Guaranteed Road Network Search with Small Unmanned Aircraft

The use of teams of small unmanned aircraft in real-world rapid-response missions is fast becoming a reality. One such application is search and detection of an evader in urban areas. This paper draws on results in graph-based pursuit-evasion, developing mappings from these abstractions to primitive motions that may be performed by aircraft, to produce search strategies providing guaranteed capture of road-bound targets. The first such strategy is applicable to evaders of arbitrary speed and agility, offering a conservative solution that is insensitive to motion constraints pursuers may possess. This is built upon to generate two strategies for capture of targets having a known speed bound that require searcher teams of much smaller size. The efficacy of these algorithms is demonstrated by evaluation in extensive simulation using realistic vehicle models across a spectrum of environment classes

Biometric information analyses using computer vision techniques.

Biometric information analysis is derived from the analysis of a series of physical and biological characteristics of a person. It is widely regarded as the most fundamental task in the realms of computer vision and machine learning. With the overwhelming power of computer vision techniques, biometric information analysis have received increasing attention in the past decades. Biometric information can be analyzed from many sources including iris, retina, voice, fingerprint, facial image or even the way one walks with. Facial image and gait, because of their easy availability, are two preferable sources of biometric information analysis. In this thesis, we investigated the development of most recent computer vision techniques and proposed various state-of-the-art models to solve the four principle problems in biometric information analysis including the age estimation, age progression, face retrieval and gait recognition. For age estimation, the modeling has always been a challenge. Existing works model the age estimation problem as either a classification or a regression problem. However, these two types of models are not able to reveal the intrinsic nature of human age. To this end, we proposed a novel hierarchical framework and a ordinal metric learning based method. In the hierarchical framework, a random forest based clustering method is introduced to find an optimal age grouping protocol. In the ordinal metric learning approach, the age estimation is solved by learning an subspace where the ordinal structure of the data is preserved. Both of them have achieved state-of-the-art performance. For face retrieval, specifically under a cross-age setting, we first proposed a novel task, that is given two images, finding the target image which is supposed to have the same identity with the first input and the same age with the second input. To tackle this task, we proposed a joint manifold learning method that can disentangle the identity with the age information. Accompanied with two independent similarity measurements, the retrieval can be easily performed. For aging progression, we also proposed a novel task that has never been considered. We devoted to fuse the identity of one image with the age of another image. By proposing a novel framework based on generative adversarial networks, our model is able to generate close-to-realistic images. Lastly, although gait recognition is an ideal long-distance biometric information task that makes up the shortfall of facial image, existing works are not able to handle large scale data with various view angles. We proposed a generative model to solve this term and achieved promising results. Moreover, our model is able to generate evidences for forensic usage

Human environment interactions and collaborative adaptive capacity building in a resilience framework

2012 Spring.Includes bibliographical references.Being firmly in the Anthropocene Era--a period in humanity's evolution where human behavior and dominance is significantly impacting the earth's systems, my research objective was in response to the concern and call of the National Science Foundation and of the International Human Dimensions Programme on Global Environmental Change that humanity needs to develop new strategies to tackle complex anthropogenic issues impacting the global environment and that there should be a focus on human behavior to effect change. Through a collaborative tri-phase dual model research initiative in the back country of Burntwater, Arizona in the Houck Chapter on the Navajo Nation, a small group of Navajo, using a photovoice and artvoice technique, began an exploration into community issues and concerns. The outcome confirmed that illegal trash dumping was a serious matter to the community in need of attention. Through multiple community gatherings the illegal trash dumping issue was discussed and explored within the workings of a Participatory Social Frame Work of Action - Collaborative Adaptive Capacity Building (PSFA-CACB) conceptual model. Using data from my field site I was able to partially inform a theoretical agent-based model Taking Care of the Land - Human Environment Interactions (TCL-HEI). Using the TCL-HEI model I was then able to theoretically illustrate within a resilience framework a social-ecological system regime basin shift from an undesirable state to a desirable state. This shift resulted from a change in the system's stability landscape variables through the introduction of a combination of consultative behavior and economic incentive model parameters. The ultimate objective of the tri-phase dual-model approach was to show how local and regional sustainable entrepreneurial and cooperative action might change illegal trash dumping behavior through a recycling and waste-to-fuels processing program. I further show how the effect of such an initiative would result in mitigating environmental degradation by lessening illegal trash dumping sites and landfill deposits while creating jobs and empowering a local population. It is my hope that the ramifications of this study might be considered at the Chapter, Agency and Nation levels on the Navajo Nation to explore possibilities of contracting-out for the development of a clean-energy waste-to-fuels processing facility and program

Distributed Systems and Mobile Computing

The book is about Distributed Systems and Mobile Computing. This is a branch of Computer Science devoted to the study of systems whose components are in different physical locations and have limited communication capabilities. Such components may be static, often organized in a network, or may be able to move in a discrete or continuous environment. The theoretical study of such systems has applications ranging from swarms of mobile robots (e.g., drones) to sensor networks, autonomous intelligent vehicles, the Internet of Things, and crawlers on the Web. The book includes five articles. Two of them are about networks: the first one studies the formation of networks by agents that interact randomly and have the ability to form connections; the second one is a study of clustering models and algorithms. The three remaining articles are concerned with autonomous mobile robots operating in continuous space. One article studies the classical gathering problem, where all robots have to reach a common location, and proposes a fast algorithm for robots that are endowed with a compass but have limited visibility. The last two articles deal with the evacuations problem, where two robots have to locate an exit point and evacuate a region in the shortest possible time

Immigration and refugee protection act : balancing individual rights and national security

Early in 2001 the federal government tabled Bill C-11, the Immigration and Refugee Protection Act (IRPA), new comprehensive legislation intended to overhaul Canada’s immigration laws. By this time, refugees had become singled out above other classes of immigrants as a threat to Canadian national security because a backlog of applicants had permitted thousands of failed refugee claimants to remain in Canada and allowed a small number of undesirable individuals to commit serious crimes and to plan and support terrorist activities. This led to public concern that refugees were a potential threat to public safety, national security, and even Canada-US relations. As a result, there were calls for Canada to tighten up its refugee system by adopting a more restrictive adjudication process for refugee claims. At the same time, there were calls for Canada to maintain a fair and open refugee system. This thesis uses discussions from parliamentary committees, an ethical analysis of the right of liberal states to exert sovereignty at the expense of their obligation to protect refugees, and key provisions in both the 1976 Immigration Acts and IRPA, to compare how the two important public goods discussed above, the rights of refugees and the need to protect national security, were balanced in the IRPA. Three major research questions guide this analysis: What provided the impetus for extra legal and security provisions in the IRPA related to refugees? Did amendments in the IRPA constitute a fundamental change to Canada’s refugee determination system? Did the IRPA strike a right balance between safeguarding the rights of refugees and safeguarding national security? These questions represent key elements of the refugee/ security nexus, a problem that the IRPA was designed to address. My thesis finds that for the most part the IRPA provided a balanced legislative response to this problem and that it protected the rights of refugees and moderately enhanced provisions related to public safety and national security, although for the latter it did not constitute a marked improvement, nor for the former did it address the outstanding issue of security certificates. But these two deficiencies in the IRPA serve to highlight the inherent tension Canada has had enacting security measures while maintaining fundamental rights for refugees in a changing geo-political environment

SCALING REINFORCEMENT LEARNING THROUGH FEUDAL MULTI-AGENT HIERARCHY

Militaries conduct wargames for training, planning, and research purposes. Artificial intelligence (AI) can improve military wargaming by reducing costs, speeding up the decision-making process, and offering new insights. Previous researchers explored using reinforcement learning (RL) for wargaming based on the successful use of RL for other human competitive games. While previous research has demonstrated that an RL agent can generate combat behavior, those experiments have been limited to small-scale wargames. This thesis investigates the feasibility and acceptability of -scaling hierarchical reinforcement learning (HRL) to support integrating AI into large military wargames. Additionally, this thesis also investigates potential complications that arise when replacing the opposing force with an intelligent agent by exploring the ways in which an intelligent agent can cause a wargame to fail. The resources required to train a feudal multi-agent hierarchy (FMH) and a standard RL agent and their effectiveness are compared in increasingly complicated wargames. While FMH fails to demonstrate the performance required for large wargames, it offers insight for future HRL research. Finally, the Department of Defense verification, validation, and accreditation process is proposed as a method to ensure that any future AI application applied to wargames are suitable.Lieutenant Colonel, United States ArmyApproved for public release. Distribution is unlimited

From third-degree to third-generation interrogation strategies: putting science into the art of criminal interviewing

The interviewing strategies of the American law-enforcement system are more than seventy-five years old. Psychologically manipulative and guilt-presumptive, these methodologies replaced the brutal third-degree interrogation tactics of the previous century, but have recently come under scrutiny for being both ethically and operationally unsound. These findings have prompted a paradigm shift toward more ethical, effective, and scientifically validated tactics. This thesis set out to explore the advantages of integrating next-generation practices into the interview-training ethos of the Department of Homeland Security (DHS) Office of Professional Responsibility (OPR)—the internal affairs component of Immigration and Customs Enforcement. An evaluation of evidence-based interrogation practices and governmental policy analyses, along with insight from subject-matter experts, provided the data for this exploration. A series of recommendations derived from the lessons learned of the U.K. PEACE model, the practices of the Federal Law Enforcement Training Center, and research by the High-Value Detainee Interrogation Group offered insight for the optimal training of interviewing techniques and their long-term retention in the field. Assuming the recommendations for OPR are both scalable and replicable, this model should be relevant and valuable for the professional practices of other DHS agencies responsible for conducting interrogations as well as for law-enforcement agencies nationwide.http://archive.org/details/fromthirddegreet1094553028Senior Special Agent, Department of Homeland Security Immigration and Customs Enforcement, Office of Professional ResponsibilityApproved for public release; distribution is unlimited

Web Archive Services Framework for Tighter Integration Between the Past and Present Web

Web archives have contained the cultural history of the web for many years, but they still have a limited capability for access. Most of the web archiving research has focused on crawling and preservation activities, with little focus on the delivery methods. The current access methods are tightly coupled with web archive infrastructure, hard to replicate or integrate with other web archives, and do not cover all the users\u27 needs. In this dissertation, we focus on the access methods for archived web data to enable users, third-party developers, researchers, and others to gain knowledge from the web archives. We build ArcSys, a new service framework that extracts, preserves, and exposes APIs for the web archive corpus. The dissertation introduces a novel categorization technique to divide the archived corpus into four levels. For each level, we will propose suitable services and APIs that enable both users and third-party developers to build new interfaces. The first level is the content level that extracts the content from the archived web data. We develop ArcContent to expose the web archive content processed through various filters. The second level is the metadata level; we extract the metadata from the archived web data and make it available to users. We implement two services, ArcLink for temporal web graph and ArcThumb for optimizing the thumbnail creation in the web archives. The third level is the URI level that focuses on using the URI HTTP redirection status to enhance the user query. Finally, the highest level in the web archiving service framework pyramid is the archive level. In this level, we define the web archive by the characteristics of its corpus and building Web Archive Profiles. The profiles are used by the Memento Aggregator for query optimization