320 research outputs found
Programmable Insight: A Computational Methodology to Explore Online News Use of Frames
abstract: The Internet is a major source of online news content. Online news is a form of large-scale narrative text with rich, complex contents that embed deep meanings (facts, strategic communication frames, and biases) for shaping and transitioning standards, values, attitudes, and beliefs of the masses. Currently, this body of narrative text remains untapped due—in large part—to human limitations. The human ability to comprehend rich text and extract hidden meanings is far superior to known computational algorithms but remains unscalable. In this research, computational treatment is given to online news framing for exposing a deeper level of expressivity coined “double subjectivity” as characterized by its cumulative amplification effects. A visual language is offered for extracting spatial and temporal dynamics of double subjectivity that may give insight into social influence about critical issues, such as environmental, economic, or political discourse. This research offers benefits of 1) scalability for processing hidden meanings in big data and 2) visibility of the entire network dynamics over time and space to give users insight into the current status and future trends of mass communication.Dissertation/ThesisDoctoral Dissertation Computer Science 201
Survivability modeling for cyber-physical systems subject to data corruption
Cyber-physical critical infrastructures are created when traditional physical infrastructure is supplemented with advanced monitoring, control, computing, and communication capability. More intelligent decision support and improved efficacy, dependability, and security are expected. Quantitative models and evaluation methods are required for determining the extent to which a cyber-physical infrastructure improves on its physical predecessors. It is essential that these models reflect both cyber and physical aspects of operation and failure. In this dissertation, we propose quantitative models for dependability attributes, in particular, survivability, of cyber-physical systems. Any malfunction or security breach, whether cyber or physical, that causes the system operation to depart from specifications will affect these dependability attributes. Our focus is on data corruption, which compromises decision support -- the fundamental role played by cyber infrastructure. The first research contribution of this work is a Petri net model for information exchange in cyber-physical systems, which facilitates i) evaluation of the extent of data corruption at a given time, and ii) illuminates the service degradation caused by propagation of corrupt data through the cyber infrastructure. In the second research contribution, we propose metrics and an evaluation method for survivability, which captures the extent of functionality retained by a system after a disruptive event. We illustrate the application of our methods through case studies on smart grids, intelligent water distribution networks, and intelligent transportation systems. Data, cyber infrastructure, and intelligent control are part and parcel of nearly every critical infrastructure that underpins daily life in developed countries. Our work provides means for quantifying and predicting the service degradation caused when cyber infrastructure fails to serve its intended purpose. It can also serve as the foundation for efforts to fortify critical systems and mitigate inevitable failures --Abstract, page iii
DEVELOPMENT OF DIAGNOSTIC AND PROGNOSTIC METHODOLOGIES FOR ELECTRONIC SYSTEMS BASED ON MAHALANOBIS DISTANCE
Diagnostic and prognostic capabilities are one aspect of the many interrelated and complementary functions in the field of Prognostic and Health Management (PHM). These capabilities are sought after by industries in order to provide maximum operational availability of their products, maximum usage life, minimum periodic maintenance inspections, lower inventory cost, accurate tracking of part life, and no false alarms. Several challenges associated with the development and implementation of these capabilities are the consideration of a system's dynamic behavior under various operating environments; complex system architecture where the components that form the overall system have complex interactions with each other with feed-forward and feedback loops of instructions; the unavailability of failure precursors; unseen events; and the absence of unique mathematical techniques that can address fault and failure events in various multivariate systems.
The Mahalanobis distance methodology distinguishes multivariable data groups in a multivariate system by a univariate distance measure calculated from the normalized value of performance parameters and their correlation coefficients. The Mahalanobis distance measure does not suffer from the scaling effect--a situation where the variability of one parameter masks the variability of another parameter, which happens when the measurement ranges or scales of two parameters are different.
A literature review showed that the Mahalanobis distance has been used for classification purposes. In this thesis, the Mahalanobis distance measure is utilized for fault detection, fault isolation, degradation identification, and prognostics.
For fault detection, a probabilistic approach is developed to establish threshold Mahalanobis distance, such that presence of a fault in a product can be identified and the product can be classified as healthy or unhealthy. A technique is presented to construct a control chart for Mahalanobis distance for detecting trends and biasness in system health or performance. An error function is defined to establish fault-specific threshold Mahalanobis distance.
A fault isolation approach is developed to isolate faults by identifying parameters that are associated with that fault. This approach utilizes the design-of-experiment concept for calculating residual Mahalanobis distance for each parameter (i.e., the contribution of each parameter to a system's health determination). An expected contribution range for each parameter estimated from the distribution of residual Mahalanobis distance is used to isolate the parameters that are responsible for a system's anomalous behavior.
A methodology to detect degradation in a system's health using a health indicator is developed. The health indicator is defined as the weighted sum of a histogram bin's fractional contribution. The histogram's optimal bin width is determined from the number of data points in a moving window. This moving window approach is utilized for progressive estimation of the health indicator over time. The health indicator is compared with a threshold value defined from the system's healthy data to indicate the system's health or performance degradation.
A symbolic time series-based health assessment approach is developed. Prognostic measures are defined for detecting anomalies in a product and predicting a product's time and probability of approaching a faulty condition. These measures are computed from a hidden Markov model developed from the symbolic representation of product dynamics. The symbolic representation of a product's dynamics is obtained by representing a Mahalanobis distance time series in symbolic form.
Case studies were performed to demonstrate the capability of the proposed methodology for real time health monitoring. Notebook computers were exposed to a set of environmental conditions representative of the extremes of their life cycle profiles. The performance parameters were monitored in situ during the experiments, and the resulting data were used as a training dataset. The dataset was also used to identify specific parameter behavior, estimate correlation among parameters, and extract features for defining a healthy baseline. Field-returned computer data and data corresponding to artificially injected faults in computers were used as test data
4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022)
Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 4th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges. Due to the covid pandemic, CARMA 2022 is planned as a virtual and face-to-face conference, simultaneouslyDoménech I De Soria, J.; Vicente Cuervo, MR. (2022). 4th. International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2022.2022.1595
A drinking water microbiome from source to tap: Community diversity, functionality, and microbial interaction
Despite the long history of water research, understanding drinking water microbiome continuum spanning from source water, treatment in the production process, distribution network, and up to the point where water enters a building is rather challenging owing to the complexity in community assembly, water matrices, physical structure, and chemical gradients from source to tap. Previous studies on drinking water microbiomes have primarily investigated “who are there” and “how do they change over time and across space” in selected stages of drinking water systems. However, it is important to ask additional questions that include but are not limited to “what are they doing?”, “why are they there?” and more critically “who is doing what?”, and “what are the interrelationships among them, and between them and their environment?”. To answer these questions, it requires not only the advent of new methods, but also the transformation of drinking water microbiology from a descriptive discipline to a hypothesis-driven science that attempts to elucidate mechanisms with the intention to predict and shape the microbiome continuum.
The studies included in this dissertation resolved the ecological patterns of a groundwater-sourced drinking water microbiome at different scales. At the community level, the treatment process could be viewed as ecological disturbances on the drinking water microbiome continuum over space in the system by combining 16S rRNA gene amplicon sequencing and metagenomics. Abstraction caused a substantial decrease in both the abundance and number of functional genes related to methanogenesis and syntrophs in raw water. The softening process reduced microbial diversity and selected an Exiguobacterium-related population, which was attributed to its ability to use the phosphotransferase system (PTS) as regulatory machinery to control the energy conditions of the cell. After disinfection and entering the distribution system, microbial populations and their functions remained relatively stable. Predation by eukaryotic populations could be another disturbance to the bacterial microbiome, which could further drive the diversification of the bacterial community. At the population level, nine draft genomes of pathogen-related species from the genera Legionella, Mycobacterium, Parachlamydia, and Leptospira were constructed and characterized in relation to their abundance, diversity, potential pathogenicity, genetic exchange, and distribution across the groundwater-sourced drinking water system. The presence/absence of specific virulence machinery could be effectively used to determine the pathogenicity potential of these genomes. Clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins (CRISPR-Cas) genetic signatures were identified as a potential biomarker in the monitoring of Legionella related strains across different drinking water systems. At the multi-species level, methano-/methylo-trophs were investigated, which were overlooked populations dominant and prevalent in drinking water microbiomes of groundwater systems. Using genome-resolved metagenomics, 34 methylotroph-related draft genomes were recovered together with another 133 draft genomes belonging to a variety of taxa. Both Type I and Type II methanotrophs dominated the finished water and distribution system. They mostly possessed methylotrophy pathways involving many enzymes rather than single enzyme systems. Network analysis determined potential species interaction between methanotrophs and a number of non-methanotrophic methylotrophs and other heterotrophs. The latter two groups had the capability to supply essential metabolites to methanotrophs as indicated by metabolic interdependency analysis.
This series of studies established a framework to understand the drinking water microbiome continuum through the inference of evolutionary and ecological processes that shape the microbiome from genomic/metagenomic data. They also offered new perspectives to some questions waiting to be answered by future studies, including “How to define a ‘healthy’ microbiome and microbial indicators?”, “How to effectively monitor opportunistic pathogens in drinking water microbiomes?”, and “Can drinking water microbiomes be predict and intentionally shaped?”
EXPLORING VOLUNTEERED GEOGRAPHIC INFORMATION WITH DATA QUALITY CONTROL FOR INTEGRATED PEST MANAGEMENT
Ph.DDOCTOR OF PHILOSOPH
A geographic knowledge discovery approach to property valuation
This thesis involves an investigation of how knowledge discovery can be applied in the area Geographic Information Science. In particular, its application in the area of
property valuation in order to reveal how different spatial entities and their interactions affect the price of the properties is explored. This approach is entirely
data driven and does not require previous knowledge of the area applied.
To demonstrate this process, a prototype system has been designed and implemented. It employs association rule mining and associative classification algorithms to uncover any existing inter-relationships and perform the valuation. Various algorithms that perform the above tasks have been proposed in the literature. The algorithm developed in this work is based on the Apriori algorithm. It has been
however, extended with an implementation of a ‘Best Rule’ classification scheme based on the Classification Based on Associations (CBA) algorithm.
For the modelling of geographic relationships a graph-theoretic approach has been employed. Graphs have been widely used as modelling tools within the geography
domain, primarily for the investigation of network-type systems. In the current context, the graph reflects topological and metric relationships between the spatial
entities depicting general spatial arrangements. An efficient graph search algorithm has been developed, based on the Djikstra shortest path algorithm that enables the
investigation of relationships between spatial entities beyond first degree connectivity.
A case study with data from three central London boroughs has been performed to validate the methodology and algorithms, and demonstrate its effectiveness for computer aided property valuation. In addition, through the case study, the influence of location in the value of properties in those boroughs has been examined. The results are encouraging as they demonstrate the effectiveness of the proposed methodology and algorithms, provided that the data is appropriately pre processed and is of high quality
Urban Informatics
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
Urban Informatics
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity
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Methods for risk and resilience evaluation in interdependent infrastructure networks
Urban infrastructure plays a key role in the structure and dynamics of every city. Besides ensuring the sustainability of communities and businesses, high-quality infrastructure services are crucial for generating jobs and attracting capital investments. Modern infrastructure systems are highly interconnected to enhance efficiency and safety of operations; however, the interconnections increase the risks of cascading failures during extreme events, such as natural disasters, acts of terrorism, and pandemics. Not only are the normal operations interrupted during such events, but prolonged operational disruptions in infrastructure services also have debilitating effects on emergency response and economic recovery in affected regions. With the emergence of new threats and intensifying climate change, the resilience of infrastructure systems has become a necessity rather than a choice for our cities.
As with any resource allocation problem, potential resilience investments require identifying priorities and evaluating project alternatives. Appropriate resilience indicators can be used to rank and prioritize infrastructure components and systems as well as to evaluate the efficacy of resilience interventions. The dissertation proposes five indicator-based methodological frameworks to assist decision-makers in analyzing the intrinsic risks and resilience in large-scale interdependent infrastructure networks.
For generic interdependent networks, an agent-based simulation approach is adopted. In this approach, the interdependent network is modeled as a weighted bi-directed network where nodes represent infrastructure components and links denote the interconnections. For evaluating the risks of cascading failures and the network's resilience, a hybrid risk measure based on the well-known Inoperability Input-Output Model (IIM) using expert judgments is developed. In the process, to handle the issue of epistemic uncertainty associated with subjective infrastructure dependency data, a method based on possibility theory is also proposed. Later, the hybrid risk measure is extended to develop two resilience indexes for quantifying the criticality and susceptibility of infrastructure components and ranking algorithms are presented. In addition, the hybrid risk measure is combined with socio-economic characteristics obtained from census data to develop a priority index to quantify the risks of cascading failures in various urban communities.
With regard to infrastructure-specific networks, the dissertation developed infrastructure ranking and prioritization methods for two distinct transportation systems, specifically road networks, and marine port systems, based on empirical disaster data. For characterizing the resilience of road networks, the dissertation proposed three indicators based on the concepts of resilience triangle and extreme travel time observations. The dissertation combined time series decomposition techniques with anomaly detection algorithms to segregate disaster effects from normal traffic patterns. For characterizing the risks of natural hazards to port systems, the dissertation employed disaster impact data along with international trade data and identified the ports with the highest risks.Civil, Architectural, and Environmental Engineerin
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