17,063 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 “service,” “security,” and “safety” fashioned “Global Korea”: an ongoing state-sponsored project aimed at promoting the economic, political, and cultural maturation of South Korea from a once notoriously inhospitable, “backward” country (hujin’guk) to a now welcoming, “advanced country” (sĹŹnjin’guk). Through physical embodiments of the culturally-specific idiom of “superior” service (sĹŹbisĹ), 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 country’s own aspirational flights from an earlier history of authoritarian rule, labor violence, and xenophobia. Against the backdrop of the Korean state’s aggressive neoliberal restructuring, globalization efforts, and current “Hell Chosun” (Helchosŏn) 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 “service mobilities.” Service mobilities refers to the participation of mostly women in a traditionally devalued but growing sector of the global labor market, the “pink collar” economy centered around “feminine” care labor. Korean female flight attendants share labor skills resembling those of other foreign labor migrants (chiefly from the “Global South”), who perform care work deemed less desirable. Yet, Korean female flight attendants elude the stigmatizing, classed, and racialized category of “labor migrant.” Moreover, within the context of South Korea’s 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 attendants’ 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 attendants’ enactments of soft, sanitized, and glamorous (hwaryŏhada) service help to purify South Korea’s 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
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
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
NF-Atlas: Multi-Volume Neural Feature Fields for Large Scale LiDAR Mapping
LiDAR Mapping has been a long-standing problem in robotics. Recent progress
in neural implicit representation has brought new opportunities to robotic
mapping. In this paper, we propose the multi-volume neural feature fields,
called NF-Atlas, which bridge the neural feature volumes with pose graph
optimization. By regarding the neural feature volume as pose graph nodes and
the relative pose between volumes as pose graph edges, the entire neural
feature field becomes both locally rigid and globally elastic. Locally, the
neural feature volume employs a sparse feature Octree and a small MLP to encode
the submap SDF with an option of semantics. Learning the map using this
structure allows for end-to-end solving of maximum a posteriori (MAP) based
probabilistic mapping. Globally, the map is built volume by volume
independently, avoiding catastrophic forgetting when mapping incrementally.
Furthermore, when a loop closure occurs, with the elastic pose graph based
representation, only updating the origin of neural volumes is required without
remapping. Finally, these functionalities of NF-Atlas are validated. Thanks to
the sparsity and the optimization based formulation, NF-Atlas shows competitive
performance in terms of accuracy, efficiency and memory usage on both
simulation and real-world datasets
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology, applications,
and challenges. Moreover, we present an outlook on how ChatGPT might evolve to
realize general-purpose AIGC (a.k.a. AI-generated content), which will be a
significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated
([email protected]
Audio-Visual Automatic Speech Recognition Towards Education for Disabilities
Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition
TransFusionOdom: Interpretable Transformer-based LiDAR-Inertial Fusion Odometry Estimation
Multi-modal fusion of sensors is a commonly used approach to enhance the
performance of odometry estimation, which is also a fundamental module for
mobile robots. However, the question of \textit{how to perform fusion among
different modalities in a supervised sensor fusion odometry estimation task?}
is still one of challenging issues remains. Some simple operations, such as
element-wise summation and concatenation, are not capable of assigning adaptive
attentional weights to incorporate different modalities efficiently, which make
it difficult to achieve competitive odometry results. Recently, the Transformer
architecture has shown potential for multi-modal fusion tasks, particularly in
the domains of vision with language. In this work, we propose an end-to-end
supervised Transformer-based LiDAR-Inertial fusion framework (namely
TransFusionOdom) for odometry estimation. The multi-attention fusion module
demonstrates different fusion approaches for homogeneous and heterogeneous
modalities to address the overfitting problem that can arise from blindly
increasing the complexity of the model. Additionally, to interpret the learning
process of the Transformer-based multi-modal interactions, a general
visualization approach is introduced to illustrate the interactions between
modalities. Moreover, exhaustive ablation studies evaluate different
multi-modal fusion strategies to verify the performance of the proposed fusion
strategy. A synthetic multi-modal dataset is made public to validate the
generalization ability of the proposed fusion strategy, which also works for
other combinations of different modalities. The quantitative and qualitative
odometry evaluations on the KITTI dataset verify the proposed TransFusionOdom
could achieve superior performance compared with other related works.Comment: Submitted to IEEE Sensors Journal with some modifications. This work
has been submitted to the IEEE for possible publication. Copyright may be
transferred without notice, after which this version may no longer be
accessibl
Grasping nothing: a study of minimal ontologies and the sense of music
If music were to have a proper sense – one in which it is truly given – one might reasonably place this in sound and aurality. I contend, however, that no such sense exists; rather, the sense of music takes place, and it does so with the impossible. To this end, this thesis – which is a work of philosophy and music – advances an ontology of the impossible (i.e., it thinks the being of what, properly speaking, can have no being) and considers its implications for music, articulating how ontological aporias – of the event, of thinking the absolute, and of sovereignty’s dismemberment – imply senses of music that are anterior to sound. John Cage’s Silent Prayer, a nonwork he never composed, compels a rerethinking of silence on the basis of its contradictory status of existence; Florian Hecker et al.’s Speculative Solution offers a basis for thinking absolute music anew to the precise extent that it is a discourse of meaninglessness; and Manfred Werder’s [yearn] pieces exhibit exemplarily that music’s sense depends on the possibility of its counterfeiting. Inso-much as these accounts produce musical senses that take the place of sound, they are also understood to be performances of these pieces. Here, then, thought is music’s organon and its instrument
Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds
This paper presents a framework for semantic segmentation on sparse
sequential point clouds of millimeter-wave radar. Compared with cameras and
lidars, millimeter-wave radars have the advantage of not revealing privacy,
having a strong anti-interference ability, and having long detection distance.
The sparsity and capturing temporal-topological features of mmWave data is
still a problem. However, the issue of capturing the temporal-topological
coupling features under the human semantic segmentation task prevents previous
advanced segmentation methods (e.g PointNet, PointCNN, Point Transformer) from
being well utilized in practical scenarios. To address the challenge caused by
the sparsity and temporal-topological feature of the data, we (i) introduce
graph structure and topological features to the point cloud, (ii) propose a
semantic segmentation framework including a global feature-extracting module
and a sequential feature-extracting module. In addition, we design an efficient
and more fitting loss function for a better training process and segmentation
results based on graph clustering. Experimentally, we deploy representative
semantic segmentation algorithms (Transformer, GCNN, etc.) on a custom dataset.
Experimental results indicate that our model achieves mean accuracy on the
custom dataset by and outperforms the state-of-the-art
algorithms. Moreover, to validate the model's robustness, we deploy our model
on the well-known S3DIS dataset. On the S3DIS dataset, our model achieves mean
accuracy by , outperforming baseline algorithms
Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
Visual simultaneous localization and mapping (SLAM) systems face challenges
in detecting loop closure under the circumstance of large viewpoint changes. In
this paper, we present an object-based loop closure detection method based on
the spatial layout and semanic consistency of the 3D scene graph. Firstly, we
propose an object-level data association approach based on the semantic
information from semantic labels, intersection over union (IoU), object color,
and object embedding. Subsequently, multi-view bundle adjustment with the
associated objects is utilized to jointly optimize the poses of objects and
cameras. We represent the refined objects as a 3D spatial graph with semantics
and topology. Then, we propose a graph matching approach to select
correspondence objects based on the structure layout and semantic property
similarity of vertices' neighbors. Finally, we jointly optimize camera
trajectories and object poses in an object-level pose graph optimization, which
results in a globally consistent map. Experimental results demonstrate that our
proposed data association approach can construct more accurate 3D semantic
maps, and our loop closure method is more robust than point-based and
object-based methods in circumstances with large viewpoint changes
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