Algorithmique & programmation en langage C - vol.2: Sujets de travaux pratiques

LicenceCe document regroupe 64 sujets de travaux pratiques (TP) et d’examen, écrits pour différents enseignements d’algorithmique et de programmation en langage C donnés à la Faculté d’ingénierie de l’Université Galatasaray (Istanbul, Turquie), entre 2005 et 2014. Il s’agit du deuxième volume d’une série de 3 documents, comprenant également le support de cours (volume 1) et un recueil des corrigés de ces sujets (volume 3).Les sujets d’examen ont été retravaillés pour prendre la forme de sujets de TP. Les 64 sujets proposés ont été ordonnés de manière à correspondre à la progression des concepts parallèlement étudiés en cours (cf. le volume 1). En ce qui concerne les concepts les plus simples, il est difficile de sortir des exercices assez classiques, d’autant plus que les étudiants ne disposent à ce moment-là du cours que d’un bagage technique très réduit. Cependant, nous avons tenté d’aborder des thèmes plus originaux dans les sujets venant plus tard. Nous nous sommes particulièrement attachés à proposer des exercices basés sur une approche graphique de l’algorithmique, grâce à l’utilisation de la bibliothèque SDL (Simple DirectMedia Layer).Le volume horaire d’un (ou même de deux) cours classique(s) ne permet bien entendu pas d’effectuer tous les TP proposés ici. Il faut remarquer que si certains sujets introduisent un concept nouveau, d’autres, au contraire, se concentrent sur l’approfondissement d’une ou plusieurs notions déjà utilisées. L’idée est plutôt, pour l’enseignant, de disposer d’un assortiment d’exercices divers, dans lequel il peut choisir ce dont il a besoin, en fonction des étudiants, de la progression effective et des objectifs de son cours. Pour les étudiants, il s’agit de proposer des exercices pouvant offrir une vision alternative à celle donnée dans le cours suivi, ou bien d’approfondir certains points vus en cours

Data Science sous Python: Algorithme, Statistique, DataViz, DataMining et Machine-Learning

Data Science is a technical discipline that associates statistical concepts to computer algorithms and calculations for processing and modeling mass data derived from observation phenomena (economic, industrial, commercial, financial, managerial, social, etc. ..). In the area of Business Intelligence, the Data Science has become an indispensable tool to help decision making for company managers in the sense that it allows to exploit and valorize the internal and external informational patrimony of the company. In recent years, Python has rapidly become one of the most used programming languages at by Data Scientists to exploit the growing potential of Big Data. The gain of popularity of this language, today, is largely explained by the numerous possibilities offered by its powerful libraries including that of numerical analysis and scientific computing (numpy, scipy, pandas), data visualization ( matplotlib) but also Machine Learning (scikit-learn). Presented in a pedagogical approach, this manuscript revisits the concepts essential for mastering Data Science with Python. The work is organized into seven chapters. The first chapter is is devoted to the presentation of the basics of programming on Python. The second chapter is devoted to the study of strings and regular expressions. The aim of this chapter is to familiarize with the processing and the use of strings values which constitute the values of variables commonly found in unstructured databases. The third chapter is devoted to presenting the methods of file management and text processing. The purpose of this chapter is to deepen the previous chapter by presenting the methods commonly used for the processing of unstructured data which are generally in the form of text files. The fourth chapter is devoted to the presentation of the methods of processing and organization of data originally stored as data tables. The fifth chapter is dedicated to presenting classical statistical analysis methods (descriptive analyzes, statistical tests, linear and logistic regression, ...). The sixth chapter is devoted to presenting of methods of datavisualization: histograms, bars graphs, pie-plots, box-plots, scatter-plots, trend curves, 3D graphs, ...). Finally, the seventh chapter is devoted to presenting of methods of data mining and machine-learning. In this chapter, we present methods such as data dimensions reductions (Principal Components Analysis, Factor Analysis, Multiple Correspondence Analysis) but also of classification methods (Hierarchical Classification, K-Means Clustering, Support Vector Machine, Random Forest)

Data Science sous Python: Algorithme, Statistique, DataViz, DataMining et Machine-Learning

Data Science is a technical discipline that associates statistical concepts to computer algorithms and calculations for processing and modeling mass data derived from observation phenomena (economic, industrial, commercial, financial, managerial, social, etc. ..). In the area of Business Intelligence, the Data Science has become an indispensable tool to help decision making for company managers in the sense that it allows to exploit and valorize the internal and external informational patrimony of the company. In recent years, Python has rapidly become one of the most used programming languages at by Data Scientists to exploit the growing potential of Big Data. The gain of popularity of this language, today, is largely explained by the numerous possibilities offered by its powerful libraries including that of numerical analysis and scientific computing (numpy, scipy, pandas), data visualization ( matplotlib) but also Machine Learning (scikit-learn). Presented in a pedagogical approach, this manuscript revisits the concepts essential for mastering Data Science with Python. The work is organized into seven chapters. The first chapter is is devoted to the presentation of the basics of programming on Python. The second chapter is devoted to the study of strings and regular expressions. The aim of this chapter is to familiarize with the processing and the use of strings values which constitute the values of variables commonly found in unstructured databases. The third chapter is devoted to presenting the methods of file management and text processing. The purpose of this chapter is to deepen the previous chapter by presenting the methods commonly used for the processing of unstructured data which are generally in the form of text files. The fourth chapter is devoted to the presentation of the methods of processing and organization of data originally stored as data tables. The fifth chapter is dedicated to presenting classical statistical analysis methods (descriptive analyzes, statistical tests, linear and logistic regression, ...). The sixth chapter is devoted to presenting of methods of datavisualization: histograms, bars graphs, pie-plots, box-plots, scatter-plots, trend curves, 3D graphs, ...). Finally, the seventh chapter is devoted to presenting of methods of data mining and machine-learning. In this chapter, we present methods such as data dimensions reductions (Principal Components Analysis, Factor Analysis, Multiple Correspondence Analysis) but also of classification methods (Hierarchical Classification, K-Means Clustering, Support Vector Machine, Random Forest)

Développement d'outils d’analyse de la motricité fine pour l’investigation de troubles neuromusculaires : théorie, prototype et mise en application dans le contexte des accidents vasculaires cérébraux

RÉSUMÉ Cette thèse examine la possibilité d’évaluer la susceptibilité à un accident vasculaire cérébral (AVC) à partir des attributs des mouvements humains. Ces travaux reposent sur l’hypothèse selon laquelle l’existence d’un état pré-AVC peut, dans certains cas, être détecté par l’évaluation de la santé neuromotrice du patient. À défaut de disposer de données longitudinales permettant d’étudier directement cette conjecture, nos résultats démontrent que le diagnostic des principaux facteurs de risque d’AVC est effectivement réalisable à partir des propriétés des mouvements. Ces conclusions sont tirées à la suite de l’analyse transversale des réponses de 120 sujets à neuf tests neuromoteurs. Par cette étude des liens entre la motricité et la présence de conditions menant potentiellement à l’AVC, on espère stimuler l’intérêt des chercheurs en santé pour l’hypothèse – rapportée de façon anecdotique par plusieurs cliniciens – de l’existence d’un état pré-AVC. Les investigations nécessaires à cette démonstration ont été menées dans le cadre de la Théorie Cinématique et suivent principalement trois axes directeurs, soit l’étude fondamentale du mouvement humain, le développement d’outils d’extraction permettant la modélisation lognormale des mouvements et l’analyse statistique des paramètres lognormaux dans le but du diagnostic des principaux facteurs de risque d’AVC (diabète, obésité, tabagisme, problèmes cardiaques, alcoolisme, hypertension et hypercholestérolémie). En introduction de la première partie de cette thèse sont répertoriés les différents indices disséminés dans la littérature scientifique étayant l’existence de liens entre les mouvements humains et les principaux facteurs de risque d’AVC. Observant que la présence de tels liens est supportée par l’état des connaissances actuelles, le paradigme offert par la Théorie Cinématique ainsi que la modélisation lognormale qui en découle sont adoptés, puis présentés. L’apparition de profils de forme lognormale au niveau des primitives du mouvement est ensuite expliquée d’un point de vue original. Une fois ces bases établies, il a été possible de procéder à l’analyse des données dont nous disposions, ce qui a mis en lumière un certain nombre de phénomènes fondamentaux relatifs à l’étude du contrôle moteur, dont trois sont particulièrement importants. En premier lieu, il a été relevé que la nature des mouvements est intrinsèquement proportionnelle.----------ABSTRACT This Ph. D. thesis investigates the brain stroke susceptibility assessment based on the movement analysis of data acquired using neuromuscular tests. This work is rooted in the hypothesis of the existence of a pre-stroke state which can sometimes be detected by looking at the properties of a patient’s neuromuscular system. As the study of this hypothesis would require longitudinal data that were unavailable, our analysis concentrates on the demonstration that the brain stroke risk factors can be diagnose from a human movement analysis. This conclusion derives from a transversal study of 120 subject’s responses to nine neuromuscular tests. It is hoped that this investigation on the links between fine motor control and brain stroke risk factors can stimulate the interest of the medical community for the anecdotic report, by some clinicians, of the possible existence of a pre-stroke state. The work presented herein was made under the Kinematic Theory and it follows three main axes which are 1) the fundamental study of human movements, 2) the design of extraction algorithms allowing the lognormal modeling of human motion, and 3) the statistical analysis of the kinematic parameters of human movements for the diagnosis of principal brain stroke risk factors. In the first part of this thesis, an overview is presented of the many observations scattered in the scientific literature concerning the link between the human movements and the main brain stroke risk factors (diabetes, obesity, cigarette smoking, cardiac problems, alcoholism, hypertension and hypercholesterolemia). Building on the observation that the existence of such a link is supported, a modeling framework is chosen and the lognormal models forming its foundations are reported from an original point of view. The application of this methodology to our database allowed the investigation of some fundamental phenomena concerning the study of motor control. Notably, the proportional nature of human motion is examined and compared to the serial representation of psychophysical processes. The delta-lognormal modeling of speed-accuracy tradeoffs (Fitts’ task) has also allowed the discovery of some fundamental aspects related to the control of this kind of movements, such as the increase of the coupling between the motor commands as the task becomes more difficult and the enhancement of the temporal coordination of the neuromuscular action as the geometrical properties of the task are scaled up

Too Fast, Too Furious? Algorithmic Trading and Financial Instability

To what extent can algorithmic trading-based strategies explain the propagation of flash crashes on financial markets? This question has to be discussed at the intersection of two disciplinary fields: management of information systems and finance. Built on realistic assumptions on traders’ strategies, on their use of algorithmic information systems and considering the role of transactions systems at the market level, an agent-based approach is presented. Final results show that speed-oriented trading strategies and the increasing use of new trading technologies can arm markets’ stability and resiliency, facing intraday operational shocks. The article also shows the central role played by transactions systems in the propagation of flash crashes, when a new regulation based on the principle of decimalization is introduced

Enacting Inquiry Learning in Mathematics through History

International audienceWe explain how history of mathematics can function as a means for enacting inquiry learning activities in mathematics as a scientific subject. It will be discussed how students develop informed conception about i) the epistemology of mathematics, ii) of how mathematicians produce mathematical knowledge, and iii) what kind of questions that drive mathematical research. We give examples from the mathematics education at Roskilde University and we show how (teacher) students from this program are themselves capable of using history to establish inquiry learning environments in mathematics in high school. The realization is argued for in the context of an explicit-reflective framework in the sense of Abd-El-Khalick (2013) and his work in science education

History of Mathematics in Mathematics Education: Recent devlopments

International audience<p>This is a concise survey on the recent developments (since 2000) concerning research on the relations between History and Pedagogy of Mathematics (the <i>HPM domain</i>). Section 1 explains the rationale of the study and formulates the key issues. Section 2 gives a brief historical account of the development of the <i>HPM domain</i> with focus on the main activities in its context and their outcomes. Section 3 provides a sufficiently comprehensive bibliographical survey of the work done in this area since 2000. Finally, section 4 summarizes the main points of this study.</p