Music In The Air : en studie av streamingtjenestene Spotify og WiMP på mobil

Denne masteroppgaven er en fortolkende studie av teknologibruk. Studien tar for seg hvordan brukergrensesnittet i streamingapplikasjonene er utformet og hvilke føringer grensesnittet legger på brukernes interaksjon med systemene. Studien er en del av forskningsprosjektet Sky & Scene. Gjennom kvalitative og kvantitative forskningsmetoder tar oppgaven for seg hvordan brukerne interagerer med spillelister, hvordan de søker og hvordan de spiller musikk offline. I tillegg studeres deling via sosiale medier, og hvordan brukerne forholder seg til koblingen mellom streamingapplikasjonen og de integrerte sosiale tjenestene. For å belyse disse spørsmålene har jeg utført en empirisk undersøkelse om bruk av musikktjenestene Spotify og WiMP for mobil. De spesifikke metodene som er benyttet er cultural probes, der seks informanter har loggført sin bruk, og en spørreundersøkelse som har blitt distribuert via sosiale medier. Utviklingen av den bærbare musikkteknologien har gjort musikk tilgjengelig overalt. Streamingtjenestene Spotify og WiMP tilbyr sine lyttere over 20 millioner låter, hvor som helst og når som helst. Undersøkelser viser at den mobile plattformen gikk forbi desktop i antall streames hos WiMP i 2012, og salget av digital musikk i Norge har for lengst gått forbi salg av fysiske formater. Denne oppgaven omhandler Spotify og WiMP på mobil – trådløst og portabelt – in the air. Mange lytter ukentlig til musikk som streames fra Internett, og deres interaksjon med streamingtjenestene på mobil er temaet for denne oppgaven

A hybridised evolutionary algorithm for multi-criterion minimum spanning tree problems

A hybridised and Knowledge-based Evolutionary Algorithm (KEA) is applied to the multi-criterion minimum spanning tree problems. Hybridisation is used across its three phases. In the first phase a deterministic single objective optimization algorithm finds the extreme points of the Pareto front. In the second phase a K-best approach finds the first neighbours of the extreme points, which serve as an elitist parent population to an evolutionary algorithm in the third phase. A knowledge-based mutation operator is applied in each generation to reproduce individuals that are at least as good as the unique parent. The advantages of KEA over previous algorithms include its speed (making it applicable to large real-world problems), its scalability to more than two criteria, and its ability to find both the supported and unsupported optimal solutions

A knowledge-based evolution strategy for the multi-objective minimum spanning tree problem

A fast Knowledge-based Evolution Strategy, KES, for the multi-objective minimum spanning tree, is presented. The proposed algorithm is validated, for the bi-objective case, with an exhaustive search for small problems (4-10 nodes), and compared with a deterministic algorithm, EPDA and NSGA-II for larger problems (up to 100 nodes) using benchmark hard instances. Experimental results show that KES finds the true Pareto fronts for small instances of the problem and calculates good approximation Pareto sets for larger instances tested. It is shown that the fronts calculated by YES are superior to NSGA-II fronts and almost as good as those established by EPDA. KES is designed to be scalable to multi-objective problems and fast due to its small complexity

Extending evolutionary algorithms to discover tri-criterion and non-supported solutions for the minimum spanning tree problem.

The study of multi-criterion minimum spanning trees is important as many optimization problems in networks, such as communication, transport and utilities can be represented by this model. Conventional evolutionary approaches struggle to discover near-optimal solutions due to the combinatorial search space, and the difficulty in discovering the non-supported solutions. Recently, a knowledge-based evolutionary approach, KEA, has been developed that overcomes some of the problems of the earlier algorithms as it is not restricted to the bi-criterion case, finds non-supported solutions and scales well to larger problems; however, the mid-point of its Pareto front is often dominated by alternative algorithms where they are applicable. Novel extensions to KEA, increasing the knowledge of the mid-point, termed KEA-W are examined, eliminating the mid-point deficiencies at the cost of computational time

Colorful ESL Test Papers And Spatial Intelligence:

The purpose of this research was to find out whether introducing color as an element which may appeal to spatially-intelligent candidates affects their performance on ESL grammar tests. 52 participants were given two parallel grammar tests, one in black and white and the other bearing the full spectrum of colors in the natural daylight. In order to identify the candidates with visual-spatial learning style, the participants and their teachers were asked to respond to Visual-Spatial Identifier rating scale. Based on the results, no significant relationship was found between the performance of candidates on the colorful and black and white grammar tests and their visual-spatial intelligence. It was concluded that other variables including the method of applying colors, the type and combination of colors as well as the differential impact of different colors on candidates with different cultural backgrounds needed to be addressed before any conclusions can be drawn about the application of color in language assessment.

Extending evolutionary algorithms to discover tri-criterion and non-supported solutions for the minimum spanning tree problem.

The study of multi-criterion minimum spanning trees is important as many optimization problems in networks, such as communication, transport and utilities can be represented by this model. Conventional evolutionary approaches struggle to discover near-optimal solutions due to the combinatorial search space, and the difficulty in discovering the non-supported solutions. Recently, a knowledge-based evolutionary approach, KEA, has been developed that overcomes some of the problems of the earlier algorithms as it is not restricted to the bi-criterion case, finds non-supported solutions and scales well to larger problems; however, the mid-point of its Pareto front is often dominated by alternative algorithms where they are applicable. Novel extensions to KEA, increasing the knowledge of the mid-point, termed KEA-W are examined, eliminating the mid-point deficiencies at the cost of computational time

Hypoxia-induced vasodilation and effects of regional phentolamine in awake patients with sleep apnea

Obstructive sleep apnea (OSA) is associated with increased sympathetic nerve activity, endothelial dysfunction, and premature cardiovascular disease. To determine whether hypoxia is associated with impaired skeletal muscle vasodilation, we compared femoral artery blood flow (ultrasound) and muscle sympathetic nerve activity (peroneal microneurography) during exposure to acute systemic hypoxia (fraction of inspired oxygen 0.1) in awake patients with OSA (n = 10) and controls (n = 8). To assess the role of elevated sympathetic nerve activity, in a separate group of patients with OSA (n = 10) and controls (n = 10) we measured brachial artery blood flow during hypoxia before and after regional α-adrenergic block with phentolamine. Despite elevated sympathetic activity, in OSA the vascular responses to hypoxia in the leg did not differ significantly from those in controls [P = not significant (NS)]. Following regional phentolamine, in both groups the hypoxia-induced increase in brachial blood flow was markedly enhanced (OSA pre vs. post, 84 ± 13 vs. 201 ± 34 ml/min, P < 0.002; controls pre vs. post 62 ± 8 vs. 140 ± 26 ml/min, P < 0.01). At end hypoxia after phentolamine, the increase of brachial blood flow above baseline was similar (OSA vs. controls +61 ± 16 vs. +48 ± 6%; P = NS). We conclude that despite high sympathetic vasoconstrictor tone and prominent sympathetic responses to acute hypoxia, hypoxia-induced limb vasodilation is preserved in OSA