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| versions
list | update_date
timestamp[s] | authors_parsed
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stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2302.01223
|
Bram Van Den Akker
|
Bram van den Akker, Olivier Jeunen, Ying Li, Ben London, Zahra Nazari,
Devesh Parekh
|
Practical Bandits: An Industry Perspective
|
Tutorial held at The Web Conference 2023 (formerly known as WWW) in
Austin, Texas (USA), on April 30 - May 4, 2023
| null | null | null |
cs.LG cs.IR
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The bandit paradigm provides a unified modeling framework for problems that
require decision-making under uncertainty. Because many business metrics can be
viewed as rewards (a.k.a. utilities) that result from actions, bandit
algorithms have seen a large and growing interest from industrial applications,
such as search, recommendation and advertising. Indeed, with the bandit lens
comes the promise of direct optimisation for the metrics we care about.
Nevertheless, the road to successfully applying bandits in production is not
an easy one. Even when the action space and rewards are well-defined,
practitioners still need to make decisions regarding multi-arm or contextual
approaches, on- or off-policy setups, delayed or immediate feedback, myopic or
long-term optimisation, etc. To make matters worse, industrial platforms
typically give rise to large action spaces in which existing approaches tend to
break down. The research literature on these topics is broad and vast, but this
can overwhelm practitioners, whose primary aim is to solve practical problems,
and therefore need to decide on a specific instantiation or approach for each
project. This tutorial will take a step towards filling that gap between the
theory and practice of bandits. Our goal is to present a unified overview of
the field and its existing terminology, concepts and algorithms -- with a focus
on problems relevant to industry. We hope our industrial perspective will help
future practitioners who wish to leverage the bandit paradigm for their
application.
|
[
{
"version": "v1",
"created": "Thu, 2 Feb 2023 17:03:40 GMT"
}
] | 2023-02-03T00:00:00 |
[
[
"Akker",
"Bram van den",
""
],
[
"Jeunen",
"Olivier",
""
],
[
"Li",
"Ying",
""
],
[
"London",
"Ben",
""
],
[
"Nazari",
"Zahra",
""
],
[
"Parekh",
"Devesh",
""
]
] |
new_dataset
| 0.996497 |
2302.01229
|
Dominik B\"ar
|
Dominik B\"ar, Nicolas Pr\"ollochs, Stefan Feuerriegel
|
New threats to society from free-speech social media platforms
| null | null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In recent years, several free-speech social media platforms (so-called
"alt-techs") have emerged, such as Parler, Gab, and Telegram. These platforms
market themselves as alternatives to mainstream social media and proclaim
"free-speech" due to the absence of content moderation, which has been
attracting a large base of partisan users, extremists, and supporters of
conspiracy theories. In this comment, we discuss some of the threats that
emerge from such social media platforms and call for more policy efforts
directed at understanding and countering the risks for society.
|
[
{
"version": "v1",
"created": "Thu, 2 Feb 2023 17:08:12 GMT"
}
] | 2023-02-03T00:00:00 |
[
[
"Bär",
"Dominik",
""
],
[
"Pröllochs",
"Nicolas",
""
],
[
"Feuerriegel",
"Stefan",
""
]
] |
new_dataset
| 0.99536 |
2302.01295
|
Cheng-Chun Hsu
|
Cheng-Chun Hsu and Zhenyu Jiang and Yuke Zhu
|
Ditto in the House: Building Articulation Models of Indoor Scenes
through Interactive Perception
|
ICRA 2023. Code and additional results are available at
https://ut-austin-rpl.github.io/HouseDitto/
| null | null | null |
cs.RO cs.AI cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Virtualizing the physical world into virtual models has been a critical
technique for robot navigation and planning in the real world. To foster
manipulation with articulated objects in everyday life, this work explores
building articulation models of indoor scenes through a robot's purposeful
interactions in these scenes. Prior work on articulation reasoning primarily
focuses on siloed objects of limited categories. To extend to room-scale
environments, the robot has to efficiently and effectively explore a
large-scale 3D space, locate articulated objects, and infer their
articulations. We introduce an interactive perception approach to this task.
Our approach, named Ditto in the House, discovers possible articulated objects
through affordance prediction, interacts with these objects to produce
articulated motions, and infers the articulation properties from the visual
observations before and after each interaction. It tightly couples affordance
prediction and articulation inference to improve both tasks. We demonstrate the
effectiveness of our approach in both simulation and real-world scenes. Code
and additional results are available at
https://ut-austin-rpl.github.io/HouseDitto/
|
[
{
"version": "v1",
"created": "Thu, 2 Feb 2023 18:22:00 GMT"
}
] | 2023-02-03T00:00:00 |
[
[
"Hsu",
"Cheng-Chun",
""
],
[
"Jiang",
"Zhenyu",
""
],
[
"Zhu",
"Yuke",
""
]
] |
new_dataset
| 0.991117 |
cs/0611111
|
Tad Hogg
|
Tad Hogg
|
Distributed Control of Microscopic Robots in Biomedical Applications
| null | null |
10.1007/978-1-4471-5113-5_8
| null |
cs.RO cs.MA
| null |
Current developments in molecular electronics, motors and chemical sensors
could enable constructing large numbers of devices able to sense, compute and
act in micron-scale environments. Such microscopic machines, of sizes
comparable to bacteria, could simultaneously monitor entire populations of
cells individually in vivo. This paper reviews plausible capabilities for
microscopic robots and the physical constraints due to operation in fluids at
low Reynolds number, diffusion-limited sensing and thermal noise from Brownian
motion. Simple distributed controls are then presented in the context of
prototypical biomedical tasks, which require control decisions on millisecond
time scales. The resulting behaviors illustrate trade-offs among speed,
accuracy and resource use. A specific example is monitoring for patterns of
chemicals in a flowing fluid released at chemically distinctive sites.
Information collected from a large number of such devices allows estimating
properties of cell-sized chemical sources in a macroscopic volume. The
microscopic devices moving with the fluid flow in small blood vessels can
detect chemicals released by tissues in response to localized injury or
infection. We find the devices can readily discriminate a single cell-sized
chemical source from the background chemical concentration, providing
high-resolution sensing in both time and space. By contrast, such a source
would be difficult to distinguish from background when diluted throughout the
blood volume as obtained with a blood sample.
|
[
{
"version": "v1",
"created": "Tue, 21 Nov 2006 23:22:20 GMT"
}
] | 2023-02-03T00:00:00 |
[
[
"Hogg",
"Tad",
""
]
] |
new_dataset
| 0.999061 |
1808.09141
|
Gaolei Li
|
Gaolei Li, Guangquan Xu, Arun Kumar Sangaiah, Jun Wu, and Jianhua Li
|
EdgeLaaS: Edge Learning as a Service for Knowledge-Centric Connected
Healthcare
|
Accepted by IEEE Network, 2019
|
vol.33, no. 6, 2019 volume={33}, number={6}, pages={37-43},
|
10.1109/MNET.001.1900019
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
By introducing networking technologies and services into healthcare
infrastructures (e.g., multimodal sensors and smart devices) that are deployed
to supervise a person's health condition, the traditional healthcare system is
being revolutionized toward knowledge-centric connected healthcare (KCCH),
where persons will take their own responsibility for their healthcare in a
knowledge-centric way. Due to the volume, velocity, and variety of healthcare
supervision data generated by these healthcare infrastructures, an urgent and
strategic issue is how to efficiently process a person's healthcare supervision
data with the right knowledge of the right guardians (e.g., relatives, nurses,
and doctors) at the right time. To solve this issue, the naming and routing
criterion of medical knowledge is studied. With this offloaded medical
knowledge, we propose an edge learning as a service (EdgeLaaS) framework for
KCCH to locally process health supervision data. In this framework, edge
learning nodes can help the patient choose better advice from the right
guardians in real time when some emergencies occur. Two application cases: 1)
fast self-help and 2) mobile help pre-calling are studied. Performance
evaluations demonstrate the superiority of KCCH and EdgeLaaS, respectively.
|
[
{
"version": "v1",
"created": "Tue, 28 Aug 2018 07:05:13 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Feb 2023 06:40:46 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Li",
"Gaolei",
""
],
[
"Xu",
"Guangquan",
""
],
[
"Sangaiah",
"Arun Kumar",
""
],
[
"Wu",
"Jun",
""
],
[
"Li",
"Jianhua",
""
]
] |
new_dataset
| 0.997514 |
1812.04861
|
Alexander Gerasimov
|
Alexander S. Gerasimov
|
A repetition-free hypersequent calculus for first-order rational Pavelka
logic
|
21 pages; corrected a misprint, added an appendix containing errata
to a cited article
| null |
10.33048/semi.2020.17.127
| null |
cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a hypersequent calculus $\text{G}^3\text{\L}\forall$ for
first-order infinite-valued {\L}ukasiewicz logic and for an extension of it,
first-order rational Pavelka logic; the calculus is intended for bottom-up
proof search. In $\text{G}^3\text{\L}\forall$, there are no structural rules,
all the rules are invertible, and designations of multisets of formulas are not
repeated in any premise of the rules. The calculus $\text{G}^3\text{\L}\forall$
proves any sentence that is provable in at least one of the previously known
hypersequent calculi for the given logics. We study proof-theoretic properties
of $\text{G}^3\text{\L}\forall$ and thereby provide foundations for proof
search algorithms.
|
[
{
"version": "v1",
"created": "Wed, 12 Dec 2018 09:17:06 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Jan 2019 13:50:26 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Gerasimov",
"Alexander S.",
""
]
] |
new_dataset
| 0.986108 |
2106.10689
|
Peng Wang
|
Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura,
Wenping Wang
|
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for
Multi-view Reconstruction
|
23 pages
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel neural surface reconstruction method, called NeuS, for
reconstructing objects and scenes with high fidelity from 2D image inputs.
Existing neural surface reconstruction approaches, such as DVR and IDR, require
foreground mask as supervision, easily get trapped in local minima, and
therefore struggle with the reconstruction of objects with severe
self-occlusion or thin structures. Meanwhile, recent neural methods for novel
view synthesis, such as NeRF and its variants, use volume rendering to produce
a neural scene representation with robustness of optimization, even for highly
complex objects. However, extracting high-quality surfaces from this learned
implicit representation is difficult because there are not sufficient surface
constraints in the representation. In NeuS, we propose to represent a surface
as the zero-level set of a signed distance function (SDF) and develop a new
volume rendering method to train a neural SDF representation. We observe that
the conventional volume rendering method causes inherent geometric errors (i.e.
bias) for surface reconstruction, and therefore propose a new formulation that
is free of bias in the first order of approximation, thus leading to more
accurate surface reconstruction even without the mask supervision. Experiments
on the DTU dataset and the BlendedMVS dataset show that NeuS outperforms the
state-of-the-arts in high-quality surface reconstruction, especially for
objects and scenes with complex structures and self-occlusion.
|
[
{
"version": "v1",
"created": "Sun, 20 Jun 2021 12:59:42 GMT"
},
{
"version": "v2",
"created": "Thu, 16 Dec 2021 11:19:23 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Feb 2023 06:00:21 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Wang",
"Peng",
""
],
[
"Liu",
"Lingjie",
""
],
[
"Liu",
"Yuan",
""
],
[
"Theobalt",
"Christian",
""
],
[
"Komura",
"Taku",
""
],
[
"Wang",
"Wenping",
""
]
] |
new_dataset
| 0.991492 |
2201.06365
|
Juan M. Gandarias
|
Alberto Giammarino, Juan M. Gandarias, Pietro Balatti, Mattia Leonori,
Marta Lorenzini, and Arash Ajoudani
|
SUPER-MAN: SUPERnumerary Robotic Bodies for Physical Assistance in
HuMAN-Robot Conjoined Actions
|
16 pages, 14 figures. Associated video: https://youtu.be/_kfhLYQjhvA
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a mobile supernumerary robotic approach to physical
assistance in human-robot conjoined actions. The study starts with a
description of the SUPER-MAN concept. The idea is to develop and utilize mobile
collaborative systems that can follow human loco-manipulation commands to
perform industrial tasks through three main components: i) an admittance-type
interface, ii) a human-robot interaction controller, and iii) a supernumerary
robotic body. Next, we present two possible implementations within the
framework from theoretical and hardware perspectives. The first system is
called MOCA-MAN and comprises a redundant torque-controlled robotic arm and an
omnidirectional mobile platform. The second one is called Kairos-MAN, formed by
a high-payload 6-DoF velocity-controlled robotic arm and an omnidirectional
mobile platform. The systems share the same admittance interface, through which
user wrenches are translated to loco-manipulation commands generated by
whole-body controllers of each system. Besides, a thorough user study with
multiple and cross-gender subjects is presented to reveal the quantitative
performance of the two systems in effort-demanding and dexterous tasks.
Moreover, we provide qualitative results from the NASA-TLX questionnaire to
demonstrate the SUPER-MAN approach's potential and its acceptability from the
users' viewpoint.
|
[
{
"version": "v1",
"created": "Mon, 17 Jan 2022 11:54:31 GMT"
},
{
"version": "v2",
"created": "Wed, 27 Jul 2022 09:37:42 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Feb 2023 15:20:58 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Giammarino",
"Alberto",
""
],
[
"Gandarias",
"Juan M.",
""
],
[
"Balatti",
"Pietro",
""
],
[
"Leonori",
"Mattia",
""
],
[
"Lorenzini",
"Marta",
""
],
[
"Ajoudani",
"Arash",
""
]
] |
new_dataset
| 0.998771 |
2206.03273
|
Guilong Li
|
Guilong Li, Yixian Chen, Yimin Wang, Zhi Yu, Peilin Nie, Zhaocheng He
|
City-scale synthetic individual-level vehicle trip data
| null | null | null | null |
cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Trip data that records each vehicle's trip activity on the road network
describes the operation of urban traffic from the individual perspective, and
it is extremely valuable for transportation research. However, restricted by
data privacy, the trip data of individual-level cannot be opened for all
researchers, while the need for it is very urgent. In this paper, we produce a
city-scale synthetic individual-level vehicle trip dataset by generating for
each individual based on the historical trip data, where the availability and
trip data privacy protection are balanced. Privacy protection inevitably
affects the availability of data. Therefore, we have conducted numerous
experiments to demonstrate the performance and reliability of the synthetic
data in different dimensions and at different granularities to help users
properly judge the tasks it can perform. The result shows that the synthetic
data is consistent with the real data (i.e., historical data) on the aggregated
level and reasonable from the individual perspective.
|
[
{
"version": "v1",
"created": "Thu, 2 Jun 2022 08:08:01 GMT"
},
{
"version": "v2",
"created": "Fri, 19 Aug 2022 07:13:56 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Feb 2023 09:11:57 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Li",
"Guilong",
""
],
[
"Chen",
"Yixian",
""
],
[
"Wang",
"Yimin",
""
],
[
"Yu",
"Zhi",
""
],
[
"Nie",
"Peilin",
""
],
[
"He",
"Zhaocheng",
""
]
] |
new_dataset
| 0.999528 |
2206.08182
|
Adrian Pfleiderer
|
Adrian Pfleiderer, Dominik M\"uller, Frank Kramer
|
Nucleus Segmentation and Analysis in Breast Cancer with the MIScnn
Framework
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The NuCLS dataset contains over 220.000 annotations of cell nuclei in breast
cancers. We show how to use these data to create a multi-rater model with the
MIScnn Framework to automate the analysis of cell nuclei. For the model
creation, we use the widespread U-Net approach embedded in a pipeline. This
pipeline provides besides the high performance convolution neural network,
several preprocessor techniques and a extended data exploration. The final
model is tested in the evaluation phase using a wide variety of metrics with a
subsequent visualization. Finally, the results are compared and interpreted
with the results of the NuCLS study. As an outlook, indications are given which
are important for the future development of models in the context of cell
nuclei.
|
[
{
"version": "v1",
"created": "Thu, 16 Jun 2022 13:51:19 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Nov 2022 09:51:07 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Feb 2023 16:53:32 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Pfleiderer",
"Adrian",
""
],
[
"Müller",
"Dominik",
""
],
[
"Kramer",
"Frank",
""
]
] |
new_dataset
| 0.963677 |
2210.06642
|
Eric Ming Chen
|
Eric Ming Chen, Jin Sun, Apoorv Khandelwal, Dani Lischinski, Noah
Snavely, Hadar Averbuch-Elor
|
What's in a Decade? Transforming Faces Through Time
|
Project Page: https://facesthroughtime.github.io
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
How can one visually characterize people in a decade? In this work, we
assemble the Faces Through Time dataset, which contains over a thousand
portrait images from each decade, spanning the 1880s to the present day. Using
our new dataset, we present a framework for resynthesizing portrait images
across time, imagining how a portrait taken during a particular decade might
have looked like, had it been taken in other decades. Our framework optimizes a
family of per-decade generators that reveal subtle changes that differentiate
decade--such as different hairstyles or makeup--while maintaining the identity
of the input portrait. Experiments show that our method is more effective in
resynthesizing portraits across time compared to state-of-the-art
image-to-image translation methods, as well as attribute-based and
language-guided portrait editing models. Our code and data will be available at
https://facesthroughtime.github.io
|
[
{
"version": "v1",
"created": "Thu, 13 Oct 2022 00:48:18 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 03:01:34 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Feb 2023 04:41:44 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Chen",
"Eric Ming",
""
],
[
"Sun",
"Jin",
""
],
[
"Khandelwal",
"Apoorv",
""
],
[
"Lischinski",
"Dani",
""
],
[
"Snavely",
"Noah",
""
],
[
"Averbuch-Elor",
"Hadar",
""
]
] |
new_dataset
| 0.975677 |
2210.11743
|
Pietro Tedeschi Dr
|
Eva Wisse and Pietro Tedeschi and Savio Sciancalepore and Roberto Di
Pietro
|
$A^2RID$ -- Anonymous Direct Authentication and Remote Identification of
Commercial Drones
| null | null |
10.1109/JIOT.2023.3240477
| null |
cs.CR cs.NI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The recent worldwide introduction of RemoteID (RID) regulations forces all
Unmanned Aircrafts (UAs), a.k.a. drones, to broadcast in plaintext on the
wireless channel their identity and real-time location, for accounting and
monitoring purposes. Although improving drones' monitoring and situational
awareness, the RID rule also generates significant privacy concerns for UAs'
operators, threatened by the ease of tracking of UAs and related
confidentiality and privacy concerns connected with the broadcasting of
plaintext identity information. In this paper, we propose $A^2RID$, a protocol
suite for anonymous direct authentication and remote identification of
heterogeneous commercial UAs. $A^2RID$ integrates and adapts protocols for
anonymous message signing to work in the UA domain, coping with the constraints
of commercial drones and the tight real-time requirements imposed by the RID
regulation. Overall, the protocols in the $A^2RID$ suite allow a UA
manufacturer to pick the configuration that best suits the capabilities and
constraints of the drone, i.e., either a processing-intensive but
memory-lightweight solution (namely, $CS-A^2RID$) or a computationally-friendly
but memory-hungry approach (namely, $DS-A^2RID$). Besides formally defining the
protocols and formally proving their security in our setting, we also implement
and test them on real heterogeneous hardware platforms, i.e., the Holybro X-500
and the ESPcopter, releasing open-source the produced code. For all the
protocols, we demonstrated experimentally the capability of generating
anonymous RemoteID messages well below the time bound of $1$ second required by
RID, while at the same time having quite a limited impact on the energy budget
of the drone.
|
[
{
"version": "v1",
"created": "Fri, 21 Oct 2022 05:43:44 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Feb 2023 05:51:56 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Wisse",
"Eva",
""
],
[
"Tedeschi",
"Pietro",
""
],
[
"Sciancalepore",
"Savio",
""
],
[
"Di Pietro",
"Roberto",
""
]
] |
new_dataset
| 0.998862 |
2211.16506
|
Rabindra Lamsal
|
Rabindra Lamsal, Aaron Harwood, Maria Rodriguez Read
|
Where did you tweet from? Inferring the origin locations of tweets based
on contextual information
|
To appear in Proceedings of the IEEE Big Data Conference 2022
| null |
10.1109/BigData55660.2022.10020460
| null |
cs.CL cs.SI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Public conversations on Twitter comprise many pertinent topics including
disasters, protests, politics, propaganda, sports, climate change,
epidemics/pandemic outbreaks, etc., that can have both regional and global
aspects. Spatial discourse analysis rely on geographical data. However, today
less than 1% of tweets are geotagged; in both cases--point location or bounding
place information. A major issue with tweets is that Twitter users can be at
location A and exchange conversations specific to location B, which we call the
Location A/B problem. The problem is considered solved if location entities can
be classified as either origin locations (Location As) or non-origin locations
(Location Bs). In this work, we propose a simple yet effective framework--the
True Origin Model--to address the problem that uses machine-level natural
language understanding to identify tweets that conceivably contain their origin
location information. The model achieves promising accuracy at country (80%),
state (67%), city (58%), county (56%) and district (64%) levels with support
from a Location Extraction Model as basic as the CoNLL-2003-based RoBERTa. We
employ a tweet contexualizer (locBERT) which is one of the core components of
the proposed model, to investigate multiple tweets' distributions for
understanding Twitter users' tweeting behavior in terms of mentioning origin
and non-origin locations. We also highlight a major concern with the currently
regarded gold standard test set (ground truth) methodology, introduce a new
data set, and identify further research avenues for advancing the area.
|
[
{
"version": "v1",
"created": "Fri, 18 Nov 2022 01:33:01 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Lamsal",
"Rabindra",
""
],
[
"Harwood",
"Aaron",
""
],
[
"Read",
"Maria Rodriguez",
""
]
] |
new_dataset
| 0.997695 |
2301.10289
|
Xinghua Lou
|
Ken Kansky, Skanda Vaidyanath, Scott Swingle, Xinghua Lou, Miguel
Lazaro-Gredilla, Dileep George
|
PushWorld: A benchmark for manipulation planning with tools and movable
obstacles
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
While recent advances in artificial intelligence have achieved human-level
performance in environments like Starcraft and Go, many physical reasoning
tasks remain challenging for modern algorithms. To date, few algorithms have
been evaluated on physical tasks that involve manipulating objects when movable
obstacles are present and when tools must be used to perform the manipulation.
To promote research on such tasks, we introduce PushWorld, an environment with
simplistic physics that requires manipulation planning with both movable
obstacles and tools. We provide a benchmark of more than 200 PushWorld puzzles
in PDDL and in an OpenAI Gym environment. We evaluate state-of-the-art
classical planning and reinforcement learning algorithms on this benchmark, and
we find that these baseline results are below human-level performance. We then
provide a new classical planning heuristic that solves the most puzzles among
the baselines, and although it is 40 times faster than the best baseline
planner, it remains below human-level performance.
|
[
{
"version": "v1",
"created": "Tue, 24 Jan 2023 20:20:17 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Feb 2023 18:16:19 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Kansky",
"Ken",
""
],
[
"Vaidyanath",
"Skanda",
""
],
[
"Swingle",
"Scott",
""
],
[
"Lou",
"Xinghua",
""
],
[
"Lazaro-Gredilla",
"Miguel",
""
],
[
"George",
"Dileep",
""
]
] |
new_dataset
| 0.999592 |
2301.12695
|
Jiahao He
|
Jiahao He, Shuangyin Li, Xinming Wang, Shing-Chi Cheung, Gansen Zhao
and Jinji Yang
|
Neural-FEBI: Accurate Function Identification in Ethereum Virtual
Machine Bytecode
|
19 pages, 13 figures
| null |
10.1016/j.jss.2023.111627
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Millions of smart contracts have been deployed onto the Ethereum platform,
posing potential attack subjects. Therefore, analyzing contract binaries is
vital since their sources are unavailable, involving identification comprising
function entry identification and detecting its boundaries. Such boundaries are
critical to many smart contract applications, e.g. reverse engineering and
profiling. Unfortunately, it is challenging to identify functions from these
stripped contract binaries due to the lack of internal function call statements
and the compiler-inducing instruction reshuffling. Recently, several existing
works excessively relied on a set of handcrafted heuristic rules which impose
several faults. To address this issue, we propose a novel neural network-based
framework for EVM bytecode Function Entries and Boundaries Identification
(neural-FEBI) that does not rely on a fixed set of handcrafted rules. Instead,
it used a two-level bi-Long Short-Term Memory network and a Conditional Random
Field network to locate the function entries. The suggested framework also
devises a control flow traversal algorithm to determine the code segments
reachable from the function entry as its boundary. Several experiments on
38,996 publicly available smart contracts collected as binary demonstrate that
neural-FEBI confirms the lowest and highest F1-scores for the function entries
identification task across different datasets of 88.3 to 99.7, respectively.
Its performance on the function boundary identification task is also increased
from 79.4% to 97.1% compared with state-of-the-art. We further demonstrate that
the identified function information can be used to construct more accurate
intra-procedural CFGs and call graphs. The experimental results confirm that
the proposed framework significantly outperforms state-of-the-art, often based
on handcrafted heuristic rules.
|
[
{
"version": "v1",
"created": "Mon, 30 Jan 2023 07:02:44 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Feb 2023 08:53:03 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"He",
"Jiahao",
""
],
[
"Li",
"Shuangyin",
""
],
[
"Wang",
"Xinming",
""
],
[
"Cheung",
"Shing-Chi",
""
],
[
"Zhao",
"Gansen",
""
],
[
"Yang",
"Jinji",
""
]
] |
new_dataset
| 0.994929 |
2302.00029
|
Mehedi Hasan Raju
|
Mehedi H. Raju, Lee Friedman, Troy M. Bouman, Oleg V. Komogortsev
|
Determining Which Sine Wave Frequencies Correspond to Signal and Which
Correspond to Noise in Eye-Tracking Time-Series
|
Pages-16, Figures-11, Tables-4. arXiv admin note: text overlap with
arXiv:2209.07657
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
The Fourier theorem proposes that any time-series can be decomposed into a
set of sinusoidal frequencies, each with its own phase and amplitude. The
literature suggests that some of these frequencies are important to reproduce
key qualities of eye-movements (``signal'') and some of these frequencies are
not important (``noise''). To understand what is signal and what is noise, we
analyzed our dataset in three ways: (1) visual inspection of plots of saccade,
microsaccade and smooth pursuit exemplars; (2) an analysis of the percentage of
variance accounted for (PVAF) in each of 1,033 unfiltered saccade trajectories
by each frequency cutoff; (3) an analysis of saccade peak velocity in the
unfiltered and various filtered conditions. Visual inspection suggested that
frequencies up to 75 Hz are required to represent microsaccades. Our PVAF
analysis indicated that data in the 0-25 Hz band are sufficient to account for
nearly 100% of the variance in unfiltered saccade trajectories. Our analysis
also indicated that frequencies below 100 Hz are sufficient to maintain peak
velocities. Therefore, our overall conclusion is that to maintain eye-movement
signal and reduce noise, a cutoff frequency of 100 Hz is appropriate. Our
results have implications for the proposed sampling rate of eye-tracking
recordings. If one is working in the frequency domain and 100 Hz needs to be
preserved, the minimum required sampling rate would be 200 Hz. However, in a
time domain analysis, a minimum 1000 Hz sampling rate is recommended.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 19:02:17 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Raju",
"Mehedi H.",
""
],
[
"Friedman",
"Lee",
""
],
[
"Bouman",
"Troy M.",
""
],
[
"Komogortsev",
"Oleg V.",
""
]
] |
new_dataset
| 0.999746 |
2302.00032
|
Deniz Oktay
|
Deniz Oktay, Mehran Mirramezani, Eder Medina, Ryan P. Adams
|
Neuromechanical Autoencoders: Learning to Couple Elastic and Neural
Network Nonlinearity
|
ICLR 2023 Spotlight
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Intelligent biological systems are characterized by their embodiment in a
complex environment and the intimate interplay between their nervous systems
and the nonlinear mechanical properties of their bodies. This coordination, in
which the dynamics of the motor system co-evolved to reduce the computational
burden on the brain, is referred to as ``mechanical intelligence'' or
``morphological computation''. In this work, we seek to develop machine
learning analogs of this process, in which we jointly learn the morphology of
complex nonlinear elastic solids along with a deep neural network to control
it. By using a specialized differentiable simulator of elastic mechanics
coupled to conventional deep learning architectures -- which we refer to as
neuromechanical autoencoders -- we are able to learn to perform morphological
computation via gradient descent. Key to our approach is the use of mechanical
metamaterials -- cellular solids, in particular -- as the morphological
substrate. Just as deep neural networks provide flexible and
massively-parametric function approximators for perceptual and control tasks,
cellular solid metamaterials are promising as a rich and learnable space for
approximating a variety of actuation tasks. In this work we take advantage of
these complementary computational concepts to co-design materials and neural
network controls to achieve nonintuitive mechanical behavior. We demonstrate in
simulation how it is possible to achieve translation, rotation, and shape
matching, as well as a ``digital MNIST'' task. We additionally manufacture and
evaluate one of the designs to verify its real-world behavior.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 19:04:28 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Oktay",
"Deniz",
""
],
[
"Mirramezani",
"Mehran",
""
],
[
"Medina",
"Eder",
""
],
[
"Adams",
"Ryan P.",
""
]
] |
new_dataset
| 0.980917 |
2302.00164
|
Gerardo Antonio Alvarez-Hernandez
|
Gerardo Antonio Alvarez Hern\'andez, Juan Carlos Olguin, Juan Irving
Vasquez, Abril Valeria Uriarte, Maria Claudia Villica\~na Torres
|
Detection of Tomato Ripening Stages using Yolov3-tiny
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the most important agricultural products in Mexico is the tomato
(Solanum lycopersicum), which occupies the 4th place national most produced
product . Therefore, it is necessary to improve its production, building
automatic detection system that detect, classify an keep tacks of the fruits is
one way to archieve it. So, in this paper, we address the design of a computer
vision system to detect tomatoes at different ripening stages. To solve the
problem, we use a neural network-based model for tomato classification and
detection. Specifically, we use the YOLOv3-tiny model because it is one of the
lightest current deep neural networks. To train it, we perform two grid
searches testing several combinations of hyperparameters. Our experiments
showed an f1-score of 90.0% in the localization and classification of ripening
stages in a custom dataset.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 00:57:58 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Hernández",
"Gerardo Antonio Alvarez",
""
],
[
"Olguin",
"Juan Carlos",
""
],
[
"Vasquez",
"Juan Irving",
""
],
[
"Uriarte",
"Abril Valeria",
""
],
[
"Torres",
"Maria Claudia Villicaña",
""
]
] |
new_dataset
| 0.998627 |
2302.00190
|
Jingyu Hu
|
Jingyu Hu, Ka-Hei Hui, Zhengzhe Liu, Ruihui Li and Chi-Wing Fu
|
Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and
Manipulation
|
arXiv admin note: substantial text overlap with arXiv:2209.08725
| null | null | null |
cs.CV cs.GR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
This paper presents a new approach for 3D shape generation, inversion, and
manipulation, through a direct generative modeling on a continuous implicit
representation in wavelet domain. Specifically, we propose a compact wavelet
representation with a pair of coarse and detail coefficient volumes to
implicitly represent 3D shapes via truncated signed distance functions and
multi-scale biorthogonal wavelets. Then, we design a pair of neural networks: a
diffusion-based generator to produce diverse shapes in the form of the coarse
coefficient volumes and a detail predictor to produce compatible detail
coefficient volumes for introducing fine structures and details. Further, we
may jointly train an encoder network to learn a latent space for inverting
shapes, allowing us to enable a rich variety of whole-shape and region-aware
shape manipulations. Both quantitative and qualitative experimental results
manifest the compelling shape generation, inversion, and manipulation
capabilities of our approach over the state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 02:47:53 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Hu",
"Jingyu",
""
],
[
"Hui",
"Ka-Hei",
""
],
[
"Liu",
"Zhengzhe",
""
],
[
"Li",
"Ruihui",
""
],
[
"Fu",
"Chi-Wing",
""
]
] |
new_dataset
| 0.955764 |
2302.00268
|
Kaifeng Gao
|
Kaifeng Gao, Long Chen, Hanwang Zhang, Jun Xiao, Qianru Sun
|
Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video
Relation Detection
|
accepted by ICLR 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Prompt tuning with large-scale pretrained vision-language models empowers
open-vocabulary predictions trained on limited base categories, e.g., object
classification and detection. In this paper, we propose compositional prompt
tuning with motion cues: an extended prompt tuning paradigm for compositional
predictions of video data. In particular, we present Relation Prompt (RePro)
for Open-vocabulary Video Visual Relation Detection (Open-VidVRD), where
conventional prompt tuning is easily biased to certain subject-object
combinations and motion patterns. To this end, RePro addresses the two
technical challenges of Open-VidVRD: 1) the prompt tokens should respect the
two different semantic roles of subject and object, and 2) the tuning should
account for the diverse spatio-temporal motion patterns of the subject-object
compositions. Without bells and whistles, our RePro achieves a new
state-of-the-art performance on two VidVRD benchmarks of not only the base
training object and predicate categories, but also the unseen ones. Extensive
ablations also demonstrate the effectiveness of the proposed compositional and
multi-mode design of prompts. Code is available at
https://github.com/Dawn-LX/OpenVoc-VidVRD.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 06:20:54 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Gao",
"Kaifeng",
""
],
[
"Chen",
"Long",
""
],
[
"Zhang",
"Hanwang",
""
],
[
"Xiao",
"Jun",
""
],
[
"Sun",
"Qianru",
""
]
] |
new_dataset
| 0.997499 |
2302.00288
|
Hao Yu
|
Hao Yu, Bo Shen, Dezhi Ran, Jiaxin Zhang, Qi Zhang, Yuchi Ma, Guangtai
Liang, Ying Li, Tao Xie, Qianxiang Wang
|
CoderEval: A Benchmark of Pragmatic Code Generation with Generative
Pre-trained Models
| null | null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Code generation models based on the pre-training and fine-tuning paradigm
have been increasingly attempted by both academia and industry, resulting in
well-known industrial models such as Codex, CodeGen, and PanGu-Coder. To
validate the performance of these models, multiple existing benchmarks (e.g.,
AiXBench and HumanEval) are proposed, including only cases of generating a
standalone function, i.e., a function that invokes or accesses only built-in
functions and standard libraries. However, standalone functions constitute only
about 30\% of functions from real open-source projects. To assess a model's
performance for pragmatic code generation (i.e., code generation for real
settings of open source or proprietary code), in this paper, we propose a
benchmark named CoderEval of pragmatic code generation with generative
pre-trained models. Compared with the widely-used HumanEval benchmark from
OpenAI, CoderEval can be used to assess the performance of models against
pragmatic code generation beyond just generating standalone functions. Through
the evaluation of three public available models (CodeGen, PanGu-Coder, and
Codex) on CoderEval, we analyze and discuss the current progress and future
directions of pragmatic code generation with a generative pre-trained model.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 07:39:28 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Yu",
"Hao",
""
],
[
"Shen",
"Bo",
""
],
[
"Ran",
"Dezhi",
""
],
[
"Zhang",
"Jiaxin",
""
],
[
"Zhang",
"Qi",
""
],
[
"Ma",
"Yuchi",
""
],
[
"Liang",
"Guangtai",
""
],
[
"Li",
"Ying",
""
],
[
"Xie",
"Tao",
""
],
[
"Wang",
"Qianxiang",
""
]
] |
new_dataset
| 0.99897 |
2302.00338
|
Yousri Daldoul
|
Yousri Daldoul
|
A Robust Certificate Management System to Prevent Evil Twin Attacks in
IEEE 802.11 Networks
| null | null | null | null |
cs.CR cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
The evil twin attack is a major security threat to WLANs. An evil twin is a
rogue AP installed by a malicious user to impersonate legitimate APs. It
intends to attract victims in order to intercept their credentials, to steal
their sensitive information, to eavesdrop on their data, etc. In this paper, we
study the security mechanisms of wireless networks and we introduce the
different authentication methods, including 802.1X authentication. We show that
802.1X has improved security through the use of digital certificates but does
not define any practical technique for the user to check the network
certificate. Therefore, it remains vulnerable to the evil twin attack. To
repair this vulnerability, we introduce Robust Certificate Management System
(RCMS) which takes advantage of the digital certificates of 802.1X to protect
the users against rogue APs. RCMS defines a new verification code to allow the
user device to check the network certificate. This practical verification
combined with the reliability of digital certificates provides a perfect
protection against rogue APs. RCMS requires a small software update on the user
terminal and does not need any modification of IEEE 802.11. It has a
significant flexibility since trusting a single AP is enough to trust all the
APs of the extended network. This allows the administrators to extend their
networks easily without the need to update any database of trusted APs on the
user devices.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 09:41:45 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Daldoul",
"Yousri",
""
]
] |
new_dataset
| 0.979355 |
2302.00369
|
Pawel Kryszkiewicz
|
Kuldeep S. Gill, Pawel Kryszkiewicz, Pawel Sroka, Adrian Kliks,
Alexander M. Wyglinski
|
Memory Enabled Bumblebee-based Dynamic Spectrum Access for Platooning
Environments
| null | null |
10.1109/TVT.2023.3236035
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a novel memory-enabled non-uniform sampling-based
bumblebee foraging algorithm (MEB) designed for optimal channel selection in a
distributed Vehicular Dynamic Spectrum Access (VDSA) framework employed in a
platoon operating environment. Given how bumblebee behavioral models are
designed to support adaptation in complex and highly time-varying environments,
these models can be employed by connected vehicles to enable their operation
within a dynamically changing network topology and support their selection of
optimal channels possessing low levels of congestion to achieve high
throughput. As a result, the proposed VDSA-based optimal channel selection
employs fundamental concepts from the bumblebee foraging model. In the proposed
approach, the Channel Busy Ratio (CBR) of all channels is computed and stored
in memory to be accessed by the MEB algorithm to make the necessary channel
switching decisions. Two averaging techniques, Sliding Window Average (SWA) and
Exponentially Weighted Moving Average (EWMA), are employed to leverage past
samples and are evaluated against the no-memory case. Due to the high
variability of the environment (e.g., high velocities, changing density of
vehicles on the road), we propose to calculate the CBR by employing non-uniform
channel sampling allocations as well as evaluate it using both simplified
numerical and realistic Vehicle-to-Vehicle (V2V) computer simulations. The
numerical simulation results show that gains in the probability of the best
channel selection can be achieved relative to a uniform sampling allocation
approach. By utilizing memory, we observe an additional increase in the channel
selection performance. Similarly, we see an increase in the probability of
successful reception when utilizing the bumblebee algorithm via a system-level
simulator.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 10:58:42 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Gill",
"Kuldeep S.",
""
],
[
"Kryszkiewicz",
"Pawel",
""
],
[
"Sroka",
"Pawel",
""
],
[
"Kliks",
"Adrian",
""
],
[
"Wyglinski",
"Alexander M.",
""
]
] |
new_dataset
| 0.996178 |
2302.00418
|
Zhuolun Li
|
Zhuolun Li, Alberto Sonnino, Philipp Jovanovic
|
Performance of EdDSA and BLS Signatures in Committee-Based Consensus
| null | null | null | null |
cs.DC cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
We present the first performance comparison of EdDSA and BLS signatures in
committee-based consensus protocols through large-scale geo-distributed
benchmarks. Contrary to popular beliefs, we find that small deployments (less
than 40 validators) can benefit from the small storage footprint of BLS
multi-signatures while larger deployments should favor EdDSA to improve
performance. As an independent contribution, we present a novel way for
committee-based consensus protocols to verify BLS multi-signed certificates by
manipulating the aggregated public key using pre-computed values.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 13:07:46 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Li",
"Zhuolun",
""
],
[
"Sonnino",
"Alberto",
""
],
[
"Jovanovic",
"Philipp",
""
]
] |
new_dataset
| 0.97414 |
2302.00424
|
Fengqing Hu
|
Fengqing Hu, Huan Yu
|
Safety-Critical Lane-Change Control for CAV Platoons in Mixed Autonomy
Traffic Using Control Barrier Functions
| null | null | null | null |
cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Platooning can serve as an effective management measure for connected and
autonomous vehicles (CAVs) to ensure overall traffic efficiency. Current study
focus on the longitudinal control of CAV platoons, however it still remains a
challenging problem to stay safe under lane-change scenarios where both
longitudinal and lateral control is required. In this paper, a safety-critical
control method is proposed conduct lane-changing maneuvers for platooning CAVs
using Control Barrier Functions (CBFs). The proposed method is composed of two
layers: a higher-level controller for general lane change decision control and
a lower-level controller for safe kinematics control. Different from
traditional kinematics controllers, this lower-level controller conducts not
only longitudinal safety-critical control but also critically ensures safety
for lateral control during the platooning lane change. To effectively design
this lower-level controller, an optimization problem is solved with constraints
defined by both CBFs and Control Lyapunov Functions (CLFs). A traffic simulator
is used to conduct numerical traffic simulations in four safety-critical
scenarios and showed the effectiveness of the proposed controller.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 13:16:16 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Hu",
"Fengqing",
""
],
[
"Yu",
"Huan",
""
]
] |
new_dataset
| 0.99668 |
2302.00455
|
Attila Nagy
|
Botond Barta, Dorina Lakatos, Attila Nagy, Mil\'an Konor Nyist, Judit
\'Acs
|
HunSum-1: an Abstractive Summarization Dataset for Hungarian
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce HunSum-1: a dataset for Hungarian abstractive summarization,
consisting of 1.14M news articles. The dataset is built by collecting, cleaning
and deduplicating data from 9 major Hungarian news sites through CommonCrawl.
Using this dataset, we build abstractive summarizer models based on huBERT and
mT5. We demonstrate the value of the created dataset by performing a
quantitative and qualitative analysis on the models' results. The HunSum-1
dataset, all models used in our experiments and our code are available open
source.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 13:59:45 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Barta",
"Botond",
""
],
[
"Lakatos",
"Dorina",
""
],
[
"Nagy",
"Attila",
""
],
[
"Nyist",
"Milán Konor",
""
],
[
"Ács",
"Judit",
""
]
] |
new_dataset
| 0.999767 |
2302.00461
|
Jiabao Gao
|
Jiabao Gao, Caijun Zhong, Geoffrey Ye Li
|
AMP-SBL Unfolding for Wideband MmWave Massive MIMO Channel Estimation
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In wideband millimeter wave (mmWave) massive multiple-input multiple-output
(MIMO) systems, channel estimation is challenging due to the hybrid
analog-digital architecture, which compresses the received pilot signal and
makes channel estimation a compressive sensing (CS) problem. However, existing
high-performance CS algorithms usually suffer from high complexity. On the
other hand, the beam squint effect caused by huge bandwidth and massive
antennas will deteriorate estimation performance. In this paper,
frequency-dependent angular dictionaries are first adopted to compensate for
beam squint. Then, the expectation-maximization (EM)-based sparse Bayesian
learning (SBL) algorithm is enhanced in two aspects, where the E-step in each
iteration is implemented by approximate message passing (AMP) to reduce
complexity while the M-step is realized by a deep neural network (DNN) to
improve performance. In simulation, the proposed AMP-SBL unfolding-based
channel estimator achieves satisfactory performance with low complexity.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 14:07:27 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Gao",
"Jiabao",
""
],
[
"Zhong",
"Caijun",
""
],
[
"Li",
"Geoffrey Ye",
""
]
] |
new_dataset
| 0.976399 |
2302.00606
|
Bennett Kleinberg
|
Isabelle van der Vegt and Bennett Kleinberg
|
The RW3D: A multi-modal panel dataset to understand the psychological
impact of the pandemic
|
preprint
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Besides far-reaching public health consequences, the COVID-19 pandemic had a
significant psychological impact on people around the world. To gain further
insight into this matter, we introduce the Real World Worry Waves Dataset
(RW3D). The dataset combines rich open-ended free-text responses with survey
data on emotions, significant life events, and psychological stressors in a
repeated-measures design in the UK over three years (2020: n=2441, 2021: n=1716
and 2022: n=1152). This paper provides background information on the data
collection procedure, the recorded variables, participants' demographics, and
higher-order psychological and text-based derived variables that emerged from
the data. The RW3D is a unique primary data resource that could inspire new
research questions on the psychological impact of the pandemic, especially
those that connect modalities (here: text data, psychological survey variables
and demographics) over time.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 17:13:06 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"van der Vegt",
"Isabelle",
""
],
[
"Kleinberg",
"Bennett",
""
]
] |
new_dataset
| 0.99954 |
2302.00635
|
Sergejs Kozlovi\v{c}s
|
Sergejs Kozlovi\v{c}s
|
Shared SAT Solvers and SAT Memory in Distributed Business Applications
|
This preprint has not undergone peer review or any post-submission
improvements or corrections. The Version of Record of this contribution is
published in CCIS vol. 1598, "Digital Business and Intelligent Systems: 15th
International Baltic Conference, Baltic DB&IS 2022, Riga, Latvia, July 4-6,
2022, Proceedings", pp.201-216, and is available online at
https://doi.org/10.1007/978-3-031-09850-5_14
|
CCIS vol. 1598, "Digital Business and Intelligent Systems: 15th
International Baltic Conference" (proceedings), pp.201-216, Riga, Latvia,
July 4-6, 2022
|
10.1007/978-3-031-09850-5_14
| null |
cs.DC cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a software architecture where SAT solvers act as a shared network
resource for distributed business applications. There can be multiple parallel
SAT solvers running either on dedicated hardware (a multi-processor system or a
system with a specific GPU) or in the cloud. In order to avoid complex message
passing between network nodes, we introduce a novel concept of the shared SAT
memory, which can be accessed (in the read/write mode) from multiple different
SAT solvers and modules implementing the business logic. As a result, our
architecture allows for the easy generation, diversification, and solving of
SAT instances from existing high-level programming languages without the need
to think about the network. We demonstrate our architecture on the use case of
transforming the integer factorization problem to SAT.
|
[
{
"version": "v1",
"created": "Tue, 24 Jan 2023 13:06:53 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Kozlovičs",
"Sergejs",
""
]
] |
new_dataset
| 0.999325 |
2302.00646
|
Jaesung Huh
|
Jaesung Huh, Jacob Chalk, Evangelos Kazakos, Dima Damen, Andrew
Zisserman
|
Epic-Sounds: A Large-scale Dataset of Actions That Sound
|
6 pages, 4 figures
| null | null | null |
cs.SD cs.AI cs.LG eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce EPIC-SOUNDS, a large-scale dataset of audio annotations
capturing temporal extents and class labels within the audio stream of the
egocentric videos. We propose an annotation pipeline where annotators
temporally label distinguishable audio segments and describe the action that
could have caused this sound. We identify actions that can be discriminated
purely from audio, through grouping these free-form descriptions of audio into
classes. For actions that involve objects colliding, we collect human
annotations of the materials of these objects (e.g. a glass object being placed
on a wooden surface), which we verify from visual labels, discarding
ambiguities. Overall, EPIC-SOUNDS includes 78.4k categorised segments of
audible events and actions, distributed across 44 classes as well as 39.2k
non-categorised segments. We train and evaluate two state-of-the-art audio
recognition models on our dataset, highlighting the importance of audio-only
labels and the limitations of current models to recognise actions that sound.
|
[
{
"version": "v1",
"created": "Wed, 1 Feb 2023 18:19:37 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Huh",
"Jaesung",
""
],
[
"Chalk",
"Jacob",
""
],
[
"Kazakos",
"Evangelos",
""
],
[
"Damen",
"Dima",
""
],
[
"Zisserman",
"Andrew",
""
]
] |
new_dataset
| 0.999654 |
2302.00654
|
Procheta Sen
|
Procheta Sen, Xi Wang, Ruiqing Xu, Emine Yilmaz
|
Task2KB: A Public Task-Oriented Knowledge Base
| null | null | null | null |
cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
Search engines and conversational assistants are commonly used to help users
complete their every day tasks such as booking travel, cooking, etc. While
there are some existing datasets that can be used for this purpose, their
coverage is limited to very few domains. In this paper, we propose a novel
knowledge base, 'Task2KB', which is constructed using data crawled from
WikiHow, an online knowledge resource offering instructional articles on a wide
range of tasks. Task2KB encapsulates various types of task-related information
and attributes, such as requirements, detailed step description, and available
methods to complete tasks. Due to its higher coverage compared to existing
related knowledge graphs, Task2KB can be highly useful in the development of
general purpose task completion assistants
|
[
{
"version": "v1",
"created": "Tue, 24 Jan 2023 18:38:09 GMT"
}
] | 2023-02-02T00:00:00 |
[
[
"Sen",
"Procheta",
""
],
[
"Wang",
"Xi",
""
],
[
"Xu",
"Ruiqing",
""
],
[
"Yilmaz",
"Emine",
""
]
] |
new_dataset
| 0.999859 |
2102.10314
|
Giacomo Giuliari
|
Giacomo Giuliari, Marc Wyss, Markus Legner, Adrian Perrig
|
GMA: A Pareto Optimal Distributed Resource-Allocation Algorithm
| null | null |
10.1007/978-3-030-79527-6_14
| null |
cs.NI cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
To address the rising demand for strong packet delivery guarantees in
networking, we study a novel way to perform graph resource allocation. We first
introduce allocation graphs, in which nodes can independently set local
resource limits based on physical constraints or policy decisions. In this
scenario we formalize the distributed path-allocation (PAdist) problem, which
consists in allocating resources to paths considering only local on-path
information -- importantly, not knowing which other paths could have an
allocation -- while at the same time achieving the global property of never
exceeding available resources.
Our core contribution, the global myopic allocation (GMA) algorithm, is a
solution to this problem. We prove that GMA can compute unconditional
allocations for all paths on a graph, while never over-allocating resources.
Further, we prove that GMA is Pareto optimal with respect to the allocation
size, and it has linear complexity in the input size. Finally, we show with
simulations that this theoretical result could be indeed applied to practical
scenarios, as the resulting path allocations are large enough to fit the
requirements of practically relevant applications.
|
[
{
"version": "v1",
"created": "Sat, 20 Feb 2021 11:15:24 GMT"
},
{
"version": "v2",
"created": "Mon, 15 Mar 2021 10:56:53 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Giuliari",
"Giacomo",
""
],
[
"Wyss",
"Marc",
""
],
[
"Legner",
"Markus",
""
],
[
"Perrig",
"Adrian",
""
]
] |
new_dataset
| 0.994557 |
2110.08196
|
Yo\`av Montacute
|
Yo\`av Montacute and Nihil Shah
|
The Pebble-Relation Comonad in Finite Model Theory
|
Appears in Logic in Computer Science (LICS) 2022 Proceedings
| null | null | null |
cs.LO math.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The pebbling comonad, introduced by Abramsky, Dawar and Wang, provides a
categorical interpretation for the k-pebble games from finite model theory. The
coKleisli category of the pebbling comonad specifies equivalences under
different fragments and extensions of infinitary k-variable logic. Moreover,
the coalgebras over this pebbling comonad characterise treewidth and correspond
to tree decompositions. In this paper we introduce the pebble-relation comonad,
which characterises pathwidth and whose coalgebras correspond to path
decompositions. We further show that the existence of a coKleisli morphism in
this comonad is equivalent to truth preservation in the restricted conjunction
fragment of k-variable infinitary logic. We do this using Dalmau's
pebble-relation game and an equivalent all-in-one pebble game. We then provide
a similar treatment to the corresponding coKleisli isomorphisms via a bijective
version of the all-in-one pebble game with a hidden pebble placement. Finally,
we show as a consequence a new Lov\'asz-type theorem relating pathwidth to the
restricted conjunction fragment of k-variable infinitary logic with counting
quantifiers.
|
[
{
"version": "v1",
"created": "Fri, 15 Oct 2021 16:52:34 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 12:03:40 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Montacute",
"Yoàv",
""
],
[
"Shah",
"Nihil",
""
]
] |
new_dataset
| 0.99876 |
2202.07835
|
Yifeng Zheng
|
Songlei Wang and Yifeng Zheng and Xiaohua Jia
|
SecGNN: Privacy-Preserving Graph Neural Network Training and Inference
as a Cloud Service
|
Accepted in IEEE Transactions on Services Computing (TSC)
| null | null | null |
cs.CR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graphs are widely used to model the complex relationships among entities. As
a powerful tool for graph analytics, graph neural networks (GNNs) have recently
gained wide attention due to its end-to-end processing capabilities. With the
proliferation of cloud computing, it is increasingly popular to deploy the
services of complex and resource-intensive model training and inference in the
cloud due to its prominent benefits. However, GNN training and inference
services, if deployed in the cloud, will raise critical privacy concerns about
the information-rich and proprietary graph data (and the resulting model).
While there has been some work on secure neural network training and inference,
they all focus on convolutional neural networks handling images and text rather
than complex graph data with rich structural information. In this paper, we
design, implement, and evaluate SecGNN, the first system supporting
privacy-preserving GNN training and inference services in the cloud. SecGNN is
built from a synergy of insights on lightweight cryptography and machine
learning techniques. We deeply examine the procedure of GNN training and
inference, and devise a series of corresponding secure customized protocols to
support the holistic computation. Extensive experiments demonstrate that SecGNN
achieves comparable plaintext training and inference accuracy, with promising
performance.
|
[
{
"version": "v1",
"created": "Wed, 16 Feb 2022 02:57:10 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 11:29:18 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Wang",
"Songlei",
""
],
[
"Zheng",
"Yifeng",
""
],
[
"Jia",
"Xiaohua",
""
]
] |
new_dataset
| 0.996846 |
2202.13468
|
Xiang Guo
|
Xiang Guo, Arash Tavakoli, Erin Robartes, Austin Angulo, T. Donna
Chen, Arsalan Heydarian
|
Roadway Design Matters: Variation in Bicyclists' Psycho-Physiological
Responses in Different Urban Roadway Designs
| null | null |
10.1016/j.trf.2022.11.015
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As a healthier and more sustainable way of mobility, cycling has been
advocated by literature and policy. However, current trends in bicyclist crash
fatalities suggest deficiencies in current roadway design in protecting these
vulnerable road users. The lack of cycling data is a common challenge for
studying bicyclists' safety, behavior, and comfort levels under different
design contexts. To understand bicyclists' behavioral and physiological
responses in an efficient and safe way, this study uses a bicycle simulator
within an immersive virtual environment (IVE). Off-the-shelf sensors are
utilized to evaluate bicyclists' cycling performance (speed and lane position)
and physiological responses (eye tracking and heart rate (HR)). Participants
bike in a simulated virtual environment modeled to scale from a real-world
street with a shared bike lane (sharrow) to evaluate how introduction of a bike
lane and a protected bike lane with pylons may impact perceptions of safety, as
well as behavioral and psycho-physiological responses. Results from 50
participants show that the protected bike lane design received the highest
perceived safety rating and exhibited the lowest average cycling speed.
Furthermore, both the bike lane and the protected bike lane scenarios show a
less dispersed gaze distribution than the as-built sharrow scenario, reflecting
a higher gaze focus among bicyclists on the biking task in the bike lane and
protected bike lane scenarios, compared to when bicyclists share right of way
with vehicles. Additionally, heart rate change point results from the study
suggest that creating dedicated zones for bicyclists (bike lanes or protected
bike lanes) has the potential to reduce bicyclists' stress levels.
|
[
{
"version": "v1",
"created": "Sun, 27 Feb 2022 22:36:10 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Guo",
"Xiang",
""
],
[
"Tavakoli",
"Arash",
""
],
[
"Robartes",
"Erin",
""
],
[
"Angulo",
"Austin",
""
],
[
"Chen",
"T. Donna",
""
],
[
"Heydarian",
"Arsalan",
""
]
] |
new_dataset
| 0.987477 |
2204.11245
|
Chao Zhang
|
Chao Zhang, Wenqiang Yi, Yuanwei Liu and Lajos Hanzo
|
Semi-Integrated-Sensing-and-Communication (Semi-ISaC): From OMA to NOMA
|
This paper has been accpeted by IEEE Transactions on Communications.
This paper also has the further content to show the detailed proofs, namely
"The Proofs in the Paper Titled by 'Semi-Integrated-Sensing-and-Communication
(Semi-ISaC): From OMA to NOMA'" followed by the journal version
| null | null | null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The new concept of semi-integrated-sensing-and-communication (Semi-ISaC) is
proposed for next-generation cellular networks. Compared to the
state-of-the-art, where the total bandwidth is used for integrated sensing and
communication (ISaC), the proposed Semi-ISaC framework provides more freedom as
it allows that a portion of the bandwidth is exclusively used for either
wireless communication or radar detection, while the rest is for ISaC
transmission. To enhance the bandwidth efficiency (BE), we investigate the
evolution of Semi-ISaC networks from orthogonal multiple access (OMA) to
non-orthogonal multiple access (NOMA). First, we evaluate the performance of an
OMA-based Semi-ISaC network. As for the communication signals, we investigate
both the outage probability (OP) and the ergodic rate. As for the radar echoes,
we characterize the ergodic radar estimation information rate (REIR). Then, we
investigate the performance of a NOMA-based Semi-ISaC network, including the OP
and the ergodic rate for communication signals and the ergodic REIR for radar
echoes. The diversity gains of OP and the high signal-to-noise ratio (SNR)
slopes of the ergodic REIR are also evaluated as insights. The analytical
results indicate that: 1) Under a two-user NOMA-based Semi-ISaC scenario, the
diversity order of the near-user is equal to the coefficient of the Nakagami-m
fading channels (m), while that of the far-user is zero; and 2) The high-SNR
slope for the ergodic REIR is based on the ratio of the radar signal's duty
cycle to the pulse duration. Our simulation results show that: 1) Semi-ISaC has
better channel capacity than the conventional ISaC; and 2) The NOMA-based
Semi-ISaC has better channel capacity than the OMA-based Semi-ISaC.
|
[
{
"version": "v1",
"created": "Sun, 24 Apr 2022 11:21:01 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 04:50:30 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Zhang",
"Chao",
""
],
[
"Yi",
"Wenqiang",
""
],
[
"Liu",
"Yuanwei",
""
],
[
"Hanzo",
"Lajos",
""
]
] |
new_dataset
| 0.982674 |
2205.12897
|
Iman Vakilinia
|
Iman Vakilinia
|
Cryptocurrency Giveaway Scam with YouTube Live Stream
| null | null |
10.1109/UEMCON54665.2022.9965686
| null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
This paper investigates the cryptocurrency giveaway scam with the YouTube
live stream carried out on 5/15/2022 and 5/16/2022. In this scam scheme, the
scammer plays a recorded video of a famous person in a YouTube live stream
annotated with a cryptocurrency giveaway announcement. In the annotated
announcement, the victims are directed to the scammer's webpage. The scammer's
webpage is designed intelligently to deceive victims such that they believe the
legitimacy of the giveaway. The scammer claims that whatever donation the
victim sends to a cryptocurrency wallet address, the giveaway scheme will
double the donated amount and immediately send it back to the victim. By
analyzing the scammers' wallet addresses, it can be seen that scammers could
steal a significant amount of money in a short time. After analyzing the
attackers' techniques, tactics, and procedures, this paper discusses the
countermeasures that can be applied to mitigate such a fraudulent activity in
the future.
|
[
{
"version": "v1",
"created": "Wed, 25 May 2022 16:30:55 GMT"
},
{
"version": "v2",
"created": "Wed, 15 Jun 2022 22:48:47 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Vakilinia",
"Iman",
""
]
] |
new_dataset
| 0.999876 |
2207.06116
|
Marc Frei
|
Marc Frei and Jonghoon Kwon and Seyedali Tabaeiaghdaei and Marc Wyss
and Christoph Lenzen and Adrian Perrig
|
G-SINC: Global Synchronization Infrastructure for Network Clocks
| null | null |
10.1109/SRDS55811.2022.00021
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many critical computing applications rely on secure and dependable time which
is reliably synchronized across large distributed systems. Today's time
synchronization architectures are commonly based on global navigation satellite
systems at the considerable risk of being exposed to outages, malfunction, or
attacks against availability and accuracy. This paper describes a practical
instantiation of a new global, Byzantine fault-tolerant clock synchronization
approach that does not place trust in any single entity and is able to tolerate
a fraction of faulty entities while still maintaining synchronization on a
global scale among otherwise sovereign network topologies. Leveraging strong
resilience and security properties provided by the path-aware SCION networking
architecture, the presented design can be implemented as a backward compatible
active standby solution for existing time synchronization deployments. Through
extensive evaluation, we demonstrate that over 94% of time servers reliably
minimize the offset of their local clocks to real-time in the presence of up to
20% malicious nodes, and all time servers remain synchronized with a skew of
only 2 ms even after one year of reference clock outage.
|
[
{
"version": "v1",
"created": "Wed, 13 Jul 2022 10:45:29 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Frei",
"Marc",
""
],
[
"Kwon",
"Jonghoon",
""
],
[
"Tabaeiaghdaei",
"Seyedali",
""
],
[
"Wyss",
"Marc",
""
],
[
"Lenzen",
"Christoph",
""
],
[
"Perrig",
"Adrian",
""
]
] |
new_dataset
| 0.987868 |
2209.03726
|
Gabriel Kasmi
|
Gabriel Kasmi, Yves-Marie Saint-Drenan, David Trebosc, Rapha\"el
Jolivet, Jonathan Leloux, Babacar Sarr, Laurent Dubus
|
A crowdsourced dataset of aerial images with annotated solar
photovoltaic arrays and installation metadata
|
12 pages, 3 figures, 7 tables, revised preprint resubmitted to
Scientific Data
| null |
10.1038/s41597-023-01951-4
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Photovoltaic (PV) energy generation plays a crucial role in the energy
transition. Small-scale PV installations are deployed at an unprecedented pace,
and their integration into the grid can be challenging since public authorities
often lack quality data about them. Overhead imagery is increasingly used to
improve the knowledge of residential PV installations with machine learning
models capable of automatically mapping these installations. However, these
models cannot be easily transferred from one region or data source to another
due to differences in image acquisition. To address this issue known as domain
shift and foster the development of PV array mapping pipelines, we propose a
dataset containing aerial images, annotations, and segmentation masks. We
provide installation metadata for more than 28,000 installations. We provide
ground truth segmentation masks for 13,000 installations, including 7,000 with
annotations for two different image providers. Finally, we provide installation
metadata that matches the annotation for more than 8,000 installations. Dataset
applications include end-to-end PV registry construction, robust PV
installations mapping, and analysis of crowdsourced datasets.
|
[
{
"version": "v1",
"created": "Thu, 8 Sep 2022 11:42:53 GMT"
},
{
"version": "v2",
"created": "Thu, 8 Dec 2022 13:38:57 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Kasmi",
"Gabriel",
""
],
[
"Saint-Drenan",
"Yves-Marie",
""
],
[
"Trebosc",
"David",
""
],
[
"Jolivet",
"Raphaël",
""
],
[
"Leloux",
"Jonathan",
""
],
[
"Sarr",
"Babacar",
""
],
[
"Dubus",
"Laurent",
""
]
] |
new_dataset
| 0.999526 |
2209.06300
|
William Hackett
|
William Hackett, Stefan Trawicki, Zhengxin Yu, Neeraj Suri, Peter
Garraghan
|
PINCH: An Adversarial Extraction Attack Framework for Deep Learning
Models
|
19 pages, 13 figures, 5 tables
| null | null | null |
cs.CR cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Adversarial extraction attacks constitute an insidious threat against Deep
Learning (DL) models in-which an adversary aims to steal the architecture,
parameters, and hyper-parameters of a targeted DL model. Existing extraction
attack literature have observed varying levels of attack success for different
DL models and datasets, yet the underlying cause(s) behind their susceptibility
often remain unclear, and would help facilitate creating secure DL systems. In
this paper we present PINCH: an efficient and automated extraction attack
framework capable of designing, deploying, and analyzing extraction attack
scenarios across heterogeneous hardware platforms. Using PINCH, we perform
extensive experimental evaluation of extraction attacks against 21 model
architectures to explore new extraction attack scenarios and further attack
staging. Our findings show (1) key extraction characteristics whereby
particular model configurations exhibit strong resilience against specific
attacks, (2) even partial extraction success enables further staging for other
adversarial attacks, and (3) equivalent stolen models uncover differences in
expressive power, yet exhibit similar captured knowledge.
|
[
{
"version": "v1",
"created": "Tue, 13 Sep 2022 21:08:13 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 15:27:26 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Hackett",
"William",
""
],
[
"Trawicki",
"Stefan",
""
],
[
"Yu",
"Zhengxin",
""
],
[
"Suri",
"Neeraj",
""
],
[
"Garraghan",
"Peter",
""
]
] |
new_dataset
| 0.992375 |
2209.14225
|
Poonam Kumari Saha
|
Poonam Kumari Saha (1), Deeksha Arya (1), Ashutosh Kumar (1), Hiroya
Maeda (2), Yoshihide Sekimoto (1) ((1) The University of Tokyo, Japan, (2)
Urban-X Technologies, Inc., Tokyo, Japan)
|
Road Rutting Detection using Deep Learning on Images
|
9 pages, 7 figures
|
2022 IEEE International Conference on Big Data (Big Data), Osaka,
Japan, 2022, pp. 6507-6515
|
10.1109/BigData55660.2022.10020642
| null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Road rutting is a severe road distress that can cause premature failure of
road incurring early and costly maintenance costs. Research on road damage
detection using image processing techniques and deep learning are being
actively conducted in the past few years. However, these researches are mostly
focused on detection of cracks, potholes, and their variants. Very few research
has been done on the detection of road rutting. This paper proposes a novel
road rutting dataset comprising of 949 images and provides both object level
and pixel level annotations. Object detection models and semantic segmentation
models were deployed to detect road rutting on the proposed dataset, and
quantitative and qualitative analysis of model predictions were done to
evaluate model performance and identify challenges faced in the detection of
road rutting using the proposed method. Object detection model YOLOX-s achieves
mAP@IoU=0.5 of 61.6% and semantic segmentation model PSPNet (Resnet-50)
achieves IoU of 54.69 and accuracy of 72.67, thus providing a benchmark
accuracy for similar work in future. The proposed road rutting dataset and the
results of our research study will help accelerate the research on detection of
road rutting using deep learning.
|
[
{
"version": "v1",
"created": "Wed, 28 Sep 2022 16:53:05 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Saha",
"Poonam Kumari",
""
],
[
"Arya",
"Deeksha",
""
],
[
"Kumar",
"Ashutosh",
""
],
[
"Maeda",
"Hiroya",
""
],
[
"Sekimoto",
"Yoshihide",
""
]
] |
new_dataset
| 0.963075 |
2210.07499
|
Jinchuan Tian
|
Jinchuan Tian, Brian Yan, Jianwei Yu, Chao Weng, Dong Yu, Shinji
Watanabe
|
Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks
| null |
International Conference on Learning Representations (ICLR), 2023
| null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a
target sequence. The Connectionist Temporal Classification (CTC) criterion is
widely used in multiple seq2seq tasks. Besides predicting the target sequence,
a side product of CTC is to predict the alignment, which is the most probable
input-long sequence that specifies a hard aligning relationship between the
input and target units. As there are multiple potential aligning sequences
(called paths) that are equally considered in CTC formulation, the choice of
which path will be most probable and become the predicted alignment is always
uncertain. In addition, it is usually observed that the alignment predicted by
vanilla CTC will drift compared with its reference and rarely provides
practical functionalities. Thus, the motivation of this work is to make the CTC
alignment prediction controllable and thus equip CTC with extra
functionalities. The Bayes risk CTC (BRCTC) criterion is then proposed in this
work, in which a customizable Bayes risk function is adopted to enforce the
desired characteristics of the predicted alignment. With the risk function, the
BRCTC is a general framework to adopt some customizable preference over the
paths in order to concentrate the posterior into a particular subset of the
paths. In applications, we explore one particular preference which yields
models with the down-sampling ability and reduced inference costs. By using
BRCTC with another preference for early emissions, we obtain an improved
performance-latency trade-off for online models. Experimentally, the proposed
BRCTC reduces the inference cost of offline models by up to 47% without
performance degradation and cuts down the overall latency of online systems to
an unseen level.
|
[
{
"version": "v1",
"created": "Fri, 14 Oct 2022 03:55:36 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 05:01:51 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Tian",
"Jinchuan",
""
],
[
"Yan",
"Brian",
""
],
[
"Yu",
"Jianwei",
""
],
[
"Weng",
"Chao",
""
],
[
"Yu",
"Dong",
""
],
[
"Watanabe",
"Shinji",
""
]
] |
new_dataset
| 0.989891 |
2211.06537
|
Tobias Fiebig
|
Florian Streibelt, Martina Lindorfer, Seda G\"urses, Carlos H.
Ga\~n\'an, Tobias Fiebig
|
Back-to-the-Future Whois: An IP Address Attribution Service for Working
with Historic Datasets
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Researchers and practitioners often face the issue of having to attribute an
IP address to an organization. For current data this is comparably easy, using
services like whois or other databases. Similarly, for historic data, several
entities like the RIPE NCC provide websites that provide access to historic
records. For large-scale network measurement work, though, researchers often
have to attribute millions of addresses. For current data, Team Cymru provides
a bulk whois service which allows bulk address attribution. However, at the
time of writing, there is no service available that allows historic bulk
attribution of IP addresses. Hence, in this paper, we introduce and evaluate
our 'Back-to-the-Future whois' service, allowing historic bulk attribution of
IP addresses on a daily granularity based on CAIDA Routeviews aggregates. We
provide this service to the community for free, and also share our
implementation so researchers can run instances themselves.
|
[
{
"version": "v1",
"created": "Sat, 12 Nov 2022 01:00:12 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 16:05:13 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Streibelt",
"Florian",
""
],
[
"Lindorfer",
"Martina",
""
],
[
"Gürses",
"Seda",
""
],
[
"Gañán",
"Carlos H.",
""
],
[
"Fiebig",
"Tobias",
""
]
] |
new_dataset
| 0.976831 |
2301.04748
|
Zhihua Liu
|
Zhihua Liu, Bin Yang, Yan Shen, Xuejun Ni, Huiyu Zhou
|
LSDM: Long-Short Diffeomorphic Motion for Weakly-Supervised Ultrasound
Landmark Tracking
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Accurate tracking of an anatomical landmark over time has been of high
interests for disease assessment such as minimally invasive surgery and tumor
radiation therapy. Ultrasound imaging is a promising modality benefiting from
low-cost and real-time acquisition. However, generating a precise landmark
tracklet is very challenging, as attempts can be easily distorted by different
interference such as landmark deformation, visual ambiguity and partial
observation. In this paper, we propose a long-short diffeomorphic motion
network, which is a multi-task framework with a learnable deformation prior to
search for the plausible deformation of landmark. Specifically, we design a
novel diffeomorphism representation in both long and short temporal domains for
delineating motion margins and reducing long-term cumulative tracking errors.
To further mitigate local anatomical ambiguity, we propose an expectation
maximisation motion alignment module to iteratively optimize both long and
short deformation, aligning to the same directional and spatial representation.
The proposed multi-task system can be trained in a weakly-supervised manner,
which only requires few landmark annotations for tracking and zero annotation
for long-short deformation learning. We conduct extensive experiments on two
ultrasound landmark tracking datasets. Experimental results show that our
proposed method can achieve better or competitive landmark tracking performance
compared with other state-of-the-art tracking methods, with a strong
generalization capability across different scanner types and different
ultrasound modalities.
|
[
{
"version": "v1",
"created": "Wed, 11 Jan 2023 22:57:31 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 12:58:19 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Liu",
"Zhihua",
""
],
[
"Yang",
"Bin",
""
],
[
"Shen",
"Yan",
""
],
[
"Ni",
"Xuejun",
""
],
[
"Zhou",
"Huiyu",
""
]
] |
new_dataset
| 0.998966 |
2301.05911
|
XueTao Jiang
|
Xuetao Jiang and Meiyu Jiang and Qingguo Zhou
|
Day-Ahead PV Power Forecasting Based on MSTL-TFT
| null | null | null | null |
cs.LG cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, renewable energy resources have accounted for an increasing
share of electricity energy.Among them, photovoltaic (PV) power generation has
received broad attention due to its economic and environmental
benefits.Accurate PV generation forecasts can reduce power dispatch from the
grid, thus increasing the supplier's profit in the day-ahead electricity
market.The power system of a PV site is affected by solar radiation, PV plant
properties and meteorological factors, resulting in uncertainty in its power
output.This study used multiple seasonal-trend decomposition using LOESS (MSTL)
and temporal fusion transformer (TFT) to perform day-ahead PV prediction on the
desert knowledge Australia solar centre (DKASC) dataset.We compare the
decomposition algorithms (VMD, EEMD and VMD-EEMD) and prediction models (BP,
LSTM and XGBoost, etc.) which are commonly used in PV prediction presently.The
results show that the MSTL-TFT method is more accurate than the aforementioned
methods, which have noticeable improvement compared to other recent day-ahead
PV predictions on desert knowledge Australia solar centre (DKASC).
|
[
{
"version": "v1",
"created": "Sat, 14 Jan 2023 12:51:10 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 08:29:22 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Jiang",
"Xuetao",
""
],
[
"Jiang",
"Meiyu",
""
],
[
"Zhou",
"Qingguo",
""
]
] |
new_dataset
| 0.97749 |
2301.10894
|
Jenny Zhuoting Zhang
|
Daniel Chee Hian Tan, Jenny Zhang, Michael (Meng Yee) Chuah, Zhibin Li
|
Perceptive Locomotion with Controllable Pace and Natural Gait
Transitions Over Uneven Terrains
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This work developed a learning framework for perceptive legged locomotion
that combines visual feedback, proprioceptive information, and active gait
regulation of foot-ground contacts. The perception requires only one
forward-facing camera to obtain the heightmap, and the active regulation of
gait paces and traveling velocity are realized through our formulation of
CPG-based high-level imitation of foot-ground contacts. Through this framework,
an end-user has the ability to command task-level inputs to control different
walking speeds and gait frequencies according to the traversal of different
terrains, which enables more reliable negotiation with encountered obstacles.
The results demonstrated that the learned perceptive locomotion policy followed
task-level control inputs with intended behaviors, and was robust in presence
of unseen terrains and external force perturbations. A video demonstration can
be found at https://youtu.be/OTzlWzDfAe8, and the codebase at
https://github.com/jennyzzt/perceptual-locomotion.
|
[
{
"version": "v1",
"created": "Thu, 26 Jan 2023 01:34:41 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 21:59:51 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Tan",
"Daniel Chee Hian",
"",
"Meng Yee"
],
[
"Zhang",
"Jenny",
"",
"Meng Yee"
],
[
"Michael",
"",
"",
"Meng Yee"
],
[
"Chuah",
"",
""
],
[
"Li",
"Zhibin",
""
]
] |
new_dataset
| 0.996679 |
2301.12231
|
Vukan Ninkovic
|
Vukan Ninkovic, Dejan Vukobratovic, Christian H\"ager, Henk Wymeersch,
Alexandre Graell i Amat
|
Rateless Autoencoder Codes: Trading off Decoding Delay and Reliability
|
6 pages, 7 figures, to appear at IEEE ICC 2023
| null | null | null |
cs.IT cs.LG eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Most of today's communication systems are designed to target reliable message
recovery after receiving the entire encoded message (codeword). However, in
many practical scenarios, the transmission process may be interrupted before
receiving the complete codeword. This paper proposes a novel rateless
autoencoder (AE)-based code design suitable for decoding the transmitted
message before the noisy codeword is fully received. Using particular dropout
strategies applied during the training process, rateless AE codes allow to
trade off between decoding delay and reliability, providing a graceful
improvement of the latter with each additionally received codeword symbol. The
proposed rateless AEs significantly outperform the conventional AE designs for
scenarios where it is desirable to trade off reliability for lower decoding
delay.
|
[
{
"version": "v1",
"created": "Sat, 28 Jan 2023 15:47:14 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 09:29:19 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Ninkovic",
"Vukan",
""
],
[
"Vukobratovic",
"Dejan",
""
],
[
"Häger",
"Christian",
""
],
[
"Wymeersch",
"Henk",
""
],
[
"Amat",
"Alexandre Graell i",
""
]
] |
new_dataset
| 0.997459 |
2301.13244
|
Haonan Chang
|
Haonan Chang, Dhruv Metha Ramesh, Shijie Geng, Yuqiu Gan, Abdeslam
Boularias
|
Mono-STAR: Mono-camera Scene-level Tracking and Reconstruction
|
This paper has been accepted by ICRA2023
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present Mono-STAR, the first real-time 3D reconstruction system that
simultaneously supports semantic fusion, fast motion tracking, non-rigid object
deformation, and topological change under a unified framework. The proposed
system solves a new optimization problem incorporating optical-flow-based 2D
constraints to deal with fast motion and a novel semantic-aware deformation
graph (SAD-graph) for handling topology change. We test the proposed system
under various challenging scenes and demonstrate that it significantly
outperforms existing state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Mon, 30 Jan 2023 19:17:03 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Chang",
"Haonan",
""
],
[
"Ramesh",
"Dhruv Metha",
""
],
[
"Geng",
"Shijie",
""
],
[
"Gan",
"Yuqiu",
""
],
[
"Boularias",
"Abdeslam",
""
]
] |
new_dataset
| 0.997815 |
2301.13280
|
Jason Wu
|
Jason Wu and Siyan Wang and Siman Shen and Yi-Hao Peng and Jeffrey
Nichols and Jeffrey P. Bigham
|
WebUI: A Dataset for Enhancing Visual UI Understanding with Web
Semantics
|
Accepted to CHI 2023. Dataset, code, and models release coming soon
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Modeling user interfaces (UIs) from visual information allows systems to make
inferences about the functionality and semantics needed to support use cases in
accessibility, app automation, and testing. Current datasets for training
machine learning models are limited in size due to the costly and
time-consuming process of manually collecting and annotating UIs. We crawled
the web to construct WebUI, a large dataset of 400,000 rendered web pages
associated with automatically extracted metadata. We analyze the composition of
WebUI and show that while automatically extracted data is noisy, most examples
meet basic criteria for visual UI modeling. We applied several strategies for
incorporating semantics found in web pages to increase the performance of
visual UI understanding models in the mobile domain, where less labeled data is
available: (i) element detection, (ii) screen classification and (iii) screen
similarity.
|
[
{
"version": "v1",
"created": "Mon, 30 Jan 2023 20:47:12 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Wu",
"Jason",
""
],
[
"Wang",
"Siyan",
""
],
[
"Shen",
"Siman",
""
],
[
"Peng",
"Yi-Hao",
""
],
[
"Nichols",
"Jeffrey",
""
],
[
"Bigham",
"Jeffrey P.",
""
]
] |
new_dataset
| 0.999852 |
2301.13308
|
Ram Vasudevan
|
Jonathan Michaux, Patrick Holmes, Bohao Zhang, Che Chen, Baiyue Wang,
Shrey Sahgal, Tiancheng Zhang, Sidhartha Dey, Shreyas Kousik, and Ram
Vasudevan
|
Can't Touch This: Real-Time, Safe Motion Planning and Control for
Manipulators Under Uncertainty
|
25 pages, 3 figures
| null | null | null |
cs.RO cs.SY eess.SY math.OC
|
http://creativecommons.org/licenses/by/4.0/
|
A key challenge to the widespread deployment of robotic manipulators is the
need to ensure safety in arbitrary environments while generating new motion
plans in real-time. In particular, one must ensure that a manipulator does not
collide with obstacles, collide with itself, or exceed its joint torque limits.
This challenge is compounded by the need to account for uncertainty in the mass
and inertia of manipulated objects, and potentially the robot itself. The
present work addresses this challenge by proposing Autonomous Robust
Manipulation via Optimization with Uncertainty-aware Reachability (ARMOUR), a
provably-safe, receding-horizon trajectory planner and tracking controller
framework for serial link manipulators. ARMOUR works by first constructing a
robust, passivity-based controller that is proven to enable a manipulator to
track desired trajectories with bounded error despite uncertain dynamics. Next,
ARMOUR uses a novel variation on the Recursive Newton-Euler Algorithm (RNEA) to
compute the set of all possible inputs required to track any trajectory within
a continuum of desired trajectories. Finally, the method computes an
over-approximation to the swept volume of the manipulator; this enables one to
formulate an optimization problem, which can be solved in real-time, to
synthesize provably-safe motion. The proposed method is compared to state of
the art methods and demonstrated on a variety of challenging manipulation
examples in simulation and on real hardware, such as maneuvering a dumbbell
with uncertain mass around obstacles.
|
[
{
"version": "v1",
"created": "Mon, 30 Jan 2023 22:02:40 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Michaux",
"Jonathan",
""
],
[
"Holmes",
"Patrick",
""
],
[
"Zhang",
"Bohao",
""
],
[
"Chen",
"Che",
""
],
[
"Wang",
"Baiyue",
""
],
[
"Sahgal",
"Shrey",
""
],
[
"Zhang",
"Tiancheng",
""
],
[
"Dey",
"Sidhartha",
""
],
[
"Kousik",
"Shreyas",
""
],
[
"Vasudevan",
"Ram",
""
]
] |
new_dataset
| 0.99923 |
2301.13382
|
David Noever
|
David Noever, Forrest McKee
|
Numeracy from Literacy: Data Science as an Emergent Skill from Large
Language Models
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Large language models (LLM) such as OpenAI's ChatGPT and GPT-3 offer unique
testbeds for exploring the translation challenges of turning literacy into
numeracy. Previous publicly-available transformer models from eighteen months
prior and 1000 times smaller failed to provide basic arithmetic. The
statistical analysis of four complex datasets described here combines
arithmetic manipulations that cannot be memorized or encoded by simple rules.
The work examines whether next-token prediction succeeds from sentence
completion into the realm of actual numerical understanding. For example, the
work highlights cases for descriptive statistics on in-memory datasets that the
LLM initially loads from memory or generates randomly using python libraries.
The resulting exploratory data analysis showcases the model's capabilities to
group by or pivot categorical sums, infer feature importance, derive
correlations, and predict unseen test cases using linear regression. To extend
the model's testable range, the research deletes and appends random rows such
that recall alone cannot explain emergent numeracy.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 03:14:57 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Noever",
"David",
""
],
[
"McKee",
"Forrest",
""
]
] |
new_dataset
| 0.984221 |
2301.13385
|
ChungI Huang
|
Chung-I Huang, Wei-Yu Chen, Wei Jan Ko, Jih-Sheng Chang, Chen-Kai Sun,
Hui Hung Yu, Fang-Pang Lin
|
Fisheye traffic data set of point center markers
|
https://youtu.be/sjUQ-Ayxxtk
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This study presents an open data-market platform and a dataset containing
160,000 markers and 18,000 images. We hope that this dataset will bring more
new data value and applications In this paper, we introduce the format and
usage of the dataset, and we show a demonstration of deep learning vehicle
detection trained by this dataset.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 03:31:43 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Huang",
"Chung-I",
""
],
[
"Chen",
"Wei-Yu",
""
],
[
"Ko",
"Wei Jan",
""
],
[
"Chang",
"Jih-Sheng",
""
],
[
"Sun",
"Chen-Kai",
""
],
[
"Yu",
"Hui Hung",
""
],
[
"Lin",
"Fang-Pang",
""
]
] |
new_dataset
| 0.982835 |
2301.13413
|
Shilin Shan
|
Shilin Shan, Quang-Cuong Pham
|
Fine Robotic Manipulation without Force/Torque Sensor
| null | null | null | null |
cs.RO cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Force Sensing and Force Control are essential to many industrial
applications. Typically, a 6-axis Force/Torque (F/T) sensor is mounted between
the robot's wrist and the end-effector in order to measure the forces and
torques exerted by the environment onto the robot (the external wrench).
Although a typical 6-axis F/T sensor can provide highly accurate measurements,
it is expensive and vulnerable to drift and external impacts. Existing methods
aiming at estimating the external wrench using only the robot's internal
signals are limited in scope: for example, wrench estimation accuracy was
mostly validated in free-space motions and simple contacts as opposed to tasks
like assembly that require high-precision force control. Here we present a
Neural Network based method and argue that by devoting particular attention to
the training data structure, it is possible to accurately estimate the external
wrench in a wide range of scenarios based solely on internal signals. As an
illustration, we demonstrate a pin insertion experiment with 100-micron
clearance and a hand-guiding experiment, both performed without external F/T
sensors or joint torque sensors. Our result opens the possibility of equipping
the existing 2.7 million industrial robots with Force Sensing and Force Control
capabilities without any additional hardware.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 05:06:04 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Shan",
"Shilin",
""
],
[
"Pham",
"Quang-Cuong",
""
]
] |
new_dataset
| 0.997704 |
2301.13455
|
Xuange Cui
|
Xuange Cui, Wei Xiong, Songlin Wang
|
ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for
E-Commerce Product Search
|
KDD Cup Workshop @ KDD 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we propose a robust multilingual model to improve the quality
of search results. Our model not only leverage the processed class-balanced
dataset, but also benefit from multitask pre-training that leads to more
general representations. In pre-training stage, we adopt mlm task,
classification task and contrastive learning task to achieve considerably
performance. In fine-tuning stage, we use confident learning, exponential
moving average method (EMA), adversarial training (FGM) and regularized dropout
strategy (R-Drop) to improve the model's generalization and robustness.
Moreover, we use a multi-granular semantic unit to discover the queries and
products textual metadata for enhancing the representation of the model. Our
approach obtained competitive results and ranked top-8 in three tasks. We
release the source code and pre-trained models associated with this work.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 07:31:34 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Cui",
"Xuange",
""
],
[
"Xiong",
"Wei",
""
],
[
"Wang",
"Songlin",
""
]
] |
new_dataset
| 0.952001 |
2301.13576
|
Pierre-Etienne Martin
|
Pierre-Etienne Martin (MPI-EVA), Jordan Calandre (MIA), Boris
Mansencal (LaBRI), Jenny Benois-Pineau (LaBRI), Renaud P\'eteri (MIA),
Laurent Mascarilla (MIA), Julien Morlier
|
Sport Task: Fine Grained Action Detection and Classification of Table
Tennis Strokes from Videos for MediaEval 2022
|
MediaEval 2022 Workshop, Jan 2023, Bergen, Norway. arXiv admin note:
substantial text overlap with arXiv:2112.11384
| null | null | null |
cs.AI cs.CV cs.HC cs.LG cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sports video analysis is a widespread research topic. Its applications are
very diverse, like events detection during a match, video summary, or
fine-grained movement analysis of athletes. As part of the MediaEval 2022
benchmarking initiative, this task aims at detecting and classifying subtle
movements from sport videos. We focus on recordings of table tennis matches.
Conducted since 2019, this task provides a classification challenge from
untrimmed videos recorded under natural conditions with known temporal
boundaries for each stroke. Since 2021, the task also provides a stroke
detection challenge from unannotated, untrimmed videos. This year, the
training, validation, and test sets are enhanced to ensure that all strokes are
represented in each dataset. The dataset is now similar to the one used in [1,
2]. This research is intended to build tools for coaches and athletes who want
to further evaluate their sport performances.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 12:03:59 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Martin",
"Pierre-Etienne",
"",
"MPI-EVA"
],
[
"Calandre",
"Jordan",
"",
"MIA"
],
[
"Mansencal",
"Boris",
"",
"LaBRI"
],
[
"Benois-Pineau",
"Jenny",
"",
"LaBRI"
],
[
"Péteri",
"Renaud",
"",
"MIA"
],
[
"Mascarilla",
"Laurent",
"",
"MIA"
],
[
"Morlier",
"Julien",
""
]
] |
new_dataset
| 0.999634 |
2301.13626
|
Venkata Sai Praneeth Karempudi
|
Venkata Sai Praneeth Karempudi, Sairam Sri Vatsavai, Ishan Thakkar and
Jeffrey Todd Hastings
|
A Polymorphic Electro-Optic Logic Gate for High-Speed Reconfigurable
Computing Circuits
| null | null | null | null |
cs.ET physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the wake of dwindling Moore's law, integrated electro-optic (E-O)
computing circuits have shown revolutionary potential to provide progressively
faster and more efficient hardware for computing. The E-O circuits for
computing from the literature can operate with minimal latency at high
bit-rates. However, they face shortcomings due to their operand handling
complexity, non-amortizable high area and static power overheads, and general
unsuitability for large-scale integration on reticle-limited chips. To
alleviate these shortcomings, in this paper, we present a microring resonator
(MRR) based polymorphic E-O logic gate (MRR-PEOLG) that can be dynamically
programmed to implement different logic functions at different times. Our
MRR-PEOLG can provide compactness and polymorphism to E-O circuits, to
consequently improve their operand handling and amortization of area and static
power overheads. We model our MRR-PEOLG using photonics foundry-validated tools
to perform frequency and time-domain analysis of its polymorphic logic
functions. Our evaluation shows that the use of our MRR-PEOLG in two E-O
circuits from prior works can reduce their area-energy-delay product by up to
82.6$\times$. A tutorial on the modeling and simulation of our MRR-PEOLG, along
with related codes and files, is available on
https://github.com/uky-UCAT/MRR-PEOLG.
|
[
{
"version": "v1",
"created": "Mon, 30 Jan 2023 05:11:17 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Karempudi",
"Venkata Sai Praneeth",
""
],
[
"Vatsavai",
"Sairam Sri",
""
],
[
"Thakkar",
"Ishan",
""
],
[
"Hastings",
"Jeffrey Todd",
""
]
] |
new_dataset
| 0.993272 |
2301.13769
|
Clara Schneidewind
|
Sebastian Holler, Sebastian Biewer, Clara Schneidewind
|
HoRStify: Sound Security Analysis of Smart Contracts
|
Accepted for CSF 2023
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The cryptocurrency Ethereum is the most widely used execution platform for
smart contracts. Smart contracts are distributed applications, which govern
financial assets and, hence, can implement advanced financial instruments, such
as decentralized exchanges or autonomous organizations (DAOs). Their financial
nature makes smart contracts an attractive attack target, as demonstrated by
numerous exploits on popular contracts resulting in financial damage of
millions of dollars. This omnipresent attack hazard motivates the need for
sound static analysis tools, which assist smart contract developers in
eliminating contract vulnerabilities a priori to deployment. Vulnerability
assessment that is sound and insightful for EVM contracts is a formidable
challenge because contracts execute low-level bytecode in a largely unknown and
potentially hostile execution environment. So far, there exists no provably
sound automated analyzer that allows for the verification of security
properties based on program dependencies, even though prevalent attack classes
fall into this category. In this work, we present HoRStify, the first automated
analyzer for dependency properties of Ethereum smart contracts based on sound
static analysis. HoRStify grounds its soundness proof on a formal proof
framework for static program slicing that we instantiate to the semantics of
EVM bytecode. We demonstrate that HoRStify is flexible enough to soundly verify
the absence of famous attack classes such as timestamp dependency and, at the
same time, performant enough to analyze real-world smart contracts.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 17:08:12 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Holler",
"Sebastian",
""
],
[
"Biewer",
"Sebastian",
""
],
[
"Schneidewind",
"Clara",
""
]
] |
new_dataset
| 0.997317 |
2301.13771
|
Nailia Mirzakhmedova
|
Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary, Maximilian
Heinrich, Nicolas Handke, Xiaoni Cai, Barriere Valentin, Doratossadat
Dastgheib, Omid Ghahroodi, Mohammad Ali Sadraei, Ehsaneddin Asgari, Lea
Kawaletz, Henning Wachsmuth, Benno Stein
|
The Touch\'e23-ValueEval Dataset for Identifying Human Values behind
Arguments
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present the Touch\'e23-ValueEval Dataset for Identifying Human Values
behind Arguments. To investigate approaches for the automated detection of
human values behind arguments, we collected 9324 arguments from 6 diverse
sources, covering religious texts, political discussions, free-text arguments,
newspaper editorials, and online democracy platforms. Each argument was
annotated by 3 crowdworkers for 54 values. The Touch\'e23-ValueEval dataset
extends the Webis-ArgValues-22. In comparison to the previous dataset, the
effectiveness of a 1-Baseline decreases, but that of an out-of-the-box BERT
model increases. Therefore, though the classification difficulty increased as
per the label distribution, the larger dataset allows for training better
models.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 17:15:33 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Mirzakhmedova",
"Nailia",
""
],
[
"Kiesel",
"Johannes",
""
],
[
"Alshomary",
"Milad",
""
],
[
"Heinrich",
"Maximilian",
""
],
[
"Handke",
"Nicolas",
""
],
[
"Cai",
"Xiaoni",
""
],
[
"Valentin",
"Barriere",
""
],
[
"Dastgheib",
"Doratossadat",
""
],
[
"Ghahroodi",
"Omid",
""
],
[
"Sadraei",
"Mohammad Ali",
""
],
[
"Asgari",
"Ehsaneddin",
""
],
[
"Kawaletz",
"Lea",
""
],
[
"Wachsmuth",
"Henning",
""
],
[
"Stein",
"Benno",
""
]
] |
new_dataset
| 0.999526 |
2301.13779
|
Harshit Joshi
|
Harshit Joshi, Abishai Ebenezer, Jos\'e Cambronero, Sumit Gulwani,
Aditya Kanade, Vu Le, Ivan Radi\v{c}ek, Gust Verbruggen
|
FLAME: A small language model for spreadsheet formulas
| null | null | null | null |
cs.PL cs.AI cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
The widespread use of spreadsheet environments by billions of users presents
a unique opportunity for formula-authoring assistance. Although large language
models, such as Codex, can assist in general-purpose languages, they are
expensive to train and challenging to deploy due to their large model sizes (up
to billions of parameters). Moreover, they require hundreds of gigabytes of
training data. We present FLAME, a T5-based model trained on Excel formulas
that leverages domain insights to achieve competitive performance with a
substantially smaller model (60M parameters) and two orders of magnitude less
training data. We curate a training dataset using sketch deduplication,
introduce an Excel-specific formula tokenizer for our model, and use
domain-specific versions of masked span prediction and noisy auto-encoding as
pretraining objectives. We evaluate FLAME on formula repair, formula
auto-completion, and a novel task called syntax reconstruction. FLAME (60M) can
outperform much larger models, such as Codex-Davinci (175B), Codex-Cushman
(12B), and CodeT5 (220M), in 6 out of 10 settings.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 17:29:43 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Joshi",
"Harshit",
""
],
[
"Ebenezer",
"Abishai",
""
],
[
"Cambronero",
"José",
""
],
[
"Gulwani",
"Sumit",
""
],
[
"Kanade",
"Aditya",
""
],
[
"Le",
"Vu",
""
],
[
"Radiček",
"Ivan",
""
],
[
"Verbruggen",
"Gust",
""
]
] |
new_dataset
| 0.981412 |
2301.13868
|
Jordan Juravsky
|
Jordan Juravsky, Yunrong Guo, Sanja Fidler, Xue Bin Peng
|
PADL: Language-Directed Physics-Based Character Control
| null | null |
10.1145/3550469.3555391
| null |
cs.LG cs.AI cs.CL cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
Developing systems that can synthesize natural and life-like motions for
simulated characters has long been a focus for computer animation. But in order
for these systems to be useful for downstream applications, they need not only
produce high-quality motions, but must also provide an accessible and versatile
interface through which users can direct a character's behaviors. Natural
language provides a simple-to-use and expressive medium for specifying a user's
intent. Recent breakthroughs in natural language processing (NLP) have
demonstrated effective use of language-based interfaces for applications such
as image generation and program synthesis. In this work, we present PADL, which
leverages recent innovations in NLP in order to take steps towards developing
language-directed controllers for physics-based character animation. PADL
allows users to issue natural language commands for specifying both high-level
tasks and low-level skills that a character should perform. We present an
adversarial imitation learning approach for training policies to map high-level
language commands to low-level controls that enable a character to perform the
desired task and skill specified by a user's commands. Furthermore, we propose
a multi-task aggregation method that leverages a language-based multiple-choice
question-answering approach to determine high-level task objectives from
language commands. We show that our framework can be applied to effectively
direct a simulated humanoid character to perform a diverse array of complex
motor skills.
|
[
{
"version": "v1",
"created": "Tue, 31 Jan 2023 18:59:22 GMT"
}
] | 2023-02-01T00:00:00 |
[
[
"Juravsky",
"Jordan",
""
],
[
"Guo",
"Yunrong",
""
],
[
"Fidler",
"Sanja",
""
],
[
"Peng",
"Xue Bin",
""
]
] |
new_dataset
| 0.999747 |
1904.01509
|
Jian Xue
|
Yanfu Yan, Ke Lu, Jian Xue, Pengcheng Gao, Jiayi Lyu
|
FEAFA: A Well-Annotated Dataset for Facial Expression Analysis and 3D
Facial Animation
|
9 pages, 7 figures
|
2019 IEEE International Conference on Multimedia & Expo Workshops
(ICMEW)
|
10.1109/ICMEW.2019.0-104
| null |
cs.LG cs.CV cs.GR eess.IV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Facial expression analysis based on machine learning requires large number of
well-annotated data to reflect different changes in facial motion. Publicly
available datasets truly help to accelerate research in this area by providing
a benchmark resource, but all of these datasets, to the best of our knowledge,
are limited to rough annotations for action units, including only their
absence, presence, or a five-level intensity according to the Facial Action
Coding System. To meet the need for videos labeled in great detail, we present
a well-annotated dataset named FEAFA for Facial Expression Analysis and 3D
Facial Animation. One hundred and twenty-two participants, including children,
young adults and elderly people, were recorded in real-world conditions. In
addition, 99,356 frames were manually labeled using Expression Quantitative
Tool developed by us to quantify 9 symmetrical FACS action units, 10
asymmetrical (unilateral) FACS action units, 2 symmetrical FACS action
descriptors and 2 asymmetrical FACS action descriptors, and each action unit or
action descriptor is well-annotated with a floating point number between 0 and
1. To provide a baseline for use in future research, a benchmark for the
regression of action unit values based on Convolutional Neural Networks are
presented. We also demonstrate the potential of our FEAFA dataset for 3D facial
animation. Almost all state-of-the-art algorithms for facial animation are
achieved based on 3D face reconstruction. We hence propose a novel method that
drives virtual characters only based on action unit value regression of the 2D
video frames of source actors.
|
[
{
"version": "v1",
"created": "Tue, 2 Apr 2019 15:50:11 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Yan",
"Yanfu",
""
],
[
"Lu",
"Ke",
""
],
[
"Xue",
"Jian",
""
],
[
"Gao",
"Pengcheng",
""
],
[
"Lyu",
"Jiayi",
""
]
] |
new_dataset
| 0.999772 |
2003.12359
|
Yuan Zhou
|
Kun Cheng, Yuan Zhou, Bihuan Chen, Rui Wang, Yuebin Bai and Yang Liu
|
Guardauto: A Decentralized Runtime Protection System for Autonomous
Driving
| null |
IEEE Transactions on Computers, Volume: 70, Issue: 10, 01 October
2021
|
10.1109/TC.2020.3018329
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Due to the broad attack surface and the lack of runtime protection, potential
safety and security threats hinder the real-life adoption of autonomous
vehicles. Although efforts have been made to mitigate some specific attacks,
there are few works on the protection of the self-driving system. This paper
presents a decentralized self-protection framework called Guardauto to protect
the self-driving system against runtime threats. First, Guardauto proposes an
isolation model to decouple the self-driving system and isolate its components
with a set of partitions. Second, Guardauto provides self-protection mechanisms
for each target component, which combines different methods to monitor the
target execution and plan adaption actions accordingly. Third, Guardauto
provides cooperation among local self-protection mechanisms to identify the
root-cause component in the case of cascading failures affecting multiple
components. A prototype has been implemented and evaluated on the open-source
autonomous driving system Autoware. Results show that Guardauto could
effectively mitigate runtime failures and attacks, and protect the control
system with acceptable performance overhead.
|
[
{
"version": "v1",
"created": "Sun, 22 Mar 2020 09:28:23 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Cheng",
"Kun",
""
],
[
"Zhou",
"Yuan",
""
],
[
"Chen",
"Bihuan",
""
],
[
"Wang",
"Rui",
""
],
[
"Bai",
"Yuebin",
""
],
[
"Liu",
"Yang",
""
]
] |
new_dataset
| 0.999717 |
2104.02598
|
Dima Kagan
|
Dima Kagan, Galit Fuhrmann Alpert, Michael Fire
|
Automatic Large Scale Detection of Red Palm Weevil Infestation using
Aerial and Street View Images
| null | null |
10.1016/j.isprsjprs.2021.10.004
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The spread of the Red Palm Weevil has dramatically affected date growers,
homeowners and governments, forcing them to deal with a constant threat to
their palm trees. Early detection of palm tree infestation has been proven to
be critical in order to allow treatment that may save trees from irreversible
damage, and is most commonly performed by local physical access for individual
tree monitoring. Here, we present a novel method for surveillance of Red Palm
Weevil infested palm trees utilizing state-of-the-art deep learning algorithms,
with aerial and street-level imagery data. To detect infested palm trees we
analyzed over 100,000 aerial and street-images, mapping the location of palm
trees in urban areas. Using this procedure, we discovered and verified infested
palm trees at various locations.
|
[
{
"version": "v1",
"created": "Tue, 6 Apr 2021 15:35:26 GMT"
},
{
"version": "v2",
"created": "Fri, 9 Apr 2021 05:35:50 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Kagan",
"Dima",
""
],
[
"Alpert",
"Galit Fuhrmann",
""
],
[
"Fire",
"Michael",
""
]
] |
new_dataset
| 0.990037 |
2105.11992
|
Danish Kashaev
|
Danish Kashaev, Richard Santiago
|
A Simple Optimal Contention Resolution Scheme for Uniform Matroids
| null |
Theoretical Computer Science 940 (2023), 81-96
|
10.1016/j.tcs.2022.10.042
| null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Contention resolution schemes (or CR schemes), introduced by Chekuri, Vondrak
and Zenklusen, are a class of randomized rounding algorithms for converting a
fractional solution to a relaxation for a down-closed constraint family into an
integer solution. A CR scheme takes a fractional point $x$ in a relaxation
polytope, rounds each coordinate $x_i$ independently to get a possibly
non-feasible set, and then drops some elements in order to satisfy the
constraints. Intuitively, a contention resolution scheme is $c$-balanced if
every element $i$ is selected with probability at least $c \cdot x_i$.
It is known that general matroids admit a $(1-1/e)$-balanced CR scheme, and
that this is (asymptotically) optimal. This is in particular true for the
special case of uniform matroids of rank one. In this work, we provide a simple
and explicit monotone CR scheme for uniform matroids of rank $k$ on $n$
elements with a balancedness of $1 -
\binom{n}{k}\:\left(1-\frac{k}{n}\right)^{n+1-k}\:\left(\frac{k}{n}\right)^k$,
and show that this is optimal. As $n$ grows, this expression converges from
above to $1 - e^{-k}k^k/k!$. While this asymptotic bound can be obtained by
combining previously known results, these require defining an exponential-sized
linear program, as well as using random sampling and the ellipsoid algorithm.
Our procedure, on the other hand, has the advantage of being simple and
explicit. This scheme extends naturally into an optimal CR scheme for partition
matroids.
|
[
{
"version": "v1",
"created": "Tue, 25 May 2021 14:55:37 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Jun 2021 15:29:22 GMT"
},
{
"version": "v3",
"created": "Tue, 27 Jul 2021 13:33:32 GMT"
},
{
"version": "v4",
"created": "Thu, 25 Nov 2021 11:53:07 GMT"
},
{
"version": "v5",
"created": "Mon, 30 Jan 2023 12:56:40 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Kashaev",
"Danish",
""
],
[
"Santiago",
"Richard",
""
]
] |
new_dataset
| 0.994099 |
2105.12882
|
Yu-Shun Hsiao
|
Yu-Shun Hsiao, Zishen Wan, Tianyu Jia, Radhika Ghosal, Abdulrahman
Mahmoud, Arijit Raychowdhury, David Brooks, Gu-Yeon Wei, Vijay Janapa Reddi
|
MAVFI: An End-to-End Fault Analysis Framework with Anomaly Detection and
Recovery for Micro Aerial Vehicles
|
6 pages, 9 figures; The first two authors have equal contributions;
Accepted as a conference paper in DATE 2023
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Safety and resilience are critical for autonomous unmanned aerial vehicles
(UAVs). We introduce MAVFI, the micro aerial vehicles (MAVs) resilience
analysis methodology to assess the effect of silent data corruption (SDC) on
UAVs' mission metrics, such as flight time and success rate, for accurately
measuring system resilience. To enhance the safety and resilience of robot
systems bound by size, weight, and power (SWaP), we offer two low-overhead
anomaly-based SDC detection and recovery algorithms based on Gaussian
statistical models and autoencoder neural networks. Our anomaly error
protection techniques are validated in numerous simulated environments. We
demonstrate that the autoencoder-based technique can recover up to all failure
cases in our studied scenarios with a computational overhead of no more than
0.0062%. Our application-aware resilience analysis framework, MAVFI, can be
utilized to comprehensively test the resilience of other Robot Operating System
(ROS)-based applications and is publicly available at
https://github.com/harvard-edge/MAVBench/tree/mavfi.
|
[
{
"version": "v1",
"created": "Thu, 27 May 2021 00:03:27 GMT"
},
{
"version": "v2",
"created": "Tue, 17 Jan 2023 00:14:31 GMT"
},
{
"version": "v3",
"created": "Mon, 30 Jan 2023 18:04:00 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Hsiao",
"Yu-Shun",
""
],
[
"Wan",
"Zishen",
""
],
[
"Jia",
"Tianyu",
""
],
[
"Ghosal",
"Radhika",
""
],
[
"Mahmoud",
"Abdulrahman",
""
],
[
"Raychowdhury",
"Arijit",
""
],
[
"Brooks",
"David",
""
],
[
"Wei",
"Gu-Yeon",
""
],
[
"Reddi",
"Vijay Janapa",
""
]
] |
new_dataset
| 0.997222 |
2110.05266
|
William Gilpin
|
William Gilpin
|
Chaos as an interpretable benchmark for forecasting and data-driven
modelling
|
10 pages, 4 figures, plus appendices
|
NeurIPS (Neural Information Processing Systems) 2021
| null | null |
cs.LG eess.SP nlin.CD
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The striking fractal geometry of strange attractors underscores the
generative nature of chaos: like probability distributions, chaotic systems can
be repeatedly measured to produce arbitrarily-detailed information about the
underlying attractor. Chaotic systems thus pose a unique challenge to modern
statistical learning techniques, while retaining quantifiable mathematical
properties that make them controllable and interpretable as benchmarks. Here,
we present a growing database currently comprising 131 known chaotic dynamical
systems spanning fields such as astrophysics, climatology, and biochemistry.
Each system is paired with precomputed multivariate and univariate time series.
Our dataset has comparable scale to existing static time series databases;
however, our systems can be re-integrated to produce additional datasets of
arbitrary length and granularity. Our dataset is annotated with known
mathematical properties of each system, and we perform feature analysis to
broadly categorize the diverse dynamics present across the collection. Chaotic
systems inherently challenge forecasting models, and across extensive
benchmarks we correlate forecasting performance with the degree of chaos
present. We also exploit the unique generative properties of our dataset in
several proof-of-concept experiments: surrogate transfer learning to improve
time series classification, importance sampling to accelerate model training,
and benchmarking symbolic regression algorithms.
|
[
{
"version": "v1",
"created": "Mon, 11 Oct 2021 13:39:41 GMT"
},
{
"version": "v2",
"created": "Sun, 29 Jan 2023 08:19:43 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Gilpin",
"William",
""
]
] |
new_dataset
| 0.999233 |
2111.02751
|
Jian Xue
|
Wei Gan, Jian Xue, Ke Lu, Yanfu Yan, Pengcheng Gao, Jiayi Lyu
|
FEAFA+: An Extended Well-Annotated Dataset for Facial Expression
Analysis and 3D Facial Animation
| null |
Proc. SPIE 12342, Fourteenth International Conference on Digital
Image Processing (ICDIP 2022), 1234211 (12 October 2022)
|
10.1117/12.2643588
| null |
cs.CV cs.GR cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Nearly all existing Facial Action Coding System-based datasets that include
facial action unit (AU) intensity information annotate the intensity values
hierarchically using A--E levels. However, facial expressions change
continuously and shift smoothly from one state to another. Therefore, it is
more effective to regress the intensity value of local facial AUs to represent
whole facial expression changes, particularly in the fields of expression
transfer and facial animation. We introduce an extension of FEAFA in
combination with the relabeled DISFA database, which is available at
https://www.iiplab.net/feafa+/ now. Extended FEAFA (FEAFA+) includes 150 video
sequences from FEAFA and DISFA, with a total of 230,184 frames being manually
annotated on floating-point intensity value of 24 redefined AUs using the
Expression Quantitative Tool. We also list crude numerical results for posed
and spontaneous subsets and provide a baseline comparison for the AU intensity
regression task.
|
[
{
"version": "v1",
"created": "Thu, 4 Nov 2021 11:03:19 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Gan",
"Wei",
""
],
[
"Xue",
"Jian",
""
],
[
"Lu",
"Ke",
""
],
[
"Yan",
"Yanfu",
""
],
[
"Gao",
"Pengcheng",
""
],
[
"Lyu",
"Jiayi",
""
]
] |
new_dataset
| 0.999577 |
2203.10583
|
Abdullah Ozbay
|
Abdullah Ozbay, Kemal Bicakci
|
Should Users Trust Their Android Devices? A Scoring System for Assessing
Security and Privacy Risks of Pre-Installed Applications
|
16 pages, 9 figures
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Android devices are equipped with many pre-installed applications which have
the capability of tracking and monitoring users. Although applications coming
pre-installed pose a great danger to user security and privacy, they have
received little attention so far among researchers in the field. In this study,
we collect a dataset comprising such applications and make it publicly
available. Using this dataset, we analyze tracker SDKs, manifest files and the
use of cloud services and report our results. We also conduct a user survey to
understand concerns and perceptions of users. Last but not least, we present a
risk scoring system which assigns scores for smart phones consolidating our
findings based on carefully weighted criteria. With this scoring system, users
could give their own trust decisions based on the available concise information
about the security and privacy impacts of applications pre-installed on their
Android devices.
|
[
{
"version": "v1",
"created": "Sun, 20 Mar 2022 15:38:52 GMT"
},
{
"version": "v2",
"created": "Sat, 28 Jan 2023 16:15:24 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Ozbay",
"Abdullah",
""
],
[
"Bicakci",
"Kemal",
""
]
] |
new_dataset
| 0.999079 |
2205.04382
|
Harry Zhang Mr.
|
Ben Eisner, Harry Zhang, David Held
|
FlowBot3D: Learning 3D Articulation Flow to Manipulate Articulated
Objects
|
Accepted to Robotics Science and Systems (RSS) 2022, Best Paper
Finalist
| null | null | null |
cs.RO cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We explore a novel method to perceive and manipulate 3D articulated objects
that generalizes to enable a robot to articulate unseen classes of objects. We
propose a vision-based system that learns to predict the potential motions of
the parts of a variety of articulated objects to guide downstream motion
planning of the system to articulate the objects. To predict the object
motions, we train a neural network to output a dense vector field representing
the point-wise motion direction of the points in the point cloud under
articulation. We then deploy an analytical motion planner based on this vector
field to achieve a policy that yields maximum articulation. We train the vision
system entirely in simulation, and we demonstrate the capability of our system
to generalize to unseen object instances and novel categories in both
simulation and the real world, deploying our policy on a Sawyer robot with no
finetuning. Results show that our system achieves state-of-the-art performance
in both simulated and real-world experiments.
|
[
{
"version": "v1",
"created": "Mon, 9 May 2022 15:35:33 GMT"
},
{
"version": "v2",
"created": "Mon, 11 Jul 2022 11:38:59 GMT"
},
{
"version": "v3",
"created": "Tue, 20 Sep 2022 21:55:06 GMT"
},
{
"version": "v4",
"created": "Wed, 26 Oct 2022 16:52:04 GMT"
},
{
"version": "v5",
"created": "Sun, 29 Jan 2023 21:02:02 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Eisner",
"Ben",
""
],
[
"Zhang",
"Harry",
""
],
[
"Held",
"David",
""
]
] |
new_dataset
| 0.988152 |
2206.00447
|
Mai Su
|
Rongfei Zeng, Mai Su, Ruiyun Yu, Xingwei Wang
|
CD$^2$: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance
|
Just accepted by TOMM
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Monocular 3D reconstruction is to reconstruct the shape of object and its
other information from a single RGB image. In 3D reconstruction, polygon mesh,
with detailed surface information and low computational cost, is the most
prevalent expression form obtained from deep learning models. However, the
state-of-the-art schemes fail to directly generate well-structured meshes, and
we identify that most meshes have severe Vertices Clustering (VC) and Illegal
Twist (IT) problems. By analyzing the mesh deformation process, we pinpoint
that the inappropriate usage of Chamfer Distance (CD) loss is a root cause of
VC and IT problems in deep learning model. In this paper, we initially
demonstrate these two problems induced by CD loss with visual examples and
quantitative analyses. Then, we propose a fine-grained reconstruction method
CD$^2$ by employing Chamfer distance twice to perform a plausible and adaptive
deformation. Extensive experiments on two 3D datasets and comparisons with five
latest schemes demonstrate that our CD$^2$ directly generates a well-structured
mesh and outperforms others in terms of several quantitative metrics.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 12:29:25 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Nov 2022 15:28:03 GMT"
},
{
"version": "v3",
"created": "Sun, 29 Jan 2023 16:02:20 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Zeng",
"Rongfei",
""
],
[
"Su",
"Mai",
""
],
[
"Yu",
"Ruiyun",
""
],
[
"Wang",
"Xingwei",
""
]
] |
new_dataset
| 0.98153 |
2206.06669
|
Sam Maesschalck
|
Sam Maesschalck, Alexander Staves, Richard Derbyshire, Benjamin Green,
David Hutchison
|
Walking Under the Ladder Logic: PLC-VBS, a PLC Control Logic
Vulnerability Discovery Tool
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Cyber security risk assessments provide a pivotal starting point towards the
understanding of existing risk exposure, through which suitable mitigation
strategies can be formed. Where risk is viewed as a product of threat,
vulnerability, and impact, understanding each element is of equal importance.
This can be a challenge in Industrial Control System (ICS) environments, where
adopted technologies are typically not only bespoke, but interact directly with
the physical world. To date, existing vulnerability identification has focused
on traditional vulnerability categories. While this provides risk assessors
with a baseline understanding, and the ability to hypothesize on potential
resulting impacts, it is high level, operating at a level of abstraction that
would be viewed as incomplete within a traditional information system context.
The work presented in this paper takes the understanding of ICS device
vulnerabilities one step further. It offers a tool, PLC-VBS, that helps
identify Programmable Logic Controller (PLC) vulnerabilities, specifically
within logic used to monitor, control, and automate operational processes.
PLC-VBS gives risk assessors a more coherent picture about the potential impact
should the identified vulnerabilities be exploited; this applies specifically
to operational process elements.
|
[
{
"version": "v1",
"created": "Tue, 14 Jun 2022 07:57:28 GMT"
},
{
"version": "v2",
"created": "Wed, 17 Aug 2022 22:38:25 GMT"
},
{
"version": "v3",
"created": "Mon, 30 Jan 2023 09:43:13 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Maesschalck",
"Sam",
""
],
[
"Staves",
"Alexander",
""
],
[
"Derbyshire",
"Richard",
""
],
[
"Green",
"Benjamin",
""
],
[
"Hutchison",
"David",
""
]
] |
new_dataset
| 0.991484 |
2206.11519
|
Luciano Freitas
|
Luciano Freitas, Andrei Tonkikh, Adda-Akram Bendoukha, Sara
Tucci-Piergiovanni, Renaud Sirdey, Oana Stan, Petr Kuznetsov
|
Homomorphic Sortition -- Secret Leader Election for PoS Blockchains
| null | null | null | null |
cs.CR cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
In a single secret leader election protocol (SSLE), one of the system
participants is chosen and, unless it decides to reveal itself, no other
participant can identify it. SSLE has a great potential in protecting
blockchain consensus protocols against denial of service (DoS) attacks.
However, all existing solutions either make strong synchrony assumptions or
have expiring registration, meaning that they require elected processes to
re-register themselves before they can be re-elected again. This, in turn,
prohibits the use of these SSLE protocols to elect leaders in
partially-synchronous consensus protocols as there may be long periods of
network instability when no new blocks are decided and, thus, no new
registrations (or re-registrations) are possible. In this paper, we propose
Homomorphic Sortition -- the first asynchronous SSLE protocol with non-expiring
registration, making it the first solution compatible with
partially-synchronous leader-based consensus protocols.
Homomorphic Sortition relies on Threshold Fully Homomorphic Encryption
(ThFHE) and is tailored to proof-of-stake (PoS) blockchains, with several
important optimizations with respect to prior proposals. In particular, unlike
most existing SSLE protocols, it works with arbitrary stake distributions and
does not require a user with multiple coins to be registered multiple times.
Our protocol is highly parallelizable and can be run completely off-chain after
setup.
Some blockchains require a sequence of rounds to have non-repeating leaders.
We define a generalization of SSLE, called Secret Leader Permutation (SLP) in
which the application can choose how many non-repeating leaders should be
output in a sequence of rounds and we show how Homomorphic Sortition also
solves this problem.
|
[
{
"version": "v1",
"created": "Thu, 23 Jun 2022 08:04:44 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 08:51:27 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Freitas",
"Luciano",
""
],
[
"Tonkikh",
"Andrei",
""
],
[
"Bendoukha",
"Adda-Akram",
""
],
[
"Tucci-Piergiovanni",
"Sara",
""
],
[
"Sirdey",
"Renaud",
""
],
[
"Stan",
"Oana",
""
],
[
"Kuznetsov",
"Petr",
""
]
] |
new_dataset
| 0.971079 |
2207.07774
|
Lucas Aimaretto
|
Lucas Aimaretto (1), Diego Dujovne (2) ((1) Facultad de Ciencias
Exactas, F\'isicas y Naturales, Universidad Nacional de C\'ordoba, (2)
Escuela de Inform\'atica y Telecomunicaciones, Universidad Diego Portales)
|
BDPC: Controlling Application Delay in 6TiSCH networks for the
Industrial Internet of Things
|
This work has been submitted to Elsevier for possible publication.
Copyright may be transferred without notice, after which this version may no
longer be accessible
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the essential requirements of wireless industrial Internet of Things
(IoT) systems is to have an extremely high packet delivery rate, generally over
99.9% and comply wih realtime deadline constraints. In industrial IoT networks,
packets arriving after the deadline become part of packet loss and lose meaning
when they arrive late. However, currently available industial IoT proposals aim
to minimize End-to-End delay without taking into account simultaneous realtime
and reliability constraints. In this paper, we propose a new mechanism, called
BDPC (Bounded Delay Packet Control) to tackle this challenge. BDPC combines the
knowledge of a node's traffic delay to the destination (root) with the time
budget of a data packet traversing the industrial IoT network, to allocate
network resources to comply the system maximum delay requirements using an
adaptive and distributed algorithm. Unlike the general aim to minimze
end-to-end delay, we propose that data packets must arrive before the deadline,
but not faster. Our results show, for example, that by using BDPC, the number
of packets arriving before the deadline can be improved more than 2.6 times
compared to the case when using the default Minimal Scheduling Function from
the standard. As a further advantage, BDPC involves minor modifications to the
6TiSCH protocol stack, which makes it compatible with current implementations.
|
[
{
"version": "v1",
"created": "Fri, 15 Jul 2022 22:44:16 GMT"
},
{
"version": "v2",
"created": "Wed, 31 Aug 2022 12:39:35 GMT"
},
{
"version": "v3",
"created": "Sun, 29 Jan 2023 01:37:39 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Aimaretto",
"Lucas",
""
],
[
"Dujovne",
"Diego",
""
]
] |
new_dataset
| 0.998903 |
2208.01508
|
Meiziniu Li
|
Meiziniu Li, Jialun Cao, Yongqiang Tian, Tsz On Li, Ming Wen,
Shing-Chi Cheung
|
COMET: Coverage-guided Model Generation For Deep Learning Library
Testing
|
34 pages, 12 figures
| null | null | null |
cs.SE cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Recent deep learning (DL) applications are mostly built on top of DL
libraries. The quality assurance of these libraries is critical to the
dependable deployment of DL applications. Techniques have been proposed to
generate various DL models and apply them to test these libraries. However,
their test effectiveness is constrained by the diversity of layer API calls in
their generated DL models. Our study reveals that these techniques can cover at
most 34.1% layer inputs, 25.9% layer parameter values, and 15.6% layer
sequences. As a result, we find that many bugs arising from specific layer API
calls (i.e., specific layer inputs, parameter values, or layer sequences) can
be missed by existing techniques. Because of this limitation, we propose COMET
to effectively generate DL models with diverse layer API calls for DL library
testing. COMET: (1) designs a set of mutation operators and a coverage-based
search algorithm to diversify layer inputs, layer parameter values, and layer
sequences in DL models. (2) proposes a model synthesis method to boost the test
efficiency without compromising the layer API call diversity. Our evaluation
result shows that COMET outperforms baselines by covering twice as many layer
inputs (69.7% vs. 34.1%), layer parameter values (50.2% vs. 25.9%), and layer
sequences (39.0% vs. 15.6%) as those by the state-of-the-art. Moreover, COMET
covers 3.4% more library branches than those by existing techniques. Finally,
COMET detects 32 new bugs in the latest version of eight popular DL libraries,
including TensorFlow and MXNet, with 21 of them confirmed by DL library
developers and 7 of those confirmed bugs have been fixed by developers.
|
[
{
"version": "v1",
"created": "Tue, 2 Aug 2022 14:53:02 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 12:01:23 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Li",
"Meiziniu",
""
],
[
"Cao",
"Jialun",
""
],
[
"Tian",
"Yongqiang",
""
],
[
"Li",
"Tsz On",
""
],
[
"Wen",
"Ming",
""
],
[
"Cheung",
"Shing-Chi",
""
]
] |
new_dataset
| 0.95863 |
2208.05697
|
Ang Lv
|
Ang Lv, Xu Tan, Tao Qin, Tie-Yan Liu, Rui Yan
|
Re-creation of Creations: A New Paradigm for Lyric-to-Melody Generation
| null | null | null | null |
cs.SD cs.AI cs.MM eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Lyric-to-melody generation is an important task in songwriting, and is also
quite challenging due to its unique characteristics: the generated melodies
should not only follow good musical patterns, but also align with features in
lyrics such as rhythms and structures. These characteristics cannot be well
handled by neural generation models that learn lyric-to-melody mapping in an
end-to-end way, due to several issues: (1) lack of aligned lyric-melody
training data to sufficiently learn lyric-melody feature alignment; (2) lack of
controllability in generation to better and explicitly align the lyric-melody
features. In this paper, we propose Re-creation of Creations (ROC), a new
paradigm for lyric-to-melody generation. ROC generates melodies according to
given lyrics and also conditions on user-designated chord progression. It
addresses the above issues through a generation-retrieval pipeline.
Specifically, our paradigm has two stages: (1) creation stage, where a huge
amount of music fragments generated by a neural melody language model are
indexed in a database through several key features (e.g., chords, tonality,
rhythm, and structural information); (2) re-creation stage, where melodies are
re-created by retrieving music fragments from the database according to the key
features from lyrics and concatenating best music fragments based on
composition guidelines and melody language model scores. ROC has several
advantages: (1) It only needs unpaired melody data to train melody language
model, instead of paired lyric-melody data in previous models. (2) It achieves
good lyric-melody feature alignment in lyric-to-melody generation. Tested by
English and Chinese lyrics, ROC outperforms previous neural based
lyric-to-melody generation models on both objective and subjective metrics.
|
[
{
"version": "v1",
"created": "Thu, 11 Aug 2022 08:44:47 GMT"
},
{
"version": "v2",
"created": "Fri, 12 Aug 2022 03:22:37 GMT"
},
{
"version": "v3",
"created": "Thu, 18 Aug 2022 08:33:48 GMT"
},
{
"version": "v4",
"created": "Sat, 28 Jan 2023 09:43:42 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Lv",
"Ang",
""
],
[
"Tan",
"Xu",
""
],
[
"Qin",
"Tao",
""
],
[
"Liu",
"Tie-Yan",
""
],
[
"Yan",
"Rui",
""
]
] |
new_dataset
| 0.998874 |
2208.10925
|
Hai Li
|
Hai Li, Xingrui Yang, Hongjia Zhai, Yuqian Liu, Hujun Bao, Guofeng
Zhang
|
Vox-Surf: Voxel-based Implicit Surface Representation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Virtual content creation and interaction play an important role in modern 3D
applications such as AR and VR. Recovering detailed 3D models from real scenes
can significantly expand the scope of its applications and has been studied for
decades in the computer vision and computer graphics community. We propose
Vox-Surf, a voxel-based implicit surface representation. Our Vox-Surf divides
the space into finite bounded voxels. Each voxel stores geometry and appearance
information in its corner vertices. Vox-Surf is suitable for almost any
scenario thanks to sparsity inherited from voxel representation and can be
easily trained from multiple view images. We leverage the progressive training
procedure to extract important voxels gradually for further optimization so
that only valid voxels are preserved, which greatly reduces the number of
sampling points and increases rendering speed.The fine voxels can also be
considered as the bounding volume for collision detection.The experiments show
that Vox-Surf representation can learn delicate surface details and accurate
color with less memory and faster rendering speed than other methods.We also
show that Vox-Surf can be more practical in scene editing and AR applications.
|
[
{
"version": "v1",
"created": "Sun, 21 Aug 2022 18:02:55 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 09:30:07 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Li",
"Hai",
""
],
[
"Yang",
"Xingrui",
""
],
[
"Zhai",
"Hongjia",
""
],
[
"Liu",
"Yuqian",
""
],
[
"Bao",
"Hujun",
""
],
[
"Zhang",
"Guofeng",
""
]
] |
new_dataset
| 0.994941 |
2209.10655
|
Xuezhe Ma
|
Xuezhe Ma, Chunting Zhou, Xiang Kong, Junxian He, Liangke Gui, Graham
Neubig, Jonathan May, Luke Zettlemoyer
|
Mega: Moving Average Equipped Gated Attention
|
Accepted by ICLR 2023. Final version (updating MT results). 13 pages,
4 figures and 7 tables
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The design choices in the Transformer attention mechanism, including weak
inductive bias and quadratic computational complexity, have limited its
application for modeling long sequences. In this paper, we introduce Mega, a
simple, theoretically grounded, single-head gated attention mechanism equipped
with (exponential) moving average to incorporate inductive bias of
position-aware local dependencies into the position-agnostic attention
mechanism. We further propose a variant of Mega that offers linear time and
space complexity yet yields only minimal quality loss, by efficiently splitting
the whole sequence into multiple chunks with fixed length. Extensive
experiments on a wide range of sequence modeling benchmarks, including the Long
Range Arena, neural machine translation, auto-regressive language modeling, and
image and speech classification, show that Mega achieves significant
improvements over other sequence models, including variants of Transformers and
recent state space models.
|
[
{
"version": "v1",
"created": "Wed, 21 Sep 2022 20:52:17 GMT"
},
{
"version": "v2",
"created": "Mon, 26 Sep 2022 06:23:38 GMT"
},
{
"version": "v3",
"created": "Sat, 28 Jan 2023 06:33:20 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Ma",
"Xuezhe",
""
],
[
"Zhou",
"Chunting",
""
],
[
"Kong",
"Xiang",
""
],
[
"He",
"Junxian",
""
],
[
"Gui",
"Liangke",
""
],
[
"Neubig",
"Graham",
""
],
[
"May",
"Jonathan",
""
],
[
"Zettlemoyer",
"Luke",
""
]
] |
new_dataset
| 0.971168 |
2211.12136
|
Laurent Viennot
|
Filippo Brunelli (UPCit\'e, IRIF (UMR\_8243)), Laurent Viennot
(UPCit\'e, IRIF (UMR\_8243))
|
Minimum-Cost Temporal Walks under Waiting-Time Constraints in Linear
Time
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a temporal graph, each edge is available at specific points in time. Such
an availability point is often represented by a ''temporal edge'' that can be
traversed from its tail only at a specific departure time, for arriving in its
head after a specific travel time. In such a graph, the connectivity from one
node to another is naturally captured by the existence of a temporal path where
temporal edges can be traversed one after the other. When imposing constraints
on how much time it is possible to wait at a node in-between two temporal
edges, it then becomes interesting to consider temporal walks where it is
allowed to visit several times the same node, possibly at different times. We
study the complexity of computing minimum-cost temporal walks from a single
source under waiting-time constraints in a temporal graph, and ask under which
conditions this problem can be solved in linear time. Our main result is a
linear time algorithm when the input temporal graph is given by its (classical)
space-time representation. We use an algebraic framework for manipulating
abstract costs, enabling the optimization of a large variety of criteria or
even combinations of these. It allows to improve previous results for several
criteria such as number of edges or overall waiting time even without waiting
constraints. It saves a logarithmic factor for all criteria under waiting
constraints. Interestingly, we show that a logarithmic factor in the time
complexity appears to be necessary with a more basic input consisting of a
single ordered list of temporal edges (sorted either by arrival times or
departure times). We indeed show equivalence between the space-time
representation and a representation with two ordered lists.
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 10:11:56 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 13:45:53 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Brunelli",
"Filippo",
"",
"UPCité, IRIF"
],
[
"Viennot",
"Laurent",
"",
"UPCité, IRIF"
]
] |
new_dataset
| 0.991978 |
2212.14565
|
Ahmed Elhadeedy
|
Ahmed Elhadeedy, Jeremy Daily
|
Using Ethernet or A Wireless Harness and Named Data Networking in
Autonomous Tractor-Trailer Communication
| null | null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous truck and trailer configurations face challenges when operating in
reverse due to the lack of sensing on the trailer. It is anticipated that
sensor packages will be installed on existing trailers to extend autonomous
operations while operating in reverse in uncontrolled environments, like a
customer's loading dock. Power Line Communication (PLC) between the trailer and
the tractor cannot support high bandwidth and low latency communication. This
paper explores the impact of using Ethernet or a wireless medium for commercial
trailer-tractor communication on the lifecycle and operation of trailer
electronic control units (ECUs) from a Systems Engineering perspective to
address system requirements, integration, and security. Additionally,
content-based and host-based networking approaches for in-vehicle
communication, such as Named Data Networking (NDN) and IP-based networking are
compared. Implementation, testing and evaluation of prototype trailer ECU
communication with the tractor ECUs over Ethernet is shown by transmitting
different data types simultaneously. The implementation is tested with two
networking approaches, Named Data Networking, and Data Distribution Service
(DDS) and the test indicated that NDN over TCP is an efficient approach that is
capable of meeting automotive communication requirements. Using Ethernet or a
wireless harness and NDN for commercial trailer Anti-Lock Braking System (ABS)
ECU provides adequate resources for the operation of autonomous trucks and the
expansion of its capabilities, and at the same time significantly reduces the
complexities compared to when new features are added to legacy communication
systems. Using a wireless medium for tractor-trailer communication will bring
new cybersecurity challenges and requirements which requires new development
and lifecycle considerations.
|
[
{
"version": "v1",
"created": "Fri, 30 Dec 2022 06:45:18 GMT"
},
{
"version": "v2",
"created": "Sun, 29 Jan 2023 08:40:03 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Elhadeedy",
"Ahmed",
""
],
[
"Daily",
"Jeremy",
""
]
] |
new_dataset
| 0.993122 |
2301.01181
|
John Nay
|
John J. Nay
|
Large Language Models as Corporate Lobbyists
|
Our open-source code available here:
https://github.com/JohnNay/llm-lobbyist
| null | null | null |
cs.CL cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
We demonstrate a proof-of-concept of a large language model conducting
corporate lobbying related activities. An autoregressive large language model
(OpenAI's text-davinci-003) determines if proposed U.S. Congressional bills are
relevant to specific public companies and provides explanations and confidence
levels. For the bills the model deems as relevant, the model drafts a letter to
the sponsor of the bill in an attempt to persuade the congressperson to make
changes to the proposed legislation. We use hundreds of novel ground-truth
labels of the relevance of a bill to a company to benchmark the performance of
the model. It outperforms the baseline of predicting the most common outcome of
irrelevance. We also benchmark the performance of the previous OpenAI GPT-3
model (text-davinci-002), which was the state-of-the-art model on many academic
natural language tasks until text-davinci-003 was recently released. The
performance of text-davinci-002 is worse than the simple baseline. Longer-term,
if AI begins to influence law in a manner that is not a direct extension of
human intentions, this threatens the critical role that law as information
could play in aligning AI with humans. Initially, AI is being used to simply
augment human lobbyists for a small portion of their daily tasks. However,
firms have an incentive to use less and less human oversight over automated
assessments of policy ideas and the written communication to regulatory
agencies and Congressional staffers. The core question raised is where to draw
the line between human-driven and AI-driven policy influence.
|
[
{
"version": "v1",
"created": "Tue, 3 Jan 2023 16:25:52 GMT"
},
{
"version": "v2",
"created": "Wed, 4 Jan 2023 16:55:35 GMT"
},
{
"version": "v3",
"created": "Thu, 5 Jan 2023 15:06:58 GMT"
},
{
"version": "v4",
"created": "Sun, 8 Jan 2023 13:54:05 GMT"
},
{
"version": "v5",
"created": "Thu, 12 Jan 2023 20:49:46 GMT"
},
{
"version": "v6",
"created": "Tue, 17 Jan 2023 14:32:05 GMT"
},
{
"version": "v7",
"created": "Sat, 28 Jan 2023 20:49:33 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Nay",
"John J.",
""
]
] |
new_dataset
| 0.995334 |
2301.02453
|
Shuangyang Li
|
Shuangyang Li, Jinhong Yuan, Paul Fitzpatrick, Taka Sakurai, and
Giuseppe Caire
|
Delay-Doppler Domain Tomlinson-Harashima Precoding for OTFS-based
Downlink MU-MIMO Transmissions: Linear Complexity Implementation and Scaling
Law Analysis
|
submitted to IEEE Transactions on Communications
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Orthogonal time frequency space (OTFS) modulation is a recently proposed
delay-Doppler (DD) domain communication scheme, which has shown promising
performance in general wireless communications, especially over high-mobility
channels. In this paper, we investigate DD domain Tomlinson-Harashima precoding
(THP) for downlink multiuser multiple-input and multiple-output OTFS
(MU-MIMO-OTFS) transmissions. Instead of directly applying THP based on the
huge equivalent channel matrix, we propose a simple implementation of THP that
does not require any matrix decomposition or inversion. Such a simple
implementation is enabled by the DD domain channel property, i.e., different
resolvable paths do not share the same delay and Doppler shifts, which makes it
possible to pre-cancel all the DD domain interference in a symbol-by-symbol
manner. We also study the achievable rate performance for the proposed scheme
by leveraging the information-theoretical equivalent models. In particular, we
show that the proposed scheme can achieve a near optimal performance in the
high signal-to-noise ratio (SNR) regime. More importantly, scaling laws for
achievable rates with respect to number of antennas and users are derived,
which indicate that the achievable rate increases logarithmically with the
number of antennas and linearly with the number of users. Our numerical results
align well with our findings and also demonstrate a significant improvement
compared to existing MU-MIMO schemes on OTFS and orthogonal frequency-division
multiplexing (OFDM).
|
[
{
"version": "v1",
"created": "Fri, 6 Jan 2023 10:32:01 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 07:12:51 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Li",
"Shuangyang",
""
],
[
"Yuan",
"Jinhong",
""
],
[
"Fitzpatrick",
"Paul",
""
],
[
"Sakurai",
"Taka",
""
],
[
"Caire",
"Giuseppe",
""
]
] |
new_dataset
| 0.99932 |
2301.04630
|
Abhishek Cauligi
|
Abhishek Cauligi and R. Michael Swan and Masahiro Ono and Shreyansh
Daftry and John Elliott and Larry Matthies and Deegan Atha
|
ShadowNav: Crater-Based Localization for Nighttime and Permanently
Shadowed Region Lunar Navigation
|
IEEE Aerospace Conference 2023
| null | null | null |
cs.RO cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
There has been an increase in interest in missions that drive significantly
longer distances per day than what has currently been performed. Further, some
of these proposed missions require autonomous driving and absolute localization
in darkness. For example, the Endurance A mission proposes to drive 1200km of
its total traverse at night. The lack of natural light available during such
missions limits what can be used as visual landmarks and the range at which
landmarks can be observed. In order for planetary rovers to traverse long
ranges, onboard absolute localization is critical to the ability of the rover
to maintain its planned trajectory and avoid known hazardous regions.
Currently, to accomplish absolute localization, a ground in the loop (GITL)
operation is performed wherein a human operator matches local maps or images
from onboard with orbital images and maps. This GITL operation limits the
distance that can be driven in a day to a few hundred meters, which is the
distance that the rover can maintain acceptable localization error via relative
methods. Previous work has shown that using craters as landmarks is a promising
approach for performing absolute localization on the moon during the day. In
this work we present a method of absolute localization that utilizes craters as
landmarks and matches detected crater edges on the surface with known craters
in orbital maps. We focus on a localization method based on a perception system
which has an external illuminator and a stereo camera. We evaluate (1) both
monocular and stereo based surface crater edge detection techniques, (2)
methods of scoring the crater edge matches for optimal localization, and (3)
localization performance on simulated Lunar surface imagery at night. We
demonstrate that this technique shows promise for maintaining absolute
localization error of less than 10m required for most planetary rover missions.
|
[
{
"version": "v1",
"created": "Wed, 11 Jan 2023 18:35:31 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Cauligi",
"Abhishek",
""
],
[
"Swan",
"R. Michael",
""
],
[
"Ono",
"Masahiro",
""
],
[
"Daftry",
"Shreyansh",
""
],
[
"Elliott",
"John",
""
],
[
"Matthies",
"Larry",
""
],
[
"Atha",
"Deegan",
""
]
] |
new_dataset
| 0.998023 |
2301.05842
|
Ross Greer
|
Ross Greer, Lulua Rakla, Samveed Desai, Afnan Alofi, Akshay
Gopalkrishnan, Mohan Trivedi
|
CHAMP: Crowdsourced, History-Based Advisory of Mapped Pedestrians for
Safer Driver Assistance Systems
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Vehicles are constantly approaching and sharing the road with pedestrians,
and as a result it is critical for vehicles to prevent any collisions with
pedestrians. Current methods for pedestrian collision prevention focus on
integrating visual pedestrian detectors with Automatic Emergency Braking (AEB)
systems which can trigger warnings and apply brakes as a pedestrian enters a
vehicle's path. Unfortunately, pedestrian-detection-based systems can be
hindered in certain situations such as nighttime or when pedestrians are
occluded. Our system, CHAMP (Crowdsourced, History-based Advisories of Mapped
Pedestrians), addresses such issues using an online, map-based pedestrian
detection system where pedestrian locations are aggregated into a dataset after
repeated passes of locations. Using this dataset, we are able to learn
pedestrian zones and generate advisory notices when a vehicle is approaching a
pedestrian despite challenges like dark lighting or pedestrian occlusion. We
collected and carefully annotated pedestrian data in La Jolla, CA to construct
training and test sets of pedestrian locations. Moreover, we use the number of
correct advisories, false advisories, and missed advisories to define precision
and recall performance metrics to evaluate CHAMP. This approach can be tuned
such that we achieve a maximum of 100% precision and 75% recall on the
experimental dataset, with performance enhancement options through further data
collection.
|
[
{
"version": "v1",
"created": "Sat, 14 Jan 2023 07:28:05 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Jan 2023 19:45:51 GMT"
},
{
"version": "v3",
"created": "Mon, 30 Jan 2023 02:00:31 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Greer",
"Ross",
""
],
[
"Rakla",
"Lulua",
""
],
[
"Desai",
"Samveed",
""
],
[
"Alofi",
"Afnan",
""
],
[
"Gopalkrishnan",
"Akshay",
""
],
[
"Trivedi",
"Mohan",
""
]
] |
new_dataset
| 0.997669 |
2301.10172
|
Archan Ghosh
|
Archan Ghosh, Debgandhar Ghosh, Madhurima Maji, Suchinta Chanda,
Kalporup Goswami
|
MTTN: Multi-Pair Text to Text Narratives for Prompt Generation
| null | null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The increased interest in diffusion models has opened up opportunities for
advancements in generative text modeling. These models can produce impressive
images when given a well-crafted prompt, but creating a powerful or meaningful
prompt can be hit-or-miss. To address this, we have created a large-scale
dataset that is derived and synthesized from real prompts and indexed with
popular image-text datasets such as MS-COCO and Flickr. We have also
implemented stages that gradually reduce context and increase complexity, which
will further enhance the output due to the complex annotations created. The
dataset, called MTTN, includes over 2.4 million sentences divided into 5
stages, resulting in a total of over 12 million pairs, and a vocabulary of over
300,000 unique words, providing ample variation. The original 2.4 million pairs
are designed to reflect the way language is used on the internet globally,
making the dataset more robust for any model trained on it.
|
[
{
"version": "v1",
"created": "Sat, 21 Jan 2023 06:55:44 GMT"
},
{
"version": "v2",
"created": "Sun, 29 Jan 2023 18:03:44 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Ghosh",
"Archan",
""
],
[
"Ghosh",
"Debgandhar",
""
],
[
"Maji",
"Madhurima",
""
],
[
"Chanda",
"Suchinta",
""
],
[
"Goswami",
"Kalporup",
""
]
] |
new_dataset
| 0.999841 |
2301.10966
|
Xuan Quang Ngo
|
Thai Nguyen Chau, Xuan Quang Ngo, Van Tu Duong, Trong Trung Nguyen,
Huy Hung Nguyen, Tan Tien Nguyen
|
Design of Mobile Manipulator for Fire Extinguisher Testing. Part II:
Design and Simulation
|
10 pages, 15 figures, the 7th International Conference on Advanced
Engineering, Theory and Applications
| null | null | null |
cs.RO cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
All flames are extinguished as early as possible, or fire services have to
deal with major conflagrations. This leads to the fact that the quality of fire
extinguishers has become a very sensitive and important issue in firefighting.
Inspired by the development of automatic fire fighting systems, this paper
presents a mobile manipulator to evaluate the power of fire extinguishers,
which is designed according to the standard of fire extinguishers named as ISO
7165:2009 and ISO 11601:2008. A detailed discussion on key specifications
solutions and mechanical design of the chassis of the mobile manipulator has
been presented in Part I: Key Specifications and Conceptual Design. The focus
of this part is on the rest of the mechanical design and controller de-sign of
the mobile manipulator.
|
[
{
"version": "v1",
"created": "Thu, 26 Jan 2023 07:19:20 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Chau",
"Thai Nguyen",
""
],
[
"Ngo",
"Xuan Quang",
""
],
[
"Duong",
"Van Tu",
""
],
[
"Nguyen",
"Trong Trung",
""
],
[
"Nguyen",
"Huy Hung",
""
],
[
"Nguyen",
"Tan Tien",
""
]
] |
new_dataset
| 0.999129 |
2301.11806
|
Arup Kumar Sarker
|
Arup Kumar Sarker, Farzana Yasmin Ahmad and Matthew B. Dwyer
|
PCV: A Point Cloud-Based Network Verifier
|
11 pages, 12 figures
| null | null | null |
cs.CV cs.SE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
3D vision with real-time LiDAR-based point cloud data became a vital part of
autonomous system research, especially perception and prediction modules use
for object classification, segmentation, and detection. Despite their success,
point cloud-based network models are vulnerable to multiple adversarial
attacks, where the certain factor of changes in the validation set causes
significant performance drop in well-trained networks. Most of the existing
verifiers work perfectly on 2D convolution. Due to complex architecture,
dimension of hyper-parameter, and 3D convolution, no verifiers can perform the
basic layer-wise verification. It is difficult to conclude the robustness of a
3D vision model without performing the verification. Because there will be
always corner cases and adversarial input that can compromise the model's
effectiveness.
In this project, we describe a point cloud-based network verifier that
successfully deals state of the art 3D classifier PointNet verifies the
robustness by generating adversarial inputs. We have used extracted properties
from the trained PointNet and changed certain factors for perturbation input.
We calculate the impact on model accuracy versus property factor and can test
PointNet network's robustness against a small collection of perturbing input
states resulting from adversarial attacks like the suggested hybrid reverse
signed attack. The experimental results reveal that the resilience property of
PointNet is affected by our hybrid reverse signed perturbation strategy
|
[
{
"version": "v1",
"created": "Fri, 27 Jan 2023 15:58:54 GMT"
},
{
"version": "v2",
"created": "Mon, 30 Jan 2023 16:07:57 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Sarker",
"Arup Kumar",
""
],
[
"Ahmad",
"Farzana Yasmin",
""
],
[
"Dwyer",
"Matthew B.",
""
]
] |
new_dataset
| 0.998446 |
2301.11927
|
Paul Diac
|
Andrei Arhire and Paul Diac
|
PACE Solver Description: A Heuristic Directed Feedback Vertex Set
Problem Algorithm
|
Second best student submission of Heuristic Track of the Feedback
Vertex Set Challenge at PACE 2022, https://pacechallenge.org/
https://github.com/AndreiiArhire/PACE2022
| null | null | null |
cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
A feedback vertex set of a graph is a set of nodes with the property that
every cycle contains at least one vertex from the set i.e. the removal of all
vertices from a feedback vertex set leads to an acyclic graph. In this short
paper, we describe the algorithm for finding a minimum directed feedback vertex
set used by the _UAIC_ANDREIARHIRE_ solver, submitted to the heuristic track of
the 2022 PACE challenge.
|
[
{
"version": "v1",
"created": "Thu, 26 Jan 2023 03:16:14 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Arhire",
"Andrei",
""
],
[
"Diac",
"Paul",
""
]
] |
new_dataset
| 0.999232 |
2301.11932
|
Muhammad Al-Barham
|
Muhammad Al-Barham and Adham Alsharkawi and Musa Al-Yaman and Mohammad
Al-Fetyani and Ashraf Elnagar and Ahmad Abu SaAleek and Mohammad Al-Odat
|
RGB Arabic Alphabets Sign Language Dataset
|
Reference for the dataset that has inspired us to create our dataset:
https://data.mendeley.com/datasets/y7pckrw6z2/1
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper introduces the RGB Arabic Alphabet Sign Language (AASL) dataset.
AASL comprises 7,856 raw and fully labelled RGB images of the Arabic sign
language alphabets, which to our best knowledge is the first publicly available
RGB dataset. The dataset is aimed to help those interested in developing
real-life Arabic sign language classification models. AASL was collected from
more than 200 participants and with different settings such as lighting,
background, image orientation, image size, and image resolution. Experts in the
field supervised, validated and filtered the collected images to ensure a
high-quality dataset. AASL is made available to the public on Kaggle.
|
[
{
"version": "v1",
"created": "Mon, 30 Jan 2023 10:21:09 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Al-Barham",
"Muhammad",
""
],
[
"Alsharkawi",
"Adham",
""
],
[
"Al-Yaman",
"Musa",
""
],
[
"Al-Fetyani",
"Mohammad",
""
],
[
"Elnagar",
"Ashraf",
""
],
[
"SaAleek",
"Ahmad Abu",
""
],
[
"Al-Odat",
"Mohammad",
""
]
] |
new_dataset
| 0.999833 |
2301.11975
|
Nathan Fradet
|
Nathan Fradet, Jean-Pierre Briot, Fabien Chhel, Amal El Fallah
Seghrouchni, Nicolas Gutowski
|
Byte Pair Encoding for Symbolic Music
|
Source code at https://github.com/Natooz/BPE-Symbolic-Music
| null | null | null |
cs.LG cs.AI cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
The symbolic music modality is nowadays mostly represented as discrete and
used with sequential models such as Transformers, for deep learning tasks.
Recent research put efforts on the tokenization, i.e. the conversion of data
into sequences of integers intelligible to such models. This can be achieved by
many ways as music can be composed of simultaneous tracks, of simultaneous
notes with several attributes. Until now, the proposed tokenizations are based
on small vocabularies describing the note attributes and time events, resulting
in fairly long token sequences. In this paper, we show how Byte Pair Encoding
(BPE) can improve the results of deep learning models while improving its
performances. We experiment on music generation and composer classification,
and study the impact of BPE on how models learn the embeddings, and show that
it can help to increase their isotropy, i.e., the uniformity of the variance of
their positions in the space.
|
[
{
"version": "v1",
"created": "Fri, 27 Jan 2023 20:22:18 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Fradet",
"Nathan",
""
],
[
"Briot",
"Jean-Pierre",
""
],
[
"Chhel",
"Fabien",
""
],
[
"Seghrouchni",
"Amal El Fallah",
""
],
[
"Gutowski",
"Nicolas",
""
]
] |
new_dataset
| 0.997462 |
2301.11995
|
Randy Kuang
|
Randy Kuang and Maria Perepechaenko and Ryan Toth
|
A New Symmetric Homomorphic Functional Encryption over a Hidden Ring for
Polynomial Public Key Encapsulations
|
21 pages, 1 figure
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper proposes a new homomorphic functional encryption using modular
multiplications over a hidden ring. Unlike traditional homomorphic encryption
where users can only passively perform ciphertext addition or multiplication,
the homomorphic functional encryption retains homomorphic addition and scalar
multiplication properties, but also allows for the user's inputs through
polynomial variables. The proposed homomorphic encryption can be applied to any
polynomials over a finite field, with their coefficients considered as their
privacy. We denote the polynomials before homomorphic encryption as plain
polynomials and after homomorphic encryption as cipher polynomials. A cipher
polynomial can be evaluated with variables from the finite field, GF(p), by
calculating the monomials of variables modulo a prime p. These properties allow
functional homomorphic encryption to be used for public key encryption of
certain asymmetric cryptosystems to hide the structure of its central map
construction. We propose a new variant of MPKC with homomorphic encryption of
its public key. We propose to use a single plaintext vector and a noise vector
of multiple variables to be associated with the central map, in place of the
secret plaintext vector to be encrypted in MPKC. We call this variant of
encrypted MPKC, a Homomorphic Polynomial Public Key algorithm or HPPK
algorithm. The HPPK algorithm holds the property of indistinguishability under
the chosen-plaintext attacks or IND-CPA. The overall classical complexity to
crack the HPPK algorithm is exponential in the size of the prime field GF(p).
We briefly report on benchmarking performance results using the SUPERCOP
toolkit. Benchmarking results demonstrate that HPPK offers rather fast
performance, which is comparable and in some cases outperforms the NIST PQC
finalists for key generation, encryption, and decryption.
|
[
{
"version": "v1",
"created": "Fri, 27 Jan 2023 21:23:44 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Kuang",
"Randy",
""
],
[
"Perepechaenko",
"Maria",
""
],
[
"Toth",
"Ryan",
""
]
] |
new_dataset
| 0.998485 |
2301.12023
|
Wonho Bae
|
Wonho Bae, Mohamed Osama Ahmed, Frederick Tung, Gabriel L. Oliveira
|
Meta Temporal Point Processes
|
Accepted to ICLR2023
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A temporal point process (TPP) is a stochastic process where its realization
is a sequence of discrete events in time. Recent work in TPPs model the process
using a neural network in a supervised learning framework, where a training set
is a collection of all the sequences. In this work, we propose to train TPPs in
a meta learning framework, where each sequence is treated as a different task,
via a novel framing of TPPs as neural processes (NPs). We introduce context
sets to model TPPs as an instantiation of NPs. Motivated by attentive NP, we
also introduce local history matching to help learn more informative features.
We demonstrate the potential of the proposed method on popular public benchmark
datasets and tasks, and compare with state-of-the-art TPP methods.
|
[
{
"version": "v1",
"created": "Fri, 27 Jan 2023 23:21:07 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Bae",
"Wonho",
""
],
[
"Ahmed",
"Mohamed Osama",
""
],
[
"Tung",
"Frederick",
""
],
[
"Oliveira",
"Gabriel L.",
""
]
] |
new_dataset
| 0.95712 |
2301.12032
|
Ali Borji
|
Ali Borji
|
BinaryVQA: A Versatile Test Set to Evaluate the Out-of-Distribution
Generalization of VQA Models
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a new test set for visual question answering (VQA) called
BinaryVQA to push the limits of VQA models. Our dataset includes 7,800
questions across 1,024 images and covers a wide variety of objects, topics, and
concepts. For easy model evaluation, we only consider binary questions.
Questions and answers are formulated and verified carefully and manually.
Around 63% of the questions have positive answers. The median number of
questions per image and question length are 7 and 5, respectively. The state of
the art OFA model achieves 75% accuracy on BinaryVQA dataset, which is
significantly lower than its performance on the VQA v2 test-dev dataset
(94.7%). We also analyze the model behavior along several dimensions including:
a) performance over different categories such as text, counting and gaze
direction, b) model interpretability, c) the effect of question length on
accuracy, d) bias of models towards positive answers and introduction of a new
score called the ShuffleAcc, and e) sensitivity to spelling and grammar errors.
Our investigation demonstrates the difficulty of our dataset and shows that it
can challenge VQA models for next few years. Data and code are publicly
available at: DATA and CODE.
|
[
{
"version": "v1",
"created": "Sat, 28 Jan 2023 00:03:44 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Borji",
"Ali",
""
]
] |
new_dataset
| 0.996747 |
2301.12055
|
Abhinit Kumar Ambastha
|
Abhinit Kumar Ambastha, Leong Tze Yun
|
TIDo: Source-free Task Incremental Learning in Non-stationary
Environments
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work presents an incremental learning approach for autonomous agents to
learn new tasks in a non-stationary environment. Updating a DNN model-based
agent to learn new target tasks requires us to store past training data and
needs a large labeled target task dataset. Few-shot task incremental learning
methods overcome the limitation of labeled target datasets by adapting trained
models to learn private target classes using a few labeled representatives and
a large unlabeled target dataset. However, the methods assume that the source
and target tasks are stationary. We propose a one-shot task incremental
learning approach that can adapt to non-stationary source and target tasks. Our
approach minimizes adversarial discrepancy between the model's feature space
and incoming incremental data to learn an updated hypothesis. We also use
distillation loss to reduce catastrophic forgetting of previously learned
tasks. Finally, we use Gaussian prototypes to generate exemplar instances
eliminating the need to store past training data. Unlike current work in task
incremental learning, our model can learn both source and target task updates
incrementally. We evaluate our method on various problem settings for
incremental object detection and disease prediction model update. We evaluate
our approach by measuring the performance of shared class and target private
class prediction. Our results show that our approach achieved improved
performance compared to existing state-of-the-art task incremental learning
methods.
|
[
{
"version": "v1",
"created": "Sat, 28 Jan 2023 02:19:45 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Ambastha",
"Abhinit Kumar",
""
],
[
"Yun",
"Leong Tze",
""
]
] |
new_dataset
| 0.994963 |
2301.12135
|
Yu Chen
|
Yu Chen, Zihao Yu, Shu Song, Tianning Yu, Jianming Li, Gim Hee Lee
|
AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from
Motion
|
accepted by ICRA 2023
| null | null | null |
cs.CV cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Despite the impressive results achieved by many existing Structure from
Motion (SfM) approaches, there is still a need to improve the robustness,
accuracy, and efficiency on large-scale scenes with many outlier matches and
sparse view graphs. In this paper, we propose AdaSfM: a coarse-to-fine adaptive
SfM approach that is scalable to large-scale and challenging datasets. Our
approach first does a coarse global SfM which improves the reliability of the
view graph by leveraging measurements from low-cost sensors such as Inertial
Measurement Units (IMUs) and wheel encoders. Subsequently, the view graph is
divided into sub-scenes that are refined in parallel by a fine local
incremental SfM regularised by the result from the coarse global SfM to improve
the camera registration accuracy and alleviate scene drifts. Finally, our
approach uses a threshold-adaptive strategy to align all local reconstructions
to the coordinate frame of global SfM. Extensive experiments on large-scale
benchmark datasets show that our approach achieves state-of-the-art accuracy
and efficiency.
|
[
{
"version": "v1",
"created": "Sat, 28 Jan 2023 09:06:50 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Chen",
"Yu",
""
],
[
"Yu",
"Zihao",
""
],
[
"Song",
"Shu",
""
],
[
"Yu",
"Tianning",
""
],
[
"Li",
"Jianming",
""
],
[
"Lee",
"Gim Hee",
""
]
] |
new_dataset
| 0.99941 |
2301.12271
|
Aitazaz Raja
|
Aitazaz Ali Raja and Sergio Grammatico
|
Bilateral Peer-to-Peer Energy Trading via Coalitional Games
| null | null | null | null |
cs.GT cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a bilateral peer-to-peer (P2P) energy trading
scheme under single-contract and multi-contract market setups, both as an
assignment game, and a special class of coalitional games. {The proposed market
formulation allows for efficient computation of a market equilibrium while
keeping the desired economic properties offered by the coalitional games.
Furthermore, our market model allows buyers to have heterogeneous preferences
(product differentiation) over the energy sellers, which can be economic,
social, or environmental. To address the problem of scalability in coalitional
games, we design a novel distributed negotiation mechanism that utilizes the
geometric structure of the equilibrium solution to improve the convergence
speed. Our algorithm enables market participants (prosumers) to reach a
consensus on a set of ``stable" and ``fair" bilateral contracts which
encourages prosumer participation.} The negotiation process is executed with
virtually minimal information requirements on a time-varying communication
network that in turn preserves privacy. We use operator-theoretic tools to
rigorously prove its convergence. Numerical simulations illustrate the benefits
of our negotiation protocol and show that the average execution time of a
negotiation step is much faster than the benchmark.
|
[
{
"version": "v1",
"created": "Sat, 28 Jan 2023 19:10:24 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Raja",
"Aitazaz Ali",
""
],
[
"Grammatico",
"Sergio",
""
]
] |
new_dataset
| 0.996021 |
2301.12293
|
Jian Wu
|
Zeba Karishma, Shaurya Rohatgi, Kavya Shrinivas Puranik, Jian Wu, C.
Lee Giles
|
ACL-Fig: A Dataset for Scientific Figure Classification
|
6 pages, 4 figures, accepted by the AAAI-23 Workshop on Scientific
Document Understanding
| null | null | null |
cs.AI cs.CV cs.DL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Most existing large-scale academic search engines are built to retrieve
text-based information. However, there are no large-scale retrieval services
for scientific figures and tables. One challenge for such services is
understanding scientific figures' semantics, such as their types and purposes.
A key obstacle is the need for datasets containing annotated scientific figures
and tables, which can then be used for classification, question-answering, and
auto-captioning. Here, we develop a pipeline that extracts figures and tables
from the scientific literature and a deep-learning-based framework that
classifies scientific figures using visual features. Using this pipeline, we
built the first large-scale automatically annotated corpus, ACL-Fig, consisting
of 112,052 scientific figures extracted from ~56K research papers in the ACL
Anthology. The ACL-Fig-Pilot dataset contains 1,671 manually labeled scientific
figures belonging to 19 categories. The dataset is accessible at
https://huggingface.co/datasets/citeseerx/ACL-fig under a CC BY-NC license.
|
[
{
"version": "v1",
"created": "Sat, 28 Jan 2023 20:27:35 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Karishma",
"Zeba",
""
],
[
"Rohatgi",
"Shaurya",
""
],
[
"Puranik",
"Kavya Shrinivas",
""
],
[
"Wu",
"Jian",
""
],
[
"Giles",
"C. Lee",
""
]
] |
new_dataset
| 0.999703 |
2301.12316
|
Akshay Mathur
|
Akshay Mathur (1) and Ella Atkins (2) ((1) University of Michigan, (2)
Virginia Tech)
|
Wind Tunnel Testing and Aerodynamic Characterization of a QuadPlane
Uncrewed Aircraft System
|
38 pages, 24 figures and 14 tables
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Electric Vertical Takeoff and Landing (eVTOL) vehicles will open new
opportunities in aviation. This paper describes the design and wind tunnel
analysis of an eVTOL uncrewed aircraft system (UAS) prototype with a
traditional aircraft wing, tail, and puller motor along with four vertical
thrust pusher motors. Vehicle design and construction are summarized. Dynamic
thrust from propulsion modules is experimentally determined at different
airspeeds over a large sweep of propeller angles of attack. Wind tunnel tests
with the vehicle prototype cover a suite of hover, transition and cruise flight
conditions. Net aerodynamic forces and moments are distinctly computed and
compared for plane, quadrotor and hybrid flight modes. Coefficient-based models
are developed. Polynomial curve fits accurately capture observed data over all
test configurations. To our knowledge, the presented wind tunnel experimental
analysis for a multi-mode eVTOL platform is novel. Increased drag and reduced
dynamic thrust likely due to flow interactions will be important to address in
future designs.
|
[
{
"version": "v1",
"created": "Sun, 29 Jan 2023 00:32:57 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Mathur",
"Akshay",
""
],
[
"Atkins",
"Ella",
""
]
] |
new_dataset
| 0.999599 |
2301.12354
|
Christopher Tralie
|
Christopher J. Tralie
|
Artistic Curve Steganography Carried by Musical Audio
|
18 pages, 14 figures, in Proceedings of EvoMUSART 2023
| null | null | null |
cs.SD cs.IR cs.MM eess.AS
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this work, we create artistic closed loop curves that trace out images and
3D shapes, which we then hide in musical audio as a form of steganography. We
use traveling salesperson art to create artistic plane loops to trace out image
contours, and we use Hamiltonian cycles on triangle meshes to create artistic
space loops that fill out 3D surfaces. Our embedding scheme is designed to
faithfully preserve the geometry of these loops after lossy compression, while
keeping their presence undetectable to the audio listener. To accomplish this,
we hide each dimension of the curve in a different frequency, and we perturb a
sliding window sum of the magnitude of that frequency to best match the target
curve at that dimension, while hiding scale information in that frequency's
phase. In the process, we exploit geometric properties of the curves to help to
more effectively hide and recover them. Our scheme is simple and encoding
happens efficiently with a nonnegative least squares framework, while decoding
is trivial. We validate our technique quantitatively on large datasets of
images and audio, and we show results of a crowd sourced listening test that
validate that the hidden information is indeed unobtrusive.
|
[
{
"version": "v1",
"created": "Sun, 29 Jan 2023 04:15:57 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Tralie",
"Christopher J.",
""
]
] |
new_dataset
| 0.997549 |
2301.12360
|
Abdurrahman Elmaghbub Mr
|
Abdurrahman Elmaghbub, Bechir Hamdaoui and Weng-Keen Wong
|
ADL-ID: Adversarial Disentanglement Learning for Wireless Device
Fingerprinting Temporal Domain Adaptation
|
The paper has been accepted at IEEE ICC'23 - MWN Symposium
| null | null | null |
cs.LG cs.CR eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
As the journey of 5G standardization is coming to an end, academia and
industry have already begun to consider the sixth-generation (6G) wireless
networks, with an aim to meet the service demands for the next decade. Deep
learning-based RF fingerprinting (DL-RFFP) has recently been recognized as a
potential solution for enabling key wireless network applications and services,
such as spectrum policy enforcement and network access control. The
state-of-the-art DL-RFFP frameworks suffer from a significant performance drop
when tested with data drawn from a domain that is different from that used for
training data. In this paper, we propose ADL-ID, an unsupervised domain
adaption framework that is based on adversarial disentanglement representation
to address the temporal domain adaptation for the RFFP task. Our framework has
been evaluated on real LoRa and WiFi datasets and showed about 24% improvement
in accuracy when compared to the baseline CNN network on short-term temporal
adaptation. It also improves the classification accuracy by up to 9% on
long-term temporal adaptation. Furthermore, we release a 5-day, 2.1TB,
large-scale WiFi 802.11b dataset collected from 50 Pycom devices to support the
research community efforts in developing and validating robust RFFP methods.
|
[
{
"version": "v1",
"created": "Sun, 29 Jan 2023 04:52:41 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Elmaghbub",
"Abdurrahman",
""
],
[
"Hamdaoui",
"Bechir",
""
],
[
"Wong",
"Weng-Keen",
""
]
] |
new_dataset
| 0.99629 |
2301.12394
|
Milan \v{S}ulc
|
\v{S}t\v{e}p\'an \v{S}imsa, Milan \v{S}ulc, Maty\'a\v{s} Skalick\'y,
Yash Patel, Ahmed Hamdi
|
DocILE 2023 Teaser: Document Information Localization and Extraction
|
Accepted to ECIR 2023
| null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The lack of data for information extraction (IE) from semi-structured
business documents is a real problem for the IE community. Publications relying
on large-scale datasets use only proprietary, unpublished data due to the
sensitive nature of such documents. Publicly available datasets are mostly
small and domain-specific. The absence of a large-scale public dataset or
benchmark hinders the reproducibility and cross-evaluation of published
methods. The DocILE 2023 competition, hosted as a lab at the CLEF 2023
conference and as an ICDAR 2023 competition, will run the first major benchmark
for the tasks of Key Information Localization and Extraction (KILE) and Line
Item Recognition (LIR) from business documents. With thousands of annotated
real documents from open sources, a hundred thousand of generated synthetic
documents, and nearly a million unlabeled documents, the DocILE lab comes with
the largest publicly available dataset for KILE and LIR. We are looking forward
to contributions from the Computer Vision, Natural Language Processing,
Information Retrieval, and other communities. The data, baselines, code and
up-to-date information about the lab and competition are available at
https://docile.rossum.ai/.
|
[
{
"version": "v1",
"created": "Sun, 29 Jan 2023 09:08:58 GMT"
}
] | 2023-01-31T00:00:00 |
[
[
"Šimsa",
"Štěpán",
""
],
[
"Šulc",
"Milan",
""
],
[
"Skalický",
"Matyáš",
""
],
[
"Patel",
"Yash",
""
],
[
"Hamdi",
"Ahmed",
""
]
] |
new_dataset
| 0.997702 |
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