4,091 research outputs found

    LIMITS OF ALGORITHMIC FAIR USE

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    In this article, we apply historical copyright principles to the evolving state of text-to-image generation and explore the implications of emerging technological constructs for copyright’s fair use doctrine. Artificial intelligence (“AI”) is frequently trained on copyrighted works, which usually involves extensive copying without owners’ authorization. Such copying could constitute prima facie copyright infringement, but existing guidance suggests fair use should apply to most machine learning contexts. Mark Lemley and Bryan Casey argue that training machine learning (“ML”) models on copyrighted material should generally be permitted under fair use when the model’s outputs transcends the purpose of its inputs. Their arguments are compelling in the domain of AI, generally. However, contemporary AI’s capacity to generate new works of art (“generative AI”) presents a unique case because it explicitly attempts to emulate the expression copyright intends to protect. Jessica Gillotte concludes that generative AI does not illicit copyright infringement because judicial guidance requires adherence to the constitutional imperative to promote the creation of new works when technological change blurs copyright’s boundaries. Even if infringement does occur, Gillotte finds that fair use would serve as a valid defense because training an AI model transforms the original work and is unlikely to damage the original artist’s market for the copyrighted work. Our paper deviates from prior scholarship by exploring specific generative AI use cases in technological detail. Ultimately, we argue that fair use’s first factor, the purpose of the use, and its fourth factor, the impact on the market for the copyrighted work, both weigh against a finding of fair use in generative AI use cases. However, even if text-to-image models aren’t found to be transformative, we argue that the potential for market usurpation alone sufficiently negates fair use. There is presently little specific guidance from courts as to whether using copyrighted works to build generative AI models constitutes either infringement or fair use, although several related lawsuits are currently pending. Text-to-art generative AIs present several scenarios that threaten substantial harm to the market for the copyrighted original, which tends to undercut the case for fair use. For example, a generative AI trained on copyrighted works has already enabled users to create works “in the style of” individual artists, which has allegedly caused business and reputational losses for the emulated copyright holder. Furthermore, past analyses have ignored the potential for a model to be non-transformative when its intended output has the same purpose and is of the same nature as its copyrighted inputs. This article contributes to the discussion by shining a technical light on text-to-art AI use cases to explore whether some uses normatively fail to qualify as fair uses. First, we examine whether text-to-image models present a prima facie infringement claim. We then distinguish text-to-image generative AIs from non-image focused AIs. In doing so, we argue that when the nature of the copyrighted work and the purpose of the infringing use are the same, it is more likely that the original artist will experience market harm. This tilts the overall analysis against a finding of fair use

    CCheXR-Attention: Clinical concept extraction and chest x-ray reports classification using modified Mogrifier and bidirectional LSTM with multihead attention

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    Radiology reports cover different aspects, from radiological observation to the diagnosis of an imaging examination, such as X-rays, MRI, and CT scans. Abundant patient information presented in radiology reports poses a few major challenges. First, radiology reports follow a free-text reporting format, which causes the loss of a large amount of information in unstructured text. Second, the extraction of important features from these reports is a huge bottleneck for machine learning models. These challenges are important, particularly the extraction of key features such as symptoms, comparison/priors, technique, finding, and impression because they facilitate the decision-making on patients’ health. To alleviate this issue, a novel architecture CCheXR-Attention is proposed to extract the clinical features from the radiological reports and classify each report into normal and abnormal categories based on the extracted information. We have proposed a modified mogrifier LSTM model and integrated a multihead attention method to extract the more relevant features. Experimental outcomes on two benchmark datasets demonstrated that the proposed model surpassed state-of-the-art models

    A knowledge graph-supported information fusion approach for multi-faceted conceptual modelling

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    It has become progressively more evident that a single data source is unable to comprehensively capture the variability of a multi-faceted concept, such as product design, driving behaviour or human trust, which has diverse semantic orientations. Therefore, multi-faceted conceptual modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is frequently applied to cope with the high dimensionality and data heterogeneity. The consideration of intra-facets relationships is also indispensable. In this context, a knowledge graph (KG), which can aggregate the relationships of multiple aspects by semantic associations, was exploited to facilitate the multi-faceted conceptual modelling based on heterogeneous and semantic-rich data. Firstly, rules of fault mechanism are extracted from the existing domain knowledge repository, and node attributes are extracted from multi-sourced data. Through abstraction and tokenisation of existing knowledge repository and concept-centric data, rules of fault mechanism were symbolised and integrated with the node attributes, which served as the entities for the concept-centric knowledge graph (CKG). Subsequently, the transformation of process data to a stack of temporal graphs was conducted under the CKG backbone. Lastly, the graph convolutional network (GCN) model was applied to extract temporal and attribute correlation features from the graphs, and a temporal convolution network (TCN) was built for conceptual modelling using these features. The effectiveness of the proposed approach and the close synergy between the KG-supported approach and multi-faceted conceptual modelling is demonstrated and substantiated in a case study using real-world data

    Recalibrating machine learning for social biases: demonstrating a new methodology through a case study classifying gender biases in archival documentation

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    This thesis proposes a recalibration of Machine Learning for social biases to minimize harms from existing approaches and practices in the field. Prioritizing quality over quantity, accuracy over efficiency, representativeness over convenience, and situated thinking over universal thinking, the thesis demonstrates an alternative approach to creating Machine Learning models. Drawing on GLAM, the Humanities, the Social Sciences, and Design, the thesis focuses on understanding and communicating biases in a specific use case. 11,888 metadata descriptions from the University of Edinburgh Heritage Collections' Archives catalog were manually annotated for gender biases and text classification models were then trained on the resulting dataset of 55,260 annotations. Evaluations of the models' performance demonstrates that annotating gender biases can be automated; however, the subjectivity of bias as a concept complicates the generalizability of any one approach. The contributions are: (1) an interdisciplinary and participatory Bias-Aware Methodology, (2) a Taxonomy of Gendered and Gender Biased Language, (3) data annotated for gender biased language, (4) gender biased text classification models, and (5) a human-centered approach to model evaluation. The contributions have implications for Machine Learning, demonstrating how bias is inherent to all data and models; more specifically for Natural Language Processing, providing an annotation taxonomy, annotated datasets and classification models for analyzing gender biased language at scale; for the Gallery, Library, Archives, and Museum sector, offering guidance to institutions seeking to reconcile with histories of marginalizing communities through their documentation practices; and for historians, who utilize cultural heritage documentation to study and interpret the past. Through a real-world application of the Bias-Aware Methodology in a case study, the thesis illustrates the need to shift away from removing social biases and towards acknowledging them, creating data and models that surface the uncertainty and multiplicity characteristic of human societies

    Advances in machine learning algorithms for financial risk management

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    In this thesis, three novel machine learning techniques are introduced to address distinct yet interrelated challenges involved in financial risk management tasks. These approaches collectively offer a comprehensive strategy, beginning with the precise classification of credit risks, advancing through the nuanced forecasting of financial asset volatility, and ending with the strategic optimisation of financial asset portfolios. Firstly, a Hybrid Dual-Resampling and Cost-Sensitive technique has been proposed to combat the prevalent issue of class imbalance in financial datasets, particularly in credit risk assessment. The key process involves the creation of heuristically balanced datasets to effectively address the problem. It uses a resampling technique based on Gaussian mixture modelling to generate a synthetic minority class from the minority class data and concurrently uses k-means clustering on the majority class. Feature selection is then performed using the Extra Tree Ensemble technique. Subsequently, a cost-sensitive logistic regression model is then applied to predict the probability of default using the heuristically balanced datasets. The results underscore the effectiveness of our proposed technique, with superior performance observed in comparison to other imbalanced preprocessing approaches. This advancement in credit risk classification lays a solid foundation for understanding individual financial behaviours, a crucial first step in the broader context of financial risk management. Building on this foundation, the thesis then explores the forecasting of financial asset volatility, a critical aspect of understanding market dynamics. A novel model that combines a Triple Discriminator Generative Adversarial Network with a continuous wavelet transform is proposed. The proposed model has the ability to decompose volatility time series into signal-like and noise-like frequency components, to allow the separate detection and monitoring of non-stationary volatility data. The network comprises of a wavelet transform component consisting of continuous wavelet transforms and inverse wavelet transform components, an auto-encoder component made up of encoder and decoder networks, and a Generative Adversarial Network consisting of triple Discriminator and Generator networks. The proposed Generative Adversarial Network employs an ensemble of unsupervised loss derived from the Generative Adversarial Network component during training, supervised loss and reconstruction loss as part of its framework. Data from nine financial assets are employed to demonstrate the effectiveness of the proposed model. This approach not only enhances our understanding of market fluctuations but also bridges the gap between individual credit risk assessment and macro-level market analysis. Finally the thesis ends with a novel proposal of a novel technique or Portfolio optimisation. This involves the use of a model-free reinforcement learning strategy for portfolio optimisation using historical Low, High, and Close prices of assets as input with weights of assets as output. A deep Capsules Network is employed to simulate the investment strategy, which involves the reallocation of the different assets to maximise the expected return on investment based on deep reinforcement learning. To provide more learning stability in an online training process, a Markov Differential Sharpe Ratio reward function has been proposed as the reinforcement learning objective function. Additionally, a Multi-Memory Weight Reservoir has also been introduced to facilitate the learning process and optimisation of computed asset weights, helping to sequentially re-balance the portfolio throughout a specified trading period. The use of the insights gained from volatility forecasting into this strategy shows the interconnected nature of the financial markets. Comparative experiments with other models demonstrated that our proposed technique is capable of achieving superior results based on risk-adjusted reward performance measures. In a nut-shell, this thesis not only addresses individual challenges in financial risk management but it also incorporates them into a comprehensive framework; from enhancing the accuracy of credit risk classification, through the improvement and understanding of market volatility, to optimisation of investment strategies. These methodologies collectively show the potential of the use of machine learning to improve financial risk management

    Leveraging semantic text analysis to improve the performance of transformer-based relation extraction

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    Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent work focuses on fine-tuned, Pre-trained Transformers. Yet, F1 scores for scientific literature achieve just 53.2, versus 69 in the general domain. The research demonstrates the failure of existing work to evidence the rationale for optimisations to finetuned classifiers. In contrast, emerging research subjectively adopts the common belief that Natural Language Processing techniques fail to derive context and shared knowledge. In fact, global context and shared knowledge account for just 10.4% and 11.2% of total relation misclassifications, respectively. In this work, the novel employment of semantic text analysis presents objective challenges for the Transformer-based classification of Joint Relation Extraction. This is the first known work to quantify that pipelined error propagation accounts for 45.3% of total relation misclassifications, the most poignant challenge in this domain. More specifically, Part-of-Speech tagging highlights the misclassification of complex noun phrases, accounting for 25.47% of relation misclassifications. Furthermore, this study identifies two limitations in the purported bidirectionality of the Bidirectional Encoder Representations from Transformers (BERT) Pre-trained Language Model. Firstly, there is a notable imbalance in the misclassification of right-to-left relations, which occurs at a rate double that of left-to-right relations. Additionally, a failure to recognise local context through determiners and prepositions contributes to 16.04% of misclassifications. Furthermore, it is highlighted that the annotation scheme of the singular dataset utilised in existing research, Scientific Entities, Relations and Coreferences (SciERC), is marred by ambiguity. Notably, two asymmetric relations within this dataset achieve recall rates of only 10% and 29

    Deep Clustering for Data Cleaning and Integration

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    Deep Learning (DL) techniques now constitute the state-of-theart for important problems in areas such as text and image processing, and there have been impactful results that deploy DL in several data management tasks. Deep Clustering (DC) has recently emerged as a sub-discipline of DL, in which data representations are learned in tandem with clustering, with a view to automatically identifying the features of the data that lead to improved clustering results. While DC has been used to good effect in several domains, particularly in image processing, the potential of DC for data management tasks remains unexplored. In this paper, we address this gap by investigating the suitability of DC for data cleaning and integration tasks, specifically schema inference, entity resolution and domain discovery, from the perspective of tables, rows and columns, respectively. In this setting, we compare and contrast several DC and non-DC clustering algorithms using standard benchmarks. The results show, among other things, that the most effective DC algorithms consistently outperform non-DC clustering algorithms for data integration tasks. Experiments also show consistently strong performance compared with state-of-the-art bespoke algorithms for each of the data integration tasks

    Improving Cross-Lingual Transfer Learning for Event Detection

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    The widespread adoption of applications powered by Artificial Intelligence (AI) backbones has unquestionably changed the way we interact with the world around us. Applications such as automated personal assistants, automatic question answering, and machine-based translation systems have become mainstays of modern culture thanks to the recent considerable advances in Natural Language Processing (NLP) research. Nonetheless, with over 7000 spoken languages in the world, there still remain a considerable number of marginalized communities that are unable to benefit from these technological advancements largely due to the language they speak. Cross-Lingual Learning (CLL) looks to address this issue by transferring the knowledge acquired from a popular, high-resource source language (e.g., English, Chinese, or Spanish) to a less favored, lower-resourced target language (e.g., Urdu or Swahili). This dissertation leverages the Event Detection (ED) sub-task of Information Extraction (IE) as a testbed and presents three novel approaches that improve cross-lingual transfer learning from distinct perspectives: (1) direct knowledge transfer, (2) hybrid knowledge transfer, and (3) few-shot learning

    Robustness, Heterogeneity and Structure Capturing for Graph Representation Learning and its Application

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    Graph neural networks (GNNs) are potent methods for graph representation learn- ing (GRL), which extract knowledge from complicated (graph) structured data in various real-world scenarios. However, GRL still faces many challenges. Firstly GNN-based node classification may deteriorate substantially by overlooking the pos- sibility of noisy data in graph structures, as models wrongly process the relation among nodes in the input graphs as the ground truth. Secondly, nodes and edges have different types in the real-world and it is essential to capture this heterogeneity in graph representation learning. Next, relations among nodes are not restricted to pairwise relations and it is necessary to capture the complex relations accordingly. Finally, the absence of structural encodings, such as positional information, deterio- rates the performance of GNNs. This thesis proposes novel methods to address the aforementioned problems: 1. Bayesian Graph Attention Network (BGAT): Developed for situations with scarce data, this method addresses the influence of spurious edges. Incor- porating Bayesian principles into the graph attention mechanism enhances robustness, leading to competitive performance against benchmarks (Chapter 3). 2. Neighbour Contrastive Heterogeneous Graph Attention Network (NC-HGAT): By enhancing a cutting-edge self-supervised heterogeneous graph neural net- work model (HGAT) with neighbour contrastive learning, this method ad- dresses heterogeneity and uncertainty simultaneously. Extra attention to edge relations in heterogeneous graphs also aids in subsequent classification tasks (Chapter 4). 3. A novel ensemble learning framework is introduced for predicting stock price movements. It adeptly captures both group-level and pairwise relations, lead- ing to notable advancements over the existing state-of-the-art. The integration of hypergraph and graph models, coupled with the utilisation of auxiliary data via GNNs before recurrent neural network (RNN), provides a deeper under- standing of long-term dependencies between similar entities in multivariate time series analysis (Chapter 5). 4. A novel framework for graph structure learning is introduced, segmenting graphs into distinct patches. By harnessing the capabilities of transformers and integrating other position encoding techniques, this approach robustly capture intricate structural information within a graph. This results in a more comprehensive understanding of its underlying patterns (Chapter 6)
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