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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2211.15521
|
Grace Luo
|
Grace Luo, Giscard Biamby, Trevor Darrell, Daniel Fried, Anna Rohrbach
|
G^3: Geolocation via Guidebook Grounding
|
Findings of EMNLP 2022
| null | null | null |
cs.CV cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We demonstrate how language can improve geolocation: the task of predicting
the location where an image was taken. Here we study explicit knowledge from
human-written guidebooks that describe the salient and class-discriminative
visual features humans use for geolocation. We propose the task of Geolocation
via Guidebook Grounding that uses a dataset of StreetView images from a diverse
set of locations and an associated textual guidebook for GeoGuessr, a popular
interactive geolocation game. Our approach predicts a country for each image by
attending over the clues automatically extracted from the guidebook.
Supervising attention with country-level pseudo labels achieves the best
performance. Our approach substantially outperforms a state-of-the-art
image-only geolocation method, with an improvement of over 5% in Top-1
accuracy. Our dataset and code can be found at
https://github.com/g-luo/geolocation_via_guidebook_grounding.
|
[
{
"version": "v1",
"created": "Mon, 28 Nov 2022 16:34:40 GMT"
}
] | 2022-11-29T00:00:00 |
[
[
"Luo",
"Grace",
""
],
[
"Biamby",
"Giscard",
""
],
[
"Darrell",
"Trevor",
""
],
[
"Fried",
"Daniel",
""
],
[
"Rohrbach",
"Anna",
""
]
] |
new_dataset
| 0.99945 |
2211.15533
|
Harm de Vries
|
Denis Kocetkov, Raymond Li, Loubna Ben Allal, Jia Li, Chenghao Mou,
Carlos Mu\~noz Ferrandis, Yacine Jernite, Margaret Mitchell, Sean Hughes,
Thomas Wolf, Dzmitry Bahdanau, Leandro von Werra, Harm de Vries
|
The Stack: 3 TB of permissively licensed source code
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Large Language Models (LLMs) play an ever-increasing role in the field of
Artificial Intelligence (AI)--not only for natural language processing but also
for code understanding and generation. To stimulate open and responsible
research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting
of permissively licensed source code in 30 programming languages. We describe
how we collect the full dataset, construct a permissively licensed subset,
present a data governance plan, discuss limitations, and show promising results
on text2code benchmarks by training 350M-parameter decoders on different Python
subsets. We find that (1) near-deduplicating the data significantly boosts
performance across all experiments, and (2) it is possible to match previously
reported HumanEval and MBPP performance using only permissively licensed data.
We make the dataset available at https://hf.co/BigCode, provide a tool called
"Am I in The Stack" (https://hf.co/spaces/bigcode/in-the-stack) for developers
to search The Stack for copies of their code, and provide a process for code to
be removed from the dataset by following the instructions at
https://www.bigcode-project.org/docs/about/the-stack/.
|
[
{
"version": "v1",
"created": "Sun, 20 Nov 2022 18:15:30 GMT"
}
] | 2022-11-29T00:00:00 |
[
[
"Kocetkov",
"Denis",
""
],
[
"Li",
"Raymond",
""
],
[
"Allal",
"Loubna Ben",
""
],
[
"Li",
"Jia",
""
],
[
"Mou",
"Chenghao",
""
],
[
"Ferrandis",
"Carlos Muñoz",
""
],
[
"Jernite",
"Yacine",
""
],
[
"Mitchell",
"Margaret",
""
],
[
"Hughes",
"Sean",
""
],
[
"Wolf",
"Thomas",
""
],
[
"Bahdanau",
"Dzmitry",
""
],
[
"von Werra",
"Leandro",
""
],
[
"de Vries",
"Harm",
""
]
] |
new_dataset
| 0.995679 |
1709.09480
|
Daniel Hein
|
Daniel Hein, Stefan Depeweg, Michel Tokic, Steffen Udluft, Alexander
Hentschel, Thomas A. Runkler, Volkmar Sterzing
|
A Benchmark Environment Motivated by Industrial Control Problems
| null |
2017 IEEE Symposium Series on Computational Intelligence (SSCI)
|
10.1109/SSCI.2017.8280935
| null |
cs.AI cs.LG cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the research area of reinforcement learning (RL), frequently novel and
promising methods are developed and introduced to the RL community. However,
although many researchers are keen to apply their methods on real-world
problems, implementing such methods in real industry environments often is a
frustrating and tedious process. Generally, academic research groups have only
limited access to real industrial data and applications. For this reason, new
methods are usually developed, evaluated and compared by using artificial
software benchmarks. On one hand, these benchmarks are designed to provide
interpretable RL training scenarios and detailed insight into the learning
process of the method on hand. On the other hand, they usually do not share
much similarity with industrial real-world applications. For this reason we
used our industry experience to design a benchmark which bridges the gap
between freely available, documented, and motivated artificial benchmarks and
properties of real industrial problems. The resulting industrial benchmark (IB)
has been made publicly available to the RL community by publishing its Java and
Python code, including an OpenAI Gym wrapper, on Github. In this paper we
motivate and describe in detail the IB's dynamics and identify prototypic
experimental settings that capture common situations in real-world industry
control problems.
|
[
{
"version": "v1",
"created": "Wed, 27 Sep 2017 13:03:52 GMT"
},
{
"version": "v2",
"created": "Tue, 6 Feb 2018 10:59:19 GMT"
},
{
"version": "v3",
"created": "Thu, 24 Nov 2022 13:27:53 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Hein",
"Daniel",
""
],
[
"Depeweg",
"Stefan",
""
],
[
"Tokic",
"Michel",
""
],
[
"Udluft",
"Steffen",
""
],
[
"Hentschel",
"Alexander",
""
],
[
"Runkler",
"Thomas A.",
""
],
[
"Sterzing",
"Volkmar",
""
]
] |
new_dataset
| 0.99898 |
1901.04056
|
Anjany Kumar Sekuboyina
|
Patrick Bilic, Patrick Christ, Hongwei Bran Li, Eugene Vorontsov, Avi
Ben-Cohen, Georgios Kaissis, Adi Szeskin, Colin Jacobs, Gabriel Efrain
Humpire Mamani, Gabriel Chartrand, Fabian Loh\"ofer, Julian Walter Holch,
Wieland Sommer, Felix Hofmann, Alexandre Hostettler, Naama Lev-Cohain, Michal
Drozdzal, Michal Marianne Amitai, Refael Vivantik, Jacob Sosna, Ivan Ezhov,
Anjany Sekuboyina, Fernando Navarro, Florian Kofler, Johannes C. Paetzold,
Suprosanna Shit, Xiaobin Hu, Jana Lipkov\'a, Markus Rempfler, Marie Piraud,
Jan Kirschke, Benedikt Wiestler, Zhiheng Zhang, Christian H\"ulsemeyer,
Marcel Beetz, Florian Ettlinger, Michela Antonelli, Woong Bae, M\'iriam
Bellver, Lei Bi, Hao Chen, Grzegorz Chlebus, Erik B. Dam, Qi Dou, Chi-Wing
Fu, Bogdan Georgescu, Xavier Gir\'o-i-Nieto, Felix Gruen, Xu Han, Pheng-Ann
Heng, J\"urgen Hesser, Jan Hendrik Moltz, Christian Igel, Fabian Isensee,
Paul J\"ager, Fucang Jia, Krishna Chaitanya Kaluva, Mahendra Khened, Ildoo
Kim, Jae-Hun Kim, Sungwoong Kim, Simon Kohl, Tomasz Konopczynski, Avinash
Kori, Ganapathy Krishnamurthi, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Li,
John Lowengrub, Jun Ma, Klaus Maier-Hein, Kevis-Kokitsi Maninis, Hans Meine,
Dorit Merhof, Akshay Pai, Mathias Perslev, Jens Petersen, Jordi Pont-Tuset,
Jin Qi, Xiaojuan Qi, Oliver Rippel, Karsten Roth, Ignacio Sarasua, Andrea
Schenk, Zengming Shen, Jordi Torres, Christian Wachinger, Chunliang Wang,
Leon Weninger, Jianrong Wu, Daguang Xu, Xiaoping Yang, Simon Chun-Ho Yu,
Yading Yuan, Miao Yu, Liping Zhang, Jorge Cardoso, Spyridon Bakas, Rickmer
Braren, Volker Heinemann, Christopher Pal, An Tang, Samuel Kadoury, Luc
Soler, Bram van Ginneken, Hayit Greenspan, Leo Joskowicz, Bjoern Menze
|
The Liver Tumor Segmentation Benchmark (LiTS)
|
Patrick Bilic, Patrick Christ, Hongwei Bran Li, and Eugene Vorontsov
made equal contributions to this work. Published in Medical Image Analysis
|
Medical Image Analysis (2022) Pg. 102680
|
10.1016/j.media.2022.102680
| null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this work, we report the set-up and results of the Liver Tumor
Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE
International Symposium on Biomedical Imaging (ISBI) 2017 and the International
Conferences on Medical Image Computing and Computer-Assisted Intervention
(MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and
secondary tumors with varied sizes and appearances with various
lesion-to-background levels (hyper-/hypo-dense), created in collaboration with
seven hospitals and research institutions. Seventy-five submitted liver and
liver tumor segmentation algorithms were trained on a set of 131 computed
tomography (CT) volumes and were tested on 70 unseen test images acquired from
different patients. We found that not a single algorithm performed best for
both liver and liver tumors in the three events. The best liver segmentation
algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the
best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI
2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional
analysis on liver tumor detection and revealed that not all top-performing
segmentation algorithms worked well for tumor detection. The best liver tumor
detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515
(MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further
research. LiTS remains an active benchmark and resource for research, e.g.,
contributing the liver-related segmentation tasks in
\url{http://medicaldecathlon.com/}. In addition, both data and online
evaluation are accessible via \url{www.lits-challenge.com}.
|
[
{
"version": "v1",
"created": "Sun, 13 Jan 2019 20:38:16 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Nov 2022 09:24:35 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Bilic",
"Patrick",
""
],
[
"Christ",
"Patrick",
""
],
[
"Li",
"Hongwei Bran",
""
],
[
"Vorontsov",
"Eugene",
""
],
[
"Ben-Cohen",
"Avi",
""
],
[
"Kaissis",
"Georgios",
""
],
[
"Szeskin",
"Adi",
""
],
[
"Jacobs",
"Colin",
""
],
[
"Mamani",
"Gabriel Efrain Humpire",
""
],
[
"Chartrand",
"Gabriel",
""
],
[
"Lohöfer",
"Fabian",
""
],
[
"Holch",
"Julian Walter",
""
],
[
"Sommer",
"Wieland",
""
],
[
"Hofmann",
"Felix",
""
],
[
"Hostettler",
"Alexandre",
""
],
[
"Lev-Cohain",
"Naama",
""
],
[
"Drozdzal",
"Michal",
""
],
[
"Amitai",
"Michal Marianne",
""
],
[
"Vivantik",
"Refael",
""
],
[
"Sosna",
"Jacob",
""
],
[
"Ezhov",
"Ivan",
""
],
[
"Sekuboyina",
"Anjany",
""
],
[
"Navarro",
"Fernando",
""
],
[
"Kofler",
"Florian",
""
],
[
"Paetzold",
"Johannes C.",
""
],
[
"Shit",
"Suprosanna",
""
],
[
"Hu",
"Xiaobin",
""
],
[
"Lipková",
"Jana",
""
],
[
"Rempfler",
"Markus",
""
],
[
"Piraud",
"Marie",
""
],
[
"Kirschke",
"Jan",
""
],
[
"Wiestler",
"Benedikt",
""
],
[
"Zhang",
"Zhiheng",
""
],
[
"Hülsemeyer",
"Christian",
""
],
[
"Beetz",
"Marcel",
""
],
[
"Ettlinger",
"Florian",
""
],
[
"Antonelli",
"Michela",
""
],
[
"Bae",
"Woong",
""
],
[
"Bellver",
"Míriam",
""
],
[
"Bi",
"Lei",
""
],
[
"Chen",
"Hao",
""
],
[
"Chlebus",
"Grzegorz",
""
],
[
"Dam",
"Erik B.",
""
],
[
"Dou",
"Qi",
""
],
[
"Fu",
"Chi-Wing",
""
],
[
"Georgescu",
"Bogdan",
""
],
[
"Giró-i-Nieto",
"Xavier",
""
],
[
"Gruen",
"Felix",
""
],
[
"Han",
"Xu",
""
],
[
"Heng",
"Pheng-Ann",
""
],
[
"Hesser",
"Jürgen",
""
],
[
"Moltz",
"Jan Hendrik",
""
],
[
"Igel",
"Christian",
""
],
[
"Isensee",
"Fabian",
""
],
[
"Jäger",
"Paul",
""
],
[
"Jia",
"Fucang",
""
],
[
"Kaluva",
"Krishna Chaitanya",
""
],
[
"Khened",
"Mahendra",
""
],
[
"Kim",
"Ildoo",
""
],
[
"Kim",
"Jae-Hun",
""
],
[
"Kim",
"Sungwoong",
""
],
[
"Kohl",
"Simon",
""
],
[
"Konopczynski",
"Tomasz",
""
],
[
"Kori",
"Avinash",
""
],
[
"Krishnamurthi",
"Ganapathy",
""
],
[
"Li",
"Fan",
""
],
[
"Li",
"Hongchao",
""
],
[
"Li",
"Junbo",
""
],
[
"Li",
"Xiaomeng",
""
],
[
"Lowengrub",
"John",
""
],
[
"Ma",
"Jun",
""
],
[
"Maier-Hein",
"Klaus",
""
],
[
"Maninis",
"Kevis-Kokitsi",
""
],
[
"Meine",
"Hans",
""
],
[
"Merhof",
"Dorit",
""
],
[
"Pai",
"Akshay",
""
],
[
"Perslev",
"Mathias",
""
],
[
"Petersen",
"Jens",
""
],
[
"Pont-Tuset",
"Jordi",
""
],
[
"Qi",
"Jin",
""
],
[
"Qi",
"Xiaojuan",
""
],
[
"Rippel",
"Oliver",
""
],
[
"Roth",
"Karsten",
""
],
[
"Sarasua",
"Ignacio",
""
],
[
"Schenk",
"Andrea",
""
],
[
"Shen",
"Zengming",
""
],
[
"Torres",
"Jordi",
""
],
[
"Wachinger",
"Christian",
""
],
[
"Wang",
"Chunliang",
""
],
[
"Weninger",
"Leon",
""
],
[
"Wu",
"Jianrong",
""
],
[
"Xu",
"Daguang",
""
],
[
"Yang",
"Xiaoping",
""
],
[
"Yu",
"Simon Chun-Ho",
""
],
[
"Yuan",
"Yading",
""
],
[
"Yu",
"Miao",
""
],
[
"Zhang",
"Liping",
""
],
[
"Cardoso",
"Jorge",
""
],
[
"Bakas",
"Spyridon",
""
],
[
"Braren",
"Rickmer",
""
],
[
"Heinemann",
"Volker",
""
],
[
"Pal",
"Christopher",
""
],
[
"Tang",
"An",
""
],
[
"Kadoury",
"Samuel",
""
],
[
"Soler",
"Luc",
""
],
[
"van Ginneken",
"Bram",
""
],
[
"Greenspan",
"Hayit",
""
],
[
"Joskowicz",
"Leo",
""
],
[
"Menze",
"Bjoern",
""
]
] |
new_dataset
| 0.999865 |
1902.04419
|
Krishna Gopal Benerjee
|
Krishna Gopal Benerjee and Sourav Deb and Manish K Gupta
|
On Conflict Free DNA Codes
|
12 pages, Draft (Table VI and Table VII are updated)
| null |
10.1007/s12095-020-00459-7
| null |
cs.IT cs.ET math.CO math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
DNA storage has emerged as an important area of research. The reliability of
DNA storage system depends on designing the DNA strings (called DNA codes) that
are sufficiently dissimilar. In this work, we introduce DNA codes that satisfy
a special constraint. Each codeword of the DNA code has a specific property
that any two consecutive sub-strings of the DNA codeword will not be the same
(a generalization of homo-polymers constraint). This is in addition to the
usual constraints such as Hamming, reverse, reverse-complement and
$GC$-content. We believe that the new constraint will help further in reducing
the errors during reading and writing data into the synthetic DNA strings. We
also present a construction (based on a variant of stochastic local search
algorithm) to calculate the size of the DNA codes with all the above
constraints, which improves the lower bounds from the existing literature, for
some specific cases. Moreover, a recursive isometric map between binary vectors
and DNA strings is proposed. Using the map and the well known binary codes we
obtain few classes of DNA codes with all the constraints including the property
that the constructed DNA codewords are free from the hairpin-like secondary
structures.
|
[
{
"version": "v1",
"created": "Tue, 12 Feb 2019 14:49:49 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Mar 2019 12:42:39 GMT"
},
{
"version": "v3",
"created": "Tue, 9 Jul 2019 03:31:03 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Benerjee",
"Krishna Gopal",
""
],
[
"Deb",
"Sourav",
""
],
[
"Gupta",
"Manish K",
""
]
] |
new_dataset
| 0.999643 |
2105.05796
|
Tomasz Stanis{\l}awek
|
Tomasz Stanis{\l}awek and Filip Grali\'nski and Anna Wr\'oblewska and
Dawid Lipi\'nski and Agnieszka Kaliska and Paulina Rosalska and Bartosz
Topolski and Przemys{\l}aw Biecek
|
Kleister: Key Information Extraction Datasets Involving Long Documents
with Complex Layouts
|
accepted to ICDAR 2021
|
International Conference on Document Analysis and Recognition
ICDAR 2021
|
10.1007/978-3-030-86549-8_36
| null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The relevance of the Key Information Extraction (KIE) task is increasingly
important in natural language processing problems. But there are still only a
few well-defined problems that serve as benchmarks for solutions in this area.
To bridge this gap, we introduce two new datasets (Kleister NDA and Kleister
Charity). They involve a mix of scanned and born-digital long formal
English-language documents. In these datasets, an NLP system is expected to
find or infer various types of entities by employing both textual and
structural layout features. The Kleister Charity dataset consists of 2,788
annual financial reports of charity organizations, with 61,643 unique pages and
21,612 entities to extract. The Kleister NDA dataset has 540 Non-disclosure
Agreements, with 3,229 unique pages and 2,160 entities to extract. We provide
several state-of-the-art baseline systems from the KIE domain (Flair, BERT,
RoBERTa, LayoutLM, LAMBERT), which show that our datasets pose a strong
challenge to existing models. The best model achieved an 81.77% and an 83.57%
F1-score on respectively the Kleister NDA and the Kleister Charity datasets. We
share the datasets to encourage progress on more in-depth and complex
information extraction tasks.
|
[
{
"version": "v1",
"created": "Wed, 12 May 2021 17:08:01 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Stanisławek",
"Tomasz",
""
],
[
"Graliński",
"Filip",
""
],
[
"Wróblewska",
"Anna",
""
],
[
"Lipiński",
"Dawid",
""
],
[
"Kaliska",
"Agnieszka",
""
],
[
"Rosalska",
"Paulina",
""
],
[
"Topolski",
"Bartosz",
""
],
[
"Biecek",
"Przemysław",
""
]
] |
new_dataset
| 0.999554 |
2108.10290
|
Martin Knoche
|
Martin Knoche, Stefan H\"ormann, Gerhard Rigoll
|
Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face
Recognition in Unconstrained Environments
|
9 pages, 4 figures, 2 tables
| null |
10.1109/FG52635.2021.9666960
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Real-world face recognition applications often deal with suboptimal image
quality or resolution due to different capturing conditions such as various
subject-to-camera distances, poor camera settings, or motion blur. This
characteristic has an unignorable effect on performance. Recent
cross-resolution face recognition approaches used simple, arbitrary, and
unrealistic down- and up-scaling techniques to measure robustness against
real-world edge-cases in image quality. Thus, we propose a new standardized
benchmark dataset and evaluation protocol derived from the famous Labeled Faces
in the Wild (LFW). In contrast to previous derivatives, which focus on pose,
age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in
the Wild (XQLFW) maximizes the quality difference. It contains only more
realistic synthetically degraded images when necessary. Our proposed dataset is
then used to further investigate the influence of image quality on several
state-of-the-art approaches. With XQLFW, we show that these models perform
differently in cross-quality cases, and hence, the generalizing capability is
not accurately predicted by their performance on LFW. Additionally, we report
baseline accuracy with recent deep learning models explicitly trained for
cross-resolution applications and evaluate the susceptibility to image quality.
To encourage further research in cross-resolution face recognition and incite
the assessment of image quality robustness, we publish the database and code
for evaluation.
|
[
{
"version": "v1",
"created": "Mon, 23 Aug 2021 17:04:32 GMT"
},
{
"version": "v2",
"created": "Thu, 26 Aug 2021 08:05:36 GMT"
},
{
"version": "v3",
"created": "Fri, 25 Nov 2022 11:44:07 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Knoche",
"Martin",
""
],
[
"Hörmann",
"Stefan",
""
],
[
"Rigoll",
"Gerhard",
""
]
] |
new_dataset
| 0.952872 |
2109.03569
|
Florent Bartoccioni
|
Florent Bartoccioni, \'Eloi Zablocki, Patrick P\'erez, Matthieu Cord,
Karteek Alahari
|
LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR
| null | null | null | null |
cs.CV cs.AI cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-based depth estimation is a key feature in autonomous systems, which
often relies on a single camera or several independent ones. In such a
monocular setup, dense depth is obtained with either additional input from one
or several expensive LiDARs, e.g., with 64 beams, or camera-only methods, which
suffer from scale-ambiguity and infinite-depth problems. In this paper, we
propose a new alternative of densely estimating metric depth by combining a
monocular camera with a light-weight LiDAR, e.g., with 4 beams, typical of
today's automotive-grade mass-produced laser scanners. Inspired by recent
self-supervised methods, we introduce a novel framework, called LiDARTouch, to
estimate dense depth maps from monocular images with the help of ``touches'' of
LiDAR, i.e., without the need for dense ground-truth depth. In our setup, the
minimal LiDAR input contributes on three different levels: as an additional
model's input, in a self-supervised LiDAR reconstruction objective function,
and to estimate changes of pose (a key component of self-supervised depth
estimation architectures). Our LiDARTouch framework achieves new state of the
art in self-supervised depth estimation on the KITTI dataset, thus supporting
our choices of integrating the very sparse LiDAR signal with other visual
features. Moreover, we show that the use of a few-beam LiDAR alleviates scale
ambiguity and infinite-depth issues that camera-only methods suffer from. We
also demonstrate that methods from the fully-supervised depth-completion
literature can be adapted to a self-supervised regime with a minimal LiDAR
signal.
|
[
{
"version": "v1",
"created": "Wed, 8 Sep 2021 12:06:31 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Nov 2022 13:12:08 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Bartoccioni",
"Florent",
""
],
[
"Zablocki",
"Éloi",
""
],
[
"Pérez",
"Patrick",
""
],
[
"Cord",
"Matthieu",
""
],
[
"Alahari",
"Karteek",
""
]
] |
new_dataset
| 0.99277 |
2112.08544
|
Revanth Reddy
|
Revanth Gangi Reddy, Sai Chetan, Zhenhailong Wang, Yi R. Fung, Kathryn
Conger, Ahmed Elsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin
Small, Heng Ji
|
NewsClaims: A New Benchmark for Claim Detection from News with Attribute
Knowledge
|
Accepted at EMNLP 2022
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Claim detection and verification are crucial for news understanding and have
emerged as promising technologies for mitigating misinformation and
disinformation in the news. However, most existing work has focused on claim
sentence analysis while overlooking additional crucial attributes (e.g., the
claimer and the main object associated with the claim). In this work, we
present NewsClaims, a new benchmark for attribute-aware claim detection in the
news domain. We extend the claim detection problem to include extraction of
additional attributes related to each claim and release 889 claims annotated
over 143 news articles. NewsClaims aims to benchmark claim detection systems in
emerging scenarios, comprising unseen topics with little or no training data.
To this end, we see that zero-shot and prompt-based baselines show promising
performance on this benchmark, while still considerably behind human
performance.
|
[
{
"version": "v1",
"created": "Thu, 16 Dec 2021 00:50:24 GMT"
},
{
"version": "v2",
"created": "Mon, 14 Mar 2022 23:19:29 GMT"
},
{
"version": "v3",
"created": "Tue, 24 May 2022 13:19:20 GMT"
},
{
"version": "v4",
"created": "Wed, 23 Nov 2022 20:43:56 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Reddy",
"Revanth Gangi",
""
],
[
"Chetan",
"Sai",
""
],
[
"Wang",
"Zhenhailong",
""
],
[
"Fung",
"Yi R.",
""
],
[
"Conger",
"Kathryn",
""
],
[
"Elsayed",
"Ahmed",
""
],
[
"Palmer",
"Martha",
""
],
[
"Nakov",
"Preslav",
""
],
[
"Hovy",
"Eduard",
""
],
[
"Small",
"Kevin",
""
],
[
"Ji",
"Heng",
""
]
] |
new_dataset
| 0.98433 |
2112.15093
|
Jingye Chen
|
Haiyang Yu, Jingye Chen, Bin Li, Jianqi Ma, Mengnan Guan, Xixi Xu,
Xiaocong Wang, Shaobo Qu, Xiangyang Xue
|
Benchmarking Chinese Text Recognition: Datasets, Baselines, and an
Empirical Study
|
Code is available at
https://github.com/FudanVI/benchmarking-chinese-text-recognition
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The flourishing blossom of deep learning has witnessed the rapid development
of text recognition in recent years. However, the existing text recognition
methods are mainly proposed for English texts. As another widely-spoken
language, Chinese text recognition (CTR) in all ways has extensive application
markets. Based on our observations, we attribute the scarce attention on CTR to
the lack of reasonable dataset construction standards, unified evaluation
protocols, and results of the existing baselines. To fill this gap, we manually
collect CTR datasets from publicly available competitions, projects, and
papers. According to application scenarios, we divide the collected datasets
into four categories including scene, web, document, and handwriting datasets.
Besides, we standardize the evaluation protocols in CTR. With unified
evaluation protocols, we evaluate a series of representative text recognition
methods on the collected datasets to provide baselines. The experimental
results indicate that the performance of baselines on CTR datasets is not as
good as that on English datasets due to the characteristics of Chinese texts
that are quite different from the Latin alphabet. Moreover, we observe that by
introducing radical-level supervision as an auxiliary task, the performance of
baselines can be further boosted. The code and datasets are made publicly
available at https://github.com/FudanVI/benchmarking-chinese-text-recognition
|
[
{
"version": "v1",
"created": "Thu, 30 Dec 2021 15:30:52 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Nov 2022 12:03:17 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Yu",
"Haiyang",
""
],
[
"Chen",
"Jingye",
""
],
[
"Li",
"Bin",
""
],
[
"Ma",
"Jianqi",
""
],
[
"Guan",
"Mengnan",
""
],
[
"Xu",
"Xixi",
""
],
[
"Wang",
"Xiaocong",
""
],
[
"Qu",
"Shaobo",
""
],
[
"Xue",
"Xiangyang",
""
]
] |
new_dataset
| 0.999544 |
2204.06979
|
Daniel Coquelin
|
Daniel Coquelin, Behnood Rasti, Markus G\"otz, Pedram Ghamisi, Richard
Gloaguen, and Achim Streit
|
HyDe: The First Open-Source, Python-Based, GPU-Accelerated Hyperspectral
Denoising Package
|
5 pages
| null |
10.1109/WHISPERS56178.2022.9955088
| null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
As with any physical instrument, hyperspectral cameras induce different kinds
of noise in the acquired data. Therefore, Hyperspectral denoising is a crucial
step for analyzing hyperspectral images (HSIs). Conventional computational
methods rarely use GPUs to improve efficiency and are not fully open-source.
Alternatively, deep learning-based methods are often open-source and use GPUs,
but their training and utilization for real-world applications remain
non-trivial for many researchers. Consequently, we propose HyDe: the first
open-source, GPU-accelerated Python-based, hyperspectral image denoising
toolbox, which aims to provide a large set of methods with an easy-to-use
environment. HyDe includes a variety of methods ranging from low-rank
wavelet-based methods to deep neural network (DNN) models. HyDe's interface
dramatically improves the interoperability of these methods and the performance
of the underlying functions. In fact, these methods maintain similar HSI
denoising performance to their original implementations while consuming nearly
ten times less energy. Furthermore, we present a method for training DNNs for
denoising HSIs which are not spatially related to the training dataset, i.e.,
training on ground-level HSIs for denoising HSIs with other perspectives
including airborne, drone-borne, and space-borne. To utilize the trained DNNs,
we show a sliding window method to effectively denoise HSIs which would
otherwise require more than 40 GB. The package can be found at:
\url{https://github.com/Helmholtz-AI-Energy/HyDe}.
|
[
{
"version": "v1",
"created": "Thu, 14 Apr 2022 14:08:55 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Coquelin",
"Daniel",
""
],
[
"Rasti",
"Behnood",
""
],
[
"Götz",
"Markus",
""
],
[
"Ghamisi",
"Pedram",
""
],
[
"Gloaguen",
"Richard",
""
],
[
"Streit",
"Achim",
""
]
] |
new_dataset
| 0.993399 |
2204.07719
|
Amnon Drory
|
Amnon Drory, Shai Avidan and Raja Giryes
|
Stress-Testing Point Cloud Registration on Automotive LiDAR
|
Accepted to the NeurIPS 2022 workshop on Machine Learning for
Autonomous Driving. Project Page:
https://github.com/AmnonDrory/LidarRegistration
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Rigid Point Cloud Registration (PCR) algorithms aim to estimate the 6-DOF
relative motion between two point clouds, which is important in various fields,
including autonomous driving. Recent years have seen a significant improvement
in global PCR algorithms, i.e. algorithms that can handle a large relative
motion. This has been demonstrated in various scenarios, including indoor
scenes, but has only been minimally tested in the Automotive setting, where
point clouds are produced by vehicle-mounted LiDAR sensors. In this work, we
aim to answer questions that are important for automotive applications,
including: which of the new algorithms is the most accurate, and which is
fastest? How transferable are deep-learning approaches, e.g. what happens when
you train a network with data from Boston, and run it in a vehicle in
Singapore? How small can the overlap between point clouds be before the
algorithms start to deteriorate? To what extent are the algorithms rotation
invariant? Our results are at times surprising. When comparing robust parameter
estimation methods for registration, we find that the fastest and most accurate
is not one of the newest approaches. Instead, it is a modern variant of the
well known RANSAC technique. We also suggest a new outlier filtering method,
Grid-Prioritized Filtering (GPF), to further improve it. An additional
contribution of this work is an algorithm for selecting challenging sets of
frame-pairs from automotive LiDAR datasets. This enables meaningful
benchmarking in the Automotive LiDAR setting, and can also improve training for
learning algorithms.
|
[
{
"version": "v1",
"created": "Sat, 16 Apr 2022 05:10:55 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Nov 2022 13:20:27 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Drory",
"Amnon",
""
],
[
"Avidan",
"Shai",
""
],
[
"Giryes",
"Raja",
""
]
] |
new_dataset
| 0.997627 |
2205.06887
|
Pritam Sarkar
|
Pritam Sarkar, Aaron Posen, Ali Etemad
|
AVCAffe: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect
for Remote Work
|
Accepted in AAAI 2023
| null | null | null |
cs.HC cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We introduce AVCAffe, the first Audio-Visual dataset consisting of Cognitive
load and Affect attributes. We record AVCAffe by simulating remote work
scenarios over a video-conferencing platform, where subjects collaborate to
complete a number of cognitively engaging tasks. AVCAffe is the largest
originally collected (not collected from the Internet) affective dataset in
English language. We recruit 106 participants from 18 different countries of
origin, spanning an age range of 18 to 57 years old, with a balanced
male-female ratio. AVCAffe comprises a total of 108 hours of video, equivalent
to more than 58,000 clips along with task-based self-reported ground truth
labels for arousal, valence, and cognitive load attributes such as mental
demand, temporal demand, effort, and a few others. We believe AVCAffe would be
a challenging benchmark for the deep learning research community given the
inherent difficulty of classifying affect and cognitive load in particular.
Moreover, our dataset fills an existing timely gap by facilitating the creation
of learning systems for better self-management of remote work meetings, and
further study of hypotheses regarding the impact of remote work on cognitive
load and affective states.
|
[
{
"version": "v1",
"created": "Fri, 13 May 2022 20:55:25 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Nov 2022 04:37:15 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Sarkar",
"Pritam",
""
],
[
"Posen",
"Aaron",
""
],
[
"Etemad",
"Ali",
""
]
] |
new_dataset
| 0.999846 |
2205.11108
|
D. Murugan
|
Petchiammal A, Briskline Kiruba S, D. Murugan, Pandarasamy A
|
Paddy Doctor: A Visual Image Dataset for Automated Paddy Disease
Classification and Benchmarking
| null | null |
10.1145/3570991.3570994
| null |
cs.CV
|
http://creativecommons.org/licenses/by-sa/4.0/
|
One of the critical biotic stress factors paddy farmers face is diseases
caused by bacteria, fungi, and other organisms. These diseases affect plants'
health severely and lead to significant crop loss. Most of these diseases can
be identified by regularly observing the leaves and stems under expert
supervision. In a country with vast agricultural regions and limited crop
protection experts, manual identification of paddy diseases is challenging.
Thus, to add a solution to this problem, it is necessary to automate the
disease identification process and provide easily accessible decision support
tools to enable effective crop protection measures. However, the lack of
availability of public datasets with detailed disease information limits the
practical implementation of accurate disease detection systems. This paper
presents \emph{Paddy Doctor}, a visual image dataset for identifying paddy
diseases. Our dataset contains 16,225 annotated paddy leaf images across 13
classes (12 diseases and normal leaf). We benchmarked the \emph{Paddy Doctor}
dataset using a Convolutional Neural Network (CNN) and four transfer learning
based models (VGG16, MobileNet, Xception, and ResNet34). The experimental
results showed that ResNet34 achieved the highest F1-score of 97.50%. We
release our dataset and reproducible code in the open source for community use.
|
[
{
"version": "v1",
"created": "Mon, 23 May 2022 07:57:40 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Nov 2022 11:23:28 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"A",
"Petchiammal",
""
],
[
"S",
"Briskline Kiruba",
""
],
[
"Murugan",
"D.",
""
],
[
"A",
"Pandarasamy",
""
]
] |
new_dataset
| 0.999763 |
2206.04510
|
Si Shen
|
Si Shen, Jiangfeng Liu, Litao Lin, Ying Huang, Lin Zhang, Chang Liu,
Yutong Feng, Dongbo Wang
|
SsciBERT: A Pre-trained Language Model for Social Science Texts
|
24 pages,2 figures
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The academic literature of social sciences records human civilization and
studies human social problems. With its large-scale growth, the ways to quickly
find existing research on relevant issues have become an urgent demand for
researchers. Previous studies, such as SciBERT, have shown that pre-training
using domain-specific texts can improve the performance of natural language
processing tasks. However, the pre-trained language model for social sciences
is not available so far. In light of this, the present research proposes a
pre-trained model based on the abstracts published in the Social Science
Citation Index (SSCI) journals. The models, which are available on GitHub
(https://github.com/S-T-Full-Text-Knowledge-Mining/SSCI-BERT), show excellent
performance on discipline classification, abstract structure-function
recognition, and named entity recognition tasks with the social sciences
literature.
|
[
{
"version": "v1",
"created": "Thu, 9 Jun 2022 13:49:04 GMT"
},
{
"version": "v2",
"created": "Sat, 11 Jun 2022 14:47:38 GMT"
},
{
"version": "v3",
"created": "Fri, 25 Nov 2022 03:28:20 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Shen",
"Si",
""
],
[
"Liu",
"Jiangfeng",
""
],
[
"Lin",
"Litao",
""
],
[
"Huang",
"Ying",
""
],
[
"Zhang",
"Lin",
""
],
[
"Liu",
"Chang",
""
],
[
"Feng",
"Yutong",
""
],
[
"Wang",
"Dongbo",
""
]
] |
new_dataset
| 0.996114 |
2206.09059
|
Tejas Srinivasan
|
Tejas Srinivasan, Ting-Yun Chang, Leticia Leonor Pinto Alva, Georgios
Chochlakis, Mohammad Rostami, Jesse Thomason
|
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
|
Accepted to NeurIPS 2022 Datasets and Benchmarks track
| null | null | null |
cs.CL cs.AI cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Current state-of-the-art vision-and-language models are evaluated on tasks
either individually or in a multi-task setting, overlooking the challenges of
continually learning (CL) tasks as they arrive. Existing CL benchmarks have
facilitated research on task adaptation and mitigating "catastrophic
forgetting", but are limited to vision-only and language-only tasks. We present
CLiMB, a benchmark to study the challenge of learning multimodal tasks in a CL
setting, and to systematically evaluate how upstream continual learning can
rapidly generalize to new multimodal and unimodal tasks. CLiMB includes
implementations of several CL algorithms and a modified Vision-Language
Transformer (ViLT) model that can be deployed on both multimodal and unimodal
tasks. We find that common CL methods can help mitigate forgetting during
multimodal task learning, but do not enable cross-task knowledge transfer. We
envision that CLiMB will facilitate research on a new class of CL algorithms
for this challenging multimodal setting.
|
[
{
"version": "v1",
"created": "Sat, 18 Jun 2022 00:16:37 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Nov 2022 21:40:45 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Srinivasan",
"Tejas",
""
],
[
"Chang",
"Ting-Yun",
""
],
[
"Alva",
"Leticia Leonor Pinto",
""
],
[
"Chochlakis",
"Georgios",
""
],
[
"Rostami",
"Mohammad",
""
],
[
"Thomason",
"Jesse",
""
]
] |
new_dataset
| 0.994955 |
2206.15153
|
Can Xiang
|
Can Xiang, Chunming Tang
|
Some $3$-designs and shortened codes from binary cyclic codes with three
zeros
|
20 pages. arXiv admin note: text overlap with arXiv:2110.03881,
arXiv:2007.05923
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Linear codes and $t$-designs are interactive with each other. It is well
known that some $t$-designs have been constructed by using certain linear codes
in recent years. However, only a small number of infinite families of the
extended codes of linear codes holding an infinite family of $t$-designs with
$t\geq 3$ are reported in the literature. In this paper, we study the extended
codes of the augmented codes of a class of binary cyclic codes with three zeros
and their dual codes, and show that those codes hold $3$-designs. Furthermore,
we obtain some shortened codes from the studied cyclic codes and explicitly
determine their parameters. Some of those shortened codes are optimal or almost
optimal.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 09:36:38 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Nov 2022 12:07:54 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Xiang",
"Can",
""
],
[
"Tang",
"Chunming",
""
]
] |
new_dataset
| 0.996548 |
2207.13345
|
Tomasz Kryjak
|
Piotr Wzorek and Tomasz Kryjak
|
Traffic Sign Detection With Event Cameras and DCNN
|
Accepted for the SPA 2022 conference, Poznan, Poland
| null |
10.23919/SPA53010.2022.9927864
| null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
In recent years, event cameras (DVS - Dynamic Vision Sensors) have been used
in vision systems as an alternative or supplement to traditional cameras. They
are characterised by high dynamic range, high temporal resolution, low latency,
and reliable performance in limited lighting conditions -- parameters that are
particularly important in the context of advanced driver assistance systems
(ADAS) and self-driving cars. In this work, we test whether these rather novel
sensors can be applied to the popular task of traffic sign detection. To this
end, we analyse different representations of the event data: event frame, event
frequency, and the exponentially decaying time surface, and apply video frame
reconstruction using a deep neural network called FireNet. We use the deep
convolutional neural network YOLOv4 as a detector. For particular
representations, we obtain a detection accuracy in the range of 86.9-88.9%
mAP@0.5. The use of a fusion of the considered representations allows us to
obtain a detector with higher accuracy of 89.9% mAP@0.5. In comparison, the
detector for the frames reconstructed with FireNet is characterised by an
accuracy of 72.67% mAP@0.5. The results obtained illustrate the potential of
event cameras in automotive applications, either as standalone sensors or in
close cooperation with typical frame-based cameras.
|
[
{
"version": "v1",
"created": "Wed, 27 Jul 2022 08:01:54 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Wzorek",
"Piotr",
""
],
[
"Kryjak",
"Tomasz",
""
]
] |
new_dataset
| 0.999072 |
2208.00001
|
Muhammad Asad
|
Muhammad Asad, Reuben Dorent, Tom Vercauteren
|
FastGeodis: Fast Generalised Geodesic Distance Transform
|
Accepted at Journal of Open Source Software (JOSS)
| null |
10.21105/joss.04532
| null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
The FastGeodis package provides an efficient implementation for computing
Geodesic and Euclidean distance transforms (or a mixture of both), targeting
efficient utilisation of CPU and GPU hardware. In particular, it implements the
paralellisable raster scan method from Criminisi et al. (2009), where elements
in a row (2D) or plane (3D) can be computed with parallel threads. This package
is able to handle 2D as well as 3D data, where it achieves up to a 20x speedup
on a CPU and up to a 74x speedup on a GPU as compared to an existing
open-source library (Wang, 2020) that uses a non-parallelisable single-thread
CPU implementation. The performance speedups reported here were evaluated using
3D volume data on an Nvidia GeForce Titan X (12 GB) with a 6-Core Intel Xeon
E5-1650 CPU. Further in-depth comparison of performance improvements are
discussed in the FastGeodis documentation: https://fastgeodis.readthedocs.io
|
[
{
"version": "v1",
"created": "Tue, 26 Jul 2022 15:01:37 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Nov 2022 23:33:37 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Asad",
"Muhammad",
""
],
[
"Dorent",
"Reuben",
""
],
[
"Vercauteren",
"Tom",
""
]
] |
new_dataset
| 0.998069 |
2208.03196
|
Vinod Kumar Chauhan
|
Vinod Kumar Chauhan, Anshul Thakur, Odhran O'Donoghue and David A.
Clifton
|
COPER: Continuous Patient State Perceiver
|
2 figures; presented in IEEE International Conference on Biomedical
and Health Informatics (IEEE BHI-2022)
| null |
10.1109/BHI56158.2022.9926807
| null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In electronic health records (EHRs), irregular time-series (ITS) occur
naturally due to patient health dynamics, reflected by irregular hospital
visits, diseases/conditions and the necessity to measure different vitals signs
at each visit etc. ITS present challenges in training machine learning
algorithms which mostly are built on assumption of coherent fixed dimensional
feature space. In this paper, we propose a novel COntinuous patient state
PERceiver model, called COPER, to cope with ITS in EHRs. COPER uses Perceiver
model and the concept of neural ordinary differential equations (ODEs) to learn
the continuous time dynamics of patient state, i.e., continuity of input space
and continuity of output space. The neural ODEs help COPER to generate regular
time-series to feed to Perceiver model which has the capability to handle
multi-modality large-scale inputs. To evaluate the performance of the proposed
model, we use in-hospital mortality prediction task on MIMIC-III dataset and
carefully design experiments to study irregularity. The results are compared
with the baselines which prove the efficacy of the proposed model.
|
[
{
"version": "v1",
"created": "Fri, 5 Aug 2022 14:32:57 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Nov 2022 13:46:38 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Chauhan",
"Vinod Kumar",
""
],
[
"Thakur",
"Anshul",
""
],
[
"O'Donoghue",
"Odhran",
""
],
[
"Clifton",
"David A.",
""
]
] |
new_dataset
| 0.993065 |
2210.08836
|
Peirong Zhang
|
Peirong Zhang, Jiajia Jiang, Yuliang Liu, Lianwen Jin
|
MSDS: A Large-Scale Chinese Signature and Token Digit String Dataset for
Handwriting Verification
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Although online handwriting verification has made great progress recently,
the verification performances are still far behind the real usage owing to the
small scale of the datasets as well as the limited biometric mediums.
Therefore, this paper proposes a new handwriting verification benchmark dataset
named Multimodal Signature and Digit String (MSDS), which consists of two
subsets: MSDS-ChS (Chinese Signatures) and MSDS-TDS (Token Digit Strings),
contributed by 402 users, with 20 genuine samples and 20 skilled forgeries per
user per subset. MSDS-ChS consists of handwritten Chinese signatures, which, to
the best of our knowledge, is the largest publicly available Chinese signature
dataset for handwriting verification, at least eight times larger than existing
online datasets. Meanwhile, MSDS-TDS consists of handwritten Token Digit
Strings, i.e, the actual phone numbers of users, which have not been explored
yet. Extensive experiments with different baselines are respectively conducted
for MSDS-ChS and MSDS-TDS. Surprisingly, verification performances of
state-of-the-art methods on MSDS-TDS are generally better than those on
MSDS-ChS, which indicates that the handwritten Token Digit String could be a
more effective biometric than handwritten Chinese signature. This is a
promising discovery that could inspire us to explore new biometric traits. The
MSDS dataset is available at https://github.com/HCIILAB/MSDS.
|
[
{
"version": "v1",
"created": "Mon, 17 Oct 2022 08:23:12 GMT"
},
{
"version": "v2",
"created": "Fri, 21 Oct 2022 04:57:21 GMT"
},
{
"version": "v3",
"created": "Thu, 17 Nov 2022 14:18:15 GMT"
},
{
"version": "v4",
"created": "Thu, 24 Nov 2022 13:25:00 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Zhang",
"Peirong",
""
],
[
"Jiang",
"Jiajia",
""
],
[
"Liu",
"Yuliang",
""
],
[
"Jin",
"Lianwen",
""
]
] |
new_dataset
| 0.99978 |
2210.15871
|
Henghui Ding
|
Henghui Ding, Chang Liu, Suchen Wang, Xudong Jiang
|
VLT: Vision-Language Transformer and Query Generation for Referring
Segmentation
|
TPAMI
| null |
10.1109/TPAMI.2022.3217852
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a Vision-Language Transformer (VLT) framework for referring
segmentation to facilitate deep interactions among multi-modal information and
enhance the holistic understanding to vision-language features. There are
different ways to understand the dynamic emphasis of a language expression,
especially when interacting with the image. However, the learned queries in
existing transformer works are fixed after training, which cannot cope with the
randomness and huge diversity of the language expressions. To address this
issue, we propose a Query Generation Module, which dynamically produces
multiple sets of input-specific queries to represent the diverse comprehensions
of language expression. To find the best among these diverse comprehensions, so
as to generate a better mask, we propose a Query Balance Module to selectively
fuse the corresponding responses of the set of queries. Furthermore, to enhance
the model's ability in dealing with diverse language expressions, we consider
inter-sample learning to explicitly endow the model with knowledge of
understanding different language expressions to the same object. We introduce
masked contrastive learning to narrow down the features of different
expressions for the same target object while distinguishing the features of
different objects. The proposed approach is lightweight and achieves new
state-of-the-art referring segmentation results consistently on five datasets.
|
[
{
"version": "v1",
"created": "Fri, 28 Oct 2022 03:36:07 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Ding",
"Henghui",
""
],
[
"Liu",
"Chang",
""
],
[
"Wang",
"Suchen",
""
],
[
"Jiang",
"Xudong",
""
]
] |
new_dataset
| 0.991777 |
2211.07521
|
Eslam Bakr
|
Eslam Mohamed Bakr, Ahmad El Sallab, Mohsen A. Rashwan
|
PKCAM: Previous Knowledge Channel Attention Module
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Recently, attention mechanisms have been explored with ConvNets, both across
the spatial and channel dimensions. However, from our knowledge, all the
existing methods devote the attention modules to capture local interactions
from a uni-scale. In this paper, we propose a Previous Knowledge Channel
Attention Module(PKCAM), that captures channel-wise relations across different
layers to model the global context. Our proposed module PKCAM is easily
integrated into any feed-forward CNN architectures and trained in an end-to-end
fashion with a negligible footprint due to its lightweight property. We
validate our novel architecture through extensive experiments on image
classification and object detection tasks with different backbones. Our
experiments show consistent improvements in performances against their
counterparts. Our code is published at https://github.com/eslambakr/EMCA.
|
[
{
"version": "v1",
"created": "Mon, 14 Nov 2022 16:49:11 GMT"
},
{
"version": "v2",
"created": "Fri, 25 Nov 2022 16:03:20 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Bakr",
"Eslam Mohamed",
""
],
[
"Sallab",
"Ahmad El",
""
],
[
"Rashwan",
"Mohsen A.",
""
]
] |
new_dataset
| 0.987758 |
2211.08788
|
Yong Hu
|
Yong Hu, Fandong Meng, Jie Zhou
|
CSCD-IME: Correcting Spelling Errors Generated by Pinyin IME
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Chinese Spelling Correction (CSC) is a task to detect and correct spelling
mistakes in texts. In fact, most of Chinese input is based on pinyin input
method, so the study of spelling errors in this process is more practical and
valuable. However, there is still no research dedicated to this essential
scenario. In this paper, we first present a Chinese Spelling Correction Dataset
for errors generated by pinyin IME (CSCD-IME), including 40,000 annotated
sentences from real posts of official media on Sina Weibo. Furthermore, we
propose a novel method to automatically construct large-scale and high-quality
pseudo data by simulating the input through pinyin IME. A series of analyses
and experiments on CSCD-IME show that spelling errors produced by pinyin IME
hold a particular distribution at pinyin level and semantic level and are
challenging enough. Meanwhile, our proposed pseudo-data construction method can
better fit this error distribution and improve the performance of CSC systems.
Finally, we provide a useful guide to using pseudo data, including the data
scale, the data source, and the training strategy.
|
[
{
"version": "v1",
"created": "Wed, 16 Nov 2022 09:25:42 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Nov 2022 09:37:41 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Hu",
"Yong",
""
],
[
"Meng",
"Fandong",
""
],
[
"Zhou",
"Jie",
""
]
] |
new_dataset
| 0.999835 |
2211.12139
|
Emily Muller
|
Emily Muller, Emily Gemmell, Ishmam Choudhury, Ricky Nathvani, Antje
Barbara Metzler, James Bennett, Emily Denton, Seth Flaxman, Majid Ezzati
|
City-Wide Perceptions of Neighbourhood Quality using Street View Images
| null | null | null | null |
cs.CV cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
The interactions of individuals with city neighbourhoods is determined, in
part, by the perceived quality of urban environments. Perceived neighbourhood
quality is a core component of urban vitality, influencing social cohesion,
sense of community, safety, activity and mental health of residents.
Large-scale assessment of perceptions of neighbourhood quality was pioneered by
the Place Pulse projects. Researchers demonstrated the efficacy of
crowd-sourcing perception ratings of image pairs across 56 cities and training
a model to predict perceptions from street-view images. Variation across cities
may limit Place Pulse's usefulness for assessing within-city perceptions. In
this paper, we set forth a protocol for city-specific dataset collection for
the perception: 'On which street would you prefer to walk?'. This paper
describes our methodology, based in London, including collection of images and
ratings, web development, model training and mapping. Assessment of within-city
perceptions of neighbourhoods can identify inequities, inform planning
priorities, and identify temporal dynamics. Code available:
https://emilymuller1991.github.io/urban-perceptions/.
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 10:16:35 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Nov 2022 11:09:23 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Muller",
"Emily",
""
],
[
"Gemmell",
"Emily",
""
],
[
"Choudhury",
"Ishmam",
""
],
[
"Nathvani",
"Ricky",
""
],
[
"Metzler",
"Antje Barbara",
""
],
[
"Bennett",
"James",
""
],
[
"Denton",
"Emily",
""
],
[
"Flaxman",
"Seth",
""
],
[
"Ezzati",
"Majid",
""
]
] |
new_dataset
| 0.99652 |
2211.13090
|
Sifeng He
|
Sifeng He, Yue He, Minlong Lu, Chen Jiang, Xudong Yang, Feng Qian,
Xiaobo Zhang, Lei Yang, Jiandong Zhang
|
TransVCL: Attention-enhanced Video Copy Localization Network with
Flexible Supervision
|
Accepted by the Thirty-Seventh AAAI Conference on Artificial
Intelligence(AAAI2023)
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video copy localization aims to precisely localize all the copied segments
within a pair of untrimmed videos in video retrieval applications. Previous
methods typically start from frame-to-frame similarity matrix generated by
cosine similarity between frame-level features of the input video pair, and
then detect and refine the boundaries of copied segments on similarity matrix
under temporal constraints. In this paper, we propose TransVCL: an
attention-enhanced video copy localization network, which is optimized directly
from initial frame-level features and trained end-to-end with three main
components: a customized Transformer for feature enhancement, a correlation and
softmax layer for similarity matrix generation, and a temporal alignment module
for copied segments localization. In contrast to previous methods demanding the
handcrafted similarity matrix, TransVCL incorporates long-range temporal
information between feature sequence pair using self- and cross- attention
layers. With the joint design and optimization of three components, the
similarity matrix can be learned to present more discriminative copied
patterns, leading to significant improvements over previous methods on
segment-level labeled datasets (VCSL and VCDB). Besides the state-of-the-art
performance in fully supervised setting, the attention architecture facilitates
TransVCL to further exploit unlabeled or simply video-level labeled data.
Additional experiments of supplementing video-level labeled datasets including
SVD and FIVR reveal the high flexibility of TransVCL from full supervision to
semi-supervision (with or without video-level annotation). Code is publicly
available at https://github.com/transvcl/TransVCL.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 16:19:45 GMT"
},
{
"version": "v2",
"created": "Thu, 24 Nov 2022 01:55:14 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"He",
"Sifeng",
""
],
[
"He",
"Yue",
""
],
[
"Lu",
"Minlong",
""
],
[
"Jiang",
"Chen",
""
],
[
"Yang",
"Xudong",
""
],
[
"Qian",
"Feng",
""
],
[
"Zhang",
"Xiaobo",
""
],
[
"Yang",
"Lei",
""
],
[
"Zhang",
"Jiandong",
""
]
] |
new_dataset
| 0.976836 |
2211.13251
|
Keqiang Sun
|
Keqiang Sun, Shangzhe Wu, Ning Zhang, Zhaoyang Huang, Quan Wang,
Hongsheng Li
|
CGOF++: Controllable 3D Face Synthesis with Conditional Generative
Occupancy Fields
|
This article is an extension of the NeurIPS'22 paper arXiv:2206.08361
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Capitalizing on the recent advances in image generation models, existing
controllable face image synthesis methods are able to generate high-fidelity
images with some levels of controllability, e.g., controlling the shapes,
expressions, textures, and poses of the generated face images. However,
previous methods focus on controllable 2D image generative models, which are
prone to producing inconsistent face images under large expression and pose
changes. In this paper, we propose a new NeRF-based conditional 3D face
synthesis framework, which enables 3D controllability over the generated face
images by imposing explicit 3D conditions from 3D face priors. At its core is a
conditional Generative Occupancy Field (cGOF++) that effectively enforces the
shape of the generated face to conform to a given 3D Morphable Model (3DMM)
mesh, built on top of EG3D [1], a recent tri-plane-based generative model. To
achieve accurate control over fine-grained 3D face shapes of the synthesized
images, we additionally incorporate a 3D landmark loss as well as a volume
warping loss into our synthesis framework. Experiments validate the
effectiveness of the proposed method, which is able to generate high-fidelity
face images and shows more precise 3D controllability than state-of-the-art
2D-based controllable face synthesis methods.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 19:02:50 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Sun",
"Keqiang",
""
],
[
"Wu",
"Shangzhe",
""
],
[
"Zhang",
"Ning",
""
],
[
"Huang",
"Zhaoyang",
""
],
[
"Wang",
"Quan",
""
],
[
"Li",
"Hongsheng",
""
]
] |
new_dataset
| 0.986775 |
2211.13376
|
Xinying Qiu
|
Xinying Qiu, Guofeng Shi
|
InDEX: Indonesian Idiom and Expression Dataset for Cloze Test
|
Accepted to "2022 International Conference on Asian Language
Processing (IALP)"
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We propose InDEX, an Indonesian Idiom and Expression dataset for cloze test.
The dataset contains 10438 unique sentences for 289 idioms and expressions for
which we generate 15 different types of distractors, resulting in a large
cloze-style corpus. Many baseline models of cloze test reading comprehension
apply BERT with random initialization to learn embedding representation. But
idioms and fixed expressions are different such that the literal meaning of the
phrases may or may not be consistent with their contextual meaning. Therefore,
we explore different ways to combine static and contextual representations for
a stronger baseline model. Experimentations show that combining definition and
random initialization will better support cloze test model performance for
idioms whether independently or mixed with fixed expressions. While for fixed
expressions with no special meaning, static embedding with random
initialization is sufficient for cloze test model.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 02:05:47 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Qiu",
"Xinying",
""
],
[
"Shi",
"Guofeng",
""
]
] |
new_dataset
| 0.999795 |
2211.13382
|
Yao Lai
|
Yao Lai, Yao Mu, Ping Luo
|
MaskPlace: Fast Chip Placement via Reinforced Visual Representation
Learning
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Placement is an essential task in modern chip design, aiming at placing
millions of circuit modules on a 2D chip canvas. Unlike the human-centric
solution, which requires months of intense effort by hardware engineers to
produce a layout to minimize delay and energy consumption, deep reinforcement
learning has become an emerging autonomous tool. However, the learning-centric
method is still in its early stage, impeded by a massive design space of size
ten to the order of a few thousand. This work presents MaskPlace to
automatically generate a valid chip layout design within a few hours, whose
performance can be superior or comparable to recent advanced approaches. It has
several appealing benefits that prior arts do not have. Firstly, MaskPlace
recasts placement as a problem of learning pixel-level visual representation to
comprehensively describe millions of modules on a chip, enabling placement in a
high-resolution canvas and a large action space. It outperforms recent methods
that represent a chip as a hypergraph. Secondly, it enables training the policy
network by an intuitive reward function with dense reward, rather than a
complicated reward function with sparse reward from previous methods. Thirdly,
extensive experiments on many public benchmarks show that MaskPlace outperforms
existing RL approaches in all key performance metrics, including wirelength,
congestion, and density. For example, it achieves 60%-90% wirelength reduction
and guarantees zero overlaps. We believe MaskPlace can improve AI-assisted chip
layout design. The deliverables are released at
https://laiyao1.github.io/maskplace.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 02:22:09 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Lai",
"Yao",
""
],
[
"Mu",
"Yao",
""
],
[
"Luo",
"Ping",
""
]
] |
new_dataset
| 0.994925 |
2211.13391
|
Zhifeng Zhu
|
Yue Xin, Kang Zhou, Xuanyao Fong, Yumeng Yang, Shenghua Gao, Zhifeng
Zhu
|
Electrical Tunable Spintronic Neuron with Trainable Activation Function
|
26 pages, 9 figures
| null | null | null |
cs.ET cond-mat.dis-nn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Spintronic devices have been widely studied for the hardware realization of
artificial neurons. The stochastic switching of magnetic tunnel junction driven
by the spin torque is commonly used to produce the sigmoid activation function.
However, the shape of the activation function in previous studies is fixed
during the training of neural network. This restricts the updating of weights
and results in a limited performance. In this work, we exploit the physics
behind the spin torque induced magnetization switching to enable the dynamic
change of the activation function during the training process. Specifically,
the pulse width and magnetic anisotropy can be electrically controlled to
change the slope of activation function, which enables a faster or slower
change of output required by the backpropagation algorithm. This is also
similar to the idea of batch normalization that is widely used in the machine
learning. Thus, this work demonstrates that the algorithms are no longer
limited to the software implementation. They can in fact be realized by the
spintronic hardware using a single device. Finally, we show that the accuracy
of hand-written digit recognition can be improved from 88% to 91.3% by using
these trainable spintronic neurons without introducing additional energy
consumption. Our proposals can stimulate the hardware realization of spintronic
neural networks.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 03:11:20 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Xin",
"Yue",
""
],
[
"Zhou",
"Kang",
""
],
[
"Fong",
"Xuanyao",
""
],
[
"Yang",
"Yumeng",
""
],
[
"Gao",
"Shenghua",
""
],
[
"Zhu",
"Zhifeng",
""
]
] |
new_dataset
| 0.998082 |
2211.13432
|
Keisuke Okumura
|
Keisuke Okumura
|
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding
|
to be presented at AAAI-23
| null | null | null |
cs.AI cs.MA cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a novel complete algorithm for multi-agent pathfinding (MAPF)
called lazy constraints addition search for MAPF (LaCAM). MAPF is a problem of
finding collision-free paths for multiple agents on graphs and is the
foundation of multi-robot coordination. LaCAM uses a two-level search to find
solutions quickly, even with hundreds of agents or more. At the low-level, it
searches constraints about agents' locations. At the high-level, it searches a
sequence of all agents' locations, following the constraints specified by the
low-level. Our exhaustive experiments reveal that LaCAM is comparable to or
outperforms state-of-the-art sub-optimal MAPF algorithms in a variety of
scenarios, regarding success rate, planning time, and solution quality of
sum-of-costs.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 06:27:18 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Okumura",
"Keisuke",
""
]
] |
new_dataset
| 0.972694 |
2211.13536
|
Nana Obayashi
|
Andrea Vicari, Nana Obayashi, Francesco Stella, Gaetan Raynaud, Karen
Mulleners, Cosimo Della Santina, and Josie Hughes
|
Proprioceptive Sensing of Soft Tentacles with Model Based Reconstruction
for Controller Optimization
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The success of soft robots in displaying emergent behaviors is tightly linked
to the compliant interaction with the environment. However, to exploit such
phenomena, proprioceptive sensing methods which do not hinder their softness
are needed. In this work we propose a new sensing approach for soft underwater
slender structures based on embedded pressure sensors and use a learning-based
pipeline to link the sensor readings to the shape of the soft structure. Using
two different modeling techniques, we compare the pose reconstruction accuracy
and identify the optimal approach. Using the proprioceptive sensing
capabilities we show how this information can be used to assess the swimming
performance over a number of metrics, namely swimming thrust, tip deflection,
and the traveling wave index. We conclude by demonstrating the robustness of
the embedded sensor on a free swimming soft robotic squid swimming at a maximum
velocity of 9.5 cm/s, with the absolute tip deflection being predicted within
an error less than 9% without the aid of external sensors.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 11:11:32 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Vicari",
"Andrea",
""
],
[
"Obayashi",
"Nana",
""
],
[
"Stella",
"Francesco",
""
],
[
"Raynaud",
"Gaetan",
""
],
[
"Mulleners",
"Karen",
""
],
[
"Della Santina",
"Cosimo",
""
],
[
"Hughes",
"Josie",
""
]
] |
new_dataset
| 0.994158 |
2211.13573
|
Ehsan Tohidi Dr
|
Fariba Armandoust, Ehsan Tohidi, Martin Kasparick, Li Wang, Ahmet
Hasim Gokceoglu, and Slawomir Stanczak
|
MIMO Systems with Reconfigurable Antennas: Joint Channel Estimation and
Mode Selection
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Reconfigurable antennas (RAs) are a promising technology to enhance the
capacity and coverage of wireless communication systems. However, RA systems
have two major challenges: (i) High computational complexity of mode selection,
and (ii) High overhead of channel estimation for all modes. In this paper, we
develop a low-complexity iterative mode selection algorithm for data
transmission in an RA-MIMO system. Furthermore, we study channel estimation of
an RA multi-user MIMO system. However, given the coherence time, it is
challenging to estimate channels of all modes. We propose a mode selection
scheme to select a subset of modes, train channels for the selected subset, and
predict channels for the remaining modes. In addition, we propose a prediction
scheme based on pattern correlation between modes. Representative simulation
results demonstrate the system's channel estimation error and achievable
sum-rate for various selected modes and different signal-to-noise ratios
(SNRs).
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 12:48:49 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Armandoust",
"Fariba",
""
],
[
"Tohidi",
"Ehsan",
""
],
[
"Kasparick",
"Martin",
""
],
[
"Wang",
"Li",
""
],
[
"Gokceoglu",
"Ahmet Hasim",
""
],
[
"Stanczak",
"Slawomir",
""
]
] |
new_dataset
| 0.97717 |
2211.13622
|
Per Erik Strandberg
|
Per Erik Strandberg
|
The Westermo test results data set
| null | null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
There is a growing body of knowledge in the computer science, software
engineering, software testing and software test automation disciplines.
However, there is a challenge for researchers to evaluate their research
findings, innovations and tools due to lack of realistic data. This paper
presents the Westermo test results data set, more than one million verdicts
from testing of embedded systems, from more than five hundred consecutive days
of nightly testing. The data also contains information on code changes in both
the software under test and the test framework used for testing. This data set
can support the research community in particular with respect to the regression
test selection problem, flaky tests, test results visualization, etc.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 14:16:56 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Strandberg",
"Per Erik",
""
]
] |
new_dataset
| 0.999503 |
2211.13680
|
Federico Benzi
|
Federico Benzi, Cristian Mancus, Cristian Secchi
|
Whole-Body Control of a Mobile Manipulator for Passive Collaborative
Transportation
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Human-robot collaborative tasks foresee interactions between humans and
robots with various degrees of complexity. Specifically, for tasks which
involve physical contact among the agents, challenges arise in the modelling
and control of such interaction. In this paper we propose a control
architecture capable of ensuring a flexible and robustly stable physical
human-robot interaction, focusing on a collaborative transportation task. The
architecture is deployed onto a mobile manipulator, modelled as a whole-body
structure, which aids the operator during the transportation of an unwieldy
load. Thanks to passivity techniques, the controller adapts its interaction
parameters online while preserving robust stability for the overall system,
thus experimentally validating the architecture.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 15:53:34 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Benzi",
"Federico",
""
],
[
"Mancus",
"Cristian",
""
],
[
"Secchi",
"Cristian",
""
]
] |
new_dataset
| 0.999322 |
2211.13776
|
Dojun Park
|
Dojun Park and Seohyun Park
|
German Phoneme Recognition with Text-to-Phoneme Data Augmentation
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In this study, we experimented to examine the effect of adding the most
frequent n phoneme bigrams to the basic vocabulary on the German phoneme
recognition model using the text-to-phoneme data augmentation strategy. As a
result, compared to the baseline model, the vowel30 model and the const20 model
showed an increased BLEU score of more than 1 point, and the total30 model
showed a significant decrease in the BLEU score of more than 20 points, showing
that the phoneme bigrams could have a positive or negative effect on the model
performance. In addition, we identified the types of errors that the models
repeatedly showed through error analysis.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 19:32:49 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Park",
"Dojun",
""
],
[
"Park",
"Seohyun",
""
]
] |
new_dataset
| 0.993497 |
2211.13812
|
Ali Sekhavati
|
Ali Sekhavati and Won-Sook Lee
|
Multi-Template Temporal Siamese Network for Long-Term Object Tracking
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Siamese Networks are one of most popular visual object tracking methods for
their high speed and high accuracy tracking ability as long as the target is
well identified. However, most Siamese Network based trackers use the first
frame as the ground truth of an object and fail when target appearance changes
significantly in next frames. They also have dif iculty distinguishing the
target from similar other objects in the frame. We propose two ideas to solve
both problems. The first idea is using a bag of dynamic templates, containing
diverse, similar, and recent target features and continuously updating it with
diverse target appearances. The other idea is to let a network learn the path
history and project a potential future target location in a next frame. This
tracker achieves state-of-the-art performance on the long-term tracking dataset
UAV20L by improving the success rate by a large margin of 15% (65.4 vs 56.6)
compared to the state-of-the-art method, HiFT. The of icial python code of this
paper is publicly available.
|
[
{
"version": "v1",
"created": "Thu, 24 Nov 2022 22:07:33 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Sekhavati",
"Ali",
""
],
[
"Lee",
"Won-Sook",
""
]
] |
new_dataset
| 0.958391 |
2211.13887
|
Yuxing Qiu
|
Yuxing Qiu, Feng Gao, Minchen Li, Govind Thattai, Yin Yang, Chenfanfu
Jiang
|
TPA-Net: Generate A Dataset for Text to Physics-based Animation
| null | null | null | null |
cs.AI cs.CL cs.CV cs.GR eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Recent breakthroughs in Vision-Language (V&L) joint research have achieved
remarkable results in various text-driven tasks. High-quality Text-to-video
(T2V), a task that has been long considered mission-impossible, was proven
feasible with reasonably good results in latest works. However, the resulting
videos often have undesired artifacts largely because the system is purely
data-driven and agnostic to the physical laws. To tackle this issue and further
push T2V towards high-level physical realism, we present an autonomous data
generation technique and a dataset, which intend to narrow the gap with a large
number of multi-modal, 3D Text-to-Video/Simulation (T2V/S) data. In the
dataset, we provide high-resolution 3D physical simulations for both solids and
fluids, along with textual descriptions of the physical phenomena. We take
advantage of state-of-the-art physical simulation methods (i) Incremental
Potential Contact (IPC) and (ii) Material Point Method (MPM) to simulate
diverse scenarios, including elastic deformations, material fractures,
collisions, turbulence, etc. Additionally, high-quality, multi-view rendering
videos are supplied for the benefit of T2V, Neural Radiance Fields (NeRF), and
other communities. This work is the first step towards fully automated
Text-to-Video/Simulation (T2V/S). Live examples and subsequent work are at
https://sites.google.com/view/tpa-net.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 04:26:41 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Qiu",
"Yuxing",
""
],
[
"Gao",
"Feng",
""
],
[
"Li",
"Minchen",
""
],
[
"Thattai",
"Govind",
""
],
[
"Yang",
"Yin",
""
],
[
"Jiang",
"Chenfanfu",
""
]
] |
new_dataset
| 0.999833 |
2211.13896
|
Xiangyu Xi
|
Xiangyu Xi, Jianwei Lv, Shuaipeng Liu, Wei Ye, Fan Yang and Guanglu
Wan
|
MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous
Informal Texts
|
Accepted at EMNLP 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Event detection (ED) identifies and classifies event triggers from
unstructured texts, serving as a fundamental task for information extraction.
Despite the remarkable progress achieved in the past several years, most
research efforts focus on detecting events from formal texts (e.g., news
articles, Wikipedia documents, financial announcements). Moreover, the texts in
each dataset are either from a single source or multiple yet relatively
homogeneous sources. With massive amounts of user-generated text accumulating
on the Web and inside enterprises, identifying meaningful events in these
informal texts, usually from multiple heterogeneous sources, has become a
problem of significant practical value. As a pioneering exploration that
expands event detection to the scenarios involving informal and heterogeneous
texts, we propose a new large-scale Chinese event detection dataset based on
user reviews, text conversations, and phone conversations in a leading
e-commerce platform for food service. We carefully investigate the proposed
dataset's textual informality and multi-source heterogeneity characteristics by
inspecting data samples quantitatively and qualitatively. Extensive experiments
with state-of-the-art event detection methods verify the unique challenges
posed by these characteristics, indicating that multi-source informal event
detection remains an open problem and requires further efforts. Our benchmark
and code are released at \url{https://github.com/myeclipse/MUSIED}.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 05:05:29 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Xi",
"Xiangyu",
""
],
[
"Lv",
"Jianwei",
""
],
[
"Liu",
"Shuaipeng",
""
],
[
"Ye",
"Wei",
""
],
[
"Yang",
"Fan",
""
],
[
"Wan",
"Guanglu",
""
]
] |
new_dataset
| 0.999836 |
2211.13925
|
Krishna Gopal Benerjee
|
Shibsankar Das, Krishna Gopal Benerjee and Adrish Banerjee
|
On DNA Codes Over the Non-Chain Ring
$\mathbb{Z}_4+u\mathbb{Z}_4+u^2\mathbb{Z}_4$ with $u^3=1$
|
This paper has been presented in IEEE Information Theory Workshop
(ITW) 2022, Mumbai, INDIA
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present a novel design strategy of DNA codes with length
$3n$ over the non-chain ring $R=\mathbb{Z}_4+u\mathbb{Z}_4+u^2\mathbb{Z}_4$
with $64$ elements and $u^3=1$, where $n$ denotes the length of a code over
$R$. We first study and analyze a distance conserving map defined over the ring
$R$ into the length-$3$ DNA sequences. Then, we derive some conditions on the
generator matrix of a linear code over $R$, which leads to a DNA code with
reversible, reversible-complement, homopolymer $2$-run-length, and
$\frac{w}{3n}$-GC-content constraints for integer $w$ ($0\leq w\leq 3n$).
Finally, we propose a new construction of DNA codes using Reed-Muller type
generator matrices. This allows us to obtain DNA codes with reversible,
reversible-complement, homopolymer $2$-run-length, and $\frac{2}{3}$-GC-content
constraints.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 06:42:04 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Das",
"Shibsankar",
""
],
[
"Benerjee",
"Krishna Gopal",
""
],
[
"Banerjee",
"Adrish",
""
]
] |
new_dataset
| 0.999675 |
2211.13930
|
Weinan He
|
Weinan He, Canming Huang, Zhanhao Xiao, Yongmei Liu
|
TRAC: A Textual Benchmark for Reasoning about Actions and Change
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Reasoning about actions and change (RAC) is essential to understand and
interact with the ever-changing environment. Previous AI research has shown the
importance of fundamental and indispensable knowledge of actions, i.e.,
preconditions and effects. However, traditional methods rely on logical
formalization which hinders practical applications. With recent
transformer-based language models (LMs), reasoning over text is desirable and
seemingly feasible, leading to the question of whether LMs can effectively and
efficiently learn to solve RAC problems. We propose four essential RAC tasks as
a comprehensive textual benchmark and generate problems in a way that minimizes
the influence of other linguistic requirements (e.g., grounding) to focus on
RAC. The resulting benchmark, TRAC, encompassing problems of various
complexities, facilitates a more granular evaluation of LMs, precisely
targeting the structural generalization ability much needed for RAC.
Experiments with three high-performing transformers indicates that additional
efforts are needed to tackle challenges raised by TRAC.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 06:54:30 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"He",
"Weinan",
""
],
[
"Huang",
"Canming",
""
],
[
"Xiao",
"Zhanhao",
""
],
[
"Liu",
"Yongmei",
""
]
] |
new_dataset
| 0.991457 |
2211.13940
|
Jiayin Sun
|
Jiayin Sun, Hong Wang and Qiulei Dong
|
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image
Recognition
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Triggered by the success of transformers in various visual tasks, the spatial
self-attention mechanism has recently attracted more and more attention in the
computer vision community. However, we empirically found that a typical vision
transformer with the spatial self-attention mechanism could not learn accurate
attention maps for distinguishing different categories of fine-grained images.
To address this problem, motivated by the temporal attention mechanism in
brains, we propose a spatial-temporal attention network for learning
fine-grained feature representations, called STAN, where the features learnt by
implementing a sequence of spatial self-attention operations corresponding to
multiple moments are aggregated progressively. The proposed STAN consists of
four modules: a self-attention backbone module for learning a sequence of
features with self-attention operations, a spatial feature self-organizing
module for facilitating the model training, a spatial-temporal feature learning
module for aggregating the re-organized features via a Long Short-Term Memory
network, and a context-aware module that is implemented as the forget block of
the spatial-temporal feature learning module for preserving/forgetting the
long-term memory by utilizing contextual information. Then, we propose a
STAN-based method for open-set fine-grained recognition by integrating the
proposed STAN network with a linear classifier, called STAN-OSFGR. Extensive
experimental results on 3 fine-grained datasets and 2 coarse-grained datasets
demonstrate that the proposed STAN-OSFGR outperforms 9 state-of-the-art
open-set recognition methods significantly in most cases.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 07:46:42 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Sun",
"Jiayin",
""
],
[
"Wang",
"Hong",
""
],
[
"Dong",
"Qiulei",
""
]
] |
new_dataset
| 0.992625 |
2211.13990
|
Muhammad Azeem Akbar
|
Muhammad Azeem Akbar, Arif Ali Khan, Sajjad Mahmood, Saima Rafi
|
Quantum Software Engineering: A New Genre of Computing
| null | null | null | null |
cs.SE cs.PL
|
http://creativecommons.org/licenses/by/4.0/
|
Quantum computing (QC) is no longer only a scientific interest but is rapidly
becoming an industrially available technology that can potentially tackle the
limitations of classical computing. Over the last few years, major technology
giants have invested in developing hardware and programming frameworks to
develop quantum-specific applications. QC hardware technologies are gaining
momentum, however, operationalizing the QC technologies trigger the need for
software-intensive methodologies, techniques, processes, tools, roles, and
responsibilities for developing industrial-centric quantum software
applications. This paper presents the vision of the quantum software
engineering (QSE) life cycle consisting of quantum requirements engineering,
quantum software design, quantum software implementation, quantum software
testing, and quantum software maintenance. This paper particularly calls for
joint contributions of software engineering research and industrial community
to present real-world solutions to support the entire quantum software
development activities. The proposed vision facilitates the researchers and
practitioners to propose new processes, reference architectures, novel tools,
and practices to leverage quantum computers and develop emerging and next
generations of quantum software.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 09:56:00 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Akbar",
"Muhammad Azeem",
""
],
[
"Khan",
"Arif Ali",
""
],
[
"Mahmood",
"Sajjad",
""
],
[
"Rafi",
"Saima",
""
]
] |
new_dataset
| 0.998132 |
2211.14054
|
Nick Michiels
|
Steven Moonen and Bram Vanherle and Joris de Hoog and Taoufik Bourgana
and Abdellatif Bey-Temsamani and Nick Michiels
|
CAD2Render: A Modular Toolkit for GPU-accelerated Photorealistic
Synthetic Data Generation for the Manufacturing Industry
|
Accepted at the Workshop on Photorealistic Image and Environment
Synthesis for Computer Vision (PIES-CV) at WACV23
| null | null | null |
cs.CV cs.GR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The use of computer vision for product and assembly quality control is
becoming ubiquitous in the manufacturing industry. Lately, it is apparent that
machine learning based solutions are outperforming classical computer vision
algorithms in terms of performance and robustness. However, a main drawback is
that they require sufficiently large and labeled training datasets, which are
often not available or too tedious and too time consuming to acquire. This is
especially true for low-volume and high-variance manufacturing. Fortunately, in
this industry, CAD models of the manufactured or assembled products are
available. This paper introduces CAD2Render, a GPU-accelerated synthetic data
generator based on the Unity High Definition Render Pipeline (HDRP). CAD2Render
is designed to add variations in a modular fashion, making it possible for high
customizable data generation, tailored to the needs of the industrial use case
at hand. Although CAD2Render is specifically designed for manufacturing use
cases, it can be used for other domains as well. We validate CAD2Render by
demonstrating state of the art performance in two industrial relevant setups.
We demonstrate that the data generated by our approach can be used to train
object detection and pose estimation models with a high enough accuracy to
direct a robot. The code for CAD2Render is available at
https://github.com/EDM-Research/CAD2Render.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 12:17:35 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Moonen",
"Steven",
""
],
[
"Vanherle",
"Bram",
""
],
[
"de Hoog",
"Joris",
""
],
[
"Bourgana",
"Taoufik",
""
],
[
"Bey-Temsamani",
"Abdellatif",
""
],
[
"Michiels",
"Nick",
""
]
] |
new_dataset
| 0.986923 |
2211.14073
|
Nathan Morsa
|
Nathan Morsa
|
EDGAR: Embedded Detection of Gunshots by AI in Real-time
|
19 pages, 4 figures, submitted to the 7th Workshop on Advanced
Analytics and Learning on Temporal Data
| null | null | null |
cs.LG eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Electronic shot counters allow armourers to perform preventive and predictive
maintenance based on quantitative measurements, improving reliability, reducing
the frequency of accidents, and reducing maintenance costs. To answer a market
pressure for both low lead time to market and increased customisation, we aim
to solve the shot detection and shot counting problem in a generic way through
machine learning.
In this study, we describe a method allowing one to construct a dataset with
minimal labelling effort by only requiring the total number of shots fired in a
time series. To our knowledge, this is the first study to propose a technique,
based on learning from label proportions, that is able to exploit these weak
labels to derive an instance-level classifier able to solve the counting
problem and the more general discrimination problem. We also show that this
technique can be deployed in heavily constrained microcontrollers while still
providing hard real-time (<100ms) inference. We evaluate our technique against
a state-of-the-art unsupervised algorithm and show a sizeable improvement,
suggesting that the information from the weak labels is successfully leveraged.
Finally, we evaluate our technique against human-generated state-of-the-art
algorithms and show that it provides comparable performance and significantly
outperforms them in some offline and real-world benchmarks.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 12:51:19 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Morsa",
"Nathan",
""
]
] |
new_dataset
| 0.9995 |
2211.14076
|
Wolfgang Steiner
|
L\'eo Poirier (ENS Lyon), Wolfgang Steiner (IRIF)
|
Factor-balanced $S$-adic languages
| null | null | null | null |
cs.FL math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A set of words, also called a language, is letter-balanced if the number of
occurrences of each letter only depends on the length of the word, up to a
constant. Similarly, a language is factor-balanced if the difference of the
number of occurrences of any given factor in words of the same length is
bounded. The most prominent example of a letter-balanced but not
factor-balanced language is given by the Thue-Morse sequence. We establish
connections between the two notions, in particular for languages given by
substitutions and, more generally, by sequences of substitutions. We show that
the two notions essentially coincide when the sequence of substitutions is
proper. For the example of Thue-Morse-Sturmian languages, we give a full
characterisation of factor-balancedness.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 12:53:06 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Poirier",
"Léo",
"",
"ENS Lyon"
],
[
"Steiner",
"Wolfgang",
"",
"IRIF"
]
] |
new_dataset
| 0.977915 |
2211.14125
|
Thomas Jantos
|
Thomas Jantos, Mohamed Amin Hamdad, Wolfgang Granig, Stephan Weiss,
Jan Steinbrener
|
PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose
Estimation
|
Supplementary material available:
https://www.aau.at/wp-content/uploads/2022/09/jantos_poet.pdf , Code
available: https://github.com/aau-cns/poet
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate 6D object pose estimation is an important task for a variety of
robotic applications such as grasping or localization. It is a challenging task
due to object symmetries, clutter and occlusion, but it becomes more
challenging when additional information, such as depth and 3D models, is not
provided. We present a transformer-based approach that takes an RGB image as
input and predicts a 6D pose for each object in the image. Besides the image,
our network does not require any additional information such as depth maps or
3D object models. First, the image is passed through an object detector to
generate feature maps and to detect objects. Then, the feature maps are fed
into a transformer with the detected bounding boxes as additional information.
Afterwards, the output object queries are processed by a separate translation
and rotation head. We achieve state-of-the-art results for RGB-only approaches
on the challenging YCB-V dataset. We illustrate the suitability of the
resulting model as pose sensor for a 6-DoF state estimation task. Code is
available at https://github.com/aau-cns/poet.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 14:07:14 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Jantos",
"Thomas",
""
],
[
"Hamdad",
"Mohamed Amin",
""
],
[
"Granig",
"Wolfgang",
""
],
[
"Weiss",
"Stephan",
""
],
[
"Steinbrener",
"Jan",
""
]
] |
new_dataset
| 0.974046 |
2211.14138
|
Ferenc Fejes
|
Ferenc Fejes, P\'eter Antal, M\'arton Kerekes
|
The TSN Building Blocks in Linux
|
Draft of the paper submitted to Netdev 0x16 conference. Link to the
submission:
https://netdevconf.info/0x16/session.html?The-TSN-building-blocks-in-Linux
| null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Various application areas e.g. industrial automation, professional
audio-video, automotive in-vehicle, aerospace on-board, and mobile fronthaul
networks require deterministic communication: loss-less forwarding with bounded
maximum latency. There is a lot of ongoing standardization activity in
different organizations to provide vendor-agnostic building blocks for
Time-Sensitive Networking (TSN), what is aimed as the universal solution for
deterministic forwarding in OSI Layer-2 networks. Furthermore, the
implementation of those standards is also happening in Linux. Some of them
require software changes only, but others have hardware support requirements.
In this paper, we give an overview of the implementation of the main TSN
standards in the mainline Linux kernel. Furthermore, we provide measurement
results on key functionality in support of TSN, e.g., scheduled transmission
and Linux bridging characteristics.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 14:30:35 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Fejes",
"Ferenc",
""
],
[
"Antal",
"Péter",
""
],
[
"Kerekes",
"Márton",
""
]
] |
new_dataset
| 0.997882 |
2211.14163
|
Xiong Lu
|
Xiong Lu, Yuxing Yan, Beibei Qi, Huang Qian, Junbin Sun, Aaron Quigley
|
Contactless Haptic Display Through Magnetic Field Control
| null |
in IEEE Transactions on Haptics, vol. 15, no. 2, pp. 328-338, 1
April-June 2022
|
10.1109/TOH.2022.3151673
| null |
cs.HC cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Haptic rendering enables people to touch, perceive, and manipulate virtual
objects in a virtual environment. Using six cascaded identical hollow disk
electromagnets and a small permanent magnet attached to an operator's finger,
this paper proposes and develops an untethered haptic interface through
magnetic field control. The concentric hole inside the six cascaded
electromagnets provides the workspace, where the 3D position of the permanent
magnet is tracked with a Microsoft Kinect sensor. The driving currents of six
cascaded electromagnets are calculated in real-time for generating the desired
magnetic force. Offline data from an FEA (finite element analysis) based
simulation, determines the relationship between the magnetic force, the driving
currents, and the position of the permanent magnet. A set of experiments
including the virtual object recognition experiment, the virtual surface
identification experiment, and the user perception evaluation experiment were
conducted to demonstrate the proposed system, where Microsoft HoloLens
holographic glasses are used for visual rendering. The proposed magnetic haptic
display leads to an untethered and non-contact interface for natural haptic
rendering applications, which overcomes the constraints of mechanical linkages
in tool-based traditional haptic devices.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 15:10:22 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Lu",
"Xiong",
""
],
[
"Yan",
"Yuxing",
""
],
[
"Qi",
"Beibei",
""
],
[
"Qian",
"Huang",
""
],
[
"Sun",
"Junbin",
""
],
[
"Quigley",
"Aaron",
""
]
] |
new_dataset
| 0.998376 |
2211.14196
|
Douglas Stebila
|
Jason Goertzen and Douglas Stebila
|
Post-Quantum Signatures in DNSSEC via Request-Based Fragmentation
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The Domain Name System Security Extensions (DNSSEC) provide authentication of
DNS responses using digital signatures. DNS operates primarily over UDP, which
leads to several constraints: notably, packets should be at most 1232 bytes
long to avoid problems during transmission. Larger DNS responses either need to
be fragmented into several UDP responses or the request would need to be
repeated over TCP, neither of which is sufficiently reliable in today's DNS
ecosystem. While RSA or elliptic curve digital signatures are sufficiently
small to avoid this problem, even for DNSSEC packets containing both a public
key and a signature, this problem is unavoidable when considering the larger
sizes of post-quantum schemes.
We propose ARRF, a method of fragmenting DNS resource records at the
application layer (rather than the transport layer) that is request-based,
meaning the initial response contains a truncated fragment and then the
requester sends follow-up requests for the remaining fragments. Using
request-based fragmentation avoids problems identified for several previously
proposed (and rejected) application-level DNS fragmentation techniques. We
implement our approach and evaluate its performance in a simulated network when
used for the three post-quantum digital signature schemes selected by NIST for
standardization (Falcon, Dilithium, and SPHINCS+) at the 128-bit security
level. Our experiments show that our request-based fragmentation approach
provides substantially lower resolution times compared to standard DNS over UDP
with TCP fallback, for all the tested post-quantum algorithms, and with less
data transmitted in the case of both Falcon and Dilithium. Furthermore, our
request-based fragmentation design can be implemented relatively easily: our
implementation is in fact a small daemon that can sit in front of a DNS name
server or resolver to fragment/reassemble transparently.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 15:54:50 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Goertzen",
"Jason",
""
],
[
"Stebila",
"Douglas",
""
]
] |
new_dataset
| 0.995038 |
2211.14233
|
\'Etienne Andr\'e
|
\'Etienne Andr\'e, Shapagat Bolat, Engel Lefaucheux, Dylan Marinho
|
strategFTO: Untimed control for timed opacity
|
This work is partially supported by the ANR-NRF French-Singaporean
research program ProMiS (ANR-19-CE25-0015 / 2019 ANR NRF 0092) and the ANR
research program BisoUS. Experiments presented in this paper were carried out
using the Grid'5000 testbed, supported by a scientific interest group hosted
by Inria and including CNRS, RENATER and several universities as well as
other organizations
|
Proceedings of the 8th International Workshop on Formal Techniques
for Safety-Critical Systems (FTSCS 2022)
|
10.1145/3563822.3568013
| null |
cs.CR cs.FL cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce a prototype tool strategFTO addressing the verification of a
security property in critical software. We consider a recent definition of
timed opacity where an attacker aims to deduce some secret while having access
only to the total execution time. The system, here modeled by timed automata,
is deemed opaque if for any execution time, there are either no corresponding
runs, or both public and private corresponding runs. We focus on the untimed
control problem: exhibiting a controller, i.e., a set of allowed actions, such
that the system restricted to those actions is fully timed-opaque. We first
show that this problem is not more complex than the full timed opacity problem,
and then we propose an algorithm, implemented and evaluated in practice.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 16:47:45 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"André",
"Étienne",
""
],
[
"Bolat",
"Shapagat",
""
],
[
"Lefaucheux",
"Engel",
""
],
[
"Marinho",
"Dylan",
""
]
] |
new_dataset
| 0.999052 |
2211.14234
|
Christopher Banks
|
Christopher J. Banks, Jessica Enright, Sibylle Mohr, Rowland R. Kao
|
Bovine Tuberculosis in Britain: identifying signatures of polarisation
and controversy on Twitter
| null | null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Approaches to disease control are influenced by and reflected in public
opinion, and the two are intrinsically entwined. Bovine tuberculosis (bTB) in
British cattle and badgers is one example where there is a high degree of
polarisation in opinion. Bovine viral diarrhoea (BVD), on the other hand, does
not have the same controversy.
In this paper we examine how language subjectivity on Twitter differs when
comparing the discourses surrounding bTB and BVD, using a combination of
network analysis and language and sentiment analysis. That data used for this
study was collected from the Twitter public API over a two-year period. We
investigated the network structure, language content, and user profiles of
tweets featuring both diseases.
While analysing network structure showed little difference between the two
disease topics, elements of the structure allowed us to better investigate the
language structure and profile of users. We found distinct differences between
the language and sentiment used in tweets about each disease, and in the
profile of the users who were doing the tweeting. We hope that this will guide
further investigation and potential avenues for surveillance or the control of
misinformation.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 16:53:33 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Banks",
"Christopher J.",
""
],
[
"Enright",
"Jessica",
""
],
[
"Mohr",
"Sibylle",
""
],
[
"Kao",
"Rowland R.",
""
]
] |
new_dataset
| 0.998318 |
2211.14259
|
\'Etienne Bamas
|
\'Etienne Bamas, Lars Rohwedder
|
Better Trees for Santa Claus
|
Abstract abridged to meet arXiv requirements
| null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We revisit the problem max-min degree arborescence, which was introduced by
Bateni et al. [STOC'09] as a central special case of the general Santa Claus
problem, which constitutes a notorious open question in approximation
algorithms. In the former problem we are given a directed graph with sources
and sinks and our goal is to find vertex disjoint arborescences rooted in the
sources such that at each non-sink vertex of an arborescence the out-degree is
at least $k$, where $k$ is to be maximized.
This problem is of particular interest, since it appears to capture much of
the difficulty of the Santa Claus problem: (1) like in the Santa Claus problem
the configuration LP has a large integrality gap in this case and (2) previous
progress by Bateni et al. was quickly generalized to the Santa Claus problem
(Chakrabarty et al. [FOCS'09]). These results remain the state-of-the-art both
for the Santa Claus problem and for max-min degree arborescence and they yield
a polylogarithmic approximation in quasi-polynomial time. We present an
exponential improvement to this, a $\mathrm{poly}(\log\log n)$-approximation in
quasi-polynomial time for the max-min degree arborescence problem. To the best
of our knowledge, this is the first example of breaking the logarithmic barrier
for a special case of the Santa Claus problem, where the configuration LP
cannot be utilized.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 17:38:15 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Bamas",
"Étienne",
""
],
[
"Rohwedder",
"Lars",
""
]
] |
new_dataset
| 0.958924 |
2211.14261
|
Nishanth Rao
|
Nishanth Rao, Suresh Sundaram, Pushpak Jagtap
|
Temporal Waypoint Navigation of Multi-UAV Payload System using Barrier
Functions
|
Submitted to ECC 2023
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Aerial package transportation often requires complex spatial and temporal
specifications to be satisfied in order to ensure safe and timely delivery from
one point to another. It is usually efficient to transport versatile payloads
using multiple UAVs that can work collaboratively to achieve the desired task.
The complex temporal specifications can be handled coherently by applying
Signal Temporal Logic (STL) to dynamical systems. This paper addresses the
problem of waypoint navigation of a multi-UAV payload system under temporal
specifications using higher-order time-varying control barrier functions
(HOCBFs). The complex nonlinear system of relative degree two is transformed
into a simple linear system using input-output feedback linearization. An
optimization-based control law is then derived to achieve the temporal waypoint
navigation of the payload. The controller's efficacy and real-time
implementability are demonstrated by simulating a package delivery scenario
inside a high-fidelity Gazebo simulation environment.
|
[
{
"version": "v1",
"created": "Fri, 25 Nov 2022 17:44:53 GMT"
}
] | 2022-11-28T00:00:00 |
[
[
"Rao",
"Nishanth",
""
],
[
"Sundaram",
"Suresh",
""
],
[
"Jagtap",
"Pushpak",
""
]
] |
new_dataset
| 0.983468 |
2102.02465
|
Lizhi Sun
|
Lizhi Sun, Shuocheng Wang, Hao Wu, Yuhang Gong, Fengyuan Xu, Yunxin
Liu, Hao Han, Sheng Zhong
|
LEAP: TrustZone Based Developer-Friendly TEE for Intelligent Mobile Apps
|
Accepted by IEEE Transactions on Mobile Computing
|
IEEE Trans. Mobile Comput.(2022)1-18
|
10.1109/TMC.2022.3207745
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
ARM TrustZone is widely deployed on commercial-off-the-shelf mobile devices
for secure execution. However, many Apps cannot enjoy this feature because it
brings many constraints to App developers. Previous works have been proposed to
build a secure execution environment for developers on top of TrustZone.
Unfortunately, these works are still not fully-fledged solutions for mobile
Apps, especially for emerging intelligent Apps. To this end, we propose LEAP,
which is a lightweight developer-friendly TEE solution for mobile Apps. LEAP
enables isolated codes to execute in parallel and access peripheral (e.g.,
mobile GPUs) with ease, flexibly manages system resources upon different
workloads, and offers the auto DevOps tool to help developers prepare the codes
running on it. We implement the LEAP prototype on the off-the-shelf ARM
platform and conduct extensive experiments on it. The experimental results show
that Apps can be adapted to run with LEAP easily and efficiently. Compared to
the state-of-the-art work along this research line, LEAP can achieve an average
3.57x speedup in supporting intelligent Apps using mobile GPU acceleration.
|
[
{
"version": "v1",
"created": "Thu, 4 Feb 2021 07:49:36 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Sep 2022 13:43:16 GMT"
},
{
"version": "v3",
"created": "Wed, 23 Nov 2022 05:31:46 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Sun",
"Lizhi",
""
],
[
"Wang",
"Shuocheng",
""
],
[
"Wu",
"Hao",
""
],
[
"Gong",
"Yuhang",
""
],
[
"Xu",
"Fengyuan",
""
],
[
"Liu",
"Yunxin",
""
],
[
"Han",
"Hao",
""
],
[
"Zhong",
"Sheng",
""
]
] |
new_dataset
| 0.999694 |
2201.11433
|
Arda Goknil
|
Ferhat Erata, Arda Goknil, Eren Y{\i}ld{\i}z, Kas{\i}m Sinan
Y{\i}ld{\i}r{\i}m, Ruzica Piskac, Jakub Szefer, and G\"ok\c{c}in Sezgin
|
ETAP: Energy-aware Timing Analysis of Intermittent Programs
|
Corrected typos in the previous submission
| null |
10.1145/3563216
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Energy harvesting battery-free embedded devices rely only on ambient energy
harvesting that enables stand-alone and sustainable IoT applications. These
devices execute programs when the harvested ambient energy in their energy
reservoir is sufficient to operate and stop execution abruptly (and start
charging) otherwise. These intermittent programs have varying timing behavior
under different energy conditions, hardware configurations, and program
structures. This paper presents Energy-aware Timing Analysis of intermittent
Programs (ETAP), a probabilistic symbolic execution approach that analyzes the
timing and energy behavior of intermittent programs at compile time. ETAP
symbolically executes the given program while taking time and energy cost
models for ambient energy and dynamic energy consumption into account. We
evaluated ETAP on several intermittent programs and compared the compile-time
analysis results with executions on real hardware. The results show that ETAP's
normalized prediction accuracy is 99.5%, and it speeds up the timing analysis
by at least two orders of magnitude compared to manual testing.
|
[
{
"version": "v1",
"created": "Thu, 27 Jan 2022 10:40:18 GMT"
},
{
"version": "v2",
"created": "Thu, 3 Feb 2022 20:15:28 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Erata",
"Ferhat",
""
],
[
"Goknil",
"Arda",
""
],
[
"Yıldız",
"Eren",
""
],
[
"Yıldırım",
"Kasım Sinan",
""
],
[
"Piskac",
"Ruzica",
""
],
[
"Szefer",
"Jakub",
""
],
[
"Sezgin",
"Gökçin",
""
]
] |
new_dataset
| 0.998524 |
2203.00314
|
Ziwei Ji
|
Ziwei Ji, Yan Xu, I-Tsun Cheng, Samuel Cahyawijaya, Rita Frieske,
Etsuko Ishii, Min Zeng, Andrea Madotto, Pascale Fung
|
VScript: Controllable Script Generation with Visual Presentation
| null |
AACL Demo (2022)
| null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In order to offer a customized script tool and inspire professional
scriptwriters, we present VScript. It is a controllable pipeline that generates
complete scripts, including dialogues and scene descriptions, as well as
presents visually using video retrieval. With an interactive interface, our
system allows users to select genres and input starting words that control the
theme and development of the generated script. We adopt a hierarchical
structure, which first generates the plot, then the script and its visual
presentation. A novel approach is also introduced to plot-guided dialogue
generation by treating it as an inverse dialogue summarization. The experiment
results show that our approach outperforms the baselines on both automatic and
human evaluations, especially in genre control.
|
[
{
"version": "v1",
"created": "Tue, 1 Mar 2022 09:43:02 GMT"
},
{
"version": "v2",
"created": "Thu, 13 Oct 2022 05:34:49 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Ji",
"Ziwei",
""
],
[
"Xu",
"Yan",
""
],
[
"Cheng",
"I-Tsun",
""
],
[
"Cahyawijaya",
"Samuel",
""
],
[
"Frieske",
"Rita",
""
],
[
"Ishii",
"Etsuko",
""
],
[
"Zeng",
"Min",
""
],
[
"Madotto",
"Andrea",
""
],
[
"Fung",
"Pascale",
""
]
] |
new_dataset
| 0.970679 |
2205.13284
|
Miguel \'A. Gonz\'alez-Santamarta
|
Miguel \'Angel Gonz\'alez-Santamarta, Francisco Javier
Rodr\'iguez-Lera, Camino Fern\'andez Llamas, Francisco Mart\'in Rico, and
Vicente Matell\'an Olivera
|
YASMIN: Yet Another State MachINe library for ROS 2
|
4 pages, 2 figures, ROSCon FR 2022
| null |
10.1007/978-3-031-21062-4_43
| null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
State machines are a common mechanism for defining behaviors in robots,
defining them based on identifiable stages. There are several libraries
available for easing the implementation of state machines in ROS 1, as SMACH or
SMACC, but there are fewer alternatives for ROS 2. YASMIN is yet another
library specifically designed for ROS 2 for easing the design of robotic
behaviors using state machines. It is available in C++ and Python, provides
some default states to speed up the development, and a web viewer for
monitoring the execution of the system and helping in the debugging.
|
[
{
"version": "v1",
"created": "Thu, 26 May 2022 11:43:02 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"González-Santamarta",
"Miguel Ángel",
""
],
[
"Rodríguez-Lera",
"Francisco Javier",
""
],
[
"Llamas",
"Camino Fernández",
""
],
[
"Rico",
"Francisco Martín",
""
],
[
"Olivera",
"Vicente Matellán",
""
]
] |
new_dataset
| 0.999729 |
2205.15712
|
Anna Wr\'oblewska
|
Micha{\l} Mo\.zd\.zonek, Anna Wr\'oblewska, Sergiy Tkachuk, Szymon
{\L}ukasik
|
Multilingual Transformers for Product Matching -- Experiments and a New
Benchmark in Polish
|
11 pages, 5 figures
|
revised version: 2022 IEEE International Conference on Fuzzy
Systems (FUZZ-IEEE) 2022
|
10.1109/fuzz-ieee55066.2022.9882843
| null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Product matching corresponds to the task of matching identical products
across different data sources. It typically employs available product features
which, apart from being multimodal, i.e., comprised of various data types,
might be non-homogeneous and incomplete. The paper shows that pre-trained,
multilingual Transformer models, after fine-tuning, are suitable for solving
the product matching problem using textual features both in English and Polish
languages. We tested multilingual mBERT and XLM-RoBERTa models in English on
Web Data Commons - training dataset and gold standard for large-scale product
matching. The obtained results show that these models perform similarly to the
latest solutions tested on this set, and in some cases, the results were even
better.
Additionally, we prepared a new dataset entirely in Polish and based on
offers in selected categories obtained from several online stores for the
research purpose. It is the first open dataset for product matching tasks in
Polish, which allows comparing the effectiveness of the pre-trained models.
Thus, we also showed the baseline results obtained by the fine-tuned mBERT and
XLM-RoBERTa models on the Polish datasets.
|
[
{
"version": "v1",
"created": "Tue, 31 May 2022 12:00:05 GMT"
},
{
"version": "v2",
"created": "Wed, 1 Jun 2022 07:59:45 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Możdżonek",
"Michał",
""
],
[
"Wróblewska",
"Anna",
""
],
[
"Tkachuk",
"Sergiy",
""
],
[
"Łukasik",
"Szymon",
""
]
] |
new_dataset
| 0.996982 |
2207.10397
|
Bei Chen
|
Bei Chen, Fengji Zhang, Anh Nguyen, Daoguang Zan, Zeqi Lin, Jian-Guang
Lou, Weizhu Chen
|
CodeT: Code Generation with Generated Tests
| null | null | null | null |
cs.CL cs.AI cs.PL cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The task of generating code solutions for a given programming problem can
benefit from the use of pre-trained language models such as Codex, which can
produce multiple diverse samples. However, a major challenge for this task is
to select the most appropriate solution from the multiple samples generated by
the pre-trained language models. A natural way to evaluate the quality and
correctness of a code solution is to run it against a set of test cases, but
the manual creation of such test cases is often costly and time-consuming. In
this paper, we propose a novel method, CodeT, that leverages the same
pre-trained language models to automatically generate test cases for the code
samples, thus reducing the human effort and increasing the coverage of the test
scenarios. CodeT then executes the code samples using the generated test cases,
and performs a dual execution agreement, which considers both the consistency
of the outputs against the generated test cases and the agreement of the
outputs with other code samples. We conduct comprehensive experiments on four
benchmarks, HumanEval, MBPP, APPS and CodeContests, using five different
pre-trained language models with varying sizes and capabilities. Our results
show that CodeT can significantly improve the performance of code solution
selection over previous methods, achieving remarkable and consistent gains
across different models and benchmarks. For instance, CodeT improves the pass@1
metric on HumanEval to 65.8%, which represents an absolute improvement of 18.8%
over the code-davinci-002 model, and an absolute improvement of more than 20%
over the previous state-of-the-art results.
|
[
{
"version": "v1",
"created": "Thu, 21 Jul 2022 10:18:37 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Nov 2022 07:42:10 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Chen",
"Bei",
""
],
[
"Zhang",
"Fengji",
""
],
[
"Nguyen",
"Anh",
""
],
[
"Zan",
"Daoguang",
""
],
[
"Lin",
"Zeqi",
""
],
[
"Lou",
"Jian-Guang",
""
],
[
"Chen",
"Weizhu",
""
]
] |
new_dataset
| 0.994424 |
2208.08846
|
Sebastian Neef
|
Sebastian Neef and Nils Wisiol
|
Oh SSH-it, what's my fingerprint? A Large-Scale Analysis of SSH Host Key
Fingerprint Verification Records in the DNS
|
Preprint; submitted to CANS 2022; accepted at CANS 2022 and published
in Springer LNCS vol 13641
|
In: Beresford, A.R., Patra, A., Bellini, E. (eds) Cryptology and
Network Security. CANS 2022. Lecture Notes in Computer Science, vol 13641.
Springer, Cham
|
10.1007/978-3-031-20974-1_4
| null |
cs.CR cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The SSH protocol is commonly used to access remote systems on the Internet,
as it provides an encrypted and authenticated channel for communication. If
upon establishing a new connection, the presented server key is unknown to the
client, the user is asked to verify the key fingerprint manually, which is
prone to errors and often blindly trusted. The SSH standard describes an
alternative to such manual key verification: using the Domain Name System (DNS)
to publish the server key information in SSHFP records.
In this paper, we conduct a large-scale Internet study to measure the
prevalence of SSHFP records among DNS domain names. We scan the Tranco 1M list
and over 500 million names from the certificate transparency log over the
course of 26 days. The results show that in two studied populations, about 1 in
10,000 domains has SSHFP records, with more than half of them deployed without
using DNSSEC, drastically reducing security benefits.
|
[
{
"version": "v1",
"created": "Thu, 18 Aug 2022 14:29:47 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Nov 2022 08:40:41 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Neef",
"Sebastian",
""
],
[
"Wisiol",
"Nils",
""
]
] |
new_dataset
| 0.997348 |
2210.15787
|
Ivan Stanojevi\'c
|
Ivan Stanojevi\'c, Vojin \v{S}enk
|
Convolutional Codes with Optimum Bidirectional Distance Profile
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we present tables of convolutional codes with an optimum
bidirectional distance profile (OBDP), defined as the minimum of the distance
profiles of the code and its corresponding "reverse" code. Such codes minimize
the average complexity of bidirectional sequential decoding algorithms. The
computer search is accelerated by the facts that optimum distance profile (ODP)
codes of larger memory must have ODP codes of smaller memory as their
"prefixes", and that OBDP codes can be obtained by "concatenating" ODP and
reverse ODP codes of smaller memory.
|
[
{
"version": "v1",
"created": "Thu, 27 Oct 2022 22:11:45 GMT"
},
{
"version": "v2",
"created": "Mon, 31 Oct 2022 21:35:48 GMT"
},
{
"version": "v3",
"created": "Tue, 22 Nov 2022 23:39:23 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Stanojević",
"Ivan",
""
],
[
"Šenk",
"Vojin",
""
]
] |
new_dataset
| 0.950269 |
2211.11294
|
Yordan Raykov
|
Kasper Claes, Valentina Ticcinelli, Reham Badawy, Yordan P. Raykov,
Luc J.W. Evers, Max A. Little
|
TSDF: A simple yet comprehensive, unified data storage and exchange
format standard for digital biosensor data in health applications
| null | null | null | null |
cs.DB
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Digital sensors are increasingly being used to monitor the change over time
of physiological processes in biological health and disease, often using
wearable devices. This generates very large amounts of digital sensor data, for
which, a consensus on a common storage, exchange and archival data format
standard, has yet to be reached. To address this gap, we propose Time Series
Data Format (TSDF): a unified, standardized format for storing all types of
physiological sensor data, across diverse disease areas. We pose a series of
format design criteria and review in detail current storage and exchange
formats. When judged against these criteria, we find these current formats
lacking, and propose a very simple, intuitive standard for both numerical
sensor data and metadata, based on raw binary data and JSON-format text files,
for sensor measurements/timestamps and metadata, respectively. By focusing on
the common characteristics of diverse biosensor data, we define a set of
necessary and sufficient metadata fields for storing, processing, exchanging,
archiving and reliably interpreting, multi-channel biological time series data.
Our aim is for this standardized format to increase the interpretability and
exchangeability of data, thereby contributing to scientific reproducibility in
studies where digital biosensor data forms a key evidence base.
|
[
{
"version": "v1",
"created": "Mon, 21 Nov 2022 09:36:22 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Nov 2022 23:18:22 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Claes",
"Kasper",
""
],
[
"Ticcinelli",
"Valentina",
""
],
[
"Badawy",
"Reham",
""
],
[
"Raykov",
"Yordan P.",
""
],
[
"Evers",
"Luc J. W.",
""
],
[
"Little",
"Max A.",
""
]
] |
new_dataset
| 0.9998 |
2211.12224
|
Igor Donevski
|
Igor Donevski, Marco Virgili, Nithin Babu, Jimmy Jessen Nielsen,
Andrew J. Forsyth, Constantinos B. Papadias, Petar Popovski
|
Sustainable Wireless Services with UAV Swarms Tailored to Renewable
Energy Sources
|
To be published in Transactions on Smart Grid
| null | null | null |
cs.NI eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Unmanned Aerial Vehicle (UAV) swarms are often required in off-grid
scenarios, such as disaster-struck, war-torn or rural areas, where the UAVs
have no access to the power grid and instead rely on renewable energy.
Considering a main battery fed from two renewable sources, wind and solar, we
scale such a system based on the financial budget, environmental
characteristics, and seasonal variations. Interestingly, the source of energy
is correlated with the energy expenditure of the UAVs, since strong winds cause
UAV hovering to become increasingly energy-hungry. The aim is to maximize the
cost efficiency of coverage at a particular location, which is a combinatorial
optimization problem for dimensioning of the multivariate energy generation
system under non-convex criteria. We have devised a customized algorithm by
lowering the processing complexity and reducing the solution space through
sampling. Evaluation is done with condensed real-world data on wind, solar
energy, and traffic load per unit area, driven by vendor-provided prices. The
implementation was tested in four locations, with varying wind or solar
intensity. The best results were achieved in locations with mild wind presence
and strong solar irradiation, while locations with strong winds and low solar
intensity require higher Capital Expenditure (CAPEX) allocation.
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 12:30:39 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Nov 2022 17:16:56 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Donevski",
"Igor",
""
],
[
"Virgili",
"Marco",
""
],
[
"Babu",
"Nithin",
""
],
[
"Nielsen",
"Jimmy Jessen",
""
],
[
"Forsyth",
"Andrew J.",
""
],
[
"Papadias",
"Constantinos B.",
""
],
[
"Popovski",
"Petar",
""
]
] |
new_dataset
| 0.991444 |
2211.12352
|
Chao Wang
|
Chao Wang, Ana Serrano, Xingang Pan, Bin Chen, Hans-Peter Seidel,
Christian Theobalt, Karol Myszkowski, Thomas Leimkuehler
|
GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild
| null | null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Most in-the-wild images are stored in Low Dynamic Range (LDR) form, serving
as a partial observation of the High Dynamic Range (HDR) visual world. Despite
limited dynamic range, these LDR images are often captured with different
exposures, implicitly containing information about the underlying HDR image
distribution. Inspired by this intuition, in this work we present, to the best
of our knowledge, the first method for learning a generative model of HDR
images from in-the-wild LDR image collections in a fully unsupervised manner.
The key idea is to train a generative adversarial network (GAN) to generate HDR
images which, when projected to LDR under various exposures, are
indistinguishable from real LDR images. The projection from HDR to LDR is
achieved via a camera model that captures the stochasticity in exposure and
camera response function. Experiments show that our method GlowGAN can
synthesize photorealistic HDR images in many challenging cases such as
landscapes, lightning, or windows, where previous supervised generative models
produce overexposed images. We further demonstrate the new application of
unsupervised inverse tone mapping (ITM) enabled by GlowGAN. Our ITM method does
not need HDR images or paired multi-exposure images for training, yet it
reconstructs more plausible information for overexposed regions than
state-of-the-art supervised learning models trained on such data.
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 15:42:08 GMT"
},
{
"version": "v2",
"created": "Wed, 23 Nov 2022 10:12:43 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Wang",
"Chao",
""
],
[
"Serrano",
"Ana",
""
],
[
"Pan",
"Xingang",
""
],
[
"Chen",
"Bin",
""
],
[
"Seidel",
"Hans-Peter",
""
],
[
"Theobalt",
"Christian",
""
],
[
"Myszkowski",
"Karol",
""
],
[
"Leimkuehler",
"Thomas",
""
]
] |
new_dataset
| 0.960065 |
2211.12544
|
Casey Peat
|
Casey Peat, Oliver Batchelor, Richard Green, James Atlas
|
Zero NeRF: Registration with Zero Overlap
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
We present Zero-NeRF, a projective surface registration method that, to the
best of our knowledge, offers the first general solution capable of alignment
between scene representations with minimal or zero visual correspondence. To do
this, we enforce consistency between visible surfaces of partial and complete
reconstructions, which allows us to constrain occluded geometry. We use a NeRF
as our surface representation and the NeRF rendering pipeline to perform this
alignment. To demonstrate the efficacy of our method, we register real-world
scenes from opposite sides with infinitesimal overlaps that cannot be
accurately registered using prior methods, and we compare these results against
widely used registration methods.
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 19:29:48 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Peat",
"Casey",
""
],
[
"Batchelor",
"Oliver",
""
],
[
"Green",
"Richard",
""
],
[
"Atlas",
"James",
""
]
] |
new_dataset
| 0.997344 |
2211.12570
|
Tharindu Ranasinghe Dr
|
Marcos Zampieri, Tharindu Ranasinghe, Mrinal Chaudhari, Saurabh
Gaikwad, Prajwal Krishna, Mayuresh Nene, Shrunali Paygude
|
Predicting the Type and Target of Offensive Social Media Posts in
Marathi
|
This is a preprint of an article published in the Journal of
Intelligent Information Systems, Springer. The final authenticated version is
available online at
https://link.springer.com/article/10.1007/s13278-022-00906-8
| null | null | null |
cs.CL cs.AI cs.CY cs.LG cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
The presence of offensive language on social media is very common motivating
platforms to invest in strategies to make communities safer. This includes
developing robust machine learning systems capable of recognizing offensive
content online. Apart from a few notable exceptions, most research on automatic
offensive language identification has dealt with English and a few other high
resource languages such as French, German, and Spanish. In this paper we
address this gap by tackling offensive language identification in Marathi, a
low-resource Indo-Aryan language spoken in India. We introduce the Marathi
Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments
on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded
annotation to the levels B (type) and C (target) of the popular OLID taxonomy.
MOLD 2.0 is the first hierarchical offensive language dataset compiled for
Marathi, thus opening new avenues for research in low-resource Indo-Aryan
languages. Finally, we also introduce SeMOLD, a larger dataset annotated
following the semi-supervised methods presented in SOLID.
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 20:36:44 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Zampieri",
"Marcos",
""
],
[
"Ranasinghe",
"Tharindu",
""
],
[
"Chaudhari",
"Mrinal",
""
],
[
"Gaikwad",
"Saurabh",
""
],
[
"Krishna",
"Prajwal",
""
],
[
"Nene",
"Mayuresh",
""
],
[
"Paygude",
"Shrunali",
""
]
] |
new_dataset
| 0.991227 |
2211.12604
|
Nataliya Shapovalova
|
Tejas Khot, Nataliya Shapovalova, Silviu Andrei, Walterio Mayol-Cuevas
|
SuperTran: Reference Based Video Transformer for Enhancing Low Bitrate
Streams in Real Time
|
4 pages
| null | null | null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
This work focuses on low bitrate video streaming scenarios (e.g. 50 -
200Kbps) where the video quality is severely compromised. We present a family
of novel deep generative models for enhancing perceptual video quality of such
streams by performing super-resolution while also removing compression
artifacts. Our model, which we call SuperTran, consumes as input a single
high-quality, high-resolution reference images in addition to the low-quality,
low-resolution video stream. The model thus learns how to borrow or copy visual
elements like textures from the reference image and fill in the remaining
details from the low resolution stream in order to produce perceptually
enhanced output video. The reference frame can be sent once at the start of the
video session or be retrieved from a gallery. Importantly, the resulting output
has substantially better detail than what has been otherwise possible with
methods that only use a low resolution input such as the SuperVEGAN method.
SuperTran works in real-time (up to 30 frames/sec) on the cloud alongside
standard pipelines.
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 22:03:11 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Khot",
"Tejas",
""
],
[
"Shapovalova",
"Nataliya",
""
],
[
"Andrei",
"Silviu",
""
],
[
"Mayol-Cuevas",
"Walterio",
""
]
] |
new_dataset
| 0.99876 |
2211.12656
|
Huangying Zhan
|
Huangying Zhan, Jiyang Zheng, Yi Xu, Ian Reid, Hamid Rezatofighi
|
ActiveRMAP: Radiance Field for Active Mapping And Planning
|
Under review
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by-sa/4.0/
|
A high-quality 3D reconstruction of a scene from a collection of 2D images
can be achieved through offline/online mapping methods. In this paper, we
explore active mapping from the perspective of implicit representations, which
have recently produced compelling results in a variety of applications. One of
the most popular implicit representations - Neural Radiance Field (NeRF), first
demonstrated photorealistic rendering results using multi-layer perceptrons,
with promising offline 3D reconstruction as a by-product of the radiance field.
More recently, researchers also applied this implicit representation for online
reconstruction and localization (i.e. implicit SLAM systems). However, the
study on using implicit representation for active vision tasks is still very
limited. In this paper, we are particularly interested in applying the neural
radiance field for active mapping and planning problems, which are closely
coupled tasks in an active system. We, for the first time, present an RGB-only
active vision framework using radiance field representation for active 3D
reconstruction and planning in an online manner. Specifically, we formulate
this joint task as an iterative dual-stage optimization problem, where we
alternatively optimize for the radiance field representation and path planning.
Experimental results suggest that the proposed method achieves competitive
results compared to other offline methods and outperforms active reconstruction
methods using NeRFs.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 01:19:30 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Zhan",
"Huangying",
""
],
[
"Zheng",
"Jiyang",
""
],
[
"Xu",
"Yi",
""
],
[
"Reid",
"Ian",
""
],
[
"Rezatofighi",
"Hamid",
""
]
] |
new_dataset
| 0.996292 |
2211.12668
|
Gong Cheng
|
Xiao Li, Yin Zhu, Sichen Liu, Jiangzhou Ju, Yuzhong Qu, Gong Cheng
|
DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical
Reasoning over Tabular and Textual Data
|
9 pages, accepted by AAAI 2023
| null | null | null |
cs.CL cs.AI cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
Numerical reasoning over hybrid data containing tables and long texts has
recently received research attention from the AI community. To generate an
executable reasoning program consisting of math and table operations to answer
a question, state-of-the-art methods use a retriever-generator pipeline.
However, their retrieval results are static, while different generation steps
may rely on different sentences. To attend to the retrieved information that is
relevant to each generation step, in this paper, we propose DyRRen, an extended
retriever-reranker-generator framework where each generation step is enhanced
by a dynamic reranking of retrieved sentences. It outperforms existing
baselines on the FinQA dataset.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 02:41:50 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Li",
"Xiao",
""
],
[
"Zhu",
"Yin",
""
],
[
"Liu",
"Sichen",
""
],
[
"Ju",
"Jiangzhou",
""
],
[
"Qu",
"Yuzhong",
""
],
[
"Cheng",
"Gong",
""
]
] |
new_dataset
| 0.994311 |
2211.12737
|
Pierre Chambon
|
Pierre Chambon, Christian Bluethgen, Jean-Benoit Delbrouck, Rogier Van
der Sluijs, Ma{\l}gorzata Po{\l}acin, Juan Manuel Zambrano Chaves, Tanishq
Mathew Abraham, Shivanshu Purohit, Curtis P. Langlotz, Akshay Chaudhari
|
RoentGen: Vision-Language Foundation Model for Chest X-ray Generation
|
19 pages
| null | null | null |
cs.CV cs.AI cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multimodal models trained on large natural image-text pair datasets have
exhibited astounding abilities in generating high-quality images. Medical
imaging data is fundamentally different to natural images, and the language
used to succinctly capture relevant details in medical data uses a different,
narrow but semantically rich, domain-specific vocabulary. Not surprisingly,
multi-modal models trained on natural image-text pairs do not tend to
generalize well to the medical domain. Developing generative imaging models
faithfully representing medical concepts while providing compositional
diversity could mitigate the existing paucity of high-quality, annotated
medical imaging datasets. In this work, we develop a strategy to overcome the
large natural-medical distributional shift by adapting a pre-trained latent
diffusion model on a corpus of publicly available chest x-rays (CXR) and their
corresponding radiology (text) reports. We investigate the model's ability to
generate high-fidelity, diverse synthetic CXR conditioned on text prompts. We
assess the model outputs quantitatively using image quality metrics, and
evaluate image quality and text-image alignment by human domain experts. We
present evidence that the resulting model (RoentGen) is able to create visually
convincing, diverse synthetic CXR images, and that the output can be controlled
to a new extent by using free-form text prompts including radiology-specific
language. Fine-tuning this model on a fixed training set and using it as a data
augmentation method, we measure a 5% improvement of a classifier trained
jointly on synthetic and real images, and a 3% improvement when trained on a
larger but purely synthetic training set. Finally, we observe that this
fine-tuning distills in-domain knowledge in the text-encoder and can improve
its representation capabilities of certain diseases like pneumothorax by 25%.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 06:58:09 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Chambon",
"Pierre",
""
],
[
"Bluethgen",
"Christian",
""
],
[
"Delbrouck",
"Jean-Benoit",
""
],
[
"Van der Sluijs",
"Rogier",
""
],
[
"Połacin",
"Małgorzata",
""
],
[
"Chaves",
"Juan Manuel Zambrano",
""
],
[
"Abraham",
"Tanishq Mathew",
""
],
[
"Purohit",
"Shivanshu",
""
],
[
"Langlotz",
"Curtis P.",
""
],
[
"Chaudhari",
"Akshay",
""
]
] |
new_dataset
| 0.956254 |
2211.12752
|
Abhilasha Sancheti
|
Abhilasha Sancheti, Aparna Garimella, Balaji Vasan Srinivasan, Rachel
Rudinger
|
Agent-Specific Deontic Modality Detection in Legal Language
|
Accepted at EMNLP 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Legal documents are typically long and written in legalese, which makes it
particularly difficult for laypeople to understand their rights and duties.
While natural language understanding technologies can be valuable in supporting
such understanding in the legal domain, the limited availability of datasets
annotated for deontic modalities in the legal domain, due to the cost of hiring
experts and privacy issues, is a bottleneck. To this end, we introduce,
LEXDEMOD, a corpus of English contracts annotated with deontic modality
expressed with respect to a contracting party or agent along with the modal
triggers. We benchmark this dataset on two tasks: (i) agent-specific
multi-label deontic modality classification, and (ii) agent-specific deontic
modality and trigger span detection using Transformer-based (Vaswani et al.,
2017) language models. Transfer learning experiments show that the linguistic
diversity of modal expressions in LEXDEMOD generalizes reasonably from lease to
employment and rental agreements. A small case study indicates that a model
trained on LEXDEMOD can detect red flags with high recall. We believe our work
offers a new research direction for deontic modality detection in the legal
domain.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 07:32:23 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Sancheti",
"Abhilasha",
""
],
[
"Garimella",
"Aparna",
""
],
[
"Srinivasan",
"Balaji Vasan",
""
],
[
"Rudinger",
"Rachel",
""
]
] |
new_dataset
| 0.999626 |
2211.12796
|
Deepa Gopinath PhD
|
Deepa P Gopinath, Thennal D K, Vrinda V Nair, Swaraj K S, Sachin G
|
IMaSC -- ICFOSS Malayalam Speech Corpus
|
18 pages, 8 figures
| null | null | null |
cs.SD cs.CL eess.AS
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Modern text-to-speech (TTS) systems use deep learning to synthesize speech
increasingly approaching human quality, but they require a database of high
quality audio-text sentence pairs for training. Malayalam, the official
language of the Indian state of Kerala and spoken by 35+ million people, is a
low resource language in terms of available corpora for TTS systems. In this
paper, we present IMaSC, a Malayalam text and speech corpora containing
approximately 50 hours of recorded speech. With 8 speakers and a total of
34,473 text-audio pairs, IMaSC is larger than every other publicly available
alternative. We evaluated the database by using it to train TTS models for each
speaker based on a modern deep learning architecture. Via subjective
evaluation, we show that our models perform significantly better in terms of
naturalness compared to previous studies and publicly available models, with an
average mean opinion score of 4.50, indicating that the synthesized speech is
close to human quality.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 09:21:01 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Gopinath",
"Deepa P",
""
],
[
"K",
"Thennal D",
""
],
[
"Nair",
"Vrinda V",
""
],
[
"S",
"Swaraj K",
""
],
[
"G",
"Sachin",
""
]
] |
new_dataset
| 0.999749 |
2211.12799
|
Mital Kinderkhedia
|
Juan Cruz Viotti, Mital Kinderkhedia
|
Benchmarking JSON BinPack
|
41 Pages. arXiv admin note: substantial text overlap with
arXiv:2201.03051
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we present benchmark results for a pre-production
implementation of a novel serialization specification: JSON BinPack. JSON
BinPack is a schema-driven and schema-less sequential binary serialization
specification based on JSON Schema. It is rich in diverse encodings, and is
developed to improve network performance and reduce the operational costs of
Internet-based software systems. We present benchmark results for 27 JSON
documents and for each plot, we show the schema-driven and schema-less
serialization specifications that produce the smallest bit-strings. Through
extensive plots and statistical comparisons, we show that JSON BinPack in
schema-driven mode is as space-efficient or more space-efficient than every
other serialization specification for the 27 documents under consideration. In
comparison to JSON, JSON BinPack in schema-driven mode provides a median and
average size reductions of 86.7% and 78.7%, respectively. We also show that the
schema-less mode of the JSON BinPack binary serialization specification is as
space-efficient or more space-efficient than every other schema-less
serialization specification for the 27 documents under consideration. In
comparison to JSON, JSON BinPack in schema-less mode provides a median and
average size reductions of 30.6% and 30.5%, respectively. Unlike other
considered schema-driven binary serialization specifications, JSON BinPack in
schema-driven mode is space-efficient in comparison to best-case compressed
JSON in terms of the median and average with size reductions of 76.1% and
66.8%, respectively. We have made our benchmark results available at
jviotti/binary-json-size-benchmark on GitHub.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 09:33:05 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Viotti",
"Juan Cruz",
""
],
[
"Kinderkhedia",
"Mital",
""
]
] |
new_dataset
| 0.999788 |
2211.12827
|
Tianyu Wang
|
Zhenghao Xing, Tianyu Wang, Xiaowei Hu, Haoran Wu, Chi-Wing Fu,
Pheng-Ann Heng
|
Video Instance Shadow Detection
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Video instance shadow detection aims to simultaneously detect, segment,
associate, and track paired shadow-object associations in videos. This work has
three key contributions to the task. First, we design SSIS-Track, a new
framework to extract shadow-object associations in videos with paired tracking
and without category specification; especially, we strive to maintain paired
tracking even the objects/shadows are temporarily occluded for several frames.
Second, we leverage both labeled images and unlabeled videos, and explore
temporal coherence by augmenting the tracking ability via an association cycle
consistency loss to optimize SSIS-Track's performance. Last, we build
$\textit{SOBA-VID}$, a new dataset with 232 unlabeled videos of ${5,863}$
frames for training and 60 labeled videos of ${1,182}$ frames for testing.
Experimental results show that SSIS-Track surpasses baselines built from SOTA
video tracking and instance-shadow-detection methods by a large margin. In the
end, we showcase several video-level applications.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 10:20:19 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Xing",
"Zhenghao",
""
],
[
"Wang",
"Tianyu",
""
],
[
"Hu",
"Xiaowei",
""
],
[
"Wu",
"Haoran",
""
],
[
"Fu",
"Chi-Wing",
""
],
[
"Heng",
"Pheng-Ann",
""
]
] |
new_dataset
| 0.976458 |
2211.12852
|
Nicholas Walker
|
Nicholas Thomas Walker, Stefan Ultes, Pierre Lison
|
GraphWOZ: Dialogue Management with Conversational Knowledge Graphs
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We present a new approach to dialogue management using conversational
knowledge graphs as core representation of the dialogue state. To this end, we
introduce a new dataset, GraphWOZ, which comprises Wizard-of-Oz dialogues in
which human participants interact with a robot acting as a receptionist. In
contrast to most existing work on dialogue management, GraphWOZ relies on a
dialogue state explicitly represented as a dynamic knowledge graph instead of a
fixed set of slots. This graph is composed of a varying number of entities
(such as individuals, places, events, utterances and mentions) and relations
between them (such as persons being part of a group or attending an event). The
graph is then regularly updated on the basis of new observations and system
actions. GraphWOZ is released along with detailed manual annotations related to
the user intents, system responses, and reference relations occurring in both
user and system turns. Based on GraphWOZ, we present experimental results for
two dialogue management tasks, namely conversational entity linking and
response ranking. For conversational entity linking, we show how to connect
utterance mentions to their corresponding entity in the knowledge graph with a
neural model relying on a combination of both string and graph-based features.
Response ranking is then performed by summarizing the relevant content of the
graph into a text, which is concatenated with the dialogue history and employed
as input to score possible responses to a given dialogue state.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 10:53:21 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Walker",
"Nicholas Thomas",
""
],
[
"Ultes",
"Stefan",
""
],
[
"Lison",
"Pierre",
""
]
] |
new_dataset
| 0.999552 |
2211.12988
|
Yuntao Wang
|
Yuntao Wang, Zhou Su, Qichao Xu, Ruidong Li, Tom H. Luan, and Pinghui
Wang
|
A Secure and Intelligent Data Sharing Scheme for UAV-Assisted Disaster
Rescue
|
Accepted by IEEE/ACM Transactions on Networking (ToN)
| null | null | null |
cs.MA cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unmanned aerial vehicles (UAVs) have the potential to establish flexible and
reliable emergency networks in disaster sites when terrestrial communication
infrastructures go down. Nevertheless, potential security threats may occur on
UAVs during data transmissions due to the untrusted environment and open-access
UAV networks. Moreover, UAVs typically have limited battery and computation
capacity, making them unaffordable for heavy security provisioning operations
when performing complicated rescue tasks. In this paper, we develop
RescueChain, a secure and efficient information sharing scheme for UAV-assisted
disaster rescue. Specifically, we first implement a lightweight
blockchain-based framework to safeguard data sharing under disasters and
immutably trace misbehaving entities. A reputation-based consensus protocol is
devised to adapt the weakly connected environment with improved consensus
efficiency and promoted UAVs' honest behaviors. Furthermore, we introduce a
novel vehicular fog computing (VFC)-based off-chain mechanism by leveraging
ground vehicles as moving fog nodes to offload UAVs' heavy data processing and
storage tasks. To offload computational tasks from the UAVs to ground vehicles
having idle computing resources, an optimal allocation strategy is developed by
choosing payoffs that achieve equilibrium in a Stackelberg game formulation of
the allocation problem. For lack of sufficient knowledge on network model
parameters and users' private cost parameters in practical environment, we also
design a two-tier deep reinforcement learning-based algorithm to seek the
optimal payment and resource strategies of UAVs and vehicles with improved
learning efficiency. Simulation results show that RescueChain can effectively
accelerate consensus process, improve offloading efficiency, reduce energy
consumption, and enhance user payoffs.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 14:49:08 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Wang",
"Yuntao",
""
],
[
"Su",
"Zhou",
""
],
[
"Xu",
"Qichao",
""
],
[
"Li",
"Ruidong",
""
],
[
"Luan",
"Tom H.",
""
],
[
"Wang",
"Pinghui",
""
]
] |
new_dataset
| 0.985098 |
2211.13067
|
Tianyu Wang
|
Tianyu Wang, Xiaowei Hu, Zhengzhe Liu, Chi-Wing Fu
|
Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection
|
Accepted to 36th Conference on Neural Information Processing Systems
(NeurIPS 2022). Code will be released on
https://github.com/stevewongv/Sparse2Dense
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
LiDAR-produced point clouds are the major source for most state-of-the-art 3D
object detectors. Yet, small, distant, and incomplete objects with sparse or
few points are often hard to detect. We present Sparse2Dense, a new framework
to efficiently boost 3D detection performance by learning to densify point
clouds in latent space. Specifically, we first train a dense point 3D detector
(DDet) with a dense point cloud as input and design a sparse point 3D detector
(SDet) with a regular point cloud as input. Importantly, we formulate the
lightweight plug-in S2D module and the point cloud reconstruction module in
SDet to densify 3D features and train SDet to produce 3D features, following
the dense 3D features in DDet. So, in inference, SDet can simulate dense 3D
features from regular (sparse) point cloud inputs without requiring dense
inputs. We evaluate our method on the large-scale Waymo Open Dataset and the
Waymo Domain Adaptation Dataset, showing its high performance and efficiency
over the state of the arts.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 16:01:06 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Wang",
"Tianyu",
""
],
[
"Hu",
"Xiaowei",
""
],
[
"Liu",
"Zhengzhe",
""
],
[
"Fu",
"Chi-Wing",
""
]
] |
new_dataset
| 0.998959 |
2211.13091
|
Zhen Hao Gan
|
Zhen Hao Gan, Yangwei You, Meng Yee (Michael) Chuah
|
Navigation with Tactile Sensor for Natural Human-Robot Interaction
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Tactile sensors have been introduced to a wide range of robotic tasks such as
robot manipulation to mimic the sense of human touch. However, there has only
been a few works that integrate tactile sensing into robot navigation. This
paper describes a navigation system which allows robots to operate in crowded
human-dense environments and behave with socially acceptable reactions by
utilizing semantic and force information collected by embedded tactile sensors,
RGB-D camera and LiDAR. Compliance control is implemented based on artificial
potential fields considering not only laser scan but also force reading from
tactile sensors which promises a fast and reliable response to any possible
collision. In contrast to cameras, LiDAR and other non-contact sensors, tactile
sensors can directly interact with humans and can be used to accept social cues
akin to natural human behavior under the same situation. Furthermore,
leveraging semantic segmentation from vision module, the robot is able to
identify and, therefore assign varying social cost to different groups of
humans enabling for socially conscious path planning. At the end of this paper,
the proposed control strategy was validated successfully by testing several
scenarios on an omni-directional robot in real world.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 16:19:56 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Gan",
"Zhen Hao",
"",
"Michael"
],
[
"You",
"Yangwei",
"",
"Michael"
],
[
"Yee",
"Meng",
"",
"Michael"
],
[
"Chuah",
"",
""
]
] |
new_dataset
| 0.995318 |
2211.13114
|
Ali Abedi
|
Shehroz S. Khan and Ali Abedi
|
Step Counting with Attention-based LSTM
| null | null | null |
EFI-94-11
|
cs.CV eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Physical activity is recognized as an essential component of overall health.
One measure of physical activity, the step count, is well known as a predictor
of long-term morbidity and mortality. Step Counting (SC) is the automated
counting of the number of steps an individual takes over a specified period of
time and space. Due to the ubiquity of smartphones and smartwatches, most
current SC approaches rely on the built-in accelerometer sensors on these
devices. The sensor signals are analyzed as multivariate time series, and the
number of steps is calculated through a variety of approaches, such as
time-domain, frequency-domain, machine-learning, and deep-learning approaches.
Most of the existing approaches rely on dividing the input signal into windows,
detecting steps in each window, and summing the detected steps. However, these
approaches require the determination of multiple parameters, including the
window size. Furthermore, most of the existing deep-learning SC approaches
require ground-truth labels for every single step, which can be arduous and
time-consuming to annotate. To circumvent these requirements, we present a
novel SC approach utilizing many-to-one attention-based LSTM. With the proposed
LSTM network, SC is solved as a regression problem, taking the entire sensor
signal as input and the step count as the output. The analysis shows that the
attention-based LSTM automatically learned the pattern of steps even in the
absence of ground-truth labels. The experimental results on three publicly
available SC datasets demonstrate that the proposed method successfully counts
the number of steps with low values of mean absolute error and high values of
SC accuracy.
|
[
{
"version": "v1",
"created": "Fri, 18 Nov 2022 03:33:57 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Khan",
"Shehroz S.",
""
],
[
"Abedi",
"Ali",
""
]
] |
new_dataset
| 0.986982 |
2211.13121
|
Onur Varol
|
Ali Najafi, Nihat Mugurtay, Ege Demirci, Serhat Demirkiran, Huseyin
Alper Karadeniz, Onur Varol
|
#Secim2023: First Public Dataset for Studying Turkish General Election
|
22 pages, 9 figures
| null | null | null |
cs.SI cs.CY cs.IR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In the context of Turkey's upcoming parliamentary and presidential elections
("se\c{c}im" in Turkish), social media is playing an important role in shaping
public debate. The increasing engagement of citizens on social media platforms
has led to the growing use of social media by political actors. It is of utmost
importance to capture the upcoming Turkish elections, as social media is
becoming an essential component of election propaganda, political debates,
smear campaigns, and election manipulation by domestic and international
actors. We provide a comprehensive dataset for social media researchers to
study the upcoming election, develop tools to prevent online manipulation, and
gather novel information to inform the public. We are committed to continually
improving the data collection and updating it regularly leading up to the
election. Using the Secim2023 dataset, researchers can examine the social and
communication networks between political actors, track current trends, and
investigate emerging threats to election integrity. Our dataset is available
at: https://github.com/ViralLab/Secim2023_Dataset
|
[
{
"version": "v1",
"created": "Tue, 22 Nov 2022 11:42:32 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Najafi",
"Ali",
""
],
[
"Mugurtay",
"Nihat",
""
],
[
"Demirci",
"Ege",
""
],
[
"Demirkiran",
"Serhat",
""
],
[
"Karadeniz",
"Huseyin Alper",
""
],
[
"Varol",
"Onur",
""
]
] |
new_dataset
| 0.999894 |
2211.13184
|
Shuming Ma
|
Shuming Ma, Hongyu Wang, Shaohan Huang, Wenhui Wang, Zewen Chi, Li
Dong, Alon Benhaim, Barun Patra, Vishrav Chaudhary, Xia Song, Furu Wei
|
TorchScale: Transformers at Scale
|
Work in progress
| null | null | null |
cs.LG cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large Transformers have achieved state-of-the-art performance across many
tasks. Most open-source libraries on scaling Transformers focus on improving
training or inference with better parallelization. In this work, we present
TorchScale, an open-source toolkit that allows researchers and developers to
scale up Transformers efficiently and effectively. TorchScale has the
implementation of several modeling techniques, which can improve modeling
generality and capability, as well as training stability and efficiency.
Experimental results on language modeling and neural machine translation
demonstrate that TorchScale can successfully scale Transformers to different
sizes without tears. The library is available at https://aka.ms/torchscale.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 17:58:51 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Ma",
"Shuming",
""
],
[
"Wang",
"Hongyu",
""
],
[
"Huang",
"Shaohan",
""
],
[
"Wang",
"Wenhui",
""
],
[
"Chi",
"Zewen",
""
],
[
"Dong",
"Li",
""
],
[
"Benhaim",
"Alon",
""
],
[
"Patra",
"Barun",
""
],
[
"Chaudhary",
"Vishrav",
""
],
[
"Song",
"Xia",
""
],
[
"Wei",
"Furu",
""
]
] |
new_dataset
| 0.999477 |
2211.13189
|
Sara Atito
|
Sara Atito, Muhammad Awais, Wenwu Wang, Mark D Plumbley, Josef Kittler
|
ASiT: Audio Spectrogram vIsion Transformer for General Audio
Representation
| null | null | null | null |
cs.SD cs.CV eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Vision transformers, which were originally developed for natural language
processing, have recently generated significant interest in the computer vision
and audio communities due to their flexibility in learning long-range
relationships. Constrained by data hungry nature of transformers and limited
labelled data most transformer-based models for audio tasks are finetuned from
ImageNet pretrained models, despite the huge gap between the natural images
domain and audio domain. This has motivated the research in self-supervised
pretraining of audio transformers, which reduces the dependency on large
amounts of labeled data and focuses on extracting concise representation of the
audio spectrograms. In this paper, we propose ASiT, a novel self-supervised
transformer for general audio representations that captures local and global
contextual information employing group masked model learning and
self-distillation. We evaluate our pretrained models on both audio and speech
classification tasks including audio event classification, keyword spotting,
and speaker identification. We further conduct comprehensive ablation studies,
including evaluations of different pretraining strategies. The proposed ASiT
framework significantly boosts the performance on all tasks and sets a new
state-of-the-art performance on five audio and speech classification tasks,
outperforming recent methods, including the approaches that use additional
datasets for pretraining. The code and pretrained weights will be made publicly
available for the scientific community.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 18:21:09 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Atito",
"Sara",
""
],
[
"Awais",
"Muhammad",
""
],
[
"Wang",
"Wenwu",
""
],
[
"Plumbley",
"Mark D",
""
],
[
"Kittler",
"Josef",
""
]
] |
new_dataset
| 0.992937 |
2211.13195
|
Maliheh Shirvanian
|
Mihai Christodorescu, Maliheh Shirvanian, and Shams Zawoad
|
Privacy-Preserving Application-to-Application Authentication Using
Dynamic Runtime Behaviors
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Application authentication is typically performed using some form of secret
credentials such as cryptographic keys, passwords, or API keys. Since clients
are responsible for securely storing and managing the keys, this approach is
vulnerable to attacks on clients. Similarly a centrally managed key store is
also susceptible to various attacks and if compromised, can leak credentials.
To resolve such issues, we propose an application authentication, where we rely
on unique and distinguishable application's behavior to lock the key during a
setup phase and unlock it for authentication. Our system add a fuzzy-extractor
layer on top of current credential authentication systems. During a key
enrollment process, the application's behavioral data collected from various
sensors in the network are used to hide the credential key. The fuzzy extractor
releases the key to the server if the application's behavior during the
authentication matches the one collected during the enrollment, with some noise
tolerance. We designed the system, analyzed its security, and implemented and
evaluated it using 10 real-life applications deployed in our network. Our
security analysis shows that the system is secure against client compromise,
vault compromise, and feature observation. The evaluation shows the scheme can
achieve 0 percent False Accept Rate with an average False Rejection Rate 14
percent and takes about 51 ms to successfully authenticate a client. In light
of these promising results, we expect our system to be of practical use, since
its deployment requires zero to minimal changes on the server.
|
[
{
"version": "v1",
"created": "Wed, 23 Nov 2022 18:28:39 GMT"
}
] | 2022-11-24T00:00:00 |
[
[
"Christodorescu",
"Mihai",
""
],
[
"Shirvanian",
"Maliheh",
""
],
[
"Zawoad",
"Shams",
""
]
] |
new_dataset
| 0.979391 |
2101.07518
|
Fu-Jen Tsai
|
Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin, Chung-Chi Tsai, and Chia-Wen
Lin
|
BANet: Blur-aware Attention Networks for Dynamic Scene Deblurring
|
TIP 2022, Code: https://github.com/pp00704831/BANet
| null |
10.1109/TIP.2022.3216216
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image motion blur results from a combination of object motions and camera
shakes, and such blurring effect is generally directional and non-uniform.
Previous research attempted to solve non-uniform blurs using self-recurrent
multiscale, multi-patch, or multi-temporal architectures with self-attention to
obtain decent results. However, using self-recurrent frameworks typically lead
to a longer inference time, while inter-pixel or inter-channel self-attention
may cause excessive memory usage. This paper proposes a Blur-aware Attention
Network (BANet), that accomplishes accurate and efficient deblurring via a
single forward pass. Our BANet utilizes region-based self-attention with
multi-kernel strip pooling to disentangle blur patterns of different magnitudes
and orientations and cascaded parallel dilated convolution to aggregate
multi-scale content features. Extensive experimental results on the GoPro and
RealBlur benchmarks demonstrate that the proposed BANet performs favorably
against the state-of-the-arts in blurred image restoration and can provide
deblurred results in real-time.
|
[
{
"version": "v1",
"created": "Tue, 19 Jan 2021 09:03:40 GMT"
},
{
"version": "v2",
"created": "Thu, 8 Jul 2021 03:40:12 GMT"
},
{
"version": "v3",
"created": "Thu, 14 Jul 2022 08:57:44 GMT"
},
{
"version": "v4",
"created": "Tue, 25 Oct 2022 12:00:50 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Tsai",
"Fu-Jen",
""
],
[
"Peng",
"Yan-Tsung",
""
],
[
"Lin",
"Yen-Yu",
""
],
[
"Tsai",
"Chung-Chi",
""
],
[
"Lin",
"Chia-Wen",
""
]
] |
new_dataset
| 0.993062 |
2103.13933
|
Hubert P. H. Shum
|
Brian K. S. Isaac-Medina, Matt Poyser, Daniel Organisciak, Chris G.
Willcocks, Toby P. Breckon, Hubert P. H. Shum
|
Unmanned Aerial Vehicle Visual Detection and Tracking using Deep Neural
Networks: A Performance Benchmark
| null | null |
10.1109/ICCVW54120.2021.00142
| null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unmanned Aerial Vehicles (UAV) can pose a major risk for aviation safety, due
to both negligent and malicious use. For this reason, the automated detection
and tracking of UAV is a fundamental task in aerial security systems. Common
technologies for UAV detection include visible-band and thermal infrared
imaging, radio frequency and radar. Recent advances in deep neural networks
(DNNs) for image-based object detection open the possibility to use visual
information for this detection and tracking task. Furthermore, these detection
architectures can be implemented as backbones for visual tracking systems,
thereby enabling persistent tracking of UAV incursions. To date, no
comprehensive performance benchmark exists that applies DNNs to visible-band
imagery for UAV detection and tracking. To this end, three datasets with varied
environmental conditions for UAV detection and tracking, comprising a total of
241 videos (331,486 images), are assessed using four detection architectures
and three tracking frameworks. The best performing detector architecture
obtains an mAP of 98.6% and the best performing tracking framework obtains a
MOTA of 96.3%. Cross-modality evaluation is carried out between visible and
infrared spectrums, achieving a maximal 82.8% mAP on visible images when
training in the infrared modality. These results provide the first public
multi-approach benchmark for state-of-the-art deep learning-based methods and
give insight into which detection and tracking architectures are effective in
the UAV domain.
|
[
{
"version": "v1",
"created": "Thu, 25 Mar 2021 15:51:53 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Mar 2021 13:50:11 GMT"
},
{
"version": "v3",
"created": "Wed, 18 Aug 2021 14:58:41 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Isaac-Medina",
"Brian K. S.",
""
],
[
"Poyser",
"Matt",
""
],
[
"Organisciak",
"Daniel",
""
],
[
"Willcocks",
"Chris G.",
""
],
[
"Breckon",
"Toby P.",
""
],
[
"Shum",
"Hubert P. H.",
""
]
] |
new_dataset
| 0.9834 |
2106.14259
|
Hitoshi Nishimura
|
Hitoshi Nishimura, Satoshi Komorita, Yasutomo Kawanishi, Hiroshi
Murase
|
SDOF-Tracker: Fast and Accurate Multiple Human Tracking by
Skipped-Detection and Optical-Flow
| null | null |
10.1587/transinf.2022EDP7022
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Multiple human tracking is a fundamental problem for scene understanding.
Although both accuracy and speed are required in real-world applications,
recent tracking methods based on deep learning have focused on accuracy and
require substantial running time. This study aims to improve running speed by
performing human detection at a certain frame interval because it accounts for
most of the running time. The question is how to maintain accuracy while
skipping human detection. In this paper, we propose a method that complements
the detection results with optical flow, based on the fact that someone's
appearance does not change much between adjacent frames. To maintain the
tracking accuracy, we introduce robust interest point selection within human
regions and a tracking termination metric calculated by the distribution of the
interest points. On the MOT20 dataset in the MOTChallenge, the proposed
SDOF-Tracker achieved the best performance in terms of the total running speed
while maintaining the MOTA metric. Our code is available at
https://github.com/hitottiez/sdof-tracker.
|
[
{
"version": "v1",
"created": "Sun, 27 Jun 2021 15:35:35 GMT"
},
{
"version": "v2",
"created": "Tue, 29 Jun 2021 04:58:45 GMT"
},
{
"version": "v3",
"created": "Sat, 30 Apr 2022 13:03:47 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Nishimura",
"Hitoshi",
""
],
[
"Komorita",
"Satoshi",
""
],
[
"Kawanishi",
"Yasutomo",
""
],
[
"Murase",
"Hiroshi",
""
]
] |
new_dataset
| 0.966388 |
2107.00420
|
Tingting Liang
|
Tingting Liang, Xiaojie Chu, Yudong Liu, Yongtao Wang, Zhi Tang, Wei
Chu, Jingdong Chen, Haibin Ling
|
CBNet: A Composite Backbone Network Architecture for Object Detection
|
IEEE Transactions on Image Processing (TIP) camera ready
| null |
10.1109/TIP.2022.3216771
| null |
cs.CV
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Modern top-performing object detectors depend heavily on backbone networks,
whose advances bring consistent performance gains through exploring more
effective network structures. In this paper, we propose a novel and flexible
backbone framework, namely CBNetV2, to construct high-performance detectors
using existing open-sourced pre-trained backbones under the pre-training
fine-tuning paradigm. In particular, CBNetV2 architecture groups multiple
identical backbones, which are connected through composite connections.
Specifically, it integrates the high- and low-level features of multiple
backbone networks and gradually expands the receptive field to more efficiently
perform object detection. We also propose a better training strategy with
assistant supervision for CBNet-based detectors. Without additional
pre-training of the composite backbone, CBNetV2 can be adapted to various
backbones (CNN-based vs. Transformer-based) and head designs of most mainstream
detectors (one-stage vs. two-stage, anchor-based vs. anchor-free-based).
Experiments provide strong evidence that, compared with simply increasing the
depth and width of the network, CBNetV2 introduces a more efficient, effective,
and resource-friendly way to build high-performance backbone networks.
Particularly, our Dual-Swin-L achieves 59.4% box AP and 51.6% mask AP on COCO
test-dev under the single-model and single-scale testing protocol, which is
significantly better than the state-of-the-art result (57.7% box AP and 50.2%
mask AP) achieved by Swin-L, while the training schedule is reduced by
6$\times$. With multi-scale testing, we push the current best single model
result to a new record of 60.1% box AP and 52.3% mask AP without using extra
training data. Code is available at https://github.com/VDIGPKU/CBNetV2.
|
[
{
"version": "v1",
"created": "Thu, 1 Jul 2021 13:05:11 GMT"
},
{
"version": "v2",
"created": "Fri, 2 Jul 2021 06:44:58 GMT"
},
{
"version": "v3",
"created": "Wed, 7 Jul 2021 16:42:55 GMT"
},
{
"version": "v4",
"created": "Mon, 12 Jul 2021 09:12:05 GMT"
},
{
"version": "v5",
"created": "Sat, 24 Jul 2021 16:50:16 GMT"
},
{
"version": "v6",
"created": "Thu, 29 Jul 2021 03:28:29 GMT"
},
{
"version": "v7",
"created": "Tue, 18 Oct 2022 05:09:09 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Liang",
"Tingting",
""
],
[
"Chu",
"Xiaojie",
""
],
[
"Liu",
"Yudong",
""
],
[
"Wang",
"Yongtao",
""
],
[
"Tang",
"Zhi",
""
],
[
"Chu",
"Wei",
""
],
[
"Chen",
"Jingdong",
""
],
[
"Ling",
"Haibin",
""
]
] |
new_dataset
| 0.999316 |
2112.09131
|
Ali Athar
|
Ali Athar, Jonathon Luiten, Alexander Hermans, Deva Ramanan, Bastian
Leibe
|
HODOR: High-level Object Descriptors for Object Re-segmentation in Video
Learned from Static Images
| null | null |
10.1109/CVPR52688.2022.00303
| null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Existing state-of-the-art methods for Video Object Segmentation (VOS) learn
low-level pixel-to-pixel correspondences between frames to propagate object
masks across video. This requires a large amount of densely annotated video
data, which is costly to annotate, and largely redundant since frames within a
video are highly correlated. In light of this, we propose HODOR: a novel method
that tackles VOS by effectively leveraging annotated static images for
understanding object appearance and scene context. We encode object instances
and scene information from an image frame into robust high-level descriptors
which can then be used to re-segment those objects in different frames. As a
result, HODOR achieves state-of-the-art performance on the DAVIS and
YouTube-VOS benchmarks compared to existing methods trained without video
annotations. Without any architectural modification, HODOR can also learn from
video context around single annotated video frames by utilizing cyclic
consistency, whereas other methods rely on dense, temporally consistent
annotations. Source code is available at: https://github.com/Ali2500/HODOR
|
[
{
"version": "v1",
"created": "Thu, 16 Dec 2021 18:59:53 GMT"
},
{
"version": "v2",
"created": "Fri, 15 Jul 2022 13:15:16 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Athar",
"Ali",
""
],
[
"Luiten",
"Jonathon",
""
],
[
"Hermans",
"Alexander",
""
],
[
"Ramanan",
"Deva",
""
],
[
"Leibe",
"Bastian",
""
]
] |
new_dataset
| 0.997769 |
2201.05047
|
Qianyu Zhou
|
Qianyu Zhou, Xiangtai Li, Lu He, Yibo Yang, Guangliang Cheng, Yunhai
Tong, Lizhuang Ma, Dacheng Tao
|
TransVOD: End-to-End Video Object Detection with Spatial-Temporal
Transformers
|
Accepted to IEEE Transactions on Pattern Analysis and Machine
Intelligence (IEEE TPAMI), extended version of arXiv:2105.10920
| null |
10.1109/TPAMI.2022.3223955
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Detection Transformer (DETR) and Deformable DETR have been proposed to
eliminate the need for many hand-designed components in object detection while
demonstrating good performance as previous complex hand-crafted detectors.
However, their performance on Video Object Detection (VOD) has not been well
explored. In this paper, we present TransVOD, the first end-to-end video object
detection system based on spatial-temporal Transformer architectures. The first
goal of this paper is to streamline the pipeline of VOD, effectively removing
the need for many hand-crafted components for feature aggregation, e.g.,
optical flow model, relation networks. Besides, benefited from the object query
design in DETR, our method does not need complicated post-processing methods
such as Seq-NMS. In particular, we present a temporal Transformer to aggregate
both the spatial object queries and the feature memories of each frame. Our
temporal transformer consists of two components: Temporal Query Encoder (TQE)
to fuse object queries, and Temporal Deformable Transformer Decoder (TDTD) to
obtain current frame detection results. These designs boost the strong baseline
deformable DETR by a significant margin (3%-4% mAP) on the ImageNet VID
dataset. Then, we present two improved versions of TransVOD including
TransVOD++ and TransVOD Lite. The former fuses object-level information into
object query via dynamic convolution while the latter models the entire video
clips as the output to speed up the inference time. We give detailed analysis
of all three models in the experiment part. In particular, our proposed
TransVOD++ sets a new state-of-the-art record in terms of accuracy on ImageNet
VID with 90.0% mAP. Our proposed TransVOD Lite also achieves the best speed and
accuracy trade-off with 83.7% mAP while running at around 30 FPS on a single
V100 GPU device.
|
[
{
"version": "v1",
"created": "Thu, 13 Jan 2022 16:17:34 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Jan 2022 07:19:08 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Jan 2022 02:06:34 GMT"
},
{
"version": "v4",
"created": "Tue, 22 Nov 2022 06:07:22 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Zhou",
"Qianyu",
""
],
[
"Li",
"Xiangtai",
""
],
[
"He",
"Lu",
""
],
[
"Yang",
"Yibo",
""
],
[
"Cheng",
"Guangliang",
""
],
[
"Tong",
"Yunhai",
""
],
[
"Ma",
"Lizhuang",
""
],
[
"Tao",
"Dacheng",
""
]
] |
new_dataset
| 0.995929 |
2202.12559
|
Xiuling Shan
|
Honglian Shen, Xiuling Shan, Zihong Tian
|
A new chaotic image encryption algorithm based on transversals in a
Latin square
|
added one reference for section 1, corrected the first author's name
in Metadata, added the author's name in reference [28]
| null |
10.3390/e24111574
| null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There are some good combinatorial structures suitable for image encryption.
In this study, a new chaotic image encryption algorithm based on transversals
in a Latin square is proposed. By means of an n-transversal of a Latin square,
we can permutate an image data group by group for the first time, then use two
Latin squares for auxiliary diffusion on the basis of a chaotic sequence,
finally make use of a pair of orthogonal Latin squares to do the second
scrambling. As a whole, the encryption process of
"scrambling-diffusion-scrambling" is formed. The experimental results indicate
that this algorithm achieves a secure and fast encryption effect. The final
information entropy is very close to 8, and the correlation coefficient is
approximate to 0. All these tests verify the robustness and practicability of
this proposed algorithm.
|
[
{
"version": "v1",
"created": "Fri, 25 Feb 2022 08:44:24 GMT"
},
{
"version": "v2",
"created": "Sat, 12 Mar 2022 02:39:22 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Shen",
"Honglian",
""
],
[
"Shan",
"Xiuling",
""
],
[
"Tian",
"Zihong",
""
]
] |
new_dataset
| 0.963322 |
2205.02678
|
Juan Pablo Carbajal
|
Stevens Paz, Roberto F. Ausas, Juan P. Carbajal, Gustavo C. Buscaglia
|
Chemoreception and chemotaxis of a three-sphere swimmer
|
20 pages, 13 figures. Submitted to "Communications in Nonlinear
Science and Numerical Simulation"
| null |
10.1016/j.cnsns.2022.106909
| null |
cs.LG nlin.AO physics.flu-dyn
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The coupled problem of hydrodynamics and solute transport for the
Najafi-Golestanian three-sphere swimmer is studied, with the Reynolds number
set to zero and P\'eclet numbers (Pe) ranging from 0.06 to 60. The adopted
method is the numerical simulation of the problem with a finite element code
based upon the FEniCS library. For the swimmer executing the optimal locomotion
gait, we report the Sherwood number as a function of Pe in homogeneous fluids
and confirm that little gain in solute flux is achieved by swimming unless Pe
is significantly larger than 10. We also consider the swimmer as an learning
agent moving inside a fluid that has a concentration gradient. The outcomes of
Q-learning processes show that learning locomotion (with the displacement as
reward) is significantly easier than learning chemotaxis (with the increase of
solute flux as reward). The chemotaxis problem, even at low Pe, has a varying
environment that renders learning more difficult. Further, the learning
difficulty increases severely with the P\'eclet number. The results demonstrate
the challenges that natural and artificial swimmers need to overcome to migrate
efficiently when exposed to chemical inhomogeneities.
|
[
{
"version": "v1",
"created": "Thu, 5 May 2022 14:34:04 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Paz",
"Stevens",
""
],
[
"Ausas",
"Roberto F.",
""
],
[
"Carbajal",
"Juan P.",
""
],
[
"Buscaglia",
"Gustavo C.",
""
]
] |
new_dataset
| 0.999466 |
2205.08180
|
Antoine Laurent
|
Sameer Khurana and Antoine Laurent and James Glass
|
SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual
Speech Representation
| null | null |
10.1109/JSTSP.2022.3192714
| null |
cs.CL cs.LG cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose the SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level
Cross-Lingual Speech Representation learning framework. Unlike previous works
on speech representation learning, which learns multilingual contextual speech
embedding at the resolution of an acoustic frame (10-20ms), this work focuses
on learning multimodal (speech-text) multilingual speech embedding at the
resolution of a sentence (5-10s) such that the embedding vector space is
semantically aligned across different languages. We combine state-of-the-art
multilingual acoustic frame-level speech representation learning model XLS-R
with the Language Agnostic BERT Sentence Embedding (LaBSE) model to create an
utterance-level multimodal multilingual speech encoder SAMU-XLSR. Although we
train SAMU-XLSR with only multilingual transcribed speech data, cross-lingual
speech-text and speech-speech associations emerge in its learned representation
space. To substantiate our claims, we use SAMU-XLSR speech encoder in
combination with a pre-trained LaBSE text sentence encoder for cross-lingual
speech-to-text translation retrieval, and SAMU-XLSR alone for cross-lingual
speech-to-speech translation retrieval. We highlight these applications by
performing several cross-lingual text and speech translation retrieval tasks
across several datasets.
|
[
{
"version": "v1",
"created": "Tue, 17 May 2022 08:58:48 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Khurana",
"Sameer",
""
],
[
"Laurent",
"Antoine",
""
],
[
"Glass",
"James",
""
]
] |
new_dataset
| 0.997862 |
2205.11169
|
Yuan Yao
|
Yuan Yao, Qianyu Chen, Ao Zhang, Wei Ji, Zhiyuan Liu, Tat-Seng Chua,
Maosong Sun
|
PEVL: Position-enhanced Pre-training and Prompt Tuning for
Vision-language Models
|
Accepted by EMNLP 2022
| null | null | null |
cs.CV cs.AI cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-language pre-training (VLP) has shown impressive performance on a wide
range of cross-modal tasks, where VLP models without reliance on object
detectors are becoming the mainstream due to their superior computation
efficiency and competitive performance. However, the removal of object
detectors also deprives the capability of VLP models in explicit object
modeling, which is essential to various position-sensitive vision-language (VL)
tasks, such as referring expression comprehension and visual commonsense
reasoning. To address the challenge, we introduce PEVL that enhances the
pre-training and prompt tuning of VLP models with explicit object position
modeling. Specifically, PEVL reformulates discretized object positions and
language in a unified language modeling framework, which facilitates explicit
VL alignment during pre-training, and also enables flexible prompt tuning for
various downstream tasks. We show that PEVL enables state-of-the-art
performance of detector-free VLP models on position-sensitive tasks such as
referring expression comprehension and phrase grounding, and also improves the
performance on position-insensitive tasks with grounded inputs. We make the
data and code for this paper publicly available at
https://github.com/thunlp/PEVL.
|
[
{
"version": "v1",
"created": "Mon, 23 May 2022 10:17:53 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Nov 2022 06:59:30 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Yao",
"Yuan",
""
],
[
"Chen",
"Qianyu",
""
],
[
"Zhang",
"Ao",
""
],
[
"Ji",
"Wei",
""
],
[
"Liu",
"Zhiyuan",
""
],
[
"Chua",
"Tat-Seng",
""
],
[
"Sun",
"Maosong",
""
]
] |
new_dataset
| 0.99761 |
2205.13879
|
Kota Dohi
|
Kota Dohi, Tomoya Nishida, Harsh Purohit, Ryo Tanabe, Takashi Endo,
Masaaki Yamamoto, Yuki Nikaido, and Yohei Kawaguchi
|
MIMII DG: Sound Dataset for Malfunctioning Industrial Machine
Investigation and Inspection for Domain Generalization Task
| null | null | null | null |
cs.SD cs.AI cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a machine sound dataset to benchmark domain generalization
techniques for anomalous sound detection (ASD). Domain shifts are differences
in data distributions that can degrade the detection performance, and handling
them is a major issue for the application of ASD systems. While currently
available datasets for ASD tasks assume that occurrences of domain shifts are
known, in practice, they can be difficult to detect. To handle such domain
shifts, domain generalization techniques that perform well regardless of the
domains should be investigated. In this paper, we present the first ASD dataset
for the domain generalization techniques, called MIMII DG. The dataset consists
of five machine types and three domain shift scenarios for each machine type.
The dataset is dedicated to the domain generalization task with features such
as multiple different values for parameters that cause domain shifts and
introduction of domain shifts that can be difficult to detect, such as shifts
in the background noise. Experimental results using two baseline systems
indicate that the dataset reproduces domain shift scenarios and is useful for
benchmarking domain generalization techniques.
|
[
{
"version": "v1",
"created": "Fri, 27 May 2022 10:19:16 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Nov 2022 02:14:02 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Dohi",
"Kota",
""
],
[
"Nishida",
"Tomoya",
""
],
[
"Purohit",
"Harsh",
""
],
[
"Tanabe",
"Ryo",
""
],
[
"Endo",
"Takashi",
""
],
[
"Yamamoto",
"Masaaki",
""
],
[
"Nikaido",
"Yuki",
""
],
[
"Kawaguchi",
"Yohei",
""
]
] |
new_dataset
| 0.999816 |
2206.08853
|
Linxi Fan
|
Linxi Fan, Guanzhi Wang, Yunfan Jiang, Ajay Mandlekar, Yuncong Yang,
Haoyi Zhu, Andrew Tang, De-An Huang, Yuke Zhu, Anima Anandkumar
|
MineDojo: Building Open-Ended Embodied Agents with Internet-Scale
Knowledge
|
Outstanding Paper Award at NeurIPS 2022. Project website:
https://minedojo.org
| null | null | null |
cs.LG cs.AI cs.CL cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Autonomous agents have made great strides in specialist domains like Atari
games and Go. However, they typically learn tabula rasa in isolated
environments with limited and manually conceived objectives, thus failing to
generalize across a wide spectrum of tasks and capabilities. Inspired by how
humans continually learn and adapt in the open world, we advocate a trinity of
ingredients for building generalist agents: 1) an environment that supports a
multitude of tasks and goals, 2) a large-scale database of multimodal
knowledge, and 3) a flexible and scalable agent architecture. We introduce
MineDojo, a new framework built on the popular Minecraft game that features a
simulation suite with thousands of diverse open-ended tasks and an
internet-scale knowledge base with Minecraft videos, tutorials, wiki pages, and
forum discussions. Using MineDojo's data, we propose a novel agent learning
algorithm that leverages large pre-trained video-language models as a learned
reward function. Our agent is able to solve a variety of open-ended tasks
specified in free-form language without any manually designed dense shaping
reward. We open-source the simulation suite, knowledge bases, algorithm
implementation, and pretrained models (https://minedojo.org) to promote
research towards the goal of generally capable embodied agents.
|
[
{
"version": "v1",
"created": "Fri, 17 Jun 2022 15:53:05 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Nov 2022 07:59:47 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Fan",
"Linxi",
""
],
[
"Wang",
"Guanzhi",
""
],
[
"Jiang",
"Yunfan",
""
],
[
"Mandlekar",
"Ajay",
""
],
[
"Yang",
"Yuncong",
""
],
[
"Zhu",
"Haoyi",
""
],
[
"Tang",
"Andrew",
""
],
[
"Huang",
"De-An",
""
],
[
"Zhu",
"Yuke",
""
],
[
"Anandkumar",
"Anima",
""
]
] |
new_dataset
| 0.999217 |
2206.08965
|
Jose Manuel Gilperez Aguilar Dr.
|
V\'ictor Corsino, Jos\'e Manuel Gilp\'erez, Luis Herrera
|
KitBit: A New AI Model for Solving Intelligence Tests and Numerical
Series
|
11 pages
| null | null | null |
cs.AI cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The resolution of intelligence tests, in particular numerical sequences, has
been of great interest in the evaluation of AI systems. We present a new
computational model called KitBit that uses a reduced set of algorithms and
their combinations to build a predictive model that finds the underlying
pattern in numerical sequences, such as those included in IQ tests and others
of much greater complexity. We present the fundamentals of the model and its
application in different cases. First, the system is tested on a set of number
series used in IQ tests collected from various sources. Next, our model is
successfully applied on the sequences used to evaluate the models reported in
the literature. In both cases, the system is capable of solving these types of
problems in less than a second using standard computing power. Finally,
KitBit's algorithms have been applied for the first time to the complete set of
entire sequences of the well-known OEIS database. We find a pattern in the form
of a list of algorithms and predict the following terms in the largest number
of series to date. These results demonstrate the potential of KitBit to solve
complex problems that could be represented numerically.
|
[
{
"version": "v1",
"created": "Fri, 17 Jun 2022 18:40:11 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Nov 2022 18:23:20 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Corsino",
"Víctor",
""
],
[
"Gilpérez",
"José Manuel",
""
],
[
"Herrera",
"Luis",
""
]
] |
new_dataset
| 0.999517 |
2207.02563
|
Xinying Ma
|
Xinying Ma, Zhi Chen, Chongwen Huang
|
Nanoscale Reconfigurable Intelligent Surface Design and Performance
Analysis for Terahertz Communications
|
9 pages, 8 figures. arXiv admin note: substantial text overlap with
arXiv:2012.06993
| null |
10.1109/TNANO.2022.3208193
| null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Terahertz (THz) communications have been envisioned as a promising enabler to
provide ultra-high data transmission for sixth generation (6G) wireless
networks. To tackle the blockage vulnerability brought by severe attenuation
and poor diffraction of THz waves, a nanoscale reconfigurable intelligent
surface (NRIS) is developed to smartly manipulate the propagation directions of
incident THz waves. In this paper, the electric properties of the graphene are
investigated by revealing the relationship between conductivity and applied
voltages, and then an efficient hardware structure of electrically-controlled
NRIS is designed based on Fabry-Perot resonance model. Particularly, the phase
response of NRIS can be programmed up to 306.82 degrees. To analyze the
hardware performance, we jointly design the passive and active beamforming for
NRIS aided THz communication system. Particularly, an adaptive gradient descent
(A-GD) algorithm is developed to optimize the phase shift matrix of NRIS by
dynamically updating the step size during the iterative process. Finally,
numerical results demonstrate the effectiveness of our designed hardware
architecture as well as the developed algorithm.
|
[
{
"version": "v1",
"created": "Wed, 6 Jul 2022 10:22:29 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Ma",
"Xinying",
""
],
[
"Chen",
"Zhi",
""
],
[
"Huang",
"Chongwen",
""
]
] |
new_dataset
| 0.995636 |
2208.02473
|
Geonho Han
|
Geonho Han, Junil Choi, and Robert W. Heath Jr
|
Radar Imaging Based on IEEE 802.11ad Waveform in V2I Communications
| null | null |
10.1109/TSP.2022.3213488
| null |
cs.IT eess.SP math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Since most of vehicular radar systems are already exploiting millimeter-wave
(mmWave) spectra, it would become much more feasible to implement a joint radar
and communication system by extending communication frequencies into the mmWave
band. In this paper, an IEEE 802.11ad waveform-based radar imaging technique is
proposed for vehicular settings. A roadside unit (RSU) transmits the IEEE
802.11ad waveform to a vehicle for communications while the RSU also listens to
the echoes of transmitted waveform to perform inverse synthetic aperture radar
(ISAR) imaging. To obtain high-resolution images of the vehicle, the RSU needs
to accurately estimate round-trip delays, Doppler shifts, and velocity of
vehicle. The proposed ISAR imaging first estimates the round-trip delays using
a good correlation property of Golay complementary sequences in the IEEE
802.11ad preamble. The Doppler shifts are then obtained using least square
estimation from the echo signals and refined to compensate phase wrapping
caused by phase rotation. The velocity of vehicle is determined using an
equation of motion and the estimated Doppler shifts. Simulation results verify
that the proposed technique is able to form high-resolution ISAR images from
point scatterer models of realistic vehicular settings with different
viewpoints. The proposed ISAR imaging technique can be used for various
vehicular applications, e.g., traffic condition analyses or advanced collision
warning systems.
|
[
{
"version": "v1",
"created": "Thu, 4 Aug 2022 05:51:21 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Han",
"Geonho",
""
],
[
"Choi",
"Junil",
""
],
[
"Heath",
"Robert W.",
"Jr"
]
] |
new_dataset
| 0.997144 |
2209.12118
|
Ali Athar
|
Ali Athar, Jonathon Luiten, Paul Voigtlaender, Tarasha Khurana, Achal
Dave, Bastian Leibe, Deva Ramanan
|
BURST: A Benchmark for Unifying Object Recognition, Segmentation and
Tracking in Video
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multiple existing benchmarks involve tracking and segmenting objects in video
e.g., Video Object Segmentation (VOS) and Multi-Object Tracking and
Segmentation (MOTS), but there is little interaction between them due to the
use of disparate benchmark datasets and metrics (e.g. J&F, mAP, sMOTSA). As a
result, published works usually target a particular benchmark, and are not
easily comparable to each another. We believe that the development of
generalized methods that can tackle multiple tasks requires greater cohesion
among these research sub-communities. In this paper, we aim to facilitate this
by proposing BURST, a dataset which contains thousands of diverse videos with
high-quality object masks, and an associated benchmark with six tasks involving
object tracking and segmentation in video. All tasks are evaluated using the
same data and comparable metrics, which enables researchers to consider them in
unison, and hence, more effectively pool knowledge from different methods
across different tasks. Additionally, we demonstrate several baselines for all
tasks and show that approaches for one task can be applied to another with a
quantifiable and explainable performance difference. Dataset annotations and
evaluation code is available at: https://github.com/Ali2500/BURST-benchmark.
|
[
{
"version": "v1",
"created": "Sun, 25 Sep 2022 01:27:35 GMT"
},
{
"version": "v2",
"created": "Tue, 22 Nov 2022 17:18:39 GMT"
}
] | 2022-11-23T00:00:00 |
[
[
"Athar",
"Ali",
""
],
[
"Luiten",
"Jonathon",
""
],
[
"Voigtlaender",
"Paul",
""
],
[
"Khurana",
"Tarasha",
""
],
[
"Dave",
"Achal",
""
],
[
"Leibe",
"Bastian",
""
],
[
"Ramanan",
"Deva",
""
]
] |
new_dataset
| 0.996778 |
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