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| versions
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timestamp[s] | authors_parsed
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value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2207.05466
|
Bruno Veloso
|
Bruno Veloso, Jo\~ao Gama, Rita P. Ribeiro, Pedro M. Pereira
|
A Benchmark dataset for predictive maintenance
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The paper describes the MetroPT data set, an outcome of a eXplainable
Predictive Maintenance (XPM) project with an urban metro public transportation
service in Porto, Portugal. The data was collected in 2022 that aimed to
evaluate machine learning methods for online anomaly detection and failure
prediction. By capturing several analogic sensor signals (pressure,
temperature, current consumption), digital signals (control signals, discrete
signals), and GPS information (latitude, longitude, and speed), we provide a
dataset that can be easily used to evaluate online machine learning methods.
This dataset contains some interesting characteristics and can be a good
benchmark for predictive maintenance models.
|
[
{
"version": "v1",
"created": "Tue, 12 Jul 2022 11:25:53 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 15:36:03 GMT"
},
{
"version": "v3",
"created": "Mon, 18 Jul 2022 09:34:24 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Veloso",
"Bruno",
""
],
[
"Gama",
"João",
""
],
[
"Ribeiro",
"Rita P.",
""
],
[
"Pereira",
"Pedro M.",
""
]
] |
new_dataset
| 0.999716 |
2207.06823
|
Harinath Krishnamoorthy
|
Nandhinee PR, Harinath Krishnamoorthy, Koushik Srivatsan, Anil Goyal,
Sudarsun Santhiappan
|
DEXTER: An end-to-end system to extract table contents from electronic
medical health documents
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we propose DEXTER, an end to end system to extract information
from tables present in medical health documents, such as electronic health
records (EHR) and explanation of benefits (EOB). DEXTER consists of four
sub-system stages: i) table detection ii) table type classification iii) cell
detection; and iv) cell content extraction. We propose a two-stage transfer
learning-based approach using CDeC-Net architecture along with Non-Maximal
suppression for table detection. We design a conventional computer vision-based
approach for table type classification and cell detection using parameterized
kernels based on image size for detecting rows and columns. Finally, we extract
the text from the detected cells using pre-existing OCR engine Tessaract. To
evaluate our system, we manually annotated a sample of the real-world medical
dataset (referred to as Meddata) consisting of wide variations of documents (in
terms of appearance) covering different table structures, such as bordered,
partially bordered, borderless, or coloured tables. We experimentally show that
DEXTER outperforms the commercially available Amazon Textract and Microsoft
Azure Form Recognizer systems on the annotated real-world medical dataset
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 11:27:02 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Jul 2022 06:52:21 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"PR",
"Nandhinee",
""
],
[
"Krishnamoorthy",
"Harinath",
""
],
[
"Srivatsan",
"Koushik",
""
],
[
"Goyal",
"Anil",
""
],
[
"Santhiappan",
"Sudarsun",
""
]
] |
new_dataset
| 0.999502 |
2207.07115
|
Ho Kei Cheng
|
Ho Kei Cheng and Alexander G. Schwing
|
XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin
Memory Model
|
Accepted to ECCV 2022. Project page:
https://hkchengrex.github.io/XMem
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present XMem, a video object segmentation architecture for long videos
with unified feature memory stores inspired by the Atkinson-Shiffrin memory
model. Prior work on video object segmentation typically only uses one type of
feature memory. For videos longer than a minute, a single feature memory model
tightly links memory consumption and accuracy. In contrast, following the
Atkinson-Shiffrin model, we develop an architecture that incorporates multiple
independent yet deeply-connected feature memory stores: a rapidly updated
sensory memory, a high-resolution working memory, and a compact thus sustained
long-term memory. Crucially, we develop a memory potentiation algorithm that
routinely consolidates actively used working memory elements into the long-term
memory, which avoids memory explosion and minimizes performance decay for
long-term prediction. Combined with a new memory reading mechanism, XMem
greatly exceeds state-of-the-art performance on long-video datasets while being
on par with state-of-the-art methods (that do not work on long videos) on
short-video datasets. Code is available at https://hkchengrex.github.io/XMem
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 17:59:37 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Jul 2022 17:56:53 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Cheng",
"Ho Kei",
""
],
[
"Schwing",
"Alexander G.",
""
]
] |
new_dataset
| 0.999707 |
2207.07490
|
Jane (Xue) Tan
|
Jane (Xue) Tan, Yong Tan
|
Crypto Rewards in Fundraising: Evidence from Crypto Donations to Ukraine
| null | null | null | null |
cs.CY econ.GN q-fin.EC
|
http://creativecommons.org/licenses/by/4.0/
|
Extrinsic incentives such as a conditional thank-you gift have shown both
positive and negative impacts on charitable fundraising. Leveraging the crypto
donations to a Ukrainian fundraising plea that accepts Ether (i.e., the
currency of the Ethereum blockchain) and Bitcoin (i.e., the currency of the
Bitcoin blockchain) over a seven-day period, we analyze the impact of crypto
rewards that lasted for more than 24 hours. Crypto rewards are newly minted
tokens that are usually valueless initially and grow in value if the
corresponding cause is well received. Separately, we find that crypto rewards
have a positive impact on the donation count but a negative impact on the
average donation size for donations from both blockchains. Comparatively, we
further find that the crypto rewards lead to an 812.48% stronger donation count
increase for Ethereum than Bitcoin, given that the crypto rewards are more
likely to be issued on the Ethereum blockchain, which has higher
programmability to support smart contracts. We also find a 30.1% stronger
decrease in average donation amount from Ethereum for small donations ($\leq
\$250$); the rewards pose similar impacts on the average donation size for the
two blockchains for large donations ($>\$250$). Our study is the first work to
look into crypto rewards as incentives for fundraising. Our findings indicate
that the positive effect of crypto rewards is more likely to manifest in
donation count, and the negative effect of crypto rewards is more likely to
manifest in donation size.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 14:22:00 GMT"
},
{
"version": "v2",
"created": "Mon, 18 Jul 2022 14:16:51 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Jane",
"",
"",
"Xue"
],
[
"Tan",
"",
""
],
[
"Tan",
"Yong",
""
]
] |
new_dataset
| 0.987711 |
2207.07712
|
Jason Wu
|
Jason Wu and Titus Barik and Xiaoyi Zhang and Colin Lea and Jeffrey
Nichols and Jeffrey P. Bigham
|
Reflow: Automatically Improving Touch Interactions in Mobile
Applications through Pixel-based Refinements
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Touch is the primary way that users interact with smartphones. However,
building mobile user interfaces where touch interactions work well for all
users is a difficult problem, because users have different abilities and
preferences. We propose a system, Reflow, which automatically applies small,
personalized UI adaptations, called refinements -- to mobile app screens to
improve touch efficiency. Reflow uses a pixel-based strategy to work with
existing applications, and improves touch efficiency while minimally disrupting
the design intent of the original application. Our system optimizes a UI by (i)
extracting its layout from its screenshot, (ii) refining its layout, and (iii)
re-rendering the UI to reflect these modifications. We conducted a user study
with 10 participants and a heuristic evaluation with 6 experts and found that
applications optimized by Reflow led to, on average, 9% faster selection time
with minimal layout disruption. The results demonstrate that Reflow's
refinements useful UI adaptations to improve touch interactions.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 19:11:49 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Wu",
"Jason",
""
],
[
"Barik",
"Titus",
""
],
[
"Zhang",
"Xiaoyi",
""
],
[
"Lea",
"Colin",
""
],
[
"Nichols",
"Jeffrey",
""
],
[
"Bigham",
"Jeffrey P.",
""
]
] |
new_dataset
| 0.999418 |
2207.07729
|
Markus Nemitz
|
Savita V. Kendre, Gus. T. Teran, Lauryn Whiteside, Tyler Looney, Ryley
Wheelock, Surya Ghai, and Markus P. Nemitz
|
Printable Flexible Robots for Remote Learning
|
9 pages, 4 figures, peer reviewed and presented paper at American
Society of Engineering Education, April 22-23rd, 2022 - Wentworth Institute
of Technology
| null | null | null |
cs.RO cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
The COVID-19 pandemic has revealed the importance of digital fabrication to
enable online learning, which remains a challenge for robotics courses. We
introduce a teaching methodology that allows students to participate remotely
in a hands-on robotics course involving the design and fabrication of robots.
Our methodology employs 3D printing techniques with flexible filaments to
create innovative soft robots; robots are made from flexible, as opposed to
rigid, materials. Students design flexible robotic components such as
actuators, sensors, and controllers using CAD software, upload their designs to
a remote 3D printing station, monitor the print with a web camera, and inspect
the components with lab staff before being mailed for testing and assembly. At
the end of the course, students will have iterated through several designs and
created fluidically-driven soft robots. Our remote teaching methodology enables
educators to utilize 3D printing resources to teach soft robotics and cultivate
creativity among students to design novel and innovative robots. Our
methodology seeks to democratize robotics engineering by decoupling hands-on
learning experiences from expensive equipment in the learning environment.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 19:51:54 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Kendre",
"Savita V.",
""
],
[
"Teran",
"Gus. T.",
""
],
[
"Whiteside",
"Lauryn",
""
],
[
"Looney",
"Tyler",
""
],
[
"Wheelock",
"Ryley",
""
],
[
"Ghai",
"Surya",
""
],
[
"Nemitz",
"Markus P.",
""
]
] |
new_dataset
| 0.998596 |
2207.07739
|
Chen Liu
|
Chen Liu, Xiaomeng Dong, Michael Potter, Hsi-Ming Chang, Ravi Soni
|
Adversarial Focal Loss: Asking Your Discriminator for Hard Examples
| null | null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Focal Loss has reached incredible popularity as it uses a simple technique to
identify and utilize hard examples to achieve better performance on
classification. However, this method does not easily generalize outside of
classification tasks, such as in keypoint detection. In this paper, we propose
a novel adaptation of Focal Loss for keypoint detection tasks, called
Adversarial Focal Loss (AFL). AFL not only is semantically analogous to Focal
loss, but also works as a plug-and-chug upgrade for arbitrary loss functions.
While Focal Loss requires output from a classifier, AFL leverages a separate
adversarial network to produce a difficulty score for each input. This
difficulty score can then be used to dynamically prioritize learning on hard
examples, even in absence of a classifier. In this work, we show AFL's
effectiveness in enhancing existing methods in keypoint detection and verify
its capability to re-weigh examples based on difficulty.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 20:26:32 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Liu",
"Chen",
""
],
[
"Dong",
"Xiaomeng",
""
],
[
"Potter",
"Michael",
""
],
[
"Chang",
"Hsi-Ming",
""
],
[
"Soni",
"Ravi",
""
]
] |
new_dataset
| 0.988122 |
2207.07771
|
Lei Zhang
|
Lei Zhang, Tianying Chen, Olivia Seow, Tim Chong, Sven Kratz, Yu Jiang
Tham, Andr\'es Monroy-Hern\'andez, Rajan Vaish, Fannie Liu
|
Auggie: Encouraging Effortful Communication through Handcrafted Digital
Experiences
|
To appear at the 25th ACM Conference On Computer-Supported
Cooperative Work And Social Computing (CSCW '22). 25 pages
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Digital communication is often brisk and automated. From auto-completed
messages to "likes," research has shown that such lightweight interactions can
affect perceptions of authenticity and closeness. On the other hand, effort in
relationships can forge emotional bonds by conveying a sense of caring and is
essential in building and maintaining relationships. To explore effortful
communication, we designed and evaluated Auggie, an iOS app that encourages
partners to create digitally handcrafted Augmented Reality (AR) experiences for
each other. Auggie is centered around crafting a 3D character with photos,
animated movements, drawings, and audio for someone else. We conducted a
two-week-long field study with 30 participants (15 pairs), who used Auggie with
their partners remotely. Our qualitative findings show that Auggie participants
engaged in meaningful effort through the handcrafting process, and felt closer
to their partners, although the tool may not be appropriate in all situations.
We discuss design implications and future directions for systems that encourage
effortful communication.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 22:31:44 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Zhang",
"Lei",
""
],
[
"Chen",
"Tianying",
""
],
[
"Seow",
"Olivia",
""
],
[
"Chong",
"Tim",
""
],
[
"Kratz",
"Sven",
""
],
[
"Tham",
"Yu Jiang",
""
],
[
"Monroy-Hernández",
"Andrés",
""
],
[
"Vaish",
"Rajan",
""
],
[
"Liu",
"Fannie",
""
]
] |
new_dataset
| 0.995177 |
2207.07790
|
Fanglin Chen
|
Fanglin Chen, Xiao Liu, Bo Tang, Feiyu Xiong, Serim Hwang, and Guomian
Zhuang
|
BCRLSP: An Offline Reinforcement Learning Framework for Sequential
Targeted Promotion
|
8 pages, DRL4IR@SIGIR
| null | null | null |
cs.LG cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
We utilize an offline reinforcement learning (RL) model for sequential
targeted promotion in the presence of budget constraints in a real-world
business environment. In our application, the mobile app aims to boost customer
retention by sending cash bonuses to customers and control the costs of such
cash bonuses during each time period. To achieve the multi-task goal, we
propose the Budget Constrained Reinforcement Learning for Sequential Promotion
(BCRLSP) framework to determine the value of cash bonuses to be sent to users.
We first find out the target policy and the associated Q-values that maximizes
the user retention rate using an RL model. A linear programming (LP) model is
then added to satisfy the constraints of promotion costs. We solve the LP
problem by maximizing the Q-values of actions learned from the RL model given
the budget constraints. During deployment, we combine the offline RL model with
the LP model to generate a robust policy under the budget constraints. Using
both online and offline experiments, we demonstrate the efficacy of our
approach by showing that BCRLSP achieves a higher long-term customer retention
rate and a lower cost than various baselines. Taking advantage of the near
real-time cost control method, the proposed framework can easily adapt to data
with a noisy behavioral policy and/or meet flexible budget constraints.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 00:10:12 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Chen",
"Fanglin",
""
],
[
"Liu",
"Xiao",
""
],
[
"Tang",
"Bo",
""
],
[
"Xiong",
"Feiyu",
""
],
[
"Hwang",
"Serim",
""
],
[
"Zhuang",
"Guomian",
""
]
] |
new_dataset
| 0.993019 |
2207.07792
|
Lin Sok
|
Lin Sok
|
Hulls of special typed linear codes and constructions of new EAQECCs
|
13 pages
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we study Euclidean and Hermitian hulls of generalized
Reed-Solomon codes and twisted generalized Reed-Solomon codes, as well as the
Hermitian hulls of Roth-Lempel typed codes. We present explicit constructions
of MDS and AMDS linear codes for which their hull dimensions are well
determined. As an application, we provide several classes of
entanglement-assisted quantum error correcting codes with new parameters.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 00:27:31 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Sok",
"Lin",
""
]
] |
new_dataset
| 0.992131 |
2207.07797
|
Lei Hsiung
|
Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho
|
CARBEN: Composite Adversarial Robustness Benchmark
|
IJCAI 2022 Demo Track; The demonstration is at
https://hsiung.cc/CARBEN/
| null | null | null |
cs.CV cs.AI cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Prior literature on adversarial attack methods has mainly focused on
attacking with and defending against a single threat model, e.g., perturbations
bounded in Lp ball. However, multiple threat models can be combined into
composite perturbations. One such approach, composite adversarial attack (CAA),
not only expands the perturbable space of the image, but also may be overlooked
by current modes of robustness evaluation. This paper demonstrates how CAA's
attack order affects the resulting image, and provides real-time inferences of
different models, which will facilitate users' configuration of the parameters
of the attack level and their rapid evaluation of model prediction. A
leaderboard to benchmark adversarial robustness against CAA is also introduced.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 01:08:44 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Hsiung",
"Lei",
""
],
[
"Tsai",
"Yun-Yun",
""
],
[
"Chen",
"Pin-Yu",
""
],
[
"Ho",
"Tsung-Yi",
""
]
] |
new_dataset
| 0.970528 |
2207.07835
|
Kevin Green
|
Fangzhou Yu, Ryan Batke, Jeremy Dao, Jonathan Hurst, Kevin Green, Alan
Fern
|
Dynamic Bipedal Maneuvers through Sim-to-Real Reinforcement Learning
|
In review for the 2022 IEEE-RAS International Conference on Humanoid
Robots. 8 pages, 8 figures, 3 tables
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For legged robots to match the athletic capabilities of humans and animals,
they must not only produce robust periodic walking and running, but also
seamlessly switch between nominal locomotion gaits and more specialized
transient maneuvers. Despite recent advancements in controls of bipedal robots,
there has been little focus on producing highly dynamic behaviors. Recent work
utilizing reinforcement learning to produce policies for control of legged
robots have demonstrated success in producing robust walking behaviors.
However, these learned policies have difficulty expressing a multitude of
different behaviors on a single network. Inspired by conventional
optimization-based control techniques for legged robots, this work applies a
recurrent policy to execute four-step, 90 degree turns trained using reference
data generated from optimized single rigid body model trajectories. We present
a novel training framework using epilogue terminal rewards for learning
specific behaviors from pre-computed trajectory data and demonstrate a
successful transfer to hardware on the bipedal robot Cassie.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 04:57:59 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Yu",
"Fangzhou",
""
],
[
"Batke",
"Ryan",
""
],
[
"Dao",
"Jeremy",
""
],
[
"Hurst",
"Jonathan",
""
],
[
"Green",
"Kevin",
""
],
[
"Fern",
"Alan",
""
]
] |
new_dataset
| 0.997623 |
2207.07836
|
Suman Banerjee
|
Mayank Singhal and Suman Banerjee
|
Envy\mbox{-}free Trip Planning in Group Trip Planning Query Problem
|
Accepted as a Full Paper @ 25th International Conference on
Network-Based Information Systems (NBiS-2022). 12 Pages. 6 Figures
| null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
In recent times, Group Trip Planning Query (henceforth referred to as GTP
Query) is one of the well\mbox{-}studied problems in Spatial Databases. The
inputs to the problem are a road network where the vertices represent the
Point-of-Interests (mentioned as POIs henceforth) and they are grouped into
different categories, edges represent the road segments, and edge weight
represents the distance and a group of users along with their source and
destination location. This problem asks to return one POI from every category
such that the aggregated distance traveled by the group is minimized. As the
objective is to minimize the aggregated distance, the existing solution
methodologies do not consider the individual distances traveled by the group
members. To address this issue, we introduce and study the \textsc{Envy Free
Group Trip Planning Query} Problem. Along with the inputs of the GTP Query
Problem, in this variant, we also have a threshold distance $D$ such that
aggregated distance traveled by the group is minimized and for any member pairs
the difference between their individual distance traveled is less than equal to
$D$. However, it may so happen that a given $D$ value no such set POIs are
found. To tackle this issue, we introduce the surrogate problem \textsc{Envy
Free Group Trip Planning Query with Minimum Additional Distance} Problem which
asks what is the minimum distance to be added with $D$ to obtain at least one
solution. For these problems, we design efficient solution approaches and
experiment with real-world datasets. From the experiments, we observe that the
proposed solution approaches lead to less aggregated distance compared to
baseline methods with reasonable computational overhead.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 04:59:55 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Singhal",
"Mayank",
""
],
[
"Banerjee",
"Suman",
""
]
] |
new_dataset
| 0.9909 |
2207.07852
|
Yuqi Liu
|
Yuqi Liu, Pengfei Xiong, Luhui Xu, Shengming Cao and Qin Jin
|
TS2-Net: Token Shift and Selection Transformer for Text-Video Retrieval
|
Accepted by ECCV2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Text-Video retrieval is a task of great practical value and has received
increasing attention, among which learning spatial-temporal video
representation is one of the research hotspots. The video encoders in the
state-of-the-art video retrieval models usually directly adopt the pre-trained
vision backbones with the network structure fixed, they therefore can not be
further improved to produce the fine-grained spatial-temporal video
representation. In this paper, we propose Token Shift and Selection Network
(TS2-Net), a novel token shift and selection transformer architecture, which
dynamically adjusts the token sequence and selects informative tokens in both
temporal and spatial dimensions from input video samples. The token shift
module temporally shifts the whole token features back-and-forth across
adjacent frames, to preserve the complete token representation and capture
subtle movements. Then the token selection module selects tokens that
contribute most to local spatial semantics. Based on thorough experiments, the
proposed TS2-Net achieves state-of-the-art performance on major text-video
retrieval benchmarks, including new records on MSRVTT, VATEX, LSMDC,
ActivityNet, and DiDeMo.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 06:50:27 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Liu",
"Yuqi",
""
],
[
"Xiong",
"Pengfei",
""
],
[
"Xu",
"Luhui",
""
],
[
"Cao",
"Shengming",
""
],
[
"Jin",
"Qin",
""
]
] |
new_dataset
| 0.974353 |
2207.07869
|
Shunli Wang
|
Shunli Wang, Shuaibing Wang, Bo Jiao, Dingkang Yang, Liuzhen Su, Peng
Zhai, Chixiao Chen, Lihua Zhang
|
CA-SpaceNet: Counterfactual Analysis for 6D Pose Estimation in Space
|
8 pages, 6 figures, IROS-2022 conference paper
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reliable and stable 6D pose estimation of uncooperative space objects plays
an essential role in on-orbit servicing and debris removal missions.
Considering that the pose estimator is sensitive to background interference,
this paper proposes a counterfactual analysis framework named CASpaceNet to
complete robust 6D pose estimation of the spaceborne targets under complicated
background. Specifically, conventional methods are adopted to extract the
features of the whole image in the factual case. In the counterfactual case, a
non-existent image without the target but only the background is imagined. Side
effect caused by background interference is reduced by counterfactual analysis,
which leads to unbiased prediction in final results. In addition, we also carry
out lowbit-width quantization for CA-SpaceNet and deploy part of the framework
to a Processing-In-Memory (PIM) accelerator on FPGA. Qualitative and
quantitative results demonstrate the effectiveness and efficiency of our
proposed method. To our best knowledge, this paper applies causal inference and
network quantization to the 6D pose estimation of space-borne targets for the
first time. The code is available at
https://github.com/Shunli-Wang/CA-SpaceNet.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 07:48:19 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Wang",
"Shunli",
""
],
[
"Wang",
"Shuaibing",
""
],
[
"Jiao",
"Bo",
""
],
[
"Yang",
"Dingkang",
""
],
[
"Su",
"Liuzhen",
""
],
[
"Zhai",
"Peng",
""
],
[
"Chen",
"Chixiao",
""
],
[
"Zhang",
"Lihua",
""
]
] |
new_dataset
| 0.997955 |
2207.07917
|
Mang Yu
|
Mang Yu, Sitao Huang and Deming Chen
|
Chimera: A Hybrid Machine Learning Driven Multi-Objective Design Space
Exploration Tool for FPGA High-Level Synthesis
|
This is an extended version of the conference paper published in the
22nd International Conference on Intelligent Data Engineering and Automated
Learning (IDEAL 2021), which won the Best Paper Award. It is supported in
part by the Xilinx Center of Excellence and Xilinx Adaptive Compute Clusters
(XACC) program at the University of Illinois Urbana-Champaign
| null | null | null |
cs.AR cs.LG cs.NE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In recent years, hardware accelerators based on field-programmable gate
arrays (FPGAs) have been widely adopted, thanks to FPGAs' extraordinary
flexibility. However, with the high flexibility comes the difficulty in design
and optimization. Conventionally, these accelerators are designed with
low-level hardware descriptive languages, which means creating large designs
with complex behavior is extremely difficult. Therefore, high-level synthesis
(HLS) tools were created to simplify hardware designs for FPGAs. They enable
the user to create hardware designs using high-level languages and provide
various optimization directives to help to improve the performance of the
synthesized hardware. However, applying these optimizations to achieve high
performance is time-consuming and usually requires expert knowledge. To address
this difficulty, we present an automated design space exploration tool for
applying HLS optimization directives, called Chimera, which significantly
reduces the human effort and expertise needed for creating high-performance HLS
designs. It utilizes a novel multi-objective exploration method that seamlessly
integrates active learning, evolutionary algorithm, and Thompson sampling,
making it capable of finding a set of optimized designs on a Pareto curve with
only a small number of design points evaluated during the exploration. In the
experiments, in less than 24 hours, this hybrid method explored design points
that have the same or superior performance compared to highly optimized
hand-tuned designs created by expert HLS users from the Rosetta benchmark
suite. In addition to discovering the extreme points, it also explores a Pareto
frontier, where the elbow point can potentially save up to 26\% of Flip-Flop
resource with negligibly higher latency.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 21:13:55 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Yu",
"Mang",
""
],
[
"Huang",
"Sitao",
""
],
[
"Chen",
"Deming",
""
]
] |
new_dataset
| 0.994687 |
2207.07932
|
Jiazhen Liu
|
Jiazhen Liu, Xirong Li, Qijie Wei, Jie Xu, Dayong Ding
|
Semi-Supervised Keypoint Detector and Descriptor for Retinal Image
Matching
|
Accepted to ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For retinal image matching (RIM), we propose SuperRetina, the first
end-to-end method with jointly trainable keypoint detector and descriptor.
SuperRetina is trained in a novel semi-supervised manner. A small set of
(nearly 100) images are incompletely labeled and used to supervise the network
to detect keypoints on the vascular tree. To attack the incompleteness of
manual labeling, we propose Progressive Keypoint Expansion to enrich the
keypoint labels at each training epoch. By utilizing a keypoint-based improved
triplet loss as its description loss, SuperRetina produces highly
discriminative descriptors at full input image size. Extensive experiments on
multiple real-world datasets justify the viability of SuperRetina. Even with
manual labeling replaced by auto labeling and thus making the training process
fully manual-annotation free, SuperRetina compares favorably against a number
of strong baselines for two RIM tasks, i.e. image registration and identity
verification. SuperRetina will be open source.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 12:55:20 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Liu",
"Jiazhen",
""
],
[
"Li",
"Xirong",
""
],
[
"Wei",
"Qijie",
""
],
[
"Xu",
"Jie",
""
],
[
"Ding",
"Dayong",
""
]
] |
new_dataset
| 0.997261 |
2207.07958
|
Javier Duarte
|
Javier Duarte and Nhan Tran and Ben Hawks and Christian Herwig and
Jules Muhizi and Shvetank Prakash and Vijay Janapa Reddi
|
FastML Science Benchmarks: Accelerating Real-Time Scientific Edge
Machine Learning
|
9 pages, 4 figures, Contribution to 3rd Workshop on Benchmarking
Machine Learning Workloads on Emerging Hardware (MLBench) at 5th Conference
on Machine Learning and Systems (MLSys)
| null | null |
FERMILAB-CONF-22-534-PPD-SCD
|
cs.LG physics.comp-ph physics.ins-det
|
http://creativecommons.org/licenses/by/4.0/
|
Applications of machine learning (ML) are growing by the day for many unique
and challenging scientific applications. However, a crucial challenge facing
these applications is their need for ultra low-latency and on-detector ML
capabilities. Given the slowdown in Moore's law and Dennard scaling, coupled
with the rapid advances in scientific instrumentation that is resulting in
growing data rates, there is a need for ultra-fast ML at the extreme edge. Fast
ML at the edge is essential for reducing and filtering scientific data in
real-time to accelerate science experimentation and enable more profound
insights. To accelerate real-time scientific edge ML hardware and software
solutions, we need well-constrained benchmark tasks with enough specifications
to be generically applicable and accessible. These benchmarks can guide the
design of future edge ML hardware for scientific applications capable of
meeting the nanosecond and microsecond level latency requirements. To this end,
we present an initial set of scientific ML benchmarks, covering a variety of ML
and embedded system techniques.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 14:30:15 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Duarte",
"Javier",
""
],
[
"Tran",
"Nhan",
""
],
[
"Hawks",
"Ben",
""
],
[
"Herwig",
"Christian",
""
],
[
"Muhizi",
"Jules",
""
],
[
"Prakash",
"Shvetank",
""
],
[
"Reddi",
"Vijay Janapa",
""
]
] |
new_dataset
| 0.997612 |
2207.08023
|
Daniel T Chang
|
Daniel T. Chang
|
Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular
Geometry
|
arXiv admin note: substantial text overlap with arXiv:2006.01785,
arXiv:2007.03513
| null | null | null |
cs.LG q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep learning for molecular science has so far mainly focused on 2D molecular
graphs. Recently, however, there has been work to extend it to 3D molecular
geometry, due to its scientific significance and critical importance in
real-world applications. The 3D distance-geometric graph representation (DG-GR)
adopts a unified scheme (distance) for representing the geometry of 3D graphs.
It is invariant to rotation and translation of the graph, and it reflects
pair-wise node interactions and their generally local nature, particularly
relevant for 3D molecular geometry. To facilitate the incorporation of 3D
molecular geometry in deep learning for molecular science, we adopt the new
graph attention network with dynamic attention (GATv2) for use with DG-GR and
propose the 3D distance-geometric graph attention network (DG-GAT). GATv2 is a
great fit for DG-GR since the attention can vary by node and by distance
between nodes. Experimental results of DG-GAT for the ESOL and FreeSolv
datasets show major improvement (31% and 38%, respectively) over those of the
standard graph convolution network based on 2D molecular graphs. The same is
true for the QM9 dataset. Our work demonstrates the utility and value of DG-GAT
for deep learning based on 3D molecular geometry.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 21:39:31 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Chang",
"Daniel T.",
""
]
] |
new_dataset
| 0.996321 |
2207.08024
|
Sumanth Gurram
|
Sumanth Gurram, Andy Fang, David Chan, John Canny
|
LAVA: Language Audio Vision Alignment for Contrastive Video Pre-Training
|
Workshop Paper at ICML 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Generating representations of video data is of key importance in advancing
the field of machine perception. Most current techniques rely on hand-annotated
data, which can be difficult to work with, expensive to generate, and hard to
scale. In this work, we propose a novel learning approach based on contrastive
learning, LAVA, which is capable of learning joint language, audio, and video
representations in a self-supervised manner. We pre-train LAVA on the Kinetics
700 dataset using transformer encoders to learn representations for each
modality. We then demonstrate that LAVA performs competitively with the current
state-of-the-art self-supervised and weakly-supervised pretraining techniques
on UCF-101 and HMDB-51 video action recognition while using a fraction of the
unlabeled data.
|
[
{
"version": "v1",
"created": "Sat, 16 Jul 2022 21:46:16 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Gurram",
"Sumanth",
""
],
[
"Fang",
"Andy",
""
],
[
"Chan",
"David",
""
],
[
"Canny",
"John",
""
]
] |
new_dataset
| 0.99812 |
2207.08081
|
Chandra Shekhar
|
Chandra Shekhar and Sudipta Saha
|
Real Time Vehicle Identification
| null | null | null | null |
cs.DC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Identification of the vehicles passing over the roads is a very important
component of an Intelligent Transportation System. However, due to the presence
of multiple vehicles together and their velocity, it gets hard to accurately
identify and record them in real-time. Solutions based on Computer-vision use
heavyweight equipment making them quiet inflexible, costly and hence unsuitable
for wide-area coverage. Solutions based on RFID, although are lightweight and
cost-effective, lack of fast and efficient communication protocol pertains to
their inability to record multiple moving vehicles at the same time. We propose
an IoT-assisted solution that leverages Synchronous-Transmission based
communication to bridge these gaps. Through extensive experiments we
demonstrate that our strategy can consistently record upto an average of 40
vehicles running at speed range 30-90 Km/h with at least 97.5% accuracy.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 05:44:14 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Shekhar",
"Chandra",
""
],
[
"Saha",
"Sudipta",
""
]
] |
new_dataset
| 0.972522 |
2207.08105
|
Marc Schmitt
|
Marc Schmitt
|
Mobile Security for the modern CEO: Attacks, Mitigations, and Future
Trends
|
25 pages
| null | null | null |
cs.CR cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Todays world is digital, global, and interconnected and mobile devices are at
the heart of modern communications in business, politics, and civil society.
However, cyber threats are an omnipresent reality in our hyper-connected world.
The world economic forum ranks cyber threats consistently among the global top
security risks. Attacks on mobile devices grow yearly in volume and magnitude
causing severe damage. This paper offers a comprehensive overview of modern
mobile attacks categorized into malware, phishing, communication, supply chain,
physical, and authentication attacks, including a section on mitigations and
limitations. It also provides security design tips to secure the mobile setup
and general recommendations to prevent the successful execution of an incoming
attack. The last section highlights future technology trends and how those will
impact and change the mobile security landscape in the future.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 08:19:24 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Schmitt",
"Marc",
""
]
] |
new_dataset
| 0.996492 |
2207.08112
|
Jonathan K\"ulz
|
Jonathan K\"ulz, Andreas Spitz, Ahmad Abu-Akel, Stephan G\"unnemann,
Robert West
|
United States Politicians' Tone Became More Negative with 2016 Primary
Campaigns
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There is a widespread belief that the tone of US political language has
become more negative recently, in particular when Donald Trump entered
politics. At the same time, there is disagreement as to whether Trump changed
or merely continued previous trends. To date, data-driven evidence regarding
these questions is scarce, partly due to the difficulty of obtaining a
comprehensive, longitudinal record of politicians' utterances. Here we apply
psycholinguistic tools to a novel, comprehensive corpus of 24 million quotes
from online news attributed to 18,627 US politicians in order to analyze how
the tone of US politicians' language evolved between 2008 and 2020. We show
that, whereas the frequency of negative emotion words had decreased
continuously during Obama's tenure, it suddenly and lastingly increased with
the 2016 primary campaigns, by 1.6 pre-campaign standard deviations, or 8% of
the pre-campaign mean, in a pattern that emerges across parties. The effect
size drops by 40% when omitting Trump's quotes, and by 50% when averaging over
speakers rather than quotes, implying that prominent speakers, and Trump in
particular, have disproportionately, though not exclusively, contributed to the
rise in negative language. This work provides the first large-scale data-driven
evidence of a drastic shift toward a more negative political tone following
Trump's campaign start as a catalyst, with important implications for the
debate about the state of US politics.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 08:41:14 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Külz",
"Jonathan",
""
],
[
"Spitz",
"Andreas",
""
],
[
"Abu-Akel",
"Ahmad",
""
],
[
"Günnemann",
"Stephan",
""
],
[
"West",
"Robert",
""
]
] |
new_dataset
| 0.999492 |
2207.08150
|
Xiao Han
|
Xiao Han, Licheng Yu, Xiatian Zhu, Li Zhang, Yi-Zhe Song, Tao Xiang
|
FashionViL: Fashion-Focused Vision-and-Language Representation Learning
|
ECCV 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Large-scale Vision-and-Language (V+L) pre-training for representation
learning has proven to be effective in boosting various downstream V+L tasks.
However, when it comes to the fashion domain, existing V+L methods are
inadequate as they overlook the unique characteristics of both the fashion V+L
data and downstream tasks. In this work, we propose a novel fashion-focused V+L
representation learning framework, dubbed as FashionViL. It contains two novel
fashion-specific pre-training tasks designed particularly to exploit two
intrinsic attributes with fashion V+L data. First, in contrast to other domains
where a V+L data point contains only a single image-text pair, there could be
multiple images in the fashion domain. We thus propose a Multi-View Contrastive
Learning task for pulling closer the visual representation of one image to the
compositional multimodal representation of another image+text. Second, fashion
text (e.g., product description) often contains rich fine-grained concepts
(attributes/noun phrases). To exploit this, a Pseudo-Attributes Classification
task is introduced to encourage the learned unimodal (visual/textual)
representations of the same concept to be adjacent. Further, fashion V+L tasks
uniquely include ones that do not conform to the common one-stream or
two-stream architectures (e.g., text-guided image retrieval). We thus propose a
flexible, versatile V+L model architecture consisting of a modality-agnostic
Transformer so that it can be flexibly adapted to any downstream tasks.
Extensive experiments show that our FashionViL achieves a new state of the art
across five downstream tasks. Code is available at
https://github.com/BrandonHanx/mmf.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 12:06:27 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Han",
"Xiao",
""
],
[
"Yu",
"Licheng",
""
],
[
"Zhu",
"Xiatian",
""
],
[
"Zhang",
"Li",
""
],
[
"Song",
"Yi-Zhe",
""
],
[
"Xiang",
"Tao",
""
]
] |
new_dataset
| 0.963831 |
2207.08178
|
Xinwei Liu
|
Xinwei Liu, Jian Liu, Yang Bai, Jindong Gu, Tao Chen, Xiaojun Jia,
Xiaochun Cao
|
Watermark Vaccine: Adversarial Attacks to Prevent Watermark Removal
|
ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As a common security tool, visible watermarking has been widely applied to
protect copyrights of digital images. However, recent works have shown that
visible watermarks can be removed by DNNs without damaging their host images.
Such watermark-removal techniques pose a great threat to the ownership of
images. Inspired by the vulnerability of DNNs on adversarial perturbations, we
propose a novel defence mechanism by adversarial machine learning for good.
From the perspective of the adversary, blind watermark-removal networks can be
posed as our target models; then we actually optimize an imperceptible
adversarial perturbation on the host images to proactively attack against
watermark-removal networks, dubbed Watermark Vaccine. Specifically, two types
of vaccines are proposed. Disrupting Watermark Vaccine (DWV) induces to ruin
the host image along with watermark after passing through watermark-removal
networks. In contrast, Inerasable Watermark Vaccine (IWV) works in another
fashion of trying to keep the watermark not removed and still noticeable.
Extensive experiments demonstrate the effectiveness of our DWV/IWV in
preventing watermark removal, especially on various watermark removal networks.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 13:50:02 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Liu",
"Xinwei",
""
],
[
"Liu",
"Jian",
""
],
[
"Bai",
"Yang",
""
],
[
"Gu",
"Jindong",
""
],
[
"Chen",
"Tao",
""
],
[
"Jia",
"Xiaojun",
""
],
[
"Cao",
"Xiaochun",
""
]
] |
new_dataset
| 0.996975 |
2207.08191
|
Zoneze Chen
|
Zongze Chen and Wenxia Yang and Xin Li
|
Stroke-Based Autoencoders: Self-Supervised Learners for Efficient
Zero-Shot Chinese Character Recognition
|
10 pages, 13 figures
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Chinese characters carry a wealth of morphological and semantic information;
therefore, the semantic enhancement of the morphology of Chinese characters has
drawn significant attention. The previous methods were intended to directly
extract information from a whole Chinese character image, which usually cannot
capture both global and local information simultaneously. In this paper, we
develop a stroke-based autoencoder(SAE), to model the sophisticated morphology
of Chinese characters with the self-supervised method. Following its canonical
writing order, we first represent a Chinese character as a series of stroke
images with a fixed writing order, and then our SAE model is trained to
reconstruct this stroke image sequence. This pre-trained SAE model can predict
the stroke image series for unseen characters, as long as their strokes or
radicals appeared in the training set. We have designed two contrasting SAE
architectures on different forms of stroke images. One is fine-tuned on
existing stroke-based method for zero-shot recognition of handwritten Chinese
characters, and the other is applied to enrich the Chinese word embeddings from
their morphological features. The experimental results validate that after
pre-training, our SAE architecture outperforms other existing methods in
zero-shot recognition and enhances the representation of Chinese characters
with their abundant morphological and semantic information.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 14:39:10 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Chen",
"Zongze",
""
],
[
"Yang",
"Wenxia",
""
],
[
"Li",
"Xin",
""
]
] |
new_dataset
| 0.999015 |
2207.08287
|
Serena Kim
|
Serena Y. Kim, Koushik Ganesan, Crystal Soderman, Raven O'Rourke
|
Spatial Distribution of Solar PV Deployment: An Application of the
Region-Based Convolutional Neural Network
| null | null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents a comprehensive analysis of the social and environmental
determinants of solar photovoltaic (PV) deployment rates in Colorado, USA.
Using 652,795 satellite imagery and computer vision frameworks based on a
convolutional neural network, we estimated the proportion of households with
solar PV systems and the roof areas covered by solar panels. At the census
block group level, 7% of Coloradan households have a rooftop PV system, and
2.5% of roof areas in Colorado are covered by solar panels as of 2021. Our
machine learning models predict solar PV deployment based on 43 natural and
social characteristics of neighborhoods. Using four algorithms (Random Forest,
CATBoost, LightGBM, XGBoost), we find that the share of Democratic party votes,
hail risks, strong wind risks, median home value, and solar PV permitting
timelines are the most important predictors of solar PV count per household. In
addition to the size of the houses, PV-to-roof area ratio is highly dependent
on solar PV permitting timelines, proportion of renters and multifamily
housing, and winter weather risks. We also find racial and ethnic disparities
in rooftop solar deployment. The average marginal effects of median household
income on solar deployment are lower in communities with a greater proportion
of African American and Hispanic residents and are higher in communities with a
greater proportion of White and Asian residents. In the ongoing energy
transition, knowing the key predictors of solar deployment can better inform
business and policy decision making for more efficient and equitable grid
infrastructure investment and distributed energy resource management.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 21:03:48 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Kim",
"Serena Y.",
""
],
[
"Ganesan",
"Koushik",
""
],
[
"Soderman",
"Crystal",
""
],
[
"O'Rourke",
"Raven",
""
]
] |
new_dataset
| 0.957449 |
2207.08292
|
Fran\c{c}ois Portet
|
Ali Can Kocabiyikoglu, Fran\c{c}ois Portet, Prudence Gibert, Herv\'e
Blanchon, Jean-Marc Babouchkine, Ga\"etan Gavazzi
|
A Spoken Drug Prescription Dataset in French for Spoken Language
Understanding
|
Ali Can Kocabiyikoglu,Fran\c{c}ois Portet, Prudence Gibert, Herv\'e
Blanchon, Jean-Marc Babouchkine, Ga\"etan Gavazzi. A Spoken Drug Prescription
Dataset in French for Spoken Language Understanding. LREC2022, Marseille,
France, 21-22-23 June 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Spoken medical dialogue systems are increasingly attracting interest to
enhance access to healthcare services and improve quality and traceability of
patient care. In this paper, we focus on medical drug prescriptions acquired on
smartphones through spoken dialogue. Such systems would facilitate the
traceability of care and would free clinicians' time. However, there is a lack
of speech corpora to develop such systems since most of the related corpora are
in text form and in English. To facilitate the research and development of
spoken medical dialogue systems, we present, to the best of our knowledge, the
first spoken medical drug prescriptions corpus, named PxSLU. It contains 4
hours of transcribed and annotated dialogues of drug prescriptions in French
acquired through an experiment with 55 participants experts and non-experts in
prescriptions. We also present some experiments that demonstrate the interest
of this corpus for the evaluation and development of medical dialogue systems.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 21:18:03 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Kocabiyikoglu",
"Ali Can",
""
],
[
"Portet",
"François",
""
],
[
"Gibert",
"Prudence",
""
],
[
"Blanchon",
"Hervé",
""
],
[
"Babouchkine",
"Jean-Marc",
""
],
[
"Gavazzi",
"Gaëtan",
""
]
] |
new_dataset
| 0.999814 |
2207.08312
|
Duncan Calvert
|
Duncan Calvert, Bhavyansh Mishra, Stephen McCrory, Sylvain Bertrand,
Robert Griffin, and Jerry Pratt
|
A Fast, Autonomous, Bipedal Walking Behavior over Rapid Regions
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In trying to build humanoid robots that perform useful tasks in a world built
for humans, we address the problem of autonomous locomotion. Humanoid robot
planning and control algorithms for walking over rough terrain are becoming
increasingly capable. At the same time, commercially available depth cameras
have been getting more accurate and GPU computing has become a primary tool in
AI research. In this paper, we present a newly constructed behavior control
system for achieving fast, autonomous, bipedal walking, without pauses or
deliberation. We achieve this using a recently published rapid planar regions
perception algorithm, a height map based body path planner, an A* footstep
planner, and a momentum-based walking controller. We put these elements
together to form a behavior control system supported by modern software
development practices and simulation tools.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 22:30:33 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Calvert",
"Duncan",
""
],
[
"Mishra",
"Bhavyansh",
""
],
[
"McCrory",
"Stephen",
""
],
[
"Bertrand",
"Sylvain",
""
],
[
"Griffin",
"Robert",
""
],
[
"Pratt",
"Jerry",
""
]
] |
new_dataset
| 0.992548 |
2207.08338
|
Hoang Le
|
Hoang Le, Liang Zhang, Amir Said, Guillaume Sautiere, Yang Yang,
Pranav Shrestha, Fei Yin, Reza Pourreza, Auke Wiggers
|
MobileCodec: Neural Inter-frame Video Compression on Mobile Devices
|
ACM MMSys 2022
| null | null | null |
cs.CV cs.MM eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Realizing the potential of neural video codecs on mobile devices is a big
technological challenge due to the computational complexity of deep networks
and the power-constrained mobile hardware. We demonstrate practical feasibility
by leveraging Qualcomm's technology and innovation, bridging the gap from
neural network-based codec simulations running on wall-powered workstations, to
real-time operation on a mobile device powered by Snapdragon technology. We
show the first-ever inter-frame neural video decoder running on a commercial
mobile phone, decoding high-definition videos in real-time while maintaining a
low bitrate and high visual quality.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 01:20:18 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Le",
"Hoang",
""
],
[
"Zhang",
"Liang",
""
],
[
"Said",
"Amir",
""
],
[
"Sautiere",
"Guillaume",
""
],
[
"Yang",
"Yang",
""
],
[
"Shrestha",
"Pranav",
""
],
[
"Yin",
"Fei",
""
],
[
"Pourreza",
"Reza",
""
],
[
"Wiggers",
"Auke",
""
]
] |
new_dataset
| 0.997798 |
2207.08420
|
David Monniaux
|
David Monniaux (VERIMAG - IMAG), Alice Pain (VERIMAG - IMAG, ENS-PSL)
|
Formally verified 32- and 64-bit integer division using double-precision
floating-point arithmetic
| null | null | null | null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Some recent processors are not equipped with an integer division unit.
Compilers then implement division by a call to a special function supplied by
the processor designers, which implements division by a loop producing one bit
of quotient per iteration. This hinders compiler optimizations and results in
non-constant time computation, which is a problem in some applications. We
advocate instead using the processor's floating-point unit, and propose code
that the compiler can easily interleave with other computations. We fully
proved the correctness of our algorithm, which mixes floating-point and
fixed-bitwidth integer computations, using the Coq proof assistant and
successfully integrated it into the CompCert formally verified compiler.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 08:01:15 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Monniaux",
"David",
"",
"VERIMAG - IMAG"
],
[
"Pain",
"Alice",
"",
"VERIMAG - IMAG, ENS-PSL"
]
] |
new_dataset
| 0.996248 |
2207.08556
|
Qifan Xiao
|
Xudong Pan, Qifan Xiao, Mi Zhang, Min Yang
|
A Certifiable Security Patch for Object Tracking in Self-Driving Systems
via Historical Deviation Modeling
| null | null | null | null |
cs.CR stat.ML
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Self-driving cars (SDC) commonly implement the perception pipeline to detect
the surrounding obstacles and track their moving trajectories, which lays the
ground for the subsequent driving decision making process. Although the
security of obstacle detection in SDC is intensively studied, not until very
recently the attackers start to exploit the vulnerability of the tracking
module. Compared with solely attacking the object detectors, this new attack
strategy influences the driving decision more effectively with less attack
budgets. However, little is known on whether the revealed vulnerability remains
effective in end-to-end self-driving systems and, if so, how to mitigate the
threat.
In this paper, we present the first systematic research on the security of
object tracking in SDC. Through a comprehensive case study on the full
perception pipeline of a popular open-sourced self-driving system, Baidu's
Apollo, we prove the mainstream multi-object tracker (MOT) based on Kalman
Filter (KF) is unsafe even with an enabled multi-sensor fusion mechanism. Our
root cause analysis reveals, the vulnerability is innate to the design of
KF-based MOT, which shall error-handle the prediction results from the object
detectors yet the adopted KF algorithm is prone to trust the observation more
when its deviation from the prediction is larger. To address this design flaw,
we propose a simple yet effective security patch for KF-based MOT, the core of
which is an adaptive strategy to balance the focus of KF on observations and
predictions according to the anomaly index of the observation-prediction
deviation, and has certified effectiveness against a generalized hijacking
attack model. Extensive evaluation on $4$ KF-based existing MOT implementations
(including 2D and 3D, academic and Apollo ones) validate the defense
effectiveness and the trivial performance overhead of our approach.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 12:30:24 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Pan",
"Xudong",
""
],
[
"Xiao",
"Qifan",
""
],
[
"Zhang",
"Mi",
""
],
[
"Yang",
"Min",
""
]
] |
new_dataset
| 0.978496 |
2207.08557
|
Ahmad Shapiro
|
Ahmad Shapiro, Ayman Khalafallah, Marwan Torki
|
AlexU-AIC at Arabic Hate Speech 2022: Contrast to Classify
| null |
Proceedings of the OSACT 2022 Workshop, LREC2022, June 2022,
200-208
| null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Online presence on social media platforms such as Facebook and Twitter has
become a daily habit for internet users. Despite the vast amount of services
the platforms offer for their users, users suffer from cyber-bullying, which
further leads to mental abuse and may escalate to cause physical harm to
individuals or targeted groups. In this paper, we present our submission to the
Arabic Hate Speech 2022 Shared Task Workshop (OSACT5 2022) using the associated
Arabic Twitter dataset. The shared task consists of 3 sub-tasks, sub-task A
focuses on detecting whether the tweet is offensive or not. Then, For offensive
Tweets, sub-task B focuses on detecting whether the tweet is hate speech or
not. Finally, For hate speech Tweets, sub-task C focuses on detecting the
fine-grained type of hate speech among six different classes. Transformer
models proved their efficiency in classification tasks, but with the problem of
over-fitting when fine-tuned on a small or an imbalanced dataset. We overcome
this limitation by investigating multiple training paradigms such as
Contrastive learning and Multi-task learning along with Classification
fine-tuning and an ensemble of our top 5 performers. Our proposed solution
achieved 0.841, 0.817, and 0.476 macro F1-average in sub-tasks A, B, and C
respectively.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 12:33:51 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Shapiro",
"Ahmad",
""
],
[
"Khalafallah",
"Ayman",
""
],
[
"Torki",
"Marwan",
""
]
] |
new_dataset
| 0.9998 |
2207.08635
|
Jiaan Wang
|
Jiaan Wang, Tingyi Zhang, Haoxiang Shi
|
GOAL: Towards Benchmarking Few-Shot Sports Game Summarization
|
work in progress
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sports game summarization aims to generate sports news based on real-time
commentaries. The task has attracted wide research attention but is still
under-explored probably due to the lack of corresponding English datasets.
Therefore, in this paper, we release GOAL, the first English sports game
summarization dataset. Specifically, there are 103 commentary-news pairs in
GOAL, where the average lengths of commentaries and news are 2724.9 and 476.3
words, respectively. Moreover, to support the research in the semi-supervised
setting, GOAL additionally provides 2,160 unlabeled commentary documents. Based
on our GOAL, we build and evaluate several baselines, including extractive and
abstractive baselines. The experimental results show the challenges of this
task still remain. We hope our work could promote the research of sports game
summarization. The dataset has been released at
https://github.com/krystalan/goal.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 14:29:18 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Wang",
"Jiaan",
""
],
[
"Zhang",
"Tingyi",
""
],
[
"Shi",
"Haoxiang",
""
]
] |
new_dataset
| 0.998143 |
2207.08766
|
David Noever
|
Samantha E. Miller Noever, David Noever
|
Word Play for Playing Othello (Reverses)
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Language models like OpenAI's Generative Pre-Trained Transformers (GPT-2/3)
capture the long-term correlations needed to generate text in a variety of
domains (such as language translators) and recently in gameplay (chess, Go, and
checkers). The present research applies both the larger (GPT-3) and smaller
(GPT-2) language models to explore the complex strategies for the game of
Othello (or Reverses). Given the game rules for rapid reversals of fortune, the
language model not only represents a candidate predictor of the next move based
on previous game moves but also avoids sparse rewards in gameplay. The language
model automatically captures or emulates championship-level strategies. The
fine-tuned GPT-2 model generates Othello games ranging from 13-71% completion,
while the larger GPT-3 model reaches 41% of a complete game. Like previous work
with chess and Go, these language models offer a novel way to generate
plausible game archives, particularly for comparing opening moves across a
larger sample than humanly possible to explore. A primary contribution of these
models magnifies (by two-fold) the previous record for player archives (120,000
human games over 45 years from 1977-2022), thus supplying the research
community with more diverse and original strategies for sampling with other
reinforcement learning techniques.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 17:13:32 GMT"
}
] | 2022-07-19T00:00:00 |
[
[
"Noever",
"Samantha E. Miller",
""
],
[
"Noever",
"David",
""
]
] |
new_dataset
| 0.955801 |
1702.06455
|
Tim Alderson
|
Tim L. Alderson
|
3-Dimensional Optical Orthogonal Codes with Ideal Autocorrelation-Bounds
and Optimal Constructions
| null |
IEEE Trans. Inform. Theory 64 (2018), no. 6, 4392-4398
|
10.1109/TIT.2017.2717538
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Several new constructions of 3-dimensional optical orthogonal codes are
presented here. In each case the codes have ideal autocorrelation $\mathbf{
\lambda_a=0} $, and in all but one case a cross correlation of $
\mathbf{\lambda_c=1} $. All codes produced are optimal with respect to the
applicable Johnson bound either presented or developed here. Thus, on one hand
the codes are as large as possible, and on the other, the bound(s) are shown to
be tight. All codes are constructed by using a particular automorphism (a
Singer cycle) of $ \mathbf{ PG(k,q)} $, the finite projective geometry of
dimension $ k $ over the field of order $ \mathbf{q} $, or by using an affine
analogue in $ AG(k,q) $.
|
[
{
"version": "v1",
"created": "Tue, 21 Feb 2017 15:56:20 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Alderson",
"Tim L.",
""
]
] |
new_dataset
| 0.989123 |
1803.04020
|
Tim Alderson
|
Tim L. Alderson and Alessandro Neri
|
Maximum Weight Spectrum Codes
|
19 pages
|
Adv. Math. Commun. 13 (2019), no. 1, 101-119
|
10.3934/amc.2019006
| null |
cs.IT math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the recent work \cite{shi18}, a combinatorial problem concerning linear
codes over a finite field $\F_q$ was introduced. In that work the authors
studied the weight set of an $[n,k]_q$ linear code, that is the set of non-zero
distinct Hamming weights, showing that its cardinality is upper bounded by
$\frac{q^k-1}{q-1}$. They showed that this bound was sharp in the case $ q=2 $,
and in the case $ k=2 $. They conjectured that the bound is sharp for every
prime power $ q $ and every positive integer $ k $. In this work quickly
establish the truth of this conjecture. We provide two proofs, each employing
different construction techniques. The first relies on the geometric view of
linear codes as systems of projective points. The second approach is purely
algebraic. We establish some lower bounds on the length of codes that satisfy
the conjecture, and the length of the new codes constructed here are discussed.
|
[
{
"version": "v1",
"created": "Sun, 11 Mar 2018 19:20:21 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Mar 2018 17:30:47 GMT"
},
{
"version": "v3",
"created": "Tue, 17 Apr 2018 18:18:46 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Alderson",
"Tim L.",
""
],
[
"Neri",
"Alessandro",
""
]
] |
new_dataset
| 0.956149 |
1807.11798
|
Tim Alderson
|
Tim L. Alderson
|
A note on full weight spectrum codes
| null |
Trans. Comb. 8 (2019), no. 3, 15-22
|
10.22108/toc.2019.112621.1584
| null |
cs.IT math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A linear $ [n,k]_q $ code $ C $ is said to be a full weight spectrum (FWS)
code if there exist codewords of each nonzero weight less than or equal to $ n
$. In this brief communication we determine necessary and sufficient conditions
for the existence of linear $ [n,k]_q $ full weight spectrum (FWS) codes.
Central to our approach is the geometric view of linear codes, whereby columns
of a generator matrix correspond to points in $ PG(k-1,q) $.
|
[
{
"version": "v1",
"created": "Tue, 31 Jul 2018 13:05:20 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Aug 2018 18:01:52 GMT"
},
{
"version": "v3",
"created": "Thu, 8 Nov 2018 19:17:44 GMT"
},
{
"version": "v4",
"created": "Wed, 3 Apr 2019 16:12:24 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Alderson",
"Tim L.",
""
]
] |
new_dataset
| 0.993724 |
2003.07311
|
Johannes C. Paetzold
|
Suprosanna Shit, Johannes C. Paetzold, Anjany Sekuboyina, Ivan Ezhov,
Alexander Unger, Andrey Zhylka, Josien P. W. Pluim, Ulrich Bauer, Bjoern H.
Menze
|
clDice -- A Novel Topology-Preserving Loss Function for Tubular
Structure Segmentation
|
* The authors Suprosanna Shit and Johannes C. Paetzold contributed
equally to the work
| null |
10.1109/CVPR46437.2021.01629
|
CVPR 2021
|
cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate segmentation of tubular, network-like structures, such as vessels,
neurons, or roads, is relevant to many fields of research. For such structures,
the topology is their most important characteristic; particularly preserving
connectedness: in the case of vascular networks, missing a connected vessel
entirely alters the blood-flow dynamics. We introduce a novel similarity
measure termed centerlineDice (short clDice), which is calculated on the
intersection of the segmentation masks and their (morphological) skeleta. We
theoretically prove that clDice guarantees topology preservation up to homotopy
equivalence for binary 2D and 3D segmentation. Extending this, we propose a
computationally efficient, differentiable loss function (soft-clDice) for
training arbitrary neural segmentation networks. We benchmark the soft-clDice
loss on five public datasets, including vessels, roads and neurons (2D and 3D).
Training on soft-clDice leads to segmentation with more accurate connectivity
information, higher graph similarity, and better volumetric scores.
|
[
{
"version": "v1",
"created": "Mon, 16 Mar 2020 16:27:49 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Mar 2020 20:45:16 GMT"
},
{
"version": "v3",
"created": "Sun, 29 Mar 2020 22:46:43 GMT"
},
{
"version": "v4",
"created": "Thu, 3 Dec 2020 19:53:43 GMT"
},
{
"version": "v5",
"created": "Mon, 29 Mar 2021 13:36:28 GMT"
},
{
"version": "v6",
"created": "Tue, 30 Mar 2021 11:51:21 GMT"
},
{
"version": "v7",
"created": "Fri, 15 Jul 2022 10:39:38 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Shit",
"Suprosanna",
""
],
[
"Paetzold",
"Johannes C.",
""
],
[
"Sekuboyina",
"Anjany",
""
],
[
"Ezhov",
"Ivan",
""
],
[
"Unger",
"Alexander",
""
],
[
"Zhylka",
"Andrey",
""
],
[
"Pluim",
"Josien P. W.",
""
],
[
"Bauer",
"Ulrich",
""
],
[
"Menze",
"Bjoern H.",
""
]
] |
new_dataset
| 0.99927 |
2004.10100
|
Shohei Hisada
|
Shohei Hisada, Taichi Murayama, Kota Tsubouchi, Sumio Fujita, Shuntaro
Yada, Shoko Wakamiya, and Eiji Aramaki
|
Syndromic surveillance using search query logs and user location
information from smartphones against COVID-19 clusters in Japan
| null | null |
10.1038/s41598-020-75771-6
| null |
cs.IR cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
[Background] Two clusters of coronavirus disease 2019 (COVID-19) were
confirmed in Hokkaido, Japan in February 2020. To capture the clusters, this
study employs Web search query logs and user location information from
smartphones. [Material and Methods] First, we anonymously identified smartphone
users who used a Web search engine (Yahoo! JAPAN Search) for the COVID-19 or
its symptoms via its companion application for smartphones (Yahoo Japan App).
We regard these searchers as Web searchers who are suspicious of their own
COVID-19 infection (WSSCI). Second, we extracted the location of the WSSCI via
the smartphone application. The spatio-temporal distribution of the number of
WSSCI are compared with the actual location of the known two clusters. [Result
and Discussion] Before the early stage of the cluster development, we could
confirm several WSSCI, which demonstrated the basic feasibility of our
WSSCI-based approach. However, it is accurate only in the early stage, and it
was biased after the public announcement of the cluster development. For the
case where the other cluster-related resources, such as fine-grained population
statistics, are not available, the proposed metric would be helpful to catch
the hint of emerging clusters.
|
[
{
"version": "v1",
"created": "Tue, 21 Apr 2020 15:21:30 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Hisada",
"Shohei",
""
],
[
"Murayama",
"Taichi",
""
],
[
"Tsubouchi",
"Kota",
""
],
[
"Fujita",
"Sumio",
""
],
[
"Yada",
"Shuntaro",
""
],
[
"Wakamiya",
"Shoko",
""
],
[
"Aramaki",
"Eiji",
""
]
] |
new_dataset
| 0.994084 |
2012.04708
|
Yusuf H. Sahin
|
Yusuf H. Sahin, Alican Mertan, Gozde Unal
|
ODFNet: Using orientation distribution functions to characterize 3D
point clouds
|
The paper is under consideration at Computer Vision and Image
Understanding
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Learning new representations of 3D point clouds is an active research area in
3D vision, as the order-invariant point cloud structure still presents
challenges to the design of neural network architectures. Recent works explored
learning either global or local features or both for point clouds, however none
of the earlier methods focused on capturing contextual shape information by
analysing local orientation distribution of points. In this paper, we leverage
on point orientation distributions around a point in order to obtain an
expressive local neighborhood representation for point clouds. We achieve this
by dividing the spherical neighborhood of a given point into predefined cone
volumes, and statistics inside each volume are used as point features. In this
way, a local patch can be represented by not only the selected point's nearest
neighbors, but also considering a point density distribution defined along
multiple orientations around the point. We are then able to construct an
orientation distribution function (ODF) neural network that involves an
ODFBlock which relies on mlp (multi-layer perceptron) layers. The new ODFNet
model achieves state-of the-art accuracy for object classification on
ModelNet40 and ScanObjectNN datasets, and segmentation on ShapeNet S3DIS
datasets.
|
[
{
"version": "v1",
"created": "Tue, 8 Dec 2020 19:54:20 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 13:02:08 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Sahin",
"Yusuf H.",
""
],
[
"Mertan",
"Alican",
""
],
[
"Unal",
"Gozde",
""
]
] |
new_dataset
| 0.978407 |
2012.06326
|
Alex B\"auerle
|
Alex B\"auerle, Patrick Albus, Raphael St\"ork, Tina Seufert, and Timo
Ropinski
|
exploRNN: Understanding Recurrent Neural Networks through Visual
Exploration
| null | null |
10.1007/s00371-022-02593-0
| null |
cs.LG cs.AI cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Due to the success of deep learning (DL) and its growing job market, students
and researchers from many areas are interested in learning about DL
technologies. Visualization has proven to be of great help during this learning
process. While most current educational visualizations are targeted towards one
specific architecture or use case, recurrent neural networks (RNNs), which are
capable of processing sequential data, are not covered yet. This is despite the
fact that tasks on sequential data, such as text and function analysis, are at
the forefront of DL research. Therefore, we propose exploRNN, the first
interactively explorable educational visualization for RNNs. On the basis of
making learning easier and more fun, we define educational objectives targeted
towards understanding RNNs. We use these objectives to form guidelines for the
visual design process. By means of exploRNN, which is accessible online, we
provide an overview of the training process of RNNs at a coarse level, while
also allowing a detailed inspection of the data flow within LSTM cells. In an
empirical study, we assessed 37 subjects in a between-subjects design to
investigate the learning outcomes and cognitive load of exploRNN compared to a
classic text-based learning environment. While learners in the text group are
ahead in superficial knowledge acquisition, exploRNN is particularly helpful
for deeper understanding of the learning content. In addition, the complex
content in exploRNN is perceived as significantly easier and causes less
extraneous load than in the text group. The study shows that for difficult
learning material such as recurrent networks, where deep understanding is
important, interactive visualizations such as exploRNN can be helpful.
|
[
{
"version": "v1",
"created": "Wed, 9 Dec 2020 15:06:01 GMT"
},
{
"version": "v2",
"created": "Wed, 5 Jan 2022 10:24:36 GMT"
},
{
"version": "v3",
"created": "Wed, 22 Jun 2022 10:52:45 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Bäuerle",
"Alex",
""
],
[
"Albus",
"Patrick",
""
],
[
"Störk",
"Raphael",
""
],
[
"Seufert",
"Tina",
""
],
[
"Ropinski",
"Timo",
""
]
] |
new_dataset
| 0.987792 |
2202.06257
|
Ling Chen
|
Pengyue Jia, Ling Chen, Dandan Lyu
|
Fine-Grained Population Mobility Data-Based Community-Level COVID-19
Prediction Model
|
Accepted by Cybernetics and Systems
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Predicting the number of infections in the anti-epidemic process is extremely
beneficial to the government in developing anti-epidemic strategies, especially
in fine-grained geographic units. Previous works focus on low spatial
resolution prediction, e.g., county-level, and preprocess data to the same
geographic level, which loses some useful information. In this paper, we
propose a fine-grained population mobility data-based model (FGC-COVID)
utilizing data of two geographic levels for community-level COVID-19
prediction. We use the population mobility data between Census Block Groups
(CBGs), which is a finer-grained geographic level than community, to build the
graph and capture the dependencies between CBGs using graph neural networks
(GNNs). To mine as finer-grained patterns as possible for prediction, a spatial
weighted aggregation module is introduced to aggregate the embeddings of CBGs
to community level based on their geographic affiliation and spatial
autocorrelation. Extensive experiments on 300 days LA city COVID-19 data
indicate our model outperforms existing forecasting models on community-level
COVID-19 prediction.
|
[
{
"version": "v1",
"created": "Sun, 13 Feb 2022 08:40:47 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Apr 2022 02:00:27 GMT"
},
{
"version": "v3",
"created": "Fri, 15 Jul 2022 07:12:56 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Jia",
"Pengyue",
""
],
[
"Chen",
"Ling",
""
],
[
"Lyu",
"Dandan",
""
]
] |
new_dataset
| 0.993937 |
2203.06585
|
Jiaqi Gu
|
Jiaqi Gu, Zhiyu Xiang, Pan Zhao, Tingming Bai, Lingxuan Wang, Xijun
Zhao, Zhiyuan Zhang
|
CVFNet: Real-time 3D Object Detection by Learning Cross View Features
|
7 pages, 5 figures, accepted by IROS 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years 3D object detection from LiDAR point clouds has made great
progress thanks to the development of deep learning technologies. Although
voxel or point based methods are popular in 3D object detection, they usually
involve time-consuming operations such as 3D convolutions on voxels or ball
query among points, making the resulting network inappropriate for time
critical applications. On the other hand, 2D view-based methods feature high
computing efficiency while usually obtaining inferior performance than the
voxel or point based methods. In this work, we present a real-time view-based
single stage 3D object detector, namely CVFNet to fulfill this task. To
strengthen the cross-view feature learning under the condition of demanding
efficiency, our framework extracts the features of different views and fuses
them in an efficient progressive way. We first propose a novel Point-Range
feature fusion module that deeply integrates point and range view features in
multiple stages. Then, a special Slice Pillar is designed to well maintain the
3D geometry when transforming the obtained deep point-view features into bird's
eye view. To better balance the ratio of samples, a sparse pillar detection
head is presented to focus the detection on the nonempty grids. We conduct
experiments on the popular KITTI and NuScenes benchmark, and state-of-the-art
performances are achieved in terms of both accuracy and speed.
|
[
{
"version": "v1",
"created": "Sun, 13 Mar 2022 06:23:18 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 03:10:58 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Gu",
"Jiaqi",
""
],
[
"Xiang",
"Zhiyu",
""
],
[
"Zhao",
"Pan",
""
],
[
"Bai",
"Tingming",
""
],
[
"Wang",
"Lingxuan",
""
],
[
"Zhao",
"Xijun",
""
],
[
"Zhang",
"Zhiyuan",
""
]
] |
new_dataset
| 0.997882 |
2203.09091
|
Inkyu Sa
|
Inkyu Sa, JongYoon Lim, Ho Seok Ahn, Bruce MacDonald
|
deepNIR: Datasets for generating synthetic NIR images and improved fruit
detection system using deep learning techniques
|
35 pages, 27 figures, published in MDPI Remote Sensing journal
| null |
10.3390/s22134721
| null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents datasets utilised for synthetic near-infrared (NIR) image
generation and bounding-box level fruit detection systems. It is undeniable
that high-calibre machine learning frameworks such as Tensorflow or Pytorch,
and large-scale ImageNet or COCO datasets with the aid of accelerated GPU
hardware have pushed the limit of machine learning techniques for more than
decades. Among these breakthroughs, a high-quality dataset is one of the
essential building blocks that can lead to success in model generalisation and
the deployment of data-driven deep neural networks. In particular, synthetic
data generation tasks often require more training samples than other supervised
approaches. Therefore, in this paper, we share the NIR+RGB datasets that are
re-processed from two public datasets (i.e., nirscene and SEN12MS) and our
novel NIR+RGB sweet pepper(capsicum) dataset. We quantitatively and
qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used
for synthetic NIR image generation. We achieved Frechet Inception Distance
(FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper
datasets respectively. In addition, we release manual annotations of 11 fruit
bounding boxes that can be exported as various formats using cloud service.
Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novel
bounding box datasets on top of our previous work presented in the deepFruits
project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The
total number of bounding box instances of the dataset is 162k and it is ready
to use from cloud service. For the evaluation of the dataset, Yolov5 single
stage detector is exploited and reported impressive
mean-average-precision,mAP[0.5:0.95] results of[min:0.49, max:0.812]. We hope
these datasets are useful and serve as a baseline for the future studies.
|
[
{
"version": "v1",
"created": "Thu, 17 Mar 2022 05:25:36 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 04:41:31 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Sa",
"Inkyu",
""
],
[
"Lim",
"JongYoon",
""
],
[
"Ahn",
"Ho Seok",
""
],
[
"MacDonald",
"Bruce",
""
]
] |
new_dataset
| 0.999779 |
2203.12184
|
Ke Shen
|
Henrique Santos, Ke Shen, Alice M. Mulvehill, Yasaman Razeghi, Deborah
L. McGuinness, Mayank Kejriwal
|
A Theoretically Grounded Benchmark for Evaluating Machine Commonsense
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Programming machines with commonsense reasoning (CSR) abilities is a
longstanding challenge in the Artificial Intelligence community. Current CSR
benchmarks use multiple-choice (and in relatively fewer cases, generative)
question-answering instances to evaluate machine commonsense. Recent progress
in transformer-based language representation models suggest that considerable
progress has been made on existing benchmarks. However, although tens of CSR
benchmarks currently exist, and are growing, it is not evident that the full
suite of commonsense capabilities have been systematically evaluated.
Furthermore, there are doubts about whether language models are 'fitting' to a
benchmark dataset's training partition by picking up on subtle, but normatively
irrelevant (at least for CSR), statistical features to achieve good performance
on the testing partition. To address these challenges, we propose a benchmark
called Theoretically-Grounded Commonsense Reasoning (TG-CSR) that is also based
on discriminative question answering, but with questions designed to evaluate
diverse aspects of commonsense, such as space, time, and world states. TG-CSR
is based on a subset of commonsense categories first proposed as a viable
theory of commonsense by Gordon and Hobbs. The benchmark is also designed to be
few-shot (and in the future, zero-shot), with only a few training and
validation examples provided. This report discusses the structure and
construction of the benchmark. Preliminary results suggest that the benchmark
is challenging even for advanced language representation models designed for
discriminative CSR question answering tasks.
Benchmark access and leaderboard:
https://codalab.lisn.upsaclay.fr/competitions/3080 Benchmark website:
https://usc-isi-i2.github.io/TGCSR/
|
[
{
"version": "v1",
"created": "Wed, 23 Mar 2022 04:06:01 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jul 2022 23:27:43 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Santos",
"Henrique",
""
],
[
"Shen",
"Ke",
""
],
[
"Mulvehill",
"Alice M.",
""
],
[
"Razeghi",
"Yasaman",
""
],
[
"McGuinness",
"Deborah L.",
""
],
[
"Kejriwal",
"Mayank",
""
]
] |
new_dataset
| 0.99439 |
2203.12268
|
Yinxiao Feng
|
Yinxiao Feng, Kaisheng Ma
|
Chiplet Actuary: A Quantitative Cost Model and Multi-Chiplet
Architecture Exploration
|
Accepted by and presented at DAC 2022
| null | null | null |
cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multi-chip integration is widely recognized as the extension of Moore's Law.
Cost-saving is a frequently mentioned advantage, but previous works rarely
present quantitative demonstrations on the cost superiority of multi-chip
integration over monolithic SoC. In this paper, we build a quantitative cost
model and put forward an analytical method for multi-chip systems based on
three typical multi-chip integration technologies to analyze the cost benefits
from yield improvement, chiplet and package reuse, and heterogeneity. We
re-examine the actual cost of multi-chip systems from various perspectives and
show how to reduce the total cost of the VLSI system through appropriate
multi-chiplet architecture.
|
[
{
"version": "v1",
"created": "Wed, 23 Mar 2022 08:30:30 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Mar 2022 08:13:57 GMT"
},
{
"version": "v3",
"created": "Fri, 15 Jul 2022 11:30:38 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Feng",
"Yinxiao",
""
],
[
"Ma",
"Kaisheng",
""
]
] |
new_dataset
| 0.972421 |
2204.03117
|
Shuo Liang
|
Shuo Liang, Wei Wei, Xian-Ling Mao, Fei Wang and Zhiyong He
|
BiSyn-GAT+: Bi-Syntax Aware Graph Attention Network for Aspect-based
Sentiment Analysis
|
Findings of ACL 2022
| null |
10.18653/v1/2022.findings-acl.144
| null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis
task that aims to align aspects and corresponding sentiments for
aspect-specific sentiment polarity inference. It is challenging because a
sentence may contain multiple aspects or complicated (e.g., conditional,
coordinating, or adversative) relations. Recently, exploiting dependency syntax
information with graph neural networks has been the most popular trend. Despite
its success, methods that heavily rely on the dependency tree pose challenges
in accurately modeling the alignment of the aspects and their words indicative
of sentiment, since the dependency tree may provide noisy signals of unrelated
associations (e.g., the "conj" relation between "great" and "dreadful" in
Figure 2). In this paper, to alleviate this problem, we propose a Bi-Syntax
aware Graph Attention Network (BiSyn-GAT+). Specifically, BiSyn-GAT+ fully
exploits the syntax information (e.g., phrase segmentation and hierarchical
structure) of the constituent tree of a sentence to model the sentiment-aware
context of every single aspect (called intra-context) and the sentiment
relations across aspects (called inter-context) for learning. Experiments on
four benchmark datasets demonstrate that BiSyn-GAT+ outperforms the
state-of-the-art methods consistently.
|
[
{
"version": "v1",
"created": "Wed, 6 Apr 2022 22:18:12 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 08:51:00 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Liang",
"Shuo",
""
],
[
"Wei",
"Wei",
""
],
[
"Mao",
"Xian-Ling",
""
],
[
"Wang",
"Fei",
""
],
[
"He",
"Zhiyong",
""
]
] |
new_dataset
| 0.968853 |
2204.07827
|
Daniel Reichman
|
Hermish Mehta and Daniel Reichman
|
Local treewidth of random and noisy graphs with applications to stopping
contagion in networks
|
Accepted to RANDOM 2022
| null | null | null |
cs.DS cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
We study the notion of local treewidth in sparse random graphs: the maximum
treewidth over all $k$-vertex subgraphs of an $n$-vertex graph. When $k$ is not
too large, we give nearly tight bounds for this local treewidth parameter; we
also derive tight bounds for the local treewidth of noisy trees, trees where
every non-edge is added independently with small probability. We apply our
upper bounds on the local treewidth to obtain fixed parameter tractable
algorithms (on random graphs and noisy trees) for edge-removal problems
centered around containing a contagious process evolving over a network. In
these problems, our main parameter of study is $k$, the number of initially
``infected'' vertices in the network. For the random graph models we consider
and a certain range of parameters the running time of our algorithms on
$n$-vertex graphs is $2^{o(k)}\textrm{poly}(n)$, improving upon the
$2^{\Omega(k)}\textrm{poly}(n)$ performance of the best-known algorithms
designed for worst-case instances of these edge deletion problems.
|
[
{
"version": "v1",
"created": "Sat, 16 Apr 2022 15:53:11 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 16:54:45 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Mehta",
"Hermish",
""
],
[
"Reichman",
"Daniel",
""
]
] |
new_dataset
| 0.997355 |
2205.01202
|
Jingxing Qian
|
Jingxing Qian, Veronica Chatrath, Jun Yang, James Servos, Angela P.
Schoellig, and Steven L. Waslander
|
POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping
in Semi-Static Scenes
|
Published in Robotics: Science and Systems (RSS) 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Maintaining an up-to-date map to reflect recent changes in the scene is very
important, particularly in situations involving repeated traversals by a robot
operating in an environment over an extended period. Undetected changes may
cause a deterioration in map quality, leading to poor localization, inefficient
operations, and lost robots. Volumetric methods, such as truncated signed
distance functions (TSDFs), have quickly gained traction due to their real-time
production of a dense and detailed map, though map updating in scenes that
change over time remains a challenge. We propose a framework that introduces a
novel probabilistic object state representation to track object pose changes in
semi-static scenes. The representation jointly models a stationarity score and
a TSDF change measure for each object. A Bayesian update rule that incorporates
both geometric and semantic information is derived to achieve consistent online
map maintenance. To extensively evaluate our approach alongside the
state-of-the-art, we release a novel real-world dataset in a warehouse
environment. We also evaluate on the public ToyCar dataset. Our method
outperforms state-of-the-art methods on the reconstruction quality of
semi-static environments.
|
[
{
"version": "v1",
"created": "Mon, 2 May 2022 20:33:11 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 13:40:04 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Qian",
"Jingxing",
""
],
[
"Chatrath",
"Veronica",
""
],
[
"Yang",
"Jun",
""
],
[
"Servos",
"James",
""
],
[
"Schoellig",
"Angela P.",
""
],
[
"Waslander",
"Steven L.",
""
]
] |
new_dataset
| 0.999281 |
2206.00204
|
Shuhao Zeng
|
Shuhao Zeng, Hongliang Zhang, Boya Di, Yuanwei Liu, Marco Di Renzo,
Zhu Han, H. Vincent Poor, Lingyang Song
|
Intelligent Omni-Surfaces: Reflection-Refraction Circuit Model,
Full-Dimensional Beamforming, and System Implementation
|
33 pages, 20 figures
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The intelligent omni-surface (IOS) is a dynamic metasurface that has recently
been proposed to achieve full-dimensional communications by realizing the dual
function of anomalous reflection and anomalous refraction. Existing research
works provide only simplified models for the reflection and refraction
responses of the IOS, which do not explicitly depend on the physical structure
of the IOS and the angle of incidence of the electromagnetic (EM) wave.
Therefore, the available reflection-refraction models are insufficient to
characterize the performance of full-dimensional communications. In this paper,
we propose a complete and detailed circuit-based reflection-refraction model
for the IOS, which is formulated in terms of the physical structure and
equivalent circuits of the IOS elements, as well as we validate it against
full-wave EM simulations. Based on the proposed circuit-based model for the
IOS, we analyze the asymmetry between the reflection and transmission
coefficients. Moreover, the proposed circuit-based model is utilized for
optimizing the hybrid beamforming of IOS-assisted networks and hence improving
the system performance. To verify the circuit-based model, the theoretical
findings, and to evaluate the performance of full-dimensional beamforming, we
implement a prototype of IOS and deploy an IOS-assisted wireless communication
testbed to experimentally measure the beam patterns and to quantify the
achievable rate. The obtained experimental results validate the theoretical
findings and the accuracy of the proposed circuit-based reflection-refraction
model for IOSs.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 03:01:23 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 01:23:04 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Zeng",
"Shuhao",
""
],
[
"Zhang",
"Hongliang",
""
],
[
"Di",
"Boya",
""
],
[
"Liu",
"Yuanwei",
""
],
[
"Di Renzo",
"Marco",
""
],
[
"Han",
"Zhu",
""
],
[
"Poor",
"H. Vincent",
""
],
[
"Song",
"Lingyang",
""
]
] |
new_dataset
| 0.991088 |
2207.00748
|
Pedro Henrique Luz de Araujo
|
Pedro H. Luz de Araujo, Ana Paula G. S. de Almeida, Fabricio A. Braz,
Nilton C. da Silva, Flavio de Barros Vidal, Teofilo E. de Campos
|
Sequence-aware multimodal page classification of Brazilian legal
documents
|
11 pages, 6 figures. This preprint, which was originally written on 8
April 2021, has not undergone peer review or any post-submission improvements
or corrections. The Version of Record of this article is published in the
International Journal on Document Analysis and Recognition, and is available
online at https://doi.org/10.1007/s10032-022-00406-7 and
https://rdcu.be/cRvvV
|
International Journal on Document Analysis and Recognition.2022
|
10.1007/s10032-022-00406-7
| null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The Brazilian Supreme Court receives tens of thousands of cases each
semester. Court employees spend thousands of hours to execute the initial
analysis and classification of those cases -- which takes effort away from
posterior, more complex stages of the case management workflow. In this paper,
we explore multimodal classification of documents from Brazil's Supreme Court.
We train and evaluate our methods on a novel multimodal dataset of 6,510
lawsuits (339,478 pages) with manual annotation assigning each page to one of
six classes. Each lawsuit is an ordered sequence of pages, which are stored
both as an image and as a corresponding text extracted through optical
character recognition. We first train two unimodal classifiers: a ResNet
pre-trained on ImageNet is fine-tuned on the images, and a convolutional
network with filters of multiple kernel sizes is trained from scratch on
document texts. We use them as extractors of visual and textual features, which
are then combined through our proposed Fusion Module. Our Fusion Module can
handle missing textual or visual input by using learned embeddings for missing
data. Moreover, we experiment with bi-directional Long Short-Term Memory
(biLSTM) networks and linear-chain conditional random fields to model the
sequential nature of the pages. The multimodal approaches outperform both
textual and visual classifiers, especially when leveraging the sequential
nature of the pages.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 06:23:25 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 07:02:55 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"de Araujo",
"Pedro H. Luz",
""
],
[
"de Almeida",
"Ana Paula G. S.",
""
],
[
"Braz",
"Fabricio A.",
""
],
[
"da Silva",
"Nilton C.",
""
],
[
"Vidal",
"Flavio de Barros",
""
],
[
"de Campos",
"Teofilo E.",
""
]
] |
new_dataset
| 0.989641 |
2207.07120
|
Ryo Eguchi
|
Ryo Eguchi, David Vacek, Cole Godzinski, Silvia Curry, Max Evans,
Allison M. Okamura
|
Between-Tactor Display Using Dynamic Tactile Stimuli
| null |
EuroHaptics 2022
| null | null |
cs.HC cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Display of illusory vibration locations between physical vibrotactile motors
(tactors) placed on the skin has the potential to reduce the number of tactors
in distributed tactile displays. This paper presents a between-tactor display
method that uses dynamic tactile stimuli to generate illusory vibration
locations. A belt with only 6 vibration motors displays 24 targets consisting
of on-tactor and between-tactor locations. On-tactor locations are represented
by simply vibrating the relevant single tactor. Between-tactor locations are
displayed by adjusting the relative vibration amplitudes of two adjacent
motors, with either (1) constant vibration amplitudes or (2) perturbed
vibration amplitudes (creating local illusory motion). User testing showed that
perturbations improve recognition accuracy for in-between tactor localization.
|
[
{
"version": "v1",
"created": "Wed, 13 Jul 2022 06:25:29 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Eguchi",
"Ryo",
""
],
[
"Vacek",
"David",
""
],
[
"Godzinski",
"Cole",
""
],
[
"Curry",
"Silvia",
""
],
[
"Evans",
"Max",
""
],
[
"Okamura",
"Allison M.",
""
]
] |
new_dataset
| 0.998366 |
2207.07262
|
Xiaoqiang Wang
|
Xiaoqiang Wang, Chunming Tang, Cunsheng Ding
|
Infinite families of cyclic and negacyclic codes supporting 3-designs
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Interplay between coding theory and combinatorial $t$-designs has been a hot
topic for many years for combinatorialists and coding theorists. Some infinite
families of cyclic codes supporting infinite families of $3$-designs have been
constructed in the past 50 years. However, no infinite family of negacyclic
codes supporting an infinite family of $3$-designs has been reported in the
literature. This is the main motivation of this paper. Let $q=p^m$, where $p$
is an odd prime and $m \geq 2$ is an integer. The objective of this paper is to
present an infinite family of cyclic codes over $\gf(q)$ supporting an infinite
family of $3$-designs and two infinite families of negacyclic codes over
$\gf(q^2)$ supporting two infinite families of $3$-designs. The parameters and
the weight distributions of these codes are determined. The subfield subcodes
of these negacyclic codes over $\gf(q)$ are studied. Three infinite families of
almost MDS codes are also presented. A constacyclic code over GF($4$)
supporting a $4$-design and six open problems are also presented in this paper.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 02:47:57 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Wang",
"Xiaoqiang",
""
],
[
"Tang",
"Chunming",
""
],
[
"Ding",
"Cunsheng",
""
]
] |
new_dataset
| 0.997074 |
2207.07386
|
Ho Yin Au
|
Ho Yin Au, Jie Chen, Junkun Jiang, Yike Guo
|
ChoreoGraph: Music-conditioned Automatic Dance Choreography over a Style
and Tempo Consistent Dynamic Graph
| null | null | null | null |
cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To generate dance that temporally and aesthetically matches the music is a
challenging problem, as the following factors need to be considered. First, the
aesthetic styles and messages conveyed by the motion and music should be
consistent. Second, the beats of the generated motion should be locally aligned
to the musical features. And finally, basic choreomusical rules should be
observed, and the motion generated should be diverse. To address these
challenges, we propose ChoreoGraph, which choreographs high-quality dance
motion for a given piece of music over a Dynamic Graph. A data-driven learning
strategy is proposed to evaluate the aesthetic style and rhythmic connections
between music and motion in a progressively learned cross-modality embedding
space. The motion sequences will be beats-aligned based on the music segments
and then incorporated as nodes of a Dynamic Motion Graph. Compatibility factors
such as the style and tempo consistency, motion context connection, action
completeness, and transition smoothness are comprehensively evaluated to
determine the node transition in the graph. We demonstrate that our
repertoire-based framework can generate motions with aesthetic consistency and
robustly extensible in diversity. Both quantitative and qualitative experiment
results show that our proposed model outperforms other baseline models.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 10:24:26 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Au",
"Ho Yin",
""
],
[
"Chen",
"Jie",
""
],
[
"Jiang",
"Junkun",
""
],
[
"Guo",
"Yike",
""
]
] |
new_dataset
| 0.973488 |
2207.07403
|
Jordi Pons
|
Nicol\'as Schmidt, Jordi Pons, Marius Miron
|
PodcastMix: A dataset for separating music and speech in podcasts
|
In proceedings of INTERSPEECH2022. Project webpage:
http://www.jordipons.me/apps/podcastmix/
| null | null | null |
cs.SD cs.DB eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce PodcastMix, a dataset formalizing the task of separating
background music and foreground speech in podcasts. We aim at defining a
benchmark suitable for training and evaluating (deep learning) source
separation models. To that end, we release a large and diverse training dataset
based on programatically generated podcasts. However, current (deep learning)
models can incur into generalization issues, specially when trained on
synthetic data. To target potential generalization issues, we release an
evaluation set based on real podcasts for which we design objective and
subjective tests. Out of our experiments with real podcasts, we find that
current (deep learning) models may have generalization issues. Yet, these can
perform competently, e.g., our best baseline separates speech with a mean
opinion score of 3.84 (rating "overall separation quality" from 1 to 5). The
dataset and baselines are accessible online.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 11:12:21 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Schmidt",
"Nicolás",
""
],
[
"Pons",
"Jordi",
""
],
[
"Miron",
"Marius",
""
]
] |
new_dataset
| 0.9988 |
2207.07413
|
Mordechai Guri
|
Mordechai Guri
|
SATAn: Air-Gap Exfiltration Attack via Radio Signals From SATA Cables
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper introduces a new type of attack on isolated, air-gapped
workstations. Although air-gap computers have no wireless connectivity, we show
that attackers can use the SATA cable as a wireless antenna to transfer radio
signals at the 6 GHz frequency band. The Serial ATA (SATA) is a bus interface
widely used in modern computers and connects the host bus to mass storage
devices such as hard disk drives, optical drives, and solid-state drives. The
prevalence of the SATA interface makes this attack highly available to
attackers in a wide range of computer systems and IT environments. We discuss
related work on this topic and provide technical background. We show the design
of the transmitter and receiver and present the implementation of these
components. We also demonstrate the attack on different computers and provide
the evaluation. The results show that attackers can use the SATA cable to
transfer a brief amount of sensitive information from highly secured, air-gap
computers wirelessly to a nearby receiver. Furthermore, we show that the attack
can operate from user mode, is effective even from inside a Virtual Machine
(VM), and can successfully work with other running workloads in the background.
Finally, we discuss defense and mitigation techniques for this new air-gap
attack.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 11:45:57 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Guri",
"Mordechai",
""
]
] |
new_dataset
| 0.999486 |
2207.07423
|
Kiran Gopinathan
|
Kiran Gopinathan
|
GopCaml: A Structural Editor for OCaml
|
Presented at OCaml workshop at ICFP 2021
| null | null | null |
cs.PL
|
http://creativecommons.org/publicdomain/zero/1.0/
|
This talk presents Gopcaml-mode, the first structural editing plugin for
OCaml. We will give a tour of the main plugin features, discussing the plugin's
internal design and its integration with existing OCaml and GNU Emacs
toolchains.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 12:02:47 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Gopinathan",
"Kiran",
""
]
] |
new_dataset
| 0.998582 |
2207.07482
|
Axel Schaffland
|
Axel Schaffland
|
The Mechanical Neural Network(MNN) -- A physical implementation of a
multilayer perceptron for education and hands-on experimentation
|
short video (30sec): https://youtu.be/zMxh3Io3hFE, full presentation
video: https://youtu.be/cEzk8JKDzy4; 8 pages, 6 figures
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper the Mechanical Neural Network(MNN) is introduced, a physical
implementation of a multilayer perceptron(MLP) with ReLU activation functions,
two input neurons, four hidden neurons and two output neurons. This physical
model of a MLP is used in education to give a hands on experience and allow
students to experience the effect of changing the parameters of the network on
the output. Neurons are small wooden levers which are connected by threads.
Students can adapt the weights between the neurons by moving the clamps
connecting a neuron via a thread to the next. The MNN can model real valued
functions and logical operators including XOR.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 14:05:44 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Schaffland",
"Axel",
""
]
] |
new_dataset
| 0.997731 |
2207.07586
|
Micha{\l} Kajstura
|
Joanna Baran, Micha{\l} Kajstura, Maciej Zi\'o{\l}kowski, Krzysztof
Rajda
|
Does Twitter know your political views? POLiTweets dataset and
semi-automatic method for political leaning discovery
| null | null | null | null |
cs.CL cs.LG cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Every day, the world is flooded by millions of messages and statements posted
on Twitter or Facebook. Social media platforms try to protect users' personal
data, but there still is a real risk of misuse, including elections
manipulation. Did you know, that only 13 posts addressing important or
controversial topics for society are enough to predict one's political
affiliation with a 0.85 F1-score? To examine this phenomenon, we created a
novel universal method of semi-automated political leaning discovery. It relies
on a heuristical data annotation procedure, which was evaluated to achieve 0.95
agreement with human annotators (counted as an accuracy metric). We also
present POLiTweets - the first publicly open Polish dataset for political
affiliation discovery in a multi-party setup, consisting of over 147k tweets
from almost 10k Polish-writing users annotated heuristically and almost 40k
tweets from 166 users annotated manually as a test set. We used our data to
study the aspects of domain shift in the context of topics and the type of
content writers - ordinary citizens vs. professional politicians.
|
[
{
"version": "v1",
"created": "Tue, 14 Jun 2022 10:28:23 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Baran",
"Joanna",
""
],
[
"Kajstura",
"Michał",
""
],
[
"Ziółkowski",
"Maciej",
""
],
[
"Rajda",
"Krzysztof",
""
]
] |
new_dataset
| 0.993333 |
2207.07629
|
Zhiruo Zhou
|
Zhiruo Zhou, Hongyu Fu, Suya You, C.-C. Jay Kuo
|
GUSOT: Green and Unsupervised Single Object Tracking for Long Video
Sequences
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Supervised and unsupervised deep trackers that rely on deep learning
technologies are popular in recent years. Yet, they demand high computational
complexity and a high memory cost. A green unsupervised single-object tracker,
called GUSOT, that aims at object tracking for long videos under a
resource-constrained environment is proposed in this work. Built upon a
baseline tracker, UHP-SOT++, which works well for short-term tracking, GUSOT
contains two additional new modules: 1) lost object recovery, and 2)
color-saliency-based shape proposal. They help resolve the tracking loss
problem and offer a more flexible object proposal, respectively. Thus, they
enable GUSOT to achieve higher tracking accuracy in the long run. We conduct
experiments on the large-scale dataset LaSOT with long video sequences, and
show that GUSOT offers a lightweight high-performance tracking solution that
finds applications in mobile and edge computing platforms.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 17:42:49 GMT"
}
] | 2022-07-18T00:00:00 |
[
[
"Zhou",
"Zhiruo",
""
],
[
"Fu",
"Hongyu",
""
],
[
"You",
"Suya",
""
],
[
"Kuo",
"C. -C. Jay",
""
]
] |
new_dataset
| 0.998307 |
1712.10222
|
Simina Br\^anzei
|
Simina Br\^anzei and Erel Segal-Halevi and Aviv Zohar
|
How to Charge Lightning: The Economics of Bitcoin Transaction Channels
|
An earlier version of the paper was presented at Scaling Bitcoin 2017
| null | null | null |
cs.CR cs.DC cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Off-chain transaction channels represent one of the leading techniques to
scale the transaction throughput in cryptocurrencies. However, the economic
effect of transaction channels on the system has not been explored much until
now.
We study the economics of Bitcoin transaction channels, and present a
framework for an economic analysis of the lightning network and its effect on
transaction fees on the blockchain. Our framework allows us to reason about
different patterns of demand for transactions and different topologies of the
lightning network, and to derive the resulting fees for transacting both on and
off the blockchain.
Our initial results indicate that while the lightning network does allow for
a substantially higher number of transactions to pass through the system, it
does not necessarily provide higher fees to miners, and as a result may in fact
lead to lower participation in mining within the system.
|
[
{
"version": "v1",
"created": "Fri, 29 Dec 2017 13:33:46 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Jul 2022 21:07:52 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Brânzei",
"Simina",
""
],
[
"Segal-Halevi",
"Erel",
""
],
[
"Zohar",
"Aviv",
""
]
] |
new_dataset
| 0.992381 |
2005.02155
|
AKM Shahariar Azad Rabby
|
Jannatul Ferdous, Suvrajit Karmaker, A K M Shahariar Azad Rabby, Syed
Akhter Hossain
|
MatriVasha: A Multipurpose Comprehensive Database for Bangla Handwritten
Compound Characters
|
19 fig, 2 table
| null |
10.1007/978-981-15-9774-9_74
| null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
At present, recognition of the Bangla handwriting compound character has been
an essential issue for many years. In recent years there have been
application-based researches in machine learning, and deep learning, which is
gained interest, and most notably is handwriting recognition because it has a
tremendous application such as Bangla OCR. MatrriVasha, the project which can
recognize Bangla, handwritten several compound characters. Currently, compound
character recognition is an important topic due to its variant application, and
helps to create old forms, and information digitization with reliability. But
unfortunately, there is a lack of a comprehensive dataset that can categorize
all types of Bangla compound characters. MatrriVasha is an attempt to align
compound character, and it's challenging because each person has a unique style
of writing shapes. After all, MatrriVasha has proposed a dataset that intends
to recognize Bangla 120(one hundred twenty) compound characters that consist of
2552(two thousand five hundred fifty-two) isolated handwritten characters
written unique writers which were collected from within Bangladesh. This
dataset faced problems in terms of the district, age, and gender-based written
related research because the samples were collected that includes a verity of
the district, age group, and the equal number of males, and females. As of now,
our proposed dataset is so far the most extensive dataset for Bangla compound
characters. It is intended to frame the acknowledgment technique for
handwritten Bangla compound character. In the future, this dataset will be made
publicly available to help to widen the research.
|
[
{
"version": "v1",
"created": "Wed, 29 Apr 2020 06:38:12 GMT"
},
{
"version": "v2",
"created": "Wed, 6 May 2020 07:59:45 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Ferdous",
"Jannatul",
""
],
[
"Karmaker",
"Suvrajit",
""
],
[
"Rabby",
"A K M Shahariar Azad",
""
],
[
"Hossain",
"Syed Akhter",
""
]
] |
new_dataset
| 0.999845 |
2101.11569
|
Marco Tarini
|
Marco Tarini
|
Closed-form Quadrangulation of N-Sided Patches
| null |
Computers & Graphics, Volume 107, Pages 60-65, ISSN 0097-8493,
2022
|
10.1016/j.cag.2022.06.015
| null |
cs.GR cs.CG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We analyze the problem of quadrangulating a $n$-sided patch, each side at its
boundary subdivided into a given number of edges, using a single irregular
vertex (or none, when $n = 4$) that breaks the otherwise fully regular lattice.
We derive, in an analytical closed-form, (1) the necessary and sufficient
conditions that a patch must meet to admit this quadrangulation, and (2) a full
description of the resulting tessellation(s).
|
[
{
"version": "v1",
"created": "Wed, 27 Jan 2021 17:54:11 GMT"
},
{
"version": "v2",
"created": "Mon, 8 Feb 2021 13:42:27 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Tarini",
"Marco",
""
]
] |
new_dataset
| 0.951485 |
2104.01821
|
Li Zhang
|
Li Zhang, Wei Lu, Jinqing Yang
|
LAGOS-AND: A Large Gold Standard Dataset for Scholarly Author Name
Disambiguation
|
33 pages, 7 tables, 7 figures
| null | null | null |
cs.DL
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present a method to automatically build large labeled
datasets for the author ambiguity problem in the academic world by leveraging
the authoritative academic resources, ORCID and DOI. Using the method, we built
LAGOS-AND, two large, gold-standard datasets for author name disambiguation
(AND), of which LAGOS-AND-BLOCK is created for clustering-based AND research
and LAGOS-AND-PAIRWISE is created for classification-based AND research. Our
LAGOS-AND datasets are substantially different from the existing ones. The
initial versions of the datasets (v1.0, released in February 2021) include 7.5M
citations authored by 798K unique authors (LAGOS-AND-BLOCK) and close to 1M
instances (LAGOS-AND-PAIRWISE). And both datasets show close similarities to
the whole Microsoft Academic Graph (MAG) across validations of six facets. In
building the datasets, we reveal the variation degrees of last names in three
literature databases, PubMed, MAG, and Semantic Scholar, by comparing author
names hosted to the authors' official last names shown on the ORCID pages.
Furthermore, we evaluate several baseline disambiguation methods as well as the
MAG's author IDs system on our datasets, and the evaluation helps identify
several interesting findings. We hope the datasets and findings will bring new
insights for future studies. The code and datasets are publicly available.
|
[
{
"version": "v1",
"created": "Mon, 5 Apr 2021 09:32:29 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jul 2022 12:50:41 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Zhang",
"Li",
""
],
[
"Lu",
"Wei",
""
],
[
"Yang",
"Jinqing",
""
]
] |
new_dataset
| 0.999656 |
2107.08336
|
Behnam Dezfouli
|
Puneet Kumar and Behnam Dezfouli
|
QuicSDN: Transitioning from TCP to QUIC for Southbound Communication in
SDNs
| null | null | null |
SIOTLAB-REV-QUICSDN-2022
|
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
In Software-Defined Networks (SDNs), the control plane and data plane
communicate for various purposes, such as applying configurations and
collecting statistical data. While various methods have been proposed to reduce
the overhead and enhance the scalability of SDNs, the impact of the transport
layer protocol used for southbound communication has not been investigated.
Existing SDNs rely on TCP (and TLS) to enforce reliability and security. In
this paper, we show that the use of TCP imposes a considerable overhead on
southbound communication, identify the causes of this overhead, and demonstrate
how replacing TCP with QUIC can enhance the performance of this communication.
We introduce the quicSDN architecture, enabling southbound communication in
SDNs via the QUIC protocol. We present a reference architecture based on the
standard, most widely used protocols by the SDN community and show how the
controller and switch are revamped to facilitate this transition. We compare,
both analytically and empirically, the performance of quicSDN versus the
traditional SDN architecture and confirm the superior performance of quicSDN.
|
[
{
"version": "v1",
"created": "Sun, 18 Jul 2021 01:09:05 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jul 2022 02:10:14 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Kumar",
"Puneet",
""
],
[
"Dezfouli",
"Behnam",
""
]
] |
new_dataset
| 0.999252 |
2107.08865
|
Siwei Chen
|
Siwei Chen, Xiao Ma, Yunfan Lu and David Hsu
|
Ab Initio Particle-based Object Manipulation
|
Robotics: Science and Systems (RSS) 2021
| null |
10.15607/RSS.2021.XVII.071
| null |
cs.RO cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents Particle-based Object Manipulation (Prompt), a new
approach to robot manipulation of novel objects ab initio, without prior object
models or pre-training on a large object data set. The key element of Prompt is
a particle-based object representation, in which each particle represents a
point in the object, the local geometric, physical, and other features of the
point, and also its relation with other particles. Like the model-based
analytic approaches to manipulation, the particle representation enables the
robot to reason about the object's geometry and dynamics in order to choose
suitable manipulation actions. Like the data-driven approaches, the particle
representation is learned online in real-time from visual sensor input,
specifically, multi-view RGB images. The particle representation thus connects
visual perception with robot control. Prompt combines the benefits of both
model-based reasoning and data-driven learning. We show empirically that Prompt
successfully handles a variety of everyday objects, some of which are
transparent. It handles various manipulation tasks, including grasping,
pushing, etc,. Our experiments also show that Prompt outperforms a
state-of-the-art data-driven grasping method on the daily objects, even though
it does not use any offline training data.
|
[
{
"version": "v1",
"created": "Mon, 19 Jul 2021 13:27:00 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Chen",
"Siwei",
""
],
[
"Ma",
"Xiao",
""
],
[
"Lu",
"Yunfan",
""
],
[
"Hsu",
"David",
""
]
] |
new_dataset
| 0.994398 |
2108.09416
|
Amir Karami
|
Amir Karami, Spring B. Clark, Anderson Mackenzie, Dorathea Lee,
Michael Zhu, Hannah R. Boyajieff, Bailey Goldschmidt
|
2020 U.S. presidential election in swing states: Gender differences in
Twitter conversations
| null | null | null | null |
cs.SI cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Social media is commonly used by the public during election campaigns to
express their opinions regarding different issues. Among various social media
channels, Twitter provides an efficient platform for researchers and
politicians to explore public opinion regarding a wide range of topics such as
the economy and foreign policy. Current literature mainly focuses on analyzing
the content of tweets without considering the gender of users. This research
collects and analyzes a large number of tweets and uses computational, human
coding, and statistical analyses to identify topics in more than 300,000 tweets
posted during the 2020 U.S. presidential election and to compare female and
male users regarding the average weight of the discussed topics. Our findings
are based upon a wide range of topics, such as tax, climate change, and the
COVID-19 pandemic. Out of the topics, there exists a significant difference
between female and male users for more than 70% of topics.
|
[
{
"version": "v1",
"created": "Sat, 21 Aug 2021 01:31:03 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jul 2022 03:28:40 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Karami",
"Amir",
""
],
[
"Clark",
"Spring B.",
""
],
[
"Mackenzie",
"Anderson",
""
],
[
"Lee",
"Dorathea",
""
],
[
"Zhu",
"Michael",
""
],
[
"Boyajieff",
"Hannah R.",
""
],
[
"Goldschmidt",
"Bailey",
""
]
] |
new_dataset
| 0.999733 |
2112.03030
|
Yinyu Nie
|
Yinyu Nie, Angela Dai, Xiaoguang Han, Matthias Nie{\ss}ner
|
Pose2Room: Understanding 3D Scenes from Human Activities
|
Accepted by ECCV'2022; Project page:
https://yinyunie.github.io/pose2room-page/ Video:
https://www.youtube.com/watch?v=MFfKTcvbM5o
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With wearable IMU sensors, one can estimate human poses from wearable devices
without requiring visual input~\cite{von2017sparse}. In this work, we pose the
question: Can we reason about object structure in real-world environments
solely from human trajectory information? Crucially, we observe that human
motion and interactions tend to give strong information about the objects in a
scene -- for instance a person sitting indicates the likely presence of a chair
or sofa. To this end, we propose P2R-Net to learn a probabilistic 3D model of
the objects in a scene characterized by their class categories and oriented 3D
bounding boxes, based on an input observed human trajectory in the environment.
P2R-Net models the probability distribution of object class as well as a deep
Gaussian mixture model for object boxes, enabling sampling of multiple,
diverse, likely modes of object configurations from an observed human
trajectory. In our experiments we show that P2R-Net can effectively learn
multi-modal distributions of likely objects for human motions, and produce a
variety of plausible object structures of the environment, even without any
visual information. The results demonstrate that P2R-Net consistently
outperforms the baselines on the PROX dataset and the VirtualHome platform.
|
[
{
"version": "v1",
"created": "Wed, 1 Dec 2021 20:54:36 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jul 2022 16:20:50 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Nie",
"Yinyu",
""
],
[
"Dai",
"Angela",
""
],
[
"Han",
"Xiaoguang",
""
],
[
"Nießner",
"Matthias",
""
]
] |
new_dataset
| 0.989928 |
2201.02179
|
Ruslan Nikolaev
|
Ruslan Nikolaev, Binoy Ravindran
|
wCQ: A Fast Wait-Free Queue with Bounded Memory Usage
| null |
Proceedings of the 34th ACM Symposium on Parallelism in Algorithms
and Architectures (SPAA 2022)
|
10.1145/3490148.3538572
| null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
The concurrency literature presents a number of approaches for building
non-blocking, FIFO, multiple-producer and multiple-consumer (MPMC) queues.
However, only a fraction of them have high performance. In addition, many queue
designs, such as LCRQ, trade memory usage for better performance. The recently
proposed SCQ design achieves both memory efficiency as well as excellent
performance. Unfortunately, both LCRQ and SCQ are only lock-free. On the other
hand, existing wait-free queues are either not very performant or suffer from
potentially unbounded memory usage. Strictly described, the latter queues, such
as Yang & Mellor-Crummey's (YMC) queue, forfeit wait-freedom as they are
blocking when memory is exhausted.
We present a wait-free queue, called wCQ. wCQ is based on SCQ and uses its
own variation of fast-path-slow-path methodology to attain wait-freedom and
bound memory usage. Our experimental studies on x86 and PowerPC architectures
validate wCQ's great performance and memory efficiency. They also show that
wCQ's performance is often on par with the best known concurrent queue designs.
|
[
{
"version": "v1",
"created": "Thu, 6 Jan 2022 18:46:53 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jul 2022 17:58:51 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Nikolaev",
"Ruslan",
""
],
[
"Ravindran",
"Binoy",
""
]
] |
new_dataset
| 0.999283 |
2203.09509
|
Thomas Hartvigsen
|
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap,
Dipankar Ray, Ece Kamar
|
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and
Implicit Hate Speech Detection
|
Published as a long paper at ACL 2022. Code:
https://github.com/microsoft/TOXIGEN
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Toxic language detection systems often falsely flag text that contains
minority group mentions as toxic, as those groups are often the targets of
online hate. Such over-reliance on spurious correlations also causes systems to
struggle with detecting implicitly toxic language. To help mitigate these
issues, we create ToxiGen, a new large-scale and machine-generated dataset of
274k toxic and benign statements about 13 minority groups. We develop a
demonstration-based prompting framework and an adversarial
classifier-in-the-loop decoding method to generate subtly toxic and benign text
with a massive pretrained language model. Controlling machine generation in
this way allows ToxiGen to cover implicitly toxic text at a larger scale, and
about more demographic groups, than previous resources of human-written text.
We conduct a human evaluation on a challenging subset of ToxiGen and find that
annotators struggle to distinguish machine-generated text from human-written
language. We also find that 94.5% of toxic examples are labeled as hate speech
by human annotators. Using three publicly-available datasets, we show that
finetuning a toxicity classifier on our data improves its performance on
human-written data substantially. We also demonstrate that ToxiGen can be used
to fight machine-generated toxicity as finetuning improves the classifier
significantly on our evaluation subset. Our code and data can be found at
https://github.com/microsoft/ToxiGen.
|
[
{
"version": "v1",
"created": "Thu, 17 Mar 2022 17:57:56 GMT"
},
{
"version": "v2",
"created": "Tue, 3 May 2022 11:54:40 GMT"
},
{
"version": "v3",
"created": "Tue, 10 May 2022 10:50:46 GMT"
},
{
"version": "v4",
"created": "Thu, 14 Jul 2022 13:04:29 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Hartvigsen",
"Thomas",
""
],
[
"Gabriel",
"Saadia",
""
],
[
"Palangi",
"Hamid",
""
],
[
"Sap",
"Maarten",
""
],
[
"Ray",
"Dipankar",
""
],
[
"Kamar",
"Ece",
""
]
] |
new_dataset
| 0.999817 |
2203.12705
|
Sagar Parekh
|
Sagar Parekh, Soheil Habibian, and Dylan P. Losey
|
RILI: Robustly Influencing Latent Intent
| null | null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
When robots interact with human partners, often these partners change their
behavior in response to the robot. On the one hand this is challenging because
the robot must learn to coordinate with a dynamic partner. But on the other
hand -- if the robot understands these dynamics -- it can harness its own
behavior, influence the human, and guide the team towards effective
collaboration. Prior research enables robots to learn to influence other robots
or simulated agents. In this paper we extend these learning approaches to now
influence humans. What makes humans especially hard to influence is that -- not
only do humans react to the robot -- but the way a single user reacts to the
robot may change over time, and different humans will respond to the same robot
behavior in different ways. We therefore propose a robust approach that learns
to influence changing partner dynamics. Our method first trains with a set of
partners across repeated interactions, and learns to predict the current
partner's behavior based on the previous states, actions, and rewards. Next, we
rapidly adapt to new partners by sampling trajectories the robot learned with
the original partners, and then leveraging those existing behaviors to
influence the new partner dynamics. We compare our resulting algorithm to
state-of-the-art baselines across simulated environments and a user study where
the robot and participants collaborate to build towers. We find that our
approach outperforms the alternatives, even when the partner follows new or
unexpected dynamics. Videos of the user study are available here:
https://youtu.be/lYsWM8An18g
|
[
{
"version": "v1",
"created": "Wed, 23 Mar 2022 19:55:49 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jul 2022 15:44:40 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Parekh",
"Sagar",
""
],
[
"Habibian",
"Soheil",
""
],
[
"Losey",
"Dylan P.",
""
]
] |
new_dataset
| 0.95205 |
2205.04047
|
Animesh Basak Chowdhury
|
Mukta Debnath, Animesh Basak Chowdhury, Debasri Saha, Susmita
Sur-Kolay
|
GreyConE: Greybox fuzzing+Concolic execution guided test generation for
high level design
|
5 pages, 5 figures, 2 tables, 2 algorithms. Accepted in International
Test Conference (ITC 2022)
| null | null | null |
cs.SE cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Exhaustive testing of high-level designs pose an arduous challenge due to
complex branching conditions, loop structures and inherent concurrency of
hardware designs. Test engineers aim to generate quality test-cases satisfying
various code coverage metrics to ensure minimal presence of bugs in a design.
Prior works in testing SystemC designs are time inefficient which obstruct
achieving the desired coverage in shorter time-span. We interleave greybox
fuzzing and concolic execution in a systematic manner and generate quality
test-cases accelerating test coverage metrics. Our results outperform
state-of-the-art methods in terms of number of test cases and branch-coverage
for some of the benchmarks, and runtime for most of them.
|
[
{
"version": "v1",
"created": "Mon, 9 May 2022 05:34:09 GMT"
},
{
"version": "v2",
"created": "Tue, 10 May 2022 09:36:02 GMT"
},
{
"version": "v3",
"created": "Wed, 13 Jul 2022 23:28:01 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Debnath",
"Mukta",
""
],
[
"Chowdhury",
"Animesh Basak",
""
],
[
"Saha",
"Debasri",
""
],
[
"Sur-Kolay",
"Susmita",
""
]
] |
new_dataset
| 0.97802 |
2207.05675
|
Laszlo Kish
|
Laszlo B. Kish
|
Time synchronization protocol for the KLJN secure key exchange scheme
|
In press at Fluctuation and Noise Letters. Coming out in the October
2022 issue
| null | null | null |
cs.CR quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The information theoretically secure Kirchhoff-law-Johnson-noise (KLJN) key
exchange scheme, similarly to quantum key distribution (QKD), is also
potentially vulnerable against clock attacks, where Eve takes over the control
of clock synchronization in the channel. This short note aims to introduce a
time synchronization protocol scheme for Alice and Bob, which is resistant
against arbitrary time delay attacks, both symmetric and asymmetric ones. We
propose and explore various ways of clock synchronization for the KLJN system
and propose an ultimate protocol that preserves time and hardware integrity
under arbitrary attacks.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 00:33:07 GMT"
},
{
"version": "v2",
"created": "Thu, 14 Jul 2022 01:02:38 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Kish",
"Laszlo B.",
""
]
] |
new_dataset
| 0.997126 |
2207.06410
|
Arijit Nandi
|
Arijit Nandi, Fatos Xhafa, Laia Subirats, Santi Fort
|
MDEAW: A Multimodal Dataset for Emotion Analysis through EDA and PPG
signals from wireless wearable low-cost off-the-shelf Devices
| null | null | null | null |
cs.HC cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We present MDEAW, a multimodal database consisting of Electrodermal Activity
(EDA) and Photoplethysmography (PPG) signals recorded during the exams for the
course taught by the teacher at Eurecat Academy, Sabadell, Barcelona in order
to elicit the emotional reactions to the students in a classroom scenario.
Signals from 10 students were recorded along with the students' self-assessment
of their affective state after each stimulus, in terms of 6 basic emotion
states. All the signals were captured using portable, wearable, wireless,
low-cost, and off-the-shelf equipment that has the potential to allow the use
of affective computing methods in everyday applications. A baseline for
student-wise affect recognition using EDA and PPG-based features, as well as
their fusion, was established through ReMECS, Fed-ReMECS, and Fed-ReMECS-U.
These results indicate the prospects of using low-cost devices for affective
state recognition applications. The proposed database will be made publicly
available in order to allow researchers to achieve a more thorough evaluation
of the suitability of these capturing devices for emotion state recognition
applications.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 07:04:29 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Nandi",
"Arijit",
""
],
[
"Xhafa",
"Fatos",
""
],
[
"Subirats",
"Laia",
""
],
[
"Fort",
"Santi",
""
]
] |
new_dataset
| 0.999796 |
2207.06440
|
Jhony Heriberto Giraldo Zuluaga
|
Jhony H. Giraldo, Sajid Javed, Naoufel Werghi, Thierry Bouwmans
|
Graph CNN for Moving Object Detection in Complex Environments from
Unseen Videos
| null |
Proceedings of the IEEE/CVF International Conference on Computer
Vision (ICCV) Workshops, 2021, pp. 225-233
|
10.1109/ICCVW54120.2021.00030
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Moving Object Detection (MOD) is a fundamental step for many computer vision
applications. MOD becomes very challenging when a video sequence captured from
a static or moving camera suffers from the challenges: camouflage, shadow,
dynamic backgrounds, and lighting variations, to name a few. Deep learning
methods have been successfully applied to address MOD with competitive
performance. However, in order to handle the overfitting problem, deep learning
methods require a large amount of labeled data which is a laborious task as
exhaustive annotations are always not available. Moreover, some MOD deep
learning methods show performance degradation in the presence of unseen video
sequences because the testing and training splits of the same sequences are
involved during the network learning process. In this work, we pose the problem
of MOD as a node classification problem using Graph Convolutional Neural
Networks (GCNNs). Our algorithm, dubbed as GraphMOD-Net, encompasses instance
segmentation, background initialization, feature extraction, and graph
construction. GraphMOD-Net is tested on unseen videos and outperforms
state-of-the-art methods in unsupervised, semi-supervised, and supervised
learning in several challenges of the Change Detection 2014 (CDNet2014) and
UCSD background subtraction datasets.
|
[
{
"version": "v1",
"created": "Wed, 13 Jul 2022 18:00:12 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Giraldo",
"Jhony H.",
""
],
[
"Javed",
"Sajid",
""
],
[
"Werghi",
"Naoufel",
""
],
[
"Bouwmans",
"Thierry",
""
]
] |
new_dataset
| 0.977302 |
2207.06464
|
Philipe Melo
|
Clara Andrade Pimentel, Joana Ziller, Philipe Melo
|
De Quem e o Jogo? Disputas Narrativas no Fandom de World of Warcraft
|
in Portuguese language
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Digital games are increasingly part of a cyberculture engendered by digital
platforms. With this in mind, we approach in this work some considerations
about World of Warcraft players as fans and content producers and the narrative
disputes that emerge about the game on fan work publishing platforms (Archive
of Our Own and DeviantArt). We analyzed a vast set of fanfics and fanarts
collected on these platforms, showing a textuality that involves not only the
digital game, but a whole network of fan production that expands beyond the act
of playing. Our observations show that, despite the popular perception that
World of Warcraft fandom is mostly male and heteronormative, women and LGBTQI+
people are a large participatory audience and produce a lot of content,
especially in the fanfic universe. The works created are also quite marked by
narratives of dissident bodies and sexualities. However, despite the presence
of these subjects and narratives in the fandom, this content is made invisible
in DeviantArt, which privileges male artists and heteronormative fanarts of a
commercial nature.
|
[
{
"version": "v1",
"created": "Wed, 13 Jul 2022 18:29:58 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Pimentel",
"Clara Andrade",
""
],
[
"Ziller",
"Joana",
""
],
[
"Melo",
"Philipe",
""
]
] |
new_dataset
| 0.998758 |
2207.06495
|
Enrico Paolini
|
Enrico Paolini, Lorenzo Valentini, Velio Tralli, Marco Chiani
|
Irregular Repetition Slotted ALOHA in an Information-Theoretic Setting
|
6 pages, 2 figures
|
2022 IEEE International Symposium on Information Theory
| null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An information-theoretic approach to irregular repetition slotted ALOHA
(IRSA) is proposed. In contrast with previous works, in which IRSA analysis is
conducted only based on quantities that are typical of collision models such as
the traffic, the new approach also captures more fundamental quantities.
Specifically, a suitable codebook construction for the adder channel model is
adopted to establish a link with successive interference cancellation over the
multi-packet reception channel. This perspective allows proving achievability
and converse results for the average sum rate of IRSA multiple access schemes.
|
[
{
"version": "v1",
"created": "Wed, 13 Jul 2022 19:37:08 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Paolini",
"Enrico",
""
],
[
"Valentini",
"Lorenzo",
""
],
[
"Tralli",
"Velio",
""
],
[
"Chiani",
"Marco",
""
]
] |
new_dataset
| 0.994274 |
2207.06553
|
Xiaodong Yang
|
Tong Su, Xishun Wang, Xiaodong Yang
|
QML for Argoverse 2 Motion Forecasting Challenge
| null | null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To safely navigate in various complex traffic scenarios, autonomous driving
systems are generally equipped with a motion forecasting module to provide
vital information for the downstream planning module. For the real-world
onboard applications, both accuracy and latency of a motion forecasting model
are essential. In this report, we present an effective and efficient solution,
which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.
|
[
{
"version": "v1",
"created": "Wed, 13 Jul 2022 23:25:30 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Su",
"Tong",
""
],
[
"Wang",
"Xishun",
""
],
[
"Yang",
"Xiaodong",
""
]
] |
new_dataset
| 0.997683 |
2207.06626
|
Tae Bok Lee
|
Tae Bok Lee, Sujy Han, Yong Seok Heo
|
Continuous Facial Motion Deblurring
| null |
IEEE Access (Early Access), 12 July 2022
|
10.1109/ACCESS.2022.3190089
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We introduce a novel framework for continuous facial motion deblurring that
restores the continuous sharp moment latent in a single motion-blurred face
image via a moment control factor. Although a motion-blurred image is the
accumulated signal of continuous sharp moments during the exposure time, most
existing single image deblurring approaches aim to restore a fixed number of
frames using multiple networks and training stages. To address this problem, we
propose a continuous facial motion deblurring network based on GAN (CFMD-GAN),
which is a novel framework for restoring the continuous moment latent in a
single motion-blurred face image with a single network and a single training
stage. To stabilize the network training, we train the generator to restore
continuous moments in the order determined by our facial motion-based
reordering process (FMR) utilizing domain-specific knowledge of the face.
Moreover, we propose an auxiliary regressor that helps our generator produce
more accurate images by estimating continuous sharp moments. Furthermore, we
introduce a control-adaptive (ContAda) block that performs spatially deformable
convolution and channel-wise attention as a function of the control factor.
Extensive experiments on the 300VW datasets demonstrate that the proposed
framework generates a various number of continuous output frames by varying the
moment control factor. Compared with the recent single-to-single image
deblurring networks trained with the same 300VW training set, the proposed
method show the superior performance in restoring the central sharp frame in
terms of perceptual metrics, including LPIPS, FID and Arcface identity
distance. The proposed method outperforms the existing single-to-video
deblurring method for both qualitative and quantitative comparisons.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 02:53:37 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Lee",
"Tae Bok",
""
],
[
"Han",
"Sujy",
""
],
[
"Heo",
"Yong Seok",
""
]
] |
new_dataset
| 0.989904 |
2207.06673
|
Pappu Yadav
|
Pappu Kumar Yadav, J. Alex Thomasson, Robert Hardin, Stephen W.
Searcy, Ulisses Braga-Neto, Sorin C. Popescu, Daniel E. Martin, Roberto
Rodriguez, Karem Meza, Juan Enciso, Jorge Solorzano Diaz, Tianyi Wang
|
Detecting Volunteer Cotton Plants in a Corn Field with Deep Learning on
UAV Remote-Sensing Imagery
|
38 Pages
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the
U.S. cotton industry that has cost more than 16 billion USD in damages since it
entered the United States from Mexico in the late 1800s. This pest has been
nearly eradicated; however, southern part of Texas still faces this issue and
is always prone to the pest reinfestation each year due to its sub-tropical
climate where cotton plants can grow year-round. Volunteer cotton (VC) plants
growing in the fields of inter-seasonal crops, like corn, can serve as hosts to
these pests once they reach pin-head square stage (5-6 leaf stage) and
therefore need to be detected, located, and destroyed or sprayed . In this
paper, we present a study to detect VC plants in a corn field using YOLOv3 on
three band aerial images collected by unmanned aircraft system (UAS). The
two-fold objectives of this paper were : (i) to determine whether YOLOv3 can be
used for VC detection in a corn field using RGB (red, green, and blue) aerial
images collected by UAS and (ii) to investigate the behavior of YOLOv3 on
images at three different scales (320 x 320, S1; 416 x 416, S2; and 512 x 512,
S3 pixels) based on average precision (AP), mean average precision (mAP) and
F1-score at 95% confidence level. No significant differences existed for mAP
among the three scales, while a significant difference was found for AP between
S1 and S3 (p = 0.04) and S2 and S3 (p = 0.02). A significant difference was
also found for F1-score between S2 and S3 (p = 0.02). The lack of significant
differences of mAP at all the three scales indicated that the trained YOLOv3
model can be used on a computer vision-based remotely piloted aerial
application system (RPAAS) for VC detection and spray application in near
real-time.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 05:59:54 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Yadav",
"Pappu Kumar",
""
],
[
"Thomasson",
"J. Alex",
""
],
[
"Hardin",
"Robert",
""
],
[
"Searcy",
"Stephen W.",
""
],
[
"Braga-Neto",
"Ulisses",
""
],
[
"Popescu",
"Sorin C.",
""
],
[
"Martin",
"Daniel E.",
""
],
[
"Rodriguez",
"Roberto",
""
],
[
"Meza",
"Karem",
""
],
[
"Enciso",
"Juan",
""
],
[
"Diaz",
"Jorge Solorzano",
""
],
[
"Wang",
"Tianyi",
""
]
] |
new_dataset
| 0.996476 |
2207.06681
|
Mart\'in Ceresa
|
Mart\'an Ceresa and C\'esar S\'anchez
|
Multi: a Formal Playground for Multi-Smart Contract Interaction
| null | null | null | null |
cs.LO cs.PL cs.SC
|
http://creativecommons.org/licenses/by/4.0/
|
Blockchains are maintained by a network of participants that run algorithms
designed to maintain collectively a distributed machine tolerant to Byzantine
attacks. From the point of view of users, blockchains provide the illusion of
centralized computers that perform trustable verifiable computations, where all
computations are deterministic and the results cannot be manipulated or undone.
Smart-contracts are written in a special-purpose programming language with
deterministic semantics. Each transaction begins with an invocation from an
external user to a smart contract. Contracts have local storage and can call
other contracts, and more importantly, they store, send and receive
cryptocurrency. It is very important to guarantee that contracts are correct
before deployment since their code cannot be modified afterward deployment.
However, the resulting ecosystem makes it very difficult to reason about
program correctness, since contracts can be executed by malicious users or
malicious contracts can be designed to exploit other contracts that call them.
Many attacks and bugs are caused by unexpected interactions between multiple
contracts, the attacked contract and unknown code that performs the exploit.
Moreover, there is a very aggressive competition between different blockchains
to expand their user base. Ideas are implemented fast and blockchains compete
to offer and adopt new features quickly. In this paper, we propose a formal
extensible playground that allows reasoning about multi-contract interactions
to ultimately prove properties before features are incorporated into the real
blockchain. We implemented a model of computation that models the execution
platform, abstracts the internal code of each individual contract and focuses
on contract interactions. Moreover, we show how many features, existing or
proposed, can be used to reason about multi-contract interactions.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 06:19:39 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Ceresa",
"Martán",
""
],
[
"Sánchez",
"César",
""
]
] |
new_dataset
| 0.999086 |
2207.06695
|
Zhanzhan Cheng
|
Liang Qiao, Hui Jiang, Ying Chen, Can Li, Pengfei Li, Zaisheng Li,
Baorui Zou, Dashan Guo, Yingda Xu, Yunlu Xu, Zhanzhan Cheng and Yi Niu
|
DavarOCR: A Toolbox for OCR and Multi-Modal Document Understanding
|
Short paper, Accept by ACM MM2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This paper presents DavarOCR, an open-source toolbox for OCR and document
understanding tasks. DavarOCR currently implements 19 advanced algorithms,
covering 9 different task forms. DavarOCR provides detailed usage instructions
and the trained models for each algorithm. Compared with the previous
opensource OCR toolbox, DavarOCR has relatively more complete support for the
sub-tasks of the cutting-edge technology of document understanding. In order to
promote the development and application of OCR technology in academia and
industry, we pay more attention to the use of modules that different
sub-domains of technology can share. DavarOCR is publicly released at
https://github.com/hikopensource/Davar-Lab-OCR.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 06:54:47 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Qiao",
"Liang",
""
],
[
"Jiang",
"Hui",
""
],
[
"Chen",
"Ying",
""
],
[
"Li",
"Can",
""
],
[
"Li",
"Pengfei",
""
],
[
"Li",
"Zaisheng",
""
],
[
"Zou",
"Baorui",
""
],
[
"Guo",
"Dashan",
""
],
[
"Xu",
"Yingda",
""
],
[
"Xu",
"Yunlu",
""
],
[
"Cheng",
"Zhanzhan",
""
],
[
"Niu",
"Yi",
""
]
] |
new_dataset
| 0.967315 |
2207.06717
|
Bowen Yu
|
Zhenyu Zhang, Bowen Yu, Haiyang Yu, Tingwen Liu, Cheng Fu, Jingyang
Li, Chengguang Tang, Jian Sun, Yongbin Li
|
Layout-Aware Information Extraction for Document-Grounded Dialogue:
Dataset, Method and Demonstration
|
Accepted to ACM Multimedia (MM) Industry Track 2022
| null | null | null |
cs.CL cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Building document-grounded dialogue systems have received growing interest as
documents convey a wealth of human knowledge and commonly exist in enterprises.
Wherein, how to comprehend and retrieve information from documents is a
challenging research problem. Previous work ignores the visual property of
documents and treats them as plain text, resulting in incomplete modality. In
this paper, we propose a Layout-aware document-level Information Extraction
dataset, LIE, to facilitate the study of extracting both structural and
semantic knowledge from visually rich documents (VRDs), so as to generate
accurate responses in dialogue systems. LIE contains 62k annotations of three
extraction tasks from 4,061 pages in product and official documents, becoming
the largest VRD-based information extraction dataset to the best of our
knowledge. We also develop benchmark methods that extend the token-based
language model to consider layout features like humans. Empirical results show
that layout is critical for VRD-based extraction, and system demonstration also
verifies that the extracted knowledge can help locate the answers that users
care about.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 07:59:45 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Zhang",
"Zhenyu",
""
],
[
"Yu",
"Bowen",
""
],
[
"Yu",
"Haiyang",
""
],
[
"Liu",
"Tingwen",
""
],
[
"Fu",
"Cheng",
""
],
[
"Li",
"Jingyang",
""
],
[
"Tang",
"Chengguang",
""
],
[
"Sun",
"Jian",
""
],
[
"Li",
"Yongbin",
""
]
] |
new_dataset
| 0.992271 |
2207.06828
|
Haozheng Zhang
|
Haozheng Zhang, Edmond S.L. Ho, Xiatian Zhang and Hubert P.H. Shum
|
Pose-based Tremor Classification for Parkinson's Disease Diagnosis from
Video
|
MICCAI 2022
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Parkinson's disease (PD) is a progressive neurodegenerative disorder that
results in a variety of motor dysfunction symptoms, including tremors,
bradykinesia, rigidity and postural instability. The diagnosis of PD mainly
relies on clinical experience rather than a definite medical test, and the
diagnostic accuracy is only about 73-84% since it is challenged by the
subjective opinions or experiences of different medical experts. Therefore, an
efficient and interpretable automatic PD diagnosis system is valuable for
supporting clinicians with more robust diagnostic decision-making. To this end,
we propose to classify Parkinson's tremor since it is one of the most
predominant symptoms of PD with strong generalizability. Different from other
computer-aided time and resource-consuming Parkinson's Tremor (PT)
classification systems that rely on wearable sensors, we propose SPAPNet, which
only requires consumer-grade non-intrusive video recording of camera-facing
human movements as input to provide undiagnosed patients with low-cost PT
classification results as a PD warning sign. For the first time, we propose to
use a novel attention module with a lightweight pyramidal
channel-squeezing-fusion architecture to extract relevant PT information and
filter the noise efficiently. This design aids in improving both classification
performance and system interpretability. Experimental results show that our
system outperforms state-of-the-arts by achieving a balanced accuracy of 90.9%
and an F1-score of 90.6% in classifying PT with the non-PT class.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 11:32:42 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Zhang",
"Haozheng",
""
],
[
"Ho",
"Edmond S. L.",
""
],
[
"Zhang",
"Xiatian",
""
],
[
"Shum",
"Hubert P. H.",
""
]
] |
new_dataset
| 0.997322 |
2207.06985
|
Mohsen Zand
|
Mohsen Zand, Ali Etemad, Michael Greenspan
|
ObjectBox: From Centers to Boxes for Anchor-Free Object Detection
|
ECCV 2022 Oral
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present ObjectBox, a novel single-stage anchor-free and highly
generalizable object detection approach. As opposed to both existing
anchor-based and anchor-free detectors, which are more biased toward specific
object scales in their label assignments, we use only object center locations
as positive samples and treat all objects equally in different feature levels
regardless of the objects' sizes or shapes. Specifically, our label assignment
strategy considers the object center locations as shape- and size-agnostic
anchors in an anchor-free fashion, and allows learning to occur at all scales
for every object. To support this, we define new regression targets as the
distances from two corners of the center cell location to the four sides of the
bounding box. Moreover, to handle scale-variant objects, we propose a tailored
IoU loss to deal with boxes with different sizes. As a result, our proposed
object detector does not need any dataset-dependent hyperparameters to be tuned
across datasets. We evaluate our method on MS-COCO 2017 and PASCAL VOC 2012
datasets, and compare our results to state-of-the-art methods. We observe that
ObjectBox performs favorably in comparison to prior works. Furthermore, we
perform rigorous ablation experiments to evaluate different components of our
method. Our code is available at: https://github.com/MohsenZand/ObjectBox.
|
[
{
"version": "v1",
"created": "Thu, 14 Jul 2022 15:10:29 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Zand",
"Mohsen",
""
],
[
"Etemad",
"Ali",
""
],
[
"Greenspan",
"Michael",
""
]
] |
new_dataset
| 0.999703 |
2207.07098
|
Martin Karp
|
Martin Karp, Daniele Massaro, Niclas Jansson, Alistair Hart, Jacob
Wahlgren, Philipp Schlatter, and Stefano Markidis
|
Large-Scale Direct Numerical Simulations of Turbulence Using GPUs and
Modern Fortran
|
13 pages, 7 figures
| null | null | null |
cs.MS cs.CE cs.DC physics.flu-dyn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present our approach to making direct numerical simulations of turbulence
with applications in sustainable shipping. We use modern Fortran and the
spectral element method to leverage and scale on supercomputers powered by the
Nvidia A100 and the recent AMD Instinct MI250X GPUs, while still providing
support for user software developed in Fortran. We demonstrate the efficiency
of our approach by performing the world's first direct numerical simulation of
the flow around a Flettner rotor at Re=30'000 and its interaction with a
turbulent boundary layer. We present one of the first performance comparisons
between the AMD Instinct MI250X and Nvidia A100 GPUs for scalable computational
fluid dynamics. Our results show that one MI250X offers performance on par with
two A100 GPUs and has a similar power efficiency.
|
[
{
"version": "v1",
"created": "Thu, 23 Jun 2022 12:41:19 GMT"
}
] | 2022-07-15T00:00:00 |
[
[
"Karp",
"Martin",
""
],
[
"Massaro",
"Daniele",
""
],
[
"Jansson",
"Niclas",
""
],
[
"Hart",
"Alistair",
""
],
[
"Wahlgren",
"Jacob",
""
],
[
"Schlatter",
"Philipp",
""
],
[
"Markidis",
"Stefano",
""
]
] |
new_dataset
| 0.999452 |
2103.09704
|
Jiaye Li
|
Shichao Zhang, Jiaye Li and Yangding Li
|
Reachable Distance Function for KNN Classification
| null |
IEEE Transactions on Knowledge and Data Engineering, 2022
|
10.1109/TKDE.2022.3185149.
| null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Distance function is a main metrics of measuring the affinity between two
data points in machine learning. Extant distance functions often provide
unreachable distance values in real applications. This can lead to incorrect
measure of the affinity between data points. This paper proposes a reachable
distance function for KNN classification. The reachable distance function is
not a geometric direct-line distance between two data points. It gives a
consideration to the class attribute of a training dataset when measuring the
affinity between data points. Concretely speaking, the reachable distance
between data points includes their class center distance and real distance. Its
shape looks like "Z", and we also call it a Z distance function. In this way,
the affinity between data points in the same class is always stronger than that
in different classes. Or, the intraclass data points are always closer than
those interclass data points. We evaluated the reachable distance with
experiments, and demonstrated that the proposed distance function achieved
better performance in KNN classification.
|
[
{
"version": "v1",
"created": "Wed, 17 Mar 2021 15:01:17 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Jun 2022 06:02:07 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Zhang",
"Shichao",
""
],
[
"Li",
"Jiaye",
""
],
[
"Li",
"Yangding",
""
]
] |
new_dataset
| 0.95695 |
2104.02527
|
Yangzheng Wu
|
Yangzheng Wu, Mohsen Zand, Ali Etemad, Michael Greenspan
|
Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial
Keypoint Voting
|
ECCV 2022 Oral
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a novel keypoint voting scheme based on intersecting spheres, that
is more accurate than existing schemes and allows for fewer, more disperse
keypoints. The scheme is based upon the distance between points, which as a 1D
quantity can be regressed more accurately than the 2D and 3D vector and offset
quantities regressed in previous work, yielding more accurate keypoint
localization. The scheme forms the basis of the proposed RCVPose method for 6
DoF pose estimation of 3D objects in RGB-D data, which is particularly
effective at handling occlusions. A CNN is trained to estimate the distance
between the 3D point corresponding to the depth mode of each RGB pixel, and a
set of 3 disperse keypoints defined in the object frame. At inference, a sphere
centered at each 3D point is generated, of radius equal to this estimated
distance. The surfaces of these spheres vote to increment a 3D accumulator
space, the peaks of which indicate keypoint locations. The proposed radial
voting scheme is more accurate than previous vector or offset schemes, and is
robust to disperse keypoints. Experiments demonstrate RCVPose to be highly
accurate and competitive, achieving state-of-the-art results on the LINEMOD
99.7% and YCB-Video 97.2% datasets, notably scoring +4.9% higher 71.1% than
previous methods on the challenging Occlusion LINEMOD dataset, and on average
outperforming all other published results from the BOP benchmark for these 3
datasets. Our code is available at http://www.github.com/aaronwool/rcvpose.
|
[
{
"version": "v1",
"created": "Tue, 6 Apr 2021 14:06:08 GMT"
},
{
"version": "v2",
"created": "Wed, 7 Apr 2021 21:29:19 GMT"
},
{
"version": "v3",
"created": "Tue, 30 Nov 2021 14:03:54 GMT"
},
{
"version": "v4",
"created": "Tue, 12 Jul 2022 23:50:22 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Wu",
"Yangzheng",
""
],
[
"Zand",
"Mohsen",
""
],
[
"Etemad",
"Ali",
""
],
[
"Greenspan",
"Michael",
""
]
] |
new_dataset
| 0.993011 |
2104.13666
|
Abbas Cheddad
|
Mengqiao Zhao, Andre G. Hochuli, Abbas Cheddad
|
End-to-End Approach for Recognition of Historical Digit Strings
|
Cite as: Mengqiao Zhao, Andre G. Hochuli and Abbas Cheddad,
End-to-End Approach for Recognition of Historical Digit Strings, to appear in
the 16th International Conference on Document Analysis and Recognition (ICDAR
2021), LNCS, Springer, Lausanne, Switzerland
| null |
10.1007/978-3-030-86334-0_39
| null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
The plethora of digitalised historical document datasets released in recent
years has rekindled interest in advancing the field of handwriting pattern
recognition. In the same vein, a recently published data set, known as ARDIS,
presents handwritten digits manually cropped from 15.000 scanned documents of
Swedish church books and exhibiting various handwriting styles. To this end, we
propose an end-to-end segmentation-free deep learning approach to handle this
challenging ancient handwriting style of dates present in the ARDIS dataset
(4-digits long strings). We show that with slight modifications in the VGG-16
deep model, the framework can achieve a recognition rate of 93.2%, resulting in
a feasible solution free of heuristic methods, segmentation, and fusion
methods. Moreover, the proposed approach outperforms the well-known CRNN method
(a model widely applied in handwriting recognition tasks).
|
[
{
"version": "v1",
"created": "Wed, 28 Apr 2021 09:39:29 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Zhao",
"Mengqiao",
""
],
[
"Hochuli",
"Andre G.",
""
],
[
"Cheddad",
"Abbas",
""
]
] |
new_dataset
| 0.998472 |
2111.04204
|
Felix Lau
|
Felix Lau, Nishant Subramani, Sasha Harrison, Aerin Kim, Elliot
Branson and Rosanne Liu
|
Natural Adversarial Objects
| null |
Advances in Neural Information Processing Systems Data Centric AI
workshop 2021
| null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Although state-of-the-art object detection methods have shown compelling
performance, models often are not robust to adversarial attacks and
out-of-distribution data. We introduce a new dataset, Natural Adversarial
Objects (NAO), to evaluate the robustness of object detection models. NAO
contains 7,934 images and 9,943 objects that are unmodified and representative
of real-world scenarios, but cause state-of-the-art detection models to
misclassify with high confidence. The mean average precision (mAP) of
EfficientDet-D7 drops 74.5% when evaluated on NAO compared to the standard
MSCOCO validation set.
Moreover, by comparing a variety of object detection architectures, we find
that better performance on MSCOCO validation set does not necessarily translate
to better performance on NAO, suggesting that robustness cannot be simply
achieved by training a more accurate model.
We further investigate why examples in NAO are difficult to detect and
classify. Experiments of shuffling image patches reveal that models are overly
sensitive to local texture. Additionally, using integrated gradients and
background replacement, we find that the detection model is reliant on pixel
information within the bounding box, and insensitive to the background context
when predicting class labels. NAO can be downloaded at
https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8.
|
[
{
"version": "v1",
"created": "Sun, 7 Nov 2021 23:42:55 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Lau",
"Felix",
""
],
[
"Subramani",
"Nishant",
""
],
[
"Harrison",
"Sasha",
""
],
[
"Kim",
"Aerin",
""
],
[
"Branson",
"Elliot",
""
],
[
"Liu",
"Rosanne",
""
]
] |
new_dataset
| 0.999714 |
2111.09046
|
Jiawei Hu
|
Jiawei Hu, Wenhang Liu, Heng Zhang, Jingang Yi, Zhenhua Xiong
|
Multi-Robot Object Transport Motion Planning with a Deformable Sheet
|
8 pages, 10 figures, accepted by RAL&CASE 2022 in June 24, 2022
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Using a deformable sheet to handle objects is convenient and found in many
practical applications. For object manipulation through a deformable sheet that
is held by multiple mobile robots, it is a challenging task to model the
object-sheet interactions. We present a computational model and algorithm to
capture the object position on the deformable sheet with changing robotic team
formations. A virtual variable cables model (VVCM) is proposed to simplify the
modeling of the robot-sheet-object system. With the VVCM, we further present a
motion planner for the robotic team to transport the object in a
three-dimensional (3D) cluttered environment. Simulation and experimental
results with different robot team sizes show the effectiveness and versatility
of the proposed VVCM. We also compare and demonstrate the planning results to
avoid the obstacle in 3D space with the other benchmark planner.
|
[
{
"version": "v1",
"created": "Wed, 17 Nov 2021 11:42:16 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Mar 2022 09:09:25 GMT"
},
{
"version": "v3",
"created": "Thu, 26 May 2022 13:37:17 GMT"
},
{
"version": "v4",
"created": "Wed, 13 Jul 2022 01:44:13 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Hu",
"Jiawei",
""
],
[
"Liu",
"Wenhang",
""
],
[
"Zhang",
"Heng",
""
],
[
"Yi",
"Jingang",
""
],
[
"Xiong",
"Zhenhua",
""
]
] |
new_dataset
| 0.995703 |
2112.03227
|
Oier Mees
|
Oier Mees, Lukas Hermann, Erick Rosete-Beas, Wolfram Burgard
|
CALVIN: A Benchmark for Language-Conditioned Policy Learning for
Long-Horizon Robot Manipulation Tasks
|
Accepted for publication at IEEE Robotics and Automation Letters
(RAL). Code, models and dataset available at http://calvin.cs.uni-freiburg.de
| null | null | null |
cs.RO cs.AI cs.CL cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
General-purpose robots coexisting with humans in their environment must learn
to relate human language to their perceptions and actions to be useful in a
range of daily tasks. Moreover, they need to acquire a diverse repertoire of
general-purpose skills that allow composing long-horizon tasks by following
unconstrained language instructions. In this paper, we present CALVIN
(Composing Actions from Language and Vision), an open-source simulated
benchmark to learn long-horizon language-conditioned tasks. Our aim is to make
it possible to develop agents that can solve many robotic manipulation tasks
over a long horizon, from onboard sensors, and specified only via human
language. CALVIN tasks are more complex in terms of sequence length, action
space, and language than existing vision-and-language task datasets and
supports flexible specification of sensor suites. We evaluate the agents in
zero-shot to novel language instructions and to novel environments and objects.
We show that a baseline model based on multi-context imitation learning
performs poorly on CALVIN, suggesting that there is significant room for
developing innovative agents that learn to relate human language to their world
models with this benchmark.
|
[
{
"version": "v1",
"created": "Mon, 6 Dec 2021 18:37:33 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Dec 2021 10:04:13 GMT"
},
{
"version": "v3",
"created": "Thu, 23 Jun 2022 11:43:49 GMT"
},
{
"version": "v4",
"created": "Wed, 13 Jul 2022 12:15:04 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Mees",
"Oier",
""
],
[
"Hermann",
"Lukas",
""
],
[
"Rosete-Beas",
"Erick",
""
],
[
"Burgard",
"Wolfram",
""
]
] |
new_dataset
| 0.999537 |
2112.08634
|
Robert Logan Iv
|
Robert L. Logan IV, Alexandre Passos, Sameer Singh and Ming-Wei Chang
|
FRUIT: Faithfully Reflecting Updated Information in Text
|
v2.0, NAACL 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Textual knowledge bases such as Wikipedia require considerable effort to keep
up to date and consistent. While automated writing assistants could potentially
ease this burden, the problem of suggesting edits grounded in external
knowledge has been under-explored. In this paper, we introduce the novel
generation task of *faithfully reflecting updated information in text* (FRUIT)
where the goal is to update an existing article given new evidence. We release
the FRUIT-WIKI dataset, a collection of over 170K distantly supervised data
produced from pairs of Wikipedia snapshots, along with our data generation
pipeline and a gold evaluation set of 914 instances whose edits are guaranteed
to be supported by the evidence. We provide benchmark results for popular
generation systems as well as EDIT5 -- a T5-based approach tailored to editing
we introduce that establishes the state of the art. Our analysis shows that
developing models that can update articles faithfully requires new capabilities
for neural generation models, and opens doors to many new applications.
|
[
{
"version": "v1",
"created": "Thu, 16 Dec 2021 05:21:24 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Jul 2022 15:01:10 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Logan",
"Robert L.",
"IV"
],
[
"Passos",
"Alexandre",
""
],
[
"Singh",
"Sameer",
""
],
[
"Chang",
"Ming-Wei",
""
]
] |
new_dataset
| 0.962398 |
2112.08910
|
Prasanna Parasurama
|
Prasanna Parasurama, Jo\~ao Sedoc
|
Degendering Resumes for Fair Algorithmic Resume Screening
|
None
| null | null | null |
cs.CL cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
We investigate whether it is feasible to remove gendered information from
resumes to mitigate potential bias in algorithmic resume screening. Using a
corpus of 709k resumes from IT firms, we first train a series of models to
classify the self-reported gender of the applicant, thereby measuring the
extent and nature of gendered information encoded in resumes. We then conduct a
series of gender obfuscation experiments, where we iteratively remove gendered
information from resumes. Finally, we train a resume screening algorithm and
investigate the trade-off between gender obfuscation and screening algorithm
performance. Results show: (1) There is a significant amount of gendered
information in resumes. (2) Lexicon-based gender obfuscation method (i.e.
removing tokens that are predictive of gender) can reduce the amount of
gendered information to a large extent. However, after a certain point, the
performance of the resume screening algorithm starts suffering. (3)
General-purpose gender debiasing methods for NLP models such as removing gender
subspace from embeddings are not effective in obfuscating gender.
|
[
{
"version": "v1",
"created": "Thu, 16 Dec 2021 14:26:36 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 19:52:35 GMT"
},
{
"version": "v3",
"created": "Tue, 12 Jul 2022 23:52:47 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Parasurama",
"Prasanna",
""
],
[
"Sedoc",
"João",
""
]
] |
new_dataset
| 0.982518 |
2201.06499
|
Vladimir Kokh
|
Pavel Blinov, Arina Reshetnikova, Aleksandr Nesterov, Galina Zubkova,
Vladimir Kokh
|
RuMedBench: A Russian Medical Language Understanding Benchmark
|
11 pages, code available at this https URL; Published in the
proceedings of 20th International Conference on Artificial Intelligence in
Medicine, Halifax, Canada; code available at
https://github.com/pavel-blinov/RuMedBench
| null |
10.1007/978-3-031-09342-5_38
| null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The paper describes the open Russian medical language understanding benchmark
covering several task types (classification, question answering, natural
language inference, named entity recognition) on a number of novel text sets.
Given the sensitive nature of the data in healthcare, such a benchmark
partially closes the problem of Russian medical dataset absence. We prepare the
unified format labeling, data split, and evaluation metrics for new tasks. The
remaining tasks are from existing datasets with a few modifications. A
single-number metric expresses a model's ability to cope with the benchmark.
Moreover, we implement several baseline models, from simple ones to neural
networks with transformer architecture, and release the code. Expectedly, the
more advanced models yield better performance, but even a simple model is
enough for a decent result in some tasks. Furthermore, for all tasks, we
provide a human evaluation. Interestingly the models outperform humans in the
large-scale classification tasks. However, the advantage of natural
intelligence remains in the tasks requiring more knowledge and reasoning.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 16:23:33 GMT"
},
{
"version": "v2",
"created": "Tue, 24 May 2022 12:39:23 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Blinov",
"Pavel",
""
],
[
"Reshetnikova",
"Arina",
""
],
[
"Nesterov",
"Aleksandr",
""
],
[
"Zubkova",
"Galina",
""
],
[
"Kokh",
"Vladimir",
""
]
] |
new_dataset
| 0.999696 |
2204.08532
|
Marcella Cornia
|
Davide Morelli, Matteo Fincato, Marcella Cornia, Federico Landi, Fabio
Cesari, Rita Cucchiara
|
Dress Code: High-Resolution Multi-Category Virtual Try-On
|
ECCV 2022 - Video Demo: https://www.youtube.com/watch?v=qr6TW3uTHG4
| null | null | null |
cs.CV cs.AI cs.GR cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image-based virtual try-on strives to transfer the appearance of a clothing
item onto the image of a target person. Prior work focuses mainly on upper-body
clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body
items. This shortcoming arises from a main factor: current publicly available
datasets for image-based virtual try-on do not account for this variety, thus
limiting progress in the field. To address this deficiency, we introduce Dress
Code, which contains images of multi-category clothes. Dress Code is more than
3x larger than publicly available datasets for image-based virtual try-on and
features high-resolution paired images (1024x768) with front-view, full-body
reference models. To generate HD try-on images with high visual quality and
rich in details, we propose to learn fine-grained discriminating features.
Specifically, we leverage a semantic-aware discriminator that makes predictions
at pixel-level instead of image- or patch-level. Extensive experimental
evaluation demonstrates that the proposed approach surpasses the baselines and
state-of-the-art competitors in terms of visual quality and quantitative
results. The Dress Code dataset is publicly available at
https://github.com/aimagelab/dress-code.
|
[
{
"version": "v1",
"created": "Mon, 18 Apr 2022 19:31:49 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Jul 2022 12:47:00 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Morelli",
"Davide",
""
],
[
"Fincato",
"Matteo",
""
],
[
"Cornia",
"Marcella",
""
],
[
"Landi",
"Federico",
""
],
[
"Cesari",
"Fabio",
""
],
[
"Cucchiara",
"Rita",
""
]
] |
new_dataset
| 0.999803 |
2204.13879
|
Ben Burgess-Limerick
|
Ben Burgess-Limerick, Chris Lehnert, Jurgen Leitner, Peter Corke
|
DGBench: An Open-Source, Reproducible Benchmark for Dynamic Grasping
|
Dynamic Grasping Benchmark available:
https://github.com/BenBurgessLimerick/DGBench
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces DGBench, a fully reproducible open-source testing
system to enable benchmarking of dynamic grasping in environments with
unpredictable relative motion between robot and object. We use the proposed
benchmark to compare several visual perception arrangements. Traditional
perception systems developed for static grasping are unable to provide feedback
during the final phase of a grasp due to sensor minimum range, occlusion, and a
limited field of view. A multi-camera eye-in-hand perception system is
presented that has advantages over commonly used camera configurations. We
quantitatively evaluate the performance on a real robot with an image-based
visual servoing grasp controller and show a significantly improved success rate
on a dynamic grasping task.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 04:37:18 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Jul 2022 05:21:56 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Burgess-Limerick",
"Ben",
""
],
[
"Lehnert",
"Chris",
""
],
[
"Leitner",
"Jurgen",
""
],
[
"Corke",
"Peter",
""
]
] |
new_dataset
| 0.98464 |
2206.04460
|
Julian Tritscher
|
Julian Tritscher, Fabian Gwinner, Daniel Schl\"or, Anna Krause,
Andreas Hotho
|
Open ERP System Data For Occupational Fraud Detection
| null | null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent estimates report that companies lose 5% of their revenue to
occupational fraud. Since most medium-sized and large companies employ
Enterprise Resource Planning (ERP) systems to track vast amounts of information
regarding their business process, researchers have in the past shown interest
in automatically detecting fraud through ERP system data. Current research in
this area, however, is hindered by the fact that ERP system data is not
publicly available for the development and comparison of fraud detection
methods. We therefore endeavour to generate public ERP system data that
includes both normal business operation and fraud. We propose a strategy for
generating ERP system data through a serious game, model a variety of fraud
scenarios in cooperation with auditing experts, and generate data from a
simulated make-to-stock production company with multiple research participants.
We aggregate the generated data into ready to used datasets for fraud detection
in ERP systems, and supply both the raw and aggregated data to the general
public to allow for open development and comparison of fraud detection
approaches on ERP system data.
|
[
{
"version": "v1",
"created": "Thu, 9 Jun 2022 12:38:29 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Jun 2022 13:04:56 GMT"
},
{
"version": "v3",
"created": "Wed, 13 Jul 2022 07:51:02 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Tritscher",
"Julian",
""
],
[
"Gwinner",
"Fabian",
""
],
[
"Schlör",
"Daniel",
""
],
[
"Krause",
"Anna",
""
],
[
"Hotho",
"Andreas",
""
]
] |
new_dataset
| 0.986711 |
2206.05728
|
Linh K\"astner
|
Linh K\"astner, Teham Bhuiyan, Tuan Anh Le, Elias Treis, Johannes Cox,
Boris Meinardus, Jacek Kmiecik, Reyk Carstens, Duc Pichel, Bassel Fatloun,
Niloufar Khorsandi, Jens Lambrecht
|
Arena-Bench: A Benchmarking Suite for Obstacle Avoidance Approaches in
Highly Dynamic Environments
|
Robotics and Automation Letters (RA-L), 2022, 8 pages, 6 figures
| null |
10.1109/LRA.2022.3190086
| null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
The ability to autonomously navigate safely, especially within dynamic
environments, is paramount for mobile robotics. In recent years, DRL approaches
have shown superior performance in dynamic obstacle avoidance. However, these
learning-based approaches are often developed in specially designed simulation
environments and are hard to test against conventional planning approaches.
Furthermore, the integration and deployment of these approaches into real
robotic platforms are not yet completely solved. In this paper, we present
Arena-bench, a benchmark suite to train, test, and evaluate navigation planners
on different robotic platforms within 3D environments. It provides tools to
design and generate highly dynamic evaluation worlds, scenarios, and tasks for
autonomous navigation and is fully integrated into the robot operating system.
To demonstrate the functionalities of our suite, we trained a DRL agent on our
platform and compared it against a variety of existing different model-based
and learning-based navigation approaches on a variety of relevant metrics.
Finally, we deployed the approaches towards real robots and demonstrated the
reproducibility of the results. The code is publicly available at
github.com/ignc-research/arena-bench.
|
[
{
"version": "v1",
"created": "Sun, 12 Jun 2022 13:00:00 GMT"
},
{
"version": "v2",
"created": "Mon, 11 Jul 2022 10:19:00 GMT"
}
] | 2022-07-14T00:00:00 |
[
[
"Kästner",
"Linh",
""
],
[
"Bhuiyan",
"Teham",
""
],
[
"Le",
"Tuan Anh",
""
],
[
"Treis",
"Elias",
""
],
[
"Cox",
"Johannes",
""
],
[
"Meinardus",
"Boris",
""
],
[
"Kmiecik",
"Jacek",
""
],
[
"Carstens",
"Reyk",
""
],
[
"Pichel",
"Duc",
""
],
[
"Fatloun",
"Bassel",
""
],
[
"Khorsandi",
"Niloufar",
""
],
[
"Lambrecht",
"Jens",
""
]
] |
new_dataset
| 0.999375 |
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