Now showing items 21-40 of 922

    • Computational searches for quadratic APN functions with subfield coefficients 

      Berg, Simon Knotten (Master thesis, 2023-06-01)
      Almost perfect nonlinear (APN) functions are important in fields such as algebra, combinatorics, cryptography, etc. Finding new APN functions is of special importance in cryptography. This is because when used in modern ...
    • Applying Gamification and Virtual Reality to an MRSA Infection Control Guideline 

      Bråten, Oskar Elias; Igesund, Trygve Eide (Master thesis, 2020-06-10)
      MRSA is a group of harmful bacteria with resistance to many important antibiotics. Infections are hard to treat and often result in prolonged hospital stays, leading to increased costs and mortality. Like with many other ...
    • Graph Algebras and Derived Graph Operations 

      Wolter, Uwe; Truong, Tam T. (Journal article, 2023)
      We revise our former definition of graph operations and correspondingly adapt the construction of graph term algebras. As a first contribution to a prospective research field, Universal Graph Algebra, we generalize some ...
    • Finding haplotypic signatures in proteins 

      Vasicek, Jakub; Skiadopoulou, Dafni; Kuznetsova, Ksenia; Wen, Bo; Johansson, Stefan; Njølstad, Pål Rasmus; Bruckner, Stefan; Käll, Lukas; Vaudel, Marc (Journal article; Peer reviewed, 2023)
      Background The nonrandom distribution of alleles of common genomic variants produces haplotypes, which are fundamental in medical and population genetic studies. Consequently, protein-coding genes with different ...
    • A Novel Evolutionary Solution Approach for Many-objective Reliability-Redundancy Allocation Problem Based on Objective Prioritization and Constraint Optimization 

      Nath, Rahul (Journal article; Peer reviewed, 2024)
      The reliability redundancy allocation problem (RRAP) has been mostly solved either as a single or as a multi-objective optimization problem. However, this problem also has numerous important constraints which play prominent ...
    • Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary 

      Kim, Hyeongji; Parviainen, Pekka; Malde, Ketil (Journal article; Peer reviewed, 2023)
      Previous studies on robustness have argued that there is a tradeoff between accuracy and adversarial accuracy. The tradeoff can be inevitable even when we neglect generalization. We argue that the tradeoff is inherent to ...
    • Further investigations on permutation based constructions of bent functions 

      Li, Kangquan; Li, Chunlei; Helleseth, Tor; Qu, Longjiang (Journal article; Peer reviewed, 2023)
      Constructing bent functions by composing a Boolean function with a permutation was introduced by Hou and Langevin in 1997. The approach appears simple but heavily depends on the construction of desirable permutations. In ...
    • FAIR+E pathogen data for surveillance and research: lessons from COVID-19 

      Neves, Aitana; Cuesta, Isabel; Hjerde, Erik; Klemetsen, Terje; Salgado, David; van Helden, Jacques; Rahman, Nadim; Fatima, Nazeefa; Karathanasis, Nestoras; Zmora, Pawel; Åkerström, Wolmar Nyberg; Grellscheid, Sushma Nagaraja; Waheed, Zahra; Blomberg, Niklas (Journal article; Peer reviewed, 2023)
      The COVID-19 pandemic has exemplified the importance of interoperable and equitable data sharing for global surveillance and to support research. While many challenges could be overcome, at least in some countries, many ...
    • Order Reconfiguration under Width Constraints 

      Arrighi, Emmanuel Jean Paul Pierre; Fernau, Henning; De Oliveira Oliveira, Mateus; Wolf, Petra Henrike Karola (Journal article; Peer reviewed, 2023)
      In this work, we consider the following order reconfiguration problem: Given a graph G together with linear orders ω and ω′ of the vertices of G, can one transform ω into ω′ by a sequence of swaps of adjacent elements in ...
    • Learning from positive and negative examples: New proof for binary alphabets 

      Lingg, Jonas; De Oliveira Oliveira, Mateus; Wolf, Petra Henrike Karola (Journal article; Peer reviewed, 2024)
      One of the most fundamental problems in computational learning theory is the problem of learning a finite automaton A consistent with a finite set P of positive examples and with a finite set N of negative examples. By ...
    • Treewidth is NP-Complete on Cubic Graphs 

      Bodlaender, Hans L.; Bonnet, Édouard; Jaffke, Lars; Knop, Dusan; Lima, Paloma Thome de; Milanič, Martin; Ordyniak, Sebastian; Pandey, Sukanya; Suchy, Ondrey (Journal article; Peer reviewed, 2023)
      In this paper, we show that Treewidth is NP-complete for cubic graphs, thereby improving the result by Bodlaender and Thilikos from 1997 that Treewidth is NP-complete on graphs with maximum degree at most 9. We add a new ...
    • Fine-grained parameterized complexity analysis of graph coloring problems 

      Jaffke, Lars; Jansen, Bart Maarten Paul (Journal article; Peer reviewed, 2023)
      The q-Coloring problem asks whether the vertices of a graph can be properly colored with q colors. In this paper we perform a fine-grained analysis of the complexity of q- Coloring with respect to a hierarchy of structural ...
    • b-Coloring Parameterized by Clique-Width 

      Jaffke, Lars; Lima, Paloma T.; Lokshtanov, Daniel (Journal article; Peer reviewed, 2023)
      We provide a polynomial-time algorithm for b- Coloring on graphs of constant clique-width. This unifies and extends nearly all previously known polynomial time results on graph classes, and answers open questions posed by ...
    • Guarding the First Order: The Rise of AES Maskings 

      Askeland, Amund; Dhooghe, S.; Petkova-Nikova, Svetla Iordanova; Rijmen, Vincent Stefaan; Zhang, Zhenda (Journal article; Peer reviewed, 2023)
      We provide three first-order hardware maskings of the AES, each allowing for a different trade-off between the number of shares and the number of register stages. All maskings use a generalization of the changing of the ...
    • Algebraic Attacks on the Encryption Scheme HADESMiMC 

      Ellingsen, Tor Kristian (Master thesis, 2023-11-20)
      HADESMiMC is a recent symmetric cryptographic algorithm working with elements in a finite field. It is proposed as a candidate cipher for secure data transfers using Multiparty Computation (MPC). MPC is particularly useful ...
    • Chatbot Generation for Open Data Accessibility 

      Heldal, Julie Marie Schnell; Hermansen, Kathrine (Master thesis, 2023-11-20)
      Open data, despite its availability, often remains inaccessible to the average person due to complex data formats and technical barriers. This challenge hinders the realization of open data’s transformative potential. ...
    • Deep Learning and Deep Reinforcement Learning for Graph Based Applications 

      Hasibi, Ramin (Doctoral thesis, 2024-01-26)
      Dyp læring har gitt state-of-the-art ytelse i mange applikasjoner som datasyn, tekstanalyse, biologi, osv. Suksessen med dyp læring har også hjulpet fremveksten av dyp forsterkende læring for optimal beslutningstaking og ...
    • Macrophage phenotype transitions in a stochastic gene-regulatory network model 

      Frank, Anna-Simone Josefine; Larripa, Kamila; Ryu, Hwayeon; Röblitz, Susanna (Journal article; Peer reviewed, 2023)
      Polarization is the process by which a macrophage cell commits to a phenotype based on external signal stimulation. To know how this process is affected by random fluctuations and events within a cell is of utmost importance ...
    • Polyhedra and algorithms for problems bridging notions of connectivity and independence 

      Samer, Phillippe (Doctoral thesis, 2023-12-21)
      I denne avhandlinga interesserer vi oss for å finne delgrafer som svarer til utvalgte modeller for begrepene sammenheng og uavhengighet. I korthet betyr dette stabile (også kalt uavhengige) mengder med gitt kardinalitet, ...
    • Causal inference in drug discovery and development 

      Michoel, Tom; Zhang, Jitao David (Journal article; Peer reviewed, 2023)
      To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and ...