Nye registreringer

  • Efficient Estimation of Generative Models Using Tukey Depth 

    Vo, Minh-Quan; Nguyen, Thu; Riegler, Michael; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2024)
    Generative models have recently received a lot of attention. However, a challenge with such models is that it is usually not possible to compute the likelihood function, which makes parameter estimation or training of ...
  • A dataset for predicting cloud cover over Europe 

    Svennevik, Hanna; Hicks, Steven; Riegler, Michael; Storelvmo, Trude; Hammer, Hugo Lewi (Peer reviewed; Journal article, 2024)
    Clouds are important factors when projecting future climate. Unfortunately, future cloud fractional cover (the portion of the sky covered by clouds) is associated with significant uncertainty, making climate projections ...
  • Peer-Mentoring for Students with Disabilities – A Preliminary Study in Norwegian Higher Education 

    Sanderson, Norun Christine; Chen, Weiqin (Lecture Notes in Computer Science;, Chapter; Peer reviewed; Conference object; Journal article, 2023)
    The number of students with disabilities in higher education is increasing. Despite governmental and institutional support, students with disabilities often have poorer progression and are at a higher risk of dropping out ...
  • Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface 

    Khan, Haroon; Khadka, Rabindra; Sultan, Malik Shahid; Yazidi, Anis; Ombao, Hernando; Mirtaheri, Peyman (Peer reviewed; Journal article, 2024)
    In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number ...
  • Estimating volcanic ash emissions using retrieved satellite ash columns and inverse ash transport modelling using VolcanicAshInversion v1.2.1, within the operational eEMEP volcanic plume forecasting system (version rv4_17) 

    Brodtkorb, André R.; Sollum, Espen; Benedictow, Anna Maria Katarina; Kylling, Arve; Klein, Heiko; Nyiri, Agnes; Valdebenito Bustamante, Alvaro Moises; Kristiansen, Nina Iren (Peer reviewed; Journal article, 2024)
    Accurate modeling of ash clouds from volcanic eruptions requires knowledge about the eruption source parameters including eruption onset, duration, mass eruption rates, particle size distribution, and vertical-emission ...
  • Exploring Multilingual Word Embedding Alignments in BERT Models: A Case Study of English and Norwegian 

    Aaby, Pernille; Biermann, Daniel; Yazidi, Anis; Borges Moreno e Mello, Gustavo; Palumbo, Fabrizio (Lecture Notes in Computer Science;, Chapter; Peer reviewed; Conference object; Journal article, 2023)
    Contextual language models, such as transformers, can solve a wide range of language tasks ranging from text classification to question answering and machine translation. Like many deep learning models, the performance ...
  • A graph neural approach for group recommendation system based on pairwise preferences 

    Abolghasemi, Roza; Viedma, Enrique Herrera; Engelstad, Paal E.; Djenouri, Youcef; Yazidi, Anis (Peer reviewed; Journal article, 2024)
    Pairwise preference information, which involves users expressing their preferences by comparing items, plays a crucial role in decision-making and has recently found application in recommendation systems. In this study, ...
  • Introducing Region Based Pooling for handling a varied number of EEG channels for deep learning models 

    Tveitstøl, Thomas; Tveter, Mats; Pérez Teseyra, Ana Silvina; Hatlestad-Hall, Christoffer; Yazidi, Anis; Hammer, Hugo Lewi; Haraldsen, Ira Hebold (Peer reviewed; Journal article, 2023)
    Introduction: A challenge when applying an artificial intelligence (AI) deep learning (DL) approach to novel electroencephalography (EEG) data, is the DL architecture’s lack of adaptability to changing numbers of EEG ...
  • Social Web in IoT: Can Evolutionary Computation and Clustering Improve Ontology Matching for Social Web of Things? 

    Belhadi, Asma; Djenouri, Djamel; Djenouri, Youcef; Belbachir, Nabil; Srivastava, Gautam (IEEE Transactions on Computational Social Systems;, Peer reviewed; Journal article, 2023)
    Many Internet of Things (IoT) applications can benefit from Social Web of Things (S-WoT) methods that enable knowledge discovery and help solving interoperability problems. The semantic modeling of S-WoT is the main emphasis ...
  • Exchange-only virial relation from the adiabatic connection 

    Laestadius, Andre; Csirik, Mihaly Andras; Penz, Markus; Tancogne-Dejean, Nicolas; Ruggenthaler, Michael; Rubio, Angel; Helgaker, Trygve (Peer reviewed; Journal article, 2024)
    The exchange-only virial relation due to Levy and Perdew is revisited. Invoking the adiabatic connection, we introduce the exchange energy in terms of the right-derivative of the universal density functional w.r.t. the ...
  • Unsupervised State Representation Learning in Partially Observable Atari Games 

    Meng, Li; Goodwin, Morten; Yazidi, Anis; Engelstad, Paal E. (Lecture Notes in Computer Science;, Chapter; Peer reviewed; Conference object; Journal article, 2023)
    State representation learning aims to capture latent factors of an environment. Although some researchers realize the connections between masked image modeling and contrastive representation learning, the effort is focused ...
  • Solving the Lunar Lander Problem with Multiple Uncertainties using a Deep Q-Learning based Short-Term Memory Agent 

    Guttulsrud, Håkon; Sandnes, Mathias; Shrestha, Raju (Chapter, 2024)
    Efficient space travel requires intelligent and robust control mechanisms during spacecraft landing scenarios. Developing a control mechanism for a rocket trajectory problem is inherently complex. This paper introduces ...
  • CELLULAR, A Cell Autophagy Imaging Dataset 

    al Outa, Amani; Hicks, Steven; Thambawita, Vajira L B; Andresen, Siri; Enserink, Jorrit Martijn; Halvorsen, Pål; Riegler, Michael Alexander; Knævelsrud, Helene (Peer reviewed; Journal article, 2023)
    Cells in living organisms are dynamic compartments that continuously respond to changes in their environment to maintain physiological homeostasis. While basal autophagy exists in cells to aid in the regular turnover of ...
  • Co-Creatives Spaces: The machine as a collaborator 

    Thelle, Notto Johannes Windju; Wærstad, Bernt Isak Grave (Chapter, 2023)
    People have always used new technology to experiment with new forms of music creation. However, the latest devel- opments in artificial intelligence (AI) suggest that machines are on the verge of becoming more than mere ...
  • Mask-conditioned latent diffusion for generating gastrointestinal polyp images 

    Macháček, Roman; Mozaffari, Leila; Sepasdar, Zahra; Parasa, Sravanthi; Halvorsen, Pål; Riegler, Michael; Thambawita, Vajira L B; Machacek, Roman (ICDAR: Intelligent Cross-Data Analysis and Retrieval;, Chapter; Peer reviewed; Conference object, 2023)
    In order to take advantage of artificial intelligence (AI) solutions in endoscopy diagnostics, we must overcome the issue of limited annotations. These limitations are caused by the high privacy concerns in the medical ...
  • Situational Disabilities in Information Systems for Situational Awareness in Flood Situations in Nigeria 

    Ogbonna, Uchenna; Paupini, Cristina; Gjøsæter, Terje (IFIP Advances in Information and Communication Technology;, Chapter; Peer reviewed; Conference object; Journal article, 2023)
    Floods are one of the major natural disasters that contribute to the high disaster death rate in the global south, especially in Nigeria. This requires an effort of collaboration from all stakeholders in designing, building, ...
  • Exploring AI Literacy Among Older Adults 

    Kaur, Amrat; Chen, Weiqin (Peer reviewed; Journal article, 2023)
  • TRANSQLATION: TRANsformer-based SQL RecommendATION 

    Tahmasebi, Shirin; Payberah, Amir H.; Soylu, Ahmet; Roman, Dumitru; Matskin, Mihhail (IEEE International Conference on Big Data;, Chapter; Peer reviewed; Conference object, 2023)
    The exponential growth of data production emphasizes the importance of database management systems (DBMS) for managing vast amounts of data. However, the complexity of writing Structured Query Language (SQL) queries requires ...
  • Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring 

    Tan, Zhipeng; Zheng, Zhuoxun; Klironomos, Antonis; Gad-Elrab, Mohamed H.; Xiao, Guohui; Soylu, Ahmet; Kharlamov, Evgeny; Zhou, Baifan (Peer reviewed; Journal article, 2023)
    Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). ...
  • Semantic Cloud System for Scaling Data Science Solutions for Welding at Bosch 

    Zheng, Zhuoxun; Zhou, Baifan; Tan, Zhipeng; Savkovic, Ognjen; Rincon-Yanez, Diego; Nikolov, Nikolay Vladimirov; Roman, Dumitru; Soylu, Ahmet; Kharlamov, Evgeny (Peer reviewed; Journal article, 2023)
    Background and Challenges. Industry 4.0 focuses on smart factories that rely on IoT tech- nology for automation. This produces massive amounts of production data, increasing the demand for data-driven solutions and cloud ...

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