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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2209.12041
|
Gabriela Ahmadi-Assalemi
|
Gabriela Ahmadi-Assalemi (1), Haider Al-Khateeb (1) ((1) Cyber Quarter
- Midlands Centre for Cyber Security, University of Wolverhampton, UK)
|
Blockchain technologies in the design of Industrial Control Systems for
Smart Cities
|
8 pages, 5 figures
|
published in IEEE Blockchain Technical Briefs, 2022,
https://blockchain.ieee.org/technicalbriefs
| null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The proliferation of sensor technologies in Industrial Control Systems (ICS)
helped to transform the environment towards better automation, process control
and monitoring. However, sensor technologies expose the smart cities of the
future to complex security challenges. Luckily, the sensing capabilities also
create opportunities to capture various data types, which apart from
operational use can add substantial value to developing mechanisms to protect
ICS and critical infrastructure. We discuss Blockchain (BC), a disruptive
technology with applications ranging from cryptocurrency to smart contracts and
the value of integrating BC technologies into the design of ICS to support
modern digital forensic readiness.
|
[
{
"version": "v1",
"created": "Sat, 24 Sep 2022 15:52:39 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Ahmadi-Assalemi",
"Gabriela",
""
],
[
"Al-Khateeb",
"Haider",
""
]
] |
new_dataset
| 0.998966 |
2209.12048
|
Andrea Carron
|
Andrea Carron, Sabrina Bodmer, Lukas Vogel, Ren\'e Zurbr\"ugg, David
Helm, Rahel Rickenbach, Simon Muntwiler, Jerome Sieber, Melanie N. Zeilinger
|
Chronos and CRS: Design of a miniature car-like robot and a software
framework for single and multi-agent robotics and control
| null | null | null | null |
cs.RO cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
From both an educational and research point of view, experiments on hardware
are a key aspect of robotics and control. In the last decade, many open-source
hardware and software frameworks for wheeled robots have been presented, mainly
in the form of unicycles and car-like robots, with the goal of making robotics
accessible to a wider audience and to support control systems development.
Unicycles are usually small and inexpensive, and therefore facilitate
experiments in a larger fleet, but they are not suited for high-speed motion.
Car-like robots are more agile, but they are usually larger and more expensive,
thus requiring more resources in terms of space and money. In order to bridge
this gap, we present Chronos, a new car-like 1/28th scale robot with customized
open-source electronics, and CRS, an open-source software framework for control
and robotics. The CRS software framework includes the implementation of various
state-of-the-art algorithms for control, estimation, and multi-agent
coordination. With this work, we aim to provide easier access to hardware and
reduce the engineering time needed to start new educational and research
projects.
|
[
{
"version": "v1",
"created": "Sat, 24 Sep 2022 16:36:21 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Carron",
"Andrea",
""
],
[
"Bodmer",
"Sabrina",
""
],
[
"Vogel",
"Lukas",
""
],
[
"Zurbrügg",
"René",
""
],
[
"Helm",
"David",
""
],
[
"Rickenbach",
"Rahel",
""
],
[
"Muntwiler",
"Simon",
""
],
[
"Sieber",
"Jerome",
""
],
[
"Zeilinger",
"Melanie N.",
""
]
] |
new_dataset
| 0.999755 |
2209.12136
|
Elijah S. Lee
|
Elijah S. Lee, Giuseppe Loianno, Dinesh Jayaraman, Vijay Kumar
|
Vision-based Perimeter Defense via Multiview Pose Estimation
|
7 pages, 10 figures
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Previous studies in the perimeter defense game have largely focused on the
fully observable setting where the true player states are known to all players.
However, this is unrealistic for practical implementation since defenders may
have to perceive the intruders and estimate their states. In this work, we
study the perimeter defense game in a photo-realistic simulator and the real
world, requiring defenders to estimate intruder states from vision. We train a
deep machine learning-based system for intruder pose detection with domain
randomization that aggregates multiple views to reduce state estimation errors
and adapt the defensive strategy to account for this. We newly introduce
performance metrics to evaluate the vision-based perimeter defense. Through
extensive experiments, we show that our approach improves state estimation, and
eventually, perimeter defense performance in both 1-defender-vs-1-intruder
games, and 2-defenders-vs-1-intruder games.
|
[
{
"version": "v1",
"created": "Sun, 25 Sep 2022 03:41:45 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Lee",
"Elijah S.",
""
],
[
"Loianno",
"Giuseppe",
""
],
[
"Jayaraman",
"Dinesh",
""
],
[
"Kumar",
"Vijay",
""
]
] |
new_dataset
| 0.990654 |
2209.12140
|
Huyen N. Nguyen
|
Huyen N. Nguyen, Caleb Trujillo, Tommy Dang
|
Modie Viewer: Protein Beasts and How to View Them
|
5 pages, 5 figures, Bio+MedVis Challenge @ IEEE VIS 2022
| null | null | null |
cs.HC cs.GR q-bio.BM
|
http://creativecommons.org/licenses/by/4.0/
|
Understanding chemical modifications on proteins opens up further
possibilities for research on rare diseases. This work proposes visualization
approaches using two-dimensional (2D) and three-dimensional (3D) visual
representations to analyze and gain insights into protein modifications. In
this work, we present the application of Modie Viewer as an attempt to address
the Bio+MedVis Challenge at IEEE VIS 2022.
|
[
{
"version": "v1",
"created": "Sun, 25 Sep 2022 04:22:01 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Nguyen",
"Huyen N.",
""
],
[
"Trujillo",
"Caleb",
""
],
[
"Dang",
"Tommy",
""
]
] |
new_dataset
| 0.991057 |
2209.12164
|
Yunlong Tang
|
Yunlong Tang, Siting Xu, Teng Wang, Qin Lin, Qinglin Lu, Feng Zheng
|
Multi-modal Segment Assemblage Network for Ad Video Editing with
Importance-Coherence Reward
|
Accepted by ACCV2022
| null | null | null |
cs.CV cs.AI cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Advertisement video editing aims to automatically edit advertising videos
into shorter videos while retaining coherent content and crucial information
conveyed by advertisers. It mainly contains two stages: video segmentation and
segment assemblage. The existing method performs well at video segmentation
stages but suffers from the problems of dependencies on extra cumbersome models
and poor performance at the segment assemblage stage. To address these
problems, we propose M-SAN (Multi-modal Segment Assemblage Network) which can
perform efficient and coherent segment assemblage task end-to-end. It utilizes
multi-modal representation extracted from the segments and follows the
Encoder-Decoder Ptr-Net framework with the Attention mechanism.
Importance-coherence reward is designed for training M-SAN. We experiment on
the Ads-1k dataset with 1000+ videos under rich ad scenarios collected from
advertisers. To evaluate the methods, we propose a unified metric,
Imp-Coh@Time, which comprehensively assesses the importance, coherence, and
duration of the outputs at the same time. Experimental results show that our
method achieves better performance than random selection and the previous
method on the metric. Ablation experiments further verify that multi-modal
representation and importance-coherence reward significantly improve the
performance. Ads-1k dataset is available at:
https://github.com/yunlong10/Ads-1k
|
[
{
"version": "v1",
"created": "Sun, 25 Sep 2022 06:51:45 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Tang",
"Yunlong",
""
],
[
"Xu",
"Siting",
""
],
[
"Wang",
"Teng",
""
],
[
"Lin",
"Qin",
""
],
[
"Lu",
"Qinglin",
""
],
[
"Zheng",
"Feng",
""
]
] |
new_dataset
| 0.999367 |
2209.12254
|
Rui Wan
|
Rui Wan, Shuangjie Xu, Wei Wu, Xiaoyi Zou, Tongyi Cao
|
From One to Many: Dynamic Cross Attention Networks for LiDAR and Camera
Fusion
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
LiDAR and cameras are two complementary sensors for 3D perception in
autonomous driving. LiDAR point clouds have accurate spatial and geometry
information, while RGB images provide textural and color data for context
reasoning. To exploit LiDAR and cameras jointly, existing fusion methods tend
to align each 3D point to only one projected image pixel based on calibration,
namely one-to-one mapping. However, the performance of these approaches highly
relies on the calibration quality, which is sensitive to the temporal and
spatial synchronization of sensors. Therefore, we propose a Dynamic Cross
Attention (DCA) module with a novel one-to-many cross-modality mapping that
learns multiple offsets from the initial projection towards the neighborhood
and thus develops tolerance to calibration error. Moreover, a \textit{dynamic
query enhancement} is proposed to perceive the model-independent calibration,
which further strengthens DCA's tolerance to the initial misalignment. The
whole fusion architecture named Dynamic Cross Attention Network (DCAN) exploits
multi-level image features and adapts to multiple representations of point
clouds, which allows DCA to serve as a plug-in fusion module. Extensive
experiments on nuScenes and KITTI prove DCA's effectiveness. The proposed DCAN
outperforms state-of-the-art methods on the nuScenes detection challenge.
|
[
{
"version": "v1",
"created": "Sun, 25 Sep 2022 16:10:14 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Wan",
"Rui",
""
],
[
"Xu",
"Shuangjie",
""
],
[
"Wu",
"Wei",
""
],
[
"Zou",
"Xiaoyi",
""
],
[
"Cao",
"Tongyi",
""
]
] |
new_dataset
| 0.99399 |
2209.12270
|
Charles Dawson
|
Charles Dawson, Austin Garrett, Falk Pollok, Yang Zhang, Chuchu Fan
|
Barrier functions enable safety-conscious force-feedback control
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In order to be effective partners for humans, robots must become increasingly
comfortable with making contact with their environment. Unfortunately, it is
hard for robots to distinguish between ``just enough'' and ``too much'' force:
some force is required to accomplish the task but too much might damage
equipment or injure humans. Traditional approaches to designing compliant
force-feedback controllers, such as stiffness control, require difficult
hand-tuning of control parameters and make it difficult to build safe,
effective robot collaborators. In this paper, we propose a novel yet
easy-to-implement force feedback controller that uses control barrier functions
(CBFs) to derive a compliant controller directly from users' specifications of
the maximum allowable forces and torques. We compare our approach to
traditional stiffness control to demonstrate potential advantages of our
control architecture, and we demonstrate the effectiveness of our controller on
an example human-robot collaboration task: cooperative manipulation of a bulky
object.
|
[
{
"version": "v1",
"created": "Sun, 25 Sep 2022 17:20:43 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Dawson",
"Charles",
""
],
[
"Garrett",
"Austin",
""
],
[
"Pollok",
"Falk",
""
],
[
"Zhang",
"Yang",
""
],
[
"Fan",
"Chuchu",
""
]
] |
new_dataset
| 0.987322 |
2209.12310
|
Cristobal A. Navarro
|
Alan Keith, H\'ector Ferrada, Crist\'obal A. Navarro
|
Accelerating the Convex Hull Computation with a Parallel GPU Algorithm
|
7 pages, in Spanish language
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The convex hull is a fundamental geometrical structure for many applications
where groups of points must be enclosed or represented by a convex polygon.
Although efficient sequential convex hull algorithms exist, and are constantly
being used in applications, their computation time is often considered an issue
for time-sensitive tasks such as real-time collision detection, clustering or
image processing for virtual reality, among others, where fast response times
are required. In this work we propose a parallel GPU-based adaptation of
heaphull, which is a state of the art CPU algorithm that computes the convex
hull by first doing a efficient filtering stage followed by the actual convex
hull computation. More specifically, this work parallelizes the filtering
stage, adapting it to the GPU programming model as a series of parallel
reductions. Experimental evaluation shows that the proposed implementation
significantly improves the performance of the convex hull computation, reaching
up to $4\times$ of speedup over the sequential CPU-based heaphull and between
$3\times \sim 4\times$ over existing GPU based approaches.
|
[
{
"version": "v1",
"created": "Sun, 25 Sep 2022 19:50:51 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Keith",
"Alan",
""
],
[
"Ferrada",
"Héctor",
""
],
[
"Navarro",
"Cristóbal A.",
""
]
] |
new_dataset
| 0.972266 |
2209.12352
|
Osama Khalid
|
Osama Khalid, Padmini Srinivasan
|
Smells like Teen Spirit: An Exploration of Sensorial Style in Literary
Genres
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
It is well recognized that sensory perceptions and language have
interconnections through numerous studies in psychology, neuroscience, and
sensorial linguistics. Set in this rich context we ask whether the use of
sensorial language in writings is part of linguistic style? This question is
important from the view of stylometrics research where a rich set of language
features have been explored, but with insufficient attention given to features
related to sensorial language.
Taking this as the goal we explore several angles about sensorial language
and style in collections of lyrics, novels, and poetry. We find, for example,
that individual use of sensorial language is not a random phenomenon; choice is
likely involved. Also, sensorial style is generally stable over time - the
shifts are extremely small. Moreover, style can be extracted from just a few
hundred sentences that have sensorial terms. We also identify representative
and distinctive features within each genre.
For example, we observe that 4 of the top 6 representative features in novels
collection involved individuals using olfactory language where we expected them
to use non-olfactory language.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 00:17:10 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Khalid",
"Osama",
""
],
[
"Srinivasan",
"Padmini",
""
]
] |
new_dataset
| 0.994797 |
2209.12386
|
Xu Yajun
|
Yajun Xu, Chuwen Huang, Yibing Nan, Shiguo Lian
|
TAD: A Large-Scale Benchmark for Traffic Accidents Detection from Video
Surveillance
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Automatic traffic accidents detection has appealed to the machine vision
community due to its implications on the development of autonomous intelligent
transportation systems (ITS) and importance to traffic safety. Most previous
studies on efficient analysis and prediction of traffic accidents, however,
have used small-scale datasets with limited coverage, which limits their effect
and applicability. Existing datasets in traffic accidents are either
small-scale, not from surveillance cameras, not open-sourced, or not built for
freeway scenes. Since accidents happened in freeways tend to cause serious
damage and are too fast to catch the spot. An open-sourced datasets targeting
on freeway traffic accidents collected from surveillance cameras is in great
need and of practical importance. In order to help the vision community address
these shortcomings, we endeavor to collect video data of real traffic accidents
that covered abundant scenes. After integration and annotation by various
dimensions, a large-scale traffic accidents dataset named TAD is proposed in
this work. Various experiments on image classification, object detection, and
video classification tasks, using public mainstream vision algorithms or
frameworks are conducted in this work to demonstrate performance of different
methods. The proposed dataset together with the experimental results are
presented as a new benchmark to improve computer vision research, especially in
ITS.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 03:00:50 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Xu",
"Yajun",
""
],
[
"Huang",
"Chuwen",
""
],
[
"Nan",
"Yibing",
""
],
[
"Lian",
"Shiguo",
""
]
] |
new_dataset
| 0.999862 |
2209.12447
|
Ozioma Collins Oguine
|
Kanyifeechukwu Jane Oguine, Ozioma Collins Oguine, Hashim Ibrahim
Bisallah
|
YOLO v3: Visual and Real-Time Object Detection Model for Smart
Surveillance Systems(3s)
|
8 pages, 12 figures, 2 tables
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Can we see it all? Do we know it All? These are questions thrown to human
beings in our contemporary society to evaluate our tendency to solve problems.
Recent studies have explored several models in object detection; however, most
have failed to meet the demand for objectiveness and predictive accuracy,
especially in developing and under-developed countries. Consequently, several
global security threats have necessitated the development of efficient
approaches to tackle these issues. This paper proposes an object detection
model for cyber-physical systems known as Smart Surveillance Systems (3s). This
research proposes a 2-phase approach, highlighting the advantages of YOLO v3
deep learning architecture in real-time and visual object detection. A transfer
learning approach was implemented for this research to reduce training time and
computing resources. The dataset utilized for training the model is the MS COCO
dataset which contains 328,000 annotated image instances. Deep learning
techniques such as Pre-processing, Data pipelining, and detection was
implemented to improve efficiency. Compared to other novel research models, the
proposed model's results performed exceedingly well in detecting WILD objects
in surveillance footages. An accuracy of 99.71% was recorded, with an improved
mAP of 61.5.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 06:34:12 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Oguine",
"Kanyifeechukwu Jane",
""
],
[
"Oguine",
"Ozioma Collins",
""
],
[
"Bisallah",
"Hashim Ibrahim",
""
]
] |
new_dataset
| 0.997981 |
2209.12475
|
Zhiming Zhang
|
Huanjing Yue, Zhiming Zhang, Jingyu Yang
|
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark
Dataset
|
Accepted by ECCV2022
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, real image super-resolution (SR) has achieved promising
results due to the development of SR datasets and corresponding real SR
methods. In contrast, the field of real video SR is lagging behind, especially
for real raw videos. Considering the superiority of raw image SR over sRGB
image SR, we construct a real-world raw video SR (Real-RawVSR) dataset and
propose a corresponding SR method. We utilize two DSLR cameras and a
beam-splitter to simultaneously capture low-resolution (LR) and high-resolution
(HR) raw videos with 2x, 3x, and 4x magnifications. There are 450 video pairs
in our dataset, with scenes varying from indoor to outdoor, and motions
including camera and object movements. To our knowledge, this is the first
real-world raw VSR dataset. Since the raw video is characterized by the Bayer
pattern, we propose a two-branch network, which deals with both the packed RGGB
sequence and the original Bayer pattern sequence, and the two branches are
complementary to each other. After going through the proposed co-alignment,
interaction, fusion, and reconstruction modules, we generate the corresponding
HR sRGB sequence. Experimental results demonstrate that the proposed method
outperforms benchmark real and synthetic video SR methods with either raw or
sRGB inputs. Our code and dataset are available at
https://github.com/zmzhang1998/Real-RawVSR.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 07:33:31 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Yue",
"Huanjing",
""
],
[
"Zhang",
"Zhiming",
""
],
[
"Yang",
"Jingyu",
""
]
] |
new_dataset
| 0.999879 |
2209.12480
|
Michael Schmitt
|
Michael Schmitt, Pedram Ghamisi, Naoto Yokoya, Ronny H\"ansch
|
EOD: The IEEE GRSS Earth Observation Database
|
This paper contains the description of the IEEE-GRSS Earth
Observation Database
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In the era of deep learning, annotated datasets have become a crucial asset
to the remote sensing community. In the last decade, a plethora of different
datasets was published, each designed for a specific data type and with a
specific task or application in mind. In the jungle of remote sensing datasets,
it can be hard to keep track of what is available already. With this paper, we
introduce EOD - the IEEE GRSS Earth Observation Database (EOD) - an interactive
online platform for cataloguing different types of datasets leveraging remote
sensing imagery.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 07:44:41 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Schmitt",
"Michael",
""
],
[
"Ghamisi",
"Pedram",
""
],
[
"Yokoya",
"Naoto",
""
],
[
"Hänsch",
"Ronny",
""
]
] |
new_dataset
| 0.997195 |
2209.12523
|
Gauthier Roussilhe
|
Gauthier Roussilhe, Thibault Pirson, Mathieu Xhonneux, David Bol
|
From Silicon Shield to Carbon Lock-in ? The Environmental Footprint of
Electronic Components Manufacturing in Taiwan (2015-2020)
|
19 pages, 9 figures, 2 tables
| null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Taiwan plans to rapidly increase its industrial production capacity of
electronic components while concurrently setting policies for its ecological
transition. Given that the island is responsible for the manufacturing of a
significant part of worldwide electronics components, the sustainability of the
Taiwanese electronics industry is therefore of critical interest. In this
paper, we survey the environmental footprint of 16 Taiwanese electronic
components manufacturers (ECM) using corporate sustainability responsibility
reports (CSR). Based on data from 2015 to 2020, this study finds out that our
sample of 16 manufacturers increased its greenhouse gases (GHG) emissions by
7.5\% per year, its final energy and electricity consumption by 8.8\% and
8.9\%, and the water usage by 6.1\%. We show that the volume of manufactured
electronic components and the environmental footprints compiled in this study
are strongly correlated, which suggests that relative efficiency gains are not
sufficient to curb the environmental footprint at the national scale. Given the
critical nature of electronics industry for Taiwan's geopolitics and economics,
the observed increase of energy consumption and the slow renewable energy
roll-out, these industrial activities could create a carbon lock-in, blocking
the Taiwanese government from achieving its carbon reduction goals and its
sustainability policies. Besides, the European Union, the USA or even China aim
at developing an industrial ecosystem targeting sub-10nm CMOS technology nodes
similar to Taiwan. This study thus provides important insights regarding the
environmental implications associated with such a technology roadmap. All data
and calculation models used in this study are provided as supplementary
material.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 08:59:45 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Roussilhe",
"Gauthier",
""
],
[
"Pirson",
"Thibault",
""
],
[
"Xhonneux",
"Mathieu",
""
],
[
"Bol",
"David",
""
]
] |
new_dataset
| 0.994508 |
2209.12587
|
Lutz Oettershagen
|
Lutz Oettershagen, Petra Mutzel
|
TGLib: An Open-Source Library for Temporal Graph Analysis
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We initiate an open-source library for the efficient analysis of temporal
graphs. We consider one of the standard models of dynamic networks in which
each edge has a discrete timestamp and transition time. Recently there has been
a massive interest in analyzing such temporal graphs. Common computational data
mining and analysis tasks include the computation of temporal distances,
centrality measures, and network statistics like topological overlap,
burstiness, or temporal diameter. To fulfill the increasing demand for
efficient and easy-to-use implementations of temporal graph algorithms, we
introduce the open-source library TGLib, which integrates efficient data
structures and algorithms for temporal graph analysis. TGLib is highly
efficient and versatile, providing simple and convenient C++ and Python
interfaces, targeting computer scientists, practitioners, students, and the
(temporal) network research community.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 11:00:51 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Oettershagen",
"Lutz",
""
],
[
"Mutzel",
"Petra",
""
]
] |
new_dataset
| 0.999065 |
2209.12650
|
Nabeel Mohammed
|
Mohammed Rakib, Md. Ismail Hossain, Nabeel Mohammed, Fuad Rahman
|
Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing
N-gram Language Models
| null | null | null | null |
cs.CL cs.AI eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although over 300M around the world speak Bangla, scant work has been done in
improving Bangla voice-to-text transcription due to Bangla being a low-resource
language. However, with the introduction of the Bengali Common Voice 9.0 speech
dataset, Automatic Speech Recognition (ASR) models can now be significantly
improved. With 399hrs of speech recordings, Bengali Common Voice is the largest
and most diversified open-source Bengali speech corpus in the world. In this
paper, we outperform the SOTA pretrained Bengali ASR models by finetuning a
pretrained wav2vec2 model on the common voice dataset. We also demonstrate how
to significantly improve the performance of an ASR model by adding an n-gram
language model as a post-processor. Finally, we do some experiments and
hyperparameter tuning to generate a robust Bangla ASR model that is better than
the existing ASR models.
|
[
{
"version": "v1",
"created": "Tue, 13 Sep 2022 17:59:21 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Rakib",
"Mohammed",
""
],
[
"Hossain",
"Md. Ismail",
""
],
[
"Mohammed",
"Nabeel",
""
],
[
"Rahman",
"Fuad",
""
]
] |
new_dataset
| 0.999156 |
2209.12655
|
Guido Governatori
|
Francesco Olivieri, Guido Governatori, Matteo Cristani, Antonino
Rotolo and Abdul Sattar
|
Deontic Meta-Rules
| null | null | null | null |
cs.AI cs.LO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The use of meta-rules in logic, i.e., rules whose content includes other
rules, has recently gained attention in the setting of non-monotonic reasoning:
a first logical formalisation and efficient algorithms to compute the
(meta)-extensions of such theories were proposed in Olivieri et al (2021) This
work extends such a logical framework by considering the deontic aspect. The
resulting logic will not just be able to model policies but also tackle
well-known aspects that occur in numerous legal systems. The use of Defeasible
Logic (DL) to model meta-rules in the application area we just alluded to has
been investigated. Within this line of research, the study mentioned above was
not focusing on the general computational properties of meta-rules.
This study fills this gap with two major contributions. First, we introduce
and formalise two variants of Defeasible Deontic Logic with Meta-Rules to
represent (1) defeasible meta-theories with deontic modalities, and (2) two
different types of conflicts among rules: Simple Conflict Defeasible Deontic
Logic, and Cautious Conflict Defeasible Deontic Logic. Second, we advance
efficient algorithms to compute the extensions for both variants.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 07:48:29 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Olivieri",
"Francesco",
""
],
[
"Governatori",
"Guido",
""
],
[
"Cristani",
"Matteo",
""
],
[
"Rotolo",
"Antonino",
""
],
[
"Sattar",
"Abdul",
""
]
] |
new_dataset
| 0.991234 |
2209.12694
|
Zian Chen
|
Xiao Cao, Zitan Chen, Canyu Le, Lei Meng
|
Multi-modal Video Chapter Generation
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Chapter generation becomes practical technique for online videos nowadays.
The chapter breakpoints enable users to quickly find the parts they want and
get the summative annotations. However, there is no public method and dataset
for this task. To facilitate the research along this direction, we introduce a
new dataset called Chapter-Gen, which consists of approximately 10k
user-generated videos with annotated chapter information. Our data collection
procedure is fast, scalable and does not require any additional manual
annotation. On top of this dataset, we design an effective baseline specificlly
for video chapters generation task. which captures two aspects of a
video,including visual dynamics and narration text. It disentangles local and
global video features for localization and title generation respectively. To
parse the long video efficiently, a skip sliding window mechanism is designed
to localize potential chapters. And a cross attention multi-modal fusion module
is developed to aggregate local features for title generation. Our experiments
demonstrate that the proposed framework achieves superior results over existing
methods which illustrate that the method design for similar task cannot be
transfered directly even after fine-tuning. Code and dataset are available at
https://github.com/czt117/MVCG.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 13:44:48 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Cao",
"Xiao",
""
],
[
"Chen",
"Zitan",
""
],
[
"Le",
"Canyu",
""
],
[
"Meng",
"Lei",
""
]
] |
new_dataset
| 0.990452 |
2209.12698
|
Daniel Esc\'anez-Exp\'osito
|
Daniel Escanez-Exposito, Pino Caballero-Gil and Francisco
Martin-Fernandez
|
QuantumSolver: A quantum tool-set for developers
|
10 pages, 4 figures, sumited to CAITS, SAM, CSCE, Springer Nature,
Indexed by Computing Research and Education (CORE) with ranking C, Indexed by
CS Conference Rankings (0.83), Indexed by GII-GRIN in Class WiP
|
CAITS, SAM, CSCE. The 2022 World Congress in Computer Science,
Computer Engineering, and Applied Computing. CSCE 2022, pg. 149. ISBN #
1-60132-516-9; American Council on Science & Education Las Vegas, USA. July
25-28, 2022
| null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper introduces a new opensource quantum tool-set called QuantumSolver
based on Qiskit to help developers without knowledge in quantum computing. The
developed library includes a set of algorithms with different features: random
number generation, Bernstein-Vazirani algorithm and quantum key distribution
using the BB84 protocol. This paper described the main details about the
implementation of the toolset, focusing in the challenges that the authors
faced. Finally, this document analyzes the results obtained with some
conclusions that authors compares with the included features.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 11:30:21 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Escanez-Exposito",
"Daniel",
""
],
[
"Caballero-Gil",
"Pino",
""
],
[
"Martin-Fernandez",
"Francisco",
""
]
] |
new_dataset
| 0.975644 |
2209.12721
|
Haocheng Hua
|
Haocheng Hua, Tony Xiao Han, and Jie Xu
|
MIMO Integrated Sensing and Communication: CRB-Rate Tradeoff
|
30 pages, 17 figures, submitted for journal publication
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper studies a multiple-input multiple-output (MIMO) integrated sensing
and communication (ISAC) system, in which a multi-antenna base station (BS)
sends unified wireless signals to estimate one sensing target and communicate
with a multi-antenna communication user (CU) simultaneously. We consider both
the point and extended target models. For the point target case, the BS
estimates the target angle and we adopt the Cram\'er-Rao bound (CRB) for angle
estimation as the sensing performance metric. For the extended target case, the
BS estimates the complete target response matrix, and we consider three
different sensing performance metrics including the trace, the maximum
eigenvalue, and the determinant of the CRB matrix for target response matrix
estimation. For each of the four scenarios with different CRB measures, we
investigate the fundamental tradeoff between the CRB for estimation and the
data rate for communication, by characterizing the Pareto boundary of the
achievable CRB-rate (C-R) region. In particular, we formulate a new MIMO rate
maximization problem for each scenario, by optimizing the transmit covariance
matrix at the BS, subject to a different form of maximum CRB constraint and its
maximum transmit power constraint. For these problems, we obtain their optimal
solutions in semi-closed forms by using advanced convex optimization
techniques. For the point target case, the optimal solution is obtained by
diagonalizing a \emph{composite channel matrix} via singular value
decomposition (SVD) together with water-filling-like power allocation over
these decomposed subchannels. For the three scenarios in the extended target
case, the optimal solutions are obtained by diagonalizing the
\emph{communication channel} via SVD, together with proper power allocation
over two orthogonal sets of subchannels. Numerical results are conducted to
validate the proposed design.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 14:23:44 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Hua",
"Haocheng",
""
],
[
"Han",
"Tony Xiao",
""
],
[
"Xu",
"Jie",
""
]
] |
new_dataset
| 0.987235 |
2209.12723
|
Yue Zhang
|
Yue Zhang, Parisa Kordjamshidi
|
LOViS: Learning Orientation and Visual Signals for Vision and Language
Navigation
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Understanding spatial and visual information is essential for a navigation
agent who follows natural language instructions. The current Transformer-based
VLN agents entangle the orientation and vision information, which limits the
gain from the learning of each information source. In this paper, we design a
neural agent with explicit Orientation and Vision modules. Those modules learn
to ground spatial information and landmark mentions in the instructions to the
visual environment more effectively. To strengthen the spatial reasoning and
visual perception of the agent, we design specific pre-training tasks to feed
and better utilize the corresponding modules in our final navigation model. We
evaluate our approach on both Room2room (R2R) and Room4room (R4R) datasets and
achieve the state of the art results on both benchmarks.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 14:26:50 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Zhang",
"Yue",
""
],
[
"Kordjamshidi",
"Parisa",
""
]
] |
new_dataset
| 0.999671 |
2209.12822
|
Leandro Passos
|
Talita A. Pereira, Regina C. Popim, Leandro A. Passos, Danillo R.
Pereira, Clayton R. Pereira, Jo\~ao P. Papa
|
ComplexWoundDB: A Database for Automatic Complex Wound Tissue
Categorization
| null | null |
10.1109/IWSSIP55020.2022.9854419
| null |
cs.CV cs.DB cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Complex wounds usually face partial or total loss of skin thickness, healing
by secondary intention. They can be acute or chronic, figuring infections,
ischemia and tissue necrosis, and association with systemic diseases. Research
institutes around the globe report countless cases, ending up in a severe
public health problem, for they involve human resources (e.g., physicians and
health care professionals) and negatively impact life quality. This paper
presents a new database for automatically categorizing complex wounds with five
categories, i.e., non-wound area, granulation, fibrinoid tissue, and dry
necrosis, hematoma. The images comprise different scenarios with complex wounds
caused by pressure, vascular ulcers, diabetes, burn, and complications after
surgical interventions. The dataset, called ComplexWoundDB, is unique because
it figures pixel-level classifications from $27$ images obtained in the wild,
i.e., images are collected at the patients' homes, labeled by four health
professionals. Further experiments with distinct machine learning techniques
evidence the challenges in addressing the problem of computer-aided complex
wound tissue categorization. The manuscript sheds light on future directions in
the area, with a detailed comparison among other databased widely used in the
literature.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 16:28:34 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Pereira",
"Talita A.",
""
],
[
"Popim",
"Regina C.",
""
],
[
"Passos",
"Leandro A.",
""
],
[
"Pereira",
"Danillo R.",
""
],
[
"Pereira",
"Clayton R.",
""
],
[
"Papa",
"João P.",
""
]
] |
new_dataset
| 0.999023 |
2209.12854
|
Sabur Baidya
|
Sumit K. Das, Mohammad Helal Uddin, Sabur Baidya
|
Edge-assisted Collaborative Digital Twin for Safety-Critical Robotics in
Industrial IoT
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Digital Twin technology is playing a pivotal role in the modern industrial
evolution. Especially, with the technological progress in the
Internet-of-Things (IoT) and the increasing trend in autonomy, multi-sensor
equipped robotics can create practical digital twin, which is particularly
useful in the industrial applications for operations, maintenance and safety.
Herein, we demonstrate a real-world digital twin of a safety-critical robotics
applications with a Franka-Emika-Panda robotic arm. We develop and showcase an
edge-assisted collaborative digital twin for dynamic obstacle avoidance which
can be useful in real-time adaptation of the robots while operating in the
uncertain and dynamic environments in industrial IoT.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 17:08:51 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Das",
"Sumit K.",
""
],
[
"Uddin",
"Mohammad Helal",
""
],
[
"Baidya",
"Sabur",
""
]
] |
new_dataset
| 0.99885 |
2209.12870
|
Xieyang Xu
|
Xieyang Xu (1), Weixin Deng (1), Ryan Beckett (2), Ratul Mahajan (1
and 3) and David Walker (4) ((1) University of Washington, (2) Microsoft, (3)
Intentionet, (4) Princeton University)
|
Test Coverage for Network Configurations
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
We develop NetCov, the first tool to reveal which network configuration lines
are being tested by a suite of network tests. It helps network engineers
improve test suites and thus increase network reliability. A key challenge in
its development is that many network tests test the data plane instead of
testing the configurations (control plane) directly. We must be able to
efficiently infer which configuration elements contribute to tested data plane
elements, even when such contributions are non-local (on remote devices) or
non-deterministic. NetCov uses an information flow graph based model that
precisely captures various forms of contributions and a scalable method to
lazily infer contributions. Using it, we show that an existing test suite for
Internet2 (a nation-wide backbone network in the USA) covers only 26% of the
configuration lines. The feedback from NetCov makes it easy to define new tests
that improve coverage. For Internet2, adding just three such tests covers an
additional 17% of the lines.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 17:39:33 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Xu",
"Xieyang",
"",
"1\n and 3"
],
[
"Deng",
"Weixin",
"",
"1\n and 3"
],
[
"Beckett",
"Ryan",
"",
"1\n and 3"
],
[
"Mahajan",
"Ratul",
"",
"1\n and 3"
],
[
"Walker",
"David",
""
]
] |
new_dataset
| 0.979445 |
2209.12882
|
Gal Katzhendler
|
Amit Daniely and Gal Katzhendler
|
Approximate Description Length, Covering Numbers, and VC Dimension
| null | null | null | null |
cs.LG cs.DS stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, Daniely and Granot [arXiv:1910.05697] introduced a new notion of
complexity called Approximate Description Length (ADL). They used it to derive
novel generalization bounds for neural networks, that despite substantial work,
were out of reach for more classical techniques such as discretization,
Covering Numbers and Rademacher Complexity. In this paper we explore how ADL
relates to classical notions of function complexity such as Covering Numbers
and VC Dimension. We find that for functions whose range is the reals, ADL is
essentially equivalent to these classical complexity measures. However, this
equivalence breaks for functions with high dimensional range.
|
[
{
"version": "v1",
"created": "Mon, 26 Sep 2022 17:53:29 GMT"
}
] | 2022-09-27T00:00:00 |
[
[
"Daniely",
"Amit",
""
],
[
"Katzhendler",
"Gal",
""
]
] |
new_dataset
| 0.960722 |
2004.09705
|
Wei Shiung Liew Mr
|
Fatai Sado, Chu Kiong Loo, Wei Shiung Liew, Matthias Kerzel, Stefan
Wermter
|
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review
| null | null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent applications of autonomous agents and robots, such as self-driving
cars, scenario-based trainers, exploration robots, and service robots have
brought attention to crucial trust-related challenges associated with the
current generation of artificial intelligence (AI) systems. AI systems based on
the connectionist deep learning neural network approach lack capabilities of
explaining their decisions and actions to others, despite their great
successes. Without symbolic interpretation capabilities, they are black boxes,
which renders their decisions or actions opaque, making it difficult to trust
them in safety-critical applications. The recent stance on the explainability
of AI systems has witnessed several approaches on eXplainable Artificial
Intelligence (XAI); however, most of the studies have focused on data-driven
XAI systems applied in computational sciences. Studies addressing the
increasingly pervasive goal-driven agents and robots are still missing. This
paper reviews approaches on explainable goal-driven intelligent agents and
robots, focusing on techniques for explaining and communicating agents
perceptual functions (example, senses, and vision) and cognitive reasoning
(example, beliefs, desires, intention, plans, and goals) with humans in the
loop. The review highlights key strategies that emphasize transparency,
understandability, and continual learning for explainability. Finally, the
paper presents requirements for explainability and suggests a roadmap for the
possible realization of effective goal-driven explainable agents and robots.
|
[
{
"version": "v1",
"created": "Tue, 21 Apr 2020 01:41:20 GMT"
},
{
"version": "v2",
"created": "Tue, 26 Jan 2021 09:29:31 GMT"
},
{
"version": "v3",
"created": "Mon, 15 Mar 2021 03:28:25 GMT"
},
{
"version": "v4",
"created": "Sat, 10 Apr 2021 06:55:15 GMT"
},
{
"version": "v5",
"created": "Thu, 22 Jul 2021 13:08:01 GMT"
},
{
"version": "v6",
"created": "Wed, 28 Jul 2021 07:35:10 GMT"
},
{
"version": "v7",
"created": "Fri, 5 Nov 2021 04:40:08 GMT"
},
{
"version": "v8",
"created": "Fri, 3 Jun 2022 11:16:52 GMT"
},
{
"version": "v9",
"created": "Fri, 23 Sep 2022 08:52:58 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Sado",
"Fatai",
""
],
[
"Loo",
"Chu Kiong",
""
],
[
"Liew",
"Wei Shiung",
""
],
[
"Kerzel",
"Matthias",
""
],
[
"Wermter",
"Stefan",
""
]
] |
new_dataset
| 0.971232 |
2105.12038
|
Nikita Drobyshev
|
Aleksandr Safin, Maxim Kan, Nikita Drobyshev, Oleg Voynov, Alexey
Artemov, Alexander Filippov, Denis Zorin, Evgeny Burnaev
|
Unpaired Depth Super-Resolution in the Wild
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Depth maps captured with commodity sensors are often of low quality and
resolution; these maps need to be enhanced to be used in many applications.
State-of-the-art data-driven methods of depth map super-resolution rely on
registered pairs of low- and high-resolution depth maps of the same scenes.
Acquisition of real-world paired data requires specialized setups. Another
alternative, generating low-resolution maps from high-resolution maps by
subsampling, adding noise and other artificial degradation methods, does not
fully capture the characteristics of real-world low-resolution images. As a
consequence, supervised learning methods trained on such artificial paired data
may not perform well on real-world low-resolution inputs. We consider an
approach to depth super-resolution based on learning from unpaired data. While
many techniques for unpaired image-to-image translation have been proposed,
most fail to deliver effective hole-filling or reconstruct accurate surfaces
using depth maps. We propose an unpaired learning method for depth
super-resolution, which is based on a learnable degradation model, enhancement
component and surface normal estimates as features to produce more accurate
depth maps. We propose a benchmark for unpaired depth SR and demonstrate that
our method outperforms existing unpaired methods and performs on par with
paired.
|
[
{
"version": "v1",
"created": "Tue, 25 May 2021 16:19:16 GMT"
},
{
"version": "v2",
"created": "Mon, 23 Aug 2021 11:21:20 GMT"
},
{
"version": "v3",
"created": "Sat, 30 Jul 2022 15:11:19 GMT"
},
{
"version": "v4",
"created": "Fri, 23 Sep 2022 15:29:08 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Safin",
"Aleksandr",
""
],
[
"Kan",
"Maxim",
""
],
[
"Drobyshev",
"Nikita",
""
],
[
"Voynov",
"Oleg",
""
],
[
"Artemov",
"Alexey",
""
],
[
"Filippov",
"Alexander",
""
],
[
"Zorin",
"Denis",
""
],
[
"Burnaev",
"Evgeny",
""
]
] |
new_dataset
| 0.956625 |
2106.06001
|
Elias Gr\"unewald
|
Elias Gr\"unewald, Paul Wille, Frank Pallas, Maria C. Borges, Max-R.
Ulbricht
|
TIRA: An OpenAPI Extension and Toolbox for GDPR Transparency in RESTful
Architectures
|
Accepted for publication at the 2021 International Workshop on
Privacy Engineering (IWPE'21). This is a preprint manuscript (authors' own
version before final copy-editing)
| null |
10.1109/EuroSPW54576.2021.00039
| null |
cs.SE cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transparency - the provision of information about what personal data is
collected for which purposes, how long it is stored, or to which parties it is
transferred - is one of the core privacy principles underlying regulations such
as the GDPR. Technical approaches for implementing transparency in practice
are, however, only rarely considered. In this paper, we present a novel
approach for doing so in current, RESTful application architectures and in line
with prevailing agile and DevOps-driven practices. For this purpose, we
introduce 1) a transparency-focused extension of OpenAPI specifications that
allows individual service descriptions to be enriched with transparency-related
annotations in a bottom-up fashion and 2) a set of higher-order tools for
aggregating respective information across multiple, interdependent services and
for coherently integrating our approach into automated CI/CD-pipelines.
Together, these building blocks pave the way for providing transparency
information that is more specific and at the same time better reflects the
actual implementation givens within complex service architectures than current,
overly broad privacy statements.
|
[
{
"version": "v1",
"created": "Thu, 10 Jun 2021 18:42:50 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Grünewald",
"Elias",
""
],
[
"Wille",
"Paul",
""
],
[
"Pallas",
"Frank",
""
],
[
"Borges",
"Maria C.",
""
],
[
"Ulbricht",
"Max-R.",
""
]
] |
new_dataset
| 0.975148 |
2201.01105
|
\'Angel Gim\'enez
|
Angel Gim\'enez, Miguel A. Murcia, Jos\'e M. Amig\'o, Oscar
Mart\'inez-Bonastre, Jos\'e Valero
|
New RED-type TCP-AQM algorithms based on beta distribution drop
functions
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, Active Queue Management (AQM) mechanisms to improve the
performance of TCP/IP networks have acquired a relevant role. In this paper we
present a simple and robust RED-type algorithm together with a couple of
dynamical variants with the ability to adapt to the specific characteristics of
different network environments, as well as to the user needs. We first present
a basic version called Beta RED (BetaRED), where the user is free to adjust the
parameters according to the network conditions. The aim is to make the
parameter setting easy and intuitive so that a good performance is obtained
over a wide range of parameters. Secondly, BetaRED is used as a framework to
design two dynamic algorithms, which we will call Adaptive Beta RED (ABetaRED)
and Dynamic Beta RED (DBetaRED). In those new algorithms certain parameters are
dynamically adjusted so that the queue length remains stable around a
predetermined reference value and according to changing network traffic
conditions. Finally, we present a battery of simulations using the Network
Simulator 3 (ns-3) software with a two-fold objective: to guide the user on how
to adjust the parameters of the BetaRED mechanism, and to show a performance
comparison of ABetaRED and DBetaRED with other representative algorithms that
pursue a similar objective.
|
[
{
"version": "v1",
"created": "Tue, 4 Jan 2022 12:14:42 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2022 04:29:36 GMT"
},
{
"version": "v3",
"created": "Thu, 22 Sep 2022 20:46:30 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Giménez",
"Angel",
""
],
[
"Murcia",
"Miguel A.",
""
],
[
"Amigó",
"José M.",
""
],
[
"Martínez-Bonastre",
"Oscar",
""
],
[
"Valero",
"José",
""
]
] |
new_dataset
| 0.999559 |
2203.02331
|
Abdul Hannan Khan
|
Abdul Hannan Khan, Mohsin Munir, Ludger van Elst and Andreas Dengel
|
F2DNet: Fast Focal Detection Network for Pedestrian Detection
|
Accepted at ICPR 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Two-stage detectors are state-of-the-art in object detection as well as
pedestrian detection. However, the current two-stage detectors are inefficient
as they do bounding box regression in multiple steps i.e. in region proposal
networks and bounding box heads. Also, the anchor-based region proposal
networks are computationally expensive to train. We propose F2DNet, a novel
two-stage detection architecture which eliminates redundancy of current
two-stage detectors by replacing the region proposal network with our focal
detection network and bounding box head with our fast suppression head. We
benchmark F2DNet on top pedestrian detection datasets, thoroughly compare it
against the existing state-of-the-art detectors and conduct cross dataset
evaluation to test the generalizability of our model to unseen data. Our F2DNet
achieves 8.7\%, 2.2\%, and 6.1\% MR-2 on City Persons, Caltech Pedestrian, and
Euro City Person datasets respectively when trained on a single dataset and
reaches 20.4\% and 26.2\% MR-2 in heavy occlusion setting of Caltech Pedestrian
and City Persons datasets when using progressive fine-tunning. Furthermore,
F2DNet have significantly lesser inference time compared to the current
state-of-the-art. Code and trained models will be available at
https://github.com/AbdulHannanKhan/F2DNet.
|
[
{
"version": "v1",
"created": "Fri, 4 Mar 2022 14:13:38 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Sep 2022 08:43:32 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Khan",
"Abdul Hannan",
""
],
[
"Munir",
"Mohsin",
""
],
[
"van Elst",
"Ludger",
""
],
[
"Dengel",
"Andreas",
""
]
] |
new_dataset
| 0.995604 |
2205.02648
|
H\'eber H. Arcolezi
|
H\'eber H. Arcolezi, Jean-Fran\c{c}ois Couchot, S\'ebastien Gambs,
Catuscia Palamidessi, Majid Zolfaghari
|
Multi-Freq-LDPy: Multiple Frequency Estimation Under Local Differential
Privacy in Python
|
Paper published in the proceedings of ESORICS 2022
| null |
10.1007/978-3-031-17143-7_40
| null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces the multi-freq-ldpy Python package for multiple
frequency estimation under Local Differential Privacy (LDP) guarantees. LDP is
a gold standard for achieving local privacy with several real-world
implementations by big tech companies such as Google, Apple, and Microsoft. The
primary application of LDP is frequency (or histogram) estimation, in which the
aggregator estimates the number of times each value has been reported. The
presented package provides an easy-to-use and fast implementation of
state-of-the-art solutions and LDP protocols for frequency estimation of:
single attribute (i.e., the building blocks), multiple attributes (i.e.,
multidimensional data), multiple collections (i.e., longitudinal data), and
both multiple attributes/collections. Multi-freq-ldpy is built on the
well-established Numpy package -- a de facto standard for scientific computing
in Python -- and the Numba package for fast execution. These features are
described and illustrated in this paper with four worked examples. This package
is open-source and publicly available under an MIT license via GitHub
(https://github.com/hharcolezi/multi-freq-ldpy) and can be installed via PyPI
(https://pypi.org/project/multi-freq-ldpy/).
|
[
{
"version": "v1",
"created": "Thu, 5 May 2022 13:48:27 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Sep 2022 08:50:16 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Arcolezi",
"Héber H.",
""
],
[
"Couchot",
"Jean-François",
""
],
[
"Gambs",
"Sébastien",
""
],
[
"Palamidessi",
"Catuscia",
""
],
[
"Zolfaghari",
"Majid",
""
]
] |
new_dataset
| 0.998412 |
2207.01059
|
Matt Groh
|
Matthew Groh
|
Identifying the Context Shift between Test Benchmarks and Production
Data
| null | null | null | null |
cs.LG cs.AI cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Machine learning models are often brittle on production data despite
achieving high accuracy on benchmark datasets. Benchmark datasets have
traditionally served dual purposes: first, benchmarks offer a standard on which
machine learning researchers can compare different methods, and second,
benchmarks provide a model, albeit imperfect, of the real world. The
incompleteness of test benchmarks (and the data upon which models are trained)
hinder robustness in machine learning, enable shortcut learning, and leave
models systematically prone to err on out-of-distribution and adversarially
perturbed data. The mismatch between a single static benchmark dataset and a
production dataset has traditionally been described as a dataset shift. In an
effort to clarify how to address the mismatch between test benchmarks and
production data, we introduce context shift to describe semantically meaningful
changes in the underlying data generation process. Moreover, we identify three
methods for addressing context shift that would otherwise lead to model
prediction errors: first, we describe how human intuition and expert knowledge
can identify semantically meaningful features upon which models systematically
fail, second, we detail how dynamic benchmarking - with its focus on capturing
the data generation process - can promote generalizability through
corroboration, and third, we highlight that clarifying a model's limitations
can reduce unexpected errors. Robust machine learning is focused on model
performance beyond benchmarks, and as such, we consider three model organism
domains - facial expression recognition, deepfake detection, and medical
diagnosis - to highlight how implicit assumptions in benchmark tasks lead to
errors in practice. By paying close attention to the role of context,
researchers can design more comprehensive benchmarks, reduce context shift
errors, and increase generalizability.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 14:54:54 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 19:33:04 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Groh",
"Matthew",
""
]
] |
new_dataset
| 0.975293 |
2207.03603
|
Jun Zhang
|
David Bombara, Revanth Konda, Steven Swanbeck, Jun Zhang
|
Anthropomorphic Twisted String-Actuated Soft Robotic Gripper with
Tendon-Based Stiffening
|
19 pages, 15 figures
| null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Realizing high-performance soft robotic grippers is challenging because of
the inherent limitations of the soft actuators and artificial muscles that
drive them, including low force output, small actuation range, and poor
compactness. Despite advances in this area, realizing compact soft grippers
with high dexterity and force output is still challenging. This paper explores
twisted string actuators (TSAs) to drive a soft robotic gripper. TSAs have been
used in numerous robotic applications, but their inclusion in soft robots has
been limited. The proposed design of the gripper was inspired by the human
hand. Tunable stiffness was implemented in the fingers with antagonistic TSAs.
The fingers' bending angles, actuation speed, blocked force output, and
stiffness tuning were experimentally characterized. The gripper achieved a
score of 6 on the Kapandji test and recreated 31 of the 33 grasps of the Feix
GRASP taxonomy. It exhibited a maximum grasping force of 72 N, which was almost
13 times its own weight. A comparison study revealed that the proposed gripper
exhibited equivalent or superior performance compared to other similar soft
grippers.
|
[
{
"version": "v1",
"created": "Thu, 7 Jul 2022 22:15:04 GMT"
},
{
"version": "v2",
"created": "Wed, 10 Aug 2022 21:57:06 GMT"
},
{
"version": "v3",
"created": "Thu, 22 Sep 2022 21:26:49 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Bombara",
"David",
""
],
[
"Konda",
"Revanth",
""
],
[
"Swanbeck",
"Steven",
""
],
[
"Zhang",
"Jun",
""
]
] |
new_dataset
| 0.995702 |
2208.11449
|
Karen Wintersperger
|
Karen Wintersperger, Hila Safi and Wolfgang Mauerer
|
QPU-System Co-Design for Quantum HPC Accelerators
| null |
Proceedings of the 35th GI/ITG International Conference on
Architecture of Computing Systems (ARCS 2022)
| null | null |
cs.AR quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The use of quantum processing units (QPUs) promises speed-ups for solving
computational problems, but the quantum devices currently available possess
only a very limited number of qubits and suffer from considerable
imperfections. One possibility to progress towards practical utility is to use
a co-design approach: Problem formulation and algorithm, but also the physical
QPU properties are tailored to the specific application. Since QPUs will likely
be used as accelerators for classical computers, details of systemic
integration into existing architectures are another lever to influence and
improve the practical utility of QPUs.
In this work, we investigate the influence of different parameters on the
runtime of quantum programs on tailored hybrid CPU-QPU-systems. We study the
influence of communication times between CPU and QPU, how adapting QPU designs
influences quantum and overall execution performance, and how these factors
interact. Using a simple model that allows for estimating which design choices
should be subjected to optimisation for a given task, we provide an intuition
to the HPC community on potentials and limitations of co-design approaches. We
also discuss physical limitations for implementing the proposed changes on real
quantum hardware devices.
|
[
{
"version": "v1",
"created": "Wed, 24 Aug 2022 11:33:48 GMT"
},
{
"version": "v2",
"created": "Mon, 5 Sep 2022 17:37:17 GMT"
},
{
"version": "v3",
"created": "Wed, 7 Sep 2022 16:41:09 GMT"
},
{
"version": "v4",
"created": "Thu, 8 Sep 2022 17:55:02 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Wintersperger",
"Karen",
""
],
[
"Safi",
"Hila",
""
],
[
"Mauerer",
"Wolfgang",
""
]
] |
new_dataset
| 0.993413 |
2209.00741
|
Tiago Fonseca
|
Tiago Fonseca, Tiago Dias, Jo\~ao Vitorino, Lu\'is Lino Ferreira,
Isabel Pra\c{c}a
|
A Low-Cost Multi-Agent System for Physical Security in Smart Buildings
|
10 pages, 2 tables, 3 figures, ICCCN 2022 conference
| null | null | null |
cs.CR cs.CV cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Modern organizations face numerous physical security threats, from fire
hazards to more intricate concerns regarding surveillance and unauthorized
personnel. Conventional standalone fire and intrusion detection solutions must
be installed and maintained independently, which leads to high capital and
operational costs. Nonetheless, due to recent developments in smart sensors,
computer vision techniques, and wireless communication technologies, these
solutions can be integrated in a modular and low-cost manner. This work
introduces Integrated Physical Security System (IP2S), a multi-agent system
capable of coordinating diverse Internet of Things (IoT) sensors and actuators
for an efficient mitigation of multiple physical security events. The proposed
system was tested in a live case study that combined fire and intrusion
detection in an industrial shop floor environment with four different sectors,
two surveillance cameras, and a firefighting robot. The experimental results
demonstrate that the integration of several events in a single automated system
can be advantageous for the security of smart buildings, reducing false alarms
and delays.
|
[
{
"version": "v1",
"created": "Thu, 1 Sep 2022 22:20:39 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Fonseca",
"Tiago",
""
],
[
"Dias",
"Tiago",
""
],
[
"Vitorino",
"João",
""
],
[
"Ferreira",
"Luís Lino",
""
],
[
"Praça",
"Isabel",
""
]
] |
new_dataset
| 0.996999 |
2209.10807
|
Eunkyu Oh
|
Eunkyu Oh, Taehun Kim, Minsoo Kim, Yunhu Ji, Sushil Khyalia
|
SR-GCL: Session-Based Recommendation with Global Context Enhanced
Augmentation in Contrastive Learning
|
11 pages. This paper has been accepted by DLG-AAAI'22
| null | null | null |
cs.IR cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Session-based recommendations aim to predict the next behavior of users based
on ongoing sessions. The previous works have been modeling the session as a
variable-length of a sequence of items and learning the representation of both
individual items and the aggregated session. Recent research has applied graph
neural networks with an attention mechanism to capture complicated item
transitions and dependencies by modeling the sessions into graph-structured
data. However, they still face fundamental challenges in terms of data and
learning methodology such as sparse supervision signals and noisy interactions
in sessions, leading to sub-optimal performance. In this paper, we propose
SR-GCL, a novel contrastive learning framework for a session-based
recommendation. As a crucial component of contrastive learning, we propose two
global context enhanced data augmentation methods while maintaining the
semantics of the original session. The extensive experiment results on two
real-world E-commerce datasets demonstrate the superiority of SR-GCL as
compared to other state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 06:18:20 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Sep 2022 04:16:12 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Oh",
"Eunkyu",
""
],
[
"Kim",
"Taehun",
""
],
[
"Kim",
"Minsoo",
""
],
[
"Ji",
"Yunhu",
""
],
[
"Khyalia",
"Sushil",
""
]
] |
new_dataset
| 0.997117 |
2209.11252
|
Shivprasad Sagare Mr
|
Shivprasad Sagare, Tushar Abhishek, Bhavyajeet Singh, Anubhav Sharma,
Manish Gupta, Vasudeva Varma
|
XF2T: Cross-lingual Fact-to-Text Generation for Low-Resource Languages
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Multiple business scenarios require an automated generation of descriptive
human-readable text from structured input data. Hence, fact-to-text generation
systems have been developed for various downstream tasks like generating soccer
reports, weather and financial reports, medical reports, person biographies,
etc. Unfortunately, previous work on fact-to-text (F2T) generation has focused
primarily on English mainly due to the high availability of relevant datasets.
Only recently, the problem of cross-lingual fact-to-text (XF2T) was proposed
for generation across multiple languages alongwith a dataset, XALIGN for eight
languages. However, there has been no rigorous work on the actual XF2T
generation problem. We extend XALIGN dataset with annotated data for four more
languages: Punjabi, Malayalam, Assamese and Oriya. We conduct an extensive
study using popular Transformer-based text generation models on our extended
multi-lingual dataset, which we call XALIGNV2. Further, we investigate the
performance of different text generation strategies: multiple variations of
pretraining, fact-aware embeddings and structure-aware input encoding. Our
extensive experiments show that a multi-lingual mT5 model which uses fact-aware
embeddings with structure-aware input encoding leads to best results on average
across the twelve languages. We make our code, dataset and model publicly
available, and hope that this will help advance further research in this
critical area.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 18:01:27 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Sagare",
"Shivprasad",
""
],
[
"Abhishek",
"Tushar",
""
],
[
"Singh",
"Bhavyajeet",
""
],
[
"Sharma",
"Anubhav",
""
],
[
"Gupta",
"Manish",
""
],
[
"Varma",
"Vasudeva",
""
]
] |
new_dataset
| 0.968297 |
2209.11266
|
Gang Liu
|
Gang Liu, Tianyan Zhou, Yong Zhao, Yu Wu, Zhuo Chen, Yao Qian, Jian Wu
|
The Microsoft System for VoxCeleb Speaker Recognition Challenge 2022
|
3 pages, 3 tables, VoxSRC2022
| null | null | null |
cs.SD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this report, we describe our submitted system for track 2 of the VoxCeleb
Speaker Recognition Challenge 2022 (VoxSRC-22). We fuse a variety of
good-performing models ranging from supervised models to self-supervised
learning(SSL) pre-trained models. The supervised models, trained using
VoxCeleb-2 dev data, consist of ECAPA-TDNN and Res2Net in a very deep
structure. The SSL pre-trained models, wav2vec and wavLM, are trained using
large scale unlabeled speech data up to million hours. These models are
cascaded with ECAPA-TDNN and further fine-tuned in a supervised fashion to
extract the speaker representations. All 13 models are applied with score
normalization and calibration and then fused into the the submitted system. We
also explore the audio quality measures in the calibration stage such as
duration, SNR, T60, and MOS. The best submitted system achieves 0.073 in minDCF
and 1.436% in EER on the VoxSRC-22 evaluation set.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 18:36:04 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Liu",
"Gang",
""
],
[
"Zhou",
"Tianyan",
""
],
[
"Zhao",
"Yong",
""
],
[
"Wu",
"Yu",
""
],
[
"Chen",
"Zhuo",
""
],
[
"Qian",
"Yao",
""
],
[
"Wu",
"Jian",
""
]
] |
new_dataset
| 0.963337 |
2209.11302
|
Ishika Singh
|
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu,
Jonathan Tremblay, Dieter Fox, Jesse Thomason, Animesh Garg
|
ProgPrompt: Generating Situated Robot Task Plans using Large Language
Models
| null | null | null | null |
cs.RO cs.AI cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Task planning can require defining myriad domain knowledge about the world in
which a robot needs to act. To ameliorate that effort, large language models
(LLMs) can be used to score potential next actions during task planning, and
even generate action sequences directly, given an instruction in natural
language with no additional domain information. However, such methods either
require enumerating all possible next steps for scoring, or generate free-form
text that may contain actions not possible on a given robot in its current
context. We present a programmatic LLM prompt structure that enables plan
generation functional across situated environments, robot capabilities, and
tasks. Our key insight is to prompt the LLM with program-like specifications of
the available actions and objects in an environment, as well as with example
programs that can be executed. We make concrete recommendations about prompt
structure and generation constraints through ablation experiments, demonstrate
state of the art success rates in VirtualHome household tasks, and deploy our
method on a physical robot arm for tabletop tasks. Website at
progprompt.github.io
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 20:29:49 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Singh",
"Ishika",
""
],
[
"Blukis",
"Valts",
""
],
[
"Mousavian",
"Arsalan",
""
],
[
"Goyal",
"Ankit",
""
],
[
"Xu",
"Danfei",
""
],
[
"Tremblay",
"Jonathan",
""
],
[
"Fox",
"Dieter",
""
],
[
"Thomason",
"Jesse",
""
],
[
"Garg",
"Animesh",
""
]
] |
new_dataset
| 0.999817 |
2209.11318
|
Charlie C.L. Wang Prof. Dr.
|
Yingjun Tian, Renbo Su, Xilong Wang, Nur Banu Altin, Guoxin Fang,
Charlie C. L. Wang
|
OpenPneu: Compact platform for pneumatic actuation with multi-channels
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents a compact system, OpenPneu, to support the pneumatic
actuation for multi-chambers on soft robots. Micro-pumps are employed in the
system to generate airflow and therefore no extra input as compressed air is
needed. Our system conducts modular design to provide good scalability, which
has been demonstrated on a prototype with ten air channels. Each air channel of
OpenPneu is equipped with both the inflation and the deflation functions to
provide a full range pressure supply from positive to negative with a maximal
flow rate at 1.7 L/min. High precision closed-loop control of pressures has
been built into our system to achieve stable and efficient dynamic performance
in actuation. An open-source control interface and API in Python are provided.
We also demonstrate the functionality of OpenPneu on three soft robotic systems
with up to 10 chambers.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 21:16:37 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Tian",
"Yingjun",
""
],
[
"Su",
"Renbo",
""
],
[
"Wang",
"Xilong",
""
],
[
"Altin",
"Nur Banu",
""
],
[
"Fang",
"Guoxin",
""
],
[
"Wang",
"Charlie C. L.",
""
]
] |
new_dataset
| 0.999703 |
2209.11321
|
Ahmed Alkhateeb
|
Shuaifeng Jiang and Ahmed Alkhateeb
|
Sensing Aided OTFS Channel Estimation for Massive MIMO Systems
|
submitted to IEEE
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Orthogonal time frequency space (OTFS) modulation has the potential to enable
robust communications in highly-mobile scenarios. Estimating the channels for
OTFS systems, however, is associated with high pilot signaling overhead that
scales with the maximum delay and Doppler spreads. This becomes particularly
challenging for massive MIMO systems where the overhead also scales with the
number of antennas. An important observation however is that the delay,
Doppler, and angle of departure/arrival information are directly related to the
distance, velocity, and direction information of the mobile user and the
various scatterers in the environment. With this motivation, we propose to
leverage radar sensing to obtain this information about the mobile users and
scatterers in the environment and leverage it to aid the OTFS channel
estimation in massive MIMO systems.
As one approach to realize our vision, this paper formulates the OTFS channel
estimation problem in massive MIMO systems as a sparse recovery problem and
utilizes the radar sensing information to determine the support (locations of
the non-zero delay-Doppler taps). The proposed radar sensing aided sparse
recovery algorithm is evaluated based on an accurate 3D ray-tracing framework
with co-existing radar and communication data. The results show that the
developed sensing-aided solution consistently outperforms the standard sparse
recovery algorithms (that do not leverage radar sensing data) and leads to a
significant reduction in the pilot overhead, which highlights a promising
direction for OTFS based massive MIMO systems.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 21:23:40 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Jiang",
"Shuaifeng",
""
],
[
"Alkhateeb",
"Ahmed",
""
]
] |
new_dataset
| 0.991704 |
2209.11345
|
Marcos V. Conde
|
Marcos V. Conde, Ui-Jin Choi, Maxime Burchi, Radu Timofte
|
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and
Restoration
|
European Conference on Computer Vision (ECCV 2022) Workshops
| null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Compression plays an important role on the efficient transmission and storage
of images and videos through band-limited systems such as streaming services,
virtual reality or videogames. However, compression unavoidably leads to
artifacts and the loss of the original information, which may severely degrade
the visual quality. For these reasons, quality enhancement of compressed images
has become a popular research topic. While most state-of-the-art image
restoration methods are based on convolutional neural networks, other
transformers-based methods such as SwinIR, show impressive performance on these
tasks.
In this paper, we explore the novel Swin Transformer V2, to improve SwinIR
for image super-resolution, and in particular, the compressed input scenario.
Using this method we can tackle the major issues in training transformer vision
models, such as training instability, resolution gaps between pre-training and
fine-tuning, and hunger on data. We conduct experiments on three representative
tasks: JPEG compression artifacts removal, image super-resolution (classical
and lightweight), and compressed image super-resolution. Experimental results
demonstrate that our method, Swin2SR, can improve the training convergence and
performance of SwinIR, and is a top-5 solution at the "AIM 2022 Challenge on
Super-Resolution of Compressed Image and Video".
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 23:25:08 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Conde",
"Marcos V.",
""
],
[
"Choi",
"Ui-Jin",
""
],
[
"Burchi",
"Maxime",
""
],
[
"Timofte",
"Radu",
""
]
] |
new_dataset
| 0.999488 |
2209.11420
|
Revanth Konda
|
Revanth Konda, David Bombara and Jun Zhang
|
Overtwisting and Coiling Highly Enhances Strain Generation of Twisted
String Actuators
| null | null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Twisted string actuators (TSAs) have exhibited great promise in robotic
applications by generating high translational force with low input torque. To
further facilitate their robotic applications, it is strongly desirable but
challenging to enhance their consistent strain generation while maintaining
compliance. Existing studies predominantly considered overtwisting and coiling
after the regular twisting stage to be undesirable non-uniform and
unpredictable knots, entanglements, and coils formed to create an unstable and
failure-prone structure. Overtwisting would work well for TSAs when uniform
coils can be consistently formed. In this study, we realize uniform and
consistent coil formation in overtwisted TSAs, which greatly increases their
strain. Furthermore, we investigate methods for enabling uniform coil formation
upon overtwisting the strings in a TSA and present a procedure to
systematically "train" the strings. To the authors' best knowledge, this is the
first study to experimentally investigate overtwisting for TSAs with different
stiffnesses and realize consistent uniform coil formation. Ultra-high
molecular-weight polyethylene (UHMWPE) strings form the stiff TSAs whereas
compliant TSAs are realized with stretchable and conductive supercoiled polymer
(SCP) strings. The strain, force, velocity, and torque of each overtwisted TSA
was studied. Overtwisting and coiling resulted in approximately 70% strain in
stiff TSAs and approximately 60% strain in compliant TSAs. This is more than
twice the strain achieved through regular twisting. Lastly, the overtwisted TSA
was successfully demonstrated in a robotic bicep.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 05:35:59 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Konda",
"Revanth",
""
],
[
"Bombara",
"David",
""
],
[
"Zhang",
"Jun",
""
]
] |
new_dataset
| 0.95936 |
2209.11500
|
Hakan Girgin
|
Hakan Girgin, Julius Jankowski and Sylvain Calinon
|
Reactive Anticipatory Robot Skills with Memory
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Optimal control in robotics has been increasingly popular in recent years and
has been applied in many applications involving complex dynamical systems.
Closed-loop optimal control strategies include model predictive control (MPC)
and time-varying linear controllers optimized through iLQR. However, such
feedback controllers rely on the information of the current state, limiting the
range of robotic applications where the robot needs to remember what it has
done before to act and plan accordingly. The recently proposed system level
synthesis (SLS) framework circumvents this limitation via a richer controller
structure with memory. In this work, we propose to optimally design reactive
anticipatory robot skills with memory by extending SLS to tracking problems
involving nonlinear systems and nonquadratic cost functions. We showcase our
method with two scenarios exploiting task precisions and object affordances in
pick-and-place tasks in a simulated and a real environment with a 7-axis Franka
Emika robot.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 09:55:41 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Girgin",
"Hakan",
""
],
[
"Jankowski",
"Julius",
""
],
[
"Calinon",
"Sylvain",
""
]
] |
new_dataset
| 0.961987 |
2209.11625
|
Jinghan Peng
|
Yu Zheng, Jinghan Peng, Yihao Chen, Yajun Zhang, Jialong Wang, Min
Liu, Minqiang Xu
|
The SpeakIn Speaker Verification System for Far-Field Speaker
Verification Challenge 2022
|
5 pages. arXiv admin note: text overlap with arXiv:2209.10846
| null | null | null |
cs.SD cs.AI eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper describes speaker verification (SV) systems submitted by the
SpeakIn team to the Task 1 and Task 2 of the Far-Field Speaker Verification
Challenge 2022 (FFSVC2022). SV tasks of the challenge focus on the problem of
fully supervised far-field speaker verification (Task 1) and semi-supervised
far-field speaker verification (Task 2). In Task 1, we used the VoxCeleb and
FFSVC2020 datasets as train datasets. And for Task 2, we only used the VoxCeleb
dataset as train set. The ResNet-based and RepVGG-based architectures were
developed for this challenge. Global statistic pooling structure and MQMHA
pooling structure were used to aggregate the frame-level features across time
to obtain utterance-level representation. We adopted AM-Softmax and AAM-Softmax
to classify the resulting embeddings. We innovatively propose a staged transfer
learning method. In the pre-training stage we reserve the speaker weights, and
there are no positive samples to train them in this stage. Then we fine-tune
these weights with both positive and negative samples in the second stage.
Compared with the traditional transfer learning strategy, this strategy can
better improve the model performance. The Sub-Mean and AS-Norm backend methods
were used to solve the problem of domain mismatch. In the fusion stage, three
models were fused in Task1 and two models were fused in Task2. On the FFSVC2022
leaderboard, the EER of our submission is 3.0049% and the corresponding minDCF
is 0.2938 in Task1. In Task2, EER and minDCF are 6.2060% and 0.5232
respectively. Our approach leads to excellent performance and ranks 1st in both
challenge tasks.
|
[
{
"version": "v1",
"created": "Fri, 23 Sep 2022 14:51:55 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Zheng",
"Yu",
""
],
[
"Peng",
"Jinghan",
""
],
[
"Chen",
"Yihao",
""
],
[
"Zhang",
"Yajun",
""
],
[
"Wang",
"Jialong",
""
],
[
"Liu",
"Min",
""
],
[
"Xu",
"Minqiang",
""
]
] |
new_dataset
| 0.959594 |
2209.11693
|
Iman Nematollahi
|
Iman Nematollahi, Erick Rosete-Beas, Seyed Mahdi B. Azad, Raghu Rajan,
Frank Hutter, Wolfram Burgard
|
T3VIP: Transformation-based 3D Video Prediction
|
Accepted at the 2022 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS)
| null | null | null |
cs.CV cs.AI cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For autonomous skill acquisition, robots have to learn about the physical
rules governing the 3D world dynamics from their own past experience to predict
and reason about plausible future outcomes. To this end, we propose a
transformation-based 3D video prediction (T3VIP) approach that explicitly
models the 3D motion by decomposing a scene into its object parts and
predicting their corresponding rigid transformations. Our model is fully
unsupervised, captures the stochastic nature of the real world, and the
observational cues in image and point cloud domains constitute its learning
signals. To fully leverage all the 2D and 3D observational signals, we equip
our model with automatic hyperparameter optimization (HPO) to interpret the
best way of learning from them. To the best of our knowledge, our model is the
first generative model that provides an RGB-D video prediction of the future
for a static camera. Our extensive evaluation with simulated and real-world
datasets demonstrates that our formulation leads to interpretable 3D models
that predict future depth videos while achieving on-par performance with 2D
models on RGB video prediction. Moreover, we demonstrate that our model
outperforms 2D baselines on visuomotor control. Videos, code, dataset, and
pre-trained models are available at http://t3vip.cs.uni-freiburg.de.
|
[
{
"version": "v1",
"created": "Mon, 19 Sep 2022 15:01:09 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"Nematollahi",
"Iman",
""
],
[
"Rosete-Beas",
"Erick",
""
],
[
"Azad",
"Seyed Mahdi B.",
""
],
[
"Rajan",
"Raghu",
""
],
[
"Hutter",
"Frank",
""
],
[
"Burgard",
"Wolfram",
""
]
] |
new_dataset
| 0.998695 |
2209.11750
|
Sannara Ek
|
Sannara EK, Fran\c{c}ois Portet, Philippe Lalanda
|
Lightweight Transformers for Human Activity Recognition on Mobile
Devices
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Human Activity Recognition (HAR) on mobile devices has shown to be achievable
with lightweight neural models learned from data generated by the user's
inertial measurement units (IMUs). Most approaches for instanced-based HAR have
used Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTMs), or a
combination of the two to achieve state-of-the-art results with real-time
performances. Recently, the Transformers architecture in the language
processing domain and then in the vision domain has pushed further the
state-of-the-art over classical architectures. However, such Transformers
architecture is heavyweight in computing resources, which is not well suited
for embedded applications of HAR that can be found in the pervasive computing
domain. In this study, we present Human Activity Recognition Transformer
(HART), a lightweight, sensor-wise transformer architecture that has been
specifically adapted to the domain of the IMUs embedded on mobile devices. Our
experiments on HAR tasks with several publicly available datasets show that
HART uses fewer FLoating-point Operations Per Second (FLOPS) and parameters
while outperforming current state-of-the-art results. Furthermore, we present
evaluations across various architectures on their performances in heterogeneous
environments and show that our models can better generalize on different
sensing devices or on-body positions.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 09:42:08 GMT"
}
] | 2022-09-26T00:00:00 |
[
[
"EK",
"Sannara",
""
],
[
"Portet",
"François",
""
],
[
"Lalanda",
"Philippe",
""
]
] |
new_dataset
| 0.988576 |
1606.06940
|
Krzysztof Fleszar
|
William S. Evans, Krzysztof Fleszar, Philipp Kindermann, Noushin
Saeedi, Chan-Su Shin, Alexander Wolff
|
Minimum Rectilinear Polygons for Given Angle Sequences
|
New result: NP-hardness of drawing polylines
|
Computational Geometry: Theory and Applications, 100:101820 (2022)
|
10.1016/j.comgeo.2021.101820
| null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A rectilinear polygon is a polygon whose edges are axis-aligned. Walking
counterclockwise on the boundary of such a polygon yields a sequence of left
turns and right turns. The number of left turns always equals the number of
right turns plus 4. It is known that any such sequence can be realized by a
rectilinear polygon. In this paper, we consider the problem of finding
realizations that minimize the perimeter or the area of the polygon or the area
of the bounding box of the polygon. We show that all three problems are NP-hard
in general. This answers an open question of Patrignani [CGTA 2001], who showed
that it is NP-hard to minimize the area of the bounding box of an orthogonal
drawing of a given planar graph. We also show that realizing polylines with
minimum bounding box area is NP-hard. Then we consider the special cases of
$x$-monotone and $xy$-monotone rectilinear polygons. For these, we can optimize
the three objectives efficiently.
|
[
{
"version": "v1",
"created": "Wed, 22 Jun 2016 13:22:18 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Dec 2018 10:17:51 GMT"
},
{
"version": "v3",
"created": "Mon, 8 Jun 2020 17:52:21 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Evans",
"William S.",
""
],
[
"Fleszar",
"Krzysztof",
""
],
[
"Kindermann",
"Philipp",
""
],
[
"Saeedi",
"Noushin",
""
],
[
"Shin",
"Chan-Su",
""
],
[
"Wolff",
"Alexander",
""
]
] |
new_dataset
| 0.996713 |
2107.00957
|
Tareq Si Salem
|
T. Si-Salem, G. Neglia, D. Carra
|
Ascent Similarity Caching with Approximate Indexes
| null | null | null | null |
cs.NI cs.LG cs.PF
|
http://creativecommons.org/licenses/by/4.0/
|
Similarity search is a key operation in multimedia retrieval systems and
recommender systems, and it will play an important role also for future machine
learning and augmented reality applications. When these systems need to serve
large objects with tight delay constraints, edge servers close to the end-user
can operate as similarity caches to speed up the retrieval. In this paper we
present A\c{C}AI, a new similarity caching policy which improves on the state
of the art by using (i) an (approximate) index for the whole catalog to decide
which objects to serve locally and which to retrieve from the remote server,
and (ii) a mirror ascent algorithm to update the set of local objects with
strong guarantees even when the request process does not exhibit any
statistical regularity.
|
[
{
"version": "v1",
"created": "Fri, 2 Jul 2021 10:40:47 GMT"
},
{
"version": "v2",
"created": "Sun, 19 Dec 2021 17:18:56 GMT"
},
{
"version": "v3",
"created": "Mon, 27 Dec 2021 14:56:49 GMT"
},
{
"version": "v4",
"created": "Thu, 22 Sep 2022 12:04:15 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Si-Salem",
"T.",
""
],
[
"Neglia",
"G.",
""
],
[
"Carra",
"D.",
""
]
] |
new_dataset
| 0.955423 |
2109.09289
|
Muhammed Sit
|
Muhammed Sit, Bong-Chul Seo and Ibrahim Demir
|
TempNet -- Temporal Super Resolution of Radar Rainfall Products with
Residual CNNs
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The temporal and spatial resolution of rainfall data is crucial for
environmental modeling studies in which its variability in space and time is
considered as a primary factor. Rainfall products from different remote sensing
instruments (e.g., radar, satellite) have different space-time resolutions
because of the differences in their sensing capabilities and post-processing
methods. In this study, we developed a deep learning approach that augments
rainfall data with increased time resolutions to complement relatively lower
resolution products. We propose a neural network architecture based on
Convolutional Neural Networks (CNNs) to improve the temporal resolution of
radar-based rainfall products and compare the proposed model with an optical
flow-based interpolation method and CNN-baseline model. The methodology
presented in this study could be used for enhancing rainfall maps with better
temporal resolution and imputation of missing frames in sequences of 2D
rainfall maps to support hydrological and flood forecasting studies.
|
[
{
"version": "v1",
"created": "Mon, 20 Sep 2021 03:58:52 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 04:14:44 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Sit",
"Muhammed",
""
],
[
"Seo",
"Bong-Chul",
""
],
[
"Demir",
"Ibrahim",
""
]
] |
new_dataset
| 0.996036 |
2203.00819
|
Duzhen Zhang
|
Duzhen Zhang, Zhen Yang, Fandong Meng, Xiuyi Chen, Jie Zhou
|
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment
| null | null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Causal Emotion Entailment (CEE) aims to discover the potential causes behind
an emotion in a conversational utterance. Previous works formalize CEE as
independent utterance pair classification problems, with emotion and speaker
information neglected. From a new perspective, this paper considers CEE in a
joint framework. We classify multiple utterances synchronously to capture the
correlations between utterances in a global view and propose a Two-Stream
Attention Model (TSAM) to effectively model the speaker's emotional influences
in the conversational history. Specifically, the TSAM comprises three modules:
Emotion Attention Network (EAN), Speaker Attention Network (SAN), and
interaction module. The EAN and SAN incorporate emotion and speaker information
in parallel, and the subsequent interaction module effectively interchanges
relevant information between the EAN and SAN via a mutual BiAffine
transformation. Extensive experimental results demonstrate that our model
achieves new State-Of-The-Art (SOTA) performance and outperforms baselines
remarkably.
|
[
{
"version": "v1",
"created": "Wed, 2 Mar 2022 02:11:41 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 08:01:56 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Zhang",
"Duzhen",
""
],
[
"Yang",
"Zhen",
""
],
[
"Meng",
"Fandong",
""
],
[
"Chen",
"Xiuyi",
""
],
[
"Zhou",
"Jie",
""
]
] |
new_dataset
| 0.998002 |
2203.12130
|
Akash Saravanan
|
Akash Saravanan and Matthew Guzdial
|
Pixel VQ-VAEs for Improved Pixel Art Representation
|
9 pages, 2 figures. Experimental AI in Games Workshop (EXAG) 2022
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine learning has had a great deal of success in image processing.
However, the focus of this work has largely been on realistic images, ignoring
more niche art styles such as pixel art. Additionally, many traditional machine
learning models that focus on groups of pixels do not work well with pixel art,
where individual pixels are important. We propose the Pixel VQ-VAE, a
specialized VQ-VAE model that learns representations of pixel art. We show that
it outperforms other models in both the quality of embeddings as well as
performance on downstream tasks.
|
[
{
"version": "v1",
"created": "Wed, 23 Mar 2022 01:47:33 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2022 20:42:00 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Saravanan",
"Akash",
""
],
[
"Guzdial",
"Matthew",
""
]
] |
new_dataset
| 0.984449 |
2203.12575
|
Wei Jiang
|
Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, Anurag Ranjan
|
NeuMan: Neural Human Radiance Field from a Single Video
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Photorealistic rendering and reposing of humans is important for enabling
augmented reality experiences. We propose a novel framework to reconstruct the
human and the scene that can be rendered with novel human poses and views from
just a single in-the-wild video. Given a video captured by a moving camera, we
train two NeRF models: a human NeRF model and a scene NeRF model. To train
these models, we rely on existing methods to estimate the rough geometry of the
human and the scene. Those rough geometry estimates allow us to create a
warping field from the observation space to the canonical pose-independent
space, where we train the human model in. Our method is able to learn subject
specific details, including cloth wrinkles and accessories, from just a 10
seconds video clip, and to provide high quality renderings of the human under
novel poses, from novel views, together with the background.
|
[
{
"version": "v1",
"created": "Wed, 23 Mar 2022 17:35:50 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 02:27:46 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Jiang",
"Wei",
""
],
[
"Yi",
"Kwang Moo",
""
],
[
"Samei",
"Golnoosh",
""
],
[
"Tuzel",
"Oncel",
""
],
[
"Ranjan",
"Anurag",
""
]
] |
new_dataset
| 0.992511 |
2203.14277
|
Dhruvil Dave
|
Aneri Dalwadi and Dhruvil Dave
|
UAST: Unicode Aware Sanskrit Transliteration
|
9 pages. Source code and implementation are available on GitHub at
https://github.com/dhruvildave/uast and https://github.com/aneri0x4f/uast-cli
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Devanagari is a writing system that is adapted by various languages like
Sanskrit. International Alphabet of Sanskrit Transliteration (IAST) is a
transliteration scheme for the romanization of the Sanskrit language. IAST
makes use of diacritics to represent various characters. On a computer, these
are defined using the Unicode standard which differs from how the Sanskrit
language behaves at a fundamental level. This results in an issue that is
encountered while designing typesetting software for Devanagari and IAST. We
discuss the problems and provide a solution that solves the issue of
incompatibilities between various transliteration and encoding schemes.
Implementation and source code are available at
https://github.com/dhruvildave/uast and https://github.com/aneri0x4f/uast-cli
|
[
{
"version": "v1",
"created": "Sun, 27 Mar 2022 11:17:00 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 03:22:56 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Dalwadi",
"Aneri",
""
],
[
"Dave",
"Dhruvil",
""
]
] |
new_dataset
| 0.997029 |
2206.07850
|
Yiqun Wang
|
Yiqun Wang, Ivan Skorokhodov, Peter Wonka
|
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
|
To appear in NeurIPS 2022. Project page:
https://github.com/yiqun-wang/HFS
| null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Neural rendering can be used to reconstruct implicit representations of
shapes without 3D supervision. However, current neural surface reconstruction
methods have difficulty learning high-frequency geometry details, so the
reconstructed shapes are often over-smoothed. We develop HF-NeuS, a novel
method to improve the quality of surface reconstruction in neural rendering. We
follow recent work to model surfaces as signed distance functions (SDFs).
First, we offer a derivation to analyze the relationship between the SDF, the
volume density, the transparency function, and the weighting function used in
the volume rendering equation and propose to model transparency as transformed
SDF. Second, we observe that attempting to jointly encode high-frequency and
low-frequency components in a single SDF leads to unstable optimization. We
propose to decompose the SDF into a base function and a displacement function
with a coarse-to-fine strategy to gradually increase the high-frequency
details. Finally, we design an adaptive optimization strategy that makes the
training process focus on improving those regions near the surface where the
SDFs have artifacts. Our qualitative and quantitative results show that our
method can reconstruct fine-grained surface details and obtain better surface
reconstruction quality than the current state of the art. Code available at
https://github.com/yiqun-wang/HFS.
|
[
{
"version": "v1",
"created": "Wed, 15 Jun 2022 23:46:48 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 14:47:38 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Wang",
"Yiqun",
""
],
[
"Skorokhodov",
"Ivan",
""
],
[
"Wonka",
"Peter",
""
]
] |
new_dataset
| 0.992905 |
2207.07285
|
Yiwei Ma
|
Yiwei Ma, Guohai Xu, Xiaoshuai Sun, Ming Yan, Ji Zhang, Rongrong Ji
|
X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text
Retrieval
|
13 pages, 6 figures, ACMMM22
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video-text retrieval has been a crucial and fundamental task in multi-modal
research. The development of video-text retrieval has been considerably
promoted by large-scale multi-modal contrastive pre-training, which primarily
focuses on coarse-grained or fine-grained contrast. However, cross-grained
contrast, which is the contrast between coarse-grained representations and
fine-grained representations, has rarely been explored in prior research.
Compared with fine-grained or coarse-grained contrasts, cross-grained contrast
calculate the correlation between coarse-grained features and each fine-grained
feature, and is able to filter out the unnecessary fine-grained features guided
by the coarse-grained feature during similarity calculation, thus improving the
accuracy of retrieval. To this end, this paper presents a novel multi-grained
contrastive model, namely X-CLIP, for video-text retrieval. However, another
challenge lies in the similarity aggregation problem, which aims to aggregate
fine-grained and cross-grained similarity matrices to instance-level
similarity. To address this challenge, we propose the Attention Over Similarity
Matrix (AOSM) module to make the model focus on the contrast between essential
frames and words, thus lowering the impact of unnecessary frames and words on
retrieval results. With multi-grained contrast and the proposed AOSM module,
X-CLIP achieves outstanding performance on five widely-used video-text
retrieval datasets, including MSR-VTT (49.3 R@1), MSVD (50.4 R@1), LSMDC (26.1
R@1), DiDeMo (47.8 R@1) and ActivityNet (46.2 R@1). It outperforms the previous
state-of-theart by +6.3%, +6.6%, +11.1%, +6.7%, +3.8% relative improvements on
these benchmarks, demonstrating the superiority of multi-grained contrast and
AOSM.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 04:23:42 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 12:27:09 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Ma",
"Yiwei",
""
],
[
"Xu",
"Guohai",
""
],
[
"Sun",
"Xiaoshuai",
""
],
[
"Yan",
"Ming",
""
],
[
"Zhang",
"Ji",
""
],
[
"Ji",
"Rongrong",
""
]
] |
new_dataset
| 0.999259 |
2209.07928
|
Paulo Pirozelli
|
Paulo Pirozelli, Ais B. R. Castro, Ana Luiza C. de Oliveira, Andr\'e
S. Oliveira, Fl\'avio N. Ca\c{c}\~ao, Igor C. Silveira, Jo\~ao G. M. Campos,
Laura C. Motheo, Leticia F. Figueiredo, Lucas F. A. O. Pellicer, Marcelo A.
Jos\'e, Marcos M. Jos\'e, Pedro de M. Ligabue, Ricardo S. Grava, Rodrigo M.
Tavares, Vin\'icius B. Matos, Yan V. Sym, Anna H. R. Costa, Anarosa A. F.
Brand\~ao, Denis D. Mau\'a, Fabio G. Cozman, Sarajane M. Peres
|
The BLue Amazon Brain (BLAB): A Modular Architecture of Services about
the Brazilian Maritime Territory
| null |
AI: Modeling Oceans and Climate Change (IJCAI-ECAI), 2022
| null | null |
cs.AI cs.CL cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
We describe the first steps in the development of an artificial agent focused
on the Brazilian maritime territory, a large region within the South Atlantic
also known as the Blue Amazon. The "BLue Amazon Brain" (BLAB) integrates a
number of services aimed at disseminating information about this region and its
importance, functioning as a tool for environmental awareness. The main service
provided by BLAB is a conversational facility that deals with complex questions
about the Blue Amazon, called BLAB-Chat; its central component is a controller
that manages several task-oriented natural language processing modules (e.g.,
question answering and summarizer systems). These modules have access to an
internal data lake as well as to third-party databases. A news reporter
(BLAB-Reporter) and a purposely-developed wiki (BLAB-Wiki) are also part of the
BLAB service architecture. In this paper, we describe our current version of
BLAB's architecture (interface, backend, web services, NLP modules, and
resources) and comment on the challenges we have faced so far, such as the lack
of training data and the scattered state of domain information. Solving these
issues presents a considerable challenge in the development of artificial
intelligence for technical domains.
|
[
{
"version": "v1",
"created": "Tue, 6 Sep 2022 18:32:08 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Pirozelli",
"Paulo",
""
],
[
"Castro",
"Ais B. R.",
""
],
[
"de Oliveira",
"Ana Luiza C.",
""
],
[
"Oliveira",
"André S.",
""
],
[
"Cação",
"Flávio N.",
""
],
[
"Silveira",
"Igor C.",
""
],
[
"Campos",
"João G. M.",
""
],
[
"Motheo",
"Laura C.",
""
],
[
"Figueiredo",
"Leticia F.",
""
],
[
"Pellicer",
"Lucas F. A. O.",
""
],
[
"José",
"Marcelo A.",
""
],
[
"José",
"Marcos M.",
""
],
[
"Ligabue",
"Pedro de M.",
""
],
[
"Grava",
"Ricardo S.",
""
],
[
"Tavares",
"Rodrigo M.",
""
],
[
"Matos",
"Vinícius B.",
""
],
[
"Sym",
"Yan V.",
""
],
[
"Costa",
"Anna H. R.",
""
],
[
"Brandão",
"Anarosa A. F.",
""
],
[
"Mauá",
"Denis D.",
""
],
[
"Cozman",
"Fabio G.",
""
],
[
"Peres",
"Sarajane M.",
""
]
] |
new_dataset
| 0.975357 |
2209.10317
|
Roxanne Pinto
|
Eugenio Marchiori, Sarah de Haas, Sergey Volnov, Ronnie Falcon,
Roxanne Pinto, Marco Zamarato
|
Android Private Compute Core Architecture
| null | null | null | null |
cs.CR cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Android's Private Compute Core (PCC) is a secure, isolated environment within
the operating system, that maintains separation from apps while enabling users
and developers to maintain control over their data. It is backed by open-source
code in the Android Framework introduced in Android 12. PCC allows features to
communicate with a server to receive model updates and contribute to global
model training through Private Compute Services (PCS), the core of which has
been open sourced. PCC is part of the OS, and by virtue of being isolated,
constrained, and trusted, it can host sophisticated ML features. The hosted
features themselves, running inside PCC, can be closed source and updatable. In
this way, PCC enables machine learning features to process ambient and OS-level
data and improve over time, while restricting the availability of information
about individual users to servers or apps.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 12:45:18 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 10:15:51 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Marchiori",
"Eugenio",
""
],
[
"de Haas",
"Sarah",
""
],
[
"Volnov",
"Sergey",
""
],
[
"Falcon",
"Ronnie",
""
],
[
"Pinto",
"Roxanne",
""
],
[
"Zamarato",
"Marco",
""
]
] |
new_dataset
| 0.99948 |
2209.10503
|
Aleksey Fedoseev
|
Aleksey Fedoseev, Ahmed Baza, Ayush Gupta, Ekaterina Dorzhieva, Riya
Neelesh Gujarathi, Dzmitry Tsetserukou
|
DandelionTouch: High Fidelity Haptic Rendering of Soft Objects in VR by
a Swarm of Drones
|
Accepted to the 2022 IEEE International Conference on Systems, Man,
and Cybernetics (SMC). Copyright 20XX IEEE. Personal use of this material is
permitted
| null | null | null |
cs.RO cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To achieve high fidelity haptic rendering of soft objects in a high mobility
virtual environment, we propose a novel haptic display DandelionTouch. The
tactile actuators are delivered to the fingertips of the user by a swarm of
drones. Users of DandelionTouch are capable of experiencing tactile feedback in
a large space that is not limited by the device's working area. Importantly,
they will not experience muscle fatigue during long interactions with virtual
objects. Hand tracking and swarm control algorithm allow guiding the swarm with
hand motions and avoid collisions inside the formation.
Several topologies of the impedance connection between swarm units were
investigated in this research. The experiment, in which drones performed a
point following task on a square trajectory in real-time, revealed that drones
connected in a Star topology performed the trajectory with low mean positional
error (RMSE decreased by 20.6% in comparison with other impedance topologies
and by 40.9% in comparison with potential field-based swarm control). The
achieved velocities of the drones in all formations with impedance behavior
were 28% higher than for the swarm controlled with the potential field
algorithm.
Additionally, the perception of several vibrotactile patterns was evaluated
in a user study with 7 participants. The study has shown that the proposed
combination of temporal delay and frequency modulation allows users to
successfully recognize the surface property and motion direction in VR
simultaneously (mean recognition rate of 70%, maximum of 93%). DandelionTouch
suggests a new type of haptic feedback in VR systems where no hand-held or
wearable interface is required.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 16:58:14 GMT"
},
{
"version": "v2",
"created": "Thu, 22 Sep 2022 11:43:51 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Fedoseev",
"Aleksey",
""
],
[
"Baza",
"Ahmed",
""
],
[
"Gupta",
"Ayush",
""
],
[
"Dorzhieva",
"Ekaterina",
""
],
[
"Gujarathi",
"Riya Neelesh",
""
],
[
"Tsetserukou",
"Dzmitry",
""
]
] |
new_dataset
| 0.997521 |
2209.10610
|
Igor Sedl\'ar
|
Igor Sedl\'ar and Johann J. Wannenburg
|
Embedding Kozen-Tiuryn Logic into Residuated One-Sorted Kleene Algebra
with Tests
| null |
I. Sedl\'ar and J.J.~Wannenburg: Embedding Kozen-Tiuryn Logic into
Residuated One-Sorted Kleene Algebra with Tests. In: A. Ciabattoni, E.
Pimentel, R. de Queiroz (Eds.): Proc. WoLLIC 2022, pp. 221-236. LNCS 13468.
Springer, 2022
|
10.1007/978-3-031-15298-6_14
| null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Kozen and Tiuryn have introduced the substructural logic $\mathsf{S}$ for
reasoning about correctness of while programs (ACM TOCL, 2003). The logic
$\mathsf{S}$ distinguishes between tests and partial correctness assertions,
representing the latter by special implicational formulas. Kozen and Tiuryn's
logic extends Kleene altebra with tests, where partial correctness assertions
are represented by equations, not terms. Kleene algebra with codomain,
$\mathsf{KAC}$, is a one-sorted alternative to Kleene algebra with tests that
expands Kleene algebra with an operator that allows to construct a Boolean
subalgebra of tests. In this paper we show that Kozen and Tiuryn's logic embeds
into the equational theory of the expansion of $\mathsf{KAC}$ with residuals of
Kleene algebra multiplication and the upper adjoint of the codomain operator.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 19:14:11 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Sedlár",
"Igor",
""
],
[
"Wannenburg",
"Johann J.",
""
]
] |
new_dataset
| 0.997807 |
2209.10687
|
Stephanie Newdick
|
Stephanie Newdick, Nitin Ongole, Tony G. Chen, Edward Schmerling, Mark
R. Cutkosky, Marco Pavone
|
Motion Planning for a Climbing Robot with Stochastic Grasps
|
7 pages, 7 figures
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motion planning for a multi-limbed climbing robot must consider the robot's
posture, joint torques, and how it uses contact forces to interact with its
environment. This paper focuses on motion planning for a robot that uses
nontraditional locomotion to explore unpredictable environments such as martian
caves. Our robotic concept, ReachBot, uses extendable and retractable booms as
limbs to achieve a large reachable workspace while climbing. Each extendable
boom is capped by a microspine gripper designed for grasping rocky surfaces.
ReachBot leverages its large workspace to navigate around obstacles, over
crevasses, and through challenging terrain. Our planning approach must be
versatile to accommodate variable terrain features and robust to mitigate risks
from the stochastic nature of grasping with spines. In this paper, we introduce
a graph traversal algorithm to select a discrete sequence of grasps based on
available terrain features suitable for grasping. This discrete plan is
complemented by a decoupled motion planner that considers the alternating
phases of body movement and end-effector movement, using a combination of
sampling-based planning and sequential convex programming to optimize
individual phases. We use our motion planner to plan a trajectory across a
simulated 2D cave environment with at least 95% probability of success and
demonstrate improved robustness over a baseline trajectory. Finally, we verify
our motion planning algorithm through experimentation on a 2D planar prototype.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 22:25:11 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Newdick",
"Stephanie",
""
],
[
"Ongole",
"Nitin",
""
],
[
"Chen",
"Tony G.",
""
],
[
"Schmerling",
"Edward",
""
],
[
"Cutkosky",
"Mark R.",
""
],
[
"Pavone",
"Marco",
""
]
] |
new_dataset
| 0.994579 |
2209.10733
|
Xinli Xu
|
Xinli Xu, Shaocong Dong, Lihe Ding, Jie Wang, Tingfa Xu, Jianan Li
|
FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection
|
7 pages, 3 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D object detection with multi-sensors is essential for an accurate and
reliable perception system of autonomous driving and robotics. Existing 3D
detectors significantly improve the accuracy by adopting a two-stage paradigm
which merely relies on LiDAR point clouds for 3D proposal refinement. Though
impressive, the sparsity of point clouds, especially for the points far away,
making it difficult for the LiDAR-only refinement module to accurately
recognize and locate objects.To address this problem, we propose a novel
multi-modality two-stage approach named FusionRCNN, which effectively and
efficiently fuses point clouds and camera images in the Regions of
Interest(RoI). FusionRCNN adaptively integrates both sparse geometry
information from LiDAR and dense texture information from camera in a unified
attention mechanism. Specifically, it first utilizes RoIPooling to obtain an
image set with a unified size and gets the point set by sampling raw points
within proposals in the RoI extraction step; then leverages an intra-modality
self-attention to enhance the domain-specific features, following by a
well-designed cross-attention to fuse the information from two
modalities.FusionRCNN is fundamentally plug-and-play and supports different
one-stage methods with almost no architectural changes. Extensive experiments
on KITTI and Waymo benchmarks demonstrate that our method significantly boosts
the performances of popular detectors.Remarkably, FusionRCNN significantly
improves the strong SECOND baseline by 6.14% mAP on Waymo, and outperforms
competing two-stage approaches. Code will be released soon at
https://github.com/xxlbigbrother/Fusion-RCNN.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 02:07:25 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Xu",
"Xinli",
""
],
[
"Dong",
"Shaocong",
""
],
[
"Ding",
"Lihe",
""
],
[
"Wang",
"Jie",
""
],
[
"Xu",
"Tingfa",
""
],
[
"Li",
"Jianan",
""
]
] |
new_dataset
| 0.995676 |
2209.10770
|
Kun Hu
|
Kun Hu, Shaohui Mei, Wei Wang, Kaylena A. Ehgoetz Martens, Liang Wang,
Simon J.G. Lewis, David D. Feng, Zhiyong Wang
|
Multi-level Adversarial Spatio-temporal Learning for Footstep Pressure
based FoG Detection
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Freezing of gait (FoG) is one of the most common symptoms of Parkinson's
disease, which is a neurodegenerative disorder of the central nervous system
impacting millions of people around the world. To address the pressing need to
improve the quality of treatment for FoG, devising a computer-aided detection
and quantification tool for FoG has been increasingly important. As a
non-invasive technique for collecting motion patterns, the footstep pressure
sequences obtained from pressure sensitive gait mats provide a great
opportunity for evaluating FoG in the clinic and potentially in the home
environment. In this study, FoG detection is formulated as a sequential
modelling task and a novel deep learning architecture, namely Adversarial
Spatio-temporal Network (ASTN), is proposed to learn FoG patterns across
multiple levels. A novel adversarial training scheme is introduced with a
multi-level subject discriminator to obtain subject-independent FoG
representations, which helps to reduce the over-fitting risk due to the high
inter-subject variance. As a result, robust FoG detection can be achieved for
unseen subjects. The proposed scheme also sheds light on improving
subject-level clinical studies from other scenarios as it can be integrated
with many existing deep architectures. To the best of our knowledge, this is
one of the first studies of footstep pressure-based FoG detection and the
approach of utilizing ASTN is the first deep neural network architecture in
pursuit of subject-independent representations. Experimental results on 393
trials collected from 21 subjects demonstrate encouraging performance of the
proposed ASTN for FoG detection with an AUC 0.85.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 04:08:23 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Hu",
"Kun",
""
],
[
"Mei",
"Shaohui",
""
],
[
"Wang",
"Wei",
""
],
[
"Martens",
"Kaylena A. Ehgoetz",
""
],
[
"Wang",
"Liang",
""
],
[
"Lewis",
"Simon J. G.",
""
],
[
"Feng",
"David D.",
""
],
[
"Wang",
"Zhiyong",
""
]
] |
new_dataset
| 0.967416 |
2209.10804
|
Rui Liu
|
Rui Liu, Berrak Sisman, Guanglai Gao, Haizhou Li
|
Controllable Accented Text-to-Speech Synthesis
|
To be submitted for possible journal publication
| null | null | null |
cs.SD cs.CL eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Accented text-to-speech (TTS) synthesis seeks to generate speech with an
accent (L2) as a variant of the standard version (L1). Accented TTS synthesis
is challenging as L2 is different from L1 in both in terms of phonetic
rendering and prosody pattern. Furthermore, there is no easy solution to the
control of the accent intensity in an utterance. In this work, we propose a
neural TTS architecture, that allows us to control the accent and its intensity
during inference. This is achieved through three novel mechanisms, 1) an accent
variance adaptor to model the complex accent variance with three prosody
controlling factors, namely pitch, energy and duration; 2) an accent intensity
modeling strategy to quantify the accent intensity; 3) a consistency constraint
module to encourage the TTS system to render the expected accent intensity at a
fine level. Experiments show that the proposed system attains superior
performance to the baseline models in terms of accent rendering and intensity
control. To our best knowledge, this is the first study of accented TTS
synthesis with explicit intensity control.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 06:13:07 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Liu",
"Rui",
""
],
[
"Sisman",
"Berrak",
""
],
[
"Gao",
"Guanglai",
""
],
[
"Li",
"Haizhou",
""
]
] |
new_dataset
| 0.971177 |
2209.10805
|
Kushagra Chatterjee
|
Kushagra Chatterjee and Prajakta Nimbhorkar
|
Popular Edges with Critical Nodes
|
Selected in ISAAC 2022 Conference
| null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
In the popular edge problem, the input is a bipartite graph $G = (A \cup
B,E)$ where $A$ and $B$ denote a set of men and a set of women respectively,
and each vertex in $A\cup B$ has a strict preference ordering over its
neighbours. A matching $M$ in $G$ is said to be {\em popular} if there is no
other matching $M'$ such that the number of vertices that prefer $M'$ to $M$ is
more than the number of vertices that prefer $M$ to $M'$. The goal is to
determine, whether a given edge $e$ belongs to some popular matching in $G$. A
polynomial-time algorithm for this problem appears in \cite{CK18}. We consider
the popular edge problem when some men or women are prioritized or critical. A
matching that matches all the critical nodes is termed as a feasible matching.
It follows from \cite{Kavitha14,Kavitha21,NNRS21,NN17} that, when $G$ admits a
feasible matching, there always exists a matching that is popular among all
feasible matchings. We give a polynomial-time algorithm for the popular edge
problem in the presence of critical men or women. We also show that an
analogous result does not hold in the many-to-one setting, which is known as
the Hospital-Residents Problem in literature, even when there are no critical
nodes.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 06:13:31 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Chatterjee",
"Kushagra",
""
],
[
"Nimbhorkar",
"Prajakta",
""
]
] |
new_dataset
| 0.996286 |
2209.10806
|
Slavomir Matuska
|
Slavomir Matuska, Martin Paralic and Robert Hudec
|
A Smart System for Sitting Posture Detection Based on Force Sensors and
Mobile Application
|
19 pages, 13 figures, 3 tables, article in journal
|
Mobile Information Systems, vol. 2020, Article ID 6625797, 13
pages, 2020
|
10.1155/2020/6625797
| null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
The employees health and wellbeing are an actual topic in our fast-moving
world. The employers losing money when their employees suffer from different
health problems and cannot work. The major problem is the spinal pain caused by
the poor sitting posture on the office chair. This paper deals with the
proposal and realization of the system for the detection of incorrect sitting
positions. The smart chair has six flexible force sensors. The Internet of
Things (IoT) node based on Arduino connects these sensors into the system. The
system detects wrong seating positions and notifies the users. In advance, we
develop a mobile application to receive those notifications. The user gets
feedback about sitting posture and additional statistical data. We defined
simple rules for processing the sensor data for recognizing wrong sitting
postures. The data from smart chairs are collecting by a private cloud solution
from QNAP and are stored in the MongoDB database. We used the Node-RED
application for whole logic implementation.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 06:13:37 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Matuska",
"Slavomir",
""
],
[
"Paralic",
"Martin",
""
],
[
"Hudec",
"Robert",
""
]
] |
new_dataset
| 0.984683 |
2209.10817
|
Xiao Han
|
Xiao Han and Lu Yang
|
SQ-SLAM: Monocular Semantic SLAM Based on Superquadric Object
Representation
|
Submitted to ICRA 2023
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object SLAM uses additional semantic information to detect and map objects in
the scene, in order to improve the system's perception and map representation
capabilities. Quadrics and cubes are often used to represent objects, but their
single shape limits the accuracy of object map and thus affects the application
of downstream tasks. In this paper, we introduce superquadrics (SQ) with shape
parameters into SLAM for representing objects, and propose a separate parameter
estimation method that can accurately estimate object pose and adapt to
different shapes. Furthermore, we present a lightweight data association
strategy for correctly associating semantic observations in multiple views with
object landmarks. We implement a monocular semantic SLAM system with real-time
performance and conduct comprehensive experiments on public datasets. The
results show that our method is able to build accurate object map and has
advantages in object representation. Code will be released upon acceptance.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 07:01:04 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Han",
"Xiao",
""
],
[
"Yang",
"Lu",
""
]
] |
new_dataset
| 0.998997 |
2209.10846
|
Jinghan Peng
|
Yu Zheng, Yihao Chen, Jinghan Peng, Yajun Zhang, Min Liu, Minqiang Xu
|
The SpeakIn System Description for CNSRC2022
|
4 pages
| null | null | null |
cs.SD cs.AI eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This report describes our speaker verification systems for the tasks of the
CN-Celeb Speaker Recognition Challenge 2022 (CNSRC 2022). This challenge
includes two tasks, namely speaker verification(SV) and speaker retrieval(SR).
The SV task involves two tracks: fixed track and open track. In the fixed
track, we only used CN-Celeb.T as the training set. For the open track of the
SV task and SR task, we added our open-source audio data. The ResNet-based,
RepVGG-based, and TDNN-based architectures were developed for this challenge.
Global statistic pooling structure and MQMHA pooling structure were used to
aggregate the frame-level features across time to obtain utterance-level
representation. We adopted AM-Softmax and AAM-Softmax combined with the
Sub-Center method to classify the resulting embeddings. We also used the
Large-Margin Fine-Tuning strategy to further improve the model performance. In
the backend, Sub-Mean and AS-Norm were used. In the SV task fixed track, our
system was a fusion of five models, and two models were fused in the SV task
open track. And we used a single system in the SR task. Our approach leads to
superior performance and comes the 1st place in the open track of the SV task,
the 2nd place in the fixed track of the SV task, and the 3rd place in the SR
task.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 08:17:47 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Zheng",
"Yu",
""
],
[
"Chen",
"Yihao",
""
],
[
"Peng",
"Jinghan",
""
],
[
"Zhang",
"Yajun",
""
],
[
"Liu",
"Min",
""
],
[
"Xu",
"Minqiang",
""
]
] |
new_dataset
| 0.997601 |
2209.10848
|
Rui Liu
|
Yifan Hu, Pengkai Yin, Rui Liu, Feilong Bao and Guanglai Gao
|
MnTTS: An Open-Source Mongolian Text-to-Speech Synthesis Dataset and
Accompanied Baseline
|
Accepted at the 2022 International Conference on Asian Language
Processing (IALP2022)
| null | null | null |
cs.SD cs.AI eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces a high-quality open-source text-to-speech (TTS)
synthesis dataset for Mongolian, a low-resource language spoken by over 10
million people worldwide. The dataset, named MnTTS, consists of about 8 hours
of transcribed audio recordings spoken by a 22-year-old professional female
Mongolian announcer. It is the first publicly available dataset developed to
promote Mongolian TTS applications in both academia and industry. In this
paper, we share our experience by describing the dataset development procedures
and faced challenges. To demonstrate the reliability of our dataset, we built a
powerful non-autoregressive baseline system based on FastSpeech2 model and
HiFi-GAN vocoder, and evaluated it using the subjective mean opinion score
(MOS) and real time factor (RTF) metrics. Evaluation results show that the
powerful baseline system trained on our dataset achieves MOS above 4 and RTF
about $3.30\times10^{-1}$, which makes it applicable for practical use. The
dataset, training recipe, and pretrained TTS models are freely available
\footnote{\label{github}\url{https://github.com/walker-hyf/MnTTS}}.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 08:24:43 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Hu",
"Yifan",
""
],
[
"Yin",
"Pengkai",
""
],
[
"Liu",
"Rui",
""
],
[
"Bao",
"Feilong",
""
],
[
"Gao",
"Guanglai",
""
]
] |
new_dataset
| 0.99986 |
2209.11048
|
Milica Petkovic
|
Milica I. Petkovic, Aleksandra Cvetkovic, Milan Narandzic, Dejan
Vukobratovic
|
Mixed RF-VLC Relaying System with Radio-Access Diversity
|
Presented at 2019 28th Wireless and Optical Communications Conference
(WOCC)
| null |
10.1109/WOCC.2019.8770633
| null |
cs.NI cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a statistical analysis of a mixed radio-frequency (RF)-visible
light communications (VLC) relaying system, where outdoor millimeter wave based
RF links are utilized to provide backhaul connectivity for indoor VLC
broadcasting. The multiple RF links are assumed to communicate with the VLC
access point through decode-and-forward relay. Novel closed-form outage
probability and average bit error rate expressions are derived and utilized to
obtain numerical results. Monte Carlo simulations validate presented numerical
results, which are further used to examine the effects of system and channel
parameters on system performance.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 14:44:06 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Petkovic",
"Milica I.",
""
],
[
"Cvetkovic",
"Aleksandra",
""
],
[
"Narandzic",
"Milan",
""
],
[
"Vukobratovic",
"Dejan",
""
]
] |
new_dataset
| 0.986716 |
2209.11070
|
Milica Petkovic
|
Milica I. Petkovic, Aleksandra M. Cvetkovic, Milan Narandzic, Nestor
D. Chatzidiamantis, Dejan Vukobratovic, George K. Karagiannidis
|
Mixed RF-VLC Relaying Systems for Interference-Sensitive Mobile
Applications
|
Published in IEEE Transactions on Vehicular Technology
| null |
10.1109/TVT.2020.3007676
| null |
cs.NI cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to their Radio-Frequency (RF) immunity, Visible Light Communications
(VLC) pose as a promising technology for interference sensitive applications
such as medical data networks. In this paper, we investigate mixed RF-VLC
relaying systems especially suited for this type of applications that support
mobility. In this system setup, the end-user, who is assumed to be on a vehicle
that is in dynamic movement, is served by an indoor VLC system, while the
outdoor data traffic is conveyed through multiple backhaul RF links.
Furthermore, it is assumed that a single backhaul RF link is activated by the
mobile relay and due to feedback delay, the RF link activation is based on
outdated channel state information (CSI). The performance of this system is
analyzed in terms of outage probability and bit error rate (BER), and novel
closed form analytical expressions are provided. Furthermore, the analysis is
extended for the case where the average SNR over the RF links and/or LED
optical power is high, and approximate analytical expressions are derived which
determine performance floors. Numerical results are provided which demonstrate
that the utilization of multiple RF backhaul links can significantly improve
overall RF-VLC system performance when outage/BER floors are avoided. This
calls upon joint design of both subsystems. Additionally, the outdated CSI
exploited for active RF selection can significantly degrade the quality of
system performance.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 15:01:19 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Petkovic",
"Milica I.",
""
],
[
"Cvetkovic",
"Aleksandra M.",
""
],
[
"Narandzic",
"Milan",
""
],
[
"Chatzidiamantis",
"Nestor D.",
""
],
[
"Vukobratovic",
"Dejan",
""
],
[
"Karagiannidis",
"George K.",
""
]
] |
new_dataset
| 0.995103 |
2209.11180
|
Artur Grigorev
|
Khaled Saleh and Artur Grigorev and Adriana-Simona Mihaita
|
Traffic Accident Risk Forecasting using Contextual Vision Transformers
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Recently, the problem of traffic accident risk forecasting has been getting
the attention of the intelligent transportation systems community due to its
significant impact on traffic clearance. This problem is commonly tackled in
the literature by using data-driven approaches that model the spatial and
temporal incident impact, since they were shown to be crucial for the traffic
accident risk forecasting problem. To achieve this, most approaches build
different architectures to capture the spatio-temporal correlations features,
making them inefficient for large traffic accident datasets. Thus, in this
work, we are proposing a novel unified framework, namely a contextual vision
transformer, that can be trained in an end-to-end approach which can
effectively reason about the spatial and temporal aspects of the problem while
providing accurate traffic accident risk predictions. We evaluate and compare
the performance of our proposed methodology against baseline approaches from
the literature across two large-scale traffic accident datasets from two
different geographical locations. The results have shown a significant
improvement with roughly 2\% in RMSE score in comparison to previous
state-of-art works (SoTA) in the literature. Moreover, our proposed approach
has outperformed the SoTA technique over the two datasets while only requiring
23x fewer computational requirements.
|
[
{
"version": "v1",
"created": "Tue, 20 Sep 2022 23:38:06 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Saleh",
"Khaled",
""
],
[
"Grigorev",
"Artur",
""
],
[
"Mihaita",
"Adriana-Simona",
""
]
] |
new_dataset
| 0.994538 |
2209.11198
|
Pinaki Prasad Guha Neogi
|
Pinaki Prasad Guha Neogi
|
A Dive into WhatsApp's End-to-End Encryption
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We live in a generation where the world around us is witnessing technological
revolutions every single day, and as a result of this, everything around us is
getting digitized with the touch of technology. In order to keep up the pace of
this technological revolution and help reaching this progress its zenith, one
of the most important aspects that needs to be taken care of is security. One
of the biggest boons of technology in the recent times has been the invention
of smartphones. As smartphones started becoming more popular, affordable and
easily accessible, hundreds of free messaging applications were launched, but
WhatsApp emerged as the ultimate winner in the race. This paper describes one
of the most important and popular features of WhatsApp, the End-to-End (E2E)
encryption system, which sets it apart from most other messaging applications
and is one of the reasons which helped it become so popular.
|
[
{
"version": "v1",
"created": "Mon, 5 Sep 2022 11:19:38 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Neogi",
"Pinaki Prasad Guha",
""
]
] |
new_dataset
| 0.994906 |
2209.11214
|
Selvarajah Thuseethan Dr.
|
Selvarajah Thuseethan, Palanisamy Vigneshwaran, Joseph Charles and
Chathrie Wimalasooriya
|
Siamese Network-based Lightweight Framework for Tomato Leaf Disease
Recognition
|
10 pages
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Automatic tomato disease recognition from leaf images is vital to avoid crop
losses by applying control measures on time. Even though recent deep
learning-based tomato disease recognition methods with classical training
procedures showed promising recognition results, they demand large labelled
data and involve expensive training. The traditional deep learning models
proposed for tomato disease recognition also consume high memory and storage
because of a high number of parameters. While lightweight networks overcome
some of these issues to a certain extent, they continue to show low performance
and struggle to handle imbalanced data. In this paper, a novel Siamese
network-based lightweight framework is proposed for automatic tomato leaf
disease recognition. This framework achieves the highest accuracy of 96.97% on
the tomato subset obtained from the PlantVillage dataset and 95.48% on the
Taiwan tomato leaf disease dataset. Experimental results further confirm that
the proposed framework is effective with imbalanced and small data. The
backbone deep network integrated with this framework is lightweight with
approximately 2.9629 million trainable parameters, which is way lower than
existing lightweight deep networks.
|
[
{
"version": "v1",
"created": "Sun, 18 Sep 2022 16:08:07 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Thuseethan",
"Selvarajah",
""
],
[
"Vigneshwaran",
"Palanisamy",
""
],
[
"Charles",
"Joseph",
""
],
[
"Wimalasooriya",
"Chathrie",
""
]
] |
new_dataset
| 0.984866 |
2209.11228
|
Gyungin Shin
|
Gyungin Shin, Weidi Xie, Samuel Albanie
|
NamedMask: Distilling Segmenters from Complementary Foundation Models
|
Tech report. Code: https://github.com/NoelShin/namedmask
| null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The goal of this work is to segment and name regions of images without access
to pixel-level labels during training. To tackle this task, we construct
segmenters by distilling the complementary strengths of two foundation models.
The first, CLIP (Radford et al. 2021), exhibits the ability to assign names to
image content but lacks an accessible representation of object structure. The
second, DINO (Caron et al. 2021), captures the spatial extent of objects but
has no knowledge of object names. Our method, termed NamedMask, begins by using
CLIP to construct category-specific archives of images. These images are
pseudo-labelled with a category-agnostic salient object detector bootstrapped
from DINO, then refined by category-specific segmenters using the CLIP archive
labels. Thanks to the high quality of the refined masks, we show that a
standard segmentation architecture trained on these archives with appropriate
data augmentation achieves impressive semantic segmentation abilities for both
single-object and multi-object images. As a result, our proposed NamedMask
performs favourably against a range of prior work on five benchmarks including
the VOC2012, COCO and large-scale ImageNet-S datasets.
|
[
{
"version": "v1",
"created": "Thu, 22 Sep 2022 17:59:55 GMT"
}
] | 2022-09-23T00:00:00 |
[
[
"Shin",
"Gyungin",
""
],
[
"Xie",
"Weidi",
""
],
[
"Albanie",
"Samuel",
""
]
] |
new_dataset
| 0.970215 |
1807.10463
|
Yang Su Mr.
|
Yang Su, Yansong Gao, Michael Chesser, Omid Kavehei, Alanson Sample
and Damith C.Ranasinghe
|
SecuCode: Intrinsic PUF Entangled Secure Wireless Code Dissemination for
Computational RFID Devices
|
Accepted to the IEEE Transactions on Dependable and Secure Computing
|
IEEE Transactions on Dependable and Secure Computing , Early
Access, 2019, pp.1-1
|
10.1109/TDSC.2019.2934438
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The simplicity of deployment and perpetual operation of energy harvesting
devices provides a compelling proposition for a new class of edge devices for
the Internet of Things. In particular, Computational Radio Frequency
Identification (CRFID) devices are an emerging class of battery-free,
computational, sensing enhanced devices that harvest all of their energy for
operation. Despite wireless connectivity and powering, secure wireless firmware
updates remains an open challenge for CRFID devices due to: intermittent
powering, limited computational capabilities, and the absence of a supervisory
operating system. We present, for the first time, a secure wireless code
dissemination (SecuCode) mechanism for CRFIDs by entangling a device intrinsic
hardware security primitive Static Random Access Memory Physical Unclonable
Function (SRAM PUF) to a firmware update protocol. The design of SecuCode: i)
overcomes the resource-constrained and intermittently powered nature of the
CRFID devices; ii) is fully compatible with existing communication protocols
employed by CRFID devices in particular, ISO-18000-6C protocol; and ii) is
built upon a standard and industry compliant firmware compilation and update
method realized by extending a recent framework for firmware updates provided
by Texas Instruments. We build an end-to-end SecuCode implementation and
conduct extensive experiments to demonstrate standards compliance, evaluate
performance and security.
|
[
{
"version": "v1",
"created": "Fri, 27 Jul 2018 07:29:04 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Jul 2019 15:42:08 GMT"
},
{
"version": "v3",
"created": "Sat, 17 Aug 2019 03:43:11 GMT"
},
{
"version": "v4",
"created": "Wed, 21 Sep 2022 06:25:43 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Su",
"Yang",
""
],
[
"Gao",
"Yansong",
""
],
[
"Chesser",
"Michael",
""
],
[
"Kavehei",
"Omid",
""
],
[
"Sample",
"Alanson",
""
],
[
"Ranasinghe",
"Damith C.",
""
]
] |
new_dataset
| 0.988705 |
2109.02122
|
Nghia Doan Mr.
|
Nghia Doan, Seyyed Ali Hashemi, Marco Mondelli, and Warren J. Gross
|
Decoding Reed-Muller Codes with Successive Codeword Permutations
|
Accepted for publication in IEEE Transactions on Communications
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A novel recursive list decoding (RLD) algorithm for Reed-Muller (RM) codes
based on successive permutations (SP) of the codeword is presented. A
low-complexity SP scheme applied to a subset of the symmetry group of RM codes
is first proposed to carefully select a good codeword permutation on the fly.
Then, the proposed SP technique is integrated into an improved RLD algorithm
that initializes different decoding paths with random codeword permutations,
which are sampled from the full symmetry group of RM codes. Finally, efficient
latency and complexity reduction schemes are introduced that virtually preserve
the error-correction performance of the proposed decoder. Simulation results
demonstrate that at the target frame error rate of $10^{-3}$ for the RM code of
length $256$ with $163$ information bits, the proposed decoder reduces $6\%$ of
the computational complexity and $22\%$ of the decoding latency of the
state-of-the-art semi-parallel simplified successive-cancellation decoder with
fast Hadamard transform (SSC-FHT) that uses $96$ permutations from the full
symmetry group of RM codes, while relatively maintaining the error-correction
performance and memory consumption of the semi-parallel permuted SSC-FHT
decoder.
|
[
{
"version": "v1",
"created": "Sun, 5 Sep 2021 16:53:07 GMT"
},
{
"version": "v2",
"created": "Sun, 23 Jan 2022 19:38:31 GMT"
},
{
"version": "v3",
"created": "Sat, 29 Jan 2022 13:16:44 GMT"
},
{
"version": "v4",
"created": "Thu, 21 Apr 2022 23:38:41 GMT"
},
{
"version": "v5",
"created": "Wed, 21 Sep 2022 00:25:13 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Doan",
"Nghia",
""
],
[
"Hashemi",
"Seyyed Ali",
""
],
[
"Mondelli",
"Marco",
""
],
[
"Gross",
"Warren J.",
""
]
] |
new_dataset
| 0.978308 |
2109.14934
|
Reza Khanmohammadi
|
Reza Khanmohammadi, Mitra Sadat Mirshafiee, Yazdan Rezaee Jouryabi,
Seyed Abolghasem Mirroshandel
|
Prose2Poem: The Blessing of Transformers in Translating Prose to Persian
Poetry
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Persian Poetry has consistently expressed its philosophy, wisdom, speech, and
rationale on the basis of its couplets, making it an enigmatic language on its
own to both native and non-native speakers. Nevertheless, the notice able gap
between Persian prose and poem has left the two pieces of literature
medium-less. Having curated a parallel corpus of prose and their equivalent
poems, we introduce a novel Neural Machine Translation (NMT) approach to
translate prose to ancient Persian poetry using transformer-based Language
Models in an extremely low-resource setting. More specifically, we trained a
Transformer model from scratch to obtain initial translations and pretrained
different variations of BERT to obtain final translations. To address the
challenge of using masked language modelling under poeticness criteria, we
heuristically joined the two models and generated valid poems in terms of
automatic and human assessments. Final results demonstrate the eligibility and
creativity of our novel heuristically aided approach among Literature
professionals and non-professionals in generating novel Persian poems.
|
[
{
"version": "v1",
"created": "Thu, 30 Sep 2021 09:04:11 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Oct 2021 07:04:49 GMT"
},
{
"version": "v3",
"created": "Sat, 27 Nov 2021 07:44:05 GMT"
},
{
"version": "v4",
"created": "Wed, 21 Sep 2022 16:29:23 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Khanmohammadi",
"Reza",
""
],
[
"Mirshafiee",
"Mitra Sadat",
""
],
[
"Jouryabi",
"Yazdan Rezaee",
""
],
[
"Mirroshandel",
"Seyed Abolghasem",
""
]
] |
new_dataset
| 0.999513 |
2112.02221
|
Nazeef Ul Haq
|
Nazeef Ul Haq and Muhammad Moazam Fraz and Tufail Sajjad Shah Hashmi
and Muhammad Shahzad
|
Orientation Aware Weapons Detection In Visual Data : A Benchmark Dataset
|
Submitted this paper in Journal
| null |
10.1007/s00607-022-01095-0
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automatic detection of weapons is significant for improving security and well
being of individuals, nonetheless, it is a difficult task due to large variety
of size, shape and appearance of weapons. View point variations and occlusion
also are reasons which makes this task more difficult. Further, the current
object detection algorithms process rectangular areas, however a slender and
long rifle may really cover just a little portion of area and the rest may
contain unessential details. To overcome these problem, we propose a CNN
architecture for Orientation Aware Weapons Detection, which provides oriented
bounding box with improved weapons detection performance. The proposed model
provides orientation not only using angle as classification problem by dividing
angle into eight classes but also angle as regression problem. For training our
model for weapon detection a new dataset comprising of total 6400 weapons
images is gathered from the web and then manually annotated with position
oriented bounding boxes. Our dataset provides not only oriented bounding box as
ground truth but also horizontal bounding box. We also provide our dataset in
multiple formats of modern object detectors for further research in this area.
The proposed model is evaluated on this dataset, and the comparative analysis
with off-the shelf object detectors yields superior performance of proposed
model, measured with standard evaluation strategies. The dataset and the model
implementation are made publicly available at this link:
https://bit.ly/2TyZICF.
|
[
{
"version": "v1",
"created": "Sat, 4 Dec 2021 02:21:02 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Haq",
"Nazeef Ul",
""
],
[
"Fraz",
"Muhammad Moazam",
""
],
[
"Hashmi",
"Tufail Sajjad Shah",
""
],
[
"Shahzad",
"Muhammad",
""
]
] |
new_dataset
| 0.977165 |
2112.13890
|
Peiyan Dong
|
Zhenglun Kong, Peiyan Dong, Xiaolong Ma, Xin Meng, Mengshu Sun, Wei
Niu, Xuan Shen, Geng Yuan, Bin Ren, Minghai Qin, Hao Tang, Yanzhi Wang
|
SPViT: Enabling Faster Vision Transformers via Soft Token Pruning
|
ECCV 2022
| null | null | null |
cs.CV cs.AI cs.AR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, Vision Transformer (ViT) has continuously established new
milestones in the computer vision field, while the high computation and memory
cost makes its propagation in industrial production difficult. Pruning, a
traditional model compression paradigm for hardware efficiency, has been widely
applied in various DNN structures. Nevertheless, it stays ambiguous on how to
perform exclusive pruning on the ViT structure. Considering three key points:
the structural characteristics, the internal data pattern of ViTs, and the
related edge device deployment, we leverage the input token sparsity and
propose a computation-aware soft pruning framework, which can be set up on
vanilla Transformers of both flatten and CNN-type structures, such as
Pooling-based ViT (PiT). More concretely, we design a dynamic attention-based
multi-head token selector, which is a lightweight module for adaptive
instance-wise token selection. We further introduce a soft pruning technique,
which integrates the less informative tokens generated by the selector module
into a package token that will participate in subsequent calculations rather
than being completely discarded. Our framework is bound to the trade-off
between accuracy and computation constraints of specific edge devices through
our proposed computation-aware training strategy. Experimental results show
that our framework significantly reduces the computation cost of ViTs while
maintaining comparable performance on image classification. Moreover, our
framework can guarantee the identified model to meet resource specifications of
mobile devices and FPGA, and even achieve the real-time execution of DeiT-T on
mobile platforms. For example, our method reduces the latency of DeiT-T to 26
ms (26%$\sim $41% superior to existing works) on the mobile device with
0.25%$\sim $4% higher top-1 accuracy on ImageNet.
|
[
{
"version": "v1",
"created": "Mon, 27 Dec 2021 20:15:25 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Sep 2022 22:20:30 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Kong",
"Zhenglun",
""
],
[
"Dong",
"Peiyan",
""
],
[
"Ma",
"Xiaolong",
""
],
[
"Meng",
"Xin",
""
],
[
"Sun",
"Mengshu",
""
],
[
"Niu",
"Wei",
""
],
[
"Shen",
"Xuan",
""
],
[
"Yuan",
"Geng",
""
],
[
"Ren",
"Bin",
""
],
[
"Qin",
"Minghai",
""
],
[
"Tang",
"Hao",
""
],
[
"Wang",
"Yanzhi",
""
]
] |
new_dataset
| 0.990744 |
2201.11438
|
Sanket Biswas
|
Sanket Biswas, Ayan Banerjee, Josep Llad\'os, and Umapada Pal
|
DocSegTr: An Instance-Level End-to-End Document Image Segmentation
Transformer
|
Preprint
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Understanding documents with rich layouts is an essential step towards
information extraction. Business intelligence processes often require the
extraction of useful semantic content from documents at a large scale for
subsequent decision-making tasks. In this context, instance-level segmentation
of different document objects (title, sections, figures etc.) has emerged as an
interesting problem for the document analysis and understanding community. To
advance the research in this direction, we present a transformer-based model
called \emph{DocSegTr} for end-to-end instance segmentation of complex layouts
in document images. The method adapts a twin attention module, for semantic
reasoning, which helps to become highly computationally efficient compared with
the state-of-the-art. To the best of our knowledge, this is the first work on
transformer-based document segmentation. Extensive experimentation on
competitive benchmarks like PubLayNet, PRIMA, Historical Japanese (HJ) and
TableBank demonstrate that our model achieved comparable or better segmentation
performance than the existing state-of-the-art approaches with the average
precision of 89.4, 40.3, 83.4 and 93.3. This simple and flexible framework
could serve as a promising baseline for instance-level recognition tasks in
document images.
|
[
{
"version": "v1",
"created": "Thu, 27 Jan 2022 10:50:22 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2022 15:58:41 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Biswas",
"Sanket",
""
],
[
"Banerjee",
"Ayan",
""
],
[
"Lladós",
"Josep",
""
],
[
"Pal",
"Umapada",
""
]
] |
new_dataset
| 0.991504 |
2202.07036
|
Felix Ott
|
Felix Ott and David R\"ugamer and Lucas Heublein and Tim Hamann and
Jens Barth and Bernd Bischl and Christopher Mutschler
|
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting
Recognition from IMU-Enhanced Pens
|
Accepted for International Journal on Document Analysis and
Recognition (IJDAR)
| null |
10.1007/s10032-022-00415-6
| null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Purpose. Handwriting is one of the most frequently occurring patterns in
everyday life and with it come challenging applications such as handwriting
recognition (HWR), writer identification, and signature verification. In
contrast to offline HWR that only uses spatial information (i.e., images),
online HWR (OnHWR) uses richer spatio-temporal information (i.e., trajectory
data or inertial data). While there exist many offline HWR datasets, there is
only little data available for the development of OnHWR methods on paper as it
requires hardware-integrated pens. Methods. This paper presents data and
benchmark models for real-time sequence-to-sequence (seq2seq) learning and
single character-based recognition. Our data is recorded by a sensor-enhanced
ballpoint pen, yielding sensor data streams from triaxial accelerometers, a
gyroscope, a magnetometer and a force sensor at 100 Hz. We propose a variety of
datasets including equations and words for both the writer-dependent and
writer-independent tasks. Our datasets allow a comparison between classical
OnHWR on tablets and on paper with sensor-enhanced pens. We provide an
evaluation benchmark for seq2seq and single character-based HWR using recurrent
and temporal convolutional networks and Transformers combined with a
connectionist temporal classification (CTC) loss and cross-entropy (CE) losses.
Results. Our convolutional network combined with BiLSTMs outperforms
Transformer-based architectures, is on par with InceptionTime for
sequence-based classification tasks, and yields better results compared to 28
state-of-the-art techniques. Time-series augmentation methods improve the
sequence-based task, and we show that CE variants can improve the single
classification task.
|
[
{
"version": "v1",
"created": "Mon, 14 Feb 2022 20:55:33 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Sep 2022 21:38:54 GMT"
},
{
"version": "v3",
"created": "Wed, 21 Sep 2022 15:17:22 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Ott",
"Felix",
""
],
[
"Rügamer",
"David",
""
],
[
"Heublein",
"Lucas",
""
],
[
"Hamann",
"Tim",
""
],
[
"Barth",
"Jens",
""
],
[
"Bischl",
"Bernd",
""
],
[
"Mutschler",
"Christopher",
""
]
] |
new_dataset
| 0.999753 |
2202.13558
|
Ziqing Yang
|
Ziqing Yang, Zihang Xu, Yiming Cui, Baoxin Wang, Min Lin, Dayong Wu,
Zhigang Chen
|
CINO: A Chinese Minority Pre-trained Language Model
|
Accepted to COLING 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multilingual pre-trained language models have shown impressive performance on
cross-lingual tasks. It greatly facilitates the applications of natural
language processing on low-resource languages. However, there are still some
languages that the current multilingual models do not perform well on. In this
paper, we propose CINO (Chinese Minority Pre-trained Language Model), a
multilingual pre-trained language model for Chinese minority languages. It
covers Standard Chinese, Yue Chinese, and six other ethnic minority languages.
To evaluate the cross-lingual ability of the multilingual model on ethnic
minority languages, we collect documents from Wikipedia and news websites, and
construct two text classification datasets, WCM (Wiki-Chinese-Minority) and
CMNews (Chinese-Minority-News). We show that CINO notably outperforms the
baselines on various classification tasks. The CINO model and the datasets are
publicly available at http://cino.hfl-rc.com.
|
[
{
"version": "v1",
"created": "Mon, 28 Feb 2022 06:02:06 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2022 01:43:35 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Yang",
"Ziqing",
""
],
[
"Xu",
"Zihang",
""
],
[
"Cui",
"Yiming",
""
],
[
"Wang",
"Baoxin",
""
],
[
"Lin",
"Min",
""
],
[
"Wu",
"Dayong",
""
],
[
"Chen",
"Zhigang",
""
]
] |
new_dataset
| 0.996896 |
2203.04548
|
Omrit Filtser
|
Omrit Filtser, Mayank Goswami, Joseph S.B. Mitchell, Valentin
Polishchuk
|
On Flipping the Fr\'{e}chet distance
| null | null | null | null |
cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
The classical and extensively-studied Fr\'echet distance between two curves
is defined as an inf max, where the infimum is over all traversals of the
curves, and the maximum is over all concurrent positions of the two agents. In
this article we investigate a "flipped" Fr\'echet measure defined by a sup min
-- the supremum is over all traversals of the curves, and the minimum is over
all concurrent positions of the two agents. This measure produces a notion of
"social distance" between two curves (or general domains), where agents
traverse curves while trying to stay as far apart as possible.
We first study the flipped Fr\'echet measure between two polygonal curves in
one and two dimensions, providing conditional lower bounds and matching
algorithms. We then consider this measure on polygons, where it denotes the
minimum distance that two agents can maintain while restricted to travel in or
on the boundary of the same polygon. We investigate several variants of the
problem in this setting, for some of which we provide linear time algorithms.
Finally, we consider this measure on graphs.
We draw connections between our proposed flipped Fr\'echet measure and
existing related work in computational geometry, hoping that our new measure
may spawn investigations akin to those performed for the Fr\'echet distance,
and into further interesting problems that arise.
|
[
{
"version": "v1",
"created": "Wed, 9 Mar 2022 06:48:11 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2022 08:43:14 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Filtser",
"Omrit",
""
],
[
"Goswami",
"Mayank",
""
],
[
"Mitchell",
"Joseph S. B.",
""
],
[
"Polishchuk",
"Valentin",
""
]
] |
new_dataset
| 0.956484 |
2206.08614
|
Luigi Celona
|
Daniel Vera Nieto and Luigi Celona and Clara Fernandez-Labrador
|
Understanding Aesthetics with Language: A Photo Critique Dataset for
Aesthetic Assessment
|
Accepted to NeurIPS Track on Datasets and Benchmarks 2022
| null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Computational inference of aesthetics is an ill-defined task due to its
subjective nature. Many datasets have been proposed to tackle the problem by
providing pairs of images and aesthetic scores based on human ratings. However,
humans are better at expressing their opinion, taste, and emotions by means of
language rather than summarizing them in a single number. In fact, photo
critiques provide much richer information as they reveal how and why users rate
the aesthetics of visual stimuli. In this regard, we propose the Reddit Photo
Critique Dataset (RPCD), which contains tuples of image and photo critiques.
RPCD consists of 74K images and 220K comments and is collected from a Reddit
community used by hobbyists and professional photographers to improve their
photography skills by leveraging constructive community feedback. The proposed
dataset differs from previous aesthetics datasets mainly in three aspects,
namely (i) the large scale of the dataset and the extension of the comments
criticizing different aspects of the image, (ii) it contains mostly UltraHD
images, and (iii) it can easily be extended to new data as it is collected
through an automatic pipeline. To the best of our knowledge, in this work, we
propose the first attempt to estimate the aesthetic quality of visual stimuli
from the critiques. To this end, we exploit the polarity of the sentiment of
criticism as an indicator of aesthetic judgment. We demonstrate how sentiment
polarity correlates positively with the aesthetic judgment available for two
aesthetic assessment benchmarks. Finally, we experiment with several models by
using the sentiment scores as a target for ranking images. Dataset and
baselines are available (https://github.com/mediatechnologycenter/aestheval).
|
[
{
"version": "v1",
"created": "Fri, 17 Jun 2022 08:16:20 GMT"
},
{
"version": "v2",
"created": "Wed, 24 Aug 2022 09:40:23 GMT"
},
{
"version": "v3",
"created": "Wed, 21 Sep 2022 15:30:50 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Nieto",
"Daniel Vera",
""
],
[
"Celona",
"Luigi",
""
],
[
"Fernandez-Labrador",
"Clara",
""
]
] |
new_dataset
| 0.999776 |
2208.00571
|
Zhihao Li
|
Zhihao Li, Jianzhuang Liu, Zhensong Zhang, Songcen Xu, and Youliang
Yan
|
CLIFF: Carrying Location Information in Full Frames into Human Pose and
Shape Estimation
|
update the related work upon v1 with small modifications
|
ECCV 2022 Oral
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Top-down methods dominate the field of 3D human pose and shape estimation,
because they are decoupled from human detection and allow researchers to focus
on the core problem. However, cropping, their first step, discards the location
information from the very beginning, which makes themselves unable to
accurately predict the global rotation in the original camera coordinate
system. To address this problem, we propose to Carry Location Information in
Full Frames (CLIFF) into this task. Specifically, we feed more holistic
features to CLIFF by concatenating the cropped-image feature with its bounding
box information. We calculate the 2D reprojection loss with a broader view of
the full frame, taking a projection process similar to that of the person
projected in the image. Fed and supervised by global-location-aware
information, CLIFF directly predicts the global rotation along with more
accurate articulated poses. Besides, we propose a pseudo-ground-truth annotator
based on CLIFF, which provides high-quality 3D annotations for in-the-wild 2D
datasets and offers crucial full supervision for regression-based methods.
Extensive experiments on popular benchmarks show that CLIFF outperforms prior
arts by a significant margin, and reaches the first place on the AGORA
leaderboard (the SMPL-Algorithms track). The code and data are available at
https://github.com/huawei-noah/noah-research/tree/master/CLIFF.
|
[
{
"version": "v1",
"created": "Mon, 1 Aug 2022 02:08:46 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2022 08:19:41 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Li",
"Zhihao",
""
],
[
"Liu",
"Jianzhuang",
""
],
[
"Zhang",
"Zhensong",
""
],
[
"Xu",
"Songcen",
""
],
[
"Yan",
"Youliang",
""
]
] |
new_dataset
| 0.986121 |
2209.09937
|
Elena Nazarova
|
Elena Nazarova, Ildar Babataev, Nipun Weerakkodi, Aleksey Fedoseev,
Dzmitry Tsetserukou
|
HyperPalm: DNN-based hand gesture recognition interface for intelligent
communication with quadruped robot in 3D space
|
6 pages, 9 figures, IEEE SMC 2022
| null | null | null |
cs.RO cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nowadays, autonomous mobile robots support people in many areas where human
presence either redundant or too dangerous. They have successfully proven
themselves in expeditions, gas industry, mines, warehouses, etc. However, even
legged robots may stuck in rough terrain conditions requiring human cognitive
abilities to navigate the system. While gamepads and keyboards are convenient
for wheeled robot control, the quadruped robot in 3D space can move along all
linear coordinates and Euler angles, requiring at least 12 buttons for
independent control of their DoF. Therefore, more convenient interfaces of
control are required.
In this paper we present HyperPalm: a novel gesture interface for intuitive
human-robot interaction with quadruped robots. Without additional devices, the
operator has full position and orientation control of the quadruped robot in 3D
space through hand gesture recognition with only 5 gestures and 6 DoF hand
motion.
The experimental results revealed to classify 5 static gestures with high
accuracy (96.5%), accurately predict the position of the 6D position of the
hand in three-dimensional space. The absolute linear deviation Root mean square
deviation (RMSD) of the proposed approach is 11.7 mm, which is almost 50% lower
than for the second tested approach, the absolute angular deviation RMSD of the
proposed approach is 2.6 degrees, which is almost 27% lower than for the second
tested approach. Moreover, the user study was conducted to explore user's
subjective experience from human-robot interaction through the proposed gesture
interface. The participants evaluated their interaction with HyperPalm as
intuitive (2.0), not causing frustration (2.63), and requiring low physical
demand (2.0).
|
[
{
"version": "v1",
"created": "Tue, 20 Sep 2022 18:28:29 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Nazarova",
"Elena",
""
],
[
"Babataev",
"Ildar",
""
],
[
"Weerakkodi",
"Nipun",
""
],
[
"Fedoseev",
"Aleksey",
""
],
[
"Tsetserukou",
"Dzmitry",
""
]
] |
new_dataset
| 0.999776 |
2209.09987
|
Domenico Bloisi
|
Domenico D. Bloisi, Andrea Pennisi, Cristian Zampino, Flavio
Biancospino, Francesco Laus, Gianluca Di Stefano, Michele Brienza, Rocchina
Romano
|
MARIO: Modular and Extensible Architecture for Computing Visual
Statistics in RoboCup SPL
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This technical report describes a modular and extensible architecture for
computing visual statistics in RoboCup SPL (MARIO), presented during the SPL
Open Research Challenge at RoboCup 2022, held in Bangkok (Thailand). MARIO is
an open-source, ready-to-use software application whose final goal is to
contribute to the growth of the RoboCup SPL community. MARIO comes with a GUI
that integrates multiple machine learning and computer vision based functions,
including automatic camera calibration, background subtraction, homography
computation, player + ball tracking and localization, NAO robot pose estimation
and fall detection. MARIO has been ranked no. 1 in the Open Research Challenge.
|
[
{
"version": "v1",
"created": "Tue, 20 Sep 2022 20:45:56 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Bloisi",
"Domenico D.",
""
],
[
"Pennisi",
"Andrea",
""
],
[
"Zampino",
"Cristian",
""
],
[
"Biancospino",
"Flavio",
""
],
[
"Laus",
"Francesco",
""
],
[
"Di Stefano",
"Gianluca",
""
],
[
"Brienza",
"Michele",
""
],
[
"Romano",
"Rocchina",
""
]
] |
new_dataset
| 0.982008 |
2209.10008
|
Ling Luo
|
Ling Luo, Yulia Gryaditskaya, Yongxin Yang, Tao Xiang, Yi-Zhe Song
|
Fine-Grained VR Sketching: Dataset and Insights
| null |
2021 International Conference on 3D Vision (3DV), pp. 1003-1013.
IEEE, 2021
| null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape
pairs of a chair category with large shapes diversity. Our dataset supports the
recent trend in the sketch community on fine-grained data analysis, and extends
it to an actively developing 3D domain. We argue for the most convenient
sketching scenario where the sketch consists of sparse lines and does not
require any sketching skills, prior training or time-consuming accurate
drawing. We then, for the first time, study the scenario of fine-grained 3D VR
sketch to 3D shape retrieval, as a novel VR sketching application and a proving
ground to drive out generic insights to inform future research. By
experimenting with carefully selected combinations of design factors on this
new problem, we draw important conclusions to help follow-on work. We hope our
dataset will enable other novel applications, especially those that require a
fine-grained angle such as fine-grained 3D shape reconstruction. The dataset is
available at tinyurl.com/VRSketch3DV21.
|
[
{
"version": "v1",
"created": "Tue, 20 Sep 2022 21:30:54 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Luo",
"Ling",
""
],
[
"Gryaditskaya",
"Yulia",
""
],
[
"Yang",
"Yongxin",
""
],
[
"Xiang",
"Tao",
""
],
[
"Song",
"Yi-Zhe",
""
]
] |
new_dataset
| 0.999849 |
2209.10016
|
Ignacio Tripodi
|
Ignacio J. Tripodi
|
Setting the rhythm scene: deep learning-based drum loop generation from
arbitrary language cues
| null | null | null | null |
cs.SD cs.AI cs.CL cs.IR cs.MM eess.AS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Generative artificial intelligence models can be a valuable aid to music
composition and live performance, both to aid the professional musician and to
help democratize the music creation process for hobbyists. Here we present a
novel method that, given an English word or phrase, generates 2 compasses of a
4-piece drum pattern that embodies the "mood" of the given language cue, or
that could be used for an audiovisual scene described by the language cue. We
envision this tool as composition aid for electronic music and audiovisual
soundtrack production, or an improvisation tool for live performance. In order
to produce the training samples for this model, besides manual annotation of
the "scene" or "mood" terms, we have designed a novel method to extract the
consensus drum track of any song. This consists of a 2-bar, 4-piece drum
pattern that represents the main percussive motif of a song, which could be
imported into any music loop device or live looping software. These two key
components (drum pattern generation from a generalizable input, and consensus
percussion extraction) present a novel approach to computer-aided composition
and provide a stepping stone for more comprehensive rhythm generation.
|
[
{
"version": "v1",
"created": "Tue, 20 Sep 2022 21:53:35 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Tripodi",
"Ignacio J.",
""
]
] |
new_dataset
| 0.994486 |
2209.10033
|
Shaoshuai Shi
|
Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele
|
MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge --
Motion Prediction
|
The 1st place solution report for Waymo Motion Prediction Challenge
of Workshop on Autonomous Driving of CVPR 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this report, we present the 1st place solution for motion prediction track
in 2022 Waymo Open Dataset Challenges. We propose a novel Motion Transformer
framework for multimodal motion prediction, which introduces a small set of
novel motion query pairs for generating better multimodal future trajectories
by jointly performing the intention localization and iterative motion
refinement. A simple model ensemble strategy with non-maximum-suppression is
adopted to further boost the final performance. Our approach achieves the 1st
place on the motion prediction leaderboard of 2022 Waymo Open Dataset
Challenges, outperforming other methods with remarkable margins. Code will be
available at https://github.com/sshaoshuai/MTR.
|
[
{
"version": "v1",
"created": "Tue, 20 Sep 2022 23:03:22 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Shi",
"Shaoshuai",
""
],
[
"Jiang",
"Li",
""
],
[
"Dai",
"Dengxin",
""
],
[
"Schiele",
"Bernt",
""
]
] |
new_dataset
| 0.998563 |
2209.10074
|
Sheng Huang
|
Wenhao Tang and Sheng Huang and Xiaoxian Zhang and Luwen Huangfu
|
PicT: A Slim Weakly Supervised Vision Transformer for Pavement Distress
Classification
|
ACM Multimedia 2022 paper, 9 pages 7 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Automatic pavement distress classification facilitates improving the
efficiency of pavement maintenance and reducing the cost of labor and
resources. A recently influential branch of this task divides the pavement
image into patches and addresses these issues from the perspective of
multi-instance learning. However, these methods neglect the correlation between
patches and suffer from a low efficiency in the model optimization and
inference. Meanwhile, Swin Transformer is able to address both of these issues
with its unique strengths. Built upon Swin Transformer, we present a vision
Transformer named \textbf{P}avement \textbf{I}mage \textbf{C}lassification
\textbf{T}ransformer (\textbf{PicT}) for pavement distress classification. In
order to better exploit the discriminative information of pavement images at
the patch level, the \textit{Patch Labeling Teacher} is proposed to leverage a
teacher model to dynamically generate pseudo labels of patches from image
labels during each iteration, and guides the model to learn the discriminative
features of patches. The broad classification head of Swin Transformer may
dilute the discriminative features of distressed patches in the feature
aggregation step due to the small distressed area ratio of the pavement image.
To overcome this drawback, we present a \textit{Patch Refiner} to cluster
patches into different groups and only select the highest distress-risk group
to yield a slim head for the final image classification. We evaluate our method
on CQU-BPDD. Extensive results show that \textbf{PicT} outperforms the
second-best performed model by a large margin of $+2.4\%$ in P@R on detection
task, $+3.9\%$ in $F1$ on recognition task, and 1.8x throughput, while enjoying
7x faster training speed using the same computing resources. Our codes and
models have been released on
\href{https://github.com/DearCaat/PicT}{https://github.com/DearCaat/PicT}.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 02:33:49 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Tang",
"Wenhao",
""
],
[
"Huang",
"Sheng",
""
],
[
"Zhang",
"Xiaoxian",
""
],
[
"Huangfu",
"Luwen",
""
]
] |
new_dataset
| 0.997474 |
2209.10098
|
Jiaqi Gu
|
Jiaqi Gu, Zhengqi Gao, Chenghao Feng, Hanqing Zhu, Ray T. Chen, Duane
S. Boning, David Z. Pan
|
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric
Photonic Device Simulation
|
13 pages. Accepted to NeurIPS 2022
| null | null | null |
cs.ET cs.LG physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Optical computing is an emerging technology for next-generation efficient
artificial intelligence (AI) due to its ultra-high speed and efficiency.
Electromagnetic field simulation is critical to the design, optimization, and
validation of photonic devices and circuits. However, costly numerical
simulation significantly hinders the scalability and turn-around time in the
photonic circuit design loop. Recently, physics-informed neural networks have
been proposed to predict the optical field solution of a single instance of a
partial differential equation (PDE) with predefined parameters. Their
complicated PDE formulation and lack of efficient parametrization mechanisms
limit their flexibility and generalization in practical simulation scenarios.
In this work, for the first time, a physics-agnostic neural operator-based
framework, dubbed NeurOLight, is proposed to learn a family of frequency-domain
Maxwell PDEs for ultra-fast parametric photonic device simulation. We balance
the efficiency and generalization of NeurOLight via several novel techniques.
Specifically, we discretize different devices into a unified domain, represent
parametric PDEs with a compact wave prior, and encode the incident light via
masked source modeling. We design our model with parameter-efficient
cross-shaped NeurOLight blocks and adopt superposition-based augmentation for
data-efficient learning. With these synergistic approaches, NeurOLight
generalizes to a large space of unseen simulation settings, demonstrates
2-orders-of-magnitude faster simulation speed than numerical solvers, and
outperforms prior neural network models by ~54% lower prediction error with
~44% fewer parameters. Our code is available at
https://github.com/JeremieMelo/NeurOLight.
|
[
{
"version": "v1",
"created": "Mon, 19 Sep 2022 21:25:26 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Gu",
"Jiaqi",
""
],
[
"Gao",
"Zhengqi",
""
],
[
"Feng",
"Chenghao",
""
],
[
"Zhu",
"Hanqing",
""
],
[
"Chen",
"Ray T.",
""
],
[
"Boning",
"Duane S.",
""
],
[
"Pan",
"David Z.",
""
]
] |
new_dataset
| 0.991315 |
2209.10125
|
Anurag Jain
|
Anurag Jain and Sujit Gujar and Kannan Srinathan
|
Interlude: Balancing Chaos And Harmony For Fair and Fast Blockchains
| null | null | null | null |
cs.CR cs.DC cs.GT
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Blockchains lie at the heart of Bitcoin and other cryptocurrencies that have
shown great promise to revolutionize finance and commerce. Although they are
gaining increasing popularity, they face technical challenges when it comes to
scaling to support greater demand while maintaining their desirable security
properties. In an exciting line of recent work, many researchers have proposed
various scalable blockchain protocols that demonstrate the potential to solve
these challenges. However, many of these protocols come with the assumptions of
honest majority and symmetric network access which may not accurately reflect
the real world where the participants may be self-interested or rational.
Secondly, these works show that their protocol works in an ideal environment
where each party has equal access to the network whereas different parties have
varying latencies and network speeds. These assumptions may render the
protocols susceptible to security threats in the real world, as highlighted by
the literature focused on exploring game-theoretic attacks on these protocols.
We propose a scalable blockchain protocol, Interlude, which comes with the
typical security guarantees while focusing on game-theoretic soundness and
network fairness. The novelty of Interlude is that it has a relatively simple
design consisting of a sequence of parallel blocks containing disjoint
transaction sets that can be mined quickly followed by a series block that is
slow to mine and gives the honest parties in the network time to synchronize.
Thus, between the chaos of parallel blocks, our blockchain protocol masquerades
an interlude moment of harmony in series blocks that synchronize the network.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 05:19:23 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Jain",
"Anurag",
""
],
[
"Gujar",
"Sujit",
""
],
[
"Srinathan",
"Kannan",
""
]
] |
new_dataset
| 0.997434 |
2209.10155
|
Zihui Guo
|
Zihui Guo, Yonghong Hou, Pichao Wang, Zhimin Gao, Mingliang Xu, and
Wanqing Li
|
FT-HID: A Large Scale RGB-D Dataset for First and Third Person Human
Interaction Analysis
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Analysis of human interaction is one important research topic of human motion
analysis. It has been studied either using first person vision (FPV) or third
person vision (TPV). However, the joint learning of both types of vision has so
far attracted little attention. One of the reasons is the lack of suitable
datasets that cover both FPV and TPV. In addition, existing benchmark datasets
of either FPV or TPV have several limitations, including the limited number of
samples, participant subjects, interaction categories, and modalities. In this
work, we contribute a large-scale human interaction dataset, namely, FT-HID
dataset. FT-HID contains pair-aligned samples of first person and third person
visions. The dataset was collected from 109 distinct subjects and has more than
90K samples for three modalities. The dataset has been validated by using
several existing action recognition methods. In addition, we introduce a novel
multi-view interaction mechanism for skeleton sequences, and a joint learning
multi-stream framework for first person and third person visions. Both methods
yield promising results on the FT-HID dataset. It is expected that the
introduction of this vision-aligned large-scale dataset will promote the
development of both FPV and TPV, and their joint learning techniques for human
action analysis. The dataset and code are available at
\href{https://github.com/ENDLICHERE/FT-HID}{here}.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 07:24:15 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Guo",
"Zihui",
""
],
[
"Hou",
"Yonghong",
""
],
[
"Wang",
"Pichao",
""
],
[
"Gao",
"Zhimin",
""
],
[
"Xu",
"Mingliang",
""
],
[
"Li",
"Wanqing",
""
]
] |
new_dataset
| 0.979474 |
2209.10170
|
Qinglan Wei
|
Qinglan Wei, Xuling Huang, Yuan Zhang
|
FV2ES: A Fully End2End Multimodal System for Fast Yet Effective Video
Emotion Recognition Inference
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the latest social networks, more and more people prefer to express their
emotions in videos through text, speech, and rich facial expressions.
Multimodal video emotion analysis techniques can help understand users' inner
world automatically based on human expressions and gestures in images, tones in
voices, and recognized natural language. However, in the existing research, the
acoustic modality has long been in a marginal position as compared to visual
and textual modalities. That is, it tends to be more difficult to improve the
contribution of the acoustic modality for the whole multimodal emotion
recognition task. Besides, although better performance can be obtained by
introducing common deep learning methods, the complex structures of these
training models always result in low inference efficiency, especially when
exposed to high-resolution and long-length videos. Moreover, the lack of a
fully end-to-end multimodal video emotion recognition system hinders its
application. In this paper, we designed a fully multimodal video-to-emotion
system (named FV2ES) for fast yet effective recognition inference, whose
benefits are threefold: (1) The adoption of the hierarchical attention method
upon the sound spectra breaks through the limited contribution of the acoustic
modality and outperforms the existing models' performance on both IEMOCAP and
CMU-MOSEI datasets; (2) the introduction of the idea of multi-scale for visual
extraction while single-branch for inference brings higher efficiency and
maintains the prediction accuracy at the same time; (3) the further integration
of data pre-processing into the aligned multimodal learning model allows the
significant reduction of computational costs and storage space.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 08:05:26 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Wei",
"Qinglan",
""
],
[
"Huang",
"Xuling",
""
],
[
"Zhang",
"Yuan",
""
]
] |
new_dataset
| 0.987724 |
2209.10198
|
Abdullah Giray Ya\u{g}l{\i}k\c{c}{\i}
|
Abdullah Giray Ya\u{g}l{\i}k\c{c}{\i}, Ataberk Olgun, Minesh Patel,
Haocong Luo, Hasan Hassan, Lois Orosa, O\u{g}uz Ergin, and Onur Mutlu
|
HiRA: Hidden Row Activation for Reducing Refresh Latency of
Off-the-Shelf DRAM Chips
|
To appear in the 55th IEEE/ACM International Symposium on
Microarchitecture (MICRO), 2022
| null | null | null |
cs.AR cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
DRAM is the building block of modern main memory systems. DRAM cells must be
periodically refreshed to prevent data loss. Refresh operations degrade system
performance by interfering with memory accesses. As DRAM chip density increases
with technology node scaling, refresh operations also increase because: 1) the
number of DRAM rows in a chip increases; and 2) DRAM cells need additional
refresh operations to mitigate bit failures caused by RowHammer, a failure
mechanism that becomes worse with technology node scaling. Thus, it is critical
to enable refresh operations at low performance overhead. To this end, we
propose a new operation, Hidden Row Activation (HiRA), and the HiRA Memory
Controller (HiRA-MC).
HiRA hides a refresh operation's latency by refreshing a row concurrently
with accessing or refreshing another row within the same bank. Unlike prior
works, HiRA achieves this parallelism without any modifications to
off-the-shelf DRAM chips. To do so, it leverages the new observation that two
rows in the same bank can be activated without data loss if the rows are
connected to different charge restoration circuitry. We experimentally
demonstrate on 56% real off-the-shelf DRAM chips that HiRA can reliably
parallelize a DRAM row's refresh operation with refresh or activation of any of
the 32% of the rows within the same bank. By doing so, HiRA reduces the overall
latency of two refresh operations by 51.4%.
HiRA-MC modifies the memory request scheduler to perform HiRA when a refresh
operation can be performed concurrently with a memory access or another
refresh. Our system-level evaluations show that HiRA-MC increases system
performance by 12.6% and 3.73x as it reduces the performance degradation due to
periodic refreshes and refreshes for RowHammer protection (preventive
refreshes), respectively, for future DRAM chips with increased density and
RowHammer vulnerability.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 08:51:03 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Yağlıkçı",
"Abdullah Giray",
""
],
[
"Olgun",
"Ataberk",
""
],
[
"Patel",
"Minesh",
""
],
[
"Luo",
"Haocong",
""
],
[
"Hassan",
"Hasan",
""
],
[
"Orosa",
"Lois",
""
],
[
"Ergin",
"Oğuz",
""
],
[
"Mutlu",
"Onur",
""
]
] |
new_dataset
| 0.992713 |
2209.10229
|
Zhanyu Guo
|
Zhanyu Guo, Shenyuan Guo, Jialong Wang, Yifan Feng
|
Intelligent wayfinding vehicle design based on visual recognition
|
in Chinese language
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Intelligent drug delivery trolley is an advanced intelligent drug delivery
equipment. Compared with traditional manual drug delivery, it has higher drug
delivery efficiency and lower error rate. In this project, an intelligent drug
delivery car is designed and manufactured, which can recognize the road route
and the room number of the target ward through visual recognition technology.
The trolley selects the corresponding route according to the identified room
number, accurately transports the drugs to the target ward, and can return to
the pharmacy after the drugs are delivered. The intelligent drug delivery car
uses DC power supply, and the motor drive module controls two DC motors, which
overcomes the problem of excessive deviation of turning angle. The trolley line
inspection function uses closed-loop control to improve the accuracy of line
inspection and the controllability of trolley speed. The identification of ward
number is completed by the camera module with microcontroller, and has the
functions of adaptive adjustment of ambient brightness, distortion correction,
automatic calibration and so on. The communication between two cooperative drug
delivery vehicles is realized by Bluetooth module, which achieves efficient and
accurate communication and interaction. Experiments show that the intelligent
drug delivery car can accurately identify the room number and plan the route to
deliver drugs to the far, middle and near wards, and has the characteristics of
fast speed and accurate judgment. In addition, two drug delivery trolleys can
cooperate to deliver drugs to the same ward, with high efficiency and high
cooperation.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 09:49:16 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Guo",
"Zhanyu",
""
],
[
"Guo",
"Shenyuan",
""
],
[
"Wang",
"Jialong",
""
],
[
"Feng",
"Yifan",
""
]
] |
new_dataset
| 0.996851 |
2209.10240
|
Ryan Shah
|
Ryan Shah, Mujeeb Ahmed, Shishir Nagaraja
|
Fingerprinting Robot Movements via Acoustic Side Channel
|
11 pages, 4 figures, 7 tables
| null | null | null |
cs.CR cs.LG cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present an acoustic side channel attack which makes use of
smartphone microphones recording a robot in operation to exploit acoustic
properties of the sound to fingerprint a robot's movements. In this work we
consider the possibility of an insider adversary who is within physical
proximity of a robotic system (such as a technician or robot operator),
equipped with only their smartphone microphone. Through the acoustic
side-channel, we demonstrate that it is indeed possible to fingerprint not only
individual robot movements within 3D space, but also patterns of movements
which could lead to inferring the purpose of the movements (i.e. surgical
procedures which a surgical robot is undertaking) and hence, resulting in
potential privacy violations. Upon evaluation, we find that individual robot
movements can be fingerprinted with around 75% accuracy, decreasing slightly
with more fine-grained movement meta-data such as distance and speed.
Furthermore, workflows could be reconstructed with around 62% accuracy as a
whole, with more complex movements such as pick-and-place or packing
reconstructed with near perfect accuracy. As well as this, in some environments
such as surgical settings, audio may be recorded and transmitted over VoIP,
such as for education/teaching purposes or in remote telemedicine. The question
here is, can the same attack be successful even when VoIP communication is
employed, and how does packet loss impact the captured audio and the success of
the attack? Using the same characteristics of acoustic sound for plain audio
captured by the smartphone, the attack was 90% accurate in fingerprinting VoIP
samples on average, 15% higher than the baseline without the VoIP codec
employed. This opens up new research questions regarding anonymous
communications to protect robotic systems from acoustic side channel attacks
via VoIP communication networks.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 10:12:37 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"Shah",
"Ryan",
""
],
[
"Ahmed",
"Mujeeb",
""
],
[
"Nagaraja",
"Shishir",
""
]
] |
new_dataset
| 0.996717 |
2209.10313
|
EPTCS
|
Hans de Nivelle (School of Engineering and Digital Sciences,
Nazarbayev University, Nursultan-City, Kazakkhstan), Dina Muktubayeva (School
of Engineering and Digital Sciences, Nazarbayev University, Nursultan-City,
Kazakhstan)
|
Generating Tokenizers with Flat Automata
|
In Proceedings GandALF 2022, arXiv:2209.09333. An implementation of
flat automata can be found on: www.compiler-tools.eu
|
EPTCS 370, 2022, pp. 66-80
|
10.4204/EPTCS.370.5
| null |
cs.FL
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce flat automata for automatic generation of tokenizers. Flat
automata are a simple representation of standard finite automata. Using the
flat representation, automata can be easily constructed, combined and printed.
Due to the use of border functions, flat automata are more compact than
standard automata in the case where intervals of characters are attached to
transitions, and the standard algorithms on automata are simpler.
We give the standard algorithms for tokenizer construction with automata,
namely construction using regular operations, determinization, and
minimization. We prove their correctness. The algorithms work with intervals of
characters, but are not more complicated than their counterparts on single
characters. It is easy to generate C++ code from the final deterministic
automaton. All procedures have been implemented in C++ and are publicly
available. The implementation has been used in applications and in teaching.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 12:44:23 GMT"
}
] | 2022-09-22T00:00:00 |
[
[
"de Nivelle",
"Hans",
"",
"School of Engineering and Digital Sciences,\n Nazarbayev University, Nursultan-City, Kazakkhstan"
],
[
"Muktubayeva",
"Dina",
"",
"School\n of Engineering and Digital Sciences, Nazarbayev University, Nursultan-City,\n Kazakhstan"
]
] |
new_dataset
| 0.969948 |
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