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The complexity of growing a graph (2024)
Journal Article
Mertzios, G., Michail, O., Skretas, G., Spirakis, P. G., & Theofilatos, M. (2025). The complexity of growing a graph. Journal of Computer and System Sciences, 147, Article 103587. https://doi.org/10.1016/j.jcss.2024.103587

We study a new algorithmic process of graph growth which starts from a single initial vertex and operates in discrete time-steps, called slots. In every slot, the graph grows via two operations (i) vertex generation and (ii) edge activation. The proc... Read More about The complexity of growing a graph.

Two-Person Interaction Augmentation with Skeleton Priors (2024)
Presentation / Conference Contribution
Li, B., Ho, E. S. L., Shum, H. P. H., & Wang, H. (2024, June). Two-Person Interaction Augmentation with Skeleton Priors. Presented at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, Washington

Close and continuous interaction with rich contacts is a crucial aspect of human activities (e.g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc. However, acquiring such skelet... Read More about Two-Person Interaction Augmentation with Skeleton Priors.

ArtAI4DS: AI Art and Its Empowering Role in Digital Storytelling (2024)
Presentation / Conference Contribution
Fernandes, T., Nisi, V., Nunes, N., & James, S. (2024, September). ArtAI4DS: AI Art and Its Empowering Role in Digital Storytelling. Presented at IFIP International Conference on Entertainment Computing, Manaus, Brazil

In an era of global interconnections, storytelling is a compelling medium for fostering understanding, building connections, and facilitating cultural exchange. Throughout history, visual imagery has been used to enrich narratives. However, this has... Read More about ArtAI4DS: AI Art and Its Empowering Role in Digital Storytelling.

Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning (2024)
Journal Article
Ezabadi, M., Zahmatkesh, M., Emami, S. A., & Castaldi, P. (2025). Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning. Advances in Space Research, 75(1), 1231-1244. https://doi.org/10.1016/j.asr.2024.09.041

This study proposes an adaptive neural command-filtered backstepping control system for precise and fast attitude control of a satellite considering different uncertain dynamics. Mission success crucially depends on robust attitude control resilient... Read More about Robust neuro-adaptive command-filtered back-stepping fault-tolerant control of satellite using composite learning.

Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving (2024)
Presentation / Conference Contribution
Tsesmelis, T., Palmieri, L., Khoroshiltseva, M., Islam, A., Elkin, G., Itzhak Shahar, O., Scarpellini, G., Fiorini, S., Ohayon, Y., Alali, N., Aslan, S., Morerio, P., Vascon, S., gravina, E., Cristina Napolitano, M., Scarpati, G., zuchtriegel, G., Spühler, A., Fuchs, M. E., James, S., …Del Bue, A. (2024, December). Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving. Presented at Conference on Neural Information Processing Systems (NeurIPS) Datasets and Benchmarks Track, Vancouver, Canada

This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for... Read More about Re-assembling the past: The RePAIR dataset and benchmark for real world 2D and 3D puzzle solving.

Towards Communication-Efficient Peer-to-Peer Networks (2024)
Presentation / Conference Contribution
Hourani, K., Moses Jr., W. K., & Pandurangan, G. (2024, September). Towards Communication-Efficient Peer-to-Peer Networks. Presented at 32nd Annual European Symposium on Algorithms (ESA 2024), Egham, United Kingdom

We focus on designing Peer-to-Peer (P2P) networks that enable efficient communication. Over the last two decades, there has been substantial algorithmic research on distributed protocols for building P2P networks with various desirable properties suc... Read More about Towards Communication-Efficient Peer-to-Peer Networks.

Scheduling with Obligatory Tests (2024)
Presentation / Conference Contribution
Dogeas, K., Erlebach, T., & Liang, Y.-C. (2024, September). Scheduling with Obligatory Tests. Presented at 32nd Annual European Symposium on Algorithms (ESA 2024), Egham, United Kingdom

Motivated by settings such as medical treatments or aircraft maintenance, we consider a scheduling problem with jobs that consist of two operations, a test and a processing part. The time required to execute the test is known in advance while the tim... Read More about Scheduling with Obligatory Tests.

Detrimental task execution patterns in mainstream OpenMP runtimes (2024)
Presentation / Conference Contribution
Weinzierl, T., Tuft, A., & Klemm, M. (2024, September). Detrimental task execution patterns in mainstream OpenMP runtimes. Presented at IWOMP 2024, Perth, Australia

The OpenMP API offers both task-based and data-parallel concepts to scientific computing. While it provides descriptive and prescriptive annotations, it is in many places deliberately unspecific how to implement its annotations. As the predomina... Read More about Detrimental task execution patterns in mainstream OpenMP runtimes.

Competitive Query Minimization for Stable Matching with One-Sided Uncertainty (2024)
Presentation / Conference Contribution
Bampis, E., Dogeas, K., Erlebach, T., Megow, N., Schlöter, J., & Trehan, A. (2024, August). Competitive Query Minimization for Stable Matching with One-Sided Uncertainty. Presented at International Conference on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2024), London, UK

We study the two-sided stable matching problem with one-sided uncertainty for two sets of agents A and B, with equal cardinality. Initially, the preference lists of the agents in A are given but the preferences of the agents in B are unknown. An algo... Read More about Competitive Query Minimization for Stable Matching with One-Sided Uncertainty.

Insights from the Use of Previously Unseen Neural Architecture Search Datasets (2024)
Presentation / Conference Contribution
Geada, R., Towers, D., Forshaw, M., Atapour-Abarghouei, A., & Mcgough, A. S. (2024, June). Insights from the Use of Previously Unseen Neural Architecture Search Datasets. Presented at IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), Seattle, WA

The boundless possibility of neural networks which can be used to solve a problem-each with different performance leads to a situation where a Deep Learning expert is required to identify the best neural network. This goes against the hope of removin... Read More about Insights from the Use of Previously Unseen Neural Architecture Search Datasets.

Skipping Spheres: SDF Scaling & Early Ray Termination for Fast Sphere Tracing (2024)
Presentation / Conference Contribution
Polychronakis, A., Koulieris, G. A., & Mania, K. (2024, September). Skipping Spheres: SDF Scaling & Early Ray Termination for Fast Sphere Tracing. Presented at Eurographics Computer Graphics & Visual Computing Conference, United Kingdom

This paper presents a rapid rendering pipeline for sphere tracing Signed Distance Functions (SDFs), showcasing a notable boost in performance compared to the current state-of-the-art. Existing methods endeavor to reduce the ray step count by adjustin... Read More about Skipping Spheres: SDF Scaling & Early Ray Termination for Fast Sphere Tracing.

Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills (2024)
Journal Article
Constable, M. D., Zhang, F. X., Conner, T., Monk, D., Rajsic, J., Ford, C., Park, L. J., Platt, A., Porteous, D., Grierson, L., & Shum, H. P. H. (2025). Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills. Advances in Health Sciences Education, 30, 15-35. https://doi.org/10.1007/s10459-024-10369-5

Health professional education stands to gain substantially from collective efforts toward building video databases of skill performances in both real and simulated settings. An accessible resource of videos that demonstrate an array of performances –... Read More about Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.

Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy (2024)
Presentation / Conference Contribution
Rafiei, M., Breckon, T. P., & Iosifidis, A. (2024, June). Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy. Presented at 2024 International Joint Conference on Neural Networks (IJCNN), Yokohama, Japan

Recent anomaly detection methods achieve high performance on commonly used image and pixel-level metrics. However, due to the imbalance in the number of normal and abnormal pixels commonly encountered in anomaly detection problems, commonly adopted p... Read More about Superpixel-based Anomaly Detection for Irregular Textures with a Focus on Pixel-level Accuracy.

One-Index Vector Quantization Based Adversarial Attack on Image Classification (2024)
Journal Article
Fan, H., Qin, X., Chen, S., Shum, H. P. H., & Li, M. (2024). One-Index Vector Quantization Based Adversarial Attack on Image Classification. Pattern Recognition Letters, 186, 47-56. https://doi.org/10.1016/j.patrec.2024.09.001

To improve storage and transmission, images are generally compressed. Vector quantization (VQ) is a popular compression method as it has a high compression ratio that suppresses other compression techniques. Despite this, existing adversarial attack... Read More about One-Index Vector Quantization Based Adversarial Attack on Image Classification.

Extracting Quantitative Streamline Information from Surface Flow Visualization Images in a Linear Cascade using Convolutional Neural Networks (2024)
Presentation / Conference Contribution
Liu, X., Ingram, G., Sims-Williams, D., & Breckon, T. P. (2024, September). Extracting Quantitative Streamline Information from Surface Flow Visualization Images in a Linear Cascade using Convolutional Neural Networks. Presented at GPPS Chania24, Chania

Surface flow visualization (SFV), specifically surface oil flow visualization, is an experimental technique that involves coating the surface with a mixture of oils and dyes before applying the flow to the subject. While investigating the surface flo... Read More about Extracting Quantitative Streamline Information from Surface Flow Visualization Images in a Linear Cascade using Convolutional Neural Networks.

Conversational Breakdown in a Customer Service Chatbot: Impact of Task Order and Criticality on User Trust and Emotion (2024)
Journal Article
Følstad, A., Law, E. L.-C., & van As, N. (2024). Conversational Breakdown in a Customer Service Chatbot: Impact of Task Order and Criticality on User Trust and Emotion. ACM Transactions on Computer-Human Interaction, 31(5), 1-52. https://doi.org/10.1145/3690383

While chatbots are increasingly used for customer service, there is a knowledge gap concerning the impact of Conversational Breakdown in such chatbot interactions. In a 2 × 4 factorial design online experiment, we studied how Conversational Breakdown... Read More about Conversational Breakdown in a Customer Service Chatbot: Impact of Task Order and Criticality on User Trust and Emotion.

SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding (2024)
Journal Article
Qiu, K., Zhang, Y., Ren, Z., Li, M., Wang, Q., Feng, Y., & Chen, F. (2024). SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding. Insects, 15(9), Article 667. https://doi.org/10.3390/insects15090667

Simple Summary: Cotton is a crucial economic crop, but it is often threatened by various pests and diseases during its growth, significantly impacting its yield and quality. Earlier image classification methods often suffer from low accuracy and stru... Read More about SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding.