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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2201.06231
|
Han Cai
|
Yajuan Liu, Han Cai, and Xiaohu Tang
|
A New Cooperative Repair Scheme with k + 1 Helper Nodes for (n, k)
Hadamard MSR codes with Small Sub-packetization
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cooperative repair model is an available technology to deal with multiple
node failures in distributed storage systems. Recently, explicit constructions
of cooperative MSR codes were given by Ye (IEEE Transactions on Information
Theory, 2020) with sub-packetization level $(d-k+h)(d-k+1)^n$. Specifically,
the sub-packetization level is $(h+1)2^n$ when $d=k+1$. In this paper, we
propose a new cooperative repair scheme by means of the inter-instance and
intra-instance pairing inherited from the perfect code which reduces the
sub-packetization to $2^n$ when $(h+1)|2^n$ and $(2\ell+1)2^n$ when
$h+1=(2\ell+1)2^m$ for $m\ge 0$, $\ell\ge 1$ with $d=k+1$ helper nodes. That is
to say, the sub-packetization is $h + 1 $ times or $2^m$ times less than Ye's.
It turned out to be the best result so far known.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 06:15:45 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Liu",
"Yajuan",
""
],
[
"Cai",
"Han",
""
],
[
"Tang",
"Xiaohu",
""
]
] |
new_dataset
| 0.979122 |
2201.06337
|
Verena Biener
|
Verena Biener, Travis Gesslein, Daniel Schneider, Felix Kawala,
Alexander Otte, Per Ola Kristensson, Michel Pahud, Eyal Ofek, Cuauhtli
Campos, Matja\v{z} Kljun, Klen \v{C}opi\v{c} Pucihar, Jens Grubert
|
PoVRPoint: Authoring Presentations in Mobile Virtual Reality
|
IEEE VR 2022; to appear in IEEE transactions on visualization and
computer graphics, 2022
|
In IEEE transactions on visualization and computer graphics, 2022
| null | null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Virtual Reality (VR) has the potential to support mobile knowledge workers by
complementing traditional input devices with a large three-dimensional output
space and spatial input. Previous research on supporting VR knowledge work
explored domains such as text entry using physical keyboards and spreadsheet
interaction using combined pen and touch input. Inspired by such work, this
paper probes the VR design space for authoring presentations in mobile
settings. We propose PoVRPoint -- a set of tools coupling pen- and touch-based
editing of presentations on mobile devices, such as tablets, with the
interaction capabilities afforded by VR. We study the utility of extended
display space to, for example, assist users in identifying target slides,
supporting spatial manipulation of objects on a slide, creating animations, and
facilitating arrangements of multiple, possibly occluded, shapes. Among other
things, our results indicate that 1) the wide field of view afforded by VR
results in significantly faster target slide identification times compared to a
tablet-only interface for visually salient targets; and 2) the
three-dimensional view in VR enables significantly faster object reordering in
the presence of occlusion compared to two baseline interfaces. A user study
further confirmed that the interaction techniques were found to be usable and
enjoyable.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 10:50:01 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Biener",
"Verena",
""
],
[
"Gesslein",
"Travis",
""
],
[
"Schneider",
"Daniel",
""
],
[
"Kawala",
"Felix",
""
],
[
"Otte",
"Alexander",
""
],
[
"Kristensson",
"Per Ola",
""
],
[
"Pahud",
"Michel",
""
],
[
"Ofek",
"Eyal",
""
],
[
"Campos",
"Cuauhtli",
""
],
[
"Kljun",
"Matjaž",
""
],
[
"Pucihar",
"Klen Čopič",
""
],
[
"Grubert",
"Jens",
""
]
] |
new_dataset
| 0.999529 |
2201.06423
|
Giseop Kim
|
Giseop Kim, Seungsang Yun, Jeongyun Kim, Ayoung Kim
|
SC-LiDAR-SLAM: a Front-end Agnostic Versatile LiDAR SLAM System
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate 3D point cloud map generation is a core task for various robot
missions or even for data-driven urban analysis. To do so, light detection and
ranging (LiDAR) sensor-based simultaneous localization and mapping (SLAM)
technology have been elaborated. To compose a full SLAM system, many odometry
and place recognition methods have independently been proposed in academia.
However, they have hardly been integrated or too tightly combined so that
exchanging (upgrading) either single odometry or place recognition module is
very effort demanding. Recently, the performance of each module has been
improved a lot, so it is necessary to build a SLAM system that can effectively
integrate them and easily replace them with the latest one. In this paper, we
release such a front-end agnostic LiDAR SLAM system, named SC-LiDAR-SLAM. We
built a complete SLAM system by designing it modular, and successfully
integrating it with Scan Context++ and diverse existing opensource LiDAR
odometry methods to generate an accurate point cloud map
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 14:20:36 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Kim",
"Giseop",
""
],
[
"Yun",
"Seungsang",
""
],
[
"Kim",
"Jeongyun",
""
],
[
"Kim",
"Ayoung",
""
]
] |
new_dataset
| 0.998256 |
2201.06496
|
Sabit Hassan
|
Hamdy Mubarak, Sabit Hassan, Shammur Absar Chowdhury, Firoj Alam
|
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination
|
8 pages, 9 figures
| null | null | null |
cs.CL cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
The emergence of the COVID-19 pandemic and the first global infodemic have
changed our lives in many different ways. We relied on social media to get the
latest information about the COVID-19 pandemic and at the same time to
disseminate information. The content in social media consisted not only health
related advises, plans, and informative news from policy makers, but also
contains conspiracies and rumors. It became important to identify such
information as soon as they are posted to make actionable decisions (e.g.,
debunking rumors, or taking certain measures for traveling). To address this
challenge, we develop and publicly release the first largest manually annotated
Arabic tweet dataset, ArCovidVac, for the COVID-19 vaccination campaign,
covering many countries in the Arab region. The dataset is enriched with
different layers of annotation, including, (i) Informativeness (more vs. less
importance of the tweets); (ii) fine-grained tweet content types (e.g., advice,
rumors, restriction, authenticate news/information); and (iii) stance towards
vaccination (pro-vaccination, neutral, anti-vaccination). Further, we performed
in-depth analysis of the data, exploring the popularity of different vaccines,
trending hashtags, topics and presence of offensiveness in the tweets. We
studied the data for individual types of tweets and temporal changes in stance
towards vaccine. We benchmarked the ArCovidVac dataset using transformer
architectures for informativeness, content types, and stance detection.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 16:19:21 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Mubarak",
"Hamdy",
""
],
[
"Hassan",
"Sabit",
""
],
[
"Chowdhury",
"Shammur Absar",
""
],
[
"Alam",
"Firoj",
""
]
] |
new_dataset
| 0.998386 |
2201.06504
|
Fabiana Zama
|
Villiam Bortolotti and Leonardo Brizi and Germana Landi and Anastasiia
Nagmutdinova and Fabiana Zama
|
MUPen2DTool: a Matlab Tool for 2D Nuclear Magnetic Resonance relaxation
data inversion
| null | null | null | null |
cs.MS
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate and efficient analysis of materials properties from Nuclear Magnetic
Resonance (NMR) relaxation data requires robust and efficient inversion
procedures. Despite the great variety of applications requiring to process
two-dimensional NMR data (2DNMR), a few software tools are freely available.
The aim of this paper is to present MUPen2DTool, an open-source MATLAB based
software tool for 2DNMR data inversion. The user can choose among several types
of NMR experiments, and the software provides codes that can be used and
extended easily. Furthermore, a MATLAB interface makes it easier to include
users own data. The practical use is demonstrated in the reported examples of
both synthetic and real NMR data.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 16:29:28 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Bortolotti",
"Villiam",
""
],
[
"Brizi",
"Leonardo",
""
],
[
"Landi",
"Germana",
""
],
[
"Nagmutdinova",
"Anastasiia",
""
],
[
"Zama",
"Fabiana",
""
]
] |
new_dataset
| 0.999202 |
2201.06517
|
Alexander Ruch
|
Alexander Ruch, Yujia Zhang, Michael Macy
|
Demographic Confounding Causes Extreme Instances of Lifestyle Politics
on Facebook
|
29 pages (27 body, 2 supplemental material), 14 figures (12 body, 2
supplemental material), 2 tables
| null | null | null |
cs.SI cs.AI cs.CL cs.IR physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lifestyle politics emerge when activities that have no substantive relevance
to ideology become politically aligned and polarized. Homophily and social
influence are able generate these fault lines on their own; however, social
identities from demographics may serve as coordinating mechanisms through which
lifestyle politics are mobilized are spread. Using a dataset of 137,661,886
observations from 299,327 Facebook interests aggregated across users of
different racial/ethnic, education, age, gender, and income demographics, we
find that the most extreme instances of lifestyle politics are those which are
highly confounded by demographics such as race/ethnicity (e.g., Black artists
and performers). After adjusting political alignment for demographic effects,
lifestyle politics decreased by 27.36% toward the political "center" and
demographically confounded interests were no longer among the most polarized
interests. Instead, after demographic deconfounding, we found that the most
liberal interests included electric cars, Planned Parenthood, and liberal
satire while the most conservative interests included the Republican Party and
conservative commentators. We validate our measures of political alignment and
lifestyle politics using the General Social Survey and find similar demographic
entanglements with lifestyle politics existed before social media such as
Facebook were ubiquitous, giving us strong confidence that our results are not
due to echo chambers or filter bubbles. Likewise, since demographic
characteristics exist prior to ideological values, we argue that the
demographic confounding we observe is causally responsible for the extreme
instances of lifestyle politics that we find among the aggregated interests. We
conclude our paper by relating our results to Simpson's paradox, cultural
omnivorousness, and network autocorrelation.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 16:48:00 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Ruch",
"Alexander",
""
],
[
"Zhang",
"Yujia",
""
],
[
"Macy",
"Michael",
""
]
] |
new_dataset
| 0.992048 |
2201.06556
|
Alexander Ruch
|
Alexander Ruch, Ari Decter-Frain, Raghav Batra
|
Millions of Co-purchases and Reviews Reveal the Spread of Polarization
and Lifestyle Politics across Online Markets
|
25 pages (21 body, 4 supplemental material), 10 figures (4 body, 6
supplemental material), 5 tables (3 body, 2 supplemental material)
| null | null | null |
cs.SI cs.AI cs.CL cs.IR physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Polarization in America has reached a high point as markets are also becoming
polarized. Existing research, however, focuses on specific market segments and
products and has not evaluated this trend's full breadth. If such fault lines
do spread into other segments that are not explicitly political, it would
indicate the presence of lifestyle politics -- when ideas and behaviors not
inherently political become politically aligned through their connections with
explicitly political things. We study the pervasiveness of polarization and
lifestyle politics over different product segments in a diverse market and test
the extent to which consumer- and platform-level network effects and morality
may explain lifestyle politics. Specifically, using graph and language data
from Amazon (82.5M reviews of 9.5M products and product and category metadata
from 1996-2014), we sample 234.6 million relations among 21.8 million market
entities to find product categories that are most politically relevant,
aligned, and polarized. We then extract moral values present in reviews' text
and use these data and other reviewer-, product-, and category-level data to
test whether individual- and platform- level network factors explain lifestyle
politics better than products' implicit morality. We find pervasive lifestyle
politics. Cultural products are 4 times more polarized than any other segment,
products' political attributes have up to 3.7 times larger associations with
lifestyle politics than author-level covariates, and morality has statistically
significant but relatively small correlations with lifestyle politics.
Examining lifestyle politics in these contexts helps us better understand the
extent and root of partisan differences, why Americans may be so polarized, and
how this polarization affects market systems.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 18:16:37 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Ruch",
"Alexander",
""
],
[
"Decter-Frain",
"Ari",
""
],
[
"Batra",
"Raghav",
""
]
] |
new_dataset
| 0.992898 |
2201.06577
|
Veena Prabhakaran
|
Jasine Babu, K. Murali Krishnan, Veena Prabhakaran, Nandini J. Warrier
|
Eternal vertex cover number of maximal outerplanar graphs
| null | null | null | null |
cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
Eternal vertex cover problem is a variant of the classical vertex cover
problem modeled as a two player attacker-defender game. Computing eternal
vertex cover number of graphs is known to be NP-hard in general and the
complexity status of the problem for bipartite graphs is open. There is a
quadratic complexity algorithm known for this problem for chordal graphs.
Maximal outerplanar graphs forms a subclass of chordal graphs, for which no
algorithm of sub-quadratic time complexity is known. In this paper, we obtain a
recursive algorithm of linear time for computing eternal vertex cover number of
maximal outerplanar graphs.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 18:58:21 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Babu",
"Jasine",
""
],
[
"Krishnan",
"K. Murali",
""
],
[
"Prabhakaran",
"Veena",
""
],
[
"Warrier",
"Nandini J.",
""
]
] |
new_dataset
| 0.999578 |
2201.06644
|
Arnav Malawade
|
Arnav Vaibhav Malawade, Trier Mortlock, Mohammad Abdullah Al Faruque
|
HydraFusion: Context-Aware Selective Sensor Fusion for Robust and
Efficient Autonomous Vehicle Perception
|
Accepted to be published in the 13th ACM/IEEE International
Conference on Cyber-Physical Systems (ICCPS 2022)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although autonomous vehicles (AVs) are expected to revolutionize
transportation, robust perception across a wide range of driving contexts
remains a significant challenge. Techniques to fuse sensor data from camera,
radar, and lidar sensors have been proposed to improve AV perception. However,
existing methods are insufficiently robust in difficult driving contexts (e.g.,
bad weather, low light, sensor obstruction) due to rigidity in their fusion
implementations. These methods fall into two broad categories: (i) early
fusion, which fails when sensor data is noisy or obscured, and (ii) late
fusion, which cannot leverage features from multiple sensors and thus produces
worse estimates. To address these limitations, we propose HydraFusion: a
selective sensor fusion framework that learns to identify the current driving
context and fuses the best combination of sensors to maximize robustness
without compromising efficiency. HydraFusion is the first approach to propose
dynamically adjusting between early fusion, late fusion, and combinations
in-between, thus varying both how and when fusion is applied. We show that, on
average, HydraFusion outperforms early and late fusion approaches by 13.66% and
14.54%, respectively, without increasing computational complexity or energy
consumption on the industry-standard Nvidia Drive PX2 AV hardware platform. We
also propose and evaluate both static and deep-learning-based context
identification strategies. Our open-source code and model implementation are
available at https://github.com/AICPS/hydrafusion.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 22:19:53 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Malawade",
"Arnav Vaibhav",
""
],
[
"Mortlock",
"Trier",
""
],
[
"Faruque",
"Mohammad Abdullah Al",
""
]
] |
new_dataset
| 0.992773 |
2201.06648
|
Haozhe Sun
|
Haozhe Sun and Wei-Wei Tu and Isabelle Guyon
|
OmniPrint: A Configurable Printed Character Synthesizer
|
Accepted at 35th Conference on Neural Information Processing Systems
(NeurIPS 2021) Track on Datasets and Benchmarks.
https://openreview.net/forum?id=R07XwJPmgpl
|
35th Conference on Neural Information Processing Systems (NeurIPS
2021) Track on Datasets and Benchmarks
| null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce OmniPrint, a synthetic data generator of isolated printed
characters, geared toward machine learning research. It draws inspiration from
famous datasets such as MNIST, SVHN and Omniglot, but offers the capability of
generating a wide variety of printed characters from various languages, fonts
and styles, with customized distortions. We include 935 fonts from 27 scripts
and many types of distortions. As a proof of concept, we show various use
cases, including an example of meta-learning dataset designed for the upcoming
MetaDL NeurIPS 2021 competition. OmniPrint is available at
https://github.com/SunHaozhe/OmniPrint.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 22:31:35 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Sun",
"Haozhe",
""
],
[
"Tu",
"Wei-Wei",
""
],
[
"Guyon",
"Isabelle",
""
]
] |
new_dataset
| 0.99974 |
2201.06696
|
Hengcan Shi
|
Hengcan Shi, Munawar Hayat, Yicheng Wu, Jianfei Cai
|
ProposalCLIP: Unsupervised Open-Category Object Proposal Generation via
Exploiting CLIP Cues
|
10 pages, 5 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Object proposal generation is an important and fundamental task in computer
vision. In this paper, we propose ProposalCLIP, a method towards unsupervised
open-category object proposal generation. Unlike previous works which require a
large number of bounding box annotations and/or can only generate proposals for
limited object categories, our ProposalCLIP is able to predict proposals for a
large variety of object categories without annotations, by exploiting CLIP
(contrastive language-image pre-training) cues. Firstly, we analyze CLIP for
unsupervised open-category proposal generation and design an objectness score
based on our empirical analysis on proposal selection. Secondly, a graph-based
merging module is proposed to solve the limitations of CLIP cues and merge
fragmented proposals. Finally, we present a proposal regression module that
extracts pseudo labels based on CLIP cues and trains a lightweight network to
further refine proposals. Extensive experiments on PASCAL VOC, COCO and Visual
Genome datasets show that our ProposalCLIP can better generate proposals than
previous state-of-the-art methods. Our ProposalCLIP also shows benefits for
downstream tasks, such as unsupervised object detection.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 01:51:35 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Shi",
"Hengcan",
""
],
[
"Hayat",
"Munawar",
""
],
[
"Wu",
"Yicheng",
""
],
[
"Cai",
"Jianfei",
""
]
] |
new_dataset
| 0.989365 |
2201.06724
|
Rongsheng Zhang
|
Rongsheng Zhang, Xiaoxi Mao, Le Li, Lin Jiang, Lin Chen, Zhiwei Hu,
Yadong Xi, Changjie Fan, Minlie Huang
|
Youling: an AI-Assisted Lyrics Creation System
|
accept by emnlp2020 demo track
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, a variety of neural models have been proposed for lyrics
generation. However, most previous work completes the generation process in a
single pass with little human intervention. We believe that lyrics creation is
a creative process with human intelligence centered. AI should play a role as
an assistant in the lyrics creation process, where human interactions are
crucial for high-quality creation. This paper demonstrates \textit{Youling}, an
AI-assisted lyrics creation system, designed to collaborate with music
creators. In the lyrics generation process, \textit{Youling} supports
traditional one pass full-text generation mode as well as an interactive
generation mode, which allows users to select the satisfactory sentences from
generated candidates conditioned on preceding context. The system also provides
a revision module which enables users to revise undesired sentences or words of
lyrics repeatedly. Besides, \textit{Youling} allows users to use multifaceted
attributes to control the content and format of generated lyrics. The demo
video of the system is available at https://youtu.be/DFeNpHk0pm4.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 03:57:04 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Zhang",
"Rongsheng",
""
],
[
"Mao",
"Xiaoxi",
""
],
[
"Li",
"Le",
""
],
[
"Jiang",
"Lin",
""
],
[
"Chen",
"Lin",
""
],
[
"Hu",
"Zhiwei",
""
],
[
"Xi",
"Yadong",
""
],
[
"Fan",
"Changjie",
""
],
[
"Huang",
"Minlie",
""
]
] |
new_dataset
| 0.96359 |
2201.06741
|
Prashant Kodali
|
Prashant Kodali, Akshala Bhatnagar, Naman Ahuja, Manish Shrivastava,
Ponnurangam Kumaraguru
|
HashSet -- A Dataset For Hashtag Segmentation
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Hashtag segmentation is the task of breaking a hashtag into its constituent
tokens. Hashtags often encode the essence of user-generated posts, along with
information like topic and sentiment, which are useful in downstream tasks.
Hashtags prioritize brevity and are written in unique ways -- transliterating
and mixing languages, spelling variations, creative named entities. Benchmark
datasets used for the hashtag segmentation task -- STAN, BOUN -- are small in
size and extracted from a single set of tweets. However, datasets should
reflect the variations in writing styles of hashtags and also account for
domain and language specificity, failing which the results will misrepresent
model performance. We argue that model performance should be assessed on a
wider variety of hashtags, and datasets should be carefully curated. To this
end, we propose HashSet, a dataset comprising of: a) 1.9k manually annotated
dataset; b) 3.3M loosely supervised dataset. HashSet dataset is sampled from a
different set of tweets when compared to existing datasets and provides an
alternate distribution of hashtags to build and validate hashtag segmentation
models. We show that the performance of SOTA models for Hashtag Segmentation
drops substantially on proposed dataset, indicating that the proposed dataset
provides an alternate set of hashtags to train and assess models.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 04:40:45 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Kodali",
"Prashant",
""
],
[
"Bhatnagar",
"Akshala",
""
],
[
"Ahuja",
"Naman",
""
],
[
"Shrivastava",
"Manish",
""
],
[
"Kumaraguru",
"Ponnurangam",
""
]
] |
new_dataset
| 0.999872 |
2201.06811
|
Mike Wu
|
Mike Wu, Will McTighe, Kaili Wang, Istvan A. Seres, Nick Bax, Manuel
Puebla, Mariano Mendez, Federico Carrone, Tom\'as De Mattey, Herman O.
Demaestri, Mariano Nicolini, Pedro Fontana
|
Tutela: An Open-Source Tool for Assessing User-Privacy on Ethereum and
Tornado Cash
|
10 pages content, 2 pages appendix
| null | null | null |
cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
A common misconception among blockchain users is that pseudonymity guarantees
privacy. The reality is almost the opposite. Every transaction one makes is
recorded on a public ledger and reveals information about one's identity.
Mixers, such as Tornado Cash, were developed to preserve privacy through
"mixing" transactions with those of others in an anonymity pool, making it
harder to link deposits and withdrawals from the pool. Unfortunately, it is
still possible to reveal information about those in the anonymity pool if users
are not careful. We introduce Tutela, an application built on expert heuristics
to report the true anonymity of an Ethereum address. In particular, Tutela has
three functionalities: first, it clusters together Ethereum addresses based on
interaction history such that for an Ethereum address, we can identify other
addresses likely owned by the same entity; second, it shows Ethereum users
their potentially compromised transactions; third, Tutela computes the true
size of the anonymity pool of each Tornado Cash mixer by excluding potentially
compromised transactions. A public implementation of Tutela can be found at
https://github.com/TutelaLabs/tutela-app. To use Tutela, visit
https://www.tutela.xyz.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 08:31:12 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Wu",
"Mike",
""
],
[
"McTighe",
"Will",
""
],
[
"Wang",
"Kaili",
""
],
[
"Seres",
"Istvan A.",
""
],
[
"Bax",
"Nick",
""
],
[
"Puebla",
"Manuel",
""
],
[
"Mendez",
"Mariano",
""
],
[
"Carrone",
"Federico",
""
],
[
"De Mattey",
"Tomás",
""
],
[
"Demaestri",
"Herman O.",
""
],
[
"Nicolini",
"Mariano",
""
],
[
"Fontana",
"Pedro",
""
]
] |
new_dataset
| 0.997408 |
2201.06835
|
Derek Xu
|
Derek Xu
|
Ray Based Distributed Autonomous Vehicle Research Platform
|
15 pages, 11 figures
| null | null | null |
cs.LG cs.DC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
My project tackles the question of whether Ray can be used to quickly train
autonomous vehicles using a simulator (Carla), and whether a platform robust
enough for further research purposes can be built around it. Ray is an
open-source framework that enables distributed machine learning applications.
Distributed computing is a technique which parallelizes computational tasks,
such as training a model, among many machines. Ray abstracts away the complex
coordination of these machines, making it rapidly scalable. Carla is a vehicle
simulator that generates data used to train a model. The bulk of the project
was writing the training logic that Ray would use to train my distributed
model. Imitation learning is the best fit for autonomous vehicles. Imitation
learning is an alternative to reinforcement learning and it works by trying to
learn the optimal policy by imitating an expert (usually a human) given a set
of demonstrations. A key deliverable for the project was showcasing my trained
agent in a few benchmark tests, such as navigating a complex turn through
traffic. Beyond that, the broader ambition was to develop a research platform
where others could quickly train and run experiments on huge amounts of Carla
vehicle data. Thus, my end product is not a single model, but a large-scale,
open-source research platform (RayCarla) for autonomous vehicle researchers to
utilize.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 09:13:27 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Xu",
"Derek",
""
]
] |
new_dataset
| 0.958039 |
2201.07000
|
Fan Meng
|
Fan Meng, Tao Song, Danya Xu
|
TCR-GAN: Predicting tropical cyclone passive microwave rainfall using
infrared imagery via generative adversarial networks
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Tropical cyclones (TC) generally carry large amounts of water vapor and can
cause large-scale extreme rainfall. Passive microwave rainfall (PMR) estimation
of TC with high spatial and temporal resolution is crucial for disaster warning
of TC, but remains a challenging problem due to the low temporal resolution of
microwave sensors. This study attempts to solve this problem by directly
forecasting PMR from satellite infrared (IR) images of TC. We develop a
generative adversarial network (GAN) to convert IR images into PMR, and
establish the mapping relationship between TC cloud-top bright temperature and
PMR, the algorithm is named TCR-GAN. Meanwhile, a new dataset that is available
as a benchmark, Dataset of Tropical Cyclone IR-to-Rainfall Prediction (TCIRRP)
was established, which is expected to advance the development of artificial
intelligence in this direction. Experimental results show that the algorithm
can effectively extract key features from IR. The end-to-end deep learning
approach shows potential as a technique that can be applied globally and
provides a new perspective tropical cyclone precipitation prediction via
satellite, which is expected to provide important insights for real-time
visualization of TC rainfall globally in operations.
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 08:22:16 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Meng",
"Fan",
""
],
[
"Song",
"Tao",
""
],
[
"Xu",
"Danya",
""
]
] |
new_dataset
| 0.999111 |
2201.07012
|
Stanislav Fort
|
Stanislav Fort
|
Adversarial vulnerability of powerful near out-of-distribution detection
|
8 pages
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There has been a significant progress in detecting out-of-distribution (OOD)
inputs in neural networks recently, primarily due to the use of large models
pretrained on large datasets, and an emerging use of multi-modality. We show a
severe adversarial vulnerability of even the strongest current OOD detection
techniques. With a small, targeted perturbation to the input pixels, we can
change the image assignment from an in-distribution to an out-distribution, and
vice versa, easily. In particular, we demonstrate severe adversarial
vulnerability on the challenging near OOD CIFAR-100 vs CIFAR-10 task, as well
as on the far OOD CIFAR-100 vs SVHN. We study the adversarial robustness of
several post-processing techniques, including the simple baseline of Maximum of
Softmax Probabilities (MSP), the Mahalanobis distance, and the newly proposed
\textit{Relative} Mahalanobis distance. By comparing the loss of OOD detection
performance at various perturbation strengths, we demonstrate the beneficial
effect of using ensembles of OOD detectors, and the use of the
\textit{Relative} Mahalanobis distance over other post-processing methods. In
addition, we show that even strong zero-shot OOD detection using CLIP and
multi-modality suffers from a severe lack of adversarial robustness as well.
Our code is available at
https://github.com/stanislavfort/adversaries_to_OOD_detection
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 14:23:07 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Fort",
"Stanislav",
""
]
] |
new_dataset
| 0.982325 |
2201.07067
|
Marco Tranzatto
|
Marco Tranzatto, Frank Mascarich, Lukas Bernreiter, Carolina Godinho,
Marco Camurri, Shehryar Khattak, Tung Dang, Victor Reijgwart, Johannes Loeje,
David Wisth, Samuel Zimmermann, Huan Nguyen, Marius Fehr, Lukas Solanka,
Russell Buchanan, Marko Bjelonic, Nikhil Khedekar, Mathieu Valceschini,
Fabian Jenelten, Mihir Dharmadhikari, Timon Homberger, Paolo De Petris,
Lorenz Wellhausen, Mihir Kulkarni, Takahiro Miki, Satchel Hirsch, Markus
Montenegro, Christos Papachristos, Fabian Tresoldi, Jan Carius, Giorgio
Valsecchi, Joonho Lee, Konrad Meyer, Xiangyu Wu, Juan Nieto, Andy Smith,
Marco Hutter, Roland Siegwart, Mark Mueller, Maurice Fallon, Kostas Alexis
|
CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel
and Urban Circuits of the DARPA Subterranean Challenge
|
50 pages, 25 figures. Accepted at Field Robotics, 2021
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Autonomous exploration of subterranean environments constitutes a major
frontier for robotic systems as underground settings present key challenges
that can render robot autonomy hard to achieve. This has motivated the DARPA
Subterranean Challenge, where teams of robots search for objects of interest in
various underground environments. In response, the CERBERUS system-of-systems
is presented as a unified strategy towards subterranean exploration using
legged and flying robots. As primary robots, ANYmal quadruped systems are
deployed considering their endurance and potential to traverse challenging
terrain. For aerial robots, both conventional and collision-tolerant
multirotors are utilized to explore spaces too narrow or otherwise unreachable
by ground systems. Anticipating degraded sensing conditions, a complementary
multi-modal sensor fusion approach utilizing camera, LiDAR, and inertial data
for resilient robot pose estimation is proposed. Individual robot pose
estimates are refined by a centralized multi-robot map optimization approach to
improve the reported location accuracy of detected objects of interest in the
DARPA-defined coordinate frame. Furthermore, a unified exploration path
planning policy is presented to facilitate the autonomous operation of both
legged and aerial robots in complex underground networks. Finally, to enable
communication between the robots and the base station, CERBERUS utilizes a
ground rover with a high-gain antenna and an optical fiber connection to the
base station, alongside breadcrumbing of wireless nodes by our legged robots.
We report results from the CERBERUS system-of-systems deployment at the DARPA
Subterranean Challenge Tunnel and Urban Circuits, along with the current
limitations and the lessons learned for the benefit of the community.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 15:48:51 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Tranzatto",
"Marco",
""
],
[
"Mascarich",
"Frank",
""
],
[
"Bernreiter",
"Lukas",
""
],
[
"Godinho",
"Carolina",
""
],
[
"Camurri",
"Marco",
""
],
[
"Khattak",
"Shehryar",
""
],
[
"Dang",
"Tung",
""
],
[
"Reijgwart",
"Victor",
""
],
[
"Loeje",
"Johannes",
""
],
[
"Wisth",
"David",
""
],
[
"Zimmermann",
"Samuel",
""
],
[
"Nguyen",
"Huan",
""
],
[
"Fehr",
"Marius",
""
],
[
"Solanka",
"Lukas",
""
],
[
"Buchanan",
"Russell",
""
],
[
"Bjelonic",
"Marko",
""
],
[
"Khedekar",
"Nikhil",
""
],
[
"Valceschini",
"Mathieu",
""
],
[
"Jenelten",
"Fabian",
""
],
[
"Dharmadhikari",
"Mihir",
""
],
[
"Homberger",
"Timon",
""
],
[
"De Petris",
"Paolo",
""
],
[
"Wellhausen",
"Lorenz",
""
],
[
"Kulkarni",
"Mihir",
""
],
[
"Miki",
"Takahiro",
""
],
[
"Hirsch",
"Satchel",
""
],
[
"Montenegro",
"Markus",
""
],
[
"Papachristos",
"Christos",
""
],
[
"Tresoldi",
"Fabian",
""
],
[
"Carius",
"Jan",
""
],
[
"Valsecchi",
"Giorgio",
""
],
[
"Lee",
"Joonho",
""
],
[
"Meyer",
"Konrad",
""
],
[
"Wu",
"Xiangyu",
""
],
[
"Nieto",
"Juan",
""
],
[
"Smith",
"Andy",
""
],
[
"Hutter",
"Marco",
""
],
[
"Siegwart",
"Roland",
""
],
[
"Mueller",
"Mark",
""
],
[
"Fallon",
"Maurice",
""
],
[
"Alexis",
"Kostas",
""
]
] |
new_dataset
| 0.999453 |
2201.07078
|
XIan Wang
|
Xian Wang, Diego Monteiro, Lik-Hang Lee, Pan Hui, and Hai-Ning Liang
|
VibroWeight: Simulating Weight and Center of Gravity Changes of Objects
in Virtual Reality for Enhanced Realism
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Haptic feedback in virtual reality (VR) allows users to perceive the physical
properties of virtual objects (e.g., their weight and motion patterns).
However, the lack of haptic sensations deteriorates users' immersion and
overall experience. In this work, we designed and implemented a low-cost
hardware prototype with liquid metal, VibroWeight, which can work in
complementarity with commercial VR handheld controllers. VibroWeight is
characterized by bimodal feedback cues in VR, driven by adaptive absolute mass
(weights) and gravity shift. To our knowledge, liquid metal is used in a VR
haptic device for the first time. Our 29 participants show that VibroWeight
delivers significantly better VR experiences in realism and comfort.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 16:01:38 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Wang",
"Xian",
""
],
[
"Monteiro",
"Diego",
""
],
[
"Lee",
"Lik-Hang",
""
],
[
"Hui",
"Pan",
""
],
[
"Liang",
"Hai-Ning",
""
]
] |
new_dataset
| 0.999454 |
2201.07170
|
Julian D. Cortes
|
Julian D. Cortes
|
What is the mission of innovation?
| null | null | null | null |
cs.SI econ.GN q-fin.EC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Governments and organizations recognize the need to revisit a mission-driven
innovation amidst national and organizational innovation policy formulations.
Notwithstanding a fertile research agenda on mission statements (hereafter
mission(s)), several lines of inquiry remain open, such as crossnational and
multisectorial studies and an examination of research knowledge intensive
institutions. In this article, we identify similarities and differences in the
content of missions from government, private, higher education, and health
research knowledge intensive institutions in a sample of over 1,900
institutions from 89 countries through the deployment of sentiment analysis,
readability, and lexical diversity; semantic networks; and a similarity
computation between document corpus. We found that missions of research
knowledge intensive institutions are challenging to read texts with lower
lexical diversity that favors positive rather than negative words. In stark
contrast to this, the non-profit sector is consonant in multiple dimensions in
its use of Corporate Social Responsibility jargon. The lexical appearance of
research in the missions varies according to mission sectorial context, and
each sector has a cluster specific focus. Utilizing the mission as a strategic
planning tool in higher income regions might serve to explain corpora
similarities shared by sectors and continents.
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 18:27:31 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Cortes",
"Julian D.",
""
]
] |
new_dataset
| 0.980451 |
2201.07189
|
Mihee Lee
|
Mihee Lee, Samuel S. Sohn, Seonghyeon Moon, Sejong Yoon, Mubbasir
Kapadia, Vladimir Pavlovic
|
MUSE-VAE: Multi-Scale VAE for Environment-Aware Long Term Trajectory
Prediction
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Accurate long-term trajectory prediction in complex scenes, where multiple
agents (e.g., pedestrians or vehicles) interact with each other and the
environment while attempting to accomplish diverse and often unknown goals, is
a challenging stochastic forecasting problem. In this work, we propose MUSE, a
new probabilistic modeling framework based on a cascade of Conditional VAEs,
which tackles the long-term, uncertain trajectory prediction task using a
coarse-to-fine multi-factor forecasting architecture. In its Macro stage, the
model learns a joint pixel-space representation of two key factors, the
underlying environment and the agent movements, to predict the long and
short-term motion goals. Conditioned on them, the Micro stage learns a
fine-grained spatio-temporal representation for the prediction of individual
agent trajectories. The VAE backbones across the two stages make it possible to
naturally account for the joint uncertainty at both levels of granularity. As a
result, MUSE offers diverse and simultaneously more accurate predictions
compared to the current state-of-the-art. We demonstrate these assertions
through a comprehensive set of experiments on nuScenes and SDD benchmarks as
well as PFSD, a new synthetic dataset, which challenges the forecasting ability
of models on complex agent-environment interaction scenarios.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 18:40:03 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Lee",
"Mihee",
""
],
[
"Sohn",
"Samuel S.",
""
],
[
"Moon",
"Seonghyeon",
""
],
[
"Yoon",
"Sejong",
""
],
[
"Kapadia",
"Mubbasir",
""
],
[
"Pavlovic",
"Vladimir",
""
]
] |
new_dataset
| 0.995943 |
2201.07201
|
Hao Li
|
Hao Li and Cor-Paul Bezemer
|
Studying Popular Open Source Machine Learning Libraries and Their
Cross-Ecosystem Bindings
|
12 pages, 10 figures, submitted to IEEE Transactions on Software
Engineering
| null | null | null |
cs.SE cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Open source machine learning (ML) libraries allow developers to integrate
advanced ML functionality into their own applications. However, popular ML
libraries, such as TensorFlow, are not available natively in all programming
languages and software package ecosystems. Hence, developers who wish to use an
ML library which is not available in their programming language or ecosystem of
choice, may need to resort to using a so-called binding library. Binding
libraries provide support across programming languages and package ecosystems
for a source library. For example, the Keras .NET binding provides support for
the Keras library in the NuGet (.NET) ecosystem even though the Keras library
was written in Python. In this paper, we conduct an in-depth study of 155
cross-ecosystem bindings and their development for 36 popular open source ML
libraries. Our study shows that for most popular ML libraries, only one package
ecosystem is officially supported (usually PyPI). Cross-ecosystem support,
which is available for 25% of the studied ML libraries, is usually provided
through community-maintained bindings, e.g., 73% of the bindings in the npm
ecosystem are community-maintained. Our study shows that the vast majority of
the studied bindings cover only a small portion of the source library releases,
and the delay for receiving support for a source library release is large.
|
[
{
"version": "v1",
"created": "Tue, 18 Jan 2022 18:53:21 GMT"
}
] | 2022-01-19T00:00:00 |
[
[
"Li",
"Hao",
""
],
[
"Bezemer",
"Cor-Paul",
""
]
] |
new_dataset
| 0.996489 |
2010.10841
|
Ran Long
|
Ran Long, Christian Rauch, Tianwei Zhang, Vladimir Ivan, Sethu
Vijayakumar
|
RigidFusion: Robot Localisation and Mapping in Environments with Large
Dynamic Rigid Objects
|
8 pages, 11 figures. IEEE Robotics and Automation Letters (2021)
|
IEEE Robotics and Automation Letters, vol. 6, no. 2, pp.
3703-3710, April 2021
|
10.1109/LRA.2021.3066375
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work presents a novel RGB-D SLAM approach to simultaneously segment,
track and reconstruct the static background and large dynamic rigid objects
that can occlude major portions of the camera view. Previous approaches treat
dynamic parts of a scene as outliers and are thus limited to a small amount of
changes in the scene, or rely on prior information for all objects in the scene
to enable robust camera tracking. Here, we propose to treat all dynamic parts
as one rigid body and simultaneously segment and track both static and dynamic
components. We, therefore, enable simultaneous localisation and reconstruction
of both the static background and rigid dynamic components in environments
where dynamic objects cause large occlusion. We evaluate our approach on
multiple challenging scenes with large dynamic occlusion. The evaluation
demonstrates that our approach achieves better motion segmentation,
localisation and mapping without requiring prior knowledge of the dynamic
object's shape and appearance.
|
[
{
"version": "v1",
"created": "Wed, 21 Oct 2020 09:04:43 GMT"
},
{
"version": "v2",
"created": "Thu, 4 Mar 2021 16:24:09 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Long",
"Ran",
""
],
[
"Rauch",
"Christian",
""
],
[
"Zhang",
"Tianwei",
""
],
[
"Ivan",
"Vladimir",
""
],
[
"Vijayakumar",
"Sethu",
""
]
] |
new_dataset
| 0.988801 |
2104.08541
|
Jiajun Deng
|
Jiajun Deng, Zhengyuan Yang, Tianlang Chen, Wengang Zhou, and Houqiang
Li
|
TransVG: End-to-End Visual Grounding with Transformers
|
This paper has been accepted by ICCV2021
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present a neat yet effective transformer-based framework
for visual grounding, namely TransVG, to address the task of grounding a
language query to the corresponding region onto an image. The state-of-the-art
methods, including two-stage or one-stage ones, rely on a complex module with
manually-designed mechanisms to perform the query reasoning and multi-modal
fusion. However, the involvement of certain mechanisms in fusion module design,
such as query decomposition and image scene graph, makes the models easily
overfit to datasets with specific scenarios, and limits the plenitudinous
interaction between the visual-linguistic context. To avoid this caveat, we
propose to establish the multi-modal correspondence by leveraging transformers,
and empirically show that the complex fusion modules e.g., modular attention
network, dynamic graph, and multi-modal tree) can be replaced by a simple stack
of transformer encoder layers with higher performance. Moreover, we
re-formulate the visual grounding as a direct coordinates regression problem
and avoid making predictions out of a set of candidates i.e., region proposals
or anchor boxes). Extensive experiments are conducted on five widely used
datasets, and a series of state-of-the-art records are set by our TransVG. We
build the benchmark of transformer-based visual grounding framework and make
the code available at \url{https://github.com/djiajunustc/TransVG}.
|
[
{
"version": "v1",
"created": "Sat, 17 Apr 2021 13:35:24 GMT"
},
{
"version": "v2",
"created": "Thu, 12 Aug 2021 08:00:27 GMT"
},
{
"version": "v3",
"created": "Sat, 9 Oct 2021 09:43:30 GMT"
},
{
"version": "v4",
"created": "Fri, 14 Jan 2022 14:46:13 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Deng",
"Jiajun",
""
],
[
"Yang",
"Zhengyuan",
""
],
[
"Chen",
"Tianlang",
""
],
[
"Zhou",
"Wengang",
""
],
[
"Li",
"Houqiang",
""
]
] |
new_dataset
| 0.967031 |
2104.12663
|
Henning Schulze
|
Henning Schulze and Dogucan Yaman and Alexander Waibel
|
CAGAN: Text-To-Image Generation with Combined Attention GANs
| null |
LNCS 13024 (2021) 392-404
|
10.1007/978-3-030-92659-5_25
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Generating images according to natural language descriptions is a challenging
task. Prior research has mainly focused to enhance the quality of generation by
investigating the use of spatial attention and/or textual attention thereby
neglecting the relationship between channels. In this work, we propose the
Combined Attention Generative Adversarial Network (CAGAN) to generate
photo-realistic images according to textual descriptions. The proposed CAGAN
utilises two attention models: word attention to draw different sub-regions
conditioned on related words; and squeeze-and-excitation attention to capture
non-linear interaction among channels. With spectral normalisation to stabilise
training, our proposed CAGAN improves the state of the art on the IS and FID on
the CUB dataset and the FID on the more challenging COCO dataset. Furthermore,
we demonstrate that judging a model by a single evaluation metric can be
misleading by developing an additional model adding local self-attention which
scores a higher IS, outperforming the state of the art on the CUB dataset, but
generates unrealistic images through feature repetition.
|
[
{
"version": "v1",
"created": "Mon, 26 Apr 2021 15:46:40 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Jun 2021 18:57:03 GMT"
},
{
"version": "v3",
"created": "Wed, 8 Sep 2021 15:48:15 GMT"
},
{
"version": "v4",
"created": "Fri, 14 Jan 2022 16:16:53 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Schulze",
"Henning",
""
],
[
"Yaman",
"Dogucan",
""
],
[
"Waibel",
"Alexander",
""
]
] |
new_dataset
| 0.989599 |
2112.13984
|
Xiaoqing Yang
|
Xiaoqing Yang, Fei Li
|
Relative velocity-based reward functions for crowd navigation of robots
| null | null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The four-wheeled Mecanum robot is widely used in various industries due to
its maneuverability and strong load capacity, which is suitable for performing
precise transportation tasks in a narrow environment, but while the Mecanum
wheel robot has mobility, it also consumes more energy than ordinary robots.
The power consumed by the Mecanum wheel mobile robot varies enormously
depending on their operating regimes and environments. Therefore, only knowing
the working environment of the robot and the accurate power consumption model
can we accurately predict the power consumption of the robot. In order to
increase the appli-cable scenarios of energy consumption modeling for Mecanum
wheel robots and improve the accuracy of energy consumption modeling, this
paper focuses on various factors that affect the energy consumption of the
Mecanum wheel robot, such as motor temperature, terrain, the center of gravity
position, etc. The model is derived from the kinematic and kinetic model
combined with electrical engineering and energy flow principles. The model has
been simulated in MATLAB and experimentally validated with the four-wheeled
Mecanum robot platform in our lab. Experimental results show that the model is
90% accurate. The results of energy consumption modeling can help robots to
save energy by helping them to perform rational path planning and task
planning.
|
[
{
"version": "v1",
"created": "Tue, 28 Dec 2021 03:49:01 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Jan 2022 08:07:04 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Yang",
"Xiaoqing",
""
],
[
"Li",
"Fei",
""
]
] |
new_dataset
| 0.962018 |
2201.05179
|
Chenning Li
|
Chenning Li, Xiuzhen Guo, Longfei Shangguan, Zhichao Cao, Kyle
Jamieson
|
CurvingLoRa to Boost LoRa Network Capacity via Concurrent Transmission
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
LoRaWAN has emerged as an appealing technology to connect IoT devices but it
functions without explicit coordination among transmitters, which can lead to
many packet collisions as the network scales. State-of-the-art work proposes
various approaches to deal with these collisions, but most functions only in
high signal-to-interference ratio (SIR) conditions and thus does not scale to
many scenarios where weak receptions are easily buried by stronger receptions
from nearby transmitters. In this paper, we take a fresh look at LoRa's
physical layer, revealing that its underlying linear chirp modulation
fundamentally limits the capacity and scalability of concurrentLoRa
transmissions. We show that by replacing linear chirps with their non-linear
counterparts, we can boost the capacity of concurrent LoRa transmissions and
empower the LoRa receiver to successfully receive weak transmissions in the
presence of strong colliding signals. Such a non-linear chirp design further
enables the receiver to demodulate fully aligned collision symbols - a case
where none of the existing approaches can deal with. We implement these ideas
in a holistic LoRaWAN stack based on the USRP N210 software-defined radio
platform. Our head-to-head comparison with two state-of-the-art research
systems and a standard LoRaWAN baseline demonstrates that CurvingLoRa improves
the network throughput by 1.6-7.6x while simultaneously sacrificing neither
power efficiency nor noise resilience. An open-source dataset and code will be
made available before publication.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 19:10:52 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Li",
"Chenning",
""
],
[
"Guo",
"Xiuzhen",
""
],
[
"Shangguan",
"Longfei",
""
],
[
"Cao",
"Zhichao",
""
],
[
"Jamieson",
"Kyle",
""
]
] |
new_dataset
| 0.999218 |
2201.05203
|
Bilal Abu-Salih
|
Bilal Abu-Salih, Dana Al Qudah, Malak Al-Hassan, Seyed Mohssen
Ghafari, Tomayess Issa, Ibrahim Aljarah, Amin Beheshti, Sulaiman Alqahtan
|
An Intelligent System for Multi-topic Social Spam Detection in
Microblogging
| null | null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
The communication revolution has perpetually reshaped the means through which
people send and receive information. Social media is an important pillar of
this revolution and has brought profound changes to various aspects of our
lives. However, the open environment and popularity of these platforms
inaugurate windows of opportunities for various cyber threats, thus social
networks have become a fertile venue for spammers and other illegitimate users
to execute their malicious activities. These activities include phishing hot
and trendy topics and posting a wide range of contents in many topics. Hence,
it is crucial to continuously introduce new techniques and approaches to detect
and stop this category of users. This paper proposes a novel and effective
approach to detect social spammers. An investigation into several attributes to
measure topic-dependent and topic-independent users' behaviours on Twitter is
carried out. The experiments of this study are undertaken on various machine
learning classifiers. The performance of these classifiers are compared and
their effectiveness is measured via a number of robust evaluation measures.
Further, the proposed approach is benchmarked against state-of-the-art social
spam and anomalous detection techniques. These experiments report the
effectiveness and utility of the proposed approach and embedded modules.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 20:39:36 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Abu-Salih",
"Bilal",
""
],
[
"Qudah",
"Dana Al",
""
],
[
"Al-Hassan",
"Malak",
""
],
[
"Ghafari",
"Seyed Mohssen",
""
],
[
"Issa",
"Tomayess",
""
],
[
"Aljarah",
"Ibrahim",
""
],
[
"Beheshti",
"Amin",
""
],
[
"Alqahtan",
"Sulaiman",
""
]
] |
new_dataset
| 0.991639 |
2201.05230
|
Daniel Bauer
|
Daniel Bauer (1), Tom Longley (2), Yueen Ma (1), Tony Wilson (2) ((1)
Department of Computer Science, Columbia University, (2) Security Force
Monitor, Human Rights Institute, Columbia Law School)
|
NLP in Human Rights Research -- Extracting Knowledge Graphs About Police
and Army Units and Their Commanders
|
Equal contributions. for associated text corpus see
https://github.com/security-force-monitor/nlp_starter_dataset
| null | null | null |
cs.CL cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
In this working paper we explore the use of an NLP system to assist the work
of Security Force Monitor (SFM). SFM creates data about the organizational
structure, command personnel and operations of police, army and other security
forces, which assists human rights researchers, journalists and litigators in
their work to help identify and bring to account specific units and personnel
alleged to have committed abuses of human rights and international criminal
law. This working paper presents an NLP system that extracts from English
language news reports the names of security force units and the biographical
details of their personnel, and infers the formal relationship between them.
Published alongside this working paper are the system's code and training
dataset. We find that the experimental NLP system performs the task at a fair
to good level. Its performance is sufficient to justify further development
into a live workflow that will give insight into whether its performance
translates into savings in time and resource that would make it an effective
technical intervention.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 21:57:21 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Bauer",
"Daniel",
""
],
[
"Longley",
"Tom",
""
],
[
"Ma",
"Yueen",
""
],
[
"Wilson",
"Tony",
""
]
] |
new_dataset
| 0.99928 |
2201.05231
|
Silviu Maniu
|
Alexandra Iacob, Bogdan Cautis, Silviu Maniu
|
Contextual Bandits for Advertising Campaigns: A Diffusion-Model
Independent Approach (Extended Version)
|
Extended version of conference article in SIAM International
Conference on Data Mining (SDM) 2022. 14 pages, 2 figures, 4 tables
| null | null | null |
cs.LG cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Motivated by scenarios of information diffusion and advertising in social
media, we study an influence maximization problem in which little is assumed to
be known about the diffusion network or about the model that determines how
information may propagate. In such a highly uncertain environment, one can
focus on multi-round diffusion campaigns, with the objective to maximize the
number of distinct users that are influenced or activated, starting from a
known base of few influential nodes. During a campaign, spread seeds are
selected sequentially at consecutive rounds, and feedback is collected in the
form of the activated nodes at each round. A round's impact (reward) is then
quantified as the number of newly activated nodes. Overall, one must maximize
the campaign's total spread, as the sum of rounds' rewards. In this setting, an
explore-exploit approach could be used to learn the key underlying diffusion
parameters, while running the campaign. We describe and compare two methods of
contextual multi-armed bandits, with upper-confidence bounds on the remaining
potential of influencers, one using a generalized linear model and the
Good-Turing estimator for remaining potential (GLM-GT-UCB), and another one
that directly adapts the LinUCB algorithm to our setting (LogNorm-LinUCB). We
show that they outperform baseline methods using state-of-the-art ideas, on
synthetic and real-world data, while at the same time exhibiting different and
complementary behavior, depending on the scenarios in which they are deployed.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 22:06:10 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Iacob",
"Alexandra",
""
],
[
"Cautis",
"Bogdan",
""
],
[
"Maniu",
"Silviu",
""
]
] |
new_dataset
| 0.99664 |
2201.05240
|
Md Atiqul Islam
|
Md Atiqul Islam, George C. Alexandropoulos, and Besma Smida
|
Integrated Sensing and Communication with Millimeter Wave Full Duplex
Hybrid Beamforming
|
6 pages, 4 figures, Submitted for publication in the Proceedings of
IEEE ICC 2022, Seoul, South Korea
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Integrated Sensing and Communication (ISAC) has attracted substantial
attraction in recent years for spectral efficiency improvement, enabling
hardware and spectrum sharing for simultaneous sensing and signaling
operations. In-band Full Duplex (FD) is being considered as a key enabling
technology for ISAC applications due to its simultaneous transmission and
reception capability. In this paper, we present an FD-based ISAC system
operating at millimeter Wave (mmWave) frequencies, where a massive
Multiple-Input Multiple-Output (MIMO) Base Station (BS) node employing hybrid
Analog and Digital (A/D) beamforming is communicating with a DownLink (DL)
multi-antenna user and the same waveform is utilized at the BS receiver for
sensing the radar targets in its coverage environment. We develop a sensing
algorithm that is capable of estimating Direction of Arrival (DoA), range, and
relative velocity of the radar targets. A joint optimization framework for
designing the A/D transmit and receive beamformers as well as the
Self-Interference (SI) cancellation is presented with the objective to maximize
the achievable DL rate and the accuracy of the radar target sensing
performance. Our simulation results, considering fifth Generation (5G)
Orthogonal Frequency Division Multiplexing (OFDM) waveforms, verify our
approach's high precision in estimating DoA, range, and velocity of multiple
radar targets, while maximizing the DL communication rate.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 23:02:02 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Islam",
"Md Atiqul",
""
],
[
"Alexandropoulos",
"George C.",
""
],
[
"Smida",
"Besma",
""
]
] |
new_dataset
| 0.996038 |
2201.05278
|
Hermes Senger
|
Jaime Freire de Souza, Jo\~ao Baptista Dias Moreira, Keith Jared
Roberts, Roussian di Ramos Alves Gaioso, Edson Satoshi Gomi, Em\'ilio Carlos
Nelli Silva and Hermes Senger
|
${\tt simwave}$ -- A Finite Difference Simulator for Acoustic Waves
Propagation
| null | null | null | null |
cs.CE
|
http://creativecommons.org/licenses/by/4.0/
|
${\tt simwave}$ is an open-source Python package to perform wave simulations
in 2D or 3D domains. It solves the constant and variable density acoustic wave
equation with the finite difference method and has support for domain
truncation techniques, several boundary conditions, and the modeling of sources
and receivers given a user-defined acquisition geometry. The architecture of
${\tt simwave}$ is designed for applications with geophysical exploration in
mind. Its Python front-end enables straightforward integration with many
existing Python scientific libraries for the composition of more complex
workflows and applications (e.g., migration and inversion problems). The
back-end is implemented in C enabling performance portability across a range of
computing hardware and compilers including both CPUs and GPUs.
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 02:21:49 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"de Souza",
"Jaime Freire",
""
],
[
"Moreira",
"João Baptista Dias",
""
],
[
"Roberts",
"Keith Jared",
""
],
[
"Gaioso",
"Roussian di Ramos Alves",
""
],
[
"Gomi",
"Edson Satoshi",
""
],
[
"Silva",
"Emílio Carlos Nelli",
""
],
[
"Senger",
"Hermes",
""
]
] |
new_dataset
| 0.996611 |
2201.05356
|
Luca Grementieri
|
Luca Grementieri, Francesco Finelli
|
StAnD: A Dataset of Linear Static Analysis Problems
|
9 pages, 1 figure
| null | null | null |
cs.LG cs.MS cs.NA math.NA
|
http://creativecommons.org/licenses/by/4.0/
|
Static analysis of structures is a fundamental step for determining the
stability of structures. Both linear and non-linear static analyses consist of
the resolution of sparse linear systems obtained by the finite element method.
The development of fast and optimized solvers for sparse linear systems
appearing in structural engineering requires data to compare existing
approaches, tune algorithms or to evaluate new ideas. We introduce the Static
Analysis Dataset (StAnD) containing 303.000 static analysis problems obtained
applying realistic loads to simulated frame structures. Along with the dataset,
we publish a detailed benchmark comparison of the running time of existing
solvers both on CPU and GPU. We release the code used to generate the dataset
and benchmark existing solvers on Github. To the best of our knowledge, this is
the largest dataset for static analysis problems and it is the first public
dataset of sparse linear systems (containing both the matrix and a realistic
constant term).
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 09:31:43 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Grementieri",
"Luca",
""
],
[
"Finelli",
"Francesco",
""
]
] |
new_dataset
| 0.999712 |
2201.05488
|
Jan Ostergaard
|
Jan {\O}stergaard
|
Stabilizing Error Correction Codes for Controlling LTI Systems over
Erasure Channels
|
Accepted and presented at the IEEE 60th Conference on Decision and
Control (CDC). arXiv admin note: substantial text overlap with
arXiv:2112.11717
| null | null | null |
cs.IT math.IT math.OC
|
http://creativecommons.org/licenses/by/4.0/
|
We propose (k,k') stabilizing codes, which is a type of delayless error
correction codes that are useful for control over networks with erasures. For
each input symbol, k output symbols are generated by the stabilizing code.
Receiving any k' of these outputs guarantees stability. Thus, the system to be
stabilized is taken into account in the design of the erasure codes. Our focus
is on LTI systems, and we construct codes based on independent encodings and
multiple descriptions. The theoretical efficiency and performance of the codes
are assessed, and their practical performances are demonstrated in a simulation
study. There is a significant gain over other delayless codes such as
repetition codes.
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 14:54:43 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Østergaard",
"Jan",
""
]
] |
new_dataset
| 0.987991 |
2201.05503
|
Leonardo Santos
|
Aurelienne A. S. Jorge, Iuri da Silva Diniz, Vander L. S. Freitas,
Izabelly C. Costa, Leonardo B. L. Santos
|
Global-threshold and backbone high-resolution weather radar networks are
significantly complementary in a watershed
|
7 pages, 6 figures To be submitted to Computers and Geosciences
(Elsevier)
| null | null | null |
cs.SI physics.soc-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
There are several criteria for building up networks from time series related
to different points in geographical space. The most used criterion is the
Global-Threshold (GT). Using a weather radar dataset, this paper shows that the
Backbone (BB) - a local-threshold criterion - generates networks whose
geographical configuration is complementary to the GT networks. We compare the
results for two well-known similarities measures: the Pearson Correlation (PC)
coefficient and the Mutual Information (MI). The extracted backbone network
(miBB), whose number of links is the same as the global MI (miGT), has the
lowest average shortest path and presents a small-world effect. Regarding the
global PC (pcGT) and its corresponding BB network (pcBB), there is a
significant linear relationship: $R2=0.77$ with a slope of $1.15$ (p-value
$<E-7$) for the pcGT network, and $R2=0.68$ with a slope of $0.76$ (p-value
$<E-7$) for the pcBB network. In relation to the MI ones, only the miGT present
a high $R2$ ($0.79$, with slope = $1.95$), whereas the miBB has an $R2$ of only
$0.20$ ($\text{slope} =0.24$). On the one hand, the GT networks present a
sizeable connected component in the central area, close to the main rivers. On
the other hand, the BB networks present a few meaningful connected components
surrounding the watershed and dominating cells close to the outlet, with
significant statistical differences in the altimetry distribution.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 17:04:59 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Jorge",
"Aurelienne A. S.",
""
],
[
"Diniz",
"Iuri da Silva",
""
],
[
"Freitas",
"Vander L. S.",
""
],
[
"Costa",
"Izabelly C.",
""
],
[
"Santos",
"Leonardo B. L.",
""
]
] |
new_dataset
| 0.999279 |
2201.05518
|
Wei Pu
|
David Guttendorf, D.W. Wilson Hamilton, Anne Harris Heckman, Herman
Herman, Felix Jonathan, Prasanna Kannappan, Nicholas Mireles, Luis
Navarro-Serment, Jean Oh, Wei Pu, Rohan Saxena, Jeff Schneider, Matt Schnur,
Carter Tiernan, Trenton Tabor
|
UGV-UAV Object Geolocation in Unstructured Environments
|
Authors are with National Robotics Engineering Center, the Robotics
Institute of Carnegie Mellon University, Pittsburgh PA, listed in
alphabetical order. E-mail: wpu@nrec.ri.cmu.edu
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A robotic system of multiple unmanned ground vehicles (UGVs) and unmanned
aerial vehicles (UAVs) has the potential for advancing autonomous object
geolocation performance. Much research has focused on algorithmic improvements
on individual components, such as navigation, motion planning, and perception.
In this paper, we present a UGV-UAV object detection and geolocation system,
which performs perception, navigation, and planning autonomously in real scale
in unstructured environment. We designed novel sensor pods equipped with
multispectral (visible, near-infrared, thermal), high resolution (181.6 Mega
Pixels), stereo (near-infrared pair), wide field of view (192 degree HFOV)
array. We developed a novel on-board software-hardware architecture to process
the high volume sensor data in real-time, and we built a custom AI subsystem
composed of detection, tracking, navigation, and planning for autonomous
objects geolocation in real-time.
This research is the first real scale demonstration of such high speed data
processing capability. Our novel modular sensor pod can boost relevant computer
vision and machine learning research. Our novel hardware-software architecture
is a solid foundation for system-level and component-level research. Our system
is validated through data-driven offline tests as well as a series of field
tests in unstructured environments. We present quantitative results as well as
discussions on key robotic system level challenges which manifest when we build
and test the system. This system is the first step toward a UGV-UAV cooperative
reconnaissance system in the future.
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 15:41:05 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Guttendorf",
"David",
""
],
[
"Hamilton",
"D. W. Wilson",
""
],
[
"Heckman",
"Anne Harris",
""
],
[
"Herman",
"Herman",
""
],
[
"Jonathan",
"Felix",
""
],
[
"Kannappan",
"Prasanna",
""
],
[
"Mireles",
"Nicholas",
""
],
[
"Navarro-Serment",
"Luis",
""
],
[
"Oh",
"Jean",
""
],
[
"Pu",
"Wei",
""
],
[
"Saxena",
"Rohan",
""
],
[
"Schneider",
"Jeff",
""
],
[
"Schnur",
"Matt",
""
],
[
"Tiernan",
"Carter",
""
],
[
"Tabor",
"Trenton",
""
]
] |
new_dataset
| 0.998989 |
2201.05541
|
Qinkang Gong
|
Qinkang Gong, Liangdao Wang, Hanjiang Lai, Yan Pan, Jian Yin
|
ViT2Hash: Unsupervised Information-Preserving Hashing
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unsupervised image hashing, which maps images into binary codes without
supervision, is a compressor with a high compression rate. Hence, how to
preserving meaningful information of the original data is a critical problem.
Inspired by the large-scale vision pre-training model, known as ViT, which has
shown significant progress for learning visual representations, in this paper,
we propose a simple information-preserving compressor to finetune the ViT model
for the target unsupervised hashing task. Specifically, from pixels to
continuous features, we first propose a feature-preserving module, using the
corrupted image as input to reconstruct the original feature from the
pre-trained ViT model and the complete image, so that the feature extractor can
focus on preserving the meaningful information of original data. Secondly, from
continuous features to hash codes, we propose a hashing-preserving module,
which aims to keep the semantic information from the pre-trained ViT model by
using the proposed Kullback-Leibler divergence loss. Besides, the quantization
loss and the similarity loss are added to minimize the quantization error. Our
method is very simple and achieves a significantly higher degree of MAP on
three benchmark image datasets.
|
[
{
"version": "v1",
"created": "Fri, 14 Jan 2022 16:25:30 GMT"
}
] | 2022-01-17T00:00:00 |
[
[
"Gong",
"Qinkang",
""
],
[
"Wang",
"Liangdao",
""
],
[
"Lai",
"Hanjiang",
""
],
[
"Pan",
"Yan",
""
],
[
"Yin",
"Jian",
""
]
] |
new_dataset
| 0.998769 |
1901.00889
|
Xing Di
|
Xing Di, He Zhang, Vishal M. Patel
|
Polarimetric Thermal to Visible Face Verification via Attribute
Preserved Synthesis
|
This work has been accepted by the 9th IEEE International Conference
on Biometrics: Theory, Applications, and Systems (BTAS 2018)
| null |
10.1109/BTAS.2018.8698554
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Thermal to visible face verification is a challenging problem due to the
large domain discrepancy between the modalities. Existing approaches either
attempt to synthesize visible faces from thermal faces or extract robust
features from these modalities for cross-modal matching. In this paper, we take
a different approach in which we make use of the attributes extracted from the
visible image to synthesize the attribute-preserved visible image from the
input thermal image for cross-modal matching. A pre-trained VGG-Face network is
used to extract the attributes from the visible image. Then, a novel Attribute
Preserved Generative Adversarial Network (AP-GAN) is proposed to synthesize the
visible image from the thermal image guided by the extracted attributes.
Finally, a deep network is used to extract features from the synthesized image
and the input visible image for verification. Extensive experiments on the ARL
Polarimetric face dataset show that the proposed method achieves significant
improvements over the state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Thu, 3 Jan 2019 19:38:33 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Di",
"Xing",
""
],
[
"Zhang",
"He",
""
],
[
"Patel",
"Vishal M.",
""
]
] |
new_dataset
| 0.999143 |
2005.11425
|
Shenshen Chen
|
Shenshen Chen (1), Geng Li (1), Dennis Duan (1), Kerim Gokarslan (1),
Bin Li (1), Qiao Xiang (1), Haitao Yu (2), Franck Le (3), Richard Yang (1),
Ying Zhang (4) ((1) Yale University, (2) College of Electronics and
Information Engineering, Tongji University, (3) Thomas J. Watson Research
Center, (4) Facebook)
|
Carbide: Highly Reliable Networks Through Real-Time Multiple Control
Plane Composition
|
12 pages + References + Appendices, 14 figures
| null | null |
YALEU/DCS/TR-1552
|
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Achieving highly reliable networks is essential for network operators to
ensure proper packet delivery in the event of software errors or hardware
failures. Networks must ensure reachability and routing correctness, such as
subnet isolation and waypoint traversal. Existing work in network verification
relies on centralized computation at the cost of fault tolerance, while other
approaches either build an over-engineered, complex control plane, or compose
multiple control planes without providing any guarantee on correctness. This
paper presents Carbide, a novel system to achieve high reliability in networks
through distributed verification and multiple control plane composition. The
core of Carbide is a simple, generic, efficient distributed verification
framework that transforms a generic network verification problem to a
reachability verification problem on a directed acyclic graph (DAG), and solves
the latter via an efficient distributed verification protocol (DV-protocol).
Equipped with verification results, Carbide allows the systematic composition
of multiple control planes and realization of operator-specified consistency.
Carbide is fully implemented. Extensive experiments show that (1) Carbide
reduces downtime by 43% over the most reliable individual underlying control
plane, while enforcing correctness requirements on all traffic; and (2) by
systematically decomposing computation to devices and pruning unnecessary
messaging between devices during verification, Carbide scales to a production
data center network.
|
[
{
"version": "v1",
"created": "Fri, 22 May 2020 23:42:21 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jan 2022 11:56:21 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Chen",
"Shenshen",
""
],
[
"Li",
"Geng",
""
],
[
"Duan",
"Dennis",
""
],
[
"Gokarslan",
"Kerim",
""
],
[
"Li",
"Bin",
""
],
[
"Xiang",
"Qiao",
""
],
[
"Yu",
"Haitao",
""
],
[
"Le",
"Franck",
""
],
[
"Yang",
"Richard",
""
],
[
"Zhang",
"Ying",
""
]
] |
new_dataset
| 0.99959 |
2104.14547
|
Anjana Deva Prasad
|
Anjana Deva Prasad, Aditya Balu, Harshil Shah, Soumik Sarkar, Chinmay
Hegde, Adarsh Krishnamurthy
|
NURBS-Diff: A Differentiable Programming Module for NURBS
| null | null | null | null |
cs.LG cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Boundary representations (B-reps) using Non-Uniform Rational B-splines
(NURBS) are the de facto standard used in CAD, but their utility in deep
learning-based approaches is not well researched. We propose a differentiable
NURBS module to integrate NURBS representations of CAD models with deep
learning methods. We mathematically define the derivatives of the NURBS curves
or surfaces with respect to the input parameters (control points, weights, and
the knot vector). These derivatives are used to define an approximate Jacobian
used for performing the "backward" evaluation to train the deep learning
models. We have implemented our NURBS module using GPU-accelerated algorithms
and integrated it with PyTorch, a popular deep learning framework. We
demonstrate the efficacy of our NURBS module in performing CAD operations such
as curve or surface fitting and surface offsetting. Further, we show its
utility in deep learning for unsupervised point cloud reconstruction and
enforce analysis constraints. These examples show that our module performs
better for certain deep learning frameworks and can be directly integrated with
any deep-learning framework requiring NURBS.
|
[
{
"version": "v1",
"created": "Thu, 29 Apr 2021 17:56:01 GMT"
},
{
"version": "v2",
"created": "Tue, 14 Sep 2021 17:30:26 GMT"
},
{
"version": "v3",
"created": "Thu, 16 Sep 2021 06:21:40 GMT"
},
{
"version": "v4",
"created": "Thu, 13 Jan 2022 15:15:01 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Prasad",
"Anjana Deva",
""
],
[
"Balu",
"Aditya",
""
],
[
"Shah",
"Harshil",
""
],
[
"Sarkar",
"Soumik",
""
],
[
"Hegde",
"Chinmay",
""
],
[
"Krishnamurthy",
"Adarsh",
""
]
] |
new_dataset
| 0.97443 |
2106.13217
|
Jing Zhang
|
Mochu Xiang, Jing Zhang, Yunqiu Lv, Aixuan Li, Yiran Zhong, Yuchao Dai
|
Exploring Depth Contribution for Camouflaged Object Detection
|
The first work in RGB-D Camouflaged object detection (COD)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Camouflaged object detection (COD) aims to segment camouflaged objects hiding
in the environment, which is challenging due to the similar appearance of
camouflaged objects and their surroundings. Research in biology suggests depth
can provide useful object localization cues for camouflaged object discovery.
In this paper, we study the depth contribution for camouflaged object
detection, where the depth maps are generated with existing monocular depth
estimation (MDE) methods. Due to the domain gap between the MDE dataset and our
COD dataset, the generated depth maps are not accurate enough to be directly
used. We then introduce two solutions to avoid the noisy depth maps from
dominating the training process. Firstly, we present an auxiliary depth
estimation branch ("ADE"), aiming to regress the depth maps. We find that "ADE"
is especially necessary for our "generated depth" scenario. Secondly, we
introduce a multi-modal confidence-aware loss function via a generative
adversarial network to weigh the contribution of depth for camouflaged object
detection. Our extensive experiments on various camouflaged object detection
datasets explain that the existing "sensor depth" based RGB-D segmentation
techniques work poorly with "generated depth", and our proposed two solutions
work cooperatively, achieving effective depth contribution exploration for
camouflaged object detection.
|
[
{
"version": "v1",
"created": "Thu, 24 Jun 2021 17:51:31 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Jun 2021 03:38:01 GMT"
},
{
"version": "v3",
"created": "Thu, 13 Jan 2022 06:05:31 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Xiang",
"Mochu",
""
],
[
"Zhang",
"Jing",
""
],
[
"Lv",
"Yunqiu",
""
],
[
"Li",
"Aixuan",
""
],
[
"Zhong",
"Yiran",
""
],
[
"Dai",
"Yuchao",
""
]
] |
new_dataset
| 0.997348 |
2107.07402
|
Anirudh Gupta
|
Anirudh Gupta, Harveen Singh Chadha, Priyanshi Shah, Neeraj Chhimwal,
Ankur Dhuriya, Rishabh Gaur, Vivek Raghavan
|
CLSRIL-23: Cross Lingual Speech Representations for Indic Languages
|
7 pages, 2 figures
| null | null | null |
cs.CL cs.LG cs.SD eess.AS
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We present a CLSRIL-23, a self supervised learning based audio pre-trained
model which learns cross lingual speech representations from raw audio across
23 Indic languages. It is built on top of wav2vec 2.0 which is solved by
training a contrastive task over masked latent speech representations and
jointly learns the quantization of latents shared across all languages. We
compare the language wise loss during pretraining to compare effects of
monolingual and multilingual pretraining. Performance on some downstream
fine-tuning tasks for speech recognition is also compared and our experiments
show that multilingual pretraining outperforms monolingual training, in terms
of learning speech representations which encodes phonetic similarity of
languages and also in terms of performance on down stream tasks. A decrease of
5% is observed in WER and 9.5% in CER when a multilingual pretrained model is
used for finetuning in Hindi. All the code models are also open sourced.
CLSRIL-23 is a model trained on $23$ languages and almost 10,000 hours of audio
data to facilitate research in speech recognition for Indic languages. We hope
that new state of the art systems will be created using the self supervised
approach, especially for low resources Indic languages.
|
[
{
"version": "v1",
"created": "Thu, 15 Jul 2021 15:42:43 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jan 2022 06:58:05 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Gupta",
"Anirudh",
""
],
[
"Chadha",
"Harveen Singh",
""
],
[
"Shah",
"Priyanshi",
""
],
[
"Chhimwal",
"Neeraj",
""
],
[
"Dhuriya",
"Ankur",
""
],
[
"Gaur",
"Rishabh",
""
],
[
"Raghavan",
"Vivek",
""
]
] |
new_dataset
| 0.999167 |
2110.05344
|
Hazel Murray
|
Hazel Murray and David Malone
|
Quantum multi-factor authentication
| null | null |
10.1007/978-3-030-93747-8_4
| null |
cs.CR quant-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present a quantum multi-factor authentication mechanism based on the
hidden-matching quantum communication complexity problem. It offers step-up
graded authentication for users via a quantum token. In this paper, we outline
the protocol, demonstrate that it can be used in a largely classical setting,
explain how it can be implemented in SASL, and discuss arising security
features. We also offer a comparison between our mechanism and current
state-of-the-art multi-factor authentication mechanisms.
|
[
{
"version": "v1",
"created": "Mon, 11 Oct 2021 15:12:39 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Murray",
"Hazel",
""
],
[
"Malone",
"David",
""
]
] |
new_dataset
| 0.992526 |
2110.05650
|
Yusheng Wang
|
Yusheng Wang, Yidong Lou, Weiwei Song, Huan Yu and Zhiyong Tu
|
GM-Livox: An Integrated Framework for Large-Scale Map Construction with
Multiple Non-repetitive Scanning LiDARs
| null | null |
10.1109/JSEN.2022.3142041
| null |
cs.RO
|
http://creativecommons.org/publicdomain/zero/1.0/
|
With the ability of providing direct and accurate enough range measurements,
light detection and ranging (LiDAR) is playing an essential role in
localization and detection for autonomous vehicles. Since single LiDAR suffers
from hardware failure and performance degradation intermittently, we present a
multi-LiDAR integration scheme in this article. Our framework tightly couples
multiple non-repetitive scanning LiDARs with inertial, encoder, and global
navigation satellite system (GNSS) into pose estimation and simultaneous global
map generation. Primarily, we formulate a precise synchronization strategy to
integrate isolated sensors, and the extracted feature points from separate
LiDARs are merged into a single sweep. The fused scans are introduced to
compute the scan-matching correspondences, which can be further refined by
additional real-time kinematic (RTK) measurements. Based thereupon, we
construct a factor graph along with the inertial preintegration result,
estimated ground constraints, and RTK data. For the purpose of maintaining a
restricted number of poses for estimation, we deploy a keyframe based
sliding-window optimization strategy in our system. The real-time performance
is guaranteed with multi-threaded computation, and extensive experiments are
conducted in challenging scenarios. Experimental results show that the
utilization of multiple LiDARs boosts the system performance in both robustness
and accuracy.
|
[
{
"version": "v1",
"created": "Mon, 11 Oct 2021 23:45:53 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Wang",
"Yusheng",
""
],
[
"Lou",
"Yidong",
""
],
[
"Song",
"Weiwei",
""
],
[
"Yu",
"Huan",
""
],
[
"Tu",
"Zhiyong",
""
]
] |
new_dataset
| 0.993004 |
2112.02448
|
Alex Shonenkov
|
Alex Shonenkov, Daria Bakshandaeva, Denis Dimitrov, Aleksandr Nikolich
|
Emojich -- zero-shot emoji generation using Russian language: a
technical report
|
5 pages, 4 figures and big figure at appendix, technical report
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This technical report presents a text-to-image neural network "Emojich" that
generates emojis using captions in Russian language as a condition. We aim to
keep the generalization ability of a pretrained big model ruDALL-E Malevich
(XL) 1.3B parameters at the fine-tuning stage, while giving special style to
the images generated. Here are presented some engineering methods, code
realization, all hyper-parameters for reproducing results and a Telegram bot
where everyone can create their own customized sets of stickers. Also, some
newly generated emojis obtained by "Emojich" model are demonstrated.
|
[
{
"version": "v1",
"created": "Sat, 4 Dec 2021 23:37:32 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jan 2022 20:15:14 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Shonenkov",
"Alex",
""
],
[
"Bakshandaeva",
"Daria",
""
],
[
"Dimitrov",
"Denis",
""
],
[
"Nikolich",
"Aleksandr",
""
]
] |
new_dataset
| 0.990135 |
2201.02121
|
Ivor van der Hoog
|
Anne Driemel, Ivor van der Hoog, Eva Rotenberg
|
On the Discrete Fr\'echet Distance in a Graph
| null | null | null | null |
cs.CG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Fr\'{e}chet distance is a well-studied similarity measure between curves
that is widely used throughout computer science. Motivated by applications
where curves stem from paths and walks on an underlying graph (such as a road
network), we define and study the Fr\'{e}chet distance for paths and walks on
graphs. When provided with a distance oracle of $G$ with $O(1)$ query time, the
classical quadratic-time dynamic program can compute the Fr\'{e}chet distance
between two walks $P$ and $Q$ in a graph $G$ in $O(|P| \cdot |Q|)$ time. We
show that there are situations where the graph structure helps with computing
Fr\'{e}chet distance: when the graph $G$ is planar, we apply existing
(approximate) distance oracles to compute a $(1+\varepsilon)$-approximation of
the Fr\'{e}chet distance between any shortest path $P$ and any walk $Q$ in
$O(|G| \log |G| / \sqrt{\varepsilon} + |P| + \frac{|Q|}{\varepsilon } )$ time.
We generalise this result to near-shortest paths, i.e. $\kappa$-straight paths,
as we show how to compute a $(1+\varepsilon)$-approximation between a
$\kappa$-straight path $P$ and any walk $Q$ in $O(|G| \log |G| /
\sqrt{\varepsilon} + |P| + \frac{\kappa|Q|}{\varepsilon } )$ time. Our
algorithmic results hold for both the strong and the weak discrete Fr\'{e}chet
distance over the shortest path metric in $G$. Finally, we show that additional
assumptions on the input, such as our assumption on path straightness, are
indeed necessary to obtain truly subquadratic running time. We provide a
conditional lower bound showing that the Fr\'{e}chet distance, or even its
$1.01$-approximation, between arbitrary \emph{paths} in a weighted planar graph
cannot be computed in $O((|P|\cdot|Q|)^{1-\delta})$ time for any $\delta > 0$
unless the Orthogonal Vector Hypothesis fails. For walks, this lower bound
holds even when $G$ is planar, unit-weight and has $O(1)$ vertices.
|
[
{
"version": "v1",
"created": "Thu, 6 Jan 2022 16:04:51 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jan 2022 12:29:01 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Driemel",
"Anne",
""
],
[
"van der Hoog",
"Ivor",
""
],
[
"Rotenberg",
"Eva",
""
]
] |
new_dataset
| 0.98716 |
2201.04649
|
Olga Doronina
|
Olga A. Doronina, Zachary J. Grey, Andrew Glaws
|
Grassmannian Shape Representations for Aerodynamic Applications
|
5 pages, 4 figures, submitted to AI for Design and
Manufacturing(ADAM) workshop of AAAI-2022 conference
| null | null | null |
cs.GR math.DG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Airfoil shape design is a classical problem in engineering and manufacturing.
Our motivation is to combine principled physics-based considerations for the
shape design problem with modern computational techniques informed by a
data-driven approach. Traditional analyses of airfoil shapes emphasize a
flow-based sensitivity to deformations which can be represented generally by
affine transformations (rotation, scaling, shearing, translation). We present a
novel representation of shapes which decouples affine-style deformations from a
rich set of data-driven deformations over a submanifold of the Grassmannian.
The Grassmannian representation, informed by a database of physically relevant
airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously
captured in the data, (ii) improved low-dimensional parameter domain for
inferential statistics informing design/manufacturing, and (iii) consistent 3D
blade representation and perturbation over a sequence of nominal shapes.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 19:01:01 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Doronina",
"Olga A.",
""
],
[
"Grey",
"Zachary J.",
""
],
[
"Glaws",
"Andrew",
""
]
] |
new_dataset
| 0.959227 |
2201.04742
|
Aryaman Pandya
|
Paul Schmitt, Nicholas Britten, JiHyun Jeong, Amelia Coffey, Kevin
Clark, Shweta Sunil Kothawade, Elena Corina Grigore, Adam Khaw, Christopher
Konopka, Linh Pham, Kim Ryan, Christopher Schmitt, Aryaman Pandya, Emilio
Frazzoli
|
nuReality: A VR environment for research of pedestrian and autonomous
vehicle interactions
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We present nuReality, a virtual reality 'VR' environment designed to test the
efficacy of vehicular behaviors to communicate intent during interactions
between autonomous vehicles 'AVs' and pedestrians at urban intersections. In
this project we focus on expressive behaviors as a means for pedestrians to
readily recognize the underlying intent of the AV's movements. VR is an ideal
tool to use to test these situations as it can be immersive and place subjects
into these potentially dangerous scenarios without risk. nuReality provides a
novel and immersive virtual reality environment that includes numerous visual
details (road and building texturing, parked cars, swaying tree limbs) as well
as auditory details (birds chirping, cars honking in the distance, people
talking). In these files we present the nuReality environment, its 10 unique
vehicle behavior scenarios, and the Unreal Engine and Autodesk Maya source
files for each scenario. The files are publicly released as open source at
www.nuReality.org, to support the academic community studying the critical
AV-pedestrian interaction.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 23:54:09 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Schmitt",
"Paul",
""
],
[
"Britten",
"Nicholas",
""
],
[
"Jeong",
"JiHyun",
""
],
[
"Coffey",
"Amelia",
""
],
[
"Clark",
"Kevin",
""
],
[
"Kothawade",
"Shweta Sunil",
""
],
[
"Grigore",
"Elena Corina",
""
],
[
"Khaw",
"Adam",
""
],
[
"Konopka",
"Christopher",
""
],
[
"Pham",
"Linh",
""
],
[
"Ryan",
"Kim",
""
],
[
"Schmitt",
"Christopher",
""
],
[
"Pandya",
"Aryaman",
""
],
[
"Frazzoli",
"Emilio",
""
]
] |
new_dataset
| 0.993648 |
2201.04851
|
Yuying Ge
|
Yuying Ge, Yibing Song, Ruimao Zhang and Ping Luo
|
MetaDance: Few-shot Dancing Video Retargeting via Temporal-aware
Meta-learning
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dancing video retargeting aims to synthesize a video that transfers the dance
movements from a source video to a target person. Previous work need collect a
several-minute-long video of a target person with thousands of frames to train
a personalized model. However, the trained model can only generate videos of
the same person. To address the limitations, recent work tackled few-shot
dancing video retargeting, which learns to synthesize videos of unseen persons
by leveraging a few frames of them. In practice, given a few frames of a
person, these work simply regarded them as a batch of individual images without
temporal correlations, thus generating temporally incoherent dancing videos of
low visual quality. In this work, we model a few frames of a person as a series
of dancing moves, where each move contains two consecutive frames, to extract
the appearance patterns and the temporal dynamics of this person. We propose
MetaDance, which utilizes temporal-aware meta-learning to optimize the
initialization of a model through the synthesis of dancing moves, such that the
meta-trained model can be efficiently tuned towards enhanced visual quality and
strengthened temporal stability for unseen persons with a few frames. Extensive
evaluations show large superiority of our method.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 09:34:20 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Ge",
"Yuying",
""
],
[
"Song",
"Yibing",
""
],
[
"Zhang",
"Ruimao",
""
],
[
"Luo",
"Ping",
""
]
] |
new_dataset
| 0.984027 |
2201.05005
|
Franca Delmastro
|
Franca Delmastro, valerio Arnaboldi, Marco Conti
|
People-centric computing and communications in Smart Cities
| null |
IEEE Communications Magazine ( Volume: 54, Issue: 7, July 2016)
|
10.1109/MCOM.2016.7509389
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The extreme pervasive nature of mobile technologies, together with the users
need to continuously interact with her personal devices and to be always
connected, strengthen the user-centric approach to design and develop new
communication and computing solutions. Nowadays users not only represent the
final utilizers of the technology, but they actively contribute to its
evolution by assuming different roles: they act as humans, by sharing contents
and experiences through social networks, and as virtual sensors, by moving
freely in the environment with their sensing devices. Smart cities represent an
important reference scenario for the active participation of users through
mobile technologies. It involves multiple application domains and defines
different levels of user engagement. Participatory sensing, opportunistic
sensing and Mobile Social Networks currently represent some of the most
promising people-centric paradigms. In addition, their integration can further
improve the user involvement through new services and applications. In this
paper we present SmartCitizen app, a MSN application designed in the framework
of a smart city project to stimulate the active participation of citizens in
generating and sharing useful contents related to the quality of life in their
city. The app has been developed on top of a context- and social-aware
middleware platform (CAMEO) able to integrate the main features of
people-centric computing paradigms, lightening the app developer effort.
Existing middleware platforms generally focus on one single people-centric
paradigm, exporting a limited set of features to mobile applications. CAMEO
overcomes these limitations. Experimental results shown in this paper can also
represent the technical guidelines for the development of heterogeneous
people-centric mobile applications, embracing different application domains.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 14:38:21 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Delmastro",
"Franca",
""
],
[
"Arnaboldi",
"valerio",
""
],
[
"Conti",
"Marco",
""
]
] |
new_dataset
| 0.99309 |
2201.05006
|
Brice Minaud
|
Brice Minaud and Michael Reichle
|
Dynamic Local Searchable Symmetric Encryption
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this article, we tackle for the first time the problem of dynamic
memory-efficient Searchable Symmetric Encryption (SSE). In the term
"memory-efficient" SSE, we encompass both the goals of local SSE, and
page-efficient SSE. The centerpiece of our approach is a novel connection
between those two goals. We introduce a map, called the Generic Local
Transform, which takes as input a page-efficient SSE scheme with certain
special features, and outputs an SSE scheme with strong locality properties. We
obtain several results.
(1) First, for page-efficient SSE, we build a dynamic scheme with page
efficiency $O(\log \log N)$ and storage efficiency $O(1)$, called LayeredSSE.
The main technical innovation behind LayeredSSE is a new weighted extension of
the two-choice allocation process, of independent interest.
(2) Second, we introduce the Generic Local Transform, and combine it with
LayeredSSE to build a dynamic SSE scheme with storage efficiency $O(1)$,
locality $O(1)$, and read efficiency $O(\log\log N)$, under the condition that
the longest list is of size $O(N^{1-1/\log \log \lambda})$. This matches, in
every respect, the purely static construction of Asharov et al. presented at
STOC 2016: dynamism comes at no extra cost.
(3) Finally, by applying the Generic Local Transform to a variant of the
Tethys scheme by Bossuat et al. from Crypto 2021, we build an unconditional
static SSE with storage efficiency $O(1)$, locality $O(1)$, and read efficiency
$O(\log^\varepsilon N)$, for an arbitrarily small constant $\varepsilon > 0$.
To our knowledge, this is the construction that comes closest to the lower
bound presented by Cash and Tessaro at Eurocrypt 2014.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 14:38:40 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Minaud",
"Brice",
""
],
[
"Reichle",
"Michael",
""
]
] |
new_dataset
| 0.997985 |
2201.05066
|
Tarik Taleb Dr.
|
Junseok Kim, Seongwon Kim, T. Taleb, and Sunghyun Choi
|
RAPID: Contention Resolution-based Random Access using Context ID for
IoT
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
With the increasing number of Internet of Things (IoT) devices, Machine Type
Communication (MTC) has become an important use case of the Fifth Generation
(5G) communication systems. Since MTC devices are mostly disconnected from Base
Station (BS) for power saving, random access procedure is required for devices
to transmit data. If many devices try random access simultaneously, preamble
collision problem occurs, thus causing latency increase. In an environment
where delay-sensitive and delay-tolerant devices coexist, the contention-based
random access procedure cannot satisfy latency requirements of delay-sensitive
devices. Therefore, we propose RAPID, a novel random access procedure, which is
completed through two message exchanges for the delay-sensitive devices. We
also develop Access Pattern Analyzer (APA), which estimates traffic
characteristics of MTC devices. When UEs, performing RAPID and contention-based
random access, coexist, it is important to determine a value which is the
number of preambles for RAPID to reduce random access load. Thus, we analyze
random access load using a Markov chain model to obtain the optimal number of
preambles for RAPID. Simulation results show RAPID achieves 99.999% reliability
with 80.8% shorter uplink latency, and also decreases random access load by
30.5% compared with state-of-the-art techniques.
|
[
{
"version": "v1",
"created": "Wed, 5 Jan 2022 13:29:09 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Kim",
"Junseok",
""
],
[
"Kim",
"Seongwon",
""
],
[
"Taleb",
"T.",
""
],
[
"Choi",
"Sunghyun",
""
]
] |
new_dataset
| 0.961944 |
2201.05075
|
Mikhail Volkov
|
Evgeniya A. Bondar and David Casas and Mikhail V. Volkov
|
Completely reachable automata: an interplay between automata, graphs,
and trees
|
29 pages, 16 figures
| null | null | null |
cs.FL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A deterministic finite automaton in which every non-empty set of states
occurs as the image of the whole state set under the action of a suitable input
word is called completely reachable. We characterize such automata in terms of
graphs and trees.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 16:58:38 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Bondar",
"Evgeniya A.",
""
],
[
"Casas",
"David",
""
],
[
"Volkov",
"Mikhail V.",
""
]
] |
new_dataset
| 0.99889 |
2201.05120
|
Carlos Rodriguez-Pardo
|
Carlos Rodriguez-Pardo and Elena Garces
|
SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps
|
12 pages. To be published in Transactions on Visualizations and
Computer Graphics. Project website:
http://carlosrodriguezpardo.es/projects/SeamlessGAN/
| null |
10.1109/TVCG.2022.3143615
| null |
cs.CV cs.GR cs.LG cs.MM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present SeamlessGAN, a method capable of automatically generating tileable
texture maps from a single input exemplar. In contrast to most existing
methods, focused solely on solving the synthesis problem, our work tackles both
problems, synthesis and tileability, simultaneously. Our key idea is to realize
that tiling a latent space within a generative network trained using
adversarial expansion techniques produces outputs with continuity at the seam
intersection that can be then be turned into tileable images by cropping the
central area. Since not every value of the latent space is valid to produce
high-quality outputs, we leverage the discriminator as a perceptual error
metric capable of identifying artifact-free textures during a sampling process.
Further, in contrast to previous work on deep texture synthesis, our model is
designed and optimized to work with multi-layered texture representations,
enabling textures composed of multiple maps such as albedo, normals, etc. We
extensively test our design choices for the network architecture, loss function
and sampling parameters. We show qualitatively and quantitatively that our
approach outperforms previous methods and works for textures of different
types.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 18:24:26 GMT"
}
] | 2022-01-14T00:00:00 |
[
[
"Rodriguez-Pardo",
"Carlos",
""
],
[
"Garces",
"Elena",
""
]
] |
new_dataset
| 0.988817 |
1902.02598
|
Matilda Rhode
|
Matilda Rhode, Pete Burnap, Adam Wedgbury
|
Real-time malware process detection and automated process killing
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Perimeter-based detection is no longer sufficient for mitigating the threat
posed by malicious software. This is evident as antivirus (AV) products are
replaced by endpoint detection and response (EDR) products, the latter allowing
visibility into live machine activity rather than relying on the AV to filter
out malicious artefacts. This paper argues that detecting malware in real-time
on an endpoint necessitates an automated response due to the rapid and
destructive nature of some malware.
The proposed model uses statistical filtering on top of a machine learning
dynamic behavioural malware detection model in order to detect individual
malicious processes on the fly and kill those which are deemed malicious. In an
experiment to measure the tangible impact of this system, we find that
fast-acting ransomware is prevented from corrupting 92% of files with a false
positive rate of 14%. Whilst the false-positive rate currently remains too high
to adopt this approach as-is, these initial results demonstrate the need for a
detection model which is able to act within seconds of the malware execution
beginning; a timescale that has not been addressed by previous work.
|
[
{
"version": "v1",
"created": "Thu, 7 Feb 2019 13:01:59 GMT"
},
{
"version": "v2",
"created": "Tue, 1 Oct 2019 10:07:05 GMT"
},
{
"version": "v3",
"created": "Wed, 12 Jan 2022 08:29:24 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Rhode",
"Matilda",
""
],
[
"Burnap",
"Pete",
""
],
[
"Wedgbury",
"Adam",
""
]
] |
new_dataset
| 0.957049 |
2004.10063
|
Maximilian Kloock
|
Maximilian Kloock, Patrick Scheffe, Janis Maczijewski, Alexandru
Kampmann, Armin Mokhtarian, Stefan Kowalewski and Bassam Alrifaee
|
Cyber-Physical Mobility Lab: An Open-Source Platform for Networked and
Autonomous Vehicles
|
This work has been presented on ECC21
| null | null | null |
cs.MA cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper introduces our Cyber-Physical Mobility Lab (CPM Lab). It is an
open-source development environment for networked and autonomous vehicles with
focus on networked decision-making, trajectory planning, and control. The CPM
Lab hosts 20 physical model-scale vehicles ({\mu}Cars) which we can seamlessly
extend by unlimited simulated vehicles. The code and construction plans are
publicly available to enable rebuilding the CPM Lab.
Our four-layered architecture enables the seamless use of the same software
in simulations and in experiments without any further adaptions. A Data
Distribution Service (DDS) based middleware allows adapting the number of
vehicles during experiments in a seamless manner. The middleware is also
responsible for synchronizing all entities following a logical execution time
approach to achieve determinism and reproducibility of experiments. This
approach makes the CPM Lab a unique platform for rapid functional prototyping
of networked decision-making algorithms.
The CPM Lab allows researchers as well as students from different disciplines
to see their ideas developing into reality. We demonstrate its capabilities
using two example experiments. We are working on a remote access to the CPM Lab
via a webinterface.
|
[
{
"version": "v1",
"created": "Tue, 21 Apr 2020 14:54:30 GMT"
},
{
"version": "v2",
"created": "Mon, 19 Apr 2021 09:56:45 GMT"
},
{
"version": "v3",
"created": "Tue, 20 Apr 2021 07:08:52 GMT"
},
{
"version": "v4",
"created": "Wed, 12 Jan 2022 10:37:05 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Kloock",
"Maximilian",
""
],
[
"Scheffe",
"Patrick",
""
],
[
"Maczijewski",
"Janis",
""
],
[
"Kampmann",
"Alexandru",
""
],
[
"Mokhtarian",
"Armin",
""
],
[
"Kowalewski",
"Stefan",
""
],
[
"Alrifaee",
"Bassam",
""
]
] |
new_dataset
| 0.985207 |
2102.03141
|
Tobias Hinz
|
Tobias Hinz and Matthew Fisher and Oliver Wang and Eli Shechtman and
Stefan Wermter
|
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing
|
Best Paper WACV 2022. Code available at
https://github.com/tohinz/CharacterGAN
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We introduce CharacterGAN, a generative model that can be trained on only a
few samples (8 - 15) of a given character. Our model generates novel poses
based on keypoint locations, which can be modified in real time while providing
interactive feedback, allowing for intuitive reposing and animation. Since we
only have very limited training samples, one of the key challenges lies in how
to address (dis)occlusions, e.g. when a hand moves behind or in front of a
body. To address this, we introduce a novel layering approach which explicitly
splits the input keypoints into different layers which are processed
independently. These layers represent different parts of the character and
provide a strong implicit bias that helps to obtain realistic results even with
strong (dis)occlusions. To combine the features of individual layers we use an
adaptive scaling approach conditioned on all keypoints. Finally, we introduce a
mask connectivity constraint to reduce distortion artifacts that occur with
extreme out-of-distribution poses at test time. We show that our approach
outperforms recent baselines and creates realistic animations for diverse
characters. We also show that our model can handle discrete state changes, for
example a profile facing left or right, that the different layers do indeed
learn features specific for the respective keypoints in those layers, and that
our model scales to larger datasets when more data is available.
|
[
{
"version": "v1",
"created": "Fri, 5 Feb 2021 12:38:15 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Mar 2021 11:12:28 GMT"
},
{
"version": "v3",
"created": "Wed, 12 Jan 2022 18:33:49 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Hinz",
"Tobias",
""
],
[
"Fisher",
"Matthew",
""
],
[
"Wang",
"Oliver",
""
],
[
"Shechtman",
"Eli",
""
],
[
"Wermter",
"Stefan",
""
]
] |
new_dataset
| 0.991084 |
2105.00374
|
Kumar Abhishek
|
Mengliu Zhao, Jeremy Kawahara, Kumar Abhishek, Sajjad Shamanian,
Ghassan Hamarneh
|
Skin3D: Detection and Longitudinal Tracking of Pigmented Skin Lesions in
3D Total-Body Textured Meshes
|
11 pages, 8 figures; Zhao and Kawahara: joint first authors;
Published in Medical Image Analysis (2021)
| null |
10.1016/j.media.2021.102329
| null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present an automated approach to detect and longitudinally track skin
lesions on 3D total-body skin surface scans. The acquired 3D mesh of the
subject is unwrapped to a 2D texture image, where a trained objected detection
model, Faster R-CNN, localizes the lesions within the 2D domain. These detected
skin lesions are mapped back to the 3D surface of the subject and, for subjects
imaged multiple times, we construct a graph-based matching procedure to
longitudinally track lesions that considers the anatomical correspondences
among pairs of meshes and the geodesic proximity of corresponding lesions and
the inter-lesion geodesic distances.
We evaluated the proposed approach using 3DBodyTex, a publicly available
dataset composed of 3D scans imaging the coloured skin (textured meshes) of 200
human subjects. We manually annotated locations that appeared to the human eye
to contain a pigmented skin lesion as well as tracked a subset of lesions
occurring on the same subject imaged in different poses. Our results, when
compared to three human annotators, suggest that the trained Faster R-CNN
detects lesions at a similar performance level as the human annotators. Our
lesion tracking algorithm achieves an average matching accuracy of 88% on a set
of detected corresponding pairs of prominent lesions of subjects imaged in
different poses, and an average longitudinal accuracy of 71% when encompassing
additional errors due to lesion detection. As there currently is no other
large-scale publicly available dataset of 3D total-body skin lesions, we
publicly release over 25,000 3DBodyTex manual annotations, which we hope will
further research on total-body skin lesion analysis.
|
[
{
"version": "v1",
"created": "Sun, 2 May 2021 01:52:28 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jan 2022 06:04:44 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Zhao",
"Mengliu",
""
],
[
"Kawahara",
"Jeremy",
""
],
[
"Abhishek",
"Kumar",
""
],
[
"Shamanian",
"Sajjad",
""
],
[
"Hamarneh",
"Ghassan",
""
]
] |
new_dataset
| 0.998727 |
2108.13359
|
Ilya Vorobyev
|
Ilya Vorobyev
|
Fast Decoding of Union-free Codes
| null | null | null | null |
cs.IT math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Union-free codes and disjunctive codes are two combinatorial structures,
which are used in nonadaptive group testing to find a set of $d$ defective
elements among $n$ samples by carrying out the minimal number of tests $t$. It
is known that union-free codes have a larger rate, whereas disjunctive codes
provide a more efficient decoding algorithm. In this paper we introduce a new
family of codes for nonadaptive group testing with fast decoding. The rate of
these codes is larger than the rate of disjunctive codes, while the decoding
algorithm has the same complexity. In addition, we derive a lower bound on the
rate of new codes for the case of $d=2$ defectives, which is significantly
better than the bound for disjunctive codes and almost as good as the bound for
union-free codes.
|
[
{
"version": "v1",
"created": "Mon, 30 Aug 2021 16:31:39 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jan 2022 14:15:40 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Vorobyev",
"Ilya",
""
]
] |
new_dataset
| 0.973131 |
2109.01188
|
Lillian Pentecost
|
Lillian Pentecost, Alexander Hankin, Marco Donato, Mark Hempstead,
Gu-Yeon Wei, and David Brooks
|
NVMExplorer: A Framework for Cross-Stack Comparisons of Embedded
Non-Volatile Memories
|
18 pages, 14 figures, 3 tables
| null | null | null |
cs.ET cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
Repeated off-chip memory accesses to DRAM drive up operating power for
data-intensive applications, and SRAM technology scaling and leakage power
limits the efficiency of embedded memories. Future on-chip storage will need
higher density and energy efficiency, and the actively expanding field of
emerging, embeddable non-volatile memory (eNVM) technologies is providing many
potential candidates to satisfy this need. Each technology proposal presents
distinct trade-offs in terms of density, read, write, and reliability
characteristics, and we present a comprehensive framework for navigating and
quantifying these design trade-offs alongside realistic system constraints and
application-level impacts. This work evaluates eNVM-based storage for a range
of application and system contexts including machine learning on the edge,
graph analytics, and general purpose cache hierarchy, in addition to describing
a freely available (http://nvmexplorer.seas.harvard.edu/) set of tools for
application experts, system designers, and device experts to better understand,
compare, and quantify the next generation of embedded memory solutions.
|
[
{
"version": "v1",
"created": "Thu, 2 Sep 2021 19:36:25 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jan 2022 00:04:25 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Pentecost",
"Lillian",
""
],
[
"Hankin",
"Alexander",
""
],
[
"Donato",
"Marco",
""
],
[
"Hempstead",
"Mark",
""
],
[
"Wei",
"Gu-Yeon",
""
],
[
"Brooks",
"David",
""
]
] |
new_dataset
| 0.997899 |
2109.13899
|
Jeremiah Johnson
|
Jeremiah W. Johnson, Swathi Hari, Donald Hampton, Hyunju K. Connor,
Amy Keesee
|
A Contrastive Learning Approach to Auroral Identification and
Classification
|
6 pages, 5 figures, 1 table
|
Proceedings of the 20th IEEE International Conference on Machine
Learning and Applications, Dec. 2021
|
10.1109/ICMLA52953.2021.00128
| null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unsupervised learning algorithms are beginning to achieve accuracies
comparable to their supervised counterparts on benchmark computer vision tasks,
but their utility for practical applications has not yet been demonstrated. In
this work, we present a novel application of unsupervised learning to the task
of auroral image classification. Specifically, we modify and adapt the Simple
framework for Contrastive Learning of Representations (SimCLR) algorithm to
learn representations of auroral images in a recently released auroral image
dataset constructed using image data from Time History of Events and Macroscale
Interactions during Substorms (THEMIS) all-sky imagers. We demonstrate that (a)
simple linear classifiers fit to the learned representations of the images
achieve state-of-the-art classification performance, improving the
classification accuracy by almost 10 percentage points over the current
benchmark; and (b) the learned representations naturally cluster into more
clusters than exist manually assigned categories, suggesting that existing
categorizations are overly coarse and may obscure important connections between
auroral types, near-earth solar wind conditions, and geomagnetic disturbances
at the earth's surface. Moreover, our model is much lighter than the previous
benchmark on this dataset, requiring in the area of fewer than 25\% of the
number of parameters. Our approach exceeds an established threshold for
operational purposes, demonstrating readiness for deployment and utilization.
|
[
{
"version": "v1",
"created": "Tue, 28 Sep 2021 17:51:25 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Sep 2021 02:08:10 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Johnson",
"Jeremiah W.",
""
],
[
"Hari",
"Swathi",
""
],
[
"Hampton",
"Donald",
""
],
[
"Connor",
"Hyunju K.",
""
],
[
"Keesee",
"Amy",
""
]
] |
new_dataset
| 0.978067 |
2110.06166
|
Shorya Sharma Mr.
|
Shorya Sharma
|
Game Theory for Adversarial Attacks and Defenses
|
With the agreement of my coauthors, I would like to withdraw the
manuscript "Game Theory for Adversarial Attacks and Defenses". Some
experimental procedures were not included in the manuscript, which makes a
part of important claims not meaningful
| null | null | null |
cs.LG cs.CR cs.GT
|
http://creativecommons.org/licenses/by/4.0/
|
Adversarial attacks can generate adversarial inputs by applying small but
intentionally worst-case perturbations to samples from the dataset, which leads
to even state-of-the-art deep neural networks outputting incorrect answers with
high confidence. Hence, some adversarial defense techniques are developed to
improve the security and robustness of the models and avoid them being
attacked. Gradually, a game-like competition between attackers and defenders
formed, in which both players would attempt to play their best strategies
against each other while maximizing their own payoffs. To solve the game, each
player would choose an optimal strategy against the opponent based on the
prediction of the opponent's strategy choice. In this work, we are on the
defensive side to apply game-theoretic approaches on defending against attacks.
We use two randomization methods, random initialization and stochastic
activation pruning, to create diversity of networks. Furthermore, we use one
denoising technique, super resolution, to improve models' robustness by
preprocessing images before attacks. Our experimental results indicate that
those three methods can effectively improve the robustness of deep-learning
neural networks.
|
[
{
"version": "v1",
"created": "Fri, 8 Oct 2021 07:38:33 GMT"
},
{
"version": "v2",
"created": "Wed, 13 Oct 2021 04:49:37 GMT"
},
{
"version": "v3",
"created": "Wed, 12 Jan 2022 14:04:54 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Sharma",
"Shorya",
""
]
] |
new_dataset
| 0.956614 |
2111.08897
|
Afshin Dehghan
|
Gilad Baruch, Zhuoyuan Chen, Afshin Dehghan, Tal Dimry, Yuri Feigin,
Peter Fu, Thomas Gebauer, Brandon Joffe, Daniel Kurz, Arik Schwartz, Elad
Shulman
|
ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene
Understanding Using Mobile RGB-D Data
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Scene understanding is an active research area. Commercial depth sensors,
such as Kinect, have enabled the release of several RGB-D datasets over the
past few years which spawned novel methods in 3D scene understanding. More
recently with the launch of the LiDAR sensor in Apple's iPads and iPhones, high
quality RGB-D data is accessible to millions of people on a device they
commonly use. This opens a whole new era in scene understanding for the
Computer Vision community as well as app developers. The fundamental research
in scene understanding together with the advances in machine learning can now
impact people's everyday experiences. However, transforming these scene
understanding methods to real-world experiences requires additional innovation
and development. In this paper we introduce ARKitScenes. It is not only the
first RGB-D dataset that is captured with a now widely available depth sensor,
but to our best knowledge, it also is the largest indoor scene understanding
data released. In addition to the raw and processed data from the mobile
device, ARKitScenes includes high resolution depth maps captured using a
stationary laser scanner, as well as manually labeled 3D oriented bounding
boxes for a large taxonomy of furniture. We further analyze the usefulness of
the data for two downstream tasks: 3D object detection and color-guided depth
upsampling. We demonstrate that our dataset can help push the boundaries of
existing state-of-the-art methods and it introduces new challenges that better
represent real-world scenarios.
|
[
{
"version": "v1",
"created": "Wed, 17 Nov 2021 04:27:01 GMT"
},
{
"version": "v2",
"created": "Fri, 31 Dec 2021 18:45:00 GMT"
},
{
"version": "v3",
"created": "Wed, 12 Jan 2022 08:19:29 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Baruch",
"Gilad",
""
],
[
"Chen",
"Zhuoyuan",
""
],
[
"Dehghan",
"Afshin",
""
],
[
"Dimry",
"Tal",
""
],
[
"Feigin",
"Yuri",
""
],
[
"Fu",
"Peter",
""
],
[
"Gebauer",
"Thomas",
""
],
[
"Joffe",
"Brandon",
""
],
[
"Kurz",
"Daniel",
""
],
[
"Schwartz",
"Arik",
""
],
[
"Shulman",
"Elad",
""
]
] |
new_dataset
| 0.999826 |
2112.11953
|
Xiao Xu
|
Xiao Xu, Libo Qin, Kaiji Chen, Guoxing Wu, Linlin Li, Wanxiang Che
|
Text is no more Enough! A Benchmark for Profile-based Spoken Language
Understanding
|
Accepted by AAAI 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current researches on spoken language understanding (SLU) heavily are limited
to a simple setting: the plain text-based SLU that takes the user utterance as
input and generates its corresponding semantic frames (e.g., intent and slots).
Unfortunately, such a simple setting may fail to work in complex real-world
scenarios when an utterance is semantically ambiguous, which cannot be achieved
by the text-based SLU models. In this paper, we first introduce a new and
important task, Profile-based Spoken Language Understanding (ProSLU), which
requires the model that not only relies on the plain text but also the
supporting profile information to predict the correct intents and slots. To
this end, we further introduce a large-scale human-annotated Chinese dataset
with over 5K utterances and their corresponding supporting profile information
(Knowledge Graph (KG), User Profile (UP), Context Awareness (CA)). In addition,
we evaluate several state-of-the-art baseline models and explore a multi-level
knowledge adapter to effectively incorporate profile information. Experimental
results reveal that all existing text-based SLU models fail to work when the
utterances are semantically ambiguous and our proposed framework can
effectively fuse the supporting information for sentence-level intent detection
and token-level slot filling. Finally, we summarize key challenges and provide
new points for future directions, which hopes to facilitate the research.
|
[
{
"version": "v1",
"created": "Wed, 22 Dec 2021 15:22:17 GMT"
},
{
"version": "v2",
"created": "Thu, 6 Jan 2022 12:26:57 GMT"
},
{
"version": "v3",
"created": "Wed, 12 Jan 2022 15:18:17 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Xu",
"Xiao",
""
],
[
"Qin",
"Libo",
""
],
[
"Chen",
"Kaiji",
""
],
[
"Wu",
"Guoxing",
""
],
[
"Li",
"Linlin",
""
],
[
"Che",
"Wanxiang",
""
]
] |
new_dataset
| 0.985728 |
2201.03817
|
Tianlang He
|
Tianlang He, Jiajie Tan, Weipeng Zhuo, Maximilian Printz, S.-H. Gary
Chan
|
Tackling Multipath and Biased Training Data for IMU-Assisted BLE
Proximity Detection
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Proximity detection is to determine whether an IoT receiver is within a
certain distance from a signal transmitter. Due to its low cost and high
popularity, Bluetooth low energy (BLE) has been used to detect proximity based
on the received signal strength indicator (RSSI). To address the fact that RSSI
can be markedly influenced by device carriage states, previous works have
incorporated RSSI with inertial measurement unit (IMU) using deep learning.
However, they have not sufficiently accounted for the impact of multipath.
Furthermore, due to the special setup, the IMU data collected in the training
process may be biased, which hampers the system's robustness and
generalizability. This issue has not been studied before. We propose PRID, an
IMU-assisted BLE proximity detection approach robust against RSSI fluctuation
and IMU data bias. PRID histogramizes RSSI to extract multipath features and
uses carriage state regularization to mitigate overfitting due to IMU data
bias. We further propose PRID-lite based on a binarized neural network to
substantially cut memory requirements for resource-constrained devices. We have
conducted extensive experiments under different multipath environments, data
bias levels, and a crowdsourced dataset. Our results show that PRID
significantly reduces false detection cases compared with the existing arts (by
over 50%). PRID-lite further reduces over 90% PRID model size and extends 60%
battery life, with a minor compromise in accuracy (7%).
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 07:46:20 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Jan 2022 03:09:25 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"He",
"Tianlang",
""
],
[
"Tan",
"Jiajie",
""
],
[
"Zhuo",
"Weipeng",
""
],
[
"Printz",
"Maximilian",
""
],
[
"Chan",
"S. -H. Gary",
""
]
] |
new_dataset
| 0.972411 |
2201.04171
|
Alejandro Macario-Rojas
|
Alejandro Macario-Rojas, Ben Parslew, Andrew Weightman, and Katharine
L. Smith
|
CLOVER Robot: A Minimally Actuated Jumping Robotic Platform for Space
Exploration
| null | null | null | null |
cs.RO physics.app-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Robots have been critical instruments to space exploration by providing
access to environments beyond human limitations. Jumping robot concepts are
attractive solutions to negotiate complex terrain. However, among the
engineering challenges to overcome to enable jumping robot concepts for
sustained operation, reduction of mechanical failure modes is one of the most
fundamental. This study set out to develop a jumping robot with focus on
minimal actuation for reduced mechanism maintenance. We present the synthesis
of a Sarrus-style linkage to constraint the system to a single translational
degree of freedom without the use of typical synchronising gears. We delimit
the present research to vertical solid jumps to assess the performance of the
fundamental main-drive linkage. A laboratory demonstrator assists the transfer
of theoretical concepts and approaches. The laboratory demonstrator performs
jumps with 63% potential-to-kinetic energy conversion efficiency, with a
theoretical maximum of 73%. Satisfactory operation opens up design optimisation
and directional jump capability towards the development of a jumping robotic
platform for space exploration.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 19:42:54 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Macario-Rojas",
"Alejandro",
""
],
[
"Parslew",
"Ben",
""
],
[
"Weightman",
"Andrew",
""
],
[
"Smith",
"Katharine L.",
""
]
] |
new_dataset
| 0.989724 |
2201.04205
|
Waleed Yousef
|
Waleed A.Yousef, Hisham E. Mohammed, Andrew A. Naguib, Rafat S. Eid,
Sherif E. Emabrak, Ahmed F. Hamed, Yusuf M. Khalifa, Shrouk T. AbdElrheem,
Eman A. Awad, Sara G. Gaafar, Alaa M. Mamdoh, Nada A. Shawky
|
JSOL: JavaScript Open-source Library for Grammar of Graphics
| null | null | null | null |
cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we introduce the JavaScript Open-source Library (\libname), a
high-level grammar for representing data in visualization graphs and plots.
\libname~perspective on the grammar of graphics is unique; it provides
state-of-art rules for encoding visual primitives that can be used to generate
a known scene or to invent a new one. \libname~has ton rules developed
specifically for data-munging, mapping, and visualization through many layers,
such as algebra, scales, and geometries. Additionally, it has a compiler that
incorporates and combines all rules specified by a user and put them in a flow
to validate it as a visualization grammar and check its requisites. Users can
customize scenes through a pipeline that either puts customized rules or comes
with new ones. We evaluated \libname~on a multitude of plots to check rules
specification of customizing a specific plot. Although the project is still
under development and many enhancements are under construction, this paper
describes the first developed version of \libname, circa 2016, where an
open-source version of it is available. One immediate practical deployment for
JSOl is to be integrated with the open-source version of the Data Visualization
Platform (DVP) \citep{Yousef2019DVP-arxiv}
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 21:23:23 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Yousef",
"Waleed A.",
""
],
[
"Mohammed",
"Hisham E.",
""
],
[
"Naguib",
"Andrew A.",
""
],
[
"Eid",
"Rafat S.",
""
],
[
"Emabrak",
"Sherif E.",
""
],
[
"Hamed",
"Ahmed F.",
""
],
[
"Khalifa",
"Yusuf M.",
""
],
[
"AbdElrheem",
"Shrouk T.",
""
],
[
"Awad",
"Eman A.",
""
],
[
"Gaafar",
"Sara G.",
""
],
[
"Mamdoh",
"Alaa M.",
""
],
[
"Shawky",
"Nada A.",
""
]
] |
new_dataset
| 0.999723 |
2201.04212
|
Chong Tang
|
Chong Tang, Wenda Li, Shelly Vishwakarma, Fangzhan Shi, Simon Julier,
Kevin Chetty
|
MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler
Signatures
| null | null | null | null |
cs.CV eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Motion tracking systems based on optical sensors typically often suffer from
issues, such as poor lighting conditions, occlusion, limited coverage, and may
raise privacy concerns. More recently, radio frequency (RF)-based approaches
using commercial WiFi devices have emerged which offer low-cost ubiquitous
sensing whilst preserving privacy. However, the output of an RF sensing system,
such as Range-Doppler spectrograms, cannot represent human motion intuitively
and usually requires further processing. In this study, MDPose, a novel
framework for human skeletal motion reconstruction based on WiFi micro-Doppler
signatures, is proposed. It provides an effective solution to track human
activities by reconstructing a skeleton model with 17 key points, which can
assist with the interpretation of conventional RF sensing outputs in a more
understandable way. Specifically, MDPose has various incremental stages to
gradually address a series of challenges: First, a denoising algorithm is
implemented to remove any unwanted noise that may affect the feature extraction
and enhance weak Doppler signatures. Secondly, the convolutional neural network
(CNN)-recurrent neural network (RNN) architecture is applied to learn
temporal-spatial dependency from clean micro-Doppler signatures and restore key
points' velocity information. Finally, a pose optimising mechanism is employed
to estimate the initial state of the skeleton and to limit the increase of
error. We have conducted comprehensive tests in a variety of environments using
numerous subjects with a single receiver radar system to demonstrate the
performance of MDPose, and report 29.4mm mean absolute error over all key
points positions, which outperforms state-of-the-art RF-based pose estimation
systems.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 21:46:28 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Tang",
"Chong",
""
],
[
"Li",
"Wenda",
""
],
[
"Vishwakarma",
"Shelly",
""
],
[
"Shi",
"Fangzhan",
""
],
[
"Julier",
"Simon",
""
],
[
"Chetty",
"Kevin",
""
]
] |
new_dataset
| 0.996306 |
2201.04235
|
Davide Callegaro
|
Davide Callegaro and Francesco Restuccia and Marco Levorato
|
SmartDet: Context-Aware Dynamic Control of Edge Task Offloading for
Mobile Object Detection
| null | null | null | null |
cs.DC cs.CV cs.LG cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mobile devices increasingly rely on object detection (OD) through deep neural
networks (DNNs) to perform critical tasks. Due to their high complexity, the
execution of these DNNs requires excessive time and energy. Low-complexity
object tracking (OT) can be used with OD, where the latter is periodically
applied to generate "fresh" references for tracking. However, the frames
processed with OD incur large delays, which may make the reference outdated and
degrade tracking quality. Herein, we propose to use edge computing in this
context, and establish parallel OT (at the mobile device) and OD (at the edge
server) processes that are resilient to large OD latency. We propose Katch-Up,
a novel tracking mechanism that improves the system resilience to excessive OD
delay. However, while Katch-Up significantly improves performance, it also
increases the computing load of the mobile device. Hence, we design SmartDet, a
low-complexity controller based on deep reinforcement learning (DRL) that
learns controlling the trade-off between resource utilization and OD
performance. SmartDet takes as input context-related information related to the
current video content and the current network conditions to optimize frequency
and type of OD offloading, as well as Katch-Up utilization. We extensively
evaluate SmartDet on a real-world testbed composed of a JetSon Nano as mobile
device and a GTX 980 Ti as edge server, connected through a Wi-Fi link.
Experimental results show that SmartDet achieves an optimal balance between
tracking performance - mean Average Recall (mAR) and resource usage. With
respect to a baseline with full Katch-Upusage and maximum channel usage, we
still increase mAR by 4% while using 50% less of the channel and 30% power
resources associated with Katch-Up. With respect to a fixed strategy using
minimal resources, we increase mAR by 20% while using Katch-Up on 1/3 of the
frames.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 23:01:35 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Callegaro",
"Davide",
""
],
[
"Restuccia",
"Francesco",
""
],
[
"Levorato",
"Marco",
""
]
] |
new_dataset
| 0.993153 |
2201.04236
|
Ethan Weber
|
Ethan Weber, Dim P. Papadopoulos, Agata Lapedriza, Ferda Ofli,
Muhammad Imran, Antonio Torralba
|
Incidents1M: a large-scale dataset of images with natural disasters,
damage, and incidents
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Natural disasters, such as floods, tornadoes, or wildfires, are increasingly
pervasive as the Earth undergoes global warming. It is difficult to predict
when and where an incident will occur, so timely emergency response is critical
to saving the lives of those endangered by destructive events. Fortunately,
technology can play a role in these situations. Social media posts can be used
as a low-latency data source to understand the progression and aftermath of a
disaster, yet parsing this data is tedious without automated methods. Prior
work has mostly focused on text-based filtering, yet image and video-based
filtering remains largely unexplored. In this work, we present the Incidents1M
Dataset, a large-scale multi-label dataset which contains 977,088 images, with
43 incident and 49 place categories. We provide details of the dataset
construction, statistics and potential biases; introduce and train a model for
incident detection; and perform image-filtering experiments on millions of
images on Flickr and Twitter. We also present some applications on incident
analysis to encourage and enable future work in computer vision for
humanitarian aid. Code, data, and models are available at
http://incidentsdataset.csail.mit.edu.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 23:03:57 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Weber",
"Ethan",
""
],
[
"Papadopoulos",
"Dim P.",
""
],
[
"Lapedriza",
"Agata",
""
],
[
"Ofli",
"Ferda",
""
],
[
"Imran",
"Muhammad",
""
],
[
"Torralba",
"Antonio",
""
]
] |
new_dataset
| 0.999847 |
2201.04255
|
Dongfang Zhao
|
Dongfang Zhao
|
Rache: Radix-additive caching for homomorphic encryption
| null | null | null | null |
cs.CR cs.DC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
One of the biggest concerns for many applications in cloud computing lies in
data privacy. A potential solution to this problem is homomorphic encryption
(HE), which supports certain operations directly over the ciphertexts.
Conventional HE schemes, however, exhibit significant performance overhead and
are hardly applicable to real-world applications. This paper presents Rache, a
caching optimization for accelerating the performance of HE schemes. The key
insights of Rache include (i) caching some homomorphic ciphertexts before
encrypting the large volume of plaintexts; (ii) expanding the plaintexts into a
summation of powers of radixes; and (iii) constructing the ciphertexts with
only homomorphic addition. The extensive evaluation shows that Rache exhibits
almost linear scalability and outperforms Paillier by orders of magnitude.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 00:54:37 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Zhao",
"Dongfang",
""
]
] |
new_dataset
| 0.999247 |
2201.04265
|
Bhargav Gokalgandhi
|
Bhargav Gokalgandhi, Ivan Seskar
|
Distributed Processing for Encoding and Decoding of Binary LDPC codes
using MPI
|
This project was funded by the NSF "COSMOS" Project under grant
number CNS-1827923. Presented in INFOCOM 2019 CNERT Workshop
| null |
10.1109/INFCOMW.2019.8845079
| null |
cs.DC cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Low Density Parity Check (LDPC) codes are linear error correcting codes used
in communication systems for Forward Error Correction (FEC). But, intensive
computation is required for encoding and decoding of LDPC codes, making it
difficult for practical usage in general purpose software based signal
processing systems. In order to accelerate the encoding and decoding of LDPC
codes, distributed processing over multiple multi-core CPUs using Message
Passing Interface (MPI) is performed. Implementation is done using Stream
Processing and Batch Processing mechanisms and the execution time for both
implementations is compared w.r.t variation in number of CPUs and number of
cores per CPU. Performance evaluation of distributed processing is shown by
variation in execution time w.r.t. increase in number of processors (CPU
cores).
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 01:40:01 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Gokalgandhi",
"Bhargav",
""
],
[
"Seskar",
"Ivan",
""
]
] |
new_dataset
| 0.992389 |
2201.04279
|
Abdelrahman Younes
|
Abdelrahman Younes
|
Dynamical Audio-Visual Navigation: Catching Unheard Moving Sound Sources
in Unmapped 3D Environments
| null | null | null | null |
cs.CV cs.LG cs.RO cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent work on audio-visual navigation targets a single static sound in
noise-free audio environments and struggles to generalize to unheard sounds. We
introduce the novel dynamic audio-visual navigation benchmark in which an
embodied AI agent must catch a moving sound source in an unmapped environment
in the presence of distractors and noisy sounds. We propose an end-to-end
reinforcement learning approach that relies on a multi-modal architecture that
fuses the spatial audio-visual information from a binaural audio signal and
spatial occupancy maps to encode the features needed to learn a robust
navigation policy for our new complex task settings. We demonstrate that our
approach outperforms the current state-of-the-art with better generalization to
unheard sounds and better robustness to noisy scenarios on the two challenging
3D scanned real-world datasets Replica and Matterport3D, for the static and
dynamic audio-visual navigation benchmarks. Our novel benchmark will be made
available at http://dav-nav.cs.uni-freiburg.de.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 03:08:03 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Younes",
"Abdelrahman",
""
]
] |
new_dataset
| 0.995688 |
2201.04280
|
Yao Su
|
Yao Su, Yuhong Jiang, Yixin Zhu, Hangxin Liu
|
Object Gathering with a Tethered Robot Duo
| null |
IEEE Robotics and Automation Letters, 2022
|
10.1109/LRA.2022.3141828
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We devise a cooperative planning framework to generate optimal trajectories
for a tethered robot duo, who is tasked to gather scattered objects spread in a
large area using a flexible net. Specifically, the proposed planning framework
first produces a set of dense waypoints for each robot, serving as the
initialization for optimization. Next, we formulate an iterative optimization
scheme to generate smooth and collision-free trajectories while ensuring
cooperation within the robot duo to efficiently gather objects and properly
avoid obstacles. We validate the generated trajectories in simulation and
implement them in physical robots using Model Reference Adaptive Controller
(MRAC) to handle unknown dynamics of carried payloads. In a series of studies,
we find that: (i) a U-shape cost function is effective in planning cooperative
robot duo, and (ii) the task efficiency is not always proportional to the
tethered net's length. Given an environment configuration, our framework can
gauge the optimal net length. To our best knowledge, ours is the first that
provides such estimation for tethered robot duo.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 03:12:40 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Su",
"Yao",
""
],
[
"Jiang",
"Yuhong",
""
],
[
"Zhu",
"Yixin",
""
],
[
"Liu",
"Hangxin",
""
]
] |
new_dataset
| 0.971617 |
2201.04353
|
Y.C. Tay
|
Y.C. Tay, Mostafa Rezazad and Hamid Sarbazi-Azad
|
A simple model for citation curve
|
13 pages, 19 figures, 2 tables
| null | null | null |
cs.DL
|
http://creativecommons.org/licenses/by/4.0/
|
There is considerable interest in the citation count for an author's
publications. This has led to many proposals for citation indices for
characterizing citation distributions. However, there is so far no tractable
model to facilitate the analysis of these distributions and the design of these
indices. This paper presents a simple equation for such design and analysis.
The equation has three parameters that are calibrated by three geometrical
characteristics of a citation distribution. Its simple form makes it tractable.
To demonstrate, the equation is used to derive closed-form expressions for
various citation indices, analyze the effect of time and identify individual
contribution to the Hirsch index for a group.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 08:01:00 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Tay",
"Y. C.",
""
],
[
"Rezazad",
"Mostafa",
""
],
[
"Sarbazi-Azad",
"Hamid",
""
]
] |
new_dataset
| 0.991734 |
2201.04409
|
Jong-Hyeok Park
|
Jong-Hyeok Park, Gihwan Oh, Sang-Won Lee
|
Enlightening Flash Storage to Stream Writes by Objects
| null | null | null | null |
cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
For a write request, today flash storage cannot distinguish the logical
object it comes from. In such object-oblivious flash devices, concurrent writes
from different objects are simply packed in their arrival order to flash memory
blocks; hence objects with different lifetimes are multiplexed onto the same
flash blocks. This multiplexing incurs write amplification, worsening the
performance. Tackling the multiplexing problem, we propose a novel interface
for flash storage, FlashAlloc. It is used to pass the logical address ranges of
logical objects to the flash storage and thus enlighten the storage to stream
writes by objects. The object-aware flash storage can de-multiplex writes from
different objects with distinct deathtimes into per-object dedicated flash
blocks. Given that popular data stores separate writes using objects (e.g.,
SSTables in RocksDB), we can achieve, unlike the existing solutions,
transparent write streaming just by calling FlashAlloc upon object creation.
Our experimental results using an open-source SSD prototype demonstrate that
FlashAlloc can reduce write amplification factor (WAF) in RocksDB, F2FS, and
MySQL by 1.5, 2.5, and 0.3, respectively and thus improve throughput by 2x,
1.8x, and 1.2x, respectively. In particular, FlashAlloc will mitigate the
interference among multitenants. When RocksDB and MySQL were run together on
the same SSD, FlashAlloc decreased WAF from 4.2 to 2.5 and doubled their
throughputs.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 10:51:15 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Park",
"Jong-Hyeok",
""
],
[
"Oh",
"Gihwan",
""
],
[
"Lee",
"Sang-Won",
""
]
] |
new_dataset
| 0.974167 |
2201.04425
|
Pavlo Mykytyn
|
Pavlo Mykytyn, Marcin Brzozowski, Zoya Dyka and Peter Langendoerfer
|
Jamming Detection for IR-UWB Ranging Technology in Autonomous UAV Swarms
|
6 pages, 1 figure
|
2021 10th MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING, p. 81-86
|
10.1109/MECO52532.2021.9460250
| null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Jamming is a form of the Denial of Service (J-DoS) attack. It is a
significant threat that causes malfunction in Unmanned Aerial Vehicle systems,
especially when used in hostile environments. The attackers mainly operate in
the wireless communication environment by following a few preexisting
scenarios. In this paper, we propose an idea for a Jamming detection mechanism.
The mechanism utilizes the network parameters available to the system and some
additional measures to distinguish between bad transmission quality and Jamming
to avoid false positive alarms. After detecting a Jamming attack, appropriate
countermeasures or mitigation techniques can be applied to keep the system
safe.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 11:45:32 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Mykytyn",
"Pavlo",
""
],
[
"Brzozowski",
"Marcin",
""
],
[
"Dyka",
"Zoya",
""
],
[
"Langendoerfer",
"Peter",
""
]
] |
new_dataset
| 0.950752 |
2201.04477
|
Giovanni Sileno
|
Giovanni Sileno, Thomas van Binsbergen, Matteo Pascucci, Tom van
Engers
|
DPCL: a Language Template for Normative Specifications
|
position paper at ProLaLa workshop @ POPL2022
| null | null | null |
cs.AI cs.FL cs.MA cs.PL cs.SC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Several solutions for specifying normative artefacts (norms, contracts,
policies) in a computational processable way have been presented in the
literature. Legal core ontologies have been proposed to systematize concepts
and relationships relevant to normative reasoning. However, no solution amongst
those has achieved general acceptance, and no common ground (representational,
computational) has been identified enabling us to easily compare them. Yet, all
these efforts share the same motivation of representing normative directives,
therefore it is plausible that there may be a representational model
encompassing all of them. This presentation will introduce DPCL, a
domain-specific language (DSL) for specifying higher-level policies (including
norms, contracts, etc.), centred on Hohfeld's framework of fundamental legal
concepts. DPCL has to be seen primarily as a "template", i.e. as an
informational model for architectural reference, rather than a fully-fledged
formal language; it aims to make explicit the general requirements that should
be expected in a language for norm specification. In this respect, it goes
rather in the direction of legal core ontologies, but differently from those,
our proposal aims to keep the character of a DSL, rather than a set of axioms
in a logical framework: it is meant to be cross-compiled to underlying
languages/tools adequate to the type of target application. We provide here an
overview of some of the language features.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 13:51:11 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Sileno",
"Giovanni",
""
],
[
"van Binsbergen",
"Thomas",
""
],
[
"Pascucci",
"Matteo",
""
],
[
"van Engers",
"Tom",
""
]
] |
new_dataset
| 0.979532 |
2201.04494
|
Qingyong Hu
|
Qingyong Hu, Bo Yang, Sheikh Khalid, Wen Xiao, Niki Trigoni, Andrew
Markham
|
SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point
Clouds
|
Accepted by IJCV 2022
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the recent availability and affordability of commercial depth sensors
and 3D scanners, an increasing number of 3D (i.e., RGBD, point cloud) datasets
have been publicized to facilitate research in 3D computer vision. However,
existing datasets either cover relatively small areas or have limited semantic
annotations. Fine-grained understanding of urban-scale 3D scenes is still in
its infancy. In this paper, we introduce SensatUrban, an urban-scale UAV
photogrammetry point cloud dataset consisting of nearly three billion points
collected from three UK cities, covering 7.6 km^2. Each point in the dataset
has been labelled with fine-grained semantic annotations, resulting in a
dataset that is three times the size of the previous existing largest
photogrammetric point cloud dataset. In addition to the more commonly
encountered categories such as road and vegetation, urban-level categories
including rail, bridge, and river are also included in our dataset. Based on
this dataset, we further build a benchmark to evaluate the performance of
state-of-the-art segmentation algorithms. In particular, we provide a
comprehensive analysis and identify several key challenges limiting urban-scale
point cloud understanding. The dataset is available at
http://point-cloud-analysis.cs.ox.ac.uk.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 14:48:11 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Hu",
"Qingyong",
""
],
[
"Yang",
"Bo",
""
],
[
"Khalid",
"Sheikh",
""
],
[
"Xiao",
"Wen",
""
],
[
"Trigoni",
"Niki",
""
],
[
"Markham",
"Andrew",
""
]
] |
new_dataset
| 0.999721 |
2201.04596
|
Dingmin Wang
|
Dingmin Wang, Pan Hu, Przemys{\l}aw Andrzej Wa{\l}\k{e}ga, Bernardo
Cuenca Grau
|
MeTeoR: Practical Reasoning in Datalog with Metric Temporal Operators
|
Accepted To AAAI 2022
| null | null | null |
cs.AI cs.DB
|
http://creativecommons.org/licenses/by/4.0/
|
DatalogMTL is an extension of Datalog with operators from metric temporal
logic which has received significant attention in recent years. It is a highly
expressive knowledge representation language that is well-suited for
applications in temporal ontology-based query answering and stream processing.
Reasoning in DatalogMTL is, however, of high computational complexity, making
implementation challenging and hindering its adoption in applications. In this
paper, we present a novel approach for practical reasoning in DatalogMTL which
combines materialisation (a.k.a. forward chaining) with automata-based
techniques. We have implemented this approach in a reasoner called MeTeoR and
evaluated its performance using a temporal extension of the Lehigh University
Benchmark and a benchmark based on real-world meteorological data. Our
experiments show that MeTeoR is a scalable system which enables reasoning over
complex temporal rules and datasets involving tens of millions of temporal
facts.
|
[
{
"version": "v1",
"created": "Wed, 12 Jan 2022 17:46:18 GMT"
}
] | 2022-01-13T00:00:00 |
[
[
"Wang",
"Dingmin",
""
],
[
"Hu",
"Pan",
""
],
[
"Wałęga",
"Przemysław Andrzej",
""
],
[
"Grau",
"Bernardo Cuenca",
""
]
] |
new_dataset
| 0.988521 |
1906.03588
|
Achintya Sarkar
|
Zheng-Hua Tan, Achintya kr. Sarkar, Najim Dehak
|
rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection
Method
| null |
Computer Speech & Language, volume 59, January 2020, Pages 1-21
| null | null |
cs.SD cs.CL cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents an unsupervised segment-based method for robust voice
activity detection (rVAD). The method consists of two passes of denoising
followed by a voice activity detection (VAD) stage. In the first pass,
high-energy segments in a speech signal are detected by using a posteriori
signal-to-noise ratio (SNR) weighted energy difference and if no pitch is
detected within a segment, the segment is considered as a high-energy noise
segment and set to zero. In the second pass, the speech signal is denoised by a
speech enhancement method, for which several methods are explored. Next,
neighbouring frames with pitch are grouped together to form pitch segments, and
based on speech statistics, the pitch segments are further extended from both
ends in order to include both voiced and unvoiced sounds and likely non-speech
parts as well. In the end, a posteriori SNR weighted energy difference is
applied to the extended pitch segments of the denoised speech signal for
detecting voice activity. We evaluate the VAD performance of the proposed
method using two databases, RATS and Aurora-2, which contain a large variety of
noise conditions. The rVAD method is further evaluated, in terms of speaker
verification performance, on the RedDots 2016 challenge database and its
noise-corrupted versions. Experiment results show that rVAD is compared
favourably with a number of existing methods. In addition, we present a
modified version of rVAD where computationally intensive pitch extraction is
replaced by computationally efficient spectral flatness calculation. The
modified version significantly reduces the computational complexity at the cost
of moderately inferior VAD performance, which is an advantage when processing a
large amount of data and running on low resource devices. The source code of
rVAD is made publicly available.
|
[
{
"version": "v1",
"created": "Sun, 9 Jun 2019 07:51:23 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Jan 2022 14:26:11 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Tan",
"Zheng-Hua",
""
],
[
"Sarkar",
"Achintya kr.",
""
],
[
"Dehak",
"Najim",
""
]
] |
new_dataset
| 0.9948 |
2011.03726
|
Xiaobo Zhou
|
Xiaobo Zhou, Shihao Yan, Qingqing Wu, Feng Shu, and Derrick Wing Kwan
Ng
|
Intelligent Reflecting Surface (IRS)-Aided Covert Wireless
Communications with Delay Constraint
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work examines the performance gain achieved by deploying an intelligent
reflecting surface (IRS) in covert communications. To this end, we formulate
the joint design of the transmit power and the IRS reflection coefficients by
taking into account the communication covertness for the cases with global
channel state information (CSI) and without a warden's instantaneous CSI. For
the case of global CSI, we first prove that perfect covertness is achievable
with the aid of the IRS even for a single-antenna transmitter, which is
impossible without an IRS. Then, we develop a penalty successive convex
approximation (PSCA) algorithm to tackle the design problem. Considering the
high complexity of the PSCA algorithm, we further propose a low-complexity
two-stage algorithm, where analytical expressions for the transmit power and
the IRS's reflection coefficients are derived. For the case without the
warden's instantaneous CSI, we first derive the covertness constraint
analytically facilitating the optimal phase shift design. Then, we consider
three hardware-related constraints on the IRS's reflection amplitudes and
determine their optimal designs together with the optimal transmit power. Our
examination shows that significant performance gain can be achieved by
deploying an IRS into covert communications.
|
[
{
"version": "v1",
"created": "Sat, 7 Nov 2020 08:49:56 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Jan 2022 13:49:06 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Zhou",
"Xiaobo",
""
],
[
"Yan",
"Shihao",
""
],
[
"Wu",
"Qingqing",
""
],
[
"Shu",
"Feng",
""
],
[
"Ng",
"Derrick Wing Kwan",
""
]
] |
new_dataset
| 0.992057 |
2103.08119
|
Guanhao Fu
|
Guanhao Fu, Ehsan Azimi, Peter Kazanzides
|
Mobile Teleoperation: Feasibility of Wireless Wearable Sensing of the
Operator's Arm Motion
|
6 pages, 11 figures. Accepted to 2021 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS)
| null |
10.1109/IROS51168.2021.9636838
| null |
cs.RO cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Teleoperation platforms often require the user to be situated at a fixed
location to both visualize and control the movement of the robot and thus do
not provide the operator with much mobility. One example is in existing robotic
surgery solutions that require the surgeons to be away from the patient,
attached to consoles where their heads must be fixed and their arms can only
move in a limited space. This creates a barrier between physicians and patients
that does not exist in normal surgery. To address this issue, we propose a
mobile telesurgery solution where the surgeons are no longer mechanically
limited to control consoles and are able to teleoperate the robots from the
patient bedside, using their arms equipped with wireless sensors and viewing
the endoscope video via optical see-through head-mounted displays (HMDs). We
evaluate the feasibility and efficiency of our user interaction method compared
to a standard surgical robotic manipulator via two tasks with different levels
of required dexterity. The results indicate that with sufficient training our
proposed platform can attain similar efficiency while providing added mobility
for the operator.
|
[
{
"version": "v1",
"created": "Mon, 15 Mar 2021 03:30:11 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Jan 2022 17:53:09 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Fu",
"Guanhao",
""
],
[
"Azimi",
"Ehsan",
""
],
[
"Kazanzides",
"Peter",
""
]
] |
new_dataset
| 0.999561 |
2105.02736
|
Daniel Paulusma
|
Giacomo Paesani and Dani\"el Paulusma and Pawe{\l} Rz\k{a}\.zewski
|
Feedback Vertex Set and Even Cycle Transversal for H-Free Graphs:
Finding Large Block Graphs
| null | null | null | null |
cs.DS cs.CC cs.DM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We prove new complexity results for Feedback Vertex Set and Even Cycle
Transversal on $H$-free graphs, that is, graphs that do not contain some fixed
graph $H$ as an induced subgraph. In particular, we prove that for every $s\geq
1$, both problems are polynomial-time solvable for $sP_3$-free graphs and
$(sP_1+P_5)$-free graphs; here, the graph $sP_3$ denotes the disjoint union of
$s$ paths on three vertices and the graph $sP_1+P_5$ denotes the disjoint union
of $s$ isolated vertices and a path on five vertices. Our new results for
Feedback Vertex Set extend all known polynomial-time results for Feedback
Vertex Set on $H$-free graphs, namely for $sP_2$-free graphs [Chiarelli et al.,
TCS 2018], $(sP_1+P_3)$-free graphs [Dabrowski et al., Algorithmica 2020] and
$P_5$-free graphs [Abrishami et al., SODA 2021]. Together, the new results also
show that both problems exhibit the same behaviour on $H$-free graphs (subject
to some open cases). This is in part due to a new general algorithm we design
for finding in a ($sP_3)$-free or $(sP_1+P_5)$-free graph $G$ a largest induced
subgraph whose blocks belong to some finite class ${\cal C}$ of graphs. We also
compare our results with the state-of-the-art results for the Odd Cycle
Transversal problem, which is known to behave differently on $H$-free graphs.
|
[
{
"version": "v1",
"created": "Thu, 6 May 2021 14:56:38 GMT"
},
{
"version": "v2",
"created": "Sat, 1 Jan 2022 19:17:20 GMT"
},
{
"version": "v3",
"created": "Mon, 10 Jan 2022 23:39:07 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Paesani",
"Giacomo",
""
],
[
"Paulusma",
"Daniël",
""
],
[
"Rzążewski",
"Paweł",
""
]
] |
new_dataset
| 0.99822 |
2105.14517
|
Jiaqi Chen
|
Jiaqi Chen, Jianheng Tang, Jinghui Qin, Xiaodan Liang, Lingbo Liu,
Eric P. Xing, Liang Lin
|
GeoQA: A Geometric Question Answering Benchmark Towards Multimodal
Numerical Reasoning
|
Accepted to Findings of ACL 2021
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automatic math problem solving has recently attracted increasing attention as
a long-standing AI benchmark. In this paper, we focus on solving geometric
problems, which requires a comprehensive understanding of textual descriptions,
visual diagrams, and theorem knowledge. However, the existing methods were
highly dependent on handcraft rules and were merely evaluated on small-scale
datasets. Therefore, we propose a Geometric Question Answering dataset GeoQA,
containing 4,998 geometric problems with corresponding annotated programs,
which illustrate the solving process of the given problems. Compared with
another publicly available dataset GeoS, GeoQA is 25 times larger, in which the
program annotations can provide a practical testbed for future research on
explicit and explainable numerical reasoning. Moreover, we introduce a Neural
Geometric Solver (NGS) to address geometric problems by comprehensively parsing
multimodal information and generating interpretable programs. We further add
multiple self-supervised auxiliary tasks on NGS to enhance cross-modal semantic
representation. Extensive experiments on GeoQA validate the effectiveness of
our proposed NGS and auxiliary tasks. However, the results are still
significantly lower than human performance, which leaves large room for future
research. Our benchmark and code are released at
https://github.com/chen-judge/GeoQA .
|
[
{
"version": "v1",
"created": "Sun, 30 May 2021 12:34:17 GMT"
},
{
"version": "v2",
"created": "Tue, 8 Jun 2021 02:53:03 GMT"
},
{
"version": "v3",
"created": "Tue, 11 Jan 2022 03:50:31 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Chen",
"Jiaqi",
""
],
[
"Tang",
"Jianheng",
""
],
[
"Qin",
"Jinghui",
""
],
[
"Liang",
"Xiaodan",
""
],
[
"Liu",
"Lingbo",
""
],
[
"Xing",
"Eric P.",
""
],
[
"Lin",
"Liang",
""
]
] |
new_dataset
| 0.99971 |
2107.09244
|
Li Shen
|
Li Shen, Yao Lu, Hao Chen, Hao Wei, Donghai Xie, Jiabao Yue, Rui Chen,
Shouye Lv, Bitao Jiang
|
S2Looking: A Satellite Side-Looking Dataset for Building Change
Detection
| null |
Remote Sens. 2021, 13, 5094
|
10.3390/rs13245094
| null |
cs.CV cs.AI eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Building-change detection underpins many important applications, especially
in the military and crisis-management domains. Recent methods used for change
detection have shifted towards deep learning, which depends on the quality of
its training data. The assembly of large-scale annotated satellite imagery
datasets is therefore essential for global building-change surveillance.
Existing datasets almost exclusively offer near-nadir viewing angles. This
limits the range of changes that can be detected. By offering larger
observation ranges, the scroll imaging mode of optical satellites presents an
opportunity to overcome this restriction. This paper therefore introduces
S2Looking, a building-change-detection dataset that contains large-scale
side-looking satellite images captured at various off-nadir angles. The dataset
consists of 5000 bitemporal image pairs of rural areas and more than 65,920
annotated instances of changes throughout the world. The dataset can be used to
train deep-learning-based change-detection algorithms. It expands upon existing
datasets by providing (1) larger viewing angles; (2) large illumination
variances; and (3) the added complexity of rural images. To facilitate {the}
use of the dataset, a benchmark task has been established, and preliminary
tests suggest that deep-learning algorithms find the dataset significantly more
challenging than the closest-competing near-nadir dataset, LEVIR-CD+. S2Looking
may therefore promote important advances in existing building-change-detection
algorithms. The dataset is available at https://github.com/S2Looking/.
|
[
{
"version": "v1",
"created": "Tue, 20 Jul 2021 03:31:00 GMT"
},
{
"version": "v2",
"created": "Sun, 26 Sep 2021 03:21:47 GMT"
},
{
"version": "v3",
"created": "Tue, 11 Jan 2022 06:54:03 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Shen",
"Li",
""
],
[
"Lu",
"Yao",
""
],
[
"Chen",
"Hao",
""
],
[
"Wei",
"Hao",
""
],
[
"Xie",
"Donghai",
""
],
[
"Yue",
"Jiabao",
""
],
[
"Chen",
"Rui",
""
],
[
"Lv",
"Shouye",
""
],
[
"Jiang",
"Bitao",
""
]
] |
new_dataset
| 0.999706 |
2108.00580
|
Weifeng Ge
|
Gangming Zhao, Weifeng Ge, and Yizhou Yu
|
GraphFPN: Graph Feature Pyramid Network for Object Detection
|
accepted by ICCV 2021, codes are updated at
https://github.com/GangmingZhao/GraphFPN-Graph-Feature-Pyramid-Network-for-Object-Detection
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Feature pyramids have been proven powerful in image understanding tasks that
require multi-scale features. State-of-the-art methods for multi-scale feature
learning focus on performing feature interactions across space and scales using
neural networks with a fixed topology. In this paper, we propose graph feature
pyramid networks that are capable of adapting their topological structures to
varying intrinsic image structures and supporting simultaneous feature
interactions across all scales. We first define an image-specific superpixel
hierarchy for each input image to represent its intrinsic image structures. The
graph feature pyramid network inherits its structure from this superpixel
hierarchy. Contextual and hierarchical layers are designed to achieve feature
interactions within the same scale and across different scales. To make these
layers more powerful, we introduce two types of local channel attention for
graph neural networks by generalizing global channel attention for
convolutional neural networks. The proposed graph feature pyramid network can
enhance the multiscale features from a convolutional feature pyramid network.
We evaluate our graph feature pyramid network in the object detection task by
integrating it into the Faster R-CNN algorithm. The modified algorithm
outperforms not only previous state-of-the-art feature pyramid-based methods
with a clear margin but also other popular detection methods on both MS-COCO
2017 validation and test datasets.
|
[
{
"version": "v1",
"created": "Mon, 2 Aug 2021 01:19:38 GMT"
},
{
"version": "v2",
"created": "Sun, 29 Aug 2021 10:34:34 GMT"
},
{
"version": "v3",
"created": "Sat, 8 Jan 2022 12:21:21 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Zhao",
"Gangming",
""
],
[
"Ge",
"Weifeng",
""
],
[
"Yu",
"Yizhou",
""
]
] |
new_dataset
| 0.993791 |
2109.07428
|
Young-Ho Kim
|
Young-Ho Kim, Ankur Kapoor, Tommaso Mansi, Ali Kamen
|
A Wide-area, Low-latency, and Power-efficient 6-DoF Pose Tracking System
for Rigid Objects
| null | null | null | null |
cs.RO cs.CV cs.SY eess.SP eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Position sensitive detectors (PSDs) offer possibility to track single active
marker's two (or three) degrees of freedom (DoF) position with a high accuracy,
while having a fast response time with high update frequency and low latency,
all using a very simple signal processing circuit. However they are not
particularly suitable for 6-DoF object pose tracking system due to lack of
orientation measurement, limited tracking range, and sensitivity to
environmental variation. We propose a novel 6-DoF pose tracking system for a
rigid object tracking requiring a single active marker. The proposed system
uses a stereo-based PSD pair and multiple Inertial Measurement Units (IMUs).
This is done based on a practical approach to identify and control the power of
Infrared-Light Emitting Diode (IR-LED) active markers, with an aim to increase
the tracking work space and reduce the power consumption. Our proposed tracking
system is validated with three different work space sizes and for static and
dynamic positional accuracy using robotic arm manipulator with three different
dynamic motion patterns. The results show that the static position
root-mean-square (RMS) error is 0.6mm. The dynamic position RMS error is
0.7-0.9mm. The orientation RMS error is between 0.04 and 0.9 degree at varied
dynamic motion. Overall, our proposed tracking system is capable of tracking a
rigid object pose with sub-millimeter accuracy at the mid range of the work
space and sub-degree accuracy for all work space under a lab setting.
|
[
{
"version": "v1",
"created": "Wed, 15 Sep 2021 17:01:37 GMT"
},
{
"version": "v2",
"created": "Mon, 10 Jan 2022 22:37:16 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Kim",
"Young-Ho",
""
],
[
"Kapoor",
"Ankur",
""
],
[
"Mansi",
"Tommaso",
""
],
[
"Kamen",
"Ali",
""
]
] |
new_dataset
| 0.995306 |
2109.08931
|
Bodin Chinthanet
|
Bodin Chinthanet, Raula Gaikovina Kula, Rodrigo Eliza Zapata, Takashi
Ishio, Kenichi Matsumoto, Akinori Ihara
|
S\=ojiTantei: Function-Call Reachability Detection of Vulnerable Code
for npm Packages
|
To be published in IEICE Transactions on Information and Systems
(Special Section on Empirical Software Engineering)
| null |
10.1587/transinf.2021MPL0001
| null |
cs.SE cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It has become common practice for software projects to adopt third-party
dependencies. Developers are encouraged to update any outdated dependency to
remain safe from potential threats of vulnerabilities. In this study, we
present an approach to aid developers show whether or not a vulnerable code is
reachable for JavaScript projects. Our prototype, S\=ojiTantei, is evaluated in
two ways (i) the accuracy when compared to a manual approach and (ii) a
larger-scale analysis of 780 clients from 78 security vulnerability cases. The
first evaluation shows that S\=ojiTantei has a high accuracy of 83.3%, with a
speed of less than a second analysis per client. The second evaluation reveals
that 68 out of the studied 78 vulnerabilities reported having at least one
clean client. The study proves that automation is promising with the potential
for further improvement.
|
[
{
"version": "v1",
"created": "Sat, 18 Sep 2021 13:17:44 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Chinthanet",
"Bodin",
""
],
[
"Kula",
"Raula Gaikovina",
""
],
[
"Zapata",
"Rodrigo Eliza",
""
],
[
"Ishio",
"Takashi",
""
],
[
"Matsumoto",
"Kenichi",
""
],
[
"Ihara",
"Akinori",
""
]
] |
new_dataset
| 0.999126 |
2110.08992
|
Dan Gordon
|
Dan Gordon, Paul Scott, Sylvie Thi\'ebaux
|
SmartGridToolbox: A Library for Simulating Modern and Future Electricity
Networks
|
20 pages, 9 figures
| null | null | null |
cs.CE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present SmartGridToolbox: a C++ library for simulating modern and future
electricity networks. SmartGridToolbox is distinguished by the fact that it is
a general purpose library (rather than an application), that emphasizes
flexibility, extensibility, and ability to interface with a wide range of other
tools, such as optimization technologies. It incorporates fully unbalanced
network modelling, fast power flow and OPF solvers, a discrete-event simulation
engine, and a component library that includes network components like lines,
cables, transformers, ZIP loads and generators, renewable and storage
components like PV generation and batteries, inverters, tap changers, PV,
generic time dependent loads and more. We anticipate that SmartGridToolbox will
be useful to researchers who require accurate simulations of electricity
networks that go beyond simple applications of load flow - for example, by
incorporating custom optimisation algorithms, controllers, devices, or network
management strategies. Being a library, it is also perfect for developing a
wide range of end use applications. We start with a comparison to existing open
source software, and move on to present its main features and benchmark
results. We conclude by discussing four applications, most notably, the use of
SmartGridToolbox in the CONSORT Bruny Island Battery Trial, conducted between
2016 and 2019.
|
[
{
"version": "v1",
"created": "Mon, 18 Oct 2021 03:10:16 GMT"
},
{
"version": "v2",
"created": "Tue, 11 Jan 2022 06:14:51 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Gordon",
"Dan",
""
],
[
"Scott",
"Paul",
""
],
[
"Thiébaux",
"Sylvie",
""
]
] |
new_dataset
| 0.996048 |
2112.13508
|
Mengjian Zhang
|
Mengjian Zhang, Guihua Wen, and Jing Yang
|
Duck swarm algorithm: a novel swarm intelligence algorithm
| null | null |
10.1016/S0550-2112(01)13508-9
| null |
cs.NE cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
A swarm intelligence-based optimization algorithm, named Duck Swarm Algorithm
(DSA), is proposed in this paper. This algorithm is inspired by the searching
for food sources and foraging behaviors of the duck swarm. The performance of
DSA is verified by using eighteen benchmark functions, where it is statistical
(best, mean, standard deviation, and average running time) results are compared
with seven well-known algorithms like Particle swarm optimization (PSO),
Firefly algorithm (FA), Chicken swarm optimization (CSO), Grey wolf optimizer
(GWO), Sine cosine algorithm (SCA), and Marine-predators algorithm (MPA), and
Archimedes optimization algorithm (AOA). Moreover, the Wilcoxon rank-sum test,
Friedman test, and convergence curves of the comparison results are used to
prove the superiority of the DSA against other algorithms. The results
demonstrate that DSA is a high-performance optimization method in terms of
convergence speed and exploration-exploitation balance for solving
high-dimension optimization functions. Also, DSA is applied for the optimal
design of two constrained engineering problems (the Three-bar truss problem,
and the Sawmill operation problem). Additionally, four engineering constraint
problems have also been used to analyze the performance of the proposed DSA.
Overall, the comparison results revealed that the DSA is a promising and very
competitive algorithm for solving different optimization problems.
|
[
{
"version": "v1",
"created": "Mon, 27 Dec 2021 04:53:36 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Zhang",
"Mengjian",
""
],
[
"Wen",
"Guihua",
""
],
[
"Yang",
"Jing",
""
]
] |
new_dataset
| 0.99087 |
2201.03710
|
Zhaohui Wang
|
Zhaohui Wang, Xiao Lin, Abhinav Mishra, Ram Sriharsha
|
Online Changepoint Detection on a Budget
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Changepoints are abrupt variations in the underlying distribution of data.
Detecting changes in a data stream is an important problem with many
applications. In this paper, we are interested in changepoint detection
algorithms which operate in an online setting in the sense that both its
storage requirements and worst-case computational complexity per observation
are independent of the number of previous observations. We propose an online
changepoint detection algorithm for both univariate and multivariate data which
compares favorably with offline changepoint detection algorithms while also
operating in a strictly more constrained computational model. In addition, we
present a simple online hyperparameter auto tuning technique for these
algorithms.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 00:20:33 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Wang",
"Zhaohui",
""
],
[
"Lin",
"Xiao",
""
],
[
"Mishra",
"Abhinav",
""
],
[
"Sriharsha",
"Ram",
""
]
] |
new_dataset
| 0.990264 |
2201.03737
|
Shin Sano
|
Shin Sano and Seiji Yamada
|
D-Graph: AI-Assisted Design Concept Exploration Graph
|
16 pages, 6 figures
| null | null | null |
cs.HC cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
We present an AI-assisted search tool, the "Design Concept Exploration Graph"
("D-Graph"). It assists automotive designers in creating an original
design-concept phrase, that is, a combination of two adjectives that conveys
product aesthetics. D-Graph retrieves adjectives from a ConceptNet knowledge
graph as nodes and visualizes them in a dynamically scalable 3D graph as users
explore words. The retrieval algorithm helps in finding unique words by ruling
out overused words on the basis of word frequency from a large text corpus and
words that are too similar between the two in a combination using the cosine
similarity from ConceptNet Numberbatch word embeddings. Our experiment with
participants in the automotive design field that used both the proposed D-Graph
and a baseline tool for design-concept-phrase creation tasks suggested a
positive difference in participants' self-evaluation on the phrases they
created, though not significant. Experts' evaluations on the phrases did not
show significant differences. Negative correlations between the cosine
similarity of the two words in a design-concept phrase and the experts'
evaluation were significant. Our qualitative analysis suggested the directions
for further development of the tool that should help users in adhering to the
strategy of creating compound phrases supported by computational linguistic
principles.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 01:42:00 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Sano",
"Shin",
""
],
[
"Yamada",
"Seiji",
""
]
] |
new_dataset
| 0.992775 |
2201.03746
|
Shunli Wang
|
Shunli Wang, Dingkang Yang, Peng Zhai, Chixiao Chen, Lihua Zhang
|
TSA-Net: Tube Self-Attention Network for Action Quality Assessment
|
9 pages, 7 figures, conference paper
| null |
10.1145/3474085.3475438
| null |
cs.CV cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In recent years, assessing action quality from videos has attracted growing
attention in computer vision community and human computer interaction. Most
existing approaches usually tackle this problem by directly migrating the model
from action recognition tasks, which ignores the intrinsic differences within
the feature map such as foreground and background information. To address this
issue, we propose a Tube Self-Attention Network (TSA-Net) for action quality
assessment (AQA). Specifically, we introduce a single object tracker into AQA
and propose the Tube Self-Attention Module (TSA), which can efficiently
generate rich spatio-temporal contextual information by adopting sparse feature
interactions. The TSA module is embedded in existing video networks to form
TSA-Net. Overall, our TSA-Net is with the following merits: 1) High
computational efficiency, 2) High flexibility, and 3) The state-of-the art
performance. Extensive experiments are conducted on popular action quality
assessment datasets including AQA-7 and MTL-AQA. Besides, a dataset named Fall
Recognition in Figure Skating (FR-FS) is proposed to explore the basic action
assessment in the figure skating scene.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 02:25:27 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Wang",
"Shunli",
""
],
[
"Yang",
"Dingkang",
""
],
[
"Zhai",
"Peng",
""
],
[
"Chen",
"Chixiao",
""
],
[
"Zhang",
"Lihua",
""
]
] |
new_dataset
| 0.999244 |
2201.03820
|
Neeldhara Misra
|
Neeldhara Misra and Saraswati Nanoti
|
Eternal Vertex Cover on Bipartite and Co-Bipartite Graphs
|
38 pages, 15 figures
| null | null | null |
cs.DS cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
Eternal Vertex Cover problem is a dynamic variant of the vertex cover
problem. We have a two player game in which guards are placed on some vertices
of a graph. In every move, one player (the attacker) attacks an edge. In
response to the attack, the second player (defender) moves the guards along the
edges of the graph in such a manner that at least one guard moves along the
attacked edge. If such a movement is not possible, then the attacker wins. If
the defender can defend the graph against an infinite sequence of attacks, then
the defender wins.
The minimum number of guards with which the defender has a winning strategy
is called the Eternal Vertex Cover Number of the graph G. On general graphs,
the computational problem of determining the minimum eternal vertex cover
number is NP-hard and admits a 2-approximation algorithm and an exponential
kernel. The complexity of the problem on bipartite graphs is open, as is the
question of whether the problem admits a polynomial kernel.
We settle both these questions by showing that Eternal Vertex Cover is
NP-hard and does not admit a polynomial compression even on bipartite graphs of
diameter six. This result also holds for split graphs. We also show that the
problem admits a polynomial time algorithm on the class of cobipartite graphs.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 07:56:48 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Misra",
"Neeldhara",
""
],
[
"Nanoti",
"Saraswati",
""
]
] |
new_dataset
| 0.999582 |
2201.03844
|
Marius Beul
|
Marius Beul, Max Schwarz, Jan Quenzel, Malte Splietker, Simon
Bultmann, Daniel Schleich, Andre Rochow, Dmytro Pavlichenko, Radu Alexandru
Rosu, Patrick Lowin, Bruno Scheider, Michael Schreiber, Finn S\"uberkr\"ub,
Sven Behnke
|
Target Chase, Wall Building, and Fire Fighting: Autonomous UAVs of Team
NimbRo at MBZIRC 2020
|
Accepted for Field Robotics, to appear 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Mohamed Bin Zayed International Robotics Challenge (MBZIRC) 2020 posed
diverse challenges for unmanned aerial vehicles (UAVs). We present our four
tailored UAVs, specifically developed for individual aerial-robot tasks of
MBZIRC, including custom hardware- and software components.
In Challenge 1, a target UAV is pursued using a high-efficiency, onboard
object detection pipeline to capture a ball from the target UAV. A second UAV
uses a similar detection method to find and pop balloons scattered throughout
the arena.
For Challenge 2, we demonstrate a larger UAV capable of autonomous aerial
manipulation: Bricks are found and tracked from camera images. Subsequently,
they are approached, picked, transported, and placed on a wall.
Finally, in Challenge 3, our UAV autonomously finds fires using LiDAR and
thermal cameras. It extinguishes the fires with an onboard fire extinguisher.
While every robot features task-specific subsystems, all UAVs rely on a
standard software stack developed for this particular and future competitions.
We present our mostly open-source software solutions, including tools for
system configuration, monitoring, robust wireless communication, high-level
control, and agile trajectory generation. For solving the MBZIRC 2020 tasks, we
advanced the state of the art in multiple research areas like machine vision
and trajectory generation.
We present our scientific contributions that constitute the foundation for
our algorithms and systems and analyze the results from the MBZIRC competition
2020 in Abu Dhabi, where our systems reached second place in the Grand
Challenge. Furthermore, we discuss lessons learned from our participation in
this complex robotic challenge.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 09:08:54 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Beul",
"Marius",
""
],
[
"Schwarz",
"Max",
""
],
[
"Quenzel",
"Jan",
""
],
[
"Splietker",
"Malte",
""
],
[
"Bultmann",
"Simon",
""
],
[
"Schleich",
"Daniel",
""
],
[
"Rochow",
"Andre",
""
],
[
"Pavlichenko",
"Dmytro",
""
],
[
"Rosu",
"Radu Alexandru",
""
],
[
"Lowin",
"Patrick",
""
],
[
"Scheider",
"Bruno",
""
],
[
"Schreiber",
"Michael",
""
],
[
"Süberkrüb",
"Finn",
""
],
[
"Behnke",
"Sven",
""
]
] |
new_dataset
| 0.991063 |
2201.03857
|
Taja Kuzman
|
Taja Kuzman, Peter Rupnik and Nikola Ljube\v{s}i\'c
|
The GINCO Training Dataset for Web Genre Identification of Documents Out
in the Wild
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This paper presents a new training dataset for automatic genre identification
GINCO, which is based on 1,125 crawled Slovenian web documents that consist of
650 thousand words. Each document was manually annotated for genre with a new
annotation schema that builds upon existing schemata, having primarily clarity
of labels and inter-annotator agreement in mind. The dataset consists of
various challenges related to web-based data, such as machine translated
content, encoding errors, multiple contents presented in one document etc.,
enabling evaluation of classifiers in realistic conditions. The initial machine
learning experiments on the dataset show that (1) pre-Transformer models are
drastically less able to model the phenomena, with macro F1 metrics ranging
around 0.22, while Transformer-based models achieve scores of around 0.58, and
(2) multilingual Transformer models work as well on the task as the monolingual
models that were previously proven to be superior to multilingual models on
standard NLP tasks.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 09:39:15 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Kuzman",
"Taja",
""
],
[
"Rupnik",
"Peter",
""
],
[
"Ljubešić",
"Nikola",
""
]
] |
new_dataset
| 0.999811 |
2201.03876
|
Hannane Yaghoubizade
|
Fahad Panolan and Hannane Yaghoubizade
|
Partial Vertex Cover on Graphs of Bounded Degeneracy
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the Partial Vertex Cover (PVC) problem, we are given an $n$-vertex graph
$G$ and a positive integer $k$, and the objective is to find a vertex subset
$S$ of size $k$ maximizing the number of edges with at least one end-point in
$S$. This problem is W[1]-hard on general graphs, but admits a parameterized
subexponential time algorithm with running time $2^{O(\sqrt{k})}n^{O(1)}$ on
planar and apex-minor free graphs [Fomin et al. (FSTTCS 2009, IPL 2011)], and a
$k^{O(k)}n^{O(1)}$ time algorithm on bounded degeneracy graphs [Amini et al.
(FSTTCS 2009, JCSS 2011)]. Graphs of bounded degeneracy contain many sparse
graph classes like planar graphs, $H$-minor free graphs, and bounded tree-width
graphs. In this work, we prove the following results:
1) There is an algorithm for PVC with running time $2^{O(k)}n^{O(1)}$ on
graphs of bounded degeneracy which is an improvement on the previous
$k^{O(k)}n^{O(1)}$ time algorithm by Amini et al.
2) PVC admits a polynomial compression on graphs of bounded degeneracy,
resolving an open problem posed by Amini et al.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 10:34:51 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Panolan",
"Fahad",
""
],
[
"Yaghoubizade",
"Hannane",
""
]
] |
new_dataset
| 0.997346 |
2201.03910
|
Noureddine Moussa
|
Noureddine Moussa and Zakaria Hamidi-Alaoui and Abdelbaki El Belrhiti
El Alaoui
|
EHRP : An effective hybrid routing protocol to compromise between energy
consumption and delay in WSNs
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Sink mobility is seen as a successful strategy to resolve the hotspot problem
in Wireless Sensor Network (WSN). Mobile sinks roam in the network and collect
data from special nodes such as Cluster Heads (CH) by means of short-range
communications which improves the energy efficiency. Numerous mobile sink based
routing protocols have been proposed, however, they incur high delays
especially in large scale networks where the mobile sink has to travel for a
long distance to collect data from CHs and consequently they failed to ensure a
tradeoff between energy efficiency and delay. To resolve this issue, we propose
in this paper an Effective Hybrid Routing Protocol termed as EHRP. The main aim
of this protocol is to combine between single-hop and multi-hop routing.
Indeed, when the mobile sink arrives at a cluster it collects its data while
the other distant CHs continue to send their data using our proposed improved
Ant Colony Optimization (ACO) algorithm to avoid the waiting-time. The existing
ACO algorithms use in the distance heuristic which is not practical in real
world and fail to consider relevant statistic information of energy (e.g.,
minimum energy, average energy) in path selection which leads to unbalanced
energy consumption in the network. To address these issues, the proposed
routing algorithm employs the Received Signal Strength Indicator (RSSI) and
statistic information of energy to consume energy efficiently and decrease the
probability of sending failure. The performance of the proposed routing
protocol is tested and compared with those of the relevant routing protocols.
The simulation results show that, in comparison with its counterparts, EHRP
succeeds to minimize energy consumption and delay as well as enhancing the
packet delivery ratio.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 12:31:10 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Moussa",
"Noureddine",
""
],
[
"Hamidi-Alaoui",
"Zakaria",
""
],
[
"Alaoui",
"Abdelbaki El Belrhiti El",
""
]
] |
new_dataset
| 0.998849 |
2201.03950
|
Gihan Ravideva Mudalige
|
Kamalavasan Kamalakkannan, Istvan Z. Reguly, Suhaib A. Fahmy, Gihan R.
Mudalige
|
High Throughput Multidimensional Tridiagonal Systems Solvers on FPGAs
|
Under review
| null | null | null |
cs.DC cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a design space exploration for synthesizing optimized,
high-throughput implementations of multiple multi-dimensional tridiagonal
system solvers on FPGAs. Re-evaluating the characteristics of algorithms for
the direct solution of tridiagonal systems, we develop a new tridiagonal solver
library aimed at implementing high-performance computing applications on Xilinx
FPGA hardware. Key new features of the library are (1) the unification of
standard state-of-the-art techniques for implementing implicit numerical
solvers with a number of novel high-gain optimizations such as vectorization
and batching, motivated by multi-dimensional systems in real-world
applications, (2) data-flow techniques that provide application specific
optimizations for both 2D and 3D problems, including integration of explicit
loops commonplace in real workloads, and (3) the development of an analytic
model to explore the design space, and obtain rapid performance estimates. The
new library provide an order of magnitude better performance for solving large
batches of systems compared to Xilinx's current tridiagonal solver library. Two
representative applications are implemented using the new solver on a Xilinx
Alveo U280 FPGA, demonstrating over 85% predictive model accuracy. These are
compared with a current state-of-the-art GPU library for solving
multi-dimensional tridiagonal systems on an Nvidia V100 GPU, analyzing time to
solution, bandwidth, and energy consumption. Results show the FPGAs achieving
competitive or better runtime performance for a range of multi-dimensional
problems compared to the V100 GPU. Additionally, the significant energy savings
offered by FPGA implementations, over 30% for the most complex application, are
quantified. We discuss the algorithmic trade-offs required to obtain good
performance on FPGAs, giving insights into the feasibility and profitability of
FPGA implementations.
|
[
{
"version": "v1",
"created": "Tue, 11 Jan 2022 13:53:13 GMT"
}
] | 2022-01-12T00:00:00 |
[
[
"Kamalakkannan",
"Kamalavasan",
""
],
[
"Reguly",
"Istvan Z.",
""
],
[
"Fahmy",
"Suhaib A.",
""
],
[
"Mudalige",
"Gihan R.",
""
]
] |
new_dataset
| 0.982733 |
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