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32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Jarvis, Padraigh Climent, Laura Arbelaez, Alejandro Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland Universitat Politècnica de València Valencia Spain

As the effects of climate change are increasingly felt worldwide, the transition to Electric Buses (EB) presents an opportunity to decarbonize the transportation sector. Several issues exist that hinder the adoption of a green bus fleet. Such as the increased cost, reduced travel distances, and required recharge times, which may negatively impact service quality. This work proposes a Mixed Integer Programming model to solve a multi-objective mixed fleet charging schedule problem. The mixed fleet considers EBs and Internal Combustion Engine Buses (ICEBs) and minimizes daily costs, such as fuel price, the Social Cost of Carbon (SCC) produced by the bus fleet, and the Value of Time (VoT) of public transport users. Non-linear charging is considered as well as alternative approaches along with Time of Use (TOU) constraints for electricity price and SCC. Empirical evaluation shows that significant savings can be made, with reductions of over 20000 in fuel costs, and reductions of over 100 tCO2eq per day. Consideration of VoT minimizes negative customer impact, limiting late arrivals to an average of 8.78 seconds per EB. The inclusion of non-linear charging makes minimal positive impact compared to limiting the total capacity of the battery, and while the inclusion of TOU constraints correlates to more savings, the amount saved is minute. © 2024 Copyright for this paper by its authors.

关键词： Integer programming

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Uncertainty-Aware Optimisation for Sustainable Multimedia Event Processing in Big data Streams

Uncertainty-Aware Optimisation for Sustainable Multimedia Ev...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Pontes, Felipe Arruda Schukat, Michael Curry, Edward University of Galway Insight Centre for Data Analytics Galway Ireland University of Galway School of Computer Science Galway Ireland

ISBN: (纸本)9798350324457

Multimedia Event Processing (MEP) systems play a critical role in various Internet of Things (IoT) applications, including Smart Cities and Health and Safety, by processing large amounts of multimedia data streams. These systems often leverage state-of-the-art Deep Neural Network (DNN) models to enhance their capabilities. However, the growth of Cloud and Edge Big data applications has imposed a significant environ-mental burden, further intensified by the substantial energy consumption associated with certain DNN model *** study addresses the environmental impact of growing Big data stream applications and focuses on optimising MEP systems to mitigate this issue. We tackle uncertainties arising from user-defined Quality of Service (QoS) interpretations and service worker measurement imprecisions by using uncertainty-aware solutions for the service selection problem in order to improve the QoS within MEP *** results reveal substantial advantages in employing uncertainty-aware strategies. These approaches consistently enhance QoS metrics, outperforming their uncertainty-oblivious counterparts. Specifically, we report improvements in more than 67%, 69%, and 20% of the scenarios, on average, for energy consumption, latency, and accuracy, respectively. These enhancements become evident within just three hours of processing, resulting in energy savings of up to 1.2 kilowatt-hours and latency reductions of 213 seconds, with a 0.29% average loss in query accuracy. These strategies improve system efficiency and ecological sustainability while incurring a small accuracy trade-off. When extrapolated over a year, the environmental benefits become even more noticeable, surpassing the energy requirements for a 1000 Km electric vehicle round-trip from Amsterdam to Paris and back. © 2023 IEEE.

关键词： Big data Deep Neural Networks Multimedia Streaming Sustainability

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Creation and Classification of Kannada Meme dataset: Exploring Domain and Troll Categories 2nd

Creation and Classification of Kannada Meme Dataset: Explo...

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2nd International Conference on Speech and Language Technologies for Low-Resource Languages, SPELLL 2023

作者： Kundargi, Sahana Y. N, Lohith M, Anand Kumar Chakravarthi, Bharathi Raja Department of Information Technology National Institute of Technology Karnataka Surathkal India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Galway Ireland

ISBN: (纸本)9783031584947

In this pioneering research, the first-ever Kannada memes dataset is established, marking a groundbreaking contribution. This dataset encompasses 2002 memes, spanning various categories such as movies, politics, sports, trolls, and non-troll memes. The classification models have been meticulously fine-tuned for memes, incorporating image-based models using DenseNet169 and text-based models with BERT for text encoding. An innovative multimodal approach combines insights from images and text, acknowledging the comprehensive nature of meme content. Throughout the study, model strengths and weaknesses are assessed, emphasizing their reliance on cutting-edge technologies like Deep Learning and Natural Language Processing. Valuable improvements are recommended, such as the implementation of oversampling techniques and regular dataset updates to enhance relevance and accuracy. This work extends beyond immediate research, contributing to the development of adaptive meme classification systems, particularly for Kannada-speaking audiences within the evolving meme culture landscape. Notably, the results indicate that multimodal models achieved the best scores for domain classification, while image-based models excelled in troll meme classification, further highlighting the significance of this approach within the field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Classification (of information)

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A Privacy-Preserving E-Voting System Using Blockchain and Linkable Ring Signatures 1

A Privacy-Preserving E-Voting System Using Blockchain and Li...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Berkani, Ahmed-Sami Moumen, Hamouma Benharzallah, Saber Achouri, Youssouf Kechadi, Mohand Tahar Lamie Laboratory University of Batna 2 Department of Computer Science Batna Algeria LaSTIC Laboratory University of Batna 2 Department of Computer Science Batna Algeria School of Computer Science University College Dublin Insight Centre for Data Analytics Belfield Ireland

ISBN: (纸本)9798331541064

In this work we propose a privacy-preserving, blockchain-enabled e-voting system that integrates Linkable Ring Signatures (LRS) to ensure a secure, scalable, and privacypreserving solution for modern e-voting, providing a framework for trusted and transparent electoral processes in the digital age. Our system ensures voter anonymity while preventing double voting through the use of a linkability tag, while blockchain technology is employed to provide an immutable and transparent ledger for vote recording. The InterPlanetary File System (IPFS) is utilized for decentralized and GDPR-compliant storage of public keys. Performance analysis shows scalability challenges for LRS in large elections, but the system remains efficient for small scale use. Future work will focus on optimizing scalability for larger deployments. © 2024 IEEE.

关键词： Differential privacy

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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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A cloud-based architecture for explainable Big data analytics using self-structuring Artificial Intelligence

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Discover Artificial Intelligence 2024年第1期4卷 33页

作者： Mills, Nishan Issadeen, Zafar Matharaarachchi, Amali Bandaragoda, Tharindu De Silva, Daswin Jennings, Andrew Manic, Milos Centre for Data Analytics and Cognition La Trobe University MelbourneVIC3086 Australia Department of Computer Science Virginia Commonwealth University RichmondVA23284 United States

Big data is steadily expanding beyond the boundaries of its foundational constructs of three primary Vs, Volume, Velocity and Variety, and two secondary Vs, Veracity and Value. The advent of 5G networks, Edge computing and IoT technologies has transformed Big data into this modern context. With these new manifestations of Big data, the focus is not only on the data itself but on the context that it applies to its immediate environment as well as the human and societal perception of this context. It is increasingly challenging for conventional AI algorithms to process and transform this data, analyse and visualise a broad spectrum of insights, and then formulate the explainability of such insights in terms of bias, transparency, safety, ethics, and causality. Self-structuring Artificial Intelligence (SSAI) addresses the limitations of conventional AI by adapting to the inherent structure of the data, incrementally learning and abstracting from this structure. SSAI has not been investigated in a cloud-based setting for generating explainable insights from these new types of Big data. In this paper we propose a cloud-based architecture for explainable Big data analytics using SSAI in highly-connected 5G and Edge computing environments. The proposed architecture is empirically evaluated on a commercial scale Big data use case of Smart Grid for Smart Cities. The results of these experiments confirm the functionality and effectiveness of the proposed architecture. © The Author(s) 2024.

关键词： data analytics

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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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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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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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Partial Compilation of SAT Using Selective Backbones 26

Partial Compilation of SAT Using Selective Backbones

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26th European Conference on Artificial Intelligence, ECAI 2023

作者： Balogh, Andrea Escamocher, Guillaume O'Sullivan, Barry Insight Sfi Research Centre for Data Analytics School of Computer Science & It University College Cork Ireland Confirm Centre for Smart Manufacturing School of Computer Science & It University College Cork Ireland

ISBN: (纸本)9781643684369

Our goal in this paper is to significantly decrease the compiled size of a given Boolean instance with a large representation, while preserving as much information about the instance as possible. We achieve this by assigning values to a subset of the variables in such a way that the resulting instance has a much smaller representation than the original one, and its number of solutions is almost as high as the starting one. We call the set of variable instantiations that we make the selective backbone of the solutions that we keep. Large selective backbones allow for smaller representations, but also eliminate more solutions. We compare different methods of computing the selective backbone that offer the best compromise. © 2023 The Authors.

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