Outputs (153)

Factors Impacting Landscape Ruggedness in Control Problems: a Case Study (2024)
Presentation / Conference Contribution
Saliby, M. E., Medvet, E., Nadizar, G., Salvato, E., & Thomson, S. L. (2024, September). Factors Impacting Landscape Ruggedness in Control Problems: a Case Study. Presented at WIVACE 2024 (XVIII International Workshop on Artificial Life and Evolutionary Computation), Namur, Belgium

Understanding fitness landscapes in evolutionary robotics (ER) can provide valuable insights into the considered robotic problems as well as into the strategies found by evolutionary algorithms (EAs) to address them, ultimately guiding practitioners... Read More about Factors Impacting Landscape Ruggedness in Control Problems: a Case Study.

DanceMark: An open telemetry framework for latency sensitive real-time networked immersive experiences (2024)
Presentation / Conference Contribution
Koniaris, B., Sinclair, D., & Mitchell, K. (2024, March). DanceMark: An open telemetry framework for latency sensitive real-time networked immersive experiences. Presented at IEEE VR Workshop on Open Access Tools and Libraries for Virtual Reality, Orlando, FL

DanceMark is an open telemetry framework designed for latency-sensitive real-time networked immersive experiences, focusing on online dancing in virtual reality within the DanceGraph platform. The goal is to minimize end-to-end latency and enhance us... Read More about DanceMark: An open telemetry framework for latency sensitive real-time networked immersive experiences.

MoodFlow: Orchestrating Conversations with Emotionally Intelligent Avatars in Mixed Reality (2024)
Presentation / Conference Contribution
Casas, L., Hannah, S., & Mitchell, K. (2024, March). MoodFlow: Orchestrating Conversations with Emotionally Intelligent Avatars in Mixed Reality. Presented at ANIVAE 2024 : 7th IEEE VR Internal Workshop on Animation in Virtual and Augmented Environments, Orlando, Florida

MoodFlow presents a novel approach at the intersection of mixed reality and conversational artificial intelligence for emotionally intelligent avatars. Through a state machine embedded in user prompts, the system decodes emotional nuances, enabling a... Read More about MoodFlow: Orchestrating Conversations with Emotionally Intelligent Avatars in Mixed Reality.

Design Considerations of Voice Articulated Generative AI Virtual Reality Dance Environments (2024)
Presentation / Conference Contribution
Casas, L., Mitchell, K., Tamariz, M., Hannah, S., Sinclair, D., Koniaris, B., & Kennedy, J. (2024, May). Design Considerations of Voice Articulated Generative AI Virtual Reality Dance Environments. Presented at SIGCHI GenAI in UGC Workshop, Honolulu, Hawaii

We consider practical and social considerations of collaborating verbally with colleagues and friends, not confined by physical distance, but through seamless networked telepresence to interactively create shared virtual dance environments. In respon... Read More about Design Considerations of Voice Articulated Generative AI Virtual Reality Dance Environments.

Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains (2024)
Journal Article
Marrero, A., Segredo, E., Leon, C., & Hart, E. (online). Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains. Evolutionary Computation, https://doi.org/10.1162/evco_a_00350

Gathering sufficient instance data to either train algorithm-selection models or understand algorithm footprints within an instance space can be challenging. We propose an approach to generating synthetic instances that are tailored to perform well w... Read More about Synthesising Diverse and Discriminatory Sets of Instances using Novelty Search in Combinatorial Domains.

Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances (2024)
Presentation / Conference Contribution
Verel, S., Thomson, S. L., & Rifki, O. (2024, April). Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances. Presented at EvoCOP 2024, Aberystwyth, UK

The Quadratic Assignment Problem (QAP) is one of the major domains in the field of evolutionary computation, and more widely in combinatorial optimization. This paper studies the phase transition of the QAP, which can be described as a dramatic chang... Read More about Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instances.

Method and system for visually seamless grafting of volumetric data (2024)
Patent
Mitchell, K. J. (2024). Method and system for visually seamless grafting of volumetric data

Visually seamless grafting of volumetric data. In some implementations, a method includes obtaining volumetric data that represents a first volume including one or more three-dimensional objects. Planar slices of the first volume are determined and f... Read More about Method and system for visually seamless grafting of volumetric data.

Frequency Fitness Assignment for Untangling Proteins in 2D (2024)
Presentation / Conference Contribution
Koutstaal, J., Kommandeur, J., Timmer, R., Horn, R., Thomson, S. L., & van den Berg, D. (2024, April). Frequency Fitness Assignment for Untangling Proteins in 2D. Presented at EvoStar 2024, Aberyswyth, UK

At the time of writing, there is no known deterministic-time algorithm to sample valid initial solutions with uniform random distribution for the HP protein folding model, because guaranteed uniform random sampling produces collisions (i.e. constrain... Read More about Frequency Fitness Assignment for Untangling Proteins in 2D.

Shape of the Waterfall: Solvability Transitions in the QAP (2024)
Presentation / Conference Contribution
Akova, S., Thomson, S. L., Verel, S., Rifki, O., & van den Berg, D. (2024, April). Shape of the Waterfall: Solvability Transitions in the QAP. Presented at EvoStar 2024, Aberyswyth, Wales

We consider a special formulation of the quadratic assignment problem (QAP): QAP-SAT, where the QAP is composed of smaller sub-problems or clauses which can be satisfied. A recent study showed a steep drop in solvability in relation to the number of... Read More about Shape of the Waterfall: Solvability Transitions in the QAP.

On the Utility of Probing Trajectories for Algorithm-Selection (2024)
Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, April). On the Utility of Probing Trajectories for Algorithm-Selection. Presented at EvoStar 2024, Aberystwyth, UK

Machine-learning approaches to algorithm-selection typically take data describing an instance as input. Input data can take the form of features derived from the instance description or fitness landscape , or can be a direct representation of the ins... Read More about On the Utility of Probing Trajectories for Algorithm-Selection.

A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control (2024)
Presentation / Conference Contribution
Montague, K., Hart, E., & Paechter, B. (2024, April). A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control. Presented at EvoStar 2024, Aberystwyth

Behaviour trees (BTs) are commonly used as controllers in robotic swarms due their modular composition and to the fact that they can be easily interpreted by humans. From an algorithmic perspective, an additional advantage is that extra modules can e... Read More about A Hierarchical Approach to Evolving Behaviour-Trees for Swarm Control.

Generalized Early Stopping in Evolutionary Direct Policy Search (2024)
Journal Article
Arza, E., Le Goff, L. K., & Hart, E. (2024). Generalized Early Stopping in Evolutionary Direct Policy Search. ACM Transactions on Evolutionary Learning and Optimization, 4(3), Article 14. https://doi.org/10.1145/3653024

Lengthy evaluation times are common in many optimization problems such as direct policy search tasks, especially when they involve conducting evaluations in the physical world, e.g. in robotics applications. Often when evaluating solution over a fixe... Read More about Generalized Early Stopping in Evolutionary Direct Policy Search.

Cluster-based oversampling with area extraction from representative points for class imbalance learning (2024)
Journal Article
Farou, Z., Wang, Y., & Horváth, T. (2024). Cluster-based oversampling with area extraction from representative points for class imbalance learning. Intelligent Systems with Applications, 22, Article 200357. https://doi.org/10.1016/j.iswa.2024.200357

Class imbalance learning is challenging in various domains where training datasets exhibit disproportionate samples in a specific class. Resampling methods have been used to adjust the class distribution, but they often have limitations for small dis... Read More about Cluster-based oversampling with area extraction from representative points for class imbalance learning.

A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system (2024)
Journal Article
Liu, Q., Pan, L., Cao, X., Gan, J., Huang, X., & Liu, X. (2024). A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system. Concurrency and Computation: Practice and Experience, 36(13), Article e8060. https://doi.org/10.1002/cpe.8060

As the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real-time (RT) load forecasting. In this article, an edge-cloud co... Read More about A spatio-temporal graph convolutional approach to real-time load forecasting in an edge-enabled distributed Internet of Smart Grids energy system.

Utilizing the Ensemble Learning and XAI for Performance Improvements in IoT Network Attack Detection (2024)
Presentation / Conference Contribution
Kalutharage, C. S., Liu, X., Chrysoulas, C., & Bamgboye, O. (2023, September). Utilizing the Ensemble Learning and XAI for Performance Improvements in IoT Network Attack Detection. Presented at The 4th International Workshop on Cyber-Physical Security for Critical Infrastructures Protection (CPS4CIP 2023) - in conjunction with ESORICS 2023, The Hague, Netherlands

Expressive Talking Avatars (2024)
Journal Article
Pan, Y., Tan, S., Cheng, S., Lin, Q., Zeng, Z., & Mitchell, K. (2024). Expressive Talking Avatars. IEEE Transactions on Visualization and Computer Graphics, 30(5), 2538-2548. https://doi.org/10.1109/TVCG.2024.3372047

Stylized avatars are common virtual representations used in VR to support interaction and communication between remote collaborators. However, explicit expressions are notoriously difficult to create, mainly because most current methods rely on geome... Read More about Expressive Talking Avatars.

Augmented Reality Methods and Systems (2024)
Patent
Cambra, L. C., & Mitchell, K. (2024). Augmented Reality Methods and Systems. US11908068

Methods and systems employing augmented reality techniques via real-world objects for various purposes. Computer implemented methods are provided for animated augmentation of real-time video of static real-world objects. A computing device receives f... Read More about Augmented Reality Methods and Systems.

An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment (2024)
Journal Article
Liu, Q., Jin, Y., Cao, X., Liu, X., Zhou, X., Zhang, Y., Xu, X., & Qi, L. (2024). An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment. IEEE Transactions on Computational Social Systems, 11(4), 5308 - 5318. https://doi.org/10.1109/TCSS.2023.3342873

Fake news is a prevalent issue in modern society, leading to misinformation and societal harm. News credibility assessment is a crucial approach for evaluating the accuracy and authenticity of news. It plays a significant role in enhancing public awa... Read More about An Entity Ontology-Based Knowledge Graph Embedding Approach to News Credibility Assessment.

DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing (2024)
Journal Article
Liu, Q., Sun, J., Zhang, Y., & Liu, X. (2024). DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing. Journal of cloud computing: advances, systems and applications, 13, Article 32. https://doi.org/10.1186/s13677-024-00607-x

In the field of meteorology, the global radar network is indispensable for detecting weather phenomena and offering early warning services. Nevertheless, radar data frequently exhibit anomalies, including gaps and clutter, arising from atmospheric re... Read More about DenMerD: a feature enhanced approach to radar beam blockage correction with edge-cloud computing.

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms (2024)
Journal Article
Mantovani, R. G., Horváth, T., Rossi, A. L. D., Cerri, R., Barbon Junior, S., Vanschoren, J., & de Carvalho, A. C. P. L. F. (2024). Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, 38, 1364–1416. https://doi.org/10.1007/s10618-024-01002-5

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex i... Read More about Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms.