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Outputs (389)

How Personalization Affects Motivation in Gamified Review Assessments (2023)
Journal Article
Rodrigues, L., Palomino, P. T., Toda, A. M., Klock, A. C., Pessoa, M., Pereira, F. D., Oliveira, E. H., Oliveira, D. F., Cristea, A. I., Gasparini, I., & Isotani, S. (2024). How Personalization Affects Motivation in Gamified Review Assessments. International Journal of Artificial Intelligence in Education, 34(2), 147-184. https://doi.org/10.1007/s40593-022-00326-x

Personalized gamification aims to address shortcomings of the one-size-fits-all (OSFA) approach in improving students’ motivations throughout the learning process. However, studies still focus on personalizing to a single user dimension, ignoring mul... Read More about How Personalization Affects Motivation in Gamified Review Assessments.

Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry (2022)
Presentation / Conference Contribution
Hodgson, R., Wang, J., Cristea, A. I., & Graham, J. (2022, December). Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry. Presented at 2022 13th International Congress on Advanced Applied Informatics Winter (IIAI-AAI-Winter), Phuket, Thailand

Barriers to the delivery of journalistic content to suitable media outlets present difficulties to both journalists and publishing houses. These may take the form of barriers to the identification of key individuals to whom the content is relevant, a... Read More about Hybrid Weighted Retrieval of Twitter Users for Temporally Relevant Full-Text Querying in the Media Industry.

Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Al Moubayed, N., & Shi, L. (2022, December). Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention. Presented at IEEE Big Data, Osaka, Japan

Medical visual question answering (Med-VQA) is to answer medical questions based on clinical images provided. This field is still in its infancy due to the complexity of the trio formed of questions, multimodal features and expert knowledge. In this... Read More about Is Unimodal Bias Always Bad for Visual Question Answering? A Medical Domain Study with Dynamic Attention.

COVID-19 and Science Advice on the ‘Grand Stage’: The Metadata and Linguistic Choices in a Scientific Advisory Groups’ Meeting Minutes. (2022)
Journal Article
Baker, H., Concannon, S., Meller, M., Cohen, K., Millington, A., Ward, S., & So, E. (2022). COVID-19 and Science Advice on the ‘Grand Stage’: The Metadata and Linguistic Choices in a Scientific Advisory Groups’ Meeting Minutes. Humanities and Social Sciences Communications, 9, Article 465. https://doi.org/10.1057/s41599-022-01403-1

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.

Automated Detection of Substance-Use Status and Related Information from Clinical Text (2022)
Journal Article
Alzubi, R., Alzoubi, H., Katsigiannis, S., West, D., & Ramzan, N. (2022). Automated Detection of Substance-Use Status and Related Information from Clinical Text. Sensors, 22(24), Article 9609. https://doi.org/10.3390/s22249609

This study aims to develop and evaluate an automated system for extracting information related to patient substance use (smoking, alcohol, and drugs) from unstructured clinical text (medical discharge records). The authors propose a four-stage system... Read More about Automated Detection of Substance-Use Status and Related Information from Clinical Text.

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.

Insights from impacts of the digital divide on children in five majority world countries during the COVID-19 pandemic (2022)
Journal Article
Law, E. L.-C., Vostanis, P., & O’Reilly, M. J. (2023). Insights from impacts of the digital divide on children in five majority world countries during the COVID-19 pandemic. Behaviour and Information Technology, 42(15), 2696-2715. https://doi.org/10.1080/0144929x.2022.2141136

The digital divide is especially pertinent in Majority World Countries (MWCs), and this was exacerbated greatly by the pandemic. Tackling the digital divide underpins the work of Human–Computer Interaction for Development (HCI4D) and remains an impor... Read More about Insights from impacts of the digital divide on children in five majority world countries during the COVID-19 pandemic.

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.

History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems (2022)
Presentation / Conference Contribution
Parra-Ullauri, J., Garcia-Dominguez, A., Bencomo, N., & Garcia Paucar, L. (2022, October). History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems. Presented at MODELS '22: 25th International Conference on Model Driven Engineering Languages and Systems, Montreal, Canada

The complexity of real-world problems requires modern software systems to autonomously adapt and modify their behaviour at run time to deal with internal and external challenges and contexts. Consequently, these self-adaptive systems (SAS) can show u... Read More about History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems.

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.

Effects of Humanlikeness and Conversational Breakdown on Trust in Chatbots for Customer Service (2022)
Presentation / Conference Contribution
Law, E. L.-C., Følstad, A., & van As, N. (2022, October). Effects of Humanlikeness and Conversational Breakdown on Trust in Chatbots for Customer Service. Presented at NordiCHI '22: Nordic Human-Computer Interaction Conference, Aarhus, Denmark

Trust in chatbots can be shaped by various factors such as humanlikeness in terms of visual appearance and conversational content, and conversational performance in terms of the chatbot’s ability to avoid conversational breakdown. The literature is i... Read More about Effects of Humanlikeness and Conversational Breakdown on Trust in Chatbots for Customer Service.

Efficient Uncertainty Quantification for Multilabel Text Classification (2022)
Presentation / Conference Contribution
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). Efficient Uncertainty Quantification for Multilabel Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

Despite rapid advances of modern artificial intelligence (AI), there is a growing concern regarding its capacity to be explainable, transparent, and accountable. One crucial step towards such AI systems involves reliable and efficient uncertainty qua... Read More about Efficient Uncertainty Quantification for Multilabel Text Classification.

Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification (2022)
Presentation / Conference Contribution
Sun, Z., Harit, A., Cristea, A. I., Yu, J., Shi, L., & Al Moubayed, N. (2022, July). Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

Graph neural networks (GNNs) have attracted extensive interest in text classification tasks due to their expected superior performance in representation learning. However, most existing studies adopted the same semi-supervised learning setting as the... Read More about Contrastive Learning with Heterogeneous Graph Attention Networks on Short Text Classification.

INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations (2022)
Presentation / Conference Contribution
Yu, J., Cristea, A. I., Harit, A., Sun, Z., Aduragba, O. T., Shi, L., & Al Moubayed, N. (2022, July). INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations. Presented at 2022 International Joint Conference on Neural Networks (IJCNN), Padova, Italy

XAI with natural language processing aims to produce human-readable explanations as evidence for AI decisionmaking, which addresses explainability and transparency. However, from an HCI perspective, the current approaches only focus on delivering a s... Read More about INTERACTION: A Generative XAI Framework for Natural Language Inference Explanations.

IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification (2022)
Journal Article
Okolo, G. I., Katsigiannis, S., & Ramzan, N. (2022). IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification. Computer Methods and Programs in Biomedicine, 226, Article 107141. https://doi.org/10.1016/j.cmpb.2022.107141

Background and Objective: Chest X-ray imaging is a relatively cheap and accessible diagnostic tool that can assist in the diagnosis of various conditions, including pneumonia, tuberculosis, COVID-19, and others. However, the requirement for expert ra... Read More about IEViT: An Enhanced Vision Transformer Architecture for Chest X-ray Image Classification.

The Implications of ‘Soft’ Requirements (2022)
Presentation / Conference Contribution
Sutcliffe, A., Sawyer, P., & Bencomo, N. (2022, August). The Implications of ‘Soft’ Requirements. Presented at IEEE International Requirements Engineering Conference (RE), Melbourne, Australia

A new focus for RE is investigated as ‘soft’ requirements which extends non-functional requirements / soft goals with a collection of people-oriented phenomena: values, motivations, emotions, and other socio-political issues that may influence the re... Read More about The Implications of ‘Soft’ Requirements.

How easy is it to eXtend Reality? A Usability Study of Authoring Toolkits (2022)
Book Chapter
Thanyadit, S., Heintz, M., Law, E. L., & Mangina, E. (2022). How easy is it to eXtend Reality? A Usability Study of Authoring Toolkits. In T. Ahram, & R. Taiar (Eds.), Human Interaction & Emerging Technologies (IHIET 2022): Artificial Intelligence & Future Applications. AHFE International. https://doi.org/10.54941/ahfe1002707

Extended Reality (XR) has the potential to be a very successful teaching tool because it enables students to engage with a learning environment that incorporates both physical and virtual objects. Nevertheless, preparing an XR lesson requires technic... Read More about How easy is it to eXtend Reality? A Usability Study of Authoring Toolkits.