TKD - Institutt for informasjonsteknologi: Nye registreringer

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    • 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 ...

    • Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration 

      Zheng, Zhuoxun; Savkovic, Ognjen; Phuc Luu, Huu; Soylu, Ahmet; Kharlamov, Evgeny; Zhou, Baifan (Peer reviewed; Journal article, 2023)
      Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges. In that context, distributed computing solutions such as cloud systems are ...

    • COCO: an annotated Twitter dataset of COVID-19 conspiracy theories 

      Langguth, Johannes; Schroeder, Daniel Thilo; Filkukova, Petra; Brenner, Stefan; Phillips, Jesper; Pogorelov, Konstantin (Peer reviewed; Journal article, 2023)
      The COVID-19 pandemic has been accompanied by a surge of misinformation on social media which covered a wide range of different topics and contained many competing narratives, including conspiracy theories. To study such ...

    • A Taxonomy for Cloud Storage Cost 

      Khan, Akif Quddus; Nikolov, Nikolay Vladimirov; Matskin, Mihhail; Prodan, Radu; Bussler, Christoph; Roman, Dumitru; Soylu, Ahmet (Communications in Computer and Information Science (CCIS);, Chapter; Peer reviewed; Conference object; Journal article, 2024)
      The cost of using cloud storage services is complex and often an unclear structure, while it is one of the important factors for organisations adopting cloud storage. Furthermore, organisations take advantage of multi-cloud ...

    • Container-Based Data Pipelines on the Computing Continuum for Remote Patient Monitoring 

      Nikolov, Nikolay Vladimirov; Solberg, Arnor; Prodan, Radu; Soylu, Ahmet; Matskin, Mihhail; Roman, Dumitru (Computer;, Peer reviewed; Journal article, 2023)
      Diagnosing, treatment, and follow-up care of patients is happening increasingly through telemedicine, especially in remote areas where direct interaction is hindered. Over the past three years, following the COVID-19 ...

    • Enhancing investigative interview training using a child avatar system: a comparative study of interactive environments 

      Zohaib Hassan, Syed; Shafiee Sabet, Saeed; Riegler, Michael Alexander; Baugerud, Gunn Astrid; Ko, Hayley Manalang; Salehi, Pegah; Røed, Ragnhild Klingenberg; Sinkerud Johnson, Miriam; Halvorsen, Pål (Peer reviewed; Journal article, 2023)
      The impact of investigative interviews by police and Child Protective Services (CPS) on abused children can be profound, making effective training vital. Quality in these interviews often falls short and current training ...

    • Household Energy Consumption Prediction: A Deep Neuroevolution Approach 

      Soudaei, Alexander; Zhang, Jianhua; Elmi, Mohamed Ahmed; Tsechoev, Mikael; Khan, Zishan; Osman, Ahmed Abbas (Chapter, 2023)
      Accurate energy consumption prediction can provide insights to make better informed decisions on energy purchase and generation. It also can prevent overloading and make it possible to store energy more efficiently. In ...

    • Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge 

      Ali, Sharib; Ghatwary, Noha; Jha, Debesh; Isik-Polat, Ece; Polat, Gorkem; Yang, Cheng; Li, Wuyang; Galdran, Adrian; Ballester, Miguel Angel Gonzalez; Thambawita, Vajira L B; Hicks, Steven; Poudel, Sahadev; Lee, Sang-Woong; Jin, Ziyi; Gan, Tianyuan; Yu, Chenghui; Yan, JiangPeng; Yeo, Doyeob; Lee, Hyunseok Lee; Tomar, Nikhil Kumar; Haitham, Mahmood; Ahmed, Amr; Riegler, Michael Alexander; Daul, Christian; Halvorsen, Pål; Rittscher, Jens; Salem, Osama E.; Lamarque, Dominique; Cannizzaro, Renato; Realdon, Stefano; de Lange, Thomas; East, James E (Peer reviewed; Journal article, 2024)
      Polyps are well‑known cancer precursors identified by colonoscopy. However, variability in their size, appearance, and location makes the detection of polyps challenging. Moreover, colonoscopy surveillance and removal ...

    • ContrastNER: Contrastive-based Prompt Tuning for Few-shot NER 

      Layegh, Amirhossein; Hossein Payberah, Amir; Soylu, Ahmet; Roman, Dumitru; Matskin, Mihhail (IEEE Annual International Computer Software and Applications Conference (COMPSAC);, Chapter; Peer reviewed; Conference object, 2023)
      Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the ...

    • Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry 

      Yahya, Muhammad; Zhou, Baifan; Breslin, John G.; Ali, Muhammad Intizar; Kharlamov, Evgeny (Peer reviewed; Journal article, 2023)
      The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, ...

    • Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry 

      Qu, Yuanwei; Zhou, Baifan; Waaler, Arild Torolv Søetorp; Cameron, David B. (Journal article, 2023)
      The petroleum industry is crucial for modern society, but the production process is complex and risky. During the production, accidents or failures, resulting from undesired production events, can cause severe environmental ...

    • Random Testing and Evolutionary Testing for Fuzzing GraphQL APIs 

      Belhadi, Asma; Zhang, Man; Arcuri, Andrea (Peer reviewed; Journal article, 2023)
      The Graph Query Language (GraphQL) is a powerful language for APIs manipulation in web services. It has been recently introduced as an alternative solution for addressing the limitations of RESTful APIs. This paper introduces ...

    • Experience with the use of a digital sleep diary in symptom management by individuals with insomnia -a pilot mixed method study 

      Thorshov, Thea Christine; Øverby, Caroline Tonje; Hansen, Diana Dobran; Bong, Way Kiat; Skifjeld, Knut; Hurlen, Petter; Dammen, Toril; Moen, Anne; Hrubos-Strøm, Harald (Peer reviewed; Journal article, 2023)
      Background: Insomnia is the most common sleep disorder. The recommended treatment is cognitive behavioural therapy for insomnia (CBTi). A sleep diary is a core tool in CBTi. We have developed a digital sleep diary with ...