Дисертації з теми "Neighborhood framework"

Thesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture, 1997.
Includes bibliographical references (leaves 109-110).
Communities and the connections between them act as the foundation for the fabric of great cities. Suburbs were originally intended as a utopian alternative to crowded urban conditions. However, the current model of residential suburban development in the United States grows less affordable for many Americans, segregates private life to a realm exclusive of community and wastefully consumes material resources . While unchecked development diminishes the rural landscape, urban neighborhoods deteriorate, lacking the resources and amenities of new developments. The transformation of blighted urban neighborhoods into dense, mixed-use communities is a viable alternative to suburban sprawl. This thesis proposes to explore methods of configuring a community within an existing urban site: its streets, lots, and buildings; to conserve land and resources, make housing affordable for a wider range of incomes, and perpetuate a sense of individual identity and community vitality. The strategies explored will be developed into a series of guidelines or urban code for the site. Layout of streets, lots, buildings and open spaces will be determined for a small community. Guidelines will be established not only for housing within the project, but those services necessary to support a viable community: commercial centers, open space, and institutional facilities. These guidelines will allow development of the site at an architectural scale.
by Samuel Joseph DeSollar.
M.Arch.

This thesis proposes a computationally efficient home energy management system to optimize the electricity payment and improve the occupant's comfort degree by appropriately scheduling all devices of the home. It incorporates solar panels, battery systems, thermostatically controlled appliances, and deferrable appliances. Also, this thesis develops a coordinated framework for the operation of multiple home energy management systems in a residential neighborhood based on the optimal and secure operation of the grid. The coordinated load scheduling framework enables customers to cooperate to optimize energy consumption at the neighborhood level and prevents any limitation violation in the grid operational constraints.

Vehicular ad-hoc networks (VANETs) are envisioned to provide many road and safety applications that will improve drivers' awareness and enhance the driving experience. Many of proposed applications are location-based that depend on sharing the location information of vehicles and events among neighboring nodes. The location-based applications should provide vehicle operators with knowledge of the current surrounding conditions to help them make appropriate traveling decisions, such as avoiding traffic congestion. Drivers expect to receive accurate and reliable information from other vehicles. Therefore, securing localization service integrity is important to support a VANET's overall system reliability. In this thesis, we study the exchanged location information in VANETs and designed a framework to prevent potential security threats that will violate users' privacy and overcome limitations that can impact the exchanged data integrity and reliability. The solution developed a secure neighborhood awareness service and shared localization information management protocol in a VANET. The proposed framework is constructed through several components: (i) a location verification protocol that will secure location information by providing a non-line-of-sight (NLOS) verification protocol to overcome moving obstacle effects; (ii) privacy-preserving location information management to detect data inconsistency and provide a recovery process while preventing attackers from tracking individual vehicles; (iii) a trust model evaluation mechanism based on neighborhood awareness; (iv) an adaptive beacon protocol that will reduce the number of messages and provide quality of service(QoS) control for network managers and authorities. We also propose a security evaluation model that quantifies the security attributes for the localization service in a VANET. The model will help evaluate an integrated security measures that are provided by different components of the network services.

The purpose of this thesis is to provide leaders with a clear explanation of commercial corridor revitalization, standard for evaluation, and justification for the support of existing and future commercial corridor revitalization districts in New Orleans, LA. This thesis begins with a definition of commercial corridor revitalization and discussion of how it has been applied in New Orleans, LA. A standard framework for commercial corridor revitalization development and assessment is also established with several steps for implementation included. To test the feasibility, significance and relevance of the proposed framework, all of the steps mentioned were implemented in the New Orleans East Town Center Case Study. Key findings of this research may contribute to the providing a tool that evaluates criteria for evaluating city-wide policies to support the sustainability and vitality of existing and future commercial corridor revitalization districts in New Orleans, LA.

Master of Regional and Community Planning
Department of Landscape Architecture/Regional and Community Planning
Huston Gibson
Often cities are in a perpetual struggle to increase or maintain community development within their districts; how can cities revitalize struggling neighborhoods? How can cities bring energy and life into portions of the downtown? How can cities encourage development for night-time destinations? There is no clear-cut answer to these questions, but this study will examine what amenities within districts may be in the presence of successful music venues. I will determine the district-based criteria for potential venue placement for success based on multiple case studies of different venues located within St. Louis, MO; Omaha, NE; and Indianapolis, IN as described in Yin’s Case Study Research (2009). These case studies will be compared using the community capital framework (Green & Haines, 2012) using a matrix based upon a) social, b) physical, c) financial, and d) cultural capitals. Each study will contain both qualitative and quantitative analysis in order to tell the story of each district for more comprehensive analysis.

This thesis proposes an object localization and image retrieval framework, which trains a discriminative feature set for each object class. For this purpose, a hierarchical learning architecture, together with a Neighborhood Tree is introduced for object labeling. Initially, a large variety of features are extracted from the regions of the pre-segmented images. These features are, then, fed to the training module, which selects the "
best set of representative features"
, suppressing relatively less important ones for each class. During this study, we attack various problems of the current image retrieval and classification systems, including feature space design, normalization and curse of dimensionality. Above all, we elaborate the semantic gap problem in comparison to human visual system. The proposed system emulates the eye-brain channel in two layers. The first layer combines relatively simple classifiers with low level, low dimensional features. Then, the second layer implements Adaptive Resonance Theory, which extracts higher level information from the first layer. This two-layer architecture reduces the curse of dimensionality and diminishes the normalization problem. The concept of Neighborhood Tree is introduced for identifying the whole object from the over-segmented image regions. The Neighborhood Tree consists of the nodes corresponding to the neighboring regions as its children and merges the regions through a search algorithm. Experiments are performed on a set of images from Corel database, using MPEG-7, Haar and Gabor features in order to observe the power and the weakness of the proposed system. The "
Best Representative Features"
are found in the training phase using Fuzzy ARTMAP [1], Feature-based AdaBoost [2], Descriptor-based AdaBoost, Best Representative Descriptor [3], majority voting and the proposed hierarchical learning architecture. During the experiments, it is observed that the proposed hierarchical learning architecture yields better retrieval rates than the existing algorithms available in the literature.

This dissertation investigates the transitions of urban design models in practice: the ways in which practitioners have adopted the urban design models and the factors that have influenced such adoption. In particular, this dissertation focused on the unexpected consequences of the adaptations of urban design models and distinguished these effects from those stemming from the inherent limitations of urban design models themselves. The major goal of this dissertation is to clarify the patterns of the transitions associated with urban design models in practice (particularly adaptation) to ensure a better understanding their impact on the urban environment. However, the transitions of urban design models in practice are complex phenomena that multiple actors with diverse interests have participated in and implemented numerous principles of the models over a long period of time and in diverse contexts. Therefore, to minimize such complexities while capturing important elements of the diffusion and adaptation processes, this dissertation presented a theoretical framework, the Model-Prototype-Adaptation (MPA) framework, based on recurring patterns of urban design movements. In the MPA framework, a "model" refers to an integrated set of urban design principles derived from a consensus of opinion of the enthusiastic proponents of an urban design movement; "prototypes" are projects developed by enthusiastic proponents who have strong commitment to the model and the movement; and "adaptations" are projects developed by eclectic followers who have weak commitment to the model and the movement and take advantage of the model for their interests and concerns. With these three key elements, the MPA framework hypothesizes two distinct transitions of urban design models in practice: "evolution," the developmental transition from old prototypes to new prototypes by enthusiastic proponents seeking to more effectively embody the model; and "divergence," a "watered down" application of the model in practice by eclectic followers responding to external factors such as market forces. This dissertation fleshed out the proposed basic MPA framework with historical reviews of the three urban design movements (Garden City, City Beautiful, and Modern) and a literature review of innovation-diffusion theories. In particular, the literature review focused on theories that present major factors influencing the adoption of innovations. The theories suggested that the ways in which adopters, who have different innovativeness and roles, perceive the attributes of innovations influence their decisions to adopt the innovations. In addition to the theoretical construction of the MPA framework, this dissertation presented a comparative case study with New Urbanist practices to test the MPA framework in a real world context. In particular, "divergence" of New Urbanism principles was examined specifically through a comparison of the six matched prototype-adaptation pairs of neighborhood developments in the Atlanta area. The case study first hypothesized three predictions about the perceptions and implementation of New Urbanism principles based on the MPA framework, that is, 1) enthusiastic proponents of New Urbanism perceive New Urbanism principles more positively than eclectic followers; 2) prototypes developed by enthusiastic proponents incorporate more New Urbanism principles and do so more thoroughly than adaptations developed by eclectic followers; and 3) New Urbanism principles that actors perceive more positively are implemented more often and more thoroughly. Data for the case study have been collected through interviews, surveys, field observations, planning documents, and local periodicals. The methods of analysis that were used in this study were pattern matching between predictions and observations, the explanation-building for the findings from pattern matching based on detailed contextual information derived from each case, and finally, cross-case synthesis. The comparative analysis showed that the case observations generally confirmed the three predictions. For example, among the New Urbanism principles, the "creation of an identifiable neighborhood" was perceived the most positively and also implemented the most often and thoroughly by both the enthusiastic proponents and the eclectic followers while "access to public transit" was perceived the least positively and implemented least often by both groups. In addition to the general confirmation of the three predictions, the analysis also revealed numerous unexpected findings, and efforts to build explanations for such findings based on the detailed contexts of each case yielded several important insights: the issue of compatibility between the thorough implementation of the New Urbanism model and the supply of affordable housing; the possibility of positive externalities from the proximity of prototypes to adaptations; two distinct flexibility arguments--flexibility for incremental accomplishment and that for contexts; the extent of public-private partnerships that broaden the influence of the New Urbanism principles beyond project boundaries; and communication problems between enthusiastic proponents and eclectic followers.

The user data in social media platforms is an excellent source of information that is beneficial for both commercial and scientific purposes. However, recent times has seen that the user data is not always used for good, which has led to higher demands on user privacy. With accurate statistical research data being just as important as the privacy of the user data, the relevance of differential privacy has increased. Differential privacy allows user data to be accessible under certain privacy conditions at the cost of accuracy in query results, which is caused by noise. The noise is based on a tuneable constant ε and the global sensitivity of a query. The query sensitivity is defined as the greatest possible difference in query result between the queried database and a neighboring database. Where the neighboring database is defined to differ by one record in a tabular database, there are multiple neighborhood notions for edge-labeled graphs. This thesis considers the notions of edge neighborhood, node neighborhood, QL-edge neighborhood and QL-outedges neighborhood. To study these notions, a framework was developed in Java to function as a query mechanism for a graph database. ArangoDB was used as a storage for graphs, which was generated by parsing data sets in the RDF format as well as through a graph synthesizer in the developed framework. Querying a database in the framework is done with Apache TinkerPop, and a Laplace distribution is used when generating noise for the query results. The framework was used to study the privacy and utility trade-off of different histogram queries on a number of data sets, while employing the different notions of neighborhood in edge-labeled graphs. The level of privacy is determined by the value on ε, and the utility is defined as a measurement based on the L1-distance between the true and noisy result. In the general case, the notions of edge neighborhood and QL-edge neighborhood are the better alternatives in terms of privacy and utility. Although, there are indications that node neighborhood and QL-outedges neighborhood are considerable options for larger graphs, where the level of privacy for edge neighborhood and QL-edge neighborhood appears to be negligible based on utility measurements.

Thesis (Ph.D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains x, 122 p.; also includes graphics. Includes bibliographical references (p. 113-122). Available online via OhioLINK's ETD Center

La prolifération des médias sociaux, malgré ses nombreux avantages, a entraîné une augmentation des propos injurieux. Ces propos, qui sont généralement blessants, toxiques ou empreints de préjugés à l'encontre d'individus ou de groupes, doivent être détectés et modérés rapidement par les plateformes en ligne. Les modèles d'apprentissage profond pour la détection de propos abusifs ont montré des niveaux de performance élevé quand ils sont évalués sur des données similaires à celles qui ont servi à entraîner les modèles, mais sont nettement moins performants s'ils sont évalués sur des données dont la distribution est différente. En outre, ils nécessitent une quantité considérable de données étiquetées coûteuses pour l'apprentissage. C'est pour cela qu'il est intéressant d'étudier le transfert efficace de connaissances à partir de corpus annotés existants de propos abusifs. Cette thèse étudie le problème de l'apprentissage par transfert pour la détection de propos abusifs et explore diverses solutions pour améliorer le transfert de connaissances dans des scénarios inter corpus.Tout d'abord, nous analysons la généralisabilité inter-corpus des modules de détection de propos abusifs sans accéder à des données cibles pendant le processus d'apprentissage. Nous examinons si la combinaison des représentations issues du thème (topic) avec des représentations contextuelles peut améliorer la généralisabilité. Nous montrons que l'association de commentaires du corpus cible avec des thèmes du corpus d'entraînement peut fournir des informations complémentaires pour un meilleur transfert inter-corpus.Ensuite, nous explorons l'adaptation au domaine non supervisée (UDA, Unsupervised Domain Adaptation), un type d'apprentissage par transfert transductif, avec accès au corpus cible non étiqueté. Nous explorons certaines approches UDA populaires dans la classification des sentiments pour la détection de propos abusifs dans le cadre de corpus croisés. Nous adaptons ensuite une variante du modèle BERT au corpus cible non étiqueté en utilisant la technique du modèle de langue avec masques (MLM Masked Language Model). Alors que cette dernière améliore les performances inter-corpus, les autres approches UDA ont des performances sous-optimales. Notre analyse révèle leurs limites et souligne le besoin de méthodes d'adaptation efficaces pour cette tâche.Comme troisième contribution, nous proposons deux approches d'adaptation au domaine utilisant les attributions de caractéristiques (feature attributions), qui sont des explications a posteriori du modèle. En particulier, nous étudions le problème des corrélations erronées (spurious correlations) spécifiques à un corpus qui limitent la généralisation pour la détection des discours de haine, un sous-ensemble des propos abusifs. Alors que les approches de la littérature reposent sur une liste de termes établie manuellement, nous extrayons et pénalisons automatiquement les termes qui causent des corrélations erronées. Nos approches dynamiques améliorent les performances dans le cas de corpus croisés par rapport aux travaux précédents, à la fois indépendamment et en combinaison avec des dictionnaires prédéfinis.Enfin, nous considérons le transfert de connaissances d'un domaine source avec beaucoup de données étiquetées vers un domaine cible, où peu d'instances étiquetées sont disponibles. Nous proposons une nouvelle stratégie d'apprentissage, qui permet une modélisation flexible de la proximité relative des voisins récupérés dans le corpus source pour apprendre la quantité de transfert utile. Nous incorporons les informations de voisinage avec une méthode de transport optimal (Optimal Transport ) qui exploite la géométrie de l'espace de représentation (embedding space) . En alignant les distributions conjointes de l'embedding et des étiquettes du voisinage, nous montrons des améliorations substantielles dans des corpus de discours haineux de taille réduite
The proliferation of social media, despite its multitude of benefits, has led to the increased spread of abusive language. Such language, being typically hurtful, toxic, or prejudiced against individuals or groups, requires timely detection and moderation by online platforms. Deep learning models for detecting abusive language have displayed great levels of in-corpus performance but underperform substantially outside the training distribution. Moreover, they require a considerable amount of expensive labeled data for training.This strongly encourages the effective transfer of knowledge from the existing annotated abusive language resources that may have different distributions to low-resource corpora. This thesis studies the problem of transfer learning for abusive language detection and explores various solutions to improve knowledge transfer in cross-corpus scenarios.First, we analyze the cross-corpus generalizability of abusive language detection models without accessing the target during training. We investigate if combining topic model representations with contextual representations can improve generalizability. The association of unseen target comments with abusive language topics in the training corpus is shown to provide complementary information for a better cross-corpus transfer.Secondly, we explore Unsupervised Domain Adaptation (UDA), a type of transductive transfer learning, with access to the unlabeled target corpus. Some popular UDA approaches from sentiment classification are analyzed for cross-corpus abusive language detection. We further adapt a BERT model variant to the unlabeled target using the Masked Language Model (MLM) objective. While the latter improves the cross-corpus performance, the other UDA methods perform sub-optimally. Our analysis reveals their limitations and emphasizes the need for effective adaptation methods suited to this task.As our third contribution, we propose two DA approaches using feature attributions, which are post-hoc model explanations. Particularly, the problem of spurious corpus-specific correlations is studied that restrict the generalizability of classifiers for detecting hate speech, a sub-category of abusive language. While the previous approaches rely on a manually curated list of terms, we automatically extract and penalize the terms causing spurious correlations. Our dynamic approaches improve the cross-corpus performanceover previous works both independently and in combination with pre-defined dictionaries.Finally, we consider transferring knowledge from a resource-rich source to a low-resource target with fewer labeled instances, across different online platforms. A novel training strategy is proposed, which allows flexible modeling of the relative proximity of neighbors retrieved from the resource-rich corpus to learn the amount of transfer. We incorporate neighborhood information with Optimal Transport that permits exploitingthe embedding space geometry. By aligning the joint embedding and label distributions of neighbors, substantial improvements are obtained in low-resource hate speech corpora

Les problèmes d’optimisation combinatoire sont devenus la cible de nombreuses recherches scientifiques pour leur importance dans la résolution de problèmes académiques et de problèmes réels rencontrés dans le domaine de l’ingénierie et dans l’industrie. La résolution de ces problèmes par des méthodes exactes ne peut être envisagée à cause des délais de traitement souvent exorbitants que nécessiteraient ces méthodes pour atteindre la (les) solution(s) optimale(s). Dans cette thèse, nous nous sommes intéressés au contexte algorithmique de résolution des problèmes combinatoires, et au contexte de modélisation de ces problèmes. Au niveau algorithmique, nous avons appréhendé les méthodes hybrides qui excellent par leur capacité à faire coopérer les méthodes exactes et les méthodes approchées afin de produire rapidement des solutions. Au niveau modélisation, nous avons travaillé sur la spécification et la résolution exacte des problématiques complexes de fouille des ensembles de motifs en étudiant tout particulièrement le passage à l’échelle sur des bases de données de grande taille. D'une part, nous avons proposé une première parallélisation de l'algorithme DGVNS, appelée CPDGVNS, qui explore en parallèle les différents clusters fournis par la décomposition arborescente en partageant la meilleure solution trouvée sur un modèle maître-travailleur. Deux autres stratégies, appelées RADGVNS et RSDGVNS, ont été proposées qui améliorent la fréquence d'échange des solutions intermédiaires entre les différents processus. Les expérimentations effectuées sur des problèmes combinatoires difficiles montrent l'adéquation et l'efficacité de nos méthodes parallèles. D'autre part, nous avons proposé une approche hybride combinant à la fois les techniques de programmation linéaire en nombres entiers (PLNE) et la fouille de motifs. Notre approche est complète et tire profit du cadre général de la PLNE (en procurant un haut niveau de flexibilité et d’expressivité) et des heuristiques spécialisées pour l’exploration et l’extraction de données (pour améliorer les temps de calcul). Outre le cadre général de l’extraction des ensembles de motifs, nous avons étudié plus particulièrement deux problèmes : le clustering conceptuel et le problème de tuilage (tiling). Les expérimentations menées ont montré l’apport de notre proposition par rapport aux approches à base de contraintes et aux heuristiques spécialisées
Combinatorial optimization problems have become the target of many scientific researches for their importance in solving academic problems and real problems encountered in the field of engineering and industry. Solving these problems by exact methods is often intractable because of the exorbitant time processing that these methods would require to reach the optimal solution(s). In this thesis, we were interested in the algorithmic context of solving combinatorial problems, and the modeling context of these problems. At the algorithmic level, we have explored the hybrid methods which excel in their ability to cooperate exact methods and approximate methods in order to produce rapidly solutions of best quality. At the modeling level, we worked on the specification and the exact resolution of complex problems in pattern set mining, in particular, by studying scaling issues in large databases. On the one hand, we proposed a first parallelization of the DGVNS algorithm, called CPDGVNS, which explores in parallel the different clusters of the tree decomposition by sharing the best overall solution on a master-worker model. Two other strategies, called RADGVNS and RSDGVNS, have been proposed which improve the frequency of exchanging intermediate solutions between the different processes. Experiments carried out on difficult combinatorial problems show the effectiveness of our parallel methods. On the other hand, we proposed a hybrid approach combining techniques of both Integer Linear Programming (ILP) and pattern mining. Our approach is comprehensive and takes advantage of the general ILP framework (by providing a high level of flexibility and expressiveness) and specialized heuristics for data mining (to improve computing time). In addition to the general framework for the pattern set mining, two problems were studied: conceptual clustering and the tiling problem. The experiments carried out showed the contribution of our proposition in relation to constraint-based approaches and specialized heuristics

abstract: The increasing isolation and segregation of children in American cities and suburbs is of special significance. This has meant a loss of freedom for children to explore their neighborhood and city as they get older, their exclusion from varied contacts with diverse adults in a variety of settings, and their consequent inability to learn from personal experience and observation, so essential to social and emotional development. The purpose of this study is to measure the differences in child-friendliness between neighborhoods with different income levels by developing an indicator framework that can be used by planning departments and other local authorities based on available data. The research also focus on what other factor (besides income) influences child-friendliness in a city at the neighborhood level. If a relationship does exist, how big is the difference in terms of child-friendliness between low-income and high-income neighborhoods, and what indicators play the most important role in creating the difference? Neighborhoods in the city of Glendale, Arizona serve as case studies to aid in refining the assessment method, and show the potential for how cities can become more child-friendly. The neighborhoods were selected based on income, same size and different location.
Dissertation/Thesis
M.U.E.P. Urban and Environmental Planning 2011

The social and neurosciences are moving toward a conceptualization of the psychological construct of sense of place in relation with spatial cognition, place imageability, and meaning. To help advance progress, this dissertation proposes a conceptual framework of sense of place that includes variables of spatial navigational strategy (i.e., egocentric and allocentric) and place imageability using notions of edges, paths, landmarks, districts, nodes. Three studies using different methods tested the proposed framework. Study 1 used a questionnaire and an interview-based protocol analysis to examine whether navigational strategy associated with participants’ levels of sense of place for recalled urban neighbourhoods. Preliminary work investigating whether sense of place and spatial navigation varied with place imageability was also done using qualitative analyses. Participants used more egocentric and allocentric strategies during cognitive map navigation when sense of place was stronger compared to when they recalled places for which they felt weak or neutral levels of sense of place. Seven categories were revealed from participants’ qualitative descriptions of urban place visualizations after completing three sense of place scales (i.e., home-sense, compactness, environment, safety, vibrancy, design, and aesthetics) and differed depending on sense of place condition. Study 2 enabled participants to articulate recollections of settings for which they felt different strengths of sense of place via a cognitive mapping task. Results reinforced the notion that individuals who experience a strong level of sense of place for an urban environment also recall more of the physical features that make it imageable. Existing literature was confirmed by this study’s results that paths and landmarks are integral to urban place imageability. Study 3 gathered information about community members’ current representations of their urban neighbourhood. Results supported hypotheses based on results of Studies 1 and 2. Nodes, edges, and landmarks were found to be particularly meaningful to residents’ spatial understanding of their neighbourhood. The fact that more allocentric strategies than egocentric strategies were used in each of the three place imageability conditions (compared to non-significant differences in sense of place conditions in Study 1) highlights compelling future research questions concerning the three variables of the proposed conceptual framework of sense of place. Similar to Study 1, qualitative analyses in Study 3 revealed paths as the predominant meaningful place imageable feature noted by residents. Thematic information about the features in each area reported to have meaning for residents indicate the categories of environment, aesthetics, and design as most prevalent. As a whole, this dissertation can inform future environmental psychology research, as well as the practices of urban planners, as they consider spatial navigation and place imageable attributes in relation to the psychological construct of sense of place in urban environments. Planners and researchers alike may benefit from this dissertation as they respond to human spatial needs while facilitating a sense of attachment and identity toward, and compatibility with, city spaces. Finally, findings may assist social scientists in clarifying how sense of place develops in urban neighbourhoods, and how it is experienced over time.
Graduate

The present dissertation develops an invariant framework for 3D gesture comparison studies. 3D gesture comparison without Lagrangian models is challenging not only because of the lack of prediction provided by physics, but also because of a dual geometry representation, spatial dimensionality and non-linearity associated to 3D-kinematics. In 3D spaces, it is difficult to compare curves without an alignment operator since it is likely that discrete curves are not synchronized and do not share a common point in space. One has to assume that each and every single trajectory in the space is unique. The common answer is to assert the similitude between two or more trajectories as estimating an average distance error from the aligned curves, provided that the alignment operator is found. In order to avoid the alignment problem, the method uses differential geometry for position and orientation curves. Differential geometry not only reduces the spatial dimensionality but also achieves view invariance. However, the nonlinear signatures may be unbounded or singular. Yet, it is shown that pattern recognition between intrinsic signatures using correlations is robust for position and orientation alike. A new mapping for orientation sequences is introduced in order to treat quaternion and Euclidean intrinsic signatures alike. The new mapping projects a 4D-hyper-sphere for orientations onto a 3D-Euclidean volume. The projection uses the quaternion invariant distance to map rotation sequences into 3D-Euclidean curves. However, quaternion spaces are sectional discrete spaces. The significance is that continuous rotation functions can be only approximated for small angles. Rotation sequences with large angle variations can only be interpolated in discrete sections. The current dissertation introduces two multi-scale approaches that improve numerical stability and bound the signal energy content of the intrinsic signatures. The first is a multilevel least squares curve fitting method similar to Haar wavelet. The second is a geodesic distance anisotropic kernel filter. The methodology testing is carried out on 3D-gestures for obstetrics training. The study quantitatively assess the process of skill acquisition and transfer of manipulating obstetric forceps gestures. The results show that the multi-scale correlations with intrinsic signatures track and evaluate gesture differences between experts and trainees.