thesis aims to improve our understanding of disruption impacts and improve the ability of metro operators to detect and manage disruptions by using large-scale automated data. The crucial precondition of any disruption analytics is to have accurate information about the location, occurrence time, duration and propagation of disruptions. In pursuit of this goal, the thesis develops statistical models to detect disruptions via deviations in trains’ headways relative to their regular services. Our method is a unique contribution in the sense that it is based on automated vehicle location data (data-driven) and the probabilistic framework is effective to detect any type of service interruptions, including minor delays that last just a few minutes. As an important research outcome, the thesis delivers novel analyses of the propagation progress of disruptions along metro lines, thus enabling us to distinguish primary and secondary disruptions as well as recovery interventions performed by operators. The other part of the thesis provides new insights for quantifying disruption impacts and measuring metro vulnerability. One of our key messages is that in metro systems there are factors influencing both the occurrence of disruptions and their outcomes. With such confounding factors, we show that causal inference is a powerful tool to estimate unbiased impacts on passenger demand and journey time, which is also capable of quantifying the spatial-temporal propagation of disruption impacts within metro networks. The causal inference approaches are applied to empirical studies based on the Hong Kong Mass Transit Railway (MTR). Our conclusions can assist researchers and practitioners in two applications: (i) the evaluation of metro performance such as service reliability, system vulnerability and resilience, and (ii) the management of future disruptions.Open Acces

An event-based conceptual model for context-aware movement analysis

Current tracking technologies enable collection of data, describing movements of various kinds of objects, including people, animals, icebergs, vehicles, containers with goods and so on. Analysis of movement data is now a hot research topic. However, most of the suggested analysis methods deal with movement data alone. Little has been done to support the analysis of movement in its spatio-temporal context, which includes various spatial and temporal objects as well as diverse properties associated with spatial locations and time moments. Comprehensive analysis of movement requires detection and analysis of relations that occur between moving objects and elements of the context in the process of the movement. We suggest a conceptual model in which movement is considered as a combination of spatial events of diverse types and extents in space and time. Spatial and temporal relations occur between movement events and elements of the spatial and temporal contexts. The model gives a ground to a generic approach based on extraction of interesting events from trajectories and treating the events as independent objects. By means of a prototype implementation, we tested the approach on complex real data about movement of wild animals. The testing showed the validity of the approach

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America

The rhetorical work of credibility-building for social scientific big data: Positioning arguments and legitimacy in empirical sociology

This article investigates the rhetorical work of building credibility for social scientific research designs with big data. Big data is discussed  as a contested concept in the social sciences, one whose meaning and implications are under dispute. Proceeding from analysis of 29 sociology articles based on empirical research, the author argues that credibility is constructed in this context through the rhetorical positioning of disciplines as legitimate interpreters of big data. The article identifies three distinct positioning strategies: conservative, reformist, and supplementarist, each of which locates the legitimacy of interpretation in its own way. While conservative positioning fixes  the locus of legitimate interpretation within the social sciences, those employing a reformist strategy seek to widen it to encompass methods from beyond established social scientific fields. Finally, supplementarist positioning portrays big data as inherently limited and ties the legitima­cy of interpretation to alternative approaches. Through identifying and addressing these respective strategies, the article discusses rhetorical positioning as part of the work of enacting big data: a performative process that can foster several visions of the future methodology of the social sciences