Reaktiv programmering är ett programmeringsparadigm som har utvecklats för att underlätta byggande av händelsedrivna reaktiva applikationer. Dess förespråkare hävdar att det kan leda till ökad kodkvalitet, men få studier har utförts för att underbygga dessa påståenden empiriskt. Denna studie syftade till att undersöka skillnaden i kodkomplexitet och kodläsbarhet mellan traditionell imperativ programmering och reaktiv programmering. En fallstudie utfördes där koden för ett befintligt öppen källkodsprojekt omstrukturerades, vilket resulterade i en imperativ version och en reaktiv version av samma kod. Verktyg för statisk källkodsanalys användes för att undersöka om några förändringar i kodläsbarhet och kodkomplexitet kunde upptäckas. Vidare diskuteras huruvida resultaten av den senaste teknikens läsbarhetsmätvärden ger en exakt förutsägelse av kodläsbarheten i reaktiva kodbaser, eller om kanske dessa mätvärden behöver modifieras för att göra dem tillämpliga på det reaktiva paradigmet. Våra resultat visar att läsbarheten påverkades negativt av reaktiv programmering när den mättes med senaste teknikens läsbarhetsmätvärden, men kodkomplexiteten minskades betydligt. / Reactive programming is a programming paradigm that has been proposed to facilitate building eventdriven reactive applications. It is claimed by its proponents to increase code quality, but few studies have been performed to substantiate these claims empirically. This study aimed to explore the difference in code complexity and code readability between traditional imperative programming and reactive programming. A case study was performed where the code of an existing open source project was restructured, resulting in an imperative version and a reactive version of the same code. Static analysis tools were used to investigate if any changes in code readability and code complexity could be detected. Furthermore, it is discussed whether the results of the state-of-the-art readability metrics give an accurate prediction of code readability in reactive code bases, or if perhaps these metrics need to be modified to make them applicable to the reactive paradigm. Our findings show that the readability was negatively affected by reactive programming when measured with state-of the-art readability metrics, however code complexity was decreased significantly

This study focused on investigating students’ complex L2 motivational systems in an equally complex educational environment. It analyzed students’ motivation while learning Spanish in a Language for Specific Purposes (LSP) course taught in a student-centered technology-enhanced classroom at a university in the Midwest. The innovative curriculum for the course emphasized student interaction, and revolved around the development of a collaborative entrepreneurial wiki project. This study addressed the expanding call for considering motivation as multidimensional, changing and contextualized (Crookes & Schmidt, 2006; Dörnyei, MacIntyre, & Henry, 2015) by steering away from simplistic cause–effect quantitative paradigms. It addressed the topic through the lens of Complex Dynamic Systems Theory (CDST) and utilized two contemporary L2 motivation frameworks for making sense of the data: the L2 Motivational Self System (L2MSS) (Dörnyei, 2005, 2009), and Directed Motivational Currents (DMCs) (Muir & Dörnyei, 2013; Dörnyei, Ibrahim, & Muir 2015). It adopted in-depth qualitative case study methodology to answer the following research questions: 1. How can students' L2 motivations be described while learning Business Spanish through an innovative curriculum? 2. What are the factors affecting students’ L2 motivations throughout the course? Four students enrolled in this class during the Fall 2015 were randomly selected as the participants for this study. Data were collected throughout the academic semester and included: 1) four in-depth interviews with each student; 2) the work students developed collaboratively on the wiki; 3) course evaluations submitted to the instructor of the course; 4) students’ academic records and 5) classroom observations of the times students worked on the wiki. Findings revealed that the self system interacted with the motivational system of students in this class, and was determinant in guiding their motivational trajectories throughout the semester. The self system was also instrumental in shaping experiences students had related to the elements of the immediate L2 learning context. Moreover, factors stemming from the immediate L2 context that fulfilled students’ self-concordant goals were also instrumental in keeping students engaged with the process of learning; and completing the wiki project became a shared goal for students in each group. These factors led students to experience a group motivational wave — with characteristics of group DMCs — as they became more and more involved with the wiki project for the course. In terms of the work completed, students’ motivations translated into detailed wiki projects that incorporated more content than specified by the project’s guidelines and requirements. Finally, the study also generated insights into areas in which the L2MSS and DMCs could be expanded or refined in order to better account for students’ complex motivational trajectories.

Distributed Approximation is a new and rapidly developing discipline that lies at the crossroads of various well-established areas of Computer Science - Distributed Computing, Approximation Algorithms, Graph Theory and often, Computational Geometry. This thesis focuses on the design and analysis of distributed algorithms to solve optimization problems that usually arise in large-scale, heavily dynamic, resource constrained networks, e.g. wireless ad-hoc and sensor networks, P2P systems, mobile networks etc. These problems can often be abstracted by variations of well-known combinatorial optimization problems, such as topology control, clustering etc. Many of these problems are known to be hard (NP-complete). But we need fast and light-weight distributed algorithms for these problems, that yield near-optimal solutions. The results presented in this thesis can be broadly divided in two parts. The first part contains a set of results that obtain improved solutions to the classic problem of computing a sparse "backbone" for Wireless Sensor Networks (WSNs). In graph-theoretic terms, the goal is to compute a spanning subgraph of the input graph, that is sparse, lightweight and has low stretch. The term "low stretch" indicates that in spite of dropping many edges, the distance between any two nodes in the graph is not increased by much. We model WSNs as geometric graphs - unit ball graphs, quasi-unit ball graphs etc. in Euclidean spaces, as well as in more general metric spaces of low doubling dimension. We identify and exploit a variety of geometric features of those models to obtain our results. In the second part of the thesis we focus on distributed algorithms for clustering problems. We present several distributed approximation algorithms for clustering problems (e.g., minimum dominating set, facility location problems) that improve on best known results so far. The main contribution here is the design of distributed algorithms where the running time is a "tunable" parameter. The advent of distributed systems of unprecedented scale and complexity motivates the question of whether it is possible to design algorithms that can provide non-trivial approximation guarantees even after very few rounds of computation and message exchanges. We call these algorithms "k-round algorithms". We design k-round algorithms for various clustering problems that yield non-trivial approximation factors even if k is a constant. Additionally, if k assumes poly-logarithmic values, our algorithms match or improve on the best-known approximation factors for these problems.

Les méthodes classiques d'apprentissage automatique reposent souvent sur une hypothèse simple mais restrictive: les données du passé et du présent sont générées selon une même distribution. Cette hypothèse permet de développer directement des garanties théoriques sur la précision de l'apprentissage. Cependant, elle n'est pas réaliste dans un grand nombre de domaines applicatifs qui ont émergé au cours des dernières années.Dans cette thèse, nous nous intéressons à quatre problèmes différents en intelligence artificielle, unis par un point commun: tous impliquent un transfer de connaissance d'un domaine vers un autre. Le premier problème est le raisonnement par analogie et s'intéresse à des assertions de la forme "A est à B ce que C est à D". Le second est l'apprentissage par transfert et se concentre sur des problèmes de classification dans des contextes où les données d'entraînement et de test ne sont pas de même distribution (ou n'appartiennent même pas au même espace). Le troisième est l'apprentissage sur flux de données, qui prend en compte des données apparaissant continument une à une à haute fréquence, avec des changements de distribution. Le dernier est le clustering collaboratif et consiste à faire échanger de l'information entre algorithmes de clusterings pour améliorer la qualité de leurs prédictions.La principale contribution de cette thèse est un cadre général pour traiter les problèmes de transfer. Ce cadre s'appuie sur la notion de complexité de Kolmogorov, qui mesure l'information continue dans un objet. Cet outil est particulièrement adapté au problème de transfert, du fait qu'il ne repose pas sur la notion de probabilité tout en étant capable de modéliser les changements de distributions.En plus de cet effort de modélisation, nous proposons dans cette thèse diverses discussions sur d'autres aspects ou applications de ces problèmes. Ces discussions s'articulent autour de la possibilité de transfert dans différents domaines et peuvent s'appuyer sur d'autres outils que la complexité. / Classical learning methods are often based on a simple but restrictive assumption: The present and future data are generated according to the same distributions. This hypothesis is particularly convenient when it comes to developing theoretical guarantees that the learning is accurate. However, it is not realistic from the point of view of applicative domains that have emerged in the last years.In this thesis, we focus on four distinct problems in artificial intelligence, that have mainly one common point: All of them imply knowledge transfer from one domain to the other. The first problem is analogical reasoning and concerns statements of the form "A is to B as C is to D". The second one is transfer learning and involves classification problem in situations where the training data and test data do not have the same distribution (nor even belong to the same space). The third one is data stream mining, ie. managing data that arrive one by one in a continuous and high-frequency stream with changes in the distributions. The last one is collaborative clustering and focuses on exchange of information between clustering algorithms to improve the quality of their predictions.The main contribution of this thesis is to present a general framework to deal with these transfer problems. This framework is based on the notion of Kolmogorov complexity, which measures the inner information of an object. This tool is particularly adapted to the problem of transfer, since it does not rely on probability distributions while being able to model the changes in the distributions.Apart from this modeling effort, we propose, in this thesis, various discussions on aspects and applications of the different problems of interest. These discussions all concern the possibility of transfer in multiple domains and are not based on complexity only.

L'Internet des objets (loT) est devenu une réalité et ses applications pressenties vont fortement augmenter la demande de vidéo mobile. En conséquence, les systèmes montent en complexité algorithmique et le portage du codage vidéo sur plates-formes embarquées devient problématique. Les nouveaux contenus vidéo 4K et 360°, venant avec des résolutions spatiales (8K, 16K) et temporelles (120 images/seconde élevées compliquent encore le problème. Il est donc nécessaire de réduire l'empreinte des nouveaux codec tels que HEVC tout en préservant les performances en compression et en qualité d'image de ces codecs, La performance énergétique limitée des batteries des systèmes embarqués pousse à proposer de nouvelle méthodes pour ajuster et contrôler la complexité et l'énergie des codecs HEVC. Ce document propose un ensemble d'études dont l'objectif est d'ajuster et de contrôler la complexité et donc la consommation énergétique de l'encodeur HEVC. Deux méthodes de prédiction de découpe de CTU sont proposées : la première basée sur une approche statistique utilisant la variance de l'image et la seconde utilisant l'intelligence artificielle. À partir de cette prédiction, une méthode est proposée pour ajuster la complexité de l'encodage HEVC. Cette solution étend l'espace de recherche autour de la prédiction et alloue la complexité dans l'image afin de minimiser les dégradations en termes de compression et de qualité. Enfin un système de contrôle temps réel de la complexité d'encodage est proposé. Il démontre l'applicabilité de contributions de ce document en maintenant la complexité d'encodage proche d'une consigne. / The Internet of Things (loT) is now a reality. Forthcoming applications will boost mobile video demand to an unprecedented level. The induced increase in computational complexity is a challenge when executing in real-time new video coding standards on embedded platforms, limited in computing, memory, and energy. New 4K UHD and 360-degree video contents coming with high spatial (SK, 16K) and temporal (120fp resolutions further complicate the problem. In this context, codecs such as HEVC (High Efficiency Vide Coding) must be worked on to reduce their complexity while preserving the bitrate and image quality. Th bounded energy density of embedded system's batteries requires designers to propose new methods scaling and controlling the complexity and energy consumption of HEVC codecs. This document presents a set of studies aiming at scaling and controlling the complexity, and therefore the energy consumption, of HEVC Intra encoding. Two methods of quad-tree partitioning prediction in "one-shot are proposed: one based on variance-aware statistic approach and one based on Machine Learning using data-mining classifiers. From the obtained prediction, a generic tunable complexity scheme of HEVC encoding is introduced. It expands the search space around the original partitioning prediction and allocates complexit in a frame while minimizing performance loss in terms of bitrate and visual quality. Finally, a real-time contr system is created that dynamically manages the encoding process to keep the encoding complexity under specific tarjet. It demonstrates the a licability of the mayor contributions of this document.

<div>Since the 1990s, grammatical complexity is a topic that has received considerable attention in various fields of applied linguistics, such as English for academic purposes, second language acquisition, language testing, and second language writing (Bulté & Housen, 2012). Many scholars in applied linguistics have recently argued that grammatical complexity has primarily been represented by clausal features (e.g., subordinate clauses), and it is important to study grammatical complexity as a multidimensional construct based on both clausal features and phrasal features (Biber, Gray & Poonpon, 2011; Norris & Ortega, 2009). Thus, this dissertation is a corpus-based investigation on how the use of noun phrases is influenced by two situational characteristics of a university context: academic level and first- and second-English language background.</div><div><br></div><div>I built my corpus by extracting 200 essays from British Academic Written English Corpus, which represents academic writing of (1) undergraduate and graduate students and (2) L1 and L2 students. Noun phrase complexity was then operationalized to the 11 noun modifiers proposed in the hypothesized developmental index of writing complexity features in Biber, Gray and Poonpon (2011). The 11 noun modifiers were extracted from the corpus and counted for statistical analysis via a set of Python programs. With a Chi-square test followed by a residual analysis, I found that both academic level and first- and second-English language background influenced noun phrase complexity but in distinct ways. The influence of academic level is primarily associated with three phrasal modifiers (i.e., attributive adjectives, premodifying nouns, and appositive NPs) and two clausal modifiers (i.e., relative clauses and noun complement clauses). The undergraduate corpus includes more of the two clausal modifiers, whereas the graduate corpus has more of the three phrasal modifiers. This suggests that, in these 200 essays, graduate students tend to build more compressed NPs than undergraduate students. However, the influence of first- and second-English language background derives from a much broader range of noun modifiers, including eight noun modifiers (e.g., attributive adjectives, relative clauses, infinitive clauses). More diverse NP patterns with different noun modifiers are in the L1 corpus than in the L2 corpus. Surprisingly, the L2 corpus has more phrasal noun modifiers (i.e., attributive adjectives, premodifying nouns), which has been argued to indicate advanced levels of academic writing. A qualitative analysis on selected essays reveals that some cases of attributive adjectives and premodifying nouns are repeatedly used by L2 students to help content development in their writing. Overall, this dissertation adds an additional piece of evidence on the importance of noun phrase complexity in writing research.</div><div><br></div>

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<p>Osnovni cilj istraživanja predstavlja ispitivanje faktora<br />kompleksnosti koji utiču na performanse međunarodnih razvojnih<br />projekata. Savremeni izazovi u upravljanju međunarodnim razvojnim<br />projektima doveli su do povećane kompleksnosti ove vrste projekata,<br />što je u poslednjih nekoliko godina podstaklo istraživače da<br />publikuju veliki broj radova na ovu temu. Dobijeni model<br />kompleksnosti razvijen je kombinovanjem rezultata Delfi<br />istraživanja sa rezultatima kvantitativnog istraživanja. Model<br />kompleksnosti može biti primenjen od strane praktičara i akademika<br />za procenu nivoa kompleksnosti međunarodnih razvojnih projekata.</p> / <p>The main goal of the research is to examine complexity factors that influence<br />International Development projects. Contemporary challenges in ID projects<br />have led to their growth in their complexity, which in recent years has driven<br />researches in recent years to publish numerous papers that deal with this<br />topic, demonstrating its importance in current project management research.<br />The research in the dissertation was done by conducting mixed research<br />method &ndash; by comparing Delphi methodology in the qualitative part, with<br />quantitative research. The model represents a novel theoretical lens for<br />assessing complexity in ID projects by both academics and practitioners.</p>

The purpose of this study was to examine cognitive complexity, extremity, and the relationship between social repertory grids and life events repertory grids (LERG) in people who report a history of trauma. Effects of type of trauma on complexity and extremity scores of each type of grid were examined. Prior research into repertory grids and trauma has used only one type of grid, predominantly social grids or LERGs. Therefore, a natural, progressive step in the grid research involved investigating how individuals integrate social and life event constructs. It was hypothesized, and results show, that there is a positive correlation between complexity scores and extremity scores of social grids and LERGs. However it was not found that there was a negative correlation between trauma history and complexity scores, and that trauma acts as a moderator for cognitive complexity. Instead, it appears that the social facet of experience is key to understanding perception of traumatic experiences. Additionally, number of traumas experienced might affect social construct elaboration.

The ongoing sustainability crisis offer numerous, multifaced societal challenges as a result of the ongoing degradation of socio-ecological systems by human activity causing massive ecological damage and human suffering. Overcoming these difficulties begs for the rapid transition of society towards sustainability. This desire for urgent action has been hindered by the lack of coordinated global leadership focused on addressing these challenges and implementing a transition towards a sustainable future. The sustainability crisis and its manifestations, which include for example climate change, air and water pollution, deforestation and social segregation, are interconnected and volatile issues whose parts influence and impact each other causing the crisis to worsen. The earth system is pushed towards tipping points from beyond which it may become impossible to maintain the human civilization. The failure of leadership to address the wicked nature of these crises means humanity has been left ill-equipped to deal with the complex problems of sustainability.  This thesis considers the role of Education for Sustainable Development (ESD) in overcoming these issues and operating as a leverage point towards sustainability. It focuses on investigating how the development of sustainability leadership education in Higher Education can contribute to addressing the sustainability crisis. It looks at the role that educators can play in designing learning environments that ensure leaders and leadership capable of addressing wicked problems posed by global unsustainability. The aim of this research is to investigate what educators should consider when designing learning environments that promote the qualities needed for leading in complexity towards sustainability. It does this by examining a number of ESD programs as case studies to investigate the efficacy of those programs at creating sustainability outcomes within their students. It also undertakes a literature review to describe and articulate the unique challenges faced by sustainability leaders from a personal and professional perspective. The study is situated closely to the ongoing ESD discussion regarding competencies-based learning for sustainability and the research aims to provide some contribution to that dialogue. It does this through the investigation of competencies acquisition and the discussion of emerging areas of leadership that may hold beneficial outcomes for the development and practice of sustainability leaders.   The results of the thesis suggest a number of outcomes for consideration by educators and include a number of main findings. Firstly, educational programs can be capable of achieving the acquisition of ‘sustainability’ competencies within their students, but if these competencies are not taught within a larger sustainability contextualization, then students can fail to see the purpose of the competencies ‘for’ sustainability. Secondly, reflective practices, developed as the result of reflective pedagogies, can provide beneficial qualities in students as future sustainability leaders and require distinct pedagogical structures in order to guide reflective practices towards sustainability outcomes. Finally, a number of unique personal and professional challenges to sustainability leadership exist and need to be overcome if the domain of sustainability is to ensure the ongoing resilience and wellbeing of individuals and groups acting as sustainability leaders.   This research suggests a novel contribution to a number of areas within ESD research, including creating knowledge within the competencies discussion regarding emerging areas of study that may influence the future of defined sustainability competencies. It also highlights the need for educators to consider the role of wellbeing and resilience in current and future sustainability leaders.