id
stringlengths 9
10
| submitter
stringlengths 2
52
⌀ | authors
stringlengths 4
6.51k
| title
stringlengths 4
246
| comments
stringlengths 1
523
⌀ | journal-ref
stringlengths 4
345
⌀ | doi
stringlengths 11
120
⌀ | report-no
stringlengths 2
243
⌀ | categories
stringlengths 5
98
| license
stringclasses 9
values | abstract
stringlengths 33
3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2308.13307
|
Doreen Jirak
|
Jasmin Bernotat, Doreen Jirak, Eduardo Benitez Sandoval, Francisco
Cruz, Alessandra Sciutti
|
Asch Meets HRI: Human Conformity to Robot Groups
|
5 pages, 2 figures
| null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We present a research outline that aims at investigating group dynamics and
peer pressure in the context of industrial robots. Our research plan was
motivated by the fact that industrial robots became already an integral part of
human-robot co-working. However, industrial robots have been sparsely
integrated into research on robot credibility, group dynamics, and potential
users' tendency to follow a robot's indication. Therefore, we aim to transfer
the classic Asch experiment (see \cite{Asch_51}) into HRI with industrial
robots. More precisely, we will test to what extent participants follow a
robot's response when confronted with a group (vs. individual) industrial robot
arms (vs. human) peers who give a false response. We are interested in
highlighting the effects of group size, perceived robot credibility,
psychological stress, and peer pressure in the context of industrial robots.
With the results of this research, we hope to highlight group dynamics that
might underlie HRI in industrial settings in which numerous robots already work
closely together with humans in shared environments.
|
[
{
"version": "v1",
"created": "Fri, 25 Aug 2023 11:14:24 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Bernotat",
"Jasmin",
""
],
[
"Jirak",
"Doreen",
""
],
[
"Sandoval",
"Eduardo Benitez",
""
],
[
"Cruz",
"Francisco",
""
],
[
"Sciutti",
"Alessandra",
""
]
] |
new_dataset
| 0.992217 |
2308.15214
|
Neeraj Cherakara
|
Neeraj Cherakara, Finny Varghese, Sheena Shabana, Nivan Nelson,
Abhiram Karukayil, Rohith Kulothungan, Mohammed Afil Farhan, Birthe Nesset,
Meriam Moujahid, Tanvi Dinkar, Verena Rieser, Oliver Lemon
|
FurChat: An Embodied Conversational Agent using LLMs, Combining Open and
Closed-Domain Dialogue with Facial Expressions
|
5 pages, 2 figures, Accepted at SIGDIAL 2023 (24th Meeting of the
Special Interest Group on Discourse and Dialogue), for the demo video, see
https://youtu.be/fwtUl1kl22s
| null | null | null |
cs.CL cs.AI cs.HC cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
We demonstrate an embodied conversational agent that can function as a
receptionist and generate a mixture of open and closed-domain dialogue along
with facial expressions, by using a large language model (LLM) to develop an
engaging conversation. We deployed the system onto a Furhat robot, which is
highly expressive and capable of using both verbal and nonverbal cues during
interaction. The system was designed specifically for the National Robotarium
to interact with visitors through natural conversations, providing them with
information about the facilities, research, news, upcoming events, etc. The
system utilises the state-of-the-art GPT-3.5 model to generate such information
along with domain-general conversations and facial expressions based on prompt
engineering.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 11:08:40 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Aug 2023 13:13:19 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Cherakara",
"Neeraj",
""
],
[
"Varghese",
"Finny",
""
],
[
"Shabana",
"Sheena",
""
],
[
"Nelson",
"Nivan",
""
],
[
"Karukayil",
"Abhiram",
""
],
[
"Kulothungan",
"Rohith",
""
],
[
"Farhan",
"Mohammed Afil",
""
],
[
"Nesset",
"Birthe",
""
],
[
"Moujahid",
"Meriam",
""
],
[
"Dinkar",
"Tanvi",
""
],
[
"Rieser",
"Verena",
""
],
[
"Lemon",
"Oliver",
""
]
] |
new_dataset
| 0.999695 |
2308.15491
|
Hung-Hsuan Chen
|
Ruei-Yuan Wang, Hung-Hsuan Chen
|
Detecting Inactive Cyberwarriors from Online Forums
| null | null | null | null |
cs.SI cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The proliferation of misinformation has emerged as a new form of warfare in
the information age. This type of warfare involves cyberwarriors, who
deliberately propagate messages aimed at defaming opponents or fostering unity
among allies. In this study, we investigate the level of activity exhibited by
cyberwarriors within a large online forum, and remarkably, we discover that
only a minute fraction of cyberwarriors are active users. Surprisingly, despite
their expected role of actively disseminating misinformation, cyberwarriors
remain predominantly silent during peacetime and only spring into action when
necessary. Moreover, we analyze the challenges associated with identifying
cyberwarriors and provide evidence that detecting inactive cyberwarriors is
considerably more challenging than identifying their active counterparts.
Finally, we discuss potential methodologies to more effectively identify
cyberwarriors during their inactive phases, offering insights into better
capturing their presence and actions. The experimental code is released for
reproducibility:
\url{https://github.com/Ryaninthegame/Detect-Inactive-Spammers-on-PTT}.
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 01:55:44 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Wang",
"Ruei-Yuan",
""
],
[
"Chen",
"Hung-Hsuan",
""
]
] |
new_dataset
| 0.957218 |
2308.15563
|
Rachel Yun Zhang
|
Irit Dinur, Siqi Liu, Rachel Yun Zhang
|
New Codes on High Dimensional Expanders
| null | null | null | null |
cs.IT cs.CC math.GR math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We describe a new parameterized family of symmetric error-correcting codes
with low-density parity-check matrices (LDPC).
Our codes can be described in two seemingly different ways. First, in
relation to Reed-Muller codes: our codes are functions on a subset of
$\mathbb{F}^n$ whose restrictions to a prescribed set of affine lines has low
degree. Alternatively, they are Tanner codes on high dimensional expanders,
where the coordinates of the codeword correspond to triangles of a
$2$-dimensional expander, such that around every edge the local view forms a
Reed-Solomon codeword.
For some range of parameters our codes are provably locally testable, and
their dimension is some fixed power of the block length. For another range of
parameters our codes have distance and dimension that are both linear in the
block length, but we do not know if they are locally testable. The codes also
have the multiplication property: the coordinate-wise product of two codewords
is a codeword in a related code.
The definition of the codes relies on the construction of a specific family
of simplicial complexes which is a slight variant on the coset complexes of
Kaufman and Oppenheim. We show a novel way to embed the triangles of these
complexes into $\mathbb{F}^n$, with the property that links of edges embed as
affine lines in $\mathbb{F}^n$.
We rely on this embedding to lower bound the rate of these codes in a way
that avoids constraint-counting and thereby achieves non-trivial rate even when
the local codes themselves have arbitrarily small rate, and in particular below
$1/2$.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 18:34:46 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Dinur",
"Irit",
""
],
[
"Liu",
"Siqi",
""
],
[
"Zhang",
"Rachel Yun",
""
]
] |
new_dataset
| 0.999321 |
2308.15614
|
Haoran Liu
|
Haoran Liu, Bokun Wang, Jianling Wang, Xiangjue Dong, Tianbao Yang,
James Caverlee
|
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks
| null | null | null | null |
cs.LG cs.CR cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As powerful tools for representation learning on graphs, graph neural
networks (GNNs) have played an important role in applications including social
networks, recommendation systems, and online web services. However, GNNs have
been shown to be vulnerable to adversarial attacks, which can significantly
degrade their effectiveness. Recent state-of-the-art approaches in adversarial
attacks rely on gradient-based meta-learning to selectively perturb a single
edge with the highest attack score until they reach the budget constraint.
While effective in identifying vulnerable links, these methods are plagued by
high computational costs. By leveraging continuous relaxation and
parameterization of the graph structure, we propose a novel attack method
called Differentiable Graph Attack (DGA) to efficiently generate effective
attacks and meanwhile eliminate the need for costly retraining. Compared to the
state-of-the-art, DGA achieves nearly equivalent attack performance with 6
times less training time and 11 times smaller GPU memory footprint on different
benchmark datasets. Additionally, we provide extensive experimental analyses of
the transferability of the DGA among different graph models, as well as its
robustness against widely-used defense mechanisms.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 20:14:42 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Liu",
"Haoran",
""
],
[
"Wang",
"Bokun",
""
],
[
"Wang",
"Jianling",
""
],
[
"Dong",
"Xiangjue",
""
],
[
"Yang",
"Tianbao",
""
],
[
"Caverlee",
"James",
""
]
] |
new_dataset
| 0.995386 |
2308.15710
|
Rafael Mosquera
|
Rafael Mosquera G\'omez, Juli\'an Eusse, Juan Ciro, Daniel Galvez,
Ryan Hileman, Kurt Bollacker, David Kanter
|
Speech Wikimedia: A 77 Language Multilingual Speech Dataset
|
Data-Centric Machine Learning Workshop at the International Machine
Learning Conference 2023 (ICML)
| null | null | null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The Speech Wikimedia Dataset is a publicly available compilation of audio
with transcriptions extracted from Wikimedia Commons. It includes 1780 hours
(195 GB) of CC-BY-SA licensed transcribed speech from a diverse set of
scenarios and speakers, in 77 different languages. Each audio file has one or
more transcriptions in different languages, making this dataset suitable for
training speech recognition, speech translation, and machine translation
models.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 02:14:49 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Gómez",
"Rafael Mosquera",
""
],
[
"Eusse",
"Julián",
""
],
[
"Ciro",
"Juan",
""
],
[
"Galvez",
"Daniel",
""
],
[
"Hileman",
"Ryan",
""
],
[
"Bollacker",
"Kurt",
""
],
[
"Kanter",
"David",
""
]
] |
new_dataset
| 0.99985 |
2308.15726
|
Fei Yu
|
Nan Che and Chenrui Liu and Fei Yu
|
AGS: An Dataset and Taxonomy for Domestic Scene Sound Event Recognition
| null | null | null | null |
cs.SD cs.AI eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Environmental sound scene and sound event recognition is important for the
recognition of suspicious events in indoor and outdoor environments (such as
nurseries, smart homes, nursing homes, etc.) and is a fundamental task involved
in many audio surveillance applications. In particular, there is no public
common data set for the research field of sound event recognition for the data
set of the indoor environmental sound scene. Therefore, this paper proposes a
data set (called as AGS) for the home environment sound. This data set
considers various types of overlapping audio in the scene, background noise.
Moreover, based on the proposed data set, this paper compares and analyzes the
advanced methods for sound event recognition, and then illustrates the
reliability of the data set proposed in this paper, and studies the challenges
raised by the new data set. Our proposed AGS and the source code of the
corresponding baselines at https://github.com/taolunzu11/AGS .
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 03:03:47 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Che",
"Nan",
""
],
[
"Liu",
"Chenrui",
""
],
[
"Yu",
"Fei",
""
]
] |
new_dataset
| 0.99659 |
2308.15784
|
Roman Jacome
|
Roman Jacome, Kumar Vijay Mishra, Brian M. Sadler and Henry Arguello
|
Octonion Phase Retrieval
|
13 pages, 3 figures
| null | null | null |
cs.IT eess.IV math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Signal processing over hypercomplex numbers arises in many optical imaging
applications. In particular, spectral image or color stereo data are often
processed using octonion algebra. Recently, the eight-band multispectral image
phase recovery has gained salience, wherein it is desired to recover the eight
bands from the phaseless measurements. In this paper, we tackle this hitherto
unaddressed hypercomplex variant of the popular phase retrieval (PR) problem.
We propose octonion Wirtinger flow (OWF) to recover an octonion signal from its
intensity-only observation. However, contrary to the complex-valued Wirtinger
flow, the non-associative nature of octonion algebra and the consequent lack of
octonion derivatives make the extension to OWF non-trivial. We resolve this
using the pseudo-real-matrix representation of octonion to perform the
derivatives in each OWF update. We demonstrate that our approach recovers the
octonion signal up to a right-octonion phase factor. Numerical experiments
validate OWF-based PR with high accuracy under both noiseless and noisy
measurements.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 06:32:31 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Jacome",
"Roman",
""
],
[
"Mishra",
"Kumar Vijay",
""
],
[
"Sadler",
"Brian M.",
""
],
[
"Arguello",
"Henry",
""
]
] |
new_dataset
| 0.957523 |
2308.15819
|
Tuukka Korhonen
|
Tuukka Korhonen, Matti J\"arvisalo
|
SharpSAT-TD in Model Counting Competitions 2021-2023
|
3 pages
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We describe SharpSAT-TD, our submission to the unweighted and weighted tracks
of the Model Counting Competition in 2021-2023, which has won in total $6$
first places in different tracks of the competition. SharpSAT-TD is based on
SharpSAT [Thurley, SAT 2006], with the primary novel modification being the use
of tree decompositions in the variable selection heuristic as introduced by the
authors in [CP 2021]. Unlike the version of SharpSAT-TD evaluated in [CP 2021],
the current version that is available in https://github.com/Laakeri/sharpsat-td
features also other significant modifications compared to the original
SharpSAT, for example, a new preprocessor.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 07:43:12 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Korhonen",
"Tuukka",
""
],
[
"Järvisalo",
"Matti",
""
]
] |
new_dataset
| 0.986857 |
2308.15823
|
Jianghong Ma
|
Kangzhe Liu, Jianghong Ma, Shanshan Feng, Haijun Zhang, Zhao Zhang
|
DRGame: Diversified Recommendation for Multi-category Video Games with
Balanced Implicit Preferences
| null | null | null | null |
cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The growing popularity of subscription services in video game consumption has
emphasized the importance of offering diversified recommendations. Providing
users with a diverse range of games is essential for ensuring continued
engagement and fostering long-term subscriptions. However, existing
recommendation models face challenges in effectively handling highly imbalanced
implicit feedback in gaming interactions. Additionally, they struggle to take
into account the distinctive characteristics of multiple categories and the
latent user interests associated with these categories. In response to these
challenges, we propose a novel framework, named DRGame, to obtain diversified
recommendation. It is centered on multi-category video games, consisting of two
{components}: Balance-driven Implicit Preferences Learning for data
pre-processing and Clustering-based Diversified Recommendation {Module} for
final prediction. The first module aims to achieve a balanced representation of
implicit feedback in game time, thereby discovering a comprehensive view of
player interests across different categories. The second module adopts
category-aware representation learning to cluster and select players and games
based on balanced implicit preferences, and then employs asymmetric neighbor
aggregation to achieve diversified recommendations. Experimental results on a
real-world dataset demonstrate the superiority of our proposed method over
existing approaches in terms of game diversity recommendations.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 07:53:27 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Liu",
"Kangzhe",
""
],
[
"Ma",
"Jianghong",
""
],
[
"Feng",
"Shanshan",
""
],
[
"Zhang",
"Haijun",
""
],
[
"Zhang",
"Zhao",
""
]
] |
new_dataset
| 0.956053 |
2308.15841
|
Johannes Zirngibl
|
Johannes Zirngibl, Florian Gebauer, Patrick Sattler, Markus Sosnowski,
Georg Carle
|
QUIC Library Hunter: Identifying Server Libraries Across the Internet
|
preprint
| null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The new QUIC protocol can be implemented in user space, and various
implementations already exist. While they follow the same specification and
general interoperability is given, differences in performance, functionality,
but also security (e.g., due to bugs) can be expected. Therefore, knowledge
about the implementation of an endpoint on the Internet can help researchers,
operators and users to better analyze connections, evaluations and findings.
We provide an approach to identify used libraries of QUIC servers based on
CONNECTION_CLOSE frames and transport parameter orders. We apply our
methodology to Internet-wide scans and identify at least one deployment for 18
QUIC libraries. In total, we can identify the library of 8.8 M IPv4 and 2.5 M
IPv6 addresses.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 08:22:05 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Zirngibl",
"Johannes",
""
],
[
"Gebauer",
"Florian",
""
],
[
"Sattler",
"Patrick",
""
],
[
"Sosnowski",
"Markus",
""
],
[
"Carle",
"Georg",
""
]
] |
new_dataset
| 0.995114 |
2308.15842
|
Sujoy Bhore
|
Sayan Bandyapadhyay, Aritra Banik, Sujoy Bhore
|
On Colorful Vertex and Edge Cover Problems
| null | null | null | null |
cs.DS cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we study two generalizations of Vertex Cover and Edge Cover,
namely Colorful Vertex Cover and Colorful Edge Cover. In the Colorful Vertex
Cover problem, given an $n$-vertex edge-colored graph $G$ with colors from
$\{1, \ldots, \omega\}$ and coverage requirements $r_1, r_2, \ldots, r_\omega$,
the goal is to find a minimum-sized set of vertices that are incident on at
least $r_i$ edges of color $i$, for each $1 \le i \le \omega$, i.e., we need to
cover at least $r_i$ edges of color $i$. Colorful Edge Cover is similar to
Colorful Vertex Cover, except here we are given a vertex-colored graph and the
goal is to cover at least $r_i$ vertices of color $i$, for each $1 \le i \le
\omega$, by a minimum-sized set of edges. These problems have several
applications in fair covering and hitting of geometric set systems involving
points and lines that are divided into multiple groups. Here, fairness ensures
that the coverage (resp. hitting) requirement of every group is fully
satisfied.
We obtain a $(2+\epsilon)$-approximation for the Colorful Vertex Cover
problem in time $n^{O(\omega/\epsilon)}$. Thus, for a constant number of
colors, the problem admits a $(2+\epsilon)$-approximation in polynomial time.
Next, for the Colorful Edge Cover problem, we design an $O(\omega n^3)$ time
exact algorithm, via a chain of reductions to a matching problem. For all
intermediate problems in this chain of reductions, we design polynomial-time
algorithms, which might be of independent interest.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 08:27:09 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Bandyapadhyay",
"Sayan",
""
],
[
"Banik",
"Aritra",
""
],
[
"Bhore",
"Sujoy",
""
]
] |
new_dataset
| 0.984031 |
2308.15846
|
Yifan Xu
|
Yifan Xu, Mengdan Zhang, Xiaoshan Yang, Changsheng Xu
|
Exploring Multi-Modal Contextual Knowledge for Open-Vocabulary Object
Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we for the first time explore helpful multi-modal contextual
knowledge to understand novel categories for open-vocabulary object detection
(OVD). The multi-modal contextual knowledge stands for the joint relationship
across regions and words. However, it is challenging to incorporate such
multi-modal contextual knowledge into OVD. The reason is that previous
detection frameworks fail to jointly model multi-modal contextual knowledge, as
object detectors only support vision inputs and no caption description is
provided at test time. To this end, we propose a multi-modal contextual
knowledge distillation framework, MMC-Det, to transfer the learned contextual
knowledge from a teacher fusion transformer with diverse multi-modal masked
language modeling (D-MLM) to a student detector. The diverse multi-modal masked
language modeling is realized by an object divergence constraint upon
traditional multi-modal masked language modeling (MLM), in order to extract
fine-grained region-level visual contexts, which are vital to object detection.
Extensive experiments performed upon various detection datasets show the
effectiveness of our multi-modal context learning strategy, where our approach
well outperforms the recent state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 08:33:13 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Xu",
"Yifan",
""
],
[
"Zhang",
"Mengdan",
""
],
[
"Yang",
"Xiaoshan",
""
],
[
"Xu",
"Changsheng",
""
]
] |
new_dataset
| 0.964102 |
2308.15870
|
EPTCS
|
Christian Hatschka (TU Vienna), Agata Ciabattoni (TU Vienna), Thomas
Eiter (TU Vienna)
|
Deontic Paradoxes in ASP with Weak Constraints
|
In Proceedings ICLP 2023, arXiv:2308.14898
|
EPTCS 385, 2023, pp. 367-380
|
10.4204/EPTCS.385.39
| null |
cs.LO cs.AI cs.CY cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The rise of powerful AI technology for a range of applications that are
sensitive to legal, social, and ethical norms demands decision-making support
in presence of norms and regulations. Normative reasoning is the realm of
deontic logics, that are challenged by well-known benchmark problems (deontic
paradoxes), and lack efficient computational tools. In this paper, we use
Answer Set Programming (ASP) for addressing these shortcomings and showcase how
to encode and resolve several well-known deontic paradoxes utilizing weak
constraints. By abstracting and generalizing this encoding, we present a
methodology for translating normative systems in ASP with weak constraints.
This methodology is applied to "ethical" versions of Pac-man, where we obtain a
comparable performance with related works, but ethically preferable results.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 08:56:54 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Hatschka",
"Christian",
"",
"TU Vienna"
],
[
"Ciabattoni",
"Agata",
"",
"TU Vienna"
],
[
"Eiter",
"Thomas",
"",
"TU Vienna"
]
] |
new_dataset
| 0.974035 |
2308.15893
|
EPTCS
|
Theresa Swift (Johns Hopkins Applied Physics Lab), Carl Andersen
|
The Janus System: Multi-paradigm Programming in Prolog and Python
|
In Proceedings ICLP 2023, arXiv:2308.14898
|
EPTCS 385, 2023, pp. 241-255
|
10.4204/EPTCS.385.24
| null |
cs.PL cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Python and Prolog express different programming paradigms, with different
strengths. Python is wildly popular because it is well-structured, easy to use,
and mixes well with thousands of scientific and machine learning programs
written in C. Prolog's logic-based approach provides powerful reasoning
capabilities, especially when combined with constraint evaluation,
probabilistic reasoning, well-founded negation, and other advances. Both
languages have commonalities as well: both are usually written in C, both are
dynamically typed, and both use data structures based on a small number of
recursive types.
This paper describes the design and implementation of Janus, a system that
tightly combines Prolog and Python into a single process. Janus bi-translates
data structures and offers performance of many hundreds of thousands of
round-trip inter-language calls per second. Although Janus is still new, it has
been used in commercial applications including natural language processing,
visual query answering and robotic automation. Janus was developed for XSB, but
porting Janus code to a second Prolog has been straightforward, indicating that
Janus is a tool that other Prologs may easily adopt.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 09:07:05 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Swift",
"Theresa",
"",
"Johns Hopkins Applied Physics Lab"
],
[
"Andersen",
"Carl",
""
]
] |
new_dataset
| 0.952637 |
2308.15917
|
Konstantin Shibin
|
Konstantin Shibin, Maksim Jenihhin, Artur Jutman, Sergei Devadze,
Anton Tsertov
|
On-Chip Sensors Data Collection and Analysis for SoC Health Management
|
6 pages, 3 figures. This paper is accepted at the 36th IEEE
International Symposium on Defect and Fault Tolerance in VLSI and
Nanotechnology Systems (DFT) 2023
| null | null | null |
cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data produced by on-chip sensors in modern SoCs contains a large amount of
information such as occurring faults, aging status, accumulated radiation dose,
performance characteristics, environmental and other operational parameters.
Such information provides insight into the overall health of a system's
hardware as well as the operability of individual modules. This gives a chance
to mitigate faults and avoid using faulty units, thus enabling hardware health
management. Raw data from embedded sensors cannot be immediately used to
perform health management tasks. In most cases, the information about occurred
faults needs to be analyzed taking into account the history of the previously
reported fault events and other collected statistics. For this purpose, we
propose a special structure called Health Map (HM) that holds the information
about functional resources, occurring faults and maps relationships between
these. In addition, we propose algorithms for aggregation and classification of
data received from on-chip sensors. The proposed Health Map contains detailed
information on a particular system level (e.g., module, SoC, board) that can be
compiled into a summary of hardware health status that in its turn enables
distributed hierarchical health management by using this information at a
higher level of system hierarchy, thus increasing the system's availability and
effective lifetime.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 09:44:28 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Shibin",
"Konstantin",
""
],
[
"Jenihhin",
"Maksim",
""
],
[
"Jutman",
"Artur",
""
],
[
"Devadze",
"Sergei",
""
],
[
"Tsertov",
"Anton",
""
]
] |
new_dataset
| 0.96082 |
2308.15939
|
Hanqiu Deng
|
Hanqiu Deng, Zhaoxiang Zhang, Jinan Bao, Xingyu Li
|
AnoVL: Adapting Vision-Language Models for Unified Zero-shot Anomaly
Localization
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Contrastive Language-Image Pre-training (CLIP) models have shown promising
performance on zero-shot visual recognition tasks by learning visual
representations under natural language supervision. Recent studies attempt the
use of CLIP to tackle zero-shot anomaly detection by matching images with
normal and abnormal state prompts. However, since CLIP focuses on building
correspondence between paired text prompts and global image-level
representations, the lack of patch-level vision to text alignment limits its
capability on precise visual anomaly localization. In this work, we introduce a
training-free adaptation (TFA) framework of CLIP for zero-shot anomaly
localization. In the visual encoder, we innovate a training-free value-wise
attention mechanism to extract intrinsic local tokens of CLIP for patch-level
local description. From the perspective of text supervision, we particularly
design a unified domain-aware contrastive state prompting template. On top of
the proposed TFA, we further introduce a test-time adaptation (TTA) mechanism
to refine anomaly localization results, where a layer of trainable parameters
in the adapter is optimized using TFA's pseudo-labels and synthetic
noise-corrupted tokens. With both TFA and TTA adaptation, we significantly
exploit the potential of CLIP for zero-shot anomaly localization and
demonstrate the effectiveness of our proposed methods on various datasets.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 10:35:36 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Deng",
"Hanqiu",
""
],
[
"Zhang",
"Zhaoxiang",
""
],
[
"Bao",
"Jinan",
""
],
[
"Li",
"Xingyu",
""
]
] |
new_dataset
| 0.959517 |
2308.15952
|
Anton Alekseev
|
Anton Alekseev, Sergey I. Nikolenko, Gulnara Kabaeva
|
Benchmarking Multilabel Topic Classification in the Kyrgyz Language
|
Accepted to AIST 2023
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Kyrgyz is a very underrepresented language in terms of modern natural
language processing resources. In this work, we present a new public benchmark
for topic classification in Kyrgyz, introducing a dataset based on collected
and annotated data from the news site 24.KG and presenting several baseline
models for news classification in the multilabel setting. We train and evaluate
both classical statistical and neural models, reporting the scores, discussing
the results, and proposing directions for future work.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 11:02:26 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Alekseev",
"Anton",
""
],
[
"Nikolenko",
"Sergey I.",
""
],
[
"Kabaeva",
"Gulnara",
""
]
] |
new_dataset
| 0.99963 |
2308.15964
|
B\'erenger Bramas
|
Paul Cardosi, B\'erenger Bramas
|
Specx: a C++ task-based runtime system for heterogeneous distributed
architectures
|
Research report. https://gitlab.inria.fr/bramas/specx
| null | null | null |
cs.DC cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Parallelization is needed everywhere, from laptops and mobile phones to
supercomputers. Among parallel programming models, task-based programming has
demonstrated a powerful potential and is widely used in high-performance
scientific computing. Not only does it allow for efficient parallelization
across distributed heterogeneous computing nodes, but it also allows for
elegant source code structuring by describing hardware-independent algorithms.
In this paper, we present Specx, a task-based runtime system written in modern
C++. Specx supports distributed heterogeneous computing by simultaneously
exploiting CPUs and GPUs (CUDA/HIP) and incorporating communication into the
task graph. We describe the specificities of Specx and demonstrate its
potential by running parallel applications.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 11:41:30 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Cardosi",
"Paul",
""
],
[
"Bramas",
"Bérenger",
""
]
] |
new_dataset
| 0.990765 |
2308.15985
|
Jianwu Fang
|
Jianwu Fang, iahuan Qiao, Jianru Xue, and Zhengguo Li
|
Vision-Based Traffic Accident Detection and Anticipation: A Survey
|
accepted in IEEE Transactions on Circuits and Systems for Video
Technology; 16 pages, 155 references
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Traffic accident detection and anticipation is an obstinate road safety
problem and painstaking efforts have been devoted. With the rapid growth of
video data, Vision-based Traffic Accident Detection and Anticipation (named
Vision-TAD and Vision-TAA) become the last one-mile problem for safe driving
and surveillance safety. However, the long-tailed, unbalanced, highly dynamic,
complex, and uncertain properties of traffic accidents form the
Out-of-Distribution (OOD) feature for Vision-TAD and Vision-TAA. Current AI
development may focus on these OOD but important problems. What has been done
for Vision-TAD and Vision-TAA? What direction we should focus on in the future
for this problem? A comprehensive survey is important. We present the first
survey on Vision-TAD in the deep learning era and the first-ever survey for
Vision-TAA. The pros and cons of each research prototype are discussed in
detail during the investigation. In addition, we also provide a critical review
of 31 publicly available benchmarks and related evaluation metrics. Through
this survey, we want to spawn new insights and open possible trends for
Vision-TAD and Vision-TAA tasks.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 12:13:41 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Fang",
"Jianwu",
""
],
[
"Qiao",
"iahuan",
""
],
[
"Xue",
"Jianru",
""
],
[
"Li",
"Zhengguo",
""
]
] |
new_dataset
| 0.998613 |
2308.15991
|
Yinda Xu
|
Yinda Xu, Lidong Yu
|
DRL-Based Trajectory Tracking for Motion-Related Modules in Autonomous
Driving
|
Technical report
| null | null | null |
cs.RO cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Autonomous driving systems are always built on motion-related modules such as
the planner and the controller. An accurate and robust trajectory tracking
method is indispensable for these motion-related modules as a primitive
routine. Current methods often make strong assumptions about the model such as
the context and the dynamics, which are not robust enough to deal with the
changing scenarios in a real-world system. In this paper, we propose a Deep
Reinforcement Learning (DRL)-based trajectory tracking method for the
motion-related modules in autonomous driving systems. The representation
learning ability of DL and the exploration nature of RL bring strong robustness
and improve accuracy. Meanwhile, it enhances versatility by running the
trajectory tracking in a model-free and data-driven manner. Through extensive
experiments, we demonstrate both the efficiency and effectiveness of our method
compared to current methods.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 12:24:30 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Xu",
"Yinda",
""
],
[
"Yu",
"Lidong",
""
]
] |
new_dataset
| 0.996981 |
2308.16052
|
Thomas H. Weisswange
|
Thomas H. Weisswange, Joel B. Schwartz, Aaron J. Horowitz, Jens
Schm\"udderich
|
Telepresence Lantern -- Designing an Immersive Video-Mediated
Communication Device for Older Adults
| null | null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We present the Telepresence Lantern concept, developed to provide
opportunities for older adults to stay in contact with remote family and
friends. It provides a new approach to video-mediated communication, designed
to facilitate natural and ambient interactions with simplified call setup.
Video communication is an established way to enhance social connectedness, but
traditional approaches create a high friction to frequent connection due to,
for example, technological barriers. Through interactive sessions with older
adult users, we created design and function prototypes to suit their needs and
preferences. The main features of our design are a curved, wide field-of-view
screen and corresponding camera and sound setup, and the affordance to easily
move the device from room-to-room. An interactive user session with a fully
functional prototype validated the potential of this concept for improving
communication among older adults and their families.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 14:19:09 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Weisswange",
"Thomas H.",
""
],
[
"Schwartz",
"Joel B.",
""
],
[
"Horowitz",
"Aaron J.",
""
],
[
"Schmüdderich",
"Jens",
""
]
] |
new_dataset
| 0.998962 |
2308.16053
|
Yu Zhang
|
Yu Zhang, Ruike Jiang, Liwenhan Xie, Yuheng Zhao, Can Liu, Tianhong
Ding, Siming Chen, Xiaoru Yuan
|
OldVisOnline: Curating a Dataset of Historical Visualizations
|
Accepted to IEEE VIS 2023
| null | null | null |
cs.HC cs.DL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the increasing adoption of digitization, more and more historical
visualizations created hundreds of years ago are accessible in digital
libraries online. It provides a unique opportunity for visualization and
history research. Meanwhile, there is no large-scale digital collection
dedicated to historical visualizations. The visualizations are scattered in
various collections, which hinders retrieval. In this study, we curate the
first large-scale dataset dedicated to historical visualizations. Our dataset
comprises 13K historical visualization images with corresponding processed
metadata from seven digital libraries. In curating the dataset, we propose a
workflow to scrape and process heterogeneous metadata. We develop a
semi-automatic labeling approach to distinguish visualizations from other
artifacts. Our dataset can be accessed with OldVisOnline, a system we have
built to browse and label historical visualizations. We discuss our vision of
usage scenarios and research opportunities with our dataset, such as textual
criticism for historical visualizations. Drawing upon our experience, we
summarize recommendations for future efforts to improve our dataset.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 14:19:31 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Zhang",
"Yu",
""
],
[
"Jiang",
"Ruike",
""
],
[
"Xie",
"Liwenhan",
""
],
[
"Zhao",
"Yuheng",
""
],
[
"Liu",
"Can",
""
],
[
"Ding",
"Tianhong",
""
],
[
"Chen",
"Siming",
""
],
[
"Yuan",
"Xiaoru",
""
]
] |
new_dataset
| 0.999796 |
2308.16055
|
Yun-Cheng Wang
|
Yun-Cheng Wang, Xiou Ge, Bin Wang, C.-C. Jay Kuo
|
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with
Auxiliary Relations
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Knowledge graph entity typing (KGET) is a task to predict the missing entity
types in knowledge graphs (KG). Previously, KG embedding (KGE) methods tried to
solve the KGET task by introducing an auxiliary relation, 'hasType', to model
the relationship between entities and their types. However, a single auxiliary
relation has limited expressiveness for diverse entity-type patterns. We
improve the expressiveness of KGE methods by introducing multiple auxiliary
relations in this work. Similar entity types are grouped to reduce the number
of auxiliary relations and improve their capability to model entity-type
patterns with different granularities. With the presence of multiple auxiliary
relations, we propose a method adopting an Asynchronous learning scheme for
Entity Typing, named AsyncET, which updates the entity and type embeddings
alternatively to keep the learned entity embedding up-to-date and informative
for entity type prediction. Experiments are conducted on two commonly used KGET
datasets to show that the performance of KGE methods on the KGET task can be
substantially improved by the proposed multiple auxiliary relations and
asynchronous embedding learning. Furthermore, our method has a significant
advantage over state-of-the-art methods in model sizes and time complexity.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 14:24:16 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Wang",
"Yun-Cheng",
""
],
[
"Ge",
"Xiou",
""
],
[
"Wang",
"Bin",
""
],
[
"Kuo",
"C. -C. Jay",
""
]
] |
new_dataset
| 0.968414 |
2308.16060
|
Raphael Schumann
|
Michael Staniek and Raphael Schumann and Maike Z\"ufle and Stefan
Riezler
|
Text-to-OverpassQL: A Natural Language Interface for Complex Geodata
Querying of OpenStreetMap
| null | null | null | null |
cs.CL cs.AI cs.CY cs.DB cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present Text-to-OverpassQL, a task designed to facilitate a natural
language interface for querying geodata from OpenStreetMap (OSM). The Overpass
Query Language (OverpassQL) allows users to formulate complex database queries
and is widely adopted in the OSM ecosystem. Generating Overpass queries from
natural language input serves multiple use-cases. It enables novice users to
utilize OverpassQL without prior knowledge, assists experienced users with
crafting advanced queries, and enables tool-augmented large language models to
access information stored in the OSM database. In order to assess the
performance of current sequence generation models on this task, we propose
OverpassNL, a dataset of 8,352 queries with corresponding natural language
inputs. We further introduce task specific evaluation metrics and ground the
evaluation of the Text-to-OverpassQL task by executing the queries against the
OSM database. We establish strong baselines by finetuning sequence-to-sequence
models and adapting large language models with in-context examples. The
detailed evaluation reveals strengths and weaknesses of the considered learning
strategies, laying the foundations for further research into the
Text-to-OverpassQL task.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 14:33:25 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Staniek",
"Michael",
""
],
[
"Schumann",
"Raphael",
""
],
[
"Züfle",
"Maike",
""
],
[
"Riezler",
"Stefan",
""
]
] |
new_dataset
| 0.987643 |
2308.16082
|
Sen Fang
|
Sen Fang, Chunyu Sui, Xuedong Zhang, Yapeng Tian
|
SignDiff: Learning Diffusion Models for American Sign Language
Production
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The field of Sign Language Production (SLP) lacked a large-scale, pre-trained
model based on deep learning for continuous American Sign Language (ASL)
production in the past decade. This limitation hampers communication for all
individuals with disabilities relying on ASL. To address this issue, we
undertook the secondary development and utilization of How2Sign, one of the
largest publicly available ASL datasets. Despite its significance, prior
researchers in the field of sign language have not effectively employed this
corpus due to the intricacies involved in American Sign Language Production
(ASLP).
To conduct large-scale ASLP, we propose SignDiff based on the latest work in
related fields, which is a dual-condition diffusion pre-training model that can
generate human sign language speakers from a skeleton pose. SignDiff has a
novel Frame Reinforcement Network called FR-Net, similar to dense human pose
estimation work, which enhances the correspondence between text lexical symbols
and sign language dense pose frames reduce the occurrence of multiple fingers
in the diffusion model. In addition, our ASLP method proposes two new improved
modules and a new loss function to improve the accuracy and quality of sign
language skeletal posture and enhance the ability of the model to train on
large-scale data.
We propose the first baseline for ASL production and report the scores of
17.19 and 12.85 on BLEU-4 on the How2Sign dev/test sets. We also evaluated our
model on the previous mainstream dataset called PHOENIX14T, and the main
experiments achieved the results of SOTA. In addition, our image quality far
exceeds all previous results by 10 percentage points on the SSIM indicator.
Finally, we conducted ablation studies and qualitative evaluations for
discussion.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 15:14:56 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Fang",
"Sen",
""
],
[
"Sui",
"Chunyu",
""
],
[
"Zhang",
"Xuedong",
""
],
[
"Tian",
"Yapeng",
""
]
] |
new_dataset
| 0.95483 |
2308.16182
|
Henghui Ding
|
Shuting He, Henghui Ding, Chang Liu, Xudong Jiang
|
GREC: Generalized Referring Expression Comprehension
|
GREC Technical Report, Project Page:
https://henghuiding.github.io/GRES
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The objective of Classic Referring Expression Comprehension (REC) is to
produce a bounding box corresponding to the object mentioned in a given textual
description. Commonly, existing datasets and techniques in classic REC are
tailored for expressions that pertain to a single target, meaning a sole
expression is linked to one specific object. Expressions that refer to multiple
targets or involve no specific target have not been taken into account. This
constraint hinders the practical applicability of REC. This study introduces a
new benchmark termed as Generalized Referring Expression Comprehension (GREC).
This benchmark extends the classic REC by permitting expressions to describe
any number of target objects. To achieve this goal, we have built the first
large-scale GREC dataset named gRefCOCO. This dataset encompasses a range of
expressions: those referring to multiple targets, expressions with no specific
target, and the single-target expressions. The design of GREC and gRefCOCO
ensures smooth compatibility with classic REC. The proposed gRefCOCO dataset, a
GREC method implementation code, and GREC evaluation code are available at
https://github.com/henghuiding/gRefCOCO.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 17:58:50 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"He",
"Shuting",
""
],
[
"Ding",
"Henghui",
""
],
[
"Liu",
"Chang",
""
],
[
"Jiang",
"Xudong",
""
]
] |
new_dataset
| 0.987949 |
2308.16184
|
Junlong Cheng
|
Junlong Cheng, Jin Ye, Zhongying Deng, Jianpin Chen, Tianbin Li, Haoyu
Wang, Yanzhou Su, Ziyan Huang, Jilong Chen, Lei Jiang, Hui Sun, Junjun He,
Shaoting Zhang, Min Zhu, Yu Qiao,
|
SAM-Med2D
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Segment Anything Model (SAM) represents a state-of-the-art research
advancement in natural image segmentation, achieving impressive results with
input prompts such as points and bounding boxes. However, our evaluation and
recent research indicate that directly applying the pretrained SAM to medical
image segmentation does not yield satisfactory performance. This limitation
primarily arises from significant domain gap between natural images and medical
images. To bridge this gap, we introduce SAM-Med2D, the most comprehensive
studies on applying SAM to medical 2D images. Specifically, we first collect
and curate approximately 4.6M images and 19.7M masks from public and private
datasets, constructing a large-scale medical image segmentation dataset
encompassing various modalities and objects. Then, we comprehensively fine-tune
SAM on this dataset and turn it into SAM-Med2D. Unlike previous methods that
only adopt bounding box or point prompts as interactive segmentation approach,
we adapt SAM to medical image segmentation through more comprehensive prompts
involving bounding boxes, points, and masks. We additionally fine-tune the
encoder and decoder of the original SAM to obtain a well-performed SAM-Med2D,
leading to the most comprehensive fine-tuning strategies to date. Finally, we
conducted a comprehensive evaluation and analysis to investigate the
performance of SAM-Med2D in medical image segmentation across various
modalities, anatomical structures, and organs. Concurrently, we validated the
generalization capability of SAM-Med2D on 9 datasets from MICCAI 2023
challenge. Overall, our approach demonstrated significantly superior
performance and generalization capability compared to SAM.
|
[
{
"version": "v1",
"created": "Wed, 30 Aug 2023 17:59:02 GMT"
}
] | 2023-08-31T00:00:00 |
[
[
"Cheng",
"Junlong",
""
],
[
"Ye",
"Jin",
""
],
[
"Deng",
"Zhongying",
""
],
[
"Chen",
"Jianpin",
""
],
[
"Li",
"Tianbin",
""
],
[
"Wang",
"Haoyu",
""
],
[
"Su",
"Yanzhou",
""
],
[
"Huang",
"Ziyan",
""
],
[
"Chen",
"Jilong",
""
],
[
"Jiang",
"Lei",
""
],
[
"Sun",
"Hui",
""
],
[
"He",
"Junjun",
""
],
[
"Zhang",
"Shaoting",
""
],
[
"Zhu",
"Min",
""
],
[
"Qiao",
"Yu",
""
]
] |
new_dataset
| 0.997066 |
2110.01005
|
Tamjid Al Rahat
|
Tamjid Al Rahat, Yu Feng, Yuan Tian
|
Cerberus: Query-driven Scalable Vulnerability Detection in OAuth Service
Provider Implementations
|
Appeared in ACM Conference on Computer and Communications Security
(CCS 2022). Please cite the conference version
|
Proceedings of the 2022 ACM SIGSAC Conference on Computer and
Communications Security
|
10.1145/3548606.3559381
| null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
OAuth protocols have been widely adopted to simplify user authentication and
service authorization for third-party applications. However, little effort has
been devoted to automatically checking the security of the libraries that
service providers widely use. In this paper, we formalize the OAuth
specifications and security best practices, and design Cerberus, an automated
static analyzer, to find logical flaws and identify vulnerabilities in the
implementation of OAuth service provider libraries. To efficiently detect
security violations in a large codebase of service provider implementation,
Cerberus employs a query-driven algorithm for answering queries about OAuth
specifications. We demonstrate the effectiveness of Cerberus by evaluating it
on datasets of popular OAuth libraries with millions of downloads. Among these
high-profile libraries, Cerberus has identified 47 vulnerabilities from ten
classes of logical flaws, 24 of which were previously unknown. We got
acknowledged by the developers of eight libraries and had three accepted CVEs.
|
[
{
"version": "v1",
"created": "Sun, 3 Oct 2021 13:43:38 GMT"
},
{
"version": "v2",
"created": "Mon, 16 May 2022 01:52:13 GMT"
},
{
"version": "v3",
"created": "Thu, 27 Oct 2022 03:49:02 GMT"
},
{
"version": "v4",
"created": "Tue, 7 Mar 2023 03:48:54 GMT"
},
{
"version": "v5",
"created": "Tue, 29 Aug 2023 09:08:27 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Rahat",
"Tamjid Al",
""
],
[
"Feng",
"Yu",
""
],
[
"Tian",
"Yuan",
""
]
] |
new_dataset
| 0.966506 |
2203.09065
|
Meida Chen
|
Meida Chen, Qingyong Hu, Zifan Yu, Hugues Thomas, Andrew Feng, Yu Hou,
Kyle McCullough, Fengbo Ren, Lucio Soibelman
|
STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point
Cloud Dataset
| null | null | null |
https://bmvc2022.mpi-inf.mpg.de/0429.pdf
|
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Although various 3D datasets with different functions and scales have been
proposed recently, it remains challenging for individuals to complete the whole
pipeline of large-scale data collection, sanitization, and annotation.
Moreover, the created datasets usually suffer from extremely imbalanced class
distribution or partial low-quality data samples. Motivated by this, we explore
the procedurally synthetic 3D data generation paradigm to equip individuals
with the full capability of creating large-scale annotated photogrammetry point
clouds. Specifically, we introduce a synthetic aerial photogrammetry point
clouds generation pipeline that takes full advantage of open geospatial data
sources and off-the-shelf commercial packages. Unlike generating synthetic data
in virtual games, where the simulated data usually have limited gaming
environments created by artists, the proposed pipeline simulates the
reconstruction process of the real environment by following the same UAV flight
pattern on different synthetic terrain shapes and building densities, which
ensure similar quality, noise pattern, and diversity with real data. In
addition, the precise semantic and instance annotations can be generated fully
automatically, avoiding the expensive and time-consuming manual annotation.
Based on the proposed pipeline, we present a richly-annotated synthetic 3D
aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16
$km^2$ of landscapes and up to 18 fine-grained semantic categories. For
verification purposes, we also provide a parallel dataset collected from four
areas in the real environment. Extensive experiments conducted on our datasets
demonstrate the effectiveness and quality of the proposed synthetic dataset.
|
[
{
"version": "v1",
"created": "Thu, 17 Mar 2022 03:50:40 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Oct 2022 17:56:28 GMT"
},
{
"version": "v3",
"created": "Fri, 14 Oct 2022 01:35:37 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Chen",
"Meida",
""
],
[
"Hu",
"Qingyong",
""
],
[
"Yu",
"Zifan",
""
],
[
"Thomas",
"Hugues",
""
],
[
"Feng",
"Andrew",
""
],
[
"Hou",
"Yu",
""
],
[
"McCullough",
"Kyle",
""
],
[
"Ren",
"Fengbo",
""
],
[
"Soibelman",
"Lucio",
""
]
] |
new_dataset
| 0.98502 |
2210.00429
|
Chee-Kheng Chng Ck
|
Chee-Kheng Chng, Alvaro Parra Bustos, Benjamin McCarthy, Tat-Jun Chin
|
ROSIA: Rotation-Search-Based Star Identification Algorithm
|
21 pages, 16 figures, Accepted to IEEE Transactions on Aerospace and
Electronic Systems
| null |
10.1109/TAES.2023.3279353
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This paper presents a rotation-search-based approach for addressing the star
identification (Star-ID) problem. The proposed algorithm, ROSIA, is a
heuristics-free algorithm that seeks the optimal rotation that maximally aligns
the input and catalog stars in their respective coordinates. ROSIA searches the
rotation space systematically with the Branch-and-Bound (BnB) method. Crucially
affecting the runtime feasibility of ROSIA is the upper bound function that
prioritizes the search space. In this paper, we make a theoretical contribution
by proposing a tight (provable) upper bound function that enables a 400x
speed-up compared to an existing formulation. Coupling the bounding function
with an efficient evaluation scheme that leverages stereographic projection and
the R-tree data structure, ROSIA achieves feasible operational speed on
embedded processors with state-of-the-art performances under different sources
of noise. The source code of ROSIA is available at
https://github.com/ckchng/ROSIA.
|
[
{
"version": "v1",
"created": "Sun, 2 Oct 2022 05:34:19 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 02:32:22 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Chng",
"Chee-Kheng",
""
],
[
"Bustos",
"Alvaro Parra",
""
],
[
"McCarthy",
"Benjamin",
""
],
[
"Chin",
"Tat-Jun",
""
]
] |
new_dataset
| 0.999244 |
2211.14308
|
Guillaume Le Moing
|
Guillaume Le Moing and Jean Ponce and Cordelia Schmid
|
WALDO: Future Video Synthesis using Object Layer Decomposition and
Parametric Flow Prediction
|
Accepted to ICCV 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents WALDO (WArping Layer-Decomposed Objects), a novel
approach to the prediction of future video frames from past ones. Individual
images are decomposed into multiple layers combining object masks and a small
set of control points. The layer structure is shared across all frames in each
video to build dense inter-frame connections. Complex scene motions are modeled
by combining parametric geometric transformations associated with individual
layers, and video synthesis is broken down into discovering the layers
associated with past frames, predicting the corresponding transformations for
upcoming ones and warping the associated object regions accordingly, and
filling in the remaining image parts. Extensive experiments on multiple
benchmarks including urban videos (Cityscapes and KITTI) and videos featuring
nonrigid motions (UCF-Sports and H3.6M), show that our method consistently
outperforms the state of the art by a significant margin in every case. Code,
pretrained models, and video samples synthesized by our approach can be found
in the project webpage https://16lemoing.github.io/waldo.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 18:59:46 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Mar 2023 15:22:30 GMT"
},
{
"version": "v3",
"created": "Tue, 29 Aug 2023 07:58:49 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Moing",
"Guillaume Le",
""
],
[
"Ponce",
"Jean",
""
],
[
"Schmid",
"Cordelia",
""
]
] |
new_dataset
| 0.998466 |
2212.01241
|
Cheng Xu
|
Cheng Xu and Xiaofeng Hou and Jiacheng Liu and Chao Li and Tianhao
Huang and Xiaozhi Zhu and Mo Niu and Lingyu Sun and Peng Tang and Tongqiao Xu
and Kwang-Ting Cheng and Minyi Guo
|
MMBench: Benchmarking End-to-End Multi-modal DNNs and Understanding
Their Hardware-Software Implications
| null | null | null | null |
cs.PF
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The explosive growth of various types of big data and advances in AI
technologies have catalyzed a new type of workloads called multi-modal DNNs.
Multi-modal DNNs are capable of interpreting and reasoning about information
from multiple modalities, making them more applicable to real-world AI
scenarios. In recent research, multi-modal DNNs have outperformed the best
uni-modal DNN in a wide range of distributed computing applications from
traditional multimedia systems to emerging autonomous edge systems. However,
despite their importance and superiority, very limited research attention has
been devoted to understand the characteristics of multi-modal DNNs and their
implications on current computing software/hardware platforms. Existing
benchmarks either target uni-modal DNNs or only focus on the algorithm
characteristics of multi-modal DNNs. There lacks representative benchmark
suites that provide comprehensive system and architecture level analysis of
multi-modal networks.
To advance the understanding of these multi-modal DNN workloads and
facilitate related research, we present MMBench, an open-source, end-to-end
benchmark suite consisting of a set of real-world multi-modal DNN workloads
with relevant performance metrics for evaluation. We then use MMBench to
conduct an in-depth analysis on the characteristics of multi-modal DNNs. We
demonstrate their unique characteristics of clear multi-stage execution,
frequent synchronization and high heterogeneity, which distinguish them from
conventional uni-modal DNNs. Finally, we conduct a case study and extend our
benchmark to edge devices. We hope that our work can provide insights for
future software/hardware design and optimization to underpin multi-modal DNNs
on both cloud and edge computing platforms.
|
[
{
"version": "v1",
"created": "Fri, 2 Dec 2022 15:35:04 GMT"
},
{
"version": "v2",
"created": "Fri, 9 Dec 2022 04:31:52 GMT"
},
{
"version": "v3",
"created": "Thu, 10 Aug 2023 06:58:16 GMT"
},
{
"version": "v4",
"created": "Tue, 29 Aug 2023 02:41:10 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Xu",
"Cheng",
""
],
[
"Hou",
"Xiaofeng",
""
],
[
"Liu",
"Jiacheng",
""
],
[
"Li",
"Chao",
""
],
[
"Huang",
"Tianhao",
""
],
[
"Zhu",
"Xiaozhi",
""
],
[
"Niu",
"Mo",
""
],
[
"Sun",
"Lingyu",
""
],
[
"Tang",
"Peng",
""
],
[
"Xu",
"Tongqiao",
""
],
[
"Cheng",
"Kwang-Ting",
""
],
[
"Guo",
"Minyi",
""
]
] |
new_dataset
| 0.998774 |
2301.00135
|
Xu Gu
|
Xu Gu, Yuchong Sun, Feiyue Ni, Shizhe Chen, Xihua Wang, Ruihua Song,
Boyuan Li, Xiang Cao
|
TeViS:Translating Text Synopses to Video Storyboards
|
Accepted to ACM Multimedia 2023
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A video storyboard is a roadmap for video creation which consists of
shot-by-shot images to visualize key plots in a text synopsis. Creating video
storyboards, however, remains challenging which not only requires cross-modal
association between high-level texts and images but also demands long-term
reasoning to make transitions smooth across shots. In this paper, we propose a
new task called Text synopsis to Video Storyboard (TeViS) which aims to
retrieve an ordered sequence of images as the video storyboard to visualize the
text synopsis. We construct a MovieNet-TeViS dataset based on the public
MovieNet dataset. It contains 10K text synopses each paired with keyframes
manually selected from corresponding movies by considering both relevance and
cinematic coherence. To benchmark the task, we present strong CLIP-based
baselines and a novel VQ-Trans. VQ-Trans first encodes text synopsis and images
into a joint embedding space and uses vector quantization (VQ) to improve the
visual representation. Then, it auto-regressively generates a sequence of
visual features for retrieval and ordering. Experimental results demonstrate
that VQ-Trans significantly outperforms prior methods and the CLIP-based
baselines. Nevertheless, there is still a large gap compared to human
performance suggesting room for promising future work. The code and data are
available at: \url{https://ruc-aimind.github.io/projects/TeViS/}
|
[
{
"version": "v1",
"created": "Sat, 31 Dec 2022 06:32:36 GMT"
},
{
"version": "v2",
"created": "Mon, 13 Feb 2023 02:09:21 GMT"
},
{
"version": "v3",
"created": "Mon, 14 Aug 2023 13:41:49 GMT"
},
{
"version": "v4",
"created": "Tue, 29 Aug 2023 13:10:56 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Gu",
"Xu",
""
],
[
"Sun",
"Yuchong",
""
],
[
"Ni",
"Feiyue",
""
],
[
"Chen",
"Shizhe",
""
],
[
"Wang",
"Xihua",
""
],
[
"Song",
"Ruihua",
""
],
[
"Li",
"Boyuan",
""
],
[
"Cao",
"Xiang",
""
]
] |
new_dataset
| 0.999846 |
2301.12457
|
Beichen Huang
|
Beichen Huang, Ran Cheng, Zhuozhao Li, Yaochu Jin, Kay Chen Tan
|
EvoX: A Distributed GPU-accelerated Framework for Scalable Evolutionary
Computation
| null | null | null | null |
cs.NE
|
http://creativecommons.org/licenses/by/4.0/
|
Evolutionary Computation (EC), drawing inspiration from natural evolutionary
processes, has solidified its place as an integral facet of Artificial
Intelligence. Its unique attributes, such as adaptability and the capability to
navigate vast problem spaces, have rendered it indispensable, especially in
domains demanding optimization like engineering design. In today's data-driven
landscape, the need for scalability in EC is more pronounced than ever,
especially with the rise in complex systems and large-scale data. However, many
existing EC libraries, designed for modest scales, fall short in catering to
the heightened demands of modern problems. The advent of some pioneering
GPU-accelerated EC libraries is a step forward, but they too grapple with
limitations, particularly in terms of flexibility, computational efficiency,
and architectural robustness. To address these challenges, this paper
introduces EvoX: a comprehensive, scalable framework tailored for the
automated, distributed, and heterogeneous execution of EC algorithms. Central
to EvoX is a functional programming model that streamlines the EC algorithm
development process, bolstered by a hierarchical state management strategy for
efficient distributed execution. Alongside this, leveraging the capabilities of
EvoX, we present a rich library of EC algorithms designed to handle a spectrum
of problem-solving scenarios. Experimental results demonstrate both the
superior system performance and model performance of EvoX. The code of EvoX is
available at https://github.com/EMI-Group/EvoX.
|
[
{
"version": "v1",
"created": "Sun, 29 Jan 2023 15:00:16 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Feb 2023 08:31:13 GMT"
},
{
"version": "v3",
"created": "Tue, 14 Feb 2023 15:23:57 GMT"
},
{
"version": "v4",
"created": "Thu, 16 Feb 2023 08:43:08 GMT"
},
{
"version": "v5",
"created": "Mon, 20 Mar 2023 07:20:22 GMT"
},
{
"version": "v6",
"created": "Sat, 26 Aug 2023 14:27:55 GMT"
},
{
"version": "v7",
"created": "Tue, 29 Aug 2023 05:49:35 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Huang",
"Beichen",
""
],
[
"Cheng",
"Ran",
""
],
[
"Li",
"Zhuozhao",
""
],
[
"Jin",
"Yaochu",
""
],
[
"Tan",
"Kay Chen",
""
]
] |
new_dataset
| 0.998548 |
2302.10469
|
Xue Xinghua
|
Xinghua Xue, Cheng Liu, Haitong Huang, Bo Liu, Ying Wang, Bing Yang,
Tao Luo, Lei Zhang, Huawei Li and Xiaowei Li
|
ApproxABFT: Approximate Algorithm-Based Fault Tolerance for Vision
Transformers
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision Transformers (ViTs) with outstanding performance becomes a popular
backbone of deep learning models for the main-stream vision tasks including
classification, object detection, and segmentation. Other than the performance,
reliability is also a critical metric for the adoption of ViTs in
safety-critical applications such as autonomous driving and robotics. With the
observation that the major computing blocks in ViTs such as multi-head
attention and feed forward are usually performed with general matrix
multiplication (GEMM), we propose a classical algorithm-based fault tolerance
(ABFT) strategy originally developed for GEMM to protect ViTs against soft
errors in the underlying computing engines. Unlike classical ABFT that will
invoke the expensive error recovery procedure whenever computing errors are
detected, we leverage the inherent fault-tolerance of ViTs and propose an
approximate ABFT, namely ApproxABFT, to invoke the error recovery procedure
only when the computing errors are significant enough, which skips many useless
error recovery procedures and simplifies the overall GEMM error recovery.
Meanwhile, it also relaxes the error threshold in error recovery procedure and
ignores minor computing errors, which reduces the error recovery complexity and
improves the error recovery quality. In addition, we also apply a fine-grained
blocking strategy to ApproxABFT and split GEMM with distinct sizes into smaller
sub blocks such that it can smooth the error thresholds across ViTs and further
improve the error recovery quality. According to our experiments, the
ApproxABFT reduces the computing overhead by 25.92\% to 81.62\% and improves
the model accuracy by 2.63\% to 72.56\% compared to the baseline ABFT while the
blocking optimization further reduces the computing overhead by 6.56\% to
73.5\% with comparable accuracy.
|
[
{
"version": "v1",
"created": "Tue, 21 Feb 2023 06:21:28 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 09:42:40 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Xue",
"Xinghua",
""
],
[
"Liu",
"Cheng",
""
],
[
"Huang",
"Haitong",
""
],
[
"Liu",
"Bo",
""
],
[
"Wang",
"Ying",
""
],
[
"Yang",
"Bing",
""
],
[
"Luo",
"Tao",
""
],
[
"Zhang",
"Lei",
""
],
[
"Li",
"Huawei",
""
],
[
"Li",
"Xiaowei",
""
]
] |
new_dataset
| 0.999022 |
2303.14672
|
Ming Qian
|
Ming Qian, Jincheng Xiong, Gui-Song Xia, Nan Xue
|
Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
|
ICCV 2023, project page: https://sat2density.github.io/, code:
https://github.com/qianmingduowan/Sat2Density
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This paper aims to develop an accurate 3D geometry representation of
satellite images using satellite-ground image pairs. Our focus is on the
challenging problem of 3D-aware ground-views synthesis from a satellite image.
We draw inspiration from the density field representation used in volumetric
neural rendering and propose a new approach, called Sat2Density. Our method
utilizes the properties of ground-view panoramas for the sky and non-sky
regions to learn faithful density fields of 3D scenes in a geometric
perspective. Unlike other methods that require extra depth information during
training, our Sat2Density can automatically learn accurate and faithful 3D
geometry via density representation without depth supervision. This advancement
significantly improves the ground-view panorama synthesis task. Additionally,
our study provides a new geometric perspective to understand the relationship
between satellite and ground-view images in 3D space.
|
[
{
"version": "v1",
"created": "Sun, 26 Mar 2023 10:15:33 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 09:33:59 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Qian",
"Ming",
""
],
[
"Xiong",
"Jincheng",
""
],
[
"Xia",
"Gui-Song",
""
],
[
"Xue",
"Nan",
""
]
] |
new_dataset
| 0.973734 |
2303.15860
|
Teng-Hui Huang
|
Teng-Hui Huang, Thilini Dahanayaka, Kanchana Thilakarathna, Philip
H.W. Leong and Hesham El Gamal
|
The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless
Fingerprinting
| null | null | null | null |
cs.IT cs.LG math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Wireless fingerprinting refers to a device identification method leveraging
hardware imperfections and wireless channel variations as signatures. Beyond
physical layer characteristics, recent studies demonstrated that user behaviors
could be identified through network traffic, e.g., packet length, without
decryption of the payload. Inspired by these results, we propose a multi-layer
fingerprinting framework that jointly considers the multi-layer signatures for
improved identification performance. In contrast to previous works, by
leveraging the recent multi-view machine learning paradigm, i.e., data with
multiple forms, our method can cluster the device information shared among the
multi-layer features without supervision. Our information-theoretic approach
can be extended to supervised and semi-supervised settings with straightforward
derivations. In solving the formulated problem, we obtain a tight surrogate
bound using variational inference for efficient optimization. In extracting the
shared device information, we develop an algorithm based on the Wyner common
information method, enjoying reduced computation complexity as compared to
existing approaches. The algorithm can be applied to data distributions
belonging to the exponential family class. Empirically, we evaluate the
algorithm in a synthetic dataset with real-world video traffic and simulated
physical layer characteristics. Our empirical results show that the proposed
method outperforms the state-of-the-art baselines in both supervised and
unsupervised settings.
|
[
{
"version": "v1",
"created": "Tue, 28 Mar 2023 10:05:06 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 03:13:32 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Huang",
"Teng-Hui",
""
],
[
"Dahanayaka",
"Thilini",
""
],
[
"Thilakarathna",
"Kanchana",
""
],
[
"Leong",
"Philip H. W.",
""
],
[
"Gamal",
"Hesham El",
""
]
] |
new_dataset
| 0.982055 |
2305.10666
|
Zelin Ying
|
Zelin Ying, Chen Li, Yu Dong, Qiuqiang Kong, Qiao Tian, Yuanyuan Huo,
Yuxuan Wang
|
a unified front-end framework for english text-to-speech synthesis
|
5 pages, 3 figures
| null | null | null |
cs.CL cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The front-end is a critical component of English text-to-speech (TTS)
systems, responsible for extracting linguistic features that are essential for
a text-to-speech model to synthesize speech, such as prosodies and phonemes.
The English TTS front-end typically consists of a text normalization (TN)
module, a prosody word prosody phrase (PWPP) module, and a grapheme-to-phoneme
(G2P) module. However, current research on the English TTS front-end focuses
solely on individual modules, neglecting the interdependence between them and
resulting in sub-optimal performance for each module. Therefore, this paper
proposes a unified front-end framework that captures the dependencies among the
English TTS front-end modules. Extensive experiments have demonstrated that the
proposed method achieves state-of-the-art (SOTA) performance in all modules.
|
[
{
"version": "v1",
"created": "Thu, 18 May 2023 02:57:54 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 07:16:52 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Ying",
"Zelin",
""
],
[
"Li",
"Chen",
""
],
[
"Dong",
"Yu",
""
],
[
"Kong",
"Qiuqiang",
""
],
[
"Tian",
"Qiao",
""
],
[
"Huo",
"Yuanyuan",
""
],
[
"Wang",
"Yuxuan",
""
]
] |
new_dataset
| 0.998769 |
2305.14594
|
Salem Lahlou
|
Salem Lahlou, Joseph D. Viviano, Victor Schmidt, Yoshua Bengio
|
torchgfn: A PyTorch GFlowNet library
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The growing popularity of generative flow networks (GFlowNets or GFNs) from a
range of researchers with diverse backgrounds and areas of expertise
necessitates a library which facilitates the testing of new features such as
training losses that can be easily compared to standard benchmark
implementations, or on a set of common environments. torchgfn is a PyTorch
library that aims to address this need. It provides users with a simple API for
environments and useful abstractions for samplers and losses. Multiple examples
are provided, replicating and unifying published results. The code is available
in https://github.com/saleml/torchgfn.
|
[
{
"version": "v1",
"created": "Wed, 24 May 2023 00:20:59 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 14:51:08 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Lahlou",
"Salem",
""
],
[
"Viviano",
"Joseph D.",
""
],
[
"Schmidt",
"Victor",
""
],
[
"Bengio",
"Yoshua",
""
]
] |
new_dataset
| 0.998064 |
2306.06826
|
Jiaxin Pei
|
Jiaxin Pei and David Jurgens
|
When Do Annotator Demographics Matter? Measuring the Influence of
Annotator Demographics with the POPQUORN Dataset
| null | null | null | null |
cs.CL cs.AI cs.CY cs.HC cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Annotators are not fungible. Their demographics, life experiences, and
backgrounds all contribute to how they label data. However, NLP has only
recently considered how annotator identity might influence their decisions.
Here, we present POPQUORN (the POtato-Prolific dataset for QUestion-Answering,
Offensiveness, text Rewriting, and politeness rating with demographic Nuance).
POPQUORN contains 45,000 annotations from 1,484 annotators, drawn from a
representative sample regarding sex, age, and race as the US population.
Through a series of analyses, we show that annotators' background plays a
significant role in their judgments. Further, our work shows that backgrounds
not previously considered in NLP (e.g., education), are meaningful and should
be considered. Our study suggests that understanding the background of
annotators and collecting labels from a demographically balanced pool of crowd
workers is important to reduce the bias of datasets. The dataset, annotator
background, and annotation interface are available at
https://github.com/Jiaxin-Pei/potato-prolific-dataset .
|
[
{
"version": "v1",
"created": "Mon, 12 Jun 2023 02:26:00 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Aug 2023 21:14:35 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Pei",
"Jiaxin",
""
],
[
"Jurgens",
"David",
""
]
] |
new_dataset
| 0.984344 |
2306.09539
|
Mahan Fathi
|
Mahan Fathi and Jonathan Pilault and Pierre-Luc Bacon and Christopher
Pal and Orhan Firat and Ross Goroshin
|
Block-State Transformer
| null | null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
State space models (SSMs) have shown impressive results on tasks that require
modeling long-range dependencies and efficiently scale to long sequences owing
to their subquadratic runtime complexity. Originally designed for continuous
signals, SSMs have shown superior performance on a plethora of tasks, in vision
and audio; however, SSMs still lag Transformer performance in Language Modeling
tasks. In this work, we propose a hybrid layer named Block-State Transformer
(BST), that internally combines an SSM sublayer for long-range
contextualization, and a Block Transformer sublayer for short-term
representation of sequences. We study three different, and completely
parallelizable, variants that integrate SSMs and block-wise attention. We show
that our model outperforms similar Transformer-based architectures on language
modeling perplexity and generalizes to longer sequences. In addition, the
Block-State Transformer demonstrates more than tenfold increase in speed at the
layer level compared to the Block-Recurrent Transformer when model
parallelization is employed.
|
[
{
"version": "v1",
"created": "Thu, 15 Jun 2023 22:48:08 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 01:08:30 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Fathi",
"Mahan",
""
],
[
"Pilault",
"Jonathan",
""
],
[
"Bacon",
"Pierre-Luc",
""
],
[
"Pal",
"Christopher",
""
],
[
"Firat",
"Orhan",
""
],
[
"Goroshin",
"Ross",
""
]
] |
new_dataset
| 0.997555 |
2307.00290
|
Can Cui
|
Can Cui, Ruining Deng, Quan Liu, Tianyuan Yao, Shunxing Bao, Lucas W.
Remedios, Yucheng Tang, Yuankai Huo
|
All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with
Prompt-based Finetuning
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Segment Anything Model (SAM) is a recently proposed prompt-based
segmentation model in a generic zero-shot segmentation approach. With the
zero-shot segmentation capacity, SAM achieved impressive flexibility and
precision on various segmentation tasks. However, the current pipeline requires
manual prompts during the inference stage, which is still resource intensive
for biomedical image segmentation. In this paper, instead of using prompts
during the inference stage, we introduce a pipeline that utilizes the SAM,
called all-in-SAM, through the entire AI development workflow (from annotation
generation to model finetuning) without requiring manual prompts during the
inference stage. Specifically, SAM is first employed to generate pixel-level
annotations from weak prompts (e.g., points, bounding box). Then, the
pixel-level annotations are used to finetune the SAM segmentation model rather
than training from scratch. Our experimental results reveal two key findings:
1) the proposed pipeline surpasses the state-of-the-art (SOTA) methods in a
nuclei segmentation task on the public Monuseg dataset, and 2) the utilization
of weak and few annotations for SAM finetuning achieves competitive performance
compared to using strong pixel-wise annotated data.
|
[
{
"version": "v1",
"created": "Sat, 1 Jul 2023 10:12:46 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 03:31:58 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Cui",
"Can",
""
],
[
"Deng",
"Ruining",
""
],
[
"Liu",
"Quan",
""
],
[
"Yao",
"Tianyuan",
""
],
[
"Bao",
"Shunxing",
""
],
[
"Remedios",
"Lucas W.",
""
],
[
"Tang",
"Yucheng",
""
],
[
"Huo",
"Yuankai",
""
]
] |
new_dataset
| 0.981917 |
2307.03854
|
B M Tazbiul Hassan Anik
|
B M Tazbiul Hassan Anik, Zubayer Islam, Mohamed Abdel-Aty
|
inTformer: A Time-Embedded Attention-Based Transformer for Crash
Likelihood Prediction at Intersections Using Connected Vehicle Data
|
29 pages, 10 figures, 8 tables
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The real-time crash likelihood prediction model is an essential component of
the proactive traffic safety management system. Over the years, numerous
studies have attempted to construct a crash likelihood prediction model in
order to enhance traffic safety, but mostly on freeways. In the majority of the
existing studies, researchers have primarily employed a deep learning-based
framework to identify crash potential. Lately, Transformer has emerged as a
potential deep neural network that fundamentally operates through
attention-based mechanisms. Transformer has several functional benefits over
extant deep learning models such as LSTM, CNN, etc. Firstly, Transformer can
readily handle long-term dependencies in a data sequence. Secondly,
Transformers can parallelly process all elements in a data sequence during
training. Finally, a Transformer does not have the vanishing gradient issue.
Realizing the immense possibility of Transformers, this paper proposes
inTersection-Transformer (inTformer), a time-embedded attention-based
Transformer model that can effectively predict intersection crash likelihood in
real-time. The proposed model was evaluated using connected vehicle data
extracted from Signal Analytics Platform. Acknowledging the complex traffic
operation mechanism at intersection, this study developed zone-specific models
by dividing the intersection region into two distinct zones:
within-intersection and approach zone. The best inTformer models in
'within-intersection,' and 'approach' zone achieved a sensitivity of 73%, and
70%, respectively. The zone-level models were also compared to earlier studies
on crash likelihood prediction at intersections and with several established
deep learning models trained on the same connected vehicle dataset.
|
[
{
"version": "v1",
"created": "Fri, 7 Jul 2023 22:00:31 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Jul 2023 05:46:11 GMT"
},
{
"version": "v3",
"created": "Mon, 28 Aug 2023 12:50:34 GMT"
},
{
"version": "v4",
"created": "Tue, 29 Aug 2023 15:51:05 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Anik",
"B M Tazbiul Hassan",
""
],
[
"Islam",
"Zubayer",
""
],
[
"Abdel-Aty",
"Mohamed",
""
]
] |
new_dataset
| 0.98255 |
2308.12651
|
Ayano Nishii
|
Yuya Higashikawa, Ayano Nishii, Junichi Teruyama, Yuki Tokuni
|
Sink Location Problems in Dynamic Flow Grid Networks
|
16 pages, 6 figures, full version of a paper accepted at COCOON 2023
| null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
A dynamic flow network consists of a directed graph, where nodes called
sources represent locations of evacuees, and nodes called sinks represent
locations of evacuation facilities. Each source and each sink are given supply
representing the number of evacuees and demand representing the maximum number
of acceptable evacuees, respectively. Each edge is given capacity and transit
time. Here, the capacity of an edge bounds the rate at which evacuees can enter
the edge per unit time, and the transit time represents the time which evacuees
take to travel across the edge. The evacuation completion time is the minimum
time at which each evacuees can arrive at one of the evacuation facilities.
Given a dynamic flow network without sinks, once sinks are located on some
nodes or edges, the evacuation completion time for this sink location is
determined. We then consider the problem of locating sinks to minimize the
evacuation completion time, called the sink location problem. The problems have
been given polynomial-time algorithms only for limited networks such as paths,
cycles, and trees, but no polynomial-time algorithms are known for more complex
network classes. In this paper, we prove that the 1-sink location problem can
be solved in polynomial-time when an input network is a grid with uniform edge
capacity and transit time.
|
[
{
"version": "v1",
"created": "Thu, 24 Aug 2023 08:47:15 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Aug 2023 16:59:10 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Higashikawa",
"Yuya",
""
],
[
"Nishii",
"Ayano",
""
],
[
"Teruyama",
"Junichi",
""
],
[
"Tokuni",
"Yuki",
""
]
] |
new_dataset
| 0.966799 |
2308.14047
|
Francesco Pirotti
|
Francesco Pirotti, Alberto Guarnieri, Sebastiano Chiodini, Carlo
Bettanini
|
Automatic coarse co-registration of point clouds from diverse scan
geometries: a test of detectors and descriptors
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Point clouds are collected nowadays from a plethora of sensors, some having
higher accuracies and higher costs, some having lower accuracies but also lower
costs. Not only there is a large choice for different sensors, but also these
can be transported by different platforms, which can provide different scan
geometries. In this work we test the extraction of four different keypoint
detectors and three feature descriptors. We benchmark performance in terms of
calculation time and we assess their performance in terms of accuracy in their
ability in coarse automatic co-registration of two clouds that are collected
with different sensors, platforms and scan geometries. One, which we define as
having the higher accuracy, and thus will be used as reference, was surveyed
via a UAV flight with a Riegl MiniVUX-3, the other on a bicycle with a Livox
Horizon over a walking path with un-even ground.The novelty in this work
consists in comparing several strategies for fast alignment of point clouds
from very different surveying geometries, as the drone has a bird's eye view
and the bicycle a ground-based view. An added challenge is related to the lower
cost of the bicycle sensor ensemble that, together with the rough terrain,
reasonably results in lower accuracy of the survey. The main idea is to use
range images to capture a simplified version of the geometry of the surveyed
area and then find the best features to match keypoints. Results show that NARF
features detected more keypoints and resulted in a faster co-registration
procedure in this scenariowhereas the accuracy of the co-registration is
similar to all the combinations of keypoint detectors and features.
|
[
{
"version": "v1",
"created": "Sun, 27 Aug 2023 08:55:22 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Pirotti",
"Francesco",
""
],
[
"Guarnieri",
"Alberto",
""
],
[
"Chiodini",
"Sebastiano",
""
],
[
"Bettanini",
"Carlo",
""
]
] |
new_dataset
| 0.993241 |
2308.14762
|
Waseem Akram
|
Waseem Akram, Muhayyuddin Ahmed, Lyes Saad Saoud, Lakmal Seneviratne,
and Irfan Hussain
|
Autonomous Underwater Robotic System for Aquaculture Applications
|
arXiv admin note: text overlap with arXiv:2308.13826
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Aquaculture is a thriving food-producing sector producing over half of the
global fish consumption. However, these aquafarms pose significant challenges
such as biofouling, vegetation, and holes within their net pens and have a
profound effect on the efficiency and sustainability of fish production.
Currently, divers and/or remotely operated vehicles are deployed for inspecting
and maintaining aquafarms; this approach is expensive and requires highly
skilled human operators. This work aims to develop a robotic-based automatic
net defect detection system for aquaculture net pens oriented to on- ROV
processing and real-time detection of different aqua-net defects such as
biofouling, vegetation, net holes, and plastic. The proposed system integrates
both deep learning-based methods for aqua-net defect detection and feedback
control law for the vehicle movement around the aqua-net to obtain a clear
sequence of net images and inspect the status of the net via performing the
inspection tasks. This work contributes to the area of aquaculture inspection,
marine robotics, and deep learning aiming to reduce cost, improve quality, and
ease of operation.
|
[
{
"version": "v1",
"created": "Sat, 26 Aug 2023 10:45:39 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Akram",
"Waseem",
""
],
[
"Ahmed",
"Muhayyuddin",
""
],
[
"Saoud",
"Lyes Saad",
""
],
[
"Seneviratne",
"Lakmal",
""
],
[
"Hussain",
"Irfan",
""
]
] |
new_dataset
| 0.996685 |
2308.14816
|
Zhipeng Cai
|
Zhipeng Cai and Matthias Mueller
|
CLNeRF: Continual Learning Meets NeRF
|
Accepted to ICCV 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Novel view synthesis aims to render unseen views given a set of calibrated
images. In practical applications, the coverage, appearance or geometry of the
scene may change over time, with new images continuously being captured.
Efficiently incorporating such continuous change is an open challenge. Standard
NeRF benchmarks only involve scene coverage expansion. To study other practical
scene changes, we propose a new dataset, World Across Time (WAT), consisting of
scenes that change in appearance and geometry over time. We also propose a
simple yet effective method, CLNeRF, which introduces continual learning (CL)
to Neural Radiance Fields (NeRFs). CLNeRF combines generative replay and the
Instant Neural Graphics Primitives (NGP) architecture to effectively prevent
catastrophic forgetting and efficiently update the model when new data arrives.
We also add trainable appearance and geometry embeddings to NGP, allowing a
single compact model to handle complex scene changes. Without the need to store
historical images, CLNeRF trained sequentially over multiple scans of a
changing scene performs on-par with the upper bound model trained on all scans
at once. Compared to other CL baselines CLNeRF performs much better across
standard benchmarks and WAT. The source code, and the WAT dataset are available
at https://github.com/IntelLabs/CLNeRF. Video presentation is available at:
https://youtu.be/nLRt6OoDGq0?si=8yD6k-8MMBJInQPs
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 18:09:13 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Cai",
"Zhipeng",
""
],
[
"Mueller",
"Matthias",
""
]
] |
new_dataset
| 0.999794 |
2308.14833
|
Derek Gloudemans
|
Derek Gloudemans, Yanbing Wang, Gracie Gumm, William Barbour, Daniel
B. Work
|
The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera
vehicle tracking
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
This work presents a novel video dataset recorded from overlapping highway
traffic cameras along an urban interstate, enabling multi-camera 3D object
tracking in a traffic monitoring context. Data is released from 3 scenes
containing video from at least 16 cameras each, totaling 57 minutes in length.
877,000 3D bounding boxes and corresponding object tracklets are fully and
accurately annotated for each camera field of view and are combined into a
spatially and temporally continuous set of vehicle trajectories for each scene.
Lastly, existing algorithms are combined to benchmark a number of 3D
multi-camera tracking pipelines on the dataset, with results indicating that
the dataset is challenging due to the difficulty of matching objects traveling
at high speeds across cameras and heavy object occlusion, potentially for
hundreds of frames, during congested traffic. This work aims to enable the
development of accurate and automatic vehicle trajectory extraction algorithms,
which will play a vital role in understanding impacts of autonomous vehicle
technologies on the safety and efficiency of traffic.
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 18:43:33 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Gloudemans",
"Derek",
""
],
[
"Wang",
"Yanbing",
""
],
[
"Gumm",
"Gracie",
""
],
[
"Barbour",
"William",
""
],
[
"Work",
"Daniel B.",
""
]
] |
new_dataset
| 0.999868 |
2308.14835
|
Robert Bridges
|
Robert A. Bridges, Brian Weber, Justin M. Beaver, Jared M. Smith, Miki
E. Verma, Savannah Norem, Kevin Spakes, Cory Watson, Jeff A. Nichols, Brian
Jewell, Michael. D. Iannacone, Chelsey Dunivan Stahl, Kelly M.T. Huffer, T.
Sean Oesch
|
AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work presents an evaluation of six prominent commercial endpoint malware
detectors, a network malware detector, and a file-conviction algorithm from a
cyber technology vendor. The evaluation was administered as the first of the
Artificial Intelligence Applications to Autonomous Cybersecurity (AI ATAC)
prize challenges, funded by / completed in service of the US Navy. The
experiment employed 100K files (50/50% benign/malicious) with a stratified
distribution of file types, including ~1K zero-day program executables
(increasing experiment size two orders of magnitude over previous work). We
present an evaluation process of delivering a file to a fresh virtual machine
donning the detection technology, waiting 90s to allow static detection, then
executing the file and waiting another period for dynamic detection; this
allows greater fidelity in the observational data than previous experiments, in
particular, resource and time-to-detection statistics. To execute all 800K
trials (100K files $\times$ 8 tools), a software framework is designed to
choreographed the experiment into a completely automated, time-synced, and
reproducible workflow with substantial parallelization. A cost-benefit model
was configured to integrate the tools' recall, precision, time to detection,
and resource requirements into a single comparable quantity by simulating costs
of use. This provides a ranking methodology for cyber competitions and a lens
through which to reason about the varied statistical viewpoints of the results.
These statistical and cost-model results provide insights on state of
commercial malware detection.
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 18:46:12 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Bridges",
"Robert A.",
""
],
[
"Weber",
"Brian",
""
],
[
"Beaver",
"Justin M.",
""
],
[
"Smith",
"Jared M.",
""
],
[
"Verma",
"Miki E.",
""
],
[
"Norem",
"Savannah",
""
],
[
"Spakes",
"Kevin",
""
],
[
"Watson",
"Cory",
""
],
[
"Nichols",
"Jeff A.",
""
],
[
"Jewell",
"Brian",
""
],
[
"Iannacone",
"Michael. D.",
""
],
[
"Stahl",
"Chelsey Dunivan",
""
],
[
"Huffer",
"Kelly M. T.",
""
],
[
"Oesch",
"T. Sean",
""
]
] |
new_dataset
| 0.992579 |
2308.14852
|
Hatef Otroshi Shahreza
|
Hatef Otroshi Shahreza, Anjith George, S\'ebastien Marcel
|
SynthDistill: Face Recognition with Knowledge Distillation from
Synthetic Data
|
Accepted in the IEEE International Joint Conference on Biometrics
(IJCB 2023)
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
State-of-the-art face recognition networks are often computationally
expensive and cannot be used for mobile applications. Training lightweight face
recognition models also requires large identity-labeled datasets. Meanwhile,
there are privacy and ethical concerns with collecting and using large face
recognition datasets. While generating synthetic datasets for training face
recognition models is an alternative option, it is challenging to generate
synthetic data with sufficient intra-class variations. In addition, there is
still a considerable gap between the performance of models trained on real and
synthetic data. In this paper, we propose a new framework (named SynthDistill)
to train lightweight face recognition models by distilling the knowledge of a
pretrained teacher face recognition model using synthetic data. We use a
pretrained face generator network to generate synthetic face images and use the
synthesized images to learn a lightweight student network. We use synthetic
face images without identity labels, mitigating the problems in the intra-class
variation generation of synthetic datasets. Instead, we propose a novel dynamic
sampling strategy from the intermediate latent space of the face generator
network to include new variations of the challenging images while further
exploring new face images in the training batch. The results on five different
face recognition datasets demonstrate the superiority of our lightweight model
compared to models trained on previous synthetic datasets, achieving a
verification accuracy of 99.52% on the LFW dataset with a lightweight network.
The results also show that our proposed framework significantly reduces the gap
between training with real and synthetic data. The source code for replicating
the experiments is publicly released.
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 19:15:27 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Shahreza",
"Hatef Otroshi",
""
],
[
"George",
"Anjith",
""
],
[
"Marcel",
"Sébastien",
""
]
] |
new_dataset
| 0.996148 |
2308.14894
|
Th\'eo Deschamps-Berger
|
Th\'eo Deschamps-Berger, Lori Lamel and Laurence Devillers
|
Multiscale Contextual Learning for Speech Emotion Recognition in
Emergency Call Center Conversations
| null | null | null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Emotion recognition in conversations is essential for ensuring advanced
human-machine interactions. However, creating robust and accurate emotion
recognition systems in real life is challenging, mainly due to the scarcity of
emotion datasets collected in the wild and the inability to take into account
the dialogue context. The CEMO dataset, composed of conversations between
agents and patients during emergency calls to a French call center, fills this
gap. The nature of these interactions highlights the role of the emotional flow
of the conversation in predicting patient emotions, as context can often make a
difference in understanding actual feelings. This paper presents a multi-scale
conversational context learning approach for speech emotion recognition, which
takes advantage of this hypothesis. We investigated this approach on both
speech transcriptions and acoustic segments. Experimentally, our method uses
the previous or next information of the targeted segment. In the text domain,
we tested the context window using a wide range of tokens (from 10 to 100) and
at the speech turns level, considering inputs from both the same and opposing
speakers. According to our tests, the context derived from previous tokens has
a more significant influence on accurate prediction than the following tokens.
Furthermore, taking the last speech turn of the same speaker in the
conversation seems useful. In the acoustic domain, we conducted an in-depth
analysis of the impact of the surrounding emotions on the prediction. While
multi-scale conversational context learning using Transformers can enhance
performance in the textual modality for emergency call recordings,
incorporating acoustic context is more challenging.
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 20:31:45 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Deschamps-Berger",
"Théo",
""
],
[
"Lamel",
"Lori",
""
],
[
"Devillers",
"Laurence",
""
]
] |
new_dataset
| 0.999052 |
2308.14898
|
EPTCS
|
Enrico Pontelli (New Mexico State University, USA), Stefania
Costantini (University of L'Aquila, Italy), Carmine Dodaro (University of
Calabria, Italy), Sarah Gaggl (TU Dresden, Germany), Roberta Calegari
(University of Bologna, Italy), Artur D'Avila Garcez (City University of
London, UK), Francesco Fabiano (University of Udine, Italy), Alessandra Mileo
(DCU, Ireland), Alessandra Russo (Imperial College London, UK), Francesca
Toni (Imperial College London, UK)
|
Proceedings 39th International Conference on Logic Programming
| null |
EPTCS 385, 2023
|
10.4204/EPTCS.385
| null |
cs.AI cs.LO cs.PL cs.SC
|
http://creativecommons.org/licenses/by/4.0/
|
This volume contains the Technical Communications presented at the 39th
International Conference on Logic Programming (ICLP 2023), held at Imperial
College London, UK from July 9 to July 15, 2023. Technical Communications
included here concern the Main Track, the Doctoral Consortium, the Application
and Systems/Demo track, the Recently Published Research Track, the
Birds-of-a-Feather track, the Thematic Tracks on Logic Programming and Machine
Learning, and Logic Programming and Explainability, Ethics, and
Trustworthiness.
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 20:46:59 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Pontelli",
"Enrico",
"",
"New Mexico State University, USA"
],
[
"Costantini",
"Stefania",
"",
"University of L'Aquila, Italy"
],
[
"Dodaro",
"Carmine",
"",
"University of\n Calabria, Italy"
],
[
"Gaggl",
"Sarah",
"",
"TU Dresden, Germany"
],
[
"Calegari",
"Roberta",
"",
"University of Bologna, Italy"
],
[
"Garcez",
"Artur D'Avila",
"",
"City University of\n London, UK"
],
[
"Fabiano",
"Francesco",
"",
"University of Udine, Italy"
],
[
"Mileo",
"Alessandra",
"",
"DCU, Ireland"
],
[
"Russo",
"Alessandra",
"",
"Imperial College London, UK"
],
[
"Toni",
"Francesca",
"",
"Imperial College London, UK"
]
] |
new_dataset
| 0.990204 |
2308.14899
|
Nathan Drenkow
|
Nathan Drenkow, Mathias Unberath
|
RobustCLEVR: A Benchmark and Framework for Evaluating Robustness in
Object-centric Learning
| null | null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Object-centric representation learning offers the potential to overcome
limitations of image-level representations by explicitly parsing image scenes
into their constituent components. While image-level representations typically
lack robustness to natural image corruptions, the robustness of object-centric
methods remains largely untested. To address this gap, we present the
RobustCLEVR benchmark dataset and evaluation framework. Our framework takes a
novel approach to evaluating robustness by enabling the specification of causal
dependencies in the image generation process grounded in expert knowledge and
capable of producing a wide range of image corruptions unattainable in existing
robustness evaluations. Using our framework, we define several causal models of
the image corruption process which explicitly encode assumptions about the
causal relationships and distributions of each corruption type. We generate
dataset variants for each causal model on which we evaluate state-of-the-art
object-centric methods. Overall, we find that object-centric methods are not
inherently robust to image corruptions. Our causal evaluation approach exposes
model sensitivities not observed using conventional evaluation processes,
yielding greater insight into robustness differences across algorithms. Lastly,
while conventional robustness evaluations view corruptions as
out-of-distribution, we use our causal framework to show that even training on
in-distribution image corruptions does not guarantee increased model
robustness. This work provides a step towards more concrete and substantiated
understanding of model performance and deterioration under complex corruption
processes of the real-world.
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 20:52:18 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Drenkow",
"Nathan",
""
],
[
"Unberath",
"Mathias",
""
]
] |
new_dataset
| 0.999238 |
2308.14936
|
Dongxiao Zhu
|
Chengyin Li, Prashant Khanduri, Yao Qiang, Rafi Ibn Sultan, Indrin
Chetty and Dongxiao Zhu
|
Auto-Prompting SAM for Mobile Friendly 3D Medical Image Segmentation
|
9 pages, 4 figures, 4 tables
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
The Segment Anything Model (SAM) has rapidly been adopted for segmenting a
wide range of natural images. However, recent studies have indicated that SAM
exhibits subpar performance on 3D medical image segmentation tasks. In addition
to the domain gaps between natural and medical images, disparities in the
spatial arrangement between 2D and 3D images, the substantial computational
burden imposed by powerful GPU servers, and the time-consuming manual prompt
generation impede the extension of SAM to a broader spectrum of medical image
segmentation applications. To address these challenges, in this work, we
introduce a novel method, AutoSAM Adapter, designed specifically for 3D
multi-organ CT-based segmentation. We employ parameter-efficient adaptation
techniques in developing an automatic prompt learning paradigm to facilitate
the transformation of the SAM model's capabilities to 3D medical image
segmentation, eliminating the need for manually generated prompts. Furthermore,
we effectively transfer the acquired knowledge of the AutoSAM Adapter to other
lightweight models specifically tailored for 3D medical image analysis,
achieving state-of-the-art (SOTA) performance on medical image segmentation
tasks. Through extensive experimental evaluation, we demonstrate the AutoSAM
Adapter as a critical foundation for effectively leveraging the emerging
ability of foundation models in 2D natural image segmentation for 3D medical
image segmentation.
|
[
{
"version": "v1",
"created": "Mon, 28 Aug 2023 23:23:53 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Li",
"Chengyin",
""
],
[
"Khanduri",
"Prashant",
""
],
[
"Qiang",
"Yao",
""
],
[
"Sultan",
"Rafi Ibn",
""
],
[
"Chetty",
"Indrin",
""
],
[
"Zhu",
"Dongxiao",
""
]
] |
new_dataset
| 0.99768 |
2308.14951
|
Homayoon Beigi
|
Mustafa Eyceoz, Justin Lee, Siddharth Pittie, Homayoon Beigi
|
Robust Open-Set Spoken Language Identification and the CU MultiLang
Dataset
|
6pages, 1 table, 6 figures
|
Recognition Technologies, Inc. Technical Report (2023),
RTI-20230328-01
|
10.13140/RG.2.2.22716.21122
|
RTI-20230828-01
|
cs.CL cs.AI cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most state-of-the-art spoken language identification models are closed-set;
in other words, they can only output a language label from the set of classes
they were trained on. Open-set spoken language identification systems, however,
gain the ability to detect when an input exhibits none of the original
languages. In this paper, we implement a novel approach to open-set spoken
language identification that uses MFCC and pitch features, a TDNN model to
extract meaningful feature embeddings, confidence thresholding on softmax
outputs, and LDA and pLDA for learning to classify new unknown languages. We
present a spoken language identification system that achieves 91.76% accuracy
on trained languages and has the capability to adapt to unknown languages on
the fly. To that end, we also built the CU MultiLang Dataset, a large and
diverse multilingual speech corpus which was used to train and evaluate our
system.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 00:44:27 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Eyceoz",
"Mustafa",
""
],
[
"Lee",
"Justin",
""
],
[
"Pittie",
"Siddharth",
""
],
[
"Beigi",
"Homayoon",
""
]
] |
new_dataset
| 0.999256 |
2308.14961
|
Beth Malmskog
|
Beth Malmskog and Na'ama Nevo
|
Lower Rate Bounds for Hermitian-Lifted Codes for Odd Prime
Characteristic
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Locally recoverable codes are error correcting codes with the additional
property that every symbol of any codeword can be recovered from a small set of
other symbols. This property is particularly desirable in cloud storage
applications. A locally recoverable code is said to have availability $t$ if
each position has $t$ disjoint recovery sets. Hermitian-lifted codes are
locally recoverable codes with high availability first described by Lopez,
Malmskog, Matthews, Pi\~nero-Gonzales, and Wootters. The codes are based on the
well-known Hermitian curve and incorporate the novel technique of lifting to
increase the rate of the code. Lopez et al. lower bounded the rate of the codes
defined over fields with characteristic 2. This paper generalizes their work to
show that the rate of Hermitian-lifted codes is bounded below by a positive
constant depending on $p$ when $q=p^l$ for any odd prime $p$.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 01:28:01 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Malmskog",
"Beth",
""
],
[
"Nevo",
"Na'ama",
""
]
] |
new_dataset
| 0.986842 |
2308.14972
|
Yaonan Zhu
|
Haokun Liu, Yaonan Zhu, Kenji Kato, Izumi Kondo, Tadayoshi Aoyama, and
Yasuhisa Hasegawa
|
LLM-Based Human-Robot Collaboration Framework for Manipulation Tasks
|
IEEE MHS 2023
| null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents a novel approach to enhance autonomous robotic
manipulation using the Large Language Model (LLM) for logical inference,
converting high-level language commands into sequences of executable motion
functions. The proposed system combines the advantage of LLM with YOLO-based
environmental perception to enable robots to autonomously make reasonable
decisions and task planning based on the given commands. Additionally, to
address the potential inaccuracies or illogical actions arising from LLM, a
combination of teleoperation and Dynamic Movement Primitives (DMP) is employed
for action correction. This integration aims to improve the practicality and
generalizability of the LLM-based human-robot collaboration system.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 01:54:49 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Liu",
"Haokun",
""
],
[
"Zhu",
"Yaonan",
""
],
[
"Kato",
"Kenji",
""
],
[
"Kondo",
"Izumi",
""
],
[
"Aoyama",
"Tadayoshi",
""
],
[
"Hasegawa",
"Yasuhisa",
""
]
] |
new_dataset
| 0.994773 |
2308.14974
|
Manar Alalfi
|
Jian Chen, Manar H. Alalfi, Thomas R. Dean, Ramesh S
|
SimSched: A tool for Simulating Autosar Implementaion in Simulink
|
21 pages
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
AUTOSAR (AUTomotive Open System ARchitecture) is an open industry standard
for the automotive sector. It defines the three-layered automotive software
architecture. One of these layers is the application layer, where functional
behaviors are encapsulated in Software Components (SW-Cs). Inside SW-Cs, a set
of runnable entities represents the internal behavior and is realized as a set
of tasks. To address AUTOSAR's lack of support for modeling behaviors of
runnables, languages such as Simulink are employed. Simulink simulations assume
Simulink block behaviors are completed in zero execution time, while real
execution requires a finite execution time. This timing mismatch can result in
failures to detect unexpected runtime behaviors during the simulation phase.
This paper extends the Simulink environment to model the timing properties of
tasks. We present a Simulink block that can schedule tasks with non-zero
simulation times. It enables a more realistic analysis during model
development.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 02:02:14 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Chen",
"Jian",
""
],
[
"Alalfi",
"Manar H.",
""
],
[
"Dean",
"Thomas R.",
""
],
[
"S",
"Ramesh",
""
]
] |
new_dataset
| 0.963932 |
2308.14994
|
Manuel Luis Delos Santos
|
Manuel Luis C. Delos Santos (1), Jerum B. Dasalla (2), Jomar C.
Feliciano (3), Dustin Red B. Cabatay (4), ((1)(3)(4) Asian Institute of
Computer Studies, Philippines, (2) Philippine State College of Aeronautics)
|
ICARUS: An Android-Based Unmanned Aerial Vehicle (UAV) Search and Rescue
Eye in the Sky
|
15 pages, 14 figures, Special Issue: IRCCETE 2023
|
International Journal of Computing Sciences Research (IJCSR),
Volume 7, pp. 2272-2286, July 14, 2023
|
10.25147/ijcsr.2017.001.1.159
|
ISSN print: 2546-0552; ISSN online: 2546-115X
|
cs.CY cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The purpose of this paper is to develop an unmanned aerial vehicle (UAV)
using a quadcopter with the capability of video surveillance, map coordinates,
a deployable parachute with a medicine kit or a food pack as a payload, a
collision warning system, remotely controlled, integrated with an android
application to assist in search and rescue operations.
Applied research for the development of the functional prototype,
quantitative and descriptive statistics to summarize data by describing the
relationship between variables in a sample or population. The quadcopter
underwent an evaluation using a survey instrument to test its acceptability
using predefined variables to select respondents within Caloocan City and
Quezon City, Philippines.
Demographic profiles and known issues and concerns were answered by 30
respondents. The results were summarized and distributed in Tables 1 and 2.
In terms of demographic profiles, the number of SAR operators within the
specified areas is distributed equally, most are male, single, and within the
age bracket of 31 and above. In issues and concerns, the most common type of
search and rescue was ground search and rescue. Human error is the primary
cause of most injuries in operating units. The prototype was useful and
everyone agreed, in terms of acceptability, drone technology will improve
search and rescue operations.
The innovative way of utilizing Android and drone technology is a new step
towards the improvement of SAR operations in the Philippines.
The LiPo battery must be replaced with a higher capacity and the drone
operator should undergo a training course and secure a permit from the Civil
Aviation Authority of the Philippines (CAAP).
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 02:49:16 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Santos",
"Manuel Luis C. Delos",
""
],
[
"Dasalla",
"Jerum B.",
""
],
[
"Feliciano",
"Jomar C.",
""
],
[
"Cabatay",
"Dustin Red B.",
""
]
] |
new_dataset
| 0.999339 |
2308.15040
|
Yung-Chin Chen
|
Yung-Chin Chen, Shimpei Ando, Daichi Fujiki, Shinya
Takamaeda-Yamazaki, Kentaro Yoshioka
|
OSA-HCIM: On-The-Fly Saliency-Aware Hybrid SRAM CIM with Dynamic
Precision Configuration
| null | null | null | null |
cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Computing-in-Memory (CIM) has shown great potential for enhancing efficiency
and performance for deep neural networks (DNNs). However, the lack of
flexibility in CIM leads to an unnecessary expenditure of computational
resources on less critical operations, and a diminished Signal-to-Noise Ratio
(SNR) when handling more complex tasks, significantly hindering the overall
performance. Hence, we focus on the integration of CIM with Saliency-Aware
Computing -- a paradigm that dynamically tailors computing precision based on
the importance of each input. We propose On-the-fly Saliency-Aware Hybrid CIM
(OSA-HCIM) offering three primary contributions: (1) On-the-fly Saliency-Aware
(OSA) precision configuration scheme, which dynamically sets the precision of
each MAC operation based on its saliency, (2) Hybrid CIM Array (HCIMA), which
enables simultaneous operation of digital-domain CIM (DCIM) and analog-domain
CIM (ACIM) via split-port 6T SRAM, and (3) an integrated framework combining
OSA and HCIMA to fulfill diverse accuracy and power demands.
Implemented on a 65nm CMOS process, OSA-HCIM demonstrates an exceptional
balance between accuracy and resource utilization. Notably, it is the first CIM
design to incorporate a dynamic digital-to-analog boundary, providing
unprecedented flexibility for saliency-aware computing. OSA-HCIM achieves a
1.95x enhancement in energy efficiency, while maintaining minimal accuracy loss
compared to DCIM when tested on CIFAR100 dataset.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 05:49:11 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Chen",
"Yung-Chin",
""
],
[
"Ando",
"Shimpei",
""
],
[
"Fujiki",
"Daichi",
""
],
[
"Takamaeda-Yamazaki",
"Shinya",
""
],
[
"Yoshioka",
"Kentaro",
""
]
] |
new_dataset
| 0.995876 |
2308.15050
|
Taotao Jing
|
Taotao Jing, Lichen Wang, Naji Khosravan, Zhiqiang Wan, Zachary
Bessinger, Zhengming Ding, Sing Bing Kang
|
iBARLE: imBalance-Aware Room Layout Estimation
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Room layout estimation predicts layouts from a single panorama. It requires
datasets with large-scale and diverse room shapes to train the models. However,
there are significant imbalances in real-world datasets including the
dimensions of layout complexity, camera locations, and variation in scene
appearance. These issues considerably influence the model training performance.
In this work, we propose the imBalance-Aware Room Layout Estimation (iBARLE)
framework to address these issues. iBARLE consists of (1) Appearance Variation
Generation (AVG) module, which promotes visual appearance domain
generalization, (2) Complex Structure Mix-up (CSMix) module, which enhances
generalizability w.r.t. room structure, and (3) a gradient-based layout
objective function, which allows more effective accounting for occlusions in
complex layouts. All modules are jointly trained and help each other to achieve
the best performance. Experiments and ablation studies based on
ZInD~\cite{cruz2021zillow} dataset illustrate that iBARLE has state-of-the-art
performance compared with other layout estimation baselines.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 06:20:36 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Jing",
"Taotao",
""
],
[
"Wang",
"Lichen",
""
],
[
"Khosravan",
"Naji",
""
],
[
"Wan",
"Zhiqiang",
""
],
[
"Bessinger",
"Zachary",
""
],
[
"Ding",
"Zhengming",
""
],
[
"Kang",
"Sing Bing",
""
]
] |
new_dataset
| 0.991214 |
2308.15061
|
Yukun Su
|
Yukun Su, Yi Yang
|
AIoT-Based Drum Transcription Robot using Convolutional Neural Networks
| null | null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the development of information technology, robot technology has made
great progress in various fields. These new technologies enable robots to be
used in industry, agriculture, education and other aspects. In this paper, we
propose a drum robot that can automatically complete music transcription in
real-time, which is based on AIoT and fog computing technology. Specifically,
this drum robot system consists of a cloud node for data storage, edge nodes
for real-time computing, and data-oriented execution application nodes. In
order to analyze drumming music and realize drum transcription, we further
propose a light-weight convolutional neural network model to classify drums,
which can be more effectively deployed in terminal devices for fast edge
calculations. The experimental results show that the proposed system can
achieve more competitive performance and enjoy a variety of smart applications
and services.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 06:50:04 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Su",
"Yukun",
""
],
[
"Yang",
"Yi",
""
]
] |
new_dataset
| 0.998876 |
2308.15069
|
Haksoo Lim
|
Haksoo Lim, Sewon Park, Minjung Kim, Jaehoon Lee, Seonkyu Lim, Noseong
Park
|
MadSGM: Multivariate Anomaly Detection with Score-based Generative
Models
| null | null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The time-series anomaly detection is one of the most fundamental tasks for
time-series. Unlike the time-series forecasting and classification, the
time-series anomaly detection typically requires unsupervised (or
self-supervised) training since collecting and labeling anomalous observations
are difficult. In addition, most existing methods resort to limited forms of
anomaly measurements and therefore, it is not clear whether they are optimal in
all circumstances. To this end, we present a multivariate time-series anomaly
detector based on score-based generative models, called MadSGM, which considers
the broadest ever set of anomaly measurement factors: i) reconstruction-based,
ii) density-based, and iii) gradient-based anomaly measurements. We also design
a conditional score network and its denoising score matching loss for the
time-series anomaly detection. Experiments on five real-world benchmark
datasets illustrate that MadSGM achieves the most robust and accurate
predictions.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 07:04:50 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Lim",
"Haksoo",
""
],
[
"Park",
"Sewon",
""
],
[
"Kim",
"Minjung",
""
],
[
"Lee",
"Jaehoon",
""
],
[
"Lim",
"Seonkyu",
""
],
[
"Park",
"Noseong",
""
]
] |
new_dataset
| 0.974699 |
2308.15075
|
Angel Martin
|
Felipe Mogoll\'on, Zaloa Fern\'andez, Josu P\'erez and \'Angel
Mart\'in
|
Benchmarking 5G MEC and Cloud infrastructures for planning IoT messaging
of CCAM data
|
6 pages, 5 figures, 6 tables, IEEE International Conference on
Intelligent Transportation Systems
| null | null | null |
cs.NI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Vehicles embed lots of sensors supporting driving and safety. Combined with
connectivity, they bring new possibilities for Connected, Cooperative and
Automated Mobility (CCAM) services that exploit local and global data for a
wide understanding beyond the myopic view of local sensors. Internet of Things
(IoT) messaging solutions are ideal for vehicular data as they ship core
features like the separation of geographic areas, the fusion of different
producers on data/sensor types, and concurrent subscription support.
Multi-access Edge Computing (MEC) and Cloud infrastructures are key to hosting
a virtualized and distributed IoT platform. Currently, the are no benchmarks
for assessing the appropriate size of an IoT platform for multiple vehicular
data types such as text, image, binary point clouds and video-formatted
samples. This paper formulates and executes the tests to get a benchmarking of
the performance of a MEC and Cloud platform according to actors' concurrency,
data volumes and business levels parameters.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 07:19:38 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Mogollón",
"Felipe",
""
],
[
"Fernández",
"Zaloa",
""
],
[
"Pérez",
"Josu",
""
],
[
"Martín",
"Ángel",
""
]
] |
new_dataset
| 0.996504 |
2308.15104
|
Johanna Ansohn McDougall
|
Johanna Ansohn McDougall, Alessandro Brighente, Willi Gro{\ss}mann,
Ben Ansohn McDougall, Joshua Stock, Hannes Federrath
|
LoVe is in the Air -- Location Verification of ADS-B Signals using
Distributed Public Sensors
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The Automatic Dependant Surveillance-Broadcast (ADS-B) message scheme was
designed without any authentication or encryption of messages in place. It is
therefore easily possible to attack it, e.g., by injecting spoofed messages or
modifying the transmitted Global Navigation Satellite System (GNSS)
coordinates. In order to verify the integrity of the received information,
various methods have been suggested, such as multilateration, the use of Kalman
filters, group certification, and many others. However, solutions based on
modifications of the standard may be difficult and too slow to be implemented
due to legal and regulatory issues. A vantage far less explored is the location
verification using public sensor data. In this paper, we propose LoVe, a
lightweight message verification approach that uses a geospatial indexing
scheme to evaluate the trustworthiness of publicly deployed sensors and the
ADS-B messages they receive. With LoVe, new messages can be evaluated with
respect to the plausibility of their reported coordinates in a location
privacy-preserving manner, while using a data-driven and lightweight approach.
By testing our approach on two open datasets, we show that LoVe achieves very
low false positive rates (between 0 and 0.00106) and very low false negative
rates (between 0.00065 and 0.00334) while providing a real-time compatible
approach that scales well even with a large sensor set. Compared to currently
existing approaches, LoVe neither requires a large number of sensors, nor for
messages to be recorded by as many sensors as possible simultaneously in order
to verify location claims. Furthermore, it can be directly applied to currently
deployed systems thus being backward compatible.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 08:13:08 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"McDougall",
"Johanna Ansohn",
""
],
[
"Brighente",
"Alessandro",
""
],
[
"Großmann",
"Willi",
""
],
[
"McDougall",
"Ben Ansohn",
""
],
[
"Stock",
"Joshua",
""
],
[
"Federrath",
"Hannes",
""
]
] |
new_dataset
| 0.998046 |
2308.15136
|
Hiroyuki Ootomo
|
Hiroyuki Ootomo, Akira Naruse, Corey Nolet, Ray Wang, Tamas Feher,
Yong Wang
|
CAGRA: Highly Parallel Graph Construction and Approximate Nearest
Neighbor Search for GPUs
| null | null | null | null |
cs.DS cs.CV cs.DB cs.DC cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Approximate Nearest Neighbor Search (ANNS) plays a critical role in various
disciplines spanning data mining and artificial intelligence, from information
retrieval and computer vision to natural language processing and recommender
systems. Data volumes have soared in recent years and the computational cost of
an exhaustive exact nearest neighbor search is often prohibitive, necessitating
the adoption of approximate techniques. The balanced performance and recall of
graph-based approaches have more recently garnered significant attention in
ANNS algorithms, however, only a few studies have explored harnessing the power
of GPUs and multi-core processors despite the widespread use of massively
parallel and general-purpose computing. To bridge this gap, we introduce a
novel parallel computing hardware-based proximity graph and search algorithm.
By leveraging the high-performance capabilities of modern hardware, our
approach achieves remarkable efficiency gains. In particular, our method
surpasses existing CPU and GPU-based methods in constructing the proximity
graph, demonstrating higher throughput in both large- and small-batch searches
while maintaining compatible accuracy. In graph construction time, our method,
CAGRA, is 2.2~27x faster than HNSW, which is one of the CPU SOTA
implementations. In large-batch query throughput in the 90% to 95% recall
range, our method is 33~77x faster than HNSW, and is 3.8~8.8x faster than the
SOTA implementations for GPU. For a single query, our method is 3.4~53x faster
than HNSW at 95% recall.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 09:10:53 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Ootomo",
"Hiroyuki",
""
],
[
"Naruse",
"Akira",
""
],
[
"Nolet",
"Corey",
""
],
[
"Wang",
"Ray",
""
],
[
"Feher",
"Tamas",
""
],
[
"Wang",
"Yong",
""
]
] |
new_dataset
| 0.959113 |
2308.15139
|
Goshgar Ismayilov
|
Goshgar Ismayilov, Can Ozturan
|
PTTS: Zero-Knowledge Proof-based Private Token Transfer System on
Ethereum Blockchain and its Network Flow Based Balance Range Privacy Attack
Analysis
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Blockchains are decentralized and immutable databases that are shared among
the nodes of the network. Although blockchains have attracted a great scale of
attention in the recent years by disrupting the traditional financial systems,
the transaction privacy is still a challenging issue that needs to be addressed
and analysed. We propose a Private Token Transfer System (PTTS) for the
Ethereum public blockchain in the first part of this paper. For the proposed
framework, zero-knowledge based protocol has been designed using Zokrates and
integrated into our private token smart contract. With the help of web user
interface designed, the end users can interact with the smart contract without
any third-party setup. In the second part of the paper, we provide security and
privacy analysis including the replay attack and the balance range privacy
attack which has been modelled as a network flow problem. It is shown that in
case some balance ranges are deliberately leaked out to particular
organizations or adversial entities, it is possible to extract meaningful
information about the user balances by employing minimum cost flow network
algorithms that have polynomial complexity. The experimental study reports the
Ethereum gas consumption and proof generation times for the proposed framework.
It also reports network solution times and goodness rates for a subset of
addresses under the balance range privacy attack with respect to number of
addresses, number of transactions and ratio of leaked transfer transaction
amounts.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 09:13:31 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Ismayilov",
"Goshgar",
""
],
[
"Ozturan",
"Can",
""
]
] |
new_dataset
| 0.99801 |
2308.15142
|
Shuxiao Ma
|
Shuxiao Ma and Linyuan Wang and Bin Yan
|
A Multimodal Visual Encoding Model Aided by Introducing Verbal Semantic
Information
| null | null | null | null |
cs.CV cs.AI q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
Biological research has revealed that the verbal semantic information in the
brain cortex, as an additional source, participates in nonverbal semantic
tasks, such as visual encoding. However, previous visual encoding models did
not incorporate verbal semantic information, contradicting this biological
finding. This paper proposes a multimodal visual information encoding network
model based on stimulus images and associated textual information in response
to this issue. Our visual information encoding network model takes stimulus
images as input and leverages textual information generated by a text-image
generation model as verbal semantic information. This approach injects new
information into the visual encoding model. Subsequently, a Transformer network
aligns image and text feature information, creating a multimodal feature space.
A convolutional network then maps from this multimodal feature space to voxel
space, constructing the multimodal visual information encoding network model.
Experimental results demonstrate that the proposed multimodal visual
information encoding network model outperforms previous models under the exact
training cost. In voxel prediction of the left hemisphere of subject 1's brain,
the performance improves by approximately 15.87%, while in the right
hemisphere, the performance improves by about 4.6%. The multimodal visual
encoding network model exhibits superior encoding performance. Additionally,
ablation experiments indicate that our proposed model better simulates the
brain's visual information processing.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 09:21:48 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Ma",
"Shuxiao",
""
],
[
"Wang",
"Linyuan",
""
],
[
"Yan",
"Bin",
""
]
] |
new_dataset
| 0.975579 |
2308.15154
|
Margherita Gambini
|
Margherita Gambini, Serena Tardelli, Maurizio Tesconi
|
The Anatomy of Conspirators: Unveiling Traits using a Comprehensive
Twitter Dataset
| null | null | null | null |
cs.SI cs.CL
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The discourse around conspiracy theories is currently thriving amidst the
rampant misinformation prevalent in online environments. Research in this field
has been focused on detecting conspiracy theories on social media, often
relying on limited datasets. In this study, we present a novel methodology for
constructing a Twitter dataset that encompasses accounts engaged in
conspiracy-related activities throughout the year 2022. Our approach centers on
data collection that is independent of specific conspiracy theories and
information operations. Additionally, our dataset includes a control group
comprising randomly selected users who can be fairly compared to the
individuals involved in conspiracy activities. This comprehensive collection
effort yielded a total of 15K accounts and 37M tweets extracted from their
timelines. We conduct a comparative analysis of the two groups across three
dimensions: topics, profiles, and behavioral characteristics. The results
indicate that conspiracy and control users exhibit similarity in terms of their
profile metadata characteristics. However, they diverge significantly in terms
of behavior and activity, particularly regarding the discussed topics, the
terminology used, and their stance on trending subjects. Interestingly, there
is no significant disparity in the presence of bot users between the two
groups, suggesting that conspiracy and automation are orthogonal concepts.
Finally, we develop a classifier to identify conspiracy users using 93
features, some of which are commonly employed in literature for troll
identification. The results demonstrate a high accuracy level (with an average
F1 score of 0.98%), enabling us to uncover the most discriminative features
associated with conspiracy-related accounts.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 09:35:23 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Gambini",
"Margherita",
""
],
[
"Tardelli",
"Serena",
""
],
[
"Tesconi",
"Maurizio",
""
]
] |
new_dataset
| 0.999286 |
2308.15161
|
Daniela P\"ohn
|
Lukas Hafner and Florian Wutz and Daniela P\"ohn and Wolfgang Hommel
|
TASEP: A Collaborative Social Engineering Tabletop Role-Playing Game to
Prevent Successful Social Engineering Attacks
| null | null |
10.1145/3600160.3605005
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data breaches resulting from targeted attacks against organizations, e.g., by
advanced persistent threat groups, often involve social engineering (SE) as the
initial attack vector before malicious software is used, e.g., for persistence,
lateral movement, and data exfiltration. While technical security controls,
such as the automated detection of phishing emails, can contribute to
mitigating SE risks, raising awareness for SE attacks through education and
motivation of personnel is an important building block to increasing an
organization's resilience. To facilitate hands-on SE awareness training as one
component of broader SE awareness campaigns, we created a SE tabletop game
called Tabletop As Social Engineering Prevention (TASEP) in two editions for
(a) small and medium enterprises and (b) large corporations, respectively. Its
game design is inspired by Dungeons & Dragons role-playing games and
facilitates LEGO models of the in-game target organizations. Participants
switch roles by playing a group of SE penetration testers and conducting a
security audit guided by the game master. We evaluated the created game with
different student groups, achieving highly immersive and flexible training,
resulting in an entertaining way of learning about SE and raising awareness.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 09:44:35 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Hafner",
"Lukas",
""
],
[
"Wutz",
"Florian",
""
],
[
"Pöhn",
"Daniela",
""
],
[
"Hommel",
"Wolfgang",
""
]
] |
new_dataset
| 0.998964 |
2308.15224
|
Tae Soo Kim
|
Tae Soo Kim, Matt Latzke, Jonathan Bragg, Amy X. Zhang, Joseph Chee
Chang
|
Papeos: Augmenting Research Papers with Talk Videos
|
Accepted to UIST 2023
| null |
10.1145/3586183.3606770
| null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Research consumption has been traditionally limited to the reading of
academic papers-a static, dense, and formally written format. Alternatively,
pre-recorded conference presentation videos, which are more dynamic, concise,
and colloquial, have recently become more widely available but potentially
under-utilized. In this work, we explore the design space and benefits for
combining academic papers and talk videos to leverage their complementary
nature to provide a rich and fluid research consumption experience. Based on
formative and co-design studies, we present Papeos, a novel reading and
authoring interface that allow authors to augment their papers by segmenting
and localizing talk videos alongside relevant paper passages with automatically
generated suggestions. With Papeos, readers can visually skim a paper through
clip thumbnails, and fluidly switch between consuming dense text in the paper
or visual summaries in the video. In a comparative lab study (n=16), Papeos
reduced mental load, scaffolded navigation, and facilitated more comprehensive
reading of papers.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 11:25:30 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Kim",
"Tae Soo",
""
],
[
"Latzke",
"Matt",
""
],
[
"Bragg",
"Jonathan",
""
],
[
"Zhang",
"Amy X.",
""
],
[
"Chang",
"Joseph Chee",
""
]
] |
new_dataset
| 0.999398 |
2308.15349
|
Vakhtang Putkaradze Dr.
|
Christopher Eldred, Fran\c{c}ois Gay-Balmaz, Sofiia Huraka, Vakhtang
Putkaradze
|
Lie-Poisson Neural Networks (LPNets): Data-Based Computing of
Hamiltonian Systems with Symmetries
|
57 pages, 13 figures
| null | null | null |
cs.LG math-ph math.MP
|
http://creativecommons.org/licenses/by/4.0/
|
An accurate data-based prediction of the long-term evolution of Hamiltonian
systems requires a network that preserves the appropriate structure under each
time step. Every Hamiltonian system contains two essential ingredients: the
Poisson bracket and the Hamiltonian. Hamiltonian systems with symmetries, whose
paradigm examples are the Lie-Poisson systems, have been shown to describe a
broad category of physical phenomena, from satellite motion to underwater
vehicles, fluids, geophysical applications, complex fluids, and plasma physics.
The Poisson bracket in these systems comes from the symmetries, while the
Hamiltonian comes from the underlying physics. We view the symmetry of the
system as primary, hence the Lie-Poisson bracket is known exactly, whereas the
Hamiltonian is regarded as coming from physics and is considered not known, or
known approximately. Using this approach, we develop a network based on
transformations that exactly preserve the Poisson bracket and the special
functions of the Lie-Poisson systems (Casimirs) to machine precision. We
present two flavors of such systems: one, where the parameters of
transformations are computed from data using a dense neural network (LPNets),
and another, where the composition of transformations is used as building
blocks (G-LPNets). We also show how to adapt these methods to a larger class of
Poisson brackets. We apply the resulting methods to several examples, such as
rigid body (satellite) motion, underwater vehicles, a particle in a magnetic
field, and others. The methods developed in this paper are important for the
construction of accurate data-based methods for simulating the long-term
dynamics of physical systems.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 14:45:23 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Eldred",
"Christopher",
""
],
[
"Gay-Balmaz",
"François",
""
],
[
"Huraka",
"Sofiia",
""
],
[
"Putkaradze",
"Vakhtang",
""
]
] |
new_dataset
| 0.954916 |
2308.15402
|
Mohammad Akhlaqur Rahman
|
Shahriar Elahi Dhruvo, Mohammad Akhlaqur Rahman, Manash Kumar Mandal,
Md. Istiak Hossain Shihab, A. A. Noman Ansary, Kaneez Fatema Shithi, Sanjida
Khanom, Rabeya Akter, Safaeid Hossain Arib, M.N. Ansary, Sazia Mehnaz,
Rezwana Sultana, Sejuti Rahman, Sayma Sultana Chowdhury, Sabbir Ahmed
Chowdhury, Farig Sadeque, Asif Sushmit
|
Bornil: An open-source sign language data crowdsourcing platform for AI
enabled dialect-agnostic communication
|
6 pages, 7 figures
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The absence of annotated sign language datasets has hindered the development
of sign language recognition and translation technologies. In this paper, we
introduce Bornil; a crowdsource-friendly, multilingual sign language data
collection, annotation, and validation platform. Bornil allows users to record
sign language gestures and lets annotators perform sentence and gloss-level
annotation. It also allows validators to make sure of the quality of both the
recorded videos and the annotations through manual validation to develop
high-quality datasets for deep learning-based Automatic Sign Language
Recognition. To demonstrate the system's efficacy; we collected the largest
sign language dataset for Bangladeshi Sign Language dialect, perform deep
learning based Sign Language Recognition modeling, and report the benchmark
performance. The Bornil platform, BornilDB v1.0 Dataset, and the codebases are
available on https://bornil.bengali.ai
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 16:00:06 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Dhruvo",
"Shahriar Elahi",
""
],
[
"Rahman",
"Mohammad Akhlaqur",
""
],
[
"Mandal",
"Manash Kumar",
""
],
[
"Shihab",
"Md. Istiak Hossain",
""
],
[
"Ansary",
"A. A. Noman",
""
],
[
"Shithi",
"Kaneez Fatema",
""
],
[
"Khanom",
"Sanjida",
""
],
[
"Akter",
"Rabeya",
""
],
[
"Arib",
"Safaeid Hossain",
""
],
[
"Ansary",
"M. N.",
""
],
[
"Mehnaz",
"Sazia",
""
],
[
"Sultana",
"Rezwana",
""
],
[
"Rahman",
"Sejuti",
""
],
[
"Chowdhury",
"Sayma Sultana",
""
],
[
"Chowdhury",
"Sabbir Ahmed",
""
],
[
"Sadeque",
"Farig",
""
],
[
"Sushmit",
"Asif",
""
]
] |
new_dataset
| 0.999291 |
2308.15403
|
Peter Manohar
|
Omar Alrabiah, Venkatesan Guruswami, Pravesh K. Kothari, Peter Manohar
|
A Near-Cubic Lower Bound for 3-Query Locally Decodable Codes from
Semirandom CSP Refutation
| null | null | null | null |
cs.CC cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A code $C \colon \{0,1\}^k \to \{0,1\}^n$ is a $q$-locally decodable code
($q$-LDC) if one can recover any chosen bit $b_i$ of the message $b \in
\{0,1\}^k$ with good confidence by randomly querying the encoding $x := C(b)$
on at most $q$ coordinates. Existing constructions of $2$-LDCs achieve $n =
\exp(O(k))$, and lower bounds show that this is in fact tight. However, when $q
= 3$, far less is known: the best constructions achieve $n = \exp(k^{o(1)})$,
while the best known results only show a quadratic lower bound $n \geq
\tilde{\Omega}(k^2)$ on the blocklength.
In this paper, we prove a near-cubic lower bound of $n \geq
\tilde{\Omega}(k^3)$ on the blocklength of $3$-query LDCs. This improves on the
best known prior works by a polynomial factor in $k$. Our proof relies on a new
connection between LDCs and refuting constraint satisfaction problems with
limited randomness. Our quantitative improvement builds on the new techniques
for refuting semirandom instances of CSPs developed in [GKM22, HKM23] and, in
particular, relies on bounding the spectral norm of appropriate Kikuchi
matrices.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 16:00:57 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Alrabiah",
"Omar",
""
],
[
"Guruswami",
"Venkatesan",
""
],
[
"Kothari",
"Pravesh K.",
""
],
[
"Manohar",
"Peter",
""
]
] |
new_dataset
| 0.95595 |
2308.15429
|
Andrew McNutt
|
Elsie Lee-Robbins, Andrew McNutt
|
Only YOU Can Make IEEE VIS Environmentally Sustainable
|
Accepted to alt.vis2023
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
The IEEE VIS Conference (or VIS) hosts more than 1000 people annually. It
brings together visualization researchers and practitioners from across the
world to share new research and knowledge. Behind the scenes, a team of
volunteers puts together the entire conference and makes sure it runs smoothly.
Organizing involves logistics of the conference, ensuring that the attendees
have an enjoyable time, allocating rooms to multiple concurrent tracks, and
keeping the conference within budget. In recent years, the COVID-19 pandemic
has abruptly disrupted plans, forcing organizers to switch to virtual, hybrid,
and satellite formats. These alternatives offer many benefits: fewer costs
(e.g., travel, venue, institutional), greater accessibility (who can physically
travel, who can get visas, who can get child care), and a lower carbon
footprint (as people do not need to fly to attend). As many conferences begin
to revert to the pre-pandemic status quo of primarily in-person conferences, we
suggest that it is an opportune moment to reflect on the benefits and drawbacks
of lower-carbon conference formats. To learn more about the logistics of
conference organizing, we talked to 6 senior executive-level VIS organizers. We
review some of the many considerations that go into planning, particularly with
regard to how they influence decisions about alternative formats. We aim to
start a discussion about the sustainability of VIS -- including sustainability
for finance, volunteers, and, central to this work, the environment -- for the
next three years and the next three hundred years.
|
[
{
"version": "v1",
"created": "Tue, 29 Aug 2023 16:43:43 GMT"
}
] | 2023-08-30T00:00:00 |
[
[
"Lee-Robbins",
"Elsie",
""
],
[
"McNutt",
"Andrew",
""
]
] |
new_dataset
| 0.989381 |
2111.11011
|
Tianlun Zheng
|
Tianlun Zheng, Zhineng Chen, Shancheng Fang, Hongtao Xie, Yu-Gang
Jiang
|
CDistNet: Perceiving Multi-Domain Character Distance for Robust Text
Recognition
|
Paper accepted for publication at IJCV 2023
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Transformer-based encoder-decoder framework is becoming popular in scene
text recognition, largely because it naturally integrates recognition clues
from both visual and semantic domains. However, recent studies show that the
two kinds of clues are not always well registered and therefore, feature and
character might be misaligned in difficult text (e.g., with a rare shape). As a
result, constraints such as character position are introduced to alleviate this
problem. Despite certain success, visual and semantic are still separately
modeled and they are merely loosely associated. In this paper, we propose a
novel module called Multi-Domain Character Distance Perception (MDCDP) to
establish a visually and semantically related position embedding. MDCDP uses
the position embedding to query both visual and semantic features following the
cross-attention mechanism. The two kinds of clues are fused into the position
branch, generating a content-aware embedding that well perceives character
spacing and orientation variants, character semantic affinities, and clues
tying the two kinds of information. They are summarized as the multi-domain
character distance. We develop CDistNet that stacks multiple MDCDPs to guide a
gradually precise distance modeling. Thus, the feature-character alignment is
well built even various recognition difficulties are presented. We verify
CDistNet on ten challenging public datasets and two series of augmented
datasets created by ourselves. The experiments demonstrate that CDistNet
performs highly competitively. It not only ranks top-tier in standard
benchmarks, but also outperforms recent popular methods by obvious margins on
real and augmented datasets presenting severe text deformation, poor linguistic
support, and rare character layouts. Code is available at
https://github.com/simplify23/CDistNet.
|
[
{
"version": "v1",
"created": "Mon, 22 Nov 2021 06:27:29 GMT"
},
{
"version": "v2",
"created": "Thu, 25 Nov 2021 02:46:11 GMT"
},
{
"version": "v3",
"created": "Wed, 22 Jun 2022 00:21:12 GMT"
},
{
"version": "v4",
"created": "Fri, 11 Aug 2023 03:17:54 GMT"
},
{
"version": "v5",
"created": "Sun, 27 Aug 2023 02:55:53 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Zheng",
"Tianlun",
""
],
[
"Chen",
"Zhineng",
""
],
[
"Fang",
"Shancheng",
""
],
[
"Xie",
"Hongtao",
""
],
[
"Jiang",
"Yu-Gang",
""
]
] |
new_dataset
| 0.997374 |
2201.06096
|
Jeremy Kepner
|
Jeremy Kepner, Kenjiro Cho, KC Claffy, Vijay Gadepally, Sarah McGuire,
Lauren Milechin, William Arcand, David Bestor, William Bergeron, Chansup
Byun, Matthew Hubbell, Michael Houle, Michael Jones, Andrew Prout, Albert
Reuther, Antonio Rosa, Siddharth Samsi, Charles Yee, Peter Michaleas
|
New Phenomena in Large-Scale Internet Traffic
|
53 pages, 27 figures, 8 tables, 121 references. Portions of this work
originally appeared as arXiv:1904.04396v1 which has been split for
publication in the book "Massive Graph Analytics" (edited by David Bader)
| null |
10.1201/9781003033707
| null |
cs.NI cs.CY cs.DC cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Internet is transforming our society, necessitating a quantitative
understanding of Internet traffic. Our team collects and curates the largest
publicly available Internet traffic data sets. An analysis of 50 billion
packets using 10,000 processors in the MIT SuperCloud reveals a new phenomenon:
the importance of otherwise unseen leaf nodes and isolated links in Internet
traffic. Our analysis further shows that a two-parameter modified
Zipf-Mandelbrot distribution accurately describes a wide variety of
source/destination statistics on moving sample windows ranging from 100{,}000
to 100{,}000{,}000 packets over collections that span years and continents. The
measured model parameters distinguish different network streams, and the model
leaf parameter strongly correlates with the fraction of the traffic in
different underlying network topologies.
|
[
{
"version": "v1",
"created": "Sun, 16 Jan 2022 17:30:10 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Kepner",
"Jeremy",
""
],
[
"Cho",
"Kenjiro",
""
],
[
"Claffy",
"KC",
""
],
[
"Gadepally",
"Vijay",
""
],
[
"McGuire",
"Sarah",
""
],
[
"Milechin",
"Lauren",
""
],
[
"Arcand",
"William",
""
],
[
"Bestor",
"David",
""
],
[
"Bergeron",
"William",
""
],
[
"Byun",
"Chansup",
""
],
[
"Hubbell",
"Matthew",
""
],
[
"Houle",
"Michael",
""
],
[
"Jones",
"Michael",
""
],
[
"Prout",
"Andrew",
""
],
[
"Reuther",
"Albert",
""
],
[
"Rosa",
"Antonio",
""
],
[
"Samsi",
"Siddharth",
""
],
[
"Yee",
"Charles",
""
],
[
"Michaleas",
"Peter",
""
]
] |
new_dataset
| 0.990778 |
2202.13799
|
Donghwee Yoon
|
Junseok Oh, Donghwee Yoon and Injung Kim
|
One-shot Ultra-high-Resolution Generative Adversarial Network That
Synthesizes 16K Images On A Single GPU
|
36 pages, 26 figures
| null | null | null |
cs.CV cs.LG eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a one-shot ultra-high-resolution generative adversarial network
(OUR-GAN) framework that generates non-repetitive 16K (16, 384 x 8, 640) images
from a single training image and is trainable on a single consumer GPU. OUR-GAN
generates an initial image that is visually plausible and varied in shape at
low resolution, and then gradually increases the resolution by adding detail
through super-resolution. Since OUR-GAN learns from a real
ultra-high-resolution (UHR) image, it can synthesize large shapes with fine
details and long-range coherence, which is difficult to achieve with
conventional generative models that rely on the patch distribution learned from
relatively small images. OUR-GAN can synthesize high-quality 16K images with
12.5 GB of GPU memory and 4K images with only 4.29 GB as it synthesizes a UHR
image part by part through seamless subregion-wise super-resolution.
Additionally, OUR-GAN improves visual coherence while maintaining diversity by
applying vertical positional convolution. In experiments on the ST4K and RAISE
datasets, OUR-GAN exhibited improved fidelity, visual coherency, and diversity
compared with the baseline one-shot synthesis models. To the best of our
knowledge, OUR-GAN is the first one-shot image synthesizer that generates
non-repetitive UHR images on a single consumer GPU. The synthesized image
samples are presented at https://our-gan.github.io.
|
[
{
"version": "v1",
"created": "Mon, 28 Feb 2022 13:48:41 GMT"
},
{
"version": "v2",
"created": "Thu, 21 Apr 2022 08:04:10 GMT"
},
{
"version": "v3",
"created": "Mon, 28 Aug 2023 04:52:53 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Oh",
"Junseok",
""
],
[
"Yoon",
"Donghwee",
""
],
[
"Kim",
"Injung",
""
]
] |
new_dataset
| 0.96298 |
2205.10292
|
Tommaso Bianchi
|
Tommaso Bianchi, Surudhi Asokraj, Alessandro Brighente, Mauro Conti,
Radha Poovendran
|
QEVSEC: Quick Electric Vehicle SEcure Charging via Dynamic Wireless
Power Transfer
|
6 pages, conference
|
2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring),
Florence, Italy, 2023, pp. 1-6
|
10.1109/VTC2023-Spring57618.2023.10199651
| null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Dynamic Wireless Power Transfer (DWPT) can be used for on-demand recharging
of Electric Vehicles (EV) while driving. However, DWPT raises numerous security
and privacy concerns. Recently, researchers demonstrated that DWPT systems are
vulnerable to adversarial attacks. In an EV charging scenario, an attacker can
prevent the authorized customer from charging, obtain a free charge by billing
a victim user and track a target vehicle. State-of-the-art authentication
schemes relying on centralized solutions are either vulnerable to various
attacks or have high computational complexity, making them unsuitable for a
dynamic scenario. In this paper, we propose Quick Electric Vehicle SEcure
Charging (QEVSEC), a novel, secure, and efficient authentication protocol for
the dynamic charging of EVs. Our idea for QEVSEC originates from multiple
vulnerabilities we found in the state-of-the-art protocol that allows tracking
of user activity and is susceptible to replay attacks. Based on these
observations, the proposed protocol solves these issues and achieves lower
computational complexity by using only primitive cryptographic operations in a
very short message exchange. QEVSEC provides scalability and a reduced cost in
each iteration, thus lowering the impact on the power needed from the grid.
|
[
{
"version": "v1",
"created": "Fri, 20 May 2022 16:42:32 GMT"
},
{
"version": "v2",
"created": "Thu, 27 Apr 2023 10:20:25 GMT"
},
{
"version": "v3",
"created": "Mon, 28 Aug 2023 08:18:28 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Bianchi",
"Tommaso",
""
],
[
"Asokraj",
"Surudhi",
""
],
[
"Brighente",
"Alessandro",
""
],
[
"Conti",
"Mauro",
""
],
[
"Poovendran",
"Radha",
""
]
] |
new_dataset
| 0.999432 |
2206.04678
|
Xi Chen
|
Xi Chen, Yun Xiong, Siqi Wang, Haofen Wang, Tao Sheng, Yao Zhang, Yu
Ye
|
ReCo: A Dataset for Residential Community Layout Planning
|
9 pages, 8 figures
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Layout planning is centrally important in the field of architecture and urban
design. Among the various basic units carrying urban functions, residential
community plays a vital part for supporting human life. Therefore, the layout
planning of residential community has always been of concern, and has attracted
particular attention since the advent of deep learning that facilitates the
automated layout generation and spatial pattern recognition. However, the
research circles generally suffer from the insufficiency of residential
community layout benchmark or high-quality datasets, which hampers the future
exploration of data-driven methods for residential community layout planning.
The lack of datasets is largely due to the difficulties of large-scale
real-world residential data acquisition and long-term expert screening. In
order to address the issues and advance a benchmark dataset for various
intelligent spatial design and analysis applications in the development of
smart city, we introduce Residential Community Layout Planning (ReCo) Dataset,
which is the first and largest open-source vector dataset related to real-world
community to date. ReCo Dataset is presented in multiple data formats with
37,646 residential community layout plans, covering 598,728 residential
buildings with height information. ReCo can be conveniently adapted for
residential community layout related urban design tasks, e.g., generative
layout design, morphological pattern recognition and spatial evaluation. To
validate the utility of ReCo in automated residential community layout
planning, two Generative Adversarial Network (GAN) based generative models are
further applied to the dataset. We expect ReCo Dataset to inspire more creative
and practical work in intelligent design and beyond. The ReCo Dataset is
published at: https://www.kaggle.com/fdudsde/reco-dataset.
|
[
{
"version": "v1",
"created": "Wed, 8 Jun 2022 17:19:55 GMT"
},
{
"version": "v2",
"created": "Mon, 15 Aug 2022 07:20:56 GMT"
},
{
"version": "v3",
"created": "Sun, 27 Aug 2023 14:35:43 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Chen",
"Xi",
""
],
[
"Xiong",
"Yun",
""
],
[
"Wang",
"Siqi",
""
],
[
"Wang",
"Haofen",
""
],
[
"Sheng",
"Tao",
""
],
[
"Zhang",
"Yao",
""
],
[
"Ye",
"Yu",
""
]
] |
new_dataset
| 0.999852 |
2206.08955
|
Sergey A. Slavnov
|
Sergey Slavnov
|
Making first order linear logic a generating grammar
|
Revised and extended version with detailed proofs. arXiv admin note:
substantial text overlap with arXiv:2112.15253
| null | null | null |
cs.CL cs.LO math.LO
|
http://creativecommons.org/licenses/by/4.0/
|
It is known that different categorial grammars have surface representation in
a fragment of first order multiplicative linear logic (MLL1). We show that the
fragment of interest is equivalent to the recently introduced extended tensor
type calculus (ETTC). ETTC is a calculus of specific typed terms, which
represent tuples of strings, more precisely bipartite graphs decorated with
strings. Types are derived from linear logic formulas, and rules correspond to
concrete operations on these string-labeled graphs, so that they can be
conveniently visualized. This provides the above mentioned fragment of MLL1
that is relevant for language modeling not only with some alternative syntax
and intuitive geometric representation, but also with an intrinsic deductive
system, which has been absent.
In this work we consider a non-trivial notationally enriched variation of the
previously introduced {\bf ETTC}, which allows more concise and transparent
computations. We present both a cut-free sequent calculus and a natural
deduction formalism.
|
[
{
"version": "v1",
"created": "Fri, 17 Jun 2022 18:11:34 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Apr 2023 13:58:26 GMT"
},
{
"version": "v3",
"created": "Wed, 23 Aug 2023 05:34:42 GMT"
},
{
"version": "v4",
"created": "Mon, 28 Aug 2023 11:19:57 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Slavnov",
"Sergey",
""
]
] |
new_dataset
| 0.987393 |
2208.09702
|
Giovanni Viglietta
|
Csaba D. T\'oth, Jorge Urrutia, and Giovanni Viglietta
|
Minimizing Visible Edges in Polyhedra
|
19 pages, 9 figures
| null | null | null |
cs.CG cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
We prove that, given a polyhedron $\mathcal P$ in $\mathbb{R}^3$, every point
in $\mathbb R^3$ that does not see any vertex of $\mathcal P$ must see eight or
more edges of $\mathcal P$, and this bound is tight. More generally, this
remains true if $\mathcal P$ is any finite arrangement of internally disjoint
polygons in $\mathbb{R}^3$. We also prove that every point in $\mathbb{R}^3$
can see six or more edges of $\mathcal{P}$ (possibly only the endpoints of some
these edges) and every point in the interior of $\mathcal{P}$ can see a
positive portion of at least six edges of $\mathcal{P}$. These bounds are also
tight.
|
[
{
"version": "v1",
"created": "Sat, 20 Aug 2022 14:59:58 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Jun 2023 01:54:28 GMT"
},
{
"version": "v3",
"created": "Mon, 28 Aug 2023 12:54:41 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Tóth",
"Csaba D.",
""
],
[
"Urrutia",
"Jorge",
""
],
[
"Viglietta",
"Giovanni",
""
]
] |
new_dataset
| 0.984652 |
2210.08423
|
Ishan Rajendrakumar Dave
|
Tushar Sangam, Ishan Rajendrakumar Dave, Waqas Sultani, Mubarak Shah
|
TransVisDrone: Spatio-Temporal Transformer for Vision-based
Drone-to-Drone Detection in Aerial Videos
|
ICRA 2023
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Drone-to-drone detection using visual feed has crucial applications, such as
detecting drone collisions, detecting drone attacks, or coordinating flight
with other drones. However, existing methods are computationally costly, follow
non-end-to-end optimization, and have complex multi-stage pipelines, making
them less suitable for real-time deployment on edge devices. In this work, we
propose a simple yet effective framework, \textit{TransVisDrone}, that provides
an end-to-end solution with higher computational efficiency. We utilize
CSPDarkNet-53 network to learn object-related spatial features and VideoSwin
model to improve drone detection in challenging scenarios by learning
spatio-temporal dependencies of drone motion. Our method achieves
state-of-the-art performance on three challenging real-world datasets (Average
[email protected]): NPS 0.95, FLDrones 0.75, and AOT 0.80, and a higher
throughput than previous methods. We also demonstrate its deployment capability
on edge devices and its usefulness in detecting drone-collision (encounter).
Project: \url{https://tusharsangam.github.io/TransVisDrone-project-page/}.
|
[
{
"version": "v1",
"created": "Sun, 16 Oct 2022 03:05:13 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Aug 2023 00:54:05 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Sangam",
"Tushar",
""
],
[
"Dave",
"Ishan Rajendrakumar",
""
],
[
"Sultani",
"Waqas",
""
],
[
"Shah",
"Mubarak",
""
]
] |
new_dataset
| 0.998885 |
2210.17262
|
Wei Day
|
Wei Day, Hao-Sheng Chen, Min-Te Sun
|
QNet: A Quantum-native Sequence Encoder Architecture
|
QCE23: 2023 IEEE International Conference on Quantum Computing &
Engineering
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This work proposes QNet, a novel sequence encoder model that entirely
inferences on the quantum computer using a minimum number of qubits. Let $n$
and $d$ represent the length of the sequence and the embedding size,
respectively. The dot-product attention mechanism requires a time complexity of
$O(n^2 \cdot d)$, while QNet has merely $O(n+d)$ quantum circuit depth. In
addition, we introduce ResQNet, a quantum-classical hybrid model composed of
several QNet blocks linked by residual connections, as an isomorph Transformer
Encoder. We evaluated our work on various natural language processing tasks,
including text classification, rating score prediction, and named entity
recognition. Our models exhibit compelling performance over classical
state-of-the-art models with a thousand times fewer parameters. In summary,
this work investigates the advantage of machine learning on near-term quantum
computers in sequential data by experimenting with natural language processing
tasks.
|
[
{
"version": "v1",
"created": "Mon, 31 Oct 2022 12:36:37 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Aug 2023 01:17:32 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Day",
"Wei",
""
],
[
"Chen",
"Hao-Sheng",
""
],
[
"Sun",
"Min-Te",
""
]
] |
new_dataset
| 0.999387 |
2211.00945
|
Xinkuang Wang
|
Xinkuang Wang, Wenjing Li, Zhongcheng Wu
|
CarDD: A New Dataset for Vision-based Car Damage Detection
|
13 pages, 10 figures, full-length paper for Transactions on
Intelligent Transportation Systems (2023)
|
in IEEE Transactions on Intelligent Transportation Systems, vol.
24, no. 7, pp. 7202-7214, July 2023
|
10.1109/TITS.2023.3258480
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automatic car damage detection has attracted significant attention in the car
insurance business. However, due to the lack of high-quality and publicly
available datasets, we can hardly learn a feasible model for car damage
detection. To this end, we contribute with Car Damage Detection (CarDD), the
first public large-scale dataset designed for vision-based car damage detection
and segmentation. Our CarDD contains 4,000 highresolution car damage images
with over 9,000 well-annotated instances of six damage categories. We detail
the image collection, selection, and annotation processes, and present a
statistical dataset analysis. Furthermore, we conduct extensive experiments on
CarDD with state-of-the-art deep methods for different tasks and provide
comprehensive analyses to highlight the specialty of car damage detection.
CarDD dataset and the source code are available at
https://cardd-ustc.github.io.
|
[
{
"version": "v1",
"created": "Wed, 2 Nov 2022 08:09:03 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Aug 2023 11:36:06 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Wang",
"Xinkuang",
""
],
[
"Li",
"Wenjing",
""
],
[
"Wu",
"Zhongcheng",
""
]
] |
new_dataset
| 0.999536 |
2211.01146
|
Masakazu Yoshimura
|
Masakazu Yoshimura, Junji Otsuka, Atsushi Irie, Takeshi Ohashi
|
DynamicISP: Dynamically Controlled Image Signal Processor for Image
Recognition
|
Accepted to ICCV2023. Several updates from v2 including additional
experiments and modification of typos in Auto Gain equation
| null | null | null |
cs.CV cs.AI cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Image Signal Processors (ISPs) play important roles in image recognition
tasks as well as in the perceptual quality of captured images. In most cases,
experts make a lot of effort to manually tune many parameters of ISPs, but the
parameters are sub-optimal. In the literature, two types of techniques have
been actively studied: a machine learning-based parameter tuning technique and
a DNN-based ISP technique. The former is lightweight but lacks expressive
power. The latter has expressive power, but the computational cost is too heavy
on edge devices. To solve these problems, we propose "DynamicISP," which
consists of multiple classical ISP functions and dynamically controls the
parameters of each frame according to the recognition result of the previous
frame. We show our method successfully controls the parameters of multiple ISP
functions and achieves state-of-the-art accuracy with low computational cost in
single and multi-category object detection tasks.
|
[
{
"version": "v1",
"created": "Wed, 2 Nov 2022 14:22:50 GMT"
},
{
"version": "v2",
"created": "Mon, 27 Mar 2023 07:02:09 GMT"
},
{
"version": "v3",
"created": "Mon, 28 Aug 2023 02:59:24 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Yoshimura",
"Masakazu",
""
],
[
"Otsuka",
"Junji",
""
],
[
"Irie",
"Atsushi",
""
],
[
"Ohashi",
"Takeshi",
""
]
] |
new_dataset
| 0.998324 |
2211.07383
|
Mathias Ibsen
|
M. Ibsen, C. Rathgeb, F. Brechtel, R. Klepp, K. P\"oppelmann, A.
George, S. Marcel, C. Busch
|
Attacking Face Recognition with T-shirts: Database, Vulnerability
Assessment and Detection
| null | null |
10.1109/ACCESS.2023.3282780
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Face recognition systems are widely deployed for biometric authentication.
Despite this, it is well-known that, without any safeguards, face recognition
systems are highly vulnerable to presentation attacks. In response to this
security issue, several promising methods for detecting presentation attacks
have been proposed which show high performance on existing benchmarks. However,
an ongoing challenge is the generalization of presentation attack detection
methods to unseen and new attack types. To this end, we propose a new T-shirt
Face Presentation Attack (TFPA) database of 1,608 T-shirt attacks using 100
unique presentation attack instruments. In an extensive evaluation, we show
that this type of attack can compromise the security of face recognition
systems and that some state-of-the-art attack detection mechanisms trained on
popular benchmarks fail to robustly generalize to the new attacks. Further, we
propose three new methods for detecting T-shirt attack images, one which relies
on the statistical differences between depth maps of bona fide images and
T-shirt attacks, an anomaly detection approach trained on features only
extracted from bona fide RGB images, and a fusion approach which achieves
competitive detection performance.
|
[
{
"version": "v1",
"created": "Mon, 14 Nov 2022 14:11:23 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Ibsen",
"M.",
""
],
[
"Rathgeb",
"C.",
""
],
[
"Brechtel",
"F.",
""
],
[
"Klepp",
"R.",
""
],
[
"Pöppelmann",
"K.",
""
],
[
"George",
"A.",
""
],
[
"Marcel",
"S.",
""
],
[
"Busch",
"C.",
""
]
] |
new_dataset
| 0.995737 |
2211.11682
|
Xiangyang Zhu
|
Xiangyang Zhu, Renrui Zhang, Bowei He, Ziyu Guo, Ziyao Zeng, Zipeng
Qin, Shanghang Zhang, Peng Gao
|
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
|
Code is available at https://github.com/yangyangyang127/PointCLIP_V2
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large-scale pre-trained models have shown promising open-world performance
for both vision and language tasks. However, their transferred capacity on 3D
point clouds is still limited and only constrained to the classification task.
In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world
learner, named as PointCLIP V2, which fully unleashes their potential for
zero-shot 3D classification, segmentation, and detection. To better align 3D
data with the pre-trained language knowledge, PointCLIP V2 contains two key
designs. For the visual end, we prompt CLIP via a shape projection module to
generate more realistic depth maps, narrowing the domain gap between projected
point clouds with natural images. For the textual end, we prompt the GPT model
to generate 3D-specific text as the input of CLIP's textual encoder. Without
any training in 3D domains, our approach significantly surpasses PointCLIP by
+42.90%, +40.44%, and +28.75% accuracy on three datasets for zero-shot 3D
classification. On top of that, V2 can be extended to few-shot 3D
classification, zero-shot 3D part segmentation, and 3D object detection in a
simple manner, demonstrating our generalization ability for unified 3D
open-world learning.
|
[
{
"version": "v1",
"created": "Mon, 21 Nov 2022 17:52:43 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Aug 2023 16:14:09 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Zhu",
"Xiangyang",
""
],
[
"Zhang",
"Renrui",
""
],
[
"He",
"Bowei",
""
],
[
"Guo",
"Ziyu",
""
],
[
"Zeng",
"Ziyao",
""
],
[
"Qin",
"Zipeng",
""
],
[
"Zhang",
"Shanghang",
""
],
[
"Gao",
"Peng",
""
]
] |
new_dataset
| 0.999779 |
2212.02053
|
Zhang Yunhua
|
Yunhua Zhang and Hazel Doughty and Cees G. M. Snoek
|
Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight
|
Under review
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper strives to recognize activities in the dark, as well as in the
day. We first establish that state-of-the-art activity recognizers are
effective during the day, but not trustworthy in the dark. The main causes are
the limited availability of labeled dark videos to learn from, as well as the
distribution shift towards the lower color contrast at test-time. To compensate
for the lack of labeled dark videos, we introduce a pseudo-supervised learning
scheme, which utilizes easy to obtain unlabeled and task-irrelevant dark videos
to improve an activity recognizer in low light. As the lower color contrast
results in visual information loss, we further propose to incorporate the
complementary activity information within audio, which is invariant to
illumination. Since the usefulness of audio and visual features differs
depending on the amount of illumination, we introduce our `darkness-adaptive'
audio-visual recognizer. Experiments on EPIC-Kitchens, Kinetics-Sound, and
Charades demonstrate our proposals are superior to image enhancement, domain
adaptation and alternative audio-visual fusion methods, and can even improve
robustness to local darkness caused by occlusions. Project page:
https://xiaobai1217.github.io/Day2Dark/
|
[
{
"version": "v1",
"created": "Mon, 5 Dec 2022 06:14:23 GMT"
},
{
"version": "v2",
"created": "Fri, 23 Jun 2023 10:37:59 GMT"
},
{
"version": "v3",
"created": "Sun, 27 Aug 2023 19:41:53 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Zhang",
"Yunhua",
""
],
[
"Doughty",
"Hazel",
""
],
[
"Snoek",
"Cees G. M.",
""
]
] |
new_dataset
| 0.997956 |
2212.04636
|
Jiaman Li
|
Jiaman Li, C. Karen Liu, Jiajun Wu
|
Ego-Body Pose Estimation via Ego-Head Pose Estimation
|
CVPR 2023 (Award Candidate)
| null | null | null |
cs.CV cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Estimating 3D human motion from an egocentric video sequence plays a critical
role in human behavior understanding and has various applications in VR/AR.
However, naively learning a mapping between egocentric videos and human motions
is challenging, because the user's body is often unobserved by the front-facing
camera placed on the head of the user. In addition, collecting large-scale,
high-quality datasets with paired egocentric videos and 3D human motions
requires accurate motion capture devices, which often limit the variety of
scenes in the videos to lab-like environments. To eliminate the need for paired
egocentric video and human motions, we propose a new method, Ego-Body Pose
Estimation via Ego-Head Pose Estimation (EgoEgo), which decomposes the problem
into two stages, connected by the head motion as an intermediate
representation. EgoEgo first integrates SLAM and a learning approach to
estimate accurate head motion. Subsequently, leveraging the estimated head pose
as input, EgoEgo utilizes conditional diffusion to generate multiple plausible
full-body motions. This disentanglement of head and body pose eliminates the
need for training datasets with paired egocentric videos and 3D human motion,
enabling us to leverage large-scale egocentric video datasets and motion
capture datasets separately. Moreover, for systematic benchmarking, we develop
a synthetic dataset, AMASS-Replica-Ego-Syn (ARES), with paired egocentric
videos and human motion. On both ARES and real data, our EgoEgo model performs
significantly better than the current state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Fri, 9 Dec 2022 02:25:20 GMT"
},
{
"version": "v2",
"created": "Sun, 2 Apr 2023 18:13:15 GMT"
},
{
"version": "v3",
"created": "Mon, 28 Aug 2023 02:51:25 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Li",
"Jiaman",
""
],
[
"Liu",
"C. Karen",
""
],
[
"Wu",
"Jiajun",
""
]
] |
new_dataset
| 0.995987 |
2301.01917
|
Ziwei Sun
|
Ziwei Sun, Zexi Hua, Hengcao Li, Haiyan Zhong
|
Flying Bird Object Detection Algorithm in Surveillance Video Based on
Motion Information
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A Flying Bird Object Detection algorithm Based on Motion Information
(FBOD-BMI) is proposed to solve the problem that the features of the object are
not obvious in a single frame, and the size of the object is small (low
Signal-to-Noise Ratio (SNR)) in surveillance video. Firstly, a ConvLSTM-PAN
model structure is designed to capture suspicious flying bird objects, in which
the Convolutional Long and Short Time Memory (ConvLSTM) network aggregated the
Spatio-temporal features of the flying bird object on adjacent multi-frame
before the input of the model and the Path Aggregation Network (PAN) located
the suspicious flying bird objects. Then, an object tracking algorithm is used
to track suspicious flying bird objects and calculate their Motion Range (MR).
At the same time, the size of the MR of the suspicious flying bird object is
adjusted adaptively according to its speed of movement (specifically, if the
bird moves slowly, its MR will be expanded according to the speed of the bird
to ensure the environmental information needed to detect the flying bird
object). Adaptive Spatio-temporal Cubes (ASt-Cubes) of the flying bird objects
are generated to ensure that the SNR of the flying bird objects is improved,
and the necessary environmental information is retained adaptively. Finally, a
LightWeight U-Shape Net (LW-USN) based on ASt-Cubes is designed to detect
flying bird objects, which rejects the false detections of the suspicious
flying bird objects and returns the position of the real flying bird objects.
The monitoring video including the flying birds is collected in the unattended
traction substation as the experimental dataset to verify the performance of
the algorithm. The experimental results show that the flying bird object
detection method based on motion information proposed in this paper can
effectively detect the flying bird object in surveillance video.
|
[
{
"version": "v1",
"created": "Thu, 5 Jan 2023 05:32:22 GMT"
},
{
"version": "v2",
"created": "Tue, 31 Jan 2023 01:17:32 GMT"
},
{
"version": "v3",
"created": "Sat, 26 Aug 2023 13:49:36 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Sun",
"Ziwei",
""
],
[
"Hua",
"Zexi",
""
],
[
"Li",
"Hengcao",
""
],
[
"Zhong",
"Haiyan",
""
]
] |
new_dataset
| 0.996592 |
2303.09551
|
Yi Wei
|
Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, Jiwen Lu
|
SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving
|
Accepted to ICCV 2023. Code is available at
https://github.com/weiyithu/SurroundOcc
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
3D scene understanding plays a vital role in vision-based autonomous driving.
While most existing methods focus on 3D object detection, they have difficulty
describing real-world objects of arbitrary shapes and infinite classes. Towards
a more comprehensive perception of a 3D scene, in this paper, we propose a
SurroundOcc method to predict the 3D occupancy with multi-camera images. We
first extract multi-scale features for each image and adopt spatial 2D-3D
attention to lift them to the 3D volume space. Then we apply 3D convolutions to
progressively upsample the volume features and impose supervision on multiple
levels. To obtain dense occupancy prediction, we design a pipeline to generate
dense occupancy ground truth without expansive occupancy annotations.
Specifically, we fuse multi-frame LiDAR scans of dynamic objects and static
scenes separately. Then we adopt Poisson Reconstruction to fill the holes and
voxelize the mesh to get dense occupancy labels. Extensive experiments on
nuScenes and SemanticKITTI datasets demonstrate the superiority of our method.
Code and dataset are available at https://github.com/weiyithu/SurroundOcc
|
[
{
"version": "v1",
"created": "Thu, 16 Mar 2023 17:59:08 GMT"
},
{
"version": "v2",
"created": "Sun, 27 Aug 2023 15:33:19 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Wei",
"Yi",
""
],
[
"Zhao",
"Linqing",
""
],
[
"Zheng",
"Wenzhao",
""
],
[
"Zhu",
"Zheng",
""
],
[
"Zhou",
"Jie",
""
],
[
"Lu",
"Jiwen",
""
]
] |
new_dataset
| 0.971582 |
2304.04760
|
Shenshen Du
|
Jun Yu, Shenshen Du, Guochen Xie, Renjie Lu, Pengwei Li, Zhongpeng
Cai, Keda Lu
|
SAR2EO: A High-resolution Image Translation Framework with Denoising
Enhancement
| null | null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is a
fundamental task in remote sensing that can enrich the dataset by fusing
information from different sources. Recently, many methods have been proposed
to tackle this task, but they are still difficult to complete the conversion
from low-resolution images to high-resolution images. Thus, we propose a
framework, SAR2EO, aiming at addressing this challenge. Firstly, to generate
high-quality EO images, we adopt the coarse-to-fine generator, multi-scale
discriminators, and improved adversarial loss in the pix2pixHD model to
increase the synthesis quality. Secondly, we introduce a denoising module to
remove the noise in SAR images, which helps to suppress the noise while
preserving the structural information of the images. To validate the
effectiveness of the proposed framework, we conduct experiments on the dataset
of the Multi-modal Aerial View Imagery Challenge (MAVIC), which consists of
large-scale SAR and EO image pairs. The experimental results demonstrate the
superiority of our proposed framework, and we win the first place in the MAVIC
held in CVPR PBVS 2023.
|
[
{
"version": "v1",
"created": "Sat, 8 Apr 2023 03:39:51 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Aug 2023 17:28:26 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Yu",
"Jun",
""
],
[
"Du",
"Shenshen",
""
],
[
"Xie",
"Guochen",
""
],
[
"Lu",
"Renjie",
""
],
[
"Li",
"Pengwei",
""
],
[
"Cai",
"Zhongpeng",
""
],
[
"Lu",
"Keda",
""
]
] |
new_dataset
| 0.997959 |
2304.06634
|
Rui Ribeiro
|
Rui Ribeiro, Joao P. Carvalho, Lu\'isa Coheur
|
PGTask: Introducing the Task of Profile Generation from Dialogues
|
Accepted at SIGDIAL 2023, 4 pages, 2 figures
| null | null | null |
cs.CL cs.AI cs.LG cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent approaches have attempted to personalize dialogue systems by
leveraging profile information into models. However, this knowledge is scarce
and difficult to obtain, which makes the extraction/generation of profile
information from dialogues a fundamental asset. To surpass this limitation, we
introduce the Profile Generation Task (PGTask). We contribute with a new
dataset for this problem, comprising profile sentences aligned with related
utterances, extracted from a corpus of dialogues. Furthermore, using
state-of-the-art methods, we provide a benchmark for profile generation on this
novel dataset. Our experiments disclose the challenges of profile generation,
and we hope that this introduces a new research direction.
|
[
{
"version": "v1",
"created": "Thu, 13 Apr 2023 16:02:19 GMT"
},
{
"version": "v2",
"created": "Sat, 26 Aug 2023 05:55:48 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Ribeiro",
"Rui",
""
],
[
"Carvalho",
"Joao P.",
""
],
[
"Coheur",
"Luísa",
""
]
] |
new_dataset
| 0.999713 |
2304.09807
|
Shaoyu Chen
|
Shaoyu Chen, Yunchi Zhang, Bencheng Liao, Jiafeng Xie, Tianheng Cheng,
Wei Sui, Qian Zhang, Chang Huang, Wenyu Liu, Xinggang Wang
|
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale
Driving Scene
|
https://github.com/hustvl/VMA
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
High-definition (HD) map serves as the essential infrastructure of autonomous
driving. In this work, we build up a systematic vectorized map annotation
framework (termed VMA) for efficiently generating HD map of large-scale driving
scene. We design a divide-and-conquer annotation scheme to solve the spatial
extensibility problem of HD map generation, and abstract map elements with a
variety of geometric patterns as unified point sequence representation, which
can be extended to most map elements in the driving scene. VMA is highly
efficient and extensible, requiring negligible human effort, and flexible in
terms of spatial scale and element type. We quantitatively and qualitatively
validate the annotation performance on real-world urban and highway scenes, as
well as NYC Planimetric Database. VMA can significantly improve map generation
efficiency and require little human effort. On average VMA takes 160min for
annotating a scene with a range of hundreds of meters, and reduces 52.3% of the
human cost, showing great application value. Code:
https://github.com/hustvl/VMA.
|
[
{
"version": "v1",
"created": "Wed, 19 Apr 2023 16:47:20 GMT"
},
{
"version": "v2",
"created": "Sun, 27 Aug 2023 13:58:18 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Chen",
"Shaoyu",
""
],
[
"Zhang",
"Yunchi",
""
],
[
"Liao",
"Bencheng",
""
],
[
"Xie",
"Jiafeng",
""
],
[
"Cheng",
"Tianheng",
""
],
[
"Sui",
"Wei",
""
],
[
"Zhang",
"Qian",
""
],
[
"Huang",
"Chang",
""
],
[
"Liu",
"Wenyu",
""
],
[
"Wang",
"Xinggang",
""
]
] |
new_dataset
| 0.95356 |
2305.08562
|
Tim Fischer
|
Tim Fischer, Michael Rogenmoser, Matheus Cavalcante, Frank K.
G\"urkaynak, Luca Benini
|
FlooNoC: A Multi-Tbps Wide NoC for Heterogeneous AXI4 Traffic
| null | null |
10.1109/MDAT.2023.3306720
| null |
cs.AR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Meeting the staggering bandwidth requirements of today's applications
challenges the traditional narrow and serialized NoCs, which hit hard bounds on
the maximum operating frequency. This paper proposes FlooNoC, an open-source,
low-latency, fully AXI4-compatible NoC with wide physical channels for
latency-tolerant high-bandwidth non-blocking transactions and decoupled
latency-critical short messages. We demonstrate the feasibility of wide
channels by integrating a 5x5 router and links within a 9-core compute cluster
in 12 nm FinFet technology. Our NoC achieves a bandwidth of 629Gbps per link
while running at only 1.23 GHz (at 0.19 pJ/B/hop), with just 10% area overhead
post layout.
|
[
{
"version": "v1",
"created": "Mon, 15 May 2023 11:42:47 GMT"
},
{
"version": "v2",
"created": "Sun, 6 Aug 2023 18:31:33 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Fischer",
"Tim",
""
],
[
"Rogenmoser",
"Michael",
""
],
[
"Cavalcante",
"Matheus",
""
],
[
"Gürkaynak",
"Frank K.",
""
],
[
"Benini",
"Luca",
""
]
] |
new_dataset
| 0.950657 |
2305.13608
|
Wenxiao Cai
|
Wenxiao Cai, Ke Jin, Jinyan Hou, Cong Guo, Letian Wu, Wankou Yang
|
VDD: Varied Drone Dataset for Semantic Segmentation
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Semantic segmentation of drone images is critical to many aerial vision tasks
as it provides essential semantic details that can compensate for the lack of
depth information from monocular cameras. However, maintaining high accuracy of
semantic segmentation models for drones requires diverse, large-scale, and
high-resolution datasets, which are rare in the field of aerial image
processing. Existing datasets are typically small and focus primarily on urban
scenes, neglecting rural and industrial areas. Models trained on such datasets
are not sufficiently equipped to handle the variety of inputs seen in drone
imagery. In the VDD-Varied Drone Dataset, we offer a large-scale and densely
labeled dataset comprising 400 high-resolution images that feature carefully
chosen scenes, camera angles, and varied light and weather conditions.
Furthermore, we have adapted existing drone datasets to conform to our
annotation standards and integrated them with VDD to create a dataset 1.5 times
the size of fine annotation of Cityscapes. We have developed a novel DeepLabT
model, which combines CNN and Transformer backbones, to provide a reliable
baseline for semantic segmentation in drone imagery. Our experiments indicate
that DeepLabT performs admirably on VDD and other drone datasets. We expect
that our dataset will generate considerable interest in drone image
segmentation and serve as a foundation for other drone vision tasks. VDD is
freely available on our website at https://vddvdd.com .
|
[
{
"version": "v1",
"created": "Tue, 23 May 2023 02:16:14 GMT"
},
{
"version": "v2",
"created": "Sun, 27 Aug 2023 14:11:34 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Cai",
"Wenxiao",
""
],
[
"Jin",
"Ke",
""
],
[
"Hou",
"Jinyan",
""
],
[
"Guo",
"Cong",
""
],
[
"Wu",
"Letian",
""
],
[
"Yang",
"Wankou",
""
]
] |
new_dataset
| 0.999786 |
2306.13192
|
Fabian Weigend
|
Fabian C Weigend, Shubham Sonawani, Michael Drolet, Heni Ben Amor
|
Anytime, Anywhere: Human Arm Pose from Smartwatch Data for Ubiquitous
Robot Control and Teleoperation
|
8 pages, 10, figures, 1 table, conference: IROS
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This work devises an optimized machine learning approach for human arm pose
estimation from a single smartwatch. Our approach results in a distribution of
possible wrist and elbow positions, which allows for a measure of uncertainty
and the detection of multiple possible arm posture solutions, i.e., multimodal
pose distributions. Combining estimated arm postures with speech recognition,
we turn the smartwatch into a ubiquitous, low-cost and versatile robot control
interface. We demonstrate in two use-cases that this intuitive control
interface enables users to swiftly intervene in robot behavior, to temporarily
adjust their goal, or to train completely new control policies by imitation.
Extensive experiments show that the approach results in a 40% reduction in
prediction error over the current state-of-the-art and achieves a mean error of
2.56cm for wrist and elbow positions.
|
[
{
"version": "v1",
"created": "Thu, 22 Jun 2023 20:29:00 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Aug 2023 16:22:24 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Weigend",
"Fabian C",
""
],
[
"Sonawani",
"Shubham",
""
],
[
"Drolet",
"Michael",
""
],
[
"Amor",
"Heni Ben",
""
]
] |
new_dataset
| 0.985366 |
2307.05016
|
Myung-Hwan Jeon
|
Jeongyun Kim, Myung-Hwan Jeon, Sangwoo Jung, Wooseong Yang, Minwoo
Jung, Jaeho Shin, Ayoung Kim
|
TRansPose: Large-Scale Multispectral Dataset for Transparent Object
|
Under review
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Transparent objects are encountered frequently in our daily lives, yet
recognizing them poses challenges for conventional vision sensors due to their
unique material properties, not being well perceived from RGB or depth cameras.
Overcoming this limitation, thermal infrared cameras have emerged as a
solution, offering improved visibility and shape information for transparent
objects. In this paper, we present TRansPose, the first large-scale
multispectral dataset that combines stereo RGB-D, thermal infrared (TIR)
images, and object poses to promote transparent object research. The dataset
includes 99 transparent objects, encompassing 43 household items, 27 recyclable
trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It
comprises a vast collection of 333,819 images and 4,000,056 annotations,
providing instance-level segmentation masks, ground-truth poses, and completed
depth information. The data was acquired using a FLIR A65 thermal infrared
(TIR) camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda
robot manipulator. Spanning 87 sequences, TRansPose covers various challenging
real-life scenarios, including objects filled with water, diverse lighting
conditions, heavy clutter, non-transparent or translucent containers, objects
in plastic bags, and multi-stacked objects. TRansPose dataset can be accessed
from the following link: https://sites.google.com/view/transpose-dataset
|
[
{
"version": "v1",
"created": "Tue, 11 Jul 2023 05:32:21 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Aug 2023 04:05:15 GMT"
}
] | 2023-08-29T00:00:00 |
[
[
"Kim",
"Jeongyun",
""
],
[
"Jeon",
"Myung-Hwan",
""
],
[
"Jung",
"Sangwoo",
""
],
[
"Yang",
"Wooseong",
""
],
[
"Jung",
"Minwoo",
""
],
[
"Shin",
"Jaeho",
""
],
[
"Kim",
"Ayoung",
""
]
] |
new_dataset
| 0.999797 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.