Biometric Data Art: Personalized Narratives and Multimodal Interaction

Biometric technology has brought enhancements to identification and access control. As more digital applications request people to input their biometric data as a more convenient and secure method of identification, the possibility of losing their personal data and identities may increase. The phenomenon of biometric data abuse causes one to question what their true identity may be and what methods can be used to define identity and hidden narratives. The questions of identification and the insecurity of biometric data have become my inspiration, providing artistic approaches to the manipulation of biometric data and having the potential to suggest new directions for solving the problems. To do so, in-depth investigation of the narratives beyond the visual features of the biometric data is necessary. This content can create a close link between an artwork and its audience by causing the latter to become deeply engaged with the artwork through their own stories.This dissertation examines narratives and artistic explorations discovered from one form of biometric data, fingerprints, drawing on insights from various fields such as genetics, hand analysis, and biology. It also presents contributions on new ways of creating interactive media artworks using fingerprint data based on visual feature analysis of the data and multimodal interaction to explore their sonic signatures. Therefore, the artwork enriches interactive media art by incorporating personalization into the artistic experience, and creates unique personalized experience for each audience member. This thesis documents developments and productions of a series of artworks, Digiti Sonus, by focusing on its conceptual approaches, design, techniques, challenges and future directions

A Survey on the Contributions of Software-Defined Networking to Traffic Engineering

Since the appearance of OpenFlow back in 2008, software-defined networking (SDN) has gained momentum. Although there are some discrepancies between the standards developing organizations working with SDN about what SDN is and how it is defined, they all outline traffic engineering (TE) as a key application. One of the most common objectives of TE is the congestion minimization, where techniques such as traffic splitting among multiple paths or advanced reservation systems are used. In such a scenario, this manuscript surveys the role of a comprehensive list of SDN protocols in TE solutions, in order to assess how these protocols can benefit TE. The SDN protocols have been categorized using the SDN architecture proposed by the open networking foundation, which differentiates among data-controller plane interfaces, application-controller plane interfaces, and management interfaces, in order to state how the interface type in which they operate influences TE. In addition, the impact of the SDN protocols on TE has been evaluated by comparing them with the path computation element (PCE)-based architecture. The PCE-based architecture has been selected to measure the impact of SDN on TE because it is the most novel TE architecture until the date, and because it already defines a set of metrics to measure the performance of TE solutions. We conclude that using the three types of interfaces simultaneously will result in more powerful and enhanced TE solutions, since they benefit TE in complementary ways.European Commission through the Horizon 2020 Research and Innovation Programme (GN4) under Grant 691567 Spanish Ministry of Economy and Competitiveness under the Secure Deployment of Services Over SDN and NFV-based Networks Project S&NSEC under Grant TEC2013-47960-C4-3-

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

Graph-based, systems approach for detecting violent extremist radicalization trajectories and other latent behaviors, A

2017 Summer.Includes bibliographical references.The number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in both the U.S and Western Europe. While detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. Recent terrorist attack successes highlight the real possibility of missed signals from, or continued radicalization by, individuals whom the authorities had formerly investigated and even interviewed. Additionally, beyond considering just the behavioral dynamics of a person of interest is the need for investigators to consider the behaviors and activities of social ties vis-à-vis the person of interest. We undertake a fundamentally systems approach in addressing these challenges by investigating the need and feasibility of a radicalization detection system, a risk assessment assistance technology for law enforcement and intelligence agencies. The proposed system first mines public data and government databases for individuals who exhibit risk indicators for extremist violence, and then enables law enforcement to monitor those individuals at the scope and scale that is lawful, and account for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. In this thesis, we first identify the operational deficiencies of current law enforcement and intelligence agency efforts, investigate the environmental conditions and stakeholders most salient to the development and operation of the proposed system, and address both programmatic and technical risks with several initial mitigating strategies. We codify this large effort into a radicalization detection system framework. The main thrust of this effort is the investigation of the technological opportunities for the identification of individuals matching a radicalization pattern of behaviors in the proposed radicalization detection system. We frame our technical approach as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT (Investigative Search for Graph Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific data sets, a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists, and a large, real-world BlogCatalog dataset serving as a proxy for the type of intelligence or law enforcement data networks that could be utilized to track the radicalization of violent extremists. We also extended INSiGHT by developing a non-combinatorial neighbor matching technique to enable analysts to maintain visibility of potential collective threats and conspiracies and account for the role close social ties have in an individual's radicalization. This enhancement was validated on small, synthetic radicalization-specific datasets as well as the large BlogCatalog dataset with real social network connections and tagging behaviors for over 80K accounts. The results showed that our algorithm returned whole and partial subgraph matches that enabled analysts to gain and maintain visibility on neighbors' activities. Overall, INSiGHT led to consistent, informed, and reliable assessments about those who pose a significant risk for some latent behavior in a variety of settings. Based upon these results, we maintain that INSiGHT is a feasible and useful supporting technology with the potential to optimize law enforcement investigative efforts and ultimately enable the prevention of individuals from carrying out extremist violence. Although the prime motivation of this research is the detection of violent extremist radicalization, we found that INSiGHT is applicable in detecting latent behaviors in other domains such as on-line student assessment and consumer analytics. This utility was demonstrated through experiments with real data. For on-line student assessment, we tested INSiGHT on a MOOC dataset of students and time-stamped on-line course activities to predict those students who persisted in the course. For consumer analytics, we tested the performance on a real, large proprietary consumer activities dataset from a home improvement retailer. Lastly, motivated by the desire to validate INSiGHT as a screening technology when ground truth is known, we developed a synthetic data generator of large population, time-stamped, individual-level consumer activities data consistent with an a priori project set designation (latent behavior). This contribution also sets the stage for future work in developing an analogous synthetic data generator for radicalization indicators to serve as a testbed for INSiGHT and other data mining algorithms

Geologic Model Parameterization for Efficient History Matching of Conventional and Unconventional Reservoirs

Proper characterization of heterogeneous rock properties and natural/induced fracture properties is essential for optimizing field development plan and reliable estimation of Estimated Ultimate Recovery (EUR) in conventional and unconventional reservoirs. It is achieved by reconciling the geologic model to the dynamic production and pressure data, otherwise known as history matching. However, history matching of high resolution reservoir models with heterogeneous features and complex fracture properties is challenging because it poses non-uniqueness and stability issues of the highly underdetermined problems. This dissertation proposes novel reservoir model parameterization methods to regularize the ill-posed problem and enhance the efficiency of history matching. We also show practical feasibility of the proposed method by various field cases. First, the spatial properties of rock and fluid are simultaneously calibrated by grid adjacency-based parameterizations to seismic and pressure data of a heavy oil reservoir in Peace River field, Canada. A novel approach is proposed to integrate frequent time lapse seismic data into high resolution reservoir models based on the seismic onset times. Multiobjective genetic algorithm (MOGA) is utilized to address potential conflicts between seismic and pressure match. We demonstrate the feasibility and robustness of the history matching workflow with MOGA and simultaneous property calibrations in the parameterized transform domain. Second, a novel multi-resolution parameterization is developed to further improve the regularization when the production data resolution is variant in a reservoir. The multiresolution parameterization adjusts the modal frequencies or resolutions of basis functions to comply with the various data resolutions. Hence, it better regularizes the undermined history matching problem compared to previous studies. Third, the grid adjacency-based parameterization is extended to parameterize reservoir models with various fracture geometries simulated by embedded discrete fracture model (EDFM). Analytical basis coefficient sensitivity to production data is calculated with the resulting basis and streamline-based sensitivity. Employing a hierarchical multi-scale workflow and the analytic sensitivity, matrix and fracture properties in EDFM are efficiently calibrated with the proposed parameterization

Habitus and Embodied Institutions; A Study of Manufacturing Enterprises in Kabul’s Conflict-Affected Market Economy and Adaptive Strategies for Enterprise Continuation during 2002-2018

This research studies the effects of embodied institutions rooted in social structures on the individual decision to enter manufacturing activities in Kabul during 2002-2018 – a political economy characterized by political conflict and a market-oriented ‘enabling environment approach’ (EEA). The EEA taken as the macro-level policy backdrop, the study draws on the dialectical relationship between the patriarchal family and Quranic injunctions pertaining to socio-economic life to draw the embodied institutions, which are then used as micro-analytic tools for explaining data patterns pertaining to the investment decision and the subsequent strategies for enterprise continuation. Using a ‘convergent parallel mixed method’, the research has adopted the Bourdieusian framework, in particular the concepts of habitus and field. Rooted in the family-religion relationship, the effects of investor habitus are observed in structuring the decision to enter manufacturing sector and the subsequent encounter with this field, including strategies for enterprise continuation. The latter, moreover, is seen as closely patterned along the patriarchal family hierarchy, reproducing in large degree the social structure where habitus is produced in the context. Building on this empirical analysis, the study relies on qualitative indicators to argue that the sector has not grown structurally significant in Kabul’s economy during 2002-2018. Habitus’ effect is also observed as a conduit for transmitting the hierarchy of power in the family to the production sector. Targeted state/bureaucratic intervention is therefore required in such a context to promote this sector’s growth within a modified EEA framework, and to moderate the uninterrupted transfer of the patriarchal family hierarchy into this sector through political and regulatory means

Diffusion-weighted magnetic resonance imaging in diagnosing graft dysfunction : a non-invasive alternative to renal biopsy.

The thesis is divided into three parts. The first part focuses on background information including how the kidney functions, diseases, and available kidney disease treatment strategies. In addition, the thesis provides information on imaging instruments and how they can be used to diagnose renal graft dysfunction. The second part focuses on elucidating the parameters linked with highly accurate diagnosis of rejection. Four parameters categories were tested: clinical biomarkers alone, individual mean apparent diffusion coefficient (ADC) at 11-different b- values, mean ADCs of certain groups of b-value, and fusion of clinical biomarkers and all b-values. The most accurate model was found to be when the b-value of b=100 s/mm2 and b=700 s/mm2 were fused. The third part of this thesis focuses on a study that uses Diffusion-Weighted MRI to diagnose and differentiate two types of renal rejection. The system was found to correctly differentiate the two types of rejection with a 98% accuracy. The last part of this thesis concludes the work that has been done and states the possible trends and future avenues