Mining Behavior of Citizen Sensor Communities to Improve Cooperation with Organizational Actors

Web 2.0 (social media) provides a natural platform for dynamic emergence of citizen (as) sensor communities, where the citizens generate content for sharing information and engaging in discussions. Such a citizen sensor community (CSC) has stated or implied goals that are helpful in the work of formal organizations, such as an emergency management unit, for prioritizing their response needs. This research addresses questions related to design of a cooperative system of organizations and citizens in CSC. Prior research by social scientists in a limited offline and online environment has provided a foundation for research on cooperative behavior challenges, including \u27articulation\u27 and \u27awareness\u27, but Web 2.0 supported CSC offers new challenges as well as opportunities. A CSC presents information overload for the organizational actors, especially in finding reliable information providers (for awareness), and finding actionable information from the data generated by citizens (for articulation). Also, we note three data level challenges: ambiguity in interpreting unconstrained natural language text, sparsity of user behaviors, and diversity of user demographics. Interdisciplinary research involving social and computer sciences is essential to address these socio-technical issues. I present a novel web information-processing framework, called the Identify-Match- Engage (IME) framework. IME allows operationalizing computation in design problems of awareness and articulation of the cooperative system between citizens and organizations, by addressing data problems of group engagement modeling and intent mining. The IME framework includes: a.) Identification of cooperation-assistive intent (seeking-offering) from short, unstructured messages using a classification model with declarative, social and contrast pattern knowledge, b.) Facilitation of coordination modeling using bipartite matching of complementary intent (seeking-offering), and c.) Identification of user groups to prioritize for engagement by defining a content-driven measure of \u27group discussion divergence\u27. The use of prior knowledge and interplay of features of users, content, and network structures efficiently captures context for computing cooperation-assistive behavior (intent and engagement) from unstructured social data in the online socio-technical systems. Our evaluation of a use-case of the crisis response domain shows improvement in performance for both intent classification and group engagement prioritization. Real world applications of this work include use of the engagement interface tool during various recent crises including the 2014 Jammu and Kashmir floods, and intent classification as a service integrated by the crisis mapping pioneer Ushahidi\u27s CrisisNET project for broader impact

Third International Conference on Technologies for Music Notation and Representation TENOR 2017

The third International Conference on Technologies for Music Notation and Representation seeks to focus on a set of specific research issues associated with Music Notation that were elaborated at the first two editions of TENOR in Paris and Cambridge. The theme of the conference is vocal music, whereas the pre-conference workshops focus on innovative technological approaches to music notation

Logic, parallelism and semantic networks : the binary predicate execution model

This thesis develops the Binary Predicate Execution Model; a distributed, massively-parallel system for semantic networks and knowledge bases that is built on a subset of first-order predicate logic. The use of logic gives the model an easily-understood programming paradigm and a well-defined semantics of execution. When expressed in binary predicates, a simple graphical interpretation can be used. All program facts are represented in an assertion graph. Each vertex is associated with a term appearing in a fact and the edges are labeled with the predicate names. Similar graphs are also associated with each rule body and the query. Finding all possible solutions corresponds to finding all possible matches between the query graph and the assertion graph. Invoking a rule corresponds to substituting the graph of its body constrained by the dependencies between its arguments. This can be implemented in a parallel, message-passing fashion where the assertion graph vertices are active processing elements which asynchronously exchange messages identifying different parts of the query that remain to be matched and containing any binding information from previous matching required to accomplish this. The model is data-driven since every message can be immediately processed without the need for any centralized control or centralized memory. By restricting how functional terms can occur, distributed data structures and remote data look-ups for unification are eliminated. Thus, the model's performance on increasingly larger problems scales-up given increasingly larger machines in most cases. Architectural support for the model is investigated and simulation results of a relatively simple software implementation are reported. This suggests performance on the order of 10^5 logical inferences per second for 256 processing elements in an n-cube configuration. Further research directions, including that of increasing efficiency, are discussed

Unsupervised Induction of Frame-Based Linguistic Forms

This thesis studies the use of bulk, structured, linguistic annotations in order to perform unsupervised induction of meaning for three kinds of linguistic forms: words, sentences, and documents. The primary linguistic annotation I consider throughout this thesis are frames, which encode core linguistic, background or societal knowledge necessary to understand abstract concepts and real-world situations. I begin with an overview of linguistically-based structured meaning representation; I then analyze available large-scale natural language processing (NLP) and linguistic resources and corpora for their abilities to accommodate bulk, automatically-obtained frame annotations. I then proceed to induce meanings of the different forms, progressing from the word level, to the sentence level, and finally to the document level. I first show how to use these bulk annotations in order to better encode linguistic- and cognitive science backed semantic expectations within word forms. I then demonstrate a straightforward approach for learning large lexicalized and refined syntactic fragments, which encode and memoize commonly used phrases and linguistic constructions. Next, I consider two unsupervised models for document and discourse understanding; one is a purely generative approach that naturally accommodates layer annotations and is the first to capture and unify a complete frame hierarchy. The other conditions on limited amounts of external annotations, imputing missing values when necessary, and can more readily scale to large corpora. These discourse models help improve document understanding and type-level understanding

Learning discrete word embeddings to achieve better interpretability and processing efficiency

L’omniprésente utilisation des plongements de mot dans le traitement des langues naturellesest la preuve de leur utilité et de leur capacité d’adaptation a une multitude de tâches. Ce-pendant, leur nature continue est une importante limite en terme de calculs, de stockage enmémoire et d’interprétation. Dans ce travail de recherche, nous proposons une méthode pourapprendre directement des plongements de mot discrets. Notre modèle est une adaptationd’une nouvelle méthode de recherche pour base de données avec des techniques dernier crien traitement des langues naturelles comme les Transformers et les LSTM. En plus d’obtenirdes plongements nécessitant une fraction des ressources informatiques nécéssaire à leur sto-ckage et leur traitement, nos expérimentations suggèrent fortement que nos représentationsapprennent des unités de bases pour le sens dans l’espace latent qui sont analogues à desmorphèmes. Nous appelons ces unités dessememes, qui, de l’anglaissemantic morphemes,veut dire morphèmes sémantiques. Nous montrons que notre modèle a un grand potentielde généralisation et qu’il produit des représentations latentes montrant de fortes relationssémantiques et conceptuelles entre les mots apparentés.The ubiquitous use of word embeddings in Natural Language Processing is proof of theirusefulness and adaptivity to a multitude of tasks. However, their continuous nature is pro-hibitive in terms of computation, storage and interpretation. In this work, we propose amethod of learning discrete word embeddings directly. The model is an adaptation of anovel database searching method using state of the art natural language processing tech-niques like Transformers and LSTM. On top of obtaining embeddings requiring a fractionof the resources to store and process, our experiments strongly suggest that our representa-tions learn basic units of meaning in latent space akin to lexical morphemes. We call theseunitssememes, i.e., semantic morphemes. We demonstrate that our model has a greatgeneralization potential and outputs representation showing strong semantic and conceptualrelations between related words

Kodikologie und Paläographie im digitalen Zeitalter 2 - Codicology and Palaeography in the Digital Age 2

Der Einsatz digitaler Technik verändert den wissenschaftlichen Umgang mit der handgeschriebenen Überlieferung. Dieser Band vertieft Fragen zu Digitalisierung und Katalogisierung, zu automatischer Schrifterkennung und Schriftanalyse, und er erweitert eine Diskussion, die mit dem im letzten Jahr erschienenen ersten Band zur digitalen Handschriftenforschung angestossen worden ist: Welche Erkenntnisse können etwa naturwissenschaftliche Methoden liefern? Welche musik- und kunsthistorischen Fragestellungen lassen sich mit Hilfe moderner Informationstechnologien beantworten? Wie lassen sich Methoden einer digitalen Auswertung lateinischer Handschriften auf griechische, glagolithische oder ägyptische Texte anwenden? Der raum-zeitliche Rahmen der hier von einer internationalen Autorenschaft zusammengetragenen 22 wissenschaftlichen Beiträge reicht vom alten Ägypten bis ins Paris der Postmoderne. Mit Beiträgen von: Pádraig Ó Macháin; Armand Tif; Alison Stones, Ken Sochats; Melissa Terras; Silke Schöttle, Ulrike Mehringer; Marilena Maniaci, Paolo Eleuteri; Ezio Ornato; Toby Burrows; Robert Kummer; Lior Wolf, Nachum Dershowitz, Liza Potikha, Tanya German, Roni Shweka, Yacov Choueka; Daniel Deckers, Leif Glaser; Timothy Stinson; Peter Meinlschmidt, Carmen Kämmerer, Volker Märgner; Peter Stokes—Dominique Stutzmann; Stephen Quirke; Markus Diem, Robert Sablatnig, Melanie Gau, Heinz Miklas; Julia Craig-McFeely; Isabelle Schürch, Martin Rüesch; Carole Dornier, Pierre-Yves Buard; Samantha Saidi, Jean-François Bert, Philippe Artières; Elena Pierazzo, Peter Stokes. Einleitung von: Franz Fischer, Patrick Sahle. Unter Mitarbeit von: Bernhard Assmann, Malte Rehbein, Patrick Sahle

Mathematical linguistics

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Voicing Kinship with Machines: Diffractive Empathetic Listening to Synthetic Voices in Performance.

This thesis contributes to the field of voice studies by analyzing the design and production of synthetic voices in performance. The work explores six case studies, consisting of different performative experiences of the last decade (2010- 2020) that featured synthetic voice design. It focusses on the political and social impact of synthetic voices, starting from yet challenging the concepts of voice in the machine and voice of the machine. The synthetic voices explored are often playing the role of simulated artificial intelligences, therefore this thesis expands its questions towards technology at large. The analysis of the case studies follows new materialist and posthumanist premises, yet it tries to confute the patriarchal and neoliberal approach towards technological development through feminist and de-colonial approaches, developing a taxonomy for synthetic voices in performance. Chapter 1 introduces terms and explains the taxonomy. Chapter 2 looks at familiar representations of fictional AI. Chapter 3 introduces headphone theatre exploring immersive practices. Chapters 4 and 5 engage with chatbots. Chapter 6 goes in depth exploring Human and Artificial Intelligence interaction, whereas chapter 7 moves slightly towards music production and live art. The body of the thesis includes the work of Pipeline Theatre, Rimini Protokoll, Annie Dorsen, Begüm Erciyas, and Holly Herndon. The analysis is informed by posthumanism, feminism, and performance studies, starting from my own practice as sound designer and singer, looking at aesthetics of reproduction, audience engagement, and voice composition. This thesis has been designed to inspire and provoke practitioners and scholars to explore synthetic voices further, question predominant biases of binarism and acknowledge their importance in redefining technology

Stranger Compass of the Stage: Difference and Desire in Early Modern City Comedy

In periods of social and political upheaval like ours, it is more important than ever to interrogate constructions of identity and difference and to understand the histories of alterity that separate us from one another. Stranger Compass of the Stage: Difference and Desire in Early Modern City Drama reimagines the cultural and social effect of alien, foreign, and stranger characters on the early modern stage and re-envisions how these characters contribute to, alter, and imaginatively build new epistemologies for understanding difference in early modern London. Resisting the field’s current critical inclination toward English identity formation, this project works intersectionally to exhume the delicate cultural and theatrical networks in which difference was negotiated. In doing so, it rescopes the limits of what counts as difference in the period. Stranger Compass addresses fundamental questions of how early modern theater navigated difference on the stage by looking to four areas of performed difference: geographic and social difference, sexual difference, physical difference/disability, and gender non-conformance. Each chapter focuses on one of these areas, and each chapter is treated with a similar analytical framework that draws on transformation and desire as socially constitutive forces. Rescoping the cultural and theatrical landscape of London allows this project to begin with geographic and social difference and to work ever closer to negotiations of individual difference in the theatrical space. Ultimately, Stranger Compass brings together methodologies that demonstrate how theatrical performance stimulated audience members to engage, participate, and revise their intimate attitudes toward difference. Looking to Thomas Middleton’s Michaelmas Term (1604) and A Trick to Catch the Old One (1605), the anonymously authored Fair Maid of the Exchange (1607), Thomas Dekker and Thomas Middleton’s The Roaring Girl (1607/10), and Ben Jonson’s Every Man in His Humour (1616 folio), I highlight that figures of difference are also often figures of familiarity and locality, who were integral to London’s most basic social relationships as a growing city with a malleable culture. Vital in their difference and desirable in their tangible divergence, the characters in these works call on us to reconsider difference, identity, and desire