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Zooplankton are an important link in marine pelagic food webs as they transfer energy from primary producers to higher trophic levels such as planktivorous fish. They migrate vertically in the water column, ascending to feed near the surface at night and descending to hide from visual predators for the day (diel vertical migration, DVM). Zooplankton are detected with Acoustic Doppler Current Profilers (ADCPs). These devices were developed for measuring water currents using acoustic pulses, a technique which requires particles such as zooplankton in the water column to scatter the sound. As a by-product of the velocity measurements, it provides information of these scatterers as echo intensity. This method has been used in researching zooplankton DVM, however, not in the northern Baltic Sea prior to this study. In this thesis, the data processing steps required to analyze echo intensity were examined for the specific environment of the Finnish Archipelago Sea. A one-year-long time-series was processed and averaged seasonally to investigate different patterns in zooplankton DVM. Vertical velocity data were used in estimating migration speed, and available reference measurements were combined to the data to examine the environmental factors affecting zooplankton DVM. Synchronized DVM was observed especially in autumn, however, indications of other migration patterns such as unsynchronized and reverse migration were detected during summer and winter, respectively. The primary cue behind zooplankton DVM was light, but additional contributing factors such as phytoplankton and currents were identified and discussed. The maximum migration speeds detected were approximately 10 cm/s downwards and 4 cm/s upwards. ADCP data are a good indicator of zooplankton migration in the northern Baltic Sea and in the future, it could prove beneficial in zooplankton monitoring and biomass estimates.

In this study we use element-level usage data that was collected from the online learning material of an university level introductory programming course for identification of areas-of-interest in the course material as well as for prediction of student learning outcomes. The data was collected in-situ using a JavaScript component embedded in the online learning material, which recorded which HTML elements were visible on the user's screen after each interaction (movement and click) and if the user's screen had been still for at least 2500 milliseconds. A visual analysis indicates that students spend large amounts of time on material sections that discuss special syntactic structures that they are unable to infer from previous experience. Overall, the analysis was able to identify areas of the online learning material that seem to be too long and in-depth for the concepts they are discussing, when the things the students have previously learned are taken into account. This high-level analysis also revealed that the time the students spent viewing an assignment's prompt was statistically significantly correlated with the perceived workload, difficulty and educational value of that same assignment. We observe that when partial correlations are considered, and multiple comparisons are corrected for, time spent with an assignment's prompt on the screen is no longer statistically significantly correlated with the three variables. The same usage data was used to investigate whether material usage statistics can predict learning outcomes or identify strong and at-risk students. The results indicate that based on just three to four weeks of data, it is possible to identify strong and at-risk students with some accuracy. Furthermore, it seems possible to identify student programming assignment scores and total course scores with a somewhat high accuracy. Models based on material usage statistics also displayed some light predictive power in predicting student exam scores. It was also shown that the predictive powers of these models are not based solely on student effort or time-on-task. All told, this thesis demonstrates that fine-grained online learning material usage data is feasible to collect and useful in understanding both the students and the learning material. The results suggest that very simple and almost entirely domain-independent data sources can be used to predict student performance to a relatively large degree, suggesting that a combination of such simple domain-independent metrics could match highly domain dependent and more complex metrics in predictive power, giving raise to more widely usable educational analytics tools.

The direct economic impacts of the global tourism industry account for 4 % of global GDP and 8 % of global greenhouse gas emissions. The industry is in transformation caused by climate change, political instability and rapid technological development. In addition, the relationship between biodiversity conservation and tourism as well as the growing popularity are considered megatrends impacting the sector. Traditional mass tourism destinations, such as the Canary Islands, may start seeing new kinds of visitors, if traveling to exotic destinations becomes difficult as a result of these transformations. Understanding transformations affecting tourism requires information about tourists’ mobilities, interests and preferences. However, traditional data collection methods may not necessarily be suited for studying quickly changing tourism. The need for Information about visitations to natural and protected areas is especially high, as traditional tourism indicators, such as flights and accommodation statistics do not tell where the tourists spend time. Social media data may enable production of new kind of knowledge and studying nature-based tourism in a new way. In this thesis, I intent to assess the role of nature in tourism in the Canary Islands, Spain using data from the photo-sharing platform Flickr. First, I compare the spatiotemporal patterns of Flickr data against official data about tourism flows to confirm the feasibility of Flickr as a data source in the Canary Islands context. I then try to understand the importance of nature visitations and differences in nature visitation patterns between visitors from different countries. Finally, I turn to analyse contents of the images to see what kinds of nature-related topics are important for each group, making use of a deep learning and cluster detection algorithms. I verify the results of my empirical analysis with data collected through interviewing experts familiar with Canary Islands tourism. The results of my research show that Flickr reflects Canary Islands tourism patterns moderately well, and that it can be used to produce information about differences in nature visitation patterns. Protected areas are shown to be important and central for Canary Islands tourism, but differences in interest toward these areas between groups are notable. Results of the content analyses show that while differences between groups exist, both nature-related content and photos of humans are important in content posted from PAs. Verification data collected through expert interviews shows that the observed differences between groups correspond to the experts’ perceptions about differences between different groups. The findings of my thesis demonstrate the importance of nature and protected areas in Canary Islands tourism and confirm earlier knowledge about the use of Flickr in studying nature visitations. The results may inform future research in the Canary Islands. More broadly, they provide information about the feasibility and limitations of the use of social media data for nature-based tourism research.

Implementing software process improvement (SPI) models or standards can be challenging for a small organization due to their limited resources compared to larger companies. The ISO/IEC 29110 series of systems and engineering standards were designed especially for very small entities (VSEs), i.e. for organizations having up to 25 employees. Company X is a small Finnish software company following a Scrum workflow. At Company X, challenges have been identified in the software testing process. Because of the company’s size, ISO/IEC 29110 out of different SPI standards was identified as a potential fit for improving this process. While the ISO/IEC 29110 standard can be applied to any software life cycle method, including agile, there is no formal guide on how to implement the standard in an agile environment. The aims of this thesis are two-fold: first, to investigate how Scrum corresponds with the standard, and second, to use the standard to identify weak points in Company X’s current software testing process and to identify action points to address them. The mappings between Scrum and the standard were investigated by carrying out a systematic literature review (SLR). A self-assessment and a software testing deployment package provided with the standard were used to assess the current testing process and to identify shortcomings in it. The shortcomings were analyzed and action points feasible in Company X’s context were suggested. The improved process containing the suggested action points was then re-assessed. The SLR yielded only a handful of papers, indicating that the topic of implementing ISO/IEC 29110 into an agile lifecycle in practice is relatively unexplored. The three papers together provided mappings for all three aspects of the standard vs. their counterparts in Scrum: activities, roles, and work products. The baseline assessment of Company X’s current process yielded a score of achieved ‘Partially’ (46,5 %). A set of seven shortcomings were identified in the assessment process and nine action points were suggested to address them. Assessing the improved process improved the score to implemented ‘Fully’ (97 %).

The usual task in music information retrieval (MIR) is to find occurrences of a monophonic query pattern within a music database, which can contain both monophonic and polyphonic content. The so-called query-by-humming systems are a famous instance of content-based MIR. In such a system, the user's hummed query is converted into symbolic form to perform search operations in a similarly encoded database. The symbolic representation (e.g., textual, MIDI or vector data) is typically a quantized and simplified version of the sampled audio data, yielding to faster search algorithms and space requirements that can be met in real-life situations. In this thesis, we investigate geometric approaches to MIR. We first study some musicological properties often needed in MIR algorithms, and then give a literature review on traditional (e.g., string-matching-based) MIR algorithms and novel techniques based on geometry. We also introduce some concepts from digital image processing, namely the mathematical morphology, which we will use to develop and implement four algorithms for geometric music retrieval. The symbolic representation in the case of our algorithms is a binary 2-D image. We use various morphological pre- and post-processing operations on the query and the database images to perform template matching / pattern recognition for the images. The algorithms are basically extensions to classic image correlation and hit-or-miss transformation techniques used widely in template matching applications. They aim to be a future extension to the retrieval engine of C-BRAHMS, which is a research project of the Department of Computer Science at University of Helsinki.

The National Library of Finland has digitized newspapers starting from late eighteenth century. Digitized data of Finnish newspapers is a heterogeneous data set, which contains the content and metadata of historical newspapers. This research work is focused to study this rich materiality data to find the data-driven categorization of newspapers. Since the data is not known beforehand, the objective is to understand the development of newspapers and use statistical methods to analyze the fluctuations in the attributes of this metadata. An important aspect of this research work is to study the computational and statistical methods which can better express the complexity of Finnish historical newspaper metadata. Exploratory analyses are performed to get an understanding of the attributes and extract the patterns among them. To explicate the attributes’ dependencies on each other, Ordinary Least Squares and Linear Regression methods are applied. The results of these regression methods confirm the significant correlation between the attributes. To categorize the data, spectral and hierarchical clustering methods are studied for grouping the newspapers with similar attributes. The clustered data further helps in dividing and understanding the data over time and place. Decision trees are constructed to split the newspapers after attributes’ logical divisions. The results of Random Forest decision trees show the paths of development of the attributes. The goal of applying various methods is to get a comprehensive interpretation of the attributes’ development based on language, time, and place and evaluate the usefulness of these methods on the newspaper data. From the features’ perspective, area appears as the most imperative feature and from language based comparison Swedish newspapers are ahead of Finnish newspapers in adapting popular trends of the time. Dividing the newspaper publishing places into regions, small towns show more fluctuations in publishing trends, while from the perspective of time the second half of twentieth century has seen a large increase in newspapers and publishing trends. This research work coordinates information on regions, language, page size, density, and area of newspapers and offers robust statistical analysis of newspapers published in Finland.

The aim of this thesis is to predict total career racing performance of Finnish trotter horses by using trotters early career racing performance and other early career variables. This thesis presents a brief introductory of harness racing and horses used in Finnish trotting sport. The data is presented and modified for predictions, with descriptive statistics of tables and visuals. The machine learning method of Random forests for regression is introduced and used in the predictions. After training the model, this thesis presents the prediction accuracy and variables of importance of the predictions of total career racing performance for both Finnhorse trotters and Finnish Standardbred trotter population. Finally, the writer discusses on the shortages and possible improvements for future research. The data for this thesis was provided by The Finnish trotting and breeding association (Suomen Hippos ry), which included all information of harness races from 1984 to the end of 2019, raced in Finland. From almost three million rows, the data was summarised to a data table of 46704 rows of trotters, that have started their career at earliest allowed three age groups. A total of 37 independent variables were used to predict three outcomes of total career earnings, total number of career starts and total number of career first placings, as separate models. The predictors are derived from other studies that estimate the environmental and genetic factors of racing performance of a trotter. The three models performed poor to moderate, with total earnings having the highest prediction accuracy. The model predicted quite well larger amounts of earnings, but was avid to predict some earnings when there in fact were none. Prediction accuracy of total number of starts was poor, especially when the true amount of starts was low. Model that predicted total number of career first placings performed the worst. This can partially be explained by the fact that winning is a rare event for a trotter in general. The models fit better for Finnish Standardbred trotters than for Finnhorse trotters. This thesis works as a good basis for future similar research, where massive amounts of data and machine learning is used to predict trotter’s career, racing performance or other factors. The results show that predicting total career racing performance as a classification problem could be a better fit than regression. These adequate classes, as well as possible better predictors and suitable imputes for missing values, should be consulted with an audience of superior knowledge in harness racing.

The lidar depolarisation ratio is used for aerosol categorisation as it is indicative of aerosol shape. Commonly, depolarisation ratio is measured in short term studies at short wavelengths such as 355 nm and 532 nm. The depolarisation ratio has a spectral dependency and so exploring values at longer wavelengths could be valuable for future studies. Here, aerosol depolarisation ratio at 1565 nm is measured across Finland’s ground based remote sensing network over a four year period. The Halo Photonics StreamLine Doppler lidars instruments were found to be stable over long time periods and cloud based calibration was used to correct for the bleed though. The depolarisation ratio of elevated aerosol layers was compared to boundary layer aerosol. A higher average depolarisation ratio was found for elevated aerosol with the exception of boreal forest sites in the summer months where values were similar. Elevated aerosols over Finland were found to originate mostly from the Arctic, Europe, Russia and North America using aerosol transport models. Four case studies were looked at in more detail: Saharan dust with a depolarisation ratio of 0.249 ± 0.018, pollen with a depolarisation ratio of 0.207 ± 0.013, anthropogenic pollution with a depolarisation ratio of 0.067 ± 0.009, and a mixed layer with a depolarisation ratio of 0.152 ± 0.019 thought to be pollen and smoke. Based on this study, Halo Doppler Lidar can be used to measure elevated aerosol at 1565 nm in the long term. Future studies could use 1565 nm depolarisation ratio alongside commonly used shorter wavelengths to aid aerosol categorisation.

The reflection seismic surveying method is useful when conducting mineral exploration in the crystalline bedrock because of its good depth extent and resolution. However, the traditional experiments with active sources are expensive and difficult to carry out, especially in remote areas or in conservation areas where mineral exploration is limited due to environmental reasons. Recently, a number of theoretical advances have proven that passive soundings utilizing ambient seismic noise can provide new opportunities for seismic imaging and contribute to data generation for reflection seismic surveys, without the need for explosive or vibratory sources. One of the most promising new methods is seismic interferometry (SI), where the impulse response between two receivers is reconstructed by correlating their signals with each other. COGITO-MIN is a joint project between the University of Helsinki, the Geological Survey of Finland, Polish Academy of Sciences, and industrial partners with the aim of investigating and developing new cost-effective seismic exploration methods in the crystalline bedrock. Within the framework of the project, a passive seismic experiment was carried out in which 45 three-component geophones were deployed for a month in the vicinity of the polymetallic Kylylahti Mine in Polvijärvi, northern Karelia, where the mining operator is the Swedish metal company Boliden. The original purpose of these geophones was to collect data suitable for detecting underground cavities related to underground nuclear explosions. The institute that collected the data was CTBTO (Comprehensive Test Ban Treaty Organization) whose task is to monitor the treaty in the pre-ratification stage. The purpose of this Master's thesis was to develop an SI workflow for the three-component data and to investigate the method's performance in an area where local geology is known after nearly 40 years of exploration and consequent mining operations. The specific scientific objectives of the thesis are (1) to demonstrate the usefulness of collecting three-component data in conjunction with or instead of single-component data, (2) to assess the noise-based SI methods used in previous studies and to improve their stability in the crystalline bedrock, and (3) to investigate the possibilities of SI from an operational perspective. Seismic velocities obtained through laboratory measurements were merged with geological and density models of the target area provided by Boliden. The resulting velocity and density grids were then used as the basis for waveform modelling, and the results from SI were validated against them. The starting point for SI was the noise-driven approach where 'each sample matters'. The interferometric workflow is built on the Seismic Unix suite together with self-written algorithms that are based on theoretical evaluations. SI is followed by an imaging workflow, which provides the basis for the reflectivity profiles. The thesis work focuses on five components of the Green's tensor and the vertical, radial and transverse component of the impulse response. With the horizontal components, one can access the S-wave patterns in addition to the P-waves. As a specialty, the so-called sign bit normalization (SBN) method was also tested. The technique involves destroying much of the amplitude information of the original seismograms by only retaining the sign bit of each sample. According to the results outlined in this thesis, SBN can make it easier to image the weak reflectors of the subsurface. This type of seismic interferometry seems particularly suitable for the early stage of mineral exploration, where the explorer does not yet fully understand the target they are studying. The most important advantage of seismic interferometry, however, is its cost effectiveness, and its potential for reducing risks for the environment.

Ohjelmiston hyvä käytettävyys varmistaa sen, että henkilöt, joille ohjelmisto on suunnattu osaavat sitä käyttää, ja että ohjelmiston käyttö on sekä tehokasta että miellyttävää. Käytettävyyden merkitys on kasvanut, kun ohjelmistojen käyttäjiksi on tullut tietotekniikan ammattilaisten lisäksi kuluttajia. Kilpailukyvyn varmistamiseksi ohjelmistojen tulee olla käytettävän lisäksi myös saavutettavia ja niiden kokonaiskäyttökokemuksen on oltava hyvä. Saavutettavuus on myös lainmukainen vaatimus ohjelmistoille. Käytettävyyden testaaminen on perinteisesti käsityötä, jota suorittaa koulutettu käytettävyysasiantuntija tai käyttäjätutkija. Suuri osa huomiosta keskittyy käyttöliittymään, jonka kautta käyttäjä on vuorovaikutuksessa ohjelmiston kanssa. Käytettävyys on kontekstisidonnaista, joten osa testaamisesta tulee aina vaatimaan ihmisen analysointia. Osa testaamisesta voidaan kuitenkin automatisoida, ja näin vapauttaa tutkijan kapasiteettia sellaisiin osa-alueisiin, jotka vaativat kontekstin ja käyttäjän toiminnan ymmärtämistä ja tulkitsemista. Lisäksi tutkija pystyy keskittymään käytettävyysongelmia korjaavien muutosehdotusten kehittämiseen. Automaattisesti voidaan testata erityisesti käyttöliittymän yhdenmukaisuutta, käyttöliittymäelementtien havaittavuutta ja kerätä automaattisesti käyttäjän raportoimia ongelmakohtia. Tässä työssä pyritään vastaamaan kysymykseen, missä määrin käytettävyyden varmistamista pystytään automatisoimaan, ja esitellään tämän tiedon pohjalta rakennettu käytettävyyden automaattisen testaamisen työkalu, Usability Spy, jonka tarkoituksena on automatisoida käytettävyystesteihin osallistuvilta henkilöiltä koottavien käytettävyystestiaineiston keräystä. Työkalun toimintaympäristönä on DOS käyttöjärjestelmän päällä toimiva Microsoft Windows tai Microsoft Windows for Workgroups ikkunointiympäristön versio 3.1 tai uudempi.