1,355 research outputs found
Adversarial Adaptation of Scene Graph Models for Understanding Civic Issues
Citizen engagement and technology usage are two emerging trends driven by
smart city initiatives. Governments around the world are adopting technology
for faster resolution of civic issues. Typically, citizens report issues, such
as broken roads, garbage dumps, etc. through web portals and mobile apps, in
order for the government authorities to take appropriate actions. Several
mediums -- text, image, audio, video -- are used to report these issues.
Through a user study with 13 citizens and 3 authorities, we found that image is
the most preferred medium to report civic issues. However, analyzing civic
issue related images is challenging for the authorities as it requires manual
effort. Moreover, previous works have been limited to identifying a specific
set of issues from images. In this work, given an image, we propose to generate
a Civic Issue Graph consisting of a set of objects and the semantic relations
between them, which are representative of the underlying civic issue. We also
release two multi-modal (text and images) datasets, that can help in further
analysis of civic issues from images. We present a novel approach for
adversarial training of existing scene graph models that enables the use of
scene graphs for new applications in the absence of any labelled training data.
We conduct several experiments to analyze the efficacy of our approach, and
using human evaluation, we establish the appropriateness of our model at
representing different civic issues.Comment: Accepted at WWW'1
Attending to Discriminative Certainty for Domain Adaptation
In this paper, we aim to solve for unsupervised domain adaptation of
classifiers where we have access to label information for the source domain
while these are not available for a target domain. While various methods have
been proposed for solving these including adversarial discriminator based
methods, most approaches have focused on the entire image based domain
adaptation. In an image, there would be regions that can be adapted better, for
instance, the foreground object may be similar in nature. To obtain such
regions, we propose methods that consider the probabilistic certainty estimate
of various regions and specify focus on these during classification for
adaptation. We observe that just by incorporating the probabilistic certainty
of the discriminator while training the classifier, we are able to obtain state
of the art results on various datasets as compared against all the recent
methods. We provide a thorough empirical analysis of the method by providing
ablation analysis, statistical significance test, and visualization of the
attention maps and t-SNE embeddings. These evaluations convincingly demonstrate
the effectiveness of the proposed approach.Comment: CVPR 2019 Accepted, Project: https://delta-lab-iitk.github.io/CADA
Curriculum CycleGAN for Textual Sentiment Domain Adaptation with Multiple Sources
Sentiment analysis of user-generated reviews or comments on products and
services in social networks can help enterprises to analyze the feedback from
customers and take corresponding actions for improvement. To mitigate
large-scale annotations on the target domain, domain adaptation (DA) provides
an alternate solution by learning a transferable model from other labeled
source domains. Existing multi-source domain adaptation (MDA) methods either
fail to extract some discriminative features in the target domain that are
related to sentiment, neglect the correlations of different sources and the
distribution difference among different sub-domains even in the same source, or
cannot reflect the varying optimal weighting during different training stages.
In this paper, we propose a novel instance-level MDA framework, named
curriculum cycle-consistent generative adversarial network (C-CycleGAN), to
address the above issues. Specifically, C-CycleGAN consists of three
components: (1) pre-trained text encoder which encodes textual input from
different domains into a continuous representation space, (2) intermediate
domain generator with curriculum instance-level adaptation which bridges the
gap across source and target domains, and (3) task classifier trained on the
intermediate domain for final sentiment classification. C-CycleGAN transfers
source samples at instance-level to an intermediate domain that is closer to
the target domain with sentiment semantics preserved and without losing
discriminative features. Further, our dynamic instance-level weighting
mechanisms can assign the optimal weights to different source samples in each
training stage. We conduct extensive experiments on three benchmark datasets
and achieve substantial gains over state-of-the-art DA approaches. Our source
code is released at: https://github.com/WArushrush/Curriculum-CycleGAN.Comment: Accepted by WWW 202
Higher Education Exchange: 2013
This annual publication serves as a forum for new ideas and dialogue between scholars and the larger public. Essays explore ways that students, administrators, and faculty can initiate and sustain an ongoing conversation about the public life they share.The Higher Education Exchange is founded on a thought articulated by Thomas Jefferson in 1820: "I know no safe depository of the ultimate powers of the society but the people themselves; and if we think them not enlightened enough to exercise their control with a wholesome discretion, the remedy is not to take it from them, but to inform their discretion by education."In the tradition of Jefferson, the Higher Education Exchange agrees that a central goal of higher education is to help make democracy possible by preparing citizens for public life. The Higher Education Exchange is part of a movement to strengthen higher education's democratic mission and foster a more democratic culture throughout American society.Working in this tradition, the Higher Education Exchange publishes interviews, case studies, analyses, news, and ideas about efforts within higher education to develop more democratic societies
Tsinghua Issue- Generative AI, Learning And New Literacies
Launched in November 2022, OpenAI\u27s ChatGPT garnered over 100 million users within two months, sparking a surge in research and concern over potential risks of extensive AI experiments. The article, originating from a conference presentation by Tsinghua University and NTHU, Taiwan, provides a nuanced overview of Generative AI. It explores the classifications, applications, governance challenges, societal implications, and development trajectory of Generative AI, emphasizing its transformative role in employment and education. The piece highlights ChatGPT\u27s significant impact and the strategic adaptations required in various sectors, including medical education, engineering, information management, and distance education. Furthermore, it explores the opportunities and challenges associated with incorporating ChatGPT in educational settings, emphasizing its support in facilitating personalized learning, developing 21st-century competencies, fostering self-directed learning, and enhancing information accessibility. It also illustrates the integration of ChatGPT and text-to-image models in high school language courses through the lens of new literacies. The text uniquely integrates three layers of discourse: introductions to Generative AI by experts, scholarly debates on its merits and drawbacks, and practical classroom applications, offering a reflective snapshot of the current and potential states of Generative AI applications while emphasizing the interconnected discussions across various layers of discourse
Urban Visual Intelligence: Studying Cities with AI and Street-level Imagery
The visual dimension of cities has been a fundamental subject in urban
studies, since the pioneering work of scholars such as Sitte, Lynch, Arnheim,
and Jacobs. Several decades later, big data and artificial intelligence (AI)
are revolutionizing how people move, sense, and interact with cities. This
paper reviews the literature on the appearance and function of cities to
illustrate how visual information has been used to understand them. A
conceptual framework, Urban Visual Intelligence, is introduced to
systematically elaborate on how new image data sources and AI techniques are
reshaping the way researchers perceive and measure cities, enabling the study
of the physical environment and its interactions with socioeconomic
environments at various scales. The paper argues that these new approaches
enable researchers to revisit the classic urban theories and themes, and
potentially help cities create environments that are more in line with human
behaviors and aspirations in the digital age
Cultural Science
This book is available as open access through the Bloomsbury Open Access programme and is available on www.bloomsburycollections.com. Cultural Science introduces a new way of thinking about culture. Adopting an evolutionary and systems approach, the authors argue that culture is the population-wide source of newness and innovation; it faces the future, not the past. Its chief characteristic is the formation of groups or 'demes' (organised and productive subpopulation; 'demos'). Demes are the means for creating, distributing and growing knowledge. However, such groups are competitive and knowledge-systems are adversarial. Starting from a rereading of Darwinian evolutionary theory, the book utilises multidisciplinary resources: Raymond Williams's 'culture is ordinary' approach; evolutionary science (e.g. Mark Pagel and Herbert Gintis); semiotics (Yuri Lotman); and economic theory (from Schumpeter to McCloskey). Successive chapters argue that: -Culture and knowledge need to be understood from an externalist ('linked brains') perspective, rather than through the lens of individual behaviour; -Demes are created by culture, especially storytelling, which in turn constitutes both politics and economics; -The clash of systems - including demes - is productive of newness, meaningfulness and successful reproduction of culture; -Contemporary urban culture and citizenship can best be explained by investigating how culture is used, and how newness and innovation emerge from unstable and contested boundaries between different meaning systems; -The evolution of culture is a process of technologically enabled 'demic concentration' of knowledge, across overlapping meaning-systems or semiospheres; a process where the number of demes accessible to any individual has increased at an accelerating rate, resulting in new problems of scale and coordination for cultural science to address. The book argues for interdisciplinary 'consilience', linking evolutionary and complexity theory in the natural sciences, economics and anthropology in the social sciences, and cultural, communication and media studies in the humanities and creative arts. It describes what is needed for a new 'modern synthesis' for the cultural sciences. It combines analytical and historical methods, to provide a framework for a general reconceptualisation of the theory of culture – one that is focused not on its political or customary aspects but rather its evolutionary significance as a generator of newness and innovation
Parliament Buildings: The architecture of politics in Europe
As political polarisation undermines confidence in the shared values and established constitutional orders of many nations, it is imperative that we explore how parliaments are to stay relevant and accessible to the citizens whom they serve. The rise of modern democracies is thought to have found physical expression in the staged unity of the parliamentary seating plan. However, the built forms alone cannot give sufficient testimony to the exercise of power in political life.
Parliament Buildings brings together architecture, history, art history, history of political thought, sociology, behavioural psychology, anthropology and political science to raise a host of challenging questions. How do parliament buildings give physical form to norms and practices, to behaviours, rituals, identities and imaginaries? How are their spatial forms influenced by the political cultures they accommodate? What kinds of histories, politics and morphologies do the diverse European parliaments share, and how do their political trajectories intersect?
This volume offers an eclectic exploration of the complex nexus between architecture and politics in Europe. Including contributions from architects who have designed or remodelled four parliament buildings in Europe, it provides the first comparative, multi-disciplinary study of parliament buildings across Europe and across history
Living with Living History: The Impact of Old Salem Museums and Gardens on the Quality of Life in Winston-Salem, North Carolina
History museums serve an important role in most communities throughout the United States as repositories of heritage and educational institutions. However, many museums neglect to consider the impact of the services they provide, especially in a holistic manner. Contemporary museum management demands better self-assessment tool than those currently in vogue. As museums struggle to survive in the twenty-first century, museum impact analysis is imperative to demonstrate their value to the communities they serve. This thesis tests the suitability of quality of life metrics for holistic evaluation of museum impacts. To do so, a pilot study was conducted at Old Salem Museums and Gardens (OSMG), an open-air living history museum located in Winston-Salem, North Carolina. OSMG demonstrates the adaptability of quality of life methods to evaluate the intricacies of the site and museum contributions to citizen well-being. Through this analysis of education, community, economic and physical character indicators, OSMG is informed to make more sustainable decisions and better demonstrate their value to the community of Winston-Salem. This thesis provides museum practitioners with an objective quality of life framework to measure museum impacts on the communities in which they are located
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Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI
We propose a method for identifying early warning signs of transformative progress in artificial intelligence (AI), and discuss how these can support the anticipatory and democratic governance of AI. We call these early
warning signs ‘canaries’, based on the use of canaries to provide early warnings of unsafe air pollution in coal
mines. Our method combines expert elicitation and collaborative causal graphs to identify key milestones
and identify the relationships between them. We present two illustrations of how this method could be
used: to identify early warnings of harmful impacts of language models; and of progress towards high-level
machine intelligence. Identifying early warning signs of transformative applications can support more efficient
monitoring and timely regulation of progress in AI: as AI advances, its impacts on society may be too great to
be governed retrospectively. It is essential that those impacted by AI have a say in how it is governed. Early
warnings can give the public time and focus to influence emerging technologies using democratic, participatory
technology assessments. We discuss the challenges in identifying early warning signals and propose directions
for future work
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