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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics Xi'an China National University of Singapore Singapore

ISBN: (纸本)9782493814104

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Extraction

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Linking of open and private data in dataspace: A case study of air quality monitoring and forecasting 2

Linking of open and private data in dataspace: A case study ...

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2nd International Workshop on Semantics in dataspaces, SDS 2024

作者： Acquier, Alex Donald, Andy Curry, Edward Ullah, Ihsan Hassan, Umairul Insight SFI Research Centre for Data Analytics University of Galway Ireland School of Computer Science University of Galway Ireland JE Cairnes School of Business and Economics University of Galway Ireland

Air pollutant monitoring and its efficient visualisation can support accurately assessing the air quality and harmful emissions;and it can guide us towards potential mitigation strategies to reduce its impact on public health and our environment. This paper presents a case study of employing dataspaces and proposing an ontology for modelling mobility to address the challenges posed by the heterogeneity of data sources in environmental monitoring, as well as using machine learning for forecasting pollutants. We employ Linked data as a powerful paradigm for harmonising and interlinking diverse and publicly available environmental data with private company data to create a dataspace for environmental monitoring. By applying semantic technologies and ontological modelling to integrate heterogeneous data, our approach fosters data interoperability and facilitates enhanced data exploration and decision support. For decision support, we demonstrate the utility of integrated data for forecasting air pollutants with the help of models developed using machine learning. Finally, a spatio-temporal visualisation platform harnesses the power of semantic relationships and contextual enrichment to support data exploration. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Linked data

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Automated Assessment of Simulated Laparoscopic Surgical Performance using 3DCNN 46

Automated Assessment of Simulated Laparoscopic Surgical Perf...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Power, David Ullah, Ihsan University College Cork Assert Centre College of Medicine and Health Ireland University of Galway School of Computer Science Insight Sfi Research Center for Data Analytics Ireland

ISBN: (纸本)9798350371499

Artificial intelligence & Computer vision have the potential to improve surgical training, especially for minimally invasive surgery by analyzing intraoperative and simulation videos for training or performance improvement purposes. Among these, techniques based on deep learning have rapidly improved, from recognizing objects, instruments, and gestures, to remembering past surgical steps and phases of surgery. However, data scarcity is a problem, particularly in surgery, where complex datasets and human annotation are expensive and time-consuming, and in most cases rely on direct intervention of clinical expertise. Laproscopic surgical assessment of performance traditionally relies on direct observation or video analysis by human experts, a costly and time-consuming undertaking. A newly collected simulated laparoscopic surgical dataset (LSPD) is presented that will initiate the research in automating this problem and avoiding manual expert assessments. LSPD statistical analyses is given to show similarity and differences between different expertise level (on Stack, Bands, and Tower Skills). Finally, a convolutional neural network is used to predict the experience level of the surgeons, where the model achieved good distinguishing results. The proposed work offers the potential to automate performance assessment and self-learn important features that can discriminate between the performance of novice, trainee, and expert levels. © 2024 IEEE.

关键词： Convolutional neural networks

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Analysis and Performance Evaluation of Credit Card Fraud by Multi-model ML 3

Analysis and Performance Evaluation of Credit Card Fraud by ...

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3rd International Conference on Advancement in Electrical and Electronic Engineering, ICAEEE 2024

作者： Islam, Md Aminul Asif Imran, A.T.M. Rahman, Md Habibur Pabel, Md Amran Hossen Mishra, Bhupesh Kumar Basu, Kashinath University of Gloucestershire Data Science School of Engineering and Technology Cheltenham United Kingdom Dhaka Bangladesh University of Chittagong Department of Mathematics Chittagong Bangladesh Wright State University Master of Science in Business Analytics United States United Kingdom Oxford Brookes University Data Science School of Engineering Computing and Mathematics United Kingdom

ISBN: (纸本)9798350388282

Using credit cards is a convenient and efficient payment mechanism. Credit card fraud significantly impacts financial loss, mental health, and the reputation of financial institutions. This study would incorporate an analysis of the preceding statement about eliminating several obstacles associated with the availability of public data, the presence of unbalanced data, the dynamic nature of fraud tendencies, and the prevalence of false alarms. The authors discuss various machine-learning techniques used to detect credit card fraud. Extreme Learning, Decision Trees, Random Forests, Support Vector Machines, Logistic Regression, and XG Boost are among these techniques on the European card benchmark dataset. A comparative evaluation of the effectiveness of machine learning has also been conducted, and precision is increased by incorporating multiple layers. The study's findings indicate significant improvements in several crucial metrics, including accuracy, f1-score, precision, and AUC curves. XGBoost model achieves optimized values of 99.9\% accuracy. The proposed model outperforms contemporary machine learning approaches in credit card fraud detection. It has been observed that the combination of data balancing techniques significantly reduces the occurrence of false negatives. This study has substantial potential for credit card fraud detection applications in the real world, providing an effective and efficient method for addressing this ongoing critical issue. © 2024 IEEE.

关键词： Decision trees

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PUnifiedNER: A Prompting-Based Unified NER System for Diverse datasets 37

PUnifiedNER: A Prompting-Based Unified NER System for Divers...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Lu, Jinghui Zhao, Rui Namee, Brian Mac Tan, Fei SenseTime Research The Insight Centre for Data Analytics University College Dublin Ireland School of Computer Science University College Dublin Ireland

ISBN: (纸本)9781577358800

Much of named entity recognition (NER) research focuses on developing dataset-specific models based on data from the domain of interest, and a limited set of related entity types. This is frustrating as each new dataset requires a new model to be trained and stored. In this work, we present a "versatile" model-the Prompting-based Unified NER system (PUnifiedNER)-that works with data from different domains and can recognise up to 37 entity types simultaneously, and theoretically it could be as many as possible. By using prompt learning, PUnifiedNER is a novel approach that is able to jointly train across multiple corpora, implementing intelligent on-demand entity recognition. Experimental results show that PUnifiedNER leads to significant prediction benefits compared to dataset-specific models with impressively reduced model deployment costs. Furthermore, the performance of PUnifiedNER can achieve competitive or even better performance than state-of-the-art domain-specific methods for some datasets. We also perform comprehensive pilot and ablation studies to support in-depth analysis of each component in PUnifiedNER. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Artificial intelligence

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Debiasing Computer Vision Models using data Augmentation based Adversarial Techniques 3

Debiasing Computer Vision Models using Data Augmentation bas...

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3rd Workshop on Bias, Risk, Ethical AI, Explainability and the Role of Logic and Logic Programming, BEWARE 2024

作者： Kumar, Teerath Mandal, Abhishek Leavy, Susan Little, Suzanne Mileo, Alessandra Bendechache, Malika CRT-AI School of Computing Dublin City University Dublin Ireland Insight SFI Research Center for Data Analytics School of Computing Dublin City University Dublin Ireland Insight SFI Research Center for Data Analytics School of Information and Communication Studies University College Dublin Dublin Ireland ADAPT & Lero Research Centres School of Computer Science University of Galway Galway Ireland

Deep learning models in computer vision, such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), have been found to exhibit significant biases related to factors such as gender and ethnicity. These biases often originate from inherent imbalances in training data predominantly sourced from the internet. In this study, we aim to address gender bias in computer vision models by curating a specialized dataset that highlights gender-related disparities. Additionally, we measure dataset diversity across six datasets (FFHQ, WIKI, IMDB, LFW, UTK Faces, diverse dataset), five professions (CEO, engineer, nurse, politician, and teacher) and different query retrieval tasks using the Image Similarity Score (ISS). To reduce learned gender biases and increase data diversity, we propose adversarial data augmentation techniques that specifically target facial regions within images. These techniques, named Partial Mix (PM), that partially mixes two gendered faces in a squared pattern, and Noise Addition (NA), that adds noise to the facial region, are designed to mitigate bias. Our experimental results demonstrate increased data diversity across the six datasets and professions, along with reduction in gender bias for CNN-based models. However, these adversarial techniques were less effective in reducing bias for Vision Transformers. This discrepancy highlights the unique challenges for bias mitigation posed by ViTs. Consistent with prior research, our findings indicate that ViTs learn from a broader set of visual cues compared to CNNs. This increased sensitivity makes ViTs more prone to amplifying biases, emphasizing the need for tailored bias mitigation strategies when deploying these models in real-world applications. © 2024 Copyright for this paper by its authors.

关键词： Adversarial machine learning

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A Machine Learning Approach to Industry Classification in Financial Markets 30th

A Machine Learning Approach to Industry Classification in ...

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30th Irish Conference on Artificial Intelligence and Cognitive science, AICS 2022

作者： Dolphin, Rian Smyth, Barry Dong, Ruihai School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics University College Dublin Dublin Ireland

ISBN: (纸本)9783031264375

Industry classification schemes provide a taxonomy for segmenting companies based on their business activities. They are relied upon in industry and academia as an integral component of many types of financial and economic analysis. However, even modern classification schemes have failed to embrace the era of big data and remain a largely subjective undertaking prone to inconsistency and misclassification. To address this, we propose a multimodal neural model for training company embeddings, which harnesses the dynamics of both historical pricing data and financial news to learn objective company representations that capture nuanced relationships. We explain our approach in detail and highlight the utility of the embeddings through several case studies and application to the downstream task of industry classification. © 2023, The Author(s).

关键词： Machine learning

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Using Blue Whale Technology: An ML Edge Self-Adaptable Vehicle Slowdown Earliest Warning Information System 8th

Using Blue Whale Technology: An ML Edge Self-Adaptable Vehic...

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8th International Conference on Microelectronics Electromagnetics and Telecommunications, ICMEET 2023

作者： Murali, N. David, D. Beulah Research Scholar Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamilnadu Chennai India Department of Data Analytics Institute of Information Technology Saveetha School of Engineering SIMATS Tamilnadu Chennai India

ISBN: (纸本)9789819784219

Human life is challenged by this main work’s ultimate goal of reducing accidents and ensuring life safety due to the enormous growth of vehicles and those based on safety. Here, cases of suspected drunk driving, reckless driving, etc., are present. In this scenario, vehicles are traveling at full speed and irresponsibly using technology on all roads. An early warning system is used to stop auto accidents. In order to accommodate the city's steadily increasing automobile population, intelligent traffic systems must be designed in a resourceful and sustainable manner using all available technology in both light and dark. The advanced ML's work on unexpected traffic flow and all road conditions resulted in significant real-time changes in the flow of traffic for every significant issue that arose. Here, the Blue Whale technology makes use of clever intelligence to quickly identify, detect, and modify warning systems. The primary objective of the study was to train CNN/BWT on the collected data sets in order to operationalize the unsupervised approach for the prediction of critical situations. Additionally, it makes an effort to clarify the situation so that guests can feel more at ease and help them decide on transfers in time-sensitive situations. IOT is a practical way to locate the nearest object using machine learning methods that are accurate and apply received data sets, resulting in significant findings that support object recognition. The paperwork fully focuses on improving driving knowledge and offering new approaches for quick object recognition. The Fast Warning System Initiative, which aims to increase daytime safety and security, is providing a prior warning system. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Backward wave tubes

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New Results on Interval Type-3 Fuzzy Control for Nonlinear Time-Delay Systems Using Convex Relaxation Technique

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3630-3641页

作者： Harikaran, B. Harshavarthini, S. Lee, Sangmoon Sakthivel, R. Sathiyaraj, T. School of Advanced Sciences Department of Mathematics Chennai600127 India Kyungpook National University School of Electronic and Electrical Engineering Daegu41566 Korea Republic of Bharathiar University Department of Applied Mathematics Coimbatore641046 India Institute of Actuarial Science and Data Analytics UCSI University Kuala Lumpur56000 Malaysia

In this article, the interval type-3 fuzzy-based state feedback control is proposed for the stabilization problem of interval type-3 fuzzy systems (IT3FSs) subject to time-varying delay. Specifically, to improve the model accuracy and control capabilities, we proposed a nonparallel distributed compensation-based controller approach, be precise, the system and controller adopts the distinct membership functions. Moreover, the stability analysis of IT3FSs in the state space form is studied newly in this article. Besides the primary membership function that exists for interval type-2 fuzzy systems (IT2FSs), the IT3FSs possess the secondary membership function to handle the overall uncertainty which enacts the superiority of the proposed work. Notably, the adequate stability criteria are derived in the form of linear matrix inequality (LMI) using the Lyapunov stability theorem. Additionally, the convex relaxation technique is used to strengthen the design flexibility and achieve the faster convergence rate, which converts an optimization problem with a vector variable to a convex program with a matrix variable, via a lifting technique. A noteworthy aspect is that the application of the convex relaxation technique in the context of IT3FSs is proposed for the first time in literature. Finally, the comparative results between IT2FSs and IT3FSs are shown via two illustrative examples, including the inverted-pendulum model, to underscore the efficacy and practical implementations of the developed control methodology. © 2013 IEEE.

关键词： Membership functions

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Beyond Correlation: Evaluating Multimedia Quality Models with the Constrained Concordance Index

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IEEE Transactions on Multimedia 2025年

作者： Ragano, Alessandro Martinez, Helard Becerra Hines, Andrew University College Dublin Insight Centre for Data Analytics the School of Computer Science Dublin Ireland

This study investigates the evaluation of multimedia quality models, focusing on the inherent uncertainties in subjective Mean Opinion Score (MOS) ratings due to factors like rater inconsistency and bias. Traditional statistical measures such as Pearson's Correlation Coefficient (PCC), Spearman's Rank Correlation Coefficient (SRCC), and Kendall's Tau (KTAU) often fail to account for these uncertainties, leading to inaccuracies in model performance assessment. We introduce the Constrained Concordance Index (CCI), a novel metric designed to overcome the limitations of existing metrics by considering the statistical significance of MOS differences and excluding comparisons where MOS confidence intervals overlap. Through comprehensive experiments across various domains including speech and image quality assessment, we demonstrate that CCI provides a more robust and accurate evaluation of instrumental quality models, especially in scenarios of low sample sizes, rater group variability, and restriction of range. Our findings suggest that incorporating rater subjectivity and focusing on statistically significant pairs can significantly enhance the evaluation framework for multimedia quality prediction models. This work not only sheds light on the overlooked aspects of subjective rating uncertainties but also proposes a methodological advancement for more reliable and accurate quality model evaluation. © 2025 IEEE.

关键词： Image quality

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