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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2212.03282
|
Darryl Hannan
|
Darryl Hannan, Steven C. Nesbit, Ximing Wen, Glen Smith, Qiao Zhang,
Alberto Goffi, Vincent Chan, Michael J. Morris, John C. Hunninghake, Nicholas
E. Villalobos, Edward Kim, Rosina O. Weber and Christopher J. MacLellan
|
MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited
Training Examples
|
IAAI 2023 (7 pages)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Point-of-Care Ultrasound (POCUS) refers to clinician-performed and
interpreted ultrasonography at the patient's bedside. Interpreting these images
requires a high level of expertise, which may not be available during
emergencies. In this paper, we support POCUS by developing classifiers that can
aid medical professionals by diagnosing whether or not a patient has
pneumothorax. We decomposed the task into multiple steps, using YOLOv4 to
extract relevant regions of the video and a 3D sparse coding model to represent
video features. Given the difficulty in acquiring positive training videos, we
trained a small-data classifier with a maximum of 15 positive and 32 negative
examples. To counteract this limitation, we leveraged subject matter expert
(SME) knowledge to limit the hypothesis space, thus reducing the cost of data
collection. We present results using two lung ultrasound datasets and
demonstrate that our model is capable of achieving performance on par with SMEs
in pneumothorax identification. We then developed an iOS application that runs
our full system in less than 4 seconds on an iPad Pro, and less than 8 seconds
on an iPhone 13 Pro, labeling key regions in the lung sonogram to provide
interpretable diagnoses.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 19:33:05 GMT"
},
{
"version": "v2",
"created": "Thu, 8 Dec 2022 03:46:45 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Hannan",
"Darryl",
""
],
[
"Nesbit",
"Steven C.",
""
],
[
"Wen",
"Ximing",
""
],
[
"Smith",
"Glen",
""
],
[
"Zhang",
"Qiao",
""
],
[
"Goffi",
"Alberto",
""
],
[
"Chan",
"Vincent",
""
],
[
"Morris",
"Michael J.",
""
],
[
"Hunninghake",
"John C.",
""
],
[
"Villalobos",
"Nicholas E.",
""
],
[
"Kim",
"Edward",
""
],
[
"Weber",
"Rosina O.",
""
],
[
"MacLellan",
"Christopher J.",
""
]
] |
new_dataset
| 0.961141 |
2212.03858
|
Ruohan Gao
|
Hao Li, Yizhi Zhang, Junzhe Zhu, Shaoxiong Wang, Michelle A Lee,
Huazhe Xu, Edward Adelson, Li Fei-Fei, Ruohan Gao, Jiajun Wu
|
See, Hear, and Feel: Smart Sensory Fusion for Robotic Manipulation
|
In CoRL 2022. Li and Zhang equal contribution; Gao and Wu equal
advising. Project page: https://ai.stanford.edu/~rhgao/see_hear_feel/
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Humans use all of their senses to accomplish different tasks in everyday
activities. In contrast, existing work on robotic manipulation mostly relies on
one, or occasionally two modalities, such as vision and touch. In this work, we
systematically study how visual, auditory, and tactile perception can jointly
help robots to solve complex manipulation tasks. We build a robot system that
can see with a camera, hear with a contact microphone, and feel with a
vision-based tactile sensor, with all three sensory modalities fused with a
self-attention model. Results on two challenging tasks, dense packing and
pouring, demonstrate the necessity and power of multisensory perception for
robotic manipulation: vision displays the global status of the robot but can
often suffer from occlusion, audio provides immediate feedback of key moments
that are even invisible, and touch offers precise local geometry for decision
making. Leveraging all three modalities, our robotic system significantly
outperforms prior methods.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 18:55:53 GMT"
},
{
"version": "v2",
"created": "Thu, 8 Dec 2022 05:52:16 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Li",
"Hao",
""
],
[
"Zhang",
"Yizhi",
""
],
[
"Zhu",
"Junzhe",
""
],
[
"Wang",
"Shaoxiong",
""
],
[
"Lee",
"Michelle A",
""
],
[
"Xu",
"Huazhe",
""
],
[
"Adelson",
"Edward",
""
],
[
"Fei-Fei",
"Li",
""
],
[
"Gao",
"Ruohan",
""
],
[
"Wu",
"Jiajun",
""
]
] |
new_dataset
| 0.974294 |
2212.03957
|
Shahrzad Haddadan
|
Suman K.Bera and Jayesh Choudhari and Shahrzad Haddadan and Sara
Ahmadian
|
DeMEtRIS: Counting (near)-Cliques by Crawling
| null | null |
10.1145/3539597.3570438
| null |
cs.DS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We study the problem of approximately counting cliques and near cliques in a
graph, where the access to the graph is only available through crawling its
vertices; thus typically seeing only a small portion of it. This model, known
as the random walk model or the neighborhood query model has been introduced
recently and captures real-life scenarios in which the entire graph is too
massive to be stored as a whole or be scanned entirely and sampling vertices
independently is non-trivial in it. We introduce DeMEtRIS: Dense Motif
Estimation through Random Incident Sampling. This method provides a scalable
algorithm for clique and near clique counting in the random walk model. We
prove the correctness of our algorithm through rigorous mathematical analysis
and extensive experiments. Both our theoretical results and our experiments
show that DeMEtRIS obtains a high precision estimation by only crawling a
sub-linear portion on vertices, thus we demonstrate a significant improvement
over previously known results.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 21:10:18 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Bera",
"Suman K.",
""
],
[
"Choudhari",
"Jayesh",
""
],
[
"Haddadan",
"Shahrzad",
""
],
[
"Ahmadian",
"Sara",
""
]
] |
new_dataset
| 0.986778 |
2212.03961
|
Gyeongmin Choe
|
Gyeongmin Choe, Beibei Du, Seonghyeon Nam, Xiaoyu Xiang, Bo Zhu,
Rakesh Ranjan
|
FSID: Fully Synthetic Image Denoising via Procedural Scene Generation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For low-level computer vision and image processing ML tasks, training on
large datasets is critical for generalization. However, the standard practice
of relying on real-world images primarily from the Internet comes with image
quality, scalability, and privacy issues, especially in commercial contexts. To
address this, we have developed a procedural synthetic data generation pipeline
and dataset tailored to low-level vision tasks. Our Unreal engine-based
synthetic data pipeline populates large scenes algorithmically with a
combination of random 3D objects, materials, and geometric transformations.
Then, we calibrate the camera noise profiles to synthesize the noisy images.
From this pipeline, we generated a fully synthetic image denoising dataset
(FSID) which consists of 175,000 noisy/clean image pairs. We then trained and
validated a CNN-based denoising model, and demonstrated that the model trained
on this synthetic data alone can achieve competitive denoising results when
evaluated on real-world noisy images captured with smartphone cameras.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 21:21:55 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Choe",
"Gyeongmin",
""
],
[
"Du",
"Beibei",
""
],
[
"Nam",
"Seonghyeon",
""
],
[
"Xiang",
"Xiaoyu",
""
],
[
"Zhu",
"Bo",
""
],
[
"Ranjan",
"Rakesh",
""
]
] |
new_dataset
| 0.998893 |
2212.03965
|
Shikhar Tuli
|
Shikhar Tuli, Chia-Hao Li, Ritvik Sharma, Niraj K. Jha
|
CODEBench: A Neural Architecture and Hardware Accelerator Co-Design
Framework
|
Published at ACM Transactions on Embedded Computing Systems. Code
available at https://github.com/jha-lab/codebench
| null |
10.1145/3575798
| null |
cs.AR cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, automated co-design of machine learning (ML) models and accelerator
architectures has attracted significant attention from both the industry and
academia. However, most co-design frameworks either explore a limited search
space or employ suboptimal exploration techniques for simultaneous design
decision investigations of the ML model and the accelerator. Furthermore,
training the ML model and simulating the accelerator performance is
computationally expensive. To address these limitations, this work proposes a
novel neural architecture and hardware accelerator co-design framework, called
CODEBench. It is composed of two new benchmarking sub-frameworks, CNNBench and
AccelBench, which explore expanded design spaces of convolutional neural
networks (CNNs) and CNN accelerators. CNNBench leverages an advanced search
technique, BOSHNAS, to efficiently train a neural heteroscedastic surrogate
model to converge to an optimal CNN architecture by employing second-order
gradients. AccelBench performs cycle-accurate simulations for a diverse set of
accelerator architectures in a vast design space. With the proposed co-design
method, called BOSHCODE, our best CNN-accelerator pair achieves 1.4% higher
accuracy on the CIFAR-10 dataset compared to the state-of-the-art pair, while
enabling 59.1% lower latency and 60.8% lower energy consumption. On the
ImageNet dataset, it achieves 3.7% higher Top1 accuracy at 43.8% lower latency
and 11.2% lower energy consumption. CODEBench outperforms the state-of-the-art
framework, i.e., Auto-NBA, by achieving 1.5% higher accuracy and 34.7x higher
throughput, while enabling 11.0x lower energy-delay product (EDP) and 4.0x
lower chip area on CIFAR-10.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 21:38:03 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Tuli",
"Shikhar",
""
],
[
"Li",
"Chia-Hao",
""
],
[
"Sharma",
"Ritvik",
""
],
[
"Jha",
"Niraj K.",
""
]
] |
new_dataset
| 0.994493 |
2212.03968
|
Michal Balazia
|
Tanay Agrawal, Michal Balazia, Philipp M\"uller, Fran\c{c}ois
Br\'emond
|
Multimodal Vision Transformers with Forced Attention for Behavior
Analysis
|
Preprint. Full paper accepted at the IEEE/CVF Winter Conference on
Applications of Computer Vision (WACV), Waikoloa, USA, Jan 2023. 11 pages
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human behavior understanding requires looking at minute details in the large
context of a scene containing multiple input modalities. It is necessary as it
allows the design of more human-like machines. While transformer approaches
have shown great improvements, they face multiple challenges such as lack of
data or background noise. To tackle these, we introduce the Forced Attention
(FAt) Transformer which utilize forced attention with a modified backbone for
input encoding and a use of additional inputs. In addition to improving the
performance on different tasks and inputs, the modification requires less time
and memory resources. We provide a model for a generalised feature extraction
for tasks concerning social signals and behavior analysis. Our focus is on
understanding behavior in videos where people are interacting with each other
or talking into the camera which simulates the first person point of view in
social interaction. FAt Transformers are applied to two downstream tasks:
personality recognition and body language recognition. We achieve
state-of-the-art results for Udiva v0.5, First Impressions v2 and MPII Group
Interaction datasets. We further provide an extensive ablation study of the
proposed architecture.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 21:56:50 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Agrawal",
"Tanay",
""
],
[
"Balazia",
"Michal",
""
],
[
"Müller",
"Philipp",
""
],
[
"Brémond",
"François",
""
]
] |
new_dataset
| 0.9989 |
2212.03992
|
Ian McQuillan
|
Oscar H. Ibarra and Ian McQuillan
|
State Grammars with Stores
|
21 pages
|
Theoretical Computer Science 798, 23-39 (2019)
|
10.1016/j.tcs.2019.06.024
| null |
cs.FL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
State grammars are context-free grammars where the productions have states
associated with them, and a production can only be applied to a nonterminal if
the current state matches the state in the production. Once states are added to
grammars, it is natural to add various stores, similar to machine models. With
such extensions, productions can only be applied if both the state and the
value read from each store matches between the current sentential form and the
production. Here, generative capacity results are presented for different
derivation modes, with and without additional stores. In particular, with the
standard derivation relation, it is shown that adding reversal-bounded counters
does not increase the capacity, and states are enough. Also, state grammars
with reversal-bounded counters that operate using leftmost derivations are
shown to coincide with languages accepted by one-way machines with a pushdown
and reversal-bounded counters, and these are surprisingly shown to be strictly
weaker than state grammars with the standard derivation relation (and no
counters). The complexity of the emptiness problem involving state grammars
with reversal-bounded counters is also studied.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 22:54:07 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Ibarra",
"Oscar H.",
""
],
[
"McQuillan",
"Ian",
""
]
] |
new_dataset
| 0.982774 |
2212.04005
|
Jinyoung Park
|
Jinyoung Park, Minseok Son, Seungju Cho, Inyoung Lee, Changick Kim
|
RainUNet for Super-Resolution Rain Movie Prediction under
Spatio-temporal Shifts
|
NeurIPS 2022, Weather4Cast core challenge
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a solution to the Weather4cast 2022 Challenge Stage 2.
The goal of the challenge is to forecast future high-resolution rainfall events
obtained from ground radar using low-resolution multiband satellite images. We
suggest a solution that performs data preprocessing appropriate to the
challenge and then predicts rainfall movies using a novel RainUNet. RainUNet is
a hierarchical U-shaped network with temporal-wise separable block (TS block)
using a decoupled large kernel 3D convolution to improve the prediction
performance. Various evaluation metrics show that our solution is effective
compared to the baseline method. The source codes are available at
https://github.com/jinyxp/Weather4cast-2022
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 23:42:39 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Park",
"Jinyoung",
""
],
[
"Son",
"Minseok",
""
],
[
"Cho",
"Seungju",
""
],
[
"Lee",
"Inyoung",
""
],
[
"Kim",
"Changick",
""
]
] |
new_dataset
| 0.998956 |
2212.04018
|
Kartik Pant
|
Kartik Anand Pant, Zhanpeng Yang, James M Goppert, and Inseok Hwang
|
An Open-Source Gazebo Plugin for GNSS Multipath Signal Emulation in
Virtual Urban Canyons
|
13 pages, 8 figures
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
One of the major errors affecting GNSS signals in urban canyons is GNSS
multipath error. In this work, we develop a Gazebo plugin which utilizes a ray
tracing technique to account for multipath effects in a virtual urban canyon
environment using virtual satellites. This software plugin balances accuracy
and computational complexity to run the simulation in real-time for both
software-in-the-loop (SITL) and hardware-in-the-loop (HITL) testing. We also
construct a 3D virtual environment of Hong Kong and compare the results from
our plugin with the GNSS data in the publicly available Urban-Nav dataset, to
validate the efficacy of the proposed Gazebo Plugin. The plugin is openly
available to all the researchers in the robotics community.
https://github.com/kpant14/multipath_sim
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 00:44:49 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Pant",
"Kartik Anand",
""
],
[
"Yang",
"Zhanpeng",
""
],
[
"Goppert",
"James M",
""
],
[
"Hwang",
"Inseok",
""
]
] |
new_dataset
| 0.963218 |
2212.04058
|
Dalin Zhang
|
Xinle Wu, Dalin Zhang, Miao Zhang, Chenjuan Guo, Shuai Zhao, Yi Zhang,
Huai Wang, Bin Yang
|
AutoPINN: When AutoML Meets Physics-Informed Neural Networks
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Physics-Informed Neural Networks (PINNs) have recently been proposed to solve
scientific and engineering problems, where physical laws are introduced into
neural networks as prior knowledge. With the embedded physical laws, PINNs
enable the estimation of critical parameters, which are unobservable via
physical tools, through observable variables. For example, Power Electronic
Converters (PECs) are essential building blocks for the green energy
transition. PINNs have been applied to estimate the capacitance, which is
unobservable during PEC operations, using current and voltage, which can be
observed easily during operations. The estimated capacitance facilitates
self-diagnostics of PECs. Existing PINNs are often manually designed, which is
time-consuming and may lead to suboptimal performance due to a large number of
design choices for neural network architectures and hyperparameters. In
addition, PINNs are often deployed on different physical devices, e.g., PECs,
with limited and varying resources. Therefore, it requires designing different
PINN models under different resource constraints, making it an even more
challenging task for manual design. To contend with the challenges, we propose
Automated Physics-Informed Neural Networks (AutoPINN), a framework that enables
the automated design of PINNs by combining AutoML and PINNs. Specifically, we
first tailor a search space that allows finding high-accuracy PINNs for PEC
internal parameter estimation. We then propose a resource-aware search strategy
to explore the search space to find the best PINN model under different
resource constraints. We experimentally demonstrate that AutoPINN is able to
find more accurate PINN models than human-designed, state-of-the-art PINN
models using fewer resources.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 03:44:08 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Wu",
"Xinle",
""
],
[
"Zhang",
"Dalin",
""
],
[
"Zhang",
"Miao",
""
],
[
"Guo",
"Chenjuan",
""
],
[
"Zhao",
"Shuai",
""
],
[
"Zhang",
"Yi",
""
],
[
"Wang",
"Huai",
""
],
[
"Yang",
"Bin",
""
]
] |
new_dataset
| 0.998084 |
2212.04061
|
Sibendu Paul
|
Sibendu Paul, Kunal Rao, Giuseppe Coviello, Murugan Sankaradas, Oliver
Po, Y. Charlie Hu, Srimat T. Chakradhar
|
Elixir: A system to enhance data quality for multiple analytics on a
video stream
| null | null | null | null |
cs.CV cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
IoT sensors, especially video cameras, are ubiquitously deployed around the
world to perform a variety of computer vision tasks in several verticals
including retail, healthcare, safety and security, transportation,
manufacturing, etc. To amortize their high deployment effort and cost, it is
desirable to perform multiple video analytics tasks, which we refer to as
Analytical Units (AUs), off the video feed coming out of every camera. In this
paper, we first show that in a multi-AU setting, changing the camera setting
has disproportionate impact on different AUs performance. In particular, the
optimal setting for one AU may severely degrade the performance for another AU,
and further the impact on different AUs varies as the environmental condition
changes. We then present Elixir, a system to enhance the video stream quality
for multiple analytics on a video stream. Elixir leverages Multi-Objective
Reinforcement Learning (MORL), where the RL agent caters to the objectives from
different AUs and adjusts the camera setting to simultaneously enhance the
performance of all AUs. To define the multiple objectives in MORL, we develop
new AU-specific quality estimator values for each individual AU. We evaluate
Elixir through real-world experiments on a testbed with three cameras deployed
next to each other (overlooking a large enterprise parking lot) running Elixir
and two baseline approaches, respectively. Elixir correctly detects 7.1%
(22,068) and 5.0% (15,731) more cars, 94% (551) and 72% (478) more faces, and
670.4% (4975) and 158.6% (3507) more persons than the default-setting and
time-sharing approaches, respectively. It also detects 115 license plates, far
more than the time-sharing approach (7) and the default setting (0).
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 04:04:58 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Paul",
"Sibendu",
""
],
[
"Rao",
"Kunal",
""
],
[
"Coviello",
"Giuseppe",
""
],
[
"Sankaradas",
"Murugan",
""
],
[
"Po",
"Oliver",
""
],
[
"Hu",
"Y. Charlie",
""
],
[
"Chakradhar",
"Srimat T.",
""
]
] |
new_dataset
| 0.978789 |
2212.04119
|
ByungSoo Ko
|
Young-Jun Lee, Byungsoo Ko, Han-Gyu Kim, Ho-Jin Choi
|
DialogCC: Large-Scale Multi-Modal Dialogue Dataset
| null | null | null | null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As sharing images in an instant message is a crucial factor, there has been
active research on learning a image-text multi-modal dialogue model. However,
training a well-generalized multi-modal dialogue model is challenging because
existing multi-modal dialogue datasets contain a small number of data, limited
topics, and a restricted variety of images per dialogue. In this paper, we
present a multi-modal dialogue dataset creation pipeline that involves matching
large-scale images to dialogues based on CLIP similarity. Using this automatic
pipeline, we propose a large-scale multi-modal dialogue dataset, DialogCC,
which covers diverse real-world topics and various images per dialogue. With
extensive experiments, we demonstrate that training a multi-modal dialogue
model with our dataset can improve generalization performance. Additionally,
existing models trained with our dataset achieve state-of-the-art performance
on image and text retrieval tasks. The source code and the dataset will be
released after publication.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 07:29:07 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Lee",
"Young-Jun",
""
],
[
"Ko",
"Byungsoo",
""
],
[
"Kim",
"Han-Gyu",
""
],
[
"Choi",
"Ho-Jin",
""
]
] |
new_dataset
| 0.999753 |
2212.04138
|
Yiannis Kantaros
|
Kaiyuan Tan, Jun Wang, Yiannis Kantaros
|
Targeted Adversarial Attacks against Neural Network Trajectory
Predictors
| null | null | null | null |
cs.LG cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Trajectory prediction is an integral component of modern autonomous systems
as it allows for envisioning future intentions of nearby moving agents. Due to
the lack of other agents' dynamics and control policies, deep neural network
(DNN) models are often employed for trajectory forecasting tasks. Although
there exists an extensive literature on improving the accuracy of these models,
there is a very limited number of works studying their robustness against
adversarially crafted input trajectories. To bridge this gap, in this paper, we
propose a targeted adversarial attack against DNN models for trajectory
forecasting tasks. We call the proposed attack TA4TP for Targeted adversarial
Attack for Trajectory Prediction. Our approach generates adversarial input
trajectories that are capable of fooling DNN models into predicting
user-specified target/desired trajectories. Our attack relies on solving a
nonlinear constrained optimization problem where the objective function
captures the deviation of the predicted trajectory from a target one while the
constraints model physical requirements that the adversarial input should
satisfy. The latter ensures that the inputs look natural and they are safe to
execute (e.g., they are close to nominal inputs and away from obstacles). We
demonstrate the effectiveness of TA4TP on two state-of-the-art DNN models and
two datasets. To the best of our knowledge, we propose the first targeted
adversarial attack against DNN models used for trajectory forecasting.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 08:34:28 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Tan",
"Kaiyuan",
""
],
[
"Wang",
"Jun",
""
],
[
"Kantaros",
"Yiannis",
""
]
] |
new_dataset
| 0.978611 |
2212.04163
|
Yijun Wang
|
Yijun Wang, Rui Lang, Rui Li and Junsong Zhang
|
NRTR: Neuron Reconstruction with Transformer from 3D Optical Microscopy
Images
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The neuron reconstruction from raw Optical Microscopy (OM) image stacks is
the basis of neuroscience. Manual annotation and semi-automatic neuron tracing
algorithms are time-consuming and inefficient. Existing deep learning neuron
reconstruction methods, although demonstrating exemplary performance, greatly
demand complex rule-based components. Therefore, a crucial challenge is
designing an end-to-end neuron reconstruction method that makes the overall
framework simpler and model training easier. We propose a Neuron Reconstruction
Transformer (NRTR) that, discarding the complex rule-based components, views
neuron reconstruction as a direct set-prediction problem. To the best of our
knowledge, NRTR is the first image-to-set deep learning model for end-to-end
neuron reconstruction. In experiments using the BigNeuron and VISoR-40
datasets, NRTR achieves excellent neuron reconstruction results for
comprehensive benchmarks and outperforms competitive baselines. Results of
extensive experiments indicate that NRTR is effective at showing that neuron
reconstruction is viewed as a set-prediction problem, which makes end-to-end
model training available.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 09:35:22 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Wang",
"Yijun",
""
],
[
"Lang",
"Rui",
""
],
[
"Li",
"Rui",
""
],
[
"Zhang",
"Junsong",
""
]
] |
new_dataset
| 0.974197 |
2212.04166
|
Yannick Schmitz
|
Marcel Wagner, Yannick Schmitz and Egon Wanke
|
On the strong metric dimension of composed graphs
| null | null | null | null |
cs.CC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Two vertices $u$ and $v$ of an undirected graph $G$ are strongly resolved by
a vertex $w$ if there is a shortest path between $w$ and $u$ containing $v$ or
a shortest path between $w$ and $v$ containing $u$. A vertex set $R$ is a
strong resolving set for $G$ if for each pair of vertices there is a vertex in
$R$ that strongly resolves them. The strong metric dimension of $G$ is the size
of a minimum strong resolving set for $G$. We show that a minimum strong
resolving set for an undirected graph $G$ can be computed efficiently if and
only if a minimum strong resolving set for each biconnected component of $G$
can be computed efficiently.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 09:41:58 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Wagner",
"Marcel",
""
],
[
"Schmitz",
"Yannick",
""
],
[
"Wanke",
"Egon",
""
]
] |
new_dataset
| 0.994811 |
2212.04175
|
Kan Huang
|
Kan Huang, Kai Zhang, Ming Liu
|
GreenEyes: An Air Quality Evaluating Model based on WaveNet
| null | null | null | null |
cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accompanying rapid industrialization, humans are suffering from serious air
pollution problems. The demand for air quality prediction is becoming more and
more important to the government's policy-making and people's daily life. In
this paper, We propose GreenEyes -- a deep neural network model, which consists
of a WaveNet-based backbone block for learning representations of sequences and
an LSTM with a Temporal Attention module for capturing the hidden interactions
between features of multi-channel inputs. To evaluate the effectiveness of our
proposed method, we carry out several experiments including an ablation study
on our collected and preprocessed air quality data near HKUST. The experimental
results show our model can effectively predict the air quality level of the
next timestamp given any segment of the air quality data from the data set. We
have also released our standalone dataset at
https://github.com/AI-Huang/IAQI_Dataset The model and code for this paper are
publicly available at https://github.com/AI-Huang/AirEvaluation
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 10:28:57 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Huang",
"Kan",
""
],
[
"Zhang",
"Kai",
""
],
[
"Liu",
"Ming",
""
]
] |
new_dataset
| 0.979828 |
2212.04197
|
Yuekai Jia
|
Yuekai Jia, Shuang Liu, Wenhao Wang, Yu Chen, Zhengde Zhai, Shoumeng
Yan, Zhengyu He
|
HyperEnclave: An Open and Cross-platform Trusted Execution Environment
| null |
In 2022 USENIX Annual Technical Conference (USENIX ATC 22), pages
437-454, Carlsbad, CA, July 2022. USENIX Association
| null | null |
cs.CR cs.AR cs.OS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A number of trusted execution environments (TEEs) have been proposed by both
academia and industry. However, most of them require specific hardware or
firmware changes and are bound to specific hardware vendors (such as Intel,
AMD, ARM, and IBM). In this paper, we propose HyperEnclave, an open and
cross-platform process-based TEE that relies on the widely-available
virtualization extension to create the isolated execution environment. In
particular, HyperEnclave is designed to support the flexible enclave operation
modes to fulfill the security and performance demands under various enclave
workloads. We provide the enclave SDK to run existing SGX programs on
HyperEnclave with little or no source code changes. We have implemented
HyperEnclave on commodity AMD servers and deployed the system in a
world-leading FinTech company to support real-world privacy-preserving
computations. The evaluation on both micro-benchmarks and application
benchmarks shows the design of HyperEnclave introduces only a small overhead.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 11:23:48 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Jia",
"Yuekai",
""
],
[
"Liu",
"Shuang",
""
],
[
"Wang",
"Wenhao",
""
],
[
"Chen",
"Yu",
""
],
[
"Zhai",
"Zhengde",
""
],
[
"Yan",
"Shoumeng",
""
],
[
"He",
"Zhengyu",
""
]
] |
new_dataset
| 0.998784 |
2212.04229
|
Prashant Hari Narayan Rajput
|
Prashant Hari Narayan Rajput (1), Constantine Doumanidis (2), Michail
Maniatakos (2) ((1) NYU Tandon School of Engineering, (2) New York University
Abu Dhabi)
|
ICSPatch: Automated Vulnerability Localization and Non-Intrusive
Hotpatching in Industrial Control Systems using Data Dependence Graphs
|
To appear in the 32nd USENIX Security Symposium, August 2023,
Anaheim, CA, USA [16 pages, 12 figures, 5 tables, code available at
https://github.com/momalab/ICSPatch]
| null | null | null |
cs.CR cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The paradigm shift of enabling extensive intercommunication between the
Operational Technology (OT) and Information Technology (IT) devices allows
vulnerabilities typical to the IT world to propagate to the OT side. Therefore,
the security layer offered in the past by air gapping is removed, making
security patching for OT devices a hard requirement. Conventional patching
involves a device reboot to load the patched code in the main memory, which
does not apply to OT devices controlling critical processes due to downtime,
necessitating in-memory vulnerability patching. Furthermore, these control
binaries are often compiled by in-house proprietary compilers, further
hindering the patching process and placing reliance on OT vendors for rapid
vulnerability discovery and patch development. The current state-of-the-art
hotpatching approaches only focus on firmware and/or RTOS. Therefore, in this
work, we develop ICSPatch, a framework to automate control logic vulnerability
localization using Data Dependence Graphs (DDGs). With the help of DDGs,
ICSPatch pinpoints the vulnerability in the control application. As an
independent second step, ICSPatch can non-intrusively hotpatch vulnerabilities
in the control application directly in the main memory of Programmable Logic
Controllers while maintaining reliable continuous operation. To evaluate our
framework, we test ICSPatch on a synthetic dataset of 24 vulnerable control
application binaries from diverse critical infrastructure sectors. Results show
that ICSPatch could successfully localize all vulnerabilities and generate
patches accordingly. Furthermore, the patch added negligible latency increase
in the execution cycle while maintaining correctness and protection against the
vulnerability.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 12:26:15 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Rajput",
"Prashant Hari Narayan",
""
],
[
"Doumanidis",
"Constantine",
""
],
[
"Maniatakos",
"Michail",
""
]
] |
new_dataset
| 0.981054 |
2212.04234
|
Xiaoyang Shan
|
Lihai Nie, Xiaoyang Shan, Laiping Zhao, Keqiu Li
|
PKDGA: A Partial Knowledge-based Domain Generation Algorithm for Botnets
|
12 pages,11 figures
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Domain generation algorithms (DGAs) can be categorized into three types:
zero-knowledge, partial-knowledge, and full-knowledge. While prior research
merely focused on zero-knowledge and full-knowledge types, we characterize
their anti-detection ability and practicality and find that zero-knowledge DGAs
present low anti-detection ability against detectors, and full-knowledge DGAs
suffer from low practicality due to the strong assumption that they are fully
detector-aware. Given these observations, we propose PKDGA, a partial
knowledge-based domain generation algorithm with high anti-detection ability
and high practicality. PKDGA employs the reinforcement learning architecture,
which makes it evolve automatically based only on the easily-observable
feedback from detectors. We evaluate PKDGA using a comprehensive set of
real-world datasets, and the results demonstrate that it reduces the detection
performance of existing detectors from 91.7% to 52.5%. We further apply PKDGA
to the Mirai malware, and the evaluations show that the proposed method is
quite lightweight and time-efficient.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 12:31:57 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Nie",
"Lihai",
""
],
[
"Shan",
"Xiaoyang",
""
],
[
"Zhao",
"Laiping",
""
],
[
"Li",
"Keqiu",
""
]
] |
new_dataset
| 0.996743 |
2212.04264
|
Kaan Ak\c{s}it
|
Ahmet G\"uzel, Jeanne Beyazian, Praneeth Chakravarthula and Kaan
Ak\c{s}it
|
ChromaCorrect: Prescription Correction in Virtual Reality Headsets
through Perceptual Guidance
|
12 pages, 9 figures, 1 table, 1 listing
| null | null | null |
cs.HC cs.GR cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A large portion of today's world population suffer from vision impairments
and wear prescription eyeglasses. However, eyeglasses causes additional bulk
and discomfort when used with augmented and virtual reality headsets, thereby
negatively impacting the viewer's visual experience. In this work, we remedy
the usage of prescription eyeglasses in Virtual Reality (VR) headsets by
shifting the optical complexity completely into software and propose a
prescription-aware rendering approach for providing sharper and immersive VR
imagery. To this end, we develop a differentiable display and visual perception
model encapsulating display-specific parameters, color and visual acuity of
human visual system and the user-specific refractive errors. Using this
differentiable visual perception model, we optimize the rendered imagery in the
display using stochastic gradient-descent solvers. This way, we provide
prescription glasses-free sharper images for a person with vision impairments.
We evaluate our approach on various displays, including desktops and VR
headsets, and show significant quality and contrast improvements for users with
vision impairments.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 13:30:17 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Güzel",
"Ahmet",
""
],
[
"Beyazian",
"Jeanne",
""
],
[
"Chakravarthula",
"Praneeth",
""
],
[
"Akşit",
"Kaan",
""
]
] |
new_dataset
| 0.996192 |
2212.04320
|
Guodong Yin
|
Guodong Yin, Mufeng Zhou, Yiming Chen, Wenjun Tang, Zekun Yang,
Mingyen Lee, Xirui Du, Jinshan Yue, Jiaxin Liu, Huazhong Yang, Yongpan Liu,
Xueqing Li
|
A 65nm 8b-Activation 8b-Weight SRAM-Based Charge-Domain
Computing-in-Memory Macro Using A Fully-Parallel Analog Adder Network and A
Single-ADC Interface
|
Accepted by IEEE 48th European Solid-State Circuits Conference
(ESSCIRC 2022)
| null | null | null |
cs.AR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Performing data-intensive tasks in the von Neumann architecture is
challenging to achieve both high performance and power efficiency due to the
memory wall bottleneck. Computing-in-memory (CiM) is a promising mitigation
approach by enabling parallel in-situ multiply-accumulate (MAC) operations
within the memory with support from the peripheral interface and datapath.
SRAM-based charge-domain CiM (CD-CiM) has shown its potential of enhanced power
efficiency and computing accuracy. However, existing SRAM-based CD-CiM faces
scaling challenges to meet the throughput requirement of high-performance
multi-bit-quantization applications. This paper presents an SRAM-based
high-throughput ReLU-optimized CD-CiM macro. It is capable of completing MAC
and ReLU of two signed 8b vectors in one CiM cycle with only one A/D
conversion. Along with non-linearity compensation for the analog computing and
A/D conversion interfaces, this work achieves 51.2GOPS throughput and
10.3TOPS/W energy efficiency, while showing 88.6% accuracy in the CIFAR-10
dataset.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 07:52:10 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Yin",
"Guodong",
""
],
[
"Zhou",
"Mufeng",
""
],
[
"Chen",
"Yiming",
""
],
[
"Tang",
"Wenjun",
""
],
[
"Yang",
"Zekun",
""
],
[
"Lee",
"Mingyen",
""
],
[
"Du",
"Xirui",
""
],
[
"Yue",
"Jinshan",
""
],
[
"Liu",
"Jiaxin",
""
],
[
"Yang",
"Huazhong",
""
],
[
"Liu",
"Yongpan",
""
],
[
"Li",
"Xueqing",
""
]
] |
new_dataset
| 0.998136 |
2212.04357
|
Kaifa Zhao
|
Kaifa Zhao, Le Yu, Shiyao Zhou, Jing Li, Xiapu Luo, Yat Fei Aemon
Chiu, Yutong Liu
|
A Fine-grained Chinese Software Privacy Policy Dataset for Sequence
Labeling and Regulation Compliant Identification
|
Accepted by EMNLP 2022 (The 2022 Conference on Empirical Methods in
Natural Language Processing)
| null | null | null |
cs.CR cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Privacy protection raises great attention on both legal levels and user
awareness. To protect user privacy, countries enact laws and regulations
requiring software privacy policies to regulate their behavior. However,
privacy policies are written in natural languages with many legal terms and
software jargon that prevent users from understanding and even reading them. It
is desirable to use NLP techniques to analyze privacy policies for helping
users understand them. Furthermore, existing datasets ignore law requirements
and are limited to English. In this paper, we construct the first Chinese
privacy policy dataset, namely CA4P-483, to facilitate the sequence labeling
tasks and regulation compliance identification between privacy policies and
software. Our dataset includes 483 Chinese Android application privacy
policies, over 11K sentences, and 52K fine-grained annotations. We evaluate
families of robust and representative baseline models on our dataset. Based on
baseline performance, we provide findings and potential research directions on
our dataset. Finally, we investigate the potential applications of CA4P-483
combing regulation requirements and program analysis.
|
[
{
"version": "v1",
"created": "Sun, 4 Dec 2022 05:59:59 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Zhao",
"Kaifa",
""
],
[
"Yu",
"Le",
""
],
[
"Zhou",
"Shiyao",
""
],
[
"Li",
"Jing",
""
],
[
"Luo",
"Xiapu",
""
],
[
"Chiu",
"Yat Fei Aemon",
""
],
[
"Liu",
"Yutong",
""
]
] |
new_dataset
| 0.999811 |
2212.04360
|
Hongwei Yi
|
Hongwei Yi, Chun-Hao P. Huang, Shashank Tripathi, Lea Hering, Justus
Thies, Michael J. Black
|
MIME: Human-Aware 3D Scene Generation
|
Project Page: https://mime.is.tue.mpg.de
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Generating realistic 3D worlds occupied by moving humans has many
applications in games, architecture, and synthetic data creation. But
generating such scenes is expensive and labor intensive. Recent work generates
human poses and motions given a 3D scene. Here, we take the opposite approach
and generate 3D indoor scenes given 3D human motion. Such motions can come from
archival motion capture or from IMU sensors worn on the body, effectively
turning human movement in a "scanner" of the 3D world. Intuitively, human
movement indicates the free-space in a room and human contact indicates
surfaces or objects that support activities such as sitting, lying or touching.
We propose MIME (Mining Interaction and Movement to infer 3D Environments),
which is a generative model of indoor scenes that produces furniture layouts
that are consistent with the human movement. MIME uses an auto-regressive
transformer architecture that takes the already generated objects in the scene
as well as the human motion as input, and outputs the next plausible object. To
train MIME, we build a dataset by populating the 3D FRONT scene dataset with 3D
humans. Our experiments show that MIME produces more diverse and plausible 3D
scenes than a recent generative scene method that does not know about human
movement. Code and data will be available for research at
https://mime.is.tue.mpg.de.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 15:56:17 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Yi",
"Hongwei",
""
],
[
"Huang",
"Chun-Hao P.",
""
],
[
"Tripathi",
"Shashank",
""
],
[
"Hering",
"Lea",
""
],
[
"Thies",
"Justus",
""
],
[
"Black",
"Michael J.",
""
]
] |
new_dataset
| 0.999553 |
2212.04408
|
Xuancheng Ren
|
Jinze Bai, Rui Men, Hao Yang, Xuancheng Ren, Kai Dang, Yichang Zhang,
Xiaohuan Zhou, Peng Wang, Sinan Tan, An Yang, Zeyu Cui, Yu Han, Shuai Bai,
Wenbin Ge, Jianxin Ma, Junyang Lin, Jingren Zhou, Chang Zhou
|
OFASys: A Multi-Modal Multi-Task Learning System for Building Generalist
Models
| null | null | null | null |
cs.CV cs.AI cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Generalist models, which are capable of performing diverse multi-modal tasks
in a task-agnostic way within a single model, have been explored recently.
Being, hopefully, an alternative to approaching general-purpose AI, existing
generalist models are still at an early stage, where modality and task coverage
is limited. To empower multi-modal task-scaling and speed up this line of
research, we release a generalist model learning system, OFASys, built on top
of a declarative task interface named multi-modal instruction. At the core of
OFASys is the idea of decoupling multi-modal task representations from the
underlying model implementations. In OFASys, a task involving multiple
modalities can be defined declaratively even with just a single line of code.
The system automatically generates task plans from such instructions for
training and inference. It also facilitates multi-task training for diverse
multi-modal workloads. As a starting point, we provide presets of 7 different
modalities and 23 highly-diverse example tasks in OFASys, with which we also
develop a first-in-kind, single model, OFA+, that can handle text, image,
speech, video, and motion data. The single OFA+ model achieves 95% performance
in average with only 16% parameters of 15 task-finetuned models, showcasing the
performance reliability of multi-modal task-scaling provided by OFASys.
Available at https://github.com/OFA-Sys/OFASys
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 17:07:09 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Bai",
"Jinze",
""
],
[
"Men",
"Rui",
""
],
[
"Yang",
"Hao",
""
],
[
"Ren",
"Xuancheng",
""
],
[
"Dang",
"Kai",
""
],
[
"Zhang",
"Yichang",
""
],
[
"Zhou",
"Xiaohuan",
""
],
[
"Wang",
"Peng",
""
],
[
"Tan",
"Sinan",
""
],
[
"Yang",
"An",
""
],
[
"Cui",
"Zeyu",
""
],
[
"Han",
"Yu",
""
],
[
"Bai",
"Shuai",
""
],
[
"Ge",
"Wenbin",
""
],
[
"Ma",
"Jianxin",
""
],
[
"Lin",
"Junyang",
""
],
[
"Zhou",
"Jingren",
""
],
[
"Zhou",
"Chang",
""
]
] |
new_dataset
| 0.968966 |
2212.04437
|
Benjamin Fele
|
Benjamin Fele and Ajda Lampe and Peter Peer and Vitomir \v{S}truc
|
C-VTON: Context-Driven Image-Based Virtual Try-On Network
|
Accepted to WACV 2022
| null |
10.1109/WACV51458.2022.00226
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Image-based virtual try-on techniques have shown great promise for enhancing
the user-experience and improving customer satisfaction on fashion-oriented
e-commerce platforms. However, existing techniques are currently still limited
in the quality of the try-on results they are able to produce from input images
of diverse characteristics. In this work, we propose a Context-Driven Virtual
Try-On Network (C-VTON) that addresses these limitations and convincingly
transfers selected clothing items to the target subjects even under challenging
pose configurations and in the presence of self-occlusions. At the core of the
C-VTON pipeline are: (i) a geometric matching procedure that efficiently aligns
the target clothing with the pose of the person in the input images, and (ii) a
powerful image generator that utilizes various types of contextual information
when synthesizing the final try-on result. C-VTON is evaluated in rigorous
experiments on the VITON and MPV datasets and in comparison to state-of-the-art
techniques from the literature. Experimental results show that the proposed
approach is able to produce photo-realistic and visually convincing results and
significantly improves on the existing state-of-the-art.
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 17:56:34 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Fele",
"Benjamin",
""
],
[
"Lampe",
"Ajda",
""
],
[
"Peer",
"Peter",
""
],
[
"Štruc",
"Vitomir",
""
]
] |
new_dataset
| 0.999727 |
2212.04498
|
Deepak Pathak
|
Kenneth Shaw, Shikhar Bahl, Deepak Pathak
|
VideoDex: Learning Dexterity from Internet Videos
|
Accepted at CoRL 2022. Website at https://video-dex.github.io
| null | null | null |
cs.RO cs.AI cs.CV cs.LG cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To build general robotic agents that can operate in many environments, it is
often imperative for the robot to collect experience in the real world.
However, this is often not feasible due to safety, time, and hardware
restrictions. We thus propose leveraging the next best thing as real-world
experience: internet videos of humans using their hands. Visual priors, such as
visual features, are often learned from videos, but we believe that more
information from videos can be utilized as a stronger prior. We build a
learning algorithm, VideoDex, that leverages visual, action, and physical
priors from human video datasets to guide robot behavior. These actions and
physical priors in the neural network dictate the typical human behavior for a
particular robot task. We test our approach on a robot arm and dexterous
hand-based system and show strong results on various manipulation tasks,
outperforming various state-of-the-art methods. Videos at
https://video-dex.github.io
|
[
{
"version": "v1",
"created": "Thu, 8 Dec 2022 18:59:59 GMT"
}
] | 2022-12-09T00:00:00 |
[
[
"Shaw",
"Kenneth",
""
],
[
"Bahl",
"Shikhar",
""
],
[
"Pathak",
"Deepak",
""
]
] |
new_dataset
| 0.955025 |
2104.12290
|
Gokhan Egri
|
Gokhan Egri, Todd Zickler
|
StegaPos: Preventing Unwanted Crops and Replacements with Imperceptible
Positional Embeddings
|
For CVPR 2022 submission, 8 pages (main)
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a learned, spatially-varying steganography system that allows
detecting when and how images have been altered by cropping, splicing or
inpainting after publication. The system comprises a learned encoder that
imperceptibly hides distinct positional signatures in every local image region
before publication, and an accompanying learned decoder that extracts the
steganographic signatures to determine, for each local image region, its 2D
positional coordinates within the originally-published image. Crop and
replacement edits become detectable by the inconsistencies they cause in the
hidden positional signatures. Using a prototype system for small $(400 \times
400)$ images, we show experimentally that simple CNN encoder and decoder
architectures can be trained jointly to achieve detection that is reliable and
robust, without introducing perceptible distortion. This approach could help
individuals and image-sharing platforms certify that an image was published by
a trusted source, and also know which parts of such an image, if any, have been
substantially altered since publication.
|
[
{
"version": "v1",
"created": "Sun, 25 Apr 2021 23:42:29 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Dec 2022 07:11:09 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Egri",
"Gokhan",
""
],
[
"Zickler",
"Todd",
""
]
] |
new_dataset
| 0.997787 |
2105.08209
|
Wojciech Kry\'sci\'nski
|
Wojciech Kry\'sci\'nski, Nazneen Rajani, Divyansh Agarwal, Caiming
Xiong, Dragomir Radev
|
BookSum: A Collection of Datasets for Long-form Narrative Summarization
|
19 pages, 12 tables, 3 figures
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The majority of available text summarization datasets include short-form
source documents that lack long-range causal and temporal dependencies, and
often contain strong layout and stylistic biases. While relevant, such datasets
will offer limited challenges for future generations of text summarization
systems. We address these issues by introducing BookSum, a collection of
datasets for long-form narrative summarization. Our dataset covers source
documents from the literature domain, such as novels, plays and stories, and
includes highly abstractive, human written summaries on three levels of
granularity of increasing difficulty: paragraph-, chapter-, and book-level. The
domain and structure of our dataset poses a unique set of challenges for
summarization systems, which include: processing very long documents,
non-trivial causal and temporal dependencies, and rich discourse structures. To
facilitate future work, we trained and evaluated multiple extractive and
abstractive summarization models as baselines for our dataset.
|
[
{
"version": "v1",
"created": "Tue, 18 May 2021 00:22:46 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 19:19:35 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Kryściński",
"Wojciech",
""
],
[
"Rajani",
"Nazneen",
""
],
[
"Agarwal",
"Divyansh",
""
],
[
"Xiong",
"Caiming",
""
],
[
"Radev",
"Dragomir",
""
]
] |
new_dataset
| 0.999591 |
2112.01238
|
Kyle McDonald
|
Kyle McDonald
|
Ethereum Emissions: A Bottom-up Estimate
|
Code at https://github.com/kylemcdonald/ethereum-emissions
| null | null | null |
cs.CY cs.CR math.OC
|
http://creativecommons.org/licenses/by/4.0/
|
The Ethereum ecosystem was maintained by a distributed global network of
computers that required massive amounts of computational power. Previous work
on estimating the energy use and emissions of the Ethereum network has relied
on top-down economic analysis and rough estimates of hardware efficiency and
emissions factors. In this work we provide a bottom-up analysis that works from
hashrate to an energy usage estimate, and from mining locations to an emissions
factor estimate, and combines these for an overall emissions estimate. We
analyze the entire history of PoW Ethereum, from creation to the merge.
|
[
{
"version": "v1",
"created": "Fri, 19 Nov 2021 11:05:48 GMT"
},
{
"version": "v2",
"created": "Fri, 3 Dec 2021 02:18:26 GMT"
},
{
"version": "v3",
"created": "Wed, 7 Dec 2022 08:24:55 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"McDonald",
"Kyle",
""
]
] |
new_dataset
| 0.997269 |
2201.09750
|
Bilge Celik
|
Bilge Celik and Prabhant Singh and Joaquin Vanschoren
|
Online AutoML: An adaptive AutoML framework for online learning
|
25 pages, 8 figures. Machine Learning S.I.: Automating Data Science
(2022)
| null |
10.1007/s10994-022-06262-0
| null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automated Machine Learning (AutoML) has been used successfully in settings
where the learning task is assumed to be static. In many real-world scenarios,
however, the data distribution will evolve over time, and it is yet to be shown
whether AutoML techniques can effectively design online pipelines in dynamic
environments. This study aims to automate pipeline design for online learning
while continuously adapting to data drift. For this purpose, we design an
adaptive Online Automated Machine Learning (OAML) system, searching the
complete pipeline configuration space of online learners, including
preprocessing algorithms and ensembling techniques. This system combines the
inherent adaptation capabilities of online learners with the fast automated
pipeline (re)optimization capabilities of AutoML. Focusing on optimization
techniques that can adapt to evolving objectives, we evaluate asynchronous
genetic programming and asynchronous successive halving to optimize these
pipelines continually. We experiment on real and artificial data streams with
varying types of concept drift to test the performance and adaptation
capabilities of the proposed system. The results confirm the utility of OAML
over popular online learning algorithms and underscore the benefits of
continuous pipeline redesign in the presence of data drift.
|
[
{
"version": "v1",
"created": "Mon, 24 Jan 2022 15:37:20 GMT"
},
{
"version": "v2",
"created": "Tue, 10 May 2022 08:57:15 GMT"
},
{
"version": "v3",
"created": "Wed, 7 Dec 2022 10:21:57 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Celik",
"Bilge",
""
],
[
"Singh",
"Prabhant",
""
],
[
"Vanschoren",
"Joaquin",
""
]
] |
new_dataset
| 0.974912 |
2202.03901
|
George Eskandar
|
George Eskandar, Sanjeev Sudarsan, Karim Guirguis, Janaranjani
Palaniswamy, Bharath Somashekar, Bin Yang
|
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous
Driving
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lidar sensors are costly yet critical for understanding the 3D environment in
autonomous driving. High-resolution sensors provide more details about the
surroundings because they contain more vertical beams, but they come at a much
higher cost, limiting their inclusion in autonomous vehicles. Upsampling lidar
pointclouds is a promising approach to gain the benefits of high resolution
while maintaining an affordable cost. Although there exist many pointcloud
upsampling frameworks, a consistent comparison of these works against each
other on the same dataset using unified metrics is still missing. In the first
part of this paper, we propose to benchmark existing methods on the Kitti
dataset. In the second part, we introduce a novel lidar upsampling model, HALS:
Height-Aware Lidar Super-resolution. HALS exploits the observation that lidar
scans exhibit a height-aware range distribution and adopts a generator
architecture with multiple upsampling branches of different receptive fields.
HALS regresses polar coordinates instead of spherical coordinates and uses a
surface-normal loss. Extensive experiments show that HALS achieves
state-of-the-art performance on 3 real-world lidar datasets.
|
[
{
"version": "v1",
"created": "Tue, 8 Feb 2022 14:43:47 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Dec 2022 17:07:54 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Eskandar",
"George",
""
],
[
"Sudarsan",
"Sanjeev",
""
],
[
"Guirguis",
"Karim",
""
],
[
"Palaniswamy",
"Janaranjani",
""
],
[
"Somashekar",
"Bharath",
""
],
[
"Yang",
"Bin",
""
]
] |
new_dataset
| 0.999345 |
2206.12864
|
Xuefei Yin
|
Xuefei Yin, Song Wang, Yanming Zhu, Jiankun Hu
|
A Novel Length-Flexible Lightweight Cancelable Fingerprint Template for
Privacy-Preserving Authentication Systems in Resource-Constrained IoT
Applications
| null |
IEEE Internet of Things Journal, 2022
|
10.1109/JIOT.2022.3204246
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fingerprint authentication techniques have been employed in various Internet
of Things (IoT) applications for access control to protect private data, but
raw fingerprint template leakage in unprotected IoT applications may render the
authentication system insecure. Cancelable fingerprint templates can
effectively prevent privacy breaches and provide strong protection to the
original templates. However, to suit resource-constrained IoT devices,
oversimplified templates would compromise authentication performance
significantly. In addition, the length of existing cancelable fingerprint
templates is usually fixed, making them difficult to be deployed in various
memory-limited IoT devices. To address these issues, we propose a novel
length-flexible lightweight cancelable fingerprint template for
privacy-preserving authentication systems in various resource-constrained IoT
applications. The proposed cancelable template design primarily consists of two
components: 1) length-flexible partial-cancelable feature generation based on
the designed re-indexing scheme; and 2) lightweight cancelable feature
generation based on the designed encoding-nested-difference-XOR scheme.
Comprehensive experimental results on public databases~FVC2002 DB1-DB4 and
FVC2004 DB1-DB4 demonstrate that the proposed cancelable fingerprint template
achieves equivalent authentication performance to state-of-the-art methods in
IoT environments, but our design substantially reduces template storage space
and computational cost. More importantly, the proposed length-flexible
lightweight cancelable template is suitable for a variety of commercial smart
cards (e.g., C5-M.O.S.T. Card Contact Microprocessor Smart Cards CLXSU064KC5).
To the best of our knowledge, the proposed method is the first length-flexible
lightweight, high-performing cancelable fingerprint template design for
resource-constrained IoT applications.
|
[
{
"version": "v1",
"created": "Sun, 26 Jun 2022 12:47:28 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Yin",
"Xuefei",
""
],
[
"Wang",
"Song",
""
],
[
"Zhu",
"Yanming",
""
],
[
"Hu",
"Jiankun",
""
]
] |
new_dataset
| 0.987155 |
2207.10805
|
Xuefei Yin
|
Xuefei Yin, Yanming Zhu, Yi Xie, Jiankun Hu
|
PowerFDNet: Deep Learning-Based Stealthy False Data Injection Attack
Detection for AC-model Transmission Systems
| null |
IEEE Open Journal of the Computer Society, 2022
|
10.1109/OJCS.2022.3199755
| null |
cs.CR cs.AI cs.LG cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent studies have demonstrated that smart grids are vulnerable to stealthy
false data injection attacks (SFDIAs), as SFDIAs can bypass residual-based bad
data detection mechanisms. The SFDIA detection has become one of the focuses of
smart grid research. Methods based on deep learning technology have shown
promising accuracy in the detection of SFDIAs. However, most existing methods
rely on the temporal structure of a sequence of measurements but do not take
account of the spatial structure between buses and transmission lines. To
address this issue, we propose a spatiotemporal deep network, PowerFDNet, for
the SFDIA detection in AC-model power grids. The PowerFDNet consists of two
sub-architectures: spatial architecture (SA) and temporal architecture (TA).
The SA is aimed at extracting representations of bus/line measurements and
modeling the spatial structure based on their representations. The TA is aimed
at modeling the temporal structure of a sequence of measurements. Therefore,
the proposed PowerFDNet can effectively model the spatiotemporal structure of
measurements. Case studies on the detection of SFDIAs on the benchmark smart
grids show that the PowerFDNet achieved significant improvement compared with
the state-of-the-art SFDIA detection methods. In addition, an IoT-oriented
lightweight prototype of size 52 MB is implemented and tested for mobile
devices, which demonstrates the potential applications on mobile devices. The
trained model will be available at
\textit{https://github.com/HubYZ/PowerFDNet}.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 00:46:43 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 23:35:01 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Yin",
"Xuefei",
""
],
[
"Zhu",
"Yanming",
""
],
[
"Xie",
"Yi",
""
],
[
"Hu",
"Jiankun",
""
]
] |
new_dataset
| 0.953908 |
2209.08565
|
Pranav Page
|
Pranav S. Page, Kaustubh S. Bhargao, Hrishikesh V. Baviskar, Gaurav S.
Kasbekar
|
Distributed Probabilistic Congestion Control in LEO Satellite Networks
|
5 pages, 5 figures, conference, poster
| null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
In a dense Low Earth Orbit (LEO) satellite constellation, using a centralized
algorithm for minimum-delay routing would incur significant signaling and
computational overhead. In this work, we exploit the deterministic topology of
the constellation to calculate the minimum-delay path between any two nodes in
a satellite network. We propose a distributed probabilistic congestion control
scheme to minimize end-to-end delay, which is built on top of the existing
Datagram Routing Algorithm (DRA). The decision to route packets is taken based
on the latest traffic information received from neighbours. We provide an
analysis of the congestion caused by a simplified DRA on a uniform infinite
mesh of nodes. We compare the proposed congestion control mechanism with the
existing congestion control used by the DRA via simulations, and show
improvements over the latter.
|
[
{
"version": "v1",
"created": "Sun, 18 Sep 2022 13:13:58 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Dec 2022 00:56:00 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Page",
"Pranav S.",
""
],
[
"Bhargao",
"Kaustubh S.",
""
],
[
"Baviskar",
"Hrishikesh V.",
""
],
[
"Kasbekar",
"Gaurav S.",
""
]
] |
new_dataset
| 0.99851 |
2210.04936
|
Zhitong Xiong
|
Zhitong Xiong, Fahong Zhang, Yi Wang, Yilei Shi, Xiao Xiang Zhu
|
EarthNets: Empowering AI in Earth Observation
|
28 pages
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Earth observation, aiming at monitoring the state of planet Earth using
remote sensing data, is critical for improving our daily lives and living
environment. With a growing number of satellites in orbit, an increasing number
of datasets with diverse sensors and research domains are being published to
facilitate the research of the remote sensing community. In this paper, we
present a comprehensive review of more than 400 publicly published datasets,
including applications like land use/cover, change/disaster monitoring, scene
understanding, agriculture, climate change, and weather forecasting. We
systematically analyze these Earth observation datasets with respect to five
aspects volume, bibliometric analysis, resolution distributions, research
domains, and the correlation between datasets. Based on the dataset attributes,
we propose to measure, rank, and select datasets to build a new benchmark for
model evaluation. Furthermore, a new platform for Earth observation, termed
EarthNets, is released as a means of achieving a fair and consistent evaluation
of deep learning methods on remote sensing data. EarthNets supports standard
dataset libraries and cutting-edge deep learning models to bridge the gap
between the remote sensing and machine learning communities. Based on this
platform, extensive deep learning methods are evaluated on the new benchmark.
The insightful results are beneficial to future research. The platform and
dataset collections are publicly available at https://earthnets.github.io/.
|
[
{
"version": "v1",
"created": "Mon, 10 Oct 2022 18:09:35 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Dec 2022 15:35:11 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Xiong",
"Zhitong",
""
],
[
"Zhang",
"Fahong",
""
],
[
"Wang",
"Yi",
""
],
[
"Shi",
"Yilei",
""
],
[
"Zhu",
"Xiao Xiang",
""
]
] |
new_dataset
| 0.998908 |
2210.11744
|
Ife Adebara
|
Ife Adebara, AbdelRahim Elmadany, Muhammad Abdul-Mageed and Alcides
Alcoba Inciarte
|
AfroLID: A Neural Language Identification Tool for African Languages
|
To appear at EMNLP 2022 Main conference
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Language identification (LID) is a crucial precursor for NLP, especially for
mining web data. Problematically, most of the world's 7000+ languages today are
not covered by LID technologies. We address this pressing issue for Africa by
introducing AfroLID, a neural LID toolkit for $517$ African languages and
varieties. AfroLID exploits a multi-domain web dataset manually curated from
across 14 language families utilizing five orthographic systems. When evaluated
on our blind Test set, AfroLID achieves 95.89 F_1-score. We also compare
AfroLID to five existing LID tools that each cover a small number of African
languages, finding it to outperform them on most languages. We further show the
utility of AfroLID in the wild by testing it on the acutely under-served
Twitter domain. Finally, we offer a number of controlled case studies and
perform a linguistically-motivated error analysis that allow us to both
showcase AfroLID's powerful capabilities and limitations.
|
[
{
"version": "v1",
"created": "Fri, 21 Oct 2022 05:45:50 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Oct 2022 18:25:36 GMT"
},
{
"version": "v3",
"created": "Wed, 7 Dec 2022 04:22:20 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Adebara",
"Ife",
""
],
[
"Elmadany",
"AbdelRahim",
""
],
[
"Abdul-Mageed",
"Muhammad",
""
],
[
"Inciarte",
"Alcides Alcoba",
""
]
] |
new_dataset
| 0.999804 |
2211.16882
|
Ashwin Rao
|
Pranjali Pathre, Anurag Sahu, Ashwin Rao, Avinash Prabhu, Meher
Shashwat Nigam, Tanvi Karandikar, Harit Pandya, and K. Madhava Krishna
|
MVRackLay: Monocular Multi-View Layout Estimation for Warehouse Racks
and Shelves
| null |
IEEE International Conference on Robotics and Biomimetics (ROBIO)
2022
| null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we propose and showcase, for the first time, monocular
multi-view layout estimation for warehouse racks and shelves. Unlike typical
layout estimation methods, MVRackLay estimates multi-layered layouts, wherein
each layer corresponds to the layout of a shelf within a rack. Given a sequence
of images of a warehouse scene, a dual-headed Convolutional-LSTM architecture
outputs segmented racks, the front and the top view layout of each shelf within
a rack. With minimal effort, such an output is transformed into a 3D rendering
of all racks, shelves and objects on the shelves, giving an accurate 3D
depiction of the entire warehouse scene in terms of racks, shelves and the
number of objects on each shelf. MVRackLay generalizes to a diverse set of
warehouse scenes with varying number of objects on each shelf, number of
shelves and in the presence of other such racks in the background. Further,
MVRackLay shows superior performance vis-a-vis its single view counterpart,
RackLay, in layout accuracy, quantized in terms of the mean IoU and mAP
metrics. We also showcase a multi-view stitching of the 3D layouts resulting in
a representation of the warehouse scene with respect to a global reference
frame akin to a rendering of the scene from a SLAM pipeline. To the best of our
knowledge, this is the first such work to portray a 3D rendering of a warehouse
scene in terms of its semantic components - Racks, Shelves and Objects - all
from a single monocular camera.
|
[
{
"version": "v1",
"created": "Wed, 30 Nov 2022 10:32:04 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Pathre",
"Pranjali",
""
],
[
"Sahu",
"Anurag",
""
],
[
"Rao",
"Ashwin",
""
],
[
"Prabhu",
"Avinash",
""
],
[
"Nigam",
"Meher Shashwat",
""
],
[
"Karandikar",
"Tanvi",
""
],
[
"Pandya",
"Harit",
""
],
[
"Krishna",
"K. Madhava",
""
]
] |
new_dataset
| 0.999592 |
2212.02936
|
Constantin Eichenberg
|
Samuel Weinbach, Marco Bellagente, Constantin Eichenberg, Andrew Dai,
Robert Baldock, Souradeep Nanda, Bj\"orn Deiseroth, Koen Oostermeijer, Hannah
Teufel, Andres Felipe Cruz-Salinas
|
M-VADER: A Model for Diffusion with Multimodal Context
|
22 pages, 14 figures, 2 tables, fixed figure 3
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce M-VADER: a diffusion model (DM) for image generation where the
output can be specified using arbitrary combinations of images and text. We
show how M-VADER enables the generation of images specified using combinations
of image and text, and combinations of multiple images. Previously, a number of
successful DM image generation algorithms have been introduced that make it
possible to specify the output image using a text prompt. Inspired by the
success of those models, and led by the notion that language was already
developed to describe the elements of visual contexts that humans find most
important, we introduce an embedding model closely related to a vision-language
model. Specifically, we introduce the embedding model S-MAGMA: a 13 billion
parameter multimodal decoder combining components from an autoregressive
vision-language model MAGMA and biases finetuned for semantic search.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 12:45:21 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Dec 2022 09:11:18 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Weinbach",
"Samuel",
""
],
[
"Bellagente",
"Marco",
""
],
[
"Eichenberg",
"Constantin",
""
],
[
"Dai",
"Andrew",
""
],
[
"Baldock",
"Robert",
""
],
[
"Nanda",
"Souradeep",
""
],
[
"Deiseroth",
"Björn",
""
],
[
"Oostermeijer",
"Koen",
""
],
[
"Teufel",
"Hannah",
""
],
[
"Cruz-Salinas",
"Andres Felipe",
""
]
] |
new_dataset
| 0.995571 |
2212.03069
|
Ngoc Tran
|
Ngoc N. Tran, Anh Tuan Bui, Dinh Phung, Trung Le
|
Multiple Perturbation Attack: Attack Pixelwise Under Different
$\ell_p$-norms For Better Adversarial Performance
|
18 pages, 8 figures, 7 tables
| null | null | null |
cs.CV cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Adversarial machine learning has been both a major concern and a hot topic
recently, especially with the ubiquitous use of deep neural networks in the
current landscape. Adversarial attacks and defenses are usually likened to a
cat-and-mouse game in which defenders and attackers evolve over the time. On
one hand, the goal is to develop strong and robust deep networks that are
resistant to malicious actors. On the other hand, in order to achieve that, we
need to devise even stronger adversarial attacks to challenge these defense
models. Most of existing attacks employs a single $\ell_p$ distance (commonly,
$p\in\{1,2,\infty\}$) to define the concept of closeness and performs steepest
gradient ascent w.r.t. this $p$-norm to update all pixels in an adversarial
example in the same way. These $\ell_p$ attacks each has its own pros and cons;
and there is no single attack that can successfully break through defense
models that are robust against multiple $\ell_p$ norms simultaneously.
Motivated by these observations, we come up with a natural approach: combining
various $\ell_p$ gradient projections on a pixel level to achieve a joint
adversarial perturbation. Specifically, we learn how to perturb each pixel to
maximize the attack performance, while maintaining the overall visual
imperceptibility of adversarial examples. Finally, through various experiments
with standardized benchmarks, we show that our method outperforms most current
strong attacks across state-of-the-art defense mechanisms, while retaining its
ability to remain clean visually.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 15:38:37 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Dec 2022 18:30:33 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Tran",
"Ngoc N.",
""
],
[
"Bui",
"Anh Tuan",
""
],
[
"Phung",
"Dinh",
""
],
[
"Le",
"Trung",
""
]
] |
new_dataset
| 0.993649 |
2212.03267
|
Congyue Deng
|
Congyue Deng, Chiyu "Max'' Jiang, Charles R. Qi, Xinchen Yan, Yin
Zhou, Leonidas Guibas, Dragomir Anguelov
|
NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as
General Image Priors
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
2D-to-3D reconstruction is an ill-posed problem, yet humans are good at
solving this problem due to their prior knowledge of the 3D world developed
over years. Driven by this observation, we propose NeRDi, a single-view NeRF
synthesis framework with general image priors from 2D diffusion models.
Formulating single-view reconstruction as an image-conditioned 3D generation
problem, we optimize the NeRF representations by minimizing a diffusion loss on
its arbitrary view renderings with a pretrained image diffusion model under the
input-view constraint. We leverage off-the-shelf vision-language models and
introduce a two-section language guidance as conditioning inputs to the
diffusion model. This is essentially helpful for improving multiview content
coherence as it narrows down the general image prior conditioned on the
semantic and visual features of the single-view input image. Additionally, we
introduce a geometric loss based on estimated depth maps to regularize the
underlying 3D geometry of the NeRF. Experimental results on the DTU MVS dataset
show that our method can synthesize novel views with higher quality even
compared to existing methods trained on this dataset. We also demonstrate our
generalizability in zero-shot NeRF synthesis for in-the-wild images.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 19:00:07 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Deng",
"Congyue",
""
],
[
"Jiang",
"Chiyu \"Max''",
""
],
[
"Qi",
"Charles R.",
""
],
[
"Yan",
"Xinchen",
""
],
[
"Zhou",
"Yin",
""
],
[
"Guibas",
"Leonidas",
""
],
[
"Anguelov",
"Dragomir",
""
]
] |
new_dataset
| 0.998365 |
2212.03273
|
Tristan Lazard
|
Tristan Lazard, Marvin Lerousseau, Etienne Decenci\`ere, Thomas Walter
|
Giga-SSL: Self-Supervised Learning for Gigapixel Images
| null | null | null | null |
cs.CV cs.LG q-bio.QM
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Whole slide images (WSI) are microscopy images of stained tissue slides
routinely prepared for diagnosis and treatment selection in medical practice.
WSI are very large (gigapixel size) and complex (made of up to millions of
cells). The current state-of-the-art (SoTA) approach to classify WSI subdivides
them into tiles, encodes them by pre-trained networks and applies Multiple
Instance Learning (MIL) to train for specific downstream tasks. However,
annotated datasets are often small, typically a few hundred to a few thousand
WSI, which may cause overfitting and underperforming models. Conversely, the
number of unannotated WSI is ever increasing, with datasets of tens of
thousands (soon to be millions) of images available. While it has been
previously proposed to use these unannotated data to identify suitable tile
representations by self-supervised learning (SSL), downstream classification
tasks still require full supervision because parts of the MIL architecture is
not trained during tile level SSL pre-training. Here, we propose a strategy of
slide level SSL to leverage the large number of WSI without annotations to
infer powerful slide representations. Applying our method to The Cancer-Genome
Atlas, one of the most widely used data resources in cancer research (16 TB
image data), we are able to downsize the dataset to 23 MB without any loss in
predictive power: we show that a linear classifier trained on top of these
embeddings maintains or improves previous SoTA performances on various
benchmark WSI classification tasks. Finally, we observe that training a
classifier on these representations with tiny datasets (e.g. 50 slides)
improved performances over SoTA by an average of +6.3 AUC points over all
downstream tasks.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 19:09:19 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Lazard",
"Tristan",
""
],
[
"Lerousseau",
"Marvin",
""
],
[
"Decencière",
"Etienne",
""
],
[
"Walter",
"Thomas",
""
]
] |
new_dataset
| 0.999427 |
2212.03287
|
Guy Amir
|
Guy Amir, Ziv Freund, Guy Katz, Elad Mandelbaum, Idan Refaeli
|
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection
System
|
To appear in Proceedings of the 25th International Symposium on
Formal Methods (FM)
| null | null | null |
cs.LO cs.LG cs.SE math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this short paper, we present our ongoing work on the veriFIRE project -- a
collaboration between industry and academia, aimed at using verification for
increasing the reliability of a real-world, safety-critical system. The system
we target is an airborne platform for wildfire detection, which incorporates
two deep neural networks. We describe the system and its properties of
interest, and discuss our attempts to verify the system's consistency, i.e.,
its ability to continue and correctly classify a given input, even if the
wildfire it describes increases in intensity. We regard this work as a step
towards the incorporation of academic-oriented verification tools into
real-world systems of interest.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 19:41:08 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Amir",
"Guy",
""
],
[
"Freund",
"Ziv",
""
],
[
"Katz",
"Guy",
""
],
[
"Mandelbaum",
"Elad",
""
],
[
"Refaeli",
"Idan",
""
]
] |
new_dataset
| 0.999539 |
2212.03297
|
Justin Xie
|
Justin Xie
|
Fine-Grained Emotional Paraphrasing along Emotion Gradients
| null | null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Paraphrase generation, a.k.a. paraphrasing, is a common and important task in
natural language processing. Emotional paraphrasing, which changes the emotion
embodied in a piece of text while preserving its meaning, has many potential
applications, e.g., moderating online dialogues and preventing cyberbullying.
We introduce a new task of fine-grained emotional paraphrasing along emotion
gradients, that is, altering the emotional intensities of the paraphrases in
fine grain following smooth variations in affective dimensions while preserving
the meanings of the originals. We propose a framework for addressing this task
by fine-tuning text-to-text Transformers through multi-task training. We
enhance several widely used paraphrasing corpus by annotating the input and
target texts with their fine-grained emotion labels. With these labels,
fine-tuning text-to-text Transformers on these corpus entails multi-task
training. Evaluations of the fine-tuned Transformers on separate test sets show
that including fine-grained emotion labels in the paraphrase task significantly
improve the chance of obtaining high-quality paraphrases of the desired
emotions, i.e., more than doubling the number of exact matches of desired
emotions while achieving consistently better scores in paraphrase metrics such
as BLEU, ROGUE, and METEOR.
|
[
{
"version": "v1",
"created": "Sun, 30 Oct 2022 05:38:22 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Xie",
"Justin",
""
]
] |
new_dataset
| 0.996144 |
2212.03308
|
Yashar Salami
|
Yashar Salami, Vahid Khajehvand, Esmaeil Zeinali
|
E3C: A Tool for Evaluating Communication and Computation Costs in
Authentication and Key Exchange Protocol
|
20 pages ,10 figures, 4 Table
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Today, with the development of blockchain and Internet of Things
technologies, we need authentication protocols and key exchanges to communicate
with these different technologies. Symmetric and asymmetric encryption methods
are used to design authentication and key exchange protocols, each of which has
different computation costs. In the Internet of Things systems, due to the
limited memory and computation power, researchers are looking the lightweight
design protocols so that the pressure caused by the computation of protocols
can be minimized. Calculating protocols' computational and communication costs
was done manually until now, which was associated with human error. In this
paper, we proposed an E3C tool that can calculate the computation and
communication costs of the authentication and key exchange protocols. E3C
provides the ability to compare several protocols in terms of communication and
processing costs and present them in separate charts. Comparing the processing
and communication costs of classical and modern protocols manually and with the
E3C indicate that the E3C can calculate the processing and communication costs
of authentication and key exchange protocols with 99.99% accuracy.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 20:14:26 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Salami",
"Yashar",
""
],
[
"Khajehvand",
"Vahid",
""
],
[
"Zeinali",
"Esmaeil",
""
]
] |
new_dataset
| 0.99065 |
2212.03357
|
Yuan Yuan
|
Hao He, Yuan Yuan, Ying-Cong Chen, Peng Cao and Dina Katabi
|
Contactless Oxygen Monitoring with Gated Transformer
|
19 pages, Workshop on Learning from Time Series for Health, NeurIPS
2022
| null | null | null |
cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the increasing popularity of telehealth, it becomes critical to ensure
that basic physiological signals can be monitored accurately at home, with
minimal patient overhead. In this paper, we propose a contactless approach for
monitoring patients' blood oxygen at home, simply by analyzing the radio
signals in the room, without any wearable devices. We extract the patients'
respiration from the radio signals that bounce off their bodies and devise a
novel neural network that infers a patient's oxygen estimates from their
breathing signal. Our model, called \emph{Gated BERT-UNet}, is designed to
adapt to the patient's medical indices (e.g., gender, sleep stages). It has
multiple predictive heads and selects the most suitable head via a gate
controlled by the person's physiological indices. Extensive empirical results
show that our model achieves high accuracy on both medical and radio datasets.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 22:43:59 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"He",
"Hao",
""
],
[
"Yuan",
"Yuan",
""
],
[
"Chen",
"Ying-Cong",
""
],
[
"Cao",
"Peng",
""
],
[
"Katabi",
"Dina",
""
]
] |
new_dataset
| 0.950186 |
2212.03383
|
Zhongtang Luo
|
Zhongtang Luo, Rohan Murukutla, Aniket Kate
|
Last Mile of Blockchains: RPC and Node-as-a-service
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
While much research focuses on different methods to secure blockchain,
information on the chain needs to be accessed by end-users to be useful. This
position paper surveys different ways that end-users may access blockchains. We
observe that between the two extremes of running a full node and fully
utilizing a trusted third-party service, many solutions regarding light nodes
are emerging. We analyze these solutions based on three basic properties of web
communication: integrity, availability and privacy. We conclude that currently,
the best way to access a blockchain while maintaining these three properties is
still to run a full node. We consider it essential that future blockchain
accessibility services should be built while considering these three
expectations.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 00:31:46 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Luo",
"Zhongtang",
""
],
[
"Murukutla",
"Rohan",
""
],
[
"Kate",
"Aniket",
""
]
] |
new_dataset
| 0.9546 |
2212.03419
|
Ruth-Ann Armstrong
|
Ruth-Ann Armstrong, John Hewitt and Christopher Manning
|
JamPatoisNLI: A Jamaican Patois Natural Language Inference Dataset
|
14 pages, 3 figures, Findings of EMNLP 2022
| null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
JamPatoisNLI provides the first dataset for natural language inference in a
creole language, Jamaican Patois. Many of the most-spoken low-resource
languages are creoles. These languages commonly have a lexicon derived from a
major world language and a distinctive grammar reflecting the languages of the
original speakers and the process of language birth by creolization. This gives
them a distinctive place in exploring the effectiveness of transfer from large
monolingual or multilingual pretrained models. While our work, along with
previous work, shows that transfer from these models to low-resource languages
that are unrelated to languages in their training set is not very effective, we
would expect stronger results from transfer to creoles. Indeed, our experiments
show considerably better results from few-shot learning of JamPatoisNLI than
for such unrelated languages, and help us begin to understand how the unique
relationship between creoles and their high-resource base languages affect
cross-lingual transfer. JamPatoisNLI, which consists of naturally-occurring
premises and expert-written hypotheses, is a step towards steering research
into a traditionally underserved language and a useful benchmark for
understanding cross-lingual NLP.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 03:07:02 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Armstrong",
"Ruth-Ann",
""
],
[
"Hewitt",
"John",
""
],
[
"Manning",
"Christopher",
""
]
] |
new_dataset
| 0.999837 |
2212.03420
|
S. Farokh Atashzar
|
Jacqueline Libby, Aniket A. Somwanshi, Federico Stancati, Gayatri
Tyagi, Aadit Patel, Naigam Bhatt, JohnRoss Rizzo, S. Farokh Atashzar
|
What Happens When Pneu-Net Soft Robotic Actuators Get Fatigued?
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Soft actuators have attracted a great deal of interest in the context of
rehabilitative and assistive robots for increasing safety and lowering costs as
compared to rigid-body robotic systems. During actuation, soft actuators
experience high levels of deformation, which can lead to microscale fractures
in their elastomeric structure, which fatigues the system over time and
eventually leads to macroscale damages and eventually failure. This paper
reports finite element modeling (FEM) of pneu-nets at high angles, along with
repetitive experimentation at high deformation rates, in order to study the
effect and behavior of fatigue in soft robotic actuators, which would result in
deviation from the ideal behavior. Comparing the FEM model and experimental
data, we show that FEM can model the performance of the actuator before fatigue
to a bending angle of 167 degrees with ~96% accuracy. We also show that the FEM
model performance will drop to 80% due to fatigue after repetitive high-angle
bending. The results of this paper objectively highlight the emergence of
fatigue over cyclic activation of the system and the resulting deviation from
the computational FEM model. Such behavior can be considered in future
controllers to adapt the system with time-variable and non-autonomous response
dynamics of soft robots.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 03:07:33 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Libby",
"Jacqueline",
""
],
[
"Somwanshi",
"Aniket A.",
""
],
[
"Stancati",
"Federico",
""
],
[
"Tyagi",
"Gayatri",
""
],
[
"Patel",
"Aadit",
""
],
[
"Bhatt",
"Naigam",
""
],
[
"Rizzo",
"JohnRoss",
""
],
[
"Atashzar",
"S. Farokh",
""
]
] |
new_dataset
| 0.99293 |
2212.03435
|
Fengyu Yang
|
Fengyu Yang, Jian Luan, Yujun Wang
|
Improve Bilingual TTS Using Dynamic Language and Phonology Embedding
|
Submitted to ICASSP2023
| null | null | null |
cs.SD cs.CL eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In most cases, bilingual TTS needs to handle three types of input scripts:
first language only, second language only, and second language embedded in the
first language. In the latter two situations, the pronunciation and intonation
of the second language are usually quite different due to the influence of the
first language. Therefore, it is a big challenge to accurately model the
pronunciation and intonation of the second language in different contexts
without mutual interference. This paper builds a Mandarin-English TTS system to
acquire more standard spoken English speech from a monolingual Chinese speaker.
We introduce phonology embedding to capture the English differences between
different phonology. Embedding mask is applied to language embedding for
distinguishing information between different languages and to phonology
embedding for focusing on English expression. We specially design an embedding
strength modulator to capture the dynamic strength of language and phonology.
Experiments show that our approach can produce significantly more natural and
standard spoken English speech of the monolingual Chinese speaker. From
analysis, we find that suitable phonology control contributes to better
performance in different scenarios.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 03:46:18 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Yang",
"Fengyu",
""
],
[
"Luan",
"Jian",
""
],
[
"Wang",
"Yujun",
""
]
] |
new_dataset
| 0.951008 |
2212.03490
|
Yue Ma
|
Yue Ma, Tianyu Yang, Yin Shan, Xiu Li
|
SimVTP: Simple Video Text Pre-training with Masked Autoencoders
|
Github: https://github.com/mayuelala/SimVTP
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents SimVTP: a Simple Video-Text Pretraining framework via
masked autoencoders. We randomly mask out the spatial-temporal tubes of input
video and the word tokens of input text and then feed them into a unified
autencoder to reconstruct the missing pixels and words. Our SimVTP has several
properties: 1) Thanks to the unified autoencoder, SimVTP reconstructs the
masked signal of one modality with the help from another modality, which
implicitly learns the cross-modal alignment between video tubes and text
tokens. 2) SimVTP not only benefits from a high video masking ratio (e.g. 90%)
due to the temporal redundancy of video, but also needs a high text masking
ratio (e.g. 75%), which is much higher than BERT (e.g. 15%), to achieve optimal
performance. This is because the aid of video modality makes text
reconstruction less challenging, which thus needs a higher mask ratio to make
the pretext harder for useful feature learning. 3) Equipping SimVTP with
video-text contrastive learning (VTC) and video-text matching (VTM), which are
two commonly used cross-modal training strategies, could further improve the
transferable performance significantly. 4) SimVTP is dataefficent, e.g.,
pre-training only on 10% data of WebVid-2M, SimVTP achieves surprisingly good
results (43.8 R@1) on MSRVTT, which is far above recent state-of-the-art
methods pre-trained on both CC3M and WebVid-2M. We transfer our pre-trained
model to various downstream tasks and achieve superior performance. The codes
and models will be released at https://github.com/mayuelala/SimVTP.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 07:14:22 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Ma",
"Yue",
""
],
[
"Yang",
"Tianyu",
""
],
[
"Shan",
"Yin",
""
],
[
"Li",
"Xiu",
""
]
] |
new_dataset
| 0.99845 |
2212.03517
|
Siwei Yang
|
Siwei Yang, Longlong Jing, Junfei Xiao, Hang Zhao, Alan Yuille,
Yingwei Li
|
AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised
Instance Segmentation
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The weakly supervised instance segmentation is a challenging task. The
existing methods typically use bounding boxes as supervision and optimize the
network with a regularization loss term such as pairwise color affinity loss
for instance segmentation. Through systematic analysis, we found that the
commonly used pairwise affinity loss has two limitations: (1) it works with
color affinity but leads to inferior performance with other modalities such as
depth gradient, (2)the original affinity loss does not prevent trivial
predictions as intended but actually accelerates this process due to the
affinity loss term being symmetric. To overcome these two limitations, in this
paper, we propose a novel asymmetric affinity loss which provides the penalty
against the trivial prediction and generalizes well with affinity loss from
different modalities. With the proposed asymmetric affinity loss, our method
outperforms the state-of-the-art methods on the Cityscapes dataset and
outperforms our baseline method by 3.5% in mask AP.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 08:47:10 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Yang",
"Siwei",
""
],
[
"Jing",
"Longlong",
""
],
[
"Xiao",
"Junfei",
""
],
[
"Zhao",
"Hang",
""
],
[
"Yuille",
"Alan",
""
],
[
"Li",
"Yingwei",
""
]
] |
new_dataset
| 0.95765 |
2212.03520
|
Mordechai Guri
|
Mordechai Guri
|
COVID-bit: Keep a Distance of (at least) 2m From My Air-Gap Computer!
|
This is an significantly extended version of a shorter paper accepted
to IEEE TrustCom 2022
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Air-gapped systems are isolated from the Internet due to the sensitive
information they handle. This paper presents COVID-bit, a new COVert channel
attack that leaks sensitive information over the air from highly isolated
systems. The information emanates from the air-gapped computer over the air to
a distance of 2m and more and can be picked up by a nearby insider or spy with
a mobile phone or laptop. Malware on an air-gapped computer can generate radio
waves by executing crafted code on the target system. The malicious code
exploits the dynamic power consumption of modern computers and manipulates the
momentary loads on CPU cores. This technique allows the malware to control the
computer's internal utilization and generate low-frequency electromagnetic
radiation in the 0 - 60 kHz band. Sensitive information (e.g., files,
encryption keys, biometric data, and keylogging) can be modulated over the
emanated signals and received by a nearby mobile phone at a max speed of 1000
bits/sec. We show that a smartphone or laptop with a small \$1 antenna carried
by a malicious insider or visitor can be used as a covert receiver. Notably,
the attack is highly evasive since it executes from an ordinary user-level
process, does not require root privileges, and is effective even within a
Virtual Machine (VM). We discuss the attack model and provide technical
details. We implement air-gap transmission of texts and files, and present
signal generation and data modulation. We test the covert channel and show
evaluation results. Finally, we present a set of countermeasures to this
air-gap attack.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 08:57:40 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Guri",
"Mordechai",
""
]
] |
new_dataset
| 0.999737 |
2212.03641
|
Michele Campobasso
|
Michele Campobasso, Luca Allodi (Eindhoven University of Technology)
|
THREAT/crawl: a Trainable, Highly-Reusable, and Extensible Automated
Method and Tool to Crawl Criminal Underground Forums
|
To be published in the Proceedings of the 17th Symposium on
Electronic Crime Research (APWG eCrime 2022). Source code of the implemented
solution available at https://gitlab.tue.nl/threat-crawl/THREATcrawl/
| null | null | null |
cs.IR cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Collecting data on underground criminal communities is highly valuable both
for security research and security operations. Unfortunately these communities
live within a constellation of diverse online forums that are difficult to
infiltrate, may adopt crawling monitoring countermeasures, and require the
development of ad-hoc scrapers for each different community, making the
endeavour increasingly technically challenging, and potentially expensive. To
address this problem we propose THREAT/crawl, a method and prototype tool for a
highly reusable crawler that can learn a wide range of (arbitrary) forum
structures, can remain under-the-radar during the crawling activity and can be
extended and configured at the user will. We showcase THREAT/crawl capabilities
and provide prime evaluation of our prototype against a range of active, live,
underground communities.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 13:54:51 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Campobasso",
"Michele",
"",
"Eindhoven University of Technology"
],
[
"Allodi",
"Luca",
"",
"Eindhoven University of Technology"
]
] |
new_dataset
| 0.988116 |
2212.03810
|
Kristina Lerman
|
Kristina Lerman
|
The Social Emotional Web
|
The 8th IEEE International Conference on Collaboration and Internet
Computing (IEEE CIC 2022)
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
The social web has linked people on a global scale, transforming how we
communicate and interact. The massive interconnectedness has created new
vulnerabilities in the form of social manipulation and misinformation. As the
social web matures, we are entering a new phase, where people share their
private feelings and emotions. This so-called social emotional web creates new
opportunities for human flourishing, but also exposes new vulnerabilities. To
reap the benefits of the social emotional web, and reduce potential harms, we
must anticipate how it will evolve and create policies that minimize risks.
|
[
{
"version": "v1",
"created": "Wed, 7 Dec 2022 17:46:22 GMT"
}
] | 2022-12-08T00:00:00 |
[
[
"Lerman",
"Kristina",
""
]
] |
new_dataset
| 0.997627 |
1910.06078
|
Fangli Xu
|
Fangli Xu, Lingfei Wu, KP Thai, Carol Hsu, Wei Wang, Richard Tong
|
MUTLA: A Large-Scale Dataset for Multimodal Teaching and Learning
Analytics
|
3 pages, 1 figure, 2 tables workshop paper
| null | null | null |
cs.CY stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automatic analysis of teacher and student interactions could be very
important to improve the quality of teaching and student engagement. However,
despite some recent progress in utilizing multimodal data for teaching and
learning analytics, a thorough analysis of a rich multimodal dataset coming for
a complex real learning environment has yet to be done. To bridge this gap, we
present a large-scale MUlti-modal Teaching and Learning Analytics (MUTLA)
dataset. This dataset includes time-synchronized multimodal data records of
students (learning logs, videos, EEG brainwaves) as they work in various
subjects from Squirrel AI Learning System (SAIL) to solve problems of varying
difficulty levels. The dataset resources include user records from the learner
records store of SAIL, brainwave data collected by EEG headset devices, and
video data captured by web cameras while students worked in the SAIL products.
Our hope is that by analyzing real-world student learning activities, facial
expressions, and brainwave patterns, researchers can better predict engagement,
which can then be used to improve adaptive learning selection and student
learning outcomes. An additional goal is to provide a dataset gathered from
real-world educational activities versus those from controlled lab environments
to benefit the educational learning community.
|
[
{
"version": "v1",
"created": "Sat, 5 Oct 2019 03:53:49 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 18:21:33 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Xu",
"Fangli",
""
],
[
"Wu",
"Lingfei",
""
],
[
"Thai",
"KP",
""
],
[
"Hsu",
"Carol",
""
],
[
"Wang",
"Wei",
""
],
[
"Tong",
"Richard",
""
]
] |
new_dataset
| 0.999799 |
2109.02580
|
Wenxi Liu
|
Wenxi Liu, Qi Li, Xindai Lin, Weixiang Yang, Shengfeng He, Yuanlong Yu
|
Ultra-high Resolution Image Segmentation via Locality-aware Context
Fusion and Alternating Local Enhancement
|
Extension of ICCV 2021 "From Contexts to Locality: Ultra-high
Resolution Image Segmentation via Locality-aware Contextual Correlation"
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Ultra-high resolution image segmentation has raised increasing interests in
recent years due to its realistic applications. In this paper, we innovate the
widely used high-resolution image segmentation pipeline, in which an ultra-high
resolution image is partitioned into regular patches for local segmentation and
then the local results are merged into a high-resolution semantic mask. In
particular, we introduce a novel locality-aware context fusion based
segmentation model to process local patches, where the relevance between local
patch and its various contexts are jointly and complementarily utilized to
handle the semantic regions with large variations. Additionally, we present the
alternating local enhancement module that restricts the negative impact of
redundant information introduced from the contexts, and thus is endowed with
the ability of fixing the locality-aware features to produce refined results.
Furthermore, in comprehensive experiments, we demonstrate that our model
outperforms other state-of-the-art methods in public benchmarks. Our released
codes are available at: https://github.com/liqiokkk/FCtL.
|
[
{
"version": "v1",
"created": "Mon, 6 Sep 2021 16:26:05 GMT"
},
{
"version": "v2",
"created": "Tue, 15 Nov 2022 14:13:49 GMT"
},
{
"version": "v3",
"created": "Tue, 6 Dec 2022 09:44:06 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Liu",
"Wenxi",
""
],
[
"Li",
"Qi",
""
],
[
"Lin",
"Xindai",
""
],
[
"Yang",
"Weixiang",
""
],
[
"He",
"Shengfeng",
""
],
[
"Yu",
"Yuanlong",
""
]
] |
new_dataset
| 0.997818 |
2203.11015
|
Xianghao Zhan
|
Xianghao Zhan, Fanjin Wang, Olivier Gevaert
|
Filter Drug-induced Liver Injury Literature with Natural Language
Processing and Ensemble Learning
|
8 pages, 4 figures
| null |
10.1109/JBHI.2022.3193365
| null |
cs.IR cs.LG stat.AP
|
http://creativecommons.org/licenses/by/4.0/
|
Drug-induced liver injury (DILI) describes the adverse effects of drugs that
damage liver. Life-threatening results including liver failure or death were
also reported in severe DILI cases. Therefore, DILI-related events are strictly
monitored for all approved drugs and the liver toxicity became important
assessments for new drug candidates. These DILI-related reports are documented
in hospital records, in clinical trial results, and also in research papers
that contain preliminary in vitro and in vivo experiments. Conventionally, data
extraction from previous publications relies heavily on resource-demanding
manual labelling, which considerably decreased the efficiency of the
information extraction process. The recent development of artificial
intelligence, particularly, the rise of natural language processing (NLP)
techniques, enabled the automatic processing of biomedical texts. In this
study, based on around 28,000 papers (titles and abstracts) provided by the
Critical Assessment of Massive Data Analysis (CAMDA) challenge, we benchmarked
model performances on filtering out DILI literature. Among four word
vectorization techniques, the model using term frequency-inverse document
frequency (TF-IDF) and logistic regression outperformed others with an accuracy
of 0.957 with our in-house test set. Furthermore, an ensemble model with
similar overall performances was implemented and was fine-tuned to lower the
false-negative cases to avoid neglecting potential DILI reports. The ensemble
model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the
hold-out validation data provided by the CAMDA committee. Moreover, important
words in positive/negative predictions were identified via model
interpretation. Overall, the ensemble model reached satisfactory classification
results, which can be further used by researchers to rapidly filter
DILI-related literature.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 23:53:07 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Zhan",
"Xianghao",
""
],
[
"Wang",
"Fanjin",
""
],
[
"Gevaert",
"Olivier",
""
]
] |
new_dataset
| 0.966045 |
2204.07874
|
Markus Borg
|
Markus Borg, Jens Henriksson, Kasper Socha, Olof Lennartsson, Elias
Sonnsj\"o L\"onegren, Thanh Bui, Piotr Tomaszewski, Sankar Raman
Sathyamoorthy, Sebastian Brink, Mahshid Helali Moghadam
|
Ergo, SMIRK is Safe: A Safety Case for a Machine Learning Component in a
Pedestrian Automatic Emergency Brake System
|
Accepted for publication in Software Quality Journal
| null | null | null |
cs.SE cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Integration of Machine Learning (ML) components in critical applications
introduces novel challenges for software certification and verification. New
safety standards and technical guidelines are under development to support the
safety of ML-based systems, e.g., ISO 21448 SOTIF for the automotive domain and
the Assurance of Machine Learning for use in Autonomous Systems (AMLAS)
framework. SOTIF and AMLAS provide high-level guidance but the details must be
chiseled out for each specific case. We initiated a research project with the
goal to demonstrate a complete safety case for an ML component in an open
automotive system. This paper reports results from an industry-academia
collaboration on safety assurance of SMIRK, an ML-based pedestrian automatic
emergency braking demonstrator running in an industry-grade simulator. We
demonstrate an application of AMLAS on SMIRK for a minimalistic operational
design domain, i.e., we share a complete safety case for its integrated
ML-based component. Finally, we report lessons learned and provide both SMIRK
and the safety case under an open-source licence for the research community to
reuse.
|
[
{
"version": "v1",
"created": "Sat, 16 Apr 2022 21:28:50 GMT"
},
{
"version": "v2",
"created": "Thu, 15 Sep 2022 12:43:05 GMT"
},
{
"version": "v3",
"created": "Tue, 6 Dec 2022 10:49:12 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Borg",
"Markus",
""
],
[
"Henriksson",
"Jens",
""
],
[
"Socha",
"Kasper",
""
],
[
"Lennartsson",
"Olof",
""
],
[
"Lönegren",
"Elias Sonnsjö",
""
],
[
"Bui",
"Thanh",
""
],
[
"Tomaszewski",
"Piotr",
""
],
[
"Sathyamoorthy",
"Sankar Raman",
""
],
[
"Brink",
"Sebastian",
""
],
[
"Moghadam",
"Mahshid Helali",
""
]
] |
new_dataset
| 0.99461 |
2205.00180
|
Mifta Sintaha
|
Mifta Sintaha, Noor Nashid, Ali Mesbah
|
Katana: Dual Slicing-Based Context for Learning Bug Fixes
| null | null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Contextual information plays a vital role for software developers when
understanding and fixing a bug. Consequently, deep learning-based program
repair techniques leverage context for bug fixes. However, existing techniques
treat context in an arbitrary manner, by extracting code in close proximity of
the buggy statement within the enclosing file, class, or method, without any
analysis to find actual relations with the bug. To reduce noise, they use a
predefined maximum limit on the number of tokens to be used as context. We
present a program slicing-based approach, in which instead of arbitrarily
including code as context, we analyze statements that have a control or data
dependency on the buggy statement. We propose a novel concept called dual
slicing, which leverages the context of both buggy and fixed versions of the
code to capture relevant repair ingredients. We present our technique and tool
called Katana, the first to apply slicing-based context for a program repair
task. The results show Katana effectively preserves sufficient information for
a model to choose contextual information while reducing noise. We compare
against four recent state-of-the-art context-aware program repair techniques.
Our results show Katana fixes between 1.5 to 3.7 times more bugs than existing
techniques.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 07:04:41 GMT"
},
{
"version": "v2",
"created": "Thu, 2 Jun 2022 20:43:53 GMT"
},
{
"version": "v3",
"created": "Mon, 5 Dec 2022 22:09:36 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Sintaha",
"Mifta",
""
],
[
"Nashid",
"Noor",
""
],
[
"Mesbah",
"Ali",
""
]
] |
new_dataset
| 0.997266 |
2205.00222
|
Randy Harsuko
|
Randy Harsuko and Tariq Alkhalifah
|
StorSeismic: A new paradigm in deep learning for seismic processing
|
18 pages, 18 figures
| null |
10.1109/TGRS.2022.3216660
| null |
cs.LG eess.SP physics.comp-ph physics.geo-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine learned tasks on seismic data are often trained sequentially and
separately, even though they utilize the same features (i.e. geometrical) of
the data. We present StorSeismic, as a framework for seismic data processing,
which consists of neural network pre-training and fine-tuning procedures. We,
specifically, utilize a neural network as a preprocessing model to store
seismic data features of a particular dataset for any downstream tasks. After
pre-training, the resulting model can be utilized later, through a fine-tuning
procedure, to perform tasks using limited additional training. Used often in
Natural Language Processing (NLP) and lately in vision tasks, BERT
(Bidirectional Encoder Representations from Transformer), a form of a
Transformer model, provides an optimal platform for this framework. The
attention mechanism of BERT, applied here on a sequence of traces within the
shot gather, is able to capture and store key geometrical features of the
seismic data. We pre-train StorSeismic on field data, along with synthetically
generated ones, in the self-supervised step. Then, we use the labeled synthetic
data to fine-tune the pre-trained network in a supervised fashion to perform
various seismic processing tasks, like denoising, velocity estimation, first
arrival picking, and NMO. Finally, the fine-tuned model is used to obtain
satisfactory inference results on the field data.
|
[
{
"version": "v1",
"created": "Sat, 30 Apr 2022 09:55:00 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Harsuko",
"Randy",
""
],
[
"Alkhalifah",
"Tariq",
""
]
] |
new_dataset
| 0.999001 |
2205.07979
|
Boro Sitnikovski
|
Boro Sitnikovski
|
Budge: a programming language and a theorem prover
| null | null | null | null |
cs.PL cs.CL cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a simple programming language based on G\"odel numbering and prime
factorization, enhanced with explicit, scoped loops, allowing for easy program
composition. Further, we will present a theorem prover that allows expressing
and working with formal systems. The theorem prover is simple as it relies
merely on a substitution rule and set equality to derive theorems. Finally, we
will represent the programming language in the theorem prover. We will show the
syntax and semantics of both, and then provide a few example programs and their
evaluation.
|
[
{
"version": "v1",
"created": "Mon, 16 May 2022 20:35:25 GMT"
},
{
"version": "v2",
"created": "Thu, 19 May 2022 12:25:22 GMT"
},
{
"version": "v3",
"created": "Tue, 24 May 2022 13:15:24 GMT"
},
{
"version": "v4",
"created": "Thu, 4 Aug 2022 11:47:22 GMT"
},
{
"version": "v5",
"created": "Tue, 23 Aug 2022 23:23:12 GMT"
},
{
"version": "v6",
"created": "Tue, 6 Dec 2022 11:48:21 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Sitnikovski",
"Boro",
""
]
] |
new_dataset
| 0.999798 |
2205.08491
|
Veli Safak
|
Veli Safak and Aniish Sridhar
|
Elon Musk's Twitter Takeover: Politician Accounts
| null | null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
We provided quantitative data supporting significant changes between the time
Twitter acceptance the offer on April 25 and the time the agreement was
finalized on October 27. Republican politicians saw significant increases in
their follower counts, while Democrat politicians saw significant decreases.
|
[
{
"version": "v1",
"created": "Mon, 16 May 2022 14:11:49 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Dec 2022 13:36:05 GMT"
},
{
"version": "v3",
"created": "Tue, 6 Dec 2022 02:57:59 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Safak",
"Veli",
""
],
[
"Sridhar",
"Aniish",
""
]
] |
new_dataset
| 0.966107 |
2205.13707
|
Shucheng Yang
|
Shucheng Yang, Xiaoping Gao, Ruoting Yang, Jie Ren, and Zhen Wang
|
A Hybrid Josephson Transmission Line and Passive Transmission Line
Routing Framework for Single Flux Quantum Logic
| null | null |
10.1109/TASC.2022.3206280
| null |
cs.ET cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
The Single Flux Quantum (SFQ) logic family is a novel digital logic as it
provides ultra-fast and energy-efficient circuits. For large-scale SFQ circuit
design, specialized electronic design automation (EDA) tools are required due
to the differences in logic type, timing constraints and circuit architecture,
in contrast to the CMOS logic. In order to improve the overall performance of
an SFQ circuit, an efficient routing algorithm should be applied during the
layout design to perform accurate timing adjustment for fixing hold violations
and optimizing critical paths. Thus, a hybrid Josephson transmission line and
passive transmission line routing framework is proposed. It consists of four
main modules and an exploration of the potential timing performance based on
the given layout placement. The proposed routing tool is demonstrated on seven
testbench circuits. The obtained results demonstrate that the operating
frequency is greatly improved, and all the hold violations are eliminated for
each circuit.
|
[
{
"version": "v1",
"created": "Fri, 27 May 2022 01:51:51 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Yang",
"Shucheng",
""
],
[
"Gao",
"Xiaoping",
""
],
[
"Yang",
"Ruoting",
""
],
[
"Ren",
"Jie",
""
],
[
"Wang",
"Zhen",
""
]
] |
new_dataset
| 0.990859 |
2208.02313
|
Monika Kwiatkowski
|
Dominik Kuhnke, Monika Kwiatkowski, Olaf Hellwich
|
Image-based Detection of Surface Defects in Concrete during Construction
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Defects increase the cost and duration of construction projects as they
require significant inspection and documentation efforts. Automating defect
detection could significantly reduce these efforts. This work focuses on
detecting honeycombs, a substantial defect in concrete structures that may
affect structural integrity. We compared honeycomb images scraped from the web
with images obtained from real construction inspections. We found that web
images do not capture the complete variance found in real-case scenarios and
that there is still a lack of data in this domain. Our dataset is therefore
freely available for further research. A Mask R-CNN and EfficientNet-B0 were
trained for honeycomb detection. The Mask R-CNN model allows detecting
honeycombs based on instance segmentation, whereas the EfficientNet-B0 model
allows a patch-based classification. Our experiments demonstrate that both
approaches are suitable for solving and automating honeycomb detection. In the
future, this solution can be incorporated into defect documentation systems.
|
[
{
"version": "v1",
"created": "Wed, 3 Aug 2022 19:05:12 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 15:19:33 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Kuhnke",
"Dominik",
""
],
[
"Kwiatkowski",
"Monika",
""
],
[
"Hellwich",
"Olaf",
""
]
] |
new_dataset
| 0.999687 |
2209.11518
|
Tuan-Anh Vu
|
Quang-Trung Truong and Tuan-Anh Vu and Tan-Sang Ha and Lokoc Jakub and
Yue Him Wong Tim and Ajay Joneja and Sai-Kit Yeung
|
Marine Video Kit: A New Marine Video Dataset for Content-based Analysis
and Retrieval
|
Camera Ready for MMM 2023, Bergen, Norway
| null | null | null |
cs.CV cs.IR cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Effective analysis of unusual domain specific video collections represents an
important practical problem, where state-of-the-art general purpose models
still face limitations. Hence, it is desirable to design benchmark datasets
that challenge novel powerful models for specific domains with additional
constraints. It is important to remember that domain specific data may be
noisier (e.g., endoscopic or underwater videos) and often require more
experienced users for effective search. In this paper, we focus on single-shot
videos taken from moving cameras in underwater environments, which constitute a
nontrivial challenge for research purposes. The first shard of a new Marine
Video Kit dataset is presented to serve for video retrieval and other computer
vision challenges. Our dataset is used in a special session during Video
Browser Showdown 2023. In addition to basic meta-data statistics, we present
several insights based on low-level features as well as semantic annotations of
selected keyframes. The analysis also contains experiments showing limitations
of respected general purpose models for retrieval. Our dataset and code are
publicly available at https://hkust-vgd.github.io/marinevideokit.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 10:57:50 GMT"
},
{
"version": "v2",
"created": "Fri, 21 Oct 2022 06:53:46 GMT"
},
{
"version": "v3",
"created": "Sat, 3 Dec 2022 15:03:32 GMT"
},
{
"version": "v4",
"created": "Tue, 6 Dec 2022 05:29:30 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Truong",
"Quang-Trung",
""
],
[
"Vu",
"Tuan-Anh",
""
],
[
"Ha",
"Tan-Sang",
""
],
[
"Jakub",
"Lokoc",
""
],
[
"Tim",
"Yue Him Wong",
""
],
[
"Joneja",
"Ajay",
""
],
[
"Yeung",
"Sai-Kit",
""
]
] |
new_dataset
| 0.999697 |
2210.07128
|
Aman Madaan
|
Aman Madaan, Shuyan Zhou, Uri Alon, Yiming Yang, Graham Neubig
|
Language Models of Code are Few-Shot Commonsense Learners
|
EMNLP 2022
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We address the general task of structured commonsense reasoning: given a
natural language input, the goal is to generate a graph such as an event -- or
a reasoning-graph. To employ large language models (LMs) for this task,
existing approaches ``serialize'' the output graph as a flat list of nodes and
edges. Although feasible, these serialized graphs strongly deviate from the
natural language corpora that LMs were pre-trained on, hindering LMs from
generating them correctly. In this paper, we show that when we instead frame
structured commonsense reasoning tasks as code generation tasks, pre-trained
LMs of code are better structured commonsense reasoners than LMs of natural
language, even when the downstream task does not involve source code at all. We
demonstrate our approach across three diverse structured commonsense reasoning
tasks. In all these natural language tasks, we show that using our approach, a
code generation LM (CODEX) outperforms natural-LMs that are fine-tuned on the
target task (e.g., T5) and other strong LMs such as GPT-3 in the few-shot
setting.
|
[
{
"version": "v1",
"created": "Thu, 13 Oct 2022 16:09:36 GMT"
},
{
"version": "v2",
"created": "Mon, 24 Oct 2022 05:29:59 GMT"
},
{
"version": "v3",
"created": "Tue, 6 Dec 2022 15:58:30 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Madaan",
"Aman",
""
],
[
"Zhou",
"Shuyan",
""
],
[
"Alon",
"Uri",
""
],
[
"Yang",
"Yiming",
""
],
[
"Neubig",
"Graham",
""
]
] |
new_dataset
| 0.953699 |
2211.00111
|
Sangdon Park
|
Sangdon Park and Xiang Cheng and Taesoo Kim
|
Unsafe's Betrayal: Abusing Unsafe Rust in Binary Reverse Engineering via
Machine Learning
| null | null | null | null |
cs.CR cs.LG cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Memory-safety bugs introduce critical software-security issues. Rust provides
memory-safe mechanisms to avoid memory-safety bugs in programming, while still
allowing unsafe escape hatches via unsafe code. However, the unsafe code that
enhances the usability of Rust provides clear spots for finding memory-safety
bugs in Rust source code. In this paper, we claim that these unsafe spots can
still be identifiable in Rust binary code via machine learning and be leveraged
for finding memory-safety bugs. To support our claim, we propose the tool
textttrustspot, that enables reverse engineering to learn an unsafe classifier
that proposes a list of functions in Rust binaries for downstream analysis. We
empirically show that the function proposals by textttrustspot can recall
$92.92\%$ of memory-safety bugs, while it covers only $16.79\%$ of the entire
binary code. As an application, we demonstrate that the function proposals are
used in targeted fuzzing on Rust packages, which contribute to reducing the
fuzzing time compared to non-targeted fuzzing.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 19:32:18 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 05:50:30 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Park",
"Sangdon",
""
],
[
"Cheng",
"Xiang",
""
],
[
"Kim",
"Taesoo",
""
]
] |
new_dataset
| 0.997736 |
2211.15350
|
Yanhui Zhang
|
Yanhui Zhang
|
Three classes of BCH codes and their duals
|
25 pages
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
BCH codes are an important class of cyclic codes, and have wide applicantions
in communication and storage systems. However, it is difficult to determine the
parameters of BCH codes and only a few cases are known. In this paper, we
mainly study three classes of BCH codes with
$n=q^{m}-1,\frac{q^{2s}-1}{q+1},\frac{q^{m}-1}{q-1}$. On one hand, we
accurately give the parameters of $\mathcal C_{(q,n,\delta,1)}$ and its dual
codes. On the other hand, we give the sufficient and necessary conditions for
$\mathcal C_{(q,n,\delta,2)}$ being dually-BCH codes.
|
[
{
"version": "v1",
"created": "Mon, 28 Nov 2022 14:31:51 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 03:37:40 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Zhang",
"Yanhui",
""
]
] |
new_dataset
| 0.955124 |
2212.01546
|
Yi Lei
|
Yi Lei, Shan Yang, Xinsheng Wang, Qicong Xie, Jixun Yao, Lei Xie, Dan
Su
|
UniSyn: An End-to-End Unified Model for Text-to-Speech and Singing Voice
Synthesis
| null | null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Text-to-speech (TTS) and singing voice synthesis (SVS) aim at generating
high-quality speaking and singing voice according to textual input and music
scores, respectively. Unifying TTS and SVS into a single system is crucial to
the applications requiring both of them. Existing methods usually suffer from
some limitations, which rely on either both singing and speaking data from the
same person or cascaded models of multiple tasks. To address these problems, a
simplified elegant framework for TTS and SVS, named UniSyn, is proposed in this
paper. It is an end-to-end unified model that can make a voice speak and sing
with only singing or speaking data from this person. To be specific, a
multi-conditional variational autoencoder (MC-VAE), which constructs two
independent latent sub-spaces with the speaker- and style-related (i.e. speak
or sing) conditions for flexible control, is proposed in UniSyn. Moreover,
supervised guided-VAE and timbre perturbation with the Wasserstein distance
constraint are leveraged to further disentangle the speaker timbre and style.
Experiments conducted on two speakers and two singers demonstrate that UniSyn
can generate natural speaking and singing voice without corresponding training
data. The proposed approach outperforms the state-of-the-art end-to-end voice
generation work, which proves the effectiveness and advantages of UniSyn.
|
[
{
"version": "v1",
"created": "Sat, 3 Dec 2022 05:58:10 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 11:28:30 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Lei",
"Yi",
""
],
[
"Yang",
"Shan",
""
],
[
"Wang",
"Xinsheng",
""
],
[
"Xie",
"Qicong",
""
],
[
"Yao",
"Jixun",
""
],
[
"Xie",
"Lei",
""
],
[
"Su",
"Dan",
""
]
] |
new_dataset
| 0.994518 |
2212.02375
|
Hankyu Jang
|
Hankyu Jang, Daeyoung Kim
|
D-TensoRF: Tensorial Radiance Fields for Dynamic Scenes
|
21 pages, 11 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neural radiance field (NeRF) attracts attention as a promising approach to
reconstructing the 3D scene. As NeRF emerges, subsequent studies have been
conducted to model dynamic scenes, which include motions or topological
changes. However, most of them use an additional deformation network, slowing
down the training and rendering speed. Tensorial radiance field (TensoRF)
recently shows its potential for fast, high-quality reconstruction of static
scenes with compact model size. In this paper, we present D-TensoRF, a
tensorial radiance field for dynamic scenes, enabling novel view synthesis at a
specific time. We consider the radiance field of a dynamic scene as a 5D
tensor. The 5D tensor represents a 4D grid in which each axis corresponds to X,
Y, Z, and time and has 1D multi-channel features per element. Similar to
TensoRF, we decompose the grid either into rank-one vector components (CP
decomposition) or low-rank matrix components (newly proposed MM decomposition).
We also use smoothing regularization to reflect the relationship between
features at different times (temporal dependency). We conduct extensive
evaluations to analyze our models. We show that D-TensoRF with CP decomposition
and MM decomposition both have short training times and significantly low
memory footprints with quantitatively and qualitatively competitive rendering
results in comparison to the state-of-the-art methods in 3D dynamic scene
modeling.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 15:57:55 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Dec 2022 04:15:10 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Jang",
"Hankyu",
""
],
[
"Kim",
"Daeyoung",
""
]
] |
new_dataset
| 0.992037 |
2212.02564
|
David Pomerenke
|
David Pomerenke
|
INCLUSIFY: A benchmark and a model for gender-inclusive German
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Gender-inclusive language is important for achieving gender equality in
languages with gender inflections, such as German. While stirring some
controversy, it is increasingly adopted by companies and political
institutions. A handful of tools have been developed to help people use
gender-inclusive language by identifying instances of the generic masculine and
providing suggestions for more inclusive reformulations. In this report, we
define the underlying tasks in terms of natural language processing, and
present a dataset and measures for benchmarking them. We also present a model
that implements these tasks, by combining an inclusive language database with
an elaborate sequence of processing steps via standard pre-trained models. Our
model achieves a recall of 0.89 and a precision of 0.82 in our benchmark for
identifying exclusive language; and one of its top five suggestions is chosen
in real-world texts in 44% of cases. We sketch how the area could be further
advanced by training end-to-end models and using large language models; and we
urge the community to include more gender-inclusive texts in their training
data in order to not present an obstacle to the adoption of gender-inclusive
language. Through these efforts, we hope to contribute to restoring justice in
language and, to a small extent, in reality.
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 19:37:48 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Pomerenke",
"David",
""
]
] |
new_dataset
| 0.999853 |
2212.02738
|
Weigang Lv
|
Weigang Lv, Jiale Bai, Qingli Yan, Hui-Ming Wang
|
RIS-Assisted Green Secure Communications: Active RIS or Passive RIS?
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reconfigurable Intelligent Surface (RIS) is one of the promising techniques
for 6G wireless communications, and recently has also been shown to be able to
improve secure communications. However, there is a "double fading" effect in
the reflection link between base station and user, thus passive RIS only
achieves a negligible secrecy gain in typical communications scenarios.In this
letter, we propose an active RIS-aided multi-antenna physical layer secrecy
transmission scheme, where the active RIS can amplify the signal actively. Our
aim is to minimize the transmit power subject to the constraint of secrecy
rate. To solve the non-convex optimization problem, a penalty-based alternating
minimization (AltMin) algorithm is proposed to optimize both the beamformer at
the transmitter and the reflection matrix at RIS. Simulation results show that
active RIS can resist the impact of "double fading" effect effectively, and is
more energy efficient than passive RIS.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 04:09:00 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Lv",
"Weigang",
""
],
[
"Bai",
"Jiale",
""
],
[
"Yan",
"Qingli",
""
],
[
"Wang",
"Hui-Ming",
""
]
] |
new_dataset
| 0.95864 |
2212.02746
|
Jiaqi Chen
|
Jiaqi Chen, Tong Li, Jinghui Qin, Pan Lu, Liang Lin, Chongyu Chen,
Xiaodan Liang
|
UniGeo: Unifying Geometry Logical Reasoning via Reformulating
Mathematical Expression
| null | null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Geometry problem solving is a well-recognized testbed for evaluating the
high-level multi-modal reasoning capability of deep models. In most existing
works, two main geometry problems: calculation and proving, are usually treated
as two specific tasks, hindering a deep model to unify its reasoning capability
on multiple math tasks. However, in essence, these two tasks have similar
problem representations and overlapped math knowledge which can improve the
understanding and reasoning ability of a deep model on both two tasks.
Therefore, we construct a large-scale Unified Geometry problem benchmark,
UniGeo, which contains 4,998 calculation problems and 9,543 proving problems.
Each proving problem is annotated with a multi-step proof with reasons and
mathematical expressions. The proof can be easily reformulated as a proving
sequence that shares the same formats with the annotated program sequence for
calculation problems. Naturally, we also present a unified multi-task Geometric
Transformer framework, Geoformer, to tackle calculation and proving problems
simultaneously in the form of sequence generation, which finally shows the
reasoning ability can be improved on both two tasks by unifying formulation.
Furthermore, we propose a Mathematical Expression Pretraining (MEP) method that
aims to predict the mathematical expressions in the problem solution, thus
improving the Geoformer model. Experiments on the UniGeo demonstrate that our
proposed Geoformer obtains state-of-the-art performance by outperforming
task-specific model NGS with over 5.6% and 3.2% accuracies on calculation and
proving problems, respectively.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 04:37:51 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Chen",
"Jiaqi",
""
],
[
"Li",
"Tong",
""
],
[
"Qin",
"Jinghui",
""
],
[
"Lu",
"Pan",
""
],
[
"Lin",
"Liang",
""
],
[
"Chen",
"Chongyu",
""
],
[
"Liang",
"Xiaodan",
""
]
] |
new_dataset
| 0.999627 |
2212.02749
|
Nariman Habili
|
Nariman Habili, Ernest Kwan, Weihao Li, Christfried Webers, Jeremy
Oorloff, Mohammad Ali Armin, Lars Petersson
|
A Hyperspectral and RGB Dataset for Building Facade Segmentation
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Hyperspectral Imaging (HSI) provides detailed spectral information and has
been utilised in many real-world applications. This work introduces an HSI
dataset of building facades in a light industry environment with the aim of
classifying different building materials in a scene. The dataset is called the
Light Industrial Building HSI (LIB-HSI) dataset. This dataset consists of nine
categories and 44 classes. In this study, we investigated deep learning based
semantic segmentation algorithms on RGB and hyperspectral images to classify
various building materials, such as timber, brick and concrete.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 04:38:44 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Habili",
"Nariman",
""
],
[
"Kwan",
"Ernest",
""
],
[
"Li",
"Weihao",
""
],
[
"Webers",
"Christfried",
""
],
[
"Oorloff",
"Jeremy",
""
],
[
"Armin",
"Mohammad Ali",
""
],
[
"Petersson",
"Lars",
""
]
] |
new_dataset
| 0.999755 |
2212.02821
|
Madhu Raka
|
Swati Bhardwaj, Mokshi Goyal and Madhu Raka
|
New Quantum codes from constacyclic codes over a general non-chain ring
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Let $q$ be a prime power and let $\mathcal{R}=\mathbb{F}_{q}[u_1,u_2, \cdots,
u_k]/\langle f_i(u_i),u_iu_j-u_ju_i\rangle$ be a finite non-chain ring, where
$f_i(u_i), 1\leq i \leq k$ are polynomials, not all linear, which split into
distinct linear factors over $\mathbb{F}_{q}$. We characterize constacyclic
codes over the ring $\mathcal{R}$ and study quantum codes from these. As an
application, some new and better quantum codes, as compared to the best known
codes, are obtained. We also prove that the choice of the polynomials
$f_i(u_i),$ $1 \leq i \leq k$ is irrelevant while constructing quantum codes
from constacyclic codes over $\mathcal{R}$, it depends only on their degrees.
It is shown that there always exists Quantum MDS code $[[n,n-2,2]]_q$ for any
$n$ with $\gcd (n,q)\neq 1.$
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 08:32:49 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Bhardwaj",
"Swati",
""
],
[
"Goyal",
"Mokshi",
""
],
[
"Raka",
"Madhu",
""
]
] |
new_dataset
| 0.999801 |
2212.02845
|
Yan Wang
|
Yan Wang, Junbo Yin, Wei Li, Pascal Frossard, Ruigang Yang, Jianbing
Shen
|
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from
Point Cloud
|
Accepted by AAAI 2023
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
LiDAR-based 3D object detection is an indispensable task in advanced
autonomous driving systems. Though impressive detection results have been
achieved by superior 3D detectors, they suffer from significant performance
degeneration when facing unseen domains, such as different LiDAR
configurations, different cities, and weather conditions. The mainstream
approaches tend to solve these challenges by leveraging unsupervised domain
adaptation (UDA) techniques. However, these UDA solutions just yield
unsatisfactory 3D detection results when there is a severe domain shift, e.g.,
from Waymo (64-beam) to nuScenes (32-beam). To address this, we present a novel
Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D),
where only a few labeled target data is available, yet can significantly
improve the adaptation performance. In particular, our SSDA3D includes an
Inter-domain Adaptation stage and an Intra-domain Generalization stage. In the
first stage, an Inter-domain Point-CutMix module is presented to efficiently
align the point cloud distribution across domains. The Point-CutMix generates
mixed samples of an intermediate domain, thus encouraging to learn
domain-invariant knowledge. Then, in the second stage, we further enhance the
model for better generalization on the unlabeled target set. This is achieved
by exploring Intra-domain Point-MixUp in semi-supervised learning, which
essentially regularizes the pseudo label distribution. Experiments from Waymo
to nuScenes show that, with only 10% labeled target data, our SSDA3D can
surpass the fully-supervised oracle model with 100% target label. Our code is
available at https://github.com/yinjunbo/SSDA3D.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 09:32:44 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Wang",
"Yan",
""
],
[
"Yin",
"Junbo",
""
],
[
"Li",
"Wei",
""
],
[
"Frossard",
"Pascal",
""
],
[
"Yang",
"Ruigang",
""
],
[
"Shen",
"Jianbing",
""
]
] |
new_dataset
| 0.99202 |
2212.02871
|
Siyuan Zhou
|
Siyuan Zhou and Chunru Zhan and Biao Wang and Tiezheng Ge and Yuning
Jiang and Li Niu
|
Video Object of Interest Segmentation
|
13 pages, 8 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we present a new computer vision task named video object of
interest segmentation (VOIS). Given a video and a target image of interest, our
objective is to simultaneously segment and track all objects in the video that
are relevant to the target image. This problem combines the traditional video
object segmentation task with an additional image indicating the content that
users are concerned with. Since no existing dataset is perfectly suitable for
this new task, we specifically construct a large-scale dataset called
LiveVideos, which contains 2418 pairs of target images and live videos with
instance-level annotations. In addition, we propose a transformer-based method
for this task. We revisit Swin Transformer and design a dual-path structure to
fuse video and image features. Then, a transformer decoder is employed to
generate object proposals for segmentation and tracking from the fused
features. Extensive experiments on LiveVideos dataset show the superiority of
our proposed method.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 10:21:10 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Zhou",
"Siyuan",
""
],
[
"Zhan",
"Chunru",
""
],
[
"Wang",
"Biao",
""
],
[
"Ge",
"Tiezheng",
""
],
[
"Jiang",
"Yuning",
""
],
[
"Niu",
"Li",
""
]
] |
new_dataset
| 0.999872 |
2212.02896
|
Pengfei Hu
|
Pengfei Hu, Zhenrong Zhang, Jianshu Zhang, Jun Du, Jiajia Wu
|
Multimodal Tree Decoder for Table of Contents Extraction in Document
Images
|
Accepted by ICPR2022
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Table of contents (ToC) extraction aims to extract headings of different
levels in documents to better understand the outline of the contents, which can
be widely used for document understanding and information retrieval. Existing
works often use hand-crafted features and predefined rule-based functions to
detect headings and resolve the hierarchical relationship between headings.
Both the benchmark and research based on deep learning are still limited.
Accordingly, in this paper, we first introduce a standard dataset, HierDoc,
including image samples from 650 documents of scientific papers with their
content labels. Then we propose a novel end-to-end model by using the
multimodal tree decoder (MTD) for ToC as a benchmark for HierDoc. The MTD model
is mainly composed of three parts, namely encoder, classifier, and decoder. The
encoder fuses the multimodality features of vision, text, and layout
information for each entity of the document. Then the classifier recognizes and
selects the heading entities. Next, to parse the hierarchical relationship
between the heading entities, a tree-structured decoder is designed. To
evaluate the performance, both the metric of tree-edit-distance similarity
(TEDS) and F1-Measure are adopted. Finally, our MTD approach achieves an
average TEDS of 87.2% and an average F1-Measure of 88.1% on the test set of
HierDoc. The code and dataset will be released at:
https://github.com/Pengfei-Hu/MTD.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 11:38:31 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Hu",
"Pengfei",
""
],
[
"Zhang",
"Zhenrong",
""
],
[
"Zhang",
"Jianshu",
""
],
[
"Du",
"Jun",
""
],
[
"Wu",
"Jiajia",
""
]
] |
new_dataset
| 0.999704 |
2212.02935
|
Richard Preen
|
Richard J. Preen and Jim Smith
|
ACRO: A multi-language toolkit for supporting Automated Checking of
Research Outputs
| null | null | null | null |
cs.CR cs.IR cs.SE stat.AP stat.ME
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper discusses the development of an open source tool ACRO, (Automatic
Checking of Research Outputs) to assist researchers and data governance teams
by distinguishing between: research output that is safe to publish; output that
requires further analysis; and output that cannot be published because it
creates substantial risk of disclosing private data. ACRO extends the
functionality and accessibility of a previous prototype by providing a
light-weight 'skin' that sits over well-known analysis tools, and enables
access in a variety of programming languages researchers might use. This adds
functionality to (i) identify potentially disclosive outputs against a range of
commonly used disclosure tests; (ii) suppress outputs where required; (iii)
report reasons for suppression; and (iv) produce simple summary documents
Trusted Research Environment (TRE) staff can use to streamline their workflow.
The ACRO code and documentation are available under an MIT license at
https://github.com/AI-SDC/ACRO
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 12:45:15 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Preen",
"Richard J.",
""
],
[
"Smith",
"Jim",
""
]
] |
new_dataset
| 0.996136 |
2212.03091
|
Pei Chen
|
Pei Chen, Wenlin Yao, Hongming Zhang, Xiaoman Pan, Dian Yu, Dong Yu,
and Jianshu Chen
|
ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base
Completion
|
ICDMW 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Knowledge base completion (KBC) aims to predict the missing links in
knowledge graphs. Previous KBC tasks and approaches mainly focus on the setting
where all test entities and relations have appeared in the training set.
However, there has been limited research on the zero-shot KBC settings, where
we need to deal with unseen entities and relations that emerge in a constantly
growing knowledge base. In this work, we systematically examine different
possible scenarios of zero-shot KBC and develop a comprehensive benchmark,
ZeroKBC, that covers these scenarios with diverse types of knowledge sources.
Our systematic analysis reveals several missing yet important zero-shot KBC
settings. Experimental results show that canonical and state-of-the-art KBC
systems cannot achieve satisfactory performance on this challenging benchmark.
By analyzing the strength and weaknesses of these systems on solving ZeroKBC,
we further present several important observations and promising future
directions.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 16:02:09 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Chen",
"Pei",
""
],
[
"Yao",
"Wenlin",
""
],
[
"Zhang",
"Hongming",
""
],
[
"Pan",
"Xiaoman",
""
],
[
"Yu",
"Dian",
""
],
[
"Yu",
"Dong",
""
],
[
"Chen",
"Jianshu",
""
]
] |
new_dataset
| 0.98969 |
2212.03222
|
William Bruno
|
William Bruno, Dan Roth
|
LawngNLI: A Long-Premise Benchmark for In-Domain Generalization from
Short to Long Contexts and for Implication-Based Retrieval
|
Findings of EMNLP 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Natural language inference has trended toward studying contexts beyond the
sentence level. An important application area is law: past cases often do not
foretell how they apply to new situations and implications must be inferred.
This paper introduces LawngNLI, constructed from U.S. legal opinions with
automatic labels with high human-validated accuracy. Premises are long and
multigranular. Experiments show two use cases. First, LawngNLI can benchmark
for in-domain generalization from short to long contexts. It has remained
unclear if large-scale long-premise NLI datasets actually need to be
constructed: near-top performance on long premises could be achievable by
fine-tuning using short premises. Without multigranularity, benchmarks cannot
distinguish lack of fine-tuning on long premises versus domain shift between
short and long datasets. In contrast, our long and short premises share the
same examples and domain. Models fine-tuned using several past NLI datasets
and/or our short premises fall short of top performance on our long premises.
So for at least certain domains (such as ours), large-scale long-premise
datasets are needed. Second, LawngNLI can benchmark for implication-based
retrieval. Queries are entailed or contradicted by target documents, allowing
users to move between arguments and evidence. Leading retrieval models perform
reasonably zero shot on a LawngNLI-derived retrieval task. We compare different
systems for re-ranking, including lexical overlap and cross-encoders fine-tuned
using a modified LawngNLI or past NLI datasets. LawngNLI can train and test
systems for implication-based case retrieval and argumentation.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 18:42:39 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Bruno",
"William",
""
],
[
"Roth",
"Dan",
""
]
] |
new_dataset
| 0.999713 |
2212.03237
|
Umar Iqbal
|
Umar Iqbal, Akin Caliskan, Koki Nagano, Sameh Khamis, Pavlo Molchanov,
Jan Kautz
|
RANA: Relightable Articulated Neural Avatars
|
project page: https://nvlabs.github.io/RANA/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We propose RANA, a relightable and articulated neural avatar for the
photorealistic synthesis of humans under arbitrary viewpoints, body poses, and
lighting. We only require a short video clip of the person to create the avatar
and assume no knowledge about the lighting environment. We present a novel
framework to model humans while disentangling their geometry, texture, and also
lighting environment from monocular RGB videos. To simplify this otherwise
ill-posed task we first estimate the coarse geometry and texture of the person
via SMPL+D model fitting and then learn an articulated neural representation
for photorealistic image generation. RANA first generates the normal and albedo
maps of the person in any given target body pose and then uses spherical
harmonics lighting to generate the shaded image in the target lighting
environment. We also propose to pretrain RANA using synthetic images and
demonstrate that it leads to better disentanglement between geometry and
texture while also improving robustness to novel body poses. Finally, we also
present a new photorealistic synthetic dataset, Relighting Humans, to
quantitatively evaluate the performance of the proposed approach.
|
[
{
"version": "v1",
"created": "Tue, 6 Dec 2022 18:59:31 GMT"
}
] | 2022-12-07T00:00:00 |
[
[
"Iqbal",
"Umar",
""
],
[
"Caliskan",
"Akin",
""
],
[
"Nagano",
"Koki",
""
],
[
"Khamis",
"Sameh",
""
],
[
"Molchanov",
"Pavlo",
""
],
[
"Kautz",
"Jan",
""
]
] |
new_dataset
| 0.974585 |
2009.14115
|
Adam Kortylewski
|
Yutong Bai, Angtian Wang, Adam Kortylewski, Alan Yuille
|
CoKe: Localized Contrastive Learning for Robust Keypoint Detection
|
Accepted to WACV 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we introduce a contrastive learning framework for keypoint
detection (CoKe). Keypoint detection differs from other visual tasks where
contrastive learning has been applied because the input is a set of images in
which multiple keypoints are annotated. This requires the contrastive learning
to be extended such that the keypoints are represented and detected
independently, which enables the contrastive loss to make the keypoint features
different from each other and from the background. Our approach has two
benefits: It enables us to exploit contrastive learning for keypoint detection,
and by detecting each keypoint independently the detection becomes more robust
to occlusion compared to holistic methods, such as stacked hourglass networks,
which attempt to detect all keypoints jointly. Our CoKe framework introduces
several technical innovations. In particular, we introduce: (i) A clutter bank
to represent non-keypoint features; (ii) a keypoint bank that stores
prototypical representations of keypoints to approximate the contrastive loss
between keypoints; and (iii) a cumulative moving average update to learn the
keypoint prototypes while training the feature extractor. Our experiments on a
range of diverse datasets (PASCAL3D+, MPII, ObjectNet3D) show that our approach
works as well, or better than, alternative methods for keypoint detection, even
for human keypoints, for which the literature is vast. Moreover, we observe
that CoKe is exceptionally robust to partial occlusion and previously unseen
object poses.
|
[
{
"version": "v1",
"created": "Tue, 29 Sep 2020 16:00:43 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Sep 2020 01:32:46 GMT"
},
{
"version": "v3",
"created": "Mon, 23 Nov 2020 16:22:35 GMT"
},
{
"version": "v4",
"created": "Mon, 5 Dec 2022 08:56:16 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Bai",
"Yutong",
""
],
[
"Wang",
"Angtian",
""
],
[
"Kortylewski",
"Adam",
""
],
[
"Yuille",
"Alan",
""
]
] |
new_dataset
| 0.955058 |
2104.09375
|
Burak Ekim
|
Burak Ekim, Elif Sertel
|
A Multi-Task Deep Learning Framework for Building Footprint Segmentation
|
International Geoscience and Remote Sensing Symposium (IGARSS), Jul
2021, Brussels, Belgium
| null |
10.1109/IGARSS47720.2021.9554766
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The task of building footprint segmentation has been well-studied in the
context of remote sensing (RS) as it provides valuable information in many
aspects, however, difficulties brought by the nature of RS images such as
variations in the spatial arrangements and in-consistent constructional
patterns require studying further, since it often causes poorly classified
segmentation maps. We address this need by designing a joint optimization
scheme for the task of building footprint delineation and introducing two
auxiliary tasks; image reconstruction and building footprint boundary
segmentation with the intent to reveal the common underlying structure to
advance the classification accuracy of a single task model under the favor of
auxiliary tasks. In particular, we propose a deep multi-task learning (MTL)
based unified fully convolutional framework which operates in an end-to-end
manner by making use of joint loss function with learnable loss weights
considering the homoscedastic uncertainty of each task loss. Experimental
results conducted on the SpaceNet6 dataset demonstrate the potential of the
proposed MTL framework as it improves the classification accuracy considerably
compared to single-task and lesser compounded tasks.
|
[
{
"version": "v1",
"created": "Mon, 19 Apr 2021 15:07:27 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Ekim",
"Burak",
""
],
[
"Sertel",
"Elif",
""
]
] |
new_dataset
| 0.997504 |
2108.07366
|
Anurag Murty Naredla
|
Anna Lubiw, Anurag Murty Naredla
|
The Visibility Center of a Simple Polygon
|
Full-length version of a paper that appeared at the European
Symposium of Algorithms 2021
| null | null | null |
cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce the \emph{visibility center} of a set of points inside a polygon
-- a point $c_V$ such that the maximum geodesic distance from $c_V$ to see any
point in the set is minimized. For a simple polygon of $n$ vertices and a set
of $m$ points inside it, we give an $O((n+m) \log {(n+m)})$ time algorithm to
find the visibility center. We find the visibility center of \emph{all} points
in a simple polygon in $O(n \log n)$ time.
Our algorithm reduces the visibility center problem to the problem of finding
the geodesic center of a set of half-polygons inside a polygon, which is of
independent interest. We give an $O((n+k) \log (n+k))$ time algorithm for this
problem, where $k$ is the number of half-polygons.
|
[
{
"version": "v1",
"created": "Mon, 16 Aug 2021 22:44:32 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Aug 2021 16:03:30 GMT"
},
{
"version": "v3",
"created": "Sun, 4 Dec 2022 23:06:46 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Lubiw",
"Anna",
""
],
[
"Naredla",
"Anurag Murty",
""
]
] |
new_dataset
| 0.975771 |
2109.00405
|
Celyn Walters
|
Celyn Walters and Simon Hadfield
|
EVReflex: Dense Time-to-Impact Prediction for Event-based Obstacle
Avoidance
|
To be published in IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS) 2021
| null |
10.1109/IROS51168.2021.9636327
| null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The broad scope of obstacle avoidance has led to many kinds of computer
vision-based approaches. Despite its popularity, it is not a solved problem.
Traditional computer vision techniques using cameras and depth sensors often
focus on static scenes, or rely on priors about the obstacles. Recent
developments in bio-inspired sensors present event cameras as a compelling
choice for dynamic scenes. Although these sensors have many advantages over
their frame-based counterparts, such as high dynamic range and temporal
resolution, event-based perception has largely remained in 2D. This often leads
to solutions reliant on heuristics and specific to a particular task. We show
that the fusion of events and depth overcomes the failure cases of each
individual modality when performing obstacle avoidance. Our proposed approach
unifies event camera and lidar streams to estimate metric time-to-impact
without prior knowledge of the scene geometry or obstacles. In addition, we
release an extensive event-based dataset with six visual streams spanning over
700 scanned scenes.
|
[
{
"version": "v1",
"created": "Wed, 1 Sep 2021 14:34:20 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Walters",
"Celyn",
""
],
[
"Hadfield",
"Simon",
""
]
] |
new_dataset
| 0.998225 |
2109.00945
|
Lynnette Hui Xian Ng
|
Lynnette Hui Xian Ng, Iain Cruickshank, Kathleen M. Carley
|
Coordinating Narratives and the Capitol Riots on Parler
| null |
Computational Mathematics Organizational Theory (2022)
|
10.1007/s10588-022-09371-2
| null |
cs.SI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Coordinated disinformation campaigns are used to influence social media
users, potentially leading to offline violence. In this study, we introduce a
general methodology to uncover coordinated messaging through analysis of user
parleys on Parler. The proposed method constructs a user-to-user coordination
network graph induced by a user-to-text graph and a text-to-text similarity
graph. The text-to-text graph is constructed based on the textual similarity of
Parler posts. We study three influential groups of users in the 6 January 2020
Capitol riots and detect networks of coordinated user clusters that are all
posting similar textual content in support of different disinformation
narratives related to the U.S. 2020 elections.
|
[
{
"version": "v1",
"created": "Thu, 2 Sep 2021 13:44:59 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Ng",
"Lynnette Hui Xian",
""
],
[
"Cruickshank",
"Iain",
""
],
[
"Carley",
"Kathleen M.",
""
]
] |
new_dataset
| 0.97697 |
2110.02929
|
Martino Sorbaro
|
Julian B\"uchel, Gregor Lenz, Yalun Hu, Sadique Sheik, Martino Sorbaro
|
Adversarial Attacks on Spiking Convolutional Neural Networks for
Event-based Vision
|
9 pages plus Supplementary Material. Accepted in Frontiers in
Neuroscience -- Neuromorphic Engineering
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Event-based dynamic vision sensors provide very sparse output in the form of
spikes, which makes them suitable for low-power applications. Convolutional
spiking neural networks model such event-based data and develop their full
energy-saving potential when deployed on asynchronous neuromorphic hardware.
Event-based vision being a nascent field, the sensitivity of spiking neural
networks to potentially malicious adversarial attacks has received little
attention so far. We show how white-box adversarial attack algorithms can be
adapted to the discrete and sparse nature of event-based visual data, and
demonstrate smaller perturbation magnitudes at higher success rates than the
current state-of-the-art algorithms. For the first time, we also verify the
effectiveness of these perturbations directly on neuromorphic hardware.
Finally, we discuss the properties of the resulting perturbations, the effect
of adversarial training as a defense strategy, and future directions.
|
[
{
"version": "v1",
"created": "Wed, 6 Oct 2021 17:20:05 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Dec 2021 15:34:41 GMT"
},
{
"version": "v3",
"created": "Mon, 5 Dec 2022 12:49:10 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Büchel",
"Julian",
""
],
[
"Lenz",
"Gregor",
""
],
[
"Hu",
"Yalun",
""
],
[
"Sheik",
"Sadique",
""
],
[
"Sorbaro",
"Martino",
""
]
] |
new_dataset
| 0.983443 |
2111.03788
|
Takuma Seno
|
Takuma Seno, Michita Imai
|
d3rlpy: An Offline Deep Reinforcement Learning Library
|
Journal of Machine Learning Research
|
Journal of Machine Learning Research 23(315) (2022) 1-20;
| null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we introduce d3rlpy, an open-sourced offline deep
reinforcement learning (RL) library for Python. d3rlpy supports a set of
offline deep RL algorithms as well as off-policy online algorithms via a fully
documented plug-and-play API. To address a reproducibility issue, we conduct a
large-scale benchmark with D4RL and Atari 2600 dataset to ensure implementation
quality and provide experimental scripts and full tables of results. The d3rlpy
source code can be found on GitHub: \url{https://github.com/takuseno/d3rlpy}.
|
[
{
"version": "v1",
"created": "Sat, 6 Nov 2021 03:09:39 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Dec 2022 12:03:07 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Seno",
"Takuma",
""
],
[
"Imai",
"Michita",
""
]
] |
new_dataset
| 0.999507 |
2112.13230
|
Yang Li
|
Yang Li, Yiting Dong, Dongcheng Zhao, Yi Zeng
|
N-Omniglot, a large-scale neuromorphic dataset for spatio-temporal
sparse few-shot learning
| null | null |
10.1038/s41597-022-01851-z
| null |
cs.NE cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Few-shot learning (learning with a few samples) is one of the most important
cognitive abilities of the human brain. However, the current artificial
intelligence systems meet difficulties in achieving this ability. Similar
challenges also exist for biologically plausible spiking neural networks
(SNNs). Datasets for traditional few-shot learning domains provide few amounts
of temporal information. and the absence of neuromorphic datasets has hindered
the development of few-shot learning for SNNs. Here, to the best of our
knowledge, we provide the first neuromorphic dataset for few-shot learning
using SNNs: N-Omniglot, based on the Dynamic Vision Sensor. It contains 1,623
categories of handwritten characters, with only 20 samples per class.
N-Omniglot eliminates the need for a neuromorphic dataset for SNNs with high
spareness and tremendous temporal coherence. Additionally, the dataset provides
a powerful challenge and a suitable benchmark for developing SNNs algorithms in
the few-shot learning domain due to the chronological information of strokes.
We also provide the improved nearest neighbor, convolutional network,
SiameseNet, and meta-learning algorithm in the spiking version for
verification.
|
[
{
"version": "v1",
"created": "Sat, 25 Dec 2021 12:41:34 GMT"
},
{
"version": "v2",
"created": "Tue, 28 Dec 2021 11:07:25 GMT"
},
{
"version": "v3",
"created": "Sat, 3 Dec 2022 15:25:33 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Li",
"Yang",
""
],
[
"Dong",
"Yiting",
""
],
[
"Zhao",
"Dongcheng",
""
],
[
"Zeng",
"Yi",
""
]
] |
new_dataset
| 0.995697 |
2202.04215
|
Omar Hamido
|
Omar Costa Hamido
|
QAC: Quantum-computing Aided Composition
|
Pre-publication draft, to appear in book 'Quantum Computer Music', E.
R. Miranda (Ed.)
| null |
10.1007/978-3-031-13909-3_8
| null |
cs.ET cs.HC cs.SD eess.AS quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this chapter I will discuss the role of quantum computing in computer
music and how it can be integrated to better serve the creative artists. I will
start by considering different approaches in current computer music and quantum
computing tools, as well as reviewing some previous attempts to integrate them.
Then, I will reflect on the meaning of this integration and present what I
coined as QAC (Quantum-computing Aided Composition) as well as an early attempt
at realizing it. This chapter will also introduce The QAC Toolkit Max package,
analyze its performance, and explore some examples of what it can offer to
realtime creative practice. Lastly, I will present a real case scenario of QAC
in the creative work Disklavier Prelude #3.
|
[
{
"version": "v1",
"created": "Wed, 9 Feb 2022 01:17:21 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Hamido",
"Omar Costa",
""
]
] |
new_dataset
| 0.99794 |
2204.00862
|
Pei Ke
|
Pei Ke, Hao Zhou, Yankai Lin, Peng Li, Jie Zhou, Xiaoyan Zhu, Minlie
Huang
|
CTRLEval: An Unsupervised Reference-Free Metric for Evaluating
Controlled Text Generation
|
Accepted by ACL 2022 (Main Conference)
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing reference-free metrics have obvious limitations for evaluating
controlled text generation models. Unsupervised metrics can only provide a
task-agnostic evaluation result which correlates weakly with human judgments,
whereas supervised ones may overfit task-specific data with poor generalization
ability to other datasets. In this paper, we propose an unsupervised
reference-free metric called CTRLEval, which evaluates controlled text
generation from different aspects by formulating each aspect into multiple text
infilling tasks. On top of these tasks, the metric assembles the generation
probabilities from a pre-trained language model without any model training.
Experimental results show that our metric has higher correlations with human
judgments than other baselines, while obtaining better generalization of
evaluating generated texts from different models and with different qualities.
|
[
{
"version": "v1",
"created": "Sat, 2 Apr 2022 13:42:49 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Dec 2022 10:11:11 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Ke",
"Pei",
""
],
[
"Zhou",
"Hao",
""
],
[
"Lin",
"Yankai",
""
],
[
"Li",
"Peng",
""
],
[
"Zhou",
"Jie",
""
],
[
"Zhu",
"Xiaoyan",
""
],
[
"Huang",
"Minlie",
""
]
] |
new_dataset
| 0.995242 |
2204.07003
|
Paolo Perrone
|
Sean Moss and Paolo Perrone
|
Probability monads with submonads of deterministic states - Extended
version
|
16 pages. Extended version of paper accepted for LICS 2022 conference
| null |
10.1145/3531130.3533355
| null |
cs.LO math.CT math.PR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Probability theory can be studied synthetically as the computational effect
embodied by a commutative monad. In the recently proposed Markov categories,
one works with an abstraction of the Kleisli category and then defines
deterministic morphisms equationally in terms of copying and discarding. The
resulting difference between 'pure' and 'deterministic' leads us to investigate
the 'sober' objects for a probability monad, for which the two concepts
coincide. We propose natural conditions on a probability monad which allow us
to identify the sober objects and define an idempotent sobrification functor.
Our framework applies to many examples of interest, including the Giry monad on
measurable spaces, and allows us to sharpen a previously given version of de
Finetti's theorem for Markov categories.
This is an extended version of the paper accepted for the Logic In Computer
Science (LICS) conference 2022. In this document we include more mathematical
details, including all the proofs, of the statements and constructions given in
the published version.
|
[
{
"version": "v1",
"created": "Thu, 14 Apr 2022 14:54:45 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Moss",
"Sean",
""
],
[
"Perrone",
"Paolo",
""
]
] |
new_dataset
| 0.977297 |
2205.10956
|
Wei Yuan
|
Wei Yuan, Quanjun Zhang, Tieke He, Chunrong Fang, Nguyen Quoc Viet
Hung, Xiaodong Hao, Hongzhi Yin
|
CIRCLE: Continual Repair across Programming Languages
|
This paper was accepted by ISSTA2022
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Automatic Program Repair (APR) aims at fixing buggy source code with less
manual debugging efforts, which plays a vital role in improving software
reliability and development productivity. Recent APR works have achieved
remarkable progress via applying deep learning (DL), particularly neural
machine translation (NMT) techniques. However, we observe that existing
DL-based APR models suffer from at least two severe drawbacks: (1) Most of them
can only generate patches for a single programming language, as a result, to
repair multiple languages, we have to build and train many repairing models.
(2) Most of them are developed in an offline manner. Therefore, they won't
function when there are new-coming requirements. To address the above problems,
a T5-based APR framework equipped with continual learning ability across
multiple programming languages is proposed, namely \emph{C}ont\emph{I}nual
\emph{R}epair a\emph{C}ross Programming \emph{L}anguag\emph{E}s
(\emph{CIRCLE}). Specifically, (1) CIRCLE utilizes a prompting function to
narrow the gap between natural language processing (NLP) pre-trained tasks and
APR. (2) CIRCLE adopts a difficulty-based rehearsal strategy to achieve
lifelong learning for APR without access to the full historical data. (3) An
elastic regularization method is employed to strengthen CIRCLE's continual
learning ability further, preventing it from catastrophic forgetting. (4)
CIRCLE applies a simple but effective re-repairing method to revise generated
errors caused by crossing multiple programming languages. We train CIRCLE for
four languages (i.e., C, JAVA, JavaScript, and Python) and evaluate it on five
commonly used benchmarks. The experimental results demonstrate that CIRCLE not
only effectively and efficiently repairs multiple programming languages in
continual learning settings, but also achieves state-of-the-art performance
with a single repair model.
|
[
{
"version": "v1",
"created": "Sun, 22 May 2022 23:34:37 GMT"
},
{
"version": "v2",
"created": "Thu, 26 May 2022 09:14:30 GMT"
},
{
"version": "v3",
"created": "Thu, 2 Jun 2022 01:16:24 GMT"
},
{
"version": "v4",
"created": "Sat, 3 Dec 2022 14:24:03 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Yuan",
"Wei",
""
],
[
"Zhang",
"Quanjun",
""
],
[
"He",
"Tieke",
""
],
[
"Fang",
"Chunrong",
""
],
[
"Hung",
"Nguyen Quoc Viet",
""
],
[
"Hao",
"Xiaodong",
""
],
[
"Yin",
"Hongzhi",
""
]
] |
new_dataset
| 0.989381 |
2207.07195
|
Duowei Li
|
Duowei Li (1 and 2), Jianping Wu (1), Feng Zhu (2), Tianyi Chen (2),
Yiik Diew Wong (2) ((1) Department of Civil Engineering, Tsinghua University,
China, (2) School of Civil and Environmental Engineering, Nanyang
Technological University, Singapore)
|
COOR-PLT: A hierarchical control model for coordinating adaptive
platoons of connected and autonomous vehicles at signal-free intersections
based on deep reinforcement learning
|
This paper has been submitted to Transportation Research Part C:
Emerging Technologies and is currently under review
|
Transportation Research Part C: Emerging Technologies 146 (2023):
103933
|
10.1016/j.trc.2022.103933
| null |
cs.LG cs.MA cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Platooning and coordination are two implementation strategies that are
frequently proposed for traffic control of connected and autonomous vehicles
(CAVs) at signal-free intersections instead of using conventional traffic
signals. However, few studies have attempted to integrate both strategies to
better facilitate the CAV control at signal-free intersections. To this end,
this study proposes a hierarchical control model, named COOR-PLT, to coordinate
adaptive CAV platoons at a signal-free intersection based on deep reinforcement
learning (DRL). COOR-PLT has a two-layer framework. The first layer uses a
centralized control strategy to form adaptive platoons. The optimal size of
each platoon is determined by considering multiple objectives (i.e.,
efficiency, fairness and energy saving). The second layer employs a
decentralized control strategy to coordinate multiple platoons passing through
the intersection. Each platoon is labeled with coordinated status or
independent status, upon which its passing priority is determined. As an
efficient DRL algorithm, Deep Q-network (DQN) is adopted to determine platoon
sizes and passing priorities respectively in the two layers. The model is
validated and examined on the simulator Simulation of Urban Mobility (SUMO).
The simulation results demonstrate that the model is able to: (1) achieve
satisfactory convergence performances; (2) adaptively determine platoon size in
response to varying traffic conditions; and (3) completely avoid deadlocks at
the intersection. By comparison with other control methods, the model manifests
its superiority of adopting adaptive platooning and DRL-based coordination
strategies. Also, the model outperforms several state-of-the-art methods on
reducing travel time and fuel consumption in different traffic conditions.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 02:22:31 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Li",
"Duowei",
"",
"1 and 2"
],
[
"Wu",
"Jianping",
""
],
[
"Zhu",
"Feng",
""
],
[
"Chen",
"Tianyi",
""
],
[
"Wong",
"Yiik Diew",
""
]
] |
new_dataset
| 0.986756 |
2208.04563
|
Tarun Rambha
|
Saumya Bhatnagar, Tarun Rambha, Gitakrishnan Ramadurai
|
An Agent-Based Fleet Management Model for First- and Last-Mile Services
| null | null |
10.1007/s11116-022-10363-z
| null |
cs.MA
|
http://creativecommons.org/licenses/by/4.0/
|
With the growth of cars and car-sharing applications, commuters in many
cities, particularly developing countries, are shifting away from public
transport. These shifts have affected two key stakeholders: transit operators
and first- and last-mile (FLM) services. Although most cities continue to
invest heavily in bus and metro projects to make public transit attractive,
ridership in these systems has often failed to reach targeted levels. FLM
service providers also experience lower demand and revenues in the wake of
shifts to other means of transport. Effective FLM options are required to
prevent this phenomenon and make public transport attractive for commuters. One
possible solution is to forge partnerships between public transport and FLM
providers that offer competitive joint mobility options. Such solutions require
prudent allocation of supply and optimised strategies for FLM operations and
ride-sharing. To this end, we build an agent- and event-based simulation model
which captures interactions between passengers and FLM services using
statecharts, vehicle routing models, and other trip matching rules. An
optimisation model for allocating FLM vehicles at different transit stations is
proposed to reduce unserved requests. Using real-world metro transit demand
data from Bengaluru, India, the effectiveness of our approach in improving FLM
connectivity and quantifying the benefits of sharing trips is demonstrated.
|
[
{
"version": "v1",
"created": "Tue, 9 Aug 2022 06:52:28 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Dec 2022 12:56:27 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Bhatnagar",
"Saumya",
""
],
[
"Rambha",
"Tarun",
""
],
[
"Ramadurai",
"Gitakrishnan",
""
]
] |
new_dataset
| 0.997786 |
2209.11388
|
Haoyu Lu
|
Haoyu Lu and Mingyu Ding and Nanyi Fei and Yuqi Huo and Zhiwu Lu
|
LGDN: Language-Guided Denoising Network for Video-Language Modeling
|
Accepted by NeurIPS2022
| null | null | null |
cs.CV cs.AI cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video-language modeling has attracted much attention with the rapid growth of
web videos. Most existing methods assume that the video frames and text
description are semantically correlated, and focus on video-language modeling
at video level. However, this hypothesis often fails for two reasons: (1) With
the rich semantics of video contents, it is difficult to cover all frames with
a single video-level description; (2) A raw video typically has
noisy/meaningless information (e.g., scenery shot, transition or teaser).
Although a number of recent works deploy attention mechanism to alleviate this
problem, the irrelevant/noisy information still makes it very difficult to
address. To overcome such challenge, we thus propose an efficient and effective
model, termed Language-Guided Denoising Network (LGDN), for video-language
modeling. Different from most existing methods that utilize all extracted video
frames, LGDN dynamically filters out the misaligned or redundant frames under
the language supervision and obtains only 2--4 salient frames per video for
cross-modal token-level alignment. Extensive experiments on five public
datasets show that our LGDN outperforms the state-of-the-arts by large margins.
We also provide detailed ablation study to reveal the critical importance of
solving the noise issue, in hope of inspiring future video-language work.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 03:35:59 GMT"
},
{
"version": "v2",
"created": "Mon, 3 Oct 2022 04:14:08 GMT"
},
{
"version": "v3",
"created": "Mon, 5 Dec 2022 07:20:42 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Lu",
"Haoyu",
""
],
[
"Ding",
"Mingyu",
""
],
[
"Fei",
"Nanyi",
""
],
[
"Huo",
"Yuqi",
""
],
[
"Lu",
"Zhiwu",
""
]
] |
new_dataset
| 0.990015 |
2211.07971
|
Charalambos Themistocleous
|
Charalambos Themistocleous
|
Discourse and conversation impairments in patients with dementia
|
Book chapter
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Neurodegeneration characterizes individuals with different dementia subtypes
(e.g., individuals with Alzheimer's Disease, Primary Progressive Aphasia, and
Parkinson's Disease), leading to progressive decline in cognitive, linguistic,
and social functioning. Speech and language impairments are early symptoms in
individuals with focal forms of neurodegenerative conditions, coupled with
deficits in cognitive, social, and behavioral domains. This paper reviews the
findings on language and communication deficits and identifies the effects of
dementia on the production and perception of discourse. It discusses findings
concerning (i) language function, cognitive representation, and impairment,
(ii) communicative competence, emotions, empathy, and theory-of-mind, and (iii)
speech-in-interaction. It argues that clinical discourse analysis can provide a
comprehensive assessment of language and communication skills in individuals,
which complements the existing neurolinguistic evaluation for (differential)
diagnosis, prognosis, and treatment efficacy evaluation.
|
[
{
"version": "v1",
"created": "Tue, 15 Nov 2022 08:18:30 GMT"
},
{
"version": "v2",
"created": "Sat, 3 Dec 2022 16:20:41 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Themistocleous",
"Charalambos",
""
]
] |
new_dataset
| 0.994021 |
2211.09210
|
Roberto Daza
|
Roberto Daza, Aythami Morales, Ruben Tolosana, Luis F. Gomez, Julian
Fierrez, Javier Ortega-Garcia
|
edBB-Demo: Biometrics and Behavior Analysis for Online Educational
Platforms
|
Accepted in "AAAI-23 Conference on Artificial Intelligence
(Demonstration Program)"
| null | null | null |
cs.HC cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present edBB-Demo, a demonstrator of an AI-powered research platform for
student monitoring in remote education. The edBB platform aims to study the
challenges associated to user recognition and behavior understanding in digital
platforms. This platform has been developed for data collection, acquiring
signals from a variety of sensors including keyboard, mouse, webcam,
microphone, smartwatch, and an Electroencephalography band. The information
captured from the sensors during the student sessions is modelled in a
multimodal learning framework. The demonstrator includes: i) Biometric user
authentication in an unsupervised environment; ii) Human action recognition
based on remote video analysis; iii) Heart rate estimation from webcam video;
and iv) Attention level estimation from facial expression analysis.
|
[
{
"version": "v1",
"created": "Wed, 16 Nov 2022 20:53:56 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Dec 2022 11:21:31 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Daza",
"Roberto",
""
],
[
"Morales",
"Aythami",
""
],
[
"Tolosana",
"Ruben",
""
],
[
"Gomez",
"Luis F.",
""
],
[
"Fierrez",
"Julian",
""
],
[
"Ortega-Garcia",
"Javier",
""
]
] |
new_dataset
| 0.989073 |
2211.11720
|
Sheng Shen
|
Sheng Shen, Shijia Yang, Tianjun Zhang, Bohan Zhai, Joseph E.
Gonzalez, Kurt Keutzer, Trevor Darrell
|
Multitask Vision-Language Prompt Tuning
|
Preprint
| null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Prompt Tuning, conditioning on task-specific learned prompt vectors, has
emerged as a data-efficient and parameter-efficient method for adapting large
pretrained vision-language models to multiple downstream tasks. However,
existing approaches usually consider learning prompt vectors for each task
independently from scratch, thereby failing to exploit the rich shareable
knowledge across different vision-language tasks. In this paper, we propose
multitask vision-language prompt tuning (MVLPT), which incorporates cross-task
knowledge into prompt tuning for vision-language models. Specifically, (i) we
demonstrate the effectiveness of learning a single transferable prompt from
multiple source tasks to initialize the prompt for each target task; (ii) we
show many target tasks can benefit each other from sharing prompt vectors and
thus can be jointly learned via multitask prompt tuning. We benchmark the
proposed MVLPT using three representative prompt tuning methods, namely text
prompt tuning, visual prompt tuning, and the unified vision-language prompt
tuning. Results in 20 vision tasks demonstrate that the proposed approach
outperforms all single-task baseline prompt tuning methods, setting the new
state-of-the-art on the few-shot ELEVATER benchmarks and cross-task
generalization benchmarks. To understand where the cross-task knowledge is most
effective, we also conduct a large-scale study on task transferability with 20
vision tasks in 400 combinations for each prompt tuning method. It shows that
the most performant MVLPT for each prompt tuning method prefers different task
combinations and many tasks can benefit each other, depending on their visual
similarity and label similarity. Code is available at
https://github.com/sIncerass/MVLPT.
|
[
{
"version": "v1",
"created": "Mon, 21 Nov 2022 18:41:44 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Nov 2022 07:24:16 GMT"
},
{
"version": "v3",
"created": "Mon, 5 Dec 2022 16:31:49 GMT"
}
] | 2022-12-06T00:00:00 |
[
[
"Shen",
"Sheng",
""
],
[
"Yang",
"Shijia",
""
],
[
"Zhang",
"Tianjun",
""
],
[
"Zhai",
"Bohan",
""
],
[
"Gonzalez",
"Joseph E.",
""
],
[
"Keutzer",
"Kurt",
""
],
[
"Darrell",
"Trevor",
""
]
] |
new_dataset
| 0.988922 |
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