Formal security analysis of registration protocols for interactive systems: a methodology and a case of study

In this work we present and formally analyze CHAT-SRP (CHAos based Tickets-Secure Registration Protocol), a protocol to provide interactive and collaborative platforms with a cryptographically robust solution to classical security issues. Namely, we focus on the secrecy and authenticity properties while keeping a high usability. In this sense, users are forced to blindly trust the system administrators and developers. Moreover, as far as we know, the use of formal methodologies for the verification of security properties of communication protocols isn't yet a common practice. We propose here a methodology to fill this gap, i.e., to analyse both the security of the proposed protocol and the pertinence of the underlying premises. In this concern, we propose the definition and formal evaluation of a protocol for the distribution of digital identities. Once distributed, these identities can be used to verify integrity and source of information. We base our security analysis on tools for automatic verification of security protocols widely accepted by the scientific community, and on the principles they are based upon. In addition, it is assumed perfect cryptographic primitives in order to focus the analysis on the exchange of protocol messages. The main property of our protocol is the incorporation of tickets, created using digests of chaos based nonces (numbers used only once) and users' personal data. Combined with a multichannel authentication scheme with some previous knowledge, these tickets provide security during the whole protocol by univocally linking each registering user with a single request. [..]Comment: 32 pages, 7 figures, 8 listings, 1 tabl

Assessment of the relationship between organizational culture and lean implementation in the aerospace industry

The aerospace industry is facing a wide range of economic and global challenges that are working together to put tremendous pressure to become more efficient. These challenges are forcing organizations to utilize the skills and competencies of its human resources more effectively. Firms must encourage behaviors and work practices that help elicit the organization’s potential. For most aerospace organizations, lean—a total quality management approach—has become a tool for addressing these challenges and meeting expectations. Many researchers see lean as a general system to improve the profitability of manufacturing, but there is some discontent in implementing lean manufacturing. Some researchers explain that implementing lean requires creating a particular culture. The purpose of the quantitative study is to examine the role that organizational culture has on successful lean implementation. The purpose of this paper is to analyze and determine if there is a relationship between the organizational culture type (Clan, Adhocracy, Hierarchy, and Market) and culture dimension (Flexibility versus Control, and Internal versus External), as the Competing Values Framework and the 3 lean implementation elements (Support, Utilization, and Infrastructure) define. Understanding the relationship between organizational culture and lean implementation elements will provide leadership with useful knowledge to facilitate the implementation of strategies that enhance the effectiveness of their lean initiatives. An exhaustive literature review on the academic and practitioner research provides a foundation for understanding lean manufacturing practices. The study uses a quantitative research approach to analyze the data gathered from an aerospace organization. The researcher utilized an online questionnaire to assess the 3 components of lean implementation and the Organizational Culture Assessment Instrument to assess the organizational cultural type. A sample of 83 completed responses were received and analyzed using one-way ANOVA tests with accompanying eta coefficients for the 3 lean implementation elements with culture type. No significant relationship was found between culture type and support (p = .26), infrastructure (p = .24) or utilization (p = .15)

Topics in Network Analysis with Applications to Brain Connectomics

Large complex network data have become common in many scientific domains, and require new statistical tools for discovering the underlying structures and features of interest. This thesis presents new methodology for network data analysis, with a focus on problems arising in the field of brain connectomics. Our overall goal is to learn parsimonious and interpretable network features, with computationally efficient and theoretically justified methods. The first project in the thesis focuses on prediction with network covariates. This setting is motivated by neuroimaging applications, in which each subject has an associated brain network constructed from fMRI data, and the goal is to derive interpretable prediction rules for a phenotype of interest or a clinical outcome. Existing approaches to this problem typically either reduce the data to a small set of global network summaries, losing a lot of local information, or treat network edges as a ``bag of features'' and use standard statistical tools without accounting for the network nature of the data. We develop a method that uses all edge weights, while still effectively incorporating network structure by using a penalty that encourages sparsity in both the number of edges and the number of nodes used. We develop efficient optimization algorithms for implementing this method and show it achieves state-of-the-art accuracy on a dataset of schizophrenic patients and healthy controls while using a smaller and more readily interpretable set of features than methods which ignore network structure. We also establish theoretical performance guarantees. Communities in networks are observed in many different domains, and in brain networks they typically correspond to regions of the brain responsible for different functions. In connectomic analyses, there are standard parcellations of the brain into such regions, typically obtained by applying clustering methods to brain connectomes of healthy subjects. However, there is now increasing evidence that these communities are dynamic, and when the goal is predicting a phenotype or distinguishing between different conditions, these static communities from an unrelated set of healthy subjects may not be the most useful for prediction. We present a method for supervised community detection, that is, a method that finds a partition of the network into communities that is most useful for predicting a particular response. We use a block-structured regularization and compute the solution with a combination of a spectral method and an ADMM optimization algorithm. The method performs well on both simulated and real brain networks, providing support for the idea of task-dependent brain regions. The last part of the thesis focuses on the problem of community detection in the general network setting. Unlike in neuroimaging, statistical network analysis is typically applied to a single network, motivated by datasets from the social sciences. While community detection has been well studied, in practice nodes in a network often belong to more than one community, leading to the much harder problem of overlapping community detection. We propose a new approach for overlapping community detection based on sparse principal component analysis, and develop efficient algorithms that are able to accurately recover community memberships, provided each node does not belong to too many communities at once. The method has a very low computational cost relative to other methods available for this problem. We show asymptotic consistency of recovering community memberships by the new method, and good empirical performance on both simulated and real-world networks.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145883/1/jarroyor_1.pd

Saberes ancestrales dentro del currículo

https://drive.google.com/drive/folders/1QlzOWaKMFY5hzeAqhEgdAbCIfG0PytrN?us p=sharingLa experiencia pedagógica de un docente se resignifica en la medida en que éste se cuestione, reflexione en torno a su rol y prácticas para lograr descubrir las dificultades que impiden el avance eficaz en el proceso. Así las cosas, es evidente que las prácticas de enseñanza se enriquecen a partir de la experiencia y sobre todo de la reflexión pedagógica en orden a la superación de dificultades y al desarrollo de alternativas que apunten a mejorar y mitigar en lo posible las problemáticas que bloqueen la adquisición de aprendizajes. La propuesta pedagógica que se presenta responde a una realidad que es evidente en contextos étnicos, donde la educación llega de manera poco aterrizada y poco pertinente, generando una desarticulación de lo que se enseña con lo que se aprende, favoreciendo las brechas en cuanto a calidad educativa se refiere.The pedagogical experience of a teacher is resignified to the extent that he is questioned, reflects on his role and practices in order to discover the difficulties that prevent effective progress in the process. Thus, it is evident that teaching practices are enriched from experience and especially from pedagogical reflection in order to overcome difficulties and the development of alternatives that aim to improve and mitigate as far as possible the problems that block the acquisition of learning. The pedagogical proposal that is presented responds to a reality that is evident in ethnic contexts, where education arrives in a poorly grounded and irrelevant way, generating a disarticulation of what is taught with what is learned, favoring gaps in terms of quality educational is concerned