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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2210.00008
|
Yash Jakhotiya
|
Yash Jakhotiya, Heramb Patil, Jugal Rawlani, Dr. Sunil B. Mane
|
Adversarial Attacks on Transformers-Based Malware Detectors
|
Accepted to the 2022 NeurIPS ML Safety Workshop. Code available at
https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers
| null | null | null |
cs.CR cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Signature-based malware detectors have proven to be insufficient as even a
small change in malignant executable code can bypass these signature-based
detectors. Many machine learning-based models have been proposed to efficiently
detect a wide variety of malware. Many of these models are found to be
susceptible to adversarial attacks - attacks that work by generating
intentionally designed inputs that can force these models to misclassify. Our
work aims to explore vulnerabilities in the current state of the art malware
detectors to adversarial attacks. We train a Transformers-based malware
detector, carry out adversarial attacks resulting in a misclassification rate
of 23.9% and propose defenses that reduce this misclassification rate to half.
An implementation of our work can be found at
https://github.com/yashjakhotiya/Adversarial-Attacks-On-Transformers.
|
[
{
"version": "v1",
"created": "Sat, 1 Oct 2022 22:23:03 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Nov 2022 17:27:59 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Jakhotiya",
"Yash",
""
],
[
"Patil",
"Heramb",
""
],
[
"Rawlani",
"Jugal",
""
],
[
"Mane",
"Dr. Sunil B.",
""
]
] |
new_dataset
| 0.966931 |
2210.04573
|
Navid Rekabsaz
|
Selim Fekih, Nicol\`o Tamagnone, Benjamin Minixhofer, Ranjan Shrestha,
Ximena Contla, Ewan Oglethorpe, Navid Rekabsaz
|
HumSet: Dataset of Multilingual Information Extraction and
Classification for Humanitarian Crisis Response
|
Published at Findings of EMNLP 2022
| null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Timely and effective response to humanitarian crises requires quick and
accurate analysis of large amounts of text data - a process that can highly
benefit from expert-assisted NLP systems trained on validated and annotated
data in the humanitarian response domain. To enable creation of such NLP
systems, we introduce and release HumSet, a novel and rich multilingual dataset
of humanitarian response documents annotated by experts in the humanitarian
response community. The dataset provides documents in three languages (English,
French, Spanish) and covers a variety of humanitarian crises from 2018 to 2021
across the globe. For each document, HUMSET provides selected snippets
(entries) as well as assigned classes to each entry annotated using common
humanitarian information analysis frameworks. HUMSET also provides novel and
challenging entry extraction and multi-label entry classification tasks. In
this paper, we take a first step towards approaching these tasks and conduct a
set of experiments on Pre-trained Language Models (PLM) to establish strong
baselines for future research in this domain. The dataset is available at
https://blog.thedeep.io/humset/.
|
[
{
"version": "v1",
"created": "Mon, 10 Oct 2022 11:28:07 GMT"
},
{
"version": "v2",
"created": "Fri, 21 Oct 2022 12:10:49 GMT"
},
{
"version": "v3",
"created": "Sun, 6 Nov 2022 10:37:03 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Fekih",
"Selim",
""
],
[
"Tamagnone",
"Nicolò",
""
],
[
"Minixhofer",
"Benjamin",
""
],
[
"Shrestha",
"Ranjan",
""
],
[
"Contla",
"Ximena",
""
],
[
"Oglethorpe",
"Ewan",
""
],
[
"Rekabsaz",
"Navid",
""
]
] |
new_dataset
| 0.999828 |
2210.05050
|
Omar Costilla Reyes
|
Jennifer J. Sun, Megan Tjandrasuwita, Atharva Sehgal, Armando
Solar-Lezama, Swarat Chaudhuri, Yisong Yue, Omar Costilla-Reyes
|
Neurosymbolic Programming for Science
|
Neural Information Processing Systems 2022 - AI for science workshop
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Neurosymbolic Programming (NP) techniques have the potential to accelerate
scientific discovery. These models combine neural and symbolic components to
learn complex patterns and representations from data, using high-level concepts
or known constraints. NP techniques can interface with symbolic domain
knowledge from scientists, such as prior knowledge and experimental context, to
produce interpretable outputs. We identify opportunities and challenges between
current NP models and scientific workflows, with real-world examples from
behavior analysis in science: to enable the use of NP broadly for workflows
across the natural and social sciences.
|
[
{
"version": "v1",
"created": "Mon, 10 Oct 2022 23:46:41 GMT"
},
{
"version": "v2",
"created": "Mon, 7 Nov 2022 15:21:32 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Sun",
"Jennifer J.",
""
],
[
"Tjandrasuwita",
"Megan",
""
],
[
"Sehgal",
"Atharva",
""
],
[
"Solar-Lezama",
"Armando",
""
],
[
"Chaudhuri",
"Swarat",
""
],
[
"Yue",
"Yisong",
""
],
[
"Costilla-Reyes",
"Omar",
""
]
] |
new_dataset
| 0.982773 |
2210.15834
|
Kunhong Liu Dr
|
Jia-Xin Ye, Xin-Cheng Wen, Xuan-Ze Wang, Yong Xu, Yan Luo, Chang-Li
Wu, Li-Yan Chen, Kun-Hong Liu
|
GM-TCNet: Gated Multi-scale Temporal Convolutional Network using Emotion
Causality for Speech Emotion Recognition
|
The source code is available at:
https://github.com/Jiaxin-Ye/GM-TCNet
|
speech communication, 145, November 2022, 21-35
|
10.1016/j.specom.2022.07.005
| null |
cs.SD cs.AI cs.HC eess.AS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In human-computer interaction, Speech Emotion Recognition (SER) plays an
essential role in understanding the user's intent and improving the interactive
experience. While similar sentimental speeches own diverse speaker
characteristics but share common antecedents and consequences, an essential
challenge for SER is how to produce robust and discriminative representations
through causality between speech emotions. In this paper, we propose a Gated
Multi-scale Temporal Convolutional Network (GM-TCNet) to construct a novel
emotional causality representation learning component with a multi-scale
receptive field. GM-TCNet deploys a novel emotional causality representation
learning component to capture the dynamics of emotion across the time domain,
constructed with dilated causal convolution layer and gating mechanism.
Besides, it utilizes skip connection fusing high-level features from different
gated convolution blocks to capture abundant and subtle emotion changes in
human speech. GM-TCNet first uses a single type of feature, mel-frequency
cepstral coefficients, as inputs and then passes them through the gated
temporal convolutional module to generate the high-level features. Finally, the
features are fed to the emotion classifier to accomplish the SER task. The
experimental results show that our model maintains the highest performance in
most cases compared to state-of-the-art techniques.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 02:00:40 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Ye",
"Jia-Xin",
""
],
[
"Wen",
"Xin-Cheng",
""
],
[
"Wang",
"Xuan-Ze",
""
],
[
"Xu",
"Yong",
""
],
[
"Luo",
"Yan",
""
],
[
"Wu",
"Chang-Li",
""
],
[
"Chen",
"Li-Yan",
""
],
[
"Liu",
"Kun-Hong",
""
]
] |
new_dataset
| 0.998945 |
2210.17146
|
Xunping Jiang
|
Ling Sun, Guiqiong Liu, Xunping Jiang, Junrui Liu, Xu Wang, Han Yang,
Shiping Yang
|
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
|
8 figures, 5 tables
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the demand for standardized large-scale livestock farming and the
development of artificial intelligence technology, a lot of research in area of
animal face recognition were carried on pigs, cattle, sheep and other
livestock. Face recognition consists of three sub-task: face detection, face
normalizing and face identification. Most of animal face recognition study
focuses on face detection and face identification. Animals are often
uncooperative when taking photos, so the collected animal face images are often
in arbitrary directions. The use of non-standard images may significantly
reduce the performance of face recognition system. However, there is no study
on normalizing of the animal face image with arbitrary directions. In this
study, we developed a light-weight angle detection and region-based
convolutional network (LAD-RCNN) containing a new rotation angle coding method
that can detect the rotation angle and the location of animal face in
one-stage. LAD-RCNN has a frame rate of 72.74 FPS (including all steps) on a
single GeForce RTX 2080 Ti GPU. LAD-RCNN has been evaluated on multiple dataset
including goat dataset and gaot infrared image. Evaluation result show that the
AP of face detection was more than 95% and the deviation between the detected
rotation angle and the ground-truth rotation angle were less than 0.036 (i.e.
6.48{\deg}) on all the test dataset. This shows that LAD-RCNN has excellent
performance on livestock face and its direction detection, and therefore it is
very suitable for livestock face detection and Normalizing. Code is available
at https://github.com/SheepBreedingLab-HZAU/LAD-RCNN/
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 08:54:21 GMT"
},
{
"version": "v2",
"created": "Sat, 5 Nov 2022 09:11:13 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Sun",
"Ling",
""
],
[
"Liu",
"Guiqiong",
""
],
[
"Jiang",
"Xunping",
""
],
[
"Liu",
"Junrui",
""
],
[
"Wang",
"Xu",
""
],
[
"Yang",
"Han",
""
],
[
"Yang",
"Shiping",
""
]
] |
new_dataset
| 0.994319 |
2211.02695
|
Hadi Salman
|
Hadi Salman, Caleb Parks, Shi Yin Hong, Justin Zhan
|
WaveNets: Wavelet Channel Attention Networks
|
IEEE BigData2022 conference
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Channel Attention reigns supreme as an effective technique in the field of
computer vision. However, the proposed channel attention by SENet suffers from
information loss in feature learning caused by the use of Global Average
Pooling (GAP) to represent channels as scalars. Thus, designing effective
channel attention mechanisms requires finding a solution to enhance features
preservation in modeling channel inter-dependencies. In this work, we utilize
Wavelet transform compression as a solution to the channel representation
problem. We first test wavelet transform as an Auto-Encoder model equipped with
conventional channel attention module. Next, we test wavelet transform as a
standalone channel compression method. We prove that global average pooling is
equivalent to the recursive approximate Haar wavelet transform. With this
proof, we generalize channel attention using Wavelet compression and name it
WaveNet. Implementation of our method can be embedded within existing channel
attention methods with a couple of lines of code. We test our proposed method
using ImageNet dataset for image classification task. Our method outperforms
the baseline SENet, and achieves the state-of-the-art results. Our code
implementation is publicly available at https://github.com/hady1011/WaveNet-C.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 18:26:47 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Salman",
"Hadi",
""
],
[
"Parks",
"Caleb",
""
],
[
"Hong",
"Shi Yin",
""
],
[
"Zhan",
"Justin",
""
]
] |
new_dataset
| 0.999143 |
2211.02903
|
Yongmao Zhang
|
Yongmao Zhang, Heyang Xue, Hanzhao Li, Lei Xie, Tingwei Guo, Ruixiong
Zhang, Caixia Gong
|
VISinger 2: High-Fidelity End-to-End Singing Voice Synthesis Enhanced by
Digital Signal Processing Synthesizer
|
Submitted to ICASSP 2023
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
End-to-end singing voice synthesis (SVS) model VISinger can achieve better
performance than the typical two-stage model with fewer parameters. However,
VISinger has several problems: text-to-phase problem, the end-to-end model
learns the meaningless mapping of text-to-phase; glitches problem, the harmonic
components corresponding to the periodic signal of the voiced segment occurs a
sudden change with audible artefacts; low sampling rate, the sampling rate of
24KHz does not meet the application needs of high-fidelity generation with the
full-band rate (44.1KHz or higher). In this paper, we propose VISinger 2 to
address these issues by integrating the digital signal processing (DSP) methods
with VISinger. Specifically, inspired by recent advances in differentiable
digital signal processing (DDSP), we incorporate a DSP synthesizer into the
decoder to solve the above issues. The DSP synthesizer consists of a harmonic
synthesizer and a noise synthesizer to generate periodic and aperiodic signals,
respectively, from the latent representation z in VISinger. It supervises the
posterior encoder to extract the latent representation without phase
information and avoid the prior encoder modelling text-to-phase mapping. To
avoid glitch artefacts, the HiFi-GAN is modified to accept the waveforms
generated by the DSP synthesizer as a condition to produce the singing voice.
Moreover, with the improved waveform decoder, VISinger 2 manages to generate
44.1kHz singing audio with richer expression and better quality. Experiments on
OpenCpop corpus show that VISinger 2 outperforms VISinger, CpopSing and
RefineSinger in both subjective and objective metrics.
|
[
{
"version": "v1",
"created": "Sat, 5 Nov 2022 13:35:00 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Zhang",
"Yongmao",
""
],
[
"Xue",
"Heyang",
""
],
[
"Li",
"Hanzhao",
""
],
[
"Xie",
"Lei",
""
],
[
"Guo",
"Tingwei",
""
],
[
"Zhang",
"Ruixiong",
""
],
[
"Gong",
"Caixia",
""
]
] |
new_dataset
| 0.998617 |
2211.02926
|
Konrad Staniszewski
|
Konrad Staniszewski (University of Warsaw, IDEAS NCBR Sp. z o.o.)
|
Parity Games of Bounded Tree-Depth
|
This is the full version of the paper that has been accepted at CSL
2023 and is going to be published in Leibniz International Proceedings in
Informatics (LIPIcs)
| null | null | null |
cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
The exact complexity of solving parity games is a major open problem. Several
authors have searched for efficient algorithms over specific classes of graphs.
In particular, Obdr\v{z}\'{a}lek showed that for graphs of bounded tree-width
or clique-width, the problem is in $\mathrm{P}$, which was later improved by
Ganardi, who showed that it is even in $\mathrm{LOGCFL}$ (with an additional
assumption for clique-width case). Here we extend this line of research by
showing that for graphs of bounded tree-depth the problem of solving parity
games is in logspace uniform $\text{AC}^0$. We achieve this by first
considering a parameter that we obtain from a modification of clique-width,
which we call shallow clique-width. We subsequently provide a suitable
reduction.
|
[
{
"version": "v1",
"created": "Sat, 5 Nov 2022 15:14:15 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Staniszewski",
"Konrad",
"",
"University of Warsaw, IDEAS NCBR Sp. z o.o."
]
] |
new_dataset
| 0.964278 |
2211.02950
|
Ivan Habernal
|
Leonard Bongard, Lena Held, Ivan Habernal
|
The Legal Argument Reasoning Task in Civil Procedure
|
Camera ready, to appear at the Natural Legal Language Processing
Workshop 2022 co-located with EMNLP
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We present a new NLP task and dataset from the domain of the U.S. civil
procedure. Each instance of the dataset consists of a general introduction to
the case, a particular question, and a possible solution argument, accompanied
by a detailed analysis of why the argument applies in that case. Since the
dataset is based on a book aimed at law students, we believe that it represents
a truly complex task for benchmarking modern legal language models. Our
baseline evaluation shows that fine-tuning a legal transformer provides some
advantage over random baseline models, but our analysis reveals that the actual
ability to infer legal arguments remains a challenging open research question.
|
[
{
"version": "v1",
"created": "Sat, 5 Nov 2022 17:41:00 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Bongard",
"Leonard",
""
],
[
"Held",
"Lena",
""
],
[
"Habernal",
"Ivan",
""
]
] |
new_dataset
| 0.99903 |
2211.03014
|
Ramviyas Parasuraman
|
Michael Starks, Aryan Gupta, Sanjay Sarma Oruganti Venkata, Ramviyas
Parasuraman
|
HeRoSwarm: Fully-Capable Miniature Swarm Robot Hardware Design With
Open-Source ROS Support
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Experiments using large numbers of miniature swarm robots are desirable to
teach, study, and test multi-robot and swarm intelligence algorithms and their
applications. To realize the full potential of a swarm robot, it should be
capable of not only motion but also sensing, computing, communication, and
power management modules with multiple options. Current swarm robot platforms
developed for commercial and academic research purposes lack several of these
critical attributes by focusing only on a few of these aspects. Therefore, in
this paper, we propose the HeRoSwarm, a fully-capable swarm robot platform with
open-source hardware and software support. The proposed robot hardware is a
low-cost design with commercial off-the-shelf components that uniquely
integrates multiple sensing, communication, and computing modalities with
various power management capabilities into a tiny footprint. Moreover, our
swarm robot with odometry capability with Robot Operating Systems (ROS) support
is unique in its kind. This simple yet powerful swarm robot design has been
extensively verified with different prototyping variants and multi-robot
experimental demonstrations.
|
[
{
"version": "v1",
"created": "Sun, 6 Nov 2022 03:07:58 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Starks",
"Michael",
""
],
[
"Gupta",
"Aryan",
""
],
[
"Venkata",
"Sanjay Sarma Oruganti",
""
],
[
"Parasuraman",
"Ramviyas",
""
]
] |
new_dataset
| 0.992402 |
2211.03250
|
Zhitong Ni
|
Zhitong Ni, J. Andrew Zhang, Kai Wu, and Ren Ping Liu
|
Uplink Sensing Using CSI Ratio in Perceptive Mobile Networks
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Uplink sensing in perceptive mobile networks (PMNs), which uses uplink
communication signals for sensing the environment around a base station, faces
challenging issues of clock asynchronism and the requirement of a line-of-sight
(LOS) path between transmitters and receivers. The channel state information
(CSI) ratio has been applied to resolve these issues, however, current research
on the CSI ratio is limited to Doppler estimation in a single dynamic path.
This paper proposes an advanced parameter estimation scheme that can extract
multiple dynamic parameters, including Doppler frequency, angle-of-arrival
(AoA), and delay, in a communication uplink channel and completes the
localization of multiple moving targets. Our scheme is based on the
multi-element Taylor series of the CSI ratio that converts a nonlinear function
of sensing parameters to linear forms and enables the applications of
traditional sensing algorithms. Using the truncated Taylor series, we develop
novel multiple-signal-classification grid searching algorithms for estimating
Doppler frequencies and AoAs and use the least-square method to obtain delays.
Both experimental and simulation results are provided, demonstrating that our
proposed scheme can achieve good performances for sensing both single and
multiple dynamic paths, without requiring the presence of a LOS path.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 00:54:12 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Ni",
"Zhitong",
""
],
[
"Zhang",
"J. Andrew",
""
],
[
"Wu",
"Kai",
""
],
[
"Liu",
"Ren Ping",
""
]
] |
new_dataset
| 0.989447 |
2211.03251
|
Olivia Hsu
|
Olivia Hsu, Alexander Rucker, Tian Zhao, Kunle Olukotun, and Fredrik
Kjolstad
|
Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow
Architecture
|
15 pages, 13 figures, 6 tables,
| null | null | null |
cs.PL cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce Stardust, a compiler that compiles sparse tensor algebra to
reconfigurable dataflow architectures (RDAs). Stardust introduces new
user-provided data representation and scheduling language constructs for
mapping to resource-constrained accelerated architectures. Stardust uses the
information provided by these constructs to determine on-chip memory placement
and to lower to the Capstan RDA through a parallel-patterns rewrite system that
targets the Spatial programming model. The Stardust compiler is implemented as
a new compilation path inside the TACO open-source system. Using cycle-accurate
simulation, we demonstrate that Stardust can generate more Capstan tensor
operations than its authors had implemented and that it results in 138$\times$
better performance than generated CPU kernels and 41$\times$ better performance
than generated GPU kernels.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 01:01:43 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Hsu",
"Olivia",
""
],
[
"Rucker",
"Alexander",
""
],
[
"Zhao",
"Tian",
""
],
[
"Olukotun",
"Kunle",
""
],
[
"Kjolstad",
"Fredrik",
""
]
] |
new_dataset
| 0.964876 |
2211.03313
|
Hojin Seo
|
Hojin Seo, Yeoun-Jae Kim, Jaesoon Choi, Youngjin Moon
|
Quasi-Static Analysis on Transoral Surgical Tendon-Driven Articulated
Robot Units
| null | null | null | null |
cs.RO physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Wire actuation in tendon-driven continuum robots enables the transmission of
force from a distance, but it is understood that tension control problems can
arise when a pulley is used to actuate two cables in a push-pull mode. This
paper analyzes the relationship between angle of rotation, pressure, as well as
variables of a single continuum unit in a quasi-static equilibrium. The primary
objective of the quasi-static analysis was to output pressure and the analysis,
given the tensions applied. Static equilibrium condition was established, and
the bisection method was carried out for the angle of rotation. The function
for the bisection method considered pressure-induced forces, friction forces,
and weight. {\theta} was 17.14{\deg}, and p was 405.6 Pa when Tl and Ts were
given the values of 1 N and 2 N, respectively. The results seemed to be
consistent with the preliminary design specification, calling for further
simulations and experiments.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 05:29:12 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Seo",
"Hojin",
""
],
[
"Kim",
"Yeoun-Jae",
""
],
[
"Choi",
"Jaesoon",
""
],
[
"Moon",
"Youngjin",
""
]
] |
new_dataset
| 0.965512 |
2211.03371
|
SeungHeon Doh
|
Taesu Kim, SeungHeon Doh, Gyunpyo Lee, Hyungseok Jeon, Juhan Nam,
Hyeon-Jeong Suk
|
Hi,KIA: A Speech Emotion Recognition Dataset for Wake-Up Words
|
Asia Pacific Signal and Information Processing Association Annual
Summit and Conference (APSIPA), 2022
| null | null | null |
cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Wake-up words (WUW) is a short sentence used to activate a speech recognition
system to receive the user's speech input. WUW utterances include not only the
lexical information for waking up the system but also non-lexical information
such as speaker identity or emotion. In particular, recognizing the user's
emotional state may elaborate the voice communication. However, there is few
dataset where the emotional state of the WUW utterances is labeled. In this
paper, we introduce Hi, KIA, a new WUW dataset which consists of 488 Korean
accent emotional utterances collected from four male and four female speakers
and each of utterances is labeled with four emotional states including anger,
happy, sad, or neutral. We present the step-by-step procedure to build the
dataset, covering scenario selection, post-processing, and human validation for
label agreement. Also, we provide two classification models for WUW speech
emotion recognition using the dataset. One is based on traditional hand-craft
features and the other is a transfer-learning approach using a pre-trained
neural network. These classification models could be used as benchmarks in
further research.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 08:57:16 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Kim",
"Taesu",
""
],
[
"Doh",
"SeungHeon",
""
],
[
"Lee",
"Gyunpyo",
""
],
[
"Jeon",
"Hyungseok",
""
],
[
"Nam",
"Juhan",
""
],
[
"Suk",
"Hyeon-Jeong",
""
]
] |
new_dataset
| 0.999828 |
2211.03375
|
Haoshu Fang
|
Hao-Shu Fang, Jiefeng Li, Hongyang Tang, Chao Xu, Haoyi Zhu, Yuliang
Xiu, Yong-Lu Li, Cewu Lu
|
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking
in Real-Time
|
Documents for AlphaPose, accepted to TPAMI
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate whole-body multi-person pose estimation and tracking is an important
yet challenging topic in computer vision. To capture the subtle actions of
humans for complex behavior analysis, whole-body pose estimation including the
face, body, hand and foot is essential over conventional body-only pose
estimation. In this paper, we present AlphaPose, a system that can perform
accurate whole-body pose estimation and tracking jointly while running in
realtime. To this end, we propose several new techniques: Symmetric Integral
Keypoint Regression (SIKR) for fast and fine localization, Parametric Pose
Non-Maximum-Suppression (P-NMS) for eliminating redundant human detections and
Pose Aware Identity Embedding for jointly pose estimation and tracking. During
training, we resort to Part-Guided Proposal Generator (PGPG) and multi-domain
knowledge distillation to further improve the accuracy. Our method is able to
localize whole-body keypoints accurately and tracks humans simultaneously given
inaccurate bounding boxes and redundant detections. We show a significant
improvement over current state-of-the-art methods in both speed and accuracy on
COCO-wholebody, COCO, PoseTrack, and our proposed Halpe-FullBody pose
estimation dataset. Our model, source codes and dataset are made publicly
available at https://github.com/MVIG-SJTU/AlphaPose.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 09:15:38 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Fang",
"Hao-Shu",
""
],
[
"Li",
"Jiefeng",
""
],
[
"Tang",
"Hongyang",
""
],
[
"Xu",
"Chao",
""
],
[
"Zhu",
"Haoyi",
""
],
[
"Xiu",
"Yuliang",
""
],
[
"Li",
"Yong-Lu",
""
],
[
"Lu",
"Cewu",
""
]
] |
new_dataset
| 0.997644 |
2211.03402
|
Liang Peng
|
Liang Peng, Jun Li, Wenbo Shao, and Hong Wang
|
PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in
Long-tail Traffic Scenarios
|
7 pages, 5 figures, 4 tables, submitted to 2023 ICRA
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Perception algorithms in autonomous driving systems confront great challenges
in long-tail traffic scenarios, where the problems of Safety of the Intended
Functionality (SOTIF) could be triggered by the algorithm performance
insufficiencies and dynamic operational environment. However, such scenarios
are not systematically included in current open-source datasets, and this paper
fills the gap accordingly. Based on the analysis and enumeration of trigger
conditions, a high-quality diverse dataset is released, including various
long-tail traffic scenarios collected from multiple resources. Considering the
development of probabilistic object detection (POD), this dataset marks trigger
sources that may cause perception SOTIF problems in the scenarios as key
objects. In addition, an evaluation protocol is suggested to verify the
effectiveness of POD algorithms in identifying the key objects via uncertainty.
The dataset never stops expanding, and the first batch of open-source data
includes 1126 frames with an average of 2.27 key objects and 2.47 normal
objects in each frame. To demonstrate how to use this dataset for SOTIF
research, this paper further quantifies the perception SOTIF entropy to confirm
whether a scenario is unknown and unsafe for a perception system. The
experimental results show that the quantified entropy can effectively and
efficiently reflect the failure of the perception algorithm.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 10:07:30 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Peng",
"Liang",
""
],
[
"Li",
"Jun",
""
],
[
"Shao",
"Wenbo",
""
],
[
"Wang",
"Hong",
""
]
] |
new_dataset
| 0.999775 |
2211.03433
|
Marco Guerini
|
Helena Bonaldi, Sara Dellantonio, Serra Sinem Tekiroglu, Marco Guerini
|
Human-Machine Collaboration Approaches to Build a Dialogue Dataset for
Hate Speech Countering
|
To appear in Proceedings of the 2022 Conference on Empirical Methods
in Natural Language Processing (long paper)
| null | null | null |
cs.CL cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Fighting online hate speech is a challenge that is usually addressed using
Natural Language Processing via automatic detection and removal of hate
content. Besides this approach, counter narratives have emerged as an effective
tool employed by NGOs to respond to online hate on social media platforms. For
this reason, Natural Language Generation is currently being studied as a way to
automatize counter narrative writing. However, the existing resources necessary
to train NLG models are limited to 2-turn interactions (a hate speech and a
counter narrative as response), while in real life, interactions can consist of
multiple turns. In this paper, we present a hybrid approach for dialogical data
collection, which combines the intervention of human expert annotators over
machine generated dialogues obtained using 19 different configurations. The
result of this work is DIALOCONAN, the first dataset comprising over 3000
fictitious multi-turn dialogues between a hater and an NGO operator, covering 6
targets of hate.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 10:37:13 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Bonaldi",
"Helena",
""
],
[
"Dellantonio",
"Sara",
""
],
[
"Tekiroglu",
"Serra Sinem",
""
],
[
"Guerini",
"Marco",
""
]
] |
new_dataset
| 0.972568 |
2211.03442
|
Prathamesh Kalamkar
|
Prathamesh Kalamkar, Astha Agarwal, Aman Tiwari, Smita Gupta, Saurabh
Karn, Vivek Raghavan
|
Named Entity Recognition in Indian court judgments
|
to be published in NLLP 2022 Workshop at EMNLP
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Identification of named entities from legal texts is an essential building
block for developing other legal Artificial Intelligence applications. Named
Entities in legal texts are slightly different and more fine-grained than
commonly used named entities like Person, Organization, Location etc. In this
paper, we introduce a new corpus of 46545 annotated legal named entities mapped
to 14 legal entity types. The Baseline model for extracting legal named
entities from judgment text is also developed.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 10:44:44 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Kalamkar",
"Prathamesh",
""
],
[
"Agarwal",
"Astha",
""
],
[
"Tiwari",
"Aman",
""
],
[
"Gupta",
"Smita",
""
],
[
"Karn",
"Saurabh",
""
],
[
"Raghavan",
"Vivek",
""
]
] |
new_dataset
| 0.96745 |
2211.03471
|
Ricardo J. Rodr\'iguez
|
Ricardo J. Rodr\'iguez and Jos\'e Luis Salazar and Juli\'an
Fern\'andez-Navajas
|
Sittin'On the Dock of the (WiFi) Bay: On the Frame Aggregation under
IEEE 802.11 DCF
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
It is well known that frame aggregation in Internet communications improves
transmission efficiency. However, it also causes a delay that for some
real-time communications is inappropriate, thus creating a trade-off between
efficiency and delay. In this paper, we establish the conditions for frame
aggregation under the IEEE 802.11 DCF protocol to be beneficial on average
delay. To do so, we first describe the transmission time in IEEE 802.11 in a
stochastic framework and then we calculate the optimal value of the frames
that, when aggregated, saves transmission time in the long term. Our findings,
discussed with numerical experimentation, show that frame aggregation reduces
transmission congestion and transmission delays.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 11:33:58 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Rodríguez",
"Ricardo J.",
""
],
[
"Salazar",
"José Luis",
""
],
[
"Fernández-Navajas",
"Julián",
""
]
] |
new_dataset
| 0.998689 |
2211.03475
|
Michele Wigger
|
Sara Faour, Mustapha Hamad, Mireille Sarkiss, and Michele Wigger
|
Testing Against Independence with an Eavesdropper
|
submitted to ITW 2023
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We study a distributed binary hypothesis testing (HT) problem with
communication and security constraints, involving three parties: a remote
sensor called Alice, a legitimate decision centre called Bob, and an
eavesdropper called Eve, all having their own source observations. In this
system, Alice conveys a rate R description of her observation to Bob, and Bob
performs a binary hypothesis test on the joint distribution underlying his and
Alice's observations. The goal of Alice and Bob is to maximise the exponential
decay of Bob's miss-detection (type II-error) probability under two
constraints: Bob's false alarm-probability (type-I error) probability has to
stay below a given threshold and Eve's uncertainty (equivocation) about Alice's
observations should stay above a given security threshold even when Eve learns
Alice's message. For the special case of testing against independence, we
characterise the largest possible type-II error exponent under the described
type-I error probability and security constraints.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 11:39:05 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Faour",
"Sara",
""
],
[
"Hamad",
"Mustapha",
""
],
[
"Sarkiss",
"Mireille",
""
],
[
"Wigger",
"Michele",
""
]
] |
new_dataset
| 0.978255 |
2211.03484
|
Gerhard Kurz
|
Gerhard Kurz and Sebastian A. Scherer and Peter Biber and David Fleer
|
When Geometry is not Enough: Using Reflector Markers in Lidar SLAM
|
Accepted at IROS 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lidar-based SLAM systems perform well in a wide range of circumstances by
relying on the geometry of the environment. However, even mature and reliable
approaches struggle when the environment contains structureless areas such as
long hallways. To allow the use of lidar-based SLAM in such environments, we
propose to add reflector markers in specific locations that would otherwise be
difficult. We present an algorithm to reliably detect these markers and two
approaches to fuse the detected markers with geometry-based scan matching. The
performance of the proposed methods is demonstrated on real-world datasets from
several industrial environments.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 12:07:11 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Kurz",
"Gerhard",
""
],
[
"Scherer",
"Sebastian A.",
""
],
[
"Biber",
"Peter",
""
],
[
"Fleer",
"David",
""
]
] |
new_dataset
| 0.986367 |
2211.03506
|
Himanshu Thapliyal
|
Jun-Cheng Chin, Tyler Cultice and Himanshu Thapliyal
|
CAN Bus: The Future of Additive Manufacturing (3D Printing)
|
6 pages
|
IEEE Consumer Electronics Magazine, 2022
| null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Additive Manufacturing (AM) is gaining renewed popularity and attention due
to low-cost fabrication systems proliferating the market. Current communication
protocols used in AM limit the connection flexibility between the control board
and peripherals; they are often complex in their wiring and thus restrict their
avenue of expansion. Thus, the Controller Area Network (CAN) bus is an
attractive pathway for inter-hardware connections due to its innate quality.
However, the combination of CAN and AM is not well explored and documented in
existing literature. This article aims to provide examples of CAN bus
applications in AM.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 13:26:53 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Chin",
"Jun-Cheng",
""
],
[
"Cultice",
"Tyler",
""
],
[
"Thapliyal",
"Himanshu",
""
]
] |
new_dataset
| 0.974458 |
2211.03589
|
Ruofan Wang
|
Juan Xu, Ruofan Wang, Yan Zhang, Hongmin Huang
|
A Reliable Multipath Routing Protocol Based on Link Stability
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Wireless NanoSensor Network (WNSN) is a new type of sensor network with broad
application prospects. In view of the limited energy of nanonodes and unstable
links in WNSNs, we propose a reliable multi-path routing based on link
stability (RMRLS). RMRLS selects the optimal path which perfects best in the
link stability evaluation model, and then selects an alternative route by the
routing similarity judgment model. RMRLS uses tew paths to cope with changes in
the network topology. The simulation shows that the RMRLS protocol has
advantages in data packet transmission success rate and average throughput,
which can improve the stability and reliability of the network.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 14:28:03 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Xu",
"Juan",
""
],
[
"Wang",
"Ruofan",
""
],
[
"Zhang",
"Yan",
""
],
[
"Huang",
"Hongmin",
""
]
] |
new_dataset
| 0.99905 |
2211.03612
|
Ming Liu
|
Ming Liu, Yaojia LV, Jingrun Zhang, Ruiji Fu, Bing Qin
|
BigCilin: An Automatic Chinese Open-domain Knowledge Graph with
Fine-grained Hypernym-Hyponym Relations
|
5 pages, 3 figures
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents BigCilin, the first Chinese open-domain knowledge graph
with fine-grained hypernym-hyponym re-lations which are extracted automatically
from multiple sources for Chinese named entities. With the fine-grained
hypernym-hyponym relations, BigCilin owns flexible semantic hierarchical
structure. Since the hypernym-hyponym paths are automati-cally generated and
one entity may have several senses, we provide a path disambi-guation solution
to map a hypernym-hyponym path of one entity to its one sense on the condition
that the path and the sense express the same meaning. In order to conveniently
access our BigCilin Knowle-dge graph, we provide web interface in two ways. One
is that it supports querying any Chinese named entity and browsing the
extracted hypernym-hyponym paths surro-unding the query entity. The other is
that it gives a top-down browsing view to illust-rate the overall hierarchical
structure of our BigCilin knowledge graph over some sam-pled entities.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 15:05:01 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Liu",
"Ming",
""
],
[
"LV",
"Yaojia",
""
],
[
"Zhang",
"Jingrun",
""
],
[
"Fu",
"Ruiji",
""
],
[
"Qin",
"Bing",
""
]
] |
new_dataset
| 0.997188 |
2211.03615
|
Ali Abedi
|
Ali Abedi, Faranak Dayyani, Charlene Chu, Shehroz S. Khan
|
MAISON -- Multimodal AI-based Sensor platform for Older Individuals
| null | null | null | null |
cs.LG cs.AI cs.DC eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
There is a global aging population requiring the need for the right tools
that can enable older adults' greater independence and the ability to age at
home, as well as assist healthcare workers. It is feasible to achieve this
objective by building predictive models that assist healthcare workers in
monitoring and analyzing older adults' behavioral, functional, and
psychological data. To develop such models, a large amount of multimodal sensor
data is typically required. In this paper, we propose MAISON, a scalable
cloud-based platform of commercially available smart devices capable of
collecting desired multimodal sensor data from older adults and patients living
in their own homes. The MAISON platform is novel due to its ability to collect
a greater variety of data modalities than the existing platforms, as well as
its new features that result in seamless data collection and ease of use for
older adults who may not be digitally literate. We demonstrated the feasibility
of the MAISON platform with two older adults discharged home from a large
rehabilitation center. The results indicate that the MAISON platform was able
to collect and store sensor data in a cloud without functional glitches or
performance degradation. This paper will also discuss the challenges faced
during the development of the platform and data collection in the homes of
older adults. MAISON is a novel platform designed to collect multimodal data
and facilitate the development of predictive models for detecting key health
indicators, including social isolation, depression, and functional decline, and
is feasible to use with older adults in the community.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 15:09:04 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Abedi",
"Ali",
""
],
[
"Dayyani",
"Faranak",
""
],
[
"Chu",
"Charlene",
""
],
[
"Khan",
"Shehroz S.",
""
]
] |
new_dataset
| 0.96775 |
2211.03662
|
William Buchanan Prof
|
Fawad Ahmed, Muneeb Ur Rehman, Jawad Ahmad, Muhammad Shahbaz Khan,
Wadii Boulila, Gautam Srivastava, Jerry Chun-Wei Lin, William J. Buchanan
|
A DNA Based Colour Image Encryption Scheme Using A Convolutional
Autoencoder
| null |
(2022) ACM Trans. Multimedia Comput. Commun. Appl
|
10.1145/3570165
| null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
With the advancement in technology, digital images can easily be transmitted
and stored over the Internet. Encryption is used to avoid illegal interception
of digital images. Encrypting large-sized colour images in their original
dimension generally results in low encryption/decryption speed along with
exerting a burden on the limited bandwidth of the transmission channel. To
address the aforementioned issues, a new encryption scheme for colour images
employing convolutional autoencoder, DNA and chaos is presented in this paper.
The proposed scheme has two main modules, the dimensionality conversion module
using the proposed convolutional autoencoder, and the encryption/decryption
module using DNA and chaos. The dimension of the input colour image is first
reduced from N $\times$ M $\times$ 3 to P $\times$ Q gray-scale image using the
encoder. Encryption and decryption are then performed in the reduced dimension
space. The decrypted gray-scale image is upsampled to obtain the original
colour image having dimension N $\times$ M $\times$ 3. The training and
validation accuracy of the proposed autoencoder is 97% and 95%, respectively.
Once the autoencoder is trained, it can be used to reduce and subsequently
increase the dimension of any arbitrary input colour image. The efficacy of the
designed autoencoder has been demonstrated by the successful reconstruction of
the compressed image into the original colour image with negligible perceptual
distortion. The second major contribution presented in this paper is an image
encryption scheme using DNA along with multiple chaotic sequences and
substitution boxes. The security of the proposed image encryption algorithm has
been gauged using several evaluation parameters, such as histogram of the
cipher image, entropy, NPCR, UACI, key sensitivity, contrast, etc. encryption.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 16:19:31 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Ahmed",
"Fawad",
""
],
[
"Rehman",
"Muneeb Ur",
""
],
[
"Ahmad",
"Jawad",
""
],
[
"Khan",
"Muhammad Shahbaz",
""
],
[
"Boulila",
"Wadii",
""
],
[
"Srivastava",
"Gautam",
""
],
[
"Lin",
"Jerry Chun-Wei",
""
],
[
"Buchanan",
"William J.",
""
]
] |
new_dataset
| 0.993674 |
2211.03688
|
Zixin Yang
|
Zixin Yang, Richard Simon, Cristian A.Linte
|
Learning Feature Descriptors for Pre- and Intra-operative Point Cloud
Matching for Laparoscopic Liver Registration
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Purpose: In laparoscopic liver surgery (LLS), pre-operative information can
be overlaid onto the intra-operative scene by registering a 3D pre-operative
model to the intra-operative partial surface reconstructed from the
laparoscopic video. To assist with this task, we explore the use of
learning-based feature descriptors, which, to our best knowledge, have not been
explored for use in laparoscopic liver registration. Furthermore, a dataset to
train and evaluate the use of learning-based descriptors does not exist.
Methods: We present the LiverMatch dataset consisting of 16 preoperative
models and their simulated intra-operative 3D surfaces. We also propose the
LiverMatch network designed for this task, which outputs per-point feature
descriptors, visibility scores, and matched points.
Results: We compare the proposed LiverMatch network with anetwork closest to
LiverMatch, and a histogram-based 3D descriptor on the testing split of the
LiverMatch dataset, which includes two unseen pre-operative models and 1400
intra-operative surfaces. Results suggest that our LiverMatch network can
predict more accurate and dense matches than the other two methods and can be
seamlessly integrated with a RANSAC-ICP-based registration algorithm to achieve
an accurate initial alignment.
Conclusion: The use of learning-based feature descriptors in LLR is
promising, as it can help achieve an accurate initial rigid alignment, which,
in turn, serves as an initialization for subsequent non-rigid registration. We
will release the dataset and code upon acceptance.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 16:58:39 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Yang",
"Zixin",
""
],
[
"Simon",
"Richard",
""
],
[
"Linte",
"Cristian A.",
""
]
] |
new_dataset
| 0.99764 |
2211.03690
|
Charles Fleming
|
Chengkai Yu and Charles Fleming and Hai-Ning Liang
|
Scale Invariant Privacy Preserving Video via Wavelet Decomposition
| null |
International Journal of Design, Analysis & Tools for Integrated
Circuits & Systems 7.1 (2018)
| null | null |
cs.CR cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Video surveillance has become ubiquitous in the modern world. Mobile devices,
surveillance cameras, and IoT devices, all can record video that can violate
our privacy. One proposed solution for this is privacy-preserving video, which
removes identifying information from the video as it is produced. Several
algorithms for this have been proposed, but all of them suffer from scale
issues: in order to sufficiently anonymize near-camera objects, distant objects
become unidentifiable. In this paper, we propose a scale-invariant method,
based on wavelet decomposition.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 17:03:23 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Yu",
"Chengkai",
""
],
[
"Fleming",
"Charles",
""
],
[
"Liang",
"Hai-Ning",
""
]
] |
new_dataset
| 0.988658 |
2211.03730
|
Mehedi Hasan Bijoy
|
Mehedi Hasan Bijoy, Nahid Hossain, Salekul Islam, Swakkhar Shatabda
|
DPCSpell: A Transformer-based Detector-Purificator-Corrector Framework
for Spelling Error Correction of Bangla and Resource Scarce Indic Languages
|
23 pages, 4 figures, and 7 tables
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Spelling error correction is the task of identifying and rectifying
misspelled words in texts. It is a potential and active research topic in
Natural Language Processing because of numerous applications in human language
understanding. The phonetically or visually similar yet semantically distinct
characters make it an arduous task in any language. Earlier efforts on spelling
error correction in Bangla and resource-scarce Indic languages focused on
rule-based, statistical, and machine learning-based methods which we found
rather inefficient. In particular, machine learning-based approaches, which
exhibit superior performance to rule-based and statistical methods, are
ineffective as they correct each character regardless of its appropriateness.
In this work, we propose a novel detector-purificator-corrector framework based
on denoising transformers by addressing previous issues. Moreover, we present a
method for large-scale corpus creation from scratch which in turn resolves the
resource limitation problem of any left-to-right scripted language. The
empirical outcomes demonstrate the effectiveness of our approach that
outperforms previous state-of-the-art methods by a significant margin for
Bangla spelling error correction. The models and corpus are publicly available
at https://tinyurl.com/DPCSpell.
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 17:59:05 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Bijoy",
"Mehedi Hasan",
""
],
[
"Hossain",
"Nahid",
""
],
[
"Islam",
"Salekul",
""
],
[
"Shatabda",
"Swakkhar",
""
]
] |
new_dataset
| 0.999021 |
2211.03779
|
Maitreya Patel
|
Maitreya Patel and Tejas Gokhale and Chitta Baral and Yezhou Yang
|
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties
via Video Question Answering
|
Accepted to EMNLP 2022; https://maitreyapatel.com/CRIPP-VQA/
| null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Videos often capture objects, their visible properties, their motion, and the
interactions between different objects. Objects also have physical properties
such as mass, which the imaging pipeline is unable to directly capture.
However, these properties can be estimated by utilizing cues from relative
object motion and the dynamics introduced by collisions. In this paper, we
introduce CRIPP-VQA, a new video question answering dataset for reasoning about
the implicit physical properties of objects in a scene. CRIPP-VQA contains
videos of objects in motion, annotated with questions that involve
counterfactual reasoning about the effect of actions, questions about planning
in order to reach a goal, and descriptive questions about visible properties of
objects. The CRIPP-VQA test set enables evaluation under several
out-of-distribution settings -- videos with objects with masses, coefficients
of friction, and initial velocities that are not observed in the training
distribution. Our experiments reveal a surprising and significant performance
gap in terms of answering questions about implicit properties (the focus of
this paper) and explicit properties of objects (the focus of prior work).
|
[
{
"version": "v1",
"created": "Mon, 7 Nov 2022 18:55:26 GMT"
}
] | 2022-11-08T00:00:00 |
[
[
"Patel",
"Maitreya",
""
],
[
"Gokhale",
"Tejas",
""
],
[
"Baral",
"Chitta",
""
],
[
"Yang",
"Yezhou",
""
]
] |
new_dataset
| 0.999863 |
1805.12262
|
Ghalia Hemrit
|
Ghalia Hemrit, Graham D. Finlayson, Arjan Gijsenij, Peter Gehler,
Simone Bianco, Brian Funt, Mark Drew and Lilong Shi
|
Rehabilitating the ColorChecker Dataset for Illuminant Estimation
|
4 pages, 3 figures, 2 tables, Proceedings of the 26th Color and
Imaging Conference
|
Color and Imaging Conference, 2018
|
10.2352/ISSN.2169-2629.2018.26.350
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a previous work, it was shown that there is a curious problem with the
benchmark ColorChecker dataset for illuminant estimation. To wit, this dataset
has at least 3 different sets of ground-truths. Typically, for a single
algorithm a single ground-truth is used. But then different algorithms, whose
performance is measured with respect to different ground-truths, are compared
against each other and then ranked. This makes no sense. We show in this paper
that there are also errors in how each ground-truth set was calculated. As a
result, all performance rankings based on the ColorChecker dataset - and there
are scores of these - are inaccurate.
In this paper, we re-generate a new 'recommended' set of ground-truth based
on the calculation methodology described by Shi and Funt. We then review the
performance evaluation of a range of illuminant estimation algorithms. Compared
with the legacy ground-truths, we find that the difference in how algorithms
perform can be large, with many local rankings of algorithms being reversed.
Finally, we draw the readers attention to our new 'open' data repository
which, we hope, will allow the ColorChecker set to be rehabilitated and once
again to become a useful benchmark for illuminant estimation algorithms.
|
[
{
"version": "v1",
"created": "Wed, 30 May 2018 23:41:17 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Sep 2018 11:30:31 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Sep 2018 16:53:27 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Hemrit",
"Ghalia",
""
],
[
"Finlayson",
"Graham D.",
""
],
[
"Gijsenij",
"Arjan",
""
],
[
"Gehler",
"Peter",
""
],
[
"Bianco",
"Simone",
""
],
[
"Funt",
"Brian",
""
],
[
"Drew",
"Mark",
""
],
[
"Shi",
"Lilong",
""
]
] |
new_dataset
| 0.999022 |
2110.04792
|
Lu Zou
|
Lu Zou, Zhangjin Huang, Naijie Gu, Guoping Wang
|
6D-ViT: Category-Level 6D Object Pose Estimation via Transformer-based
Instance Representation Learning
|
13 pages, 12 figures
|
IEEE Transactions on Image Processing 2022
|
10.1109/TIP.2022.3216980
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents 6D-ViT, a transformer-based instance representation
learning network, which is suitable for highly accurate category-level object
pose estimation on RGB-D images. Specifically, a novel two-stream
encoder-decoder framework is dedicated to exploring complex and powerful
instance representations from RGB images, point clouds and categorical shape
priors. For this purpose, the whole framework consists of two main branches,
named Pixelformer and Pointformer. The Pixelformer contains a pyramid
transformer encoder with an all-MLP decoder to extract pixelwise appearance
representations from RGB images, while the Pointformer relies on a cascaded
transformer encoder and an all-MLP decoder to acquire the pointwise geometric
characteristics from point clouds. Then, dense instance representations (i.e.,
correspondence matrix, deformation field) are obtained from a multi-source
aggregation network with shape priors, appearance and geometric information as
input. Finally, the instance 6D pose is computed by leveraging the
correspondence among dense representations, shape priors, and the instance
point clouds. Extensive experiments on both synthetic and real-world datasets
demonstrate that the proposed 3D instance representation learning framework
achieves state-of-the-art performance on both datasets, and significantly
outperforms all existing methods.
|
[
{
"version": "v1",
"created": "Sun, 10 Oct 2021 13:34:16 GMT"
},
{
"version": "v2",
"created": "Sat, 30 Oct 2021 07:44:57 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Zou",
"Lu",
""
],
[
"Huang",
"Zhangjin",
""
],
[
"Gu",
"Naijie",
""
],
[
"Wang",
"Guoping",
""
]
] |
new_dataset
| 0.997696 |
2112.07471
|
Yufeng Zheng
|
Yufeng Zheng, Victoria Fern\'andez Abrevaya, Marcel C. B\"uhler, Xu
Chen, Michael J. Black, Otmar Hilliges
|
I M Avatar: Implicit Morphable Head Avatars from Videos
|
Accepted at CVPR 2022 as an oral presentation. Project page
https://ait.ethz.ch/projects/2022/IMavatar/ ; Github page:
https://github.com/zhengyuf/IMavatar
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Traditional 3D morphable face models (3DMMs) provide fine-grained control
over expression but cannot easily capture geometric and appearance details.
Neural volumetric representations approach photorealism but are hard to animate
and do not generalize well to unseen expressions. To tackle this problem, we
propose IMavatar (Implicit Morphable avatar), a novel method for learning
implicit head avatars from monocular videos. Inspired by the fine-grained
control mechanisms afforded by conventional 3DMMs, we represent the expression-
and pose- related deformations via learned blendshapes and skinning fields.
These attributes are pose-independent and can be used to morph the canonical
geometry and texture fields given novel expression and pose parameters. We
employ ray marching and iterative root-finding to locate the canonical surface
intersection for each pixel. A key contribution is our novel analytical
gradient formulation that enables end-to-end training of IMavatars from videos.
We show quantitatively and qualitatively that our method improves geometry and
covers a more complete expression space compared to state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Tue, 14 Dec 2021 15:30:32 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Dec 2021 15:55:34 GMT"
},
{
"version": "v3",
"created": "Wed, 30 Mar 2022 11:43:27 GMT"
},
{
"version": "v4",
"created": "Mon, 4 Apr 2022 14:59:07 GMT"
},
{
"version": "v5",
"created": "Tue, 19 Apr 2022 08:48:23 GMT"
},
{
"version": "v6",
"created": "Fri, 4 Nov 2022 12:01:17 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Zheng",
"Yufeng",
""
],
[
"Abrevaya",
"Victoria Fernández",
""
],
[
"Bühler",
"Marcel C.",
""
],
[
"Chen",
"Xu",
""
],
[
"Black",
"Michael J.",
""
],
[
"Hilliges",
"Otmar",
""
]
] |
new_dataset
| 0.981622 |
2204.13483
|
Lianqing Zheng
|
Lianqing Zheng, Zhixiong Ma, Xichan Zhu, Bin Tan, Sen Li, Kai Long,
Weiqi Sun, Sihan Chen, Lu Zhang, Mengyue Wan, Libo Huang, Jie Bai
|
TJ4DRadSet: A 4D Radar Dataset for Autonomous Driving
|
2022 IEEE International Intelligent Transportation Systems Conference
(ITSC 2022)
| null |
10.1109/ITSC55140.2022.9922539
| null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The next-generation high-resolution automotive radar (4D radar) can provide
additional elevation measurement and denser point clouds, which has great
potential for 3D sensing in autonomous driving. In this paper, we introduce a
dataset named TJ4DRadSet with 4D radar points for autonomous driving research.
The dataset was collected in various driving scenarios, with a total of 7757
synchronized frames in 44 consecutive sequences, which are well annotated with
3D bounding boxes and track ids. We provide a 4D radar-based 3D object
detection baseline for our dataset to demonstrate the effectiveness of deep
learning methods for 4D radar point clouds. The dataset can be accessed via the
following link: https://github.com/TJRadarLab/TJ4DRadSet.
|
[
{
"version": "v1",
"created": "Thu, 28 Apr 2022 13:17:06 GMT"
},
{
"version": "v2",
"created": "Sat, 30 Apr 2022 06:15:11 GMT"
},
{
"version": "v3",
"created": "Wed, 27 Jul 2022 09:46:06 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Zheng",
"Lianqing",
""
],
[
"Ma",
"Zhixiong",
""
],
[
"Zhu",
"Xichan",
""
],
[
"Tan",
"Bin",
""
],
[
"Li",
"Sen",
""
],
[
"Long",
"Kai",
""
],
[
"Sun",
"Weiqi",
""
],
[
"Chen",
"Sihan",
""
],
[
"Zhang",
"Lu",
""
],
[
"Wan",
"Mengyue",
""
],
[
"Huang",
"Libo",
""
],
[
"Bai",
"Jie",
""
]
] |
new_dataset
| 0.99983 |
2204.14264
|
Jinlan Fu
|
Jinlan Fu, See-Kiong Ng, Pengfei Liu
|
Polyglot Prompt: Multilingual Multitask PrompTraining
|
EMNLP 2022 (Main Conference)
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper aims for a potential architectural improvement for multilingual
learning and asks: Can different tasks from different languages be modeled in a
monolithic framework, i.e. without any task/language-specific module? The
benefit of achieving this could open new doors for future multilingual
research, including allowing systems trained on low resources to be further
assisted by other languages as well as other tasks. We approach this goal by
developing a learning framework named Polyglot Prompting to exploit prompting
methods for learning a unified semantic space for different languages and tasks
with multilingual prompt engineering. We performed a comprehensive evaluation
of 6 tasks, namely topic classification, sentiment classification, named entity
recognition, question answering, natural language inference, and summarization,
covering 24 datasets and 49 languages. The experimental results demonstrated
the efficacy of multilingual multitask prompt-based learning and led to
inspiring observations. We also present an interpretable multilingual
evaluation methodology and show how the proposed framework, multilingual
multitask prompt training, works. We release all datasets prompted in the best
setting and code.
|
[
{
"version": "v1",
"created": "Fri, 29 Apr 2022 17:40:50 GMT"
},
{
"version": "v2",
"created": "Fri, 4 Nov 2022 06:01:05 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Fu",
"Jinlan",
""
],
[
"Ng",
"See-Kiong",
""
],
[
"Liu",
"Pengfei",
""
]
] |
new_dataset
| 0.995668 |
2205.12496
|
Harsh Trivedi
|
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, Ashish Sabharwal
|
Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard
Contexts
|
Accepted at EMNLP'22
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Question-answering datasets require a broad set of reasoning skills. We show
how to use question decompositions to teach language models these broad
reasoning skills in a robust fashion. Specifically, we use widely available
QDMR representations to programmatically create hard-to-cheat synthetic
contexts for real questions in six multi-step reasoning datasets. These
contexts are carefully designed to avoid reasoning shortcuts prevalent in real
contexts that prevent models from learning the right skills. This results in a
pretraining dataset, named TeaBReaC, containing 525K multi-step questions (with
associated formal programs) covering about 900 reasoning patterns. We show that
pretraining standard language models (LMs) on TeaBReaC before fine-tuning them
on target datasets improves their performance by up to 13 F1 points across 4
multi-step QA datasets, with up to 21 point gain on more complex questions. The
resulting models also demonstrate higher robustness, with a 5-8 F1 point
improvement on two contrast sets. Furthermore, TeaBReaC pretraining
substantially improves model performance and robustness even when starting with
numerate LMs pretrained using recent methods (e.g., PReasM, POET). Our work
thus shows how to effectively use decomposition-guided contexts to robustly
teach multi-step reasoning.
|
[
{
"version": "v1",
"created": "Wed, 25 May 2022 05:13:21 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 19:38:06 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Trivedi",
"Harsh",
""
],
[
"Balasubramanian",
"Niranjan",
""
],
[
"Khot",
"Tushar",
""
],
[
"Sabharwal",
"Ashish",
""
]
] |
new_dataset
| 0.981168 |
2210.14136
|
Fazlourrahman Balouchzahi
|
Fazlourrahman Balouchzahi and Grigori Sidorov and Alexander Gelbukh
|
PolyHope: Two-Level Hope Speech Detection from Tweets
|
20 pages, 9 figures
| null | null | null |
cs.CL cs.AI cs.CY cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Hope is characterized as openness of spirit toward the future, a desire,
expectation, and wish for something to happen or to be true that remarkably
affects human's state of mind, emotions, behaviors, and decisions. Hope is
usually associated with concepts of desired expectations and
possibility/probability concerning the future. Despite its importance, hope has
rarely been studied as a social media analysis task. This paper presents a hope
speech dataset that classifies each tweet first into "Hope" and "Not Hope",
then into three fine-grained hope categories: "Generalized Hope", "Realistic
Hope", and "Unrealistic Hope" (along with "Not Hope"). English tweets in the
first half of 2022 were collected to build this dataset. Furthermore, we
describe our annotation process and guidelines in detail and discuss the
challenges of classifying hope and the limitations of the existing hope speech
detection corpora. In addition, we reported several baselines based on
different learning approaches, such as traditional machine learning, deep
learning, and transformers, to benchmark our dataset. We evaluated our
baselines using weighted-averaged and macro-averaged F1-scores. Observations
show that a strict process for annotator selection and detailed annotation
guidelines enhanced the dataset's quality. This strict annotation process
resulted in promising performance for simple machine learning classifiers with
only bi-grams; however, binary and multiclass hope speech detection results
reveal that contextual embedding models have higher performance in this
dataset.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 16:34:03 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 19:54:01 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Balouchzahi",
"Fazlourrahman",
""
],
[
"Sidorov",
"Grigori",
""
],
[
"Gelbukh",
"Alexander",
""
]
] |
new_dataset
| 0.999811 |
2211.02141
|
Mohammad Imrul Jubair
|
Simanta Deb Turja, Mohammad Imrul Jubair, Md. Shafiur Rahman, Md.
Hasib Al Zadid, Mohtasim Hossain Shovon, Md. Faraz Kabir Khan
|
Shapes2Toon: Generating Cartoon Characters from Simple Geometric Shapes
|
Accepted as a full paper in AICCSA2022 (19th ACS/IEEE International
Conference on Computer Systems and Applications)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Cartoons are an important part of our entertainment culture. Though drawing a
cartoon is not for everyone, creating it using an arrangement of basic
geometric primitives that approximates that character is a fairly frequent
technique in art. The key motivation behind this technique is that human bodies
- as well as cartoon figures - can be split down into various basic geometric
primitives. Numerous tutorials are available that demonstrate how to draw
figures using an appropriate arrangement of fundamental shapes, thus assisting
us in creating cartoon characters. This technique is very beneficial for
children in terms of teaching them how to draw cartoons. In this paper, we
develop a tool - shape2toon - that aims to automate this approach by utilizing
a generative adversarial network which combines geometric primitives (i.e.
circles) and generate a cartoon figure (i.e. Mickey Mouse) depending on the
given approximation. For this purpose, we created a dataset of geometrically
represented cartoon characters. We apply an image-to-image translation
technique on our dataset and report the results in this paper. The experimental
results show that our system can generate cartoon characters from input layout
of geometric shapes. In addition, we demonstrate a web-based tool as a
practical implication of our work.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 20:52:19 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Turja",
"Simanta Deb",
""
],
[
"Jubair",
"Mohammad Imrul",
""
],
[
"Rahman",
"Md. Shafiur",
""
],
[
"Zadid",
"Md. Hasib Al",
""
],
[
"Shovon",
"Mohtasim Hossain",
""
],
[
"Khan",
"Md. Faraz Kabir",
""
]
] |
new_dataset
| 0.999588 |
2211.02175
|
Bing Shuai
|
Bing Shuai, Alessandro Bergamo, Uta Buechler, Andrew Berneshawi,
Alyssa Boden, Joseph Tighe
|
Large Scale Real-World Multi-Person Tracking
|
ECCV 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a new large scale multi-person tracking dataset --
\texttt{PersonPath22}, which is over an order of magnitude larger than
currently available high quality multi-object tracking datasets such as MOT17,
HiEve, and MOT20 datasets. The lack of large scale training and test data for
this task has limited the community's ability to understand the performance of
their tracking systems on a wide range of scenarios and conditions such as
variations in person density, actions being performed, weather, and time of
day. \texttt{PersonPath22} dataset was specifically sourced to provide a wide
variety of these conditions and our annotations include rich meta-data such
that the performance of a tracker can be evaluated along these different
dimensions. The lack of training data has also limited the ability to perform
end-to-end training of tracking systems. As such, the highest performing
tracking systems all rely on strong detectors trained on external image
datasets. We hope that the release of this dataset will enable new lines of
research that take advantage of large scale video based training data.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 23:03:13 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Shuai",
"Bing",
""
],
[
"Bergamo",
"Alessandro",
""
],
[
"Buechler",
"Uta",
""
],
[
"Berneshawi",
"Andrew",
""
],
[
"Boden",
"Alyssa",
""
],
[
"Tighe",
"Joseph",
""
]
] |
new_dataset
| 0.999315 |
2211.02179
|
Kevin Cheang
|
Kevin Cheang, Cameron Rasmussen, Dayeol Lee, David W. Kohlbrenner,
Krste Asanovi\'c, Sanjit A. Seshia
|
Verifying RISC-V Physical Memory Protection
|
SECRISC-V 2019 Workshop
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We formally verify an open-source hardware implementation of physical memory
protection (PMP) in RISC-V, which is a standard feature used for memory
isolation in security critical systems such as the Keystone trusted execution
environment. PMP provides per-hardware-thread machine-mode control registers
that specify the access privileges for physical memory regions. We first
formalize the functional property of the PMP rules based on the RISC-V ISA
manual. Then, we use the LIME tool to translate an open-source implementation
of the PMP hardware module written in Chisel to the UCLID5 formal verification
language. We encode the formal specification in UCLID5 and verify the
functional correctness of the hardware. This is an initial effort towards
verifying the Keystone framework, where the trusted computing base (TCB) relies
on PMP to provide security guarantees such as integrity and confidentiality.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 23:12:28 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Cheang",
"Kevin",
""
],
[
"Rasmussen",
"Cameron",
""
],
[
"Lee",
"Dayeol",
""
],
[
"Kohlbrenner",
"David W.",
""
],
[
"Asanović",
"Krste",
""
],
[
"Seshia",
"Sanjit A.",
""
]
] |
new_dataset
| 0.998765 |
2211.02223
|
Chunming Jiang
|
Chunming Jiang, Yilei Zhang
|
Adversarial Defense via Neural Oscillation inspired Gradient Masking
| null | null | null | null |
cs.LG cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Spiking neural networks (SNNs) attract great attention due to their low power
consumption, low latency, and biological plausibility. As they are widely
deployed in neuromorphic devices for low-power brain-inspired computing,
security issues become increasingly important. However, compared to deep neural
networks (DNNs), SNNs currently lack specifically designed defense methods
against adversarial attacks. Inspired by neural membrane potential oscillation,
we propose a novel neural model that incorporates the bio-inspired oscillation
mechanism to enhance the security of SNNs. Our experiments show that SNNs with
neural oscillation neurons have better resistance to adversarial attacks than
ordinary SNNs with LIF neurons on kinds of architectures and datasets.
Furthermore, we propose a defense method that changes model's gradients by
replacing the form of oscillation, which hides the original training gradients
and confuses the attacker into using gradients of 'fake' neurons to generate
invalid adversarial samples. Our experiments suggest that the proposed defense
method can effectively resist both single-step and iterative attacks with
comparable defense effectiveness and much less computational costs than
adversarial training methods on DNNs. To the best of our knowledge, this is the
first work that establishes adversarial defense through masking surrogate
gradients on SNNs.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 02:13:19 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Jiang",
"Chunming",
""
],
[
"Zhang",
"Yilei",
""
]
] |
new_dataset
| 0.964993 |
2211.02269
|
Winston Wu
|
Changyuan Qiu, Winston Wu, Xinliang Frederick Zhang, Lu Wang
|
Late Fusion with Triplet Margin Objective for Multimodal Ideology
Prediction and Analysis
|
EMNLP 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Prior work on ideology prediction has largely focused on single modalities,
i.e., text or images. In this work, we introduce the task of multimodal
ideology prediction, where a model predicts binary or five-point scale
ideological leanings, given a text-image pair with political content. We first
collect five new large-scale datasets with English documents and images along
with their ideological leanings, covering news articles from a wide range of US
mainstream media and social media posts from Reddit and Twitter. We conduct
in-depth analyses of news articles and reveal differences in image content and
usage across the political spectrum. Furthermore, we perform extensive
experiments and ablation studies, demonstrating the effectiveness of targeted
pretraining objectives on different model components. Our best-performing
model, a late-fusion architecture pretrained with a triplet objective over
multimodal content, outperforms the state-of-the-art text-only model by almost
4% and a strong multimodal baseline with no pretraining by over 3%.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 05:45:26 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Qiu",
"Changyuan",
""
],
[
"Wu",
"Winston",
""
],
[
"Zhang",
"Xinliang Frederick",
""
],
[
"Wang",
"Lu",
""
]
] |
new_dataset
| 0.95154 |
2211.02295
|
Tao Yu
|
Tao Yu, Kento Kajiwara, Kiyomichi Araki, Kei Sakaguchi
|
Experiment of Multi-UAV Full-Duplex System Equipped with Directional
Antennas
|
The paper was accepted by IEEE Consumer Communications & Networking
Conference (CCNC) 2023
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the key enablers for the realization of a variety of unmanned aerial
vehicle (UAV)-based systems is the high-performance communication system
linking many UAVs and ground station. We have proposed a spectrum-efficient
full-duplex directional-antennas-equipped multi-UAV communication system with
low hardware complexity to address the issues of low spectrum efficiency caused
by co-channel interference in areal channels. In this paper, by using the
prototype system including UAVs and ground station, field experiments are
carried out to confirm the feasibility and effectiveness of the proposed
system's key feature, i.e., co-channel interference cancellation among UAVs by
directional antennas and UAV relative position control, instead of
energy-consuming dedicated self-interference cancellers on UAVs in traditional
full-duplex systems. Both uplink and downlink performance are tested.
Specially, in downlink experiment, channel power of interference between a pair
of two UAVs is measured when UAVs are in different positional relationships.
The experiment results agree well with the designs and confirm that the
proposed system can greatly improve the system performance.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 07:28:16 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Yu",
"Tao",
""
],
[
"Kajiwara",
"Kento",
""
],
[
"Araki",
"Kiyomichi",
""
],
[
"Sakaguchi",
"Kei",
""
]
] |
new_dataset
| 0.982254 |
2211.02321
|
Zhao Zhang
|
Bo Wang, Zhao Zhang, Mingbo Zhao, Xiaojie Jin, Mingliang Xu, Meng Wang
|
OSIC: A New One-Stage Image Captioner Coined
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mainstream image caption models are usually two-stage captioners, i.e.,
calculating object features by pre-trained detector, and feeding them into a
language model to generate text descriptions. However, such an operation will
cause a task-based information gap to decrease the performance, since the
object features in detection task are suboptimal representation and cannot
provide all necessary information for subsequent text generation. Besides,
object features are usually represented by the last layer features that lose
the local details of input images. In this paper, we propose a novel One-Stage
Image Captioner (OSIC) with dynamic multi-sight learning, which directly
transforms input image into descriptive sentences in one stage. As a result,
the task-based information gap can be greatly reduced. To obtain rich features,
we use the Swin Transformer to calculate multi-level features, and then feed
them into a novel dynamic multi-sight embedding module to exploit both global
structure and local texture of input images. To enhance the global modeling of
encoder for caption, we propose a new dual-dimensional refining module to
non-locally model the interaction of the embedded features. Finally, OSIC can
obtain rich and useful information to improve the image caption task. Extensive
comparisons on benchmark MS-COCO dataset verified the superior performance of
our method.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 08:50:09 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Wang",
"Bo",
""
],
[
"Zhang",
"Zhao",
""
],
[
"Zhao",
"Mingbo",
""
],
[
"Jin",
"Xiaojie",
""
],
[
"Xu",
"Mingliang",
""
],
[
"Wang",
"Meng",
""
]
] |
new_dataset
| 0.993275 |
2211.02356
|
Rajat Tandon
|
Jeffrey Liu, Rajat Tandon, Uma Durairaj, Jiani Guo, Spencer
Zahabizadeh, Sanjana Ilango, Jeremy Tang, Neelesh Gupta, Zoe Zhou, Jelena
Mirkovic
|
Did your child get disturbed by an inappropriate advertisement on
YouTube?
|
In Proceedings of KDD Undergraduate Consortium (KDD-UC 2022)
| null | null | null |
cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
YouTube is a popular video platform for sharing creative content and ideas,
targeting different demographics. Adults, older children, and young children
are all avid viewers of YouTube videos. Meanwhile, countless young-kid-oriented
channels have produced numerous instructional and age appropriate videos for
young children. However, inappropriate content for young children, such as
violent or sexually suggestive content, still exists. And children lack the
ability to decide whether a video is appropriate for them or not, which then
causes a huge risk to children's mental health. Prior works have focused on
identifying YouTube videos that are inappropriate for children. However, these
works ignore that not only the actual video content influences children, but
also the advertisements that are shown with those videos.
In this paper, we quantify the influence of inappropriate advertisements on
YouTube videos that are appropriate for young children to watch. We analyze the
advertising patterns of 24.6 K diverse YouTube videos appropriate for young
children. We find that 9.9% of the 4.6 K unique advertisements shown on these
24.6 K videos contain inappropriate content for young children. Moreover, we
observe that 26.9% of all the 24.6 K appropriate videos include at least one ad
that is inappropriate for young children. Additionally, we publicly release our
datasets and provide recommendations about how to address this issue.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 10:28:54 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Liu",
"Jeffrey",
""
],
[
"Tandon",
"Rajat",
""
],
[
"Durairaj",
"Uma",
""
],
[
"Guo",
"Jiani",
""
],
[
"Zahabizadeh",
"Spencer",
""
],
[
"Ilango",
"Sanjana",
""
],
[
"Tang",
"Jeremy",
""
],
[
"Gupta",
"Neelesh",
""
],
[
"Zhou",
"Zoe",
""
],
[
"Mirkovic",
"Jelena",
""
]
] |
new_dataset
| 0.999453 |
2211.02443
|
Yuhang Gai
|
Yuhang Gai, Bing Wang, Jiwen Zhang, Dan Wu, and Ken Chen
|
Robotic Assembly Control Reconfiguration Based on Transfer Reinforcement
Learning for Objects with Different Geometric Features
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Robotic force-based compliance control is a preferred approach to achieve
high-precision assembly tasks. When the geometric features of assembly objects
are asymmetric or irregular, reinforcement learning (RL) agents are gradually
incorporated into the compliance controller to adapt to complex force-pose
mapping which is hard to model analytically. Since force-pose mapping is
strongly dependent on geometric features, a compliance controller is only
optimal for current geometric features. To reduce the learning cost of assembly
objects with different geometric features, this paper is devoted to answering
how to reconfigure existing controllers for new assembly objects with different
geometric features. In this paper, model-based parameters are first
reconfigured based on the proposed Equivalent Theory of Compliance Law (ETCL).
Then the RL agent is transferred based on the proposed Weighted Dimensional
Policy Distillation (WDPD) method. The experiment results demonstrate that the
control reconfiguration method costs less time and achieves better control
performance, which confirms the validity of proposed methods.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 13:31:11 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Gai",
"Yuhang",
""
],
[
"Wang",
"Bing",
""
],
[
"Zhang",
"Jiwen",
""
],
[
"Wu",
"Dan",
""
],
[
"Chen",
"Ken",
""
]
] |
new_dataset
| 0.952423 |
2211.02567
|
Dazhen Deng
|
Dazhen Deng, Aoyu Wu, Haotian Li, Ji Lan, Yong Wang, Huamin Qu,
Yingcai Wu
|
KB4VA: A Knowledge Base of Visualization Designs for Visual Analytics
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Visual analytics (VA) systems have been widely used to facilitate
decision-making and analytical reasoning in various application domains. VA
involves visual designs, interaction designs, and data mining, which is a
systematic and complex paradigm. In this work, we focus on the design of
effective visualizations for complex data and analytical tasks, which is a
critical step in designing a VA system. This step is challenging because it
requires extensive knowledge about domain problems and visualization to design
effective encodings. Existing visualization designs published in top venues are
valuable resources to inspire designs for problems with similar data structures
and tasks. However, those designs are hard to understand, parse, and retrieve
due to the lack of specifications. To address this problem, we build KB4VA, a
knowledge base of visualization designs in VA systems with comprehensive labels
about their analytical tasks and visual encodings. Our labeling scheme is
inspired by a workshop study with 12 VA researchers to learn user requirements
in understanding and retrieving professional visualization designs in VA
systems. The theme extends Vega-Lite specifications for describing advanced and
composited visualization designs in a declarative manner, thus facilitating
human understanding and automatic indexing. To demonstrate the usefulness of
our knowledge base, we present a user study about design inspirations for VA
tasks. In summary, our work opens new perspectives for enhancing the
accessibility and reusability of professional visualization designs.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 01:58:13 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Deng",
"Dazhen",
""
],
[
"Wu",
"Aoyu",
""
],
[
"Li",
"Haotian",
""
],
[
"Lan",
"Ji",
""
],
[
"Wang",
"Yong",
""
],
[
"Qu",
"Huamin",
""
],
[
"Wu",
"Yingcai",
""
]
] |
new_dataset
| 0.999033 |
2211.02598
|
Paolo Gibertini
|
Paolo Gibertini, Luca Fehlings, Suzanne Lancaster, Quang Duong, Thomas
Mikolajick, Catherine Dubourdieu, Stefan Slesazeck, Erika Covi, Veeresh
Deshpande
|
A Ferroelectric Tunnel Junction-based Integrate-and-Fire Neuron
| null | null | null | null |
cs.ET cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
Event-based neuromorphic systems provide a low-power solution by using
artificial neurons and synapses to process data asynchronously in the form of
spikes. Ferroelectric Tunnel Junctions (FTJs) are ultra low-power memory
devices and are well-suited to be integrated in these systems. Here, we present
a hybrid FTJ-CMOS Integrate-and-Fire neuron which constitutes a fundamental
building block for new-generation neuromorphic networks for edge computing. We
demonstrate electrically tunable neural dynamics achievable by tuning the
switching of the FTJ device.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 17:13:58 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Gibertini",
"Paolo",
""
],
[
"Fehlings",
"Luca",
""
],
[
"Lancaster",
"Suzanne",
""
],
[
"Duong",
"Quang",
""
],
[
"Mikolajick",
"Thomas",
""
],
[
"Dubourdieu",
"Catherine",
""
],
[
"Slesazeck",
"Stefan",
""
],
[
"Covi",
"Erika",
""
],
[
"Deshpande",
"Veeresh",
""
]
] |
new_dataset
| 0.999708 |
2211.02648
|
Juan Carlos Dibene Simental
|
Juan C. Dibene, Enrique Dunn
|
HoloLens 2 Sensor Streaming
|
Technical report
| null | null | null |
cs.MM
|
http://creativecommons.org/licenses/by/4.0/
|
We present a HoloLens 2 server application for streaming device data via TCP
in real time. The server can stream data from the four grayscale cameras, depth
sensor, IMU, front RGB camera, microphone, head tracking, eye tracking, and
hand tracking. Each sent data frame has a timestamp and, optionally, the
instantaneous pose of the device in 3D space. The server allows downloading
device calibration data, such as camera intrinsics, and can be integrated into
Unity projects as a plugin, with support for basic upstream capabilities. To
achieve real time video streaming at full frame rate, we leverage the video
encoding capabilities of the HoloLens 2. Finally, we present a Python library
for receiving and decoding the data, which includes utilities that facilitate
passing the data to other libraries. The source code, Python demos, and
precompiled binaries are available at https://github.com/jdibenes/hl2ss.
|
[
{
"version": "v1",
"created": "Fri, 4 Nov 2022 17:58:52 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Dibene",
"Juan C.",
""
],
[
"Dunn",
"Enrique",
""
]
] |
new_dataset
| 0.998505 |
2211.02652
|
Jianfei Zhou
|
Jianfei Zhou and Tianxing Jiang and Shuwei Song and Ting Chen
|
AntFuzzer: A Grey-Box Fuzzing Framework for EOSIO Smart Contracts
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the past few years, several attacks against the vulnerabilities of EOSIO
smart contracts have caused severe financial losses to this prevalent
blockchain platform. As a lightweight test-generation approach, grey-box
fuzzing can open up the possibility of improving the security of EOSIO smart
contracts. However, developing a practical grey-box fuzzer for EOSIO smart
contracts from scratch is time-consuming and requires a deep understanding of
EOSIO internals. In this work, we proposed AntFuzzer, the first highly
extensible grey-box fuzzing framework for EOSIO smart contracts. AntFuzzer
implements a novel approach that interfaces AFL to conduct AFL-style grey-box
fuzzing on EOSIO smart contracts. Compared to black-box fuzzing tools,
AntFuzzer can effectively trigger those hard-to-cover branches. It achieved an
improvement in code coverage on 37.5% of smart contracts in our benchmark
dataset. AntFuzzer provides unified interfaces for users to easily develop new
detection plugins for continually emerging vulnerabilities. We have implemented
6 detection plugins on AntFuzzer to detect major vulnerabilities of EOSIO smart
contracts. In our large-scale fuzzing experiments on 4,616 real-world smart
contracts, AntFuzzer successfully detected 741 vulnerabilities. The results
demonstrate the effectiveness and efficiency of AntFuzzer and our detection pl
|
[
{
"version": "v1",
"created": "Wed, 2 Nov 2022 08:29:21 GMT"
}
] | 2022-11-07T00:00:00 |
[
[
"Zhou",
"Jianfei",
""
],
[
"Jiang",
"Tianxing",
""
],
[
"Song",
"Shuwei",
""
],
[
"Chen",
"Ting",
""
]
] |
new_dataset
| 0.996203 |
1906.11898
|
L\'eonard Boussioux
|
L\'eonard Boussioux, Tom\'as Giro-Larraz, Charles Guille-Escuret,
Mehdi Cherti, Bal\'azs K\'egl
|
InsectUp: Crowdsourcing Insect Observations to Assess Demographic Shifts
and Improve Classification
|
Appearing at the International Conference on Machine Learning, AI for
Social Good Workshop, Long Beach, United States, 2019 Appearing at the
International Conference on Computer Vision, AI for Wildlife Conservation
Workshop, Seoul, South Korea, 2019 5 pages, 6 figures
| null | null | null |
cs.CV cs.AI cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Insects play such a crucial role in ecosystems that a shift in demography of
just a few species can have devastating consequences at environmental, social
and economic levels. Despite this, evaluation of insect demography is strongly
limited by the difficulty of collecting census data at sufficient scale. We
propose a method to gather and leverage observations from bystanders, hikers,
and entomology enthusiasts in order to provide researchers with data that could
significantly help anticipate and identify environmental threats. Finally, we
show that there is indeed interest on both sides for such collaboration.
|
[
{
"version": "v1",
"created": "Thu, 30 May 2019 00:57:15 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Jan 2020 18:39:03 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Boussioux",
"Léonard",
""
],
[
"Giro-Larraz",
"Tomás",
""
],
[
"Guille-Escuret",
"Charles",
""
],
[
"Cherti",
"Mehdi",
""
],
[
"Kégl",
"Balázs",
""
]
] |
new_dataset
| 0.999484 |
2102.01468
|
Yinbo Yu
|
Yinbo Yu and Jiajia Liu
|
TAPInspector: Safety and Liveness Verification of Concurrent
Trigger-Action IoT Systems
| null |
IEEE Transactions on Information Forensics and Security 2022
|
10.1109/TIFS.2022.3214084
| null |
cs.CR cs.HC cs.NI cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Trigger-action programming (TAP) is a popular end-user programming framework
that can simplify the Internet of Things (IoT) automation with simple
trigger-action rules. However, it also introduces new security and safety
threats. A lot of advanced techniques have been proposed to address this
problem. Rigorously reasoning about the security of a TAP-based IoT system
requires a well-defined model and verification method both against rule
semantics and physical-world features, e.g., concurrency, rule latency,
extended action, tardy attributes, and connection-based rule interactions,
which has been missing until now. By analyzing these features, we find 9 new
types of rule interaction vulnerabilities and validate them on two commercial
IoT platforms. We then present TAPInspector, a novel system to detect these
interaction vulnerabilities in concurrent TAP-based IoT systems. It
automatically extracts TAP rules from IoT apps, translates them into a hybrid
model by model slicing and state compression, and performs semantic analysis
and model checking with various safety and liveness properties. Our experiments
corroborate that TAPInspector is practical: it identifies 533 violations
related to rule interaction from 1108 real-world market IoT apps and is at
least 60000 times faster than the baseline without optimization.
|
[
{
"version": "v1",
"created": "Tue, 2 Feb 2021 12:39:59 GMT"
},
{
"version": "v2",
"created": "Fri, 6 May 2022 01:17:07 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Yu",
"Yinbo",
""
],
[
"Liu",
"Jiajia",
""
]
] |
new_dataset
| 0.993593 |
2103.14027
|
Yosuke Shinya
|
Yosuke Shinya
|
USB: Universal-Scale Object Detection Benchmark
|
BMVC 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Benchmarks, such as COCO, play a crucial role in object detection. However,
existing benchmarks are insufficient in scale variation, and their protocols
are inadequate for fair comparison. In this paper, we introduce the
Universal-Scale object detection Benchmark (USB). USB has variations in object
scales and image domains by incorporating COCO with the recently proposed Waymo
Open Dataset and Manga109-s dataset. To enable fair comparison and inclusive
research, we propose training and evaluation protocols. They have multiple
divisions for training epochs and evaluation image resolutions, like weight
classes in sports, and compatibility across training protocols, like the
backward compatibility of the Universal Serial Bus. Specifically, we request
participants to report results with not only higher protocols (longer training)
but also lower protocols (shorter training). Using the proposed benchmark and
protocols, we conducted extensive experiments using 15 methods and found
weaknesses of existing COCO-biased methods. The code is available at
https://github.com/shinya7y/UniverseNet .
|
[
{
"version": "v1",
"created": "Thu, 25 Mar 2021 17:59:15 GMT"
},
{
"version": "v2",
"created": "Wed, 8 Dec 2021 18:32:00 GMT"
},
{
"version": "v3",
"created": "Wed, 2 Nov 2022 19:12:01 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Shinya",
"Yosuke",
""
]
] |
new_dataset
| 0.999858 |
2105.06763
|
EPTCS
|
Matteo Capucci (University of Strathclyde), Neil Ghani (University of
Strathclyde), J\'er\'emy Ledent (University of Strathclyde), Fredrik Nordvall
Forsberg (University of Strathclyde)
|
Translating Extensive Form Games to Open Games with Agency
|
In Proceedings ACT 2021, arXiv:2211.01102
|
EPTCS 372, 2022, pp. 221-234
|
10.4204/EPTCS.372.16
| null |
cs.GT cs.MA math.CT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show open games cover extensive form games with both perfect and imperfect
information. Doing so forces us to address two current weaknesses in open
games: the lack of a notion of player and their agency within open games, and
the lack of choice operators. Using the former we construct the latter, and
these choice operators subsume previous proposed operators for open games,
thereby making progress towards a core, canonical and ergonomic calculus of
game operators. Collectively these innovations increase the level of
compositionality of open games, and demonstrate their expressiveness.
|
[
{
"version": "v1",
"created": "Fri, 14 May 2021 11:15:25 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 14:09:57 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Capucci",
"Matteo",
"",
"University of Strathclyde"
],
[
"Ghani",
"Neil",
"",
"University of\n Strathclyde"
],
[
"Ledent",
"Jérémy",
"",
"University of Strathclyde"
],
[
"Forsberg",
"Fredrik Nordvall",
"",
"University of Strathclyde"
]
] |
new_dataset
| 0.982387 |
2106.07763
|
EPTCS
|
Guillaume Boisseau (University of Oxford, UK), Pawe{\l} Soboci\'nski
(Tallinn University of Technology, Estonia)
|
String Diagrammatic Electrical Circuit Theory
|
In Proceedings ACT 2021, arXiv:2211.01102
|
EPTCS 372, 2022, pp. 178-191
|
10.4204/EPTCS.372.13
| null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
We develop a comprehensive string diagrammatic treatment of electrical
circuits. Building on previous, limited case studies, we introduce controlled
sources and meters as elements, and the impedance calculus, a powerful toolbox
for diagrammatic reasoning on circuit diagrams. We demonstrate the power of our
approach by giving idiomatic proofs of several textbook results, including the
superposition theorem and Thevenin's theorem.
|
[
{
"version": "v1",
"created": "Mon, 14 Jun 2021 21:21:52 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 14:18:10 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Boisseau",
"Guillaume",
"",
"University of Oxford, UK"
],
[
"Sobociński",
"Paweł",
"",
"Tallinn University of Technology, Estonia"
]
] |
new_dataset
| 0.999164 |
2202.06633
|
Jianqiao Zhao
|
Jianqiao Zhao, Yanyang Li, Wanyu Du, Yangfeng Ji, Dong Yu, Michael R.
Lyu, Liwei Wang
|
FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment
Act Flows
|
EMNLP 2022 camera-ready version
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite recent progress in open-domain dialogue evaluation, how to develop
automatic metrics remains an open problem. We explore the potential of dialogue
evaluation featuring dialog act information, which was hardly explicitly
modeled in previous methods. However, defined at the utterance level in
general, dialog act is of coarse granularity, as an utterance can contain
multiple segments possessing different functions. Hence, we propose segment
act, an extension of dialog act from utterance level to segment level, and
crowdsource a large-scale dataset for it. To utilize segment act flows,
sequences of segment acts, for evaluation, we develop the first consensus-based
dialogue evaluation framework, FlowEval. This framework provides a
reference-free approach for dialog evaluation by finding pseudo-references.
Extensive experiments against strong baselines on three benchmark datasets
demonstrate the effectiveness and other desirable characteristics of our
FlowEval, pointing out a potential path for better dialogue evaluation.
|
[
{
"version": "v1",
"created": "Mon, 14 Feb 2022 11:37:20 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 07:36:50 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Zhao",
"Jianqiao",
""
],
[
"Li",
"Yanyang",
""
],
[
"Du",
"Wanyu",
""
],
[
"Ji",
"Yangfeng",
""
],
[
"Yu",
"Dong",
""
],
[
"Lyu",
"Michael R.",
""
],
[
"Wang",
"Liwei",
""
]
] |
new_dataset
| 0.998644 |
2204.11235
|
Ga\"etan Dou\'eneau-Tabot
|
Olivier Carton, Ga\"etan Dou\'eneau-Tabot
|
Continuous rational functions are deterministic regular
|
41 pages
| null | null | null |
cs.FL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
A word-to-word function is rational if it can be realized by a
non-deterministic one-way transducer. Over finite words, it is a classical
result that any rational function is regular, i.e. it can be computed by a
deterministic two-way transducer, or equivalently, by a deterministic streaming
string transducer (a one-way automaton which manipulates string registers).
This result no longer holds for infinite words, since a non-deterministic
one-way transducer can guess, and check along its run, properties such as
infinitely many occurrences of some pattern, which is impossible for a
deterministic machine. In this paper, we identify the class of rational
functions over infinite words which are also computable by a deterministic
two-way transducer. It coincides with the class of rational functions which are
continuous, and this property can thus be decided. This solves an open question
raised in a previous paper of Dave et al.
|
[
{
"version": "v1",
"created": "Sun, 24 Apr 2022 10:07:21 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 07:11:57 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Carton",
"Olivier",
""
],
[
"Douéneau-Tabot",
"Gaëtan",
""
]
] |
new_dataset
| 0.99191 |
2207.04908
|
Aldi Piroli
|
Aldi Piroli, Vinzenz Dallabetta, Marc Walessa, Daniel Meissner,
Johannes Kopp, Klaus Dietmayer
|
Detection of Condensed Vehicle Gas Exhaust in LiDAR Point Clouds
|
Accepted for ITSC2022
|
2022 IEEE 25th International Conference on Intelligent
Transportation Systems (ITSC)
|
10.1109/ITSC55140.2022.9922475
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
LiDAR sensors used in autonomous driving applications are negatively affected
by adverse weather conditions. One common, but understudied effect, is the
condensation of vehicle gas exhaust in cold weather. This everyday phenomenon
can severely impact the quality of LiDAR measurements, resulting in a less
accurate environment perception by creating artifacts like ghost object
detections. In the literature, the semantic segmentation of adverse weather
effects like rain and fog is achieved using learning-based approaches. However,
such methods require large sets of labeled data, which can be extremely
expensive and laborious to get. We address this problem by presenting a
two-step approach for the detection of condensed vehicle gas exhaust. First, we
identify for each vehicle in a scene its emission area and detect gas exhaust
if present. Then, isolated clouds are detected by modeling through time the
regions of space where gas exhaust is likely to be present. We test our method
on real urban data, showing that our approach can reliably detect gas exhaust
in different scenarios, making it appealing for offline pre-labeling and online
applications such as ghost object detection.
|
[
{
"version": "v1",
"created": "Mon, 11 Jul 2022 14:36:27 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Piroli",
"Aldi",
""
],
[
"Dallabetta",
"Vinzenz",
""
],
[
"Walessa",
"Marc",
""
],
[
"Meissner",
"Daniel",
""
],
[
"Kopp",
"Johannes",
""
],
[
"Dietmayer",
"Klaus",
""
]
] |
new_dataset
| 0.995557 |
2209.13511
|
Yanbing Mao
|
Yanbing Mao, Lui Sha, Huajie Shao, Yuliang Gu, Qixin Wang, Tarek
Abdelzaher
|
Phy-Taylor: Physics-Model-Based Deep Neural Networks
|
Working Paper
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Purely data-driven deep neural networks (DNNs) applied to physical
engineering systems can infer relations that violate physics laws, thus leading
to unexpected consequences. To address this challenge, we propose a
physics-model-based DNN framework, called Phy-Taylor, that accelerates learning
compliant representations with physical knowledge. The Phy-Taylor framework
makes two key contributions; it introduces a new architectural
Physics-compatible neural network (PhN), and features a novel compliance
mechanism, we call {\em Physics-guided Neural Network Editing\}. The PhN aims
to directly capture nonlinearities inspired by physical quantities, such as
kinetic energy, potential energy, electrical power, and aerodynamic drag force.
To do so, the PhN augments neural network layers with two key components: (i)
monomials of Taylor series expansion of nonlinear functions capturing physical
knowledge, and (ii) a suppressor for mitigating the influence of noise. The
neural-network editing mechanism further modifies network links and activation
functions consistently with physical knowledge. As an extension, we also
propose a self-correcting Phy-Taylor framework that introduces two additional
capabilities: (i) physics-model-based safety relationship learning, and (ii)
automatic output correction when violations of safety occur. Through
experiments, we show that (by expressing hard-to-learn nonlinearities directly
and by constraining dependencies) Phy-Taylor features considerably fewer
parameters, and a remarkably accelerated training process, while offering
enhanced model robustness and accuracy.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 16:30:35 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 04:44:33 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Mao",
"Yanbing",
""
],
[
"Sha",
"Lui",
""
],
[
"Shao",
"Huajie",
""
],
[
"Gu",
"Yuliang",
""
],
[
"Wang",
"Qixin",
""
],
[
"Abdelzaher",
"Tarek",
""
]
] |
new_dataset
| 0.995443 |
2210.02890
|
Soujanya Poria
|
Siqi Shen, Deepanway Ghosal, Navonil Majumder, Henry Lim, Rada
Mihalcea, Soujanya Poria
|
Multiview Contextual Commonsense Inference: A New Dataset and Task
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Contextual commonsense inference is the task of generating various types of
explanations around the events in a dyadic dialogue, including cause,
motivation, emotional reaction, and others. Producing a coherent and
non-trivial explanation requires awareness of the dialogue's structure and of
how an event is grounded in the context. In this work, we create CICEROv2, a
dataset consisting of 8,351 instances from 2,379 dialogues, containing multiple
human-written answers for each contextual commonsense inference question,
representing a type of explanation on cause, subsequent event, motivation, and
emotional reaction. We show that the inferences in CICEROv2 are more
semantically diverse than other contextual commonsense inference datasets. To
solve the inference task, we propose a collection of pre-training objectives,
including concept denoising and utterance sorting to prepare a pre-trained
model for the downstream contextual commonsense inference task. Our results
show that the proposed pre-training objectives are effective at adapting the
pre-trained T5-Large model for the contextual commonsense inference task.
|
[
{
"version": "v1",
"created": "Thu, 6 Oct 2022 13:08:41 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 00:33:48 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Shen",
"Siqi",
""
],
[
"Ghosal",
"Deepanway",
""
],
[
"Majumder",
"Navonil",
""
],
[
"Lim",
"Henry",
""
],
[
"Mihalcea",
"Rada",
""
],
[
"Poria",
"Soujanya",
""
]
] |
new_dataset
| 0.999891 |
2210.11703
|
Kaiyuan Chen
|
Kaiyuan Chen, Alexander Thomas, Hanming Lu, William Mullen, Jeffery
Ichnowski, Rahul Arya, Nivedha Krishnakumar, Ryan Teoh, Willis Wang, Anthony
Joseph, John Kubiatowicz
|
SCL: A Secure Concurrency Layer For Paranoid Stateful Lambdas
|
updated with acknowledgement; 14 pages, 11 figures, 2 tables
| null | null | null |
cs.CR cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a federated Function-as-a-Service (FaaS) execution model that
provides secure and stateful execution in both Cloud and Edge environments. The
FaaS workers, called Paranoid Stateful Lambdas (PSLs), collaborate with one
another to perform large parallel computations. We exploit cryptographically
hardened and mobile bundles of data, called DataCapsules, to provide persistent
state for our PSLs, whose execution is protected using hardware-secured TEEs.
To make PSLs easy to program and performant, we build the familiar Key-Value
Store interface on top of DataCapsules in a way that allows amortization of
cryptographic operations. We demonstrate PSLs functioning in an edge
environment running on a group of Intel NUCs with SGXv2.
As described, our Secure Concurrency Layer (SCL), provides
eventually-consistent semantics over written values using untrusted and
unordered multicast. All SCL communication is encrypted, unforgeable, and
private. For durability, updates are recorded in replicated DataCapsules, which
are append-only cryptographically-hardened blockchain with confidentiality,
integrity, and provenance guarantees. Values for inactive keys are stored in a
log-structured merge-tree (LSM) in the same DataCapsule. SCL features a variety
of communication optimizations, such as an efficient message passing framework
that reduces the latency up to 44x from the Intel SGX SDK, and an actor-based
cryptographic processing architecture that batches cryptographic operations and
increases throughput by 81x.
|
[
{
"version": "v1",
"created": "Fri, 21 Oct 2022 03:10:03 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Nov 2022 23:52:02 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Chen",
"Kaiyuan",
""
],
[
"Thomas",
"Alexander",
""
],
[
"Lu",
"Hanming",
""
],
[
"Mullen",
"William",
""
],
[
"Ichnowski",
"Jeffery",
""
],
[
"Arya",
"Rahul",
""
],
[
"Krishnakumar",
"Nivedha",
""
],
[
"Teoh",
"Ryan",
""
],
[
"Wang",
"Willis",
""
],
[
"Joseph",
"Anthony",
""
],
[
"Kubiatowicz",
"John",
""
]
] |
new_dataset
| 0.998671 |
2211.01226
|
Artem Reshetnikov
|
Artem Reshetnikov, Maria-Cristina Marinescu, Joaquim More Lopez
|
DEArt: Dataset of European Art
|
VISART VI. Workshop at the European Conference of Computer Vision
(ECCV)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Large datasets that were made publicly available to the research community
over the last 20 years have been a key enabling factor for the advances in deep
learning algorithms for NLP or computer vision. These datasets are generally
pairs of aligned image / manually annotated metadata, where images are
photographs of everyday life. Scholarly and historical content, on the other
hand, treat subjects that are not necessarily popular to a general audience,
they may not always contain a large number of data points, and new data may be
difficult or impossible to collect. Some exceptions do exist, for instance,
scientific or health data, but this is not the case for cultural heritage (CH).
The poor performance of the best models in computer vision - when tested over
artworks - coupled with the lack of extensively annotated datasets for CH, and
the fact that artwork images depict objects and actions not captured by
photographs, indicate that a CH-specific dataset would be highly valuable for
this community. We propose DEArt, at this point primarily an object detection
and pose classification dataset meant to be a reference for paintings between
the XIIth and the XVIIIth centuries. It contains more than 15000 images, about
80% non-iconic, aligned with manual annotations for the bounding boxes
identifying all instances of 69 classes as well as 12 possible poses for boxes
identifying human-like objects. Of these, more than 50 classes are CH-specific
and thus do not appear in other datasets; these reflect imaginary beings,
symbolic entities and other categories related to art. Additionally, existing
datasets do not include pose annotations. Our results show that object
detectors for the cultural heritage domain can achieve a level of precision
comparable to state-of-art models for generic images via transfer learning.
|
[
{
"version": "v1",
"created": "Wed, 2 Nov 2022 16:05:35 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 07:33:46 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Reshetnikov",
"Artem",
""
],
[
"Marinescu",
"Maria-Cristina",
""
],
[
"Lopez",
"Joaquim More",
""
]
] |
new_dataset
| 0.999764 |
2211.01551
|
Faisal Tareque Shohan
|
Faisal Tareque Shohan, Abu Ubaida Akash, Muhammad Ibrahim, Mohammad
Shafiul Alam
|
Crime Prediction using Machine Learning with a Novel Crime Dataset
|
24 pages
| null | null | null |
cs.LG cs.AI cs.CY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Crime is an unlawful act that carries legal repercussions. Bangladesh has a
high crime rate due to poverty, population growth, and many other
socio-economic issues. For law enforcement agencies, understanding crime
patterns is essential for preventing future criminal activity. For this
purpose, these agencies need structured crime database. This paper introduces a
novel crime dataset that contains temporal, geographic, weather, and
demographic data about 6574 crime incidents of Bangladesh. We manually gather
crime news articles of a seven year time span from a daily newspaper archive.
We extract basic features from these raw text. Using these basic features, we
then consult standard service-providers of geo-location and weather data in
order to garner these information related to the collected crime incidents.
Furthermore, we collect demographic information from Bangladesh National Census
data. All these information are combined that results in a standard machine
learning dataset. Together, 36 features are engineered for the crime prediction
task. Five supervised machine learning classification algorithms are then
evaluated on this newly built dataset and satisfactory results are achieved. We
also conduct exploratory analysis on various aspects the dataset. This dataset
is expected to serve as the foundation for crime incidence prediction systems
for Bangladesh and other countries. The findings of this study will help law
enforcement agencies to forecast and contain crime as well as to ensure optimal
resource allocation for crime patrol and prevention.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 01:55:52 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Shohan",
"Faisal Tareque",
""
],
[
"Akash",
"Abu Ubaida",
""
],
[
"Ibrahim",
"Muhammad",
""
],
[
"Alam",
"Mohammad Shafiul",
""
]
] |
new_dataset
| 0.986548 |
2211.01559
|
Yifan Gao
|
Yifan Gao, Danni Zhang and Haoyue Li
|
The ProfessionAl Go annotation datasEt (PAGE)
|
Journal version of arXiv:2205.00254, under review
| null | null | null |
cs.AI cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The game of Go has been highly under-researched due to the lack of game
records and analysis tools. In recent years, the increasing number of
professional competitions and the advent of AlphaZero-based algorithms provide
an excellent opportunity for analyzing human Go games on a large scale. In this
paper, we present the ProfessionAl Go annotation datasEt (PAGE), containing
98,525 games played by 2,007 professional players and spans over 70 years. The
dataset includes rich AI analysis results for each move. Moreover, PAGE
provides detailed metadata for every player and game after manual cleaning and
labeling. Beyond the preliminary analysis of the dataset, we provide sample
tasks that benefit from our dataset to demonstrate the potential application of
PAGE in multiple research directions. To the best of our knowledge, PAGE is the
first dataset with extensive annotation in the game of Go. This work is an
extended version of [1] where we perform a more detailed description, analysis,
and application.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 02:41:41 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Gao",
"Yifan",
""
],
[
"Zhang",
"Danni",
""
],
[
"Li",
"Haoyue",
""
]
] |
new_dataset
| 0.998482 |
2211.01566
|
Ramchander Rao Bhaskara
|
Roshan Thomas Eapen, Ramchander Rao Bhaskara, Manoranjan Majji
|
NaRPA: Navigation and Rendering Pipeline for Astronautics
|
49 pages, 22 figures
| null | null | null |
cs.GR cs.CV cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents Navigation and Rendering Pipeline for Astronautics
(NaRPA) - a novel ray-tracing-based computer graphics engine to model and
simulate light transport for space-borne imaging. NaRPA incorporates lighting
models with attention to atmospheric and shading effects for the synthesis of
space-to-space and ground-to-space virtual observations. In addition to image
rendering, the engine also possesses point cloud, depth, and contour map
generation capabilities to simulate passive and active vision-based sensors and
to facilitate the designing, testing, or verification of visual navigation
algorithms. Physically based rendering capabilities of NaRPA and the efficacy
of the proposed rendering algorithm are demonstrated using applications in
representative space-based environments. A key demonstration includes NaRPA as
a tool for generating stereo imagery and application in 3D coordinate
estimation using triangulation. Another prominent application of NaRPA includes
a novel differentiable rendering approach for image-based attitude estimation
is proposed to highlight the efficacy of the NaRPA engine for simulating
vision-based navigation and guidance operations.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 03:07:21 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Eapen",
"Roshan Thomas",
""
],
[
"Bhaskara",
"Ramchander Rao",
""
],
[
"Majji",
"Manoranjan",
""
]
] |
new_dataset
| 0.999449 |
2211.01585
|
Ao Zhang
|
Ao Zhang, Fan Yu, Kaixun Huang, Lei Xie, Longbiao Wang, Eng Siong
Chng, Hui Bu, Binbin Zhang, Wei Chen, Xin Xu
|
The ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge
(ICSRC): Dataset, Tracks, Baseline and Results
|
Accepted by ISCSLP2022
| null | null | null |
cs.SD eess.AS
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This paper summarizes the outcomes from the ISCSLP 2022 Intelligent Cockpit
Speech Recognition Challenge (ICSRC). We first address the necessity of the
challenge and then introduce the associated dataset collected from a new-energy
vehicle (NEV) covering a variety of cockpit acoustic conditions and linguistic
contents. We then describe the track arrangement and the baseline system.
Specifically, we set up two tracks in terms of allowed model/system size to
investigate resource-constrained and -unconstrained setups, targeting to
vehicle embedded as well as cloud ASR systems respectively. Finally we
summarize the challenge results and provide the major observations from the
submitted systems.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 04:45:28 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Zhang",
"Ao",
""
],
[
"Yu",
"Fan",
""
],
[
"Huang",
"Kaixun",
""
],
[
"Xie",
"Lei",
""
],
[
"Wang",
"Longbiao",
""
],
[
"Chng",
"Eng Siong",
""
],
[
"Bu",
"Hui",
""
],
[
"Zhang",
"Binbin",
""
],
[
"Chen",
"Wei",
""
],
[
"Xu",
"Xin",
""
]
] |
new_dataset
| 0.999676 |
2211.01589
|
Yuan Hu
|
Yuan Hu, Zhibin Wang, Zhou Huang, Yu Liu
|
PolyBuilding: Polygon Transformer for End-to-End Building Extraction
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present PolyBuilding, a fully end-to-end polygon Transformer for building
extraction. PolyBuilding direct predicts vector representation of buildings
from remote sensing images. It builds upon an encoder-decoder transformer
architecture and simultaneously outputs building bounding boxes and polygons.
Given a set of polygon queries, the model learns the relations among them and
encodes context information from the image to predict the final set of building
polygons with fixed vertex numbers. Corner classification is performed to
distinguish the building corners from the sampled points, which can be used to
remove redundant vertices along the building walls during inference. A 1-d
non-maximum suppression (NMS) is further applied to reduce vertex redundancy
near the building corners. With the refinement operations, polygons with
regular shapes and low complexity can be effectively obtained. Comprehensive
experiments are conducted on the CrowdAI dataset. Quantitative and qualitative
results show that our approach outperforms prior polygonal building extraction
methods by a large margin. It also achieves a new state-of-the-art in terms of
pixel-level coverage, instance-level precision and recall, and geometry-level
properties (including contour regularity and polygon complexity).
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 04:53:17 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Hu",
"Yuan",
""
],
[
"Wang",
"Zhibin",
""
],
[
"Huang",
"Zhou",
""
],
[
"Liu",
"Yu",
""
]
] |
new_dataset
| 0.996229 |
2211.01600
|
Lily Goli
|
Lily Goli, Daniel Rebain, Sara Sabour, Animesh Garg, Andrea
Tagliasacchi
|
nerf2nerf: Pairwise Registration of Neural Radiance Fields
| null | null | null | null |
cs.CV cs.AI cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a technique for pairwise registration of neural fields that
extends classical optimization-based local registration (i.e. ICP) to operate
on Neural Radiance Fields (NeRF) -- neural 3D scene representations trained
from collections of calibrated images. NeRF does not decompose illumination and
color, so to make registration invariant to illumination, we introduce the
concept of a ''surface field'' -- a field distilled from a pre-trained NeRF
model that measures the likelihood of a point being on the surface of an
object. We then cast nerf2nerf registration as a robust optimization that
iteratively seeks a rigid transformation that aligns the surface fields of the
two scenes. We evaluate the effectiveness of our technique by introducing a
dataset of pre-trained NeRF scenes -- our synthetic scenes enable quantitative
evaluations and comparisons to classical registration techniques, while our
real scenes demonstrate the validity of our technique in real-world scenarios.
Additional results available at: https://nerf2nerf.github.io
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 06:04:59 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Goli",
"Lily",
""
],
[
"Rebain",
"Daniel",
""
],
[
"Sabour",
"Sara",
""
],
[
"Garg",
"Animesh",
""
],
[
"Tagliasacchi",
"Andrea",
""
]
] |
new_dataset
| 0.989798 |
2211.01604
|
Alex Beatson
|
Tian Qin, Alex Beatson, Deniz Oktay, Nick McGreivy, Ryan P. Adams
|
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Partial differential equations (PDEs) are often computationally challenging
to solve, and in many settings many related PDEs must be be solved either at
every timestep or for a variety of candidate boundary conditions, parameters,
or geometric domains. We present a meta-learning based method which learns to
rapidly solve problems from a distribution of related PDEs. We use
meta-learning (MAML and LEAP) to identify initializations for a neural network
representation of the PDE solution such that a residual of the PDE can be
quickly minimized on a novel task. We apply our meta-solving approach to a
nonlinear Poisson's equation, 1D Burgers' equation, and hyperelasticity
equations with varying parameters, geometries, and boundary conditions. The
resulting Meta-PDE method finds qualitatively accurate solutions to most
problems within a few gradient steps; for the nonlinear Poisson and
hyper-elasticity equation this results in an intermediate accuracy
approximation up to an order of magnitude faster than a baseline finite element
analysis (FEA) solver with equivalent accuracy. In comparison to other learned
solvers and surrogate models, this meta-learning approach can be trained
without supervision from expensive ground-truth data, does not require a mesh,
and can even be used when the geometry and topology varies between tasks.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 06:17:52 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Qin",
"Tian",
""
],
[
"Beatson",
"Alex",
""
],
[
"Oktay",
"Deniz",
""
],
[
"McGreivy",
"Nick",
""
],
[
"Adams",
"Ryan P.",
""
]
] |
new_dataset
| 0.990197 |
2211.01629
|
Omkar Ranadive
|
Omkar Ranadive, Jisu Kim, Serin Lee, Youngseo Cha, Heechan Park,
Minkook Cho, Young K. Hwang
|
Image-based Early Detection System for Wildfires
|
Published in Tackling Climate Change with Machine Learning workshop,
Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS
2022)
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Wildfires are a disastrous phenomenon which cause damage to land, loss of
property, air pollution, and even loss of human life. Due to the warmer and
drier conditions created by climate change, more severe and uncontrollable
wildfires are expected to occur in the coming years. This could lead to a
global wildfire crisis and have dire consequences on our planet. Hence, it has
become imperative to use technology to help prevent the spread of wildfires.
One way to prevent the spread of wildfires before they become too large is to
perform early detection i.e, detecting the smoke before the actual fire starts.
In this paper, we present our Wildfire Detection and Alert System which use
machine learning to detect wildfire smoke with a high degree of accuracy and
can send immediate alerts to users. Our technology is currently being used in
the USA to monitor data coming in from hundreds of cameras daily. We show that
our system has a high true detection rate and a low false detection rate. Our
performance evaluation study also shows that on an average our system detects
wildfire smoke faster than an actual person.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 07:38:30 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Ranadive",
"Omkar",
""
],
[
"Kim",
"Jisu",
""
],
[
"Lee",
"Serin",
""
],
[
"Cha",
"Youngseo",
""
],
[
"Park",
"Heechan",
""
],
[
"Cho",
"Minkook",
""
],
[
"Hwang",
"Young K.",
""
]
] |
new_dataset
| 0.992941 |
2211.01644
|
Kai Chen
|
Kai Chen, Stephen James, Congying Sui, Yun-Hui Liu, Pieter Abbeel, Qi
Dou
|
StereoPose: Category-Level 6D Transparent Object Pose Estimation from
Stereo Images via Back-View NOCS
|
7 pages, 6 figures, Project homepage:
https://appsrv.cse.cuhk.edu.hk/~kaichen/stereopose.html
| null | null | null |
cs.RO cs.AI cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most existing methods for category-level pose estimation rely on object point
clouds. However, when considering transparent objects, depth cameras are
usually not able to capture meaningful data, resulting in point clouds with
severe artifacts. Without a high-quality point cloud, existing methods are not
applicable to challenging transparent objects. To tackle this problem, we
present StereoPose, a novel stereo image framework for category-level object
pose estimation, ideally suited for transparent objects. For a robust
estimation from pure stereo images, we develop a pipeline that decouples
category-level pose estimation into object size estimation, initial pose
estimation, and pose refinement. StereoPose then estimates object pose based on
representation in the normalized object coordinate space~(NOCS). To address the
issue of image content aliasing, we further define a back-view NOCS map for the
transparent object. The back-view NOCS aims to reduce the network learning
ambiguity caused by content aliasing, and leverage informative cues on the back
of the transparent object for more accurate pose estimation. To further improve
the performance of the stereo framework, StereoPose is equipped with a parallax
attention module for stereo feature fusion and an epipolar loss for improving
the stereo-view consistency of network predictions. Extensive experiments on
the public TOD dataset demonstrate the superiority of the proposed StereoPose
framework for category-level 6D transparent object pose estimation.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 08:36:09 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Chen",
"Kai",
""
],
[
"James",
"Stephen",
""
],
[
"Sui",
"Congying",
""
],
[
"Liu",
"Yun-Hui",
""
],
[
"Abbeel",
"Pieter",
""
],
[
"Dou",
"Qi",
""
]
] |
new_dataset
| 0.969713 |
2211.01705
|
Jihyun Mun
|
Jihyun Mun, Sunhee Kim, Myeong Ju Kim, Jiwon Ryu, Sejoong Kim, Minhwa
Chung
|
A speech corpus for chronic kidney disease
| null | null | null | null |
cs.CL
|
http://creativecommons.org/publicdomain/zero/1.0/
|
In this study, we present a speech corpus of patients with chronic kidney
disease (CKD) that will be used for research on pathological voice analysis,
automatic illness identification, and severity prediction. This paper
introduces the steps involved in creating this corpus, including the choice of
speech-related parameters and speech lists as well as the recording technique.
The speakers in this corpus, 289 CKD patients with varying degrees of severity
who were categorized based on estimated glomerular filtration rate (eGFR),
delivered sustained vowels, sentence, and paragraph stimuli. This study
compared and analyzed the voice characteristics of CKD patients with those of
the control group; the results revealed differences in voice quality,
phoneme-level pronunciation, prosody, glottal source, and aerodynamic
parameters.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 10:57:48 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Mun",
"Jihyun",
""
],
[
"Kim",
"Sunhee",
""
],
[
"Kim",
"Myeong Ju",
""
],
[
"Ryu",
"Jiwon",
""
],
[
"Kim",
"Sejoong",
""
],
[
"Chung",
"Minhwa",
""
]
] |
new_dataset
| 0.970115 |
2211.01730
|
Mehmet Emre Ozfatura
|
Emre Ozfatura and Yulin Shao and Amin Ghazanfari and Alberto Perotti
and Branislav Popovic and Deniz Gunduz
|
Feedback is Good, Active Feedback is Better: Block Attention Active
Feedback Codes
| null | null | null | null |
cs.IT cs.AI cs.LG eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep neural network (DNN)-assisted channel coding designs, such as
low-complexity neural decoders for existing codes, or end-to-end
neural-network-based auto-encoder designs are gaining interest recently due to
their improved performance and flexibility; particularly for communication
scenarios in which high-performing structured code designs do not exist.
Communication in the presence of feedback is one such communication scenario,
and practical code design for feedback channels has remained an open challenge
in coding theory for many decades. Recently, DNN-based designs have shown
impressive results in exploiting feedback. In particular, generalized block
attention feedback (GBAF) codes, which utilizes the popular transformer
architecture, achieved significant improvement in terms of the block error rate
(BLER) performance. However, previous works have focused mainly on passive
feedback, where the transmitter observes a noisy version of the signal at the
receiver. In this work, we show that GBAF codes can also be used for channels
with active feedback. We implement a pair of transformer architectures, at the
transmitter and the receiver, which interact with each other sequentially, and
achieve a new state-of-the-art BLER performance, especially in the low SNR
regime.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 11:44:06 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Ozfatura",
"Emre",
""
],
[
"Shao",
"Yulin",
""
],
[
"Ghazanfari",
"Amin",
""
],
[
"Perotti",
"Alberto",
""
],
[
"Popovic",
"Branislav",
""
],
[
"Gunduz",
"Deniz",
""
]
] |
new_dataset
| 0.99281 |
2211.01812
|
Hadi Hajieghrary
|
Sevag Tafnakaji and Hadi Hajieghrary and Quentin Teixeira and Yasemin
Bekiroglu
|
Benchmarking local motion planners for navigation of mobile manipulators
|
Accepted to be presented at 2023 IEEE/SICE International Symposium on
System Integration
| null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
There are various trajectory planners for mobile manipulators. It is often
challenging to compare their performance under similar circumstances due to
differences in hardware, dissimilarity of tasks and objectives, as well as
uncertainties in measurements and operating environments. In this paper, we
propose a simulation framework to evaluate the performance of the local
trajectory planners to generate smooth, and dynamically and kinematically
feasible trajectories for mobile manipulators in the same environment. We focus
on local planners as they are key components that provide smooth trajectories
while carrying a load, react to dynamic obstacles, and avoid collisions. We
evaluate two prominent local trajectory planners, Dynamic-Window Approach (DWA)
and Time Elastic Band (TEB) using the metrics that we introduce. Moreover, our
software solution is applicable to any other local planners used in the Robot
Operating System (ROS) framework, without additional programming effort.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 13:45:55 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Tafnakaji",
"Sevag",
""
],
[
"Hajieghrary",
"Hadi",
""
],
[
"Teixeira",
"Quentin",
""
],
[
"Bekiroglu",
"Yasemin",
""
]
] |
new_dataset
| 0.998955 |
2211.01829
|
Seulbae Kim
|
Seulbae Kim and Major Liu and Junghwan "John" Rhee and Yuseok Jeon and
Yonghwi Kwon and Chung Hwan Kim
|
DriveFuzz: Discovering Autonomous Driving Bugs through Driving
Quality-Guided Fuzzing
|
This is the full version of the paper published at ACM CCS 2022. This
version includes the appendices (pages 14 and 15)
| null |
10.1145/3548606.3560558
| null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous driving has become real; semi-autonomous driving vehicles in an
affordable price range are already on the streets, and major automotive vendors
are actively developing full self-driving systems to deploy them in this
decade. Before rolling the products out to the end-users, it is critical to
test and ensure the safety of the autonomous driving systems, consisting of
multiple layers intertwined in a complicated way. However, while
safety-critical bugs may exist in any layer and even across layers, relatively
little attention has been given to testing the entire driving system across all
the layers. Prior work mainly focuses on white-box testing of individual layers
and preventing attacks on each layer.
In this paper, we aim at holistic testing of autonomous driving systems that
have a whole stack of layers integrated in their entirety. Instead of looking
into the individual layers, we focus on the vehicle states that the system
continuously changes in the driving environment. This allows us to design
DriveFuzz, a new systematic fuzzing framework that can uncover potential
vulnerabilities regardless of their locations. DriveFuzz automatically
generates and mutates driving scenarios based on diverse factors leveraging a
high-fidelity driving simulator. We build novel driving test oracles based on
the real-world traffic rules to detect safety-critical misbehaviors, and guide
the fuzzer towards such misbehaviors through driving quality metrics referring
to the physical states of the vehicle.
DriveFuzz has discovered 30 new bugs in various layers of two autonomous
driving systems (Autoware and CARLA Behavior Agent) and three additional bugs
in the CARLA simulator. We further analyze the impact of these bugs and how an
adversary may exploit them as security vulnerabilities to cause critical
accidents in the real world.
|
[
{
"version": "v1",
"created": "Tue, 25 Oct 2022 19:31:55 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Kim",
"Seulbae",
""
],
[
"Liu",
"Major",
""
],
[
"Rhee",
"Junghwan \"John\"",
""
],
[
"Jeon",
"Yuseok",
""
],
[
"Kwon",
"Yonghwi",
""
],
[
"Kim",
"Chung Hwan",
""
]
] |
new_dataset
| 0.98524 |
2211.01839
|
Filip Szatkowski
|
Filip Szatkowski, Karol J. Piczak, Przemys{\l}aw Spurek, Jacek Tabor,
Tomasz Trzci\'nski
|
HyperSound: Generating Implicit Neural Representations of Audio Signals
with Hypernetworks
| null | null | null | null |
cs.SD cs.AI cs.LG cs.NE eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Implicit neural representations (INRs) are a rapidly growing research field,
which provides alternative ways to represent multimedia signals. Recent
applications of INRs include image super-resolution, compression of
high-dimensional signals, or 3D rendering. However, these solutions usually
focus on visual data, and adapting them to the audio domain is not trivial.
Moreover, it requires a separately trained model for every data sample. To
address this limitation, we propose HyperSound, a meta-learning method
leveraging hypernetworks to produce INRs for audio signals unseen at training
time. We show that our approach can reconstruct sound waves with quality
comparable to other state-of-the-art models.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 14:20:32 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Szatkowski",
"Filip",
""
],
[
"Piczak",
"Karol J.",
""
],
[
"Spurek",
"Przemysław",
""
],
[
"Tabor",
"Jacek",
""
],
[
"Trzciński",
"Tomasz",
""
]
] |
new_dataset
| 0.98776 |
2211.01859
|
Ramtin Gharleghi
|
Ramtin Gharleghi, Dona Adikari, Katy Ellenberger, Mark Webster, Chris
Ellis, Arcot Sowmya, Sze-Yuan Ooi, Susann Beier
|
Computed tomography coronary angiogram images, annotations and
associated data of normal and diseased arteries
|
10 pages, 3 figures. Submitted to the journal Scientific Data. For
associated challenge, see https://asoca.grand-challenge.org/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to
evaluate coronary artery anatomy and disease. CTCA is ideal for geometry
reconstruction to create virtual models of coronary arteries. To our knowledge
there is no public dataset that includes centrelines and segmentation of the
full coronary tree.
We provide anonymized CTCA images, voxel-wise annotations and associated data
in the form of centrelines, calcification scores and meshes of the coronary
lumen in 20 normal and 20 diseased cases. Images were obtained along with
patient information with informed, written consent as part of Coronary Atlas
(https://www.coronaryatlas.org/). Cases were classified as normal (zero calcium
score with no signs of stenosis) or diseased (confirmed coronary artery
disease). Manual voxel-wise segmentations by three experts were combined using
majority voting to generate the final annotations.
Provided data can be used for a variety of research purposes, such as 3D
printing patient-specific models, development and validation of segmentation
algorithms, education and training of medical personnel and in-silico analyses
such as testing of medical devices.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 14:50:43 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Gharleghi",
"Ramtin",
""
],
[
"Adikari",
"Dona",
""
],
[
"Ellenberger",
"Katy",
""
],
[
"Webster",
"Mark",
""
],
[
"Ellis",
"Chris",
""
],
[
"Sowmya",
"Arcot",
""
],
[
"Ooi",
"Sze-Yuan",
""
],
[
"Beier",
"Susann",
""
]
] |
new_dataset
| 0.997954 |
2211.01917
|
Joel Brogan
|
David Cornett III and Joel Brogan and Nell Barber and Deniz Aykac and
Seth Baird and Nick Burchfield and Carl Dukes and Andrew Duncan and Regina
Ferrell and Jim Goddard and Gavin Jager and Matt Larson and Bart Murphy and
Christi Johnson and Ian Shelley and Nisha Srinivas and Brandon Stockwell and
Leanne Thompson and Matt Yohe and Robert Zhang and Scott Dolvin and Hector J.
Santos-Villalobos and David S. Bolme
|
Expanding Accurate Person Recognition to New Altitudes and Ranges: The
BRIAR Dataset
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Face recognition technology has advanced significantly in recent years due
largely to the availability of large and increasingly complex training datasets
for use in deep learning models. These datasets, however, typically comprise
images scraped from news sites or social media platforms and, therefore, have
limited utility in more advanced security, forensics, and military
applications. These applications require lower resolution, longer ranges, and
elevated viewpoints. To meet these critical needs, we collected and curated the
first and second subsets of a large multi-modal biometric dataset designed for
use in the research and development (R&D) of biometric recognition technologies
under extremely challenging conditions. Thus far, the dataset includes more
than 350,000 still images and over 1,300 hours of video footage of
approximately 1,000 subjects. To collect this data, we used Nikon DSLR cameras,
a variety of commercial surveillance cameras, specialized long-rage R&D
cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the
development of algorithms capable of accurately recognizing people at ranges up
to 1,000 m and from high angles of elevation. These advances will include
improvements to the state of the art in face recognition and will support new
research in the area of whole-body recognition using methods based on gait and
anthropometry. This paper describes methods used to collect and curate the
dataset, and the dataset's characteristics at the current stage.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 15:51:39 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Cornett",
"David",
"III"
],
[
"Brogan",
"Joel",
""
],
[
"Barber",
"Nell",
""
],
[
"Aykac",
"Deniz",
""
],
[
"Baird",
"Seth",
""
],
[
"Burchfield",
"Nick",
""
],
[
"Dukes",
"Carl",
""
],
[
"Duncan",
"Andrew",
""
],
[
"Ferrell",
"Regina",
""
],
[
"Goddard",
"Jim",
""
],
[
"Jager",
"Gavin",
""
],
[
"Larson",
"Matt",
""
],
[
"Murphy",
"Bart",
""
],
[
"Johnson",
"Christi",
""
],
[
"Shelley",
"Ian",
""
],
[
"Srinivas",
"Nisha",
""
],
[
"Stockwell",
"Brandon",
""
],
[
"Thompson",
"Leanne",
""
],
[
"Yohe",
"Matt",
""
],
[
"Zhang",
"Robert",
""
],
[
"Dolvin",
"Scott",
""
],
[
"Santos-Villalobos",
"Hector J.",
""
],
[
"Bolme",
"David S.",
""
]
] |
new_dataset
| 0.967951 |
2211.01941
|
Wei Sun
|
Rushmian Annoy Wadud, Wei Sun
|
DyOb-SLAM : Dynamic Object Tracking SLAM System
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Simultaneous Localization & Mapping (SLAM) is the process of building a
mutual relationship between localization and mapping of the subject in its
surrounding environment. With the help of different sensors, various types of
SLAM systems have developed to deal with the problem of building the
relationship between localization and mapping. A limitation in the SLAM process
is the lack of consideration of dynamic objects in the mapping of the
environment. We propose the Dynamic Object Tracking SLAM (DyOb-SLAM), which is
a Visual SLAM system that can localize and map the surrounding dynamic objects
in the environment as well as track the dynamic objects in each frame. With the
help of a neural network and a dense optical flow algorithm, dynamic objects
and static objects in an environment can be differentiated. DyOb-SLAM creates
two separate maps for both static and dynamic contents. For the static
features, a sparse map is obtained. For the dynamic contents, a trajectory
global map is created as output. As a result, a frame to frame real-time based
dynamic object tracking system is obtained. With the pose calculation of the
dynamic objects and camera, DyOb-SLAM can estimate the speed of the dynamic
objects with time. The performance of DyOb-SLAM is observed by comparing it
with a similar Visual SLAM system, VDO-SLAM and the performance is measured by
calculating the camera and object pose errors as well as the object speed
error.
|
[
{
"version": "v1",
"created": "Thu, 3 Nov 2022 16:28:19 GMT"
}
] | 2022-11-04T00:00:00 |
[
[
"Wadud",
"Rushmian Annoy",
""
],
[
"Sun",
"Wei",
""
]
] |
new_dataset
| 0.956951 |
1906.04376
|
Xuewen Yang
|
Xuewen Yang, Xin Wang
|
Recognizing License Plates in Real-Time
|
License Plate Detection and Recognition, Computer Vision, Supervised
Learning
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
License plate detection and recognition (LPDR) is of growing importance for
enabling intelligent transportation and ensuring the security and safety of the
cities. However, LPDR faces a big challenge in a practical environment. The
license plates can have extremely diverse sizes, fonts and colors, and the
plate images are usually of poor quality caused by skewed capturing angles,
uneven lighting, occlusion, and blurring. In applications such as surveillance,
it often requires fast processing. To enable real-time and accurate license
plate recognition, in this work, we propose a set of techniques: 1) a contour
reconstruction method along with edge-detection to quickly detect the candidate
plates; 2) a simple zero-one-alternation scheme to effectively remove the fake
top and bottom borders around plates to facilitate more accurate segmentation
of characters on plates; 3) a set of techniques to augment the training data,
incorporate SIFT features into the CNN network, and exploit transfer learning
to obtain the initial parameters for more effective training; and 4) a
two-phase verification procedure to determine the correct plate at low cost, a
statistical filtering in the plate detection stage to quickly remove unwanted
candidates, and the accurate CR results after the CR process to perform further
plate verification without additional processing. We implement a complete LPDR
system based on our algorithms. The experimental results demonstrate that our
system can accurately recognize license plate in real-time. Additionally, it
works robustly under various levels of illumination and noise, and in the
presence of car movement. Compared to peer schemes, our system is not only
among the most accurate ones but is also the fastest, and can be easily applied
to other scenarios.
|
[
{
"version": "v1",
"created": "Tue, 11 Jun 2019 03:45:49 GMT"
},
{
"version": "v2",
"created": "Sun, 5 Apr 2020 15:44:44 GMT"
},
{
"version": "v3",
"created": "Tue, 14 Sep 2021 05:16:37 GMT"
},
{
"version": "v4",
"created": "Thu, 19 May 2022 23:33:06 GMT"
},
{
"version": "v5",
"created": "Mon, 13 Jun 2022 05:56:06 GMT"
},
{
"version": "v6",
"created": "Wed, 2 Nov 2022 16:04:38 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Yang",
"Xuewen",
""
],
[
"Wang",
"Xin",
""
]
] |
new_dataset
| 0.999586 |
1910.06452
|
Gabriele Dragotto
|
Margarida Carvalho, Gabriele Dragotto, Felipe Feijoo, Andrea Lodi,
Sriram Sankaranarayanan
|
When Nash Meets Stackelberg
| null | null | null | null |
cs.GT math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This article introduces a class of $Nash$ games among $Stackelberg$ players
($NASPs$), namely, a class of simultaneous non-cooperative games where the
players solve sequential Stackelberg games. Specifically, each player solves a
Stackelberg game where a leader optimizes a (parametrized) linear objective
function subject to linear constraints while its followers solve convex
quadratic problems subject to the standard optimistic assumption. Although we
prove that deciding if a $NASP$ instance admits a Nash equilibrium is generally
a $\Sigma^2_p$-hard decision problem, we devise two exact and
computationally-efficient algorithms to compute and select Nash equilibria or
certify that no equilibrium exists. We apply $NASPs$ to model the hierarchical
interactions of international energy markets where climate-change aware
regulators oversee the operations of profit-driven energy producers. By
combining real-world data with our models, we find that Nash equilibria provide
informative, and often counterintuitive, managerial insights for market
regulators.
|
[
{
"version": "v1",
"created": "Mon, 14 Oct 2019 22:32:13 GMT"
},
{
"version": "v2",
"created": "Sun, 22 Dec 2019 10:23:53 GMT"
},
{
"version": "v3",
"created": "Tue, 21 Apr 2020 16:12:53 GMT"
},
{
"version": "v4",
"created": "Thu, 18 Jun 2020 14:34:43 GMT"
},
{
"version": "v5",
"created": "Tue, 7 Sep 2021 22:13:26 GMT"
},
{
"version": "v6",
"created": "Wed, 2 Nov 2022 16:22:00 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Carvalho",
"Margarida",
""
],
[
"Dragotto",
"Gabriele",
""
],
[
"Feijoo",
"Felipe",
""
],
[
"Lodi",
"Andrea",
""
],
[
"Sankaranarayanan",
"Sriram",
""
]
] |
new_dataset
| 0.984416 |
2002.01924
|
Remi Chou
|
Remi A. Chou
|
Explicit Wiretap Channel Codes via Source Coding, Universal Hashing, and
Distribution Approximation, When the Channels' Statistics are Uncertain
|
16 pages, two-column, 3 figures, accepted to IEEE Transactions on
Information Forensics and Security
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider wiretap channels with uncertainty on the eavesdropper channel
under (i) noisy blockwise type II, (ii) compound, or (iii) arbitrarily varying
models. We present explicit wiretap codes that can handle these models in a
unified manner and only rely on three primitives, namely source coding with
side information, universal hashing, and distribution approximation. Our
explicit wiretap codes achieve the best known single-letter achievable rates,
previously obtained non-constructively, for the models considered. Our results
are obtained for strong secrecy, do not require a pre-shared secret between the
legitimate users, and do not require any symmetry properties on the channel. An
extension of our results to compound main channels is also derived via new
capacity-achieving polar coding schemes for compound settings.
|
[
{
"version": "v1",
"created": "Wed, 5 Feb 2020 18:59:09 GMT"
},
{
"version": "v2",
"created": "Sun, 13 Dec 2020 01:05:59 GMT"
},
{
"version": "v3",
"created": "Tue, 1 Nov 2022 18:52:32 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Chou",
"Remi A.",
""
]
] |
new_dataset
| 0.992945 |
2105.01208
|
Sanja Rukavina
|
Sara Ban, Sanja Rukavina
|
Type IV-II codes over Z4 constructed from generalized bent functions
|
16 pages
|
Australas. J. Combin., 84 (3) (2022), 341-356
| null | null |
cs.IT math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A Type IV-II Z4-code is a self-dual code over Z4 with the property that all
Euclidean weights are divisible by eight and all codewords have even Hamming
weight. In this paper we use generalized bent functions for a construction of
self-orthogonal codes over Z4 of length $2^m$, for $m$ odd, $m \geq 3$, and
prove that for $m \geq 5$ those codes can be extended to Type IV-II Z4-codes.
From that family of Type IV-II Z4-codes, we obtain a family of self-dual Type
II binary codes by using Gray map. We also consider the weight distributions of
the obtained codes and the structure of the supports of the minimum weight
codewords.
|
[
{
"version": "v1",
"created": "Mon, 3 May 2021 22:56:08 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Ban",
"Sara",
""
],
[
"Rukavina",
"Sanja",
""
]
] |
new_dataset
| 0.99214 |
2106.01601
|
Jiao Sun
|
Jiao Sun and Nanyun Peng
|
Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia
|
ACL 2021
| null | null | null |
cs.CL cs.AI cs.CY cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human activities can be seen as sequences of events, which are crucial to
understanding societies. Disproportional event distribution for different
demographic groups can manifest and amplify social stereotypes, and potentially
jeopardize the ability of members in some groups to pursue certain goals. In
this paper, we present the first event-centric study of gender biases in a
Wikipedia corpus. To facilitate the study, we curate a corpus of career and
personal life descriptions with demographic information consisting of 7,854
fragments from 10,412 celebrities. Then we detect events with a
state-of-the-art event detection model, calibrate the results using
strategically generated templates, and extract events that have asymmetric
associations with genders. Our study discovers that the Wikipedia pages tend to
intermingle personal life events with professional events for females but not
for males, which calls for the awareness of the Wikipedia community to
formalize guidelines and train the editors to mind the implicit biases that
contributors carry. Our work also lays the foundation for future works on
quantifying and discovering event biases at the corpus level.
|
[
{
"version": "v1",
"created": "Thu, 3 Jun 2021 05:22:16 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Sun",
"Jiao",
""
],
[
"Peng",
"Nanyun",
""
]
] |
new_dataset
| 0.990737 |
2110.15221
|
Ivan Carvalho
|
Matthew Treinish and Ivan Carvalho and Georgios Tsilimigkounakis and
Nahum S\'a
|
rustworkx: A High-Performance Graph Library for Python
| null | null |
10.21105/joss.03968
| null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
In rustworkx, we provide a high-performance, flexible graph library for
Python. rustworkx is inspired by NetworkX but addresses many performance
concerns of the latter. rustworkx is written in Rust and is particularly suited
for performance-sensitive applications that use graph representations.
|
[
{
"version": "v1",
"created": "Thu, 28 Oct 2021 15:34:21 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Feb 2022 16:39:47 GMT"
},
{
"version": "v3",
"created": "Tue, 2 Aug 2022 00:40:57 GMT"
},
{
"version": "v4",
"created": "Wed, 2 Nov 2022 00:29:03 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Treinish",
"Matthew",
""
],
[
"Carvalho",
"Ivan",
""
],
[
"Tsilimigkounakis",
"Georgios",
""
],
[
"Sá",
"Nahum",
""
]
] |
new_dataset
| 0.999704 |
2111.02276
|
Diancheng Li
|
Diancheng Li, Dongliang Fan, Renjie Zhu, Qiaozhi Lei, Yuxuan Liao, Xin
Yang, Yang Pan, Zheng Wang, Yang Wu, Sicong Liu, Hongqiang Wang
|
Origami-inspired soft twisting actuator
|
9 figures. Soft Robotics (2022)
| null |
10.1089/soro.2021.0185
| null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Soft actuators have shown great advantages in compliance and morphology
matched for manipulation of delicate objects and inspection in a confined
space. There is an unmet need for a soft actuator that can provide torsional
motion to e.g. enlarge working space and increase degrees of freedom. Towards
this goal, we present origami-inspired soft pneumatic actuators (OSPAs) made
from silicone. The prototype can output a rotation of more than one revolution
(up to 435{\deg}), more significant than its counterparts. Its rotation ratio
(=rotation angle/ aspect ratio) is more than 136{\deg}, about twice the largest
one in other literature. We describe the design and fabrication method, build
the analytical model and simulation model, and analyze and optimize the
parameters. Finally, we demonstrate the potentially extensive utility of the
OSPAs through their integration into a gripper capable of simultaneously
grasping and lifting fragile or flat objects, a versatile robot arm capable of
picking and placing items at the right angle with the twisting actuators, and a
soft snake robot capable of changing attitude and directions by torsion of the
twisting actuators.
|
[
{
"version": "v1",
"created": "Wed, 3 Nov 2021 15:13:27 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Nov 2022 15:11:18 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Li",
"Diancheng",
""
],
[
"Fan",
"Dongliang",
""
],
[
"Zhu",
"Renjie",
""
],
[
"Lei",
"Qiaozhi",
""
],
[
"Liao",
"Yuxuan",
""
],
[
"Yang",
"Xin",
""
],
[
"Pan",
"Yang",
""
],
[
"Wang",
"Zheng",
""
],
[
"Wu",
"Yang",
""
],
[
"Liu",
"Sicong",
""
],
[
"Wang",
"Hongqiang",
""
]
] |
new_dataset
| 0.971612 |
2203.07852
|
DeLesley Hutchins
|
DeLesley Hutchins, Imanol Schlag, Yuhuai Wu, Ethan Dyer, Behnam
Neyshabur
|
Block-Recurrent Transformers
|
Update to NeurIPS camera-ready version
| null | null | null |
cs.LG cs.AI cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce the Block-Recurrent Transformer, which applies a transformer
layer in a recurrent fashion along a sequence, and has linear complexity with
respect to sequence length. Our recurrent cell operates on blocks of tokens
rather than single tokens during training, and leverages parallel computation
within a block in order to make efficient use of accelerator hardware. The cell
itself is strikingly simple. It is merely a transformer layer: it uses
self-attention and cross-attention to efficiently compute a recurrent function
over a large set of state vectors and tokens. Our design was inspired in part
by LSTM cells, and it uses LSTM-style gates, but it scales the typical LSTM
cell up by several orders of magnitude. Our implementation of recurrence has
the same cost in both computation time and parameter count as a conventional
transformer layer, but offers dramatically improved perplexity in language
modeling tasks over very long sequences. Our model out-performs a long-range
Transformer XL baseline by a wide margin, while running twice as fast. We
demonstrate its effectiveness on PG19 (books), arXiv papers, and GitHub source
code. Our code has been released as open source.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 23:44:33 GMT"
},
{
"version": "v2",
"created": "Sat, 17 Sep 2022 01:31:49 GMT"
},
{
"version": "v3",
"created": "Wed, 2 Nov 2022 00:35:56 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Hutchins",
"DeLesley",
""
],
[
"Schlag",
"Imanol",
""
],
[
"Wu",
"Yuhuai",
""
],
[
"Dyer",
"Ethan",
""
],
[
"Neyshabur",
"Behnam",
""
]
] |
new_dataset
| 0.999198 |
2204.02550
|
Neekon Vafa
|
Aparna Gupte, Neekon Vafa, Vinod Vaikuntanathan
|
Continuous LWE is as Hard as LWE & Applications to Learning Gaussian
Mixtures
|
Fixed bugs in Lemma 9 and Section 6
| null | null | null |
cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We show direct and conceptually simple reductions between the classical
learning with errors (LWE) problem and its continuous analog, CLWE (Bruna,
Regev, Song and Tang, STOC 2021). This allows us to bring to bear the powerful
machinery of LWE-based cryptography to the applications of CLWE. For example,
we obtain the hardness of CLWE under the classical worst-case hardness of the
gap shortest vector problem. Previously, this was known only under quantum
worst-case hardness of lattice problems. More broadly, with our reductions
between the two problems, any future developments to LWE will also apply to
CLWE and its downstream applications.
As a concrete application, we show an improved hardness result for density
estimation for mixtures of Gaussians. In this computational problem, given
sample access to a mixture of Gaussians, the goal is to output a function that
estimates the density function of the mixture. Under the (plausible and widely
believed) exponential hardness of the classical LWE problem, we show that
Gaussian mixture density estimation in $\mathbb{R}^n$ with roughly $\log n$
Gaussian components given $\mathsf{poly}(n)$ samples requires time
quasi-polynomial in $n$. Under the (conservative) polynomial hardness of LWE,
we show hardness of density estimation for $n^{\epsilon}$ Gaussians for any
constant $\epsilon > 0$, which improves on Bruna, Regev, Song and Tang (STOC
2021), who show hardness for at least $\sqrt{n}$ Gaussians under polynomial
(quantum) hardness assumptions.
Our key technical tool is a reduction from classical LWE to LWE with
$k$-sparse secrets where the multiplicative increase in the noise is only
$O(\sqrt{k})$, independent of the ambient dimension $n$.
|
[
{
"version": "v1",
"created": "Wed, 6 Apr 2022 03:03:39 GMT"
},
{
"version": "v2",
"created": "Tue, 7 Jun 2022 18:45:32 GMT"
},
{
"version": "v3",
"created": "Wed, 2 Nov 2022 05:06:35 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Gupte",
"Aparna",
""
],
[
"Vafa",
"Neekon",
""
],
[
"Vaikuntanathan",
"Vinod",
""
]
] |
new_dataset
| 0.97441 |
2204.11641
|
Maryam Motallebighomi
|
Maryam Motallebighomi, Harshad Sathaye, Mridula Singh, Aanjhan
Ranganathan
|
Cryptography Is Not Enough: Relay Attacks on Authenticated GNSS Signals
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Civilian-GNSS is vulnerable to signal spoofing attacks, and countermeasures
based on cryptographic authentication are being proposed to protect against
these attacks. Both Galileo and GPS are currently testing broadcast
authentication techniques based on the delayed key disclosure to validate the
integrity of navigation messages. These authentication mechanisms have proven
secure against record now and replay later attacks, as navigation messages
become invalid after keys are released. This work analyzes the security
guarantees of cryptographically protected GNSS signals and shows the
possibility of spoofing a receiver to an arbitrary location without breaking
any cryptographic operation. In contrast to prior work, we demonstrate the
ability of an attacker to receive signals close to the victim receiver and
generate spoofing signals for a different target location without modifying the
navigation message contents. Our strategy exploits the essential common
reception and transmission time method used to estimate pseudorange in GNSS
receivers, thereby rendering any cryptographic authentication useless. We
evaluate our attack on a commercial receiver (ublox M9N) and a software-defined
GNSS receiver (GNSS-SDR) using a combination of open-source tools, commercial
GNSS signal generators, and software-defined radio hardware platforms. Our
results show that it is possible to spoof a victim receiver to locations around
4000 km away from the true location without requiring any high-speed
communication networks or modifying the message contents. Through this work, we
further highlight the fundamental limitations in securing a broadcast
signaling-based localization system even if all communications are
cryptographically protected.
|
[
{
"version": "v1",
"created": "Mon, 25 Apr 2022 13:19:57 GMT"
},
{
"version": "v2",
"created": "Wed, 28 Sep 2022 19:38:09 GMT"
},
{
"version": "v3",
"created": "Wed, 2 Nov 2022 01:42:30 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Motallebighomi",
"Maryam",
""
],
[
"Sathaye",
"Harshad",
""
],
[
"Singh",
"Mridula",
""
],
[
"Ranganathan",
"Aanjhan",
""
]
] |
new_dataset
| 0.979475 |
2206.00006
|
Baoyu Jing
|
Baoyu Jing, Yuchen Yan, Yada Zhu and Hanghang Tong
|
COIN: Co-Cluster Infomax for Bipartite Graphs
|
NeurIPS 2022 GLFrontiers Workshop
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Bipartite graphs are powerful data structures to model interactions between
two types of nodes, which have been used in a variety of applications, such as
recommender systems, information retrieval, and drug discovery. A fundamental
challenge for bipartite graphs is how to learn informative node embeddings.
Despite the success of recent self-supervised learning methods on bipartite
graphs, their objectives are discriminating instance-wise positive and negative
node pairs, which could contain cluster-level errors. In this paper, we
introduce a novel co-cluster infomax (COIN) framework, which captures the
cluster-level information by maximizing the mutual information of co-clusters.
Different from previous infomax methods which estimate mutual information by
neural networks, COIN could easily calculate mutual information. Besides, COIN
is an end-to-end coclustering method which can be trained jointly with other
objective functions and optimized via back-propagation. Furthermore, we also
provide theoretical analysis for COIN. We theoretically prove that COIN is able
to effectively increase the mutual information of node embeddings and COIN is
upper-bounded by the prior distributions of nodes. We extensively evaluate the
proposed COIN framework on various benchmark datasets and tasks to demonstrate
the effectiveness of COIN.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 10:20:07 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Nov 2022 16:38:37 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Jing",
"Baoyu",
""
],
[
"Yan",
"Yuchen",
""
],
[
"Zhu",
"Yada",
""
],
[
"Tong",
"Hanghang",
""
]
] |
new_dataset
| 0.977094 |
2206.00208
|
Kun Song
|
Kun Song, Heyang Xue, Xinsheng Wang, Jian Cong, Yongmao Zhang, Lei
Xie, Bing Yang, Xiong Zhang, Dan Su
|
AdaVITS: Tiny VITS for Low Computing Resource Speaker Adaptation
|
Accepted by ISCSLP 2022
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Speaker adaptation in text-to-speech synthesis (TTS) is to finetune a
pre-trained TTS model to adapt to new target speakers with limited data. While
much effort has been conducted towards this task, seldom work has been
performed for low computational resource scenarios due to the challenges raised
by the requirement of the lightweight model and less computational complexity.
In this paper, a tiny VITS-based TTS model, named AdaVITS, for low computing
resource speaker adaptation is proposed. To effectively reduce parameters and
computational complexity of VITS, an iSTFT-based wave construction decoder is
proposed to replace the upsampling-based decoder which is resource-consuming in
the original VITS. Besides, NanoFlow is introduced to share the density
estimate across flow blocks to reduce the parameters of the prior encoder.
Furthermore, to reduce the computational complexity of the textual encoder,
scaled-dot attention is replaced with linear attention. To deal with the
instability caused by the simplified model, instead of using the original text
encoder, phonetic posteriorgram (PPG) is utilized as linguistic feature via a
text-to-PPG module, which is then used as input for the encoder. Experiment
shows that AdaVITS can generate stable and natural speech in speaker adaptation
with 8.97M model parameters and 0.72GFlops computational complexity.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 03:09:18 GMT"
},
{
"version": "v2",
"created": "Mon, 31 Oct 2022 14:17:47 GMT"
},
{
"version": "v3",
"created": "Wed, 2 Nov 2022 13:04:35 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Song",
"Kun",
""
],
[
"Xue",
"Heyang",
""
],
[
"Wang",
"Xinsheng",
""
],
[
"Cong",
"Jian",
""
],
[
"Zhang",
"Yongmao",
""
],
[
"Xie",
"Lei",
""
],
[
"Yang",
"Bing",
""
],
[
"Zhang",
"Xiong",
""
],
[
"Su",
"Dan",
""
]
] |
new_dataset
| 0.988637 |
2206.04186
|
Hanyang Jiang
|
Hanyang Jiang, Yuehaw Khoo, Haizhao Yang
|
Reinforced Inverse Scattering
| null | null | null | null |
cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inverse wave scattering aims at determining the properties of an object using
data on how the object scatters incoming waves. In order to collect
information, sensors are put in different locations to send and receive waves
from each other. The choice of sensor positions and incident wave frequencies
determines the reconstruction quality of scatterer properties. This paper
introduces reinforcement learning to develop precision imaging that decides
sensor positions and wave frequencies adaptive to different scatterers in an
intelligent way, thus obtaining a significant improvement in reconstruction
quality with limited imaging resources. Extensive numerical results will be
provided to demonstrate the superiority of the proposed method over existing
methods.
|
[
{
"version": "v1",
"created": "Wed, 8 Jun 2022 22:56:09 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Nov 2022 15:10:16 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Jiang",
"Hanyang",
""
],
[
"Khoo",
"Yuehaw",
""
],
[
"Yang",
"Haizhao",
""
]
] |
new_dataset
| 0.952727 |
2208.00627
|
Yilan Zhang
|
Yilan Zhang, Fengying Xie, Xuedong Song, Hangning Zhou, Yiguang Yang,
Haopeng Zhang, Jie Liu
|
A Rotation Meanout Network with Invariance for Dermoscopy Image
Classification and Retrieval
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The computer-aided diagnosis (CAD) system can provide a reference basis for
the clinical diagnosis of skin diseases. Convolutional neural networks (CNNs)
can not only extract visual elements such as colors and shapes but also
semantic features. As such they have made great improvements in many tasks of
dermoscopy images. The imaging of dermoscopy has no principal orientation,
indicating that there are a large number of skin lesion rotations in the
datasets. However, CNNs lack rotation invariance, which is bound to affect the
robustness of CNNs against rotations. To tackle this issue, we propose a
rotation meanout (RM) network to extract rotation-invariant features from
dermoscopy images. In RM, each set of rotated feature maps corresponds to a set
of outputs of the weight-sharing convolutions and they are fused using meanout
strategy to obtain the final feature maps. Through theoretical derivation, the
proposed RM network is rotation-equivariant and can extract rotation-invariant
features when followed by the global average pooling (GAP) operation. The
extracted rotation-invariant features can better represent the original data in
classification and retrieval tasks for dermoscopy images. The RM is a general
operation, which does not change the network structure or increase any
parameter, and can be flexibly embedded in any part of CNNs. Extensive
experiments are conducted on a dermoscopy image dataset. The results show our
method outperforms other anti-rotation methods and achieves great improvements
in dermoscopy image classification and retrieval tasks, indicating the
potential of rotation invariance in the field of dermoscopy images.
|
[
{
"version": "v1",
"created": "Mon, 1 Aug 2022 06:15:52 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Nov 2022 09:06:47 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Zhang",
"Yilan",
""
],
[
"Xie",
"Fengying",
""
],
[
"Song",
"Xuedong",
""
],
[
"Zhou",
"Hangning",
""
],
[
"Yang",
"Yiguang",
""
],
[
"Zhang",
"Haopeng",
""
],
[
"Liu",
"Jie",
""
]
] |
new_dataset
| 0.983747 |
2209.02577
|
Yixue Zhao
|
Yixue Zhao, Saghar Talebipour, Kesina Baral, Hyojae Park, Leon Yee,
Safwat Ali Khan, Yuriy Brun, Nenad Medvidovic, Kevin Moran
|
Avgust: Automating Usage-Based Test Generation from Videos of App
Executions
| null |
ESEC/FSE 2022
|
10.1145/3540250.3549134
| null |
cs.SE cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Writing and maintaining UI tests for mobile apps is a time-consuming and
tedious task. While decades of research have produced automated approaches for
UI test generation, these approaches typically focus on testing for crashes or
maximizing code coverage. By contrast, recent research has shown that
developers prefer usage-based tests, which center around specific uses of app
features, to help support activities such as regression testing. Very few
existing techniques support the generation of such tests, as doing so requires
automating the difficult task of understanding the semantics of UI screens and
user inputs. In this paper, we introduce Avgust, which automates key steps of
generating usage-based tests. Avgust uses neural models for image understanding
to process video recordings of app uses to synthesize an app-agnostic
state-machine encoding of those uses. Then, Avgust uses this encoding to
synthesize test cases for a new target app. We evaluate Avgust on 374 videos of
common uses of 18 popular apps and show that 69% of the tests Avgust generates
successfully execute the desired usage, and that Avgust's classifiers
outperform the state of the art.
|
[
{
"version": "v1",
"created": "Tue, 6 Sep 2022 15:36:03 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Oct 2022 23:03:31 GMT"
},
{
"version": "v3",
"created": "Mon, 31 Oct 2022 15:56:55 GMT"
},
{
"version": "v4",
"created": "Tue, 1 Nov 2022 18:52:39 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Zhao",
"Yixue",
""
],
[
"Talebipour",
"Saghar",
""
],
[
"Baral",
"Kesina",
""
],
[
"Park",
"Hyojae",
""
],
[
"Yee",
"Leon",
""
],
[
"Khan",
"Safwat Ali",
""
],
[
"Brun",
"Yuriy",
""
],
[
"Medvidovic",
"Nenad",
""
],
[
"Moran",
"Kevin",
""
]
] |
new_dataset
| 0.990692 |
2209.03625
|
Devarsh Patel
|
Devarsh Patel, Sarthak Patel, Megh Patel
|
Application of image-to-image translation in improving pedestrian
detection
|
This is a working draft and not indented for publication
| null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The lack of effective target regions makes it difficult to perform several
visual functions in low intensity light, including pedestrian recognition, and
image-to-image translation. In this situation, with the accumulation of
high-quality information by the combined use of infrared and visible images it
is possible to detect pedestrians even in low light. In this study we are going
to use advanced deep learning models like pix2pixGAN and YOLOv7 on LLVIP
dataset, containing visible-infrared image pairs for low light vision. This
dataset contains 33672 images and most of the images were captured in dark
scenes, tightly synchronized with time and location.
|
[
{
"version": "v1",
"created": "Thu, 8 Sep 2022 08:07:01 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Nov 2022 12:22:44 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Patel",
"Devarsh",
""
],
[
"Patel",
"Sarthak",
""
],
[
"Patel",
"Megh",
""
]
] |
new_dataset
| 0.996732 |
2209.14156
|
Jaemin Cho
|
Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal
|
TVLT: Textless Vision-Language Transformer
|
NeurIPS 2022 Oral (21 pages; the first three authors contributed
equally)
| null | null | null |
cs.CV cs.AI cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we present the Textless Vision-Language Transformer (TVLT),
where homogeneous transformer blocks take raw visual and audio inputs for
vision-and-language representation learning with minimal modality-specific
design, and do not use text-specific modules such as tokenization or automatic
speech recognition (ASR). TVLT is trained by reconstructing masked patches of
continuous video frames and audio spectrograms (masked autoencoding) and
contrastive modeling to align video and audio. TVLT attains performance
comparable to its text-based counterpart on various multimodal tasks, such as
visual question answering, image retrieval, video retrieval, and multimodal
sentiment analysis, with 28x faster inference speed and only 1/3 of the
parameters. Our findings suggest the possibility of learning compact and
efficient visual-linguistic representations from low-level visual and audio
signals without assuming the prior existence of text. Our code and checkpoints
are available at: https://github.com/zinengtang/TVLT
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 15:08:03 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Nov 2022 16:48:00 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Tang",
"Zineng",
""
],
[
"Cho",
"Jaemin",
""
],
[
"Nie",
"Yixin",
""
],
[
"Bansal",
"Mohit",
""
]
] |
new_dataset
| 0.999381 |
2210.17349
|
Kun Song
|
Kun Song, Jian Cong, Xinsheng Wang, Yongmao Zhang, Lei Xie, Ning
Jiang, Haiying Wu
|
Robust MelGAN: A robust universal neural vocoder for high-fidelity TTS
|
Accepted by ISCSLP 2022
| null | null | null |
cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In current two-stage neural text-to-speech (TTS) paradigm, it is ideal to
have a universal neural vocoder, once trained, which is robust to imperfect
mel-spectrogram predicted from the acoustic model. To this end, we propose
Robust MelGAN vocoder by solving the original multi-band MelGAN's metallic
sound problem and increasing its generalization ability. Specifically, we
introduce a fine-grained network dropout strategy to the generator. With a
specifically designed over-smooth handler which separates speech signal intro
periodic and aperiodic components, we only perform network dropout to the
aperodic components, which alleviates metallic sounding and maintains good
speaker similarity. To further improve generalization ability, we introduce
several data augmentation methods to augment fake data in the discriminator,
including harmonic shift, harmonic noise and phase noise. Experiments show that
Robust MelGAN can be used as a universal vocoder, significantly improving sound
quality in TTS systems built on various types of data.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 14:24:10 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Nov 2022 03:30:50 GMT"
},
{
"version": "v3",
"created": "Wed, 2 Nov 2022 13:05:46 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Song",
"Kun",
""
],
[
"Cong",
"Jian",
""
],
[
"Wang",
"Xinsheng",
""
],
[
"Zhang",
"Yongmao",
""
],
[
"Xie",
"Lei",
""
],
[
"Jiang",
"Ning",
""
],
[
"Wu",
"Haiying",
""
]
] |
new_dataset
| 0.967835 |
2211.00718
|
Andrew J
|
Jomin Jose, Andrew J, Kumudha Raimond, Shweta Vincent
|
SleepyWheels: An Ensemble Model for Drowsiness Detection leading to
Accident Prevention
|
20 pages
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Around 40 percent of accidents related to driving on highways in India occur
due to the driver falling asleep behind the steering wheel. Several types of
research are ongoing to detect driver drowsiness but they suffer from the
complexity and cost of the models. In this paper, SleepyWheels a revolutionary
method that uses a lightweight neural network in conjunction with facial
landmark identification is proposed to identify driver fatigue in real time.
SleepyWheels is successful in a wide range of test scenarios, including the
lack of facial characteristics while covering the eye or mouth, the drivers
varying skin tones, camera placements, and observational angles. It can work
well when emulated to real time systems. SleepyWheels utilized EfficientNetV2
and a facial landmark detector for identifying drowsiness detection. The model
is trained on a specially created dataset on driver sleepiness and it achieves
an accuracy of 97 percent. The model is lightweight hence it can be further
deployed as a mobile application for various platforms.
|
[
{
"version": "v1",
"created": "Tue, 1 Nov 2022 19:36:47 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Jose",
"Jomin",
""
],
[
"J",
"Andrew",
""
],
[
"Raimond",
"Kumudha",
""
],
[
"Vincent",
"Shweta",
""
]
] |
new_dataset
| 0.99401 |
2211.00746
|
Jyoti Kini
|
Jyoti Kini, Ajmal Mian, Mubarak Shah
|
3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D
Point Clouds
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We propose a method for joint detection and tracking of multiple objects in
3D point clouds, a task conventionally treated as a two-step process comprising
object detection followed by data association. Our method embeds both steps
into a single end-to-end trainable network eliminating the dependency on
external object detectors. Our model exploits temporal information employing
multiple frames to detect objects and track them in a single network, thereby
making it a utilitarian formulation for real-world scenarios. Computing
affinity matrix by employing features similarity across consecutive point cloud
scans forms an integral part of visual tracking. We propose an attention-based
refinement module to refine the affinity matrix by suppressing erroneous
correspondences. The module is designed to capture the global context in
affinity matrix by employing self-attention within each affinity matrix and
cross-attention across a pair of affinity matrices. Unlike competing
approaches, our network does not require complex post-processing algorithms,
and processes raw LiDAR frames to directly output tracking results. We
demonstrate the effectiveness of our method on the three tracking benchmarks:
JRDB, Waymo, and KITTI. Experimental evaluations indicate the ability of our
model to generalize well across datasets.
|
[
{
"version": "v1",
"created": "Tue, 1 Nov 2022 20:59:38 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Kini",
"Jyoti",
""
],
[
"Mian",
"Ajmal",
""
],
[
"Shah",
"Mubarak",
""
]
] |
new_dataset
| 0.995852 |
2211.00752
|
Artem Lykov
|
Artem Lykov, Aleksey Fedoseev, and Dzmitry Tsetserukou
|
DeltaFinger: a 3-DoF Wearable Haptic Display Enabling High-Fidelity
Force Vector Presentation at a User Finger
|
13 pages, 8 figures, accepted version to AsiaHaptics 2022
| null | null | null |
cs.HC cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents a novel haptic device DeltaFinger designed to deliver the
force of interaction with virtual objects by guiding user's finger with
wearable delta mechanism. The developed interface is capable to deliver 3D
force vector to the fingertip of the index finger of the user, allowing complex
rendering of virtual reality (VR) environment. The developed device is able to
produce the kinesthetic feedback up to 1.8 N in vertical projection and 0.9 N
in horizontal projection without restricting the motion freedom of of the
remaining fingers. The experimental results showed a sufficient precision in
perception of force vector with DeltaFinger (mean force vector error of 0.6
rad). The proposed device potentially can be applied to VR communications,
medicine, and navigation of the people with vision problems.
|
[
{
"version": "v1",
"created": "Tue, 1 Nov 2022 21:15:49 GMT"
}
] | 2022-11-03T00:00:00 |
[
[
"Lykov",
"Artem",
""
],
[
"Fedoseev",
"Aleksey",
""
],
[
"Tsetserukou",
"Dzmitry",
""
]
] |
new_dataset
| 0.999255 |
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