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Abstract This dissertation aims to uncover emerging practices in how people seek information while on the move in augmented urban spaces. The backdrop for a majority of the work presented here is the City of Oulu in Finland, where we have installed a variety of new ubiquitous computing infrastructure and services including, among others, a network of large interactive public displays called UBI-hotspots. The hotspots serve as a versatile platform on top of which new types of services can be developed, deployed and tested in an authentic urban setting with real, non-coached users and a sufficiently long timespan to truly evaluate the impact of such services on the everyday life and practices of the city and its citizens. The case studies presented in this dissertation aim at understanding the effect of such highly visible additions to the urban space from the point of view of human information behaviour. I seek to understand the underlying information seeking strategies people employ while foraging the hotspots for information, and the types of information people see as valuable while attending their daily business in the downtown area of the City. Questions such as how do people utilize the new sources of information in their daily information seeking tasks, and what is the preferred medium for information delivery, are addressed. The theoretical framework for the studies is derived from both ubiquitous and urban computing, and from the field of human information behaviour research. The main findings of the presented studies indicate that people have adapted the new infrastructure and services as parts of their daily information seeking tasks. The detailed usage data logged by all hotspots provide insight into the browsing habits of users, and analysis of inter-session navigation show that various latent strategies of information seeking exist. Further, findings indicate that there is a clear difference between the types of services people perceive as useful prior to using the hotspots, and services that people actually use on the hotspots. Also, findings indicate that people are willing to download information items from the hotspots to their mobile devices for later reference, thus adding information to their personal information repository. / Tiivistelmä Tämä väitöskirja pyrkii löytämään ja selittämään uusia tapoja joilla ihmiset etsivät informaatiota älykkäissä kaupunkitiloissa. Tausta suurelle osalle työstä on Oulun kaupunki, jonne olemme asentaneet erilaisia jokapaikan tietotekniikan laitteistoja sekä palveluja. Erityisesti väitöskirjassa tutkitaan suurten julkisten näyttöjen – ”UBI-näyttöjen” – verkostoa. UBI-näytöt toimivat monipuolisena alustana jonka päällä uusia palveluja voidaan kehittää sekä testata autenttisessa kaupunkitilassa todellisten käyttäjien toimesta riittävän pitkällä aikavälillä, joka puolestaan mahdollistaa palveluiden todellisen merkittävyyden arvioimisen suhteessa ihmisten jokapäiväiseen informaatiokäyttäytymiseen sekä informaatiotarpeisiin. Väitöskirjassa esitetyt tapaustutkimukset pyrkivät ymmärtämään tällaisten erittäin näkyvien tietoteknisten laitteiden vaikutusta ihmisten käyttäytymiseen informaatiotutkimuksen näkökulmasta. Tarkastelun kohteena ovat ihmisten jokapäiväiseen tiedonhakuun liittyvät strategiat heidän käyttäessään UBI-näyttöjä, sekä erilaiset informaatiotyypit joita ihmiset pitävät tärkeinä hoitaessaan jokapäiväisiä asioitaan kaupunkitiloissa. Kysymykset kuten kuinka ihmiset käyttävät uusia informaation lähteitä etsiessään tietoa jokapäiväisiin tarpeisiinsa sekä millä laitteilla ihmiset mieluiten etsivät ko. tietoa ohjaavat suurta osaa tutkimuksesta. Tutkimuksen teoreettinen viitekehys muodostuu jokapaikan tietotekniikan tutkimuksesta, urbaanin tietotekniikan tutkimuksesta, sekä ihmisten informaatiokäyttäytymisen tutkimuksesta. Tutkimuksen tärkeimmät löydökset osoittavat että ihmiset ovat ottaneet uudet tietotekniset resurssit osaksi päivittäistä informaatiokäyttäytymistään. Yksityiskohtainen lokitieto yhdistettynä haastattelu- ja havainnointidataan tarjoaa syvällisen näkemyksen käyttäjien tiedontarpeisiin. Dataa analysoimalla olemme havainneet joukon strategioita joita ihmiset käyttävät etsiessään tietoa kaupunkitiloissa. Tutkimus osoittaa myös, että ihmisten oletettujen tiedontarpeiden sekä havainnoidun käyttäytymisen välillä on suuria eroavaisuuksia. Käyttäjät ovat myös halukkaita lataamaan tietosisältöä matkapuhelimiinsa myöhempää käyttöä varten, täten lisäten tietoa omaan henkilökohtaiseen tietovarastoonsa.

Prolonged-release (PR) fampridine is the only approved medication to improve walking in multiple sclerosis (MS), having been shown to produce a clinically meaningful improvement in walking ability in the subset of MS patients with Expanded Disability Status Scale 4–7. Recent responder subgroup analyses in the phase III ENHANCE study show a large effect size in terms of an increase of 20.58 points on the patient-reported 12-item MS Walking Scale in the 43% of patients classified as responders to PR-fampridine, corresponding to a standardized response mean of 1.68. Use of PR-fampridine in clinical practice varies across Europe, depending partly on whether it is reimbursed. A group of European MS experts met in June 2017 to discuss their experience with using PR-fampridine, including their views on the patient population for treatment, assessment of treatment response, re-testing and retreatment, and stopping criteria. This article summarizes the experts’ opinions on how PRfampridine can be used in real-world clinical practice to optimize the benefits to people with MS with impaired walking ability.

Physical asset management in the electric power sector encompasses the scheduling of the maintenance and replacement of grid components, as well as decisions about investments in new components. Data plays a crucial role in these decisions. The importance of data is increasing with the transformation of the power system and its evolution toward smart grids. This thesis deals with questions related to data management as a way to improve the performance of asset management decisions. Data management is defined as the collection, processing, and storage of data. Here, the focus is on the collection and processing of data. First, the influence of data on the decisions related to assets is explored. In particular, the impacts of data quality on the replacement time of a generic component (a line for example) are quantified using a scenario approach, and failure modeling. In fact, decisions based on data of poor quality are most likely not optimal. In this case, faulty data related to the age of the component leads to a non-optimal scheduling of component replacement. The corresponding costs are calculated for different levels of data quality. A framework has been developed to evaluate the amount of investment needed into data quality improvement, and its profitability. Then, the ways to use available data efficiently are investigated. Especially, the possibility to use machine learning algorithms on real-world datasets is examined. New approaches are developed to use only available data for component ranking and failure prediction, which are two important concepts often used to prioritize components and schedule maintenance and replacement. A large part of the scientific literature assumes that the future of smart grids lies in big data collection, and in developing algorithms to process huge amounts of data. On the contrary, this work contributes to show how automatization and machine learning techniques can actually be used to reduce the need to collect huge amount of data, by using the available data more efficiently. One major challenge is the trade-offs needed between precision of modeling results, and costs of data management. / <p>QC 20210330</p>

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When performing fingerprinting attacks against websites in a controlled environment astudy may achieve very promising results. However, these can be misleading as the closedworld setting may not accurately represent the real-world. This is a problem many priorworks have been critiqued for, the inability to transfer their results from the closed-worldsetting to the real-world. Being able to do so is of great importance to establish what thereal-world consequences would be of fingerprint attacks. If unable to apply one’s findingsoutside of a tightly controlled environment it is difficult to gauge if these attacks types posea real threat or not. Thereby, this thesis has, contrary to previous work, based its settingon a real-world scenario to provide tangible insights into vulnerabilities of news websites.Furthermore, it targeted internal pages of websites, something understudied by previousliterature. All of this while presenting a novel classifier that is lightweight and requireslittle training, and a framework for automatically collecting and labelling encrypted TCPtraffic without the use of a proxy.

The crew scheduling problem with attendance rates is highly relevant for regional passenger rail transport in Germany. Its major characteristic is that only a certain percentage of trains have to be covered by crew members or conductors, causing a significant increase in complexity. Despite being commonly found in regional transport networks, discussions regarding this issue remain relatively rare in the literature. We propose a novel hybrid column generation approach for a real-world problem in railway passenger transport. To the best of our knowledge, several realistic requirements that are necessary for successful application of generated schedules in practice have been integrated for the first time in this study. A mixed integer programming model is used to solve the master problem, whereas a genetic algorithm is applied for the pricing problem. Several improvement strategies are applied to accelerate the solution process; these strategies are analyzed in detail and are exemplified. The effectiveness of the proposed algorithm is proven by a comprehensive computational study using real-world instances, which are made publicly available. Further we provide real optimality gaps on average less than 10 % based on lower bounds generated by solving an arc flow formulation. The developed approach is successfully used in practice by DB Regio AG.

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