305,912 research outputs found
A systematic analysis of user experience dimensions for interactive digital narratives
No full textProviding intelligent feedback to aid authoring has been proposed as a way to speed up authoring, give the author more control, and to enable the authoring of more complex interactive narratives. However, there is little research investigating what concrete feedback items would be useful for interactive digital narrative (IDN) creators. In this paper, we discuss potentially useful feedback items in relation to authoring goals and concerns. We perform a systematic literature review to make a list of concrete feedback items of interest related to the most emphasised concern of authoring - the effect of the interactive narrative on the user. We identify 47 User Experience (UX) dimensions in the IDN literature that could serve as useful feedback items, covering 8 categories - Agency, Cognition, Immersion, Affect, Drama, Rewards, Motivation and Dissonance. This list combines and untangles how different IDN researchers have interpreted and expressed interest in the complex idea of UX in the past decade and gives us insight into what concrete aspects of UX might be useful to estimate via automated feedback
Translating Global Vision into Local Reality: Building the Right Knowledge and Policy Infrastructure to Support Local Sustainable Development: SDSN Contribution to the U20 Process, Buenos Aires
No full textTranslating Global Vision into Local Reality: Building the right knowledge and policy infrastructure to support local sustainable development” is a White Paper prepared by SDSN as a voluntary contribution to enrich the discussions of the Urban 20 process
An investigation into NLP techniques for generating intelligent narrative feedback to support IDN authoring
No full textAuthoring Interactive Digital Narratives (IDN) is challenging since past a certain size, it becomes hard to keep track of the user’s experience along all the different storylines. Natural Language Processing (NLP) provides us with the opportunity to generate such intelligent feedback that can help authors keep better track of the story space. This is what this PhD addresses. In the first phase a systematic review of IDN literature is performed and list of User experience (UX) dimensions that could form the basis of feedback to authors is compiled. The second phase then maps these onto related areas of NLP research to see where these could be estimated automatically. This reveals 47 dimensions of UX covering 8 categories—23 of these map to 12 areas of NLP research, leading on to 5 specific examples of how they might help IDN authors: plotting emotional arcs, visualising emotion type and intensity, revealing the predictability of events, debugging internal story logic, and branch-wise summarization. One of these NLP areas (Automatic Text Summarisation) is chosen for deeper investigation in Phase 3. A dataset is generated by simulating playthroughs of eight episodes from two narrative games - Before the Storm and Wolf Among Us using fan-created transcripts online. Annotations for extractive summarisation were created automatically by aligning extracts with fan-made abstractive summaries available online. The dataset is released as open source for future researchers to train and test their approaches for IDN text. On applying common baseline extractive text summarization approaches to this dataset, several shortcomings in standard approaches are revealed when applied to narrative and interactive narrative datasets. The last phase of this work experiments with using rationale-based learning with word-level and sentence-level rationales indicating the proximity of words and sentences to choice points. The results indicate that rationale-based learning can improve the ability of attention-based text summarisation models to create higher quality summaries that encode key narrative information better suggesting a promising new direction for narrative-based text summarisation models. In this way, this thesis takes a step toward generating authoring feedback to assist IDN authors as well as understanding the complexities and unique challenges posed by the domain
Democracia e administração da justiça
No full textDissertação (mestrado) - Universidade Federal de Santa Catarina. Centro de Ciencias JuridicasO presente trabalho tem como objetivo analisar criticamente os princípios da Democracia representativa que inspiraram as práticas institucionais da administração da justiça, bem como as possibilidades ou não de uma justiça democrática que implique num maior controle e participação da sociedade civil. Assim, após os delineamentos históricos da Democracia, a natureza da expressão e conteúdo, bem como seus princípios básicos, busca-se demarcar, de um lado, os liames entre o Liberalismo clássico e a Democracia moderna e, de outro, a insuficiência de seus pressupostos na inter-relação com o Estado e o Direito moderno. Avançando, aborda-se os aspectos históricos-críticos da Divisão dos Poderes, enquanto um dos fundamentos nucleares da Democracia representativa. Tal questionamento privilegia a função judicial que, mesmo sendo considerada um poder separado e independente, na verdade, não tem sido independente, já que o aparato jurisdicional, enquanto instância de poder, constitui sempre uma articulação do que é dominante na sociedade. Por fim, tratar-se-á das insuficiências do Poder Judiciário que é reflexo de uma crise maior de natureza política, pois está em crise o paradigma de Democracia representativa, que sempre ofereceu uma aparência de racionalidade à expropriação política da sociedade e ao domínio de fato da classe economicamente dominante. A pesquisa ressalta a vinculação necessária entre administração da Justiça e valores democráticos, bem como os critérios para uma atividade judicial democrática. Em síntese, a implementação de práticas e disposições relativas à Democracia participativa na esfera da adminstração da Justiça determina, por um lado, uma nova postura dos operadores jurídicos nos tribunais e, por outro, com a descentralização, apresenta-se como a possibilidade de se estabelecer uma nova relação entre sociedade civil e sociedade política
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Dispelling the Myths Behind First-author Citation Counts
No full textWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
IDN-Sum:a new dataset for interactive digital narrative extractive text summarisation
No full textSummarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text summarization algorithms. Our dataset is generated using random playthroughs of 8 IDN episodes, taken from 2 different IDN games, and consists of 10,000 documents. Playthrough documents are annotated through automatic alignment with fan-sourced summaries using a commonly used alignment algorithm. We also report and discuss results from experiments applying common baseline extractive text summarization algorithms to this dataset. Qualitative analysis of the results reveal shortcomings in common annotation approaches and evaluation methods when applied to narrative and interactive narrative datasets. The dataset is released as open source for future researchers to train and test their own approaches for IDN text
Rationale-based learning using self-supervised narrative events for text summarisation of interactive digital narratives
No full textThis paper explores using rationale-based learning with supervised attention to focus the training of text summarisation models on words and sentences surrounding choice points for Interactive Digital Narratives (IDNs). IDNs allow players to interact with the story via choice points, making choices central to these narratives. Exploiting such knowledge about narrative structure during model training can help ensure key narrative information appears in generated summaries of narrative-based text and thus improve the quality of these summaries. We experiment with using word-level and sentence-level rationales indicating the proximity of words and sentences to self-supervised choice points. Our results indicate that rationale-based learning can improve the ability of attention-based text summarisation models to create higher quality summaries that encode key narrative information better for different playthroughs of the same interactive narrative. These results suggest a promising new direction for narrative-based text summarisation models
IDN-Sum: Novel dataset for interactive digital narrative extractive text summarisation
No full textSummarizing Interactive Digital Narratives (IDN) presents some unique challenges to existing text summarization models especially around capturing interactive elements in addition to important plot points. In this paper we describe the first IDN dataset (IDN-Sum) designed specifically for training and testing IDN text summarization algorithms. Our dataset is generated using random playthroughs of 8 IDN episodes, taken from 2 different IDN games, and consists of 10,000 documents. Playthrough documents are annotated through automatic alignment with fan-sourced summaries using a commonly used alignment algorithm. The dataset is released as open source for future researchers to train and test their own approaches for IDN text.</span
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