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3.33k
| versions
list | update_date
timestamp[s] | authors_parsed
list | prediction
stringclasses 1
value | probability
float64 0.95
1
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2207.00688
|
Perez Ogayo
|
Perez Ogayo, Graham Neubig, Alan W Black
|
Building African Voices
| null | null | null | null |
cs.CL cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Modern speech synthesis techniques can produce natural-sounding speech given
sufficient high-quality data and compute resources. However, such data is not
readily available for many languages. This paper focuses on speech synthesis
for low-resourced African languages, from corpus creation to sharing and
deploying the Text-to-Speech (TTS) systems. We first create a set of
general-purpose instructions on building speech synthesis systems with minimum
technological resources and subject-matter expertise. Next, we create new
datasets and curate datasets from "found" data (existing recordings) through a
participatory approach while considering accessibility, quality, and breadth.
We demonstrate that we can develop synthesizers that generate intelligible
speech with 25 minutes of created speech, even when recorded in suboptimal
environments. Finally, we release the speech data, code, and trained voices for
12 African languages to support researchers and developers.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 23:28:16 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Ogayo",
"Perez",
""
],
[
"Neubig",
"Graham",
""
],
[
"Black",
"Alan W",
""
]
] |
new_dataset
| 0.998447 |
2207.00691
|
Robert Wolfe
|
Robert Wolfe and Aylin Caliskan
|
American == White in Multimodal Language-and-Image AI
|
Accepted to AI Ethics and Society 2022
| null | null | null |
cs.CY cs.AI cs.CL cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Three state-of-the-art language-and-image AI models, CLIP, SLIP, and BLIP,
are evaluated for evidence of a bias previously observed in social and
experimental psychology: equating American identity with being White. Embedding
association tests (EATs) using standardized images of self-identified Asian,
Black, Latina/o, and White individuals from the Chicago Face Database (CFD)
reveal that White individuals are more associated with collective in-group
words than are Asian, Black, or Latina/o individuals. In assessments of three
core aspects of American identity reported by social psychologists,
single-category EATs reveal that images of White individuals are more
associated with patriotism and with being born in America, but that, consistent
with prior findings in psychology, White individuals are associated with being
less likely to treat people of all races and backgrounds equally. Three
downstream machine learning tasks demonstrate biases associating American with
White. In a visual question answering task using BLIP, 97% of White individuals
are identified as American, compared to only 3% of Asian individuals. When
asked in what state the individual depicted lives in, the model responds China
53% of the time for Asian individuals, but always with an American state for
White individuals. In an image captioning task, BLIP remarks upon the race of
Asian individuals as much as 36% of the time, but never remarks upon race for
White individuals. Finally, provided with an initialization image from the CFD
and the text "an American person," a synthetic image generator (VQGAN) using
the text-based guidance of CLIP lightens the skin tone of individuals of all
races (by 35% for Black individuals, based on pixel brightness). The results
indicate that biases equating American identity with being White are learned by
language-and-image AI, and propagate to downstream applications of such models.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 23:45:56 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Wolfe",
"Robert",
""
],
[
"Caliskan",
"Aylin",
""
]
] |
new_dataset
| 0.995197 |
2207.00750
|
Ru He
|
Chao Yang, Ru He, Fangquan Lin, Suoyuan Song, Jingqiao Zhang, Cheng
Yang
|
GUIM -- General User and Item Embedding with Mixture of Representation
in E-commerce
|
10 pages, 3 figures
| null | null | null |
cs.AI cs.IR
|
http://creativecommons.org/licenses/by/4.0/
|
Our goal is to build general representation (embedding) for each user and
each product item across Alibaba's businesses, including Taobao and Tmall which
are among the world's biggest e-commerce websites. The representation of users
and items has been playing a critical role in various downstream applications,
including recommendation system, search, marketing, demand forecasting and so
on. Inspired from the BERT model in natural language processing (NLP) domain,
we propose a GUIM (General User Item embedding with Mixture of representation)
model to achieve the goal with massive, structured, multi-modal data including
the interactions among hundreds of millions of users and items. We utilize
mixture of representation (MoR) as a novel representation form to model the
diverse interests of each user. In addition, we use the InfoNCE from
contrastive learning to avoid intractable computational costs due to the
numerous size of item (token) vocabulary. Finally, we propose a set of
representative downstream tasks to serve as a standard benchmark to evaluate
the quality of the learned user and/or item embeddings, analogous to the GLUE
benchmark in NLP domain. Our experimental results in these downstream tasks
clearly show the comparative value of embeddings learned from our GUIM model.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 06:27:54 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Yang",
"Chao",
""
],
[
"He",
"Ru",
""
],
[
"Lin",
"Fangquan",
""
],
[
"Song",
"Suoyuan",
""
],
[
"Zhang",
"Jingqiao",
""
],
[
"Yang",
"Cheng",
""
]
] |
new_dataset
| 0.969775 |
2207.00758
|
Akari Asai
|
Akari Asai, Shayne Longpre, Jungo Kasai, Chia-Hsuan Lee, Rui Zhang,
Junjie Hu, Ikuya Yamada, Jonathan H. Clark, Eunsol Choi
|
MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question
Answering for 16 Diverse Languages
|
NAACL Workshop on Multilingual Information Access
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
We present the results of the Workshop on Multilingual Information Access
(MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question
answering (QA) systems in 16 typologically diverse languages. In this task, we
adapted two large-scale cross-lingual open-retrieval QA datasets in 14
typologically diverse languages, and newly annotated open-retrieval QA data in
2 underrepresented languages: Tagalog and Tamil. Four teams submitted their
systems. The best system leveraging iteratively mined diverse negative examples
and larger pretrained models achieves 32.2 F1, outperforming our baseline by
4.5 points. The second best system uses entity-aware contextualized
representations for document retrieval, and achieves significant improvements
in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 06:54:10 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Asai",
"Akari",
""
],
[
"Longpre",
"Shayne",
""
],
[
"Kasai",
"Jungo",
""
],
[
"Lee",
"Chia-Hsuan",
""
],
[
"Zhang",
"Rui",
""
],
[
"Hu",
"Junjie",
""
],
[
"Yamada",
"Ikuya",
""
],
[
"Clark",
"Jonathan H.",
""
],
[
"Choi",
"Eunsol",
""
]
] |
new_dataset
| 0.996485 |
2207.00785
|
Ebrahim Chekol Jibril
|
Ebrahim Chekol Jibril and A. C\"uneyd Tant\u{g}
|
ANEC: An Amharic Named Entity Corpus and Transformer Based Recognizer
|
22 pages including references and indexes, 10 figures and 6 tables
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Named Entity Recognition is an information extraction task that serves as a
preprocessing step for other natural language processing tasks, such as machine
translation, information retrieval, and question answering. Named entity
recognition enables the identification of proper names as well as temporal and
numeric expressions in an open domain text. For Semitic languages such as
Arabic, Amharic, and Hebrew, the named entity recognition task is more
challenging due to the heavily inflected structure of these languages. In this
paper, we present an Amharic named entity recognition system based on
bidirectional long short-term memory with a conditional random fields layer. We
annotate a new Amharic named entity recognition dataset (8,070 sentences, which
has 182,691 tokens) and apply Synthetic Minority Over-sampling Technique to our
dataset to mitigate the imbalanced classification problem. Our named entity
recognition system achieves an F_1 score of 93%, which is the new
state-of-the-art result for Amharic named entity recognition.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 09:50:37 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Jibril",
"Ebrahim Chekol",
""
],
[
"Tantğ",
"A. Cüneyd",
""
]
] |
new_dataset
| 0.999495 |
2207.00794
|
Tian-Zhu Xiang
|
Yujia Sun, Shuo Wang, Chenglizhao Chen, Tian-Zhu Xiang
|
Boundary-Guided Camouflaged Object Detection
|
Accepted by IJCAI2022
|
IJCAI2022
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Camouflaged object detection (COD), segmenting objects that are elegantly
blended into their surroundings, is a valuable yet challenging task. Existing
deep-learning methods often fall into the difficulty of accurately identifying
the camouflaged object with complete and fine object structure. To this end, in
this paper, we propose a novel boundary-guided network (BGNet) for camouflaged
object detection. Our method explores valuable and extra object-related edge
semantics to guide representation learning of COD, which forces the model to
generate features that highlight object structure, thereby promoting
camouflaged object detection of accurate boundary localization. Extensive
experiments on three challenging benchmark datasets demonstrate that our BGNet
significantly outperforms the existing 18 state-of-the-art methods under four
widely-used evaluation metrics. Our code is publicly available at:
https://github.com/thograce/BGNet.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 10:48:35 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Sun",
"Yujia",
""
],
[
"Wang",
"Shuo",
""
],
[
"Chen",
"Chenglizhao",
""
],
[
"Xiang",
"Tian-Zhu",
""
]
] |
new_dataset
| 0.993206 |
2207.00796
|
Yuping Ye
|
Yuping Ye, Siyuan Chen and Zhan Song
|
Benchmarks for Industrial Inspection Based on Structured Light
| null | null | null | null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Robustness and accuracy are two critical metrics for industrial inspection.
In this paper, we propose benchmarks that can evaluate the structured light
method's performance. Our evaluation metric was learning from a lot of
inspection tasks from the factories. The metric we proposed consists of four
detailed criteria such as flatness, length, height and sphericity. Then we can
judge whether the structured light method/device can be applied to a specified
inspection task by our evaluation metric quickly. A structured light device
built for TypeC pin needles inspection performance is evaluated via our metrics
in the final experimental section.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 11:09:05 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Ye",
"Yuping",
""
],
[
"Chen",
"Siyuan",
""
],
[
"Song",
"Zhan",
""
]
] |
new_dataset
| 0.953717 |
2207.00801
|
Keita Ishizuka
|
Keita Ishizuka
|
Construction of quaternary Hermitian LCD codes
|
15 pages
| null | null | null |
cs.IT cs.DM math.CO math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a general construction of many Hermitian LCD $[n, k]$ codes from
a given Hermitian LCD $[n, k]$ code. Furthermore, we present some results on
punctured codes and shortened codes of quaternary Hermitian LCD codes. As an
application, we improve some of the previously known lower bounds on the
largest minimum weights of quaternary Hermitian LCD codes of length $12 \le n
\le 30$.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 11:30:53 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Ishizuka",
"Keita",
""
]
] |
new_dataset
| 0.999612 |
2207.00804
|
Xingyu Wu
|
Xingyu Wu and Jinyang Li
|
An AIoT-enabled Autonomous Dementia Monitoring System
| null | null | null | null |
cs.LG cs.AI cs.NA math.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An autonomous Artificial Internet of Things (AIoT) system for elderly
dementia patients monitoring in a smart home is presented. The system mainly
implements two functions based on the activity inference of the sensor data,
which are real time abnormal activity monitoring and trend prediction of
disease related activities. Specifically, CASAS dataset is employed to train a
Random Forest (RF) model for activity inference. Then, another RF model trained
by the output data of activity inference is used for abnormal activity
monitoring. Particularly, RF is chosen for these tasks because of its balanced
trade offs between accuracy, time efficiency, flexibility, and
interpretability. Moreover, Long Short Term Memory (LSTM) is utilised to
forecast the disease related activity trend of a patient. Consequently, the
accuracy of two RF classifiers designed for activity inference and abnormal
activity detection is greater than 99 percent and 94 percent, respectively.
Furthermore, using the duration of sleep as an example, the LSTM model achieves
accurate and evident future trends prediction.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 11:36:16 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Wu",
"Xingyu",
""
],
[
"Li",
"Jinyang",
""
]
] |
new_dataset
| 0.993888 |
2207.00806
|
\'Agoston Sipos
|
\'Agoston Sipos
|
Corner-based implicit patches
| null | null | null | null |
cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multi-sided surfaces are often defined by side interpolants (also called
ribbons), i.e. the surface has to connect to the ribbons with a prescribed
degree of smoothness. The I-patch is such a family of implicit surfaces capable
of interpolating an arbitrary number of ribbons and can be used in design and
approximation. While in the case of parametric surfaces describing ribbons is a
well-discussed problem, defining implicit ribbons is a different task. This
paper will introduce corner I-patches, a new representation that describes
implicit surfaces based on corner interpolants. Those may be defined with much
simpler surfaces, while the shape of the patch will depend on a handful of
scalar parameters. Continuity between patches will be enforced via constraints
on these parameters. Corner I-patches have several favorable properties that
can be exploited for example in volume rendering or approximation.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 11:44:35 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Sipos",
"Ágoston",
""
]
] |
new_dataset
| 0.999285 |
2207.00837
|
Jingyao Wang
|
Jingyao Wang, Naigong Yu
|
UTD-Yolov5: A Real-time Underwater Targets Detection Method based on
Attention Improved YOLOv5
| null | null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As the treasure house of nature, the ocean contains abundant resources. But
the coral reefs, which are crucial to the sustainable development of marine
life, are facing a huge crisis because of the existence of COTS and other
organisms. The protection of society through manual labor is limited and
inefficient. The unpredictable nature of the marine environment also makes
manual operations risky. The use of robots for underwater operations has become
a trend. However, the underwater image acquisition has defects such as weak
light, low resolution, and many interferences, while the existing target
detection algorithms are not effective. Based on this, we propose an underwater
target detection algorithm based on Attention Improved YOLOv5, called
UTD-Yolov5. It can quickly and efficiently detect COTS, which in turn provides
a prerequisite for complex underwater operations. We adjusted the original
network architecture of YOLOv5 in multiple stages, including: replacing the
original Backbone with a two-stage cascaded CSP (CSP2); introducing the visual
channel attention mechanism module SE; designing random anchor box similarity
calculation method etc. These operations enable UTD-Yolov5 to detect more
flexibly and capture features more accurately. In order to make the network
more efficient, we also propose optimization methods such as WBF and iterative
refinement mechanism. This paper conducts a lot of experiments based on the
CSIRO dataset [1]. The results show that the average accuracy of our UTD-Yolov5
reaches 78.54%, which is a great improvement compared to the baseline.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 14:09:08 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Wang",
"Jingyao",
""
],
[
"Yu",
"Naigong",
""
]
] |
new_dataset
| 0.983767 |
2207.00843
|
EPTCS
|
Joris Ceulemans (KU Leuven), Andreas Nuyts (KU Leuven), Dominique
Devriese (KU Leuven)
|
Sikkel: Multimode Simple Type Theory as an Agda Library
|
In Proceedings MSFP 2022, arXiv:2206.09534
|
EPTCS 360, 2022, pp. 93-112
|
10.4204/EPTCS.360.5
| null |
cs.PL cs.LO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many variants of type theory extend a basic theory with additional primitives
or properties like univalence, guarded recursion or parametricity, to enable
constructions or proofs that would be harder or impossible to do in the
original theory. However, implementing such extended type theories (either from
scratch or by modifying an existing implementation) is a big hurdle for their
wider adoption. In this paper we present Sikkel, a library in the dependently
typed programming language Agda that allows users to program in extended type
theories. It uses a deeply embedded language that can be easily extended with
additional type and term constructors, thus supporting a wide variety of type
theories. Moreover, Sikkel has a type checker that is sound by construction in
the sense that all well-typed programs are automatically translated to their
semantics in a shallow embedding based on presheaf models. Additionally, our
model supports combining different base categories by using modalities to
transport definitions between them. This enables in particular a general
approach for extracting definitions to the meta-level, so that we can use the
extended type theories to define regular Agda functions and prove properties of
them. In this paper, we demonstrate Sikkel theories with guarded recursion and
parametricity, but other extensions can be easily plugged in. For now, Sikkel
supports only simple type theories but its model already anticipates the future
addition of dependent types and a universe.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 14:24:04 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Ceulemans",
"Joris",
"",
"KU Leuven"
],
[
"Nuyts",
"Andreas",
"",
"KU Leuven"
],
[
"Devriese",
"Dominique",
"",
"KU Leuven"
]
] |
new_dataset
| 0.998358 |
2207.00851
|
EPTCS
|
Dylan McDermott, Tarmo Uustalu
|
What Makes a Strong Monad?
|
In Proceedings MSFP 2022, arXiv:2206.09534
|
EPTCS 360, 2022, pp. 113-133
|
10.4204/EPTCS.360.6
| null |
cs.LO cs.PL math.CT
|
http://creativecommons.org/licenses/by/4.0/
|
Strong monads are important for several applications, in particular, in the
denotational semantics of effectful languages, where strength is needed to
sequence computations that have free variables. Strength is non-trivial: it can
be difficult to determine whether a monad has any strength at all, and monads
can be strong in multiple ways. We therefore review some of the most important
known facts about strength and prove some new ones. In particular, we present a
number of equivalent characterizations of strong functor and strong monad, and
give some conditions that guarantee existence or uniqueness of strengths. We
look at strength from three different perspectives: actions of a monoidal
category V, enrichment over V, and powering over V. We are primarily motivated
by semantics of effects, but the results are also useful in other contexts.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 14:36:10 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"McDermott",
"Dylan",
""
],
[
"Uustalu",
"Tarmo",
""
]
] |
new_dataset
| 0.998 |
2207.00896
|
Klen \v{C}opi\v{c} Pucihar
|
Maheshya Weerasinghe, Verena Biener, Jens Grubert, Aaron J Quigley,
Alice Toniolo, Klen \v{C}opi\v{c} Pucihar and Matja\v{z} Kljun
|
VocabulARy: Learning Vocabulary in AR Supported by Keyword
Visualisations
| null | null | null | null |
cs.HC cs.GR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Learning vocabulary in a primary or secondary language is enhanced when we
encounter words in context. This context can be afforded by the place or
activity we are engaged with. Existing learning environments include formal
learning, mnemonics, flashcards, use of a dictionary or thesaurus, all leading
to practice with new words in context. In this work, we propose an enhancement
to the language learning process by providing the user with words and learning
tools in context, with VocabulARy. VocabulARy visually annotates objects in AR,
in the user's surroundings, with the corresponding English (first language) and
Japanese (second language) words to enhance the language learning process. In
addition to the written and audio description of each word, we also present the
user with a keyword and its visualisation to enhance memory retention. We
evaluate our prototype by comparing it to an alternate AR system that does not
show an additional visualisation of the keyword, and, also, we compare it to
two non-AR systems on a tablet, one with and one without visualising the
keyword. Our results indicate that AR outperforms the tablet system regarding
immediate recall, mental effort and task completion time. Additionally, the
visualisation approach scored significantly higher than showing only the
written keyword with respect to immediate and delayed recall and learning
efficiency, mental effort and task-completion time.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 18:35:22 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Weerasinghe",
"Maheshya",
""
],
[
"Biener",
"Verena",
""
],
[
"Grubert",
"Jens",
""
],
[
"Quigley",
"Aaron J",
""
],
[
"Toniolo",
"Alice",
""
],
[
"Pucihar",
"Klen Čopič",
""
],
[
"Kljun",
"Matjaž",
""
]
] |
new_dataset
| 0.996669 |
2207.00913
|
Yuhao Nie
|
Yuhao Nie, Xiatong Li, Andea Scott, Yuchi Sun, Vignesh Venugopal, Adam
Brandt
|
SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for
Short-term Solar Forecasting
| null | null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Large-scale integration of photovoltaics (PV) into electricity grids is
challenged by the intermittent nature of solar power. Sky-image-based solar
forecasting using deep learning has been recognized as a promising approach to
predicting the short-term fluctuations. However, there are few publicly
available standardized benchmark datasets for image-based solar forecasting,
which limits the comparison of different forecasting models and the exploration
of forecasting methods. To fill these gaps, we introduce SKIPP'D -- a SKy
Images and Photovoltaic Power Generation Dataset. The dataset contains three
years (2017-2019) of quality-controlled down-sampled sky images and PV power
generation data that is ready-to-use for short-term solar forecasting using
deep learning. In addition, to support the flexibility in research, we provide
the high resolution, high frequency sky images and PV power generation data as
well as the concurrent sky video footage. We also include a code base
containing data processing scripts and baseline model implementations for
researchers to reproduce our previous work and accelerate their research in
solar forecasting.
|
[
{
"version": "v1",
"created": "Sat, 2 Jul 2022 21:52:50 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Nie",
"Yuhao",
""
],
[
"Li",
"Xiatong",
""
],
[
"Scott",
"Andea",
""
],
[
"Sun",
"Yuchi",
""
],
[
"Venugopal",
"Vignesh",
""
],
[
"Brandt",
"Adam",
""
]
] |
new_dataset
| 0.99888 |
2207.00928
|
Jing Li
|
Qidan Zhu, Jing Li, Fei Yuan, Quan Gan
|
Continuous Sign Language Recognition via Temporal Super-Resolution
Network
|
13 pages, 11 figures
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Aiming at the problem that the spatial-temporal hierarchical continuous sign
language recognition model based on deep learning has a large amount of
computation, which limits the real-time application of the model, this paper
proposes a temporal super-resolution network(TSRNet). The data is reconstructed
into a dense feature sequence to reduce the overall model computation while
keeping the final recognition accuracy loss to a minimum. The continuous sign
language recognition model(CSLR) via TSRNet mainly consists of three parts:
frame-level feature extraction, time series feature extraction and TSRNet,
where TSRNet is located between frame-level feature extraction and time-series
feature extraction, which mainly includes two branches: detail descriptor and
rough descriptor. The sparse frame-level features are fused through the
features obtained by the two designed branches as the reconstructed dense
frame-level feature sequence, and the connectionist temporal
classification(CTC) loss is used for training and optimization after the
time-series feature extraction part. To better recover semantic-level
information, the overall model is trained with the self-generating adversarial
training method proposed in this paper to reduce the model error rate. The
training method regards the TSRNet as the generator, and the frame-level
processing part and the temporal processing part as the discriminator. In
addition, in order to unify the evaluation criteria of model accuracy loss
under different benchmarks, this paper proposes word error rate
deviation(WERD), which takes the error rate between the estimated word error
rate (WER) and the reference WER obtained by the reconstructed frame-level
feature sequence and the complete original frame-level feature sequence as the
WERD. Experiments on two large-scale sign language datasets demonstrate the
effectiveness of the proposed model.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 00:55:45 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Zhu",
"Qidan",
""
],
[
"Li",
"Jing",
""
],
[
"Yuan",
"Fei",
""
],
[
"Gan",
"Quan",
""
]
] |
new_dataset
| 0.995005 |
2207.00942
|
Nathaniel Hanson
|
Nathaniel Hanson, Tarik Kelestemur, Deniz Erdogmus, Taskin Padir
|
Pregrasp Object Material Classification by a Novel Gripper Design with
Integrated Spectroscopy
| null | null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
Robots benefit from being able to classify objects they interact with or
manipulate based on their material properties. This capability ensures fine
manipulation of complex objects through proper grasp pose and force selection.
Prior work has focused on haptic or visual processing to determine material
type at grasp time. In this work, we introduce a novel parallel robot gripper
design and a method for collecting spectral readings and visual images from
within the gripper finger. We train a nonlinear Support Vector Machine (SVM)
that can classify the material of the object about to be grasped through
recursive estimation, with increasing confidence as the distance from the
finger tips to the object decreases. In order to validate the hardware design
and classification method, we collect samples from 16 real and fake fruit
varieties (composed of polystyrene/plastic) resulting in a dataset containing
spectral curves, scene images, and high-resolution texture images as the
objects are grasped, lifted, and released. Our modeling method demonstrates an
accuracy of 96.4% in classifying objects in a 32 class decision problem. This
represents a performance improvement by 29.4% over the state of the art
computer vision algorithms at distinguishing between visually similar
materials. In contrast to prior work, our recursive estimation model accounts
for increasing spectral signal strength and allows for decisions to be made as
the gripper approaches an object. We conclude that spectroscopy is a promising
sensing modality for enabling robots to not only classify grasped objects but
also understand their underlying material composition.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 03:14:45 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Hanson",
"Nathaniel",
""
],
[
"Kelestemur",
"Tarik",
""
],
[
"Erdogmus",
"Deniz",
""
],
[
"Padir",
"Taskin",
""
]
] |
new_dataset
| 0.996429 |
2207.00960
|
Dhruv Makwana
|
Subhrajit Nag, Dhruv Makwana, Sai Chandra Teja R, Sparsh Mittal, C
Krishna Mohan
|
WaferSegClassNet -- A Light-weight Network for Classification and
Segmentation of Semiconductor Wafer Defects
|
11 pages, 2 figures, 7 tables, Published in Computers in Industry
|
Volume 142, 2022, 103720, ISSN 0166-3615,
|
10.1016/j.compind.2022.103720
| null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
As the integration density and design intricacy of semiconductor wafers
increase, the magnitude and complexity of defects in them are also on the rise.
Since the manual inspection of wafer defects is costly, an automated artificial
intelligence (AI) based computer-vision approach is highly desired. The
previous works on defect analysis have several limitations, such as low
accuracy and the need for separate models for classification and segmentation.
For analyzing mixed-type defects, some previous works require separately
training one model for each defect type, which is non-scalable. In this paper,
we present WaferSegClassNet (WSCN), a novel network based on encoder-decoder
architecture. WSCN performs simultaneous classification and segmentation of
both single and mixed-type wafer defects. WSCN uses a "shared encoder" for
classification, and segmentation, which allows training WSCN end-to-end. We use
N-pair contrastive loss to first pretrain the encoder and then use BCE-Dice
loss for segmentation, and categorical cross-entropy loss for classification.
Use of N-pair contrastive loss helps in better embedding representation in the
latent dimension of wafer maps. WSCN has a model size of only 0.51MB and
performs only 0.2M FLOPS. Thus, it is much lighter than other state-of-the-art
models. Also, it requires only 150 epochs for convergence, compared to 4,000
epochs needed by a previous work. We evaluate our model on the MixedWM38
dataset, which has 38,015 images. WSCN achieves an average classification
accuracy of 98.2% and a dice coefficient of 0.9999. We are the first to show
segmentation results on the MixedWM38 dataset. The source code can be obtained
from https://github.com/ckmvigil/WaferSegClassNet.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 05:46:19 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Nag",
"Subhrajit",
""
],
[
"Makwana",
"Dhruv",
""
],
[
"R",
"Sai Chandra Teja",
""
],
[
"Mittal",
"Sparsh",
""
],
[
"Mohan",
"C Krishna",
""
]
] |
new_dataset
| 0.966847 |
2207.00964
|
Jiajun Chai
|
Jiajun Chai, Yuanheng Zhu, Dongbin Zhao
|
NVIF: Neighboring Variational Information Flow for Large-Scale
Cooperative Multi-Agent Scenarios
| null | null | null | null |
cs.MA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Communication-based multi-agent reinforcement learning (MARL) provides
information exchange between agents, which promotes the cooperation. However,
existing methods cannot perform well in the large-scale multi-agent system. In
this paper, we adopt neighboring communication and propose a Neighboring
Variational Information Flow (NVIF) to provide efficient communication for
agents. It employs variational auto-encoder to compress the shared information
into a latent state. This communication protocol does not rely dependently on a
specific task, so that it can be pre-trained to stabilize the MARL training.
Besides. we combine NVIF with Proximal Policy Optimization (NVIF-PPO) and Deep
Q Network (NVIF-DQN), and present a theoretical analysis to illustrate NVIF-PPO
can promote cooperation. We evaluate the NVIF-PPO and NVIF-DQN on MAgent, a
widely used large-scale multi-agent environment, by two tasks with different
map sizes. Experiments show that our method outperforms other compared methods,
and can learn effective and scalable cooperation strategies in the large-scale
multi-agent system.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 06:15:16 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Chai",
"Jiajun",
""
],
[
"Zhu",
"Yuanheng",
""
],
[
"Zhao",
"Dongbin",
""
]
] |
new_dataset
| 0.979413 |
2207.00973
|
Tian-Zhu Xiang
|
Lin Li, Jingyi Liu, Shuo Wang, Xunkun Wang, Tian-Zhu Xiang
|
Trichomonas Vaginalis Segmentation in Microscope Images
|
Accepted by MICCAI2022
|
MICCAI2022
| null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Trichomoniasis is a common infectious disease with high incidence caused by
the parasite Trichomonas vaginalis, increasing the risk of getting HIV in
humans if left untreated. Automated detection of Trichomonas vaginalis from
microscopic images can provide vital information for the diagnosis of
trichomoniasis. However, accurate Trichomonas vaginalis segmentation (TVS) is a
challenging task due to the high appearance similarity between the Trichomonas
and other cells (e.g., leukocyte), the large appearance variation caused by
their motility, and, most importantly, the lack of large-scale annotated data
for deep model training. To address these challenges, we elaborately collected
the first large-scale Microscopic Image dataset of Trichomonas Vaginalis, named
TVMI3K, which consists of 3,158 images covering Trichomonas of various
appearances in diverse backgrounds, with high-quality annotations including
object-level mask labels, object boundaries, and challenging attributes.
Besides, we propose a simple yet effective baseline, termed TVNet, to
automatically segment Trichomonas from microscopic images, including
high-resolution fusion and foreground-background attention modules. Extensive
experiments demonstrate that our model achieves superior segmentation
performance and outperforms various cutting-edge object detection models both
quantitatively and qualitatively, making it a promising framework to promote
future research in TVS tasks. The dataset and results will be publicly
available at: https://github.com/CellRecog/cellRecog.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 07:29:05 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Li",
"Lin",
""
],
[
"Liu",
"Jingyi",
""
],
[
"Wang",
"Shuo",
""
],
[
"Wang",
"Xunkun",
""
],
[
"Xiang",
"Tian-Zhu",
""
]
] |
new_dataset
| 0.997808 |
2207.01058
|
Weiming Zhuang
|
Weiming Zhuang, Chongjie Ye, Ying Xu, Pengzhi Mao, Shuai Zhang
|
Chat-to-Design: AI Assisted Personalized Fashion Design
| null | null | null | null |
cs.AI cs.CV cs.HC cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this demo, we present Chat-to-Design, a new multimodal interaction system
for personalized fashion design. Compared to classic systems that recommend
apparel based on keywords, Chat-to-Design enables users to design clothes in
two steps: 1) coarse-grained selection via conversation and 2) fine-grained
editing via an interactive interface. It encompasses three sub-systems to
deliver an immersive user experience: A conversation system empowered by
natural language understanding to accept users' requests and manages dialogs; A
multimodal fashion retrieval system empowered by a large-scale pretrained
language-image network to retrieve requested apparel; A fashion design system
empowered by emerging generative techniques to edit attributes of retrieved
clothes.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 14:54:39 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Zhuang",
"Weiming",
""
],
[
"Ye",
"Chongjie",
""
],
[
"Xu",
"Ying",
""
],
[
"Mao",
"Pengzhi",
""
],
[
"Zhang",
"Shuai",
""
]
] |
new_dataset
| 0.96846 |
2207.01092
|
Alexander Sch\"afer
|
Alexander Sch\"afer, Gerd Reis, Didier Stricker
|
The Gesture Authoring Space: Authoring Customised Hand Gestures for
Grasping Virtual Objects in Immersive Virtual Environments
| null | null |
10.1145/3543758.3543766
| null |
cs.HC cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Natural user interfaces are on the rise. Manufacturers for Augmented,
Virtual, and Mixed Reality head mounted displays are increasingly integrating
new sensors into their consumer grade products, allowing gesture recognition
without additional hardware. This offers new possibilities for bare handed
interaction within virtual environments. This work proposes a hand gesture
authoring tool for object specific grab gestures allowing virtual objects to be
grabbed as in the real world. The presented solution uses template matching for
gesture recognition and requires no technical knowledge to design and create
custom tailored hand gestures. In a user study, the proposed approach is
compared with the pinch gesture and the controller for grasping virtual
objects. The different grasping techniques are compared in terms of accuracy,
task completion time, usability, and naturalness. The study showed that
gestures created with the proposed approach are perceived by users as a more
natural input modality than the others.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 18:33:33 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Schäfer",
"Alexander",
""
],
[
"Reis",
"Gerd",
""
],
[
"Stricker",
"Didier",
""
]
] |
new_dataset
| 0.995989 |
2207.01124
|
Mohayeminul Islam
|
Mohayeminul Islam and Ajay Kumar Jha and Sarah Nadi
|
PyMigBench and PyMigTax: A Benchmark and Taxonomy for Python Library
Migration
|
40 pages, 21 figures, submitted to Empirical Software Engineering
| null | null | null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
Developers heavily rely on Application Programming Interfaces (APIs) from
libraries to build their projects. However, libraries might become obsolete, or
new libraries with better APIs might become available. In such cases,
developers need to replace the used libraries with alternative libraries, a
process referred to as library migration. When done manually, library migration
can be tedious, time-consuming, and error-prone. Most of the current research
on automated library migration techniques focus on Java libraries, and even
more so on version migrations of the same library. Despite the increasing
popularity of Python, limited research work has investigated migration between
Python libraries. In this paper, we investigate the nature of Python library
migrations in open-source systems. We analyze the code changes that happen
during library migration and build PyMigBench, a manually verified migration
benchmark. PyMigBench contains 436 migration-related code changes from 74
commits in 57 client repositories, and includes migrations between 34 unique
pairs of libraries. Additionally, we manually analyze the migration-related
code changes and create a taxonomy of migrations, PyMigTax, that categorizes
migrations across various dimensions. Our contributions provide the necessary
foundations for developing automated Python library migration tools and
techniques.
|
[
{
"version": "v1",
"created": "Sun, 3 Jul 2022 21:00:08 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Islam",
"Mohayeminul",
""
],
[
"Jha",
"Ajay Kumar",
""
],
[
"Nadi",
"Sarah",
""
]
] |
new_dataset
| 0.998576 |
2207.01204
|
Eugene Ang
|
Eugene P.W. Ang, Shan Lin, Rahul Ahuja, Nemath Ahmed, Alex C. Kot
|
Adversarial Pairwise Reverse Attention for Camera Performance Imbalance
in Person Re-identification: New Dataset and Metrics
|
Accepted into the IEEE International Conference on Image Processing
(ICIP) 2022
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Existing evaluation metrics for Person Re-Identification (Person ReID) models
focus on system-wide performance. However, our studies reveal weaknesses due to
the uneven data distributions among cameras and different camera properties
that expose the ReID system to exploitation. In this work, we raise the
long-ignored ReID problem of camera performance imbalance and collect a
real-world privacy-aware dataset from 38 cameras to assist the study of the
imbalance issue. We propose new metrics to quantify camera performance
imbalance and further propose the Adversarial Pairwise Reverse Attention (APRA)
Module to guide the model learning the camera invariant feature with a novel
pairwise attention inversion mechanism.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 05:16:16 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Ang",
"Eugene P. W.",
""
],
[
"Lin",
"Shan",
""
],
[
"Ahuja",
"Rahul",
""
],
[
"Ahmed",
"Nemath",
""
],
[
"Kot",
"Alex C.",
""
]
] |
new_dataset
| 0.972848 |
2207.01216
|
Cheng Zou
|
Cheng Zou, Furong Xu, Meng Wang, Wen Li, Yuan Cheng
|
Solutions for Fine-grained and Long-tailed Snake Species Recognition in
SnakeCLEF 2022
|
Top solutions for FGVC9, accepted to CLEF2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automatic snake species recognition is important because it has vast
potential to help lower deaths and disabilities caused by snakebites. We
introduce our solution in SnakeCLEF 2022 for fine-grained snake species
recognition on a heavy long-tailed class distribution. First, a network
architecture is designed to extract and fuse features from multiple modalities,
i.e. photograph from visual modality and geographic locality information from
language modality. Then, logit adjustment based methods are studied to relieve
the impact caused by the severe class imbalance. Next, a combination of
supervised and self-supervised learning method is proposed to make full use of
the dataset, including both labeled training data and unlabeled testing data.
Finally, post processing strategies, such as multi-scale and multi-crop
test-time-augmentation, location filtering and model ensemble, are employed for
better performance. With an ensemble of several different models, a private
score 82.65%, ranking the 3rd, is achieved on the final leaderboard.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 05:55:58 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Zou",
"Cheng",
""
],
[
"Xu",
"Furong",
""
],
[
"Wang",
"Meng",
""
],
[
"Li",
"Wen",
""
],
[
"Cheng",
"Yuan",
""
]
] |
new_dataset
| 0.998825 |
2207.01220
|
Oshri Naparstek
|
Oshri Naparstek, Ophir Azulai, Daniel Rotman, Yevgeny Burshtein, Peter
Staar, Udi Barzelay
|
BusiNet -- a Light and Fast Text Detection Network for Business
Documents
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For digitizing or indexing physical documents, Optical Character Recognition
(OCR), the process of extracting textual information from scanned documents, is
a vital technology. When a document is visually damaged or contains non-textual
elements, existing technologies can yield poor results, as erroneous detection
results can greatly affect the quality of OCR. In this paper we present a
detection network dubbed BusiNet aimed at OCR of business documents. Business
documents often include sensitive information and as such they cannot be
uploaded to a cloud service for OCR. BusiNet was designed to be fast and light
so it could run locally preventing privacy issues. Furthermore, BusiNet is
built to handle scanned document corruption and noise using a specialized
synthetic dataset. The model is made robust to unseen noise by employing
adversarial training strategies. We perform an evaluation on publicly available
datasets demonstrating the usefulness and broad applicability of our model.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 06:08:49 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Naparstek",
"Oshri",
""
],
[
"Azulai",
"Ophir",
""
],
[
"Rotman",
"Daniel",
""
],
[
"Burshtein",
"Yevgeny",
""
],
[
"Staar",
"Peter",
""
],
[
"Barzelay",
"Udi",
""
]
] |
new_dataset
| 0.999763 |
2207.01227
|
Shahid Alam
|
Shahid Alam
|
Cybersecurity: Past, Present and Future
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
The digital transformation has created a new digital space known as
cyberspace. This new cyberspace has improved the workings of businesses,
organizations, governments, society as a whole, and day to day life of an
individual. With these improvements come new challenges, and one of the main
challenges is security. The security of the new cyberspace is called
cybersecurity. Cyberspace has created new technologies and environments such as
cloud computing, smart devices, IoTs, and several others. To keep pace with
these advancements in cyber technologies there is a need to expand research and
develop new cybersecurity methods and tools to secure these domains and
environments. This book is an effort to introduce the reader to the field of
cybersecurity, highlight current issues and challenges, and provide future
directions to mitigate or resolve them. The main specializations of
cybersecurity covered in this book are software security, hardware security,
the evolution of malware, biometrics, cyber intelligence, and cyber forensics.
We must learn from the past, evolve our present and improve the future. Based
on this objective, the book covers the past, present, and future of these main
specializations of cybersecurity. The book also examines the upcoming areas of
research in cyber intelligence, such as hybrid augmented and explainable
artificial intelligence (AI). Human and AI collaboration can significantly
increase the performance of a cybersecurity system. Interpreting and explaining
machine learning models, i.e., explainable AI is an emerging field of study and
has a lot of potentials to improve the role of AI in cybersecurity.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 06:47:50 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Alam",
"Shahid",
""
]
] |
new_dataset
| 0.984365 |
2207.01239
|
Zhongxiang Chang
|
Zhongxiang Chang and Yuning Chen and Zhongbao Zhou
|
Satellite downlink scheduling under breakpoint resume mode
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
A novel problem called satellite downlink scheduling problem (SDSP) under
breakpoint resume mode (SDSP-BRM) is studied in our paper. Compared to the
traditional SDSP where an imaging data has to be completely downloaded at one
time, SDSP-BRM allows the data of an imaging data be broken into a number of
pieces which can be downloaded in different playback windows. By analyzing the
characteristics of SDSP-BRM, we first propose a mixed integer programming model
for its formulation and then prove the NP-hardness of SDSP-BRM. To solve the
problem, we design a simple and effective heuristic algorithm (SEHA) where a
number of problem-tailored move operators are proposed for local searching.
Numerical results on a set of well-designed scenarios demonstrate the
efficiency of the proposed algorithm in comparison to the general purpose CPLEX
solver. We conduct additional experiments to shed light on the impact of the
segmental strategy on the overall performance of the proposed SEHA.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 07:30:51 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Chang",
"Zhongxiang",
""
],
[
"Chen",
"Yuning",
""
],
[
"Zhou",
"Zhongbao",
""
]
] |
new_dataset
| 0.987488 |
2207.01255
|
Guochen Yu
|
Yuansheng Guan, Guochen Yu, Andong Li, Chengshi Zheng, Jie Wang
|
TMGAN-PLC: Audio Packet Loss Concealment using Temporal Memory
Generative Adversarial Network
|
accepted by INTERSPEECH 2022
| null | null | null |
cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Real-time communications in packet-switched networks have become widely used
in daily communication, while they inevitably suffer from network delays and
data losses in constrained real-time conditions. To solve these problems, audio
packet loss concealment (PLC) algorithms have been developed to mitigate voice
transmission failures by reconstructing the lost information. Limited by the
transmission latency and device memory, it is still intractable for PLC to
accomplish high-quality voice reconstruction using a relatively small packet
buffer. In this paper, we propose a temporal memory generative adversarial
network for audio PLC, dubbed TMGAN-PLC, which is comprised of a novel
nested-UNet generator and the time-domain/frequency-domain discriminators.
Specifically, a combination of the nested-UNet and temporal feature-wise linear
modulation is elaborately devised in the generator to finely adjust the
intra-frame information and establish inter-frame temporal dependencies. To
complement the missing speech content caused by longer loss bursts, we employ
multi-stage gated vector quantizers to capture the correct content and
reconstruct the near-real smooth audio. Extensive experiments on the PLC
Challenge dataset demonstrate that the proposed method yields promising
performance in terms of speech quality, intelligibility, and PLCMOS.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 08:27:19 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Guan",
"Yuansheng",
""
],
[
"Yu",
"Guochen",
""
],
[
"Li",
"Andong",
""
],
[
"Zheng",
"Chengshi",
""
],
[
"Wang",
"Jie",
""
]
] |
new_dataset
| 0.991411 |
2207.01256
|
Ran Yu
|
Ran Yu, Limock, Stefan Dietze
|
Still Haven't Found What You're Looking For -- Detecting the Intent of
Web Search Missions from User Interaction Features
| null | null | null | null |
cs.IR cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Web search is among the most frequent online activities. Whereas traditional
information retrieval techniques focus on the information need behind a user
query, previous work has shown that user behaviour and interaction can provide
important signals for understanding the underlying intent of a search mission.
An established taxonomy distinguishes between transactional, navigational and
informational search missions, where in particular the latter involve a
learning goal, i.e. the intent to acquire knowledge about a particular topic.
We introduce a supervised approach for classifying online search missions into
either of these categories by utilising a range of features obtained from the
user interactions during an online search mission. Applying our model to a
dataset of real-world query logs, we show that search missions can be
categorised with an average F1 score of 63% and accuracy of 69%, while
performance on informational and navigational missions is particularly
promising (F1>75%). This suggests the potential to utilise such supervised
classification during online search to better facilitate retrieval and ranking
as well as to improve affiliated services, such as targeted online ads.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 08:30:18 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Yu",
"Ran",
""
],
[
"Limock",
"",
""
],
[
"Dietze",
"Stefan",
""
]
] |
new_dataset
| 0.974846 |
2207.01296
|
Ester Gonzalez-Sosa
|
Ester Gonzalez-Sosa, Andrija Gajic, Diego Gonzalez-Morin, Guillermo
Robledo, Pablo Perez and Alvaro Villegas
|
Real Time Egocentric Segmentation for Video-self Avatar in Mixed Reality
|
9 pages, 9 figures
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In this work we present our real-time egocentric body segmentation algorithm.
Our algorithm achieves a frame rate of 66 fps for an input resolution of
640x480, thanks to our shallow network inspired in Thundernet's architecture.
Besides, we put a strong emphasis on the variability of the training data. More
concretely, we describe the creation process of our Egocentric Bodies
(EgoBodies) dataset, composed of almost 10,000 images from three datasets,
created both from synthetic methods and real capturing. We conduct experiments
to understand the contribution of the individual datasets; compare Thundernet
model trained with EgoBodies with simpler and more complex previous approaches
and discuss their corresponding performance in a real-life setup in terms of
segmentation quality and inference times. The described trained semantic
segmentation algorithm is already integrated in an end-to-end system for Mixed
Reality (MR), making it possible for users to see his/her own body while being
immersed in a MR scene.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 10:00:16 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Gonzalez-Sosa",
"Ester",
""
],
[
"Gajic",
"Andrija",
""
],
[
"Gonzalez-Morin",
"Diego",
""
],
[
"Robledo",
"Guillermo",
""
],
[
"Perez",
"Pablo",
""
],
[
"Villegas",
"Alvaro",
""
]
] |
new_dataset
| 0.992401 |
2207.01404
|
Ling Gao
|
Ling Gao and Yuxuan Liang and Jiaqi Yang and Shaoxun Wu and Chenyu
Wang and Jiaben Chen and Laurent Kneip
|
VECtor: A Versatile Event-Centric Benchmark for Multi-Sensor SLAM
| null |
IEEE Robotics and Automation Letters, 2022
|
10.1109/LRA.2022.3186770
| null |
cs.RO cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Event cameras have recently gained in popularity as they hold strong
potential to complement regular cameras in situations of high dynamics or
challenging illumination. An important problem that may benefit from the
addition of an event camera is given by Simultaneous Localization And Mapping
(SLAM). However, in order to ensure progress on event-inclusive multi-sensor
SLAM, novel benchmark sequences are needed. Our contribution is the first
complete set of benchmark datasets captured with a multi-sensor setup
containing an event-based stereo camera, a regular stereo camera, multiple
depth sensors, and an inertial measurement unit. The setup is fully
hardware-synchronized and underwent accurate extrinsic calibration. All
sequences come with ground truth data captured by highly accurate external
reference devices such as a motion capture system. Individual sequences include
both small and large-scale environments, and cover the specific challenges
targeted by dynamic vision sensors.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 13:37:26 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Gao",
"Ling",
""
],
[
"Liang",
"Yuxuan",
""
],
[
"Yang",
"Jiaqi",
""
],
[
"Wu",
"Shaoxun",
""
],
[
"Wang",
"Chenyu",
""
],
[
"Chen",
"Jiaben",
""
],
[
"Kneip",
"Laurent",
""
]
] |
new_dataset
| 0.99955 |
2207.01406
|
Bjorn Lindqvist Mr.
|
Bj\"orn Lindqvist, Sina Sharif Mansouri, Jakub Halu\v{s}ka, and George
Nikolakopoulos
|
Reactive Navigation of an Unmanned Aerial Vehicle with Perception-based
Obstacle Avoidance Constraints
|
16 pages, 28 figures
|
IEEE Transactions on Control Systems Technology (2021) Early
Access
|
10.1109/TCST.2021.3124820
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this article we propose a reactive constrained navigation scheme, with
embedded obstacles avoidance for an Unmanned Aerial Vehicle (UAV), for enabling
navigation in obstacle-dense environments. The proposed navigation architecture
is based on Nonlinear Model Predictive Control (NMPC), and utilizes an on-board
2D LiDAR to detect obstacles and translate online the key geometric information
of the environment into parametric constraints for the NMPC that constrain the
available position-space for the UAV. This article focuses also on the
real-world implementation and experimental validation of the proposed reactive
navigation scheme, and it is applied in multiple challenging laboratory
experiments, where we also conduct comparisons with relevant methods of
reactive obstacle avoidance. The solver utilized in the proposed approach is
the Optimization Engine (OpEn) and the Proximal Averaged Newton for Optimal
Control (PANOC) algorithm, where a penalty method is applied to properly
consider obstacles and input constraints during the navigation task. The
proposed novel scheme allows for fast solutions, while using limited on-board
computational power, that is a required feature for the overall closed loop
performance of an UAV and is applied in multiple real-time scenarios. The
combination of built-in obstacle avoidance and real-time applicability makes
the proposed reactive constrained navigation scheme an elegant framework for
UAVs that is able to perform fast nonlinear control, local path-planning and
obstacle avoidance, all embedded in the control layer.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 13:38:07 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Lindqvist",
"Björn",
""
],
[
"Mansouri",
"Sina Sharif",
""
],
[
"Haluška",
"Jakub",
""
],
[
"Nikolakopoulos",
"George",
""
]
] |
new_dataset
| 0.953303 |
2207.01434
|
Yue Qin
|
Yue Qin and Xiaojing Liao
|
Cybersecurity Entity Alignment via Masked Graph Attention Networks
| null | null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cybersecurity vulnerability information is often recorded by multiple
channels, including government vulnerability repositories,
individual-maintained vulnerability-gathering platforms, or
vulnerability-disclosure email lists and forums. Integrating vulnerability
information from different channels enables comprehensive threat assessment and
quick deployment to various security mechanisms. Efforts to automatically
gather such information, however, are impeded by the limitations of today's
entity alignment techniques. In our study, we annotate the first
cybersecurity-domain entity alignment dataset and reveal the unique
characteristics of security entities. Based on these observations, we propose
the first cybersecurity entity alignment model, CEAM, which equips GNN-based
entity alignment with two mechanisms: asymmetric masked aggregation and
partitioned attention. Experimental results on cybersecurity-domain entity
alignment datasets demonstrate that CEAM significantly outperforms
state-of-the-art entity alignment methods.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 14:19:32 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Qin",
"Yue",
""
],
[
"Liao",
"Xiaojing",
""
]
] |
new_dataset
| 0.997493 |
2207.01452
|
Jun Cen
|
Jun Cen, Peng Yun, Shiwei Zhang, Junhao Cai, Di Luan, Michael Yu Wang,
Ming Liu, Mingqian Tang
|
Open-world Semantic Segmentation for LIDAR Point Clouds
|
Accepted by ECCV 2022. arXiv admin note: text overlap with
arXiv:2011.10033, arXiv:2109.05441 by other authors
| null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Current methods for LIDAR semantic segmentation are not robust enough for
real-world applications, e.g., autonomous driving, since it is closed-set and
static. The closed-set assumption makes the network only able to output labels
of trained classes, even for objects never seen before, while a static network
cannot update its knowledge base according to what it has seen. Therefore, in
this work, we propose the open-world semantic segmentation task for LIDAR point
clouds, which aims to 1) identify both old and novel classes using open-set
semantic segmentation, and 2) gradually incorporate novel objects into the
existing knowledge base using incremental learning without forgetting old
classes. For this purpose, we propose a REdundAncy cLassifier (REAL) framework
to provide a general architecture for both the open-set semantic segmentation
and incremental learning problems. The experimental results show that REAL can
simultaneously achieves state-of-the-art performance in the open-set semantic
segmentation task on the SemanticKITTI and nuScenes datasets, and alleviate the
catastrophic forgetting problem with a large margin during incremental
learning.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 14:40:35 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Cen",
"Jun",
""
],
[
"Yun",
"Peng",
""
],
[
"Zhang",
"Shiwei",
""
],
[
"Cai",
"Junhao",
""
],
[
"Luan",
"Di",
""
],
[
"Wang",
"Michael Yu",
""
],
[
"Liu",
"Ming",
""
],
[
"Tang",
"Mingqian",
""
]
] |
new_dataset
| 0.982246 |
2207.01483
|
Alexander Wang
|
Alexander Wang, Jerry Sun, Kaitlyn Chen, Kevin Zhou, Edward Li Gu,
Chenxin Fang
|
"COVID-19 was a FIFA conspiracy #curropt": An Investigation into the
Viral Spread of COVID-19 Misinformation
|
Winner of the 2021 ProjectX undergraduate research competition hosted
by the University of Toronto under the category of Epidemiology. Accepted by
the University of Toronto AI Conference 2022. 8 pages, 4 figures
| null | null | null |
cs.CY cs.CL cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
The outbreak of the infectious and fatal disease COVID-19 has revealed that
pandemics assail public health in two waves: first, from the contagion itself
and second, from plagues of suspicion and stigma. Now, we have in our hands and
on our phones an outbreak of moral controversy. Modern dependency on social
medias has not only facilitated access to the locations of vaccine clinics and
testing sites but also-and more frequently-to the convoluted explanations of
how "COVID-19 was a FIFA conspiracy"[1]. The MIT Media Lab finds that false
news "diffuses significantly farther, faster, deeper, and more broadly than
truth, in all categories of information, and by an order of magnitude"[2]. The
question is, how does the spread of misinformation interact with a physical
epidemic disease? In this paper, we estimate the extent to which misinformation
has influenced the course of the COVID-19 pandemic using natural language
processing models and provide a strategy to combat social media posts that are
likely to cause widespread harm.
|
[
{
"version": "v1",
"created": "Sun, 12 Jun 2022 19:41:01 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Wang",
"Alexander",
""
],
[
"Sun",
"Jerry",
""
],
[
"Chen",
"Kaitlyn",
""
],
[
"Zhou",
"Kevin",
""
],
[
"Gu",
"Edward Li",
""
],
[
"Fang",
"Chenxin",
""
]
] |
new_dataset
| 0.99814 |
2207.01492
|
Ruchita Bhadre
|
Ruchita Bhadre, Prathamesh Yeole, Tejas Ranka, Rohini Mudhalwadkar
|
SmartMask- Developing an automated self-care system
|
Presented at SIG Healthcare Indagation IEEE
| null | null | null |
cs.CY cs.LG cs.RO eess.SP
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
COVID-19 has changed our world and has filled people with fear and anxiety.
Everyone has a fear of coming in contact with people having the Coronavirus. In
Spite of releasing full lockdowns, there is still a pressing need to maintain
social distancing in the short- to medium-term to control the spread of
coronavirus. Due to lack of self discipline or obviously pulling down the mask
to get some fresh air, might pose a threat when you come near a person showing
COVID symptoms. Abiding to WHO guidelines to avoid touching the mask while
wearing it, we propose a wearable device for no contact pulling up of mask on
face and additionally to implement social distancing with sensors mounted on
the device. The SmartMask will detect if we are in the vicinity of any other
person and will pull itself up. With sensors for detecting the closeness of
objects around you and prompting you to take a proper action or pull the mask
automatically. Along with the automated mask we will incorporate a temperature
sensor to check vitals of an individual at all times and give an alert to the
peers around him. This will ensure social distancing and help in avoiding
spread of the virus.
|
[
{
"version": "v1",
"created": "Wed, 15 Jun 2022 03:17:01 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Bhadre",
"Ruchita",
""
],
[
"Yeole",
"Prathamesh",
""
],
[
"Ranka",
"Tejas",
""
],
[
"Mudhalwadkar",
"Rohini",
""
]
] |
new_dataset
| 0.99552 |
2207.01505
|
Bohan Jiang
|
Bohan Jiang, Paras Sheth, Baoxin Li, Huan Liu
|
CoVaxNet: An Online-Offline Data Repository for COVID-19 Vaccine
Hesitancy Research
|
10 pages
| null | null | null |
cs.CY cs.LG cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
Despite the astonishing success of COVID-19 vaccines against the virus, a
substantial proportion of the population is still hesitant to be vaccinated,
undermining governmental efforts to control the virus. To address this problem,
we need to understand the different factors giving rise to such a behavior,
including social media discourses, news media propaganda, government responses,
demographic and socioeconomic statuses, and COVID-19 statistics, etc. However,
existing datasets fail to cover all these aspects, making it difficult to form
a complete picture in inferencing about the problem of vaccine hesitancy. In
this paper, we construct a multi-source, multi-modal, and multi-feature
online-offline data repository CoVaxNet. We provide descriptive analyses and
insights to illustrate critical patterns in CoVaxNet. Moreover, we propose a
novel approach for connecting online and offline data so as to facilitate the
inference tasks that exploit complementary information sources.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 05:58:35 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Jiang",
"Bohan",
""
],
[
"Sheth",
"Paras",
""
],
[
"Li",
"Baoxin",
""
],
[
"Liu",
"Huan",
""
]
] |
new_dataset
| 0.9993 |
2207.01600
|
Jin Wan
|
Jin Wan and Hui Yin and Zhenyao Wu and Xinyi Wu and Zhihao Liu and
Song Wang
|
CRFormer: A Cross-Region Transformer for Shadow Removal
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Aiming to restore the original intensity of shadow regions in an image and
make them compatible with the remaining non-shadow regions without a trace,
shadow removal is a very challenging problem that benefits many downstream
image/video-related tasks. Recently, transformers have shown their strong
capability in various applications by capturing global pixel interactions and
this capability is highly desirable in shadow removal. However, applying
transformers to promote shadow removal is non-trivial for the following two
reasons: 1) The patchify operation is not suitable for shadow removal due to
irregular shadow shapes; 2) shadow removal only needs one-way interaction from
the non-shadow region to the shadow region instead of the common two-way
interactions among all pixels in the image. In this paper, we propose a novel
cross-region transformer, namely CRFormer, for shadow removal which differs
from existing transformers by only considering the pixel interactions from the
non-shadow region to the shadow region without splitting images into patches.
This is achieved by a carefully designed region-aware cross-attention operation
that can aggregate the recovered shadow region features conditioned on the
non-shadow region features. Extensive experiments on ISTD, AISTD, SRD, and
Video Shadow Removal datasets demonstrate the superiority of our method
compared to other state-of-the-art methods.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 17:33:02 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Wan",
"Jin",
""
],
[
"Yin",
"Hui",
""
],
[
"Wu",
"Zhenyao",
""
],
[
"Wu",
"Xinyi",
""
],
[
"Liu",
"Zhihao",
""
],
[
"Wang",
"Song",
""
]
] |
new_dataset
| 0.998712 |
2207.01605
|
Roland Kromes
|
Ilya Grishkov, Roland Kromes, Thanassis Giannetsos and Kaitai Liang
|
ID-based self-encryption via Hyperledger Fabric based smart contract
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by/4.0/
|
This paper offers a prototype of a Hyperledger Fabric-IPFS based network
architecture including a smart contract based encryption scheme that meant to
improve the security of user's data that is being uploaded to the distributed
ledger. A new extension to the self-encryption scheme was deployed by
integrating data owner's identity into the encryption process. Such integration
allows to permanently preserve ownership of the original file and link it to
the person/entity who originally uploaded it. Moreover, self-encryption
provides strong security guarantees that decryption of a file is
computationally not feasible under the condition that the encrypted file and
the key are safely stored.
|
[
{
"version": "v1",
"created": "Mon, 4 Jul 2022 17:37:03 GMT"
}
] | 2022-07-05T00:00:00 |
[
[
"Grishkov",
"Ilya",
""
],
[
"Kromes",
"Roland",
""
],
[
"Giannetsos",
"Thanassis",
""
],
[
"Liang",
"Kaitai",
""
]
] |
new_dataset
| 0.998806 |
2104.10340
|
Wangzhi Li
|
Mobin Zhao, Wangzhi Li, Yongjie Fu, Kangrui Ruan, Xuan Di
|
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with
Connected Vehicles
|
29 pages, 14 figures
|
Transportation Research Part C: Emerging Technologies, 141 (2022):
103728
| null | null |
cs.LG cs.AI cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
This paper develops a decentralized reinforcement learning (RL) scheme for
multi-intersection adaptive traffic signal control (TSC), called "CVLight",
that leverages data collected from connected vehicles (CVs). The state and
reward design facilitates coordination among agents and considers travel delays
collected by CVs. A novel algorithm, Asymmetric Advantage Actor-critic
(Asym-A2C), is proposed where both CV and non-CV information is used to train
the critic network, while only CV information is used to execute optimal signal
timing. Comprehensive experiments show the superiority of CVLight over
state-of-the-art algorithms under a 2-by-2 synthetic road network with various
traffic demand patterns and penetration rates. The learned policy is then
visualized to further demonstrate the advantage of Asym-A2C. A pre-train
technique is applied to improve the scalability of CVLight, which significantly
shortens the training time and shows the advantage in performance under a
5-by-5 road network. A case study is performed on a 2-by-2 road network located
in State College, Pennsylvania, USA, to further demonstrate the effectiveness
of the proposed algorithm under real-world scenarios. Compared to other
baseline models, the trained CVLight agent can efficiently control multiple
intersections solely based on CV data and achieve the best performance,
especially under low CV penetration rates.
|
[
{
"version": "v1",
"created": "Wed, 21 Apr 2021 03:38:11 GMT"
},
{
"version": "v2",
"created": "Fri, 10 Dec 2021 22:21:45 GMT"
},
{
"version": "v3",
"created": "Fri, 1 Jul 2022 03:28:09 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Zhao",
"Mobin",
""
],
[
"Li",
"Wangzhi",
""
],
[
"Fu",
"Yongjie",
""
],
[
"Ruan",
"Kangrui",
""
],
[
"Di",
"Xuan",
""
]
] |
new_dataset
| 0.976769 |
2105.09847
|
Micha\"el Fonder
|
Micha\"el Fonder and Damien Ernst and Marc Van Droogenbroeck
|
M4Depth: Monocular depth estimation for autonomous vehicles in unseen
environments
|
Main paper: 9 pages, Appendix: 4 pages, References: 2 pages. Code
available on GitHub: https://github.com/michael-fonder/M4Depth
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Estimating the distance to objects is crucial for autonomous vehicles when
using depth sensors is not possible. In this case, the distance has to be
estimated from on-board mounted RGB cameras, which is a complex task especially
in environments such as natural outdoor landscapes. In this paper, we present a
new method named M4Depth for depth estimation. First, we establish a bijective
relationship between depth and the visual disparity of two consecutive frames
and show how to exploit it to perform motion-invariant pixel-wise depth
estimation. Then, we detail M4Depth which is based on a pyramidal convolutional
neural network architecture where each level refines an input disparity map
estimate by using two customized cost volumes. We use these cost volumes to
leverage the visual spatio-temporal constraints imposed by motion and to make
the network robust for varied scenes. We benchmarked our approach both in test
and generalization modes on public datasets featuring synthetic camera
trajectories recorded in a wide variety of outdoor scenes. Results show that
our network outperforms the state of the art on these datasets, while also
performing well on a standard depth estimation benchmark. The code of our
method is publicly available at https://github.com/michael-fonder/M4Depth.
|
[
{
"version": "v1",
"created": "Thu, 20 May 2021 15:46:02 GMT"
},
{
"version": "v2",
"created": "Fri, 21 May 2021 09:13:23 GMT"
},
{
"version": "v3",
"created": "Fri, 1 Jul 2022 10:08:30 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Fonder",
"Michaël",
""
],
[
"Ernst",
"Damien",
""
],
[
"Van Droogenbroeck",
"Marc",
""
]
] |
new_dataset
| 0.988732 |
2106.14405
|
Andrew Szot
|
Andrew Szot, Alex Clegg, Eric Undersander, Erik Wijmans, Yili Zhao,
John Turner, Noah Maestre, Mustafa Mukadam, Devendra Chaplot, Oleksandr
Maksymets, Aaron Gokaslan, Vladimir Vondrus, Sameer Dharur, Franziska Meier,
Wojciech Galuba, Angel Chang, Zsolt Kira, Vladlen Koltun, Jitendra Malik,
Manolis Savva, Dhruv Batra
|
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
| null | null | null | null |
cs.LG cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce Habitat 2.0 (H2.0), a simulation platform for training virtual
robots in interactive 3D environments and complex physics-enabled scenarios. We
make comprehensive contributions to all levels of the embodied AI stack - data,
simulation, and benchmark tasks. Specifically, we present: (i) ReplicaCAD: an
artist-authored, annotated, reconfigurable 3D dataset of apartments (matching
real spaces) with articulated objects (e.g. cabinets and drawers that can
open/close); (ii) H2.0: a high-performance physics-enabled 3D simulator with
speeds exceeding 25,000 simulation steps per second (850x real-time) on an
8-GPU node, representing 100x speed-ups over prior work; and, (iii) Home
Assistant Benchmark (HAB): a suite of common tasks for assistive robots (tidy
the house, prepare groceries, set the table) that test a range of mobile
manipulation capabilities. These large-scale engineering contributions allow us
to systematically compare deep reinforcement learning (RL) at scale and
classical sense-plan-act (SPA) pipelines in long-horizon structured tasks, with
an emphasis on generalization to new objects, receptacles, and layouts. We find
that (1) flat RL policies struggle on HAB compared to hierarchical ones; (2) a
hierarchy with independent skills suffers from 'hand-off problems', and (3) SPA
pipelines are more brittle than RL policies.
|
[
{
"version": "v1",
"created": "Mon, 28 Jun 2021 05:42:15 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 05:29:15 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Szot",
"Andrew",
""
],
[
"Clegg",
"Alex",
""
],
[
"Undersander",
"Eric",
""
],
[
"Wijmans",
"Erik",
""
],
[
"Zhao",
"Yili",
""
],
[
"Turner",
"John",
""
],
[
"Maestre",
"Noah",
""
],
[
"Mukadam",
"Mustafa",
""
],
[
"Chaplot",
"Devendra",
""
],
[
"Maksymets",
"Oleksandr",
""
],
[
"Gokaslan",
"Aaron",
""
],
[
"Vondrus",
"Vladimir",
""
],
[
"Dharur",
"Sameer",
""
],
[
"Meier",
"Franziska",
""
],
[
"Galuba",
"Wojciech",
""
],
[
"Chang",
"Angel",
""
],
[
"Kira",
"Zsolt",
""
],
[
"Koltun",
"Vladlen",
""
],
[
"Malik",
"Jitendra",
""
],
[
"Savva",
"Manolis",
""
],
[
"Batra",
"Dhruv",
""
]
] |
new_dataset
| 0.99969 |
2110.01073
|
Ori Shapira
|
Ori Shapira, Ramakanth Pasunuru, Ido Dagan, Yael Amsterdamer
|
Multi-Document Keyphrase Extraction: Dataset, Baselines and Review
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Keyphrase extraction has been extensively researched within the
single-document setting, with an abundance of methods, datasets and
applications. In contrast, multi-document keyphrase extraction has been
infrequently studied, despite its utility for describing sets of documents, and
its use in summarization. Moreover, no prior dataset exists for multi-document
keyphrase extraction, hindering the progress of the task. Recent advances in
multi-text processing make the task an even more appealing challenge to pursue.
To stimulate this pursuit, we present here the first dataset for the task,
MK-DUC-01, which can serve as a new benchmark, and test multiple keyphrase
extraction baselines on our data. In addition, we provide a brief, yet
comprehensive, literature review of the task.
|
[
{
"version": "v1",
"created": "Sun, 3 Oct 2021 19:10:28 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 13:32:21 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Shapira",
"Ori",
""
],
[
"Pasunuru",
"Ramakanth",
""
],
[
"Dagan",
"Ido",
""
],
[
"Amsterdamer",
"Yael",
""
]
] |
new_dataset
| 0.995841 |
2111.11730
|
Hitesh Tewari Dr
|
Matthew Chun, Stefan Weber and Hitesh Tewari
|
A Lightweight Encryption Scheme for IoT Devices in the Fog
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The Internet of Things (IoT) is the collection of everyday smart devices
which connect to the Cloud, often through Fog nodes, to transmit and receive
information. These everyday devices are distinct from traditional computers
because they typically have notable constraints on their RAM, flash memory, and
computational power. Due to these constraints, we believe that many of the
proposed encryption schemes are too heavyweight to be employed in the IoT. In
this paper we present a lightweight, flexible encryption scheme that relies on
the one-way information loss property of a secure hash function. Our scheme
imposes minimal computational and storage requirements, and imposes no
non-negligible burdens on the encrypting device, except for the hash itself. We
find that the encryption algorithm is particularly lightweight, and holds up
strongly in terms of its speed and memory efficiency.
|
[
{
"version": "v1",
"created": "Tue, 23 Nov 2021 08:56:10 GMT"
},
{
"version": "v2",
"created": "Mon, 29 Nov 2021 11:22:52 GMT"
},
{
"version": "v3",
"created": "Wed, 1 Dec 2021 23:09:15 GMT"
},
{
"version": "v4",
"created": "Fri, 1 Jul 2022 08:26:19 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Chun",
"Matthew",
""
],
[
"Weber",
"Stefan",
""
],
[
"Tewari",
"Hitesh",
""
]
] |
new_dataset
| 0.996091 |
2112.01097
|
Hitesh Tewari Dr
|
Philip Bradish, Sarang Chaudhari, Michael Clear and Hitesh Tewari
|
CoviChain: A Blockchain Based COVID-19 Vaccination Passport
| null | null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Vaccination passports are being issued by governments around the world in
order to open up their travel and hospitality sectors. Civil liberty
campaigners on the other hand argue that such mandatory instruments encroach
upon our fundamental right to anonymity, freedom of movement, and are a
backdoor to issuing "identity documents" to citizens by their governments. In
this paper we present a privacy-preserving framework that uses two-factor
authentication to create a unique identifier that can be used to locate a
person's vaccination record on a blockchain, but does not store any personal
information about them. Our main contribution is the employment of a locality
sensitive hashing algorithm over an iris extraction technique, that can be used
to authenticate users and anonymously locate vaccination records on the
blockchain, without leaking any personally identifiable information to the
blockchain. Our proposed system allows for the safe reopening of society, while
maintaining the privacy of citizens.
|
[
{
"version": "v1",
"created": "Thu, 2 Dec 2021 10:17:23 GMT"
},
{
"version": "v2",
"created": "Sun, 5 Dec 2021 12:23:04 GMT"
},
{
"version": "v3",
"created": "Tue, 7 Dec 2021 10:18:40 GMT"
},
{
"version": "v4",
"created": "Wed, 16 Feb 2022 09:21:33 GMT"
},
{
"version": "v5",
"created": "Fri, 1 Jul 2022 08:54:30 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Bradish",
"Philip",
""
],
[
"Chaudhari",
"Sarang",
""
],
[
"Clear",
"Michael",
""
],
[
"Tewari",
"Hitesh",
""
]
] |
new_dataset
| 0.999675 |
2202.00443
|
Pierre Lison
|
Ildik\'o Pil\'an, Pierre Lison, Lilja {\O}vrelid, Anthi Papadopoulou,
David S\'anchez and Montserrat Batet
|
The Text Anonymization Benchmark (TAB): A Dedicated Corpus and
Evaluation Framework for Text Anonymization
| null | null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel benchmark and associated evaluation metrics for assessing
the performance of text anonymization methods. Text anonymization, defined as
the task of editing a text document to prevent the disclosure of personal
information, currently suffers from a shortage of privacy-oriented annotated
text resources, making it difficult to properly evaluate the level of privacy
protection offered by various anonymization methods. This paper presents TAB
(Text Anonymization Benchmark), a new, open-source annotated corpus developed
to address this shortage. The corpus comprises 1,268 English-language court
cases from the European Court of Human Rights (ECHR) enriched with
comprehensive annotations about the personal information appearing in each
document, including their semantic category, identifier type, confidential
attributes, and co-reference relations. Compared to previous work, the TAB
corpus is designed to go beyond traditional de-identification (which is limited
to the detection of predefined semantic categories), and explicitly marks which
text spans ought to be masked in order to conceal the identity of the person to
be protected. Along with presenting the corpus and its annotation layers, we
also propose a set of evaluation metrics that are specifically tailored towards
measuring the performance of text anonymization, both in terms of privacy
protection and utility preservation. We illustrate the use of the benchmark and
the proposed metrics by assessing the empirical performance of several baseline
text anonymization models. The full corpus along with its privacy-oriented
annotation guidelines, evaluation scripts and baseline models are available on:
https://github.com/NorskRegnesentral/text-anonymisation-benchmark
|
[
{
"version": "v1",
"created": "Tue, 25 Jan 2022 14:34:42 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 10:27:00 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Pilán",
"Ildikó",
""
],
[
"Lison",
"Pierre",
""
],
[
"Øvrelid",
"Lilja",
""
],
[
"Papadopoulou",
"Anthi",
""
],
[
"Sánchez",
"David",
""
],
[
"Batet",
"Montserrat",
""
]
] |
new_dataset
| 0.995162 |
2202.01340
|
Anthony Ortiz
|
Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker,
Shivaprakash K Nagaraju, Caleb Robinson, Priyal Bhatia, Aditi Khurana, Jane
Wang, Felipe Oviedo, Juan Lavista Ferres
|
An Artificial Intelligence Dataset for Solar Energy Locations in India
|
Accepted for publication in Nature Scientific Data
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Rapid development of renewable energy sources, particularly solar
photovoltaics (PV), is critical to mitigate climate change. As a result, India
has set ambitious goals to install 500 gigawatts of solar energy capacity by
2030. Given the large footprint projected to meet renewables energy targets,
the potential for land use conflicts over environmental values is high. To
expedite development of solar energy, land use planners will need access to
up-to-date and accurate geo-spatial information of PV infrastructure. In this
work, we developed a spatially explicit machine learning model to map
utility-scale solar projects across India using freely available satellite
imagery with a mean accuracy of 92%. Our model predictions were validated by
human experts to obtain a dataset of 1363 solar PV farms. Using this dataset,
we measure the solar footprint across India and quantified the degree of
landcover modification associated with the development of PV infrastructure.
Our analysis indicates that over 74% of solar development In India was built on
landcover types that have natural ecosystem preservation, or agricultural
value.
|
[
{
"version": "v1",
"created": "Mon, 31 Jan 2022 23:53:19 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 00:11:54 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Ortiz",
"Anthony",
""
],
[
"Negandhi",
"Dhaval",
""
],
[
"Mysorekar",
"Sagar R",
""
],
[
"Kiesecker",
"Joseph",
""
],
[
"Nagaraju",
"Shivaprakash K",
""
],
[
"Robinson",
"Caleb",
""
],
[
"Bhatia",
"Priyal",
""
],
[
"Khurana",
"Aditi",
""
],
[
"Wang",
"Jane",
""
],
[
"Oviedo",
"Felipe",
""
],
[
"Ferres",
"Juan Lavista",
""
]
] |
new_dataset
| 0.999756 |
2203.06147
|
Dovydas Joksas
|
Dovydas Joksas, AbdulAziz AlMutairi, Oscar Lee, Murat Cubukcu, Antonio
Lombardo, Hidekazu Kurebayashi, Anthony J. Kenyon, Adnan Mehonic
|
Memristive, Spintronic, and 2D-Materials-Based Devices to Improve and
Complement Computing Hardware
|
28 pages, 7 figures
|
Adv. Intell. Syst. 2022, 2200068
|
10.1002/aisy.202200068
| null |
cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In a data-driven economy, virtually all industries benefit from advances in
information technology -- powerful computing systems are critically important
for rapid technological progress. However, this progress might be at risk of
slowing down if we do not address the discrepancy between our current computing
power demands and what the existing technologies can offer. Key limitations to
improving energy efficiency are the excessive growth of data transfer costs
associated with the von Neumann architecture and the fundamental limits of
complementary metal-oxide-semiconductor (CMOS) technologies, such as
transistors. In this perspective article, we discuss three technologies that
will likely play an essential role in future computing systems: memristive
electronics, spintronics, and electronics based on 2D materials. We present how
these may transform conventional digital computers and contribute to the
adoption of new paradigms, like neuromorphic computing.
|
[
{
"version": "v1",
"created": "Fri, 11 Mar 2022 18:18:25 GMT"
},
{
"version": "v2",
"created": "Mon, 23 May 2022 15:07:24 GMT"
},
{
"version": "v3",
"created": "Fri, 1 Jul 2022 17:28:32 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Joksas",
"Dovydas",
""
],
[
"AlMutairi",
"AbdulAziz",
""
],
[
"Lee",
"Oscar",
""
],
[
"Cubukcu",
"Murat",
""
],
[
"Lombardo",
"Antonio",
""
],
[
"Kurebayashi",
"Hidekazu",
""
],
[
"Kenyon",
"Anthony J.",
""
],
[
"Mehonic",
"Adnan",
""
]
] |
new_dataset
| 0.999545 |
2203.14883
|
Hongkuan Zhou
|
Hongkuan Zhou, Da Zheng, Israt Nisa, Vasileios Ioannidis, Xiang Song,
George Karypis
|
TGL: A General Framework for Temporal GNN Training on Billion-Scale
Graphs
|
VLDB'22
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Many real world graphs contain time domain information. Temporal Graph Neural
Networks capture temporal information as well as structural and contextual
information in the generated dynamic node embeddings. Researchers have shown
that these embeddings achieve state-of-the-art performance in many different
tasks. In this work, we propose TGL, a unified framework for large-scale
offline Temporal Graph Neural Network training where users can compose various
Temporal Graph Neural Networks with simple configuration files. TGL comprises
five main components, a temporal sampler, a mailbox, a node memory module, a
memory updater, and a message passing engine. We design a Temporal-CSR data
structure and a parallel sampler to efficiently sample temporal neighbors to
formtraining mini-batches. We propose a novel random chunk scheduling technique
that mitigates the problem of obsolete node memory when training with a large
batch size. To address the limitations of current TGNNs only being evaluated on
small-scale datasets, we introduce two large-scale real-world datasets with 0.2
and 1.3 billion temporal edges. We evaluate the performance of TGL on four
small-scale datasets with a single GPU and the two large datasets with multiple
GPUs for both link prediction and node classification tasks. We compare TGL
with the open-sourced code of five methods and show that TGL achieves similar
or better accuracy with an average of 13x speedup. Our temporal parallel
sampler achieves an average of 173x speedup on a multi-core CPU compared with
the baselines. On a 4-GPU machine, TGL can train one epoch of more than one
billion temporal edges within 1-10 hours. To the best of our knowledge, this is
the first work that proposes a general framework for large-scale Temporal Graph
Neural Networks training on multiple GPUs.
|
[
{
"version": "v1",
"created": "Mon, 28 Mar 2022 16:41:18 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 18:35:43 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Zhou",
"Hongkuan",
""
],
[
"Zheng",
"Da",
""
],
[
"Nisa",
"Israt",
""
],
[
"Ioannidis",
"Vasileios",
""
],
[
"Song",
"Xiang",
""
],
[
"Karypis",
"George",
""
]
] |
new_dataset
| 0.998555 |
2205.02048
|
Nicholas Popovic
|
Nicholas Popovic, Michael F\"arber
|
Few-Shot Document-Level Relation Extraction
|
Published at NAACL 2022
| null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present FREDo, a few-shot document-level relation extraction (FSDLRE)
benchmark. As opposed to existing benchmarks which are built on sentence-level
relation extraction corpora, we argue that document-level corpora provide more
realism, particularly regarding none-of-the-above (NOTA) distributions.
Therefore, we propose a set of FSDLRE tasks and construct a benchmark based on
two existing supervised learning data sets, DocRED and sciERC. We adapt the
state-of-the-art sentence-level method MNAV to the document-level and develop
it further for improved domain adaptation. We find FSDLRE to be a challenging
setting with interesting new characteristics such as the ability to sample NOTA
instances from the support set. The data, code, and trained models are
available online (https://github.com/nicpopovic/FREDo).
|
[
{
"version": "v1",
"created": "Wed, 4 May 2022 13:16:19 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 15:38:00 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Popovic",
"Nicholas",
""
],
[
"Färber",
"Michael",
""
]
] |
new_dataset
| 0.997914 |
2205.06779
|
Qiuhui Chen
|
Qiuhui Chen, Yi Hong
|
Scribble2D5: Weakly-Supervised Volumetric Image Segmentation via
Scribble Annotations
|
Accepted by MICCAI 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, weakly-supervised image segmentation using weak annotations like
scribbles has gained great attention, since such annotations are much easier to
obtain compared to time-consuming and label-intensive labeling at the
pixel/voxel level. However, because scribbles lack structure information of
region of interest (ROI), existing scribble-based methods suffer from poor
boundary localization. Furthermore, most current methods are designed for 2D
image segmentation, which do not fully leverage the volumetric information if
directly applied to image slices. In this paper, we propose a scribble-based
volumetric image segmentation, Scribble2D5, which tackles 3D anisotropic image
segmentation and improves boundary prediction. To achieve this, we augment a
2.5D attention UNet with a proposed label propagation module to extend semantic
information from scribbles and a combination of static and active boundary
prediction to learn ROI's boundary and regularize its shape. Extensive
experiments on three public datasets demonstrate Scribble2D5 significantly
outperforms current scribble-based methods and approaches the performance of
fully-supervised ones. Our code is available online.
|
[
{
"version": "v1",
"created": "Fri, 13 May 2022 17:04:10 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 04:54:54 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Chen",
"Qiuhui",
""
],
[
"Hong",
"Yi",
""
]
] |
new_dataset
| 0.992973 |
2206.07934
|
Chen Zhang
|
Chen Zhang, Honglin Sun, Chen Chen, Yandong Guo
|
BANet: Motion Forecasting with Boundary Aware Network
| null | null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a motion forecasting model called BANet, which means
Boundary-Aware Network, and it is a variant of LaneGCN. We believe that it is
not enough to use only the lane centerline as input to obtain the embedding
features of the vector map nodes. The lane centerline can only provide the
topology of the lanes, and other elements of the vector map also contain rich
information. For example, the lane boundary can provide traffic rule constraint
information such as whether it is possible to change lanes which is very
important. Therefore, we achieved better performance by encoding more vector
map elements in the motion forecasting model.We report our results on the 2022
Argoverse2 Motion Forecasting challenge and rank 1st on the test leaderboard.
|
[
{
"version": "v1",
"created": "Thu, 16 Jun 2022 05:56:24 GMT"
},
{
"version": "v2",
"created": "Tue, 21 Jun 2022 03:15:36 GMT"
},
{
"version": "v3",
"created": "Fri, 1 Jul 2022 03:19:40 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Zhang",
"Chen",
""
],
[
"Sun",
"Honglin",
""
],
[
"Chen",
"Chen",
""
],
[
"Guo",
"Yandong",
""
]
] |
new_dataset
| 0.989686 |
2206.15147
|
Asier Guti\'errez-Fandi\~no
|
Asier Guti\'errez-Fandi\~no, David P\'erez-Fern\'andez, Jordi
Armengol-Estap\'e, David Griol, Zoraida Callejas
|
esCorpius: A Massive Spanish Crawling Corpus
|
esCorpius is available on
https://huggingface.co/datasets/LHF/escorpius
| null | null | null |
cs.CL cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
In the recent years, transformer-based models have lead to significant
advances in language modelling for natural language processing. However, they
require a vast amount of data to be (pre-)trained and there is a lack of
corpora in languages other than English. Recently, several initiatives have
presented multilingual datasets obtained from automatic web crawling. However,
the results in Spanish present important shortcomings, as they are either too
small in comparison with other languages, or present a low quality derived from
sub-optimal cleaning and deduplication. In this paper, we introduce esCorpius,
a Spanish crawling corpus obtained from near 1 Pb of Common Crawl data. It is
the most extensive corpus in Spanish with this level of quality in the
extraction, purification and deduplication of web textual content. Our data
curation process involves a novel highly parallel cleaning pipeline and
encompasses a series of deduplication mechanisms that together ensure the
integrity of both document and paragraph boundaries. Additionally, we maintain
both the source web page URL and the WARC shard origin URL in order to complain
with EU regulations. esCorpius has been released under CC BY-NC-ND 4.0 license
and is available on HuggingFace.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 09:29:18 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 08:22:32 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Gutiérrez-Fandiño",
"Asier",
""
],
[
"Pérez-Fernández",
"David",
""
],
[
"Armengol-Estapé",
"Jordi",
""
],
[
"Griol",
"David",
""
],
[
"Callejas",
"Zoraida",
""
]
] |
new_dataset
| 0.999131 |
2206.15211
|
Ricardo Grando
|
Junior Costa de Jesus, Victor Augusto Kich, Alisson Henrique Kolling,
Ricardo Bedin Grando, Rodrigo da Silva Guerra, Paulo Lilles Jorge Drews Jr
|
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized
Representations in Reinforcement Learning for Mapless Navigation of Unmanned
Aerial Vehicles
|
Accepted to the IEEE International Conference on Intelligent Robots
and Systems (IROS) 2022
| null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Reinforcement Learning (RL) has presented an impressive performance in video
games through raw pixel imaging and continuous control tasks. However, RL
performs poorly with high-dimensional observations such as raw pixel images. It
is generally accepted that physical state-based RL policies such as laser
sensor measurements give a more sample-efficient result than learning by
pixels. This work presents a new approach that extracts information from a
depth map estimation to teach an RL agent to perform the mapless navigation of
Unmanned Aerial Vehicle (UAV). We propose the Depth-Imaged Contrastive
Unsupervised Prioritized Representations in Reinforcement Learning(Depth-CUPRL)
that estimates the depth of images with a prioritized replay memory. We used a
combination of RL and Contrastive Learning to lead with the problem of RL based
on images. From the analysis of the results with Unmanned Aerial Vehicles
(UAVs), it is possible to conclude that our Depth-CUPRL approach is effective
for the decision-making and outperforms state-of-the-art pixel-based approaches
in the mapless navigation capability.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 11:54:01 GMT"
},
{
"version": "v2",
"created": "Fri, 1 Jul 2022 01:27:15 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"de Jesus",
"Junior Costa",
""
],
[
"Kich",
"Victor Augusto",
""
],
[
"Kolling",
"Alisson Henrique",
""
],
[
"Grando",
"Ricardo Bedin",
""
],
[
"Guerra",
"Rodrigo da Silva",
""
],
[
"Drews",
"Paulo Lilles Jorge",
"Jr"
]
] |
new_dataset
| 0.992788 |
2207.00035
|
Simon Pietro Romano
|
Maurizio D'Arienzo and Simon Pietro Romano
|
GOSPF: An energy efficient implementation of the OSPF routing protocol
|
18 pages
|
Journal of Network and Computer Applications, Volume 75, 2016,
Pages 110-127, ISSN 1084-8045
|
10.1016/j.jnca.2016.07.011.
| null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Energy saving is currently one of the most challenging issues for the
Internet research community. Indeed, the exponential growth of applications and
services induces a remarkable increase in power consumption and hence calls for
novel solutions which are capable to preserve energy of the infrastructures, at
the same time maintaining the required Quality of Service guarantees. In this
paper we introduce a new mechanism for saving energy through intelligent switch
off of network links. The mechanism has been implemented as an extension to the
Open Shortest Path First routing protocol. We first show through simulations
that our solution is capable to dramatically reduce energy consumption when
compared to the standard OSPF implementation. We then illustrate a real-world
implementation of the proposed protocol within the Quagga routing software
suite.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 18:01:34 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"D'Arienzo",
"Maurizio",
""
],
[
"Romano",
"Simon Pietro",
""
]
] |
new_dataset
| 0.992254 |
2207.00038
|
Sanaz Taheri Boshrooyeh
|
Oskar Thor\'en, Sanaz Taheri-Boshrooyeh, Hanno Cornelius
|
Waku: A Family of Modular P2P Protocols For Secure &
Censorship-Resistant Communication
|
IEEE ICDCSW 2022
| null | null | null |
cs.CR cs.DC
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Waku is a family of modular protocols that enable secure,
censorship-resistant, and anonymous peer-to-peer communication. Waku protocols
provide capabilities that make them suitable to run in resource-restricted
environments e.g., mobile devices and web browsers. Such capabilities include
(i) retrieving historical messaging for mostly-offline devices (ii) adaptive
nodes; allowing for heterogeneous nodes to contribute to the network (iii)
preserving bandwidth usage for resource-restricted devices, (iv) minimizing
connectivity requirements for devices with a limited connection, and (v)
enabling efficient, private, economic spam protection for heterogeneous nodes.
Waku's modular design and resource-efficient protocols make it superior to its
predecessor i.e., Whisper. In this paper, we give an overview of the Waku
protocols stack, its architecture, and protocols interaction along with a
sample demo scenario on configuring and running a Waku node using nwaku i.e.,
Waku client written in Nim.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 18:13:10 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Thorén",
"Oskar",
""
],
[
"Taheri-Boshrooyeh",
"Sanaz",
""
],
[
"Cornelius",
"Hanno",
""
]
] |
new_dataset
| 0.99859 |
2207.00106
|
Mark Endo
|
Mark Endo, Kathleen L. Poston, Edith V. Sullivan, Li Fei-Fei, Kilian
M. Pohl, Ehsan Adeli
|
GaitForeMer: Self-Supervised Pre-Training of Transformers via Human
Motion Forecasting for Few-Shot Gait Impairment Severity Estimation
|
Accepted as a conference paper at MICCAI (Medical Image Computing and
Computer Assisted Intervention) 2022
| null | null | null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Parkinson's disease (PD) is a neurological disorder that has a variety of
observable motor-related symptoms such as slow movement, tremor, muscular
rigidity, and impaired posture. PD is typically diagnosed by evaluating the
severity of motor impairments according to scoring systems such as the Movement
Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS).
Automated severity prediction using video recordings of individuals provides a
promising route for non-intrusive monitoring of motor impairments. However, the
limited size of PD gait data hinders model ability and clinical potential.
Because of this clinical data scarcity and inspired by the recent advances in
self-supervised large-scale language models like GPT-3, we use human motion
forecasting as an effective self-supervised pre-training task for the
estimation of motor impairment severity. We introduce GaitForeMer, Gait
Forecasting and impairment estimation transforMer, which is first pre-trained
on public datasets to forecast gait movements and then applied to clinical data
to predict MDS-UPDRS gait impairment severity. Our method outperforms previous
approaches that rely solely on clinical data by a large margin, achieving an F1
score of 0.76, precision of 0.79, and recall of 0.75. Using GaitForeMer, we
show how public human movement data repositories can assist clinical use cases
through learning universal motion representations. The code is available at
https://github.com/markendo/GaitForeMer .
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 21:29:47 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Endo",
"Mark",
""
],
[
"Poston",
"Kathleen L.",
""
],
[
"Sullivan",
"Edith V.",
""
],
[
"Fei-Fei",
"Li",
""
],
[
"Pohl",
"Kilian M.",
""
],
[
"Adeli",
"Ehsan",
""
]
] |
new_dataset
| 0.998892 |
2207.00116
|
Sanaz Taheri Boshrooyeh
|
Sanaz Taheri-Boshrooyeh, Oskar Thor\'en, Barry Whitehat, Wei Jie Koh,
Onur Kilic, Kobi Gurkan
|
Privacy-Preserving Spam-Protected Gossip-Based Routing
|
IEEE ICDCS 2022
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
WAKU-RLN-RELAY is an anonymous peer-to-peer gossip-based routing protocol
that features a privacy-preserving spam-protection with cryptographically
guaranteed economic incentives. While being an anonymous routing protocol where
routed messages are not attributable to their origin, it allows global
identification and removal of spammers. It addresses the performance and
privacy issues of its counterparts including proof-of-work and reputation-based
schemes. Its light computational overhead makes it suitable for
resource-limited environments. The spam protection works by limiting the
messaging rate of each network participant where rate violation results in
financial punishment. We deploy the novel construct of rate-limiting nullifier
to enforce the message rate limit. We provide a proof-of-concept implementation
of WAKU-RLN-RELAY to prove the efficiency and feasibility of our solution.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 22:21:51 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Taheri-Boshrooyeh",
"Sanaz",
""
],
[
"Thorén",
"Oskar",
""
],
[
"Whitehat",
"Barry",
""
],
[
"Koh",
"Wei Jie",
""
],
[
"Kilic",
"Onur",
""
],
[
"Gurkan",
"Kobi",
""
]
] |
new_dataset
| 0.998897 |
2207.00117
|
Sanaz Taheri Boshrooyeh
|
Sanaz Taheri-Boshrooyeh, Oskar Thor\'en, Barry Whitehat, Wei Jie Koh,
Onur Kilic, Kobi Gurkan
|
WAKU-RLN-RELAY: Privacy-Preserving Peer-to-Peer Economic Spam Protection
|
IEEE ICDCSW 2022
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
In this paper, we propose WAKU-RLN-RELAY as a spam-protected gossip-based
routing protocol that can run in heterogeneous networks. It features a
privacy-preserving peer-to-peer (p2p) economic spam protection mechanism.
WAKU-RLN-RELAY addresses the performance and privacy issues of the
state-of-the-art p2p spam prevention techniques including peer scoring utilized
by libp2p, and proof-of-work used by e.g., Whisper, the p2p messaging layer of
Ethereum. In WAKU-RLN-RELAY, spam protection works by limiting the messaging
rate of each network participant. Rate violation is disincentivized since it
results in financial punishment where the punishment is cryptographically
guaranteed. Peers who identify spammers are also rewarded. To enforce the rate
limit, we adopt the suggested framework of Semaphore and its extended version,
however, we modify that framework to properly address the unique requirements
of a network of p2p resource-restricted users. The current work dives into the
end-to-end integration of Semaphore into WAKU-RLN-RELAY, the modifications
required to make it suitable for resource-limited users, and the open problems
and future research directions. We also provide a proof-of-concept open-source
implementation of WAKU-RLN-RELAY, and its specifications together with a rough
performance evaluation.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 22:22:24 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Taheri-Boshrooyeh",
"Sanaz",
""
],
[
"Thorén",
"Oskar",
""
],
[
"Whitehat",
"Barry",
""
],
[
"Koh",
"Wei Jie",
""
],
[
"Kilic",
"Onur",
""
],
[
"Gurkan",
"Kobi",
""
]
] |
new_dataset
| 0.998677 |
2207.00119
|
Ana Ozaki
|
Tiziano Dalmonte, Andrea Mazzullo, and Ana Ozaki
|
Reasoning in Non-normal Modal Description Logics
|
ARQNL 2022
| null | null | null |
cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Non-normal modal logics, interpreted on neighbourhood models which generalise
the usual relational semantics, have found application in several areas, such
as epistemic, deontic, and coalitional reasoning. We present here preliminary
results on reasoning in a family of modal description logics obtained by
combining ALC with non-normal modal operators. First, we provide a framework of
terminating, correct, and complete tableau algorithms to check satisfiability
of formulas in such logics with the semantics based on varying domains. We then
investigate the satisfiability problems in fragments of these languages
obtained by restricting the application of modal operators to formulas only,
and interpreted on models with constant domains, providing tight complexity
results.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 22:30:30 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Dalmonte",
"Tiziano",
""
],
[
"Mazzullo",
"Andrea",
""
],
[
"Ozaki",
"Ana",
""
]
] |
new_dataset
| 0.967082 |
2207.00147
|
Sunyi Zheng
|
Sunyi Zheng, Jingxiong Li, Zhongyi Shui, Chenglu Zhu, Yunlong Zhang,
Pingyi Chen, Lin Yang
|
ChrSNet: Chromosome Straightening using Self-attention Guided Networks
|
Accepted to MICCAI 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Karyotyping is an important procedure to assess the possible existence of
chromosomal abnormalities. However, because of the non-rigid nature,
chromosomes are usually heavily curved in microscopic images and such deformed
shapes hinder the chromosome analysis for cytogeneticists. In this paper, we
present a self-attention guided framework to erase the curvature of
chromosomes. The proposed framework extracts spatial information and local
textures to preserve banding patterns in a regression module. With
complementary information from the bent chromosome, a refinement module is
designed to further improve fine details. In addition, we propose two dedicated
geometric constraints to maintain the length and restore the distortion of
chromosomes. To train our framework, we create a synthetic dataset where curved
chromosomes are generated from the real-world straight chromosomes by
grid-deformation. Quantitative and qualitative experiments are conducted on
synthetic and real-world data. Experimental results show that our proposed
method can effectively straighten bent chromosomes while keeping banding
details and length.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 02:19:49 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Zheng",
"Sunyi",
""
],
[
"Li",
"Jingxiong",
""
],
[
"Shui",
"Zhongyi",
""
],
[
"Zhu",
"Chenglu",
""
],
[
"Zhang",
"Yunlong",
""
],
[
"Chen",
"Pingyi",
""
],
[
"Yang",
"Lin",
""
]
] |
new_dataset
| 0.975826 |
2207.00152
|
Chemseddine Benkalfate
|
Chemseddine Benkalfate (1 and 2), Mohammed Feham (1), Achour Ouslimani
(2) and Abed-Elhak Kasbari (2) ((1) STIC laboratory, Telecommunications
Department, Universitu of Tlemcen, (2) Quartz laboratory, Electrical and
Electronics Engineering, ENSEA)
|
UWB patch antenna design and realization in the bandwidth 780 MHz to
4.22 GHz
|
10 Pages, 13 Figures
| null | null | null |
cs.NI eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
The proposed UWB antenna covers mobile communications (GSM, EDG, UMTS(3G),
LTE(4G)) and wireless networks (WIFI, WiMAX), within a theoretical bandwidth
defined from 780MHz to 4.22GHz. The UWB antenna is designed and realized on a
FR-4 substrate with an electrical permittivity of 4.4. It presents a 98.75%
average analytical efficiency and an omnidirectional radiation within the
previous bandwidth. The impedance excitation port is fixed at 50 Ohm according
with the SMA impedance used in the practical part. The measured results are in
good agreement with those obtained using CST and ADS softwares. The measured
bandwidth, defined from 980MHz to 4.2GHz, presents an efficiency of 94.14%.
Furthermore, the practical radiation diagram and the excitation port impedance
stay the same as that the simulation one.
|
[
{
"version": "v1",
"created": "Wed, 25 May 2022 12:44:36 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Benkalfate",
"Chemseddine",
"",
"1 and 2"
],
[
"Feham",
"Mohammed",
""
],
[
"Ouslimani",
"Achour",
""
],
[
"Kasbari",
"Abed-Elhak",
""
]
] |
new_dataset
| 0.999343 |
2207.00155
|
Andrea Bedin
|
Leonardo Badia, Andrea Bedin
|
Blockage-Peeking Game of Mobile Strategic Nodes in Millimeter Wave
Communications
|
8 pages, 6 figures. Published on MedComNet 2022
| null | null | null |
cs.NI cs.GT
|
http://creativecommons.org/licenses/by/4.0/
|
Given the importance of line-of-sight in mmWave communications, a strategic
adversary can harm a transmission by obstructing the receiver, which in turn
can react by trying to move around this hurdle. To expand on this point, we
study one such scenario from the perspective of game theory, considering a
mobile mmWave receiver and an adversary interacting strategically as players in
a zero-sum game, where they want to maximize, or respectively minimize, the
spectral efficiency of the communication. To do so, the adversary attempts at
screening the receiver's line of sight as an obstacle, while the receiver can
move around so as to avoid the blockage. We consider preset distances and the
choices available to the players are to change their angular coordinates to go
around each other. This is framed as a static game of complete information, for
which we numerically find the Nash equilibrium in mixed strategies, drawing
some interesting conclusions such as connecting it with the beamforming pattern
of the transmitter.
|
[
{
"version": "v1",
"created": "Fri, 10 Jun 2022 07:41:09 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Badia",
"Leonardo",
""
],
[
"Bedin",
"Andrea",
""
]
] |
new_dataset
| 0.972462 |
2207.00251
|
Gangming Zhao
|
Chengwei Pan, Gangming Zhao, Junjie Fang, Baolian Qi, Jiaheng Liu,
Chaowei Fang, Dingwen Zhang, Jinpeng Li, and Yizhou Yu
|
Computer-aided Tuberculosis Diagnosis with Attribute Reasoning
Assistance
|
Provisionally Accepted for Medical Image Computing and Computer
Assisted Interventions 2022 (MICCAI 2022). arXiv admin note: text overlap
with arXiv:2010.04483
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Although deep learning algorithms have been intensively developed for
computer-aided tuberculosis diagnosis (CTD), they mainly depend on carefully
annotated datasets, leading to much time and resource consumption. Weakly
supervised learning (WSL), which leverages coarse-grained labels to accomplish
fine-grained tasks, has the potential to solve this problem. In this paper, we
first propose a new large-scale tuberculosis (TB) chest X-ray dataset, namely
the tuberculosis chest X-ray attribute dataset (TBX-Att), and then establish an
attribute-assisted weakly-supervised framework to classify and localize TB by
leveraging the attribute information to overcome the insufficiency of
supervision in WSL scenarios. Specifically, first, the TBX-Att dataset contains
2000 X-ray images with seven kinds of attributes for TB relational reasoning,
which are annotated by experienced radiologists. It also includes the public
TBX11K dataset with 11200 X-ray images to facilitate weakly supervised
detection. Second, we exploit a multi-scale feature interaction model for TB
area classification and detection with attribute relational reasoning. The
proposed model is evaluated on the TBX-Att dataset and will serve as a solid
baseline for future research. The code and data will be available at
https://github.com/GangmingZhao/tb-attribute-weak-localization.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 07:50:35 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Pan",
"Chengwei",
""
],
[
"Zhao",
"Gangming",
""
],
[
"Fang",
"Junjie",
""
],
[
"Qi",
"Baolian",
""
],
[
"Liu",
"Jiaheng",
""
],
[
"Fang",
"Chaowei",
""
],
[
"Zhang",
"Dingwen",
""
],
[
"Li",
"Jinpeng",
""
],
[
"Yu",
"Yizhou",
""
]
] |
new_dataset
| 0.999396 |
2207.00272
|
Linjie Yang
|
Linjie Yang, Pingzhi Fan, Li Li, Zhiguo Ding, Li Hao
|
Grant-Free Transmission by LDPC Matrix Mapping and Integrated Cover-MPA
Detector
|
30pages, 11 figures
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, a novel transceiver architecture is proposed to simultaneously
achieve efficient random access and reliable data transmission in massive IoT
networks. At the transmitter side, each user is assigned a unique protocol
sequence which is used to identify the user and also indicate the user's
channel access pattern. Hence, user identification is completed by the
detection of channel access patterns. Particularly, the columns of a parity
check matrix of low-density-parity-check (LDPC) code are employed as protocol
sequences. The design guideline of this LDPC parity check matrix and the
associated performance analysis are provided in this paper.At the receiver
side, a two-stage iterative detection architecture is designed, which consists
of a group testing component and a payload data decoding component. They
collaborate in a way that the group testing component maps detected protocol
sequences to a tanner graph, on which the second component could execute its
message passing algorithm. In turn, zero symbols detected by the message
passing algorithm of the second component indicate potential false alarms made
by the first group testing component. Hence, the tanner graph could iteratively
evolve.The provided simulation results demonstrate that our transceiver design
realizes a practical one-step grant-free transmission and has a compelling
performance.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 08:55:17 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Yang",
"Linjie",
""
],
[
"Fan",
"Pingzhi",
""
],
[
"Li",
"Li",
""
],
[
"Ding",
"Zhiguo",
""
],
[
"Hao",
"Li",
""
]
] |
new_dataset
| 0.959488 |
2207.00421
|
Mark Stamp
|
Huy Nguyen and Fabio Di Troia and Genya Ishigaki and Mark Stamp
|
Generative Adversarial Networks and Image-Based Malware Classification
| null | null | null | null |
cs.CR cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
For efficient malware removal, determination of malware threat levels, and
damage estimation, malware family classification plays a critical role. In this
paper, we extract features from malware executable files and represent them as
images using various approaches. We then focus on Generative Adversarial
Networks (GAN) for multiclass classification and compare our GAN results to
other popular machine learning techniques, including Support Vector Machine
(SVM), XGBoost, and Restricted Boltzmann Machines (RBM). We find that the
AC-GAN discriminator is generally competitive with other machine learning
techniques. We also evaluate the utility of the GAN generative model for
adversarial attacks on image-based malware detection. While AC-GAN generated
images are visually impressive, we find that they are easily distinguished from
real malware images using any of several learning techniques. This result
indicates that our GAN generated images would be of little value in adversarial
attacks.
|
[
{
"version": "v1",
"created": "Wed, 8 Jun 2022 20:59:47 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Nguyen",
"Huy",
""
],
[
"Di Troia",
"Fabio",
""
],
[
"Ishigaki",
"Genya",
""
],
[
"Stamp",
"Mark",
""
]
] |
new_dataset
| 0.984919 |
2207.00423
|
Alberto Carrasco-Casado
|
Alberto Carrasco-Casado, Koichi Shiratama, Phuc V. Trinh, Dimitar
Kolev, Femi Ishola, Tetsuharu Fuse, Hiroyuki Tsuji, Morio Toyoshima
|
NICT's versatile miniaturized lasercom terminals for moving platforms
|
5 pages, 6 figures, 1 table
|
Proceedings of the 2022 IEEE International Conference on Space
Optical Systems and Applications (ICSOS)
|
10.1109/ICSOS53063.2022.9749711
| null |
cs.NI eess.SP
|
http://creativecommons.org/licenses/by/4.0/
|
With the goal of meeting the diverse requirements of many different types of
platforms, ranging from small drones to big satellites, and being applied in a
variety of diverse scenarios, ranging from fixed terrestrial links to moving
platforms in general, and operating within a wide range of conditions and
distances, the Japanese National Institute of Information and Communications
Technology (NICT) is currently working towards the development of a series of
versatile miniaturized free-space laser-communication terminals. By choosing
the appropriate terminal configuration for any given scenario, the basic
conditions of operations can be satisfied without the need of customization,
and the adaptive design of the terminals can close the gap to achieve an
optimum solution that meets the communication requirements. This paper presents
NICT's current efforts regarding the development of this series of lasercom
terminals and introduces the first prototypes developed for validation and test
purposes.
|
[
{
"version": "v1",
"created": "Mon, 9 May 2022 06:57:02 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Carrasco-Casado",
"Alberto",
""
],
[
"Shiratama",
"Koichi",
""
],
[
"Trinh",
"Phuc V.",
""
],
[
"Kolev",
"Dimitar",
""
],
[
"Ishola",
"Femi",
""
],
[
"Fuse",
"Tetsuharu",
""
],
[
"Tsuji",
"Hiroyuki",
""
],
[
"Toyoshima",
"Morio",
""
]
] |
new_dataset
| 0.997446 |
2207.00459
|
Jingxiao Ma
|
Jingxiao Ma and Sherief Reda
|
RUCA: RUntime Configurable Approximate Circuits with Self-Correcting
Capability
|
8 pages, 7 figures, to be published in 30th International Workshop on
Logic & Synthesis
| null | null | null |
cs.AR
|
http://creativecommons.org/licenses/by/4.0/
|
Approximate computing is an emerging computing paradigm that offers improved
power consumption by relaxing the requirement for full accuracy. Since
real-world applications may have different requirements for design accuracy,
one trend of approximate computing is to design runtime quality-configurable
circuits, which are able to operate under different accuracy modes with
different power consumption. In this paper, we present a novel framework RUCA
which aims to approximate an arbitrary input circuit in a runtime configurable
fashion. By factorizing and decomposing the truth table, our approach aims to
approximate and separate the input circuit into multiple configuration blocks
which support different accuracy levels, including a corrector circuit to
restore full accuracy. By activating different blocks, the approximate circuit
is able to operate at different accuracy-power configurations. To improve the
scalability of our algorithm, we also provide a design space exploration scheme
with circuit partitioning to navigate the search space of possible
approximations of subcircuits during design time. We thoroughly evaluate our
methodology on a set of benchmarks and compare against another
quality-configurable approach, showcasing the benefits and flexibility of RUCA.
For 3-level designs, RUCA saves power consumption by 36.57% within 1% error and
by 51.32% within 2% error on average.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 14:32:42 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Ma",
"Jingxiao",
""
],
[
"Reda",
"Sherief",
""
]
] |
new_dataset
| 0.991273 |
2207.00477
|
Yang Xing
|
Karan Kheta, Claire Delgove, Ruolin Liu, Adeola Aderogba, Marc-Olivier
Pokam, Muhammed Mehmet Unal, Yang Xing, Weisi Guo
|
Vision-based Conflict Detection within Crowds based on High-Resolution
Human Pose Estimation for Smart and Safe Airport
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Future airports are becoming more complex and congested with the increasing
number of travellers. While the airports are more likely to become hotspots for
potential conflicts to break out which can cause serious delays to flights and
several safety issues. An intelligent algorithm which renders security
surveillance more effective in detecting conflicts would bring many benefits to
the passengers in terms of their safety, finance, and travelling efficiency.
This paper details the development of a machine learning model to classify
conflicting behaviour in a crowd. HRNet is used to segment the images and then
two approaches are taken to classify the poses of people in the frame via
multiple classifiers. Among them, it was found that the support vector machine
(SVM) achieved the most performant achieving precision of 94.37%. Where the
model falls short is against ambiguous behaviour such as a hug or losing track
of a subject in the frame. The resulting model has potential for deployment
within an airport if improvements are made to cope with the vast number of
potential passengers in view as well as training against further ambiguous
behaviours which will arise in an airport setting. In turn, will provide the
capability to enhance security surveillance and improve airport safety.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 14:54:12 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Kheta",
"Karan",
""
],
[
"Delgove",
"Claire",
""
],
[
"Liu",
"Ruolin",
""
],
[
"Aderogba",
"Adeola",
""
],
[
"Pokam",
"Marc-Olivier",
""
],
[
"Unal",
"Muhammed Mehmet",
""
],
[
"Xing",
"Yang",
""
],
[
"Guo",
"Weisi",
""
]
] |
new_dataset
| 0.980167 |
2207.00499
|
Arij Bouazizi
|
Arij Bouazizi and Adrian Holzbock and Ulrich Kressel and Klaus
Dietmayer and Vasileios Belagiannis
|
MotionMixer: MLP-based 3D Human Body Pose Forecasting
|
Accepted by IJCAI-ECAI'22 (Oral-Long presentation)
| null | null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work, we present MotionMixer, an efficient 3D human body pose
forecasting model based solely on multi-layer perceptrons (MLPs). MotionMixer
learns the spatial-temporal 3D body pose dependencies by sequentially mixing
both modalities. Given a stacked sequence of 3D body poses, a spatial-MLP
extracts fine grained spatial dependencies of the body joints. The interaction
of the body joints over time is then modelled by a temporal MLP. The
spatial-temporal mixed features are finally aggregated and decoded to obtain
the future motion. To calibrate the influence of each time step in the pose
sequence, we make use of squeeze-and-excitation (SE) blocks. We evaluate our
approach on Human3.6M, AMASS, and 3DPW datasets using the standard evaluation
protocols. For all evaluations, we demonstrate state-of-the-art performance,
while having a model with a smaller number of parameters. Our code is available
at: https://github.com/MotionMLP/MotionMixer
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 15:36:08 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Bouazizi",
"Arij",
""
],
[
"Holzbock",
"Adrian",
""
],
[
"Kressel",
"Ulrich",
""
],
[
"Dietmayer",
"Klaus",
""
],
[
"Belagiannis",
"Vasileios",
""
]
] |
new_dataset
| 0.999134 |
2207.00526
|
Matthew Earnshaw
|
Matthew Earnshaw, Pawe{\l} Soboci\'nski
|
Regular Monoidal Languages
|
Full version of a paper accepted for MFCS 2022
| null | null | null |
cs.FL math.CT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce regular languages of morphisms in free monoidal categories, with
their associated grammars and automata. These subsume the classical theory of
regular languages of words and trees, but also open up a much wider class of
languages over string diagrams. We use the algebra of monoidal and cartesian
restriction categories to investigate the properties of regular monoidal
languages, and provide sufficient conditions for their recognizability by
deterministic monoidal automata.
|
[
{
"version": "v1",
"created": "Fri, 1 Jul 2022 16:18:52 GMT"
}
] | 2022-07-04T00:00:00 |
[
[
"Earnshaw",
"Matthew",
""
],
[
"Sobociński",
"Paweł",
""
]
] |
new_dataset
| 0.998658 |
1706.06696
|
Mat\'ias Mattamala
|
Mat\'ias Mattamala, Gonzalo Olave, Clayder Gonz\'alez, Nicol\'as
Hasb\'un and Javier Ruiz-del-Solar
|
The NAO Backpack: An Open-hardware Add-on for Fast Software Development
with the NAO Robot
|
Accepted in the RoboCup Symposium 2017. Final version will be
published at Springer
| null |
10.1007/978-3-030-00308-1_25
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present an open-source accessory for the NAO robot, which enables to test
computationally demanding algorithms in an external platform while preserving
robot's autonomy and mobility. The platform has the form of a backpack, which
can be 3D printed and replicated, and holds an ODROID XU4 board to process
algorithms externally with ROS compatibility. We provide also a software bridge
between the B-Human's framework and ROS to have access to the robot's sensors
close to real-time. We tested the platform in several robotics applications
such as data logging, visual SLAM, and robot vision with deep learning
techniques. The CAD model, hardware specifications and software are available
online for the benefit of the community:
https://github.com/uchile-robotics/nao-backpack
|
[
{
"version": "v1",
"created": "Tue, 20 Jun 2017 22:53:16 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Mattamala",
"Matías",
""
],
[
"Olave",
"Gonzalo",
""
],
[
"González",
"Clayder",
""
],
[
"Hasbún",
"Nicolás",
""
],
[
"Ruiz-del-Solar",
"Javier",
""
]
] |
new_dataset
| 0.997232 |
1711.02513
|
Jose-Luis Aragon
|
E. Alejandra Ortiz-Duran and Jose L. Aragon
|
CGAlgebra: a Mathematica package for conformal geometric algebra. v.2.0
|
Improved version, one figure
| null | null | null |
cs.MS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A tutorial of the Mathematica package CGAlgebra, for conformal geometric
algebra calculations is presented. Using rule-based programming, the
5-dimensional conformal geometric algebra is implemented and defined functions
simplify the calculations of geometric, outer and inner products, as well as
many other calculations related with geometric transformations. CGAlgebra is
available from https://github.com/jlaragonvera/Geometric-Algebra
|
[
{
"version": "v1",
"created": "Fri, 3 Nov 2017 23:29:00 GMT"
},
{
"version": "v2",
"created": "Thu, 23 Aug 2018 01:28:24 GMT"
},
{
"version": "v3",
"created": "Wed, 29 Jun 2022 22:52:58 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Ortiz-Duran",
"E. Alejandra",
""
],
[
"Aragon",
"Jose L.",
""
]
] |
new_dataset
| 0.99973 |
2003.05691
|
Yiduo Wang
|
Milad Ramezani, Yiduo Wang, Marco Camurri, David Wisth, Matias
Mattamala and Maurice Fallon
|
The Newer College Dataset: Handheld LiDAR, Inertial and Vision with
Ground Truth
| null | null |
10.1109/IROS45743.2020.9340849
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we present a large dataset with a variety of mobile mapping
sensors collected using a handheld device carried at typical walking speeds for
nearly 2.2 km through New College, Oxford. The dataset includes data from two
commercially available devices - a stereoscopic-inertial camera and a
multi-beam 3D LiDAR, which also provides inertial measurements. Additionally,
we used a tripod-mounted survey grade LiDAR scanner to capture a detailed
millimeter-accurate 3D map of the test location (containing $\sim$290 million
points). Using the map we inferred centimeter-accurate 6 Degree of Freedom
(DoF) ground truth for the position of the device for each LiDAR scan to enable
better evaluation of LiDAR and vision localisation, mapping and reconstruction
systems. This ground truth is the particular novel contribution of this dataset
and we believe that it will enable systematic evaluation which many similar
datasets have lacked. The dataset combines both built environments, open spaces
and vegetated areas so as to test localization and mapping systems such as
vision-based navigation, visual and LiDAR SLAM, 3D LIDAR reconstruction and
appearance-based place recognition. The dataset is available at:
ori.ox.ac.uk/datasets/newer-college-dataset
|
[
{
"version": "v1",
"created": "Thu, 12 Mar 2020 10:17:16 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 14:33:50 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Ramezani",
"Milad",
""
],
[
"Wang",
"Yiduo",
""
],
[
"Camurri",
"Marco",
""
],
[
"Wisth",
"David",
""
],
[
"Mattamala",
"Matias",
""
],
[
"Fallon",
"Maurice",
""
]
] |
new_dataset
| 0.999883 |
2004.12502
|
Paulo Almeida
|
Paulo Almeida, Manuel Marques-Pita and Joana Gon\c{c}alves-S\'a
|
PTPARL-D: Annotated Corpus of 44 years of Portuguese Parliament debates
| null |
Corpora, Volume 16 Issue 3, Page 337-348, ISSN 1749-5032 Available
Online Nov 2021
|
10.3366/cor.2021.0226
| null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In a representative democracy, some decide in the name of the rest, and these
elected officials are commonly gathered in public assemblies, such as
parliaments, where they discuss policies, legislate, and vote on fundamental
initiatives. A core aspect of such democratic processes are the plenary
debates, where important public discussions take place. Many parliaments around
the world are increasingly keeping the transcripts of such debates, and other
parliamentary data, in digital formats accessible to the public, increasing
transparency and accountability. Furthermore, some parliaments are bringing old
paper transcripts to semi-structured digital formats. However, these records
are often only provided as raw text or even as images, with little to no
annotation, and inconsistent formats, making them difficult to analyze and
study, reducing both transparency and public reach. Here, we present PTPARL-D,
an annotated corpus of debates in the Portuguese Parliament, from 1976 to 2019,
covering the entire period of Portuguese democracy.
|
[
{
"version": "v1",
"created": "Sun, 26 Apr 2020 23:22:41 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Almeida",
"Paulo",
""
],
[
"Marques-Pita",
"Manuel",
""
],
[
"Gonçalves-Sá",
"Joana",
""
]
] |
new_dataset
| 0.999565 |
2101.07383
|
Kai Yao
|
Kai Yao, Alberto Ortiz, Francisco Bonnin-Pascual
|
A DCNN-based Arbitrarily-Oriented Object Detector for Quality Control
and Inspection Application
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Following the success of machine vision systems for on-line automated quality
control and inspection processes, an object recognition solution is presented
in this work for two different specific applications, i.e., the detection of
quality control items in surgery toolboxes prepared for sterilizing in a
hospital, as well as the detection of defects in vessel hulls to prevent
potential structural failures. The solution has two stages. First, a feature
pyramid architecture based on Single Shot MultiBox Detector (SSD) is used to
improve the detection performance, and a statistical analysis based on ground
truth is employed to select parameters of a range of default boxes. Second, a
lightweight neural network is exploited to achieve oriented detection results
using a regression method. The first stage of the proposed method is capable of
detecting the small targets considered in the two scenarios. In the second
stage, despite the simplicity, it is efficient to detect elongated targets
while maintaining high running efficiency.
|
[
{
"version": "v1",
"created": "Tue, 19 Jan 2021 00:23:27 GMT"
},
{
"version": "v2",
"created": "Mon, 28 Feb 2022 12:12:54 GMT"
},
{
"version": "v3",
"created": "Thu, 30 Jun 2022 05:41:36 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Yao",
"Kai",
""
],
[
"Ortiz",
"Alberto",
""
],
[
"Bonnin-Pascual",
"Francisco",
""
]
] |
new_dataset
| 0.971955 |
2104.09647
|
Alexander Spangher
|
Alexander Spangher and Jonathan May
|
NewsEdits: A Dataset of Revision Histories for News Articles (Technical
Report: Data Processing)
|
11 pages
| null | null | null |
cs.CL cs.DL
|
http://creativecommons.org/licenses/by/4.0/
|
News article revision histories have the potential to give us novel insights
across varied fields of linguistics and social sciences. In this work, we
present, to our knowledge, the first publicly available dataset of news article
revision histories, or NewsEdits.
Our dataset is multilingual; it contains 1,278,804 articles with 4,609,430
versions from over 22 English- and French-language newspaper sources based in
three countries. Across version pairs, we count 10.9 million added sentences;
8.9 million changed sentences and 6.8 million removed sentences. Within the
changed sentences, we derive 72 million atomic edits. NewsEdits is, to our
knowledge, the largest corpus of revision histories of any domain.
|
[
{
"version": "v1",
"created": "Mon, 19 Apr 2021 21:15:30 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 16:58:41 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Spangher",
"Alexander",
""
],
[
"May",
"Jonathan",
""
]
] |
new_dataset
| 0.99985 |
2106.15314
|
Gareth Simons
|
Gareth D. Simons
|
The cityseer Python package for pedestrian-scale network-based urban
analysis
|
Revision incorporating additional figure
| null | null | null |
cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
cityseer-api is a Python package consisting of computational tools for
fine-grained street-network and land-use analysis, helpful in assessing the
morphological precursors to vibrant neighbourhoods. It is underpinned by
network-based methods developed specifically for urban analysis at the
pedestrian scale. cityseer-api computes a variety of node and segment-based
network centrality methods, land-use accessibility and mixed-use measures, and
statistical aggregations. Accessibilities and aggregations are computed
dynamically over the street-network while taking walking distance thresholds
and the direction of approach into account, and can optionally incorporate
spatial impedances and network decomposition to increase spatial precision. The
use of Python facilitates compatibility with popular computational tools for
network manipulation (NetworkX), geospatial topology (shapely), geospatial data
state management (GeoPandas), and the NumPy stack of scientific packages. The
provision of robust network cleaning tools aids the use of OpenStreetMap data
for network analysis. Underlying loop-intensive algorithms are implemented in
Numba JIT compiled code so that the methods scale efficiently to larger cities
and regions.
Online documentation is available from
https://cityseer.benchmarkurbanism.com, and the Github repository is available
at https://github.com/benchmark-urbanism/cityseer. Example notebooks are
available at https://cityseer.benchmarkurbanism.com/examples/.
|
[
{
"version": "v1",
"created": "Sat, 26 Jun 2021 14:51:38 GMT"
},
{
"version": "v2",
"created": "Wed, 30 Jun 2021 20:12:07 GMT"
},
{
"version": "v3",
"created": "Wed, 29 Jun 2022 16:54:43 GMT"
},
{
"version": "v4",
"created": "Thu, 30 Jun 2022 17:06:50 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Simons",
"Gareth D.",
""
]
] |
new_dataset
| 0.999783 |
2111.12423
|
Jiaming Ye
|
Yinxing Xue, Jiaming Ye, Wei Zhang, Jun Sun, Lei Ma, Haijun Wang,
Jianjun Zhao
|
xFuzz: Machine Learning Guided Cross-Contract Fuzzing
|
IEEE Transactions on Dependable and Secure Computing (2022)
| null |
10.1109/TDSC.2022.3182373
| null |
cs.CR cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Smart contract transactions are increasingly interleaved by cross-contract
calls. While many tools have been developed to identify a common set of
vulnerabilities, the cross-contract vulnerability is overlooked by existing
tools. Cross-contract vulnerabilities are exploitable bugs that manifest in the
presence of more than two interacting contracts. Existing methods are however
limited to analyze a maximum of two contracts at the same time. Detecting
cross-contract vulnerabilities is highly non-trivial. With multiple interacting
contracts, the search space is much larger than that of a single contract. To
address this problem, we present xFuzz, a machine learning guided smart
contract fuzzing framework. The machine learning models are trained with novel
features (e.g., word vectors and instructions) and are used to filter likely
benign program paths. Comparing with existing static tools, machine learning
model is proven to be more robust, avoiding directly adopting manually-defined
rules in specific tools. We compare xFuzz with three state-of-the-art tools on
7,391 contracts. xFuzz detects 18 exploitable cross-contract vulnerabilities,
of which 15 vulnerabilities are exposed for the first time. Furthermore, our
approach is shown to be efficient in detecting non-cross-contract
vulnerabilities as well -- using less than 20% time as that of other fuzzing
tools, xFuzz detects twice as many vulnerabilities.
|
[
{
"version": "v1",
"created": "Wed, 24 Nov 2021 11:09:49 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 05:54:51 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Xue",
"Yinxing",
""
],
[
"Ye",
"Jiaming",
""
],
[
"Zhang",
"Wei",
""
],
[
"Sun",
"Jun",
""
],
[
"Ma",
"Lei",
""
],
[
"Wang",
"Haijun",
""
],
[
"Zhao",
"Jianjun",
""
]
] |
new_dataset
| 0.997547 |
2202.11703
|
Shouchang Guo
|
Shouchang Guo, Valentin Deschaintre, Douglas Noll, Arthur Roullier
|
U-Attention to Textures: Hierarchical Hourglass Vision Transformer for
Universal Texture Synthesis
| null | null | null | null |
cs.CV cs.GR eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a novel U-Attention vision Transformer for universal texture
synthesis. We exploit the natural long-range dependencies enabled by the
attention mechanism to allow our approach to synthesize diverse textures while
preserving their structures in a single inference. We propose a hierarchical
hourglass backbone that attends to the global structure and performs patch
mapping at varying scales in a coarse-to-fine-to-coarse stream. Completed by
skip connection and convolution designs that propagate and fuse information at
different scales, our hierarchical U-Attention architecture unifies attention
to features from macro structures to micro details, and progressively refines
synthesis results at successive stages. Our method achieves stronger 2$\times$
synthesis than previous work on both stochastic and structured textures while
generalizing to unseen textures without fine-tuning. Ablation studies
demonstrate the effectiveness of each component of our architecture.
|
[
{
"version": "v1",
"created": "Wed, 23 Feb 2022 18:58:56 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 07:16:09 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Guo",
"Shouchang",
""
],
[
"Deschaintre",
"Valentin",
""
],
[
"Noll",
"Douglas",
""
],
[
"Roullier",
"Arthur",
""
]
] |
new_dataset
| 0.998169 |
2203.04406
|
Alex Berke
|
Geoffrey Ding, Alex Berke, Karthik Gopalakrishnan, Kwassi H. Degue,
Hamsa Balakrishnan, Max Z. Li
|
Routing with Privacy for Drone Package Delivery Systems
| null |
International Conference on Research in Air Transportation (ICRAT)
2022
| null | null |
cs.CR cs.CY cs.SI cs.SY eess.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Unmanned aerial vehicles (UAVs), or drones, are increasingly being used to
deliver goods from vendors to customers. To safely conduct these operations at
scale, drones are required to broadcast position information as codified in
remote identification (remote ID) regulations. However, location broadcast of
package delivery drones introduces a privacy risk for customers using these
delivery services: Third-party observers may leverage broadcast drone
trajectories to link customers with their purchases, potentially resulting in a
wide range of privacy risks. We propose a probabilistic definition of privacy
risk based on the likelihood of associating a customer to a vendor given a
package delivery route. Next, we quantify these risks, enabling drone operators
to assess privacy risks when planning delivery routes. We then evaluate the
impacts of various factors (e.g., drone capacity) on privacy and consider the
trade-offs between privacy and delivery wait times. Finally, we propose
heuristics for generating routes with privacy guarantees to avoid exhaustive
enumeration of all possible routes and evaluate their performance on several
realistic delivery scenarios.
|
[
{
"version": "v1",
"created": "Fri, 4 Mar 2022 18:50:53 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 02:11:25 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Ding",
"Geoffrey",
""
],
[
"Berke",
"Alex",
""
],
[
"Gopalakrishnan",
"Karthik",
""
],
[
"Degue",
"Kwassi H.",
""
],
[
"Balakrishnan",
"Hamsa",
""
],
[
"Li",
"Max Z.",
""
]
] |
new_dataset
| 0.998161 |
2203.07060
|
Joey Wilson
|
Joey Wilson, Jingyu Song, Yuewei Fu, Arthur Zhang, Andrew Capodieci,
Paramsothy Jayakumar, Kira Barton, and Maani Ghaffari
|
MotionSC: Data Set and Network for Real-Time Semantic Mapping in Dynamic
Environments
| null | null | null | null |
cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
This work addresses a gap in semantic scene completion (SSC) data by creating
a novel outdoor data set with accurate and complete dynamic scenes. Our data
set is formed from randomly sampled views of the world at each time step, which
supervises generalizability to complete scenes without occlusions or traces. We
create SSC baselines from state-of-the-art open source networks and construct a
benchmark real-time dense local semantic mapping algorithm, MotionSC, by
leveraging recent 3D deep learning architectures to enhance SSC with temporal
information. Our network shows that the proposed data set can quantify and
supervise accurate scene completion in the presence of dynamic objects, which
can lead to the development of improved dynamic mapping algorithms. All
software is available at https://github.com/UMich-CURLY/3DMapping.
|
[
{
"version": "v1",
"created": "Mon, 14 Mar 2022 13:00:33 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 16:28:39 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Wilson",
"Joey",
""
],
[
"Song",
"Jingyu",
""
],
[
"Fu",
"Yuewei",
""
],
[
"Zhang",
"Arthur",
""
],
[
"Capodieci",
"Andrew",
""
],
[
"Jayakumar",
"Paramsothy",
""
],
[
"Barton",
"Kira",
""
],
[
"Ghaffari",
"Maani",
""
]
] |
new_dataset
| 0.996851 |
2204.02090
|
Venkatesh Shenoy Kadandale
|
Venkatesh S. Kadandale, Juan F. Montesinos, Gloria Haro
|
VocaLiST: An Audio-Visual Synchronisation Model for Lips and Voices
|
Paper accepted to Interspeech 2022; Project Page:
https://ipcv.github.io/VocaLiST/
| null | null | null |
cs.CV cs.IR cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we address the problem of lip-voice synchronisation in videos
containing human face and voice. Our approach is based on determining if the
lips motion and the voice in a video are synchronised or not, depending on
their audio-visual correspondence score. We propose an audio-visual cross-modal
transformer-based model that outperforms several baseline models in the
audio-visual synchronisation task on the standard lip-reading speech benchmark
dataset LRS2. While the existing methods focus mainly on lip synchronisation in
speech videos, we also consider the special case of the singing voice. The
singing voice is a more challenging use case for synchronisation due to
sustained vowel sounds. We also investigate the relevance of lip
synchronisation models trained on speech datasets in the context of singing
voice. Finally, we use the frozen visual features learned by our lip
synchronisation model in the singing voice separation task to outperform a
baseline audio-visual model which was trained end-to-end. The demos, source
code, and the pre-trained models are available on
https://ipcv.github.io/VocaLiST/
|
[
{
"version": "v1",
"created": "Tue, 5 Apr 2022 10:02:39 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 11:46:24 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Kadandale",
"Venkatesh S.",
""
],
[
"Montesinos",
"Juan F.",
""
],
[
"Haro",
"Gloria",
""
]
] |
new_dataset
| 0.985322 |
2204.07763
|
Jiangeng Chang
|
Jiangeng Chang, Yucheng Ruan, Cui Shaoze, John Soong Tshon Yit,
Mengling Feng
|
UFRC: A Unified Framework for Reliable COVID-19 Detection on
Crowdsourced Cough Audio
| null | null | null | null |
cs.SD cs.LG eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We suggested a unified system with core components of data augmentation,
ImageNet-pretrained ResNet-50, cost-sensitive loss, deep ensemble learning, and
uncertainty estimation to quickly and consistently detect COVID-19 using
acoustic evidence. To increase the model's capacity to identify a minority
class, data augmentation and cost-sensitive loss are incorporated (infected
samples). In the COVID-19 detection challenge, ImageNet-pretrained ResNet-50
has been found to be effective. The unified framework also integrates deep
ensemble learning and uncertainty estimation to integrate predictions from
various base classifiers for generalisation and reliability. We ran a series of
tests using the DiCOVA2021 challenge dataset to assess the efficacy of our
proposed method, and the results show that our method has an AUC-ROC of 85.43
percent, making it a promising method for COVID-19 detection. The unified
framework also demonstrates that audio may be used to quickly diagnose
different respiratory disorders.
|
[
{
"version": "v1",
"created": "Sat, 16 Apr 2022 09:24:16 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 06:19:42 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Chang",
"Jiangeng",
""
],
[
"Ruan",
"Yucheng",
""
],
[
"Shaoze",
"Cui",
""
],
[
"Yit",
"John Soong Tshon",
""
],
[
"Feng",
"Mengling",
""
]
] |
new_dataset
| 0.989842 |
2205.04108
|
Claudio Soriente
|
Alessandro Sforzin, Matteo Maso, Claudio Soriente, Ghassan Karame
|
On the Storage Overhead of Proof-of-Work Blockchains
| null | null | null | null |
cs.CR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Permissionless blockchains such as Bitcoin have long been criticized for
their high computational and storage overhead. Unfortunately, while a number of
proposals address the energy consumption of existing Proof-of-Work deployments,
little attention has been given so far to remedy the storage overhead incurred
by those blockchains. In fact, it seems widely acceptable that full nodes
supporting the blockchains have to volunteer hundreds of GBs of their storage,
to store and verify all transactions exchanged in the system.
In this paper, we explore the solution space to effectively reduce the
storage footprint of Proof-of-Work based blockchains. To do so, we analyze, by
means of thorough empirical measurements, how existing full blockchain nodes
utilize data from the shared ledger to validate incoming transactions/blocks.
Based on this analysis, we show that it is possible for full nodes to locally
reduce their storage footprint to approximately 15 GB, without any modification
to the underlying protocol. We also discuss other client-side strategies to
further reduce the storage footprint while incurring negligible computational
overhead on the nodes.
|
[
{
"version": "v1",
"created": "Mon, 9 May 2022 08:19:35 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 08:00:45 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Sforzin",
"Alessandro",
""
],
[
"Maso",
"Matteo",
""
],
[
"Soriente",
"Claudio",
""
],
[
"Karame",
"Ghassan",
""
]
] |
new_dataset
| 0.954933 |
2206.14286
|
Felix Chern
|
Felix Chern, Blake Hechtman, Andy Davis, Ruiqi Guo, David Majnemer,
Sanjiv Kumar
|
TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s
| null | null | null | null |
cs.PF cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This paper presents a novel nearest neighbor search algorithm achieving TPU
(Google Tensor Processing Unit) peak performance, outperforming
state-of-the-art GPU algorithms with similar level of recall. The design of the
proposed algorithm is motivated by an accurate accelerator performance model
that takes into account both the memory and instruction bottlenecks. Our
algorithm comes with an analytical guarantee of recall in expectation and does
not require maintaining sophisticated index data structure or tuning, making it
suitable for applications with frequent updates. Our work is available in the
open-source package of Jax and Tensorflow on TPU.
|
[
{
"version": "v1",
"created": "Tue, 28 Jun 2022 20:53:25 GMT"
},
{
"version": "v2",
"created": "Thu, 30 Jun 2022 10:48:01 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Chern",
"Felix",
""
],
[
"Hechtman",
"Blake",
""
],
[
"Davis",
"Andy",
""
],
[
"Guo",
"Ruiqi",
""
],
[
"Majnemer",
"David",
""
],
[
"Kumar",
"Sanjiv",
""
]
] |
new_dataset
| 0.959687 |
2206.14898
|
Fabrizio Montecchiani
|
Patrizio Angelini, Michael A. Bekos, Giordano Da Lozzo, Martin
Gronemann, Fabrizio Montecchiani, Alessandra Tappini
|
Recognizing Map Graphs of Bounded Treewidth
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A map graph is a graph admitting a representation in which vertices are
nations on a spherical map and edges are shared curve segments or points
between nations. We present an explicit fixed-parameter tractable algorithm for
recognizing map graphs parameterized by treewidth. The algorithm has time
complexity that is linear in the size of the graph and, if the input is a
yes-instance, it reports a certificate in the form of a so-called witness.
Furthermore, this result is developed within a more general algorithmic
framework that allows to test, for any $k$, if the input graph admits a $k$-map
(where at most $k$ nations meet at a common point) or a hole-free~$k$-map
(where each point of the sphere is covered by at least one nation). We point
out that, although bounding the treewidth of the input graph also bounds the
size of its largest clique, the latter alone does not seem to be a strong
enough structural limitation to obtain an efficient time complexity. In fact,
while the largest clique in a $k$-map graph is $\lfloor 3k/2 \rfloor$, the
recognition of $k$-map graphs is still open for any fixed $k \ge 5$.
|
[
{
"version": "v1",
"created": "Wed, 29 Jun 2022 20:35:01 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Angelini",
"Patrizio",
""
],
[
"Bekos",
"Michael A.",
""
],
[
"Da Lozzo",
"Giordano",
""
],
[
"Gronemann",
"Martin",
""
],
[
"Montecchiani",
"Fabrizio",
""
],
[
"Tappini",
"Alessandra",
""
]
] |
new_dataset
| 0.999491 |
2206.14909
|
Maximilian Pfister
|
Patrizio Angelini, Michael A. Bekos, Julia Katheder, Michael Kaufmann,
Maximilian Pfister
|
RAC Drawings of Graphs with Low Degree
|
Extended version of a paper presented at MFCS 2022
| null | null | null |
cs.CG cs.DM cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Motivated by cognitive experiments providing evidence that large
crossing-angles do not impair the readability of a graph drawing, RAC (Right
Angle Crossing) drawings were introduced to address the problem of producing
readable representations of non-planar graphs by supporting the optimal case in
which all crossings form 90{\deg} angles. In this work, we make progress on the
problem of finding RAC drawings of graphs of low degree. In this context, a
long-standing open question asks whether all degree-3 graphs admit
straight-line RAC drawings. This question has been positively answered for the
Hamiltonian degree-3 graphs. We improve on this result by extending to the
class of 3-edge-colorable degree-3 graphs. When each edge is allowed to have
one bend, we prove that degree-4 graphs admit such RAC drawings, a result which
was previously known only for degree-3 graphs. Finally, we show that
7-edge-colorable degree-7 graphs admit RAC drawings with two bends per edge.
This improves over the previous result on degree-6 graphs.
|
[
{
"version": "v1",
"created": "Wed, 29 Jun 2022 20:51:44 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Angelini",
"Patrizio",
""
],
[
"Bekos",
"Michael A.",
""
],
[
"Katheder",
"Julia",
""
],
[
"Kaufmann",
"Michael",
""
],
[
"Pfister",
"Maximilian",
""
]
] |
new_dataset
| 0.991034 |
2206.14913
|
Pawan Sahu
|
Pawan Kumar Sahu, Saksham Aggarwal, Taneesh Gupta, Gyanendra Das
|
GPTs at Factify 2022: Prompt Aided Fact-Verification
|
Accepted in AAAI'22: First Workshop on Multimodal Fact-Checking and
Hate Speech Detection, Februrary 22 - March 1, 2022,Vancouver, BC, Canada
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
One of the most pressing societal issues is the fight against false news. The
false claims, as difficult as they are to expose, create a lot of damage. To
tackle the problem, fact verification becomes crucial and thus has been a topic
of interest among diverse research communities. Using only the textual form of
data we propose our solution to the problem and achieve competitive results
with other approaches. We present our solution based on two approaches - PLM
(pre-trained language model) based method and Prompt based method. The
PLM-based approach uses the traditional supervised learning, where the model is
trained to take 'x' as input and output prediction 'y' as P(y|x). Whereas,
Prompt-based learning reflects the idea to design input to fit the model such
that the original objective may be re-framed as a problem of (masked) language
modeling. We may further stimulate the rich knowledge provided by PLMs to
better serve downstream tasks by employing extra prompts to fine-tune PLMs. Our
experiments showed that the proposed method performs better than just
fine-tuning PLMs. We achieved an F1 score of 0.6946 on the FACTIFY dataset and
a 7th position on the competition leader-board.
|
[
{
"version": "v1",
"created": "Wed, 29 Jun 2022 21:07:39 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Sahu",
"Pawan Kumar",
""
],
[
"Aggarwal",
"Saksham",
""
],
[
"Gupta",
"Taneesh",
""
],
[
"Das",
"Gyanendra",
""
]
] |
new_dataset
| 0.989438 |
2206.14977
|
Hongliang Liang
|
Hongliang Liang, Xianglin Cheng, Jie Liu, Jin Li
|
Multiple Targets Directed Greybox Fuzzing
|
14 pages, 5 figures, 10 tables
| null | null | null |
cs.CR cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Directed greybox fuzzing (DGF) can quickly discover or reproduce bugs in
programs by seeking to reach a program location or explore some locations in
order. However, due to their static stage division and coarse-grained energy
scheduling, prior DGF tools perform poorly when facing multiple target
locations (targets for short).
In this paper, we present multiple targets directed greybox fuzzing which
aims to reach multiple programs locations in a fuzzing campaign. Specifically,
we propose a novel strategy to adaptively coordinate exploration and
exploitation stages, and a novel energy scheduling strategy by considering more
relations between seeds and target locations. We implement our approaches in a
tool called LeoFuzz and evaluate it on crash reproduction, true positives
verification, and vulnerability exposure in real-world programs. Experimental
results show that LeoFuzz outperforms six state-of-the-art fuzzers, i.e., QYSM,
AFLGo, Lolly, Berry, Beacon and WindRanger in terms of effectiveness and
efficiency. Moreover, LeoFuzz has detected 23 new vulnerabilities in real-world
programs, and 11 of them have been assigned CVE IDs.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 02:01:26 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Liang",
"Hongliang",
""
],
[
"Cheng",
"Xianglin",
""
],
[
"Liu",
"Jie",
""
],
[
"Li",
"Jin",
""
]
] |
new_dataset
| 0.979964 |
2206.14992
|
Brian Hempel
|
Brian Hempel and Ravi Chugh
|
Maniposynth: Bimodal Tangible Functional Programming
|
ECOOP 2022 Paper + Appendices. 34 pages, 15 figures. For video figure
and artifact, see https://maniposynth.org/
| null |
10.4230/LIPIcs.ECOOP.2022.16
| null |
cs.PL cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Traditionally, writing code is a non-graphical, abstract, and linear process.
Not everyone is comfortable with this way of thinking at all times. Can
programming be transformed into a graphical, concrete, non-linear activity?
While nodes-and-wires and blocks-based programming environments do leverage
graphical direct manipulation, users perform their manipulations on abstract
syntax tree elements, which are still abstract. Is it possible to be more
concrete - could users instead directly manipulate live program values to
create their program?
We present a system, Maniposynth, that reimagines functional programming as a
non-linear workflow where program expressions are spread on a 2D canvas. The
live results of those expressions are continuously displayed and available for
direct manipulation. The non-linear canvas liberates users to work
out-of-order, and the live values can be interacted with via drag-and-drop.
Incomplete programs are gracefully handled via hole expressions, which allow
Maniposynth to offer program synthesis. Throughout the workflow, the program is
valid OCaml code which the user may inspect and edit in their preferred text
editor at any time.
With Maniposynth's direct manipulation features, we created 38 programs drawn
from a functional data structures course. We additionally hired two
professional OCaml developers to implement a subset of these programs. We
report on these experiences and discuss to what degree Maniposynth meets its
goals of providing a non-linear, concrete, graphical programming workflow.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 02:52:46 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Hempel",
"Brian",
""
],
[
"Chugh",
"Ravi",
""
]
] |
new_dataset
| 0.997674 |
2206.15007
|
Zhiying Zhu
|
Zhiying Zhu, Weixin Liang, James Zou
|
GSCLIP : A Framework for Explaining Distribution Shifts in Natural
Language
|
Accepted by ICML 2022 DataPerf
| null | null | null |
cs.CL cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Helping end users comprehend the abstract distribution shifts can greatly
facilitate AI deployment. Motivated by this, we propose a novel task, dataset
explanation. Given two image data sets, dataset explanation aims to
automatically point out their dataset-level distribution shifts with natural
language. Current techniques for monitoring distribution shifts provide
inadequate information to understand datasets with the goal of improving data
quality. Therefore, we introduce GSCLIP, a training-free framework to solve the
dataset explanation task. In GSCLIP, we propose the selector as the first
quantitative evaluation method to identify explanations that are proper to
summarize dataset shifts. Furthermore, we leverage this selector to demonstrate
the superiority of a generator based on language model generation. Systematic
evaluation on natural data shift verifies that GSCLIP, a combined system of a
hybrid generator group and an efficient selector is not only easy-to-use but
also powerful for dataset explanation at scale.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 04:06:26 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Zhu",
"Zhiying",
""
],
[
"Liang",
"Weixin",
""
],
[
"Zou",
"James",
""
]
] |
new_dataset
| 0.999737 |
2206.15086
|
Ameya Pore
|
Ameya Pore, Martina Finocchiaro, Diego Dall'Alba, Albert Hernansanz,
Gastone Ciuti, Alberto Arezzo, Arianna Menciassi, Alicia Casals, Paolo
Fiorini
|
Colonoscopy Navigation using End-to-End Deep Visuomotor Control: A User
Study
|
Accepted in IROS2022
| null | null | null |
cs.RO cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Flexible endoscopes for colonoscopy present several limitations due to their
inherent complexity, resulting in patient discomfort and lack of intuitiveness
for clinicians. Robotic devices together with autonomous control represent a
viable solution to reduce the workload of endoscopists and the training time
while improving the overall procedure outcome. Prior works on autonomous
endoscope control use heuristic policies that limit their generalisation to the
unstructured and highly deformable colon environment and require frequent human
intervention. This work proposes an image-based control of the endoscope using
Deep Reinforcement Learning, called Deep Visuomotor Control (DVC), to exhibit
adaptive behaviour in convoluted sections of the colon tract. DVC learns a
mapping between the endoscopic images and the control signal of the endoscope.
A first user study of 20 expert gastrointestinal endoscopists was carried out
to compare their navigation performance with DVC policies using a realistic
virtual simulator. The results indicate that DVC shows equivalent performance
on several assessment parameters, being more safer. Moreover, a second user
study with 20 novice participants was performed to demonstrate easier human
supervision compared to a state-of-the-art heuristic control policy. Seamless
supervision of colonoscopy procedures would enable interventionists to focus on
the medical decision rather than on the control problem of the endoscope.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 07:42:21 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Pore",
"Ameya",
""
],
[
"Finocchiaro",
"Martina",
""
],
[
"Dall'Alba",
"Diego",
""
],
[
"Hernansanz",
"Albert",
""
],
[
"Ciuti",
"Gastone",
""
],
[
"Arezzo",
"Alberto",
""
],
[
"Menciassi",
"Arianna",
""
],
[
"Casals",
"Alicia",
""
],
[
"Fiorini",
"Paolo",
""
]
] |
new_dataset
| 0.997617 |
2206.15091
|
Viktoriia Korchemna
|
Robert Ganian and Viktoriia Korchemna
|
Slim Tree-Cut Width
|
18 pages, 5 figures, 1 table
| null | null | null |
cs.CC cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Tree-cut width is a parameter that has been introduced as an attempt to
obtain an analogue of treewidth for edge cuts. Unfortunately, in spite of its
desirable structural properties, it turned out that tree-cut width falls short
as an edge-cut based alternative to treewidth in algorithmic aspects. This has
led to the very recent introduction of a simple edge-based parameter called
edge-cut width [WG 2022], which has precisely the algorithmic applications one
would expect from an analogue of treewidth for edge cuts, but does not have the
desired structural properties. In this paper, we study a variant of tree-cut
width obtained by changing the threshold for so-called thin nodes in tree-cut
decompositions from 2 to 1. We show that this "slim tree-cut width" satisfies
all the requirements of an edge-cut based analogue of treewidth, both
structural and algorithmic, while being less restrictive than edge-cut width.
Our results also include an alternative characterization of slim tree-cut width
via an easy-to-use spanning-tree decomposition akin to the one used for
edge-cut width, a characterization of slim tree-cut width in terms of forbidden
immersions as well as approximation algorithm for computing the parameter.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 07:51:08 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Ganian",
"Robert",
""
],
[
"Korchemna",
"Viktoriia",
""
]
] |
new_dataset
| 0.996872 |
2206.15102
|
Tingxiang Fan
|
Tingxiang Fan, Bowen Shen, Hua Chen, Wei Zhang and Jia Pan
|
DynamicFilter: an Online Dynamic Objects Removal Framework for Highly
Dynamic Environments
|
ICRA 2022
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Emergence of massive dynamic objects will diversify spatial structures when
robots navigate in urban environments. Therefore, the online removal of dynamic
objects is critical. In this paper, we introduce a novel online removal
framework for highly dynamic urban environments. The framework consists of the
scan-to-map front-end and the map-to-map back-end modules. Both the front- and
back-ends deeply integrate the visibility-based approach and map-based
approach. The experiments validate the framework in highly dynamic simulation
scenarios and real-world datasets.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 08:07:47 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Fan",
"Tingxiang",
""
],
[
"Shen",
"Bowen",
""
],
[
"Chen",
"Hua",
""
],
[
"Zhang",
"Wei",
""
],
[
"Pan",
"Jia",
""
]
] |
new_dataset
| 0.996673 |
2206.15154
|
Georgi Pramatarov
|
Georgi Pramatarov, Daniele De Martini, Matthew Gadd, Paul Newman
|
BoxGraph: Semantic Place Recognition and Pose Estimation from 3D LiDAR
|
Accepted for publication at the IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS) 2022
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper is about extremely robust and lightweight localisation using LiDAR
point clouds based on instance segmentation and graph matching. We model 3D
point clouds as fully-connected graphs of semantically identified components
where each vertex corresponds to an object instance and encodes its shape.
Optimal vertex association across graphs allows for full 6-Degree-of-Freedom
(DoF) pose estimation and place recognition by measuring similarity. This
representation is very concise, condensing the size of maps by a factor of 25
against the state-of-the-art, requiring only 3kB to represent a 1.4MB laser
scan. We verify the efficacy of our system on the SemanticKITTI dataset, where
we achieve a new state-of-the-art in place recognition, with an average of
88.4% recall at 100% precision where the next closest competitor follows with
64.9%. We also show accurate metric pose estimation performance - estimating
6-DoF pose with median errors of 10 cm and 0.33 deg.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 09:39:08 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Pramatarov",
"Georgi",
""
],
[
"De Martini",
"Daniele",
""
],
[
"Gadd",
"Matthew",
""
],
[
"Newman",
"Paul",
""
]
] |
new_dataset
| 0.997858 |
2206.15170
|
Ardi Tampuu
|
Ardi Tampuu, Romet Aidla, Jan Are van Gent, Tambet Matiisen
|
LiDAR-as-Camera for End-to-End Driving
| null | null | null | null |
cs.AI cs.CV cs.RO
|
http://creativecommons.org/licenses/by/4.0/
|
The core task of any autonomous driving system is to transform sensory inputs
into driving commands. In end-to-end driving, this is achieved via a neural
network, with one or multiple cameras as the most commonly used input and
low-level driving command, e.g. steering angle, as output. However,
depth-sensing has been shown in simulation to make the end-to-end driving task
easier. On a real car, combining depth and visual information can be
challenging, due to the difficulty of obtaining good spatial and temporal
alignment of the sensors. To alleviate alignment problems, Ouster LiDARs can
output surround-view LiDAR-images with depth, intensity, and ambient radiation
channels. These measurements originate from the same sensor, rendering them
perfectly aligned in time and space. We demonstrate that such LiDAR-images are
sufficient for the real-car road-following task and perform at least equally to
camera-based models in the tested conditions, with the difference increasing
when needing to generalize to new weather conditions. In the second direction
of study, we reveal that the temporal smoothness of off-policy prediction
sequences correlates equally well with actual on-policy driving ability as the
commonly used mean absolute error.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 10:06:49 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Tampuu",
"Ardi",
""
],
[
"Aidla",
"Romet",
""
],
[
"van Gent",
"Jan Are",
""
],
[
"Matiisen",
"Tambet",
""
]
] |
new_dataset
| 0.999761 |
2206.15219
|
alexander lerch
|
Alexander Lerch
|
libACA, pyACA, and ACA-Code: Audio Content Analysis in 3 Languages
|
Preprint submitted to "Software Impacts"
| null | null | null |
cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
The three packages libACA, pyACA, and ACA-Code provide reference
implementations for basic approaches and algorithms for the analysis of musical
audio signals in three different languages: C++, Python, and Matlab. All three
packages cover the same algorithms, such as extraction of low level audio
features, fundamental frequency estimation, as well as simple approaches to
chord recognition, musical key detection, and onset detection. In addition, it
implementations of more generic algorithms useful in audio content analysis
such as dynamic time warping and the Viterbi algorithm are provided. The three
packages thus provide a practical cross-language and cross-platform reference
to students and engineers implementing audio analysis algorithms and enable
implementation-focused learning of algorithms for audio content analysis and
music information retrieval.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 12:09:41 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Lerch",
"Alexander",
""
]
] |
new_dataset
| 0.999687 |
2206.15276
|
Kyle Kastner
|
Kyle Kastner, Aaron Courville
|
R-MelNet: Reduced Mel-Spectral Modeling for Neural TTS
| null | null | null | null |
cs.SD cs.LG eess.AS
|
http://creativecommons.org/licenses/by-sa/4.0/
|
This paper introduces R-MelNet, a two-part autoregressive architecture with a
frontend based on the first tier of MelNet and a backend WaveRNN-style audio
decoder for neural text-to-speech synthesis. Taking as input a mixed sequence
of characters and phonemes, with an optional audio priming sequence, this model
produces low-resolution mel-spectral features which are interpolated and used
by a WaveRNN decoder to produce an audio waveform. Coupled with half precision
training, R-MelNet uses under 11 gigabytes of GPU memory on a single commodity
GPU (NVIDIA 2080Ti). We detail a number of critical implementation details for
stable half precision training, including an approximate, numerically stable
mixture of logistics attention. Using a stochastic, multi-sample per step
inference scheme, the resulting model generates highly varied audio, while
enabling text and audio based controls to modify output waveforms. Qualitative
and quantitative evaluations of an R-MelNet system trained on a single speaker
TTS dataset demonstrate the effectiveness of our approach.
|
[
{
"version": "v1",
"created": "Thu, 30 Jun 2022 13:29:31 GMT"
}
] | 2022-07-01T00:00:00 |
[
[
"Kastner",
"Kyle",
""
],
[
"Courville",
"Aaron",
""
]
] |
new_dataset
| 0.963633 |
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