Sökning: "Network Effects"

Visar resultat 6 - 10 av 883 uppsatser innehållade orden Network Effects.

1. 6. POLITISKT DELTAGANDEIRESURSSVAGA OMRÅDEN : En kvantitativ studie om CVM-modellens förklaringsgrad på politiskt deltagande i resurssvaga områden

   Kandidat-uppsats, Örebro universitet/Institutionen för humaniora, utbildnings- och samhällsvetenskap

   Författare :Cornelia Bacic; Rebecca Venäläinen; [2024]
   Nyckelord :Civic Voluntarism Model; political participation; disadvantaged areas; resources; motivation; recruiting networks; local associations; context; quantitative method;

   Sammanfattning : Political participation is a vital part of modern democracies which can be described as a way to convey the interests and the preferences of citizens and exert pressure on the government to align with the will of the citizens. For this sake it could be considered a democracy problem that citizens participate to varying extents, where people living in disadvantaged areas participate to a lesser extent compared to people in areas with a higher degree of resources. LÄS MER

3. 7. (Swift) sanctions and the rise of parallel payment systems: A qualitative study of financial infrastructure and power dynamics in times of FinTech

   Kandidat-uppsats, Göteborgs universitet/Institutionen för globala studier

   Författare :Jacqueline Klehr; [2023-10-05]
   Nyckelord :Financial infrastructure; economic sanctions; Swift; payment systems; ; cross border payments; financial network;

   Sammanfattning : In 2022, the West imposed sanctions of unprecedented scale on Russia following the war in Ukraine, including severing Moscow from the main global financial message provider, Swift, with the objective to harm the ability of Russian banks to operate globally. As the global financial system is centralised, being severed from Swift significantly complicates the process of conducting cross border payments. LÄS MER

4. 8. An Empirical Survey of Bandits in an Industrial Recommender System Setting

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Tobias Schwarz; Johan Brandby; [2023-09-21]
   Nyckelord :computer science; industrial application; machine learning; reinforcement learning; multi-armed bandits; MAB; contextual multi-armed bandits; survey; batch learning;

   Sammanfattning : In this thesis, the effects of incorporating unstructured data—images in the wild—in contextual multi-armed bandits are investigated, when used within a recommender system setting, which focuses on picture-based content suggestion. The idea is to employ image features, extracted by a pre-trained convolutional neural network, and study the resulting bandit behaviors when including respective excluding this information in the typical context creation, which normally relies on structured data sources—such as metadata. LÄS MER

5. 9. Evaluation and Optimization of LTE-V2X Mode 4 under Aperiodic Messages of Variable Size

   Master-uppsats, Lunds universitet/Institutionen för elektro- och informationsteknik

   Författare :Md Mamunur Rashid; [2023]
   Nyckelord :LTE-V2X; aperiodic; variable size; CAM; Technology and Engineering;

   Sammanfattning : Vehicular networks connect vehicles for improved road safety and efficiency with the assistance of wireless information exchange. Vehicular networks are based on the frequent broadcast of awareness messages referred to as CAM (Cooperative Awareness Messages) or BSM (Basic Safety Message) in the ETSI and SAE standards, respectively. LÄS MER

7. 10. Combining Cell Painting, Gene Expression and Structure-Activity Data for Mechanism of Action Prediction

   Master-uppsats, Uppsala universitet/Nationellt resurscentrum för biologi och bioteknik

   Författare :Erik Everett Palm; [2023]
   Nyckelord :bioinformatics; deep learning; machine learning; joint model; tabular data; image data;

   Sammanfattning : The rapid progress in high-throughput omics methods and high-resolution morphological profiling, coupled with the significant advances in machine learning (ML) and deep learning (DL), has opened new avenues for tackling the notoriously difficult problem of predicting the Mechanism of Action (MoA) for a drug of clinical interest. Understanding a drug's MoA can enrich our knowledge of its biological activity, shed light on potential side effects, and serve as a predictor of clinical success. LÄS MER