8,306 research outputs found

    Tourism and heritage in the Chornobyl Exclusion Zone

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    Tourism and Heritage in the Chornobyl Exclusion Zone (CEZ) uses an ethnographic lens to explore the dissonances associated with the commodification of Chornobyl's heritage. The book considers the role of the guides as experience brokers, focusing on the synergy between tourists and guides in the performance of heritage interpretation. Banaszkiewicz proposes to perceive tour guides as important actors in the bottom-up construction of heritage discourse contributing to more inclusive and participatory approach to heritage management. Demonstrating that the CEZ has been going through a dynamic transformation into a mass tourism attraction, the book offers a critical reflection on heritagisation as a meaning-making process in which the resources of the past are interpreted, negotiated, and recognised as a valuable legacy. Applying the concepts of dissonant heritage to describe the heterogeneous character of the CEZ, the book broadens the interpretative scope of dark tourism which takes on a new dimension in the context of the war in Ukraine. Tourism and Heritage in the Chornobyl Exclusion Zone argues that post-disaster sites such as Chornobyl can teach us a great deal about the importance of preserving cultural and natural heritage for future generations. The book will be of interest to academics and students who are engaged in the study of heritage, tourism, memory, disasters and Eastern Europe

    Comedians without a Cause: The Politics and Aesthetics of Humour in Dutch Cabaret (1966-2020)

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    Comedians play an important role in society and public debate. While comedians have been considered important cultural critics for quite some time, comedy has acquired a new social and political significance in recent years, with humour taking centre stage in political and social debates around issues of identity, social justice, and freedom of speech. To understand the shifting meanings and political implications of humour within a Dutch context, this PhD thesis examines the political and aesthetic workings of humour in the highly popular Dutch cabaret genre, focusing on cabaret performances from the 1960s to the present. The central questions of the thesis are: how do comedians use humour to deliver social critique, and how does their humour resonate with political ideologies? These questions are answered by adopting a cultural studies approach to humour, which is used to analyse Dutch cabaret performances, and by studying related materials such as reviews and media interviews with comedians. This thesis shows that, from the 1960s onwards, Dutch comedians have been considered ‘progressive rebels’ – politically engaged, subversive, and carrying a left-wing political agenda – but that this image is in need of correction. While we tend to look for progressive political messages in the work of comedians who present themselves as being anti-establishment rebels – such as Youp van ‘t Hek, Hans Teeuwen, and Theo Maassen – this thesis demonstrates that their transgressive and provocative humour tends to protect social hierarchies and relationships of power. Moreover, it shows that, paradoxically, both the deliberately moderate and nuanced humour of Wim Kan and Claudia de Breij, and the seemingly past-oriented nostalgia of Alex Klaasen, are more radical and progressive than the transgressive humour of van ‘t Hek, Teeuwen and Maassen. Finally, comedians who present absurdist or deconstructionist forms of humour, such as the early student cabarets, Freek de Jonge, and Micha Wertheim, tend to disassociate themselves from an explicit political engagement. By challenging the dominant image of the Dutch comedian as a ‘progressive rebel,’ this thesis contributes to a better understanding of humour in the present cultural moment, in which humour is often either not taken seriously, or one-sidedly celebrated as being merely pleasurable, innocent, or progressively liberating. In so doing, this thesis concludes, the ‘dark’ and more conservative sides of humour tend to get obscured

    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

    A novel Auto-ML Framework for Sarcasm Detection

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    Many domains have sarcasm or verbal irony presented in the text of reviews, tweets, comments, and dialog discussions. The purpose of this research is to classify sarcasm for multiple domains using the deep learning based AutoML framework. The proposed AutoML framework has five models in the model search pipeline, these five models are the combination of convolutional neural network (CNN), Long Short-Term Memory (LSTM), deep neural network (DNN), and Bidirectional Long Short-Term Memory (BiLSTM). The hybrid combination of CNN, LSTM, and DNN models are presented as CNN-LSTM-DNN, LSTM-DNN, BiLSTM-DNN, and CNN-BiLSTM-DNN. This work has proposed the algorithms that contrast polarities between terms and phrases, which are categorized into implicit and explicit incongruity categories. The incongruity and pragmatic features like punctuation, exclamation marks, and others integrated into the AutoML DeepConcat framework models. That integration was possible when the DeepConcat AutoML framework initiate a model search pipeline for five models to achieve better performance. Conceptually, DeepConcat means that model will integrate with generalized features. It was evident that the pretrain model BiLSTM achieved a better performance of 0.98 F1 when compared with the other five model performances. Similarly, the AutoML based BiLSTM-DNN model achieved the best performance of 0.98 F1, which is better than core approaches and existing state-of-the-art Tweeter tweet dataset, Amazon reviews, and dialog discussion comments. The proposed AutoML framework has compared performance metrics F1 and AUC and discovered that F1 is better than AUC. The integration of all feature categories achieved a better performance than the individual category of pragmatic and incongruity features. This research also evaluated the performance of the dropout layer hyperparameter and it achieved better performance than the fixed percentage like 10% of dropout parameter of the AutoML based Bayesian optimization. Proposed AutoML framework DeepConcat evaluated best pretrain models BiLSTM-DNN and CNN-CNN-DNN to transfer knowledge across domains like Amazon reviews and Dialog discussion comments (text) using the last strategy, full layer, and our fade-out freezing strategies. In the transfer learning fade-out strategy outperformed the existing state-of-the-art model BiLSTM-DNN, the performance is 0.98 F1 on tweets, 0.85 F1 on Amazon reviews, and 0.87 F1 on the dialog discussion SCV2-Gen dataset. Further, all strategies with various domains can be compared for the best model selection

    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

    AIUCD 2022 - Proceedings

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    L’undicesima edizione del Convegno Nazionale dell’AIUCD-Associazione di Informatica Umanistica ha per titolo Culture digitali. Intersezioni: filosofia, arti, media. Nel titolo è presente, in maniera esplicita, la richiesta di una riflessione, metodologica e teorica, sull’interrelazione tra tecnologie digitali, scienze dell’informazione, discipline filosofiche, mondo delle arti e cultural studies

    Chinese Benteng Women’s Participation in Local Development Affairs in Indonesia: Appropriate means for struggle and a pathway to claim citizen’ right?

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    It had been more than two decades passing by aftermath the devastating Asia’s Financial Crisis in 1997, subsequently followed by Suharto’s step down from his presidential throne which he occupied for more than three decades. The financial turmoil turned to a political disaster furthermore has led to massive looting that severely impacted Indonesians of Chinese descendant, including unresolved mystery of the most atrocious sexual violation against women and covert killings of students and democracy activists in this country. Since then, precisely aftermath May 1998, which publicly known as “Reformasi”1, Indonesia underwent political reform that eventually corresponded positively to its macroeconomic growth. Twenty years later, in 2018, Indonesia captured worldwide attention because it has successfully hosted two internationally renowned events, namely the Asian Games 2018 – the most prestigious sport events in Asia – conducted in Jakarta and Palembang; and the IMF/World Bank Annual Meeting 2018 in Bali. Particularly in the IMF/World Bank Annual Meeting, this event has significantly elevated Indonesia’s credibility and international prestige in the global economic powerplay as one of the nations with promising growth and openness. However, the narrative about poverty and inequality, including increasing racial tension, religious conservatism, and sexual violation against women are superseded by friendly climate for foreign investment and eventually excessive glorification of the nation’s economic growth. By portraying the image of promising new economic power, as rhetorically promised by President Joko Widodo during his presidential terms, Indonesia has swept the growing inequality in this highly stratified society that historically compounded with religious and racial tension under the carpet of digital economy.Arte y Humanidade

    Extraction of transforming sequences and sentence histories from writing process data : a first step towards linguistic modeling of writing

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    Online first, part of special issue "Methods for understanding writing process by analysis of writing timecourse" Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)Producing written texts is a non-linear process: in contrast to speech, writers are free to change already written text at any place at any point in time. Linguistic considerations are likely to play an important role, but so far, no linguistic models of the writing process exist. We present an approach for the analysis of writing processes with a focus on linguistic structures based on the novel concepts of transforming sequences, text history, and sentence history. The processing of raw keystroke logging data and the application of natural language processing tools allows for the extraction and filtering of product and process data to be stored in a hierarchical data structure. This structure is used to re-create and visualize the genesis and history for a text and its individual sentences. Focusing on sentences as primary building blocks of written language and full texts, we aim to complement established writing process analyses and, ultimately, to interpret writing timecourse data with respect to linguistic structures. To enable researchers to explore this view, we provide a fully functional implementation of our approach as an open-source software tool and visualizations of the results. We report on a small scale exploratory study in German where we used our tool. The results indicate both the feasibility of the approach and that writers actually revise on a linguistic level. The latter confirms the need for modeling written text production from the perspective of linguistic structures beyond the word level

    Graphical scaffolding for the learning of data wrangling APIs

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    In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction
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