9,212 research outputs found

    Documenting Knowledge Graph Embedding and Link Prediction using Knowledge Graphs

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    In recent years, sub-symbolic learning, i.e., Knowledge Graph Embedding (KGE) incorporated with Knowledge Graphs (KGs) has gained significant attention in various downstream tasks (e.g., Link Prediction (LP)). These techniques learn a latent vector representation of KG's semantical structure to infer missing links. Nonetheless, the KGE models remain a black box, and the decision-making process behind them is not clear. Thus, the trustability and reliability of the model's outcomes have been challenged. While many state-of-the-art approaches provide data-driven frameworks to address these issues, they do not always provide a complete understanding, and the interpretations are not machine-readable. That is why, in this work, we extend a hybrid interpretable framework, InterpretME, in the field of the KGE models, especially for translation distance models, which include TransE, TransH, TransR, and TransD. The experimental evaluation on various benchmark KGs supports the validity of this approach, which we term Trace KGE. Trace KGE, in particular, contributes to increased interpretability and understanding of the perplexing KGE model's behavior

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Location Reference Recognition from Texts: A Survey and Comparison

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    A vast amount of location information exists in unstructured texts, such as social media posts, news stories, scientific articles, web pages, travel blogs, and historical archives. Geoparsing refers to recognizing location references from texts and identifying their geospatial representations. While geoparsing can benefit many domains, a summary of its specific applications is still missing. Further, there is a lack of a comprehensive review and comparison of existing approaches for location reference recognition, which is the first and core step of geoparsing. To fill these research gaps, this review first summarizes seven typical application domains of geoparsing: geographic information retrieval, disaster management, disease surveillance, traffic management, spatial humanities, tourism management, and crime management. We then review existing approaches for location reference recognition by categorizing these approaches into four groups based on their underlying functional principle: rule-based, gazetteer matching–based, statistical learning-–based, and hybrid approaches. Next, we thoroughly evaluate the correctness and computational efficiency of the 27 most widely used approaches for location reference recognition based on 26 public datasets with different types of texts (e.g., social media posts and news stories) containing 39,736 location references worldwide. Results from this thorough evaluation can help inform future methodological developments and can help guide the selection of proper approaches based on application needs

    Modular lifelong machine learning

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    Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge. Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand. This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems. First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures. Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations. Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods. Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer

    How Different Is Stereotypical Bias Across Languages?

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    Recent studies have demonstrated how to assess the stereotypical bias in pre-trained English language models. In this work, we extend this branch of research in multiple different dimensions by systematically investigating (a) mono- and multilingual models of (b) different underlying architectures with respect to their bias in (c) multiple different languages. To that end, we make use of the English StereoSet data set (Nadeem et al., 2021), which we semi-automatically translate into German, French, Spanish, and Turkish. We find that it is of major importance to conduct this type of analysis in a multilingual setting, as our experiments show a much more nuanced picture as well as notable differences from the English-only analysis. The main takeaways from our analysis are that mGPT-2 (partly) shows surprising anti-stereotypical behavior across languages, English (monolingual) models exhibit the strongest bias, and the stereotypes reflected in the data set are least present in Turkish models. Finally, we release our codebase alongside the translated data sets and practical guidelines for the semi-automatic translation to encourage a further extension of our work to other languages.Comment: Accepted @ "3rd Workshop on Bias and Fairness in AI" (co-located with ECML PKDD 2023). This is the author's version of the work. The definite version of record will be published in the proceeding

    The Globalization of Artificial Intelligence: African Imaginaries of Technoscientific Futures

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    Imaginaries of artificial intelligence (AI) have transcended geographies of the Global North and become increasingly entangled with narratives of economic growth, progress, and modernity in Africa. This raises several issues such as the entanglement of AI with global technoscientific capitalism and its impact on the dissemination of AI in Africa. The lack of African perspectives on the development of AI exacerbates concerns of raciality and inclusion in the scientific research, circulation, and adoption of AI. My argument in this dissertation is that innovation in AI, in both its sociotechnical imaginaries and political economies, excludes marginalized countries, nations and communities in ways that not only bar their participation in the reception of AI, but also as being part and parcel of its creation. Underpinned by decolonial thinking, and perspectives from science and technology studies and African studies, this dissertation looks at how AI is reconfiguring the debate about development and modernization in Africa and the implications for local sociotechnical practices of AI innovation and governance. I examined AI in international development and industry across Kenya, Ghana, and Nigeria, by tracing Canada’s AI4D Africa program and following AI start-ups at AfriLabs. I used multi-sited case studies and discourse analysis to examine the data collected from interviews, participant observations, and documents. In the empirical chapters, I first examine how local actors understand the notion of decolonizing AI and show that it has become a sociotechnical imaginary. I then investigate the political economy of AI in Africa and argue that despite Western efforts to integrate the African AI ecosystem globally, the AI epistemic communities in the continent continue to be excluded from dominant AI innovation spaces. Finally, I examine the emergence of a Pan-African AI imaginary and argue that AI governance can be understood as a state-building experiment in post-colonial Africa. The main issue at stake is that the lack of African perspectives in AI leads to negative impacts on innovation and limits the fair distribution of the benefits of AI across nations, countries, and communities, while at the same time excludes globally marginalized epistemic communities from the imagination and creation of AI

    An Exploration of the Suitability of Pharmacy Education in Saudi Arabia to Prepare Graduates to Meet Healthcare Needs: a Mixed-Methods Study

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    The key role of pharmacists within the health system, particularly in optimising safe, responsible and effective use of medicines, underpins the demand for a highly skilled and competent workforce. Therefore, developing the capacity of pharmacists to attain and maintain essential competencies relevant to the population’s health needs is required to ensure a high standard of patient care, thereby helping to improve patient and population health. In Saudi Arabia, little evidence exists regarding the assessment of national educational programmes’ structure and outcomes. Moreover, no national competency framework exists for pharmacists in any sector or stage of practice. In the absence of such core quality elements to inform pharmacy education assessment and development, the extent to which pharmacy schools in Saudi Arabia prepare competent pharmacists to address societal needs from pharmacy services is unclear. Therefore, this study aimed to explore the extent to which pharmacy education can prepare competent pharmacists to address the healthcare needs for pharmacy practice in Saudi Arabia. An exploratory sequential mixed methods research design was used to address the aim of this study in three phases: individual interviews and focus groups were employed with a purposively selected sample of pharmacy policy makers, pharmacists and the public to explore societal healthcare needs and the roles required of pharmacists to meet those needs; a national online survey of pharmacists and an online nominal group consensus method of pharmacy experts were used to identify competencies considered essential to develop a profession-wide national foundation level competency framework; and a case study in which curriculum mapping of two purposively selected Doctor of Pharmacy (PharmD) curricula was used to assess the extent to which the current pharmacy programme in Saudi Arabia meets the identified competencies of the developed national competency framework. Based on qualitative and quantitative analyses of societal healthcare needs, pharmacists’ roles, core competencies and curricular contents within the local context of Saudi Arabia, findings showed that there is a mismatch between initial education and real practice needs and expectations. While the country’s current needs from pharmacists are to optimise health system capacity and increase access to primary care services and medicines expertise in community pharmacies, the study indicated local education is product-oriented with a focus of curricular content and experiential training opportunities in most schools on preparing future pharmacists for hospital pharmacy practice. The study also identified several gaps between current initial education programmes and the competencies required to practise the expected roles, suggesting that current initial education might not prepare the students sufficiently to provide the full range of quality pharmaceutical services as per the country’s pharmacy practice needs. The study provided a new understanding of graduates’ readiness to practise as per the country’s pharmacy practice needs, the quality of educational programmes and pharmacists' professional development opportunities in Saudi Arabia. Findings maybe used to inform the development of competency-based education and maximise graduates’ capacity to deliver and develop pharmaceutical services effectively to best meet societal healthcare needs in Saudi Arabia

    The Effects of Different Types of Unfocused Corrective Feedback on Complexity, Accuracy and Fluency in L2 English Academic Writing

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    Research on written corrective feedback in second language (L2) learning has made progress, answering the unknowns regarding its effectiveness. Currently, debate focuses on the most effective way of giving feedback. Controversy, however, remains and there is a scarcity of research on unfocused feedback. The present study examines the effects of unfocused direct, indirect and metalinguistic written corrective feedback (WCF) on the complexity, accuracy and fluency (CAF) of 139 L1 Arabic or Urdu – L2 English students' writing. The study also investigates if the moderating variables of aptitude, attitudes and proficiency affect the uptake of feedback. Students in four intact groups were designated as feedback groups, plus one control group. They wrote argument essays and were given four rounds of feedback and feedback support sessions over fourteen weeks; whereas learners in the control group received no feedback or support sessions. Students wrote both text revisions and new texts. Results showed that on text revisions, the direct and metalinguistic feedback groups had losses in fluency compared to the indirect and control groups. The indirect feedback group had significantly lower lexical diversity than the direct and metalinguistic groups. On new texts, there were no significant gains or losses from the unfocused feedback. The moderating variables of proficiency and aptitude had no significant relationships with CAF gains or losses, but positive attitudes towards feedback had a negative relationship with gains in complexity and fluency on text revisions. These results reveal that on text revisions, some forms of unfocused feedback have effects on fluency and lexical diversity, but on new texts there are no effects. Future work should examine if increasing the number of treatment sessions has positive effects on CAF, and discover at what point unfocused WCF may become too cognitively demanding. The results provide useful information for practitioners who could use a more blended approach between focused and unfocused WCF and increase the treatment sessions

    Measuring the Severity of Depression from Text using Graph Representation Learning

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    The common practice of psychology in measuring the severity of a patient's depressive symptoms is based on an interactive conversation between a clinician and the patient. In this dissertation, we focus on predicting a score representing the severity of depression from such a text. We first present a generic graph neural network (GNN) to automatically rate severity using patient transcripts. We also test a few sequence-based deep models in the same task. We then propose a novel form for node attributes within a GNN-based model that captures node-specific embedding for every word in the vocabulary. This provides a global representation of each node, coupled with node-level updates according to associations between words in a transcript. Furthermore, we evaluate the performance of our GNN-based model on a Twitter sentiment dataset to classify three different sentiments and on Alzheimer's data to differentiate Alzheimer’s disease from healthy individuals respectively. In addition to applying the GNN model to learn a prediction model from the text, we provide post-hoc explanations of the model's decisions for all three tasks using the model's gradients
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