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All Outputs (35)

SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection (2025)
Presentation / Conference Contribution
Maviya, S., Arnau-González, P., Arevalillo-Herráez, M., & Katsigiannis, S. (2025, February). SKDU at De-Factify 4.0: Natural language features for AI-Generated Text-Detection. Presented at De-factify 4.0 Workshop at 39th Annual AAAI Conference on Artificial Intelligence, Philadelphia, PA, USA

SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task (2024)
Presentation / Conference Contribution
Malviya, S., & Katsigiannis, S. (2024, November). SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task. Presented at 7th Fact Extraction and VERification Workshop (FEVER), Miami, Florida, USA

As part of the AVeriTeC shared task, we developed a pipelined system comprising robust and finely tuned models. Our system integrates advanced techniques for evidence retrieval and question generation, leveraging cross-encoders and large language mod... Read More about SK_DU Team: Cross-Encoder based Evidence Retrieval and Question Generation with Improved Prompt for the AVeriTeC Shared Task.

Evidence Retrieval for Fact Verification using Multi-stage Reranking (2024)
Presentation / Conference Contribution
Malviya, S., & Katsigiannis, S. (2024, November). Evidence Retrieval for Fact Verification using Multi-stage Reranking. Presented at 2024 Conference on Empirical Methods in Natural Language Processing (EMNLP), Miami, FL, USA

Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography (2024)
Presentation / Conference Contribution
Jayasinghe, J., Katsigiannis, S., & Malasinghe, L. (2023, November). Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography. Presented at International Conference on Electrical, Computer and Energy Technologies (ICECET 2023), Cape Town, South Africa

Remote Photoplethysmography (rPPG) is a non-invasive approach for monitoring Heart Rate (HR) that can be used in various applications in healthcare and biometrics. rPPG measurements acquired using facial videos have become very popular and one of the... Read More about Comparative Study of Face Tracking Algorithms for Remote Photoplethysmography.

Towards Automatic Tutoring of Custom Student-Stated Math Word Problems (2023)
Presentation / Conference Contribution
Arnau-González, P., Serrano-Mamolar, A., Katsigiannis, S., & Arevalillo-Herráez, M. (2023, July). Towards Automatic Tutoring of Custom Student-Stated Math Word Problems. Presented at International Conference on Artificial Intelligence in Education (AIED), Tokyo, Japan

Math Word Problem (MWP) solving for teaching math with Intelligent Tutoring Systems (ITSs) faces a major limitation: ITSs only supervise pre-registered problems, requiring substantial manual effort to add new ones. ITSs cannot assist with student-gen... Read More about Towards Automatic Tutoring of Custom Student-Stated Math Word Problems.

Multi-modal lung ultrasound image classification by fusing image-based features and probe information (2022)
Presentation / Conference Contribution
Okolo, G. I., Katsigiannis, S., & Ramzan, N. (2022, November). Multi-modal lung ultrasound image classification by fusing image-based features and probe information. Presented at IEEE International Conference on BioInformatics and BioEngineering (BIBE 2022), Taichung, Taiwan

Lung ultrasound is a widely used portable, cheap, and non-invasive medical imaging technology that can be used to identify various lung pathologies. In this work, we propose a multi-modal approach for lung ultrasound image classification that combine... Read More about Multi-modal lung ultrasound image classification by fusing image-based features and probe information.

A Localisation Study of Deep Learning Models for Chest X-ray Image Classification (2022)
Presentation / Conference Contribution
Gascoigne-Burns, J., & Katsigiannis, S. (2022, September). A Localisation Study of Deep Learning Models for Chest X-ray Image Classification. Presented at 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Ioannina, Greece

Deep learning models have demonstrated superhuman performance in a multitude of image classification tasks, including the classification of chest X-ray images. Despite this, medical professionals are reluctant to embrace these models in clinical sett... Read More about A Localisation Study of Deep Learning Models for Chest X-ray Image Classification.

SOS: Systematic Offensive Stereotyping Bias in Word Embeddings (2022)
Presentation / Conference Contribution
Elsafoury, F., Wilson, S. R., Katsigiannis, S., & Ramzan, N. (2022, October). SOS: Systematic Offensive Stereotyping Bias in Word Embeddings. Presented at 29th International Conference on Computational Linguistics (COLING 2022), Gyeongju, Republic of Korea

Systematic Offensive stereotyping (SOS) in word embeddings could lead to associating marginalised groups with hate speech and profanity, which might lead to blocking and silencing those groups, especially on social media platforms. In this [id=stk]wo... Read More about SOS: Systematic Offensive Stereotyping Bias in Word Embeddings.

Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding (2022)
Presentation / Conference Contribution
Li, R., Katsigiannis, S., & Shum, H. P. (2022, October). Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding. Presented at ICIP 2022: IEEE International Conference in Image Processing, Bordeaux, France

Trajectory prediction of road users in real-world scenarios is challenging because their movement patterns are stochastic and complex. Previous pedestrian-oriented works have been successful in modelling the complex interactions among pedestrians, bu... Read More about Multiclass-SGCN: Sparse Graph-based Trajectory Prediction with Agent Class Embedding.

On the benefits of using Hidden Markov Models to predict emotions (2022)
Presentation / Conference Contribution
Wu, Y., Arevalillo-Herráez, M., Katsigiannis, S., & Ramzan, N. (2022, July). On the benefits of using Hidden Markov Models to predict emotions. Presented at ACM Conference on User Modeling, Adaptation and Personalization (UMAP), Barcelona

The availability of low-cost wireless physiological sensors has allowed the use of emotion recognition technologies in various applications. In this work, we describe a technique to predict emotional states in Russell’s two-dimensional emotion space... Read More about On the benefits of using Hidden Markov Models to predict emotions.

Single-channel EEG-based subject identification using visual stimuli (2021)
Presentation / Conference Contribution
Katsigiannis, S., Arnau-González, P., Arevalillo-Herráez, M., & Ramzan, N. (2021, July). Single-channel EEG-based subject identification using visual stimuli. Presented at 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), Online

Electroencephalography (EEG) signals have been recently proposed as a biometrics modality due to some inherent advantages over traditional biometric approaches. In this work, we studied the performance of individual EEG channels for the task of subje... Read More about Single-channel EEG-based subject identification using visual stimuli.

Does BERT pay attention to cyberbullying? (2021)
Presentation / Conference Contribution
Elsafoury, F., Katsigiannis, S., Wilson, S., & Ramzan, N. (2021, July). Does BERT pay attention to cyberbullying?. Presented at 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, Online

Social media have brought threats like cyberbullying, which can lead to stress, anxiety, depression and in some severe cases, suicide attempts. Detecting cyberbullying can help to warn/ block bullies and provide support to victims. However, very few... Read More about Does BERT pay attention to cyberbullying?.

Defining gaze tracking metrics by observing a growing divide between 2D and 3D tracking (2020)
Presentation / Conference Contribution
Blakey, W. A., Katsigiannis, S., Hajimirza, N., & Ramzan, N. (2020, December). Defining gaze tracking metrics by observing a growing divide between 2D and 3D tracking. Presented at IS&T International Symposium on Electronic Imaging, Burlingame, CA, USA

This work examines the different terminology used for defining gaze tracking technology and explores the different methodologies used for describing their respective accuracy. Through a comparative study of different gaze tracking technologies, such... Read More about Defining gaze tracking metrics by observing a growing divide between 2D and 3D tracking.

Image-Evoked Affect and its Impact on EEG-Based Biometrics (2019)
Presentation / Conference Contribution
Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., & Ramzan, N. (2019, December). Image-Evoked Affect and its Impact on EEG-Based Biometrics. Presented at IEEE International Conference on Image Processing (IEEE ICIP), Taipei, Taiwan

On using EEG signals for emotion modeling and biometrics (2019)
Presentation / Conference Contribution
Arevalillo-Herráez, M., Chicote-Huete, G., Ferri, F., Ayesh, A., Boticario, J., Katsigiannis, S., Ramzan, N., & Arnau-González, P. (2019, October). On using EEG signals for emotion modeling and biometrics. Presented at 33rd European Simulation and Modelling Conference (ESM), Palma de Mallorca, Spain

SNPs-based Hypertension Disease Detection via Machine Learning Techniques (2018)
Presentation / Conference Contribution
Alzubi, R., Ramzan, N., Alzoubi, H., & Katsigiannis, S. (2018, December). SNPs-based Hypertension Disease Detection via Machine Learning Techniques. Presented at 24th International Conference on Automation and Computing (ICAC), Newcastle upon Tyne, United Kingdom