Extracting Food Substitutes From Food Diary via Distributional Similarity

Genetic ancestry admixture of patients infected with Influenza A(H1N1)pdm09 sorted by African ancestry. Each individual ancestry is depicted as a column, whereas color represents the proportion of ancestry estimated for that individual (African = blue; European = brown; Native American = green). (A) Non-hospitalized patients and (B) Hospitalized patients

The tricot citizen science approach applied to on-farm variety evaluation: methodological progress and perspectives

Tricot (triadic comparisons of technologies) is a citizen science approach for testing technology options in their use environments, which is being applied to on-farm testing of crop varieties. Over the last years, important progress has been made on the tricot methodology of which an overview is given. Trial dimensions depend on several factors but tricot implies that plot size is as small as possible to include farmers with small plots (yet avoiding excessive interplot competition) while many locations are included to ensure representativeness of trials. Gender and socio-economic work is focused on better household characterization and recruitment strategies that move beyond sex-aggregation to address aspects of intersectionality. Ethics, privacy and traditional knowledge aspects will be addressed through expanding digital support in this direction. Genetic gain estimates need to be addressed by yield measurements, which can be generated by farmers themselves. There is conceptual clarity about the needs for documentation of trials and publishing data but this aspect requires further digital development. Much progress has been made on the ClimMob digital platform already, which is user friendly and supports trials in the main steps and includes open-source data analytics packages. Further improvements need to be made to ensure better integration with other tools. A next step will be the development of scaling strategies that involve business development. An important input into these strategies are economic studies, which are ongoing

Empirical studies of factors affecting opinion dynamics

The advent of new online services has an enormous potential to impact the opinion of users. Two main drivers of this impact are crowdsourced evaluations and ratings, and algorithmically-chosen recommendations. However, understanding the relationship between these systems and their impacts is very challenging due the complex nature of recommender systems and due to the heterogeneous nature of crowdsourced reviews. In this thesis, we explore how these two drivers affect opinion dynamics with respect to two potential impacts: reliability of information and polarization of user opinion. First, we analyze the reliability of online ratings. By performing an empirical analysis of a large corpus of online ratings, we point out how different influences such as shifts in population or platform characteristics are correlated with changes in the perception of an item over time. Second, we investigate polarization in the context of recommender systems. We define three metrics - intensity, simplification, and divergence - to capture essential traits of user opinions and explore how they vary in a closed-loop with recommender systems. Finally, we examine reliability in recommendations via an empirical exploration on YouTube. We quantify changes in the nature of the recommended content, and we show how YouTube recommendations lead users - especially privacy-seeking users - away from reliable information. Taken together, these studies shed light on important factors that affect how user opinion is shaped by online systems.2020-08-24T00:00:00

The accuracy of farmer-generated data in an agricultural citizen science methodology.

Over the last decades, participatory approaches involving on-farm experimentation have become more prevalent in agricultural research. Nevertheless, these approaches remain difficult to scale because they usually require close attention from well-trained professionals. Novel large-N participatory trials, building on recent advances in citizen science and crowdsourcing methodologies, involve large numbers of participants and little researcher supervision. Reduced supervision may affect data quality, but the “Wisdom of Crowds” principle implies that many independent observations from a diverse group of people often lead to highly accurate results when taken together. In this study, we test whether farmer-generated data in agricultural citizen science are good enough to generate valid statements about the research topic. We experimentally assess the accuracy of farmer observations in trials of crowdsourced crop variety selection that use triadic comparisons of technologies (tricot). At five sites in Honduras, 35 farmers (women and men) participated in tricot experiments. They ranked three varieties of common bean (Phaseolus vulgaris L.) for Plant vigor, Plant architecture, Pest resistance, and Disease resistance. Furthermore, with a simulation approach using the empirical data, we did an order-of-magnitude estimation of the sample size of participants needed to produce relevant results. Reliability of farmers’ experimental observations was generally low (Kendall’s W0.174 to 0.676). But aggregated observations contained information and had sufficient validity (Kendall’s tau coefficient 0.33 to 0.76) to identify the correct ranking orders of varieties by fitting Mallows-Bradley-Terry models to the data. Our sample size simulation shows that low reliability can be compensated by engaging higher numbers of observers to generate statistically meaningful results, demonstrating the usefulness of the Wisdom of Crowds principle in agricultural research. In this first study on data quality from a farmer citizen science methodology, we show that realistic numbers of less than 200 participants can produce meaningful results for agricultural research by tricot-style trials

The emotional review–reward effect: how do reviews increase impulsivity?

A growing reliance on customer reviews prompts firms to develop strategies to encourage customers to post online reviews of their products. However, little research investigates the behavioral consequences of writing a review. The act of sharing personal opinions through reviews is a rewarding experience and makes customers feel socially connected. With an application of reverse alliesthesia theory, the current study predicts that such rewarding experiences drive online reviewers to seek other rewards, such as impulsive buying. Three lab-based and two field studies demonstrate such an emotional review–reward effect: sharing emotional inf ormation in the public realm of customer reviews, rather than forming similar opinions privately, drives participants to make more impulsive buying decisions

Understanding and Mitigating Multi-sided Exposure Bias in Recommender Systems

Fairness is a critical system-level objective in recommender systems that has been the subject of extensive recent research. It is especially important in multi-sided recommendation platforms where it may be crucial to optimize utilities not just for the end user, but also for other actors such as item sellers or producers who desire a fair representation of their items. Existing solutions do not properly address various aspects of multi-sided fairness in recommendations as they may either solely have one-sided view (i.e. improving the fairness only for one side), or do not appropriately measure the fairness for each actor involved in the system. In this thesis, I aim at first investigating the impact of unfair recommendations on the system and how these unfair recommendations can negatively affect major actors in the system. Then, I seek to propose solutions to tackle the unfairness of recommendations. I propose a rating transformation technique that works as a pre-processing step before building the recommendation model to alleviate the inherent popularity bias in the input data and consequently to mitigate the exposure unfairness for items and suppliers in the recommendation lists. Also, as another solution, I propose a general graph-based solution that works as a post-processing approach after recommendation generation for mitigating the multi-sided exposure bias in the recommendation results. For evaluation, I introduce several metrics for measuring the exposure fairness for items and suppliers, and show that these metrics better capture the fairness properties in the recommendation results. I perform extensive experiments to evaluate the effectiveness of the proposed solutions. The experiments on different publicly-available datasets and comparison with various baselines confirm the superiority of the proposed solutions in improving the exposure fairness for items and suppliers.Comment: Doctoral thesi

Agrobiodiversity and climate adaptation: insights for risk management in small-scale farming

Agriculture is a dynamic activity that sustains food and other goods for global human population. Aiming to ensure global food security, the sector has evolved dramatically, especially over the last century with the introduction of high-yielding crops, improved technology and pathogen resistant varieties, to name a few. According to the Food and Agriculture Organisation of the United Nations (FAO), the food security issue is still present. In 2019, around 26% of the world population experienced either a moderate or severe level of food insecurity. Climate change makes the challenge of food security even more pressing. It is argued that increased agrobiodiversity through farm diversification and varietal selection can help farmers to cope with the negative effects of climate change while ensuring food security. However, such approaches have been difficult to scale up. One could argue that we often lack information to understand the contexts that drive farmers’ adaptation decisions and how to develop recommendations for adaptation. In this thesis, I developed methods and tools to support farmers and stakeholders in adapting to a changing climate. I present results from three continents to improve the understanding of the food systems at the farm level, and specifically in smallholder farming. I provide insights for the different biological levels: species level, focusing on trees as slow grower organisms for interspecific diversification; varieties level, looking for locally adapted phenotypes; and genotype level, focusing on G × E interactions to support crop breeding for intraspecific diversification. From the first part of the study, conducted in Central America, the results showed that farmers have a clear preference to a set of adaptation strategies, with reforestation (agroforestry) as the first choice (Paper 1). Crop variety management is the least preferred choice of the top-5. In the second part of the study, I assessed the impacts of climate change on the habitats of the 100 most common tree species used in coffee (Coffea arabica L.) and cocoa (Theobroma cacao L.) agroforestry in Central America (Paper 2). The results showed that the most preferred trees are the most vulnerable, but farmers could re-think the agroforestry designs using a portfolio of underutilised species already present in low densities at the current systems. In the third part of the study, I employed a citizen science approach that can scale variety testing and help farmers to select the right crop variety for their farms (Paper 3). I tested this approach with common bean (Phaseolus vulgaris L.) in Nicaragua, bread wheat (Triticum aestivum L.) in India and durum wheat (Triticum durum Desf.) in Ethiopia. The results showed that the approach reduces geographic sampling bias and could be scaled to provide tailored recommendations for crop variety management. I also show, with durum wheat genotypes in Ethiopia, that linking the farmer-generated data to scientist-generated data can support breeding programs targeting challenging crop production environments using a data-driven decentralised approach (Paper 4). The approach is fully replicable, and part of its workflow is presented in this thesis (Paper 5 and Paper 6). Overall, the results of this thesis should be seen as starting point to develop lines of research that support recommendations to adapt agricultural systems to a changing climate.Landbruk er en dynamisk aktivitet som skal sikre mat og andre varer som verdens befolkning til enhver tid trenger. Med global matsikkerhet som mål har landbruket utviklet seg mye, særlig det siste århundre, blant annet ved å ta i bruk høytytende grøder, forbedret dyrkningsteknikk og sorter som er resistente mot sykdommer. I følge FNs organisasjon for ernæring og landbruk (FAO) er matsikkerhet fortsatt et aktuelt tema. I 2019 opplevde rundt 26% av verdens befolkning en moderat eller alvorlig grad av usikkerhet rundt tilgangen på mat. Klimaendringer gjør utfordringene rundt matsikkerhet enda mer krevende. Hele matvaresystemet må endres for å takle klimaendringer og samtidig sikre nok mat til alle. Det hevdes at økt biologisk mangfold i landbruket kan hjelpe bønder i å takle klimaendringene og samtidig sikre matproduksjonen, dette gjennom mer variasjon i hva som dyrkes på gårdsnivå og gjennom et bedre utvalg av sorter. Slike tilnærminger har imidlertid vist seg å være vanskelige å skalere opp. Man kan hevde at vi ofte mangler tilstrekkelig med informasjon for fullt ut kunne forstå hva som avgjør bønders valg knytta til klimatilpasning - og videre hvordan man så skal kunne utvikle rådgivingen for dette. I denne avhandlingen har jeg utviklet metoder og verktøy som kan brukes for å hjelpe bønder og andre i å tilpasse seg til endringer i klima. Jeg presenterer resultater fra tre ulike kontinent, dette for gi eksempel på hvordan en økt forståelse av matvaresystemene kan fungere på gårdsnivå, og særlig på små gårder. Jeg går inn på ulike biologiske nivå: på artsnivå, med fokus på trær som vokser langsomt og som gir stor diversitet mellom arter; på sortsnivå, ved å søke å finne lokalt tilpassa fenotyper; og på genotypenivå, ved å fokusere på samspillet mellom gener og miljø (G × E), dette for å støtte foredlingsarbeid for økt diversitet innenfor arter. Resultater fra første delen av studiet som ble gjennomført i Mellom-Amerika viste at bønder har en klar preferanse for et sett av strategier i forhold til klimatilpasning, med agroskogbruk som førstevalg (Artikkel 1). Sortsvalg er det minst foretrukne valget av topp fem. I andre del av studiet undersøkte jeg hvor egnet dagens vokseplasser i Mellom-Amerika er for de 100 vanligste trærne som anvendes innenfor agroskogbruket med kaffe (Coffea arabica L.) og kakao (Theobroma cacao L.), dette med tanke på framtidige klimascenarier (Artikkel 2). Resultatene viste at de mest foretrukne trærne er de mest sårbare og at bøndene burde tenke nytt i forhold til utforming av agroskogbruket, dette ved å ta i bruk en rekke mindre anvendte arter som likevel finnes i dagens system. I tredje del av studiet anvendte jeg grasrotforskning som tilnærming for å skalere opp sortsforsøk og hjelpe bønder med å velge riktig sort for gårdene sine (Artikkel 3). Jeg undersøkte dette i bønner (Phaseolus vulgaris L.) i Nicaragua, vanlig brødhvete (Triticum aestivum L.) i India og durumhvete (Triticum durum Desf.) i Etiopia. Resultatene viste at en slik tilnærming reduserer feilkilder knyttet til geografisk representasjon og kan skaleres opp for å gi mer skreddersydde løsninger for bruk av sorter. I arbeidet med ulike genotyper av durumhvete i Etiopia viser jeg at foredlingsprogrammer kan styrkes at ved å koble grasrot-genererte data til forsker-genererte data gjennom en desentralisert tilnærming (Artikkel 4). Tilnærmingen er fullt mulig å gjenta, og en del av arbeidsflyten er presentert i avhandlingen (Artikkel 5 og 6). Samlet sett bør resultatene fra denne avhandlingen sees som en start på å utvikle en forskningen som kan bistå med anbefalinger slik at landbruket bedre kan tilpasse seg til et klima i endring.publishedVersio