8,609 research outputs found
Serving to secure "Global Korea": Gender, mobility, and flight attendant labor migrants
This dissertation is an ethnography of mobility and modernity in contemporary South Korea (the Republic of Korea) following neoliberal restructuring precipitated by the Asian Financial Crisis (1997). It focuses on how comparative âservice,â âsecurity,â and âsafetyâ fashioned âGlobal Koreaâ: an ongoing state-sponsored project aimed at promoting the economic, political, and cultural maturation of South Korea from a once notoriously inhospitable, âbackwardâ country (hujinâguk) to a now welcoming, âadvanced countryâ (sĹnjinâguk). Through physical embodiments of the culturally-specific idiom of âsuperiorâ service (sĹbisĹ), I argue that aspiring, current, and former Korean flight attendants have driven the production and maintenance of this national project.
More broadly, as a driver of this national project, this occupation has emerged out of the countryâs own aspirational flights from an earlier history of authoritarian rule, labor violence, and xenophobia. Against the backdrop of the Korean stateâs aggressive neoliberal restructuring, globalization efforts, and current âHell Chosunâ (HelchosĹn) economy, a group of largely academically and/or class disadvantaged young women have been able secure individualized modes of pleasure, self-fulfillment, and class advancement via what I deem âservice mobilities.â Service mobilities refers to the participation of mostly women in a traditionally devalued but growing sector of the global labor market, the âpink collarâ economy centered around âfeminineâ care labor. Korean female flight attendants share labor skills resembling those of other foreign labor migrants (chiefly from the âGlobal Southâ), who perform care work deemed less desirable. Yet, Korean female flight attendants elude the stigmatizing, classed, and racialized category of âlabor migrant.â Moreover, within the context of South Koreaâs unique history of rapid modernization, the flight attendant occupation also commands considerable social prestige.
Based on ethnographic and archival research on aspiring, current, and former Korean flight attendants, this dissertation asks how these unique care laborers negotiate a metaphorical and literal series of sustained border crossings and inspections between Korean flight attendantsâ contingent status as lowly care-laboring migrants, on the one hand, and ostensibly glamorous, globetrotting elites, on the other. This study contends the following: first, the flight attendant occupation in South Korea represents new politics of pleasure and pain in contemporary East Asia. Second, Korean female flight attendantsâ enactments of soft, sanitized, and glamorous (hwaryĹhada) service help to purify South Koreaâs less savory past. In so doing, Korean flight attendants reconstitute the historical role of female laborers as burden bearers and caretakers of the Korean state.U of I OnlyAuthor submitted a 2-year U of I restriction extension request
Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
Generalizable person re-identification (Re-ID) is a very hot research topic
in machine learning and computer vision, which plays a significant role in
realistic scenarios due to its various applications in public security and
video surveillance. However, previous methods mainly focus on the visual
representation learning, while neglect to explore the potential of semantic
features during training, which easily leads to poor generalization capability
when adapted to the new domain. In this paper, we propose a Multi-Modal
Equivalent Transformer called MMET for more robust visual-semantic embedding
learning on visual, textual and visual-textual tasks respectively. To further
enhance the robust feature learning in the context of transformer, a dynamic
masking mechanism called Masked Multimodal Modeling strategy (MMM) is
introduced to mask both the image patches and the text tokens, which can
jointly works on multimodal or unimodal data and significantly boost the
performance of generalizable person Re-ID. Extensive experiments on benchmark
datasets demonstrate the competitive performance of our method over previous
approaches. We hope this method could advance the research towards
visual-semantic representation learning. Our source code is also publicly
available at https://github.com/JeremyXSC/MMET
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Sign Language Translation from Instructional Videos
The advances in automatic sign language translation (SLT) to spoken languages
have been mostly benchmarked with datasets of limited size and restricted
domains. Our work advances the state of the art by providing the first baseline
results on How2Sign, a large and broad dataset.
We train a Transformer over I3D video features, using the reduced BLEU as a
reference metric for validation, instead of the widely used BLEU score. We
report a result of 8.03 on the BLEU score, and publish the first open-source
implementation of its kind to promote further advances.Comment: Paper accepted at WiCV @CVPR2
Procedure-Aware Pretraining for Instructional Video Understanding
Our goal is to learn a video representation that is useful for downstream
procedure understanding tasks in instructional videos. Due to the small amount
of available annotations, a key challenge in procedure understanding is to be
able to extract from unlabeled videos the procedural knowledge such as the
identity of the task (e.g., 'make latte'), its steps (e.g., 'pour milk'), or
the potential next steps given partial progress in its execution. Our main
insight is that instructional videos depict sequences of steps that repeat
between instances of the same or different tasks, and that this structure can
be well represented by a Procedural Knowledge Graph (PKG), where nodes are
discrete steps and edges connect steps that occur sequentially in the
instructional activities. This graph can then be used to generate pseudo labels
to train a video representation that encodes the procedural knowledge in a more
accessible form to generalize to multiple procedure understanding tasks. We
build a PKG by combining information from a text-based procedural knowledge
database and an unlabeled instructional video corpus and then use it to
generate training pseudo labels with four novel pre-training objectives. We
call this PKG-based pre-training procedure and the resulting model Paprika,
Procedure-Aware PRe-training for Instructional Knowledge Acquisition. We
evaluate Paprika on COIN and CrossTask for procedure understanding tasks such
as task recognition, step recognition, and step forecasting. Paprika yields a
video representation that improves over the state of the art: up to 11.23%
gains in accuracy in 12 evaluation settings. Implementation is available at
https://github.com/salesforce/paprika.Comment: CVPR 202
SViTT: Temporal Learning of Sparse Video-Text Transformers
Do video-text transformers learn to model temporal relationships across
frames? Despite their immense capacity and the abundance of multimodal training
data, recent work has revealed the strong tendency of video-text models towards
frame-based spatial representations, while temporal reasoning remains largely
unsolved. In this work, we identify several key challenges in temporal learning
of video-text transformers: the spatiotemporal trade-off from limited network
size; the curse of dimensionality for multi-frame modeling; and the diminishing
returns of semantic information by extending clip length. Guided by these
findings, we propose SViTT, a sparse video-text architecture that performs
multi-frame reasoning with significantly lower cost than naive transformers
with dense attention. Analogous to graph-based networks, SViTT employs two
forms of sparsity: edge sparsity that limits the query-key communications
between tokens in self-attention, and node sparsity that discards uninformative
visual tokens. Trained with a curriculum which increases model sparsity with
the clip length, SViTT outperforms dense transformer baselines on multiple
video-text retrieval and question answering benchmarks, with a fraction of
computational cost. Project page: http://svcl.ucsd.edu/projects/svitt.Comment: CVPR 202
Strategies for Early Learners
Welcome to learning about how to effectively plan curriculum for young children. This textbook will address: ⢠Developing curriculum through the planning cycle ⢠Theories that inform what we know about how children learn and the best ways for teachers to support learning ⢠The three components of developmentally appropriate practice ⢠Importance and value of play and intentional teaching ⢠Different models of curriculum ⢠Process of lesson planning (documenting planned experiences for children) ⢠Physical, temporal, and social environments that set the stage for childrenâs learning ⢠Appropriate guidance techniques to support childrenâs behaviors as the self-regulation abilities mature. ⢠Planning for preschool-aged children in specific domains including o Physical development o Language and literacy o Math o Science o Creative (the visual and performing arts) o Diversity (social science and history) o Health and safety ⢠Making childrenâs learning visible through documentation and assessmenthttps://scholar.utc.edu/open-textbooks/1001/thumbnail.jp
Image classification over unknown and anomalous domains
A longstanding goal in computer vision research is to develop methods that are simultaneously applicable to a broad range of prediction problems. In contrast to this, models often perform best when they are specialized to some task or data type. This thesis investigates the challenges of learning models that generalize well over multiple unknown or anomalous modes and domains in data, and presents new solutions for learning robustly in this setting.
Initial investigations focus on normalization for distributions that contain multiple sources (e.g. images in different styles like cartoons or photos). Experiments demonstrate the extent to which existing modules, batch normalization in particular, struggle with such heterogeneous data, and a new solution is proposed that can better handle data from multiple visual modes, using differing sample statistics for each.
While ideas to counter the overspecialization of models have been formulated in sub-disciplines of transfer learning, e.g. multi-domain and multi-task learning, these usually rely on the existence of meta information, such as task or domain labels. Relaxing this assumption gives rise to a new transfer learning setting, called latent domain learning in this thesis, in which training and inference are carried out over data from multiple visual domains, without domain-level annotations. Customized solutions are required for this, as the performance of standard models degrades: a new data augmentation technique that interpolates between latent domains in an unsupervised way is presented, alongside a dedicated module that sparsely accounts for hidden domains in data, without requiring domain labels to do so.
In addition, the thesis studies the problem of classifying previously unseen or anomalous modes in data, a fundamental problem in one-class learning, and anomaly detection in particular. While recent ideas have been focused on developing self-supervised solutions for the one-class setting, in this thesis new methods based on transfer learning are formulated. Extensive experimental evidence demonstrates that a transfer-based perspective benefits new problems that have recently been proposed in anomaly detection literature, in particular challenging semantic detection tasks
Graphical scaffolding for the learning of data wrangling APIs
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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