Outputs (220)

Computing Balanced Solutions for Large International Kidney Exchange Schemes (2022)
Presentation / Conference Contribution
Benedek, M., Biró, P., Kern, W., & Paulusma, D. (2022, May). Computing Balanced Solutions for Large International Kidney Exchange Schemes. Presented at AAMAS ' 22: International Conference on Autonomous Agents and Multi-Agent Systems, Virtual Event / New Zealand

To overcome incompatibility issues, kidney patients may swap their donors. In international kidney exchange programmes (IKEPs), countries merge their national patient–donor pools. We consider a recently introduced credit system. In each round, countr... Read More about Computing Balanced Solutions for Large International Kidney Exchange Schemes.

Vibration-based communication for deafblind people (2022)
Presentation / Conference Contribution
Kutner, D. C., & Hadžidedić, S. (2022, March). Vibration-based communication for deafblind people. Presented at 2022 IEEE Haptics Symposium (HAPTICS), Santa Barbara, CA

Deafblind people have both hearing and visual impairments, which makes communication with other people often dependent on expensive technologies e.g., Braille displays, or on caregivers acting as interpreters. This paper presents Morse I/O (MIO), a v... Read More about Vibration-based communication for deafblind people.

A Bayesian Network-based model to understand the role of soft requirements in technology acceptance: the Case of the NHS COVID-19 Test and Trace App in England and Wales (2022)
Presentation / Conference Contribution
Garcia-Paucar, L., Bencomo, N., Sutcliffe, A., & Sawyer, P. (2022, April). A Bayesian Network-based model to understand the role of soft requirements in technology acceptance: the Case of the NHS COVID-19 Test and Trace App in England and Wales. Presented at 37th Annual ACM Symposium on Applied Computing (ACM SAC'2022), Brno, Czech Republic

Soft requirements (such as human values, motivations, and personal attitudes) can strongly influence technology acceptance. As such, we need to understand, model and predict decisions made by end users regarding the adoption and utilization of softwa... Read More about A Bayesian Network-based model to understand the role of soft requirements in technology acceptance: the Case of the NHS COVID-19 Test and Trace App in England and Wales.

Multi-view 3D Objects Localization from Street-level Scenes (2022)
Presentation / Conference Contribution
Ahmad, J., Toso, M., Taiana, M., James, S., & Del Bue, A. (2022, May). Multi-view 3D Objects Localization from Street-level Scenes. Presented at Image Analysis and Processing – ICIAP 2022, Lecce, Italy

Parameterized temporal exploration problems (2022)
Presentation / Conference Contribution
Erlebach, T., & Spooner, J. T. (2022, March). Parameterized temporal exploration problems. Presented at 1st Symposium on Algorithmic Foundations of Dynamic Networks (SAND 2022), Virtual Conference

In this paper we study the fixed-parameter tractability of the problem of deciding whether a given temporal graph G admits a temporal walk that visits all vertices (temporal exploration) or, in some problem variants, a certain subset of the vertices.... Read More about Parameterized temporal exploration problems.

A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees (2022)
Journal Article
Stefanec, M., Hofstadler, D. N., Krajník, T., Turgut, A. E., Alemdar, H., Lennox, B., Şahin, E., Arvin, F., & Schmickl, T. (2022). A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees. Frontiers in Robotics and AI, 9, Article 791921. https://doi.org/10.3389/frobt.2022.791921

Honey bees live in colonies of thousands of individuals, that not only need to collaborate with each other but also to interact intensively with their ecosystem. A small group of robots operating in a honey bee colony and interacting with the queen b... Read More about A Minimally Invasive Approach Towards “Ecosystem Hacking” With Honeybees.

RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis (2022)
Journal Article
Organisciak, D., Shum, H. P., Nwoye, E., & Woo, W. L. (2022). RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis. Expert Systems with Applications, 201, Article 117158. https://doi.org/10.1016/j.eswa.2022.117158

Schizophrenia is a severe mental health condition that requires a long and complicated diagnostic process. However, early diagnosis is vital to control symptoms. Deep learning has recently become a popular way to analyse and interpret medical data. P... Read More about RobIn: A Robust Interpretable Deep Network for Schizophrenia Diagnosis.

On the Complexity of Optimising Variants of Phylogenetic Diversity on Phylogenetic Networks (2022)
Journal Article
Bordewich, M., Semple, C., & Wicke, K. (2022). On the Complexity of Optimising Variants of Phylogenetic Diversity on Phylogenetic Networks. Theoretical Computer Science, 917, 66-80. https://doi.org/10.1016/j.tcs.2022.03.012

Phylogenetic Diversity (PD) is a prominent quantitative measure of the biodiversity of a collection of present-day species (taxa). This measure is based on the evolutionary distance among the species in the collection. Loosely speaking, if T is a roo... Read More about On the Complexity of Optimising Variants of Phylogenetic Diversity on Phylogenetic Networks.

Dynamic Bandwidth Slicing for Time-Critical IoT Data Streams in the Edge-Cloud Continuum (2022)
Journal Article
Habeeb, F., Alwasel, K., Noor, A., Jha, D. N., Alqattan, D., Li, Y., Aujla, G. S., Szydlo, T., & Ranjan, R. (2022). Dynamic Bandwidth Slicing for Time-Critical IoT Data Streams in the Edge-Cloud Continuum. IEEE Transactions on Industrial Informatics, 18(11), 8017-8026. https://doi.org/10.1109/tii.2022.3169971

Edge computing has gained momentum in recent years, as complementary to cloud computing, for supporting applications (e.g. industrial control systems) that require Time-Critical communication guarantees. While edge computing can provide immediate ana... Read More about Dynamic Bandwidth Slicing for Time-Critical IoT Data Streams in the Edge-Cloud Continuum.

ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR (2022)
Journal Article
Shakeel, A., Walters, R. J., Ebmeier, S. K., & Moubayed, N. A. (2022). ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR. IEEE Transactions on Geoscience and Remote Sensing, 60, https://doi.org/10.1109/tgrs.2022.3169455

In this study, we address the challenging problem of automatic detection of transient deformation of the Earth’s crust in time series of differential satellite radar [interferometric synthetic aperture radar (InSAR)] images. The detection of these ev... Read More about ALADDIn: Autoencoder-LSTM based Anomaly Detector of Deformation in InSAR.