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stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2210.10483
|
Tiago Matos Santos
|
Tiago Matos Santos
|
O Problema do Roteamento de Interliga\c{c}\~oes El\'etricas em Circuitos
Integrados
|
in Portuguese language
| null | null | null |
cs.OH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Integrated circuit design automation tools are essential for the feasibility
of complex designs with millions of transistors. One of the steps performed
within the process is the routing of interconnections between components of a
circuit. This problem, which also aims to optimize the utilization of
connection resources, has been shown to be NP-Complete and requires heuristic
algorithms to look for the best achievable solutions. In this work, we present
a definition of this problem in context with a brief review of existing
solutions in the literature. Then, we propose a methodology for the development
of an original algorithm, which aims to differentiate itself, in certain
domains, from the solutions already proposed.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 12:41:35 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Santos",
"Tiago Matos",
""
]
] |
new_dataset
| 0.990336 |
2210.10515
|
Pouria Mehrabi
|
Pouria Mehrabi, Hamid D. Taghirad
|
A Segment-Wise Gaussian Process-Based Ground Segmentation With Local
Smoothness Estimation
| null | null | null | null |
cs.LG cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Both in terrestrial and extraterrestrial environments, the precise and
informative model of the ground and the surface ahead is crucial for navigation
and obstacle avoidance. The ground surface is not always flat and it may be
sloped, bumpy and rough specially in off-road terrestrial scenes. In bumpy and
rough scenes the functional relationship of the surface-related features may
vary in different areas of the ground, as the structure of the ground surface
may vary suddenly and further the measured point cloud of the ground does not
bear smoothness. Thus, the ground-related features must be obtained based on
local estimates or even point estimates. To tackle this problem, the
segment-wise GP-based ground segmentation method with local smoothness
estimation is proposed. This method is an extension to our previous method in
which a realistic measurement of the length-scale values were provided for the
covariance kernel in each line-segment to give precise estimation of the ground
for sloped terrains. In this extension, the value of the length-scale is
estimated locally for each data point which makes it much more precise for the
rough scenes while being not computationally complex and more robust to
under-segmentation, sparsity and under-represent-ability. The segment-wise task
is performed to estimate a partial continuous model of the ground for each
radial range segment. Simulation results show the effectiveness of the proposed
method to give a continuous and precise estimation of the ground surface in
rough and bumpy scenes while being fast enough for real-world applications.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 12:42:21 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Mehrabi",
"Pouria",
""
],
[
"Taghirad",
"Hamid D.",
""
]
] |
new_dataset
| 0.997275 |
2210.10523
|
Theodor Schnitzler
|
Theodor Schnitzler, Katharina Kohls, Evangelos Bitsikas, Christina
P\"opper
|
Hope of Delivery: Extracting User Locations From Mobile Instant
Messengers
|
33 pages, 23 figures, 9 tables, NDSS 2023
| null |
10.14722/ndss.2023.23188
| null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
Mobile instant messengers such as WhatsApp use delivery status notifications
in order to inform users if a sent message has successfully reached its
destination. This is useful and important information for the sender due to the
often asynchronous use of the messenger service. However, as we demonstrate in
this paper, this standard feature opens up a timing side channel with
unexpected consequences for user location privacy. We investigate this threat
conceptually and experimentally for three widely spread instant messengers. We
validate that this information leak even exists in privacy-friendly messengers
such as Signal and Threema.
Our results show that, after a training phase, a messenger user can
distinguish different locations of the message receiver. Our analyses involving
multiple rounds of measurements and evaluations show that the timing side
channel persists independent of distances between receiver locations -- the
attack works both for receivers in different countries as well as at small
scale in one city. For instance, out of three locations within the same city,
the sender can determine the correct one with more than 80% accuracy. Thus,
messenger users can secretly spy on each others' whereabouts when sending
instant messages. As our countermeasure evaluation shows, messenger providers
could effectively disable the timing side channel by randomly delaying delivery
confirmations within the range of a few seconds. For users themselves, the
threat is harder to prevent since there is no option to turn off delivery
confirmations.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 12:57:47 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Schnitzler",
"Theodor",
""
],
[
"Kohls",
"Katharina",
""
],
[
"Bitsikas",
"Evangelos",
""
],
[
"Pöpper",
"Christina",
""
]
] |
new_dataset
| 0.994527 |
2210.10542
|
Thomas Lucas
|
Thomas Lucas, Fabien Baradel, Philippe Weinzaepfel, Gr\'egory Rogez
|
PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting
|
ECCV'22 Conference paper
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We address the problem of action-conditioned generation of human motion
sequences. Existing work falls into two categories: forecast models conditioned
on observed past motions, or generative models conditioned on action labels and
duration only. In contrast, we generate motion conditioned on observations of
arbitrary length, including none. To solve this generalized problem, we propose
PoseGPT, an auto-regressive transformer-based approach which internally
compresses human motion into quantized latent sequences. An auto-encoder first
maps human motion to latent index sequences in a discrete space, and
vice-versa. Inspired by the Generative Pretrained Transformer (GPT), we propose
to train a GPT-like model for next-index prediction in that space; this allows
PoseGPT to output distributions on possible futures, with or without
conditioning on past motion. The discrete and compressed nature of the latent
space allows the GPT-like model to focus on long-range signal, as it removes
low-level redundancy in the input signal. Predicting discrete indices also
alleviates the common pitfall of predicting averaged poses, a typical failure
case when regressing continuous values, as the average of discrete targets is
not a target itself. Our experimental results show that our proposed approach
achieves state-of-the-art results on HumanAct12, a standard but small scale
dataset, as well as on BABEL, a recent large scale MoCap dataset, and on GRAB,
a human-object interactions dataset.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 13:30:39 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Lucas",
"Thomas",
""
],
[
"Baradel",
"Fabien",
""
],
[
"Weinzaepfel",
"Philippe",
""
],
[
"Rogez",
"Grégory",
""
]
] |
new_dataset
| 0.969041 |
2210.10561
|
Philipp Richter
|
Philipp Richter and Oliver Gasser and Arthur Berger
|
Illuminating Large-Scale IPv6 Scanning in the Internet
| null |
in Proceedings of the ACM Internet Measurement Conference (IMC),
2022
|
10.1145/3517745.3561452
| null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
While scans of the IPv4 space are ubiquitous, today little is known about
scanning activity in the IPv6 Internet. In this work, we present a longitudinal
and detailed empirical study on large-scale IPv6 scanning behavior in the
Internet, based on firewall logs captured at some 230,000 hosts of a major
Content Distribution Network (CDN). We develop methods to identify IPv6 scans,
assess current and past levels of IPv6 scanning activity, and study dominant
characteristics of scans, including scanner origins, targeted services, and
insights on how scanners find target IPv6 addresses. Where possible, we compare
our findings to what can be assessed from publicly available traces. Our work
identifies and highlights new challenges to detect scanning activity in the
IPv6 Internet, and uncovers that today's scans of the IPv6 space show widely
different characteristics when compared to the more well-known IPv4 scans.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 14:00:59 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Richter",
"Philipp",
""
],
[
"Gasser",
"Oliver",
""
],
[
"Berger",
"Arthur",
""
]
] |
new_dataset
| 0.994871 |
2210.10565
|
Margherita Ronchini
|
Margherita Ronchini, Yasser Rezaeiyan, Milad Zamani, Gabriella
Panuccio, Farshad Moradi
|
NET-TEN: a silicon neuromorphic network for low-latency detection of
seizures in local field potentials
|
14 pages, 6 figures
| null | null | null |
cs.HC cs.AR eess.SP q-bio.NC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Therapeutic intervention in neurological disorders still relies heavily on
pharmacological solutions, while the treatment of patients with drug resistance
remains an open challenge. This is particularly true for patients with
epilepsy, 30% of whom are refractory to medications. Implantable devices for
chronic recording and electrical modulation of brain activity have proved a
viable alternative in such cases. To operate, the device should detect the
relevant electrographic biomarkers from Local Field Potentials (LFPs) and
determine the right time for stimulation. To enable timely interventions, the
ideal device should attain biomarker detection with low latency while operating
under low power consumption to prolong the battery life. Neuromorphic networks
have progressively gained reputation as low-latency low-power computing
systems, which makes them a promising candidate as processing core of
next-generation implantable neural interfaces. Here we introduce a fully-analog
neuromorphic device implemented in CMOS technology for analyzing LFP signals in
an in vitro model of acute ictogenesis. We show that the system can detect
ictal and interictal events with ms-latency and with high precision, consuming
on average 3.50 nW during the task. Our work paves the way to a new generation
of brain implantable devices for personalized closed-loop stimulation for
epilepsy treatment.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 14:07:07 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Ronchini",
"Margherita",
""
],
[
"Rezaeiyan",
"Yasser",
""
],
[
"Zamani",
"Milad",
""
],
[
"Panuccio",
"Gabriella",
""
],
[
"Moradi",
"Farshad",
""
]
] |
new_dataset
| 0.991353 |
2210.10581
|
Peipei Liu
|
Peipei Liu, Hong Li, Zhiyu Wang, Yimo Ren, Jie Liu, Fei Lyu, Hongsong
Zhu, Limin Sun
|
CEntRE: A paragraph-level Chinese dataset for Relation Extraction among
Enterprises
| null | null | null | null |
cs.CL cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Enterprise relation extraction aims to detect pairs of enterprise entities
and identify the business relations between them from unstructured or
semi-structured text data, and it is crucial for several real-world
applications such as risk analysis, rating research and supply chain security.
However, previous work mainly focuses on getting attribute information about
enterprises like personnel and corporate business, and pays little attention to
enterprise relation extraction. To encourage further progress in the research,
we introduce the CEntRE, a new dataset constructed from publicly available
business news data with careful human annotation and intelligent data
processing. Extensive experiments on CEntRE with six excellent models
demonstrate the challenges of our proposed dataset.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 14:22:10 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Liu",
"Peipei",
""
],
[
"Li",
"Hong",
""
],
[
"Wang",
"Zhiyu",
""
],
[
"Ren",
"Yimo",
""
],
[
"Liu",
"Jie",
""
],
[
"Lyu",
"Fei",
""
],
[
"Zhu",
"Hongsong",
""
],
[
"Sun",
"Limin",
""
]
] |
new_dataset
| 0.999844 |
2210.10606
|
Royi Rassin
|
Royi Rassin, Shauli Ravfogel, Yoav Goldberg
|
DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image
Models
|
5 pages, BlackboxNLP @ EMNLP 2022
| null | null | null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We study the way DALLE-2 maps symbols (words) in the prompt to their
references (entities or properties of entities in the generated image). We show
that in stark contrast to the way human process language, DALLE-2 does not
follow the constraint that each word has a single role in the interpretation,
and sometimes re-use the same symbol for different purposes. We collect a set
of stimuli that reflect the phenomenon: we show that DALLE-2 depicts both
senses of nouns with multiple senses at once; and that a given word can modify
the properties of two distinct entities in the image, or can be depicted as one
object and also modify the properties of another object, creating a semantic
leakage of properties between entities. Taken together, our study highlights
the differences between DALLE-2 and human language processing and opens an
avenue for future study on the inductive biases of text-to-image models.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 14:52:40 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Rassin",
"Royi",
""
],
[
"Ravfogel",
"Shauli",
""
],
[
"Goldberg",
"Yoav",
""
]
] |
new_dataset
| 0.996091 |
2210.10732
|
Clifford Broni-Bediako
|
Junshi Xia, Naoto Yokoya, Bruno Adriano, Clifford Broni-Bediako
|
OpenEarthMap: A Benchmark Dataset for Global High-Resolution Land Cover
Mapping
|
Accepted by WACV 2023
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We introduce OpenEarthMap, a benchmark dataset, for global high-resolution
land cover mapping. OpenEarthMap consists of 2.2 million segments of 5000
aerial and satellite images covering 97 regions from 44 countries across 6
continents, with manually annotated 8-class land cover labels at a 0.25--0.5m
ground sampling distance. Semantic segmentation models trained on the
OpenEarthMap generalize worldwide and can be used as off-the-shelf models in a
variety of applications. We evaluate the performance of state-of-the-art
methods for unsupervised domain adaptation and present challenging problem
settings suitable for further technical development. We also investigate
lightweight models using automated neural architecture search for limited
computational resources and fast mapping. The dataset is available at
https://open-earth-map.org.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 17:20:16 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Xia",
"Junshi",
""
],
[
"Yokoya",
"Naoto",
""
],
[
"Adriano",
"Bruno",
""
],
[
"Broni-Bediako",
"Clifford",
""
]
] |
new_dataset
| 0.999748 |
2210.10770
|
Paul-Edouard Sarlin
|
Paul-Edouard Sarlin, Mihai Dusmanu, Johannes L. Sch\"onberger, Pablo
Speciale, Lukas Gruber, Viktor Larsson, Ondrej Miksik, Marc Pollefeys
|
LaMAR: Benchmarking Localization and Mapping for Augmented Reality
|
Accepted at ECCV 2022, website at https://lamar.ethz.ch/
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Localization and mapping is the foundational technology for augmented reality
(AR) that enables sharing and persistence of digital content in the real world.
While significant progress has been made, researchers are still mostly driven
by unrealistic benchmarks not representative of real-world AR scenarios. These
benchmarks are often based on small-scale datasets with low scene diversity,
captured from stationary cameras, and lack other sensor inputs like inertial,
radio, or depth data. Furthermore, their ground-truth (GT) accuracy is mostly
insufficient to satisfy AR requirements. To close this gap, we introduce LaMAR,
a new benchmark with a comprehensive capture and GT pipeline that co-registers
realistic trajectories and sensor streams captured by heterogeneous AR devices
in large, unconstrained scenes. To establish an accurate GT, our pipeline
robustly aligns the trajectories against laser scans in a fully automated
manner. As a result, we publish a benchmark dataset of diverse and large-scale
scenes recorded with head-mounted and hand-held AR devices. We extend several
state-of-the-art methods to take advantage of the AR-specific setup and
evaluate them on our benchmark. The results offer new insights on current
research and reveal promising avenues for future work in the field of
localization and mapping for AR.
|
[
{
"version": "v1",
"created": "Wed, 19 Oct 2022 17:58:17 GMT"
}
] | 2022-10-20T00:00:00 |
[
[
"Sarlin",
"Paul-Edouard",
""
],
[
"Dusmanu",
"Mihai",
""
],
[
"Schönberger",
"Johannes L.",
""
],
[
"Speciale",
"Pablo",
""
],
[
"Gruber",
"Lukas",
""
],
[
"Larsson",
"Viktor",
""
],
[
"Miksik",
"Ondrej",
""
],
[
"Pollefeys",
"Marc",
""
]
] |
new_dataset
| 0.992532 |
2109.00356
|
Evan Calabrese
|
Evan Calabrese, Javier E. Villanueva-Meyer, Jeffrey D. Rudie, Andreas
M. Rauschecker, Ujjwal Baid, Spyridon Bakas, Soonmee Cha, John T. Mongan,
Christopher P. Hess
|
The University of California San Francisco Preoperative Diffuse Glioma
MRI (UCSF-PDGM) Dataset
|
7 pages, 2 figures, 2 tables
|
Radiology: Artificial Intelligence 4.6 (2022): e220058
|
10.1148/ryai.220058
| null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Here we present the University of California San Francisco Preoperative
Diffuse Glioma MRI (UCSF-PDGM) dataset. The UCSF-PDGM dataset includes 500
subjects with histopathologically-proven diffuse gliomas who were imaged with a
standardized 3 Tesla preoperative brain tumor MRI protocol featuring
predominantly 3D imaging, as well as advanced diffusion and perfusion imaging
techniques. The dataset also includes isocitrate dehydrogenase (IDH) mutation
status for all cases and O6-methylguanine-DNA methyltransferase (MGMT) promotor
methylation status for World Health Organization (WHO) grade III and IV
gliomas. The UCSF-PDGM has been made publicly available in the hopes that
researchers around the world will use these data to continue to push the
boundaries of AI applications for diffuse gliomas.
|
[
{
"version": "v1",
"created": "Mon, 30 Aug 2021 22:54:12 GMT"
},
{
"version": "v2",
"created": "Wed, 16 Mar 2022 00:35:58 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Calabrese",
"Evan",
""
],
[
"Villanueva-Meyer",
"Javier E.",
""
],
[
"Rudie",
"Jeffrey D.",
""
],
[
"Rauschecker",
"Andreas M.",
""
],
[
"Baid",
"Ujjwal",
""
],
[
"Bakas",
"Spyridon",
""
],
[
"Cha",
"Soonmee",
""
],
[
"Mongan",
"John T.",
""
],
[
"Hess",
"Christopher P.",
""
]
] |
new_dataset
| 0.999738 |
2109.03564
|
Yi Sun
|
Yi Sun, Yu Zheng, Chao Hao, Hangping Qiu
|
NSP-BERT: A Prompt-based Few-Shot Learner Through an Original
Pre-training Task--Next Sentence Prediction
|
Published at COLING2022, long paper
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Using prompts to utilize language models to perform various downstream tasks,
also known as prompt-based learning or prompt-learning, has lately gained
significant success in comparison to the pre-train and fine-tune paradigm.
Nonetheless, virtually all prompt-based methods are token-level, meaning they
all utilize GPT's left-to-right language model or BERT's masked language model
to perform cloze-style tasks. In this paper, we attempt to accomplish several
NLP tasks in the zero-shot scenario using a BERT original pre-training task
abandoned by RoBERTa and other models--Next Sentence Prediction (NSP). Unlike
token-level techniques, our sentence-level prompt-based method NSP-BERT does
not need to fix the length of the prompt or the position to be predicted,
allowing it to handle tasks such as entity linking with ease. Based on the
characteristics of NSP-BERT, we offer several quick building templates for
various downstream tasks. We suggest a two-stage prompt method for word sense
disambiguation tasks in particular. Our strategies for mapping the labels
significantly enhance the model's performance on sentence pair tasks. On the
FewCLUE benchmark, our NSP-BERT outperforms other zero-shot methods on most of
these tasks and comes close to the few-shot methods.
|
[
{
"version": "v1",
"created": "Wed, 8 Sep 2021 11:57:08 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 09:40:35 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Sun",
"Yi",
""
],
[
"Zheng",
"Yu",
""
],
[
"Hao",
"Chao",
""
],
[
"Qiu",
"Hangping",
""
]
] |
new_dataset
| 0.983655 |
2110.13638
|
George Onoufriou
|
George Onoufriou, Marc Hanheide, Georgios Leontidis
|
EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for
Vision and Private Strawberry Yield Forecasting
|
13 pages, 6 figures, journal
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We present automatically parameterised Fully Homomorphic Encryption (FHE) for
encrypted neural network inference and exemplify our inference over FHE
compatible neural networks with our own open-source framework and reproducible
examples. We use the 4th generation Cheon, Kim, Kim and Song (CKKS) FHE scheme
over fixed points provided by the Microsoft Simple Encrypted Arithmetic Library
(MS-SEAL). We significantly enhance the usability and applicability of FHE in
deep learning contexts, with a focus on the constituent graphs, traversal, and
optimisation. We find that FHE is not a panacea for all privacy preserving
machine learning (PPML) problems, and that certain limitations still remain,
such as model training. However we also find that in certain contexts FHE is
well suited for computing completely private predictions with neural networks.
The ability to privately compute sensitive problems more easily, while lowering
the barriers to entry, can allow otherwise too-sensitive fields to begin
advantaging themselves of performant third-party neural networks. Lastly we
show how encrypted deep learning can be applied to a sensitive real world
problem in agri-food, i.e. strawberry yield forecasting, demonstrating
competitive performance. We argue that the adoption of encrypted deep learning
methods at scale could allow for a greater adoption of deep learning
methodologies where privacy concerns exists, hence having a large positive
potential impact within the agri-food sector and its journey to net zero.
|
[
{
"version": "v1",
"created": "Tue, 26 Oct 2021 12:43:35 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 10:07:07 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Onoufriou",
"George",
""
],
[
"Hanheide",
"Marc",
""
],
[
"Leontidis",
"Georgios",
""
]
] |
new_dataset
| 0.954015 |
2203.02882
|
Ran Long
|
Ran Long, Christian Rauch, Tianwei Zhang, Vladimir Ivan, Tin Lun Lam
and Sethu Vijayakumar
|
RGB-D SLAM in Indoor Planar Environments with Multiple Large Dynamic
Objects
|
8 papges, 9 figures
|
IEEE Robotics and Automation Letters 2022
|
10.1109/LRA.2022.3186091
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This work presents a novel dense RGB-D SLAM approach for dynamic planar
environments that enables simultaneous multi-object tracking, camera
localisation and background reconstruction. Previous dynamic SLAM methods
either rely on semantic segmentation to directly detect dynamic objects; or
assume that dynamic objects occupy a smaller proportion of the camera view than
the static background and can, therefore, be removed as outliers. Our approach,
however, enables dense SLAM when the camera view is largely occluded by
multiple dynamic objects with the aid of camera motion prior. The dynamic
planar objects are separated by their different rigid motions and tracked
independently. The remaining dynamic non-planar areas are removed as outliers
and not mapped into the background. The evaluation demonstrates that our
approach outperforms the state-of-the-art methods in terms of localisation,
mapping, dynamic segmentation and object tracking. We also demonstrate its
robustness to large drift in the camera motion prior.
|
[
{
"version": "v1",
"created": "Sun, 6 Mar 2022 05:54:25 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 16:44:27 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Long",
"Ran",
""
],
[
"Rauch",
"Christian",
""
],
[
"Zhang",
"Tianwei",
""
],
[
"Ivan",
"Vladimir",
""
],
[
"Lam",
"Tin Lun",
""
],
[
"Vijayakumar",
"Sethu",
""
]
] |
new_dataset
| 0.984866 |
2203.12602
|
Zhan Tong
|
Zhan Tong, Yibing Song, Jue Wang, Limin Wang
|
VideoMAE: Masked Autoencoders are Data-Efficient Learners for
Self-Supervised Video Pre-Training
|
NeurIPS 2022 camera-ready version
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Pre-training video transformers on extra large-scale datasets is generally
required to achieve premier performance on relatively small datasets. In this
paper, we show that video masked autoencoders (VideoMAE) are data-efficient
learners for self-supervised video pre-training (SSVP). We are inspired by the
recent ImageMAE and propose customized video tube masking with an extremely
high ratio. This simple design makes video reconstruction a more challenging
self-supervision task, thus encouraging extracting more effective video
representations during this pre-training process. We obtain three important
findings on SSVP: (1) An extremely high proportion of masking ratio (i.e., 90%
to 95%) still yields favorable performance of VideoMAE. The temporally
redundant video content enables a higher masking ratio than that of images. (2)
VideoMAE achieves impressive results on very small datasets (i.e., around 3k-4k
videos) without using any extra data. (3) VideoMAE shows that data quality is
more important than data quantity for SSVP. Domain shift between pre-training
and target datasets is an important issue. Notably, our VideoMAE with the
vanilla ViT can achieve 87.4% on Kinetics-400, 75.4% on Something-Something V2,
91.3% on UCF101, and 62.6% on HMDB51, without using any extra data. Code is
available at https://github.com/MCG-NJU/VideoMAE.
|
[
{
"version": "v1",
"created": "Wed, 23 Mar 2022 17:55:10 GMT"
},
{
"version": "v2",
"created": "Thu, 7 Jul 2022 14:38:38 GMT"
},
{
"version": "v3",
"created": "Tue, 18 Oct 2022 09:15:42 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Tong",
"Zhan",
""
],
[
"Song",
"Yibing",
""
],
[
"Wang",
"Jue",
""
],
[
"Wang",
"Limin",
""
]
] |
new_dataset
| 0.999622 |
2205.08121
|
Francis Lau C.M.
|
Jia Zhan and Francis C.M. Lau
|
Design of Joint Source-Channel Codes Based on a Generic Protograph
|
26 pages, 15 figures, 5 tables
| null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this paper, we propose using a generic protograph to design joint
source-channel codes (JSCCs). We present a generalized algorithm, called
protograph extrinsic information transfer for JSCC algorithm (PEXIT-JSCC
algorithm), for analyzing the channel threshold of the proposed JSCC. We also
propose a source generic protograph EXIT (SGP-EXIT) algorithm, which is more
appropriate than the generalized source protograph extrinsic information
transfer (GSP-EXIT) algorithm, for evaluating the source threshold of a generic
protograph. Moreover, a collaborative optimization method based on the SGP-EXIT
and PEXIT-JSCC algorithms is proposed to construct generic-protograph JSCCs
with good source and channel thresholds. Finally, we construct
generic-protograph JSCCs, analyze their decoding thresholds, and compare their
theoretical and error performance with JSCC systems based on optimized
double-protographs. Results show that our proposed codes can attain channel
thresholds within 1 dB from the Shannon limit and outperform
double-protograph-based JSCCs.
|
[
{
"version": "v1",
"created": "Tue, 17 May 2022 06:42:13 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Oct 2022 14:38:21 GMT"
},
{
"version": "v3",
"created": "Tue, 18 Oct 2022 08:00:59 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Zhan",
"Jia",
""
],
[
"Lau",
"Francis C. M.",
""
]
] |
new_dataset
| 0.99794 |
2205.14292
|
Dian Wang
|
Dian Wang, Colin Kohler, Xupeng Zhu, Mingxi Jia, Robert Platt
|
BulletArm: An Open-Source Robotic Manipulation Benchmark and Learning
Framework
|
Published at ISRR 2022
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present BulletArm, a novel benchmark and learning-environment for robotic
manipulation. BulletArm is designed around two key principles: reproducibility
and extensibility. We aim to encourage more direct comparisons between robotic
learning methods by providing a set of standardized benchmark tasks in
simulation alongside a collection of baseline algorithms. The framework
consists of 31 different manipulation tasks of varying difficulty, ranging from
simple reaching and picking tasks to more realistic tasks such as bin packing
and pallet stacking. In addition to the provided tasks, BulletArm has been
built to facilitate easy expansion and provides a suite of tools to assist
users when adding new tasks to the framework. Moreover, we introduce a set of
five benchmarks and evaluate them using a series of state-of-the-art baseline
algorithms. By including these algorithms as part of our framework, we hope to
encourage users to benchmark their work on any new tasks against these
baselines.
|
[
{
"version": "v1",
"created": "Sat, 28 May 2022 01:19:50 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 19:25:45 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Wang",
"Dian",
""
],
[
"Kohler",
"Colin",
""
],
[
"Zhu",
"Xupeng",
""
],
[
"Jia",
"Mingxi",
""
],
[
"Platt",
"Robert",
""
]
] |
new_dataset
| 0.999558 |
2206.00629
|
Zixin Guo
|
Zixin Guo, Tzu-Jui Julius Wang, Jorma Laaksonen
|
CLIP4IDC: CLIP for Image Difference Captioning
|
Accepted to AACL-IJCNLP 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Image Difference Captioning (IDC) aims at generating sentences to describe
differences between two similar-looking images. Conventional approaches learn
an IDC model with a pre-trained and usually frozen visual feature extractor.
Accordingly, two major issues may arise: (1) a large domain gap usually exists
between the pre-training datasets used for training such a visual encoder and
that of the downstream IDC task, and (2) the visual feature extractor, when
separately encoding two images, often does not effectively encode the visual
changes between two images. Due to the excellent zero-shot performance of the
recently proposed CLIP, we thus propose CLIP4IDC to transfer a CLIP model for
the IDC task to address those issues. Different from directly fine-tuning CLIP
to generate sentences, we introduce an adaptation training process to adapt
CLIP's visual encoder to capture and align differences in image pairs based on
the textual descriptions. Experiments on three IDC benchmark datasets,
CLEVR-Change, Spot-the-Diff, and Image-Editing-Request, demonstrate the
effectiveness of CLIP4IDC.
|
[
{
"version": "v1",
"created": "Wed, 1 Jun 2022 17:02:08 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 12:30:01 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Guo",
"Zixin",
""
],
[
"Wang",
"Tzu-Jui Julius",
""
],
[
"Laaksonen",
"Jorma",
""
]
] |
new_dataset
| 0.988865 |
2206.12469
|
Atijit Anuchitanukul
|
Atijit Anuchitanukul and Lucia Specia
|
Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion,
Age, and Origin from Vocal Bursts
| null | null | null | null |
cs.SD cs.CL eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present Burst2Vec, our multi-task learning approach to predict emotion,
age, and origin (i.e., native country/language) from vocal bursts. Burst2Vec
utilises pre-trained speech representations to capture acoustic information
from raw waveforms and incorporates the concept of model debiasing via
adversarial training. Our models achieve a relative 30 % performance gain over
baselines using pre-extracted features and score the highest amongst all
participants in the ICML ExVo 2022 Multi-Task Challenge.
|
[
{
"version": "v1",
"created": "Fri, 24 Jun 2022 18:57:41 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 05:48:23 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Anuchitanukul",
"Atijit",
""
],
[
"Specia",
"Lucia",
""
]
] |
new_dataset
| 0.96643 |
2207.08980
|
Siamul Karim Khan
|
Siamul Karim Khan, Patrick Tinsley and Adam Czajka
|
DeformIrisNet: An Identity-Preserving Model of Iris Texture Deformation
|
Accepted to the IEEE/CVF Winter Conference on Applications of
Computer Vision (WACV) 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Nonlinear iris texture deformations due to pupil size variations are one of
the main factors responsible for within-class variance of genuine comparison
scores in iris recognition. In dominant approaches to iris recognition, the
size of a ring-shaped iris region is linearly scaled to a canonical rectangle,
used further in encoding and matching. However, the biological complexity of
the iris sphincter and dilator muscles causes the movements of iris features to
be nonlinear in a function of pupil size, and not solely organized along radial
paths. Alternatively to the existing theoretical models based on the
biomechanics of iris musculature, in this paper we propose a novel deep
autoencoder-based model that can effectively learn complex movements of iris
texture features directly from the data. The proposed model takes two inputs,
(a) an ISO-compliant near-infrared iris image with initial pupil size, and (b)
the binary mask defining the target shape of the iris. The model makes all the
necessary nonlinear deformations to the iris texture to match the shape of the
iris in an image (a) with the shape provided by the target mask (b). The
identity-preservation component of the loss function helps the model in finding
deformations that preserve identity and not only the visual realism of the
generated samples. We also demonstrate two immediate applications of this
model: better compensation for iris texture deformations in iris recognition
algorithms, compared to linear models, and the creation of a generative
algorithm that can aid human forensic examiners, who may need to compare iris
images with a large difference in pupil dilation. We offer the source codes and
model weights available along with this paper.
|
[
{
"version": "v1",
"created": "Mon, 18 Jul 2022 23:23:23 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 07:32:34 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Khan",
"Siamul Karim",
""
],
[
"Tinsley",
"Patrick",
""
],
[
"Czajka",
"Adam",
""
]
] |
new_dataset
| 0.999628 |
2208.03430
|
Anjul Tyagi
|
Anjul Tyagi, Tyler Estro, Geoff Kuenning, Erez Zadok, Klaus Mueller
|
PC-Expo: A Metrics-Based Interactive Axes Reordering Method for Parallel
Coordinate Displays
|
Pre-print of the accepted paper at Transactions of Visualization and
Computer Graphics Forum (TVCG), 2022
|
IEEE Transactions of Visualization and Computer Graphics, 2022
|
10.1109/TVCG.2022.3209392
| null |
cs.GR
|
http://creativecommons.org/licenses/by/4.0/
|
The axes ordering in PCP presents a particular story from the data based on
the user perception of PCP polylines. Existing works focus on directly
optimizing for PCP axes ordering based on some common analysis tasks like
clustering, neighborhood, and correlation. However, direct optimization for PCP
axes based on these common properties is restrictive because it does not
account for multiple properties occurring between the axes, and for local
properties that occur in small regions in the data. Also, many of these
techniques do not support the human-in-the-loop (HIL) paradigm, which is
crucial (i) for explainability and (ii) in cases where no single reordering
scheme fits the user goals. To alleviate these problems, we present PC-Expo, a
real-time visual analytics framework for all-in-one PCP line pattern detection,
and axes reordering. We studied the connection of line patterns in PCPs with
different data analysis tasks and datasets. PC-Expo expands prior work on PCP
axes reordering by developing real-time, local detection schemes for the 12
most common analysis tasks (properties). Users can choose the story they want
to present with PCPs by optimizing directly over their choice of properties.
These properties can be ranked, or combined using individual weights, creating
a custom optimization scheme for axes reordering. Users can control the
granularity at which they want to work with their detection scheme in the data,
allowing exploration of local regions. PC-Expo also supports HIL axes
reordering via local-property visualization, which shows the regions of
granular activity for every axis pair. Local-property visualization is helpful
for PCP axes reordering based on multiple properties, when no single reordering
scheme fits the user goals.
|
[
{
"version": "v1",
"created": "Sat, 6 Aug 2022 02:36:30 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Tyagi",
"Anjul",
""
],
[
"Estro",
"Tyler",
""
],
[
"Kuenning",
"Geoff",
""
],
[
"Zadok",
"Erez",
""
],
[
"Mueller",
"Klaus",
""
]
] |
new_dataset
| 0.966798 |
2208.06063
|
Nantheera Anantrasirichai
|
Nantheera Anantrasirichai and Thanarat H. Chalidabhongse and Duangdao
Palasuwan and Korranat Naruenatthanaset and Thananop Kobchaisawat and
Nuntiporn Nunthanasup and Kanyarat Boonpeng and Xudong Ma and Alin Achim
|
ICIP 2022 Challenge on Parasitic Egg Detection and Classification in
Microscopic Images: Dataset, Methods and Results
|
The 29th IEEE International Conference on Image Processing
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Manual examination of faecal smear samples to identify the existence of
parasitic eggs is very time-consuming and can only be done by specialists.
Therefore, an automated system is required to tackle this problem since it can
relate to serious intestinal parasitic infections. This paper reviews the ICIP
2022 Challenge on parasitic egg detection and classification in microscopic
images. We describe a new dataset for this application, which is the largest
dataset of its kind. The methods used by participants in the challenge are
summarised and discussed along with their results.
|
[
{
"version": "v1",
"created": "Thu, 11 Aug 2022 22:50:51 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 21:55:42 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Anantrasirichai",
"Nantheera",
""
],
[
"Chalidabhongse",
"Thanarat H.",
""
],
[
"Palasuwan",
"Duangdao",
""
],
[
"Naruenatthanaset",
"Korranat",
""
],
[
"Kobchaisawat",
"Thananop",
""
],
[
"Nunthanasup",
"Nuntiporn",
""
],
[
"Boonpeng",
"Kanyarat",
""
],
[
"Ma",
"Xudong",
""
],
[
"Achim",
"Alin",
""
]
] |
new_dataset
| 0.999581 |
2208.06305
|
Ejup Hoxha
|
Ejup Hoxha, Jinglun Feng, Diar Sanakov, Ardian Gjinofci, Jizhong Xiao
|
Robotic Inspection and Characterization of Subsurface Defects on
Concrete Structures Using Impact Sounding
| null |
Structural Health Monitorign 2021
|
10.12783/shm2021/36339
| null |
cs.RO eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
Impact-sounding (IS) and impact-echo (IE) are well-developed non-destructive
evaluation (NDE) methods that are widely used for inspections of concrete
structures to ensure the safety and sustainability. However, it is a tedious
work to collect IS and IE data along grid lines covering a large target area
for characterization of subsurface defects. On the other hand, data processing
is very complicated that requires domain experts to interpret the results. To
address the above problems, we present a novel robotic inspection system named
as Impact-Rover to automate the data collection process and introduce data
analytics software to visualize the inspection result allowing regular
non-professional people to understand. The system consists of three modules: 1)
a robotic platform with vertical mobility to collect IS and IE data in
hard-to-reach locations, 2) vision-based positioning module that fuses the
RGB-D camera, IMU and wheel encoder to estimate the 6-DOF pose of the robot, 3)
a data analytics software module for processing the IS data to generate defect
maps. The Impact-Rover hosts both IE and IS devices on a sliding mechanism and
can perform move-stop-sample operations to collect multiple IS and IE data at
adjustable spacing. The robot takes samples much faster than the manual data
collection method because it automatically takes the multiple measurements
along a straight line and records the locations. This paper focuses on
reporting experimental results on IS. We calculate features and use
unsupervised learning methods for analyzing the data. By combining the pose
generated by our vision-based localization module and the position of the head
of the sliding mechanism we can generate maps of possible defects. The results
on concrete slabs demonstrate that our impact-sounding system can effectively
reveal shallow defects.
|
[
{
"version": "v1",
"created": "Fri, 12 Aug 2022 14:43:52 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Hoxha",
"Ejup",
""
],
[
"Feng",
"Jinglun",
""
],
[
"Sanakov",
"Diar",
""
],
[
"Gjinofci",
"Ardian",
""
],
[
"Xiao",
"Jizhong",
""
]
] |
new_dataset
| 0.995751 |
2208.10859
|
Colin Groth
|
Colin Groth, Sascha Fricke, Susana Castillo, Marcus Magnor
|
Wavelet-Based Fast Decoding of 360-Degree Videos
| null | null | null | null |
cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose a wavelet-based video codec specifically designed
for VR displays that enables real-time playback of high-resolution 360{\deg}
videos. Our codec exploits the fact that only a fraction of the full 360{\deg}
video frame is visible on the display at any time. To load and decode the video
viewport-dependently in real time, we make use of the wavelet transform for
intra- as well as inter-frame coding. Thereby, the relevant content is directly
streamed from the drive, without the need to hold the entire frames in memory.
With an average of 193 frames per second at 8192x8192-pixel full-frame
resolution, the conducted evaluation demonstrates that our codec's decoding
performance is up to 272% higher than that of the state-of-the-art video codecs
H.265 and AV1 for typical VR displays. By means of a perceptual study, we
further illustrate the necessity of high frame rates for a better VR
experience. Finally, we demonstrate how our wavelet-based codec can also
directly be used in conjunction with foveation for further performance
increase.
|
[
{
"version": "v1",
"created": "Tue, 23 Aug 2022 10:35:26 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 14:41:43 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Groth",
"Colin",
""
],
[
"Fricke",
"Sascha",
""
],
[
"Castillo",
"Susana",
""
],
[
"Magnor",
"Marcus",
""
]
] |
new_dataset
| 0.99892 |
2209.00213
|
Moseli Mots'oehli
|
Moseli Mots'oehli and Yao Chao Yang
|
Public Parking Spot Detection And Geo-localization Using Transfer
Learning
|
Accepted for presentation at SACAIR 2022. 11 pages,5 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In cities around the world, locating public parking lots with vacant parking
spots is a major problem, costing commuters time and adding to traffic
congestion. This work illustrates how a dataset of Geo-tagged images from a
mobile phone camera, can be used in navigating to the most convenient public
parking lot in Johannesburg with an available parking space, detected by a
neural network powered-public camera. The images are used to fine-tune a
Detectron2 model pre-trained on the ImageNet dataset to demonstrate detection
and segmentation of vacant parking spots, we then add the parking lot's
corresponding longitude and latitude coordinates to recommend the most
convenient parking lot to the driver based on the Haversine distance and number
of available parking spots. Using the VGG Image Annotation (VIA) we use images
from an expanding dataset of images, and annotate these with polygon outlines
of the four different types of objects of interest: cars, open parking spots,
people, and car number plates. We use the segmentation model to ensure number
plates can be occluded in production for car registration anonymity purposes.
We get an 89% and 82% intersection over union cover score on cars and parking
spaces respectively. This work has the potential to help reduce the amount of
time commuters spend searching for free public parking, hence easing traffic
congestion in and around shopping complexes and other public places, and
maximize people's utility with respect to driving on public roads.
|
[
{
"version": "v1",
"created": "Thu, 1 Sep 2022 04:09:51 GMT"
},
{
"version": "v2",
"created": "Sun, 4 Sep 2022 04:29:17 GMT"
},
{
"version": "v3",
"created": "Tue, 18 Oct 2022 03:59:29 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Mots'oehli",
"Moseli",
""
],
[
"Yang",
"Yao Chao",
""
]
] |
new_dataset
| 0.998564 |
2209.13464
|
Zhijian Ou
|
Hong Liu, Hao Peng, Zhijian Ou, Juanzi Li, Yi Huang and Junlan Feng
|
Information Extraction and Human-Robot Dialogue towards Real-life Tasks:
A Baseline Study with the MobileCS Dataset
|
Accepted by EMNLP 2022 SereTOD Workshop
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, there have merged a class of task-oriented dialogue (TOD) datasets
collected through Wizard-of-Oz simulated games. However, the Wizard-of-Oz data
are in fact simulated data and thus are fundamentally different from real-life
conversations, which are more noisy and casual. Recently, the SereTOD challenge
is organized and releases the MobileCS dataset, which consists of real-world
dialog transcripts between real users and customer-service staffs from China
Mobile. Based on the MobileCS dataset, the SereTOD challenge has two tasks, not
only evaluating the construction of the dialogue system itself, but also
examining information extraction from dialog transcripts, which is crucial for
building the knowledge base for TOD. This paper mainly presents a baseline
study of the two tasks with the MobileCS dataset. We introduce how the two
baselines are constructed, the problems encountered, and the results. We
anticipate that the baselines can facilitate exciting future research to build
human-robot dialogue systems for real-life tasks.
|
[
{
"version": "v1",
"created": "Tue, 27 Sep 2022 15:30:43 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 06:15:28 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Liu",
"Hong",
""
],
[
"Peng",
"Hao",
""
],
[
"Ou",
"Zhijian",
""
],
[
"Li",
"Juanzi",
""
],
[
"Huang",
"Yi",
""
],
[
"Feng",
"Junlan",
""
]
] |
new_dataset
| 0.993393 |
2210.07873
|
Amir Zeldes
|
Amir Zeldes, Nick Howell, Noam Ordan and Yifat Ben Moshe
|
A Second Wave of UD Hebrew Treebanking and Cross-Domain Parsing
|
Proceedings of EMNLP 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Foundational Hebrew NLP tasks such as segmentation, tagging and parsing, have
relied to date on various versions of the Hebrew Treebank (HTB, Sima'an et al.
2001). However, the data in HTB, a single-source newswire corpus, is now over
30 years old, and does not cover many aspects of contemporary Hebrew on the
web. This paper presents a new, freely available UD treebank of Hebrew
stratified from a range of topics selected from Hebrew Wikipedia. In addition
to introducing the corpus and evaluating the quality of its annotations, we
deploy automatic validation tools based on grew (Guillaume, 2021), and conduct
the first cross domain parsing experiments in Hebrew. We obtain new
state-of-the-art (SOTA) results on UD NLP tasks, using a combination of the
latest language modelling and some incremental improvements to existing
transformer based approaches. We also release a new version of the UD HTB
matching annotation scheme updates from our new corpus.
|
[
{
"version": "v1",
"created": "Fri, 14 Oct 2022 14:52:07 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 14:53:07 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Zeldes",
"Amir",
""
],
[
"Howell",
"Nick",
""
],
[
"Ordan",
"Noam",
""
],
[
"Moshe",
"Yifat Ben",
""
]
] |
new_dataset
| 0.998302 |
2210.08305
|
Runkai Zhao
|
Runkai Zhao, Heng Wang, Chaoyi Zhang, Weidong Cai
|
PointNeuron: 3D Neuron Reconstruction via Geometry and Topology Learning
of Point Clouds
|
WACV 2023
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Digital neuron reconstruction from 3D microscopy images is an essential
technique for investigating brain connectomics and neuron morphology. Existing
reconstruction frameworks use convolution-based segmentation networks to
partition the neuron from noisy backgrounds before applying the tracing
algorithm. The tracing results are sensitive to the raw image quality and
segmentation accuracy. In this paper, we propose a novel framework for 3D
neuron reconstruction. Our key idea is to use the geometric representation
power of the point cloud to better explore the intrinsic structural information
of neurons. Our proposed framework adopts one graph convolutional network to
predict the neural skeleton points and another one to produce the connectivity
of these points. We finally generate the target SWC file through the
interpretation of the predicted point coordinates, radius, and connections.
Evaluated on the Janelia-Fly dataset from the BigNeuron project, we show that
our framework achieves competitive neuron reconstruction performance. Our
geometry and topology learning of point clouds could further benefit 3D medical
image analysis, such as cardiac surface reconstruction. Our code is available
at https://github.com/RunkaiZhao/PointNeuron.
|
[
{
"version": "v1",
"created": "Sat, 15 Oct 2022 14:11:56 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 01:59:13 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Zhao",
"Runkai",
""
],
[
"Wang",
"Heng",
""
],
[
"Zhang",
"Chaoyi",
""
],
[
"Cai",
"Weidong",
""
]
] |
new_dataset
| 0.996496 |
2210.09267
|
Jyh-Jing Hwang
|
Jyh-Jing Hwang and Henrik Kretzschmar and Joshua Manela and Sean
Rafferty and Nicholas Armstrong-Crews and Tiffany Chen and Dragomir Anguelov
|
CramNet: Camera-Radar Fusion with Ray-Constrained Cross-Attention for
Robust 3D Object Detection
|
ECCV 2022
| null | null | null |
cs.CV cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Robust 3D object detection is critical for safe autonomous driving. Camera
and radar sensors are synergistic as they capture complementary information and
work well under different environmental conditions. Fusing camera and radar
data is challenging, however, as each of the sensors lacks information along a
perpendicular axis, that is, depth is unknown to camera and elevation is
unknown to radar. We propose the camera-radar matching network CramNet, an
efficient approach to fuse the sensor readings from camera and radar in a joint
3D space. To leverage radar range measurements for better camera depth
predictions, we propose a novel ray-constrained cross-attention mechanism that
resolves the ambiguity in the geometric correspondences between camera features
and radar features. Our method supports training with sensor modality dropout,
which leads to robust 3D object detection, even when a camera or radar sensor
suddenly malfunctions on a vehicle. We demonstrate the effectiveness of our
fusion approach through extensive experiments on the RADIATE dataset, one of
the few large-scale datasets that provide radar radio frequency imagery. A
camera-only variant of our method achieves competitive performance in monocular
3D object detection on the Waymo Open Dataset.
|
[
{
"version": "v1",
"created": "Mon, 17 Oct 2022 17:18:47 GMT"
},
{
"version": "v2",
"created": "Tue, 18 Oct 2022 01:46:28 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Hwang",
"Jyh-Jing",
""
],
[
"Kretzschmar",
"Henrik",
""
],
[
"Manela",
"Joshua",
""
],
[
"Rafferty",
"Sean",
""
],
[
"Armstrong-Crews",
"Nicholas",
""
],
[
"Chen",
"Tiffany",
""
],
[
"Anguelov",
"Dragomir",
""
]
] |
new_dataset
| 0.999481 |
2210.09345
|
Elisa Bassignana
|
Elisa Bassignana and Barbara Plank
|
CrossRE: A Cross-Domain Dataset for Relation Extraction
|
Accepted in Findings of the Association for Computational
Linguistics: EMNLP 2022
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Relation Extraction (RE) has attracted increasing attention, but current RE
evaluation is limited to in-domain evaluation setups. Little is known on how
well a RE system fares in challenging, but realistic out-of-distribution
evaluation setups. To address this gap, we propose CrossRE, a new,
freely-available cross-domain benchmark for RE, which comprises six distinct
text domains and includes multi-label annotations. An additional innovation is
that we release meta-data collected during annotation, to include explanations
and flags of difficult instances. We provide an empirical evaluation with a
state-of-the-art model for relation classification. As the meta-data enables us
to shed new light on the state-of-the-art model, we provide a comprehensive
analysis on the impact of difficult cases and find correlations between model
and human annotations. Overall, our empirical investigation highlights the
difficulty of cross-domain RE. We release our dataset, to spur more research in
this direction.
|
[
{
"version": "v1",
"created": "Mon, 17 Oct 2022 18:33:14 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Bassignana",
"Elisa",
""
],
[
"Plank",
"Barbara",
""
]
] |
new_dataset
| 0.999443 |
2210.09389
|
Rashid Mehmood PhD
|
Istiak Ahmad, Fahad AlQurashi, Rashid Mehmood
|
Potrika: Raw and Balanced Newspaper Datasets in the Bangla Language with
Eight Topics and Five Attributes
|
10 pages, 5 figures
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Knowledge is central to human and scientific developments. Natural Language
Processing (NLP) allows automated analysis and creation of knowledge. Data is a
crucial NLP and machine learning ingredient. The scarcity of open datasets is a
well-known problem in machine and deep learning research. This is very much the
case for textual NLP datasets in English and other major world languages. For
the Bangla language, the situation is even more challenging and the number of
large datasets for NLP research is practically nil. We hereby present Potrika,
a large single-label Bangla news article textual dataset curated for NLP
research from six popular online news portals in Bangladesh (Jugantor,
Jaijaidin, Ittefaq, Kaler Kontho, Inqilab, and Somoyer Alo) for the period
2014-2020. The articles are classified into eight distinct categories
(National, Sports, International, Entertainment, Economy, Education, Politics,
and Science \& Technology) providing five attributes (News Article, Category,
Headline, Publication Date, and Newspaper Source). The raw dataset contains
185.51 million words and 12.57 million sentences contained in 664,880 news
articles. Moreover, using NLP augmentation techniques, we create from the raw
(unbalanced) dataset another (balanced) dataset comprising 320,000 news
articles with 40,000 articles in each of the eight news categories. Potrika
contains both the datasets (raw and balanced) to suit a wide range of NLP
research. By far, to the best of our knowledge, Potrika is the largest and the
most extensive dataset for news classification.
|
[
{
"version": "v1",
"created": "Mon, 17 Oct 2022 19:37:42 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Ahmad",
"Istiak",
""
],
[
"AlQurashi",
"Fahad",
""
],
[
"Mehmood",
"Rashid",
""
]
] |
new_dataset
| 0.999179 |
2210.09396
|
Sky CH-Wang
|
Sky CH-Wang, Evan Li, Oliver Li, Smaranda Muresan, Zhou Yu
|
Affective Idiosyncratic Responses to Music
|
EMNLP 2022 Main Conference; see Github
https://github.com/skychwang/music-emotions
| null | null | null |
cs.CL cs.AI cs.CY cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Affective responses to music are highly personal. Despite consensus that
idiosyncratic factors play a key role in regulating how listeners emotionally
respond to music, precisely measuring the marginal effects of these variables
has proved challenging. To address this gap, we develop computational methods
to measure affective responses to music from over 403M listener comments on a
Chinese social music platform. Building on studies from music psychology in
systematic and quasi-causal analyses, we test for musical, lyrical, contextual,
demographic, and mental health effects that drive listener affective responses.
Finally, motivated by the social phenomenon known as w\v{a}ng-y\`i-y\'un, we
identify influencing factors of platform user self-disclosures, the social
support they receive, and notable differences in discloser user activity.
|
[
{
"version": "v1",
"created": "Mon, 17 Oct 2022 19:57:46 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"CH-Wang",
"Sky",
""
],
[
"Li",
"Evan",
""
],
[
"Li",
"Oliver",
""
],
[
"Muresan",
"Smaranda",
""
],
[
"Yu",
"Zhou",
""
]
] |
new_dataset
| 0.997082 |
2210.09411
|
Kenechukwu Mbanisi
|
Kenechukwu C. Mbanisi and Michael A. Gennert
|
Multimodal Shared Autonomy for Social Navigation Assistance of
Telepresence Robots
|
10 pages, 4 figures
| null | null | null |
cs.RO cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Mobile telepresence robots (MTRs) have become increasingly popular in the
expanding world of remote work, providing new avenues for people to actively
participate in activities at a distance. However, humans operating MTRs often
have difficulty navigating in densely populated environments due to limited
situation awareness and narrow field-of-view, which reduces user acceptance and
satisfaction. Shared autonomy in navigation has been studied primarily in
static environments or in situations where only one pedestrian interacts with
the robot. We present a multimodal shared autonomy approach, leveraging visual
and haptic guidance, to provide navigation assistance for remote operators in
densely-populated environments. It uses a modified form of reciprocal velocity
obstacles for generating safe control inputs while taking social proxemics
constraints into account. Two different visual guidance designs, as well as
haptic force rendering, were proposed to convey safe control input. We
conducted a user study to compare the merits and limitations of multimodal
navigation assistance to haptic or visual assistance alone on a shared
navigation task. The study involved 15 participants operating a virtual
telepresence robot in a virtual hall with moving pedestrians, using the
different assistance modalities. We evaluated navigation performance,
transparency and cooperation, as well as user preferences. Our results showed
that participants preferred multimodal assistance with a visual guidance
trajectory over haptic or visual modalities alone, although it had no impact on
navigation performance. Additionally, we found that visual guidance
trajectories conveyed a higher degree of understanding and cooperation than
equivalent haptic cues in a navigation task.
|
[
{
"version": "v1",
"created": "Mon, 17 Oct 2022 20:23:32 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Mbanisi",
"Kenechukwu C.",
""
],
[
"Gennert",
"Michael A.",
""
]
] |
new_dataset
| 0.966532 |
2210.09460
|
Matthew Sotoudeh
|
Matthew Sotoudeh
|
System-Specific Interpreters Make Megasystems Friendlier
|
To appear at the Eight Workshop on Live Programming (LIVE 2022)
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Modern operating systems, browsers, and office suites have become megasystems
built on millions of lines of code. Their sheer size can intimidate even
experienced users and programmers away from attempting to understand and modify
the software running on their machines. This paper introduces system-specific
interpreters (SSIs) as a tool to help users regain knowledge of and control
over megasystems. SSIs directly execute individual modules of a megasystem in a
gdb-like environment without forcing the user to build, run, and trace the
entire system. A prototype framework to help write SSIs is described in this
paper and available for download at https://github.com/matthewsot/ssi-live22.
|
[
{
"version": "v1",
"created": "Mon, 17 Oct 2022 22:19:22 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Sotoudeh",
"Matthew",
""
]
] |
new_dataset
| 0.972882 |
2210.09495
|
Noriaki Ota
|
Noriaki Ota, Shingo Yokoi, Shinsuke Yamaoka
|
5th Place Solution to Kaggle Google Universal Image Embedding
Competition
|
3 pages, 1 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present our solution, which placed 5th in the kaggle Google
Universal Image Embedding Competition in 2022. We use the ViT-H visual encoder
of CLIP from the openclip repository as a backbone and train a head model
composed of BatchNormalization and Linear layers using ArcFace. The dataset
used was a subset of products10K, GLDv2, GPR1200, and Food101. And applying TTA
for part of images also improves the score. With this method, we achieve a
score of 0.684 on the public and 0.688 on the private leaderboard. Our code is
available.
https://github.com/riron1206/kaggle-Google-Universal-Image-Embedding-Competition-5th-Place-Solution
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 00:34:09 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Ota",
"Noriaki",
""
],
[
"Yokoi",
"Shingo",
""
],
[
"Yamaoka",
"Shinsuke",
""
]
] |
new_dataset
| 0.998506 |
2210.09729
|
Zan Wang
|
Zan Wang, Yixin Chen, Tengyu Liu, Yixin Zhu, Wei Liang, Siyuan Huang
|
HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes
|
Accepted by NeurIPS 2022
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning to generate diverse scene-aware and goal-oriented human motions in
3D scenes remains challenging due to the mediocre characteristics of the
existing datasets on Human-Scene Interaction (HSI); they only have limited
scale/quality and lack semantics. To fill in the gap, we propose a large-scale
and semantic-rich synthetic HSI dataset, denoted as HUMANISE, by aligning the
captured human motion sequences with various 3D indoor scenes. We automatically
annotate the aligned motions with language descriptions that depict the action
and the unique interacting objects in the scene; e.g., sit on the armchair near
the desk. HUMANISE thus enables a new generation task, language-conditioned
human motion generation in 3D scenes. The proposed task is challenging as it
requires joint modeling of the 3D scene, human motion, and natural language. To
tackle this task, we present a novel scene-and-language conditioned generative
model that can produce 3D human motions of the desirable action interacting
with the specified objects. Our experiments demonstrate that our model
generates diverse and semantically consistent human motions in 3D scenes.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 10:14:11 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Wang",
"Zan",
""
],
[
"Chen",
"Yixin",
""
],
[
"Liu",
"Tengyu",
""
],
[
"Zhu",
"Yixin",
""
],
[
"Liang",
"Wei",
""
],
[
"Huang",
"Siyuan",
""
]
] |
new_dataset
| 0.999698 |
2210.09765
|
Fernando Alonso-Fernandez
|
Fernando Alonso-Fernandez, Reuben A. Farrugia, Josef Bigun
|
Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution
and Matcher Fusion
|
Published at Intl Conf on Biometrics: Theory, Apps and Systems, BTAS
2016
| null |
10.1109/BTAS.2016.7791208
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Current research in iris recognition is moving towards enabling more relaxed
acquisition conditions. This has effects on the quality of acquired images,
with low resolution being a predominant issue. Here, we evaluate a
super-resolution algorithm used to reconstruct iris images based on
Eigen-transformation of local image patches. Each patch is reconstructed
separately, allowing better quality of enhanced images by preserving local
information. Contrast enhancement is used to improve the reconstruction
quality, while matcher fusion has been adopted to improve iris recognition
performance. We validate the system using a database of 1,872 near-infrared
iris images. The presented approach is superior to bilinear or bicubic
interpolation, especially at lower resolutions, and the fusion of the two
systems pushes the EER to below 5% for down-sampling factors up to a image size
of only 13x13.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 11:25:19 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Alonso-Fernandez",
"Fernando",
""
],
[
"Farrugia",
"Reuben A.",
""
],
[
"Bigun",
"Josef",
""
]
] |
new_dataset
| 0.998417 |
2210.09778
|
Fernando Alonso-Fernandez
|
Fernando Alonso-Fernandez, Anna Mikaelyan, Josef Bigun
|
Compact multi-scale periocular recognition using SAFE features
|
Published at IEEE/IAPR Intl Conf on Pattern Recognition, ICPR 2016
| null |
10.1109/ICPR.2016.7899842
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present a new approach for periocular recognition based on
the Symmetry Assessment by Feature Expansion (SAFE) descriptor, which encodes
the presence of various symmetric curve families around image key points. We
use the sclera center as single key point for feature extraction, highlighting
the object-like identity properties that concentrates to this unique point of
the eye. As it is demonstrated, such discriminative properties can be encoded
with a reduced set of symmetric curves. Experiments are done with a database of
periocular images captured with a digital camera. We test our system against
reference periocular features, achieving top performance with a considerably
smaller feature vector (given by the use of a single key point). All the
systems tested also show a nearly steady correlation between acquisition
distance and performance, and they are also able to cope well when enrolment
and test images are not captured at the same distance. Fusion experiments among
the available systems are also provided.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 11:46:38 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Alonso-Fernandez",
"Fernando",
""
],
[
"Mikaelyan",
"Anna",
""
],
[
"Bigun",
"Josef",
""
]
] |
new_dataset
| 0.996313 |
2210.09790
|
Kazuya Tsubokura
|
Kazuya Tsubokura, Fumiya Kishi, Kotomi Narita, Takuya Takeda, Yurie
Iribe
|
Hospitable Travel Agent Dialogue Robot: Team Irisapu Project Description
for DRC2022
|
5 pages, 5 figures, This paper is part of the proceedings of the
Dialogue Robot Competition 2022
| null | null | null |
cs.RO cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
This paper describes the dialog robot system designed by Team Irisapu for the
preliminary round of the Dialogue Robot Competition 2022 (DRC2022). Our
objective was to design a hospitable travel agent robot. The system we
developed was ranked 8th out of 13 systems in the preliminary round of the
competition, but our robot received high marks for its naturalness and
likeability.Our next challenge is to create a system that can provide more
useful information to users.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 12:04:59 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Tsubokura",
"Kazuya",
""
],
[
"Kishi",
"Fumiya",
""
],
[
"Narita",
"Kotomi",
""
],
[
"Takeda",
"Takuya",
""
],
[
"Iribe",
"Yurie",
""
]
] |
new_dataset
| 0.998164 |
2210.09843
|
Emanuele Maiorana
|
Emanuele Maiorana, Chiara Romano, Emiliano Schena, and Carlo Massaroni
|
BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting
Heart Activity
| null | null | null | null |
cs.CV eess.SP
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Wearable devices are increasingly used, thanks to the wide set of
applications that can be deployed exploiting their ability to monitor physical
activity and health-related parameters. Their usage has been recently proposed
to perform biometric recognition, leveraging on the uniqueness of the recorded
traits to generate discriminative identifiers. Most of the studies conducted on
this topic have considered signals derived from cardiac activity, detecting it
mainly using electrical measurements thorugh electrocardiography, or optical
recordings employing photoplethysmography. In this paper we instead propose a
BIOmetric recognition approach using Wearable Inertial Sensors detecting Heart
activity (BIOWISH). In more detail, we investigate the feasibility of
exploiting mechanical measurements obtained through seismocardiography and
gyrocardiography to recognize a person. Several feature extractors and
classifiers, including deep learning techniques relying on transfer learning
and siamese training, are employed to derive distinctive characteristics from
the considered signals, and differentiate between legitimate and impostor
subjects. An multi-session database, comprising acquisitions taken from
subjects performing different activities, is employed to perform experimental
tests simulating a verification system. The obtained results testify that
identifiers derived from measurements of chest vibrations, collected by
wearable inertial sensors, could be employed to guarantee high recognition
performance, even when considering short-time recordings.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 13:26:49 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Maiorana",
"Emanuele",
""
],
[
"Romano",
"Chiara",
""
],
[
"Schena",
"Emiliano",
""
],
[
"Massaroni",
"Carlo",
""
]
] |
new_dataset
| 0.954899 |
2210.09873
|
Yong Niu
|
Lei Wang, Bo Ai, Yong Niu, Zhangdui Zhong, Shiwen Mao, Ning Wang, and
Zhu Han
|
Energy Efficient Train-Ground mmWave Mobile Relay System for High Speed
Railways
|
13 pages, 12 figures, IEEE TGCN
| null |
10.1109/TGCN.2022.3194036
| null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The rapid development of high-speed railways (HSRs) puts forward high
requirements on the corresponding communication system. Millimeter wave
(mmWave) can be a promising solution due to its wide bandwidth, narrow beams,
and rich spectrum resources. However, with the large number of antenna elements
employed, energy-efficient solutions at mmWave frequencies are in great demand.
Based on a mmWave HSR communication system with multiple mobile relays (MRs) on
top of the train, a dynamic power-control scheme for train-ground
communications is proposed. The scheme follows the regular movement
characteristics of high-speed trains and considers three phases of train
movement: the train enters the cell, all MRs are covered in the cell, and the
train leaves the cell. The transmit power is further refined according to the
number of MRs in the cell and the distance between the train and the remote
radio head. By minimizing energy consumption under the constraints of the
transmitted data and transmit power budget, the transmit power is allocated to
multiple MRs through the multiplier punitive function-based algorithm.
Comprehensive simulation results, where the velocity estimation error is taken
into account, are provided to demonstrate the effectiveness of the proposed
scheme over several baseline schemes.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 14:07:02 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Wang",
"Lei",
""
],
[
"Ai",
"Bo",
""
],
[
"Niu",
"Yong",
""
],
[
"Zhong",
"Zhangdui",
""
],
[
"Mao",
"Shiwen",
""
],
[
"Wang",
"Ning",
""
],
[
"Han",
"Zhu",
""
]
] |
new_dataset
| 0.998588 |
2210.09940
|
Tarun Kumar Yadav
|
Tarun Kumar Yadav, Devashish Gosain, Amir Herzberg, Daniel Zappala and
Kent Seamons
|
Automatic Detection of Fake Key Attacks in Secure Messaging
|
An extended version of our paper published at ACM CCS 2022
| null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Popular instant messaging applications such as WhatsApp and Signal provide
end-to-end encryption for billions of users. They rely on a centralized,
application-specific server to distribute public keys and relay encrypted
messages between the users. Therefore, they prevent passive attacks but are
vulnerable to some active attacks. A malicious or hacked server can distribute
fake keys to users to perform man-in-the-middle or impersonation attacks. While
typical secure messaging applications provide a manual method for users to
detect these attacks, this burdens users, and studies show it is ineffective in
practice. This paper presents KTACA, a completely automated approach for key
verification that is oblivious to users and easy to deploy. We motivate KTACA
by designing two approaches to automatic key verification. One approach uses
client auditing (KTCA) and the second uses anonymous key monitoring (AKM). Both
have relatively inferior security properties, leading to KTACA, which combines
these approaches to provide the best of both worlds. We provide a security
analysis of each defense, identifying which attacks they can automatically
detect. We implement the active attacks to demonstrate they are possible, and
we also create a prototype implementation of all the defenses to measure their
performance and confirm their feasibility. Finally, we discuss the strengths
and weaknesses of each defense, the overhead on clients and service providers,
and deployment considerations.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 15:44:09 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Yadav",
"Tarun Kumar",
""
],
[
"Gosain",
"Devashish",
""
],
[
"Herzberg",
"Amir",
""
],
[
"Zappala",
"Daniel",
""
],
[
"Seamons",
"Kent",
""
]
] |
new_dataset
| 0.989652 |
2210.09956
|
Selvarajah Thuseethan Dr.
|
Sivasubramaniam Janarthan, Selvarajah Thuseethan, Sutharshan
Rajasegarar and John Yearwood
|
Double Attention-based Lightweight Network for Plant Pest Recognition
|
14 pages
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Timely recognition of plant pests from field images is significant to avoid
potential losses of crop yields. Traditional convolutional neural network-based
deep learning models demand high computational capability and require large
labelled samples for each pest type for training. On the other hand, the
existing lightweight network-based approaches suffer in correctly classifying
the pests because of common characteristics and high similarity between
multiple plant pests. In this work, a novel double attention-based lightweight
deep learning architecture is proposed to automatically recognize different
plant pests. The lightweight network facilitates faster and small data training
while the double attention module increases performance by focusing on the most
pertinent information. The proposed approach achieves 96.61%, 99.08% and 91.60%
on three variants of two publicly available datasets with 5869, 545 and 500
samples, respectively. Moreover, the comparison results reveal that the
proposed approach outperforms existing approaches on both small and large
datasets consistently.
|
[
{
"version": "v1",
"created": "Tue, 4 Oct 2022 09:25:09 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Janarthan",
"Sivasubramaniam",
""
],
[
"Thuseethan",
"Selvarajah",
""
],
[
"Rajasegarar",
"Sutharshan",
""
],
[
"Yearwood",
"John",
""
]
] |
new_dataset
| 0.956037 |
2210.09962
|
Anirudh Chakravarthy
|
Harshan Baskar, Anirudh S Chakravarthy, Prateek Garg, Divyam Goel,
Abhijith S Raj, Kshitij Kumar, Lakshya, Ravichandra Parvatham, V Sushant,
Bijay Kumar Rout
|
Nighttime Dehaze-Enhancement
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we introduce a new computer vision task called nighttime
dehaze-enhancement. This task aims to jointly perform dehazing and lightness
enhancement. Our task fundamentally differs from nighttime dehazing -- our goal
is to jointly dehaze and enhance scenes, while nighttime dehazing aims to
dehaze scenes under a nighttime setting. In order to facilitate further
research on this task, we release a new benchmark dataset called Reside-$\beta$
Night dataset, consisting of 4122 nighttime hazed images from 2061 scenes and
2061 ground truth images. Moreover, we also propose a new network called NDENet
(Nighttime Dehaze-Enhancement Network), which jointly performs dehazing and
low-light enhancement in an end-to-end manner. We evaluate our method on the
proposed benchmark and achieve SSIM of 0.8962 and PSNR of 26.25. We also
compare our network with other baseline networks on our benchmark to
demonstrate the effectiveness of our approach. We believe that nighttime
dehaze-enhancement is an essential task particularly for autonomous navigation
applications, and hope that our work will open up new frontiers in research.
Our dataset and code will be made publicly available upon acceptance of our
paper.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 16:19:25 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Baskar",
"Harshan",
""
],
[
"Chakravarthy",
"Anirudh S",
""
],
[
"Garg",
"Prateek",
""
],
[
"Goel",
"Divyam",
""
],
[
"Raj",
"Abhijith S",
""
],
[
"Kumar",
"Kshitij",
""
],
[
"Lakshya",
"",
""
],
[
"Parvatham",
"Ravichandra",
""
],
[
"Sushant",
"V",
""
],
[
"Rout",
"Bijay Kumar",
""
]
] |
new_dataset
| 0.99973 |
2210.09984
|
Jimmy Lin
|
Xinyu Zhang, Nandan Thakur, Odunayo Ogundepo, Ehsan Kamalloo, David
Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Mehdi Rezagholizadeh, Jimmy Lin
|
Making a MIRACL: Multilingual Information Retrieval Across a Continuum
of Languages
| null | null | null | null |
cs.IR cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
MIRACL (Multilingual Information Retrieval Across a Continuum of Languages)
is a multilingual dataset we have built for the WSDM 2023 Cup challenge that
focuses on ad hoc retrieval across 18 different languages, which collectively
encompass over three billion native speakers around the world. These languages
have diverse typologies, originate from many different language families, and
are associated with varying amounts of available resources -- including what
researchers typically characterize as high-resource as well as low-resource
languages. Our dataset is designed to support the creation and evaluation of
models for monolingual retrieval, where the queries and the corpora are in the
same language. In total, we have gathered over 700k high-quality relevance
judgments for around 77k queries over Wikipedia in these 18 languages, where
all assessments have been performed by native speakers hired by our team. Our
goal is to spur research that will improve retrieval across a continuum of
languages, thus enhancing information access capabilities for diverse
populations around the world, particularly those that have been traditionally
underserved. This overview paper describes the dataset and baselines that we
share with the community. The MIRACL website is live at http://miracl.ai/.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 16:47:18 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Zhang",
"Xinyu",
""
],
[
"Thakur",
"Nandan",
""
],
[
"Ogundepo",
"Odunayo",
""
],
[
"Kamalloo",
"Ehsan",
""
],
[
"Alfonso-Hermelo",
"David",
""
],
[
"Li",
"Xiaoguang",
""
],
[
"Liu",
"Qun",
""
],
[
"Rezagholizadeh",
"Mehdi",
""
],
[
"Lin",
"Jimmy",
""
]
] |
new_dataset
| 0.999789 |
2210.10036
|
Shaofei Wang
|
Shaofei Wang and Katja Schwarz and Andreas Geiger and Siyu Tang
|
ARAH: Animatable Volume Rendering of Articulated Human SDFs
|
Accepted to ECCV 2022. Project page:
https://neuralbodies.github.io/arah/
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Combining human body models with differentiable rendering has recently
enabled animatable avatars of clothed humans from sparse sets of multi-view RGB
videos. While state-of-the-art approaches achieve realistic appearance with
neural radiance fields (NeRF), the inferred geometry often lacks detail due to
missing geometric constraints. Further, animating avatars in
out-of-distribution poses is not yet possible because the mapping from
observation space to canonical space does not generalize faithfully to unseen
poses. In this work, we address these shortcomings and propose a model to
create animatable clothed human avatars with detailed geometry that generalize
well to out-of-distribution poses. To achieve detailed geometry, we combine an
articulated implicit surface representation with volume rendering. For
generalization, we propose a novel joint root-finding algorithm for
simultaneous ray-surface intersection search and correspondence search. Our
algorithm enables efficient point sampling and accurate point canonicalization
while generalizing well to unseen poses. We demonstrate that our proposed
pipeline can generate clothed avatars with high-quality pose-dependent geometry
and appearance from a sparse set of multi-view RGB videos. Our method achieves
state-of-the-art performance on geometry and appearance reconstruction while
creating animatable avatars that generalize well to out-of-distribution poses
beyond the small number of training poses.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 17:56:59 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Wang",
"Shaofei",
""
],
[
"Schwarz",
"Katja",
""
],
[
"Geiger",
"Andreas",
""
],
[
"Tang",
"Siyu",
""
]
] |
new_dataset
| 0.990263 |
2210.10045
|
Sharon Levy
|
Sharon Levy, Emily Allaway, Melanie Subbiah, Lydia Chilton, Desmond
Patton, Kathleen McKeown, William Yang Wang
|
SafeText: A Benchmark for Exploring Physical Safety in Language Models
|
Accepted to EMNLP 2022
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Understanding what constitutes safe text is an important issue in natural
language processing and can often prevent the deployment of models deemed
harmful and unsafe. One such type of safety that has been scarcely studied is
commonsense physical safety, i.e. text that is not explicitly violent and
requires additional commonsense knowledge to comprehend that it leads to
physical harm. We create the first benchmark dataset, SafeText, comprising
real-life scenarios with paired safe and physically unsafe pieces of advice. We
utilize SafeText to empirically study commonsense physical safety across
various models designed for text generation and commonsense reasoning tasks. We
find that state-of-the-art large language models are susceptible to the
generation of unsafe text and have difficulty rejecting unsafe advice. As a
result, we argue for further studies of safety and the assessment of
commonsense physical safety in models before release.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 17:59:31 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Levy",
"Sharon",
""
],
[
"Allaway",
"Emily",
""
],
[
"Subbiah",
"Melanie",
""
],
[
"Chilton",
"Lydia",
""
],
[
"Patton",
"Desmond",
""
],
[
"McKeown",
"Kathleen",
""
],
[
"Wang",
"William Yang",
""
]
] |
new_dataset
| 0.999878 |
2210.10046
|
Guanqi Zhan
|
Guanqi Zhan, Weidi Xie, Andrew Zisserman
|
A Tri-Layer Plugin to Improve Occluded Detection
|
BMVC 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Detecting occluded objects still remains a challenge for state-of-the-art
object detectors. The objective of this work is to improve the detection for
such objects, and thereby improve the overall performance of a modern object
detector.
To this end we make the following four contributions: (1) We propose a simple
'plugin' module for the detection head of two-stage object detectors to improve
the recall of partially occluded objects. The module predicts a tri-layer of
segmentation masks for the target object, the occluder and the occludee, and by
doing so is able to better predict the mask of the target object. (2) We
propose a scalable pipeline for generating training data for the module by
using amodal completion of existing object detection and instance segmentation
training datasets to establish occlusion relationships. (3) We also establish a
COCO evaluation dataset to measure the recall performance of partially occluded
and separated objects. (4) We show that the plugin module inserted into a
two-stage detector can boost the performance significantly, by only fine-tuning
the detection head, and with additional improvements if the entire architecture
is fine-tuned. COCO results are reported for Mask R-CNN with Swin-T or Swin-S
backbones, and Cascade Mask R-CNN with a Swin-B backbone.
|
[
{
"version": "v1",
"created": "Tue, 18 Oct 2022 17:59:51 GMT"
}
] | 2022-10-19T00:00:00 |
[
[
"Zhan",
"Guanqi",
""
],
[
"Xie",
"Weidi",
""
],
[
"Zisserman",
"Andrew",
""
]
] |
new_dataset
| 0.99482 |
2007.08224
|
Enrico Meloni
|
Enrico Meloni, Luca Pasqualini, Matteo Tiezzi, Marco Gori, Stefano
Melacci
|
SAILenv: Learning in Virtual Visual Environments Made Simple
|
8 pages, 7 figures, submitted to ICPR 2020
| null |
10.1109/ICPR48806.2021.9412909
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, researchers in Machine Learning algorithms, Computer Vision
scientists, engineers and others, showed a growing interest in 3D simulators as
a mean to artificially create experimental settings that are very close to
those in the real world. However, most of the existing platforms to interface
algorithms with 3D environments are often designed to setup navigation-related
experiments, to study physical interactions, or to handle ad-hoc cases that are
not thought to be customized, sometimes lacking a strong photorealistic
appearance and an easy-to-use software interface. In this paper, we present a
novel platform, SAILenv, that is specifically designed to be simple and
customizable, and that allows researchers to experiment visual recognition in
virtual 3D scenes. A few lines of code are needed to interface every algorithm
with the virtual world, and non-3D-graphics experts can easily customize the 3D
environment itself, exploiting a collection of photorealistic objects. Our
framework yields pixel-level semantic and instance labeling, depth, and, to the
best of our knowledge, it is the only one that provides motion-related
information directly inherited from the 3D engine. The client-server
communication operates at a low level, avoiding the overhead of HTTP-based data
exchanges. We perform experiments using a state-of-the-art object detector
trained on real-world images, showing that it is able to recognize the
photorealistic 3D objects of our environment. The computational burden of the
optical flow compares favourably with the estimation performed using modern
GPU-based convolutional networks or more classic implementations. We believe
that the scientific community will benefit from the easiness and high-quality
of our framework to evaluate newly proposed algorithms in their own customized
realistic conditions.
|
[
{
"version": "v1",
"created": "Thu, 16 Jul 2020 09:50:23 GMT"
},
{
"version": "v2",
"created": "Mon, 20 Jul 2020 15:42:02 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Meloni",
"Enrico",
""
],
[
"Pasqualini",
"Luca",
""
],
[
"Tiezzi",
"Matteo",
""
],
[
"Gori",
"Marco",
""
],
[
"Melacci",
"Stefano",
""
]
] |
new_dataset
| 0.993278 |
2012.09700
|
Xuanhong Chen
|
Xuanhong Chen, Kairui Feng, Naiyuan Liu, Bingbing Ni, Yifan Lu,
Zhengyan Tong, Ziang Liu
|
RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial
Precipitation Downscaling
|
Accepted at NeurIPS 2022. Project page:
https://neuralchen.github.io/RainNet/
|
Conference on Neural Information Processing Systems (NeurIPS) 2022
| null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
AI-for-science approaches have been applied to solve scientific problems
(e.g., nuclear fusion, ecology, genomics, meteorology) and have achieved highly
promising results. Spatial precipitation downscaling is one of the most
important meteorological problem and urgently requires the participation of AI.
However, the lack of a well-organized and annotated large-scale dataset hinders
the training and verification of more effective and advancing deep-learning
models for precipitation downscaling. To alleviate these obstacles, we present
the first large-scale spatial precipitation downscaling dataset named RainNet,
which contains more than $62,400$ pairs of high-quality low/high-resolution
precipitation maps for over $17$ years, ready to help the evolution of deep
learning models in precipitation downscaling. Specifically, the precipitation
maps carefully collected in RainNet cover various meteorological phenomena
(e.g., hurricane, squall), which is of great help to improve the model
generalization ability. In addition, the map pairs in RainNet are organized in
the form of image sequences ($720$ maps per month or 1 map/hour), showing
complex physical properties, e.g., temporal misalignment, temporal sparse, and
fluid properties. Furthermore, two deep-learning-oriented metrics are
specifically introduced to evaluate or verify the comprehensive performance of
the trained model (e.g., prediction maps reconstruction accuracy). To
illustrate the applications of RainNet, 14 state-of-the-art models, including
deep models and traditional approaches, are evaluated. To fully explore
potential downscaling solutions, we propose an implicit physical estimation
benchmark framework to learn the above characteristics. Extensive experiments
demonstrate the value of RainNet in training and evaluating downscaling models.
Our dataset is available at https://neuralchen.github.io/RainNet/.
|
[
{
"version": "v1",
"created": "Thu, 17 Dec 2020 16:12:17 GMT"
},
{
"version": "v2",
"created": "Fri, 18 Dec 2020 03:22:57 GMT"
},
{
"version": "v3",
"created": "Fri, 14 Oct 2022 19:17:05 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Chen",
"Xuanhong",
""
],
[
"Feng",
"Kairui",
""
],
[
"Liu",
"Naiyuan",
""
],
[
"Ni",
"Bingbing",
""
],
[
"Lu",
"Yifan",
""
],
[
"Tong",
"Zhengyan",
""
],
[
"Liu",
"Ziang",
""
]
] |
new_dataset
| 0.999573 |
2107.08146
|
Peter Jansen
|
Peter Jansen and Jordan Boyd-Graber
|
Picard understanding Darmok: A Dataset and Model for Metaphor-Rich
Translation in a Constructed Language
|
Accepted to the the 2022 Workshop on Figurative Language Processing
(at EMNLP 2022)
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Tamarian, a fictional language introduced in the Star Trek episode Darmok,
communicates meaning through utterances of metaphorical references, such as
"Darmok and Jalad at Tanagra" instead of "We should work together." This work
assembles a Tamarian-English dictionary of utterances from the original episode
and several follow-on novels, and uses this to construct a parallel corpus of
456 English-Tamarian utterances. A machine translation system based on a large
language model (T5) is trained using this parallel corpus, and is shown to
produce an accuracy of 76% when translating from English to Tamarian on known
utterances.
|
[
{
"version": "v1",
"created": "Fri, 16 Jul 2021 23:35:45 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Oct 2022 20:35:02 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Jansen",
"Peter",
""
],
[
"Boyd-Graber",
"Jordan",
""
]
] |
new_dataset
| 0.999829 |
2108.07622
|
Cunhua Pan
|
Kangda Zhi, Cunhua Pan, Hong Ren, Kezhi Wang, Maged Elkashlan, Marco
Di Renzo, Robert Schober, H. Vincent Poor, Jiangzhou Wang, and Lajos Hanzo
|
Two-Timescale Design for Reconfigurable Intelligent Surface-Aided
Massive MIMO Systems with Imperfect CSI
|
Revision in IEEE TIT. Keywords: Reconfigurable Intelligent Surface,
Intelligent Reflecting Surface, Massive MIMO, Channel estimation, etc
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
This paper investigates the two-timescale transmission design for
reconfigurable intelligent surface (RIS)-aided massive multiple-input
multiple-output (MIMO) systems, where the beamforming at the base station (BS)
is adapted to the rapidly-changing instantaneous channel state information
(CSI), while the passive beamforming at the RIS is adapted to the
slowly-changing statistical CSI.
Specifically, we first propose a linear minimum mean square error (LMMSE)
estimator to obtain the aggregated channel from the users to the BS in each
channel coherence interval. Based on the estimated channel, we apply the
low-complexity maximal ratio combining (MRC) beamforming at the BS, and then
derive the ergodic achievable rate in a closed form expression.
To draw design insights, we perform a detailed theoretical analysis departing
from the derived ergodic achievable rate. If the BS-RIS channel is Rician
distributed, we prove that the transmit power can be scaled proportionally to
$1/M$, as the number of BS antennas, $M$, grows to infinity while maintaining a
non-zero rate.
If the BS-RIS channel is Rayleigh distributed, the transmit power can be
scaled either proportionally to $1/\sqrt{M}$ as $M$ grows large, or
proportionally to $1/N$ as the number of reflecting elements, $N$, grows large,
while still maintaining a non-zero rate.
By capitalizing on the derived expression of the data rate under the
statistical knowledge of the CSI, we maximize the minimum user rate by
designing the passive beamforming at the RIS.
Numerical results confirm that, even in the presence of imperfect CSI, the
integration of an RIS in massive MIMO systems results in promising performance
gains. In addition, the obtained results reveal that it is favorable to place
the RIS close to the users rather than close to the BS.
|
[
{
"version": "v1",
"created": "Tue, 17 Aug 2021 13:51:12 GMT"
},
{
"version": "v2",
"created": "Wed, 18 Aug 2021 00:45:01 GMT"
},
{
"version": "v3",
"created": "Sat, 28 May 2022 15:09:47 GMT"
},
{
"version": "v4",
"created": "Sat, 15 Oct 2022 07:45:57 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Zhi",
"Kangda",
""
],
[
"Pan",
"Cunhua",
""
],
[
"Ren",
"Hong",
""
],
[
"Wang",
"Kezhi",
""
],
[
"Elkashlan",
"Maged",
""
],
[
"Di Renzo",
"Marco",
""
],
[
"Schober",
"Robert",
""
],
[
"Poor",
"H. Vincent",
""
],
[
"Wang",
"Jiangzhou",
""
],
[
"Hanzo",
"Lajos",
""
]
] |
new_dataset
| 0.994245 |
2109.03631
|
Mohammad Ridwan Kabir
|
Mohammad Ridwan Kabir (1), Mohammad Anas Jawad (1), Mohaimin Ehsan
(1), Hasan Mahmud (1), Md. Kamrul Hasan (1) ((1) Department of Computer
Science and Engineering (CSE), Islamic University of Technology (IUT),
Gazipur, Bangladesh.)
|
Renovo: Prototype of a Low-Cost Sensor-Based Therapeutic System for
Upper Limb Rehabilitation
|
27 pages, 10 figures, 5 tables
| null | null | null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Stroke patients with Upper Limb Disability (ULD) are re-acclimated to their
lost motor capability through therapeutic interventions, following assessment
by Physiotherapists (PTs) using various qualitative assessment protocols.
However, the assessments are often biased and prone to errors. Real-time
visualization and quantitative analysis of various Performance Metrics (PMs) of
patient's motion data, such as - Range of Motion (RoM), Repetition Rate (RR),
Velocity (V), etc., may be vital for proper assessment. In this study, we
present Renovo, a wearable inertial sensor-based therapeutic system, which
assists PTs with real-time visualization and quantitative patient assessment,
while providing patients with progress feedback. We showcase the results of a
three-week pilot study on the rehabilitation of ULD patients (N=16), in 3
successive sessions at one-week interval, following evaluation both by Renovo
and PTs (N=5). Results suggest that sensor-based quantitative assessment
reduces the possibility of human error and bias, enhancing efficiency of
rehabilitation.
|
[
{
"version": "v1",
"created": "Wed, 8 Sep 2021 13:23:25 GMT"
},
{
"version": "v2",
"created": "Sun, 12 Sep 2021 16:28:11 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Oct 2022 05:14:59 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Kabir",
"Mohammad Ridwan",
""
],
[
"Jawad",
"Mohammad Anas",
""
],
[
"Ehsan",
"Mohaimin",
""
],
[
"Mahmud",
"Hasan",
""
],
[
"Hasan",
"Md. Kamrul",
""
]
] |
new_dataset
| 0.999244 |
2109.07846
|
Md. Mohi Uddin Khan
|
Abdullah Bin Shams, Md. Mohsin Sarker Raihan, Md. Mohi Uddin Khan,
Ocean Monjur and Rahat Bin Preo
|
Telehealthcare and Telepathology in Pandemic: A Noninvasive, Low-Cost
Micro-Invasive and Multimodal Real-Time Online Application for Early
Diagnosis of COVID-19 Infection
|
32 Pages. This article has been submitted for review to a prestigious
journal
| null | null | null |
cs.LG cs.SD eess.AS q-bio.BM
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
To contain the spread of the virus and stop the overcrowding of hospitalized
patients, the coronavirus pandemic crippled healthcare facilities, mandating
lockdowns and promoting remote work. As a result, telehealth has become
increasingly popular for offering low-risk care to patients. However, the
difficulty of preventing the next potential waves of infection has increased by
constant virus mutation into new forms and a general lack of test kits,
particularly in developing nations. In this research, a unique cloud-based
application for the early identification of individuals who may have COVID-19
infection is proposed. The application provides five modes of diagnosis from
possible symptoms (f1), cough sound (f2), specific blood biomarkers (f3), Raman
spectral data of blood specimens (f4), and ECG signal paper-based image (f5).
When a user selects an option and enters the information, the data is sent to
the cloud server. The deployed machine learning (ML) and deep learning (DL)
models classify the data in real time and inform the user of the likelihood of
COVID-19 infection. Our deployed models can classify with an accuracy of 100%,
99.80%, 99.55%, 95.65%, and 77.59% from f3, f4, f5, f2, and f1 respectively.
Moreover, the sensitivity for f2, f3, and f4 is 100%, which indicates the
correct identification of COVID positive patients. This is significant in
limiting the spread of the virus. Additionally, another ML model, as seen to
offer 92% accuracy serves to identify patients who, out of a large group of
patients admitted to the hospital cohort, need immediate critical care support
by estimating the mortality risk of patients from blood parameters. The
instantaneous multimodal nature of our technique offers multiplex and accurate
diagnostic methods, highlighting the effectiveness of telehealth as a simple,
widely available, and low-cost diagnostic solution, even for future pandemics.
|
[
{
"version": "v1",
"created": "Thu, 16 Sep 2021 10:22:31 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Oct 2022 20:10:21 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Shams",
"Abdullah Bin",
""
],
[
"Raihan",
"Md. Mohsin Sarker",
""
],
[
"Khan",
"Md. Mohi Uddin",
""
],
[
"Monjur",
"Ocean",
""
],
[
"Preo",
"Rahat Bin",
""
]
] |
new_dataset
| 0.998632 |
2109.07989
|
Lalli Myllyaho
|
Lalli Myllyaho, Mikko Raatikainen, Tomi M\"annist\"o, Jukka K.
Nurminen, Tommi Mikkonen
|
On Misbehaviour and Fault Tolerance in Machine Learning Systems
|
15 pages, 1 figure, 2 tables. The manuscript has been accepted to the
Journal of Systems and Software
| null |
10.1016/j.jss.2021.111096
| null |
cs.SE
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Machine learning (ML) provides us with numerous opportunities, allowing ML
systems to adapt to new situations and contexts. At the same time, this
adaptability raises uncertainties concerning the run-time product quality or
dependability, such as reliability and security, of these systems. Systems can
be tested and monitored, but this does not provide protection against faults
and failures in adapted ML systems themselves. We studied software designs that
aim at introducing fault tolerance in ML systems so that possible problems in
ML components of the systems can be avoided. The research was conducted as a
case study, and its data was collected through five semi-structured interviews
with experienced software architects. We present a conceptualisation of the
misbehaviour of ML systems, the perceived role of fault tolerance, and the
designs used. Common patterns to incorporating ML components in design in a
fault tolerant fashion have started to emerge. ML models are, for example,
guarded by monitoring the inputs and their distribution, and enforcing business
rules on acceptable outputs. Multiple, specialised ML models are used to adapt
to the variations and changes in the surrounding world, and simpler fall-over
techniques like default outputs are put in place to have systems up and running
in the face of problems. However, the general role of these patterns is not
widely acknowledged. This is mainly due to the relative immaturity of using ML
as part of a complete software system: the field still lacks established
frameworks and practices beyond training to implement, operate, and maintain
the software that utilises ML. ML software engineering needs further analysis
and development on all fronts.
|
[
{
"version": "v1",
"created": "Thu, 16 Sep 2021 13:58:18 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Myllyaho",
"Lalli",
""
],
[
"Raatikainen",
"Mikko",
""
],
[
"Männistö",
"Tomi",
""
],
[
"Nurminen",
"Jukka K.",
""
],
[
"Mikkonen",
"Tommi",
""
]
] |
new_dataset
| 0.993484 |
2110.08057
|
Zihan Zhang
|
Zihan Zhang, Xiangyang Ji, Yuan Zhou
|
Almost Optimal Batch-Regret Tradeoff for Batch Linear Contextual Bandits
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the optimal batch-regret tradeoff for batch linear contextual
bandits. For any batch number $M$, number of actions $K$, time horizon $T$, and
dimension $d$, we provide an algorithm and prove its regret guarantee, which,
due to technical reasons, features a two-phase expression as the time horizon
$T$ grows. We also prove a lower bound theorem that surprisingly shows the
optimality of our two-phase regret upper bound (up to logarithmic factors) in
the \emph{full range} of the problem parameters, therefore establishing the
exact batch-regret tradeoff.
Compared to the recent work \citep{ruan2020linear} which showed that $M =
O(\log \log T)$ batches suffice to achieve the asymptotically minimax-optimal
regret without the batch constraints, our algorithm is simpler and easier for
practical implementation. Furthermore, our algorithm achieves the optimal
regret for all $T \geq d$, while \citep{ruan2020linear} requires that $T$
greater than an unrealistically large polynomial of $d$.
Along our analysis, we also prove a new matrix concentration inequality with
dependence on their dynamic upper bounds, which, to the best of our knowledge,
is the first of its kind in literature and maybe of independent interest.
|
[
{
"version": "v1",
"created": "Fri, 15 Oct 2021 12:32:33 GMT"
},
{
"version": "v2",
"created": "Tue, 9 Nov 2021 09:14:19 GMT"
},
{
"version": "v3",
"created": "Sat, 15 Oct 2022 04:21:31 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Zhang",
"Zihan",
""
],
[
"Ji",
"Xiangyang",
""
],
[
"Zhou",
"Yuan",
""
]
] |
new_dataset
| 0.992288 |
2112.01047
|
Taolin Zhang
|
Taolin Zhang, Chengyu Wang, Nan Hu, Minghui Qiu, Chengguang Tang,
Xiaofeng He, Jun Huang
|
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for
Natural Language Understanding
|
Accepted by AAAI22
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained
models with relation triples injecting from knowledge graphs to improve
language understanding abilities. To guarantee effective knowledge injection,
previous studies integrate models with knowledge encoders for representing
knowledge retrieved from knowledge graphs. The operations for knowledge
retrieval and encoding bring significant computational burdens, restricting the
usage of such models in real-world applications that require high inference
speed. In this paper, we propose a novel KEPLM named DKPLM that Decomposes
Knowledge injection process of the Pre-trained Language Models in pre-training,
fine-tuning and inference stages, which facilitates the applications of KEPLMs
in real-world scenarios. Specifically, we first detect knowledge-aware
long-tail entities as the target for knowledge injection, enhancing the KEPLMs'
semantic understanding abilities and avoiding injecting redundant information.
The embeddings of long-tail entities are replaced by "pseudo token
representations" formed by relevant knowledge triples. We further design the
relational knowledge decoding task for pre-training to force the models to
truly understand the injected knowledge by relation triple reconstruction.
Experiments show that our model outperforms other KEPLMs significantly over
zero-shot knowledge probing tasks and multiple knowledge-aware language
understanding tasks. We further show that DKPLM has a higher inference speed
than other competing models due to the decomposing mechanism.
|
[
{
"version": "v1",
"created": "Thu, 2 Dec 2021 08:19:42 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Oct 2022 02:51:32 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Zhang",
"Taolin",
""
],
[
"Wang",
"Chengyu",
""
],
[
"Hu",
"Nan",
""
],
[
"Qiu",
"Minghui",
""
],
[
"Tang",
"Chengguang",
""
],
[
"He",
"Xiaofeng",
""
],
[
"Huang",
"Jun",
""
]
] |
new_dataset
| 0.990947 |
2201.13230
|
\'Ad\'am Kov\'acs
|
\'Ad\'am Kov\'acs, Kinga G\'emes, Eszter Ikl\'odi, G\'abor Recski
|
POTATO: exPlainable infOrmation exTrAcTion framewOrk
|
4 pages
| null |
10.1145/3511808.3557196
| null |
cs.CL cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We present POTATO, a task- and languageindependent framework for
human-in-the-loop (HITL) learning of rule-based text classifiers using
graph-based features. POTATO handles any type of directed graph and supports
parsing text into Abstract Meaning Representations (AMR), Universal
Dependencies (UD), and 4lang semantic graphs. A streamlit-based user interface
allows users to build rule systems from graph patterns, provides real-time
evaluation based on ground truth data, and suggests rules by ranking graph
features using interpretable machine learning models. Users can also provide
patterns over graphs using regular expressions, and POTATO can recommend
refinements of such rules. POTATO is applied in projects across domains and
languages, including classification tasks on German legal text and English
social media data. All components of our system are written in Python, can be
installed via pip, and are released under an MIT License on GitHub.
|
[
{
"version": "v1",
"created": "Mon, 31 Jan 2022 13:43:02 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Oct 2022 22:57:26 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Kovács",
"Ádám",
""
],
[
"Gémes",
"Kinga",
""
],
[
"Iklódi",
"Eszter",
""
],
[
"Recski",
"Gábor",
""
]
] |
new_dataset
| 0.997208 |
2202.02394
|
Yash Jakhotiya
|
Yash Jakhotiya, Vaibhav Kumar, Ashwin Pathak, Raj Shah
|
JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity
Detection using Zero and One-Shot Learning
|
Accepted at the 16th International Workshop on Semantic Evaluation
(SemEval-2022), NAACL. Best Project Award for Georgia Tech CS 7650. Code
available at
https://github.com/ashwinpathak20/Idiomaticity_Detection_Using_Few_Shot_Learning
| null |
10.18653/v1/2022.semeval-1.19
| null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large Language Models have been successful in a wide variety of Natural
Language Processing tasks by capturing the compositionality of the text
representations. In spite of their great success, these vector representations
fail to capture meaning of idiomatic multi-word expressions (MWEs). In this
paper, we focus on the detection of idiomatic expressions by using binary
classification. We use a dataset consisting of the literal and idiomatic usage
of MWEs in English and Portuguese. Thereafter, we perform the classification in
two different settings: zero shot and one shot, to determine if a given
sentence contains an idiom or not. N shot classification for this task is
defined by N number of common idioms between the training and testing sets. In
this paper, we train multiple Large Language Models in both the settings and
achieve an F1 score (macro) of 0.73 for the zero shot setting and an F1 score
(macro) of 0.85 for the one shot setting. An implementation of our work can be
found at
https://github.com/ashwinpathak20/Idiomaticity_Detection_Using_Few_Shot_Learning.
|
[
{
"version": "v1",
"created": "Fri, 4 Feb 2022 21:17:41 GMT"
},
{
"version": "v2",
"created": "Wed, 2 Mar 2022 17:28:34 GMT"
},
{
"version": "v3",
"created": "Fri, 13 May 2022 18:20:16 GMT"
},
{
"version": "v4",
"created": "Thu, 2 Jun 2022 23:40:33 GMT"
},
{
"version": "v5",
"created": "Tue, 21 Jun 2022 22:20:54 GMT"
},
{
"version": "v6",
"created": "Thu, 23 Jun 2022 05:15:17 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Jakhotiya",
"Yash",
""
],
[
"Kumar",
"Vaibhav",
""
],
[
"Pathak",
"Ashwin",
""
],
[
"Shah",
"Raj",
""
]
] |
new_dataset
| 0.999817 |
2202.05599
|
Jiaan Wang
|
Jiaan Wang, Fandong Meng, Ziyao Lu, Duo Zheng, Zhixu Li, Jianfeng Qu,
Jie Zhou
|
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization
|
Accepted to EMNLP 2022 (main conference)
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present ClidSum, a benchmark dataset for building cross-lingual
summarization systems on dialogue documents. It consists of 67k+ dialogue
documents from two subsets (i.e., SAMSum and MediaSum) and 112k+ annotated
summaries in different target languages. Based on the proposed ClidSum, we
introduce two benchmark settings for supervised and semi-supervised scenarios,
respectively. We then build various baseline systems in different paradigms
(pipeline and end-to-end) and conduct extensive experiments on ClidSum to
provide deeper analyses. Furthermore, we propose mDialBART which extends
mBART-50 (a multi-lingual BART) via further pre-training. The multiple
objectives used in the further pre-training stage help the pre-trained model
capture the structural characteristics as well as important content in
dialogues and the transformation from source to the target language.
Experimental results show the superiority of mDialBART, as an end-to-end model,
outperforms strong pipeline models on ClidSum. Finally, we discuss specific
challenges that current approaches faced with this task and give multiple
promising directions for future research. We have released the dataset and code
at https://github.com/krystalan/ClidSum.
|
[
{
"version": "v1",
"created": "Fri, 11 Feb 2022 13:32:14 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Oct 2022 09:29:30 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Wang",
"Jiaan",
""
],
[
"Meng",
"Fandong",
""
],
[
"Lu",
"Ziyao",
""
],
[
"Zheng",
"Duo",
""
],
[
"Li",
"Zhixu",
""
],
[
"Qu",
"Jianfeng",
""
],
[
"Zhou",
"Jie",
""
]
] |
new_dataset
| 0.999821 |
2204.02915
|
Lo\"ic Bidoux
|
Lo\"ic Bidoux, Philippe Gaborit
|
Compact Post-Quantum Signatures from Proofs of Knowledge leveraging
Structure for the PKP, SD and RSD Problems
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
The MPC-in-the-head introduced in [IKOS07] has established itself as an
important paradigm to design efficient digital signatures. It has been
leveraged in the Picnic scheme [CDG+ 20] that reached the third round of the
NIST PQC Standardization process. It has also been used in [Beu20] to introduce
the Proof of Knowledge (PoK) with Helper paradigm. This construction permits to
design shorter signatures but induces a non negligible performance overhead as
it uses cut-and-choose. In this paper, we introduce the PoK leveraging
structure paradigm along with its associated challenge space amplification
technique. Our new approach to design PoK brings some improvements over the PoK
with Helper one. Indeed, we show how one can substitute the Helper in these
constructions by leveraging the underlying structure of the considered problem.
This approach does not suffer from the performance overhead inherent to the PoK
with Helper paradigm hence offers different trade-offs between security,
signature sizes and performances. We also present four new post-quantum
signature schemes. The first one is based on a new PoK with Helper for the
Syndrome Decoding problem. It relies on ideas from [BGKM22] and [FJR21] and
improve the latter using a new technique that can be seen as performing some
cut-and-choose with a meet in the middle approach. The three other signatures
are based on our new PoK leveraging structure approach and as such illustrate
its versatility. We provide new PoK related to the Permuted Kernel Problem
(PKP), Syndrome Decoding (SD) problem and Rank Syndrome Decoding (RSD) problem.
In practice, these PoK lead to comparable or shorter signatures than existing
ones. Indeed, considering (public key + signature), we get sizes below 9kB for
our signature related to the PKP problem, below 15kB for our signature related
to the SD problem and below 7kB for our signature related to the RSD problem.
|
[
{
"version": "v1",
"created": "Wed, 6 Apr 2022 16:09:26 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 14:07:31 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Bidoux",
"Loïc",
""
],
[
"Gaborit",
"Philippe",
""
]
] |
new_dataset
| 0.979362 |
2204.10321
|
Adam Tonderski
|
Adam Tonderski, Joakim Johnander, Christoffer Petersson, and Kalle
{\AA}str\"om
|
Future Object Detection with Spatiotemporal Transformers
|
14 pages, 6 figures
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We propose the task Future Object Detection, in which the goal is to predict
the bounding boxes for all visible objects in a future video frame. While this
task involves recognizing temporal and kinematic patterns, in addition to the
semantic and geometric ones, it only requires annotations in the standard form
for individual, single (future) frames, in contrast to expensive full sequence
annotations. We propose to tackle this task with an end-to-end method, in which
a detection transformer is trained to directly output the future objects. In
order to make accurate predictions about the future, it is necessary to capture
the dynamics in the scene, both object motion and the movement of the
ego-camera. To this end, we extend existing detection transformers in two ways.
First, we experiment with three different mechanisms that enable the network to
spatiotemporally process multiple frames. Second, we provide ego-motion
information to the model in a learnable manner. We show that both of these
extensions improve the future object detection performance substantially. Our
final approach learns to capture the dynamics and makes predictions on par with
an oracle for prediction horizons up to 100 ms, and outperforms all baselines
for longer prediction horizons. By visualizing the attention maps, we observe
that a form of tracking emerges within the network. Code is available at
github.com/atonderski/future-object-detection.
|
[
{
"version": "v1",
"created": "Thu, 21 Apr 2022 17:58:36 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 13:45:29 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Tonderski",
"Adam",
""
],
[
"Johnander",
"Joakim",
""
],
[
"Petersson",
"Christoffer",
""
],
[
"Åström",
"Kalle",
""
]
] |
new_dataset
| 0.980153 |
2205.12404
|
Tuhin Chakrabarty Mr
|
Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh and Smaranda
Muresan
|
FLUTE: Figurative Language Understanding through Textual Explanations
|
EMNLP 2022 Main Conference (Long Paper)
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Figurative language understanding has been recently framed as a recognizing
textual entailment (RTE) task (a.k.a. natural language inference, or NLI).
However, similar to classical RTE/NLI datasets, the current benchmarks suffer
from spurious correlations and annotation artifacts. To tackle this problem,
work on NLI has built explanation-based datasets such as e-SNLI, allowing us to
probe whether language models are right for the right reasons.Yet no such data
exists for figurative language, making it harder to assess genuine
understanding of such expressions. To address this issue, we release FLUTE, a
dataset of 9,000 figurative NLI instances with explanations, spanning four
categories: Sarcasm, Simile, Metaphor, and Idioms. We collect the data through
a model-in-the-loop framework based on GPT-3, crowd workers, and expert
annotators. We show how utilizing GPT-3 in conjunction with human annotators
(novices and experts) can aid in scaling up the creation of datasets even for
such complex linguistic phenomena as figurative language. The baseline
performance of the T5 model fine-tuned on FLUTE shows that our dataset can
bring us a step closer to developing models that understand figurative language
through textual explanations.
|
[
{
"version": "v1",
"created": "Tue, 24 May 2022 23:25:02 GMT"
},
{
"version": "v2",
"created": "Fri, 7 Oct 2022 19:43:36 GMT"
},
{
"version": "v3",
"created": "Fri, 14 Oct 2022 18:40:00 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Chakrabarty",
"Tuhin",
""
],
[
"Saakyan",
"Arkadiy",
""
],
[
"Ghosh",
"Debanjan",
""
],
[
"Muresan",
"Smaranda",
""
]
] |
new_dataset
| 0.999598 |
2205.13011
|
Dario Tscholl
|
Dario Tscholl, Stephan-Daniel Gravert, Aurel X. Appius and Robert K.
Katzschmann
|
Flying Hydraulically Amplified Electrostatic Gripper System for Aerial
Object Manipulation
|
16 pages, 12 figures, accepted and presented at the International
Symposium on Robotics Research (ISRR) 2022. Video:
youtube.com/watch?v=7PmZ8C0Ji08
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Rapid and versatile object manipulation in air is an open challenge. An
energy-efficient and adaptive soft gripper combined with an agile aerial
vehicle could revolutionize aerial robotic manipulation in areas such as
warehousing. This paper presents a bio-inspired gripper powered by
hydraulically amplified electrostatic actuators mounted to a quadcopter that
can interact safely and naturally with its environment. Our gripping concept is
motivated by an eagle's foot. Our custom multi-actuator concept is inspired by
a scorpion tail design (consisting of a base electrode with pouches stacked
adjacently) and spider-inspired joints (classic pouch motors with a flexible
hinge layer). A hybrid of these two designs realizes a higher force output
under moderate deflections of up to 25{\deg} compared to single-hinge concepts.
In addition, sandwiching the hinge layer improves the robustness of the
gripper. For the first time, we show that soft manipulation in air is possible
using electrostatic actuation. This study demonstrates the potential of
untethered hydraulically amplified actuators in aerial robotic manipulation.
Our proof of concept opens up the use of hydraulic electrostatic actuators in
mobile aerial systems.
|
[
{
"version": "v1",
"created": "Wed, 25 May 2022 18:44:28 GMT"
},
{
"version": "v2",
"created": "Wed, 21 Sep 2022 07:51:09 GMT"
},
{
"version": "v3",
"created": "Sat, 15 Oct 2022 21:08:29 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Tscholl",
"Dario",
""
],
[
"Gravert",
"Stephan-Daniel",
""
],
[
"Appius",
"Aurel X.",
""
],
[
"Katzschmann",
"Robert K.",
""
]
] |
new_dataset
| 0.997074 |
2205.13634
|
Yuhao Zhang
|
Yuhao Zhang, Aws Albarghouthi, Loris D'Antoni
|
BagFlip: A Certified Defense against Data Poisoning
|
Neurips 2022
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Machine learning models are vulnerable to data-poisoning attacks, in which an
attacker maliciously modifies the training set to change the prediction of a
learned model. In a trigger-less attack, the attacker can modify the training
set but not the test inputs, while in a backdoor attack the attacker can also
modify test inputs. Existing model-agnostic defense approaches either cannot
handle backdoor attacks or do not provide effective certificates (i.e., a proof
of a defense). We present BagFlip, a model-agnostic certified approach that can
effectively defend against both trigger-less and backdoor attacks. We evaluate
BagFlip on image classification and malware detection datasets. BagFlip is
equal to or more effective than the state-of-the-art approaches for
trigger-less attacks and more effective than the state-of-the-art approaches
for backdoor attacks.
|
[
{
"version": "v1",
"created": "Thu, 26 May 2022 21:09:24 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Oct 2022 15:48:46 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Zhang",
"Yuhao",
""
],
[
"Albarghouthi",
"Aws",
""
],
[
"D'Antoni",
"Loris",
""
]
] |
new_dataset
| 0.987431 |
2206.01724
|
Chengliang Zhong
|
Chengliang Zhong, Peixing You, Xiaoxue Chen, Hao Zhao, Fuchun Sun,
Guyue Zhou, Xiaodong Mu, Chuang Gan, Wenbing Huang
|
SNAKE: Shape-aware Neural 3D Keypoint Field
|
Accepted by NeurIPS 2022. Codes are available at
https://github.com/zhongcl-thu/SNAKE
| null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Detecting 3D keypoints from point clouds is important for shape
reconstruction, while this work investigates the dual question: can shape
reconstruction benefit 3D keypoint detection? Existing methods either seek
salient features according to statistics of different orders or learn to
predict keypoints that are invariant to transformation. Nevertheless, the idea
of incorporating shape reconstruction into 3D keypoint detection is
under-explored. We argue that this is restricted by former problem
formulations. To this end, a novel unsupervised paradigm named SNAKE is
proposed, which is short for shape-aware neural 3D keypoint field. Similar to
recent coordinate-based radiance or distance field, our network takes 3D
coordinates as inputs and predicts implicit shape indicators and keypoint
saliency simultaneously, thus naturally entangling 3D keypoint detection and
shape reconstruction. We achieve superior performance on various public
benchmarks, including standalone object datasets ModelNet40, KeypointNet, SMPL
meshes and scene-level datasets 3DMatch and Redwood. Intrinsic shape awareness
brings several advantages as follows. (1) SNAKE generates 3D keypoints
consistent with human semantic annotation, even without such supervision. (2)
SNAKE outperforms counterparts in terms of repeatability, especially when the
input point clouds are down-sampled. (3) the generated keypoints allow accurate
geometric registration, notably in a zero-shot setting. Codes are available at
https://github.com/zhongcl-thu/SNAKE
|
[
{
"version": "v1",
"created": "Fri, 3 Jun 2022 17:58:43 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 07:45:17 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Zhong",
"Chengliang",
""
],
[
"You",
"Peixing",
""
],
[
"Chen",
"Xiaoxue",
""
],
[
"Zhao",
"Hao",
""
],
[
"Sun",
"Fuchun",
""
],
[
"Zhou",
"Guyue",
""
],
[
"Mu",
"Xiaodong",
""
],
[
"Gan",
"Chuang",
""
],
[
"Huang",
"Wenbing",
""
]
] |
new_dataset
| 0.978998 |
2206.10071
|
Kay Liu
|
Kay Liu, Yingtong Dou, Yue Zhao, Xueying Ding, Xiyang Hu, Ruitong
Zhang, Kaize Ding, Canyu Chen, Hao Peng, Kai Shu, Lichao Sun, Jundong Li,
George H. Chen, Zhihao Jia, Philip S. Yu
|
BOND: Benchmarking Unsupervised Outlier Node Detection on Static
Attributed Graphs
|
NeurIPS 2022. Benchmark available at
https://github.com/pygod-team/pygod/tree/main/benchmark
| null | null | null |
cs.LG cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Detecting which nodes in graphs are outliers is a relatively new machine
learning task with numerous applications. Despite the proliferation of
algorithms developed in recent years for this task, there has been no standard
comprehensive setting for performance evaluation. Consequently, it has been
difficult to understand which methods work well and when under a broad range of
settings. To bridge this gap, we present--to the best of our knowledge--the
first comprehensive benchmark for unsupervised outlier node detection on static
attributed graphs called BOND, with the following highlights. (1) We benchmark
the outlier detection performance of 14 methods ranging from classical matrix
factorization to the latest graph neural networks. (2) Using nine real
datasets, our benchmark assesses how the different detection methods respond to
two major types of synthetic outliers and separately to "organic" (real
non-synthetic) outliers. (3) Using an existing random graph generation
technique, we produce a family of synthetically generated datasets of different
graph sizes that enable us to compare the running time and memory usage of the
different outlier detection algorithms. Based on our experimental results, we
discuss the pros and cons of existing graph outlier detection algorithms, and
we highlight opportunities for future research. Importantly, our code is freely
available and meant to be easily extendable:
https://github.com/pygod-team/pygod/tree/main/benchmark
|
[
{
"version": "v1",
"created": "Tue, 21 Jun 2022 01:46:38 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Oct 2022 01:18:45 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Liu",
"Kay",
""
],
[
"Dou",
"Yingtong",
""
],
[
"Zhao",
"Yue",
""
],
[
"Ding",
"Xueying",
""
],
[
"Hu",
"Xiyang",
""
],
[
"Zhang",
"Ruitong",
""
],
[
"Ding",
"Kaize",
""
],
[
"Chen",
"Canyu",
""
],
[
"Peng",
"Hao",
""
],
[
"Shu",
"Kai",
""
],
[
"Sun",
"Lichao",
""
],
[
"Li",
"Jundong",
""
],
[
"Chen",
"George H.",
""
],
[
"Jia",
"Zhihao",
""
],
[
"Yu",
"Philip S.",
""
]
] |
new_dataset
| 0.995685 |
2206.10910
|
Xiao-Feng Zhang
|
Xiao Feng Zhang and Chao Chen Gu and Shan Ying Zhu
|
SpA-Former: Transformer image shadow detection and removal via spatial
attention
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose an end-to-end SpA-Former to recover a shadow-free
image from a single shaded image. Unlike traditional methods that require two
steps for shadow detection and then shadow removal, the SpA-Former unifies
these steps into one, which is a one-stage network capable of directly learning
the mapping function between shadows and no shadows, it does not require a
separate shadow detection. Thus, SpA-former is adaptable to real image
de-shadowing for shadows projected on different semantic regions. SpA-Former
consists of transformer layer and a series of joint Fourier transform residual
blocks and two-wheel joint spatial attention. The network in this paper is able
to handle the task while achieving a very fast processing efficiency.
Our code is relased on
https://github.com/zhangbaijin/SpA-Former-shadow-removal
|
[
{
"version": "v1",
"created": "Wed, 22 Jun 2022 08:30:22 GMT"
},
{
"version": "v2",
"created": "Wed, 29 Jun 2022 04:36:52 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Oct 2022 03:27:55 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Zhang",
"Xiao Feng",
""
],
[
"Gu",
"Chao Chen",
""
],
[
"Zhu",
"Shan Ying",
""
]
] |
new_dataset
| 0.980004 |
2206.13597
|
Jiepeng Wang
|
Jiepeng Wang, Peng Wang, Xiaoxiao Long, Christian Theobalt, Taku
Komura, Lingjie Liu, Wenping Wang
|
NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Reconstructing 3D indoor scenes from 2D images is an important task in many
computer vision and graphics applications. A main challenge in this task is
that large texture-less areas in typical indoor scenes make existing methods
struggle to produce satisfactory reconstruction results. We propose a new
method, named NeuRIS, for high quality reconstruction of indoor scenes. The key
idea of NeuRIS is to integrate estimated normal of indoor scenes as a prior in
a neural rendering framework for reconstructing large texture-less shapes and,
importantly, to do this in an adaptive manner to also enable the reconstruction
of irregular shapes with fine details. Specifically, we evaluate the
faithfulness of the normal priors on-the-fly by checking the multi-view
consistency of reconstruction during the optimization process. Only the normal
priors accepted as faithful will be utilized for 3D reconstruction, which
typically happens in the regions of smooth shapes possibly with weak texture.
However, for those regions with small objects or thin structures, for which the
normal priors are usually unreliable, we will only rely on visual features of
the input images, since such regions typically contain relatively rich visual
features (e.g., shade changes and boundary contours). Extensive experiments
show that NeuRIS significantly outperforms the state-of-the-art methods in
terms of reconstruction quality.
|
[
{
"version": "v1",
"created": "Mon, 27 Jun 2022 19:22:03 GMT"
},
{
"version": "v2",
"created": "Sun, 16 Oct 2022 14:30:57 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Wang",
"Jiepeng",
""
],
[
"Wang",
"Peng",
""
],
[
"Long",
"Xiaoxiao",
""
],
[
"Theobalt",
"Christian",
""
],
[
"Komura",
"Taku",
""
],
[
"Liu",
"Lingjie",
""
],
[
"Wang",
"Wenping",
""
]
] |
new_dataset
| 0.99711 |
2207.06025
|
Domenico Lof\`u
|
Domenico Lof\`u, Pietro Tedeschi, Tommaso Di Noia and Eugenio Di
Sciascio
|
URANUS: Radio Frequency Tracking, Classification and Identification of
Unmanned Aircraft Vehicles
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Safety and security issues for Critical Infrastructures (CI) are growing as
attackers increasingly adopt drones as an attack vector flying in sensitive
airspace, such as airports, military bases, city centres, and crowded places.
The rapid proliferation of drones for merchandise, shipping recreations
activities, and other commercial applications poses severe concerns on the CI
operators due to the violations and the invasions of the restricted airspaces.
A cost-effective framework is needed to detect, classify and identify the
presence of drones in such cases. In this paper, we demonstrate that CI
operators can detect, classify and identify timely and efficiently drones
(multi-copter and fixed-wings) invading no-drone zones, with an inexpensive
RF-based detection framework named URANUS. Our experiments show that by using
Random Forest classifier, we achieved a classification accuracy of 93.4% in the
classification of one or multiple specific drones. The tracking performance
achieves an accuracy with an average of MAE=0.3650, MSE=0.9254 and R2 = 0.7502.
Our framework has been released as open-source, to enable the community to
verify our findings and use URANUS as a ready-to-use basis for further
analysis.
|
[
{
"version": "v1",
"created": "Wed, 13 Jul 2022 08:14:18 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 08:27:44 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Lofù",
"Domenico",
""
],
[
"Tedeschi",
"Pietro",
""
],
[
"Di Noia",
"Tommaso",
""
],
[
"Di Sciascio",
"Eugenio",
""
]
] |
new_dataset
| 0.999657 |
2207.08192
|
Zeyi Liu
|
Zeyi Liu, Zhenjia Xu, Shuran Song
|
BusyBot: Learning to Interact, Reason, and Plan in a BusyBoard
Environment
|
CoRL 2022 camera-ready; Website: https://busybot.cs.columbia.edu/
| null | null | null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce BusyBoard, a toy-inspired robot learning environment that
leverages a diverse set of articulated objects and inter-object functional
relations to provide rich visual feedback for robot interactions. Based on this
environment, we introduce a learning framework, BusyBot, which allows an agent
to jointly acquire three fundamental capabilities (interaction, reasoning, and
planning) in an integrated and self-supervised manner. With the rich sensory
feedback provided by BusyBoard, BusyBot first learns a policy to efficiently
interact with the environment; then with data collected using the policy,
BusyBot reasons the inter-object functional relations through a causal
discovery network; and finally by combining the learned interaction policy and
relation reasoning skill, the agent is able to perform goal-conditioned
manipulation tasks. We evaluate BusyBot in both simulated and real-world
environments, and validate its generalizability to unseen objects and
relations. Video is available at https://youtu.be/EJ98xBJZ9ek.
|
[
{
"version": "v1",
"created": "Sun, 17 Jul 2022 14:43:06 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 03:23:15 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Liu",
"Zeyi",
""
],
[
"Xu",
"Zhenjia",
""
],
[
"Song",
"Shuran",
""
]
] |
new_dataset
| 0.999045 |
2207.09639
|
Justin Cui
|
Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh
|
DC-BENCH: Dataset Condensation Benchmark
| null | null | null | null |
cs.LG cs.AI cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Dataset Condensation is a newly emerging technique aiming at learning a tiny
dataset that captures the rich information encoded in the original dataset. As
the size of datasets contemporary machine learning models rely on becomes
increasingly large, condensation methods become a prominent direction for
accelerating network training and reducing data storage. Despite numerous
methods have been proposed in this rapidly growing field, evaluating and
comparing different condensation methods is non-trivial and still remains an
open issue. The quality of condensed dataset are often shadowed by many
critical contributing factors to the end performance, such as data augmentation
and model architectures. The lack of a systematic way to evaluate and compare
condensation methods not only hinders our understanding of existing techniques,
but also discourages practical usage of the synthesized datasets. This work
provides the first large-scale standardized benchmark on Dataset Condensation.
It consists of a suite of evaluations to comprehensively reflect the
generability and effectiveness of condensation methods through the lens of
their generated dataset. Leveraging this benchmark, we conduct a large-scale
study of current condensation methods, and report many insightful findings that
open up new possibilities for future development. The benchmark library,
including evaluators, baseline methods, and generated datasets, is open-sourced
to facilitate future research and application.
|
[
{
"version": "v1",
"created": "Wed, 20 Jul 2022 03:54:05 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 07:47:01 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Cui",
"Justin",
""
],
[
"Wang",
"Ruochen",
""
],
[
"Si",
"Si",
""
],
[
"Hsieh",
"Cho-Jui",
""
]
] |
new_dataset
| 0.999824 |
2207.10894
|
Julie Jiang
|
Julie Jiang, Emily Chen, Luca Luceri, Goran Muri\'c, Francesco Pierri,
Ho-Chun Herbert Chang, Emilio Ferrara
|
What are Your Pronouns? Examining Gender Pronoun Usage on Twitter
|
13 pages, 10 figures, 2 tables
| null | null | null |
cs.SI
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Stating your gender pronouns, along with your name, is becoming the new norm
of self-introductions at school, at the workplace, and online. The increasing
prevalence and awareness of nonconforming gender identities put discussions of
developing gender-inclusive language at the forefront. This work presents the
first empirical research on gender pronoun usage on large-scale social media.
Leveraging a Twitter dataset of over 2 billion tweets collected continuously
over two years, we find that the public declaration of gender pronouns is on
the rise, with most people declaring as using she series pronouns, followed by
he series pronouns, and a smaller but considerable amount of non-binary
pronouns. From analyzing Twitter posts and sharing activities, we can discern
users who use gender pronouns from those who do not and also distinguish users
of various gender identities. We further illustrate the relationship between
explicit forms of social network exposure to gender pronouns and their eventual
gender pronoun adoption. This work carries crucial implications for
gender-identity studies and initiates new research directions in gender-related
fairness and inclusion, as well as support against online harassment and
discrimination on social media.
|
[
{
"version": "v1",
"created": "Fri, 22 Jul 2022 06:13:45 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Oct 2022 21:14:46 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Jiang",
"Julie",
""
],
[
"Chen",
"Emily",
""
],
[
"Luceri",
"Luca",
""
],
[
"Murić",
"Goran",
""
],
[
"Pierri",
"Francesco",
""
],
[
"Chang",
"Ho-Chun Herbert",
""
],
[
"Ferrara",
"Emilio",
""
]
] |
new_dataset
| 0.998733 |
2207.12126
|
Mathilde Papillon
|
Mathilde Papillon, Mariel Pettee, Nina Miolane
|
PirouNet: Creating Dance through Artist-Centric Deep Learning
|
18 pages, 6 figures
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Using Artificial Intelligence (AI) to create dance choreography with
intention is still at an early stage. Methods that conditionally generate dance
sequences remain limited in their ability to follow choreographer-specific
creative direction, often relying on external prompts or supervised learning.
In the same vein, fully annotated dance datasets are rare and labor intensive.
To fill this gap and help leverage deep learning as a meaningful tool for
choreographers, we propose "PirouNet", a semi-supervised conditional recurrent
variational autoencoder together with a dance labeling web application.
PirouNet allows dance professionals to annotate data with their own subjective
creative labels and subsequently generate new bouts of choreography based on
their aesthetic criteria. Thanks to the proposed semi-supervised approach,
PirouNet only requires a small portion of the dataset to be labeled, typically
on the order of 1%. We demonstrate PirouNet's capabilities as it generates
original choreography based on the "Laban Time Effort", an established dance
notion describing intention for a movement's time dynamics. We extensively
evaluate PirouNet's dance creations through a series of qualitative and
quantitative metrics, validating its applicability as a tool for
choreographers.
|
[
{
"version": "v1",
"created": "Thu, 21 Jul 2022 18:04:59 GMT"
},
{
"version": "v2",
"created": "Fri, 14 Oct 2022 23:49:17 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Papillon",
"Mathilde",
""
],
[
"Pettee",
"Mariel",
""
],
[
"Miolane",
"Nina",
""
]
] |
new_dataset
| 0.999061 |
2208.08738
|
Chang Xu
|
Chang Xu, Jinwang Wang, Wen Yang, Huai Yu, Lei Yu, Gui-Song Xia
|
RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object
Detection
|
ECCV2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Detecting tiny objects is one of the main obstacles hindering the development
of object detection. The performance of generic object detectors tends to
drastically deteriorate on tiny object detection tasks. In this paper, we point
out that either box prior in the anchor-based detector or point prior in the
anchor-free detector is sub-optimal for tiny objects. Our key observation is
that the current anchor-based or anchor-free label assignment paradigms will
incur many outlier tiny-sized ground truth samples, leading to detectors
imposing less focus on the tiny objects. To this end, we propose a Gaussian
Receptive Field based Label Assignment (RFLA) strategy for tiny object
detection. Specifically, RFLA first utilizes the prior information that the
feature receptive field follows Gaussian distribution. Then, instead of
assigning samples with IoU or center sampling strategy, a new Receptive Field
Distance (RFD) is proposed to directly measure the similarity between the
Gaussian receptive field and ground truth. Considering that the IoU-threshold
based and center sampling strategy are skewed to large objects, we further
design a Hierarchical Label Assignment (HLA) module based on RFD to achieve
balanced learning for tiny objects. Extensive experiments on four datasets
demonstrate the effectiveness of the proposed methods. Especially, our approach
outperforms the state-of-the-art competitors with 4.0 AP points on the AI-TOD
dataset. Codes are available at https://github.com/Chasel-Tsui/mmdet-rfla
|
[
{
"version": "v1",
"created": "Thu, 18 Aug 2022 09:35:56 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 06:25:59 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Xu",
"Chang",
""
],
[
"Wang",
"Jinwang",
""
],
[
"Yang",
"Wen",
""
],
[
"Yu",
"Huai",
""
],
[
"Yu",
"Lei",
""
],
[
"Xia",
"Gui-Song",
""
]
] |
new_dataset
| 0.994689 |
2208.10004
|
Muying Luo
|
Muying Luo, Shunping Ji, Shiqing Wei
|
A diverse large-scale building dataset and a novel plug-and-play domain
generalization method for building extraction
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we introduce a new building dataset and propose a novel domain
generalization method to facilitate the development of building extraction from
high-resolution remote sensing images. The problem with the current building
datasets involves that they lack diversity, the quality of the labels is
unsatisfactory, and they are hardly used to train a building extraction model
with good generalization ability, so as to properly evaluate the real
performance of a model in practical scenes. To address these issues, we built a
diverse, large-scale, and high-quality building dataset named the WHU-Mix
building dataset, which is more practice-oriented. The WHU-Mix building dataset
consists of a training/validation set containing 43,727 diverse images
collected from all over the world, and a test set containing 8402 images from
five other cities on five continents. In addition, to further improve the
generalization ability of a building extraction model, we propose a domain
generalization method named batch style mixing (BSM), which can be embedded as
an efficient plug-and-play module in the frond-end of a building extraction
model, providing the model with a progressively larger data distribution to
learn data-invariant knowledge. The experiments conducted in this study
confirmed the potential of the WHU-Mix building dataset to improve the
performance of a building extraction model, resulting in a 6-36% improvement in
mIoU, compared to the other existing datasets. The adverse impact of the
inaccurate labels in the other datasets can cause about 20% IoU decrease. The
experiments also confirmed the high performance of the proposed BSM module in
enhancing the generalization ability and robustness of a model, exceeding the
baseline model without domain generalization by 13% and the recent domain
generalization methods by 4-15% in mIoU.
|
[
{
"version": "v1",
"created": "Mon, 22 Aug 2022 01:43:13 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 13:32:55 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Luo",
"Muying",
""
],
[
"Ji",
"Shunping",
""
],
[
"Wei",
"Shiqing",
""
]
] |
new_dataset
| 0.966063 |
2209.05434
|
Junshu Tang
|
Junshu Tang, Bo Zhang, Binxin Yang, Ting Zhang, Dong Chen, Lizhuang
Ma, Fang Wen
|
3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation
|
Project webpage: https://junshutang.github.io/control/index.html
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
In contrast to the traditional avatar creation pipeline which is a costly
process, contemporary generative approaches directly learn the data
distribution from photographs. While plenty of works extend unconditional
generative models and achieve some levels of controllability, it is still
challenging to ensure multi-view consistency, especially in large poses. In
this work, we propose a network that generates 3D-aware portraits while being
controllable according to semantic parameters regarding pose, identity,
expression and illumination. Our network uses neural scene representation to
model 3D-aware portraits, whose generation is guided by a parametric face model
that supports explicit control. While the latent disentanglement can be further
enhanced by contrasting images with partially different attributes, there still
exists noticeable inconsistency in non-face areas, e.g., hair and background,
when animating expressions. Wesolve this by proposing a volume blending
strategy in which we form a composite output by blending dynamic and static
areas, with two parts segmented from the jointly learned semantic field. Our
method outperforms prior arts in extensive experiments, producing realistic
portraits with vivid expression in natural lighting when viewed from free
viewpoints. It also demonstrates generalization ability to real images as well
as out-of-domain data, showing great promise in real applications.
|
[
{
"version": "v1",
"created": "Mon, 12 Sep 2022 17:40:08 GMT"
},
{
"version": "v2",
"created": "Tue, 20 Sep 2022 07:35:50 GMT"
},
{
"version": "v3",
"created": "Mon, 17 Oct 2022 07:02:29 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Tang",
"Junshu",
""
],
[
"Zhang",
"Bo",
""
],
[
"Yang",
"Binxin",
""
],
[
"Zhang",
"Ting",
""
],
[
"Chen",
"Dong",
""
],
[
"Ma",
"Lizhuang",
""
],
[
"Wen",
"Fang",
""
]
] |
new_dataset
| 0.997465 |
2209.09874
|
Fei Xia
|
Boyuan Chen and Fei Xia and Brian Ichter and Kanishka Rao and
Keerthana Gopalakrishnan and Michael S. Ryoo and Austin Stone and Daniel
Kappler
|
Open-vocabulary Queryable Scene Representations for Real World Planning
|
v2, added references to concurrent work and acknowledgments
| null | null | null |
cs.RO cs.AI cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large language models (LLMs) have unlocked new capabilities of task planning
from human instructions. However, prior attempts to apply LLMs to real-world
robotic tasks are limited by the lack of grounding in the surrounding scene. In
this paper, we develop NLMap, an open-vocabulary and queryable scene
representation to address this problem. NLMap serves as a framework to gather
and integrate contextual information into LLM planners, allowing them to see
and query available objects in the scene before generating a
context-conditioned plan. NLMap first establishes a natural language queryable
scene representation with Visual Language models (VLMs). An LLM based object
proposal module parses instructions and proposes involved objects to query the
scene representation for object availability and location. An LLM planner then
plans with such information about the scene. NLMap allows robots to operate
without a fixed list of objects nor executable options, enabling real robot
operation unachievable by previous methods. Project website:
https://nlmap-saycan.github.io
|
[
{
"version": "v1",
"created": "Tue, 20 Sep 2022 17:29:56 GMT"
},
{
"version": "v2",
"created": "Sat, 15 Oct 2022 07:05:36 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Chen",
"Boyuan",
""
],
[
"Xia",
"Fei",
""
],
[
"Ichter",
"Brian",
""
],
[
"Rao",
"Kanishka",
""
],
[
"Gopalakrishnan",
"Keerthana",
""
],
[
"Ryoo",
"Michael S.",
""
],
[
"Stone",
"Austin",
""
],
[
"Kappler",
"Daniel",
""
]
] |
new_dataset
| 0.996954 |
2210.05236
|
Lin Ma
|
Lin Ma, Jiangtao Gong, Hao Xu, Hao Chen, Hao Zhao, Wenbing Huang and
Guyue Zhou
|
Planning Assembly Sequence with Graph Transformer
|
Submitted to ICRA2023
| null | null | null |
cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Assembly sequence planning (ASP) is the essential process for modern
manufacturing, proven to be NP-complete thus its effective and efficient
solution has been a challenge for researchers in the field. In this paper, we
present a graph-transformer based framework for the ASP problem which is
trained and demonstrated on a self-collected ASP database. The ASP database
contains a self-collected set of LEGO models. The LEGO model is abstracted to a
heterogeneous graph structure after a thorough analysis of the original
structure and feature extraction. The ground truth assembly sequence is first
generated by brute-force search and then adjusted manually to in line with
human rational habits. Based on this self-collected ASP dataset, we propose a
heterogeneous graph-transformer framework to learn the latent rules for
assembly planning. We evaluated the proposed framework in a series of
experiment. The results show that the similarity of the predicted and ground
truth sequences can reach 0.44, a medium correlation measured by Kendall's
$\tau$. Meanwhile, we compared the different effects of node features and edge
features and generated a feasible and reasonable assembly sequence as a
benchmark for further research. Our data set and code is available on
https://github.com/AIR-DISCOVER/ICRA\_ASP.
|
[
{
"version": "v1",
"created": "Tue, 11 Oct 2022 08:06:16 GMT"
},
{
"version": "v2",
"created": "Wed, 12 Oct 2022 15:00:34 GMT"
},
{
"version": "v3",
"created": "Sat, 15 Oct 2022 08:26:28 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Ma",
"Lin",
""
],
[
"Gong",
"Jiangtao",
""
],
[
"Xu",
"Hao",
""
],
[
"Chen",
"Hao",
""
],
[
"Zhao",
"Hao",
""
],
[
"Huang",
"Wenbing",
""
],
[
"Zhou",
"Guyue",
""
]
] |
new_dataset
| 0.985104 |
2210.06570
|
Yuekun Dai
|
Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Chen Change Loy
|
Flare7K: A Phenomenological Nighttime Flare Removal Dataset
|
Camera-ready version for NeurIPS 2022 Track Datasets and Benchmarks
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Artificial lights commonly leave strong lens flare artifacts on images
captured at night. Nighttime flare not only affects the visual quality but also
degrades the performance of vision algorithms. Existing flare removal methods
mainly focus on removing daytime flares and fail in nighttime. Nighttime flare
removal is challenging because of the unique luminance and spectrum of
artificial lights and the diverse patterns and image degradation of the flares
captured at night. The scarcity of nighttime flare removal datasets limits the
research on this crucial task. In this paper, we introduce, Flare7K, the first
nighttime flare removal dataset, which is generated based on the observation
and statistics of real-world nighttime lens flares. It offers 5,000 scattering
and 2,000 reflective flare images, consisting of 25 types of scattering flares
and 10 types of reflective flares. The 7,000 flare patterns can be randomly
added to flare-free images, forming the flare-corrupted and flare-free image
pairs. With the paired data, we can train deep models to restore
flare-corrupted images taken in the real world effectively. Apart from abundant
flare patterns, we also provide rich annotations, including the labeling of
light source, glare with shimmer, reflective flare, and streak, which are
commonly absent from existing datasets. Hence, our dataset can facilitate new
work in nighttime flare removal and more fine-grained analysis of flare
patterns. Extensive experiments show that our dataset adds diversity to
existing flare datasets and pushes the frontier of nighttime flare removal.
|
[
{
"version": "v1",
"created": "Wed, 12 Oct 2022 20:17:24 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Dai",
"Yuekun",
""
],
[
"Li",
"Chongyi",
""
],
[
"Zhou",
"Shangchen",
""
],
[
"Feng",
"Ruicheng",
""
],
[
"Loy",
"Chen Change",
""
]
] |
new_dataset
| 0.999791 |
2210.06909
|
Georg W\"olflein
|
Georg W\"olflein, In Hwa Um, David J Harrison, Ognjen Arandjelovi\'c
|
HoechstGAN: Virtual Lymphocyte Staining Using Generative Adversarial
Networks
|
Accepted at IEEE/CVF Winter Conference on Applications of Computer
Vision (WACV) 2023
| null | null | null |
cs.CV cs.LG q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
The presence and density of specific types of immune cells are important to
understand a patient's immune response to cancer. However, immunofluorescence
staining required to identify T cell subtypes is expensive, time-consuming, and
rarely performed in clinical settings. We present a framework to virtually
stain Hoechst images (which are cheap and widespread) with both CD3 and CD8 to
identify T cell subtypes in clear cell renal cell carcinoma using generative
adversarial networks. Our proposed method jointly learns both staining tasks,
incentivising the network to incorporate mutually beneficial information from
each task. We devise a novel metric to quantify the virtual staining quality,
and use it to evaluate our method.
|
[
{
"version": "v1",
"created": "Thu, 13 Oct 2022 11:23:19 GMT"
},
{
"version": "v2",
"created": "Mon, 17 Oct 2022 12:21:42 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Wölflein",
"Georg",
""
],
[
"Um",
"In Hwa",
""
],
[
"Harrison",
"David J",
""
],
[
"Arandjelović",
"Ognjen",
""
]
] |
new_dataset
| 0.996652 |
2210.08015
|
Juan Heredia
|
Juan Heredia, Christian Schlette, and Mikkel Baun Kj{\ae}rgaard
|
AR Training App for Energy Optimal Programming of Cobots
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Worldwide most factories aim for low-cost and fast production ignoring
resources and energy consumption. But, high revenues have been accompanied by
environmental degradation. The United Nations reacted to the ecological problem
and proposed the Sustainable Development Goals, and one of them is Sustainable
Production (Goal 12). In addition, the participation of lightweight robots,
such as collaborative robots, in modern industrial production is increasing.
The energy consumption of a single collaborative robot is not significant,
however, the consumption of more and more cobots worldwide is representative.
Consequently, our research focuses on strategies to reduce the energy
consumption of lightweight robots aiming for sustainable production. Firstly,
the energy consumption of the lightweight robot UR10e is assessed by a set of
experiments. We analyzed the results of the experiments to describe the
relationship between the energy consumption and the evaluation parameters, thus
paving the way to optimization strategies. Next, we propose four strategies to
reduce energy consumption: 1) optimal standby position, 2) optimal robot
instruction, 3) optimal motion time, and 4) reduction of dissipative energy.
The results show that cobots potentially reduce from 3\% up to 37\% of their
energy consumption, depending on the optimization technique. To disseminate the
results of our research, we developed an AR game in which the users learn how
to energy-efficiently program cobots.
|
[
{
"version": "v1",
"created": "Fri, 14 Oct 2022 15:10:43 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Heredia",
"Juan",
""
],
[
"Schlette",
"Christian",
""
],
[
"Kjærgaard",
"Mikkel Baun",
""
]
] |
new_dataset
| 0.983048 |
2210.08041
|
Song Gao
|
Yunlei Liang, Jiawei Zhu, Wen Ye, Song Gao
|
Region2Vec: Community Detection on Spatial Networks Using Graph
Embedding with Node Attributes and Spatial Interactions
|
4 pages, 1 page
|
The 30th International Conference on Advances in Geographic
Information Systems (SIGSPATIAL'22), November 1-4, 2022, Seattle, WA, USA
|
10.1145/3557915.3560974
| null |
cs.SI cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Community Detection algorithms are used to detect densely connected
components in complex networks and reveal underlying relationships among
components. As a special type of networks, spatial networks are usually
generated by the connections among geographic regions. Identifying the spatial
network communities can help reveal the spatial interaction patterns,
understand the hidden regional structures and support regional development
decision-making. Given the recent development of Graph Convolutional Networks
(GCN) and its powerful performance in identifying multi-scale spatial
interactions, we proposed an unsupervised GCN-based community detection method
"region2vec" on spatial networks. Our method first generates node embeddings
for regions that share common attributes and have intense spatial interactions,
and then applies clustering algorithms to detect communities based on their
embedding similarity and spatial adjacency. Experimental results show that
while existing methods trade off either attribute similarities or spatial
interactions for one another, "region2vec" maintains a great balance between
both and performs the best when one wants to maximize both attribute
similarities and spatial interactions within communities.
|
[
{
"version": "v1",
"created": "Mon, 10 Oct 2022 02:32:55 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Liang",
"Yunlei",
""
],
[
"Zhu",
"Jiawei",
""
],
[
"Ye",
"Wen",
""
],
[
"Gao",
"Song",
""
]
] |
new_dataset
| 0.979923 |
2210.08061
|
Mahyar Najibi
|
Mahyar Najibi, Jingwei Ji, Yin Zhou, Charles R. Qi, Xinchen Yan, Scott
Ettinger, Dragomir Anguelov
|
Motion Inspired Unsupervised Perception and Prediction in Autonomous
Driving
|
ECCV 2022
| null | null | null |
cs.CV cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning-based perception and prediction modules in modern autonomous driving
systems typically rely on expensive human annotation and are designed to
perceive only a handful of predefined object categories. This closed-set
paradigm is insufficient for the safety-critical autonomous driving task, where
the autonomous vehicle needs to process arbitrarily many types of traffic
participants and their motion behaviors in a highly dynamic world. To address
this difficulty, this paper pioneers a novel and challenging direction, i.e.,
training perception and prediction models to understand open-set moving
objects, with no human supervision. Our proposed framework uses self-learned
flow to trigger an automated meta labeling pipeline to achieve automatic
supervision. 3D detection experiments on the Waymo Open Dataset show that our
method significantly outperforms classical unsupervised approaches and is even
competitive to the counterpart with supervised scene flow. We further show that
our approach generates highly promising results in open-set 3D detection and
trajectory prediction, confirming its potential in closing the safety gap of
fully supervised systems.
|
[
{
"version": "v1",
"created": "Fri, 14 Oct 2022 18:55:44 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Najibi",
"Mahyar",
""
],
[
"Ji",
"Jingwei",
""
],
[
"Zhou",
"Yin",
""
],
[
"Qi",
"Charles R.",
""
],
[
"Yan",
"Xinchen",
""
],
[
"Ettinger",
"Scott",
""
],
[
"Anguelov",
"Dragomir",
""
]
] |
new_dataset
| 0.977721 |
2210.08116
|
Md. Nayem Hasan Muntasir
|
Md. Nayem Hasan Muntasir, Tariqul Islam Siam, Md. Kamruzzaman Sarker
|
A Low-cost Humanoid Prototype Intended to assist people with disability
using Raspberry Pi
|
The number of total pages is 8, number of figures is 3, and number of
tables is 2
| null | null | null |
cs.RO cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This paper will try to delineate the making of a Humanoid prototype intended
to assist people with disability (PWD). The assistance that this prototype will
offer is rather rudimentary. However, our key focus is to make the prototype
cost-friendly while pertaining to its humanoid-like functionalities.
Considering growing needs of Robots, facilities for further installment of
features have been made available in this project. The prototype will be of
humanoid shape harnessing the power of Artificial Neural Network (ANN) to
converse with the users. The prototype uses a raspberry pi and as the
computational capability of a raspberry pi is minimal, we cut corners to
squeeze the last drop of performance and make it as efficient as possible.
|
[
{
"version": "v1",
"created": "Tue, 4 Oct 2022 20:05:03 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Muntasir",
"Md. Nayem Hasan",
""
],
[
"Siam",
"Tariqul Islam",
""
],
[
"Sarker",
"Md. Kamruzzaman",
""
]
] |
new_dataset
| 0.999439 |
2210.08129
|
Shubhanshu Mishra
|
Shubhanshu Mishra, Aman Saini, Raheleh Makki, Sneha Mehta, Aria
Haghighi, Ali Mollahosseini
|
TweetNERD -- End to End Entity Linking Benchmark for Tweets
|
19 pages, 2 figures. Accepted to Thirty-sixth Conference on Neural
Information Processing Systems Datasets and Benchmarks Track 2022. Data
available at: https://doi.org/10.5281/zenodo.6617192 under Creative Commons
Attribution 4.0 International (CC BY 4.0) license. Check out more details at
https://github.com/twitter-research/TweetNERD
| null | null | null |
cs.CL cs.AI cs.IR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Named Entity Recognition and Disambiguation (NERD) systems are foundational
for information retrieval, question answering, event detection, and other
natural language processing (NLP) applications. We introduce TweetNERD, a
dataset of 340K+ Tweets across 2010-2021, for benchmarking NERD systems on
Tweets. This is the largest and most temporally diverse open sourced dataset
benchmark for NERD on Tweets and can be used to facilitate research in this
area. We describe evaluation setup with TweetNERD for three NERD tasks: Named
Entity Recognition (NER), Entity Linking with True Spans (EL), and End to End
Entity Linking (End2End); and provide performance of existing publicly
available methods on specific TweetNERD splits. TweetNERD is available at:
https://doi.org/10.5281/zenodo.6617192 under Creative Commons Attribution 4.0
International (CC BY 4.0) license. Check out more details at
https://github.com/twitter-research/TweetNERD.
|
[
{
"version": "v1",
"created": "Fri, 14 Oct 2022 21:55:07 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Mishra",
"Shubhanshu",
""
],
[
"Saini",
"Aman",
""
],
[
"Makki",
"Raheleh",
""
],
[
"Mehta",
"Sneha",
""
],
[
"Haghighi",
"Aria",
""
],
[
"Mollahosseini",
"Ali",
""
]
] |
new_dataset
| 0.99938 |
2210.08132
|
Weili Wang
|
Weili Wang, Omid Abbasi, Halim Yanikomeroglu, Chengchao Liang, Lun
Tang, and Qianbin Chen
|
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for
Ubiquitous IoT
| null | null | null | null |
cs.NI cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vertical heterogenous networks (VHetNets) and artificial intelligence (AI)
play critical roles in 6G and beyond networks. This article presents an
AI-native VHetNets architecture to enable the synergy of VHetNets and AI,
thereby supporting varieties of AI services while facilitating automatic and
intelligent network management. Anomaly detection in Internet of Things (IoT)
is a major AI service required by many fields, including intrusion detection,
state monitoring, device-activity analysis, security supervision and so on.
Conventional anomaly detection technologies mainly consider the anomaly
detection as a standalone service that is independent of any other network
management functionalities, which cannot be used directly in ubiquitous IoT due
to the resource constrained end nodes and decentralized data distribution. In
this article, we develop an AI-native VHetNets-enabled framework to provide the
anomaly detection service for ubiquitous IoT, whose implementation is assisted
by intelligent network management functionalities. We first discuss the
possibilities of VHetNets used for distributed AI model training to provide
anomaly detection service for ubiquitous IoT, i.e., VHetNets for AI. After
that, we study the application of AI approaches in helping provide automatic
and intelligent network management functionalities for VHetNets, i.e., AI for
VHetNets, whose aim is to facilitate the efficient implementation of anomaly
detection service. Finally, a case study is presented to demonstrate the
efficiency and effectiveness of the proposed AI-native VHetNets-enabled anomaly
detection framework.
|
[
{
"version": "v1",
"created": "Fri, 14 Oct 2022 21:55:57 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Wang",
"Weili",
""
],
[
"Abbasi",
"Omid",
""
],
[
"Yanikomeroglu",
"Halim",
""
],
[
"Liang",
"Chengchao",
""
],
[
"Tang",
"Lun",
""
],
[
"Chen",
"Qianbin",
""
]
] |
new_dataset
| 0.967973 |
2210.08137
|
Ahalya Prabhakar
|
Ahalya Prabhakar and Aude Billard
|
User-specific, Adaptable Safety Controllers Facilitate User Adoption in
Human-Robot Collaboration
|
Presented at the AI-HRI Symposium at AAAI Fall Symposium Series (FSS)
2022 (arXiv:2209.14292)
| null | null |
AIHRI/2022/5084
|
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As assistive and collaborative robots become more ubiquitous in the
real-world, we need to develop interfaces and controllers that are safe for
users to build trust and encourage adoption. In this Blue Sky paper, we discuss
the need for co-evolving task and user-specific safety controllers that can
accommodate people's safety preferences. We argue that while most adaptive
controllers focus on behavioral adaptation, safety adaptation is also a major
consideration for building trust in collaborative systems. Furthermore, we
highlight the need for adaptation over time, to account for user's changes in
preferences as experience and trust builds. We provide a general formulation
for what these interfaces should look like and what features are necessary for
making them feasible and successful. In this formulation, users provide
demonstrations and labelled safety ratings from which a safety value function
is learned. These value functions can be updated by updating the safety labels
on demonstrations to learn an updated function. We discuss how this can be
implemented at a high-level, as well as some promising approaches and
techniques for enabling this.
|
[
{
"version": "v1",
"created": "Fri, 14 Oct 2022 22:05:39 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Prabhakar",
"Ahalya",
""
],
[
"Billard",
"Aude",
""
]
] |
new_dataset
| 0.996917 |
2210.08249
|
Yongwei Zhou
|
Yongwei Zhou, Junwei Bao, Chaoqun Duan, Youzheng Wu, Xiaodong He,
Tiejun Zhao
|
UniRPG: Unified Discrete Reasoning over Table and Text as Program
Generation
|
Accepted to EMNLP 2022
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Question answering requiring discrete reasoning, e.g., arithmetic computing,
comparison, and counting, over knowledge is a challenging task. In this paper,
we propose UniRPG, a semantic-parsing-based approach advanced in
interpretability and scalability, to perform unified discrete reasoning over
heterogeneous knowledge resources, i.e., table and text, as program generation.
Concretely, UniRPG consists of a neural programmer and a symbolic program
executor, where a program is the composition of a set of pre-defined general
atomic and higher-order operations and arguments extracted from table and text.
First, the programmer parses a question into a program by generating operations
and copying arguments, and then the executor derives answers from table and
text based on the program. To alleviate the costly program annotation issue, we
design a distant supervision approach for programmer learning, where pseudo
programs are automatically constructed without annotated derivations. Extensive
experiments on the TAT-QA dataset show that UniRPG achieves tremendous
improvements and enhances interpretability and scalability compared with
state-of-the-art methods, even without derivation annotation. Moreover, it
achieves promising performance on the textual dataset DROP without derivations.
|
[
{
"version": "v1",
"created": "Sat, 15 Oct 2022 10:17:52 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Zhou",
"Yongwei",
""
],
[
"Bao",
"Junwei",
""
],
[
"Duan",
"Chaoqun",
""
],
[
"Wu",
"Youzheng",
""
],
[
"He",
"Xiaodong",
""
],
[
"Zhao",
"Tiejun",
""
]
] |
new_dataset
| 0.996141 |
2210.08274
|
Xixi Wu
|
Xixi Wu, Yun Xiong, Yao Zhang, Yizhu Jiao, Caihua Shan, Yiheng Sun,
Yangyong Zhu, and Philip S. Yu
|
CLARE: A Semi-supervised Community Detection Algorithm
|
Accepted by KDD'2022
| null |
10.1145/3534678.3539370
| null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Community detection refers to the task of discovering closely related
subgraphs to understand the networks. However, traditional community detection
algorithms fail to pinpoint a particular kind of community. This limits its
applicability in real-world networks, e.g., distinguishing fraud groups from
normal ones in transaction networks. Recently, semi-supervised community
detection emerges as a solution. It aims to seek other similar communities in
the network with few labeled communities as training data. Existing works can
be regarded as seed-based: locate seed nodes and then develop communities
around seeds. However, these methods are quite sensitive to the quality of
selected seeds since communities generated around a mis-detected seed may be
irrelevant. Besides, they have individual issues, e.g., inflexibility and high
computational overhead. To address these issues, we propose CLARE, which
consists of two key components, Community Locator and Community Rewriter. Our
idea is that we can locate potential communities and then refine them.
Therefore, the community locator is proposed for quickly locating potential
communities by seeking subgraphs that are similar to training ones in the
network. To further adjust these located communities, we devise the community
rewriter. Enhanced by deep reinforcement learning, it suggests intelligent
decisions, such as adding or dropping nodes, to refine community structures
flexibly. Extensive experiments verify both the effectiveness and efficiency of
our work compared with prior state-of-the-art approaches on multiple real-world
datasets.
|
[
{
"version": "v1",
"created": "Sat, 15 Oct 2022 12:37:46 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Wu",
"Xixi",
""
],
[
"Xiong",
"Yun",
""
],
[
"Zhang",
"Yao",
""
],
[
"Jiao",
"Yizhu",
""
],
[
"Shan",
"Caihua",
""
],
[
"Sun",
"Yiheng",
""
],
[
"Zhu",
"Yangyong",
""
],
[
"Yu",
"Philip S.",
""
]
] |
new_dataset
| 0.993162 |
2210.08281
|
Felix Klement
|
Felix Klement, Henrich C. P\"ohls, Stefan Katzenbeisser
|
Man-in-the-OBD: A modular, protocol agnostic firewall for automotive
dongles to enhance privacy and security
|
22 pages
| null | null | null |
cs.CR cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Third-party dongles for cars, e.g. from insurance companies, can extract
sensitive data and even send commands to the car via the standardized OBD-II
interface. Due to the lack of message authentication mechanisms, this leads to
major security vulnerabilities for example regarding the connection with
malicious devices. Therefore, we apply a modular, protocol-independent firewall
approach by placing a man-in-the-middle between the third-party dongle and the
car's OBD-II interface. With this privileged network position, we demonstrate
how the data flow accessible through the OBD-II interface can be modified or
restricted. We can modify the messages contents or delay the arrival of
messages by using our fine-granular configurable rewriting rules, specifically
designed to work protocol agnostic. We have implemented our modular approach
for a configurable firewall at the OBD-II interface and successfully tested it
against third-party dongles available on the market. Thus, our approach enables
a security layer to enhance automotive privacy and security of dongle users,
which is of high relevance due to missing message authentications on the level
of the electronic control units.
|
[
{
"version": "v1",
"created": "Sat, 15 Oct 2022 13:07:23 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Klement",
"Felix",
""
],
[
"Pöhls",
"Henrich C.",
""
],
[
"Katzenbeisser",
"Stefan",
""
]
] |
new_dataset
| 0.980705 |
2210.08284
|
Muhammad Al-Qurishi Dr
|
Muhammad AL-Qurishi and Sarah AlQaseemi and Riad Soussi
|
AraLegal-BERT: A pretrained language model for Arabic Legal text
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
The effectiveness of the BERT model on multiple linguistic tasks has been
well documented. On the other hand, its potentials for narrow and specific
domains such as Legal, have not been fully explored. In this paper, we examine
how BERT can be used in the Arabic legal domain and try customizing this
language model for several downstream tasks using several different
domain-relevant training and testing datasets to train BERT from scratch. We
introduce the AraLegal-BERT, a bidirectional encoder Transformer-based model
that have been thoroughly tested and carefully optimized with the goal to
amplify the impact of NLP-driven solution concerning jurisprudence, legal
documents, and legal practice. We fine-tuned AraLegal-BERT and evaluated it
against three BERT variations for Arabic language in three natural languages
understanding (NLU) tasks. The results show that the base version of
AraLegal-BERT achieve better accuracy than the general and original BERT over
the Legal text.
|
[
{
"version": "v1",
"created": "Sat, 15 Oct 2022 13:08:40 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"AL-Qurishi",
"Muhammad",
""
],
[
"AlQaseemi",
"Sarah",
""
],
[
"Soussi",
"Riad",
""
]
] |
new_dataset
| 0.964188 |
2210.08307
|
Panagiotis Kasnesis
|
Panagiotis Kasnesis, Christos Chatzigeorgiou, Dimitrios G. Kogias,
Charalampos Z. Patrikakis, Harris V. Georgiou and Aspasia Tzeletopoulou
|
MoRSE: Deep Learning-based Arm Gesture Recognition for Search and Rescue
Operations
|
Accepted for presentation in the IEEE 8th World Forum on Internet of
Things
| null | null | null |
cs.LG cs.HC
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Efficient and quick remote communication in search and rescue operations can
be life-saving for the first responders. However, while operating on the field
means of communication based on text, image and audio are not suitable for
several disaster scenarios. In this paper, we present a smartwatch-based
application, which utilizes a Deep Learning (DL) model, to recognize a set of
predefined arm gestures, maps them into Morse code via vibrations enabling
remote communication amongst first responders. The model performance was
evaluated by training it using 4,200 gestures performed by 7 subjects
(cross-validation) wearing a smartwatch on their dominant arm. Our DL model
relies on convolutional pooling and surpasses the performance of existing DL
approaches and common machine learning classifiers, obtaining gesture
recognition accuracy above 95%. We conclude by discussing the results and
providing future directions.
|
[
{
"version": "v1",
"created": "Sat, 15 Oct 2022 14:23:54 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Kasnesis",
"Panagiotis",
""
],
[
"Chatzigeorgiou",
"Christos",
""
],
[
"Kogias",
"Dimitrios G.",
""
],
[
"Patrikakis",
"Charalampos Z.",
""
],
[
"Georgiou",
"Harris V.",
""
],
[
"Tzeletopoulou",
"Aspasia",
""
]
] |
new_dataset
| 0.962328 |
2210.08353
|
Juncheng Liu
|
Juncheng Liu, Bryan Hooi, Kenji Kawaguchi, Xiaokui Xiao
|
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
|
NeurIPS 2022
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, implicit graph neural networks (GNNs) have been proposed to capture
long-range dependencies in underlying graphs. In this paper, we introduce and
justify two weaknesses of implicit GNNs: the constrained expressiveness due to
their limited effective range for capturing long-range dependencies, and their
lack of ability to capture multiscale information on graphs at multiple
resolutions. To show the limited effective range of previous implicit GNNs, We
first provide a theoretical analysis and point out the intrinsic relationship
between the effective range and the convergence of iterative equations used in
these models. To mitigate the mentioned weaknesses, we propose a multiscale
graph neural network with implicit layers (MGNNI) which is able to model
multiscale structures on graphs and has an expanded effective range for
capturing long-range dependencies. We conduct comprehensive experiments for
both node classification and graph classification to show that MGNNI
outperforms representative baselines and has a better ability for multiscale
modeling and capturing of long-range dependencies.
|
[
{
"version": "v1",
"created": "Sat, 15 Oct 2022 18:18:55 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Liu",
"Juncheng",
""
],
[
"Hooi",
"Bryan",
""
],
[
"Kawaguchi",
"Kenji",
""
],
[
"Xiao",
"Xiaokui",
""
]
] |
new_dataset
| 0.998951 |
2210.08394
|
Sizhe An
|
Sizhe An, Yin Li, Umit Ogras
|
mRI: Multi-modal 3D Human Pose Estimation Dataset using mmWave, RGB-D,
and Inertial Sensors
|
Thirty-sixth Conference on Neural Information Processing Systems
(NeurIPS 2022). Project page: https://sizhean.github.io/mri
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The ability to estimate 3D human body pose and movement, also known as human
pose estimation (HPE), enables many applications for home-based health
monitoring, such as remote rehabilitation training. Several possible solutions
have emerged using sensors ranging from RGB cameras, depth sensors,
millimeter-Wave (mmWave) radars, and wearable inertial sensors. Despite
previous efforts on datasets and benchmarks for HPE, few dataset exploits
multiple modalities and focuses on home-based health monitoring. To bridge the
gap, we present mRI, a multi-modal 3D human pose estimation dataset with
mmWave, RGB-D, and Inertial Sensors. Our dataset consists of over 160k
synchronized frames from 20 subjects performing rehabilitation exercises and
supports the benchmarks of HPE and action detection. We perform extensive
experiments using our dataset and delineate the strength of each modality. We
hope that the release of mRI can catalyze the research in pose estimation,
multi-modal learning, and action understanding, and more importantly facilitate
the applications of home-based health monitoring.
|
[
{
"version": "v1",
"created": "Sat, 15 Oct 2022 23:08:44 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"An",
"Sizhe",
""
],
[
"Li",
"Yin",
""
],
[
"Ogras",
"Umit",
""
]
] |
new_dataset
| 0.999817 |
2210.08402
|
Jenia Jitsev
|
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross
Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell
Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig
Schmidt, Robert Kaczmarczyk and Jenia Jitsev
|
LAION-5B: An open large-scale dataset for training next generation
image-text models
|
36th Conference on Neural Information Processing Systems (NeurIPS
2022), Track on Datasets and Benchmarks. OpenReview:
https://openreview.net/forum?id=M3Y74vmsMcY
| null | null | null |
cs.CV cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Groundbreaking language-vision architectures like CLIP and DALL-E proved the
utility of training on large amounts of noisy image-text data, without relying
on expensive accurate labels used in standard vision unimodal supervised
learning. The resulting models showed capabilities of strong text-guided image
generation and transfer to downstream tasks, while performing remarkably at
zero-shot classification with noteworthy out-of-distribution robustness. Since
then, large-scale language-vision models like ALIGN, BASIC, GLIDE, Flamingo and
Imagen made further improvements. Studying the training and capabilities of
such models requires datasets containing billions of image-text pairs. Until
now, no datasets of this size have been made openly available for the broader
research community. To address this problem and democratize research on
large-scale multi-modal models, we present LAION-5B - a dataset consisting of
5.85 billion CLIP-filtered image-text pairs, of which 2.32B contain English
language. We show successful replication and fine-tuning of foundational models
like CLIP, GLIDE and Stable Diffusion using the dataset, and discuss further
experiments enabled with an openly available dataset of this scale.
Additionally we provide several nearest neighbor indices, an improved
web-interface for dataset exploration and subset generation, and detection
scores for watermark, NSFW, and toxic content detection. Announcement page
https://laion.ai/laion-5b-a-new-era-of-open-large-scale-multi-modal-datasets/
|
[
{
"version": "v1",
"created": "Sun, 16 Oct 2022 00:08:18 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Schuhmann",
"Christoph",
""
],
[
"Beaumont",
"Romain",
""
],
[
"Vencu",
"Richard",
""
],
[
"Gordon",
"Cade",
""
],
[
"Wightman",
"Ross",
""
],
[
"Cherti",
"Mehdi",
""
],
[
"Coombes",
"Theo",
""
],
[
"Katta",
"Aarush",
""
],
[
"Mullis",
"Clayton",
""
],
[
"Wortsman",
"Mitchell",
""
],
[
"Schramowski",
"Patrick",
""
],
[
"Kundurthy",
"Srivatsa",
""
],
[
"Crowson",
"Katherine",
""
],
[
"Schmidt",
"Ludwig",
""
],
[
"Kaczmarczyk",
"Robert",
""
],
[
"Jitsev",
"Jenia",
""
]
] |
new_dataset
| 0.999579 |
2210.08414
|
Alan Wagner
|
Alan R. Wagner, Colin Holbrook, Daniel Holman, Brett Sheeran, Vidullan
Surendran, Jared Armagost, Savanna Spazak, Yinxuan Yin
|
Using Virtual Reality to Simulate Human-Robot Emergency Evacuation
Scenarios
|
Accepted at AAAI Fall Symposium AI-HRI Workshop
| null | null |
AIHRI/2022/8997
|
cs.RO cs.AI cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
This paper describes our recent effort to use virtual reality to simulate
threatening emergency evacuation scenarios in which a robot guides a person to
an exit. Our prior work has demonstrated that people will follow a robot's
guidance, even when the robot is faulty, during an emergency evacuation. Yet,
because physical in-person emergency evacuation experiments are difficult and
costly to conduct and because we would like to evaluate many different factors,
we are motivated to develop a system that immerses people in the simulation
environment to encourage genuine subject reactions. We are working to complete
experiments verifying the validity of our approach.
|
[
{
"version": "v1",
"created": "Sun, 16 Oct 2022 02:29:30 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Wagner",
"Alan R.",
""
],
[
"Holbrook",
"Colin",
""
],
[
"Holman",
"Daniel",
""
],
[
"Sheeran",
"Brett",
""
],
[
"Surendran",
"Vidullan",
""
],
[
"Armagost",
"Jared",
""
],
[
"Spazak",
"Savanna",
""
],
[
"Yin",
"Yinxuan",
""
]
] |
new_dataset
| 0.978799 |
2210.08455
|
Luiza Labazanova Miss
|
Luiza Labazanova, Shuang Peng, Liuming Qiu, Hoi-Yin Lee, Thrishantha
Nanayakkara and David Navarro-Alarcon
|
Self-Reconfigurable Soft-Rigid Mobile Agent with Variable Stiffness and
Adaptive Morphology
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we propose a novel design of a hybrid mobile robot with
controllable stiffness and deformable shape. Compared to conventional mobile
agents, our system can switch between rigid and compliant phases by solidifying
or melting Field's metal in its structure and, thus, alter its shape through
the motion of its active components. In the soft state, the robot's main body
can bend into circular arcs, which enables it to conform to surrounding curved
objects. This variable geometry of the robot creates new motion modes which
cannot be described by standard (i.e., fixed geometry) models. To this end, we
develop a unified mathematical model that captures the differential kinematics
of both rigid and soft states. An optimised control strategy is further
proposed to select the most appropriate phase states and motion modes needed to
reach a target pose-shape configuration. The performance of our new method is
validated with numerical simulations and experiments conducted on a prototype
system. The simulation source code is available at
https://github.com/Louashka/2sr-agent-simulation.git}{GitHub repository.
|
[
{
"version": "v1",
"created": "Sun, 16 Oct 2022 06:07:50 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Labazanova",
"Luiza",
""
],
[
"Peng",
"Shuang",
""
],
[
"Qiu",
"Liuming",
""
],
[
"Lee",
"Hoi-Yin",
""
],
[
"Nanayakkara",
"Thrishantha",
""
],
[
"Navarro-Alarcon",
"David",
""
]
] |
new_dataset
| 0.999352 |
2210.08463
|
Xiaoqiang Wang
|
Xiaoqiang Wang, Jiaojiao Wang, Chengju Li, Yansheng Wu
|
Two classes of narrow-sense BCH codes and their duals
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
BCH codes and their dual codes are two special subclasses of cyclic codes and
are the best linear codes in many cases. A lot of progress on the study of BCH
cyclic codes has been made, but little is known about the minimum distances of
the duals of BCH codes. Recently, a new concept called dually-BCH code was
introduced to investigate the duals of BCH codes and the lower bounds on their
minimum distances in \cite{GDL21}. For a prime power $q$ and an integer $m \ge
4$, let $n=\frac{q^m-1}{q+1}$ \ ($m$ even), or $n=\frac{q^m-1}{q-1}$ \ ($q>2$).
In this paper, some sufficient and necessary conditions in terms of the
designed distance will be given to ensure that the narrow-sense BCH codes of
length $n$ are dually-BCH codes, which extended the results in \cite{GDL21}.
Lower bounds on the minimum distances of their dual codes are developed for
$n=\frac{q^m-1}{q+1}$ \ ($m$ even). As byproducts, we present the largest coset
leader $\delta_1$ modulo $n$ being of two types, which proves a conjecture in
\cite{WLP19} and partially solves an open problem in \cite{Li2017}. We also
investigate the parameters of the narrow-sense BCH codes of length $n$ with
design distance $\delta_1$. The BCH codes presented in this paper have good
parameters in general.
|
[
{
"version": "v1",
"created": "Sun, 16 Oct 2022 06:41:57 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Wang",
"Xiaoqiang",
""
],
[
"Wang",
"Jiaojiao",
""
],
[
"Li",
"Chengju",
""
],
[
"Wu",
"Yansheng",
""
]
] |
new_dataset
| 0.992738 |
2210.08472
|
Yuan-Gen Wang
|
Chao Zhou, Yuan-Gen Wang, Guopu Zhu
|
Object-Attentional Untargeted Adversarial Attack
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep neural networks are facing severe threats from adversarial attacks. Most
existing black-box attacks fool target model by generating either global
perturbations or local patches. However, both global perturbations and local
patches easily cause annoying visual artifacts in adversarial example. Compared
with some smooth regions of an image, the object region generally has more
edges and a more complex texture. Thus small perturbations on it will be more
imperceptible. On the other hand, the object region is undoubtfully the
decisive part of an image to classification tasks. Motivated by these two
facts, we propose an object-attentional adversarial attack method for
untargeted attack. Specifically, we first generate an object region by
intersecting the object detection region from YOLOv4 with the salient object
detection (SOD) region from HVPNet. Furthermore, we design an activation
strategy to avoid the reaction caused by the incomplete SOD. Then, we perform
an adversarial attack only on the detected object region by leveraging Simple
Black-box Adversarial Attack (SimBA). To verify the proposed method, we create
a unique dataset by extracting all the images containing the object defined by
COCO from ImageNet-1K, named COCO-Reduced-ImageNet in this paper. Experimental
results on ImageNet-1K and COCO-Reduced-ImageNet show that under various system
settings, our method yields the adversarial example with better perceptual
quality meanwhile saving the query budget up to 24.16\% compared to the
state-of-the-art approaches including SimBA.
|
[
{
"version": "v1",
"created": "Sun, 16 Oct 2022 07:45:13 GMT"
}
] | 2022-10-18T00:00:00 |
[
[
"Zhou",
"Chao",
""
],
[
"Wang",
"Yuan-Gen",
""
],
[
"Zhu",
"Guopu",
""
]
] |
new_dataset
| 0.987098 |
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