5,603 research outputs found

    Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review

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    Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)

    International Academic Symposium of Social Science 2022

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    This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate

    Reparation in Transitional Justice: A Normative Framework

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    Nach bewaffneten Konflikten, Genoziden und anderen Formen systemischen Unrechts müssen Staaten die Überlebenden schwerster Menschenrechtsverletzungen entschädigen. Als Teil eines Transitional-Justice-Prozesses soll so auch Unrecht aufgearbeitet und eine gesellschaftliche Transformation erreicht werden. Die Studie bietet den bisher umfassendsten Vorschlag rechtlicher Standards für diese Lage. Sie beruht auf interviewbasierten Fallstudien zu Entschädigungsprogrammen in Sierra Leone, Kolumbien und beim Internationalen Strafgerichtshof, sowie theoretischen Überlegungen zu den Zielen von Entschädigung in der Transitional Justice. Dadurch gibt sie tiefe Einblicke in Probleme und Chancen der Aufarbeitung systemischen Unrechts durch Entschädigung.In the aftermath of armed conflicts, genocide and other forms of systemic injustice, states - increasingly international courts - must repair a large number of survivors of grave human rights violations. As part of a transitional justice process, such reparation should also enable societal transformation. This study offers the most comprehensive proposal for legal standards in this complex situation to date. It comprises interview-based case studies of the reparation programs in Sierra Leone, Colombia, and at the International Criminal Court, as well as theoretical reflections on the goals and role of reparation in transitional justice. With that, the study provides deep insights into the problems and opportunities of this instrument

    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

    Religion, Education, and the ‘East’. Addressing Orientalism and Interculturality in Religious Education Through Japanese and East Asian Religions

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    This work addresses the theme of Japanese religions in order to rethink theories and practices pertaining to the field of Religious Education. Through an interdisciplinary framework that combines the study of religions, didactics and intercultural education, this book puts the case study of Religious Education in England in front of two ‘challenges’ in order to reveal hidden spots, tackle unquestioned assumptions and highlight problematic areas. These ‘challenges’, while focusing primarily on Japanese religions, are addressed within the wider contexts of other East Asian traditions and of the modern historical exchanges with the Euro-American societies. As result, a model for teaching Japanese and other East Asian religions is discussed and proposed in order to fruitfully engage issues such as orientalism, occidentalism, interculturality and critical thinking

    Women Philosophers in Nineteenth-Century Britain

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    Many women wrote philosophy in nineteenth-century Britain, and they wrote across the full range of philosophical topics. Yet these important women thinkers have been left out of the philosophical canon and many of them are barely known today. The aim of this book is to put them back on the map. It introduces twelve women philosophers - Mary Shepherd, Harriet Martineau, Ada Lovelace, George Eliot, Frances Power Cobbe, Helena Blavatsky, Julia Wedgwood, Victoria Welby, Arabella Buckley, Annie Besant, Vernon Lee, and Constance Naden. Alison Stone looks at their views on naturalism, philosophy of mind, evolution, morality and religion, and progress in history. She shows how these women interacted and developed their philosophical views in conversation with one another, not only with their male contemporaries. The rich print and periodical culture of the period enabled these women to publish philosophy in forms accessible to a general readership, despite the restrictions women faced, such as having limited or no access to university education. Stone explains how these women became excluded from the history of philosophy because there was a cultural shift at the end of the nineteenth century towards specialised forms of philosophical writing, which depended on academic credentials that were still largely unavailable to women

    Evaluating the generalisability of neural rumour verification models

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    Research on automated social media rumour verification, the task of identifying the veracity of questionable information circulating on social media, has yielded neural models achieving high performance, with accuracy scores that often exceed 90%. However, none of these studies focus on the real-world generalisability of the proposed approaches, that is whether the models perform well on datasets other than those on which they were initially trained and tested. In this work we aim to fill this gap by assessing the generalisability of top performing neural rumour verification models covering a range of different architectures from the perspectives of both topic and temporal robustness. For a more complete evaluation of generalisability, we collect and release COVID-RV, a novel dataset of Twitter conversations revolving around COVID-19 rumours. Unlike other existing COVID-19 datasets, our COVID-RV contains conversations around rumours that follow the format of prominent rumour verification benchmarks, while being different from them in terms of topic and time scale, thus allowing better assessment of the temporal robustness of the models. We evaluate model performance on COVID-RV and three popular rumour verification datasets to understand limitations and advantages of different model architectures, training datasets and evaluation scenarios. We find a dramatic drop in performance when testing models on a different dataset from that used for training. Further, we evaluate the ability of models to generalise in a few-shot learning setup, as well as when word embeddings are updated with the vocabulary of a new, unseen rumour. Drawing upon our experiments we discuss challenges and make recommendations for future research directions in addressing this important problem

    Utilizing Multi-modal Weak Signals to Improve User Stance Inference in Social Media

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    Social media has become an integral component of the daily life. There are millions of various types of content being released into social networks daily. This allows for an interesting view into a users\u27 view on everyday life. Exploring the opinions of users in social media networks has always been an interesting subject for the Natural Language Processing researchers. Knowing the social opinions of a mass will allow anyone to make informed policy or marketing related decisions. This is exactly why it is desirable to find comprehensive social opinions. The nature of social media is complex and therefore obtaining the social opinion becomes a challenging task. Because of how diverse and complex social media networks are, they typically resonate with the actual social connections but in a digital platform. Similar to how users make friends and companions in the real world, the digital platforms enable users to mimic similar social connections. This work mainly looks at how to obtain a comprehensive social opinion out of social media network. Typical social opinion quantifiers will look at text contributions made by users to find the opinions. Currently, it is challenging because the majority of users on social media will be consuming content rather than expressing their opinions out into the world. This makes natural language processing based methods impractical due to not having linguistic features. In our work we look to improve a method named stance inference which can utilize multi-domain features to extract the social opinion. We also introduce a method which can expose users opinions even though they do not have on-topical content. We also note how by introducing weak supervision to an unsupervised task of stance inference we can improve the performance. The weak supervision we bring into the pipeline is through hashtags. We show how hashtags are contextual indicators added by humans which will be much likelier to be related than a topic model. Lastly we introduce disentanglement methods for chronological social media networks which allows one to utilize the methods we introduce above to be applied in these type of platforms

    Human Extinction in the Pessimist Tradition

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    Faced with countless threats that pose a danger to the continued existence of the human species, there has emerged a new field in philosophy known as Existential Risk Management. This discipline proposes to understand, quantify, classify and ultimately defeat the risks that exist and that threaten our continued existence. These philosophers accept that human life is good and that it should be promoted. And because of the tremendous value that is found in human life, they argue we should do whatever we can to avert our disappearance. Philosophical pessimists hold that life is always filled with suffering and that because of this to not exist is better than to exist. Yet once we are here, once we exist, it is in our interest to reduce the amount of suffering. So while pessimists sustain that nonexistence and the disappearance of the human species is the ultimate goal (insofar as it is the only way to defeat and terminate the suffering that conditions all of existence), this nonexistence is to be obtained by nonviolent means, voluntarily and only in full understanding of our existential predicament. In this dissertation I do two things. First, I define pessimism. This is essential because pessimism means different things to different people. Therefore, an important purpose of my work is to delimitate and establish an historically grounded definition that carves out the unique contributions that pessimism makes to philosophy. To do this, I survey the history of pessimism and lay out the main arguments made by pessimists. Two, I argue that philosophical pessimists can support the efforts made by the Existential Risk philosophers even though the reasons for doing so are different. And in so arguing I highlight the relevance and importance of the history of philosophy for contemporary debates

    Learning to express, learning as self-expression: a multimethod investigation of the L2 selves of distance adult Irish L2 learners

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    This multimethod study is an exploration of the validity and interpretive utility of Dörnyei’s (2009) ‘L2 motivational self-system’ (L2MSS), as it applies to adult, non-formal learners of Irish, who are learning through Massive Open Online Courses (MOOCs). It is grounded in the psychology of language learning motivation (LLM), assessing whether non-formal adult Irish L2 learners are motivated by future L2 guides, both Ideal, reflecting hopes and dreams, and Ought-to, representing obligations and responsibilities. Three research questions are addressed, i) exploring the theory’s validity at a general level and examining whether ii) the L2 learning experience and iii) learner heritage background, are meaningful in predicting, and understanding, the motivations of learners. Using distinct samples from an iterated quantitative survey instrument (final n=638) and narrative interviews (n=42), evidence demonstrates the theory’s utility in an underexplored context, while raising questions regarding adult Irish language learners and theories of self. Learners endorsed and articulated internalised reasons to learn, encompassing personal hopes and obligations, with social others less directly impactful on their motivation. The futures learners described often referenced non-L2 related aspirations of self, and were less-directly related to L2 proficiency, in many cases. Challenges in relation to the latter, due to contextual difficulties, low efficacy beliefs, and limited contact with L2 speakers and learners, are described. Recommendations to encourage sustained L2 learning and support adult learners in fostering and developing L2 selves are made, to aid them in realising their personal language learning goals
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