El aprendizaje distribuido como estrategia didáctica en la enseñanza del vocabulario de ELE : Un acercamiento a su uso en el salón escolar sueco

Aprender nuevas palabras en un idioma extranjero, es decir, el léxico necesario que fundamenta la posibilidad del desarrollo de las destrezas comunicativas, constituye uno de los problemas más complejos en el proceso tanto de enseñanza como de aprendizaje del español como lengua extranjera. En relación con el aprendizaje del vocabulario identificamos un posible problema; el riesgo de que el número de palabras aprendidas se olvide aumenta después de la prueba o los ejercicios. Si nuestros alumnos no pueden ampliar su vocabulario su competencia comunicativa tampoco va a desarrollar.Para poder entender por qué ocurre el problema y cómo se podría encontrar otros recursos didácticos que contribuyan a un cambio en el proceso, investigamos un fenómeno conocido por la psicología de la educación como el efecto de la memoria espaciada - un fenómeno cognitivo que se benéfica de las repeticiones, pero siempre distribuidas en el tiempo. Estrategias de enseñanza que utilizan dicho efecto se refiere como aprendizaje distribuido.Mediante un pequeño estudio analizamos el efecto de la memoria espaciada (ME) como método alternativa. De este estudio podemos inferir que existe un efecto de memoria espaciada tangible en el aprendizaje de los alumnos que estudiaron según un modelo distribuido, es decir con repeticiones.Pudimos constatar un resultado positivo en este pequeño estudio piloto. Los alumnos lograron recordar en la examinación el 85% de las palabras ejercitadas en la clase un mes después. Este resultado abre nuevas perspectivas de estudio e indica que puede haber alternativas didácticas en la enseñanza del vocabulario de ELE en el salón escolar sueco.

Studies of Protein-Protein and Protein-Water Interactions by Small Angle X-Ray Scattering, Terahertz Spectroscopy, ASMOS, And Computer Simulation

The protein folding problem has been one of the most challenging subjects in biological physics due to its complexity. Energy landscape theory based on statistical mechanics provides a thermodynamic interpretation of the protein folding process. We have been working to answer fundamental questions about protein-protein and protein-water interactions, which are very important for describing the energy landscape surface of proteins correctly. At first, we present a new method for computing protein-protein interaction potentials of solvated proteins directly from SAXS data. An ensemble of proteins was modeled by Metropolis Monte Carlo and Molecular Dynamics simulations, and the global X-ray scattering of the whole model ensemble was computed at each snapshot of the simulation. The interaction potential model was optimized and iterated by a Levenberg-Marquardt algorithm. Secondly, we report that terahertz spectroscopy directly probes hydration dynamics around proteins and determines the size of the dynamical hydration shell. We also present the sequence and pH-dependence of the hydration shell and the effect of the hydrophobicity. On the other hand, kinetic terahertz absorption (KITA) spectroscopy is introduced to study the refolding kinetics of ubiquitin and its mutants. KITA results are compared to small angle X-ray scattering, tryptophan fluorescence, and circular dichroism results. We propose that KITA monitors the rearrangement of hydrogen bonding during secondary structure formation. Finally, we present development of the automated single molecule operating system (ASMOS) for a high throughput single molecule detector, which levitates a single protein molecule in a 10 µm diameter droplet by the laser guidance. I also have performed supporting calculations and simulations with my own program codes.

Applied web traffic analysis for numerical encoding of SQL Injection attack features.

SQL Injection Attack (SQLIA) remains a technique used by a computer network intruder to pilfer an organisation’s confidential data. This is done by an intruder re-crafting web form’s input and query strings used in web requests with malicious intent to compromise the security of an organisation’s confidential data stored at the back-end database. The database is the most valuable data source, and thus, intruders are unrelenting in constantly evolving new techniques to bypass the signature’s solutions currently provided in Web Application Firewalls (WAF) to mitigate SQLIA. There is therefore a need for an automated scalable methodology in the pre-processing of SQLIA features fit for a supervised learning model. However, obtaining a ready-made scalable dataset that is feature engineered with numerical attributes dataset items to train Artificial Neural Network (ANN) and Machine Leaning (ML) models is a known issue in applying artificial intelligence to effectively address ever evolving novel SQLIA signatures. This proposed approach applies numerical attributes encoding ontology to encode features (both legitimate web requests and SQLIA) to numerical data items as to extract scalable dataset for input to a supervised learning model in moving towards a ML SQLIA detection and prevention model. In numerical attributes encoding of features, the proposed model explores a hybrid of static and dynamic pattern matching by implementing a Non-Deterministic Finite Automaton (NFA). This combined with proxy and SQL parser Application Programming Interface (API) to intercept and parse web requests in transition to the back-end database. In developing a solution to address SQLIA, this model allows processed web requests at the proxy deemed to contain injected query string to be excluded from reaching the target back-end database. This paper is intended for evaluating the performance metrics of a dataset obtained by numerical encoding of features ontology in Microsoft Azure Machine Learning (MAML) studio using Two-Class Support Vector Machines (TCSVM) binary classifier. This methodology then forms the subject of the empirical evaluation.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed timevarying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible realtime term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Shape and chemically anisotropic colloidal particles: A theoretical study of their structure, phase behavior, and slow dynamics

A detailed non-equilibrium state diagram of shape-anisotropic particle fluids is constructed. The effects of particle shape are explored using Naive Mode Coupling Theory (NMCT), and a single particle Non-linear Langevin Equation (NLE) theory. The dynamical behavior of non-ergodic fluids are discussed. We employ a rotationally frozen approach to NMCT in order to determine a transition to center of mass (translational) localization. Both ideal and kinetic glass transitions are found to be highly shape dependent, and uniformly increase with particle dimensionality. The glass transition volume fraction of quasi 1- and 2- dimensional particles fall monotonically with the number of sites (aspect ratio), while 3-dimensional particles display a non-monotonic dependence of glassy vitrification on the number of sites. Introducing interparticle attractions results in a far more complex state diagram. The ideal non-ergodic boundary shows a glass-fluid-gel re-entrance previously predicted for spherical particle fluids. The non-ergodic region of the state diagram presents qualitatively different dynamics in different regimes. They are qualified by the different behaviors of the NLE dynamic free energy. The caging dominated, repulsive glass regime is characterized by long localization lengths and barrier locations, dictated by repulsive hard core interactions, while the bonding dominated gel region has short localization lengths (commensurate with the attraction range), and barrier locations. There exists a small region of the state diagram which is qualified by both glassy and gel localization lengths in the dynamic free energy. A much larger (high volume fraction, and high attraction strength) region of phase space is characterized by short gel-like localization lengths, and long barrier locations. The region is called the attractive glass and represents a 2-step relaxation process whereby a particle first breaks attractive physical bonds, and then escapes its topological cage. The dynamic fragility of fluids are highly particle shape dependent. It increases with particle dimensionality and falls with aspect ratio for quasi 1- and 2- dimentional particles. An ultralocal limit analysis of the NLE theory predicts universalities in the behavior of relaxation times, and elastic moduli. The equlibrium phase diagram of chemically anisotropic Janus spheres and Janus rods are calculated employing a mean field Random Phase Approximation. The calculations for Janus rods are corroborated by the full liquid state Reference Interaction Site Model theory. The Janus particles consist of attractive and repulsive regions. Both rods and spheres display rich phase behavior. The phase diagrams of these systems display fluid, macrophase separated, attraction driven microphase separated, repulsion driven microphase separated and crystalline regimes. Macrophase separation is predicted in highly attractive low volume fraction systems. Attraction driven microphase separation is charaterized by long length scale divergences, where the ordering length scale determines the microphase ordered structures. The ordering length scale of repulsion driven microphase separation is determined by the repulsive range. At the high volume fractions, particles forgo the enthalpic considerations of attractions and repulsions to satisfy hard core constraints and maximize vibrational entropy. This results in site length scale ordering in rods, and the sphere length scale ordering in Janus spheres, i.e., crystallization. A change in the Janus balance of both rods and spheres results in quantitative changes in spinodal temperatures and the position of phase boundaries. However, a change in the block sequence of Janus rods causes qualitative changes in the type of microphase ordered state, and induces prominent features (such as the Lifshitz point) in the phase diagrams of these systems. A detailed study of the number of nearest neighbors in Janus rod systems reflect a deep connection between this local measure of structure, and the structure factor which represents the most global measure of order.

Essays on the term structure of interest rates

This PhD thesis contains three main chapters on macro finance, with a focus on the term structure of interest rates and the applications of state-of-the-art Bayesian econometrics. Except for Chapter 1 and Chapter 5, which set out the general introduction and conclusion, each of the chapters can be considered as a standalone piece of work. In Chapter 2, we model and predict the term structure of US interest rates in a data rich environment. We allow the model dimension and parameters to change over time, accounting for model uncertainty and sudden structural changes. The proposed time-varying parameter Nelson-Siegel Dynamic Model Averaging (DMA) predicts yields better than standard benchmarks. DMA performs better since it incorporates more macro-finance information during recessions. The proposed method allows us to estimate plausible real-time term premia, whose countercyclicality weakened during the financial crisis. Chapter 3 investigates global term structure dynamics using a Bayesian hierarchical factor model augmented with macroeconomic fundamentals. More than half of the variation in the bond yields of seven advanced economies is due to global co-movement. Our results suggest that global inflation is the most important factor among global macro fundamentals. Non-fundamental factors are essential in driving global co-movements, and are closely related to sentiment and economic uncertainty. Lastly, we analyze asymmetric spillovers in global bond markets connected to diverging monetary policies. Chapter 4 proposes a no-arbitrage framework of term structure modeling with learning and model uncertainty. The representative agent considers parameter instability, as well as the uncertainty in learning speed and model restrictions. The empirical evidence shows that apart from observational variance, parameter instability is the dominant source of predictive variance when compared with uncertainty in learning speed or model restrictions. When accounting for ambiguity aversion, the out-of-sample predictability of excess returns implied by the learning model can be translated into significant and consistent economic gains over the Expectations Hypothesis benchmark.

Fragebogen zur Erfassung von Störungen im Unterricht: die Faktorenstruktur der Schülerinnen- und Schülerversion

In diesem Beitrag wird ein neu entwickelter Schülerinnen- und Schülerfragebogen zur Erfassung aggressiver und nicht aggressiver Schülerstörungen, aggressiven Lehrerverhaltens, Störungen des methodisch-didaktischen Settings sowie Klassenführung und Beziehung vorgestellt und die testtheoretischen Kennwerte diskutiert. Die faktorielle Struktur wurde an einer Stichprobe von N=1341 Schülerinnen und Schüler der fünften und sechsten Klasse ermittelt. Eine explorative Faktorenanalyse mit Oblimin-Rotation ergab sieben eindeutige, gut interpretierbare Faktoren, welche den theoretisch postulierten Konstrukten entsprechen. Vier Faktoren erfassen Störungen und drei Faktoren umfassen störungspräventive Merkmale des Unterrichts. Die internen Konsistenzen der Skalen liegen zwischen .60 und .88. (DIPF/Orig.)

Paths to peace: conflict management trajectories in militarized interstate disputes

When multiple third-parties (states, coalitions, and international organizations) intervene in the same conflict, do their efforts inform one another? Anecdotal evidence suggests such a possibility, but research to date has not attempted to model this interdependence directly. The current project breaks with that tradition. In particular, it proposes three competing explanations of how previous intervention efforts affect current intervention decisions: a cost model (and a variant on it, a limited commitments model), a learning model, and a random model. After using a series of Markov transition (regime-switching) models to evaluate conflict management behavior within militarized interstate disputes in the 1946-2001 period, this study concludes that third-party intervention efforts inform one another. More specifically, third-parties examine previous efforts and balance their desire to manage conflict with their need to minimize intervention costs (the cost and limited commitments models). As a result, third-parties intervene regularly using verbal pleas and mediation, but rely significantly less frequently on legal, administrative, or peace operations strategies. This empirical threshold to the intervention costs that third-parties are willing to bear has strong theoretical foundations and holds across different time periods and third-party actors. Furthermore, the analysis indicates that the first third-party to intervene in a conflict is most likely to use a strategy designed to help the disputants work toward a resolution of their dispute. After this initial intervention, the level of third-party involvement declines and often devolves into a series of verbal pleas for peace. Such findings cumulatively suggest that disputants hold the key to effective conflict management. If the disputants adopt and maintain an extreme bargaining position or fail to encourage third-parties to accept greater intervention costs, their dispute will receive little more than verbal pleas for negotiations and peace.

Conceptual guidelines for the international expansion of SMEs from Curaçao

Internationalization and moving to new markets can create many opportunities for small businesses across the globe, but also presents a number of new challenges they will face, which may influence their competitive advantage in the global market -- Present paper aims to provide an internationalization guide for SMEs from Curaçao -- Also the determinants that can impact internationalization will be discussed -- In this paper, three widely researched internationalization models form the basis of the theoretical perspectives of this paper; the traditional Uppsala model, the Network model and the Linkage, Leverage and Learning model

L’interaction comme méthode d’apprentissage du Français langue étrangère (FLE) en Suède

Mon travail s’inspire de l’idée de la Direction nationale de l’enseignement scolaire suédoise (Skolverket, 2011) qui suggère que « les élèves doivent avoir la possibilité de développer une capacité de communication et de compréhension de la langue ». Mon mémoire commence par une introduction générale, continue avec une analyse des manuels utilisés dans l’apprentissage du FLE et des activités favorisant l’interaction - échange réciproque verbale ou non verbale, pour terminer par une conclusion générale. Comme il n’existe pas d’enseignement sans méthodes appropriées, il existe des manuels qu’on peut utiliser afin de faciliter l’apprentissage par l’interaction de la langue cible. Les activités de communication orale et écrite d’ordre pédagogique sont indispensables. Tout comme l’écrit Revue française de pédagogie (1994 :133) « La classe est un système social complexe dont les parties sont en interaction dynamique, acteurs (enseignant et élève[s]), situation, matière selon des statuts sociaux. ». Pour effectuer mes recherches, j’ai porté mon attention sur l’analyse des manuels scolaires qu’il m’a été permis de consulter. Il s’agit des manuels d’entraide comme : Mais oui 3 et Escalade littéraire. Concernant l’analyse du matériel didactique favorisant l’interaction, ces deux manuels proposent des exercices de réflexion selon le niveau de l’apprenant. Dans mon travail, je m’intéresse à l’aspect didactique et linguistique de ces deux manuels scolaires.

La cronémimca de reuniones multipartitas en videoconferencia : Observaciones en torno al silencio de la fase de preparación

En este estudio se examinan los silencios que aparecen durante la fase de preparación de dieciocho reuniones multipartitas por videoconferencia. Partiendo de la metodología del Análisis Conversacional, el análisis del material muestra que la duración de los silencios entre turnos se reduce considerablemente en dos momentos cruciales de la interacción en línea. Esto a su vez confirma la existencia de tres etapas diferenciadas en la preparación: etapa inicial, etapa de transición y etapa audiovisual, en cada una de las cuales los silencios presentan unas características concretas. Se muestra que en el chat escrito también se producen intervalos y lapsos y que el nivel de tolerancia de los participantes hacia el silencio varía en cada una de las etapas, lo cual puede relacionarse con su nivel de atención y disponibilidad. Se sugiere un estándar máximo aproximado para los intervalos que ocurren en la etapa de transición y se demuestra que, en el canal audio-oral, el límite de tolerancia se sitúa por encima de los tres segundos de duración. Por último, este estudio pone de manifiesto la aparición de diferentes ritmos cronémicos a lo largo de la preparación en videoconferencia, donde elementos como la apertura de la cámara web parecen poseer una gran relevancia interaccional.

Virtual knowledge brokering : describing the roles and strategies used by knowledge brokers in a pediatric physiotherapy virtual community of practice

Introduction: Knowledge transfer in pediatric rehabilitation is challenging and requires active, multifaceted strategies. The use of knowledge brokers (KBs) is one such strategy noted to promote clinician behavior change. The success of using KBs to transfer knowledge relies on their ability to adapt to ever-changing clinical contexts. In addition, with the rapid growth of online platforms as knowledge transfer forums, KBs must become effective in virtual environments. Although the role of KBs has been studied in various clinical contexts, their emerging role in specific online environments designed to support evidence-based behavior change has not yet been described. Our objective is to describe the roles of, and strategies used by, four KBs involved in a virtual community of practice to guide and inform future online KB interventions. Methods: A descriptive design guided this study and a thematic content analysis process was used to analyze online KB postings. The Promoting Action on Research in Health Sciences knowledge transfer framework and online andragogical learning theories assisted in the coding. A thematic map was created illustrating the links between KBs' strategies and emerging roles in the virtual environment. Results: We analyzed 95 posts and identified three roles: 1) context architect: promoting a respectful learning environment, 2) knowledge sharing promoter: building capacity, and 3) linkage creator: connecting research-to-practice. Strategies used by KBs reflected invitational, constructivism, and connectivism approaches, with roles and strategies changing over time. Discussion: This study increases our understanding of the actions of KBs in virtual contexts to foster uptake of research evidence in pediatric physiotherapy. Our results provide valuable information about the knowledge and skills required by individuals to fulfill this role in virtual environments.

Tensor-Train decomposition for image classification problems

In these last years a great effort has been put in the development of new techniques for automatic object classification, also due to the consequences in many applications such as medical imaging or driverless cars. To this end, several mathematical models have been developed from logistic regression to neural networks. A crucial aspect of these so called classification algorithms is the use of algebraic tools to represent and approximate the input data. In this thesis, we examine two different models for image classification based on a particular tensor decomposition named Tensor-Train (TT) decomposition. The use of tensor approaches preserves the multidimensional structure of the data and the neighboring relations among pixels. Furthermore the Tensor-Train, differently from other tensor decompositions, does not suffer from the curse of dimensionality making it an extremely powerful strategy when dealing with high-dimensional data. It also allows data compression when combined with truncation strategies that reduce memory requirements without spoiling classification performance. The first model we propose is based on a direct decomposition of the database by means of the TT decomposition to find basis vectors used to classify a new object. The second model is a tensor dictionary learning model, based on the TT decomposition where the terms of the decomposition are estimated using a proximal alternating linearized minimization algorithm with a spectral stepsize.

Computational methods applied to undeciphered scripts from the Aegean and Cyprus: Linear A and Cypro-Minoan

The study of ancient, undeciphered scripts presents unique challenges, that depend both on the nature of the problem and on the peculiarities of each writing system. In this thesis, I present two computational approaches that are tailored to two different tasks and writing systems. The first of these methods is aimed at the decipherment of the Linear A afraction signs, in order to discover their numerical values. This is achieved with a combination of constraint programming, ad-hoc metrics and paleographic considerations. The second main contribution of this thesis regards the creation of an unsupervised deep learning model which uses drawings of signs from ancient writing system to learn to distinguish different graphemes in the vector space. This system, which is based on techniques used in the field of computer vision, is adapted to the study of ancient writing systems by incorporating information about sequences in the model, mirroring what is often done in natural language processing. In order to develop this model, the Cypriot Greek Syllabary is used as a target, since this is a deciphered writing system. Finally, this unsupervised model is adapted to the undeciphered Cypro-Minoan and it is used to answer open questions about this script. In particular, by reconstructing multiple allographs that are not agreed upon by paleographers, it supports the idea that Cypro-Minoan is a single script and not a collection of three script like it was proposed in the literature. These results on two different tasks shows that computational methods can be applied to undeciphered scripts, despite the relatively low amount of available data, paving the way for further advancement in paleography using these methods.

Addressing challenges in heterogeneous embedded systems programming: security and performance

Embedded systems are increasingly integral to daily life, improving and facilitating the efficiency of modern Cyber-Physical Systems which provide access to sensor data, and actuators. As modern architectures become increasingly complex and heterogeneous, their optimization becomes a challenging task. Additionally, ensuring platform security is important to avoid harm to individuals and assets. This study primarily addresses challenges in contemporary Embedded Systems, focusing on platform optimization and security enforcement. The initial section of this study delves into the application of machine learning methods to efficiently determine the optimal number of cores for a parallel RISC-V cluster to minimize energy consumption using static source code analysis. Results demonstrate that automated platform configuration is not only viable but also that there is a moderate performance trade-off when relying solely on static features. The second part focuses on addressing the problem of heterogeneous device mapping, which involves assigning tasks to the most suitable computational device in a heterogeneous platform for optimal runtime. The contribution of this section lies in the introduction of novel pre-processing techniques, along with a training framework called Siamese Networks, that enhances the classification performance of DeepLLVM, an advanced approach for task mapping. Importantly, these proposed approaches are independent from the specific deep-learning model used. Finally, this research work focuses on addressing issues concerning the binary exploitation of software running in modern Embedded Systems. It proposes an architecture to implement Control-Flow Integrity in embedded platforms with a Root-of-Trust, aiming to enhance security guarantees with limited hardware modifications. The approach involves enhancing the architecture of a modern RISC-V platform for autonomous vehicles by implementing a side-channel communication mechanism that relays control-flow changes executed by the process running on the host core to the Root-of-Trust. This approach has limited impact on performance and it is effective in enhancing the security of embedded platforms.