Global ETD Search

1	Proceedings of the Workshop on Membrane Computing, WMC 2016. Konur, Savas, Gheorghe, Marian 08 1900 (has links) yes / This Workshop on Membrane Computing, at the Conference of Unconventional Computation and Natural Computation (UCNC), 12th July 2016, Manchester, UK, is the second event of this type after the Workshop at UCNC 2015 in Auckland, New Zealand. Following the tradition of the 2015 Workshop the Proceedings are published as technical report. The Workshop consisted of one invited talk and six contributed presentations (three full papers and three extended abstracts) covering a broad spectrum of topics in Membrane Computing, from computational and complexity theory to formal verification, simulation and applications in robotics. All these papers – see below, but the last extended abstract, are included in this volume. The invited talk given by Rudolf Freund, “P SystemsWorking in Set Modes”, presented a general overview on basic topics in the theory of Membrane Computing as well as new developments and future research directions in this area. Radu Nicolescu in “Distributed and Parallel Dynamic Programming Algorithms Modelled on cP Systems” presented an interesting dynamic programming algorithm in a distributed and parallel setting based on P systems enriched with adequate data structure and programming concepts representation. Omar Belingheri, Antonio E. Porreca and Claudio Zandron showed in “P Systems with Hybrid Sets” that P systems with negative multiplicities of objects are less powerful than Turing machines. Artiom Alhazov, Rudolf Freund and Sergiu Ivanov presented in “Extended Spiking Neural P Systems with States” new results regading the newly introduced topic of spiking neural P systems where states are considered. “Selection Criteria for Statistical Model Checker”, by Mehmet E. Bakir and Mike Stannett, presented some early experiments in selecting adequate statistical model checkers for biological systems modelled with P systems. In “Towards Agent-Based Simulation of Kernel P Systems using FLAME and FLAME GPU”, Raluca Lefticaru, Luis F. Macías-Ramos, Ionuţ M. Niculescu, Laurenţiu Mierlă presented some of the advatages of implementing kernel P systems simulations in FLAME. Andrei G. Florea and Cătălin Buiu, in “An Efficient Implementation and Integration of a P Colony Simulator for Swarm Robotics Applications" presented an interesting and efficient implementation based on P colonies for swarms of Kilobot robots. http://ucnc15.wordpress.fos.auckland.ac.nz/workshop-on-membrane-computingwmc- at-the-conference-on-unconventional-computation-natural-computation/
2	On learning and visualizing lexicographic preference trees Moussa, Ahmed S. 01 January 2019 (has links) Preferences are very important in research fields such as decision making, recommendersystemsandmarketing. The focus of this thesis is on preferences over combinatorial domains, which are domains of objects configured with categorical attributes. For example, the domain of cars includes car objects that are constructed withvaluesforattributes, such as ‘make’, ‘year’, ‘model’, ‘color’, ‘body type’ and ‘transmission’.Different values can instantiate an attribute. For instance, values for attribute ‘make’canbeHonda, Toyota, Tesla or BMW, and attribute ‘transmission’ can haveautomaticormanual. To this end,thisthesis studiesproblemsonpreference visualization and learning for lexicographic preference trees, graphical preference models that often are compact over complex domains of objects built of categorical attributes. Visualizing preferences is essential to provide users with insights into the process of decision making, while learning preferences from data is practically important, as it is ineffective to elicit preference models directly from users. The results obtained from this thesis are two parts: 1) for preference visualization, aweb- basedsystem is created that visualizes various types of lexicographic preference tree models learned by a greedy learning algorithm; 2) for preference learning, a genetic algorithm is designed and implemented, called GA, that learns a restricted type of lexicographic preference tree, called unconditional importance and unconditional preference tree, or UIUP trees for short. Experiments show that GA achieves higher accuracy compared to the greedy algorithm at the cost of more computational time. Moreover, a Dynamic Programming Algorithm (DPA) was devised and implemented that computes an optimal UIUP tree model in the sense that it satisfies as many examples as possible in the dataset. This novel exact algorithm (DPA), was used to evaluate the quality of models computed by GA, and it was found to reduce the factorial time complexity of the brute force algorithm to exponential. The major contribution to the field of machine learning and data mining in this thesis would be the novel learning algorithm (DPA) which is an exact algorithm. DPA learns and finds the best UIUP tree model in the huge search space which classifies accurately the most number of examples in the training dataset; such model is referred to as the optimal model in this thesis. Finally, using datasets produced from randomly generated UIUP trees, this thesis presents experimental results on the performances (e.g., accuracy and computational time) of GA compared to the existent greedy algorithm and DPA. Thesis University of North Florida UNF MACHINE LEARNING VISUALIZING LEXICOGRAPHIC PREFERENCE TREES DATA MINING CLASSIFICATION PREFERENCE LEARNING Novel algorithms -- Design – Testing Computational Engineering Computer and Systems Architecture Computer Engineering Other Computer Engineering
3	Energy-aware scheduling : complexity and algorithms Renaud-Goud, Paul 05 July 2012 (has links) (PDF) In this thesis we have tackled a few scheduling problems under energy constraint, since the energy issue is becoming crucial, for both economical and environmental reasons. In the first chapter, we exhibit tight bounds on the energy metric of a classical algorithm that minimizes the makespan of independent tasks. In the second chapter, we schedule several independent but concurrent pipelined applications and address problems combining multiple criteria, which are period, latency and energy. We perform an exhaustive complexity study and describe the performance of new heuristics. In the third chapter, we study the replica placement problem in a tree network. We try to minimize the energy consumption in a dynamic frame. After a complexity study, we confirm the quality of our heuristics through a complete set of simulations. In the fourth chapter, we come back to streaming applications, but in the form of series-parallel graphs, and try to map them onto a chip multiprocessor. The design of a polynomial algorithm on a simple problem allows us to derive heuristics on the most general problem, whose NP-completeness has been proven. In the fifth chapter, we study energy bounds of different routing policies in chip multiprocessors, compared to the classical XY routing, and develop new routing heuristics. In the last chapter, we compare the performance of different algorithms of the literature that tackle the problem of mapping DAG applications to minimize the energy consumption. [INFO:INFO_OH] Computer Science/Other Energy minimization Power Scheduling Complexity Makespan Heuristics Optimal algorithms Greedy algorithm Independent jobs Parallel processors Mapping Concurrent streaming applications Heterogeneous platforms Resource sharing Energy Latency Period Workflow Replica placement Tree networks Update strategies Dynamic programming algorithms Series-parallel graph Routing Chip multiprocesseur Manhattan Single path Multiple paths Slack reclamation DAG
4	Energy-aware scheduling : complexity and algorithms / Ordonnancement sous contrainte d'énergie : complexité et algorithmes Renaud-Goud, Paul 05 July 2012 (has links) Dans cette thèse, nous nous sommes intéressés à des problèmes d'ordonnancement sous contrainte d'énergie, puisque la réduction de l'énergie est devenue une nécessité, tant sur le plan économique qu'écologique. Dans le premier chapitre, nous exhibons des bornes strictes sur l'énergie d'un algorithme classique qui minimise le temps d'exécution de tâches indépendantes. Dans le second chapitre, nous ordonnançons plusieurs applications chaînées de type « streaming », et nous étudions des problèmes contraignant l'énergie, la période et la latence. Nous effectuons une étude de complexité exhaustive, et décrivons les performances de nouvelles heuristiques. Dans le troisième chapitre, nous étudions le problème de placement de répliques dans un réseau arborescent. Nous nous plaçons dans un cadre dynamique, et nous bornons à minimiser l'énergie. Après une étude de complexité, nous confirmons la qualité de nos heuristiques grâce à un jeu complet de simulations. Dans le quatrième chapitre, nous revenons aux applications « streaming », mais sous forme de graphes série-parallèles, et nous tentons de les placer sur un processeur multi-cœur. La découverte d'un algorithme polynomial sur un problème simple nous permet la conception d'heuristiques sur le problème le plus général dont nous avons établi la NP-complétude. Dans le cinquième chapitre, nous étudions des bornes énergétiques de politiques de routage dans des processeurs multi-cœurs, en comparaison avec le routage classique XY, et développons de nouvheuristiques de routage. Dans le dernier chapitre, nous étudions expérimentalement le placement d'applications sous forme de DAG sur des machines réelles. / In this thesis we have tackled a few scheduling problems under energy constraint, since the energy issue is becoming crucial, for both economical and environmental reasons. In the first chapter, we exhibit tight bounds on the energy metric of a classical algorithm that minimizes the makespan of independent tasks. In the second chapter, we schedule several independent but concurrent pipelined applications and address problems combining multiple criteria, which are period, latency and energy. We perform an exhaustive complexity study and describe the performance of new heuristics. In the third chapter, we study the replica placement problem in a tree network. We try to minimize the energy consumption in a dynamic frame. After a complexity study, we confirm the quality of our heuristics through a complete set of simulations. In the fourth chapter, we come back to streaming applications, but in the form of series-parallel graphs, and try to map them onto a chip multiprocessor. The design of a polynomial algorithm on a simple problem allows us to derive heuristics on the most general problem, whose NP-completeness has been proven. In the fifth chapter, we study energy bounds of different routing policies in chip multiprocessors, compared to the classical XY routing, and develop new routing heuristics. In the last chapter, we compare the performance of different algorithms of the literature that tackle the problem of mapping DAG applications to minimize the energy consumption. Minimisation d'énergie Puissance Ordonnancement Complexité Heuristique Algorithmes optimaux Algorithme glouton Travaux indépendants Processeurs parallèles Placement Applications concurrentes Plate-forme hétérogène Partage de ressources Énergie Latence Période Placement de répliques Réseau arborescent Stratégies de mise à jour Algorithme de programmation dynamique Graphe série-parallèle Routage Multiprocesseur Manhattan Chemin unique Chemin multiples DAG Energy minimization Power Scheduling Complexity Makespan Heuristics Optimal algorithms Greedy algorithm Independent jobs Parallel processors Mapping Concurrent streaming applications Heterogeneous platforms Resource sharing Energy Latency Period Workflow Replica placement Tree networks Update strategies Dynamic programming algorithms Series-parallel graph Routing Chip multiprocesseur Manhattan Single path Multiple paths Slack reclamation DAG

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