Network usage is an important quality metric for mobile apps. Slow networks, low monthly traffic quotas and high roaming fees restrict mobile users’ amount of usable Internet traffic. Companies wanting their apps to stay competitive must be aware of their network usage and changes to it. Short feedback loops for the impact of code changes are key in agile software development. To notify stakeholders of changes when they happen without being prohibitively expensive in terms of manpower the change detection must be fully automated. To further decrease the manpower overhead cost of implementing network usage change detection the system need to have low configuration requirements, and keep the false positive rate low while managing to detect larger changes. This thesis proposes an automated change detection method for network activity to quickly notify stakeholders with relevant information to begin a root cause analysis after a change in the network activity is introduced. With measurements of the Spotify’s iOS app we show that the tool achieves a low rate of false positives while detecting relevant changes in the network activity even for apps with dynamic network usage patterns as Spotify. / Nätverksaktivitet är ett viktigt kvalitetsmått för mobilappar. Mobilanvändare begränsas ofta av långsamma nätverk, låg månatlig trafikkvot och höga roamingavgifter. Företag som vill ha konkurrenskraftiga appar behöver vara medveten om deras nätverksaktivitet och förändringar av den. Snabb återkoppling för effekten av kodändringar är vitalt för agil programutveckling. För att underrätta intressenter om ändringar när de händer utan att vara avskräckande dyrt med avseende på arbetskraft måste ändringsdetekteringen vara fullständigt automatiserad. För att ytterligare minska arbetskostnaderna för ändringsdetektering av nätverksaktivitet måste detekteringssystemet vara snabbt att konfigurera, hålla en låg grad av felaktig detektering samtidigt som den lyckas identifiera stora ändringar. Den här uppsatsen föreslår ett automatiserat förändringsdetekteringsverktyg för nätverksaktivitet för att snabbt meddela stakeholders med relevant information för påbörjan av grundorsaksanalys när en ändring som påverkar nätverksaktiviteten introduceras. Med hjälp av mätningar på Spotifys iOS-app visar vi att verktyget når en låg grad av felaktiga detekteringar medan den identifierar ändringar i nätverksaktiviteten även för appar med så dynamisk nätverksanvändning som Spotify.

Inferences from scientific success to the approximate truth of successful theories remain central to the most influential arguments for scientific realism. Challenges to such inferences, however, based on radical discontinuities within the history of science, have motivated a distinctive style of revision to the original argument. Conceding the historical claim, selective realists argue that accompanying even the most revolutionary change is the retention of significant parts of replaced theories, and that a realist attitude towards the systematically retained constituents of our scientific theories can still be defended. Selective realists thereby hope to secure the argument from success against apparent historical counterexamples. Independently of that objective, historical considerations have inspired a further argument for selective realism, where evidence for the retention of parts of theories is itself offered as justification for adopting a realist attitude towards them. Given the nature of these arguments from success and from retention, a reasonable expectation is that they would complement and reinforce one another, but although several theses purport to provide such a synthesis the results are often unconvincing. In this paper I reconsider the realist's favoured type of scientific success, novel success, offer a revised interpretation of the concept, and argue that a significant consequence of reconfiguring the realist's argument from success accordingly is a greater potential for its unification with the argument from retention.

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Teaching simulated biped robots to walk is a popular problem in machine learning. However, until this thesis, evolving a biped controller has not been attempted through an indirect encoding, i.e. a compressed representation of the solution, despite the fact that natural bipeds such as humans evolved through such an indirect encoding (i.e. DNA). Thus the promise for indirect encoding is to evolve gaits that rival those seen in nature. In this thesis, an indirect encoding called HyperNEAT evolves a controller for a biped robot in a computer simulation. To most effectively explore the deceptive behavior space of biped walkers, novelty search is applied as a fitness metric. The result is that although the indirect encoding can evolve a stable bipedal gait, the overall neural architecture is brittle to small mutations. This result suggests that some capabilities might be necessary to include beyond indirect encoding, such as lifetime adaptation. Thus this thesis provides fresh insight into the requisite ingredients for the eventual achievement of fluid bipedal walking through artificial evolution.

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No / This paper examines the continuous intention by healthcare professionals to use the Internet of Medical Things (IoMT) in combination with responsible artificial intelligence (AI). Using the theory of Diffusion of Innovation (DOI), a model was developed to determine the continuous intention to use IoMT taking into account the risks and complexity involved in using AI. Data was gathered from 276 healthcare professionals through a survey questionnaire across hospitals in Bahrain. Empirical outcomes reveal nine significant relationships amongst the constructs. The findings show that despite contradictions associated with AI, continuous intention to use behaviour can be predicted during the diffusion of IoMT. This study advances the under- standing of the role of responsible AI in the continued use of IoMT in healthcare and extends DOI to address the diffusion of two innovations concurrently.

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