“Neighborly Adoption” examines the predominance of adoption narratives in canonical transatlantic novels and highlights their intervention in a relatively unknown and surprisingly contentious discourse on child adoption. In the 1840s and 1850s, American and British reformers, politicians, and authors were thinking through issues surrounding a growing population of abandoned children in metropolitan cities and the flourishing practice and legal codification of private adoption. While institutional care was still on the rise in this period, writers like Harriet Beecher Stowe, Charles Dickens, and Maria Susanna Cummins criticized these methods, endorsing adoption as a more appropriate model of displaced child care. While critics like Carol J. Singley and Mark C. Jerng have read nineteenth-century adoption narratives as commentaries on whom might be included or excluded from national citizenship, I argue that the adoption plot should be understood as a thoroughly transatlantic phenomenon. American and British authors whose novels were popular on both sides of the Atlantic promote and interrogate what I call “neighborly adoption,” a practice in which a local community of individuals or families collectively raise a displaced child. In these narratives, varied members of the neighborhood—single, married, male, female, poor, and rich—have beneficial and empowering relationships with the children in their community, regardless of biological relation to them. Though adoption today is largely associated with individualistic values—i.e. completing one’s family, a child’s best interest—this project reveals the collective interests at the heart of adoption in nineteenth-century transatlantic literature.

This thesis work presents a non-parametric learning method, the Extended Nearest Neighbor (ENN) algorithm, as a tool for data disaggregation in smart grids. The ENN algorithm makes the prediction according to the maximum gain of intra-class coherence. This algorithm not only considers the K nearest neighbors of the test sample but also considers whether these K data points consider the test sample as their nearest neighbor or not. So far, ENN has shown noticeable improvement in the classification accuracy for various real-life applications. To further enhance its prediction capability, in this thesis we propose to incorporate a metric learning algorithm, namely the Large Margin Nearest Neighbor (LMNN) algorithm, as a training stage in ENN. Our experiments on real-life energy data sets have shown significant performance improvement compared to several other traditional classification algorithms, including the classic KNN method and Support Vector Machines.

<p>Entering the era of big data, human beings are faced with an unprecedented amount of geometric data today. Many computational challenges arise in processing the new deluge of geometric data. A critical one is data uncertainty: the data is inherently noisy and inaccuracy, and often lacks of completeness. The past few decades have witnessed the influence of geometric algorithms in various fields including GIS, spatial databases, and computer vision, etc. Yet most of the existing geometric algorithms are built on the assumption of the data being precise and are incapable of properly handling data in the presence of uncertainty. This thesis explores a few algorithmic challenges in what we call geometric computing over uncertain data.</p><p>We study the nearest-neighbor searching problem, which returns the nearest neighbor of a query point in a set of points, in a probabilistic framework. This thesis investigates two different nearest-neighbor formulations: expected nearest neighbor (ENN), where we consider the expected distance between each input point and a query point, and probabilistic nearest neighbor (PNN), where we estimate the probability of each input point being the nearest neighbor of a query point.</p><p>For the ENN problem, we consider a probabilistic framework in which the location of each input point and/or query point is specified as a probability density function and the goal is to return the point that minimizes the expected distance. We present methods for computing an exact ENN or an \\eps-approximate ENN, for a given error parameter 0 < \\eps < 1, under different distance functions. These methods build an index of near-linear size and answer ENN queries in polylogarithmic or sublinear time, depending on the underlying function. As far as we know, these are the first nontrivial methods for answering exact or \\eps-approximate ENN queries with provable performance guarantees. Moreover, we extend our results to answer exact or \\eps-approximate k-ENN queries. Notably, when only the query points are uncertain, we obtain state-of-the-art results for top-k aggregate (group) nearest-neighbor queries in the L1 metric using the weighted SUM operator.</p><p>For the PNN problem, we consider a probabilistic framework in which the location of each input point is specified as a probability distribution function. We present efficient algorithms for (i) computing all points that are nearest neighbors of a query point with nonzero probability; (ii) estimating, within a specified additive error, the probability of a point being the nearest neighbor of a query point; (iii) using it to return the point that maximizes the probability being the nearest neighbor, or all the points with probabilities greater than some threshold to be the nearest neighbor. We also present some experimental results to demonstrate the effectiveness of our approach.</p><p>We study the convex-hull problem, which asks for the smallest convex set that contains a given point set, in a probabilistic setting. In our framework, the uncertainty of each input point is described by a probability distribution over a finite number of possible locations including a null location to account for non-existence of the point. Our results include both exact and approximation algorithms for computing the probability of a query point lying inside the convex hull of the input, time-space tradeoffs for the membership queries, a connection between Tukey depth and membership queries, as well as a new notion of \\beta-hull that may be a useful representation of uncertain hulls.</p><p>We study contour trees of terrains, which encode the topological changes of the level set of the height value \\ell as we raise \\ell from -\\infty to +\\infty on the terrains, in a probabilistic setting. We consider a terrain that is defined by linearly interpolating each triangle of a triangulation. In our framework, the uncertainty lies in the height of each vertex in the triangulation, and we assume that it is described by a probability distribution. We first show that the probability of a vertex being a critical point, and the expected number of nodes (resp. edges) of the contour tree, can be computed exactly efficiently. Then we present efficient sampling-based methods for estimating, with high probability, (i) the probability that two points lie on an edge of the contour tree, within additive error; (ii) the expected distance of two points p, q and the probability that the distance of p, q is at least \\ell on the contour tree, within additive error and/or relative error, where the distance of p, q on a contour tree is defined to be the difference between the maximum height and the minimum height on the unique path from p to q on the contour tree.</p> / Dissertation

This report deals with designing and implementing of a complete SEND protocol for operating systems GNU/Linux. The first part of the document contains a description of ND and SEND protocols. The second part of the document defines security threats connected with unsecured ND. The third part of the report describes a design and implementation of SEND protocol named sendd . Conclusion of the document is dedicated to a summary of accomplished results and information about future development of this project.

The IPv6 protocol offers with some new functions, one of them is auto configuration. With auto configuration it is possible for nodes, i.e. hosts and routers, for automatically associated with IPv6 addresses without manual configuration. Auto configuration it is another protocol as it uses Neighbor Discovery protocol (ND) messages (ND is mandatory in the IPv6 stack). The main purpose of ND is that nodes can discover other nodes on the local link, perform address resolution, check that addresses are unique, and check the reachability with active nodes. There are exactly the same vulnerabilities of IPv6 as IPv4 and is now exception, ND if not properly secured. IPsec is a standard security mechanism for IPv6 but it does not solve the problem of secure auto configuration due the bootstrapping problem. Therefore the Internet Engineering Task Force (IETF) introduced Secure Neighbor Discovery (SEND). SEND is a mechanism for authentication, message protection, and router authentication. One important element of SEND is the use of Cryptographically Generated Address (CGA) an important mechanism to prove that the sender of the ND message is the actual owner of the address it claims NDprotector is an open-source implementation of SEND served as the basis for the analysis presented in this report. This implementation was evaluated in a small lab environment against some attacks in order to establish if it can defend itself from these attacks. / IPv6 protokollet kom det ett par nya funktioner där en av dem är autokonfiguration. Autokonfiguration gör det möjligt för noder, d.v.s. hostar och routrar för att automatiskt bli tilldelade IPv6 adresser manuell konfigurering. För att autokonfiguration ska fungera så används Neighbor Discovery (ND) meddelanden som är ett obligatoriskt protokoll i IPv6- stacken. ND har till huvudsaklig uppgift att noder kan upptäcka andra noder på den lokala länken, utföra adressöversättningar, kolltrollera så att adresser är unika samt kontrollera tillgängligheten hos aktiva noder. Precis som IPv4 så har IPv6 en hel del sårbarheter och med ND så är det inget undantag då det inte är säkrat. IPsec som är en den standard säkerhets mekanism till IPv6 löser inte problemet på grund av bootstrapping problemet. Det var därför Internet Engineering Task Force (IETF) introducerade Secure Neighbor Discovery (SEND). SEND är en mekanism för autentisering, meddelande skydd och router autentisering. En viktig del av SEND är Cryptographilcally Generated Address (CGA), en teknik som används för att försäkra sig så att det är den sändaren av ND meddelandet som är den riktiga ägaren av den hävdade adressen. NDprotector är en öppen källkods implementation av SEND som jag har valt att ha som grund för denna rapport. Jag kommer att sätta upp NDprotector i en liten labbmiljö där jag kommer att utföra olika attacker samt se efter om det klarar att försvara sig emot attackerna.

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This master's thesis analyses and demonstrates selected IPv6 attacks including two Man-in-the-Middle attacks and one Denial of Service attack - Rogue Router Advertisement, Neighbor Cache Poisoning and Duplicate Address Detection DoS, respectively. In the first part the author presents necessary information related to the issue and provides detailed information on how to realize these attacks in practice using publicly available tools. The second part of the thesis presents various ways of mitigating presented attacks, analyses implementations of some of those countermeasures on Cisco and H3C devices and discussess their applicability.

This paper presents the overall sinkhole distributions and conducts hypothesis tests of sinkhole distributions and sinkhole formation using data stored in the Karst Feature Database (KFD) of Minnesota. Nearest neighbor analysis (NNA) was extended to include different orders of NNA, different scales of concentrated zones of sinkholes, and directions to the nearest sinkholes. The statistical results, along with the sinkhole density distribution, indicate that sinkholes tend to form in highly concentrated zones instead of scattered individuals. The pattern changes from clustered to random to regular as the scale of the analysis decreases from 10-100 km2 to 5-30 km 2 to 2-10 km2. Hypotheses that may explain this phenomenon are: (1) areas in the highly concentrated zones of sinkholes have similar geologic and topographical settings that favor sinkhole formation; (2) existing sinkholes change the hydraulic gradient in the surrounding area and increase the solution and erosional processes that eventually form more new sinkholes.

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy