10,439 research outputs found

    Knowledge Graph Building Blocks: An easy-to-use Framework for developing FAIREr Knowledge Graphs

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    Knowledge graphs and ontologies provide promising technical solutions for implementing the FAIR Principles for Findable, Accessible, Interoperable, and Reusable data and metadata. However, they also come with their own challenges. Nine such challenges are discussed and associated with the criterion of cognitive interoperability and specific FAIREr principles (FAIR + Explorability raised) that they fail to meet. We introduce an easy-to-use, open source knowledge graph framework that is based on knowledge graph building blocks (KGBBs). KGBBs are small information modules for knowledge-processing, each based on a specific type of semantic unit. By interrelating several KGBBs, one can specify a KGBB-driven FAIREr knowledge graph. Besides implementing semantic units, the KGBB Framework clearly distinguishes and decouples an internal in-memory data model from data storage, data display, and data access/export models. We argue that this decoupling is essential for solving many problems of knowledge management systems. We discuss the architecture of the KGBB Framework as we envision it, comprising (i) an openly accessible KGBB-Repository for different types of KGBBs, (ii) a KGBB-Engine for managing and operating FAIREr knowledge graphs (including automatic provenance tracking, editing changelog, and versioning of semantic units); (iii) a repository for KGBB-Functions; (iv) a low-code KGBB-Editor with which domain experts can create new KGBBs and specify their own FAIREr knowledge graph without having to think about semantic modelling. We conclude with discussing the nine challenges and how the KGBB Framework provides solutions for the issues they raise. While most of what we discuss here is entirely conceptual, we can point to two prototypes that demonstrate the principle feasibility of using semantic units and KGBBs to manage and structure knowledge graphs

    Endogenous measures for contextualising large-scale social phenomena: a corpus-based method for mediated public discourse

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    This work presents an interdisciplinary methodology for developing endogenous measures of group membership through analysis of pervasive linguistic patterns in public discourse. Focusing on political discourse, this work critiques the conventional approach to the study of political participation, which is premised on decontextualised, exogenous measures to characterise groups. Considering the theoretical and empirical weaknesses of decontextualised approaches to large-scale social phenomena, this work suggests that contextualisation using endogenous measures might provide a complementary perspective to mitigate such weaknesses. This work develops a sociomaterial perspective on political participation in mediated discourse as affiliatory action performed through language. While the affiliatory function of language is often performed consciously (such as statements of identity), this work is concerned with unconscious features (such as patterns in lexis and grammar). This work argues that pervasive patterns in such features that emerge through socialisation are resistant to change and manipulation, and thus might serve as endogenous measures of sociopolitical contexts, and thus of groups. In terms of method, the work takes a corpus-based approach to the analysis of data from the Twitter messaging service whereby patterns in users’ speech are examined statistically in order to trace potential community membership. The method is applied in the US state of Michigan during the second half of 2018—6 November having been the date of midterm (i.e. non-Presidential) elections in the United States. The corpus is assembled from the original posts of 5,889 users, who are nominally geolocalised to 417 municipalities. These users are clustered according to pervasive language features. Comparing the linguistic clusters according to the municipalities they represent finds that there are regular sociodemographic differentials across clusters. This is understood as an indication of social structure, suggesting that endogenous measures derived from pervasive patterns in language may indeed offer a complementary, contextualised perspective on large-scale social phenomena

    An exploration of the language within Ofsted reports and their influence on primary school performance in mathematics: a mixed methods critical discourse analysis

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    This thesis contributes to the understanding of the language of Ofsted reports, their similarity to one another and associations between different terms used within ‘areas for improvement’ sections and subsequent outcomes for pupils. The research responds to concerns from serving headteachers that Ofsted reports are overly similar, do not capture the unique story of their school, and are unhelpful for improvement. In seeking to answer ‘how similar are Ofsted reports’ the study uses two tools, a plagiarism detection software (Turnitin) and a discourse analysis tool (NVivo) to identify trends within and across a large corpus of reports. The approach is based on critical discourse analysis (Van Dijk, 2009; Fairclough, 1989) but shaped in the form of practitioner enquiry seeking power in the form of impact on pupils and practitioners, rather than a more traditional, sociological application of the method. The research found that in 2017, primary school section 5 Ofsted reports had more than half of their content exactly duplicated within other primary school inspection reports published that same year. Discourse analysis showed the quality assurance process overrode variables such as inspector designation, gender, or team size, leading to three distinct patterns of duplication: block duplication, self-referencing, and template writing. The most unique part of a report was found to be the ‘area for improvement’ section, which was tracked to externally verified outcomes for pupils using terms linked to ‘mathematics’. Those required to improve mathematics in their areas for improvement improved progress and attainment in mathematics significantly more than national rates. These findings indicate that there was a positive correlation between the inspection reporting process and a beneficial impact on pupil outcomes in mathematics, and that the significant similarity of one report to another had no bearing on the usefulness of the report for school improvement purposes within this corpus

    Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules

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    We target the problem of automatically synthesizing proofs of semantic equivalence between two programs made of sequences of statements. We represent programs using abstract syntax trees (AST), where a given set of semantics-preserving rewrite rules can be applied on a specific AST pattern to generate a transformed and semantically equivalent program. In our system, two programs are equivalent if there exists a sequence of application of these rewrite rules that leads to rewriting one program into the other. We propose a neural network architecture based on a transformer model to generate proofs of equivalence between program pairs. The system outputs a sequence of rewrites, and the validity of the sequence is simply checked by verifying it can be applied. If no valid sequence is produced by the neural network, the system reports the programs as non-equivalent, ensuring by design no programs may be incorrectly reported as equivalent. Our system is fully implemented for a given grammar which can represent straight-line programs with function calls and multiple types. To efficiently train the system to generate such sequences, we develop an original incremental training technique, named self-supervised sample selection. We extensively study the effectiveness of this novel training approach on proofs of increasing complexity and length. Our system, S4Eq, achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent programsComment: 30 pages including appendi

    Examples of works to practice staccato technique in clarinet instrument

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    Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır. Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur. Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir. Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır. Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    A Case Study Examining Japanese University Students' Digital Literacy and Perceptions of Digital Tools for Academic English learning

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    Current Japanese youth are constantly connected to the Internet and using digital devices, but predominantly for social media and entertainment. According to literature on the Japanese digital native, tertiary students do not—and cannot—use technology with any reasonable fluency, but the likely reasons are rarely addressed. To fill the gap in the literature, this study, by employing a case study methodology, explores students’ experience with technology for English learning through the introduction of digital tools. First-year Japanese university students in an Academic English Program (AEP) were introduced to a variety of easily available digital tools. The instruction was administered online, and each tool was accompanied by a task directly related to classwork. Both quantitative and qualitative data were collected in the form of a pre-course Computer Literacy Survey, a post-course open-ended Reflection Activity survey, and interviews. The qualitative data was reviewed drawing on the Technology Acceptance Model (TAM) and its educational variants as an analytical framework. Educational, social, and cultural factors were also examined to help identify underlying factors that would influence students’ perceptions. The results suggest that the subjects’ lack of awareness of, and experience with, the use of technology for learning are the fundamental causes of their perceptions of initial difficulty. Based on these findings, this study proposes a possible technology integration model that enhances digital literacy for more effective language learning in the context of Japanese education

    The interpretation of Islam and nationalism by the elite through the English language media in Pakistan.

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    The media is constructed and interpreted through what people 'know'. That knowledge is, forthe most part, created through day to day experiences. In Pakistan, Islam and nationalism aretwo components of this social knowledge which are intrinsically tied to the experiences of thePakistani people. Censorship and selection are means through which this knowledge isarticulated and interpreted.General conceptions of partially shared large scale bodies of knowledge and ideas reinforce,and are reinforced by, general medium of mass communication: the print and electronic media.Focusing on the govermnent, media institutions and Pakistani elites, I describe and analyse thedifferent, sometimes conflicting, interpretations of Islam and Pakistani nationalism manifest inand through media productions presented in Pakistan.The media means many things, not least of which is power. It is the media as a source ofpower that is so frequently controlled, directed and manipulated. The terminology may beslightly different according to the context within which one is talking - propaganda, selection,etc. - but ultimately it comes down to the same thing - censorship. Each of the three groups:government, media institutions and Pakistani elites - have the power to interpret and censormedia content and consideration must be taken of each of the other power holders consequentlyrestricting the power of each group in relation to the other two. The processes of thismanipulation and their consequences form the major themes of this thesis

    Machine learning for managing structured and semi-structured data

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    As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen
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