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23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Wallace, Richard J. Insight Centre for Data Analytics and Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9783031734991

We still do not have an adequate understanding of heuristic methods used for solving constraint satisfaction problems (CSPs). An example involves the effects of preprocessing, an essential means of improving CSP search. The traditional explanation for its beneficial effect is that the resulting "problem reduction" leaves fewer possibilities to explore. Recently, however, it was shown that when dynamic variable ordering heuristics are used, other factors related to domain size reduction are much more important. This paper extends this analysis and explores some implications of this new perspective. The key idea is that pattern of domain reductions produced by preprocessing transmits information that guides heuristic decisions. Treating domain reduction as a code and the set of reductions as a message of length n transmitted to the search algorithm, we distinguish two factors, message discriminability and code quality, and show that greater problem reduction enhances both. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Constraint satisfaction problems

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Information Flows during Constraint Satisfaction Search with Dynamic Heuristics: Information from Preprocessing 1

Information Flows during Constraint Satisfaction Search with...

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1st International Workshop on Artificial Intelligence for Climate Change, 12th Italian Workshop on Planning and Scheduling, 31st RCRA Workshop on Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion, and SPIRIT Workshop on Strategies, Prediction, Interaction, and Reasoning in Italy, AI4CC-IPS-RCRA-SPIRIT 2024

作者： Wallace, Richard J. Insight Centre for Data Analytics School of Computer Science and IT University College Cork Ireland

We still do not have an adequate understanding of heuristic methods used for solving constraint satisfaction problems (CSPs). An example of this involves the effects of preprocessing, an essential means of improving CSP search. The traditional explanation for its beneficial effect is that the resulting "problem reduction" leaves fewer possibilities to explore. Recently, however, it was shown that when dynamic variable ordering heuristics are used, other factors related to domain size reduction are much more important. This paper extends this earlier work and clarifies what is involved. The key idea is that the pattern of domain reductions produced by preprocessing constitutes information that by itself enables heuristics to make better decisions. Treating domain reduction as a code and the set of reductions as a message of length n transmitted to the search algorithm, we can distinguish two factors, message discriminability and code quality. We provide evidence that greater problem reduction enhances both. © 2024 Copyright for this paper by its authors.

关键词： Constraint satisfaction problems

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Synthetically Augmented Self-Supervised Fine-Tuning for Diverse Text OCR Correction 27

Synthetically Augmented Self-Supervised Fine-Tuning for Dive...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Guan, Shuhao Greene, Derek Insight Centre for Data Analytics Dublin School of Computer Science University College Dublin Ireland

ISBN: (纸本)9781643685489

The adoption of Optical Character Recognition (OCR) tools has been central to the increased digitization of historical documents. However, the errors introduced during OCR, particularly in texts with a specialized vocabulary (SV), necessitate effective post-OCR correction methodologies. This study introduces a novel approach that leverages weak supervision and self-supervised finetuning to enhance post-OCR correction without the need for substantial manual annotations. By using multi-noise-level synthetic data, generated through automatically-extracted OCR errors and applied to clean texts, we can train robust models tailored for post-OCR tasks. Furthermore, we propose a unique self-supervised fine-tuning strategy, applied specifically to long texts, enables models to adeptly handle out-of-vocabulary problems and SV. Additionally, we tested the performance of the GPT model on post-OCR tasks. © 2024 The Authors.

关键词： Optical character recognition

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Explaining the Effects of Preprocessing on Constraint Satisfaction Search 30th

Explaining the Effects of Preprocessing on Constraint Sat...

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

作者： Wallace, Richard J. Insight Centre for Data Analytics and Department of Computer Science University College Cork Cork Ireland

ISBN: (纸本)9783031264375

Preprocessing constraint satisfaction problems is a much studied method for improving the performance of subsequent solution search. The traditional explanation for its beneficial effects is "problem reduction", where possible values that cannot take part in a solution are discarded, leaving fewer possibilities to explore during search. Here, we show that this is not the only or even the main factor when dynamic variable ordering heuristics are used. Multiple lines of evidence indicate that under these conditions domain reductions effected by preprocessing serve to inform the heuristic as to which variables should be chosen for instantiation before others. It is suggested that an information transmission model is needed to account for such effects, and it is argued that an extension of this approach can incorporate simple domain reduction effects as well. © 2023, The Author(s).

关键词： Constraint satisfaction problems

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On the Effect of Cross-Channel Normalization on Wide-Shallow Convolutional Networks 26

On the Effect of Cross-Channel Normalization on Wide-Shallow...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Alakkari, Salaheddin Mileo, Alessandra Department of Computer Science and Information Systems University of Limerick Ireland Insight Centre for Data Analytics Dublin City University Ireland

ISBN: (纸本)9781837242672

Cross-channel Normalization (CN) was first proposed in AlexNet paper as a biologically inspired normalization process mimicking the lateral inhibition phenomenon in biological neurons. However, the effect of such a normalization was not well explored in the literature due to the wide popularity of batch normalization. In this paper, we show that such a type of normalization can significantly enhance the network accuracy when applied to wide-shallow convolutional networks. Wide-shallow networks are more advantageous compared to deep CNNs since they significantly increase parallelism and reduce the sequential computation across layers. Our experiments show that using cross-channel normalization not only improves the accuracy of residual and standard convolutional networks on benchmark datasets but also significantly enhances the performance when considering limited and reduced representation examples. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Convolution

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Testing Most Distant Neighbor (MDN) Variants for Semi-Factual Explanations in XAI 32

Testing Most Distant Neighbor (MDN) Variants for Semi-Factua...

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Workshops at the 32nd International Conference on Case-Based Reasoning, ICCBR-WS 2024

作者： Aryal, Saugat Keane, Mark T. School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

Recently, Semi-factual explanations have gained popularity in the eXplainable AI (XAI) community. They provide "even if" justifications to indicate what key input features could change without changing the outcome. Although several methods have now been proposed to compute semi-factuals, the instance-based, Most Distant Neighbors (MDN) method has emerged in recent comprehensive tests to be quite competitive, even though was originally proposed as a naive benchmark. However, on some metrics MDN comes bottom of the class (e.g., sparsity). In this paper, we explore nine variants of the MDN method to determine whether its performance on some metrics can be improved relative to older methods by performing comprehensive tests on key metrics. The results show that there are MDN variants that perform better on some key metrics, but that some of the historical methods still do better. © 2024 Copyright for this paper by its authors.

关键词： Most Distant Neighbor Semi-Factual XAI XCBR

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ML-KFHE: Multi-label Ensemble Classification Algorithm Exploiting Sensor Fusion Properties of the Kalman Filter

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SN computer science 2023年第6期4卷 821页

作者： Pakrashi, Arjun Mac Namee, Brian School of Computer Science University College Dublin Dublin Ireland Insight Centre for Data Analytics Dublin Ireland

Despite the success of ensemble classification methods in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman filter-based Heuristic ensemble (KFHE) is an ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This work proposes a multi-label version of KFHE, ML-KFHE, demonstrating the effectiveness of the KFHE method on multi-label datasets. Two variants are introduced based on the underlying component classifier algorithm, ML-KFHE-HOMER, and ML-KFHE-CC which uses HOMER and classifier chain (CC) as the underlying multi-label algorithms, respectively. ML-KFHE-HOMER and ML-KFHE-CC sequentially train multiple HOMER and CC multi-label classifiers and aggregate their outputs using the sensor fusion properties of the Kalman filter. Extensive experiments and detailed analysis were performed on thirteen multi-label datasets and eight other algorithms, which included state-of-the-art ensemble methods. The results show, for both versions, the ML-KFHE framework improves the predictive performance significantly with respect to bagged combinations of HOMER (named E-HOMER), also introduced in this paper, and bagged combination of CC, ensemble classifier chains (ECC), thus demonstrating the effectiveness of ML-KFHE. Also, the ML-KFHE-HOMER variant was found to perform consistently and significantly better than the compared multi-label methods including existing approaches based on ensembles. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Classifier fusion Ensembles Kalman filter Multi-label classification

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An artificial intelligence framework for explainable drift detection in energy forecasting

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Energy and AI 2024年第3期17卷 368-379页

作者： Chamod Samarajeewa Daswin De Silva Milos Manic Nishan Mills Harsha Moraliyage Damminda Alahakoon Andrew Jennings Centre for Data Analytics and Cognition La Trobe UniversityAustralia Department of Computer Science Virginia Commonwealth UniversityRichmondUSA

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

关键词： Building energy consumption forecasting Explainable drift detection Energy embedding Dimensional modeling

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Towards Graph-Based Semi-Supervised Learning on Audio Embeddings for Label Classification 22

Towards Graph-Based Semi-Supervised Learning on Audio Embedd...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Chandaliya, Rishabh Timilsina, Mohan Breslin, John Serrano, Martin University of Galway Ireland Insight Sfi Research Centre for Data Analytics Department of Computer Science Galway Ireland

ISBN: (纸本)9798350345346

The increasing importance of audio-based healthcare diagnostics, particularly in chronic respiratory problems, has prompted the development of novel approaches for disease classification using the full spectrum analysis of cough sounds. This paper presents an innovative study exploring the potential of employing supervised and semi-supervised learning methodologies for disease categorization based on cough audio samples. Specifically, this study focuses on scenarios characterized by a shortage of annotated data related to chronic diseases. The objectives of our study involved the utilization of standard machine learning algorithms for direct classification based on embeddings, as well as the integration of Graph Neural Networks (GNNs) on the KNN graph. Preliminary results indicate that GNN models consistently outperformed traditional classifiers. For instance, with just 1 % of the data, GAT and GCN achieved AUC PR values of 0.84 and 0.87, respectively, surpassing all traditional methods. The superiority mentioned above was sustained even when the data fraction was augmented to 3% and 5%. The usefulness of graph neural networks (GNNs) was further supported by comprehensive performance measures, wherein the graph convolutional network (GCN) exhibited exceptional preci-sion and PR (precision-recall). In summary, the AudioVecDiagnosis framework presents a promising opportunity for further investigation in audio-based healthcare diagnostics. It provides an optimum approach for situations with a limited availability of labeled data. © 2023 IEEE.

关键词： Audio-based medical diagnostics AudioVec-Diagnosis Cough noises Disease classification Semi-supervised learning Wav2Vec2.0 model

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CouRGe: Counterfactual Reviews Generator for Sentiment Analysis 30th

CouRGe: Counterfactual Reviews Generator for Sentiment Anal...

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

作者： Carraro, Diego Brown, Kenneth N. Insight Centre for Data Analytics School of Computer Science and IT University College Cork Cork Ireland

ISBN: (纸本)9783031264375

Past literature in Natural Language Processing (NLP) has demonstrated that counterfactual data points are useful, for example, for increasing model generalisation, enhancing model interpretability, and as a data augmentation approach. However, obtaining counterfactual examples often requires human annotation effort, which is an expensive and highly skilled process. For these reasons, solutions that resort to transformer-based language models have been recently proposed to generate counterfactuals automatically, but such solutions show limitations. In this paper, we present CouRGe, a language model that, given a movie review (i.e. a seed review) and its sentiment label, generates a counterfactual review that is close (similar) to the seed review but of the opposite sentiment. CouRGe is trained by supervised fine-tuning of GPT-2 on a task-specific dataset of paired movie reviews, and its generation is prompt-based. The model does not require any modification to the network’s architecture or the design of a specific new task for fine-tuning. Experiments show that CouRGe’s generation is effective at flipping the seed sentiment and produces counterfactuals reasonably close to the seed review. This proves once again the great flexibility of language models towards downstream tasks as hard as counterfactual reasoning and opens up the use of CouRGe’s generated counterfactuals for the applications mentioned above. © 2023, The Author(s).

关键词： Sentiment analysis

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