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Macroeconomic forecasting is a classic problem, today most often modeled using time series analysis. Few attempts have been made using machine learning methods, and even fewer incorporating unconventional data, such as that from social media. In this thesis, a Generative Adversarial Network (GAN) is used to predict U.S. unemployment, beating the ARIMA benchmark on all horizons. Furthermore, attempts at using Twitter data and the Natural Language Processing (NLP) model DistilBERT are performed. While these attempts do not beat the benchmark, they do show promising results with predictive power. The models are also tested at predicting the U.S. stock index S&P 500. For these models, the Twitter data does improve the accuracy and shows the potential of social media data when predicting a more erratic index with less seasonality that is more responsive to current trends in public discourse. The results also show that Twitter data can be used to predict trends in both unemployment and the S&P 500 index. This sets the stage for further research into NLP-GAN models for macroeconomic predictions using social media data. Makroekonomiska prognoser är sedan länge en svår utmaning. Idag löses de oftast med tidsserieanalys och få försök har gjorts med maskininlärning. I denna uppsats används ett generativt motstridande nätverk (GAN) för att förutspå amerikansk arbetslöshet, med resultat som slår samtliga riktmärken satta av en ARIMA. Ett försök görs också till att använda data från Twitter och den datorlingvistiska (NLP) modellen DistilBERT. Dessa modeller slår inte riktmärkena men visar lovande resultat. Modellerna testas vidare på det amerikanska börsindexet S&P 500. För dessa modeller förbättrade Twitterdata resultaten vilket visar på den potential data från sociala medier har när de appliceras på mer oregelbunda index, utan tydligt säsongsberoende och som är mer känsliga för trender i det offentliga samtalet. Resultaten visar på att Twitterdata kan användas för att hitta trender i både amerikansk arbetslöshet och S&P 500 indexet. Detta lägger grunden för fortsatt forskning inom NLP-GAN modeller för makroekonomiska prognoser baserade på data från sociala medier.

The field of natural language processing has received increased attention lately, but less focus is put on comparing models, which differ in complexity. This thesis compares Random Forest to LSTM, for the task of classifying a message as question or non-question. The comparison was done by training and optimizing the models on historic chat data from the Swedish insurance company Hedvig. Different types of word embedding were also tested, such as Word2vec and Bag of Words. The results demonstrated that LSTM achieved slightly higher scores than Random Forest, in terms of F1 and accuracy. The models’ performance were not significantly improved after optimization and it was also dependent on which corpus the models were trained on. An investigation of how a chatbot would affect Hedvig’s adoption rate was also conducted, mainly by reviewing previous studies about chatbots’ effects on user experience. The potential effects on the innovation’s five attributes, relative advantage, compatibility, complexity, trialability and observability were analyzed to answer the problem statement. The results showed that the adoption rate of Hedvig could be positively affected, by improving the first two attributes. The effects a chatbot would have on complexity, trialability and observability were however suggested to be negligible, if not negative. Det vetenskapliga området språkteknologi har fått ökad uppmärksamhet den senaste tiden, men mindre fokus riktas på att jämföra modeller som skiljer sig i komplexitet. Den här kandidatuppsatsen jämför Random Forest med LSTM, genom att undersöka hur väl modellerna kan användas för att klassificera ett meddelande som fråga eller icke-fråga. Jämförelsen gjordes genom att träna och optimera modellerna på historisk chattdata från det svenska försäkringsbolaget Hedvig. Olika typer av word embedding, så som Word2vec och Bag of Words, testades också. Resultaten visade att LSTM uppnådde något högre F1 och accuracy än Random Forest. Modellernas prestanda förbättrades inte signifikant efter optimering och resultatet var också beroende av vilket korpus modellerna tränades på. En undersökning av hur en chattbot skulle påverka Hedvigs adoption rate genomfördes också, huvudsakligen genom att granska tidigare studier om chattbotars effekt på användarupplevelsen. De potentiella effekterna på en innovations fem attribut, relativ fördel, kompatibilitet, komplexitet, prövbarhet and observerbarhet analyserades för att kunna svara på frågeställningen. Resultaten visade att Hedvigs adoption rate kan påverkas positivt, genom att förbättra de två första attributen. Effekterna en chattbot skulle ha på komplexitet, prövbarhet och observerbarhet ansågs dock vara försumbar, om inte negativ.

The use of Deep Learning methods for Document Understanding has been embraced by the research community in recent years. A requirement for Deep Learning methods and especially Transformer Networks, is access to large datasets. The objective of this thesis was to evaluate a state-of-the-art model for Document Layout Analysis on a public and custom dataset. Additionally, the objective was to build a pipeline for building a dataset specifically for Visually Rich Documents. The research methodology consisted of a literature study to find the state-of-the-art model for Document Layout Analysis and a relevant dataset used to evaluate the chosen model. The literature study also included research on how existing datasets in the domain were collected and processed. Finally, an evaluation framework was created. The evaluation showed that the chosen multi-modal transformer network, LayoutLMv2, performed well on the Docbank dataset. The custom build dataset was limited by class imbalance, although good performance for the larger classes. The annotator tool and its auto-tagging feature performed well and the proposed pipelined showed great promise for creating datasets with Visually Rich Documents. In conclusion, this thesis project answers the research questions and suggests two main opportunities. The first is to encourage others to build datasets with Visually Rich Documents using a similar pipeline to the one presented in this paper. The second is to evaluate the possibility of creating the visual token information for LayoutLMv2 as part of the transformer network rather than using a separate CNN. Användningen av Deep Learning-metoder för dokumentförståelse har anammats av forskarvärlden de senaste åren. Ett krav för Deep Learning-metoder och speciellt Transformer Networks är tillgång till stora datamängder. Syftet med denna avhandling var att utvärdera en state-of-the-art modell för analys av dokumentlayout på en offentligt tillgängligt dataset. Dessutom var målet att bygga en pipeline för att bygga en dataset specifikt för Visuallt Rika Dokument. Forskningsmetodiken bestod av en litteraturstudie för att hitta modellen för Document Layout Analys och ett relevant dataset som användes för att utvärdera den valda modellen. Litteraturstudien omfattade också forskning om hur befintliga dataset i domänen samlades in och bearbetades. Slutligen skapades en utvärderingsram. Utvärderingen visade att det valda multimodala transformatornätverket, LayoutLMv2, fungerade bra på Docbank-datasetet. Den skapade datasetet begränsades av klassobalans även om bra prestanda för de större klasserna erhölls. Annotatorverktyget och dess autotaggningsfunktion fungerade bra och den föreslagna pipelinen visade sig vara mycket lovande för att skapa dataset med VVisuallt Rika Dokument.svis besvarar detta examensarbete forskningsfrågorna och föreslår två huvudsakliga möjligheter. Den första är att uppmuntra andra att bygga datauppsättningar med Visuallt Rika Dokument med en liknande pipeline som den som presenteras i denna uppsats. Det andra är att utvärdera möjligheten att skapa den visuella tokeninformationen för LayoutLMv2 som en del av transformatornätverket snarare än att använda en separat CNN.

Part of book: ISBN 978-1-009-10023-6QC 20221219

QC 20160318

This article explores how the waste crisis in Naples, which has been occurring since the 1990s, has stimulated the political creativeness of local activists who have started to experiment new ways for participation and community building. In particular, we investigate how environmental justice struggles have evolved in commoning processes (that is, in the creation of participatory institutions and in the defense of commons) by studying the social mobilization in Chiaiano, a neighborhood at the northern periphery of Naples (Italy). Using oral history interviews, documents produced by grassroots organizations, mass media reports, and our participants’ observation notes, we have analyzed the evolution of the mobilization in Chiaiano, the connections between environmental concerns and commoning, and the results in terms of social experimentation. Keywords: Environmental Justice. Commoning. Participatory Democracy. Waste. Naples (Italy).

Podcasts are an exponentially growing audio medium where useful and relevant content should be served, which requires new methods of information sorting. This thesis is the first to look into the state-of-art problem of segmenting podcasts into chapters (structurally and topically coherent sections). Podcast segmentation is a more difficult problem than segmenting structured text due to spontaneous speech and transcription errors from automatic speech recognition systems. This thesis used author-provided timestamps from podcast descriptions as labels to perform supervised learning. Binary classification is performed on sentences from podcast transcripts. A general framework is delivered for creating a dataset with 21 436 podcast episodes, training a supervised model, and for evaluation. The framework managed to address technical challenges such as a high data imbalance (there are few chapter transitions per episode), and finding an appropriate context size (how many sentences are shown to the model during inference). The proposed model outperformed a baseline model in quantitative metrics and in a human evaluation with 100 transitions. The solution provided in this thesis can be used to chapterize podcasts, which has many downstream applications, such as segment sorting, summarization, and information retrieval. Podcasts är ett exponentiellt växande ljudmedium där användbart och relevant innehåll är viktigt, vilket kräver nya metoder för sortering av information. Detta examensarbete är det första projektet som antar utmaningen att segmentera podcasts in i kapitel (strukturellt och tematiskt sammanhängande avsnitt). Podcastsegmentering är ett svårare problem än att segmentera strukturerad text på grund av spontant tal och fel i transkriberingssystem. Detta projekt använde kapiteltider från podcastbeskrivningar som signaler för att kunna göra supervised learning. Binär klassificering görs på meningar från podcast-transkript. Denna uppsats levererar ett ramverk för att skapa ett dataset med 21 436 podcasts, träna en supervised maskininlärningsmodell samt för utvärdering. Ramverket lyckades lösa tekniska utmaningar såsom obalanserad data (det är få kapitelövergångar i varje podcast) och att hitta en rimlig kontextstorlek (hur många meningar som modellen ser för varje inferens). Den tränade modellen var bättre än en slumpmässig referensmodell i både kvantitativa mätningar samt i en mänsklig utvärdering för 100 kapitelövergångar. Slutligen, detta examensarbete har resulterat i en lösning som kan kapitelindela podcasts, vilket har många applikationer såsom sortering av segment, summering, och informationssökning.

Can walking trails be understood not only as routes to history and heritage, but also as heritage in and of themselves? The paper explores the articulation of trails as a distinct landscape and mobility heritage, bridging the nature-culture divide and building on physical and intellectual movements over time. The authors aim to contribute to a better understanding of the geography of trails and trailscapes by analysing the emergence of the Swedish-Norwegian trail Finnskogleden. The trail is situated in the border region spanning the former county of Hedmark in present-day Innlandet County, south-eastern Norway, and Värmland County in mid-western Sweden, a forested area where Finnish-speaking immigrants settled from the 16th century to the early 20th century. Archives, literature, interviews, and field visits were used to analyse the emergence and governance of the trail. The main finding is the importance of continuous articulation work by local and regional stakeholders, through texts, maps, maintenance, and mobility. In conclusion, the Finn forest trailscape and its mobility heritage can be seen as an articulation of territory over time, a multilayered process drawing on various environing technologies, making the trail a transformative part of a trans-border political geography. Rörelsearvet: stigar och leder i hållbar och inkluderande kulturarvsförvaltning

The agricultural sector is the main contributor for the warming from non-CO2 gases, especially methane and nitrous oxide. Existing measures to mitigate these emissions can only reduce but not eliminate these emissions. Owing to the diffused nature of these emissions, it is hard to design a single point measure to address the emissions from the agricultural sector. In our work, we present the first-of-a-kind direct air capture-based process to mitigate these diverse emissions. The process is designed based on thermal catalytic route for the methane conversion, which is coupled to a direct air capture unit for CO2 capture. The process was modelled based on steady state assumptions to estimate the energy requirement per tonne of CO2 equivalent mitigated. Energy estimations were later compared for the two methane removal systems with and without CO2 capture unit. The energy demand per tonne CO2-equivalent removed from the system without CO2 capture unit (only CH4 removal) was found to be 16.54 GJ. For the methane removal system with CO2 capture unit (co-removal of CO2 and CH4), the energy demand is 15.42 GJ per tonne-CO2 equivalent. QC 20230120

The long legacy of colonization that is rooted in how plants are known is mostly out of sight. But at times the colonial legacy of botany becomes all too apparent. This article draws upon ethnograhic field work in Cape Town, South Africa, over several years to contribute knoweldge how colonial and imperial forms of science and colonial management influenced urban botany and later urban ecology. But it points towards a more general argument that is often forgotten when the history of urban ecology and “urban nature knowledge” is written up. This works to decenter or on-stage what has often been silenced in the now taken-for-granted "success" story of the growth of modern urban ecology. What are the colonial remains within urban ecology and urban environmental knowledge today? QC 20201105 Visual Environmental Humanities